/**
* 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();
}
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();
}
};
}
/**
* 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;
}
