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 = np.sum(distances < threshold*2) coverage_rate = covered_points / target_point_cloud.shape[0] return coverage_rate @staticmethod def compute_overlap_rate(new_point_cloud, combined_point_cloud, threshold=0.01): kdtree = cKDTree(combined_point_cloud) distances, _ = kdtree.query(new_point_cloud) overlapping_points = np.sum(distances < threshold) if new_point_cloud.shape[0] == 0: overlap_rate = 0 else: overlap_rate = overlapping_points / new_point_cloud.shape[0] return overlap_rate @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_with_overlap(target_point_cloud, point_cloud_list, scan_points_indices_list, threshold=0.01, soft_overlap_threshold=0.5, hard_overlap_threshold=0.7, 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) max_rec_pts_num = downsampled_max_rec_pts.shape[0] max_rec_pts_coverage = ReconstructionUtil.compute_coverage_rate(target_point_cloud, downsampled_max_rec_pts, threshold) new_coverage = ReconstructionUtil.compute_coverage_rate(downsampled_max_rec_pts, combined_point_cloud, threshold) current_coverage = new_coverage 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] import time while remaining_views: best_view = None best_coverage_increase = -1 best_combined_point_cloud = None 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): overlap_threshold = hard_overlap_threshold else: overlap_threshold = soft_overlap_threshold start = time.time() overlap_rate = ReconstructionUtil.compute_overlap_rate(point_cloud_list[view_index],combined_point_cloud, threshold) end = time.time() # print(f"overlap_rate Time: {end-start}") if overlap_rate < overlap_threshold: continue start = time.time() 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 = ReconstructionUtil.compute_coverage_rate(downsampled_max_rec_pts, new_downsampled_combined_point_cloud, threshold) end = time.time() #print(f"compute_coverage_rate Time: {end-start}") coverage_increase = new_coverage - current_coverage if coverage_increase > best_coverage_increase: best_coverage_increase = coverage_increase best_view = view_index best_combined_point_cloud = new_downsampled_combined_point_cloud if best_view is not None: if best_coverage_increase <=1e-3: 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}, Max rec pts num: {max_rec_pts_num}") print(f"Current coverage: {current_coverage}, Best coverage increase: {best_coverage_increase}, Max coverage: {max_rec_pts_coverage}") 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 # ----- Debug Trace ----- # import ipdb; ipdb.set_trace() # ------------------------ # 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 compute_covered_scan_points(scan_points, point_cloud, threshold=0.01): tree = cKDTree(point_cloud) covered_points = [] indices = [] for i, scan_point in enumerate(scan_points): if tree.query_ball_point(scan_point, threshold): covered_points.append(scan_point) indices.append(i) return covered_points, indices @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