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demo.py
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demo.py
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import argparse
import copy
import numpy as np
import os
import torch
from easydict import EasyDict as edict
import sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from data import collate_fn
from models import architectures, NgeNet, vote
from utils import decode_config, npy2pcd, pcd2npy, execute_global_registration, \
npy2feat, setup_seed, get_blue, get_yellow, voxel_ds, normal, \
read_cloud, vis_plys
CUR = os.path.dirname(os.path.abspath(__file__))
class NgeNet_pipeline():
def __init__(self, ckpt_path, voxel_size, vote_flag, cuda=True):
self.voxel_size_3dmatch = 0.025
self.voxel_size = voxel_size
self.scale = self.voxel_size / self.voxel_size_3dmatch
self.cuda = cuda
self.vote_flag = vote_flag
config = self.prepare_config()
self.neighborhood_limits = [38, 36, 35, 38]
model = NgeNet(config)
if self.cuda:
model = model.cuda()
model.load_state_dict(torch.load(ckpt_path))
else:
model.load_state_dict(
torch.load(ckpt_path, map_location=torch.device('cpu')))
self.model = model
self.config = config
self.model.eval()
def prepare_config(self):
config = decode_config(os.path.join(CUR, 'configs', 'threedmatch.yaml'))
config = edict(config)
# config.first_subsampling_dl = self.voxel_size
config.architecture = architectures[config.dataset]
return config
def prepare_inputs(self, source, target):
src_pcd_input = pcd2npy(voxel_ds(copy.deepcopy(source), self.voxel_size))
tgt_pcd_input = pcd2npy(voxel_ds(copy.deepcopy(target), self.voxel_size))
src_pcd_input /= self.scale
tgt_pcd_input /= self.scale
src_feats = np.ones_like(src_pcd_input[:, :1])
tgt_feats = np.ones_like(tgt_pcd_input[:, :1])
src_pcd = normal(npy2pcd(src_pcd_input), radius=4*self.voxel_size_3dmatch, max_nn=30, loc=(0, 0, 0))
tgt_pcd = normal(npy2pcd(tgt_pcd_input), radius=4*self.voxel_size_3dmatch, max_nn=30, loc=(0, 0, 0))
src_normals = np.array(src_pcd.normals).astype(np.float32)
tgt_normals = np.array(tgt_pcd.normals).astype(np.float32)
T = np.eye(4)
coors = np.array([[0, 0], [1, 1]])
src_pcd = pcd2npy(source)
tgt_pcd = pcd2npy(target)
pair = dict(
src_points=src_pcd_input,
tgt_points=tgt_pcd_input,
src_feats=src_feats,
tgt_feats=tgt_feats,
src_normals=src_normals,
tgt_normals=tgt_normals,
transf=T,
coors=coors,
src_points_raw=src_pcd,
tgt_points_raw=tgt_pcd)
dict_inputs = collate_fn([pair], self.config, self.neighborhood_limits)
if self.cuda:
for k, v in dict_inputs.items():
if isinstance(v, list):
for i in range(len(v)):
dict_inputs[k][i] = dict_inputs[k][i].cuda()
else:
dict_inputs[k] = dict_inputs[k].cuda()
return dict_inputs
def pipeline(self, source, target, npts=20000):
inputs = self.prepare_inputs(source, target)
batched_feats_h, batched_feats_m, batched_feats_l = self.model(inputs)
stack_points = inputs['points']
stack_lengths = inputs['stacked_lengths']
coords_src = stack_points[0][:stack_lengths[0][0]]
coords_tgt = stack_points[0][stack_lengths[0][0]:]
feats_src_h = batched_feats_h[:stack_lengths[0][0]]
feats_tgt_h = batched_feats_h[stack_lengths[0][0]:]
feats_src_m = batched_feats_m[:stack_lengths[0][0]]
feats_tgt_m = batched_feats_m[stack_lengths[0][0]:]
feats_src_l = batched_feats_l[:stack_lengths[0][0]]
feats_tgt_l = batched_feats_l[stack_lengths[0][0]:]
source_npy = coords_src.detach().cpu().numpy() * self.scale
target_npy = coords_tgt.detach().cpu().numpy() * self.scale
source_feats_h = feats_src_h[:, :-2].detach().cpu().numpy()
