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python opencv肤色检测的实现示例
脚本专栏 2024/12/23 佚名
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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

python opencv肤色检测的实现示例python opencv肤色检测的实现示例

椭圆模型

python opencv肤色检测的实现示例

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

python opencv肤色检测的实现示例

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

python opencv肤色检测的实现示例

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

python opencv肤色检测的实现示例

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

python opencv肤色检测的实现示例

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')

 到此这篇关于python opencv肤色检测的实现示例的文章就介绍到这了,更多相关opencv 肤色检测内容请搜索以前的文章或继续浏览下面的相关文章希望大家以后多多支持！

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opencv,肤色检测
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