Delete TestANN.java

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);
-
-    }    
-}