def assign_gt_to_ws(self, gt):
ws_nopad = morpho.juicy_center(self.watershed, self.pad_thickness)
bdrymap = morpho.pad(morpho.seg_to_bdry(ws_nopad),
[0]*self.pad_thickness)
gt_bdrymap_nopad = morpho.seg_to_bdry(gt)
gt_bdrymap = morpho.pad(gt_bdrymap_nopad, [0]*self.pad_thickness)
k = distance_transform_cdt(1-bdrymap, return_indices=True)
ind = nonzero(gt_bdrymap.ravel())[0]
closest_sub = numpy.concatenate(
[i.ravel()[:,newaxis] for i in k[1]],axis=1)
closest_sub = closest_sub[ind,:]
closest_ind = [dot(bdrymap.strides, i)/bdrymap.itemsize
for i in closest_sub]
M = zeros_like(bdrymap).astype(float)
M.ravel()[closest_ind]=1.0
bdrymap.ravel()[closest_ind] = False
k = distance_transform_cdt(1-bdrymap, return_indices=True)
ind = nonzero(gt_bdrymap.ravel())[0]
closest_sub = numpy.concatenate(
[i.ravel()[:,newaxis] for i in k[1]],axis=1)
closest_sub = closest_sub[ind,:]
closest_ind = [dot(bdrymap.strides, i)/bdrymap.itemsize
for i in closest_sub]
M.ravel()[closest_ind]=1.0
return M
def assign_gt_to_ws(self, gt):
ws_nopad = morpho.juicy_center(self.watershed, self.pad_thickness)
bdrymap = morpho.pad(morpho.seg_to_bdry(ws_nopad),
[0] * self.pad_thickness)
gt_bdrymap_nopad = morpho.seg_to_bdry(gt)
gt_bdrymap = morpho.pad(gt_bdrymap_nopad, [0] * self.pad_thickness)
k = distance_transform_cdt(1 - bdrymap, return_indices=True)
ind = nonzero(gt_bdrymap.ravel())[0]
closest_sub = numpy.concatenate([i.ravel()[:, newaxis] for i in k[1]],
axis=1)
closest_sub = closest_sub[ind, :]
closest_ind = [
dot(bdrymap.strides, i) / bdrymap.itemsize for i in closest_sub
]
M = zeros_like(bdrymap).astype(float)
M.ravel()[closest_ind] = 1.0
bdrymap.ravel()[closest_ind] = False
k = distance_transform_cdt(1 - bdrymap, return_indices=True)
ind = nonzero(gt_bdrymap.ravel())[0]
closest_sub = numpy.concatenate([i.ravel()[:, newaxis] for i in k[1]],
axis=1)
closest_sub = closest_sub[ind, :]
closest_ind = [
dot(bdrymap.strides, i) / bdrymap.itemsize for i in closest_sub
]
M.ravel()[closest_ind] = 1.0
return M
def show_merge_3D(g, n1, n2, **kwargs):
im = kwargs.get('image', None)
alpha = kwargs.get('alpha', 0.5)
fignum = kwargs.get('fignum', 10)
bdr = np.zeros(g.segmentation.shape, np.uint8)
bdri = list(g[n1][n2]['boundary'])
bdr.ravel()[bdri] = 3
bdr.ravel()[list(g.node[n1]['extent'])] = 1
bdr.ravel()[list(g.node[n2]['extent'])] = 2
bdr = morpho.juicy_center(bdr, g.pad_thickness)
x, y, z = np.array(center_of_mass(bdr == 3)).round().astype(np.uint32)
fig = plt.figure(fignum)
bdr_cmapped = jet_transparent(bdr, alpha)
plt.subplot(221)
imshow_grey(im[:, :, z]) \
plt.imshow(bdr_cmapped[:,:,z], interpolation='nearest')
plt.subplot(222)
imshow_grey(im[:, y, :]) \
plt.imshow(bdr_cmapped[:,y,:], interpolation='nearest')
plt.subplot(223)
imshow_grey(im[x, :, :]) \
plt.imshow(bdr_cmapped[x,:,:], interpolation='nearest')
plt.subplot(224)
_ = plt.hist(g.probabilities_r[bdri][:, 0], bins=25)
return bdr, (x, y, z)
def build_volume(self, nbunch=None):
"""Return the segmentation (numpy.ndarray) induced by the graph."""
