def run_find_border_contacts(yamlfile, logging=True):
ipl = IPL(yaml=yamlfile)
ipl.set_indent(1)
params = rdict(data=ipl.get_params())
if logging:
ipl.startlogger(filename=params['resultfolder'] + 'find_border_contacts.log', type='w', name='FindBorderContacts')
else:
ipl.startlogger()
try:
# # Copy the script file and the parameters to the scriptsfolder
# copy(inspect.stack()[0][1], params['scriptsfolder'])
# copy(yamlfile, params['scriptsfolder'] + 'find_border_contacts.parameters.yml')
# ipl.logging('\nInitial datastructure: \n\n{}', ipl.datastructure2string(maxdepth=3))
find_border_contacts(ipl)
# ipl.logging('\nFinal datastructure: \n\n{}', ipl.datastructure2string(maxdepth=3))
# ipl.write(filepath=params['intermedfolder'] + params['largeobjfile'])
ipl.logging('')
ipl.stoplogger()
except:
ipl.errout('Unexpected error')
def run_remove_small_objects(yamlfile):
ipl = IPL(
yaml=yamlfile,
yamlspec={'path': 'datafolder', 'filename': 'labelsfile', 'skeys': 'labelsname'},
recursive_search=True,
nodata=True
)
# Set indentation of the logging
ipl.set_indent(1)
params = ipl.get_params()
ipl.startlogger(filename=params['resultfolder'] + 'remove_small_objects.log', type='w', name='RemoveSmallObjects')
try:
# # Copy the script file and the parameters to the scriptsfolder
# copy(inspect.stack()[0][1], params['scriptsfolder'])
# copy(yamlfile, params['scriptsfolder'] + 'remove_small_objects.parameters.yml')
ipl.logging('\nipl datastructure: \n\n{}', ipl.datastructure2string(maxdepth=3))
remove_small_objects(ipl)
ipl.logging('\nFinal datastructure: \n\n{}', ipl.datastructure2string(maxdepth=3))
# ipl.write(filepath=params['intermedfolder'] + params['largeobjfile'])
ipl.logging('')
ipl.stoplogger()
except:
ipl.errout('Unexpected error')
def run_compute_feature_images(yamlfile):
ipl = IPL(yaml=yamlfile)
ipl.set_indent(1)
params = rdict(data=ipl.get_params())
ipl.startlogger(filename=params['resultfolder'] + 'compute_feature_images.log', type='w', name='ComputeFeatureImages')
try:
# # Copy the script file and the parameters to the scriptsfolder
# copy(inspect.stack()[0][1], params['scriptsfolder'])
# copy(yamlfile, params['scriptsfolder'] + 'compute_feature_images.parameters.yml')
# ipl.logging('\nInitial datastructure: \n\n{}', ipl.datastructure2string(maxdepth=3))
compute_feature_images(ipl)
# ipl.logging('\nFinal datastructure: \n\n{}', ipl.datastructure2string(maxdepth=3))
# ipl.write(filepath=params['intermedfolder'] + params['largeobjfile'])
ipl.logging('')
ipl.stoplogger()
except:
ipl.errout('Unexpected error')
def run_paths_of_merges(yamlfile, logging=True):
ipl = IPL(yaml=yamlfile)
ipl.set_indent(1)
params = rdict(data=ipl.get_params())
if logging:
ipl.startlogger(filename=params['resultfolder'] +
'paths_of_merges.log',
type='w',
name='PathsOfMerges')
else:
ipl.startlogger()
try:
# # Copy the script file and the parameters to the scriptsfolder
# copy(inspect.stack()[0][1], params['scriptsfolder'])
# copy(yamlfile, params['scriptsfolder'] + 'paths_of_merges.parameters.yml')
# ipl.logging('\nInitial datastructure: \n\n{}', ipl.datastructure2string(maxdepth=3))
paths_of_merges(ipl, params['debug'])
# ipl.logging('\nFinal datastructure: \n\n{}', ipl.datastructure2string(maxdepth=3))
# ipl.write(filepath=params['intermedfolder'] + params['largeobjfile'])
ipl.logging('')
ipl.stoplogger()
except:
ipl.errout('Unexpected error')
def run_random_forest(yamlfile,
logging=True,
make_only_feature_array=False,
debug=False,
write=True):
