Changeset 33324

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Timestamp:
11.07.2019 17:49:42 (6 days ago)
Author:
cpb16
Message:

Backup for 4th crash of the day. Need to reimplement x corrodinate checker

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

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

    r33319 r33324  
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     1208        <state relative-caret-position="570"> 
     1209          <caret line="212" column="30" selection-start-line="212" selection-start-column="30" selection-end-line="212" selection-end-column="30" /> 
     1210          <folding> 
     1211            <element signature="e#0#31950#0" expanded="true" /> 
     1212            <element signature="imports" expanded="true" /> 
     1213            <element signature="class#StartAndEndPoint#0;class#Main#0" /> 
     1214            <element signature="e#2039#2040#0" expanded="true" /> 
     1215            <element signature="e#2073#2074#0" expanded="true" /> 
     1216            <element signature="e#2103#2104#0" expanded="true" /> 
     1217            <element signature="e#2138#2139#0" expanded="true" /> 
     1218            <element signature="e#2193#2194#0" expanded="true" /> 
     1219            <element signature="e#2225#2226#0" expanded="true" /> 
     1220            <element signature="e#2262#2263#0" expanded="true" /> 
     1221            <element signature="e#2294#2295#0" expanded="true" /> 
     1222            <element signature="e#2349#2350#0" expanded="true" /> 
     1223            <element signature="e#2412#2413#0" expanded="true" /> 
     1224            <element signature="method#removeDuplicates#0;class#Main#0" /> 
     1225            <element signature="method#StandardDeviation#0;class#Main#0" /> 
     1226            <element signature="method#toBufferedImage#0;class#Main#0" /> 
     1227            <element signature="method#ClassifierPixelCount#0;class#Main#0" /> 
     1228            <element signature="method#ClassifierLineCount#0;class#Main#0" /> 
     1229            <element signature="method#ClassifierLineClusterOLD#0;class#Main#0" /> 
     1230            <element signature="e#9112#9121#0" expanded="true" /> 
     1231            <element signature="method#ClassifierLineCluster#0;class#Main#0" /> 
    7021232          </folding> 
    7031233        </state> 
  • other-projects/is-sheet-music-encore/trunk/image-identification-development/src/Main.java

    r33310 r33324  
     1/* 
     2                StartAndEndPoint l1 = parseArray[i]; 
     3                StartAndEndPoint l2 = parseArray[i+ 1]; 
     4                //CHECK WHICH line starts after the other 
     5                //If l1 is starting after, then comparisons are based around l1.s 
     6                //System.out.println("l1: " + l1.getP1().x); 
     7                //System.out.println("l2: " + l2.getP1().x); 
     8 
     9                System.out.println("1.0: L1S: " + l1.getP1().x + " larger than L2S: " + l2.getP1().x); 
     10                if(l1.getP1().x > l2.getP1().x) { 
     11                    System.out.println("1.1: Comparing L1S: " + l1.getP1().x + " less than L2E: " + l2.getP2().x); 
     12                    if (l1.getP1().x < l2.getP2().x) { 
     13                        //AND 
     14                        System.out.println("1.2: Comparing L1S: " + l1.getP1().x + " larger than L2S: " + l2.getP1().x); 
     15                        if (l1.getP1().x > l2.getP1().x) { 
     16                            System.out.println("1: Success. NEXT"); 
     17                            //IT IS INTERSECTED 
     18                            continue; 
     19                        } 
     20                        else { 
     21                            //FAILED SECOND COMPARISON 
     22                            System.out.println("1: Fail"); 
     23                        } 
     24                    } 
     25                    else { 
     26                        System.out.println("Checking other line"); 
     27                    } 
     28                    System.out.println("2.0: L2S: " + l2.getP1().x + " larger than  L1S: " + l1.getP1().x); 
     29                } 
     30                //If l2 is starting after, then comparisons are based around l2.s 
     31                else if(l2.getP1().x > l1.getP1().x) { 
     32                    System.out.println("2.1: Comparing L2S: " + l1.getP1().x + " less than L1E: " + l2.getP2().x); 
     33                    if (l2.getP1().x < l1.getP2().x) { 
     34                        //AND 
     35                        System.out.println("2.2: Comparing L2S: " + l2.getP1().x + " larger than L1S: " + l1.getP1().x); 
     36                        if (l2.getP1().x > l1.getP1().x) { 
     37                            System.out.println("2: Success"); 
     38                            //IT IS INTERSECTED 
     39                            //continue; 
     40                        } 
     41                        else { 
     42                            //FAILED SECOND COMPARISON 
     43                            System.out.println("2: Fail"); 
     44                            //return false; 
     45                        } 
     46                    } 
     47                    else { 
     48                        System.out.println("Failed second comparison RETURN FALSE"); 
     49                        return false; 
     50                    } 
     51                    //return false; 
     52                } 
     53                else{ 
     54                    System.out.println("NEITHER RETURN FALSE"); 
     55                    return false; 
     56                } 
     57  */ 
     58 
    159import org.opencv.core.*; 
    260import org.opencv.core.Point; 
     
