python opencv肤色检测的实现示例

1 椭圆肤色检测模型

原理：将RGB图像转换到YCRCB空间，肤色像素点会聚集到一个椭圆区域。先定义一个椭圆模型，然后将每个RGB像素点转换到YCRCB空间比对是否再椭圆区域，是的话判断为皮肤。

YCRCB颜色空间

椭圆模型

代码

1. def ellipse_detect(image):
2. """
3. :param image: 图片路径
4. :return: None
5. """
6. img = cv2.imread(image,cv2.IMREAD_COLOR)
7. skinCrCbHist = np.zeros((256,256), dtype= np.uint8 )
8. cv2.ellipse(skinCrCbHist ,(113,155),(23,15),43,0, 360, (255,255,255),-1)
9.  
10. YCRCB = cv2.cvtColor(img,cv2.COLOR_BGR2YCR_CB)
11. (y,cr,cb)= cv2.split(YCRCB)
12. skin = np.zeros(cr.shape, dtype=np.uint8)
13. (x,y)= cr.shape
14. for i in range(0,x):
15. for j in range(0,y):
16. CR= YCRCB[i,j,1]
17. CB= YCRCB[i,j,2]
18. if skinCrCbHist [CR,CB]>0:
19. skin[i,j]= 255
20. cv2.namedWindow(image, cv2.WINDOW_NORMAL)
21. cv2.imshow(image, img)
22. dst = cv2.bitwise_and(img,img,mask= skin)
23. cv2.namedWindow("cutout", cv2.WINDOW_NORMAL)
24. cv2.imshow("cutout",dst)
25. cv2.waitKey()

效果

2 YCrCb颜色空间的Cr分量+Otsu法阈值分割算法

原理

针对YCRCB中CR分量的处理，将RGB转换为YCRCB，对CR通道单独进行otsu处理，otsu方法opencv里用threshold

代码

1. def cr_otsu(image):
2. """YCrCb颜色空间的Cr分量+Otsu阈值分割
3. :param image: 图片路径
4. :return: None
5. """
6. img = cv2.imread(image, cv2.IMREAD_COLOR)
7. ycrcb = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB)
8.  
9. (y, cr, cb) = cv2.split(ycrcb)
10. cr1 = cv2.GaussianBlur(cr, (5, 5), 0)
11. _, skin = cv2.threshold(cr1,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
12.  
13. cv2.namedWindow("image raw", cv2.WINDOW_NORMAL)
14. cv2.imshow("image raw", img)
15. cv2.namedWindow("image CR", cv2.WINDOW_NORMAL)
16. cv2.imshow("image CR", cr1)
17. cv2.namedWindow("Skin Cr+OTSU", cv2.WINDOW_NORMAL)
18. cv2.imshow("Skin Cr+OTSU", skin)
19.  
20. dst = cv2.bitwise_and(img, img, mask=skin)
21. cv2.namedWindow("seperate", cv2.WINDOW_NORMAL)
22. cv2.imshow("seperate", dst)
23. cv2.waitKey()

效果

3 基于YCrCb颜色空间Cr, Cb范围筛选法

原理

类似于第二种方法，只不过是对CR和CB两个通道综合考虑

代码

1. def crcb_range_sceening(image):
2. """
3. :param image: 图片路径
4. :return: None
5. """
6. img = cv2.imread(image,cv2.IMREAD_COLOR)
7. ycrcb=cv2.cvtColor(img,cv2.COLOR_BGR2YCR_CB)
8. (y,cr,cb)= cv2.split(ycrcb)
9.  
10. skin = np.zeros(cr.shape,dtype= np.uint8)
11. (x,y)= cr.shape
12. for i in range(0,x):
13. for j in range(0,y):
14. if (cr[i][j]>140)and(cr[i][j])<175 and (cr[i][j]>100) and (cb[i][j])<120:
15. skin[i][j]= 255
16. else:
17. skin[i][j] = 0
18. cv2.namedWindow(image,cv2.WINDOW_NORMAL)
19. cv2.imshow(image,img)
20. cv2.namedWindow(image+"skin2 cr+cb",cv2.WINDOW_NORMAL)
21. cv2.imshow(image+"skin2 cr+cb",skin)
22.  
23. dst = cv2.bitwise_and(img,img,mask=skin)
24. cv2.namedWindow("cutout",cv2.WINDOW_NORMAL)
25. cv2.imshow("cutout",dst)
26.  
27. cv2.waitKey()

效果

4 HSV颜色空间H,S,V范围筛选法

原理

还是转换空间然后每个通道设置一个阈值综合考虑，进行二值化操作。

代码

1. def hsv_detect(image):
2. """
3. :param image: 图片路径
4. :return: None
5. """
6. img = cv2.imread(image,cv2.IMREAD_COLOR)
7. hsv=cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
8. (_h,_s,_v)= cv2.split(hsv)
9. skin= np.zeros(_h.shape,dtype=np.uint8)
10. (x,y)= _h.shape
11.  
12. for i in range(0,x):
13. for j in range(0,y):
14. if(_h[i][j]>7) and (_h[i][j]<20) and (_s[i][j]>28) and (_s[i][j]<255) and (_v[i][j]>50 ) and (_v[i][j]<255):
15. skin[i][j] = 255
16. else:
17. skin[i][j] = 0
18. cv2.namedWindow(image, cv2.WINDOW_NORMAL)
19. cv2.imshow(image, img)
20. cv2.namedWindow(image + "hsv", cv2.WINDOW_NORMAL)
21. cv2.imshow(image + "hsv", skin)
22. dst = cv2.bitwise_and(img, img, mask=skin)
23. cv2.namedWindow("cutout", cv2.WINDOW_NORMAL)
24. cv2.imshow("cutout", dst)
25. cv2.waitKey()

效果

示例

1. import cv2
2. import numpy as np
3.  
4. def ellipse_detect(image):
5. """
6. :param image: img path
7. :return: None
8. """
9. img = cv2.imread(image, cv2.IMREAD_COLOR)
10. skinCrCbHist = np.zeros((256, 256), dtype=np.uint8)
11. cv2.ellipse(skinCrCbHist, (113, 155), (23, 15), 43, 0, 360, (255, 255, 255), -1)
12.  
13. YCRCB = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB)
14. (y, cr, cb) = cv2.split(YCRCB)
15. skin = np.zeros(cr.shape, dtype=np.uint8)
16. (x, y) = cr.shape
17. for i in range(0, x):
18. for j in range(0, y):
19. CR = YCRCB[i, j, 1]
20. CB = YCRCB[i, j, 2]
21. if skinCrCbHist[CR, CB] > 0:
22. skin[i, j] = 255
23. cv2.namedWindow(image, cv2.WINDOW_NORMAL)
24. cv2.imshow(image, img)
25. dst = cv2.bitwise_and(img, img, mask=skin)
26. cv2.namedWindow("cutout", cv2.WINDOW_NORMAL)
27. cv2.imshow("cutout", dst)
28. cv2.waitKey()
29.  
30. if __name__ == '__main__':
31. ellipse_detect('./test.png')

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原文链接：https://blog.csdn.net/weixin_40893939/article/details/84527037