OpenCV Template Matching example in Android

Question

I'm a beginner to OpenCV. I'm trying to do a sample android application to match a template image in a given image using OpenCV Template matching. I searched in the internet and I couldn't find a proper android or java code which satisfy my requirement. But I have C++ code. I dont know how to translate it. http://docs.opencv.org/doc/tutorials/imgproc/histograms/template_matching/template_matching.html

Can you please help me to find a proper java or android code. Or else please help me with translate this C++ code into java, which I can use inside android application.

Thank you in advance.

C++ code

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <stdio.h>

using namespace std;
using namespace cv;

/// Global Variables
Mat img; Mat templ; Mat result;
char* image_window = "Source Image";
char* result_window = "Result window";

int match_method;
int max_Trackbar = 5;

/// Function Headers
void MatchingMethod( int, void* );

/** @function main */
int main( int argc, char** argv )
{
  /// Load image and template
  img = imread( argv[1], 1 );
  templ = imread( argv[2], 1 );

  /// Create windows
  namedWindow( image_window, CV_WINDOW_AUTOSIZE );
  namedWindow( result_window, CV_WINDOW_AUTOSIZE );

  /// Create Trackbar
  char* trackbar_label = "Method: \n 0: SQDIFF \n 1: SQDIFF NORMED \n 2: TM CCORR \n 3: TM CCORR NORMED \n 4: TM COEFF \n 5: TM COEFF NORMED";
  createTrackbar( trackbar_label, image_window, &match_method, max_Trackbar, MatchingMethod );

  MatchingMethod( 0, 0 );

  waitKey(0);
  return 0;
}

/**
 * @function MatchingMethod
 * @brief Trackbar callback
 */
void MatchingMethod( int, void* )
{
  /// Source image to display
  Mat img_display;
  img.copyTo( img_display );

  /// Create the result matrix
  int result_cols =  img.cols - templ.cols + 1;
  int result_rows = img.rows - templ.rows + 1;

  result.create( result_cols, result_rows, CV_32FC1 );

  /// Do the Matching and Normalize
  matchTemplate( img, templ, result, match_method );
  normalize( result, result, 0, 1, NORM_MINMAX, -1, Mat() );

  /// Localizing the best match with minMaxLoc
  double minVal; double maxVal; Point minLoc; Point maxLoc;
  Point matchLoc;

  minMaxLoc( result, &minVal, &maxVal, &minLoc, &maxLoc, Mat() );

  /// For SQDIFF and SQDIFF_NORMED, the best matches are lower values. For all the other methods, the higher the better
  if( match_method  == CV_TM_SQDIFF || match_method == CV_TM_SQDIFF_NORMED )
    { matchLoc = minLoc; }
  else
    { matchLoc = maxLoc; }

  /// Show me what you got
  rectangle( img_display, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
  rectangle( result, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );

  imshow( image_window, img_display );
  imshow( result_window, result );

  return;
}

Answer 1

I was facing the same problem you did. No source in Java available. Some search in the JavaDoc and some hints for const values later, I wrote this, which is almost the sample code above written in Java:

package opencv;

import org.opencv.core.Core;
import org.opencv.core.Core.MinMaxLocResult;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.Point;
import org.opencv.core.Scalar;
import org.opencv.highgui.Highgui;
import org.opencv.imgproc.Imgproc;

class MatchingDemo {
    public void run(String inFile, String templateFile, String outFile, int match_method) {
        System.out.println("\nRunning Template Matching");

        Mat img = Highgui.imread(inFile);
        Mat templ = Highgui.imread(templateFile);

        // / Create the result matrix
        int result_cols = img.cols() - templ.cols() + 1;
        int result_rows = img.rows() - templ.rows() + 1;
        Mat result = new Mat(result_rows, result_cols, CvType.CV_32FC1);

        // / Do the Matching and Normalize
        Imgproc.matchTemplate(img, templ, result, match_method);
        Core.normalize(result, result, 0, 1, Core.NORM_MINMAX, -1, new Mat());

        // / Localizing the best match with minMaxLoc
        MinMaxLocResult mmr = Core.minMaxLoc(result);

        Point matchLoc;
        if (match_method == Imgproc.TM_SQDIFF || match_method == Imgproc.TM_SQDIFF_NORMED) {
            matchLoc = mmr.minLoc;
        } else {
            matchLoc = mmr.maxLoc;
        }

        // / Show me what you got
        Core.rectangle(img, matchLoc, new Point(matchLoc.x + templ.cols(),
                matchLoc.y + templ.rows()), new Scalar(0, 255, 0));

        // Save the visualized detection.
        System.out.println("Writing "+ outFile);
        Highgui.imwrite(outFile, img);

    }
}

public class TemplateMatching {
    public static void main(String[] args) {
        System.loadLibrary("opencv_java246");
        new MatchingDemo().run(args[0], args[1], args[2], Imgproc.TM_CCOEFF);
    }
}

Now, run the program with the following options: lena.png template.png templatematch.png and you should receive the same result I did. Make sure the files are accessible by your runtime and, of course, opencv 2.4.6 library is registered to your classpath.

Answer 2

If you want to use OpenCV 3 and more you should use this code

because there is no Highgui in OpenCV 3 and you should use imgcodecs instead.

import org.opencv.core.Core;
import org.opencv.core.Core.MinMaxLocResult;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.Point;
import org.opencv.core.Scalar;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;

class MatchingDemo {
    public void run(String inFile, String templateFile, String outFile,
        int match_method) {
    System.out.println("\nRunning Template Matching");

    Mat img = Imgcodecs.imread(inFile);
    Mat templ = Imgcodecs.imread(templateFile);

    // / Create the result matrix
    int result_cols = img.cols() - templ.cols() + 1;
    int result_rows = img.rows() - templ.rows() + 1;
    Mat result = new Mat(result_rows, result_cols, CvType.CV_32FC1);

    // / Do the Matching and Normalize
    Imgproc.matchTemplate(img, templ, result, match_method);
    Core.normalize(result, result, 0, 1, Core.NORM_MINMAX, -1, new Mat());

    // / Localizing the best match with minMaxLoc
    MinMaxLocResult mmr = Core.minMaxLoc(result);

    Point matchLoc;
    if (match_method == Imgproc.TM_SQDIFF
            || match_method == Imgproc.TM_SQDIFF_NORMED) {
        matchLoc = mmr.minLoc;
    } else {
        matchLoc = mmr.maxLoc;
    }

    // / Show me what you got
    Imgproc.rectangle(img, matchLoc, new Point(matchLoc.x + templ.cols(),
            matchLoc.y + templ.rows()), new Scalar(0, 255, 0));

    // Save the visualized detection.
    System.out.println("Writing " + outFile);
    Imgcodecs.imwrite(outFile, img);

}
}

public class TemplateMatching {

public static void main(String[] args) {
    System.loadLibrary("opencv_java300");
    new MatchingDemo().run(args[0], args[1], args[2], Imgproc.TM_CCOEFF);
}

}