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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, 'KolektorSDD')
dirs = os.listdir(dataset_root)
normal_images = list()
normal_labels = list()
normal_fname = list()
outlier_images = list()
outlier_labels = list()
outlier_fname = list()
for d in dirs:
files = os.listdir(os.path.join(dataset_root, d))
images = list()
for f in files:
if 'jpg' in f[-3:]:
images.append(f)
for image in images:
split_images = list()
split_labels = list()
image_name = image.split('.')[0]
image_data = cv2.imread(os.path.join(dataset_root, d, image))
label_data = cv2.imread(os.path.join(dataset_root, d, image_name + '_label.bmp'))
if image_data.shape != label_data.shape:
raise ValueError
image_length = image_data.shape[0]
split_images.append(image_data[:image_length // 3, :, :])
split_images.append(image_data[image_length // 3:image_length * 2 // 3, :, :])
split_images.append(image_data[image_length * 2 // 3:, :, :])
split_labels.append(label_data[:image_length // 3, :, :])
split_labels.append(label_data[image_length // 3:image_length * 2 // 3, :, :])
split_labels.append(label_data[image_length * 2 // 3:, :, :])
for i, (im, la) in enumerate(zip(split_images, split_labels)):
if np.max(la) != 0:
outlier_images.append(im)
outlier_labels.append(la)
outlier_fname.append(d + '_' + image_name + '_' + str(i))
else:
normal_images.append(im)
normal_labels.append(la)
normal_fname.append(d + '_' + image_name + '_' + str(i))
normal_train, normal_test, normal_name_train, normal_name_test = train_test_split(normal_images, normal_fname, test_size=0.25, random_state=42)
target_root = '../datasets/SDD_anomaly_detection/SDD'
train_root = os.path.join(target_root, 'train/good')
if not os.path.exists(train_root):
os.makedirs(train_root)
for image, name in zip(normal_train, normal_name_train):
cv2.imwrite(os.path.join(train_root, name + '.png'), image)
test_root = os.path.join(target_root, 'test/good')
if not os.path.exists(test_root):
os.makedirs(test_root)
for image, name in zip(normal_test, normal_name_test):
cv2.imwrite(os.path.join(test_root, name + '.png'), image)
defect_root = os.path.join(target_root, 'test/defect')
label_root = os.path.join(target_root, 'ground_truth/defect')
if not os.path.exists(defect_root):
os.makedirs(defect_root)
if not os.path.exists(label_root):
os.makedirs(label_root)
for image, label, name in zip(outlier_images, outlier_labels, outlier_fname):
cv2.imwrite(os.path.join(defect_root, name + '.png'), image)
cv2.imwrite(os.path.join(label_root, name + '_mask.png'), label)
print("Done")