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import open3d as o3d |
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import numpy as np |
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def downsample_and_filter(pcd_file): |
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pcd = o3d.io.read_point_cloud(pcd_file, max_bound_div = 750, neighbor_num = 8) |
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point_num = len(pcd.points) |
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if (point_num > 10000000): |
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voxel_down_pcd = o3d.geometry.PointCloud.uniform_down_sample(pcd, int(point_num / 10000000)+1) |
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else: |
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voxel_down_pcd = pcd |
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max_bound = voxel_down_pcd.get_max_bound() |
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ball_radius = np.linalg.norm(max_bound) / max_bound_div |
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pcd_filter, _ = voxel_down_pcd.remove_radius_outlier(neighbor_num, ball_radius) |
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print('filtered size', len(pcd_filter.points), 'pre size:', len(pcd.points)) |
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o3d.io.write_point_cloud(pcd_file[:-4] + '_filtered.ply', pcd_filter) |
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if __name__ == "__main__": |
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import os |
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dir_path = './data/demo_pcd' |
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for pcd_file in os.listdir(dir_path): |
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downsample_and_filter(os.path.join(dir_path, pcd_file)) |
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