Ejemplo n.º 1
0
    def isProperDetector(self,
                         configString,
                         detectorData=None,
                         detectorProperties=None):
        '''Try to create or use the specified detector.
        Create a test runset and call the detector with it.
        Check for proper handling of intentional problems.
        '''
        detector = easy.getDetector(configString)
        if not detector:
            print("cannot find or connect to detector")
            return False

        # make a runset including erroneous files
        rs = cvac.RunSet()
        file_normal = easy.getLabelable("testImg/italia.jpg")
        file_notexist = easy.getLabelable("testImg/doesnotexist.jpg")
        file_notconvertible = easy.getLabelable("testImg/notconvertible.xxx")
        easy.addToRunSet(rs, file_normal,
                         cvac.Purpose(cvac.PurposeType.UNPURPOSED, -1))
        easy.addToRunSet(rs, file_notexist,
                         cvac.Purpose(cvac.PurposeType.UNPURPOSED, -1))
        easy.addToRunSet(rs, file_notconvertible,
                         cvac.Purpose(cvac.PurposeType.UNPURPOSED, -1))
        results = easy.detect(detector, detectorData, rs, detectorProperties)

        # check for number of results: must be the same as runset length
        nRunset = 0
        for pur in rs.purposedLists:
            nRunset += len(pur.labeledArtifacts)
        if len(results) != nRunset:
            print("incorrect result set size")
            return False

        # check that the doesnotexist file caused hasLabel==False
        found = False
        for lbl in results:
            # for an erroneous file
            if lbl.original.sub == img_notexist.sub or lbl.original.sub == img_notconvertible.sub:
                for foundlbl in lbl.foundLabels:
                    if foundlbl.lab.hasLabel:
                        print(
                            "Incorrectly assigned label for an erroneous file."
                        )
                        return False
            else:  #for a normal file
                for foundlbl in lbl.foundLabels:
                    # confidence 0..1
                    if not 0.0 <= foundlbl.confidence and foundlbl.confidence <= 1.0:
                        print("Label confidence out of bounds ({0}).".format(
                            foundlbl.confidence))
                        return False
                    # check that either this was not assigned a label, or the
                    # assigned label is of proper syntax
                    if foundlbl.lab.hasLabel:
                        if not isinstance(foundlbl.lab.name, str):
                            print("Label name must be of string type.")
                            return False
        return True
Ejemplo n.º 2
0
 def isProperDetector( self, configString, detectorData=None, detectorProperties=None ):
     '''Try to create or use the specified detector.
     Create a test runset and call the detector with it.
     Check for proper handling of intentional problems.
     '''
     detector = easy.getDetector( configString )
     if not detector:
         print("cannot find or connect to detector")
         return False
     
     # make a runset including erroneous files
     rs = cvac.RunSet()
     file_normal = easy.getLabelable( "testImg/italia.jpg" )
     file_notexist = easy.getLabelable( "testImg/doesnotexist.jpg" )
     file_notconvertible = easy.getLabelable( "testImg/notconvertible.xxx")
     easy.addToRunSet( rs, file_normal, cvac.Purpose( cvac.PurposeType.UNPURPOSED, -1 ))
     easy.addToRunSet( rs, file_notexist, cvac.Purpose( cvac.PurposeType.UNPURPOSED, -1 ))
     easy.addToRunSet( rs, file_notconvertible, cvac.Purpose( cvac.PurposeType.UNPURPOSED, -1 ) )
     results = easy.detect( detector, detectorData, rs, detectorProperties )
     
     # check for number of results: must be the same as runset length
     nRunset = 0 
     for pur in rs.purposedLists:
         nRunset += len(pur.labeledArtifacts)
     if len(results)!=nRunset:
         print("incorrect result set size")
         return False
     
     # check that the doesnotexist file caused hasLabel==False
     found=False
     for lbl in results:
         # for an erroneous file
         if lbl.original.sub==img_notexist.sub or lbl.original.sub==img_notconvertible.sub:
             for foundlbl in lbl.foundLabels:
                 if foundlbl.lab.hasLabel:
                     print("Incorrectly assigned label for an erroneous file.")
                     return False
         else:   #for a normal file
             for foundlbl in lbl.foundLabels:
                 # confidence 0..1
                 if not 0.0<=foundlbl.confidence and foundlbl.confidence<=1.0:
                     print("Label confidence out of bounds ({0}).".format(foundlbl.confidence))
                     return False
                 # check that either this was not assigned a label, or the
                 # assigned label is of proper syntax
                 if foundlbl.lab.hasLabel:
                     if not isinstance(foundlbl.lab.name, str):
                         print("Label name must be of string type.")
                         return False
     return True
Ejemplo n.º 3
0
def evaluate( contender, runset, printVerbose=False ):
    if type(runset) is dict and not runset['runset'] is None\
        and isinstance(runset['runset'], cvac.RunSet):
        runset = runset['runset']
    if not runset or not isinstance(runset, cvac.RunSet) or not runset.purposedLists:
        raise RuntimeError("no proper runset")
    evalset = runset

    print( "---- evaluation:" )
    easy.printRunSetInfo( evalset, printArtifacts=printVerbose )
    detector = contender.getDetector()
    detections = easy.detect( detector, contender.detectorData, evalset,
                              detectorProperties=contender.detectorProps )
    ct = getConfusionTable( detections, origSet=evalset, foundMap=contender.foundMap )

