使用Pytorch和OpenCV实现视频人脸替换

“DeepFaceLab”项目已经发布了很长时间了，作为研究的目的，本文将介绍他的原理，并使用Pytorch和OpenCV创建一个简化版本。

本文将分成3个部分，第一部分从两个视频中提取人脸并构建标准人脸数据集。第二部分使用数据集与神经网络一起学习如何在潜在空间中表示人脸，并从该表示中重建人脸图像。最后部分使用神经网络在视频的每一帧中创建与源视频中相同但具有目标视频中人物表情的人脸。然后将原人脸替换为假人脸，并将新帧保存为新的假视频。

项目的基本结构(在第一次运行之前)如下所示

 ├── face_masking.py
 ├── main.py
 ├── face_extraction_tools.py
 ├── quick96.py
 ├── merge_frame_to_fake_video.py
 ├── data
 │ ├── data_dst.mp4
 │ ├── data_src.mp4

main.py是主脚本，data文件夹包含程序需要的的data_dst.mp4和data_src.mp4文件。

提取和对齐-构建数据集

在第一部分中，我们主要介绍face_extraction_tools.py文件中的代码。

因为第一步是从视频中提取帧，所以需要构建一个将帧保存为JPEG图像的函数。这个函数接受一个视频的路径和另一个输出文件夹的路径。

 def extract_frames_from_video(video_path: Union[str, Path], output_folder: Union[str, Path], frames_to_skip: int=0) -> None:
     """
     Extract frame from video as a JPG images.
     Args:
         video_path (str | Path): the path to the input video from it the frame will be extracted
         output_folder (str | Path): the folder where the frames will be saved
         frames_to_skip (int): how many frames to skip after a frame which is saved. 0 will save all the frames.
             If, for example, this value is 2, the first frame will be saved, then frame 2 and 3 will be skipped,
             the 4th frame will be saved, and so on.
 
     Returns:
 
     """
 
     video_path = Path(video_path)
     output_folder = Path(output_folder)
 
     if not video_path.exists():
         raise ValueError(f'The path to the video file {video_path.absolute()} is not exist')
     if not output_folder.exists():
         output_folder.mkdir(parents=True)
 
     video_capture = cv2.VideoCapture(str(video_path))
 
     extract_frame_counter = 0
     saved_frame_counter = 0
     while True:
         ret, frame = video_capture.read()
         if not ret:
             break
 
         if extract_frame_counter % (frames_to_skip + 1) == 0:
             cv2.imwrite(str(output_folder / f'{saved_frame_counter:05d}.jpg'), frame, [cv2.IMWRITE_JPEG_QUALITY, 90])
             saved_frame_counter += 1
 
         extract_frame_counter += 1
 
     print(f'{saved_frame_counter} of {extract_frame_counter} frames saved')

函数首先检查视频文件是否存在，以及输出文件夹是否存在，如果不存在则自动创建。然后使用OpenCV 的videoccapture类来创建一个对象来读取视频，然后逐帧保存为输出文件夹中的JPEG文件。也可以根据frames_to_skip参数跳过帧。

然后就是需要构建人脸提取器。该工具应该能够检测图像中的人脸，提取并对齐它。构建这样一个工具的最佳方法是创建一个FaceExtractor类，其中包含检测、提取和对齐的方法。

对于检测部分，我们将使用带有OpenCV的YuNet。YuNet是一个快速准确的基于cnn的人脸检测器，可以由OpenCV中的FaceDetectorYN类使用。要创建这样一个FaceDetectorYN对象，我们需要一个带有权重的ONNX文件。该文件可以在OpenCV Zoo中找到，当前版本名为“face_detection_yunet_2023mar.onnx”。

