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')
if __name__ == '__main__':
resultsfolder = '/mnt/localdata02/jhennies/neuraldata/results/cremi_2016/161110_random_forest_of_paths/'
yamlfile = resultsfolder + '/parameters.yml'
features = IPL(yaml=yamlfile,
yamlspec={
'path': 'intermedfolder',
'filename': 'featurefile'
})
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))
yamlfile = os.path.dirname(os.path.abspath(__file__)) + '/parameters.yml'
hfp = IPL(yaml=yamlfile,
yamlspec={
'path': 'intermedfolder',
'filename': 'locmaxborderfile',
'skeys': {
'locmaxbordernames': (2, 3)
}
},
tkeys=('disttransf', 'disttransfm'),
castkey=None)
params = hfp.get_params()
thisparams = params['localmax_on_disttransf']
hfp.startlogger(filename=params['resultfolder'] +
'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{}',
if __name__ == '__main__':
resultsfolder = '/mnt/localdata02/jhennies/neuraldata/results/cremi_2016/161110_random_forest_of_paths/'
yamlfile = resultsfolder + '/parameters.yml'
ipl = IPL(yaml=yamlfile,
yamlspec={
'path': 'datafolder',
'filename': 'labelsfile'
})
params = ipl.get_params()
ipl.startlogger(filename=params['resultfolder'] +
'remove_small_objects.log',
type='a')
try:
# Create folder for scripts
if not os.path.exists(params['scriptsfolder']):
os.makedirs(params['scriptsfolder'])
else:
if params['overwriteresults']:
ipl.logging(
'remove_small_objects: Warning: Scriptsfolder already exists and content will be overwritten...\n'
)
else:
raise IOError(
'remove_small_objects: Error: Scriptsfolder already exists!'
tkeys='true',
castkey=None)
params = hfp.get_params()
hfp.data_from_file(filepath=params['intermedfolder'] +
params['pathsfalsefile'],
tkeys='false',
castkey=None)
hfp.data_from_file(filepath=params['intermedfolder'] +
params['locmaxfile'],
skeys=('disttransf', 'disttransfm'),
tkeys=('disttransf', 'disttransfm'))
hfp.startlogger()
try:
hfp.logging('hfp datastructure:\n---\n{}---',
hfp.datastructure2string(maxdepth=2))
hfp.anytask(lib.getvaluesfromcoords,
reciprocal=True,
keys='disttransfm',
indict=hfp['false', '6155_9552'],
tkeys='result_false')
hfp.logging('hfp datastructure:\n---\n{}---',
hfp.datastructure2string(maxdepth=2))
if __name__ == '__main__':
resultsfolder = '/mnt/localdata02/jhennies/neuraldata/results/cremi_2016/161110_random_forest_of_paths/'
yamlfile = resultsfolder + '/parameters.yml'
ipl = IPL(yaml=yamlfile,
yamlspec={
'path': 'intermedfolder',
'filename': 'largeobjfile'
})
params = ipl.get_params()
ipl.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
ipl.code2log(inspect.stack()[0][1])
ipl.logging('')
ipl.yaml2log()
ipl.logging('')
ipl.logging('\nipl datastructure: \n\n{}',
# ipl['true', 'locmax'] = IPL(data=ipl['largeobj', 'locmax', 'path'])
# ipl.pop('largeobj')
ipl.data_from_file(filepath=params['intermedfolder'] + params['pathsfalsefile'],
skeys='path', recursive_search=True, integrate=True)
ipl.rename_layer('largeobjm', 'false')
ipl.remove_layer('path')
# ipl['false', 'border'] = IPL(data=ipl['largeobjm', 'border_locmax_m', 'path'])
# ipl['false', 'locmax'] = IPL(data=ipl['largeobjm', 'locmaxm', 'path'])
# ipl.pop('largeobjm')
#
# ipl.pop('pathsim')
# ipl.pop('overlay')
ipl.startlogger(filename=params['resultfolder']+'features_of_paths.log', type='w')
try:
ipl.code2log(inspect.stack()[0][1])
ipl.logging('')
ipl.yaml2log()
ipl.logging('')
ipl.logging('\nipl datastructure: \n\n{}', ipl.datastructure2string(maxdepth=4))