    # create result structure
    r = EvaluationResult( 0, ct[0], nores=ct[1],
                          detail=None, name=contender.name )
    return r
Ejemplo n.º 4
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    def test_cvacdatadir(self):
        print("testing cvac data dir")
        if sys.platform == 'win32':
            datadir = os.getenv("CVAC_DATADIR", None)
            datadir = datadir.replace("/", "\\")
            easy.CVAC_DataDir = datadir
            print("Testing using back slashes")
            print("Using CVAC_DATADIR as " + datadir)
            testset = []
            easy.misc.searchDir(testset,
                                datadir + '\\testImg',
                                recursive=True,
                                video=False,
                                image=True)
            runset = cvac.RunSet()
            easy.addToRunSet(runset, testset, 'pos')
            modelfile = 'detectors/bowUSKOCA.zip'
            detector = easy.getDetector("BOW_Detector")
            props = easy.getDetectorProperties(detector)
            props.verbosity = 3
            results = easy.detect(detector,
                                  modelfile,
                                  runset,
                                  detectorProperties=props)
            easy.printResults(results)
        else:
            print("Skipping back slash test on this platform")

        #run it again with all forward slashes
        datadir = os.getenv("CVAC_DATADIR", None)
        datadir = datadir.replace("\\", "/")
        print("Testing using all forward slashes")
        print("Using CVAC_DATADIR as " + datadir)
        easy.CVAC_DataDir = datadir
        testset = []
        easy.misc.searchDir(testset,
                            datadir + '/testImg',
                            recursive=True,
                            video=False,
                            image=True)
        runset = cvac.RunSet()
        easy.addToRunSet(runset, testset, 'pos')
        modelfile = 'detectors/bowUSKOCA.zip'
        detector = easy.getDetector("BOW_Detector")
        props = easy.getDetectorProperties(detector)
        props.verbosity = 3
        results = easy.detect(detector,
                              modelfile,
                              runset,
                              detectorProperties=props)
        easy.printResults(results)

        #run it for forward slashes and relative path
        origDir = datadir
        curDir = os.getcwd()
        curDir = curDir.replace("\\", "/")
        datadir = datadir.replace("\\", "/")
        print("using relative paths for " + curDir + " in  data dir " +
              datadir)
        if datadir.startswith(curDir):
            datadir = datadir[len(curDir) + 1:]
            easy.CVAC_DataDir = datadir
            print("Using CVAC_DataDir as " + datadir)
            testset = []
            easy.misc.searchDir(testset,
                                origDir + '/testImg',
                                recursive=True,
                                video=False,
                                image=True)
            runset = cvac.RunSet()
            easy.addToRunSet(runset, testset, 'pos')
            modelfile = origDir + '/detectors/bowUSKOCA.zip'
            detector = easy.getDetector("BOW_Detector")
            props = easy.getDetectorProperties(detector)
            props.verbosity = 3
            results = easy.detect(detector,
                                  modelfile,
                                  runset,
                                  detectorProperties=props)
            easy.printResults(results)
        else:
            RuntimeError("Bad datadir")
Ejemplo n.º 5
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def doDetect():
    easy.detect( detector, modelfile, runset, callbackRecv = callbackRecv, detectorProperties = props)
Ejemplo n.º 6
0
'''

import easy

# obtain a reference to a Bag of Words (BOW) detector
detector = easy.getDetector( "BOW_Detector" )

# a model for distinguishing Canadian, Korean, and US flags,
# trained previously with a BOW-specific trainer and stored in a file
modelfile = "detectors/bowUSKOCA.zip"

# a test image; the location is relative to the "CVAC.DataDir"
imgfile = "testImg/TestKrFlag.jpg"

# apply the detector type, using the model and testing the imgfile
results = easy.detect( detector, modelfile, imgfile )

# you can print the results with Easy!'s pretty-printer;
# we will explain the meaning of the "unlabeled" and the number
# of the found label later.
# or you can always print variables directly (uncomment the next line):
# print("{0}".format( results ))
print("------- Bag of Words results for flags: -------")
easy.printResults( results )

# let's try a different model, pre-trained not for
# flags but for various corporate logos
print("------- Bag of Words results for corporate logos: -------")
modelfile = "detectors/bowCorporateLogoModel.zip"
imgfile = "corporate_logos/shell/shell2.png"
results = easy.detect( detector, modelfile, imgfile )
Ejemplo n.º 7
0
    rocZip = easy.makeROCdata(rocData_optimal)
    
