def apply_C_filter(image, dll, c_function, filter_size, mode='constant', cval=0):
"""
Apply a C function based filter on image
Parameters
----------
image : 2D array
Input image.
dll: str or a ctypes dll object
If str: A dll name (including full path if not on the default search
path).
c_function: str or int
If str: The name of the variable in the dll pointing to the function.
If int: A Function pointer.
filter_size : (2,) array
Filter shape.
mode : str
Padding mode.
cval : a scalar
Padding fill value (applicable is mode == 'const')
Returns
-------
output : array
A 2D array of the same dtype and shape as the input array.
"""
# A temporary confinment to float32
image = convert(image, np.float32)
if type(dll) is str:
dll = ctypes.cdll.LoadLibrary(dll)
if type(c_function) is str:
# pfcn = ctypes.c_voidp.in_dll(dll, c_function).value
pfcn = ctypes.cast(getattr(dll, c_function), ctypes.c_void_p).value
#pfcn = getattr(dll, c_function)
else:
pfcn = c_function
#pfcn = ctypes.cast(pfcn, ctypes.pointer)
# Prepare paded data
padded = fiter.gen_filter_matrix(image, filter_size, mode=mode, cval=cval)
output = np.empty_like(image)
padded.filter_with_C_callback_float(pfcn, output)
return output
import gen_filter
import _filter_iter as fiter
def py_rand_select(buffer):
buf = buffer.ravel()
return buf[np.random.randint(len(buf))]
x = np.linspace(-3,3,51).astype(np.float32)
z = np.exp(-(x**2+x[:,None]**2))
z[::2, ::2] = 0.0
C_filtered = gen_filter.apply_C_filter(z, './example_C_filters.so',
'rand_select_addr', (3,3))
prepare = fiter.gen_filter_matrix(z, (3,3))
Py_filtered = prepare.filter_with_py_callback(py_rand_select)
fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(9, 4))
ax1.imshow(z, interpolation='nearest')
ax1.set_title('Original data')
ax2.imshow(C_filtered, interpolation='nearest')
ax2.set_title('C filtered data')
ax3.imshow(Py_filtered, interpolation='nearest')
ax3.set_title('Python filtered data')
plt.show()
