def main():
# parse cmd arguments
parser = getParser()
parser.parse_args()
args = getArguments(parser)
# prepare logger
logger = Logger.getInstance()
if args.debug: logger.setLevel(logging.DEBUG)
elif args.verbose: logger.setLevel(logging.INFO)
# build output image name
image_bg_name = args.folder + '/' + args.mask.split('/')[-1][:-4] + '.bg'
image_bg_name += args.mask.split('/')[-1][-4:]
# check if output image exists
if not args.force:
if os.path.exists(image_bg_name):
logger.warning('The output image {} already exists. Breaking.'.format(image_bg_name))
exit(1)
# load mask
logger.info('Loading mask {}...'.format(args.mask))
try:
mask_image = load(args.mask)
mask_image_data = numpy.squeeze(mask_image.get_data()).astype(scipy.bool_)
except ImageFileError as e:
logger.critical('The mask image does not exist or its file type is unknown.')
raise ArgumentError('The mask image does not exist or its file type is unknown.', e)
# array of indices to access desired slices
sls = [(slice(1), slice(None), slice(None)),
(slice(-1, None), slice(None), slice(None)),
(slice(None), slice(1), slice(None)),
(slice(None), slice(-1, None), slice(None)),
(slice(None), slice(None), slice(1)),
(slice(None), slice(None), slice(-1, None))]
# security check
logger.info('Determine if the slices are not intersection with the reference liver mask...')
for sl in sls:
if not 0 == len(mask_image_data[sl].nonzero()[0]):
logger.critical('Reference mask reaches till the image border.')
raise ArgumentError('Reference mask reaches till the image border.')
# create and save background marker image
logger.info('Creating background marker image...')
image_bg_data = scipy.zeros(mask_image_data.shape, dtype=scipy.bool_)
for sl in sls:
image_bg_data[sl] = True
logger.info('Saving background marker image...')
mask_image.get_header().set_data_dtype(scipy.int8)
save(image_like(image_bg_data, mask_image), image_bg_name)
logger.info('Successfully terminated.')
def main():
# parse cmd arguments
parser = getParser()
parser.parse_args()
args = getArguments(parser)
# prepare logger
logger = Logger.getInstance()
if args.debug: logger.setLevel(logging.DEBUG)
elif args.verbose: logger.setLevel(logging.INFO)
# build output image name
image_fg_name = args.folder + '/' + args.mask.split('/')[-1][:-4] + '.fg'
image_fg_name += args.mask.split('/')[-1][-4:]
image_bg_name = args.folder + '/' + args.mask.split('/')[-1][:-4] + '.bg'
image_bg_name += args.mask.split('/')[-1][-4:]
# check if output image exists
if not args.force:
if os.path.exists(image_fg_name):
logger.warning(
'The output image {} already exists. Breaking.'.format(
image_fg_name))
exit(1)
elif os.path.exists(image_bg_name):
logger.warning(
'The output image {} already exists. Breaking.'.format(
image_bg_name))
exit(1)
# load mask
logger.info('Loading mask {}...'.format(args.mask))
try:
mask_image = load(args.mask)
mask_image_data = numpy.squeeze(mask_image.get_data()).astype(
scipy.bool_)
except ImageFileError as e:
logger.critical(
'The mask image does not exist or its file type is unknown.')
raise ArgumentError(
'The mask image does not exist or its file type is unknown.', e)
# erode mask stepwise
logger.info('Step-wise reducing mask to find center...')
mask_remains = mask_image_data.copy()
while (True):
mask_remains_next = ndimage.binary_erosion(mask_remains, iterations=2)
if 0 == len(mask_remains_next.nonzero()[0]):
break
mask_remains = mask_remains_next
# extract one of the remaining voxels
voxels = mask_remains.nonzero()
marker = (voxels[0][0], voxels[1][0], voxels[2][0])
logger.debug('Extracted foreground seed is {}.'.format(marker))
# check suitability of corners as background markers
logger.info(
'Checking if the corners are suitable background seed candidates...')
if True == mask_image_data[0,0,0] or \
True == mask_image_data[-1,0,0] or \
True == mask_image_data[0,-1,0] or \
True == mask_image_data[0,0,-1] or \
True == mask_image_data[-1,-1,0] or \
True == mask_image_data[-1,0,-1] or \
True == mask_image_data[0,-1,-1] or \
True == mask_image_data[-1,-1,-1]:
logger.critical(
'The corners of the image do not correspond to background voxels.')
raise ArgumentError(
'The corners of the image do not correspond to background voxels.')
