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())
# For multiple labels
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:
# Seed the randomize function
random.seed(1)
# # Choose one label
# lbl = random.choice(labels)
# ifp.logging('Choice: {}', lbl)
#
# ifp.astype(np.uint32, ids='labels')
# (grag, rag) = ifp.anytask_rtrn(graphs.gridRegionAdjacencyGraph, ignoreLabel=0, ids='labels')
# ifp.logging('RAG: {}', rag)
#
# ifp.logging('Node ids: {}', rag.nodeIds())
ifp.astype(np.uint32, ids='labels')
(grag, rag) = ifp.anytask_rtrn(graphs.gridRegionAdjacencyGraph, ignoreLabel=0, ids='labels')
edge_ids = rag.edgeIds()
ifp.logging('Edge ids: {}', edge_ids)
# Type 1:
# Select edges by size (smallest edges)
ifp.logging('Number of edgeLengths = {}', len(rag.edgeLengths()))
edgelen_ids = dict(zip(edge_ids, rag.edgeLengths()))
ifp.logging('edgelen_ids = {}', edgelen_ids)
sorted_edgelens = np.sort(rag.edgeLengths())
#
smallest_merge_lens = sorted_edgelens[0:numberbysize]
ifp.logging('Lengths selected for merging: {}', smallest_merge_lens)
#
smallest_merge_ids = []
for x in smallest_merge_lens:
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())
# For multiple labels
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)
