Difference between revisions of "Example Template Matching"
(Updated for v0.16) |
m |
||
(6 intermediate revisions by the same user not shown) | |||
Line 1: | Line 1: | ||
<center> | <center> | ||
<gallery widths=250px heights=200px> | <gallery widths=250px heights=200px> | ||
File:Example_template_matches.jpg | Found matches for | File:Example_template_matches.jpg | Found matches for the cursor with and without a mask. | ||
File:Example_template_intensity.jpg | Match intensity for a template. | File:Example_template_intensity.jpg | Match intensity for a template. | ||
</gallery> | </gallery> | ||
</center> | </center> | ||
Template matching | 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. | 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: | Example Code: | ||
* [https://github.com/lessthanoptimal/BoofCV/blob/v0. | * [https://github.com/lessthanoptimal/BoofCV/blob/v0.25/examples/src/boofcv/examples/features/ExampleTemplateMatching.java ExampleTemplateMatching.java] | ||
Concepts: | Concepts: | ||
Line 21: | Line 21: | ||
/** | /** | ||
* Example of how to find objects inside an image using template matching. Template matching works | * 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. | * 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 | * @author Peter Abeles | ||
Line 32: | Line 33: | ||
* @param image Image being searched | * @param image Image being searched | ||
* @param template Template being looked for | * @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 | * @param expectedMatches Number of expected matches it hopes to find | ||
* @return List of match location and scores | * @return List of match location and scores | ||
*/ | */ | ||
private static List<Match> findMatches( | private static List<Match> findMatches(GrayF32 image, GrayF32 template, GrayF32 mask, | ||
int expectedMatches) { | int expectedMatches) { | ||
// create template matcher. | // create template matcher. | ||
TemplateMatching< | TemplateMatching<GrayF32> matcher = | ||
FactoryTemplateMatching.createMatcher(TemplateScoreType.SUM_DIFF_SQ, | FactoryTemplateMatching.createMatcher(TemplateScoreType.SUM_DIFF_SQ, GrayF32.class); | ||
// Find the points which match the template the best | // Find the points which match the template the best | ||
matcher.setTemplate(template, expectedMatches); | matcher.setImage(image); | ||
matcher.process( | matcher.setTemplate(template, mask,expectedMatches); | ||
matcher.process(); | |||
return matcher.getResults().toList(); | return matcher.getResults().toList(); | ||
Line 53: | Line 56: | ||
* a better match to the template. | * a better match to the template. | ||
*/ | */ | ||
public static void showMatchIntensity( | public static void showMatchIntensity(GrayF32 image, GrayF32 template, GrayF32 mask) { | ||
// create algorithm for computing intensity image | // create algorithm for computing intensity image | ||
TemplateMatchingIntensity< | TemplateMatchingIntensity<GrayF32> matchIntensity = | ||
FactoryTemplateMatching.createIntensity(TemplateScoreType.SUM_DIFF_SQ, | FactoryTemplateMatching.createIntensity(TemplateScoreType.SUM_DIFF_SQ, GrayF32.class); | ||
// apply the template to the image | // apply the template to the image | ||
matchIntensity.process( | matchIntensity.setInputImage(image); | ||
matchIntensity.process(template, mask); | |||
// get the results | // get the results | ||
GrayF32 intensity = matchIntensity.getIntensity(); | |||
// adjust the intensity image so that white indicates a good match and black a poor match | // adjust the intensity image so that white indicates a good match and black a poor match | ||
Line 76: | Line 80: | ||
BufferedImage output = new BufferedImage(image.width, image.height, BufferedImage.TYPE_INT_BGR); | BufferedImage output = new BufferedImage(image.width, image.height, BufferedImage.TYPE_INT_BGR); | ||
VisualizeImageData.grayMagnitude(intensity, output, -1); | VisualizeImageData.grayMagnitude(intensity, output, -1); | ||
ShowImages.showWindow(output, "Match Intensity"); | ShowImages.showWindow(output, "Match Intensity", true); | ||
} | } | ||
Line 82: | Line 86: | ||
// Load image and templates | // Load image and templates | ||
String directory = " | 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 | // create output image to show results | ||
Line 94: | Line 98: | ||
Graphics2D g2 = output.createGraphics(); | 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)); | |||
g2.setColor(Color.RED); | drawRectangles(g2, image, templateCursor, maskCursor, 3); | ||
drawRectangles(g2, image, | |||
// show match intensity image for this template | // show match intensity image for this template | ||
showMatchIntensity(image, | 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 | // Now it searches for a specific icon for which there is only one match | ||
g2.setColor(Color. | g2.setColor(Color.ORANGE); g2.setStroke(new BasicStroke(3)); | ||
drawRectangles(g2, image, templatePaint, null, 1); | |||
drawRectangles(g2, image, | |||
ShowImages.showWindow(output, "Found Matches"); | ShowImages.showWindow(output, "Found Matches",true); | ||
} | } | ||
Line 119: | Line 119: | ||
*/ | */ | ||
private static void drawRectangles(Graphics2D g2, | private static void drawRectangles(Graphics2D g2, | ||
GrayF32 image, GrayF32 template, GrayF32 mask, | |||
int expectedMatches) { | int expectedMatches) { | ||
List<Match> found = findMatches(image, template, expectedMatches); | List<Match> found = findMatches(image, template, mask, expectedMatches); | ||
int r = 2; | int r = 2; | ||
Line 127: | Line 127: | ||
int h = template.height + 2 * r; | int h = template.height + 2 * r; | ||
for (Match m : found) { | 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 | // the return point is the template's top left corner | ||
int x0 = m.x - r; | int x0 = m.x - r; |
Revision as of 15:40, 1 December 2016
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:
Concepts:
- 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.setImage(image);
matcher.setTemplate(template, mask,expectedMatches);
matcher.process();
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.setInputImage(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);
}
}
}