target_feats_h = feats_tgt_h[:, :-2].detach().cpu().numpy()
source_feats_m = feats_src_m.detach().cpu().numpy()
target_feats_m = feats_tgt_m.detach().cpu().numpy()
source_feats_l = feats_src_l.detach().cpu().numpy()
target_feats_l = feats_tgt_l.detach().cpu().numpy()
source_overlap_scores = feats_src_h[:, -2].detach().cpu().numpy()
target_overlap_scores = feats_tgt_h[:, -2].detach().cpu().numpy()
source_scores = source_overlap_scores
target_scores = target_overlap_scores
npoints = npts
if npoints > 0:
if source_npy.shape[0] > npoints:
p = source_scores / np.sum(source_scores)
idx = np.random.choice(len(source_npy), size=npoints, replace=False, p=p)
source_npy = source_npy[idx]
source_feats_h = source_feats_h[idx]
source_feats_m = source_feats_m[idx]
source_feats_l = source_feats_l[idx]
if target_npy.shape[0] > npoints:
p = target_scores / np.sum(target_scores)
idx = np.random.choice(len(target_npy), size=npoints, replace=False, p=p)
target_npy = target_npy[idx]
target_feats_h = target_feats_h[idx]
target_feats_m = target_feats_m[idx]
target_feats_l = target_feats_l[idx]
if self.vote_flag:
after_vote = vote(source_npy=source_npy,
target_npy=target_npy,
source_feats=[source_feats_h, source_feats_m, source_feats_l],
target_feats=[target_feats_h, target_feats_m, target_feats_l],
voxel_size=self.voxel_size * 2,
use_cuda=self.cuda)
source_npy, target_npy, source_feats_npy, target_feats_npy = after_vote
else:
source_feats_npy, target_feats_npy = source_feats_h, target_feats_h
source, target = npy2pcd(source_npy), npy2pcd(target_npy)
source_feats, target_feats = npy2feat(source_feats_npy), npy2feat(target_feats_npy)
pred_T, estimate = execute_global_registration(source=source,
target=target,
source_feats=source_feats,
target_feats=target_feats,
voxel_size=self.voxel_size*2)
torch.cuda.empty_cache()
return pred_T
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Configuration Parameters')
parser.add_argument('--src_path', required=True, help='source point cloud path')
parser.add_argument('--tgt_path', required=True, help='target point cloud path')
parser.add_argument('--checkpoint', required=True, help='checkpoint path')
parser.add_argument('--voxel_size', type=float, required=True, help='voxel size')
parser.add_argument('--npts', type=int, default=20000,
help='the number of sampled points for registration')
parser.add_argument('--no_vote', action='store_true',
help='whether to use multi-level consistent voting')
parser.add_argument('--no_vis', action='store_true',
help='whether to visualize the point clouds')
parser.add_argument('--no_cuda', action='store_true',
help='whether to use cuda')
args = parser.parse_args()
# input data
source, target = read_cloud(args.src_path), read_cloud(args.tgt_path)
# loading model
cuda = not args.no_cuda
vote_flag = not args.no_vote
model = NgeNet_pipeline(
ckpt_path=args.checkpoint,
voxel_size=args.voxel_size,
vote_flag=vote_flag,
cuda=cuda)
# registration
T = model.pipeline(source, target, npts=args.npts)
print('Estimated transformation matrix: ', T)
# vis
if not args.no_vis:
# voxelization for fluent visualization
source = voxel_ds(source, args.voxel_size)
target = voxel_ds(target, args.voxel_size)
estimate = copy.deepcopy(source).transform(T)
source.paint_uniform_color(get_yellow())
source.estimate_normals()
target.paint_uniform_color(get_blue())
target.estimate_normals()
vis_plys([source, target], need_color=False)
estimate.paint_uniform_color(get_yellow())
estimate.estimate_normals()
vis_plys([estimate, target], need_color=False)