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point_pillars_check_input.py
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import os
from glob import glob
import numpy as np
import tensorflow as tf
# from processors import SimpleDataGenerator
# from custom_processors import CustomDataGenerator, AnalyseCustomDataGenerator
from point_pillars_custom_processors_v2 import CustomDataGenerator, AnalyseCustomDataGenerator
from inference_utils_v2 import generate_bboxes_from_pred, GroundTruthGenerator, focal_loss_checker
from inference_utils_v2 import rotational_nms, generate_bboxes_from_pred_and_np_array
from readers import KittiDataReader
from config_v2 import Parameters
from network import build_point_pillar_graph
from point_viz.converter import PointvizConverter
DATA_ROOT = "/media/data3/tjtanaa/kitti_dataset/"
# MODEL_ROOT = "./logs_Car_Pedestrian_Custom_Dataset_single_process"
MODEL_ROOT = "./logs_Car_Pedestrian_Custom_Dataset_No_Early_Stopping_Input_Coordinate_Analysis_v2"
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
def limit_period(val, offset=0.5, period=np.pi):
return val - np.floor(val / period + offset) * period
if __name__ == "__main__":
params = Parameters()
# save_viz_path = "/home/tan/tjtanaa/PointPillars/visualization/custom_prediction_multiprocessing"
# save_viz_path = "/home/tan/tjtanaa/PointPillars/visualization/input_coordinate_analysis_point_pillar_v2_gt_only"
# save_viz_path = "/home/tan/tjtanaa/PointPillars/visualization/input_coordinate_analysis_point_pillar_v2_labels_only"
save_viz_path = "/home/tan/tjtanaa/PointPillars/visualization/pedestrian_input_coordinate_analysis_point_pillar_v2_gt_and_labels"
# Initialize and setup output directory.
Converter = PointvizConverter(save_viz_path)
gt_database_dir = os.path.join(DATA_ROOT, "gt_database")
validation_gen = AnalyseCustomDataGenerator(batch_size=params.batch_size, root_dir=DATA_ROOT,
npoints=16384, split='train_val_test',random_select=False, classes=list(params.classes_map.keys()))
for sample_id in validation_gen.sample_id_list:
print(sample_id)
# for batch_idx in range(0,20):
# [pillars, voxels], [occupancy_, position_, size_, angle_, heading_, classification_], [pts_input, gt_boxes3d, sample] = validation_gen[batch_idx]
# set_boxes, confidences = [], []
# loop_range = occupancy_.shape[0] if len(occupancy_.shape) == 4 else 1
# for i in range(loop_range):
# # set_box, predicted_boxes3d = generate_bboxes_from_pred_and_np_array(occupancy[i], position[i], size[i], angle[i],
# # heading[i],
# # classification[i], params.anchor_dims, occ_threshold=0.15)
# gt_set_box, decoded_gt_boxes3d = generate_bboxes_from_pred_and_np_array(occupancy_[i], position_[i], size_[i], angle_[i],
# heading_[i],
# classification_[i], params.anchor_dims, occ_threshold=0.5)
# # exit()
# gt_boxes3d_ = gt_boxes3d[i]
# print(gt_boxes3d_.shape)
# gt_bbox_params = np.stack([gt_boxes3d_[:,3], gt_boxes3d_[:,5], gt_boxes3d_[:,4],
# gt_boxes3d_[:,1], gt_boxes3d_[:,2] ,
# gt_boxes3d_[:,0],
# gt_boxes3d_[:,6]], axis=1)
# gt_bbox_params_list = gt_bbox_params.tolist()
# # gt_bbox_params_list = []
# # print(gt_bbox_params_list)
