def clearScalars( trkFile1, outputFile ):
'''
Copy scalars from trkFile1 to trkFile2
'''
s = io.loadTrk( trkFile1 )
tracks = s[0]
origHeader = s[1]
tracksHeader = numpy.copy( s[1] )
newTracks = []
for tCounter, t in enumerate( tracks ):
tCoordinates = t[0]
tScalars = t[1]
tProperties = t[2]
# clear scalars
newTracks.append( ( tCoordinates, None, tProperties ) )
# write trkFile2 with update scalars
io.saveTrk( outputFile, newTracks, tracksHeader, None, True )
c.info( 'Cleared scalars from ' + trkFile1 + ' and saved as ' + outputFile )
def transform( tracks, matrix, outputFile=None, verbose=False, threadName='Global' ):
'''
'''
# O(Tracks x Points)
#
# loop through all tracks and transform'em!!
for t in xrange( len( tracks ) ):
track = tracks[t]
points = track[0]
newPoints = numpy.copy( points )
# loop through all points of the current track
for p in xrange( len( points ) ):
pointBefore = points[p]
pointAfter = numpy.append( pointBefore, 1 )
pointAfter = numpy.dot( matrix, pointAfter )
pointAfter = numpy.delete( pointAfter, -1 )
newPoints[p] = pointAfter
# create a new track with the transformed points
newTrack = ( newPoints, track[1], track[2] )
# replace the old track with the newTrack
tracks[t] = newTrack
if not outputFile:
return tracks
else:
# write it out to disk
io.saveTrk( outputFile, tracks, None, None, True )
def sub(master,
tracks,
outputFile=None,
verbose=False,
threadName='Global'):
'''
Subtract tracks from master. Both parameters are nibabel.trackvis.streamlines objects.
Calculation cost: O(M*N)
Returns the result as a nibabel.trackvis.streamlines object or writes it to the file system if an outputFile is specified.
'''
masterSizeBefore = len(master)
subtractedCount = 0
# O(M*N)
for t in xrange(masterSizeBefore):
if subtractedCount == len(tracks):
# no way we can subtract more.. stop the loop
return master
c.debug(
threadName +
': Looking for more tracks to subtract.. [Check #' + str(t) +
'/' + str(masterSizeBefore) + ']', verbose)
if master[t] == -1:
# this fiber was already removed, skip to next one
continue
for u in xrange(len(tracks)):
if tracks[u] == -1:
# this fiber was already removed, skip to next one
continue
# compare fiber
if [p for points in master[t][0] for p in points
] == [p for points in tracks[u][0] for p in points]:
# fibers are equal, set them as dirty
master[t] = -1
tracks[u] = -1
subtractedCount += 1
# ... and jump out
break
master = filter(lambda t: t != -1, master)
if not outputFile:
return master
else:
# write it out to disk
io.saveTrk(outputFile, master, None, None, True)
def sub(master, tracks, outputFile=None, verbose=False, threadName="Global"):
"""
Subtract tracks from master. Both parameters are nibabel.trackvis.streamlines objects.
Calculation cost: O(M*N)
Returns the result as a nibabel.trackvis.streamlines object or writes it to the file system if an outputFile is specified.
"""
masterSizeBefore = len(master)
subtractedCount = 0
# O(M*N)
for t in xrange(masterSizeBefore):
if subtractedCount == len(tracks):
# no way we can subtract more.. stop the loop
return master
c.debug(
threadName
+ ": Looking for more tracks to subtract.. [Check #"
+ str(t)
+ "/"
+ str(masterSizeBefore)
+ "]",
verbose,
)
if master[t] == -1:
# this fiber was already removed, skip to next one
continue
for u in xrange(len(tracks)):
if tracks[u] == -1:
# this fiber was already removed, skip to next one
continue
# compare fiber
if [p for points in master[t][0] for p in points] == [p for points in tracks[u][0] for p in points]:
# fibers are equal, set them as dirty
master[t] = -1
tracks[u] = -1
subtractedCount += 1
# ... and jump out
break
master = filter(lambda t: t != -1, master)
if not outputFile:
return master
else:
# write it out to disk
io.saveTrk(outputFile, master, None, None, True)
def __init__(self, scalarName, volume):
"""
"""
super(FyMapAction, self).__init__(scalarName)
# load volume
self._image = io.readImage(volume)
self._imageHeader = self._image.header
self._imageDimensions = self._image.shape[:3]
self._imageSpacing = self._imageHeader.get_zooms()[:3]
def run( self, files ):
