Ignore:
Timestamp:
2019-06-27T15:19:39+12:00 (5 years ago)
Author:
cpb16
Message:

Had break through with the refined houghlinesP algorithm overall accurarcy rate of 93%

Location:
other-projects/is-sheet-music-encore/trunk
Files:
8 edited

Legend:

Unmodified
Added
Removed

  • other-projects/is-sheet-music-encore/trunk/Makefile

    r33221 r33243  
    9090#HoughLinesP_refined
    9191run-classifier-houghlinesP-refined-test:
     92    #cd image-identification-terminal; rm log-houghlinesP-refined-test.txt
    9293        #Compile
    9394    cd image-identification-terminal; javac javaAccuracyCalculator.java
    … …  
    100101    @date
    101102        #Calculate the Accuracy of the classifier
    102     cd image-identification-terminal; java javaAccuracyCalculator log-houghlinesP-refined-test.txt houghlinesP-refined results.txt
     103    cd image-identification-terminal; java javaAccuracyCalculator log-houghlinesP-refined-test.txt houghlinesP-refined-test results.txt
    103104        #Display output of javaImageClassifier
    104105    cd image-identification-terminal; less log-houghlinesP-refined-test.txt
    … …  
    107108
    108109run-classifier-houghlinesP-refined-all:
     110    cd image-identification-terminal; rm log-houghlinesP-refined-MU.txt
    109111        #Compile
    110112    cd image-identification-terminal; javac javaAccuracyCalculator.java
    … …  
    113115    @echo starting classfier
    114116    @date
    115     cd image-identification-terminal; ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/download-images/MU houghlinesP log-houghlinesP-refined-MU.txt -all
     117    cd image-identification-terminal; ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/download-images/MU houghlinesP-refined log-houghlinesP-refined-MU.txt -all
    116118    @echo finished classifier
    117119    @date
    118120        #Calculate the Accuracy of the classifier
    119     cd image-identification-terminal; java javaAccuracyCalculator log-houghlinesP-refined-MU.txt houghlinesP results.txt
     121    cd image-identification-terminal; java javaAccuracyCalculator log-houghlinesP-refined-MU.txt houghlinesP-refined-all results.txt
    120122        #Display output of javaImageClassifier
    121123    cd image-identification-terminal; less log-houghlinesP-refined-MU.txt
    … …  
    145147    cd image-identification-terminal; less log-houghlinesP-refined-MU.txt
    146148
    147 run-classifier-houghlinesP-refined-test-remote:
    148         #Compile
    149     cd image-identification-terminal; javac javaAccuracyCalculator.java
    150     cd image-identification-terminal; javac -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar javaImageClassifier.java;
    151         #Run javaImageClassifier on all images in TestImages
    152     @echo starting classfier
    153     @date
    154     cd image-identification-terminal; ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/download-images/TestImages houghlinesP-refined log-TestImages.txt -all
    155     @echo finished classifier
    156     @date
    157         #Calculate the Accuracy of the classifier
    158     cd image-identification-terminal; java javaAccuracyCalculator log-TestImages.txt houghlinesP-refined-test results.txt
    159         #Display output of javaImageClassifier
    160     cd image-identification-terminal; less log-TestImages.txt
    161         #Display output of javaAccuracyCalculator output
    162     cd image-identification-terminal; less results.txt
    163     cd image-identification-terminal; rm log-TestImages.txt
    164 

  • other-projects/is-sheet-music-encore/trunk/image-identification-development/.idea/workspace.xml

    r33170 r33243  
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    … …  
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    … …  
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    … …  
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    … …  
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  • other-projects/is-sheet-music-encore/trunk/image-identification-development/src/Main.java

