169 lines
8.2 KiB
Python
169 lines
8.2 KiB
Python
import numpy as np
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from scipy.spatial import cKDTree
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from utils.pts import PtsUtil
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class ReconstructionUtil:
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@staticmethod
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def compute_coverage_rate(target_point_cloud, combined_point_cloud, threshold=0.01):
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kdtree = cKDTree(combined_point_cloud)
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distances, _ = kdtree.query(target_point_cloud)
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covered_points = np.sum(distances < threshold*2)
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coverage_rate = covered_points / target_point_cloud.shape[0]
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return coverage_rate
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@staticmethod
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def compute_overlap_rate(new_point_cloud, combined_point_cloud, threshold=0.01):
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kdtree = cKDTree(combined_point_cloud)
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distances, _ = kdtree.query(new_point_cloud)
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overlapping_points = np.sum(distances < threshold)
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if new_point_cloud.shape[0] == 0:
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overlap_rate = 0
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else:
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overlap_rate = overlapping_points / new_point_cloud.shape[0]
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return overlap_rate
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@staticmethod
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def combine_point_with_view_sequence(point_list, view_sequence):
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selected_views = []
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for view_index, _ in view_sequence:
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selected_views.append(point_list[view_index])
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return np.vstack(selected_views)
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@staticmethod
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def compute_next_view_coverage_list(views, combined_point_cloud, target_point_cloud, threshold=0.01):
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best_view = None
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best_coverage_increase = -1
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current_coverage = ReconstructionUtil.compute_coverage_rate(target_point_cloud, combined_point_cloud, threshold)
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for view_index, view in enumerate(views):
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candidate_views = combined_point_cloud + [view]
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down_sampled_combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(candidate_views, threshold)
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new_coverage = ReconstructionUtil.compute_coverage_rate(target_point_cloud, down_sampled_combined_point_cloud, threshold)
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coverage_increase = new_coverage - current_coverage
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if coverage_increase > best_coverage_increase:
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best_coverage_increase = coverage_increase
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best_view = view_index
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return best_view, best_coverage_increase
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@staticmethod
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def get_new_added_points(old_combined_pts, new_pts, threshold=0.005):
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if old_combined_pts.size == 0:
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return new_pts
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if new_pts.size == 0:
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return np.array([])
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tree = cKDTree(old_combined_pts)
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distances, _ = tree.query(new_pts, k=1)
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new_added_points = new_pts[distances > threshold]
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return new_added_points
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@staticmethod
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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):
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selected_views = [point_cloud_list[init_view]]
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combined_point_cloud = np.vstack(selected_views)
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history_indices = [scan_points_indices_list[init_view]]
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down_sampled_combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(combined_point_cloud,threshold)
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new_coverage = ReconstructionUtil.compute_coverage_rate(target_point_cloud, down_sampled_combined_point_cloud, threshold)
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current_coverage = new_coverage
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remaining_views = list(range(len(point_cloud_list)))
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view_sequence = [(init_view, current_coverage)]
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cnt_processed_view = 0
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remaining_views.remove(init_view)
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while remaining_views:
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best_view = None
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best_coverage_increase = -1
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for view_index in remaining_views:
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if point_cloud_list[view_index].shape[0] == 0:
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continue
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if selected_views:
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new_scan_points_indices = scan_points_indices_list[view_index]
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if not ReconstructionUtil.check_scan_points_overlap(history_indices, new_scan_points_indices, scan_points_threshold):
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overlap_threshold = hard_overlap_threshold
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else:
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overlap_threshold = soft_overlap_threshold
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combined_old_point_cloud = np.vstack(selected_views)
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down_sampled_old_point_cloud = PtsUtil.voxel_downsample_point_cloud(combined_old_point_cloud,threshold)
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down_sampled_new_view_point_cloud = PtsUtil.voxel_downsample_point_cloud(point_cloud_list[view_index],threshold)
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overlap_rate = ReconstructionUtil.compute_overlap_rate(down_sampled_new_view_point_cloud,down_sampled_old_point_cloud, threshold)
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if overlap_rate < overlap_threshold:
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continue
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candidate_views = selected_views + [point_cloud_list[view_index]]
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combined_point_cloud = np.vstack(candidate_views)
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down_sampled_combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(combined_point_cloud,threshold)
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new_coverage = ReconstructionUtil.compute_coverage_rate(target_point_cloud, down_sampled_combined_point_cloud, threshold)
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coverage_increase = new_coverage - current_coverage
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if coverage_increase > best_coverage_increase:
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best_coverage_increase = coverage_increase
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best_view = view_index
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if best_view is not None:
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if best_coverage_increase <=3e-3:
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break
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selected_views.append(point_cloud_list[best_view])
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remaining_views.remove(best_view)
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history_indices.append(scan_points_indices_list[best_view])
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current_coverage += best_coverage_increase
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cnt_processed_view += 1
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if status_info is not None:
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sm = status_info["status_manager"]
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app_name = status_info["app_name"]
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runner_name = status_info["runner_name"]
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sm.set_status(app_name, runner_name, "current coverage", current_coverage)
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sm.set_progress(app_name, runner_name, "processed view", cnt_processed_view, len(point_cloud_list))
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view_sequence.append((best_view, current_coverage))
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else:
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break
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if status_info is not None:
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sm = status_info["status_manager"]
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app_name = status_info["app_name"]
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runner_name = status_info["runner_name"]
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sm.set_progress(app_name, runner_name, "processed view", len(point_cloud_list), len(point_cloud_list))
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return view_sequence, remaining_views, down_sampled_combined_point_cloud
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@staticmethod
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def generate_scan_points(display_table_top, display_table_radius, min_distance=0.03, max_points_num = 500, max_attempts = 1000):
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points = []
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attempts = 0
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while len(points) < max_points_num and attempts < max_attempts:
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angle = np.random.uniform(0, 2 * np.pi)
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r = np.random.uniform(0, display_table_radius)
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x = r * np.cos(angle)
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y = r * np.sin(angle)
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z = display_table_top
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new_point = (x, y, z)
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if all(np.linalg.norm(np.array(new_point) - np.array(existing_point)) >= min_distance for existing_point in points):
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points.append(new_point)
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attempts += 1
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return points
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@staticmethod
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def compute_covered_scan_points(scan_points, point_cloud, threshold=0.01):
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tree = cKDTree(point_cloud)
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covered_points = []
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indices = []
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for i, scan_point in enumerate(scan_points):
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if tree.query_ball_point(scan_point, threshold):
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covered_points.append(scan_point)
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indices.append(i)
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return covered_points, indices
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@staticmethod
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def check_scan_points_overlap(history_indices, indices2, threshold=5):
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for indices1 in history_indices:
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if len(set(indices1).intersection(set(indices2))) >= threshold:
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return True
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return False
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