def test():
classifierLocation = "/home/amb/dev/mrf/classifiers/logisticRegression/superpixel/logReg_miniMSRC.pkl"
classifier = pomio.unpickleObject(classifierLocation)
carFile = "7_3_s.bmp"
msrcData = "/home/amb/dev/mrf/data/MSRC_ObjCategImageDatabase_v2"
car = pomio.msrc_loadImages(msrcData, ["Images/" + carFile])[0]
groundTruth = car.m_gt
mask = superPixels.getSuperPixels_SLIC(car.m_img, 400, 10)
spLabels = SuperPixelClassifier.predictSuperPixelLabels(
classifier, car.m_img, 400, 10, True)[0]
prediction = SuperPixelClassifier.getSuperPixelLabelledImage(
car.m_img, mask, spLabels)
# save prediction to file
pomio.writeMatToCSV(prediction,
"/home/amb/dev/mrf/eval/testPrediction1.labels")
results = evaluatePrediction(prediction, groundTruth, carFile)
print "\nINFO: Car test eval results::\n\t", results
classResults = evaluateClassPerformance(prediction, groundTruth)
print "\nINFO: Car test eval class results::\n\t", classResults
confusionResults = evaluateConfusionMatrix(prediction, groundTruth)
print "\nINFO: Car test eval confusion matrix results::\n\t", "Just sum up entries... ", np.sum(
confusionResults)
def test():
# TODO use reference classifier
classifierName = "/home/amb/dev/mrf/classifiers/randomForest/superpixel/randyForest_superPixel_maxDepth15_0.6Data.pkl"
classifier = pomio.unpickleObject(classifierName)
carFile = "7_3_s.bmp"
msrcData = "/home/amb/dev/mrf/data/MSRC_ObjCategImageDatabase_v2"
car = pomio.msrc_loadImages(msrcData, ["Images/" + carFile])[0]
groundTruth = car.m_gt
mask = SuperPixels.getSuperPixels_SLIC(car.m_img, 400, 10)
spLabels = SuperPixelClassifier.predictSuperPixelLabels(
classifier, car.m_img, 400, 10)[0]
prediction = SuperPixelClassifier.getSuperPixelLabelledImage(
car.m_img, mask, spLabels)
# save prediction to file
pomio.writeMatToCSV(
prediction, "/home/amb/dev/eval/test/predict/testPrediction1.labels")
results = evaluatePrediction(prediction, groundTruth)
print "\nINFO: Car test eval results::\n\t", results
def test():
# TODO use reference classifier
classifierName = "/home/amb/dev/mrf/classifiers/randomForest/superpixel/randyForest_superPixel_maxDepth15_0.6Data.pkl"
classifier = pomio.unpickleObject(classifierName)
carFile = "7_3_s.bmp"
msrcData = "/home/amb/dev/mrf/data/MSRC_ObjCategImageDatabase_v2"
car = pomio.msrc_loadImages(msrcData , [ "Images/" + carFile ] )[0]
groundTruth = car.m_gt
mask = SuperPixels.getSuperPixels_SLIC(car.m_img, 400, 10)
spLabels = SuperPixelClassifier.predictSuperPixelLabels(classifier, car.m_img,400,10)[0]
prediction = SuperPixelClassifier.getSuperPixelLabelledImage(car.m_img, mask, spLabels)
# save prediction to file
pomio.writeMatToCSV(prediction, "/home/amb/dev/eval/test/predict/testPrediction1.labels")
results = evaluatePrediction(prediction, groundTruth)
print "\nINFO: Car test eval results::\n\t" , results
def createAndSaveFeatureLabelData(
msrcData, outfileBase, dataType, outfileType, nbSuperPixels, superPixelCompactness
):
if dataType == None or dataType == "":
outfileFtrs = '%s_ftrs.%s' % ( outfileBase, outfileType )
outfileLabs = '%s_labs.%s' % ( outfileBase, outfileType )
outfileAdj = '%s_adj.%s' % ( outfileBase, outfileType )
else:
outfileFtrs = '%s_%s_ftrs.%s' % ( outfileBase, dataType, outfileType )
outfileLabs = '%s_%s_labs.%s' % ( outfileBase, dataType, outfileType )
outfileAdj = '%s_%s_adj.%s' % ( outfileBase, dataType, outfileType )