我们的init()方法如下：

 def __init__(self, image_size):
         """
         Create a YuNet face detector to get face from image of size 'image_size'. The YuNet model
         will be downloaded from opencv zoo, if it's not already exist.
         Args:
             image_size (tuple): a tuple of (width: int, height: int) of the image to be analyzed
         """
         detection_model_path = Path('models/face_detection_yunet_2023mar.onnx')
         if not detection_model_path.exists():
             detection_model_path.parent.mkdir(parents=True, exist_ok=True)
             url = "https://github.com/opencv/opencv_zoo/blob/main/models/face_detection_yunet/face_detection_yunet_2023mar.onnx"
             print('Downloading face detection model...')
             filename, headers = urlretrieve(url, filename=str(detection_model_path))
             print('Download finish!')
 
         self.detector = cv2.FaceDetectorYN.create(str(detection_model_path), "", image_size)

函数首先检查权重文件是否存在，如果不存在，则从web下载。然后使用权重文件和要分析的图像大小创建FaceDetectorYN对象。检测方法采用YuNet检测方法在图像中寻找人脸

     def detect(self, image):
         ret, faces = self.detector.detect(image)
         return ret, faces

YuNet的输出是一个大小为[num_faces, 15]的2D数组，包含以下信息:

0-1:边界框左上角的x, y
2-3:边框的宽度、高度
4-5:右眼的x, y(样图中蓝点)
6-7:左眼x, y(样图中红点)
8-9:鼻尖x, y(示例图中绿色点)
10-11:嘴巴右角的x, y(样例图像中的粉色点)
12-13:嘴角左角x, y(样例图中黄色点)
14:面部评分

现在已经有了脸部位置数据，我们可以用它来获得脸部的对齐图像。这里主要利用眼睛位置的信息。我们希望眼睛在对齐后的图像中处于相同的水平(相同的y坐标)。

  @staticmethod
     def align(image, face, desired_face_width=256, left_eye_desired_coordinate=np.array((0.37, 0.37))):
         """
         Align the face so the eyes will be at the same level
         Args:
             image (np.ndarray): image with face
             face (np.ndarray):  face coordinates from the detection step
             desired_face_width (int): the final width of the aligned face image
             left_eye_desired_coordinate (np.ndarray): a length 2 array of values between
              0 and 1 where the left eye should be in the aligned image
 
         Returns:
             (np.ndarray): aligned face image
         """
         desired_face_height = desired_face_width
         right_eye_desired_coordinate = np.array((1 - left_eye_desired_coordinate[0], left_eye_desired_coordinate[1]))
 
         # get coordinate of the center of the eyes in the image
         right_eye = face[4:6]
         left_eye = face[6:8]
 
         # compute the angle of the right eye relative to the left eye
         dist_eyes_x = right_eye[0] - left_eye[0]
         dist_eyes_y = right_eye[1] - left_eye[1]
         dist_between_eyes = np.sqrt(dist_eyes_x ** 2 + dist_eyes_y ** 2)
         angles_between_eyes = np.rad2deg(np.arctan2(dist_eyes_y, dist_eyes_x) - np.pi)
         eyes_center = (left_eye + right_eye) // 2
 
         desired_dist_between_eyes = desired_face_width * (
                     right_eye_desired_coordinate[0] - left_eye_desired_coordinate[0])
         scale = desired_dist_between_eyes / dist_between_eyes
 
         M = cv2.getRotationMatrix2D(eyes_center, angles_between_eyes, scale)
 
         M[0, 2] += 0.5 * desired_face_width - eyes_center[0]
         M[1, 2] += left_eye_desired_coordinate[1] * desired_face_height - eyes_center[1]
 
         face_aligned = cv2.warpAffine(image, M, (desired_face_width, desired_face_height), flags=cv2.INTER_CUBIC)
         return face_aligned

这个方法获取单张人脸的图像和信息，输出图像的宽度和期望的左眼相对位置。我们假设输出图像是平方的，并且右眼的期望位置具有相同的y位置和x位置的1 - left_eye_x。计算两眼之间的距离和角度，以及两眼之间的中心点。

最后一个方法是extract方法，它类似于align方法，但没有转换，它也返回图像中人脸的边界框。

 def extract_and_align_face_from_image(input_dir: Union[str, Path], desired_face_width: int=256) -> None:
     """
     Extract the face from an image, align it and save to a directory inside in the input directory
     Args:
         input_dir (str|Path): path to the directory contains the images extracted from a video
         desired_face_width (int): the width of the aligned imaged in pixels
 
     Returns:
 
     """
 
     input_dir = Path(input_dir)
     output_dir = input_dir / 'aligned'
     if output_dir.exists():
         rmtree(output_dir)
     output_dir.mkdir()
 
 
     image = cv2.imread(str(input_dir / '00000.jpg'))
     image_height = image.shape[0]
     image_width = image.shape[1]
 
     detector = FaceExtractor((image_width, image_height))
 
     for image_path in tqdm(list(input_dir.glob('*.jpg'))):
         image = cv2.imread(str(image_path))
 
         ret, faces = detector.detect(image)
         if faces is None:
             continue
 
         face_aligned = detector.align(image, faces[0, :], desired_face_width)
         cv2.imwrite(str(output_dir / f'{image_path.name}'), face_aligned, [cv2.IMWRITE_JPEG_QUALITY, 90])

训练

对于网络，我们将使用AutoEncoder。在AutoEncoder中，有两个主要组件——编码器和解码器。编码器获取原始图像并找到它的潜在表示，解码器利用潜在表示重构原始图像。

对于我们的任务，要训练一个编码器来找到一个潜在的人脸表示和两个解码器——一个可以重建源人脸，另一个可以重建目标人脸。

在这三个组件被训练之后，我们回到最初的目标:创建一个源面部但具有目标表情的图像。也就是说使用解码器A和人脸B的图像。

面孔的潜在空间保留了面部的主要特征，如位置、方向和表情。解码器获取这些编码信息并学习如何构建全脸图像。由于解码器A只知道如何构造A类型的脸，因此它从编码器中获取图像B的特征并从中构造A类型的图像。

在本文中，我们将使用来自原始DeepFaceLab项目的Quick96架构的一个小修改版本。

模型的全部细节可以在quick96.py文件中。

在我们训练模型之前，还需要处理数据。为了使模型具有鲁棒性并避免过拟合，我们还需要在原始人脸图像上应用两种类型的增强。第一个是一般的转换，包括旋转，缩放，在x和y方向上的平移，以及水平翻转。对于每个转换，我们为参数或概率定义一个范围(例如，我们可以用来旋转的角度范围)，然后从范围中选择一个随机值来应用于图像。

 random_transform_args = {
     'rotation_range': 10,
     'zoom_range': 0.05,
     'shift_range': 0.05,
     'random_flip': 0.5,
   }
 
 def random_transform(image, rotation_range, zoom_range, shift_range, random_flip):
     """
     Make a random transformation for an image, including rotation, zoom, shift and flip.
     Args:
         image (np.array): an image to be transformed
         rotation_range (float): the range of possible angles to rotate - [-rotation_range, rotation_range]
         zoom_range (float): range of possible scales - [1 - zoom_range, 1 + zoom_range]
         shift_range (float): the percent of translation for x  and y
         random_flip (float): the probability of horizontal flip
 
     Returns:
         (np.array): transformed image
     """
     h, w = image.shape[0:2]
     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
     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 np.random.random() < random_flip:
         result = result[:, ::-1]
     return result

第2个是通过使用带噪声的插值图产生的失真。这种扭曲将迫使模型理解人脸的关键特征，并使其更加一般化。

 def random_warp(image):
     """
     Create a distorted face image and a target undistorted image
     Args:
         image  (np.array): image to warp
 
     Returns:
         (np.array): warped version of the image
         (np.array): target image to construct from the warped version
     """
     h, w = image.shape[:2]
 
