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“DeepFaceLab”我的项目曾经公布了很长时间了，作为钻研的目标，本文将介绍他的原理，并应用 Pytorch 和 OpenCV 创立一个简化版本。
本文将分成 3 个局部，第一局部从两个视频中提取人脸并构建规范人脸数据集。第二局部应用数据集与神经网络一起学习如何在潜在空间中示意人脸，并从该示意中重建人脸图像。最初局部应用神经网络在视频的每一帧中创立与源视频中雷同但具备指标视频中人物表情的人脸。而后将原人脸替换为假人脸，并将新帧保留为新的假视频。
我的项目的根本构造 (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.mp4main.py 是主脚本，data 文件夹蕴含程序须要的的 data_dst.mp4 和 data_src.mp4 文件。
提取和对齐 - 构建数据集
在第一局部中，咱们次要介绍 face_extraction_tools.py 文件中的代码。
因为第一步是从视频中提取帧，所以须要构建一个将帧保留为 JPEG 图像的函数。这个函数承受一个视频的门路和另一个输入文件夹的门路。
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')函数首先查看视频文件是否存在，以及输入文件夹是否存在，如果不存在则主动创立。而后应用 OpenCV 的 videoccapture 类来创立一个对象来读取视频，而后逐帧保留为输入文件夹中的 JPEG 文件。也能够依据 frames_to_skip 参数跳过帧。
而后就是须要构建人脸提取器。该工具应该可能检测图像中的人脸，提取并对齐它。构建这样一个工具的最佳办法是创立一个 FaceExtractor 类，其中蕴含检测、提取和对齐的办法。
对于检测局部，咱们将应用带有 OpenCV 的 YuNet。YuNet 是一个疾速精确的基于 cnn 的人脸检测器，能够由 OpenCV 中的 FaceDetectorYN 类应用。要创立这样一个 FaceDetectorYN 对象，咱们须要一个带有权重的 ONNX 文件。该文件能够在 OpenCV Zoo 中找到，以后版本名为“face_detection_yunet_2023mar.onnx”。
咱们的 init() 办法如下：
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)函数首先查看权重文件是否存在，如果不存在，则从 web 下载。而后使用权重文件和要剖析的图像大小创立 FaceDetectorYN 对象。检测办法采纳 YuNet 检测办法在图像中寻找人脸
def detect(self, image):
ret, faces = self.detector.detect(image)
return ret, facesYuNet 的输入是一个大小为 [num_faces, 15] 的 2D 数组，蕴含以下信息:
- 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
当初曾经有了脸部地位数据，咱们能够用它来取得脸部的对齐图像。这里次要利用眼睛地位的信息。咱们心愿眼睛在对齐后的图像中处于雷同的程度(雷同的 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这个办法获取单张人脸的图像和信息，输入图像的宽度和冀望的左眼绝对地位。咱们假如输入图像是平方的，并且右眼的冀望地位具备雷同的 y 地位和 x 地位的 1 – left_eye_x。计算两眼之间的间隔和角度，以及两眼之间的中心点。
最初一个办法是 extract 办法，它相似于 align 办法，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，咱们将应用 AutoEncoder。在 AutoEncoder 中，有两个次要组件——编码器和解码器。编码器获取原始图像并找到它的潜在示意，解码器利用潜在示意重构原始图像。
对于咱们的工作，要训练一个编码器来找到一个潜在的人脸示意和两个解码器——一个能够重建源人脸，另一个能够重建指标人脸。
在这三个组件被训练之后，咱们回到最后的指标: 创立一个源面部但具备指标表情的图像。也就是说应用解码器 A 和人脸 B 的图像。
脸孔的潜在空间保留了面部的次要特色，如地位、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。因为解码器 A 只晓得如何结构 A 类型的脸，因而它从编码器中获取图像 B 的特色并从中结构 A 类型的图像。
在本文中，咱们将应用来自原始 DeepFaceLab 我的项目的 Quick96 架构的一个小批改版本。
模型的全副细节能够在 quick96.py 文件中。
