boofcv.abst.tracker.DdaManagerGeneralPoint Maven / Gradle / Ivy

Go to download
Show more of this group Show more artifacts with this name
Show all versions of boofcv-feature Show documentation
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) 2011-2020, 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.abst.tracker;

import boofcv.abst.feature.describe.DescribeRegionPoint;
import boofcv.alg.descriptor.UtilFeature;
import boofcv.alg.feature.detect.interest.EasyGeneralFeatureDetector;
import boofcv.struct.QueueCorner;
import boofcv.struct.feature.TupleDesc;
import boofcv.struct.image.ImageGray;
import georegression.struct.point.Point2D_F64;
import georegression.struct.point.Point2D_I16;
import org.ddogleg.struct.FastArray;
import org.ddogleg.struct.FastQueue;

/**
 * Detects simple features (corners and blobs) whose location if fully described by a pixel coordinate.  Unlike more
 * generalized implementations, previously detected features can be excluded automatically when detecting new
 * features.
 *
 * @author Peter Abeles
 */
public class DdaManagerGeneralPoint, D extends ImageGray, Desc extends TupleDesc>
		implements DdaFeatureManager {

	// feature detector
	private EasyGeneralFeatureDetector detector;
	// feature descriptor
	private DescribeRegionPoint describe;
	// scale that features should be created at
	private double scale;

	// storage for descriptors
	private FastQueue descriptors;
	private FastQueue locations = new FastQueue<>(100, Point2D_F64::new);

	int numSets;

	public DdaManagerGeneralPoint(EasyGeneralFeatureDetector detector,
								  DescribeRegionPoint describe,
								  double scale) {
		this.detector = detector;
		this.describe = describe;
		this.scale = scale;

		numSets = detector.getDetector().isDetectMinimums() ? 1 : 0;
		numSets += detector.getDetector().isDetectMaximums() ? 1 : 0;

		descriptors = UtilFeature.createQueue(describe,100);
	}

	@Override
	public void detectFeatures(I input) {
		// detect features in the image
		detector.detect(input,null);
		describe.setImage(input);

		descriptors.reset();
		locations.reset();
	}

	@Override
	public void getFeatures(int set, FastArray locations, FastArray descriptions) {
		if( numSets == 2 ) {
			if( set == 0 ) {
				computeDescriptions(locations, descriptions, detector.getMinimums());
			} else if( set == 1 ) {
				computeDescriptions(locations, descriptions, detector.getMaximums());
			}
		} else if( detector.getDetector().isDetectMinimums() ) {
			computeDescriptions(locations, descriptions, detector.getMinimums());
		} else {
			computeDescriptions(locations, descriptions, detector.getMaximums());
		}
	}

	@Override
	public int getNumberOfSets() {
		return numSets;
	}

	private void computeDescriptions(FastArray locDst, FastArray featDst, QueueCorner found) {
		for( int i = 0; i < found.size; i++ ) {
			Point2D_I16 p = found.get(i);
			Desc desc = descriptors.grow();

			if( describe.process(p.x,p.y,0,scale,desc) ) {
				Point2D_F64 loc = locations.grow();
				loc.set(p.x,p.y);
				describe.process(loc.x,loc.y,0,scale,desc);
				featDst.add(desc);
				locDst.add( loc );
			} else {
				descriptors.removeTail();
			}
		}
	}

	@Override
	public Desc createDescription() {
		return describe.createDescription();
	}

	@Override
	public Class getDescriptionType() {
		return describe.getDescriptionType();
	}


}