piqa 1.0.2

PyTorch Image Quality Assessment

PyTorch Image Quality Assessment

This package is a collection of measures and metrics for image quality assessment in various image processing tasks such as denoising, super-resolution, image interpolation, etc. It relies heavily on PyTorch and takes advantage of its efficiency and automatic differentiation.

It should noted that piqa is directly inspired from the piq project. However, it focuses on the conciseness, readability and understandability of its (sub-)modules, such that anyone can freely and easily reuse and/or adapt them to its needs.

piqa should be pronounced pika (like Pikachu ⚡️)

Installation

The piqa package is available on PyPI, which means it is installable with pip:

pip install piqa

Alternatively, if you need the lastest features, you can install it using

git clone https://github.com/francois-rozet/piqa
cd piqa
python setup.py install

or copy the package directly to your project, with

git clone https://github.com/francois-rozet/piqa
cd piqa
cp -R piqa <path/to/project>/piqa

Getting started

import torch
import piqa.psnr as psnr
import piqa.ssim as ssim

x = torch.rand(3, 3, 256, 256)
y = torch.rand(3, 3, 256, 256)

# PSNR function
l = psnr.psnr(x, y)

# SSIM instantiable object
criterion = ssim.SSIM().cuda()
l = criterion(x, y)

Documentation

The documentation of this package is generated automatically using pdoc.

The code follows the Google Python style and is compliant with YAPF.