CaffeGooglenet.java

/*
 * JavaCV version of OpenCV caffe_googlenet.cpp 
 * https://github.com/ludv1x/opencv_contrib/blob/master/modules/dnn/samples/caffe_googlenet.cpp
 *
 * Paolo Bolettieri <paolo.bolettieri@gmail.com>
 */

import static org.bytedeco.javacpp.opencv_core.minMaxLoc;
import static org.bytedeco.javacpp.opencv_dnn.createCaffeImporter;
import static org.bytedeco.javacpp.opencv_imgcodecs.imread;
import static org.bytedeco.javacpp.opencv_imgproc.resize;

import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;

import org.bytedeco.javacpp.opencv_core.Mat;
import org.bytedeco.javacpp.opencv_core.Point;
import org.bytedeco.javacpp.opencv_core.Size;
import org.bytedeco.javacpp.opencv_dnn.Blob;
import org.bytedeco.javacpp.opencv_dnn.Importer;
import org.bytedeco.javacpp.opencv_dnn.Net;

public class CaffeGooglenet {
	
	/* Find best class for the blob (i. e. class with maximal probability) */
	public static void getMaxClass(Blob probBlob, Point classId, double[] classProb) {
		Mat probMat =probBlob.matRefConst().reshape(1, 1); //reshape the blob to 1x1000 matrix
	    minMaxLoc(probMat, null, classProb, null, classId, null);
	}

	public static List<String> readClassNames() {
		String filename = "synset_words.txt";
		List<String> classNames = null;
		
		try (BufferedReader br = new BufferedReader(new FileReader(new File(filename)))) {
			classNames = new ArrayList<String>();
			String name = null;
			while ((name = br.readLine()) != null) {
				classNames.add(name.substring(name.indexOf(' ')+1));
			} 
		} catch (IOException ex) {  
		  System.err.println("File with classes labels not found " + filename);  
		  System.exit(-1);
		} 
	    return classNames;
	}

	public static void main(String[] args) throws Exception {
	    String modelTxt = "bvlc_googlenet.prototxt";
	    String modelBin = "bvlc_googlenet.caffemodel";
	    String imageFile = (args.length > 0) ? args[0] : "space_shuttle.jpg";

	    //! [Create the importer of Caffe model]
	    Importer importer = null;
	  //Try to import Caffe GoogleNet model
	    try {
	        importer = createCaffeImporter(modelTxt, modelBin);
	    	//Importer can throw errors, we will catch them
	    } catch (Exception e) {
	        e.printStackTrace();
	    }
	    //! [Create the importer of Caffe model]

	    if (importer == null) {
	        System.err.println("Can't load network by using the following files: ");
	        System.err.println("prototxt:   " + modelTxt);
	        System.err.println("caffemodel: " + modelBin);
	        System.err.println("bvlc_googlenet.caffemodel can be downloaded here:");
	        System.err.println("http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel");
	        System.exit(-1);
	    }

	    //! [Initialize network]
	    Net net = new Net();
	    importer.populateNet(net);
	    importer.close();
	    //We don't need importer anymore
	    //! [Initialize network]

	    //! [Prepare blob]
	    Mat img = imread(imageFile);
	    
	    if (img.empty()) {
	    	System.err.println("Can't read image from the file: " + imageFile);
	    	System.exit(-1);
	    }

	    resize(img, img, new Size(224, 224));       //GoogLeNet accepts only 224x224 RGB-images
	    Blob inputBlob = new Blob(img);   //Convert Mat to dnn::Blob image batch
	    //! [Prepare blob]

	    //! [Set input blob]
	    net.setBlob(".data", inputBlob);        //set the network input
	    //! [Set input blob]

	    //! [Make forward pass]
	    net.forward();                          //compute output
	    //! [Make forward pass]

	    //! [Gather output]
	    Blob prob = net.getBlob("prob");   //gather output of "prob" layer

	    Point classId = new Point();
	    double[] classProb = new double[1];
	    getMaxClass(prob, classId, classProb);//find the best class
	    //! [Gather output]

	    //! [Print results]
	    List<String> classNames = readClassNames();

	    System.out.println("Best class: #" + classId.x() + " '" + classNames.get(classId.x()) + "'");
	    System.out.println("Best class: #" + classId.x());
	    System.out.println("Probability: " + classProb[0] * 100 + "%");

	    //! [Print results]
	} //main

}