    r33170 r33243  
    44import org.opencv.imgcodecs.Imgcodecs;
    55import org.opencv.imgproc.Imgproc;
     6import org.opencv.photo.Photo;
    67import static org.opencv.imgcodecs.Imgcodecs.imwrite;
    78import java.awt.image.BufferedImage;
    89import java.awt.image.DataBufferByte;
    910import java.io.File;
     11import java.util.ArrayList;
    1012import javax.imageio.ImageIO;
    1113
    … …  
    3739
    3840    //GLOBAL_CONSTANTS
     41    static int CLASSIFIER_HOUGHLINESP_MIN   = 10;
     42    static int CLASSIFIER_HOUGHLINESP_MAX   = 65;
     43    static int HOUGHLINEP_THRESHOLD         = 10;
     44    static int MINLINECOUNT                 = 40;
     45    static double MAXLINEGAP                = 4;
     46    static double SLOPEGRADIENT             = 0.02;
    3947    //SHOULD TURN INTO ARGS
    4048
    … …  
    5967        return null;
    6068    }
    61 
    62     private static boolean Classifier(BufferedImage img){
     69    private static boolean ClassifierPixelCount(BufferedImage img){
    6370        try {
    6471            //Read file
    … …  
    8592            percentage = ((float)redCount/(float)pixelCount)*(float)100;
    8693            //If more than %10 and less than %50 then its sheet music!
    87             if(percentage > 10 && percentage < 50){ //MAKE THESE CONSTANTS!!
     94            if(percentage > CLASSIFIER_HOUGHLINESP_MIN && percentage < CLASSIFIER_HOUGHLINESP_MAX){ //MAKE THESE CONSTANTS!!
    8895                return true;}
    8996        }
    … …  
    93100        return false;
    94101    }
     102    private static boolean Classifier(int lineCount){
     103
     104           if(lineCount>MINLINECOUNT){
     105                return true;
     106            }
     107            else{
     108                return false;
     109            }
     110    }
     111
    95112
    96113    public static void main(String[] args) {
    97114        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
    98115        try {
     116            //temp array for terminalversion
     117
     118            ArrayList returnArray = new ArrayList();
     119            returnArray.add(true);
     120            returnArray.add(10);
     121
     122
    99123            //Variables
    100124            Mat edgesDetected = new Mat();
    101125            Mat edgesDetectedRGB = new Mat();
     126            Mat edgesExtra = new Mat();
    102127            Mat edgesDetectedRGBProb;
    103128            Mat edgeDoesntMakeSense;
    104129            Mat justEdges; //TESTING
     130
     131            String directory = "/Scratch/cpb16/is-sheet-music-encore/download-images/MU/";
     132            //!!!!!!!!!!!!!!!!!!!!!!!!!!!NOT!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
     133            //mdp.39015097852365-2.png 176 lines    Contents page.
     134            //mdp.39015097852555-3.png 76  lines
     135            String default_file = directory+"SheetMusic/mdp.39015080972303-3.png";
     136            //String default_file ="TestImages/NotNot/mdp.39015080972303-3.png";
     137
     138
     139            //System.out.println(default_file);
    105140            //String default_file = "TestImages/NotSheetMusic01.png";
    106             String default_file = "TestImages/NotSheetMusic02.png";
     141            //String default_file = "TestImages/NotSheetMusic02.png";
    107142            //String default_file = "TestImages/SheetMusic01.png";
    108143            //String default_file = "TestImages/SheetMusic02.png";
    109144            //String default_file = "TestImages/vLine.png";
    110145            String filename = ((args.length > 0) ? args[0] : default_file);
     146            File file = new File(filename);
