def parse_classifier_file(self, workspace):
d = self.get_dictionary(workspace.image_set_list)
if all([d.has_key(k) for k in (CLASSIFIERS_KEY, FEATURE_ITEMS_KEY)]):
return
# Load classifier from hdf5
fileName = str(os.path.join(self.h5_directory.get_absolute_path(),
self.classifier_file_name.value))
hf = h5py.File(fileName,'r')
temp = hf['classifiers'].keys()
# If hf is not closed this leads to an error in win64 and mac os x
hf.close()
del hf
classifiers = []
for cid in temp:
cidpath = 'classifiers/' + cid
try:
classifiers.append(ClassifierRandomForest.deserialize(fileName, cidpath))
except:
classifiers.append(ClassifierRandomForest.loadRFfromFile(fileName, cidpath))
d[CLASSIFIERS_KEY] = classifiers
# Restore user selection of feature items from hdf5
featureItems = []
f = h5py.File(fileName,'r')
for fgrp in f['features'].values():
featureItems.append(FeatureBase.deserialize(fgrp))
f.close()
del f
d[FEATURE_ITEMS_KEY] = featureItems
def load_features(self, filename=None):
# if filename is None:
# filename = self.classifier_name
featureItems = []
hf = h5py.File(filename, "r")
for fgrp in hf["features"].values():
featureItems.append(FeatureBase.deserialize(fgrp))
hf.close()
del hf
self.features = featureItems
def load_features(self, filename=None):
#if filename is None:
# filename = self.classifier_name
featureItems = []
hf = h5py.File(filename, 'r')
for fgrp in hf['features'].values():
featureItems.append(FeatureBase.deserialize(fgrp))
hf.close()
del hf
self.features = featureItems
def parse_classifier_file(self, workspace):
global classifier_dict
# Load classifier from hdf5
fileName = str(
os.path.join(self.h5_directory.get_absolute_path(),
self.classifier_file_name.value))
modtime = os.stat(fileName).st_mtime
if fileName in classifier_dict:
last_modtime, d = classifier_dict[fileName]
if modtime == last_modtime:
return d
d = {}
hf = h5py.File(fileName, 'r')
temp = hf['classifiers'].keys()
# If hf is not closed this leads to an error in win64 and mac os x
hf.close()
del hf
classifiers = []
for cid in temp:
cidpath = 'classifiers/' + cid
try:
classifiers.append(
ClassifierRandomForest.deserialize(fileName, cidpath))
except:
classifiers.append(
ClassifierRandomForest.loadRFfromFile(fileName, cidpath))
d[CLASSIFIERS_KEY] = classifiers
# Restore user selection of feature items from hdf5
featureItems = []
f = h5py.File(fileName, 'r')
for fgrp in f['features'].values():
featureItems.append(FeatureBase.deserialize(fgrp))
f.close()
del f
d[FEATURE_ITEMS_KEY] = featureItems
classifier_dict[fileName] = (modtime, d)
return d
def parse_classifier_hdf5(self, filename):
'''Parse the classifiers out of the HDF5 file
filename - name of classifier file
returns a dictionary
CLASSIFIERS_KEY - the random forest classifiers
FEATURE_ITEMS_KEY - the features needed by the classifier
'''
d = {}
if not isinstance(filename, str):
filename = filename.encode('utf-8')
hf = h5py.File(filename,'r')
temp = hf['classifiers'].keys()
# If hf is not closed this leads to an error in win64 and mac os x
hf.close()
del hf
classifiers = []
for cid in temp:
if isinstance(cid, unicode):
cid = cid.encode('utf-8')
cidpath = 'classifiers/' + cid
try:
classifiers.append(
ClassifierRandomForest.deserialize(filename, cidpath))
except:
classifiers.append(
ClassifierRandomForest.loadRFfromFile(filename, cidpath))
d[CLASSIFIERS_KEY] = classifiers
# Restore user selection of feature items from hdf5
featureItems = []
f = h5py.File(filename,'r')
for fgrp in f['features'].values():
featureItems.append(FeatureBase.deserialize(fgrp))
d[FEATURE_ITEMS_KEY] = featureItems
f.close()
del f
return d
def parse_classifier_hdf5(self, filename):
'''Parse the classifiers out of the HDF5 file
filename - name of classifier file
returns a dictionary
CLASSIFIERS_KEY - the random forest classifiers
FEATURE_ITEMS_KEY - the features needed by the classifier
'''
d = {}
if not isinstance(filename, str):
filename = filename.encode('utf-8')
hf = h5py.File(filename, 'r')
temp = hf['classifiers'].keys()
# If hf is not closed this leads to an error in win64 and mac os x
hf.close()
del hf
classifiers = []
for cid in temp:
if isinstance(cid, unicode):
cid = cid.encode('utf-8')
cidpath = 'classifiers/' + cid
try:
classifiers.append(
ClassifierRandomForest.deserialize(filename, cidpath))
except:
classifiers.append(
ClassifierRandomForest.loadRFfromFile(filename, cidpath))
d[CLASSIFIERS_KEY] = classifiers
# Restore user selection of feature items from hdf5
featureItems = []
f = h5py.File(filename, 'r')
for fgrp in f['features'].values():