'''
'''
for f in files:
header = io.loadTrkHeaderOnly( f )
dimensions = header['dim']
spacing = header['voxel_size']
origin = header['origin']
numberOfScalars = header['n_scalars']
scalarNames = header['scalar_name']
numberOfProperties = header['n_properties']
propertyNames = header['property_name']
vox2rasMatrix = header['vox_to_ras']
voxelOrder = header['voxel_order']
pad1 = header['pad1']
pad2 = header['pad2']
imageOrientation = header['image_orientation_patient']
numberOfTracks = header['n_count']
version = header['version']
c.info( 'FILE: ' + f )
c.info( ' TRACKVIS VERSION: ' + str( version ) )
c.info( ' NUMBER OF TRACKS: ' + str( numberOfTracks ) )
c.info( ' DIMENSIONS: ' + str( dimensions ) )
c.info( ' SPACING: ' + str( spacing ) )
c.info( ' ORIGIN: ' + str( origin ) )
c.info( ' NUMBER OF SCALARS: ' + str( numberOfScalars ) )
if numberOfScalars > 0:
c.info( ' SCALARS: ' + str( scalarNames ) )
c.info( ' NUMBER OF PROPERTIES: ' + str( numberOfProperties ) )
if numberOfProperties > 0:
c.info( ' PROPERTIES: ' + str( propertyNames ) )
if version == 2:
# only in trackvis v2
c.info( ' VOX2RAS Matrix:' )
c.info( ' ' + str( vox2rasMatrix[0] ) )
c.info( ' ' + str( vox2rasMatrix[1] ) )
c.info( ' ' + str( vox2rasMatrix[2] ) )
c.info( ' ' + str( vox2rasMatrix[3] ) )
c.info( ' VOXEL ORDER: ' + str( voxelOrder ) )
#c.info( ' IMAGE ORIENTATION: ' )
#c.info( ' ' + str( imageOrientation ) )
#c.info( ' PADDING 1: ' + str( pad1 ) )
#c.info( ' PADDING 2: ' + str( pad2 ) )
print
def run(self, files):
'''
'''
for f in files:
header = io.loadTrkHeaderOnly(f)
dimensions = header['dim']
spacing = header['voxel_size']
origin = header['origin']
numberOfScalars = header['n_scalars']
scalarNames = header['scalar_name']
numberOfProperties = header['n_properties']
propertyNames = header['property_name']
vox2rasMatrix = header['vox_to_ras']
voxelOrder = header['voxel_order']
pad1 = header['pad1']
pad2 = header['pad2']
imageOrientation = header['image_orientation_patient']
numberOfTracks = header['n_count']
version = header['version']
c.info('FILE: ' + f)
c.info(' TRACKVIS VERSION: ' + str(version))
c.info(' NUMBER OF TRACKS: ' + str(numberOfTracks))
c.info(' DIMENSIONS: ' + str(dimensions))
c.info(' SPACING: ' + str(spacing))
c.info(' ORIGIN: ' + str(origin))
c.info(' NUMBER OF SCALARS: ' + str(numberOfScalars))
if numberOfScalars > 0:
c.info(' SCALARS: ' + str(scalarNames))
c.info(' NUMBER OF PROPERTIES: ' + str(numberOfProperties))
if numberOfProperties > 0:
c.info(' PROPERTIES: ' + str(propertyNames))
if version == 2:
# only in trackvis v2
c.info(' VOX2RAS Matrix:')
c.info(' ' + str(vox2rasMatrix[0]))
c.info(' ' + str(vox2rasMatrix[1]))
c.info(' ' + str(vox2rasMatrix[2]))
c.info(' ' + str(vox2rasMatrix[3]))
c.info(' VOXEL ORDER: ' + str(voxelOrder))
#c.info( ' IMAGE ORIENTATION: ' )
#c.info( ' ' + str( imageOrientation ) )
#c.info( ' PADDING 1: ' + str( pad1 ) )
#c.info( ' PADDING 2: ' + str( pad2 ) )
print
def copyScalars( trkFile1, trkFile2, outputFile ):
'''
Copy scalars from trkFile1 to trkFile2
'''
s = io.loadTrk( trkFile1 )
s2 = io.loadTrk( trkFile2 )
tracks = s[0]
tracks2 = s2[0]
origHeader = s[1]
origHeader2 = s2[1]
tracksHeader = numpy.copy( s[1] )
tracksHeader2 = numpy.copy( s2[1] )
#if tracksHeader['n_count'] != tracksHeader2['n_count']:
# c.error( 'The track counts do not match!' )
# sys.exit( 2 )
# now copy
tracksHeader2['n_scalars'] = tracksHeader['n_scalars']
tracksHeader2['scalar_name'] = tracksHeader['scalar_name']
newTracks2 = []
for tCounter, t in enumerate( tracks ):
tCoordinates = t[0]
tScalars = t[1]
# copy scalars over
#tracks2[tCounter][1] = numpy.copy( tScalars )
newTracks2.append( ( tracks2[tCounter][0], tScalars[:], tracks2[tCounter][2] ) )
# write trkFile2 with update scalars
io.saveTrk( outputFile, newTracks2, tracksHeader2, None, True )
c.info( 'Copied Scalars from ' + trkFile1 + ' to ' + trkFile2 + ' and saved as ' + outputFile )
def createSampleTrkFile( outputfile ):
'''
Create a sample track file which contains fibers with random points.
The fiber coordinates match the sample volume from above.
