opencv 查找连通区域 最大面积实例

今天在弄一个查找连通的最大面积的问题。

要把图像弄成黑底，白字，这样才可以正确找到。

然后调用下边的方法：

RETR_CCOMP:提取所有轮廓，并将轮廓组织成双层结构(two-level hierarchy),顶层为连通域的外围边界，次层位内层边界

#include 
#include 
 
using namespace cv;
using namespace std;
 
int main( int argc, char** argv )
{
  Mat src = imread( argv[1] );
 
  int largest_area=0;
  int largest_contour_index=0;
  Rect bounding_rect;
 
  Mat thr;
  cvtColor( src, thr, COLOR_BGR2GRAY ); //Convert to gray
  threshold( thr, thr, 125, 255, THRESH_BINARY ); //Threshold the gray
  bitwise_not(thr,thr); //这里先变反转颜色
 
  vector > contours; // Vector for storing contours
 
  findContours( thr, contours, RETR_CCOMP, CHAIN_APPROX_SIMPLE ); // Find the contours in the image
 
  for( size_t i = 0; i< contours.size(); i++ ) // iterate through each contour.
  {
    double area = contourArea( contours[i] ); // Find the area of contour
 
    if( area > largest_area )
    {
      largest_area = area;
      largest_contour_index = i; //Store the index of largest contour
      bounding_rect = boundingRect( contours[i] ); // Find the bounding rectangle for biggest contour
    }
  }
 
  drawContours( src, contours,largest_contour_index, Scalar( 0, 255, 0 ), 2 ); // Draw the largest contour using previously stored index.
 
  imshow( "result", src );
  waitKey();
  return 0;
}

方法二： connectedComponentsWithStats

 std::pair< int , int > MaxAreaFromSource(Mat srcImage, Mat &dstImage, int index)
{
  
  
  cv::Mat img_bool, labels, stats, centroids, img_color, img_gray;
  
  //连通域计算
  int nccomps = cv::connectedComponentsWithStats (
     srcImage, //二值图像
     labels,   //和原图一样大的标记图
     stats, //nccomps×5的矩阵 表示每个连通区域的外接矩形和面积（pixel）
     centroids //nccomps×2的矩阵 表示每个连通区域的质心
     );
  //cv::imshow("labels", labels);
  //cv::waitKey();
  
  vector colors(nccomps);
  colors[0] = cv::Vec3b(0,0,0); // background pixels remain black.
  
   printf( "index:%d==================n",index );
  
  vector< int >vec_width,vec_area,vec_height;
  
  for(int label = 1; label < nccomps; ++label)
  {
    colors[label] = cv::Vec3b( (std::rand()&255), (std::rand()&255), (std::rand()&255) );
    std::cout << "Component "<< label << std::endl;
    std::cout << "CC_STAT_LEFT  = " << stats.at(label,cv::CC_STAT_LEFT) << std::endl;
    std::cout << "CC_STAT_TOP  = " << stats.at(label,cv::CC_STAT_TOP) << std::endl;
    std::cout << "CC_STAT_WIDTH = " << stats.at(label,cv::CC_STAT_WIDTH) << std::endl;
    std::cout << "CC_STAT_HEIGHT = " << stats.at(label,cv::CC_STAT_HEIGHT) << std::endl;
    std::cout << "CC_STAT_AREA  = " << stats.at(label,cv::CC_STAT_AREA) << std::endl;
    std::cout << "CENTER  = (" << centroids.at(label, 0) <<","<< centroids.at(label, 1) << ")"<< std::endl << std::endl;
    
    int area = stats.at(label,cv::CC_STAT_AREA);
    int left = stats.at(label,cv::CC_STAT_LEFT);
    int top = stats.at(label,cv::CC_STAT_TOP);
    int width = stats.at(label,cv::CC_STAT_WIDTH);
    int height = stats.at(label,cv::CC_STAT_HEIGHT);
    
    vec_area.push_back(area);
    vec_width.push_back(width);
    vec_height.push_back(height);
  }
  
  vector::iterator bigwidth = std::max_element(std::begin(vec_width), std::end(vec_width));
  vector::iterator bigheight = std::max_element(std::begin(vec_height), std::end(vec_height));
  vector::iterator bigarea = std::max_element(std::begin(vec_area), std::end(vec_area));
  
  //printf( "area:%d------------width:%d height:%d n", *bigarea, *bigwidth, *bigheight );
  
  //按照label值，对不同的连通域进行着色
  img_color = cv::Mat::zeros(srcImage.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(y, x);
      CV_Assert(0 <= label && label <= nccomps);
      img_color.at(y, x) = colors[label];
    }
  
  cv::imshow("color", img_color);
  cv::waitKey();
   
  return make_pair( *bigarea , index );
}

我先用这个函数实现了一下，效果正确，还是opencv demo 是正确的，网上找了个例子，害死我了。

说明一下：方法一 比 第二种方法 运行速度快很多哦！ 这一点很重要。

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