Changeset 33304


Ignore:
Timestamp:
2019-07-06T15:45:02+12:00 (5 years ago)
Author:
cpb16
Message:

Backup for computer crash, only lost 5 lines of code in development section. They have been rewritten.

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

Legend:

Unmodified
Added
Removed

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

    r33243 r33304  
    125125    cd image-identification-terminal; less results.txt
    126126
    127 run-all:
     127run-both-MU:
    128128        #Compile
    129129    cd image-identification-terminal; javac javaAccuracyCalculator.java
    … …  
    147147    cd image-identification-terminal; less log-houghlinesP-refined-MU.txt
    148148
     149run-houghlineP-refined-corpus:
     150    cd image-identification-terminal; cd log-all; rm *.txt
     151        #Compile
     152    cd image-identification-terminal; javac javaAccuracyCalculator.java
     153    cd image-identification-terminal; javac -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar javaImageClassifier.java;
     154        #Run javaImageClassifier on all images in Corpus
     155    cd image-identification-terminal; ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/download-images/MU houghlinesP-refined log-all/log-houghlinesP-refined-MU.txt -all
     156    cd image-identification-terminal; ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/download-images/BK houghlinesP-refined log-all/log-houghlinesP-refined-BK.txt -all
     157    cd image-identification-terminal; ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/download-images/MP houghlinesP-refined log-all/log-houghlinesP-refined-MP.txt -all
     158    cd image-identification-terminal; ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/download-images/SE houghlinesP-refined log-all/log-houghlinesP-refined-SE.txt -all
     159        #Calculate the Accuracy of the classifier
     160    cd image-identification-terminal; java javaAccuracyCalculator log-all/log-houghlinesP-refined-MU.txt houghlinesP-refined-MU log-all/results.txt
     161    cd image-identification-terminal; java javaAccuracyCalculator log-all/log-houghlinesP-refined-BK.txt houghlinesP-refined-BK log-all/results.txt
     162    cd image-identification-terminal; java javaAccuracyCalculator log-all/log-houghlinesP-refined-MP.txt houghlinesP-refined-MP log-all/results.txt
     163    cd image-identification-terminal; java javaAccuracyCalculator log-all/log-houghlinesP-refined-SE.txt houghlinesP-refined-SE log-all/results.txt
     164        #Display the results
     165    cd image-identification-terminal; less log-all/results.txt
     166

