def test_multiplicity_stats():
stats1 = csr.summarize(csr.Skeleton(skeleton0))
stats2 = csr.summarize(csr.Skeleton(skeleton0, spacing=2))
assert_almost_equal(2 * stats1['branch-distance'].values,
stats2['branch-distance'].values)
assert_almost_equal(2 * stats1['euclidean-distance'].values,
stats2['euclidean-distance'].values)
def bench_suite():
times = OrderedDict()
skeleton = np.load(os.path.join(rundir, 'infected3.npz'))['skeleton']
with timer() as t_build_graph:
g, indices = csr.skeleton_to_csgraph(skeleton, spacing=2.24826)
times['build graph'] = t_build_graph[0]
with timer() as t_build_graph2:
g, indices = csr.skeleton_to_csgraph(skeleton, spacing=2.24826)
times['build graph again'] = t_build_graph2[0]
with timer() as t_stats:
stats = csr.branch_statistics(g)
times['compute statistics'] = t_stats[0]
with timer() as t_stats2:
stats = csr.branch_statistics(g)
times['compute statistics again'] = t_stats2[0]
with timer() as t_skeleton:
skel_obj = csr.Skeleton(skeleton)
times['skeleton object'] = t_skeleton[0]
with timer() as t_skeleton2:
skel_obj = csr.Skeleton(skeleton)
times['skeleton object again'] = t_skeleton2[0]
with timer() as t_summary:
summary = csr.summarize(skel_obj)
times['compute per-skeleton statistics'] = t_summary[0]
return times
def test_summarize_spacing():
df = csr.summarize(csr.Skeleton(skeleton2))
df2 = csr.summarize(csr.Skeleton(skeleton2, spacing=2))
assert_equal(np.array(df['node-id-src']), np.array(df2['node-id-src']))
assert_almost_equal(np.array(df2['euclidean-distance']),
np.array(2 * df['euclidean-distance']))
assert_almost_equal(np.array(df2['branch-distance']),
np.array(2 * df['branch-distance']))
def test_fast_graph_center_idx():
s = csr.Skeleton(skeleton0)
i = csr._fast_graph_center_idx(s)
assert i == 6
s = csr.Skeleton(skeleton4)
i = csr._fast_graph_center_idx(s)
assert i == 1
def test_topograph_summary():
stats = csr.summarize(
csr.Skeleton(topograph1d, spacing=2.5, value_is_height=True),
value_is_height=True,
)
assert stats.loc[0, 'euclidean-distance'] == 5.0
columns = ['coord-src-0', 'coord-src-1', 'coord-dst-0', 'coord-dst-1']
assert_almost_equal(sorted(stats.loc[0, columns]), [0, 3, 3, 5])
def test_diagonal():
s = csr.Skeleton(skeleton4)
# We choose the shells so that we catch all three, then two, then one arm
# of the skeleton, while not triggering the "shell spacing too small"
# warning
c, r, counts = csr.sholl_analysis(s,
center=[1, 1],
shells=np.arange(0.09, 5, 1.45))
np.testing.assert_equal(counts, [3, 2, 1, 0])
def _old_branch_statistics(skeleton_image,
*,
spacing=1,
value_is_height=False):
skel = csr.Skeleton(skeleton_image,
spacing=spacing,
value_is_height=value_is_height)
summary = csr.summarize(skel, value_is_height=value_is_height)
columns = ['node-id-src', 'node-id-dst', 'branch-distance', 'branch-type']
return summary[columns].to_numpy()
def test_transpose_image():
image = np.zeros((10, 10))
rr, cc = line(4, 0, 4, 2)
image[rr, cc] = 1
rr, cc = line(3, 2, 3, 5)
image[rr, cc] = 1
rr, cc = line(1, 2, 8, 2)
image[rr, cc] = 1
rr, cc = line(1, 0, 1, 8)
image[rr, cc] = 1
skeleton1 = csr.Skeleton(image)
skeleton2 = csr.Skeleton(image.T)
assert (skeleton1.n_paths == skeleton2.n_paths)
np.testing.assert_allclose(
np.sort(skeleton1.path_lengths()),
np.sort(skeleton2.path_lengths()),
)
def test_sholl_spacing():
s = csr.Skeleton(skeleton0, spacing=(1, 5))
with pytest.warns(UserWarning):
c, r, counts = csr.sholl_analysis(s,
center=[3, 15],
shells=np.arange(17))
for i in range(4):
assert np.isin(i, counts)
c, r, counts = csr.sholl_analysis(s,
center=[3, 15],
shells=np.arange(1, 20, 6))
np.testing.assert_equal(counts, [3, 2, 2, 0])
def skeletonize(self, threshold):
fin_mask = (self.z_scores > threshold).astype(int)
skel = skeletonize(fin_mask).astype(int)
sk_obj = csr.Skeleton(skel, spacing=1, keep_images=True)
path_lengths = sk_obj.path_lengths()
this_one = np.argmax(path_lengths)
path_coordinates = sk_obj.path_coordinates(this_one).astype(int)
pruned_skel = np.zeros(fin_mask.shape)
for pair in path_coordinates:
pruned_skel[pair[0], pair[1]] = 1
return pruned_skel, path_coordinates[::-1, :]
def mask_2_swc(TIFFileName, SWCFileName, smp=4, ZRatio=1):
# Read binary mask
Skl_ImFile = tiff.TiffFile(TIFFileName)
Skl_np = Skl_ImFile.asarray()
branch_data = csr.summarise(Skl_np)
skel_obj = csr.Skeleton(Skl_np)
Brch_vox = skel_obj.path_coordinates
NSkeletons = np.unique(branch_data['skeleton-id']).shape[0]
# Check that the mask only holds one skeleton
if NSkeletons > 1:
exit("Error: more than one skeleton found in the mask!")
# Extract relevant data from skeleton branches
NBranches = branch_data.shape[0]
id_0 = np.zeros(NBranches, dtype=int)
id_1 = np.zeros(NBranches, dtype=int)
list = skel_obj.paths_list()
for i in range(NBranches):
id_0[i] = list[i][0]
id_1[i] = list[i][-1]
# Count number of unique vertices and build renumbering LUT
AllNodes = (np.unique([id_0, id_1]))
MaxVertexIdx = (np.amax(AllNodes))
NVertices = AllNodes.size
LUTVertices = np.zeros(MaxVertexIdx + 1, dtype=int)
VertExist = np.zeros(MaxVertexIdx + 1, dtype=int)
# Estimate total number of segments
TotSegments = 1
for i in range(NBranches):
TotSegments = TotSegments + (
1 + np.floor(Brch_vox(i).shape[0] / smp)).astype(int)
print("Number of branches: %i" % NBranches)
print("Number of nodes: %i" % NVertices)
print("Estimated number of segments: %i" % TotSegments)
# Fill SWC array
global SWC_data
SWC_data = np.ones((TotSegments, 7), dtype=int)
BranchOrphaned = np.zeros(NBranches, dtype=int)
cntVert = 0
# Insert first branch
Idx0 = id_0[0]
Idx1 = id_1[0]
SWC_data[cntVert, 0] = cntVert
LUTVertices[Idx0] = cntVert
VertExist[Idx0] = 1
SWC_data[cntVert, 6] = -1
SWC_data[cntVert, 2] = Brch_vox(0)[0, 2]
SWC_data[cntVert, 3] = Brch_vox(0)[0, 1]
SWC_data[cntVert, 4] = Brch_vox(0)[0, 0] * ZRatio
Vox = Brch_vox(0)
(SWC_data, cntVert) = insertNodes(cntVert, LUTVertices[Idx0], Vox, smp,
ZRatio)
LUTVertices[Idx1] = cntVert
VertExist[Idx1] = 1
# Main loop
BrchToBeAdded = np.arange(NBranches, dtype=int)
cntIt = 0
for it in range(10):
if np.sum(BrchToBeAdded) == 0:
break
cntIt = cntIt + 1
for j in range(1, NBranches):
i = BrchToBeAdded[j]
if i > 0:
Idx0 = id_0[i]
Idx1 = id_1[i]
if VertExist[Idx0]:
# First node exists, it is then an ancestor
Vox = Brch_vox(i)
(SWC_data, cntVert) = insertNodes(cntVert,
LUTVertices[Idx0], Vox,
smp, ZRatio)
LUTVertices[Idx1] = cntVert
VertExist[Idx1] = 1
BrchToBeAdded[j] = 0
else:
if VertExist[Idx1]:
# Second node exists, it is then an ancestor
Vox = np.flip(Brch_vox(i), 0)
(SWC_data,
cntVert) = insertNodes(cntVert, LUTVertices[Idx1],
Vox, smp, ZRatio)
LUTVertices[Idx0] = cntVert
VertExist[Idx0] = 1
BrchToBeAdded[j] = 0
# Truncate SWC array and add 1 to all IDs (SWC convention)
SWC_data = SWC_data[0:cntVert + 1, :]
for i in range(SWC_data.shape[0]):
SWC_data[i, 0] = SWC_data[i, 0] + 1
if SWC_data[i, 6] > -1:
SWC_data[i, 6] = SWC_data[i, 6] + 1
# Check for duplicated nodes
unique_rows = np.unique(SWC_data[:, 2:4], axis=0)
if unique_rows.shape[0] != SWC_data.shape[0]:
#print("Warning: the skeleton holds loop(s), this is incompatible with SWC format and it will be encoded with duplicated nodes!")
exit(
"Error: the skeleton holds loop(s), this is incompatible with SWC format!"
)
# Display status
print("Performed %i iterations" % cntIt)
print("Remaining branches: %i " % np.count_nonzero(BrchToBeAdded))
print("Number of segments: %i" % SWC_data.shape[0])
# Write SWC file
with open(SWCFileName, "w") as f:
f.write("# ORIGINAL_SOURCE Mask2SWC 1.0\n# SCALE 1.0 1.0 1.0\n\n")
f.close()
with open(SWCFileName, "a") as f:
np.savetxt(f, SWC_data, fmt='%i', delimiter=" ")
f.close()
def mask_2_obj(TIFFileName, OBJFileName, smp=4, ZRatio=1):
# Read skeleton image
Skl_ImFile = tiff.TiffFile(TIFFileName)
Skl_np = Skl_ImFile.asarray()
# Analyze skeleton
branch_data = csr.summarise(Skl_np)
NBranches = branch_data.shape[0]
skel_obj = csr.Skeleton(Skl_np)
Brch_vox = skel_obj.path_coordinates
NSkeletons = np.unique(branch_data['skeleton-id']).shape[0]
# Extract relevant data from skeleton branches
NBranches = branch_data.shape[0]
id_0 = np.zeros(NBranches, dtype=int)
id_1 = np.zeros(NBranches, dtype=int)
list = skel_obj.paths_list()
for i in range(NBranches):
id_0[i] = list[i][0]
id_1[i] = list[i][-1]
# Parse all nodes to find unique vertices
AllNodes = (np.unique([id_0, id_1]))
MaxVertexIdx = (np.amax(AllNodes))
NVertices = AllNodes.size
# Estimate total number of segments
TotSegments = 1
for i in range(NBranches):
TotSegments = TotSegments + (
1 + np.floor(Brch_vox(i).shape[0] / smp)).astype(int)
# Display model information
print("Number of skeletons: %i" % NSkeletons)
print("Number of branches: %i" % NBranches)
print("Number of nodes: %i" % NVertices)
print("Estimated number of segments: %i" % TotSegments)
# Build re-indexing LUT
LUTVertices = np.zeros([MaxVertexIdx + 1, 1], dtype=int)
for i in range(NVertices):
LUTVertices[AllNodes[i]] = i
# Fill OBJ v-data
OBJ_Vdata = np.zeros([TotSegments, 3], dtype=int)
for i in range(NBranches):
OBJ_Vdata[LUTVertices[id_0[i]], 0] = Brch_vox(i)[0, 2]
OBJ_Vdata[LUTVertices[id_0[i]], 1] = Brch_vox(i)[0, 1]
OBJ_Vdata[LUTVertices[id_0[i]], 2] = Brch_vox(i)[0, 0] * ZRatio
OBJ_Vdata[LUTVertices[id_1[i]], 0] = Brch_vox(i)[-1, 2]
OBJ_Vdata[LUTVertices[id_1[i]], 1] = Brch_vox(i)[-1, 1]
OBJ_Vdata[LUTVertices[id_1[i]], 2] = Brch_vox(i)[-1, 0] * ZRatio
cntVertices = NVertices
# Fill OBJ l-data
OBJ_Ldata = np.ones((TotSegments, 2), dtype=int)
cntSegments = 0
for i in range(NBranches):
Vox = Brch_vox(i)
L = Vox.shape[0]
nNodes = (1 + np.floor(L / smp)).astype(int)
PrevNode = LUTVertices[id_0[i]] + 1
for s in range(1, nNodes - 1):
OBJ_Vdata[cntVertices, 0] = Vox[np.round(s * (L - 1) /
(nNodes - 1)).astype(int),
2]
OBJ_Vdata[cntVertices, 1] = Vox[np.round(s * (L - 1) /
(nNodes - 1)).astype(int),
1]
OBJ_Vdata[cntVertices,
2] = Vox[np.round(s * (L - 1) /
(nNodes - 1)).astype(int), 0] * ZRatio
OBJ_Ldata[cntSegments, 0] = PrevNode
OBJ_Ldata[cntSegments, 1] = cntVertices + 1
PrevNode = cntVertices + 1
cntVertices = cntVertices + 1
cntSegments = cntSegments + 1
OBJ_Ldata[cntSegments, 0] = PrevNode
OBJ_Ldata[cntSegments, 1] = LUTVertices[id_1[i]] + 1
cntSegments = cntSegments + 1
# Display status
print("Actual number of vertices: %i" % cntVertices)
print("Actual number of segments: %i" % cntSegments)
# Export OBJ file
with open(OBJFileName, "w") as f:
f.write("")
f.close()
with open(OBJFileName, "a") as f:
for i in range(cntVertices):
f.write("v %i %i %i\n" %
(OBJ_Vdata[i, 0], OBJ_Vdata[i, 1], OBJ_Vdata[i, 2]))
f.close()
with open(OBJFileName, "a") as f:
for i in range(cntSegments):
f.write("l %i %i\n" % (OBJ_Ldata[i, 0], OBJ_Ldata[i, 1]))
f.close()
def test_2skeletons():
df = csr.summarize(csr.Skeleton(skeleton2))
assert_almost_equal(np.unique(df['euclidean-distance']), np.sqrt([5, 10]))
assert_equal(np.unique(df['skeleton-id']), [0, 1])
assert_equal(np.bincount(df['branch-type']), [0, 4, 4])
def test_sholl():
s = csr.Skeleton(skeleton0)
c, r, counts = csr.sholl_analysis(s, shells=np.arange(0, 5, 1.5))
np.testing.assert_equal(c, [3, 3])
np.testing.assert_equal(counts, [0, 3, 3, 0])
def test_tip_junction_edges():
stats1 = csr.summarize(csr.Skeleton(skeleton4))
assert stats1.shape[0] == 3 # ensure all three branches are counted
def test_pixel_values():
image = np.random.random((45, ))
expected = np.mean(image)
stats = csr.summarize(csr.Skeleton(image))
assert_almost_equal(stats.loc[0, 'mean-pixel-value'], expected)
def test_stats(test_skeleton):
stats = csr.summarize(csr.Skeleton(test_skeleton))
return stats
def skl2obj(Skl_np, smp, ZRatio, OBJToExport):
# Analyze skeleton
branch_data = csr.summarise(Skl_np)
NBranches = branch_data.shape[0]
skel_obj = csr.Skeleton(Skl_np)
Brch_vox = skel_obj.path_coordinates
NSkeletons = np.unique(branch_data['skeleton-id']).shape[0]
# Extract required information on skeleton branches
#NBranches = branch_data.shape[0]
brclist = skel_obj.paths_list()
NBranches = len(brclist)
id_0 = np.zeros(NBranches, dtype=int)
id_1 = np.zeros(NBranches, dtype=int)
for i in range(NBranches):
id_0[i] = brclist[i][0]
id_1[i] = brclist[i][-1]
# Parse all nodes to find unique vertices
AllNodes = (np.unique([id_0, id_1]))
MaxVertexIdx = (np.amax(AllNodes))
NVertices = AllNodes.size
# Over-estimate of total number of segments after skeleton sampling (node to node links)
TotSegments = 1
for i in range(NBranches):
TotSegments = TotSegments + (
1 + np.floor(Brch_vox(i).shape[0] / smp)).astype(int)
# Display model information
#print("Number of skeletons: %i"%NSkeletons)
#print("Number of branches: %i"%NBranches)
#print("Number of nodes: %i"%NVertices)
#print("Estimated number of segments: %i"%TotSegments)
# Build re-indexing LUT
LUTVertices = np.zeros([MaxVertexIdx + 1, 1], dtype=int)
for i in range(NVertices):
LUTVertices[AllNodes[i]] = i
# Fill OBJ v-data
OBJ_Vdata = np.zeros([TotSegments, 3], dtype=int)
for i in range(NBranches):
OBJ_Vdata[LUTVertices[id_0[i]], 0] = Brch_vox(i)[0, 2]
OBJ_Vdata[LUTVertices[id_0[i]], 1] = Brch_vox(i)[0, 1]
OBJ_Vdata[LUTVertices[id_0[i]], 2] = Brch_vox(i)[0, 0] * ZRatio
OBJ_Vdata[LUTVertices[id_1[i]], 0] = Brch_vox(i)[-1, 2]
OBJ_Vdata[LUTVertices[id_1[i]], 1] = Brch_vox(i)[-1, 1]
OBJ_Vdata[LUTVertices[id_1[i]], 2] = Brch_vox(i)[-1, 0] * ZRatio
cntVertices = NVertices
# Fill OBJ l-data
OBJ_Ldata = np.ones((TotSegments, 2), dtype=int)
cntSegments = 0
for i in range(NBranches):
Vox = Brch_vox(i)
L = Vox.shape[0]
nNodes = (1 + np.floor(L / smp)).astype(int)
PrevNode = LUTVertices[id_0[i]] + 1
for s in range(1, nNodes - 1):
OBJ_Vdata[cntVertices, 0] = Vox[np.round(s * (L - 1) /
(nNodes - 1)).astype(int),
2]
OBJ_Vdata[cntVertices, 1] = Vox[np.round(s * (L - 1) /
(nNodes - 1)).astype(int),
1]
OBJ_Vdata[cntVertices,
2] = Vox[np.round(s * (L - 1) /
(nNodes - 1)).astype(int), 0] * ZRatio
OBJ_Ldata[cntSegments, 0] = PrevNode
OBJ_Ldata[cntSegments, 1] = cntVertices + 1
PrevNode = cntVertices + 1
cntVertices = cntVertices + 1
cntSegments = cntSegments + 1
OBJ_Ldata[cntSegments, 0] = PrevNode
OBJ_Ldata[cntSegments, 1] = LUTVertices[id_1[i]] + 1
cntSegments = cntSegments + 1
# Display model statistics
#print("Actual number of vertices: %i" %cntVertices)
#print("Actual number of segments: %i" %cntSegments)
with open(OBJToExport, "w") as f:
f.write("")
f.close()
with open(OBJToExport, "a") as f:
for i in range(cntVertices):
f.write("v %i %i %i\n" %
(OBJ_Vdata[i, 0], OBJ_Vdata[i, 1], OBJ_Vdata[i, 2]))
f.close()
with open(OBJToExport, "a") as f:
for i in range(cntSegments):
f.write("l %i %i\n" % (OBJ_Ldata[i, 0], OBJ_Ldata[i, 1]))
f.close()
