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 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
