Changeset 33589


Ignore:
Timestamp:
2019-10-21T21:45:10+13:00 (5 years ago)
Author:
cpb16
Message:

final01. Need Map results still

Location:
other-projects/is-sheet-music-encore/trunk
Files:
34 added
12 edited

Legend:

Unmodified
Added
Removed

  • other-projects/is-sheet-music-encore/trunk/EndToEndSystem.sh

    r33458 r33589  
    1414#Test/Dev Scripts
    1515#****************************************************************************************************
    16 
    17 
    18 
     16if [ $type == "argTest" ] ; then
     17    if [ $# != 3 ] ; then
     18        echo "Usage:./EndToEndSystem <ClassifierType> <ResultSubDirectory> <borderIgnoreAmount> "
     19        exit 1
     20    fi
     21    location=$2
     22    borderIgnoreAmount=$3
     23    cd image-identification-terminal   
     24    mkdir log-EndToEnd/$location/
     25    #Delete files if exists
     26    if [ -d log-EndToEnd/$location ] ; then
     27        echo "removing folder logEndToEnd/$location"
     28        rm -r log-EndToEnd/$location/
     29        mkdir log-EndToEnd/$location/
     30    fi
     31
     32    echo $'\n***********************************************\n' >> log-EndToEnd/$location/results.txt
     33    echo "Start Time: $(date +"%T")" >> log-EndToEnd/$location/results.txt #Add End Time
     34    echo >> log-EndToEnd/$location/results.txt #Add Space
     35    cat configClassifierComparison.properties >> log-EndToEnd/$location/results.txt #Add config
     36    echo $'ARGUEMENT DETAILS: \n' >> log-EndToEnd/$location/results.txt
     37    echo "borderIgnoreAmount: $borderIgnoreAmount.   That is to say that outer $borderIgnoreAmount% of the image was not considered by the algorithm." >> log-EndToEnd/$location/results.txt
     38    echo "[NYI]" >> log-EndToEnd/$location/results.txt
     39    echo >> log-EndToEnd/$location/results.txt #Space
     40   
     41    #Compile
     42    echo "Compiling java programs..."
     43    javac javaAccuracyCalculator.java
     44    javac -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar javaClassifierComparison.java;
     45   
     46    #Classifiers
     47    echo "Successfuly Compiled, running classifer now..."
     48    ./runClassiferV02.sh -argTest /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MU morphology log-EndToEnd/$location/log-morph-MU.txt $borderIgnoreAmount
     49   
     50    #Accuracy Calculation
     51    echo "Successfuly Classified, writing results now..."
     52    java javaAccuracyCalculator log-EndToEnd/$location/log-morph-MU.txt morph-MU log-EndToEnd/$location/results.txt
     53    echo >>log-EndToEnd/$location/results.txt
     54    echo "End Time: $(date +"%T")" >> log-EndToEnd/$location/results.txt #Add End Time
     55    #.././calculateCorpusAccuracy.sh
     56   
     57    #less log-EndToEnd/$location/results.txt
     58    exit $?
     59fi
    1960
    2061#****************************************************************************************************
    … …  
    3071    fi
    3172    echo $new_page_num
    32     exit 1
     73    exit $?
    3374fi
    3475#****************************************************************************************************
    3576#Classifier Scripts [HIGHRES BY DEFUALT]
    3677#****************************************************************************************************
     78
     79#Morphology - No args
     80
    3781if [ $type == "morph" ] ; then
    3882    if [ $# != 2 ] ; then
    39         echo "Usage:./EndToEndSystem cluster <ResultSubDirectory> "
     83        echo "Usage:./EndToEndSystem <ClassifierType> <ResultSubDirectory> "
    4084        exit 1
    4185    fi
    … …  
    4993    #Classifiers
    5094    echo "Successfuly Compiled, running classifer now..."
    51     ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MU morphology log-EndToEnd/$location/log-morph-MU.txt -allNew
    52     ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/BK morphology log-EndToEnd/$location/log-morph-BK.txt -allNew
    53     ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MP morphology log-EndToEnd/$location/log-morph-MP.txt -allNew
    54     ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/SE morphology log-EndToEnd/$location/log-morph-SE.txt -allNew
     95    ./runClassiferV02.sh -all /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MU morphology log-EndToEnd/$location/log-morph-MU.txt
     96    ./runClassiferV02.sh -all /Scratch/cpb16/is-sheet-music-encore/hires-download-images/BK morphology log-EndToEnd/$location/log-morph-BK.txt
     97    ./runClassiferV02.sh -all /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MP morphology log-EndToEnd/$location/log-morph-MP.txt
     98    ./runClassiferV02.sh -all /Scratch/cpb16/is-sheet-music-encore/hires-download-images/SE morphology log-EndToEnd/$location/log-morph-SE.txt
    5599    #Results
    56100    echo "Successfuly Classified, writing results now..."
    … …  
    67111    .././calculateCorpusAccuracy.sh
    68112    less log-EndToEnd/$location/results.txt
    69     exit 1
    70 fi
     113    exit $?
     114fi
     115
     116#Cluster - No args
    71117
    72118if [ $type == "cluster" ] ; then
    73119   
    74120    if [ $# != 2 ] ; then
    75         echo "Usage:./EndToEndSystem cluster <ResultSubDirectory>"
     121        echo "Usage:./EndToEndSystem <ClassifierType> <ResultSubDirectory>"
    76122        exit 1
    77123    fi
    … …  
    84130    #Classifiers
    85131    echo "Successfuly Compiled, running classifer now..."
    86     #./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MU cluster log-EndToEnd/$location/log-cluster-MU.txt -allNew
    87     #./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/BK cluster log-EndToEnd/$location/log-cluster-BK.txt -allNew
    88     #./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MP cluster log-EndToEnd/$location/log-cluster-MP.txt -allNew
    89     #./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/SE cluster log-EndToEnd/$location/log-cluster-SE.txt -allNew
     132    #./runClassiferV02.sh -all /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MU cluster log-EndToEnd/$location/log-cluster-MU.txt
     133    #./runClassiferV02.sh -all /Scratch/cpb16/is-sheet-music-encore/hires-download-images/BK cluster log-EndToEnd/$location/log-cluster-BK.txt
     134    #./runClassiferV02.sh -all /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MP cluster log-EndToEnd/$location/log-cluster-MP.txt
     135    #./runClassiferV02.sh -all /Scratch/cpb16/is-sheet-music-encore/hires-download-images/SE cluster log-EndToEnd/$location/log-cluster-SE.txt
    90136    #Results
    91137    echo "Successfuly Classified, writing results now..."
    … …  
    102148    .././calculateCorpusAccuracy.sh
    103149    less log-EndToEnd/$location/results.txt
    104     exit 1
    105 fi
     150    exit $?
     151fi
     152
     153#Morphology - Border Args -Corpus - No Map
     154
     155if [ $type == "morph-border" ] ; then
     156    if [ $# != 3 ] ; then
     157        echo "Usage:./EndToEndSystem <ClassifierType> <ResultSubDirectory> <borderIgnoreAmount> "
     158        exit 1
     159    fi
     160    location=$2
     161    borderIgnoreAmount=$3
     162    cd image-identification-terminal   
     163    mkdir log-EndToEnd/$location/
     164    #Delete files if exists
     165    if [ -d log-EndToEnd/$location ] ; then
     166        echo "removing folder logEndToEnd/$location"
     167        rm -r log-EndToEnd/$location/
     168        mkdir log-EndToEnd/$location/
     169    fi
     170
     171    echo $'\n***********************************************\n' >> log-EndToEnd/$location/results.txt
     172    echo "Start Time: $(date +"%T")" >> log-EndToEnd/$location/results.txt #Add End Time
     173    echo >> log-EndToEnd/$location/results.txt #Add Space
     174    cat configClassifierComparison.properties >> log-EndToEnd/$location/results.txt #Add config
     175    echo $'ARGUEMENT DETAILS: \n' >> log-EndToEnd/$location/results.txt
     176    echo "borderIgnoreAmount: $borderIgnoreAmount.   That is to say that outer $borderIgnoreAmount% of the image was not considered by the algorithm." >> log-EndToEnd/$location/results.txt
     177    echo "[NYI]" >> log-EndToEnd/$location/results.txt
     178    echo >> log-EndToEnd/$location/results.txt #Space
     179   
     180    #Compile
     181    echo "Compiling java programs..."
     182    javac javaAccuracyCalculator.java
     183    javac -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar javaClassifierComparison.java;
     184   
     185    #Classifiers
     186    echo "Successfuly Compiled, running classifer now..."
     187    ./runClassiferV02.sh -borderAll /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MU morphology log-EndToEnd/$location/log-morph-MU.txt $borderIgnoreAmount
     188    ./runClassiferV02.sh -borderAll /Scratch/cpb16/is-sheet-music-encore/hires-download-images/BK morphology log-EndToEnd/$location/log-morph-BK.txt $borderIgnoreAmount
     189    ./runClassiferV02.sh -borderAll /Scratch/cpb16/is-sheet-music-encore/hires-download-images/SE morphology log-EndToEnd/$location/log-morph-SE.txt $borderIgnoreAmount
     190   
     191    #Accuracy Calculation
     192    echo "Successfuly Classified, writing results now..."
     193    java javaAccuracyCalculator log-EndToEnd/$location/log-morph-MU.txt morph-MU log-EndToEnd/$location/results.txt
     194    java javaAccuracyCalculator log-EndToEnd/$location/log-morph-BK.txt morph-BK log-EndToEnd/$location/results.txt
     195    java javaAccuracyCalculator log-EndToEnd/$location/log-morph-SE.txt morph-SE log-EndToEnd/$location/results.txt
     196    echo >>log-EndToEnd/$location/results.txt
     197    echo "End Time: $(date +"%T")" >> log-EndToEnd/$location/results.txt #Add End Time
     198    #.././calculateCorpusAccuracy.sh
     199   
     200    #less log-EndToEnd/$location/results.txt
     201    exit $?
     202fi
     203
     204#HoughTransform - Cluster Args -Corpus - No Map
     205
     206if [ $type == "cluster-border" ] ; then
     207    if [ $# != 3 ] ; then
     208        echo "Usage:./EndToEndSystem <ClassifierType> <ResultSubDirectory> <borderIgnoreAmount> "
     209        exit 1
     210    fi
     211    location=$2
     212    borderIgnoreAmount=$3
     213    cd image-identification-terminal   
     214    mkdir log-EndToEnd/$location/
     215    #Delete files if exists
     216    if [ -d log-EndToEnd/$location ] ; then
     217        echo "removing folder logEndToEnd/$location"
     218        rm -r log-EndToEnd/$location/
     219        mkdir log-EndToEnd/$location/
     220    fi
     221
     222    echo $'\n***********************************************\n' >> log-EndToEnd/$location/results.txt
     223    echo "Start Time: $(date +"%T")" >> log-EndToEnd/$location/results.txt #Add End Time
     224    echo >> log-EndToEnd/$location/results.txt #Add Space
     225    cat configClassifierComparison.properties >> log-EndToEnd/$location/results.txt #Add config
     226    echo $'ARGUEMENT DETAILS: \n' >> log-EndToEnd/$location/results.txt
     227    echo "borderIgnoreAmount: $borderIgnoreAmount.   That is to say that outer $borderIgnoreAmount% of the image was not considered by the algorithm." >> log-EndToEnd/$location/results.txt
     228    echo "[NYI]" >> log-EndToEnd/$location/results.txt
     229    echo >> log-EndToEnd/$location/results.txt #Space
     230   
     231    #Compile
     232    echo "Compiling java programs..."
     233    javac javaAccuracyCalculator.java
     234    javac -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar javaClassifierComparison.java;
     235   
     236    #Classifiers
     237    echo "Successfuly Compiled, running classifer now..."
     238    ./runClassiferV02.sh -borderAll /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MU cluster log-EndToEnd/$location/log-cluster-MU.txt $borderIgnoreAmount
     239    ./runClassiferV02.sh -borderAll /Scratch/cpb16/is-sheet-music-encore/hires-download-images/BK cluster log-EndToEnd/$location/log-cluster-BK.txt $borderIgnoreAmount
     240    ./runClassiferV02.sh -borderAll /Scratch/cpb16/is-sheet-music-encore/hires-download-images/SE cluster log-EndToEnd/$location/log-cluster-SE.txt $borderIgnoreAmount
     241   
     242    #Accuracy Calculation
     243    echo "Successfuly Classified, writing results now..."
     244    java javaAccuracyCalculator log-EndToEnd/$location/log-cluster-MU.txt cluster-MU log-EndToEnd/$location/results.txt
     245    java javaAccuracyCalculator log-EndToEnd/$location/log-cluster-BK.txt cluster-BK log-EndToEnd/$location/results.txt
     246    java javaAccuracyCalculator log-EndToEnd/$location/log-cluster-SE.txt cluster-SE log-EndToEnd/$location/results.txt
     247    echo >>log-EndToEnd/$location/results.txt
     248    echo "End Time: $(date +"%T")" >> log-EndToEnd/$location/results.txt #Add End Time
     249    #.././calculateCorpusAccuracy.sh
     250   
     251    #less log-EndToEnd/$location/results.txt
     252    exit $?
     253fi
     254
     255
     256
     257#HoughTransform - Count Args -Corpus - No Map                                                                                                                                                                 
     258
     259if [ $type == "count-border" ] ; then
     260        if [ $# != 3 ] ; then
     261                echo "Usage:./EndToEndSystem <ClassifierType> <ResultSubDirectory> <borderIgnoreAmount> "
     262                exit 1
     263        fi
     264        location=$2
     265        borderIgnoreAmount=$3
     266        cd image-identification-terminal
     267        mkdir log-EndToEnd/$location/
     268        #Delete files if exists                                                                                                                                                                                 
     269        if [ -d log-EndToEnd/$location ] ; then
     270                echo "removing folder logEndToEnd/$location"
     271                rm -r log-EndToEnd/$location/
     272                mkdir log-EndToEnd/$location/
     273        fi
     274
     275        echo $'\n***********************************************\n' >> log-EndToEnd/$location/results.txt
     276        echo "Start Time: $(date +"%T")" >> log-EndToEnd/$location/results.txt #Add End Time                                                                                                                   
     277        echo >> log-EndToEnd/$location/results.txt #Add Space                                                                                                                                                   
     278        cat configClassifierComparison.properties >> log-EndToEnd/$location/results.txt #Add config                                                                                                             
     279        echo $'ARGUEMENT DETAILS: \n' >> log-EndToEnd/$location/results.txt
     280        echo "borderIgnoreAmount: $borderIgnoreAmount.   That is to say that outer $borderIgnoreAmount% of the image was not considered by the algorithm." >> log-EndToEnd/$location/results.txt
     281        echo "[NYI]" >> log-EndToEnd/$location/results.txt
     282        echo >> log-EndToEnd/$location/results.txt #Space                                                                                                                                                       
     283
     284        #Compile                                                                                                                                                                                               
     285        echo "Compiling java programs..."
     286        javac javaAccuracyCalculator.java
     287        javac -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar javaClassifierComparison.java;
     288
     289        #Classifiers                                                                                                                                                                                           
     290        echo "Successfuly Compiled, running classifer now..."
     291        ./runClassiferV02.sh -borderAll /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MU count log-EndToEnd/$location/log-count-MU.txt $borderIgnoreAmount
     292        ./runClassiferV02.sh -borderAll /Scratch/cpb16/is-sheet-music-encore/hires-download-images/BK count log-EndToEnd/$location/log-count-BK.txt $borderIgnoreAmount
     293        ./runClassiferV02.sh -borderAll /Scratch/cpb16/is-sheet-music-encore/hires-download-images/SE count log-EndToEnd/$location/log-count-SE.txt $borderIgnoreAmount
     294
     295        #Accuracy Calculation                                                                                                                                                                                   
     296        echo "Successfuly Classified, writing results now..."
     297        java javaAccuracyCalculator log-EndToEnd/$location/log-count-MU.txt count-MU log-EndToEnd/$location/results.txt
     298        java javaAccuracyCalculator log-EndToEnd/$location/log-count-BK.txt count-BK log-EndToEnd/$location/results.txt
     299        java javaAccuracyCalculator log-EndToEnd/$location/log-count-SE.txt count-SE log-EndToEnd/$location/results.txt
     300        echo >>log-EndToEnd/$location/results.txt
     301        echo "End Time: $(date +"%T")" >> log-EndToEnd/$location/results.txt #Add End Time                                                                                                                     
     302        #.././calculateCorpusAccuracy.sh                                                                                                                                                                       
     303
     304        #less log-EndToEnd/$location/results.txt                                                                                                                                                               
     305        exit $?
     306fi
     307
     308
     309
     310
     311
     312#Map SCRIPT                                                                                                                                                                 
     313
     314if [ $type == "map-hough-morph" ] ; then
     315        if [ $# != 3 ] ; then
     316                echo "Usage:./EndToEndSystem <ClassifierType> <ResultSubDirectory> <borderIgnoreAmount> "
     317                exit 1
     318        fi
     319        location=$2
     320        borderIgnoreAmount=$3
     321        cd image-identification-terminal
     322        mkdir log-EndToEnd/$location/
     323        #Delete files if exists                                                                                                                                                                                 
     324        if [ -d log-EndToEnd/$location ] ; then
     325                echo "removing folder logEndToEnd/$location"
     326                rm -r log-EndToEnd/$location/
     327                mkdir log-EndToEnd/$location/
     328        fi
     329
     330        echo $'\n***********************************************\n' >> log-EndToEnd/$location/results.txt
     331        echo "Start Time: $(date +"%T")" >> log-EndToEnd/$location/results.txt #Add End Time                                                                                                                   
     332        echo >> log-EndToEnd/$location/results.txt #Add Space                                                                                                                                                   
     333        cat configClassifierComparison.properties >> log-EndToEnd/$location/results.txt #Add  config                                                                                                             
     334        echo $'ARGUEMENT DETAILS: \n' >> log-EndToEnd/$location/results.txt
     335        echo "borderIgnoreAmount: $borderIgnoreAmount.   That is to say that outer $borderIgnoreAmount% of the image was not considered by the algorithm." >> log-EndToEnd/$location/results.txt
     336        echo "[NYI]" >> log-EndToEnd/$location/results.txt
     337        echo >> log-EndToEnd/$location/results.txt                                                                                                                                                       
     338
     339        #Compile                                                                                                                                                                                               
     340        echo "Compiling java programs..."
     341        javac javaAccuracyCalculator.java
     342        javac -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar javaClassifierComparison.java;
     343
     344        #Classifiers                                                                                                                                                                                           
     345        echo "Successfuly Compiled, running classifer now..."
     346        ./runClassiferV02.sh -borderAll /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MP count log-EndToEnd/$location/log-count-MP.txt $borderIgnoreAmount
     347        ./runClassiferV02.sh -borderAll /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MP morphology log-EndToEnd/$location/log-morph-MP.txt $borderIgnoreAmount
     348
     349        #Accuracy Calculation                                                                                                                                                                                   
     350        echo "Successfuly Classified, writing results now..."
     351        java javaAccuracyCalculator log-EndToEnd/$location/log-count-MP.txt count-MP log-EndToEnd/$location/results.txt
     352        java javaAccuracyCalculator log-EndToEnd/$location/log-morph-MP.txt morph-MP log-EndToEnd/$location/results.txt
     353       
     354        echo >>log-EndToEnd/$location/results.txt
     355        echo "End Time: $(date +"%T")" >> log-EndToEnd/$location/results.txt #Add End Time                                                                                                                     
     356        #.././calculateCorpusAccuracy.sh                                                                                                                                                                       
     357
     358        #less log-EndToEnd/$location/results.txt                                                                                                                                                               
     359        exit $?
     360fi
     361
     362
     363
     364
    106365
    107366