    1270import java.util.Collection; 
    1371import java.util.Collections; 
     72import java.util.Comparator; 
    1473import javax.imageio.ImageIO; 
    1574 
     
    2281//https://www.programiz.com/java-programming/examples/standard-deviation 
    2382//https://www.geeksforgeeks.org/how-to-remove-duplicates-from-arraylist-in-java/ 
     83//https://stackoverflow.com/questions/7988486/how-do-you-calculate-the-variance-median-and-standard-deviation-in-c-or-java/7988556 
     84//https://stackoverflow.com/questions/10396970/sort-a-list-that-contains-a-custom-class 
    2485 
    2586 
     
    43104 
    44105public class Main { 
    45  
    46106    //DEPENDENT FUNCTIONS AND CLASSES 
    47107    static class StartAndEndPoint { 
     
    101161        //DIRECTLY COPIED//DIRECTLY COPIED//DIRECTLY COPIED//DIRECTLY COPIED//DIRECTLY COPIED//DIRECTLY COPIED 
    102162    } 
    103     public static double StandardDeviation(double parseArray[]) 
    104     { 
     163    public static double StandardDeviation(double parseArray[]) { 
    105164 
    106165        double mean; 
     
    121180 
    122181    } 
     182    public static Boolean ClusterCheck(StartAndEndPoint parseArray[]){ 
     183        System.out.println("LENGTH: " + parseArray.length); 
     184        //MAKE THREE COMPARISONS 
     185        //After clusters have been found. 
     186        //Check if  their x positions intersect 
     187        //Logic being 
     188        //(L1.S < L2.E && L1.S > L2.S) 
     189        //or 
     190        //(L2.S < L1.E && L2.S > L1.S) 
     191        //Variance is using Start of line point. 
     192        //USING VARIANTS 
     193        double sum =0; 
     194        double temp =0; 
     195        double mean, variance; 
     196        int size = parseArray.length; 
     197        //Calculate sum of array 
     198        for(int i =0; i < parseArray.length; i++){ 
     199            sum += parseArray[i].getP1().y; 
     200        } 
     201        //Calculate mean of array 
     202        mean = sum/parseArray.length; 
     203        //Calculate variants 
     204        for(int i =0; i < size; i++){ 
     205            temp += Math.pow((parseArray[i].getP1().y-mean),2); 
     206        } 
     207        variance = Math.abs(temp/(size -1)); 
     208        System.out.println("VARIANCE: " + variance); 
     209        if(variance <= CLUSTER_DISTANCE_MAX && variance > CLUSTER_DISTANCE_MIN){ 
     210            for(int i = 0; i < 3; i++){ 
     211                double l1_S = parseArray[i].getP1().x; 
     212                double l1_E = parseArray[i].getP2().x; 
     213                double l2_S =  
     214 
     215            } 
     216        } 
     217        return false; 
     218    } 
    123219 
    124220    //GLOBAL_CONSTANTS 
     221    static double CLUSTER_DISTANCE_MAX      = 15; 
     222    static double CLUSTER_DISTANCE_MIN      = 2; 
    125223    static int CLASSIFIER_HOUGHLINESP_MIN   = 10; 
    126224    static int CLASSIFIER_HOUGHLINESP_MAX   = 65; 
     
    128226    static int STANDARD_DEVIATION_THRESHOLD = 6; 
    129227    static int MINLINECOUNT                 = 40; 
    130     static double MAXLINEGAP                = 1;  //4 
     228    static int MAXLINEGAP                   = 1;  //4 
    131229    static double SLOPEGRADIENT             = 0.02; 
    132230    //SHOULD TURN INTO ARGS 
     
    265363 
    266364        /* 
     365        ADDITION: 
     366        After clusters have been found. 
     367        Check if x positions intersect at all 
     368            StartXPos of p1 
     369 
    267370        This will check for a cluster of lines that are close together. 
    268371        1. Go through the list of Y positions(start point) in parsed array. 
     