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

    r33243 r33304  
    1010import java.io.File;
    1111import java.util.ArrayList;
     12import java.util.Collection;
     13import java.util.Collections;
    1214import javax.imageio.ImageIO;
    1315
    … …  
    1719//https://www.wikihow.com/Calculate-Percentage-in-Java
    1820//https://riptutorial.com/opencv/example/21963/converting-an-mat-object-to-an-bufferedimage-object
     21//https://beginnersbook.com/2013/12/java-arraylist-of-object-sort-example-comparable-and-comparator/
     22//https://www.programiz.com/java-programming/examples/standard-deviation
     23//https://www.geeksforgeeks.org/how-to-remove-duplicates-from-arraylist-in-java/
     24
    1925
    2026
    … …  
    3844public class Main {
    3945
     46    //DEPENDENT FUNCTIONS AND CLASSES
     47    static class StartAndEndPoint {
     48        //PRIVATES
     49        private Point _p1;
     50        private Point _p2;
     51        //CONSTRUCTOR
     52        public StartAndEndPoint(Point p1, Point p2){
     53            _p1 = p1;
     54            _p2 = p2;
     55        }
     56        //GETTERS
     57        public Point getP1(){
     58            return _p1;
     59        }
     60        public Point getP2(){
     61            return  _p2;
     62        }
     63        //SETTERS
     64        public void setP1(Point p1){
     65            _p1 = p1;
     66        }
     67        public void setP2(Point p2){
     68            _p2 = p2;
     69        }
     70
     71        //ToString
     72        public  String toString(){
     73            return "Start: " + _p1 + " End: " + _p2;
     74        }
     75        /*
     76        //CompareToOverride
     77        //Compares start point y co ordinates of input PointArray
     78        //With this. start point y co ordinate
     79        @Override
     80        public double compareTo(StartAndEndPoint comparePointArray){
     81            Point comparePoint = (comparePointArray.getP1());
     82            return (this.getP1().y) - (comparePoint.y);
     83        }
     84        */
     85    }
     86    public static <T> ArrayList<T> removeDuplicates(ArrayList<T> list) {
     87        //DIRECTLY COPIED//DIRECTLY COPIED//DIRECTLY COPIED//DIRECTLY COPIED//DIRECTLY COPIED//DIRECTLY COPIED
     88        // Function to remove duplicates from an ArrayList
     89        // Create a new ArrayList
     90        ArrayList<T> newList = new ArrayList();
     91        // Traverse through the first list
     92        for (T element : list) {
     93            // If this element is not present in newList
     94            // then add it
     95            if (!newList.contains(element)) {
     96                newList.add(element);
     97            }
     98        }
     99        // return the new list
     100        return newList;
     101        //DIRECTLY COPIED//DIRECTLY COPIED//DIRECTLY COPIED//DIRECTLY COPIED//DIRECTLY COPIED//DIRECTLY COPIED
     102    }
     103    public static double StandardDeviation(double parseArray[])
     104    {
     105        double mean;
     106        double sum =0;
     107        double standardDeviation = 0;
     108        //calculate sum of array
     109        for(int i =0; i > parseArray.length; i++){
     110            sum += parseArray[i];
     111        }
     112        //calculate mean of array
     113        mean = sum/parseArray.length;
     114        //calculate SD of array
     115        for(int j =0; j > parseArray.length; j++){
     116            standardDeviation += Math.pow(parseArray[j]-mean, 2);
     117        }
     118        return Math.sqrt(standardDeviation/parseArray.length);
     119    }
     120
    40121    //GLOBAL_CONSTANTS
    41122    static int CLASSIFIER_HOUGHLINESP_MIN   = 10;
    … …  
    43124    static int HOUGHLINEP_THRESHOLD         = 10;
    44125    static int MINLINECOUNT                 = 40;
    45     static double MAXLINEGAP                = 4;
     126    static double MAXLINEGAP                = 1;  //4
    46127    static double SLOPEGRADIENT             = 0.02;
    47128    //SHOULD TURN INTO ARGS
    48129
     130    //CLASSIFYING FUNCTIONS
    49131    private static  BufferedImage toBufferedImage(Mat mat){
    50132        //MOSTLY COPY PASTE!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
    … …  
    100182        return false;
    101183    }
    102     private static boolean Classifier(int lineCount){
     184    private static boolean ClassifierLineCount(int lineCount){
    103185
    104186           if(lineCount>MINLINECOUNT){
    … …  
    109191            }
    110192    }
     193    private static ArrayList ClassifierLineClusterOLD(BufferedImage img){
     194        ArrayList returnArray = new ArrayList();
     195        try {
     196
     197            //IF THIS WORKS THEN IMPLEMENT A VERSION THAT USES POINTS from the draw line code.
     198            //ALSO CHECK OUT K NEAREST NEIGHBOR?
     199            //0xFFFF0000 = RED
     200
     201            //go thru every pixel until find red pixel
