OpenCV 磨皮-Python

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#!/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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