polimi-ispl / python_patch_extractor

N-dimensional patch extractor

Patch Extractor

A simple yet powerful N-dimensional patch extractor, in pure python!

Authors: Nicolò Bonettini, Luca Bondi, Francesco Picetti

Requirements and Installation

To use this Patch Extractor, simply clone this repo in your work directory.

Actually you will need only PatchExtractor.py.

The extractor is built upon numpy and scikit-image modules.

Usage Instructions

1. First you need to import the extractor class:

   from python_patch_extractor import PatchExtractor as PE

2. Then, instantiate an object:

   pe = PE.PatchExtractor(dim=patch_shape)

   PatchExtractor class accepts a number of parameters:

   • dim: tuple - the shape of every single patch [required].
   • offset: tuple - offset to be applied during extraction [None].
   • stride: tuple - step length (in samples) between two adjacent patches [None, i.e. equal to dim].
   • rand: bool - random shuffling the extracted patches [None]. It is concurrent with function argument.
   • function: function - score function for ordering the patches [None]. It requires the thresholdargument and it is concurrent with rand argument.
   • threshold: float - threshold value to be applied to scores computed by function [None]. Only the patches that have a score ≥ threshold are returned.
   • num: int - number of extracted patches to return [None]. It is concurrent with indexes argument.
   • indexes: list or 1D array - indexes of the extracted patches to return [None]. It is concurrent with num argument.
   • tapering: str - tapering function applied to the overlapping portion; must be rect, hanning, cosine, cosinesquare [rect]. For now it works only for 2D patches.
   • padding: str - padding strategy (taken from np.pad). If the patch is bigger than the input, the original content is centered in the resulting patch. If the patch is smaller than the input, it adds some samples at the end of each axis in order to keep all the available data.

3. Once you have your data vector (e.g., an image), you can extract patches via:

   patches = pe.extract(data)

   patches will have a shape that is a tuple made by two contributions: first you have the number of patches extracted for each data dimension, then the patch dimension. For examples, if your image is 128 x 128 and you extract patches of 64 x 64 (with same stride), your patch array shape will be (2, 2, 64, 64). The number of extracted patches will be 4.

   You can get the shape of the extracted patches array by using PE.patch_array_shape(in_size, patch_shape, patch_stride).

   You can get the number of the extracted patches by using PE.count_patches(in_size, patch_shape, patch_stride).

4. If you need to reconstruct the whole data from the patches, you can use:

   assembled_data = pe.reconstruct(patches)

   For now it works for data with up to 4 dimensions.

   Please note that patches must have the shape provided by the extract method. If you have ravelled your patch array, you have lost the spatial information of how many patches for each data dimension you extracted. However, this information is provided by PE.patch_array_shape(in_size, patch_shape, patch_stride).

   Depending on the data shape, patch shape and stride you can loose some data elements (at the axes ends). Thus the reconstructed data could be smaller that the original data.