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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.tracker.meanshift;
import boofcv.alg.interpolate.InterpolatePixelMB;
import boofcv.misc.BoofMiscOps;
import boofcv.struct.RectangleRotate_F32;
import boofcv.struct.image.ImageBase;
import georegression.struct.point.Point2D_F32;
import org.ddogleg.stats.UtilGaussian;
import java.util.ArrayList;
import java.util.List;
/**
* Computes a local histogram weighted using a Gaussian function. The weighting function is shaped using a rotated
* rectangle, where the sigma along each axis is set by the rectangle's width and height. For use with
* {@link TrackerMeanShiftComaniciu2003}.
*
* The histogram is stored in a row major format.
*
* @author Peter Abeles
**/
public class LocalWeightedHistogramRotRect> {
// Interpolation function
private InterpolatePixelMB interpolate;
// storage for interpolated pixel
private float value[];
// maximum value of a pixel in any band
protected float maxPixelValue;
// number of binds in the histogram
private int numBins;
// cosine and sine of rotation rectangle angle
protected float c,s;
// output of conversion from region to image coordinates
public float imageX,imageY;
// which element in the histogram does a coordinate in the grid belong to
protected int sampleHistIndex[];
// storage for sample weights and the histogram
protected float weights[];
protected float histogram[];
// list of sample points. in square coordinates. where 0.5 is 1/2 the width or height
protected List samplePts = new ArrayList<>();
/**
* Configures histogram calculation.
*
* @param numSamples Number of points it samples along each axis of the rectangle.
* @param numSigmas Number of standard deviations away the sides will be from the center. Try 3
* @param numHistogramBins Number of bins in the histogram
* @param numBands Number of bands in the input image
* @param maxPixelValue Maximum value of a pixel across all bands
* @param interpolate Function used to interpolate the image
*/
public LocalWeightedHistogramRotRect(int numSamples, double numSigmas,
int numHistogramBins, int numBands,
float maxPixelValue,
InterpolatePixelMB interpolate) {
this.numBins = numHistogramBins;
this.maxPixelValue = maxPixelValue*1.0001f; // avoid the possibility exceeding the max histogram size
this.interpolate = interpolate;
sampleHistIndex = new int[ numSamples*numSamples ];
histogram = new float[ (int)Math.pow(numHistogramBins,numBands) ];
value = new float[ numBands ];
createSamplePoints(numSamples);
computeWeights(numSamples, numSigmas);
}
/**
* compute the weights by convolving 1D gaussian kernel
*/
protected void computeWeights(int numSamples, double numSigmas) {
weights = new float[ numSamples*numSamples ];
float w[] = new float[ numSamples ];
for( int i = 0; i < numSamples; i++ ) {
float x = i/(float)(numSamples-1);
w[i] = (float) UtilGaussian.computePDF(0, 1, 2f*numSigmas * (x - 0.5f));
}
for( int y = 0; y < numSamples; y++ ) {
for( int x = 0; x < numSamples; x++ ) {
weights[y*numSamples + x] = w[y]*w[x];
}
}
}
/**
* create the list of points in square coordinates that it will sample. values will range
* from -0.5 to 0.5 along each axis.
*/
protected void createSamplePoints(int numSamples) {
for( int y = 0; y < numSamples; y++ ) {
float regionY = (y/(numSamples-1.0f) - 0.5f);
for( int x = 0; x < numSamples; x++ ) {
float regionX = (x/(numSamples-1.0f) - 0.5f);
samplePts.add( new Point2D_F32(regionX,regionY));
}
}
}
/**
* Computes the histogram inside the specified region. Results are returned by calling {@link #getHistogram()}.
*
* @param image Input image
* @param region Region which is to be sampled
*/
public void computeHistogram( T image , RectangleRotate_F32 region ) {
interpolate.setImage(image);
c = (float)Math.cos( region.theta );
s = (float)Math.sin( region.theta );
for( int i = 0; i < histogram.length; i++ ) {
histogram[i] = 0;
}
// if it is entirely inside the image, interpolate using a faster technique
if( isInFastBounds(region) ) {
computeHistogramInside( region);
} else {
computeHistogramBorder(image, region);
}
normalizeHistogram();
}
/**
* Computes the histogram quickly inside the image
*/
protected void computeHistogramInside( RectangleRotate_F32 region) {
for( int i = 0; i < samplePts.size(); i++ ) {
Point2D_F32 p = samplePts.get(i);
squareToImageSample(p.x, p.y, region);
interpolate.get_fast(imageX,imageY,value);
int indexHistogram = computeHistogramBin(value);
sampleHistIndex[ i ] = indexHistogram;
histogram[indexHistogram] += weights[i];
}
}
/**
* Computes the histogram and skips pixels which are outside the image border
*/
protected void computeHistogramBorder(T image, RectangleRotate_F32 region) {
for( int i = 0; i < samplePts.size(); i++ ) {
Point2D_F32 p = samplePts.get(i);
squareToImageSample(p.x, p.y, region);
// make sure its inside the image
if( !BoofMiscOps.checkInside(image, imageX, imageY)) {
sampleHistIndex[ i ] = -1;
} else {
// use the slower interpolation which can handle the border
interpolate.get(imageX, imageY, value);
int indexHistogram = computeHistogramBin(value);
sampleHistIndex[ i ] = indexHistogram;
histogram[indexHistogram] += weights[i];
}
}
}
/**
* Given the value of a pixel, compute which bin in the histogram it belongs in
*/
protected int computeHistogramBin( float value[] ) {
int indexHistogram = 0;
int binStride = 1;
for( int bandIndex = 0; bandIndex < value.length; bandIndex++ ) {
int bin = (int)(numBins*value[bandIndex]/maxPixelValue);
indexHistogram += bin*binStride;
binStride *= numBins;
}
return indexHistogram;
}
/**
* Checks to see if the region can be sampled using the fast algorithm
*/
protected boolean isInFastBounds(RectangleRotate_F32 region) {
squareToImageSample(-0.5f, -0.5f, region);
if( !interpolate.isInFastBounds(imageX, imageY))
return false;
squareToImageSample(-0.5f, 0.5f, region);
if( !interpolate.isInFastBounds(imageX, imageY))
return false;
squareToImageSample(0.5f, 0.5f, region);
if( !interpolate.isInFastBounds(imageX, imageY))
return false;
squareToImageSample(0.5f, -0.5f, region);
if( !interpolate.isInFastBounds(imageX, imageY))
return false;
return true;
}
protected void normalizeHistogram() {
float total = 0;
for( int i = 0; i < histogram.length; i++ ) {
total += histogram[i];
}
for( int i = 0; i < histogram.length; i++ ) {
histogram[i] /= total;
}
}
/**
* Converts a point from square coordinates into image coordinates
*/
protected void squareToImageSample(float x, float y, RectangleRotate_F32 region) {
// -1 because it starts counting at 0. otherwise width+1 samples are made
x *= region.width-1;
y *= region.height-1;
imageX = x*c - y*s + region.cx;
imageY = x*s + y*c + region.cy;
}
public float[] getHistogram() {
return histogram;
}
public int[] getSampleHistIndex() {
return sampleHistIndex;
}
public List getSamplePts() {
return samplePts;
}
}
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