/**
* 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();
}
/**
* Process all the data in the training data set to learn the classifications. See code for
* details.
*/
public void learnAndSave() {
System.out.println("======== Learning Classifier");
// Either load pre-computed words or compute the words from the training images
AssignCluster<double[]> assignment;
if (new File(CLUSTER_FILE_NAME).exists()) {
assignment = UtilIO.load(CLUSTER_FILE_NAME);
} else {
System.out.println(" Computing clusters");
assignment = computeClusters();
}
// Use these clusters to assign features to words
FeatureToWordHistogram_F64 featuresToHistogram =
new FeatureToWordHistogram_F64(assignment, HISTOGRAM_HARD);
// Storage for the work histogram in each image in the training set and their label
List<HistogramScene> memory;
if (!new File(HISTOGRAM_FILE_NAME).exists()) {
System.out.println(" computing histograms");
memory = computeHistograms(featuresToHistogram);
UtilIO.save(memory, HISTOGRAM_FILE_NAME);
}
}