    307410                            if(linePointsArray.get(j).getP1().y != linePointsArray.get(i).getP1().y){ 
    308411                                closeLineYPos.add(linePointsArray.get(j).getP1().y); 
     412 
    309413                            } 
    310414                        } 
     
    325429                //Sort array and remove duplicates 
    326430                Collections.sort(closeLineYPos); 
     431 
     432 
     433 
    327434                closeLineYPos = removeDuplicates(closeLineYPos); 
     435 
     436                //DISPLAYING AS EXCEPTED! WOO! 
     437 
     438                for (double y : closeLineYPos){ 
     439                    System.out.println("CloseLineYPos: " + y); 
     440                } 
    328441                if(closeLineYPos.size() >= 4) { 
    329442                    //FOR every item in array of CloseLines 
     
    334447                        } 
    335448                        else{ 
    336                             //Add 4 values of Close Line Array to a tempArray 
     449 
     450                            //Add 4 values of CloseLine Array to a tempArray 
    337451                            double[] tempArray = new double[4]; 
    338452                            tempArray[0] = closeLineYPos.get(i); 
     
    344458                            //If it SD is less than 5 then it is considered to be a cluster of lines. 
    345459 
     460 
    346461                            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)); 
     462                                //System.out.println("tempArray PT: "+tempArray[0] + " , " + tempArray[1] + " , " + tempArray[2] + " , " + tempArray[3]); 
     463                                //System.out.println("tempArray SD: " + StandardDeviation(tempArray)); 
    349464                                //Store array 
    350465                                clusterArray.add(tempArray); 
     
    352467                                //Go down +4 positions in closeLineYPos array 
    353468                                if((i + 4 < closeLineYPos.size())){ 
    354                                     System.out.println("IF, i = " + i + " -> "+ (i+4) + ", CloseLineYpos size= " + closeLineYPos.size()); 
     469                                    //System.out.println("IF, i = " + i + " -> "+ (i+4) + ", CloseLineYpos size= " + closeLineYPos.size()); 
    355470                                    i = i+4; 
    356471                                } 
    357472                                else{ 
    358473                                    //break 
    359                                     System.out.println("ELSE, i = " + i+ " closeLineYpos size= " + closeLineYPos.size()); 
     474                                    //System.out.println("ELSE, i = " + i+ " closeLineYpos size= " + closeLineYPos.size()); 
    360475                                    Thread.sleep(2000); 
    361476                                    break; 
     
