Example Template Matching

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Template matching compares a smaller image (the template) against every possible location in a larger target image. A match is declared the fit score is a local peak and above a threshold. Typically template matching is only used in highly controlled environments and doesn't work to well in natural scenes. It's also extremely computationally expensive and larger images/templates are likely to take an excessive amount of time to process.

The example below is intended to demonstrate the strengths and weaknesses of template matching. For each template the number of matches returned needs to be specified. If the number of matches is known then the results are good in this example, but if too many are requested the some of the results are noise. The intensity image is shown for a match. Notice how ambiguous the results are.

Example Code:


  • Template Matching

Example Code

 * Example of how to find objects inside an image using template matching.  Template matching works
 * well when there is little noise in the image and the object's appearance is known and static.  It can
 * also be very slow to compute, depending on the image and template size.
 * @author Peter Abeles
public class ExampleTemplateMatching {
	 * Demonstrates how to search for matches of a template inside an image
	 * @param image           Image being searched
	 * @param template        Template being looked for
	 * @param mask            Mask which determines the weight of each template pixel in the match score
	 * @param expectedMatches Number of expected matches it hopes to find
	 * @return List of match location and scores
	private static List<Match> findMatches(GrayF32 image, GrayF32 template, GrayF32 mask,
										   int expectedMatches) {
		// create template matcher.
		TemplateMatching<GrayF32> matcher =
				FactoryTemplateMatching.createMatcher(TemplateScoreType.SUM_DIFF_SQ, GrayF32.class);
		// Find the points which match the template the best
		matcher.setTemplate(template, mask,expectedMatches);
		return matcher.getResults().toList();
	 * Computes the template match intensity image and displays the results. Brighter intensity indicates
	 * a better match to the template.
	public static void showMatchIntensity(GrayF32 image, GrayF32 template, GrayF32 mask) {
		// create algorithm for computing intensity image
		TemplateMatchingIntensity<GrayF32> matchIntensity =
				FactoryTemplateMatching.createIntensity(TemplateScoreType.SUM_DIFF_SQ, GrayF32.class);
		// apply the template to the image
		matchIntensity.process(template, mask);
		// get the results
		GrayF32 intensity = matchIntensity.getIntensity();
		// adjust the intensity image so that white indicates a good match and black a poor match
		// the scale is kept linear to highlight how ambiguous the solution is
		float min = ImageStatistics.min(intensity);
		float max = ImageStatistics.max(intensity);
		float range = max - min;
		PixelMath.plus(intensity, -min, intensity);
		PixelMath.divide(intensity, range, intensity);
		PixelMath.multiply(intensity, 255.0f, intensity);
		BufferedImage output = new BufferedImage(image.width, image.height, BufferedImage.TYPE_INT_BGR);
		VisualizeImageData.grayMagnitude(intensity, output, -1);
		ShowImages.showWindow(output, "Match Intensity", true);
	public static void main(String args[]) {
		// Load image and templates
		String directory = UtilIO.pathExample("template");
		GrayF32 image = UtilImageIO.loadImage(directory ,"desktop.png", GrayF32.class);
		GrayF32 templateCursor = UtilImageIO.loadImage(directory , "cursor.png", GrayF32.class);
		GrayF32 maskCursor = UtilImageIO.loadImage(directory , "cursor_mask.png", GrayF32.class);
		GrayF32 templatePaint = UtilImageIO.loadImage(directory , "paint.png", GrayF32.class);
		// create output image to show results
		BufferedImage output = new BufferedImage(image.width, image.height, BufferedImage.TYPE_INT_BGR);
		ConvertBufferedImage.convertTo(image, output);
		Graphics2D g2 = output.createGraphics();
		// Search for the cursor in the image.  For demonstration purposes it has been pasted 3 times
		g2.setColor(Color.RED); g2.setStroke(new BasicStroke(5));
		drawRectangles(g2, image, templateCursor, maskCursor, 3);
		// show match intensity image for this template
		showMatchIntensity(image, templateCursor, maskCursor);
		// Now it's try finding the cursor without a mask.  it will get confused when the background is black
		g2.setColor(Color.BLUE); g2.setStroke(new BasicStroke(2));
		drawRectangles(g2, image, templateCursor, null, 3);
		// Now it searches for a specific icon for which there is only one match
		g2.setColor(Color.ORANGE); g2.setStroke(new BasicStroke(3));
		drawRectangles(g2, image, templatePaint, null, 1);
		ShowImages.showWindow(output, "Found Matches",true);
	 * Helper function will is finds matches and displays the results as colored rectangles
	private static void drawRectangles(Graphics2D g2,
									   GrayF32 image, GrayF32 template, GrayF32 mask,
									   int expectedMatches) {
		List<Match> found = findMatches(image, template, mask, expectedMatches);
		int r = 2;
		int w = template.width + 2 * r;
		int h = template.height + 2 * r;
		for (Match m : found) {
			System.out.println("Match "+m.x+" "+m.y+"    score "+m.score);
			// this demonstrates how to filter out false positives
			// the meaning of score will depend on the template technique
//			if( m.score < -1000 )  // This line is commented out for demonstration purposes
//				continue;
			// the return point is the template's top left corner
			int x0 = m.x - r;
			int y0 = m.y - r;
			int x1 = x0 + w;
			int y1 = y0 + h;
			g2.drawLine(x0, y0, x1, y0);
			g2.drawLine(x1, y0, x1, y1);
			g2.drawLine(x1, y1, x0, y1);
			g2.drawLine(x0, y1, x0, y0);