Changeset 33310 for other-projects

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Timestamp:
08.07.2019 16:36:39 (3 months ago)
Author:
cpb16
Message:

developing line clustering. Have completed line cluster algorithm. need to check it is calculating correctly

Location:
other-projects/is-sheet-music-encore/trunk/image-identification-development
Files:
3 modified

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

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    715707    <entry file="file://$PROJECT_DIR$/src/Main.java"> 
    716708      <provider selected="true" editor-type-id="text-editor"> 
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  • other-projects/is-sheet-music-encore/trunk/image-identification-development/src/Main.java

    r33304 r33310  
    103103    public static double StandardDeviation(double parseArray[]) 
    104104    { 
     105 
    105106        double mean; 
    106         double sum =0; 
    107         double standardDeviation = 0; 
     107        double sum =0.0; 
     108        double standardDeviation = 0.0; 
    108109        //calculate sum of array 
    109         for(int i =0; i > parseArray.length; i++){ 
     110        for(int i =0; i < parseArray.length; i++){ 
    110111            sum += parseArray[i]; 
    111112        } 
     
    113114        mean = sum/parseArray.length; 
    114115        //calculate SD of array 
    115         for(int j =0; j > parseArray.length; j++){ 
     116        for(int j =0; j < parseArray.length; j++){ 
    116117            standardDeviation += Math.pow(parseArray[j]-mean, 2); 
    117118        } 
    118119        return Math.sqrt(standardDeviation/parseArray.length); 
     120 
     121 
    119122    } 
    120123 
     
    123126    static int CLASSIFIER_HOUGHLINESP_MAX   = 65; 
    124127    static int HOUGHLINEP_THRESHOLD         = 10; 
     128    static int STANDARD_DEVIATION_THRESHOLD = 6; 
    125129    static int MINLINECOUNT                 = 40; 
    126130    static double MAXLINEGAP                = 1;  //4 
     
    210214            ArrayList<Integer> redPixelYpos = new ArrayList<Integer>(); 
    211215 
    212  
    213  
    214216            //Go Thru every pixel 
    215217            for(int i=0; i < y; i++){ 
     
    260262    return returnArray; 
    261263    } 
    262  
    263     private static ArrayList ClassifierLineCluster(ArrayList<StartAndEndPoint> linePointsArray){ 
     264    private static ArrayList ClassifierLineCluster(ArrayList<StartAndEndPoint> linePointsArray, Mat clustersFoundRGB){ 
     265 
     266        /* 
     267        This will check for a cluster of lines that are close together. 
     268        1. Go through the list of Y positions(start point) in parsed array. 
     269            If, there is a small distance between them, 
     270                then, add to closeLineArray. 
     271 
     272        Have all Y positions that are close to each other now. 
     273        Need to find the lines that are clustered together. 
     274 
     275        Now check if there are four of these are close to each other. 
     276        2.  Go through list of closeLine. 
     277                Get first four lines, traversing down a step each iteration {0,1,2,3} -> {1,2,3,4} -> {2,3,4,5} 
     278                If, those 4 lines are close together, 
     279                    Then, add them to a new array that holds Line Cluster Values. 
     280                    Go to line 4 positions down since, as do not want duplicates. 
     281 
     282        3. 
     283        */ 
     284 
    264285        ArrayList returnArray = new ArrayList(); 
    265286        ArrayList<Double> closeLineYPos = new ArrayList(); 
     
    269290            if(linePointsArray.size()> 1) { 
    270291 
     292                /* 
    271293                //Display input array TESTING PURPOSES 
    272294                for (int i = 0; i < linePointsArray.size(); i++) { 
    273295                    System.out.println(linePointsArray.get(i).toString()); 
    274296                } 
    275  
    276  
    277                 //Check if y points are close together 
     297                */ 
     298 
     299                //1. Check if y points are close together 
    278300                //go thru list and compare values against each other 
    279301                for (int i = 0; i < linePointsArray.size(); i++){ 
     
