Example Color Histogram Lookup - Revision history

Peter at 23:15, 17 January 2022

2022-01-17T23:15:43Z

← Older revision		Revision as of 23:15, 17 January 2022
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	</center>		</center>

	Image color histograms can be treated as features. These features can then be used to look up similar images. The example below shows how different color spaces can be used to look up similar images from a data set of vacation photos. The animated image above show the results from a query.		Image color histograms can be treated as features. These features can then be used to look up similar images. The example below shows how different color spaces can be used to look up similar images from a data set of vacation photos. The animated image above show the results from a query.

	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.38/examples/src/main/java/boofcv/examples/recognition/ExampleColorHistogramLookup.java ExampleColorHistogramLookup.java ]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.40/examples/src/main/java/boofcv/examples/recognition/ExampleColorHistogramLookup.java ExampleColorHistogramLookup.java ]

	Concepts:		Concepts:
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	for (String path : images) {		for (String path : images) {
	BufferedImage buffered = UtilImageIO.~~loadImage~~(path);		BufferedImage buffered = UtilImageIO.loadImageNotNull(path);
	~~if (buffered == null) throw new RuntimeException("Can't load image!"~~);

	rgb.reshape(buffered.getWidth(), buffered.getHeight());		rgb.reshape(buffered.getWidth(), buffered.getHeight());
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	for (File f : images) {		for (File f : images) {
	BufferedImage buffered = UtilImageIO.~~loadImage~~(f.getPath());		BufferedImage buffered = UtilImageIO.loadImageNotNull(f.getPath());
	~~if (buffered == null) throw new RuntimeException("Can't load image!"~~);

	rgb.reshape(buffered.getWidth(), buffered.getHeight());		rgb.reshape(buffered.getWidth(), buffered.getHeight());
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	for (File f : images) {		for (File f : images) {
	BufferedImage buffered = UtilImageIO.~~loadImage~~(f.getPath());		BufferedImage buffered = UtilImageIO.loadImageNotNull(f.getPath());
	~~if (buffered == null) throw new RuntimeException("Can't load image!"~~);

	rgb.reshape(buffered.getWidth(), buffered.getHeight());		rgb.reshape(buffered.getWidth(), buffered.getHeight());
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	GrayU8 gray = new GrayU8(1, 1);		GrayU8 gray = new GrayU8(1, 1);
	for (File f : images) {		for (File f : images) {
	BufferedImage buffered = UtilImageIO.~~loadImage~~(f.getPath());		BufferedImage buffered = UtilImageIO.loadImageNotNull(f.getPath());
	~~if (buffered == null) throw new RuntimeException("Can't load image!"~~);

	gray.reshape(buffered.getWidth(), buffered.getHeight());		gray.reshape(buffered.getWidth(), buffered.getHeight());
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	// Add the target which the other images are being matched against		// Add the target which the other images are being matched against
	gui.addImage(UtilImageIO.~~loadImage~~(images.get(target)), "Target", ScaleOptions.ALL);		gui.addImage(UtilImageIO.loadImageNotNull(images.get(target)), "Target", ScaleOptions.ALL);

	// The results will be the 10 best matches, but their order can be arbitrary. For display purposes		// The results will be the 10 best matches, but their order can be arbitrary. For display purposes
	// it's better to do it from best fit to worst fit		// it's better to do it from best fit to worst fit
	Collections.sort(results.toList(), (Comparator~~<NnData>)~~( ~~o1, o2 ) -> {~~		Collections.sort(results.toList(), Comparator.comparingDouble(( NnData o ) -> o.distance));
	if (~~o1.distance < o2.distance~~)
	~~return~~ -1;
	~~else if (o1.distance~~ > o2.distance)
	~~return 1;~~
	~~else~~
	~~return 0;~~
	});

	// Add images to GUI -- first match is always the target image, so skip it		// Add images to GUI -- first match is always the target image, so skip it