# Check can write these files.
f=open(outfileFtrs,'w')
f.close()
f=open(outfileLabs,'w')
f.close()
# Edited getSuperPixelTrainingData to allow user-defined data split ratios
superPixelData = SuperPixelClassifier.getSuperPixelData(msrcData,nbSuperPixels,superPixelCompactness)
superPixelFeatures = superPixelData[0]
superPixelLabels = superPixelData[1].astype(np.int32)
superPixelClassAdj = superPixelData[2]
assert np.all( np.isfinite( superPixelFeatures ) )
#print superPixelClassAdj
# Output
if outfileType == 'pkl':
pomio.pickleObject( superPixelFeatures, outfileFtrs )
pomio.pickleObject( superPixelLabels, outfileLabs )
pomio.pickleObject( superPixelClassAdj, outfileAdj )
elif outfileType == 'csv':
pomio.writeMatToCSV( superPixelFeatures, outfileFtrs )
pomio.writeMatToCSV( superPixelLabels, outfileLabs )
pomio.writeMatToCSV( superPixelClassAdj, outfileAdj )
else:
assert False, 'unknown output file format ' + outfileType
print 'Output written to file ', outfileFtrs, ' and ', outfileLabs, ' and ', outfileAdj
def createAndSaveFeatureLabelData(msrcData, outfileBase, dataType, outfileType,
nbSuperPixels, superPixelCompactness):
if dataType == None or dataType == "":
outfileFtrs = '%s_ftrs.%s' % (outfileBase, outfileType)
outfileLabs = '%s_labs.%s' % (outfileBase, outfileType)
outfileAdj = '%s_adj.%s' % (outfileBase, outfileType)
else:
outfileFtrs = '%s_%s_ftrs.%s' % (outfileBase, dataType, outfileType)
outfileLabs = '%s_%s_labs.%s' % (outfileBase, dataType, outfileType)
outfileAdj = '%s_%s_adj.%s' % (outfileBase, dataType, outfileType)
# Check can write these files.
f = open(outfileFtrs, 'w')
f.close()
f = open(outfileLabs, 'w')
f.close()
# Edited getSuperPixelTrainingData to allow user-defined data split ratios
superPixelData = SuperPixelClassifier.getSuperPixelData(
msrcData, nbSuperPixels, superPixelCompactness)
superPixelFeatures = superPixelData[0]
superPixelLabels = superPixelData[1].astype(np.int32)
superPixelClassAdj = superPixelData[2]
assert np.all(np.isfinite(superPixelFeatures))
#print superPixelClassAdj
# Output
if outfileType == 'pkl':
pomio.pickleObject(superPixelFeatures, outfileFtrs)
pomio.pickleObject(superPixelLabels, outfileLabs)
pomio.pickleObject(superPixelClassAdj, outfileAdj)
elif outfileType == 'csv':
pomio.writeMatToCSV(superPixelFeatures, outfileFtrs)
pomio.writeMatToCSV(superPixelLabels, outfileLabs)
pomio.writeMatToCSV(superPixelClassAdj, outfileAdj)
else:
assert False, 'unknown output file format ' + outfileType
print 'Output written to file ', outfileFtrs, ' and ', outfileLabs, ' and ', outfileAdj
def createAndSaveFeatureLabelData(
msrcData, outfileBase, dataType, outfileType, nbSuperPixels, superPixelCompactness, ftype, aggtype, verbose
):
if verbose:
print "Creating feature set ", outfileBase
if dataType == None or dataType == "":
outfileFtrs = "%s_ftrs.%s" % (outfileBase, outfileType)
outfileLabs = "%s_labs.%s" % (outfileBase, outfileType)
else:
outfileFtrs = "%s_%s_ftrs.%s" % (outfileBase, dataType, outfileType)
outfileLabs = "%s_%s_labs.%s" % (outfileBase, dataType, outfileType)
