浅谈OpenCV中的新函数connectedComponentsWithStats用法

//寻找最大的轮廓
 VP FindBigestContour(Mat src){ 
  int imax = 0; //代表最大轮廓的序号
  int imaxcontour = -1; //代表最大轮廓的大小
  std::vector<std::vector<Point>>contours; 
  findContours(src,contours,CV_RETR_LIST,CV_CHAIN_APPROX_SIMPLE);
  for (int i=0;i<contours.size();i++){
   int itmp = contourArea(contours[i]);//这里采用的是轮廓大小
   if (imaxcontour < itmp ){
    imax = i;
    imaxcontour = itmp;
   }
  }
  return contours[imax];
 }
 //寻找并绘制出彩色联通区域
 vector<VP> connection2(Mat src,Mat& draw){ 
  draw = Mat::zeros(src.rows,src.cols,CV_8UC3);
  vector<VP>contours; 
  findContours(src.clone(),contours,CV_RETR_LIST,CV_CHAIN_APPROX_SIMPLE);
  //由于给大的区域着色会覆盖小的区域，所以首先进行排序操作
  //冒泡排序，由小到大排序
  VP vptmp;
  for(int i=1;i<contours.size();i++){
   for(int j=contours.size()-1;j>=i;j--){
    if (contourArea(contours[j]) < contourArea(contours[j-1]))
    {
     vptmp = contours[j-1];
     contours[j-1] = contours[j];
     contours[j] = vptmp;
    }
   }
  }

int cv::connectedComponents (
 cv::InputArrayn image,    // input 8-bit single-channel (binary)
 cv::OutputArray labels,    // output label map
 int    connectivity = 8,  // 4- or 8-connected components
 int    ltype  = CV_32S // Output label type (CV_32S or CV_16U)
 );
int cv::connectedComponentsWithStats (
 cv::InputArrayn image,    // input 8-bit single-channel (binary)
 cv::OutputArray labels,    // output label map
 cv::OutputArray stats,    // Nx5 matrix (CV_32S) of statistics:
               // [x0, y0, width0, height0, area0;
               // ... ; x(N-1), y(N-1), width(N-1),
               // height(N-1), area(N-1)]
 cv::OutputArray centroids,   // Nx2 CV_64F matrix of centroids:
               // [ cx0, cy0; ... ; cx(N-1), cy(N-1)]
 int    connectivity = 8,  // 4- or 8-connected components
 int    ltype  = CV_32S // Output label type (CV_32S or CV_16U)
 );

Mat img = cv::imread( "e:/sandbox/rect.png",0); 
 cv::Mat img_edge, labels, img_color, stats,centroids;
 cv::threshold(img, img_edge, 128, 255, cv::THRESH_BINARY);
 bitwise_not(img_edge,img_edge);
 cv::imshow("Image after threshold", img_edge);
 int i, nccomps = cv::connectedComponentsWithStats (
  img_edge, labels,
  stats, centroids
  );
 cout << "Total Connected Components Detected: " << nccomps << endl;
 vector<cv::Vec3b> colors(nccomps+1);
 colors[0] = Vec3b(0,0,0); // background pixels remain black.
 for( i = 1; i < nccomps; i++ ) {
  colors[i] = Vec3b(rand()%256, rand()%256, rand()%256);
  if( stats.at<int>(i, cv::CC_STAT_AREA) < 200 )
   colors[i] = Vec3b(0,0,0); // small regions are painted with black too.
 }
 img_color = Mat::zeros(img.size(), CV_8UC3);
 for( int y = 0; y < img_color.rows; y++ )
  for( int x = 0; x < img_color.cols; x++ )
  {
   int label = labels.at<int>(y, x);
   CV_Assert(0 <= label && label <= nccomps);
   img_color.at<cv::Vec3b>(y, x) = colors[label];
  }
 cv::imshow("Labeled map", img_color);
 cv::waitKey();