1 橢圓膚色檢測模型

原理：將RGB影象轉換到YCRCB空間，膚色畫素點會聚集到一個橢圓區域。先定義一個橢圓模型，然後將每個RGB畫素點轉換到YCRCB空間比對是否再橢圓區域，是的話判斷為面板。

YCRCB顏色空間

橢圓模型

程式碼

def ellipse_detect(image):
  """
  :param image: 圖片路徑
  :return: None
  """
  img = cv2.imread(image,cv2.IMREAD_COLOR)
  skinCrCbHist = np.zeros((256,256),dtype= np.uint8 )
  cv2.ellipse(skinCrCbHist,(113,155),(23,15),43,360,(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,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

2YCrCb顏色空間的Cr分量+Otsu法閾值分割演算法

原理

針對YCRCB中CR分量的處理，將RGB轉換為YCRCB，對CR通道單獨進行otsu處理，otsu方法opencv裡用threshold

程式碼

def cr_otsu(image):
  """YCrCb顏色空間的Cr分量+Otsu閾值分割
  :param image: 圖片路徑
  :return: None
  """
  img = cv2.imread(image,cv2.IMREAD_COLOR)
  ycrcb = cv2.cvtColor(img,cv2.COLOR_BGR2YCR_CB)
 
  (y,cb) = cv2.split(ycrcb)
  cr1 = cv2.GaussianBlur(cr,(5,5),0)
  _,skin = cv2.threshold(cr1,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,mask=skin)
  cv2.namedWindow("seperate",cv2.WINDOW_NORMAL)
  cv2.imshow("seperate",dst)
  cv2.waitKey()

效果

3 基於YCrCb顏色空間Cr,Cb範圍篩選法

原理

類似於第二種方法，只不過是對CR和CB兩個通道綜合考慮

程式碼

def crcb_range_sceening(image):
  """
  :param image: 圖片路徑
  :return: None
  """
  img = cv2.imread(image,cv2.IMREAD_COLOR)
  ycrcb=cv2.cvtColor(img,cb)= cv2.split(ycrcb)
 
  skin = np.zeros(cr.shape,dtype= np.uint8)
  (x,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,img)
  cv2.namedWindow(image+"skin2 cr+cb",cv2.WINDOW_NORMAL)
  cv2.imshow(image+"skin2 cr+cb",mask=skin)
  cv2.namedWindow("cutout",dst)
 
  cv2.waitKey()

效果

4 HSV顏色空間H,S,V範圍篩選法

原理

還是轉換空間然後每個通道設定一個閾值綜合考慮，進行二值化操作。

程式碼

def hsv_detect(image):
  """
  :param image: 圖片路徑
  :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,y)= _h.shape
 
  for i 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,img)
  cv2.namedWindow(image + "hsv",cv2.WINDOW_NORMAL)
  cv2.imshow(image + "hsv",skin)
  dst = cv2.bitwise_and(img,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,dtype=np.uint8)
  cv2.ellipse(skinCrCbHist,cb) = cv2.split(YCRCB)
  skin = np.zeros(cr.shape,y) = cr.shape
  for i in range(0,y):
      CR = YCRCB[i,1]
      CB = YCRCB[i,2]
      if skinCrCbHist[CR,CB] > 0:
        skin[i,j] = 255
  cv2.namedWindow(image,dst)
  cv2.waitKey()
 
 
 
if __name__ == '__main__':
  ellipse_detect('./test.png')

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