'''
fibers = []
numberOfFibers = NUMBER_OF_FIBERS
print Colors.PURPLE + 'Creating a sample trkFile (' + Colors.CYAN + str( numberOfFibers ) + ' fibers' + Colors.PURPLE + '): ' + Colors.ORANGE + outputfile + Colors.PURPLE + '..' + Colors._CLEAR
for f in range( numberOfFibers ):
# from 3 to 10 points, randomly chosen
numberOfPoints = random.randint( 3, 10 )
# the point array reserves 3 components for each point (x,y,z)
points = numpy.empty( shape=( numberOfPoints, 3 ), dtype=numpy.float32 )
for p in range( numberOfPoints ):
# create random points with coordinates in the range 0..9 to match the sample volume's dimensions
points[p] = [random.randint( 0, int( SAMPLE_VOLUME_DIMENSION_X * SAMPLE_VOLUME_SPACING_X ) - 1 ),
random.randint( 0, int( SAMPLE_VOLUME_DIMENSION_Y * SAMPLE_VOLUME_SPACING_Y ) - 1 ),
random.randint( 0, int( SAMPLE_VOLUME_DIMENSION_Z * SAMPLE_VOLUME_SPACING_Z ) - 1 )]
# create an appropriate header
header = eH()
header['voxel_size'] = ( SAMPLE_VOLUME_SPACING_X, SAMPLE_VOLUME_SPACING_Y, SAMPLE_VOLUME_SPACING_Z )
header['dim'] = ( SAMPLE_VOLUME_DIMENSION_X, SAMPLE_VOLUME_DIMENSION_Y, SAMPLE_VOLUME_DIMENSION_Z )
# store the trk file
fibers.append( ( points, None, None ) )
io.saveTrk( outputfile, fibers, header, None, True )
return numberOfFibers
from _common import FNNDSCConsole as c
from _common import FNNDSCFileIO as io
from _common import FNNDSCUtil as u
import sys
import numpy
# ENTRYPOINT
if __name__ == "__main__":
track = sys.argv[1]
trackId = int(sys.argv[2])
volume = sys.argv[3]
s = io.loadTrk(track)
tracks = s[0]
origHeader = s[1]
image = io.readImage(volume)
imageHeader = image.header
imageDimensions = image.shape[:3]
imageSpacing = imageHeader.get_zooms()
singleTrack = tracks[trackId]
coords = singleTrack[0]
valueSum = 0
length = 0
_last = None
def run(self, input, output, mode, verbose, jobs):
if len(input) < 2:
c.error("Please specify at least two *.trk files as input!")
sys.exit(2)
if os.path.exists(output):
# abort if file already exists
c.error("File " + str(output) + " already exists..")
c.error("Aborting..")
sys.exit(2)
jobs = int(jobs)
if jobs < 1 or jobs > 32:
jobs = 1
# load 'master'
mTracks = io.loadTrk(input[0])
# copy the tracks and the header from the 'master'
c.info("Master is " + input[0])
outputTracks = mTracks[0]
c.info("Number of tracks: " + str(len(outputTracks)))
header = mTracks[1]
# remove the first input
input.pop(0)
if mode == "add":
#
# ADD
#
for i in input:
iTracks = io.loadTrk(i)
# add the tracks
c.debug("Adding " + str(len(iTracks[0])) + " tracks from " + i + " to master..", verbose)
outputTracks = TrackvisCalcLogic.add(outputTracks, iTracks[0])
c.debug("Number of output tracks after final addition: " + str(len(outputTracks)), verbose)
elif mode == "sub":
#
# SUB
#
c.debug("Using " + str(jobs) + " threads..", verbose)
mergedOutputTracks = outputTracks[:]
for i in input:
iTracks = io.loadTrk(i)
# subtract the tracks
c.info("Subtracting " + i + " (" + str(len(iTracks[0])) + " tracks) from master..")
#
# THREADED COMPONENT
#
numberOfThreads = jobs
c.info("Splitting master into " + str(jobs) + " pieces..")
splittedOutputTracks = u.split_list(mergedOutputTracks, numberOfThreads)
# list of threads
t = [None] * numberOfThreads
# list of alive flags
a = [None] * numberOfThreads
# list of tempFiles
f = [None] * numberOfThreads
for n in xrange(numberOfThreads):
# mark thread as alive
a[n] = True
# fire the thread and give it a filename based on the number
tmpFile = tempfile.mkstemp(".trk", "t_calc")[1]
f[n] = tmpFile
t[n] = Process(
target=TrackvisCalcLogic.sub,
args=(splittedOutputTracks[n][:], iTracks[0][:], tmpFile, verbose, "Thread-" + str(n + 1)),
)
c.info("Starting Thread-" + str(n + 1) + "...")
t[n].start()
allDone = False
while not allDone:
time.sleep(1)
for n in xrange(numberOfThreads):
a[n] = t[n].is_alive()
if not any(a):
# if no thread is alive
allDone = True
#
# END OF THREADED COMPONENT
#
c.info("All Threads done!")
c.info("Merging output..")
# now read all the created tempFiles and merge'em to one
# first thread output is the master here
tmpMaster = f[0]
tMasterTracks = io.loadTrk(tmpMaster)
for tmpFileNo in xrange(1, len(f)):
tTracks = io.loadTrk(f[tmpFileNo])
# add them
mergedOutputTracks = TrackvisCalcLogic.add(tMasterTracks[0], tTracks[0])
c.info("Merging done!")
# some stats
c.info("Number of output tracks after final removal: " + str(len(mergedOutputTracks)))
outputTracks = mergedOutputTracks
# now save the outputTracks
io.saveTrk(output, outputTracks, header)
c.info("All done!")
def makeMatrix(inputs, outputs, no_cortex):
"""
Make 1/ADC, ADC, FA, FiberNumber, FiberLength, E1, E2, E3 connectivity matrices.