# Check can write these files.
f = open(outfileFtrs, "w")
f.close()
f = open(outfileLabs, "w")
f.close()
if verbose:
print " - computing superpixels"
allSuperPixels = superPixels.computeSuperPixelGraphMulti(
[z.m_img for z in msrcData], "slic", [nbSuperPixels, superPixelCompactness], nbCores=args.nbCores
)
if verbose:
print " - computing features"
superPixelFeatures = features.computeSuperPixelFeaturesMulti(
[z.m_img for z in msrcData], allSuperPixels, ftype, aggtype, asMatrix=True, nbCores=args.nbCores
)
if verbose:
print " - extracting labels"
superPixelLabels = classification.computeSuperPixelLabelsMulti([z.m_gt for z in msrcData], allSuperPixels)
assert np.all(np.isfinite(superPixelFeatures))
# Don't save features with void labels.
good = superPixelLabels != pomio.getVoidIdx()
if not np.all(good):
if verbose:
print " - discarding %d superpixels with void labels" % np.count_nonzero(np.logical_not(good))
superPixelLabels = superPixelLabels[good]
superPixelFeatures = superPixelFeatures[good, :]
if verbose:
print " - writing %d feature vectors of dimension %d to output files" % (
superPixelFeatures.shape[0],
superPixelFeatures.shape[1],
)
# Output
if outfileType == "pkl":
pomio.pickleObject(superPixelFeatures, outfileFtrs)
pomio.pickleObject(superPixelLabels, outfileLabs)
elif outfileType == "csv":
pomio.writeMatToCSV(superPixelFeatures, outfileFtrs)
pomio.writeMatToCSV(superPixelLabels, outfileLabs)
else:
assert False, "unknown output file format " + outfileType
print "Output written to file ", outfileFtrs, " and ", outfileLabs
def createAndSaveFeatureLabelData(msrcData, outfileBase, dataType, outfileType,
nbSuperPixels, superPixelCompactness, ftype,
aggtype, verbose):
if verbose:
print 'Creating feature set ', outfileBase
if dataType == None or dataType == "":
outfileFtrs = '%s_ftrs.%s' % (outfileBase, outfileType)
outfileLabs = '%s_labs.%s' % (outfileBase, outfileType)
else:
outfileFtrs = '%s_%s_ftrs.%s' % (outfileBase, dataType, outfileType)
outfileLabs = '%s_%s_labs.%s' % (outfileBase, dataType, outfileType)
# Check can write these files.
f = open(outfileFtrs, 'w')
f.close()
f = open(outfileLabs, 'w')
f.close()
if verbose:
print ' - computing superpixels'
allSuperPixels = superPixels.computeSuperPixelGraphMulti( \
[ z.m_img for z in msrcData ],
'slic',
[nbSuperPixels, superPixelCompactness], nbCores=args.nbCores )
if verbose:
print ' - computing features'
superPixelFeatures = features.computeSuperPixelFeaturesMulti(
[z.m_img for z in msrcData],
allSuperPixels,
ftype,
aggtype,
asMatrix=True,
nbCores=args.nbCores)
if verbose:
print ' - extracting labels'
superPixelLabels = classification.computeSuperPixelLabelsMulti( \
[z.m_gt for z in msrcData], allSuperPixels )
assert np.all(np.isfinite(superPixelFeatures))
# Don't save features with void labels.
good = (superPixelLabels != pomio.getVoidIdx())
if not np.all(good):
if verbose:
print ' - discarding %d superpixels with void labels' % np.count_nonzero(
np.logical_not(good))
superPixelLabels = superPixelLabels[good]
superPixelFeatures = superPixelFeatures[good, :]
if verbose:
print ' - writing %d feature vectors of dimension %d to output files' % \
(superPixelFeatures.shape[0], superPixelFeatures.shape[1])
# Output
if outfileType == 'pkl':
pomio.pickleObject(superPixelFeatures, outfileFtrs)
pomio.pickleObject(superPixelLabels, outfileLabs)
elif outfileType == 'csv':
pomio.writeMatToCSV(superPixelFeatures, outfileFtrs)
pomio.writeMatToCSV(superPixelLabels, outfileLabs)
else:
assert False, 'unknown output file format ' + outfileType
print 'Output written to file ', outfileFtrs, ' and ', outfileLabs
