OpenCV图像分割中的分水岭算法 OpenCV图像分割中的分水岭算法原理与应用详解

#if !defined WATERSHS 
#define WATERSHS 
 
#include <opencv2/core/core.hpp> 
#include <opencv2/imgproc/imgproc.hpp> 
 
class WatershedSegmenter { 
 
 private: 
 
   cv::Mat markers; 
 
 public: 
 
   void setMarkers(const cv::Mat& markerImage) { 
 
    // Convert to image of ints 
    markerImage.convertTo(markers,CV_32S); 
   } 
 
   cv::Mat process(const cv::Mat &image) { 
 
    // Apply watershed 
    cv::watershed(image,markers); 
 
    return markers; 
   } 
 
   // Return result in the form of an image 
   cv::Mat getSegmentation() { 
      
    cv::Mat tmp; 
    // all segment with label higher than 255 
    // will be assigned value 255 
    markers.convertTo(tmp,CV_8U); 
 
    return tmp; 
   } 
 
   // Return watershed in the form of an image以图像的形式返回分水岭 
   cv::Mat getWatersheds() { 
   
    cv::Mat tmp; 
    //在变换前，把每个像素p转换为255p+255（在conertTo中实现） 
    markers.convertTo(tmp,CV_8U,255,255); 
 
    return tmp; 
   } 
}; 
#endif 

// Read input image 
  cv::Mat image1= cv::imread("image.jpg"); 
  if (!image1.data) 
    return 0;  
// Display the color image 
  cv::resize(image1, image1, cv::Size(), 0.7, 0.7); 
  cv::namedWindow("Original Image1"); 
  cv::imshow("Original Image1",image1); 

// Identify image pixels with object 
   
  Mat binary; 
  cv::cvtColor(image1,binary,COLOR_BGRA2GRAY); 
  cv::threshold(binary,binary,30,255,THRESH_BINARY_INV);//阈值分割原图的灰度图，获得二值图像 
  // Display the binary image 
  cv::namedWindow("binary Image1"); 
  cv::imshow("binary Image1",binary); 
  waitKey(); 
   
  // CLOSE operation 
  cv::Mat element5(5,5,CV_8U,cv::Scalar(1));//5*5正方形，8位uchar型，全1结构元素 
  cv::Mat fg1; 
  cv::morphologyEx(binary, fg1,cv::MORPH_CLOSE,element5,Point(-1,-1),1);// 闭运算填充物体内细小空洞、连接邻近物体 
 
  // Display the foreground image 
  cv::namedWindow("Foreground Image"); 
  cv::imshow("Foreground Image",fg1); 
  waitKey(); 

// Identify image pixels without objects 
   
  cv::Mat bg1; 
  cv::dilate(binary,bg1,cv::Mat(),cv::Point(-1,-1),4);//膨胀4次，锚点为结构元素中心点 
  cv::threshold(bg1,bg1,1,128,cv::THRESH_BINARY_INV);//>=1的像素设置为128（即背景） 
  // Display the background image 
  cv::namedWindow("Background Image"); 
  cv::imshow("Background Image",bg1); 
  waitKey(); 

//Get markers image 
 
  Mat markers1 = fg1 + bg1; //使用Mat类的重载运算符+来合并图像。 
  cv::namedWindow("markers Image"); 
  cv::imshow("markers Image",markers1); 
  waitKey(); 

// Apply watershed segmentation 
 
  WatershedSegmenter segmenter1; //实例化一个分水岭分割方法的对象 
  segmenter1.setMarkers(markers1);//设置算法的标记图像，使得水淹过程从这组预先定义好的标记像素开始 
  segmenter1.process(image1);   //传入待分割原图 
    
  // Display segmentation result 
  cv::namedWindow("Segmentation1"); 
  cv::imshow("Segmentation1",segmenter1.getSegmentation());//将修改后的标记图markers转换为可显示的8位灰度图并返回分割结果（白色为前景，灰色为背景，0为边缘） 
  waitKey(); 
    // Display watersheds 
  cv::namedWindow("Watersheds1"); 
  cv::imshow("Watersheds1",segmenter1.getWatersheds());//以图像的形式返回分水岭（分割线条） 
  waitKey(); 

// Get the masked image 
  Mat maskimage = segmenter1.getSegmentation(); 
  cv::threshold(maskimage,maskimage,250,1,THRESH_BINARY); 
  cv::cvtColor(maskimage,maskimage,COLOR_GRAY2BGR); 
 
  maskimage = image1.mul(maskimage); 
  cv::namedWindow("maskimage"); 
  cv::imshow("maskimage",maskimage); 
  waitKey(); 
 
  // Turn background (0) to white (255) 
  int nl= maskimage.rows; // number of lines 
  int nc= maskimage.cols * maskimage.channels(); // total number of elements per line 
 
  for (int j=0; j<nl; j++) { 
     uchar* data= maskimage.ptr<uchar>(j); 
    for (int i=0; i<nc; i++)  
    { 
      // process each pixel --------------------- 
      if (*data==0) //将背景由黑色改为白色显示 
        *data=255; 
      data++;//指针操作：如为uchar型指针则移动1个字节，即移动到下1列 
    } 
   } 
  cv::namedWindow("result"); 
  cv::imshow("result",maskimage); 
  waitKey();