"""
s = io.loadTrk(outputs["fibers_final"])
tracks = s[0]
header = s[1]
scalarNames = header["scalar_name"].tolist()
matrix = {}
# check if the segmentation is mapped
try:
scalarNames.index("segmentation")
except:
c.error(Colors.RED)
for s in scalarNames:
if not s:
continue
print s
return
for i in inputs:
if i == "fibers" or i == "segmentation" or i == "T1" or i == "b0":
# we do not map these
continue
# for tCounter, t in enumerate( tracks ):
try:
labelIndex = scalarNames.index("segmentation")
adcIndex = scalarNames.index("adc")
faIndex = scalarNames.index("fa")
e1Index = scalarNames.index("e1")
e2Index = scalarNames.index("e2")
e3Index = scalarNames.index("e3")
lengthIndex = scalarNames.index("length")
except:
c.error("Not all scalars were found: aparc_aseg_endlabel, adc, fa, length, e1, e2, e3")
sys.exit(2)
m_fn = numpy.zeros([68, 68])
m_fa = numpy.zeros([68, 68])
m_adc = numpy.zeros([68, 68])
m_adcinv = numpy.zeros([68, 68])
m_len = numpy.zeros([68, 68])
m_e1 = numpy.zeros([68, 68])
m_e2 = numpy.zeros([68, 68])
m_e3 = numpy.zeros([68, 68])
fslabel_vol = [
2012,
2019,
2032,
2014,
2020,
2018,
2027,
2028,
2003,
2024,
2017,
2026,
2002,
2023,
2010,
2022,
2031,
2029,
2008,
2025,
2005,
2021,
2011,
2013,
2007,
2016,
2006,
2033,
2009,
2015,
2001,
2030,
2034,
2035,
1012,
1019,
1032,
1014,
1020,
1018,
1027,
1028,
1003,
1024,
1017,
1026,
1002,
1023,
1010,
1022,
1031,
1029,
1008,
1025,
1005,
1021,
1011,
1013,
1007,
1016,
1006,
1033,
1009,
1015,
1001,
1030,
1034,
1035,
]
for tCounter, t in enumerate(tracks):
tCoordinates = t[0]
tScalars = t[1]
fa = numpy.mean(tScalars[:, faIndex])
adc = numpy.mean(tScalars[:, adcIndex])
e1 = numpy.mean(tScalars[:, e1Index])
e2 = numpy.mean(tScalars[:, e2Index])
e3 = numpy.mean(tScalars[:, e3Index])
len = tScalars[0, lengthIndex]
firstLabel = tScalars[0, labelIndex]
lastLabel = tScalars[-1, labelIndex]
try:
fIndex = fslabel_vol.index(firstLabel)
lIndex = fslabel_vol.index(lastLabel)
except:
continue
print "found", firstLabel, lastLabel
m_fn[fIndex, lIndex] += 1
m_fa[fIndex, lIndex] += fa
m_adc[fIndex, lIndex] += adc
m_e1[fIndex, lIndex] += e1
m_e2[fIndex, lIndex] += e2
m_e3[fIndex, lIndex] += e3
m_adcinv[fIndex, lIndex] += 1 / adc
m_len[fIndex, lIndex] += len
# symmetrize matrices
m_fn = m_fn + m_fn.T - numpy.diag(m_fn.diagonal())
m_fa = m_fa + m_fa.T - numpy.diag(m_fa.diagonal())
m_adc = m_adc + m_adc.T - numpy.diag(m_adc.diagonal())
m_e1 = m_e1 + m_e1.T - numpy.diag(m_e1.diagonal())
m_e2 = m_e2 + m_e2.T - numpy.diag(m_e2.diagonal())
m_e3 = m_e3 + m_e3.T - numpy.diag(m_e3.diagonal())
m_adcinv = m_adcinv + m_adcinv.T - numpy.diag(m_adcinv.diagonal())
m_len = m_len + m_len.T - numpy.diag(m_len.diagonal())
# normalize matrices
m_fa[:] /= m_fn[:]
m_adc[:] /= m_fn[:]
m_e1[:] /= m_fn[:]
m_e2[:] /= m_fn[:]
m_e3[:] /= m_fn[:]
m_adcinv[:] /= m_fn[:]
m_len[:] /= m_fn[:]
m_fa = numpy.nan_to_num(m_fa)
m_e1 = numpy.nan_to_num(m_e1)
m_e2 = numpy.nan_to_num(m_e2)
m_e3 = numpy.nan_to_num(m_e3)
m_adc = numpy.nan_to_num(m_adc)
m_adcinv = numpy.nan_to_num(m_adcinv)
m_len = numpy.nan_to_num(m_len)
# save as .mat and .csv
sio.savemat(
outputDirectory + "fibmap_all_cMatrix.mat",
{
"m_fiberNumber": m_fn,
"m_fa": m_fa,
"m_adc": m_adc,
"m_adcInverse": m_adcinv,
"m_fiberLength": m_len,
"m_e1": m_e1,
"m_e2": m_e2,
"m_e3": m_e3,
},
)
numpy.savetxt(outputDirectory + "fibmap_fibernumber_cMatrix.csv", m_fn, delimiter=",")
numpy.savetxt(outputDirectory + "fibmap_fa_cMatrix.csv", m_fa, delimiter=",")
numpy.savetxt(outputDirectory + "fibmap_e1_cMatrix.csv", m_e1, delimiter=",")
numpy.savetxt(outputDirectory + "fibmap_e2_cMatrix.csv", m_e2, delimiter=",")
numpy.savetxt(outputDirectory + "fibmap_e3_cMatrix.csv", m_e3, delimiter=",")
numpy.savetxt(outputDirectory + "fibmap_adc_cMatrix.csv", m_adc, delimiter=",")
numpy.savetxt(outputDirectory + "fibmap_adcinv_cMatrix.csv", m_adcinv, delimiter=",")
numpy.savetxt(outputDirectory + "fibmap_fiberlength_cMatrix.csv", m_len, delimiter=",")
c.info("Connectivity matrices generated and stored.")
