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')
return result
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('')
from hdf5_image_processing import Hdf5ImageProcessingLib as IPL
import os
import numpy as np
__author__ = 'jhennies'
if __name__ == '__main__':
yamlfile = os.path.dirname(os.path.abspath(__file__)) + '/parameters.yml'
ipl = IPL(yaml=yamlfile)
ipl.logging('Parameters: {}', ipl.get_params())
params = ipl.get_params()
ipl.data_from_file(filepath=params['datafolder'] +
'cremi.splA.raw_neurons.crop.h5',
skeys='raw',
tkeys='raw')
ipl.crop_bounding_rect(np.s_[10:110, 200:712, 200:712], keys='raw')
ipl.write(filepath=params['datafolder'] +
'cremi.splA.raw_neurons.crop.crop_10-200-200_110-712-712.h5')
if __name__ == '__main__':
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()
ipl[kl].setlogger(ipl.getlogger())
ipl[kl] = accumulate_small_objects(ipl[kl], k, thisparams)
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:
