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BoofCV is an open source Java library for real-time computer vision and robotics applications.
/*
* 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.alg.flow;
import boofcv.alg.tracker.klt.KltTrackFault;
import boofcv.alg.tracker.klt.KltTracker;
import boofcv.alg.tracker.klt.PyramidKltFeature;
import boofcv.alg.tracker.klt.PyramidKltTracker;
import boofcv.struct.flow.ImageFlow;
import boofcv.struct.image.ImageGray;
import boofcv.struct.pyramid.ImagePyramid;
import java.util.Arrays;
/**
* Computes the dense optical flow using {@link KltTracker}. A feature is computed from each pixel in the prev
* image and it is tracked into the curr image. The flow assigned to a pixel is the template with the lowest error
* which overlaps it. In other words, a pixel is assigned the flow with the lowest error with in 'radius' pixels
* of it. A pixel is marked as invalid if all tracks around the pixel fail.
*
* @author Peter Abeles
*/
public class DenseOpticalFlowKlt {
// Amount it adjusts the score for the center of a region.
// Visually this looks better, but only makes a small difference in benchmark performance
private static float MAGIC_ADJUSTMENT = 0.7f;
private PyramidKltTracker tracker;
private PyramidKltFeature feature;
// goodness of fit for each template
float scores[] = new float[1];
// size of template
private int regionRadius;
// image shape
private int width,height;
public DenseOpticalFlowKlt(PyramidKltTracker tracker , int numLayers , int radius ) {
this.tracker = tracker;
feature = new PyramidKltFeature(numLayers,radius);
this.regionRadius = radius;
}
public void process( ImagePyramid prev, D[] prevDerivX, D[] prevDerivY,
ImagePyramid curr , ImageFlow output ) {
this.width = output.width;
this.height = output.height;
// initialize and set the score for each pixel to be very high
int N = width*height;
if( scores.length < N)
scores = new float[N];
Arrays.fill(scores,0,N,Float.MAX_VALUE);
for (int i = 0; i < N; i++) {
output.data[i].markInvalid();
}
for( int y = 0; y < output.height; y++ ) {
for( int x = 0; x < output.width; x++ ) {
tracker.setImage(prev,prevDerivX,prevDerivY);
feature.setPosition(x,y);
if( tracker.setDescription(feature) ) {
// derivX and derivY are not used, but can't be null for setImage()
tracker.setImage(curr);
KltTrackFault fault = tracker.track(feature);
if( fault == KltTrackFault.SUCCESS ) {
float score = tracker.getError();
// bias the result to prefer the central template
scores[y*output.width+x] = score*MAGIC_ADJUSTMENT;
output.get(x,y).set(feature.x-x,feature.y-y);
// see if this flow should be assigned to any of its neighbors
checkNeighbors(x, y, score, feature.x-x,feature.y-y, output);
}
}
}
}
}
/**
* Examines every pixel inside the region centered at (cx,cy) to see if their optical flow has a worse
* score the one specified in 'flow'
*/
protected void checkNeighbors( int cx , int cy , float score , float flowX , float flowY , ImageFlow output ) {
int x0 = Math.max(0,cx-regionRadius);
int x1 = Math.min(output.width, cx + regionRadius + 1);
int y0 = Math.max(0,cy-regionRadius);
int y1 = Math.min(output.height, cy + regionRadius + 1);
for( int i = y0; i < y1; i++ ) {
int index = width*i + x0;
for( int j = x0; j < x1; j++ , index++ ) {
float s = scores[ index ];
ImageFlow.D f = output.data[index];
if( s > score ) {
f.set(flowX,flowY);
scores[index] = score;
} else if( s == score ) {
// Pick solution with the least motion when ambiguous
float m0 = f.x*f.x + f.y*f.y;
float m1 = flowX*flowX + flowY*flowY;
if( m1 < m0 ) {
f.set(flowX,flowY);
scores[index] = score;
}
}
}
}
}
}