import os import open3d as o3d import numpy as np import shutil class PointCloudDownsampler: def __init__(self, input_dir, output_dir, temp_dir, N, voxel_start=0.0001, voxel_step=0.0005): self.input_dir = input_dir self.output_dir = output_dir self.temp_dir = temp_dir self.N = N self.voxel_start = voxel_start self.voxel_step = voxel_step # Ensure output and temp directories exist if not os.path.exists(self.output_dir): os.makedirs(self.output_dir) if not os.path.exists(self.temp_dir): os.makedirs(self.temp_dir) def downsample_point_cloud(self, point_cloud, voxel_size): return point_cloud.voxel_down_sample(voxel_size) def process_point_clouds(self): for filename in os.listdir(self.input_dir): if filename.endswith(".pcd") or filename.endswith(".ply"): file_path = os.path.join(self.input_dir, filename) pcd = o3d.io.read_point_cloud(file_path) num_points = len(pcd.points) voxel_size = self.voxel_start best_voxel_size = None best_num_points = num_points print(f"Processing {filename} with {num_points} points") while True: downsampled_pcd = self.downsample_point_cloud(pcd, voxel_size) downsampled_num_points = len(downsampled_pcd.points) print(f"Trying voxel size: {voxel_size:.5f} -> {downsampled_num_points} points") if downsampled_num_points < self.N: if best_num_points > self.N: print(f"Found optimal voxel size: {best_voxel_size:.5f} with {best_num_points} points") break print(f"Breaking at voxel size {voxel_size:.5f} with {downsampled_num_points} points") break else: best_voxel_size = voxel_size best_num_points = downsampled_num_points voxel_size += self.voxel_step if best_voxel_size: optimal_pcd = self.downsample_point_cloud(pcd, best_voxel_size) temp_path = os.path.join(self.temp_dir, filename) o3d.io.write_point_cloud(temp_path, optimal_pcd) print(f"Temporarily saved {filename} with {len(optimal_pcd.points)} points to {self.temp_dir}") print("-" * 50) def random_downsample_point_cloud(self, pcd, target_size): num_points = len(pcd.points) if num_points <= target_size: print(f"No downsampling needed. Point cloud has {num_points} points, which is less than or equal to {target_size}.") return pcd indices = np.random.choice(num_points, target_size, replace=False) downsampled_pcd = pcd.select_by_index(indices) return downsampled_pcd def downsample_all_to_target_size(self): for filename in os.listdir(self.temp_dir): if filename.endswith(".pcd") or filename.endswith(".ply"): file_path = os.path.join(self.temp_dir, filename) try: pcd = o3d.io.read_point_cloud(file_path) original_size = len(pcd.points) print(f"Processing {filename}: Original size = {original_size} points") downsampled_pcd = self.random_downsample_point_cloud(pcd, self.N) downsampled_size = len(downsampled_pcd.points) output_path = os.path.join(self.output_dir, f"{filename}") o3d.io.write_point_cloud(output_path, downsampled_pcd) print(f"Downsampled {filename}: New size = {downsampled_size} points") print(f"Saved downsampled point cloud to {output_path}") print("-" * 50) except Exception as e: print(f"Failed to process {filename}: {e}") # Clean up the temp directory shutil.rmtree(self.temp_dir) print(f"Temporary files in {self.temp_dir} have been deleted.") input_dir = "/path/to/input/directory" output_dir = "/path/to/output/directory" temp_dir = "/path/to/temp/directory" N = 50000 # Target number of points processor = PointCloudDownsampler(input_dir, output_dir, temp_dir, N) processor.process_point_clouds() processor.downsample_all_to_target_size()