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

def ellipse_detect(image):

"""

:param image: 图片路径

:return: None

"""

img = cv2.imread(image,cv2.IMREAD_COLOR)

skinCrCbHist = np.zeros((256,256), dtype= np.uint8 )

cv2.ellipse(skinCrCbHist ,(113,155),(23,15),43,0, 360, (255,255,255),-1)

YCRCB = cv2.cvtColor(img,cv2.COLOR_BGR2YCR_CB)

(y,cr,cb)= cv2.split(YCRCB)

skin = np.zeros(cr.shape, dtype=np.uint8)

(x,y)= cr.shape

for i in range(0,x):

for j in range(0,y):

CR= YCRCB[i,j,1]

CB= YCRCB[i,j,2]

if skinCrCbHist [CR,CB]>0:

skin[i,j]= 255

cv2.namedWindow(image, cv2.WINDOW_NORMAL)

cv2.imshow(image, img)

dst = cv2.bitwise_and(img,img,mask= skin)

cv2.namedWindow("cutout", cv2.WINDOW_NORMAL)

cv2.imshow("cutout",dst)

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

def cr_otsu(image):

"""YCrCb颜色空间的Cr分量+Otsu阈值分割

:param image: 图片路径

:return: None

"""

img = cv2.imread(image, cv2.IMREAD_COLOR)

ycrcb = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB)

(y, cr, cb) = cv2.split(ycrcb)

cr1 = cv2.GaussianBlur(cr, (5, 5), 0)

_, skin = cv2.threshold(cr1,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

cv2.namedWindow("image raw", cv2.WINDOW_NORMAL)

cv2.imshow("image raw", img)

cv2.namedWindow("image CR", cv2.WINDOW_NORMAL)

cv2.imshow("image CR", cr1)

cv2.namedWindow("Skin Cr+OTSU", cv2.WINDOW_NORMAL)

cv2.imshow("Skin Cr+OTSU", skin)

dst = cv2.bitwise_and(img, img, mask=skin)

cv2.namedWindow("seperate", cv2.WINDOW_NORMAL)

cv2.imshow("seperate", dst)

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

def crcb_range_sceening(image):

"""

:param image: 图片路径

:return: None

"""

img = cv2.imread(image,cv2.IMREAD_COLOR)

ycrcb=cv2.cvtColor(img,cv2.COLOR_BGR2YCR_CB)

(y,cr,cb)= cv2.split(ycrcb)

skin = np.zeros(cr.shape,dtype= np.uint8)

(x,y)= cr.shape

for i in range(0,x):

for j in range(0,y):

if (cr[i][j]>140)and(cr[i][j])<175 and (cr[i][j]>100) and (cb[i][j])<120:

skin[i][j]= 255

else:

skin[i][j] = 0

cv2.namedWindow(image,cv2.WINDOW_NORMAL)

cv2.imshow(image,img)

cv2.namedWindow(image+"skin2 cr+cb",cv2.WINDOW_NORMAL)

cv2.imshow(image+"skin2 cr+cb",skin)

dst = cv2.bitwise_and(img,img,mask=skin)

cv2.namedWindow("cutout",cv2.WINDOW_NORMAL)

cv2.imshow("cutout",dst)

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

def hsv_detect(image):

"""

:param image: 图片路径

:return: None

"""

img = cv2.imread(image,cv2.IMREAD_COLOR)

hsv=cv2.cvtColor(img,cv2.COLOR_BGR2HSV)

(_h,_s,_v)= cv2.split(hsv)

skin= np.zeros(_h.shape,dtype=np.uint8)

(x,y)= _h.shape

for i in range(0,x):

for j in range(0,y):

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):

skin[i][j] = 255

else:

skin[i][j] = 0

cv2.namedWindow(image, cv2.WINDOW_NORMAL)

cv2.imshow(image, img)

cv2.namedWindow(image + "hsv", cv2.WINDOW_NORMAL)

cv2.imshow(image + "hsv", skin)

dst = cv2.bitwise_and(img, img, mask=skin)

cv2.namedWindow("cutout", cv2.WINDOW_NORMAL)

cv2.imshow("cutout", dst)

cv2.waitKey()

效果

示例

import cv2

import numpy as np

def ellipse_detect(image):

"""

:param image: img path

:return: None

"""

img = cv2.imread(image, cv2.IMREAD_COLOR)

skinCrCbHist = np.zeros((256, 256), dtype=np.uint8)

cv2.ellipse(skinCrCbHist, (113, 155), (23, 15), 43, 0, 360, (255, 255, 255), -1)

YCRCB = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB)

(y, cr, cb) = cv2.split(YCRCB)

skin = np.zeros(cr.shape, dtype=np.uint8)

(x, y) = cr.shape

for i in range(0, x):

for j in range(0, y):

CR = YCRCB[i, j, 1]

CB = YCRCB[i, j, 2]

if skinCrCbHist[CR, CB] > 0:

skin[i, j] = 255

cv2.namedWindow(image, cv2.WINDOW_NORMAL)

cv2.imshow(image, img)

dst = cv2.bitwise_and(img, img, mask=skin)

cv2.namedWindow("cutout", cv2.WINDOW_NORMAL)

cv2.imshow("cutout", dst)

cv2.waitKey()

if __name__ == '__main__':

ellipse_detect('./test.png')

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