分类： Python/Ruby
2022-03-18 17:21:36
# -*- coding: utf-8 -*-
import cv2
import dlib
import numpy as np
detector = dlib.get_frontal_face_detector() # dlib的正向人脸检测器
predictor = dlib.shape_predictor(r'shape_predictor_68_face_landmarks.dat') # 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_size
def 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)
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):
"""
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)
return mask
def 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_point
def 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 mask
def 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 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('{}: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 外汇跟单gendan5.com= get_mask_union(im2_mask, affine_im1_mask) # 掩模合并
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('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()
