How to draw contours of each segmented object

Question

I apply watershed segmentation to detect touching objects and it works okay doing that. Now, I would like to draw contours of each object, so I can get their length, area, moments etc.. But the objects in the result of the segmentation are still touching. So, I fail to draw contours of each one. How can I draw contours of each object?

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

using namespace std;
using namespace cv;

int main()
{
    Mat src = imread("source.png");

    // Create binary image from source image
    Mat srcGray;
    cvtColor(src, srcGray, CV_BGR2GRAY);

    Mat srcThresh;
    threshold(srcGray, srcThresh, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);

    // Perform the distance transform algorithm
    Mat dist;
    distanceTransform(srcThresh, dist, CV_DIST_L2, 3);

    // Normalize the distance image for range = {0.0, 1.0}
    normalize(dist, dist, 0, 1., NORM_MINMAX);

    // Threshold to obtain the peaks 
    threshold(dist, dist, 0.1, 3.5, CV_THRESH_BINARY);

    // Create the CV_8U version of the distance image
    Mat dist_8u;
    dist.convertTo(dist_8u, CV_8U);

    // Find total markers
    std::vector<std::vector<Point> > contours;
    findContours(dist_8u, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
    int ncomp = contours.size();

    // Create the marker image for the watershed algorithm
    Mat markers = Mat::zeros(dist.size(), CV_32SC1);

    // Draw the foreground markers
    for (int i = 0; i < ncomp; i++)
        drawContours(markers, contours, i, Scalar::all(i + 1), -1);

    // Draw the background marker
    circle(markers, Point(5, 5), 3, CV_RGB(255, 255, 255), -1);

    // Perform the watershed algorithm
    watershed(src, markers);

    Mat wgResult = (markers.clone()) * 10000;

    imshow("Watershed", wgResult);

    waitKey(0);
    return 0;
}

Source image:

Watershed Result:

Answer 1

The markers matrix returned by watershed contains the indices of the segmented regions, according to the seed. So each component will have the same seed value. You can then create a binary matrix for each seed like:

Mat1b mask = (markers == seed);

Once you have the binary mask for each component, you can easily compute its area, moments, etc...

Code:

#include <opencv2/opencv.hpp>
#include <iostream>
using namespace std;
using namespace cv;

int main()
{
    Mat src = imread("D:\\SO\\img\\postit.png");

    // Create binary image from source image
    Mat srcGray;
    cvtColor(src, srcGray, CV_BGR2GRAY);

    Mat srcThresh;
    threshold(srcGray, srcThresh, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);

    // Perform the distance transform algorithm
    Mat dist;
    distanceTransform(srcThresh, dist, CV_DIST_L2, 3);

    // Normalize the distance image for range = {0.0, 1.0}
    normalize(dist, dist, 0, 1., NORM_MINMAX);

    // Threshold to obtain the peaks 
    threshold(dist, dist, 0.1, 3.5, CV_THRESH_BINARY);

    // Create the CV_8U version of the distance image
    Mat dist_8u;
    dist.convertTo(dist_8u, CV_8U);

    // Find total markers
    std::vector<std::vector<Point> > contours;
    findContours(dist_8u, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
    int ncomp = contours.size();

    // Create the marker image for the watershed algorithm
    Mat markers = Mat::zeros(dist.size(), CV_32SC1);

    // Draw the foreground markers
    for (int i = 0; i < ncomp; i++)
        drawContours(markers, contours, i, Scalar::all(i + 1), -1);

    // Draw the background marker
    circle(markers, Point(5, 5), 3, CV_RGB(255, 255, 255), -1);

    // Perform the watershed algorithm
    watershed(src, markers);

    for (int seed = 1; seed <= ncomp; ++seed)
    {
        Mat1b mask = (markers == seed);

        // Now you have the mask, you can compute your statistics

        imshow("Mask", mask);
        waitKey();
    }

    return 0;
}

Answer 2

There are many way to do this. Depending on the current image that have been shown, you can simply do erosion and dilation operation on order to separate them. However this will not work if the elapse area is bigger.

You need a closing operation: http://docs.opencv.org/2.4/doc/tutorials/imgproc/opening_closing_hats/opening_closing_hats.html

1. threshold it.
2. apply closing operation.
3. get contours