     202            //get y pos of red pixel
     203            //continue with loop until find another red pixel
     204            //get y pos of red pixel
     205            //compare y pos (if close together then continue loop) else break
     206
     207            int x = img.getWidth();
     208            int y = img.getHeight();
     209            int closeLineCount = 0;
     210            ArrayList<Integer> redPixelYpos = new ArrayList<Integer>();
     211
     212
     213
     214            //Go Thru every pixel
     215            for(int i=0; i < y; i++){
     216                for(int j=0;j < x; j++){
     217                    //Get value for current pixels RGB value
     218                    int currPixelRGB = img.getRGB(j, i);
     219                    //Check if pixel is red (hex value of red)
     220                    if(currPixelRGB == 0xFFFF0000) {
     221
     222                        //Store y pos of red pixel if there is no duplicate
     223                        if(!redPixelYpos.contains(i)){
     224                            redPixelYpos.add(i);
     225                            //System.out.println(i );
     226                        }
     227                    }
     228                }
     229            }
     230            //Check if any of the lines found are close together and that there has been more than one line found
     231            if(redPixelYpos.size()>1){
     232                //go through list and compare every value
     233                for(int i =0; i< redPixelYpos.size(); i++){
     234                    //System.out.println("i: " +redPixelYpos.get(i));
     235                    for(int j=0; j< redPixelYpos.size(); j++){
     236                        //System.out.println("j: "+redPixelYpos.get(j));
     237                        //Check if difference is less than 4 and the values are not duplicates.
     238                        if(Math.abs(redPixelYpos.get(i) - redPixelYpos.get(j)) < 4 && !redPixelYpos.get(j).equals(redPixelYpos.get(i))){
     239                            closeLineCount++;
     240                        }
     241                    }
     242                }
     243            }
     244            int clusterCount = closeLineCount/4;
     245
     246            if(closeLineCount >= 4){
     247                returnArray.add(true);
     248                returnArray.add(closeLineCount);
     249                returnArray.add(clusterCount);
     250            }
     251            else{
     252                returnArray.add(false);
     253                returnArray.add(closeLineCount);
     254                returnArray.add(clusterCount);
     255            }
     256        }
     257        catch (Exception e) {
     258            System.err.println(e);
     259        }
     260    return returnArray;
     261    }
     262
     263    private static ArrayList ClassifierLineCluster(ArrayList<StartAndEndPoint> linePointsArray){
     264        ArrayList returnArray = new ArrayList();
     265        ArrayList<Double> closeLineYPos = new ArrayList();
     266        ArrayList<double[]> clusterArray = new ArrayList();
     267        int clusterCount = 0;
     268        try {
     269            if(linePointsArray.size()> 1) {
     270
     271                //Display input array TESTING PURPOSES
     272                for (int i = 0; i < linePointsArray.size(); i++) {
     273                    System.out.println(linePointsArray.get(i).toString());
     274                }
     275
     276
     277                //Check if y points are close together
     278                //go thru list and compare values against each other
     279                for (int i = 0; i < linePointsArray.size(); i++){
     280                    //System.out.println("i: "+ linePointsArray.get(i).getP1().y);
     281                    for (int j = 0; j < linePointsArray.size(); j++) {
     282                        //System.out.println("j: "+ linePointsArray.get(j).getP1().y);
     283                        //Check if difference is less than 4 and the values are not duplicates.
     284                        if(Math.abs(linePointsArray.get(j).getP1().y - linePointsArray.get(i).getP1().y) < 5){
     285                            if(linePointsArray.get(j).getP1().y != linePointsArray.get(i).getP1().y){
     286                                closeLineYPos.add(linePointsArray.get(j).getP1().y);
     287                            }
     288                        }
     289                    }
     290                }
     291
     292                System.out.println(" ");
     293
     294                //Have all y coordinates that close to each other.
     295                //Now check if there are four of these are close to each other.
     296                if(closeLineYPos.size() >= 4) {
     297                    //Sort array and remove duplicates
     298                    Collections.sort(closeLineYPos);
     299                    closeLineYPos = removeDuplicates(closeLineYPos);
     300
     301