# create and save foreground marker image
logger.info('Creating foreground marker image...')
image_fg_data = scipy.zeros(mask_image_data.shape, dtype=scipy.bool_)
image_fg_data[marker[0], marker[1], marker[2]] = True
logger.info('Saving foreground marker image...')
mask_image.get_header().set_data_dtype(scipy.int8)
save(image_like(image_fg_data, mask_image), image_fg_name)
# create and save background marker image
logger.info('Creating background marker image...')
image_bg_data = scipy.zeros(mask_image_data.shape, dtype=scipy.bool_)
image_bg_data[0, 0, 0] = True
image_bg_data[-1, 0, 0] = True
image_bg_data[0, -1, 0] = True
image_bg_data[0, 0, -1] = True
image_bg_data[-1, -1, 0] = True
image_bg_data[-1, 0, -1] = True
image_bg_data[0, -1, -1] = True
image_bg_data[-1, -1, -1] = True
logger.info('Saving background marker image...')
mask_image.get_header().set_data_dtype(scipy.int8)
save(image_like(image_bg_data, mask_image), image_bg_name)
logger.info('Successfully terminated.')
def main():
# parse cmd arguments
parser = getParser()
parser.parse_args()
args = getArguments(parser)
# prepare logger
logger = Logger.getInstance()
if args.debug: logger.setLevel(logging.DEBUG)
elif args.verbose: logger.setLevel(logging.INFO)
# build output image name
image_fg_name = args.folder + '/' + args.mask.split('/')[-1][:-4] + '.fg'
image_fg_name += args.mask.split('/')[-1][-4:]
image_bg_name = args.folder + '/' + args.mask.split('/')[-1][:-4] + '.bg'
image_bg_name += args.mask.split('/')[-1][-4:]
# check if output image exists
if not args.force:
if os.path.exists(image_fg_name):
logger.warning('The output image {} already exists. Breaking.'.format(image_fg_name))
exit(1)
elif os.path.exists(image_bg_name):
logger.warning('The output image {} already exists. Breaking.'.format(image_bg_name))
exit(1)
# load mask
logger.info('Loading mask {}...'.format(args.mask))
try:
mask_image = load(args.mask)
mask_image_data = numpy.squeeze(mask_image.get_data()).astype(scipy.bool_)
except ImageFileError as e:
logger.critical('The mask image does not exist or its file type is unknown.')
raise ArgumentError('The mask image does not exist or its file type is unknown.', e)
# erode mask stepwise
logger.info('Step-wise reducing mask to find center...')
mask_remains = mask_image_data.copy()
while (True):
mask_remains_next = ndimage.binary_erosion(mask_remains, iterations=2)
if 0 == len(mask_remains_next.nonzero()[0]):
break
mask_remains = mask_remains_next
# extract one of the remaining voxels
voxels = mask_remains.nonzero()
marker = (voxels[0][0], voxels[1][0], voxels[2][0])
logger.debug('Extracted foreground seed is {}.'.format(marker))
# check suitability of corners as background markers
logger.info('Checking if the corners are suitable background seed candidates...')
if True == mask_image_data[0,0,0] or \
True == mask_image_data[-1,0,0] or \
True == mask_image_data[0,-1,0] or \
True == mask_image_data[0,0,-1] or \
True == mask_image_data[-1,-1,0] or \
True == mask_image_data[-1,0,-1] or \
True == mask_image_data[0,-1,-1] or \
True == mask_image_data[-1,-1,-1]:
logger.critical('The corners of the image do not correspond to background voxels.')
raise ArgumentError('The corners of the image do not correspond to background voxels.')
# create and save foreground marker image
logger.info('Creating foreground marker image...')
image_fg_data = scipy.zeros(mask_image_data.shape, dtype=scipy.bool_)
image_fg_data[marker[0], marker[1], marker[2]] = True
logger.info('Saving foreground marker image...')
mask_image.get_header().set_data_dtype(scipy.int8)
save(image_like(image_fg_data, mask_image), image_fg_name)
# create and save background marker image
logger.info('Creating background marker image...')
image_bg_data = scipy.zeros(mask_image_data.shape, dtype=scipy.bool_)
image_bg_data[0,0,0] = True
image_bg_data[-1,0,0] = True
image_bg_data[0,-1,0] = True
image_bg_data[0,0,-1] = True
image_bg_data[-1,-1,0] = True
image_bg_data[-1,0,-1] = True
image_bg_data[0,-1,-1] = True
image_bg_data[-1,-1,-1] = True
logger.info('Saving background marker image...')
mask_image.get_header().set_data_dtype(scipy.int8)
save(image_like(image_bg_data, mask_image), image_bg_name)
logger.info('Successfully terminated.')
