Java FImage.addInplace方法代码示例

import org.openimaj.image.FImage; //导入方法依赖的package包/类
static FImage loadPositive() throws IOException {
	final String base = "/Users/jsh2/Data/att_faces/s%d/%d.pgm";

	final FImage image = new FImage(400, 400);
	for (int j = 1; j <= 40; j++) {
		for (int i = 1; i <= 10; i++) {
			final File file = new File(String.format(base, j, i));

			FImage img = ImageUtilities.readF(file);
			img = img.extractCenter(50, 50);
			img = ResizeProcessor.resample(img, 400, 400);
			image.addInplace(img);
		}
	}

	return image.normalise();
}
 

import org.openimaj.image.FImage; //导入方法依赖的package包/类
@Override
public void beforeUpdate(MBFImage frame) {
	// Rectangle extractionArea = new
	// Rectangle(100,100,IMAGE_WIDTH-200,IMAGE_HEIGHT-200);

	if (this.mode == DebugMode.DEBUG_DISPLAY)
		return;
	final FImage grey = frame.extractROI(extractionArea).flatten();
	if (!this.backgroundLearner.ready()) {
		grey.process(this.backgroundLearner);
		frame.fill(RGBColour.BLACK);
		frame.drawImage(new MBFImage(grey, grey, grey), (int) extractionArea.x, (int) extractionArea.y);
		return;
	}
	grey.addInplace(this.backgroundLearner.getBackground());
	grey.threshold(0.07f);
	// grey.processInplace(new OtsuThreshold());
	// if(grey.sum() > BIGGEST_POINT_AREA * 2 ){
	// this.backgroundLearner.relearn();
	// return;
	// }

	// List<Circle> filtered = getFilteredCircles(grey);
	final List<Touch> filtered = getFilteredTouchesFast(grey);
	if (filtered.size() != 0)
		this.fireTouchEvent(filtered);
	frame.fill(RGBColour.BLACK);
	frame.drawImage(new MBFImage(grey, grey, grey), (int) extractionArea.x, (int) extractionArea.y);
}
 

import org.openimaj.image.FImage; //导入方法依赖的package包/类
/**
 * Test consistency between modes.
 */
@Test
public void testConsistency() {
	FImage kernel = new FImage(3,3);
	kernel.addInplace(1f);
	
	FImage kernelRow = new FImage(3,1);
	FImage kernelCol = new FImage(1,3);
	
	kernelRow.addInplace(3f);
	kernelCol.addInplace(3f);
	
	FImage im = new FImage(10,10);
	im.addInplace(1f);
	
	FConvolution conAutoSep = new FConvolution(kernel);
	FConvolution conBrute = new FConvolution(kernel);
	FConvolution conAutoRow = new FConvolution(kernelRow);
	FConvolution conAutoCol = new FConvolution(kernelCol);
	
	conBrute.setBruteForce(true);
	
	assertTrue(im.process(conAutoSep).equalsThresh(im.multiply(9f), 0.001f));
	assertTrue(im.process(conAutoRow).equalsThresh(im.multiply(9f), 0.001f));
	assertTrue(im.process(conAutoCol).equalsThresh(im.multiply(9f), 0.001f));
	assertTrue(im.process(conBrute).extractROI(1, 1, im.width-3, im.height-3).equalsThresh(im.multiply(9f).extractROI(1, 1, im.width-3, im.height-3), 0.001f));
}
 

import org.openimaj.image.FImage; //导入方法依赖的package包/类
/**
 *	{@inheritDoc}
 * 	@see org.openimaj.image.analysis.pyramid.PyramidProcessor#process(org.openimaj.image.analysis.pyramid.gaussian.GaussianPyramid)
 */
@Override
public void process( final GaussianPyramid<FImage> pyramid )
{
	FImage fmap = null;

	// Process each of the octaves in the pyramid
	for( final GaussianOctave<FImage> octave : pyramid )
	{
		// Extract text regions using the basic text extractor
		FImage octaveFMap = LiuSamarabanduTextExtractorMultiscale.this.basicTextExtractor.textRegionDetection(
				octave.getNextOctaveImage() );

		if( fmap == null )
			fmap = octaveFMap;
		else
		{
			// Fuse across scales
			octaveFMap = ResizeProcessor.resample( octaveFMap,
					fmap.getWidth(), fmap.getHeight() ).normalise();

			if( LiuSamarabanduTextExtractorMultiscale.DEBUG )
				DisplayUtilities.display( octaveFMap, "Resized feature map" );

			fmap.addInplace( octaveFMap );
		}
	}

	this.featureMap = fmap;
}
 

import org.openimaj.image.FImage; //导入方法依赖的package包/类
@Override
public FImage createInterestPointMap() {
	FImage lxx = l.process(BasicDerivativeKernels.DXX_KERNEL).multiplyInplace(detectionScale*detectionScale);
	FImage lyy = l.process(BasicDerivativeKernels.DYY_KERNEL).multiplyInplace(detectionScale*detectionScale);
	
	FImage add = lxx.addInplace(lyy);
	
	return add.multiply(add);
}
 

import org.openimaj.image.FImage; //导入方法依赖的package包/类
public static void main(String[] args) throws IOException {
	final TestImageClass tic = new TestImageClass();

	final FImage trainPhoto = ResizeProcessor.halfSize(ResizeProcessor.halfSize(ImageUtilities.readF(new File(
			"/Users/jon/Desktop/images50cm4band/sp7034.jpeg"))));
	final FImage trainClass = ResizeProcessor.halfSize(ResizeProcessor.halfSize(ImageUtilities.readF(new File(
			"/Users/jon/Desktop/images50cm4band/sp7034-classes.PNG"))));

	tic.extractFeaturePatches(trainPhoto, 20000, 8);
	tic.extractClassifierTrainingPatches(trainPhoto, trainClass, 1000, 32);
	tic.learnDictionary(100);

