ipfml.processing.reconstruction

Functions for reconstruction process of image using reduction/compression methods

Functions

fast_ica(image, components)	Reconstruct an image from Fast ICA compression using specific number of components to use
ipca(image, components[, _batch_size])	Reconstruct an image from IPCA compression using specific number of components to use and batch size
svd(image, interval)	Reconstruct an image from SVD compression using specific interval of Singular Values

ipfml.processing.reconstruction.fast_ica(image, components)

Reconstruct an image from Fast ICA compression using specific number of components to use

Parameters:

• image – PIL Image, Numpy array or path of 3D image
• components – Number of components used for reconstruction

Returns:

Reconstructed image

Example:

>>> from PIL import Image
>>> import numpy as np
>>> from ipfml.processing import reconstruction
>>> image_values = Image.open('./images/test_img.png')
>>> reconstructed_image = reconstruction.fast_ica(image_values, 25)
>>> reconstructed_image.shape
(200, 200)

ipfml.processing.reconstruction.ipca(image, components, _batch_size=25)

Reconstruct an image from IPCA compression using specific number of components to use and batch size

Parameters:

• image – PIL Image, Numpy array or path of 3D image
• components – Number of components used for reconstruction
• batch_size – Batch size used for learn (default 25)

Returns:

Reconstructed image

Example:

>>> from PIL import Image
>>> import numpy as np
>>> from ipfml.processing import reconstruction
>>> image_values = Image.open('./images/test_img.png')
>>> reconstructed_image = reconstruction.ipca(image_values, 20)
>>> reconstructed_image.shape
(200, 200)

ipfml.processing.reconstruction.svd(image, interval)

Reconstruct an image from SVD compression using specific interval of Singular Values

Parameters:

• image – PIL Image, Numpy array or path of 3D image
• interval – Interval used for reconstruction

Returns:

Reconstructed image

Example:

>>> from PIL import Image
>>> import numpy as np
>>> from ipfml.processing import reconstruction
>>> image_values = Image.open('./images/test_img.png')
>>> reconstructed_image = reconstruction.svd(image_values, (100, 200))
>>> reconstructed_image.shape
(200, 200)