Peter at 19:08, 12 July 2021

2021-07-12T19:08:39Z

Peter at 02:33, 22 December 2020

2020-12-22T02:33:47Z

← Older revision		Revision as of 02:33, 22 December 2020
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	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.36/examples/src/main/java/boofcv/examples/recognition/ExampleColorHistogramLookup.java ExampleColorHistogramLookup.java ]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.37/examples/src/main/java/boofcv/examples/recognition/ExampleColorHistogramLookup.java ExampleColorHistogramLookup.java ]

	Concepts:		Concepts:
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	* from hue and saturation only, which makes it lighting independent.		* from hue and saturation only, which makes it lighting independent.
	*/		*/
	public static List<double[]> coupledHueSat( List<String> images ) {		public static List<double[]> coupledHueSat( List<String> images ) {
	List<double[]> points = new ArrayList<>();		List<double[]> points = new ArrayList<>();

	Planar<GrayF32> rgb = new Planar<>(GrayF32.class,1,1,3);		Planar<GrayF32> rgb = new Planar<>(GrayF32.class, 1, 1, 3);
	Planar<GrayF32> hsv = new Planar<>(GrayF32.class,1,1,3);		Planar<GrayF32> hsv = new Planar<>(GrayF32.class, 1, 1, 3);

	for( String path : images ) {		for (String path : images) {
	BufferedImage buffered = UtilImageIO.loadImage(path);		BufferedImage buffered = UtilImageIO.loadImage(path);
	if( buffered == null ) throw new RuntimeException("Can't load image!");		if (buffered == null) throw new RuntimeException("Can't load image!");

	rgb.reshape(buffered.getWidth(), buffered.getHeight());		rgb.reshape(buffered.getWidth(), buffered.getHeight());
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	ColorHsv.rgbToHsv(rgb, hsv);		ColorHsv.rgbToHsv(rgb, hsv);

	Planar<GrayF32> hs = hsv.partialSpectrum(0,1);		Planar<GrayF32> hs = hsv.partialSpectrum(0, 1);

	// The number of bins is an important parameter. Try adjusting it		// The number of bins is an important parameter. Try adjusting it
	Histogram_F64 histogram = new Histogram_F64(12,12);		Histogram_F64 histogram = new Histogram_F64(12, 12);
	histogram.setRange(0, 0, 2.0*Math.PI); // range of hue is from 0 to 2PI		histogram.setRange(0, 0, 2.0*Math.PI); // range of hue is from 0 to 2PI
	histogram.setRange(1, 0, 1.0); // range of saturation is from 0 to 1		histogram.setRange(1, 0, 1.0); // range of saturation is from 0 to 1

	// Compute the histogram		// Compute the histogram
	GHistogramFeatureOps.histogram(hs,histogram);		GHistogramFeatureOps.histogram(hs, histogram);

	UtilFeature.normalizeL2(histogram); // normalize so that image size doesn't matter		UtilFeature.normalizeL2(histogram); // normalize so that image size doesn't matter
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	* worse results since the basic assumption that hue and saturation are decoupled is most of the time false.		* worse results since the basic assumption that hue and saturation are decoupled is most of the time false.
	*/		*/
	public static List<double[]> independentHueSat( List<File> images ) {		public static List<double[]> independentHueSat( List<File> images ) {
	List<double[]> points = new ArrayList<>();		List<double[]> points = new ArrayList<>();

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	List<TupleDesc_F64> histogramList = new ArrayList<>();		List<TupleDesc_F64> histogramList = new ArrayList<>();
	histogramList.add(histogramHue); histogramList.add(histogramValue);		histogramList.add(histogramHue);
			histogramList.add(histogramValue);

