# 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())
# Boundary distance transform
# a) Boundaries
ifp.logging('Finding boundaries ...')
ifp.pixels_at_boundary(
axes=(np.array(thisparams['anisotropy']).astype(np.float32)**-1
).astype(np.uint8))
ifp.astype(np.float32)
# b) Distance transform
ifp.logging('Computing distance transform on boundaries ...')
ifp.distance_transform(pixel_pitch=thisparams['anisotropy'],
background=True)
locmaxnames = params['locmaxnames']
ifp.rename_entries(ids=('largeobj', 'largeobjm'),
targetids=(locmaxnames[0], locmaxnames[1]))
# Gaussian smoothing
ifp.logging('Dragging Carl Friedrich over the image ...')
ifp.gaussian_smoothing(thisparams['sigma'] /
np.array(thisparams['anisotropy']),
ids=(locmaxnames[0], locmaxnames[1]),
targetids=(locmaxnames[2], locmaxnames[3]))
# 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())
# Boundary distance transform
# a) Boundaries
ifp.logging('Finding boundaries ...')
ifp.pixels_at_boundary(
axes=(np.array(thisparams['anisotropy']).astype(np.float32) ** -1).astype(np.uint8)
)
ifp.astype(np.float32)
# b) Distance transform
ifp.logging('Computing distance transform on boundaries ...')
ifp.distance_transform(
pixel_pitch=thisparams['anisotropy'],
background=True
)
locmaxnames = params['locmaxnames']
ifp.rename_entries(ids = ('largeobj', 'largeobjm'), targetids = (locmaxnames[0], locmaxnames[1]))
# Gaussian smoothing
ifp.logging('Dragging Carl Friedrich over the image ...')
ifp.gaussian_smoothing(thisparams['sigma'] / np.array(thisparams['anisotropy']),
ids=(locmaxnames[0], locmaxnames[1]), targetids=(locmaxnames[2], locmaxnames[3]))
ifp.logging('labels = {}', labels)
# 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 = []
