def test_getLabelableList(self):
print("testing getLabelableList")
sys.stdout.flush()
labelList = easy.getLabelableList("easyTestData")
'''verify that the labels are correct
They should be , the root directory "easyTestData",
"kitchen" and "MITmountain"
'''
for entry in labelList:
print("label name " + entry.lab.name)
if entry.lab.name != "easyTestData" and \
entry.lab.name != "kitchen" and \
entry.lab.name != "MITmountain":
raise RuntimeError(
"Not a valid label name in test getLabelableList")
if entry.lab.name == "kitchen":
# should find easyTestData, inside, house properties
if "easyTestData" not in entry.lab.properties:
raise RuntimeError("easyTestData not in properties")
if "inside" not in entry.lab.properties:
raise RuntimeError("inside not in properties")
if "house" not in entry.lab.properties:
raise RuntimeError("house not in properties")
''' verify that we can make a runset and use it with the labelableList
'''
runset = cvac.RunSet()
posPurpose = easy.getPurpose('pos')
easy.addToRunSet(runset, labelList, posPurpose)
if not easy.isProperRunSet(runset):
raise RuntimeError(
"test getLabelableList failed with an invalid runset")
labelList = easy.getLabelableList("easyTestData", recursive=False)
''' should only have one label "easyTestData"
'''
for entry in labelList:
if entry.lab.name != "easyTestData":
raise RuntimeError(
"recursive option failed in test getLabelableList")
if len(entry.lab.properties) > 0:
raise RuntimeError("labelable should not have any properties")
if not easy.isProperRunSet(runset):
raise RuntimeError(
"test getLabelableList failed with an invalid runset with non-recursive call"
)
def test_getLabelableList(self):
print("testing getLabelableList")
sys.stdout.flush()
labelList = easy.getLabelableList("easyTestData")
'''verify that the labels are correct
They should be , the root directory "easyTestData",
"kitchen" and "MITmountain"
'''
for entry in labelList:
print ("label name " + entry.lab.name)
if entry.lab.name != "easyTestData" and \
entry.lab.name != "kitchen" and \
entry.lab.name != "MITmountain":
raise RuntimeError("Not a valid label name in test getLabelableList")
if entry.lab.name == "kitchen":
# should find easyTestData, inside, house properties
if "easyTestData" not in entry.lab.properties:
raise RuntimeError("easyTestData not in properties")
if "inside" not in entry.lab.properties:
raise RuntimeError("inside not in properties")
if "house" not in entry.lab.properties:
raise RuntimeError("house not in properties")
''' verify that we can make a runset and use it with the labelableList
'''
runset = cvac.RunSet()
posPurpose = easy.getPurpose('pos')
easy.addToRunSet(runset, labelList, posPurpose)
if not easy.isProperRunSet(runset):
raise RuntimeError("test getLabelableList failed with an invalid runset")
labelList = easy.getLabelableList("easyTestData", recursive=False)
''' should only have one label "easyTestData"
'''
for entry in labelList:
if entry.lab.name != "easyTestData":
raise RuntimeError("recursive option failed in test getLabelableList")
if len(entry.lab.properties) > 0:
raise RuntimeError("labelable should not have any properties")
if not easy.isProperRunSet(runset):
raise RuntimeError("test getLabelableList failed with an invalid runset with non-recursive call")
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
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
matz 6/18/2013
'''
import cvac
import easy
import zipfile
#
# create a RunSet from corporate logo images
#
print("==== Training runset: ====")
#trainset = easy.createRunSet( "trainImg" )
trainset = cvac.RunSet()
caset = easy.createRunSet("trainImg/ca")
krset = easy.createRunSet("trainImg/kr")
usset = easy.createRunSet("trainImg/us")
easy.addToRunSet(trainset, caset, cvac.Purpose(cvac.PurposeType.MULTICLASS, 0))
easy.addToRunSet(trainset, krset, cvac.Purpose(cvac.PurposeType.MULTICLASS, 1))
easy.addToRunSet(trainset, usset, cvac.Purpose(cvac.PurposeType.MULTICLASS, 2))
easy.printRunSetInfo(trainset, printLabels=True)
#
# 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
c.detectorData = "detectors/dpmStarbucksLogo.zip"
c.foundMap = {
'Positive': easy.getPurpose('pos'),
'Negative': easy.getPurpose('neg')
}
contenders.append(c)
# OpenCVCascade, with special settings for anticipated poor performance
if (easy.getTrainer("OpenCVCascadeTrainer") == None):
print("Cascade service(s) are insufficiently configured, skipping.")