     # build coordinate map to wrap the image according to
     range_ = np.linspace(h / 2 - h * 0.4, h / 2 + h * 0.4, 5)
     mapx = np.broadcast_to(range_, (5, 5))
     mapy = mapx.T
 
     # add noise to get a distortion of the face while warp the image
     mapx = mapx + np.random.normal(size=(5, 5), scale=5*h/256)
     mapy = mapy + np.random.normal(size=(5, 5), scale=5*h/256)
 
     # get interpolation map for the center of the face with size of (96, 96)
     interp_mapx = cv2.resize(mapx, (int(w / 2 * (1 + 0.25)) , int(h / 2 * (1 + 0.25))))[int(w/2 * 0.25/2):int(w / 2 * (1 + 0.25) - w/2 * 0.25/2), int(w/2 * 0.25/2):int(w / 2 * (1 + 0.25) - w/2 * 0.25/2)].astype('float32')
     interp_mapy = cv2.resize(mapy, (int(w / 2 * (1 + 0.25)) , int(h / 2 * (1 + 0.25))))[int(w/2 * 0.25/2):int(w / 2 * (1 + 0.25) - w/2 * 0.25/2), int(w/2 * 0.25/2):int(w / 2 * (1 + 0.25) - w/2 * 0.25/2)].astype('float32')
 
     # remap the face image according to the interpolation map to get warp version
     warped_image = cv2.remap(image, interp_mapx, interp_mapy, cv2.INTER_LINEAR)
 
     # create the target (undistorted) image
     # find a transformation to go from the source coordinates to the destination coordinate
     src_points = np.stack([mapx.ravel(), mapy.ravel()], axis=-1)
     dst_points = np.mgrid[0:w//2+1:w//8, 0:h//2+1:h//8].T.reshape(-1, 2)
 
     # We want to find a similarity matrix (scale rotation and translation) between the
     # source and destination points. The matrix should have the structure
     # [[a, -b, c],
     #  [b,  a, d]]
     # so we can construct unknown vector [a, b, c, d] and solve for it using least
     # squares with the source and destination x and y points.
     A = np.zeros((2 * src_points.shape[0], 2))
     A[0::2, :] = src_points  # [x, y]
     A[0::2, 1] = -A[0::2, 1] # [x, -y]
     A[1::2, :] = src_points[:, ::-1]  # [y, x]
     A = np.hstack((A, np.tile(np.eye(2), (src_points.shape[0], 1))))  # [x, -y, 1, 0] for x coordinate and [y, x, 0 ,1] for y coordinate
     b = dst_points.flatten()  # arrange as [x0, y0, x1, y1, ..., xN, yN]
 
     similarity_mat = np.linalg.lstsq(A, b, rcond=None)[0] # get the similarity matrix elements as vector [a, b, c, d]
     # construct the similarity matrix from the result vector of the least squares
     similarity_mat = np.array([[similarity_mat[0], -similarity_mat[1], similarity_mat[2]],
                                [similarity_mat[1], similarity_mat[0], similarity_mat[3]]])
     # use the similarity matrix to construct the target image using affine transformation
     target_image = cv2.warpAffine(image, similarity_mat, (w // 2, h // 2))
 
     return warped_image, target_image

这个函数有两个部分，我们首先在面部周围的区域创建图像的坐标图。有一个x坐标的映射和一个y坐标的映射。mapx和mapy变量中的值是以像素为单位的坐标。然后在图像上添加一些噪声，使坐标在随机方向上移动。我们添加的噪声，得到了一个扭曲的坐标(像素在随机方向上移动一点)。然后裁剪了插值后的贴图，使其包含脸部的中心，大小为96x96像素。现在我们可以使用扭曲的映射来重新映射图像，得到一个新的扭曲的图像。

在第二部分创建未扭曲的图像，这是模型应该从扭曲的图像中创建的目标图像。使用噪声作为源坐标，并为目标图像定义一组目标坐标。然后我们使用最小二乘法找到一个相似变换矩阵(尺度旋转和平移)，将其从源坐标映射到目标坐标，并将其应用于图像以获得目标图像。

然后就可以创建一个Dataset类来处理数据了。FaceData类非常简单。它获取包含src和dst文件夹的文件夹的路径，其中包含我们在前一部分中创建的数据，并返回大小为(2 * 96,2 * 96)归一化为1的随机源和目标图像。我们的网络将得到的是一个经过变换和扭曲的图像，以及源脸和目标脸的目标图像。所以还需要实现了一个collate_fn

 def collate_fn(self, batch):
         """
         Collate function to arrange the data returns from a batch. The batch returns a list
         of tuples contains pairs of source and destination images, which is the input of this
         function, and the function returns a tuple with 4 4D tensors of the warp and target
         images for the source and destination
         Args:
             batch (list): a list of tuples contains pairs of source and destination images
                 as numpy array
 
         Returns:
             (torch.Tensor): a 4D tensor of the wrap version of the source images
             (torch.Tensor): a 4D tensor of the target source images
             (torch.Tensor): a 4D tensor of the wrap version of the destination images
             (torch.Tensor): a 4D tensor of the target destination images
         """
         images_src, images_dst = list(zip(*batch))  # convert list of tuples with pairs of images into tuples of source and destination images
         warp_image_src, target_image_src = get_training_data(images_src, len(images_src))
         warp_image_src = torch.tensor(warp_image_src, dtype=torch.float32).permute(0, 3, 1, 2).to(device)
         target_image_src = torch.tensor(target_image_src, dtype=torch.float32).permute(0, 3, 1, 2).to(device)
         warp_image_dst, target_image_dst = get_training_data(images_dst, len(images_dst))
         warp_image_dst = torch.tensor(warp_image_dst, dtype=torch.float32).permute(0, 3, 1, 2).to(device)
         target_image_dst = torch.tensor(target_image_dst, dtype=torch.float32).permute(0, 3, 1, 2).to(device)
 
         return warp_image_src, target_image_src, warp_image_dst, target_image_dst

当我们从Dataloader对象获取数据时，它将返回一个元组，其中包含来自FaceData对象的源图像和目标图像对。collate_fn接受这个结果，并对图像进行变换和失真，得到目标图像，并为扭曲的源图像、目标源图像、扭曲的目标图像和目标目标图像返回四个4D张量。

训练使用的损失函数是MSE (L2)损失和DSSIM的组合

训练的指标和结果如上图所示

生成视频

在最后一步就是创建视频。处理此任务的函数称为merge_frame_to_fake_video.py。我们使用MediaPipe创建了facemask类。

当初始化facemask对象时，初始化MediaPipe人脸检测器。

 class FaceMasking:
     def __init__(self):
         landmarks_model_path = Path('models/face_landmarker.task')
         if not landmarks_model_path.exists():
             landmarks_model_path.parent.mkdir(parents=True, exist_ok=True)
             url = "https://storage.googleapis.com/mediapipe-models/face_landmarker/face_landmarker/float16/latest/face_landmarker.task"
             print('Downloading face landmarks model...')
             filename, headers = urlretrieve(url, filename=str(landmarks_model_path))
             print('Download finish!')
 
         base_options = python_mp.BaseOptions(model_asset_path=str(landmarks_model_path))
         options = vision.FaceLandmarkerOptions(base_options=base_options,
                                                output_face_blendshapes=False,
                                                output_facial_transformation_matrixes=False,
                                                num_faces=1)
         self.detector = vision.FaceLandmarker.create_from_options(options)

这个类也有一个从人脸图像中获取掩码的方法:

 def get_mask(self, image):
         """
         return uint8 mask of the face in image
         Args:
             image (np.ndarray): RGB image with single face
 
         Returns:
             (np.ndarray): single channel uint8 mask of the face
         """
         im_mp = mp.Image(image_format=mp.ImageFormat.SRGB, data=image.astype(np.uint8).copy())
         detection_result = self.detector.detect(im_mp)
 
         x = np.array([landmark.x * image.shape[1] for landmark in detection_result.face_landmarks[0]], dtype=np.float32)
         y = np.array([landmark.y * image.shape[0] for landmark in detection_result.face_landmarks[0]], dtype=np.float32)
 
         hull = np.round(np.squeeze(cv2.convexHull(np.column_stack((x, y))))).astype(np.int32)
         mask = np.zeros(image.shape[:2], dtype=np.uint8)
         mask = cv2.fillConvexPoly(mask, hull, 255)
         kernel = np.ones((7, 7), np.uint8)
         mask = cv2.erode(mask, kernel)
 
         return mask

该函数首先将输入图像转换为MediaPipe图像结构，然后使用人脸检测器查找人脸。然后使用OpenCV找到点的凸包，并使用OpenCV的fillConvexPoly函数填充凸包的区域，从而得到一个二进制掩码。最后，我们应用侵蚀操作来缩小遮蔽。

  def get_mask(self, image):
         """
         return uint8 mask of the face in image
         Args:
             image (np.ndarray): RGB image with single face
 
         Returns:
             (np.ndarray): single channel uint8 mask of the face
         """
         im_mp = mp.Image(image_format=mp.ImageFormat.SRGB, data=image.astype(np.uint8).copy())
         detection_result = self.detector.detect(im_mp)
 
         x = np.array([landmark.x * image.shape[1] for landmark in detection_result.face_landmarks[0]], dtype=np.float32)
         y = np.array([landmark.y * image.shape[0] for landmark in detection_result.face_landmarks[0]], dtype=np.float32)
 
         hull = np.round(np.squeeze(cv2.convexHull(np.column_stack((x, y))))).astype(np.int32)
         mask = np.zeros(image.shape[:2], dtype=np.uint8)
         mask = cv2.fillConvexPoly(mask, hull, 255)
         kernel = np.ones((7, 7), np.uint8)
         mask = cv2.erode(mask, kernel)
 
         return mask

merge_frame_to_fake_video函数就是将上面所有的步骤整合，创建一个新的视频对象，一个FaceExtracot对象，一个facemask对象，创建神经网络组件，并加载它们的权重。

 def merge_frames_to_fake_video(dst_frames_path, model_name='Quick96', saved_models_dir='saved_model'):
     model_path = Path(saved_models_dir) / f'{model_name}.pth'
     dst_frames_path = Path(dst_frames_path)
     image = Image.open(next(dst_frames_path.glob('*.jpg')))
     image_size = image.size
     result_video = cv2.VideoWriter(str(dst_frames_path.parent / 'fake.mp4'), cv2.VideoWriter_fourcc(*'MJPG'), 30, image.size)
 
     face_extractor = FaceExtractor(image_size)
     face_masker = FaceMasking()
 
     encoder = Encoder().to(device)
     inter = Inter().to(device)
     decoder = Decoder().to(device)
 
     saved_model = torch.load(model_path)
     encoder.load_state_dict(saved_model['encoder'])
     inter.load_state_dict(saved_model['inter'])
     decoder.load_state_dict(saved_model['decoder_src'])
 
     model = torch.nn.Sequential(encoder, inter, decoder)