     302                   /*for (double num : closeLineYPos){
     303                      System.out.println(num);
     304                   } */
     305
     306
     307                    //Check first four items, traverse down a step {0,1,2,3} -> {1,2,3,4} -> {2,3,4,5}
     308                    for(int i= 0; i< closeLineYPos.size(); i++){
     309                        //If last comparator is within the array bounds.
     310                        if(i + 3 == closeLineYPos.size()){
     311                            break;
     312                        }
     313                        else{
     314                            double[] tempArray = new double[4];
     315                            tempArray[0] = closeLineYPos.get(i + 0);
     316                            tempArray[1] = closeLineYPos.get(i + 1);
     317                            tempArray[2] = closeLineYPos.get(i + 2);
     318                            tempArray[3] = closeLineYPos.get(i + 3);
     319                            System.out.println(tempArray[0] + " , " + tempArray[1] + " , " + tempArray[2] + " , " + tempArray[3]);
     320                            //Check standard deviation
     321                            if(StandardDeviation(tempArray) < 5){
     322                                //Store array
     323                                clusterArray.add(tempArray);
     324                                //Check if more than one item in array
     325                                if(clusterArray.size() > 1){
     326                                    //check for duplicate yPos in stored arrays (tempArray)
     327
     328                                }
     329
     330
     331                            }
     332                        }
     333                    }
     334
     335                    //for (double num : closeLineYPos){
     336                    //    System.out.println(num);
     337                    //}
     338                }
     339
     340                //PROBLEM. Definition of cluster. Need to check if cluster.
     341                //check if four lines are close to each other.(four for loops)
     342                //  then store these four items in an array and add one to the counter.
     343                //  (will need to check if found 5th item. - DONT NEED TO? Value gained from finding the 5th line? The staffline height?)
     344                //
     345
     346
     347
     348                //SETUP RETURN ARRAY
     349                if(closeLineYPos.size() >= 4){
     350                    returnArray.add(true);
     351                    returnArray.add(closeLineYPos.size());
     352                    returnArray.add(clusterCount);
     353                }
     354                else{
     355                    returnArray.add(false);
     356                    returnArray.add(closeLineYPos.size());
     357                    returnArray.add(clusterCount);
     358                }
     359            }
     360        }
     361        catch (Exception e) {
     362            System.err.println(e);
     363        }
     364        return returnArray;
     365    }
     366
    111367
    112368
    113369    public static void main(String[] args) {
     370
    114371        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
     372
    115373        try {
    116             //temp array for terminalversion
    117 
    118             ArrayList returnArray = new ArrayList();
    119             returnArray.add(true);
    120             returnArray.add(10);
    121 
     374            ArrayList<StartAndEndPoint> pointArrayList = new ArrayList<>();
    122375
    123376            //Variables
    124377            Mat edgesDetected = new Mat();
    125378            Mat edgesDetectedRGB = new Mat();
    126             Mat edgesExtra = new Mat();
    127             Mat edgesDetectedRGBProb;
    128             Mat edgeDoesntMakeSense;
    129             Mat justEdges; //TESTING
    130 
    131379            String directory = "/Scratch/cpb16/is-sheet-music-encore/download-images/MU/";
    132380            //!!!!!!!!!!!!!!!!!!!!!!!!!!!NOT!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
    133381            //mdp.39015097852365-2.png 176 lines    Contents page.
    134382            //mdp.39015097852555-3.png 76  lines
    135             String default_file = directory+"SheetMusic/mdp.39015080972303-3.png";
     383            String default_file = directory+"SheetMusic/coo.31924062612282-9.png";
    136384            //String default_file ="TestImages/NotNot/mdp.39015080972303-3.png";
    137385
    … …  
    152400            Mat original = Imgcodecs.imread(filename, Imgcodecs.IMREAD_GRAYSCALE);
    153401            // Edge detection
    154             //01 CANNY
    155             //Imgproc.Canny(original, edgesDetected, 50, 200, 3, false);
    156             //Imgproc.Canny(original, edgesDetected,0, 100, 3, false );
    157             //Imgproc.Canny(original, edgesDetected,80, 120);
    158             //02 BINARYINV
    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);