    289311                    } 
    290312                } 
    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. 
     313                /*for (double num : closeLineYPos){ 
     314                      System.out.println(num); 
     315                } */ 
     316 
     317                //2. Now check if there are four of these are close to each other. 
     318                //Go through all of the items in this list and check if four of them are close together 
     319                //Check first four items, traverse down a step {0,1,2,3} -> {1,2,3,4} -> {2,3,4,5} 
     320                //If 4 items are close together, 
     321                    //Then add them to a new array that holds Line Cluster Values. 
     322                    //Go down 4 positions down since, as do not want duplicates. 
     323 
     324                //Now have an array of at least four lines that are close together. 
     325                //Sort array and remove duplicates 
     326                Collections.sort(closeLineYPos); 
     327                closeLineYPos = removeDuplicates(closeLineYPos); 
    296328                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} 
     329                    //FOR every item in array of CloseLines 
    308330                    for(int i= 0; i< closeLineYPos.size(); i++){ 
    309                         //If last comparator is within the array bounds. 
    310                         if(i + 3 == closeLineYPos.size()){ 
     331                        //If last comparator is at end of array. 
     332                        if(i + 4 >= closeLineYPos.size()){ 
    311333                            break; 
    312334                        } 
    313335                        else{ 
     336                            //Add 4 values of Close Line Array to a tempArray 
    314337                            double[] tempArray = new double[4]; 
    315                             tempArray[0] = closeLineYPos.get(i + 0); 
     338                            tempArray[0] = closeLineYPos.get(i); 
    316339                            tempArray[1] = closeLineYPos.get(i + 1); 
    317340                            tempArray[2] = closeLineYPos.get(i + 2); 
    318341                            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){ 
     342 
     343                            //Check standard deviation between these 4 values. 
     344                            //If it SD is less than 5 then it is considered to be a cluster of lines. 
     345 
     346                            if(StandardDeviation(tempArray) < STANDARD_DEVIATION_THRESHOLD){ 
     347                                System.out.println("tempArray PT: "+tempArray[0] + " , " + tempArray[1] + " , " + tempArray[2] + " , " + tempArray[3]); 
     348                                System.out.println("tempArray SD: " + StandardDeviation(tempArray)); 
    322349                                //Store array 
    323350                                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  
     351                                //If I + 4 is less than the size of the array then increment by 4 
     352                                //Go down +4 positions in closeLineYPos array 
     353                                if((i + 4 < closeLineYPos.size())){ 
     354                                    System.out.println("IF, i = " + i + " -> "+ (i+4) + ", CloseLineYpos size= " + closeLineYPos.size()); 
     355                                    i = i+4; 
    328356                                } 
    329  
    330  
     357                                else{ 
     358                                    //break 
     359                                    System.out.println("ELSE, i = " + i+ " closeLineYpos size= " + closeLineYPos.size()); 
     360                                    Thread.sleep(2000); 
     361                                    break; 
     362                                } 
    331363                            } 
    332364                        } 
    333365                    } 
    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  
     366                } 
     367                /* 
     368                System.out.println("Cluster Coordinates: "); 
     369                for(double[] items : clusterArray){ 
     370                    for(int i = 0; i <items.length; i++){ 
     371                        System.out.println("ITEMS: "+ items[i]); 
     372                    } 
     373                } 
     374                */ 
     375                //Setup Drawing clusters found. 
     376                //For every pt given the input array 
     377                for(StartAndEndPoint pt : linePointsArray){ 
     378                    //Go through every the Arrays in the clusterArray(clustered lines) 
     379                    for(int i =0; i < clusterArray.size(); i++){ 
     380                        //Go through every item in the array 
     381                        for(double item : clusterArray.get(i)) { 
     382                            //Check if the curr item is equal to current pt 
     383                            if (item == pt.getP1().y){ 
     384                                //calculate a different colour for each line 
     385 
     386                                //Draw a line 
     387                                Imgproc.line(clustersFoundRGB, pt.getP1(), pt.getP2(), new Scalar(0, 255, 0), 1, Imgproc.LINE_4, 0); 
     388                            } 
     389                        } 
     390                    } 
     391 
     392                } 
     393 
     394                clusterCount = clusterArray.size(); 
    348395                //SETUP RETURN ARRAY 
    349                 if(closeLineYPos.size() >= 4){ 
     396                if(clusterCount >= 1){ 
    350397                    returnArray.add(true); 
    351398                    returnArray.add(closeLineYPos.size()); 
    352399                    returnArray.add(clusterCount); 
     400                    returnArray.add(clustersFoundRGB); 
    353401                } 
    354402                else{ 
     