from _common import FNNDSCFileIO as io from _common import FNNDSCUtil as u import sys import numpy # ENTRYPOINT if __name__ == "__main__": track = sys.argv[1] trackId = int( sys.argv[2] ) volume = sys.argv[3] s = io.loadTrk( track ) tracks = s[0] origHeader = s[1] image = io.readImage( volume ) imageHeader = image.header imageDimensions = image.shape[:3] imageSpacing = imageHeader.get_zooms() singleTrack = tracks[trackId] coords = singleTrack[0] valueSum = 0
save_image(img, volFile)
# trk file
fibers = []
# 2,5,6
# 3,5,7
# 2,6,7
# 8,7,3
# 9,5,4
points = np.array([[2, 5, 6], [3, 5, 7], [2, 6, 7], [8, 7, 3], [9, 5, 4]],
dtype=np.float32)
fibers.append((points, None, None))
io.saveTrk(trkFile, fibers, None, None, True)
# with fyborg
fyborg.fyborg(trkFile, mappedTrkFile, [fyborg.FyMapAction('test', volFile)])
# now validate
s = io.loadTrk(mappedTrkFile)
tracks = s[0]
origHeader = s[1]
scalars = tracks[0][1]
print scalars[0], '==', testArr[2][5][6]
print scalars[1], '==', testArr[3][5][7]
print scalars[2], '==', testArr[2][6][7]
print scalars[3], '==', testArr[8][7][3]
print scalars[4], '==', testArr[9][5][4]
def run( self, input, output, matrix, jobs ):
'''
'''
if os.path.exists( output ):
# abort if file already exists
c.error( 'File ' + str( output ) + ' already exists..' )
c.error( 'Aborting..' )
sys.exit( 2 )
if not os.path.isfile( matrix ):
# abort if the matrix does not exist
c.error( 'Matrix-File ' + str( matrix ) + ' does not exist..' )
c.error( 'Aborting..' )
sys.exit( 2 )
jobs = int( jobs )
if jobs < 1 or jobs > 32:
jobs = 1
# read
c.info( 'Loading ' + input + '..' )
t = io.loadTrk( input )
tracks = t[0]
header = t[1]
#.. copy the current header
newHeader = numpy.copy( header )
# print old matrix in header
#
# WARNING: this matrix is actually never used by TrackVis (see email from Ruopeng).
# We still modify it to keep it in sync with the transformations which we apply point wise.
#
if hasattr( header, 'vox_to_ras' ):
oldMatrix = header['vox_to_ras']
c.info( 'Old transformation matrix:' )
c.info( ' ' + str( oldMatrix[0] ) )
c.info( ' ' + str( oldMatrix[1] ) )
c.info( ' ' + str( oldMatrix[2] ) )
c.info( ' ' + str( oldMatrix[3] ) )
#
# load our transformation Matrix
#
newMatrix = numpy.loadtxt( matrix, float, '#', ' ' )
#
# THREADED COMPONENT
#
numberOfThreads = jobs
c.info( 'Splitting the input into ' + str( jobs ) + ' pieces..' )
splittedOutputTracks = u.split_list( tracks, numberOfThreads )
# list of threads
t = [None] * numberOfThreads
# list of alive flags
a = [None] * numberOfThreads
# list of tempFiles
f = [None] * numberOfThreads
for n in xrange( numberOfThreads ):
# mark thread as alive
a[n] = True
# fire the thread and give it a filename based on the number
tmpFile = tempfile.mkstemp( '.trk', 't_transform' )[1]
f[n] = tmpFile
t[n] = Process( target=TrackvisTransformLogic.transform, args=( splittedOutputTracks[n][:], newMatrix, tmpFile, False, 'Thread-' + str( n + 1 ) ) )
c.info( "Starting Thread-" + str( n + 1 ) + "..." )
t[n].start()
allDone = False
while not allDone:
time.sleep( 1 )
for n in xrange( numberOfThreads ):
a[n] = t[n].is_alive()
if not any( a ):
# if no thread is alive
allDone = True
#
# END OF THREADED COMPONENT
#
c.info( "All Threads done!" )
c.info( "Merging output.." )
# now read all the created tempFiles and merge'em to one
# first thread output is the master here
tmpMaster = f[0]
tMasterTracks = io.loadTrk( tmpMaster )
for tmpFileNo in xrange( 1, len( f ) ):
tTracks = io.loadTrk( f[tmpFileNo] )
# add them
tracks = TrackvisCalcLogic.add( tMasterTracks[0], tTracks[0] )
c.info( "Merging done!" )
#
# replace the matrix in the header with a transformed one even if it will never be used by TrackVis
#
if hasattr( header, 'vox_to_ras' ):
result = numpy.dot( oldMatrix, newMatrix )
c.info( 'New transformation matrix:' )
c.info( ' ' + str( result[0] ) )
c.info( ' ' + str( result[1] ) )
c.info( ' ' + str( result[2] ) )
c.info( ' ' + str( result[3] ) )
newHeader['vox_to_ras'] = result
# write
c.info( 'Saving ' + output + '..' )
io.saveTrk( output, tracks, newHeader )
c.info( 'All done!' )
def fyborg( trkFile, outputTrkFile, actions, *args ):
if not actions:
c.error( "We gotta do something.." )
return
showDebug = 'debug' in args
singleThread = 'singlethread' in args
c.debug( "trkFile:" + str( trkFile ), showDebug )
c.debug( "outputTrkFile:" + str( outputTrkFile ), showDebug )
