| import os | |
| import json | |
| import pandas as pd | |
| class HyperSolver(object): | |
| CLSNAMES = [ | |
| 'colon', | |
| ] | |
| def __init__(self, root='data/mvtec'): | |
| self.root = root | |
| self.meta_path = f'{root}/meta.json' | |
| def run(self): | |
| info = dict(train={}, test={}) | |
| anomaly_samples = 0 | |
| normal_samples = 0 | |
| for cls_name in self.CLSNAMES: | |
| cls_dir = f'{self.root}' | |
| for phase in ['test']: | |
| cls_info = [] | |
| species = set(os.listdir(f'{cls_dir}'))-set(['masks']) | |
| print("species", species) | |
| for specie in species: | |
| is_abnormal = True if specie not in ['good'] else False | |
| img_names = os.listdir(f'{cls_dir}/{specie}') | |
| mask_names = os.listdir(f'{cls_dir}/masks/') if is_abnormal else None | |
| img_names.sort() | |
| mask_names.sort() if mask_names is not None else None | |
| assert len(img_names) == len(mask_names) if mask_names is not None else True | |
| for idx, img_name in enumerate(img_names): | |
| info_img = dict( | |
| img_path=f'{cls_dir}/{specie}/{img_name}', | |
| mask_path=f'{cls_dir}/masks/{mask_names[idx]}' if is_abnormal else '', | |
| cls_name=cls_name, | |
| specie_name=specie, | |
| anomaly=1 if is_abnormal else 0, | |
| ) | |
| cls_info.append(info_img) | |
| if phase == 'test': | |
| if is_abnormal: | |
| anomaly_samples = anomaly_samples + 1 | |
| else: | |
| normal_samples = normal_samples + 1 | |
| info[phase][cls_name] = cls_info | |
| with open(self.meta_path, 'w') as f: | |
| f.write(json.dumps(info, indent=4) + "\n") | |
| print('normal_samples', normal_samples, 'anomaly_samples', anomaly_samples) | |
| if __name__ == '__main__': | |
| runner = HyperSolver(root='/remote-home/iot_zhouqihang/data/medical/EndoTect_2020_Segmentation_Test_Dataset') | |
| runner.run() | |