PyStretch 0.2.1

Introduction

The package author’s goal is to provide an image processing toolset which allows for persistent manipulation and analysis of large, spatially enabled raster datasets, on machines which are traditionally RAM constrained. The RAM constraint can be imposed either by a lack of memory or by the size of the raster for analysis. Additionally, this package allows for the systematic removal spatially definable camera error.

What about bugs or feature requests?

With any initial release, bugs are undoubtably present. To report a bug or request a feature, open a ticket at http://github.com/jlaura/PyStretch

The code can also be cloned and forked via git, if you are a github user.

Getting started immediately - a super brief overview

This package is intended to be run primarily from the pystretch.py script located in Python##/bin/. Ideally this directory is already added to your Path of your PYTHONPATH. If not, you either need to run the script from the bin directory or add that directroy to your PATH.

For help use:

python pystretch.py –help

Typical usage often looks like this:

python pystretch.py <stretch> <optional segmentation> <input> <optional output>

or

python pystretch.py -l -t 5 input.tif -o output_image.tif

The code above performs a linear stretch <-l> by segmenting the image into 5 horizontal sections <-t 5> and writes to the output <output_image.tif>.

Note that segmenting the image into 5 pieces will likely result in 6 total sections as the image is subdivided into 5 sections of equal size and one ‘remainder’ section.

For a much more in depth look at the functionality checkout: http://packages.python.org/PyStretch

Known Issues

1. When reading against the intrinsic image block size rad and write times are quite long due to thrashing. This is a known issue with the way in which GDAL RasterIO works. It is therefore suggested that images be read in either the block size or multiples of the block size.

For example, the block size on a GTiff is most often one scanline. Therefore, using horizontal segments will read in multiples of the scanline and avoid Band Thrashing. Reading the same image in the vertical direction can increase processing time threefold.

2. The numpy implementation of ndarray.std(), which calculates the standard deviation, creates an in memory copy of the array. Be aware of this when deciding on image segmentation size. The standard deviation of the image or image segment is only calculated when performing a standard deviation or gaussian stretch. In all other cases this calculation is omitted.

Accessing classes via imports

It is hoped that this package will provide a number of tools to leverage the segmentation and image analysis algorithms in your own work. For this reason the majority of the functionality is importable.

Imports are intentionally left explicit. That is to say that the following will not work

import * from pystretch.core import * will not work.

Instead explicitly import the modules you want to use

from pystretch.core import ArrayConvert from pystretch.linear import Linear from pystretch.plot import Plot