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

    r33458 r33589  
     1#**********
     2#Quick CMD for EndToEndSystem.sh
     3#**********
     4ete-twentyMU-borders-quick:
     5    ./EndToEndSystem.sh argTest argTest0p 0
     6    ./EndToEndSystem.sh argTest argTest5p 5
     7    ./EndToEndSystem.sh argTest argTest10p 10
     8    ./EndToEndSystem.sh argTest argTest20p 20
     9    ./EndToEndSystem.sh argTest argTest40p 40
     10    cd image-identification-terminal/log-EndToEnd; cat */results.txt > BatchResults.txt
     11
     12ete-all-borders-morph:
     13    ./EndToEndSystem.sh morph-border morph-border-at-0 0
     14    ./EndToEndSystem.sh morph-border morph-border-at-5 5
     15    ./EndToEndSystem.sh morph-border morph-border-at-10 10
     16    ./EndToEndSystem.sh morph-border morph-border-at-20 20
     17    ./EndToEndSystem.sh morph-border morph-border-at-40 40
     18    cd image-identification-terminal/log-EndToEnd; cat morph*/results.txt > BatchResultsMorph.txt
     19
     20ete-all-borders-cluster:
     21    ./EndToEndSystem.sh cluster-border cluster-border-at-0 0
     22    ./EndToEndSystem.sh cluster-border cluster-border-at-5 5
     23    ./EndToEndSystem.sh cluster-border cluster-border-at-10 10
     24    ./EndToEndSystem.sh cluster-border cluster-border-at-20 20
     25    ./EndToEndSystem.sh cluster-border cluster-border-at-40 40
     26    cd image-identification-terminal/log-EndToEnd; cat cluster*/results.txt > BatchResultsCluster.txt
     27
     28ete-all-borders-count:
     29    ./EndToEndSystem.sh count-border count-border-at-0 0
     30    ./EndToEndSystem.sh count-border count-border-at-5 5
     31    ./EndToEndSystem.sh count-border count-border-at-10 10
     32    ./EndToEndSystem.sh count-border count-border-at-20 20
     33    ./EndToEndSystem.sh count-border count-border-at-40 40
     34    cd image-identification-terminal/log-EndToEnd; cat count*/results.txt > BatchResultsCount.txt
     35
     36#BLOCKSIZE (CHANGED MAIN COMPARISION JAVA FILE FROM 15 to BLOCKSIZE)
     37
     38ete-all-borders-count-BLOCKSIZED:
     39    ./EndToEndSystem.sh count-border count-borderBLOCKSIZED-at-0 0
     40    ./EndToEndSystem.sh count-border count-borderBLOCKSIZED-at-5 5
     41    ./EndToEndSystem.sh count-border count-borderBLOCKSIZED-at-10 10
     42    ./EndToEndSystem.sh count-border count-borderBLOCKSIZED-at-20 20
     43    ./EndToEndSystem.sh count-border count-borderBLOCKSIZED-at-40 40
     44    cd image-identification-terminal/log-EndToEnd; cat count-borderBLOCKSIZED*/results.txt > BatchResultsCountBLOCKSIZED.txt
     45ete-all-borders-morph-BLOCKSIZED:
     46    ./EndToEndSystem.sh morph-border morph-borderBLOCKSIZED-at-0 0
     47    ./EndToEndSystem.sh morph-border morph-borderBLOCKSIZED-at-5 5
     48    ./EndToEndSystem.sh morph-border morph-borderBLOCKSIZED-at-10 10
     49    ./EndToEndSystem.sh morph-border morph-borderBLOCKSIZED-at-20 20
     50    ./EndToEndSystem.sh morph-border morph-borderBLOCKSIZED-at-40 40
     51    cd image-identification-terminal/log-EndToEnd; cat morph-borderBLOCKSIZED*/results.txt > BatchResultsMorphBLOCKSIZED.txt
     52   
     53ete-blocksize-both-ob-args:
     54    ./EndToEndSystem.sh count-border count-borderBLOCKSIZED-at-0 0
     55    ./EndToEndSystem.sh morph-border morph-borderBLOCKSIZED-at-0 0
     56   
     57    ./EndToEndSystem.sh count-border count-borderBLOCKSIZED-at-5 5
     58    ./EndToEndSystem.sh morph-border morph-borderBLOCKSIZED-at-5 5
     59   
     60    ./EndToEndSystem.sh count-border count-borderBLOCKSIZED-at-10 10
     61    ./EndToEndSystem.sh morph-border morph-borderBLOCKSIZED-at-10 10
     62   
     63    ./EndToEndSystem.sh count-border count-borderBLOCKSIZED-at-20 20   
     64    ./EndToEndSystem.sh morph-border morph-borderBLOCKSIZED-at-20 20
     65   
     66    ./EndToEndSystem.sh count-border count-borderBLOCKSIZED-at-40 40           
     67    ./EndToEndSystem.sh morph-border morph-borderBLOCKSIZED-at-40 40
     68           
     69    cd image-identification-terminal/log-EndToEnd; cat count-borderBLOCKSIZED*/results.txt > BatchResultsCountBLOCKSIZED.txt   
     70    cd image-identification-terminal/log-EndToEnd; cat morph-borderBLOCKSIZED*/results.txt > BatchResultsMorphBLOCKSIZED.txt   
    171#**********
    272#Utilities/Test
    … …  
    1080download-single:
    1181    ./COMPX520-RUN-PNG-hi-res.sh coo1.ark:/13960/t2t448j81 1 /..
    12    
     82
    1383test-get-page-numbers-map:
    1484    cd java-gen-corpus; javac javaGenMapPageCountChecker.java
    … …  
    1787
    1888test-gen-new-5000-map:
    19     #MAKE SURE TO RUN source SETUP.bash prior to this cmd
     89        #MAKE SURE TO RUN source SETUP.bash prior to this cmd
    2090    cd java-gen-corpus; javac javaGenMap5000ValidIDList.java
    2191    cd java-gen-corpus; java javaGenMap5000ValidIDList gen-500-MP-hathiFullIDList.txt gen-5000-MapList02.txt
    22    
     92
    2393test-gen-new-5000-local-map:
    24     #MAKE SURE TO RUN source SETUP.bash prior to this cmd
     94        #MAKE SURE TO RUN source SETUP.bash prior to this cmd
    2595    cd java-gen-corpus; javac javaGenMapLocal5000ValidIDList.java
    2696    cd java-gen-corpus; java javaGenMapLocal5000ValidIDList MapListNumPages.txt gen-5000-Local-MapList.txt
    27    
    28    
    29 run-morphology-20-test:
    30     cd image-identification-terminal/log-all-hires-new/test; rm *.txt
    31     cd image-identification-terminal; javac javaAccuracyCalculator.java
    32     cd image-identification-terminal; javac -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar javaClassifierComparison.java;
    33     cd image-identification-terminal; ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MU morphology log-all-hires-new/test/log-morphology-MU-test.txt -testNew
     97
     98run-morphology-20-test-time:
     99    time make run-morphology-20-test   
     100run-morphology-20-test:     
     101        #cd image-identification-terminal/log-all-hires-new/test; rm *.txt
     102    cd image-identification-terminal; javac javaAccuracyCalculator.java
     103    cd image-identification-terminal; javac -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar javaClassifierComparison.java;
     104    cd image-identification-terminal; ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/SE morphology log-all-hires-new/test/log-morphology-SE-test.txt -testNew
    34105    cd image-identification-terminal; date >> log-all-hires-new/test/results.txt
    35106    cd image-identification-terminal; cat configClassifierComparison.properties >> log-all-hires-new/test/results.txt
    36     cd image-identification-terminal; java javaAccuracyCalculator log-all-hires-new/test/log-morphology-MU-test.txt morphology-MU log-all-hires-new/test/results.txt
     107    cd image-identification-terminal; java javaAccuracyCalculator log-all-hires-new/test/log-morphology-SE-test.txt morphology-SE log-all-hires-new/test/results.txt
    37108    cd image-identification-terminal; less log-all-hires-new/test/results.txt
    38     cd image-identification-terminal; less log-all-hires-new/test/log-morphology-MU-test.txt
     109    cd image-identification-terminal; less log-all-hires-new/test/log-morphology-SE-test.txt
    39110
    40111run-morph-single:
    … …  
    56127    make hi-res-download-map
    57128hi-res-download-map:
    58     ./FirstAndLastTest-DOWNLOADER-PNG.sh java-gen-corpus/gen-5000-Local-MapList.txt MP
     129    ./COMPX520-MAP-DOWNLOADER-PNG.sh MapListGen/gen-500-Local-MapList-final.txt MP
    59130
    60131######################################
    … …  
    66137    cd java-gen-corpus; java javaGenFullIDList hathiFull.txt gen-10000-MU-hathiFullIDList.txt MU
    67138    cd java-gen-corpus; java javaGenFirstAndLastTenIDList gen-10000-MU-hathiFullIDList.txt 10000 gen-10000-MU-hathiValidIDList.txt
    68     #./FirstAndLastTest-DOWNLOADER-PNG.sh java-gen-corpus/gen-10000-MU-hathiValidIDList.txt MU
     139#./FirstAndLastTest-DOWNLOADER-PNG.sh java-gen-corpus/gen-10000-MU-hathiValidIDList.txt MU
    69140
    70141
    … …  
    79150
    80151run-cluster-corpus-hires:
    81     #Compile
    82     cd image-identification-terminal; javac javaAccuracyCalculator.java
    83     cd image-identification-terminal; javac -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar javaClassifierComparison.java;
    84     #Run classifier
     152#Compile
     153    cd image-identification-terminal; javac javaAccuracyCalculator.java
     154    cd image-identification-terminal; javac -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar javaClassifierComparison.java;
     155#Run classifier
    85156    cd image-identification-terminal; ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MU cluster log-all-hires-new/log-cluster-MU.txt -allNew
    86157    cd image-identification-terminal; ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/BK cluster log-all-hires-new/log-cluster-BK.txt -allNew
    87158    cd image-identification-terminal; ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MP cluster log-all-hires-new/log-cluster-MP.txt -allNew
    88159    cd image-identification-terminal; ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/SE cluster log-all-hires-new/log-cluster-SE.txt -allNew
    89     #Calculate accuracy
     160#Calculate accuracy
    90161    cd image-identification-terminal; echo configClassifierComparison.properties >> log-all-hires-new/results.txt
    91162    cd image-identification-terminal; java javaAccuracyCalculator log-all-hires-new/log-cluster-MU.txt cluster-MU log-all-hires-new/results.txt
    … …  
    97168
    98169run-count-corpus-hires:
    99     #Compile
    100     cd image-identification-terminal; javac javaAccuracyCalculator.java
    101     cd image-identification-terminal; javac -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar javaClassifierComparison.java;
    102     #Run classifier
     170#Compile
     171    cd image-identification-terminal; javac javaAccuracyCalculator.java
     172    cd image-identification-terminal; javac -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar javaClassifierComparison.java;
     173#Run classifier
    103174    cd image-identification-terminal; ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MU count log-all-hires-new/log-count-MU.txt -allNew
    104175    cd image-identification-terminal; ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/BK count log-all-hires-new/log-count-BK.txt -allNew
    105176    cd image-identification-terminal; ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MP count log-all-hires-new/log-count-MP.txt -allNew
    106177    cd image-identification-terminal; ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/SE count log-all-hires-new/log-count-SE.txt -allNew
    107     #Calculate accuracy
     178#Calculate accuracy
    108179    cd image-identification-terminal; java javaAccuracyCalculator log-all-hires-new/log-count-MU.txt count-MU log-all-hires-new/results.txt
    109180    cd image-identification-terminal; java javaAccuracyCalculator log-all-hires-new/log-count-BK.txt count-BK log-all-hires-new/results.txt
    … …  
    115186#Cluster and Count
    116187run-combo-corpus-hires:
    117     #Compile
    118     cd image-identification-terminal; javac javaAccuracyCalculator.java
    119     cd image-identification-terminal; javac -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar javaClassifierComparison.java;
    120     #Run classifier
     188#Compile
     189    cd image-identification-terminal; javac javaAccuracyCalculator.java
     190    cd image-identification-terminal; javac -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar javaClassifierComparison.java;
     191#Run classifier
    121192    cd image-identification-terminal; ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MU combo log-all-hires-new/log-combo-MU.txt -allNew
    122193    cd image-identification-terminal; ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/BK combo log-all-hires-new/log-combo-BK.txt -allNew
    123     #cd image-identification-terminal; ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MP combo log-all-hires-new/log-combo-MP.txt -allNew
     194#cd image-identification-terminal; ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MP combo log-all-hires-new/log-combo-MP.txt -allNew
    124195    cd image-identification-terminal; ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/SE combo log-all-hires-new/log-combo-SE.txt -allNew
    125     #Calculate accuracy
     196#Calculate accuracy
    126197    cd image-identification-terminal; java javaAccuracyCalculator log-all-hires-new/log-combo-MU.txt combo-MU log-all-hires-new/results.txt
    127198    cd image-identification-terminal; java javaAccuracyCalculator log-all-hires-new/log-combo-BK.txt combo-BK log-all-hires-new/results.txt
    128     #cd image-identification-terminal; java javaAccuracyCalculator log-all-hires-new/log-combo-MP.txt combo-MP log-all-hires-new/results.txt
     199#cd image-identification-terminal; java javaAccuracyCalculator log-all-hires-new/log-combo-MP.txt combo-MP log-all-hires-new/results.txt
    129200    cd image-identification-terminal; java javaAccuracyCalculator log-all-hires-new/log-combo-SE.txt combo-SE log-all-hires-new/results.txt
    130201    #Display the results
    … …  
    150221#**************
    151222run-houghlinesP-refined-corpus:
    152     #cd image-identification-terminal; cd log-all; rm *.txt
     223#cd image-identification-terminal; cd log-all; rm *.txt
    153224        #Compile
    154225    cd image-identification-terminal; javac javaAccuracyCalculator.java
    … …  
    184255        #Display the results
    185256    cd image-identification-terminal; less log-all-hires/results.txt   
    186    
     257
    187258#***********
    188259#OLDGEN
    189260#***********   
    190     #MUSIC, Including Sheet music
     261#MUSIC, Including Sheet music
    191262gen-500-MU:
    192263    cd java-gen-corpus; javac *.java

