def load_images(filepath, skeys=None, recursive_search=False, logger=None):
if logger is not None:
logger.logging('Loading data from \n{}', filepath)
else:
print 'Loading data from \n{}'.format(filepath)
data = IPL()
data.data_from_file(filepath=filepath,
skeys=skeys,
recursive_search=recursive_search,
nodata=True)
return data
def load_images(ipl):
"""
These images are loaded:
paths_true (paths within single label objects)
paths_false (paths of merged objects which cross the merging site)
featureims_true
featureims_false
:param ipl:
:return:
"""
paths_true = IPL()
paths_false = IPL()
featureims_true = IPL()
featureims_false = IPL()
params = ipl.get_params()
ipl.logging('Loading true paths ...')
# Paths within labels (true paths)
paths_true.data_from_file(
filepath=params['intermedfolder'] + params['pathstruefile'],
skeys='path',
recursive_search=True, nodata=True
)
ipl.logging('Loading false paths ...')
# Paths of merges (false paths)
paths_false.data_from_file(
filepath=params['intermedfolder'] + params['pathsfalsefile'],
skeys='path',
recursive_search=True, nodata=True
)
ipl.logging('Loading features for true paths ...')
# Load features for true paths
featureims_true.data_from_file(
filepath=params['intermedfolder'] + params['featureimsfile'],
nodata=True
)
featureims_true.delete_items(params['largeobjmnames'][0])
ipl.logging('Loading features for false paths ...')
# Load features for false paths
featureims_false.data_from_file(
filepath=params['intermedfolder'] + params['featureimsfile'],
nodata=True
)
featureims_false.delete_items(params['largeobjname'])
return (paths_true, paths_false, featureims_true, featureims_false)
def load_images(ipl):
"""
These images are loaded:
paths_true (paths within single label objects)
paths_false (paths of merged objects which cross the merging site)
featureims_true
featureims_false
:param ipl:
:return:
"""
paths_true = IPL()
paths_false = IPL()
featureims_true = IPL()
featureims_false = IPL()
params = ipl.get_params()
ipl.logging('Loading true paths ...')
# Paths within labels (true paths)
paths_true.data_from_file(filepath=params['intermedfolder'] +
params['pathstruefile'],
skeys='path',
recursive_search=True,
nodata=True)
ipl.logging('Loading false paths ...')
# Paths of merges (false paths)
paths_false.data_from_file(filepath=params['intermedfolder'] +
params['pathsfalsefile'],
skeys='path',
recursive_search=True,
nodata=True)
ipl.logging('Loading features for true paths ...')
# Load features for true paths
featureims_true.data_from_file(filepath=params['intermedfolder'] +
params['featureimsfile'],
nodata=True)
featureims_true.delete_items(params['largeobjmnames'][0])
ipl.logging('Loading features for false paths ...')
# Load features for false paths
featureims_false.data_from_file(filepath=params['intermedfolder'] +
params['featureimsfile'],
nodata=True)
featureims_false.delete_items(params['largeobjname'])
return (paths_true, paths_false, featureims_true, featureims_false)
if __name__ == '__main__':
yamlfile = os.path.dirname(os.path.abspath(__file__)) + '/parameters.yml'
hfp = IPL(
yaml=yamlfile,
yamlspec={'path': 'intermedfolder', 'filename': 'pathstruefile'},
castkey=None
)
# hfp.logging('datastructure:\n---\n{}', hfp.datastructure2string())
params = hfp.get_params()
hfp['true', 'border'] = IPL(data=hfp['largeobj', 'border_locmax', 'path'])
hfp['true', 'locmax'] = IPL(data=hfp['largeobj', 'locmax', 'path'])
hfp.pop('largeobj')
hfp.data_from_file(filepath=params['intermedfolder'] + params['pathsfalsefile'])
hfp['false', 'border'] = IPL(data=hfp['largeobjm', 'border_locmax_m', 'path'])
hfp['false', 'locmax'] = IPL(data=hfp['largeobjm', 'locmaxm', 'path'])
hfp.pop('largeobjm')
hfp.pop('pathsim')
hfp.pop('overlay')
# hfp.data_from_file(
# filepath=params['intermedfolder'] + params['pathsfalsefile'],
# tkeys='false',
# castkey=None
# )
hfp.data_from_file(
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')
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', 'skeys': 'largeobjname'},
recursive_search=True
)
params = ipl.get_params()
thisparams = params['paths_within_labels']
ipl.startlogger(filename=params['resultfolder'] + 'paths_within_labels.log', type='w')
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()
if __name__ == '__main__':
yamlfile = os.path.dirname(os.path.abspath(__file__)) + '/parameters.yml'
hfp = IPL(yaml=yamlfile,
yamlspec={
'path': 'intermedfolder',
'filename': 'pathstruefile'
},
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,
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')
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',
'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()
yamlfile = resultsfolder + '/parameters.yml'
ipl = IPL(
yaml=yamlfile,
yamlspec={'path': 'intermedfolder', 'filename': 'pathstruefile'},
skeys='path', recursive_search=True
)
# ipl.logging('datastructure:\n---\n{}', ipl.datastructure2string())
params = ipl.get_params()
ipl.rename_layer('largeobj', 'true')
# ipl['true', 'border'] = IPL(data=ipl['largeobj', 'border_locmax', 'path'])
# 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])
return bordercontacts
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))
yamlfile = os.path.dirname(os.path.abspath(__file__)) + '/parameters.yml'
hfp = IPL(yaml=yamlfile,
yamlspec={
'path': 'intermedfolder',
'filename': 'pathstruefile'
},
castkey=None)
# hfp.logging('datastructure:\n---\n{}', hfp.datastructure2string())
params = hfp.get_params()
hfp['true', 'border'] = IPL(data=hfp['largeobj', 'border_locmax', 'path'])
hfp['true', 'locmax'] = IPL(data=hfp['largeobj', 'locmax', 'path'])
hfp.pop('largeobj')
hfp.data_from_file(filepath=params['intermedfolder'] +
params['pathsfalsefile'])
hfp['false', 'border'] = IPL(data=hfp['largeobjm', 'border_locmax_m',
'path'])
hfp['false', 'locmax'] = IPL(data=hfp['largeobjm', 'locmaxm', 'path'])
hfp.pop('largeobjm')
hfp.pop('pathsim')
hfp.pop('overlay')
# # TODO: Insert code here
# hfp = Hdf5ImageProcessingLib(
# yaml=yamlfile,
# yamlspec={'path': 'intermedfolder', 'filename': 'pathstruefile'},
# tkeys='true',
# castkey=None
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': 'locmaxborderfile', 'skeys': {'locmaxbordernames': (1, 3)}},
recursive_search=True
)
params = ipl.get_params()
thisparams = params['paths_of_partners']
ipl.startlogger(filename=params['resultfolder'] + 'paths_of_partners.log', type='w')
ipl.data_from_file(params['intermedfolder'] + params['largeobjfile'],
skeys=params['largeobjname'],
recursive_search=True, integrate=True)
ipl.data_from_file(params['intermedfolder'] + params['largeobjmfile'],
skeys=(params['largeobjmnames'][0], params['largeobjmnames'][4]),
recursive_search=True, integrate=True)
ipl.data_from_file(params['intermedfolder'] + params['locmaxfile'],
skeys=params['locmaxnames'][1],
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_of_partners.parameters.yml')
# Write script and parameters to the logfile
ipl.code2log(inspect.stack()[0][1])
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
hfp.code2log(inspect.stack()[0][1])
hfp.logging('')
hfp.yaml2log()
if __name__ == "__main__":
yamlfile = os.path.dirname(os.path.abspath(__file__)) + "/parameters.yml"
yamlfile = os.path.dirname(os.path.abspath(__file__)) + "/parameters.yml"
hfp = IPL(yaml=yamlfile, yamlspec={"path": "intermedfolder", "filename": "pathstruefile"}, castkey=None)
# hfp.logging('datastructure:\n---\n{}', hfp.datastructure2string())
params = hfp.get_params()
hfp["true", "border"] = IPL(data=hfp["largeobj", "border_locmax", "path"])
hfp["true", "locmax"] = IPL(data=hfp["largeobj", "locmax", "path"])
hfp.pop("largeobj")
hfp.data_from_file(filepath=params["intermedfolder"] + params["pathsfalsefile"])
hfp["false", "border"] = IPL(data=hfp["largeobjm", "border_locmax_m", "path"])
hfp["false", "locmax"] = IPL(data=hfp["largeobjm", "locmaxm", "path"])
hfp.pop("largeobjm")
hfp.pop("pathsim")
hfp.pop("overlay")
# # TODO: Insert code here
# hfp = Hdf5ImageProcessingLib(
# yaml=yamlfile,
# yamlspec={'path': 'intermedfolder', 'filename': 'pathstruefile'},
# tkeys='true',
# castkey=None
# )
ipl = IPL(yaml=yamlfile,
yamlspec={
'path': 'intermedfolder',
'filename': 'largeobjfile',
'skeys': 'largeobjname'
},
recursive_search=True)
params = ipl.get_params()
thisparams = params['paths_within_labels']
ipl.startlogger(filename=params['resultfolder'] +
'paths_within_labels.log',
type='w')
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])
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')
# Write script and parameters to the logfile
hfp.code2log(inspect.stack()[0][1])
