def createWeights(ftb, ctb, project, featureThreshold, imagesOnly):
# Build the classifier (basically a set of weights)
message = ''
trainFts = wndcharm.FeatureSet.FeatureSet_Discrete()
classId = 0
for ds in project.listChildren():
message += 'Processing dataset id:%d\n' % ds.getId()
message += addToFeatureSet(ftb, ds, trainFts, classId, imagesOnly)
classId += 1
tmp = trainFts.ContiguousDataMatrix()
weights = wndcharm.FeatureSet.FisherFeatureWeights.NewFromFeatureSet(trainFts)
if featureThreshold < 1.0:
nFeatures = ceil(len(weights.names) * featureThreshold)
message += 'Selecting top %d features\n' % nFeatures
weights = weights.Threshold(nFeatures)
trainFts = reduceFeatures(trainFts, weights)
version = trainFts.feature_vector_version
# Save the features, weights and classes to tables
# TODO:Delete existing tables
#if getProjectTableFile(tcOutF, tcF.tableName, proj):
ctb.createClassifierTables(weights.names, version)
message += 'Created classifier tables ids: %d %d %d version:%s\n' % (
ctb.tcF.tableId, ctb.tcW.tableId, ctb.tcL.tableId, version)
# We've (ab)used imagenames_list to hold the image ids
ids = [long(a) for b in trainFts.imagenames_list for a in b]
classIds = [a for b in [[i] * len(z) for i, z in izip(xrange(
len(trainFts.imagenames_list)), trainFts.imagenames_list)]
for a in b]
featureMatrix = trainFts.data_matrix
featureNames = weights.names
featureWeights = weights.values
classNames = trainFts.classnames_list
ctb.saveClassifierTables(ids, classIds, featureMatrix,
featureNames, featureWeights, classNames)
WndcharmStorage.addFileAnnotationTo(ctb.tcF, project)
WndcharmStorage.addFileAnnotationTo(ctb.tcW, project)
WndcharmStorage.addFileAnnotationTo(ctb.tcL, project)
message += 'Saved classifier\n'
classifierName = WndcharmStorage.CLASSIFIER_WNDCHARM_NAMESPACE
ns = WndcharmStorage.createClassifierTagSet(
ctb.tcL.conn, classifierName, project.getName(), classNames, project)
message += 'Created tagset: %s\n' % ns
return trainFts, weights, message
def extractFeatures(ftb, ds, newOnly, chNames, imageId = None, im = None):
message = ''
tc = ftb.tc
# dataset must be explicitly provided because an image can be linked to
# multiple datasets in which case im.getDataset() doesn't work
if not im:
if not imageId:
#raise Exception('No input image')
raise omero.ServerError('No input image')
im = ftb.conn.getObject('Image', imageId)
if not im:
return 'Image id:%d not found\n' % imageId
else:
imageId = im.getId()
tid = WndcharmStorage.getAttachedTableFile(ftb.tc, ds)
if tid:
if not ftb.openTable(tid):
return message + '\nERROR: Table not opened\n'
version = unwrap(ftb.versiontag.getTextValue())
# version seems to be in unicode
message += 'Opened table id:%d version:%s\n' % (tid, str(version))
if newOnly and ftb.tableContainsId(imageId):
return message + 'Image id:%d features already in table' % imageId
# FIXME: default is convert multichannel to greyscale unless user input
# Calculate features for an image channel
# Optionally prepend the channel label to each feature name and combine
ftall = None
for c in xrange( len( chNames ) ):
wndcharm_matrix = PyImageMatrix()
wndcharm_matrix.allocate( im.getSizeX(), im.getSizeY() )
numpy_matrix = wndcharm_matrix.as_ndarray()
numpy_matrix[:] = im.getPrimaryPixels().getPlane(theZ=0,theC=c,theT=0)
feature_plan = wndcharm.StdFeatureComputationPlans.getFeatureSet();
options = "" # This is where you can tell wnd-charm to normalize pixel intensities,
# take ROIs etc. ... leave blank for now.
ft = Signatures.NewFromFeatureComputationPlan( wndcharm_matrix, feature_plan, options )
ft.names = [WndcharmStorage.insert_channel_name(
n, chNames[c]) for n in ft.names]
ft.source_path = im.getName()
if not ftall:
ftall = ft
else:
ftall.names += ft.names
ftall.values += ft.values
# Save the features to a table
if not tid:
ftb.createTable(ftall.names, ft.version)
version = unwrap(ftb.versiontag.getTextValue())
message += 'Created new table id:%d version:%s\n' % (
ftb.tc.tableId, version)
message += WndcharmStorage.addFileAnnotationTo(tc, ds)
if version != ft.version:
return message + 'Incompatible version: Stored=%s Calculated=%s' % (
version, ft.version)
ftb.saveFeatures(imageId, ftall)
return message + 'Extracted features from Image id:%d\n' % imageId
