boofcv.alg.feature.detect.interest.UnrollSiftScaleSpaceGradient 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
Show all versions of boofcv-feature Show documentation
BoofCV is an open source Java library for real-time computer vision and robotics applications.
/*
* Copyright (c) 2011-2017, 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.detect.interest;
import boofcv.abst.filter.derivative.ImageGradient;
import boofcv.factory.filter.derivative.FactoryDerivative;
import boofcv.struct.image.GrayF32;
import java.util.ArrayList;
import java.util.List;
/**
* Precomputes the gradient for all scales in the scale-space and saves them in a list. Since it saves the entire
* scale space it can take up a bit of memory, but allows quick random look up of images.
*
* @author Peter Abeles
*/
public class UnrollSiftScaleSpaceGradient {
// input scale space
SiftScaleSpace scaleSpace;
// scale images that are using
List usedScales = new ArrayList<>();
// storage for all possible scales
List allScales = new ArrayList<>();
// used to compute the image gradient
ImageGradient gradient = FactoryDerivative.three(GrayF32.class,null);
public UnrollSiftScaleSpaceGradient(SiftScaleSpace scaleSpace) {
this.scaleSpace = scaleSpace;
// create one image for each scale to minimize memory being created/destroyed
int numScales = scaleSpace.getNumScales()*scaleSpace.getTotalOctaves();
for (int i = 0; i < numScales; i++) {
allScales.add( new ImageScale());
}
}
/**
* Sets the input image. Scale-space is computed and unrolled from this image
* @param image
*/
public void setImage(GrayF32 image) {
scaleSpace.initialize(image);
usedScales.clear();
do {
for (int i = 0; i < scaleSpace.getNumScales(); i++) {
GrayF32 scaleImage = scaleSpace.getImageScale(i);
double sigma = scaleSpace.computeSigmaScale(i);
double pixelCurrentToInput = scaleSpace.pixelScaleCurrentToInput();
ImageScale scale = allScales.get(usedScales.size());
scale.derivX.reshape(scaleImage.width,scaleImage.height);
scale.derivY.reshape(scaleImage.width,scaleImage.height);
gradient.process(scaleImage,scale.derivX,scale.derivY);
scale.imageToInput = pixelCurrentToInput;
scale.sigma = sigma;
usedScales.add(scale);
}
} while( scaleSpace.computeNextOctave() );
}
/**
* Looks up the image which is closest specified sigma
*/
public ImageScale lookup( double sigma ) {
ImageScale best = null;
double bestValue = Double.MAX_VALUE;
for (int i = 0; i < usedScales.size(); i++) {
ImageScale image = usedScales.get(i);
double difference = Math.abs(sigma-image.sigma);
if( difference < bestValue ) {
bestValue = difference;
best = image;
}
}
return best;
}
public static class ImageScale {
public GrayF32 derivX = new GrayF32(1,1);
public GrayF32 derivY = new GrayF32(1,1);
public double imageToInput;
public double sigma;
}
}
© 2015 - 2025 Weber Informatics LLC | Privacy Policy