  • other-projects/is-sheet-music-encore/trunk/image-identification-dev-02/.idea/workspace.xml

    r33458 r33589  
    1515        <entry file="file://$PROJECT_DIR$/image-identification-development/src/Main.java">
    1616          <provider selected="true" editor-type-id="text-editor">
    17             <state relative-caret-position="557">
    18               <caret line="779" column="12" selection-start-line="779" selection-start-column="12" selection-end-line="779" selection-end-column="12" />
     17            <state relative-caret-position="2014">
     18              <caret line="760" selection-start-line="760" selection-end-line="760" />
    1919              <folding>
    2020                <element signature="imports" expanded="true" />
    … …  
    4040        <entry file="file://$PROJECT_DIR$/image-identification-development/src/MainMorph.java">
    4141          <provider selected="true" editor-type-id="text-editor">
    42             <state relative-caret-position="383">
    43               <caret line="236" column="54" selection-start-line="236" selection-start-column="54" selection-end-line="236" selection-end-column="54" />
     42            <state relative-caret-position="106">
     43              <caret line="1351" column="52" selection-start-line="1351" selection-start-column="52" selection-end-line="1351" selection-end-column="52" />
    4444              <folding>
    4545                <element signature="imports" expanded="true" />
    4646                <element signature="method#imageViewer#0;class#MainMorph#0" />
    47                 <element signature="e#14232#16729#1" />
    48                 <element signature="e#16814#18826#1" />
    49                 <element signature="e#18895#21536#1" />
    50                 <element signature="e#21620#27308#1" />
    51                 <element signature="e#27393#31396#1" />
    52                 <element signature="e#31464#32641#1" />
    53                 <element signature="e#32710#36690#1" />
    54                 <element signature="e#36800#48520#1" />
    55                 <element signature="e#45567#45616#0" />
    56                 <element signature="e#48614#59515#1" />
    57                 <element signature="e#59847#68195#1" />
    58                 <element signature="e#61117#61159#0" />
    5947              </folding>
    6048            </state>
    … …  
    6553        <entry file="file://$PROJECT_DIR$/../image-identification-terminal/javaClassifierComparison.java">
    6654          <provider selected="true" editor-type-id="text-editor">
    67             <state relative-caret-position="225">
    68               <caret line="592" column="12" selection-start-line="592" selection-start-column="12" selection-end-line="592" selection-end-column="12" />
     55            <state relative-caret-position="6897">
     56              <caret line="442" column="23" selection-start-line="442" selection-start-column="23" selection-end-line="442" selection-end-column="23" />
    6957              <folding>
    7058                <element signature="imports" expanded="true" />
    71                 <element signature="method#init#0;class#javaClassifierComparison#0" />
    7259                <element signature="class#StartAndEndPoint#0;class#javaClassifierComparison#0" />
    7360                <element signature="class#Pair#0;class#javaClassifierComparison#0" />
    74                 <element signature="method#main#0;class#javaClassifierComparison#0" />
    75                 <element signature="method#Algorithm_HoughLinesP_Single#0;class#javaClassifierComparison#0" />
    76                 <element signature="method#Algorithm_HoughLinesP_Combo#0;class#javaClassifierComparison#0" />
    77                 <element signature="method#Algorithm_MorphologyOLD#0;class#javaClassifierComparison#0" />
    78                 <element signature="method#Algorithm_Morphology#0;class#javaClassifierComparison#0" />
     61                <element signature="method#init#0;class#javaClassifierComparison#0" />
    7962                <element signature="method#Classifier_LineCounter#0;class#javaClassifierComparison#0" />
    8063                <element signature="method#Classifier_ClusterDetection#0;class#javaClassifierComparison#0" />
    … …  
    8366                <element signature="method#lineComparison#0;class#javaClassifierComparison#0" />
    8467                <element signature="method#ClusterCheck#0;class#javaClassifierComparison#0" />
     68                <element signature="method#Algorithm_MorphologyOLD2#0;class#javaClassifierComparison#0" />
     69                <element signature="method#Algorithm_MorphologyOLD#0;class#javaClassifierComparison#0" />
    8570              </folding>
    8671            </state>
    … …  
    10287      <find>FILLED</find>
    10388      <find>test</find>
     89      <find>binarized</find>
     90      <find>THRESHOLD_C</find>
    10491    </findStrings>
    10592  </component>
    … …  
    10996        <option value="$PROJECT_DIR$/../image-identification-terminal/javaImageClassifier.java" />
    11097        <option value="$PROJECT_DIR$/../image-identification-terminal/javaClassifierComparison" />
     98        <option value="$PROJECT_DIR$/../image-identification-terminal/javaClassifierComparison.java" />
    11199        <option value="$PROJECT_DIR$/image-identification-development/src/Main.java" />
    112         <option value="$PROJECT_DIR$/../image-identification-terminal/javaClassifierComparison.java" />
    113100        <option value="$PROJECT_DIR$/image-identification-development/src/MainMorph.java" />
    114101      </list>
    … …  
    191178      <window_info anchor="bottom" id="Message" order="0" />
    192179      <window_info anchor="bottom" id="Find" order="1" weight="0.32962137" />
    193       <window_info anchor="bottom" id="Run" order="2" weight="0.42316258" />
     180      <window_info anchor="bottom" id="Run" order="2" weight="0.2037862" />
    194181      <window_info anchor="bottom" id="Debug" order="3" weight="0.3997773" />
    195182      <window_info anchor="bottom" id="Cvs" order="4" weight="0.25" />
    … …  
    207194      <window_info anchor="right" id="Maven" order="5" weight="0.3299298" />
    208195    </layout>
     196    <layout-to-restore>
     197      <window_info content_ui="combo" id="Project" order="0" weight="0.19783024" />
     198      <window_info id="Structure" order="1" side_tool="true" weight="0.25" />
     199      <window_info id="Designer" order="2" />
     200      <window_info id="UI Designer" order="3" />
     201      <window_info id="Favorites" order="4" side_tool="true" />
     202      <window_info anchor="bottom" id="Message" order="0" />
     203      <window_info anchor="bottom" id="Find" order="1" weight="0.32962137" />
     204      <window_info active="true" anchor="bottom" id="Run" order="2" visible="true" weight="0.42316258" />
     205      <window_info anchor="bottom" id="Debug" order="3" weight="0.3997773" />
     206      <window_info anchor="bottom" id="Cvs" order="4" weight="0.25" />
     207      <window_info anchor="bottom" id="Inspection" order="5" weight="0.4" />
     208      <window_info anchor="bottom" id="TODO" order="6" />
     209      <window_info anchor="bottom" id="Version Control" order="7" />
     210      <window_info anchor="bottom" id="Terminal" order="8" weight="0.32962137" />
     211      <window_info anchor="bottom" id="Event Log" order="9" side_tool="true" weight="0.32962137" />
     212      <window_info anchor="bottom" id="Messages" order="10" weight="0.32962137" />
     213      <window_info anchor="right" id="Commander" internal_type="SLIDING" order="0" type="SLIDING" weight="0.4" />
     214      <window_info anchor="right" id="Ant Build" order="1" weight="0.24952137" />
     215      <window_info anchor="right" content_ui="combo" id="Hierarchy" order="2" weight="0.25" />
     216      <window_info anchor="right" id="Palette" order="3" />
     217      <window_info anchor="right" id="Palette&#9;" order="4" />
     218      <window_info anchor="right" id="Maven" order="5" weight="0.3299298" />
     219    </layout-to-restore>
    209220  </component>
    210221  <component name="antWorkspaceConfiguration">
    … …  
    238249    <entry file="file://$PROJECT_DIR$/image-identification-development/src/Main.java">
    239250      <provider selected="true" editor-type-id="text-editor">
    240         <state relative-caret-position="557">
    241           <caret line="779" column="12" selection-start-line="779" selection-start-column="12" selection-end-line="779" selection-end-column="12" />
     251        <state relative-caret-position="2014">
     252          <caret line="760" selection-start-line="760" selection-end-line="760" />
    242253          <folding>
    243254            <element signature="imports" expanded="true" />
    … …  
    261272    <entry file="file://$PROJECT_DIR$/../image-identification-terminal/javaClassifierComparison.java">
    262273      <provider selected="true" editor-type-id="text-editor">
    263         <state relative-caret-position="225">
    264           <caret line="592" column="12" selection-start-line="592" selection-start-column="12" selection-end-line="592" selection-end-column="12" />
     274        <state relative-caret-position="6897">
     275          <caret line="442" column="23" selection-start-line="442" selection-start-column="23" selection-end-line="442" selection-end-column="23" />
    265276          <folding>
    266277            <element signature="imports" expanded="true" />
    267             <element signature="method#init#0;class#javaClassifierComparison#0" />
    268278            <element signature="class#StartAndEndPoint#0;class#javaClassifierComparison#0" />
    269279            <element signature="class#Pair#0;class#javaClassifierComparison#0" />
    270             <element signature="method#main#0;class#javaClassifierComparison#0" />
    271             <element signature="method#Algorithm_HoughLinesP_Single#0;class#javaClassifierComparison#0" />
    272             <element signature="method#Algorithm_HoughLinesP_Combo#0;class#javaClassifierComparison#0" />
    273             <element signature="method#Algorithm_MorphologyOLD#0;class#javaClassifierComparison#0" />
    274             <element signature="method#Algorithm_Morphology#0;class#javaClassifierComparison#0" />
     280            <element signature="method#init#0;class#javaClassifierComparison#0" />
    275281            <element signature="method#Classifier_LineCounter#0;class#javaClassifierComparison#0" />
    276282            <element signature="method#Classifier_ClusterDetection#0;class#javaClassifierComparison#0" />
    … …  
    279285            <element signature="method#lineComparison#0;class#javaClassifierComparison#0" />
    280286            <element signature="method#ClusterCheck#0;class#javaClassifierComparison#0" />
     287            <element signature="method#Algorithm_MorphologyOLD2#0;class#javaClassifierComparison#0" />
     288            <element signature="method#Algorithm_MorphologyOLD#0;class#javaClassifierComparison#0" />
    281289          </folding>
    282290        </state>
    … …  
    285293    <entry file="file://$PROJECT_DIR$/image-identification-development/src/MainMorph.java">
    286294      <provider selected="true" editor-type-id="text-editor">
    287         <state relative-caret-position="383">
    288           <caret line="236" column="54" selection-start-line="236" selection-start-column="54" selection-end-line="236" selection-end-column="54" />
     295        <state relative-caret-position="106">
     296          <caret line="1351" column="52" selection-start-line="1351" selection-start-column="52" selection-end-line="1351" selection-end-column="52" />
    289297          <folding>
    290298            <element signature="imports" expanded="true" />
    291299            <element signature="method#imageViewer#0;class#MainMorph#0" />
    292             <element signature="e#14232#16729#1" />
    293             <element signature="e#16814#18826#1" />
    294             <element signature="e#18895#21536#1" />
    295             <element signature="e#21620#27308#1" />
    296             <element signature="e#27393#31396#1" />
    297             <element signature="e#31464#32641#1" />
    298             <element signature="e#32710#36690#1" />
    299             <element signature="e#36800#48520#1" />
    300             <element signature="e#45567#45616#0" />
    301             <element signature="e#48614#59515#1" />
    302             <element signature="e#59847#68195#1" />
    303             <element signature="e#61117#61159#0" />
    304300          </folding>
    305301        </state>