    163             //03 BINARY
    164             //Imgproc.adaptiveThreshold(original, edgesDetected,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY,15, 2);
    165             //04 NO PROC
    166             //edgesDetected = original.clone();
    167             //05 OTSU THRESHOLD
    168             //Imgproc.threshold(original, edgesDetected,0,255,Imgproc.THRESH_BINARY_INV+Imgproc.THRESH_OTSU);
    169 
    170 
    171 
    172 
     402            Imgproc.adaptiveThreshold(original, edgesDetected,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 15, 4);
    173403
    174404            //Convert to RGB for future use
    175405            Imgproc.cvtColor(edgesDetected, edgesDetectedRGB, Imgproc.COLOR_GRAY2BGR);
    176             justEdges = edgesDetectedRGB.clone();//TESTING
    177             edgesDetectedRGBProb = edgesDetectedRGB.clone();
    178             edgeDoesntMakeSense = edgesDetectedRGB.clone();
    179406
    180407            Mat linesP = new Mat(); // will hold the results of the detection
    … …  
    184411
    185412            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());
    188 
    189             //Imgproc.HoughLinesP(edgesDetected,linesP,1,Math.PI/2, 50, 80, 5);
     413            System.out.println("Before Gradient Filtering num lines: " + linesP.rows());
     414
    190415            // Draw the lines
    191416            for (int x = 0; x < linesP.rows(); x++) {
    192417                double[] l = linesP.get(x, 0);
    193 
    194                 //Find angle that line is at
    195                 //double rho = linesP.get(x, 0)[0];
    196                 //double theta = linesP.get(x, 0)[1];
    197                 //double cosTheta = Math.cos(theta);
    198                 //double sinTheta = Math.sin(theta);
    199                 //double x0 = cosTheta * rho;
    200                 //double y0 = sinTheta * rho;
    201                 //double xpt1 = x0 + 1000 * (-sinTheta);
    202                 //double ypt1 = y0 + 1000 * (cosTheta);
    203                 //double xpt2 = x0 - 1000 * (-sinTheta);
    204                 //double ypt2 = y0 - 1000 * (cosTheta);
    205                 //double angle =  Math.atan2((float)ypt2 - (float)ypt1, (float)xpt2 - (float)xpt1)*(Math.PI);
    206                 //double testAngle = (ypt2 - ypt1)/(xpt2 - xpt1);
    207 
    208                 //New angles
    209418                Point p1 = new Point(l[0], l[1]);
    210419                Point p2 = new Point(l[2], l[3]);
    211420                double m = Math.abs(p2.y - p1.y)/(p2.x - p1.x);
    212                 //System.out.println(l[0]);
    213                 //System.out.println(l[1]);
    214                 //System.out.println(l[2]);
    215                 //System.out.println(l[3]);
    216 
    217421
    218422                if(m<=SLOPEGRADIENT) {
    219                     //System.out.println("m: " + m);
    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);
     423                    Imgproc.line(edgesDetectedRGB, p1, p2, new Scalar(0, 0, 255), 1, Imgproc.LINE_4, 0);
    221424                    horizontalLineCount++;
     425                    pointArrayList.add(new StartAndEndPoint(p1, p2));
    222426                }
    223427
    … …  
    226430            //Prove by finding number of points from one end to other:
    227431            //Get width of image.
    228             System.out.println("every matrix widths: "+edgesDetectedRGB.size().width);
    229432            File filenameTest = new File("TestImages/NotSheetMusic02.png");
    230433            BufferedImage i = ImageIO.read(filenameTest);
    231             System.out.println("input image width: "+ i.getWidth());
    232 
    233434            BufferedImage toBeClassifiedImg = toBufferedImage(edgesDetectedRGB);
    234             System.out.println("Result: " + Classifier(horizontalLineCount));
    235 
    236 
    237             System.out.println();
     435
     436            System.out.println("LINE COUNT RESULT:   " +  ClassifierLineCount(horizontalLineCount) + '\t' +"LineCount: " + horizontalLineCount); //COUNT OF LINES CLASSIFICATION
     437            System.out.println("LINE CLUSTER RESULT: " +  ClassifierLineClusterOLD(toBeClassifiedImg).get(0) + '\t' + "LinesFound: " + ClassifierLineClusterOLD(toBeClassifiedImg).get(1) + '\t' + "ClustersFound: " + ClassifierLineClusterOLD(toBeClassifiedImg).get(2));
     438            //System.out.println("NEW CLUSTER RESULTS: " +  ClassifierLineCluster(pointArrayList).get(0) + '\t' + "LinesFound: " + ClassifierLineCluster(pointArrayList).get(1) + '\t' + "ClustersFound: " + ClassifierLineCluster(pointArrayList).get(2));
     439            System.out.println(ClassifierLineCluster(pointArrayList));
     440
    238441            //Display Results
    239             HighGui.imshow("Source", original);
     442            //HighGui.imshow("Source", original);
    240443            //HighGui.imshow("Just Edges", justEdges); //TESTING
    241444            HighGui.imshow("Detected Lines (in red) - positive", edgesDetectedRGB);

