Test the ANN

TestANN.java

+package opencvapp;
+
+import java.io.File;
+import java.util.Arrays;
+import org.opencv.core.Core;
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.MatOfFloat;
+import org.opencv.core.MatOfInt;
+import org.opencv.imgcodecs.Imgcodecs;
+import org.opencv.imgproc.Imgproc;
+import org.opencv.ml.ANN_MLP;
+
+public class PredictANN {
+    public static void main(String[] args) {
+        String currentDirectory = System.getProperty("user.dir");
+        System.load(currentDirectory + "\\OpenCVDLL\\x64\\" + Core.NATIVE_LIBRARY_NAME + ".dll");
+        Mat datasetHist = new Mat();
+        Mat datasetLabels = new Mat();
+        
+        String [] classesNames = {"a", "b", "c", "d"};
+        
+        for(int classIdx=0; classIdx<classesNames.length; classIdx++){
+            String currClassName = classesNames[classIdx];
+            String currClassDir = currentDirectory + "\\Dataset\\Test\\" + currClassName + "\\";
+            System.out.println("Current Class Directory : " + currClassDir);
+            
+            File folder = new File(currClassDir);
+            File[] listOfFiles = folder.listFiles();
+            
+            int imgCount = 0;
+
+            for (File listOfFile : listOfFiles) {
+                // Make sure we are working with a file and its extension is JPG
+                if (listOfFile.isFile() && (currClassDir + listOfFile.getName()).endsWith(".jpg")) {
+                    System.out.println("Class Index " + classIdx + "(" + currClassName + ")" + ", Image Index " + imgCount + "(" + listOfFile.getName() + ")");
+                    
+                    String currImgPath = currClassDir + listOfFile.getName();
+                    System.out.println(currImgPath);
+                    Mat imgBGR = Imgcodecs.imread(currImgPath);
+    //                int numRows = imgBGR.rows();
+    //                int numCols = imgBGR.cols();
+    //                int numChannels = imgRimgBGRGB.channels();
+    //                System.out.println("Image Size : (" + numRows + ", " + numCols + ", " + numChannels + ")");
+
+                    Mat imgHSV = new Mat();
+                    Imgproc.cvtColor(imgBGR, imgHSV, Imgproc.COLOR_BGR2HSV);
+
+                    // Preparing parameters of Imgproc.calcHist().
+                    MatOfInt selectedChannels = new MatOfInt(0);
+                    Mat imgHist = new Mat();
+                    MatOfInt histSize = new MatOfInt(180);
+                    MatOfFloat ranges = new MatOfFloat(0f, 180f);
+
+                    // Doc: https://docs.opencv.org/3.1.0/d6/dc7/group__imgproc__hist.html#ga4b2b5fd75503ff9e6844cc4dcdaed35d
+                    Imgproc.calcHist(Arrays.asList(imgHSV), selectedChannels, new Mat(), imgHist, histSize, ranges);
+
+                    // Transposing the histogram Mat from being 1D column vector to be 1D row vector.
+                    imgHist = imgHist.t();
+                    System.out.println("Hue Channel Hist : " + imgHist.dump());
+                System.out.println("Image Hist Size : (" + imgHist.rows() + ", " + imgHist.cols()+ ")\n");
+
+                    // Inserting the extracted histogram of the current image into the Mat collecting the histograms of all images.
+                    datasetHist.push_back(imgHist);
+                    datasetLabels.push_back(new MatOfInt(classIdx));
+                    
+                    imgCount++;
+                }
+            }
+        }
+        
+        // Converting the type of the features & labels Mats into CV_32F because ANN accepts data of this type.
+        datasetHist.convertTo(datasetHist, CvType.CV_32F);
+        datasetLabels.convertTo(datasetLabels, CvType.CV_32F);
+        
+        System.out.println("Dataset Hist Size : (" + datasetHist.rows() + ", " + datasetHist.cols()+ ")");
+        System.out.println("Dataset Label Size : (" + datasetLabels.rows() + ", " + datasetLabels.cols()+ ")");
+
+        ANN_MLP ANN = ANN_MLP.load(currentDirectory + "\\OpenCV_ANN_Fruits.yml");
+        
+        double num_correct_predictions = 0;
+        for (int i = 0; i < datasetHist.rows(); i++) {
+            Mat sample = datasetHist.row(i);
+            double correct_label = datasetLabels.get(i, 0)[0];
+
+            Mat results = new Mat();
+            ANN.predict(sample, results, 0);
+
+            double response = results.get(0, 0)[0];
+            int predicted_label = (int) Math.round(response);
+            
+            System.out.println("Predicted Score : " + response + ", Predicted Label : " + predicted_label + ", Correct Label : " + correct_label);
+
+            if (predicted_label == correct_label) {
+                num_correct_predictions += 1;
+            }
+        }
+        
+        double accuracy = (num_correct_predictions / datasetHist.rows()) * 100;
+        System.out.println("Accuracy : " + accuracy);
+
+    }    
+}