def createSuperPixelRandomForestClassifier(msrcDataDirectory,
classifierDirectory, nbSuperPixels,
superPixelCompactness, scale):
# Get training data
superPixelTrainData = getSuperPixelTrainingData(msrcDataDirectory,
nbSuperPixels,
superPixelCompactness,
scale)
superPixelTrainFeatures = superPixelTrainData[0]
superPixelTrainLabels = superPixelTrainData[1]
# now train random forest classifier on labelled superpixel data
print '\n\n**Training a random forest on %d examples...' % len(
superPixelTrainLabels)
print "Training feature data shape=", np.shape(
superPixelTrainFeatures), "type:", type(superPixelTrainFeatures)
print "Training labels shape=", np.shape(
superPixelTrainLabels), "type:", type(superPixelTrainLabels)
numEstimators = 100
classifier = sklearn.ensemble.RandomForestClassifier(
n_estimators=numEstimators)
classifier = classifier.fit(superPixelTrainData, superPixelTrainLabels)
# Now serialise the classifier to file
classifierFilename = classifierDirectory + "/" + "randForest_superPixel_nEst" + str(
numEstimators) + ".pkl"
pomio.pickleObject(classifier, classifierFilename)
print "Rand forest classifier saved @ " + str(classifierFilename)
def createSuperPixelRandomForestClassifier(msrcDataDirectory, classifierDirectory,nbSuperPixels,superPixelCompactness, scale):
# Get training data
superPixelTrainData = getSuperPixelTrainingData(msrcDataDirectory,nbSuperPixels,superPixelCompactness, scale)
superPixelTrainFeatures = superPixelTrainData[0]
superPixelTrainLabels = superPixelTrainData[1]
# now train random forest classifier on labelled superpixel data
print '\n\n**Training a random forest on %d examples...' % len(superPixelTrainLabels)
print "Training feature data shape=" , np.shape(superPixelTrainFeatures) , "type:", type(superPixelTrainFeatures)
print "Training labels shape=" , np.shape(superPixelTrainLabels), "type:" , type(superPixelTrainLabels)
numEstimators = 100
classifier = sklearn.ensemble.RandomForestClassifier(n_estimators=numEstimators)
classifier = classifier.fit( superPixelTrainData, superPixelTrainLabels)
# Now serialise the classifier to file
classifierFilename = classifierDirectory + "/" + "randForest_superPixel_nEst" + str(numEstimators) + ".pkl"
pomio.pickleObject(classifier, classifierFilename)
print "Rand forest classifier saved @ " + str(classifierFilename)
def createSuperPixelLogisticRegressionClassifier(msrcDataDirectory, classifierDirectory, scale):
# Get training data
superPixelTrainData = getSuperPixelTrainingData(msrcDataDirectory,nbSuperPixels,superPixelCompactness, scale)
superPixelTrainFeatures = superPixelTrainData[0]
superPixelTrainLabels = superPixelTrainData[1]
# now train random forest classifier on labelled superpixel data
print '\n\n**Training a LR classifier on %d examples...' % len(superPixelTrainLabels)
print "Training feature data shape=" , np.shape(superPixelTrainFeatures) , "type:", type(superPixelTrainFeatures)
print "Training labels shape=" , np.shape(superPixelTrainLabels), "type:" , type(superPixelTrainLabels)
Cvalue = 0.5
# sklearn.linear_model.LogisticRegression(penalty='l2', dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None)
classifier = LogisticRegression(penalty='l1' , dual=False, tol=0.0001, C=Cvalue, fit_intercept=True, intercept_scaling=1)
classifier.fit(superPixelTrainFeatures, superPixelTrainLabels)
# Now serialise the classifier to file
classifierFilename = classifierDirectory + "/" + "LR_superPixel_C" + str(Cvalue) + ".pkl"
pomio.pickleObject(classifier, classifierFilename)
print "LR classifier saved @ " + str(classifierFilename)
def createSuperPixelLogisticRegressionClassifier(msrcDataDirectory,
classifierDirectory, scale):
# Get training data
superPixelTrainData = getSuperPixelTrainingData(msrcDataDirectory,
nbSuperPixels,
superPixelCompactness,
scale)
superPixelTrainFeatures = superPixelTrainData[0]
superPixelTrainLabels = superPixelTrainData[1]
# now train random forest classifier on labelled superpixel data
print '\n\n**Training a LR classifier on %d examples...' % len(
superPixelTrainLabels)
print "Training feature data shape=", np.shape(
superPixelTrainFeatures), "type:", type(superPixelTrainFeatures)
print "Training labels shape=", np.shape(
superPixelTrainLabels), "type:", type(superPixelTrainLabels)
Cvalue = 0.5
# sklearn.linear_model.LogisticRegression(penalty='l2', dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None)
classifier = LogisticRegression(penalty='l1',
dual=False,
tol=0.0001,
C=Cvalue,
fit_intercept=True,
intercept_scaling=1)
classifier.fit(superPixelTrainFeatures, superPixelTrainLabels)
# Now serialise the classifier to file
classifierFilename = classifierDirectory + "/" + "LR_superPixel_C" + str(
Cvalue) + ".pkl"
pomio.pickleObject(classifier, classifierFilename)
print "LR classifier saved @ " + str(classifierFilename)
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
reportAccuracy('Training set', labs, predlabs)
# optionally test classifier on hold-out test set
if infileFtrsTest != None and infileLabsTest != None:
# Load the features and labels
if infileFtrsTest.endswith('.pkl'):
ftrsTest = pomio.unpickleObject(infileFtrsTest)
else:
ftrsTest = pomio.readMatFromCSV(infileFtrsTest)
if infileLabsTest.endswith('.pkl'):
labsTest = pomio.unpickleObject(infileLabsTest)
else:
labsTest = pomio.readMatFromCSV(infileLabsTest).astype(np.int32)
ntest = len(labsTest)
assert ntest == ftrsTest.shape[0], 'Error: for TEST set, there are %d labels and %d features' \
% ( ntest, ftrsTest.shape[0] )
assert np.all(np.isfinite(ftrsTest))
predlabs = getlabs(clfr, ftrsTest)
reportAccuracy('Test set', labsTest, predlabs)
# Write the classifier
if clfr != None and outfile != None:
pomio.pickleObject(clfr, outfile)
print 'Output written to file ', outfile
else:
print "No classifier to persist or no output filename; review input parameters."