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

    r33170 r33304  
    7070               
    7171                //Correctly identified SheetMusic as SheetMusic
    72                 trueAccuracyRate = ((float)truePositive/(float)sheetMusicCount)*(float)100;
    73 
    74                 //Amount of Correctly identified NotSheetSheetMusic as NotSheetMusic
    75                 falseAccuracyRate = ((float)trueNegative/(float)notSheetMusicCount)*(float)100;
     72                if(sheetMusicCount != 0){
     73                    trueAccuracyRate = ((float)truePositive/(float)sheetMusicCount)*(float)100;
     74                }
     75                else {
     76                    trueAccuracyRate = 0;
     77                }
    7678               
    77                 overallAccuracyRate = (truePositive + trueNegative)/(float)(sheetMusicCount+notSheetMusicCount)*(float)100;
     79                if(notSheetMusicCount != 0){
     80                    //Amount of Correctly identified NotSheetSheetMusic as NotSheetMusic
     81                    falseAccuracyRate = ((float)trueNegative/(float)notSheetMusicCount)*(float)100;
     82                }
     83                else {
     84                    falseAccuracyRate = 0;
     85                }
     86               
     87                if(notSheetMusicCount != 0 && sheetMusicCount != 0) {
     88                    overallAccuracyRate = (truePositive + trueNegative)/(float)(sheetMusicCount+notSheetMusicCount)*(float)100;
     89                }
     90                else if (sheetMusicCount = 0) {
     91                    overallAccuracyRate = falseAccuracyRate/2;
     92                }
     93                else {
     94                    overallAccuracyRate = trueAccuracyRate/2;
     95                }
    7896               
    7997                fw.write("Date: " + d.toString() + '\n'

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

    r33243 r33304  
    3333    static int CLASSIFIER_HOUGHLINESP_MAX   = 65;
    3434    static int HOUGHLINEP_THRESHOLD         = 10;
    35     static int MINLINECOUNT                 = 40; //50
     35    static int MINLINECOUNT                 = 40;
    3636    static double MAXLINEGAP                = 4;
    37     static double SLOPEGRADIENT             = 0.02; //0.01
     37    static double SLOPEGRADIENT             = 0.02;
    3838
    3939    public static void main(String[] args) {   
    … …  
    136136        //Imgproc.Canny(original, edgesDetected, 50, 200, 3, false);
    137137        Imgproc.adaptiveThreshold(original, edgesDetected,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV,15, 2);
     138        //Imgproc.blur(edgesExtra, edgesDetected, new Size(3,1));
    138139        //Imgproc.medianBlur(edgesExtra, edgesDetected, 3);
    139140       
    … …  
    160161            if(m<SLOPEGRADIENT) {
    161162                //System.out.println("m: " + m);
    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                //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); SINCE NOT SAVING IMAGES OR COUNTING PIXELS
    163164                horizontalLineCount++;
    164165            }
    … …  
    166167       
    167168        //Convert MAT into a BufferedImage
    168         BufferedImage toBeClassifiedImg = toBufferedImage(edgesDetectedRGB);
     169        //BufferedImage toBeClassifiedImg = toBufferedImage(edgesDetectedRGB); SINCE NOT SAVING IMAGES OR COUNTING PIXELS
    169170        //Calculate if its sheet music or not
    170171        isSheetMusic = classifier_HoughLinesP_refined(horizontalLineCount); 

