def test_get_relative_volume_difference(self):
i1, i2, i3, i4, i5 = self._createTestImages()
# same image
v = Volume(i1, i1)
self.assertEqual(v.get_relative_volume_difference(), 0)
# same image with both offsets the same
v = Volume(i1, i1, [1,2,3], [1,2,3])
self.assertEqual(v.get_relative_volume_difference(), 0)
# same image with different offsets
v = Volume(i1, i1, [0,0,0], [1,1,1])
self.assertEqual(v.get_relative_volume_difference(), 0)
# overlapping image with objects of same volume
v = Volume(i1, i2)
self.assertEqual(v.get_relative_volume_difference(), 0)
# not overlapping image with objects of same volume
v = Volume(i1, i3)
self.assertEqual(v.get_relative_volume_difference(), 0)
# included object with different volume
v = Volume(i1, i4)
self.assertEqual(v.get_relative_volume_difference(), 700)
# included object with different volume in image of different size
v = Volume(i1, i5)
self.assertEqual(v.get_relative_volume_difference(), 700)
# included object with different volume / reversed
v = Volume(i4, i1)
self.assertEqual(v.get_relative_volume_difference(), -87.5)
# included object with different volume in image of different size / reversed
v = Volume(i5, i1)
self.assertEqual(v.get_relative_volume_difference(), -87.5)
def test_get_volumetric_overlap_error(self):
i1, i2, i3, i4, i5 = self._createTestImages()
# same image
v = Volume(i1, i1)
self.assertEqual(v.get_volumetric_overlap_error(), 0)
# same image with both offsets the same
v = Volume(i1, i1, [1,2,3], [1,2,3])
self.assertEqual(v.get_volumetric_overlap_error(), 0)
# same image with different offsets
v = Volume(i1, i1, [0,0,0], [1,1,1])
self.assertAlmostEqual(v.get_volumetric_overlap_error(), 37.364705882)
# overlapping image with objects of same volume
v = Volume(i1, i2)
self.assertAlmostEqual(v.get_volumetric_overlap_error(), 93.333333333)
# not overlapping image with objects of same volume
v = Volume(i1, i3)
self.assertEqual(v.get_volumetric_overlap_error(), 100)
# included object with different volume
v = Volume(i1, i4)
self.assertEqual(v.get_volumetric_overlap_error(), 87.5)
# included object with different volume in image of different size
v = Volume(i1, i5)
self.assertEqual(v.get_volumetric_overlap_error(), 87.5)
# reversed versions where sensible #
# same image with different offsets
v = Volume(i1, i1, [1,1,1], [0,0,0])
self.assertAlmostEqual(v.get_volumetric_overlap_error(), 37.364705882)
# overlapping image with objects of same volume
v = Volume(i2, i1)
self.assertAlmostEqual(v.get_volumetric_overlap_error(), 93.333333333)
# not overlapping image with objects of same volume
v = Volume(i3, i1)
self.assertEqual(v.get_volumetric_overlap_error(), 100)
# included object with different volume
v = Volume(i4, i1)
self.assertEqual(v.get_volumetric_overlap_error(), 87.5)
# included object with different volume in image of different size
v = Volume(i5, i1)
self.assertEqual(v.get_volumetric_overlap_error(), 87.5)
def test_get_volumetric_overlap_error(self):
i1, i2, i3, i4, i5 = self._createTestImages()
# same image
v = Volume(i1, i1)
self.assertEqual(v.get_volumetric_overlap_error(), 0)
# same image with both offsets the same
v = Volume(i1, i1, [1, 2, 3], [1, 2, 3])
self.assertEqual(v.get_volumetric_overlap_error(), 0)
# same image with different offsets
v = Volume(i1, i1, [0, 0, 0], [1, 1, 1])
self.assertAlmostEqual(v.get_volumetric_overlap_error(), 37.364705882)
# overlapping image with objects of same volume
v = Volume(i1, i2)
self.assertAlmostEqual(v.get_volumetric_overlap_error(), 93.333333333)
# not overlapping image with objects of same volume
v = Volume(i1, i3)
self.assertEqual(v.get_volumetric_overlap_error(), 100)
# included object with different volume
v = Volume(i1, i4)
self.assertEqual(v.get_volumetric_overlap_error(), 87.5)
# included object with different volume in image of different size
v = Volume(i1, i5)
self.assertEqual(v.get_volumetric_overlap_error(), 87.5)
# reversed versions where sensible #
# same image with different offsets
v = Volume(i1, i1, [1, 1, 1], [0, 0, 0])
self.assertAlmostEqual(v.get_volumetric_overlap_error(), 37.364705882)
# overlapping image with objects of same volume
v = Volume(i2, i1)
self.assertAlmostEqual(v.get_volumetric_overlap_error(), 93.333333333)
# not overlapping image with objects of same volume
v = Volume(i3, i1)
self.assertEqual(v.get_volumetric_overlap_error(), 100)
# included object with different volume
v = Volume(i4, i1)
self.assertEqual(v.get_volumetric_overlap_error(), 87.5)
# included object with different volume in image of different size
v = Volume(i5, i1)
self.assertEqual(v.get_volumetric_overlap_error(), 87.5)
def test_get_relative_volume_difference(self):
i1, i2, i3, i4, i5 = self._createTestImages()
# same image
v = Volume(i1, i1)
self.assertEqual(v.get_relative_volume_difference(), 0)
# same image with both offsets the same
v = Volume(i1, i1, [1, 2, 3], [1, 2, 3])
self.assertEqual(v.get_relative_volume_difference(), 0)
# same image with different offsets
v = Volume(i1, i1, [0, 0, 0], [1, 1, 1])
self.assertEqual(v.get_relative_volume_difference(), 0)
# overlapping image with objects of same volume
v = Volume(i1, i2)
self.assertEqual(v.get_relative_volume_difference(), 0)
# not overlapping image with objects of same volume
v = Volume(i1, i3)
self.assertEqual(v.get_relative_volume_difference(), 0)
# included object with different volume
v = Volume(i1, i4)
self.assertEqual(v.get_relative_volume_difference(), 700)
# included object with different volume in image of different size
v = Volume(i1, i5)
self.assertEqual(v.get_relative_volume_difference(), 700)
# included object with different volume / reversed
v = Volume(i4, i1)
self.assertEqual(v.get_relative_volume_difference(), -87.5)
# included object with different volume in image of different size / reversed
v = Volume(i5, i1)
self.assertEqual(v.get_relative_volume_difference(), -87.5)
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)
# load reference image
image_reference_data, image_reference_header = load(args.reference)
# prepare reference image data
image_reference_data = (0 != image_reference_data) # ensures that the reference mask is of type bool
image_reference_size = len(image_reference_data.nonzero()[0])
# raise exception when the input mask is zero
if 0 >= image_reference_size:
raise Exception('The reference mask if of size <= 0.')
# extract pyhsical pixel spacing
spacing = numpy.array(get_pixel_spacing(image_reference_header))
# print header if requested
if args.header:
print '{}/{};'.format(args.reference.split('/')[-1], image_reference_size) ,
print 'mask-size;VolumetricOverlapError;RelativeVolumeDifference;AverageSymmetricSurfaceDistance;MaximumSymmetricSurfaceDistance;RootMeanSquareSymmetricSurfaceDistance'
# load mask using nibabel
image_mask_data, image_mask_header = load(args.input)
# check if physical pixel spacing is coherent
mask_spacing = numpy.array(get_pixel_spacing(image_mask_header))
if not (spacing == mask_spacing).all():
if not args.ignore:
print 'Stopped. Incoherent pixel spacing (reference={}/mask={})\n'.format(spacing, mask_spacing)
logger.warning('The physical pixel spacings of reference ({}) and mask ({}) do not comply. Breaking evaluation.'.format(spacing, mask_spacing))
sys.exit(-1)
else:
logger.warning('The physical pixel spacings of reference ({}) and mask ({}) do not comply. Evaluation continued nevertheless, as ignore flag is set.'.format(spacing, mask_spacing))
# prepare mask data
image_mask_data = (0 != image_mask_data) # ensures that the mask is of type bool
image_mask_size = len(image_mask_data.nonzero()[0])
# write mask name and size into file
print '{};{}'.format(args.input.split('/')[-1], image_mask_size) ,
# warn when the mask is of size 0 or less
if 0 >= image_mask_size:
print ';Skipped: mask size is 0'
logger.warning('The mask is of size <= 0. Breaking evaluation.')
sys.exit(-1)
# skip if reference mask ratio hints to bad results
if 0.75 > 1. * image_reference_size / image_mask_size or 1.25 < 1. * image_reference_size / image_mask_size:
print ';Skipped: reference/mask <0.075 or >1.25'
logger.warning('The reference/mask ration of the mask is <0.075 or >1.25. Breaking evaluation.')
sys.exit(-1)
# volume metrics
logger.info('Calculating volume metrics...')
v = Volume(image_mask_data, image_reference_data)
print ';{};{}'.format(v.get_volumetric_overlap_error(),
v.get_relative_volume_difference()) ,
logger.info('Calculating surface metrics...')
s = Surface(image_mask_data, image_reference_data, spacing)
print ';{};{};{}'.format(s.get_average_symmetric_surface_distance(),
s.get_maximum_symmetric_surface_distance(),
s.get_root_mean_square_symmetric_surface_distance())
logger.info('Successfully terminated.')
