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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()
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