def test_gradient_magnitude(self):
"""
Test if the gradient magnitude filter produces the same results, independent from
the dtype of the input array.
"""
dtypes = [
scipy.int8, scipy.int16, scipy.int32, scipy.int64, scipy.uint8,
scipy.uint16, scipy.uint32, scipy.uint64, scipy.float32,
scipy.float64, scipy.float128
]
arr_base = scipy.random.randint(0, 100, (20, 30, 40, 50))
arr_reference = gradient_magnitude(arr_base)
# test different dtypes
for dtype in dtypes:
arr = gradient_magnitude(arr_base.astype(dtype))
self.assertTrue(
(arr == arr_reference).all(),
'Difference for dtype {} encountered.'.format(dtype))
# test if voxel spacing is taken into account
arr = gradient_magnitude(arr_base, [1.1, 1.2, 1.3, 4])
self.assertFalse((arr == arr_reference).all(),
'Implementation ignores the passed voxel spacing.')
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)
# check if output image exists (will also be performed before saving, but as the gradient might be time intensity, a initial check can save frustration)
if not args.force:
if os.path.exists(args.output):
raise ArgumentError('The output image {} already exists.'.format(
args.output))
# loading image
data_input, header_input = load(args.input)
logger.debug('Input array: dtype={}, shape={}'.format(
data_input.dtype, data_input.shape))
# execute the gradient map filter
logger.info('Applying gradient map filter...')
data_output = filter.gradient_magnitude(
data_input, header.get_pixel_spacing(header_input))
logger.debug('Resulting array: dtype={}, shape={}'.format(
data_output.dtype, data_output.shape))
# save image
save(data_output, args.output, header_input, args.force)
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)
# check if output image exists (will also be performed before saving, but as the gradient might be time intensity, a initial check can save frustration)
if not args.force:
if os.path.exists(args.output):
raise ArgumentError('The output image {} already exists.'.format(args.output))
# loading image
data_input, header_input = load(args.input)
logger.debug('Input array: dtype={}, shape={}'.format(data_input.dtype, data_input.shape))
# execute the gradient map filter
logger.info('Applying gradient map filter...')
data_output = filter.gradient_magnitude(data_input, header.get_pixel_spacing(header_input))
logger.debug('Resulting array: dtype={}, shape={}'.format(data_output.dtype, data_output.shape))
# save image
save(data_output, args.output, header_input, args.force)
logger.info('Successfully terminated.')
def test_gradient_magnitude(self):
"""
Test if the gradient magnitude filter produces the same results, independent from
the dtype of the input array.
"""
dtypes = [scipy.int8, scipy.int16, scipy.int32, scipy.int64,
scipy.uint8, scipy.uint16, scipy.uint32, scipy.uint64,
scipy.float32, scipy.float64, scipy.float128]
arr_base = scipy.random.randint(0, 100, (20, 30, 40, 50))
arr_reference = gradient_magnitude(arr_base)
# test different dtypes
for dtype in dtypes:
arr = gradient_magnitude(arr_base.astype(dtype))
self.assertTrue((arr == arr_reference).all(), 'Difference for dtype {} encountered.'.format(dtype))
# test if voxel spacing is taken into account
arr = gradient_magnitude(arr_base, [1.1, 1.2, 1.3, 4])
self.assertFalse((arr == arr_reference).all(), 'Implementation ignores the passed voxel spacing.')
