boofcv.examples.recognition.ExampleClassifySceneKnn Maven / Gradle / Ivy

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/*
 * Copyright (c) 2022, Peter Abeles. All Rights Reserved.
 *
 * This file is part of BoofCV (http://boofcv.org).
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package boofcv.examples.recognition;

import boofcv.abst.feature.dense.DescribeImageDense;
import boofcv.alg.bow.ClusterVisualWords;
import boofcv.alg.bow.LearnSceneFromFiles;
import boofcv.alg.scene.ClassifierKNearestNeighborsBow;
import boofcv.alg.scene.FeatureToWordHistogram_F64;
import boofcv.alg.scene.HistogramScene;
import boofcv.alg.scene.KdTreeHistogramScene_F64;
import boofcv.factory.feature.dense.ConfigDenseSurfFast;
import boofcv.factory.feature.dense.DenseSampling;
import boofcv.factory.feature.dense.FactoryDescribeImageDense;
import boofcv.gui.image.ShowImages;
import boofcv.gui.learning.ConfusionMatrixPanel;
import boofcv.io.UtilIO;
import boofcv.io.image.UtilImageIO;
import boofcv.struct.feature.TupleDesc_F64;
import boofcv.struct.image.GrayU8;
import boofcv.struct.learning.Confusion;
import deepboof.io.DeepBoofDataBaseOps;
import org.ddogleg.clustering.AssignCluster;
import org.ddogleg.clustering.ComputeClusters;
import org.ddogleg.clustering.ConfigKMeans;
import org.ddogleg.clustering.FactoryClustering;
import org.ddogleg.nn.FactoryNearestNeighbor;
import org.ddogleg.nn.NearestNeighbor;

import java.io.File;
import java.io.IOException;
import java.io.UncheckedIOException;
import java.util.ArrayList;
import java.util.List;

/**
 * 
 * Example of how to train a K-NN bow-of-word classifier for scene recognition. The resulting classifier
 * produces results which are correct 52.2% of the time. To provide a point of comparison, randomly selecting
 * a scene is about 6.7% accurate, SVM One vs One RBF classifier can produce accuracy of around 74% and
 * other people using different techniques claim to have achieved around 85% accurate with more advanced
 * techniques.
 * 
 *
 * Training Steps:
 * 
 * Compute dense SURF features across the training data set.
 * Cluster using k-means to create works.
 * For each image compute the histogram of words found in the image
 * Save word histograms and image scene labels in a classifier
 * 
 *
 * Testing Steps:
 * 
 * For each image in the testing data set compute its histogram
 * Look up the k-nearest-neighbors for that histogram
 * Classify an image by by selecting the scene type with the most neighbors
 * 
 *
 * NOTE: Scene recognition is still very much a work in progress in BoofCV and the code is likely to be
 * significantly modified in the future.
 *
 * @author Peter Abeles
 */
public class ExampleClassifySceneKnn extends LearnSceneFromFiles {

	// Tuning parameters
	public static int NUMBER_OF_WORDS = 100;
	public static boolean HISTOGRAM_HARD = true;
	public static int NUM_NEIGHBORS = 10;
	public static int MAX_KNN_ITERATIONS = 100;

	// Files intermediate results are stored in
	public static final String CLUSTER_FILE_NAME = "clusters.obj";
	public static final String HISTOGRAM_FILE_NAME = "histograms.obj";

	// Algorithms
	ClusterVisualWords cluster;
	DescribeImageDense describeImage;
	NearestNeighbor nn;

	ClassifierKNearestNeighborsBow classifier;

	public ExampleClassifySceneKnn( final DescribeImageDense describeImage,
									ComputeClusters clusterer,
									NearestNeighbor nn ) {
		this.describeImage = describeImage;
		this.cluster = new ClusterVisualWords(clusterer, 0xFEEDBEEF);
		this.nn = nn;
	}

	/**
	 * 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 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
		var featuresToHistogram = new FeatureToWordHistogram_F64(assignment, HISTOGRAM_HARD);

		// Storage for the work histogram in each image in the training set and their label
		List memory;

		if (!new File(HISTOGRAM_FILE_NAME).exists()) {
			System.out.println(" computing histograms");
			memory = computeHistograms(featuresToHistogram);
			UtilIO.save(memory, HISTOGRAM_FILE_NAME);
		}
	}

	/**
	 * Extract dense features across the training set. Then clusters are found within those features.
	 */
	private AssignCluster computeClusters() {
		System.out.println("Image Features");

		// Compute features in the training image set
		var features = new ArrayList();
		for (String scene : train.keySet()) {
			List imagePaths = train.get(scene);
			System.out.println("   " + scene);

			for (String path : imagePaths) {
				GrayU8 image = UtilImageIO.loadImage(path, GrayU8.class);
				describeImage.process(image);

				// the descriptions will get recycled on the next call, so create a copy
				for (TupleDesc_F64 d : describeImage.getDescriptions()) {
					features.add(d.copy());
				}
			}
		}
		// 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 void loadAndCreateClassifier() {
		// load results from a file
		List memory = UtilIO.load(HISTOGRAM_FILE_NAME);
		AssignCluster assignment = UtilIO.load(CLUSTER_FILE_NAME);

		var featuresToHistogram = new FeatureToWordHistogram_F64(assignment, HISTOGRAM_HARD);


		// Provide the training results to K-NN and it will preprocess these results for quick lookup later on
		// Can use this classifier with saved results and avoid the

		classifier = new ClassifierKNearestNeighborsBow<>(nn, describeImage, featuresToHistogram);
		classifier.setClassificationData(memory, getScenes().size());
		classifier.setNumNeighbors(NUM_NEIGHBORS);
	}

	/**
	 * 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 computeHistograms( FeatureToWordHistogram_F64 featuresToHistogram ) {

		List scenes = getScenes();

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

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

			for (String path : imagePaths) {
				GrayU8 image = UtilImageIO.loadImage(path, GrayU8.class);

				// reset before processing a new image
				featuresToHistogram.reset();
				describeImage.process(image);
				for (TupleDesc_F64 d : describeImage.getDescriptions()) {
					featuresToHistogram.addFeature(d);
				}
				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;
	}

	@Override
	protected int classify( String path ) {
		GrayU8 image = UtilImageIO.loadImage(path, GrayU8.class);

		return classifier.classify(image);
	}

	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 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 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 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
	}
}