v = zeros_like(self.watershed)
vr = v.ravel()
if nbunch is None:
nbunch = self.nodes()
for n in nbunch:
vr[list(self.node[n]['extent'])] = n
return morpho.juicy_center(v, self.pad_thickness)
def build_volume(self, nbunch=None):
"""Return the segmentation (numpy.ndarray) induced by the graph."""
v = zeros_like(self.watershed)
vr = v.ravel()
if nbunch is None:
nbunch = self.nodes()
for n in nbunch:
vr[list(self.node[n]['extent'])] = n
return morpho.juicy_center(v,self.pad_thickness)
def remove_merged_boundaries(ar):
import morpho
arp = morpho.pad(ar, [0,ar.max()+1])
arpr = arp.ravel()
zero_idxs = (arpr == 0).nonzero()[0]
ns = arpr[morpho.get_neighbor_idxs(arp, zero_idxs)]
ns_compl = ns.copy()
ns_compl[ns==0] = ns.max()+1
merged_boundaries = (ns.max(axis=1) == ns_compl.min(axis=1)).nonzero()[0]
arpr[zero_idxs[merged_boundaries]] = ns.max(axis=1)[merged_boundaries]
return morpho.juicy_center(arp, 2)
def remove_merged_boundaries(ar):
import morpho
arp = morpho.pad(ar, [0, ar.max() + 1])
arpr = arp.ravel()
zero_idxs = (arpr == 0).nonzero()[0]
ns = arpr[morpho.get_neighbor_idxs(arp, zero_idxs)]
ns_compl = ns.copy()
ns_compl[ns == 0] = ns.max() + 1
merged_boundaries = (ns.max(axis=1) == ns_compl.min(axis=1)).nonzero()[0]
arpr[zero_idxs[merged_boundaries]] = ns.max(axis=1)[merged_boundaries]
return morpho.juicy_center(arp, 2)
def build_boundary_map(self, ebunch=None):
if len(self.merge_queue) == 0:
self.rebuild_merge_queue()
m = zeros(self.watershed.shape, double)
mr = m.ravel()
if ebunch is None:
ebunch = self.real_edges_iter()
ebunch = sorted([(self[u][v]['weight'], u, v) for u, v in ebunch])
for w, u, v in ebunch:
b = list(self[u][v]['boundary'])
mr[b] = w
if hasattr(self, 'ignored_boundary'):
m[self.ignored_boundary] = inf
return morpho.juicy_center(m, self.pad_thickness)
def build_boundary_map(self, ebunch=None):
if len(self.merge_queue) == 0:
self.rebuild_merge_queue()
m = zeros(self.watershed.shape, double)
mr = m.ravel()
if ebunch is None:
ebunch = self.real_edges_iter()
ebunch = sorted([(self[u][v]['weight'], u, v) for u, v in ebunch])
for w, u, v in ebunch:
b = list(self[u][v]['boundary'])
mr[b] = w
if hasattr(self, 'ignored_boundary'):
m[self.ignored_boundary] = inf
return morpho.juicy_center(m, self.pad_thickness)
def show_merge_3D(g, n1, n2, **kwargs):
im = kwargs.get('image', None)
alpha = kwargs.get('alpha', 0.5)
fignum = kwargs.get('fignum', 10)
bdr = np.zeros(g.segmentation.shape, np.uint8)
bdri = list(g[n1][n2]['boundary'])
bdr.ravel()[bdri] = 3
bdr.ravel()[list(g.node[n1]['extent'])] = 1
bdr.ravel()[list(g.node[n2]['extent'])] = 2
bdr = morpho.juicy_center(bdr, g.pad_thickness)
x, y, z = np.array(center_of_mass(bdr==3)).round().astype(np.uint32)
fig = plt.figure(fignum)
bdr_cmapped = jet_transparent(bdr, alpha)
plt.subplot(221); imshow_grey(im[:,:,z]); \
plt.imshow(bdr_cmapped[:,:,z], interpolation='nearest')
plt.subplot(222); imshow_grey(im[:,y,:]); \
plt.imshow(bdr_cmapped[:,y,:], interpolation='nearest')
plt.subplot(223); imshow_grey(im[x,:,:]); \
plt.imshow(bdr_cmapped[x,:,:], interpolation='nearest')
