class GenerateGraph:
def __init__(self, Label):
self.Label = Label
self.Shape = np.shape(self.Label) # size of image
self.Length = self.Shape[0] * self.Shape[1] * self.Shape[
2] # number of voxels
self.__ComputeArea()
# private
def __ComputeArea(self):
self.Area = np.sum(self.Label > 0)
def __CalculateDistMap(self):
#
# XY=[self.Label[i,:,:] for i in range(self.Shape[0])] #Z-XY
# ZX=[self.Label[:,:,i] for i in range(self.Shape[2])] #Y-ZX
# ZY=[self.Label[:,i,:] for i in range(self.Shape[1])] #X-ZY
#
# DistXY=np.array([image.morphology.distance_transform_edt(i) for i in XY])
# DistZX=np.array([image.morphology.distance_transform_edt(i) for i in ZX])
# DistZY=np.array([image.morphology.distance_transform_edt(i) for i in ZY])
#
# DistZX=np.rollaxis(DistZX, 0, 3)
# DistZY=np.rollaxis(DistZY, 0, 2)
#
# DistMap_=np.maximum(DistXY, DistZX)
# DistMap=np.maximum(DistMap_, DistZY)
#
# DistMap=filt.maximum_filter(DistMap, size=(3,3,3))
DistMap = image.morphology.distance_transform_edt(self.Label)
self.DistMap = DistMap
def __AssignDistMapToGraph(self):
'''
Assign dist values to graph nodes
'''
Nodes = self.Graph.GetNodes()
for i in Nodes:
Pos = tuple(self.Graph.node[i]['pos'].astype(int))
if Pos[0] < self.Shape[0] and Pos[1] < self.Shape[1] and Pos[
2] < self.Shape[2]:
Dist = self.DistMap[Pos]
if Dist < 1:
Dist = 1
self.Graph.node[i]['r'] = Dist
else:
self.Graph.node[i]['r'] = 1
def __GenerateRandomGraphFromLabel(self):
#random sampling
x = np.random.uniform(low=0,
high=self.Shape[0],
size=self.NInitialNodes).tolist()
y = np.random.uniform(low=0,
high=self.Shape[1],
size=self.NInitialNodes).tolist()
z = np.random.uniform(low=0,
high=self.Shape[2],
size=self.NInitialNodes).tolist()
NodesIndices = self.Label[(np.floor(x).astype('int'),
np.floor(y).astype('int'),
np.floor(z).astype('int'))] > 0
Index = np.array([x, y, z]).T
NodesPos = Index[NodesIndices]
# build graph
self.NNodes = len(NodesPos)
self.Graph = Graph()
self.Graph.add_nodes_from(range(self.NNodes))
# assign positions to nodes
for i, p in zip(self.Graph.GetNodes(), NodesPos):
self.Graph.node[i]['pos'] = p
# build connectivity
Tree = sp.spatial.cKDTree(NodesPos)
NeigborsIndices = Tree.query(NodesPos, k=self.Connection + 1)[1]
Edges = []
for ind, i in enumerate(NeigborsIndices):
Neigbours = np.unique(i)
c = [[ind, j] for j in Neigbours if j != ind and j != self.NNodes]
if c:
Edges.append(c)
#assign connections
Edges = [j for i in Edges for j in i] # unravel
self.Graph.add_edges_from(Edges)
def __GenerateRandomGridGraphFromLabel(self):
IndexTrueVoxels = np.where(self.Label)
Index = np.array(IndexTrueVoxels).T
#Limit NNodes to # of ture voxels
if self.NNodes > len(Index): self.NNodes = len(Index)
# probibility of true voxels
Probability = (self.Label).astype(float) / np.sum(self.Label)
Probability = Probability[IndexTrueVoxels]
# obtain nodes
NodesIndices = np.random.choice(range(len(Probability)),
self.NNodes,
p=Probability)
NodesPos = Index[NodesIndices]
# build graph
self.Graph = Graph()
self.Graph.add_nodes_from(range(self.NNodes))
# assign positions to nodes
for i, p in zip(self.Graph.GetNodes(), NodesPos):
self.Graph.node[i]['pos'] = p
# build connectivity
Tree = sp.spatial.cKDTree(NodesPos)
NeigborsIndices = Tree.query(NodesPos, k=self.Connection + 1)[1]
Edges = []
for ind, i in enumerate(NeigborsIndices):
Neigbours = np.unique(i)
c = [[ind, j] for j in Neigbours if j != ind and j != self.NNodes]
if c:
Edges.append(c)
#assign connections
Edges = [j for i in Edges for j in i] # unravel
self.Graph.add_edges_from(Edges)
