OpenCV Template Matching example in Android
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;
}
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.
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);
}
}
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