Exemplo n.º 1
0
  /**
   * Extract dense features across the training set. Then clusters are found within those features.
   */
  private AssignCluster<double[]> computeClusters() {
    System.out.println("Image Features");

    // computes features in the training image set
    features.reset();
    for (String scene : train.keySet()) {
      List<String> imagePaths = train.get(scene);
      System.out.println("   " + scene);

      for (String path : imagePaths) {
        ImageUInt8 image = UtilImageIO.loadImage(path, ImageUInt8.class);
        describeImage.process(image, features, null);
      }
    }
    // add the features to the overall list which the clusters will be found inside of
    for (int i = 0; i < features.size; i++) {
      cluster.addReference(features.get(i));
    }

    System.out.println("Clustering");
    // Find the clusters.  This can take a bit
    cluster.process(NUMBER_OF_WORDS);

    UtilIO.save(cluster.getAssignment(), CLUSTER_FILE_NAME);

    return cluster.getAssignment();
  }
Exemplo n.º 2
0
  public ExampleClassifySceneKnn(
      final DescribeImageDense<ImageUInt8, TupleDesc_F64> describeImage,
      ComputeClusters<double[]> clusterer,
      NearestNeighbor<HistogramScene> nn) {
    this.describeImage = describeImage;
    this.cluster =
        new ClusterVisualWords(clusterer, describeImage.createDescription().size(), 0xFEEDBEEF);
    this.nn = nn;

    // This list can be dynamically grown.  However TupleDesc doesn't have a no argument constructor
    // so
    // you must to it how to cosntruct the data
    features =
        new FastQueue<TupleDesc_F64>(TupleDesc_F64.class, true) {
          @Override
          protected TupleDesc_F64 createInstance() {
            return describeImage.createDescription();
          }
        };
  }
Exemplo n.º 3
0
  /**
   * For all the images in the training data set it computes a {@link HistogramScene}. That data
   * structure contains the word histogram and the scene that the histogram belongs to.
   */
  private List<HistogramScene> computeHistograms(FeatureToWordHistogram_F64 featuresToHistogram) {

    List<String> scenes = getScenes();

    List<HistogramScene> memory; // Processed results which will be passed into the k-NN algorithm
    memory = new ArrayList<HistogramScene>();

    for (int sceneIndex = 0; sceneIndex < scenes.size(); sceneIndex++) {
      String scene = scenes.get(sceneIndex);
      System.out.println("   " + scene);
      List<String> imagePaths = train.get(scene);

      for (String path : imagePaths) {
        ImageUInt8 image = UtilImageIO.loadImage(path, ImageUInt8.class);

        // reset before processing a new image
        featuresToHistogram.reset();
        features.reset();
        describeImage.process(image, features, null);
        for (int i = 0; i < features.size; i++) {
          featuresToHistogram.addFeature(features.get(i));
        }
        featuresToHistogram.process();

        // The histogram is already normalized so that it sums up to 1.  This provides invariance
        // against the overall number of features changing.
        double[] histogram = featuresToHistogram.getHistogram();

        // Create the data structure used by the KNN classifier
        HistogramScene imageHist = new HistogramScene(NUMBER_OF_WORDS);
        imageHist.setHistogram(histogram);
        imageHist.type = sceneIndex;

        memory.add(imageHist);
      }
    }
    return memory;
  }