  • other-projects/is-sheet-music-encore/trunk/image-identification-dev-02/image-identification-development/src/Main.java

    r33455 r33589  
    111111    static int STANDARD_DEVIATION_THRESHOLD = 6;
    112112    static int MINLINECOUNT                 = 40;
    113     static int MAXLINEGAP                   = 1;  //4
     113    static int MAXLINEGAP                   = 1;
    114114    static double THRESHOLD_C               = 4;
    115115    static double SLOPEGRADIENT             = 0.02; //0.02
    … …  
    757757            //!!!!!!!!!!!!!!!!!!!!!!!!!!!NOTNOT!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
    758758            //coo.31924062612282-9.png 8   lines
    759             //String default_file = hiresDirectory+ "MU/SheetMusic/mdp.39015096400919-9.png";
     759            String default_file = hiresDirectory+ "MU/SheetMusic/mdp.39015096400919-9.png";
     760            //String default_file = hiresDirectory+ "MU/SheetMusic/coo.31924062612282-9.png";
    760761
    761762            //String default_file = hiresDirectory+ "MU/SheetMusic/mdp.39015096402204-2.png";
    … …  
    778779            //String default_file = "TestImages/SheetMusic01.png";
    779780            //String default_file = "TestImages/SheetMusic02.png";
    780             String default_file = "TestImages/vLine.png";
     781            //String default_file = "TestImages/vLine.png";
    781782            String filename = ((args.length > 0) ? args[0] : default_file);
    782783            File file = new File(filename);
    … …  
    791792            // Edge detection
    792793
    793             Imgproc.adaptiveThreshold(original, edgesDetected,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 15, THRESHOLD_C);
     794            Imgproc.adaptiveThreshold(original, edgesDetected,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 1001, THRESHOLD_C);
    794795            //TEST PARAMETERSImgproc.adaptiveThreshold(original, edgesDetected,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 531,1);
    795796
    796797
    797798//            //IGNORE BORDERS OF IMAGE (using crop)
    798 //            Imgproc.adaptiveThreshold(original, mid,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 15, 2);
     799//            Imgproc.adaptiveThreshold(original, mid,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 1001, 2);
    799800//            double maxX = mid.size().width;
    800801//            double maxY = mid.size().height;
    801 //            Point cp1 = new Point(maxX/10, maxY/10);
     802//            Point cp1 = new Point(maxX/5, maxY/5);
    802803//            Point cp2 = new Point(maxX - cp1.x, maxY -cp1.y);
    803804//            Rect rectCrop = new Rect(cp1, cp2);
    804805//            edgesDetected = mid.submat(rectCrop);
    805806
    806 
     807//            System.out.println("Width: " + edgesDetected.width() + " Height: " + edgesDetected.height());
    807808            //****************MORPHOLOGY****************************************************************************************
    808809            //ADDIOTIONAL FILTERING TO STOP STREAKS
    … …  
    833834
    834835            double minLineLength = edgesDetectedRGB.size().width/8;
    835 
     836            imwrite("houghtest-bin.jpg", edgesDetectedRGB);
    836837            Imgproc.HoughLinesP(edgesDetected, linesP, 1, Math.PI / 720, HOUGHLINEP_THRESHOLD, minLineLength,MAXLINEGAP); // runs the actual detection
    837838            //System.out.println("Before Gradient Filtering num lines: " + linesP.rows());
    … …  
    856857            //File filenameTest = new File("TestImages/NotSheetMusic02.png");
    857858            //BufferedImage i = ImageIO.read(filenameTest);
    858             BufferedImage toBeClassifiedImg = toBufferedImage(edgesDetectedRGB);
     859            //BufferedImage toBeClassifiedImg = toBufferedImage(edgesDetectedRGB);
    859860
    860861
    861862            //Display Results
     863
     864            imwrite("houghtest-lines.jpg", edgesDetectedRGB);
    862865            //HighGui.imshow("Source", original);
    863866            //HighGui.imshow("Just Edges", justEdges); //TESTING
    864867
    865 
    866             HighGui.imshow("LINESFOUND", edgesDetectedRGB);
    867             HighGui.resizeWindow("LINESFOUND", 1000,1000);
    868            
    869             HighGui.imshow("CLUSTERSFOUND", clustersFoundRGB);
    870             HighGui.resizeWindow("CLUSTERSFOUND", 1000,1000);
     868//            HighGui.namedWindow("LINESFOUND", HighGui.WINDOW_AUTOSIZE);
     869//            HighGui.imshow("LINESFOUND", edgesDetectedRGB);
     870//            HighGui.resizeWindow("LINESFOUND", 1000,1000);
     871//
     872//
     873//            HighGui.imshow("CLUSTERSFOUND", clustersFoundRGB);
     874//            HighGui.namedWindow("CLUSTERSFOUND", HighGui.WINDOW_AUTOSIZE);
     875//            HighGui.resizeWindow("CLUSTERSFOUND", 1000,1000);
    871876
    872877            //HighGui.imshow("Detected Lines (in red) - negative", edgesDetectedRGBProb);
    … …  
    874879            System.out.println("LINE COUNT RESULT:   " +  ClassifierLineCount(horizontalLineCount) + '\t' +"LinesFound: " + horizontalLineCount); //COUNT OF LINES CLASSIFICATION
    875880            //System.out.println("LINE CLUSTER RESULT: " +  ClassifierLineClusterOLD(toBeClassifiedImg).get(0) + '\t' + "LinesFound: " + ClassifierLineClusterOLD(toBeClassifiedImg).get(1) + '\t' + "ClustersFound: " + ClassifierLineClusterOLD(toBeClassifiedImg).get(2));
    876             System.out.println("NEW CLUSTER RESULTS: " +  ClassifierLineClusterPt(pointArrayList,clustersFoundRGB).get(0) + '\t' + "LinesFound: " + horizontalLineCount + '\t' + "ClustersFound: " + ClassifierLineClusterPt(pointArrayList,clustersFoundRGB).get(1));
     881            //System.out.println("NEW CLUSTER RESULTS: " +  ClassifierLineClusterPt(pointArrayList,clustersFoundRGB).get(0) + '\t' + "LinesFound: " + horizontalLineCount + '\t' + "ClustersFound: " + ClassifierLineClusterPt(pointArrayList,clustersFoundRGB).get(1));
    877882            //System.out.println(ClassifierLineClusterPt(pointArrayList, clustersFoundRGB));
    878883
    … …  
    880885
    881886            // Wait and Exit
    882             HighGui.waitKey();
     887            //HighGui.waitKey();
    883888            System.exit(0);
    884889        }

  • other-projects/is-sheet-music-encore/trunk/image-identification-dev-02/image-identification-development/src/MainMorph.java

