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update normal in computing strategy

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This commit is contained in:
hofee 2024-10-23 11:13:18 -05:00
parent 9d0119549e
commit a1226eb294
5 changed files with 38 additions and 12 deletions
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2
configs/local/strategy_generate_config.yaml
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@ -13,7 +13,7 @@ runner:
generate:
voxel_threshold: 0.003
overlap_area_threshold: 25
compute_with_normal: True
compute_with_normal: False
scan_points_threshold: 10
overwrite: False
seq_num: 15

4
preprocess/preprocessor.py
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@ -164,9 +164,9 @@ def save_scene_data(root, scene, scene_idx=0, scene_total=1,file_type="txt"):
if __name__ == "__main__":
#root = "/media/hofee/repository/new_data_with_normal"
root = r"C:\Document\Local Project\nbv_rec\nbv_reconstruction\temp"
root = r"C:\Document\Datasets\nbv_rec_part2"
scene_list = os.listdir(root)
from_idx = 0 # 1000
from_idx = 600 # 1000
to_idx = len(scene_list) # 1500

11
utils/pts.py
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@ -7,8 +7,15 @@ class PtsUtil:
@staticmethod
def voxel_downsample_point_cloud(point_cloud, voxel_size=0.005, require_idx=False):
voxel_indices = np.floor(point_cloud / voxel_size).astype(np.int32)
unique_voxels = np.unique(voxel_indices, axis=0, return_inverse=True)
return unique_voxels[0]*voxel_size
if require_idx:
_, inverse, counts = np.unique(voxel_indices, axis=0, return_inverse=True, return_counts=True)
idx_sort = np.argsort(inverse)
idx_unique = idx_sort[np.cumsum(counts)-counts]
downsampled_points = point_cloud[idx_unique]
return downsampled_points, idx_unique
else:
unique_voxels = np.unique(voxel_indices, axis=0, return_inverse=True)
return unique_voxels[0]*voxel_size
@staticmethod
def random_downsample_point_cloud(point_cloud, num_points, require_idx=False):

9
utils/reconstruction.py
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@ -19,6 +19,12 @@ class ReconstructionUtil:
is_covered_by_distance = distances < threshold*2
normal_dots = np.einsum('ij,ij->i', target_normal, combined_normal[indices])
is_covered_by_normal = normal_dots > normal_threshold
pts_nrm_target = np.hstack([target_point_cloud, target_normal])
np.savetxt("pts_nrm_target.txt", pts_nrm_target)
pts_nrm_combined = np.hstack([combined_point_cloud, combined_normal])
np.savetxt("pts_nrm_combined.txt", pts_nrm_combined)
import ipdb; ipdb.set_trace()
covered_points_num = np.sum(is_covered_by_distance & is_covered_by_normal)
coverage_rate = covered_points_num / target_point_cloud.shape[0]
@ -145,7 +151,6 @@ class ReconstructionUtil:
max_rec_nrm = np.vstack(normal_list)
downsampled_max_rec_pts, idx = PtsUtil.voxel_downsample_point_cloud(max_rec_pts, threshold, require_idx=True)
downsampled_max_rec_nrm = max_rec_nrm[idx]
max_rec_pts_num = downsampled_max_rec_pts.shape[0]
try:
max_real_rec_pts_coverage, _ = ReconstructionUtil.compute_coverage_rate_with_normal(target_point_cloud, downsampled_max_rec_pts, target_normal, downsampled_max_rec_nrm, threshold)
@ -184,7 +189,7 @@ class ReconstructionUtil:
new_combined_point_cloud = np.vstack([combined_point_cloud, point_cloud_list[view_index]])
new_combined_normal = np.vstack([combined_normal, normal_list[view_index]])
new_downsampled_combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(new_combined_point_cloud,threshold)
new_downsampled_combined_point_cloud, idx = PtsUtil.voxel_downsample_point_cloud(new_combined_point_cloud,threshold, require_idx=True)
new_downsampled_combined_normal = new_combined_normal[idx]
new_coverage, new_covered_num = ReconstructionUtil.compute_coverage_rate_with_normal(downsampled_max_rec_pts, new_downsampled_combined_point_cloud, downsampled_max_rec_nrm, new_downsampled_combined_normal, threshold)
coverage_increase = new_coverage - current_coverage

24
utils/vis.py
View File

@ -156,7 +156,18 @@ class visualizeUtil:
sampled_visualized_normal = np.array(sampled_visualized_normal).reshape(-1, 3)
np.savetxt(os.path.join(output_dir, "target_pts.txt"), sampled_target_points)
np.savetxt(os.path.join(output_dir, "target_normal.txt"), sampled_visualized_normal)
@staticmethod
def save_pts_nrm(pts_nrm, output_dir):
pts = pts_nrm[:, :3]
nrm = pts_nrm[:, 3:]
visualized_nrm = []
num_samples = 10
for i in range(len(pts)):
visualized_nrm.append(pts[i] + 0.02*t * nrm[i] for t in range(num_samples))
visualized_nrm = np.array(visualized_nrm).reshape(-1, 3)
np.savetxt(os.path.join(output_dir, "nrm.txt"), visualized_nrm)
np.savetxt(os.path.join(output_dir, "pts.txt"), pts)
# ------ Debug ------
@ -168,8 +179,11 @@ if __name__ == "__main__":
output_dir = r"C:\Document\Local Project\nbv_rec\nbv_reconstruction\test"
#visualizeUtil.save_all_cam_pos_and_cam_axis(root, scene, output_dir)
visualizeUtil.save_all_combined_pts(root, scene, output_dir)
visualizeUtil.save_seq_combined_pts(root, scene, [0, 121, 286, 175, 111,366,45,230,232,225,255,17,199,78,60], output_dir)
visualizeUtil.save_seq_cam_pos_and_cam_axis(root, scene, [0, 121, 286, 175, 111,366,45,230,232,225,255,17,199,78,60], output_dir)
visualizeUtil.save_target_mesh_at_world_space(root, model_dir, scene)
# visualizeUtil.save_all_combined_pts(root, scene, output_dir)
# visualizeUtil.save_seq_combined_pts(root, scene, [0, 121, 286, 175, 111,366,45,230,232,225,255,17,199,78,60], output_dir)
# visualizeUtil.save_seq_cam_pos_and_cam_axis(root, scene, [0, 121, 286, 175, 111,366,45,230,232,225,255,17,199,78,60], output_dir)
# visualizeUtil.save_target_mesh_at_world_space(root, model_dir, scene)
#visualizeUtil.save_points_and_normals(root, scene,"10", output_dir, binocular=True)
pts_nrm = np.loadtxt(r"C:\Document\Local Project\nbv_rec\nbv_reconstruction\pts_nrm_target.txt")
visualizeUtil.save_pts_nrm(pts_nrm, output_dir)