Java UtilFeature类代码示例

import boofcv.alg.descriptor.UtilFeature; //导入依赖的package包/类
public static ImageDesc readIn(ByteBuffer buf)
{
	FastQueue<BrightFeature> des = UtilFeature.createQueue(detDesc,0);
	int dts = buf.getInt();
	for(int i=0;i<dts;i++)
	{
		int vs = buf.getInt();
		BrightFeature f = new BrightFeature(vs);
		double[] vls = new double[vs];
		for(int j=0;j<vs;j++)
		{
			vls[j]=buf.getDouble();
		}
		f.set(vls);
		des.add(f);
	}
	AverageHash h = AverageHash.readIn(buf);
	return new ImageDesc(des,h);
}
 

import boofcv.alg.descriptor.UtilFeature; //导入依赖的package包/类
/**
 * 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.
 */
public static double[] coupledHueSat(BufferedImage image) {
    Planar<GrayF32> rgb = new Planar<>(GrayF32.class, image.getWidth(), image.getHeight(), 3);
    Planar<GrayF32> hsv = new Planar<>(GrayF32.class, image.getWidth(), image.getHeight(), 3);

    ConvertBufferedImage.convertFrom(image, rgb, true);
    ColorHsv.rgbToHsv_F32(rgb, hsv);

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

    // The number of bins is an important parameter.  Try adjusting it
    Histogram_F64 histogram = new Histogram_F64(10, 10);
    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

    // Compute the histogram
    GHistogramFeatureOps.histogram(hs, histogram);
    histogram.value[0] = 0.0; // remove black

    UtilFeature.normalizeL2(histogram); // normalize so that image size doesn't matter

    return histogram.value;
}
 

import boofcv.alg.descriptor.UtilFeature; //导入依赖的package包/类
/**
 * 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.
 */
public static double[] coupledRGB(BufferedImage image) {

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

    rgb.reshape(image.getWidth(), image.getHeight());
    ConvertBufferedImage.convertFrom(image, rgb, true);

    // The number of bins is an important parameter.  Try adjusting it
    Histogram_F64 histogram = new Histogram_F64(5,5,5);
    histogram.setRange(0, 0, 255);
    histogram.setRange(1, 0, 255);
    histogram.setRange(2, 0, 255);

    GHistogramFeatureOps.histogram(rgb,histogram);
    histogram.value[0] = 0.0; // remove black

    UtilFeature.normalizeL2(histogram); // normalize so that image size doesn't matter

    return histogram.value;
}
 

import boofcv.alg.descriptor.UtilFeature; //导入依赖的package包/类
/**
 * 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.
 */
public static double[] independentHueSat(BufferedImage image) {
    // The number of bins is an important parameter.  Try adjusting it
    TupleDesc_F64 histogramHue = new TupleDesc_F64(5);
    TupleDesc_F64 histogramValue = new TupleDesc_F64(5);

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

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

    rgb.reshape(image.getWidth(), image.getHeight());
    hsv.reshape(image.getWidth(), image.getHeight());
    ConvertBufferedImage.convertFrom(image, rgb, true);
    ColorHsv.rgbToHsv_F32(rgb, hsv);

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

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

    UtilFeature.normalizeL2(imageHist); // normalize so that image size doesn't matter

    return imageHist.value;
}
 

import boofcv.alg.descriptor.UtilFeature; //导入依赖的package包/类
/**
 * 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.
 */
public double[] coupledHueSat(byte[] image) throws IOException {
	Planar<GrayF32> rgb = new Planar<GrayF32>(GrayF32.class,1,1,3);
	Planar<GrayF32> hsv = new Planar<GrayF32>(GrayF32.class,1,1,3);

	BufferedImage buffered = ImageIO.read(new ByteArrayInputStream(image));
	if (buffered == null) {
		throw new RuntimeException("Can't load image!");
	}

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

	ConvertBufferedImage.convertFrom(buffered, rgb, true);
	ColorHsv.rgbToHsv_F32(rgb, hsv);

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

	// The number of bins is an important parameter.  Try adjusting it
	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(1, 0, 1.0);         // range of saturation is from 0 to 1

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

	UtilFeature.normalizeL2(histogram); // normalize so that image size doesn't matter

	return histogram.value;
}