	Planar<GrayF32> rgb = new Planar<>(GrayF32.class,1,1,3);		Planar<GrayF32> rgb = new Planar<>(GrayF32.class, 1, 1, 3);
	Planar<GrayF32> hsv = new Planar<>(GrayF32.class,1,1,3);		Planar<GrayF32> hsv = new Planar<>(GrayF32.class, 1, 1, 3);

	for( File f : images ) {		for (File f : images) {
	BufferedImage buffered = UtilImageIO.loadImage(f.getPath());		BufferedImage buffered = UtilImageIO.loadImage(f.getPath());
	if( buffered == null ) throw new RuntimeException("Can't load image!");		if (buffered == null) throw new RuntimeException("Can't load image!");

	rgb.reshape(buffered.getWidth(), buffered.getHeight());		rgb.reshape(buffered.getWidth(), buffered.getHeight());
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	ColorHsv.rgbToHsv(rgb, hsv);		ColorHsv.rgbToHsv(rgb, hsv);

	GHistogramFeatureOps.histogram(hsv.getBand(0), 0, 2*Math.PI,histogramHue);		GHistogramFeatureOps.histogram(hsv.getBand(0), 0, 2*Math.PI, histogramHue);
	GHistogramFeatureOps.histogram(hsv.getBand(1), 0, 1, histogramValue);		GHistogramFeatureOps.histogram(hsv.getBand(1), 0, 1, histogramValue);

	// need to combine them into a single descriptor for processing later on		// need to combine them into a single descriptor for processing later on
	TupleDesc_F64 imageHist = UtilFeature.combine(histogramList,null);		TupleDesc_F64 imageHist = UtilFeature.combine(histogramList, null);

	UtilFeature.normalizeL2(imageHist); // normalize so that image size doesn't matter		UtilFeature.normalizeL2(imageHist); // normalize so that image size doesn't matter
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	List<double[]> points = new ArrayList<>();		List<double[]> points = new ArrayList<>();

	Planar<GrayF32> rgb = new Planar<>(GrayF32.class,1,1,3);		Planar<GrayF32> rgb = new Planar<>(GrayF32.class, 1, 1, 3);

	for( File f : images ) {		for (File f : images) {
	BufferedImage buffered = UtilImageIO.loadImage(f.getPath());		BufferedImage buffered = UtilImageIO.loadImage(f.getPath());
	if( buffered == null ) throw new RuntimeException("Can't load image!");		if (buffered == null) throw new RuntimeException("Can't load image!");

	rgb.reshape(buffered.getWidth(), buffered.getHeight());		rgb.reshape(buffered.getWidth(), buffered.getHeight());
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	// The number of bins is an important parameter. Try adjusting it		// The number of bins is an important parameter. Try adjusting it
	Histogram_F64 histogram = new Histogram_F64(10,10,10);		Histogram_F64 histogram = new Histogram_F64(10, 10, 10);
	histogram.setRange(0, 0, 255);		histogram.setRange(0, 0, 255);
	histogram.setRange(1, 0, 255);		histogram.setRange(1, 0, 255);
	histogram.setRange(2, 0, 255);		histogram.setRange(2, 0, 255);

	GHistogramFeatureOps.histogram(rgb,histogram);		GHistogramFeatureOps.histogram(rgb, histogram);

	UtilFeature.normalizeL2(histogram); // normalize so that image size doesn't matter		UtilFeature.normalizeL2(histogram); // normalize so that image size doesn't matter
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	List<double[]> points = new ArrayList<>();		List<double[]> points = new ArrayList<>();

	GrayU8 gray = new GrayU8(1,1);		GrayU8 gray = new GrayU8(1, 1);
	for( File f : images ) {		for (File f : images) {
	BufferedImage buffered = UtilImageIO.loadImage(f.getPath());		BufferedImage buffered = UtilImageIO.loadImage(f.getPath());
	if( buffered == null ) throw new RuntimeException("Can't load image!");		if (buffered == null) throw new RuntimeException("Can't load image!");

	gray.reshape(buffered.getWidth(), buffered.getHeight());		gray.reshape(buffered.getWidth(), buffered.getHeight());
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	}		}

	public static void main(String[] args) {		public static void main( String[] args ) {

	String imagePath = UtilIO.pathExample("recognition/vacation");		String imagePath = UtilIO.pathExample("recognition/vacation");
	List<String> images = UtilIO.listByPrefix(imagePath,null,".jpg");		List<String> images = UtilIO.listByPrefix(imagePath, null, ".jpg");
	Collections.sort(images);		Collections.sort(images);