    '''
    Extract only optimal ROC points
    '''
    detector = easy.getDetector( strDetector )
    detectorProps = easy.getDetectorProperties(detector)
    #specify either a desired falseAlarmRate or recall, but not both:
    priority = "recall"
    if priority=="falseAlarmRate":
        detectorProps.falseAlarmRate = 0.01 #x-axis
        print("falseAlarmRate = {0}".format(detectorProps.falseAlarmRate))
    else:
        detectorProps.recall = 0.90 #y-axis
        print("recall = {0}".format(detectorProps.recall))
    results = easy.detect( detector, rocZip, runset, detectorProperties = detectorProps)
    easy.printResults( results )
else:
    ###############################################################
    # Without background data
    '''
    Execute jousting for generating ROC points
    '''
    contenders = []
    for nWord in list_nWord:
        c1 = evaluate.Contender("bowROC_oneclass_"+str(nWord))
        c1.trainerString = strTrainer
        c1.detectorString = strDetector
        trainer = easy.getTrainer(c1.trainerString)
        trainerProps = easy.getTrainerProperties(trainer)
        trainerProps.props["RejectClassStrategy"] = "ignore"    
Ejemplo n.º 8
0
    rocZip = easy.makeROCdata(rocData_optimal)
    '''
    Extract only optimal ROC points
    '''
    detector = easy.getDetector(strDetector)
    detectorProps = easy.getDetectorProperties(detector)
    #specify either a desired falseAlarmRate or recall, but not both:
    priority = "recall"
    if priority == "falseAlarmRate":
        detectorProps.falseAlarmRate = 0.01  #x-axis
        print("falseAlarmRate = {0}".format(detectorProps.falseAlarmRate))
    else:
        detectorProps.recall = 0.90  #y-axis
        print("recall = {0}".format(detectorProps.recall))
    results = easy.detect(detector,
                          rocZip,
                          runset,
                          detectorProperties=detectorProps)
    easy.printResults(results)
else:
    ###############################################################
    # Without background data
    '''
    Execute jousting for generating ROC points
    '''
    contenders = []
    for nWord in list_nWord:
        c1 = evaluate.Contender("bowROC_oneclass_" + str(nWord))
        c1.trainerString = strTrainer
        c1.detectorString = strDetector
        trainer = easy.getTrainer(c1.trainerString)
        trainerProps = easy.getTrainerProperties(trainer)
Ejemplo n.º 9
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def crossValidate( contender, runset, folds=10, printVerbose=False ):
    '''Returns summary statistics tp, fp, tn, fn, recall, trueNegRate,
    and a detailed matrix of results with one row for
    each fold, and one column each for true positive, false
    positive, true negative, and false negative counts'''

    # sanity checks:
    # only positive and negative purposes,
    # count number of entries for each purpose
    runset_pos = asList( runset, purpose="pos" )
    runset_neg = asList( runset, purpose="neg" )
    num_items = ( len(runset_pos), len(runset_neg) )
    # check that there are no other purposes
    all_items = len( asList( runset ) )
    if sum(num_items)!=all_items:
        raise RuntimeError("crossValidate can only handle Positive and Negative purposes")
    if min(num_items)<2:
        raise RuntimeError("need more than 1 labeled item per purpose to cross validate")

    # make sure there are enough items for xval to make sense
    if folds>min(num_items):
        print("warning: cannot do "+folds+"-fold validation with only "+str(num_items)+" labeled items")
        folds = min(num_items)

    # calculate the size of the training and evaluation sets.
    # if the number of labeled items in the runset divided
    # by the number of folds isn't an even
    # division, use more items for the evaluation
    chunksize = (int(math.floor( num_items[0]/folds )), int(math.floor( num_items[1]/folds )))
    trainsize = (chunksize[0] * (folds-1), chunksize[1] * (folds-1))
    evalsize  = (num_items[0]-trainsize[0], num_items[1]-trainsize[1])
    print( "Will perform a {0}-fold cross-validation with {1} training samples and "
           "{2} evaluation samples".format( folds, trainsize, evalsize ) )

    # randomize the order of the elements in the runset, once and for all folds
    rndidx = ( range( num_items[0] ), range( num_items[1] ) )
    random.shuffle( rndidx[0] ) # shuffles items in place
    random.shuffle( rndidx[1] ) # shuffles items in place

    #confusionTables = numpy.empty( [folds, 5], dtype=int )
    confusionTables = []
    
    for fold in range( folds ):
        # split the runset
        trainset, evalset = splitRunSet( runset_pos, runset_neg, fold, chunksize, evalsize, rndidx )
        print( "-------- fold number {0} --------".format(fold) )

