Examples with TupleDesc_F64 boofcv.struct.feature.TupleDesc_F64 used on opensource projects

Example 56 with TupleDesc_F64

use of boofcv.struct.feature.TupleDesc_F64 in project BoofCV by lessthanoptimal.

the class ExampleClassifySceneKnn method main.

public static void main(String[] args) {
    ConfigDenseSurfFast surfFast = new ConfigDenseSurfFast(new DenseSampling(8, 8));
    ConfigDenseSurfStable surfStable = new ConfigDenseSurfStable(new DenseSampling(8, 8));
    ConfigDenseSift sift = new ConfigDenseSift(new DenseSampling(6, 6));
    ConfigDenseHoG hog = new ConfigDenseHoG();
    DescribeImageDense<GrayU8, TupleDesc_F64> desc = (DescribeImageDense) FactoryDescribeImageDense.surfFast(surfFast, GrayU8.class);
    // FactoryDescribeImageDense.surfStable(surfStable, GrayU8.class);
    // FactoryDescribeImageDense.sift(sift, GrayU8.class);
    // FactoryDescribeImageDense.hog(hog, ImageType.single(GrayU8.class));
    ComputeClusters<double[]> clusterer = FactoryClustering.kMeans_F64(null, MAX_KNN_ITERATIONS, 20, 1e-6);
    clusterer.setVerbose(true);
    NearestNeighbor<HistogramScene> nn = FactoryNearestNeighbor.exhaustive();
    ExampleClassifySceneKnn example = new ExampleClassifySceneKnn(desc, clusterer, nn);
    File trainingDir = new File(UtilIO.pathExample("learning/scene/train"));
    File testingDir = new File(UtilIO.pathExample("learning/scene/test"));
    if (!trainingDir.exists() || !testingDir.exists()) {
        String addressSrc = "http://boofcv.org/notwiki/largefiles/bow_data_v001.zip";
        File dst = new File(trainingDir.getParentFile(), "bow_data_v001.zip");
        try {
            DeepBoofDataBaseOps.download(addressSrc, dst);
            DeepBoofDataBaseOps.decompressZip(dst, dst.getParentFile(), true);
            System.out.println("Download complete!");
        } catch (IOException e) {
            throw new RuntimeException(e);
        }
    } else {
        System.out.println("Delete and download again if there are file not found errors");
        System.out.println("   " + trainingDir);
        System.out.println("   " + testingDir);
    }
    example.loadSets(trainingDir, null, testingDir);
    // train the classifier
    example.learnAndSave();
    // now load it for evaluation purposes from the files
    example.loadAndCreateClassifier();
    // test the classifier on the test set
    Confusion confusion = example.evaluateTest();
    confusion.getMatrix().print();
    System.out.println("Accuracy = " + confusion.computeAccuracy());
    // Show confusion matrix
    // Not the best coloration scheme...  perfect = red diagonal and blue elsewhere.
    ShowImages.showWindow(new ConfusionMatrixPanel(confusion.getMatrix(), example.getScenes(), 400, true), "Confusion Matrix", true);
// For SIFT descriptor the accuracy is          54.0%
// For  "fast"  SURF descriptor the accuracy is 52.2%
// For "stable" SURF descriptor the accuracy is 49.4%
// For HOG                                      53.3%
// SURF results are interesting. "Stable" is significantly better than "fast"!
// One explanation is that the descriptor for "fast" samples a smaller region than "stable", by a
// couple of pixels at scale of 1.  Thus there is less overlap between the features.
// Reducing the size of "stable" to 0.95 does slightly improve performance to 50.5%, can't scale it down
// much more without performance going down
}

Example 57 with TupleDesc_F64

use of boofcv.struct.feature.TupleDesc_F64 in project BoofCV by lessthanoptimal.

the class ExampleColorHistogramLookup method independentHueSat.

/**
 * 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 List<double[]> independentHueSat(List<File> images) {
    List<double[]> points = new ArrayList<>();
    // The number of bins is an important parameter.  Try adjusting it
    TupleDesc_F64 histogramHue = new TupleDesc_F64(30);
    TupleDesc_F64 histogramValue = new TupleDesc_F64(30);
    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);
    for (File f : images) {
        BufferedImage buffered = UtilImageIO.loadImage(f.getPath());
        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);
        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);
        // normalize so that image size doesn't matter
        UtilFeature.normalizeL2(imageHist);
        points.add(imageHist.value);
    }
    return points;
}

Example 58 with TupleDesc_F64

use of boofcv.struct.feature.TupleDesc_F64 in project BoofCV by lessthanoptimal.

the class TestDescribeRegionPointConvert method basic.

@Test
public void basic() {
    DummyConvert convert = new DummyConvert();
    DummyDescribe original = new DummyDescribe();
    DescribeRegionPointConvert<GrayF32, TupleDesc_F64, TupleDesc_S8> alg = new DescribeRegionPointConvert<>(original, convert);
    TupleDesc_S8 found = alg.createDescription();
    assertTrue(found.value.length == 5);
    assertFalse(original.calledImageSet);
    alg.setImage(null);
    assertTrue(original.calledImageSet);
    alg.process(1, 2, 2, 2, found);
    assertEquals(5, found.value[0]);
    assertTrue(alg.requiresOrientation() == original.requiresOrientation());
    assertTrue(alg.requiresRadius() == original.requiresRadius());
    assertTrue(alg.getDescriptionType() == TupleDesc_S8.class);
}

Example 59 with TupleDesc_F64

use of boofcv.struct.feature.TupleDesc_F64 in project BoofCV by lessthanoptimal.

the class TestDescribeRegionPoint_SIFT method process.

@Test
public void process() {
    GrayF32 image = new GrayF32(640, 480);
    GImageMiscOps.fillUniform(image, rand, 0, 200);
    DescribeRegionPoint_SIFT<GrayF32> alg = declare();
    alg.setImage(image);
    TupleDesc_F64 desc0 = alg.createDescription();
    TupleDesc_F64 desc1 = alg.createDescription();
    TupleDesc_F64 desc2 = alg.createDescription();
    // same location, but different orientations and scales
    assertTrue(alg.process(100, 120, 0.5, 10, desc0));
    assertTrue(alg.process(100, 50, -1.1, 10, desc1));
    assertTrue(alg.process(100, 50, 0.5, 7, desc2));
    // should be 3 different descriptions
    assertNotEquals(desc0.getDouble(0), desc1.getDouble(0), 1e-6);
    assertNotEquals(desc0.getDouble(0), desc2.getDouble(0), 1e-6);
    assertNotEquals(desc1.getDouble(0), desc2.getDouble(0), 1e-6);
    // see if it blows up along the image border
    assertTrue(alg.process(0, 120, 0.5, 10, desc0));
    assertTrue(alg.process(100, 0, 0.5, 10, desc0));
    assertTrue(alg.process(639, 120, 0.5, 10, desc0));
    assertTrue(alg.process(100, 479, 0.5, 10, desc0));
}

Example 60 with TupleDesc_F64

use of boofcv.struct.feature.TupleDesc_F64 in project BoofCV by lessthanoptimal.

the class TestDescribeRegionPoint_SIFT method flags.

@Test
public void flags() {
    DescribeRegionPoint_SIFT<GrayF32> alg = declare();
    assertTrue(alg.requiresOrientation());
    assertTrue(alg.requiresRadius());
    TupleDesc_F64 desc = alg.createDescription();
    assertEquals(128, desc.size());
    assertEquals(2 * alg.describe.getCanonicalRadius(), alg.getCanonicalWidth(), 1e-8);
}