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	NearestNeighbor<double[]> nn = FactoryNearestNeighbor.exhaustive(new KdTreeEuclideanSq_F64(targetPoint.length));		NearestNeighbor<double[]> nn = FactoryNearestNeighbor.exhaustive(new KdTreeEuclideanSq_F64(targetPoint.length));
	NearestNeighbor.Search<double[]> search = nn.createSearch();		NearestNeighbor.Search<double[]> search = nn.createSearch();
	~~FastQueue~~<NnData<double[]>> results = new ~~FastQueue~~(NnData::new);		DogArray<NnData<double[]>> results = new DogArray(NnData::new);

	nn.setPoints(points, true);		nn.setPoints(points, true);
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	// The results will be the 10 best matches, but their order can be arbitrary. For display purposes		// The results will be the 10 best matches, but their order can be arbitrary. For display purposes
	// it's better to do it from best fit to worst fit		// it's better to do it from best fit to worst fit
	Collections.sort(results.toList(), (Comparator<NnData>) (o1, o2) -> {		Collections.sort(results.toList(), (Comparator<NnData>)( o1, o2 ) -> {
	if( o1.distance < o2.distance)		if (o1.distance < o2.distance)
	return -1;		return -1;
	else if( o1.distance > o2.distance )		else if (o1.distance > o2.distance)
	return 1;		return 1;
	else		else
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	}		}

	ShowImages.showWindow(gui,"Similar Images",true);		ShowImages.showWindow(gui, "Similar Images", true);
	}		}
	}		}
	</syntaxhighlight>		</syntaxhighlight>

Peter at 05:16, 18 May 2020

2020-05-18T05:16:40Z

← Older revision		Revision as of 05:16, 18 May 2020
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	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.34/examples/src/main/java/boofcv/examples/recognition/ExampleColorHistogramLookup.java ExampleColorHistogramLookup.java ]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.36/examples/src/main/java/boofcv/examples/recognition/ExampleColorHistogramLookup.java ExampleColorHistogramLookup.java ]

	Concepts:		Concepts:
Line 200:		Line 200:
	NearestNeighbor<double[]> nn = FactoryNearestNeighbor.exhaustive(new KdTreeEuclideanSq_F64(targetPoint.length));		NearestNeighbor<double[]> nn = FactoryNearestNeighbor.exhaustive(new KdTreeEuclideanSq_F64(targetPoint.length));
	NearestNeighbor.Search<double[]> search = nn.createSearch();		NearestNeighbor.Search<double[]> search = nn.createSearch();
	FastQueue<NnData<double[]>> results = new FastQueue(NnData~~.class,true~~);		FastQueue<NnData<double[]>> results = new FastQueue(NnData::new);

	nn.setPoints(points, true);		nn.setPoints(points, true);

Peter at 18:19, 7 July 2019

2019-07-07T18:19:15Z

← Older revision		Revision as of 18:19, 7 July 2019
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	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.33/examples/src/main/java/boofcv/examples/recognition/ExampleColorHistogramLookup.java ExampleColorHistogramLookup.java ]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.34/examples/src/main/java/boofcv/examples/recognition/ExampleColorHistogramLookup.java ExampleColorHistogramLookup.java ]

	Concepts:		Concepts:
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	// Use a generic NN search algorithm. This uses Euclidean distance as a distance metric.		// Use a generic NN search algorithm. This uses Euclidean distance as a distance metric.
	NearestNeighbor<double[]> nn = FactoryNearestNeighbor.exhaustive(new KdTreeEuclideanSq_F64(targetPoint.length));		NearestNeighbor<double[]> nn = FactoryNearestNeighbor.exhaustive(new KdTreeEuclideanSq_F64(targetPoint.length));
			NearestNeighbor.Search<double[]> search = nn.createSearch();
	FastQueue<NnData<double[]>> results = new FastQueue(NnData.class,true);		FastQueue<NnData<double[]>> results = new FastQueue(NnData.class,true);

	nn.setPoints(points, true);		nn.setPoints(points, true);
	nn.findNearest(targetPoint, -1, 10, results);		search.findNearest(targetPoint, -1, 10, results);