        # training
        print( "---- training:" )
        easy.printRunSetInfo( trainset, printArtifacts=printVerbose )
        trainer = contender.getTrainer()
        
        model = easy.train( trainer, trainset,
                            trainerProperties=contender.trainerProps)

        # detection
        print( "---- evaluation:" )
        easy.printRunSetInfo( evalset, printArtifacts=printVerbose )
        detector = contender.getDetector()
        detections = easy.detect( detector, model, evalset,
                                  detectorProperties=contender.detectorProps )
        confusionTables.append( \
            getConfusionTable( detections, origSet=evalset, foundMap=contender.foundMap ))

    # calculate statistics of our tuple TestResult,nores
    
    sumTestResult = TestResult()
    sumNoRes = 0;
    for entry in confusionTables:
        sumTestResult.tp += entry[0].tp
        sumTestResult.tn += entry[0].tn
        sumTestResult.fp += entry[0].fp
        sumTestResult.fn += entry[0].fn
        sumNoRes += entry[1]
    r = EvaluationResult(folds, sumTestResult, sumNoRes, detail=None, name=contender.name)
    return r
Ejemplo n.º 10
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# Make sure all files in the RunSet are available on the remote site;
# it is the client's responsibility to upload them if not.
# The putResult contains information about which files were actually transferred.
#
fileserver = easy.getFileServer( "FileService:default -p 10110 " + host )
putResult = easy.putAllFiles( fileserver, rs1 )
modelfile = "detectors/haarcascade_frontalface_alt.xml"
if not fileserver.exists( easy.getCvacPath(modelfile) ):
    easy.putFile( fileserver, easy.getCvacPath(modelfile) )

#
# detect remotely: note the host specification
#
print("------- Remote detection, local result display: -------")
detector = easy.getDetector( "OpenCVCascadeDetector:default -p 10102 "+host )
results = easy.detect( detector, modelfile, rs1 )
easy.printResults( results )

#
# Example 2:
# Train on a remote machine, obtain the model file, and test locally.
# Assume the files are on the remote machine, or transfer with putAllFiles.
#
trainer = easy.getTrainer( "bowTrain:default -p 10103 "+ host) # remote
trainset = easy.createRunSet( "trainImg" );
trainedModel = easy.train( trainer, trainset )
easy.getFile( fileserver, trainedModel.file )  # downloads the model from remote
print("{0}".format(trainedModel))
detector = easy.getDetector( "bowTest:default -p 10104" ) # local service
testset = easy.createRunSet("testImg","UNPURPOSED"  )
results = easy.detect( detector, trainedModel, testset )
Ejemplo n.º 11
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    def test_cvacdatadir(self):
        print("testing cvac data dir")
        if sys.platform == 'win32':
            datadir = os.getenv("CVAC_DATADIR", None) 
            datadir = datadir.replace("/", "\\")
            easy.CVAC_DataDir = datadir
            print("Testing using back slashes")
            print("Using CVAC_DATADIR as " + datadir)
            testset = []
            easy.misc.searchDir(testset, datadir + '\\testImg', recursive=True, video=False, image=True)
            runset = cvac.RunSet()
            easy.addToRunSet(runset, testset, 'pos')
            modelfile = 'detectors/bowUSKOCA.zip'
            detector = easy.getDetector("BOW_Detector")
            props = easy.getDetectorProperties(detector)
            props.verbosity = 3
            results = easy.detect(detector, modelfile, runset, 
                            detectorProperties=props)
            easy.printResults(results)
        else:
            print("Skipping back slash test on this platform")
        
        #run it again with all forward slashes
        datadir = os.getenv("CVAC_DATADIR", None) 
        datadir = datadir.replace("\\", "/")
        print("Testing using all forward slashes")
        print("Using CVAC_DATADIR as " + datadir)
        easy.CVAC_DataDir = datadir
        testset = []
        easy.misc.searchDir(testset, datadir + '/testImg', recursive=True, video=False, image=True)
        runset = cvac.RunSet()
        easy.addToRunSet(runset, testset, 'pos')
        modelfile = 'detectors/bowUSKOCA.zip'
        detector = easy.getDetector("BOW_Detector")
        props = easy.getDetectorProperties(detector)
        props.verbosity = 3
        results = easy.detect(detector, modelfile, runset,
                               detectorProperties=props)
        easy.printResults(results)

