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hofee
2025-05-13 09:03:38 +08:00
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import numpy as np
from scipy.spatial import cKDTree
from utils.pts import PtsUtil
class ReconstructionUtil:
@staticmethod
def compute_coverage_rate(target_point_cloud, combined_point_cloud, threshold=0.01):
kdtree = cKDTree(combined_point_cloud)
distances, _ = kdtree.query(target_point_cloud)
covered_points_num = np.sum(distances < threshold*2)
coverage_rate = covered_points_num / target_point_cloud.shape[0]
return coverage_rate, covered_points_num
@staticmethod
def compute_coverage_rate_with_normal(target_point_cloud, combined_point_cloud, target_normal, combined_normal, threshold=0.01, normal_threshold=0.1):
kdtree = cKDTree(combined_point_cloud)
distances, indices = kdtree.query(target_point_cloud)
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]
return coverage_rate, covered_points_num
@staticmethod
def check_overlap(new_point_cloud, combined_point_cloud, overlap_area_threshold=25, voxel_size=0.01, require_new_added_pts_num=False):
kdtree = cKDTree(combined_point_cloud)
distances, _ = kdtree.query(new_point_cloud)
overlapping_points_num = np.sum(distances < voxel_size*2)
cm = 0.01
voxel_size_cm = voxel_size / cm
overlap_area = overlapping_points_num * voxel_size_cm * voxel_size_cm
if require_new_added_pts_num:
return overlap_area > overlap_area_threshold, len(new_point_cloud)-np.sum(distances < voxel_size*1.2)
return overlap_area > overlap_area_threshold
@staticmethod
def get_new_added_points(old_combined_pts, new_pts, threshold=0.005):
if old_combined_pts.size == 0:
return new_pts
if new_pts.size == 0:
return np.array([])
tree = cKDTree(old_combined_pts)
distances, _ = tree.query(new_pts, k=1)
new_added_points = new_pts[distances > threshold]
return new_added_points
@staticmethod
def compute_next_best_view_sequence(target_point_cloud, point_cloud_list, scan_points_indices_list, threshold=0.01, overlap_area_threshold=25, init_view = 0, scan_points_threshold=5, status_info=None):
selected_views = [init_view]
combined_point_cloud = point_cloud_list[init_view]
history_indices = [scan_points_indices_list[init_view]]
max_rec_pts = np.vstack(point_cloud_list)
downsampled_max_rec_pts = PtsUtil.voxel_downsample_point_cloud(max_rec_pts, threshold)
combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(combined_point_cloud, threshold)
max_rec_pts_num = downsampled_max_rec_pts.shape[0]
max_real_rec_pts_coverage, _ = ReconstructionUtil.compute_coverage_rate(target_point_cloud, downsampled_max_rec_pts, threshold)
new_coverage, new_covered_num = ReconstructionUtil.compute_coverage_rate(downsampled_max_rec_pts, combined_point_cloud, threshold)
current_coverage = new_coverage
current_covered_num = new_covered_num
remaining_views = list(range(len(point_cloud_list)))
view_sequence = [(init_view, current_coverage)]
cnt_processed_view = 0
remaining_views.remove(init_view)
curr_rec_pts_num = combined_point_cloud.shape[0]
drop_output_ratio = 0.4
import time
while remaining_views:
best_view = None
best_coverage_increase = -1
best_combined_point_cloud = None
best_covered_num = 0
for view_index in remaining_views:
if np.random.rand() < drop_output_ratio:
continue
if point_cloud_list[view_index].shape[0] == 0:
continue
if selected_views:
new_scan_points_indices = scan_points_indices_list[view_index]
if not ReconstructionUtil.check_scan_points_overlap(history_indices, new_scan_points_indices, scan_points_threshold):
curr_overlap_area_threshold = overlap_area_threshold
else:
curr_overlap_area_threshold = overlap_area_threshold * 0.5
if not ReconstructionUtil.check_overlap(point_cloud_list[view_index], combined_point_cloud, overlap_area_threshold = curr_overlap_area_threshold, voxel_size=threshold):
continue
new_combined_point_cloud = np.vstack([combined_point_cloud, point_cloud_list[view_index]])
new_downsampled_combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(new_combined_point_cloud,threshold)
new_coverage, new_covered_num = ReconstructionUtil.compute_coverage_rate(downsampled_max_rec_pts, new_downsampled_combined_point_cloud, threshold)
coverage_increase = new_coverage - current_coverage
if coverage_increase > best_coverage_increase:
best_coverage_increase = coverage_increase
best_view = view_index
best_covered_num = new_covered_num
best_combined_point_cloud = new_downsampled_combined_point_cloud
if best_view is not None:
if best_coverage_increase <=1e-3 or best_covered_num - current_covered_num <= 5:
break
selected_views.append(best_view)
best_rec_pts_num = best_combined_point_cloud.shape[0]
print(f"Current rec pts num: {curr_rec_pts_num}, Best rec pts num: {best_rec_pts_num}, Best cover pts: {best_covered_num}, Max rec pts num: {max_rec_pts_num}")
print(f"Current coverage: {current_coverage+best_coverage_increase}, Best coverage increase: {best_coverage_increase}, Max Real coverage: {max_real_rec_pts_coverage}")
current_covered_num = best_covered_num
curr_rec_pts_num = best_rec_pts_num
combined_point_cloud = best_combined_point_cloud
remaining_views.remove(best_view)
history_indices.append(scan_points_indices_list[best_view])
