物体颜色同一化——手势识别

""" 从视频读取帧保存为图片"""
import cv2
import numpy as np
# cap = cv2.VideoCapture('people.mp4')#读取文件
cap = cv2.VideoCapture(0)#读取摄像头

#皮肤检测
def A(img):

  YCrCb = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB) #转换至YCrCb空间
  (y,cr,cb) = cv2.split(YCrCb) #拆分出Y,Cr,Cb值
  cr1 = cv2.GaussianBlur(cr, (5,5), 0)
  _, skin = cv2.threshold(cr1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) #Ostu处理
  res = cv2.bitwise_and(img,img, mask = skin)
  return res

def B(img):

  #binaryimg = cv2.Canny(Laplacian, 50, 200) #二值化，canny检测
  h = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE) #寻找轮廓
  contour = h[0]
  contour = sorted(contour, key = cv2.contourArea, reverse=True)#已轮廓区域面积进行排序
  #contourmax = contour[0][:, 0, :]#保留区域面积最大的轮廓点坐标
  bg = np.ones(dst.shape, np.uint8) *255#创建白色幕布
  ret = cv2.drawContours(bg,contour[0],-1,(0,0,0),3) #绘制黑色轮廓
  return ret


while(True):

  ret, frame = cap.read()
  #下面三行可以根据自己的电脑进行调节
  src = cv2.resize(frame,(400,350), interpolation=cv2.INTER_CUBIC)#窗口大小
  cv2.rectangle(src, (90, 60), (300, 300 ), (0, 255, 0))#框出截取位置
  roi = src[60:300 , 90:300] # 获取手势框图

  res = A(roi) # 进行肤色检测
  cv2.imshow("0",cv2.resize(roi,(500,500)))

  gray = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY)
  dst = cv2.Laplacian(gray, cv2.CV_16S, ksize = 3)
  Laplacian = cv2.convertScaleAbs(dst)

  contour = B(Laplacian)#轮廓处理
  cv2.imshow("2",cv2.resize(contour,(500,500)))


  key = cv2.waitKey(50) & 0xFF
  if key == ord('q'):
      break
cap.release()
cv2.destroyAllWindows()