        #run it for forward slashes and relative path
        origDir = datadir
        curDir = os.getcwd()
        curDir = curDir.replace("\\", "/")
        datadir = datadir.replace("\\", "/")
        print("using relative paths for " + curDir + " in  data dir " + datadir)
        if datadir.startswith(curDir):
            datadir = datadir[len(curDir)+1:]
            easy.CVAC_DataDir = datadir
            print("Using CVAC_DataDir as "  + datadir)
            testset = []
            easy.misc.searchDir(testset, origDir + '/testImg', recursive=True, video=False, image=True)
            runset = cvac.RunSet()
            easy.addToRunSet(runset, testset, 'pos')
            modelfile = origDir + '/detectors/bowUSKOCA.zip'
            detector = easy.getDetector("BOW_Detector")
            props = easy.getDetectorProperties(detector)
            props.verbosity = 3
            results = easy.detect(detector, modelfile, runset,
                                   detectorProperties=props)
            easy.printResults(results)
        else:
            RuntimeError("Bad datadir")
Ejemplo n.º 12
0
# pick a subset: all license plates
license_plates = categories['license plate']
print("There are {0} license plate labels.".format( len(license_plates) ))

# another subset: all labels starting with "car"
cars = []
for key in categories.keys():
    if key.startswith("car"):
        cars.append( categories[key] )
print("There are {0} car-related labels.".format( len(cars) ))

# Note that Labels are cached in the CorpusServer, but the corpus currently
# needs to re-mirror if the CorpusServer is restarted because Labels are
# not stored to disk.  Images are stored to disk.

quit()  # done for now

# Train a detector on license plates
trainer = easy.getTrainer( "BOW_Trainer:default -p 10103 ")
trainset = easy.createRunSet( license_plates, "pos" )
easy.printRunSetInfo( trainset )
licenseplateModel = easy.train( trainer, trainset )

# test the license plate detector on the known locations of cars;
# this will only try to detect within the boundaries of cars.
testset = easy.createRunSet( cars, "unpurposed" )
detector = easy.getDetector( "BOW_Detector:default -p 10104" )
results = easy.detect( detector, licenseplateModel, testset )

printResults( results )
Ejemplo n.º 13
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import sys, os

thisPath = os.path.dirname(os.path.abspath(__file__))
srcPath = os.path.abspath(thisPath+"/../../lib/python")
sys.path.append(srcPath)
import paths
import cvac

import easy


#
# First, a teaser for detection:
#
detector = easy.getDetector( "bowTest:default -p 10104" )
results = easy.detect( detector, "detectors/bowUSKOCA.zip", "testImg/TestCaFlag.jpg" )
easy.printResults( results )

#
# Second, a quick way to train a detector.  The resulting model
# can be used in place of the detector above.
#
# TODO: currently breaks because Caltech101 doesn't get extracted as expected
#categories, lablist = easy.getDataSet( "corpus/CvacCorpusTest" )
# categories, lablist = easy.getDataSet( "corpus/Caltech101.properties", createMirror=False )
#easy.printCategoryInfo( categories )
#runset = easy.createRunSet( categories["car_side"] )
#trainer = easy.getTrainer( "bowTrain:default -p 10103" )
#carSideModel = easy.train( trainer, runset )

#
Ejemplo n.º 14
0
#
# Create a training set from one sample each of 9 corporate logos
#
trainset1 = easy.createRunSet("corporate_logos")

# train, round 1
trainer = easy.getTrainer("BOW_Trainer")
model1 = easy.train(trainer, trainset1)

# evaluate the model on a separate test set, images and videos
# in DataDir/testImg
testset1 = easy.createRunSet("testImg", "UNPURPOSED")
easy.printRunSetInfo(testset1)
detector = easy.getDetector("BOW_Detector")
result1 = easy.detect(detector, model1, testset1)
easy.printResults(result1)

# sort the images from the testdata1 folder into subfolders of
# "testresults1" corresponding to the found labels;
# if multiple labels were found per original, consider only
# the label with the highest confidence
easy.sortIntoFolders(result1, outfolder="testresults1", multi="highest")

# Now manually sort through the created testresults1 and move
# _incorrectly_ classified samples into correctly labeled subfolders
# of a new folder "corporate_logos_round2".  Found labels on locations
# that are not one of the 9 logos have to be sorted into a 10th class
# (subfolder), generally called the "reject" class.
#
# We simulate this manual process here.  Note that the new folder needs to be
Ejemplo n.º 15
0
# Ex. for image files in a directory 
tFileSuffixes = ["jpg","bmp"]
directory_images = cvac.PurposedDirectory(cvac.Purpose(cvac.PurposeType.MULTICLASS,1),
                                          cvac.DirectoryPath("testImg"),
                                          tFileSuffixes,
                                          1)
runset.purposedLists.append(directory_images)
 

#===============================================================================
# # If you have video files, you may comment out this part for testing the video files
# # Ex. for video files in a directory 
# tFileSuffixes = ["mpg","mpeg","avi"]
# directory_videos = cvac.PurposedDirectory(cvac.Purpose(cvac.PurposeType.MULTICLASS,1),
#                                           cvac.DirectoryPath("trainVideo"),
#                                           tFileSuffixes,
#                                           1)
# runset.purposedLists.append(directory_videos)
#===============================================================================
 
host = "-h localhost"
detector = easy.getDetector( "RSItrTest_Detector:default -p 10109 " + host )
modelfile = ''  #empty detectorData only for testing
results = easy.detect( detector, modelfile, runset )
#results = easy.detect( detector, trainedModel, runset )
#easy.printResults( results, foundMap=classmap )
easy.printResults( results)

quit()
Ejemplo n.º 16
0
# pick a subset: all license plates
license_plates = categories['license plate']
print("There are {0} license plate labels.".format(len(license_plates)))