c.debug( "args:" + str( args ), showDebug )
# load trk file
s = io.loadTrk( trkFile )
tracks = s[0]
origHeader = s[1]
tracksHeader = numpy.copy( s[1] )
numberOfScalars = origHeader['n_scalars']
scalars = origHeader['scalar_name'].tolist()
numberOfTracks = origHeader['n_count']
# show some file informations
printTrkInfo( tracksHeader, trkFile )
# grab the scalarNames
scalarNames = []
for a in actions:
if a.scalarName() != FyAction.NoScalar:
scalarNames.append( a.scalarName() )
# increase the number of scalars
tracksHeader['n_scalars'] += len( scalarNames )
# .. attach the new scalar names
for i in range( len( scalarNames ) ):
tracksHeader['scalar_name'][numberOfScalars + i] = scalarNames[i]
#
# THREADED COMPONENT
#
if singleThread:
numberOfThreads = 1
else:
numberOfThreads = multiprocessing.cpu_count()
c.info( 'Splitting master into ' + str( numberOfThreads ) + ' pieces..' )
splittedOutputTracks = u.split_list( tracks[:], numberOfThreads )
# list of threads
t = [None] * numberOfThreads
# list of alive flags
a = [None] * numberOfThreads
# list of tempFiles
f = [None] * numberOfThreads
for n in xrange( numberOfThreads ):
# configure actions
__actions = []
for act in actions:
__actions.append( act )
# mark thread as alive
a[n] = True
# fire the thread and give it a filename based on the number
tmpFile = tempfile.mkstemp( '.trk', 'fyborg' )[1]
f[n] = tmpFile
t[n] = Process( target=fyborgLooper_, args=( splittedOutputTracks[n][:], tracksHeader, tmpFile, __actions, showDebug, n + 1 ) )
c.info( "Starting Thread-" + str( n + 1 ) + "..." )
t[n].start()
allDone = False
while not allDone:
time.sleep( 1 )
for n in xrange( numberOfThreads ):
a[n] = t[n].is_alive()
if not any( a ):
# if no thread is alive
allDone = True
#
# END OF THREADED COMPONENT
#
c.info( "All Threads done!" )
#
# Merging stage
#
c.info( "Merging tracks.." )
outputTracks = []
# now read all the created tempFiles and merge'em to one
for tmpFileNo in xrange( 0, len( f ) ):
tTracks = io.loadTrk( f[tmpFileNo] )
# add them
outputTracks.extend( tTracks[0] )
c.info( "Merging done!" )
io.saveTrk( outputTrkFile, outputTracks, tracksHeader, None, True )
c.info( "All done!" )
def run(self, input, output, mode, verbose, jobs):
if len(input) < 2:
c.error('Please specify at least two *.trk files as input!')
sys.exit(2)
if os.path.exists(output):
# abort if file already exists
c.error('File ' + str(output) + ' already exists..')
c.error('Aborting..')
sys.exit(2)
jobs = int(jobs)
if jobs < 1 or jobs > 32:
jobs = 1
# load 'master'
mTracks = io.loadTrk(input[0])
# copy the tracks and the header from the 'master'
c.info('Master is ' + input[0])
outputTracks = mTracks[0]
c.info('Number of tracks: ' + str(len(outputTracks)))
header = mTracks[1]
# remove the first input
input.pop(0)
if mode == 'add':
#
# ADD
#
for i in input:
iTracks = io.loadTrk(i)
# add the tracks
c.debug(
'Adding ' + str(len(iTracks[0])) + ' tracks from ' + i +
' to master..', verbose)
outputTracks = TrackvisCalcLogic.add(outputTracks, iTracks[0])
c.debug(
'Number of output tracks after final addition: ' +
str(len(outputTracks)), verbose)
elif mode == 'sub':
#
# SUB
#
c.debug('Using ' + str(jobs) + ' threads..', verbose)
mergedOutputTracks = outputTracks[:]
for i in input:
iTracks = io.loadTrk(i)
# subtract the tracks
c.info('Subtracting ' + i + ' (' + str(len(iTracks[0])) +
' tracks) from master..')
#
# THREADED COMPONENT
#
numberOfThreads = jobs
c.info('Splitting master into ' + str(jobs) + ' pieces..')
splittedOutputTracks = u.split_list(mergedOutputTracks,
numberOfThreads)
# list of threads
t = [None] * numberOfThreads
# list of alive flags
a = [None] * numberOfThreads
# list of tempFiles
f = [None] * numberOfThreads
for n in xrange(numberOfThreads):
# mark thread as alive
a[n] = True
# fire the thread and give it a filename based on the number
tmpFile = tempfile.mkstemp('.trk', 't_calc')[1]
f[n] = tmpFile
t[n] = Process(target=TrackvisCalcLogic.sub,
args=(splittedOutputTracks[n][:],
iTracks[0][:], tmpFile, verbose,
'Thread-' + str(n + 1)))
c.info("Starting Thread-" + str(n + 1) + "...")
t[n].start()
allDone = False
while not allDone:
time.sleep(1)
for n in xrange(numberOfThreads):
a[n] = t[n].is_alive()
if not any(a):
# if no thread is alive
allDone = True
#
# END OF THREADED COMPONENT
#
c.info("All Threads done!")
c.info("Merging output..")