# # print(len(gt_bbox_params_list))
# # print(len(gt_bbox_params_list[0]))
# for k in range(len(gt_bbox_params_list)):
# msg = "%.5f, %.5f"%(gt_bbox_params_list[k][3],gt_bbox_params_list[k][5])
# gt_bbox_params_list[k].append("Green")
# gt_bbox_params_list[k].append(msg)
# if len(gt_set_box) > 0:
# decoded_gt_boxes3d_ = decoded_gt_boxes3d
# # bbox_params = validation_gen.convert_predictions_into_point_viz_format(predicted_boxes3d[:,[1, 2, 0, 5, 3, 4, 6 ]])
# print(decoded_gt_boxes3d_.shape)
# # print(predicted_boxes3d_)
# # print(size[i])
# bbox_params = np.stack([decoded_gt_boxes3d_[:,3], decoded_gt_boxes3d_[:,5], decoded_gt_boxes3d_[:,4],
# decoded_gt_boxes3d_[:,1], decoded_gt_boxes3d_[:,2] ,
# decoded_gt_boxes3d_[:,0],
# decoded_gt_boxes3d_[:,6]], axis=1)
# # bbox_params = np.stack([predicted_boxes3d[:,4], predicted_boxes3d[:,5], predicted_boxes3d[:,3],
# # predicted_boxes3d[:,1], -(predicted_boxes3d[:,2] - predicted_boxes3d[:,5] / 2),
# # predicted_boxes3d[:,0],
# # predicted_boxes3d[:,6]], axis=1)
# bbox_params_list = bbox_params.tolist()
# # bbox_labels_conf = [str(predicted_boxes3d[k,9]) for k in range(predicted_boxes3d.shape[0])]
# for k in range(decoded_gt_boxes3d.shape[0]):
# msg = "%.5f, %.5f"%(bbox_params_list[k][3],bbox_params_list[k][5])
# # msg = (str(bbox_params_list[k][3:5]))
# bbox_params_list[k].append("Magenta")
# bbox_params_list[k].append(msg)
# # bbox_params_list[k].append(str(decoded_gt_boxes3d[k,9]) + params.map_classes[int(decoded_gt_boxes3d[k,8])])
# gt_bbox_params_list.append(bbox_params_list[k])
# # print(gt_bbox_params_list)
# # print(gt_bbox_params.tolist())
# coor = pts_input[i][:,[1,2,0]]
# # coor[:,1] *= -1
# Converter.compile("val_custom_sample_{}".format(batch_idx * params.batch_size+i), coors=coor, intensity=pts_input[i][:,3],
# bbox_params=gt_bbox_params_list)
# exit()
# set_boxes.append(set_box)
# # set_boxes.append(generate_bboxes_from_pred(occupancy, position, size, angle, heading,
# # classification, params.anchor_dims, occ_threshold=0.1))
# # confidences.append([float(boxes.conf) for boxes in set_boxes[-1]])
# sum_bboxes = 0
# for h in range(len(set_boxes)):
# sum_bboxes += len(set_boxes[h])
# print('Batch ', str(batch_idx) ,': Box predictions with occupancy > occ_thr: ', sum_bboxes)
# print('Scene 1: Box predictions with occupancy > occ_thr: ', len(set_boxes[0]))
# exit()
# print(set_boxes[-1])
# # NMS
# nms_boxes = rotational_nms(set_boxes, confidences, occ_threshold=0.7, nms_iou_thr=0.5)
# print('Scene 1: Boxes after NMS with iou_thr: ', len(nms_boxes[0]))
# # Do all the further operations on predicted_boxes array, which contains the predicted bounding boxes
# gt_gen = GroundTruthGenerator(data_reader, label_files, calibration_files, network_format=False)
# gt_gen0 = GroundTruthGenerator(data_reader, label_files, calibration_files, network_format=True)
# for seq_boxes, gt_label, gt0 in zip(nms_boxes, gt_gen, gt_gen0):
# print("---------- New Scenario ---------- ")
# focal_loss_checker(gt0[0], occupancy[0], n_occs=-1)
# print("---------- ------------ ---------- ")
# for gt in gt_label:
# print(gt)
# for pred in seq_boxes:
# print(pred)