else:
c = ev.Contender("cascade")
c.trainerString = "OpenCVCascadeTrainer"
c.detectorString = "OpenCVCascadeDetector"
# c.foundMap = {'any':easy.getPurpose('pos')}
c.foundMap = {'positive': posPurpose, 'negative': negPurpose}
detector = easy.getDetector(c.detectorString)
detectorProps = easy.getDetectorProperties(detector)
c.detectorProps = detectorProps
c.detectorProps.props["maxRectangles"] = "200"
c.detectorProps.minNeighbors = 0
# This prevents hang up in evaluator when training has too few samples
contenders.append(c)
runset = easy.createRunSet("trainImg/kr", "pos")
easy.addToRunSet(runset, "trainImg/ca", "neg")
easy.printRunSetInfo(runset, printArtifacts=False, printLabels=True)
perfdata = ev.joust(contenders, runset, folds=3)
ev.printEvaluationResults(perfdata[0])
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")
c3.detectorString = "DPM_Detector:default -p 10116"
c3.detectorData = "detectors/dpmStarbucksLogo.zip"
c3.foundMap = {'Positive':easy.getPurpose('pos'), 'Negative':easy.getPurpose('neg')}
# OpenCVCascade
c2 = Contender("cascade")
c2.trainerString = "OpenCVCascadeTrainer:default -p 10107"
c2.detectorString = "OpenCVCascadeDetector:default -p 10102"
c2.foundMap = {'positive':posPurpose, 'negative':negPurpose}
detector = easy.getDetector(c2.detectorString)
detectorProps = easy.getDetectorProperties(detector)
c2.detectorProps = detectorProps;
c2.detectorProps.props["maxRectangles"] = "200"
c2.detectorProps.minNeighbors = 0; # This prevents hang up in evaluator when training has too few samples
c4 = Contender("cascade")
c4.trainerString = "OpenCVCascadeTrainer:default -p 10107"
c4.detectorString = "OpenCVCascadeDetector:default -p 10102"
c4.foundMap = {'any':easy.getPurpose('pos')}
runset = easy.createRunSet( "trainImg/kr/Kr001.jpg", "pos" )
easy.addToRunSet( runset, "trainImg/kr/Kr002.jpg", "pos" )
easy.addToRunSet( runset, "trainImg/kr/Kr003.jpg", "pos" )
easy.addToRunSet( runset, "trainImg/ca/ca0003.jpg", "neg" )
easy.addToRunSet( runset, "trainImg/ca/ca0004.jpg", "neg" )
easy.addToRunSet( runset, "trainImg/ca/ca0005.jpg", "neg" )
easy.printRunSetInfo( runset, printArtifacts=False, printLabels=True )
perfdata = joust( [c1, c2], runset, folds=3 )
printEvaluationResults(perfdata)
import evaluate
import cvac
import time #for computing computation time
import os #for chdir
stime = time.clock()
'''
Making training data
'''
#trainImg_roc_simple
#trainImg_roc
trainsetPos = easy.createRunSet( 'corporate_logos' )
trainsetNeg = easy.createRunSet( 'trainImg' )
runset = cvac.RunSet()
easy.addToRunSet(runset, trainsetPos, 'pos')
easy.addToRunSet(runset, trainsetNeg, 'neg')
easy.printRunSetInfo( runset, printLabels=True, )
strTrainer = "BOW_Trainer"
strDetector = "BOW_Detector"
list_nWord = [5,10,15,20]
doWithNegativeSample = True
if doWithNegativeSample:
###############################################################
# With background data
'''
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")
import easy
import evaluate
import cvac
import time #for computing computation time
import os #for chdir
stime = time.clock()
'''
Making training data
'''
#trainImg_roc_simple
#trainImg_roc
trainsetPos = easy.createRunSet('corporate_logos')
trainsetNeg = easy.createRunSet('trainImg')
runset = cvac.RunSet()
easy.addToRunSet(runset, trainsetPos, 'pos')
easy.addToRunSet(runset, trainsetNeg, 'neg')
easy.printRunSetInfo(
runset,
printLabels=True,
)
strTrainer = "BOW_Trainer"
strDetector = "BOW_Detector"
list_nWord = [5, 10, 15, 20]
doWithNegativeSample = True
if doWithNegativeSample:
###############################################################
# Create a list of labelable files under a directory.
lablist2 = easy.getLabelableList("trainImg")
categories2 = easy.getCategories(lablist2)
print("\n=== Corpus 2: ===")
print('Obtained {0} labeled artifact{1} from trainImg directory:'.format(
len(lablist2), ("s", "")[len(lablist2) == 1]))
easy.printCategoryInfo(categories2)
# Note how both corpora contain flag images, but they have different
# 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
matz 6/18/2013
'''
import cvac
import easy
import zipfile
#
# create a RunSet from corporate logo images
#
print("==== Training runset: ====")
#trainset = easy.createRunSet( "trainImg" )
trainset = cvac.RunSet()
caset = easy.createRunSet("trainImg/ca")
krset = easy.createRunSet("trainImg/kr")
usset = easy.createRunSet("trainImg/us")
easy.addToRunSet(trainset, caset, cvac.Purpose(cvac.PurposeType.MULTICLASS,0))
easy.addToRunSet(trainset, krset, cvac.Purpose(cvac.PurposeType.MULTICLASS,1))
easy.addToRunSet(trainset, usset, cvac.Purpose(cvac.PurposeType.MULTICLASS,2))
easy.printRunSetInfo( trainset, printLabels=True )
#
# 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
else:
c = ev.Contender("DPM")
c.detectorString = "DPM_Detector"
c.detectorData = "detectors/dpmStarbucksLogo.zip"
c.foundMap = {'Positive':easy.getPurpose('pos'), 'Negative':easy.getPurpose('neg')}
contenders.append( c );
# OpenCVCascade, with special settings for anticipated poor performance
if (easy.getTrainer("OpenCVCascadeTrainer")==None):
print("Cascade service(s) are insufficiently configured, skipping.")
else:
c = ev.Contender("cascade")
c.trainerString = "OpenCVCascadeTrainer"
c.detectorString = "OpenCVCascadeDetector"
# c.foundMap = {'any':easy.getPurpose('pos')}
c.foundMap = {'positive':posPurpose, 'negative':negPurpose}
detector = easy.getDetector(c.detectorString)
detectorProps = easy.getDetectorProperties(detector)
c.detectorProps = detectorProps;
c.detectorProps.props["maxRectangles"] = "200"
c.detectorProps.minNeighbors = 0; # This prevents hang up in evaluator when training has too few samples
contenders.append( c );
runset = easy.createRunSet( "trainImg/kr", "pos" )
easy.addToRunSet( runset, "trainImg/ca", "neg" )
easy.printRunSetInfo( runset, printArtifacts=False, printLabels=True )
perfdata = ev.joust( contenders, runset, folds=3 )
ev.printEvaluationResults(perfdata[0])
cs = easy.getCorpusServer( "PythonCorpusService:default -p 10021")
corpus = easy.openCorpus( corpus_fname, corpusServer=cs )
categories, lablist = easy.getDataSet( corpus, corpusServer=cs )
print('Obtained {0} labeled artifact{1} from corpus "{2}":'.format(
len(lablist), ("s","")[len(lablist)==1], corpus.name ));
easy.printCategoryInfo( categories )
# if desired, you can draw the images and their annotations,
# one image at a time, at a given maximum size (width, height)
#easy.drawLabelables( lablist, (512, 512) )
print("==== Training runset: ====")
posPurpose = easy.getPurpose('pos')
negPurpose = easy.getPurpose('neg')
trainset = cvac.RunSet()
easy.addToRunSet(trainset, categories[objname], posPurpose);
#trainset = easy.createRunSet( categories, purpose=posPurpose );
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")
