example using matchShapes in OpenCV

#include <opencv2/highgui/highgui.hpp>

#include <opencv2/imgproc/imgproc.hpp>

#include <cmath>

#include <iostream>

#include <cstdio>

using namespace std;

using namespace cv;

int main(int argc, char*argv[])

{

     if(argc!=3)

    {

        cout<<"usage: ./ms main.png match.png"<<endl;

        return 1;

     }

    Mat src = imread(argv[1]);

    if (src.empty())

   {

      cout<<"figuire "<<argv[1]<< "is not located!"<<endl;

      return 1;

    }

    Mat match = imread(argv[2]);

     if (match.empty())

     {

          cout<<"figuire "<<argv[2]<< "is not located!"<<endl;

          return 1;

     }

      Mat srcGray;

      Mat matchGray;

      cvtColor(src, srcGray, CV_BGR2GRAY);

      cvtColor(match, matchGray, CV_BGR2GRAY);

      Mat src_th, match_th;

      threshold(srcGray, src_th,125, 255,THRESH_BINARY_INV);

      threshold(matchGray, match_th,125, 255,THRESH_BINARY_INV);

      vector<vector<Point> > src_contours;

      vector<Vec4i> src_hierarchy;

      findContours(src_th, src_contours, src_hierarchy,CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE);

      vector<vector<Point> > match_contours;

      vector<Vec4i> match_hierarchy;

      findContours(match_th, match_contours, match_hierarchy,CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE);

      double matchR;  

     for(int i=0;i<src_contours.size();++i)

    {

        matchR = matchShapes(approx, match_contours[1],CV_CONTOURS_MATCH_I1,0);

       cout<<"match resul of contour "<<i<<" is: "<<matchR<<endl;

     }

}

However, matchShapes function does not work very well.

We are using the 2nd figure to match any one in the 1st figure. Here is the result:

match resul of contour 0 is: 0.11592
match resul of contour 1 is: 0.151644
match resul of contour 2 is: 0.282304
match resul of contour 3 is: 0.390383
match resul of contour 4 is: 0.540419
match resul of contour 5 is: 0.757443

different stages of using OpenCV

1: read in images.

Mat src = imread("and.png");

2: convert to a gray image.

 Mat gray;

cvtColor(src, gray, CV_BGR2GRAY);

3: find edges. There are two ways: threshold and canny. This step is optional. If the picture is only black and white, then this step is not necessary. We can just use findContours.

Mat th; Mat bw;

//method 1:  threshold(gray, th,125, 255,THRESH_BINARY);

//method 2:  Canny(gray, bw, 100, 200); 

       Note the differences between the above two images.
4: find contours and draw contours.

4.1:  findContours(th, contours, hierarchy,CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE);

 Note the red rectangle. If we change :

threshold(gray, th,125, 255,THRESH_BINARY);

to

threshold(gray, th,125, 255,THRESH_BINARY_INV);

You will not see that red  rectangle.

   4.2: findContours(bw, contours2, hierarchy2,CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE);

Why do we get 4 contours instead of 2 contours since we only two shapes in the canny output?
Because Canny creates two contours for each edge, one outside and one inside of that edge.


4: write out the image.

imwrite("output.png", color);