append=True)
return paths
if __name__ == '__main__':
try:
yamlfile = os.path.dirname(
os.path.abspath(__file__)) + '/parameters.yml'
ifp = ImageFileProcessing(yaml=yamlfile,
yamlspec={
'image_path': 'intermedfolder',
'image_file': 'locmaxfile',
'image_names': ('locmaxnames', 1, 3)
},
asdict=True,
keys=('disttransf', 'locmax'))
params = ifp.get_params()
thisparams = params['paths_of_partners']
ifp.addfromfile(params['intermedfolder'] + params['largeobjmfile'],
image_names=params['largeobjmnames'],
ids=[
'largeobjm', 'mergeids_small', 'mergeids_random',
'mergeids_all'
])
ifp.addfromfile(params['intermedfolder'] + params['largeobjfile'],
image_names=params['largeobjname'],
ids='largeobj')
return d - it
elif type == 'it/d':
return it / d
if __name__ == "__main__":
# sys.stdout = open('/media/julian/Daten/neuraldata/isbi_2013/mc_crop_cache/log.txt', "a")
# Parameters
tapering_tolerance_rel = 0.5
tapering_tolerance_abs = 5
# object = 191
ifp = ImageFileProcessing(
"/media/julian/Daten/neuraldata/isbi_2013/mc_crop_cache/",
"multicut_segmentation.h5", asdict=True, keys=('labels',))
# for obj in [191]:
for obj in xrange(1, ifp.amax(ids=('labels',))['labels'] + 1):
# sys.stdout = open('/media/julian/Daten/neuraldata/isbi_2013/mc_crop_cache/log.txt', "a")
print '_____________________________________________________________________'
print 'Object label: ' + str(obj)
ifp.deepcopy_entry('labels', 'disttransf')
if True:
# Start with one object only
if __name__ == "__main__":
folder = '/media/julian/Daten/neuraldata/cremi_2016/'
file = 'cremi.splA.raw_neurons.crop.h5'
names = ('neuron_ids', )
keys = ('labels', )
tolerance_rel = 0.5
tolerance_abs = 5
pixel_pitch = (1, 1, 10)
ifp = ImageFileProcessing(folder,
file,
asdict=True,
image_names=names,
keys=keys)
ifp.startlogger('{}tapering.log'.format(folder), type='w')
ifp.logging("######################################################")
ifp.logging('# Starting tapering_violation_detection with: #')
ifp.logging("# tolerance_rel = {0: <31} #".format(tolerance_rel))
ifp.logging("# tolerance_abs = {0: <31} #".format(tolerance_abs))
ifp.logging("# pixel_pitch = {0: <34}#".format(pixel_pitch))
ifp.logging("######################################################\n")
tapering_violation_detection(ifp,
tolerance_rel=tolerance_rel,
tolerance_abs=tolerance_abs,
def find_large_objects():
anisotropy = np.array([10, 1, 1])
folder = '/media/julian/Daten/neuraldata/cremi_2016/'
file = 'cremi.splA.raw_neurons.crop.crop_10-200-200_110-712-712.disttransf.h5'
names = ('labels', 'disttransf')
keys = ('labels', 'disttransf')
ifp = ImageFileProcessing(
folder,
file, asdict=True,
image_names=names,
keys=keys)
ifp.startlogger(filename=None, type='a')
ifp.addtodict([], 'largeobjects')
c = 0
for lblo in ifp.label_bounds_iterator('labels', 'curlabel', ids='disttransf', targetids='curdisttransf',
maskvalue=0, value=0):
ifp.logging('------------\nCurrent label {} in iteration {}', lblo['label'], c)
ifp.logging('Bounding box = {}', lblo['bounds'])
local_maxima_found = find_local_maxima(ifp, anisotropy)
ifp.logging('Local maxima found: {}', local_maxima_found)
if local_maxima_found:
ifp.get_image('largeobjects').append(lblo['label'])
# if ifp.amax('locmax') != 0:
#
# ifp.write(filename='{0}input_{1}.h5'.format(resultfolder, lblo['label']), ids=('curdisttransf', 'curlabel', 'smoothed'))
# ifp.write(filename='{0}paths_over_dist_{1}.h5'.format(resultfolder, lblo['label']), ids=('paths_over_dist',))
#
# else:
#
# ifp.logging('No maxima found!')
else:
ifp.logging('Maxima detection not successful!')
c += 1
# if c == 10:
# break
ifp.write(filename='largeobjects.h5', ids=('largeobjects',))
ifp.logging('')
ifp.stoplogger()
def remove_small_objects():
folder = '/media/julian/Daten/neuraldata/cremi_2016/'
file = 'cremi.splA.raw_neurons.crop.crop_10-200-200_110-712-712.disttransf.h5'
names = ('labels',)
keys = ('labels',)
ifp = ImageFileProcessing(
folder,
file, asdict=True,
image_names=names,
keys=keys)
ifp.startlogger(filename=None)
# Find all relevant labels
ifp.addfromfile('{}largeobjects.h5'.format(folder), image_ids=(0,))
for lbl in ifp.label_image_iterator(
'labels', 'largeobjects', accumulate=True, labellist=ifp.get_image('largeobjects')):
ifp.logging('Label {} done!', lbl)
ifp.getlabel(0, ids='largeobjects', targetids='background')
ifp.write(filename='largeobjectimage.h5')
ifp.logging('')
ifp.stoplogger()
import random
import vigra
import numpy as np
import os
import inspect
__author__ = 'jhennies'
if __name__ == '__main__':
yamlfile = os.path.dirname(os.path.abspath(__file__)) + '/parameters.yml'
ifp = ImageFileProcessing(yaml=yamlfile,
yamlspec={
'image_path': 'intermedfolder',
'image_file': 'largeobjfile',
'image_names': 'largeobjname'
},
asdict=True,
keys=('largeobj', ))
params = ifp.get_params()
thisparams = params['localmax_on_disttransf']
ifp.addfromfile(params['intermedfolder'] + params['largeobjmfile'],
image_names=params['largeobjmnames'][0],
ids='largeobjm')
ifp.startlogger(filename=ifp.get_params()['intermedfolder'] +
'locmax_on_disttransf.log',
type='a')
# ifp.code2log(__file__)
ifp.code2log(inspect.stack()[0][1])
from image_processing import ImageFileProcessing
ifp = ImageFileProcessing(
image_path='/media/julian/Daten/neuraldata/cremi_2016/',
image_file='cremi.splA.raw_neurons.crop.crop_10-200-200_110-712-712.largeobjects.h5'
)
ifp.crop((16, 320, 180), (19, 350, 220), ids='largeobjects', targetids='crop')
ifp.filter_values(14994, type='eq', setto=0, ids='crop')
ifp.get_image('largeobjects')[16:19, 320:350, 180:220] = ifp.get_image('crop')
ifp.write(filename='largeobj_test.h5', ids=('background', 'labels', 'largeobjects'))
ifp.anytask(vigra.filters.discDilation, 2, ids='pathsim')
ifp.logging('ifp.shape = {}', ifp.shape(ids=('pathsim', 'curlabelpair', 'curdisttransf')))
ifp.set_data_dict({'paths_over_dist': np.array([ifp.get_image('pathsim'), ifp.get_image('curlabelpair'), ifp.get_image('curdisttransf')])}, append=True)
return paths
if __name__ == '__main__':
try:
yamlfile = os.path.dirname(os.path.abspath(__file__)) + '/parameters.yml'
ifp = ImageFileProcessing(
yaml=yamlfile,
yamlspec={'image_path': 'intermedfolder', 'image_file': 'locmaxfile', 'image_names': ('locmaxnames', 1, 3)},
asdict=True,
keys=('disttransf', 'locmax')
)
params = ifp.get_params()
thisparams = params['paths_of_partners']
ifp.addfromfile(params['intermedfolder']+params['largeobjmfile'], image_names=params['largeobjmnames'],
ids=['largeobjm', 'mergeids_small', 'mergeids_random', 'mergeids_all'])
ifp.addfromfile(params['intermedfolder']+params['largeobjfile'], image_names=params['largeobjname'], ids='largeobj')
ifp.startlogger(filename=params['intermedfolder'] + 'paths_of_partners.log', type='a')
# ifp.code2log(__file__)
ifp.code2log(inspect.stack()[0][1])
ifp.logging('')
ifp.logging('yamlfile = {}', yamlfile)
# ifp = ImageFileProcessing.empty()
crop = True
anisotropy = np.array([10, 1, 1])
folder = '/media/julian/Daten/neuraldata/cremi_2016/'
file = 'cremi.splA.raw_neurons.crop.crop_10-200-200_110-712-712.disttransf.h5'
names = ('labels','disttransf')
keys = ('labels','disttransf')
resultfolder = 'develop/160927_determine_shortest_paths/160929_pthsovrdist_pow10/'
if True:
ifp = ImageFileProcessing(
folder,
file, asdict=True,
image_names=names,
keys=keys)
ifp.startlogger(filename='/media/julian/Daten/neuraldata/cremi_2016/develop/160927_determine_shortest_paths/160929_pthsovrdist_pow10/160929.log', type='a')
# # Cropping
# if crop:
# ifp.crop([10, 200, 200], [110, 712, 712])
# ifp.write()
# ifp.logging('ifp.get_image = {}', ifp.get_image('labels')[0, 0, 0])
# ifp.logging('ifp.amax = {}\n', ifp.amax('labels'))
ifp.logging('keys() = {}', ifp.get_data().keys())
# ifp = ImageFileProcessing.empty()
crop = True
anisotropy = np.array([10, 1, 1])
folder = '/media/julian/Daten/neuraldata/cremi_2016/'
file = 'cremi.splA.raw_neurons.crop.crop_10-200-200_110-712-712.disttransf.h5'
names = ('labels', 'disttransf')
keys = ('labels', 'disttransf')
resultfolder = 'develop/160927_determine_shortest_paths/160929_pthsovrdist_pow10/'
if True:
ifp = ImageFileProcessing(folder,
file,
asdict=True,
image_names=names,
keys=keys)
ifp.startlogger(
filename=
'/media/julian/Daten/neuraldata/cremi_2016/develop/160927_determine_shortest_paths/160929_pthsovrdist_pow10/160929.log',
type='a')
# # Cropping
# if crop:
# ifp.crop([10, 200, 200], [110, 712, 712])
# ifp.write()
# ifp.logging('ifp.get_image = {}', ifp.get_image('labels')[0, 0, 0])
elif type == 'it/d':
return it / d
if __name__ == "__main__":
# sys.stdout = open('/media/julian/Daten/neuraldata/isbi_2013/mc_crop_cache/log.txt', "a")
# Parameters
tapering_tolerance_rel = 0.5
tapering_tolerance_abs = 5
# object = 191
ifp = ImageFileProcessing(
"/media/julian/Daten/neuraldata/isbi_2013/mc_crop_cache/",
"multicut_segmentation.h5",
asdict=True,
keys=('labels', ))
# for obj in [191]:
for obj in xrange(1, ifp.amax(ids=('labels', ))['labels'] + 1):
# sys.stdout = open('/media/julian/Daten/neuraldata/isbi_2013/mc_crop_cache/log.txt', "a")
print '_____________________________________________________________________'
print 'Object label: ' + str(obj)
ifp.deepcopy_entry('labels', 'disttransf')
if True:
#
# return image
if __name__ == '__main__':
crop = True
anisotropy = np.array([10, 1, 1])
folder = '/media/julian/Daten/neuraldata/cremi_2016/'
file = 'cremi.splA.raw_neurons.crop.h5'
names = ('neuron_ids', )
keys = ('labels', )
ifp = ImageFileProcessing(folder,
file,
asdict=True,
image_names=names,
keys=keys)
ifp.startlogger(
filename='/media/julian/Daten/neuraldata/cremi_2016/disttransf.log',
type='a')
ifp.logging('keys = {}', ifp.get_data().keys())
if crop:
ifp.crop([10, 200, 200], [110, 712, 712])
ifp_boundary_distance_transform(ifp,
'labels',
'disttransf',
def remove_small_objects():
folder = '/media/julian/Daten/neuraldata/cremi_2016/'
file = 'cremi.splA.raw_neurons.crop.crop_10-200-200_110-712-712.disttransf.h5'
names = ('labels', )
keys = ('labels', )
ifp = ImageFileProcessing(folder,
file,
asdict=True,
image_names=names,
keys=keys)
ifp.startlogger(filename=None)
# Find all relevant labels
ifp.addfromfile('{}largeobjects.h5'.format(folder), image_ids=(0, ))
for lbl in ifp.label_image_iterator(
'labels',
'largeobjects',
accumulate=True,
labellist=ifp.get_image('largeobjects')):
ifp.logging('Label {} done!', lbl)
ifp.getlabel(0, ids='largeobjects', targetids='background')
ifp.write(filename='largeobjectimage.h5')
ifp.logging('')
ifp.stoplogger()
def find_large_objects():
anisotropy = np.array([10, 1, 1])
folder = '/media/julian/Daten/neuraldata/cremi_2016/'
file = 'cremi.splA.raw_neurons.crop.crop_10-200-200_110-712-712.disttransf.h5'
names = ('labels', 'disttransf')
keys = ('labels', 'disttransf')
ifp = ImageFileProcessing(folder,
file,
asdict=True,
image_names=names,
keys=keys)
ifp.startlogger(filename=None, type='a')
ifp.addtodict([], 'largeobjects')
c = 0
for lblo in ifp.label_bounds_iterator('labels',
'curlabel',
ids='disttransf',
targetids='curdisttransf',
maskvalue=0,
value=0):
ifp.logging('------------\nCurrent label {} in iteration {}',
lblo['label'], c)
ifp.logging('Bounding box = {}', lblo['bounds'])
local_maxima_found = find_local_maxima(ifp, anisotropy)
ifp.logging('Local maxima found: {}', local_maxima_found)
if local_maxima_found:
ifp.get_image('largeobjects').append(lblo['label'])
# if ifp.amax('locmax') != 0:
#
# ifp.write(filename='{0}input_{1}.h5'.format(resultfolder, lblo['label']), ids=('curdisttransf', 'curlabel', 'smoothed'))
# ifp.write(filename='{0}paths_over_dist_{1}.h5'.format(resultfolder, lblo['label']), ids=('paths_over_dist',))
#
# else:
#
# ifp.logging('No maxima found!')
else:
ifp.logging('Maxima detection not successful!')
c += 1
# if c == 10:
# break
ifp.write(filename='largeobjects.h5', ids=('largeobjects', ))
ifp.logging('')
ifp.stoplogger()
# return image
if __name__ == '__main__':
crop = True
anisotropy = np.array([10, 1, 1])
folder = '/media/julian/Daten/neuraldata/cremi_2016/'
file = 'cremi.splA.raw_neurons.crop.h5'
names = ('neuron_ids',)
keys = ('labels',)
ifp = ImageFileProcessing(
folder,
file, asdict=True,
image_names=names,
keys=keys)
ifp.startlogger(filename='/media/julian/Daten/neuraldata/cremi_2016/disttransf.log', type='a')
ifp.logging('keys = {}', ifp.get_data().keys())
if crop:
ifp.crop([10, 200, 200], [110, 712, 712])
ifp_boundary_distance_transform(ifp, 'labels', 'disttransf', pixel_pitch=anisotropy)
ifp.logging('ifp.shape() = {}', ifp.shape())
if __name__ == "__main__":
folder = '/media/julian/Daten/neuraldata/cremi_2016/'
file = 'cremi.splA.raw_neurons.crop.h5'
names = ('neuron_ids',)
keys = ('labels',)
tolerance_rel = 0.5
tolerance_abs = 5
pixel_pitch = (1, 1, 10)
ifp = ImageFileProcessing(
folder,
file, asdict=True,
image_names=names,
keys=keys)
ifp.startlogger('{}tapering.log'.format(folder), type='w')
ifp.logging("######################################################")
ifp.logging('# Starting tapering_violation_detection with: #')
ifp.logging("# tolerance_rel = {0: <31} #".format(tolerance_rel))
ifp.logging("# tolerance_abs = {0: <31} #".format(tolerance_abs))
ifp.logging("# pixel_pitch = {0: <34}#".format(pixel_pitch))
ifp.logging("######################################################\n")
tapering_violation_detection(ifp,
tolerance_rel=tolerance_rel,
tolerance_abs=tolerance_abs,
ifp.get_image('curdisttransf')
])
},
append=True)
return paths
if __name__ == '__main__':
yamlfile = os.path.dirname(os.path.abspath(__file__)) + '/parameters.yml'
ifp = ImageFileProcessing(yaml=yamlfile,
yamlspec={
'image_path': 'intermedfolder',
'image_file': 'locmaxfile',
'image_names': ('locmaxnames', 0, 2)
},
asdict=True,