	ListDisplayPanel gui = new ListDisplayPanel();		ListDisplayPanel gui = new ListDisplayPanel();

Peter at 14:51, 15 March 2019

2019-03-15T14:51:36Z

← Older revision		Revision as of 14:51, 15 March 2019
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	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.32/examples/src/main/java/boofcv/examples/recognition/ExampleColorHistogramLookup.java ExampleColorHistogramLookup.java ]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.33/examples/src/main/java/boofcv/examples/recognition/ExampleColorHistogramLookup.java ExampleColorHistogramLookup.java ]

	Concepts:		Concepts:
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	ConvertBufferedImage.convertFrom(buffered, rgb, true);		ConvertBufferedImage.convertFrom(buffered, rgb, true);
	ColorHsv.~~rgbToHsv_F32~~(rgb, hsv);		ColorHsv.rgbToHsv(rgb, hsv);

	Planar<GrayF32> hs = hsv.partialSpectrum(0,1);		Planar<GrayF32> hs = hsv.partialSpectrum(0,1);
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	hsv.reshape(buffered.getWidth(), buffered.getHeight());		hsv.reshape(buffered.getWidth(), buffered.getHeight());
	ConvertBufferedImage.convertFrom(buffered, rgb, true);		ConvertBufferedImage.convertFrom(buffered, rgb, true);
	ColorHsv.~~rgbToHsv_F32~~(rgb, hsv);		ColorHsv.rgbToHsv(rgb, hsv);

	GHistogramFeatureOps.histogram(hsv.getBand(0), 0, 2*Math.PI,histogramHue);		GHistogramFeatureOps.histogram(hsv.getBand(0), 0, 2*Math.PI,histogramHue);

Peter at 03:58, 27 December 2018

2018-12-27T03:58:53Z

← Older revision		Revision as of 03:58, 27 December 2018
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	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.31/examples/src/main/java/boofcv/examples/recognition/ExampleColorHistogramLookup.java ExampleColorHistogramLookup.java ]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.32/examples/src/main/java/boofcv/examples/recognition/ExampleColorHistogramLookup.java ExampleColorHistogramLookup.java ]

	Concepts:		Concepts:
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	* from hue and saturation only, which makes it lighting independent.		* from hue and saturation only, which makes it lighting independent.
	*/		*/
	public static List<double[]> coupledHueSat( List<~~File~~> images ) {		public static List<double[]> coupledHueSat( List<String> images ) {
	List<double[]> points = new ArrayList<>();		List<double[]> points = new ArrayList<>();

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	Planar<GrayF32> hsv = new Planar<>(GrayF32.class,1,1,3);		Planar<GrayF32> hsv = new Planar<>(GrayF32.class,1,1,3);

	for( ~~File f~~ : images ) {		for( String path : images ) {
	BufferedImage buffered = UtilImageIO.loadImage(~~f.getPath()~~);		BufferedImage buffered = UtilImageIO.loadImage(path);
	if( buffered == null ) throw new RuntimeException("Can't load image!");		if( buffered == null ) throw new RuntimeException("Can't load image!");

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	String imagePath = UtilIO.pathExample("recognition/vacation");		String imagePath = UtilIO.pathExample("recognition/vacation");
	List<~~File~~> images = ~~Arrays.asList(~~UtilIO.~~findMatches(new File~~(imagePath),"\\w*.jpg"));		List<String> images = UtilIO.listByPrefix(imagePath,null,".jpg");
	Collections.sort(images);		Collections.sort(images);

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	// Add the target which the other images are being matched against		// Add the target which the other images are being matched against
	gui.addImage(UtilImageIO.loadImage(images.get(target~~).getPath(~~)), "Target", ScaleOptions.ALL);		gui.addImage(UtilImageIO.loadImage(images.get(target)), "Target", ScaleOptions.ALL);

