org.ddogleg.struct.FastQueue Java Examples

public static FastQueue<BrightFeature> readDescIn(ByteBuffer buf,DetectDescribePoint<GrayF32,BrightFeature> ddp)
{
	FastQueue<BrightFeature> d = UtilFeature.createQueue(ddp,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);
		d.add(f);
	}
	return d;
}
 

public void writeDescOut(DataOutputStream out, FastQueue<BrightFeature> d) throws IOException
{
	out.writeInt(d.data.length);
	for(BrightFeature ft:d.data)
	{
		out.writeInt(ft.value.length);
		for(double val:ft.value)
		{
			out.writeDouble(val);
		}
	}
}
 

/**
 * Self API. Load an image from the URL and find the closest matching image.
 */
@SuppressWarnings("unchecked")
public Vertex matchImage(byte[] image, Vertex tag, double error, Network network) throws IOException {
	double[] histogram = coupledHueSat(image);
	
	List<double[]> points = new ArrayList<double[]>();
	List<Vertex> images = tag.orderedRelations(Primitive.IMAGE);
	for (Vertex vertex : images) {
		Object value = vertex.getData();
		if (!(value instanceof BinaryData)) {
			continue;
		}
		BinaryData data = (BinaryData)network.findData((BinaryData)value);
		points.add(coupledHueSat(data.getBytes()));
	}

	// Use a generic NN search algorithm.  This uses Euclidean distance as a distance metric.
	NearestNeighbor<Vertex> nn = FactoryNearestNeighbor.exhaustive();
	FastQueue<NnData<Vertex>> results = new FastQueue(NnData.class, true);

	nn.init(histogram.length);
	nn.setPoints(points, images);
	nn.findNearest(histogram, -1, 1, results);
	NnData<Vertex> best = results.get(0);
	log("Image match", Level.FINE, best.distance);
	if (best.distance > error) {
		return null;
	}
	return best.data;
}
 

public ImageDesc(FastQueue<BrightFeature> d, AverageHash h)
{
	desc = d;
	hash = h;
}
 

public static ImageDesc readIn(ByteBuffer buf)
{
	FastQueue<BrightFeature> d = readDescIn(buf,detDesc);
	AverageHash h = AverageHash.readIn(buf);
	return new ImageDesc(d,h);
}
 

private void describeImage(GrayF32 input, FastQueue<BrightFeature> descs) {
	detDesc.detect(input);
	for (int i = 0; i < detDesc.getNumberOfFeatures(); i++) {
		descs.grow().setTo(detDesc.getDescription(i));
	}
}