AI换脸-白红宇

AI换脸

阅读量：2135 次

发布时间：2019-04-30

本文共 6402 字，大约阅读时间需要 21 分钟。

使用opencv-python和dlib实现的简单换脸程序

faceswap.py

# -*- coding: utf-8 -*-import osimport cv2import dlibimport numpy as nphere = os.path.dirname(os.path.abspath(__file__))models_folder_path = os.path.join(here, 'models')  # 模型保存文件夹faces_folder_path = os.path.join(here, 'faces')  # 人脸图片保存文件夹predictor_path = os.path.join(models_folder_path, 'shape_predictor_68_face_landmarks.dat')  # 模型路径image_face_path = os.path.join(faces_folder_path, 'JayChou.png')  # 人脸图片路径detector = dlib.get_frontal_face_detector()  # dlib的正向人脸检测器predictor = dlib.shape_predictor(predictor_path)  # dlib的人脸形状检测器def get_image_size(image):    """    获取图片大小（高度,宽度）    :param image: image    :return: （高度,宽度）    """    image_size = (image.shape[0], image.shape[1])    return image_sizedef get_face_landmarks(image, face_detector, shape_predictor):    """    获取人脸标志，68个特征点    :param image: image    :param face_detector: dlib.get_frontal_face_detector    :param shape_predictor: dlib.shape_predictor    :return: np.array([[],[]]), 68个特征点    """    dets = face_detector(image, 1)    num_faces = len(dets)    if num_faces == 0:        print("Sorry, there were no faces found.")        return None    shape = shape_predictor(image, dets[0])    face_landmarks = np.array([[p.x, p.y] for p in shape.parts()])    return face_landmarksdef get_face_mask(image_size, face_landmarks):    """    获取人脸掩模    :param image_size: 图片大小    :param face_landmarks: 68个特征点    :return: image_mask, 掩模图片    """    mask = np.zeros(image_size, dtype=np.uint8)    points = np.concatenate([face_landmarks[0:16], face_landmarks[26:17:-1]])    cv2.fillPoly(img=mask, pts=[points], color=255)    # mask = np.zeros(image_size, dtype=np.uint8)    # points = cv2.convexHull(face_landmarks)  # 凸包    # cv2.fillConvexPoly(mask, points, color=255)    return maskdef get_affine_image(image1, image2, face_landmarks1, face_landmarks2):    """    获取图片1仿射变换后的图片    :param image1: 图片1, 要进行仿射变换的图片    :param image2: 图片2, 只要用来获取图片大小，生成与之大小相同的仿射变换图片    :param face_landmarks1: 图片1的人脸特征点    :param face_landmarks2: 图片2的人脸特征点    :return: 仿射变换后的图片    """    three_points_index = [18, 8, 25]    M = cv2.getAffineTransform(face_landmarks1[three_points_index].astype(np.float32),                               face_landmarks2[three_points_index].astype(np.float32))    dsize = (image2.shape[1], image2.shape[0])    affine_image = cv2.warpAffine(image1, M, dsize)    return affine_image.astype(np.uint8)def get_mask_center_point(image_mask):    """    获取掩模的中心点坐标    :param image_mask: 掩模图片    :return: 掩模中心    """    image_mask_index = np.argwhere(image_mask > 0)    miny, minx = np.min(image_mask_index, axis=0)    maxy, maxx = np.max(image_mask_index, axis=0)    center_point = ((maxx + minx) // 2, (maxy + miny) // 2)    return center_pointdef get_mask_union(mask1, mask2):    """    获取两个掩模掩盖部分的并集    :param mask1: mask_image, 掩模1    :param mask2: mask_image, 掩模2    :return: 两个掩模掩盖部分的并集    """    mask = np.min([mask1, mask2], axis=0)  # 掩盖部分并集    mask = ((cv2.blur(mask, (5, 5)) == 255) * 255).astype(np.uint8)  # 缩小掩模大小    mask = cv2.blur(mask, (3, 3)).astype(np.uint8)  # 模糊掩模    return maskdef skin_color_adjustment(im1, im2, mask=None):    """    肤色调整    :param im1: 图片1    :param im2: 图片2    :param mask: 人脸 mask. 如果存在，使用人脸部分均值来求肤色变换系数；否则，使用高斯模糊来求肤色变换系数    :return: 根据图片2的颜色调整的图片1    """    if mask is None:        im1_ksize = 55        im2_ksize = 55        im1_factor = cv2.GaussianBlur(im1, (im1_ksize, im1_ksize), 0).astype(np.float)        im2_factor = cv2.GaussianBlur(im2, (im2_ksize, im2_ksize), 0).astype(np.float)    else:        im1_face_image = cv2.bitwise_and(im1, im1, mask=mask)        im2_face_image = cv2.bitwise_and(im2, im2, mask=mask)        im1_factor = np.mean(im1_face_image, axis=(0, 1))        im2_factor = np.mean(im2_face_image, axis=(0, 1))    im1 = np.clip((im1.astype(np.float) * im2_factor / np.clip(im1_factor, 1e-6, None)), 0, 255).astype(np.uint8)    return im1def main():    im1 = cv2.imread(image_face_path)  # face_image    im1 = cv2.resize(im1, (600, im1.shape[0] * 600 // im1.shape[1]))    landmarks1 = get_face_landmarks(im1, detector, predictor)  # 68_face_landmarks    if landmarks1 is None:        print('{}:检测不到人脸'.format(image_face_path))        exit(1)    im1_size = get_image_size(im1)  # 脸图大小    im1_mask = get_face_mask(im1_size, landmarks1)  # 脸图人脸掩模    cam = cv2.VideoCapture(0)    while True:        ret_val, im2 = cam.read()  # camera_image        landmarks2 = get_face_landmarks(im2, detector, predictor)  # 68_face_landmarks        if landmarks2 is not None:            im2_size = get_image_size(im2)  # 摄像头图片大小            im2_mask = get_face_mask(im2_size, landmarks2)  # 摄像头图片人脸掩模            affine_im1 = get_affine_image(im1, im2, landmarks1, landmarks2)  # im1（脸图）仿射变换后的图片            affine_im1_mask = get_affine_image(im1_mask, im2, landmarks1, landmarks2)  # im1（脸图）仿射变换后的图片的人脸掩模            union_mask = get_mask_union(im2_mask, affine_im1_mask)  # 掩模合并            # affine_im1_face_image = cv2.bitwise_and(affine_im1, affine_im1, mask=union_mask)  # im1（脸图）的脸            # im2_face_image = cv2.bitwise_and(im2, im2, mask=union_mask)  # im2（摄像头图片）的脸            # cv2.imshow('affine_im1_face_image', affine_im1_face_image)            # cv2.imshow('im2_face_image', im2_face_image)            affine_im1 = skin_color_adjustment(affine_im1, im2, mask=union_mask)  # 肤色调整            point = get_mask_center_point(affine_im1_mask)  # im1（脸图）仿射变换后的图片的人脸掩模的中心点            seamless_im = cv2.seamlessClone(affine_im1, im2, mask=union_mask, p=point, flags=cv2.NORMAL_CLONE)  # 进行泊松融合            # cv2.imshow('affine_im1', affine_im1)            # cv2.imshow('im2', im2)            cv2.imshow('seamless_im', seamless_im)        else:            cv2.imshow('seamless_im', im2)        if cv2.waitKey(1) == 27:  # 按Esc退出            break    cv2.destroyAllWindows()if __name__ == '__main__':    main()