plt.subplot(224); _ = plt.hist(g.probabilities_r[bdri][:,0], bins=25)
return bdr, (x,y,z)
thickness = args.thickness
zcrop1 = [0, thickness]
overlaps = [2**i + 1 for i in range(1, 8)]
results_table = zeros([len(args.thresholds), len(range(1, 8))], dtype=bool)
for j, overlap in enumerate(overlaps):
zcrop2 = [thickness - overlap, 2 * thickness - overlap]
# pdb.set_trace()
probs1, ws1 = crop_probs_and_ws(args.xy_crop + zcrop1, probs, ws)
probs2, ws2 = crop_probs_and_ws(args.xy_crop + zcrop2, probs, ws)
g1 = Rag(ws1,
probs1,
show_progress=args.show_progress,
lowmem=args.low_memory)
g2 = Rag(ws2,
probs2,
show_progress=args.show_progress,
lowmem=args.low_memory)
for i, t in enumerate(args.thresholds):
g1.agglomerate(t)
g2.agglomerate(t)
results_table[i, j] = (juicy_center(
g1.segmentation,
2)[..., -overlap / 2].astype(bool) == juicy_center(
g2.segmentation, 2)[..., overlap / 2].astype(bool)).all()
savetxt('debug.txt', results_table, delimiter='\t')
results_table = hstack([array(args.thresholds)[:, newaxis], results_table])
results_table = \
vstack([array([0]+overlaps), results_table, results_table.all(axis=0)])
savetxt(args.fout, results_table, delimiter='\t', fmt='%i')
probs = median_filter(probs, 3)
elif args.gaussian_filter is not None:
probs = gaussian_filter(probs, args.gaussian_filter)
if args.watershed is None:
args.watershed = watershed(probs, show_progress=args.show_progress)
ws = args.watershed
thickness = args.thickness
zcrop1 = [0, thickness]
overlaps = [2 ** i + 1 for i in range(1, 8)]
results_table = zeros([len(args.thresholds), len(range(1, 8))], dtype=bool)
for j, overlap in enumerate(overlaps):
zcrop2 = [thickness - overlap, 2 * thickness - overlap]
# pdb.set_trace()
probs1, ws1 = crop_probs_and_ws(args.xy_crop + zcrop1, probs, ws)
probs2, ws2 = crop_probs_and_ws(args.xy_crop + zcrop2, probs, ws)
g1 = Rag(ws1, probs1, show_progress=args.show_progress, lowmem=args.low_memory)
g2 = Rag(ws2, probs2, show_progress=args.show_progress, lowmem=args.low_memory)
for i, t in enumerate(args.thresholds):
g1.agglomerate(t)
g2.agglomerate(t)
results_table[i, j] = (
juicy_center(g1.segmentation, 2)[..., -overlap / 2].astype(bool)
== juicy_center(g2.segmentation, 2)[..., overlap / 2].astype(bool)
).all()
savetxt("debug.txt", results_table, delimiter="\t")
results_table = hstack([array(args.thresholds)[:, newaxis], results_table])
results_table = vstack([array([0] + overlaps), results_table, results_table.all(axis=0)])
savetxt(args.fout, results_table, delimiter="\t", fmt="%i")
def get_ucm(self):
if hasattr(self, 'ignored_boundary'):
self.ucm[self.ignored_boundary] = self.max_merge_score
return morpho.juicy_center(self.ucm, self.pad_thickness)
def get_segmentation(self):
return morpho.juicy_center(self.segmentation, self.pad_thickness)
def get_ucm(self):
if hasattr(self, 'ignored_boundary'):
self.ucm[self.ignored_boundary] = self.max_merge_score
return morpho.juicy_center(self.ucm, self.pad_thickness)
def get_segmentation(self):
return morpho.juicy_center(self.segmentation, self.pad_thickness)
