Python實現攝像頭實時換臉詳解

# -*- coding: utf-8 -*-

import cv2
import dlib
import numpy as np

detector = dlib.get_frontal_face_detector()  # dlib的正向人臉檢測器
predictor = dlib.shape_predictor(r'shape_predictor_68_face_landmarks.dat')  # dlib的人臉形狀檢測器


def get_image_size(image):
    """
    獲取圖片大小（高度,寬度）
    :param image: image
    :return: （高度,寬度）
    """
    image_size = (image.shape[0], image.shape[1])
    return image_size


def get_face_landmarks(image, face_detector, shape_predictor):
    """
    獲取人臉標誌，68個特徵點
    :param image: image
    :param face_detector: dlib.get_frontal_face_detector
    :param shape_predictor: dlib.shape_predictor
    :return: np.array([[],[]]), 68個特徵點
    """
    dets = face_detector(image, 1)

    shape = shape_predictor(image, dets[0])
    face_landmarks = np.array([[p.x, p.y] for p in shape.parts()])
    return face_landmarks


def get_face_mask(image_size, face_landmarks):
    """
    獲取人臉掩模
    :param image_size: 圖片大小
    :param face_landmarks: 68個特徵點
    :return: image_mask, 掩模圖片
    """
    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)
    return mask


def get_affine_image(image1, image2, face_landmarks1, face_landmarks2):
    """
    獲取圖片1仿射變換後的圖片
    :param image1: 圖片1, 要進行仿射變換的圖片
    :param image2: 圖片2, 只要用來獲取圖片大小，生成與之大小相同的仿射變換圖片
    :param face_landmarks1: 圖片1的人臉特徵點
    :param face_landmarks2: 圖片2的人臉特徵點
    :return: 仿射變換後的圖片
    """
    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):
    """
    獲取掩模的中心點座標
    :param image_mask: 掩模圖片
    :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_point


def get_mask_union(mask1, mask2):
    """
    獲取兩個掩模掩蓋部分的並集
    :param mask1: mask_image, 掩模1
    :param mask2: mask_image, 掩模2
    :return: 兩個掩模掩蓋部分的並集
    """
    mask = np.min([mask1, mask2], axis=0)  # 掩蓋部分並集
    mask = ((cv2.blur(mask, (5, 5)) == 255) * 255).astype(np.uint8)  # 縮小掩模大小
    mask = cv2.blur(mask, (3, 3)).astype(np.uint8)  # 模糊掩模
    return mask


def skin_color_adjustment(im1, im2, mask=None):
    """
    膚色調整
    :param im1: 圖片1
    :param im2: 圖片2
    :param mask: 人臉 mask. 如果存在，使用人臉部分均值來求膚色變換系數；否則，使用高斯模糊來求膚色變換系數
    :return: 根據圖片2的顏色調整的圖片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 im1


def main():
    im1 = cv2.imread('1.png')  # 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('{}:檢測不到人臉'.format(image_face_path))
        exit(1)
    im1_size = get_image_size(im1)  # 臉圖大小
    im1_mask = get_face_mask(im1_size, landmarks1)  # 臉圖人臉掩模

    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)  # 攝像頭圖片大小
            im2_mask = get_face_mask(im2_size, landmarks2)  # 攝像頭圖片人臉掩模

            affine_im1 = get_affine_image(im1, im2, landmarks1, landmarks2)  # im1（臉圖）仿射變換後的圖片
            affine_im1_mask = get_affine_image(im1_mask, im2, landmarks1, landmarks2)  # im1（臉圖）仿射變換後的圖片的人臉掩模

            union_mask = get_mask_union(im2_mask, affine_im1_mask)  # 掩模合併

            affine_im1 = skin_color_adjustment(affine_im1, im2, mask=union_mask)  # 膚色調整
            point = get_mask_center_point(affine_im1_mask)  # im1（臉圖）仿射變換後的圖片的人臉掩模的中心點
            seamless_im = cv2.seamlessClone(affine_im1, im2, mask=union_mask, p=point, flags=cv2.NORMAL_CLONE)  # 進行泊松融合

            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()