    413528    } 
    414529 
     530    private static ArrayList ClassifierLineClusterPt(ArrayList<StartAndEndPoint> linePointsArray, Mat clustersFoundRGB){ 
     531        /* 
     532        ADDITION: 
     533        This will check for a cluster of lines that are close together. 
     534        1. Go through the list of Y positions(start point) in parsed array. 
     535            If, there is a small distance between them, 
     536                then, add to closeLineArray. 
     537 
     538        Have all Y positions that are close to each other now. 
     539        Need to find the lines that are clustered together. 
     540 
     541        Now check if there are four of these are close to each other. 
     542        2.  Go through list of closeLine. 
     543                Get first four lines, traversing down a step each iteration {0,1,2,3} -> {1,2,3,4} -> {2,3,4,5} 
     544                If, those 4 lines are close together, 
     545                    Then, add them to a new array that holds Line Cluster Values. 
     546                    Go to line 4 positions down since, as do not want duplicates. 
     547 
     548        3. 
     549        */ 
     550        ArrayList returnArray = new ArrayList(); 
     551        ArrayList<StartAndEndPoint> closeLinePts = new ArrayList(); 
     552        ArrayList<StartAndEndPoint[]> clusterPtArray = new ArrayList(); 
     553        int clusterCount = 0; 
     554        try { 
     555            if(linePointsArray.size()> 1) { 
     556                /* 
     557                //Display input array TESTING PURPOSES 
     558                for (int i = 0; i < linePointsArray.size(); i++) { 
     559                    System.out.println(linePointsArray.get(i).toString()); 
     560                } 
     561                */ 
     562                //1. Check if y points are close together 
     563                //go thru list and compare values against each other 
     564                for (int i = 0; i < linePointsArray.size(); i++){ 
     565                    //System.out.println("i: "+ linePointsArray.get(i).getP1().y); 
     566                    for (int j = 0; j < linePointsArray.size(); j++) { 
     567                        //System.out.println("j: "+ linePointsArray.get(j).getP1().y); 
     568                        //Check if difference is less than 4 and the values are not duplicates. 
     569                        if(Math.abs(linePointsArray.get(j).getP1().y - linePointsArray.get(i).getP1().y) < 5){ 
     570                            if(linePointsArray.get(j).getP1().y != linePointsArray.get(i).getP1().y){ 
     571                                closeLinePts.add(linePointsArray.get(i)); 
     572                            } 
     573                        } 
     574                    } 
     575                } 
     576                /*for (double num : closeLineYPos){ 
     577                      System.out.println(num); 
     578                } */ 
     579 
     580                //2. Now check if there are four of these are close to each other. 
     581                //Go through all of the items in this list and check if four of them are close together 
     582                //Check first four items, traverse down a step {0,1,2,3} -> {1,2,3,4} -> {2,3,4,5} 
     583                //If 4 items are close together, 
     584                //Then add them to a new array that holds Line Cluster Values. 
     585                //Go down 4 positions down since, as do not want duplicates. 
     586 
     587                //Now have an array of at least four lines that are close together. 
     588                //Sort array and remove duplicates 
     589                Collections.sort(closeLinePts, new Comparator<StartAndEndPoint>() { 
     590                    @Override 
     591                    public int compare(StartAndEndPoint p1, StartAndEndPoint p2) { 
     592                        return (int)(p1.getP1().y - p2.getP1().y); 
     593                    } 
     594                }); 
     595                closeLinePts = removeDuplicates(closeLinePts); 
     596                /*DISPLAYING AS EXCEPTED! WOO! 
     597                for (StartAndEndPoint pt : closeLinePts) { 
     598                    System.out.println("CloseLinePTs: " + pt.getP1().y); 
     599                } 
     600                */ 
     601 
     602                if(closeLinePts.size() >= 4) { 
     603                    //FOR every item in array of CloseLines 
     604                    for(int i= 0; i< closeLinePts.size(); i++){ 
     605                        //If last comparator is at end of array. 
     606                        if(i + 4 >= closeLinePts.size()){ 
     607                            break; 
     608                        } 
     609                        else{ 
     610                            //Add 4 values of CloseLinePt Array to a tempArray 
     611                            StartAndEndPoint[] tempPtArray = new StartAndEndPoint[4]; 
     612                            tempPtArray[0] = closeLinePts.get(i); 
     613                            tempPtArray[1] = closeLinePts.get(i + 1); 
     614                            tempPtArray[2] = closeLinePts.get(i + 2); 
     615                            tempPtArray[3] = closeLinePts.get(i + 3); 
     616 
     617                            //Check standard deviation between these 4 values. 
     618                            //If it SD is less than 5 then it is considered to be a cluster of lines. 
     619                            if(ClusterCheck(tempPtArray)){ 
     620                                //System.out.println("tempArray PT: "+tempArray[0] + " , " + tempArray[1] + " , " + tempArray[2] + " , " + tempArray[3]); 
     621                                //System.out.println("tempArray SD: " + StandardDeviation(tempArray)); 
     622                                //Store array 
     623                                clusterPtArray.add(tempPtArray); 
     624                                //If I + 4 is less than the size of the array then increment by 4 
     625                                //Go down +4 positions in closeLineYPos array 
     626                                if((i + 4 < closeLinePts.size())){ 
     627                                    //System.out.println("IF, i = " + i + " -> "+ (i+4) + ", CloseLineYpos size= " + closeLineYPos.size()); 
     628                                    i = i+4; 
     629                                } 
     630                                else{ 
     631                                    //break 
     632                                    //System.out.println("ELSE, i = " + i+ " closeLineYpos size= " + closeLineYPos.size()); 
     633                                    Thread.sleep(2000); 
     634                                    break; 
     635                                } 
     636                            } 
     637                        } 
     638                    } 
     639                } 
     640                /* 
     641                System.out.println("Cluster Coordinates: "); 
     642                for(double[] items : clusterArray){ 
     643                    for(int i = 0; i <items.length; i++){ 
     644                        System.out.println("ITEMS: "+ items[i]); 
     645                    } 
     646                } 
     647                */ 
     648                //Setup Drawing clusters found. 
     649                //For every pt given the input array 
     650                for(StartAndEndPoint pt : linePointsArray){ 
     651                    //Go through every the Arrays in the clusterArray(clustered lines) 
     652                    for(int i =0; i < clusterPtArray.size(); i++){ 
     653                        //Go through every item in the array 
     654                        for(StartAndEndPoint item : clusterPtArray.get(i)) { 
     655                            //Check if the curr item is equal to current pt 
     656                            if (item.getP1().y == pt.getP1().y){ 
     657                                //calculate a different colour for each line 
     658                                //Draw a line 
     659                                Imgproc.line(clustersFoundRGB, pt.getP1(), pt.getP2(), new Scalar(0, 255, 0), 1, Imgproc.LINE_4, 0); 
     660                            } 
     661                        } 
     662                    } 
     663 
     664                } 
     665 
     666                clusterCount = clusterPtArray.size(); 
     667                //SETUP RETURN ARRAY 
     668                if(clusterCount >= 1){ 
     669                    returnArray.add(true); 
     670                    returnArray.add(closeLinePts.size()); 
     671                    returnArray.add(clusterCount); 
     672                    returnArray.add(clustersFoundRGB); 
     673                } 
     674                else{ 
     675                    returnArray.add(false); 
     676                    returnArray.add(closeLinePts.size()); 
     677                    returnArray.add(clusterCount); 
     678                } 
     679            } 
     680        } 
     681        catch (Exception e) { 
     682            System.err.println(e); 
     683        } 
     684        return returnArray; 
     685    } 
     686 
    415687 
    416688 
     