    r33458 r33589  
    6666
    6767import static org.opencv.core.Core.FILLED;
     68import static org.opencv.core.Core.calcCovarMatrix;
    6869import static org.opencv.core.CvType.CV_8UC3;
    6970import static org.opencv.highgui.HighGui.createJFrame;
    … …  
    193194            //Variables
    194195            System.out.println("Running Code version: " + CODE_VERSION + " Image Version: " +IMAGE_VERSION);
    195             Mat mid = new Mat();
    196196            Mat edgesDetectedRGB = new Mat();
    197197            Mat clustersFoundRGB = new Mat();
    … …  
    209209            //String default_file =hiresDirectory+"BK/NotSheetMusic/aeu.ark+=13960=t2q53nq6w-6.png";
    210210            //String default_file =hiresDirectory+"BK/NotSheetMusic/aeu.ark+=13960=t9z03w65z-4.png";
     211            //String default_file =hiresDirectory+"BK/NotSheetMusic/loc.ark+=13960=t50g4443d-5.png";
    211212            //String default_file =hiresDirectory+"MU/NotSheetMusic/aeu.ark+=13960=t0dv28v9r-1.png";
    212213            //String default_file =hiresDirectory+"MU/SheetMusic/emu.010001066823-9.png";
    … …  
    234235            //String default_file = hiresDirectory+"/MU/NotSheetMusic/aeu.ark+=13960=t0dv28v9r-1.png";  //Image evaluation
    235236            //String default_file = hiresDirectory+"/MU/NotSheetMusic/aeu.ark+=13960=t0dv28v9r-2.png";  //large numbers
     237            //String default_file = hiresDirectory+ "MU/SheetMusic/mdp.39015096400919-9.png";
    236238            //String default_file = hiresDirectory+"/MU/NotSheetMusic/aeu.ark+=13960=t0dv28v9r-3.png";
    237             String default_file = hiresDirectory+"/MU/SheetMusic/mdp.39015080921409-9.png";
     239            //String default_file = hiresDirectory+"/MU/SheetMusic/mdp.39015080921409-9.png";
    238240            //String default_file = hiresDirectory+"/MU/NotSheetMusic/aeu.ark+=13960=t0dv28v9r-4.png";
    239241            //String default_file = hiresDirectory+"/MU/NotSheetMusic/aeu.ark+=13960=t0dv28v9r-5.png";
    … …  
    242244            //String default_file = hiresDirectory+"/MU/NotSheetMusic/aeu.ark+=13960=t0dv28v9r-8.png";
    243245            //String default_file = hiresDirectory+"/MU/NotSheetMusic/aeu.ark+=13960=t0dv28v9r-9.png";
     246            String default_file = hiresDirectory+"/MU/SheetMusic/mdp.39015096398899-4.png";
    244247
    245248
    … …  
    260263            Mat original = Imgcodecs.imread(filename, Imgcodecs.IMREAD_GRAYSCALE);
    261264            System.out.println("Width: " + original.width() + " Height: " + original.height());
    262             Mat binarizedOriginal = original.clone();
     265            Mat binarized = original.clone();
    263266           
    264             Imgproc.adaptiveThreshold(original, binarizedOriginal,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 15, THRESHOLD_C);
     267            //Imgproc.adaptiveThreshold(original, binarized,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 15, THRESHOLD_C);
    265268            //TEST PARAMETERSImgproc.adaptiveThreshold(original, edgesDetected,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 531,1);
    266269            //Imgproc.threshold(original,original, 127, 255, Imgproc.THRESH_BINARY);
    267 
     270            //************************
     271            //Argument Setup
     272            //************************
     273            int tempvariable = 0;
     274            if (tempvariable ==1){
     275                Imgproc.adaptiveThreshold(original, binarized, 255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C, Imgproc.THRESH_BINARY_INV, 1001, 5);
     276            }
     277            else {
     278
     279                int borderIgnoreAmount = 0;
     280                double divisor;
     281                switch (borderIgnoreAmount) {
     282                    case 5:
     283                        divisor = 40;
     284                        break;
     285                    case 10:
     286                        divisor = 20;
     287                        break;
     288                    case 20:
     289                        divisor = 10;
     290                        break;
     291                    case 40:
     292                        divisor = 5;
     293                        break;
     294                    case 0:
     295                        divisor = -1;
     296                        break;
     297                    default:
     298                        divisor = -1;
     299                }
     300                if (divisor != -1) {
     301                    Mat mid = new Mat();
     302                    Imgproc.adaptiveThreshold(original, mid, 255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C, Imgproc.THRESH_BINARY_INV, 15, THRESHOLD_C);
     303                    double maxX = mid.size().width;
     304                    double maxY = mid.size().height;
     305                    Point cp1 = new Point(maxX / divisor, maxY / divisor);
     306                    Point cp2 = new Point(maxX - cp1.x, maxY - cp1.y);
     307                    Rect rectCrop = new Rect(cp1, cp2);
     308                    binarized = mid.submat(rectCrop);
     309                } else {
     310                    Imgproc.adaptiveThreshold(original, binarized, 255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C, Imgproc.THRESH_BINARY_INV, 15, THRESHOLD_C);
     311                }
     312
     313                System.out.println("Width: " + binarized.width() + " Height: " + binarized.height());
     314            }
    268315
    269316            //****************MORPHOLOGY****************************************************************************************
    … …  
    276323            //dynamic morphology??
    277324            if(CODE_VERSION == 1) {
    278                 int hori = binarizedOriginal.width();
    279                 int vert = binarizedOriginal.height();
     325                int hori = binarized.width();
     326                int vert = binarized.height();
    280327                //Find ratio between 100 and width and 100 and height
    281328                int divX = hori/10;
    … …  
    284331                int sizeY = (vert/divY) * 10;
    285332
    286                 Mat test = binarizedOriginal.clone();
     333                Mat test = binarized.clone();
    287334                imageViewer("Original", test);
    288335
    … …  
    326373
    327374                //MAKE SURE BLACK & WHITE
    328                 Mat test = binarizedOriginal.clone();
     375                Mat test = binarized.clone();
    329376                imageViewer("00 Binarized Original", test);
    330377
    … …  
    366413            //Tutorial/Demo Code
    367414            if (CODE_VERSION == 3) {
    368                 Mat horizontal = binarizedOriginal.clone();
    369                 Mat vertical = binarizedOriginal.clone();
     415                Mat horizontal = binarized.clone();
     416                Mat vertical = binarized.clone();
    370417                // Specify size on horizontal axis
    371418                int horizontal_size = horizontal.cols() / 50;
    … …  
    420467                imageViewer("original", original);
    421468
    422                 Mat test = binarizedOriginal.clone();
    423                 Mat pre = binarizedOriginal.clone();
     469                Mat test = binarized.clone();
     470                Mat pre = binarized.clone();
    424471                Mat dst = new Mat();
    425472
    … …  
    534581            //Better morphology attempt - dynamic
    535582            if(CODE_VERSION == 5) {
    536                 int hori = binarizedOriginal.width();
    537                 int vert = binarizedOriginal.height();
     583                int hori = binarized.width();
     584                int vert = binarized.height();
    538585                //Find ratio between 100 and width and 100 and height
    539586                int sizeX100 = 10 * (hori/68);
    … …  
    551598                imageViewer("original", original);
    552599
    553                 Mat test = binarizedOriginal.clone();
     600                Mat test = binarized.clone();
    554601                imageViewer("00 Inverse Binarized Original", test);
    555602
    … …  
    643690                imageViewer("original", original);
    644691
    645                 Mat src = binarizedOriginal.clone();
    646                 Mat test = binarizedOriginal.clone();
     692                Mat src = binarized.clone();
     693                Mat test = binarized.clone();
    647694                Mat mask = new Mat();
    648695                Mat dst = new Mat();
    … …  
    729776                imageViewer("original", original);
    730777
    731                 Mat test = binarizedOriginal.clone();
     778                Mat test = binarized.clone();
    732779
    733780
    … …  
    738785                //Large Object Removal
    739786                //************************************
    740                 Mat srcLOR = binarizedOriginal.clone();
     787                Mat srcLOR = binarized.clone();
    741788                Mat maskLOR = new Mat();
    742789                Mat dstLOR = new Mat();
    … …  
    9681015                    //Imgproc.adaptiveThreshold(original1, original,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 15, THRESHOLD_C);
    9691016
    970                     Mat test = binarizedOriginal.clone();
     1017                    Mat test = binarized.clone();
    9711018                    //************************************
    9721019                    //Large Object Removal
    9731020                    //************************************
    974                     Mat srcLOR = binarizedOriginal.clone();
     1021                    Mat srcLOR = binarized.clone();
    9751022                    Mat maskLOR = new Mat();
    9761023                    Mat dstLOR = new Mat();
    … …  
    11581205            //Reset after david chat
    11591206            if(CODE_VERSION == 9){
     1207                int areaCounter=0;
    11601208                //Display Original
    11611209                imageViewer("original", original);
    1162                 imageViewer("000 Inverse Binarized Original", binarizedOriginal);
     1210                imageViewer("000 Inverse Binarized Original", binarized);
    11631211
    11641212
    … …  
    11661214                //1. Large Object Remover
    11671215                //************************************
    1168                 Mat srcLOR = binarizedOriginal.clone();
     1216                Mat srcLOR = binarized.clone();
    11691217                Mat maskLOR = new Mat();
    11701218                Mat dstLOR = new Mat();
    … …  
    11761224
    11771225                //Heal the large items
    1178                 Mat healLOR = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(25,25));
     1226                Mat healLOR = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(25,25)); //5,5
    11791227                Imgproc.morphologyEx(maskLOR,maskLOR, Imgproc.MORPH_CLOSE, healLOR);
    11801228                imageViewer("002 heal objects in mask", maskLOR);
    11811229
    11821230                //IsolateLarge
    1183                 Mat isolateLargeLOR = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(30,30));
     1231                Mat isolateLargeLOR = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(30,30)); //8,8
    11841232                Imgproc.morphologyEx(maskLOR,maskLOR, Imgproc.MORPH_OPEN, isolateLargeLOR);
    11851233                imageViewer("003 Isolate large", maskLOR);
    … …  
    12541302
    12551303                //4. Classify like line clusters.
    1256                 Mat base = binarizedOriginal.clone();
     1304                Mat base = binarized.clone();
    12571305                //***********************************
    12581306                //3. Isolate straight lines
    12591307                //***********************************
    12601308                //Heal lines
    1261                 Mat healISL = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(82,1));
     1309                Mat healISL = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(86,1));
    12621310                Imgproc.morphologyEx(dstLOR,base, Imgproc.MORPH_OPEN, healISL);
    12631311                imageViewer("202 heal objects in mask", base);
    … …  
    13101358                        System.out.println("i:"+ i + " Height: " + boundRect[i].height);
    13111359                        if(boundRect[i].width > 300){
     1360                            areaCounter++;
    13121361                            System.out.println("i:"+ i + " Width: " +  boundRect[i].width);
    13131362                            Imgproc.rectangle(rectOutput, boundRect[i].tl(), boundRect[i].br(), new Scalar(255, 0, 0), 20);
    … …  
    13181367
    13191368                imageViewer("4000 Rect found", rectOutput);
     1369                System.out.println(areaCounter);
    13201370
    13211371
    … …  
    13271377                Pair returnVariables = new Pair();
    13281378                int areaCounter=0;
     1379
     1380
    13291381                //Mat original = Imgcodecs.imread(filename, Imgcodecs.IMREAD_GRAYSCALE);
    1330                 //Mat binarizedOriginal = original.clone();
    1331                 //Imgproc.adaptiveThreshold(original, binarizedOriginal,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 15, THRESHOLD_C);
     1382                //Mat binarized = original.clone();
     1383                //Mat mid = new Mat();
     1384                //if(borderCrop > 0){
     1385                    //Imgproc.adaptiveThreshold(original, mid,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 15, 2);
     1386                    //double maxX = mid.size().width;
     1387                    //double maxY = mid.size().height;
     1388                    //Point cp1 = new Point(maxX/10, maxY/10);
     1389                    //Point cp2 = new Point(maxX - cp1.x, maxY -cp1.y);
     1390                    //Rect rectCrop = new Rect(cp1, cp2);
     1391                    //binarized = mid.submat(rectCrop);
     1392                //}
     1393                //else{
     1394                //Imgproc.adaptiveThreshold(original, binarized,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 15, THRESHOLD_C);
     1395                //}
     1396
     1397
    13321398                try{
    13331399                    //************************************
    13341400                    //1. Large Object Remover
    13351401                    //************************************
    1336                     Mat srcLOR = binarizedOriginal.clone();
     1402                    Mat srcLOR = binarized.clone();
    13371403                    Mat maskLOR = new Mat();
    13381404                    Mat dstLOR = new Mat();
    … …  
    13531419                    srcLOR.copyTo(dstLOR, maskLOR);
    13541420
    1355                     Mat base = binarizedOriginal.clone();
     1421                    Mat base = binarized.clone();
    13561422                    //***********************************
    13571423                    //3. Isolate straight lines

  • other-projects/is-sheet-music-encore/trunk/image-identification-terminal/Makefile

    r33449 r33589  
    3636   
    3737testrun-classifier-single-combo:
    38     ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/hires-download-images/MU/SheetMusic/coo.31924062612282-9.png combo logSingle.txt -singleNew
     38    ./runClassifer.sh /Scratch/cpb16/is-sheet-music-encore/image-identification-terminal/TestImages/SheetMusic02.png combo logSingle.txt -singleNew 40
    3939    echo "Displaying javaImageClassifier output"
    4040        #Calculate the Accuracy of the classifier

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

    r33439 r33589  
    2828                float falseAccuracyRate  =0;
    2929                float overallAccuracyRate=0;
     30                String notApplicable = "N/A";
    3031                //
    3132                int sheetMusicCount = 0;
    … …  
    9394                }
    9495                else if (sheetMusicCount == 0) {
     96                    trueAccuracyRate = -1;
    9597                    overallAccuracyRate = falseAccuracyRate;
    9698                }
    9799                else if(notSheetMusicCount == 0) {
     100                    falseAccuracyRate = -1;
    98101                    overallAccuracyRate = trueAccuracyRate;
    99102                }
    … …  
    102105                    System.out.println("There is nothing to compare");
    103106                }
    104                
    105                 fw.write("Date: " + d.toString() + '\n'
    106                     + "Classifier: " + classifierType + '\n'
    107                     + "truePositive: "  + truePositive + '\n'
    108                     + "falseNegative: " + falseNegative + '\n'
    109                     + "falsePositve: "  + falsePositive + '\n' 
    110                     + "trueNegative: "  + trueNegative + '\n'
    111                     + "SheetMusicAccuracyRate: "    + trueAccuracyRate + "%" + '\n'
    112                     + "NotSheetMusicAccuracyRate: " + falseAccuracyRate + "%" + '\n'
    113                     + "OverallAccuracyRate: "       + overallAccuracyRate + "%" + '\n' +
    114                     "****************************************************************" + '\n');     
     107                if(falseAccuracyRate ==-1){
     108                    fw.write("Date: " + d.toString() + '\n'
     109                        + "Classifier: " + classifierType + '\n'
     110                        + "truePositive: "  + truePositive + '\n'
     111                        + "falseNegative: " + falseNegative + '\n'
     112                        + "falsePositve: "  + falsePositive + '\n' 
     113                        + "trueNegative: "  + trueNegative + '\n'
     114                        + "SheetMusicAccuracyRate: "    + trueAccuracyRate + "%" + '\n'
     115                        + "NotSheetMusicAccuracyRate: " + notApplicable + '\n'
     116                        + "OverallAccuracyRate: "       + overallAccuracyRate + "%" + '\n');                   
     117                }
     118                else if(trueAccuracyRate == -1){   
     119                    fw.write("Date: " + d.toString() + '\n'
     120                        + "Classifier: " + classifierType + '\n'
     121                        + "truePositive: "  + truePositive + '\n'
     122                        + "falseNegative: " + falseNegative + '\n'
     123                        + "falsePositve: "  + falsePositive + '\n' 
     124                        + "trueNegative: "  + trueNegative + '\n'
     125                        + "SheetMusicAccuracyRate: "    + notApplicable + '\n'
     126                        + "NotSheetMusicAccuracyRate: " + falseAccuracyRate + "%" + '\n'
     127                        + "OverallAccuracyRate: "       + overallAccuracyRate + "%" + '\n');                   
     128                }
     129                else{
     130                    fw.write("Date: " + d.toString() + '\n'
     131                        + "Classifier: " + classifierType + '\n'
     132                        + "truePositive: "  + truePositive + '\n'
     133                        + "falseNegative: " + falseNegative + '\n'
     134                        + "falsePositve: "  + falsePositive + '\n' 
     135                        + "trueNegative: "  + trueNegative + '\n'
     136                        + "SheetMusicAccuracyRate: "    + trueAccuracyRate + "%" + '\n'
     137                        + "NotSheetMusicAccuracyRate: " + falseAccuracyRate + "%" + '\n'
     138                        + "OverallAccuracyRate: "       + overallAccuracyRate + "%" + '\n');
     139                }       
    115140                buf.close();
    116141                fw.close();                             

