diff --git a/cfg/exp/ablation/feature_threshold/slic100_dino224_16.yaml b/.deprecated/cfg/exp/ablation/feature_threshold/slic100_dino224_16.yaml similarity index 100% rename from cfg/exp/ablation/feature_threshold/slic100_dino224_16.yaml rename to .deprecated/cfg/exp/ablation/feature_threshold/slic100_dino224_16.yaml diff --git a/cfg/exp/ablation/feature_threshold/slic100_efficientnet_b4.yaml b/.deprecated/cfg/exp/ablation/feature_threshold/slic100_efficientnet_b4.yaml similarity index 100% rename from cfg/exp/ablation/feature_threshold/slic100_efficientnet_b4.yaml rename to .deprecated/cfg/exp/ablation/feature_threshold/slic100_efficientnet_b4.yaml diff --git a/cfg/exp/ablation/feature_threshold/slic100_resnet18.yaml b/.deprecated/cfg/exp/ablation/feature_threshold/slic100_resnet18.yaml similarity index 100% rename from cfg/exp/ablation/feature_threshold/slic100_resnet18.yaml rename to .deprecated/cfg/exp/ablation/feature_threshold/slic100_resnet18.yaml diff --git a/cfg/exp/ablation/feature_threshold/slic100_resnet50.yaml b/.deprecated/cfg/exp/ablation/feature_threshold/slic100_resnet50.yaml similarity index 100% rename from cfg/exp/ablation/feature_threshold/slic100_resnet50.yaml rename to .deprecated/cfg/exp/ablation/feature_threshold/slic100_resnet50.yaml diff --git a/cfg/exp/ablation/feature_threshold/slic100_resnet50_dino.yaml b/.deprecated/cfg/exp/ablation/feature_threshold/slic100_resnet50_dino.yaml similarity index 100% rename from cfg/exp/ablation/feature_threshold/slic100_resnet50_dino.yaml rename to .deprecated/cfg/exp/ablation/feature_threshold/slic100_resnet50_dino.yaml diff --git a/cfg/exp/ablation/feature_threshold/slic100_sift.yaml b/.deprecated/cfg/exp/ablation/feature_threshold/slic100_sift.yaml similarity index 100% rename from cfg/exp/ablation/feature_threshold/slic100_sift.yaml rename to .deprecated/cfg/exp/ablation/feature_threshold/slic100_sift.yaml diff --git a/cfg/exp/ablation/loss_function/anomaly_only.yaml b/.deprecated/cfg/exp/ablation/loss_function/anomaly_only.yaml similarity index 100% rename from cfg/exp/ablation/loss_function/anomaly_only.yaml rename to .deprecated/cfg/exp/ablation/loss_function/anomaly_only.yaml diff --git a/cfg/exp/ablation/loss_function/mse_and_anomaly.yaml b/.deprecated/cfg/exp/ablation/loss_function/mse_and_anomaly.yaml similarity index 100% rename from cfg/exp/ablation/loss_function/mse_and_anomaly.yaml rename to .deprecated/cfg/exp/ablation/loss_function/mse_and_anomaly.yaml diff --git a/cfg/exp/ablation/loss_function/mse_and_anomaly_threshold.yaml b/.deprecated/cfg/exp/ablation/loss_function/mse_and_anomaly_threshold.yaml similarity index 100% rename from cfg/exp/ablation/loss_function/mse_and_anomaly_threshold.yaml rename to .deprecated/cfg/exp/ablation/loss_function/mse_and_anomaly_threshold.yaml diff --git a/cfg/exp/ablation/loss_function/mse_only.yaml b/.deprecated/cfg/exp/ablation/loss_function/mse_only.yaml similarity index 100% rename from cfg/exp/ablation/loss_function/mse_only.yaml rename to .deprecated/cfg/exp/ablation/loss_function/mse_only.yaml diff --git a/cfg/exp/ablation/scene_adaptation/default.yaml b/.deprecated/cfg/exp/ablation/scene_adaptation/default.yaml similarity index 100% rename from cfg/exp/ablation/scene_adaptation/default.yaml rename to .deprecated/cfg/exp/ablation/scene_adaptation/default.yaml diff --git a/.deprecated/dataset/__init__.py b/.deprecated/dataset/__init__.py new file mode 100644 index 00000000..7e048bc8 --- /dev/null +++ b/.deprecated/dataset/__init__.py @@ -0,0 +1 @@ +from .graph_trav_dataset import get_ablation_module diff --git a/wild_visual_navigation/learning/dataset/graph_trav_dataset.py b/.deprecated/dataset/graph_trav_dataset.py similarity index 85% rename from wild_visual_navigation/learning/dataset/graph_trav_dataset.py rename to .deprecated/dataset/graph_trav_dataset.py index efc49f80..54348a78 100644 --- a/wild_visual_navigation/learning/dataset/graph_trav_dataset.py +++ b/.deprecated/dataset/graph_trav_dataset.py @@ -1,14 +1,13 @@ -from torch_geometric.data import InMemoryDataset, DataListLoader, DataLoader -from torch_geometric.data import LightningDataset +# TODO +# from torch_geometric.data import InMemoryDataset, DataLoader +# from torch_geometric.data import LightningDataset +# from torch_geometric.data import Dataset from wild_visual_navigation import WVN_ROOT_DIR import os import torch -from pathlib import Path -from torch_geometric.data import Dataset from torchvision import transforms as T from typing import Optional, Callable -from torch_geometric.data import Data import random @@ -46,7 +45,6 @@ def __init__( print("Not found path", img_p) if training_data_percentage < 100: - if int(len(ls) * training_data_percentage / 100) == 0: raise Exception("Defined Training Data Perentage to small !") @@ -187,13 +185,28 @@ def get_test_dataset(perugia_root, env, feature_key, test_all_datasets, training ) ] else: - val_dataset = get_test_dataset(perugia_root, env, feature_key, test_all_datasets, training_data_percentage) + val_dataset = get_test_dataset( + perugia_root, + env, + feature_key, + test_all_datasets, + training_data_percentage, + ) train_loader = DataLoader( - dataset=train_dataset, batch_size=batch_size, num_workers=num_workers, pin_memory=False + dataset=train_dataset, + batch_size=batch_size, + num_workers=num_workers, + pin_memory=False, ) val_loader = [ - DataLoader(dataset=v, batch_size=batch_size, num_workers=num_workers, pin_memory=False) for v in val_dataset + DataLoader( + dataset=v, + batch_size=batch_size, + num_workers=num_workers, + pin_memory=False, + ) + for v in val_dataset ] else: @@ -213,9 +226,20 @@ def get_test_dataset(perugia_root, env, feature_key, test_all_datasets, training ) ] else: - test_dataset = get_test_dataset(perugia_root, env, feature_key, test_all_datasets, training_data_percentage) + test_dataset = get_test_dataset( + perugia_root, + env, + feature_key, + test_all_datasets, + training_data_percentage, + ) test_loader = [ - DataLoader(dataset=t, batch_size=batch_size, num_workers=num_workers, pin_memory=False) + DataLoader( + dataset=t, + batch_size=batch_size, + num_workers=num_workers, + pin_memory=False, + ) for t in test_dataset ] else: diff --git a/wild_visual_navigation/learning/general/__init__.py b/.deprecated/general/__init__.py similarity index 100% rename from wild_visual_navigation/learning/general/__init__.py rename to .deprecated/general/__init__.py diff --git a/.deprecated/general/training_routine.py b/.deprecated/general/training_routine.py new file mode 100644 index 00000000..b2023080 --- /dev/null +++ b/.deprecated/general/training_routine.py @@ -0,0 +1,120 @@ +import os +import warnings +from os.path import join +from omegaconf import read_write + +warnings.filterwarnings("ignore", ".*does not have many workers.*") + +# Frameworks +import torch +from pytorch_lightning import seed_everything, Trainer +from pytorch_lightning.callbacks import EarlyStopping +from pytorch_lightning.callbacks import ModelCheckpoint +from pytorch_lightning.callbacks import LearningRateMonitor +from pytorch_lightning.profiler import AdvancedProfiler + +# Costume Modules +from wild_visual_navigation.utils import get_logger +from wild_visual_navigation.lightning import LightningTrav +from wild_visual_navigation.utils import create_experiment_folder, save_omega_cfg +from wild_visual_navigation.dataset import get_ablation_module +from wild_visual_navigation.cfg import ExperimentParams + +__all__ = ["training_routine"] + + +def training_routine(exp: ExperimentParams, seed=42) -> torch.Tensor: + exp + seed_everything(seed) + + if exp.general.log_to_disk: + model_path = create_experiment_folder(exp) + else: + model_path = exp.general.model_path + + with read_write(exp): + # Update model paths + exp.general.model_path = model_path + exp.general.name = os.path.relpath(model_path, exp.env.results) + exp.trainer.default_root_dir = model_path + exp.visu.learning_visu.p_visu = join(model_path, "visu") + + logger = get_logger(exp) + + # Set gpus + exp.trainer.gpus = 1 if torch.cuda.is_available() else None + + # Profiler + if exp.trainer.get("profiler", False) == "advanced": + exp.trainer.profiler = AdvancedProfiler(dirpath=model_path, filename="profile.txt") + + # Callbacks + cb_ls = [] + if logger is not None: + cb_ls.append(LearningRateMonitor(**exp.lr_monitor)) + + if exp.cb_early_stopping.active: + early_stop_callback = EarlyStopping(**exp.cb_early_stopping.cfg) + cb_ls.appned(early_stop_callback) + + if exp.cb_checkpoint.active: + checkpoint_callback = ModelCheckpoint( + dirpath=model_path, + save_top_k=1, + monitor="epoch", + mode="max", + save_last=True, + ) + cb_ls.append(checkpoint_callback) + + train_dl, val_dl, test_dl = get_ablation_module( + **exp.ablation_data_module, + perugia_root=exp.general.perugia_root, + get_train_val_dataset=not exp.general.skip_train, + get_test_dataset=not exp.ablation_data_module.val_equals_test, + ) + + # Set correct input feature dimension + if train_dl is not None: + sample = train_dl.dataset[0] + else: + sample = test_dl[0].dataset[0] + input_feature_dimension = sample.x.shape[1] + + with read_write(exp): + exp.model.simple_mlp_cfg.input_size = input_feature_dimension + exp.model.simple_gcn_cfg.input_size = input_feature_dimension + exp.model.double_mlp_cfg.input_size = input_feature_dimension + exp.model.linear_rnvp_cfg.input_size = input_feature_dimension + + if exp.general.log_to_disk: + save_omega_cfg(exp, os.path.join(model_path, "experiment_params.yaml")) + + # Model + model = LightningTrav(exp=exp) + if type(exp.model.load_ckpt) == str: + ckpt = torch.load(exp.model.load_ckpt) + try: + res = model.load_state_dict(ckpt.state_dict, strict=False) + except Exception: + res = model.load_state_dict(ckpt, strict=False) + print("Loaded model checkpoint:", res) + trainer = Trainer(**exp.trainer, callbacks=cb_ls, logger=logger) + + if not exp.general.skip_train: + trainer.fit(model=model, train_dataloaders=train_dl, val_dataloaders=val_dl) + + if exp.ablation_data_module.val_equals_test: + return model.accumulated_val_results, model + + # TODO Verify that this makes sense here + test_envs = [] + for j, dl in enumerate(test_dl): + if exp.loss.w_trav == 0: + model._traversability_loss._anomaly_threshold = None + + model.nr_test_run = j + res = trainer.test(model=model, dataloaders=dl)[0] + test_envs.append(dl.dataset.env) + + return {k: v for k, v in zip(test_envs, model.accumulated_test_results)}, model diff --git a/wild_visual_navigation/learning/lightning/__init__.py b/.deprecated/lightning/__init__.py similarity index 100% rename from wild_visual_navigation/learning/lightning/__init__.py rename to .deprecated/lightning/__init__.py diff --git a/wild_visual_navigation/learning/lightning/lightning_module.py b/.deprecated/lightning/lightning_module.py similarity index 80% rename from wild_visual_navigation/learning/lightning/lightning_module.py rename to .deprecated/lightning/lightning_module.py index 69e09fce..5e49bea8 100644 --- a/wild_visual_navigation/learning/lightning/lightning_module.py +++ b/.deprecated/lightning/lightning_module.py @@ -1,27 +1,25 @@ -from wild_visual_navigation.learning.model import get_model +from wild_visual_navigation.model import get_model import pytorch_lightning as pl import torch import torch.nn.functional as F -from os.path import join from wild_visual_navigation.visu import LearningVisualizer from pytorch_lightning.utilities.types import EPOCH_OUTPUT -from torch_geometric.data import Data + +# from torch_geometric.data import Data from torchmetrics import ROC -from wild_visual_navigation.learning.utils import TraversabilityLoss, MetricLogger +from wild_visual_navigation.utils import TraversabilityLoss, MetricLogger import os -import pickle class LightningTrav(pl.LightningModule): - def __init__(self, exp: dict, env: dict, log: bool = False): + def __init__(self, exp: dict, log: bool = False): super().__init__() self._model = get_model(exp["model"]) self._visu_count = {"val": 0, "test": 0, "train": 0} self._visualizer = LearningVisualizer(**exp["visu"]["learning_visu"], pl_model=self) self._exp = exp - self._env = env self._mode = "train" self._log = log @@ -71,18 +69,30 @@ def training_step(self, batch: any, batch_idx: int) -> torch.Tensor: for k, v in loss_aux.items(): if k.find("loss") != -1: - self.log(f"{self._mode}_{k}", v.item(), on_epoch=True, prog_bar=True, batch_size=BS) - self.log(f"{self._mode}_loss", loss.item(), on_epoch=True, prog_bar=True, batch_size=BS) + self.log( + f"{self._mode}_{k}", + v.item(), + on_epoch=True, + prog_bar=True, + batch_size=BS, + ) + self.log( + f"{self._mode}_loss", + loss.item(), + on_epoch=True, + prog_bar=True, + batch_size=BS, + ) self.visu(graph, res_updated, loss_aux["confidence"]) # This mask should contain all the segments corrosponding to trees. mask_anomaly = loss_aux["confidence"] < 0.5 - mask_proprioceptive = graph.y == 1 + mask_supervision = graph.y == 1 # Remove the segments that are for sure not an anomalies given that we have walked on them. - mask_anomaly[mask_proprioceptive] = False + mask_anomaly[mask_supervision] = False # Elements are valid if they are either an anomaly or we have walked on them to fit the ROC - mask_valid = mask_anomaly | mask_proprioceptive + mask_valid = mask_anomaly | mask_supervision self._auxiliary_training_roc(res_updated[mask_valid, 0], graph.y[mask_valid].type(torch.long)) return loss @@ -113,9 +123,21 @@ def validation_step(self, batch: any, batch_idx: int, dataloader_id: int = 0) -> for k, v in loss_aux.items(): if k.find("loss") != -1: - self.log(f"{self._mode}_{k}", v.item(), on_epoch=True, prog_bar=True, batch_size=BS) + self.log( + f"{self._mode}_{k}", + v.item(), + on_epoch=True, + prog_bar=True, + batch_size=BS, + ) - self.log(f"{self._mode}_loss", loss.item(), on_epoch=True, prog_bar=True, batch_size=BS) + self.log( + f"{self._mode}_loss", + loss.item(), + on_epoch=True, + prog_bar=True, + batch_size=BS, + ) self.visu(graph, res_updated, loss_aux["confidence"]) return loss @@ -134,7 +156,7 @@ def update_threshold(self): fpr, tpr, thresholds = self._auxiliary_training_roc.compute() index = torch.where(fpr > 0.15)[0][0] self.threshold[0] = thresholds[index] - except: + except Exception: pass else: self.threshold[0] = 0.5 @@ -159,13 +181,25 @@ def test_step(self, batch: any, batch_idx: int, dataloader_id: int = 0) -> torch for k, v in loss_aux.items(): if k.find("loss") != -1: - self.log(f"{self._mode}_{k}", v.item(), on_epoch=True, prog_bar=True, batch_size=BS) - self.log(f"{self._mode}_loss", loss.item(), on_epoch=True, prog_bar=True, batch_size=BS) + self.log( + f"{self._mode}_{k}", + v.item(), + on_epoch=True, + prog_bar=True, + batch_size=BS, + ) + self.log( + f"{self._mode}_loss", + loss.item(), + on_epoch=True, + prog_bar=True, + batch_size=BS, + ) self.visu(graph, res_updated, loss_aux["confidence"]) return loss def test_epoch_end(self, outputs: any, plot=False): - ################ NEW VERSION ################ + # NEW VERSION res = self._metric_logger.get_epoch_results("test") dic2 = { "trainer_logged_metric" + k: v.item() @@ -200,7 +234,7 @@ def visu(self, graph: Data, res: torch.tensor, confidence: torch.tensor): on_epoch=True, batch_size=graph.ptr.shape[0] - 1, ) - except: + except Exception: pass for b in range(graph.ptr.shape[0] - 1): @@ -233,14 +267,22 @@ def visu(self, graph: Data, res: torch.tensor, confidence: torch.tensor): # Visualize Graph with Segmentation t1 = self._visualizer.plot_traversability_graph_on_seg( - pred[:, 0], seg, graph[b], center, img, not_log=True, colorize_invalid_centers=True + pred[:, 0], + seg, + graph[b], + center, + img, + not_log=True, + colorize_invalid_centers=True, ) t2 = self._visualizer.plot_traversability_graph_on_seg( graph[b].y, seg, graph[b], center, img, not_log=True ) t_img = self._visualizer.plot_image(img, not_log=True) self._visualizer.plot_list( - imgs=[t1, t2, t_img], tag=f"C{c}_{self._mode}_GraphTrav", store_folder=f"{self._mode}/graph_trav" + imgs=[t1, t2, t_img], + tag=f"C{c}_{self._mode}_GraphTrav", + store_folder=f"{self._mode}/graph_trav", ) nr_channel_reco = graph[b].x.shape[1] @@ -249,14 +291,27 @@ def visu(self, graph: Data, res: torch.tensor, confidence: torch.tensor): # # Visualize Graph with Segmentation c1 = self._visualizer.plot_traversability_graph_on_seg( - conf, seg, graph[b], center, img, not_log=True, colorize_invalid_centers=True + conf, + seg, + graph[b], + center, + img, + not_log=True, + colorize_invalid_centers=True, ) c2 = self._visualizer.plot_traversability_graph_on_seg( - graph[b].y_valid.type(torch.float32), seg, graph[b], center, img, not_log=True + graph[b].y_valid.type(torch.float32), + seg, + graph[b], + center, + img, + not_log=True, ) c_img = self._visualizer.plot_image(img, not_log=True) self._visualizer.plot_list( - imgs=[c1, c2, c_img], tag=f"C{c}_{self._mode}_GraphConf", store_folder=f"{self._mode}/graph_conf" + imgs=[c1, c2, c_img], + tag=f"C{c}_{self._mode}_GraphConf", + store_folder=f"{self._mode}/graph_conf", ) # if self._mode == "test": @@ -317,10 +372,18 @@ def visu(self, graph: Data, res: torch.tensor, confidence: torch.tensor): reco_loss = F.mse_loss(pred[:, -nr_channel_reco:], graph[b].x, reduction="none").mean(dim=1) self._visualizer.plot_histogram( - reco_loss, graph[b].y, mean, std, tag=f"C{c}_{self._mode}__confidence_generator_prop" + reco_loss, + graph[b].y, + mean, + std, + tag=f"C{c}_{self._mode}__confidence_generator_prop", ) if hasattr(graph[b], "y_gt"): self._visualizer.plot_histogram( - reco_loss, graph[b].y_gt, mean, std, tag=f"C{c}_{self._mode}__confidence_generator_gt" + reco_loss, + graph[b].y_gt, + mean, + std, + tag=f"C{c}_{self._mode}__confidence_generator_gt", ) diff --git a/scripts/ablations/sklearn_ablation.py b/.deprecated/scripts/ablations/sklearn_ablation.py similarity index 87% rename from scripts/ablations/sklearn_ablation.py rename to .deprecated/scripts/ablations/sklearn_ablation.py index 68704184..c2d3636e 100644 --- a/scripts/ablations/sklearn_ablation.py +++ b/.deprecated/scripts/ablations/sklearn_ablation.py @@ -1,7 +1,6 @@ +raise ValueError("TODO: Not tested with new configuration!") # Use the same config to load the data using the dataloader -from wild_visual_navigation.learning.dataset import get_ablation_module -from wild_visual_navigation.learning.utils import load_env -from wild_visual_navigation import WVN_ROOT_DIR +from wild_visual_navigation.dataset import get_ablation_module from wild_visual_navigation.cfg import ExperimentParams import torch from torchmetrics import Accuracy, AUROC @@ -10,17 +9,14 @@ patch_sklearn() -from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.ensemble import RandomForestClassifier -from sklearn.neural_network import MLPClassifier import numpy as np import copy import os import pickle from pathlib import Path -env = load_env() number_training_runs = 5 test_all_datasets = False @@ -45,7 +41,7 @@ "training_in_memory": True, } train_dataset, val_dataset, test_datasets = get_ablation_module( - **ablation_data_module, perugia_root=env["perugia_root"] + **ablation_data_module, perugia_root=exp.env.perugia_root ) test_scenes = [a.dataset.env for a in test_datasets] @@ -83,7 +79,6 @@ for model_name, v in models.items(): run_results = {} for run in range(number_training_runs): - test_auroc_gt_image = AUROC("binary") test_auroc_prop_image = AUROC("binary") test_acc_gt_image = Accuracy("binary") @@ -127,8 +122,14 @@ for features_test, y_gt, y_prop, test_scene in zip(features_tests, y_gts, y_props, test_scenes): y_pred = v.predict_proba(features_test) - test_auroc_gt.update(torch.from_numpy(y_pred[:, 1]), torch.from_numpy(y_gt).type(torch.long)) - test_auroc_prop.update(torch.from_numpy(y_pred[:, 1]), torch.from_numpy(y_prop).type(torch.long)) + test_auroc_gt.update( + torch.from_numpy(y_pred[:, 1]), + torch.from_numpy(y_gt).type(torch.long), + ) + test_auroc_prop.update( + torch.from_numpy(y_pred[:, 1]), + torch.from_numpy(y_prop).type(torch.long), + ) res = { "test_auroc_gt_seg": test_auroc_gt.compute().item(), @@ -145,10 +146,11 @@ model_results[model_name] = copy.deepcopy(run_results) results_epoch[scene] = copy.deepcopy(model_results) - ws = os.environ["ENV_WORKSTATION_NAME"] + ws = os.environ.get("ENV_WORKSTATION_NAME", "default") # Store epoch output to disk. p = os.path.join( - env["base"], f"ablations/classicial_learning_ablation_{ws}/classicial_learning_ablation_test_results.pkl" + exp.env.results, + f"ablations/classicial_learning_ablation_{ws}/classicial_learning_ablation_test_results.pkl", ) print(results_epoch) Path(p).parent.mkdir(parents=True, exist_ok=True) @@ -156,7 +158,7 @@ try: os.remove(p) except OSError as error: - pass + print(error) with open(p, "wb") as handle: pickle.dump(results_epoch, handle, protocol=pickle.HIGHEST_PROTOCOL) diff --git a/scripts/ablations/stepwise_ablation.py b/.deprecated/scripts/ablations/stepwise_ablation.py similarity index 85% rename from scripts/ablations/stepwise_ablation.py rename to .deprecated/scripts/ablations/stepwise_ablation.py index 2b94f184..cb3e486a 100644 --- a/scripts/ablations/stepwise_ablation.py +++ b/.deprecated/scripts/ablations/stepwise_ablation.py @@ -1,16 +1,14 @@ +raise ValueError("TODO: Not tested with new configuration!") import os import torch from pathlib import Path -import yaml import time import pickle import copy import argparse import shutil -from wild_visual_navigation import WVN_ROOT_DIR -from wild_visual_navigation.learning.utils import load_yaml, load_env from wild_visual_navigation.cfg import ExperimentParams -from wild_visual_navigation.learning.general import training_routine +from wild_visual_navigation.general import training_routine if __name__ == "__main__": """Test how much time and data it takes for a model to convergee on a scene. @@ -18,7 +16,6 @@ After training we run for all model checkpoints the test routine. """ exp = ExperimentParams() - env = load_env() parser = argparse.ArgumentParser() parser.add_argument("--output_key", type=str, default="learning_curve", help="Name of the run.") @@ -29,7 +26,10 @@ parser.add_argument("--test_all_datasets", dest="test_all_datasets", action="store_true", help="") parser.set_defaults(test_all_datasets=False) parser.add_argument( - "--scenes", default="forest,hilly,grassland", type=str, help="List of scenes seperated by comma without spaces." + "--scenes", + default="forest,hilly,grassland", + type=str, + help="List of scenes seperated by comma without spaces.", ) parser.add_argument("--store_model_every_n_steps", type=int, default=100) parser.add_argument("--max_steps", type=int, default=1000) @@ -65,8 +65,8 @@ exp.visu.val = 0 exp.visu.test = 0 exp.verify_params() - ws = os.environ["ENV_WORKSTATION_NAME"] - exp.general.model_path = os.path.join(env["base"], f"ablations/{output_key}_{ws}") + ws = os.environ.get("ENV_WORKSTATION_NAME", "default") + exp.general.model_path = os.path.join(exp.env.results, f"ablations/{output_key}_{ws}") # If check_val_every_n_epoch in the current setting the test dataloader is used for validation. # All results during validation are stored and returned by the training routine. @@ -79,7 +79,9 @@ Path(exp.general.model_path).mkdir(parents=True, exist_ok=True) percent = range( - data_start_percentage, data_stop_percentage + data_percentage_increment, data_percentage_increment + data_start_percentage, + data_stop_percentage + data_percentage_increment, + data_percentage_increment, ) with open(os.path.join(exp.general.model_path, "experiment_params.pkl"), "wb") as handle: @@ -106,7 +108,7 @@ try: os.remove(p) except OSError as error: - pass + print(error) with open(p, "wb") as handle: pickle.dump(results_epoch, handle, protocol=pickle.HIGHEST_PROTOCOL) @@ -118,11 +120,18 @@ p_inter = os.path.join(exp.general.model_path, f"{output_key}_steps.pkl") total_paths = [str(s) for s in Path(exp.general.model_path).rglob("*.pt")] print("total_paths", len(total_paths)) - import time st = time.time() for j, p in enumerate(Path(exp.general.model_path).rglob("*.pt")): - print("XXXXXXXXXXXXXXXXXXXXX PROGRESS ", j, " of ", len(total_paths), " in ", time.time() - st, " seconds.") + print( + "XXXXXXXXXXXXXXXXXXXXX PROGRESS ", + j, + " of ", + len(total_paths), + " in ", + time.time() - st, + " seconds.", + ) res = str(p).split("/")[-1].split("_") scene, percentage, run, steps = res[-4], res[-3], res[-2], res[-1] @@ -132,7 +141,14 @@ res, _ = training_routine(exp, seed=run) results_step.append( - {"scene": scene, "percentage": percentage, "run": run, "steps": steps, "results": res, "model_path": str(p)} + { + "scene": scene, + "percentage": percentage, + "run": run, + "steps": steps, + "results": res, + "model_path": str(p), + } ) torch.cuda.empty_cache() @@ -141,7 +157,7 @@ try: os.remove(p_inter.replace(".pkl", f"_inter_{j}.pkl")) except OSError as error: - pass + print(error) with open(p_inter.replace(".pkl", f"_inter_{j}.pkl"), "wb") as handle: pickle.dump(results_step, handle, protocol=pickle.HIGHEST_PROTOCOL) @@ -151,7 +167,7 @@ try: os.remove(p) except OSError as error: - pass + print(error) with open(p, "wb") as handle: pickle.dump(results_step, handle, protocol=pickle.HIGHEST_PROTOCOL) diff --git a/scripts/ablations/training_ablation.py b/.deprecated/scripts/ablations/training_ablation.py similarity index 84% rename from scripts/ablations/training_ablation.py rename to .deprecated/scripts/ablations/training_ablation.py index d554e73c..b12b548d 100644 --- a/scripts/ablations/training_ablation.py +++ b/.deprecated/scripts/ablations/training_ablation.py @@ -1,21 +1,18 @@ +raise ValueError("TODO: Not tested with new configuration!") import os import torch from pathlib import Path -import yaml -import time import pickle import copy from wild_visual_navigation import WVN_ROOT_DIR -from wild_visual_navigation.learning.utils import load_yaml +from wild_visual_navigation.utils import load_yaml from wild_visual_navigation.cfg import ExperimentParams -from wild_visual_navigation.learning.general import training_routine +from wild_visual_navigation.general import training_routine from wild_visual_navigation.utils import override_params import argparse -import logging import shutil from dataclasses import asdict -import copy if __name__ == "__main__": """Test how much time and data it takes for a model to convergee on a scene. @@ -29,16 +26,30 @@ """ parser = argparse.ArgumentParser() - parser.add_argument("--ablation_type", type=str, default="network", help="Folder containing the ablation configs.") + parser.add_argument( + "--ablation_type", + type=str, + default="network", + help="Folder containing the ablation configs.", + ) parser.add_argument("--number_training_runs", type=int, default=1, help="Number of run per config.") parser.add_argument( - "--test_all_datasets", dest="test_all_datasets", action="store_true", help="Test on all datasets." + "--test_all_datasets", + dest="test_all_datasets", + action="store_true", + help="Test on all datasets.", ) parser.add_argument( - "--store_final_model", dest="store_final_model", action="store_true", help="store_final_model on all datasets." + "--store_final_model", + dest="store_final_model", + action="store_true", + help="store_final_model on all datasets.", ) parser.add_argument( - "--scenes", default="forest,hilly,grassland", type=str, help="List of scenes seperated by comma without spaces." + "--scenes", + default="forest,hilly,grassland", + type=str, + help="List of scenes seperated by comma without spaces.", ) parser.add_argument("--special_key", type=str, default="", help="Test on all datasets.") parser.set_defaults(test_all_datasets=False) @@ -80,7 +91,7 @@ def get_exp(args, model_path, p, scene, stored_params): number_training_runs = args.number_training_runs folder = args.ablation_type special_key = args.special_key - ws = os.environ["ENV_WORKSTATION_NAME"] + ws = os.environ.get("ENV_WORKSTATION_NAME", "default") model_path = os.path.join(WVN_ROOT_DIR, f"results/ablations/{folder}_ablation_{ws}") shutil.rmtree(model_path, ignore_errors=True) Path(model_path).mkdir(parents=True, exist_ok=True) @@ -122,7 +133,7 @@ def get_exp(args, model_path, p, scene, stored_params): try: os.remove(p) except OSError as error: - pass + print(error) with open(p, "wb") as handle: pickle.dump(results_epoch, handle, protocol=pickle.HIGHEST_PROTOCOL) diff --git a/scripts/dataset_generation/convert_test_images_for_labeling.py b/.deprecated/scripts/dataset_generation/convert_test_images_for_labeling.py similarity index 69% rename from scripts/dataset_generation/convert_test_images_for_labeling.py rename to .deprecated/scripts/dataset_generation/convert_test_images_for_labeling.py index d87ac0d3..2c40f01f 100644 --- a/scripts/dataset_generation/convert_test_images_for_labeling.py +++ b/.deprecated/scripts/dataset_generation/convert_test_images_for_labeling.py @@ -1,5 +1,5 @@ import numpy as np -from wild_visual_navigation.utils import perguia_dataset, ROOT_DIR +from wild_visual_navigation.utils import ROOT_DIR import os @@ -24,4 +24,11 @@ res = torch.load(p) img = Image.fromarray(np.uint8(res.permute(1, 2, 0).cpu().numpy() * 255)) - img.save(os.path.join(ROOT_DIR, "wvn_output/labeling", env, l.split("/")[-1].replace(".pt", ".png"))) + img.save( + os.path.join( + ROOT_DIR, + "wvn_output/labeling", + env, + l.split("/")[-1].replace(".pt", ".png"), + ) + ) diff --git a/scripts/dataset_generation/create_gnn_dataset.py b/.deprecated/scripts/dataset_generation/create_gnn_dataset.py similarity index 98% rename from scripts/dataset_generation/create_gnn_dataset.py rename to .deprecated/scripts/dataset_generation/create_gnn_dataset.py index 5b51f10d..e4d5b7aa 100644 --- a/scripts/dataset_generation/create_gnn_dataset.py +++ b/.deprecated/scripts/dataset_generation/create_gnn_dataset.py @@ -7,8 +7,8 @@ import argparse import cv2 - -from torch_geometric.data import Data +# TODO +# from torch_geometric.data import Data if __name__ == "__main__": """Converts a folder with images to a torch_geometric dataformat. diff --git a/scripts/dataset_generation/create_train_val_test_lists.py b/.deprecated/scripts/dataset_generation/create_train_val_test_lists.py similarity index 99% rename from scripts/dataset_generation/create_train_val_test_lists.py rename to .deprecated/scripts/dataset_generation/create_train_val_test_lists.py index cce39ea6..fc93b324 100644 --- a/scripts/dataset_generation/create_train_val_test_lists.py +++ b/.deprecated/scripts/dataset_generation/create_train_val_test_lists.py @@ -17,7 +17,6 @@ scenes[d["env"]] = [] for d in perguia_dataset: - p = [ str(s).replace(ROOT_DIR + "/", "") for s in Path(ROOT_DIR, "wvn_output", d["name"].replace("mission_data/", ""), "image").rglob("*.pt") diff --git a/scripts/dataset_generation/download_bitmaps_from_segments_ai.py b/.deprecated/scripts/dataset_generation/download_bitmaps_from_segments_ai.py similarity index 100% rename from scripts/dataset_generation/download_bitmaps_from_segments_ai.py rename to .deprecated/scripts/dataset_generation/download_bitmaps_from_segments_ai.py diff --git a/scripts/dataset_generation/extract_all.py b/.deprecated/scripts/dataset_generation/extract_all.py similarity index 100% rename from scripts/dataset_generation/extract_all.py rename to .deprecated/scripts/dataset_generation/extract_all.py diff --git a/.deprecated/scripts/dataset_generation/extract_binary_images_and_labels.py b/.deprecated/scripts/dataset_generation/extract_binary_images_and_labels.py new file mode 100644 index 00000000..a7f77d14 --- /dev/null +++ b/.deprecated/scripts/dataset_generation/extract_binary_images_and_labels.py @@ -0,0 +1,220 @@ +import sys +import os +import time +from tqdm import tqdm +import subprocess +import yaml + +from tf_bag import BagTfTransformer +import rospy +import rosparam +from sensor_msgs.msg import CameraInfo +import rosbag + +from postprocessing_tools_ros.merging import merge_bags_single + +# from py_image_proc_cuda import ImageProcCuda +# from cv_bridge import CvBridge + +from wild_visual_navigation import WVN_ROOT_DIR +from wild_visual_navigation.utils import perugia_dataset, ROOT_DIR + +sys.path.append(f"{WVN_ROOT_DIR}/wild_visual_navigation_ros/scripts") +from wild_visual_navigation_node import WvnRosInterface + +sys.path.append(f"{WVN_ROOT_DIR}/wild_visual_navigation_anymal/scripts") +from anymal_msg_converter_node import anymal_msg_callback + +# We need to do the following +# 1. Debayering cam4 -> send via ros and wait for result ("correct params") +# 2. anymal_state_topic -> /wild_visual_navigation_node/robot_state +# 3. Feed into wild_visual_navigation_node ("correct params") +# # Iterate rosbags + + +def get_bag_info(rosbag_path: str) -> dict: + # This queries rosbag info using subprocess and get the YAML output to parse the topics + info_dict = yaml.safe_load( + subprocess.Popen(["rosbag", "info", "--yaml", rosbag_path], stdout=subprocess.PIPE).communicate()[0] + ) + return info_dict + + +class BagTfTransformerWrapper: + def __init__(self, bag): + self.tf_listener = BagTfTransformer(bag) + + def waitForTransform(self, parent_frame, child_frame, time, duration): + return self.tf_listener.waitForTransform(parent_frame, child_frame, time) + + def lookupTransform(self, parent_frame, child_frame, time): + try: + return self.tf_listener.lookupTransform(parent_frame, child_frame, time) + except Exception: + return (None, None) + + +def do(n, dry_run): + d = perugia_dataset[n] + + if bool(dry_run): + print(d) + return + + s = os.path.join(ROOT_DIR, d["name"]) + + valid_topics = ["/state_estimator/anymal_state", "/wide_angle_camera_front/img_out"] + + rosbags = [ + "/home/rschmid/RosBags/6/images.bag", + "/home/rschmid/RosBags/6/2023-03-02-11-13-08_anymal-d020-lpc_mission_0.bag", + "/home/rschmid/RosBags/6/2023-03-02-11-13-08_anymal-d020-lpc_mission_1.bag", + ] + + output_bag_wvn = s + "_wvn.bag" + output_bag_tf = s + "_tf.bag" + + if not os.path.exists(output_bag_tf): + total_included_count, total_skipped_count = merge_bags_single( + input_bag=rosbags, + output_bag=output_bag_tf, + topics="/tf /tf_static", + verbose=True, + ) + if not os.path.exists(output_bag_wvn): + total_included_count, total_skipped_count = merge_bags_single( + input_bag=rosbags, + output_bag=output_bag_wvn, + topics=" ".join(valid_topics), + verbose=True, + ) + + # Setup WVN node + rospy.init_node("wild_visual_navigation_node") + + mission = s.split("/")[-1] + + running_store_folder = f"/home/rschmid/RosBags/output/{mission}" + + if os.path.exists(running_store_folder): + print("Folder already exists, but proceeding!") + # return + + rosparam.set_param("wild_visual_navigation_node/mode", "extract_labels") + rosparam.set_param("wild_visual_navigation_node/running_store_folder", running_store_folder) + + # for supervision callback + state_msg_valid = False + # desired_twist_msg_valid = False + + wvn_ros_interface = WvnRosInterface() + print("-" * 80) + + print("start loading tf") + tf_listener = BagTfTransformerWrapper(output_bag_tf) + wvn_ros_interface.setup_rosbag_replay(tf_listener) + print("done loading tf") + + # Höngg new + info_msg = CameraInfo() + info_msg.height = 1080 + info_msg.width = 1440 + info_msg.distortion_model = "equidistant" + info_msg.D = [ + 0.4316922809468283, + 0.09279900476637248, + -0.4010909691803734, + 0.4756163338479413, + ] + info_msg.K = [ + 575.6050407221768, + 0.0, + 745.7312198525915, + 0.0, + 578.564849365178, + 519.5207040671075, + 0.0, + 0.0, + 1.0, + ] + info_msg.P = [ + 575.6050407221768, + 0.0, + 745.7312198525915, + 0.0, + 0.0, + 578.564849365178, + 519.5207040671075, + 0.0, + 0.0, + 0.0, + 1.0, + 0.0, + ] + info_msg.R = [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0] + + rosbag_info_dict = get_bag_info(output_bag_wvn) + total_msgs = sum([x["messages"] for x in rosbag_info_dict["topics"] if x["topic"] in valid_topics]) + total_time_img = 0 + total_time_state = 0 + n = 0 + + with rosbag.Bag(output_bag_wvn, "r") as bag: + start_time = rospy.Time.from_sec(bag.get_start_time() + d["start"]) + end_time = rospy.Time.from_sec(bag.get_start_time() + d["stop"]) + with tqdm( + total=total_msgs, + desc="Total", + colour="green", + position=1, + bar_format="{desc:<13}{percentage:3.0f}%|{bar:20}{r_bar}", + ) as pbar: + for topic, msg, ts in bag.read_messages(topics=None, start_time=start_time, end_time=end_time): + if rospy.is_shutdown(): + return + pbar.update(1) + st = time.time() + if topic == "/state_estimator/anymal_state": + state_msg = anymal_msg_callback(msg, return_msg=True) + state_msg_valid = True + + elif topic == "/wide_angle_camera_front/img_out": + image_msg = msg + # print("Received /wide_angle_camera_front/img_out") + + info_msg.header = msg.header + camera_options = {} + camera_options["name"] = "wide_angle_camera_front" + camera_options["use_for_training"] = True + + info_msg.header = msg.header + try: + wvn_ros_interface.image_callback(image_msg, info_msg, camera_options) + except Exception as e: + print("Bad image_callback", e) + + total_time_img += time.time() - st + # print(f"image time: {total_time_img} , state time: {total_time_state}") + # print("add image") + if state_msg_valid: + try: + wvn_ros_interface.robot_state_callback(state_msg, None) + except Exception as e: + print("Bad robot_state callback ", e) + + state_msg_valid = False + total_time_state += time.time() - st + + print("Finished with converting the dataset") + rospy.signal_shutdown("stop the node") + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--n", type=int, default=0, help="Store data") + parser.add_argument("--dry_run", type=int, default=0, help="Store data") + args = parser.parse_args() + + do(args.n, args.dry_run) diff --git a/scripts/dataset_generation/extract_features_for_dataset.py b/.deprecated/scripts/dataset_generation/extract_features_for_dataset.py similarity index 94% rename from scripts/dataset_generation/extract_features_for_dataset.py rename to .deprecated/scripts/dataset_generation/extract_features_for_dataset.py index b5c10fa5..617919ea 100644 --- a/scripts/dataset_generation/extract_features_for_dataset.py +++ b/.deprecated/scripts/dataset_generation/extract_features_for_dataset.py @@ -3,7 +3,9 @@ import os from pathlib import Path import torch -from torch_geometric.data import Data + +# TODO +# from torch_geometric.data import Data from wild_visual_navigation.utils import KLTTrackerOpenCV import numpy as np from tqdm import tqdm @@ -62,7 +64,13 @@ # ) fes["slic200_dino112_8"] = FeatureExtractor( - device, "slic", "dino", 112, model_type="vit_small", patch_size=8, slic_num_components=200 + device, + "slic", + "dino", + 112, + model_type="vit_small", + patch_size=8, + slic_num_components=200, ) fes["grid32_dino112_8"] = FeatureExtractor( device, "slic", "dino", 112, model_type="vit_small", patch_size=8, cell_size=32 @@ -87,7 +95,13 @@ form = "{desc:<13}{percentage:3.0f}%|{bar:20}{r_bar}" - with tqdm(total=len(needed_images), desc="Total", colour="green", position=1, bar_format=form) as pbar: + with tqdm( + total=len(needed_images), + desc="Total", + colour="green", + position=1, + bar_format=form, + ) as pbar: for m_nr, mission in enumerate(mission_folders): assert os.path.isdir( os.path.join(mission, "image") @@ -151,7 +165,6 @@ key = image.split("/")[-1][:-3] # remove .pt img = torch.load(image) for name, feature_extractor in fes.items(): - edges, feat, seg, center = feature_extractor.extract(img.clone()[None], return_centers=True) filename = os.path.join(mission, "features", name, "seg", key + ".pt") @@ -209,7 +222,8 @@ from wild_visual_navigation.visu import LearningVisualizer visu = LearningVisualizer( - p_visu=os.path.join(WVN_ROOT_DIR, "results/extract_features"), store=True + p_visu=os.path.join(WVN_ROOT_DIR, "results/extract_features"), + store=True, ) visu.plot_sparse_optical_flow( pre_pos, @@ -261,7 +275,12 @@ feature_edges_buffer[name] = edges feature_buffer[name] = feat else: - data = Data(x=feat, edge_index=edges, y=supervision_signal, y_valid=supervision_signal_valid) + data = Data( + x=feat, + edge_index=edges, + y=supervision_signal, + y_valid=supervision_signal_valid, + ) filename = os.path.join(mission, "features", name, "graph", key + ".pt") if store and store_idx: diff --git a/scripts/dataset_generation/extract_images_and_labels.py b/.deprecated/scripts/dataset_generation/extract_images_and_labels.py similarity index 87% rename from scripts/dataset_generation/extract_images_and_labels.py rename to .deprecated/scripts/dataset_generation/extract_images_and_labels.py index 1c2702b2..6090c0c6 100644 --- a/scripts/dataset_generation/extract_images_and_labels.py +++ b/.deprecated/scripts/dataset_generation/extract_images_and_labels.py @@ -12,7 +12,7 @@ from sensor_msgs.msg import Image, CameraInfo import rosbag -from postprocessing_tools_ros.merging import merge_bags_single, merge_bags_all +from postprocessing_tools_ros.merging import merge_bags_single # from py_image_proc_cuda import ImageProcCuda # from cv_bridge import CvBridge @@ -51,7 +51,7 @@ def waitForTransform(self, parent_frame, child_frame, time, duration): def lookupTransform(self, parent_frame, child_frame, time): try: return self.tf_listener.lookupTransform(parent_frame, child_frame, time) - except: + except Exception: return (None, None) @@ -64,7 +64,11 @@ def do(n, dry_run): s = os.path.join(ROOT_DIR, d["name"]) - valid_topics = ["/state_estimator/anymal_state", "/alphasense_driver_ros/cam4", "/log/state/desiredRobotTwist"] + valid_topics = [ + "/state_estimator/anymal_state", + "/alphasense_driver_ros/cam4", + "/log/state/desiredRobotTwist", + ] # Merge rosbags if necessary rosbags = [ @@ -76,7 +80,7 @@ def do(n, dry_run): ] try: rosbags.sort(key=lambda x: int(x.split("/")[-1][-5])) - except: + except Exception: pass output_bag_wvn = s + "_wvn.bag" @@ -86,11 +90,17 @@ def do(n, dry_run): # jetson locomotion robot if not os.path.exists(output_bag_tf): total_included_count, total_skipped_count = merge_bags_single( - input_bag=tf_bags, output_bag=output_bag_tf, topics="/tf /tf_static", verbose=True + input_bag=tf_bags, + output_bag=output_bag_tf, + topics="/tf /tf_static", + verbose=True, ) if not os.path.exists(output_bag_wvn): total_included_count, total_skipped_count = merge_bags_single( - input_bag=rosbags, output_bag=output_bag_wvn, topics=" ".join(valid_topics), verbose=True + input_bag=rosbags, + output_bag=output_bag_wvn, + topics=" ".join(valid_topics), + verbose=True, ) # Setup WVN node @@ -109,7 +119,7 @@ def do(n, dry_run): rosparam.set_param("wild_visual_navigation_node/mode", "extract_labels") rosparam.set_param("wild_visual_navigation_node/extraction_store_folder", extraction_store_folder) - # for proprioceptive callback + # for supervision callback state_msg_valid = False desired_twist_msg_valid = False @@ -126,7 +136,17 @@ def do(n, dry_run): info_msg.height = 540 info_msg.width = 720 info_msg.distortion_model = "plumb_bob" - info_msg.K = [347.548139773951, 0.0, 342.454373227748, 0.0, 347.434712422309, 271.368057185649, 0.0, 0.0, 1.0] + info_msg.K = [ + 347.548139773951, + 0.0, + 342.454373227748, + 0.0, + 347.434712422309, + 271.368057185649, + 0.0, + 0.0, + 1.0, + ] info_msg.P = [ 347.548139773951, 0.0, @@ -159,7 +179,7 @@ def do(n, dry_run): position=1, bar_format="{desc:<13}{percentage:3.0f}%|{bar:20}{r_bar}", ) as pbar: - for (topic, msg, ts) in bag.read_messages(topics=None, start_time=start_time, end_time=end_time): + for topic, msg, ts in bag.read_messages(topics=None, start_time=start_time, end_time=end_time): pbar.update(1) st = time.time() if topic == "/state_estimator/anymal_state": @@ -177,10 +197,12 @@ def do(n, dry_run): # Change this for service call try: image_msg = rospy.wait_for_message( - "/alphasense_driver_ros/cam4/debayered", Image, timeout=0.01 + "/alphasense_driver_ros/cam4/debayered", + Image, + timeout=0.01, ) suc = True - except Exception as e: + except Exception: suc = False pass if suc: diff --git a/scripts/dataset_generation/validate_extract_images_and_labels.py b/.deprecated/scripts/dataset_generation/validate_extract_images_and_labels.py similarity index 90% rename from scripts/dataset_generation/validate_extract_images_and_labels.py rename to .deprecated/scripts/dataset_generation/validate_extract_images_and_labels.py index 8eb1f358..63f922d7 100644 --- a/scripts/dataset_generation/validate_extract_images_and_labels.py +++ b/.deprecated/scripts/dataset_generation/validate_extract_images_and_labels.py @@ -46,7 +46,14 @@ def get_bag_info(rosbag_path: str) -> dict: p = str(os.path.join(ouput_dir, d["name"].replace("mission_data/", ""), "image", dele)) if os.path.exists(p): os.remove(p) - p = str(os.path.join(ouput_dir, d["name"].replace("mission_data/", ""), "supervision_mask", dele)) + p = str( + os.path.join( + ouput_dir, + d["name"].replace("mission_data/", ""), + "supervision_mask", + dele, + ) + ) if os.path.exists(p): os.remove(p) diff --git a/scripts/dataset_generation/visu.py b/.deprecated/scripts/dataset_generation/visu.py similarity index 100% rename from scripts/dataset_generation/visu.py rename to .deprecated/scripts/dataset_generation/visu.py diff --git a/.deprecated/scripts/train.py b/.deprecated/scripts/train.py new file mode 100644 index 00000000..32d7b16a --- /dev/null +++ b/.deprecated/scripts/train.py @@ -0,0 +1,8 @@ +from wild_visual_navigation.cfg import ExperimentParams +from wild_visual_navigation.general import training_routine + +from omegaconf import OmegaConf + +if __name__ == "__main__": + params = OmegaConf.structured(ExperimentParams) + training_routine(params) diff --git a/.deprecated/scripts/train_optuna.py b/.deprecated/scripts/train_optuna.py new file mode 100644 index 00000000..acd0fb62 --- /dev/null +++ b/.deprecated/scripts/train_optuna.py @@ -0,0 +1,75 @@ +import optuna +from argparse import ArgumentParser +import copy +import os +import neptune.integrations.optuna as optuna_utils +import torch + +from wild_visual_navigation.cfg import ExperimentParams +from wild_visual_navigation.general import training_routine +from wild_visual_navigation.utils import get_neptune_run +from omegaconf import read_write, OmegaConf + + +def objective(trial, params: ExperimentParams): + exp = copy.deepcopy(params) + + with read_write(exp): + # Parameter to sweep + exp.optimizer.lr = trial.suggest_float("lr", 0.0001, 0.01, log=True) + exp.loss.w_trav = trial.suggest_float("w_trav", 0.0, 1.0) + exp.loss.w_temp = trial.suggest_float("w_temp", 0.0, 1.0) + exp.loss.w_reco = trial.suggest_float("w_reco", 0.0, 1.0) + exp.loss.anomaly_balanced = trial.suggest_categorical("anomaly_balanced", [True]) + + res, _ = training_routine(exp) + + torch.cuda.empty_cache() + return list(res.values())[0]["test_auroc_gt_image"] + + +if __name__ == "__main__": + parser = ArgumentParser() + + parser.add_argument("--name", type=str, default="sweep_loss_function", help="Name of sweep") + parser.add_argument("--n_trials", type=int, default=100, help="Number Trials") + + args = parser.parse_args() + params = OmegaConf.structured(ExperimentParams) + + with read_write(): + params.general.name = os.path.join(args.name, params.general.name) + + run = get_neptune_run( + neptune_project_name=params.logger.neptune_project_name, + tags=["optuna", args.name], + ) + + neptune_callback = optuna_utils.NeptuneCallback( + run, + plots_update_freq=2, + log_plot_slice=True, + log_plot_contour=True, + ) + + params.logger.name = "skip" + params.trainer.enable_checkpointing = False + params.cb_checkpoint.active = False + params.visu.train = 0 + params.visu.val = 0 + params.visu.test = 0 + params.trainer.check_val_every_n_epoch = 100000 + params.general.store_model_every_n_steps = None + params.ablation_data_module.training_in_memory = True + params.trainer.max_steps = 1000 + params.trainer.max_epochs = None + params.general.log_to_disk = False + params.trainer.progress_bar_refresh_rate = 0 + params.trainer.weights_summary = None + params.trainer.enable_progress_bar = False + + binded_objective = lambda trial: objective(trial=trial, experiment=args.experiment) + study = optuna.create_study(direction=optuna.study.StudyDirection.MAXIMIZE) + study.optimize(binded_objective, n_trials=args.n_trials, callbacks=[neptune_callback]) + + trial = study.best_trial diff --git a/wild_visual_navigation/utils/dataset_info.py b/.deprecated/utils/dataset_info.py similarity index 95% rename from wild_visual_navigation/utils/dataset_info.py rename to .deprecated/utils/dataset_info.py index 2d680f85..4ff388da 100644 --- a/wild_visual_navigation/utils/dataset_info.py +++ b/.deprecated/utils/dataset_info.py @@ -133,7 +133,7 @@ def dataset_play(env="forest", mode="train", nr=0, rate=1.0, ignore_tf=False, ot stop = d["stop"] duration = stop - start bags = os.path.join(ROOT_DIR, d["name"]) + "/*.bag" - comment = d["comment"] + # comment = d["comment"] if bool(ignore_tf): bags += " /tf:=/tf_trash" cmd = f"rosbag play -s {start} -u {duration} -r {rate} {other_args} {bags}" @@ -153,5 +153,10 @@ def dataset_play(env="forest", mode="train", nr=0, rate=1.0, ignore_tf=False, ot args = parser.parse_args() dataset_play( - env=args.env, mode=args.mode, nr=args.nr, rate=args.rate, ignore_tf=args.ignore_tf, other_args=args.other_args + env=args.env, + mode=args.mode, + nr=args.nr, + rate=args.rate, + ignore_tf=args.ignore_tf, + other_args=args.other_args, ) diff --git a/wild_visual_navigation/utils/klt_tracker.py b/.deprecated/utils/klt_tracker.py similarity index 87% rename from wild_visual_navigation/utils/klt_tracker.py rename to .deprecated/utils/klt_tracker.py index 0acef608..4a02f330 100644 --- a/wild_visual_navigation/utils/klt_tracker.py +++ b/.deprecated/utils/klt_tracker.py @@ -1,6 +1,5 @@ import torch import kornia -from wild_visual_navigation.utils import Timer def interp2_torch_batch(v, xq, yq): @@ -48,11 +47,17 @@ class KLTTrackerOpenCV(torch.nn.Module): def __init__(self, device="cpu", window_size=25, levels=15) -> None: super().__init__() self.lk_params = dict( - winSize=(7, 7), maxLevel=2, criteria=(cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.03) + winSize=(7, 7), + maxLevel=2, + criteria=(cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.03), ) def forward( - self, t_startXs: torch.Tensor, t_startYs: torch.Tensor, img_prev: torch.Tensor, img_next: torch.Tensor + self, + t_startXs: torch.Tensor, + t_startYs: torch.Tensor, + img_prev: torch.Tensor, + img_next: torch.Tensor, ) -> torch.Tensor: """_summary_ @@ -82,11 +87,16 @@ def __init__(self, device="cpu", window_size=25, levels=15) -> None: self.window_size = torch.tensor(window_size, device=device) self.levels = levels self.t_mesh_x, self.t_mesh_y = torch.meshgrid( - torch.arange(self.window_size, device=self.device), torch.arange(self.window_size, device=self.device) + torch.arange(self.window_size, device=self.device), + torch.arange(self.window_size, device=self.device), ) def forward( - self, t_startXs: torch.Tensor, t_startYs: torch.Tensor, img_prev: torch.Tensor, img_next: torch.Tensor + self, + t_startXs: torch.Tensor, + t_startYs: torch.Tensor, + img_prev: torch.Tensor, + img_next: torch.Tensor, ) -> torch.Tensor: """_summary_ @@ -113,7 +123,12 @@ def forward( t_newYs = torch.full(t_startYs_flat.shape, -1, dtype=torch.float32, device=self.device) t_newXs, t_newYs = self.estimateFeatureTranslationBatch( - t_startXs_flat, t_startYs_flat, t_Ix, t_Iy, (t_img_prev_gray * 255)[0], (t_img_next_gray * 255)[0] + t_startXs_flat, + t_startYs_flat, + t_Ix, + t_Iy, + (t_img_prev_gray * 255)[0], + (t_img_next_gray * 255)[0], ) t_newXs = torch.reshape(t_newXs, t_startXs.shape) @@ -149,7 +164,9 @@ def estimateFeatureTranslationBatch(self, t_startX, t_startY, t_Ix, t_Iy, t_img1 t_coor = torch.stack((t_mesh_x_flat, t_mesh_y_flat), dim=1) t_I2_value = interp2_torch_batch( - t_img2_gray.clone(), t_coor[:, 0, :].contiguous(), t_coor[:, 1, :].contiguous() + t_img2_gray.clone(), + t_coor[:, 0, :].contiguous(), + t_coor[:, 1, :].contiguous(), ).contiguous() t_Ip = (t_I2_value - t_I1_value)[:, :, None] @@ -162,10 +179,6 @@ def estimateFeatureTranslationBatch(self, t_startX, t_startY, t_Ix, t_Iy, t_img1 if __name__ == "__main__": - import cv2 as cv - from PIL import Image, ImageDraw - import numpy as np - # TODO write a test for it pre = torch.load("/home/jonfrey/git/wild_visual_navigation/previous.pt") cur = torch.load("/home/jonfrey/git/wild_visual_navigation/current.pt") diff --git a/wild_visual_navigation/learning/utils/metric_logger.py b/.deprecated/utils/metric_logger.py similarity index 89% rename from wild_visual_navigation/learning/utils/metric_logger.py rename to .deprecated/utils/metric_logger.py index c82439d8..5892fa86 100644 --- a/wild_visual_navigation/learning/utils/metric_logger.py +++ b/.deprecated/utils/metric_logger.py @@ -83,19 +83,37 @@ def log_image(self, graph, res, mode, threshold=0.5): self.auroc_self_image[mode](preds=bp, target=bpro.type(torch.long)) self.log_handel( - f"{mode}_auroc_gt_image", self.auroc_gt_image[mode], on_epoch=True, on_step=False, batch_size=BS + f"{mode}_auroc_gt_image", + self.auroc_gt_image[mode], + on_epoch=True, + on_step=False, + batch_size=BS, ) self.log_handel( - f"{mode}_auroc_self_image", self.auroc_self_image[mode], on_epoch=True, on_step=False, batch_size=BS + f"{mode}_auroc_self_image", + self.auroc_self_image[mode], + on_epoch=True, + on_step=False, + batch_size=BS, ) # GT self.acc_gt_image[mode](preds=bp > threshold, target=graph.label.type(torch.long)) self.acc_self_image[mode](preds=bp > threshold, target=bpro.type(torch.long)) - self.log_handel(f"{mode}_acc_gt_image", self.acc_gt_image[mode], on_epoch=True, on_step=False, batch_size=BS) self.log_handel( - f"{mode}_acc_self_image", self.acc_self_image[mode], on_epoch=True, on_step=False, batch_size=BS + f"{mode}_acc_gt_image", + self.acc_gt_image[mode], + on_epoch=True, + on_step=False, + batch_size=BS, + ) + self.log_handel( + f"{mode}_acc_self_image", + self.acc_self_image[mode], + on_epoch=True, + on_step=False, + batch_size=BS, ) @torch.no_grad() @@ -110,7 +128,7 @@ def log_confidence(self, graph, res, confidence, mode): bpro = graph.label.clone().type(torch.float32).flatten() buffer_conf = graph.label.clone().type(torch.float32).flatten() - bp = res[seg_pixel_index, 0].reshape(BS, H, W) + bp = res[seg_pixel_index, 0].reshape(BS, H, W) # noqa: F841 bpro = graph.y[seg_pixel_index].reshape(BS, H, W) buffer_conf = confidence[seg_pixel_index].reshape(BS, H, W) @@ -134,7 +152,11 @@ def log_confidence(self, graph, res, confidence, mode): self.acc_anomaly_gt_image[mode](preds=buffer_conf, target=graph.label.type(torch.long)) self.acc_anomaly_self_image[mode](preds=buffer_conf, target=bpro.type(torch.long)) self.log_handel( - f"{mode}_acc_anomaly_gt_image", self.acc_anomaly_gt_image[mode], on_epoch=True, on_step=False, batch_size=BS + f"{mode}_acc_anomaly_gt_image", + self.acc_anomaly_gt_image[mode], + on_epoch=True, + on_step=False, + batch_size=BS, ) self.log_handel( f"{mode}_acc_anomaly_self_image", @@ -154,7 +176,7 @@ def get_epoch_results(self, mode): roc_gt_image = [None, None, None] auroc_gt_image = self.auroc_gt_image[mode].compute().item() - # generate proprioceptive label + # generate supervision label if self.log_roc_image: roc_self_image = [a.cpu().numpy() for a in self.roc_self_image[mode].compute()] else: diff --git a/.docker/Dockerfile b/.docker/Dockerfile deleted file mode 100644 index ddeebd91..00000000 --- a/.docker/Dockerfile +++ /dev/null @@ -1,21 +0,0 @@ -FROM pytorchlightning/pytorch_lightning:base-conda-py3.8-torch1.10 - -# nvidia-container-runtime -ENV NVIDIA_VISIBLE_DEVICES \ - ${NVIDIA_VISIBLE_DEVICES:-all} -ENV NVIDIA_DRIVER_CAPABILITIES \ - ${NVIDIA_DRIVER_CAPABILITIES:+$NVIDIA_DRIVER_CAPABILITIES,}graphics - -LABEL maintainer="Jonas Frey" mail="jonfrey@ethz.ch" - -SHELL ["/bin/bash", "--login", "-c"] - -ARG DEBIAN_FRONTEND=noninteractive -ENV DEBIAN_frontend noninteractive - -RUN mkdir /home/git -RUN mkdir /home/results -RUN mkdir /home/code -RUN mkdir /home/dataset -RUN mkdir /home/work -RUN mkdir /home/tmpdir \ No newline at end of file diff --git a/.docker/bsub.sh b/.docker/bsub.sh deleted file mode 100755 index 9dbb166e..00000000 --- a/.docker/bsub.sh +++ /dev/null @@ -1,16 +0,0 @@ -#!/bin/bash - -# Is executed either from your local workstation or on the cluster. -# Syncs the repostiory to the cluster (only on local workstation) and schedules the job on the cluster. -echo "Execute cluster_bsub.sh" -echo "Args: $@" -h=`echo $@` - -if [[ ! $ENV_WORKSTATION_NAME = "euler" ]] -then - rsync -r -v --exclude=.git/* --exclude=results/* --exclude=.neptune/* /home/$USERNAME/git/wild_visual_navigation/ $USERNAME@euler:/cluster/home/$USERNAME/wild_visual_navigation - ssh $USERNAME@euler " source /cluster/home/$USERNAME/.bashrc; bsub -n 24 -R singularity -R \"rusage[mem=2596,ngpus_excl_p=1]\" -W $TIME -o $OUTFILE_NAME -R \"select[gpu_mtotal0>=10000]\" -R \"rusage[scratch=1500]\" -R \"select[gpu_driver>=470]\" /cluster/home/$USERNAME/wild_visual_navigation/.docker/cluster_run.sh $h" -else - TIME=24:00 - bsub -n 24 -R singularity -R "rusage[mem=1596,ngpus_excl_p=1]" -W $TIME -R "select[gpu_mtotal0>=10000]" -R "rusage[scratch=1500]" -R "select[gpu_driver>=470]" /cluster/home/$USERNAME/wild_visual_navigation/.docker/cluster_run.sh $h -fi \ No newline at end of file diff --git a/.docker/build.sh b/.docker/build.sh deleted file mode 100755 index 40f0b3e0..00000000 --- a/.docker/build.sh +++ /dev/null @@ -1,3 +0,0 @@ -ssh_key_name = "id_rsa" - -eval $(ssh-agent) && ssh-add ~/.ssh/id_rsa && export DOCKER_BUILDKIT=1 && docker build --progress=plain --ssh default -f Dockerfile ./ -t wvn \ No newline at end of file diff --git a/.docker/cluster_run.sh b/.docker/cluster_run.sh deleted file mode 100755 index 8d31dd7a..00000000 --- a/.docker/cluster_run.sh +++ /dev/null @@ -1,14 +0,0 @@ -#!/bin/bash - -# Is executed on a node on the cluster, started with the bsub command. - -echo "Execute cluster_run.sh" -echo $@ - -module load gcc/6.3.0 cuda/11.4.2 -tar -xf /cluster/work/rsl/$USERNAME/wvn/containers/wvn.tar -C $TMPDIR - -singularity exec -B $TMPDIR:/home/tmpdir -B $WORK/wvn:/home/work -B $HOME/wvn:/home/git --nv --writable --containall $TMPDIR/wvn.sif /home/wild_visual_navigation/.docker/cluster_run.sh $@ -echo "Execute cluster_run.sh done" -bkill $LSB_JOBID -exit 0 \ No newline at end of file diff --git a/.docker/container_run.sh b/.docker/container_run.sh deleted file mode 100755 index 0dec3285..00000000 --- a/.docker/container_run.sh +++ /dev/null @@ -1,12 +0,0 @@ -#!/bin/bash - -# Is executed inside the singularity container. -# Start the training. - -echo "Execute cluster_run_container.sh" -h=`echo $@` -echo $h -export ENV_WORKSTATION_NAME=euler - -cd /home/git/wvn -exec bash -c "/root/miniconda3/envs/lightning/bin/python3 -u scripts/train.py --headless $h" \ No newline at end of file diff --git a/.flake8 b/.flake8 index 9888251d..b41810cd 100644 --- a/.flake8 +++ b/.flake8 @@ -1,5 +1,5 @@ [flake8] -ignore=E402,E501,W503 +ignore=E402,E501,W503,C901,E731,W605 extend-ignore = E203 max-line-length = 120 max-complexity = 18 diff --git a/.gitignore b/.gitignore index 133a267b..88488fd1 100644 --- a/.gitignore +++ b/.gitignore @@ -7,6 +7,8 @@ assets/stego/cocostuff27_vit_base_5.ckpt .pytest_cache/** .vscode/** notebooks/** +*.code-workspace + # DrawIO **.dtmp **.bkp @@ -143,3 +145,5 @@ dmypy.json # Pyre type checker .pyre/ +assets/virtual_env/* + diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..ff6efa3b --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,24 @@ +# .pre-commit-config.yaml +# From https://sbarnea.com/lint/black/ +--- +repos: + - repo: https://github.com/python/black.git + rev: 22.12.0 + hooks: + - id: black + language_version: python3 + - repo: https://github.com/pycqa/flake8 + rev: 3.7.9 + hooks: + - id: flake8 + additional_dependencies: + - flake8-black>=0.1.1 + language_version: python3 + - repo: local + hooks: + - id: jupyter-nb-clear-output + name: jupyter-nb-clear-output + files: \.ipynb$ + stages: [commit] + language: system + entry: jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 00000000..288c58ea --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022-2024 ETH Zurich, Matias Mattamala, Jonas Frey. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/README.md b/README.md index 015904b6..d6c40739 100644 --- a/README.md +++ b/README.md @@ -2,10 +2,10 @@

Citation • - Setup • + Installation • + Overview • Experiments • - Contributing • - Credits + Development ![Formatting](https://github.com/leggedrobotics/wild_visual_navigation/actions/workflows/formatting.yml/badge.svg)

@@ -15,218 +15,182 @@ Dino ## Citation +``` +@INPROCEEDINGS{frey23fast, + AUTHOR = {Jonas, Frey and Matias, Mattamala and Nived, Chebrolu and Cesar, Cadena and Maurice, Fallon and Marco, Hutter}, + TITLE = {\href{https://arxiv.org/abs/2305.08510}{Fast Traversability Estimation for Wild Visual Navigation}}, + BOOKTITLE = {Proceedings of Robotics: Science and Systems}, + YEAR = {2023}, + ADDRESS = {Daegu, Republic of Korea}, + MONTH = {July}, + DOI = {10.15607/RSS.2023.XIX.054} +} +``` +If you are also building up on the STEGO integration or using the pre-trained models for a comparision please cite: ``` -@INPROCEEDINGS{FreyMattamala23, - AUTHOR = {Jonas Frey and Matias Mattamala and Nived Chebrolu and Cesar Cadena and Maurice Fallon and Marco Hutter}, - TITLE = {{Fast Traversability Estimation for Wild Visual Navigation}}, - BOOKTITLE = {Proceedings of Robotics: Science and Systems}, - YEAR = {2023}, - ADDRESS = {Daegu, Republic of Korea}, - MONTH = {June}, - DOI = {TBD} +@INPROCEEDINGS{mattamala24wild, + AUTHOR = {Mattamala, Matias and Jonas, Frey and Piotr Libera and Chebrolu, Nived and Georg Martius and Cadena, Cesar and Hutter, Marco and Fallon, Maurice}, + TITLE = {{Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision}}, + BOOKTITLE = {under review for Autonomous Robots}, + YEAR = {2024} } ``` + +If you are using the elevation_mapping integration +``` +@INPROCEEDINGS{erni23mem, + AUTHOR={Erni, Gian and Frey, Jonas and Miki, Takahiro and Mattamala, Matias and Hutter, Marco}, + TITLE={\href{https://arxiv.org/abs/2309.16818}{MEM: Multi-Modal Elevation Mapping for Robotics and Learning}}, + BOOKTITLE={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, + YEAR={2023}, + PAGES={11011-11018}, + DOI={10.1109/IROS55552.2023.10342108} +} +``` + Checkout out also our other works. Dino -## Setup +## Installation -1. Clone the repository. +1. Clone the WVN and our STEGO reimplementation. ```shell mkdir ~/git && cd ~/git git clone git@github.com:leggedrobotics/wild_visual_navigation.git +git clone git@github.com:leggedrobotics/self_supervised_segmentation.git ``` -2. Install the conda environment. +2. Install the virtual environment. ```shell -# Make sure to be in the base conda environment cd ~/git/wild_visual_navigation -conda env create -f environment.yaml +# TODO ``` 3. Install the wild_visual_navigation package. ```shell -conda activate wvn cd ~/git pip3 install -e ./wild_visual_navigation ``` -4. Configure global paths. +4. [Optionally] Configure custom paths +Set your custom global paths by defining the ENV_WORKSTATION_NAME and exporting the variable in your `~/.bashrc`. + + ```shell + export ENV_WORKSTATION_NAME=your_workstation_name + ``` +The paths can be specified by modifying `wild_visual_navigation/wild_visual_navigation/cfg/gloabl_params.py`. +Add your desired global paths. +Per default, all results are stored in `wild_visual_navigation/results`. -      All global paths are stored within a single yaml-file (`cfg\env\ge76.yaml`) for each machine. -      The correct yaml-file for a machine is identified based on the environment variable `ENV_WORKSTATION_NAME`. -      You can generate a new configuration environment file in the same directory: -      Example: `cfg/env/your_workstation_name.yaml` -      Content: -```yaml -# If a relative path is given it is relative the the wild_visual_navigation project directory. -base: results/learning -perugia_root: /media/Data/Datasets/2022_Perugia -``` +Dino +## Overview +![Overview](./assets/drawings/software_overview.jpg) +What we provide: +- Learning and Feature Extraction Nodes integrated in ROS1 +- Gazebo Test Simulation Envrionment +- Example ROSbags +- Pre-trained models with minimalistic inference script (can be used as a easy baseline) +- Integration into elevation_mapping_cupy -      We recommend to directly set the environment variable in your `~/.bashrc` by adding the following: - - ```shell - export ENV_WORKSTATION_NAME=your_workstation_name - ``` Dino ## Experiments -### Robot Usage [Online] -Mode to run the pipeline either fully online on the robot or to simply replay rosbags on your system. -- Launch ANYmal Converter: -```shell -rosrun wild_visual_navigation_anymal anymal_msg_converter_node.py -``` +### Inference pre-trained model -- Run WVN Node: -```shell -python wild_visual_navigation_ros/scripts/wild_visual_navigation_node.py _mode:=debug -``` -There are multiple parameters you can change via the ros-parameter server. -Optionally the node offers services to store the created graph/trained network to the disk. -- (optionally) RVIZ: -```shell -roslaunch wild_visual_navigation_ros view.launch -``` +### Online adaptation [Simulation] + -- (replay only) Run Debayer: +### Online adaptation [Rosbag] + +MPI Outdoor +MPI Indoor +Bahnhofstrasse +Mountain Bike + + +#### Setup +Let`s set up a new catkin_ws: ```shell -roslaunch image_proc_cuda_ros image_proc_cuda_node.launch cam0:=false cam1:=false cam2:=false cam3:=false cam4:=true cam5:=false cam6:=false run_gamma_correction:=false run_white_balance:=true run_vignetting_correction:=false run_color_enhancer:=false run_color_calibration:=false run_undistortion:=true run_clahe:=false dump_images:=false needs_rotation_cam4:=true debayer_option:=bayer_gbrg8 +# Create Workspace +source /opt/ros/noetic/setup.bash +mkdir -r ~/catkin_ws/src && cd ~/catkin_ws/src +catkin init +catkin config --extend /opt/ros/noetic +catkin config --cmake-args -DCMAKE_BUILD_TYPE=RelWithDebInfo + +# Clone Repos +git clone git@github.com:ANYbotics/anymal_d_simple_description.git +git clone git@github.com:ori-drs/procman_ros.git + +# Symlink WVN +ln -s ~/git/wild_visual_navigation ~/catkin_ws/src + +# Dependencies +rosdep install -ryi --from-paths . --ignore-src + +# Build +cd ~/catkin_ws +catkin build anymal_d_simple_description +catkin build procman_ros +catkin build wild_visual_navigation_ros + +# Source +source /opt/ros/noetic/setup.bash +source ~/catkin_ws/devel/setup.bash ``` -- (replay only) Replay Rosbag: +After successfully building the ros workspace you can run the full pipeline by either using the launch file (this requires all packages to be installed into your system python installation), or by running the nodes from the virtual environment as plain python scripts. + + +- Run wild_visual_navigation ```shell -rosbag play --clock path_to_mission/*.bag +roslaunch wild_visual_navigation_ros wild_visual_navigation.launch ``` -### Replay Usage [Online] -We provide a launch file to start all required nodes for close-loop integration. +- (ANYmal replay only) Load ANYmal description for RViZ ```shell -roslaunch wild_visual_navigation_ros replay_launch.launch -``` -The launch file allows to toggle the individual modules on and off. -```xml - - - - - - +roslaunch anymal_d_simple_description load.launch ``` -- Run WVN Node: +- (ANYmal replay only) Replay Rosbag: ```shell -python wild_visual_navigation_ros/scripts/wild_visual_navigation_node.py _mode:=default +robag play --clock path_to_mission/*.bag ``` -- Replay Rosbag: +- RVIZ: ```shell -rosbag play --clock path_to_mission/*.bag +roslaunch wild_visual_navigation_ros view.launch ``` -### Learning Usage [Offline] -#### Dataset Generation -Sometimes it`s useful to just analyze the network training therefore we provide the tools to extract a dataset useful for learning from a given rosbag. -In the following we explain how you can generate the dataset with the following structure: +Degugging (sometimes it is desirable to run the nodes seperate): +```shell +python wild_visual_navigation_ros/scripts/wvn_feature_extractor_node.py ``` -dataset_name - split - forest_train.txt - forest_val.txt - forest_test.txt - day3 - date_time_mission_0_day_3 - features - slic100_dino224_16 - center - time_stamp.pt - ... - graph - time_stamp.pt - ... - seg - time_stamp.pt - ... - slic_sift - ... - ... - image - time_stamp.pt - ... - supervision_mask - time_stamp.pt - ... +```shell +python wild_visual_navigation_ros/scripts/wvn_learning_node.py ``` -1. Dataset is configured in **wild_visual_navigation/utils/dataset_info.py** -2. Run extract_images_and_labels.py (can be also done for multiple missions with **extract_all.py**) - - Will at first merge all bags within the provided folders from dataset_info into bags containing the `_tf.bag` and other useful data `_wvn.bag`. - - Steps blocking through the bag - - Currently, this is done by storing a binary mask and images as .pt files (maybe change to png for storage) -3. Validate if images and labels are generated correctly with **validate_extract_images_and_labels.py** - - This script will remove images if no label is available and the other way around - - The stopping early times should be quite small within the seconds -4. Create lists with the training and train/val/test images: **create_train_val_test_lists.py** -5. Convert the correct `.pt` files to `.png` such that you can upload them for the test set to segments.ai **convert_test_images_for_labelling.py** -6. Label them online -7. Fetch the results using **download_bitmaps_from_segments_ai.py** -8. Extract the features segments and graph from the image **extract_features_for_dataset.py** - - -#### Training the Network -##### Training -We provide scripts for training the network for a single run where a parameter configuration yaml-file can be passed to override the parameters configured within `cfg/experiments_params.py`. -Training from the final dataset. -`python3 scripts/train_gnn.py --exp=exp_forest.yaml` - -##### Hyperparameter -We also provide scripts to use optuna for hyperparameter-searching: - -`python3 scripts/train_optuna.py --exp=exp_forest.yaml` - -Within the objective function you can easily adjust the trail parameter suggestions. - -##### Ablations -Finally, we categorize our ablations into `loss`, `network`, `feature`, `time_adaptation` and `knn_evaluation`. - -##### `loss`, `network`, and `feature` -For `loss`, `network`, and `feature` we can simply run a training script and pass the correct keyword. -We provide the configurations for those experiments within the `cfg/exp/ablation` folder. -``` -python3 scripts/ablation/training_ablation.py --ablation_type=network -``` -After running the training the results are stored respectively in `scripts/ablations/_ablation` as a pickle file. -For each training run the trained network is evaluate on all testing scenes and the AUROC and ROC values are stored with respect to the hand labeled gt-labels and self-supervised proprioceptive-labels. -We provide a jupyter notebook to interpret the training results. -``` -python3 scripts/ablation/training_ablation_visu.ipynb -``` +- The general configuration files can be found under: `wild_visual_navigation/cfg/experiment_params.py` +- This configuration is used in the `offline-model-training` and in the `online-ros` mode. +- When running the `online-ros` mode additional configurations for the individual nodes are defined in `wild_visual_navigation/cfg/ros_params.py`. +- These configuration file is filled based on the rosparameter-server during runtime. +- The default values for this configuration can be found under `wild_visual_navigation/wild_visual_navigation_ros/config/wild_visual_navigation`. +- We set an environment variable to automatically load the correct global paths and trigger some special behavior e.g. when training on a cluster. -##### `time_adaptation` -For the `time_adaptation` run simply run: -``` -python3 scripts/ablation/time_adaptation.py -``` -and for visualization: -``` -python3 scripts/ablation/time_adaptation_visu.py -``` -done. Dino -## Contributing +## Development ### Code formatting ```shell @@ -241,12 +205,29 @@ Code format is checked on push. ### Testing Introduction to [pytest](https://github.com/pluralsight/intro-to-pytest). - ```shell pytest ``` -Pytest is not checked on push. -Dino +### Open-Sourcing +Generating headers +```shell +pip3 install adheader -## Credits +# If your are using zsh otherwise remove \ +addheader wild_visual_navigation -t header.txt -p \*.py --sep-len 79 --comment='#' --sep=' ' +addheader wild_visual_navigation_ros -t header.txt -p \*.py -sep-len 79 --comment='#' --sep=' ' +addheader wild_visual_navigation_anymal -t header.txt -p \*.py --sep-len 79 --comment='#' --sep=' ' + +addheader wild_visual_navigation_ros -t header.txt -p \*CMakeLists.txt --sep-len 79 --comment='#' --sep=' ' +addheader wild_visual_navigation_anymal -t header.txt -p \*.py -p \*CMakeLists.txt --sep-len 79 --comment='#' --sep=' ' +``` + +### Releasing ANYmal data +```shell +rosrun procman_ros sheriff -l ~/git/wild_visual_navigation/wild_visual_navigation_anymal/config/procman/record_rosbags.pmd --start-roscore +``` + +```shell +rosbag_play --tf --sem --flp --wvn mission/*.bag +``` diff --git a/assets/drawings/software_overview.jpg b/assets/drawings/software_overview.jpg new file mode 100644 index 00000000..38d5b0e0 Binary files /dev/null and b/assets/drawings/software_overview.jpg differ diff --git a/assets/images/bahnhofstrasse_raw.png b/assets/images/bahnhofstrasse_raw.png new file mode 100644 index 00000000..9ed1f053 Binary files /dev/null and b/assets/images/bahnhofstrasse_raw.png differ diff --git a/assets/images/bahnhofstrasse_trav.png b/assets/images/bahnhofstrasse_trav.png new file mode 100644 index 00000000..15917325 Binary files /dev/null and b/assets/images/bahnhofstrasse_trav.png differ diff --git a/assets/images/mountain_bike_trail_raw.png b/assets/images/mountain_bike_trail_raw.png new file mode 100644 index 00000000..099617d2 Binary files /dev/null and b/assets/images/mountain_bike_trail_raw.png differ diff --git a/assets/images/mountain_bike_trail_trav.png b/assets/images/mountain_bike_trail_trav.png new file mode 100644 index 00000000..30bece6c Binary files /dev/null and b/assets/images/mountain_bike_trail_trav.png differ diff --git a/assets/images/mpi_indoor_raw.png b/assets/images/mpi_indoor_raw.png new file mode 100644 index 00000000..39d0aeea Binary files /dev/null and b/assets/images/mpi_indoor_raw.png differ diff --git a/assets/images/mpi_indoor_trav.png b/assets/images/mpi_indoor_trav.png new file mode 100644 index 00000000..58ed6fee Binary files /dev/null and b/assets/images/mpi_indoor_trav.png differ diff --git a/assets/images/mpi_outdoor_raw.png b/assets/images/mpi_outdoor_raw.png new file mode 100644 index 00000000..1af3e066 Binary files /dev/null and b/assets/images/mpi_outdoor_raw.png differ diff --git a/assets/images/mpi_outdoor_trav.png b/assets/images/mpi_outdoor_trav.png new file mode 100644 index 00000000..6f1024ef Binary files /dev/null and b/assets/images/mpi_outdoor_trav.png differ diff --git a/cfg/env/jetson.yaml b/cfg/env/jetson.yaml deleted file mode 100644 index 8e38b327..00000000 --- a/cfg/env/jetson.yaml +++ /dev/null @@ -1 +0,0 @@ -base: results \ No newline at end of file diff --git a/docker/Dockerfile b/docker/Dockerfile new file mode 100644 index 00000000..a78b68ec --- /dev/null +++ b/docker/Dockerfile @@ -0,0 +1,126 @@ +##################### +# Base image # +##################### +FROM nvidia/cuda:11.8.0-runtime-ubuntu20.04 as base + +# Labels +LABEL maintainer="Matias Mattamala;Jonas Frey" +LABEL contact="matias@robots.ox.ac.uk;jonfrey@ethz.ch" +LABEL description="WVN Docker" + +ARG ROS_VERSION="noetic" + +# == +# Disable dialog frontend +# == +ARG DEBIAN_FRONTEND=noninteractive + +# == +# Select shell +# == +SHELL ["/bin/bash", "-c"] + +# == +# Install ROS +# == +RUN apt update \ + && apt install --no-install-recommends -y curl gnupg lsb-release git build-essential nano \ + && sh -c 'echo "deb http://packages.ros.org/ros/ubuntu $(lsb_release -sc) main" > /etc/apt/sources.list.d/ros-latest.list' \ + && curl -s https://raw.githubusercontent.com/ros/rosdistro/master/ros.asc | apt-key add - \ + && apt update \ + && apt install --no-install-recommends -y ros-${ROS_VERSION}-desktop-full \ + && apt install --no-install-recommends -y \ + python3-pip \ + python3-venv \ + python3-rosdep \ + python3-rosinstall \ + python3-rosinstall-generator \ + python3-wstool \ + python3-catkin-tools \ + python3-osrf-pycommon \ + && rm -f "/etc/ros/rosdep/sources.list.d/20-default.list" \ + && rosdep init \ + && rosdep update \ + && echo "source /opt/ros/noetic/setup.bash" >> ~/.bashrc + +# == +# Install python packages +# == +RUN cd /root \ + && python3 -m venv env --system-site-packages \ + && source /root/env/bin/activate \ + && pip install --upgrade pip \ + && pip3 install --no-cache-dir \ + torch==2.1.0 torchvision --extra-index-url https://download.pytorch.org/whl/cu121 \ + && pip3 install --no-cache-dir \ + black \ + flake8 \ + jupyter \ + wget \ + numpy \ + tqdm \ + kornia \ + torchmetrics \ + pytorch_lightning \ + pytest \ + scipy \ + scikit-image \ + scikit-learn \ + matplotlib \ + seaborn \ + pandas \ + pytictac \ + torch_geometric \ + omegaconf \ + optuna \ + neptune \ + fast-slic \ + hydra-core \ + prettytable \ + termcolor \ + pydensecrf@git+https://github.com/lucasb-eyer/pydensecrf.git \ + liegroups@git+https://github.com/mmattamala/liegroups \ + opencv-python>=4.6 + +# == +# Remove cache and extra files +# == +RUN rm -rf /var/lib/apt/lists/* && apt clean + + +# == +# Enable dialog again +# == +ARG DEBIAN_FRONTEND=dialog + +# == +# Run bash +# == +CMD ["/bin/bash"] +WORKDIR /root + + +############################################################### +# Development image +# This adds display access and simulation support +############################################################### + +FROM base as dev + +ENV DEBIAN_FRONTEND=noninteractive + +RUN mkdir -p catkin_ws/src \ + && source /opt/ros/noetic/setup.bash \ + && source "/root/.bashrc" \ + && cd /root/catkin_ws && catkin build \ + && apt-get update \ + && apt-get install -y \ + ros-noetic-jackal-simulator \ + ros-noetic-jackal-desktop \ + ros-noetic-teleop-twist-keyboard \ + ros-noetic-rqt-robot-steering \ + && rm -rf /var/lib/apt/lists/* + +RUN echo "source /root/catkin_ws/devel/setup.bash" >> ~/.bashrc +RUN echo "source /root/env/bin/activate" >> ~/.bashrc +ENV DEBIAN_FRONTEND=dialog \ No newline at end of file diff --git a/docker/Dockerfile.pyg b/docker/Dockerfile.pyg deleted file mode 100644 index ab23f9c6..00000000 --- a/docker/Dockerfile.pyg +++ /dev/null @@ -1,54 +0,0 @@ -ARG BASE_IMAGE=rslethz/jetpack-5:r34.1.1-ml -FROM ${BASE_IMAGE} - -# Labels -LABEL maintainer="Matias Mattamala" -LABEL contact="matias@robots.ox.ac.uk" -LABEL description="Image with custom pytorch geometric (pyg) builds for Jetson" -LABEL example_usage="docker run --cidfile /tmp/jetson_docker.cid -it --rm --net=host --runtime nvidia -e DISPLAY=$DISPLAY -v /tmp/.X11-unix/:/tmp/.X11-unix -v ${YOUR_GIT_WS}:/root/git -v ${YOUR_CATKIN_WS}:/root/catkin_ws/ rslethz/jetpack-5:r34.1.1-wvn" - -# Set CUDA_ARCH_LIST for torch-geometric compilation -ARG TORCH_CUDA_ARCH_LIST="5.3;6.2;7.2;8.7" - -# Pytorch geometric -RUN echo "Installing pytorch-geometric. It requires to compile each module to enable CUDA 11.4 support" - -# Jetson architectures -ENV TORCH_CUDA_ARG_LIST=$TORCH_CUDA_ARCH_LIST -RUN echo TORCH_CUDA_ARG_LIST=$TORCH_CUDA_ARG_LIST - -ENV PATH="/usr/local/cuda/bin:${PATH}" -RUN echo PATH=$PATH - -ENV CPATH="/usr/local/cuda/include:${CPATH}" -RUN echo CPATH=$CPATH - -ENV LD_LIBRARY_PATH="/usr/local/cuda/lib64:${LD_LIBRARY_PATH}" -RUN echo LD_LIBRARY_PATH=$LD_LIBRARY_PATH - -# Library path -ENV LIBRARY_PATH="/usr/local/cuda/lib64:${LIBRARY_PATH}" -RUN echo LIBRARY_PATH=$LIBRARY_PATH - -# CUDA_HOME -ENV CUDA_HOME="/usr/local/cuda" - -# torch-scatter -RUN echo "Compiling torch-scatter" -RUN pip3 -v install torch-scatter - -# torch-sparse -RUN echo "Compiling torch-sparse" -RUN pip3 -v install torch-sparse - -# torch-cluster -RUN echo "Compiling torch-cluster" -RUN pip3 -v install torch-cluster - -# torch-spline-conv -RUN echo "Compiling torch-spline-conv" -RUN pip3 -v install torch-spline-conv - -# torch-geometric -RUN echo "Compiling torch-geometric" -RUN pip3 -v install --no-cache-dir torch-geometric diff --git a/docker/Dockerfile.wvn b/docker/Dockerfile.wvn deleted file mode 100644 index 990e798f..00000000 --- a/docker/Dockerfile.wvn +++ /dev/null @@ -1,66 +0,0 @@ -ARG BASE_IMAGE=rslethz/jetpack-5:r34.1.1-ml-pyg -FROM ${BASE_IMAGE} -ARG BASE_IMAGE -RUN echo BASE_IMAGE=${BASE_IMAGE} - -# Labels -LABEL maintainer="Matias Mattamala" -LABEL contact="matias@robots.ox.ac.uk" -LABEL description="Image with WVN dependencies" -LABEL example_usage="docker run --cidfile /tmp/jetson_docker.cid -it --rm --net=host --runtime nvidia -e DISPLAY=$DISPLAY -v /tmp/.X11-unix/:/tmp/.X11-unix -v ${YOUR_GIT_WS}:/root/git -v ${YOUR_CATKIN_WS}:/root/catkin_ws/ rslethz/jetpack-5:wvn-latest" - -# Extra libraries for other ROS dependencies -RUN pip3 install --no-cache-dir ruamel.yaml -RUN pip3 install --no-cache-dir black -RUN pip3 install --no-cache-dir flake8 -RUN pip3 install --no-cache-dir shapely==1.7.1 - -# Install Python dependencies -RUN pip3 install --no-cache-dir pillow==9.2 -RUN pip3 install --no-cache-dir wget -RUN pip3 install --no-cache-dir colorama -RUN pip3 install --no-cache-dir simple-parsing -RUN pip3 install --no-cache-dir kornia -RUN pip3 install --no-cache-dir pytest -RUN pip3 install --no-cache-dir scipy -RUN pip3 install --no-cache-dir scikit-image -RUN pip3 install --no-cache-dir scikit-learn -RUN pip3 install --no-cache-dir seaborn -RUN pip3 install --no-cache-dir pandas -RUN pip3 install --no-cache-dir fast_slic - -# Stuff below only applies to Jetson -# Environment variables to make scikit-learn work -# If not added, it will raise an error: -# ImportError: /usr/local/lib/python3.8/dist-packages/skimage/_shared/../../scikit_image.libs/libgomp-d22c30c5.so.1.0.0: cannot allocate memory in static TLS block -# It seems that scikit-image has not been built correctly. -# -# Your install of scikit-image appears to be broken. -# Try re-installing the package following the instructions at: -# https://scikit-image.org/docs/stable/install.html -# -ENV LD_PRELOAD=/usr/local/lib/python3.8/dist-packages/skimage/_shared/../../scikit_image.libs/libgomp-d22c30c5.so.1.0.0 - -# Pytorch geometric -RUN pip3 -v install torch-scatter -RUN pip3 -v install torch-sparse -RUN pip3 -v install torch-cluster -RUN pip3 -v install torch-spline-conv -RUN pip3 -v install torch-geometric - -# Other learning packages -RUN pip3 install --no-cache-dir neptune-client[optuna] -RUN pip3 install --no-cache-dir hydra-core -RUN pip3 install --no-cache-dir pytorch_lightning==1.6.5 - -# Other dependencies -RUN pip3 install --no-cache-dir git+https://github.com/lucasb-eyer/pydensecrf.git#egg=pydensecrf -RUN pip3 install --no-cache-dir git+https://github.com/mmattamala/liegroups#egg=liegroups -RUN pip3 install --no-cache-dir git+https://github.com/leggedrobotics/stego.git#egg=stego -RUN pip3 install --no-cache-dir git+https://github.com/JonasFrey96/pytorch_pwc.git#egg=pytorch_pwc==0.0.1 - -# Setup entrypoint -COPY entrypoint.sh /entrypoint.sh -ENTRYPOINT ["/entrypoint.sh"] -CMD ["bash"] -WORKDIR /root/ diff --git a/docker/README.md b/docker/README.md index 83851998..4723f414 100644 --- a/docker/README.md +++ b/docker/README.md @@ -1,32 +1,34 @@ -## Docker build instructions -### General overview -This folder stores basic Dockerfiles and scripts to build the dependencies of the wild_visual_navigation package for (1) desktop machines and (2) Jetson platforms. +# Docker files -The main difference between both are the source images. While the one for desktop/laptops rely on NVidia's images with Pytorch support `nvcr.io/nvidia/pytorch:20.06-py3`, the Jetson ones are more involved and rely on internal images prepared by RSL. Additionally, Jetson requires to build pytorch geometric (`pyg`) from scratch, so we also add Dockerfiles to build an intermediate image for this purpose only. +## Build and run containers -### Usage -While staying in the docker folder, you can build the images for desktop platforms (`rslethz/desktop:r34.1.1-wvn`) by using: +To build the container: ```sh -./bin/build.sh --target=desktop +docker compose -f docker-compose.yaml build ``` -Similarly, for Jetson ones you must use: +To run the container (terminal-based): + +```sh +docker compose -f docker-compose.yaml up -d +``` + +To launch bash on the container: + ```sh -./bin/build.sh --target=jetson +docker compose exec wvn bash ``` -The Jetson version will generate the images `rslethz/jetpack-5:r34.1.1-ml-pyg` and `rslethz/jetpack-5:r34.1.1-wvn`, with pytorch geometric and full wild_visual_navigation dependencies respectively. -### Pushing images to ORI server -For internal use at the Oxford Robotics Institute (ORI), we also provide a helper script to upload the images to the docker server. For example, after building the images you can run: +To stop the container: ```sh -./bin/push_ori.sh --target=jetson +docker compose -f docker-compose.yaml stop ``` -This will create new tags for the previously mentioned images: +To run the GUI-enabled version and check the Gazebo environment: + ```sh -rslethz/jetpack-5:r34.1.1-ml-pyg -> ori-ci-gateway.robots.ox.ac.uk:12002/drs/jetson:r34.1.1-ml-pyg-latest -rslethz/jetpack-5:r34.1.1-wvn -> ori-ci-gateway.robots.ox.ac.uk:12002/drs/jetson:r34.1.1-wvn-latest +docker compose -f docker-compose-gui.yaml up -d +docker compose exec wvn_gui bash ``` -which comply with the naming used internally at ORI. \ No newline at end of file diff --git a/docker/bin/build.sh b/docker/bin/build.sh deleted file mode 100755 index c3620e0b..00000000 --- a/docker/bin/build.