pyrtools: tools for multi-scale image processing

Briefly, the tools include:

Recursive multi-scale image decompositions (pyramids), including Laplacian pyramids, QMFs, Wavelets, and steerable pyramids. These operate on 1D or 2D signals of arbitrary dimension.
Fast 2D convolution routines, with subsampling and boundary-handling.
Fast point-operations, histograms, histogram-matching.
Fast synthetic image generation: sine gratings, zone plates, fractals, etc.
Display routines for images and pyramids. These include several auto-scaling options, rounding to integer zoom factors to avoid resampling artifacts, and useful labeling (dimensions and gray-range).

This is a python 3 port of Eero Simoncelli's matlabPyrTools, but it does not attempt to recreate all of the matlab code from matlabPyrTools. The goal is to create a Python interface for the C code at the heart of matlabPyrTools.

NOTE: If you are only interested in the complex steerable pyramid, we have a pytorch implementation in the plenoptic package; the implementation in plenoptic is differentiable.

Citing us

If you use pyrtools in a published academic article or presentation, please cite us! You can find the link to the most recent release on Zenodo here (though please specify the version you used not the most recent one!). You can also get a formatted citation at the top right of our GitHub repo

Installation

It's recommended you install from pip: pip install pyrtools.

If you wish to install from the main branch, it's still recommended to use pip, just run pip install . (or pip install -e . if you want the changes you make in the directory to be reflected in your install) from the root directory of this project. The core of this code is the C code, and the pip install will compile it nicely.

Dependencies

Dependencies are documented in setup.py.

IPython is optional. If it's not installed, pyrtools.display_tools.animshow must be called with as_html5=False (but since this is for displaying the animated image in a Jupyter / IPython notebook, you probably won't need that functionality).

For the C code to compile, we require gcc version >= 6, because of this issue

Pyramid resources

If you would like to learn more about pyramids and why they're helpful for image processing, here are some resources to get you started:

Brian Wandell's Foundations of Vision, chapter 8 (the rest of the book is helpful if you want to understand the basics of the visual system).
Adelson et al, 1984, "Pyramid methods in image processing".
Notes from David Heeger on steerable filters
Notes from Eero Simoncelli on the Steerable Pyramid

Authors

Rob Young and Eero Simoncelli, 7/13

William Broderick, 6/17

William Broderick, Pierre-Étienne Fiquet, Zhuo Wang, Zahra Kadkhodaie, Nikhil Parthasarathy, and the Lab for Computational Vision, 4/19

Usage:

method parameters mimic the matlab function parameters except that there's no need to pass pyr or pind, since the pyPyrTools version pyr and pyrSize are properties of the class.

load modules (note that if you installed via pip, you can skip the first two lines):

import pyrtools as pt

create pyramid:

pyr = pt.pyramids.LaplacianPyramid(img)

reconstruct image from pyramid:

recon_img = pyr.recon_pyr()

Please see TUTORIALS/02_pyramids.ipynb for more examples. You can start this with: jupyter notebook 02_pyramids.ipynb if you have iPython and Jupyter installed.

Testing

All code should be considered a beta release. By that we mean that it is being actively developed and tested. You can find unit tests in TESTS/unitTests.py and run them with python TESTS/unitTests.py.

If you're using functions or parameters that do not have associated unit tests you should test this yourself to make sure the results are correct. You could then submit your test code, so that we can build more complete unit tests.

Build the documentation

NOTE: If you just want to read the documentation, you do not need to do this; documentation is built automatically on readthedocs.

However, it can be built locally as well. You would do this if you've made changes locally to the documentation (or the docstrings) that you would like to examine before pushing. The virtual environment required to do so is defined in docs/environment.yml, so to create that environment and build the docs, do the following from the project's root directory:

# install sphinx and required packages to build documentation
conda env create -f docs/environment.yml
# activate the environment
conda activate pyrtools_docs
# install pyrtools
pip install -e .
# build documentation
cd docs/
make html

The index page of the documentation will then be located at docs/_build/html/index.html, open it in your browser to navigate around.

The pyrtools_docs environment you're creating contains the package sphinx and several extensions for it that are required to build the documentation. You also need to install pyrtools from your local version so that sphinx can import the library and grab all of the docstrings (you're installing the local version so you can see all the changes you've made).

LabForComputationalVision / pyrtools