def main():
#fo = file("../Skeletons/PE00000_edited_skeleton_graphs.pckle","rb")
fo = file("PE00000_edited_bc_333_skeleton_graphs.pckle", "rb")
data = cPickle.load(fo)
ogs = data["orderedGraphs"]
g = ogs[(0, u'0')]
oldSize = g.size()
iter = 0
while (True):
prune(g)
iter += 1
newSize = g.size()
if (newSize == oldSize):
break
else:
oldSize = newSize
print iter, oldSize
dg = np.array(g.degree().values())
print dg.max(), dg.min(), dg.mean()
imgitk = io.readImage("PE00000.mha", returnITK=True, imgMode='sshort')
img = itk.PyBuffer.ISS3.GetArrayFromImage(imgitk)
spacing = imgitk.GetSpacing()
#img2 = img.copy()
#vimg = insertGraphInImage(g,img2)
#a3d.call(data = [vimg])
#for i in range(20):
# randomNode = pickRandomNodeOfDegree(g)
# h = getNodeSubgraph(g,randomNode)
#
# visualizeXY_MPL(h,img,buffer=20)
fo = file("PE00000_edited_bc_333_skeleton_graphs_pruned.pckle", "wb")
cPickle.dump(g, fo)
def main():
#fo = file("../Skeletons/PE00000_edited_skeleton_graphs.pckle","rb")
fo = file("PE00000_edited_bc_333_skeleton_graphs.pckle","rb")
data = cPickle.load(fo)
ogs = data["orderedGraphs"]
g = ogs[(0,u'0')]
oldSize = g.size()
iter = 0
while(True):
prune(g)
iter += 1
newSize = g.size()
if( newSize == oldSize ):
break
else:
oldSize = newSize
print iter, oldSize
dg = np.array(g.degree().values())
print dg.max(), dg.min(), dg.mean()
imgitk = io.readImage("PE00000.mha",returnITK=True,imgMode='sshort')
img = itk.PyBuffer.ISS3.GetArrayFromImage(imgitk)
spacing = imgitk.GetSpacing()
#img2 = img.copy()
#vimg = insertGraphInImage(g,img2)
#a3d.call(data = [vimg])
#for i in range(20):
# randomNode = pickRandomNodeOfDegree(g)
# h = getNodeSubgraph(g,randomNode)
#
# visualizeXY_MPL(h,img,buffer=20)
fo = file("PE00000_edited_bc_333_skeleton_graphs_pruned.pckle","wb")
cPickle.dump(g,fo)
def test_critEnds():
import nxvasc
import dicom
import fomFuncs
import costFuncs
import imageTools.ITKUtils.io as io
#img=dicom.read_file("data/tree2.dcm")
#array=img.pixel_array
#mask = na.where(array>0, 1, 0)
img = io.readImage("data/p16Mask.mha",returnITK=False,imgMode="uchar")
mask = na.where(img>0,1,0)
#mask = createPhantom3()
vx = nxvasc.nxvasc()
vx.setOutputFile("testnx.out")
vx.setMask(mask)
vx.getMaskMips()
vx.endPointsFromCritPoints("data/ends.pckle")
vx.generateRawFigureOfMeritData()
vx.setFigureOfMeritF(fomFuncs.fomf1)
vx.setCostFunction(costFuncs.cf1)
vx.createMaskDictionary()
vx.fillFigureOfMerit()
vx.createNXGraph(verbose=True)
vx.getEndPointPaths()
print("trace back paths")
vx.traceBackPaths5()
print("plot paths")
vx.showPaths(vx.getSGPaths(),label="Orthogonal MIP Images with Centerlines-SG")
input('continue')
def test_critEnds():
import nxvasc
import dicom
import fomFuncs
import costFuncs
import imageTools.ITKUtils.io as io
#img=dicom.read_file("data/tree2.dcm")
#array=img.pixel_array
#mask = na.where(array>0, 1, 0)
img = io.readImage("data/p16Mask.mha", returnITK=False, imgMode="uchar")
mask = na.where(img > 0, 1, 0)
#mask = createPhantom3()
vx = nxvasc.nxvasc()
vx.setOutputFile("testnx.out")
vx.setMask(mask)
vx.getMaskMips()
vx.endPointsFromCritPoints("data/ends.pckle")
vx.generateRawFigureOfMeritData()
vx.setFigureOfMeritF(fomFuncs.fomf1)
vx.setCostFunction(costFuncs.cf1)
vx.createMaskDictionary()
vx.fillFigureOfMerit()
vx.createNXGraph(verbose=True)
vx.getEndPointPaths()
print("trace back paths")
vx.traceBackPaths5()
print("plot paths")
vx.showPaths(vx.getSGPaths(),
label="Orthogonal MIP Images with Centerlines-SG")
input('continue')
def A_Star():
img=io.readImage("PE00025Filter0_seg.mha", returnITK=False, imgMode='uchar')
mask=na.where(img>0,1,0)
instance=nxvasc.nxvasc()
instance.setmask(mask)
indexes=open("Indexes.pckle",'rb')
inds=pickle.load(indexes)
graph=open("PE00025NXgraph.pckle",'rb')
G=pickle.load(graph)
instance.generateRawFigureOfMerit()
instance.setFigureOfMeritF(fomFuncs.fom1)
instance.setCostFunction(costFuncs.cf1)
instance.GenerateAstarPaths(G, inds)
path=open("PE00025A_StarPaths.pckle", 'wb')
pickle.dump(self.paths, path)
def GenerateNonEndpointPaths():
graph=open("PE000026NENXGraph.pckle", "wb") #stores the graph
#paths=open("PE00026Paths.pckle", "wb") #stores the paths
#indexes=open("Indexes.pckle", "wb") #stores the index locations for each set of crds.