# another subset: all labels starting with "car"
cars = []
for key in categories.keys():
    if key.startswith("car"):
        cars.append(categories[key])
print("There are {0} car-related labels.".format(len(cars)))

# Note that Labels are cached in the CorpusServer, but the corpus currently
# needs to re-mirror if the CorpusServer is restarted because Labels are
# not stored to disk.  Images are stored to disk.

quit()  # done for now

# Train a detector on license plates
trainer = easy.getTrainer("BOW_Trainer:default -p 10103 ")
trainset = easy.createRunSet(license_plates, "pos")
easy.printRunSetInfo(trainset)
licenseplateModel = easy.train(trainer, trainset)

# test the license plate detector on the known locations of cars;
# this will only try to detect within the boundaries of cars.
testset = easy.createRunSet(cars, "unpurposed")
detector = easy.getDetector("BOW_Detector:default -p 10104")
results = easy.detect(detector, licenseplateModel, testset)

printResults(results)
Ejemplo n.º 17
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    0, 0)
singleImages.labeledArtifacts.append(cvac.Labelable(0, tlab, tsub))

# Ex. for image files in a directory
tFileSuffixes = ["jpg", "bmp"]
directory_images = cvac.PurposedDirectory(
    cvac.Purpose(cvac.PurposeType.MULTICLASS, 1),
    cvac.DirectoryPath("testImg"), tFileSuffixes, 1)
runset.purposedLists.append(directory_images)

#===============================================================================
# # If you have video files, you may comment out this part for testing the video files
# # Ex. for video files in a directory
# tFileSuffixes = ["mpg","mpeg","avi"]
# directory_videos = cvac.PurposedDirectory(cvac.Purpose(cvac.PurposeType.MULTICLASS,1),
#                                           cvac.DirectoryPath("trainVideo"),
#                                           tFileSuffixes,
#                                           1)
# runset.purposedLists.append(directory_videos)
#===============================================================================

host = "-h localhost"
detector = easy.getDetector("RSItrTest_Detector:default -p 10109 " + host)
modelfile = ''  #empty detectorData only for testing
results = easy.detect(detector, modelfile, runset)
#results = easy.detect( detector, trainedModel, runset )
#easy.printResults( results, foundMap=classmap )
easy.printResults(results)

quit()
Ejemplo n.º 18
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# Make sure all files in the RunSet are available on the remote site;
# it is the client's responsibility to upload them if not.
# The putResult contains information about which files were actually transferred.
#
print("------- Remote detection, local result display: -------")
fileserver = easy.getFileServer("PythonFileService:default -p 10111 " + host)
putResult = easy.putAllFiles(fileserver, rs1)
modelfile = "detectors/haarcascade_frontalface_alt.xml"
if not fileserver.exists(easy.getCvacPath(modelfile)):
    easy.putFile(fileserver, easy.getCvacPath(modelfile))

#
# detect remotely: note the host specification
#
detector = easy.getDetector("OpenCVCascadeDetector:default -p 10102 " + host)
results = easy.detect(detector, modelfile, rs1)
easy.printResults(results)

#
# Example 2:
# Train on a remote machine, obtain the model file, and test locally.
# Assume the files are on the remote machine, or transfer with putAllFiles.
# If no local services are installed, this will be skipped.
#
print("------- Remote training, local detection: -------")
try:
    detector = easy.getDetector(
        "BOW_Detector:default -p 10104")  # local service
    trainer = easy.getTrainer("BOW_Trainer:default -p 10103 " + host)  # remote
    trainset = easy.createRunSet("trainImg")
    trainedModel = easy.train(trainer, trainset)
Ejemplo n.º 19
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'''
Easy!  mini tutorial
Kalman tracker via Matlab Bridge
matz 10/2014
'''

import easy

#
# create a test set as in the Matlab sample at
# http://www.mathworks.com/help/vision/ug/object-tracking.html
# You need to have the Matlab vision toolbox installed.  The
# singleball.avi movie is here:
# YourMatlabProgramPath/toolbox/vision/visiondemos/singleball.avi
# You should move it to the Easy! data directory for the demo
#

runset = easy.createRunSet( "singleball.avi" )

# Optionally, specify a folder that contains a sequence of the video's
# frames as image files
# runset = easy.createRunSet( "singleball.avi_frames" )