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

    r33243 r33304  
    1 Date: Thu Jun 20 18:40:41 NZST 2019
    2 Classifier: houghlinesP
    3 truePositive: 3098
    4 falseNegative: 63
    5 falsePositve: 624
    6 trueNegative: 1216
    7 SheetMusicAccuracyRate: 98.00696%
    8 NotSheetMusicAccuracyRate: 66.08695%
    9 OverallAccuracyRate: 86.26274%
     1BEST RESULTS TO DATE
    102
    11 Date: Thu Jun 20 18:40:42 NZST 2019
    12 Classifier: houghlinesP-refined
    13 truePositive: 1666
    14 falseNegative: 1495
    15 falsePositve: 123
    16 trueNegative: 1717
    17 SheetMusicAccuracyRate: 52.70484%
    18 NotSheetMusicAccuracyRate: 93.315216%
    19 OverallAccuracyRate: 67.64648%
    20 
    21 
    22 
    23 
    24 BEST SO FAR
    25 Date: Tue Jun 25 16:02:11 NZST 2019
    26 Classifier: houghlinesP-refined
    27 truePositive: 80
    28 falseNegative: 20
    29 falsePositve: 11
    30 trueNegative: 89
    31 SheetMusicAccuracyRate: 80.0%
    32 NotSheetMusicAccuracyRate: 89.0%
    33 OverallAccuracyRate: 84.5%
    34 
    35 Date: Tue Jun 25 16:21:55 NZST 2019
    36 Classifier: houghlinesP
    37 truePositive: 3063
    38 falseNegative: 98
    39 falsePositve: 596
    40 trueNegative: 1244
    41 SheetMusicAccuracyRate: 96.89972%
    42 NotSheetMusicAccuracyRate: 67.608696%
    43 OverallAccuracyRate: 86.12277%
    44 
    45 Date: Tue Jun 25 20:06:21 NZST 2019
    46 Classifier: houghlinesP-refined-all
    47 truePositive: 1723
    48 falseNegative: 1438
    49 falsePositve: 86
    50 trueNegative: 1754
    51 SheetMusicAccuracyRate: 54.508064%
    52 NotSheetMusicAccuracyRate: 95.32609%
    53 OverallAccuracyRate: 69.52609%
    54 
    55 Date: Tue Jun 25 20:13:22 NZST 2019
    56 Classifier: houghlinesP-refined
    57 truePositive: 80
    58 falseNegative: 20
    59 falsePositve: 11
    60 trueNegative: 89
    61 SheetMusicAccuracyRate: 80.0%
    62 NotSheetMusicAccuracyRate: 89.0%
    63 OverallAccuracyRate: 84.5%
    64 
    65 Date: Tue Jun 25 22:40:22 NZST 2019
    66 Classifier: houghlinesP-refined-all-canny
    67 truePositive: 1124
    68 falseNegative: 2037
    69 falsePositve: 61
    70 trueNegative: 1779
    71 SheetMusicAccuracyRate: 35.558365%
    72 NotSheetMusicAccuracyRate: 96.684784%
    73 OverallAccuracyRate: 58.048393%
    74 
    75 MAX 65
    76 MIN 10
    77 ALL MU
    78 RESULT:
    79 
    80 
    81 Date: Wed Jun 26 20:08:09 NZST 2019
    82 Classifier: houghlinesP-refined-all-canny
    83 truePositive: 1124
    84 falseNegative: 2037
    85 falsePositve: 61
    86 trueNegative: 1779
    87 SheetMusicAccuracyRate: 35.558365%
    88 NotSheetMusicAccuracyRate: 96.684784%
    89 OverallAccuracyRate: 58.048393%
    90 
    91 Date: Wed Jun 26 20:44:04 NZST 2019
    92 Classifier: houghlinesP-refined-all
    93 truePositive: 1725
    94 falseNegative: 1436
    95 falsePositve: 87
    96 trueNegative: 1753
    97 SheetMusicAccuracyRate: 54.571335%
    98 NotSheetMusicAccuracyRate: 95.271736%
    99 OverallAccuracyRate: 69.54609%
    100 
    101 BOTH PRE THURSDAY MEETING
