| """
|
| Exposing DeepFake Videos By Detecting Face Warping Artifacts
|
| Yuezun Li, Siwei Lyu
|
| https://arxiv.org/abs/1811.00656
|
| """
|
| import cv2
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| import numpy as np
|
| from PIL import Image, ImageEnhance
|
|
|
|
|
|
|
|
|
| '''
|
| these two function is implemented by myself, may have some errors QAQ
|
| '''
|
|
|
|
|
| def change_res(img):
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| init_res = img.shape[0]
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| fake_res = np.random.randint(init_res//4, init_res*2)
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| img = cv2.resize(img, (fake_res, fake_res))
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| img = cv2.resize(img, (init_res, init_res))
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| return img, fake_res
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|
|
|
|
| def aug_one_im(img,
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| random_transform_args=None,
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| color_rng=[0.9, 1.1]):
|
| """
|
| Augment operation for image list
|
| :param images: image list
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| :param random_transform_args: shape transform arguments
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| :param color_rng: color transform arguments
|
| :return:
|
| """
|
| images = [img]
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| images = aug(images, random_transform_args, color_rng)
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|
|
| return images[0]
|
|
|
|
|
| def aug(images,
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| random_transform_args={
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| 'rotation_range': 10,
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| 'zoom_range': 0.05,
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| 'shift_range': 0.05,
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| 'random_flip': 0.5,
|
| },
|
| color_rng=[0.9, 1.1]):
|
| """
|
| Augment operation for image list
|
| :param images: image list
|
| :param random_transform_args: shape transform arguments
|
| :param color_rng: color transform arguments
|
| :return:
|
| """
|
| if random_transform_args is not None:
|
|
|
| images = random_transform(images, **random_transform_args)
|
|
|
| if color_rng is not None:
|
| for i, im in enumerate(images):
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|
|
| im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
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| im = Image.fromarray(np.uint8(im))
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|
|
|
|
| factor = np.random.uniform(color_rng[0], color_rng[1])
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| enhancer = ImageEnhance.Brightness(im)
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| im = enhancer.enhance(factor)
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|
|
| factor = np.random.uniform(color_rng[0], color_rng[1])
|
| enhancer = ImageEnhance.Contrast(im)
|
| im = enhancer.enhance(factor)
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|
|
| factor = np.random.uniform(color_rng[0], color_rng[1])
|
| enhancer = ImageEnhance.Color(im)
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| im = enhancer.enhance(factor)
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|
|
|
|
| factor = np.random.uniform(color_rng[0], color_rng[1])
|
| enhancer = ImageEnhance.Sharpness(im)
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| im = enhancer.enhance(factor)
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| im = np.array(im).astype(np.uint8)
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|
|
| im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
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| images[i] = im.copy()
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|
|
| return images
|
|
|
|
|
| def random_transform(images, rotation_range, zoom_range, shift_range, random_flip):
|
| """
|
| Random transform images in a list
|
| :param images:
|
| :param rotation_range:
|
| :param zoom_range:
|
| :param shift_range:
|
| :param random_flip:
|
| :return:
|
| """
|
| h, w = images[0].shape[:2]
|
| rotation = np.random.uniform(-rotation_range, rotation_range)
|
| scale = np.random.uniform(1 - zoom_range, 1 + zoom_range)
|
| tx = np.random.uniform(-shift_range, shift_range) * w
|
| ty = np.random.uniform(-shift_range, shift_range) * h
|
| flip_prob = np.random.random()
|
| for i, image in enumerate(images):
|
| mat = cv2.getRotationMatrix2D((w / 2, h / 2), rotation, scale)
|
| mat[:, 2] += (tx, ty)
|
| result = cv2.warpAffine(
|
| image, mat, (w, h), borderMode=cv2.BORDER_REPLICATE)
|
| if flip_prob < random_flip:
|
| result = result[:, ::-1]
|
| images[i] = result.copy()
|
| return images
|
|
|
|
|
| if __name__ == "__main__":
|
| dirr = '/FaceXray/dataset/utils/'
|
| test_im = cv2.imread('{}test.png'.format(dirr))
|
| resample_res, fake_res = change_res(test_im)
|
| cv2.imwrite('{}res_{}.png'.format(dirr, fake_res), resample_res)
|
| aug_im = aug_one_im(test_im)
|
| cv2.imwrite('{}auged.png'.format(dirr), aug_im)
|
|
|
