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| import os | |
| import json | |
| import random | |
| from config import DATA_ROOT | |
| DTD_ROOT = os.path.join(DATA_ROOT, 'DTD-Synthetic') | |
| class DTDSolver(object): | |
| CLSNAMES = [ | |
| 'Blotchy_099', 'Fibrous_183', 'Marbled_078', 'Matted_069', 'Mesh_114','Perforated_037','Stratified_154','Woven_001','Woven_068','Woven_104','Woven_125','Woven_127', | |
| ] | |
| def __init__(self, root=DTD_ROOT, train_ratio=0.5): | |
| self.root = root | |
| self.meta_path = f'{root}/meta.json' | |
| self.train_ratio = train_ratio | |
| def run(self): | |
| self.generate_meta_info() | |
| def generate_meta_info(self): | |
| info = dict(train={}, test={}) | |
| for cls_name in self.CLSNAMES: | |
| cls_dir = f'{self.root}/{cls_name}' | |
| for phase in ['train', 'test']: | |
| cls_info = [] | |
| species = os.listdir(f'{cls_dir}/{phase}') | |
| for specie in species: | |
| is_abnormal = True if specie not in ['good'] else False | |
| img_names = os.listdir(f'{cls_dir}/{phase}/{specie}') | |
| mask_names = os.listdir(f'{cls_dir}/ground_truth/{specie}') if is_abnormal else None | |
| img_names.sort() | |
| mask_names.sort() if mask_names is not None else None | |
| for idx, img_name in enumerate(img_names): | |
| info_img = dict( | |
| img_path=f'{cls_name}/{phase}/{specie}/{img_name}', | |
| mask_path=f'{cls_name}/ground_truth/{specie}/{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) | |
| info[phase][cls_name] = cls_info | |
| with open(self.meta_path, 'w') as f: | |
| f.write(json.dumps(info, indent=4) + "\n") | |
| if __name__ == '__main__': | |
| runner = DTDSolver(root=DTD_ROOT) | |
| runner.run() | |