def erosion_filter(
in_raster,
width=3,
iterations=1,
circular=True,
weighted_distance=True,
sigma=3.5,
_kernel=False,
):
kernel = (_kernel if _kernel is not False else create_kernel(
width,
circular=circular,
weighted_edges=True,
weighted_distance=weighted_distance,
sigma=sigma,
normalise=False,
))
if iterations == 1:
return kernel_filter(in_raster, kernel,
"erode").astype(in_raster.dtype)
else:
result = kernel_filter(in_raster, kernel,
"erode").astype(in_raster.dtype)
for _ in range(iterations - 1):
result = kernel_filter(result, kernel,
"erode").astype(in_raster.dtype)
return result
def sum_filter(
in_raster,
width=3,
circular=True,
holed=False,
weighted_edges=True,
weighted_distance=False,
distance_calc="gaussian",
sigma=2,
dtype=False,
_kernel=False,
):
kernel = (_kernel if _kernel is not False else create_kernel(
width,
circular=circular,
holed=holed,
normalise=False,
weighted_edges=weighted_edges,
weighted_distance=weighted_distance,
distance_calc=distance_calc,
sigma=sigma,
))
if dtype is False:
return kernel_filter(in_raster, kernel, "mean").astype(in_raster.dtype)
return kernel_filter(in_raster, kernel, "mean").astype(dtype)
def median_filter(
in_raster,
width=3,
iterations=1,
circular=True,
holed=False,
weighted_edges=True,
weighted_distance=True,
distance_calc="gaussian",
sigma=2,
dtype='float32',
_kernel=False,
):
kernel = (_kernel if _kernel is not False else create_kernel(
width,
circular=circular,
holed=holed,
weighted_edges=weighted_edges,
weighted_distance=weighted_distance,
distance_calc=distance_calc,
sigma=sigma,
))
if iterations == 1:
return kernel_filter(in_raster, kernel, "median", dtype)
else:
result = kernel_filter(in_raster, kernel, "median", dtype)
for _ in range(iterations - 1):
result = kernel_filter(result, kernel, "median", dtype)
return result
def close_filter(
in_raster,
width=3,
circular=True,
weighted_distance=True,
sigma=3.5,
_kernel=False,
):
kernel = (_kernel if _kernel is not False else create_kernel(
width,
circular=circular,
weighted_edges=True,
weighted_distance=weighted_distance,
sigma=sigma,
normalise=False,
))
return kernel_filter(
kernel_filter(in_raster, kernel, "dilate"),
kernel,
"erode",
).astype(in_raster.dtype)
def cdef_difference_filter(
in_raster_master,
in_raster_slave,
width=3,
circular=True,
holed=False,
weighted_edges=True,
weighted_distance=False,
distance_calc="gaussian",
sigma=2,
dtype='float32',
_kernel=False,
):
kernel = (_kernel if _kernel is not False else create_kernel(
width,
circular=circular,
holed=holed,
normalise=True,
weighted_edges=weighted_edges,
weighted_distance=weighted_distance,
distance_calc=distance_calc,
sigma=sigma,
))
master_mean = kernel_filter(in_raster_master, kernel, 'mean')
slave_mean = kernel_filter(in_raster_slave, kernel, 'mean')
master_median = kernel_filter(in_raster_master, kernel, "median")
slave_median = kernel_filter(in_raster_slave, kernel, 'median')
abs_median_diff = np.abs(master_median - slave_median)
master_mean_difference = np.abs(master_mean -
np.abs(master_median - slave_median))
slave_mean_difference = np.abs(slave_mean -
np.abs(master_median - slave_median))
master_mad_std = kernel_filter(in_raster_master, kernel, 'mad_std')
slave_mad_std = kernel_filter(in_raster_slave, kernel, 'mad_std')
with np.errstate(divide='ignore', invalid='ignore'):
zscores_master = (master_mean -
master_mean_difference) / master_mad_std
zscores_master[master_mad_std == 0] = 0
zscores_slave = (slave_mean - slave_mean_difference) / slave_mad_std
zscores_slave[slave_mad_std == 0] = 0
return np.min([zscores_master, zscores_slave], axis=0)
def kurtosis_filter(
in_raster,
width=3,
circular=True,
holed=False,
weighted_edges=True,
weighted_distance=True,
distance_calc="gaussian",
sigma=2,
dtype='float32',
_kernel=False,
):
kernel = (_kernel if _kernel is not False else create_kernel(
width,
circular=circular,
holed=holed,
weighted_edges=weighted_edges,
weighted_distance=weighted_distance,
distance_calc=distance_calc,
sigma=sigma,
))
return kernel_filter(in_raster, kernel, "kurtosis", dtype)
