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BoofCV is an open source Java library for real-time computer vision and robotics applications.
There is a newer version: 1.1.7
/*
 * Copyright (c) 2011-2019, 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.alg.feature.detdesc;

import boofcv.abst.feature.orientation.OrientationIntegral;
import boofcv.alg.feature.describe.DescribePointSurfPlanar;
import boofcv.alg.feature.detect.interest.FastHessianFeatureDetector;
import boofcv.struct.feature.BrightFeature;
import boofcv.struct.feature.ScalePoint;
import boofcv.struct.feature.SurfFeatureQueue;
import boofcv.struct.image.ImageGray;
import boofcv.struct.image.Planar;
import georegression.struct.point.Point2D_F64;
import org.ddogleg.struct.GrowQueue_F64;

import java.util.List;

/**
 * Computes a color SURF descriptor from a {@link Planar} image.  Features are detected,
 * orientation estimated, and laplacian sign computed using a gray scale image.  The gray scale image is found by
 * computing the average across all bands for each pixel.  A descriptor is computed inside band individually
 * and stored in a descriptor which is N*length long.  N = number of bands and length = number of
 * elements in normal descriptor.
 *
 * @see boofcv.alg.feature.describe.DescribePointSurfPlanar
 *
 * @param  Type of integral image
 *
 * @author Peter Abeles
 */
public class DetectDescribeSurfPlanar>
{
	// SURF algorithms
	protected FastHessianFeatureDetector detector;
	protected OrientationIntegral orientation;
	protected DescribePointSurfPlanar describe;


	// storage for computed features
	protected SurfFeatureQueue descriptions;
	// detected scale points
	protected List foundPoints;
	// orientation of features
	protected GrowQueue_F64 featureAngles = new GrowQueue_F64(10);

	public DetectDescribeSurfPlanar(FastHessianFeatureDetector detector,
									OrientationIntegral orientation,
									DescribePointSurfPlanar describe )
	{
		this.detector = detector;
		this.orientation = orientation;
		this.describe = describe;

		descriptions = new SurfFeatureQueue(describe.getDescriptorLength());
	}

	public BrightFeature createDescription() {
		return describe.createDescription();
	}

	public BrightFeature getDescription(int index) {
		return descriptions.get(index);
	}

	/**
	 * Detects and describes features inside provide images.  All images are integral images.
	 *
	 * @param grayII Gray-scale integral image
	 * @param colorII Color integral image
	 */
	public void detect( II grayII , Planar colorII ) {

		descriptions.reset();
		featureAngles.reset();

		// detect features
		detector.detect(grayII);

		// describe the found interest points
		foundPoints = detector.getFoundPoints();

		descriptions.resize(foundPoints.size());
		featureAngles.resize(foundPoints.size());

		describe(grayII, colorII);
	}

	protected void describe(II grayII, Planar colorII) {
		orientation.setImage(grayII);
		describe.setImage(grayII,colorII);
		for( int i = 0; i < foundPoints.size(); i++ ) {
			ScalePoint p = foundPoints.get(i);
			orientation.setObjectRadius(p.scale);
			double angle = orientation.compute(p.x,p.y);

			describe.describe(p.x, p.y, angle, p.scale, descriptions.get(i));

			featureAngles.set(i,angle);
		}
	}

	public DescribePointSurfPlanar getDescribe() {
		return describe;
	}

	public int getNumberOfFeatures() {
		return foundPoints.size();
	}

	public Point2D_F64 getLocation(int featureIndex) {
		return foundPoints.get(featureIndex);
	}

	public double getRadius(int featureIndex) {
		return foundPoints.get(featureIndex).scale;
	}

	public double getOrientation(int featureIndex) {
		return featureAngles.get(featureIndex);
	}
}