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

    r33458 r33589  
    7272    static double THRESHOLD_AREA_COUNT;
    7373
    74 //********************************************************************************************
    75 //CLASSES
    76 //********************************************************************************************
    77    
    78     //Sets globals based off properties file
    79     public static void init(){
    80         try{
    81             Properties config = new Properties();
    82             FileInputStream input = new FileInputStream("configClassifierComparison.properties");
    83             config.load(input);
    84             CLASSIFIER_HOUGHLINESP_MIN =    Integer.parseInt(config.getProperty("CLASSIFIER_HOUGHLINESP_MIN"));
    85             CLASSIFIER_HOUGHLINESP_MAX =    Integer.parseInt(config.getProperty("CLASSIFIER_HOUGHLINESP_MAX"));
    86             HOUGHLINEP_THRESHOLD =          Integer.parseInt(config.getProperty("HOUGHLINEP_THRESHOLD"));
    87             STANDARD_DEVIATION_THRESHOLD =  Integer.parseInt(config.getProperty("STANDARD_DEVIATION_THRESHOLD"));
    88             MINLINECOUNT =                  Integer.parseInt(config.getProperty("MINLINECOUNT"));
    89             MAXLINEGAP =                    Integer.parseInt(config.getProperty("MAXLINEGAP"));
    90             THRESHOLD_C =                   Double.parseDouble(config.getProperty("THRESHOLD_C"));
    91             SLOPEGRADIENT =                 Double.parseDouble(config.getProperty("SLOPEGRADIENT"));
    92             CLUSTER_DISTANCE_MAX =          Double.parseDouble(config.getProperty("CLUSTER_DISTANCE_MAX"));
    93             CLUSTER_DISTANCE_MIN =          Double.parseDouble(config.getProperty("CLUSTER_DISTANCE_MIN"));
    94             THRESHOLD_AREA_SIZE =           Double.parseDouble(config.getProperty("THRESHOLD_AREA_SIZE"));
    95             THRESHOLD_AREA_COUNT =          Double.parseDouble(config.getProperty("THRESHOLD_AREA_COUNT"));
    96         }
    97         catch(Exception e){
    98             e.printStackTrace();
    99         }
    100     }
    101    
     74    //******************
     75    //CLASSES
     76    //******************
    10277    static public class StartAndEndPoint {
    10378        //PRIVATES
    … …  
    161136    }
    162137
     138    //******************
     139    //TRIGGERS
     140    //******************
     141    /**Sets globals based off properties file*/
     142    public static void init(){
     143        try{
     144            Properties config = new Properties();
     145            FileInputStream input = new FileInputStream("configClassifierComparison.properties");
     146            config.load(input);
     147            CLASSIFIER_HOUGHLINESP_MIN =    Integer.parseInt(config.getProperty("CLASSIFIER_HOUGHLINESP_MIN"));
     148            CLASSIFIER_HOUGHLINESP_MAX =    Integer.parseInt(config.getProperty("CLASSIFIER_HOUGHLINESP_MAX"));
     149            HOUGHLINEP_THRESHOLD =          Integer.parseInt(config.getProperty("HOUGHLINEP_THRESHOLD"));
     150            STANDARD_DEVIATION_THRESHOLD =  Integer.parseInt(config.getProperty("STANDARD_DEVIATION_THRESHOLD"));
     151            MINLINECOUNT =                  Integer.parseInt(config.getProperty("MINLINECOUNT"));
     152            MAXLINEGAP =                    Integer.parseInt(config.getProperty("MAXLINEGAP"));
     153            THRESHOLD_C =                   Double.parseDouble(config.getProperty("THRESHOLD_C"));
     154            SLOPEGRADIENT =                 Double.parseDouble(config.getProperty("SLOPEGRADIENT"));
     155            CLUSTER_DISTANCE_MAX =          Double.parseDouble(config.getProperty("CLUSTER_DISTANCE_MAX"));
     156            CLUSTER_DISTANCE_MIN =          Double.parseDouble(config.getProperty("CLUSTER_DISTANCE_MIN"));
     157            THRESHOLD_AREA_SIZE =           Double.parseDouble(config.getProperty("THRESHOLD_AREA_SIZE"));
     158            THRESHOLD_AREA_COUNT =          Double.parseDouble(config.getProperty("THRESHOLD_AREA_COUNT"));
     159        }
     160        catch(Exception e){
     161            e.printStackTrace();
     162        }
     163    }
     164    /***/
    163165    public static void main(String[] args) {
    164166        init();
    165167        try {
    166168       
    167             if (args.length != 3) {
     169            if (args.length < 3) {
    168170                System.out.println("Usage: imageClassifier <inputFilename> <classifierType> <outputFilename>");
     171                System.out.println("+ (Optional)<BorderIgnoreAmount[0,5, 10, 20, 40]>");
    169172            } else {
    170173                Pair algorithmResult = new Pair();
    171 
    172174                Boolean result = null;
    173175                String result_cluster = "";
    … …  
    176178                String outputFilename = args[2];
    177179                Boolean enableLineClustering = null;
     180
    178181                //Prep Writing output to disc
    179182                File log = new File(outputFilename);
    180183                FileWriter fileWriter = new FileWriter(log, true);
    181184                BufferedWriter bw = new BufferedWriter(fileWriter);
    182                 //Execute classifierType defined from arguement
    183 
    184185                File imgCheck = new File(imageFilename);
    185186                if(!imgCheck.exists()){System.err.println("Image not found: "+ imageFilename);}
    186187
    187                 //Split output by tab for processing in next java program
    188                 //imageFilename = 1, result = 3, classifierType = 4
    189                
     188//
     189
     190
     191                //Arguement Preps
     192                int borderIgnoreAmount;
     193                if(args.length >= 4){borderIgnoreAmount =Integer.parseInt(args[3]);}
     194                else {borderIgnoreAmount=-1;}
     195
     196                //imageFilename = 1, result = 3, classifierType = 4 ?????
    190197                switch (classifierType) {
     198                    //Splits output by tab for processing in next java program
    191199                    case "count":
    192200                        enableLineClustering = false;
    193                         algorithmResult = Algorithm_HoughLinesP_Single(imageFilename, enableLineClustering);
     201                        algorithmResult = Algorithm_HoughLinesP_Single(imageFilename, enableLineClustering, borderIgnoreAmount);
    194202                        bw.write("Filename:" + '\t' + imageFilename + '\t' + "Classified as:" + '\t' + algorithmResult.getBoolean() + '\t' + "Number of lines:" + '\t' + algorithmResult.getInteger() + '\t' + classifierType + '\n');
    195203                        break;
    196204                    case "cluster":
    197205                        enableLineClustering = true;
    198                         algorithmResult = Algorithm_HoughLinesP_Single(imageFilename, enableLineClustering);
     206                        algorithmResult = Algorithm_HoughLinesP_Single(imageFilename, enableLineClustering, borderIgnoreAmount);
    199207                        bw.write("Filename:" + '\t' + imageFilename + '\t' + "Classified as:" + '\t' + algorithmResult.getBoolean() + '\t' + "Number of lines:" + '\t' + algorithmResult.getInteger() + '\t' + classifierType + '\n');
    200208                        break;
    201209                    case "combo":
    202                         algorithmResult = Algorithm_HoughLinesP_Combo(imageFilename);
     210                        algorithmResult = Algorithm_HoughLinesP_Combo(imageFilename, borderIgnoreAmount);
    203211                        bw.write("Filename:" + '\t' + imageFilename + '\t' + "Classified as:" + '\t' + algorithmResult.getBoolean() + '\t' + "Number of lines:" + '\t' + algorithmResult.getInteger() + '\t' + classifierType + '\n');
    204212                        break;
    205213                    case "morphology":
    206                         algorithmResult = Algorithm_Morphology(imageFilename);
     214                        algorithmResult = Algorithm_Morphology(imageFilename, borderIgnoreAmount);
    207215                        bw.write("Filename:" + '\t' + imageFilename + '\t' + "Classified as:" + '\t' + algorithmResult.getBoolean() + '\t' + "Number of areas:" + '\t' + algorithmResult.getInteger() + '\t' + classifierType + '\n');
    208216                        break;
    … …  
    210218                        System.out.println("unknown algorithm");
    211219                        break;
    212                 }     
    213                
     220                }
    214221                bw.close();
    215222            }
    … …  
    222229    //ALGORITHM FUNCTIONS
    223230    //******************
    224     private static Pair Algorithm_HoughLinesP_Single(String filename, Boolean enableLineClusterDetection){
     231    private static Pair Algorithm_HoughLinesP_Single(String filename, Boolean enableLineClusterDetection, int borderIgnoreAmount){
     232        //****************EXPLANATION**************************************************
     233        //
     234        //Load an image in greyscale
     235        //Additional matrix to hold results of line detection
     236        //Inversed Binarization of image
     237        //Detect lines in image
     238        //Go thru every line detected and check its gradient is less than SLOPEGRADIENT
     239        //
     240        //****************EXPLANATION**************************************************
    225241        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
    226242        Boolean isSheetMusic = null;
    … …  
    235251            ArrayList<StartAndEndPoint> pointArrayList = new ArrayList<StartAndEndPoint>();
    236252
    237             //****************EXPLANATION**************************************************
    238             //
    239             //Load an image in greyscale
    240             //Additional matrix to hold results of line detection
    241             //Inversed Binarization of image
    242             //Detect lines in image
    243             //Go thru every line detected and check its gradient is less than SLOPEGRADIENT
    244             //
    245             //****************EXPLANATION**************************************************
    246 
    247253            Mat original = Imgcodecs.imread(filename, Imgcodecs.IMREAD_GRAYSCALE);
     254           
     255            Mat binarized = original.clone();
     256            int blockSize = binarized.height()/3;
     257            //BlockSize must be blockSize % 2 == 1 && blockSize > 1
     258            while (!(blockSize % 2 == 1 && blockSize > 1)) {
     259                blockSize++;
     260            }
     261           
     262           
     263            System.out.println("Width: " + binarized.width() + " Height: " + binarized.height());
     264            //************************
     265            //Argument Setup
     266            //************************
     267            double divisor;
     268            switch (borderIgnoreAmount){
     269                case 5:
     270                    divisor = 40;
     271                    break;
     272                case 10:
     273                    divisor = 20;
     274                    break;
     275                case 20:
     276                    divisor = 10;
     277                    break;
     278                case 40:
     279                    divisor = 5;
     280                    break;
     281                case -1:
     282                    divisor = -1;
     283                    break;
     284                default: divisor = -1;
     285            }
     286            if(divisor != -1 ) {
     287                Mat mid = new Mat();
     288                Imgproc.adaptiveThreshold(original, mid,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, blockSize, THRESHOLD_C);
     289                //Remove borders from Mat
     290                //Get width and
     291                double maxX = mid.size().width;
     292                double maxY = mid.size().height;
     293                Point cp1 = new Point(maxX / divisor, maxY / divisor);
     294                Point cp2 = new Point(maxX - cp1.x, maxY - cp1.y);
     295                Rect rectCrop = new Rect(cp1, cp2);
     296                binarized = mid.submat(rectCrop);
     297            }
     298            else{Imgproc.adaptiveThreshold(original, binarized,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, blockSize, THRESHOLD_C);}
     299            System.out.println("Width: " + binarized.width() + " Height: " + binarized.height());
     300            edgesDetected = binarized.clone();
     301           
     302           
    248303            Mat linesP = new Mat(); //will hold the results of the detection
    249             Imgproc.adaptiveThreshold(original, edgesDetected,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV,15, THRESHOLD_C);
    250304            double minLineLength = edgesDetected.size().width/8;
    251305            Imgproc.HoughLinesP(edgesDetected, linesP, 1, Math.PI / 720, HOUGHLINEP_THRESHOLD, minLineLength, MAXLINEGAP);
    … …  
    274328        return returnVariables;
    275329    }
    276     private static Pair Algorithm_HoughLinesP_Combo(String filename){
     330    private static Pair Algorithm_HoughLinesP_Combo(String filename, int borderIgnoreAmount){
     331        //****************EXPLANATION**************************************************
     332        //
     333        //Load an image in greyscale
     334        //Additional matrix to hold results of line detection
     335        //Inversed Binarization of image
     336        //Detect lines in image
     337        //Go thru every line detected and check its gradient is less than SLOPEGRADIENT
     338        //
     339        //****************EXPLANATION**************************************************
     340       
    277341        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
    278342        Boolean isSheetMusic = null;
    … …  
    281345            //Variables
    282346            int horizontalLineCount =0;
     347           
    283348            Mat edgesDetected = new Mat();
    284349            Mat edgesDetectedRGB = new Mat();
    … …  
    286351            Mat edgesDetectedRGBProb;
    287352            ArrayList<StartAndEndPoint> pointArrayList = new ArrayList<StartAndEndPoint>();
    288 
    289             //****************EXPLANATION**************************************************
    290             //
    291             //Load an image in greyscale
    292             //Additional matrix to hold results of line detection
    293             //Inversed Binarization of image
    294             //Detect lines in image
    295             //Go thru every line detected and check its gradient is less than SLOPEGRADIENT
    296             //
    297             //****************EXPLANATION**************************************************
    298 
    299353            Mat original = Imgcodecs.imread(filename, Imgcodecs.IMREAD_GRAYSCALE);
     354           
     355            Mat binarized = original.clone();
     356            int blockSize = binarized.height()/3;
     357            while (!(blockSize % 2 == 1 && blockSize > 1)) {
     358                blockSize++;
     359            }
     360            System.out.println("Width: " + binarized.width() + " Height: " + binarized.height());
     361            //************************
     362            //Argument Setup
     363            //************************
     364            double divisor;
     365            switch (borderIgnoreAmount){
     366                case 5:
     367                    divisor = 40;
     368                    break;
     369                case 10:
     370                    divisor = 20;
     371                    break;
     372                case 20:
     373                    divisor = 10;
     374                    break;
     375                case 40:
     376                    divisor = 5;
     377                    break;
     378                case -1:
     379                    divisor = -1;
     380                    break;
     381                default: divisor = -1;
     382            }
     383            if(divisor != -1 ) {
     384                Mat mid = new Mat();
     385                Imgproc.adaptiveThreshold(original, mid,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, blockSize, THRESHOLD_C);
     386                //Remove borders from Mat
     387                //Get width and
     388                double maxX = mid.size().width;
     389                double maxY = mid.size().height;
     390                Point cp1 = new Point(maxX / divisor, maxY / divisor);
     391                Point cp2 = new Point(maxX - cp1.x, maxY - cp1.y);
     392                Rect rectCrop = new Rect(cp1, cp2);
     393                binarized = mid.submat(rectCrop);
     394            }
     395            else{Imgproc.adaptiveThreshold(original, binarized,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, blockSize, THRESHOLD_C);}
     396            System.out.println("Width: " + binarized.width() + " Height: " + binarized.height());
     397            edgesDetected = binarized.clone();
     398
    300399            Mat linesP = new Mat(); //will hold the results of the detection
    301             Imgproc.adaptiveThreshold(original, edgesDetected,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV,15, THRESHOLD_C);
    302400            double minLineLength = edgesDetected.size().width/8;
    303401            Imgproc.HoughLinesP(edgesDetected, linesP, 1, Math.PI / 720, HOUGHLINEP_THRESHOLD, minLineLength, MAXLINEGAP);
    … …  
    320418            }
    321419            else if (isSheetMusic == false){
     420                System.out.println("TEST");
    322421                returnVariables = Classifier_ClusterDetection(pointArrayList);
    323422            }
    … …  
    328427        return returnVariables;
    329428    }
    330     private static Pair Algorithm_MorphologyOLD2(String filename){
    331 
     429    private static Pair Algorithm_Morphology(String filename, int borderIgnoreAmount){
    332430        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
    333         Boolean isSheetMusic = null;
    334431        Pair returnVariables = new Pair();
    335432        int areaCounter = 0;
    336         try{
    337             Mat edgesDetectedRGB = new Mat();
    338             Mat original = Imgcodecs.imread(filename, Imgcodecs.IMREAD_GRAYSCALE);
    339 
    340             System.out.println("Width: " + original.width() + " Height: " + original.height());
    341 
    342             ArrayList<MatOfPoint> contours = new ArrayList<MatOfPoint>();
    343             Mat hierarchy = new Mat();
    344 
    345             //Thresholds
    346             Imgproc.adaptiveThreshold(original, original,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 15, THRESHOLD_C);
    347             Mat processed = original.clone();
    348             //Morphological Processing
    349             Mat kernelErode = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,1));
    350             Imgproc.erode(processed,processed,kernelErode);
    351 
    352             Mat kernelDilate = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(20,3));
    353             Imgproc.dilate(processed,processed,kernelDilate);
    354 
    355             Mat kernelOpening = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(4,4));
    356             Imgproc.morphologyEx(processed, processed, Imgproc.MORPH_CLOSE, kernelOpening);
    357 
    358             Mat kernelErode02 = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(8,8));
    359             Imgproc.erode(processed,processed,kernelErode02);
    360 
    361             //Detect contours
    362             Imgproc.findContours(processed, contours, hierarchy, Imgproc.RETR_TREE, Imgproc.CHAIN_APPROX_SIMPLE);
    363 
    364             //Record areas
    365             for (int i = 0; i < contours.size(); i++) {
    366                 double area = Imgproc.contourArea(contours.get(i));
    367                 System.out.println("AREA: " + area);
    368                 //Check if area detected meets threshold
    369                 if(area > THRESHOLD_AREA_SIZE) {
    370                     areaCounter++;
    371 
    372                 }
    373             }
    374             System.out.println("areacount = " + areaCounter);
    375 
    376             //Calculates if sheet music or not
    377             if(areaCounter >= THRESHOLD_AREA_COUNT){
    378                 isSheetMusic = true;
    379                 returnVariables.setBoolean(isSheetMusic);
    380                 returnVariables.setInteger(areaCounter);
    381             }
    382         }
    383         catch(Exception e){
    384             System.err.println(e);
    385         }
    386         return returnVariables;
    387     }
    388     private static Pair Algorithm_MorphologyOLD(String filename){
    389 
    390         System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
    391         Boolean isSheetMusic = null;
    392         Pair returnVariables = new Pair();
    393         int FILLED = -1;
    394         try{
    395             Mat original1 = Imgcodecs.imread(filename, Imgcodecs.IMREAD_GRAYSCALE);
    396             System.out.println("Width: " + original1.width() + " Height: " + original1.height());
    397             Mat original = original1.clone();
    398             Imgproc.adaptiveThreshold(original1, original,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 15, THRESHOLD_C);
    399             Mat test = original.clone();
    400             //************************************
    401             //Large Object Removal
    402             //************************************
    403             Mat srcLOR = original.clone();
    404             Mat maskLOR = new Mat();
    405             Mat dstLOR = new Mat();
    406 
    407             //denoize
    408             Mat denoize = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(5,5));
    409             Imgproc.morphologyEx(srcLOR,maskLOR, Imgproc.MORPH_OPEN, denoize);
    410 
    411             //close up gaps
    412             Mat gapCloser = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(5,5));
    413             Imgproc.morphologyEx(maskLOR,maskLOR,Imgproc.MORPH_CLOSE, gapCloser);
    414 
    415             //Isolate large items
    416             Mat isolateLarge = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(8, 8));
    417             Imgproc.morphologyEx(maskLOR,maskLOR,Imgproc.MORPH_OPEN, isolateLarge);
    418 
    419             //Remove large items from image
    420             Core.bitwise_not(maskLOR,maskLOR);
    421             srcLOR.copyTo(dstLOR, maskLOR);
    422 
    423             //****************************************
    424             //Small object removal (SOR)
    425             //****************************************
    426 
    427             Mat srcSOR = dstLOR.clone();
    428             Mat maskSOR = new Mat();
    429             Mat dstSOR = new Mat();
    430 
    431             Mat startSOR =Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(7,7));
    432             Imgproc.morphologyEx(srcSOR,maskSOR, Imgproc.MORPH_OPEN, startSOR);
    433 
    434             Mat highlightSmall = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(7,7));
    435             Imgproc.dilate(maskSOR, maskSOR, highlightSmall);
    436 
    437 
    438 /*              Mat isolateSmall =Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,10));
    439                 Imgproc.morphologyEx(maskSOR,maskSOR,Imgproc.MORPH_CLOSE, isolateSmall);
    440                 imageViewer("13 isolate small - mask", maskSOR);
    441 */
    442 
    443             //Remove small items from image
    444             Core.bitwise_not(maskSOR, maskSOR);
    445             srcSOR.copyTo(dstSOR, maskSOR);
    446 
    447 
    448             //****************************************
    449             //start staff line detection
    450             //****************************************
    451 
    452             Mat kernelErode = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(15,2)); //10,2
    453             Imgproc.erode(dstSOR,test,kernelErode);
    454 
    455 
    456             Mat kernelDilate = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,4)); //20,3
    457             Imgproc.dilate(test,test,kernelDilate);