current_coverage += best_coverage_increase
cnt_processed_view += 1
if status_info is not None:
sm = status_info["status_manager"]
app_name = status_info["app_name"]
runner_name = status_info["runner_name"]
sm.set_status(app_name, runner_name, "current coverage", current_coverage)
sm.set_progress(app_name, runner_name, "processed view", cnt_processed_view, len(point_cloud_list))
view_sequence.append((best_view, current_coverage))
else:
break
if status_info is not None:
sm = status_info["status_manager"]
app_name = status_info["app_name"]
runner_name = status_info["runner_name"]
sm.set_progress(app_name, runner_name, "processed view", len(point_cloud_list), len(point_cloud_list))
return view_sequence, remaining_views, combined_point_cloud
@staticmethod
def compute_next_best_view_sequence_with_normal(target_point_cloud, target_normal, point_cloud_list, normal_list, scan_points_indices_list, threshold=0.01, overlap_area_threshold=25, init_view = 0, scan_points_threshold=5, status_info=None):
selected_views = [init_view]
combined_point_cloud = point_cloud_list[init_view]
combined_normal = normal_list[init_view]
history_indices = [scan_points_indices_list[init_view]]
max_rec_pts = np.vstack(point_cloud_list)
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)
except:
import ipdb; ipdb.set_trace()
new_coverage, new_covered_num = ReconstructionUtil.compute_coverage_rate_with_normal(downsampled_max_rec_pts, combined_point_cloud, downsampled_max_rec_nrm, combined_normal, threshold)
current_coverage = new_coverage
current_covered_num = new_covered_num
remaining_views = list(range(len(point_cloud_list)))
view_sequence = [(init_view, current_coverage)]
cnt_processed_view = 0
remaining_views.remove(init_view)
curr_rec_pts_num = combined_point_cloud.shape[0]
while remaining_views:
best_view = None
best_coverage_increase = -1
best_combined_point_cloud = None
best_combined_normal = None
best_covered_num = 0
for view_index in remaining_views:
if point_cloud_list[view_index].shape[0] == 0:
continue
if selected_views:
new_scan_points_indices = scan_points_indices_list[view_index]
if not ReconstructionUtil.check_scan_points_overlap(history_indices, new_scan_points_indices, scan_points_threshold):
curr_overlap_area_threshold = overlap_area_threshold
else:
curr_overlap_area_threshold = overlap_area_threshold * 0.5
if not ReconstructionUtil.check_overlap(point_cloud_list[view_index], combined_point_cloud, overlap_area_threshold = curr_overlap_area_threshold, voxel_size=threshold):
continue
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, 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
if coverage_increase > best_coverage_increase:
best_coverage_increase = coverage_increase
best_view = view_index
best_covered_num = new_covered_num
best_combined_point_cloud = new_downsampled_combined_point_cloud
best_combined_normal = new_downsampled_combined_normal
if best_view is not None:
if best_coverage_increase <=1e-3 or best_covered_num - current_covered_num <= 5:
break
selected_views.append(best_view)
best_rec_pts_num = best_combined_point_cloud.shape[0]
print(f"Current rec pts num: {curr_rec_pts_num}, Best rec pts num: {best_rec_pts_num}, Best cover pts: {best_covered_num}, Max rec pts num: {max_rec_pts_num}")
print(f"Current coverage: {current_coverage}, Best coverage increase: {best_coverage_increase}, Max Real coverage: {max_real_rec_pts_coverage}")
current_covered_num = best_covered_num
curr_rec_pts_num = best_rec_pts_num
combined_point_cloud = best_combined_point_cloud
combined_normal = best_combined_normal
remaining_views.remove(best_view)
history_indices.append(scan_points_indices_list[best_view])
current_coverage += best_coverage_increase
cnt_processed_view += 1
if status_info is not None:
sm = status_info["status_manager"]
app_name = status_info["app_name"]
runner_name = status_info["runner_name"]
sm.set_status(app_name, runner_name, "current coverage", current_coverage)
sm.set_progress(app_name, runner_name, "processed view", cnt_processed_view, len(point_cloud_list))
view_sequence.append((best_view, current_coverage))
else:
break
if status_info is not None:
sm = status_info["status_manager"]
app_name = status_info["app_name"]
runner_name = status_info["runner_name"]
sm.set_progress(app_name, runner_name, "processed view", len(point_cloud_list), len(point_cloud_list))
return view_sequence, remaining_views, combined_point_cloud
@staticmethod
def generate_scan_points(display_table_top, display_table_radius, min_distance=0.03, max_points_num = 500, max_attempts = 1000):
points = []
attempts = 0
while len(points) < max_points_num and attempts < max_attempts:
angle = np.random.uniform(0, 2 * np.pi)
r = np.random.uniform(0, display_table_radius)
x = r * np.cos(angle)
y = r * np.sin(angle)
z = display_table_top
new_point = (x, y, z)
if all(np.linalg.norm(np.array(new_point) - np.array(existing_point)) >= min_distance for existing_point in points):
points.append(new_point)
attempts += 1
return points
@staticmethod
def check_scan_points_overlap(history_indices, indices2, threshold=5):
for indices1 in history_indices:
if len(set(indices1).intersection(set(indices2))) >= threshold:
return True
return False