	// Note: should really use sparse version!!
	/*
	 * final LiblinearAnnotator<FImage, Boolean> ann = new
	 * LiblinearAnnotator<FImage, Boolean>(tic, Mode.MULTICLASS,
	 * SolverType.L2R_L2LOSS_SVC, 1, 0.0001);
	 * 
	 * final MapBackedDataset<Boolean, ListBackedDataset<FImage>, FImage>
	 * data = new MapBackedDataset<Boolean, ListBackedDataset<FImage>,
	 * FImage>(); data.add(true, new
	 * ListBackedDataset<FImage>(Arrays.asList(tic.ruralPatches)));
	 * data.add(false, new
	 * ListBackedDataset<FImage>(Arrays.asList(tic.urbanPatches)));
	 * ann.train(data);
	 */
	final FImage test = ResizeProcessor.halfSize(ResizeProcessor.halfSize(ImageUtilities.readF(new File(
			"/Users/jon/Desktop/images50cm4band/test.jpeg")))).normalise();

	/*
	 * final FImage result = test.extractCenter(test.width - 32, test.height
	 * - 32); final FImage tmp = new FImage(32, 32); for (int y = 0; y <
	 * test.height - 32; y++) { for (int x = 0; x < test.width - 32; x++) {
	 * test.extractROI(x, y, tmp);
	 * 
	 * final ClassificationResult<Boolean> r = ann.classify(tmp); final
	 * Boolean clz = r.getPredictedClasses().iterator().next();
	 * 
	 * if (clz) result.pixels[y][x] = 1;
	 * 
	 * DisplayUtilities.displayName(result, "result"); } }
	 */

	final FImage tmp = new FImage(8 * 10, 8 * 10);
	for (int i = 0, y = 0; y < 10; y++) {
		for (int x = 0; x < 10; x++, i++) {
			final FImage p = new FImage(tic.dictionary[i], 8, 8);
			p.divideInplace(2 * Math.max(p.min(), p.max()));
			p.addInplace(0.5f);
			tmp.drawImage(p, x * 8, y * 8);
		}
	}
	DisplayUtilities.display(tmp);
}
 

import org.openimaj.image.FImage; //导入方法依赖的package包/类
public static void main(String[] args) throws IOException {
	final File patchesFile = new File("patches.bin");

	// final RandomPatchSampler sampler =
	// new
	// RandomPatchSampler(Caltech101.getImages(ImageUtilities.FIMAGE_READER),
	// 8, 8, 100000);
	// sampler.save(patchesFile);
	final List<FImage> patches = RandomPatchSampler.loadPatches(patchesFile);

	final double[][] data = new double[patches.size()][];
	for (int i = 0; i < data.length; i++)
		data[i] = patches.get(i).getDoublePixelVector();

	// final PCAWhitening whitening = new PCAWhitening();
	final WhiteningTransform whitening = new ZCAWhitening(0.1, new PerExampleMeanCenterVar(10f / 255f));
	whitening.train(data);
	final double[][] wd = whitening.whiten(data);

	// final double[][] comps =
	// whitening.getTransform().transpose().getArray();
	// for (int i = 0; i < comps.length; i++)
	// DisplayUtilities.di play(ResizeProcessor.resample(new
	// FImage(comps[i], 8, 8).normalise(), 128, 128));

	// final FImage tmp1 = new FImage(100 * 8, 100 * 8);
	// final FImage tmp2 = new FImage(100 * 8, 100 * 8);
	// final FImage tmp3 = new FImage(100 * 8, 100 * 8);
	// for (int i = 0; i < 100; i++) {
	// for (int j = 0; j < 100; j++) {
	// final double[] d = new PerExampleMeanCenterVar(10f /
	// 255f).normalise(patches.get(i * 100 + j)
	// .getDoublePixelVector());
	// FImage patch = new FImage(d, 8, 8);
	// patch.divideInplace(2 * Math.max(patch.min(), patch.max()));
	// patch.addInplace(0.5f);
	// tmp2.drawImage(patch, i * 8, j * 8);
	//
	// tmp3.drawImage(patches.get(i * 100 + j), i * 8, j * 8);
	//
	// patch = new FImage(wd[i * 100 + j], 8, 8);
	// patch.divideInplace(2 * Math.max(patch.min(), patch.max()));
	// patch.addInplace(0.5f);
	// tmp1.drawImage(patch, i * 8, j * 8);
	// }
	// }
	// DisplayUtilities.display(tmp3);
	// DisplayUtilities.display(tmp2);
	// DisplayUtilities.display(tmp1);

	final SphericalKMeans skm = new SphericalKMeans(2500, 10);
	final SphericalKMeansResult res = skm.cluster(wd);
	final FImage tmp = new FImage(50 * (8 + 1) + 1, 50 * (8 + 1) + 1);
	tmp.fill(1f);
	for (int i = 0; i < 50; i++) {
		for (int j = 0; j < 50; j++) {
			final FImage patch = ResizeProcessor
					.resample(
							new FImage(res.centroids[i * 50 + j], 8, 8),
							8, 8);
			patch.divideInplace(2 * Math.max(Math.abs(patch.min()),
					Math.abs(patch.max())));
			patch.addInplace(0.5f);
			tmp.drawImage(patch, i * (8 + 1) + 1, j * (8 + 1) + 1);
		}
	}
	DisplayUtilities.display(tmp);
}