'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))
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('')
hfp = IPL(
yaml=yamlfile,
yamlspec={'path': 'datafolder', 'filename': 'largeobjfile', 'skeys': 'largeobjname'},
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:
ipl.data_from_file(params['intermedfolder'] + params['locmaxfile'],
skeys=params['locmaxnames'][0],
recursive_search=True,
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:
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{}',
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()
else:
if params['overwriteresults']:
ipl.logging(
'remove_small_objects: Warning: Intermedfolder already exists and content will be overwritten...\n'
)
else:
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('')
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))
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:
