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BoofCV is an open source Java library for real-time computer vision and robotics applications.
/*
* Copyright (c) 2011-2013, 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.feature.tracker;
import boofcv.abst.feature.describe.DescribeRegionPoint;
import boofcv.alg.feature.UtilFeature;
import boofcv.alg.feature.detect.interest.EasyGeneralFeatureDetector;
import boofcv.struct.QueueCorner;
import boofcv.struct.feature.TupleDesc;
import boofcv.struct.image.ImageSingleBand;
import georegression.struct.point.Point2D_F64;
import georegression.struct.point.Point2D_I16;
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
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.class,true);
public DdaManagerGeneralPoint(EasyGeneralFeatureDetector detector,
DescribeRegionPoint describe,
double scale) {
this.detector = detector;
this.describe = describe;
this.scale = scale;
descriptors = UtilFeature.createQueue(describe,100);
}
@Override
public void detectFeatures(I input, FastQueue locDst, FastQueue featDst) {
// detect features in the image
detector.detect(input,null);
describe.setImage(input);
QueueCorner found = detector.getMaximums();
// compute descriptors and populate results list
descriptors.reset();
locations.reset();
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();
}
}