    377425            Mat edgesDetected = new Mat(); 
    378426            Mat edgesDetectedRGB = new Mat(); 
     427            Mat clustersFoundRGB = new Mat(); 
    379428            String directory = "/Scratch/cpb16/is-sheet-music-encore/download-images/MU/"; 
    380429            //!!!!!!!!!!!!!!!!!!!!!!!!!!!NOT!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 
    381430            //mdp.39015097852365-2.png 176 lines    Contents page. 
    382431            //mdp.39015097852555-3.png 76  lines 
    383             String default_file = directory+"SheetMusic/coo.31924062612282-9.png"; 
     432            //String default_file = directory+"SheetMusic/coo.31924062612282-9.png"; 
    384433            //String default_file ="TestImages/NotNot/mdp.39015080972303-3.png"; 
    385434 
     
    389438            //String default_file = "TestImages/NotSheetMusic02.png"; 
    390439            //String default_file = "TestImages/SheetMusic01.png"; 
    391             //String default_file = "TestImages/SheetMusic02.png"; 
     440            String default_file = "TestImages/SheetMusic02.png"; 
    392441            //String default_file = "TestImages/vLine.png"; 
    393442            String filename = ((args.length > 0) ? args[0] : default_file); 
     
    404453            //Convert to RGB for future use 
    405454            Imgproc.cvtColor(edgesDetected, edgesDetectedRGB, Imgproc.COLOR_GRAY2BGR); 
     455            clustersFoundRGB = edgesDetectedRGB.clone(); 
    406456 
    407457            Mat linesP = new Mat(); // will hold the results of the detection 
     
    434484            BufferedImage toBeClassifiedImg = toBufferedImage(edgesDetectedRGB); 
    435485 
    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)); 
     486 
    440487 
    441488            //Display Results 
     
    443490            //HighGui.imshow("Just Edges", justEdges); //TESTING 
    444491            HighGui.imshow("Detected Lines (in red) - positive", edgesDetectedRGB); 
     492            if(ClassifierLineCluster(pointArrayList, clustersFoundRGB).get(3) != null) { 
     493                HighGui.imshow("CLUSTERS FOUND", clustersFoundRGB); 
     494            } 
    445495            //HighGui.imshow("Detected Lines (in red) - negative", edgesDetectedRGBProb); 
    446             //HighGui.imshow("Detected Lines (in red) - edgeDoesntMakeSense", edgeDoesntMakeSense); 
     496 
     497 
     498 
     499            //System.out.println("LINE COUNT RESULT:   " +  ClassifierLineCount(horizontalLineCount) + '\t' +"LineCount: " + horizontalLineCount); //COUNT OF LINES CLASSIFICATION 
     500            //System.out.println("LINE CLUSTER RESULT: " +  ClassifierLineClusterOLD(toBeClassifiedImg).get(0) + '\t' + "LinesFound: " + ClassifierLineClusterOLD(toBeClassifiedImg).get(1) + '\t' + "ClustersFound: " + ClassifierLineClusterOLD(toBeClassifiedImg).get(2)); 
     501            System.out.println("NEW CLUSTER RESULTS: " +  ClassifierLineCluster(pointArrayList,clustersFoundRGB).get(0) + '\t' + "LinesFound: " + ClassifierLineCluster(pointArrayList,clustersFoundRGB).get(1) + '\t' + "ClustersFound: " + ClassifierLineCluster(pointArrayList,clustersFoundRGB).get(2)); 
     502            //System.out.println(ClassifierLineCluster(pointArrayList, clustersFoundRGB)); 
    447503 
    448504            // Wait and Exit