def __GenerateGridGraphFromLabel(self):
def VoxelsPositions(Label, Shape, Length):
'''
Shape: shape of array
indexing in order: rows by row->depth
'''
# positions of each voxel
z, x, y = np.meshgrid(range(Shape[0]),
range(Shape[1]),
range(Shape[2]),
indexing='ij')
x = x[Label.astype(bool)]
y = y[Label.astype(bool)]
z = z[Label.astype(bool)]
VoxelsPos = np.transpose([z, x, y])
return VoxelsPos
def GetConnections(Label, Shape, Length):
# connections from pathways on array grid
Array = (np.reshape(range(Length), Shape) + 1) * Label
# incides of voxels in the Array
VoxelsIndices = Array[Label.astype(bool)]
#--------
path1 = iter(
np.transpose(
[Array[:, :, 0:-1].ravel(), Array[:, :, 1:].ravel()]))
path1 = (i for i in path1 if all(i))
#--------
path2 = iter(
np.transpose([
np.swapaxes(Array[:, 0:-1, :], 1, 2).ravel(),
np.swapaxes(Array[:, 1:, :], 1, 2).ravel()
]))
path2 = (i for i in path2 if all(i))
#--------
path3 = iter(
np.transpose([
np.swapaxes(Array[0:-1, :, :], 0, 2).ravel(),
np.swapaxes(Array[1:, :, :], 0, 2).ravel()
]))
path3 = (i for i in path3 if all(i))
return VoxelsIndices, path1, path2, path3
if self.Sampling is not None:
Scale = (1.0 / self.Sampling, 1.0 / self.Sampling,
1.0 / self.Sampling)
Label = image.zoom(self.Label.astype(int), Scale)
Shape = np.shape(Label) # size of image
Length = Shape[0] * Shape[1] * Shape[2] # number of voxels
else:
Label = self.Label
Shape = self.Shape
Length = self.Length
# voxel indices and thier positions
t1 = time()
VoxelsPos = VoxelsPositions(Label, Shape, Length)
print('create nodes: ' + str(time() - t1))
t1 = time()
VoxelsIndices, Connections1, Connections2, Connections3 = GetConnections(
Label, Shape, Length)
print('create connections: ' + str(time() - t1))
# build graph
t1 = time()
self.Graph = Graph()
self.Graph.add_nodes_from(VoxelsIndices)
for ind, p in zip(VoxelsIndices, VoxelsPos):
self.Graph.node[ind]['pos'] = p
self.Graph.add_edges_from(Connections1)
self.Graph.add_edges_from(Connections2)
self.Graph.add_edges_from(Connections3)
#exclude nodes with less than 2 neighbors
NNodesToExclude = 1
while NNodesToExclude > 0:
NodesToExclude = [
i for i in self.Graph.GetNodes()
if len(self.Graph.GetNeighbors(i)) <= 2
]
self.Graph.remove_nodes_from(NodesToExclude)
NNodesToExclude = len(NodesToExclude)
if self.Sampling is not None:
for i in self.Graph.GetNodes():
self.Graph.node[i][
'pos'] = self.Graph.node[i]['pos'] * self.Sampling
print('create graph: ' + str(time() - t1))
# public
def UpdateRandomGraph(self, connection=8, nInitialNodes=100000):
self.Connection = connection
self.NInitialNodes = nInitialNodes
self.__GenerateRandomGraphFromLabel()
def UpdateRandomGridGraph(self, connection=8, nNodes=100000):
self.Connection = connection
self.NNodes = nNodes
self.__GenerateRandomGridGraphFromLabel()
def UpdateGridGraph(self, Sampling=None):
if Sampling is not None:
self.Sampling = float(Sampling)
else:
self.Sampling = Sampling
self.__GenerateGridGraphFromLabel()
def GetOutput(self):
self.__CalculateDistMap()
self.__AssignDistMapToGraph()
self.Graph = fixG(self.Graph)
self.Graph.Area = self.Area
return self.Graph
def GetArea(self):
return self.Area
def GetDistMap(self):
self.__CalculateDistMap()
return self.DistMap
class GenerateGraph:
def __init__(self, Label, DisMap=None, label_ext=False):
self.Label = Label
self.label_ext = label_ext
self.Shape = np.shape(self.Label) # size of image
self.Length = self.Shape[0] * self.Shape[1] * self.Shape[
2] # number of voxels
self.__ComputeArea()
self.DistMap = DisMap
# private
def __ComputeArea(self):
