watershed segmentation opencv xcode

Question

I am now learning a code from the opencv codebook (OpenCV 2 Computer Vision Application Programming Cookbook): Chapter 5, Segmenting images using watersheds, page 131.

Here is my main code:

#include "opencv2/opencv.hpp"
#include <string>

using namespace cv;
using namespace std;

class WatershedSegmenter {
    private:
    cv::Mat markers;
    public:
    void setMarkers(const cv::Mat& markerImage){
        markerImage.convertTo(markers, CV_32S);
    }

    cv::Mat process(const cv::Mat &image){
        cv::watershed(image,markers);
        return markers;
    }
};

int main ()
{
    cv::Mat image = cv::imread("/Users/yaozhongsong/Pictures/IMG_1648.JPG");

    // Eliminate noise and smaller objects
    cv::Mat fg;
    cv::erode(binary,fg,cv::Mat(),cv::Point(-1,-1),6);

    // 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);

    // Create markers image
    cv::Mat markers(binary.size(),CV_8U,cv::Scalar(0));
    markers= fg+bg;

    // Create watershed segmentation object
    WatershedSegmenter segmenter;
    // Set markers and process
    segmenter.setMarkers(markers);
    segmenter.process(image);

    imshow("a",image);
    std::cout<<".";
    cv::waitKey(0);
}

However, it doesn't work. How could I initialize a binary image? And how could I make this segmentation code work?

I am not very clear about this part of the book. Thanks in advance!

Answer 1

Below is the simplified version of your code, and it works fine for me. Check it out :

#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"

using namespace cv;
using namespace std;

int main ()
{
    Mat image = imread("sofwatershed.jpg");
    Mat binary = imread("sofwsthresh.png",0);

    // Eliminate noise and smaller objects
    Mat fg;
    erode(binary,fg,Mat(),Point(-1,-1),2);

    // Identify image pixels without objects
    Mat bg;
    dilate(binary,bg,Mat(),Point(-1,-1),3);
    threshold(bg,bg,1,128,THRESH_BINARY_INV);

// Create markers image
    Mat markers(binary.size(),CV_8U,Scalar(0));
    markers= fg+bg;

markers.convertTo(markers, CV_32S);
watershed(image,markers);

markers.convertTo(markers,CV_8U);
imshow("a",markers);
waitKey(0);
}

Below is my input image :

Below is my output image :

See the code explanation here : Simple watershed Sample in OpenCV

Answer 2

I had the same problem as you, following the exact same code sample of the cookbook (great book btw).

Just to place the matter I was coding under Visual Studio 2013 and OpenCV 2.4.8. After a lot of searching and no solutions I decided to change the IDE.

It's still Visual Studio BUT it's 2010!!!! And boom it works!

Becareful of how you configure Visual Studio with OpenCV. Here's a great tutorial for installation here

Good day to all

Answer 3

There's a couple of things that should be mentioned about your code:

• Watershed expects the input and the output image to have the same size;
• You probably want to get rid of the const parameters in the methods;
• Notice that the result of watershed is actually markers and not image as your code suggests; About that, you need to grab the return of process()!

This is your code, with the fixes above:

// Usage: ./app input.jpg
#include "opencv2/opencv.hpp"
#include <string>

using namespace cv;
using namespace std;

class WatershedSegmenter{
private:
    cv::Mat markers;
public:
    void setMarkers(cv::Mat& markerImage)
    {
        markerImage.convertTo(markers, CV_32S);
    }

    cv::Mat process(cv::Mat &image)
    {
        cv::watershed(image, markers);
        markers.convertTo(markers,CV_8U);
        return markers;
    }
};


int main(int argc, char* argv[])
{
    cv::Mat image = cv::imread(argv[1]);
    cv::Mat binary;// = cv::imread(argv[2], 0);
    cv::cvtColor(image, binary, CV_BGR2GRAY);
    cv::threshold(binary, binary, 100, 255, THRESH_BINARY);

    imshow("originalimage", image);
    imshow("originalbinary", binary);

    // Eliminate noise and smaller objects
    cv::Mat fg;
    cv::erode(binary,fg,cv::Mat(),cv::Point(-1,-1),2);
    imshow("fg", fg);

    // Identify image pixels without objects
    cv::Mat bg;
    cv::dilate(binary,bg,cv::Mat(),cv::Point(-1,-1),3);
    cv::threshold(bg,bg,1, 128,cv::THRESH_BINARY_INV);
    imshow("bg", bg);

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

    // Create watershed segmentation object
    WatershedSegmenter segmenter;
    segmenter.setMarkers(markers);

    cv::Mat result = segmenter.process(image);
    result.convertTo(result,CV_8U);
    imshow("final_result", result);

    cv::waitKey(0);

    return 0;
}

I took the liberty of using Abid's input image for testing and this is what I got: