def isbp_walk_for_bps(I, bpi):
bpi = set(bpi)
# Use raveled image
Ir = np.ravel(I)
# Get edge pixels
edgeidcs = iu.edge_coords(I.shape, dtype='flat')
# Get emanators from first bp
do_first = set() # These are the 4-connected emanators
emanators = set()
for bp in bpi:
emanators = emanators | set(iu.neighbors_flat(bp, Ir, I.shape[1])[0])
do_first.update(iu.four_conn([bp], I)[0])
# Create set containing pixels that have already been visited
walked = bpi | emanators
while emanators:
if do_first:
idx = do_first.pop()
emanators.remove(idx)
else:
idx = emanators.pop()
walking = 1
while walking:
walked.add(idx)
neighs = set(iu.neighbors_flat(idx, Ir, I.shape[1])[0])
neighs = neighs - walked
if len(neighs) == 0:
walking = 0
elif len(neighs) == 1:
idx = neighs.pop()
if idx in edgeidcs:
walking = 0
else:
bpi.add(idx)
fourconn = iu.four_conn([idx], I)[0]
fourconn = [f for f in fourconn if f in neighs]
do_first.update(fourconn)
emanators = emanators | neighs
walked.add(idx)
walked.update(neighs)
return bpi
def get_neighbors(idx, Iskel):
"""
Get a flattened array of neighboring pixel indices.
Returns a flattened array of the neighboring pixel indices within Iskel
that are True. Only looks at 8-connected neighbors (i.e. a 3x3 kernel with
centered on idx).
Parameters
----------
idx : np.int
Index within Iskel to get neighbors.
Iskel : np.ndarray
Image of the skeletonized mask, but can be any image array.
Returns
-------
neighbor_idcs_gloal : list
Indices within Iskel of True pixels bordering idx.
"""
size = (3, 3)
cent_idx = 4 # OR int((size[0]*size[1] - 1) / 2)
# Pull square with idx at center
I, row_offset, col_offset = iu.get_array(idx, Iskel, size)
I_flat = np.ravel(I)
# Find its neighbors (next possible steps)
neighbor_idcs, _ = iu.neighbors_flat(cent_idx, I_flat, size[1])
neighbor_idcs_gloal = iu.reglobalize_flat_idx(neighbor_idcs, size,
row_offset, col_offset,
Iskel.shape)
return neighbor_idcs_gloal
def test_neighbors_flatmid(self):
"""Test 3."""
I = np.zeros((5, 5))
I[1, 1] = 1.0
Iflat = np.ravel(I)
idx = 10
ncols = 5
filt = 'none'
idcs, vals = im_utils.neighbors_flat(idx, Iflat, ncols, filt=filt)
# make assertion
assert np.all(idcs == [5, 6, 11, 15, 16])
assert np.all(vals == [0., 1., 0., 0., 0.])
def test_neighbors_flatsmall(self):
"""Test 2."""
I = np.zeros((5, 5))
I[1, 2] = 1.0
Iflat = np.ravel(I)
idx = 3
ncols = 5
filt = 'none'
idcs, vals = im_utils.neighbors_flat(idx, Iflat, ncols, filt=filt)
# make assertion
assert np.all(idcs == [2, 4, 7, 8, 9])
assert np.all(vals == [0., 0., 1., 0., 0.])
def test_neighbors_bottomwrow(self):
"""Test 9."""
I = np.zeros((20, 10))
I[-1, :] = 2.0
Iflat = np.ravel(I)
ncols = 10
idx = 190
filt = 'none'
idcs, vals = im_utils.neighbors_flat(idx, Iflat, ncols, filt=filt)
# make assertion
assert np.all(idcs == [180, 181, 191])
assert np.all(vals == [0., 0., 2.])
def test_neighbors_flatbottom(self):
"""Test 8."""
I = np.zeros((5, 5))
I[3, 4] = 1.0
Iflat = np.ravel(I)
idx = 24
ncols = 5
filt = 'none'
idcs, vals = im_utils.neighbors_flat(idx, Iflat, ncols, filt=filt)
# make assertion
assert np.all(idcs == [18, 19, 23])
assert np.all(vals == [0., 1., 0.])
def test_neighbors_flatlast(self):
"""Test 6."""
I = np.zeros((5, 5))
I[4, 3] = 3.0
Iflat = np.ravel(I)
idx = 22
ncols = 5
filt = 'none'
idcs, vals = im_utils.neighbors_flat(idx, Iflat, ncols, filt=filt)
# make assertion
assert np.all(idcs == [16, 17, 18, 21, 23])
assert np.all(vals == [0., 0., 0., 0., 3.])
def test_neighbors_midcol(self):
"""Test 4."""
I = np.zeros((5, 5))
I[4, 3] = 1.0
Iflat = np.ravel(I)
idx = 19
ncols = 5
filt = 'none'
idcs, vals = im_utils.neighbors_flat(idx, Iflat, ncols, filt=filt)
# make assertion
assert np.all(idcs == [13, 14, 18, 23, 24])
assert np.all(vals == [0., 0., 0., 1., 0.])
def test_neighbors_flat(self):
"""Test 1."""
I = np.zeros((5, 5))
I[1, 1] = 1.0
I[2, 2] = 3.0
Iflat = np.ravel(I)
idx = 11
ncols = 5
filt = 'none'
idcs, vals = im_utils.neighbors_flat(idx, Iflat, ncols, filt=filt)
# make assertion
assert np.all(idcs == [5, 6, 7, 10, 12, 15, 16, 17])
assert np.all(vals == [0., 1., 0., 0., 3., 0., 0., 0.])
def test_neighbors_flattop(self):
"""Test 7."""
I = np.zeros((5, 5))
I[0, 3] = 1.0
I[1, 4] = 3.0
Iflat = np.ravel(I)
idx = 4
ncols = 5
filt = 'none'
idcs, vals = im_utils.neighbors_flat(idx, Iflat, ncols, filt=filt)
# make assertion
assert np.all(idcs == [3, 8, 9])
assert np.all(vals == [1., 0., 3.])
def test_neighbors_flatfirst(self):
"""Test 5."""
I = np.zeros((5, 5))
I[1, 1] = 1.0
I[2, 2] = 3.0
Iflat = np.ravel(I)
idx = 0
ncols = 5
filt = 'none'
idcs, vals = im_utils.neighbors_flat(idx, Iflat, ncols, filt=filt)
# make assertion
assert np.all(idcs == [1, 5, 6])
assert np.all(vals == [0., 0., 1.])
def get_neighbors(idx, Iskel):
size = (3, 3)
cent_idx = 4 # OR int((size[0]*size[1] - 1) / 2)
# Pull square with idx at center
I, row_offset, col_offset = iu.get_array(idx, Iskel, size)
I_flat = np.ravel(I)
# Find its neighbors (next possible steps)
neighbor_idcs, _ = iu.neighbors_flat(cent_idx, I_flat, size[1])
neighbor_idcs_gloal = iu.reglobalize_flat_idx(neighbor_idcs, size,
row_offset, col_offset,
Iskel.shape)
return neighbor_idcs_gloal
def isbp_walk_for_bps(I, bpi):
"""
Find branchpoints by walking from a pixel.
Finds branchpoints by ensuring that all pixels in the sub-skeleton can be
walked to from the set of already-found branchpoints, without visiting
the same pixel more than once.
Parameters
----------
I : np.ndarray
Binary image of a skeleton. In RivGraph, the skeleton is a reduced and
padded version of Iskel.
bpi : list
Indices within I of the branchpoint to begin walk.
Returns
-------
bpi : list
Branchpoint indices in I.
"""
bpi = set(bpi)
# Use raveled image
Ir = np.ravel(I)
# Get edge pixels
edgeidcs = iu.edge_coords(I.shape, dtype='flat')
# Get emanators from first bp
do_first = set() # These are the 4-connected emanators
emanators = set()
for bp in bpi:
emanators = emanators | set(iu.neighbors_flat(bp, Ir, I.shape[1])[0])
do_first.update(iu.four_conn([bp], I)[0])
# Create set containing pixels that have already been visited
walked = bpi | emanators
while emanators:
if do_first:
idx = do_first.pop()
emanators.remove(idx)
else:
idx = emanators.pop()
walking = 1
while walking:
walked.add(idx)
neighs = set(iu.neighbors_flat(idx, Ir, I.shape[1])[0])
neighs = neighs - walked
if len(neighs) == 0:
walking = 0
elif len(neighs) == 1:
idx = neighs.pop()
if idx in edgeidcs:
walking = 0
else:
bpi.add(idx)
fourconn = iu.four_conn([idx], I)[0]
fourconn = [f for f in fourconn if f in neighs]
do_first.update(fourconn)
emanators = emanators | neighs
walked.add(idx)
walked.update(neighs)
return bpi
