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import os |
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import json |
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import random |
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from dataset import MVTEC_ROOT |
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class MVTecSolver(object): |
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CLSNAMES = [ |
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'bottle', 'cable', 'capsule', 'carpet', 'grid', |
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'hazelnut', 'leather', 'metal_nut', 'pill', 'screw', |
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'tile', 'toothbrush', 'transistor', 'wood', 'zipper', |
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] |
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def __init__(self, root=MVTEC_ROOT, train_ratio=0.5): |
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self.root = root |
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self.meta_path = f'{root}/meta.json' |
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self.train_ratio = train_ratio |
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def run(self): |
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self.generate_meta_info() |
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def generate_meta_info(self): |
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info = dict(train={}, test={}) |
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for cls_name in self.CLSNAMES: |
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cls_dir = f'{self.root}/{cls_name}' |
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for phase in ['train', 'test']: |
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cls_info = [] |
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species = os.listdir(f'{cls_dir}/{phase}') |
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for specie in species: |
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is_abnormal = True if specie not in ['good'] else False |
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img_names = os.listdir(f'{cls_dir}/{phase}/{specie}') |
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mask_names = os.listdir(f'{cls_dir}/ground_truth/{specie}') if is_abnormal else None |
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img_names.sort() |
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mask_names.sort() if mask_names is not None else None |
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for idx, img_name in enumerate(img_names): |
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info_img = dict( |
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img_path=f'{cls_name}/{phase}/{specie}/{img_name}', |
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mask_path=f'{cls_name}/ground_truth/{specie}/{mask_names[idx]}' if is_abnormal else '', |
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cls_name=cls_name, |
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specie_name=specie, |
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anomaly=1 if is_abnormal else 0, |
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) |
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cls_info.append(info_img) |
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info[phase][cls_name] = cls_info |
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with open(self.meta_path, 'w') as f: |
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f.write(json.dumps(info, indent=4) + "\n") |
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if __name__ == '__main__': |
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runner = MVTecSolver(root=MVTEC_ROOT) |
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runner.run() |
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