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

Example 91 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.loadImageNotNull(f.getPath());
        rgb.reshape(buffered.getWidth(), buffered.getHeight());
        hsv.reshape(buffered.getWidth(), buffered.getHeight());
        ConvertBufferedImage.convertFrom(buffered, rgb, true);
        ColorHsv.rgbToHsv(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.data);
    }
    return points;
}

Example 92 with TupleDesc_F64

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

the class ExampleComputeFundamentalMatrix method computeMatches.

/**
 * Use the associate point feature example to create a list of {@link AssociatedPair} for use in computing the
 * fundamental matrix.
 */
public static List<AssociatedPair> computeMatches(BufferedImage left, BufferedImage right) {
    DetectDescribePoint<GrayF32, TupleDesc_F64> detDesc = FactoryDetectDescribe.surfStable(new ConfigFastHessian(0, 2, 400, 1, 9, 4, 4), null, null, GrayF32.class);
    // DetectDescribePoint detDesc = FactoryDetectDescribe.sift(null,new ConfigSiftDetector(2,0,200,5),null,null);
    ScoreAssociation<TupleDesc_F64> scorer = FactoryAssociation.scoreEuclidean(TupleDesc_F64.class, true);
    AssociateDescription<TupleDesc_F64> associate = FactoryAssociation.greedy(new ConfigAssociateGreedy(true, 0.1), scorer);
    var findMatches = new ExampleAssociatePoints<>(detDesc, associate, GrayF32.class);
    findMatches.associate(left, right);
    List<AssociatedPair> matches = new ArrayList<>();
    FastAccess<AssociatedIndex> matchIndexes = associate.getMatches();
    for (int i = 0; i < matchIndexes.size; i++) {
        AssociatedIndex a = matchIndexes.get(i);
        var p = new AssociatedPair(findMatches.pointsA.get(a.src), findMatches.pointsB.get(a.dst));
        matches.add(p);
    }
    return matches;
}

Example 93 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) {
    var 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 = FactoryDescribeImageDense.surfFast(surfFast, GrayU8.class);
    // FactoryDescribeImageDense.surfStable(surfStable, GrayU8.class);
    // FactoryDescribeImageDense.sift(sift, GrayU8.class);
    // FactoryDescribeImageDense.hog(hog, ImageType.single(GrayU8.class));
    var configKMeans = new ConfigKMeans();
    configKMeans.maxIterations = MAX_KNN_ITERATIONS;
    configKMeans.reseedAfterIterations = 20;
    ComputeClusters<double[]> clusterer = FactoryClustering.kMeans_MT(configKMeans, desc.createDescription().size(), 200, double[].class);
    clusterer.setVerbose(true);
    // The _MT tells it to use the threaded version. This can run MUCH faster.
    int pointDof = desc.createDescription().size();
    NearestNeighbor<HistogramScene> nn = FactoryNearestNeighbor.exhaustive(new KdTreeHistogramScene_F64(pointDof));
    ExampleClassifySceneKnn example = new ExampleClassifySceneKnn(desc, clusterer, nn);
    var trainingDir = new File(UtilIO.pathExample("learning/scene/train"));
    var 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 UncheckedIOException(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 94 with TupleDesc_F64

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

the class TestDetectDescribeSurfPlanar_MT method compare_Single_to_MT.

