def processImages(client, scriptParams):
message = ''
# for params with default values, we can get the value directly
dataType = scriptParams['Data_Type']
ids = scriptParams['IDs']
contextName = scriptParams['Context_Name']
tableName = '/Wndcharm/' + contextName + '/SmallFeatureSet.h5'
message += 'tableName:' + tableName + '\n'
ftb = WndcharmStorage.FeatureTable(client, tableName)
try:
# Get the datasets
objects, logMessage = script_utils.getObjects(ftb.conn, scriptParams)
message += logMessage
if not objects:
return message
datasets = WndcharmStorage.datasetGenerator(ftb.conn, dataType, ids)
for ds in datasets:
message += 'Processing dataset id:%d\n' % ds.getId()
msg = countCompleted(ftb, ds)
message += msg
except:
print message
raise
finally:
ftb.close()
return message
def predict(client, scriptParams):
message = ""
# for params with default values, we can get the value directly
projectId = scriptParams["Training_Project_ID"]
dataType = scriptParams["Data_Type"]
predictIds = scriptParams["IDs"]
commentImages = scriptParams["Comment_Images"]
tagImages = scriptParams["Tag_Images"]
contextName = scriptParams["Context_Name"]
tableNameIn = "/Wndcharm/" + contextName + WndcharmStorage.SMALLFEATURES_TABLE
tableNameF = "/Wndcharm/" + contextName + WndcharmStorage.CLASS_FEATURES_TABLE
tableNameW = "/Wndcharm/" + contextName + WndcharmStorage.CLASS_WEIGHTS_TABLE
tableNameL = "/Wndcharm/" + contextName + WndcharmStorage.CLASS_LABELS_TABLE
message += "tableNameIn:" + tableNameIn + "\n"
message += "tableNameF:" + tableNameF + "\n"
message += "tableNameW:" + tableNameW + "\n"
message += "tableNameL:" + tableNameL + "\n"
ftb = WndcharmStorage.FeatureTable(client, tableNameIn)
ctb = WndcharmStorage.ClassifierTables(client, tableNameF, tableNameW, tableNameL)
try:
message += "Loading classifier\n"
trainProject = ftb.conn.getObject("Project", projectId)
trainFts, weights = loadClassifier(ctb, trainProject)
classifierName = WndcharmStorage.CLASSIFIER_WNDCHARM_NAMESPACE
tagSet = WndcharmStorage.getClassifierTagSet(classifierName, trainProject.getName(), trainProject)
# Predict
message += "Predicting\n"
predDatasets = WndcharmStorage.datasetGenerator(ftb.conn, dataType, predictIds)
for ds in predDatasets:
message += "Predicting dataset id:%d\n" % ds.getId()
pred, msg = predictDataset(ftb, trainFts, ds, weights)
message += msg
message += addPredictionsToImages(ftb.conn, pred, ds.getId(), commentImages, tagSet)
except:
print message
raise
finally:
ftb.close()
ctb.close()
return message
def processImages(client, scriptParams):
message = ''
# for params with default values, we can get the value directly
dataType = scriptParams['Data_Type']
ids = scriptParams['IDs']
contextName = scriptParams['Context_Name']
newOnly = scriptParams['New_Images_Only']
tableName = '/Wndcharm/' + contextName + '/SmallFeatureSet.h5'
message += 'tableName:' + tableName + '\n'
ftb = WndcharmStorage.FeatureTable(client, tableName)
try:
nimages = 0
# Get the datasets
objects, logMessage = script_utils.getObjects(ftb.conn, scriptParams)
message += logMessage
if not objects:
return message
datasets = list(WndcharmStorage.datasetGenerator(
ftb.conn, dataType, ids))
good, chNames, msg = checkChannels(datasets)
message += msg
if not good:
raise omero.ServerError(
'Channel check failed, ' +
'all images must have the same channels: %s' % message)
for d in datasets:
message += 'Processing dataset id:%d\n' % d.getId()
for image in d.listChildren():
message += 'Processing image id:%d\n' % image.getId()
msg = extractFeatures(ftb, d, newOnly, chNames, im=image)
message += msg + '\n'
except:
print message
raise
finally:
ftb.close()
return message