def main():
# parse cmd arguments
parser = getParser()
parser.parse_args()
args = getArguments(parser)
# prepare logger
logger = Logger.getInstance()
if args.debug: logger.setLevel(logging.DEBUG)
elif args.verbose: logger.setLevel(logging.INFO)
# iterate over input images
for image in args.images:
# build output image name
image_real_name = image.split('/')[-1][:-4] + '_real' + image.split('/')[-1][-4:]
image_imag_name = image.split('/')[-1][:-4] + '_imag' + image.split('/')[-1][-4:]
# check if output images exists
if not args.force:
if os.path.exists(image_real_name):
logger.warning('The output image {} already exists. Skipping this step.'.format(image_real_name))
continue
elif os.path.exists(image_imag_name):
logger.warning('The output image {} already exists. Skipping this step.'.format(image_imag_name))
continue
# load image using nibabel
logger.info('Loading image {} using NiBabel...'.format(image))
image_original = load(image)
# get and prepare image data
image_original_data = scipy.squeeze(image_original.get_data())
# apply the discrete fast Fourier transformation
logger.info('Executing the discrete fast Fourier transformation...')
image_fft_data = scipy.fftpack.fftn(image_original_data)
# transform to logarithmic scale
logger.info('To logarithmic space...')
image_real_data = image_fft_data.real
print image_real_data.min(), image_real_data.max()
image_real_data = image_real_data + abs(image_real_data.min())
constant = 65535./(math.log(1 + image_real_data.max())) # scale by 0.0001, log and then scale to fir uint16
myfunc = lambda x: constant * math.log(1 + x * 0.0001)
new_func = numpy.vectorize(myfunc)
logger.info('Apply...')
image_real_data = new_func(image_real_data)
print image_real_data.min(), image_real_data.max()
image_imag_data = image_fft_data.imag
# save resulting images
logger.info('Saving resulting images real part as {} in the same format as input image, only with data-type float32...'.format(image_real_name))
image_real = image_like(image_real_data, image_original)
image_real.get_header().set_data_dtype(scipy.uint16)
save(image_real, image_real_name)
logger.info('Saving resulting images real part as {} in the same format as input image, only with data-type float32...'.format(image_imag_name))
image_imag = image_like(image_imag_data, image_original)
image_imag.get_header().set_data_dtype(scipy.float32)
save(image_imag, image_imag_name)
logger.info('Successfully terminated.')
def main():
# parse cmd arguments
parser = getParser()
parser.parse_args()
args = getArguments(parser)
# prepare logger
logger = Logger.getInstance()
if args.debug:
logger.setLevel(logging.DEBUG)
elif args.verbose:
logger.setLevel(logging.INFO)
# iterate over input images
for image in args.images:
# build output image name
image_real_name = image.split("/")[-1][:-4] + "_real" + image.split("/")[-1][-4:]
image_imag_name = image.split("/")[-1][:-4] + "_imag" + image.split("/")[-1][-4:]
# check if output images exists
if not args.force:
if os.path.exists(image_real_name):
logger.warning("The output image {} already exists. Skipping this step.".format(image_real_name))
continue
elif os.path.exists(image_imag_name):
logger.warning("The output image {} already exists. Skipping this step.".format(image_imag_name))
continue
# load image using nibabel
logger.info("Loading image {} using NiBabel...".format(image))
image_original = load(image)
# get and prepare image data
image_original_data = scipy.squeeze(image_original.get_data())
# apply the discrete fast Fourier transformation
logger.info("Executing the discrete fast Fourier transformation...")
image_fft_data = scipy.fftpack.fftn(image_original_data)
# transform to logarithmic scale
logger.info("To logarithmic space...")
image_real_data = image_fft_data.real
print image_real_data.min(), image_real_data.max()
image_real_data = image_real_data + abs(image_real_data.min())
constant = 65535.0 / (math.log(1 + image_real_data.max())) # scale by 0.0001, log and then scale to fir uint16
myfunc = lambda x: constant * math.log(1 + x * 0.0001)
new_func = numpy.vectorize(myfunc)
logger.info("Apply...")
image_real_data = new_func(image_real_data)
print image_real_data.min(), image_real_data.max()
image_imag_data = image_fft_data.imag
# save resulting images
logger.info(
"Saving resulting images real part as {} in the same format as input image, only with data-type float32...".format(
image_real_name
)
)
image_real = image_like(image_real_data, image_original)
image_real.get_header().set_data_dtype(scipy.uint16)
save(image_real, image_real_name)
logger.info(
"Saving resulting images real part as {} in the same format as input image, only with data-type float32...".format(
image_imag_name
)
)
image_imag = image_like(image_imag_data, image_original)
image_imag.get_header().set_data_dtype(scipy.float32)
save(image_imag, image_imag_name)
logger.info("Successfully terminated.")