使用分水岭算法对图像进行分割

最新推荐文章于 2025-07-14 17:52:40 发布

_noname

最新推荐文章于 2025-07-14 17:52:40 发布

阅读量2.5k

点赞数

CC 4.0 BY-SA版权

分类专栏： opencv2 文章标签： opencv2 算法

本文链接：https://blog.youkuaiyun.com/williamfan21c/article/details/24405589

opencv2 专栏收录该内容

29 篇文章

订阅专栏

本文探讨了如何利用OpenCV库中的分水岭算法进行图像分割。通过介绍分水岭变换的基本原理和步骤，阐述了其在图像处理中的重要性。详细讲解了在实际操作中如何实现算法，并给出了实例分析，展示了分水岭算法在复杂图像分割任务中的效果。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

#if !defined WATERSHS
#define WATERSHS

#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.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

#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("d:/test/opencv/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("d:/test/opencv/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);

	// 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);

	// Show markers image
	cv::Mat markers(binary.size(),CV_8U,cv::Scalar(0));
	markers= fg+bg;
	cv::namedWindow("Markers");
	cv::imshow("Markers",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());

	// Display watersheds
	cv::namedWindow("Watersheds");
	cv::imshow("Watersheds",segmenter.getWatersheds());

	
	cv::waitKey();
	return 0;
}