	// The results will be the 10 best matches, but their order can be arbitrary. For display purposes		// The results will be the 10 best matches, but their order can be arbitrary. For display purposes
	// it's better to do it from best fit to worst fit		// it's better to do it from best fit to worst fit
	Collections.sort(results.toList(), ~~new~~ Comparator<NnData>() {		Collections.sort(results.toList(), (Comparator<NnData>) (o1, o2) -> {
	~~@Override~~		if( o1.distance < o2.distance)
	~~public int compare~~(~~NnData~~ o1, ~~NnData~~ o2) {		return -1;
	if( o1.distance < o2.distance)		else if( o1.distance > o2.distance )
	return -1;		return 1;
	else if( o1.distance > o2.distance )		else
	return 1;		return 0;
	else
	return 0;
	}
	});		});

	// Add images to GUI -- first match is always the target image, so skip it		// Add images to GUI -- first match is always the target image, so skip it
	for (int i = 1; i < results.size; i++) {		for (int i = 1; i < results.size; i++) {
	~~File~~ file = images.get(results.get(i).index);		String file = images.get(results.get(i).index);
	double error = results.get(i).distance;		double error = results.get(i).distance;
	BufferedImage image = UtilImageIO.loadImage(file~~.getPath()~~);		BufferedImage image = UtilImageIO.loadImage(file);
	gui.addImage(image, String.format("Error %6.3f", error), ScaleOptions.ALL);		gui.addImage(image, String.format("Error %6.3f", error), ScaleOptions.ALL);
	}		}

Peter at 02:59, 17 October 2018

2018-10-17T02:59:34Z

← Older revision		Revision as of 02:59, 17 October 2018
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	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.27/examples/src/boofcv/examples/recognition/ExampleColorHistogramLookup.java ExampleColorHistogramLookup.java ]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.31/examples/src/main/java/boofcv/examples/recognition/ExampleColorHistogramLookup.java ExampleColorHistogramLookup.java ]

	Concepts:		Concepts:
Line 198:		Line 198:

	// Use a generic NN search algorithm. This uses Euclidean distance as a distance metric.		// Use a generic NN search algorithm. This uses Euclidean distance as a distance metric.
	NearestNeighbor<~~File~~> nn = FactoryNearestNeighbor.exhaustive();		NearestNeighbor<double[]> nn = FactoryNearestNeighbor.exhaustive(new KdTreeEuclideanSq_F64(targetPoint.length));
	FastQueue<NnData<~~File~~>> results = new FastQueue(NnData.class,true);		FastQueue<NnData<double[]>> results = new FastQueue(NnData.class,true);

	~~nn.init(targetPoint.length);~~		nn.setPoints(points, true);
	nn.setPoints(points, ~~images~~);
	nn.findNearest(targetPoint, -1, 10, results);		nn.findNearest(targetPoint, -1, 10, results);

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	// Add images to GUI -- first match is always the target image, so skip it		// Add images to GUI -- first match is always the target image, so skip it
	for (int i = 1; i < results.size; i++) {		for (int i = 1; i < results.size; i++) {
	File file = results.get(i).~~data~~;		File file = images.get(results.get(i).index);
	double error = results.get(i).distance;		double error = results.get(i).distance;
	BufferedImage image = UtilImageIO.loadImage(file.getPath());		BufferedImage image = UtilImageIO.loadImage(file.getPath());

Peter at 16:05, 17 August 2017

2017-08-17T16:05:13Z

← Older revision		Revision as of 16:05, 17 August 2017
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	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.25/examples/src/boofcv/examples/recognition/ExampleColorHistogramLookup.java ExampleColorHistogramLookup.java ]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.27/examples/src/boofcv/examples/recognition/ExampleColorHistogramLookup.java ExampleColorHistogramLookup.java ]

	Concepts:		Concepts:
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	public static void main(String[] args) {		public static void main(String[] args) {