ipl = IPL(yaml=yamlfile)
ipl.set_indent(1)
params = rdict(data=ipl.get_params())
if logging:
ipl.startlogger(filename=params['resultfolder'] + 'random_forest.log',
type='w',
name='RandomForest')
else:
ipl.startlogger()
try:
# # Copy the script file and the parameters to the scriptsfolder
# copy(inspect.stack()[0][1], params['scriptsfolder'])
# copy(yamlfile, params['scriptsfolder'] + 'random_forest.parameters.yml')
# ipl.logging('\nInitial datastructure: \n\n{}', ipl.datastructure2string(maxdepth=3))
if make_only_feature_array:
make_feature_arrays(ipl)
else:
result = IPL()
result['result'], result['evaluation'] = random_forest(ipl,
debug=debug)
# ipl.logging('\nFinal datastructure: \n\n{}', ipl.datastructure2string(maxdepth=3))
if write:
result.write(filepath=params['resultfolder'] +
params['resultsfile'])
ipl.logging('')
ipl.stoplogger()
except:
ipl.errout('Unexpected error')
def run_remove_small_objects(yamlfile):
ipl = IPL(yaml=yamlfile,
yamlspec={
'path': 'datafolder',
'filename': 'labelsfile',
'skeys': 'labelsname'
},
recursive_search=True,
nodata=True)
# Set indentation of the logging
ipl.set_indent(1)
params = ipl.get_params()
ipl.startlogger(filename=params['resultfolder'] +
'remove_small_objects.log',
type='w',
name='RemoveSmallObjects')
try:
# # Copy the script file and the parameters to the scriptsfolder
# copy(inspect.stack()[0][1], params['scriptsfolder'])
# copy(yamlfile, params['scriptsfolder'] + 'remove_small_objects.parameters.yml')
ipl.logging('\nipl datastructure: \n\n{}',
ipl.datastructure2string(maxdepth=3))
remove_small_objects(ipl)
ipl.logging('\nFinal datastructure: \n\n{}',
ipl.datastructure2string(maxdepth=3))
# ipl.write(filepath=params['intermedfolder'] + params['largeobjfile'])
ipl.logging('')
ipl.stoplogger()
except:
ipl.errout('Unexpected error')
def run_random_forest(yamlfile, logging=True, make_only_feature_array=False, debug=False, write=True):
ipl = IPL(yaml=yamlfile)
ipl.set_indent(1)
params = rdict(data=ipl.get_params())
if logging:
ipl.startlogger(filename=params['resultfolder'] + 'random_forest.log', type='w', name='RandomForest')
else:
ipl.startlogger()
try:
# # Copy the script file and the parameters to the scriptsfolder
# copy(inspect.stack()[0][1], params['scriptsfolder'])
# copy(yamlfile, params['scriptsfolder'] + 'random_forest.parameters.yml')
# ipl.logging('\nInitial datastructure: \n\n{}', ipl.datastructure2string(maxdepth=3))
if make_only_feature_array:
make_feature_arrays(ipl)
else:
result = IPL()
result['result'], result['evaluation'] = random_forest(ipl, debug=debug)
# ipl.logging('\nFinal datastructure: \n\n{}', ipl.datastructure2string(maxdepth=3))
if write:
result.write(filepath=params['resultfolder'] + params['resultsfile'])
ipl.logging('')
ipl.stoplogger()
except:
ipl.errout('Unexpected error')
)
params = features.get_params()
thisparams = params['random_forest']
features.startlogger(filename=params['resultfolder'] + 'random_forest.log', type='w')
try:
# Copy the script file and the parameters to the scriptsfolder
copy(inspect.stack()[0][1], params['scriptsfolder'])
copy(yamlfile, params['scriptsfolder'] + 'random_forest.parameters.yml')
# Write script and parameters to the logfile
features.code2log(inspect.stack()[0][1])
features.logging('')
features.yaml2log()
features.logging('')
features.logging('\nfeatures datastructure: \n---\n{}', features.datastructure2string(maxdepth=2))
result = random_forest_iteration(features)
result.write(filepath=params['intermedfolder'] + params['randforestfile'])
