Example Scene Recognition

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Scene recognition
Example of scene recognition in action. The two closest matches to the top image are found. This works well even with very large databases, e.g. 100,000 images.

Example Code:


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Example Code

 * In BoofCV, scene recognition [1] refers to the problem of trying to identify photos of the same scene (not a single
 * object in the image) from different perspectives. This is of interest if you want to organize your photos, find
 * images to create a mosaic from, or cluster photos for 3D reconstruction. Solutions to this problem tend to
 * emphasise fast accurate retrieval from large databases.
 * [1] As far as I can tell there is no universal terminology for this specific sub problem. It is sometimes lumped
 * under Content Based Image Retrieval (CBIR), which is a very generic term.
 * @author Peter Abeles
public class ExampleSceneRecognition {
	public static void main( String[] args ) {
		String imagePath = UtilIO.pathExample("recognition/scene");
		List<String> images = UtilIO.listByPrefix(imagePath, null, ".jpg");

		SceneRecognition<GrayU8> recognizer;

		// Except for real-time applications or when there are more than a few hundred images, you might want to
		// just learn the dictionary from scratch
		File saveDirectory = new File("example_recognition");

		// Tell it to process gray U8 images
		ImageType<GrayU8> imageType = ImageType.SB_U8;

		// Used to look up images one at a time from various sources. In this case a list of images.
		var imageIterator = new ImageFileListIterator<>(images, imageType);

		if (false) {
			// Set the line above to true and it will download a pre-built model. Useful when you have a lot of images
			// or simply want to skip the learning step
			System.out.println("Downloading pre-built model");
			recognizer = RecognitionIO.downloadDefaultSceneRecognition(new File("downloaded_models"), imageType);
			recognizer.setVerbose(System.out, BoofMiscOps.hashSet(BoofVerbose.RECURSIVE));
		} else if (saveDirectory.exists()) {
			System.out.println("Loading previously generated model");
			recognizer = RecognitionIO.loadFeatureToScene(saveDirectory, imageType);
			recognizer.setVerbose(System.out, BoofMiscOps.hashSet(BoofVerbose.RECURSIVE));
		} else {
			// If many applications, learning a new model is a small fraction of the compute time and since its
			// fit to the images it will be more accurate than a generic pre-built model
			System.out.println("Creating a new model");
			var config = new ConfigFeatureToSceneRecognition();
			// Use a hierarchical vocabulary tree, which is very fast and also one of the more accurate approaches
			config.typeRecognize = ConfigFeatureToSceneRecognition.Type.NISTER_2006;

			recognizer = FactorySceneRecognition.createFeatureToScene(config, imageType);
			// This will print out a lot of debugging information to stdout
			recognizer.setVerbose(System.out, BoofMiscOps.hashSet(BoofVerbose.RECURSIVE));

			// Learn the model from the initial set of images

		// See if the recognition algorithm already has images loaded in to it
		if (recognizer.getImageIds(null).isEmpty()) {
			// Add images to the data base
			System.out.println("Adding images to the database");
			while (imageIterator.hasNext()) {
				GrayU8 image = imageIterator.next();
				recognizer.addImage(images.get(imageIterator.getIndex()), image);

			// This saves the model with the image database to disk
			System.out.println("Saving model");
			BoofMiscOps.profile(() -> RecognitionIO.saveFeatureToScene(
					(WrapFeatureToSceneRecognition<GrayU8, ?>)recognizer, saveDirectory), "");

		ListDisplayPanel gui = new ListDisplayPanel();

		// Specifies which image it will try to look up. In the example, related images are in sets of 3.
		int queryImage = 9;

		// Add the target which the other images are being matched against
		gui.addImage(UtilImageIO.loadImage(images.get(queryImage)), "Query", ScaleOptions.ALL);

		// Look up images
		DogArray<SceneRecognition.Match> matches = new DogArray<>(SceneRecognition.Match::new);
		recognizer.query(imageIterator.loadImage(queryImage),/* filter */ ( id ) -> true,/* limit */ 5, matches);
		for (int i = 0; i < matches.size; i++) {
			String file = matches.get(i).id;
			double error = matches.get(i).error;
			BufferedImage image = UtilImageIO.loadImage(file);
			String name = FilenameUtils.getBaseName(new File(file).getName());
			gui.addImage(image, String.format("%20s Error %6.3f", name, error), ScaleOptions.ALL);

		System.out.println("Total images = " + images.size());
		System.out.println(images.get(queryImage) + " -> " + matches.get(0).id + " matches.size=" + matches.size);

		ShowImages.showWindow(gui, "Similar Images by Features", true);