def std(image, size):
"""
a moving std calculation on an nd image
"""
import numpy as np
import ctypes
import scipy.ndimage as ndi
from numba import cfunc, carray
from numba.core.types import intc, intp, float64, voidptr, CPointer
from scipy import LowLevelCallable
@cfunc(intc(CPointer(float64), intp, CPointer(float64), voidptr),
fastmath=True)
def _std(values_ptr, len_values, result):
values = carray(values_ptr, (len_values, ), dtype=float64)
accumx = 0
accumx2 = 0
for x in values:
accumx += x
accumx2 += x * x
mean = accumx / len_values
std = np.sqrt((accumx2 / len_values) - mean**2)
result[0] = std
return 1
res = ndi.generic_filter(image, LowLevelCallable(_std.ctypes), size)
return res
def filter_std(image, size, sigma=1):
"""
explicitly filters an nd image based on deviation by local stdev over local mean.
return mask of points that are sigma local stdev over(under) the local mean if sigma is positiv(negativ)
"""
import numpy as np
import ctypes
import scipy.ndimage as ndi
from numba import cfunc, carray
from numba.core.types import intc, intp, float64, voidptr, CPointer
from scipy import LowLevelCallable
@cfunc(intc(CPointer(float64), intp, CPointer(float64), voidptr),
fastmath=True)
def _std(values_ptr, len_values, result, data):
values = carray(values_ptr, (len_values, ), dtype=float64)
accumx = 0
accumx2 = 0
sigma = carray(data, (1, ), dtype=float64)[0]
for x in values:
accumx += x
accumx2 += x * x
mean = accumx / len_values
std = np.sqrt((accumx2 / len_values) - mean**2)
result[0] = sigma * std + mean
return 1
csigma = ctypes.c_double(sigma)
ptr = ctypes.cast(ctypes.pointer(csigma), ctypes.c_void_p)
res = image > ndi.generic_filter(image, LowLevelCallable(_std.ctypes, ptr),
size)
if sigma < 0:
res = ~res
return res
def jit_filter_function(filter_function):
"""Decorator for use with scipy.ndimage.generic_filter."""
jitted_function = numba.jit(filter_function, nopython=True)
@cfunc(intc(CPointer(float64), intp, CPointer(float64), voidptr))
def wrapped(values_ptr, len_values, result, data):
values = carray(values_ptr, (len_values, ), dtype=float64)
result[0] = jitted_function(values)
return 1
return LowLevelCallable(wrapped.ctypes)
def jit_geometric_function(geometric_function):
jitted_function = numba.jit(geometric_function, nopython=True)
@cfunc(intc(CPointer(intp), CPointer(float64), intc, intc, voidptr))
def wrapped(output_ptr, input_ptr, output_rank, input_rank, user_data):
output_coords = carray(output_ptr, (output_rank, ), dtype=intp)
input_coords = carray(input_ptr, (output_rank, ), dtype=float64)
jitted_function(output_coords, input_coords)
return 1
return LowLevelCallable(wrapped.ctypes)
def jit_filter_function(filter_function):
"""Decorator for use with scipy.ndimage.generic_filter."""
jitted_function = numba.jit(filter_function, nopython=True)
@cfunc(intc(CPointer(float64), intp, CPointer(float64), voidptr))
def wrapped(values_ptr, len_values, result, data):
values = carray(values_ptr, (len_values,), dtype=float64)
result[0] = jitted_function(values)
return 1
sig = None
if sys.platform == "win32":
sig = "int (double *, npy_intp, double *, void *)"
return LowLevelCallable(wrapped.ctypes, signature=sig)
def jit_geometric_function(geometric_function):
jitted_function = numba.jit(geometric_function, nopython=True)
@cfunc(intc(CPointer(intp), CPointer(float64), intc, intc, voidptr))
def wrapped(output_ptr, input_ptr, output_rank, input_rank, user_data):
output_coords = carray(output_ptr, (output_rank,), dtype=intp)
input_coords = carray(input_ptr, (output_rank,), dtype=float64)
jitted_function(output_coords, input_coords)
return 1
# needs to be tested
# sig = None
# if sys.platform == "win32":
# sig = "int (double *, double *, int, int, void *)"
return LowLevelCallable(wrapped.ctypes, signature=sig)