#
# evaluate your tracking algorithm with a common match scoring method
#
tracker = easy.getDetector( "KalmanTracker" )
results = easy.detect( tracker, None, runset )
easy.printResults( results )
easy.printLabeledTrack( results )
Ejemplo n.º 20
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#
# Connect to the trainer for a Bag of Words algorithm, then
# train with the given runset
#
print("starting training, this might take a few minutes...")
trainer = easy.getTrainer("BOW_Trainer")
trainedModel = easy.train(trainer, trainset)

#
# Display information about the file in which the model is stored;
# this is generally no concern to algorithm users and only of
# interest to algorithm developers since it is algorithm-specific
#
zipfname = easy.getFSPath(trainedModel)
print("{0}".format(zipfname))
zipf = zipfile.ZipFile(zipfname)
print("Training model stored in file {0}".format(zipfname))
# print("file contents:\n{0}".format(zipf.namelist()))

#
# test the trained model on a separate set of images
#
print("==== Test runset: ====")
testset = easy.createRunSet("testImg")
easy.printRunSetInfo(testset, printLabels=True)
detector = easy.getDetector("BOW_Detector")
results = easy.detect(detector, trainedModel, testset)
print("==== Results: ====")
easy.printResults(results)
Ejemplo n.º 21
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# The tracker needs a labeled location that defines the location
# within the first frame of the video  of the item we want to track.
# In this we define a bounding box around the toy soldiers initial location.
vlab = easy.getLabelable( vfile, labelText="soldier" )
labloc = cvac.BBox(255, 195,70,120)
lab = cvac.LabeledLocation()
lab.loc = labloc
lab.confidence = 0
lab.sub = vlab.sub
easy.addToRunSet(runset, lab, easy.getPurpose('pos'))
# Setting the verbosity level gives us some feedback while the tracking is done.
props = easy.getDetectorProperties(detector)
props.verbosity = 7
# A model file is not required for this detector
modelfile = None
results = easy.detect( detector, modelfile, runset, detectorProperties = props)

# <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
'''
Now we run it again this time setting up a callback to display the
tracking frames on the images.
'''

class MyDetectorCallbackReceiverI(easy.DetectorCallbackReceiverI):
    def __init__(self):
        import easy
        easy.DetectorCallbackReceiverI.__init__(self)

    def foundNewResults(self, r2, current=None):
        easy.DetectorCallbackReceiverI.foundNewResults(self, r2, current)
        # Draw the result video frame and the bounding box
Ejemplo n.º 22
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correct = easy.isProperRunSet(trainset, deleteInvalid=True)
if not correct:
    print("failed Integrity test!!!!")
    exit()
#trainset = easy.createRunSet( categories, purpose=posPurpose );
#easy.printRunSetInfo( trainset, printLabels=True )
easy.printRunSetInfo( trainset )

#
# Connect to the trainer for a Bag of Words algorithm, then
# train with the given runset
#
#trainer = easy.getTrainer( "BOW_Trainer")
trainer = easy.getTrainer( "BOW_Trainer:default -p 10103")
trainedModel = easy.train( trainer, trainset )
zipfname = easy.getFSPath( trainedModel )
print("Training model stored in file {0}".format( zipfname ))

#
# test the trained model on a separate set of images
#
print("==== Test runset: ====")
testset = easy.createRunSet( "testImg" )
easy.printRunSetInfo( testset, printLabels=True )
detector = easy.getDetector( "BOW_Detector" )
results = easy.detect( detector, trainedModel, testset )
print("==== Results: ====")
easy.printResults( results )


Ejemplo n.º 23
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#
# Create a training set from one sample each of 9 corporate logos
#
trainset1 = easy.createRunSet( "corporate_logos" )

# train, round 1
trainer = easy.getTrainer( "BOW_Trainer")
model1 = easy.train( trainer, trainset1 )

# evaluate the model on a separate test set, images and videos
# in DataDir/testdata1
testset1 = easy.createRunSet( "testImg", "UNPURPOSED" )
easy.printRunSetInfo( testset1 )
detector = easy.getDetector( "BOW_Detector" )
result1 = easy.detect( detector, model1, testset1 )
easy.printResults(result1)

# sort the images from the testdata1 folder into subfolders of
# "testresults1" corresponding to the found labels;
# if multiple labels were found per original, consider only
# the label with the highest confidence
easy.sortIntoFolders( result1, outfolder="testresults1", multi="highest")

# Now manually sort through the created testresults1 and move
# _incorrectly_ classified samples into correctly labeled subfolders
# of a new folder "corporate_logos_round2".  Found labels on locations
# that are not one of the 9 logos have to be sorted into a 10th class
# (subfolder), generally called the "reject" class.
#
# We simulate this manual process here.  Note that the new folder needs to be
Ejemplo n.º 24
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# labels.  To use them for evaluation, let's assign the same purpose
# to syntactically different but semantically identical labels.
# Because we don't specify it, this guesses the specific Purpose that
# is assigned to the labels.
# Also obtain this mapping from Purpose to label name, called "classmap."
rs1 = easy.createRunSet( categories1['CA_flag']+categories2['ca'], "0" )
easy.addToRunSet( rs1, categories1['KO_flag']+categories2['kr'], "1" )
easy.addToRunSet( rs1, categories1['US_flag']+categories2['us'], "2" )
print("\n=== The Corpora combined into one RunSet: ===");
easy.printRunSetInfo( rs1 )

# A runset can be used for training or for testing
print("------- Bag of Words results for corporate logos: -------")
detector = easy.getDetector( "BOW_Detector" )
modelfile = "detectors/bowUSKOCA.zip"
results1 = easy.detect( detector, modelfile, rs1 )
print("Note that both original and found labels are printed:")
easy.printResults( results1 )

# Print again, this time replacing the found labels with a double
# mapping from foundLabel -> guessed Purpose -> classmap label;
# Note that this fails if multiple original labels mapped to the same
# Purpose.
wait()
print("------- Same results, but found labels replaced with guessed original labels: -------")
easy.printResults( results1, foundMap=rs1['classmap'], inverseMap=True )

# Print again, this time replacing all labels with their assigned
# purposes, bot original and found labels.  Note the "identical label"
# matches.
wait()
Ejemplo n.º 25
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'''

import easy

# obtain a reference to a Bag of Words (BOW) detector
detector = easy.getDetector( "BOW_Detector" )

# a model for distinguishing Canadian, Korean, and US flags,
# trained previously with a BOW-specific trainer and stored in a file
modelfile = "detectors/bowUSKOCA.zip"

# a test image; the location is relative to the "CVAC.DataDir"
imgfile = "testImg/TestKrFlag.jpg"

# apply the detector type, using the model and testing the imgfile
results = easy.detect( detector, modelfile, imgfile )

# you can print the results with Easy!'s pretty-printer;
# we will explain the meaning of the "unlabeled" and the number
# of the found label later.
# or you can always print variables directly (uncomment the next line):
# print("{0}".format( results ))
print("------- Bag of Words results for flags: -------")
easy.printResults( results )

# let's try a different model, pre-trained not for
# flags but for various corporate logos
print("------- Bag of Words results for corporate logos: -------")
modelfile = "detectors/bowCorporateLogoModel.zip"
imgfile = "corporate_logos/shell/shell2.png"
results = easy.detect( detector, modelfile, imgfile )
Ejemplo n.º 26
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'''
Easy!  mini tutorial
Kalman tracker via Matlab Bridge
matz 10/2014
'''

import easy

#
# create a test set as in the Matlab sample at
# http://www.mathworks.com/help/vision/ug/object-tracking.html
# You need to have the Matlab vision toolbox installed.  The
# singleball.avi movie is here:
# YourMatlabProgramPath/toolbox/vision/visiondemos/singleball.avi
# You should move it to the Easy! data directory for the demo
#

runset = easy.createRunSet("singleball.avi")

# Optionally, specify a folder that contains a sequence of the video's
# frames as image files
# runset = easy.createRunSet( "singleball.avi_frames" )

#
# evaluate your tracking algorithm with a common match scoring method
#
tracker = easy.getDetector("KalmanTracker")
results = easy.detect(tracker, None, runset)
easy.printResults(results)
easy.printLabeledTrack(results)
Ejemplo n.º 27
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# labels.  To use them for evaluation, let's assign the same purpose
# to syntactically different but semantically identical labels.
# Because we don't specify it, this guesses the specific Purpose that
# is assigned to the labels.
# Also obtain this mapping from Purpose to label name, called "classmap."
rs1 = easy.createRunSet(categories1['CA_flag'] + categories2['ca'], "0")
easy.addToRunSet(rs1, categories1['KO_flag'] + categories2['kr'], "1")
easy.addToRunSet(rs1, categories1['US_flag'] + categories2['us'], "2")
print("\n=== The Corpora combined into one RunSet: ===")
easy.printRunSetInfo(rs1)

# A runset can be used for training or for testing
print("------- Bag of Words results for corporate logos: -------")
detector = easy.getDetector("BOW_Detector")
modelfile = "detectors/bowUSKOCA.zip"
results1 = easy.detect(detector, modelfile, rs1)
print("Note that both original and found labels are printed:")
easy.printResults(results1)

# Print again, this time replacing the found labels with a double
# mapping from foundLabel -> guessed Purpose -> classmap label;
# Note that this fails if multiple original labels mapped to the same
# Purpose.
wait()
print(
    "------- Same results, but found labels replaced with guessed original labels: -------"
)
easy.printResults(results1, foundMap=rs1['classmap'], inverseMap=True)

# Print again, this time replacing all labels with their assigned
# purposes, bot original and found labels.  Note the "identical label"