endpoints=open("PE00026Nonendpoints.pckle", 'rb')
inds=pickle.load(endpoints)
img=io.readImage("PE000026Filter0_seg.mha", returnITK=False, imgMode="uchar")
mask = na.where(img>0, 1, 0)
instance=VascMask.VascMask()
instance.setMask(mask)
instance.setNeighborhoodSize(sz=3)
instance.getFOM()
#inds=instance.getInds(crds)
instance.createNXGraph()
pickle.dump(inds, indexes)
pickle.dump(instance.G, graph)
def GenerateGraph():
graph=open("PE00025CompleteNXGraph.pckle", "wb") #stores the graph
img=io.readImage("PE00025Filter0_seg.mha",returnITK=False,imgMode="uchar")
print("image loaded")
mask = na.where(img>0, 1, 0)
print("mask created")
instance=VascMask.VascMask()
print("class instance generated")
instance.setMask(mask)
print("mask set")
instance.setNeighborhoodSize(sz=3)
print("Neighborhood gathered")
instance.getFOM()
print("FOM obtained")
instance.createNXGraph()
pickle.dump(instance.G, graph)
def A_Star():
img = io.readImage("PE00025Filter0_seg.mha",
returnITK=False,
imgMode='uchar')
mask = na.where(img > 0, 1, 0)
instance = nxvasc.nxvasc()
instance.setmask(mask)
indexes = open("Indexes.pckle", 'rb')
inds = pickle.load(indexes)
graph = open("PE00025NXgraph.pckle", 'rb')
G = pickle.load(graph)
instance.generateRawFigureOfMerit()
instance.setFigureOfMeritF(fomFuncs.fom1)
instance.setCostFunction(costFuncs.cf1)
instance.GenerateAstarPaths(G, inds)
path = open("PE00025A_StarPaths.pckle", 'wb')
pickle.dump(self.paths, path)
def GenerateGraph():
graph = open("PE00025CompleteNXGraph.pckle", "wb") #stores the graph
img = io.readImage("PE00025Filter0_seg.mha",
returnITK=False,
imgMode="uchar")
print("image loaded")
mask = na.where(img > 0, 1, 0)
print("mask created")
instance = VascMask.VascMask()
print("class instance generated")
instance.setMask(mask)
print("mask set")
instance.setNeighborhoodSize(sz=3)
print("Neighborhood gathered")
instance.getFOM()
print("FOM obtained")
instance.createNXGraph()
pickle.dump(instance.G, graph)
def main2():
#fo = file("../Skeletons/PE00000_edited_skeleton_graphs_pruned.pckle","rb")
fo = file("PE00000_edited_bc_333_skeleton_graphs_pruned.pckle", "rb")
#fo = file("PE00000_edited_bc_333_skeleton_graphs.pckle","rb")
g = cPickle.load(fo)
oldSize = g.size()
iter = 0
#print "drop short edges"
#pruner(g,prune)
#while(True):
# pruneTwosers(g)
# iter += 1
# newSize = g.size()
# if( newSize == oldSize ):
# break
# else:
# oldSize = newSize
# print iter, oldSize
#raw_input('continue')
imgitk = io.readImage("PE00000_edited_bc_333.mha",
returnITK=True,
imgMode='sshort')
img = itk.PyBuffer.ISS3.GetArrayFromImage(imgitk)
spacing = imgitk.GetSpacing()
#viewImg(img,[1,1,1], [1,],g)
#viewImg(img,spacing, [1,])
print "eliminate degree two nodes"
pruner(g, pruneTwosers)
nodes = [(94, 301, 275), (58, 221, 168), (76, 300, 183), (97, 270, 118),
(341, 319, 256)]
for i in range(20):
randomNode = pickRandomNodeOfDegree(g)
#for randomNode in nodes:
print "randomly selected node is", randomNode
h = getNodeSubgraph(g, randomNode)
visualizeXY_MPL(h, img, randomNode, spacing, buffer=20)
def main2():
#fo = file("../Skeletons/PE00000_edited_skeleton_graphs_pruned.pckle","rb")
fo = file("PE00000_edited_bc_333_skeleton_graphs_pruned.pckle","rb")
#fo = file("PE00000_edited_bc_333_skeleton_graphs.pckle","rb")
g = cPickle.load(fo)
oldSize = g.size()
iter = 0
#print "drop short edges"
#pruner(g,prune)
#while(True):
# pruneTwosers(g)
# iter += 1
# newSize = g.size()
# if( newSize == oldSize ):
# break
# else:
# oldSize = newSize
# print iter, oldSize
#raw_input('continue')
imgitk = io.readImage("PE00000_edited_bc_333.mha",returnITK=True,imgMode='sshort')
img = itk.PyBuffer.ISS3.GetArrayFromImage(imgitk)
spacing = imgitk.GetSpacing()
#viewImg(img,[1,1,1], [1,],g)