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -set -e - -# Source variables -source bin/env_variables.sh - -# Default target -TARGET=none - -# Read arguments -for i in "$@" -do -case $i in - -t=*|--target=*) - TARGET=${i#*=} - echo "[build.sh]: User-set target type is: '$TARGET'" - shift - ;; -esac -done - -# Handle different target cases -if [[ "$TARGET" == "jetson" ]]; then - echo "Building images for target [$TARGET]" - - # Build pytorch geometric docker - echo "Building ${PYG_TAG} from $PYG_DOCKERFILE" - sudo docker build --build-arg BASE_IMAGE=$ML_JETSON_TAG --network=host -t $PYG_JETSON_TAG -f $PYG_DOCKERFILE . - - # Build wvn docker - echo "Building ${WVN_TAG} from $WVN_DOCKERFILE" - sudo docker build --build-arg BASE_IMAGE=$PYG_JETSON_TAG --network=host -t $WVN_JETSON_TAG -f $WVN_DOCKERFILE . - -elif [[ "$TARGET" == "desktop" ]]; then - echo "Building images for target [$TARGET]" - - # Build wvn docker - echo "Building ${WVN_DESKTOP_TAG} from $WVN_DOCKERFILE" - sudo docker build --build-arg BASE_IMAGE=$ML_DESKTOP_TAG --network=host -t $WVN_DESKTOP_TAG -f $WVN_DOCKERFILE . - -else - echo "Error: unsupported target [$TARGET]" -fi \ No newline at end of file diff --git a/docker/bin/env_variables.sh b/docker/bin/env_variables.sh deleted file mode 100755 index c9cb1122..00000000 --- a/docker/bin/env_variables.sh +++ /dev/null @@ -1,19 +0,0 @@ -#!/bin/bash - -# Docker images -PYG_DOCKERFILE=Dockerfile.pyg -WVN_DOCKERFILE=Dockerfile.wvn - -# Source ML images (with Pytorch support) -ML_JETSON_TAG="rslethz/jetpack-5:r34.1.1-ml" -ML_DESKTOP_TAG="nvcr.io/nvidia/pytorch:20.06-py3" - -# Export tags -PYG_JETSON_TAG="rslethz/jetpack-5:r34.1.1-ml-pyg" -WVN_JETSON_TAG="rslethz/jetpack-5:r34.1.1-wvn" -WVN_DESKTOP_TAG="rslethz/desktop:r34.1.1-wvn" - -# ORI-compliant tags for built images -ORI_PYG_JETSON_TAG="ori-ci-gateway.robots.ox.ac.uk:12002/drs/jetson:r34.1.1-ml-pyg-latest" -ORI_WVN_JETSON_TAG="ori-ci-gateway.robots.ox.ac.uk:12002/drs/jetson:r34.1.1-wvn-latest" -ORI_WVN_DESKTOP_TAG="ori-ci-gateway.robots.ox.ac.uk:12002/drs/desktop:wvn-latest" \ No newline at end of file diff --git a/docker/bin/push_ori.sh b/docker/bin/push_ori.sh deleted file mode 100755 index b7121cc0..00000000 --- a/docker/bin/push_ori.sh +++ /dev/null @@ -1,50 +0,0 @@ -#!/bin/bash -# This is an Oxford Robotics Institute (ORI)-specific executable -# It makes tags that are compliant with the internal naming for the images -# and pushes the images to our local server - -set -e - -# Source variables -source bin/env_variables.sh - -# Default target -TARGET=none - -# Read arguments -for i in "$@" -do -case $i in - -t=*|--target=*) - TARGET=${i#*=} - echo "[build.sh]: User-set target type is: '$TARGET'" - shift - ;; -esac -done - -update_tags_and_push() -{ - local original_tag=$1 - local new_tag=$2 - - # Build pytorch geometric docker - echo "Making new tag $new_tag" - docker tag $original_tag $new_tag - echo "Pushing $new_tag to server..." - docker push $new_tag - #echo "Removing tag $original_tag" - #docker image rm $original_tag -} - -# Handle different target cases -if [[ "$TARGET" == "jetson" ]]; then - update_tags_and_push "$PYG_JETSON_TAG" "$ORI_PYG_JETSON_TAG" - update_tags_and_push "$WVN_JETSON_TAG" "$ORI_WVN_JETSON_TAG" - -elif [[ "$TARGET" == "desktop" ]]; then - update_tags_and_push $WVN_DESKTOP_TAG $ORI_WVN_DESKTOP_TAG - -else - echo "Error: unsupported target [$TARGET]" -fi diff --git a/docker/bin/run.sh b/docker/bin/run.sh deleted file mode 100755 index c9d167da..00000000 --- a/docker/bin/run.sh +++ /dev/null @@ -1,38 +0,0 @@ -#!/bin/bash -# This script launches a WVN container for the target platform - -set -e - -# Source variables -source bin/env_variables.sh - -# Default target -TARGET=none -GIT_WS=$HOME/git -CATKIN_WS=$HOME/catkin_ws - -# Read arguments -for i in "$@" -do -case $i in - -t=*|--target=*) - TARGET=${i#*=} - echo "[build.sh]: User-set target type is: '$TARGET'" - shift - ;; -esac -done - - -# Handle different target cases -if [[ "$TARGET" == "jetson" ]]; then - docker run -it --rm --net=host --runtime nvidia -e DISPLAY=$DISPLAY -v /tmp/.X11-unix/:/tmp/.X11-unix -v ${GIT_WS}:/root/git -v ${CATKIN_WS}:/root/catkin_ws/ $WVN_JETSON_TAG - -elif [[ "$TARGET" == "desktop" ]]; then - docker run -it --rm --net=host --gpus all --runtime nvidia -e DISPLAY=$DISPLAY -v /tmp/.X11-unix/:/tmp/.X11-unix -v ${GIT_WS}:/root/git -v ${CATKIN_WS}:/root/catkin_ws/ $WVN_DESKTOP_TAG - -else - echo "Error: unsupported target [$TARGET]" -fi - - diff --git a/docker/docker-compose-gui-nvidia.yaml b/docker/docker-compose-gui-nvidia.yaml new file mode 100644 index 00000000..23414ae0 --- /dev/null +++ b/docker/docker-compose-gui-nvidia.yaml @@ -0,0 +1,10 @@ +version: "3.9" +services: + wvn: + extends: + file: docker-compose-gui.yaml + service: wvn + environment: + - NVIDIA_VISIBLE_DEVICES=all + - NVIDIA_DRIVER_CAPABILITIES=all + runtime: nvidia \ No newline at end of file diff --git a/docker/docker-compose-gui.yaml b/docker/docker-compose-gui.yaml new file mode 100644 index 00000000..0cc35c5f --- /dev/null +++ b/docker/docker-compose-gui.yaml @@ -0,0 +1,13 @@ +version: "3.9" +services: + wvn: + extends: + file: docker-compose.yaml + service: wvn + environment: + - DISPLAY=${DISPLAY} + - QT_X11_NO_MITSHM=1 + volumes: + - /dev/dri:/dev/dri + - /tmp/.X11-unix:/tmp/.X11-unix:rw + - /tmp/.docker.xauth:/tmp/.docker.xauth:rw \ No newline at end of file diff --git a/docker/docker-compose-nvidia.yaml b/docker/docker-compose-nvidia.yaml new file mode 100644 index 00000000..4b82f8df --- /dev/null +++ b/docker/docker-compose-nvidia.yaml @@ -0,0 +1,9 @@ +version: "3.9" +services: + wvn: + extends: + file: docker-compose.yaml + service: wvn + environment: + - NVIDIA_VISIBLE_DEVICES=all + runtime: nvidia \ No newline at end of file diff --git a/docker/docker-compose.yaml b/docker/docker-compose.yaml new file mode 100644 index 00000000..4a1058e0 --- /dev/null +++ b/docker/docker-compose.yaml @@ -0,0 +1,23 @@ +version: "3.9" +services: + wvn: + build: + context: .. + dockerfile: docker/Dockerfile + target: dev + stdin_open: true + tty: true + network_mode: "host" + volumes: + - ../../wild_visual_navigation:/root/catkin_ws/src/wild_visual_navigation + - ../../self_supervised_segmentation:/root/catkin_ws/src/self_supervised_segmentation + - ../../segment-anything:/root/catkin_ws/src/segment-anything + environment: + - ROS_IP=127.0.0.1 + command: "/bin/bash" + # command: > + # /bin/bash -c "pip3 install -e /root/catkin_ws/src/wild_visual_navigation + # && pip3 install -e /root/catkin_ws/src/self_supervised_segmentation + # && cd /root/catkin_ws && catkin build + # && cd /root + # && source /root/catkin_ws/devel/setup.bash" \ No newline at end of file diff --git a/docker/entrypoint.sh b/docker/entrypoint.sh deleted file mode 100755 index 4a608ff3..00000000 --- a/docker/entrypoint.sh +++ /dev/null @@ -1,14 +0,0 @@ -#!/bin/bash -set -e - -# Installing wild visual navigation -install_wvn="pip3 install -e /root/git/wild_visual_navigation" - - -echo "Installing wvn: ${install_wvn}..." -$install_wvn > /dev/null -echo "Done!" - -echo "source /root/catkin_ws/devel/setup.bash" >> /root/.bashrc - -exec "$@" diff --git a/environment.yaml b/environment.yaml deleted file mode 100644 index 7ba355e1..00000000 --- a/environment.yaml +++ /dev/null @@ -1,50 +0,0 @@ -name: wvn -channels: - - pytorch - - robostack - - robostack-experimental - - conda-forge - - pyg - - defaults -dependencies: - - python=3.8 - - kornia=0.6.6 - - pip - - torchvision=0.12 - - pytorch=1.11.0 - - torchaudio - - cudatoolkit=11.3 - - pytest - - scipy - - scikit-image - - scikit-learn - - ros-noetic-ros-base - - ros-noetic-cv-bridge - - libopencv - - compilers - - cmake - - pkg-config - - make - - ninja - - catkin_tools - - pyg - - neptune-client - - simple-parsing - - matplotlib - - seaborn - - pandas - - torchmetrics==0.11.0 - - pip: - - neptune-client[optuna] - - wandb - - wget - - hydra-core - - pytorch_lightning==1.6.5 - - colorama - - kornia_rs - - fast_slic - - git+https://github.com/lucasb-eyer/pydensecrf.git#egg=pydensecrf - - git+https://github.com/kornia/kornia#egg=kornia - - git+https://github.com/mmattamala/liegroups#egg=liegroups - - --editable git+https://github.com/leggedrobotics/stego.git#egg=stego==0.0.1 - - --editable git+https://github.com/JonasFrey96/pytorch_pwc.git#egg=pytorch_pwc==0.0.1 diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..e34796ec --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,2 @@ +[tool.black] +line-length = 120 \ No newline at end of file diff --git a/scripts/confidence_generator/plot_confidence.ipynb b/scripts/confidence_generator/plot_confidence.ipynb deleted file mode 100644 index 2ba20dbe..00000000 --- a/scripts/confidence_generator/plot_confidence.ipynb +++ /dev/null @@ -1,166 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import wild_visual_navigation.visu.paper_colors as pc\n", - "import torch\n", - "import os\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from pathlib import Path\n", - "\n", - "%matplotlib inline\n", - "\n", - "WORKING_DIR = \"/home/matias/git_rsl/wild_visual_navigation/results/learning/2023-01-21T15-39-12_test\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "samples_directory = Path(os.path.join(WORKING_DIR, \"confidence_generator\"))\n", - "\n", - "# Check confidence generator folder\n", - "if not samples_directory.exists():\n", - " raise FileNotFoundError(f\"Confidence generator path selected does not exists: {confidence_generator_path}\")\n", - "\n", - "# Load all the .pt files\n", - "steps = []\n", - "all_samples = []\n", - "positive_samples = []\n", - "gaussian_mean = []\n", - "gaussian_std = []\n", - "\n", - "for s in sorted(samples_directory.rglob(\"*.pt\")):\n", - " data = torch.load(s)\n", - "\n", - " # Get step\n", - " step = int(s.stem.replace(\"samples_\", \"\"))\n", - "\n", - " # Save step\n", - " steps.append(step)\n", - " all_samples.append(data[\"x\"].numpy())\n", - " positive_samples.append(data[\"x_positive\"].numpy())\n", - " gaussian_mean.append(data[\"mean\"].item())\n", - " gaussian_std.append(data[\"std\"].item())\n", - "\n", - "steps = np.asarray(steps)\n", - "gaussian_mean = np.asarray(gaussian_mean)\n", - "gaussian_std = np.asarray(gaussian_std)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from datetime import datetime\n", - "\n", - "N_steps = len(steps)\n", - "N_skip = 5\n", - "fig, axs = plt.subplots(N_steps, constrained_layout=True, figsize=(5, N_steps * 2))\n", - "\n", - "for s in range(N_steps):\n", - " N = 30\n", - " bins = np.linspace(0, 15, N)\n", - " all_hist = np.histogram(all_samples[s], bins)\n", - " pos_hist = np.histogram(positive_samples[s], bins)\n", - "\n", - " axs[s].bar(all_hist[1][:-1], all_hist[0] / all_hist[0].max(), alpha=0.5, color=\"k\", width=0.4)\n", - " axs[s].bar(pos_hist[1][:-1], pos_hist[0] / pos_hist[0].max(), alpha=0.5, color=\"b\", width=0.4)\n", - " axs[s].plot(bins, np.exp(-((bins - gaussian_mean[s]) ** 2) / (2 * gaussian_std[s] ** 2)), color=\"b\", linewidth=2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot distribution over time\n", - "# samples as scatterplot\n", - "# Positive Gaussian as GP (mean + std)\n", - "\n", - "# Plot stuff\n", - "fig_scale = 0.5\n", - "fig, axs = plt.subplots(1, constrained_layout=True, figsize=(fig_scale * 13, fig_scale * 9))\n", - "\n", - "for s in range(len(steps)):\n", - " step = steps[s]\n", - "\n", - " N = len(all_samples[s])\n", - " axs.scatter(\n", - " step * np.ones(N), all_samples[s], color=(0.2, 0.2, 0.2), alpha=0.05, linewidths=0\n", - " ) # axs.set_xlabel(\"Steps\")\n", - "\n", - " M = len(positive_samples[s])\n", - " axs.scatter(\n", - " step * np.ones(M), positive_samples[s], color=pc.paper_colors_rgb_f[\"blue\"], alpha=0.3, linewidths=0\n", - " ) # label=\"positive_samples\"\n", - "\n", - "std_factor = 1.5\n", - "axs.fill_between(\n", - " steps,\n", - " gaussian_mean * 0,\n", - " gaussian_mean + std_factor * gaussian_std,\n", - " color=pc.darken(pc.paper_colors_rgb_f[\"blue\"], 0.3),\n", - " alpha=0.2,\n", - " linewidth=0,\n", - ")\n", - "axs.plot(\n", - " steps,\n", - " gaussian_mean,\n", - " label=\"Gaussian approximation\",\n", - " color=pc.darken(pc.paper_colors_rgb_f[\"blue\"], 0.3),\n", - " linewidth=2,\n", - ")\n", - "axs.set_xlabel(\"Steps\")\n", - "axs.set_ylabel(\"Reconstruction loss\")\n", - "axs.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "wvn", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "afa7ba36e378b18fb9d9622f0c65b59a17b022d718aa019edc90e2573a229775" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/scripts/helpers/visu_data.py b/scripts/helpers/visu_data.py deleted file mode 100644 index 66792a43..00000000 --- a/scripts/helpers/visu_data.py +++ /dev/null @@ -1,64 +0,0 @@ -# Use the same config to load the data using the dataloader -from wild_visual_navigation.learning.dataset import get_ablation_module -from wild_visual_navigation.learning.utils import load_env -from wild_visual_navigation import WVN_ROOT_DIR -from wild_visual_navigation.visu import LearningVisualizer -import torch -from torchmetrics import Accuracy, AUROC -from PIL import Image -import numpy as np -import copy -import os -import pickle -from pathlib import Path -import time - -env = load_env() -test_all_datasets = True - -for scene in ["forest", "hilly", "grassland"]: - ablation_data_module = { - "batch_size": 1, - "num_workers": 0, - "env": scene, - "feature_key": "slic_dino", - "test_equals_val": False, - "val_equals_test": False, - "test_all_datasets": test_all_datasets, - "training_data_percentage": 100, - "training_in_memory": False, - } - train_loader, val_loader, test_loader = get_ablation_module( - **ablation_data_module, perugia_root=env["perugia_root"] - ) - test_scenes = [a.dataset.env for a in test_loader] - - visualizer = LearningVisualizer() - import matplotlib.pyplot as plt - - # You probably won't need this if you're embedding things in a tkinter plot... - plt.ion() - - fig = plt.figure() - ax = fig.add_subplot(111) - obj = ax.imshow(np.zeros((100, 100, 3))) - for t in test_loader: - for j, d in enumerate(t): - graph = d[0] - b = 0 - center = graph.center[:] - seg = graph.seg - img = graph.img - # res = visualizer.plot_traversability_graph_on_seg( - # graph.y_gt.type(torch.float32), seg[0], graph, center, img[0], not_log=True - # ) - - res = visualizer.plot_detectron(img[0], graph.label[0].type(torch.long), not_log=True, max_seg=2) - i1 = Image.fromarray(res) - - obj.set_data(i1) - - fig.canvas.draw() - fig.canvas.flush_events() - print(j) - time.sleep(0.25) diff --git a/scripts/paper/convert_to_pandas.ipynb b/scripts/paper/convert_to_pandas.ipynb deleted file mode 100644 index 66074b62..00000000 --- a/scripts/paper/convert_to_pandas.ipynb +++ /dev/null @@ -1,198 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "dfe70020", - "metadata": {}, - "source": [ - "# Load run data and convert to pandas dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d0b50d34", - "metadata": {}, - "outputs": [], - "source": [ - "# %matplotlib notebook\n", - "\n", - "from wild_visual_navigation import WVN_ROOT_DIR\n", - "import os\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import pickle\n", - "\n", - "folder, model_name = \"confidence_fn\", False\n", - "folder, model_name = \"lr\", False\n", - "folder, model_name = \"feature\", False\n", - "folder, model_name = \"loss\", False\n", - "folder, model_name = \"network\", False\n", - "\n", - "\n", - "folder, model_name = \"threshold\", False\n", - "\n", - "folder, model_name = \"anomaly_detection_only\", False\n", - "folder, model_name = \"classicial_learning\", True\n", - "# folder, model_name = \"debug\", False\n", - "# p = os.path.join(WVN_ROOT_DIR, f\"scripts/ablations/{folder}_ablation/{folder}_ablation_test_results.pkl\")\n", - "ws = \"_\" + os.environ[\"ENV_WORKSTATION_NAME\"]\n", - "p = os.path.join(WVN_ROOT_DIR, f\"results/ablations/{folder}_ablation{ws}/{folder}_ablation_test_results.pkl\")\n", - "with open(p, \"rb\") as f:\n", - " res = pickle.load(f)\n", - "\n", - "import scipy\n", - "import pandas as pd\n", - "import pickle\n", - "import copy\n", - "\n", - "data = {\n", - " \"run_nr\": [],\n", - " \"cfg_name\": [],\n", - " \"train_scene\": [],\n", - " \"test_scene\": [],\n", - "}\n", - "\n", - "for train_scene_name, scene_data in res.items():\n", - " for config_path, config_data in scene_data.items():\n", - " if model_name:\n", - " name = config_path\n", - " else:\n", - " # its a path\n", - " name = config_path.split(\"/\")[-1][:-5]\n", - " for run_nr, run_data in config_data.items():\n", - " for test_scene_name, test_scene_data in run_data.items():\n", - " data[\"cfg_name\"].append(name)\n", - " data[\"run_nr\"].append(run_nr)\n", - " data[\"train_scene\"].append(train_scene_name)\n", - " data[\"test_scene\"].append(test_scene_name)\n", - " for k in test_scene_data.keys():\n", - " if type(test_scene_data[k]) is float:\n", - " try:\n", - " data[k].append(test_scene_data[k])\n", - " except:\n", - " data[k] = [test_scene_data[k]]\n", - "\n", - "df = pd.DataFrame.from_dict(data)\n", - "\n", - "data_acc = {\n", - " \"cfg_name\": [],\n", - " \"train_scene\": [],\n", - " \"test_scene\": [],\n", - "}\n", - "\n", - "data_auroc = {\n", - " \"cfg_name\": [],\n", - " \"train_scene\": [],\n", - " \"test_scene\": [],\n", - "}\n", - "keys = []\n", - "for i in range(len(df)):\n", - " n = data[\"cfg_name\"][i]\n", - " tr = data[\"train_scene\"][i]\n", - " te = data[\"test_scene\"][i]\n", - "\n", - " a = df[(df[\"train_scene\"] == tr) * (df[\"test_scene\"] == te) * (df[\"cfg_name\"] == n)]\n", - " k = n + tr + te\n", - " if k in keys:\n", - " continue\n", - " keys.append(k)\n", - "\n", - " data_auroc[\"cfg_name\"].append(n)\n", - " data_auroc[\"train_scene\"].append(tr)\n", - " data_auroc[\"test_scene\"].append(te)\n", - "\n", - " data_acc[\"cfg_name\"].append(n)\n", - " data_acc[\"train_scene\"].append(tr)\n", - " data_acc[\"test_scene\"].append(te)\n", - "\n", - " for k in a.keys():\n", - "\n", - " if k.find(\"auroc\") != -1:\n", - " try:\n", - " data_auroc[k + \"_std\"].append(a[k].std())\n", - " data_auroc[k + \"_m\"].append(a[k].mean())\n", - " except:\n", - " data_auroc[k + \"_std\"] = [a[k].std()]\n", - " data_auroc[k + \"_m\"] = [a[k].mean()]\n", - " if k.find(\"acc\") != -1:\n", - " try:\n", - " data_acc[k + \"_std\"].append(a[k].std())\n", - " data_acc[k + \"_m\"].append(a[k].mean())\n", - " except:\n", - " data_acc[k + \"_std\"] = [a[k].std()]\n", - " data_acc[k + \"_m\"] = [a[k].mean()]\n", - "df_auroc = pd.DataFrame.from_dict(data_auroc)\n", - "df_auroc = df_auroc.round(decimals=4)\n", - "df_auroc[df_auroc.select_dtypes(include=[\"number\"]).columns] = (\n", - " df_auroc[df_auroc.select_dtypes(include=[\"number\"]).columns] * 100\n", - ")\n", - "\n", - "df_acc = pd.DataFrame.from_dict(data_acc)\n", - "df_acc = df_acc.round(decimals=4)\n", - "df_acc[df_acc.select_dtypes(include=[\"number\"]).columns] = (\n", - " df_acc[df_acc.select_dtypes(include=[\"number\"]).columns] * 100\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "08841994", - "metadata": {}, - "outputs": [], - "source": [ - "df_acc = df_acc.sort_values(\"test_acc_gt_image_m\", ascending=False)\n", - "scene = \"hilly\"\n", - "df_acc[(df_acc[\"train_scene\"].str.find(scene) != -1)] # * (df_acc[\"test_scene\"].str.find(scene) != -1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "57a22059", - "metadata": {}, - "outputs": [], - "source": [ - "df_acc = df_acc.sort_values(\"test_acc_gt_image_m\", ascending=False)\n", - "scene = \"forest\"\n", - "df_acc[(df_acc[\"train_scene\"].str.find(scene) != -1)] # (df[\"test_scene\"].str.find(scene) != -1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a5cc7cee", - "metadata": {}, - "outputs": [], - "source": [ - "df_acc = df_acc.sort_values(\"test_acc_gt_image_m\", ascending=False)\n", - "scene = \"grassland\"\n", - "df_acc[(df_acc[\"train_scene\"].str.find(scene) != -1)] # (df[\"test_scene\"].str.find(scene) != -1)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "wvn", - "language": "python", - "name": "wvn" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/scripts/paper/data_percentage.ipynb b/scripts/paper/data_percentage.ipynb deleted file mode 100644 index e36e6605..00000000 --- a/scripts/paper/data_percentage.ipynb +++ /dev/null @@ -1,169 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "bd542ea1", - "metadata": {}, - "outputs": [], - "source": [ - "import pickle\n", - "from wild_visual_navigation import WVN_ROOT_DIR\n", - "import os\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "ws = \"_\" + os.environ[\"ENV_WORKSTATION_NAME\"]\n", - "name = \"data_percentage_all\"\n", - "with open(os.path.join(WVN_ROOT_DIR, f\"results/ablations/{name}{ws}/{name}_steps.pkl\"), \"rb\") as f:\n", - " res = pickle.load(f)\n", - "\n", - "\n", - "# GET RUN CONFIGURATION\n", - "all_percentages = []\n", - "all_steps = []\n", - "all_runs = []\n", - "for data in res:\n", - " data[\"model_path\"]\n", - " percentage, steps, run = data[\"percentage\"], data[\"steps\"], data[\"run\"]\n", - " all_runs.append(run)\n", - " all_percentages.append(percentage)\n", - " all_steps.append(steps)\n", - "NR_RUNS = np.unique(np.array(all_runs)).shape[0]\n", - "NR_TRAINIG_DATA = np.unique(np.array(all_percentages)).shape[0]\n", - "STEPS_DIV = np.unique(all_steps)[1] - np.unique(all_steps)[0]\n", - "NR_STEPS = np.unique(all_steps).shape[0]\n", - "\n", - "# CHANGE STORAGE FORMAT\n", - "\n", - "auroc_gt = {}\n", - "auroc_gt[\"forest\"] = np.zeros((NR_TRAINIG_DATA, NR_STEPS, NR_RUNS))\n", - "auroc_gt[\"grassland\"] = np.zeros((NR_TRAINIG_DATA, NR_STEPS, NR_RUNS))\n", - "auroc_gt[\"hilly\"] = np.zeros((NR_TRAINIG_DATA, NR_STEPS, NR_RUNS))\n", - "auroc_prop = {}\n", - "auroc_prop[\"forest\"] = np.zeros((NR_TRAINIG_DATA, NR_STEPS, NR_RUNS))\n", - "auroc_prop[\"grassland\"] = np.zeros((NR_TRAINIG_DATA, NR_STEPS, NR_RUNS))\n", - "auroc_prop[\"hilly\"] = np.zeros((NR_TRAINIG_DATA, NR_STEPS, NR_RUNS))\n", - "\n", - "\n", - "for data in res:\n", - " percentage, steps, run = data[\"percentage\"], data[\"steps\"], data[\"run\"]\n", - " try:\n", - " da = [v for v in data[\"results\"].values()][0]\n", - " auroc_gt[data[\"scene\"]][int(percentage / 10) - 1, int(steps / STEPS_DIV), run] = da[\"test_acc_gt_image\"]\n", - " auroc_prop[data[\"scene\"]][int(percentage / 10) - 1, int(steps / STEPS_DIV), run] = da[\"test_acc_self_image\"]\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "df98e062", - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "fontdict = {\n", - " \"fontsize\": 16,\n", - " \"fontweight\": plt.rcParams[\"axes.titleweight\"],\n", - " \"verticalalignment\": \"baseline\",\n", - " \"horizontalalignment\": \"center\",\n", - "}\n", - "\n", - "\n", - "def plot(title, data, env, ax0, ax1, plot_y, scene):\n", - " ax1.tick_params(axis=\"both\", which=\"major\", labelsize=12)\n", - "\n", - " # Plot Matrix\n", - " label_y = [str(k) for k in range(10, 101, 10)]\n", - " label_x = [str(200)] + [str(int(j / 1000)) + \"k\" for j in range(300, data.shape[1] * 102, 100)]\n", - "\n", - " im = ax1.imshow(data, cmap=sns.color_palette(\"RdYlBu\", as_cmap=True))\n", - " ax1.set_xticks([1, 21, 21 + 25, 21 + 50, 21 + 75])\n", - " ax1.set_yticks([0, 5, 9])\n", - " ax1.set_xticklabels([50, 250, 500, 750, 1000])\n", - " ax1.set_yticklabels([10, 50, 100])\n", - "\n", - " ax1.invert_yaxis()\n", - " ax1.set_xlabel(f\"{scene} - Training Steps\")\n", - "\n", - " if plot_y:\n", - " ax1.set_ylabel(\"Data Percentage\")\n", - "\n", - " ax0.xaxis.set_label_coords(0.5, -3)\n", - "\n", - " plt.setp(ax1.get_xticklabels(), rotation=0, ha=\"center\", rotation_mode=\"anchor\")\n", - " ax1.set_aspect(3.5)\n", - "\n", - " # Plot color bar\n", - " nr = 100\n", - " bar = np.arange(0, nr)[None]\n", - " im = ax0.imshow(bar, cmap=sns.color_palette(\"RdYlBu\", as_cmap=True))\n", - " ax0.set_aspect(2)\n", - " ax0.set_xticks(np.array([0, nr - 1]))\n", - " ax0.set_yticks(np.array([]))\n", - " ax0.set_xticklabels(np.array([round(data.min() * 100, 1), round(data.max() * 100, 1)]))\n", - " ax0.set_yticklabels([])\n", - "\n", - " ax1.tick_params(axis=\"both\", which=\"major\", labelsize=12)\n", - " ax1.tick_params(axis=\"both\", which=\"minor\", labelsize=12)\n", - " ax0.tick_params(axis=\"both\", which=\"major\", labelsize=12)\n", - " ax0.tick_params(axis=\"both\", which=\"minor\", labelsize=12)\n", - "\n", - " ax0.set_xlabel(\"ACC in percentage\", fontdict=fontdict)\n", - " ax0.xaxis.set_label_coords(0.5, -3)\n", - "\n", - "\n", - "scale = 2\n", - "width_half_inch, width_double_inch = 88.9 / 25.4, 182.0 / 25.4\n", - "height_inch = 33 / 25.4\n", - "\n", - "fig = plt.figure(figsize=(width_double_inch * scale, height_inch * scale), dpi=300)\n", - "\n", - "fig.set_tight_layout(False)\n", - "\n", - "gs1 = fig.add_gridspec(nrows=2, ncols=3, left=0.05, right=0.98, top=1, wspace=0.15, height_ratios=[1, 15])\n", - "ax0, ax1, ax2 = fig.add_subplot(gs1[0, 0]), fig.add_subplot(gs1[0, 1]), fig.add_subplot(gs1[0, 2])\n", - "gs2 = fig.add_gridspec(nrows=2, ncols=3, left=0.05, right=0.98, top=1.2, wspace=0.15, height_ratios=[1, 5])\n", - "\n", - "ax3 = fig.add_subplot(gs2[1, 0])\n", - "ax4, ax5 = fig.add_subplot(gs2[1, 1], sharex=ax3), fig.add_subplot(gs2[1, 2], sharex=ax3)\n", - "\n", - "plot(\"Hilly\", auroc_gt[\"hilly\"][:, 3:].mean(axis=2), \"hilly\", ax0, ax3, True, \"Hilly\")\n", - "plot(\"Forest\", auroc_gt[\"forest\"][:, 3:].mean(axis=2), \"forest\", ax1, ax4, False, \"Forest\")\n", - "\n", - "da = auroc_gt[\"grassland\"][:, 3:].mean(axis=2)\n", - "plot(\"Grass\", da, \"grassland\", ax2, ax5, False, \"Grass\")\n", - "\n", - "fig.savefig(f\"/tmp/img.png\")\n", - "fig.savefig(f\"/tmp/img.pdf\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "wvn", - "language": "python", - "name": "wvn" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/scripts/paper/get_times_of_perugia_dataset.py b/scripts/paper/get_times_of_perugia_dataset.py deleted file mode 100644 index e60f896c..00000000 --- a/scripts/paper/get_times_of_perugia_dataset.py +++ /dev/null @@ -1,27 +0,0 @@ -from pathlib import Path -from wild_visual_navigation.learning.utils import load_env -import os - -perugia_root = os.path.join(load_env()["perugia_root"], "wvn_output/split") -ls = [str(s) for s in Path(perugia_root).rglob("*.txt") if str(s).find("desc") == -1] -import numpy as np - -for l in ls: - pa = [] - with open(l, "r") as f: - while True: - - line = f.readline() - if not line: - break - - pa.append(line.strip()) - folder = np.unique(np.array([s.split("/")[:-2] for s in pa])) - - mission = np.unique(np.array([s.split("/")[-3] for s in pa])) - arr = np.array([float(s.split("/")[-1][:-3].replace("_", ".")) for s in pa]) - delta_t = 0 - for m in mission: - mask = np.array([s.split("/")[-3] for s in pa]) == m - delta_t += arr[mask][-1] - arr[mask][0] - print(delta_t) diff --git a/scripts/paper/learning_curve.ipynb b/scripts/paper/learning_curve.ipynb deleted file mode 100644 index d739381d..00000000 --- a/scripts/paper/learning_curve.ipynb +++ /dev/null @@ -1,363 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "38cca5da", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib\n", - "import pickle\n", - "from wild_visual_navigation import WVN_ROOT_DIR\n", - "import os\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# prev learning_curve\n", - "base = \"results/ablations/time_adaptation-1000_ge76\"\n", - "\n", - "with open(os.path.join(WVN_ROOT_DIR, base, \"time_adaptation-1000_steps.pkl\"), \"rb\") as f:\n", - " runs = pickle.load(f)\n", - "scene = \"forest\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5548cbce", - "metadata": {}, - "outputs": [], - "source": [ - "store_every_n_steps = 1\n", - "nr_runs = int(np.array([run[\"run\"] for run in runs]).max()) + 1\n", - "max_step = np.array([run[\"steps\"] for run in runs]).max()\n", - "max_steps = int(max_step / store_every_n_steps) + 1\n", - "\n", - "y_names = [\n", - " \"test_auroc_gt_image\",\n", - " \"test_auroc_self_image\",\n", - " \"test_auroc_anomaly_gt_image\",\n", - " \"test_auroc_anomaly_self_image\",\n", - " \"test_acc_gt_image\",\n", - " \"test_acc_self_image\",\n", - " \"test_acc_anomaly_gt_image\",\n", - " \"test_acc_anomaly_self_image\",\n", - " \"trainer_logged_metrictest_loss_reco\",\n", - " \"trainer_logged_metrictest_loss_trav\",\n", - " \"trainer_logged_metrictest_loss\",\n", - "]\n", - "\n", - "y = {k: np.zeros((max_steps, nr_runs)) for k in y_names}\n", - "x_steps = np.arange(0, max_step + store_every_n_steps, store_every_n_steps)\n", - "\n", - "model_paths = [\"nan\"] * max_steps\n", - "for run in runs:\n", - " step = int(run[\"steps\"] / store_every_n_steps)\n", - " for y_name in y_names:\n", - " try:\n", - " y[y_name][step, run[\"run\"]] = run[\"results\"][scene][y_name]\n", - " except:\n", - " pass\n", - " model_paths[step] = run[\"model_path\"]\n", - "\n", - "y[\"trainer_logged_metrictest_loss_trav\"] *= 16.666666" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5a3ee9c8", - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "###################################### AUROC ####################################################\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from wild_visual_navigation.visu import paper_colors_rgb_u8, paper_colors_rgba_u8\n", - "from wild_visual_navigation.visu import paper_colors_rgb_f, paper_colors_rgba_f\n", - "\n", - "width_singel_inch, width_double_inch = 88.9 / 25.4, 182.0 / 25.4\n", - "height_inch = 82 / 25.4\n", - "scale = 2\n", - "sns.set_style(\"darkgrid\")\n", - "plt.rcParams.update({\"font.size\": 16})\n", - "\n", - "fig = plt.figure(figsize=(width_double_inch * scale, height_inch * scale), dpi=300)\n", - "gs = fig.add_gridspec(nrows=3, ncols=8, left=0.035, right=0.98, top=0.97, wspace=0.9, hspace=0.0)\n", - "gs = fig.add_gridspec(nrows=3, ncols=8, left=0.035, right=0.98, top=0.97, wspace=0.9, height_ratios=[1.4, 1, 1])\n", - "ax0 = fig.add_subplot(gs[0, 0:4])\n", - "ax1 = fig.add_subplot(gs[0, 4:])\n", - "\n", - "# fig, ax = plt.subplots(1, 1, figsize=(width_singel_inch*scale, height_inch*scale), dpi=300)\n", - "ax0.tick_params(axis=\"both\", which=\"major\", labelsize=12)\n", - "ax1.tick_params(axis=\"both\", which=\"major\", labelsize=12)\n", - "\n", - "\n", - "fig.set_tight_layout(True)\n", - "\n", - "\n", - "def plot_auroc(keys, x_steps, y, lim_min, lim_max, ax, y_tags, y_axis_labels, two_axis=False):\n", - " x = [x_steps for k in keys]\n", - " y_mean = [y[k].mean(axis=1) for k in keys]\n", - " y_lower = [y[k].mean(axis=1) - y[k].std(axis=1) for k in keys]\n", - " y_upper = [y[k].mean(axis=1) + y[k].std(axis=1) for k in keys]\n", - " # not used\n", - " ax_ori = ax\n", - "\n", - " for j, (_x, _y, _y_lower, _y_upper, _y_tag) in enumerate(zip(x, y_mean, y_lower, y_upper, y_tags)):\n", - " k = [k for k in paper_colors_rgb_f.keys()][j]\n", - " if two_axis and j == 1:\n", - " ax.plot([0], [0], label=_y_tag, color=paper_colors_rgb_f[k])\n", - " ax.legend()\n", - "\n", - " ax.set_ylabel(y_axis_labels[j])\n", - "\n", - " if two_axis and j == 1:\n", - " ax = ax.twinx()\n", - "\n", - " ax.plot(_x, _y, label=_y_tag, color=paper_colors_rgb_f[k])\n", - " if not (_y_lower is None):\n", - " ax.plot(_x, _y_lower, color=paper_colors_rgb_f[k + \"_light\"], alpha=0.1)\n", - " ax.plot(_x, _y_upper, color=paper_colors_rgb_f[k + \"_light\"], alpha=0.1)\n", - " ax.fill_between(_x, _y_lower, _y_upper, color=paper_colors_rgb_f[k + \"_light\"], alpha=0.2)\n", - "\n", - " if not (two_axis and j == 1):\n", - " ax.legend()\n", - "\n", - " ax.tick_params(axis=\"both\", which=\"major\", labelsize=12)\n", - "\n", - " # ax.plot(np.linspace(0, 1, 100), np.linspace(0, 1, 100), linestyle=\"--\", color=\"gray\")\n", - "\n", - " ax_ori.set_xlabel(\"Training Steps\")\n", - " ax_ori.spines[\"top\"].set_visible(False)\n", - " ax_ori.spines[\"right\"].set_visible(False)\n", - " label_x = [0, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000]\n", - " ax_ori.set_xticks([0, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000])\n", - " ax_ori.set_xticklabels(label_x)\n", - "\n", - " if not two_axis:\n", - " ax_ori.set_ylabel(y_axis_labels[0])\n", - "\n", - " # plt.xlim(0, 1)\n", - " ax_ori.set_ylim(lim_min, lim_max)\n", - "\n", - "\n", - "# plot_auroc(\n", - "# [\"test_auroc_gt_image\", \"test_auroc_self_image\"],\n", - "# x_steps,\n", - "# y,\n", - "# 0.6,\n", - "# 1,\n", - "# ax0,\n", - "# [\"GT AUROC\", \"SELF AUROC\"],\n", - "# y_axis_labels=[\"AUROC\"],\n", - "# )\n", - "plot_auroc(\n", - " [\"trainer_logged_metrictest_loss_reco\", \"trainer_logged_metrictest_loss_trav\"],\n", - " x_steps,\n", - " y,\n", - " None,\n", - " None,\n", - " ax0,\n", - " [\"Reco\", \"Trav\"],\n", - " y_axis_labels=[\"Loss\"],\n", - ")\n", - "\n", - "plot_auroc(\n", - " [\"test_acc_gt_image\", \"test_acc_self_image\"],\n", - " x_steps,\n", - " y,\n", - " None,\n", - " None,\n", - " ax1,\n", - " [\"GT Acc\", \"SELF Acc\"],\n", - " y_axis_labels=[\"Accuracy\", \"Self Acc\"],\n", - " two_axis=False,\n", - ")\n", - "\n", - "\n", - "###################################### Images ####################################################\n", - "\n", - "\n", - "# Setup dataloader\n", - "from wild_visual_navigation.learning.dataset import get_ablation_module\n", - "from wild_visual_navigation.learning.utils import load_env\n", - "from wild_visual_navigation import WVN_ROOT_DIR\n", - "from wild_visual_navigation.cfg import ExperimentParams\n", - "\n", - "from wild_visual_navigation.visu import LearningVisualizer\n", - "from PIL import Image\n", - "from wild_visual_navigation.learning.model import get_model\n", - "from dataclasses import asdict\n", - "import torch\n", - "\n", - "with open(os.path.join(WVN_ROOT_DIR, base, \"experiment_params.pkl\"), \"rb\") as f:\n", - " exp = pickle.load(f)\n", - "exp.ablation_data_module\n", - "\n", - "env = load_env()\n", - "\n", - "exp.ablation_data_module\n", - "ablation_data_module = {\n", - " \"batch_size\": 1,\n", - " \"num_workers\": 0,\n", - " \"env\": scene,\n", - " \"feature_key\": exp.ablation_data_module.feature_key,\n", - " \"test_equals_val\": False,\n", - " \"val_equals_test\": False,\n", - " \"test_all_datasets\": False,\n", - " \"training_data_percentage\": 100,\n", - " \"training_in_memory\": False,\n", - "}\n", - "train_loader, val_loader, test_loader = get_ablation_module(**ablation_data_module, perugia_root=env[\"perugia_root\"])\n", - "test_scenes = [a.dataset.env for a in test_loader]\n", - "test_all_datasets = True\n", - "\n", - "\n", - "def load_model(model_cfg: ExperimentParams.ModelParams, checkpoint_path: str):\n", - " model = get_model(asdict(model_cfg))\n", - " ckpt = torch.load(checkpoint_path)\n", - " ckpt = {\n", - " k.replace(\"_model.\", \"\"): v\n", - " for k, v in ckpt.items()\n", - " if k.find(\"_traversability\") == -1 and k.find(\"threshold\") == -1\n", - " }\n", - " res = model.load_state_dict(ckpt, strict=True)\n", - " return model\n", - "\n", - "\n", - "fontdict = {\n", - " \"fontsize\": 16,\n", - " \"fontweight\": plt.rcParams[\"axes.titleweight\"],\n", - " \"verticalalignment\": \"baseline\",\n", - " \"horizontalalignment\": \"center\",\n", - "}\n", - "\n", - "if True:\n", - " for l, img_nr in enumerate([20, 32]):\n", - " if l == 0:\n", - " gs2 = fig.add_gridspec(nrows=3, ncols=8, left=0.06, right=0.97, top=1.0, wspace=0.1, hspace=0.1)\n", - " gs2 = fig.add_gridspec(\n", - " nrows=3, ncols=8, left=0.03, right=0.98, top=1.0, wspace=0.1, height_ratios=[1.7, 3, 0.1]\n", - " )\n", - "\n", - " if l == 1:\n", - " gs2 = fig.add_gridspec(nrows=3, ncols=8, left=0.06, right=0.97, top=1.0, wspace=0.1, hspace=0.1)\n", - " gs2 = fig.add_gridspec(\n", - " nrows=3, ncols=8, left=0.03, right=0.98, top=1.0, bottom=0, wspace=0.1, height_ratios=[1.3, 2.15, 2.0]\n", - " )\n", - "\n", - " thresholds = []\n", - " models = [0, 99, 249, 499, 999]\n", - " for j, training_step in enumerate(models):\n", - " model = load_model(exp.model, model_paths[training_step])\n", - " model.to(\"cuda\")\n", - " graph = test_loader[0].dataset[img_nr]\n", - " pred = model(graph)\n", - " visualizer = LearningVisualizer()\n", - " img = graph.img\n", - " seg = graph.seg\n", - " res = visualizer.plot_detectron(img[0], graph.label[0].type(torch.long), not_log=True, max_seg=2)\n", - " center = graph.center\n", - "\n", - " threshold = torch.load(model_paths[training_step])[\"threshold\"].item()\n", - " thresholds.append(threshold)\n", - " traversability = pred[:, 0]\n", - " m = traversability < threshold\n", - " # Scale untraversable\n", - " traversability[m] *= 0.5 / threshold\n", - " # Scale traversable\n", - " traversability[~m] -= threshold\n", - " traversability[~m] *= 0.5 / (1 - threshold)\n", - " traversability[~m] += 0.5\n", - " traversability = traversability.clip(0, 1)\n", - "\n", - " buffer_traversability = graph.seg.clone().type(torch.float32).flatten()\n", - " BS, H, W = graph.seg.shape\n", - " seg_pixel_index = (graph.seg).flatten()\n", - " buffer_traversability = traversability[seg_pixel_index].reshape(BS, H, W)\n", - " res_pred = visualizer.plot_detectron_classification(img[0], buffer_traversability, not_log=True)\n", - "\n", - " # res_pred = visualizer.plot_traversability_graph_on_seg(\n", - " # traversability, seg[0], graph, center, img[0], not_log=True, colorize_invalid_centers=True\n", - " # )\n", - "\n", - " ax = fig.add_subplot(gs2[1 + l, j + 2])\n", - " ax.imshow(res_pred)\n", - " ax.axis(\"off\")\n", - " step = int((training_step + 1) * 1)\n", - " if l == 1:\n", - " ax.set_title(f\"Step-{step}\", fontdict=fontdict, loc=\"center\", y=0, pad=-16)\n", - "\n", - " # graph.seg[0]\n", - " res_img = visualizer.plot_detectron(\n", - " img[0],\n", - " graph.label[0].type(torch.long) * 99,\n", - " not_log=True,\n", - " max_seg=100,\n", - " colormap=\"RdYlBu\",\n", - " boundary_seg=None,\n", - " alpha=0,\n", - " )\n", - " res_gt = visualizer.plot_detectron(\n", - " img[0],\n", - " graph.label[0].type(torch.long) * 99,\n", - " not_log=True,\n", - " max_seg=100,\n", - " colormap=\"RdYlBu\",\n", - " boundary_seg=None,\n", - " )\n", - " res_prop = visualizer.plot_traversability_graph_on_seg(\n", - " graph.y, seg[0], graph, center, img[0], not_log=True, colorize_invalid_centers=True\n", - " )\n", - "\n", - " ax0 = fig.add_subplot(gs2[1 + l, 0])\n", - " ax1 = fig.add_subplot(gs2[1 + l, 1])\n", - " ax7 = fig.add_subplot(gs2[1 + l, 7])\n", - "\n", - " res = ax0.text(-110, 370, s=f\"Example {l+1}\", fontsize=16, rotation=90)\n", - "\n", - " if l == 1:\n", - " ax0.set_title(\"Image\", fontdict=fontdict, loc=\"center\", y=0, pad=-16)\n", - " ax1.set_title(\"Supervision\", fontdict=fontdict, loc=\"center\", y=0, pad=-16)\n", - " ax7.set_title(\"Label\", fontdict=fontdict, loc=\"center\", y=0, pad=-16)\n", - "\n", - " ax0.axis(\"off\")\n", - " ax1.axis(\"off\")\n", - " ax7.axis(\"off\")\n", - "\n", - " ax0.imshow(res_img)\n", - " ax1.imshow(res_prop)\n", - " ax7.imshow(res_gt)\n", - "\n", - "fig.set_tight_layout(False)\n", - "fig.savefig(\"/tmp/img.png\", dpi=300)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "wvn", - "language": "python", - "name": "wvn" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/scripts/paper/model_timing.ipynb b/scripts/paper/model_timing.ipynb deleted file mode 100644 index d0c0845c..00000000 --- a/scripts/paper/model_timing.ipynb +++ /dev/null @@ -1,77 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "dcd980a3", - "metadata": {}, - "outputs": [], - "source": [ - "# %matplotlib notebook\n", - "\n", - "from wild_visual_navigation import WVN_ROOT_DIR\n", - "import os\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import pickle\n", - "\n", - "folder = \"time_extractor\"\n", - "ws = os.environ[\"ENV_WORKSTATION_NAME\"]\n", - "p = os.path.join(WVN_ROOT_DIR, f\"results/ablations/{folder}_ablation_{ws}/{folder}_ablation_test_results.pkl\")\n", - "\n", - "with open(p, \"rb\") as f:\n", - " res = pickle.load(f)\n", - "\n", - "import scipy\n", - "import pandas as pd\n", - "import pickle\n", - "import copy\n", - "\n", - "data = {\n", - " \"cfg_name\": [],\n", - " \"runtime_mean\": [],\n", - " \"runtime_std\": [],\n", - " \"runtime_warmup_mean\": [],\n", - " \"runtime_warmup_std\": [],\n", - " \"parameter\": [],\n", - "}\n", - "\n", - "for name, res in res.items():\n", - " data[\"cfg_name\"].append(name)\n", - " a = np.array(res[\"times_eval\"])\n", - " data[\"runtime_mean\"].append(a.mean())\n", - " data[\"runtime_std\"].append(a.std())\n", - " a = np.array(res[\"times_warmup\"])\n", - " data[\"runtime_warmup_mean\"].append(a.mean())\n", - " data[\"runtime_warmup_std\"].append(a.std())\n", - " data[\"parameter\"].append(res[\"parameter\"])\n", - "\n", - "df = pd.DataFrame.from_dict(data)\n", - "df" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "wvn", - "language": "python", - "name": "wvn" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/scripts/paper/ros_gps_to_tf_publisher.py b/scripts/paper/ros_gps_to_tf_publisher.py deleted file mode 100644 index de05a49a..00000000 --- a/scripts/paper/ros_gps_to_tf_publisher.py +++ /dev/null @@ -1,67 +0,0 @@ -import rospy -import tf -from geometry_msgs.msg import PointStamped -import os -import subprocess -import time - - -def callback(point_stamped, nr): - br = tf.TransformBroadcaster() - br.sendTransform( - (-point_stamped.point.x, -point_stamped.point.y, -point_stamped.point.z), - (0, 0, 0, 1), - rospy.Time.now(), - "map", - f"sensor{str(nr)}", - ) - - -root = "/media/Data/Datasets/2022_Perugia/day3/mission_data" -bags = { - "3": "2022-05-12T09-45-07_mission_0_day_3", - "2": "2022-05-12T09-57-13_mission_0_day_3", - "1": "2022-05-12T10-18-16_mission_0_day_3", - "4": "2022-05-12T10-34-03_mission_0_day_3", - "5": "2022-05-12T10-45-20_mission_0_day_3", -} - -bags = { - "1": "2022-05-12T17-36-33_mission_0_day_3", - "2": "2022-05-12T15-36-30_mission_0_day_3", - "3": "2022-05-12T15-52-37_mission_0_day_3", - "4": "2022-05-12T18-21-23_mission_0_day_3", -} - -bags = { - "1": "2022-05-12T11-56-13_mission_0_day_3", - "2": "2022-05-12T11-44-56_mission_0_day_3", - "3": "2022-05-12T12-08-09_mission_0_day_3", -} - -# Launch rosmaster -# roscore_process = subprocess.Popen(["roscore"], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, env=env) -# roscore_process.kill() -print(" roslaunch wild_visual_navigation_ros trajectory_server.launch") -# trajectory_process = subprocess.Popen(["source /home/jonfrey/catkin_ws/devel/setup.bash && roslaunch wild_visual_navigation_ros trajectory_server.launch"], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) -# trajectory_process.kill() -for i, m in bags.items(): - cmd = f"rosbag play {root}/{m}/*gps*.bag -r 10 /rover/piksi/position_receiver_0/ros/pos_enu:=/rover/piksi/position_receiver_0/ros/pos_enu{i}" - print(cmd) - # Run simulation - # try: - # subprocess.call( - # ["rosbag", "play", f"{root}/{m}/*tf*.bag", "-r", "10", f"/rover/piksi/position_receiver_0/ros/pos_enu:=/rover/piksi/position_receiver_0/ros/pos_enu{i}"], - # stdout=subprocess.DEVNULL, - # stderr=subprocess.DEVNULL, - # )rosbag play /media/Data/Datasets/2022_Perugia/day3/mission_data/2022-05-12T09-45-07_mission_0_day_3/*tf*.bag -r 10 /rover/piksi/position_receiver_0/ros/pos_enu:=/rover/piksi/position_receiver_0/ros/pos_enu1 - # except: - # print("error") - -rospy.init_node("point_to_tf") -sub = rospy.Subscriber("/rover/piksi/position_receiver_0/ros/pos_enu1", PointStamped, callback, 1) -sub = rospy.Subscriber("/rover/piksi/position_receiver_0/ros/pos_enu2", PointStamped, callback, 2) -sub = rospy.Subscriber("/rover/piksi/position_receiver_0/ros/pos_enu3", PointStamped, callback, 3) -sub = rospy.Subscriber("/rover/piksi/position_receiver_0/ros/pos_enu4", PointStamped, callback, 4) -sub = rospy.Subscriber("/rover/piksi/position_receiver_0/ros/pos_enu5", PointStamped, callback, 5) -rospy.spin() diff --git a/scripts/paper/ros_odom_to_tf_publisher.py b/scripts/paper/ros_odom_to_tf_publisher.py deleted file mode 100644 index 39fe1aeb..00000000 --- a/scripts/paper/ros_odom_to_tf_publisher.py +++ /dev/null @@ -1,25 +0,0 @@ -import rospy -import tf -from geometry_msgs.msg import PoseStamped - -i = 0 - - -def pose_callback(data): - br = tf.TransformBroadcaster() - br.sendTransform( - (data.pose.position.x, data.pose.position.y, data.pose.position.z), - (data.pose.orientation.x, data.pose.orientation.y, data.pose.orientation.z, data.pose.orientation.w), - data.header.stamp, - "base", - "abc", - ) - print(data.header.frame_id) - print(data.header.stamp) - print("published") - - -if __name__ == "__main__": - rospy.init_node("pose_to_tf_node_new") - rospy.Subscriber("/mapping_node/o3d_slam_lidar_pose_in_map", PoseStamped, pose_callback) - rospy.spin() diff --git a/scripts/paper/time_extractor.py b/scripts/paper/time_extractor.py deleted file mode 100644 index e2e1b386..00000000 --- a/scripts/paper/time_extractor.py +++ /dev/null @@ -1,97 +0,0 @@ -from wild_visual_navigation.feature_extractor import FeatureExtractor -import os -from pathlib import Path -import torch -import pickle -from wild_visual_navigation import WVN_ROOT_DIR - -if __name__ == "__main__": - - folder = "time_extractor" - warm_up_runs = 10 - hot_runs = 10 - special_key = "" - - # Setup folder - ws = os.environ["ENV_WORKSTATION_NAME"] - res_folder = os.path.join(WVN_ROOT_DIR, f"results/ablations/{folder}_ablation_{ws}") - p = os.path.join(res_folder, f"{folder}_ablation_test_results{special_key}.pkl") - Path(p).parent.mkdir(parents=True, exist_ok=True) - try: - os.remove(p) - except OSError as error: - pass - device = "cuda" if torch.cuda.is_available() else "cpu" - - # Create a dictionary of feature extractors. - fes = {} - fes["slic100_dino224_16"] = FeatureExtractor( - device, "slic", "dino", 224, model_type="vit_small", patch_size=16, slic_num_components=100 - ) - fes["slic100_sift"] = FeatureExtractor(device, "slic", "sift", slic_num_components=100) - fes["slic100_efficientnet_b4"] = FeatureExtractor( - device, "slic", "torchvision", (384, 380), model_type="efficientnet_b4", slic_num_components=100 - ) - fes["slic100_resnet50"] = FeatureExtractor( - device, "slic", "torchvision", 448, model_type="resnet50", slic_num_components=100 - ) - fes["slic100_resnet50_dino"] = FeatureExtractor( - device, "slic", "torchvision", 448, model_type="resnet50_dino", slic_num_components=100 - ) - - res = {} - for extractor_name, fe in fes.items(): - print(extractor_name) - input_size = fe._input_size - if not isinstance(input_size, tuple): - input_size = (input_size, input_size) - - times_warmup = [] - for i in range(warm_up_runs): - img = torch.rand((1, 3, input_size[0], input_size[1])) - img.to(device) - start = torch.cuda.Event(enable_timing=True) - end = torch.cuda.Event(enable_timing=True) - start.record() - feat = fe.compute_features(img, None, None) - end.record() - torch.cuda.synchronize() - t = start.elapsed_time(end) - times_warmup.append(t) - - times_eval = [] - for i in range(hot_runs): - img = torch.rand((1, 3, input_size[0], input_size[1])) - img.to(device) - start = torch.cuda.Event(enable_timing=True) - end = torch.cuda.Event(enable_timing=True) - start.record() - feat = fe.compute_features(img, None, None) - end.record() - torch.cuda.synchronize() - t = start.elapsed_time(end) - times_eval.append(t) - try: - model = fe.extractor.model - model.train() - params = sum(p.numel() for p in model.parameters() if p.requires_grad) - - except: - try: - model = fe._extractor - params = sum(p.numel() for p in model.parameters() if p.requires_grad) - except: - params = 0 - - res[extractor_name] = { - "times_eval": times_eval, - "times_warmup": times_warmup, - "parameter": params, - "warm_up_runs": warm_up_runs, - "hot_runs": hot_runs, - "device": torch.cuda.get_device_name(), - "input_size": input_size, - } - - with open(p, "wb") as handle: - pickle.dump(res, handle, protocol=pickle.HIGHEST_PROTOCOL) diff --git a/scripts/train.py b/scripts/train.py deleted file mode 100644 index f59e29aa..00000000 --- a/scripts/train.py +++ /dev/null @@ -1,26 +0,0 @@ -from simple_parsing import ArgumentParser -from wild_visual_navigation.cfg import ExperimentParams -from wild_visual_navigation.learning.general import training_routine -from wild_visual_navigation.learning.utils import load_yaml -from wild_visual_navigation import WVN_ROOT_DIR -from wild_visual_navigation.utils import override_params -import os - - -if __name__ == "__main__": - """ - WARNING: the command line arguments are overriden based on the enviornment file! Not the inutive other way around - """ - - parser = ArgumentParser() - parser.add_arguments(ExperimentParams, dest="experiment") - - parser.add_argument("--exp", type=str, default="nan", help="Overwrite params") - args = parser.parse_args() - - p = os.path.join(WVN_ROOT_DIR, "cfg/exp", args.exp) - if args.exp != "nan" and os.path.isfile(p): - exp_override = load_yaml(p) - args.experiment = override_params(args.experiment, exp_override) - - training_routine(args.experiment) diff --git a/scripts/train_optuna.py b/scripts/train_optuna.py deleted file mode 100644 index 1b4815c1..00000000 --- a/scripts/train_optuna.py +++ /dev/null @@ -1,82 +0,0 @@ -import optuna -from simple_parsing import ArgumentParser -import copy -import os -import neptune.new.integrations.optuna as optuna_utils -import torch - -from wild_visual_navigation.cfg import ExperimentParams -from wild_visual_navigation.learning.general import training_routine -from wild_visual_navigation.learning.utils import get_neptune_run -from wild_visual_navigation.utils import override_params -from wild_visual_navigation.learning.utils import load_yaml -from wild_visual_navigation import WVN_ROOT_DIR - - -def objective(trial, experiment: ExperimentParams): - exp = copy.deepcopy(experiment) - - # exp.optimizer.lr = trial.suggest_float("lr", 0.0001, 0.01, log=True) - exp.loss.w_trav = trial.suggest_float("w_trav", 0.0, 1.0) - exp.loss.w_temp = trial.suggest_float("w_temp", 0.0, 1.0) - exp.loss.w_reco = trial.suggest_float("w_reco", 0.0, 1.0) - exp.loss.anomaly_balanced = trial.suggest_categorical("anomaly_balanced", [True]) - - # if not trial.suggest_categorical("use_temporal_consistency", [True, False]): - # exp.loss.w_temp = 0.0 - # exp.trainer.max_epochs = trial.suggest_int("max_epochs", 1, 10) - - res, _ = training_routine(exp) - - torch.cuda.empty_cache() - return list(res.values())[0]["test_auroc_gt_image"] - - -if __name__ == "__main__": - parser = ArgumentParser() - - parser.add_arguments(ExperimentParams, dest="experiment") - parser.add_argument("--exp", type=str, default="nan", help="Overwrite params") - - parser.add_argument("--name", type=str, default="sweep_loss_function", help="Name of sweep") - parser.add_argument("--n_trials", type=int, default=100, help="Number Trials") - - args = parser.parse_args() - - p = os.path.join(WVN_ROOT_DIR, "cfg/exp", args.exp) - if args.exp != "nan" and os.path.isfile(p): - exp_override = load_yaml(p) - args.experiment = override_params(args.experiment, exp_override) - - args.experiment.general.name = os.path.join(args.name, args.experiment.general.name) - - run = get_neptune_run(neptune_project_name=args.experiment.logger.neptune_project_name, tags=["optuna", args.name]) - - neptune_callback = optuna_utils.NeptuneCallback( - run, - plots_update_freq=2, - log_plot_slice=True, - log_plot_contour=True, - ) - - args.experiment.logger.name = "skip" - args.experiment.trainer.enable_checkpointing = False - args.experiment.cb_checkpoint.active = False - args.experiment.visu.train = 0 - args.experiment.visu.val = 0 - args.experiment.visu.test = 0 - args.experiment.trainer.check_val_every_n_epoch = 100000 - args.experiment.general.store_model_every_n_steps = None - args.experiment.ablation_data_module.training_in_memory = True - args.experiment.trainer.max_steps = 1000 - args.experiment.trainer.max_epochs = None - args.experiment.general.log_to_disk = False - args.experiment.trainer.progress_bar_refresh_rate = 0 - args.experiment.trainer.weights_summary = None - args.experiment.trainer.enable_progress_bar = False - - binded_objective = lambda trial: objective(trial=trial, experiment=args.experiment) - study = optuna.create_study(direction=optuna.study.StudyDirection.MAXIMIZE) - study.optimize(binded_objective, n_trials=args.n_trials, callbacks=[neptune_callback]) - - trial = study.best_trial diff --git a/setup.py b/setup.py index 7b31d434..6d0d5d64 100644 --- a/setup.py +++ b/setup.py @@ -1,13 +1,49 @@ from setuptools import find_packages from distutils.core import setup +# Minimum dependencies required prior to installation +# TODO: If we add the "opencv-python>=4.6", it wont build on the jetson + +INSTALL_REQUIRES = [ + # generic + "numpy", + "tqdm", + "kornia>=0.6.5", + "pip", + "torchvision", + "torch>=1.21", + "torchmetrics", + "pytorch_lightning>=1.6.5", + "pytest", + "scipy", + "scikit-image", + "scikit-learn", + "matplotlib", + "seaborn", + "pandas", + "pytictac", + "torch_geometric", + "omegaconf", + "optuna", + "neptune", + "fast-slic", + "hydra-core", + "prettytable", + "termcolor", + "pydensecrf@git+https://github.com/lucasb-eyer/pydensecrf.git", + "liegroups@git+https://github.com/mmattamala/liegroups", + "wget", + "rospkg", +] setup( name="wild_visual_navigation", version="0.0.1", author="Jonas Frey, Matias Mattamala", - author_email="jonfrey@ethz.ch, matias@leggedrobotics.com", + author_email="jonfrey@ethz.ch, matias@robots.oex.ac.uk", packages=find_packages(), - python_requires=">=3.6", + python_requires=">=3.7", description="A small example package", - install_requires=["kornia>=0.6.5"], + install_requires=[INSTALL_REQUIRES], + dependencies=["https://download.pytorch.org/whl/torch-2.1.0+cu121-cp38-cp38-linux_x86_64.whl"], + dependency_links=["https://download.pytorch.org/whl/torch-2.1.0+cu121-cp38-cp38-linux_x86_64.whl"], ) diff --git a/tests/test_confidence_generator.py b/tests/test_confidence_generator.py index f69d0d26..e2ab2bb1 100644 --- a/tests/test_confidence_generator.py +++ b/tests/test_confidence_generator.py @@ -2,7 +2,6 @@ import matplotlib.pyplot as plt import torch import torch.nn.functional as F -import numpy as np def generate_traversability_test_signal(N=500, T=10, events=[3, 8], event_length=0.5, device=torch.device("cpu")): @@ -13,7 +12,7 @@ def generate_traversability_test_signal(N=500, T=10, events=[3, 8], event_length event_length = [event_length] * len(events) for e, l in zip(events, event_length): - half_event = l / 2 + # half_event = l / 2 ini_event = e end_event = e + l x += torch.sigmoid(10 * (t - ini_event)) - torch.sigmoid(10 * (t - end_event)) @@ -21,6 +20,13 @@ def generate_traversability_test_signal(N=500, T=10, events=[3, 8], event_length def test_confidence_generator(): + from wild_visual_navigation.utils.testing import make_results_folder + from wild_visual_navigation.visu import get_img_from_fig + from os.path import join + + # Create test directory + outpath = make_results_folder("test_confidence_generator") + device = torch.device("cpu") N = 1000 sigma_factor = 0.5 @@ -67,7 +73,7 @@ def test_confidence_generator(): conf = torch.zeros((3, N), device=device) # Naive confidence generator - cg = ConfidenceGenerator(std_factor=sigma_factor).to(device) + cg = ConfidenceGenerator(std_factor=sigma_factor, method="latest_measurement").to(device) # Run for i in range(x.shape[0]): @@ -104,7 +110,13 @@ def test_confidence_generator(): # Signal subplot axs[0].plot(t_np, x_noisy_np, label="Reconstructed signal", color="r") axs[0].plot(t_np, x_np, label="Target signal", color="k") - axs[0].plot(t_np, x_noisy_positive_np, label="Positive samples", color=(0.5, 0, 0), marker=".") + axs[0].plot( + t_np, + x_noisy_positive_np, + label="Positive samples", + color=(0.5, 0, 0), + marker=".", + ) axs[0].set_ylabel("Signals") axs[0].legend(loc="upper right") @@ -144,7 +156,13 @@ def test_confidence_generator(): axs[2].set_ylabel("Confidence") axs[2].legend(loc="upper right") - plt.show() + img = get_img_from_fig(fig) + img.save( + join( + outpath, + "confidence_generator_test.png", + ) + ) if __name__ == "__main__": diff --git a/tests/test_configuration.py b/tests/test_configuration.py new file mode 100644 index 00000000..13055398 --- /dev/null +++ b/tests/test_configuration.py @@ -0,0 +1,17 @@ +from wild_visual_navigation.cfg import RosLearningNodeParams +from omegaconf import OmegaConf +from omegaconf import read_write +from omegaconf.dictconfig import DictConfig + + +def test_configuration(): + cfg = OmegaConf.structured(RosLearningNodeParams) + print(cfg) + with read_write(cfg): + cfg.image_callback_rate = 1.0 + + print(cfg.image_callback_rate) + + +if __name__ == "__main__": + test_configuration() diff --git a/tests/test_dino_time.py b/tests/test_dino_time.py index d96f4ce8..ce6b34a0 100644 --- a/tests/test_dino_time.py +++ b/tests/test_dino_time.py @@ -1,36 +1,38 @@ -from wild_visual_navigation import WVN_ROOT_DIR -from wild_visual_navigation.utils import Timer -from wild_visual_navigation.feature_extractor import DinoInterface, DinoTrtInterface, TrtModel -from collections import namedtuple, OrderedDict -from torchvision import transforms as T -import cv2 -import os +# from wild_visual_navigation import WVN_ROOT_DIR +from pytictac import Timer +from wild_visual_navigation.feature_extractor import ( + DinoInterface, + # TrtModel, +) + +# from collections import namedtuple, OrderedDict +# from torchvision import transforms as T +# import os import torch -import tensorrt as trt -import numpy as np -import os +from wild_visual_navigation.utils.testing import load_test_image, get_dino_transform + +# import tensorrt as trt +# import numpy as np def test_dino_interfacer(): device = "cuda" if torch.cuda.is_available() else "cpu" di = DinoInterface(device) + transform = get_dino_transform() - np_img = cv2.imread(os.path.join(WVN_ROOT_DIR, "assets/images/forest_clean.png")) - img = torch.from_numpy(cv2.cvtColor(np_img, cv2.COLOR_BGR2RGB)).to(device) - img = img.permute(2, 0, 1) - img = (img.type(torch.float32) / 255)[None] + img = load_test_image().to(device) ##################################################################################### for i in range(5): im = img + torch.rand(img.shape, device=img.device) / 100 - di.inference(di.transform(im)) + di.inference(transform(im)) ##################################################################################### with Timer("BS1 Dino Inference: "): for i in range(5): im = img + torch.rand(img.shape, device=img.device) / 100 with Timer("BS1 Dino Single: "): - res = di.inference(di.transform(im)) + di.inference(transform(im)) ##################################################################################### # img = img.repeat(4, 1, 1, 1) @@ -41,14 +43,15 @@ def test_dino_interfacer(): # res = di.inference(di.transform(im)) ##################################################################################### - # Conversion from ONNX model (https://github.com/facebookresearch/dino) - exported_trt_file = "dino_exported.trt" - exported_trt_path = os.path.join(WVN_ROOT_DIR, "assets/dino", exported_trt_file) - di_trt = DinoTrtInterface(exported_trt_path, device) + # # Conversion from ONNX model (https://github.com/facebookresearch/dino) + # from wild_visual_navigation.feature_extractor import DinoTrtInterface + # exported_trt_file = "dino_exported.trt" + # exported_trt_path = os.path.join(WVN_ROOT_DIR, "assets/dino", exported_trt_file) + # di_trt = DinoTrtInterface(exported_trt_path, device) - with Timer("TensorRT Inference: "): - im = img + torch.rand(img.shape, device=img.device) / 100 - y = di_trt.inference(di.transform(im).contiguous()) + # with Timer("TensorRT Inference: "): + # im = img + torch.rand(img.shape, device=img.device) / 100 + # di_trt.inference(di.transform(im).contiguous()) ##################################################################################### # Conversion using the torch_tensorrt library: https://github.com/pytorch/TensorRT diff --git a/tests/test_dino_time_settings.py b/tests/test_dino_time_settings.py index 02ca5cd5..22cc87f6 100644 --- a/tests/test_dino_time_settings.py +++ b/tests/test_dino_time_settings.py @@ -1,16 +1,20 @@ from wild_visual_navigation import WVN_ROOT_DIR -from wild_visual_navigation.utils import Timer -from wild_visual_navigation.feature_extractor import DinoInterface, DinoTrtInterface, TrtModel -from collections import namedtuple, OrderedDict -from torchvision import transforms as T -import cv2 +from wild_visual_navigation.feature_extractor import ( + DinoInterface, + # DinoTrtInterface, + # TrtModel, +) + +# from collections import namedtuple, OrderedDict +# from torchvision import transforms as T +# import cv2 import os from os.path import join import numpy as np import pandas as pd -import os import torch -import tensorrt as trt + +# import tensorrt as trt from tqdm import tqdm import contextlib @@ -35,10 +39,7 @@ def capture_output(): def run_dino_interfacer(): """Performance inference using stego and stores result as an image.""" - from wild_visual_navigation.utils import Timer - from wild_visual_navigation.visu import get_img_from_fig - import matplotlib.pyplot as plt - from stego.src import remove_axes + from pytictac import Timer import cv2 # Create test directory @@ -85,7 +86,7 @@ def run_dino_interfacer(): for model in MODELS: for patch in PATCHES: di = None - with capture_output() as out: + with capture_output(): # Create DINO di = DinoInterface( device=device, @@ -99,12 +100,12 @@ def run_dino_interfacer(): print(size, interp, model, patch, img_name) for n in tqdm(range(TRIALS)): - with capture_output() as out: + with capture_output(): try: timer.tic() - feat_dino = di.inference(di.transform(img), interpolate=False) + di.inference(di.transform(img), interpolate=False) t = timer.toc() - except Exception as e: + except Exception: t = np.nan samples.append([size, interp, model, patch, img_name, n, t]) @@ -114,7 +115,16 @@ def run_dino_interfacer(): # Make pandas dataframe df = pd.DataFrame( - samples, columns=["image_size", "interpolation", "model", "patch_size", "img_name", "sample", "time"] + samples, + columns=[ + "image_size", + "interpolation", + "model", + "patch_size", + "img_name", + "sample", + "time", + ], ) df.to_pickle(join(test_path, f"dino_time_settings_{TRIALS}iter.pkl")) df.to_csv(join(test_path, f"dino_time_settings_{TRIALS}iter.csv")) diff --git a/tests/test_feature_extractor.py b/tests/test_feature_extractor.py index 06776035..c0785f42 100644 --- a/tests/test_feature_extractor.py +++ b/tests/test_feature_extractor.py @@ -1,51 +1,52 @@ -from wild_visual_navigation import WVN_ROOT_DIR from wild_visual_navigation.feature_extractor import FeatureExtractor from wild_visual_navigation.visu import get_img_from_fig +from wild_visual_navigation.utils.testing import load_test_image, get_dino_transform, make_results_folder +from os.path import join +from pytictac import Timer import matplotlib.pyplot as plt import torch -from torchvision import transforms as T -from pathlib import PurePath, Path -import os -import cv2 +import itertools def test_feature_extractor(): - segmentation_type = "none" - feature_type = "dino" - device = "cuda" if torch.cuda.is_available() else "cpu" - fe = FeatureExtractor(device, segmentation_type=segmentation_type, feature_type=feature_type) - - transform = T.Compose( - [ - T.Resize(448, T.InterpolationMode.NEAREST), - T.CenterCrop(448), - ] - ) - - np_img = cv2.imread(os.path.join(WVN_ROOT_DIR, "assets/images/forest_clean.png")) - img = torch.from_numpy(cv2.cvtColor(np_img, cv2.COLOR_BGR2RGB)).to(device) - img = img.permute(2, 0, 1) - img = (img.type(torch.float32) / 255)[None] - adj, feat, seg, center = fe.extract(transform(img.clone())) - - p = PurePath(WVN_ROOT_DIR).joinpath( - "results", "test_feature_extractor", f"forest_clean_graph_{segmentation_type}.png" - ) - Path(p.parent).mkdir(parents=True, exist_ok=True) - - # Plot result as in colab - fig, ax = plt.subplots(1, 2, figsize=(5 * 3, 5)) - - ax[0].imshow(transform(img).permute(0, 2, 3, 1)[0].cpu()) - ax[0].set_title("Image") - ax[1].imshow(seg.cpu(), cmap=plt.colormaps.get("inferno")) - ax[1].set_title("Segmentation") - plt.tight_layout() - - # Store results to test directory - img = get_img_from_fig(fig) - img.save(str(p)) + segmentation_types = ["none", "grid", "slic", "random", "stego"] + feature_types = ["dino", "dinov2", "stego"] + backbone_types = ["vit_small", "vit_base"] # "vit_small_reg", "vit_base_reg"] + + for seg_type, feat_type, back_type in itertools.product(segmentation_types, feature_types, backbone_types): + if seg_type == "stego" and feat_type != "stego": + continue + + with Timer(f"Running seg [{seg_type}], feat [{feat_type}], backbone [{back_type}]"): + try: + fe = FeatureExtractor( + device, segmentation_type=seg_type, feature_type=feat_type, backbone_type=back_type + ) + except Exception: + print("Not available") + continue + + img = load_test_image().to(device) + transform = get_dino_transform() + outpath = make_results_folder("test_feature_extractor") + + # Compute + edges, feat, seg, center, dense_feat = fe.extract(transform(img.clone())) + + # Plot result as in colab + fig, ax = plt.subplots(1, 2, figsize=(5 * 3, 5)) + + ax[0].imshow(transform(img).permute(0, 2, 3, 1)[0].cpu()) + ax[0].set_title("Image") + ax[1].imshow(seg.cpu(), cmap=plt.colormaps.get("gray")) + ax[1].set_title("Segmentation") + plt.tight_layout() + + # Store results to test directory + img = get_img_from_fig(fig) + img.save(join(outpath, f"forest_clean_graph_{seg_type}_{feat_type}.png")) + plt.close() if __name__ == "__main__": diff --git a/tests/test_image_projector.py b/tests/test_image_projector.py index 1472af4b..61eea3c7 100644 --- a/tests/test_image_projector.py +++ b/tests/test_image_projector.py @@ -1,32 +1,25 @@ from wild_visual_navigation.image_projector import run_image_projector -from wild_visual_navigation.traversability_estimator import ProprioceptionNode +from wild_visual_navigation.traversability_estimator import SupervisionNode def test_image_projector(): run_image_projector() -def test_proprioceptive_projection(): - from wild_visual_navigation import WVN_ROOT_DIR +def test_supervision_projection(): from wild_visual_navigation.image_projector import ImageProjector from wild_visual_navigation.visu import get_img_from_fig - from wild_visual_navigation.utils import Timer - from wild_visual_navigation.utils import make_plane, make_box, make_dense_plane, make_polygon_from_points - from PIL import Image + from wild_visual_navigation.utils.testing import load_test_image, make_results_folder from liegroups.torch import SE3, SO3 import matplotlib.pyplot as plt import torch - import torchvision.transforms as transforms - import os from os.path import join from kornia.utils import tensor_to_image - from stego.src import remove_axes + from stego.utils import remove_axes import random - to_tensor = transforms.ToTensor() - # Create test directory - os.makedirs(join(WVN_ROOT_DIR, "results", "test_image_projector"), exist_ok=True) + outpath = make_results_folder("test_image_projector") # Define number of cameras (batch) B = 100 @@ -38,19 +31,17 @@ def test_proprioceptive_projection(): # Extrisics pose_camera_in_world = torch.eye(4)[None] - # Image size - H = 1080 - W = 1440 + H = torch.tensor(1080) + W = torch.tensor(1440) # Create projector im = ImageProjector(K, H, W) # Load image - pil_image = Image.open(join(WVN_ROOT_DIR, "assets/images/forest_clean.png")) + torch_image = load_test_image() - # Convert to torch - torch_image = to_tensor(pil_image) + # Resize torch_image = im.resize_image(torch_image) mask = (torch_image * 0.0)[None] @@ -70,15 +61,15 @@ def test_proprioceptive_projection(): delta = SE3(R_WC, rho).as_matrix()[None] # Pose matrix of camera in world frame pose_base_in_world = pose_base_in_world @ delta pose_footprint_in_base = torch.eye(4)[None] - print(delta, pose_base_in_world) + # print(delta, pose_base_in_world) twist = torch.rand((3,)) - proprioception = torch.rand((10,)) + supervision = torch.rand((10,)) traversability = torch.rand(1) traversability_var = torch.tensor([0.2]) color = torch.rand((3,))[None] - proprio_node = ProprioceptionNode( + supervision_node = SupervisionNode( timestamp=i, pose_base_in_world=pose_base_in_world, pose_footprint_in_base=pose_footprint_in_base, @@ -86,15 +77,15 @@ def test_proprioceptive_projection(): width=0.5, length=0.8, height=0.1, - proprioception=proprioception, + supervision=supervision, traversability=traversability, traversability_var=traversability_var, is_untraversable=torch.BoolTensor([False]), ) - nodes.append(proprio_node) + nodes.append(supervision_node) if i > 0: - footprint = proprio_node.make_footprint_with_node(nodes[i - 1])[None] + footprint = supervision_node.make_footprint_with_node(nodes[i - 1])[None] # project footprint k_mask, torch_image_overlay, k_points, k_valid = im.project_and_render( @@ -107,7 +98,7 @@ def test_proprioceptive_projection(): fig, ax = plt.subplots(1, 2, figsize=(2 * 5, 5)) ax[0].imshow(tensor_to_image(torch_image)) ax[0].set_title("Image") - ax[1].imshow(tensor_to_image(mask)) + ax[1].imshow(tensor_to_image(mask[0])) ax[1].set_title("Labels") remove_axes(ax) @@ -115,8 +106,8 @@ def test_proprioceptive_projection(): # Store results to test directory img = get_img_from_fig(fig) - img.save(join(WVN_ROOT_DIR, "results", "test_image_projector", "forest_clean_proprioceptive_projection.png")) + img.save(join(outpath, "forest_clean_supervision_projection.png")) if __name__ == "__main__": - test_proprioceptive_projection() + test_supervision_projection() diff --git a/tests/test_kornia.py b/tests/test_kornia.py index c44cedfe..cd599ac4 100644 --- a/tests/test_kornia.py +++ b/tests/test_kornia.py @@ -1,6 +1,5 @@ -from wild_visual_navigation import WVN_ROOT_DIR from wild_visual_navigation.visu import get_img_from_fig -import os +from wild_visual_navigation.utils.testing import make_results_folder from os.path import join import matplotlib.pyplot as plt import torch @@ -47,8 +46,8 @@ def test_draw_polygon(): # Draw k_out = draw_convex_polygon(img, poly, color) # Show - os.makedirs(join(WVN_ROOT_DIR, "results", "test_kornia"), exist_ok=True) + outpath = make_results_folder("test_kornia") fig, ax = plt.subplots(1, 1, figsize=(5, 5)) ax.imshow(tensor_to_image(k_out)) out_img = get_img_from_fig(fig) - out_img.save(join(WVN_ROOT_DIR, "results", "test_kornia", "polygon_test.png")) + out_img.save(join(outpath, "polygon_test.png")) diff --git a/tests/test_monitoring.py b/tests/test_monitoring.py index c5a38b5e..91c36fe2 100644 --- a/tests/test_monitoring.py +++ b/tests/test_monitoring.py @@ -1,4 +1,5 @@ -from wild_visual_navigation.utils import SystemLevelGpuMonitor, SystemLevelTimer, accumulate_memory, accumulate_time +from wild_visual_navigation.utils import SystemLevelGpuMonitor, accumulate_memory +from pytictac import accumulate_time import time import torch @@ -14,26 +15,31 @@ def __init__(self): @accumulate_time def test_memory_then_timing(self, s): time.sleep(s / 1000) - self.tensors.append(torch.zeros((s, s), device="cuda")) + self.tensors.append(torch.zeros((10 * s, 10 * s), device="cuda")) @accumulate_time @accumulate_memory def test_timing_then_memory(self, s): time.sleep(s / 1000) - self.tensors.append(torch.zeros((4, s, s), device="cuda")) + self.tensors.append(torch.zeros((4, 10 * s, 10 * s), device="cuda")) # Create objects my_test = MyTest() gpu_monitor = SystemLevelGpuMonitor( - objects=[my_test], names=["test"], enabled=True, device="cuda", store_samples=True, skip_n_samples=1 - ) - time_monitor = SystemLevelTimer( objects=[my_test], names=["test"], + enabled=True, + device="cuda", + store_samples=True, + skip_n_samples=1, ) + # time_monitor = ClassContextTimer( + # objects=[my_test], + # names=["test"], + # ) # Run loop - for n in range(400): + for n in range(100): print(f"step {n}") step = n t = n / 10 @@ -42,7 +48,7 @@ def test_timing_then_memory(self, s): my_test.test_timing_then_memory(n) gpu_monitor.store("/tmp") - time_monitor.store("/tmp") + # time_monitor.store("/tmp") if __name__ == "__main__": diff --git a/tests/test_optical_flow.py b/tests/test_optical_flow.py index c4bd8b3c..065b5b0b 100644 --- a/tests/test_optical_flow.py +++ b/tests/test_optical_flow.py @@ -1,15 +1,22 @@ -from pytorch_pwc.network import PwcFlowEstimator -from pytorch_pwc import PWC_ROOT_DIR -import torch -from wild_visual_navigation import WVN_ROOT_DIR -from wild_visual_navigation.visu import LearningVisualizer -import numpy as np -import PIL -import os -from wild_visual_navigation.utils import Timer +import pytest +import sys -if __name__ == "__main__": +try: + from pytorch_pwc.network import PwcFlowEstimator + from pytorch_pwc import PWC_ROOT_DIR +except ImportError: + pass + + +@pytest.mark.skipif("pytorch_pwc" not in sys.modules, reason="requires the pytorch_pwc library") +def pytorch_pwc_test(): import os + import torch + from wild_visual_navigation import WVN_ROOT_DIR + from wild_visual_navigation.visu import LearningVisualizer + import numpy as np + import PIL + from pytictac import Timer tenOne = torch.FloatTensor( np.ascontiguousarray( @@ -39,3 +46,7 @@ visu = LearningVisualizer(p_visu=os.path.join(WVN_ROOT_DIR, "results/test_visu"), store=True) visu.plot_optical_flow(res, tenOne, tenTwo) print("done") + + +if __name__ == "__main__": + pytorch_pwc_test() diff --git a/tests/test_traversability_estimator.py b/tests/test_traversability_estimator.py index 984e4ef6..fd3d2d51 100644 --- a/tests/test_traversability_estimator.py +++ b/tests/test_traversability_estimator.py @@ -1,4 +1,7 @@ -from wild_visual_navigation.traversability_estimator import run_base_state, run_base_graph +from wild_visual_navigation.traversability_estimator import ( + run_base_state, + run_base_graph, +) def test_base_state(): diff --git a/wild_visual_navigation/__init__.py b/wild_visual_navigation/__init__.py index c3c60af9..1c859e93 100644 --- a/wild_visual_navigation/__init__.py +++ b/wild_visual_navigation/__init__.py @@ -1,4 +1,9 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# import os WVN_ROOT_DIR = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) -"""Absolute path to the wild_visual_navigation repository.""" +"""Absolute path to the wild_visual_navigation repository.""" diff --git a/wild_visual_navigation/cfg/__init__.py b/wild_visual_navigation/cfg/__init__.py index 829340ea..590af461 100644 --- a/wild_visual_navigation/cfg/__init__.py +++ b/wild_visual_navigation/cfg/__init__.py @@ -1 +1,8 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# +from .global_params import get_global_env_params, GlobalEnvironmentParams from .experiment_params import ExperimentParams +from .ros_params import RosLearningNodeParams, RosFeatureExtractorNodeParams diff --git a/wild_visual_navigation/cfg/experiment_params.py b/wild_visual_navigation/cfg/experiment_params.py index f786e995..d8cba169 100644 --- a/wild_visual_navigation/cfg/experiment_params.py +++ b/wild_visual_navigation/cfg/experiment_params.py @@ -1,10 +1,19 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# from dataclasses import dataclass, field -from typing import Tuple, Dict, List, Optional -from simple_parsing.helpers import Serializable +from typing import List, Optional +from typing import Any +import os +from wild_visual_navigation.cfg import get_global_env_params, GlobalEnvironmentParams @dataclass -class ExperimentParams(Serializable): +class ExperimentParams: + env: GlobalEnvironmentParams = get_global_env_params(os.environ.get("ENV_WORKSTATION_NAME", "default")) + @dataclass class GeneralParams: name: str = "debug/debug" @@ -42,14 +51,22 @@ class LossParams: w_trav: float = 0.03 w_reco: float = 0.5 w_temp: float = 0.0 # 0.75 - method: str = "latest_measurment" - confidence_std_factor: float = 1.0 + method: str = "latest_measurement" + confidence_std_factor: float = 0.5 trav_cross_entropy: bool = False loss: LossParams = LossParams() + @dataclass + class LossAnomalyParams: + method: str = "latest_measurement" + confidence_std_factor: float = 0.5 + + loss_anomaly: LossAnomalyParams = LossAnomalyParams() + @dataclass class TrainerParams: + default_root_dir: Optional[str] = None precision: int = 32 accumulate_grad_batches: int = 1 fast_dev_run: bool = False @@ -57,7 +74,7 @@ class TrainerParams: limit_val_batches: float = 1.0 limit_test_batches: float = 1.0 max_epochs: Optional[int] = None - profiler: bool = False + profiler: Any = False num_sanity_val_steps: int = 0 check_val_every_n_epoch: int = 1 enable_checkpointing: bool = True @@ -65,6 +82,7 @@ class TrainerParams: enable_progress_bar: bool = True weights_summary: Optional[str] = "top" progress_bar_refresh_rate: Optional[int] = None + gpus: int = -1 trainer: TrainerParams = TrainerParams() @@ -84,12 +102,12 @@ class AblationDataModuleParams: @dataclass class ModelParams: - name: str = "SimpleMLP" + name: str = "SimpleMLP" # LinearRnvp, SimpleMLP, SimpleGCN, DoubleMLP load_ckpt: Optional[str] = None @dataclass class SimpleMlpCfgParams: - input_size: int = 90 + input_size: int = 90 # 90 for stego, 384 for dino hidden_sizes: List[int] = field(default_factory=lambda: [256, 32, 1]) reconstruction: bool = True @@ -97,19 +115,30 @@ class SimpleMlpCfgParams: @dataclass class DoubleMlpCfgParams: - input_size: int = 90 + input_size: int = 384 hidden_sizes: List[int] = field(default_factory=lambda: [64, 32, 1]) double_mlp_cfg: DoubleMlpCfgParams = DoubleMlpCfgParams() @dataclass class SimpleGcnCfgParams: - input_size: int = 90 + input_size: int = 384 reconstruction: bool = True hidden_sizes: List[int] = field(default_factory=lambda: [256, 128, 1]) simple_gcn_cfg: SimpleGcnCfgParams = SimpleGcnCfgParams() + @dataclass + class LinearRnvpCfgParams: + input_size: int = 384 + coupling_topology: List[int] = field(default_factory=lambda: [200]) + mask_type: str = "odds" + conditioning_size: int = 0 + use_permutation: bool = True + single_function: bool = False + + linear_rnvp_cfg: LinearRnvpCfgParams = LinearRnvpCfgParams() + model: ModelParams = ModelParams() @dataclass @@ -142,20 +171,10 @@ class VisuParams: @dataclass class LearningVisuParams: - p_visu: Optional[bool] = None + p_visu: Optional[str] = None store: bool = True log: bool = True learning_visu: LearningVisuParams = LearningVisuParams() visu: VisuParams = VisuParams() - - def verify_params(self): - if not self.general.log_to_disk: - assert self.trainer.profiler != "advanced", "Should not be advanced if not logging to disk" - assert self.cb_checkpoint.active == False, "Should be False if not logging to disk" - - if self.loss.trav_cross_entropy: - self.model.simple_mlp_cfg.hidden_sizes[-1] = 2 - self.model.double_mlp_cfg.hidden_sizes[-1] = 2 - self.model.simple_gcn_cfg.hidden_sizes[-1] = 2 diff --git a/wild_visual_navigation/cfg/global_params.py b/wild_visual_navigation/cfg/global_params.py new file mode 100644 index 00000000..159edd1f --- /dev/null +++ b/wild_visual_navigation/cfg/global_params.py @@ -0,0 +1,21 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# +from dataclasses import dataclass + + +@dataclass +class GlobalEnvironmentParams: + perugia_root: str + results: str + + +def get_global_env_params(name): + configs = { + "default": GlobalEnvironmentParams(perugia_root="TBD", results="results"), + "ge76": GlobalEnvironmentParams(perugia_root="TBD", results="results"), + "jetson": GlobalEnvironmentParams(perugia_root="TBD", results="results"), + } + return configs[name] diff --git a/wild_visual_navigation/cfg/ros_params.py b/wild_visual_navigation/cfg/ros_params.py new file mode 100644 index 00000000..64a057d3 --- /dev/null +++ b/wild_visual_navigation/cfg/ros_params.py @@ -0,0 +1,94 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# +from dataclasses import dataclass +from typing import Dict +from typing import Any + + +@dataclass +class RosLearningNodeParams: + # Input topics + camera_topics: Dict[str, Any] + robot_state_topic: str + desired_twist_topic: str + + # Relevant frames + fixed_frame: str + base_frame: str + footprint_frame: str + + # Robot size + robot_length: float + robot_width: float + robot_height: float + + # Traversability estimation params + traversability_radius: float # meters + image_graph_dist_thr: float # meters + supervision_graph_dist_thr: float # meters + confidence_std_factor: float + min_samples_for_training: int + network_input_image_height: int + network_input_image_width: int + vis_node_index: int + + # Supervision Generator + untraversable_thr: float + + mission_name: str + mission_timestamp: bool + + # Threads + image_callback_rate: float # hertz + supervision_callback_rate: float # hertz + learning_thread_rate: float # hertz + logging_thread_rate: float # hertz + load_save_checkpoint_rate: float # hert + + # Runtime options + device: str + mode: Any # check out comments in the class WVNMode + colormap: str + + print_image_callback_time: bool + print_supervision_callback_time: bool + log_time: bool + log_confidence: bool + verbose: bool + + extraction_store_folder: str + + +@dataclass +class RosFeatureExtractorNodeParams: + # Input topics + camera_topics: Dict[str, Any] + + # FeatureExtractor + network_input_image_height: int # 448 + network_input_image_width: int # 448 + segmentation_type: str + feature_type: str + dino_patch_size: int # 8 or 16; 8 is finer + dino_backbone: str # vit_small, vit_base + slic_num_components: int + + # ConfidenceGenerator + confidence_std_factor: float + + # Output setting + prediction_per_pixel: bool + + # Runtime options + mode: Any # check out comments in the class WVNMode + status_thread_rate: float # hertz + device: str + log_confidence: bool + verbose: bool + + # Threads + image_callback_rate: float # hertz + load_save_checkpoint_rate: float # hertz diff --git a/wild_visual_navigation/feature_extractor/__init__.py b/wild_visual_navigation/feature_extractor/__init__.py index c986125c..ba75ba13 100644 --- a/wild_visual_navigation/feature_extractor/__init__.py +++ b/wild_visual_navigation/feature_extractor/__init__.py @@ -1,3 +1,8 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# from .dino_interface import DinoInterface, run_dino_interfacer from .torchvision_interface import TorchVisionInterface diff --git a/wild_visual_navigation/feature_extractor/dino_interface.py b/wild_visual_navigation/feature_extractor/dino_interface.py index 35f00ab4..816ad25e 100644 --- a/wild_visual_navigation/feature_extractor/dino_interface.py +++ b/wild_visual_navigation/feature_extractor/dino_interface.py @@ -1,256 +1,201 @@ -from wild_visual_navigation import WVN_ROOT_DIR -import os +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# from os.path import join import torch.nn.functional as F import torch -from omegaconf import DictConfig from torchvision import transforms as T -from stego.src.train_segmentation import DinoFeaturizer +from omegaconf import OmegaConf + +from stego.backbones.backbone import get_backbone class DinoInterface: def __init__( self, device: str, + backbone: str = "dino", input_size: int = 448, - input_interp: str = "bilinear", - model_type: str = "vit_small", + backbone_type: str = "vit_small", patch_size: int = 8, + projection_type: str = None, # nonlinear or None + dropout_p: float = 0, # True or False + pretrained_weights: str = None, + cfg: OmegaConf = OmegaConf.create({}), ): - self.dim = 90 - self.cfg = DictConfig( - { - "dino_patch_size": patch_size, - "dino_feat_type": "feat", - "model_type": model_type, - "projection_type": "nonlinear", - "pretrained_weights": None, - "dropout": True, - } - ) - - # Pretrained weights - if self.cfg.pretrained_weights is None: - self.cfg.pretrained_weights = self.download_pretrained_model(self.cfg) + # Load config + if cfg.is_empty(): + self._cfg = OmegaConf.create( + { + "backbone": backbone, + "backbone_type": backbone_type, + "input_size": input_size, + "patch_size": patch_size, + "projection_type": projection_type, + "dropout_p": dropout_p, + "pretrained_weights": pretrained_weights, + } + ) + else: + self._cfg = cfg # Initialize DINO - self.model = DinoFeaturizer(self.dim, self.cfg) + self._model = get_backbone(self._cfg) # Send to device - self.model.to(device) - self.device = device - - self._input_size = input_size - self._input_interp = input_interp - - # Interpolation type - if self._input_interp == "bilinear": - interp = T.InterpolationMode.BILINEAR - elif self._input_interp == "nearest": - interp = T.InterpolationMode.NEAREST - - # Transformation for testing - self.transform = T.Compose( - [ - T.Resize(self._input_size, interp), - T.CenterCrop(self._input_size), - T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), - ] - ) + self._model.to(device) + self._device = device - self.crop = T.Compose( + # Other + normalization = T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) + self._transform = T.Compose( [ - T.Resize(self._input_size, interp), - T.CenterCrop(self._input_size), + T.Resize(input_size, T.InterpolationMode.NEAREST), + T.CenterCrop(input_size), + normalization, ] ) - # Just normalization - self.norm = T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - def change_device(self, device): """Changes the device of all the class members Args: device (str): new device """ - self.model.to(device) - self.device = device - - def download_pretrained_model(self, cfg: DictConfig): - """Loads model. - - Returns: - model (stego.src.train_segmentation.DinoFeaturizer): Pretrained model - """ - - arch = cfg.model_type - patch_size = cfg.dino_patch_size - - if arch == "vit_small" and patch_size == 16: - model = "dino_deitsmall16_pretrain" - elif arch == "vit_small" and patch_size == 8: - model = "dino_deitsmall8_300ep_pretrain" - elif arch == "vit_base" and patch_size == 16: - model = "dino_vitbase16_pretrain" - elif arch == "vit_base" and patch_size == 8: - model = "dino_vitbase8_pretrain" - elif arch == "resnet": - model = "dino_resnet50_pretrain" - else: - raise ValueError("Unknown arch and patch size") - - url = f"{model}/{model}.pth" - - # Download model - model_path = join(WVN_ROOT_DIR, "assets", "dino") - if not os.path.exists(model_path): - os.makedirs(model_path, exist_ok=True) - state_dict = torch.hub.load_state_dict_from_url( - url="https://dl.fbaipublicfiles.com/dino/" + url, model_dir=model_path - ) - - return join(model_path, f"{model}.pth") - - def get_feature_dim(self): - return self.dim + self._model.to(device) + self._device = device @torch.no_grad() - def inference(self, img: torch.tensor, interpolate: bool = False): + def inference(self, img: torch.tensor): """Performance inference using DINO Args: - img (torch.tensor, dtype=type.torch.float32, shape=(BS,3,H.W)): Input image + img (torch.tensor, dtype=type.torch.float32, shape=(B,3,H.W)): Input image Returns: - features (torch.tensor, dtype=torch.float32, shape=(BS,D,H,W)): per-pixel D-dimensional features + features (torch.tensor, dtype=torch.float32, shape=(B,D,H,W)): per-pixel D-dimensional features """ - # assert 1 == img.shape[0] - img = self.norm(img).to(self.device) + + # Resize image and normalize + resized_img = self._transform(img).to(self._device) # Extract features - features = self.model(img)[1] + features = self._model(resized_img) # resize and interpolate features B, D, H, W = img.shape - new_size = (H, H) - pad = int((W - H) / 2) - features = F.interpolate(features, new_size, mode="bilinear", align_corners=True) - features = F.pad(features, pad=[pad, pad, 0, 0]) - + new_features_size = (H, H) + # pad = int((W - H) / 2) + features = F.interpolate(features, new_features_size, mode="bilinear", align_corners=True) + # features = F.pad(features, pad=[pad, pad, 0, 0]) return features @property def input_size(self): - return self._input_size + return self._cfg.input_size @property - def input_interpolation(self): - return self._input_interp + def backbone(self): + return self._cfg.backbone @property - def model_type(self): - return self.cfg.model_type + def backbone_type(self): + return self._cfg.backbone_type @property def vit_patch_size(self): - return self.cfg.dino_patch_size + return self._cfg.patch_size def run_dino_interfacer(): """Performance inference using stego and stores result as an image.""" - from wild_visual_navigation.utils import Timer + from pytictac import Timer from wild_visual_navigation.visu import get_img_from_fig + from wild_visual_navigation.utils.testing import load_test_image, make_results_folder import matplotlib.pyplot as plt - from stego.src import remove_axes - import cv2 + from stego.utils import remove_axes # Create test directory - os.makedirs(join(WVN_ROOT_DIR, "results", "test_dino_interfacer"), exist_ok=True) + outpath = make_results_folder("test_dino_interfacer") # Inference model device = "cuda" if torch.cuda.is_available() else "cpu" - p = join(WVN_ROOT_DIR, "assets/images/forest_clean.png") - np_img = cv2.imread(p) - img = torch.from_numpy(cv2.cvtColor(np_img, cv2.COLOR_BGR2RGB)).to(device) - img = img.permute(2, 0, 1) - img = (img.type(torch.float32) / 255)[None] + img = load_test_image().to(device) + img = F.interpolate(img, scale_factor=0.25) plot = False save_features = True # Settings size = 448 - interp = "bilinear" model = "vit_small" patch = 8 + backbone = "dinov2" # Inference with DINO # Create DINO - di = DinoInterface(device=device, input_size=size, input_interp=interp, model_type=model, patch_size=patch) - - with Timer( - f"DINO, input_size, {di.input_size}, interp, {di.input_interpolation}, model, {di.model_type}, patch_size, {di.vit_patch_size}" - ): - try: - feat_dino = di.inference(di.transform(img), interpolate=False) - - if save_features: - for i in range(90): - fig = plt.figure(frameon=False) - fig.set_size_inches(2, 2) - ax = plt.Axes(fig, [0.0, 0.0, 1.0, 1.0]) - ax.set_axis_off() - fig.add_axes(ax) - ax.imshow(feat_dino[0][i].cpu(), cmap=plt.colormaps.get("inferno")) - - # Store results to test directory - out_img = get_img_from_fig(fig) - out_img.save( - join( - WVN_ROOT_DIR, - "results", - "test_dino_interfacer", - f"forest_clean_dino_feat{i:02}_{di.input_size}_{di.input_interpolation}_{di.model_type}_{di.vit_patch_size}.png", - ) - ) - plt.close("all") - - if plot: - # Plot result as in colab - fig, ax = plt.subplots(10, 11, figsize=(1 * 11, 1 * 11)) - - for i in range(10): - for j in range(11): - if i == 0 and j == 0: - continue - - elif (i == 0 and j != 0) or (i != 0 and j == 0): - ax[i][j].imshow(img.permute(0, 2, 3, 1)[0].cpu()) - ax[i][j].set_title("Image") - else: - n = (i - 1) * 10 + (j - 1) - if n >= di.get_feature_dim(): - break - ax[i][j].imshow(feat_dino[0][n].cpu(), cmap=plt.colormaps.get("inferno")) - ax[i][j].set_title("Features [0]") - remove_axes(ax) - plt.tight_layout() - - # Store results to test directory - out_img = get_img_from_fig(fig) - out_img.save( - join( - WVN_ROOT_DIR, - "results", - "test_dino_interfacer", - f"forest_clean_dino_{di.input_size}_{di.input_interpolation}_{di.model_type}_{di.vit_patch_size}.png", - ) + di = DinoInterface( + device=device, + backbone=backbone, + input_size=size, + backbone_type=model, + patch_size=patch, + ) + + with Timer(f"DINO, input_size, {di.input_size}, model, {di.backbone_type}, patch_size, {di.vit_patch_size}"): + feat_dino = di.inference(img) + + if save_features: + for i in range(90): + fig = plt.figure(frameon=False) + fig.set_size_inches(2, 2) + ax = plt.Axes(fig, [0.0, 0.0, 1.0, 1.0]) + ax.set_axis_off() + fig.add_axes(ax) + ax.imshow(feat_dino[0][i].cpu(), cmap=plt.colormaps.get("inferno")) + + # Store results to test directory + out_img = get_img_from_fig(fig) + out_img.save( + join( + outpath, + f"forest_clean_dino_feat{i:02}_{di.input_size}_{di.backbone_type}_{di.vit_patch_size}.png", ) - plt.close("all") - - except Exception as e: - pass + ) + plt.close("all") + + if plot: + # Plot result as in colab + fig, ax = plt.subplots(10, 11, figsize=(1 * 11, 1 * 11)) + + for i in range(10): + for j in range(11): + if i == 0 and j == 0: + continue + + elif (i == 0 and j != 0) or (i != 0 and j == 0): + ax[i][j].imshow(img.permute(0, 2, 3, 1)[0].cpu()) + ax[i][j].set_title("Image") + else: + n = (i - 1) * 10 + (j - 1) + if n >= di.get_feature_dim(): + break + ax[i][j].imshow(feat_dino[0][n].cpu(), cmap=plt.colormaps.get("inferno")) + ax[i][j].set_title("Features [0]") + remove_axes(ax) + plt.tight_layout() + + # Store results to test directory + out_img = get_img_from_fig(fig) + out_img.save( + join( + outpath, + f"forest_clean_{di.backbone}_{di.input_size}_{di.backbone_type}_{di.vit_patch_size}.png", + ) + ) + plt.close("all") if __name__ == "__main__": diff --git a/wild_visual_navigation/feature_extractor/dino_trt_interface.py b/wild_visual_navigation/feature_extractor/dino_trt_interface.py index b71dcbc4..adfcbc09 100644 --- a/wild_visual_navigation/feature_extractor/dino_trt_interface.py +++ b/wild_visual_navigation/feature_extractor/dino_trt_interface.py @@ -1,3 +1,8 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# from wild_visual_navigation import WVN_ROOT_DIR from os.path import join from omegaconf import DictConfig @@ -55,7 +60,9 @@ def __call__(self, x: torch.tensor, batch_size=1): class DinoTrtInterface: def __init__( - self, trt_model_path: str = os.path.join(WVN_ROOT_DIR, "assets/dino/dino_exported.trt"), device: str = "cuda" + self, + trt_model_path: str = os.path.join(WVN_ROOT_DIR, "assets/dino/dino_exported.trt"), + device: str = "cuda", ): self.device = device self.dim = 90 @@ -122,24 +129,22 @@ def run_dino_trt_interfacer(): """Performance inference using stego and stores result as an image.""" from wild_visual_navigation.visu import get_img_from_fig + from wild_visual_navigation.testing import load_test_image, get_dino_transform + from wild_visual_navigation.utils.testing import make_results_folder import matplotlib.pyplot as plt from stego.src import remove_axes - import cv2 # Create test directory - os.makedirs(join(WVN_ROOT_DIR, "results", "test_dino_trt_interfacer"), exist_ok=True) + outpath = make_results_folder("test_dino_trt_interfacer") # Inference model device = "cuda" if torch.cuda.is_available() else "cpu" di = DinoTrtInterface(device=device) - p = join(WVN_ROOT_DIR, "assets/images/forest_clean.png") - np_img = cv2.imread(p) - img = torch.from_numpy(cv2.cvtColor(np_img, cv2.COLOR_BGR2RGB)).to(device) - img = img.permute(2, 0, 1) - img = (img.type(torch.float32) / 255)[None] + img = load_test_image().to(device) + transform = get_dino_transform() # Inference with DINO - feat_dino = di.inference(di.transform(img), interpolate=False) + feat_dino = di.inference(transform(img), interpolate=False) # Fix size of DINO features to match input image's size B, D, H, W = img.shape @@ -169,7 +174,7 @@ def run_dino_trt_interfacer(): # Store results to test directory img = get_img_from_fig(fig) - img.save(join(WVN_ROOT_DIR, "results", "test_dino_trt_interfacer", "forest_clean_dino.png")) + img.save(join(outpath, "forest_clean_dino.png")) if __name__ == "__main__": diff --git a/wild_visual_navigation/feature_extractor/feature_extractor.py b/wild_visual_navigation/feature_extractor/feature_extractor.py index 74c42801..053a2e82 100644 --- a/wild_visual_navigation/feature_extractor/feature_extractor.py +++ b/wild_visual_navigation/feature_extractor/feature_extractor.py @@ -1,23 +1,29 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# from wild_visual_navigation.feature_extractor import ( StegoInterface, DinoInterface, SegmentExtractor, TorchVisionInterface, ) -from wild_visual_navigation.utils import Timer -import skimage import torch import numpy as np import kornia from kornia.feature import DenseSIFTDescriptor from kornia.contrib import extract_tensor_patches, combine_tensor_patches -from torchvision import transforms as T -from PIL import Image, ImageDraw class FeatureExtractor: def __init__( - self, device: str, segmentation_type: str = "slic", feature_type: str = "dino", input_size: int = 448, **kwargs + self, + device: str, + segmentation_type: str = "slic", + feature_type: str = "dino", + input_size: int = 448, + **kwargs, ): """Feature extraction from image @@ -30,37 +36,79 @@ def __init__( self._segmentation_type = segmentation_type self._feature_type = feature_type self._input_size = input_size + # Prepare segment extractor self.segment_extractor = SegmentExtractor().to(self._device) # Prepare extractor depending on the type if self._feature_type == "stego": self._feature_dim = 90 - self.extractor = StegoInterface(device=device, input_size=input_size) - elif self._feature_type == "dino": - self._feature_dim = 90 + self._extractor = StegoInterface( + device=device, + input_size=input_size, + n_image_clusters=kwargs.get("n_image_clusters", 20), + run_clustering=kwargs.get("run_clustering", True), + run_crf=kwargs.get("run_crf", False), + ) + + elif "dino" in self._feature_type: + self._feature_dim = 384 + self._extractor = DinoInterface( + device=device, + input_size=input_size, + patch_size=kwargs.get("patch_size", 8), + backbone=kwargs.get("backbone", self._feature_type), + backbone_type=kwargs.get("backbone_type", "vit_base"), + ) - self.extractor = DinoInterface(device=device, input_size=input_size, patch_size=kwargs.get("patch_size", 8)) elif self._feature_type == "sift": self._feature_dim = 128 - self.extractor = DenseSIFTDescriptor().to(device) + self._extractor = DenseSIFTDescriptor().to(device) + elif self._feature_type == "torchvision": self._extractor = TorchVisionInterface( device=device, model_type=kwargs["model_type"], input_size=input_size ) + elif self._feature_type == "histogram": self._feature_dim = 90 + + elif self._feature_type == "none": + pass + else: raise f"Extractor[{self._feature_type}] not supported!" + # Segmentation if self.segmentation_type == "slic": from fast_slic import Slic self.slic = Slic( - num_components=kwargs.get("slic_num_components", 100), compactness=kwargs.get("slic_compactness", 10) + num_components=kwargs.get("slic_num_components", 100), + compactness=kwargs.get("slic_compactness", 10), ) + elif self.segmentation_type == "random": + pass + def extract(self, img, **kwargs): + if self._segmentation_type == "random": + dense_feat = self.compute_features(img, None, None, **kwargs) + + H, W = img.shape[2:] + nr = kwargs.get("n_random_pixels", 100) + + seg = torch.full((H * W,), -1, dtype=torch.long, device=self._device) + indices = torch.randperm(H * W, device=self._device)[:nr] + seg[indices] = torch.arange(0, nr, device=self._device) + seg = seg.reshape(H, W) + feat = dense_feat[0].reshape(dense_feat.shape[1], H * W)[:, indices].T + + if kwargs.get("return_dense_features", False): + return None, feat, seg, None, dense_feat + + return None, feat, seg, None, None + # Compute segments, their centers, and edges connecting them (graph structure) # with Timer("feature_extractor - compute_segments"): edges, seg, center = self.compute_segments(img, **kwargs) @@ -69,10 +117,14 @@ def extract(self, img, **kwargs): # with Timer("feature_extractor - compute_features"): dense_feat = self.compute_features(img, seg, center, **kwargs) + # with Timer("feature_extractor - compute_features"): # Sparsify features to match the centers if required feat = self.sparsify_features(dense_feat, seg) - return edges, feat, seg, center + if kwargs.get("return_dense_features", False): + return edges, feat, seg, center, dense_feat + + return edges, feat, seg, center, None @property def feature_type(self): @@ -93,7 +145,7 @@ def change_device(self, device): device (str): new device """ self._device = device - self.extractor.change_device(device) + self._extractor.change_device(device) def compute_segments(self, img: torch.tensor, **kwargs): if self._segmentation_type == "none" or self._segmentation_type is None: @@ -107,17 +159,21 @@ def compute_segments(self, img: torch.tensor, **kwargs): elif self._segmentation_type == "stego": seg = self.segment_stego(img, **kwargs) + + elif self._segmentation_type == "random": + seg = self.segment_random(img, **kwargs) + else: raise f"segmentation_type [{self._segmentation_type}] not supported" # Compute edges and centers if self._segmentation_type != "none" and self._segmentation_type is not None: # Extract adjacency_list based on segments - edges = self.segment_extractor.adjacency_list(seg[None, None]) + edges = self.segment_extractor.adjacency_list(seg) # Extract centers - centers = self.segment_extractor.centers(seg[None, None]) + centers = self.segment_extractor.centers(seg) - return edges.T, seg, centers + return edges.T, seg[0, 0], centers def segment_pixelwise(self, img, **kwargs): # Generate pixel-wise segmentation @@ -136,7 +192,7 @@ def segment_pixelwise(self, img, **kwargs): ver_edges = torch.cat((seg[:-1, :].reshape(-1, 1), seg[1:, :].reshape(-1, 1)), dim=1) edges = torch.cat((hor_edges, ver_edges), dim=0) - return edges, seg, centers + return edges, seg[None, None], centers def segment_grid(self, img, **kwargs): cell_size = kwargs.get("cell_size", 32) @@ -151,29 +207,42 @@ def segment_grid(self, img, **kwargs): for i in range(patches.shape[1]): patches[:, i, :, :, :] = i - combine_patch_size = (int(H / cell_size), int(W / cell_size)) + # combine_patch_size = (int(H / cell_size), int(W / cell_size)) seg = combine_tensor_patches( - patches=patches, original_size=(H, W), window_size=combine_patch_size, stride=combine_patch_size + patches=patches, + original_size=(H, W), + window_size=patch_size, + stride=patch_size, ) - return seg[0, 0].to(self._device) + return seg.to(self._device) def segment_slic(self, img, **kwargs): # Get slic clusters img_np = kornia.utils.tensor_to_image(img) - seg = self.slic.iterate(np.uint8(np.ascontiguousarray(img_np) * 255)) + seg = self.slic.iterate(np.uint8(np.ascontiguousarray(img_np) * 255))[None, None] return torch.from_numpy(seg).to(self._device).type(torch.long) + def segment_random(self, img, **kwargs): + # Randomly select + H, W = img.shape[2:] + nr = kwargs.get("n_random_pixels", 100) + seg = torch.full((H * W,), -1, dtype=torch.long, device=self._device) + indices = torch.randperm(H * W, device=self._device)[:nr] + seg[indices] = torch.arange(0, nr, device=self._device) + seg = seg.reshape(H, W)[None, None] + return seg + def segment_stego(self, img, **kwargs): # Prepare input image img_internal = img.clone() - self.extractor.inference_crf(img_internal) - seg = torch.from_numpy(self.extractor.cluster_segments).to(self._device) + self._extractor.inference(img_internal) + seg = self._extractor.cluster_segments.to(self._device) + # seg = torch.from_numpy(self._extractor.cluster_segments).to(self._device) # Change the segment indices by numbers from 0 to N for i, k in enumerate(seg.unique()): seg[seg == k.item()] = i - return seg def compute_features(self, img: torch.tensor, seg: torch.tensor, center: torch.tensor, **kwargs): @@ -183,7 +252,7 @@ def compute_features(self, img: torch.tensor, seg: torch.tensor, center: torch.t elif self._feature_type == "sift": feat = self.compute_sift(img, seg, center, **kwargs) - elif self._feature_type == "dino": + elif "dino" in self._feature_type: feat = self.compute_dino(img, seg, center, **kwargs) elif self._feature_type == "stego": @@ -191,7 +260,8 @@ def compute_features(self, img: torch.tensor, seg: torch.tensor, center: torch.t elif self._feature_type == "torchvision": feat = self.compute_torchvision(img, seg, center, **kwargs) - + elif self._feature_type == "none": + feat = None else: raise f"segmentation_type [{self._segmentation_type}] not supported" @@ -204,18 +274,18 @@ def compute_histogram(self, img: torch.tensor, seg: torch.tensor, **kwargs): def compute_sift(self, img: torch.tensor, seg: torch.tensor, center: torch.tensor, **kwargs): B, C, H, W = img.shape if C == 3: - feat_r = self.extractor(img[:, 0, :, :][None]) - feat_g = self.extractor(img[:, 1, :, :][None]) - feat_b = self.extractor(img[:, 2, :, :][None]) - features = torch.cat([feat_r, feat_r, feat_b], dim=1) + feat_r = self._extractor(img[:, 0, :, :][None]) + feat_g = self._extractor(img[:, 1, :, :][None]) + feat_b = self._extractor(img[:, 2, :, :][None]) + features = torch.cat([feat_r, feat_g, feat_b], dim=1) else: - features = self.extractor(img) + features = self._extractor(img) return features @torch.no_grad() def compute_dino(self, img: torch.tensor, seg: torch.tensor, center: torch.tensor, **kwargs): img_internal = img.clone() - features = self.extractor.inference(img_internal) + features = self._extractor.inference(img_internal) return features @torch.no_grad() @@ -226,7 +296,11 @@ def compute_torchvision(self, img: torch.tensor, seg: torch.tensor, center: torc @torch.no_grad() def compute_stego(self, img: torch.tensor, seg: torch.tensor, center: torch.tensor, **kwargs): - return self.extractor.features + try: + return self._extractor.features + except Exception: + self.segment_stego(img, **kwargs) + return self._extractor.features def sparsify_features(self, dense_features: torch.tensor, seg: torch.tensor, cumsum_trick=False): if self._feature_type not in ["histogram"] and self._segmentation_type not in ["none"]: @@ -239,7 +313,8 @@ def sparsify_features(self, dense_features: torch.tensor, seg: torch.tensor, cum segs = [ torch.nn.functional.interpolate( - seg[None, None, :, :].type(torch.float32), scale_factor=(scale_x, scale_y) + seg[None, None, :, :].type(torch.float32), + scale_factor=(scale_x, scale_y), )[0, 0].type(torch.long) for scale_x, scale_y in zip(scales_x, scales_y) ] @@ -252,6 +327,11 @@ def sparsify_features(self, dense_features: torch.tensor, seg: torch.tensor, cum # Iterate over each scale for dense_feature, seg_scaled in zip(dense_features.values(), segs): m = seg_scaled == i + prev_scale_x = 1.0 + prev_scale_y = 1.0 + prev_x = 1.0 + prev_y = 1.0 + # When downscaling the mask it becomes 0 therfore calculate x,y # Based on the previous scale if m.sum() == 0: diff --git a/wild_visual_navigation/feature_extractor/segment_extractor.py b/wild_visual_navigation/feature_extractor/segment_extractor.py index 1aec152b..7095bfdb 100644 --- a/wild_visual_navigation/feature_extractor/segment_extractor.py +++ b/wild_visual_navigation/feature_extractor/segment_extractor.py @@ -1,3 +1,8 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# # author: Jonas Frey import torch @@ -41,7 +46,7 @@ def adjacency_list(self, seg: torch.tensor): Returns: adjacency_list (torch.Tensor, dtype=torch.long, shape=(N, 2): Adjacency list of undirected graph """ - assert seg.shape[0] == 1 and len(seg.shape) == 4 + assert seg.shape[0] == 1 and len(seg.shape) == 4, f"{seg.shape}" res = self.f1(seg.type(torch.float32)) boundary_mask = (res != 0)[0, :, 2:-2, 2:-2] diff --git a/wild_visual_navigation/feature_extractor/stego_interface.py b/wild_visual_navigation/feature_extractor/stego_interface.py index 6bd9b0b2..d399603a 100644 --- a/wild_visual_navigation/feature_extractor/stego_interface.py +++ b/wild_visual_navigation/feature_extractor/stego_interface.py @@ -1,48 +1,62 @@ -from wild_visual_navigation import WVN_ROOT_DIR -import os +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# from os.path import join import torch.nn.functional as F import torch -import wget from torchvision import transforms as T -from stego.src import LitUnsupervisedSegmenter +from omegaconf import OmegaConf -# from stego.src import get_transform -from stego.src import dense_crf +from pytictac import Timer +from stego import STEGO_ROOT_DIR +from stego.stego import Stego +from stego.data import create_cityscapes_colormap class StegoInterface: - def __init__(self, device: str, input_size: int = 448, input_interp: str = "bilinear"): - self.model = self.load() - self.model.to(device) - self.device = device - self._input_size = input_size - self._input_interp = input_interp - - if self._input_interp == "bilinear": - interp = T.InterpolationMode.BILINEAR - elif self._input_interp == "nearest": - interp = T.InterpolationMode.NEAREST - - # Transformation for testing - self.transform = T.Compose( + def __init__( + self, + device: str, + input_size: int = 448, + model_path: str = f"{STEGO_ROOT_DIR}/models/stego_cocostuff27_vit_base_5_cluster_linear_fine_tuning.ckpt", + n_image_clusters: int = 40, + run_crf: bool = True, + run_clustering: bool = False, + cfg: OmegaConf = OmegaConf.create({}), + ): + # Load config + if cfg.is_empty(): + self._cfg = OmegaConf.create( + { + "model_path": model_path, + "input_size": input_size, + "run_crf": run_crf, + "run_clustering": run_clustering, + "n_image_clusters": n_image_clusters, + } + ) + else: + self._cfg = cfg + + self._model = Stego.load_from_checkpoint(self._cfg.model_path, n_image_clusters=self._cfg.n_image_clusters) + self._model.eval().to(device) + self._device = device + + # Colormap + self._cmap = create_cityscapes_colormap() + + # Other + normalization = T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) + self._transform = T.Compose( [ - T.Resize(self._input_size, interp), - T.CenterCrop(self._input_size), - T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), + T.Resize(input_size, T.InterpolationMode.NEAREST), + T.CenterCrop(input_size), + normalization, ] ) - self.crop = T.Compose( - [ - T.Resize(self._input_size, interp), - T.CenterCrop(self._input_size), - ] - ) - - # Just normalization - self.norm = T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - # Internal variables to access internal data self._features = None self._segments = None @@ -53,25 +67,8 @@ def change_device(self, device): Args: device (str): new device """ - self.model.to(device) - self.device = device - - def load(self): - """Loads model. - - Returns: - model (stego.src.train_segmentation.LitUnsupervisedSegmenter): Pretrained model - """ - model_name = "cocostuff27_vit_base_5.ckpt" - model_path = join(WVN_ROOT_DIR, "assets", "stego", model_name) - if not os.path.exists(model_path): - os.makedirs(join(WVN_ROOT_DIR, "assets", "stego"), exist_ok=True) - saved_model_url_root = "https://marhamilresearch4.blob.core.windows.net/stego-public/saved_models/" - saved_model_name = model_name - wget.download(saved_model_url_root + saved_model_name, model_path) - - model = LitUnsupervisedSegmenter.load_from_checkpoint(model_path) - return model + self._model.to(device) + self._device = device @torch.no_grad() def inference(self, img: torch.tensor): @@ -83,47 +80,47 @@ def inference(self, img: torch.tensor): linear_probs (torch.tensor, dtype=torch.float32, shape=(BS,C,H,W)): Linear prediction cluster_probs (torch.tensor, dtype=torch.float32, shape=(BS,C,H,W)): Cluster prediction """ - assert 1 == img.shape[0] - - img = self.norm(img).to(self.device) - code1 = self.model(img) - code2 = self.model(img.flip(dims=[3])) - code = (code1 + code2.flip(dims=[3])) / 2 - code = F.interpolate(code, img.shape[-2:], mode="bilinear", align_corners=False) - linear_probs = torch.log_softmax(self.model.linear_probe(code), dim=1) - cluster_probs = self.model.cluster_probe(code, 2, log_probs=True) - - # Save segments - self._code = code - self._segments = linear_probs - - return linear_probs, cluster_probs - - @torch.no_grad() - def inference_crf(self, img: torch.tensor): - """ - Args: - img (torch.tensor, dtype=type.torch.float32): Input image - - Returns: - linear_pred (torch.tensor, dtype=torch.int64): Linear prediction - cluster_pred (torch.tensor, dtype=torch.int64): Cluster prediction - """ + # assert 1 == img.shape[0] + + # Resize image and normalize + # with Timer("input normalization"): + resized_img = self._transform(img).to(self._device) + + # Run STEGO + # with Timer("compute code"): + self._code = self._model.get_code(resized_img) + + # with Timer("compute postprocess"): + self._cluster_pred, self._linear_pred = self._model.postprocess( + code=self._code, + img=resized_img, + use_crf_cluster=self._cfg.run_crf, + use_crf_linear=self._cfg.run_crf, + image_clustering=self._cfg.run_clustering, + ) - linear_probs, cluster_probs = self.inference(img) - single_img = img[0].cpu() - self._linear_pred = dense_crf(single_img, linear_probs[0]).argmax(0) - self._cluster_pred = dense_crf(single_img, cluster_probs[0]).argmax(0) + # resize and interpolate features + # with Timer("interpolate output"): + B, D, H, W = img.shape + new_features_size = (H, H) + # pad = int((W - H) / 2) + self._code = F.interpolate(self._code, new_features_size, mode="bilinear", align_corners=True) + self._cluster_pred = F.interpolate(self._cluster_pred[None].float(), new_features_size, mode="nearest").int() + self._linear_pred = F.interpolate(self._linear_pred[None].float(), new_features_size, mode="nearest").int() return self._linear_pred, self._cluster_pred @property - def input_size(self): - return self._input_size + def model(self): + return self._model + + @property + def cmap(self): + return self._cmap @property - def input_interpolation(self): - return self._input_interp + def input_size(self): + return self._cfg.input_size @property def linear_segments(self): @@ -140,38 +137,39 @@ def features(self): def run_stego_interfacer(): """Performance inference using stego and stores result as an image.""" - - from wild_visual_navigation.utils import Timer from wild_visual_navigation.visu import get_img_from_fig + from wild_visual_navigation.utils.testing import load_test_image, make_results_folder + from stego.utils import remove_axes import matplotlib.pyplot as plt - from stego.src import unnorm, remove_axes - import cv2 # Create test directory - os.makedirs(join(WVN_ROOT_DIR, "results", "test_stego_interfacer"), exist_ok=True) + outpath = make_results_folder("test_stego_interfacer") # Inference model device = "cuda" if torch.cuda.is_available() else "cpu" - si = StegoInterface(device=device, input_size=448, input_interp="bilinear") + si = StegoInterface( + device=device, + input_size=448, + run_crf=False, + run_clustering=True, + n_image_clusters=20, + ) - p = join(WVN_ROOT_DIR, "assets/images/forest_clean.png") - np_img = cv2.imread(p) - img = torch.from_numpy(cv2.cvtColor(np_img, cv2.COLOR_BGR2RGB)).to(device) - img = img.permute(2, 0, 1) - img = (img.type(torch.float32) / 255)[None] + img = load_test_image().to(device) + img = F.interpolate(img, scale_factor=0.5) - with Timer(f"Stego (input {si.input_size}, interp: {si.input_interpolation})"): - linear_pred, cluster_pred = si.inference_crf(si.crop(img)) + with Timer(f"Stego input {si.input_size}\n"): + linear_pred, cluster_pred = si.inference(img) # Plot result as in colab fig, ax = plt.subplots(1, 3, figsize=(5 * 3, 5)) - ax[0].imshow(si.crop(img).permute(0, 2, 3, 1)[0].cpu()) + ax[0].imshow(img[0].permute(1, 2, 0).cpu().numpy()) ax[0].set_title("Image") - ax[1].imshow(si.model.label_cmap[cluster_pred]) + ax[1].imshow(si.cmap[cluster_pred[0, 0].cpu() % si.cmap.shape[0]]) ax[1].set_title("Cluster Predictions") - ax[2].imshow(si.model.label_cmap[linear_pred]) + ax[2].imshow(si.cmap[linear_pred[0, 0].cpu()]) ax[2].set_title("Linear Probe Predictions") remove_axes(ax) @@ -179,10 +177,8 @@ def run_stego_interfacer(): img = get_img_from_fig(fig) img.save( join( - WVN_ROOT_DIR, - "results", - "test_stego_interfacer", - f"forest_clean_stego_{si.input_size}_{si.input_interpolation}.png", + outpath, + f"forest_clean_stego_{si.input_size}.png", ) ) diff --git a/wild_visual_navigation/feature_extractor/torchvision_interface.py b/wild_visual_navigation/feature_extractor/torchvision_interface.py index b9d2e683..bccf80cd 100644 --- a/wild_visual_navigation/feature_extractor/torchvision_interface.py +++ b/wild_visual_navigation/feature_extractor/torchvision_interface.py @@ -1,9 +1,12 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# from wild_visual_navigation import WVN_ROOT_DIR -import os from os.path import join from torch import nn -import torch.nn.functional as F import torch import torchvision.models as models @@ -120,31 +123,19 @@ def inference(self, img: torch.tensor, interpolate: bool = False): def run_torch_vision_model_interfacer(): """Performance inference using stego and stores result as an image.""" - - from wild_visual_navigation.utils import Timer - from wild_visual_navigation.visu import get_img_from_fig - import matplotlib.pyplot as plt - from stego.src import remove_axes - import cv2 - - # Create test directory - # os.makedirs(join(WVN_ROOT_DIR, "results", "test_torchvision_interfacer"), exist_ok=True) + from wild_visual_navigation.utils.testing import load_test_image # Inference model device = "cuda" if torch.cuda.is_available() else "cpu" - p = join(WVN_ROOT_DIR, "assets/images/forest_clean.png") - np_img = cv2.imread(p) - img = torch.from_numpy(cv2.cvtColor(np_img, cv2.COLOR_BGR2RGB)).to(device) - img = img.permute(2, 0, 1) - img = (img.type(torch.float32) / 255)[None] + img = load_test_image().to(device) - plot = False - save_features = True + # plot = False + # save_features = True size = 448 model_type = "resnet18" - di = TorchVisionInterface(model_type=model_type, input_size=488) + di = TorchVisionInterface(model_type=model_type, input_size=size) di.to(device) img.to(device) res = di(img) diff --git a/wild_visual_navigation/image_projector/__init__.py b/wild_visual_navigation/image_projector/__init__.py index be10e47c..3c571a78 100644 --- a/wild_visual_navigation/image_projector/__init__.py +++ b/wild_visual_navigation/image_projector/__init__.py @@ -1 +1,6 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# from .image_projector import ImageProjector, run_image_projector diff --git a/wild_visual_navigation/image_projector/image_projector.py b/wild_visual_navigation/image_projector/image_projector.py index 0fde0707..f1d8a3c2 100644 --- a/wild_visual_navigation/image_projector/image_projector.py +++ b/wild_visual_navigation/image_projector/image_projector.py @@ -1,6 +1,9 @@ -from wild_visual_navigation import WVN_ROOT_DIR -from wild_visual_navigation.utils import Timer -import os +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# +from pytictac import Timer from os.path import join import torch from torchvision import transforms as T @@ -147,7 +150,11 @@ def project(self, pose_camera_in_world: torch.tensor, points_W: torch.tensor): return projected_points, valid_points, valid_z def project_and_render( - self, pose_camera_in_world: torch.tensor, points: torch.tensor, colors: torch.tensor, image: torch.tensor = None + self, + pose_camera_in_world: torch.tensor, + points: torch.tensor, + colors: torch.tensor, + image: torch.tensor = None, ): """Projects the points and returns an image with the projection @@ -197,19 +204,17 @@ def run_image_projector(): """Projects 3D points to example images and returns an image with the projection""" from wild_visual_navigation.visu import get_img_from_fig - from wild_visual_navigation.utils import Timer - from wild_visual_navigation.utils import make_plane, make_box, make_dense_plane, make_polygon_from_points - from PIL import Image + from wild_visual_navigation.utils import ( + make_polygon_from_points, + ) + from wild_visual_navigation.utils.testing import load_test_image, make_results_folder import matplotlib.pyplot as plt import torch - import torchvision.transforms as transforms from kornia.utils import tensor_to_image - from stego.src import remove_axes - - to_tensor = transforms.ToTensor() + from stego.utils import remove_axes # Create test directory - os.makedirs(join(WVN_ROOT_DIR, "results", "test_image_projector"), exist_ok=True) + outpath = make_results_folder("test_image_projector") # Define number of cameras (batch) B = 10 @@ -229,17 +234,14 @@ def run_image_projector(): R_WC = SO3.from_rpy(phi) # Rotation matrix from roll-pitch-yaw pose_camera_in_world[i] = SE3(R_WC, rho).as_matrix() # Pose matrix of camera in world frame # Image size - H = 1080 - W = 1440 + H = torch.tensor(1080) + W = torch.tensor(1440) # Create projector im = ImageProjector(K, H, W) # Load image - pil_img = Image.open(join(WVN_ROOT_DIR, "assets/images/forest_clean.png")) - - # Convert to torch - k_img = to_tensor(pil_img) + k_img = load_test_image() k_img = k_img.expand(B, 3, H, W) k_img = im.resize_image(k_img) @@ -269,7 +271,7 @@ def run_image_projector(): for x in range(-3, 3, 1): try: k_points_overlay[:, idx[1].item() + y, idx[0].item() + x] = torch.tensor([0, 255, 0]) - except Exception as e: + except Exception: continue ax[i, 0].imshow(tensor_to_image(k_img[i])) @@ -285,7 +287,12 @@ def run_image_projector(): # Store results to test directory img = get_img_from_fig(fig) - img.save(join(WVN_ROOT_DIR, "results", "test_image_projector", "forest_clean_image_projector.png")) + img.save( + join( + outpath, + "forest_clean_image_projector.png", + ) + ) if __name__ == "__main__": diff --git a/wild_visual_navigation/learning/dataset/__init__.py b/wild_visual_navigation/learning/dataset/__init__.py deleted file mode 100644 index d44093de..00000000 --- a/wild_visual_navigation/learning/dataset/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .graph_trav_dataset import get_ablation_module -from .twist_dataset import TwistDataset, TwistDataModule diff --git a/wild_visual_navigation/learning/general/training_routine.py b/wild_visual_navigation/learning/general/training_routine.py deleted file mode 100644 index 28f1451d..00000000 --- a/wild_visual_navigation/learning/general/training_routine.py +++ /dev/null @@ -1,124 +0,0 @@ -import os -import yaml -import dataclasses -import pickle -import warnings - -warnings.filterwarnings("ignore", ".*does not have many workers.*") - -# Frameworks -import torch -import pytorch_lightning -from pytorch_lightning import seed_everything, Trainer -from pytorch_lightning.callbacks import EarlyStopping -from pytorch_lightning.callbacks import ModelCheckpoint -from pytorch_lightning.callbacks import LearningRateMonitor -from pytorch_lightning.profiler import AdvancedProfiler -from pytorch_lightning.utilities import rank_zero_warn, rank_zero_only -from pytorch_lightning.plugins import DDP2Plugin, DDPPlugin, DDPSpawnPlugin - -# Costume Modules -from wild_visual_navigation.learning.utils import get_logger -from wild_visual_navigation.learning.lightning import LightningTrav -from wild_visual_navigation.learning.utils import load_yaml, load_env, create_experiment_folder -from wild_visual_navigation.learning.dataset import get_ablation_module -from wild_visual_navigation.cfg import ExperimentParams - -__all__ = ["training_routine"] - - -def training_routine(experiment: ExperimentParams, seed=42) -> torch.Tensor: - experiment.verify_params() - seed_everything(seed) - exp = dataclasses.asdict(experiment) - env = load_env() - - if exp["general"]["log_to_disk"]: - model_path = create_experiment_folder(exp, env) - exp["general"]["name"] = os.path.relpath(model_path, env["base"]) - - with open(os.path.join(model_path, "experiment_params.yaml"), "w") as f: - yaml.dump(exp, f, default_flow_style=False) - - exp["trainer"]["default_root_dir"] = model_path - exp["visu"]["learning_visu"]["p_visu"] = os.path.join(exp["general"]["model_path"], "visu") - - logger = get_logger(exp, env) - - # Set gpus - if (exp["trainer"]).get("gpus", -1) == -1: - nr = torch.cuda.device_count() - print(f"Set GPU Count for Trainer to {nr}!") - for i in range(nr): - print(f"Device {i}: " + str(torch.cuda.get_device_name(i))) - exp["trainer"]["gpus"] = -1 - - # Profiler - if exp["trainer"].get("profiler", False) == "advanced": - exp["trainer"]["profiler"] = AdvancedProfiler(dirpath=model_path, filename="profile.txt") - else: - exp["trainer"]["profiler"] = False - - # Callbacks - cb_ls = [] - if logger is not None: - cb_ls.append(LearningRateMonitor(**exp["lr_monitor"])) - - if exp["cb_early_stopping"]["active"]: - early_stop_callback = EarlyStopping(**exp["cb_early_stopping"]["cfg"]) - cb_ls.appned(early_stop_callback) - - if exp["cb_checkpoint"]["active"]: - checkpoint_callback = ModelCheckpoint( - dirpath=model_path, save_top_k=1, monitor="epoch", mode="max", save_last=True - ) - cb_ls.append(checkpoint_callback) - - gpus = 1 if torch.cuda.is_available() else None - exp["trainer"]["gpus"] = gpus - - train_dl, val_dl, test_dl = get_ablation_module( - **exp["ablation_data_module"], - perugia_root=env["perugia_root"], - get_train_val_dataset=not exp["general"]["skip_train"], - get_test_dataset=not exp["ablation_data_module"]["val_equals_test"], - ) - - # Set correct input feature dimension - if train_dl is not None: - sample = train_dl.dataset[0] - else: - sample = test_dl[0].dataset[0] - - input_feature_dimension = sample.x.shape[1] - - exp["model"]["simple_mlp_cfg"]["input_size"] = input_feature_dimension - exp["model"]["simple_gcn_cfg"]["input_size"] = input_feature_dimension - - # Model - model = LightningTrav(exp=exp, env=env) - if type(exp["model"]["load_ckpt"]) == str: - ckpt = torch.load(exp["model"]["load_ckpt"]) - try: - res = model.load_state_dict(ckpt["state_dict"], strict=False) - except: - res = model.load_state_dict(ckpt, strict=False) - print("Loaded model checkpoint:", res) - trainer = Trainer(**exp["trainer"], callbacks=cb_ls, logger=logger) - - if not exp["general"]["skip_train"]: - trainer.fit(model=model, train_dataloaders=train_dl, val_dataloaders=val_dl) - - if exp["ablation_data_module"]["val_equals_test"]: - return model.accumulated_val_results, model - - test_envs = [] - for j, dl in enumerate(test_dl): - if exp["loss"]["w_trav"] == 0: - model._traversability_loss._anomaly_threshold = None - - model.nr_test_run = j - res = trainer.test(model=model, dataloaders=dl)[0] - test_envs.append(dl.dataset.env) - - return {k: v for k, v in zip(test_envs, model.accumulated_test_results)}, model diff --git a/wild_visual_navigation/learning/model/__init__.py b/wild_visual_navigation/learning/model/__init__.py deleted file mode 100644 index 5fc47920..00000000 --- a/wild_visual_navigation/learning/model/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .simple_mlp import SimpleMLP, DoubleMLP -from .simple_gcn import SimpleGCN -from .network_register import get_model diff --git a/wild_visual_navigation/learning/utils/__init__.py b/wild_visual_navigation/learning/utils/__init__.py deleted file mode 100644 index 5872fd1d..00000000 --- a/wild_visual_navigation/learning/utils/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -from .flatten_dict import * -from .get_logger import get_logger, get_neptune_run -from .loading import load_env, load_yaml, file_path -from .create_experiment_folder import create_experiment_folder -from .get_confidence import get_confidence -from .loss import TraversabilityLoss -from .metric_logger import MetricLogger diff --git a/wild_visual_navigation/learning/utils/get_logger.py b/wild_visual_navigation/learning/utils/get_logger.py deleted file mode 100644 index 00353184..00000000 --- a/wild_visual_navigation/learning/utils/get_logger.py +++ /dev/null @@ -1,110 +0,0 @@ -from pytorch_lightning.loggers.neptune import NeptuneLogger -from pytorch_lightning.loggers import TensorBoardLogger, WandbLogger -from wild_visual_navigation.learning.utils import flatten_dict -import inspect -import os -import neptune.new as neptune - -__all__ = ["get_neptune_logger", "get_wandb_logger", "get_tensorboard_logger", "get_neptune_run"] - -PROXIES = {"http": "http://proxy.ethz.ch:3128", "https": "http://proxy.ethz.ch:3128"} - - -def get_neptune_run(neptune_project_name: str, tags: [str]) -> any: - """Get neptune run - - Args: - neptune_project_name (str): Neptune project name - tags (list of str): Tags to identify the project - """ - proxies = None - if os.environ["ENV_WORKSTATION_NAME"] == "euler": - proxies = PROXIES - - run = neptune.init( - api_token=os.environ["NEPTUNE_API_TOKEN"], - project=neptune_project_name, - tags=[os.environ["ENV_WORKSTATION_NAME"]] + tags, - proxies=proxies, - ) - return run - - -def get_neptune_logger(exp: dict, env: dict) -> NeptuneLogger: - """Returns NeptuneLogger - - Args: - exp (dict): Content of environment file - env (dict): Content of experiment file - Returns: - (logger): Logger - """ - project_name = exp["logger"]["neptune_project_name"] # Neptune AI project_name "username/project" - - params = flatten_dict(exp) - - name_full = exp["general"]["name"] - name_short = "__".join(name_full.split("/")[-2:]) - - proxies = None - if os.environ["ENV_WORKSTATION_NAME"] == "euler": - proxies = PROXIES - - return NeptuneLogger( - api_key=os.environ["NEPTUNE_API_TOKEN"], - project=project_name, - name=name_short, - tags=[os.environ["ENV_WORKSTATION_NAME"], name_full.split("/")[-2], name_full.split("/")[-1]], - proxies=proxies, - ) - - -def get_wandb_logger(exp: dict, env: dict) -> WandbLogger: - """Returns NeptuneLogger - - Args: - exp (dict): Content of environment file - - Returns: - (logger): Logger - """ - project_name = exp["logger"]["wandb_project_name"] # project_name (str): W&B project_name - save_dir = os.path.join(exp["general"]["model_path"]) # save_dir (str): File path to save directory - params = flatten_dict(exp) - name_full = exp["general"]["name"] - name_short = "__".join(name_full.split("/")[-2:]) - return WandbLogger( - name=name_short, project=project_name, entity=exp["logger"]["wandb_entity"], save_dir=save_dir, offline=False - ) - - -def get_tensorboard_logger(exp: dict, env: dict) -> TensorBoardLogger: - """Returns TensorboardLoggers - - Args: - exp (dict): Content of environment file - - Returns: - (logger): Logger - """ - params = flatten_dict(exp) - return TensorBoardLogger(save_dir=exp["name"], name="tensorboard", default_hp_metric=params) - - -def get_skip_logger(exp: dict, env: dict) -> None: - """Returns None - - Args: - exp (dict): Content of environment file - - Returns: - (logger): Logger - """ - return None - - -def get_logger(exp: dict, env: dict) -> any: - name = exp["logger"]["name"] - save_dir = os.path.join(env["base"], exp["general"]["name"]) - register = {k: v for k, v in globals().items() if inspect.isfunction(v)} - return register[f"get_{name}_logger"](exp, env) diff --git a/wild_visual_navigation/model/__init__.py b/wild_visual_navigation/model/__init__.py new file mode 100644 index 00000000..d21a5c70 --- /dev/null +++ b/wild_visual_navigation/model/__init__.py @@ -0,0 +1,9 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# +from .simple_mlp import SimpleMLP, DoubleMLP +from .simple_gcn import SimpleGCN +from .linear_rnvp import LinearRnvp +from .network_register import get_model diff --git a/wild_visual_navigation/model/linear_rnvp.py b/wild_visual_navigation/model/linear_rnvp.py new file mode 100644 index 00000000..fa63b286 --- /dev/null +++ b/wild_visual_navigation/model/linear_rnvp.py @@ -0,0 +1,296 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# +from wild_visual_navigation.utils import Data +import copy +import torch +from torch import nn, distributions + + +class LinearBatchNorm(nn.Module): + """ + An (invertible) batch normalization layer. + This class is mostly inspired from this one: + https://github.com/kamenbliznashki/normalizing_flows/blob/master/maf.py + """ + + def __init__(self, input_size, momentum=0.9, eps=1e-5): + super().__init__() + self.momentum = momentum + self.eps = eps + + self.log_gamma = nn.Parameter(torch.zeros(input_size)) + self.beta = nn.Parameter(torch.zeros(input_size)) + + self.register_buffer("running_mean", torch.zeros(input_size)) + self.register_buffer("running_var", torch.ones(input_size)) + + def forward(self, x, **kwargs): + if self.training: + self.batch_mean = x.mean(0) + self.batch_var = x.var(0) + + self.running_mean.mul_(self.momentum).add_(self.batch_mean.data * (1 - self.momentum)) + self.running_var.mul_(self.momentum).add_(self.batch_var.data * (1 - self.momentum)) + + mean = self.batch_mean + var = self.batch_var + else: + mean = self.running_mean + var = self.running_var + + x_hat = (x - mean) / torch.sqrt(var + self.eps) + y = self.log_gamma.exp() * x_hat + self.beta + + log_det = self.log_gamma - 0.5 * torch.log(var + self.eps) + + return y, log_det.expand_as(x).sum(1) + + def backward(self, x, **kwargs): + if self.training: + mean = self.batch_mean + var = self.batch_var + else: + mean = self.running_mean + var = self.running_var + + x_hat = (x - self.beta) * torch.exp(-self.log_gamma) + x = x_hat * torch.sqrt(var + self.eps) + mean + + log_det = 0.5 * torch.log(var + self.eps) - self.log_gamma + + return x, log_det.expand_as(x).sum(1) + + +class LinearCouplingLayer(nn.Module): + """ + Linear coupling layer. + (i) Split the input x into 2 parts x1 and x2 according to a given mask. + (ii) Compute s(x2) and t(x2) with given neural network. + (iii) Final output is [exp(s(x2))*x1 + t(x2); x2]. + The inverse is trivially [(x1 - t(x2))*exp(-s(x2)); x2]. + """ + + def __init__( + self, + input_size, + mask, + network_topology, + conditioning_size=None, + single_function=True, + ): + super().__init__() + + if conditioning_size is None: + conditioning_size = 0 + + if network_topology is None or len(network_topology) == 0: + network_topology = [input_size] + + self.register_buffer("mask", mask) + + self.dim = input_size + + self.s = [ + nn.Linear(input_size + conditioning_size, network_topology[0]), + nn.ReLU(), + ] + + for i in range(len(network_topology)): + t = network_topology[i] + t_p = network_topology[i - 1] + self.s.extend([nn.Linear(t_p, t), nn.ReLU()]) + + if single_function: + input_size = input_size * 2 + + ll = nn.Linear(network_topology[-1], input_size) + + self.s.append(ll) + self.s = nn.Sequential(*self.s) + + if single_function: + self.st = lambda x: (self.s(x).chunk(2, 1)) + else: + self.t = copy.deepcopy(self.s) + self.st = lambda x: (self.s(x), self.t(x)) + + def backward(self, x, y=None): + mx = x * self.mask + + if y is not None: + _mx = torch.cat([y, mx], dim=1) + else: + _mx = mx + + s, t = self.st(_mx) + s = torch.tanh(s) # Adding an activation function here with non-linearities + + u = mx + (1 - self.mask) * (x - t) * torch.exp(-s) + + log_abs_det_jacobian = -(1 - self.mask) * s + + return u, log_abs_det_jacobian.sum(1) + + def forward(self, u, y=None): + mu = u * self.mask + + if y is not None: + _mu = torch.cat([y, mu], dim=1) + else: + _mu = mu + + s, t = self.st(_mu) + s = torch.tanh(s) + + x = mu + (1 - self.mask) * (u * s.exp() + t) + + log_abs_det_jacobian = (1 - self.mask) * s + + return x, log_abs_det_jacobian.sum(1) + + +class Permutation(nn.Module): + """ + A permutation layer. + """ + + def __init__(self, in_ch): + super().__init__() + self.in_ch = in_ch + self.register_buffer("p", torch.randperm(in_ch)) + self.register_buffer("invp", torch.argsort(self.p)) + + def forward(self, x, y=None): + assert x.shape[1] == self.in_ch + out = x[:, self.p] + return out, 0 + + def backward(self, x, y=None): + assert x.shape[1] == self.in_ch + out = x[:, self.invp] + return out, 0 + + +class SequentialFlow(nn.Sequential): + """ + Utility class to build a normalizing flow from a sequence of base transformations. + During forward and inverse steps, aggregates the sum of the log determinants of the Jacobians. + """ + + def forward(self, x, y=None): + log_det = 0 + for module in self: + x, _log_det = module(x, y=y) + log_det = log_det + _log_det + return x, log_det + + def backward(self, u, y=None): + log_det = 0 + for module in reversed(self): + u, _log_det = module.backward(u, y=y) + log_det = log_det + _log_det + return u, log_det + + def forward_steps(self, x, y=None): + log_det = 0 + xs = [x] + for module in self: + x, _log_det = module(x, y=y) + xs.append(x) + log_det = log_det + _log_det + return xs, log_det + + def backward_steps(self, u, y=None): + log_det = 0 + us = [u] + for module in reversed(self): + u, _log_det = module.backward(u, y=y) + us.append(u) + log_det = log_det + _log_det + return us, log_det + + +class LinearRnvp(nn.Module): + """ + Main RNVP model, alternating affine coupling layers + with permutations and/or batch normalization steps. + """ + + def __init__( + self, + input_size, + coupling_topology, + flow_n=2, + use_permutation=False, + batch_norm=False, + mask_type="odds", + conditioning_size=None, + single_function=False, + **kwargs + ): + super().__init__() + + self.register_buffer("prior_mean", torch.zeros(input_size)) # Normal Gaussian with zero mean + self.register_buffer("prior_var", torch.ones(input_size)) # Normal Gaussian with unit variance + + if mask_type == "odds": + mask = torch.arange(0, input_size).float() % 2 + elif mask_type == "half": + mask = torch.zeros(input_size) + mask[: input_size // 2] = 1 + else: + assert False + + if coupling_topology is None: + coupling_topology = [input_size // 2, input_size // 2] + + blocks = [] + + for i in range(flow_n): + blocks.append( + LinearCouplingLayer( + input_size, + mask, + network_topology=coupling_topology, + conditioning_size=conditioning_size, + single_function=single_function, + ) + ) + if use_permutation: + blocks.append(Permutation(input_size)) + else: + mask = 1 - mask + + if batch_norm: + blocks.append(LinearBatchNorm(input_size)) + + self.flows = SequentialFlow(*blocks) + + def logprob(self, x): + return self.prior.log_prob(x) # Compute log probability of the input at the Gaussian distribution + + @property + def prior(self): + return distributions.Normal(self.prior_mean, self.prior_var) # Normal Gaussian with zero mean and unit variance + + def forward(self, data: Data): + x = data.x + z, log_det = self.flows.forward(x, y=None) + log_prob = self.logprob(z) + return {"z": z, "log_det": log_det, "logprob": log_prob} + + def backward(self, u, y=None, return_step=False): + if return_step: + return self.flows.backward_steps(u, y) + return self.flows.backward(u, y) + + def sample(self, samples=1, y=None, return_step=False, return_logdet=False): + u = self.prior.sample((samples,)) + z, d = self.backward(u, y=y, return_step=return_step) + if return_logdet: + d = self.logprob(u).sum(1) + d + return z, d + return z diff --git a/wild_visual_navigation/learning/model/network_register.py b/wild_visual_navigation/model/network_register.py similarity index 85% rename from wild_visual_navigation/learning/model/network_register.py rename to wild_visual_navigation/model/network_register.py index 7883cda5..fb991df8 100644 --- a/wild_visual_navigation/learning/model/network_register.py +++ b/wild_visual_navigation/model/network_register.py @@ -1,4 +1,9 @@ -from wild_visual_navigation.learning.model import * +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# +from wild_visual_navigation.model import * import inspect import torch @@ -52,7 +57,7 @@ def get_model(model_cfg: dict) -> torch.nn.Module: if __name__ == "__main__": from wild_visual_navigation import WVN_ROOT_DIR - from wild_visual_navigation.learning.utils import load_yaml + from wild_visual_navigation.utils import load_yaml from os.path import join exp = load_yaml(join(WVN_ROOT_DIR, "cfg/exp/exp.yaml")) diff --git a/wild_visual_navigation/learning/model/simple_gcn.py b/wild_visual_navigation/model/simple_gcn.py similarity index 79% rename from wild_visual_navigation/learning/model/simple_gcn.py rename to wild_visual_navigation/model/simple_gcn.py index 4beb7955..419e4f24 100644 --- a/wild_visual_navigation/learning/model/simple_gcn.py +++ b/wild_visual_navigation/model/simple_gcn.py @@ -1,7 +1,13 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# import torch import torch.nn.functional as F -from torch_geometric.nn import GCNConv -from torch_geometric.data import Data + +# from torch_geometric.nn import GCNConv +from wild_visual_navigation.utils import Data class SimpleGCN(torch.nn.Module): diff --git a/wild_visual_navigation/learning/model/simple_mlp.py b/wild_visual_navigation/model/simple_mlp.py similarity index 84% rename from wild_visual_navigation/learning/model/simple_mlp.py rename to wild_visual_navigation/model/simple_mlp.py index dea1e40c..bbf0a28a 100644 --- a/wild_visual_navigation/learning/model/simple_mlp.py +++ b/wild_visual_navigation/model/simple_mlp.py @@ -1,10 +1,19 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# +from wild_visual_navigation.utils import Data import torch -from torch_geometric.data import Data -import torch.nn.functional as F class SimpleMLP(torch.nn.Module): - def __init__(self, input_size: int = 64, hidden_sizes: [int] = [255], reconstruction: bool = False): + def __init__( + self, + input_size: int = 64, + hidden_sizes: [int] = [255], + reconstruction: bool = False, + ): super(SimpleMLP, self).__init__() layers = [] self.nr_sigmoid_layers = hidden_sizes[-1] diff --git a/wild_visual_navigation/supervision_generator/__init__.py b/wild_visual_navigation/supervision_generator/__init__.py index 7d990007..cb5e5f91 100644 --- a/wild_visual_navigation/supervision_generator/__init__.py +++ b/wild_visual_navigation/supervision_generator/__init__.py @@ -1 +1,7 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# +from .twist_dataset import TwistDataset, TwistDataModule from .supervision_generator import SupervisionGenerator, run_supervision_generator diff --git a/wild_visual_navigation/supervision_generator/supervision_generator.py b/wild_visual_navigation/supervision_generator/supervision_generator.py index b44190f6..be32f62a 100644 --- a/wild_visual_navigation/supervision_generator/supervision_generator.py +++ b/wild_visual_navigation/supervision_generator/supervision_generator.py @@ -1,27 +1,30 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# from wild_visual_navigation import WVN_ROOT_DIR from wild_visual_navigation.traversability_estimator.graphs import DistanceWindowGraph from wild_visual_navigation.traversability_estimator.nodes import TwistNode from wild_visual_navigation.utils import KalmanFilter -from liegroups import SE2, SE3 -from os.path import join +from liegroups import SE3 import os import torch -import torch.nn as nn class SupervisionGenerator: def __init__( self, - device: str = "cuda", - kf_process_cov: float = 0.01, - kf_meas_cov: float = 10, - kf_outlier_rejection: str = "none", - kf_outlier_rejection_delta: float = 1.0, - sigmoid_slope: float = 15, - sigmoid_cutoff: float = 0.2, - untraversable_thr: float = 0.1, - time_horizon: float = 1, - graph_max_length: float = 1, + device: str, + kf_process_cov, + kf_meas_cov, + kf_outlier_rejection, + kf_outlier_rejection_delta, + sigmoid_slope, + sigmoid_cutoff, + untraversable_thr, + time_horizon, + graph_max_length, ): """Generates traversability signals/labels from different sources @@ -102,9 +105,6 @@ def update_velocity_tracking( S = self.get_velocity_selection_matrix(velocities).to(self.device) - # Get dimensionality of input - N = current_velocity.shape[-1] - # Compute discrepancy error = (torch.nn.functional.mse_loss(S @ current_velocity, S @ desired_velocity)) / max_velocity @@ -185,7 +185,7 @@ def untraversable_thr(self): def run_supervision_generator(): """Projects 3D points to example images and returns an image with the projection""" - from wild_visual_navigation.learning.dataset import TwistDataset + from wild_visual_navigation.supervision_generator import TwistDataset import matplotlib.pyplot as plt import numpy as np import pandas as pd @@ -206,6 +206,8 @@ def run_supervision_generator(): sigmoid_slope=30, sigmoid_cutoff=0.2, untraversable_thr=0.05, + time_horizon=0.05, + graph_max_length=1, ) # Saved data list @@ -220,9 +222,21 @@ def run_supervision_generator(): trav, trav_cov, is_untrav = ag.update_velocity_tracking( curr[0], des[0], max_velocity=0.8, velocities=["vx", "vy"] ) - saved_data.append([t.item(), curr.norm().item(), des.norm().item(), trav.item(), trav_cov.item(), is_untrav]) + saved_data.append( + [ + t.item(), + curr.norm().item(), + des.norm().item(), + trav.item(), + trav_cov.item(), + is_untrav, + ] + ) - df = pd.DataFrame(saved_data, columns=["ts", "curr", "des", "trav", "trav_cov", "is_untraversable"]) + df = pd.DataFrame( + saved_data, + columns=["ts", "curr", "des", "trav", "trav_cov", "is_untraversable"], + ) df["ts"] = df["ts"] - df["ts"][0] df["trav_upper"] = df["trav"] + df["trav_cov"] diff --git a/wild_visual_navigation/learning/dataset/twist_dataset.py b/wild_visual_navigation/supervision_generator/twist_dataset.py similarity index 88% rename from wild_visual_navigation/learning/dataset/twist_dataset.py rename to wild_visual_navigation/supervision_generator/twist_dataset.py index 602ddd4f..2e0c906c 100644 --- a/wild_visual_navigation/learning/dataset/twist_dataset.py +++ b/wild_visual_navigation/supervision_generator/twist_dataset.py @@ -1,3 +1,8 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# from wild_visual_navigation import WVN_ROOT_DIR import os import torch @@ -5,8 +10,8 @@ import pandas as pd import pytorch_lightning as pl from pathlib import Path -from torch.utils.data import Dataset, DataLoader, random_split -from typing import Optional, Callable +from torch.utils.data import Dataset, DataLoader +from typing import Optional class TwistDataset(Dataset): @@ -78,7 +83,12 @@ def __init__( # Find closest samples tol = pd.Timedelta(ts_matching_thr) merged_df = pd.merge_asof( - left=current_df, right=desired_df, right_index=True, left_index=True, direction="nearest", tolerance=tol + left=current_df, + right=desired_df, + right_index=True, + left_index=True, + direction="nearest", + tolerance=tol, ) # Reindex to integer indices self.size = merged_df.index.size @@ -129,7 +139,12 @@ def __getitem__(self, idx: int): class TwistDataModule(pl.LightningDataModule): def __init__( - self, root: str, current_filename: str, desired_filename: str, seq_size: int = 16, batch_size: int = 32 + self, + root: str, + current_filename: str, + desired_filename: str, + seq_size: int = 16, + batch_size: int = 32, ): super().__init__() self.root = root @@ -138,10 +153,18 @@ def __init__( self.seq_size = seq_size self.batch_size = batch_size self.data_train = TwistDataset( - self.root, self.current_filename, self.desired_filename, mode="train", seq_size=self.seq_size + self.root, + self.current_filename, + self.desired_filename, + mode="train", + seq_size=self.seq_size, ) self.data_val = TwistDataset( - self.root, self.current_filename, self.desired_filename, mode="val", seq_size=self.seq_size + self.root, + self.current_filename, + self.desired_filename, + mode="val", + seq_size=self.seq_size, ) def prepare_data(self): diff --git a/wild_visual_navigation/traversability_estimator/__init__.py b/wild_visual_navigation/traversability_estimator/__init__.py index 43c2cd3b..f3c6973a 100644 --- a/wild_visual_navigation/traversability_estimator/__init__.py +++ b/wild_visual_navigation/traversability_estimator/__init__.py @@ -1,3 +1,8 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# from .graphs import ( BaseGraph, TemporalWindowGraph, @@ -6,5 +11,5 @@ run_base_graph, run_temporal_window_graph, ) -from .nodes import BaseNode, ProprioceptionNode, MissionNode, TwistNode, run_base_state +from .nodes import BaseNode, SupervisionNode, MissionNode, TwistNode, run_base_state from .traversability_estimator import TraversabilityEstimator diff --git a/wild_visual_navigation/traversability_estimator/graphs.py b/wild_visual_navigation/traversability_estimator/graphs.py index 4064f63d..d4709001 100644 --- a/wild_visual_navigation/traversability_estimator/graphs.py +++ b/wild_visual_navigation/traversability_estimator/graphs.py @@ -1,10 +1,14 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# from .nodes import BaseNode import networkx as nx import random from threading import Lock import networkx import torch -from wild_visual_navigation.utils import Timer class BaseGraph: @@ -69,7 +73,11 @@ def add_node(self, node: BaseNode): # Add edge to latest if self._last_added_node is not None: - self._graph.add_edge(node, self._last_added_node, distance=node.distance_to(self._last_added_node)) + self._graph.add_edge( + node, + self._last_added_node, + distance=node.distance_to(self._last_added_node), + ) else: self._first_node = node @@ -144,7 +152,12 @@ def approximate_timestamp_filter(node): return nodes[0] if len(nodes) > 0 else None def get_nodes_within_radius_range( - self, node: BaseNode, min_radius: float, max_radius: float, time_eps: float = 1, metric: str = "dijkstra" + self, + node: BaseNode, + min_radius: float, + max_radius: float, + time_eps: float = 1, + metric: str = "dijkstra", ): # Find closest node in the graph (timestamp). This is useful when we are finding nodes corresponding to another graph closest_node = self.get_node_with_timestamp(node.timestamp, eps=time_eps) @@ -192,7 +205,11 @@ def remove_nodes(self, nodes: list): self._graph.remove_nodes_from(nodes) def remove_nodes_within_radius_range( - self, node: BaseNode, min_radius: float = 0, max_radius: float = float("inf"), metric: str = "dijkstra" + self, + node: BaseNode, + min_radius: float = 0, + max_radius: float = float("inf"), + metric: str = "dijkstra", ): # Significantly faster then checking all the nodes nodes_to_remove = [] @@ -310,7 +327,10 @@ def run_base_graph(): nodes_list = [] for i in range(N): - s = BaseNode(timestamp=i, pose_base_in_world=SE3(SO3.identity(), torch.Tensor([i / 10.0, 0, 0])).as_matrix()) + s = BaseNode( + timestamp=i, + pose_base_in_world=SE3(SO3.identity(), torch.Tensor([i / 10.0, 0, 0])).as_matrix(), + ) nodes_list.append(s) graph.add_node(s) @@ -363,7 +383,10 @@ def run_temporal_window_graph(): nodes_list = [] for i in range(N): t = i - s = BaseNode(timestamp=t, pose_base_in_world=SE3(SO3.identity(), torch.Tensor([i / 10.0, 0, 0])).as_matrix()) + s = BaseNode( + timestamp=t, + pose_base_in_world=SE3(SO3.identity(), torch.Tensor([i / 10.0, 0, 0])).as_matrix(), + ) nodes_list.append(s) graph.add_node(s) assert graph.get_first_node().timestamp >= t - W diff --git a/wild_visual_navigation/traversability_estimator/nodes.py b/wild_visual_navigation/traversability_estimator/nodes.py index 2addb156..42a9ac0b 100644 --- a/wild_visual_navigation/traversability_estimator/nodes.py +++ b/wild_visual_navigation/traversability_estimator/nodes.py @@ -1,10 +1,20 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# from wild_visual_navigation.image_projector import ImageProjector -from wild_visual_navigation.utils import make_box, make_plane, make_polygon_from_points, make_dense_plane +from wild_visual_navigation.utils import ( + make_box, + make_plane, + make_polygon_from_points, + make_dense_plane, +) from liegroups.torch import SE3, SO3 -from torch_geometric.data import Data +from wild_visual_navigation.utils import Data + import os import torch -import torch.nn.functional as F from typing import Optional @@ -74,7 +84,10 @@ def distance_to(self, other): """ # Compute pose difference, then log() to get a vector, then extract position coordinates, finally get norm return ( - SE3.from_matrix(self.pose_base_in_world.inverse() @ other.pose_base_in_world, normalize=True) + SE3.from_matrix( + self.pose_base_in_world.inverse() @ other.pose_base_in_world, + normalize=True, + ) .log()[:3] .norm() ) @@ -114,7 +127,6 @@ def __init__( pose_cam_in_world: torch.tensor = None, image: torch.tensor = None, image_projector: ImageProjector = None, - correspondence=None, camera_name="cam", use_for_training=True, ): @@ -131,7 +143,6 @@ def __init__( # Uninitialized members self._features = None - self._feature_type = None self._feature_edges = None self._feature_segments = None self._feature_positions = None @@ -139,7 +150,6 @@ def __init__( self._supervision_mask = None self._supervision_signal = None self._supervision_signal_valid = None - self._correspondence = correspondence self._confidence = None def clear_debug_data(self): @@ -161,10 +171,12 @@ def change_device(self, device): """ super().change_device(device) self._image_projector.change_device(device) - self._image = self._image.to(device) + self._pose_cam_in_base = self._pose_cam_in_base.to(device) self._pose_cam_in_world = self._pose_cam_in_world.to(device) + if self._image is not None: + self._image = self._image.to(device) if self._features is not None: self._features = self._features.to(device) if self._feature_edges is not None: @@ -181,38 +193,57 @@ def change_device(self, device): self._supervision_signal = self._supervision_signal.to(device) if self._supervision_signal_valid is not None: self._supervision_signal_valid = self._supervision_signal_valid.to(device) - if self._correspondence is not None: - self._correspondence = self._correspondence.to(device) if self._confidence is not None: self._confidence = self._confidence.to(device) - def as_pyg_data(self, previous_node: Optional[BaseNode] = None, aux: bool = False): + def as_pyg_data( + self, + previous_node: Optional[BaseNode] = None, + anomaly_detection: bool = False, + aux: bool = False, + ): if aux: return Data(x=self.features, edge_index=self._feature_edges) if previous_node is None: - return Data( - x=self.features, - edge_index=self._feature_edges, - y=self._supervision_signal, - y_valid=self._supervision_signal_valid, - ) + if anomaly_detection: + return Data( + x=self.features[self._supervision_signal_valid], + edge_index=self._feature_edges, + y=self._supervision_signal[self._supervision_signal_valid], + y_valid=self._supervision_signal_valid[self._supervision_signal_valid], + ) + else: + return Data( + x=self.features, + edge_index=self._feature_edges, + y=self._supervision_signal, + y_valid=self._supervision_signal_valid, + ) + else: - return Data( - x=self.features, - edge_index=self._feature_edges, - y=self._supervision_signal, - y_valid=self._supervision_signal_valid, - x_previous=previous_node.features, - edge_index_previous=previous_node._feature_edges, - correspondence=self._correspondence, - ) + if anomaly_detection: + return Data( + x=self.features[self._supervision_signal_valid], + edge_index=self._feature_edges, + y=self._supervision_signal[self._supervision_signal_valid], + y_valid=self._supervision_signal_valid[self._supervision_signal_valid], + x_previous=previous_node.features, + edge_index_previous=previous_node._feature_edges, + ) + else: + return Data( + x=self.features, + edge_index=self._feature_edges, + y=self._supervision_signal, + y_valid=self._supervision_signal_valid, + x_previous=previous_node.features, + edge_index_previous=previous_node._feature_edges, + ) def is_valid(self): - valid_members = ( isinstance(self._features, torch.Tensor) and isinstance(self._supervision_signal, torch.Tensor) - and isinstance(self._correspondence, torch.Tensor) and isinstance(self._supervision_signal_valid, torch.Tensor) ) valid_signals = self._supervision_signal_valid.any() if valid_members else False @@ -227,18 +258,10 @@ def camera_name(self): def confidence(self): return self._confidence - @property - def correspondence(self): - return self._correspondence - @property def features(self): return self._features - @property - def feature_type(self): - return self._feature_type - @property def feature_edges(self): return self._feature_edges @@ -291,18 +314,10 @@ def camera_name(self, camera_name): def confidence(self, confidence): self._confidence = confidence - @correspondence.setter - def correspondence(self, correspondence): - self._correspondence = correspondence - @features.setter def features(self, features): self._features = features - @feature_type.setter - def feature_type(self, feature_type): - self._feature_type = feature_type - @feature_edges.setter def feature_edges(self, feature_edges): self._feature_edges = feature_edges @@ -347,7 +362,13 @@ def supervision_mask(self, supervision_mask): def use_for_training(self, use_for_training): self._use_for_training = use_for_training - def save(self, output_path: str, index: int, graph_only: bool = False, previous_node: Optional[BaseNode] = None): + def save( + self, + output_path: str, + index: int, + graph_only: bool = False, + previous_node: Optional[BaseNode] = None, + ): if self._feature_positions is not None: graph_data = self.as_pyg_data(previous_node) path = os.path.join(output_path, "graph", f"graph_{index:06d}.pt") @@ -362,10 +383,17 @@ def save(self, output_path: str, index: int, graph_only: bool = False, previous_ p = path.replace("graph", "seg") torch.save(self._feature_segments.cpu(), p) - def project_footprint(self, footprint: torch.tensor, color: torch.tensor = torch.FloatTensor([1.0, 1.0, 1.0])): - mask, image_overlay, projected_points, valid_points = self._image_projector.project_and_render( - self._pose_cam_in_world[None], footprint, color - ) + def project_footprint( + self, + footprint: torch.tensor, + color: torch.tensor = torch.FloatTensor([1.0, 1.0, 1.0]), + ): + ( + mask, + image_overlay, + projected_points, + valid_points, + ) = self._image_projector.project_and_render(self._pose_cam_in_world[None], footprint, color) return mask, image_overlay, projected_points, valid_points @@ -412,11 +440,12 @@ def update_supervision_signal(self): self._supervision_signal_valid = self._supervision_signal > 0 -class ProprioceptionNode(BaseNode): +class SupervisionNode(BaseNode): """Local node stores all the information required for traversability estimation and debugging - All the information matches a real frame that must be respected to keep consistency""" + All the information matches a real frame that must be respected to keep consistency + """ - _name = "proprioception_node" + _name = "supervision_node" def __init__( self, @@ -429,7 +458,7 @@ def __init__( length: float = 0.1, width: float = 0.1, height: float = 0.1, - proprioception: torch.tensor = None, + supervision: torch.tensor = None, traversability: torch.tensor = torch.FloatTensor([0.0]), traversability_var: torch.tensor = torch.FloatTensor([1.0]), is_untraversable: bool = False, @@ -449,7 +478,7 @@ def __init__( self._length = length self._width = width self._height = height - self._proprioceptive_state = proprioception + self._supervision_state = supervision self._traversability = traversability self._traversability_var = traversability_var self._is_untraversable = is_untraversable @@ -465,17 +494,24 @@ def change_device(self, device): self._pose_footprint_in_world = self._pose_footprint_in_world.to(device) self._twist_in_base = self._twist_in_base.to(device) self._desired_twist_in_base = self._desired_twist_in_base.to(device) - self._proprioceptive_state = self._proprioceptive_state.to(device) + self._supervision_state = self._supervision_state.to(device) def get_bounding_box_points(self): - return make_box(self._length, self._width, self._height, pose=self._pose_base_in_world, grid_size=5).to( - self._pose_base_in_world.device - ) + return make_box( + self._length, + self._width, + self._height, + pose=self._pose_base_in_world, + grid_size=5, + ).to(self._pose_base_in_world.device) def get_footprint_points(self): - return make_plane(x=self._length, y=self._width, pose=self._pose_footprint_in_world, grid_size=25).to( - self._pose_footprint_in_world.device - ) + return make_plane( + x=self._length, + y=self._width, + pose=self._pose_footprint_in_world, + grid_size=25, + ).to(self._pose_footprint_in_world.device) def get_side_points(self): return make_plane(x=0.0, y=self._width, pose=self._pose_footprint_in_world, grid_size=2).to( @@ -495,7 +531,11 @@ def get_untraversable_plane(self, grid_size=5): # Prepare transformation of plane in base frame rho = torch.FloatTensor( - [0.5 * self._length * motion_direction[0], 0.5 * self._length * motion_direction[1], -self._height / 2] + [ + 0.5 * self._length * motion_direction[0], + 0.5 * self._length * motion_direction[1], + -self._height / 2, + ] ) # Translation vector (x, y, z) phi = torch.FloatTensor([0.0, 0.0, z_angle]) # roll-pitch-yaw R_BP = SO3.from_rpy(phi) @@ -503,9 +543,12 @@ def get_untraversable_plane(self, grid_size=5): pose_plane_in_world = self._pose_base_in_world @ pose_plane_in_base # Pose of plane in world frame # Make plane - return make_dense_plane(y=0.5 * self._width, z=self._height, pose=pose_plane_in_world, grid_size=grid_size).to( - device - ) + return make_dense_plane( + y=0.5 * self._width, + z=self._height, + pose=pose_plane_in_world, + grid_size=grid_size, + ).to(device) def make_footprint_with_node(self, other: BaseNode, grid_size: int = 10): if self.is_untraversable: @@ -559,8 +602,8 @@ def pose_footprint_in_world(self): return self._pose_footprint_in_world @property - def propropioceptive_state(self): - return self._proprioceptive_state + def supervision_state(self): + return self._supervision_state @traversability.setter def traversability(self, traversability): @@ -571,7 +614,7 @@ def traversability_var(self, variance): self._traversability_var = variance def is_valid(self): - return isinstance(self._proprioceptive_state, torch.Tensor) + return isinstance(self._supervision_state, torch.Tensor) class TwistNode(BaseNode): diff --git a/wild_visual_navigation/traversability_estimator/traversability_estimator.py b/wild_visual_navigation/traversability_estimator/traversability_estimator.py index e8bbbd0b..7d2aebf9 100644 --- a/wild_visual_navigation/traversability_estimator/traversability_estimator.py +++ b/wild_visual_navigation/traversability_estimator/traversability_estimator.py @@ -1,34 +1,31 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# from wild_visual_navigation.feature_extractor import FeatureExtractor from wild_visual_navigation.image_projector import ImageProjector -from wild_visual_navigation.learning.model import get_model +from wild_visual_navigation.model import get_model from wild_visual_navigation.cfg import ExperimentParams -from wild_visual_navigation.utils import Timer, accumulate_time +from pytictac import accumulate_time from wild_visual_navigation.traversability_estimator import ( BaseGraph, DistanceWindowGraph, MissionNode, - ProprioceptionNode, + SupervisionNode, MaxElementsGraph, ) from wild_visual_navigation.utils import WVNMode -from wild_visual_navigation.learning.utils import TraversabilityLoss -from wild_visual_navigation.utils import make_polygon_from_points +from wild_visual_navigation.utils import TraversabilityLoss, AnomalyLoss from wild_visual_navigation.visu import LearningVisualizer -from wild_visual_navigation.utils import KLTTracker, KLTTrackerOpenCV - -from wild_visual_navigation import WVN_ROOT_DIR from pytorch_lightning import seed_everything -from torch_geometric.data import Data, Batch +from wild_visual_navigation.utils import Data, Batch from threading import Lock -import dataclasses import os import pickle -import time import torch -import torch.nn.functional as F import torchvision.transforms as transforms -from torchmetrics import ROC to_tensor = transforms.ToTensor() @@ -37,37 +34,26 @@ class TraversabilityEstimator: def __init__( self, params: ExperimentParams, - scale_traversability: bool, - device: str = "cuda", - max_distance: float = 3, - image_size: int = 448, - image_distance_thr: float = None, - proprio_distance_thr: float = None, - segmentation_type: str = "slic", - feature_type: str = "dino", - optical_flow_estimator_type: str = "none", - min_samples_for_training: int = 10, - vis_node_index: int = 10, - mode: bool = False, - extraction_store_folder=None, - **kwargs, + device: str, + max_distance: float, + image_distance_thr: float, + supervision_distance_thr: float, + min_samples_for_training: int, + vis_node_index: int, + mode: bool, + extraction_store_folder, + anomaly_detection: bool, ): self._device = device self._mode = mode self._extraction_store_folder = extraction_store_folder self._min_samples_for_training = min_samples_for_training self._vis_node_index = vis_node_index - self._scale_traversability = scale_traversability self._params = params - self._scale_traversability_threshold = 0 - - if self._scale_traversability: - # Use 500 bins for constant memory usuage - self._auxiliary_training_roc = ROC(task="binary", thresholds=5000) - self._auxiliary_training_roc.to(self._device) + self._anomaly_detection = anomaly_detection # Local graphs - self._proprio_graph = DistanceWindowGraph(max_distance=max_distance, edge_distance=proprio_distance_thr) + self._supervision_graph = DistanceWindowGraph(max_distance=max_distance, edge_distance=supervision_distance_thr) # Experience graph if mode == WVNMode.EXTRACT_LABELS: @@ -78,29 +64,12 @@ def __init__( # Visualization node self._vis_mission_node = None - # Feature extractor - self._segmentation_type = segmentation_type - self._feature_type = feature_type - - self._feature_extractor = FeatureExtractor( - self._device, - segmentation_type=self._segmentation_type, - feature_type=self._feature_type, - input_size=image_size, - **kwargs, - ) - # Optical flow - self._optical_flow_estimator_type = optical_flow_estimator_type - - if optical_flow_estimator_type == "sparse": - self._optical_flow_estimator = KLTTrackerOpenCV(device=self._device) - # Mutex self._learning_lock = Lock() self._pause_training = False self._pause_mission_graph = False - self._pause_proprio_graph = False + self._pause_supervision_graph = False # Visualization self._visualizer = LearningVisualizer() @@ -108,21 +77,30 @@ def __init__( # Lightning module seed_everything(42) - self._exp_cfg = dataclasses.asdict(self._params) - self._model = get_model(self._exp_cfg["model"]).to(self._device) + self._model = get_model(self._params.model).to(self._device) self._model.train() - self._traversability_loss = TraversabilityLoss( - **self._exp_cfg["loss"], - model=self._model, - log_enabled=self._exp_cfg["general"]["log_confidence"], - log_folder=self._exp_cfg["general"]["model_path"], - ) - self._traversability_loss.to(self._device) + if self._anomaly_detection: + self._traversability_loss = AnomalyLoss( + **self._params["loss_anomaly"], + log_enabled=self._params["general"]["log_confidence"], + log_folder=self._params["general"]["model_path"], + ) + self._traversability_loss.to(self._device) + + else: + self._traversability_loss = TraversabilityLoss( + **self._params["loss"], + model=self._model, + log_enabled=self._params["general"]["log_confidence"], + log_folder=self._params["general"]["model_path"], + ) + self._traversability_loss.to(self._device) - self._optimizer = torch.optim.Adam(self._model.parameters(), lr=self._exp_cfg["optimizer"]["lr"]) + self._optimizer = torch.optim.Adam(self._model.parameters(), lr=self._params["optimizer"]["lr"]) self._loss = torch.tensor([torch.inf]) self._step = 0 + self._debug_info_node_count = 0 torch.set_grad_enabled(True) @@ -141,48 +119,6 @@ def __setstate__(self, state: dict): def reset(self): print("[WARNING] Resetting the traversability estimator is not fully tested") - # with self._learning_lock: - # self._pause_training = True - # self._pause_mission_graph = True - # self._pause_proprio_graph = True - # time.sleep(2.0) - - # self._proprio_graph.clear() - # self._mission_graph.clear() - - # # Reset all the learning stuff - # self._step = 0 - # self._loss = torch.tensor([torch.inf]) - - # # Re-create model - # self._exp_cfg = dataclasses.asdict(self._params) - # self._model = get_model(self._exp_cfg["model"]).to(self._device) - # self._model.train() - - # # Re-create optimizer - # self._optimizer = torch.optim.Adam(self._model.parameters(), lr=self._exp_cfg["optimizer"]["lr"]) - - # # Re-create traversability loss - # self._traversability_loss = TraversabilityLoss( - # **self._exp_cfg["loss"], - # model=self._model, - # log_enabled=self._exp_cfg["general"]["log_confidence"], - # log_folder=self._exp_cfg["general"]["model_path"], - # ) - # self._traversability_loss.to(self._device) - - # # Resume training - # self._pause_training = False - # self._pause_mission_graph = False - # self._pause_proprio_graph = False - - @property - def scale_traversability_threshold(self): - return self._scale_traversability_threshold - - @scale_traversability_threshold.setter - def scale_traversability_threshold(self, scale_traversability_threshold): - self._scale_traversability_threshold = scale_traversability_threshold @property def loss(self): @@ -207,44 +143,12 @@ def change_device(self, device: str): Args: device (str): new device """ - self._proprio_graph.change_device(device) + self._supervision_graph.change_device(device) self._mission_graph.change_device(device) - self._feature_extractor.change_device(device) self._model = self._model.to(device) - if self._optical_flow_estimator_type != "none": - self._optical_flow_estimator = self._optical_flow_estimator.to(device) - - if self._scale_traversability: - # Use 500 bins for constant memory usuage - self._auxiliary_training_roc.to(device) - - @accumulate_time - def update_features(self, node: MissionNode): - """Extracts visual features from a node that stores an image - Args: - node (MissionNode): new node in the mission graph - """ - if self._mode != WVNMode.EXTRACT_LABELS: - # Extract features - # Check do we need to add here the .clone() in - edges, feat, seg, center = self._feature_extractor.extract(img=node.image[None], return_centers=True) - - # Set features in node - node.feature_type = self._feature_extractor.feature_type - node.features = feat - node.feature_edges = edges - node.feature_segments = seg - node.feature_positions = center - - @accumulate_time - def update_prediction(self, node: MissionNode): - data = Data(x=node.features, edge_index=node.feature_edges) - with torch.inference_mode(): - with self._learning_lock: - node.prediction = self._model(data) - reco_loss = F.mse_loss(node.prediction[:, 1:], node.features, reduction="none").mean(dim=1) - node.confidence = self._traversability_loss._confidence_generator.inference_without_update(reco_loss) + if self._use_feature_extractor: + self._feature_extractor.change_device(device) @accumulate_time def update_visualization_node(self): @@ -258,116 +162,6 @@ def update_visualization_node(self): self._vis_mission_node = self._mission_graph.get_nodes()[-self._vis_node_index] - @accumulate_time - def add_correspondences_dense_optical_flow(self, node: MissionNode, previous_node: MissionNode, debug=False): - flow_previous_to_current = self._optical_flow_estimator.forward(previous_node.image.clone(), node.image.clone()) - grid_x, grid_y = torch.meshgrid( - torch.arange(0, previous_node.image.shape[1], device=self._device, dtype=torch.float32), - torch.arange(0, previous_node.image.shape[2], device=self._device, dtype=torch.float32), - indexing="ij", - ) - positions = torch.stack([grid_x, grid_y]) - positions += flow_previous_to_current - positions = positions.type(torch.long) - start_seg = torch.unique(previous_node.feature_segments) - - previous = [] - current = [] - for el in start_seg: - m = previous_node.feature_segments == el - candidates = positions[:, m] - m = ( - (candidates[0, :] >= 0) - * (candidates[0, :] < m.shape[0]) - * (candidates[1, :] >= 0) - * (candidates[1, :] < m.shape[1]) - ) - if m.sum() == 0: - continue - candidates = candidates[:, m] - res = node.feature_segments[candidates[0, :], candidates[1, :]] - previous.append(el) - current.append(torch.unique(res, sorted=True)[0]) - - if len(current) != 0: - current = torch.stack(current) - previous = torch.stack(previous) - correspondence = torch.stack([previous, current], dim=1) - node.correspondence = correspondence - - if debug: - from wild_visual_navigation import WVN_ROOT_DIR - from wild_visual_navigation.visu import LearningVisualizer - - visu = LearningVisualizer(p_visu=os.path.join(WVN_ROOT_DIR, "results/test_visu"), store=True) - visu.plot_correspondence_segment( - seg_prev=previous_node.feature_segments, - seg_current=node.feature_segments, - img_prev=previous_node.image, - img_current=node.image, - center_prev=previous_node.feature_positions, - center_current=node.feature_positions, - correspondence=node.correspondence, - tag="centers", - ) - - visu.plot_optical_flow( - flow=flow_previous_to_current, img1=previous_node.image, img2=node.image, tag="flow", s=50 - ) - - @accumulate_time - def add_correspondences_sparse_optical_flow(self, node: MissionNode, previous_node: MissionNode, debug=False): - # Transform previous_nodes feature locations into current image using KLT - pre_pos = previous_node.feature_positions - cur_pos = self._optical_flow_estimator( - previous_node.feature_positions[:, 0].clone(), - previous_node.feature_positions[:, 1].clone(), - previous_node.image, - node.image, - ) - cur_pos = torch.stack(cur_pos, dim=1) - # Use forward propagated cluster centers to get segment index of current image - - # Only compute for forward propagated centers that fall onto current image plane - m = ( - (cur_pos[:, 0] >= 0) - * (cur_pos[:, 1] >= 0) - * (cur_pos[:, 0] < node.image.shape[1]) - * (cur_pos[:, 1] < node.image.shape[2]) - ) - - # Can enumerate previous segments and use mask to get segment index - cor_pre = torch.arange(previous_node.feature_positions.shape[0], device=self._device)[m] - # cor_pre = pre[m] - - # Check feature_segmentation mask to index correct segment index - cur_pos = cur_pos[m].type(torch.long) - cor_cur = node.feature_segments[cur_pos[:, 1], cur_pos[:, 0]] - node.correspondence = torch.stack([cor_pre, cor_cur], dim=1) - - if debug: - from wild_visual_navigation import WVN_ROOT_DIR - from wild_visual_navigation.visu import LearningVisualizer - - visu = LearningVisualizer(p_visu=os.path.join(WVN_ROOT_DIR, "results/test_visu"), store=True) - visu.plot_sparse_optical_flow( - pre_pos, - cur_pos, - img1=previous_node.image, - img2=node.image, - tag="flow", - ) - visu.plot_correspondence_segment( - seg_prev=previous_node.feature_segments, - seg_current=node.feature_segments, - img_prev=previous_node.image, - img_current=node.image, - center_prev=previous_node.feature_positions, - center_current=node.feature_positions, - correspondence=node.correspondence, - tag="centers", - ) - @accumulate_time def add_mission_node(self, node: MissionNode, verbose: bool = False): """Adds a node to the mission graph to images and training info @@ -379,12 +173,6 @@ def add_mission_node(self, node: MissionNode, verbose: bool = False): if self._pause_mission_graph: return False - # Compute image features - self.update_features(node) - - # Get last added node - previous_node = self._mission_graph.get_last_node() - # Add image node success = self._mission_graph.add_node(node) @@ -395,49 +183,36 @@ def add_mission_node(self, node: MissionNode, verbose: bool = False): s += " " * (48 - len(s)) + f"total nodes [{total_nodes}]" if verbose: print(s) - - if self._mode != WVNMode.EXTRACT_LABELS: - # Set optical flow - if self._optical_flow_estimator_type == "dense" and previous_node is not None: - raise Exception("Not working") - self.add_correspondences_dense_optical_flow(node, previous_node, debug=False) - - elif self._optical_flow_estimator_type == "sparse" and previous_node is not None: - self.add_correspondences_sparse_optical_flow(node, previous_node, debug=False) - + h, w = node._feature_segments.shape[0], node._feature_segments.shape[1] # Project past footprints on current image - supervision_mask = torch.ones(node.image.shape).to(self._device) * torch.nan + supervision_mask = torch.ones((3, h, w)).to(self._device) * torch.nan # Finally overwrite the current mask node.supervision_mask = supervision_mask node.update_supervision_signal() - if self._mode == WVNMode.EXTRACT_LABELS: - p = os.path.join(self._extraction_store_folder, "image", str(node.timestamp).replace(".", "_") + ".pt") - torch.save(node.image, p) - return True - else: - return False + + return False @accumulate_time @torch.no_grad() - def add_proprio_node(self, pnode: ProprioceptionNode): - """Adds a node to the proprioceptive graph to store proprioception + def add_supervision_node(self, pnode: SupervisionNode): + """Adds a node to the supervision graph to store supervision Args: - node (BaseNode): new node in the proprioceptive graph + node (BaseNode): new node in the supervision graph """ - if self._pause_proprio_graph: + if self._pause_supervision_graph: return False # If the node is not valid, we do nothing if not pnode.is_valid(): return False - # Get last added proprio node - last_pnode = self._proprio_graph.get_last_node() - success = self._proprio_graph.add_node(pnode) + # Get last added supervision node + last_pnode = self._supervision_graph.get_last_node() + success = self._supervision_graph.add_node(pnode) if not success: # Update traversability of latest node @@ -446,6 +221,7 @@ def add_proprio_node(self, pnode: ProprioceptionNode): return False else: + # If the previous node doesn't exist or it's invalid, we do nothing if last_pnode is None or not last_pnode.is_valid(): return False @@ -460,9 +236,19 @@ def add_proprio_node(self, pnode: ProprioceptionNode): if (not hasattr(last_mission_node, "supervision_mask")) or (last_mission_node.supervision_mask is None): return False + for j, ele in enumerate( + list(self._mission_graph._graph.nodes._nodes.items())[self._debug_info_node_count :] + ): + node, values = ele + if last_mission_node.timestamp - values["timestamp"] > 30: + node.clear_debug_data() + self._debug_info_node_count += 1 + else: + break + # Get all mission nodes within a range mission_nodes = self._mission_graph.get_nodes_within_radius_range( - last_mission_node, 0, self._proprio_graph.max_distance + last_mission_node, 0, self._supervision_graph.max_distance ) if len(mission_nodes) < 1: @@ -473,7 +259,6 @@ def add_proprio_node(self, pnode: ProprioceptionNode): # New implementation B = len(mission_nodes) - # Prepare batches K = torch.eye(4, device=self._device).repeat(B, 1, 1) supervision_masks = torch.zeros(last_mission_node.supervision_mask.shape, device=self._device).repeat( @@ -488,9 +273,7 @@ def add_proprio_node(self, pnode: ProprioceptionNode): K[i] = mnode.image_projector.camera.intrinsics pose_camera_in_world[i] = mnode.pose_cam_in_world - if (not hasattr(mnode, "supervision_mask")) or (mnode.supervision_mask is None): - continue - else: + if not ((not hasattr(mnode, "supervision_mask")) or (mnode.supervision_mask is None)): supervision_masks[i] = mnode.supervision_mask im = ImageProjector(K, H, W) @@ -519,8 +302,8 @@ def add_proprio_node(self, pnode: ProprioceptionNode): def get_mission_nodes(self): return self._mission_graph.get_nodes() - def get_proprio_nodes(self): - return self._proprio_graph.get_nodes() + def get_supervision_nodes(self): + return self._supervision_graph.get_nodes() def get_last_valid_mission_node(self): last_valid_node = None @@ -530,10 +313,6 @@ def get_last_valid_mission_node(self): return last_valid_node def get_mission_node_for_visualization(self): - # print(f"get_mission_node_for_visualization: {self._vis_mission_node}") - # if self._vis_mission_node is not None: - # print(f" has image {hasattr(self._vis_mission_node, 'image')}") - # print(f" has supervision_mask {hasattr(self._vis_mission_node, 'supervision_mask')}") return self._vis_mission_node def save(self, mission_path: str, filename: str): @@ -586,7 +365,12 @@ def save_graph(self, mission_path: str, export_debug: bool = False): i = 0 for node in mission_nodes: if node.is_valid(): - node.save(mission_path, i, graph_only=False, previous_node=self._mission_graph.get_previous_node(node)) + node.save( + mission_path, + i, + graph_only=False, + previous_node=self._mission_graph.get_previous_node(node), + ) i += 1 self._pause_training = False @@ -645,39 +429,19 @@ def load_checkpoint(self, checkpoint_path: str): self._pause_training = False @accumulate_time - def make_batch(self, batch_size: int = 8): + def make_batch( + self, + batch_size: int = 8, + ): """Samples a batch from the mission_graph Args: batch_size (int): Size of the batch """ - if self._optical_flow_estimator_type != "none": - raise ValueError( - "Currently not supported the auxilary graph implementation is not needed and the features and edge index of previous node should be added to the mission graph directly." - ) - # Sample a batch of nodes and their previous node, for temporal consistency - mission_nodes = self._mission_graph.get_n_random_valid_nodes(n=batch_size) - ls = [ - x.as_pyg_data(self._mission_graph.get_previous_node(x)) - for x in mission_nodes - if x.correspondence is not None - ] - - ls_aux = [self._mission_graph.get_previous_node(x).as_pyg_data(aux=True) for x in mission_nodes] - - # Make sure to only use nodes with valid correspondences - while len(ls) < batch_size: - mn = self._mission_graph.get_n_random_valid_nodes(n=1)[0] - if mn.correspondence is not None: - ls.append(mn.as_pyg_data(self._mission_graph.get_previous_node(mn))) - ls_aux.append(self._mission_graph.get_previous_node(mn).as_pyg_data(aux=True)) - - batch = Batch.from_data_list(ls) - else: - # Just sample N random nodes - mission_nodes = self._mission_graph.get_n_random_valid_nodes(n=batch_size) - batch = Batch.from_data_list([x.as_pyg_data() for x in mission_nodes]) + # Just sample N random nodes + mission_nodes = self._mission_graph.get_n_random_valid_nodes(n=batch_size) + batch = Batch.from_data_list([x.as_pyg_data(anomaly_detection=self._anomaly_detection) for x in mission_nodes]) return batch @@ -691,51 +455,46 @@ def train(self): if self._pause_training: return {} - return_dict = {} - if self._mission_graph.get_num_valid_nodes() > self._min_samples_for_training: + num_valid_nodes = self._mission_graph.get_num_valid_nodes() + return_dict = {"mission_graph_num_valid_node": num_valid_nodes} + if num_valid_nodes > self._min_samples_for_training: # Prepare new batch - graph = self.make_batch(self._exp_cfg["ablation_data_module"]["batch_size"]) - - with self._learning_lock: - # Forward pass - res = self._model(graph) - - log_step = (self._step % 20) == 0 - self._loss, loss_aux = self._traversability_loss(graph, res, step=self._step, log_step=log_step) - - # Keep track of ROC during training for rescaling the loss when publishing - if self._scale_traversability: - # This mask should contain all the segments corrosponding to trees. - mask_anomaly = loss_aux["confidence"] < 0.5 - mask_proprioceptive = graph.y == 1 - # Remove the segments that are for sure not an anomalies given that we have walked on them. - mask_anomaly[mask_proprioceptive] = False - # Elements are valid if they are either an anomaly or we have walked on them to fit the ROC - mask_valid = mask_anomaly | mask_proprioceptive - self._auxiliary_training_roc(res[mask_valid, 0], graph.y[mask_valid].type(torch.long)) - return_dict["scale_traversability_threshold"] = self._scale_traversability_threshold - # Backprop - self._optimizer.zero_grad() - self._loss.backward() - self._optimizer.step() - - # Print losses - if log_step: - loss_trav = loss_aux["loss_trav"] - loss_reco = loss_aux["loss_reco"] - print( - f"step: {self._step} | loss: {self._loss:5f} | loss_trav: {loss_trav:5f} | loss_reco: {loss_reco:5f}" - ) + graph = self.make_batch(self._params["ablation_data_module"]["batch_size"]) + if graph is not None: + with self._learning_lock: + # Forward pass + + res = self._model(graph) + + log_step = (self._step % 20) == 0 + self._loss, loss_aux, trav = self._traversability_loss( + graph, res, step=self._step, log_step=log_step + ) + + # Backprop + self._optimizer.zero_grad() + self._loss.backward() + self._optimizer.step() + + # Print losses + if log_step: + loss_trav = loss_aux["loss_trav"] + loss_reco = loss_aux["loss_reco"] + print( + f"step: {self._step} | loss: {self._loss.item():5f} | loss_trav: {loss_trav.item():5f} | loss_reco: {loss_reco.item():5f}" + ) + + # Update steps + self._step += 1 - # Update steps - self._step += 1 + # Return loss + return_dict["loss_total"] = self._loss.item() + return_dict["loss_trav"] = loss_aux["loss_trav"].item() + return_dict["loss_reco"] = loss_aux["loss_reco"].item() - # Return loss - return_dict["loss_total"] = self._loss.item() - return_dict["loss_trav"] = loss_aux["loss_trav"].item() - return_dict["loss_reco"] = loss_aux["loss_reco"].item() - return return_dict - return {"loss_total": -1} + return return_dict + return_dict["loss_total"] = -1 + return return_dict @accumulate_time def plot_mission_node_prediction(self, node: MissionNode): diff --git a/wild_visual_navigation/utils/__init__.py b/wild_visual_navigation/utils/__init__.py index a60f90ca..b44f8835 100644 --- a/wild_visual_navigation/utils/__init__.py +++ b/wild_visual_navigation/utils/__init__.py @@ -1,9 +1,30 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# +from .data import Data, Batch +from .flatten_dict import flatten_dict +from .get_logger import get_logger, get_neptune_run +from .loading import load_yaml, file_path, save_omega_cfg +from .create_experiment_folder import create_experiment_folder +from .get_confidence import get_confidence from .kalman_filter import KalmanFilter -from .meshes import make_box, make_rounded_box, make_ellipsoid, make_plane, make_polygon_from_points, make_dense_plane -from .timing import Timer, accumulate_time, SystemLevelTimer, SystemLevelContextTimer -from .klt_tracker import KLTTracker, KLTTrackerOpenCV from .confidence_generator import ConfidenceGenerator +from .meshes import ( + make_box, + make_rounded_box, + make_ellipsoid, + make_plane, + make_polygon_from_points, + make_dense_plane, +) from .operation_modes import WVNMode -from .dataset_info import perugia_dataset, ROOT_DIR -from .override_params import override_params -from .gpu_monitor import GpuMonitor, SystemLevelGpuMonitor, SystemLevelContextGpuMonitor, accumulate_memory +from .gpu_monitor import ( + GpuMonitor, + SystemLevelGpuMonitor, + SystemLevelContextGpuMonitor, + accumulate_memory, +) +from .loss import TraversabilityLoss, AnomalyLoss +from .testing import load_test_image, get_dino_transform, make_results_folder diff --git a/wild_visual_navigation/utils/confidence_generator.py b/wild_visual_navigation/utils/confidence_generator.py index ec6bc69a..1d920c5b 100644 --- a/wild_visual_navigation/utils/confidence_generator.py +++ b/wild_visual_navigation/utils/confidence_generator.py @@ -1,21 +1,27 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# from wild_visual_navigation.utils import KalmanFilter +from wild_visual_navigation import WVN_ROOT_DIR import torch import os +from collections import deque class ConfidenceGenerator(torch.nn.Module): def __init__( self, - std_factor: float = 0.7, - method: str = "running_mean", + std_factor, + method, log_enabled: bool = False, - log_folder: str = "/tmp", + log_folder: str = f"{WVN_ROOT_DIR}/results", ): """Returns a confidence value for each number Args: - std_factor (float, optional): _description_. Defaults to 2.0. - device (str, optional): _description_. Defaults to "cpu". + std_factor (float, optional): _description_. Defaults to 0.7. """ super(ConfidenceGenerator, self).__init__() self.std_factor = std_factor @@ -51,21 +57,36 @@ def __init__( self.running_n = torch.nn.Parameter(running_n, requires_grad=False) self.running_sum = torch.nn.Parameter(running_sum, requires_grad=False) self.running_sum_of_squares = torch.nn.Parameter(running_sum_of_squares, requires_grad=False) + + # self.running_n = running_n.to("cuda") + # self.running_sum = running_sum.to("cuda") + # self.running_sum_of_squares = running_sum_of_squares.to("cuda") + self._update = self.update_running_mean self._reset = self.reset_running_mean - elif method == "latest_measurment": - self._update = self.update_latest_measurment - self._reset = self.reset_latest_measurment + elif method == "latest_measurement": + self._update = self.update_latest_measurement + self._reset = self.reset_latest_measurement + elif method == "moving_average": + window_size = 5 + self.data_window = deque(maxlen=window_size) + self._update = self.update_moving_average + self._reset = self.reset_moving_average else: raise ValueError("Unknown method") - def update_latest_measurment(self, x: torch.Tensor, x_positive: torch.Tensor): + def update_latest_measurement(self, x: torch.Tensor, x_positive: torch.Tensor): # Then the confidence is computed as the distance to the center of the Gaussian given factor*sigma self.mean[0] = x_positive.mean() self.std[0] = x_positive.std() return self.inference_without_update(x) - def reset_latest_measurment(self, x: torch.Tensor, x_positive: torch.Tensor): + def reset_latest_measurement(self, x: torch.Tensor, x_positive: torch.Tensor): + self.mean[0] = 0 + self.var[0] = 1 + self.std[0] = 1 + + def reset_moving_average(self, x: torch.Tensor, x_positive: torch.Tensor): self.mean[0] = 0 self.var[0] = 1 self.std[0] = 1 @@ -81,9 +102,29 @@ def update_running_mean(self, x: torch.tensor, x_positive: torch.tensor): self.var[0] = self.running_sum_of_squares / self.running_n - self.mean**2 self.std[0] = torch.sqrt(self.var) - # Then the confidence is computed as the distance to the center of the Gaussian given factor*sigma - confidence = torch.exp(-(((x - self.mean) / (self.std * self.std_factor)) ** 2) * 0.5) - confidence[x < self.mean] = 1.0 + if x.device != self.mean.device: + return torch.zeros_like(x) + + shifted_mean = self.mean + self.std * self.std_factor + std_fac = 1 + interval_min = max(shifted_mean - std_fac * self.std, 0) + interval_max = shifted_mean + std_fac * self.std + x = torch.clip(x, interval_min, interval_max) + confidence = 1 - ((x - interval_min) / (interval_max - interval_min)) + + return confidence.type(torch.float32) + + def update_moving_average(self, x: torch.tensor, x_positive: torch.tensor): + self.data_window.append(x_positive) + + data_window_tensor = list(self.data_window) + data_window_tensor = torch.cat(data_window_tensor, dim=0) + + self.mean[0] = data_window_tensor.mean() + self.std[0] = data_window_tensor.std() + + x = torch.clip(x, self.mean - 2 * self.std, self.mean + 2 * self.std) + confidence = (x - torch.min(x)) / (torch.max(x) - torch.min(x)) return confidence.type(torch.float32) @@ -94,7 +135,7 @@ def update_kalman_filter(self, x: torch.tensor, x_positive: torch.tensor): self.var[0, 0] = var[0, 0] self.mean[0] = mean[0] - assert torch.isnan(self.mean).any() == False, "Nan Value in mean detected" + assert not torch.isnan(self.mean).any(), "Nan Value in mean detected" self.std[0] = torch.sqrt(self.var)[0, 0] # Then the confidence is computed as the distance to the center of the Gaussian given factor*sigma @@ -103,7 +144,13 @@ def update_kalman_filter(self, x: torch.tensor, x_positive: torch.tensor): return confidence.type(torch.float32) - def update(self, x: torch.tensor, x_positive: torch.tensor, step: int, log_step: bool = False): + def update( + self, + x: torch.tensor, + x_positive: torch.tensor, + step: int, + log_step: bool = False, + ): """Input a tensor with multiple error predictions. Returns the estimated confidence score within 2 standard deviations based on the running mean and variance. @@ -121,17 +168,28 @@ def update(self, x: torch.tensor, x_positive: torch.tensor, step: int, log_step: with torch.no_grad(): torch.save( - {"x": x.cpu(), "x_positive": x_positive.cpu(), "mean": self.mean.cpu(), "std": self.std.cpu()}, + { + "x": x.cpu(), + "x_positive": x_positive.cpu(), + "mean": self.mean.cpu(), + "std": self.std.cpu(), + }, os.path.join(base_folder, f"samples_{step:06}.pt"), ) return output def inference_without_update(self, x: torch.tensor): + if x.device != self.mean.device: return torch.zeros_like(x) + shifted_mean = self.mean + self.std * self.std_factor + std_fac = 1 + interval_min = max(shifted_mean - std_fac * self.std, 0) + interval_max = shifted_mean + std_fac * self.std + x = torch.clip(x, interval_min, interval_max) + confidence = 1 - ((x - interval_min) / (interval_max - interval_min)) - confidence = torch.exp(-(((x - self.mean) / (self.std * self.std_factor)) ** 2) * 0.5) return confidence.type(torch.float32) def forward(self, x: torch.tensor): @@ -150,15 +208,13 @@ def reset_kalman_filter(self): self.var[0] = 1 self.std[0] = 1 + def get_dict(self): + return {"mean": self.mean, "var": self.var, "std": self.std} + if __name__ == "__main__": - cg = ConfidenceGenerator() - for i in range(100000): - inp = ( - torch.rand( - 10, - ) - * 10 - ) - res = cg.update(inp, inp) - print("inp ", inp, " res ", res, "std", cg.std) + cg = ConfidenceGenerator(std_factor=0.5, method="latest_measurement") + for i in range(1000): + inp = torch.rand(10) * 10 + res = cg.update(inp, inp, step=i) + # print("inp ", inp, " res ", res, "std", cg.std) diff --git a/wild_visual_navigation/learning/utils/create_experiment_folder.py b/wild_visual_navigation/utils/create_experiment_folder.py similarity index 53% rename from wild_visual_navigation/learning/utils/create_experiment_folder.py rename to wild_visual_navigation/utils/create_experiment_folder.py index 64f0b70f..a879fd8b 100644 --- a/wild_visual_navigation/learning/utils/create_experiment_folder.py +++ b/wild_visual_navigation/utils/create_experiment_folder.py @@ -1,47 +1,48 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# import datetime import os import shutil from pathlib import Path from pytorch_lightning.utilities import rank_zero_only from wild_visual_navigation.cfg import ExperimentParams -from typing import Union +from omegaconf import read_write +from wild_visual_navigation import WVN_ROOT_DIR @rank_zero_only -def create_experiment_folder(exp: Union[dict, ExperimentParams], env: dict) -> str: +def create_experiment_folder(exp: ExperimentParams) -> str: """Creates an experiment folder if rank=0 with optional unique timestamp. Inplace sets the correct model_path in the experiment cfg. + Updateds the results path correctly Args: - exp (dict or ExperimentParams): Experiment cfg - env (dict): Environment cfg + exp (ExperimentParams): Experiment cfg Returns: str: model_path of the run """ - if isinstance(exp, ExperimentParams): - name = exp.general.name - timestamp = exp.general.timestamp - else: - name = exp["general"]["name"] - timestamp = exp["general"]["timestamp"] + name = exp.general.name + timestamp = exp.general.timestamp + + if not os.path.isabs(exp.env.results): + with read_write(exp): + exp.env.results = os.path.join(WVN_ROOT_DIR, exp.env.results) # Set in name the correct model path if timestamp: timestamp = datetime.datetime.now().replace(microsecond=0).isoformat() - model_path = os.path.join(env["base"], name) + model_path = os.path.join(exp.env.results, name) p = model_path.split("/") model_path = os.path.join("/", *p[:-1], str(timestamp).replace(":", "-") + "_" + p[-1]) else: - model_path = os.path.join(env["base"], name) + model_path = os.path.join(exp.env.results, name) shutil.rmtree(model_path, ignore_errors=True) # Create the directory Path(model_path).mkdir(parents=True, exist_ok=True) - if isinstance(exp, ExperimentParams): - exp.general.model_path = model_path - else: - exp["general"]["model_path"] = model_path - return model_path diff --git a/wild_visual_navigation/utils/data.py b/wild_visual_navigation/utils/data.py new file mode 100644 index 00000000..a6aa28af --- /dev/null +++ b/wild_visual_navigation/utils/data.py @@ -0,0 +1,73 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# +from typing import List +from typing_extensions import Self +import torch + + +class Data: + def __init__(self, **kwargs): + for k, v in kwargs.items(): + setattr(self, k, v) + + +class Batch: + def __init__(self): + pass + + @classmethod + def from_data_list(cls, list_of_data: List[Data]) -> Self: + if len(list_of_data) == 0: + return None + + base = ["x"] + + tensors_to_concatenate = [ + k for k in dir(list_of_data[0]) if k[0] != "_" and getattr(list_of_data[0], k) is not None and not k in base + ] + base = base + tensors_to_concatenate + + for k in base: + if k == "edge_index": + ls = [] + for j, data in enumerate(list_of_data): + ls.append(getattr(data, k) + cls.ptr[j]) + + cls.edge_index = torch.cat(ls, dim=-1) + else: + + if k == "x": + running = 0 + ptrs = [running] + batches = [] + + for j, data in enumerate(list_of_data): + running = running + getattr(data, k).shape[0] + ptrs.append(running) + batches += [j] * int(getattr(data, k).shape[0]) + + cls.ptr = torch.tensor(ptrs, dtype=torch.long) + cls.batch = torch.tensor(batches, dtype=torch.long) + + setattr(cls, k, torch.cat([getattr(data, k) for data in list_of_data], dim=0)) + + cls.ba = cls.x.shape[0] + return cls + + +if __name__ == "__main__": + from torch_geometric.data import Data as DataTorchGeometric + from torch_geometric.data import Batch as BatchTorchGeometric + + edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long) + x = torch.tensor([[-1], [0], [1]], dtype=torch.float) + data_tg1 = DataTorchGeometric(x=x, edge_index=edge_index) + data1 = Data(x=x, edge_index=edge_index) + edge_index2 = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long) + x2 = torch.tensor([[-1], [12], [1]], dtype=torch.float) + data_tg2 = DataTorchGeometric(x=x2, edge_index=edge_index2) + data2 = Data(x=x2, edge_index=edge_index2) + batch = BatchTorchGeometric.from_data_list([data_tg1, data_tg2]) diff --git a/wild_visual_navigation/learning/utils/flatten_dict.py b/wild_visual_navigation/utils/flatten_dict.py similarity index 82% rename from wild_visual_navigation/learning/utils/flatten_dict.py rename to wild_visual_navigation/utils/flatten_dict.py index 0446b895..6e788635 100644 --- a/wild_visual_navigation/learning/utils/flatten_dict.py +++ b/wild_visual_navigation/utils/flatten_dict.py @@ -1,3 +1,8 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# import collections __all__ = ["flatten_dict"] diff --git a/wild_visual_navigation/learning/utils/get_confidence.py b/wild_visual_navigation/utils/get_confidence.py similarity index 57% rename from wild_visual_navigation/learning/utils/get_confidence.py rename to wild_visual_navigation/utils/get_confidence.py index 3594aa7c..dee10778 100644 --- a/wild_visual_navigation/learning/utils/get_confidence.py +++ b/wild_visual_navigation/utils/get_confidence.py @@ -1,3 +1,8 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# import torch.nn.functional as F import torch diff --git a/wild_visual_navigation/utils/get_logger.py b/wild_visual_navigation/utils/get_logger.py new file mode 100644 index 00000000..c1154622 --- /dev/null +++ b/wild_visual_navigation/utils/get_logger.py @@ -0,0 +1,127 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# +from pytorch_lightning.loggers.neptune import NeptuneLogger +from pytorch_lightning.loggers import TensorBoardLogger, WandbLogger +from wild_visual_navigation.utils import flatten_dict +import inspect +import os +import neptune + +__all__ = [ + "get_neptune_logger", + "get_wandb_logger", + "get_tensorboard_logger", + "get_neptune_run", +] + +PROXIES = {"http": "http://proxy.ethz.ch:3128", "https": "http://proxy.ethz.ch:3128"} + + +def get_neptune_run(neptune_project_name: str, tags: [str]) -> any: + """Get neptune run + + Args: + neptune_project_name (str): Neptune project name + tags (list of str): Tags to identify the project + """ + proxies = None + if os.environ.get("ENV_WORKSTATION_NAME", "default") == "euler": + proxies = PROXIES + + run = neptune.init( + api_token=os.environ["NEPTUNE_API_TOKEN"], + project=neptune_project_name, + tags=[os.environ.get("ENV_WORKSTATION_NAME", "default")] + tags, + proxies=proxies, + ) + return run + + +def get_neptune_logger(exp: dict) -> NeptuneLogger: + """Returns NeptuneLogger + + Args: + exp (dict): Content of environment file + Returns: + (logger): Logger + """ + project_name = exp.logger.neptune_project_name # Neptune AI project_name "username/project" + + params = flatten_dict(exp) # noqa: F841 + + name_full = exp.general.name + name_short = "__".join(name_full.split("/")[-2:]) + + proxies = None + if os.environ.get("ENV_WORKSTATION_NAME", "default") == "euler": + proxies = PROXIES + + return NeptuneLogger( + api_key=os.environ["NEPTUNE_API_TOKEN"], + project=project_name, + name=name_short, + tags=[ + os.environ.get("ENV_WORKSTATION_NAME", "default"), + name_full.split("/")[-2], + name_full.split("/")[-1], + ], + proxies=proxies, + ) + + +def get_wandb_logger(exp: dict) -> WandbLogger: + """Returns NeptuneLogger + + Args: + exp (dict): Content of environment file + + Returns: + (logger): Logger + """ + project_name = exp.logger.wandb_project_name # project_name (str): W&B project_name + save_dir = os.path.join(exp.general.model_path) # save_dir (str): File path to save directory + params = flatten_dict(exp) # noqa: F841 + name_full = exp.general.name + name_short = "__".join(name_full.split("/")[-2:]) + return WandbLogger( + name=name_short, + project=project_name, + entity=exp.logger.wandb_entity, + save_dir=save_dir, + offline=False, + ) + + +def get_tensorboard_logger(exp: dict) -> TensorBoardLogger: + """Returns TensorboardLoggers + + Args: + exp (dict): Content of environment file + + Returns: + (logger): Logger + """ + params = flatten_dict(exp) + return TensorBoardLogger(save_dir=exp.name, name="tensorboard", default_hp_metric=params) + + +def get_skip_logger(exp: dict) -> None: + """Returns None + + Args: + exp (dict): Content of environment file + + Returns: + (logger): Logger + """ + return None + + +def get_logger(exp: dict) -> any: + name = exp.logger.name + save_dir = os.path.join(exp.env.folder, exp.general.name) # noqa: F841 + register = {k: v for k, v in globals().items() if inspect.isfunction(v)} + return register[f"get_{name}_logger"](exp) diff --git a/wild_visual_navigation/utils/gpu_monitor.py b/wild_visual_navigation/utils/gpu_monitor.py index c2c00031..a553349e 100644 --- a/wild_visual_navigation/utils/gpu_monitor.py +++ b/wild_visual_navigation/utils/gpu_monitor.py @@ -1,6 +1,10 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# import torch import os -import numpy as np import pandas as pd from functools import wraps @@ -27,7 +31,6 @@ def pynvml_gpu_memory_query(device, pid): def jtop_gpu_memory_query(device, pid): - from jtop import jtop import psutil process = psutil.Process(pid) @@ -181,7 +184,15 @@ def toc(self): class SystemLevelGpuMonitor: - def __init__(self, objects, names, enabled=True, device=None, store_samples=False, skip_n_samples=1) -> None: + def __init__( + self, + objects, + names, + enabled=True, + device=None, + store_samples=False, + skip_n_samples=1, + ) -> None: self.objects = objects self.names = names @@ -211,7 +222,7 @@ def store(self, folder): if hasattr(o, "slg_memory_samples"): # Each object has a slg_memory_samples dict # Each entry of the dict is a numpy array with samples - for (k, v) in o.slg_memory_samples.items(): + for k, v in o.slg_memory_samples.items(): out_filename = f"{base_folder}/{n}_{k}.csv" print(f" {out_filename}") df = pd.DataFrame(v) @@ -219,7 +230,6 @@ def store(self, folder): if __name__ == "__main__": - print("GPU memory measuring using context manager") tensors = [] @@ -263,7 +273,12 @@ def test2(self, s): t = 0.1 my_test = MyTest() gpu_monitor = SystemLevelGpuMonitor( - objects=[my_test], names=["test"], enabled=True, device="cuda", store_samples=True, skip_n_samples=1 + objects=[my_test], + names=["test"], + enabled=True, + device="cuda", + store_samples=True, + skip_n_samples=1, ) for n in range(400): step = n diff --git a/wild_visual_navigation/utils/kalman_filter.py b/wild_visual_navigation/utils/kalman_filter.py index fe049511..1486cb05 100644 --- a/wild_visual_navigation/utils/kalman_filter.py +++ b/wild_visual_navigation/utils/kalman_filter.py @@ -1,3 +1,8 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# import torch import torch.nn as nn @@ -138,7 +143,13 @@ def run_kalman_filter(): kf1.init_meas_model(meas_model=torch.eye(1), meas_cov=torch.eye(1) * 2) # Outlier-robust KF - kf2 = KalmanFilter(dim_state=1, dim_control=1, dim_meas=1, outlier_rejection="huber", outlier_delta=0.5) + kf2 = KalmanFilter( + dim_state=1, + dim_control=1, + dim_meas=1, + outlier_rejection="huber", + outlier_delta=0.5, + ) kf2.init_process_model(proc_model=torch.eye(1) * 1, proc_cov=torch.eye(1) * 0.5) kf2.init_meas_model(meas_model=torch.eye(1), meas_cov=torch.eye(1) * 2) diff --git a/wild_visual_navigation/learning/utils/loading.py b/wild_visual_navigation/utils/loading.py similarity index 61% rename from wild_visual_navigation/learning/utils/loading.py rename to wild_visual_navigation/utils/loading.py index 7a94f4cf..e31da012 100644 --- a/wild_visual_navigation/learning/utils/loading.py +++ b/wild_visual_navigation/utils/loading.py @@ -1,6 +1,13 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# +from wild_visual_navigation import WVN_ROOT_DIR + import os import yaml -from wild_visual_navigation import WVN_ROOT_DIR + __all__ = ["file_path", "load_yaml"] @@ -40,18 +47,11 @@ def load_yaml(path: str) -> dict: return res -def load_env() -> dict: - """Uses ENV_WORKSTATION_NAME variable to load specified environment yaml file. - - Returns: - (dict): Returns content of environment file +def save_omega_cfg(cfg, path): + """ + Args: + cfg (omegaconf): Cfg file + path (str): File path """ - env_cfg_path = os.path.join(WVN_ROOT_DIR, "cfg/env", os.environ["ENV_WORKSTATION_NAME"] + ".yaml") - env = load_yaml(env_cfg_path) - for k in env.keys(): - if k == "workstation": - continue - if not os.path.isabs(env[k]): - env[k] = os.path.join(WVN_ROOT_DIR, env[k]) - - return env + with open(path, "rb") as file: + OmegaConf.save(config=cfg, f=file) diff --git a/wild_visual_navigation/learning/utils/loss.py b/wild_visual_navigation/utils/loss.py similarity index 55% rename from wild_visual_navigation/learning/utils/loss.py rename to wild_visual_navigation/utils/loss.py index f68ce8d5..1e6343d9 100644 --- a/wild_visual_navigation/learning/utils/loss.py +++ b/wild_visual_navigation/utils/loss.py @@ -1,25 +1,72 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# +from wild_visual_navigation.utils import ConfidenceGenerator + import torch.nn.functional as F -from torch_geometric.data import Data +from wild_visual_navigation.utils import Data + import torch from typing import Optional -from wild_visual_navigation.utils import ConfidenceGenerator from torch import nn -from torchmetrics import ROC, AUROC, Accuracy + + +class AnomalyLoss(nn.Module): + def __init__( + self, + confidence_std_factor: float, + method: str, + log_enabled: bool, + log_folder: str, + ): + super(AnomalyLoss, self).__init__() + + self._confidence_generator = ConfidenceGenerator( + std_factor=confidence_std_factor, method=method, log_enabled=log_enabled, log_folder=log_folder + ) + + def forward( + self, + graph: Optional[Data], + res: dict, + update_generator: bool = True, + step: int = 0, + log_step: bool = False, + ): + loss_aux = {} + loss_aux["loss_trav"] = torch.tensor([0.0]) + loss_aux["loss_reco"] = torch.tensor([0.0]) + + losses = res["logprob"].sum(1) + res["log_det"] # Sum over all channels, resulting in h*w output dimensions + + if update_generator: + confidence = self._confidence_generator.update( + x=-losses.clone().detach(), x_positive=-losses.clone().detach(), step=step + ) + + loss_aux["confidence"] = confidence + + return -torch.mean(losses), loss_aux, confidence + + def update_node_confidence(self, node): + node.confidence = 0 class TraversabilityLoss(nn.Module): def __init__( self, - w_trav, - w_reco, - w_temp, - anomaly_balanced, - model, - method, - confidence_std_factor, + w_trav: float, + w_reco: float, + w_temp: float, + anomaly_balanced: bool, + model: nn.Module, + method: str, + confidence_std_factor: float, + log_enabled: bool, + log_folder: str, trav_cross_entropy=False, - log_enabled: bool = False, - log_folder: str = "/tmp", ): # TODO remove trav_cross_entropy default param when running in online mode super(TraversabilityLoss, self).__init__() @@ -36,10 +83,7 @@ def __init__( self._trav_loss_func = F.mse_loss self._confidence_generator = ConfidenceGenerator( - std_factor=confidence_std_factor, - method=method, - log_enabled=log_enabled, - log_folder=log_folder, + std_factor=confidence_std_factor, method=method, log_enabled=log_enabled, log_folder=log_folder ) def reset(self): @@ -47,7 +91,12 @@ def reset(self): self._confidence_generator.reset() def forward( - self, graph: Data, res: torch.Tensor, update_generator: bool = True, step: int = 0, log_step: bool = False + self, + graph: Data, + res: torch.Tensor, + update_generator: bool = True, + step: int = 0, + log_step: bool = False, ): # Compute reconstruction loss nr_channel_reco = graph.x.shape[1] @@ -56,7 +105,10 @@ def forward( with torch.no_grad(): if update_generator: confidence = self._confidence_generator.update( - x=loss_reco, x_positive=loss_reco[graph.y_valid], step=step, log_step=log_step + x=loss_reco, + x_positive=loss_reco[graph.y_valid], + step=step, + log_step=log_step, ) else: confidence = self._confidence_generator.inference_without_update(x=loss_reco) @@ -65,7 +117,9 @@ def forward( if self._trav_cross_entropy: label = label.type(torch.long) loss_trav_raw = self._trav_loss_func( - res[:, :-nr_channel_reco].squeeze()[:, 0], label.type(torch.float32), reduction="none" + res[:, :-nr_channel_reco].squeeze()[:, 0], + label.type(torch.float32), + reduction="none", ) else: loss_trav_raw = self._trav_loss_func(res[:, :-nr_channel_reco].squeeze(), label, reduction="none") @@ -104,3 +158,7 @@ def forward( }, res_updated, ) + + def update_node_confidence(self, node): + reco_loss = F.mse_loss(node.prediction[:, 1:], node.features, reduction="none").mean(dim=1) + node.confidence = self._confidence_generator.inference_without_update(reco_loss) diff --git a/wild_visual_navigation/utils/meshes.py b/wild_visual_navigation/utils/meshes.py index ad12512f..383e4596 100644 --- a/wild_visual_navigation/utils/meshes.py +++ b/wild_visual_navigation/utils/meshes.py @@ -1,3 +1,8 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# import torch from kornia.geometry.linalg import transform_points @@ -72,15 +77,30 @@ def make_ellipsoid(length, width, height, pose=torch.eye(4), grid_size=11): def make_plane(x=None, y=None, z=None, pose=torch.eye(4), grid_size=10): if x is None: points = torch.FloatTensor( - [[0.0, y / 2, z / 2], [0.0, -y / 2, z / 2], [0.0, -y / 2, -z / 2], [0.0, y / 2, -z / 2]] + [ + [0.0, y / 2, z / 2], + [0.0, -y / 2, z / 2], + [0.0, -y / 2, -z / 2], + [0.0, y / 2, -z / 2], + ] ).to(pose.device) elif y is None: points = torch.FloatTensor( - [[x / 2, 0.0, z / 2], [x / 2, 0.0, -z / 2], [-x / 2, 0.0, -z / 2], [-x / 2, 0.0, z / 2]] + [ + [x / 2, 0.0, z / 2], + [x / 2, 0.0, -z / 2], + [-x / 2, 0.0, -z / 2], + [-x / 2, 0.0, z / 2], + ] ).to(pose.device) elif z is None: points = torch.FloatTensor( - [[x / 2, y / 2, 0.0], [x / 2, -y / 2, 0.0], [-x / 2, -y / 2, 0.0], [-x / 2, y / 2, 0.0]] + [ + [x / 2, y / 2, 0.0], + [x / 2, -y / 2, 0.0], + [-x / 2, -y / 2, 0.0], + [-x / 2, y / 2, 0.0], + ] ).to(pose.device) else: raise "make_plane requires just 2 inputs to be set" diff --git a/wild_visual_navigation/utils/operation_modes.py b/wild_visual_navigation/utils/operation_modes.py index 776e4475..6fd761e3 100644 --- a/wild_visual_navigation/utils/operation_modes.py +++ b/wild_visual_navigation/utils/operation_modes.py @@ -1,3 +1,8 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# from enum import Enum diff --git a/wild_visual_navigation/utils/override_params.py b/wild_visual_navigation/utils/override_params.py deleted file mode 100644 index 3cc686dc..00000000 --- a/wild_visual_navigation/utils/override_params.py +++ /dev/null @@ -1,11 +0,0 @@ -import dataclasses - - -def override_params(dc, exp): - for k, v in exp.items(): - if hasattr(dc, k): - if dataclasses.is_dataclass(getattr(dc, k)): - setattr(dc, k, override_params(getattr(dc, k), v)) - else: - setattr(dc, k, v) - return dc diff --git a/wild_visual_navigation/utils/testing.py b/wild_visual_navigation/utils/testing.py new file mode 100644 index 00000000..03ef26a1 --- /dev/null +++ b/wild_visual_navigation/utils/testing.py @@ -0,0 +1,34 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# +import os +import cv2 +import torch +from torchvision import transforms as T +from wild_visual_navigation import WVN_ROOT_DIR + + +def load_test_image(): + np_img = cv2.imread(os.path.join(WVN_ROOT_DIR, "assets/images/forest_clean.png")) + img = torch.from_numpy(cv2.cvtColor(np_img, cv2.COLOR_BGR2RGB)) + img = img.permute(2, 0, 1) + img = (img.type(torch.float32) / 255)[None] + return img + + +def get_dino_transform(): + transform = T.Compose( + [ + T.Resize(224, T.InterpolationMode.NEAREST), + T.CenterCrop(224), + ] + ) + return transform + + +def make_results_folder(name): + path = os.path.join(WVN_ROOT_DIR, "results", name) + os.makedirs(path, exist_ok=True) + return path diff --git a/wild_visual_navigation/utils/timing.py b/wild_visual_navigation/utils/timing.py deleted file mode 100644 index 0a8bfd26..00000000 --- a/wild_visual_navigation/utils/timing.py +++ /dev/null @@ -1,204 +0,0 @@ -import torch -import time -import copy -import pickle -import os -from functools import wraps - - -class CpuTimer: - def __init__(self, name="") -> None: - self.name = name - - def __enter__(self): - self.tic() - - def __exit__(self, exc_type, exc_value, exc_tb): - print(f"Time {self.name}: ", self.toc(), "ms") - - def tic(self): - self.start = time.perf_counter() - - def toc(self): - self.end_time = time.perf_counter() - return self.end - self.start - - -class Timer: - def __init__(self, name="") -> None: - self.name = name - self.start = torch.cuda.Event(enable_timing=True) - self.end = torch.cuda.Event(enable_timing=True) - - def __enter__(self): - self.tic() - - def __exit__(self, exc_type, exc_value, exc_tb): - print(f"Time {self.name}: ", self.toc(), "ms") - - def tic(self): - self.start.record() - - def toc(self): - self.end.record() - torch.cuda.synchronize() - return self.start.elapsed_time(self.end) - - -def accumulate_time(method): - @wraps(method) - def timed(*args, **kw): - if not hasattr(args[0], "slt_enabled"): - return method(*args, **kw) - elif not args[0].slt_enabled: - return method(*args, **kw) - - start = torch.cuda.Event(enable_timing=True) - end = torch.cuda.Event(enable_timing=True) - start.record() - result = method(*args, **kw) - end.record() - torch.cuda.synchronize() - - st = start.elapsed_time(end) - if not hasattr(args[0], "slt_time_summary"): - args[0].slt_time_summary = {} - args[0].slt_n_summary = {} - args[0].slt_n_level = {} - args[0].slt_time_squared_summary = {} - - if method.__name__ in args[0].slt_time_summary: - args[0].slt_time_summary[method.__name__] += st - args[0].slt_time_squared_summary[method.__name__] += st**2 - args[0].slt_n_summary[method.__name__] += 1 - else: - args[0].slt_time_summary[method.__name__] = st - args[0].slt_time_squared_summary[method.__name__] = st**2 - args[0].slt_n_summary[method.__name__] = 1 - args[0].slt_n_level[method.__name__] = 0 - - return result - - return timed - - -class SystemLevelContextTimer: - def __init__(self, parent, name="") -> None: - self.parent = parent - if not hasattr(self.parent, "slt_enabled"): - return - elif not self.parent.slt_enabled: - return - - self.name = name - self.start = torch.cuda.Event(enable_timing=True) - self.end = torch.cuda.Event(enable_timing=True) - - def __enter__(self): - if not hasattr(self.parent, "slt_enabled"): - return - elif not self.parent.slt_enabled: - return - - self.tic() - - def __exit__(self, exc_type, exc_value, exc_tb): - if not hasattr(self.parent, "slt_enabled"): - return - elif not self.parent.slt_enabled: - return - - st = self.toc() - - if not hasattr(self.parent, "slt_time_summary"): - self.parent.slt_time_summary = {} - self.parent.slt_time_squared_summary = {} - self.parent.slt_n_summary = {} - self.parent.slt_n_level = {} - - if self.name in self.parent.slt_time_summary: - self.parent.slt_time_summary[self.name] += st - self.parent.slt_time_squared_summary[self.name] += st**2 - self.parent.slt_n_summary[self.name] += 1 - else: - self.parent.slt_time_summary[self.name] = st - self.parent.slt_time_squared_summary[self.name] = st**2 - self.parent.slt_n_summary[self.name] = 1 - self.parent.slt_n_level[self.name] = 1 - - def tic(self): - self.start.record() - - def toc(self): - self.end.record() - torch.cuda.synchronize() - return self.start.elapsed_time(self.end) - - -class SystemLevelTimer: - def __init__(self, objects, names, enabled=True) -> None: - self.objects = objects - self.names = names - - for o in self.objects: - o.slt_enabled = enabled - - def __str__(self): - s = "" - for n, o in zip(self.names, self.objects): - if hasattr(o, "slt_time_summary"): - s += n - for (k, v) in o.slt_time_summary.items(): - n = o.slt_n_summary[k] - spacing = int(o.slt_n_level[k] * 5) - mean = v / n - std = round(((o.slt_time_squared_summary[k] / n) - (mean**2)) ** 0.5, 3) - s += ( - "\n +" - + "-" * spacing - + f"- {k}:".ljust(35 - spacing) - + f"{round(v,2)}ms".ljust(18) - + f"counts: {n} ".ljust(18) - + f"std: {std} ".ljust(18) - + f"mean: {round(mean,3)} ".ljust(18) - ) - s += "\n" - return s - - def store(self, folder, key="system_level_timer"): - res = {} - for n, o in zip(self.names, self.objects): - if hasattr(o, "slt_time_summary"): - mean = {k: o.slt_time_summary[k] / o.slt_n_summary[k] for k in o.slt_time_summary.keys()} - std = { - k: (o.slt_time_squared_summary[k] / o.slt_n_summary[k] - mean[k] ** 2) ** 0.5 - for k in o.slt_time_summary.keys() - } - store = [o.slt_n_level, o.slt_time_summary, std, mean] - res[n] = copy.deepcopy(store) - - with open(os.path.join(folder, key + ".pkl"), "wb") as f: - pickle.dump(res, f, protocol=pickle.HIGHEST_PROTOCOL) - - s = self.__str__() - with open(os.path.join(folder, key + ".txt"), "w") as f: - for l in s.split("\n"): - f.write(l + "\n") - - -if __name__ == "__main__": - - print("Start timing using context manager") - - with Timer("Test1"): - s1, s2 = 1000, 1000 - a = torch.zeros((s1, s2)) - for x in range(s1): - for y in range(s2): - a[x, y] = x * y - with Timer("Test2"): - s1, s2 = 1000, 1000 - a = torch.zeros((s1, s2)) - for x in range(s1): - for y in range(s2): - a[y, x] = x * y diff --git a/wild_visual_navigation/visu/__init__.py b/wild_visual_navigation/visu/__init__.py index be43c69e..e6172047 100644 --- a/wild_visual_navigation/visu/__init__.py +++ b/wild_visual_navigation/visu/__init__.py @@ -1,3 +1,8 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# from .paper_colors import ( paper_colors_rgb_u8, paper_colors_rgba_u8, diff --git a/wild_visual_navigation/visu/image_functionality.py b/wild_visual_navigation/visu/image_functionality.py index 77b92211..ef4ba477 100644 --- a/wild_visual_navigation/visu/image_functionality.py +++ b/wild_visual_navigation/visu/image_functionality.py @@ -1,3 +1,8 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# # TODO: Jonas doc strings import os import imageio @@ -54,7 +59,7 @@ def wrap(*args, **kwargs): ds = cv2.resize(img, dsize=(int(W / 2), int(H / 2)), interpolation=cv2.INTER_CUBIC) try: # logger == neptuneai - from neptune.new.types import File + from neptune.types import File args[0]._pl_model.logger.experiment[tag].log(File.as_image(np.float32(ds) / 255), step=epoch) except Exception: diff --git a/wild_visual_navigation/visu/paper_colors.py b/wild_visual_navigation/visu/paper_colors.py index 44d26463..c5b934e9 100644 --- a/wild_visual_navigation/visu/paper_colors.py +++ b/wild_visual_navigation/visu/paper_colors.py @@ -1,3 +1,8 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# paper_colors_rgb_u8 = { "orange": (251, 151, 39), "mangenta": (150, 36, 145), diff --git a/wild_visual_navigation/visu/plotting.py b/wild_visual_navigation/visu/plotting.py index 7df721a7..c3559716 100644 --- a/wild_visual_navigation/visu/plotting.py +++ b/wild_visual_navigation/visu/plotting.py @@ -1,3 +1,8 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# from matplotlib.backends.backend_agg import FigureCanvasAgg from PIL import Image import numpy as np diff --git a/wild_visual_navigation/visu/visualizer.py b/wild_visual_navigation/visu/visualizer.py index 80bae295..d67fbc35 100644 --- a/wild_visual_navigation/visu/visualizer.py +++ b/wild_visual_navigation/visu/visualizer.py @@ -1,3 +1,8 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Jonas Frey, Matias Mattamala. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# # TODO: Jonas doc strings, rework visualiation functions import os @@ -15,11 +20,10 @@ matplotlib.use("Agg") from wild_visual_navigation.visu import image_functionality -from wild_visual_navigation.learning.utils import get_confidence +from wild_visual_navigation.utils import get_confidence from wild_visual_navigation.visu import get_img_from_fig -from wild_visual_navigation.visu import paper_colors_rgb_u8, paper_colors_rgba_u8 -from wild_visual_navigation.visu import paper_colors_rgb_f, paper_colors_rgba_f -from wild_visual_navigation.utils import Timer, accumulate_time +from wild_visual_navigation.visu import paper_colors_rgb_f +from pytictac import Timer, accumulate_time __all__ = ["LearningVisualizer"] @@ -85,7 +89,7 @@ def plot_roc(self, x, y, y_lower=None, y_upper=None, title=None, y_tag=None, **k sns.set_style("darkgrid") fig, ax = plt.subplots(figsize=(3, 3)) - l = len(x) + l = len(x) # noqa: E741 assert len(y) == l assert len(y_lower) == l assert len(y_upper) == l @@ -99,7 +103,13 @@ def plot_roc(self, x, y, y_lower=None, y_upper=None, title=None, y_tag=None, **k if not (_y_lower is None): ax.plot(_x, _y_lower, color=paper_colors_rgb_f[k + "_light"], alpha=0.1) ax.plot(_x, _y_upper, color=paper_colors_rgb_f[k + "_light"], alpha=0.1) - ax.fill_between(_x, _y_lower, _y_upper, color=paper_colors_rgb_f[k + "_light"], alpha=0.2) + ax.fill_between( + _x, + _y_lower, + _y_upper, + color=paper_colors_rgb_f[k + "_light"], + alpha=0.2, + ) ax.plot(np.linspace(0, 1, 100), np.linspace(0, 1, 100), linestyle="--", color="gray") ax.set_xlabel("False positive rate") @@ -221,7 +231,7 @@ def plot_traversability_graph_on_seg( boundary_seg=seg, draw_bound=False, ) - i2 = (torch.from_numpy(i1).type(torch.float32) / 255).permute(2, 0, 1) + i2 = (torch.from_numpy(i1).type(torch.float32) / 255).permute(2, 0, 1) # noqa: F841 # Plot Graph on Image return i1 @@ -239,7 +249,15 @@ def plot_traversability_graph_on_seg( @accumulate_time @image_functionality def plot_traversability_graph( - self, prediction, graph, center, img, max_val=1.0, colormap="RdYlBu", colorize_invalid_centers=False, **kwargs + self, + prediction, + graph, + center, + img, + max_val=1.0, + colormap="RdYlBu", + colorize_invalid_centers=False, + **kwargs, ): """Plot prediction on graph @@ -355,15 +373,35 @@ def plot_detectron_classification( overlay_mask=None, **kwargs, ): - cmap = cm.get_cmap("RdYlBu", 256) - cmap = np.concatenate([cmap(np.linspace(0, 0.3, 128)), cmap(np.linspace(0.7, 1.0, 128))]) - cmap = torch.from_numpy(cmap).to(seg)[:, :3] + if kwargs.get("cmap", None): + cmap = kwargs["cmap"] + else: + s = 0.3 # If bigger, get more fine-grained green, if smaller get more fine-grained red + cmap = cm.get_cmap("RdYlBu", 256) # or RdYlGn + cmap = np.concatenate( + [cmap(np.linspace(0, s, 128)), cmap(np.linspace(1 - s, 1.0, 128))] + ) # Stretch the colormap + cmap = torch.from_numpy(cmap).to(seg)[:, :3] img = self.plot_image(img, not_log=True) seg_img = self.plot_segmentation( - (seg * 255).type(torch.long).clip(0, 255), max_seg=256, colormap=cmap, store=False, not_log=True + (seg * 255).type(torch.long).clip(0, 255), + max_seg=256, + colormap=cmap, + store=False, + not_log=True, ) + # plt.hist(seg_img.ravel(), bins=500) + # # Get current ros time + # now = rospy.Time.now() + # # Create a unique filename + # filename = f"{now.secs}_{now.nsecs}.png" + # # Save the figure + # plt.savefig(f"/home/rschmid/overlays/{filename}") + # # Close the figure + # plt.close() + H, W = img.shape[:2] back = np.zeros((H, W, 4)) back[:, :, :3] = img @@ -527,14 +565,19 @@ def plot_optical_flow(self, flow: torch.Tensor, img1: torch.Tensor, img2: torch. dv = (flow[1, u, v] + i2.shape[1]).item() try: draw.line([(v, u), (v + dv, u + du)], fill=col, width=2) - except: + except Exception: pass return np.array(pil_img).astype(np.uint8) @accumulate_time @image_functionality def plot_sparse_optical_flow( - self, pre_pos: torch.Tensor, cur_pos: torch.Tensor, img1: torch.Tensor, img2: torch.Tensor, **kwargs + self, + pre_pos: torch.Tensor, + cur_pos: torch.Tensor, + img1: torch.Tensor, + img2: torch.Tensor, + **kwargs, ): """Draws line connection between to images based on estimated flow @@ -557,8 +600,15 @@ def plot_sparse_optical_flow( col = (0, 255, 0) for p, c in zip(pre_pos, cur_pos): try: - draw.line([(p[0].item(), p[1].item()), ((i2.shape[1] + c[0]).item(), c[1].item())], fill=col, width=2) - except: + draw.line( + [ + (p[0].item(), p[1].item()), + ((i2.shape[1] + c[0]).item(), c[1].item()), + ], + fill=col, + width=2, + ) + except Exception: pass return np.array(pil_img).astype(np.uint8) @@ -685,7 +735,13 @@ def plot_sparse_optical_flow( with Timer("plot_detectron_quick"): for i in range(N): visu.plot_detectron( - img=img, seg=seg, store=store, max_seg=ele, tag="5_quick", not_log=not_log, draw_bound=False + img=img, + seg=seg, + store=store, + max_seg=ele, + tag="5_quick", + not_log=not_log, + draw_bound=False, ) print("Start seg") diff --git a/wild_visual_navigation_anymal/CMakeLists.txt b/wild_visual_navigation_anymal/CMakeLists.txt index 8af4e78e..c0be04f0 100644 --- a/wild_visual_navigation_anymal/CMakeLists.txt +++ b/wild_visual_navigation_anymal/CMakeLists.txt @@ -1,3 +1,8 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Matias Mattamala, Jonas Frey. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# cmake_minimum_required(VERSION 3.0.2) project(wild_visual_navigation_anymal) set (CMAKE_CXX_STANDARD 14) @@ -5,7 +10,6 @@ set (CMAKE_CXX_STANDARD 14) set(CATKIN_PACKAGE_LIST rospy roscpp - anymal_msgs sensor_msgs std_msgs wild_visual_navigation_msgs @@ -29,13 +33,16 @@ include_directories( ${catkin_INCLUDE_DIRS} ) -# Declare node -add_executable(anymal_msg_converter_cpp_node - src/anymal_msg_converter_cpp_node.cpp) +if(BUILD_ANYMAL) + find_package( anymal_msgs ) + # Declare node + add_executable( anymal_msg_converter_cpp_node + src/anymal_msg_converter_cpp_node.cpp) -target_link_libraries(anymal_msg_converter_cpp_node + target_link_libraries(anymal_msg_converter_cpp_node ${catkin_LIBRARIES} -) + ) +endif() ############# ## Install ## diff --git a/wild_visual_navigation_anymal/README.md b/wild_visual_navigation_anymal/README.md new file mode 100644 index 00000000..292b50b4 --- /dev/null +++ b/wild_visual_navigation_anymal/README.md @@ -0,0 +1,15 @@ +Setup on ANYmal: + +NPC: +``` +sudo apt-get install ros-noetic-anymal-msgs-dev +catkin build wild_visual_navigation_anymal + +catkin build wild_visual_navigation_anymal --cmake-args -DBUILD_ANYMAL=1 +``` + +Jetson: +``` +``` + +Currently the resizing is not working which is pretty bad for the wide angle camera. \ No newline at end of file diff --git a/wild_visual_navigation_ros/config/elevation_mapping_cupy/anymal_parameters.yaml b/wild_visual_navigation_anymal/config/elevation_mapping_cupy/anymal_parameters.yaml similarity index 89% rename from wild_visual_navigation_ros/config/elevation_mapping_cupy/anymal_parameters.yaml rename to wild_visual_navigation_anymal/config/elevation_mapping_cupy/anymal_parameters.yaml index b275acf0..73959ac4 100644 --- a/wild_visual_navigation_ros/config/elevation_mapping_cupy/anymal_parameters.yaml +++ b/wild_visual_navigation_anymal/config/elevation_mapping_cupy/anymal_parameters.yaml @@ -55,42 +55,53 @@ enable_normal_color: false # If true, the map contains 'col #### Traversability filter ######## use_chainer: false # Use chainer as a backend of traversability filter or pytorch. If false, it uses pytorch. pytorch requires ~2GB more GPU memory compared to chainer but runs faster. -weight_file: '$(rospack find elevation_mapping_cupy)/config/weights.dat' # Weight file for traversability filter +weight_file: '$(rospack find elevation_mapping_cupy)/config/core/weights.dat' # Weight file for traversability filter #### Upper bound ######## use_only_above_for_upper_bound: false +#### Initializer ######## +initialize_method: 'linear' # Choose one from 'nearest', 'linear', 'cubic' +initialize_frame_id: ['RF_FOOT', 'LF_FOOT', 'RH_FOOT', 'LH_FOOT'] # One tf (like ['footprint'] ) initializes a square around it. +initialize_tf_offset: [0.0, 0.0, 0.0, 0.0] # z direction. Should be same number as initialize_frame_id. +dilation_size_initialize: 5 # dilation size after the init. +initialize_tf_grid_size: 0.4 # This is not used if number of tf is more than 3. +use_initializer_at_start: true # Use initializer when the node starts. + #### Plugins ######## -plugin_config_file: '$(rospack find wild_visual_navigation_ros)/config/elevation_mapping_cupy/anymal_plugin_config.yaml' +plugin_config_file: '$(rospack find wild_visual_navigation_anymal)/config/elevation_mapping_cupy/anymal_plugin_config.yaml' + +pointcloud_channel_fusions: + rgb: 'color' + default: 'average' + +image_channel_fusions: + rgb: 'color' + default: 'exponential' + feat_.*: 'exponential' + sem_.*: 'exponential' + #### Publishers ######## # topic_name: # layers: # Choose from 'elevation', 'variance', 'traversability', 'time', 'normal_x', 'normal_y', 'normal_z', 'color', plugin_layer_names # basic_layers: # basic_layers for valid cell computation (e.g. Rviz): Choose a subset of `layers`. # fps: # Publish rate. Use smaller value than `map_acquire_fps`. + publishers: elevation_map_raw: - layers: ['elevation', 'traversability', 'variance'] + layers: ['elevation', 'traversability', 'variance', 'rgb'] basic_layers: ['elevation', 'traversability'] fps: 5.0 - semantic_map_raw: - layers: ['elevation', 'traversability', 'visual_traversability', 'elevation_with_semantic'] + semantic_map: + layers: ['elevation', 'traversability', 'visual_traversability', 'visual_confidence', 'elevation_with_semantics', 'rgb'] basic_layers: ['elevation', 'traversability'] fps: 5.0 - # elevation_map_recordable: - # layers: ['elevation', 'traversability'] - # basic_layers: ['elevation', 'traversability'] - # fps: 2.0 - # elevation_map_filter: - # layers: ['min_filter', 'smooth', 'inpaint', 'upper_bound','rgb'] - # basic_layers: ['min_filter'] - # fps: 3.0 -#### Initializer ######## -initialize_method: 'linear' # Choose one from 'nearest', 'linear', 'cubic' -initialize_frame_id: ['RF_FOOT', 'LF_FOOT', 'RH_FOOT', 'LH_FOOT'] # One tf (like ['footprint'] ) initializes a square around it. -initialize_tf_offset: [0.0, 0.0, 0.0, 0.0] # z direction. Should be same number as initialize_frame_id. -dilation_size_initialize: 5 # dilation size after the init. -initialize_tf_grid_size: 0.4 # This is not used if number of tf is more than 3. -use_initializer_at_start: true # Use initializer when the node starts. + local_planning_map: + layers: ['elevation', 'traversability', 'visual_traversability'] + basic_layers: ['elevation', 'traversability'] + fps: 5.0 + + \ No newline at end of file diff --git a/wild_visual_navigation_ros/config/elevation_mapping_cupy/anymal_plugin_config.yaml b/wild_visual_navigation_anymal/config/elevation_mapping_cupy/anymal_plugin_config.yaml similarity index 91% rename from wild_visual_navigation_ros/config/elevation_mapping_cupy/anymal_plugin_config.yaml rename to wild_visual_navigation_anymal/config/elevation_mapping_cupy/anymal_plugin_config.yaml index 20ef3f98..9e41a17c 100644 --- a/wild_visual_navigation_ros/config/elevation_mapping_cupy/anymal_plugin_config.yaml +++ b/wild_visual_navigation_anymal/config/elevation_mapping_cupy/anymal_plugin_config.yaml @@ -29,10 +29,10 @@ inpainting: method: "telea" # telea or ns # Apply inpainting using opencv -elevation_semantic: +mask_elevation_semantics: enable: True fill_nan: False is_height_layer: True - layer_name: "elevation_with_semantic" + layer_name: "elevation_with_semantics" extra_params: - method: "telea" \ No newline at end of file + semantic_reference_layer_name: "visual_traversability" \ No newline at end of file diff --git a/wild_visual_navigation_anymal/config/elevation_mapping_cupy/anymal_sensor_parameter.yaml b/wild_visual_navigation_anymal/config/elevation_mapping_cupy/anymal_sensor_parameter.yaml new file mode 100644 index 00000000..bad469c6 --- /dev/null +++ b/wild_visual_navigation_anymal/config/elevation_mapping_cupy/anymal_sensor_parameter.yaml @@ -0,0 +1,60 @@ +#### Subscribers ######## +subscribers: + front_upper_depth: + topic_name: /depth_camera_front_upper/point_cloud_self_filtered + data_type: pointcloud + + front_lower_depth: + topic_name: /depth_camera_front_lower/point_cloud_self_filtered + data_type: pointcloud + + rear_upper_depth: + topic_name: /depth_camera_rear_upper/point_cloud_self_filtered + data_type: pointcloud + + rear_lower_depth: + topic_name: /depth_camera_rear_lower/point_cloud_self_filtered + data_type: pointcloud + + left_depth: + topic_name: /depth_camera_left/point_cloud_self_filtered + data_type: pointcloud + + right_depth: + topic_name: /depth_camera_right/point_cloud_self_filtered + data_type: pointcloud + + velodyne: + topic_name: /point_cloud_filter/lidar/point_cloud_filtered + data_type: pointcloud + + # Semantics + front_wide_angle: + topic_name: /wide_angle_camera_front/image_color_rect + camera_info_topic_name: /wide_angle_camera_front/camera_info + data_type: image + + rear_wide_angle: + topic_name: /wide_angle_camera_rear/image_color_rect + camera_info_topic_name: /wide_angle_camera_rear/camera_info + data_type: image + + # Traversability + # channels: ["visual_traversability"] + sem_wvn_front_traversability: + topic_name: '/wild_visual_navigation_node/front/traversability' + camera_info_topic_name: '/wild_visual_navigation_node/front/camera_info' + channels: ['visual_traversability'] + data_type: image + + sem_wvn_rear_traversability: + topic_name: '/wild_visual_navigation_node/rear/traversability' + camera_info_topic_name: '/wild_visual_navigation_node/rear/camera_info' + channels: ['visual_traversability'] + data_type: image + + # sem_wvn_front_confidence: + # topic_name: '/wild_visual_navigation_node/front/confidence' + # camera_info_topic_name: '/wild_visual_navigation_node/front/camera_info' + # channels: ['visual_traversability'] + # data_type: image \ No newline at end of file diff --git a/wild_visual_navigation_ros/config/field_local_planner/visual/filter_chain.yaml b/wild_visual_navigation_anymal/config/field_local_planner/geometric/filter_chain.yaml similarity index 53% rename from wild_visual_navigation_ros/config/field_local_planner/visual/filter_chain.yaml rename to wild_visual_navigation_anymal/config/field_local_planner/geometric/filter_chain.yaml index a0dbfb42..4c21672c 100644 --- a/wild_visual_navigation_ros/config/field_local_planner/visual/filter_chain.yaml +++ b/wild_visual_navigation_anymal/config/field_local_planner/geometric/filter_chain.yaml @@ -11,18 +11,14 @@ grid_map_filters: output_layer: unknown_space # Inpainting - - name: denoise_inpaint - type: gridMapFiltersDrs/DenoiseAndInpaintFilter + - name: inpaint + type: gridMapFiltersDrs/InpaintFilter params: input_layer: elevation output_layer: elevation_inpainted radius: 0.1 # in m inpainting_type: telea # 'ns' or 'telea' allowed - pre_denoising: false # enables denoising before inpainting - denoising_radius: 0.5 # m - denoising_type: non_local # 'total_variation', 'non_local', 'gaussian', and 'median' supported - total_variation_lambda: 1.0 - + # Compute surface normals (fast) - name: surface_normals type: gridMapFiltersDrs/FastNormalsVectorFilter @@ -56,55 +52,61 @@ grid_map_filters: - name: traversability type: gridMapFilters/MathExpressionFilter params: - output_layer: geometric_traversability + output_layer: raw_traversability # expression: (0.5 * (1.0 - (slope / 0.6)) + 0.5 * height_traversability) .* (1.0 - unknown_space) expression: (0.5 * (1.0 - (slope / 0.6)) + 0.5 * height_traversability) - - - name: footprint_mask - type: gridMapFiltersDrs/FootprintMaskFilter - params: - output_layer: footprint_mask - footprint_frame: base - type: rectangle - length: 1.0 - width: 0.5 - clearance: 0.1 - - # Inpaint visual traversability - - name: inpaint_visual_traversability - type: gridMapFiltersDrs/DenoiseAndInpaintFilter + + # Denoising + - name: smooth_traversability + type: gridMapFiltersDrs/DenoiseFilter params: - input_layer: visual_traversability - output_layer: visual_traversability_inpainted - radius: 0.1 # in m - inpainting_type: telea # 'ns' or 'telea' allowed - pre_denoising: true # enables denoising before inpainting - denoising_radius: 0.12 # m - denoising_type: median # 'total_variation', 'non_local', 'gaussian', and 'median' supported - total_variation_lambda: 1.0 + input_layer: raw_traversability + output_layer: traversability_smoothed + radius: 0.5 # m + type: gaussian # 'total_variation', 'non_local', 'gaussian', and 'median' supported - - name: visual_traversability_footprint_fix + # Denoising + - name: rescale_traversability type: gridMapFilters/MathExpressionFilter params: - output_layer: visual_traversability_inpainted - expression: (1.0 - footprint_mask) .* visual_traversability_inpainted + footprint_mask + output_layer: traversability + #expression: (traversability_smoothed - minOfFinites(traversability_smoothed)) / maxOfFinites(traversability_smoothed) - minOfFinites(traversability_smoothed)) .* (maxOfFinites(traversability_smoothed) - minOfFinites(traversability_smoothed)) + minOfFinites(traversability_smoothed) + expression: traversability_smoothed + + # - name: inflate_traversability + # type: gridMapFilters/SlidingWindowMathExpressionFilter + # params: + # input_layer: traversability + # output_layer: traversability + # expression: minOfFinites(traversability) #minOfFinites(traversability) + # compute_empty_cells: false + # edge_handling: empty # options: inside, crop, empty, mean + # # window_size: 5 # in number of cells (optional, default: 3), make sure to make this compatible with the kernel matrix + # window_length: 0.4 # instead of window_size, in m # Compute 2D Signed Distance Field - name: sdf type: gridMapFiltersDrs/SignedDistanceField2dFilter params: - input_layer: visual_traversability_inpainted + input_layer: traversability output_layer: sdf normalize_gradients: true threshold: 0.1 + # Compute cost using traversability + - name: traversability_to_cost + type: gridMapFilters/MathExpressionFilter + params: + output_layer: cost + expression: 10*(1.0 - traversability) + 1.0 + - name: geodesic - type: gridMapFiltersDrs/GeodesicDistanceField2dFilter + # type: gridMapFiltersDrs/GeodesicDistanceField2dFilter + type: gridMapFiltersDrs/GeodesicFieldFilter params: - input_layer: visual_traversability_inpainted + input_layer: cost output_layer: geodesic normalize_gradients: true attractor_topic: /field_local_planner/current_goal - threshold: 0.1 # to be applied to the input layer - use_field_smoothing: false - field_smoothing_radius: 0.1 # m \ No newline at end of file + publish_path: true + path_topic: /geodesic_distance_filter/path \ No newline at end of file diff --git a/wild_visual_navigation_anymal/config/field_local_planner/geometric/filter_chain_wifi.yaml b/wild_visual_navigation_anymal/config/field_local_planner/geometric/filter_chain_wifi.yaml new file mode 100644 index 00000000..97367dcb --- /dev/null +++ b/wild_visual_navigation_anymal/config/field_local_planner/geometric/filter_chain_wifi.yaml @@ -0,0 +1,6 @@ +grid_map_filters: + + - name: delete + type: gridMapFiltersDrs/DeleteAllButFilter + params: + layers: [elevation, elevation_inpainted, traversability, cost, geodesic, sdf] # List of layers. \ No newline at end of file diff --git a/wild_visual_navigation_ros/config/field_local_planner/rmp.yaml b/wild_visual_navigation_anymal/config/field_local_planner/rmp.yaml similarity index 90% rename from wild_visual_navigation_ros/config/field_local_planner/rmp.yaml rename to wild_visual_navigation_anymal/config/field_local_planner/rmp.yaml index 8045a444..003bf63c 100644 --- a/wild_visual_navigation_ros/config/field_local_planner/rmp.yaml +++ b/wild_visual_navigation_anymal/config/field_local_planner/rmp.yaml @@ -53,7 +53,8 @@ rmp: - geodesic_heading - goal_position - goal_orientation - - damping + # - damping + - regularization - id: front_left point_factor: [0.6, 0.3] @@ -61,6 +62,7 @@ rmp: radius: 0.3 affected_by: - sdf_obstacle + - sdf_obstacle_damping - id: front_right point_factor: [0.6, -0.3] @@ -68,6 +70,7 @@ rmp: radius: 0.3 affected_by: - sdf_obstacle + - sdf_obstacle_damping - id: back_left point_factor: [-0.6, 0.3] @@ -75,6 +78,7 @@ rmp: radius: 0.3 affected_by: - sdf_obstacle + - sdf_obstacle_damping - id: back_right point_factor: [-0.6, -0.3] @@ -82,6 +86,7 @@ rmp: radius: 0.3 affected_by: - sdf_obstacle + - sdf_obstacle_damping # This defines the parameters for the RMPs policies: @@ -141,6 +146,14 @@ rmp: type: projector_invlogistic offset: 1.0 steepness: 1.0 + sdf_obstacle_damping: + weight: 1.0 # 1.0 + gain: 0.1 + color: [1.0, 0.0, 0.0] # red + metric: + type: projector_invlogistic + offset: 1.0 + steepness: 1.0 regularization: weight: 0.0 # 1.0 gain: 0.001 @@ -148,4 +161,4 @@ rmp: metric: type: constant offset: 0.0 - steepness: 1.0 + steepness: 1.0 \ No newline at end of file diff --git a/wild_visual_navigation_ros/config/field_local_planner/geometric/filter_chain.yaml b/wild_visual_navigation_anymal/config/field_local_planner/visual/filter_chain.yaml similarity index 70% rename from wild_visual_navigation_ros/config/field_local_planner/geometric/filter_chain.yaml rename to wild_visual_navigation_anymal/config/field_local_planner/visual/filter_chain.yaml index 118e8a27..0284fe5c 100644 --- a/wild_visual_navigation_ros/config/field_local_planner/geometric/filter_chain.yaml +++ b/wild_visual_navigation_anymal/config/field_local_planner/visual/filter_chain.yaml @@ -11,17 +11,13 @@ grid_map_filters: output_layer: unknown_space # Inpainting - - name: denoise_inpaint - type: gridMapFiltersDrs/DenoiseAndInpaintFilter + - name: inpaint + type: gridMapFiltersDrs/InpaintFilter params: input_layer: elevation output_layer: elevation_inpainted radius: 0.1 # in m inpainting_type: telea # 'ns' or 'telea' allowed - pre_denoising: false # enables denoising before inpainting - denoising_radius: 0.5 # m - denoising_type: non_local # 'total_variation', 'non_local', 'gaussian', and 'median' supported - total_variation_lambda: 1.0 # Compute surface normals (fast) - name: surface_normals @@ -59,23 +55,40 @@ grid_map_filters: output_layer: geometric_traversability # expression: (0.5 * (1.0 - (slope / 0.6)) + 0.5 * height_traversability) .* (1.0 - unknown_space) expression: (0.5 * (1.0 - (slope / 0.6)) + 0.5 * height_traversability) + + # Inpaint visual traversability + - name: inpaint_visual_traversability + type: gridMapFiltersDrs/DenoiseFilter + params: + input_layer: visual_traversability + output_layer: visual_traversability_inpainted + radius: 0.12 # m + type: median # 'total_variation', 'non_local', 'gaussian', and 'median' supported + total_variation_lambda: 1.0 # Compute 2D Signed Distance Field - name: sdf type: gridMapFiltersDrs/SignedDistanceField2dFilter params: - input_layer: geometric_traversability + input_layer: visual_traversability_inpainted output_layer: sdf normalize_gradients: true threshold: 0.1 + # Compute cost using traversability + - name: traversability_to_cost + type: gridMapFilters/MathExpressionFilter + params: + output_layer: cost + expression: 10*(1.0 - visual_traversability_inpainted) + 1.0 + - name: geodesic - type: gridMapFiltersDrs/GeodesicDistanceField2dFilter + # type: gridMapFiltersDrs/GeodesicDistanceField2dFilter + type: gridMapFiltersDrs/GeodesicFieldFilter params: - input_layer: geometric_traversability + input_layer: cost output_layer: geodesic normalize_gradients: true attractor_topic: /field_local_planner/current_goal - threshold: 0.1 # to be applied to the input layer - use_field_smoothing: false - field_smoothing_radius: 0.1 # m \ No newline at end of file + publish_path: true + path_topic: /geodesic_distance_filter/path \ No newline at end of file diff --git a/wild_visual_navigation_ros/config/field_local_planner/geometric/filter_chain_wifi.yaml b/wild_visual_navigation_anymal/config/field_local_planner/visual/filter_chain_wifi.yaml similarity index 66% rename from wild_visual_navigation_ros/config/field_local_planner/geometric/filter_chain_wifi.yaml rename to wild_visual_navigation_anymal/config/field_local_planner/visual/filter_chain_wifi.yaml index ef49d560..ea53314a 100644 --- a/wild_visual_navigation_ros/config/field_local_planner/geometric/filter_chain_wifi.yaml +++ b/wild_visual_navigation_anymal/config/field_local_planner/visual/filter_chain_wifi.yaml @@ -3,4 +3,4 @@ grid_map_filters: - name: delete type: gridMapFiltersDrs/DeleteAllButFilter params: - layers: [elevation, elevation_inpainted, geometric_traversability, geodesic, sdf] # List of layers. \ No newline at end of file + layers: [elevation, elevation_inpainted, geometric_traversability, visual_traversability_inpainted, cost, geodesic, sdf] # List of layers. \ No newline at end of file diff --git a/wild_visual_navigation_anymal/config/procman/record_rosbags.pmd b/wild_visual_navigation_anymal/config/procman/record_rosbags.pmd new file mode 100644 index 00000000..3dc5ab4e --- /dev/null +++ b/wild_visual_navigation_anymal/config/procman/record_rosbags.pmd @@ -0,0 +1,61 @@ +group "0 - Setup" { + cmd "0.0 - anymal_rsl_replay" { + exec = "rosrun anymal_rsl_launch replay.py d /Data/2024_02_06_Dodo_MPI_WVN/dodo_mission_2024_02_06/2024-02-06-14-39-01_Small_MPI_Indoor/2024-02-06-14-39-01_lpc.yaml"; + host = "localhost"; + } + cmd "0.1 - anymal_msg_converter" { + exec = "rosrun wild_visual_navigation_anymal anymal_msg_converter_node.py"; + host = "localhost"; + } + cmd "0.2 - resize_images_wide_angle_dual" { + exec = "roslaunch wild_visual_navigation_anymal resize_images_wide_angle_dual.launch"; + host = "localhost"; + } + cmd "0.3 - resize_hdr" { + exec = "roslaunch wild_visual_navigation_anymal resize_hdr.launch"; + host = "localhost"; + } + cmd "0.4 - uncompress_front" { + exec = "rosrun image_transport republish compressed in:=/wide_angle_camera_front/image_color_rect raw out:=/wide_angle_camera_front/image_color_rect"; + host = "localhost"; + } + cmd "0.5 - uncompress_rear" { + exec = "rosrun image_transport republish compressed in:=/wide_angle_camera_rear/image_color_rect raw out:=/wide_angle_camera_rear/image_color_rect"; + host = "localhost"; + } + cmd "0.6 - uncompress_hdr" { + exec = "rosrun image_transport republish compressed in:=/hdr_camera/image_raw raw out:=/hdr_camera/image_raw"; + host = "localhost"; + } + cmd "0.7 - record_coordinate" { + exec = "rosrun anymal_rsl_recording rosbag_record_coordinator.py"; + host = "localhost"; + } + cmd "0.8 - record_node" { + exec = "rosrun anymal_rsl_recording rosbag_record_node.py __name:=rosbag_record_node_ge76"; + host = "localhost"; + } + cmd "0.9 - rviz" { + exec = "roslaunch wild_visual_navigation_anymal view.launch"; + host = "localhost"; + } +} + + +group "1 - Recording" { + cmd "1.1 - start recording" { + exec = "rosservice call /rosbag_record_robot_coordinator/start_recording \"yaml_file: '/home/jonfrey/workspaces/catkin_ws/src/wild_visual_navigation/wild_visual_navigation_anymal/config/recording/create_open_source_rosbags.yaml'\""; + host = "localhost"; + } + cmd "1.2 - stop recording" { + exec = "rosservice call /rosbag_record_robot_coordinator/stop_recording \"verbose: false\""; + host = "localhost"; + } +} + +group "2 - bag play" { + cmd "2.0 - rosbag play" { + exec = "rosbag_play --sem --tf --flp --wvn --sem /Data/2024_02_06_Dodo_MPI_WVN/dodo_mission_2024_02_06/2024-02-06-14-39-01_Small_MPI_Indoor/*.bag -r 0.5 -l "; + host = "localhost"; + } +} \ No newline at end of file diff --git a/wild_visual_navigation_anymal/config/procman/replay.pmd b/wild_visual_navigation_anymal/config/procman/replay.pmd new file mode 100644 index 00000000..a9f4a7c2 --- /dev/null +++ b/wild_visual_navigation_anymal/config/procman/replay.pmd @@ -0,0 +1,99 @@ +group "0 - Setup" { + cmd "0.0 - anymal_rsl_replay" { + exec = "rosrun anymal_rsl_launch replay.py d /Data/2024_02_06_Dodo_MPI_WVN/dodo_mission_2024_02_06/2024-02-06-14-39-01_Small_MPI_Indoor/2024-02-06-14-39-01_lpc.yaml"; + host = "localhost"; + } + cmd "0.1 - anymal_msg_converter" { + exec = "rosrun wild_visual_navigation_anymal anymal_msg_converter_node.py"; + host = "localhost"; + } + cmd "0.2 - rviz anymal" { + exec = "roslaunch wild_visual_navigation_anymal view.launch"; + host = "localhost"; + } + cmd "0.3 - resize_images_wide_angle_front" { + exec = "roslaunch wild_visual_navigation_anymal resize_images_wide_angle_front.launch"; + host = "localhost"; + } + cmd "0.4 - resize_images_wide_angle_rear" { + exec = "roslaunch wild_visual_navigation_anymal resize_images_wide_angle_rear.launch"; + host = "localhost"; + } + cmd "0.5 - rqt learning graph" { + exec = "rosrun rqt_multiplot rqt_multiplot"; + host = "localhost"; + } + cmd "0.6 - rosbag play" { + exec = "rosbag_play --sem --tf --flp --wvn --sem /Data/2024_02_06_Dodo_MPI_WVN/dodo_mission_2024_02_06/2024-02-06-14-39-01_Small_MPI_Indoor/*.bag -r 0.5 -l "; + host = "localhost"; + } +} + +group "1 - WVN" { + cmd "1.1 - wild_visual_navigation - resize - elevation mapping - overlay" { + exec = "roslaunch wild_visual_navigation_anymal robot.launch camera:=wide_angle_dual_resize"; + host = "localhost"; + } + + cmd "1.2 - overlay" { + exec = "roslaunch wild_visual_navigation_anymal overlay_images.launch"; + host = "localhost"; + } + cmd "1.3 - elevation_mapping_cupy" { + exec = "roslaunch wild_visual_navigation_anymal elevation_mapping_cupy.launch"; + host = "localhost"; + } + cmd "1.4 - wild_visual_navigation_learning" { + exec = "python3 /home/jonfrey/workspaces/catkin_ws/src/wild_visual_navigation/wild_visual_navigation_ros/scripts/wvn_learning_node.py"; + host = "localhost"; + } + cmd "1.5 - wild_visual_navigation_feature_extractor" { + exec = "python3 /home/jonfrey/workspaces/catkin_ws/src/wild_visual_navigation/wild_visual_navigation_ros/scripts/wvn_feature_extractor_node.py"; + host = "localhost"; + } + cmd "1.6 - kill_wvn" { + exec = "rosnode kill /wvn_learning_node /wvn_feature_extractor_node"; + host = "localhost"; + } +} + +group "3 - Config" { + cmd "3.1 - dynamic_reconfigure" { + exec = "rosrun rqt_reconfigure rqt_reconfigure"; + host = "localhost"; + } +} + +group "4 - Uncompress" { + cmd "4.1 - front" { + exec = "rosrun image_transport republish compressed in:=/wide_angle_camera_front/image_color_rect raw out:=/wide_angle_camera_front/image_color_rect"; + host = "localhost"; + } + cmd "4.2 - rear" { + exec = "rosrun image_transport republish compressed in:=/wide_angle_camera_rear/image_color_rect raw out:=/wide_angle_camera_rear/image_color_rect"; + host = "localhost"; + } + cmd "4.3 - hdr" { + exec = "rosrun image_transport republish compressed in:=/hdr_camera/image_raw raw out:=/hdr_camera/image_raw"; + host = "localhost"; + } +} + +group "5 - Interface" { + cmd "5.1 - pause_training" { + exec = "rosservice call /wild_visual_navigation_node/pause_learning 'data: true'"; + host = "localhost"; + } + cmd "5.2 - resume_training" { + exec = "rosservice call /wild_visual_navigation_node/pause_learning 'data: false'"; + host = "localhost"; + } + cmd "5.3 - save_checkpoint" { + exec = "rosservice call /wild_visual_navigation_node/save_checkpoint ''"; + host = "localhost"; + } + cmd "5.4 - load_checkpoint" { + exec = "rosservice call /wild_visual_navigation_node/load_checkpoint 'path: 'absolute_path_in_robot_filesystem'"; + host = "localhost"; + } +} \ No newline at end of file diff --git a/wild_visual_navigation_ros/config/procman/robot.pmd b/wild_visual_navigation_anymal/config/procman/robot.pmd similarity index 78% rename from wild_visual_navigation_ros/config/procman/robot.pmd rename to wild_visual_navigation_anymal/config/procman/robot.pmd index 05e40b96..749eadba 100644 --- a/wild_visual_navigation_ros/config/procman/robot.pmd +++ b/wild_visual_navigation_anymal/config/procman/robot.pmd @@ -33,10 +33,17 @@ group "0.npc" { } } -group "1.jetson" { +group "1.xavier" { + cmd "elevation_mapping" { + exec = "roslaunch wild_visual_navigation_ros elevation_mapping_cupy.launch"; + host = "anymal_coyote_xavier"; + } +} + +group "2.orin" { cmd "wild_visual_navigation" { exec = "roslaunch wild_visual_navigation_ros robot.launch"; - host = "apc-digiforest"; + host = "anymal_coyote_orin"; } cmd "kill_wvn" { @@ -45,33 +52,33 @@ group "1.jetson" { } } -group "2.visualization" { +group "3.visualization" { cmd "rviz" { exec = "roslaunch wild_visual_navigation_ros view.launch"; host = "localhost"; } } -group "3.recording" { - cmd "3.1.rosbag_record" { +group "4.recording" { + cmd "4.1.rosbag_record" { exec = "rosservice call /rosbag_record_robot_coordinator/record_bag {}"; host = "localhost"; } - cmd "3.2.rosbag_stop" { + cmd "4.2.rosbag_stop" { exec = "rosservice call /rosbag_record_robot_coordinator/stop_bag {}"; host = "localhost"; } - cmd "3.3.fetch_bags" { + cmd "4.3.fetch_bags" { exec = "/home/ori/git/anymal_rsl/anymal_rsl/anymal_rsl_utils/anymal_rsl_recording/anymal_rsl_recording/bin/copy_mission_data_from_robot.sh digiforest coyote /home/ori/logs"; host = "localhost"; } - cmd "3.4.delete_bags" { + cmd "4.4.delete_bags" { exec = "/home/ori/git/anymal_rsl/anymal_rsl/anymal_rsl_utils/anymal_rsl_recording/anymal_rsl_recording/bin/remove_mission_data_from_robot.sh digiforest coyote"; host = "localhost"; } } -group "4.monitoring" { +group "5.monitoring" { cmd "lpc_disk" { exec = "rostopic echo /disk_monitor_lpc/status/disks[1]"; host = "localhost"; @@ -90,13 +97,28 @@ group "4.monitoring" { } } -group "5.configuration" { - cmd "5.1.dynamic_reconfigure" { +group "6.configuration" { + cmd "6.1.dynamic_reconfigure" { exec = "rosrun rqt_reconfigure rqt_reconfigure"; host = "localhost"; } } +group "7.white_balance" { + cmd "7.1.white_balance_front_reset" { + exec = "rosservice call /alphasense_raw_image_pipeline_front/reset_white_balance"; + host = "localhost"; + } + cmd "7.2.white_balance_left_reset" { + exec = "rosservice call /alphasense_raw_image_pipeline_left/reset_white_balance"; + host = "localhost"; + } + cmd "7.3.white_balance_right_reset" { + exec = "rosservice call /alphasense_raw_image_pipeline_right/reset_white_balance"; + host = "localhost"; + } +} + group "x.learning_utils" { cmd "x.1.pause_training" { exec = "rosservice call /wild_visual_navigation_node/pause_learning 'data: true'"; @@ -108,7 +130,7 @@ group "x.learning_utils" { } cmd "x.3.save_checkpoint" { exec = "rosservice call /wild_visual_navigation_node/save_checkpoint ''"; - host = "apc-digiforest"; + host = "anymal_coyote_orin"; } cmd "x.4.load_checkpoint" { exec = "rosservice call /wild_visual_navigation_node/load_checkpoint 'path: 'absolute_path_in_robot_filesystem'"; diff --git a/wild_visual_navigation_anymal/config/recording/create_open_source_rosbags.yaml b/wild_visual_navigation_anymal/config/recording/create_open_source_rosbags.yaml new file mode 100644 index 00000000..4fdb5b2a --- /dev/null +++ b/wild_visual_navigation_anymal/config/recording/create_open_source_rosbags.yaml @@ -0,0 +1,23 @@ +ge76: + open_source: + - /tf + - /tf_static + - /clock + - /motion_reference/command_twist + - /debug_info + - /twist_mux/twist + - /depth_camera_front_upper/point_cloud_self_filtered + - /depth_camera_front_lower/point_cloud_self_filtered + - /depth_camera_rear_upper/point_cloud_self_filtered + - /depth_camera_rear_lower/point_cloud_self_filtered + - /depth_camera_left/point_cloud_self_filtered + - /depth_camera_right/point_cloud_self_filtered + - /state_estimator/pose_in_odom + - /point_cloud_filter/lidar/point_cloud_filtered + - /wide_angle_camera_front/image_color_rect_resize + - /wide_angle_camera_front_resize/camera_info + - /wide_angle_camera_rear/image_color_rect_resize + - /wide_angle_camera_rear_resize/camera_info + - /wild_visual_navigation_node/robot_state + - /hdr_camera_resize/image_raw + - /hdr_camera_resize/camera_info diff --git a/wild_visual_navigation_anymal/config/recording/dodo.yaml b/wild_visual_navigation_anymal/config/recording/dodo.yaml new file mode 100644 index 00000000..203a100f --- /dev/null +++ b/wild_visual_navigation_anymal/config/recording/dodo.yaml @@ -0,0 +1,157 @@ +lpc: + generic: + - /anymal_low_level_controller/actuator_readings + - /anymal_low_level_controller/actuator_readings_extended_throttled + - /chrony_monitor_lpc/status + - /cpu_loupe_lpc/cpu_loupe + - /cpu_monitor_lpc/status + - /anyjoy/rcu + - /motion_reference/command_pose + - /motion_reference/command_twist + - /debug_info + - /twist_mux/rcu + - /twist_mux/twist + - /disk_monitor_lpc/status + - /distance_tracker/total_distance + - /distance_tracker/display + - /foot_scan + - /memory_monitor_lpc/status + - /path_logger/logged_path + - /path_logger/reverse_path + - /ping_monitor_anymal/markers + - /pdb/battery_state + - /pdb/intrinsic_state + - /pdb/power_state + - /resource_database/reports + - /sensors/battery_voltage + - /soft_emcy_stop + + depth_cameras_front_back: + - /depth_camera_front_upper/point_cloud_self_filtered + - /depth_camera_front_lower/point_cloud_self_filtered + - /depth_camera_rear_upper/point_cloud_self_filtered + - /depth_camera_rear_lower/point_cloud_self_filtered + # Front Upper + # - /depth_camera_front_upper/depth/camera_info + # - /depth_camera_front_upper/depth/image_rect_raw + # - /depth_camera_front_upper/infra1/image_rect_raw + # Front Lower + # - /depth_camera_front_lower/depth/camera_info + # - /depth_camera_front_lower/depth/image_rect_raw + # - /depth_camera_front_lower/infra1/image_rect_raw + # Rear Lower + # - /depth_camera_rear_lower/depth/camera_info + # - /depth_camera_rear_lower/depth/image_rect_raw + # - /depth_camera_rear_lower/infra1/image_rect_raw + # Rear Upper + # - /depth_camera_rear_upper/depth/camera_info + # - /depth_camera_rear_upper/depth/image_rect_raw + # - /depth_camera_rear_upper/infra1/image_rect_raw + + imu: + - /sensors/imu + state_estimator: + - /state_estimator/actuator_readings + - /state_estimator/anymal_state + - /state_estimator/contact_force_lf_foot + - /state_estimator/contact_force_lh_foot + - /state_estimator/contact_force_rf_foot + - /state_estimator/contact_force_rh_foot + - /state_estimator/disable_change_notification + - /state_estimator/enable_change_notification + - /state_estimator/force_calibrator_commands + - /state_estimator/imu + - /state_estimator/imu_angular_velocity_bias + - /state_estimator/imu_linear_acceleration_bias + - /state_estimator/init_change_notification + - /state_estimator/joint_states + - /state_estimator/odometry + - /state_estimator/pose_in_odom + - /state_estimator/reset_notification + - /state_estimator/twist + - /state_estimator/twist_throttle + - /state_estimator/zero_velocity_update_notification + tf: + - /tf + - /tf_static + clock: + - /clock +npc: + lidar: + - /lidar/packets + - /point_cloud_filter/lidar/point_cloud_filtered + # - /point_cloud_filter/lidar/point_cloud_filtered + wide_angle_camera: + - /wide_angle_camera_front/image_color_rect/compressed + - /wide_angle_camera_front/camera_info + - /wide_angle_camera_rear/image_color_rect/compressed + - /wide_angle_camera_rear/camera_info + compslam: + - /compslam_lio/full_path + - /compslam_lio/odometry + - /compslam_lio/map + - /compslam_lio/map_optimized + depth_cameras_side: + # Right + # - /depth_camera_right/depth/camera_info + # - /depth_camera_right/depth/image_rect_raw + #- /depth_camera_right/infra1/image_rect_raw + # Left + # - /depth_camera_left/depth/camera_info + # - /depth_camera_left/depth/image_rect_raw + # - /depth_camera_left/infra1/image_rect_raw + - /depth_camera_left/point_cloud_self_filtered + - /depth_camera_right/point_cloud_self_filtered + generic: + - /anymal/absolute_reference + - /behavior_engine/status + - /chrony_monitor_npc/status + - /clicked_point + - /colorless_uniform_mapper/local_point_cloud + - /transform_aft_mapped_to_init_CORRECTED + - /disk_monitor_npc/status + - /integrated_to_init_CORRECTED + - /local_guidance_path_follower/controller_reference_marker + - /local_guidance_path_manager/received_global_path + - /memory_monitor_npc/status + - /msf_compslam_lio_body_imu/msf_core/odometry + - /network_monitor_npc/status + - /pinger_npc_to_jetson/ping + - /pinger_npc_to_lpc/ping + - /cpu_monitor_npc/status + - /cpu_loupe_npc/cpu_loupe + - /resource_database/reports +jetson: + elevation_mapping: + - /elevation_mapping/elevation_map_raw + - /elevation_mapping/semantic_map + generic: + - /chrony_monitor_jetson/status + - /cpu_loupe_jetson/cpu_loupe + - /disk_monitor_jetson/status + - /gpu_monitor_jetson + - /memory_monitor_jetson/status + - /network_monitor_jetson/status + - /safety_checker/traversabiliy_safety_status + - /safety_checker/footprint_polygon + - /safety_checker/untraversable_polygon + - /pinger_jetson_to_lpc/ping + - /pinger_jetson_to_npc/ping + - /pinger_jetson_to_apc/ping + hdr_camera: + - /hdr_camera/image_raw/compressed + - /hdr_camera/camera_info + wvn: + - /wild_visual_navigation_node/front/camera_info + - /wild_visual_navigation_node/front/confidence + - /wild_visual_navigation_node/front/feat + - /wild_visual_navigation_node/front/image_input + - /wild_visual_navigation_node/front/traversability + - /wild_visual_navigation_node/graph_footprints + - /wild_visual_navigation_node/graph_footprints_array + - /wild_visual_navigation_node/rear/camera_info + - /wild_visual_navigation_node/rear/confidence + - /wild_visual_navigation_node/rear/image_input + - /wild_visual_navigation_node/rear/traversability + - /wild_visual_navigation_node/robot_state + - /wild_visual_navigation_node/supervision_graph \ No newline at end of file diff --git a/wild_visual_navigation_anymal/config/recording/start_recording.sh b/wild_visual_navigation_anymal/config/recording/start_recording.sh new file mode 100755 index 00000000..383db851 --- /dev/null +++ b/wild_visual_navigation_anymal/config/recording/start_recording.sh @@ -0,0 +1 @@ +rosservice call /rosbag_record_coordinator/start_recording "yaml_file: '/home/jonfrey/git/wild_visual_navigation/wild_visual_navigation_anymal/config/recording/dodo.yaml'" \ No newline at end of file diff --git a/wild_visual_navigation_anymal/config/recording/stop_recording.sh b/wild_visual_navigation_anymal/config/recording/stop_recording.sh new file mode 100755 index 00000000..afe1a69e --- /dev/null +++ b/wild_visual_navigation_anymal/config/recording/stop_recording.sh @@ -0,0 +1 @@ +rosservice call /rosbag_record_coordinator/stop_recording "verbose: false" \ No newline at end of file diff --git a/wild_visual_navigation_anymal/config/rviz/anymal.rviz b/wild_visual_navigation_anymal/config/rviz/anymal.rviz new file mode 100644 index 00000000..4ab62272 --- /dev/null +++ b/wild_visual_navigation_anymal/config/rviz/anymal.rviz @@ -0,0 +1,1272 @@ +Panels: + - Class: rviz/Displays + Help Height: 0 + Name: Displays + Property Tree Widget: + Expanded: + - /Global Options1 + - /TF1/Tree1 + - /Robot Info1 + - /Wild Visual Navigation1 + - /Wild Visual Navigation1/Self Supervision1 + - /Wild Visual Navigation1/Prediction1 + - /GridMaps1 + - /GridMaps1/Visual Traversability1 + Splitter Ratio: 0.6433260440826416 + Tree Height: 1310 + - Class: rviz/Selection + Name: Selection + - Class: rviz/Tool Properties + Expanded: + - /2D Pose Estimate1 + - /2D Nav Goal1 + - /Publish Point1 + Name: Tool Properties + Splitter Ratio: 0.5886790156364441 + - Class: rviz/Views + Expanded: + - /Current View1 + - /ThirdPersonFollower1 + Name: Views + Splitter Ratio: 0.5 + - Class: rviz/Time + Name: Time + SyncMode: 0 + SyncSource: AB Wide Rear Front +Preferences: + PromptSaveOnExit: true +Toolbars: + toolButtonStyle: 2 +Visualization Manager: + Class: "" + Displays: + - Alpha: 0.5 + Cell Size: 1 + Class: rviz/Grid + Color: 160; 160; 164 + Enabled: false + Line Style: + Line Width: 0.029999999329447746 + Value: Lines + Name: Grid + Normal Cell Count: 0 + Offset: + X: 0 + Y: 0 + Z: 0 + Plane: XY + Plane Cell Count: 20 + Reference Frame: + Value: false + - Class: rviz/TF + Enabled: false + Frame Timeout: 1000 + Frames: + All Enabled: false + Marker Alpha: 1 + Marker Scale: 0.5 + Name: TF + Show Arrows: true + Show Axes: true + Show Names: true + Tree: + {} + Update Interval: 0 + Value: false + - Class: rviz/Group + Displays: + - Angle Tolerance: 0.10000000149011612 + Class: rviz/Odometry + Covariance: + Orientation: + Alpha: 0.5 + Color: 255; 255; 127 + Color Style: Unique + Frame: Local + Offset: 1 + Scale: 1 + Value: true + Position: + Alpha: 0.30000001192092896 + Color: 204; 51; 204 + Scale: 1 + Value: true + Value: true + Enabled: false + Keep: 200 + Name: Odometry + Position Tolerance: 1 + Queue Size: 10 + Shape: + Alpha: 1 + Axes Length: 1 + Axes Radius: 0.10000000149011612 + Color: 255; 25; 0 + Head Length: 0.30000001192092896 + Head Radius: 0.10000000149011612 + Shaft Length: 1 + Shaft Radius: 0.05000000074505806 + Value: Arrow + Topic: /loam/odometry + Unreliable: false + Value: false + - Alpha: 1 + Class: rviz/RobotModel + Collision Enabled: false + Enabled: true + Links: + All Links Enabled: true + Expand Joint Details: false + Expand Link Details: false + Expand Tree: false + LF_FOOT: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LF_HAA_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LF_HFE_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LF_HFE_output: + Alpha: 1 + Show Axes: false + Show Trail: false + LF_HIP: + Alpha: 1 + Show Axes: false + Show Trail: false + LF_KFE_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LF_SHANK: + Alpha: 1 + Show Axes: false + Show Trail: false + LF_THIGH: + Alpha: 1 + Show Axes: false + Show Trail: false + LF_hip_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LF_shank_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LF_thigh_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LH_FOOT: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LH_HAA_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LH_HFE_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LH_HFE_output: + Alpha: 1 + Show Axes: false + Show Trail: false + LH_HIP: + Alpha: 1 + Show Axes: false + Show Trail: false + LH_KFE_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LH_SHANK: + Alpha: 1 + Show Axes: false + Show Trail: false + LH_THIGH: + Alpha: 1 + Show Axes: false + Show Trail: false + LH_hip_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LH_shank_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LH_thigh_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + Link Tree Style: Links in Alphabetic Order + RF_FOOT: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RF_HAA_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RF_HFE_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RF_HFE_output: + Alpha: 1 + Show Axes: false + Show Trail: false + RF_HIP: + Alpha: 1 + Show Axes: false + Show Trail: false + RF_KFE_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RF_SHANK: + Alpha: 1 + Show Axes: false + Show Trail: false + RF_THIGH: + Alpha: 1 + Show Axes: false + Show Trail: false + RF_hip_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RF_shank_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RF_thigh_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RH_FOOT: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RH_HAA_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RH_HFE_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RH_HFE_output: + Alpha: 1 + Show Axes: false + Show Trail: false + RH_HIP: + Alpha: 1 + Show Axes: false + Show Trail: false + RH_KFE_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RH_SHANK: + Alpha: 1 + Show Axes: false + Show Trail: false + RH_THIGH: + Alpha: 1 + Show Axes: false + Show Trail: false + RH_hip_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RH_shank_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RH_thigh_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + base: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + base_inertia: + Alpha: 1 + Show Axes: false + Show Trail: false + battery: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + body_top: + Alpha: 1 + Show Axes: false + Show Trail: false + bottom_shell: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + depth_camera_front_lower_camera: + Alpha: 1 + Show Axes: false + Show Trail: false + depth_camera_front_lower_camera_parent: + Alpha: 1 + Show Axes: false + Show Trail: false + depth_camera_front_upper_camera: + Alpha: 1 + Show Axes: false + Show Trail: false + depth_camera_front_upper_camera_parent: + Alpha: 1 + Show Axes: false + Show Trail: false + depth_camera_left_camera: + Alpha: 1 + Show Axes: false + Show Trail: false + depth_camera_left_camera_parent: + Alpha: 1 + Show Axes: false + Show Trail: false + depth_camera_rear_lower_camera: + Alpha: 1 + Show Axes: false + Show Trail: false + depth_camera_rear_lower_camera_parent: + Alpha: 1 + Show Axes: false + Show Trail: false + depth_camera_rear_upper_camera: + Alpha: 1 + Show Axes: false + Show Trail: false + depth_camera_rear_upper_camera_parent: + Alpha: 1 + Show Axes: false + Show Trail: false + depth_camera_right_camera: + Alpha: 1 + Show Axes: false + Show Trail: false + depth_camera_right_camera_parent: + Alpha: 1 + Show Axes: false + Show Trail: false + docking_socket: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + face_front: + Alpha: 1 + Show Axes: false + Show Trail: false + face_rear: + Alpha: 1 + Show Axes: false + Show Trail: false + face_shell_front: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + face_shell_rear: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + hatch_shell: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + hbc_receiver: + Alpha: 1 + Show Axes: false + Show Trail: false + imu_link: + Alpha: 1 + Show Axes: false + Show Trail: false + lidar: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + lidar_parent: + Alpha: 1 + Show Axes: false + Show Trail: false + top_shell: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + wide_angle_camera_front_camera: + Alpha: 1 + Show Axes: false + Show Trail: false + wide_angle_camera_front_camera_parent: + Alpha: 1 + Show Axes: false + Show Trail: false + wide_angle_camera_rear_camera: + Alpha: 1 + Show Axes: false + Show Trail: false + wide_angle_camera_rear_camera_parent: + Alpha: 1 + Show Axes: false + Show Trail: false + Name: RobotModel + Robot Description: anymal_description + TF Prefix: "" + Update Interval: 0 + Value: true + Visual Enabled: true + Enabled: true + Name: Robot Info + - Class: rviz/Group + Displays: + - Class: rviz/Group + Displays: + - Class: rviz/Marker + Enabled: true + Marker Topic: /wild_visual_navigation_node/graph_footprints + Name: Footprint + Namespaces: + footprints: true + Queue Size: 100 + Value: true + - Alpha: 1 + Buffer Length: 1 + Class: rviz/Path + Color: 67; 110; 176 + Enabled: true + Head Diameter: 0.30000001192092896 + Head Length: 0.20000000298023224 + Length: 0.30000001192092896 + Line Style: Billboards + Line Width: 0.029999999329447746 + Name: Supervision Graph + Offset: + X: 0 + Y: 0 + Z: 0 + Pose Color: 255; 85; 255 + Pose Style: None + Queue Size: 10 + Radius: 0.029999999329447746 + Shaft Diameter: 0.10000000149011612 + Shaft Length: 0.10000000149011612 + Topic: /wild_visual_navigation_node/supervision_graph + Unreliable: false + Value: true + - Alpha: 1 + Buffer Length: 1 + Class: rviz/Path + Color: 25; 255; 0 + Enabled: true + Head Diameter: 0.30000001192092896 + Head Length: 0.20000000298023224 + Length: 0.30000001192092896 + Line Style: Billboards + Line Width: 0.029999999329447746 + Name: Mission Graph + Offset: + X: 0 + Y: 0 + Z: 0 + Pose Color: 255; 85; 255 + Pose Style: Axes + Queue Size: 10 + Radius: 0.029999999329447746 + Shaft Diameter: 0.10000000149011612 + Shaft Length: 0.10000000149011612 + Topic: /wild_visual_navigation_node/mission_graph + Unreliable: false + Value: true + Enabled: true + Name: Self Supervision + - Class: rviz/Group + Displays: + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/front/debug/last_image_traversability + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Traversability + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/front/debug/last_image_confidence + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Uncertainty + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Alpha: 1 + Buffer Length: 1 + Class: rviz/Path + Color: 136; 138; 133 + Enabled: true + Head Diameter: 0.30000001192092896 + Head Length: 0.20000000298023224 + Length: 0.10000000149011612 + Line Style: Billboards + Line Width: 0.05000000074505806 + Name: Mission Graph + Offset: + X: 0 + Y: 0 + Z: 0 + Pose Color: 255; 85; 255 + Pose Style: Axes + Queue Size: 10 + Radius: 0.009999999776482582 + Shaft Diameter: 0.10000000149011612 + Shaft Length: 0.10000000149011612 + Topic: /wild_visual_navigation_node/mission_graph + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/front/debug/last_node_image_mask + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Reprojected Path + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/front/debug/last_node_image_labeled + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Supervision Signal + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: "" + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Placeholder + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + Enabled: false + Name: Self Supervision (Debug) + - Class: rviz/Group + Displays: + - Class: rviz/Image + Enabled: true + Image Topic: /wide_angle_camera_front/image_color_rect + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: AB Wide Angle Front + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wide_angle_camera_rear/image_color_rect + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: AB Wide Rear Front + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + Enabled: true + Name: Cameras + - Class: rviz/Group + Displays: + - Alpha: 1 + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 21.55472183227539 + Min Value: -7.581119537353516 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 239; 41; 41 + Color Transformer: FlatColor + Decay Time: 0 + Enabled: true + Invert Rainbow: false + Max Color: 255; 255; 255 + Min Color: 0; 0; 0 + Name: realsense-front + Position Transformer: XYZ + Queue Size: 10 + Selectable: true + Size (Pixels): 3 + Size (m): 0.009999999776482582 + Style: Squares + Topic: /depth_camera_front_upper/point_cloud_self_filtered + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: true + - Alpha: 1 + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 21.55472183227539 + Min Value: -7.581119537353516 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 239; 41; 41 + Color Transformer: FlatColor + Decay Time: 0 + Enabled: true + Invert Rainbow: false + Max Color: 255; 255; 255 + Min Color: 0; 0; 0 + Name: realsense-rear + Position Transformer: XYZ + Queue Size: 10 + Selectable: true + Size (Pixels): 3 + Size (m): 0.009999999776482582 + Style: Squares + Topic: /depth_camera_rear_upper/point_cloud_self_filtered + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: true + - Alpha: 1 + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 21.55472183227539 + Min Value: -7.581119537353516 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 239; 41; 41 + Color Transformer: FlatColor + Decay Time: 0 + Enabled: true + Invert Rainbow: false + Max Color: 255; 255; 255 + Min Color: 0; 0; 0 + Name: realsense-left + Position Transformer: XYZ + Queue Size: 10 + Selectable: true + Size (Pixels): 3 + Size (m): 0.009999999776482582 + Style: Squares + Topic: /depth_camera_left/point_cloud_self_filtered + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: true + - Alpha: 1 + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 21.55472183227539 + Min Value: -7.581119537353516 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 239; 41; 41 + Color Transformer: FlatColor + Decay Time: 0 + Enabled: true + Invert Rainbow: false + Max Color: 255; 255; 255 + Min Color: 0; 0; 0 + Name: realsense-right + Position Transformer: XYZ + Queue Size: 10 + Selectable: true + Size (Pixels): 3 + Size (m): 0.009999999776482582 + Style: Squares + Topic: /depth_camera_right/point_cloud_self_filtered + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: true + - Alpha: 1 + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 7.054330825805664 + Min Value: -0.766709566116333 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 239; 41; 41 + Color Transformer: AxisColor + Decay Time: 0 + Enabled: true + Invert Rainbow: false + Max Color: 255; 255; 255 + Min Color: 0; 0; 0 + Name: velodyne + Position Transformer: XYZ + Queue Size: 10 + Selectable: true + Size (Pixels): 3 + Size (m): 0.019999999552965164 + Style: Squares + Topic: /point_cloud_filter/lidar/point_cloud_filtered + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: true + Enabled: false + Name: Depth Sensors + - Class: rviz/Group + Displays: + - Class: rviz/Image + Enabled: false + Image Topic: /wild_visual_navigation_node/front/image_input + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Input Image + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false + - Class: rviz/Image + Enabled: false + Image Topic: /wild_visual_navigation_node/front/traversability + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Traversability Raw + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false + - Class: rviz/Image + Enabled: false + Image Topic: /wild_visual_navigation_node/front/debug/last_node_image_overlay + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Learning Mask + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_visu_front_trav/traversability + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: F Traversability Overlay + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_visu_rear_trav/traversability + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: R Traversability Overlay + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + Enabled: true + Name: Prediction + - Class: rviz/Group + Displays: + - Class: rviz/Marker + Enabled: true + Marker Topic: /field_local_planner/real_carrot + Name: Goal (carrot) + Namespaces: + {} + Queue Size: 100 + Value: true + - Alpha: 1 + Axes Length: 1 + Axes Radius: 0.10000000149011612 + Class: rviz/PoseWithCovariance + Color: 255; 25; 0 + Covariance: + Orientation: + Alpha: 0.5 + Color: 255; 255; 127 + Color Style: Unique + Frame: Local + Offset: 1 + Scale: 1 + Value: true + Position: + Alpha: 0.30000001192092896 + Color: 204; 51; 204 + Scale: 1 + Value: true + Value: true + Enabled: true + Head Length: 0.30000001192092896 + Head Radius: 0.10000000149011612 + Name: Goal (pose) + Queue Size: 10 + Shaft Length: 1 + Shaft Radius: 0.05000000074505806 + Shape: Arrow + Topic: /field_local_planner/current_goal + Unreliable: false + Value: true + - Alpha: 1 + Autocompute Intensity Bounds: false + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: geodesic + Color Transformer: IntensityLayer + ColorMap: plasma + Enabled: false + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation_inpainted + Height Transformer: "" + History Length: 1 + Invert ColorMap: true + Max Color: 255; 255; 255 + Max Intensity: 5 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: Geodesic Field (GDF) + Show Grid Lines: true + Topic: /elevation_mapping/elevation_map_wifi + Unreliable: false + Use ColorMap: true + Value: false + Enabled: true + Name: Local planner + - Class: rviz/Group + Displays: + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/left/image_input + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: L Input Image + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/left/traversability + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: L Traversability Raw + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/left/confidence + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: L Confidence Raw + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/right/image_input + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: R Input Image + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/right/traversability + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: R Traversability Raw + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/right/confidence + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: R Confidence Raw + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + Enabled: false + Name: LR Prediction + Enabled: true + Name: Wild Visual Navigation + - Class: rviz/Group + Displays: + - Alpha: 1 + Autocompute Intensity Bounds: true + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: elevation + Color Transformer: GridMapLayer + ColorMap: terrain + Enabled: false + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation + Height Transformer: GridMapLayer + History Length: 1 + Invert ColorMap: false + Max Color: 255; 255; 255 + Max Intensity: 10 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: Elevation + Show Grid Lines: true + Topic: /elevation_mapping/semantic_map + Unreliable: false + Use ColorMap: true + Value: false + - Alpha: 1 + Autocompute Intensity Bounds: false + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: geodesic + Color Transformer: IntensityLayer + ColorMap: plasma + Enabled: false + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation_inpainted + Height Transformer: "" + History Length: 1 + Invert ColorMap: true + Max Color: 255; 255; 255 + Max Intensity: 5 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: Local Planner (GDF) + Show Grid Lines: true + Topic: /elevation_mapping/elevation_map_wifi + Unreliable: false + Use ColorMap: true + Value: false + - Alpha: 1 + Autocompute Intensity Bounds: false + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: sdf + Color Transformer: IntensityLayer + ColorMap: turbo + Enabled: false + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation + Height Transformer: Layer + History Length: 1 + Invert ColorMap: true + Max Color: 255; 255; 255 + Max Intensity: 2 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: Local Planner (SDF) + Show Grid Lines: true + Topic: /elevation_mapping/elevation_map_wifi + Unreliable: false + Use ColorMap: true + Value: false + - Alpha: 1 + Autocompute Intensity Bounds: true + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: rgb + Color Transformer: ColorLayer + ColorMap: terrain + Enabled: false + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation_with_semantics + Height Transformer: "" + History Length: 1 + Invert ColorMap: true + Max Color: 255; 255; 255 + Max Intensity: 1 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: RGB + Show Grid Lines: true + Topic: /elevation_mapping/semantic_map + Unreliable: false + Use ColorMap: true + Value: false + - Alpha: 1 + Autocompute Intensity Bounds: false + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: visual_traversability + Color Transformer: "" + ColorMap: coolwarm + Enabled: true + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation_with_semantics + Height Transformer: GridMapLayer + History Length: 1 + Invert ColorMap: true + Max Color: 255; 255; 255 + Max Intensity: 1 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: Visual Traversability + Show Grid Lines: false + Topic: /elevation_mapping/semantic_map + Unreliable: false + Use ColorMap: true + Value: true + - Alpha: 1 + Autocompute Intensity Bounds: false + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: traversability + Color Transformer: "" + ColorMap: coolwarm + Enabled: false + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation + Height Transformer: GridMapLayer + History Length: 1 + Invert ColorMap: true + Max Color: 255; 255; 255 + Max Intensity: 1 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: Geom Traversability + Show Grid Lines: true + Topic: /elevation_mapping/semantic_map + Unreliable: false + Use ColorMap: true + Value: false + Enabled: true + Name: GridMaps + Enabled: true + Global Options: + Background Color: 255; 255; 255 + Default Light: true + Fixed Frame: base + Frame Rate: 30 + Name: root + Tools: + - Class: rviz/Interact + Hide Inactive Objects: true + - Class: rviz/MoveCamera + - Class: rviz/Select + - Class: rviz/FocusCamera + - Class: rviz/Measure + - Class: rviz/SetInitialPose + Theta std deviation: 0.2617993950843811 + Topic: /initialpose + X std deviation: 0.5 + Y std deviation: 0.5 + - Class: rviz/SetGoal + Topic: /move_base_simple/goal + - Class: rviz/PublishPoint + Single click: true + Topic: /clicked_point + Value: true + Views: + Current: + Class: rviz/XYOrbit + Distance: 12.523152351379395 + Enable Stereo Rendering: + Stereo Eye Separation: 0.05999999865889549 + Stereo Focal Distance: 1 + Swap Stereo Eyes: false + Value: false + Field of View: 0.7853981852531433 + Focal Point: + X: -2.2413411140441895 + Y: -0.2522427439689636 + Z: 0 + Focal Shape Fixed Size: true + Focal Shape Size: 0.05000000074505806 + Invert Z Axis: false + Name: Current View + Near Clip Distance: 0.009999999776482582 + Pitch: 1.5697963237762451 + Target Frame: base + Yaw: 3.1414999961853027 + Saved: + - Class: rviz/ThirdPersonFollower + Distance: 22.303850173950195 + Enable Stereo Rendering: + Stereo Eye Separation: 0.05999999865889549 + Stereo Focal Distance: 1 + Swap Stereo Eyes: false + Value: false + Field of View: 0.7853981852531433 + Focal Point: + X: 3.3520777225494385 + Y: 3.0211095809936523 + Z: -5.593323707580566 + Focal Shape Fixed Size: false + Focal Shape Size: 0.05000000074505806 + Invert Z Axis: false + Name: ThirdPersonFollower + Near Clip Distance: 0.009999999776482582 + Pitch: 1.5697963237762451 + Target Frame: base + Yaw: 0.0003485679626464844 +Window Geometry: + AB Wide Angle Front: + collapsed: false + AB Wide Rear Front: + collapsed: false + Displays: + collapsed: false + F Traversability Overlay: + collapsed: false + Height: 2096 + Hide Left Dock: false + Hide Right Dock: false + Input Image: + collapsed: false + L Confidence Raw: + collapsed: false + L Input Image: + collapsed: false + L Traversability Raw: + collapsed: false + Learning Mask: + collapsed: false + Placeholder: + collapsed: false + QMainWindow State: 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+ R Confidence Raw: + collapsed: false + R Input Image: + collapsed: false + R Traversability Overlay: + collapsed: false + R Traversability Raw: + collapsed: false + Reprojected Path: + collapsed: false + Selection: + collapsed: false + Supervision Signal: + collapsed: false + Time: + collapsed: false + Tool Properties: + collapsed: false + Traversability: + collapsed: false + Traversability Raw: + collapsed: false + Uncertainty: + collapsed: false + Views: + collapsed: false + Width: 3768 + X: 1992 + Y: 27 diff --git a/wild_visual_navigation_anymal/config/rviz/deprecated/anymal.rviz b/wild_visual_navigation_anymal/config/rviz/deprecated/anymal.rviz new file mode 100644 index 00000000..5dabe460 --- /dev/null +++ b/wild_visual_navigation_anymal/config/rviz/deprecated/anymal.rviz @@ -0,0 +1,1540 @@ +Panels: + - Class: rviz/Displays + Help Height: 0 + Name: Displays + Property Tree Widget: + Expanded: + - /Global Options1 + - /TF1/Tree1 + - /Wild Visual Navigation1 + - /Wild Visual Navigation1/Cameras1 + - /Wild Visual Navigation1/Prediction1 + - /Wild Visual Navigation1/Prediction1/F Traversability Raw1 + - /Wild Visual Navigation1/Prediction1/F Learning Mask1 + - /Wild Visual Navigation1/Prediction1/F Confidence Overlay1 + - /Wild Visual Navigation1/LR Prediction1 + - /Elevation Map WIFI1/Local Planner (SDF)1 + - /Elevation Map RAW1 + Splitter Ratio: 0.4659898579120636 + Tree Height: 698 + - Class: rviz/Selection + Name: Selection + - Class: rviz/Tool Properties + Expanded: + - /2D Pose Estimate1 + - /2D Nav Goal1 + - /Publish Point1 + Name: Tool Properties + Splitter Ratio: 0.5886790156364441 + - Class: rviz/Views + Expanded: + - /Current View1 + Name: Views + Splitter Ratio: 0.5 + - Class: rviz/Time + Name: Time + SyncMode: 0 + SyncSource: velodyne +Preferences: + PromptSaveOnExit: true +Toolbars: + toolButtonStyle: 2 +Visualization Manager: + Class: "" + Displays: + - Alpha: 0.5 + Cell Size: 1 + Class: rviz/Grid + Color: 160; 160; 164 + Enabled: true + Line Style: + Line Width: 0.029999999329447746 + Value: Lines + Name: Grid + Normal Cell Count: 0 + Offset: + X: 0 + Y: 0 + Z: 0 + Plane: XY + Plane Cell Count: 20 + Reference Frame: + Value: true + - Class: rviz/TF + Enabled: false + Frame Timeout: 1000 + Frames: + All Enabled: false + Marker Alpha: 1 + Marker Scale: 0.5 + Name: TF + Show Arrows: true + Show Axes: true + Show Names: true + Tree: + {} + Update Interval: 0 + Value: false + - Class: rviz/Group + Displays: + - Angle Tolerance: 0.10000000149011612 + Class: rviz/Odometry + Covariance: + Orientation: + Alpha: 0.5 + Color: 255; 255; 127 + Color Style: Unique + Frame: Local + Offset: 1 + Scale: 1 + Value: true + Position: + Alpha: 0.30000001192092896 + Color: 204; 51; 204 + Scale: 1 + Value: true + Value: true + Enabled: false + Keep: 200 + Name: Odometry + Position Tolerance: 1 + Queue Size: 10 + Shape: + Alpha: 1 + Axes Length: 1 + Axes Radius: 0.10000000149011612 + Color: 255; 25; 0 + Head Length: 0.30000001192092896 + Head Radius: 0.10000000149011612 + Shaft Length: 1 + Shaft Radius: 0.05000000074505806 + Value: Arrow + Topic: /loam/odometry + Unreliable: false + Value: false + - Alpha: 1 + Class: rviz/RobotModel + Collision Enabled: false + Enabled: true + Links: + All Links Enabled: true + Expand Joint Details: false + Expand Link Details: false + Expand Tree: false + LF_FOOT: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LF_HAA_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LF_HFE_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LF_HFE_output: + Alpha: 1 + Show Axes: false + Show Trail: false + LF_HIP: + Alpha: 1 + Show Axes: false + Show Trail: false + LF_KFE_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LF_SHANK: + Alpha: 1 + Show Axes: false + Show Trail: false + LF_THIGH: + Alpha: 1 + Show Axes: false + Show Trail: false + LF_hip_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LF_shank_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LF_thigh_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LH_FOOT: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LH_HAA_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LH_HFE_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LH_HFE_output: + Alpha: 1 + Show Axes: false + Show Trail: false + LH_HIP: + Alpha: 1 + Show Axes: false + Show Trail: false + LH_KFE_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LH_SHANK: + Alpha: 1 + Show Axes: false + Show Trail: false + LH_THIGH: + Alpha: 1 + Show Axes: false + Show Trail: false + LH_hip_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LH_shank_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + LH_thigh_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + Link Tree Style: Links in Alphabetic Order + RF_FOOT: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RF_HAA_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RF_HFE_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RF_HFE_output: + Alpha: 1 + Show Axes: false + Show Trail: false + RF_HIP: + Alpha: 1 + Show Axes: false + Show Trail: false + RF_KFE_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RF_SHANK: + Alpha: 1 + Show Axes: false + Show Trail: false + RF_THIGH: + Alpha: 1 + Show Axes: false + Show Trail: false + RF_hip_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RF_shank_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RF_thigh_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RH_FOOT: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RH_HAA_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RH_HFE_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RH_HFE_output: + Alpha: 1 + Show Axes: false + Show Trail: false + RH_HIP: + Alpha: 1 + Show Axes: false + Show Trail: false + RH_KFE_drive: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RH_SHANK: + Alpha: 1 + Show Axes: false + Show Trail: false + RH_THIGH: + Alpha: 1 + Show Axes: false + Show Trail: false + RH_hip_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RH_shank_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + RH_thigh_fixed: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + base: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + base_inertia: + Alpha: 1 + Show Axes: false + Show Trail: false + battery: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + body_top: + Alpha: 1 + Show Axes: false + Show Trail: false + bottom_shell: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + depth_camera_front_lower_camera: + Alpha: 1 + Show Axes: false + Show Trail: false + depth_camera_front_lower_camera_parent: + Alpha: 1 + Show Axes: false + Show Trail: false + depth_camera_front_upper_camera: + Alpha: 1 + Show Axes: false + Show Trail: false + depth_camera_front_upper_camera_parent: + Alpha: 1 + Show Axes: false + Show Trail: false + depth_camera_left_camera: + Alpha: 1 + Show Axes: false + Show Trail: false + depth_camera_left_camera_parent: + Alpha: 1 + Show Axes: false + Show Trail: false + depth_camera_rear_lower_camera: + Alpha: 1 + Show Axes: false + Show Trail: false + depth_camera_rear_lower_camera_parent: + Alpha: 1 + Show Axes: false + Show Trail: false + depth_camera_rear_upper_camera: + Alpha: 1 + Show Axes: false + Show Trail: false + depth_camera_rear_upper_camera_parent: + Alpha: 1 + Show Axes: false + Show Trail: false + depth_camera_right_camera: + Alpha: 1 + Show Axes: false + Show Trail: false + depth_camera_right_camera_parent: + Alpha: 1 + Show Axes: false + Show Trail: false + docking_socket: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + face_front: + Alpha: 1 + Show Axes: false + Show Trail: false + face_rear: + Alpha: 1 + Show Axes: false + Show Trail: false + face_shell_front: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + face_shell_rear: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + hatch_shell: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + hbc_receiver: + Alpha: 1 + Show Axes: false + Show Trail: false + imu_link: + Alpha: 1 + Show Axes: false + Show Trail: false + lidar: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + lidar_parent: + Alpha: 1 + Show Axes: false + Show Trail: false + top_shell: + Alpha: 1 + Show Axes: false + Show Trail: false + Value: true + wide_angle_camera_front_camera: + Alpha: 1 + Show Axes: false + Show Trail: false + wide_angle_camera_front_camera_parent: + Alpha: 1 + Show Axes: false + Show Trail: false + wide_angle_camera_rear_camera: + Alpha: 1 + Show Axes: false + Show Trail: false + wide_angle_camera_rear_camera_parent: + Alpha: 1 + Show Axes: false + Show Trail: false + Name: RobotModel + Robot Description: anymal_description + TF Prefix: "" + Update Interval: 0 + Value: true + Visual Enabled: true + Enabled: true + Name: Robot Info + - Class: rviz/Group + Displays: + - Class: rviz/Group + Displays: + - Class: rviz/Marker + Enabled: true + Marker Topic: /wild_visual_navigation_node/graph_footprints + Name: Footprint + Namespaces: + footprints: true + Queue Size: 100 + Value: true + - Alpha: 1 + Buffer Length: 1 + Class: rviz/Path + Color: 40; 87; 244 + Enabled: true + Head Diameter: 0.30000001192092896 + Head Length: 0.20000000298023224 + Length: 0.30000001192092896 + Line Style: Billboards + Line Width: 0.029999999329447746 + Name: Supervision Graph + Offset: + X: 0 + Y: 0 + Z: 0 + Pose Color: 255; 85; 255 + Pose Style: None + Queue Size: 10 + Radius: 0.029999999329447746 + Shaft Diameter: 0.10000000149011612 + Shaft Length: 0.10000000149011612 + Topic: /wild_visual_navigation_node/supervision_graph + Unreliable: false + Value: true + Enabled: true + Name: Self Supervision + - Class: rviz/Group + Displays: + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/front/debug/last_image_traversability + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Traversability + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/front/debug/last_image_confidence + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Uncertainty + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Alpha: 1 + Buffer Length: 1 + Class: rviz/Path + Color: 136; 138; 133 + Enabled: true + Head Diameter: 0.30000001192092896 + Head Length: 0.20000000298023224 + Length: 0.10000000149011612 + Line Style: Billboards + Line Width: 0.05000000074505806 + Name: Mission Graph + Offset: + X: 0 + Y: 0 + Z: 0 + Pose Color: 255; 85; 255 + Pose Style: Axes + Queue Size: 10 + Radius: 0.009999999776482582 + Shaft Diameter: 0.10000000149011612 + Shaft Length: 0.10000000149011612 + Topic: /wild_visual_navigation_node/mission_graph + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/front/debug/last_node_image_mask + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Reprojected Path + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/front/debug/last_node_image_labeled + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Supervision Signal + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: "" + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Placeholder + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + Enabled: false + Name: Self Supervision (Debug) + - Class: rviz/Group + Displays: + - Class: rviz/Image + Enabled: false + Image Topic: /alphasense_driver_ros/cam4/debayered + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: AS Front (cam4) + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false + - Class: rviz/Image + Enabled: false + Image Topic: /alphasense_driver_ros/cam3/debayered + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: AS Left (cam3) + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false + - Class: rviz/Image + Enabled: false + Image Topic: /alphasense_driver_ros/cam5/debayered + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: AS Right (cam5) + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false + - Class: rviz/Image + Enabled: true + Image Topic: /wide_angle_camera_front/image_color + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: AB Wide Angle Front + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: false + Image Topic: "" + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Placeholder + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false + Enabled: true + Name: Cameras + - Class: rviz/Group + Displays: + - Alpha: 1 + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 21.55472183227539 + Min Value: -7.581119537353516 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 239; 41; 41 + Color Transformer: FlatColor + Decay Time: 0 + Enabled: false + Invert Rainbow: false + Max Color: 255; 255; 255 + Min Color: 0; 0; 0 + Name: rear-bpearl + Position Transformer: XYZ + Queue Size: 10 + Selectable: true + Size (Pixels): 3 + Size (m): 0.009999999776482582 + Style: Squares + Topic: /robot_self_filter/bpearl_rear/point_cloud + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: false + - Alpha: 1 + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 22.831743240356445 + Min Value: -4.732121467590332 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 239; 41; 41 + Color Transformer: FlatColor + Decay Time: 0 + Enabled: false + Invert Rainbow: false + Max Color: 255; 255; 255 + Min Color: 0; 0; 0 + Name: front-bpearl + Position Transformer: XYZ + Queue Size: 10 + Selectable: true + Size (Pixels): 3 + Size (m): 0.009999999776482582 + Style: Squares + Topic: /robot_self_filter/bpearl_front/point_cloud + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: false + - Alpha: 1 + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 21.55472183227539 + Min Value: -7.581119537353516 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 239; 41; 41 + Color Transformer: FlatColor + Decay Time: 0 + Enabled: false + Invert Rainbow: false + Max Color: 255; 255; 255 + Min Color: 0; 0; 0 + Name: realsense-front + Position Transformer: XYZ + Queue Size: 10 + Selectable: true + Size (Pixels): 3 + Size (m): 0.009999999776482582 + Style: Squares + Topic: /depth_camera_front/point_cloud_self_filtered + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: false + - Alpha: 1 + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 21.55472183227539 + Min Value: -7.581119537353516 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 239; 41; 41 + Color Transformer: FlatColor + Decay Time: 0 + Enabled: false + Invert Rainbow: false + Max Color: 255; 255; 255 + Min Color: 0; 0; 0 + Name: realsense-rear + Position Transformer: XYZ + Queue Size: 10 + Selectable: true + Size (Pixels): 3 + Size (m): 0.009999999776482582 + Style: Squares + Topic: /depth_camera_rear/point_cloud_self_filtered + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: false + - Alpha: 1 + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 21.55472183227539 + Min Value: -7.581119537353516 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 239; 41; 41 + Color Transformer: FlatColor + Decay Time: 0 + Enabled: false + Invert Rainbow: false + Max Color: 255; 255; 255 + Min Color: 0; 0; 0 + Name: realsense-left + Position Transformer: XYZ + Queue Size: 10 + Selectable: true + Size (Pixels): 3 + Size (m): 0.009999999776482582 + Style: Squares + Topic: /depth_camera_left/point_cloud_self_filtered + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: false + - Alpha: 1 + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 21.55472183227539 + Min Value: -7.581119537353516 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 239; 41; 41 + Color Transformer: FlatColor + Decay Time: 0 + Enabled: false + Invert Rainbow: false + Max Color: 255; 255; 255 + Min Color: 0; 0; 0 + Name: realsense-right + Position Transformer: XYZ + Queue Size: 10 + Selectable: true + Size (Pixels): 3 + Size (m): 0.009999999776482582 + Style: Squares + Topic: /depth_camera_right/point_cloud_self_filtered + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: false + - Alpha: 1 + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 15.389507293701172 + Min Value: -1.5122853517532349 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 239; 41; 41 + Color Transformer: AxisColor + Decay Time: 0 + Enabled: true + Invert Rainbow: false + Max Color: 255; 255; 255 + Min Color: 0; 0; 0 + Name: velodyne + Position Transformer: XYZ + Queue Size: 10 + Selectable: true + Size (Pixels): 3 + Size (m): 0.05000000074505806 + Style: Squares + Topic: /point_cloud_filter/lidar/point_cloud_filtered + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: true + Enabled: true + Name: Depth Sensors + - Class: rviz/Group + Displays: + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/front/image_input + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: F Input Image + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: false + Image Topic: /wild_visual_navigation_node/front/traversability + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: F Traversability Raw + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/front/debug/last_node_image_overlay + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: F Learning Mask + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: false + Image Topic: /wild_visual_navigation_visu_traversability/traversability_overlayed + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: F Traversability Overlay + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false + - Class: rviz/Image + Enabled: false + Image Topic: /wild_visual_navigation_visu_0/traversability_overlayed + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: F Traversability Overlay Replay + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false + - Class: rviz/Image + Enabled: false + Image Topic: /wild_visual_navigation_visu_confidence/confidence_overlayed + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: F Confidence Overlay + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false + - Class: rviz/Image + Enabled: false + Image Topic: /wild_visual_navigation_visu_1/confidence_overlayed + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: F Confidence Overlay Replay + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false + Enabled: true + Name: Prediction + - Class: rviz/Group + Displays: + - Class: rviz/Marker + Enabled: true + Marker Topic: /field_local_planner/real_carrot + Name: Goal (carrot) + Namespaces: + {} + Queue Size: 100 + Value: true + - Alpha: 1 + Axes Length: 1 + Axes Radius: 0.10000000149011612 + Class: rviz/PoseWithCovariance + Color: 255; 25; 0 + Covariance: + Orientation: + Alpha: 0.5 + Color: 255; 255; 127 + Color Style: Unique + Frame: Local + Offset: 1 + Scale: 1 + Value: true + Position: + Alpha: 0.30000001192092896 + Color: 204; 51; 204 + Scale: 1 + Value: true + Value: true + Enabled: true + Head Length: 0.30000001192092896 + Head Radius: 0.10000000149011612 + Name: Goal (pose) + Queue Size: 10 + Shaft Length: 1 + Shaft Radius: 0.05000000074505806 + Shape: Arrow + Topic: /field_local_planner/current_goal + Unreliable: false + Value: true + - Alpha: 1 + Autocompute Intensity Bounds: false + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: geodesic + Color Transformer: IntensityLayer + ColorMap: plasma + Enabled: false + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation_inpainted + Height Transformer: "" + History Length: 1 + Invert ColorMap: true + Max Color: 255; 255; 255 + Max Intensity: 5 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: Geodesic Field (GDF) + Show Grid Lines: true + Topic: /elevation_mapping/elevation_map_wifi + Unreliable: false + Use ColorMap: true + Value: false + Enabled: true + Name: Local planner + - Class: rviz/Group + Displays: + - Class: rviz/Image + Enabled: false + Image Topic: /wild_visual_navigation_node/left/image_input + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: L Input Image + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false + - Class: rviz/Image + Enabled: false + Image Topic: /wild_visual_navigation_node/left/traversability + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: L Traversability Raw + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false + - Class: rviz/Image + Enabled: false + Image Topic: /wild_visual_navigation_node/left/confidence + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: L Confidence Raw + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false + - Class: rviz/Image + Enabled: false + Image Topic: /wild_visual_navigation_node/right/image_input + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: R Input Image + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false + - Class: rviz/Image + Enabled: false + Image Topic: /wild_visual_navigation_node/right/traversability + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: R Traversability Raw + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false + - Class: rviz/Image + Enabled: false + Image Topic: /wild_visual_navigation_node/right/confidence + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: R Confidence Raw + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false + Enabled: false + Name: LR Prediction + Enabled: true + Name: Wild Visual Navigation + - Class: rviz/Group + Displays: + - Alpha: 1 + Autocompute Intensity Bounds: true + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: elevation + Color Transformer: IntensityLayer + ColorMap: terrain + Enabled: false + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation + Height Transformer: "" + History Length: 1 + Invert ColorMap: true + Max Color: 255; 255; 255 + Max Intensity: 1 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: Elevation + Show Grid Lines: true + Topic: /elevation_mapping/elevation_map_wifi + Unreliable: false + Use ColorMap: true + Value: false + - Alpha: 1 + Autocompute Intensity Bounds: true + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: elevation_inpainted + Color Transformer: IntensityLayer + ColorMap: terrain + Enabled: false + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation_inpainted + Height Transformer: "" + History Length: 1 + Invert ColorMap: true + Max Color: 255; 255; 255 + Max Intensity: 1 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: Elevation (Inpainted) + Show Grid Lines: true + Topic: /elevation_mapping/elevation_map_wifi + Unreliable: false + Use ColorMap: true + Value: false + - Alpha: 1 + Autocompute Intensity Bounds: false + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: geodesic + Color Transformer: IntensityLayer + ColorMap: plasma + Enabled: false + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation_inpainted + Height Transformer: "" + History Length: 1 + Invert ColorMap: true + Max Color: 255; 255; 255 + Max Intensity: 5 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: Local Planner (GDF) + Show Grid Lines: true + Topic: /elevation_mapping/elevation_map_wifi + Unreliable: false + Use ColorMap: true + Value: false + - Alpha: 0.5 + Autocompute Intensity Bounds: false + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: sdf + Color Transformer: GridMapLayer + ColorMap: turbo + Enabled: true + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation + Height Transformer: GridMapLayer + History Length: 1 + Invert ColorMap: true + Max Color: 255; 255; 255 + Max Intensity: 2 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: Local Planner (SDF) + Show Grid Lines: false + Topic: /elevation_mapping/elevation_map_wifi + Unreliable: false + Use ColorMap: true + Value: true + - Alpha: 1 + Autocompute Intensity Bounds: true + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: rgb_image + Color Transformer: ColorLayer + ColorMap: terrain + Enabled: false + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation + Height Transformer: "" + History Length: 1 + Invert ColorMap: true + Max Color: 255; 255; 255 + Max Intensity: 1 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: RGB + Show Grid Lines: true + Topic: /elevation_mapping/semantic_map_raw + Unreliable: false + Use ColorMap: true + Value: false + - Alpha: 1 + Autocompute Intensity Bounds: true + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: visual_traversability_inpainted + Color Transformer: GridMapLayer + ColorMap: coolwarm + Enabled: false + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation_inpainted + Height Transformer: GridMapLayer + History Length: 1 + Invert ColorMap: true + Max Color: 255; 255; 255 + Max Intensity: 10 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: Visual Traversability Inpainted + Show Grid Lines: true + Topic: /elevation_mapping/elevation_map_wifi + Unreliable: false + Use ColorMap: true + Value: false + Enabled: false + Name: Elevation Map WIFI + - Class: rviz/Group + Displays: + - Alpha: 1 + Autocompute Intensity Bounds: true + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: elevation + Color Transformer: GridMapLayer + ColorMap: terrain + Enabled: false + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation + Height Transformer: GridMapLayer + History Length: 1 + Invert ColorMap: false + Max Color: 255; 255; 255 + Max Intensity: 10 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: Elevation + Show Grid Lines: true + Topic: /elevation_mapping/semantic_map_raw + Unreliable: false + Use ColorMap: true + Value: false + - Alpha: 1 + Autocompute Intensity Bounds: false + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: geodesic + Color Transformer: IntensityLayer + ColorMap: plasma + Enabled: false + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation_inpainted + Height Transformer: "" + History Length: 1 + Invert ColorMap: true + Max Color: 255; 255; 255 + Max Intensity: 5 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: Local Planner (GDF) + Show Grid Lines: true + Topic: /elevation_mapping/elevation_map_wifi + Unreliable: false + Use ColorMap: true + Value: false + - Alpha: 1 + Autocompute Intensity Bounds: true + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: elevation_inpainted + Color Transformer: IntensityLayer + ColorMap: terrain + Enabled: false + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation_inpainted + Height Transformer: "" + History Length: 1 + Invert ColorMap: true + Max Color: 255; 255; 255 + Max Intensity: 1 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: Elevation (Inpainted) + Show Grid Lines: true + Topic: /elevation_mapping/elevation_map_wifi + Unreliable: false + Use ColorMap: true + Value: false + - Alpha: 1 + Autocompute Intensity Bounds: false + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: sdf + Color Transformer: IntensityLayer + ColorMap: turbo + Enabled: false + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation + Height Transformer: Layer + History Length: 1 + Invert ColorMap: true + Max Color: 255; 255; 255 + Max Intensity: 2 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: Local Planner (SDF) + Show Grid Lines: true + Topic: /elevation_mapping/elevation_map_wifi + Unreliable: false + Use ColorMap: true + Value: false + - Alpha: 1 + Autocompute Intensity Bounds: true + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: rgb_image + Color Transformer: ColorLayer + ColorMap: terrain + Enabled: false + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation + Height Transformer: "" + History Length: 1 + Invert ColorMap: true + Max Color: 255; 255; 255 + Max Intensity: 1 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: RGB + Show Grid Lines: true + Topic: /elevation_mapping/semantic_map_raw + Unreliable: false + Use ColorMap: true + Value: false + - Alpha: 1 + Autocompute Intensity Bounds: true + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: visual_traversability + Color Transformer: "" + ColorMap: coolwarm + Enabled: true + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation_with_semantic + Height Transformer: GridMapLayer + History Length: 1 + Invert ColorMap: true + Max Color: 255; 255; 255 + Max Intensity: 10 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: Visual Traversability + Show Grid Lines: true + Topic: /elevation_mapping/semantic_map_raw + Unreliable: false + Use ColorMap: true + Value: true + - Alpha: 1 + Autocompute Intensity Bounds: true + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: traversability + Color Transformer: "" + ColorMap: coolwarm + Enabled: false + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation + Height Transformer: GridMapLayer + History Length: 1 + Invert ColorMap: true + Max Color: 255; 255; 255 + Max Intensity: 10 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: Geom Traversability + Show Grid Lines: true + Topic: /elevation_mapping/elevation_map_raw + Unreliable: false + Use ColorMap: true + Value: false + Enabled: true + Name: Elevation Map RAW + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_visu_0/traversability_overlayed + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: F Trav Over + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + Enabled: true + Global Options: + Background Color: 255; 255; 255 + Default Light: true + Fixed Frame: base + Frame Rate: 30 + Name: root + Tools: + - Class: rviz/Interact + Hide Inactive Objects: true + - Class: rviz/MoveCamera + - Class: rviz/Select + - Class: rviz/FocusCamera + - Class: rviz/Measure + - Class: rviz/SetInitialPose + Theta std deviation: 0.2617993950843811 + Topic: /initialpose + X std deviation: 0.5 + Y std deviation: 0.5 + - Class: rviz/SetGoal + Topic: /move_base_simple/goal + - Class: rviz/PublishPoint + Single click: true + Topic: /clicked_point + Value: true + Views: + Current: + Class: rviz/ThirdPersonFollower + Distance: 18.592376708984375 + Enable Stereo Rendering: + Stereo Eye Separation: 0.05999999865889549 + Stereo Focal Distance: 1 + Swap Stereo Eyes: false + Value: false + Field of View: 0.7853981852531433 + Focal Point: + X: 0.8738794326782227 + Y: 1.1860613822937012 + Z: -5.593326091766357 + Focal Shape Fixed Size: false + Focal Shape Size: 0.05000000074505806 + Invert Z Axis: false + Name: Current View + Near Clip Distance: 0.009999999776482582 + Pitch: 0.6897974610328674 + Target Frame: base + Yaw: 5.176687240600586 + Saved: ~ +Window Geometry: + AB Wide Angle Front: + collapsed: false + AS Front (cam4): + collapsed: false + AS Left (cam3): + collapsed: false + AS Right (cam5): + collapsed: false + Displays: + collapsed: false + F Confidence Overlay: + collapsed: false + F Confidence Overlay Replay: + collapsed: false + F Input Image: + collapsed: false + F Learning Mask: + collapsed: false + F Trav Over: + collapsed: false + F Traversability Overlay: + collapsed: false + F Traversability Overlay Replay: + collapsed: false + F Traversability Raw: + collapsed: false + Height: 1376 + Hide Left Dock: false + Hide Right Dock: true + L Confidence Raw: + collapsed: false + L Input Image: + collapsed: false + L Traversability Raw: + collapsed: false + Placeholder: + collapsed: false + QMainWindow State: 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+ R Confidence Raw: + collapsed: false + R Input Image: + collapsed: false + R Traversability Raw: + collapsed: false + Reprojected Path: + collapsed: false + Selection: + collapsed: false + Supervision Signal: + collapsed: false + Time: + collapsed: false + Tool Properties: + collapsed: false + Traversability: + collapsed: false + Uncertainty: + collapsed: false + Views: + collapsed: true + Width: 2488 + X: 72 + Y: 27 diff --git a/wild_visual_navigation_ros/config/rviz/anymal_debug.rviz b/wild_visual_navigation_anymal/config/rviz/deprecated/anymal_debug.rviz similarity index 99% rename from wild_visual_navigation_ros/config/rviz/anymal_debug.rviz rename to wild_visual_navigation_anymal/config/rviz/deprecated/anymal_debug.rviz index 9c9f3c06..adb8ecd4 100644 --- a/wild_visual_navigation_ros/config/rviz/anymal_debug.rviz +++ b/wild_visual_navigation_anymal/config/rviz/deprecated/anymal_debug.rviz @@ -524,7 +524,7 @@ Visualization Manager: Length: 0.30000001192092896 Line Style: Billboards Line Width: 0.029999999329447746 - Name: Proprio Graph + Name: Supervision Graph Offset: X: 0 Y: 0 @@ -535,7 +535,7 @@ Visualization Manager: Radius: 0.029999999329447746 Shaft Diameter: 0.10000000149011612 Shaft Length: 0.10000000149011612 - Topic: /wild_visual_navigation_node/proprioceptive_graph + Topic: /wild_visual_navigation_node/supervision_graph Unreliable: false Value: true Enabled: true diff --git a/wild_visual_navigation_ros/config/rviz/anymal.rviz b/wild_visual_navigation_anymal/config/rviz/deprecated/anymal_dodo.rviz similarity index 80% rename from wild_visual_navigation_ros/config/rviz/anymal.rviz rename to wild_visual_navigation_anymal/config/rviz/deprecated/anymal_dodo.rviz index b8fef33f..3f4ec12c 100644 --- a/wild_visual_navigation_ros/config/rviz/anymal.rviz +++ b/wild_visual_navigation_anymal/config/rviz/deprecated/anymal_dodo.rviz @@ -7,12 +7,12 @@ Panels: - /Global Options1 - /TF1/Tree1 - /Wild Visual Navigation1 - - /Wild Visual Navigation1/Depth Sensors1 - - /Wild Visual Navigation1/Prediction1 - - /Elevation Map WIFI1 + - /Wild Visual Navigation1/Depth Sensors1/realsense-front1 - /Elevation Map WIFI1/Local Planner (SDF)1 - Splitter Ratio: 0.6102783679962158 - Tree Height: 546 + - /Elevation Map RAW1 + - /Elevation Map RAW1/Visual Traversability1 + Splitter Ratio: 0.5789473652839661 + Tree Height: 685 - Class: rviz/Selection Name: Selection - Class: rviz/Tool Properties @@ -28,10 +28,9 @@ Panels: Name: Views Splitter Ratio: 0.5 - Class: rviz/Time - Experimental: false Name: Time SyncMode: 0 - SyncSource: Input Image + SyncSource: Traversability Raw Preferences: PromptSaveOnExit: true Toolbars: @@ -123,21 +122,25 @@ Visualization Manager: Show Axes: false Show Trail: false Value: true - LF_HAA: + LF_HAA_drive: Alpha: 1 Show Axes: false Show Trail: false Value: true - LF_HFE: + LF_HFE_drive: Alpha: 1 Show Axes: false Show Trail: false Value: true + LF_HFE_output: + Alpha: 1 + Show Axes: false + Show Trail: false LF_HIP: Alpha: 1 Show Axes: false Show Trail: false - LF_KFE: + LF_KFE_drive: Alpha: 1 Show Axes: false Show Trail: false @@ -170,21 +173,25 @@ Visualization Manager: Show Axes: false Show Trail: false Value: true - LH_HAA: + LH_HAA_drive: Alpha: 1 Show Axes: false Show Trail: false Value: true - LH_HFE: + LH_HFE_drive: Alpha: 1 Show Axes: false Show Trail: false Value: true + LH_HFE_output: + Alpha: 1 + Show Axes: false + Show Trail: false LH_HIP: Alpha: 1 Show Axes: false Show Trail: false - LH_KFE: + LH_KFE_drive: Alpha: 1 Show Axes: false Show Trail: false @@ -218,21 +225,25 @@ Visualization Manager: Show Axes: false Show Trail: false Value: true - RF_HAA: + RF_HAA_drive: Alpha: 1 Show Axes: false Show Trail: false Value: true - RF_HFE: + RF_HFE_drive: Alpha: 1 Show Axes: false Show Trail: false Value: true + RF_HFE_output: + Alpha: 1 + Show Axes: false + Show Trail: false RF_HIP: Alpha: 1 Show Axes: false Show Trail: false - RF_KFE: + RF_KFE_drive: Alpha: 1 Show Axes: false Show Trail: false @@ -265,21 +276,25 @@ Visualization Manager: Show Axes: false Show Trail: false Value: true - RH_HAA: + RH_HAA_drive: Alpha: 1 Show Axes: false Show Trail: false Value: true - RH_HFE: + RH_HFE_drive: Alpha: 1 Show Axes: false Show Trail: false Value: true + RH_HFE_output: + Alpha: 1 + Show Axes: false + Show Trail: false RH_HIP: Alpha: 1 Show Axes: false Show Trail: false - RH_KFE: + RH_KFE_drive: Alpha: 1 Show Axes: false Show Trail: false @@ -307,11 +322,6 @@ Visualization Manager: Show Axes: false Show Trail: false Value: true - alphasense_mesh: - Alpha: 1 - Show Axes: false - Show Trail: false - Value: true base: Alpha: 1 Show Axes: false @@ -335,74 +345,43 @@ Visualization Manager: Show Axes: false Show Trail: false Value: true - cam0_sensor_frame: - Alpha: 1 - Show Axes: false - Show Trail: false - cam1_sensor_frame: - Alpha: 1 - Show Axes: false - Show Trail: false - cam2_sensor_frame: - Alpha: 1 - Show Axes: false - Show Trail: false - cam3_sensor_frame: - Alpha: 1 - Show Axes: false - Show Trail: false - cam3_sensor_frame_helper: + depth_camera_front_lower_camera: Alpha: 1 Show Axes: false Show Trail: false - cam4_sensor_frame: + depth_camera_front_lower_camera_parent: Alpha: 1 Show Axes: false Show Trail: false - cam4_sensor_frame_helper: + depth_camera_front_upper_camera: Alpha: 1 Show Axes: false Show Trail: false - cam5_sensor_frame: + depth_camera_front_upper_camera_parent: Alpha: 1 Show Axes: false Show Trail: false - cam5_sensor_frame_helper: - Alpha: 1 - Show Axes: false - Show Trail: false - cam6_sensor_frame: - Alpha: 1 - Show Axes: false - Show Trail: false - cam6_sensor_frame_helper: - Alpha: 1 - Show Axes: false - Show Trail: false - depth_camera_front_camera: + depth_camera_left_camera: Alpha: 1 Show Axes: false Show Trail: false - Value: true - depth_camera_front_camera_parent: + depth_camera_left_camera_parent: Alpha: 1 Show Axes: false Show Trail: false - depth_camera_left_camera: + depth_camera_rear_lower_camera: Alpha: 1 Show Axes: false Show Trail: false - Value: true - depth_camera_left_camera_parent: + depth_camera_rear_lower_camera_parent: Alpha: 1 Show Axes: false Show Trail: false - depth_camera_rear_camera: + depth_camera_rear_upper_camera: Alpha: 1 Show Axes: false Show Trail: false - Value: true - depth_camera_rear_camera_parent: + depth_camera_rear_upper_camera_parent: Alpha: 1 Show Axes: false Show Trail: false @@ -410,68 +389,55 @@ Visualization Manager: Alpha: 1 Show Axes: false Show Trail: false - Value: true depth_camera_right_camera_parent: Alpha: 1 Show Axes: false Show Trail: false - docking_hatch_cover: + docking_socket: Alpha: 1 Show Axes: false Show Trail: false + Value: true face_front: Alpha: 1 Show Axes: false Show Trail: false - Value: true face_rear: Alpha: 1 Show Axes: false Show Trail: false - Value: true - front_handle: + face_shell_front: Alpha: 1 Show Axes: false Show Trail: false Value: true - handle: + face_shell_rear: Alpha: 1 Show Axes: false Show Trail: false Value: true - hatch: + hatch_shell: Alpha: 1 Show Axes: false Show Trail: false Value: true - imu_link: + hbc_receiver: Alpha: 1 Show Axes: false Show Trail: false - imu_sensor_frame: - Alpha: 1 - Show Axes: false - Show Trail: false - interface_hatch: + imu_link: Alpha: 1 Show Axes: false Show Trail: false - Value: true lidar: Alpha: 1 Show Axes: false Show Trail: false Value: true - perception_head_cage: - Alpha: 1 - Show Axes: false - Show Trail: false - Value: true - remote: + lidar_parent: Alpha: 1 Show Axes: false Show Trail: false - Value: true top_shell: Alpha: 1 Show Axes: false @@ -481,7 +447,6 @@ Visualization Manager: Alpha: 1 Show Axes: false Show Trail: false - Value: true wide_angle_camera_front_camera_parent: Alpha: 1 Show Axes: false @@ -490,7 +455,6 @@ Visualization Manager: Alpha: 1 Show Axes: false Show Trail: false - Value: true wide_angle_camera_rear_camera_parent: Alpha: 1 Show Axes: false @@ -508,13 +472,13 @@ Visualization Manager: - Class: rviz/Group Displays: - Class: rviz/Marker - Enabled: true + Enabled: false Marker Topic: /wild_visual_navigation_node/graph_footprints Name: Footprint Namespaces: {} Queue Size: 100 - Value: true + Value: false - Alpha: 1 Buffer Length: 1 Class: rviz/Path @@ -525,7 +489,7 @@ Visualization Manager: Length: 0.30000001192092896 Line Style: Billboards Line Width: 0.029999999329447746 - Name: Proprio Graph + Name: Supervision Graph Offset: X: 0 Y: 0 @@ -536,7 +500,7 @@ Visualization Manager: Radius: 0.029999999329447746 Shaft Diameter: 0.10000000149011612 Shaft Length: 0.10000000149011612 - Topic: /wild_visual_navigation_node/proprioceptive_graph + Topic: /wild_visual_navigation_node/supervision_graph Unreliable: false Value: true Enabled: true @@ -631,42 +595,6 @@ Visualization Manager: Name: Self Supervision (Debug) - Class: rviz/Group Displays: - - Class: rviz/Image - Enabled: true - Image Topic: /alphasense_driver_ros/cam4/debayered - Max Value: 1 - Median window: 5 - Min Value: 0 - Name: AS Front (cam4) - Normalize Range: true - Queue Size: 2 - Transport Hint: raw - Unreliable: false - Value: true - - Class: rviz/Image - Enabled: false - Image Topic: /alphasense_driver_ros/cam3/debayered - Max Value: 1 - Median window: 5 - Min Value: 0 - Name: AS Left (cam3) - Normalize Range: true - Queue Size: 2 - Transport Hint: raw - Unreliable: false - Value: false - - Class: rviz/Image - Enabled: false - Image Topic: /alphasense_driver_ros/cam5/debayered - Max Value: 1 - Median window: 5 - Min Value: 0 - Name: AS Right (cam5) - Normalize Range: true - Queue Size: 2 - Transport Hint: raw - Unreliable: false - Value: false - Class: rviz/Image Enabled: false Image Topic: /wide_angle_camera_front/image_color @@ -774,7 +702,7 @@ Visualization Manager: Size (Pixels): 3 Size (m): 0.009999999776482582 Style: Squares - Topic: /depth_camera_front/point_cloud_self_filtered + Topic: /depth_camera_front_upper/point_cloud_self_filtered Unreliable: false Use Fixed Frame: true Use rainbow: true @@ -866,8 +794,8 @@ Visualization Manager: - Alpha: 1 Autocompute Intensity Bounds: true Autocompute Value Bounds: - Max Value: 12.096750259399414 - Min Value: -2.868344783782959 + Max Value: 15.884197235107422 + Min Value: -2.124349355697632 Value: true Axis: Z Channel Name: intensity @@ -875,7 +803,7 @@ Visualization Manager: Color: 239; 41; 41 Color Transformer: AxisColor Decay Time: 0 - Enabled: true + Enabled: false Invert Rainbow: false Max Color: 255; 255; 255 Min Color: 0; 0; 0 @@ -890,7 +818,7 @@ Visualization Manager: Unreliable: false Use Fixed Frame: true Use rainbow: true - Value: true + Value: false Enabled: true Name: Depth Sensors - Class: rviz/Group @@ -908,7 +836,7 @@ Visualization Manager: Unreliable: false Value: true - Class: rviz/Image - Enabled: false + Enabled: true Image Topic: /wild_visual_navigation_node/front/traversability Max Value: 1 Median window: 5 @@ -918,38 +846,74 @@ Visualization Manager: Queue Size: 2 Transport Hint: raw Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/front/debug/last_node_image_overlay + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Learning Mask + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: false + Image Topic: /wild_visual_navigation_visu_traversability/traversability_overlayed + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: F Traversability Overlay + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false Value: false - Class: rviz/Image Enabled: false - Image Topic: /wild_visual_navigation_node/front/confidence + Image Topic: /wild_visual_navigation_visu_0/traversability_overlayed Max Value: 1 Median window: 5 Min Value: 0 - Name: Confidence Raw + Name: F Traversability Overlay Replay Normalize Range: true Queue Size: 2 Transport Hint: raw Unreliable: false Value: false - Class: rviz/Image - Enabled: true - Image Topic: /wild_visual_navigation_visu_traversability/traversability_overlayed + Enabled: false + Image Topic: /wild_visual_navigation_visu_confidence/confidence_overlayed Max Value: 1 Median window: 5 Min Value: 0 - Name: Traversability Overlay + Name: F Confidence Overlay Normalize Range: true Queue Size: 2 Transport Hint: raw Unreliable: false - Value: true + Value: false + - Class: rviz/Image + Enabled: false + Image Topic: /wild_visual_navigation_visu_1/confidence_overlayed + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: F Confidence Overlay Replay + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false - Class: rviz/Image Enabled: true - Image Topic: /wild_visual_navigation_visu_confidence/confidence_overlayed + Image Topic: /wild_visual_navigation_visu_0/traversability_overlayed Max Value: 1 Median window: 5 Min Value: 0 - Name: Confidence Overlay + Name: Traversability Overlay Normalize Range: true Queue Size: 2 Transport Hint: raw @@ -964,7 +928,7 @@ Visualization Manager: Marker Topic: /field_local_planner/real_carrot Name: Goal (carrot) Namespaces: - carrot: true + {} Queue Size: 100 Value: true - Alpha: 1 @@ -1024,6 +988,82 @@ Visualization Manager: Value: false Enabled: true Name: Local planner + - Class: rviz/Group + Displays: + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/left/image_input + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: L Input Image + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/left/traversability + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: L Traversability Raw + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/left/confidence + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: L Confidence Raw + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/right/image_input + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: R Input Image + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/right/traversability + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: R Traversability Raw + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/right/confidence + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: R Confidence Raw + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + Enabled: false + Name: LR Prediction Enabled: true Name: Wild Visual Navigation - Class: rviz/Group @@ -1172,7 +1212,7 @@ Visualization Manager: Unreliable: false Use ColorMap: true Value: false - Enabled: true + Enabled: false Name: Elevation Map WIFI - Class: rviz/Group Displays: @@ -1303,13 +1343,13 @@ Visualization Manager: Color Layer: visual_traversability Color Transformer: "" ColorMap: coolwarm - Enabled: false + Enabled: true Grid Cell Decimation: 1 Grid Line Thickness: 0.10000000149011612 - Height Layer: visual_traversability + Height Layer: elevation_semantic Height Transformer: GridMapLayer History Length: 1 - Invert ColorMap: true + Invert ColorMap: false Max Color: 255; 255; 255 Max Intensity: 10 Min Color: 0; 0; 0 @@ -1319,7 +1359,7 @@ Visualization Manager: Topic: /elevation_mapping/semantic_map_raw Unreliable: false Use ColorMap: true - Value: false + Value: true - Alpha: 1 Autocompute Intensity Bounds: true Class: grid_map_rviz_plugin/GridMap @@ -1374,7 +1414,7 @@ Visualization Manager: Views: Current: Class: rviz/ThirdPersonFollower - Distance: 17.14068031311035 + Distance: 12.582637786865234 Enable Stereo Rendering: Stereo Eye Separation: 0.05999999865889549 Stereo Focal Distance: 1 @@ -1382,41 +1422,53 @@ Visualization Manager: Value: false Field of View: 0.7853981852531433 Focal Point: - X: 3.1985225677490234 - Y: 1.6920582056045532 - Z: -5.593326568603516 + X: 1.0811700820922852 + Y: -0.22880220413208008 + Z: -5.593325614929199 Focal Shape Fixed Size: false Focal Shape Size: 0.05000000074505806 Invert Z Axis: false Name: Current View Near Clip Distance: 0.009999999776482582 - Pitch: 1.5297969579696655 + Pitch: 0.8147973418235779 Target Frame: base - Yaw: 0.04853395000100136 + Yaw: 3.7585225105285645 Saved: ~ Window Geometry: AB Wide Angle Front: collapsed: false - AS Front (cam4): - collapsed: false - AS Left (cam3): + Displays: collapsed: false - AS Right (cam5): + F Confidence Overlay: collapsed: false - Confidence Overlay: + F Confidence Overlay Replay: collapsed: false - Confidence Raw: + F Traversability Overlay: collapsed: false - Displays: + F Traversability Overlay Replay: collapsed: false - Height: 1016 + Height: 1376 Hide Left Dock: false Hide Right Dock: true Input Image: collapsed: false + L Confidence Raw: + collapsed: false + L Input Image: + collapsed: false + L Traversability Raw: + collapsed: false + Learning Mask: + collapsed: false Placeholder: collapsed: false - QMainWindow State: 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+ R Confidence Raw: + collapsed: false + R Input Image: + collapsed: false + R Traversability Raw: + collapsed: false Reprojected Path: collapsed: false Selection: @@ -1437,6 +1489,6 @@ Window Geometry: collapsed: false Views: collapsed: true - Width: 1848 + Width: 2488 X: 72 Y: 27 diff --git a/wild_visual_navigation_ros/config/rviz/anymal_rerun_wvn.rviz b/wild_visual_navigation_anymal/config/rviz/deprecated/anymal_rerun_wvn.rviz similarity index 99% rename from wild_visual_navigation_ros/config/rviz/anymal_rerun_wvn.rviz rename to wild_visual_navigation_anymal/config/rviz/deprecated/anymal_rerun_wvn.rviz index 662f7156..7afa2bab 100644 --- a/wild_visual_navigation_ros/config/rviz/anymal_rerun_wvn.rviz +++ b/wild_visual_navigation_anymal/config/rviz/deprecated/anymal_rerun_wvn.rviz @@ -525,7 +525,7 @@ Visualization Manager: Length: 0.30000001192092896 Line Style: Billboards Line Width: 0.029999999329447746 - Name: Proprio Graph + Name: Supervision Graph Offset: X: 0 Y: 0 @@ -536,7 +536,7 @@ Visualization Manager: Radius: 0.029999999329447746 Shaft Diameter: 0.10000000149011612 Shaft Length: 0.10000000149011612 - Topic: /wild_visual_navigation_node/proprioceptive_graph + Topic: /wild_visual_navigation_node/supervision_graph Unreliable: false Value: true Enabled: true diff --git a/wild_visual_navigation_ros/config/rviz/gps_trajectory.rviz b/wild_visual_navigation_anymal/config/rviz/deprecated/gps_trajectory.rviz similarity index 100% rename from wild_visual_navigation_ros/config/rviz/gps_trajectory.rviz rename to wild_visual_navigation_anymal/config/rviz/deprecated/gps_trajectory.rviz diff --git a/wild_visual_navigation_ros/config/rviz/video.rviz b/wild_visual_navigation_anymal/config/rviz/deprecated/video.rviz similarity index 99% rename from wild_visual_navigation_ros/config/rviz/video.rviz rename to wild_visual_navigation_anymal/config/rviz/deprecated/video.rviz index b0050252..19b5b88f 100644 --- a/wild_visual_navigation_ros/config/rviz/video.rviz +++ b/wild_visual_navigation_anymal/config/rviz/deprecated/video.rviz @@ -486,7 +486,7 @@ Visualization Manager: Length: 0.30000001192092896 Line Style: Billboards Line Width: 0.029999999329447746 - Name: Proprio Graph + Name: Supervision Graph Offset: X: 0 Y: 0 @@ -497,7 +497,7 @@ Visualization Manager: Radius: 0.029999999329447746 Shaft Diameter: 0.10000000149011612 Shaft Length: 0.10000000149011612 - Topic: /wild_visual_navigation_node/proprioceptive_graph + Topic: /wild_visual_navigation_node/supervision_graph Unreliable: false Value: true Enabled: true diff --git a/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/alphasense.yaml b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/alphasense.yaml new file mode 100644 index 00000000..82ab4cb2 --- /dev/null +++ b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/alphasense.yaml @@ -0,0 +1,26 @@ +camera_topics: + front: + image_topic: "/alphasense_driver_ros/cam4/debayered" + info_topic: "/alphasense_driver_ros/cam4/camera_info" + use_for_training: true + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 + left: + image_topic: "/alphasense_driver_ros/cam3/debayered" + info_topic: "/alphasense_driver_ros/cam3/camera_info" + use_for_training: false + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 + right: + image_topic: "/alphasense_driver_ros/cam5/debayered" + info_topic: "/alphasense_driver_ros/cam5/camera_info" + use_for_training: false + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 + +# Provides 1080 (height) x 1920 (width) images +network_input_image_height: 224 # 448 +network_input_image_width: 224 # 448 \ No newline at end of file diff --git a/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/alphasense_compressed.yaml b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/alphasense_compressed.yaml new file mode 100644 index 00000000..3017f436 --- /dev/null +++ b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/alphasense_compressed.yaml @@ -0,0 +1,26 @@ +camera_topics: + front: + image_topic: "/alphasense_driver_ros/cam4/color_rect/image/compressed" + info_topic: "/alphasense_driver_ros/cam4/color_rect/camera_info" + use_for_training: true + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 + left: + image_topic: "/alphasense_driver_ros/cam3/color_rect/image/compressed" + info_topic: "/alphasense_driver_ros/cam3/color_rect/camera_info" + use_for_training: false + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 + right: + image_topic: "/alphasense_driver_ros/cam5/color_rect/image/compressed" + info_topic: "/alphasense_driver_ros/cam5/color_rect/camera_info" + use_for_training: false + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 + +# Provides 1080 (height) x 1920 (width) images +network_input_image_height: 224 # 448 +network_input_image_width: 224 # 448 \ No newline at end of file diff --git a/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/alphasense_compressed_front.yaml b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/alphasense_compressed_front.yaml new file mode 100644 index 00000000..6e284945 --- /dev/null +++ b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/alphasense_compressed_front.yaml @@ -0,0 +1,12 @@ +camera_topics: + front: + image_topic: "/alphasense_driver_ros/cam4/color_rect/image/compressed" + info_topic: "/alphasense_driver_ros/cam4/color_rect/camera_info" + use_for_training: true + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 + +# Provides 1080 (height) x 1920 (width) images +network_input_image_height: 224 # 448 +network_input_image_width: 224 # 448 \ No newline at end of file diff --git a/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/alphasense_resize.yaml b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/alphasense_resize.yaml new file mode 100644 index 00000000..bd7d2c61 --- /dev/null +++ b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/alphasense_resize.yaml @@ -0,0 +1,26 @@ +camera_topics: + front: + image_topic: "/alphasense_driver_ros/cam4/color_rect_resize/image" + info_topic: "/alphasense_driver_ros/cam4/color_rect_resize/camera_info" + use_for_training: true + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 + left: + image_topic: "/alphasense_driver_ros/cam3/color_rect_resize/image" + info_topic: "/alphasense_driver_ros/cam3/color_rect_resize/camera_info" + use_for_training: false + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 + right: + image_topic: "/alphasense_driver_ros/cam5/color_rect_resize/image" + info_topic: "/alphasense_driver_ros/cam5/color_rect_resize/camera_info" + use_for_training: false + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 + +# Provides 1080 (height) x 1920 (width) images +network_input_image_height: 224 # 448 +network_input_image_width: 224 # 448 \ No newline at end of file diff --git a/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/hdr.yaml b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/hdr.yaml new file mode 100644 index 00000000..90c26af9 --- /dev/null +++ b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/hdr.yaml @@ -0,0 +1,12 @@ +camera_topics: + # Because this is the anybotics rear + rear: + image_topic: "/hdr_camera/image_raw" + info_topic: "/hdr_camera/camera_info" + use_for_training: true + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 +# Provides 1080 (height) x 1920 (width) images +network_input_image_height: 224 # 448 +network_input_image_width: 224 # 448 \ No newline at end of file diff --git a/wild_visual_navigation_ros/config/wild_visual_navigation/inputs/none.yaml b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/none.yaml similarity index 100% rename from wild_visual_navigation_ros/config/wild_visual_navigation/inputs/none.yaml rename to wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/none.yaml diff --git a/wild_visual_navigation_ros/config/wild_visual_navigation/inputs/realsense_front.yaml b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/realsense_front.yaml similarity index 56% rename from wild_visual_navigation_ros/config/wild_visual_navigation/inputs/realsense_front.yaml rename to wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/realsense_front.yaml index 10d880e5..c98d812d 100644 --- a/wild_visual_navigation_ros/config/wild_visual_navigation/inputs/realsense_front.yaml +++ b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/realsense_front.yaml @@ -3,7 +3,10 @@ camera_topics: image_topic: "/depth_camera_front/color/image_raw" info_topic: "/depth_camera_front/color/camera_info" use_for_training: true + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 # Provides 480 (height) x 848 (width) images -network_input_image_height: 224 # 448 -network_input_image_width: 224 # 448 \ No newline at end of file +network_input_image_height: 448 # 448 +network_input_image_width: 448 # 448 \ No newline at end of file diff --git a/wild_visual_navigation_ros/config/wild_visual_navigation/inputs/realsense_rear.yaml b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/realsense_rear.yaml similarity index 77% rename from wild_visual_navigation_ros/config/wild_visual_navigation/inputs/realsense_rear.yaml rename to wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/realsense_rear.yaml index 074f5116..1cb0ea2c 100644 --- a/wild_visual_navigation_ros/config/wild_visual_navigation/inputs/realsense_rear.yaml +++ b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/realsense_rear.yaml @@ -3,6 +3,9 @@ camera_topics: image_topic: "/depth_camera_rear/color/image_raw" info_topic: "/depth_camera_rear/color/camera_info" use_for_training: true + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 # Provides 480 (height) x 848 (width) images network_input_image_height: 224 # 448 diff --git a/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/wide_angle_dual.yaml b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/wide_angle_dual.yaml new file mode 100644 index 00000000..54235d12 --- /dev/null +++ b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/wide_angle_dual.yaml @@ -0,0 +1,18 @@ +camera_topics: + front: + image_topic: "/wide_angle_camera_front/image_color_rect" + info_topic: "/wide_angle_camera_front/camera_info" + use_for_training: false + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 + rear: + image_topic: "/wide_angle_camera_rear/image_color_rect" + info_topic: "/wide_angle_camera_rear/camera_info" + use_for_training: true + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 +# Provides 1080 (height) x 1920 (width) images +network_input_image_height: 224 # 448 +network_input_image_width: 224 # 448 \ No newline at end of file diff --git a/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/wide_angle_dual_resize.yaml b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/wide_angle_dual_resize.yaml new file mode 100644 index 00000000..b119c22d --- /dev/null +++ b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/wide_angle_dual_resize.yaml @@ -0,0 +1,18 @@ +camera_topics: + front: + image_topic: "/wide_angle_camera_front/image_color_rect_resize" + info_topic: "/wide_angle_camera_front_resize/camera_info" + use_for_training: false + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 + rear: + image_topic: "/wide_angle_camera_rear/image_color_rect_resize" + info_topic: "/wide_angle_camera_rear_resize/camera_info" + use_for_training: true + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 +# Provides 1080 (height) x 1920 (width) images +network_input_image_height: 224 # 448 +network_input_image_width: 224 # 448 \ No newline at end of file diff --git a/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/wide_angle_front.yaml b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/wide_angle_front.yaml new file mode 100644 index 00000000..a8b8ca62 --- /dev/null +++ b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/wide_angle_front.yaml @@ -0,0 +1,12 @@ +camera_topics: + rear: + image_topic: "/wide_angle_camera_rear/image_color" + info_topic: "/wide_angle_camera_rear/camera_info" + use_for_training: true + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 + +# Provides 1080 (height) x 1920 (width) images +network_input_image_height: 448 # 448 +network_input_image_width: 448 # 448 \ No newline at end of file diff --git a/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/wide_angle_front_compressed.yaml b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/wide_angle_front_compressed.yaml new file mode 100644 index 00000000..41ac9801 --- /dev/null +++ b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/wide_angle_front_compressed.yaml @@ -0,0 +1,12 @@ +camera_topics: + front: + image_topic: "/wide_angle_camera_rear/image_color_rect/compressed" + info_topic: "/wide_angle_camera_rear/camera_info" + use_for_training: true + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 + +# Provides 1080 (height) x 1920 (width) images +network_input_image_height: 224 # 448 +network_input_image_width: 224 # 448 \ No newline at end of file diff --git a/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/wide_angle_front_resize.yaml b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/wide_angle_front_resize.yaml new file mode 100644 index 00000000..c3d8ae6b --- /dev/null +++ b/wild_visual_navigation_anymal/config/wild_visual_navigation/inputs/wide_angle_front_resize.yaml @@ -0,0 +1,12 @@ +camera_topics: + rear: + image_topic: "/wide_angle_camera_rear/image_color_resize" + info_topic: "/wide_angle_camera_rear_resize/camera_info" + use_for_training: true + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 + +# Provides 1080 (height) x 1920 (width) images +network_input_image_height: 224 # 448 +network_input_image_width: 224 # 448 \ No newline at end of file diff --git a/wild_visual_navigation_anymal/launch/deprecated/convert_to_public_release.launch b/wild_visual_navigation_anymal/launch/deprecated/convert_to_public_release.launch new file mode 100644 index 00000000..0424ed7c --- /dev/null +++ b/wild_visual_navigation_anymal/launch/deprecated/convert_to_public_release.launch @@ -0,0 +1,39 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/wild_visual_navigation_anymal/launch/deprecated/raw_image_pipeline_perugia.launch b/wild_visual_navigation_anymal/launch/deprecated/raw_image_pipeline_perugia.launch new file mode 100644 index 00000000..fc44183d --- /dev/null +++ b/wild_visual_navigation_anymal/launch/deprecated/raw_image_pipeline_perugia.launch @@ -0,0 +1,287 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - 'image_transport/compressedDepth' + - 'image_transport/theora' + + + - 'image_transport/compressedDepth' + - 'image_transport/theora' + + + - 'image_transport/compressedDepth' + - 'image_transport/theora' + + + - 'image_transport/compressedDepth' + - 'image_transport/theora' + + + - 'image_transport/compressedDepth' + - 'image_transport/theora' + + + - 'image_transport/compressedDepth' + - 'image_transport/theora' + + + + + + + - 'image_transport/compressedDepth' + - 'image_transport/theora' + + + - 'image_transport/compressedDepth' + - 'image_transport/theora' + + + - 'image_transport/compressedDepth' + - 'image_transport/theora' + + + - 'image_transport/compressedDepth' + - 'image_transport/theora' + + + - 'image_transport/compressedDepth' + - 'image_transport/theora' + + + - 'image_transport/compressedDepth' + - 'image_transport/theora' + + + + + + + + - 'image_transport/compressedDepth' + - 'image_transport/theora' + + + - 'image_transport/compressedDepth' + - 'image_transport/theora' + + + - 'image_transport/compressedDepth' + - 'image_transport/theora' + + + - 'image_transport/compressedDepth' + - 'image_transport/theora' + + + - 'image_transport/compressedDepth' + - 'image_transport/theora' + + + - 'image_transport/compressedDepth' + - 'image_transport/theora' + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/wild_visual_navigation_ros/launch/replay_launch.launch b/wild_visual_navigation_anymal/launch/deprecated/replay_launch.launch similarity index 78% rename from wild_visual_navigation_ros/launch/replay_launch.launch rename to wild_visual_navigation_anymal/launch/deprecated/replay_launch.launch index c2ee5612..12c12f93 100644 --- a/wild_visual_navigation_ros/launch/replay_launch.launch +++ b/wild_visual_navigation_anymal/launch/deprecated/replay_launch.launch @@ -2,18 +2,21 @@ - - + + - - + + - + - - - + + + + + + @@ -26,7 +29,7 @@ - + @@ -72,4 +75,9 @@ + + + + + \ No newline at end of file diff --git a/wild_visual_navigation_ros/launch/shift_gridmaps.launch b/wild_visual_navigation_anymal/launch/deprecated/shift_gridmaps.launch similarity index 100% rename from wild_visual_navigation_ros/launch/shift_gridmaps.launch rename to wild_visual_navigation_anymal/launch/deprecated/shift_gridmaps.launch diff --git a/wild_visual_navigation_ros/launch/debug/trajectory_base_map_new.launch b/wild_visual_navigation_anymal/launch/deprecated/trajectory_base_map_new.launch similarity index 100% rename from wild_visual_navigation_ros/launch/debug/trajectory_base_map_new.launch rename to wild_visual_navigation_anymal/launch/deprecated/trajectory_base_map_new.launch diff --git a/wild_visual_navigation_ros/launch/debug/trajectory_server.launch b/wild_visual_navigation_anymal/launch/deprecated/trajectory_server.launch similarity index 100% rename from wild_visual_navigation_ros/launch/debug/trajectory_server.launch rename to wild_visual_navigation_anymal/launch/deprecated/trajectory_server.launch diff --git a/wild_visual_navigation_anymal/launch/elevation_mapping_cupy.launch b/wild_visual_navigation_anymal/launch/elevation_mapping_cupy.launch new file mode 100644 index 00000000..7f222389 --- /dev/null +++ b/wild_visual_navigation_anymal/launch/elevation_mapping_cupy.launch @@ -0,0 +1,9 @@ + + + + + + + + + \ No newline at end of file diff --git a/wild_visual_navigation_ros/launch/field_local_planner.launch b/wild_visual_navigation_anymal/launch/field_local_planner.launch similarity index 100% rename from wild_visual_navigation_ros/launch/field_local_planner.launch rename to wild_visual_navigation_anymal/launch/field_local_planner.launch diff --git a/wild_visual_navigation_anymal/launch/overlay_images.launch b/wild_visual_navigation_anymal/launch/overlay_images.launch new file mode 100644 index 00000000..bf323deb --- /dev/null +++ b/wild_visual_navigation_anymal/launch/overlay_images.launch @@ -0,0 +1,28 @@ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/wild_visual_navigation_anymal/launch/resize_hdr.launch b/wild_visual_navigation_anymal/launch/resize_hdr.launch new file mode 100644 index 00000000..dff77a73 --- /dev/null +++ b/wild_visual_navigation_anymal/launch/resize_hdr.launch @@ -0,0 +1,12 @@ + + + + + + + + + + + + \ No newline at end of file diff --git a/wild_visual_navigation_anymal/launch/resize_images_alphasense.launch b/wild_visual_navigation_anymal/launch/resize_images_alphasense.launch new file mode 100644 index 00000000..c51df980 --- /dev/null +++ b/wild_visual_navigation_anymal/launch/resize_images_alphasense.launch @@ -0,0 +1,55 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/wild_visual_navigation_anymal/launch/resize_images_wide_angle_dual.launch b/wild_visual_navigation_anymal/launch/resize_images_wide_angle_dual.launch new file mode 100644 index 00000000..7a3503c0 --- /dev/null +++ b/wild_visual_navigation_anymal/launch/resize_images_wide_angle_dual.launch @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/wild_visual_navigation_anymal/launch/resize_images_wide_angle_front.launch b/wild_visual_navigation_anymal/launch/resize_images_wide_angle_front.launch new file mode 100644 index 00000000..b772ecfc --- /dev/null +++ b/wild_visual_navigation_anymal/launch/resize_images_wide_angle_front.launch @@ -0,0 +1,12 @@ + + + + + + + + + + + + \ No newline at end of file diff --git a/wild_visual_navigation_anymal/launch/resize_images_wide_angle_rear.launch b/wild_visual_navigation_anymal/launch/resize_images_wide_angle_rear.launch new file mode 100644 index 00000000..8927054e --- /dev/null +++ b/wild_visual_navigation_anymal/launch/resize_images_wide_angle_rear.launch @@ -0,0 +1,12 @@ + + + + + + + + + + + + \ No newline at end of file diff --git a/wild_visual_navigation_anymal/launch/robot_jetson.launch b/wild_visual_navigation_anymal/launch/robot_jetson.launch new file mode 100644 index 00000000..8f152d76 --- /dev/null +++ b/wild_visual_navigation_anymal/launch/robot_jetson.launch @@ -0,0 +1,16 @@ + + + + + + + + + + + + + + + + diff --git a/wild_visual_navigation_anymal/launch/robot_npc.launch b/wild_visual_navigation_anymal/launch/robot_npc.launch new file mode 100644 index 00000000..7007dedf --- /dev/null +++ b/wild_visual_navigation_anymal/launch/robot_npc.launch @@ -0,0 +1,10 @@ + + + + + + + + + + diff --git a/wild_visual_navigation_anymal/launch/robot_replay.launch b/wild_visual_navigation_anymal/launch/robot_replay.launch new file mode 100644 index 00000000..394bcab7 --- /dev/null +++ b/wild_visual_navigation_anymal/launch/robot_replay.launch @@ -0,0 +1,21 @@ + + + + + + + + + + + + + + + + + + + + + diff --git a/wild_visual_navigation_anymal/launch/view.launch b/wild_visual_navigation_anymal/launch/view.launch new file mode 100644 index 00000000..214b6d15 --- /dev/null +++ b/wild_visual_navigation_anymal/launch/view.launch @@ -0,0 +1,7 @@ + + + + + + + diff --git a/wild_visual_navigation_anymal/launch/wild_visual_navigation.launch b/wild_visual_navigation_anymal/launch/wild_visual_navigation.launch new file mode 100644 index 00000000..26831988 --- /dev/null +++ b/wild_visual_navigation_anymal/launch/wild_visual_navigation.launch @@ -0,0 +1,27 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/wild_visual_navigation_anymal/package.xml b/wild_visual_navigation_anymal/package.xml index 5714f941..ad4523b2 100644 --- a/wild_visual_navigation_anymal/package.xml +++ b/wild_visual_navigation_anymal/package.xml @@ -14,9 +14,9 @@ rospy roscpp - anymal_msgs nav_msgs sensor_msgs std_msgs wild_visual_navigation_msgs + wild_visual_navigation_ros diff --git a/wild_visual_navigation_anymal/scripts/anymal_msg_converter_node.py b/wild_visual_navigation_anymal/scripts/anymal_msg_converter_node.py old mode 100755 new mode 100644 index ad993880..8f101291 --- a/wild_visual_navigation_anymal/scripts/anymal_msg_converter_node.py +++ b/wild_visual_navigation_anymal/scripts/anymal_msg_converter_node.py @@ -1,4 +1,9 @@ #!/usr/bin/python3 +# +# Copyright (c) 2022-2024, ETH Zurich, Matias Mattamala, Jonas Frey. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# from wild_visual_navigation_msgs.msg import CustomState, RobotState from anymal_msgs.msg import AnymalState import rospy @@ -132,8 +137,8 @@ def anymal_msg_callback(anymal_state, return_msg=False): output_topic = rospy.get_param("~output_topic", "/wild_visual_navigation_node/robot_state") # Set publishers and subscribers - anymal_state_sub = rospy.Subscriber(anymal_state_topic, AnymalState, anymal_msg_callback, queue_size=20) robot_state_pub = rospy.Publisher(output_topic, RobotState, queue_size=20) + anymal_state_sub = rospy.Subscriber(anymal_state_topic, AnymalState, anymal_msg_callback, queue_size=20) rospy.loginfo("[anymal_msg_converter_node] ready") rospy.spin() diff --git a/wild_visual_navigation_anymal/scripts/anymal_msg_with_latent_converter_node.py b/wild_visual_navigation_anymal/scripts/anymal_msg_with_latent_converter_node.py old mode 100755 new mode 100644 index 3d40005c..2b47ce57 --- a/wild_visual_navigation_anymal/scripts/anymal_msg_with_latent_converter_node.py +++ b/wild_visual_navigation_anymal/scripts/anymal_msg_with_latent_converter_node.py @@ -1,4 +1,9 @@ #!/usr/bin/python3 +# +# Copyright (c) 2022-2024, ETH Zurich, Matias Mattamala, Jonas Frey. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# from wild_visual_navigation_msgs.msg import CustomState, RobotState from anymal_msgs.msg import AnymalState from std_msgs.msg import Float32MultiArray diff --git a/wild_visual_navigation_anymal/scripts/open_source_rosbag_converter/convert_to_public_format.py b/wild_visual_navigation_anymal/scripts/open_source_rosbag_converter/convert_to_public_format.py new file mode 100644 index 00000000..586f0402 --- /dev/null +++ b/wild_visual_navigation_anymal/scripts/open_source_rosbag_converter/convert_to_public_format.py @@ -0,0 +1,133 @@ +#!/usr/bin/env python +# +# Copyright (c) 2022-2024, ETH Zurich, Matias Mattamala, Jonas Frey. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# + +import rospy +from anymal_msgs.msg import AnymalState +from sensor_msgs.msg import JointState +import tf2_ros +import geometry_msgs.msg + +# for wvn +from wild_visual_navigation_msgs.msg import CustomState, RobotState + +labels_initialized = False +# Preallocate messages +robot_state_msg = RobotState() +# Extract joint states +joint_position = CustomState() +joint_position.name = "joint_position" +joint_position.dim = 12 +joint_position.labels = [""] * joint_position.dim +joint_position.values = [0] * joint_position.dim +robot_state_msg.states.append(joint_position) + +# Joint velocity +joint_velocity = CustomState() +joint_velocity.name = "joint_velocity" +joint_velocity.dim = 12 +joint_velocity.labels = [""] * joint_velocity.dim +joint_velocity.values = [0] * joint_velocity.dim +robot_state_msg.states.append(joint_velocity) + +# Acceleration +joint_acceleration = CustomState() +joint_acceleration.name = "joint_acceleration" +joint_acceleration.dim = 12 +joint_acceleration.labels = [""] * joint_acceleration.dim +joint_acceleration.values = [0] * joint_acceleration.dim +robot_state_msg.states.append(joint_acceleration) + +# Effort +joint_effort = CustomState() +joint_effort.name = "joint_effort" +joint_effort.dim = 12 +joint_effort.labels = [""] * joint_effort.dim +joint_effort.values = [0] * joint_effort.dim +robot_state_msg.states.append(joint_effort) + +# Vector state +vector_state = CustomState() +vector_state.name = "vector_state" +vector_state.dim = 7 + 6 # + 4 * 12 +vector_state.values = [0] * vector_state.dim +vector_state.labels = [""] * vector_state.dim +vector_state.values = [0] * vector_state.dim +robot_state_msg.states.append(vector_state) + + +i = 0 +for x in ["tx", "ty", "tz", "qx", "qy", "qz", "qw", "vx", "vy", "vz", "wx", "wy", "wz"]: + robot_state_msg.states[4].labels[i] = x + i += 1 + + +def anymal_state_callback(anymal_state_msg): + global labels_initialized + + # Joint states for URDF + joint_state_msg = JointState() + joint_state_msg.header = anymal_state_msg.joints.header + joint_state_msg.header.frame_id = "" + joint_state_msg.name = anymal_state_msg.joints.name + joint_state_msg.position = anymal_state_msg.joints.position + # joint_state_msg.velocity = anymal_state_msg.joints.velocity + # joint_state_msg.effort = anymal_state_msg.joints.effort + joint_state_publisher.publish(joint_state_msg) + # Pose for odometry + pose = anymal_state_msg.pose.pose + + # Create a TransformStamped message + transform_stamped = geometry_msgs.msg.TransformStamped() + transform_stamped.header = anymal_state_msg.pose.header + transform_stamped.child_frame_id = "base" + transform_stamped.transform.translation = pose.position + transform_stamped.transform.rotation = pose.orientation + + # Publish the TF transform + tf_broadcaster.sendTransform(transform_stamped) + + # WVN + + # For RobotState msg + robot_state_msg.header = anymal_state_msg.header + + # Extract pose + robot_state_msg.pose = anymal_state_msg.pose + + # Extract twist + robot_state_msg.twist = anymal_state_msg.twist + # Vector state + robot_state_msg.states[4].values[0] = anymal_state_msg.pose.pose.position.x + robot_state_msg.states[4].values[1] = anymal_state_msg.pose.pose.position.y + robot_state_msg.states[4].values[2] = anymal_state_msg.pose.pose.position.z + robot_state_msg.states[4].values[3] = anymal_state_msg.pose.pose.orientation.x + robot_state_msg.states[4].values[4] = anymal_state_msg.pose.pose.orientation.y + robot_state_msg.states[4].values[5] = anymal_state_msg.pose.pose.orientation.z + robot_state_msg.states[4].values[6] = anymal_state_msg.pose.pose.orientation.w + robot_state_msg.states[4].values[7] = anymal_state_msg.twist.twist.linear.x + robot_state_msg.states[4].values[8] = anymal_state_msg.twist.twist.linear.y + robot_state_msg.states[4].values[9] = anymal_state_msg.twist.twist.linear.z + robot_state_msg.states[4].values[10] = anymal_state_msg.twist.twist.angular.x + robot_state_msg.states[4].values[11] = anymal_state_msg.twist.twist.angular.y + robot_state_msg.states[4].values[12] = anymal_state_msg.twist.twist.angular.z + robot_state_pub.publish(robot_state_msg) + + +if __name__ == "__main__": + rospy.init_node("anymal_state_republisher") + tf_broadcaster = tf2_ros.TransformBroadcaster() + + # Read parameters + joint_states_topic = rospy.get_param("~joint_states") + anymal_state_topic = rospy.get_param( + "~anymal_state_topic", + ) + output_topic = rospy.get_param("~output_topic") + joint_state_publisher = rospy.Publisher(joint_states_topic, JointState, queue_size=10) + robot_state_pub = rospy.Publisher(output_topic, RobotState, queue_size=10) + rospy.Subscriber(anymal_state_topic, AnymalState, anymal_state_callback, queue_size=1) + rospy.spin() diff --git a/wild_visual_navigation_anymal/scripts/open_source_rosbag_converter/encoding_fixer.py b/wild_visual_navigation_anymal/scripts/open_source_rosbag_converter/encoding_fixer.py new file mode 100644 index 00000000..48d2f2a1 --- /dev/null +++ b/wild_visual_navigation_anymal/scripts/open_source_rosbag_converter/encoding_fixer.py @@ -0,0 +1,39 @@ +#!/env/bin/python3 +# +# Copyright (c) 2022-2024, ETH Zurich, Matias Mattamala, Jonas Frey. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# + +from __future__ import print_function + +import sys +import rospy +from sensor_msgs.msg import Image +from cv_bridge import CvBridge + + +class ImageConverter: + def __init__(self, cam): + self.image_pub = rospy.Publisher(f"/alphasense_driver_ros/{cam}_corrected", Image, queue_size=10) + + self.bridge = CvBridge() + self.image_sub = rospy.Subscriber(f"/alphasense_driver_ros/{cam}", Image, self.callback) + + def callback(self, msg): + msg.encoding = "bayer_gbrg8" + self.image_pub.publish(msg) + + +def main(args): + rospy.init_node("image_converter", anonymous=True) + cam = rospy.get_param("~cam", "cam4") + ic = ImageConverter(cam) # noqa: F841 + try: + rospy.spin() + except KeyboardInterrupt: + print("Shutting down") + + +if __name__ == "__main__": + main(sys.argv) diff --git a/wild_visual_navigation_anymal/scripts/open_source_rosbag_converter/store_images_readme_preview.py b/wild_visual_navigation_anymal/scripts/open_source_rosbag_converter/store_images_readme_preview.py new file mode 100644 index 00000000..bf74818c --- /dev/null +++ b/wild_visual_navigation_anymal/scripts/open_source_rosbag_converter/store_images_readme_preview.py @@ -0,0 +1,55 @@ +#!/usr/bin/env python + +import rospy +import cv2 +import os +from sensor_msgs.msg import Image +from cv_bridge import CvBridge +from message_filters import ApproximateTimeSynchronizer, Subscriber +from PIL import Image as PILImage + + +class ImageSaver: + def __init__(self): + rospy.init_node("image_saver", anonymous=True) + self.bridge = CvBridge() + self.count = 0 + + self.dir_path = "/Data/open_source_dataset" + # Create directories to store images + # Subscriber for the images with approximate time synchronization + self.trav_sub = Subscriber("/wild_visual_navigation_visu_traversability_rear/traversability_overlayed", Image) + self.raw_sub = Subscriber("/wide_angle_camera_rear/image_color_rect_resize", Image) + self.sync = ApproximateTimeSynchronizer([self.trav_sub, self.raw_sub], queue_size=1, slop=0.3) + self.sync.registerCallback(self.callback) + + def callback(self, trav_msg, raw_msg): + try: + trav_cv2 = self.bridge.imgmsg_to_cv2(trav_msg, desired_encoding="passthrough") + raw_cv2 = self.bridge.imgmsg_to_cv2(raw_msg, desired_encoding="passthrough") + + # Convert images to RGBA format + trav_rgba = cv2.cvtColor(trav_cv2, cv2.COLOR_RGB2BGR) + raw_rgba = cv2.cvtColor(raw_cv2, cv2.COLOR_BGR2BGRA) + + # Save traversability image + trav_filename = os.path.join(self.dir_path, f"{self.count}_trav.png") + cv2.imwrite(trav_filename, trav_rgba) + rospy.loginfo(f"Saved traversability image: {trav_filename}") + + # Save raw image + raw_filename = os.path.join(self.dir_path, f"{self.count}_raw.png") + cv2.imwrite(raw_filename, raw_rgba) + rospy.loginfo(f"Saved raw image: {raw_filename}") + + self.count += 1 + except Exception as e: + rospy.logerr(f"Error processing images: {e}") + + +if __name__ == "__main__": + try: + ImageSaver() + rospy.spin() + except rospy.ROSInterruptException: + pass diff --git a/wild_visual_navigation_anymal/scripts/policy_debug_info_converter_node.py b/wild_visual_navigation_anymal/scripts/policy_debug_info_converter_node.py index 988fbb8c..82773fb5 100644 --- a/wild_visual_navigation_anymal/scripts/policy_debug_info_converter_node.py +++ b/wild_visual_navigation_anymal/scripts/policy_debug_info_converter_node.py @@ -1,3 +1,8 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Matias Mattamala, Jonas Frey. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# import rospy from geometry_msgs.msg import TwistStamped from std_msgs.msg import Float32MultiArray @@ -16,7 +21,12 @@ def policy_debug_info_callback(debug_info_msg: Float32MultiArray): if __name__ == "__main__": rospy.init_node("policy_debug_info_converter_node") # Set publishers and subscribers - sub = rospy.Subscriber("/debug_info", Float32MultiArray, queue_size=10, callback=policy_debug_info_callback) + sub = rospy.Subscriber( + "/debug_info", + Float32MultiArray, + queue_size=10, + callback=policy_debug_info_callback, + ) pub = rospy.Publisher("/log/state/desiredRobotTwist", TwistStamped, queue_size=10) rospy.loginfo("[policy_debug_info_converter_node] ready") rospy.spin() diff --git a/wild_visual_navigation_anymal/src/anymal_msg_converter_cpp_node.cpp b/wild_visual_navigation_anymal/src/anymal_msg_converter_cpp_node.cpp index 2b536a1c..b4e82a3c 100644 --- a/wild_visual_navigation_anymal/src/anymal_msg_converter_cpp_node.cpp +++ b/wild_visual_navigation_anymal/src/anymal_msg_converter_cpp_node.cpp @@ -43,7 +43,7 @@ class AnymalMsgConverter { }; int main(int argc, char** argv) { - ros::init(argc, argv, "field_local_planner"); + ros::init(argc, argv, "anymal_msg_converter"); ros::NodeHandle nh("~"); AnymalMsgConverter converter(nh); return 0; diff --git a/wild_visual_navigation_msgs/CMakeLists.txt b/wild_visual_navigation_msgs/CMakeLists.txt index 20aaf45a..f8f47572 100644 --- a/wild_visual_navigation_msgs/CMakeLists.txt +++ b/wild_visual_navigation_msgs/CMakeLists.txt @@ -6,6 +6,7 @@ find_package(catkin REQUIRED COMPONENTS message_generation message_runtime std_msgs + sensor_msgs ) add_message_files( @@ -13,6 +14,7 @@ add_message_files( CustomState.msg RobotState.msg SystemState.msg + ImageFeatures.msg ) add_service_files( @@ -25,6 +27,7 @@ generate_messages( DEPENDENCIES geometry_msgs std_msgs + sensor_msgs ) catkin_package( @@ -32,4 +35,5 @@ catkin_package( message_generation message_runtime std_msgs + sensor_msgs ) diff --git a/wild_visual_navigation_msgs/msg/ImageFeatures.msg b/wild_visual_navigation_msgs/msg/ImageFeatures.msg new file mode 100644 index 00000000..8360f6a6 --- /dev/null +++ b/wild_visual_navigation_msgs/msg/ImageFeatures.msg @@ -0,0 +1,5 @@ +# Header +std_msgs/Header header +sensor_msgs/Image feature_segments +std_msgs/Float32MultiArray features + diff --git a/wild_visual_navigation_msgs/msg/SystemState.msg b/wild_visual_navigation_msgs/msg/SystemState.msg index 2b1e314d..03e12f9c 100644 --- a/wild_visual_navigation_msgs/msg/SystemState.msg +++ b/wild_visual_navigation_msgs/msg/SystemState.msg @@ -1,5 +1,7 @@ # Pipeline operation mode uint32 mode +# Current valid samples +uint32 mission_graph_num_valid_node # Training loss float32 loss_total # Training loss @@ -10,5 +12,3 @@ float32 loss_reco uint32 step # Pause learning flag bool pause_learning -# Traversability thereshold scaling -float32 scale_traversability_threshold \ No newline at end of file diff --git a/wild_visual_navigation_msgs/package.xml b/wild_visual_navigation_msgs/package.xml index c1352f70..41fe2074 100644 --- a/wild_visual_navigation_msgs/package.xml +++ b/wild_visual_navigation_msgs/package.xml @@ -16,4 +16,5 @@ message_generation message_runtime std_msgs + sensor_msgs diff --git a/wild_visual_navigation_ros/CMakeLists.txt b/wild_visual_navigation_ros/CMakeLists.txt index 830d02f5..8953d5e1 100644 --- a/wild_visual_navigation_ros/CMakeLists.txt +++ b/wild_visual_navigation_ros/CMakeLists.txt @@ -1,3 +1,8 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Matias Mattamala, Jonas Frey. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# cmake_minimum_required(VERSION 3.0.2) project(wild_visual_navigation_ros) @@ -14,9 +19,9 @@ catkin_package( ) catkin_python_setup() -catkin_install_python(PROGRAMS scripts/wild_visual_navigation_node.py +catkin_install_python(PROGRAMS scripts/wvn_feature_extractor_node.py + scripts/wvn_learning_node.py scripts/overlay_images.py - scripts/shift_gridmap.py scripts/smart_carrot.py scripts/rosbag_play.sh DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}) diff --git a/wild_visual_navigation_ros/README.md b/wild_visual_navigation_ros/README.md new file mode 100644 index 00000000..4eda8845 --- /dev/null +++ b/wild_visual_navigation_ros/README.md @@ -0,0 +1,13 @@ +Commands for deployment: + +# Start/stop recording +$HOME/workspaces/catkin_ws/src/wild_visual_navigation/wild_visual_navigation_ros/config/recording/start_recording.sh +$HOME/workspaces/catkin_ws/src/wild_visual_navigation/wild_visual_navigation_ros/config/recording/stop_recording.sh + +# Copying the data +$HOME/workspaces/catkin_ws/src/anymal_rsl/anymal_rsl/anymal_rsl_utils/anymal_rsl_recording/anymal_rsl_recording/bin/copy_mission_data_from_robot.sh jonfrey dodo 2024-02-06-15-20-33 /Data/2024_02_06_Dodo_MPI_WVN + +ssh jonfrey@anymal-dodo-jetson -t 'chown -R jonfrey ~/git/anymal_rsl/anymal_rsl/anymal_rsl_utils/anymal_rsl_recording/anymal_rsl_recording/data/' +ssh jonfrey@anymal-dodo-jetson -t 'chgrp -R jonfrey ~/git/anymal_rsl/anymal_rsl/anymal_rsl_utils/anymal_rsl_recording/anymal_rsl_recording/data/' + +$HOME/workspaces/catkin_ws/src/anymal_rsl/anymal_rsl/anymal_rsl_utils/anymal_rsl_recording/anymal_rsl_recording/bin/remove_mission_data_from_robot.sh jonfrey dodo \ No newline at end of file diff --git a/wild_visual_navigation_ros/config/elevation_mapping_cupy/anymal_sensor_parameter.yaml b/wild_visual_navigation_ros/config/elevation_mapping_cupy/anymal_sensor_parameter.yaml deleted file mode 100644 index 5810011a..00000000 --- a/wild_visual_navigation_ros/config/elevation_mapping_cupy/anymal_sensor_parameter.yaml +++ /dev/null @@ -1,141 +0,0 @@ -#### Subscribers ######## -subscribers: - # RGB cameras - # alphasense_front_rgb: - # fusion: ['image_color'] - # topic_name_camera: /alphasense_driver_ros/cam4/debayered - # topic_name_camera_info: /alphasense_driver_ros/cam4/camera_info - # channels: ["rgb_image"] - # data_type: image - - # alphasense_left_rgb: - # fusion: ['image_color'] - # topic_name_camera: /alphasense_driver_ros/cam3/debayered - # topic_name_camera_info: /alphasense_driver_ros/cam3/camera_info - # channels: ["rgb_image"] - # data_type: image - - # alphasense_right_rgb: - # fusion: ['image_color'] - # topic_name_camera: /alphasense_driver_ros/cam5/debayered - # topic_name_camera_info: /alphasense_driver_ros/cam5/camera_info - # channels: ["rgb_image"] - # data_type: image - - # wide_angle_camera_rear: - # fusion: ['image_color'] - # topic_name_camera: /wide_angle_camera_rear/image_color_rect/compressed - # topic_name_camera_info: /wide_angle_camera_rear/camera_info - # channels: ["rgb_image"] - # data_type: image - - # wide_angle_camera_front: - # fusion: ['image_color'] - # topic_name_camera: /wide_angle_camera_front/image_color_rect/compressed - # topic_name_camera_info: /wide_angle_camera_front/camera_info - # channels: ["rgb_image"] - # data_type: image - - # Traversability - wvn_front_traversability: - fusion: ['image_exponential'] - topic_name_camera: '/wild_visual_navigation_node/front/traversability' - topic_name_camera_info: '/wild_visual_navigation_node/front/camera_info' - channels: ["visual_traversability"] - data_type: image - - # wvn_left_traversability: - # fusion: ['image_exponential'] - # topic_name_camera: '/wild_visual_navigation_node/left/traversability' - # topic_name_camera_info: '/wild_visual_navigation_node/left/camera_info' - # channels: ["visual_traversability"] - # data_type: image - - # wvn_right_traversability: - # fusion: ['image_exponential'] - # topic_name_camera: '/wild_visual_navigation_node/right/traversability' - # topic_name_camera_info: '/wild_visual_navigation_node/right/camera_info' - # channels: ["visual_traversability"] - # data_type: image - - # Confidence - # wvn_front_confidence: - # fusion: ['image_exponential'] - # topic_name_camera: '/wild_visual_navigation_node/front/confidence' - # topic_name_camera_info: '/wild_visual_navigation_node/front/camera_info' - # channels: ["visual_confidence"] - # data_type: image - - # wvn_left_confidence: - # fusion: ['image_exponential'] - # topic_name_camera: '/wild_visual_navigation_node/left/confidence' - # topic_name_camera_info: '/wild_visual_navigation_node/left/camera_info' - # channels: ["visual_confidence"] - # data_type: image - - # wvn_right_confidence: - # fusion: ['image_exponential'] - # topic_name_camera: '/wild_visual_navigation_node/right/confidence_raw' - # topic_name_camera_info: '/wild_visual_navigation_node/right/camera_info' - # channels: ["visual_confidence"] - # data_type: image - - - front_depth: - channels: [] - fusion: [] - topic_name: /depth_camera_front/point_cloud_self_filtered - data_type: pointcloud - - rear_depth: - channels: [] - fusion: [] - topic_name: /depth_camera_rear/point_cloud_self_filtered - data_type: pointcloud - - left_depth: - channels: [] - fusion: [] - topic_name: /depth_camera_left/point_cloud_self_filtered - data_type: pointcloud - - right_depth: - channels: [] - fusion: [] - topic_name: /depth_camera_right/point_cloud_self_filtered - data_type: pointcloud - - - # velodyne: - # channels: [] - # fusion: [] - # topic_name: /point_cloud_filter/lidar/point_cloud_filtered - # data_type: pointcloud - - - # Cerberus - front_bpearl: - channels: [] - fusion: [] - topic_name: /robot_self_filter/bpearl_front/point_cloud - data_type: pointcloud - - rear_bpearl: - channels: [] - fusion: [] - topic_name: /robot_self_filter/bpearl_rear/point_cloud - data_type: pointcloud - - - # Cameras for D - # front_depth_d: - # channels: [] - # fusion: [] - # topic_name: /depth_camera_front_upper/point_cloud_self_filtered - # data_type: pointcloud - - # rear_depth_d: - # channels: [] - # fusion: [] - # topic_name: /depth_camera_rear_upper/point_cloud_self_filtered - # data_type: pointcloud \ No newline at end of file diff --git a/wild_visual_navigation_ros/config/elevation_mapping_cupy/wvn_parameters.yaml b/wild_visual_navigation_ros/config/elevation_mapping_cupy/wvn_parameters.yaml new file mode 100644 index 00000000..a9440421 --- /dev/null +++ b/wild_visual_navigation_ros/config/elevation_mapping_cupy/wvn_parameters.yaml @@ -0,0 +1,102 @@ +#### Basic parameters ######## +resolution: 0.04 # resolution in m. +map_length: 8 # map's size in m. +sensor_noise_factor: 0.05 # point's noise is sensor_noise_factor*z^2 (z is distance from sensor). +mahalanobis_thresh: 2.0 # points outside this distance is outlier. +outlier_variance: 0.01 # if point is outlier, add this value to the cell. +drift_compensation_variance_inler: 0.05 # cells under this value is used for drift compensation. +max_drift: 0.1 # drift compensation happens only the drift is smaller than this value (for safety) +drift_compensation_alpha: 0.1 # drift compensation alpha for smoother update of drift compensation +time_variance: 0.0001 # add this value when update_variance is called. +max_variance: 100.0 # maximum variance for each cell. +initial_variance: 1000.0 # initial variance for each cell. +traversability_inlier: 0.9 # cells with higher traversability are used for drift compensation. +dilation_size: 3 # dilation filter size before traversability filter. +wall_num_thresh: 20 # if there are more points than this value, only higher points than the current height are used to make the wall more sharp. +min_height_drift_cnt: 100 # drift compensation only happens if the valid cells are more than this number. +position_noise_thresh: 0.01 # if the position change is bigger than this value, the drift compensation happens. +orientation_noise_thresh: 0.01 # if the orientation change is bigger than this value, the drift compensation happens. +position_lowpass_alpha: 0.2 # lowpass filter alpha used for detecting movements. +orientation_lowpass_alpha: 0.2 # lowpass filter alpha used for detecting movements. +min_valid_distance: 0.5 # points with shorter distance will be filtered out. +max_height_range: 1.0 # points higher than this value from sensor will be filtered out to disable ceiling. +ramped_height_range_a: 0.3 # if z > max(d - ramped_height_range_b, 0) * ramped_height_range_a + ramped_height_range_c, reject. +ramped_height_range_b: 1.0 # if z > max(d - ramped_height_range_b, 0) * ramped_height_range_a + ramped_height_range_c, reject. +ramped_height_range_c: 0.2 # if z > max(d - ramped_height_range_b, 0) * ramped_height_range_a + ramped_height_range_c, reject. +update_variance_fps: 5.0 # fps for updating variance. +update_pose_fps: 10.0 # fps for updating pose and shift the center of map. +time_interval: 0.1 # Time layer is updated with this interval. +map_acquire_fps: 5.0 # Raw map is fetched from GPU memory in this fps. +publish_statistics_fps: 1.0 # Publish statistics topic in this fps. + +max_ray_length: 10.0 # maximum length for ray tracing. +cleanup_step: 0.1 # subtitute this value from validity layer at visibiltiy cleanup. +cleanup_cos_thresh: 0.1 # subtitute this value from validity layer at visibiltiy cleanup. + +safe_thresh: 0.7 # if traversability is smaller, it is counted as unsafe cell. +safe_min_thresh: 0.4 # polygon is unsafe if there exists lower traversability than this. +max_unsafe_n: 10 # if the number of cells under safe_thresh exceeds this value, polygon is unsafe. + +overlap_clear_range_xy: 4.0 # xy range [m] for clearing overlapped area. this defines the valid area for overlap clearance. (used for multi floor setting) +overlap_clear_range_z: 2.0 # z range [m] for clearing overlapped area. cells outside this range will be cleared. (used for multi floor setting) + +map_frame: 'odom' # The map frame where the odometry source uses. +base_frame: 'base' # The robot's base frame. This frame will be a center of the map. +corrected_map_frame: 'odom_corrected' + +#### Feature toggles ######## +enable_edge_sharpen: true +enable_visibility_cleanup: true +enable_drift_compensation: true +enable_overlap_clearance: true +enable_pointcloud_publishing: false +enable_drift_corrected_TF_publishing: false +enable_normal_color: false # If true, the map contains 'color' layer corresponding to normal. Add 'color' layer to the publishers setting if you want to visualize. + +#### Traversability filter ######## +use_chainer: false # Use chainer as a backend of traversability filter or pytorch. If false, it uses pytorch. pytorch requires ~2GB more GPU memory compared to chainer but runs faster. +weight_file: '$(rospack find elevation_mapping_cupy)/config/core/weights.dat' # Weight file for traversability filter + +#### Upper bound ######## +use_only_above_for_upper_bound: false + +#### Initializer ######## +initialize_method: 'linear' # Choose one from 'nearest', 'linear', 'cubic' +initialize_frame_id: ['RF_FOOT', 'LF_FOOT', 'RH_FOOT', 'LH_FOOT'] # One tf (like ['footprint'] ) initializes a square around it. +initialize_tf_offset: [0.0, 0.0, 0.0, 0.0] # z direction. Should be same number as initialize_frame_id. +dilation_size_initialize: 5 # dilation size after the init. +initialize_tf_grid_size: 0.4 # This is not used if number of tf is more than 3. +use_initializer_at_start: true # Use initializer when the node starts. + +#### Plugins ######## +plugin_config_file: '$(rospack find wild_visual_navigation_ros)/config/elevation_mapping_cupy/anymal_plugin_config.yaml' + +pointcloud_channel_fusions: + rgb: 'color' + default: 'average' + +image_channel_fusions: + rgb: 'color' + default: 'exponential' + feat_.*: 'exponential' + sem_.*: 'exponential' + + +#### Publishers ######## +# topic_name: +# layers: # Choose from 'elevation', 'variance', 'traversability', 'time', 'normal_x', 'normal_y', 'normal_z', 'color', plugin_layer_names +# basic_layers: # basic_layers for valid cell computation (e.g. Rviz): Choose a subset of `layers`. +# fps: # Publish rate. Use smaller value than `map_acquire_fps`. + +publishers: + elevation_map_raw: + layers: ['elevation', 'traversability', 'variance', 'rgb'] + basic_layers: ['elevation', 'traversability'] + fps: 5.0 + + semantic_map_raw: + layers: ['elevation', 'traversability', 'visual_traversability', 'visual_confidence', 'elevation_with_semantics'] + basic_layers: ['elevation', 'traversability'] + fps: 5.0 + + \ No newline at end of file diff --git a/wild_visual_navigation_ros/config/elevation_mapping_cupy/wvn_plugin_config.yaml b/wild_visual_navigation_ros/config/elevation_mapping_cupy/wvn_plugin_config.yaml new file mode 100644 index 00000000..9e41a17c --- /dev/null +++ b/wild_visual_navigation_ros/config/elevation_mapping_cupy/wvn_plugin_config.yaml @@ -0,0 +1,38 @@ +# Settings of the plugins. (The plugins should be stored in script/plugins) + +# min_filter fills in minimum value around the invalid cell. +min_filter: + enable: True # weather to load this plugin + fill_nan: False # Fill nans to invalid cells of elevation layer. + is_height_layer: True # If this is a height layer (such as elevation) or not (such as traversability) + layer_name: "min_filter" # The layer name. + extra_params: # This params are passed to the plugin class on initialization. + dilation_size: 1 # The patch size to apply + iteration_n: 30 # The number of iterations + +# Apply smoothing. +smooth_filter: + enable: True + fill_nan: False + is_height_layer: True + layer_name: "smooth" + extra_params: + input_layer_name: "min_filter" + +# Apply inpainting using opencv +inpainting: + enable: True + fill_nan: False + is_height_layer: True + layer_name: "inpaint" + extra_params: + method: "telea" # telea or ns + +# Apply inpainting using opencv +mask_elevation_semantics: + enable: True + fill_nan: False + is_height_layer: True + layer_name: "elevation_with_semantics" + extra_params: + semantic_reference_layer_name: "visual_traversability" \ No newline at end of file diff --git a/wild_visual_navigation_ros/config/elevation_mapping_cupy/wvn_sensor_parameter.yaml b/wild_visual_navigation_ros/config/elevation_mapping_cupy/wvn_sensor_parameter.yaml new file mode 100644 index 00000000..3b034e76 --- /dev/null +++ b/wild_visual_navigation_ros/config/elevation_mapping_cupy/wvn_sensor_parameter.yaml @@ -0,0 +1,67 @@ +#### Subscribers ######## +subscribers: + front_upper_depth: + topic_name: /depth_camera_front_upper/point_cloud_self_filtered + data_type: pointcloud + + front_lower_depth: + topic_name: /depth_camera_front_lower/point_cloud_self_filtered + data_type: pointcloud + + rear_upper_depth: + topic_name: /depth_camera_rear_upper/point_cloud_self_filtered + data_type: pointcloud + + rear_lower_depth: + topic_name: /depth_camera_rear_lower/point_cloud_self_filtered + data_type: pointcloud + + left_depth: + topic_name: /depth_camera_left/point_cloud_self_filtered + data_type: pointcloud + + right_depth: + topic_name: /depth_camera_right/point_cloud_self_filtered + data_type: pointcloud + + velodyne: + topic_name: /point_cloud_filter/lidar/point_cloud_filtered + data_type: pointcloud + + # Semantics + front_wide_angle: + topic_name: /wide_angle_camera_front/image_color_rect/compressed + camera_info_topic_name: /wide_angle_camera_front/camera_info + data_type: image + + rear_wide_angle: + topic_name: /wide_angle_camera_rear/image_color_rect/compressed + camera_info_topic_name: /wide_angle_camera_rear/camera_info + data_type: image + + + # Traversability + # channels: ["visual_traversability"] + sem_wvn_front_traversability: + topic_name: '/wild_visual_navigation_node/front/traversability' + camera_info_topic_name: '/wild_visual_navigation_node/front/camera_info' + channels: ['visual_traversability'] + data_type: image + + sem_wvn_left_traversability: + topic_name: '/wild_visual_navigation_node/left/traversability' + camera_info_topic_name: '/wild_visual_navigation_node/left/camera_info' + channels: ['visual_traversability'] + data_type: image + + sem_wvn_right_traversability: + topic_name: '/wild_visual_navigation_node/right/traversability' + camera_info_topic_name: '/wild_visual_navigation_node/right/camera_info' + channels: ['visual_traversability'] + data_type: image + + # sem_wvn_front_confidence: + # topic_name: '/wild_visual_navigation_node/front/confidence' + # camera_info_topic_name: '/wild_visual_navigation_node/front/camera_info' + # channels: ['visual_traversability'] + # data_type: image \ No newline at end of file diff --git a/wild_visual_navigation_ros/config/field_local_planner/visual/filter_chain_wifi.yaml b/wild_visual_navigation_ros/config/field_local_planner/visual/filter_chain_wifi.yaml deleted file mode 100644 index f36d72c3..00000000 --- a/wild_visual_navigation_ros/config/field_local_planner/visual/filter_chain_wifi.yaml +++ /dev/null @@ -1,6 +0,0 @@ -grid_map_filters: - - - name: delete - type: gridMapFiltersDrs/DeleteAllButFilter - params: - layers: [elevation, elevation_inpainted, geometric_traversability, visual_traversability_inpainted, geodesic, sdf] # List of layers. \ No newline at end of file diff --git a/wild_visual_navigation_ros/config/rviz/open_source.rviz b/wild_visual_navigation_ros/config/rviz/open_source.rviz new file mode 100644 index 00000000..baafb702 --- /dev/null +++ b/wild_visual_navigation_ros/config/rviz/open_source.rviz @@ -0,0 +1,1030 @@ +Panels: + - Class: rviz/Displays + Help Height: 0 + Name: Displays + Property Tree Widget: + Expanded: + - /Global Options1 + - /TF1/Tree1 + - /Robot Info1 + - /Wild Visual Navigation1 + - /Wild Visual Navigation1/Self Supervision1 + - /Wild Visual Navigation1/Cameras Resized1 + - /Wild Visual Navigation1/Depth Sensors1 + - /Wild Visual Navigation1/Prediction1 + Splitter Ratio: 0.6411483287811279 + Tree Height: 863 + - Class: rviz/Selection + Name: Selection + - Class: rviz/Tool Properties + Expanded: + - /2D Pose Estimate1 + - /2D Nav Goal1 + - /Publish Point1 + Name: Tool Properties + Splitter Ratio: 0.5886790156364441 + - Class: rviz/Views + Expanded: + - /Current View1 + Name: Views + Splitter Ratio: 0.5 + - Class: rviz/Time + Name: Time + SyncMode: 0 + SyncSource: AB Wide Angle Front +Preferences: + PromptSaveOnExit: true +Toolbars: + toolButtonStyle: 2 +Visualization Manager: + Class: "" + Displays: + - Alpha: 0.5 + Cell Size: 1 + Class: rviz/Grid + Color: 160; 160; 164 + Enabled: false + Line Style: + Line Width: 0.029999999329447746 + Value: Lines + Name: Grid + Normal Cell Count: 0 + Offset: + X: 0 + Y: 0 + Z: 0 + Plane: XY + Plane Cell Count: 20 + Reference Frame: + Value: false + - Class: rviz/TF + Enabled: true + Frame Timeout: 1000 + Frames: + All Enabled: false + LF_FOOT: + Value: true + LF_HAA_drive: + Value: true + LF_HFE_drive: + Value: true + LF_HFE_output: + Value: true + LF_HIP: + Value: true + LF_KFE_drive: + Value: true + LF_SHANK: + Value: true + LF_THIGH: + Value: true + LF_hip_fixed: + Value: true + LF_shank_fixed: + Value: true + LF_thigh_fixed: + Value: true + LH_FOOT: + Value: true + LH_HAA_drive: + Value: true + LH_HFE_drive: + Value: true + LH_HFE_output: + Value: true + LH_HIP: + Value: true + LH_KFE_drive: + Value: true + LH_SHANK: + Value: true + LH_THIGH: + Value: true + LH_hip_fixed: + Value: true + LH_shank_fixed: + Value: true + LH_thigh_fixed: + Value: true + RF_FOOT: + Value: true + RF_HAA_drive: + Value: true + RF_HFE_drive: + Value: true + RF_HFE_output: + Value: true + RF_HIP: + Value: true + RF_KFE_drive: + Value: true + RF_SHANK: + Value: true + RF_THIGH: + Value: true + RF_hip_fixed: + Value: true + RF_shank_fixed: + Value: true + RF_thigh_fixed: + Value: true + RH_FOOT: + Value: true + RH_HAA_drive: + Value: true + RH_HFE_drive: + Value: true + RH_HFE_output: + Value: true + RH_HIP: + Value: true + RH_KFE_drive: + Value: true + RH_SHANK: + Value: true + RH_THIGH: + Value: true + RH_hip_fixed: + Value: true + RH_shank_fixed: + Value: true + RH_thigh_fixed: + Value: true + base: + Value: true + base_inertia: + Value: true + battery: + Value: true + body_top: + Value: true + bottom_shell: + Value: true + camera_init: + Value: true + camera_init_CORRECTED: + Value: true + depth_camera_front_lower_camera: + Value: true + depth_camera_front_lower_camera_parent: + Value: true + depth_camera_front_upper_camera: + Value: true + depth_camera_front_upper_camera_parent: + Value: true + depth_camera_left_camera: + Value: true + depth_camera_left_camera_parent: + Value: true + depth_camera_rear_lower_camera: + Value: true + depth_camera_rear_lower_camera_parent: + Value: true + depth_camera_rear_upper_camera: + Value: true + depth_camera_rear_upper_camera_parent: + Value: true + depth_camera_right_camera: + Value: true + depth_camera_right_camera_parent: + Value: true + docking_socket: + Value: true + face_front: + Value: true + face_rear: + Value: true + face_shell_front: + Value: true + face_shell_rear: + Value: true + feetcenter: + Value: true + footprint: + Value: true + hatch_shell: + Value: true + hbc_receiver: + Value: true + imu_link: + Value: true + lidar: + Value: true + lidar_parent: + Value: true + map: + Value: true + map_o3d_localization_manager: + Value: true + msf_body_imu_map: + Value: true + odom: + Value: true + top_shell: + Value: true + wide_angle_camera_front_camera: + Value: true + wide_angle_camera_front_camera_parent: + Value: true + wide_angle_camera_rear_camera: + Value: true + wide_angle_camera_rear_camera_parent: + Value: true + Marker Alpha: 1 + Marker Scale: 0.5 + Name: TF + Show Arrows: true + Show Axes: true + Show Names: true + Tree: + odom: + base: + LF_HAA_drive: + LF_HIP: + LF_hip_fixed: + LF_HFE_output: + LF_THIGH: + LF_HFE_drive: + LF_thigh_fixed: + LF_KFE_drive: + LF_SHANK: + LF_shank_fixed: + LF_FOOT: + {} + LH_HAA_drive: + LH_HIP: + LH_hip_fixed: + LH_HFE_output: + LH_THIGH: + LH_HFE_drive: + LH_thigh_fixed: + LH_KFE_drive: + LH_SHANK: + LH_shank_fixed: + LH_FOOT: + {} + RF_HAA_drive: + RF_HIP: + RF_hip_fixed: + RF_HFE_output: + RF_THIGH: + RF_HFE_drive: + RF_thigh_fixed: + RF_KFE_drive: + RF_SHANK: + RF_shank_fixed: + RF_FOOT: + {} + RH_HAA_drive: + RH_HIP: + RH_hip_fixed: + RH_HFE_output: + RH_THIGH: + RH_HFE_drive: + RH_thigh_fixed: + RH_KFE_drive: + RH_SHANK: + RH_shank_fixed: + RH_FOOT: + {} + base_inertia: + {} + battery: + {} + body_top: + {} + bottom_shell: + {} + depth_camera_left_camera: + depth_camera_left_camera_parent: + {} + depth_camera_right_camera: + depth_camera_right_camera_parent: + {} + docking_socket: + {} + face_front: + depth_camera_front_lower_camera: + depth_camera_front_lower_camera_parent: + {} + depth_camera_front_upper_camera: + depth_camera_front_upper_camera_parent: + {} + face_shell_front: + {} + wide_angle_camera_front_camera: + wide_angle_camera_front_camera_parent: + {} + face_rear: + depth_camera_rear_lower_camera: + depth_camera_rear_lower_camera_parent: + {} + depth_camera_rear_upper_camera: + depth_camera_rear_upper_camera_parent: + {} + face_shell_rear: + {} + wide_angle_camera_rear_camera: + wide_angle_camera_rear_camera_parent: + {} + hatch_shell: + {} + hbc_receiver: + {} + imu_link: + {} + lidar_parent: + lidar: + {} + top_shell: + {} + feetcenter: + {} + footprint: + {} + map_o3d_localization_manager: + {} + msf_body_imu_map: + {} + Update Interval: 0 + Value: true + - Class: rviz/Group + Displays: + - Angle Tolerance: 0.10000000149011612 + Class: rviz/Odometry + Covariance: + Orientation: + Alpha: 0.5 + Color: 255; 255; 127 + Color Style: Unique + Frame: Local + Offset: 1 + Scale: 1 + Value: true + Position: + Alpha: 0.30000001192092896 + Color: 204; 51; 204 + Scale: 1 + Value: true + Value: true + Enabled: false + Keep: 200 + Name: Odometry + Position Tolerance: 1 + Queue Size: 10 + Shape: + Alpha: 1 + Axes Length: 1 + Axes Radius: 0.10000000149011612 + Color: 255; 25; 0 + Head Length: 0.30000001192092896 + Head Radius: 0.10000000149011612 + Shaft Length: 1 + Shaft Radius: 0.05000000074505806 + Value: Arrow + Topic: /loam/odometry + Unreliable: false + Value: false + - Alpha: 1 + Class: rviz/RobotModel + Collision Enabled: false + Enabled: true + Links: + All Links Enabled: true + Expand Joint Details: false + Expand Link Details: false + Expand Tree: false + Link Tree Style: Links in Alphabetic Order + Name: RobotModel + Robot Description: anymal_description + TF Prefix: "" + Update Interval: 0 + Value: true + Visual Enabled: true + Enabled: true + Name: Robot Info + - Class: rviz/Group + Displays: + - Class: rviz/Group + Displays: + - Class: rviz/Marker + Enabled: true + Marker Topic: /wild_visual_navigation_node/graph_footprints + Name: Footprint + Namespaces: + footprints: true + Queue Size: 100 + Value: true + - Alpha: 1 + Buffer Length: 1 + Class: rviz/Path + Color: 67; 110; 176 + Enabled: true + Head Diameter: 0.30000001192092896 + Head Length: 0.20000000298023224 + Length: 0.30000001192092896 + Line Style: Billboards + Line Width: 0.029999999329447746 + Name: Supervision Graph + Offset: + X: 0 + Y: 0 + Z: 0 + Pose Color: 255; 85; 255 + Pose Style: None + Queue Size: 10 + Radius: 0.029999999329447746 + Shaft Diameter: 0.10000000149011612 + Shaft Length: 0.10000000149011612 + Topic: /wild_visual_navigation_node/supervision_graph + Unreliable: false + Value: true + - Alpha: 1 + Buffer Length: 1 + Class: rviz/Path + Color: 25; 255; 0 + Enabled: true + Head Diameter: 0.30000001192092896 + Head Length: 0.20000000298023224 + Length: 0.30000001192092896 + Line Style: Billboards + Line Width: 0.029999999329447746 + Name: Mission Graph + Offset: + X: 0 + Y: 0 + Z: 0 + Pose Color: 255; 85; 255 + Pose Style: Axes + Queue Size: 10 + Radius: 0.029999999329447746 + Shaft Diameter: 0.10000000149011612 + Shaft Length: 0.10000000149011612 + Topic: /wild_visual_navigation_node/mission_graph + Unreliable: false + Value: true + Enabled: true + Name: Self Supervision + - Class: rviz/Group + Displays: + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/front/debug/last_image_traversability + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Traversability + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/front/debug/last_image_confidence + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Uncertainty + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Alpha: 1 + Buffer Length: 1 + Class: rviz/Path + Color: 136; 138; 133 + Enabled: true + Head Diameter: 0.30000001192092896 + Head Length: 0.20000000298023224 + Length: 0.10000000149011612 + Line Style: Billboards + Line Width: 0.05000000074505806 + Name: Mission Graph + Offset: + X: 0 + Y: 0 + Z: 0 + Pose Color: 255; 85; 255 + Pose Style: Axes + Queue Size: 10 + Radius: 0.009999999776482582 + Shaft Diameter: 0.10000000149011612 + Shaft Length: 0.10000000149011612 + Topic: /wild_visual_navigation_node/mission_graph + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/front/debug/last_node_image_mask + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Reprojected Path + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_node/front/debug/last_node_image_labeled + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Supervision Signal + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + Enabled: false + Name: Self Supervision (Debug) + - Class: rviz/Group + Displays: + - Class: rviz/Image + Enabled: true + Image Topic: /wide_angle_camera_front/image_color_rect_resize + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: AB Wide Angle Front + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wide_angle_camera_rear/image_color_rect_resize + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: AB Wide Rear Front + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: false + Image Topic: /hdr_camera_resize/image_raw + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: HDR + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false + Enabled: true + Name: Cameras Resized + - Class: rviz/Group + Displays: + - Alpha: 1 + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 21.55472183227539 + Min Value: -7.581119537353516 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 239; 41; 41 + Color Transformer: FlatColor + Decay Time: 0 + Enabled: true + Invert Rainbow: false + Max Color: 255; 255; 255 + Min Color: 0; 0; 0 + Name: realsense-front + Position Transformer: XYZ + Queue Size: 10 + Selectable: true + Size (Pixels): 3 + Size (m): 0.009999999776482582 + Style: Squares + Topic: /depth_camera_front_upper/point_cloud_self_filtered + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: true + - Alpha: 1 + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 21.55472183227539 + Min Value: -7.581119537353516 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 239; 41; 41 + Color Transformer: FlatColor + Decay Time: 0 + Enabled: true + Invert Rainbow: false + Max Color: 255; 255; 255 + Min Color: 0; 0; 0 + Name: realsense-rear + Position Transformer: XYZ + Queue Size: 10 + Selectable: true + Size (Pixels): 3 + Size (m): 0.009999999776482582 + Style: Squares + Topic: /depth_camera_rear_upper/point_cloud_self_filtered + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: true + - Alpha: 1 + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 21.55472183227539 + Min Value: -7.581119537353516 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 239; 41; 41 + Color Transformer: FlatColor + Decay Time: 0 + Enabled: true + Invert Rainbow: false + Max Color: 255; 255; 255 + Min Color: 0; 0; 0 + Name: realsense-left + Position Transformer: XYZ + Queue Size: 10 + Selectable: true + Size (Pixels): 3 + Size (m): 0.009999999776482582 + Style: Squares + Topic: /depth_camera_left/point_cloud_self_filtered + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: true + - Alpha: 1 + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 21.55472183227539 + Min Value: -7.581119537353516 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 239; 41; 41 + Color Transformer: FlatColor + Decay Time: 0 + Enabled: true + Invert Rainbow: false + Max Color: 255; 255; 255 + Min Color: 0; 0; 0 + Name: realsense-right + Position Transformer: XYZ + Queue Size: 10 + Selectable: true + Size (Pixels): 3 + Size (m): 0.009999999776482582 + Style: Squares + Topic: /depth_camera_right/point_cloud_self_filtered + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: true + - Alpha: 1 + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 4.33540153503418 + Min Value: -0.6894725561141968 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 239; 41; 41 + Color Transformer: AxisColor + Decay Time: 0 + Enabled: false + Invert Rainbow: false + Max Color: 255; 255; 255 + Min Color: 0; 0; 0 + Name: velodyne + Position Transformer: XYZ + Queue Size: 10 + Selectable: true + Size (Pixels): 3 + Size (m): 0.019999999552965164 + Style: Squares + Topic: /point_cloud_filter/lidar/point_cloud_filtered + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: false + Enabled: false + Name: Depth Sensors + - Class: rviz/Group + Displays: + - Class: rviz/Image + Enabled: false + Image Topic: /wild_visual_navigation_node/front/image_input + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Input Image + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false + - Class: rviz/Image + Enabled: false + Image Topic: /wild_visual_navigation_node/front/traversability + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Traversability Raw + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false + - Class: rviz/Image + Enabled: false + Image Topic: /wild_visual_navigation_node/front/debug/last_node_image_overlay + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: Learning Mask + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: false + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_visu_traversability_front/traversability_overlayed + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: F Traversability Overlay + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_visu_traversability_rear/traversability_overlayed + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: R Traversability Overlay + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_visu_confidence_front/confidence_overlayed + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: F Confidence Overlay + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + - Class: rviz/Image + Enabled: true + Image Topic: /wild_visual_navigation_visu_confidence_rear/confidence_overlayed + Max Value: 1 + Median window: 5 + Min Value: 0 + Name: R Confidence Overlay + Normalize Range: true + Queue Size: 2 + Transport Hint: raw + Unreliable: false + Value: true + Enabled: true + Name: Prediction + - Class: rviz/Group + Displays: + - Class: rviz/Marker + Enabled: false + Marker Topic: /field_local_planner/real_carrot + Name: Goal (carrot) + Namespaces: + {} + Queue Size: 100 + Value: false + - Alpha: 1 + Axes Length: 1 + Axes Radius: 0.10000000149011612 + Class: rviz/PoseWithCovariance + Color: 255; 25; 0 + Covariance: + Orientation: + Alpha: 0.5 + Color: 255; 255; 127 + Color Style: Unique + Frame: Local + Offset: 1 + Scale: 1 + Value: true + Position: + Alpha: 0.30000001192092896 + Color: 204; 51; 204 + Scale: 1 + Value: true + Value: true + Enabled: false + Head Length: 0.30000001192092896 + Head Radius: 0.10000000149011612 + Name: Goal (pose) + Queue Size: 10 + Shaft Length: 1 + Shaft Radius: 0.05000000074505806 + Shape: Arrow + Topic: /field_local_planner/current_goal + Unreliable: false + Value: false + Enabled: true + Name: Local planner + Enabled: true + Name: Wild Visual Navigation + - Class: rviz/Group + Displays: + - Alpha: 1 + Autocompute Intensity Bounds: true + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: elevation + Color Transformer: GridMapLayer + ColorMap: terrain + Enabled: false + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation + Height Transformer: GridMapLayer + History Length: 1 + Invert ColorMap: false + Max Color: 255; 255; 255 + Max Intensity: 10 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: Elevation + Show Grid Lines: true + Topic: /elevation_mapping/semantic_map + Unreliable: false + Use ColorMap: true + Value: false + - Alpha: 1 + Autocompute Intensity Bounds: false + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: visual_traversability + Color Transformer: "" + ColorMap: coolwarm + Enabled: false + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation_with_semantics + Height Transformer: GridMapLayer + History Length: 1 + Invert ColorMap: true + Max Color: 255; 255; 255 + Max Intensity: 1 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: Visual Traversability + Show Grid Lines: false + Topic: /elevation_mapping/semantic_map + Unreliable: false + Use ColorMap: true + Value: false + - Alpha: 1 + Autocompute Intensity Bounds: false + Class: grid_map_rviz_plugin/GridMap + Color: 200; 200; 200 + Color Layer: traversability + Color Transformer: "" + ColorMap: coolwarm + Enabled: false + Grid Cell Decimation: 1 + Grid Line Thickness: 0.10000000149011612 + Height Layer: elevation + Height Transformer: GridMapLayer + History Length: 1 + Invert ColorMap: true + Max Color: 255; 255; 255 + Max Intensity: 1 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: Geom Traversability + Show Grid Lines: true + Topic: /elevation_mapping/semantic_map + Unreliable: false + Use ColorMap: true + Value: false + Enabled: true + Name: GridMaps + Enabled: true + Global Options: + Background Color: 255; 255; 255 + Default Light: true + Fixed Frame: odom + Frame Rate: 30 + Name: root + Tools: + - Class: rviz/Interact + Hide Inactive Objects: true + - Class: rviz/MoveCamera + - Class: rviz/Select + - Class: rviz/FocusCamera + - Class: rviz/Measure + - Class: rviz/SetInitialPose + Theta std deviation: 0.2617993950843811 + Topic: /initialpose + X std deviation: 0.5 + Y std deviation: 0.5 + - Class: rviz/SetGoal + Topic: /move_base_simple/goal + - Class: rviz/PublishPoint + Single click: true + Topic: /clicked_point + Value: true + Views: + Current: + Class: rviz/ThirdPersonFollower + Distance: 13.404160499572754 + Enable Stereo Rendering: + Stereo Eye Separation: 0.05999999865889549 + Stereo Focal Distance: 1 + Swap Stereo Eyes: false + Value: false + Field of View: 0.7853981852531433 + Focal Point: + X: 2.1661949157714844 + Y: -0.48903560638427734 + Z: 0 + Focal Shape Fixed Size: true + Focal Shape Size: 0.05000000074505806 + Invert Z Axis: false + Name: Current View + Near Clip Distance: 0.009999999776482582 + Pitch: 0.4953981041908264 + Target Frame: base + Yaw: 0.4853982925415039 + Saved: ~ +Window Geometry: + AB Wide Angle Front: + collapsed: false + AB Wide Rear Front: + collapsed: false + Displays: + collapsed: false + F Confidence Overlay: + collapsed: false + F Traversability Overlay: + collapsed: false + HDR: + collapsed: false + Height: 1376 + Hide Left Dock: false + Hide Right Dock: false + Input Image: + collapsed: false + Learning Mask: + collapsed: false + QMainWindow State: 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+ R Confidence Overlay: + collapsed: false + R Traversability Overlay: + collapsed: false + Reprojected Path: + collapsed: false + Selection: + collapsed: false + Supervision Signal: + collapsed: false + Time: + collapsed: false + Tool Properties: + collapsed: false + Traversability: + collapsed: false + Traversability Raw: + collapsed: false + Uncertainty: + collapsed: false + Views: + collapsed: false + Width: 2488 + X: 72 + Y: 27 diff --git a/wild_visual_navigation_ros/config/wild_visual_navigation/default.yaml b/wild_visual_navigation_ros/config/wild_visual_navigation/default.yaml index 20032e13..29406bc3 100644 --- a/wild_visual_navigation_ros/config/wild_visual_navigation/default.yaml +++ b/wild_visual_navigation_ros/config/wild_visual_navigation/default.yaml @@ -1,79 +1,57 @@ # Input topics robot_state_topic: "/wild_visual_navigation_node/robot_state" -# desired_twist_topic: "/motion_reference/command_twist" -desired_twist_topic: "/log/state/desiredRobotTwist" - -camera_topics: - front: - image_topic: "/alphasense_driver_ros/cam4/debayered" - info_topic: "/alphasense_driver_ros/cam4/camera_info" - use_for_training: true - # left: - # image_topic: "/alphasense_driver_ros/cam3/debayered" - # info_topic: "/alphasense_driver_ros/cam3/camera_info" - # use_for_training: false - # right: - # image_topic: "/alphasense_driver_ros/cam5/debayered" - # info_topic: "/alphasense_driver_ros/cam5/camera_info" - # use_for_training: false - +desired_twist_topic: "/motion_reference/command_twist" # Relevant frames fixed_frame: odom base_frame: base footprint_frame: footprint -# Robot size -robot_length: 1.0 -robot_width: 0.6 +# Robot size used to project the footprint +robot_length: 0.8 +robot_width: 0.3 robot_height: 0.3 -# Robot specs -robot_max_velocity: 0.8 - -# Traversability estimation params -traversability_radius: 3.0 # meters -image_graph_dist_thr: 0.2 # meters -proprio_graph_dist_thr: 0.1 # meters -network_input_image_height: 224 # 448 -network_input_image_width: 224 # 448 -segmentation_type: "slic" -feature_type: "dino" -dino_patch_size: 16 # DINO only -confidence_std_factor: 1.0 -scale_traversability: True -scale_traversability_max_fpr: 0.15 -min_samples_for_training: 5 - -# Optical flow params -optical_flow_estimator_type: "none" +# Traversability estimation parameters +traversability_radius: 3.0 # Maximum distance in meters of the supervision graph +image_graph_dist_thr: 0.2 # Minimum distance between mission nodes being added +supervision_graph_dist_thr: 0.1 # Minimum distance between supervision nodes being added +network_input_image_height: 224 # Unput image height +network_input_image_width: 224 # Input image width +segmentation_type: "stego" # Options: slic, grid, random, stego +feature_type: "stego" # Options: dino, dinov2, stego +dino_patch_size: 8 # Options: 8, 16; We found 8 is sufficient and faster +dino_backbone: vit_small # Options: vit_small +slic_num_components: 100 # Number of segments for slic +confidence_std_factor: 1.0 # Tuning parameter to change the confidence computation / explained in confidence_generator.py +min_samples_for_training: 5 # Minimum number of mission nodes with successfull reprojection before start training +prediction_per_pixel: True # If true, trained network is inferenced for each pixel, otherwise per segment +vis_node_index: 10 # Defines node which is used for visualization, can help to debug the projected footprint + # 1 -> indicates latest added mission node; 10 -> indicates the node added 10 steps in the past # Supervision Generator -untraversable_thr: 0.01 - -mission_name: "test" -mission_timestamp: True +untraversable_thr: 0.0 # Threshold for untraversable area -> this threshold is used to spawn special "collision walls" -> not used in the paper +mission_name: "test" # Name of the mission_folder used for logging +mission_timestamp: True # If true current timestamp is used as a prefix for the mission_folder # Threads -image_callback_rate: 3 # hertz -proprio_callback_rate: 4 # hertz -learning_thread_rate: 10 # hertz -logging_thread_rate: 2 # hertz +image_callback_rate: 10 # Maximum rate at which images are processed in hertz +supervision_callback_rate: 10 # Maximum rate at which supervision_signals are processed in hertz +learning_thread_rate: 10 # Gradient steps per second in hertz +logging_thread_rate: 2 # Logging of learning_node in hertz +status_thread_rate: 0.5 # Status of feature_extractor_node in hertz +load_save_checkpoint_rate: 1.0 # Rate at which checkpoints are saved / loaded between learning and feature extraction + # Runtime options device: "cuda" -mode: "online" # check out comments in the class WVNMode +mode: "online" # Options: "online", "debug", "extract_labels"; Sets operation mode of both nodes colormap: "RdYlBu" print_image_callback_time: false -print_proprio_callback_time: false -log_time: true -log_memory: true -log_confidence: true -verbose: false -debug_supervision_node_index_from_last: 10 - -use_debug_for_desired: true -extraction_store_folder: "nan" -exp: "nan" +print_supervision_callback_time: false +log_time: false +log_confidence: false +verbose: true +extraction_store_folder: "nan" \ No newline at end of file diff --git a/wild_visual_navigation_ros/config/wild_visual_navigation/inputs/alphasense.yaml b/wild_visual_navigation_ros/config/wild_visual_navigation/inputs/alphasense.yaml deleted file mode 100644 index 6eb15dde..00000000 --- a/wild_visual_navigation_ros/config/wild_visual_navigation/inputs/alphasense.yaml +++ /dev/null @@ -1,17 +0,0 @@ -camera_topics: - front: - image_topic: "/alphasense_driver_ros/cam4/debayered" - info_topic: "/alphasense_driver_ros/cam4/camera_info" - use_for_training: true - # left: - # image_topic: "/alphasense_driver_ros/cam3/debayered" - # info_topic: "/alphasense_driver_ros/cam3/camera_info" - # use_for_training: false - # right: - # image_topic: "/alphasense_driver_ros/cam5/debayered" - # info_topic: "/alphasense_driver_ros/cam5/camera_info" - # use_for_training: false - -# Provides 1080 (height) x 1920 (width) images -network_input_image_height: 224 # 448 -network_input_image_width: 224 # 448 \ No newline at end of file diff --git a/wild_visual_navigation_ros/config/wild_visual_navigation/inputs/alphasense_compressed.yaml b/wild_visual_navigation_ros/config/wild_visual_navigation/inputs/alphasense_compressed.yaml deleted file mode 100644 index b8af1e27..00000000 --- a/wild_visual_navigation_ros/config/wild_visual_navigation/inputs/alphasense_compressed.yaml +++ /dev/null @@ -1,17 +0,0 @@ -camera_topics: - front: - image_topic: "/alphasense_driver_ros/cam4/debayered/compressed" - info_topic: "/alphasense_driver_ros/cam4/camera_info" - use_for_training: true - # left: - # image_topic: "/alphasense_driver_ros/cam3/debayered" - # info_topic: "/alphasense_driver_ros/cam3/camera_info" - # use_for_training: false - # right: - # image_topic: "/alphasense_driver_ros/cam5/debayered" - # info_topic: "/alphasense_driver_ros/cam5/camera_info" - # use_for_training: false - -# Provides 1080 (height) x 1920 (width) images -network_input_image_height: 224 # 448 -network_input_image_width: 224 # 448 \ No newline at end of file diff --git a/wild_visual_navigation_ros/config/wild_visual_navigation/inputs/wide_angle_front.yaml b/wild_visual_navigation_ros/config/wild_visual_navigation/inputs/wide_angle_front.yaml deleted file mode 100644 index 970bcf6d..00000000 --- a/wild_visual_navigation_ros/config/wild_visual_navigation/inputs/wide_angle_front.yaml +++ /dev/null @@ -1,9 +0,0 @@ -camera_topics: - front: - image_topic: "/wide_angle_camera_front/image_color" - info_topic: "/wide_angle_camera_front/camera_info" - use_for_training: true - -# Provides 1080 (height) x 1920 (width) images -network_input_image_height: 224 # 448 -network_input_image_width: 224 # 448 \ No newline at end of file diff --git a/wild_visual_navigation_ros/config/wild_visual_navigation/resized_all.yaml b/wild_visual_navigation_ros/config/wild_visual_navigation/resized_all.yaml new file mode 100644 index 00000000..ffd199db --- /dev/null +++ b/wild_visual_navigation_ros/config/wild_visual_navigation/resized_all.yaml @@ -0,0 +1,26 @@ +camera_topics: + front: + image_topic: "/wide_angle_camera_front/image_color_rect_resize" + info_topic: "/wide_angle_camera_front_resize/camera_info" + use_for_training: false + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 + rear: + image_topic: "/wide_angle_camera_rear/image_color_rect_resize" + info_topic: "/wide_angle_camera_rear_resize/camera_info" + use_for_training: true + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 + + hdr: + image_topic: "/hdr_camera_resize/image_raw" + info_topic: "/hdr_camera_resize/camera_info" + use_for_training: false + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 +# Provides 1080 (height) x 1920 (width) images +network_input_image_height: 224 # 448 +network_input_image_width: 224 # 448 \ No newline at end of file diff --git a/wild_visual_navigation_ros/config/wild_visual_navigation/resized_wide_angle_dual.yaml b/wild_visual_navigation_ros/config/wild_visual_navigation/resized_wide_angle_dual.yaml new file mode 100644 index 00000000..ace08bcc --- /dev/null +++ b/wild_visual_navigation_ros/config/wild_visual_navigation/resized_wide_angle_dual.yaml @@ -0,0 +1,19 @@ +camera_topics: + front: + image_topic: "/wide_angle_camera_front/image_color_rect_resize" + info_topic: "/wide_angle_camera_front_resize/camera_info" + use_for_training: false + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 + rear: + image_topic: "/wide_angle_camera_rear/image_color_rect_resize" + info_topic: "/wide_angle_camera_rear_resize/camera_info" + use_for_training: true + publish_confidence: true + publish_input_image: true + scheduler_weight: 1 + +# Provides 1080 (height) x 1920 (width) images +network_input_image_height: 224 # 448 +network_input_image_width: 224 # 448 \ No newline at end of file diff --git a/wild_visual_navigation_ros/launch/elevation_mapping_cupy.launch b/wild_visual_navigation_ros/launch/elevation_mapping_cupy.launch index f741481c..db6a5619 100644 --- a/wild_visual_navigation_ros/launch/elevation_mapping_cupy.launch +++ b/wild_visual_navigation_ros/launch/elevation_mapping_cupy.launch @@ -2,8 +2,8 @@ - - + + \ No newline at end of file diff --git a/wild_visual_navigation_ros/launch/overlay_images.launch b/wild_visual_navigation_ros/launch/overlay_images.launch index 6d8c0288..39db6505 100644 --- a/wild_visual_navigation_ros/launch/overlay_images.launch +++ b/wild_visual_navigation_ros/launch/overlay_images.launch @@ -1,12 +1,23 @@ - + - + + + + + + + + + + + + \ No newline at end of file diff --git a/wild_visual_navigation_ros/launch/record_rosbag_debug.launch b/wild_visual_navigation_ros/launch/record_rosbag_debug.launch deleted file mode 100644 index 4f3b9549..00000000 --- a/wild_visual_navigation_ros/launch/record_rosbag_debug.launch +++ /dev/null @@ -1,95 +0,0 @@ - - - - - - - - - - diff --git a/wild_visual_navigation_ros/launch/robot.launch b/wild_visual_navigation_ros/launch/robot.launch deleted file mode 100644 index 9eb92dff..00000000 --- a/wild_visual_navigation_ros/launch/robot.launch +++ /dev/null @@ -1,16 +0,0 @@ - - - - - - - - - - - - - - - - diff --git a/wild_visual_navigation_ros/launch/smart_carrot.launch b/wild_visual_navigation_ros/launch/smart_carrot.launch new file mode 100644 index 00000000..75655e21 --- /dev/null +++ b/wild_visual_navigation_ros/launch/smart_carrot.launch @@ -0,0 +1,16 @@ + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/wild_visual_navigation_ros/launch/view.launch b/wild_visual_navigation_ros/launch/view.launch index 6fd0912e..ade9d973 100644 --- a/wild_visual_navigation_ros/launch/view.launch +++ b/wild_visual_navigation_ros/launch/view.launch @@ -1,5 +1,5 @@ - + diff --git a/wild_visual_navigation_ros/launch/wild_visual_navigation.launch b/wild_visual_navigation_ros/launch/wild_visual_navigation.launch index cd75f09c..0e7f8042 100644 --- a/wild_visual_navigation_ros/launch/wild_visual_navigation.launch +++ b/wild_visual_navigation_ros/launch/wild_visual_navigation.launch @@ -1,22 +1,28 @@ - - + - + + + + - - - + + + + - - - + + + + + + - + diff --git a/wild_visual_navigation_ros/scripts/histogram.py b/wild_visual_navigation_ros/scripts/histogram.py deleted file mode 100644 index 2f7b09b3..00000000 --- a/wild_visual_navigation_ros/scripts/histogram.py +++ /dev/null @@ -1,29 +0,0 @@ -import rospy -import cv2 -import matplotlib.pyplot as plt -from cv_bridge import CvBridge -from sensor_msgs.msg import Image -import numpy as np - - -class HistogramNode: - def __init__(self): - self.bridge = CvBridge() - self.image_sub = rospy.Subscriber("/wild_visual_navigation_node/front/traversability", Image, self.callback) - - def callback(self, msg): - # Convert the ROS Image message to a numpy array - img = self.bridge.imgmsg_to_cv2(msg, desired_encoding="passthrough") - - # Compute the histogram of the image - hist = np.histogram(img.flatten(), bins=100) - - # Plot the histogram using matplotlib - plt.hist(img, bins=100, range=[0, 1]) - plt.savefig("histogram.png") - - -if __name__ == "__main__": - rospy.init_node("histogram_node") - node = HistogramNode() - rospy.spin() diff --git a/wild_visual_navigation_ros/scripts/overlay_images.py b/wild_visual_navigation_ros/scripts/overlay_images.py index 943cfc36..b5c32371 100644 --- a/wild_visual_navigation_ros/scripts/overlay_images.py +++ b/wild_visual_navigation_ros/scripts/overlay_images.py @@ -1,6 +1,11 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Matias Mattamala, Jonas Frey. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# import message_filters import rospy -from sensor_msgs.msg import Image, CameraInfo, CompressedImage +from sensor_msgs.msg import Image # , CameraInfo # , CompressedImage import wild_visual_navigation_ros.ros_converter as rc from wild_visual_navigation.visu import LearningVisualizer import sys @@ -12,8 +17,9 @@ def __init__(self): self.value_sub_topic = rospy.get_param("~value_sub_topic") self.image_pub_topic = rospy.get_param("~image_pub_topic") - self.input_pub = rospy.Publisher(f"~{self.image_pub_topic}", Image, queue_size=10) + self._pub = rospy.Publisher(f"~{self.image_pub_topic}", Image, queue_size=1) self._visualizer = LearningVisualizer() + image_sub = message_filters.Subscriber(self.image_sub_topic, Image) trav_sub = message_filters.Subscriber(self.value_sub_topic, Image) sync = message_filters.ApproximateTimeSynchronizer([image_sub, trav_sub], queue_size=2, slop=0.5) @@ -23,15 +29,22 @@ def callback(self, image_msg, trav_msgs): torch_image = rc.ros_image_to_torch(image_msg, device="cpu") torch_trav = rc.ros_image_to_torch(trav_msgs, device="cpu", desired_encoding="passthrough") img_out = self._visualizer.plot_detectron_classification(torch_image, torch_trav.clip(0, 1)) - self.input_pub.publish(rc.numpy_to_ros_image(img_out)) + ros_msg = rc.numpy_to_ros_image(img_out) + ros_msg.header.stamp = image_msg.header.stamp + self._pub.publish(ros_msg) if __name__ == "__main__": print("Stated ImageOverlayNode") try: - nr = "_" + rospy.myargv(argv=sys.argv)[-1].split(" ")[-1] - rospy.init_node(f"wild_visual_navigation_visu{nr}") - except: + args = rospy.myargv(argv=sys.argv) + if "--nr" in args: + nr_index = args.index("--nr") + nr = args[nr_index + 1] + else: + nr = "0" # Handle case when no arg is set + rospy.init_node(f"wild_visual_navigation_visu_{nr}") + except Exception: rospy.init_node("wild_visual_navigation_visu") wvn = ImageOverlayNode() diff --git a/wild_visual_navigation_ros/scripts/path_length.py b/wild_visual_navigation_ros/scripts/path_length.py deleted file mode 100644 index 85065225..00000000 --- a/wild_visual_navigation_ros/scripts/path_length.py +++ /dev/null @@ -1,24 +0,0 @@ -import rospy -from nav_msgs.msg import Path - -import numpy as np - - -def callback(data, args): - (topic_name,) = args - path_length = 0 - for i in range(len(data.poses) - 1): - p1 = np.array([data.poses[i].pose.position.x, data.poses[i].pose.position.y, data.poses[i].pose.position.z]) - p2 = np.array( - [data.poses[i + 1].pose.position.x, data.poses[i + 1].pose.position.y, data.poses[i + 1].pose.position.z] - ) - path_length += np.linalg.norm(p2 - p1) - print("Received path on topic {} with length {} meters".format(topic_name, path_length)) - - -if __name__ == "__main__": - rospy.init_node("path_subscriber") - topic_names = ["/gps_trajectory1", "/gps_trajectory2", "/gps_trajectory3", "/gps_trajectory4", "/gps_trajectory5"] - for topic_name in topic_names: - rospy.Subscriber(topic_name, Path, callback, callback_args=(topic_name,)) - rospy.spin() diff --git a/wild_visual_navigation_ros/scripts/pickle_traversability_estimator.py b/wild_visual_navigation_ros/scripts/pickle_traversability_estimator.py deleted file mode 100755 index 6fa74bc5..00000000 --- a/wild_visual_navigation_ros/scripts/pickle_traversability_estimator.py +++ /dev/null @@ -1,24 +0,0 @@ -#!/usr/bin/python3 -from wild_visual_navigation.traversability_estimator import TraversabilityEstimator -from os.path import exists -import argparse -import torch - -torch.cuda.empty_cache() - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Interface to debug traversability estimator training via pickle") - parser.add_argument("--pickle_file", type=str, help="Pickle file path") - parser.add_argument("--device", type=str, help="device where to load the traversability estimator", default="cuda") - args = parser.parse_args() - - # Check if file exists - if not exists(args.pickle_file): - raise ValueError(f"Argument pickle file [{args.pickle_file}] doesn't exist") - - te = TraversabilityEstimator.load(args.pickle_file, device=args.device) - - # Make training thread - while True: - print("New iteration") - te.train() diff --git a/wild_visual_navigation_ros/scripts/ros_visualizer.py b/wild_visual_navigation_ros/scripts/ros_visualizer.py deleted file mode 100644 index 5b1d3e50..00000000 --- a/wild_visual_navigation_ros/scripts/ros_visualizer.py +++ /dev/null @@ -1,140 +0,0 @@ -import rospy -import tf2_ros -from sensor_msgs.msg import PointCloud2, CameraInfo, Image, Image -from grid_map_msgs.msg import GridMap, GridMapInfo -from std_msgs.msg import Float32MultiArray, MultiArrayDimension -from geometry_msgs.msg import TransformStamped -import numpy as np -import ros_numpy - - -class SimpleNumpyToRviz: - def __init__(self, init_node=True, postfix=""): - rospy.init_node("numpy_to_rviz", anonymous=False) - self.pub_pointcloud = rospy.Publisher(f"~pointcloud{postfix}", PointCloud2, queue_size=1) - self.pub_gridmap = rospy.Publisher(f"~gridmap{postfix}", GridMap, queue_size=1) - self.pub_image = rospy.Publisher(f"~image{postfix}", Image, queue_size=1) - self.pub_camera_info = rospy.Publisher(f"~camera_info{postfix}", CameraInfo, queue_size=1) - self.br = tf2_ros.TransformBroadcaster() - - def camera_info(self, camera_info): - pass - - def tf(self, msg, reference_frame="crl_rzr/map"): - t = TransformStamped() - t.header.stamp = rospy.Time.now() - t.header.frame_id = reference_frame - t.child_frame_id = msg[3] - t.transform.translation.x = msg[4][0] - t.transform.translation.y = msg[4][1] - t.transform.translation.z = msg[4][2] - t.transform.rotation.x = msg[5][0] - t.transform.rotation.y = msg[5][1] - t.transform.rotation.z = msg[5][2] - t.transform.rotation.w = msg[5][3] - self.br.sendTransform(t) - - def image(self, img): - pass - - def pointcloud(self, points, reference_frame="sensor_gravity"): - data = np.zeros(points.shape[0], dtype=[("x", np.float32), ("y", np.float32), ("z", np.float32)]) - data["x"] = points[:, 0] - data["y"] = points[:, 1] - data["z"] = points[:, 2] - msg = ros_numpy.msgify(PointCloud2, data) - - msg.header.frame_id = reference_frame - - self.pub_pointcloud.publish(msg) - - def gridmap(self, msg, publish=True): - data_in = msg[0] - - size_x = data_in["data"].shape[1] - size_y = data_in["data"].shape[2] - - data_dim_0 = MultiArrayDimension() - data_dim_0.label = "column_index" # y dimension - data_dim_0.size = size_y # number of columns which is y - data_dim_0.stride = size_y * size_x # rows*cols - data_dim_1 = MultiArrayDimension() - data_dim_1.label = "row_index" # x dimension - data_dim_1.size = size_x # number of rows which is x - data_dim_1.stride = size_x # number of rows - layers = [] - data = [] - - for i in range(data_in["data"].shape[0]): - data_tmp = Float32MultiArray() - data_tmp.layout.dim.append(data_dim_0) - data_tmp.layout.dim.append(data_dim_1) - data_tmp.data = data_in["data"][i, ::-1, ::-1].transpose().ravel() - data.append(data_tmp) - - info = GridMapInfo() - info.pose.position.x = data_in["position"][0] - info.pose.position.y = data_in["position"][1] - info.pose.position.z = data_in["position"][2] - info.pose.orientation.x = data_in["orientation_xyzw"][0] - info.pose.orientation.y = data_in["orientation_xyzw"][1] - info.pose.orientation.z = data_in["orientation_xyzw"][2] - info.pose.orientation.z = data_in["orientation_xyzw"][3] - info.header.seq = msg[1] - # info.header.stamp.secs = msg[2]vis - info.header.stamp = rospy.Time.now() - info.resolution = data_in["resolution"] - info.length_x = size_x * data_in["resolution"] - info.length_y = size_y * data_in["resolution"] - - gm_msg = GridMap(info=info, layers=data_in["layers"], basic_layers=data_in["basic_layers"], data=data) - - if publish: - self.pub_gridmap.publish(gm_msg) - - return gm_msg - - def gridmap_arr(self, arr, res, layers, reference_frame="sensor_gravity", publish=True, x=0, y=0): - size_x = arr.shape[1] - size_y = arr.shape[2] - - data_dim_0 = MultiArrayDimension() - data_dim_0.label = "column_index" # y dimension - data_dim_0.size = size_y # number of columns which is y - data_dim_0.stride = size_y * size_x # rows*cols - data_dim_1 = MultiArrayDimension() - data_dim_1.label = "row_index" # x dimension - data_dim_1.size = size_x # number of rows which is x - data_dim_1.stride = size_x # number of rows - data = [] - - for i in range(arr.shape[0]): - data_tmp = Float32MultiArray() - data_tmp.layout.dim.append(data_dim_0) - data_tmp.layout.dim.append(data_dim_1) - data_tmp.data = arr[i, ::-1, ::-1].transpose().ravel() - data.append(data_tmp) - - info = GridMapInfo() - info.pose.orientation.w = 1 - info.header.seq = 0 - info.header.stamp = rospy.Time.now() - info.resolution = res - info.length_x = size_x * res - info.length_y = size_y * res - info.header.frame_id = reference_frame - info.pose.position.x = x - info.pose.position.y = y - gm_msg = GridMap(info=info, layers=layers, basic_layers=[], data=data) - if publish: - self.pub_gridmap.publish(gm_msg) - return gm_msg - - -if __name__ == "__main__": - import time - import pickle as pkl - from os.path import join - import torch - - vis = SimpleNumpyToRviz() diff --git a/wild_visual_navigation_ros/scripts/rosbag_play.sh b/wild_visual_navigation_ros/scripts/rosbag_play.sh index 902b2470..a6a39dad 100755 --- a/wild_visual_navigation_ros/scripts/rosbag_play.sh +++ b/wild_visual_navigation_ros/scripts/rosbag_play.sh @@ -4,37 +4,39 @@ args="" for option in "$@"; do if [ "$option" == "--sem" ]; then args="$args /elevation_mapping/elevation_map_raw:=/recorded/elevation_mapping/elevation_map_raw \ - /elevation_mapping/semantic_map_raw:=/recorded/elevation_mapping/semantic_map_raw" + /elevation_mapping/semantic_map:=/recorded/elevation_mapping/semantic_map" elif [ "$option" == "--wvn" ]; then - args="$args /wild_visual_navigation_node/front/camera_info:=/recorded_wvn/wild_visual_navigation_node/front/camera_info \ - /wild_visual_navigation_node/front/confidence:=/recorded_wvn/wild_visual_navigation_node/front/confidence \ - /wild_visual_navigation_node/front/image_input:=/recorded_wvn/wild_visual_navigation_node/front/image_input \ - /wild_visual_navigation_node/front/traversability:=/recorded_wvn/wild_visual_navigation_node/front/traversability \ - /wild_visual_navigation_node/graph_footprints:=/recorded_wvn/wild_visual_navigation_node/graph_footprints \ - /wild_visual_navigation_node/instant_traversability:=/recorded_wvn/wild_visual_navigation_node/instant_traversability \ - /wild_visual_navigation_node/proprioceptive_graph:=/recorded_wvn/wild_visual_navigation_node/proprioceptive_graph \ - /wild_visual_navigation_node/robot_state:=/recorded_wvn/wild_visual_navigation_node/robot_state \ - /wild_visual_navigation_node/system_state:=/recorded_wvn/wild_visual_navigation_node/system_state \ - /wild_visual_navigation_visu_confidence/confidence_overlayed:=/recorded_wvn/wild_visual_navigation_visu_confidence/confidence_overlayed \ - /wild_visual_navigation_visu_traversability/traversability_overlayed:=/recorded_wvn/wild_visual_navigation_visu_traversability/traversability_overlayed" + args="$args /wild_visual_navigation_node/front/camera_info:=/recorded/wild_visual_navigation_node/front/camera_info \ + /wild_visual_navigation_node/front/confidence:=/recorded/wild_visual_navigation_node/front/confidence \ + /wild_visual_navigation_node/front/feat:=/recorded/wild_visual_navigation_node/front/feat \ + /wild_visual_navigation_node/front/image_input:=/recorded/wild_visual_navigation_node/front/image_input \ + /wild_visual_navigation_node/front/traversability:=/recorded/wild_visual_navigation_node/front/traversability \ + /wild_visual_navigation_node/graph_footprints:=/recorded/wild_visual_navigation_node/graph_footprints \ + /wild_visual_navigation_node/graph_footprints_array:=/recorded/wild_visual_navigation_node/graph_footprints_array \ + /wild_visual_navigation_node/mission_graph:=/recorded/wild_visual_navigation_node/mission_graph \ + /wild_visual_navigation_node/rear/camera_info:=/recorded/wild_visual_navigation_node/rear/camera_info \ + /wild_visual_navigation_node/rear/confidence:=/recorded/wild_visual_navigation_node/rear/confidence \ + /wild_visual_navigation_node/rear/image_input:=/recorded/wild_visual_navigation_node/rear/image_input \ + /wild_visual_navigation_node/rear/traversability:=/recorded/wild_visual_navigation_node/rear/traversability \ + /wild_visual_navigation_node/supervision_graph:=/recorded/wild_visual_navigation_node/supervision_graph \ + /wild_visual_navigation_visu_front_trav/traversability:=/recorded/wild_visual_navigation_visu_front_trav/traversability \ + /wild_visual_navigation_visu_rear_trav/traversability:=/recorded/wild_visual_navigation_visu_rear_trav/traversability" elif [ "$option" == "--flp" ]; then - args="$args /field_local_planner/action_server/status:=/recorded_flp/field_local_planner/action_server/status \ - /field_local_planner/current_base:=/recorded_flp/field_local_planner/current_base \ - /field_local_planner/current_goal:=/recorded_flp/field_local_planner/current_goal \ - /field_local_planner/parameter_descriptions:=/recorded_flp/field_local_planner/parameter_descriptions \ - /field_local_planner/parameter_updates:=/recorded_flp/field_local_planner/parameter_updates \ - /field_local_planner/path:=/recorded_flp/field_local_planner/path \ - /field_local_planner/real_carrot:=/recorded_flp/field_local_planner/real_carrot \ - /field_local_planner/rmp/control_points:=/recorded_flp/field_local_planner/rmp/control_points \ - /field_local_planner/rmp/parameter_descriptions:=/recorded_flp/field_local_planner/rmp/parameter_descriptions \ - /field_local_planner/rmp/parameter_updates:=/recorded_flp/field_local_planner/rmp/parameter_updates \ - /field_local_planner/status:=/recorded_flp/field_local_planner/status \ - /elevation_mapping/elevation_map_wifi:=/recorded_flp/elevation_mapping/elevation_map_wifi" + args="$args /field_local_planner/action_server/status:=/recorded/field_local_planner/action_server/status \ + /field_local_planner/current_base:=/recorded/field_local_planner/current_base \ + /field_local_planner/current_goal:=/recorded/field_local_planner/current_goal \ + /field_local_planner/parameter_descriptions:=/recorded/field_local_planner/parameter_descriptions \ + /field_local_planner/parameter_updates:=/recorded/field_local_planner/parameter_updates \ + /field_local_planner/path:=/recorded/field_local_planner/path \ + /field_local_planner/real_carrot:=/recorded/field_local_planner/real_carrot \ + /field_local_planner/rmp/control_points:=/recorded/field_local_planner/rmp/control_points \ + /field_local_planner/rmp/parameter_descriptions:=/recorded/field_local_planner/rmp/parameter_descriptions \ + /field_local_planner/rmp/parameter_updates:=/recorded/field_local_planner/rmp/parameter_updates \ + /field_local_planner/status:=/recorded/field_local_planner/status \ + /elevation_mapping/elevation_map_wifi:=/recorded/elevation_mapping/elevation_map_wifi" elif [ "$option" == "--tf" ]; then - args="$args /tf:=/recorded_flp/tf" - - # /tf_static:=/recorded_flp/tf_static" - + args="$args /tf:=/recorded/tf" + # /tf_static:=/recorded/tf_static" echo "rosrun anymal_rsl_launch replay.py c /media/Data/Datasets/2023_Oxford_Testing/2023_01_27_Oxford_Park/mission_data/2023-01-27-11-00-22/2023-01-27-11-00-22_anymal-coyote-lpc_mission.yaml" else args="$args $option" diff --git a/wild_visual_navigation_ros/scripts/shift_gridmap.py b/wild_visual_navigation_ros/scripts/shift_gridmap.py deleted file mode 100644 index ea1efa79..00000000 --- a/wild_visual_navigation_ros/scripts/shift_gridmap.py +++ /dev/null @@ -1,56 +0,0 @@ -from grid_map_msgs.msg import GridMap, GridMapInfo -import rospy -import sys -import tf -from tf.transformations import euler_from_quaternion -import math -import numpy as np - - -class ShiftGridMapNode: - def __init__(self): - self.gridmap_sub_topic = rospy.get_param("~gridmap_sub_topic", "/recorded/elevation_map_wifi") - print("Subscribed", self.gridmap_sub_topic) - self.gridmap_pub_topic = rospy.get_param("~gridmap_pub_topic", "elevation_map_wifi_shifted") - self.offset_factor_x = rospy.get_param("~offset_factor_x") - self.offset_factor_y = rospy.get_param("~offset_factor_y") - self.compensate_tf = rospy.get_param("~compensate_tf", False) - - if self.compensate_tf: - self.listener = tf.TransformListener() - - self.pub = rospy.Publisher(f"~{self.gridmap_pub_topic}", GridMap, queue_size=10) - rospy.Subscriber(f"~{self.gridmap_sub_topic}", GridMap, self.callback, queue_size=1) - - def callback(self, msg): - delta_x = msg.info.length_x * self.offset_factor_x - delta_y = msg.info.length_y * self.offset_factor_y - - if self.compensate_tf: - try: - (trans, rot) = self.listener.lookupTransform(msg.info.header.frame_id, "base", msg.info.header.stamp) - except (tf.LookupException, tf.ConnectivityException, tf.ExtrapolationException): - return - yaw = euler_from_quaternion(rot)[2] - else: - yaw = 0 - - msg.info.pose.position.x += delta_x * math.cos(yaw) - delta_y * math.sin(yaw) - msg.info.pose.position.y += delta_x * math.sin(yaw) + delta_y * math.cos(yaw) - self.pub.publish(msg) - - -if __name__ == "__main__": - print("Starting Shift GridMap Node!") - # rosrun wild_visual_navigation_ros shift_gridmap.py 0 _gridmap_sub_topic:=/elevation_mapping/elevation_map_wifi _gridmap_pub_topic:=/left _offset_factor_x:=10 _offset_factor_y:=0 - - try: - nr = "_" + rospy.myargv(argv=sys.argv)[-1].split(" ")[-1] - rospy.init_node(f"wild_visual_navigation_gridmap_{nr}") - except: - rospy.init_node("wild_visual_navigation_gridmap") - - wvn = ShiftGridMapNode() - rospy.spin() - - print("Finished Shift GridMap Node!") diff --git a/wild_visual_navigation_ros/scripts/smart_carrot.py b/wild_visual_navigation_ros/scripts/smart_carrot.py index f2773d58..e46e0d63 100644 --- a/wild_visual_navigation_ros/scripts/smart_carrot.py +++ b/wild_visual_navigation_ros/scripts/smart_carrot.py @@ -1,117 +1,167 @@ -from grid_map_msgs.msg import GridMap, GridMapInfo -import rospy import sys import numpy as np -import tf2_ros from scipy.spatial.transform import Rotation as R import cv2 import math -from geometry_msgs.msg import PoseWithCovarianceStamped import time +import rospy +from grid_map_msgs.msg import GridMap, GridMapInfo +from geometry_msgs.msg import PoseWithCovarianceStamped +from dynamic_reconfigure.server import Server +import tf2_ros + class SmartCarrotNode: def __init__(self): - self.gridmap_sub_topic = "/elevation_mapping/elevation_map_wifi" # rospy.get_param("~gridmap_sub_topic", "/elevation_mapping/elevation_map_wifi") - self.pub = rospy.Publisher(f"~binary_mask", GridMap, queue_size=5) - self.pub_goal = rospy.Publisher(f"/initialpose", PoseWithCovarianceStamped, queue_size=5) - self.tf_buffer = tf2_ros.Buffer(cache_time=rospy.Duration(3.0)) + # ROS topics + self.gridmap_sub_topic = rospy.get_param("~gridmap_sub_topic") + self.debug_pub_topic = rospy.get_param("~debug_pub_topic") + self.goal_pub_topic = rospy.get_param("~goal_pub_topic") + self.map_frame = rospy.get_param("~map_frame") + self.base_frame = rospy.get_param("~base_frame") + # Operation Mode + self.debug = rospy.get_param("~debug") + + # Parameters + self.distance_force_factor = rospy.get_param("~distance_force_factor") + self.center_force_factor = rospy.get_param("~center_force_factor") + + # TODO this could be implemented + self.filter_chain_funcs = [] + if self.distance_force_factor > 0: + self.filter_chain_funcs.append(self.apply_distance_force) + self.distance_force = None + + if self.center_force_factor > 0: + self.filter_chain_funcs.append(self.apply_center_force) + + def distance_to_line(array, x_cor, y_cor, yaw, start_x, start_y): + return np.abs(np.cos(yaw) * (x_cor - start_x) - np.sin(yaw) * (y_cor - start_y)) + + self.vdistance_to_line = np.vectorize(distance_to_line) + + # Initialize ROS publishers + self.pub = rospy.Publisher(f"~{self.debug_pub_topic}", GridMap, queue_size=5) + self.pub_goal = rospy.Publisher(self.goal_pub_topic, PoseWithCovarianceStamped, queue_size=5) + + # Initialize TF listener + self.tf_buffer = tf2_ros.Buffer(cache_time=rospy.Duration(10.0)) self.tf_listener = tf2_ros.TransformListener(self.tf_buffer) + self.sub = rospy.Subscriber(self.gridmap_sub_topic, GridMap, self.callback, queue_size=5) + + def apply_distance_force(self, yaw, sdf): + if self.distance_force is None: + # Create the distance force + self.distance_force = np.zeros((sdf.shape[0], sdf.shape[1])) + for x in range(sdf.shape[0]): + for y in range(sdf.shape[1]): + self.distance_force[x, y] = math.sqrt( + (x - int(sdf.shape[0] / 2)) ** 2 + (y - int(sdf.shape[1] / 2)) ** 2 + ) + self.distance_force /= self.distance_force.max() + self.distance_force *= self.distance_force_factor + return sdf + self.distance_force + + def apply_center_force(self, yaw, sdf): + xv, yv = np.meshgrid(np.arange(0, sdf.shape[0]), np.arange(0, sdf.shape[1])) + center_force = self.vdistance_to_line(sdf, xv, yv, yaw, int(sdf.shape[0] / 2), int(sdf.shape[1] / 2)) + return sdf - center_force * self.center_force_factor + + def get_pattern_mask(self, H, W, yaw): + # Defines a pattern based on the yaw of the robot where we search for a minimum within the SDF + binary_mask = np.zeros((H, W), dtype=np.uint8) + distance = 30 + center_x = int(H / 2 + math.sin(yaw) * distance) + center_y = int(W / 2 + math.cos(yaw) * distance) + binary_mask = cv2.circle(binary_mask, (center_x, center_y), 0, 255, 30) + distance = 55 + center_x = int(H / 2 + math.sin(yaw) * distance) + center_y = int(W / 2 + math.cos(yaw) * distance) + binary_mask = cv2.circle(binary_mask, (center_x, center_y), 0, 255, 40) + distance = 90 + center_x = int(H / 2 + math.sin(yaw) * distance) + center_y = int(W / 2 + math.cos(yaw) * distance) + binary_mask = cv2.circle(binary_mask, (center_x, center_y), 0, 255, 50) - self.sub = rospy.Subscriber(f"~{self.gridmap_sub_topic}", GridMap, self.callback, queue_size=10) - - self.offset = np.zeros((200, 200)) - for x in range(200): - for y in range(200): - self.offset[x, y] = math.sqrt((x - 100) ** 2 + (y - 100) ** 2) + return binary_mask == 0 - self.offset /= self.offset.max() - self.offset *= 0.5 - self.debug = True + def get_elevation_mask(self, elevation_layer): + invalid_elevation = np.isnan(elevation_layer) + # Increase the size of the invalid elevation to reduce noise + kernel = np.ones((3, 3), np.uint8) + invalid_elevation = cv2.dilate(np.uint8(invalid_elevation) * 255, kernel, iterations=1) == 255 + return invalid_elevation def callback(self, msg): - st = time.time() - target_layer = "sdf" - if target_layer in msg.layers: - # extract grid_map layer as numpy array - data_list = msg.data[msg.layers.index(target_layer)].data - layout_info = msg.data[msg.layers.index(target_layer)].layout - n_cols = layout_info.dim[0].size - n_rows = layout_info.dim[1].size - sdf = np.reshape(np.array(data_list), (n_rows, n_cols)) - sdf = sdf[::-1, ::-1].transpose().astype(np.float32) - - target_layer = "elevation" - if target_layer in msg.layers: - # extract grid_map layer as numpy array - data_list = msg.data[msg.layers.index(target_layer)].data - layout_info = msg.data[msg.layers.index(target_layer)].layout - n_cols = layout_info.dim[0].size - n_rows = layout_info.dim[1].size - elevation = np.reshape(np.array(data_list), (n_rows, n_cols)) - elevation = elevation[::-1, ::-1].transpose().astype(np.float32) + print("called callback") + # Convert GridMap to numpy array + layers = {} + for layer_name in ["sdf", "elevation"]: + if layer_name in msg.layers: + data_list = msg.data[msg.layers.index(layer_name)].data + layout_info = msg.data[msg.layers.index(layer_name)].layout + n_cols = layout_info.dim[0].size + n_rows = layout_info.dim[1].size + layer = np.reshape(np.array(data_list), (n_rows, n_cols)) + layer = layer[::-1, ::-1].transpose().astype(np.float32) + layers[layer_name] = layer + else: + rospy.logwarn(f"Layer {layer_name} not found in GridMap") + return False try: res = self.tf_buffer.lookup_transform( - "odom", "base_inverted_field_local_planner", msg.info.header.stamp, timeout=rospy.Duration(0.01) + self.map_frame, self.base_frame, msg.info.header.stamp, timeout=rospy.Duration(0.01) ) except Exception as e: - print("error") - print("Error in query tf: ", e) - rospy.logwarn(f"Couldn't get between odom and base") - return + error = str(e) + rospy.logwarn(f"Couldn't get between odom and base {error}") + return False - yaw = R.from_quat( - [res.transform.rotation.x, res.transform.rotation.y, res.transform.rotation.z, res.transform.rotation.w] - ).as_euler("zxy", degrees=False)[0] + H, W = layers["sdf"].shape + rot = res.transform.rotation + yaw = R.from_quat([rot.x, rot.y, rot.z, rot.w]).as_euler("zxy", degrees=False)[0] - binary_mask = np.zeros((sdf.shape[0], sdf.shape[1]), dtype=np.uint8) + mask_pattern = self.get_pattern_mask(H, W, yaw) + mask_elevation = self.get_elevation_mask(layers["elevation"]) - distance = 30 # sdf.shape[0] / 5 - center_x = int(sdf.shape[0] / 2 + math.sin(yaw) * distance) - center_y = int(sdf.shape[1] / 2 + math.cos(yaw) * distance) - binary_mask = cv2.circle(binary_mask, (center_x, center_y), 0, 255, 30) - distance = 55 # sdf.shape[0] / 3 - center_x = int(sdf.shape[0] / 2 + math.sin(yaw) * distance) - center_y = int(sdf.shape[1] / 2 + math.cos(yaw) * distance) - binary_mask = cv2.circle(binary_mask, (center_x, center_y), 0, 255, 40) - distance = 90 # sdf.shape[0] / 2 - center_x = int(sdf.shape[0] / 2 + math.sin(yaw) * distance) - center_y = int(sdf.shape[1] / 2 + math.cos(yaw) * distance) - binary_mask = cv2.circle(binary_mask, (center_x, center_y), 0, 255, 50) - m = binary_mask == 0 - sdf += self.offset - m2 = np.isnan(elevation) + for filter_func in self.filter_chain_funcs: + layers["sdf"] = filter_func(yaw, layers["sdf"]) - kernel = np.ones((3, 3), np.uint8) - m2 = cv2.dilate(np.uint8(m2) * 255, kernel, iterations=1) == 255 - sdf[m] = sdf.min() - sdf[m2] = sdf.min() + layers["sdf"][mask_pattern] = -np.inf + layers["sdf"][mask_elevation] = -np.inf - if sdf.min() == sdf.max(): + if layers["sdf"].min() == layers["sdf"].max(): + rospy.logwarn(f"No valid elevation within the SDF of the defined pattern {e}") return - x, y = np.where(sdf == sdf.max()) + # Get index of the maximum gridmax cell index within the SDF + x, y = np.where(layers["sdf"] == layers["sdf"].max()) x = x[0] y = y[0] - x -= sdf.shape[0] / 2 - y -= sdf.shape[1] / 2 + + # Convert the GridMap index to a map frame position + x -= H / 2 + y -= W / 2 x *= msg.info.resolution y *= msg.info.resolution x += msg.info.pose.position.x y += msg.info.pose.position.y + # Publish the goal goal = PoseWithCovarianceStamped() goal.header.stamp = msg.info.header.stamp - goal.header.frame_id = "odom" + goal.header.frame_id = self.map_frame goal.pose.pose.position.x = x goal.pose.pose.position.y = y goal.pose.pose.position.z = res.transform.translation.z + 0.3 - goal.pose.pose.orientation = res.transform.rotation + goal.pose.pose.orientation = rot self.pub_goal.publish(goal) + if self.debug: - target_layer = "sdf" - msg.data[msg.layers.index(target_layer)].data = sdf[::-1, ::-1].transpose().ravel() + # Republish the SDF used to search for the maximum goal + msg.data[msg.layers.index("sdf")].data = layers["sdf"][::-1, ::-1].transpose().ravel() self.pub.publish(msg) diff --git a/wild_visual_navigation_ros/scripts/wild_visual_navigation_node.py b/wild_visual_navigation_ros/scripts/wild_visual_navigation_node.py deleted file mode 100644 index 3212b3e2..00000000 --- a/wild_visual_navigation_ros/scripts/wild_visual_navigation_node.py +++ /dev/null @@ -1,978 +0,0 @@ -from wild_visual_navigation import WVN_ROOT_DIR -from wild_visual_navigation.image_projector import ImageProjector -from wild_visual_navigation.supervision_generator import SupervisionGenerator -from wild_visual_navigation.traversability_estimator import TraversabilityEstimator -from wild_visual_navigation.traversability_estimator import MissionNode, ProprioceptionNode -import wild_visual_navigation_ros.ros_converter as rc -from wild_visual_navigation.utils import accumulate_time -from wild_visual_navigation_msgs.msg import RobotState, SystemState -from wild_visual_navigation_msgs.srv import ( - LoadCheckpoint, - SaveCheckpoint, - LoadCheckpointResponse, - SaveCheckpointResponse, -) -from std_srvs.srv import SetBool, Trigger, TriggerResponse -from wild_visual_navigation.utils import SystemLevelTimer, SystemLevelContextTimer -from wild_visual_navigation.utils import SystemLevelGpuMonitor, SystemLevelContextGpuMonitor, accumulate_memory -from wild_visual_navigation.utils import WVNMode -from wild_visual_navigation.cfg import ExperimentParams -from wild_visual_navigation.utils import override_params -from wild_visual_navigation.learning.utils import load_yaml, load_env, create_experiment_folder -from geometry_msgs.msg import PoseStamped, Point, TwistStamped -from nav_msgs.msg import Path -from sensor_msgs.msg import Image, CameraInfo, CompressedImage -from std_msgs.msg import ColorRGBA, Float32, Float32MultiArray -from threading import Thread, Event -from visualization_msgs.msg import Marker -import message_filters -import os -import rospy -import seaborn as sns -import tf -import torch -import numpy as np -from typing import Optional -import traceback -import signal -import sys -import tf2_ros - -if torch.cuda.is_available(): - torch.cuda.empty_cache() - - -class WvnRosInterface: - def __init__(self): - # Timers to control the rate of the publishers - self.last_image_ts = rospy.get_time() - self.last_proprio_ts = rospy.get_time() - - # Read params - self.read_params() - - # Visualization - self.color_palette = sns.color_palette(self.colormap, as_cmap=True) - - # Setup Mission Folder - create_experiment_folder(self.params, load_env()) - - # Initialize traversability estimator - self.traversability_estimator = TraversabilityEstimator( - params=self.params, - device=self.device, - image_size=self.network_input_image_height, # Note: we assume height == width - segmentation_type=self.segmentation_type, - feature_type=self.feature_type, - max_distance=self.traversability_radius, - image_distance_thr=self.image_graph_dist_thr, - proprio_distance_thr=self.proprio_graph_dist_thr, - optical_flow_estimator_type=self.optical_flow_estimator_type, - min_samples_for_training=self.min_samples_for_training, - vis_node_index=self.vis_node_index, - mode=self.mode, - extraction_store_folder=self.extraction_store_folder, - patch_size=self.dino_patch_size, - scale_traversability=self.scale_traversability, - ) - - # Initialize traversability generator to process velocity commands - self.supervision_generator = SupervisionGenerator( - self.device, - kf_process_cov=0.1, - kf_meas_cov=10, - kf_outlier_rejection="huber", - kf_outlier_rejection_delta=0.5, - sigmoid_slope=20, - sigmoid_cutoff=0.25, # 0.2 - untraversable_thr=self.untraversable_thr, # 0.1 - time_horizon=0.05, - ) - # Initialize camera handler for subscription/publishing - self.camera_handler = {} - self.system_events = {} - # Setup ros - self.setup_ros(setup_fully=self.mode != WVNMode.EXTRACT_LABELS) - - # Setup Timer if needed - self.timer = SystemLevelTimer( - objects=[ - self, - self.traversability_estimator, - self.traversability_estimator._visualizer, - self.supervision_generator, - ], - names=["WVN", "TraversabilityEstimator", "Visualizer", "SupervisionGenerator"], - enabled=(self.print_image_callback_time or self.print_proprio_callback_time or self.log_time), - ) - - self.gpu_monitor = SystemLevelGpuMonitor( - objects=[ - self, - self.traversability_estimator, - self.traversability_estimator._visualizer, - self.supervision_generator, - ], - names=["WVN", "TraversabilityEstimator", "Visualizer", "SupervisionGenerator"], - enabled=self.log_memory, - device=self.device, - store_samples=True, - skip_n_samples=1, - ) - # Register shotdown callbacks - rospy.on_shutdown(self.shutdown_callback) - signal.signal(signal.SIGINT, self.shutdown_callback) - signal.signal(signal.SIGTERM, self.shutdown_callback) - - # Launch processes - print("─" * 80) - print("Launching [learning] thread") - if self.mode != WVNMode.EXTRACT_LABELS: - self.learning_thread_stop_event = Event() - self.learning_thread = Thread(target=self.learning_thread_loop, name="learning") - self.learning_thread.start() - - self.logging_thread_stop_event = Event() - self.logging_thread = Thread(target=self.logging_thread_loop, name="logging") - self.logging_thread.start() - print("[WVN] System ready") - - def shutdown_callback(self, *args, **kwargs): - # Write stuff to files - rospy.logwarn("Shutdown callback called") - if self.mode != WVNMode.EXTRACT_LABELS: - self.learning_thread_stop_event.set() - self.logging_thread_stop_event.set() - - print("Storing learned checkpoint...", end="") - self.traversability_estimator.save_checkpoint(self.params.general.model_path, "last_checkpoint.pt") - print("done") - - if self.log_time: - print("Storing timer data...", end="") - self.timer.store(folder=self.params.general.model_path) - print("done") - if self.log_memory: - print("Storing memory data...", end="") - self.gpu_monitor.store(folder=self.params.general.model_path) - print("done") - - print("Joining learning thread...", end="") - if self.mode != WVNMode.EXTRACT_LABELS: - self.learning_thread_stop_event.set() - self.learning_thread.join() - - self.logging_thread_stop_event.set() - self.logging_thread.join() - print("done") - - rospy.signal_shutdown(f"Wild Visual Navigation killed {args}") - sys.exit(0) - - @accumulate_time - def learning_thread_loop(self): - """This implements the main thread that runs the training procedure - We can only set the rate using rosparam - """ - # Set rate - rate = rospy.Rate(self.learning_thread_rate) - - # Learning loop - while True: - self.system_events["learning_thread_loop"] = {"time": rospy.get_time(), "value": "running"} - self.learning_thread_stop_event.wait(timeout=0.01) - if self.learning_thread_stop_event.is_set(): - rospy.logwarn("Stopped learning thread") - break - - # Optimize model - # with SystemLevelContextGpuMonitor(self, "training_step_time"): - with SystemLevelContextTimer(self, "training_step_time"): - res = self.traversability_estimator.train() - - if self.step != self.traversability_estimator.step: - self.step_time = rospy.get_time() - self.step = self.traversability_estimator.step - self.gpu_monitor.update(self.step, self.step_time) - - # Publish loss - system_state = SystemState() - for k in res.keys(): - if hasattr(system_state, k): - setattr(system_state, k, res[k]) - - system_state.pause_learning = self.traversability_estimator.pause_learning - system_state.mode = self.mode.value - system_state.step = self.step - self.pub_system_state.publish(system_state) - - rate.sleep() - - self.system_events["learning_thread_loop"] = {"time": rospy.get_time(), "value": "finished"} - self.learning_thread_stop_event.clear() - - def logging_thread_loop(self): - rate = rospy.Rate(self.logging_thread_rate) - # Learning loop - while True: - self.learning_thread_stop_event.wait(timeout=0.01) - if self.learning_thread_stop_event.is_set(): - rospy.logwarn("Stopped logging thread") - break - - current_time = rospy.get_time() - tmp = self.system_events.copy() - rospy.loginfo("System Events:") - for k, v in tmp.items(): - value = v["value"] - msg = ( - (k + ": ").ljust(35, " ") - + (str(round(current_time - v["time"], 4)) + "s ").ljust(10, " ") - + f" {value}" - ) - rospy.loginfo(msg) - rate.sleep() - rospy.loginfo("--------------") - self.learning_thread_stop_event.clear() - - @accumulate_time - def read_params(self): - """Reads all the parameters from the parameter server""" - # Topics - self.robot_state_topic = rospy.get_param("~robot_state_topic") - self.desired_twist_topic = rospy.get_param("~desired_twist_topic") - self.camera_topics = rospy.get_param("~camera_topics") - - # Frames - self.fixed_frame = rospy.get_param("~fixed_frame") - self.base_frame = rospy.get_param("~base_frame") - self.footprint_frame = rospy.get_param("~footprint_frame") - - # Robot size and specs - self.robot_length = rospy.get_param("~robot_length") - self.robot_width = rospy.get_param("~robot_width") - self.robot_height = rospy.get_param("~robot_height") - - # Traversability estimation params - self.traversability_radius = rospy.get_param("~traversability_radius") - self.image_graph_dist_thr = rospy.get_param("~image_graph_dist_thr") - self.proprio_graph_dist_thr = rospy.get_param("~proprio_graph_dist_thr") - self.network_input_image_height = rospy.get_param("~network_input_image_height") - self.network_input_image_width = rospy.get_param("~network_input_image_width") - self.segmentation_type = rospy.get_param("~segmentation_type") - self.feature_type = rospy.get_param("~feature_type") - self.dino_patch_size = rospy.get_param("~dino_patch_size") - self.confidence_std_factor = rospy.get_param("~confidence_std_factor") - self.scale_traversability = rospy.get_param("~scale_traversability") - self.scale_traversability_max_fpr = rospy.get_param("~scale_traversability_max_fpr") - self.min_samples_for_training = rospy.get_param("~min_samples_for_training") - self.vis_node_index = rospy.get_param("~debug_supervision_node_index_from_last") - - # Supervision Generator - self.robot_max_velocity = rospy.get_param("~robot_max_velocity") - self.untraversable_thr = rospy.get_param("~untraversable_thr") - - # Optical flow params - self.optical_flow_estimator_type = rospy.get_param("~optical_flow_estimator_type") - - # Threads - self.image_callback_rate = rospy.get_param("~image_callback_rate") # hertz - self.proprio_callback_rate = rospy.get_param("~proprio_callback_rate") # hertz - self.learning_thread_rate = rospy.get_param("~learning_thread_rate") # hertz - self.logging_thread_rate = rospy.get_param("~logging_thread_rate") # hertz - - # Data storage - self.mission_name = rospy.get_param("~mission_name") - self.mission_timestamp = rospy.get_param("~mission_timestamp") - - # Print timings - self.print_image_callback_time = rospy.get_param("~print_image_callback_time") - self.print_proprio_callback_time = rospy.get_param("~print_proprio_callback_time") - self.log_time = rospy.get_param("~log_time") - self.log_memory = rospy.get_param("~log_memory") - self.log_confidence = rospy.get_param("~log_confidence") - self.verbose = rospy.get_param("~verbose") - - # Select mode: # debug, online, extract_labels - self.use_debug_for_desired = rospy.get_param("~use_debug_for_desired") # Note: Unused parameter - self.mode = WVNMode.from_string(rospy.get_param("~mode", "debug")) - self.extraction_store_folder = rospy.get_param("~extraction_store_folder") - - # Parse operation modes - if self.mode == WVNMode.ONLINE: - print("\nWARNING: online_mode enabled. The graph will not store any debug/training data such as images\n") - - elif self.mode == WVNMode.EXTRACT_LABELS: - self.image_callback_rate = 3 - self.proprio_callback_rate = 4 - self.optical_flow_estimator_type = False - self.image_graph_dist_thr = 0.2 - self.proprio_graph_dist_thr = 0.1 - - os.makedirs(os.path.join(self.extraction_store_folder, "image"), exist_ok=True) - os.makedirs(os.path.join(self.extraction_store_folder, "supervision_mask"), exist_ok=True) - - # Experiment file - exp_file = rospy.get_param("~exp") - - # Torch device - self.device = rospy.get_param("~device") - - # Visualization - self.colormap = rospy.get_param("~colormap") - - # Initialize traversability estimator parameters - self.params = ExperimentParams() - if exp_file != "nan": - exp_override = load_yaml(os.path.join(WVN_ROOT_DIR, "cfg/exp", exp_file)) - self.params = override_params(self.params, exp_override) - - self.params.general.name = self.mission_name - self.params.general.timestamp = self.mission_timestamp - self.params.general.log_confidence = self.log_confidence - self.params.loss.confidence_std_factor = self.confidence_std_factor - self.params.loss.w_temp = 0 - self.step = -1 - self.step_time = rospy.get_time() - - assert self.optical_flow_estimator_type == "none", "Optical flow estimator not tested due to changes" - - def setup_rosbag_replay(self, tf_listener): - self.tf_listener = tf_listener - - def setup_ros(self, setup_fully=True): - """Main function to setup ROS-related stuff: publishers, subscribers and services""" - if setup_fully: - # Initialize TF listener - self.tf_buffer = tf2_ros.Buffer(cache_time=rospy.Duration(20.0)) - self.tf_listener = tf2_ros.TransformListener(self.tf_buffer) - - # Robot state callback - robot_state_sub = message_filters.Subscriber(self.robot_state_topic, RobotState) - cache1 = message_filters.Cache(robot_state_sub, 10) - desired_twist_sub = message_filters.Subscriber(self.desired_twist_topic, TwistStamped) - cache2 = message_filters.Cache(desired_twist_sub, 10) - - self.robot_state_sub = message_filters.ApproximateTimeSynchronizer( - [robot_state_sub, desired_twist_sub], queue_size=10, slop=0.5 - ) - - print("Start waiting for RobotState topic being published!") - rospy.wait_for_message(self.robot_state_topic, RobotState) - print("Start waiting for TwistStamped topic being published!") - rospy.wait_for_message(self.desired_twist_topic, TwistStamped) - - self.robot_state_sub.registerCallback(self.robot_state_callback) - - # Image callback - for cam in self.camera_topics: - # Initialize camera handler for given cam - self.camera_handler[cam] = {} - # Store camera name - self.camera_topics[cam]["name"] = cam - # Set subscribers - base_topic = self.camera_topics[cam]["image_topic"].replace("/compressed", "") - is_compressed = self.camera_topics[cam]["image_topic"] != base_topic - if is_compressed: - image_sub = message_filters.Subscriber(self.camera_topics[cam]["image_topic"], CompressedImage) - else: - image_sub = message_filters.Subscriber(self.camera_topics[cam]["image_topic"], Image) - - info_sub = message_filters.Subscriber(self.camera_topics[cam]["info_topic"], CameraInfo) - sync = message_filters.ApproximateTimeSynchronizer([image_sub, info_sub], queue_size=2, slop=0.5) - sync.registerCallback(self.image_callback, self.camera_topics[cam]) - self.camera_handler[cam]["image_sub"] = image_sub - self.camera_handler[cam]["info_sub"] = info_sub - self.camera_handler[cam]["sync"] = sync - - # Set publishers - input_pub = rospy.Publisher(f"/wild_visual_navigation_node/{cam}/image_input", Image, queue_size=10) - trav_pub = rospy.Publisher(f"/wild_visual_navigation_node/{cam}/traversability", Image, queue_size=10) - conf_pub = rospy.Publisher(f"/wild_visual_navigation_node/{cam}/confidence", Image, queue_size=10) - info_pub = rospy.Publisher(f"/wild_visual_navigation_node/{cam}/camera_info", CameraInfo, queue_size=10) - self.camera_handler[cam]["input_pub"] = input_pub - self.camera_handler[cam]["trav_pub"] = trav_pub - self.camera_handler[cam]["conf_pub"] = conf_pub - self.camera_handler[cam]["info_pub"] = info_pub - - if self.mode == WVNMode.DEBUG: - # Debugging publishers - last_image_labeled_pub = rospy.Publisher( - f"/wild_visual_navigation_node/{cam}/debug/last_node_image_labeled", Image, queue_size=10 - ) - last_image_mask_pub = rospy.Publisher( - f"/wild_visual_navigation_node/{cam}/debug/last_node_image_mask", Image, queue_size=10 - ) - last_image_trav_pub = rospy.Publisher( - f"/wild_visual_navigation_node/{cam}/debug/last_image_traversability", Image, queue_size=10 - ) - last_image_conf_pub = rospy.Publisher( - f"/wild_visual_navigation_node/{cam}/debug/last_image_confidence", Image, queue_size=10 - ) - self.camera_handler[cam]["debug"] = {} - self.camera_handler[cam]["debug"]["image_labeled"] = last_image_labeled_pub - self.camera_handler[cam]["debug"]["image_mask"] = last_image_mask_pub - self.camera_handler[cam]["debug"]["image_trav"] = last_image_trav_pub - self.camera_handler[cam]["debug"]["image_conf"] = last_image_conf_pub - - # 3D outputs - self.pub_debug_proprio_graph = rospy.Publisher( - "/wild_visual_navigation_node/proprioceptive_graph", Path, queue_size=10 - ) - self.pub_mission_graph = rospy.Publisher("/wild_visual_navigation_node/mission_graph", Path, queue_size=10) - self.pub_graph_footprints = rospy.Publisher( - "/wild_visual_navigation_node/graph_footprints", Marker, queue_size=10 - ) - # 1D outputs - self.pub_instant_traversability = rospy.Publisher( - "/wild_visual_navigation_node/instant_traversability", Float32, queue_size=10 - ) - self.pub_system_state = rospy.Publisher("/wild_visual_navigation_node/system_state", SystemState, queue_size=10) - - # Services - # Like, reset graph or the like - self.save_checkpt_service = rospy.Service("~save_checkpoint", SaveCheckpoint, self.save_checkpoint_callback) - self.load_checkpt_service = rospy.Service("~load_checkpoint", LoadCheckpoint, self.load_checkpoint_callback) - - self.pause_learning_service = rospy.Service("~pause_learning", SetBool, self.pause_learning_callback) - self.reset_service = rospy.Service("~reset", Trigger, self.reset_callback) - - def pause_learning_callback(self, req): - """Start and stop the network training""" - prev_state = self.traversability_estimator.pause_learning - self.traversability_estimator.pause_learning = req.data - if not req.data and prev_state: - message = "Resume training!" - elif req.data and prev_state: - message = "Training was already paused!" - elif not req.data and not prev_state: - message = "Training was already running!" - elif req.data and not prev_state: - message = "Pause training!" - message += f" Updated the network for {self.traversability_estimator.step} steps" - - return True, message - - def reset_callback(self, req): - """Resets the system""" - print("WARNING: System reset!") - - print("Storing learned checkpoint...", end="") - self.traversability_estimator.save_checkpoint(self.params.general.model_path, "last_checkpoint.pt") - print("done") - - if self.log_time: - print("Storing timer data...", end="") - self.timer.store(folder=self.params.general.model_path) - print("done") - if self.log_memory: - print("Storing memory data...", end="") - self.gpu_monitor.store(folder=self.params.general.model_path) - print("done") - - # Create new mission folder - create_experiment_folder(self.params, load_env()) - - # Reset traversability estimator - self.traversability_estimator.reset() - - print("Reset done") - return TriggerResponse(True, "Reset done!") - - @accumulate_time - def save_checkpoint_callback(self, req): - """Service call to store the learned checkpoint - - Args: - req (TriggerRequest): Trigger request service - """ - if req.checkpoint_name == "": - req.checkpoint_name = "last_checkpoint.pt" - - if req.mission_path == "": - message = f"[WARNING] Store checkpoint {req.checkpoint_name} default mission path: {self.params.general.model_path}/{req.checkpoint_name}" - req.mission_path = self.params.general.model_path - else: - message = f"Store checkpoint {req.checkpoint_name} to: {req.mission_path}/{req.checkpoint_name}" - - self.traversability_estimator.save_checkpoint(req.mission_path, req.checkpoint_name) - return SaveCheckpointResponse(success=True, message=message) - - def load_checkpoint_callback(self, req): - """Service call to load a learned checkpoint - - Args: - req (TriggerRequest): Trigger request service - """ - if req.checkpoint_path == "": - return LoadCheckpointResponse( - success=False, - message=f"Path [{req.checkpoint_path}] is empty. Please check and try again", - ) - checkpoint_path = req.checkpoint_path - self.traversability_estimator.load_checkpoint(checkpoint_path) - return LoadCheckpointResponse(success=True, message=f"Checkpoint [{checkpoint_path}] loaded successfully") - - @accumulate_time - def query_tf(self, parent_frame: str, child_frame: str, stamp: Optional[rospy.Time] = None): - """Helper function to query TFs - - Args: - parent_frame (str): Frame of the parent TF - child_frame (str): Frame of the child - """ - - if stamp is None: - stamp = rospy.Time(0) - - try: - res = self.tf_buffer.lookup_transform(parent_frame, child_frame, stamp, timeout=rospy.Duration(0.03)) - trans = (res.transform.translation.x, res.transform.translation.y, res.transform.translation.z) - rot = np.array( - [res.transform.rotation.x, res.transform.rotation.y, res.transform.rotation.z, res.transform.rotation.w] - ) - rot /= np.linalg.norm(rot) - return (trans, tuple(rot)) - except Exception as e: - print("Error in query tf: ", e) - rospy.logwarn(f"Couldn't get between {parent_frame} and {child_frame}") - return (None, None) - - @accumulate_memory - @accumulate_time - def robot_state_callback(self, state_msg, desired_twist_msg: TwistStamped): - """Main callback to process proprioceptive info (robot state) - - Args: - state_msg (wild_visual_navigation_msgs/RobotState): Robot state message - desired_twist_msg (geometry_msgs/TwistStamped): Desired twist message - """ - self.system_events["robot_state_callback_received"] = {"time": rospy.get_time(), "value": "message received"} - try: - ts = state_msg.header.stamp.to_sec() - if abs(ts - self.last_proprio_ts) < 1.0 / self.proprio_callback_rate: - self.system_events["robot_state_callback_cancled"] = { - "time": rospy.get_time(), - "value": "cancled due to rate", - } - return - self.last_propio_ts = ts - - # Query transforms from TF - suc, pose_base_in_world = rc.ros_tf_to_torch( - self.query_tf(self.fixed_frame, self.base_frame, state_msg.header.stamp), device=self.device - ) - if not suc: - self.system_events["robot_state_callback_cancled"] = { - "time": rospy.get_time(), - "value": "cancled due to pose_base_in_world", - } - return - - suc, pose_footprint_in_base = rc.ros_tf_to_torch( - self.query_tf(self.base_frame, self.footprint_frame, state_msg.header.stamp), device=self.device - ) - if not suc: - self.system_events["robot_state_callback_cancled"] = { - "time": rospy.get_time(), - "value": "cancled due to pose_footprint_in_base", - } - return - - # The footprint requires a correction: we use the same orientation as the base - pose_footprint_in_base[:3, :3] = torch.eye(3, device=self.device) - - # Convert state to tensor - proprio_tensor, proprio_labels = rc.wvn_robot_state_to_torch(state_msg, device=self.device) - current_twist_tensor = rc.twist_stamped_to_torch(state_msg.twist, device=self.device) - desired_twist_tensor = rc.twist_stamped_to_torch(desired_twist_msg, device=self.device) - - # Update traversability - traversability, traversability_var, is_untraversable = self.supervision_generator.update_velocity_tracking( - current_twist_tensor, desired_twist_tensor, velocities=["vx", "vy"] - ) - - # Create proprioceptive node for the graph - proprio_node = ProprioceptionNode( - timestamp=ts, - pose_base_in_world=pose_base_in_world, - pose_footprint_in_base=pose_footprint_in_base, - twist_in_base=current_twist_tensor, - desired_twist_in_base=desired_twist_tensor, - width=self.robot_width, - length=self.robot_length, - height=self.robot_width, - proprioception=proprio_tensor, - traversability=traversability, - traversability_var=traversability_var, - is_untraversable=is_untraversable, - ) - - # Add node to the graph - self.traversability_estimator.add_proprio_node(proprio_node) - - if self.mode == WVNMode.DEBUG or self.mode == WVNMode.ONLINE: - self.visualize_proprioception() - - if self.print_proprio_callback_time: - print(self.timer) - - self.system_events["robot_state_callback_state"] = { - "time": rospy.get_time(), - "value": "executed successfully", - } - - except Exception as e: - traceback.print_exc() - print("error state callback", e) - self.system_events["robot_state_callback_state"] = { - "time": rospy.get_time(), - "value": f"failed to execute {e}", - } - - raise Exception("Error in robot state callback") - - @accumulate_memory - @accumulate_time - def image_callback(self, image_msg: Image, info_msg: CameraInfo, camera_options: dict): - """Main callback to process incoming images - - Args: - image_msg (sensor_msgs/Image): Incoming image - info_msg (sensor_msgs/CameraInfo): Camera info message associated to the image - """ - self.system_events["image_callback_received"] = {"time": rospy.get_time(), "value": "message received"} - if self.verbose: - print(f"\nImage callback: {camera_options['name']}... ", end="") - try: - - # Run the callback so as to match the desired rate - ts = image_msg.header.stamp.to_sec() - if abs(ts - self.last_image_ts) < 1.0 / self.image_callback_rate: - if self.verbose: - print("skip") - return - else: - if self.verbose: - print("process") - self.last_image_ts = ts - - # Query transforms from TF - suc, pose_base_in_world = rc.ros_tf_to_torch( - self.query_tf(self.fixed_frame, self.base_frame, image_msg.header.stamp), device=self.device - ) - if not suc: - self.system_events["image_callback_cancled"] = { - "time": rospy.get_time(), - "value": "cancled due to pose_base_in_world", - } - return - suc, pose_cam_in_base = rc.ros_tf_to_torch( - self.query_tf(self.base_frame, image_msg.header.frame_id, image_msg.header.stamp), device=self.device - ) - if not suc: - self.system_events["image_callback_cancled"] = { - "time": rospy.get_time(), - "value": "cancled due to pose_cam_in_base", - } - return - - # Prepare image projector - K, H, W = rc.ros_cam_info_to_tensors(info_msg, device=self.device) - image_projector = ImageProjector( - K=K, h=H, w=W, new_h=self.network_input_image_height, new_w=self.network_input_image_width - ) - - # Add image to base node - # convert image message to torch image - torch_image = rc.ros_image_to_torch(image_msg, device=self.device) - torch_image = image_projector.resize_image(torch_image) - - # Create mission node for the graph - mission_node = MissionNode( - timestamp=ts, - pose_base_in_world=pose_base_in_world, - pose_cam_in_base=pose_cam_in_base, - image=torch_image, - image_projector=image_projector, - correspondence=torch.zeros((1,)) if self.optical_flow_estimator_type != "sparse" else None, - camera_name=camera_options["name"], - use_for_training=camera_options["use_for_training"], - ) - - # Add node to graph - added_new_node = self.traversability_estimator.add_mission_node(mission_node) - - with SystemLevelContextGpuMonitor(self, "update_prediction"): - with SystemLevelContextTimer(self, "update_prediction"): - # Update prediction - self.traversability_estimator.update_prediction(mission_node) - - if self.mode == WVNMode.ONLINE or self.mode == WVNMode.DEBUG: - self.publish_predictions(mission_node, image_msg, info_msg, image_projector.scaled_camera_matrix) - - if self.mode == WVNMode.DEBUG: - # Publish current predictions - self.visualize_mission() - # Publish supervision data depending on the mode - self.visualize_debug() - - # If a new node was added, update the node is used to visualize the supervision signals - if added_new_node: - self.traversability_estimator.update_visualization_node() - - # Print callback time if required - if self.print_image_callback_time: - print(self.timer) - - self.system_events["image_callback_state"] = {"time": rospy.get_time(), "value": "executed successfully"} - - except Exception as e: - traceback.print_exc() - print("error image callback", e) - self.system_events["image_callback_state"] = {"time": rospy.get_time(), "value": f"failed to execute {e}"} - raise Exception("Error in image callback") - - @accumulate_memory - @accumulate_time - def publish_predictions( - self, mission_node: MissionNode, image_msg: Image, info_msg: CameraInfo, scaled_camera_matrix: torch.Tensor - ): - """Publish predictions for the current image - - Args: - mission_node (MissionNode): Mission node - """ - # Publish predictions - if mission_node is not None and self.mode != WVNMode.EXTRACT_LABELS: - # Get camera name - cam = mission_node.camera_name - - # Traversability - out_trav = torch.zeros(mission_node.feature_segments.shape, device=self.device) - out_conf = torch.zeros(mission_node.feature_segments.shape, device=self.device) - fs = mission_node.feature_segments.reshape(-1) - out_trav = out_trav.reshape(-1) - out_conf = out_conf.reshape(-1) - traversability = mission_node.prediction[:, 0] - - # Optionally rescale the traversability output before publishing - if self.scale_traversability: - # Compute ROC Threshold - if self.traversability_estimator._auxiliary_training_roc._update_count != 0: - fpr, tpr, thresholds = self.traversability_estimator._auxiliary_training_roc.compute() - index = torch.where(fpr > self.scale_traversability_max_fpr)[0][0] - threshold = thresholds[index] - self.traversability_estimator.scale_traversability_threshold = threshold - - # Apply piecewise linear scaling 0->0; threshold->0.5; 1->1 - traversability = traversability.clone() - m = traversability < threshold - # Scale untraversable - traversability[m] *= 0.5 / threshold - # Scale traversable - traversability[~m] -= threshold - traversability[~m] *= 0.5 / (1 - threshold) - traversability[~m] += 0.5 - traversability = traversability.clip(0, 1) - - out_trav = traversability[fs] - out_conf = mission_node.confidence[fs] - out_trav = out_trav.reshape(mission_node.feature_segments.shape) - out_conf = out_conf.reshape(mission_node.feature_segments.shape) - - # Color, resized image used as the network input - msg = rc.torch_to_ros_image(mission_node.image.cpu(), "rgb8") - msg.header = image_msg.header - msg.width = out_trav.shape[0] - msg.height = out_trav.shape[1] - self.camera_handler[cam]["input_pub"].publish(msg) - - # Output traversability - msg = rc.numpy_to_ros_image(out_trav.cpu().numpy(), "passthrough") - msg.header = image_msg.header - msg.width = out_trav.shape[0] - msg.height = out_trav.shape[1] - self.camera_handler[cam]["trav_pub"].publish(msg) - - # Output confidence - # out_conf[:,:] = 1 - # out_conf[int(out_conf.shape[0]/2):,:] = 0 - # out_conf[:,int(out_conf.shape[1]/2):] = 0 - msg = rc.numpy_to_ros_image(out_conf.cpu().numpy(), "passthrough") - msg.header = image_msg.header - msg.width = out_trav.shape[0] - msg.height = out_trav.shape[1] - self.camera_handler[cam]["conf_pub"].publish(msg) - - # Output camera info - # The header stays the same, only the image, width, K and P change due to the different resolution - info_msg.width = out_trav.shape[0] - info_msg.height = out_trav.shape[1] - info_msg.K = scaled_camera_matrix[0, :3, :3].cpu().numpy().flatten().tolist() - info_msg.P = scaled_camera_matrix[0, :3, :4].cpu().numpy().flatten().tolist() - self.camera_handler[cam]["info_pub"].publish(info_msg) - - @accumulate_memory - @accumulate_time - def visualize_proprioception(self): - """Publishes all the visualizations related to proprioceptive info, - like footprints and the sliding graph - """ - # Get current time for later - now = rospy.Time.now() - - proprio_graph_msg = Path() - proprio_graph_msg.header.frame_id = self.fixed_frame - proprio_graph_msg.header.stamp = now - - # Footprints - footprints_marker = Marker() - footprints_marker.id = 0 - footprints_marker.ns = "footprints" - footprints_marker.header.frame_id = self.fixed_frame - footprints_marker.header.stamp = now - footprints_marker.type = Marker.TRIANGLE_LIST - footprints_marker.action = Marker.ADD - footprints_marker.scale.x = 1 - footprints_marker.scale.y = 1 - footprints_marker.scale.z = 1 - footprints_marker.color.a = 1.0 - footprints_marker.pose.orientation.w = 1.0 - footprints_marker.pose.position.x = 0.0 - footprints_marker.pose.position.y = 0.0 - footprints_marker.pose.position.z = 0.0 - - last_points = [None, None] - for node in self.traversability_estimator.get_proprio_nodes(): - # Path - pose = PoseStamped() - pose.header.stamp = now - pose.header.frame_id = self.fixed_frame - pose.pose = rc.torch_to_ros_pose(node.pose_base_in_world) - proprio_graph_msg.poses.append(pose) - - # Color for traversability - r, g, b, _ = self.color_palette(node.traversability.item()) - c = ColorRGBA(r, g, b, 0.95) - - # Rainbow path - side_points = node.get_side_points() - - # if the last points are empty, fill and continue - if None in last_points: - for i in range(2): - last_points[i] = Point( - x=side_points[i, 0].item(), y=side_points[i, 1].item(), z=side_points[i, 2].item() - ) - continue - else: - # here we want to add 2 triangles: from the last saved points (lp) - # and the current side points (sp): - # triangle 1: [lp0, lp1, sp0] - # triangle 2: [lp1, sp0, sp1] - - points_to_add = [] - # add the last points points - for lp in last_points: - points_to_add.append(lp) - # Add first from new side points - points_to_add.append( - Point(x=side_points[i, 0].item(), y=side_points[i, 1].item(), z=side_points[i, 2].item()) - ) - # Add last of last points - points_to_add.append(last_points[0]) - # Add new side points and update last points - for i in range(2): - last_points[i] = Point( - x=side_points[i, 0].item(), y=side_points[i, 1].item(), z=side_points[i, 2].item() - ) - points_to_add.append(last_points[i]) - - # Add all the points and colors - for p in points_to_add: - footprints_marker.points.append(p) - footprints_marker.colors.append(c) - - # Untraversable plane - if node.is_untraversable: - untraversable_plane = node.get_untraversable_plane(grid_size=2) - N, D = untraversable_plane.shape - for n in [0, 1, 3, 2, 0, 3]: # this is a hack to show the triangles correctly - p = Point() - p.x = untraversable_plane[n, 0] - p.y = untraversable_plane[n, 1] - p.z = untraversable_plane[n, 2] - footprints_marker.points.append(p) - footprints_marker.colors.append(c) - - # Publish - if len(footprints_marker.points) % 3 != 0: - if self.verbose: - print(f"number of points for footprint is {len(footprints_marker.points)}") - return - self.pub_graph_footprints.publish(footprints_marker) - self.pub_debug_proprio_graph.publish(proprio_graph_msg) - - # Publish latest traversability - self.pub_instant_traversability.publish(self.supervision_generator.traversability) - self.system_events["visualize_proprioception"] = {"time": rospy.get_time(), "value": f"executed successfully"} - - @accumulate_memory - @accumulate_time - def visualize_mission(self): - """Publishes all the visualizations related to the mission graph""" - # Get current time for later - now = rospy.Time.now() - - # Publish mission graph - mission_graph_msg = Path() - mission_graph_msg.header.frame_id = self.fixed_frame - mission_graph_msg.header.stamp = now - - for node in self.traversability_estimator.get_mission_nodes(): - pose = PoseStamped() - pose.header.stamp = now - pose.header.frame_id = self.fixed_frame - pose.pose = rc.torch_to_ros_pose(node.pose_cam_in_world) - mission_graph_msg.poses.append(pose) - - self.pub_mission_graph.publish(mission_graph_msg) - - @accumulate_memory - @accumulate_time - def visualize_debug(self): - """Publishes all the debugging, slow visualizations""" - - # Get visualization node - vis_node = self.traversability_estimator.get_mission_node_for_visualization() - - # Publish predictions - if vis_node is not None and self.mode != WVNMode.EXTRACT_LABELS: - cam = vis_node.camera_name - ( - np_prediction_image, - np_uncertainty_image, - ) = self.traversability_estimator.plot_mission_node_prediction(vis_node) - - # self.pub_image_input.publish(rc.torch_to_ros_image(vis_node.image)) - self.camera_handler[cam]["debug"]["image_trav"].publish(rc.numpy_to_ros_image(np_prediction_image)) - self.camera_handler[cam]["debug"]["image_conf"].publish(rc.numpy_to_ros_image(np_uncertainty_image)) - - # Publish reprojections of last node in graph - if vis_node is not None: - cam = vis_node.camera_name - - np_labeled_image, np_mask_image = self.traversability_estimator.plot_mission_node_training(vis_node) - - if np_labeled_image is None or np_mask_image is None: - return - self.camera_handler[cam]["debug"]["image_labeled"].publish(rc.numpy_to_ros_image(np_labeled_image)) - self.camera_handler[cam]["debug"]["image_mask"].publish(rc.numpy_to_ros_image(np_mask_image)) - - -if __name__ == "__main__": - node_name = "wild_visual_navigation_node" - rospy.init_node("wild_visual_navigation_node") - wvn = WvnRosInterface() - rospy.spin() diff --git a/wild_visual_navigation_ros/scripts/wvn_feature_extractor_node.py b/wild_visual_navigation_ros/scripts/wvn_feature_extractor_node.py new file mode 100644 index 00000000..e6d4caa5 --- /dev/null +++ b/wild_visual_navigation_ros/scripts/wvn_feature_extractor_node.py @@ -0,0 +1,464 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Matias Mattamala, Jonas Frey. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# +from wild_visual_navigation import WVN_ROOT_DIR +from wild_visual_navigation.feature_extractor import FeatureExtractor +from wild_visual_navigation.cfg import ExperimentParams, RosFeatureExtractorNodeParams +from wild_visual_navigation.image_projector import ImageProjector +from wild_visual_navigation_msgs.msg import ImageFeatures +import wild_visual_navigation_ros.ros_converter as rc +from wild_visual_navigation_ros.scheduler import Scheduler +from wild_visual_navigation_ros.reload_rosparams import reload_rosparams +from wild_visual_navigation.model import get_model +from wild_visual_navigation.utils import ConfidenceGenerator +from wild_visual_navigation.utils import AnomalyLoss + +import rospy +from sensor_msgs.msg import Image, CameraInfo, CompressedImage +from std_msgs.msg import MultiArrayDimension + +import torch +import numpy as np +import torch.nn.functional as F +import signal +import sys +import traceback +from omegaconf import OmegaConf, read_write +from wild_visual_navigation.utils import Data +from os.path import join +from threading import Thread, Event +from prettytable import PrettyTable +from termcolor import colored +import os + + +class WvnFeatureExtractor: + def __init__(self, node_name): + # Read params + self.read_params() + + # Initialize variables + self._node_name = node_name + self._load_model_counter = 0 + + # Timers to control the rate of the subscriber + self._last_checkpoint_ts = rospy.get_time() + + # Setup modules + self._feature_extractor = FeatureExtractor( + self._ros_params.device, + segmentation_type=self._ros_params.segmentation_type, + feature_type=self._ros_params.feature_type, + patch_size=self._ros_params.dino_patch_size, + backbone_type=self._ros_params.dino_backbone, + input_size=self._ros_params.network_input_image_height, + slic_num_components=self._ros_params.slic_num_components, + ) + + # Load model + # We manually update the input size to the models depending on the chosen features + self._params.model.simple_mlp_cfg.input_size = self._feature_extractor.feature_dim + self._params.model.double_mlp_cfg.input_size = self._feature_extractor.feature_dim + self._params.model.simple_gcn_cfg.input_size = self._feature_extractor.feature_dim + self._params.model.linear_rnvp_cfg.input_size = self._feature_extractor.feature_dim + self._model = get_model(self._params.model).to(self._ros_params.device) + self._model.eval() + + if self.anomaly_detection: + self._confidence_generator = ConfidenceGenerator( + method=self._params.loss_anomaly.method, std_factor=self._params.loss_anomaly.confidence_std_factor + ) + + else: + self._confidence_generator = ConfidenceGenerator( + method=self._params.loss.method, std_factor=self._params.loss.confidence_std_factor + ) + self._log_data = {} + self.setup_ros() + + # Setup verbosity levels + if self._ros_params.verbose: + + self._status_thread_stop_event = Event() + self._status_thread = Thread(target=self.status_thread_loop, name="status") + self._run_status_thread = True + self._status_thread.start() + + rospy.on_shutdown(self.shutdown_callback) + signal.signal(signal.SIGINT, self.shutdown_callback) + signal.signal(signal.SIGTERM, self.shutdown_callback) + + def shutdown_callback(self, *args, **kwargs): + self._run_status_thread = False + self._status_thread_stop_event.set() + self._status_thread.join() + + rospy.signal_shutdown(f"Wild Visual Navigation Feature Extraction killed {args}") + sys.exit(0) + + def read_params(self): + """Reads all the parameters from the parameter server""" + self._params = OmegaConf.structured(ExperimentParams) + self._ros_params = OmegaConf.structured(RosFeatureExtractorNodeParams) + + # Override the empty dataclass with values from rosparm server + with read_write(self._ros_params): + for k in self._ros_params.keys(): + self._ros_params[k] = rospy.get_param(f"~{k}") + + with read_write(self._params): + self._params.loss.confidence_std_factor = self._ros_params.confidence_std_factor + self._params.loss_anomaly.confidence_std_factor = self._ros_params.confidence_std_factor + + self.anomaly_detection = self._params.model.name == "LinearRnvp" + + def setup_ros(self, setup_fully=True): + """Main function to setup ROS-related stuff: publishers, subscribers and services""" + # Image callback + + self._camera_handler = {} + self._camera_scheduler = Scheduler() + + if self._ros_params.verbose: + # DEBUG Logging + self._log_data[f"time_last_model"] = -1 + self._log_data[f"nr_model_updates"] = -1 + + self._last_image_ts = {} + + for cam in self._ros_params.camera_topics: + self._last_image_ts[cam] = rospy.get_time() + if self._ros_params.verbose: + # DEBUG Logging + self._log_data[f"nr_images_{cam}"] = 0 + self._log_data[f"time_last_image_{cam}"] = -1 + + # Initialize camera handler for given cam + self._camera_handler[cam] = {} + # Store camera name + self._ros_params.camera_topics[cam]["name"] = cam + + # Add to scheduler + self._camera_scheduler.add_process(cam, self._ros_params.camera_topics[cam]["scheduler_weight"]) + + # Camera info + t = self._ros_params.camera_topics[cam]["info_topic"] + rospy.loginfo(f"[{self._node_name}] Waiting for camera info topic {t}") + camera_info_msg = rospy.wait_for_message(self._ros_params.camera_topics[cam]["info_topic"], CameraInfo) + rospy.loginfo(f"[{self._node_name}] Done") + K, H, W = rc.ros_cam_info_to_tensors(camera_info_msg, device=self._ros_params.device) + + self._camera_handler[cam]["camera_info"] = camera_info_msg + self._camera_handler[cam]["K"] = K + self._camera_handler[cam]["H"] = H + self._camera_handler[cam]["W"] = W + + image_projector = ImageProjector( + K=self._camera_handler[cam]["K"], + h=self._camera_handler[cam]["H"], + w=self._camera_handler[cam]["W"], + new_h=self._ros_params.network_input_image_height, + new_w=self._ros_params.network_input_image_width, + ) + msg = self._camera_handler[cam]["camera_info"] + msg.width = self._ros_params.network_input_image_width + msg.height = self._ros_params.network_input_image_height + msg.K = image_projector.scaled_camera_matrix[0, :3, :3].cpu().numpy().flatten().tolist() + msg.P = image_projector.scaled_camera_matrix[0, :3, :4].cpu().numpy().flatten().tolist() + + with read_write(self._ros_params): + self._camera_handler[cam]["camera_info_msg_out"] = msg + self._camera_handler[cam]["image_projector"] = image_projector + + # Set subscribers + base_topic = self._ros_params.camera_topics[cam]["image_topic"].replace("/compressed", "") + is_compressed = self._ros_params.camera_topics[cam]["image_topic"] != base_topic + if is_compressed: + # TODO study the effect of the buffer size + image_sub = rospy.Subscriber( + self._ros_params.camera_topics[cam]["image_topic"], + CompressedImage, + self.image_callback, + callback_args=cam, + queue_size=1, + ) + else: + image_sub = rospy.Subscriber( + self._ros_params.camera_topics[cam]["image_topic"], + Image, + self.image_callback, + callback_args=cam, + queue_size=1, + ) + self._camera_handler[cam]["image_sub"] = image_sub + + # Set publishers + trav_pub = rospy.Publisher( + f"/wild_visual_navigation_node/{cam}/traversability", + Image, + queue_size=1, + ) + info_pub = rospy.Publisher( + f"/wild_visual_navigation_node/{cam}/camera_info", + CameraInfo, + queue_size=1, + ) + self._camera_handler[cam]["trav_pub"] = trav_pub + self._camera_handler[cam]["info_pub"] = info_pub + if self.anomaly_detection and self._ros_params.camera_topics[cam]["publish_confidence"]: + rospy.logwarn(f"[{self._node_name}] Warning force set public confidence to false") + self._ros_params.camera_topics[cam]["publish_confidence"] = False + + if self._ros_params.camera_topics[cam]["publish_input_image"]: + input_pub = rospy.Publisher( + f"/wild_visual_navigation_node/{cam}/image_input", + Image, + queue_size=1, + ) + self._camera_handler[cam]["input_pub"] = input_pub + + if self._ros_params.camera_topics[cam]["publish_confidence"]: + conf_pub = rospy.Publisher( + f"/wild_visual_navigation_node/{cam}/confidence", + Image, + queue_size=1, + ) + self._camera_handler[cam]["conf_pub"] = conf_pub + + if self._ros_params.camera_topics[cam]["use_for_training"]: + imagefeat_pub = rospy.Publisher( + f"/wild_visual_navigation_node/{cam}/feat", + ImageFeatures, + queue_size=1, + ) + self._camera_handler[cam]["imagefeat_pub"] = imagefeat_pub + + def status_thread_loop(self): + rate = rospy.Rate(self._ros_params.status_thread_rate) + # Learning loop + while self._run_status_thread: + self._status_thread_stop_event.wait(timeout=0.01) + if self._status_thread_stop_event.is_set(): + rospy.logwarn(f"[{self._node_name}] Stopped learning thread") + break + + t = rospy.get_time() + x = PrettyTable() + x.field_names = ["Key", "Value"] + + for k, v in self._log_data.items(): + if "time" in k: + d = t - v + if d < 0: + c = "red" + if d < 0.2: + c = "green" + elif d < 1.0: + c = "yellow" + else: + c = "red" + x.add_row([k, colored(round(d, 2), c)]) + else: + x.add_row([k, v]) + print(f"[{self._node_name}]\n{x}") + try: + rate.sleep() + except Exception: + rate = rospy.Rate(self._ros_params.status_thread_rate) + print(f"[{self._node_name}] Ignored jump pack in time!") + self._status_thread_stop_event.clear() + + @torch.no_grad() + def image_callback(self, image_msg: Image, cam: str): # info_msg: CameraInfo + """Main callback to process incoming images. + + Args: + image_msg (sensor_msgs/Image): Incoming image + info_msg (sensor_msgs/CameraInfo): Camera info message associated to the image + cam (str): Camera name + """ + # Check the rate + ts = image_msg.header.stamp.to_sec() + if abs(ts - self._last_image_ts[cam]) < 1.0 / self._ros_params.image_callback_rate: + return + + # Check the scheduler + if self._camera_scheduler.get() != cam: + return + else: + if self._ros_params.verbose: + rospy.loginfo(f"[{self._node_name}] Image callback: {cam} -> Process") + + self._last_image_ts[cam] = ts + + # If all the checks are passed, process the image + try: + if self._ros_params.verbose: + # DEBUG Logging + self._log_data[f"nr_images_{cam}"] += 1 + self._log_data[f"time_last_image_{cam}"] = rospy.get_time() + + # Update model from file if possible + self.load_model(image_msg.header.stamp) + + # Convert image message to torch image + torch_image = rc.ros_image_to_torch(image_msg, device=self._ros_params.device) + torch_image = self._camera_handler[cam]["image_projector"].resize_image(torch_image) + C, H, W = torch_image.shape + + # Extract features + _, feat, seg, center, dense_feat = self._feature_extractor.extract( + img=torch_image[None], + return_centers=False, + return_dense_features=True, + n_random_pixels=100, + ) + + # Forward pass to predict traversability + if self._ros_params.prediction_per_pixel: + # Pixel-wise traversability prediction using the dense features + data = Data(x=dense_feat[0].permute(1, 2, 0).reshape(-1, dense_feat.shape[1])) + else: + # input_feat = dense_feat[0].permute(1, 2, 0).reshape(-1, dense_feat.shape[1]) + # Segment-wise traversability prediction using the average feature per segment + input_feat = feat[seg.reshape(-1)] + data = Data(x=input_feat) + + # Predict traversability per feature + prediction = self._model.forward(data) + + if not self.anomaly_detection: + out_trav = prediction.reshape(H, W, -1)[:, :, 0] + else: + losses = prediction["logprob"].sum(1) + prediction["log_det"] + confidence = self._confidence_generator.inference_without_update(x=-losses) + trav = confidence + out_trav = trav.reshape(H, W, -1)[:, :, 0] + + msg = rc.numpy_to_ros_image(out_trav.cpu().numpy(), "passthrough") + msg.header = image_msg.header + msg.width = out_trav.shape[0] + msg.height = out_trav.shape[1] + self._camera_handler[cam]["trav_pub"].publish(msg) + + msg = self._camera_handler[cam]["camera_info_msg_out"] + msg.header = image_msg.header + self._camera_handler[cam]["info_pub"].publish(msg) + + # Publish image + if self._ros_params.camera_topics[cam]["publish_input_image"]: + msg = rc.numpy_to_ros_image( + (torch_image.permute(1, 2, 0) * 255).cpu().numpy().astype(np.uint8), + "rgb8", + ) + msg.header = image_msg.header + msg.width = torch_image.shape[1] + msg.height = torch_image.shape[2] + self._camera_handler[cam]["input_pub"].publish(msg) + + # Publish confidence + if self._ros_params.camera_topics[cam]["publish_confidence"]: + loss_reco = F.mse_loss(prediction[:, 1:], data.x, reduction="none").mean(dim=1) + confidence = self._confidence_generator.inference_without_update(x=loss_reco) + out_confidence = confidence.reshape(H, W) + msg = rc.numpy_to_ros_image(out_confidence.cpu().numpy(), "passthrough") + msg.header = image_msg.header + msg.width = out_confidence.shape[0] + msg.height = out_confidence.shape[1] + self._camera_handler[cam]["conf_pub"].publish(msg) + + # Publish features and feature_segments + if self._ros_params.camera_topics[cam]["use_for_training"]: + msg = ImageFeatures() + msg.header = image_msg.header + msg.feature_segments = rc.numpy_to_ros_image(seg.cpu().numpy().astype(np.int32), "passthrough") + msg.feature_segments.header = image_msg.header + feat_np = feat.cpu().numpy() + + mad1 = MultiArrayDimension() + mad1.label = "n" + mad1.size = feat_np.shape[0] + mad1.stride = feat_np.shape[0] * feat_np.shape[1] + + mad2 = MultiArrayDimension() + mad2.label = "feat" + mad2.size = feat_np.shape[1] + mad2.stride = feat_np.shape[1] + + msg.features.data = feat_np.flatten().tolist() + msg.features.layout.dim.append(mad1) + msg.features.layout.dim.append(mad2) + self._camera_handler[cam]["imagefeat_pub"].publish(msg) + + except Exception as e: + traceback.print_exc() + rospy.logerr(f"[self._node_name] error image callback", e) + self.system_events["image_callback_state"] = { + "time": rospy.get_time(), + "value": f"failed to execute {e}", + } + raise Exception("Error in image callback") + + # Step scheduler + self._camera_scheduler.step() + + def load_model(self, stamp): + """Method to load the new model weights to perform inference on the incoming images + + Args: + None + """ + ts = stamp.to_sec() + if abs(ts - self._last_checkpoint_ts) < 1.0 / self._ros_params.load_save_checkpoint_rate: + return + + self._last_checkpoint_ts = ts + + # self._load_model_counter += 1 + # if self._load_model_counter % 10 == 0: + p = join(WVN_ROOT_DIR, ".tmp_state_dict.pt") + # p = join(WVN_ROOT_DIR,"assets/checkpoints/mountain_bike_trail_fpr_0.25.pt") + + if os.path.exists(p): + new_model_state_dict = torch.load(p) + k = list(self._model.state_dict().keys())[-1] + + # check if the key is in state dict - this may be not the case if switched between models + # assumption first key within state_dict is unique and sufficient to identify if a model has changed + if k in new_model_state_dict: + # check if the model has changed + if (self._model.state_dict()[k] != new_model_state_dict[k]).any(): + if self._ros_params.verbose: + self._log_data[f"time_last_model"] = rospy.get_time() + self._log_data[f"nr_model_updates"] += 1 + + self._model.load_state_dict(new_model_state_dict, strict=False) + if "confidence_generator" in new_model_state_dict.keys(): + cg = new_model_state_dict["confidence_generator"] + self._confidence_generator.var = cg["var"] + self._confidence_generator.mean = cg["mean"] + self._confidence_generator.std = cg["std"] + + if self._ros_params.verbose: + m, s, v = cg["mean"].item(), cg["std"].item(), cg["var"].item() + rospy.loginfo(f"[{self._node_name}] Loaded Confidence Generator {m}, std {s} var {v}") + + else: + if self._ros_params.verbose: + rospy.logerr(f"[{self._node_name}] Model Loading Failed") + + +if __name__ == "__main__": + node_name = "wvn_feature_extractor_node" + rospy.init_node(node_name) + + reload_rosparams( + enabled=rospy.get_param("~reload_default_params", True), + node_name=node_name, + camera_cfg="wide_angle_dual", + ) + + wvn = WvnFeatureExtractor(node_name) + rospy.spin() diff --git a/wild_visual_navigation_ros/scripts/wvn_learning_node.py b/wild_visual_navigation_ros/scripts/wvn_learning_node.py new file mode 100644 index 00000000..9bcb3a55 --- /dev/null +++ b/wild_visual_navigation_ros/scripts/wvn_learning_node.py @@ -0,0 +1,962 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Matias Mattamala, Jonas Frey. