def wiener(im, mysize=None, noise=None):
"""Perform a Wiener filter on an N-dimensional array.
Apply a Wiener filter to the N-dimensional array `im`.
Args:
im (cupy.ndarray): An N-dimensional array.
mysize (int or cupy.ndarray, optional): A scalar or an N-length list
giving the size of the Wiener filter window in each dimension.
Elements of mysize should be odd. If mysize is a scalar, then this
scalar is used as the size in each dimension.
noise (float, optional): The noise-power to use. If None, then noise is
estimated as the average of the local variance of the input.
Returns:
cupy.ndarray: Wiener filtered result with the same shape as `im`.
.. seealso:: :func:`scipy.signal.wiener`
"""
if im.dtype.kind == 'c':
# TODO: adding support for complex types requires ndimage filters
# to support complex types (which they could easily if not for the
# scipy compatibility requirement of forbidding complex and using
# float64 intermediates)
raise TypeError("complex types not currently supported")
if mysize is None:
mysize = 3
mysize = _util._fix_sequence_arg(mysize, im.ndim, 'mysize', int)
im = im.astype(float, copy=False)
# Estimate the local mean
local_mean = filters.uniform_filter(im, mysize, mode='constant')
# Estimate the local variance
local_var = filters.uniform_filter(im * im, mysize, mode='constant')
local_var -= local_mean * local_mean
# Estimate the noise power if needed.
if noise is None:
noise = local_var.mean()
# Perform the filtering
res = im - local_mean
res *= 1 - noise / local_var
res += local_mean
return cupy.where(local_var < noise, local_mean, res)
def wiener(im, mysize=None, noise=None):
"""Perform a Wiener filter on an N-dimensional array.
Apply a Wiener filter to the N-dimensional array `im`.
Args:
im (cupy.ndarray): An N-dimensional array.
mysize (int or cupy.ndarray, optional): A scalar or an N-length list
giving the size of the Wiener filter window in each dimension.
Elements of mysize should be odd. If mysize is a scalar, then this
scalar is used as the size in each dimension.
noise (float, optional): The noise-power to use. If None, then noise is
estimated as the average of the local variance of the input.
Returns:
cupy.ndarray: Wiener filtered result with the same shape as `im`.
.. seealso:: :func:`scipy.signal.wiener`
"""
if mysize is None:
mysize = 3
mysize = _util._fix_sequence_arg(mysize, im.ndim, 'mysize', int)
im = im.astype(cupy.complex128 if im.dtype.kind == 'c' else cupy.float64,
copy=False)
# Estimate the local mean
local_mean = filters.uniform_filter(im, mysize, mode='constant')
# Estimate the local variance
local_var = filters.uniform_filter(im * im, mysize, mode='constant')
local_var -= local_mean * local_mean
# Estimate the noise power if needed.
if noise is None:
noise = local_var.mean()
# Perform the filtering
res = im - local_mean
res *= 1 - noise / local_var
res += local_mean
return cupy.where(local_var < noise, local_mean, res)
def diffuse_uniform(self, size=3, mode="wrap"):
"""Pheromones get distributed using uniform smoothing. This can lead to nice artefacts,
but also to diagonal drift at high values for size (?)"""
self.trail_map = uniform_filter(self.trail_map, size=size, mode=mode)
def diffuse_uniform(self, size=3, mode="wrap"):
"""Pheromones get distributed using uniform smoothing. """
self.trail_map = uniform_filter(self.trail_map, size=size, mode=mode)