	String ~~regex~~ = UtilIO.pathExample("recognition/vacation")~~+"/^\\w*.jpg"~~;		String imagePath = UtilIO.pathExample("recognition/vacation");
	List<File> images = Arrays.asList(~~BoofMiscOps~~.findMatches(~~regex~~));		List<File> images = Arrays.asList(UtilIO.findMatches(new File(imagePath),"\\w*.jpg"));
	Collections.sort(images);		Collections.sort(images);

Peter at 22:37, 12 December 2016

2016-12-12T22:37:23Z

← Older revision		Revision as of 22:37, 12 December 2016
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	Related Examples:		Related Examples:
	* [Example_Image_Classification\|Image Classification]]		* [[Example_Image_Classification\|Image Classification]]

	= Example Code =		= Example Code =

← Older revision		Revision as of 19:08, 12 July 2021
Line 8:		Line 8:

	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.37/examples/src/main/java/boofcv/examples/recognition/ExampleColorHistogramLookup.java ExampleColorHistogramLookup.java ]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.38/examples/src/main/java/boofcv/examples/recognition/ExampleColorHistogramLookup.java ExampleColorHistogramLookup.java ]

	Concepts:		Concepts:
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	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	/**		/**
	* Demonstration of how to find similar images using color histograms. Image color histograms here are treated as		* Demonstration of how to find similar images using color histograms. Image color histograms here are treated as
	* features and extracted using a more flexible algorithm than when they are used for image processing. It's		* features and extracted using a more flexible algorithm than when they are used for image processing. It's
	* more flexible in that the bin size can be varied and n-dimensions are supported.		* more flexible in that the bin size can be varied and n-dimensions are supported.
	*		*
	* In this example, histograms for about 150 images are generated. A target image is selected and the 10 most		* In this example, histograms for about 150 images are generated. A target image is selected and the 10 most
	* similar images, according to Euclidean distance of the histograms, are found. This illustrates several concepts;		* similar images, according to Euclidean distance of the histograms, are found. This illustrates several concepts;
	* 1) How to construct a histogram in 1D, 2D, 3D, ..etc, 2) Histograms are just feature descriptors.		* 1) How to construct a histogram in 1D, 2D, 3D, ..etc, 2) Histograms are just feature descriptors.
	* 3) Advantages of different color spaces.		* 3) Advantages of different color spaces.
	*		*
	* Euclidean distance is used here since that's what the nearest-neighbor search uses. It's possible to compare		* Euclidean distance is used here since that's what the nearest-neighbor search uses. It's possible to compare
	* two histograms using any of the distance metrics in DescriptorDistance too.		* two histograms using any of the distance metrics in DescriptorDistance too.
	*		*
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	/**		/**
	* HSV stores color information in Hue and Saturation while intensity is in Value. This computes a 2D histogram		* HSV stores color information in Hue and Saturation while intensity is in Value. This computes a 2D histogram
	* from hue and saturation only, which makes it lighting independent.		* from hue and saturation only, which makes it lighting independent.
	*/		*/
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	Planar<GrayF32> hs = hsv.partialSpectrum(0, 1);		Planar<GrayF32> hs = hsv.partialSpectrum(0, 1);

	// The number of bins is an important parameter. Try adjusting it		// The number of bins is an important parameter. Try adjusting it
	Histogram_F64 histogram = new Histogram_F64(12, 12);		Histogram_F64 histogram = new Histogram_F64(12, 12);
	histogram.setRange(0, 0, 2.0*Math.PI); // range of hue is from 0 to 2PI		histogram.setRange(0, 0, 2.0*Math.PI); // range of hue is from 0 to 2PI
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	UtilFeature.normalizeL2(histogram); // normalize so that image size doesn't matter		UtilFeature.normalizeL2(histogram); // normalize so that image size doesn't matter

	points.add(histogram.~~value~~);		points.add(histogram.data);
	}		}

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	/**		/**
	* Computes two independent 1D histograms from hue and saturation. Less affects by sparsity, but can produce		* Computes two independent 1D histograms from hue and saturation. Less affects by sparsity, but can produce
	* worse results since the basic assumption that hue and saturation are decoupled is most of the time false.		* worse results since the basic assumption that hue and saturation are decoupled is most of the time false.
	*/		*/
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	List<double[]> points = new ArrayList<>();		List<double[]> points = new ArrayList<>();