features.logging('\nFinal dictionary structure:\n---\n{}', features.datastructure2string(maxdepth=2))
features.logging('')
features.stoplogger()
except:
raise
features.errout('Unexpected error')
type='w')
try:
# Copy the script file and the parameters to the scriptsfolder
copy(inspect.stack()[0][1], params['scriptsfolder'])
copy(yamlfile,
params['scriptsfolder'] + 'random_forest.parameters.yml')
# Write script and parameters to the logfile
features.code2log(inspect.stack()[0][1])
features.logging('')
features.yaml2log()
features.logging('')
features.logging('\nfeatures datastructure: \n---\n{}',
features.datastructure2string(maxdepth=2))
result = random_forest_iteration(features)
result.write(filepath=params['intermedfolder'] +
params['randforestfile'])
features.logging('\nFinal dictionary structure:\n---\n{}',
features.datastructure2string(maxdepth=2))
features.logging('')
features.stoplogger()
except:
raise
features.errout('Unexpected error')
tkeys='largeobj',
castkey=None
)
params = hfp.get_params()
hfp.startlogger(filename=params['resultfolder'] + 'merge_adjacent_objects.log', type='w')
try:
# Copy the script file and the parameters to the scriptsfolder
copy(inspect.stack()[0][1], params['scriptsfolder'])
copy(yamlfile, params['scriptsfolder'] + 'merge_adjacent_objects.parameters.yml')
# Write script and parameters to the logfile
hfp.code2log(inspect.stack()[0][1])
hfp.logging('')
hfp.yaml2log()
hfp.logging('')
hfp.logging('\nhfp datastructure: \n\n{}', hfp.datastructure2string(maxdepth=1))
merge_adjacent_objects(hfp)
hfp.write(filepath=params['intermedfolder'] + params['largeobjmfile'])
hfp.logging('\nFinal dictionary structure:\n---\n{}', hfp.datastructure2string())
hfp.logging('')
hfp.stoplogger()
except:
hfp.errout('Unexpected error')
integrate=True)
ipl.data_from_file(params['intermedfolder'] + params['locmaxborderfile'],
skeys=(params['locmaxbordernames'][0], params['locmaxbordernames'][2]),
recursive_search=True,
integrate=True)
try:
# Copy the script file and the parameters to the scriptsfolder
copy(inspect.stack()[0][1], params['scriptsfolder'])
copy(yamlfile, params['scriptsfolder'] + 'paths_within_labels.parameters.yml')
# Write script and parameters to the logfile
ipl.code2log(inspect.stack()[0][1])
ipl.logging('')
ipl.yaml2log()
ipl.logging('')
ipl.logging('\nipl datastructure: \n\n{}', ipl.datastructure2string(maxdepth=3))
paths = paths_within_labels_image_iteration(ipl)
paths.write(filepath=params['intermedfolder'] + params['pathstruefile'])
ipl.logging('\nFinal dictionary structure:\n---\n{}', ipl.datastructure2string())
ipl.logging('')
ipl.stoplogger()
except:
ipl.errout('Unexpected error')
'localmax_on_disttransf.log',
type='w')
try:
# Copy the script file and the parameters to the scriptsfolder
copy(inspect.stack()[0][1], params['scriptsfolder'])
copy(yamlfile,
params['scriptsfolder'] + 'localmax_on_disttransf.parameters.yml')
# Write script and parameters to the logfile
hfp.code2log(inspect.stack()[0][1])
hfp.logging('')
hfp.yaml2log()
hfp.logging('')
hfp.logging('\nhfp datastructure: \n\n{}',
hfp.datastructure2string(maxdepth=1))
localmax_on_disttransf(hfp, ('disttransf', 'disttransfm'))
hfp.write(filepath=params['intermedfolder'] + params['locmaxfile'])
hfp.logging('\nFinal dictionary structure:\n---\n{}',
hfp.datastructure2string())
hfp.logging('')
hfp.stoplogger()
except:
hfp.errout('Unexpected error')
raise IOError(
'remove_small_objects: Error: Intermedfolder already exists!'