     147            if(!file.exists()){System.err.println("Image not found: "+ filename);}
     148
     149            int horizontalLineCount =0;
    111150
    112151            // Load an image
    … …  
    114153            // Edge detection
    115154            //01 CANNY
    116             Imgproc.Canny(original, edgesDetected, 50, 200, 3, false);
     155            //Imgproc.Canny(original, edgesDetected, 50, 200, 3, false);
     156            //Imgproc.Canny(original, edgesDetected,0, 100, 3, false );
    117157            //Imgproc.Canny(original, edgesDetected,80, 120);
    118158            //02 BINARYINV
    119             //Imgproc.adaptiveThreshold(original, edgesDetected,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV,15, 2);
     159            Imgproc.adaptiveThreshold(original, edgesDetected,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV,15, 2);
     160
     161            //Imgproc.adaptiveThreshold(original, edgesExtra,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV,15, 2);
     162            //Imgproc.medianBlur(edgesExtra, edgesDetected, 3);
    120163            //03 BINARY
    121164            //Imgproc.adaptiveThreshold(original, edgesDetected,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY,15, 2);
    … …  
    127170
    128171
     172
     173
    129174            //Convert to RGB for future use
    130175            Imgproc.cvtColor(edgesDetected, edgesDetectedRGB, Imgproc.COLOR_GRAY2BGR);
    … …  
    136181            //(edgeDetectedImage, outputOfDetection(r,Ξ), resolution of rho, resolution of theta, threshold (minimum num of intersections)
    137182
    138             double minLineLength = edgesDetectedRGB.size().width/4;
    139             Imgproc.HoughLinesP(edgesDetected, linesP, 1, Math.PI / 180, 50, minLineLength, 10); // runs the actual detection
    140 
    141 
     183            double minLineLength = edgesDetectedRGB.size().width/8;
     184
     185            Imgproc.HoughLinesP(edgesDetected, linesP, 1, Math.PI / 720, HOUGHLINEP_THRESHOLD, minLineLength,MAXLINEGAP); // runs the actual detection
     186            //Imgproc.HoughLinesP(edgesDetected, linesP, 1, Math.PI / 180, HOUGHLINEP_THRESHOLD, minLineLength,MAXLINEGAP); // runs the actual detection
     187            System.out.println("Before Graident Filtering num lines: " + linesP.rows());
    142188
    143189            //Imgproc.HoughLinesP(edgesDetected,linesP,1,Math.PI/2, 50, 80, 5);
    … …  
    170216
    171217
    172 
    173 
    174 
    175 
    176                 if(m<=0.1) {
     218                if(m<=SLOPEGRADIENT) {
    177219                    //System.out.println("m: " + m);
    178                     Imgproc.line(edgesDetectedRGB, new Point(l[0], l[1]), new Point(l[2], l[3]), new Scalar(0, 0, 255), 3, Imgproc.LINE_AA, 0);
     220                    Imgproc.line(edgesDetectedRGB, new Point(l[0], l[1]), new Point(l[2], l[3]), new Scalar(0, 0, 255), 1, Imgproc.LINE_AA, 0);
     221                    horizontalLineCount++;
    179222                }
    180223
    … …  
    189232
    190233            BufferedImage toBeClassifiedImg = toBufferedImage(edgesDetectedRGB);
    191             System.out.println("Result: " + Classifier(toBeClassifiedImg));
    192 
     234            System.out.println("Result: " + Classifier(horizontalLineCount));
     235
     236
     237            System.out.println();
    193238            //Display Results
    194             //HighGui.imshow("Source", original);
     239            HighGui.imshow("Source", original);
    195240            //HighGui.imshow("Just Edges", justEdges); //TESTING
    196241            HighGui.imshow("Detected Lines (in red) - positive", edgesDetectedRGB);

