使用Pytorch和OpenCV實現(xiàn)視頻人臉替換
作者：DZ
“DeepFaceLab”項目已經(jīng)發(fā)布了很長時間了，作為研究的目的，本文將介紹他的原理，并使用Pytorch和OpenCV創(chuàng)建一個簡化版本。
“DeepFaceLab”項目已經(jīng)發(fā)布了很長時間了，作為研究的目的，本文將介紹他的原理，并使用Pytorch和OpenCV創(chuàng)建一個簡化版本。
本文將分成3個部分，第一部分從兩個視頻中提取人臉并構建標準人臉數(shù)據(jù)集。第二部分使用數(shù)據(jù)集與神經(jīng)網(wǎng)絡一起學習如何在潛在空間中表示人臉，并從該表示中重建人臉圖像。最后部分使用神經(jīng)網(wǎng)絡在視頻的每一幀中創(chuàng)建與源視頻中相同但具有目標視頻中人物表情的人臉。然后將原人臉替換為假人臉，并將新幀保存為新的假視頻。
項目的基本結構(在第一次運行之前)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是主腳本，data文件夾包含程序需要的的data_dst.mp4和data_src.mp4文件。
提取和對齊-構建數(shù)據(jù)集
在第一部分中，我們主要介紹face_extraction_tools.py文件中的代碼。
因為第一步是從視頻中提取幀，所以需要構建一個將幀保存為JPEG圖像的函數(shù)。這個函數(shù)接受一個視頻的路徑和另一個輸出文件夾的路徑。
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')函數(shù)首先檢查視頻文件是否存在，以及輸出文件夾是否存在，如果不存在則自動創(chuàng)建。然后使用OpenCV 的videoccapture類來創(chuàng)建一個對象來讀取視頻，然后逐幀保存為輸出文件夾中的JPEG文件。也可以根據(jù)frames_to_skip參數(shù)跳過幀。
然后就是需要構建人臉提取器。該工具應該能夠檢測圖像中的人臉，提取并對齊它。構建這樣一個工具的最佳方法是創(chuàng)建一個FaceExtractor類，其中包含檢測、提取和對齊的方法。
對于檢測部分，我們將使用帶有OpenCV的YuNet。YuNet是一個快速準確的基于cnn的人臉檢測器，可以由OpenCV中的FaceDetectorYN類使用。要創(chuàng)建這樣一個FaceDetectorYN對象，我們需要一個帶有權重的ONNX文件。該文件可以在OpenCV Zoo中找到，當前版本名為“face_detection_yunet_2023mar.onnx”。
我們的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)函數(shù)首先檢查權重文件是否存在，如果不存在，則從web下載。然后使用權重文件和要分析的圖像大小創(chuàng)建FaceDetectorYN對象。檢測方法采用YuNet檢測方法在圖像中尋找人臉
def detect(self, image):
ret, faces = self.detector.detect(image)
return ret, facesYuNet的輸出是一個大小為[num_faces, 15]的2D數(shù)組，包含以下信息:
- 0-1:邊界框左上角的x, y
- 2-3:邊框的寬度、高度
- 4-5:右眼的x, y(樣圖中藍點)
- 6-7:左眼x, y(樣圖中紅點)
- 8-9:鼻尖x, y(示例圖中綠色點)
- 10-11:嘴巴右角的x, y(樣例圖像中的粉色點)
- 12-13:嘴角左角x, y(樣例圖中黃色點)
- 14:面部評分
現(xiàn)在已經(jīng)有了臉部位置數(shù)據(jù)，我們可以用它來獲得臉部的對齊圖像。這里主要利用眼睛位置的信息。我們希望眼睛在對齊后的圖像中處于相同的水平(相同的y坐標)。
@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這個方法獲取單張人臉的圖像和信息，輸出圖像的寬度和期望的左眼相對位置。我們假設輸出圖像是平方的，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。計算兩眼之間的距離和角度，以及兩眼之間的中心點。
最后一個方法是extract方法，它類似于align方法，但沒有轉換，它也返回圖像中人臉的邊界框。
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])
訓練
對于網(wǎng)絡，我們將使用AutoEncoder。在AutoEncoder中，有兩個主要組件——編碼器和解碼器。編碼器獲取原始圖像并找到它的潛在表示，解碼器利用潛在表示重構原始圖像。
對于我們的任務，要訓練一個編碼器來找到一個潛在的人臉表示和兩個解碼器——一個可以重建源人臉，另一個可以重建目標人臉。
在這三個組件被訓練之后，我們回到最初的目標:創(chuàng)建一個源面部但具有目標表情的圖像。也就是說使用解碼器A和人臉B的圖像。
面孔的潛在空間保留了面部的主要特征，如位置、方向和表情。解碼器獲取這些編碼信息并學習如何構建全臉圖像。由于解碼器A只知道如何構造A類型的臉，因此它從編碼器中獲取圖像B的特征并從中構造A類型的圖像。
在本文中，我們將使用來自原始DeepFaceLab項目的Quick96架構的一個小修改版本。
模型的全部細節(jié)可以在quick96.py文件中。
