https://github.com/ashkarin/ndpatch

Package to extract arbitrary regions from an N-dimensional numpy array assuming it mirrored infinitely.
https://github.com/ashkarin/ndpatch

array data-structures deep-neural-networks image-processing numpy patches region-of-interest

Last synced: 23 days ago
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Package to extract arbitrary regions from an N-dimensional numpy array assuming it mirrored infinitely.

• Host: GitHub
• URL: https://github.com/ashkarin/ndpatch
• Owner: ashkarin
• License: mit
• Created: 2018-09-24T20:29:10.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2019-05-08T17:32:04.000Z (over 5 years ago)
• Last Synced: 2024-09-28T09:14:28.305Z (about 1 month ago)
• Topics: array, data-structures, deep-neural-networks, image-processing, numpy, patches, region-of-interest
• Language: Python
• Homepage:
• Size: 20.5 KB
• Stars: 1
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE.txt

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README

        
.. image:: static/ndpatch.svg

    :height: 120

    :align: center

    

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.. image:: https://travis-ci.org/ashkarin/ndpatch.svg?branch=master 

    :target: https://travis-ci.org/ashkarin/ndpatch

**NDPatch** is the package for extracting arbitrary regions from an N-dimensional numpy array assuming it mirrored infinitely.

Installation

------------

The easiest way to install the latest version is by using pip::

    $ pip install ndpatch

You may also use Git to clone the repository and install it manually::

    $ git clone https://github.com/ashkarin/ndpatch.git

    $ cd ndpatch

    $ python setup.py install

Usage

-----

To take a patch from the array:

.. code-block:: python

  import numpy as np

  import ndpatch

  array = np.arange(25).reshape((5,5))

  index = (1, 2)

  shape = (3, 3)

  patch = ndpatch.get_ndpatch(array, shape, index)

  # patch =

  # [[ 7,  8,  9],

  #  [12, 13, 14],

  #  [17, 18, 19]]

To take get a random patch index:

.. code-block:: python

  import numpy as np

  import ndpatch

  array_shape = (5, 5)

  index = ndpatch.get_random_patch_index(array_shape)

To extract random patches from the array:

.. code-block:: python

  import numpy as np

  import ndpatch

  npatches = 10

  patch_shape = (3, 3)

  array = np.arange(100).reshape((10,10))

  patches = [ndpatch.get_random_ndpatch(array, patch_shape) for _ in range(npatches)]

To split the 3D array on set of overlapping 3D patches and rebuild it back:

.. code-block:: python

  import numpy as np

  import ndpatch

  array = np.arange(0, 125).reshape((5,5,5))

  patch_shape = (4, 3, 3)

  overlap = 2

  indices = ndpatch.get_patches_indices(array.shape, patch_shape, overlap)

  patches = [ndpatch.get_ndpatch(array, patch_shape, index) for index in indices]

  reconstructed = ndpatch.reconstruct_from_patches(patches, indices, array.shape, default_value=0)

  # Validate

  equal = (reconstructed == array)

  assert (np.all(equal))