• OpenCV 磨皮-Python


    --

    #!/bin/python
    # 祛痘美白 
    
    import numpy as np
    import cv2
    
    def beauty_face(img):
        '''
        Dest =(Src * (100 - Opacity) + (Src + 2 * GuassBlur(EPFFilter(Src) - Src + 128) - 256) * Opacity) /100 ;
        https://my.oschina.net/wujux/blog/1563461
        '''
    
        dst = np.zeros_like(img)
        #int value1 = 3, value2 = 1; 磨皮程度与细节程度的确定
        v1 = 3
        v2 = 1
        dx = v1 * 5 # 双边滤波参数之一 
        fc = v1 * 12.5 # 双边滤波参数之一 
        p = 0.1
       
        temp4 = np.zeros_like(img)
        
        temp1 = cv2.bilateralFilter(img,dx,fc,fc)
        temp2 = cv2.subtract(temp1,img)
        temp2 = cv2.add(temp2,(10,10,10,128))
        temp3 = cv2.GaussianBlur(temp2,(2*v2 - 1,2*v2-1),0)
        temp4 = cv2.add(img,temp3)
        dst = cv2.addWeighted(img,p,temp4,1-p,0.0)
        dst = cv2.add(dst,(10, 10, 10,255))
        return dst
    
    def beauty_face2(src):
        '''
        Dest =(Src * (100 - Opacity) + (Src + 2 * GuassBlur(EPFFilter(Src) - Src + 128) - 256) * Opacity) /100 ;
        '''
    
        dst = np.zeros_like(src)
        #int value1 = 3, value2 = 1; 磨皮程度与细节程度的确定
        v1 = 3
        v2 = 1
        dx = v1 * 5 # 双边滤波参数之一 
        fc = v1 * 12.5 # 双边滤波参数之一 
        p = 0.1
       
        temp4 = np.zeros_like(src)
        
        temp1 = cv2.bilateralFilter(src,dx,fc,fc)
        temp2 = cv2.subtract(temp1,src)
        temp2 = cv2.add(temp2, (10,10,10,128))
        temp3 = cv2.GaussianBlur(temp2,(2*v2 - 1,2*v2-1),0)
        temp4 = cv2.subtract(cv2.add(cv2.add(temp3, temp3), src), (10, 10, 10, 255))
        
        dst = cv2.addWeighted(src,p,temp4,1-p,0.0)
        dst = cv2.add(dst, (10, 10, 10,255))
        return dst
    
    
    def init():
        img = cv2.imread('testimg.jpg')
    
        # blur1 = cv2.GaussianBlur(img, (5,5),0)
        # blur2 = cv2.bilateralFilter(img, 9 , 75, 75)
        blur3 = beauty_face(img)
        blur4 = beauty_face2(img)
    
        cv2.imshow('image0', img)
        # cv2.imshow('image1', blur1)
        # cv2.imshow('image2', blur2)
        cv2.imshow('image3', blur3)
        cv2.imshow('image4', blur4)
    
        #cv2.namedWindow('image', cv2.WINDOW_NORMAL)
        #cv2.resizeWindow('image', 1000, 1000) #定义frame的大小
    
        cv2.waitKey(0)
        cv2.imwrite('result1.png', blur3)
        cv2.imwrite('result2.png', blur4)
        cv2.destroyAllWindows()
    
    if __name__ == "__main__":
        init()

    实验效果图

    原图(只用于算法实验侵权联系作者删除)、beauty_face 与 beauty_face2 对比如下:

     
    原图
     
    beauty_face
     
    beauty_face2

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  • 原文地址:https://www.cnblogs.com/Ph-one/p/12153414.html
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