# Create a list of labelable files under a directory.
lablist2 = easy.getLabelableList( "trainImg" )
categories2 = easy.getCategories(lablist2)
print("\n=== Corpus 2: ===");
print('Obtained {0} labeled artifact{1} from trainImg directory:'.format(
len(lablist2), ("s","")[len(lablist2)==1]));
easy.printCategoryInfo( categories2 )
# Note how both corpora contain flag images, but they have different
# 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
detector = easy.getDetector( "StruckTracker" )
# a test video; the location is relative to the "CVAC.DataDir"
vfile = "tracks/VTS_01_2.mpg"
runset = cvac.RunSet()
# 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="car" )
labloc = cvac.BBox(290, 280,60,30)
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
class MyDetectorCallbackReceiverI(easy.DetectorCallbackReceiverI):
def __init__(self):
import easy
easy.DetectorCallbackReceiverI.__init__(self)
self.video = cv2.VideoWriter("results.mpg", cv2.cv.CV_FOURCC('P','I','M','1'),25, (720, 480))
if self.video.isOpened() == False:
print("Could not create video file!")
def foundNewResults(self, r2, current=None):
import cvac
# a simple RunSet with just one unlabeled image;
# remember that paths are relative to CVAC.DataDir
rs1 = easy.createRunSet( "testImg/italia.jpg" )
print("=== RunSet 1: ===");
easy.printRunSetInfo( rs1, printLabels=True )
# to give samples a purpose, state the purpose:
rs2 = easy.createRunSet( "testImg/italia.jpg", "POSITIVE" )
print("\n=== RunSet 2: ===");
easy.printRunSetInfo( rs2, printLabels=True )
# add more samples to a runset; anything starting with "pos"
# will be added into the POSITIVE sequence of labeled items
easy.addToRunSet( rs2, "testImg/TestKrFlag.jpg", "POS" )
easy.addToRunSet( rs2, "testImg/TestCaFlag.jpg", "neg" )
easy.addToRunSet( rs2, "testImg/TestUsFlag.jpg", "0" )
print("\n=== RunSet 2, after appending: ===");
easy.printRunSetInfo( rs2, printLabels=True )
# create a runset from a folder with sub-folders
rs3 = easy.createRunSet( "trainImg" )
print("\n=== RunSet 3: ===");
easy.printRunSetInfo( rs3, printLabels=True )
# you can check wether the runset has been properly constructed:
if easy.isProperRunSet( rs3 ):
print("correctly constructed runset")
else:
print("improperly constructed runset")
cs = easy.getCorpusServer("PythonCorpusService:default -p 10021")
corpus = easy.openCorpus(corpus_fname, corpusServer=cs)
categories, lablist = easy.getDataSet(corpus, corpusServer=cs)
print('Obtained {0} labeled artifact{1} from corpus "{2}":'.format(
len(lablist), ("s", "")[len(lablist) == 1], corpus.name))
easy.printCategoryInfo(categories)
# if desired, you can draw the images and their annotations,
# one image at a time, at a given maximum size (width, height)
#easy.drawLabelables( lablist, (512, 512) )
print("==== Training runset: ====")
posPurpose = easy.getPurpose('pos')
negPurpose = easy.getPurpose('neg')
trainset = cvac.RunSet()
easy.addToRunSet(trainset, categories[objname], posPurpose)
#trainset = easy.createRunSet( categories, purpose=posPurpose );
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")
