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, 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!')
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
for _current in coords:
if _last != None:
# we have the previous point
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
# 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
for _current in coords:
if _last != None:
# we have the previous point
