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BoofCV is an open source Java library for real-time computer vision and robotics applications.
There is a newer version: 1.1.6
/*
 * Copyright (c) 2021, Peter Abeles. All Rights Reserved.
 *
 * This file is part of BoofCV (http://boofcv.org).
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package boofcv.examples.features;

import boofcv.abst.feature.associate.AssociateDescription;
import boofcv.abst.feature.associate.ScoreAssociation;
import boofcv.abst.feature.detdesc.DetectDescribePoint;
import boofcv.abst.feature.detect.interest.ConfigFastHessian;
import boofcv.alg.descriptor.UtilFeature;
import boofcv.factory.feature.associate.ConfigAssociateGreedy;
import boofcv.factory.feature.associate.FactoryAssociation;
import boofcv.factory.feature.detdesc.FactoryDetectDescribe;
import boofcv.gui.feature.AssociationPanel;
import boofcv.gui.image.ShowImages;
import boofcv.io.UtilIO;
import boofcv.io.image.ConvertBufferedImage;
import boofcv.io.image.UtilImageIO;
import boofcv.struct.feature.TupleDesc;
import boofcv.struct.image.GrayF32;
import boofcv.struct.image.ImageGray;
import georegression.struct.point.Point2D_F64;
import org.ddogleg.struct.DogArray;

import java.awt.image.BufferedImage;
import java.util.ArrayList;
import java.util.List;

/**
 * After interest points have been detected in two images the next step is to associate the two
 * sets of images so that the relationship can be found. This is done by computing descriptors for
 * each detected feature and associating them together. In the code below abstracted interfaces are
 * used to allow different algorithms to be easily used. The cost of this abstraction is that detector/descriptor
 * specific information is thrown away, potentially slowing down or degrading performance.
 *
 * @author Peter Abeles
 */
public class ExampleAssociatePoints, TD extends TupleDesc> {

	// algorithm used to detect and describe interest points
	DetectDescribePoint detDesc;
	// Associated descriptions together by minimizing an error metric
	AssociateDescription associate;

	// location of interest points
	public List pointsA;
	public List pointsB;

	Class imageType;

	public ExampleAssociatePoints( DetectDescribePoint detDesc,
								   AssociateDescription associate,
								   Class imageType ) {
		this.detDesc = detDesc;
		this.associate = associate;
		this.imageType = imageType;
	}

	/**
	 * Detect and associate point features in the two images. Display the results.
	 */
	public void associate( BufferedImage imageA, BufferedImage imageB ) {
		T inputA = ConvertBufferedImage.convertFromSingle(imageA, null, imageType);
		T inputB = ConvertBufferedImage.convertFromSingle(imageB, null, imageType);

		// stores the location of detected interest points
		pointsA = new ArrayList<>();
		pointsB = new ArrayList<>();

		// stores the description of detected interest points
		DogArray descA = UtilFeature.createArray(detDesc, 100);
		DogArray descB = UtilFeature.createArray(detDesc, 100);

		// describe each image using interest points
		describeImage(inputA, pointsA, descA);
		describeImage(inputB, pointsB, descB);

		// Associate features between the two images
		associate.setSource(descA);
		associate.setDestination(descB);
		associate.associate();

		// display the results
		AssociationPanel panel = new AssociationPanel(20);
		panel.setAssociation(pointsA, pointsB, associate.getMatches());
		panel.setImages(imageA, imageB);

		ShowImages.showWindow(panel, "Associated Features", true);
	}

	/**
	 * Detects features inside the two images and computes descriptions at those points.
	 */
	private void describeImage( T input, List points, DogArray descs ) {
		detDesc.detect(input);

		for (int i = 0; i < detDesc.getNumberOfFeatures(); i++) {
			points.add(detDesc.getLocation(i).copy());
			descs.grow().setTo(detDesc.getDescription(i));
		}
	}

	public static void main( String[] args ) {

		Class imageType = GrayF32.class;
//		Class imageType = GrayU8.class;

		// select which algorithms to use
		DetectDescribePoint detDesc = FactoryDetectDescribe.
				surfStable(new ConfigFastHessian(1, 2, 300, 1, 9, 4, 4), null, null, imageType);
//				sift(new ConfigCompleteSift(0,5,600));

		ScoreAssociation scorer = FactoryAssociation.defaultScore(detDesc.getDescriptionType());
		AssociateDescription associate = FactoryAssociation.greedy(new ConfigAssociateGreedy(true), scorer);

		// load and match images
		ExampleAssociatePoints app = new ExampleAssociatePoints(detDesc, associate, imageType);

		BufferedImage imageA = UtilImageIO.loadImageNotNull(UtilIO.pathExample("stitch/kayak_01.jpg"));
		BufferedImage imageB = UtilImageIO.loadImageNotNull(UtilIO.pathExample("stitch/kayak_03.jpg"));

		app.associate(imageA, imageB);
	}
}