)
# Copy the script file and the parameters to the scriptsfolder
copy(inspect.stack()[0][1], params['scriptsfolder'])
copy(yamlfile,
params['scriptsfolder'] + 'remove_small_objects.parameters.yml')
# Write script and parameters to the logfile
ipl.code2log(inspect.stack()[0][1])
ipl.logging('')
ipl.yaml2log()
ipl.logging('')
ipl.logging('\nipl datastructure: \n\n{}',
ipl.datastructure2string(maxdepth=3))
remove_small_objects(ipl)
ipl.logging('\nFinal datastructure: \n\n{}',
ipl.datastructure2string(maxdepth=3))
ipl.write(filepath=params['intermedfolder'] + params['largeobjfile'])
ipl.logging('')
ipl.stoplogger()
except:
ipl.errout('Unexpected error')
# hfp.pop('result_true')
hfp.pop('true')
hfp.pop('false')
hfp.logging('hfp datastructure:\n---\n{}---',
hfp.datastructure2string(maxdepth=2))
print hfp['result.6720_13067.8']
y = []
for k, v in hfp.iteritems():
if v:
try:
y.append(v)
x = range(0, len(v))
plt.plot(x, v)
# plt.show()
lab.savefig(params['intermedfolder'] + 'plots/' + k +
'.png')
plt.clf()
except ValueError:
pass
except:
hfp.errout('Unexpected error', traceback)
hfp.stoplogger()
# Done: This is total bullshit! I need to iterate over all paths and extract the region features individually!
# Store all feature images in here
disttransf_images = IPL(
yaml=yamlfile,
yamlspec={'path': 'intermedfolder', 'filename': 'locmaxborderfile', 'skeys': {'locmaxbordernames': (2, 3)}},
recursive_search=True
)
feature_images = IPL(
yaml=yamlfile,
yamlspec={'path': 'datafolder', 'filename': 'rawdatafile', 'skeys': 'rawdataname'},
recursive_search=True
)
ipl.logging('\nDisttransf images datastructure: \n---\n{}', disttransf_images.datastructure2string(maxdepth=4))
ipl.logging('\nFeature images datastructure: \n---\n{}', feature_images.datastructure2string(maxdepth=4))
feature_images.astype(np.float32)
# features = IPL()
features = features_of_paths_image_iteration(ipl, disttransf_images, feature_images)
features.write(filepath=params['intermedfolder'] + params['featurefile'])
ipl.logging('\nFinal datastructure:\n---\n{}', features.datastructure2string())
ipl.logging('')
ipl.stoplogger()
except ValueError:
ipl.errout('Unexpected error', traceback)
# hfp.pop('result_true')
hfp.pop('true')
hfp.pop('false')
hfp.pop('raw')
hfp.logging('hfp datastructure:\n---\n{}---', hfp.datastructure2string(maxdepth=2))
# print hfp['result.6720_13067.8']
y = []
for k, v in hfp.iteritems():
if v.values()[0]:
try:
# y.append(v)
x = range(0, len(v.values()[0]))
y = np.swapaxes(np.array(v.values()), 0, 1)
plt.plot(x, y)
# plt.show()
lab.savefig(params['intermedfolder'] + 'plots/' + k + '.png')
plt.clf()
except ValueError:
pass
except:
hfp.errout('Unexpected error', traceback)
hfp.stoplogger()
# # feature_images.data_from_file(params[''])
#
# hfp.logging('\ndisttransf_images datastructure: \n\n{}', disttransf_images.datastructure2string(maxdepth=1))
#
# # This is for the path images
# paths = IPL()
# # # Add the feature images to the paths dictionary
# # paths.set_data_dict(feature_images.get_data(), append=True)
#
# # Create the path images for feature accumulator
# make_path_images(paths, hfp, disttransf_images['disttransf'].shape)
# # paths.write(filepath='/media/julian/Daten/neuraldata/cremi_2016/test.h5')
#
# # This is for the features
# features = IPL()
#
# # Get features along the paths
# make_features_paths(paths, disttransf_images, feature_images, features)
#
# hfp.logging('\nCalculated features: \n-------------------\n{}-------------------\n', features.datastructure2string())
# hfp.logging('Possible features: \n{}', features['paths_false', 'disttransfm'].supportedFeatures())
features.write(filepath=params["intermedfolder"] + params["featurefile"])
hfp.logging("")
hfp.stoplogger()
except ValueError:
hfp.errout("Unexpected error", traceback)