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())
ifp.logging('{}', ifp.amax())
hfp = Hdf5Processing()
c = 0
for lblo in ifp.label_bounds_iterator('largeobj',
'curlabel',
ids=('locmax', 'disttransf'),
targetids=('curlocmax',
'curdisttransf'),
maskvalue=0,
value=0,
background=0):
ifp.logging('------------\nCurrent label {} in iteration {}',
lblo['label'], c)
ifp.logging('Bounding box = {}', lblo['bounds'])
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
ifp.getlabel(obj, ids=('disttransf',))
if ifp.amax(ids=('disttransf',)) == 0:
print 'Skiping object ' + str(obj) + ': Not found.'
# sys.exit()
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.logging('ifp.get_data().keys() = {}', ifp.get_data().keys())
ifp.logging('ifp.shape() = {}', ifp.shape())
ifp.logging('ifp.amax() = {}', ifp.amax())
hfp = Hdf5Processing()
c = 0
# These are the labels which were merged with a respective partner
# labellist = ifp.get_image('mergeids_all')[:, 0]
labellist = ifp.get_image('mergeids_all')
# labellist = [9988]
ifp.logging('labellist = {}', labellist)
# for lblo in ifp.label_bounds_iterator('largeobjm', 'curlabel',
# ids=('locmax', 'disttransf', 'largeobj'),
# targetids=('curlocmax', 'curdisttransf', 'curlargeobj'),
# maskvalue=0, value=0, background=0, labellist=labellist,
# forcecontinue=True):
for lblp in ifp.labelpair_bounds_iterator('largeobj',
ifp.logging('Starting label iterator for {} labels', len(ifp.anytask_rtrn(np.unique, ids='labels')))
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)
ifp.logging('Local maxima found: {}', local_maxima_found)
if local_maxima_found:
if ifp.amax('locmax') != 0:
find_shortest_path(ifp)
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!')
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.logging('ifp.get_data().keys() = {}', ifp.get_data().keys())
ifp.logging('ifp.shape() = {}', ifp.shape())
ifp.logging('ifp.amax() = {}', ifp.amax())
hfp = Hdf5Processing()
c = 0
# These are the labels which were merged with a respective partner
# labellist = ifp.get_image('mergeids_all')[:, 0]
labellist = ifp.get_image('mergeids_all')
# labellist = [9988]
ifp.logging('labellist = {}', labellist)
# for lblo in ifp.label_bounds_iterator('largeobjm', 'curlabel',
# ids=('locmax', 'disttransf', 'largeobj'),
# targetids=('curlocmax', 'curdisttransf', 'curlargeobj'),
# maskvalue=0, value=0, background=0, labellist=labellist,
# forcecontinue=True):
for lblp in ifp.labelpair_bounds_iterator('largeobj', 'curlabelpair',
# 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
ifp.getlabel(obj, ids=('disttransf', ))
if ifp.amax(ids=('disttransf', )) == 0:
print 'Skiping object ' + str(obj) + ': Not found.'
# sys.exit()
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)
ifp.logging('Local maxima found: {}', local_maxima_found)
if local_maxima_found:
if ifp.amax('locmax') != 0:
find_shortest_path(ifp)
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:
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())
ifp.logging('{}', ifp.amax())
hfp = Hdf5Processing()
c = 0
for lblo in ifp.label_bounds_iterator('largeobj', 'curlabel', ids=('locmax', 'disttransf'), targetids=('curlocmax', 'curdisttransf'),
maskvalue=0, value=0, background=0):
ifp.logging('------------\nCurrent label {} in iteration {}', lblo['label'], c)
ifp.logging('Bounding box = {}', lblo['bounds'])
if ifp.amax('curlocmax') == 1:
ps = find_shortest_path(ifp, thisparams['penaltypower'], lblo['bounds'])
ifp.logging('Number of paths found: {}', len(ps))
if ps:
hfp.setdata({lblo['label']: ps}, append=True)
ifp.write(filename='paths_over_dist_true_{}.h5'.format(lblo['label']), ids='paths_over_dist')