在我們訓練模型之前，還需要處理數(shù)據(jù)。為了使模型具有魯棒性并避免過擬合，我們還需要在原始人臉圖像上應用兩種類型的增強。第一個是一般的轉換，包括旋轉，縮放，在x和y方向上的平移，以及水平翻轉。對于每個轉換，我們?yōu)閰?shù)或概率定義一個范圍(例如，我們可以用來旋轉的角度范圍)，然后從范圍中選擇一個隨機值來應用于圖像。
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個是通過使用帶噪聲的插值圖產(chǎn)生的失真。這種扭曲將迫使模型理解人臉的關鍵特征，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這個函數(shù)有兩個部分，我們首先在面部周圍的區(qū)域創(chuàng)建圖像的坐標圖。有一個x坐標的映射和一個y坐標的映射。mapx和mapy變量中的值是以像素為單位的坐標。然后在圖像上添加一些噪聲，使坐標在隨機方向上移動。我們添加的噪聲，得到了一個扭曲的坐標(像素在隨機方向上移動一點)。然后裁剪了插值后的貼圖，使其包含臉部的中心，大小為96x96像素?，F(xiàn)在我們可以使用扭曲的映射來重新映射圖像，得到一個新的扭曲的圖像。
在第二部分創(chuàng)建未扭曲的圖像，這是模型應該從扭曲的圖像中創(chuàng)建的目標圖像。使用噪聲作為源坐標，并為目標圖像定義一組目標坐標。然后我們使用最小二乘法找到一個相似變換矩陣(尺度旋轉和平移)，將其從源坐標映射到目標坐標，并將其應用于圖像以獲得目標圖像。
然后就可以創(chuàng)建一個Dataset類來處理數(shù)據(jù)了。FaceData類非常簡單。它獲取包含src和dst文件夾的文件夾的路徑，其中包含我們在前一部分中創(chuàng)建的數(shù)據(jù)，并返回大小為(2 * 96,2 * 96)歸一化為1的隨機源和目標圖像。我們的網(wǎng)絡將得到的是一個經(jīng)過變換和扭曲的圖像，以及源臉和目標臉的目標圖像。所以還需要實現(xiàn)了一個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當我們從Dataloader對象獲取數(shù)據(jù)時，它將返回一個元組，其中包含來自FaceData對象的源圖像和目標圖像對。collate_fn接受這個結果，并對圖像進行變換和失真，得到目標圖像，并為扭曲的源圖像、目標源圖像、扭曲的目標圖像和目標目標圖像返回四個4D張量。
訓練使用的損失函數(shù)是MSE (L2)損失和DSSIM的組合
訓練的指標和結果如上圖所示
生成視頻
在最后一步就是創(chuàng)建視頻。處理此任務的函數(shù)稱為merge_frame_to_fake_video.py。我們使用MediaPipe創(chuàng)建了facemask類。
當初始化facemask對象時，初始化MediaPipe人臉檢測器。
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)這個類也有一個從人臉圖像中獲取掩碼的方法:
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該函數(shù)首先將輸入圖像轉換為MediaPipe圖像結構，然后使用人臉檢測器查找人臉。然后使用OpenCV找到點的凸包，并使用OpenCV的fillConvexPoly函數(shù)填充凸包的區(qū)域，從而得到一個二進制掩碼。最后，我們應用侵蝕操作來縮小遮蔽。
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 maskmerge_frame_to_fake_video函數(shù)就是將上面所有的步驟整合，創(chuàng)建一個新的視頻對象，一個FaceExtracot對象，一個facemask對象，創(chuàng)建神經(jīng)網(wǎng)絡組件，并加載它們的權重。
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)然后針對目標視頻中的所有幀，找到臉。如果沒有人臉就把畫面寫入視頻。如果有人臉，將其提取出來，轉換為網(wǎng)絡的適當輸入，并生成新的人臉。
對原人臉和新人臉進行遮蔽，利用遮蔽圖像上的矩量找到原人臉的中心。使用無縫克隆，以逼真的方式將新臉代替原來的臉(例如，改變假臉的膚色，以適應原來的臉皮膚)。最后將結果作為一個新的幀放回原始幀，并將其寫入視頻文件。
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()一幀的結果是這樣的
模型并不完美，面部的某些角度，特別是側面視圖，會導致圖像不那么好，但總體效果不錯。
整合
為了運行整個過程，還需要創(chuàng)建一個主腳本。
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')總結 在這篇文章中，我們介紹了DeepFaceLab的運行流程，并使用我們自己的方法實現(xiàn)了該過程。我們首先從視頻中提取幀，然后從幀中提取人臉并對齊它們以創(chuàng)建一個數(shù)據(jù)庫。使用神經(jīng)網(wǎng)絡來學習如何在潛在空間中表示人臉以及如何重建人臉。遍歷了目標視頻的幀，找到了人臉并替換，這就是這個項目的完整流程。
本文只做學習研究，實際項目請參見：
責任編輯：華軒
來源：
DeepHub IMBA