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# +from wild_visual_navigation import WVN_ROOT_DIR +from wild_visual_navigation.image_projector import ImageProjector +from wild_visual_navigation.supervision_generator import SupervisionGenerator +from wild_visual_navigation.traversability_estimator import TraversabilityEstimator +from wild_visual_navigation.traversability_estimator import MissionNode, SupervisionNode +import wild_visual_navigation_ros.ros_converter as rc +from wild_visual_navigation_ros.reload_rosparams import reload_rosparams +from wild_visual_navigation_msgs.msg import RobotState, SystemState, ImageFeatures +from wild_visual_navigation.visu import LearningVisualizer +from wild_visual_navigation_msgs.srv import ( + LoadCheckpoint, + SaveCheckpoint, + LoadCheckpointResponse, + SaveCheckpointResponse, +) +from wild_visual_navigation.utils import WVNMode, create_experiment_folder +from wild_visual_navigation.cfg import ExperimentParams, RosLearningNodeParams + +from std_srvs.srv import SetBool, Trigger, TriggerResponse +from geometry_msgs.msg import PoseStamped, Point, TwistStamped +from nav_msgs.msg import Path +from sensor_msgs.msg import Image, CameraInfo +from std_msgs.msg import ColorRGBA, Float32 +from visualization_msgs.msg import Marker +import tf2_ros +import rospy +import message_filters + +from pytictac import ClassTimer, ClassContextTimer, accumulate_time +from omegaconf import OmegaConf, read_write +from threading import Thread, Event +import os +import seaborn as sns +import torch +import numpy as np +from typing import Optional +import traceback +import signal +import sys +import time + + +def time_func(): + return rospy.get_time() + + +class WvnLearning: + def __init__(self, node_name): + # Timers to control the rate of the publishers + self._last_image_ts = time_func() + self._last_supervision_ts = time_func() + self._last_checkpoint_ts = time_func() + self._setup_ready = False + + # Prepare variables + self._node_name = node_name + + # Read params + self.read_params() + + # Initialize camera handler for subscription/publishing + self._system_events = {} + + # Setup ros + self.setup_ros(setup_fully=self._ros_params.mode != WVNMode.EXTRACT_LABELS) + + # Visualization + self._color_palette = sns.color_palette(self._ros_params.colormap, as_cmap=True) + + # Setup Mission Folder + model_path = create_experiment_folder(self._params) + + with read_write(self._params): + self._params.general.model_path = model_path + + # Initialize traversability estimator + self._traversability_estimator = TraversabilityEstimator( + params=self._params, + device=self._ros_params.device, + max_distance=self._ros_params.traversability_radius, + image_distance_thr=self._ros_params.image_graph_dist_thr, + supervision_distance_thr=self._ros_params.supervision_graph_dist_thr, + min_samples_for_training=self._ros_params.min_samples_for_training, + vis_node_index=self._ros_params.vis_node_index, + mode=self._ros_params.mode, + extraction_store_folder=self._ros_params.extraction_store_folder, + anomaly_detection=self.anomaly_detection, + ) + + # Initialize traversability generator to process velocity commands + self._supervision_generator = SupervisionGenerator( + device=self._ros_params.device, + kf_process_cov=0.1, + kf_meas_cov=10, + kf_outlier_rejection="huber", + kf_outlier_rejection_delta=0.5, + sigmoid_slope=20, + sigmoid_cutoff=0.25, # 0.2 + untraversable_thr=self._ros_params.untraversable_thr, # 0.1 + time_horizon=0.05, + graph_max_length=1, + ) + + # Setup Timer if needed + self._timer = ClassTimer( + objects=[ + self, + self._traversability_estimator, + self._traversability_estimator._visualizer, + self._supervision_generator, + ], + names=[ + "WVN", + "TraversabilityEstimator", + "Visualizer", + "SupervisionGenerator", + ], + enabled=( + self._ros_params.print_image_callback_time + or self._ros_params.print_supervision_callback_time + or self._ros_params.log_time + ), + ) + + # Register shutdown callbacks + rospy.on_shutdown(self.shutdown_callback) + signal.signal(signal.SIGINT, self.shutdown_callback) + signal.signal(signal.SIGTERM, self.shutdown_callback) + + # Launch processes + print("-" * 80) + self._setup_ready = True + rospy.loginfo(f"[{self._node_name}] Launching [learning] thread") + if self._ros_params.mode != WVNMode.EXTRACT_LABELS: + self._learning_thread_stop_event = Event() + self.learning_thread = Thread(target=self.learning_thread_loop, name="learning") + self.learning_thread.start() + + # self.logging_thread_stop_event = Event() + # self.logging_thread = Thread(target=self.logging_thread_loop, name="logging") + # self.logging_thread.start() + rospy.loginfo(f"[{self._node_name}] [WVN] System ready") + + def shutdown_callback(self, *args, **kwargs): + # Write stuff to files + rospy.logwarn("Shutdown callback called") + if self._ros_params.mode != WVNMode.EXTRACT_LABELS: + self._learning_thread_stop_event.set() + # self.logging_thread_stop_event.set() + + print(f"[{self._node_name}] Storing learned checkpoint...", end="") + self._traversability_estimator.save_checkpoint(self._params.general.model_path, "last_checkpoint.pt") + print("done") + + if self._ros_params.log_time: + print(f"[{self._node_name}] Storing timer data...", end="") + self._timer.store(folder=self._params.general.model_path) + print("done") + + print(f"[{self._node_name}] Joining learning thread...", end="") + if self._ros_params.mode != WVNMode.EXTRACT_LABELS: + self._learning_thread_stop_event.set() + self.learning_thread.join() + + # self.logging_thread_stop_event.set() + # self.logging_thread.join() + print("done") + + rospy.signal_shutdown(f"[{self._node_name}] Wild Visual Navigation killed {args}") + sys.exit(0) + + @accumulate_time + def read_params(self): + """Reads all the parameters from the parameter server""" + self._params = OmegaConf.structured(ExperimentParams) + self._ros_params = OmegaConf.structured(RosLearningNodeParams) + + # Override the empty dataclass with values from ros parmeter server + with read_write(self._ros_params): + for k in self._ros_params.keys(): + self._ros_params[k] = rospy.get_param(f"~{k}") + + self._ros_params.robot_height = rospy.get_param("~robot_height") # TODO robot_height currently not used + + with read_write(self._ros_params): + self._ros_params.mode = WVNMode.from_string(self._ros_params.mode) + + with read_write(self._params): + self._params.general.name = self._ros_params.mission_name + self._params.general.timestamp = self._ros_params.mission_timestamp + self._params.general.log_confidence = self._ros_params.log_confidence + self._params.loss.confidence_std_factor = self._ros_params.confidence_std_factor + self._params.loss.w_temp = 0 + + # Parse operation modes + if self._ros_params.mode == WVNMode.ONLINE: + rospy.logwarn( + f"[{self._node_name}] WARNING: online_mode enabled. The graph will not store any debug/training data such as images\n" + ) + + elif self._ros_params.mode == WVNMode.EXTRACT_LABELS: + with read_write(self._ros_params): + # TODO verify if this is needed + self._ros_params.image_callback_rate = 3 + self._ros_params.supervision_callback_rate = 4 + self._ros_params.image_graph_dist_thr = 0.2 + self._ros_params.supervision_graph_dist_thr = 0.1 + os.makedirs( + os.path.join(self._ros_params.extraction_store_folder, "image"), + exist_ok=True, + ) + os.makedirs( + os.path.join(self._ros_params.extraction_store_folder, "supervision_mask"), + exist_ok=True, + ) + + self._step = -1 + self._step_time = time_func() + self.anomaly_detection = self._params.model.name == "LinearRnvp" + + def setup_ros(self, setup_fully=True): + """Main function to setup ROS-related stuff: publishers, subscribers and services""" + if setup_fully: + # Initialize TF listener + self.tf_buffer = tf2_ros.Buffer(cache_time=rospy.Duration(30.0)) + self.tf_listener = tf2_ros.TransformListener(self.tf_buffer) + + # Robot state callback + robot_state_sub = message_filters.Subscriber(self._ros_params.robot_state_topic, RobotState) + cache1 = message_filters.Cache(robot_state_sub, 10) # noqa: F841 + desired_twist_sub = message_filters.Subscriber(self._ros_params.desired_twist_topic, TwistStamped) + cache2 = message_filters.Cache(desired_twist_sub, 10) # noqa: F841 + + self._robot_state_sub = message_filters.ApproximateTimeSynchronizer( + [robot_state_sub, desired_twist_sub], queue_size=10, slop=0.5 + ) + + rospy.loginfo( + f"[{self._node_name}] Start waiting for RobotState topic {self._ros_params.robot_state_topic} being published!" + ) + rospy.wait_for_message(self._ros_params.robot_state_topic, RobotState) + rospy.loginfo( + f"[{self._node_name}] Start waiting for TwistStamped topic {self._ros_params.desired_twist_topic} being published!" + ) + rospy.wait_for_message(self._ros_params.desired_twist_topic, TwistStamped) + self._robot_state_sub.registerCallback(self.robot_state_callback) + + self._camera_handler = {} + # Image callback + for cam in self._ros_params.camera_topics: + # Initialize camera handler for given cam + self._camera_handler[cam] = {} + # Store camera name + self._ros_params.camera_topics[cam]["name"] = cam + + # Set subscribers + if self._ros_params.mode == WVNMode.DEBUG: + # In debug mode additionally send the image to the callback function + self._visualizer = LearningVisualizer() + + imagefeat_sub = message_filters.Subscriber( + f"/wild_visual_navigation_node/{cam}/feat", ImageFeatures + ) + info_sub = message_filters.Subscriber(f"/wild_visual_navigation_node/{cam}/camera_info", CameraInfo) + image_sub = message_filters.Subscriber(f"/wild_visual_navigation_node/{cam}/image_input", Image) + sync = message_filters.ApproximateTimeSynchronizer( + [imagefeat_sub, info_sub, image_sub], queue_size=4, slop=0.5 + ) + sync.registerCallback(self.imagefeat_callback, self._ros_params.camera_topics[cam]) + + last_image_overlay_pub = rospy.Publisher( + f"/wild_visual_navigation_node/{cam}/debug/last_node_image_overlay", + Image, + queue_size=10, + ) + + self._camera_handler[cam]["debug"] = {} + self._camera_handler[cam]["debug"]["image_overlay"] = last_image_overlay_pub + + else: + print(f"/wild_visual_navigation_node/{cam}/feat") + imagefeat_sub = message_filters.Subscriber( + f"/wild_visual_navigation_node/{cam}/feat", ImageFeatures + ) + info_sub = message_filters.Subscriber(f"/wild_visual_navigation_node/{cam}/camera_info", CameraInfo) + sync = message_filters.ApproximateTimeSynchronizer( + [imagefeat_sub, info_sub], queue_size=4, slop=0.5 + ) + sync.registerCallback(self.imagefeat_callback, self._ros_params.camera_topics[cam]) + + # Wait for features message to determine the input size of the model + cam = list(self._ros_params.camera_topics.keys())[0] + + for cam in self._ros_params.camera_topics: + rospy.loginfo(f"[{self._node_name}] Waiting for feat topic {cam}...") + if self._ros_params.camera_topics[cam]["use_for_training"]: + feat_msg = rospy.wait_for_message(f"/wild_visual_navigation_node/{cam}/feat", ImageFeatures) + + feature_dim = int(feat_msg.features.layout.dim[1].size) + # Modify the parameters + with read_write(self._params): + self._params.model.simple_mlp_cfg.input_size = feature_dim + self._params.model.double_mlp_cfg.input_size = feature_dim + self._params.model.simple_gcn_cfg.input_size = feature_dim + self._params.model.linear_rnvp_cfg.input_size = feature_dim + rospy.loginfo(f"[{self._node_name}] Done") + + # 3D outputs + self._pub_debug_supervision_graph = rospy.Publisher( + "/wild_visual_navigation_node/supervision_graph", Path, queue_size=10 + ) + self._pub_mission_graph = rospy.Publisher("/wild_visual_navigation_node/mission_graph", Path, queue_size=10) + self._pub_graph_footprints = rospy.Publisher( + "/wild_visual_navigation_node/graph_footprints", Marker, queue_size=10 + ) + # 1D outputs + self._pub_instant_traversability = rospy.Publisher( + "/wild_visual_navigation_node/instant_traversability", + Float32, + queue_size=10, + ) + self._pub_system_state = rospy.Publisher( + "/wild_visual_navigation_node/system_state", SystemState, queue_size=10 + ) + + # Services + # Like, reset graph or the like + self._save_checkpt_service = rospy.Service("~save_checkpoint", SaveCheckpoint, self.save_checkpoint_callback) + self._load_checkpt_service = rospy.Service("~load_checkpoint", LoadCheckpoint, self.load_checkpoint_callback) + + self._pause_learning_service = rospy.Service("~pause_learning", SetBool, self.pause_learning_callback) + self._reset_service = rospy.Service("~reset", Trigger, self.reset_callback) + + @accumulate_time + def learning_thread_loop(self): + """This implements the main thread that runs the training procedure + We can only set the rate using rosparam + """ + # Set rate + rate = rospy.Rate(self._ros_params.learning_thread_rate) + # Learning loop + while True: + self._system_events["learning_thread_loop"] = { + "time": time_func(), + "value": "running", + } + self._learning_thread_stop_event.wait(timeout=0.01) + if self._learning_thread_stop_event.is_set(): + rospy.logwarn("Stopped learning thread") + break + + # Optimize model + with ClassContextTimer(parent_obj=self, block_name="training_step_time"): + res = self._traversability_estimator.train() + + if self._step != self._traversability_estimator.step: + self._step_time = time_func() + self._step = self._traversability_estimator.step + + # Publish loss + system_state = SystemState() + for k in res.keys(): + if hasattr(system_state, k): + setattr(system_state, k, res[k]) + + system_state.pause_learning = self._traversability_estimator.pause_learning + system_state.mode = self._ros_params.mode.value + system_state.step = self._step + self._pub_system_state.publish(system_state) + + # Get current weights + new_model_state_dict = self._traversability_estimator._model.state_dict() + + # Check the rate + ts = time_func() + if abs(ts - self._last_checkpoint_ts) > 1.0 / self._ros_params.load_save_checkpoint_rate: + + cg = self._traversability_estimator._traversability_loss._confidence_generator + new_model_state_dict["confidence_generator"] = cg.get_dict() + + fn = os.path.join(WVN_ROOT_DIR, ".tmp_state_dict.pt") + if os.path.exists(fn): + os.remove(fn) + torch.save(new_model_state_dict, fn) + self._last_checkpoint_ts = ts + print( + "Update model. Valid Nodes: ", + self._traversability_estimator._mission_graph.get_num_valid_nodes(), + " steps: ", + self._traversability_estimator._step, + ) + + rate.sleep() + + self._system_events["learning_thread_loop"] = { + "time": time_func(), + "value": "finished", + } + self._learning_thread_stop_event.clear() + + def logging_thread_loop(self): + rate = rospy.Rate(self._ros_params.logging_thread_rate) + + # Learning loop + while True: + self._learning_thread_stop_event.wait(timeout=0.01) + if self._learning_thread_stop_event.is_set(): + rospy.logwarn("Stopped logging thread") + break + + current_time = time_func() + tmp = self._system_events.copy() + rospy.loginfo("System Events:") + for k, v in tmp.items(): + value = v["value"] + msg = ( + (k + ": ").ljust(35, " ") + + (str(round(current_time - v["time"], 4)) + "s ").ljust(10, " ") + + f" {value}" + ) + rospy.loginfo(msg) + rate.sleep() + rospy.loginfo("--------------") + self._learning_thread_stop_event.clear() + + @accumulate_time + def robot_state_callback(self, state_msg, desired_twist_msg: TwistStamped): + """Main callback to process supervision info (robot state) + + Args: + state_msg (wild_visual_navigation_msgs/RobotState): Robot state message + desired_twist_msg (geometry_msgs/TwistStamped): Desired twist message + """ + if not self._setup_ready: + return + + self._system_events["robot_state_callback_received"] = { + "time": time_func(), + "value": "message received", + } + try: + ts = state_msg.header.stamp.to_sec() + if abs(ts - self._last_supervision_ts) < 1.0 / self._ros_params.supervision_callback_rate: + self._system_events["robot_state_callback_canceled"] = { + "time": time_func(), + "value": "canceled due to rate", + } + return + self._last_supervision_ts = ts + + # Query transforms from TF + success, pose_base_in_world = rc.ros_tf_to_torch( + self.query_tf( + self._ros_params.fixed_frame, + self._ros_params.base_frame, + state_msg.header.stamp, + ), + device=self._ros_params.device, + ) + if not success: + self._system_events["robot_state_callback_canceled"] = { + "time": time_func(), + "value": "canceled due to pose_base_in_world", + } + return + + success, pose_footprint_in_base = rc.ros_tf_to_torch( + self.query_tf( + self._ros_params.base_frame, + self._ros_params.footprint_frame, + state_msg.header.stamp, + ), + device=self._ros_params.device, + ) + if not success: + self._system_events["robot_state_callback_canceled"] = { + "time": time_func(), + "value": "canceled due to pose_footprint_in_base", + } + return + + # The footprint requires a correction: we use the same orientation as the base + pose_footprint_in_base[:3, :3] = torch.eye(3, device=self._ros_params.device) + + # Convert state to tensor + supervision_tensor, supervision_labels = rc.wvn_robot_state_to_torch( + state_msg, device=self._ros_params.device + ) + current_twist_tensor = rc.twist_stamped_to_torch(state_msg.twist, device=self._ros_params.device) + desired_twist_tensor = rc.twist_stamped_to_torch(desired_twist_msg, device=self._ros_params.device) + + # Update traversability + ( + traversability, + traversability_var, + is_untraversable, + ) = self._supervision_generator.update_velocity_tracking( + current_twist_tensor, desired_twist_tensor, velocities=["vx", "vy"] + ) + + # Create supervision node for the graph + supervision_node = SupervisionNode( + timestamp=ts, + pose_base_in_world=pose_base_in_world, + pose_footprint_in_base=pose_footprint_in_base, + twist_in_base=current_twist_tensor, + desired_twist_in_base=desired_twist_tensor, + width=self._ros_params.robot_width, + length=self._ros_params.robot_length, + height=self._ros_params.robot_height, + supervision=supervision_tensor, + traversability=traversability, + traversability_var=traversability_var, + is_untraversable=is_untraversable, + ) + + # Add node to the graph + self._traversability_estimator.add_supervision_node(supervision_node) + + if self._ros_params.mode == WVNMode.DEBUG or self._ros_params.mode == WVNMode.ONLINE: + self.visualize_supervision() + + if self._ros_params.print_supervision_callback_time: + print(f"[{self._node_name}]\n{self._timer}") + + self._system_events["robot_state_callback_state"] = { + "time": time_func(), + "value": "executed successfully", + } + + except Exception as e: + traceback.print_exc() + rospy.logerr(f"[{self._node_name}] error state callback", e) + self._system_events["robot_state_callback_state"] = { + "time": time_func(), + "value": f"failed to execute {e}", + } + + raise Exception("Error in robot state callback") + + @accumulate_time + def imagefeat_callback(self, *args): + """Main callback to process incoming images + + Args: + imagefeat_msg (wild_visual_navigation_msg/ImageFeatures): Incoming imagefeatures + info_msg (sensor_msgs/CameraInfo): Camera info message associated to the image + """ + if not self._setup_ready: + return + + if self._ros_params.mode == WVNMode.DEBUG: + assert len(args) == 4 + imagefeat_msg, info_msg, image_msg, camera_options = tuple(args) + else: + assert len(args) == 3 + imagefeat_msg, info_msg, camera_options = tuple(args) + + self._system_events["image_callback_received"] = { + "time": time_func(), + "value": "message received", + } + + if self._ros_params.verbose: + print(f"[{self._node_name}] Image callback: {camera_options['name']}... ", end="") + + try: + # Run the callback so as to match the desired rate + ts = imagefeat_msg.header.stamp.to_sec() + if abs(ts - self._last_image_ts) < 1.0 / self._ros_params.image_callback_rate: + return + self._last_image_ts = ts + + # Query transforms from TF + success, pose_base_in_world = rc.ros_tf_to_torch( + self.query_tf( + self._ros_params.fixed_frame, + self._ros_params.base_frame, + imagefeat_msg.header.stamp, + ), + device=self._ros_params.device, + ) + if not success: + self._system_events["image_callback_canceled"] = { + "time": time_func(), + "value": "canceled due to pose_base_in_world", + } + return + + success, pose_cam_in_base = rc.ros_tf_to_torch( + self.query_tf( + self._ros_params.base_frame, + imagefeat_msg.header.frame_id, + imagefeat_msg.header.stamp, + ), + device=self._ros_params.device, + ) + if not success: + self._system_events["image_callback_canceled"] = { + "time": time_func(), + "value": "canceled due to pose_cam_in_base", + } + return + + # Prepare image projector + K, H, W = rc.ros_cam_info_to_tensors(info_msg, device=self._ros_params.device) + image_projector = ImageProjector( + K=K, + h=H, + w=W, + new_h=self._ros_params.network_input_image_height, + new_w=self._ros_params.network_input_image_width, + ) + # Add image to base node + # convert image message to torch image + feature_segments = rc.ros_image_to_torch( + imagefeat_msg.feature_segments, + desired_encoding="passthrough", + device=self._ros_params.device, + ).clone() + h_small, w_small = feature_segments.shape[1:3] + + torch_image = None + # convert image message to torch image + if self._ros_params.mode == WVNMode.DEBUG: + torch_image = rc.ros_image_to_torch( + image_msg, + desired_encoding="passthrough", + device=self._ros_params.device, + ).clone() + + # Create mission node for the graph + mission_node = MissionNode( + timestamp=ts, + pose_base_in_world=pose_base_in_world, + pose_cam_in_base=pose_cam_in_base, + image=torch_image, + image_projector=image_projector, + camera_name=camera_options["name"], + use_for_training=camera_options["use_for_training"], + ) + ma = imagefeat_msg.features + dims = tuple(map(lambda x: x.size, ma.layout.dim)) + mission_node.features = torch.from_numpy( + np.array(ma.data, dtype=float).reshape(dims).astype(np.float32) + ).to(self._ros_params.device) + mission_node.feature_segments = feature_segments[0] + + # Add node to graph + added_new_node = self._traversability_estimator.add_mission_node(mission_node) + + if self._ros_params.mode == WVNMode.DEBUG: + # Publish current predictions + + # Publish supervision data depending on the mode + self.visualize_image_overlay() + + if added_new_node: + self._traversability_estimator.update_visualization_node() + + self.visualize_mission_graph() + + # Print callback time if required + if self._ros_params.print_image_callback_time: + rospy.loginfo(f"[{self._node_name}]\n{self._timer}") + + self._system_events["image_callback_state"] = { + "time": time_func(), + "value": "executed successfully", + } + + except Exception as e: + traceback.print_exc() + rospy.logerr(f"[{self._node_name}] error image callback", e) + self._system_events["image_callback_state"] = { + "time": time_func(), + "value": f"failed to execute {e}", + } + raise Exception("Error in image callback") + + @accumulate_time + def visualize_supervision(self): + """Publishes all the visualizations related to supervision info, + like footprints and the sliding graph + """ + # Get current time for later + now = rospy.Time.now() + + supervision_graph_msg = Path() + supervision_graph_msg.header.frame_id = self._ros_params.fixed_frame + supervision_graph_msg.header.stamp = now + + # Footprints + footprints_marker = Marker() + footprints_marker.id = 0 + footprints_marker.ns = "footprints" + footprints_marker.header.frame_id = self._ros_params.fixed_frame + footprints_marker.header.stamp = now + footprints_marker.type = Marker.TRIANGLE_LIST + footprints_marker.action = Marker.ADD + footprints_marker.scale.x = 1 + footprints_marker.scale.y = 1 + footprints_marker.scale.z = 1 + footprints_marker.color.a = 1.0 + footprints_marker.pose.orientation.w = 1.0 + footprints_marker.pose.position.x = 0.0 + footprints_marker.pose.position.y = 0.0 + footprints_marker.pose.position.z = 0.0 + + last_points = [None, None] + for node in self._traversability_estimator.get_supervision_nodes(): + # Path + pose = PoseStamped() + pose.header.stamp = now + pose.header.frame_id = self._ros_params.fixed_frame + pose.pose = rc.torch_to_ros_pose(node.pose_base_in_world) + supervision_graph_msg.poses.append(pose) + + # Color for traversability + r, g, b, _ = self._color_palette(node.traversability.item()) + c = ColorRGBA(r, g, b, 0.95) + + # Rainbow path + side_points = node.get_side_points() + + # if the last points are empty, fill and continue + if None in last_points: + for i in range(2): + last_points[i] = Point( + x=side_points[i, 0].item(), + y=side_points[i, 1].item(), + z=side_points[i, 2].item(), + ) + continue + else: + # here we want to add 2 triangles: from the last saved points (lp) + # and the current side points (sp): + # triangle 1: [lp0, lp1, sp0] + # triangle 2: [lp1, sp0, sp1] + + points_to_add = [] + # add the last points points + for lp in last_points: + points_to_add.append(lp) + # Add first from new side points + points_to_add.append( + Point( + x=side_points[i, 0].item(), + y=side_points[i, 1].item(), + z=side_points[i, 2].item(), + ) + ) + # Add last of last points + points_to_add.append(last_points[0]) + # Add new side points and update last points + for i in range(2): + last_points[i] = Point( + x=side_points[i, 0].item(), + y=side_points[i, 1].item(), + z=side_points[i, 2].item(), + ) + points_to_add.append(last_points[i]) + + # Add all the points and colors + for p in points_to_add: + footprints_marker.points.append(p) + footprints_marker.colors.append(c) + + # Untraversable plane + if node.is_untraversable: + untraversable_plane = node.get_untraversable_plane(grid_size=2) + N, D = untraversable_plane.shape + # the following is a 'hack' to show the triangles correctly + for n in [0, 1, 3, 2, 0, 3]: + p = Point() + p.x = untraversable_plane[n, 0] + p.y = untraversable_plane[n, 1] + p.z = untraversable_plane[n, 2] + footprints_marker.points.append(p) + footprints_marker.colors.append(c) + + # Publish + if len(footprints_marker.points) % 3 != 0: + if self._ros_params.verbose: + rospy.loginfo(f"[{self._node_name}] number of points for footprint is {len(footprints_marker.points)}") + return + self._pub_graph_footprints.publish(footprints_marker) + self._pub_debug_supervision_graph.publish(supervision_graph_msg) + + # Publish latest traversability + self._pub_instant_traversability.publish(self._supervision_generator.traversability) + self._system_events["visualize_supervision"] = { + "time": time_func(), + "value": f"executed successfully", + } + + @accumulate_time + def visualize_mission_graph(self): + """Publishes all the visualizations related to the mission graph""" + # Get current time for later + now = rospy.Time.now() + + # Publish mission graph + mission_graph_msg = Path() + mission_graph_msg.header.frame_id = self._ros_params.fixed_frame + mission_graph_msg.header.stamp = now + + for node in self._traversability_estimator.get_mission_nodes(): + pose = PoseStamped() + pose.header.stamp = now + pose.header.frame_id = self._ros_params.fixed_frame + pose.pose = rc.torch_to_ros_pose(node.pose_cam_in_world) + mission_graph_msg.poses.append(pose) + + self._pub_mission_graph.publish(mission_graph_msg) + + @accumulate_time + def visualize_image_overlay(self): + """Publishes all the debugging, slow visualizations""" + + # Get visualization node + vis_node = self._traversability_estimator.get_mission_node_for_visualization() + + # Publish reprojections of last node in graph + if vis_node is not None: + cam = vis_node.camera_name + torch_image = vis_node._image + torch_mask = vis_node._supervision_mask + torch_mask = torch.nan_to_num(torch_mask.nanmean(axis=0)) != 0 + torch_mask = torch_mask.float() + + image_out = self._visualizer.plot_detectron_classification(torch_image, torch_mask, cmap="Blues") + self._camera_handler[cam]["debug"]["image_overlay"].publish(rc.numpy_to_ros_image(image_out)) + + def pause_learning_callback(self, req): + """Start and stop the network training""" + prev_state = self._traversability_estimator.pause_learning + self._traversability_estimator.pause_learning = req.data + if not req.data and prev_state: + message = "Resume training!" + elif req.data and prev_state: + message = "Training was already paused!" + elif not req.data and not prev_state: + message = "Training was already running!" + elif req.data and not prev_state: + message = "Pause training!" + message += f" Updated the network for {self._traversability_estimator.step} steps" + + return True, message + + def reset_callback(self, req): + """Resets the system""" + rospy.logwarn(f"[{self._node_name}] System reset!") + + print(f"[{self._node_name}] Storing learned checkpoint...", end="") + self._traversability_estimator.save_checkpoint(self._params.general.model_path, "last_checkpoint.pt") + print("done") + + if self._ros_params.log_time: + print(f"[{self._node_name}] Storing timer data...", end="") + self._timer.store(folder=self._params.general.model_path) + print("done") + + # Create new mission folder + create_experiment_folder(self._params) + + # Reset traversability estimator + self._traversability_estimator.reset() + + print(f"[{self._node_name}] Reset done") + return TriggerResponse(True, "Reset done!") + + @accumulate_time + def save_checkpoint_callback(self, req): + """Service call to store the learned checkpoint + + Args: + req (TriggerRequest): Trigger request service + """ + if req.checkpoint_name == "": + req.checkpoint_name = "last_checkpoint.pt" + + if req.mission_path == "": + message = f"[WARNING] Store checkpoint {req.checkpoint_name} default mission path: {self._params.general.model_path}/{req.checkpoint_name}" + req.mission_path = self._params.general.model_path + else: + message = f"Store checkpoint {req.checkpoint_name} to: {req.mission_path}/{req.checkpoint_name}" + + self._traversability_estimator.save_checkpoint(req.mission_path, req.checkpoint_name) + return SaveCheckpointResponse(success=True, message=message) + + def load_checkpoint_callback(self, req): + """Service call to load a learned checkpoint + + Args: + req (TriggerRequest): Trigger request service + """ + if req.checkpoint_path == "": + return LoadCheckpointResponse( + success=False, + message=f"Path [{req.checkpoint_path}] is empty. Please check and try again", + ) + checkpoint_path = req.checkpoint_path + self._traversability_estimator.load_checkpoint(checkpoint_path) + return LoadCheckpointResponse(success=True, message=f"Checkpoint [{checkpoint_path}] loaded successfully") + + @accumulate_time + def query_tf(self, parent_frame: str, child_frame: str, stamp: Optional[rospy.Time] = None): + """Helper function to query TFs + + Args: + parent_frame (str): Frame of the parent TF + child_frame (str): Frame of the child + """ + + if stamp is None: + stamp = rospy.Time(0) + + try: + res = self.tf_buffer.lookup_transform(parent_frame, child_frame, stamp, timeout=rospy.Duration(0.03)) + trans = ( + res.transform.translation.x, + res.transform.translation.y, + res.transform.translation.z, + ) + rot = np.array( + [ + res.transform.rotation.x, + res.transform.rotation.y, + res.transform.rotation.z, + res.transform.rotation.w, + ] + ) + rot /= np.linalg.norm(rot) + return (trans, tuple(rot)) + except Exception: + if self._ros_params.verbose: + # print("Error in query tf: ", e) + rospy.logwarn(f"[{self._node_name}] Couldn't get between {parent_frame} and {child_frame}") + return (None, None) + + +if __name__ == "__main__": + fn = os.path.join(WVN_ROOT_DIR, ".tmp_state_dict.pt") + if os.path.exists(fn): + os.remove(fn) + + node_name = "wvn_learning_node" + rospy.init_node(node_name) + + reload_rosparams( + enabled=rospy.get_param("~reload_default_params", True), + node_name=node_name, + camera_cfg="wide_angle_dual", + ) + wvn = WvnLearning(node_name) + rospy.spin() diff --git a/wild_visual_navigation_ros/setup.py b/wild_visual_navigation_ros/setup.py index f376a2ec..33c335b0 100644 --- a/wild_visual_navigation_ros/setup.py +++ b/wild_visual_navigation_ros/setup.py @@ -1,3 +1,8 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Matias Mattamala, Jonas Frey. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# from setuptools import setup from catkin_pkg.python_setup import generate_distutils_setup diff --git a/wild_visual_navigation_ros/src/wild_visual_navigation_ros/__init__.py b/wild_visual_navigation_ros/src/wild_visual_navigation_ros/__init__.py index ca21baa5..b8dda2bc 100644 --- a/wild_visual_navigation_ros/src/wild_visual_navigation_ros/__init__.py +++ b/wild_visual_navigation_ros/src/wild_visual_navigation_ros/__init__.py @@ -1,3 +1,8 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Matias Mattamala, Jonas Frey. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# from .ros_converter import ( robot_state_to_torch, wvn_robot_state_to_torch, @@ -5,3 +10,5 @@ ros_tf_to_torch, ros_image_to_torch, ) +from .scheduler import Scheduler +from .reload_rosparams import reload_rosparams \ No newline at end of file diff --git a/wild_visual_navigation_ros/src/wild_visual_navigation_ros/ros_converter.py b/wild_visual_navigation_ros/src/wild_visual_navigation_ros/ros_converter.py index a8dff961..93ce778c 100644 --- a/wild_visual_navigation_ros/src/wild_visual_navigation_ros/ros_converter.py +++ b/wild_visual_navigation_ros/src/wild_visual_navigation_ros/ros_converter.py @@ -1,3 +1,8 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Matias Mattamala, Jonas Frey. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# import cv2 from geometry_msgs.msg import Pose from nav_msgs.msg import Odometry @@ -8,7 +13,7 @@ import numpy as np import torch import torchvision.transforms as transforms - +from pytictac import Timer CV_BRIDGE = CvBridge() TO_TENSOR = transforms.ToTensor() TO_PIL_IMAGE = transforms.ToPILImage() @@ -106,15 +111,18 @@ def ros_tf_to_torch(tf_pose, device="cpu"): def ros_image_to_torch(ros_img, desired_encoding="rgb8", device="cpu"): - if isinstance(ros_img, Image): + if type(ros_img).__name__ == "_sensor_msgs__Image" or isinstance(ros_img, Image): np_image = CV_BRIDGE.imgmsg_to_cv2(ros_img, desired_encoding=desired_encoding) - elif isinstance(ros_img, CompressedImage): + elif type(ros_img).__name__ == "_sensor_msgs__CompressedImage" or isinstance(ros_img, CompressedImage): np_arr = np.fromstring(ros_img.data, np.uint8) np_image = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) if "bgr" in ros_img.format: np_image = cv2.cvtColor(np_image, cv2.COLOR_BGR2RGB) - + + else: + raise ValueError("Image message type is not implemented.") + return TO_TENSOR(np_image).to(device) diff --git a/wild_visual_navigation_ros/src/wild_visual_navigation_ros/scheduler.py b/wild_visual_navigation_ros/src/wild_visual_navigation_ros/scheduler.py new file mode 100644 index 00000000..8183cfc6 --- /dev/null +++ b/wild_visual_navigation_ros/src/wild_visual_navigation_ros/scheduler.py @@ -0,0 +1,106 @@ +# +# Copyright (c) 2022-2024, ETH Zurich, Matias Mattamala, Jonas Frey. +# All rights reserved. Licensed under the MIT license. +# See LICENSE file in the project root for details. +# +class Scheduler: + """ + Implements a modified weighted round-robin scheduler + https://en.wikipedia.org/wiki/Weighted_round_robin + + Given a list of processes and corresponding weights, + it will produce a process schedule that assigns a fair ordering for + the processes given their weight + + Note: This does not handle process execution but only the schedule + """ + + def __init__(self): + self._processes = {} + self._schedule = [] + self._idx = 0 + + def add_process(self, name, weight: int = 1): + # Adds a process to the schedule with some weight (int) + self._processes[name] = weight + self._make_schedule() + + def step(self): + # Move the queue to the next process + self._idx = (self._idx + 1) % len(self._schedule) + + def get(self): + # Gets the current process in the schedule + if len(self._schedule) == 0: + return None + else: + # Get the current scheduled process + return self._schedule[self._idx] + + @property + def schedule(self): + return self._schedule + + def _make_schedule(self): + # Reset schedule + self._schedule = [] + + # Prepare some lists + weights = list(self._processes.values()) + processes = list(self._processes.keys()) + + # Get the total weight + w_total = sum(weights) + + # Get the number of processes + num_processes = len(processes) + + # Prepare queues + queues = [[p] * w for p, w in zip(processes, weights)] + + # Fill schedule + for w in range(w_total): + for i in range(num_processes): + if len(queues[i]) > 0 and weights[i] > w: + self._schedule.append(queues[i].pop()) + + +def run_scheduler(): + s = Scheduler() + s.add_process("p1", 1) + s.add_process("p2", 1) + s.add_process("p3", 1) + print(f"Schedule: {s.schedule}") + + assert s.get() == "p1" + s.step() + assert s.get() == "p2" + s.step() + assert s.get() == "p3" + s.step() + assert s.get() == "p1" + s.step() + + s = Scheduler() + s.add_process("p1", 2) + s.add_process("p2", 1) + s.add_process("p3", 2) + s.add_process("p4", 1) + print(f"Schedule: {s.schedule}") + + assert s.get() == "p1" + s.step() + assert s.get() == "p2" + s.step() + assert s.get() == "p3" + s.step() + assert s.get() == "p4" + s.step() + assert s.get() == "p1" + s.step() + assert s.get() == "p3" + s.step() + + +if __name__ == "__main__": + run_scheduler() diff --git a/wild_visual_navigation_sim/CMakeLists.txt b/wild_visual_navigation_sim/CMakeLists.txt new file mode 100644 index 00000000..d451ee18 --- /dev/null +++ b/wild_visual_navigation_sim/CMakeLists.txt @@ -0,0 +1,27 @@ +cmake_minimum_required(VERSION 3.0.2) +project(wild_visual_navigation_sim) + +find_package(catkin REQUIRED COMPONENTS + rospy + nav_msgs + sensor_msgs + std_msgs + wild_visual_navigation_ros +) + +catkin_package( + CATKIN_DEPENDS +) + +# catkin_python_setup() +# catkin_install_python(PROGRAMS scripts/wvn_feature_extractor_node.py +# scripts/wvn_learning_node.py +# scripts/overlay_images.py +# scripts/smart_carrot.py +# scripts/rosbag_play.sh +# DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}) + +message(${CATKIN_PACKAGE_SHARE_DESTINATION}) +install(DIRECTORY launch Media worlds + DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION} +) \ No newline at end of file diff --git a/wild_visual_navigation_sim/Media/models/OliveTree1.dae b/wild_visual_navigation_sim/Media/models/OliveTree1.dae new file mode 100644 index 00000000..6583bc3e --- /dev/null +++ b/wild_visual_navigation_sim/Media/models/OliveTree1.dae @@ -0,0 +1,4065 @@ + + + + + Google SketchUp 7.0.8657 + + 2010-03-16T17:45:56Z + 2010-03-16T17:45:56Z + + Z_UP + + + + textures/texture0.jpg + + + textures/texture0.png + + + + + + + + + + + + + + + + material_0_1_0-image + + + + + material_0_1_0-image-surface + + + + + + 0.000000 0.000000 0.000000 1 + + + 0.000000 0.000000 0.000000 1 + + + + + + 1 1 1 1 + + + 0.050000 + + + + + + 1 + + + + + + + + + material_1_2_0-image + + + + + material_1_2_0-image-surface + + + + + + 0.000000 0.000000 0.000000 1 + + + 0.000000 0.000000 0.000000 1 + + + + + + 1 1 1 1 + + + 0.010000 + + + + + + 1 + + + + + + + + + + 60.510791 32.918764 68.437903 61.241531 44.984435 54.582557 60.592489 45.240710 55.022876 60.646860 44.968439 54.000801 + + + + + + + + + + -0.402852 -0.768615 -0.496932 0.998781 -0.041493 -0.026754 -0.422988 0.441994 0.791026 -0.402852 -0.768615 -0.496932 -0.382975 -0.490402 -0.782838 -0.402852 -0.768615 -0.496932 + + + + + + + + + + -39.720115 12.765230 -1.736267 0.259387 -0.035743 0.832450 -31.484872 15.003045 -1.503014 0.031632 -0.114239 0.687616 48.508210 8.992771 0.731048 0.777517 1.064867 -0.011546 + + + + + + + + + + + + + + +

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1140 40.98016 510 1140 40.98016 540 1140 41.90391 540 1140 41.90391 570 1140 42.54355 570 1140 42.54355 600 1140 42.87029 600 1140 42.87029 630 1140 42.91062 630 1140 42.91062 990 780 17.82801 990 780 17.82801 990 750 10.69445 990 750 10.69445 990 720 6.804693 990 720 6.804693 990 690 6.152544 990 690 6.152544 990 660 7.835781 990 660 7.835781 990 630 10.08404 990 630 10.08404 990 600 12.48875 990 600 12.48875 990 570 15.7144 990 570 15.7144 990 540 19.46885 990 540 19.46885 990 510 23.76349 990 510 23.76349 990 480 27.92428 990 480 27.92428 300 810 70.06299 300 810 70.06299 300 840 63.76248 300 840 63.76248 300 870 57.37818 300 870 57.37818 300 900 51.16201 300 900 51.16201 300 930 45.359 300 930 45.359 300 960 40.19776 300 960 40.19776 300 990 35.88166 300 990 35.88166 300 1020 32.58087 300 1020 32.58087 300 1050 30.42574 300 1050 30.42574 300 1080 29.50173 300 1080 29.50173 300 1110 29.83815 300 1110 29.83815 660 450 68.48664 660 450 68.48664 690 450 75.63672 690 450 75.63672 720 450 83.3451 720 450 83.3451 750 450 89.92067 750 450 89.92067 780 450 93.70007 780 450 93.70007 810 450 93.9145 810 450 93.9145 840 450 89.76984 840 450 89.76984 870 450 81.40951 870 450 81.40951 900 450 69.83741 900 450 69.83741 930 450 56.50795 930 450 56.50795 960 450 43.11915 960 450 43.11915 300 1140 30.8072 300 1140 30.8072 660 1140 42.91062 660 1140 42.91062 690 1110 45.08063 690 1110 45.08063 720 1080 50.73693 720 1080 50.73693 750 1050 57.81658 750 1050 57.81658 780 1020 64.26923 780 1020 64.26923 810 990 68.4953 810 990 68.4953 840 960 68.19495 840 960 68.19495 870 930 62.66437 870 930 62.66437 900 900 53.91066 900 900 53.91066 930 870 44.80638 930 870 44.80638 960 840 36.16482 960 840 36.16482 990 810 27.95096 990 810 27.95096 990 450 31.36964 990 450 31.36964 300 780 76.0307 300 780 76.0307 330 750 83.62625 330 750 83.62625 360 720 89.35453 360 720 89.35453 390 690 92.7682 390 690 92.7682 420 660 93.59723 420 660 93.59723 450 630 91.80338 450 630 91.80338 480 600 88.01651 480 600 88.01651 510 570 82.70958 510 570 82.70958 540 540 76.29463 540 540 76.29463 570 510 69.26947 570 510 69.26947 600 480 63.94483 600 480 63.94483 630 450 63.31864 630 450 63.31864 300 1170 31.98293 300 1170 31.98293 330 1170 33.31662 330 1170 33.31662 360 1170 34.75286 360 1170 34.75286 390 1170 36.23173 390 1170 36.23173 420 1170 37.69119 420 1170 37.69119 450 1170 39.06951 450 1170 39.06951 480 1170 40.30775 480 1170 40.30775 510 1170 41.35211 510 1170 41.35211 540 1170 42.15609 540 1170 42.15609 570 1170 42.68236 570 1170 42.68236 600 1170 42.90428 600 1170 42.90428 630 1170 42.91062 630 1170 42.91062 1020 780 20.70066 1020 780 20.70066 1020 750 12.47489 1020 750 12.47489 1020 720 7.375809 1020 720 7.375809 1020 690 5.445051 1020 690 5.445051 1020 660 6.230351 1020 660 6.230351 1020 630 7.782293 1020 630 7.782293 1020 600 9.460534 1020 600 9.460534 1020 570 10.93704 1020 570 10.93704 1020 540 12.91595 1020 540 12.91595 1020 510 15.92204 1020 510 15.92204 1020 480 19.34747 1020 480 19.34747 1020 450 22.7155 1020 450 22.7155 270 810 67.15836 270 810 67.15836 270 840 60.93082 270 840 60.93082 270 870 54.64669 270 870 54.64669 270 900 48.55575 270 900 48.55575 270 930 42.90044 270 930 42.90044 270 960 37.9064 270 960 37.9064 270 990 33.77366 270 990 33.77366 270 1020 30.66885 270 1020 30.66885 270 1050 28.71854 270 1050 28.71854 270 1080 28.0043 270 1080 28.0043 270 1110 28.51954 270 1110 28.51954 270 1140 29.55228 270 1140 29.55228 660 420 65.15848 660 420 65.15848 690 420 72.48213 690 420 72.48213 720 420 80.40982 720 420 80.40982 750 420 87.4587 750 420 87.4587 780 420 91.92159 780 420 91.92159 810 420 92.73139 810 420 92.73139 840 420 89.0367 840 420 89.0367 870 420 81.2103 870 420 81.2103 900 420 70.25376 900 420 70.25376 930 420 57.59272 930 420 57.59272 960 420 44.8905 960 420 44.8905 990 420 33.80872 990 420 33.80872 270 1170 30.8072 270 1170 30.8072 660 1170 42.91062 660 1170 42.91062 690 1140 44.05533 690 1140 44.05533 720 1110 48.23812 720 1110 48.23812 750 1080 54.99628 750 1080 54.99628 780 1050 62.35217 780 1050 62.35217 810 1020 68.57125 810 1020 68.57125 840 990 72.21984 840 990 72.21984 870 960 71.16364 870 960 71.16364 900 930 65.64694 900 930 65.64694 930 900 58.04881 930 900 58.04881 960 870 49.58645 960 870 49.58645 990 840 40.54727 990 840 40.54727 1020 810 31.76783 1020 810 31.76783 1020 420 25.76762 1020 420 25.76762 270 780 73.08181 270 780 73.08181 300 750 81.42907 300 750 81.42907 330 720 88.16192 330 720 88.16192 360 690 92.75629 360 690 92.75629 390 660 94.84954 390 660 94.84954 420 630 94.26993 420 630 94.26993 450 600 91.31878 450 600 91.31878 480 570 86.6485 480 570 86.6485 510 540 80.62571 510 540 80.62571 540 510 73.71424 540 510 73.71424 570 480 66.44639 570 480 66.44639 600 450 61.30073 600 450 61.30073 630 420 59.84544 630 420 59.84544 270 1200 32.23374 270 1200 32.23374 300 1200 33.31662 300 1200 33.31662 330 1200 34.54146 330 1200 34.54146 360 1200 35.85542 360 1200 35.85542 390 1200 37.20139 390 1200 37.20139 420 1200 38.52036 420 1200 38.52036 450 1200 39.75377 450 1200 39.75377 480 1200 40.84588 480 1200 40.84588 510 1200 41.74603 510 1200 41.74603 540 1200 42.41076 540 1200 42.41076 570 1200 42.80555 570 1200 42.80555 600 1200 42.91062 600 1200 42.91062 630 1200 42.91062 630 1200 42.91062 1050 780 23.61371 1050 780 23.61371 1050 750 14.46178 1050 750 14.46178 1050 720 8.27981 1050 720 8.27981 1050 690 5.158763 1050 690 5.158763 1050 660 4.900972 1050 660 4.900972 1050 630 5.689189 1050 630 5.689189 1050 600 6.647724 1050 600 6.647724 1050 570 7.772603 1050 570 7.772603 1050 540 9.554898 1050 540 9.554898 1050 510 11.94859 1050 510 11.94859 1050 480 14.85725 1050 480 14.85725 1050 450 18.1637 1050 450 18.1637 1050 420 21.73452 1050 420 21.73452 240 810 63.76751 240 810 63.76751 240 840 57.6982 240 840 57.6982 240 870 51.60288 240 870 51.60288 240 900 45.72719 240 900 45.72719 240 930 40.3089 240 930 40.3089 240 960 35.56866 240 960 35.56866 240 990 31.70133 240 990 31.70133 240 1020 28.86828 240 1020 28.86828 240 1050 27.19084 240 1050 27.19084 240 1080 26.74534 240 1080 26.74534 240 1110 27.43555 240 1110 27.43555 240 1140 28.51954 240 1140 28.51954 240 1170 29.83815 240 1170 29.83815 660 390 59.95279 660 390 59.95279 690 390 66.84312 690 390 66.84312 720 390 74.4528 720 390 74.4528 750 390 81.41394 750 390 81.41394 780 390 86.316 780 390 86.316 810 390 87.51821 810 390 87.51821 840 390 84.48605 840 390 84.48605 870 390 77.57743 870 390 77.57743 900 390 67.7269 900 390 67.7269 930 390 56.3418 930 390 56.3418 960 390 45.00275 960 390 45.00275 990 390 35.27752 990 390 35.27752 1020 390 28.49504 1020 390 28.49504 240 1200 31.33935 240 1200 31.33935 660 1200 42.91062 660 1200 42.91062 690 1170 43.9806 690 1170 43.9806 720 1140 47.44552 720 1140 47.44552 750 1110 53.27648 750 1110 53.27648 780 1080 60.83369 780 1080 60.83369 810 1050 68.338 810 1050 68.338 840 1020 74.40621 840 1020 74.40621 870 990 77.68336 870 990 77.68336 900 960 76.22981 900 960 76.22981 930 930 71.28427 930 930 71.28427 960 900 63.9323 960 900 63.9323 990 870 54.87149 990 870 54.87149 1020 840 45.14836 1020 840 45.14836 1050 810 35.42167 1050 810 35.42167 1050 390 25.42533 1050 390 25.42533 240 780 69.56688 240 780 69.56688 270 750 78.46563 270 750 78.46563 300 720 86.04328 300 720 86.04328 330 690 91.71017 330 690 91.71017 360 660 95.02192 360 660 95.02192 390 630 95.70617 390 630 95.70617 420 600 93.77491 420 600 93.77491 450 570 89.88439 450 570 89.88439 480 540 84.43 480 540 84.43 510 510 77.82991 510 510 77.82991 540 480 70.59006 540 480 70.59006 570 450 63.15011 570 450 63.15011 600 420 57.69396 600 420 57.69396 630 390 55.11619 630 390 55.11619 1080 780 25.98549 1080 780 25.98549 1080 750 16.10557 1080 750 16.10557 1080 720 9.017711 1080 720 9.017711 1080 690 4.861352 1080 690 4.861352 1080 660 3.5771 1080 660 3.5771 1080 630 3.605434 1080 630 3.605434 1080 600 4.020609 1080 600 4.020609 1080 570 5.172964 1080 570 5.172964 1080 540 7.042416 1080 540 7.042416 1080 510 9.554898 1080 510 9.554898 1080 480 12.61118 1080 480 12.61118 1080 450 16.09067 1080 450 16.09067 1080 420 19.85604 1080 420 19.85604 1080 390 23.75852 1080 390 23.75852 210 810 60.03718 210 810 60.03718 210 840 54.20751 210 840 54.20751 210 870 48.38496 210 870 48.38496 210 900 42.80904 210 900 42.80904 210 930 37.71089 210 930 37.71089 210 960 33.30422 210 960 33.30422 210 990 29.7769 210 990 29.7769 210 1020 27.28358 210 1020 27.28358 210 1050 25.93903 210 1050 25.93903 210 1080 25.81303 210 1080 25.81303 210 1110 26.62899 210 1110 26.62899 210 1140 27.75058 210 1140 27.75058 210 1170 29.11586 210 1170 29.11586 210 1200 30.67173 210 1200 30.67173 660 360 53.59819 660 360 53.59819 690 360 59.53424 690 360 59.53424 720 360 66.35981 720 360 66.35981 750 360 72.81788 750 360 72.81788 780 360 77.58941 780 360 77.58941 810 360 79.05938 810 360 79.05938 840 360 76.80065 840 360 76.80065 870 360 71.13463 870 360 71.13463 900 360 62.8961 900 360 62.8961 930 360 53.31695 930 360 53.31695 960 360 43.90894 960 360 43.90894 990 360 36.11303 990 360 36.11303 1020 360 31.11776 1020 360 31.11776 1050 360 29.08639 1050 360 29.08639 690 1200 43.77228 690 1200 43.77228 720 1170 47.2872 720 1170 47.2872 750 1140 52.95104 750 1140 52.95104 780 1110 60.18205 780 1110 60.18205 810 1080 68.34431 810 1080 68.34431 840 1050 75.97388 840 1050 75.97388 870 1020 82.023 870 1020 82.023 900 990 84.98143 900 990 84.98143 930 960 83.51103 930 960 83.51103 960 930 78.19131 960 930 78.19131 990 900 70.01068 990 900 70.01068 1020 870 60.10287 1020 870 60.10287 1050 840 49.35154 1050 840 49.35154 1080 810 38.31818 1080 810 38.31818 1080 360 27.64367 1080 360 27.64367 210 780 65.63558 210 780 65.63558 240 750 74.86365 240 750 74.86365 270 720 83.09535 270 720 83.09535 300 690 89.6884 300 690 89.6884 330 660 94.12519 330 660 94.12519 360 630 96.05278 360 630 96.05278 390 600 95.28977 390 600 95.28977 420 570 92.29127 420 570 92.29127 450 540 87.5574 450 540 87.5574 480 510 81.45046 480 510 81.45046 510 480 74.43785 510 480 74.43785 540 450 66.96341 540 450 66.96341 570 420 59.39901 570 420 59.39901 600 390 53.41372 600 390 53.41372 630 360 49.76463 630 360 49.76463 1110 780 27.3351 1110 780 27.3351 1110 750 16.9518 1110 750 16.9518 1110 720 9.179739 1110 720 9.179739 1110 690 4.203458 1110 690 4.203458 1110 660 2.047406 1110 660 2.047406 1110 630 1.644623 1110 630 1.644623 1110 600 2.047406 1110 600 2.047406 1110 570 3.23933 1110 570 3.23933 1110 540 5.172964 1110 540 5.172964 1110 510 7.772603 1110 510 7.772603 1110 480 10.93704 1110 480 10.93704 1110 450 14.54326 1110 450 14.54326 1110 420 18.45111 1110 420 18.45111 1110 390 22.50866 1110 390 22.50866 1110 360 26.55803 1110 360 26.55803 180 810 56.12859 180 810 56.12859 180 840 50.61422 180 840 50.61422 180 870 45.14175 180 870 45.14175 180 900 39.94266 180 900 39.94266 180 930 35.23957 180 930 35.23957 180 960 31.23744 180 960 31.23744 180 990 28.11534 180 990 28.11534 180 1020 26.01983 180 1020 26.01983 180 1050 25.05854 180 1050 25.05854 180 1080 25.28049 180 1080 25.28049 180 1110 26.13181 180 1110 26.13181 180 1140 27.27642 180 1140 27.27642 180 1170 28.67018 180 1170 28.67018 180 1200 30.25942 180 1200 30.25942 660 330 47.10303 660 330 47.10303 690 330 51.70267 690 330 51.70267 720 330 57.39434 720 330 57.39434 750 330 62.98643 750 330 62.98643 780 330 67.01031 780 330 67.01031 810 330 68.62599 810 330 68.62599 840 330 67.21588 840 330 67.21588 870 330 62.99195 870 330 62.99195 900 330 56.68956 900 330 56.68956 930 330 49.38576 930 330 49.38576 960 330 42.33849 960 330 42.33849 990 330 36.87064 990 330 36.87064 1020 330 34.00767 1020 330 34.00767 1050 330 32.56846 1050 330 32.56846 1080 330 31.35733 1080 330 31.35733 720 1200 46.83077 720 1200 46.83077 750 1170 52.70285 750 1170 52.70285 780 1140 60.36053 780 1140 60.36053 810 1110 68.94959 810 1110 68.94959 840 1080 77.60854 840 1080 77.60854 870 1050 85.38519 870 1050 85.38519 900 1020 91.49625 900 1020 91.49625 930 990 93.83227 930 990 93.83227 960 960 91.35965 960 960 91.35965 990 930 84.9683 990 930 84.9683 1020 900 75.73964 1020 900 75.73964 1050 870 64.6794 1050 870 64.6794 1080 840 52.56901 1080 840 52.56901 1110 810 39.96919 1110 810 39.96919 1110 330 30.44153 1110 330 30.44153 180 780 61.45378 180 780 61.45378 210 750 70.77471 210 750 70.77471 240 720 79.44542 240 720 79.44542 270 690 86.7858 270 690 86.7858 300 660 92.21641 300 660 92.21641 330 630 95.30262 330 630 95.30262 360 600 95.78096 360 600 95.78096 390 570 93.77491 390 570 93.77491 420 540 89.88439 420 540 89.88439 450 510 84.44534 450 510 84.44534 480 480 77.85726 480 480 77.85726 510 450 70.54434 510 450 70.54434 540 420 62.84293 540 420 62.84293 570 390 55.36224 570 390 55.36224 600 360 48.99586 600 360 48.99586 630 330 44.64707 630 330 44.64707 1140 780 27.35395 1140 780 27.35395 1140 750 16.714 1140 750 16.714 1140 720 8.555513 1140 720 8.555513 1140 690 3.192118 1140 690 3.192118 1140 660 0.8304025 1140 660 0.8304025 1140 630 0.418504 1140 630 0.418504 1140 600 0.8304025 1140 600 0.8304025 1140 570 2.047406 1140 570 2.047406 1140 540 4.020609 1140 540 4.020609 1140 510 6.673576 1140 510 6.673576 1140 480 9.904043 1140 480 9.904043 1140 450 13.58759 1140 450 13.58759 1140 420 17.58236 1140 420 17.58236 1140 390 21.73452 1140 390 21.73452 1140 360 25.88412 1140 360 25.88412 1140 330 29.87131 1140 330 29.87131 150 810 52.21092 150 810 52.21092 150 840 47.08007 150 840 47.08007 150 870 42.02653 150 870 42.02653 150 900 37.27202 150 900 37.27202 150 930 33.02898 150 930 33.02898 150 960 29.49198 150 960 29.49198 150 990 26.82968 150 990 26.82968 150 1020 25.17794 150 1020 25.17794 150 1050 24.6398 150 1050 24.6398 150 1080 25.10671 150 1080 25.10671 150 1110 25.96384 150 1110 25.96384 150 1140 27.1162 150 1140 27.1162 150 1170 28.51954 150 1170 28.51954 150 1200 30.11999 150 1200 30.11999 660 300 41.59899 660 300 41.59899 690 300 44.65469 690 300 44.65469 720 300 48.96977 720 300 48.96977 750 300 53.18696 750 300 53.18696 780 300 56.32395 780 300 56.32395 810 300 61.76778 810 300 61.76778 840 300 67.34968 840 300 67.34968 870 300 58.53529 870 300 58.53529 900 300 50.339 900 300 50.339 930 300 45.52262 930 300 45.52262 960 300 41.13489 960 300 41.13489 990 300 38.19938 990 300 38.19938 1020 300 36.87767 1020 300 36.87767 1050 300 36.13717 1050 300 36.13717 1080 300 36.17876 1080 300 36.17876 1110 300 36.37339 1110 300 36.37339 750 1200 51.97951 750 1200 51.97951 780 1170 60.01786 780 1170 60.01786 810 1140 69.38953 810 1140 69.38953 840 1110 79.15337 840 1110 79.15337 870 1080 88.3626 870 1080 88.3626 900 1050 96.14384 900 1050 96.14384 930 1020 101.5463 930 1020 101.5463 960 990 102.5394 960 990 102.5394 990 960 98.75945 990 960 98.75945 1020 930 91.094 1020 930 91.094 1050 900 80.547 1050 900 80.547 1080 870 68.03724 1080 870 68.03724 1110 840 54.32405 1110 840 54.32405 1140 810 40.06649 1140 810 40.06649 1140 300 36.27911 1140 300 36.27911 150 780 57.19703 150 780 57.19703 180 750 66.36828 180 750 66.36828 210 720 75.24601 210 720 75.24601 240 690 83.12838 240 690 83.12838 270 660 89.38832 270 660 89.38832 300 630 93.52175 300 630 93.52175 330 600 95.18471 330 600 95.18471 360 570 94.32818 360 570 94.32818 390 540 91.54913 390 540 91.54913 420 510 87.07115 420 510 87.07115 450 480 81.16715 450 480 81.16715 480 450 74.22768 480 450 74.22768 510 420 66.52198 510 420 66.52198 540 390 58.63514 540 390 58.63514 570 360 51.27459 570 360 51.27459 600 330 45.13308 600 330 45.13308 630 300 40.7051 630 300 40.7051 1170 780 26.66214 1170 780 26.66214 1170 750 16.09446 1170 750 16.09446 1170 720 8.007869 1170 720 8.007869 1170 690 2.7146 1170 690 2.7146 1170 660 0.418504 1170 660 0.418504 1170 630 0 1170 630 0 1170 600 0.418504 1170 600 0.418504 1170 570 1.644623 1170 570 1.644623 1170 540 3.631286 1170 540 3.631286 1170 510 6.302224 1170 510 6.302224 1170 480 9.554898 1170 480 9.554898 1170 450 13.26443 1170 450 13.26443 1170 420 17.28842 1170 420 17.28842 1170 390 21.47238 1170 390 21.47238 1170 360 25.65568 1170 360 25.65568 1170 330 29.67773 1170 330 29.67773 1170 300 35.74983 1170 300 35.74983 120 810 48.45451 120 810 48.45451 120 840 43.7664 120 840 43.7664 120 870 39.19061 120 870 39.19061 120 900 34.93757 120 900 34.93757 120 930 31.20806 120 930 31.20806 120 960 28.18492 120 960 28.18492 120 990 26.02551 120 990 26.02551 120 1020 24.85556 120 1020 24.85556 120 1050 24.75542 120 1050 24.75542 120 1080 25.28049 120 1080 25.28049 120 1110 26.13181 120 1110 26.13181 120 1140 27.27642 120 1140 27.27642 120 1170 28.67018 120 1170 28.67018 120 1200 30.25942 120 1200 30.25942 660 270 38.16046 660 270 38.16046 690 270 39.6142 690 270 39.6142 720 270 42.14758 720 270 42.14758 750 270 44.88641 750 270 44.88641 780 270 47.11303 780 270 47.11303 810 270 58.46037 810 270 58.46037 840 270 68.60725 840 270 68.60725 870 270 57.30749 870 270 57.30749 900 270 45.091 900 270 45.091 930 270 42.7759 930 270 42.7759 960 270 41.0525 960 270 41.0525 990 270 40.3067 990 270 40.3067 1020 270 41.00982 1020 270 41.00982 1050 270 42.96388 1050 270 42.96388 1080 270 45.19611 1080 270 45.19611 1110 270 46.83496 1110 270 46.83496 1140 270 47.28645 1140 270 47.28645 780 1200 59.01376 780 1200 59.01376 810 1170 68.94959 810 1170 68.94959 840 1140 79.69141 840 1140 79.69141 870 1110 90.23573 870 1110 90.23573 900 1080 99.61783 900 1080 99.61783 930 1050 106.9908 930 1050 106.9908 960 1020 111.0674 960 1020 111.0674 990 990 110.5053 990 990 110.5053 1020 960 105.2182 1020 960 105.2182 1050 930 96.03559 1050 930 96.03559 1080 900 83.90737 1080 900 83.90737 1110 870 69.72792 1110 870 69.72792 1140 840 54.36315 1140 840 54.36315 1170 810 39.3034 1170 810 39.3034 1170 270 46.35043 1170 270 46.35043 120 780 53.04346 120 780 53.04346 150 750 61.82496 150 750 61.82496 180 720 70.66891 180 720 70.66891 210 690 78.86847 210 690 78.86847 240 660 85.76449 240 660 85.76449 270 630 90.79667 270 630 90.79667 300 600 93.6085 300 600 93.6085 330 570 94.17024 330 570 94.17024 360 540 92.6687 360 540 92.6687 390 510 89.3361 390 510 89.3361 420 480 84.35085 420 480 84.35085 450 450 78.04707 450 450 78.04707 480 420 70.65587 480 420 70.65587 510 390 62.64433 510 390 62.64433 540 360 54.70665 540 360 54.70665 570 330 47.56472 570 330 47.56472 600 300 41.91282 600 300 41.91282 630 270 38.32275 630 270 38.32275 1200 780 26.00718 1200 780 26.00718 1200 750 15.74247 1200 750 15.74247 1200 720 7.940756 1200 720 7.940756 1200 690 2.909063 1200 690 2.909063 1200 660 0.8304025 1200 660 0.8304025 1200 630 0.418504 1200 630 0.418504 1200 600 0.8304025 1200 600 0.8304025 1200 570 2.047406 1200 570 2.047406 1200 540 4.020609 1200 540 4.020609 1200 510 6.673576 1200 510 6.673576 1200 480 9.904043 1200 480 9.904043 1200 450 13.58759 1200 450 13.58759 1200 420 17.58236 1200 420 17.58236 1200 390 21.73452 1200 390 21.73452 1200 360 25.88412 1200 360 25.88412 1200 330 29.87131 1200 330 29.87131 1200 300 34.96949 1200 300 34.96949 1200 270 44.26106 1200 270 44.26106 90 810 45.02399 90 810 45.02399 90 840 40.82755 90 840 40.82755 90 870 36.77701 90 870 36.77701 90 900 33.07016 90 900 33.07016 90 930 29.89482 90 930 29.89482 90 960 27.42087 90 960 27.42087 90 990 25.79313 90 990 25.79313 90 1020 25.12501 90 1020 25.12501 90 1050 25.28049 90 1050 25.28049 90 1080 25.79474 90 1080 25.79474 90 1110 26.62899 90 1110 26.62899 90 1140 27.75058 90 1140 27.75058 90 1170 29.11586 90 1170 29.11586 90 1200 30.67173 90 1200 30.67173 660 240 36.79895 660 240 36.79895 690 240 37.17336 690 240 37.17336 720 240 37.97745 720 240 37.97745 750 240 39.33806 750 240 39.33806 780 240 40.71164 780 240 40.71164 810 240 45.69826 810 240 45.69826 840 240 52.28063 840 240 52.28063 870 240 46.128 870 240 46.128 900 240 41.97903 900 240 41.97903 930 240 41.98842 930 240 41.98842 960 240 42.07745 960 240 42.07745 990 240 44.05091 990 240 44.05091 1020 240 48.00231 1020 240 48.00231 1050 240 52.85049 1050 240 52.85049 1080 240 57.40407 1080 240 57.40407 1110 240 60.56305 1110 240 60.56305 1140 240 61.58398 1140 240 61.58398 1170 240 60.21352 1170 240 60.21352 810 1200 67.65635 810 1200 67.65635 840 1170 79.15337 840 1170 79.15337 870 1140 90.87068 870 1140 90.87068 900 1110 101.7696 900 1110 101.7696 930 1080 110.8958 930 1080 110.8958 960 1050 117.4067 960 1050 117.4067 990 1020 119.7263 990 1020 119.7263 1020 990 117.2726 1020 990 117.2726 1050 960 110.2488 1050 960 110.2488 1080 930 99.31663 1080 930 99.31663 1110 900 85.41697 1110 900 85.41697 1140 870 69.69458 1140 870 69.69458 1170 840 53.53117 1170 840 53.53117 1200 810 38.33332 1200 810 38.33332 1200 240 56.73516 1200 240 56.73516 90 780 49.16681 90 780 49.16681 120 750 57.3294 120 750 57.3294 150 720 65.89847 150 720 65.89847 180 690 74.1789 180 690 74.1789 210 660 81.49582 210 660 81.49582 240 630 87.25118 240 630 87.25118 270 600 91.27073 270 600 91.27073 300 570 93.27594 300 570 93.27594 330 540 93.13499 330 540 93.13499 360 510 91.09118 360 510 91.09118 390 480 87.22658 390 480 87.22658 420 450 81.78841 420 450 81.78841 450 420 75.01687 450 420 75.01687 480 390 67.20755 480 390 67.20755 510 360 59.01787 510 360 59.01787 540 330 51.1676 540 330 51.1676 570 300 44.38081 570 300 44.38081 600 270 39.33079 600 270 39.33079 630 240 36.58916 630 240 36.58916 60 810 42.07165 60 810 42.07165 60 840 38.40449 60 840 38.40449 60 870 34.9144 60 870 34.9144 60 900 31.78537 60 900 31.78537 60 930 29.19111 60 930 29.19111 60 960 27.28753 60 960 27.28753 60 990 26.20585 60 990 26.20585 60 1020 25.96384 60 1020 25.96384 60 1050 26.13181 60 1050 26.13181 60 1080 26.62899 60 1080 26.62899 60 1110 27.43555 60 1110 27.43555 60 1140 28.51954 60 1140 28.51954 60 1170 29.83815 60 1170 29.83815 60 1200 31.33935 60 1200 31.33935 660 210 35.58346 660 210 35.58346 690 210 36.00432 690 210 36.00432 720 210 36.56705 720 210 36.56705 750 210 37.24645 750 210 37.24645 780 210 38.01199 780 210 38.01199 810 210 38.82897 810 210 38.82897 840 210 39.66 840 210 39.66 870 210 40.4665 870 210 40.4665 900 210 41.21031 900 210 41.21031 930 210 41.85525 930 210 41.85525 960 210 44.03029 960 210 44.03029 990 210 49.31972 990 210 49.31972 1020 210 56.54349 1020 210 56.54349 1050 210 64.2562 1050 210 64.2562 1080 210 71.08126 1080 210 71.08126 1110 210 75.72177 1110 210 75.72177 1140 210 77.29577 1140 210 77.29577 1170 210 75.5006 1170 210 75.5006 1200 210 70.66523 1200 210 70.66523 840 1200 77.56844 840 1200 77.56844 870 1170 90.23573 870 1170 90.23573 900 1140 102.4982 900 1140 102.4982 930 1110 113.3105 930 1110 113.3105 960 1080 121.7576 960 1080 121.7576 990 1050 126.8843 990 1050 126.8843 1020 1020 127.2207 1020 1020 127.2207 1050 990 122.6534 1050 990 122.6534 1080 960 113.5987 1080 960 113.5987 1110 930 100.8414 1110 930 100.8414 1140 900 85.47166 1140 900 85.47166 1170 870 68.79749 1170 870 68.79749 1200 840 52.24018 1200 840 52.24018 60 780 45.72949 60 780 45.72949 90 750 53.06306 90 750 53.06306 120 720 61.12439 120 720 61.12439 150 690 69.24634 150 690 69.24634 180 660 76.75488 180 660 76.75488 210 630 83.10669 210 630 83.10669 240 600 88.22825 240 600 88.22825 270 570 91.6009 270 570 91.6009 300 540 92.87514 300 540 92.87514 330 510 92.21739 330 510 92.21739 360 480 89.63579 360 480 89.63579 390 450 85.27714 390 450 85.27714 420 420 79.38055 420 420 79.38055 450 390 72.09303 450 390 72.09303 480 360 63.98369 480 360 63.98369 510 330 55.74929 510 330 55.74929 540 300 48.12304 540 300 48.12304 570 270 41.81893 570 270 41.81893 600 240 37.47797 600 240 37.47797 630 210 35.40761 630 210 35.40761 30 810 39.73129 30 810 39.73129 30 840 36.61901 30 840 36.61901 30 870 33.71164 30 870 33.71164 30 900 31.17842 30 900 31.17842 30 930 29.17809 30 930 29.17809 30 960 27.85174 30 960 27.85174 30 990 27.27642 30 990 27.27642 30 1020 27.1162 30 1020 27.1162 30 1050 27.27642 30 1050 27.27642 30 1080 27.75058 30 1080 27.75058 30 1110 28.51954 30 1110 28.51954 30 1140 29.55228 30 1140 29.55228 30 1170 30.8072 30 1170 30.8072 30 1200 32.23374 30 1200 32.23374 660 180 34.41281 660 180 34.41281 690 180 34.87636 690 180 34.87636 720 180 35.49736 720 180 35.49736 750 180 36.24923 750 180 36.24923 780 180 37.09968 780 180 37.09968 810 180 38.01199 810 180 38.01199 840 180 38.94654 840 180 38.94654 870 180 39.86234 870 180 39.86234 900 180 40.71873 900 180 40.71873 930 180 41.88548 930 180 41.88548 960 180 46.6297 960 180 46.6297 990 180 54.83678 990 180 54.83678 1020 180 65.01935 1020 180 65.01935 1050 180 75.35479 1050 180 75.35479 1080 180 84.2164 1080 180 84.2164 1110 180 90.18302 1110 180 90.18302 1140 180 92.25313 1140 180 92.25313 1170 180 90.08118 1170 180 90.08118 1200 180 84.03528 1200 180 84.03528 870 1200 88.3626 870 1200 88.3626 900 1170 101.7696 900 1170 101.7696 930 1140 114.1275 930 1140 114.1275 960 1110 124.4139 960 1110 124.4139 990 1080 131.7811 990 1080 131.7811 1020 1050 135.0937 1020 1050 135.0937 1050 1020 133.2894 1050 1020 133.2894 1080 990 126.5614 1080 990 126.5614 1110 960 115.511 1110 960 115.511 1140 930 101.0881 1140 930 101.0881 1170 900 84.51462 1170 900 84.51462 1200 870 67.1865 1200 870 67.1865 30 780 42.8762 30 780 42.8762 60 750 49.19716 60 750 49.19716 90 720 56.53439 90 720 56.53439 120 690 64.26401 120 690 64.26401 150 660 71.72922 150 660 71.72922 180 630 78.52294 180 630 78.52294 210 600 84.55958 210 600 84.55958 240 570 89.17299 240 570 89.17299 270 540 91.85341 270 540 91.85341 300 510 92.6298 300 510 92.6298 330 480 91.44859 330 480 91.44859 360 450 88.34991 360 450 88.34991 390 420 83.53398 390 420 83.53398 420 390 77.06988 420 390 77.06988 450 360 69.36932 450 360 69.36932 480 330 61.08573 480 330 61.08573 510 300 52.94046 510 300 52.94046 540 270 45.66793 540 270 45.66793 570 240 39.96027 570 240 39.96027 600 210 36.40784 600 210 36.40784 630 180 34.35688 630 180 34.35688 0 810 38.11278 0 810 38.11278 0 840 35.56859 0 840 35.56859 0 870 33.25302 0 870 33.25302 0 900 31.31984 0 900 31.31984 0 930 29.91218 0 930 29.91218 0 960 29.11586 0 960 29.11586 0 990 28.67018 0 990 28.67018 0 1020 28.51954 0 1020 28.51954 0 1050 28.67018 0 1050 28.67018 0 1080 29.11586 0 1080 29.11586 0 1110 29.83815 0 1110 29.83815 0 1140 30.8072 0 1140 30.8072 0 1170 31.98293 0 1170 31.98293 0 1200 33.31662 0 1200 33.31662 660 150 33.33252 660 150 33.33252 690 150 33.83403 690 150 33.83403 720 150 34.50687 720 150 34.50687 750 150 35.32321 750 150 35.32321 780 150 36.24923 780 150 36.24923 810 150 37.24645 810 150 37.24645 840 150 38.27328 840 150 38.27328 870 150 39.28662 870 150 39.28662 900 150 40.24363 900 150 40.24363 930 150 42.53047 930 150 42.53047 960 150 49.33374 960 150 49.33374 990 150 59.7343 990 150 59.7343 1020 150 72.08134 1020 150 72.08134 1050 150 84.41583 1050 150 84.41583 1080 150 94.84523 1080 150 94.84523 1110 150 101.8499 1110 150 101.8499 1140 150 104.3162 1140 150 104.3162 1170 150 101.8499 1170 150 101.8499 1200 150 94.84523 1200 150 94.84523 900 1200 99.61783 900 1200 99.61783 930 1170 113.3105 930 1170 113.3105 960 1140 125.3121 960 1140 125.3121 990 1110 134.6523 990 1110 134.6523 1020 1080 140.562 1020 1080 140.562 1050 1050 141.7554 1050 1050 141.7554 1080 1020 137.7226 1080 1020 137.7226 1110 990 128.8588 1110 990 128.8588 1140 960 115.9439 1140 960 115.9439 1170 930 100.0776 1170 930 100.0776 1200 900 82.59134 1200 900 82.59134 0 780 40.72814 0 780 40.72814 30 750 45.88593 30 750 45.88593 60 720 52.30694 60 720 52.30694 90 690 59.42431 90 690 59.42431 120 660 66.61403 120 660 66.61403 150 630 73.64055 150 630 73.64055 180 600 80.37224 180 600 80.37224 210 570 86.05766 210 570 86.05766 240 540 90.07156 240 540 90.07156 270 510 92.27995 270 510 92.27995 300 480 92.56803 300 480 92.56803 330 450 90.86622 330 450 90.86622 360 420 87.28525 360 420 87.28525 390 390 81.91637 390 390 81.91637 420 360 74.93835 420 360 74.93835 450 330 66.94016 450 330 66.94016 480 300 58.61061 480 300 58.61061 510 270 50.68357 510 270 50.68357 540 240 43.88263 540 240 43.88263 570 210 38.86713 570 210 38.86713 600 180 35.67099 600 180 35.67099 630 150 33.41841 630 150 33.41841 660 120 32.38458 660 120 32.38458 690 120 32.91849 690 120 32.91849 720 120 33.63555 720 120 33.63555 750 120 34.50687 750 120 34.50687 780 120 35.49736 780 120 35.49736 810 120 36.56705 810 120 36.56705 840 120 37.67268 840 120 37.67268 870 120 38.76939 870 120 38.76939 900 120 39.81249 900 120 39.81249 930 120 43.1249 930 120 43.1249 960 120 51.28813 960 120 51.28813 990 120 63.12685 990 120 63.12685 1020 120 76.87728 1020 120 76.87728 1050 120 90.46502 1050 120 90.46502 1080 120 101.8499 1080 120 101.8499 1110 120 109.4538 1110 120 109.4538 1140 120 112.1287 1140 120 112.1287 1170 120 109.4538 1170 120 109.4538 1200 120 101.8499 1200 120 101.8499 930 1200 110.8958 930 1200 110.8958 960 1170 124.4139 960 1170 124.4139 990 1140 135.6228 990 1140 135.6228 1020 1110 143.6314 1020 1110 143.6314 1050 1080 147.7089 1050 1080 147.7089 1080 1050 146.6438 1080 1050 146.6438 1110 1020 140.3686 1110 1020 140.3686 1140 990 129.468 1140 990 129.468 1170 960 114.8874 1170 960 114.8874 1200 930 97.85646 1200 930 97.85646 0 750 43.2607 0 750 43.2607 30 720 48.60439 30 720 48.60439 60 690 54.91134 60 690 54.91134 90 660 61.60469 90 660 61.60469 120 630 68.61723 120 630 68.61723 150 600 75.79828 150 600 75.79828 180 570 82.35069 180 570 82.35069 210 540 87.56795 210 540 87.56795 240 510 91.15672 240 510 91.15672 270 480 92.93274 270 480 92.93274 300 450 92.71711 300 450 92.71711 330 420 90.51166 330 420 90.51166 360 390 86.42767 360 390 86.42767 390 360 80.46108 390 360 80.46108 420 330 73.07167 420 330 73.07167 450 300 64.8958 450 300 64.8958 480 270 56.64594 480 270 56.64594 510 240 49.05649 510 240 49.05649 540 210 42.82883 540 210 42.82883 570 180 38.42255 570 180 38.42255 600 150 34.98283 600 150 34.98283 630 120 32.59679 630 120 32.59679 660 90 31.60582 660 90 31.60582 690 90 32.16584 690 90 32.16584 720 90 32.91849 720 90 32.91849 750 90 33.83403 750 90 33.83403 780 90 34.87636 780 90 34.87636 810 90 36.00432 810 90 36.00432 840 90 37.17336 840 90 37.17336 870 90 38.33721 870 90 38.33721 900 90 39.44968 900 90 39.44968 930 90 43.20321 930 90 43.20321 960 90 51.87761 960 90 51.87761 990 90 64.25201 990 90 64.25201 1020 90 78.5233 1020 90 78.5233 1050 90 92.57702 1050 90 92.57702 1080 90 104.3162 1080 90 104.3162 1110 90 112.1287 1110 90 112.1287 1140 90 114.8874 1140 90 114.8874 1170 90 112.1287 1170 90 112.1287 1200 90 104.3162 1200 90 104.3162 960 1200 121.7576 960 1200 121.7576 990 1170 134.6523 990 1170 134.6523 1020 1140 144.6636 1020 1140 144.6636 1050 1110 151.0053 1050 1110 151.0053 1080 1080 152.9731 1080 1080 152.9731 1110 1050 149.5949 1110 1050 149.5949 1140 1020 141.1406 1140 1020 141.1406 1170 990 128.3743 1170 990 128.3743 1200 960 112.3894 1200 960 112.3894 0 720 45.56675 0 720 45.56675 30 690 50.89394 30 690 50.89394 60 660 56.88922 60 660 56.88922 90 630 63.62154 90 630 63.62154 120 600 70.98885 120 600 70.98885 150 570 78.174 150 570 78.174 180 540 84.4156 180 540 84.4156 210 510 89.2858 210 510 89.2858 240 480 92.51822 240 480 92.51822 270 450 93.82822 270 450 93.82822 300 420 93.10742 300 420 93.10742 330 390 90.42266 330 390 90.42266 360 360 85.72235 360 360 85.72235 390 330 79.24346 390 330 79.24346 420 300 71.55158 420 300 71.55158 450 270 63.31746 450 270 63.31746 480 240 55.26542 480 240 55.26542 510 210 48.11876 510 210 48.11876 540 180 42.53378 540 180 42.53378 570 150 38.01829 570 150 38.01829 600 120 34.34283 600 120 34.34283 630 90 31.90124 630 90 31.90124 660 60 31.02658 660 60 31.02658 690 60 31.60582 690 60 31.60582 720 60 32.38458 720 60 32.38458 750 60 33.33252 750 60 33.33252 780 60 34.41281 780 60 34.41281 810 60 35.58346 810 60 35.58346 840 60 36.79895 840 60 36.79895 870 60 38.01199 870 60 38.01199 900 60 39.17534 900 60 39.17534 930 60 42.60935 930 60 42.60935 960 60 50.89297 960 60 50.89297 990 60 62.84843 990 60 62.84843 1020 60 76.70965 1020 60 76.70965 1050 60 90.40016 1050 60 90.40016 1080 60 101.8499 1080 60 101.8499 1110 60 109.4538 1110 60 109.4538 1140 60 112.1287 1140 60 112.1287 1170 60 109.4538 1170 60 109.4538 1200 60 101.8499 1200 60 101.8499 990 1200 131.7811 990 1200 131.7811 1020 1170 143.6314 1020 1170 143.6314 1050 1140 152.0874 1050 1140 152.0874 1080 1110 156.4898 1080 1110 156.4898 1110 1080 156.1798 1110 1080 156.1798 1140 1050 150.5129 1140 1050 150.5129 1170 1020 140.0191 1170 1020 140.0191 1200 990 125.6263 1200 990 125.6263 0 690 47.51923 0 690 47.51923 30 660 52.64122 30 660 52.64122 60 630 58.82643 60 630 58.82643 90 600 66.1082 90 600 66.1082 120 570 73.67056 120 570 73.67056 150 540 80.71878 150 540 80.71878 180 510 86.72784 180 510 86.72784 210 480 91.33663 210 480 91.33663 240 450 94.16126 240 450 94.16126 270 420 94.9851 270 420 94.9851 300 390 93.76959 300 390 93.76959 330 360 90.52855 330 360 90.52855 360 330 85.23086 360 330 85.23086 390 300 78.33509 390 300 78.33509 420 270 70.45122 420 270 70.45122 450 240 62.27295 450 240 62.27295 480 210 54.52485 480 210 54.52485 510 180 47.90826 510 180 47.90826 540 150 42.4436 540 150 42.4436 570 120 37.58613 570 120 37.58613 600 90 33.75642 600 90 33.75642 630 60 31.34514 630 60 31.34514 660 30 30.66952 660 30 30.66952 690 30 31.26062 690 30 31.26062 720 30 32.05535 720 30 32.05535 750 30 33.02308 750 30 33.02308 780 30 34.12653 780 30 34.12653 810 30 35.32321 810 30 35.32321 840 30 36.56705 840 30 36.56705 870 30 37.81011 870 30 37.81011 900 30 39.00453 900 30 39.00453 930 30 41.53134 930 30 41.53134 960 30 48.57211 960 30 48.57211 990 30 59.20282 990 30 59.20282 1020 30 71.76814 1020 30 71.76814 1050 30 84.30492 1050 30 84.30492 1080 30 94.84523 1080 30 94.84523 1110 30 101.8499 1110 30 101.8499 1140 30 104.3162 1140 30 104.3162 1170 30 101.8499 1170 30 101.8499 1200 30 94.84523 1200 30 94.84523 1020 1200 140.5767 1020 1200 140.5767 1050 1170 151.0053 1050 1170 151.0053 1080 1140 157.6094 1080 1140 157.6094 1110 1110 159.8725 1110 1110 159.8725 1140 1080 157.2244 1140 1080 157.2244 1170 1050 149.3738 1170 1050 149.3738 1200 1020 137.0536 1200 1020 137.0536 0 660 49.01326 0 660 49.01326 30 630 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+ + + + + + + + + + + + + + + + + + + 1 1 1 1 + + + + + + + + + + ID11 + + + + + ID12 + + + + + + + + + + + + + + + textures/Vegetation_Grass1.jpg + + + + + + diff --git a/wild_visual_navigation_sim/Media/models/textures/Vegetation_Grass1 (copy).jpg b/wild_visual_navigation_sim/Media/models/textures/Vegetation_Grass1 (copy).jpg new file mode 100644 index 00000000..6be377f3 Binary files /dev/null and b/wild_visual_navigation_sim/Media/models/textures/Vegetation_Grass1 (copy).jpg differ diff --git a/wild_visual_navigation_sim/Media/models/textures/Vegetation_Grass1.jpg b/wild_visual_navigation_sim/Media/models/textures/Vegetation_Grass1.jpg new file mode 100644 index 00000000..90775be1 Binary files /dev/null and b/wild_visual_navigation_sim/Media/models/textures/Vegetation_Grass1.jpg differ diff --git a/wild_visual_navigation_sim/Media/models/textures/mtt.png b/wild_visual_navigation_sim/Media/models/textures/mtt.png new file mode 100644 index 00000000..07485fa4 Binary files /dev/null and b/wild_visual_navigation_sim/Media/models/textures/mtt.png differ diff --git a/wild_visual_navigation_sim/Media/models/textures/texture0.jpg b/wild_visual_navigation_sim/Media/models/textures/texture0.jpg new file mode 100644 index 00000000..96adb41c Binary files /dev/null and b/wild_visual_navigation_sim/Media/models/textures/texture0.jpg differ diff --git a/wild_visual_navigation_sim/Media/models/textures/texture0.png b/wild_visual_navigation_sim/Media/models/textures/texture0.png new file mode 100644 index 00000000..23fd62ff Binary files /dev/null and b/wild_visual_navigation_sim/Media/models/textures/texture0.png differ diff --git a/wild_visual_navigation_sim/README.md b/wild_visual_navigation_sim/README.md new file mode 100644 index 00000000..bb92611e --- /dev/null +++ b/wild_visual_navigation_sim/README.md @@ -0,0 +1,20 @@ +# Wild Visual Navigation Sim + +Simulation environment to test Wild Visual Navigation (WVN). We use a modified Clearpath Jackal (adding a camera). + +## Requirements + +```sh +$ sudo apt update +$ sudo apt install -y \ + ros-noetic-jackal-simulator \ + ros-noetic-jackal-desktop \ + ros-noetic-teleop-twist-keyboard \ + ros-noetic-rqt-robot-steering \ +``` + +## Running + +```sh +$ roslaunch wild_visual_navigation_sim sim.launch +``` diff --git a/wild_visual_navigation_sim/launch/sim.launch b/wild_visual_navigation_sim/launch/sim.launch new file mode 100644 index 00000000..68e83fa8 --- /dev/null +++ b/wild_visual_navigation_sim/launch/sim.launch @@ -0,0 +1,34 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/wild_visual_navigation_sim/package.xml b/wild_visual_navigation_sim/package.xml new file mode 100644 index 00000000..921c777c --- /dev/null +++ b/wild_visual_navigation_sim/package.xml @@ -0,0 +1,26 @@ + + + wild_visual_navigation_sim + 0.0.1 + Simulation environment for wild_visual_navigation + Matias Mattamala + Jonas Frey + Matias Mattamala + Jonas Frey + + Proprietary + + catkin + + rospy + nav_msgs + sensor_msgs + std_msgs + wild_visual_navigation_ros + + gazebo_ros + + + + + diff --git a/wild_visual_navigation_sim/worlds/outdoor.world b/wild_visual_navigation_sim/worlds/outdoor.world new file mode 100644 index 00000000..a0bb69ba --- /dev/null +++ b/wild_visual_navigation_sim/worlds/outdoor.world @@ -0,0 +1,314 @@ + + + + + 0.2 0.2 0.2 1 + 1 + 0 + + 0 0 -9.81 + + 1 + 0 0 100 0 -0 0 + 0.8 0.8 0.8 1 + 0.9 0.9 0.9 1 + + 1000 + 0.1 + 0.01 + 0.001 + + 0.2 0.2 -0.9 + + + 1 + + + + + + file://heightmap.png + 30 30 12 + 0 0 -1 + + 10 + __default__ + __default__ + + + 0 + 0 + + + + 10 + + + + + + + + + + + + + + + + + + file://media/materials/textures/dirt_diffusespecular.png + file://media/materials/textures/flat_normal.png + 7 + + + file://media/materials/textures/grass_diffusespecular.png + file://media/materials/textures/flat_normal.png + 3 + + + file://media/materials/textures/fungus_diffusespecular.png + file://media/materials/textures/flat_normal.png + 4 + + + 0 + 0.05 + + + -1 + 7 + + + file://heightmap.png + 30 30 12 + 0 0 -1 + + + + 0 + 0 + 0 + + + + + + true + + 3 3 -1 0 0 0 + + + + file://OliveTree1.dae + 1 1 1 + + + + + + + file://OliveTree1.dae + 1 1 1 + + + false + + + + + + true + + 5 6 -1.0 0.0 0 0.3 + + + + file://OliveTree1.dae + 1 1 1 + + + + + + + file://OliveTree1.dae + 1 1 1 + + + false + + + + + + true + + 1 8 -0.2 0.0 0 0 + + + + file://OliveTree2.dae + 1 1 1 + + + + + + + file://OliveTree2.dae + 1 1 1 + + + false + + + + + + true + + -5 4 -1.0 0 0.1 + + + + file://OliveTree1.dae + 1 1 1 + + + + + + + file://OliveTree1.dae + 1 1 1 + + + false + + + + + + true + + -10 -3 -1.0 0 0 0.3 + + + + file://OliveTree1.dae + 1 1 1 + + + + + + + file://OliveTree1.dae + 1 1 1 + + + false + + + + + + true + + -6 -1 -0.8 0 0 0.5 + + + + file://OliveTree1.dae + 1 1 1 + + + + + + + file://OliveTree1.dae + 1 1 1 + + + false + + + + + + true + + -1 1 -1.0 0.0 0 0.2 + + + + file://OliveTree2.dae + 1 1 1 + + + + + + + file://OliveTree2.dae + 1 1 1 + + + false + + + + + + true + + 9 -4 -0.5 0 0 0.5 + + + + file://OliveTree1.dae + 1 1 1 + + + + + + + file://OliveTree1.dae + 1 1 1 + + + false + + + + + + true + + 6 -0.5 -1.0 0 0 0.5 + + + + file://OliveTree1.dae + 1 1 1 + + + + + + + file://OliveTree1.dae + 1 1 1 + + + false + + + + + \ No newline at end of file diff --git a/wild_visual_navigation_sim/worlds/scenario.world b/wild_visual_navigation_sim/worlds/scenario.world new file mode 100644 index 00000000..46cfec7b --- /dev/null +++ b/wild_visual_navigation_sim/worlds/scenario.world @@ -0,0 +1,282 @@ + + + + + + 0.5 0.5 0.5 1 + 0.5 0.5 0.5 1 + false + + + 0 0 -9.81 + + + quick + 10 + 1.3 + + + 0.0 + 0.2 + 10 + 0.001 + + + 0.001 + + + 0 20 20 0.1 0.1 0 + 1 1 1 1 + 1 1 1 1 + + 300 + + 0.1 0.1 -1 + false + + + + -15.0 -15.0 0.5 0 0 0 + + + + file://scenario.dae + 1 1 0.4 + + + + + + + file://scenario.dae + 1 1 0.4 + + + false + + + true + + + + + true + + 3 3 0.6 0 0 0 + + + + file://OliveTree1.dae + 1 1 1 + + + + + + + file://OliveTree1.dae + 1 1 1 + + + false + + + + + + true + + 5 6 0.7 0 0 0.3 + + + + file://OliveTree1.dae + 1 1 1 + + + + + + + file://OliveTree1.dae + 1 1 1 + + + false + + + + + + true + + 1 8 0.9 0 0 0 + + + + file://OliveTree2.dae + 1 1 1 + + + + + + + file://OliveTree2.dae + 1 1 1 + + + false + + + + + + true + + -5 4 0.8 0 0 0.1 + + + + file://OliveTree1.dae + 1 1 1 + + + + + + + file://OliveTree1.dae + 1 1 1 + + + false + + + + + + true + + -10 -3 1.1 0 0 0.3 + + + + file://OliveTree1.dae + 1 1 1 + + + + + + + file://OliveTree1.dae + 1 1 1 + + + false + + + + + + true + + -6 -7 1.02 0 0 0.5 + + + + file://OliveTree1.dae + 1 1 1 + + + + + + + file://OliveTree1.dae + 1 1 1 + + + false + + + + + + true + + -1 -6 0.8 0 0 0.2 + + + + file://OliveTree2.dae + 1 1 1 + + + + + + + file://OliveTree2.dae + 1 1 1 + + + false + + + + + + true + + 9 -4 0.5 0 0 0.5 + + + + file://OliveTree1.dae + 1 1 1 + + + + + + + file://OliveTree1.dae + 1 1 1 + + + false + + + + + + true + + 6 -10 0.8 0 0 0.5 + + + + file://OliveTree1.dae + 1 1 1 + + + + + + + file://OliveTree1.dae + 1 1 1 + + + false + + + + + + \ No newline at end of file