self.Area = np.sum(self.Label > 0)
def __CalculateDistMap(self):
if self.DistMap is None:
#self.DistMap=image.morphology.distance_transform_edt(self.Label)
self.DistMap = DistMap3D(self.Label)
def __AssignDistMapToGraph(self):
'''
Assign dist values to graph nodes
'''
Nodes = self.Graph.GetNodes()
for i in Nodes:
Pos = tuple(self.Graph.node[i]['pos'].astype(int))
if Pos[0] < self.Shape[0] and Pos[1] < self.Shape[1] and Pos[
2] < self.Shape[2]:
Dist = self.DistMap[Pos]
if Dist < 1:
Dist = 1
self.Graph.node[i]['r'] = Dist
else:
self.Graph.node[i]['r'] = 1
def __GenerateRandomGraphFromLabel(self):
#random sampling
x = np.random.uniform(low=0,
high=self.Shape[0],
size=self.NInitialNodes).tolist()
y = np.random.uniform(low=0,
high=self.Shape[1],
size=self.NInitialNodes).tolist()
z = np.random.uniform(low=0,
high=self.Shape[2],
size=self.NInitialNodes).tolist()
NodesIndices = self.Label[(np.floor(x).astype('int'),
np.floor(y).astype('int'),
np.floor(z).astype('int'))] > 0
Index = np.array([x, y, z]).T
NodesPos = Index[NodesIndices]
# build graph
self.NNodes = len(NodesPos)
self.Graph = Graph()
self.Graph.add_nodes_from(range(self.NNodes))
# assign positions to nodes
for i, p in zip(self.Graph.GetNodes(), NodesPos):
self.Graph.node[i]['pos'] = p
# build connectivity
Tree = sp.spatial.cKDTree(NodesPos)
NeigborsIndices = Tree.query(NodesPos, k=self.Connection + 1)[1]
Edges = []
for ind, i in enumerate(NeigborsIndices):
Neigbours = np.unique(i)
c = [[ind, j] for j in Neigbours if j != ind and j != self.NNodes]
if c:
Edges.append(c)
#assign connections
Edges = [j for i in Edges for j in i] # unravel
self.Graph.add_edges_from(Edges)
def __GenerateRandomGridGraphFromLabel(self):
IndexTrueVoxels = np.where(self.Label)
Index = np.array(IndexTrueVoxels).T
#Limit NNodes to # of ture voxels
if self.NNodes > len(Index): self.NNodes = len(Index)
# probibility of true voxels
Probability = (self.Label).astype(float) / np.sum(self.Label)
Probability = Probability[IndexTrueVoxels]
# obtain nodes
NodesIndices = np.random.choice(range(len(Probability)),
self.NNodes,
p=Probability)
NodesPos = Index[NodesIndices]
# build graph
self.Graph = Graph()
self.Graph.add_nodes_from(range(self.NNodes))
# assign positions to nodes
for i, p in zip(self.Graph.GetNodes(), NodesPos):
self.Graph.node[i]['pos'] = p
# build connectivity
Tree = sp.spatial.cKDTree(NodesPos)
NeigborsIndices = Tree.query(NodesPos, k=self.Connection + 1)[1]
Edges = []
for ind, i in enumerate(NeigborsIndices):
Neigbours = np.unique(i)
c = [[ind, j] for j in Neigbours if j != ind and j != self.NNodes]
if c:
Edges.append(c)
#assign connections
Edges = [j for i in Edges for j in i] # unravel
self.Graph.add_edges_from(Edges)
def __GenerateGridGraphFromLabel(self):
def VoxelsPositions(Label, Shape, Length):
'''
Shape: shape of array
indexing in order: rows by row->depth
'''
# positions of each voxel
z, x, y = np.meshgrid(range(Shape[0]),
range(Shape[1]),
range(Shape[2]),
indexing='ij')
x = x[Label.astype(bool)]
y = y[Label.astype(bool)]
z = z[Label.astype(bool)]
VoxelsPos = np.transpose([z, x, y])
return VoxelsPos
def GetConnections(Label, Shape, Length):
# connections from pathways on array grid
Array = (np.reshape(range(Length), Shape) + 1) * Label
# incides of voxels in the Array
VoxelsIndices = Array[Label.astype(bool)]
#--------
path1 = iter(
np.transpose(
[Array[:, :, 0:-1].ravel(), Array[:, :, 1:].ravel()]))
path1 = (i for i in path1 if all(i))
#--------
path2 = iter(
np.transpose([
np.swapaxes(Array[:, 0:-1, :], 1, 2).ravel(),
