| import os | |
| import numpy as np | |
| from sklearn.model_selection import train_test_split | |
| import cv2 | |
| import argparse | |
| from config import DATA_ROOT | |
| dataset_root = os.path.join(DATA_ROOT, 'DAGM2007') | |
| class_names = os.listdir(dataset_root) | |
| for class_name in class_names: | |
| states = os.listdir(os.path.join(dataset_root, class_name)) | |
| for state in states: | |
| images = list() | |
| mask = list() | |
| files = os.listdir(os.path.join(dataset_root, class_name,state)) | |
| for f in files: | |
| if 'PNG' in f[-3:]: | |
| images.append(f) | |
| files = os.listdir(os.path.join(dataset_root, class_name, state,'Label')) | |
| for f in files: | |
| if 'PNG' in f[-3:]: | |
| mask.append(f) | |
| normal_image_path_train = list() | |
| normal_image_path_test = list() | |
| normal_image_path = list() | |
| abnormal_image_path = list() | |
| abnormal_image_label = list() | |
| for f in images: | |
| id = f[-8:-4] | |
| flag = 0 | |
| for y in mask: | |
| if id in y: | |
| abnormal_image_path.append(f) | |
| abnormal_image_label.append(y) | |
| flag = 1 | |
| break | |
| if flag == 0: | |
| normal_image_path.append(f) | |
| if len(abnormal_image_path) != len(abnormal_image_label): | |
| raise ValueError | |
| length = len(abnormal_image_path) | |
| normal_image_path_test = normal_image_path[:length] | |
| normal_image_path_train = normal_image_path[length:] | |
| target_root = '../datasets/DAGM_anomaly_detection' | |
| train_root = os.path.join(target_root, class_name, 'train','good') | |
| if not os.path.exists(train_root): | |
| os.makedirs(train_root) | |
| for f in normal_image_path_train: | |
| image_data = cv2.imread(os.path.join(dataset_root, class_name, state,f)) | |
| cv2.imwrite(os.path.join(train_root,f), image_data) | |
| test_root = os.path.join(target_root, class_name, 'test','good') | |
| if not os.path.exists(test_root): | |
| os.makedirs(test_root) | |
| for f in normal_image_path_test: | |
| image_data = cv2.imread(os.path.join(dataset_root, class_name, state,f)) | |
| cv2.imwrite(os.path.join(test_root,f), image_data) | |
| test_root = os.path.join(target_root, class_name, 'test','defect') | |
| if not os.path.exists(test_root): | |
| os.makedirs(test_root) | |
| for f in abnormal_image_path: | |
| image_data = cv2.imread(os.path.join(dataset_root, class_name, state,f)) | |
| cv2.imwrite(os.path.join(test_root,f), image_data) | |
| test_root = os.path.join(target_root, class_name, 'ground_truth','defect') | |
| if not os.path.exists(test_root): | |
| os.makedirs(test_root) | |
| for f in mask: | |
| image_data = cv2.imread(os.path.join(dataset_root, class_name, state,'Label',f)) | |
| cv2.imwrite(os.path.join(test_root,f), image_data) | |
| print("Done") |