
boofcv.abst.flow.FlowKlt_to_DenseOpticalFlow Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of feature Show documentation
Show all versions of feature Show documentation
BoofCV is an open source Java library for real-time computer vision and robotics applications.
The newest version!
/*
* Copyright (c) 2011-2016, 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.flow;
import boofcv.abst.filter.derivative.ImageGradient;
import boofcv.alg.flow.DenseOpticalFlowKlt;
import boofcv.alg.transform.pyramid.PyramidOps;
import boofcv.core.image.GeneralizedImageOps;
import boofcv.struct.flow.ImageFlow;
import boofcv.struct.image.ImageGray;
import boofcv.struct.image.ImageType;
import boofcv.struct.pyramid.ImagePyramid;
import java.lang.reflect.Array;
/**
* Wrapper around {@link DenseOpticalFlowKlt} for {@link DenseOpticalFlow}.
*
* @author Peter Abeles
*/
public class FlowKlt_to_DenseOpticalFlow
implements DenseOpticalFlow
{
DenseOpticalFlowKlt flowKlt;
ImageGradient gradient;
ImagePyramid pyramidSrc;
ImagePyramid pyramidDst;
D[] srcDerivX;
D[] srcDerivY;
ImageType imageType;
public FlowKlt_to_DenseOpticalFlow(DenseOpticalFlowKlt flowKlt,
ImageGradient gradient,
ImagePyramid pyramidSrc,
ImagePyramid pyramidDst,
Class inputType , Class derivType ) {
if( pyramidSrc.getNumLayers() != pyramidDst.getNumLayers() )
throw new IllegalArgumentException("Pyramids do not have the same number of layers!");
this.flowKlt = flowKlt;
this.gradient = gradient;
this.pyramidSrc = pyramidSrc;
this.pyramidDst = pyramidDst;
srcDerivX = (D[])Array.newInstance(derivType,pyramidSrc.getNumLayers());
srcDerivY = (D[])Array.newInstance(derivType,pyramidSrc.getNumLayers());
for( int i = 0; i < srcDerivX.length; i++ ) {
srcDerivX[i] = GeneralizedImageOps.createSingleBand(derivType,1,1);
srcDerivY[i] = GeneralizedImageOps.createSingleBand(derivType,1,1);
}
imageType = ImageType.single(inputType);
}
@Override
public void process(I source, I destination, ImageFlow flow) {
pyramidSrc.process(source);
pyramidDst.process(destination);
PyramidOps.reshapeOutput(pyramidSrc,srcDerivX);
PyramidOps.reshapeOutput(pyramidSrc,srcDerivY);
PyramidOps.gradient(pyramidSrc, gradient, srcDerivX,srcDerivY);
flowKlt.process(pyramidSrc,srcDerivX,srcDerivY,pyramidDst,flow);
}
@Override
public ImageType getInputType() {
return imageType;
}
}
© 2015 - 2025 Weber Informatics LLC | Privacy Policy