然后针对目标视频中的所有帧，找到脸。如果没有人脸就把画面写入视频。如果有人脸，将其提取出来，转换为网络的适当输入，并生成新的人脸。

对原人脸和新人脸进行遮蔽，利用遮蔽图像上的矩量找到原人脸的中心。使用无缝克隆，以逼真的方式将新脸代替原来的脸(例如，改变假脸的肤色，以适应原来的脸皮肤)。最后将结果作为一个新的帧放回原始帧，并将其写入视频文件。

     frames_list = sorted(dst_frames_path.glob('*.jpg'))
     for ii, frame_path in enumerate(frames_list, 1):
         print(f'Working om {ii}/{len(frames_list)}')
         frame = cv2.imread(str(frame_path))
         retval, face = face_extractor.detect(frame)
         if face is None:
             result_video.write(frame)
             continue
         face_image, face = face_extractor.extract(frame, face[0])
         face_image = face_image[..., ::-1].copy()
         face_image_cropped = cv2.resize(face_image, (96, 96)) #face_image_resized[96//2:96+96//2, 96//2:96+96//2]
         face_image_cropped_torch = torch.tensor(face_image_cropped / 255., dtype=torch.float32).permute(2, 0, 1).unsqueeze(0).to(device)
         generated_face_torch = model(face_image_cropped_torch)
         generated_face = (generated_face_torch.squeeze().permute(1,2,0).detach().cpu().numpy() * 255).astype(np.uint8)
 
 
         mask_origin = face_masker.get_mask(face_image_cropped)
         mask_fake = face_masker.get_mask(generated_face)
 
         origin_moments = cv2.moments(mask_origin)
         cx = np.round(origin_moments['m10'] / origin_moments['m00']).astype(int)
         cy = np.round(origin_moments['m01'] / origin_moments['m00']).astype(int)
         try:
             output_face = cv2.seamlessClone(generated_face, face_image_cropped, mask_fake, (cx, cy), cv2.NORMAL_CLONE)
         except:
             print('Skip')
             continue
 
         fake_face_image = cv2.resize(output_face, (face_image.shape[1], face_image.shape[0]))
         fake_face_image = fake_face_image[..., ::-1] # change to BGR
         frame[face[1]:face[1]+face[3], face[0]:face[0]+face[2]] = fake_face_image
         result_video.write(frame)
 
     result_video.release()

一帧的结果是这样的

模型并不完美，面部的某些角度，特别是侧面视图，会导致图像不那么好，但总体效果不错。

整合

为了运行整个过程，还需要创建一个主脚本。

 from pathlib import Path
 import face_extraction_tools as fet
 import quick96 as q96
 from merge_frame_to_fake_video import merge_frames_to_fake_video
 
 ##### user parameters #####
 # True for executing the step
 extract_and_align_src = True
 extract_and_align_dst = True
 train = True
 eval = False
 
 model_name = 'Quick96'  # use this name to save and load the model
 new_model = False  # True for creating a new model even if a model with the same name already exists
 
 ##### end of user parameters #####
 
 # the path for the videos to process
 data_root = Path('./data')
 src_video_path = data_root / 'data_src.mp4'
 dst_video_path = data_root / 'data_dst.mp4'
 
 # path to folders where the intermediate product will be saved
 src_processing_folder = data_root / 'src'
 dst_processing_folder = data_root / 'dst'
 
 # step 1: extract the frames from the videos
 if extract_and_align_src:
     fet.extract_frames_from_video(video_path=src_video_path, output_folder=src_processing_folder, frames_to_skip=0)
 if extract_and_align_dst:
     fet.extract_frames_from_video(video_path=dst_video_path, output_folder=dst_processing_folder, frames_to_skip=0)
 
 # step 2: extract and align face from frames
 if extract_and_align_src:
     fet.extract_and_align_face_from_image(input_dir=src_processing_folder, desired_face_width=256)
 if extract_and_align_dst:
     fet.extract_and_align_face_from_image(input_dir=dst_processing_folder, desired_face_width=256)
 
 # step 3: train the model
 if train:
     q96.train(str(data_root), model_name, new_model, saved_models_dir='saved_model')
 
 # step 4: create the fake video
 if eval:
     merge_frames_to_fake_video(dst_processing_folder, model_name, saved_models_dir='saved_model')