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
# optionally test classifier on hold-out test set
if infileFtrsTest != None and infileLabsTest != None:
# Load the features and labels
if infileFtrsTest.endswith('.pkl'):
ftrsTest = pomio.unpickleObject( infileFtrsTest )
else:
ftrsTest = pomio.readMatFromCSV( infileFtrsTest )
if infileLabsTest.endswith('.pkl'):
labsTest = pomio.unpickleObject( infileLabsTest )
else:
labsTest = pomio.readMatFromCSV( infileLabsTest ).astype(np.int32)
ntest = len(labsTest)
assert ntest == ftrsTest.shape[0], 'Error: for TEST set, there are %d labels and %d features' \
% ( ntest, ftrsTest.shape[0] )
assert np.all( np.isfinite( ftrsTest ) )
predlabs = getlabs(clfr,ftrsTest)
reportAccuracy( 'Test set', labsTest, predlabs )
# Write the classifier
if clfr != None and outfile != None:
pomio.pickleObject( clfr, outfile )
print 'Output written to file ', outfile
else:
print "No classifier to persist or no output filename; review input parameters."
image, 'slic', [numberSuperPixels, superPixelCompactness])
[spClassPreds, spClassProbs
] = classification.classifyImageSuperPixels(image, clfr, spGraph, ftype,
aggtype, makeProbs)
spClassPredsImage = spGraph.imageFromSuperPixelData(
spClassPreds.reshape((len(spClassPreds), 1)))
if args.verbose:
plt.subplot(1, 2, 1)
plt.imshow(image)
plt.title(args.infile)
plt.subplot(1, 2, 2)
pomio.showLabels(spClassPredsImage)
plt.waitforbuttonpress()
if args.outfile and len(args.outfile) > 0:
print 'Writing output label file %s' % args.outfile
outimg = pomio.msrc_convertLabelsToRGB(spClassPredsImage)
skimage.io.imsave(args.outfile, outimg)
print ' done.'
if makeProbs:
print 'Writing output (superpixels, class probabilities) to pickle file %s' % \
args.outprobsfile
assert spClassProbs != None
pomio.pickleObject((spGraph, spClassProbs), args.outprobsfile)
if args.verbose:
plt.interactive(0)
plt.show()
print 'Classifying file ', args.infile
image = amntools.readImage(args.infile)
spGraph = superPixels.computeSuperPixelGraph(image,'slic',[numberSuperPixels, superPixelCompactness])
[spClassPreds, spClassProbs] = classification.classifyImageSuperPixels( image, clfr, spGraph, ftype, aggtype, makeProbs)
spClassPredsImage = spGraph.imageFromSuperPixelData( spClassPreds.reshape( (len(spClassPreds),1) ) )
if args.verbose:
plt.subplot(1,2,1)
plt.imshow(image)
plt.title(args.infile)
plt.subplot(1,2,2)
pomio.showLabels(spClassPredsImage)
plt.waitforbuttonpress()
if args.outfile and len(args.outfile)>0:
print 'Writing output label file %s' % args.outfile
outimg = pomio.msrc_convertLabelsToRGB( spClassPredsImage )
skimage.io.imsave(args.outfile, outimg)
print ' done.'
if makeProbs:
print 'Writing output (superpixels, class probabilities) to pickle file %s' % \
args.outprobsfile
assert spClassProbs != None
pomio.pickleObject( (spGraph,spClassProbs), args.outprobsfile )
if args.verbose:
plt.interactive(0)
plt.show()