# now read all the created tempFiles and merge'em to one
# first thread output is the master here
tmpMaster = f[0]
tMasterTracks = io.loadTrk(tmpMaster)
for tmpFileNo in xrange(1, len(f)):
tTracks = io.loadTrk(f[tmpFileNo])
# add them
mergedOutputTracks = TrackvisCalcLogic.add(
tMasterTracks[0], tTracks[0])
c.info("Merging done!")
# some stats
c.info('Number of output tracks after final removal: ' +
str(len(mergedOutputTracks)))
outputTracks = mergedOutputTracks
# now save the outputTracks
io.saveTrk(output, outputTracks, header)
c.info('All done!')
save_image( img, volFile ) # trk file fibers = [] # 2,5,6 # 3,5,7 # 2,6,7 # 8,7,3 # 9,5,4 points = np.array( [[2, 5, 6], [3, 5, 7], [2, 6, 7], [8, 7, 3], [9, 5, 4]], dtype=np.float32 ) fibers.append( ( points, None, None ) ) io.saveTrk( trkFile, fibers, None, None, True ) # with fyborg fyborg.fyborg( trkFile, mappedTrkFile, [fyborg.FyMapAction( 'test', volFile )] ) # now validate s = io.loadTrk( mappedTrkFile ) tracks = s[0] origHeader = s[1] scalars = tracks[0][1] print scalars[0], '==', testArr[2][5][6] print scalars[1], '==', testArr[3][5][7] print scalars[2], '==', testArr[2][6][7] print scalars[3], '==', testArr[8][7][3] print scalars[4], '==', testArr[9][5][4]
def validateMapping( volumefile, trkfile, radius=0, map_intermediate=True ):
'''
Check if a trk file has correctly mapped scalar values from a volume file.
If radius is > 0 take it into account by looking for the most common value in a sphere
around the original point. This only happens on start and end points so.
If map_intermediate is active, also the points between end points are validated but never
using the radius.
Returns TRUE if everything is fine, FALSE if there were errors.
'''
# load the mapped trk file
s = io.loadTrk( trkfile )
volume = io.readImage( volumefile )
imageHeader = volume.header
image_size = volume.shape[:3]
# grab the tracks
tracks = s[0]
# pad the image with zeros
image = ap.pad( volume, radius, 'constant', constant_values=( 0 ) )
# any errors?
any_errors = False
# incorporate spacing
spacing = imageHeader.get_zooms()[:3]
# .. and loop through them
for t in tracks:
points = t[0] # the points of this fiber track
scalars = t[1] # the mapped scalars
for index, p in enumerate( points ):
current_point = [ int( a / b ) for a, b in zip( [p[0], p[1], p[2]], spacing )]
#print 'ORIG', volume[current_point[0], current_point[1], current_point[2]]
is_first_point = ( index == 0 )
is_last_point = ( index == len( points ) - 1 )
# if this is
if not map_intermediate and not is_first_point and not is_last_point:
real_scalar = 0.0
else:
# here we check for the neighborhood if radius > 0
if radius > 0 and ( is_first_point or is_last_point ):
# neighborhood search!
r = radius
a, b, c = current_point
# crop the image according to the neighborhood look-up
# since we zero-padded the image, we don't need boundary checks here
min_x = a - r
max_x = a + r + 1
min_y = b - r
max_y = b + r + 1
min_z = c - r
max_z = c + r + 1
cropped_image = numpy.asarray( image[min_x + r:max_x + r, min_y + r:max_y + r, min_z + r:max_z + r] )
# create a sphere mask
x, y, z = numpy.ogrid[0:2 * r + 1, 0:2 * r + 1, 0:2 * r + 1]
mask = ( x - r ) ** 2 + ( y - r ) ** 2 + ( z - r ) ** 2 <= r * r # 3d sphere mask
# apply the mask
masked_container = cropped_image[mask]
# throw away all zeros (0)
masked_container = masked_container[numpy.nonzero( masked_container )]
# find the most frequent label in the masked container
from collections import Counter
# by default, we use the original one
mostFrequentLabel = volume[a, b, c]
if len( masked_container ) != 0:
counter = Counter( masked_container )
all_labels = counter.most_common()
best_match_label = counter.most_common( 1 )
original_pos = [i for i, v in enumerate( all_labels ) if v[0] == mostFrequentLabel]
if not original_pos or all_labels[original_pos[0]][1] != best_match_label[0][1]:
# the original label appears less often as the new best_match_label
# in this case, we use the new best matched label
mostFrequentLabel = best_match_label[0][0]
# we don't need an else here since the original label is already set
real_scalar = mostFrequentLabel
else:
# simple mapping without radius incorporation and make sure we are inside the volume
real_scalar = volume[min( current_point[0], image_size[0] - 1 ), min( current_point[1], image_size[1] - 1 ) , min( current_point[2], image_size[2] - 1 )]
if type( scalars ) is types.NoneType:
mapped_scalar = -1
else:
mapped_scalar = scalars[index][0]
# now check if the mapped scalar from the trk file matches the real scalar
compare = ( mapped_scalar == real_scalar )
if compare:
compare = Colors.GREEN + 'OK'
else:
compare = Colors.RED + 'WRONG!!!'