在咱们训练模型之前，还须要解决数据。为了使模型具备鲁棒性并防止过拟合，咱们还须要在原始人脸图像上利用两种类型的加强。第一个是个别的转换，包含旋转，缩放，在 x 和 y 方向上的平移，以及程度翻转。对于每个转换，咱们为参数或概率定义一个范畴(例如，咱们能够用来旋转的角度范畴)，而后从范畴中抉择一个随机值来利用于图像。
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 个是通过应用带噪声的插值图产生的失真。这种扭曲将迫使模型了解人脸的要害特色，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这个函数有两个局部，咱们首先在面部四周的区域创立图像的坐标图。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。而后在图像上增加一些噪声，使坐标在随机方向上挪动。咱们增加的噪声，失去了一个扭曲的坐标(像素在随机方向上挪动一点)。而后裁剪了插值后的贴图，使其蕴含脸部的核心，大小为 96×96 像素。当初咱们能够应用扭曲的映射来从新映射图像，失去一个新的扭曲的图像。
在第二局部创立未扭曲的图像，这是模型应该从扭曲的图像中创立的指标图像。应用噪声作为源坐标，并为指标图像定义一组指标坐标。而后咱们应用最小二乘法找到一个类似变换矩阵(尺度旋转和平移)，将其从源坐标映射到指标坐标，并将其利用于图像以取得指标图像。
而后就能够创立一个 Dataset 类来解决数据了。FaceData 类非常简单。它获取蕴含 src 和 dst 文件夹的文件夹的门路，其中蕴含咱们在前一部分中创立的数据，并返回大小为 (2 96,2 96) 归一化为 1 的随机源和指标图像。咱们的网络将失去的是一个通过变换和扭曲的图像，以及源脸和指标脸的指标图像。所以还须要实现了一个 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当咱们从 Dataloader 对象获取数据时，它将返回一个元组，其中蕴含来自 FaceData 对象的源图像和指标图像对。collate_fn 承受这个后果，并对图像进行变换和失真，失去指标图像，并为扭曲的源图像、指标源图像、扭曲的指标图像和指标指标图像返回四个 4D 张量。
训练应用的损失函数是 MSE (L2)损失和 DSSIM 的组合
训练的指标和后果如上图所示
生成视频
在最初一步就是创立视频。解决此工作的函数称为 merge_frame_to_fake_video.py。咱们应用 MediaPipe 创立了 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办法:
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该函数首先将输出图像转换为 MediaPipe 图像构造，而后应用人脸检测器查找人脸。而后应用 OpenCV 找到点的凸包，并应用 OpenCV 的 fillConvexPoly 函数填充凸包的区域，从而失去一个二进制掩码。最初，咱们利用侵蚀操作来放大遮蔽。
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 maskmerge_frame_to_fake_video 函数就是将下面所有的步骤整合，创立一个新的视频对象，一个 FaceExtracot 对象，一个 facemask 对象，创立神经网络组件，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)而后针对指标视频中的所有帧，找到脸。如果没有人脸就把画面写入视频。如果有人脸，将其提取进去，转换为网络的适当输出，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核心。应用无缝克隆，以真切的形式将新脸代替原来的脸(例如，扭转假脸的肤色，以适应原来的脸皮肤)。最初将后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()一帧的后果是这样的
模型并不完满，面部的某些角度，特地是侧面视图，会导致图像不那么好，但总体成果不错。
整合
为了运行整个过程，还须要创立一个主脚本。
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')总结
在这篇文章中，咱们介绍了 DeepFaceLab 的运行流程，并应用咱们本人的办法实现了该过程。咱们首先从视频中提取帧，而后从帧中提取人脸并对齐它们以创立一个数据库。应用神经网络来学习如何在潜在空间中示意人脸以及如何重建人脸。遍历了指标视频的帧，找到了人脸并替换，这就是这个我的项目的残缺流程。
本文只做学习钻研，理论我的项目请参见：
https://avoid.overfit.cn/post/ec72d69b57464a08803c86db8720e3e9
作者：DZ
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