  • other-projects/is-sheet-music-encore/trunk/image-identification-terminal/javaImageClassifier.java

    r33221 r33243  
    1212import javax.imageio.ImageIO;
    1313import java.util.logging.Logger;
     14import java.util.ArrayList;
    1415
    1516//REFERENCES:
    … …  
    2829public class javaImageClassifier{
    2930    //Constants
    30     static int CLASSIFIER_HOUGHLINESP_MIN = 5;
    31     static int CLASSIFIER_HOUGHLINESP_MAX = 40;
     31
     32    static int CLASSIFIER_HOUGHLINESP_MIN   = 10;
     33    static int CLASSIFIER_HOUGHLINESP_MAX   = 65;
     34    static int HOUGHLINEP_THRESHOLD         = 10;
     35    static int MINLINECOUNT                 = 40; //50
     36    static double MAXLINEGAP                = 4;
     37    static double SLOPEGRADIENT             = 0.02; //0.01
    3238
    3339    public static void main(String[] args) {   
    … …  
    3743            }
    3844            else {
     45                ArrayList result_refined = null;
    3946                Boolean result = null;
    4047                String imageFilename = args[0];
    4148                String classifierType = args[1];
    42                 String outputFilename = args[2];           
     49                String outputFilename = args[2];
     50                //Prep Writing output to disc
     51                File log = new File(outputFilename);
     52                FileWriter fileWriter = new FileWriter(log, true);
     53                BufferedWriter bw = new BufferedWriter(fileWriter);         
    4354                //Execute classifierType defined from arguement
     55               
     56                //Split output by tab for processing in next java program
     57                //imageFilename = 1, result = 3, classifierType = 4
    4458                switch(classifierType){
    4559                case "houghlinesP":
    4660                    result = setup_HoughLinesP(imageFilename); //true or false
     61                    bw.write("Filename:" + '\t' + imageFilename + '\t' + "Classified as:" + '\t' + result + '\t' + classifierType + '\n');
    4762                    break;
    4863                case "houghlinesP-refined":
    49                     result = setup_HoughLinesP_refined(imageFilename);
     64                    result_refined = setup_HoughLinesP_refined(imageFilename);
     65                    bw.write("Filename:" + '\t' + imageFilename + '\t' + "Classified as:" + '\t' + result_refined.get(0) + '\t' + "Number of lines:" + '\t' + result_refined.get(1) + '\t' + classifierType + '\n');
    5066                    break;
    5167                default:
    … …  
    5369                    break;
    5470                }           
    55                 //Write output to disc
    56                 File log = new File(outputFilename);
    57                 FileWriter fileWriter = new FileWriter(log, true);
    58                 BufferedWriter bw = new BufferedWriter(fileWriter);
    59                 //Split output by tab for processing in next java program
    60                 //imageFilename = 1, result = 3, classifierType = 4
    61                 bw.write("Filename:" + '\t' + imageFilename + '\t' + "Classified as:" + '\t' + result + '\t' + classifierType + '\n');
     71
    6272                bw.close();             
    6373            }
    … …  
    8191        Mat edgesDetected = new Mat();
    8292        Mat edgesDetectedRGB = new Mat();
     93        Mat edgesExtra = new Mat();
    8394        Mat edgesDetectedRGBProb;
    8495        // Load an image
    … …  
    108119 }
    109120 
    110      private static Boolean setup_HoughLinesP_refined(String filename){
     121     private static ArrayList setup_HoughLinesP_refined(String filename){
    111122    System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
    112123    Boolean isSheetMusic = null;
     124    ArrayList returnArray = new ArrayList();
    113125    try{
     126       
    114127        //Variables
     128        int horizontalLineCount =0;
    115129        Mat edgesDetected = new Mat();
    116130        Mat edgesDetectedRGB = new Mat();
     131        Mat edgesExtra = new Mat();
    117132        Mat edgesDetectedRGBProb;
    118133        // Load an image
    119134        Mat original = Imgcodecs.imread(filename, Imgcodecs.IMREAD_GRAYSCALE);
    120135        // Edge detection
    121         Imgproc.Canny(original, edgesDetected, 50, 200, 3, false);
     136        //Imgproc.Canny(original, edgesDetected, 50, 200, 3, false);
     137        Imgproc.adaptiveThreshold(original, edgesDetected,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV,15, 2);
     138        //Imgproc.medianBlur(edgesExtra, edgesDetected, 3);
     139       
     140       
    122141        //Copy edges to the images that will display the results in BGR
    123142        Imgproc.cvtColor(edgesDetected, edgesDetectedRGB, Imgproc.COLOR_GRAY2BGR);
    124143        // Probabilistic Line Transform
    125144        Mat linesP = new Mat(); // will hold the results of the detection
    126         double minLineLength = edgesDetectedRGB.size().width/4;
    127         //Imgproc.HoughLinesP(edgesDetected, linesP, 1, Math.PI / 180, 50, minLineLength, 10);// runs the actual detection
    128             Imgproc.HoughLinesP(edgesDetected, linesP, 1, Math.PI / 180, 10, minLineLength, 5);// remote testing
     145        double minLineLength = edgesDetectedRGB.size().width/8;
     146        //Imgproc.HoughLinesP(edgesDetected, linesP, 1, Math.PI / 180, 10, minLineLength, MAXLINEGAP);// runs the actual detection
     147        Imgproc.HoughLinesP(edgesDetected, linesP, 1, Math.PI / 720, HOUGHLINEP_THRESHOLD, minLineLength, MAXLINEGAP); //TESTING
    129148        // Draw the lines
    130149       
    … …  
    139158            //System.out.println(l[2]);
    140159            //System.out.println(l[3]);
    141             if(m<0.1) {
     160            if(m<SLOPEGRADIENT) {
    142161                //System.out.println("m: " + m);
    143                 Imgproc.line(edgesDetectedRGB, new Point(l[0], l[1]), new Point(l[2], l[3]), new Scalar(0, 0, 255), 3, Imgproc.LINE_AA, 0);
     162                Imgproc.line(edgesDetectedRGB, new Point(l[0], l[1]), new Point(l[2], l[3]), new Scalar(0, 0, 255), 1, Imgproc.LINE_AA, 0);
     163                horizontalLineCount++;
    144164            }
    145165        }
    … …  
    148168        BufferedImage toBeClassifiedImg = toBufferedImage(edgesDetectedRGB);
    149169        //Calculate if its sheet music or not
    150         isSheetMusic = classifier_HoughLinesP(toBeClassifiedImg); 
    151 
     170        isSheetMusic = classifier_HoughLinesP_refined(horizontalLineCount); 
     171        returnArray.add(isSheetMusic);
     172        returnArray.add(horizontalLineCount);
    152173           
    153174    }
    … …  
    155176            System.err.println(e);
    156177        }
    157         return isSheetMusic;
     178        return returnArray;
    158179 }
    159180 
    … …  
    193214        return false;
    194215    }
     216   
     217    private static boolean classifier_HoughLinesP_refined(int lineCount){
     218        if(lineCount>MINLINECOUNT){
     219            return true;
     220        }
     221        else{
     222            return false;
     223        }
     224    }
     225   
    195226        private static  BufferedImage toBufferedImage(Mat mat){
    196227        //MOSTLY COPY PASTE!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

