medpy.iterators.patchwise.CentredPatchIteratorOverlapping

class medpy.iterators.patchwise.CentredPatchIteratorOverlapping(array, psize, offset=None, cval=0)

Iterated patch-wise over the array, where the central patch is centred on the image centre.

All yielded patches will be of size psize. Areas outside of the array are filled with cval. Besides the patch, a patch mask is returned, that denoted the outside values. Additionally, the n-dimensional grid id and a slicer object are returned.

To extract the same patch from another array of the same size as array, use the applyslicer method.

The following schematic overview explains the behaviour to expect for even and odd images respectively patches. All O denote image voxels, | the patch borders and # padded voxels.

One-dimensional image of size 5 with patch sizes 1, 2, 3, 4 and 5:

|O|O|O|O|O|

|#O|OO|OO|

|##O|OOO|O##|

|OOOO|O###|

|OOOOO|

One-dimensional image of size 4 with patch sizes 1, 2, 3 and 4:

|O|O|O|O|

|#O|OO|O#|

|OOO|O##|

|OOOO|

Parameters:

arrayarray_like

A n-dimensional array.

psizeint or sequence of ints

The patch size. If a single integer interpreted as hyper-cube.

offsetNone, int or sequence of ints

The patch offset. If None interpreted as non-overlapping patches. If a single integer interpreted as hyper-cube.

cvalnumber

Value to fill undefined positions.

Examples

>>> import numpy
>>> from medpy.iterators import CentredPatchIterator
>>> arr = numpy.arange(0, 25).reshape((5,5))
>>> arr
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24]])
>>> patches, pmasks, gridids, slicers = zip(*CentredPatchIterator(arr, 3))
Total number of patches:
>>> len(patches)
9
Central patch:
>>> patches[4]
array([[ 6,  7,  8],
       [11, 12, 13],
       [16, 17, 18]])
Bottom-right corner patch:
>>> patches[-1]
array([[24,  0,  0],
       [ 0,  0,  0],
       [ 0,  0,  0]])
And its definition mask:
>>> pmasks[-1]
array([[ True, False, False],
       [False, False, False],
       [False, False, False]], dtype=bool)
One dimensional behaviour examples:
>>> arr = range(1, 5)
>>> len(arr)
4
>>> patches, pmasks, _, _ = zip(*CentredPatchIterator(arr, 1))
>>> arr, patches
([1, 2, 3, 4], (array([1]), array([2]), array([3]), array([4])))
>>> patches, _, _, _ = zip(*CentredPatchIterator(arr, 2))
>>> arr, patches
([1, 2, 3, 4], (array([0, 1]), array([2, 3]), array([4, 0])))
>>> patches, _, _, _ = zip(*CentredPatchIterator(arr, 3))
>>> arr, patches
([1, 2, 3, 4], (array([1, 2, 3]), array([4, 0, 0])))
>>> patches, _, _, _ = zip(*CentredPatchIterator(arr, 4))
>>> arr, patches
([1, 2, 3, 4], (array([1, 2, 3, 4]),))

__init__(array, psize, offset=None, cval=0)

Methods

__init__(array, psize[, offset, cval])
applyslicer(array, slicer, pmask[, cval])	Apply a slicer returned by the iterator to a new array of the same dimensionality as the one used to initialize the iterator.
assembleimage(patches, pmasks, gridids)	Assemble an image from a number of patches, patch masks and their grid ids.
next()	Yields the next patch.