np.swapaxes(Array[:, 1:, :], 1, 2).ravel()
]))
path2 = (i for i in path2 if all(i))
#--------
path3 = iter(
np.transpose([
np.swapaxes(Array[0:-1, :, :], 0, 2).ravel(),
np.swapaxes(Array[1:, :, :], 0, 2).ravel()
]))
path3 = (i for i in path3 if all(i))
return VoxelsIndices, path1, path2, path3
if self.Sampling is not None:
Scale = (1.0 / self.Sampling, 1.0 / self.Sampling,
1.0 / self.Sampling)
Label = image.zoom(self.Label.astype(int), Scale)
Shape = np.shape(Label) # size of image
Length = Shape[0] * Shape[1] * Shape[2] # number of voxels
else:
Label = self.Label
Shape = self.Shape
Length = self.Length
# voxel indices and thier positions
t1 = time()
VoxelsPos = VoxelsPositions(Label, Shape, Length)
print('create nodes: ' + str(time() - t1))
t1 = time()
VoxelsIndices, Connections1, Connections2, Connections3 = GetConnections(
Label, Shape, Length)
print('create connections: ' + str(time() - t1))
# build graph
t1 = time()
self.Graph = Graph()
self.Graph.add_nodes_from(VoxelsIndices)
for ind, p in zip(VoxelsIndices, VoxelsPos):
self.Graph.node[ind]['pos'] = p
self.Graph.add_edges_from(Connections1)
self.Graph.add_edges_from(Connections2)
self.Graph.add_edges_from(Connections3)
#exclude nodes with less than 2 neighbors
# NNodesToExclude=1
# while NNodesToExclude>0:
# NodesToExclude=[i for i in self.Graph.GetNodes() if len(self.Graph.GetNeighbors(i))<=2]
# self.Graph.remove_nodes_from(NodesToExclude)
# NNodesToExclude=len(NodesToExclude)
# reconnect nodes with less that 2 edges (request from Sreekanth )
pos = np.array(self.Graph.GetNodesPos())
nodes = np.array(self.Graph.GetNodes())
NodesToModify = [
i for i in self.Graph.GetNodes()
if len(self.Graph.GetNeighbors(i)) <= 2
]
pos_xc_nodes = np.array(
[self.Graph.node[k]['pos'] for k in NodesToModify])
new_edges = []
for nn, pp in zip(NodesToModify, pos_xc_nodes):
checkp = pos - pp[None, :]
checkp = np.sum(checkp**2, axis=1)**0.5
ed_nodes = nodes[checkp <= 2**0.5]
new_ed = [[nn, kk] for kk in ed_nodes if kk != nn]
#print(len(new_ed))
new_edges.append(new_ed)
new_edges = [k2 for k1 in new_edges for k2 in k1]
self.Graph.add_edges_from(new_edges)
# label extremity nodes at imageborders
if self.label_ext:
maxx, maxy, maxz = Label.shape
maxx -= 1
maxy -= 1
maxz -= 1
pos = self.Graph.GetNodesPos()
ext = []
for n, p in zip(self.Graph.GetNodes(), self.Graph.GetNodesPos()):
if p[0] == 0 or p[0] == maxx:
self.Graph.node[n]['ext'] = 1
if p[1] == 0 or p[1] == maxy:
self.Graph.node[n]['ext'] = 1
if p[2] == 0 or p[2] == maxz:
self.Graph.node[n]['ext'] = 1
try:
dumb = self.Graph.node[n]['ext']
except:
self.Graph.node[n]['ext'] = 0
if self.Sampling is not None:
for i in self.Graph.GetNodes():
self.Graph.node[i][
'pos'] = self.Graph.node[i]['pos'] * self.Sampling
print('create graph: ' + str(time() - t1))
# public
def UpdateRandomGraph(self, connection=8, nInitialNodes=100000):
self.Connection = connection
self.NInitialNodes = nInitialNodes
self.__GenerateRandomGraphFromLabel()
def UpdateRandomGridGraph(self, connection=8, nNodes=100000):
self.Connection = connection
self.NNodes = nNodes
self.__GenerateRandomGridGraphFromLabel()
def UpdateGridGraph(self, Sampling=None):
if Sampling is not None:
self.Sampling = float(Sampling)
else:
self.Sampling = Sampling
self.__GenerateGridGraphFromLabel()
def GetOutput(self):
self.__CalculateDistMap()
self.__AssignDistMapToGraph()
self.Graph = fixG(self.Graph)
self.Graph.Area = self.Area
return self.Graph
def GetArea(self):
return self.Area
def GetDistMap(self):
self.__CalculateDistMap()
return self.DistMap