  • other-projects/is-sheet-music-encore/trunk/image-identification-terminal/results.txt

    r33221 r33243  
    2222
    2323
    24 Date: Sun Jun 23 17:48:16 NZST 2019
     24BEST SO FAR
     25Date: Tue Jun 25 16:02:11 NZST 2019
     26Classifier: houghlinesP-refined
     27truePositive: 80
     28falseNegative: 20
     29falsePositve: 11
     30trueNegative: 89
     31SheetMusicAccuracyRate: 80.0%
     32NotSheetMusicAccuracyRate: 89.0%
     33OverallAccuracyRate: 84.5%
     34
     35Date: Tue Jun 25 16:21:55 NZST 2019
     36Classifier: houghlinesP
     37truePositive: 3063
     38falseNegative: 98
     39falsePositve: 596
     40trueNegative: 1244
     41SheetMusicAccuracyRate: 96.89972%
     42NotSheetMusicAccuracyRate: 67.608696%
     43OverallAccuracyRate: 86.12277%
     44
     45Date: Tue Jun 25 20:06:21 NZST 2019
     46Classifier: houghlinesP-refined-all
     47truePositive: 1723
     48falseNegative: 1438
     49falsePositve: 86
     50trueNegative: 1754
     51SheetMusicAccuracyRate: 54.508064%
     52NotSheetMusicAccuracyRate: 95.32609%
     53OverallAccuracyRate: 69.52609%
     54
     55Date: Tue Jun 25 20:13:22 NZST 2019
     56Classifier: houghlinesP-refined
     57truePositive: 80
     58falseNegative: 20
     59falsePositve: 11
     60trueNegative: 89
     61SheetMusicAccuracyRate: 80.0%
     62NotSheetMusicAccuracyRate: 89.0%
     63OverallAccuracyRate: 84.5%
     64
     65Date: Tue Jun 25 22:40:22 NZST 2019
     66Classifier: houghlinesP-refined-all-canny
     67truePositive: 1124
     68falseNegative: 2037
     69falsePositve: 61
     70trueNegative: 1779
     71SheetMusicAccuracyRate: 35.558365%
     72NotSheetMusicAccuracyRate: 96.684784%
     73OverallAccuracyRate: 58.048393%
     74
     75MAX 65
     76MIN 10
     77ALL MU
     78RESULT:
     79
     80
     81Date: Wed Jun 26 20:08:09 NZST 2019
     82Classifier: houghlinesP-refined-all-canny
     83truePositive: 1124
     84falseNegative: 2037
     85falsePositve: 61
     86trueNegative: 1779
     87SheetMusicAccuracyRate: 35.558365%
     88NotSheetMusicAccuracyRate: 96.684784%
     89OverallAccuracyRate: 58.048393%
     90
     91Date: Wed Jun 26 20:44:04 NZST 2019
     92Classifier: houghlinesP-refined-all
     93truePositive: 1725
     94falseNegative: 1436
     95falsePositve: 87
     96trueNegative: 1753
     97SheetMusicAccuracyRate: 54.571335%
     98NotSheetMusicAccuracyRate: 95.271736%
     99OverallAccuracyRate: 69.54609%
     100
     101BOTH PRE THURSDAY MEETING
     102Date: Wed Jun 26 21:40:37 NZST 2019
     103Classifier: houghlinesP
     104truePositive: 3103
     105falseNegative: 58
     106falsePositve: 660
     107trueNegative: 1180
     108SheetMusicAccuracyRate: 98.16514%
     109NotSheetMusicAccuracyRate: 64.13044%
     110OverallAccuracyRate: 85.642876%
     111
     112Date: Wed Jun 26 21:40:37 NZST 2019
     113Classifier: houghlinesP-refined
     114truePositive: 1725
     115falseNegative: 1436
     116falsePositve: 87
     117trueNegative: 1753
     118SheetMusicAccuracyRate: 54.571335%
     119NotSheetMusicAccuracyRate: 95.271736%
     120OverallAccuracyRate: 69.54609%
     121
     122
     123POST MEETING
     124
     125
     126Date: Thu Jun 27 11:56:02 NZST 2019
     127Classifier: houghlinesP-refined
     128truePositive: 0
     129falseNegative: 0
     130falsePositve: 0
     131trueNegative: 0
     132SheetMusicAccuracyRate: 0.0%
     133NotSheetMusicAccuracyRate: 0.0%
     134OverallAccuracyRate: 0.0%
     135
     136Date: Thu Jun 27 12:04:40 NZST 2019
    25137Classifier: houghlinesP-refined-test
    26 truePositive: 5
    27 falseNegative: 5
    28 falsePositve: 0
    29 trueNegative: 10
    30 SheetMusicAccuracyRate: 50.0%
    31 NotSheetMusicAccuracyRate: 100.0%
    32 OverallAccuracyRate: 75.0%
     138truePositive: 7
     139falseNegative: 3
     140falsePositve: 5
     141trueNegative: 5
     142SheetMusicAccuracyRate: 70.0%
     143NotSheetMusicAccuracyRate: 50.0%
     144OverallAccuracyRate: 60.000004%
    33145
    34 Date: Sun Jun 23 17:50:39 NZST 2019
    35 Classifier: houghlinesP-refined-test
    36 truePositive: 5
    37 falseNegative: 5
    38 falsePositve: 0
    39 trueNegative: 10
    40 SheetMusicAccuracyRate: 50.0%
    41 NotSheetMusicAccuracyRate: 100.0%
    42 OverallAccuracyRate: 75.0%
     146LINE GAP OF 10
    43147
     148Date: Thu Jun 27 12:24:59 NZST 2019
     149Classifier: houghlinesP-refined-all
     150truePositive: 2808
     151falseNegative: 353
     152falsePositve: 307
     153trueNegative: 1533
     154SheetMusicAccuracyRate: 88.83265%
     155NotSheetMusicAccuracyRate: 83.315216%
     156OverallAccuracyRate: 86.802635%
     157
     158
     159LINE GAP OF 4
     160
     161
     162Date: Thu Jun 27 14:35:24 NZST 2019
     163Classifier: houghlinesP-refined-all
     164truePositive: 2626
     165falseNegative: 535
     166falsePositve: 77
     167trueNegative: 1763
     168SheetMusicAccuracyRate: 83.074974%
     169NotSheetMusicAccuracyRate: 95.815216%
     170OverallAccuracyRate: 87.76244%
     171
     172MINLINECOUNT=40
     173gradient = 0.02
     174Date: Thu Jun 27 15:10:14 NZST 2019
     175Classifier: houghlinesP-refined-all
     176truePositive: 2961
     177falseNegative: 200
     178falsePositve: 136
     179trueNegative: 1704
     180SheetMusicAccuracyRate: 93.67289%
     181NotSheetMusicAccuracyRate: 92.608696%
     182OverallAccuracyRate: 93.28135%
     183