#viewImg(img,spacing, [1,])
print "eliminate degree two nodes"
pruner(g,pruneTwosers)
nodes = [(94, 301, 275), (58, 221, 168),(76, 300, 183),(97, 270, 118),(341, 319, 256)]
for i in range(20):
randomNode = pickRandomNodeOfDegree(g)
#for randomNode in nodes:
print "randomly selected node is",randomNode
h = getNodeSubgraph(g,randomNode)
visualizeXY_MPL(h,img,randomNode, spacing, buffer=20)
pyplot.title("mipy")
pyplot.subplot(223)
pyplot.title("mipx")
pyplot.imshow(slice2, interpolation = 'nearest')
figure = pyplot.show()
input('continue')
#Read in modified .pckle file
endpoints=open('Modifiedf0MipEndpoints.pckle','rb')
#endpoints=sys.argv[1]
#fo = open(endpoints,'rb')
#crds=cPickle.load(fo)
crds = pickle.load(endpoints)
#Read in image
#img= io.readImage(str(sys.argv[2]), returnITK=False, imgMode='uchar')
img= io.readImage('PE00026Filter0_seg.mha', returnITK=False, imgMode='uchar')
mask = na.where(img>0, 1, 0) #create the mask
point=nxvasc.nxvasc() #create an instance of the class
point.setMask(mask) #Set the mask using the numpy image mask. In addition to
#reading the mask, the 1D indicies into the 3D image are generate and stored
point.createMaskDictionary() #Create dictionary that maps the index into the 3D
#volume to an index into the mask. The 1D array into the 3D volume is the key &
#the ordinal position of the index is the value
point.get3x3matrix() #define relationship between neighbors
inds=point.getInds(crds)#get corresponding indexes
#get the crds of all pts within the 3x3 or 26-point neighborhood of index (i)
for crd in crds:
#grab the 3x3 neighborhood of i and convert to list
neighbors = mask[crd[2]-1:crd[2]+2, crd[1]-1:crd[1]+2, crd[0]-1:crd[0]+2]
#plot=showPoints(point, neighbors)
# get the orthogonal (x,y,z) MIPS of the mask.
#5x5identifyNonEndpoints.py
import os
import sys
"""Goes and grabs the 5x5 neighborhoods of the non endpoints"""
sys.path.append("../vasctrees")
import numpy as na
import random
import pickle
import imageTools.ITKUtils.io as io
import nxvasc
from sliceOrientation import OrientMask
img = io.readImage("PE00001Filter0_seg.mha", returnITK=False, imgMode="uchar")
nonPoints = open("PE00001NonEndpoints.pckle", 'rb')
nonendpoints = pickle.load(
nonPoints) #points from ModifiedGenerateNonEndpoints.py
output2 = open("PE00001NeighborsNonPoints5x5.pckle",
'wb') #store 5x5 neighborhood
#Must create the mask so we can identify the points that are not endpoints
mask = na.where(img > 0, 1, 0)
point = nxvasc.nxvasc()
point.setMask(mask)
point.createMaskDictionary()
point.get5x5matrix()
crds = point.get_crds(nonendpoints)
ModifiedSampledPoints = []
ModifiedPoints = []
for crd in crds:
print(crd)
#get the neighbors to add up the neighborhood
neighbors = mask[crd[2] - 2:crd[2] + 3, crd[1] - 2:crd[1] + 3,
"""The purpose of this code is to perform random sampling of non-endpoints
and grab the 7x7 neighborhoods"""
import os
import sys
sys.path.append("../vasctrees")
import nxvasc
from sliceOrientation import OrientMask
import imageTools.ITKUtils.io as io
import scipy.ndimage as ndi
import numpy as na
import pickle
fle=open("PE00001NonEndpoints.pckle",'wb')
output2=open("PE00001NeighborsNonPoints7x7.pckle", 'wb')
img=io.readImage("PE00001Filter0_seg.mha", returnITK=False, imgMode="uchar")
#Must create the mask so we can identify the points that are not endpoints
mask=na.where(img>0, 1, 0)
point=nxvasc.nxvasc()
point.setMask(mask)
point.createMaskDictionary()
point.get7x7matrix()
f1=ndi.distance_transform_cdt(img)
surface=na.where(f1==1,1,0)
indsSurface =na.nonzero(surface.flat==1)[0]
length=len(indsSurface)
print("length",length)
ModifiedSampledPoints=[]
ModifiedPoints=[]
points=[]
for i in indsSurface:
if len(ModifiedPoints)<112:
