opencv图像融合（给人脸添加一个眼镜）

基于dlib68点人脸检测的小功能实现

图像旋转找的现成的方法，稍稍麻烦点的地方就是mask处理，虽然目的达到了，但是效果一般

 import numpy as np
 import cv2 as cv
 import dlib
 import math

 # 做一个戴眼镜的滤镜效果

 detector = dlib.get_frontal_face_detector()
 predictor = dlib.shape_predictor('dlib/shape_predictor_68_face_landmarks.dat')

 # 图像旋转，保持原来大小
 def rotate_bound(image, angle):
     # grab the dimensions of the image and then determine the
     # center
     (h, w) = image.shape[:2]
     (cX, cY) = (w // 2, h // 2)

     # grab the rotation matrix (applying the negative of the
     # angle to rotate clockwise), then grab the sine and cosine
     # (i.e., the rotation components of the matrix)
     M = cv.getRotationMatrix2D((cX, cY), -angle, 1.0)
     cos = np.abs(M[0, 0])
     sin = np.abs(M[0, 1])

     # compute the new bounding dimensions of the image
     nW = int((h * sin) + (w * cos))
     nH = int((h * cos) + (w * sin))

     # adjust the rotation matrix to take into account translation
     M[0, 2] += (nW / 2) - cX
     M[1, 2] += (nH / 2) - cY

     # perform the actual rotation and return the image
     return cv.warpAffine(image, M, (nW, nH))

 def detect_face(camera_idx):
     # camera_idx: 电脑自带摄像头或者usb摄像头
     cv.namedWindow('detect')
     cap = cv.VideoCapture(camera_idx)

     while cap.isOpened():
         cv.namedWindow('detect', cv.WINDOW_AUTOSIZE)
         ok, frame = cap.read()
         # 为摄像头的时候，翻转画面
         if camera_idx == 0 or camera_idx == 1:
             frame = cv.flip(frame, 1, dst=None)
         if not ok:
             break
         gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
         rects = detector(gray, 0)
         for i in range(len(rects)):
             landmarks = np.matrix([[p.x, p.y] for p in predictor(frame, rects[i]).parts()])
             # 脸轮廓：1~17
             # 眉毛：18~22, 23~27
             # 鼻梁：28~31
             # 鼻子：31~36
             # 眼睛：37~42, 43~48
             # 嘴唇：49~68
             # 左眼角和右眼角的位置
             pos_left = (landmarks[0][0, 0], landmarks[36][0, 1])
             pos_right = (landmarks[16][0, 0], landmarks[45][0, 1])
             face_center = (landmarks[27][0, 0], landmarks[27][0, 1])
             src = cv.imread('images/glasses.jpg')
             # 433x187眼镜图片原始大小，按人脸比例缩放一下
             length = pos_right[0] - pos_left[0]
             width = int(187/(433/length))
             src = cv.resize(src, (length, width), interpolation=cv.INTER_CUBIC)

             # 角度旋转，通过计算两个眼角和水平方向的夹角来旋转眼镜
             sx = landmarks[36][0, 0] - landmarks[45][0, 0]
             sy = landmarks[36][0, 1] - landmarks[45][0, 1]
             # 夹角正切值
             r = sy/sx
             # 求正切角,弧度转为度
             degree = math.degrees(math.atan(r))
             # 调用旋转方法
             src = rotate_bound(src, degree)

             # mask处理，去掉旋转后的无关区域，初始化一个全0mask，用或运算处理mask
             src_mask = np.zeros(src.shape, src.dtype)
             src_mask = cv.bitwise_or(src, src_mask)
             # 泊松融合
             output = cv.seamlessClone(src, frame, src_mask, face_center, cv.MIXED_CLONE)
         cv.imshow('detect', output)
         c = cv.waitKey(10)
         if c & 0xFF == ord('q'):
             break
     cap.release()
     cv.destroyAllWindows()

 if __name__ == '__main__':
     video = 'video/face.mp4'
     detect_face(video)

眼镜图片

效果

泊松融合三种参数效果在这里一样

除了眼镜图片较浅其他的还算可以吧

还可以扩展面部其他装饰

参考：

泊松融合：https://www.smwenku.com/a/5b7aec012b7177392c97200d

图像旋转：https://blog.csdn.net/hui3909/article/details/78854387