    435707 
    436708            //System.out.println(default_file); 
    437             //String default_file = "TestImages/NotSheetMusic01.png"; 
     709            String default_file = "TestImages/NotSheetMusic01.png"; 
    438710            //String default_file = "TestImages/NotSheetMusic02.png"; 
    439711            //String default_file = "TestImages/SheetMusic01.png"; 
    440             String default_file = "TestImages/SheetMusic02.png"; 
     712            //String default_file = "TestImages/SheetMusic02.png"; 
    441713            //String default_file = "TestImages/vLine.png"; 
    442714            String filename = ((args.length > 0) ? args[0] : default_file); 
     
    461733 
    462734            Imgproc.HoughLinesP(edgesDetected, linesP, 1, Math.PI / 720, HOUGHLINEP_THRESHOLD, minLineLength,MAXLINEGAP); // runs the actual detection 
    463             System.out.println("Before Gradient Filtering num lines: " + linesP.rows()); 
     735            //System.out.println("Before Gradient Filtering num lines: " + linesP.rows()); 
    464736 
    465737            // Draw the lines 
     
    489761            //HighGui.imshow("Source", original); 
    490762            //HighGui.imshow("Just Edges", justEdges); //TESTING 
    491             HighGui.imshow("Detected Lines (in red) - positive", edgesDetectedRGB); 
    492             if(ClassifierLineCluster(pointArrayList, clustersFoundRGB).get(3) != null) { 
     763            //HighGui.imshow("Detected Lines (in red) - positive", edgesDetectedRGB); 
     764            if(ClassifierLineClusterPt(pointArrayList, clustersFoundRGB).get(3) != null) { 
    493765                HighGui.imshow("CLUSTERS FOUND", clustersFoundRGB); 
    494766            } 
     
    499771            //System.out.println("LINE COUNT RESULT:   " +  ClassifierLineCount(horizontalLineCount) + '\t' +"LineCount: " + horizontalLineCount); //COUNT OF LINES CLASSIFICATION 
    500772            //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)); 
     773            //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)); 
     774            System.out.println(ClassifierLineClusterPt(pointArrayList, clustersFoundRGB)); 
    503775 
    504776            // Wait and Exit