operation = []
fold_rounds = int(deform_rounds * 0.7)
for _ in range(fold_rounds):
operation.append(0)
for _ in range(deform_rounds - fold_rounds):
operation.append(1)
random.shuffle(operation)
file_name = img[:img.index('.')] + '-' + uuid.uuid1().hex[0:8]
# generate
label_x, label_y = gen_deform_label(img_path,
resize_shape_before_deform,
operation)
img_b, img_g, img_r, label_x, label_y = from_label_deform(
label_x, label_y, img_path, resize_shape_before_deform)
img_b, img_g, img_r = texture(label_x, img_b, img_g, img_r,
texture_path)
# resize image and label
label_x, label_y, img_b, img_g, img_r = resize_img_label(
label_x, label_y, img_b, img_g, img_r, resize_shape_after_deform)
img = np.dstack([img_b, img_g, img_r])
cv2.imwrite(os.path.join(target_img_folder, file_name) + '.png', img)
# change label precision to lower the volumn of label npz
label_x, label_y = label_x.astype(np.float16), label_y.astype(
np.float16)
np.savez_compressed(os.path.join(target_label_folder, file_name),
x=label_x,
if __name__ == '__main__':
img_path = '/home/wulei/DocUNet/data_gen/scan/25.png'
texture_path = '/home/wulei/DocUNet/data_gen/scan/dtd_3.jpg'
operation = [1, 0, 0, 0, 1]
data_path = '/home/wulei/DocUNet/data_gen'
filename = os.path.basename(img_path)
label_x, label_y = gen_deform_label(img_path, operation)
# label_visualization(label_x, "x")
# label_visualization(label_y, 'y')
# cv2.waitKey(0)
# cv2.destroyAllWindows()
print(f'start deformation...')
start = time.time()
img_b, img_g, img_r, label_x, label_y = from_label_deform(label_x, label_y, img_path)
img = texture(label_x, img_b, img_g, img_r, texture_path)
cv2.imwrite(os.path.join(data_path, 'img', filename), img)
label_path = os.path.join(data_path, 'labels')
if not os.path.exists(label_path):
print(f'path {label_path} not exists, mkdir')
os.mkdir(label_path)
np.savez_compressed(os.path.join(label_path, filename[: filename.index('.')]), x = label_x, y = label_y)
print(f'deformation finished. time: {time.time() - start}')