    458 
    459 
    460             Mat kernelClose = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,4)); //4,4
    461             Imgproc.morphologyEx(test, test, Imgproc.MORPH_CLOSE, kernelClose);
    462 
    463 
    464 
    465             Mat kernelErode02 = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,4)); //10,1
    466             Imgproc.erode(test,test,kernelErode02);
    467 
    468 
    469             //********************************************************************************
    470             //DETECT OUTLINE AND FIND AREA OF THESE LINES.
    471             //********************************************************************************
    472             ArrayList<MatOfPoint> contours = new ArrayList<MatOfPoint>();
    473             ArrayList<MatOfPoint> largeContours = new ArrayList<MatOfPoint>();
    474             ArrayList<MatOfPoint> postContours = new ArrayList<MatOfPoint>();
    475             Mat hierarchy = new Mat();
    476 
    477             //PARAMETERS: input image, output array of arrays, output array, contour retrieval mode, contour approximation method.
    478             //(contours)  output array of arrays: Detected contours. Each contour is stored as a vector of points
    479             //(hierarchy) output array:           Optional output vector, containing information about the image topology.
    480             //https://docs.opencv.org/3.3.1/d3/dc0/group__imgproc__shape.html#ga17ed9f5d79ae97bd4c7cf18403e1689a
    481 
    482             Imgproc.findContours(test, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
    483 
    484             System.out.println(contours.size());
    485             //Draw contours and record areas
    486             Mat allContoursFound = Mat.zeros(test.size(), CvType.CV_8UC3);
    487             Mat largeContoursFound = allContoursFound.clone() ;
    488             Mat postContoursFound = allContoursFound.clone();
    489             int areaCounter = 0;
    490 
    491             //Have created a preprocess to remove large objects.
    492             //Need to now finalized Classifier, re try area detection.
    493             //Paths to take - rectangle boxes around detected contours over threshold (area or perimeter)
    494             //Just use area and periemter to determine if sheet music
    495             //Discuss with david before weekend perhaps?
    496 
    497             Imgproc.drawContours(allContoursFound, contours, -1, new Scalar(0, 255, 0), 1); //USES LINE_8
    498             for (int i = 0; i < contours.size(); i++) {
    499                 double area = Imgproc.contourArea(contours.get(i));
    500                 if(area > 100) {
    501                     //System.out.println("AREA: " + area);
    502                     Imgproc.drawContours(largeContoursFound, contours, i, new Scalar(255, 0, 0), FILLED);
    503                     //create list of large coutours found
    504                     largeContours.add(contours.get(i));
    505                 }
    506             }
    507 
    508             //*****************************************************************
    509             //Circles and centres on processed images
    510             //*****************************************************************
    511 
    512             //Init arrays
    513             Mat circleOutput = allContoursFound.clone();
    514             MatOfPoint2f[] contoursPoly  = new MatOfPoint2f[largeContours.size()];
    515             Point[] centers = new Point[largeContours.size()];
    516             float[][] radius = new float[largeContours.size()][1];
    517 
    518             //Fill arrays
    519             for (int i = 0; i < largeContours.size(); i++) {
    520                 contoursPoly[i] = new MatOfPoint2f();
    521                 Imgproc.approxPolyDP(new MatOfPoint2f(largeContours.get(i).toArray()), contoursPoly[i], 1, true);
    522                 centers[i] = new Point();
    523                 Imgproc.minEnclosingCircle(contoursPoly[i], centers[i], radius[i]);
    524 
    525             }
    526             //Draw circle for each large contour
    527             for (int i = 0; i < largeContours.size(); i++) {
    528                 Imgproc.circle(circleOutput, centers[i], (int) radius[i][0],new Scalar(255, 0, 0), 1);
    529             }
    530 
    531 
    532             //********************************************************************************
    533             //Centroids - Everything must be to scale
    534             //********************************************************************************
    535 
    536             ArrayList<Moments> mu = new ArrayList<Moments>(largeContours.size());
    537             Mat centreOutput = Mat.zeros(largeContoursFound.size(), CvType.CV_8UC3);
    538             for (int i = 0; i < largeContours.size(); i++) {
    539                 mu.add(i, Imgproc.moments(largeContours.get(i), false));
    540                 Moments p = mu.get(i);
    541                 int x = (int) (p.get_m10() / p.get_m00());
    542                 int y = (int) (p.get_m01() / p.get_m00());
    543                 Imgproc.circle(centreOutput, new Point(x, y), 4, new Scalar(255, 255, 255), 30);
    544             }
    545 
    546             //***********************************************
    547             //PostProcessing - Morphology Classifier
    548             //  Use dilation to "Connect the dots"
    549             //  Testing showed the centroids were clustered together
    550             //  Then use area or perimeter as a classifier filter
    551             //***********************************************
    552 
    553             Mat postDilate = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(150,15));
    554             Imgproc.dilate(centreOutput,centreOutput,postDilate);
    555 
    556             Mat postClose = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,4)); //4,4
    557             Imgproc.morphologyEx(centreOutput, centreOutput, Imgproc.MORPH_CLOSE, postClose);
    558 
    559             Mat postDenoize = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(100,100));
    560             Imgproc.morphologyEx(centreOutput,centreOutput, Imgproc.MORPH_OPEN, postDenoize);
    561 
    562             //Find area
    563             Mat centreOutputGrey = new Mat();
    564             Imgproc.cvtColor(centreOutput, centreOutputGrey, Imgproc.COLOR_RGB2GRAY);
    565             Imgproc.findContours(centreOutputGrey, postContours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
    566 
    567             for (int i = 0; i < postContours.size(); i++) {
    568                 double area = Imgproc.contourArea(postContours.get(i));
    569                 if(area > THRESHOLD_AREA_SIZE) {
    570                     //System.err.println("POST AREA: " + area);
    571                     Imgproc.drawContours(postContoursFound, postContours, i, new Scalar(0, 255, 0), FILLED);
    572                     areaCounter++;
    573                 }
    574             }
    575             //Classifier Calculation
    576             if(areaCounter >= THRESHOLD_AREA_COUNT){
    577                 returnVariables.setBoolean(true);
    578                 returnVariables.setInteger(areaCounter);
    579             }
    580         }
    581         catch(Exception e){
    582             System.err.println(e);
    583         }
    584         return returnVariables;
    585     }
    586     private static Pair Algorithm_Morphology(String filename){
    587         System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
    588         Pair returnVariables = new Pair();
    589         int areaCounter=0;
    590433        Mat original = Imgcodecs.imread(filename, Imgcodecs.IMREAD_GRAYSCALE);
    591         Mat binarizedOriginal = original.clone();
    592         Imgproc.adaptiveThreshold(original, binarizedOriginal,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 15, THRESHOLD_C);
     434        Mat binarized = original.clone();
     435        int blockSize = binarized.height()/3;   
     436        while (!(blockSize % 2 == 1 && blockSize > 1)) {
     437            blockSize++;
     438        }
     439        System.out.println("Width: " + binarized.width() + " Height: " + binarized.height());
     440        //************************
     441        //Argument Setup
     442        //************************
     443        double divisor;
     444        switch (borderIgnoreAmount){
     445            case 5:
     446                divisor = 40;
     447                break;
     448            case 10:
     449                divisor = 20;
     450                break;
     451            case 20:
     452                divisor = 10;
     453                break;
     454            case 40:
     455                divisor = 5;
     456                break;
     457            case -1:
     458                divisor = -1;
     459                break;
     460            default: divisor = -1;
     461        }
     462        if(divisor != -1 ) {
     463            Mat mid = new Mat();
     464            Imgproc.adaptiveThreshold(original, mid,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, blockSize, THRESHOLD_C);
     465            //Remove borders from Mat
     466            //Get width and
     467            double maxX = mid.size().width;
     468            double maxY = mid.size().height;
     469            Point cp1 = new Point(maxX / divisor, maxY / divisor);
     470            Point cp2 = new Point(maxX - cp1.x, maxY - cp1.y);
     471            Rect rectCrop = new Rect(cp1, cp2);
     472            binarized = mid.submat(rectCrop);
     473        }
     474        else{Imgproc.adaptiveThreshold(original, binarized,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, blockSize, THRESHOLD_C);}
     475        System.out.println("Width: " + binarized.width() + " Height: " + binarized.height());
    593476        try{
    594477            //************************************
    595478            //1. Large Object Remover
    596479            //************************************
    597             Mat srcLOR = binarizedOriginal.clone();
     480            Mat srcLOR = binarized.clone();
    598481            Mat maskLOR = new Mat();
    599482            Mat dstLOR = new Mat();
    … …  
    614497            srcLOR.copyTo(dstLOR, maskLOR);
    615498
    616             Mat base = binarizedOriginal.clone();
     499            Mat base = binarized.clone();
    617500            //***********************************
    618501            //3. Isolate straight lines
    … …  
    872755        }
    873756    }
     757
     758    //******************
     759    //OLD FUNCTIONS
     760    //******************
     761    private static Pair Algorithm_MorphologyOLD2(String filename){
     762
     763        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
     764        Boolean isSheetMusic = null;
     765        Pair returnVariables = new Pair();
     766        int areaCounter = 0;
     767        try{
     768            Mat edgesDetectedRGB = new Mat();
     769            Mat original = Imgcodecs.imread(filename, Imgcodecs.IMREAD_GRAYSCALE);
     770
     771            System.out.println("Width: " + original.width() + " Height: " + original.height());
     772
     773            ArrayList<MatOfPoint> contours = new ArrayList<MatOfPoint>();
     774            Mat hierarchy = new Mat();
     775
     776            //Thresholds
     777            Imgproc.adaptiveThreshold(original, original,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 15, THRESHOLD_C);
     778            Mat processed = original.clone();
     779            //Morphological Processing
     780            Mat kernelErode = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,1));
     781            Imgproc.erode(processed,processed,kernelErode);
     782
     783            Mat kernelDilate = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(20,3));
     784            Imgproc.dilate(processed,processed,kernelDilate);
     785
     786            Mat kernelOpening = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(4,4));
     787            Imgproc.morphologyEx(processed, processed, Imgproc.MORPH_CLOSE, kernelOpening);
     788
     789            Mat kernelErode02 = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(8,8));
     790            Imgproc.erode(processed,processed,kernelErode02);
     791
     792            //Detect contours
     793            Imgproc.findContours(processed, contours, hierarchy, Imgproc.RETR_TREE, Imgproc.CHAIN_APPROX_SIMPLE);
     794
     795            //Record areas
     796            for (int i = 0; i < contours.size(); i++) {
     797                double area = Imgproc.contourArea(contours.get(i));
     798                System.out.println("AREA: " + area);
     799                //Check if area detected meets threshold
     800                if(area > THRESHOLD_AREA_SIZE) {
     801                    areaCounter++;
     802
     803                }
     804            }
     805            System.out.println("areacount = " + areaCounter);
     806
     807            //Calculates if sheet music or not
     808            if(areaCounter >= THRESHOLD_AREA_COUNT){
     809                isSheetMusic = true;
     810                returnVariables.setBoolean(isSheetMusic);
     811                returnVariables.setInteger(areaCounter);
     812            }
     813        }
     814        catch(Exception e){
     815            System.err.println(e);
     816        }
     817        return returnVariables;
     818    }
     819    private static Pair Algorithm_MorphologyOLD(String filename){
     820
     821        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
     822        Boolean isSheetMusic = null;
     823        Pair returnVariables = new Pair();
     824        int FILLED = -1;
     825        try{
     826            Mat original1 = Imgcodecs.imread(filename, Imgcodecs.IMREAD_GRAYSCALE);
     827            System.out.println("Width: " + original1.width() + " Height: " + original1.height());
     828            Mat original = original1.clone();
     829            Imgproc.adaptiveThreshold(original1, original,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 15, THRESHOLD_C);
     830            Mat test = original.clone();
     831            //************************************
     832            //Large Object Removal
     833            //************************************
     834            Mat srcLOR = original.clone();
     835            Mat maskLOR = new Mat();
     836            Mat dstLOR = new Mat();
     837
     838            //denoize
     839            Mat denoize = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(5,5));
     840            Imgproc.morphologyEx(srcLOR,maskLOR, Imgproc.MORPH_OPEN, denoize);
     841
     842            //close up gaps
     843            Mat gapCloser = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(5,5));
     844            Imgproc.morphologyEx(maskLOR,maskLOR,Imgproc.MORPH_CLOSE, gapCloser);
     845
     846            //Isolate large items
     847            Mat isolateLarge = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(8, 8));
     848            Imgproc.morphologyEx(maskLOR,maskLOR,Imgproc.MORPH_OPEN, isolateLarge);
     849
     850            //Remove large items from image
     851            Core.bitwise_not(maskLOR,maskLOR);
     852            srcLOR.copyTo(dstLOR, maskLOR);
     853
     854            //****************************************
     855            //Small object removal (SOR)
     856            //****************************************
     857
     858            Mat srcSOR = dstLOR.clone();
     859            Mat maskSOR = new Mat();
     860            Mat dstSOR = new Mat();
     861
     862            Mat startSOR =Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(7,7));
     863            Imgproc.morphologyEx(srcSOR,maskSOR, Imgproc.MORPH_OPEN, startSOR);
     864
     865            Mat highlightSmall = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(7,7));
     866            Imgproc.dilate(maskSOR, maskSOR, highlightSmall);
     867
     868
     869/*              Mat isolateSmall =Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,10));
     870                Imgproc.morphologyEx(maskSOR,maskSOR,Imgproc.MORPH_CLOSE, isolateSmall);
     871                imageViewer("13 isolate small - mask", maskSOR);
     872*/
     873
     874            //Remove small items from image
     875            Core.bitwise_not(maskSOR, maskSOR);
     876            srcSOR.copyTo(dstSOR, maskSOR);
     877
     878
     879            //****************************************
     880            //start staff line detection
     881            //****************************************
     882
     883            Mat kernelErode = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(15,2)); //10,2
     884            Imgproc.erode(dstSOR,test,kernelErode);
     885
     886
     887            Mat kernelDilate = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,4)); //20,3
     888            Imgproc.dilate(test,test,kernelDilate);
     889
     890
     891            Mat kernelClose = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,4)); //4,4
     892            Imgproc.morphologyEx(test, test, Imgproc.MORPH_CLOSE, kernelClose);
     893
     894
     895
     896            Mat kernelErode02 = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,4)); //10,1
     897            Imgproc.erode(test,test,kernelErode02);
     898
     899
     900            //********************************************************************************
     901            //DETECT OUTLINE AND FIND AREA OF THESE LINES.
     902            //********************************************************************************
     903            ArrayList<MatOfPoint> contours = new ArrayList<MatOfPoint>();
     904            ArrayList<MatOfPoint> largeContours = new ArrayList<MatOfPoint>();
     905            ArrayList<MatOfPoint> postContours = new ArrayList<MatOfPoint>();
     906            Mat hierarchy = new Mat();
     907
     908            //PARAMETERS: input image, output array of arrays, output array, contour retrieval mode, contour approximation method.
     909            //(contours)  output array of arrays: Detected contours. Each contour is stored as a vector of points
     910            //(hierarchy) output array:           Optional output vector, containing information about the image topology.
     911            //https://docs.opencv.org/3.3.1/d3/dc0/group__imgproc__shape.html#ga17ed9f5d79ae97bd4c7cf18403e1689a
     912
     913            Imgproc.findContours(test, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
     914
     915            System.out.println(contours.size());
     916            //Draw contours and record areas
     917            Mat allContoursFound = Mat.zeros(test.size(), CvType.CV_8UC3);
     918            Mat largeContoursFound = allContoursFound.clone() ;
     919            Mat postContoursFound = allContoursFound.clone();
     920            int areaCounter = 0;
     921
     922            //Have created a preprocess to remove large objects.
     923            //Need to now finalized Classifier, re try area detection.
     924            //Paths to take - rectangle boxes around detected contours over threshold (area or perimeter)
     925            //Just use area and periemter to determine if sheet music
     926            //Discuss with david before weekend perhaps?
     927
     928            Imgproc.drawContours(allContoursFound, contours, -1, new Scalar(0, 255, 0), 1); //USES LINE_8
     929            for (int i = 0; i < contours.size(); i++) {
     930                double area = Imgproc.contourArea(contours.get(i));
     931                if(area > 100) {
     932                    //System.out.println("AREA: " + area);
     933                    Imgproc.drawContours(largeContoursFound, contours, i, new Scalar(255, 0, 0), FILLED);
     934                    //create list of large coutours found
     935                    largeContours.add(contours.get(i));
     936                }
     937            }
     938
     939            //*****************************************************************
     940            //Circles and centres on processed images
     941            //*****************************************************************
     942
     943            //Init arrays
     944            Mat circleOutput = allContoursFound.clone();
     945            MatOfPoint2f[] contoursPoly  = new MatOfPoint2f[largeContours.size()];
     946            Point[] centers = new Point[largeContours.size()];
     947            float[][] radius = new float[largeContours.size()][1];
     948
     949            //Fill arrays
     950            for (int i = 0; i < largeContours.size(); i++) {
     951                contoursPoly[i] = new MatOfPoint2f();
     952                Imgproc.approxPolyDP(new MatOfPoint2f(largeContours.get(i).toArray()), contoursPoly[i], 1, true);
     953                centers[i] = new Point();
     954                Imgproc.minEnclosingCircle(contoursPoly[i], centers[i], radius[i]);
     955
     956            }
     957            //Draw circle for each large contour
     958            for (int i = 0; i < largeContours.size(); i++) {
     959                Imgproc.circle(circleOutput, centers[i], (int) radius[i][0],new Scalar(255, 0, 0), 1);
     960            }
     961
     962
     963            //********************************************************************************
     964            //Centroids - Everything must be to scale
     965            //********************************************************************************
     966
     967            ArrayList<Moments> mu = new ArrayList<Moments>(largeContours.size());
     968            Mat centreOutput = Mat.zeros(largeContoursFound.size(), CvType.CV_8UC3);
     969            for (int i = 0; i < largeContours.size(); i++) {
     970                mu.add(i, Imgproc.moments(largeContours.get(i), false));
     971                Moments p = mu.get(i);
     972                int x = (int) (p.get_m10() / p.get_m00());
     973                int y = (int) (p.get_m01() / p.get_m00());
     974                Imgproc.circle(centreOutput, new Point(x, y), 4, new Scalar(255, 255, 255), 30);
     975            }
     976
     977            //***********************************************
     978            //PostProcessing - Morphology Classifier
     979            //  Use dilation to "Connect the dots"
     980            //  Testing showed the centroids were clustered together
     981            //  Then use area or perimeter as a classifier filter
     982            //***********************************************
     983
     984            Mat postDilate = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(150,15));
     985            Imgproc.dilate(centreOutput,centreOutput,postDilate);
     986
     987            Mat postClose = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,4)); //4,4
     988            Imgproc.morphologyEx(centreOutput, centreOutput, Imgproc.MORPH_CLOSE, postClose);
     989
     990            Mat postDenoize = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(100,100));
     991            Imgproc.morphologyEx(centreOutput,centreOutput, Imgproc.MORPH_OPEN, postDenoize);
     992
     993            //Find area
     994            Mat centreOutputGrey = new Mat();
     995            Imgproc.cvtColor(centreOutput, centreOutputGrey, Imgproc.COLOR_RGB2GRAY);
     996            Imgproc.findContours(centreOutputGrey, postContours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
     997
     998            for (int i = 0; i < postContours.size(); i++) {
     999                double area = Imgproc.contourArea(postContours.get(i));
     1000                if(area > THRESHOLD_AREA_SIZE) {
     1001                    //System.err.println("POST AREA: " + area);
     1002                    Imgproc.drawContours(postContoursFound, postContours, i, new Scalar(0, 255, 0), FILLED);
     1003                    areaCounter++;
     1004                }
     1005            }
     1006            //Classifier Calculation
     1007            if(areaCounter >= THRESHOLD_AREA_COUNT){
     1008                returnVariables.setBoolean(true);
     1009                returnVariables.setInteger(areaCounter);
     1010            }
     1011        }
     1012        catch(Exception e){
     1013            System.err.println(e);
     1014        }
     1015        return returnVariables;
     1016    }
    8741017}

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

    r33458 r33589  
    11#!/bin/bash
     2#https://www.cyberciti.biz/faq/linux-unix-shell-check-if-directory-empty/
    23
    3 if [ $# != 4 ] ; then
    4   echo "Usage: ./testClassifier.sh folder/file classifier_type output_filename  type(-a:all -test:100 -single:1)" 1>&2
     4if [ $# -lt 4 ] ; then
     5  echo "Usage: ./testClassifier.sh  <folder/file> <classifier_type> <output_filename> <amount>" 1>&2
     6  echo "<amount> options: -a (all) -test (100) -single (1)"
    57  exit 1
    68fi
    7 
     9if [ $# -eq 5 ] ; then
     10    borderIgnore=$5
     11fi
    812source=$1
    913classifier_type=$2
    1014output=$3
     15
     16
     17
    1118i=0
    1219j=0
    … …  
    6875        javac -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar -Xlint javaClassifierComparison.java;
    6976        #Runs javaImageClassifier on 10 images of each
    70         for file in $source/SheetMusic/*.png; do
    71             if [ $i -lt 100 ] ; then
    72                 echo $file
    73                 echo $output
    74                 echo $i
    75                 echo ""
    76                 java -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar:. -Djava.library.path=/Scratch/cpb16/opencv-3.4.2/build/lib/  javaClassifierComparison $file $classifier_type $output
    77                 i=$[$i+1]       
    78             fi
    79         done
    80         for file in $source/NotSheetMusic/*.png; do
    81             if [ $j -lt 100 ] ; then
    82                 echo $file
    83                 echo $output
    84                 echo $j
    85                 echo ""
    86                 java -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar:. -Djava.library.path=/Scratch/cpb16/opencv-3.4.2/build/lib/  javaClassifierComparison $file $classifier_type $output
    87                 j=$[$j+1]       
    88             fi
    89         done
     77        if [ "$(ls -A $source/SheetMusic)" ]; then
     78            #Not Empty
     79            for file in $source/SheetMusic/*.png; do
     80                if [ $i -lt 10 ] ; then
     81                    echo $file
     82                    echo $output
     83                    echo $i
     84                    echo ""
     85                    java -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar:. -Djava.library.path=/Scratch/cpb16/opencv-3.4.2/build/lib/  javaClassifierComparison $file $classifier_type $output
     86                    i=$[$i+1]       
     87                fi
     88            done
     89        else
     90            echo "$source/SheetMusic is Empty"
     91        fi
     92       
     93        if [ "$(ls -A $source/NotSheetMusic)" ]; then
     94            #Not Empty
     95            for file in $source/NotSheetMusic/*.png; do
     96                if [ $j -lt 10 ] ; then
     97                    echo $file
     98                    echo $output
     99                    echo $j
     100                    echo ""
     101                    java -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar:. -Djava.library.path=/Scratch/cpb16/opencv-3.4.2/build/lib/  javaClassifierComparison $file $classifier_type $output
     102                    j=$[$j+1]       
     103                fi
     104            done
     105        else
     106            echo "$source/NotSheetMusic is Empty"
     107        fi
    90108        ;;
    91109#________________________________________________________________________________________________________________________________________________________________________________________
    … …  
    107125        echo $output
    108126        echo $classifier_type
    109         java -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar:. -Djava.library.path=/Scratch/cpb16/opencv-3.4.2/build/lib/  javaClassifierComparison $source $classifier_type $output
     127        echo $borderIgnore
     128        java -cp /Scratch/cpb16/opencv-3.4.2/build/bin/opencv-342.jar:. -Djava.library.path=/Scratch/cpb16/opencv-3.4.2/build/lib/  javaClassifierComparison $source $classifier_type $output $borderIgnore
    110129        ;; 
    111130esac       
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