	// The number of bins is an important parameter. Try adjusting it		// The number of bins is an important parameter. Try adjusting it
	TupleDesc_F64 histogramHue = new TupleDesc_F64(30);		TupleDesc_F64 histogramHue = new TupleDesc_F64(30);
	TupleDesc_F64 histogramValue = new TupleDesc_F64(30);		TupleDesc_F64 histogramValue = new TupleDesc_F64(30);
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	UtilFeature.normalizeL2(imageHist); // normalize so that image size doesn't matter		UtilFeature.normalizeL2(imageHist); // normalize so that image size doesn't matter

	points.add(imageHist.~~value~~);		points.add(imageHist.data);
	}		}

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	/**		/**
	* Constructs a 3D histogram using RGB. RGB is a popular color space, but the resulting histogram will		* Constructs a 3D histogram using RGB. RGB is a popular color space, but the resulting histogram will
	* depend on lighting conditions and might not produce the accurate results.		* depend on lighting conditions and might not produce the accurate results.
	*/		*/
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	ConvertBufferedImage.convertFrom(buffered, rgb, true);		ConvertBufferedImage.convertFrom(buffered, rgb, true);

	// The number of bins is an important parameter. Try adjusting it		// The number of bins is an important parameter. Try adjusting it
	Histogram_F64 histogram = new Histogram_F64(10, 10, 10);		Histogram_F64 histogram = new Histogram_F64(10, 10, 10);
	histogram.setRange(0, 0, 255);		histogram.setRange(0, 0, 255);
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	UtilFeature.normalizeL2(histogram); // normalize so that image size doesn't matter		UtilFeature.normalizeL2(histogram); // normalize so that image size doesn't matter

	points.add(histogram.~~value~~);		points.add(histogram.data);
	}		}

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	/**		/**
	* Computes a histogram from the gray scale intensity image alone. Probably the least effective at looking up		* Computes a histogram from the gray scale intensity image alone. Probably the least effective at looking up
	* similar images.		* similar images.
	*/		*/
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	UtilFeature.normalizeL2(imageHist); // normalize so that image size doesn't matter		UtilFeature.normalizeL2(imageHist); // normalize so that image size doesn't matter

	points.add(imageHist.~~value~~);		points.add(imageHist.data);
	}		}

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	double[] targetPoint = points.get(target);		double[] targetPoint = points.get(target);

	// Use a generic NN search algorithm. This uses Euclidean distance as a distance metric.		// Use a generic NN search algorithm. This uses Euclidean distance as a distance metric.
	NearestNeighbor<double[]> nn = FactoryNearestNeighbor.exhaustive(new KdTreeEuclideanSq_F64(targetPoint.length));		NearestNeighbor<double[]> nn = FactoryNearestNeighbor.exhaustive(new KdTreeEuclideanSq_F64(targetPoint.length));
	NearestNeighbor.Search<double[]> search = nn.createSearch();		NearestNeighbor.Search<double[]> search = nn.createSearch();
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	gui.addImage(UtilImageIO.loadImage(images.get(target)), "Target", ScaleOptions.ALL);		gui.addImage(UtilImageIO.loadImage(images.get(target)), "Target", ScaleOptions.ALL);

	// The results will be the 10 best matches, but their order can be arbitrary. For display purposes		// The results will be the 10 best matches, but their order can be arbitrary. For display purposes
	// it's better to do it from best fit to worst fit		// it's better to do it from best fit to worst fit
	Collections.sort(results.toList(), (Comparator<NnData>)( o1, o2 ) -> {		Collections.sort(results.toList(), (Comparator<NnData>)( o1, o2 ) -> {