any_errors = True
print Colors.PURPLE + 'Probing ' + Colors.CYAN + str( current_point ) + Colors.PURPLE + ' for scalar.. SHOULD BE: ' + Colors.CYAN + str( real_scalar ) + Colors.PURPLE + ' WAS: ' + Colors.CYAN + str( mapped_scalar ) + Colors.PURPLE + ' ... ' + str( compare ) + Colors._CLEAR
# return TRUE if everything went fine and FALSE if there were errors
return not any_errors
def run(self, masterFile, inputFiles, outputDirectory, spacing, dimensions,
likefreesurfer, nii):
'''
Performs the equalization
'''
# sanity checks
outputDirectory = os.path.normpath(outputDirectory)
# prepare the output directory
if os.path.exists(outputDirectory):
c.error('The output directory already exists!')
c.error('Aborting..')
sys.exit(2)
# create the output directory
os.mkdir(outputDirectory)
# MASTER
masterFile = os.path.normpath(masterFile)
# read the master
master = io.readImage(masterFile)
c.info('MASTER IMAGE: ' + str(masterFile))
# INPUTS
for i in range(len(inputFiles)):
inputFiles[i] = os.path.normpath(inputFiles[i])
c.info('INPUT IMAGE ' + str(i + 1) + ': ' + str(inputFiles[i]))
# print more info
c.info('OUTPUT DIRECTORY: ' + str(outputDirectory))
if likefreesurfer:
spacing = '1,1,1'
dimensions = '256,256,256'
if spacing != 'no':
c.info('SET SPACINGS: ' + str(spacing))
if dimensions != 'no':
c.info('SET DIMENSIONS: ' + str(dimensions))
# re-sample master to obtain an isotropic dataset
master = self.aniso2iso(master, spacing, dimensions)
masterFileBasename = os.path.split(masterFile)[1]
masterFileBasenameWithoutExt = os.path.splitext(masterFileBasename)[0]
if not nii:
masterOutputFileName = os.path.join(outputDirectory,
masterFileBasename)
else:
masterOutputFileName = os.path.join(
outputDirectory, masterFileBasenameWithoutExt) + '.nii'
io.saveImage(masterOutputFileName, master)
# equalize all images to the master
for i in range(len(inputFiles)):
currentInputFile = inputFiles[i]
c.info('Equalizing ' + str(currentInputFile) + ' to ' +
str(masterFile) + "...")
# load the image
currentImage = io.readImage(currentInputFile)
currentImageHeader = currentImage.header
c.info(' old spacing: ' + str(currentImageHeader.get_zooms()))
c.info(' old dimensions: ' + str(currentImage.shape[:3]))
# now resample
resampledImage = resampler.resample_img2img(currentImage, master)
# .. and save it
currentInputFileBasename = os.path.split(currentInputFile)[1]
currentInputFileBasenameWithoutExt = os.path.splitext(
currentInputFileBasename)[0]
if not nii:
outputFileName = os.path.join(outputDirectory,
currentInputFileBasename)
else:
outputFileName = os.path.join(
outputDirectory, currentInputFileBasenameWithoutExt)
savedImage = io.saveImage(outputFileName, resampledImage)
#c.info( ' new spacing: ' + str( savedImageHeader.get_zooms() ) )
c.info(' new dimensions: ' + str(savedImage.shape[:3]))
c.info('All done!')
def fyborgLooper_( tracks, tracksHeader, outputTrkFile, actions, showDebug, threadNumber ):
import numpy
numberOfTracks = len( tracks )
# the buffer for the new tracks
newTracks = []
# now loop through the tracks
for tCounter, t in enumerate( tracks ):
# some debug stats
c.debug( 'Thread-' + str( threadNumber ) + ': Processing ' + str( tCounter + 1 ) + '/' + str( numberOfTracks ), showDebug )
# generate a unique ID for this track
uniqueId = str( threadNumber ) + str( tCounter )
tCoordinates = t[0]
tScalars = t[1]
# buffer for fiberScalars
_fiberScalars = {}
# first round: mapping per fiber
# .. execute each action and buffer return value (scalar)
for a in actions:
value = a.scalarPerFiber( uniqueId, tCoordinates, tScalars )
_fiberScalars[a.scalarName()] = value
#
# Coordinate Loop
#
# buffer for coordinate scalars)
scalars = []
# second round: mapping per coordinate
for cCounter, coords in enumerate( tCoordinates ):
_coordScalars = {}
_mergedScalars = [] # this is the actual buffer for ordered fiber and coord scalars merged together
# .. execute each action and buffer return value (scalar)
for a in actions:
value = a.scalarPerCoordinate( uniqueId, coords[0], coords[1], coords[2] ) # pass x,y,z
_coordScalars[a.scalarName()] = value
# now merge the old scalars and the fiber and coord scalars
# this preserves the ordering of the configured actions
if tScalars != None:
_mergedScalars.extend( tScalars[cCounter] )
for a in actions:
value = _fiberScalars[a.scalarName()]
if value != FyAction.NoScalar:
_mergedScalars.append( value )
else:
# no fiber scalar, check if there is a coord scalar
value = _coordScalars[a.scalarName()]
if value != FyAction.NoScalar:
_mergedScalars.append( value )
# attach scalars
scalars.append( _mergedScalars )
# validate the fibers using the action's validate methods
validator = []
for a in actions:
validator.append( a.validate( uniqueId ) )
if all( validator ):
# this is a valid fiber
# .. add the new track with the coordinates, the new scalar array and the properties
newScalars = numpy.asarray( scalars )
newTracks.append( ( t[0], newScalars, t[2] ) )
# save everything
io.saveTrk( outputTrkFile, newTracks, tracksHeader, None, True )