ipfml.filters.kernels

Kernel to apply on images using convolution

Functions

bilateral_diff(window[, func])	Bilaeral difference kernel to use with convolution process on image
plane_max_error(window)	Plane max error kernel to use with convolution process on image
plane_mean(window)	Plane mean kernel to use with convolution process on image

ipfml.filters.kernels.bilateral_diff(window, func=<built-in function max>)

Bilaeral difference kernel to use with convolution process on image

Apply difference pixel to pixel and keep max on min difference before applying mean

Parameters:

• window – the window part to use from image
• func – max or min function to get difference between pixels

Returns:

mean of max or min difference of pixels

Example:

>>> from ipfml.filters.kernels import bilateral_diff
>>> import numpy as np
>>> window = np.arange(9).reshape([3, 3])
>>> result = bilateral_diff(window)
>>> result
3.0

ipfml.filters.kernels.plane_max_error(window)

Plane max error kernel to use with convolution process on image

Parameters:window – the window part to use from image Returns:Difference between max and min error from mean plane

Example:

>>> from ipfml.filters.kernels import plane_max_error
>>> import numpy as np
>>> window = np.arange(9).reshape([3, 3])
>>> result = plane_max_error(window)
>>> (result < 0.0001)
True

ipfml.filters.kernels.plane_mean(window)

Plane mean kernel to use with convolution process on image

Parameters:window – the window part to use from image Returns:Normalized residual error from mean plane

Example:

>>> from ipfml.filters.kernels import plane_mean
>>> import numpy as np
>>> window = np.arange(9).reshape([3, 3])
>>> result = plane_mean(window)
>>> (result < 0.0001)
True