102 lines
3 KiB
Python
102 lines
3 KiB
Python
import os
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import torch
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import time
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import multiprocessing
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import numpy as np
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from tqdm import tqdm
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from src.dpsr import DPSR
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data_path = 'data/ShapeNet' # path for ShapeNet from ONet
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base = 'data' # output base directory
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dataset_name = 'shapenet_psr'
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multiprocess = True
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njobs = 8
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save_pointcloud = True
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save_psr_field = True
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resolution = 128
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zero_level = 0.0
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num_points = 100000
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padding = 1.2
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dpsr = DPSR(res=(resolution, resolution, resolution), sig=0)
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def process_one(obj):
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obj_name = obj.split('/')[-1]
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c = obj.split('/')[-2]
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# create new for the current object
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out_path_cur = os.path.join(base, dataset_name, c)
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out_path_cur_obj = os.path.join(out_path_cur, obj_name)
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os.makedirs(out_path_cur_obj, exist_ok=True)
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gt_path = os.path.join(data_path, c, obj_name, 'pointcloud.npz')
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data = np.load(gt_path)
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points = data['points']
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normals = data['normals']
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# normalize the point to [0, 1)
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points = points / padding + 0.5
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# to scale back during inference, we should:
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#! p = (p - 0.5) * padding
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if save_pointcloud:
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outdir = os.path.join(out_path_cur_obj, 'pointcloud.npz')
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# np.savez(outdir, points=points, normals=normals)
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np.savez(outdir, points=data['points'], normals=data['normals'])
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# return
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if save_psr_field:
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psr_gt = dpsr(torch.from_numpy(points.astype(np.float32))[None],
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torch.from_numpy(normals.astype(np.float32))[None]).squeeze().cpu().numpy().astype(np.float16)
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outdir = os.path.join(out_path_cur_obj, 'psr.npz')
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np.savez(outdir, psr=psr_gt)
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def main(c):
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print('---------------------------------------')
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print('Processing {} {}'.format(c, split))
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print('---------------------------------------')
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for split in ['train', 'val', 'test']:
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fname = os.path.join(data_path, c, split+'.lst')
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with open(fname, 'r') as f:
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obj_list = f.read().splitlines()
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obj_list = [c+'/'+s for s in obj_list]
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if multiprocess:
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# multiprocessing.set_start_method('spawn', force=True)
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pool = multiprocessing.Pool(njobs)
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try:
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for _ in tqdm(pool.imap_unordered(process_one, obj_list), total=len(obj_list)):
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pass
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# pool.map_async(process_one, obj_list).get()
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except KeyboardInterrupt:
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# Allow ^C to interrupt from any thread.
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exit()
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pool.close()
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else:
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for obj in tqdm(obj_list):
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process_one(obj)
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print('Done Processing {} {}!'.format(c, split))
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if __name__ == "__main__":
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classes = ['02691156', '02828884', '02933112',
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'02958343', '03211117', '03001627',
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'03636649', '03691459', '04090263',
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'04256520', '04379243', '04401088', '04530566']
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t_start = time.time()
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for c in classes:
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main(c)
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t_end = time.time()
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print('Total processing time: ', t_end - t_start)
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