keys=('disttransf', 'locmax'))
params = ifp.get_params()
thisparams = params['paths_within_labels']
ifp.addfromfile(params['intermedfolder'] + params['largeobjfile'],
image_names=params['largeobjname'],
ids='largeobj')
ifp.startlogger(filename=params['intermedfolder'] +
'paths_within_labels.log',
type='a')
# ifp.code2log(__file__)
ifp.code2log(inspect.stack()[0][1])
__author__ = 'jhennies'
if __name__ == '__main__':
numberbysize = 10
numberbyrandom = 5
folder = '/media/julian/Daten/neuraldata/cremi_2016/'
file = 'cremi.splA.raw_neurons.crop.crop_10-200-200_110-712-712.largeobjects.h5'
names = ('largeobjects',)
keys = ('labels',)
targetfolder = 'cremi.splA.raw_neurons.crop.crop_10-200-200_110-712-712_merges/'
ifp = ImageFileProcessing(
folder,
file, asdict=True,
image_names=names,
keys=keys)
ifp.startlogger(filename=None, type='a')
# Done: Randomly select an edge
# Done: Find adjacent objects
# Done: Extract both adjacent objects as an image (needed for subsequent learning)
# TODO: Merge the objects
# Done: Select edges by size? Small edges are more relevant for our algorithm
# # Find all relevant labels
# ifp.addfromfile('{}largeobjects.h5'.format(folder), image_ids=(0,))
ifp.logging('keys = {}', ifp.get_data().keys())
import random
import vigra
import numpy as np
import os
import inspect
__author__ = 'jhennies'
if __name__ == '__main__':
yamlfile = os.path.dirname(os.path.abspath(__file__)) + '/parameters.yml'
ifp = ImageFileProcessing(
yaml=yamlfile,
yamlspec={'image_path': 'intermedfolder', 'image_file': 'largeobjfile', 'image_names': 'largeobjname'},
asdict=True,
keys=('largeobj',)
)
params = ifp.get_params()
thisparams = params['localmax_on_disttransf']
ifp.addfromfile(params['intermedfolder']+params['largeobjmfile'], image_names=params['largeobjmnames'][0], ids='largeobjm')
ifp.startlogger(filename=ifp.get_params()['intermedfolder'] + 'locmax_on_disttransf.log', type='a')
# ifp.code2log(__file__)
ifp.code2log(inspect.stack()[0][1])
ifp.logging('')
ifp.logging('yamlfile = {}', yamlfile)
ifp.logging('ifp.get_data().keys() = {}', ifp.get_data().keys())
ifp.logging('ifp.shape() = {}', ifp.shape())
ifp.astype(np.uint8, ('pathsim', 'curlocmax'))
# ifp.anytask(vigra.filters.multiBinaryDilation, ('paths', 'locmax'), 3)
ifp.swapaxes(0, 2, ids=('pathsim', 'curlocmax', 'curdisttransf'))
ifp.anytask(vigra.filters.discDilation, 2, ids=('pathsim', 'curlocmax'))
ifp.set_data_dict({'paths_over_dist': np.array([ifp.get_image('pathsim'), ifp.get_image('curlocmax'), ifp.get_image('curdisttransf')])}, append=True)
return paths
if __name__ == '__main__':
yamlfile = os.path.dirname(os.path.abspath(__file__)) + '/parameters.yml'
ifp = ImageFileProcessing(
yaml=yamlfile,
yamlspec={'image_path': 'intermedfolder', 'image_file': 'locmaxfile', 'image_names': ('locmaxnames', 0, 2)},
asdict=True,
keys=('disttransf', 'locmax')
)
params = ifp.get_params()
thisparams = params['paths_within_labels']
ifp.addfromfile(params['intermedfolder']+params['largeobjfile'], image_names=params['largeobjname'], ids='largeobj')
ifp.startlogger(filename=params['intermedfolder'] + 'paths_within_labels.log', type='a')
# ifp.code2log(__file__)
ifp.code2log(inspect.stack()[0][1])
ifp.logging('')
ifp.logging('yamlfile = {}', yamlfile)
ifp.logging('ifp.get_data().keys() = {}', ifp.get_data().keys())
ifp.logging('ifp.shape() = {}', ifp.shape())