  • other-projects/is-sheet-music-encore/trunk/image-identification-terminal/runClassifer.sh

    r33221 r33243  
    22
    33if [ $# != 4 ] ; then
    4   echo "Usage: ./testClassifier.sh file_name classifier_type output_filename num_doc(-a = all -test = 100)" 1>&2
     4  echo "Usage: ./testClassifier.sh file_name classifier_type output_filename type(-a:all -test:100)" 1>&2
    55  exit 1
    66fi
    … …  
    88folder_name=$1
    99classifier_type=$2
    10 output_filename=$3
    11 num_doc=$4
     10output=$3
    1211i=0
     12j=0
    1313case "$4" in
    1414    -all)
    … …  
    1616        for file_name in $folder_name/*/*.png; do
    1717            echo $file_name
    18             echo $output_filename
     18            echo $output
    1919            echo $i
    2020            echo ""
    21             java -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar:. -Djava.library.path=/Scratch/cpb16/opencv-3.4.2/build/lib/  javaImageClassifier $file_name $classifier_type $output_filename
     21            java -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar:. -Djava.library.path=/Scratch/cpb16/opencv-3.4.2/build/lib/  javaImageClassifier $file_name $classifier_type $output
    2222            i=$[$i+1]
    2323        done
    24         ;;
     24    ;;
     25       
    2526    -test)
    26         #Runs javaImageClassifier on 100 images
    27         for file_name in $folder_name/*/*.png; do
    28         if [ $i -lt 200 ] ; then
    29             echo $file_name
    30             echo $output_filename
    31             echo $i
    32             echo ""
    33             java -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar:. -Djava.library.path=/Scratch/cpb16/opencv-3.4.2/build/lib/  javaImageClassifier $file_name $classifier_type $output_filename
    34             i=$[$i+1]
    35         else
    36             exit 0
    37         fi     
     27        #Runs javaImageClassifier on 100 images of each
     28        for file_name in $folder_name/SheetMusic/*.png; do
     29            if [ $i -lt 10 ] ; then
     30                echo $file_name
     31                echo $output
     32                echo $i
     33                echo ""
     34                java -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar:. -Djava.library.path=/Scratch/cpb16/opencv-3.4.2/build/lib/  javaImageClassifier $file_name $classifier_type $output
     35                i=$[$i+1]       
     36            fi
    3837        done
    39         ;;
    40     -small)
    41         #Runs javaImageClassifier on 100 images
    42         for file_name in $folder_name/*/*.png; do
    43         if [ $i -lt 50 ] ; then
    44             echo $file_name
    45             echo $output_filename
    46             echo $i
    47             echo ""
    48             java -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar:. -Djava.library.path=/Scratch/cpb16/opencv-3.4.2/build/lib/  javaImageClassifier $file_name $classifier_type $output_filename
    49             i=$[$i+1]
    50         else
    51             exit 0
    52         fi     
     38        for file_name in $folder_name/NotSheetMusic/*.png; do
     39            if [ $j -lt 10 ] ; then
     40                echo $file_name
     41                echo $output
     42                echo $j
     43                echo ""
     44                java -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar:. -Djava.library.path=/Scratch/cpb16/opencv-3.4.2/build/lib/  javaImageClassifier $file_name $classifier_type $output
     45                j=$[$j+1]       
     46            fi
    5347        done
    54         ;;
     48    ;;
     49           
    5550esac       
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