@Test
void compare_Single_to_MT() {
    Planar<GrayF32> input = new Planar<>(GrayF32.class, width, height, 3);
    GImageMiscOps.addUniform(input, rand, 0, 200);
    DetectDescribeSurfPlanar<GrayF32> desc_ST;
    DetectDescribeSurfPlanar_MT<GrayF32> desc_MT;
    {
        DescribePointSurf<GrayF32> desc = new DescribePointSurf<>(GrayF32.class);
        DescribePointSurfPlanar<GrayF32> descMulti = new DescribePointSurfPlanar<>(desc, 3);
        FastHessianFeatureDetector<GrayF32> detector = FactoryInterestPointAlgs.fastHessian(null);
        OrientationIntegral<GrayF32> orientation = FactoryOrientationAlgs.sliding_ii(null, GrayF32.class);
        desc_ST = new DetectDescribeSurfPlanar<>(detector, orientation, descMulti);
    }
    {
        DescribePointSurf<GrayF32> desc = new DescribePointSurf<>(GrayF32.class);
        DescribePointSurfPlanar<GrayF32> descMulti = new DescribePointSurfPlanar<>(desc, 3);
        FastHessianFeatureDetector<GrayF32> detector = FactoryInterestPointAlgs.fastHessian(null);
        OrientationIntegral<GrayF32> orientation = FactoryOrientationAlgs.sliding_ii(null, GrayF32.class);
        desc_MT = new DetectDescribeSurfPlanar_MT<>(detector, orientation, descMulti);
    }
    GrayF32 gray = ConvertImage.average(input, null);
    desc_ST.detect(gray, input);
    desc_MT.detect(gray, input);
    assertEquals(desc_ST.getNumberOfFeatures(), desc_MT.getNumberOfFeatures());
    int N = desc_ST.getNumberOfFeatures();
    for (int idx_st = 0; idx_st < N; idx_st++) {
        Point2D_F64 loc_st = desc_ST.getLocation(idx_st);
        // order isn't guaranteed. Do an exhaustive search
        boolean matched = false;
        for (int idx_mt = 0; idx_mt < N; idx_mt++) {
            Point2D_F64 loc_mt = desc_MT.getLocation(idx_mt);
            if (loc_st.x != loc_mt.x || loc_st.y != loc_mt.y) {
                continue;
            }
            if (desc_ST.getRadius(idx_st) != desc_MT.getRadius(idx_mt)) {
                continue;
            }
            if (desc_ST.getOrientation(idx_st) != desc_MT.getOrientation(idx_mt)) {
                continue;
            }
            if (desc_ST.isWhite(idx_st) != desc_MT.isWhite(idx_mt)) {
                continue;
            }
            TupleDesc_F64 fd_st = desc_ST.getDescription(idx_st);
            TupleDesc_F64 fd_mt = desc_MT.getDescription(idx_mt);
            assertEquals(0, DescriptorDistance.sad(fd_st, fd_mt));
            matched = true;
            break;
        }
        assertTrue(matched, "No match " + idx_st);
    }
}

Example 95 with TupleDesc_F64

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

the class TestCompleteSift_MT method compareToSingleThread.

@Test
void compareToSingleThread() {
    GrayF32 image = new GrayF32(300, 290);
    GImageMiscOps.fillUniform(image, rand, 0, 200);
    BoofConcurrency.USE_CONCURRENT = false;
    CompleteSift single = FactoryDetectDescribeAlgs.sift(config);
    BoofConcurrency.USE_CONCURRENT = true;
    CompleteSift multi = FactoryDetectDescribeAlgs.sift(config);
    single.process(image);
    multi.process(image);
    assertEquals(128, single.getDescriptorLength());
    assertEquals(128, multi.getDescriptorLength());
    assertEquals(single.getLocations().size, multi.getLocations().size);
    int N = single.getLocations().size;
    for (int i = 0; i < N; i++) {
        ScalePoint sp = single.getLocations().get(i);
        ScalePoint mp = multi.getLocations().get(i);
        assertEquals(sp.intensity, mp.intensity);
        assertEquals(sp.scale, mp.scale);
        assertEquals(0.0, sp.pixel.distance(mp.pixel));
        assertEquals(single.getOrientations().get(i), multi.getOrientations().get(i));
        TupleDesc_F64 sd = single.getDescriptions().get(i);
        TupleDesc_F64 md = multi.getDescriptions().get(i);
        assertEquals(0.0, DescriptorDistance.euclidean(sd, md));
    }
}