JavaCV version of OpenCV caffe_googlenet.cpp

samples/CaffeGooglenet.java

@@ -0,0 +1,122 @@
+/*
+ * 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
+
+}