def percentile_filter(input,
percentile,
size=None,
footprint=None,
mode='reflect',
cval=0.0,
origin=0):
footprint = _utils._get_footprint(input.ndim, size, footprint)
origin = _utils._get_origin(footprint.shape, origin)
depth = _utils._get_depth(footprint.shape, origin)
depth, boundary = _utils._get_depth_boundary(footprint.ndim, depth, "none")
result = input.map_overlap(
scipy.ndimage.filters.percentile_filter,
depth=depth,
boundary=boundary,
dtype=input.dtype,
percentile=percentile,
footprint=footprint,
mode=mode,
cval=cval,
origin=origin
)
return result
def _wrapped_ordering_filter(input,
size=None,
footprint=None,
mode='reflect',
cval=0.0,
origin=0):
footprint = _utils._get_footprint(input.ndim, size, footprint)
origin = _utils._get_origin(footprint.shape, origin)
depth = _utils._get_depth(footprint.shape, origin)
depth, boundary = _utils._get_depth_boundary(footprint.ndim,
depth,
"none")
result = input.map_overlap(
func,
depth=depth,
boundary=boundary,
dtype=input.dtype,
footprint=footprint,
mode=mode,
cval=cval,
origin=origin
)
return result
def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
selem = np.ones(2 * [entropy_filter_size])
depth = 5
depth, boundary = _utils._get_depth_boundary(image.ndim, depth, "none")
if (image.ndim == 2):
result = image.map_overlap(
skimage.filters.rank.generic.entropy,
depth = depth,
boundary = boundary,
dtype = image.dtype,
selem = selem,
out = out,
mask = mask,
shift_x = shift_x,
shift_y = shift_y
)
else:
lst = []
for i in range(np.size(stack, axis = 0)):
lst.append(entropy(stack[i], selem))
result = np.stack(lst, axis =0)
return result
def uniform_filter(input, size=3, mode='reflect', cval=0.0, origin=0):
size = _utils._get_size(input.ndim, size)
depth = _utils._get_depth(size, origin)
depth, boundary = _utils._get_depth_boundary(input.ndim, depth, "none")
result = input.map_overlap(scipy.ndimage.filters.uniform_filter,
depth=depth,
boundary=boundary,
dtype=input.dtype,
size=size,
mode=mode,
cval=cval,
origin=origin)
return result
def convolve(input,
weights,
mode='reflect',
cval=0.0,
origin=0):
origin = _utils._get_origin(weights.shape, origin)
depth = _utils._get_depth(weights.shape, origin)
depth, boundary = _utils._get_depth_boundary(input.ndim, depth, "none")
result = input.map_overlap(
scipy.ndimage.filters.convolve,
depth=depth,
boundary=boundary,
dtype=input.dtype,
weights=weights,
mode=mode,
cval=cval,
origin=origin
)
return result
def gaussian_laplace(input,
sigma,
mode='reflect',
cval=0.0,
truncate=4.0,
**kwargs):
sigma = _get_sigmas(input, sigma)
depth = _get_border(input, sigma, truncate)
depth, boundary = _utils._get_depth_boundary(input.ndim, depth, "none")
result = input.map_overlap(scipy.ndimage.filters.gaussian_laplace,
depth=depth,
boundary=boundary,
dtype=input.dtype,
sigma=sigma,
mode=mode,
cval=cval,
truncate=truncate,
**kwargs)
return result
def test_errs__get_depth_boundary(err_type, ndim, depth, boundary):
with pytest.raises(err_type):
_utils._get_depth_boundary(ndim, depth, boundary)
def test__get_depth_boundary(expected, ndim, depth, boundary):
assert expected == _utils._get_depth_boundary(ndim, depth, boundary)