'''
Easy! mini tutorial
Build a model for object detection
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)
# 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 )
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])
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)
Invoke a remote service, send files, receive files, receive messages
matz 6/17/2013
'''
import easy
#
# Example 1: Test on a remote machine.
# specify the host name of the service
#
host = "-h vision.nps.edu"
#
# create a simple RunSet with just one unlabeled image
#
rs1 = easy.createRunSet("testImg/italia.jpg")
#
# 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
# 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;
'''
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)
'''
Easy! mini tutorial
Repeatedly train and evaluate for efficient label use; bootstrap.
matz 6/21/2013
'''
import os
import easy
#
# 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")
'''
Easy! mini tutorial
Build a model for object detection
matz 6/18/2013
'''
import easy
import zipfile
#
# create a RunSet from corporate logo images
#
print("==== Training runset: ====")
trainset = easy.createRunSet( "trainImg" );
easy.printRunSetInfo( trainset, printLabels=True )
#
# Connect to the trainer for a Bag of Words algorithm, then
# train with the given runset
#
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 dataset information about this corpus
#
cs = easy.getCorpusServer("CorpusServer:default -p 10011")
#corpus = easy.openCorpus( cs, "corpus/CvacCorpusTest.properties" )
#corpus = easy.openCorpus( cs, "corporate_logos" );
corpus = easy.openCorpus( cs, "trainImg" );
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 )
#
# add all samples from corpus to a RunSet,
# also obtain a mapping from class ID to label name
#
res = easy.createRunSet( categories )
runset = res['runset']
classmap = res['classmap']
#
# Make sure all files in the RunSet are available on the remote site;
# it is the client's responsibility to upload them if not.
#
host = "-h localhost"
#host = "-h vision.nps.edu"
fileserver = easy.getFileServer( "FileService:default -p 10110 " + host )
putResult = easy.putAllFiles( fileserver, runset )
#
# Connect to a trainer service, train on the RunSet
#
# 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)
generated by matz 7/16/2013
updated by k.lee May/2014
'''
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]
# 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;
'''
Easy! mini tutorial
Build a model for object detection
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 )
Invoke a remote service, send files, receive files, receive messages
matz 6/17/2013
'''
import easy
#
# Example 1: Test on a remote machine.
# specify the host name of the service
#
host = "-h vision.nps.edu"
#
# create a simple RunSet with just one unlabeled image
#
rs1 = easy.createRunSet( "testImg/italia.jpg" )
#
# 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
#
''' 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 )
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])
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 )
''' 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
''' Easy! mini tutorial Repeatedly train and evaluate for efficient label use; bootstrap. matz 6/21/2013 ''' import os import easy # # 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")
'''
Easy! mini tutorial
Create a RunSet in several ways, use it for detection and evaluation
matz 6/18/2013
'''
import easy
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.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")
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)
