cv::Mat src = cv::imread("F:\\t13.bmp",CV_BGR2GRAY);
  cv::Mat hsv;
  GaussianBlur(src,hsv,cv::Size(5,5),0,0);

// Quantize the gray scale to 30 levels 
int gbins = 16; 
int histSize[] = {gbins}; 
  // gray scale varies from 0 to 256 
float granges[] = {0,256}; 
const float* ranges[] = { granges }; 
cv::MatND hist; 
// we compute the histogram from the 0-th and 1-st channels 
int channels[] = {0}; 
 
//calculate hist 
calcHist( &hsv, 1, channels, cv::Mat(), // do not use mask 
      hist, 1, histSize, ranges, 
      true, // the histogram is uniform 
      false ); 
//find the max value of hist 
double maxVal=0; 
minMaxLoc(hist, 0, &maxVal, 0, 0); 
 
int scale = 20; 
cv::Mat histImg; 
histImg.create(500,gbins*scale,CV_8UC3); 
 
//show gray scale of hist image 
for(int g=0;g<gbins;g++){ 
  float binVal = hist.at<float>(g,0); 
  int intensity = cvRound(binVal*255); 
  rectangle( histImg, cv::Point(g*scale,0), 
            cv::Point((g+1)*scale - 1,binVal/maxVal*400), 
            CV_RGB(0,0,0), 
            CV_FILLED ); 
} 
cv::imshow("histImg",histImg); 
 
//threshold processing 
cv::Mat hsvRe; 
threshold( hsv, hsvRe, 64, 255,cv::THRESH_BINARY); 

vector<Point> approx; 
 
for (size_t i = 0; i < contours.size(); i++) 
{ 
  approxPolyDP(Mat(contours[i]), approx,  
         arcLength(Mat(contours[i]), true)*0.02, true); 
 
  if (approx.size() == 4 && 
    fabs(contourArea(Mat(approx))) > 1000 && 
    isContourConvex(Mat(approx))) 
  { 
    double maxCosine = 0; 
 
    for( int j = 2; j < 5; j++ ) 
    { 
      double cosine = fabs(angle(approx[j%4], approx[j-2], approx[j-1])); 
      maxCosine = MAX(maxCosine, cosine); 
    } 
 
    if( maxCosine < 0.3 ) 
      squares.push_back(approx); 
  } 
}