# Done: Make path image (true)
# Done: Make path image (false)
# TODO: Get topological features
# TODO: Topological feature: Length (with respect to anisotropy!)
# TODO: Topological feature: Statistics on curvature
ipl = IPL(yaml=yamlfile,
yamlspec={
'path': 'intermedfolder',
'filename': 'largeobjfile',
'skeys': 'largeobjname'
},
recursive_search=True)
params = ipl.get_params()
thisparams = params['find_border_contacts']
ipl.data_from_file(params['intermedfolder'] + params['largeobjmfile'],
skeys=params['largeobjmnames'][0],
recursive_search=True,
integrate=True)
ipl.startlogger(filename=params['resultfolder'] +
'find_border_contacts.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'] + 'find_border_contacts.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{}',
if __name__ == '__main__':
yamlfile = os.path.dirname(os.path.abspath(__file__)) + '/parameters.yml'
hfp = IPL(
yaml=yamlfile,
yamlspec={'path': 'intermedfolder', 'filename': 'largeobjfile', 'skeys': 'largeobjname'},
tkeys='largeobj',
castkey=None
)
params = hfp.get_params()
thisparams = params['find_border_contacts']
hfp.data_from_file(params['intermedfolder'] + params['largeobjmfile'],
skeys=params['largeobjmnames'][0], tkeys='largeobjm')
hfp.startlogger(filename=params['resultfolder'] + 'find_orphans.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'] + 'find_orphans.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))
if thisparams['return_bordercontact_images']:
yamlfile = os.path.dirname(os.path.abspath(__file__)) + '/parameters.yml'
hfp = IPL(yaml=yamlfile,
yamlspec={
'path': 'intermedfolder',
'filename': 'locmaxborderfile',
'skeys': {
'locmaxbordernames': (0, 2)
}
},
tkeys=('border_locmax', 'disttransf'),
castkey=None)
params = hfp.get_params()
thisparams = params['paths_within_labels']
hfp.startlogger(filename=params['resultfolder'] +
'paths_within_labels.log',
type='w')
hfp.data_from_file(params['intermedfolder'] + params['largeobjfile'],
skeys=params['largeobjname'],
tkeys='largeobj')
hfp.data_from_file(params['intermedfolder'] + params['locmaxfile'],
skeys=params['locmaxnames'][0],
tkeys='locmax')
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
# pass
if __name__ == '__main__':
yamlfile = os.path.dirname(os.path.abspath(__file__)) + '/parameters.yml'
hfp = IPL(
yaml=yamlfile,
yamlspec={'path': 'intermedfolder', 'filename': 'locmaxborderfile', 'skeys': {'locmaxbordernames': (1, 3)}},
tkeys=('border_locmax_m', 'disttransfm'),
castkey=None
)
params = hfp.get_params()
thisparams = params['paths_of_partners']
hfp.startlogger(filename=params['resultfolder'] + 'paths_of_partners.log', type='w')
hfp.data_from_file(params['intermedfolder'] + params['largeobjfile'],
skeys=params['largeobjname'],
tkeys='largeobj')
hfp.data_from_file(params['intermedfolder'] + params['largeobjmfile'],
skeys=(params['largeobjmnames'][0], params['largeobjmnames'][4]),
tkeys=('largeobjm', 'change_hash'))
hfp.data_from_file(params['intermedfolder'] + params['locmaxfile'],
skeys=params['locmaxnames'][0],
tkeys='locmaxm')
try:
# Copy the script file and the parameters to the scriptsfolder
copy(inspect.stack()[0][1], params['scriptsfolder'])
copy(yamlfile, params['scriptsfolder'] + 'paths_of_partners.parameters.yml')
# # TODO: Insert code here
# hfp = Hdf5ImageProcessingLib(
# yaml=yamlfile,
# yamlspec={'path': 'intermedfolder', 'filename': 'pathstruefile'},
# tkeys='true',
# castkey=None
# )
# params = hfp.get_params()
# hfp.logging('params = {}', params)
# hfp.data_from_file(
# filepath=params['intermedfolder'] + params['pathsfalsefile'],
# tkeys='false',
# castkey=None
# )
hfp.startlogger(filename=params['intermedfolder'] +
'features_of_paths.log',
type='a')
try:
hfp.code2log(inspect.stack()[0][1])
hfp.logging('')
hfp.yaml2log()
hfp.logging('')
hfp.logging('\nhfp datastructure: \n\n{}',
hfp.datastructure2string(maxdepth=1))
# Done: Iterate over paths and accumulate features
# Done: Implement data iterator
