一、分水岭算法简介

分水岭分割方法，是一种基于拓扑理论的数学形态学的分割方法，其基本思想是把图像看作是测地学上的拓扑地貌，图像中每一点像素的灰度值表示该点的海拔高度，每一个局部极小值及其影响区域称为集水盆，而集水盆的边界则形成分水岭。分水岭的概念和形成可以通过模拟浸入过程来说明。在每一个局部极小值表面，刺穿一个小孔，然后把整个模型慢慢浸入水中，随着浸入的加深，每一个局部极小值的影响域慢慢向外扩展，在两个集水盆汇合处构筑大坝，即形成分水岭。 分水岭算法一般和区域生长法或聚类分析法相结合。
分水岭算法一般用于分割感兴趣的图像区域，应用如细胞边界的分割，分割出相片中的头像等等。
二、分水岭用opencv函数实现
分水岭算法在opencv2库中实现函数是：
头文件：#include <opencv2/imgproc/imgproc.hpp>
函数声明：CV_EXPORTS_W void watershed( InputArray image, InputOutputArray markers );
参数：
InputArray image  要分割的原始图片
InputOutputArray markers 标记数组，非零的32位有符号的int型数组，用于标记出要分割的关键点，进而区域生长，扩展出感兴趣的区域。
实现程序： watershedSegmenter.h

#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  
      cv::Mat getWatersheds() {  
      
        cv::Mat tmp;  
        markers.convertTo(tmp,CV_8U,255,255);  
  
        return tmp;  
      }  
};  
  
  
#endif  

segment.cpp

#include <iostream>  
#include <opencv2/core/core.hpp>  
#include <opencv2/imgproc/imgproc.hpp>  
#include <opencv2/highgui/highgui.hpp>  
#include "watershedSegmentation.h"  
  
  
int main()  
{  
    // Read input image  
    cv::Mat image= cv::imread("group.jpg");  
    if (!image.data)  
        return 0;   
  
    // Display the image  
    cv::namedWindow("Original Image");  
    cv::imshow("Original Image",image);  
  
    // Get the binary map  
    cv::Mat binary;  
    binary= cv::imread("binary.bmp",0);  
  
    // Display the binary image  
    cv::namedWindow("Binary Image");  
    cv::imshow("Binary Image",binary);  
  
    // Eliminate noise and smaller objects  
    cv::Mat fg;  
    cv::erode(binary,fg,cv::Mat(),cv::Point(-1,-1),6);  
  
    // Display the foreground image  
    cv::namedWindow("Foreground Image");  
    cv::imshow("Foreground Image",fg);  
    cv::imwrite("ForegroundImage.jpg",fg);  
    // Identify image pixels without objects  
    cv::Mat bg;  
    cv::dilate(binary,bg,cv::Mat(),cv::Point(-1,-1),6);  
    cv::threshold(bg,bg,1,128,cv::THRESH_BINARY_INV);  
  
    // Display the background image  
    cv::namedWindow("Background Image");  
    cv::imshow("Background Image",bg);  
    cv::imwrite("BackgroundImage.jpg",bg);  
    // Show markers image  
    cv::Mat markers(binary.size(),CV_8U,cv::Scalar(0));  
    markers= fg+bg;  
    cv::namedWindow("Markers");  
    cv::imshow("Markers",markers);  
    cv::imwrite("Markers.jpg",markers);  
    // Create watershed segmentation object  
    WatershedSegmenter segmenter;  
  
    // Set markers and process  
    segmenter.setMarkers(markers);  
    segmenter.process(image);  
  
    // Display segmentation result  
    cv::namedWindow("Segmentation");  
    cv::imshow("Segmentation",segmenter.getSegmentation());  
    cv::imwrite("Segmentation.jpg",segmenter.getSegmentation());  
    // Display watersheds  
    cv::namedWindow("Watersheds");  
    cv::imshow("Watersheds",segmenter.getWatersheds());  
    cv::imwrite("Watersheds.jpg",segmenter.getWatersheds());      
    // Open another image  
    image= cv::imread("tower.jpg");  
  
    // Identify background pixels  
    cv::Mat imageMask(image.size(),CV_8U,cv::Scalar(0));  
    cv::rectangle(imageMask,cv::Point(5,5),cv::Point(image.cols-5,image.rows-5),cv::Scalar(255),3);  
    // Identify foreground pixels (in the middle of the image)  
    cv::rectangle(imageMask,cv::Point(image.cols/2-10,image.rows/2-10),  
                         cv::Point(image.cols/2+10,image.rows/2+10),cv::Scalar(1),10);  
  
    // Set markers and process  
    segmenter.setMarkers(imageMask);  
    segmenter.process(image);  
  
    // Display the image with markers  
    cv::rectangle(image,cv::Point(5,5),cv::Point(image.cols-5,image.rows-5),cv::Scalar(255,255,255),3);  
    cv::rectangle(image,cv::Point(image.cols/2-10,image.rows/2-10),  
                         cv::Point(image.cols/2+10,image.rows/2+10),cv::Scalar(1,1,1),10);  
    cv::namedWindow("Image with marker");  
    cv::imshow("Image with marker",image);  
    cv::imwrite("Image with marker.jpg",image);  
    // Display watersheds  
    cv::namedWindow("Watersheds of foreground object");  
    cv::imshow("Watersheds of foreground object",segmenter.getWatersheds());  
    cv::imwrite("Watersheds of foreground object.jpg",segmenter.getWatersheds());  
  
    cv::waitKey();  
    return 0;  
}  

程序运行结果：
group.jpg
       

binary.bmp

fg

bg

markers

Segmentation

Watersheds

tower.jpg

Image with marker.jpg

Watersheds of foreground object.jpg

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