    102 Date: Wed Jun 26 21:40:37 NZST 2019
     3Date: Tue Jul 02 12:35:16 NZST 2019
    1034Classifier: houghlinesP
    1045truePositive: 3103
    … …  
    11011OverallAccuracyRate: 85.642876%
    11112
    112 Date: Wed Jun 26 21:40:37 NZST 2019
     13Date: Tue Jul 02 12:35:16 NZST 2019
    11314Classifier: houghlinesP-refined
    114 truePositive: 1725
    115 falseNegative: 1436
    116 falsePositve: 87
    117 trueNegative: 1753
    118 SheetMusicAccuracyRate: 54.571335%
    119 NotSheetMusicAccuracyRate: 95.271736%
    120 OverallAccuracyRate: 69.54609%
    121 
    122 
    123 POST MEETING
    124 
    125 
    126 Date: Thu Jun 27 11:56:02 NZST 2019
    127 Classifier: houghlinesP-refined
    128 truePositive: 0
    129 falseNegative: 0
    130 falsePositve: 0
    131 trueNegative: 0
    132 SheetMusicAccuracyRate: 0.0%
    133 NotSheetMusicAccuracyRate: 0.0%
    134 OverallAccuracyRate: 0.0%
    135 
    136 Date: Thu Jun 27 12:04:40 NZST 2019
    137 Classifier: houghlinesP-refined-test
    138 truePositive: 7
    139 falseNegative: 3
    140 falsePositve: 5
    141 trueNegative: 5
    142 SheetMusicAccuracyRate: 70.0%
    143 NotSheetMusicAccuracyRate: 50.0%
    144 OverallAccuracyRate: 60.000004%
    145 
    146 LINE GAP OF 10
    147 
    148 Date: Thu Jun 27 12:24:59 NZST 2019
    149 Classifier: houghlinesP-refined-all
    150 truePositive: 2808
    151 falseNegative: 353
    152 falsePositve: 307
    153 trueNegative: 1533
    154 SheetMusicAccuracyRate: 88.83265%
    155 NotSheetMusicAccuracyRate: 83.315216%
    156 OverallAccuracyRate: 86.802635%
    157 
    158 
    159 LINE GAP OF 4
    160 
    161 
    162 Date: Thu Jun 27 14:35:24 NZST 2019
    163 Classifier: houghlinesP-refined-all
    164 truePositive: 2626
    165 falseNegative: 535
    166 falsePositve: 77
    167 trueNegative: 1763
    168 SheetMusicAccuracyRate: 83.074974%
    169 NotSheetMusicAccuracyRate: 95.815216%
    170 OverallAccuracyRate: 87.76244%
    171 
    172 MINLINECOUNT=40
    173 gradient = 0.02
    174 Date: Thu Jun 27 15:10:14 NZST 2019
    175 Classifier: houghlinesP-refined-all
    17615truePositive: 2961
    17716falseNegative: 200
    … …  
    18221OverallAccuracyRate: 93.28135%
    18322
     23REFINEMENT IMPROVEMENT TESTS
     24BINARY_INV, 15, 4
     25
     26Date: Tue Jul 02 12:56:53 NZST 2019
     27Classifier: houghlinesP-refined-all
     28truePositive: 2952
     29falseNegative: 209
     30falsePositve: 128
     31trueNegative: 1712
     32SheetMusicAccuracyRate: 93.38817%
     33NotSheetMusicAccuracyRate: 93.04348%
     34OverallAccuracyRate: 93.26135%
     35
     36BINARY_INV, 15, 4
     37BLUR 3,1
     38 
     39Date: Tue Jul 02 13:27:00 NZST 2019
     40Classifier: houghlinesP-refined-all
     41truePositive: 2989
     42falseNegative: 172
     43falsePositve: 211
     44trueNegative: 1628
     45SheetMusicAccuracyRate: 94.558685%
     46NotSheetMusicAccuracyRate: 88.526375%
     47OverallAccuracyRate: 92.34%
     48
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