106 lines
3.7 KiB
106 lines
3.7 KiB
import os
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from multiprocessing import Pool
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import platform
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in_path = 'raw_input'
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out_path = 'training_data'
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument('-r', action = 'store_true', help = 'generating training data for repaired data')
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args = parser.parse_args()
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sys_info = platform.system()
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if sys_info == 'Linux':
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#linux version
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feature_sampling_path = './Bin/FeatureSample'
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para_sample_path = './Bin/ParametricSample'
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else:
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#windows version
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feature_sampling_path = r'.\Bin\FeatureSample.exe'
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para_sample_path = r'.\Bin\ParametricSample.exe'
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def gen_one_para_sample(yamlfile):
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yaml_path = os.path.join(in_path, yamlfile)
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obj_path = os.path.join(in_path, yamlfile.replace('.yml', '.obj'))
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os.system("{} -y {} -o {}".format(para_sample_path, yaml_path, obj_path))
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fea_path = obj_path.replace('.obj','.fea')
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xyz_path = os.path.join(out_path, yamlfile.replace('.yml', '_50k.xyz'))
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mask_path = os.path.join(out_path, yamlfile.replace('.yml', '_50k_mask.txt'))
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para_pts_path = obj_path.replace('.obj', '_50k.xyz')
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para_ptsface_path = obj_path.replace('.obj', '_50k_tris.txt')
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os.system("{} -i {} -o {} -f {} -k {} -m 1 --ns 50000 --fs 0 -c 1 --csg 1 --repairtree --convex 1 -r 0 --mp 6 -p {} --pf {}".format(feature_sampling_path, obj_path, xyz_path, fea_path, mask_path, para_pts_path, para_ptsface_path))
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#copy points data sampled from points to target directory
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if sys_info == 'Linux':
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os.system('cp {} {}'.format(para_pts_path, xyz_path))
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else:
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os.system('copy {} {}'.format(para_pts_path, xyz_path))
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def gen_para_sample():
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if not os.path.exists(out_path):
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os.mkdir(out_path)
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allfs = os.listdir(in_path)
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tasks = []
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for f in allfs:
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if f.endswith('.yml'):
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tasks.append(f)
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flag_parallel = False
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if not flag_parallel:
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for task in tasks:
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gen_one_para_sample(task)
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return
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with Pool(40) as p:
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p.map(gen_one_para_sample, tasks)
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def gen_one_para_sample_repair(prefix, in_folder, out_folder):
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obj_normalize = os.path.join(in_folder, prefix + '_50k_fixtree.obj')
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fea_path = obj_normalize.replace('.obj','.fea')
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xyz_path = os.path.join(out_folder, prefix + '_50k.xyz')
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mask_path = os.path.join(out_folder, prefix + '_50k_mask.txt')
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para_pts_path = os.path.join(in_path, prefix + '_50k.xyz')
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para_ptsface_path = os.path.join(in_path, prefix + '_50k_tris.txt')
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os.system("{} -i {} -o {} -f {} -k {} -m 1 --ns 50000 --fs 0 -c 0 --csg 1 --convex 1 -r 0 --repairtree -p {} --pf {}".format(feature_sampling_path, obj_normalize, xyz_path, fea_path, mask_path, para_pts_path, para_ptsface_path))
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#copy points data sampled from points to target directory
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if sys_info == 'Linux':
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os.system('cp {} {}'.format(para_pts_path, xyz_path))
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else:
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os.system('copy {} {}'.format(para_pts_path, xyz_path))
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def gen_para_sample_repair():
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out_path_repair = 'training_data_repair'
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failure_path = 'raw_input' #path that containing the *objtreefail files
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in_path_repair = 'training_data' #path that containing the *fixtree.obj/fea files
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if not os.path.exists(out_path_repair):
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os.mkdir(out_path_repair)
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allfs = os.listdir(failure_path)
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tasks = []
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for f in allfs:
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if f.endswith('.objtreefail'):
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f_split = f.split('.')[0].split('_')
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name = f_split[0] + '_' + f_split[1]
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tasks.append(name)
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for t in tasks:
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gen_one_para_sample_repair(t, in_path_repair, out_path_repair)
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if __name__ == '__main__':
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if not args.r:
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gen_para_sample()
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else:
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gen_para_sample_repair()
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