featureItems.append(FeatureBase.deserialize(fgrp))
d[FEATURE_ITEMS_KEY] = featureItems
f.close()
del f
return d
def parse_classifier_file(self, workspace):
global classifier_dict
# Load classifier from hdf5
fileName = str(os.path.join(self.h5_directory.get_absolute_path(),
self.classifier_file_name.value))
modtime = os.stat(fileName).st_mtime
if fileName in classifier_dict:
last_modtime, d = classifier_dict[fileName]
if modtime == last_modtime:
return d
d = {}
hf = h5py.File(fileName,'r')
temp = hf['classifiers'].keys()
# If hf is not closed this leads to an error in win64 and mac os x
hf.close()
del hf
classifiers = []
for cid in temp:
cidpath = 'classifiers/' + cid
try:
classifiers.append(ClassifierRandomForest.deserialize(fileName, cidpath))
except:
classifiers.append(ClassifierRandomForest.loadRFfromFile(fileName, cidpath))
d[CLASSIFIERS_KEY] = classifiers
# Restore user selection of feature items from hdf5
featureItems = []
f = h5py.File(fileName,'r')
for fgrp in f['features'].values():
featureItems.append(FeatureBase.deserialize(fgrp))
f.close()
del f
d[FEATURE_ITEMS_KEY] = featureItems
classifier_dict[fileName] = (modtime, d)
return d
def parse_classifier_file(self, workspace):
d = self.get_dictionary(workspace.image_set_list)
if all([d.has_key(k) for k in (CLASSIFIERS_KEY, FEATURE_ITEMS_KEY)]):
return
# Load classifier from hdf5
fileName = str(
os.path.join(self.h5_directory.get_absolute_path(),
self.classifier_file_name.value))
hf = h5py.File(fileName, 'r')
temp = hf['classifiers'].keys()
# If hf is not closed this leads to an error in win64 and mac os x
hf.close()
del hf
classifiers = []
for cid in temp:
cidpath = 'classifiers/' + cid
try:
classifiers.append(
ClassifierRandomForest.deserialize(fileName, cidpath))
except:
classifiers.append(
ClassifierRandomForest.loadRFfromFile(fileName, cidpath))
d[CLASSIFIERS_KEY] = classifiers
# Restore user selection of feature items from hdf5
featureItems = []
f = h5py.File(fileName, 'r')
for fgrp in f['features'].values():
featureItems.append(FeatureBase.deserialize(fgrp))
f.close()
del f
d[FEATURE_ITEMS_KEY] = featureItems
def _predict_image_with_ilastik(self, image_):
import ilastik
from ilastik.core.dataMgr import DataMgr, DataItemImage
from ilastik.modules.classification.core.featureMgr import FeatureMgr
from ilastik.modules.classification.core.classificationMgr import ClassificationMgr
from ilastik.modules.classification.core.features.featureBase import FeatureBase
from ilastik.modules.classification.core.classifiers.classifierRandomForest import ClassifierRandomForest
from ilastik.modules.classification.core.classificationMgr import ClassifierPredictThread
from ilastik.core.volume import DataAccessor
import numpy, h5py
dataMgr = DataMgr()
# Transform input image to ilastik convention s
# 3D = (time,x,y,z,channel)
# 2D = (time,1,x,y,channel)
# Note, this work for 2D images right now. Is there a need for 3D
image_.shape = (1,1) + image_.shape
# Check if image_ has channels, if not add singelton dimension
if len(image_.shape) == 4:
image_.shape = image_.shape + (1,)
# Add data item di to dataMgr
di = DataItemImage('')
di.setDataVol(DataAccessor(image_))
dataMgr.append(di, alreadyLoaded=True)
fileName = self.params["ilastik_classifier"]
ilastik_class = self.params["ilastik_class_selector"]
hf = h5py.File(fileName,'r')
temp = hf['classifiers'].keys()
# If hf is not closed this leads to an error in win64 and mac os x
hf.close()
del hf
classifiers = []
for cid in temp:
cidpath = 'classifiers/' + cid
classifiers.append(ClassifierRandomForest.loadRFfromFile(fileName, str(cidpath)))
dataMgr.module["Classification"]["classificationMgr"].classifiers = classifiers
# Restore user selection of feature items from hdf5
featureItems = []
f = h5py.File(fileName,'r')
for fgrp in f['features'].values():
featureItems.append(FeatureBase.deserialize(fgrp))
f.close()
del f
fm = FeatureMgr(dataMgr, featureItems)
# Create FeatureMgr
# Compute features
fm.prepareCompute(dataMgr)
fm.triggerCompute()
fm.joinCompute(dataMgr)
# Predict with loaded classifier
classificationPredict = ClassifierPredictThread(dataMgr)
classificationPredict.start()
classificationPredict.wait()
if ilastik_class >= classificationPredict._prediction[0].shape[-1]:
raise RuntimeError('ilastik output class not valid...')
# Produce output image and select the probability map
probMap = (classificationPredict._prediction[0][0,0,:,:, ilastik_class] * 255).astype(numpy.uint8)
img_out = ccore.numpy_to_image(probMap, True)
return img_out
