photosynthesis-team / Piq

.. image:: docs/source/_static/piq_logo_main.png :target: https://github.com/photosynthesis-team/piq

..

PyTorch Image Quality (PIQ) is not endorsed by Facebook, Inc.;

PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.

|pypy| |conda| |flake8| |tests| |codecov| |quality_gate|

.. |pypy| image:: https://badge.fury.io/py/piq.svg :target: https://pypi.org/project/piq/ :alt: Pypi Version .. |conda| image:: https://anaconda.org/photosynthesis-team/piq/badges/version.svg :target: https://anaconda.org/photosynthesis-team/piq :alt: Conda Version .. |flake8| image:: https://github.com/photosynthesis-team/piq/workflows/flake-8%20style%20check/badge.svg :alt: CI flake-8 style check .. |tests| image:: https://github.com/photosynthesis-team/piq/workflows/testing/badge.svg :alt: CI testing .. |codecov| image:: https://codecov.io/gh/photosynthesis-team/piq/branch/master/graph/badge.svg :target: https://codecov.io/gh/photosynthesis-team/piq :alt: codecov .. |quality_gate| image:: https://sonarcloud.io/api/project_badges/measure?project=photosynthesis-team_photosynthesis.metrics&metric=alert_status :target: https://sonarcloud.io/dashboard?id=photosynthesis-team_photosynthesis.metrics :alt: Quality Gate Status

.. intro-section-start

PyTorch Image Quality (PIQ) <https://github.com/photosynthesis-team/piq>_ is a collection of measures and metrics for image quality assessment. PIQ helps you to concentrate on your experiments without the boilerplate code. The library contains a set of measures and metrics that is continually getting extended. For measures/metrics that can be used as loss functions, corresponding PyTorch modules are implemented.

We provide:

• Unified interface, which is easy to use and extend.
• Written on pure PyTorch with bare minima of additional dependencies.
• Extensive user input validation. You code will not crash in the middle of the training.
• Fast (GPU computations available) and reliable.
• Most metrics can be backpropagated for model optimization.
• Supports python 3.6-3.8.

PIQ was initially named PhotoSynthesis.Metrics <https://pypi.org/project/photosynthesis-metrics/0.4.0/>_.

.. intro-section-end

.. installation-section-start

Installation

PyTorch Image Quality (PIQ) <https://github.com/photosynthesis-team/piq>_ can be installed using pip, conda or git.

If you use pip, you can install it with:

.. code-block:: sh

$ pip install piq

If you use conda, you can install it with:

.. code-block:: sh

$ conda install piq -c photosynthesis-team -c conda-forge -c PyTorch

If you want to use the latest features straight from the master, clone PIQ repo <https://github.com/photosynthesis-team/piq>_:

.. code-block:: sh

git clone https://github.com/photosynthesis-team/piq.git cd piq python setup.py install

.. installation-section-end

.. documentation-section-start

Documentation

The full documentation is available at https://piq.readthedocs.io.

.. documentation-section-end

.. usage-examples-start

Usage Examples

Image-based metrics ^^^^^^^^^^^^^^^^^^^ The group of metrics (such as PSNR, SSIM, BRISQUE) takes image or images as input. We have a functional interface, which returns a metric value, and a class interface, which allows to use any metric as a loss function.

.. code-block:: python

import torch from piq import ssim, SSIMLoss

x = torch.rand(4, 3, 256, 256, requires_grad=True) y = torch.rand(4, 3, 256, 256)

ssim_index: torch.Tensor = ssim(x, y, data_range=1.)

loss = SSIMLoss(data_range=1.) output: torch.Tensor = loss(x, y) output.backward()

For a full list of examples, see image metrics <https://github.com/photosynthesis-team/piq/blob/master/examples/image_metrics.py>_ examples.

Feature-based metrics ^^^^^^^^^^^^^^^^^^^^^

The group of metrics (such as IS, FID, KID) takes a list of image features. Image features can be extracted by some feature extractor network separately or by using the compute_feats method of a class.

Note: compute_feats consumes a data loader of a predefined format.

.. code-block:: python

import torch from torch.utils.data import DataLoader from piq import FID

first_dl, second_dl = DataLoader(), DataLoader() fid_metric = FID() first_feats = fid_metric.compute_feats(first_dl) second_feats = fid_metric.compute_feats(second_dl) fid: torch.Tensor = fid_metric(first_feats, second_feats)

If you already have image features, use the class interface for score computation:

.. code-block:: python

import torch
from piq import FID

x_feats = torch.rand(10000, 1024)
y_feats = torch.rand(10000, 1024)
msid_metric = MSID()
msid: torch.Tensor = msid_metric(x_feats, y_feats)

For a full list of examples, see feature metrics <https://github.com/photosynthesis-team/piq/blob/master/examples/feature_metrics.py>_ examples.

.. usage-examples-end

.. list-of-metrics-start

List of metrics

Full Reference ^^^^^^^^^^^^^^

=========== ====== ========== Acronym Year Metric =========== ====== ========== PSNR - Peak Signal-to-Noise Ratio <https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio>_ SSIM 2003 Structural Similarity <https://en.wikipedia.org/wiki/Structural_similarity>_ MS-SSIM 2004 Multi-Scale Structural Similarity <https://ieeexplore.ieee.org/abstract/document/1292216>_ VIFp 2004 Visual Information Fidelity <https://ieeexplore.ieee.org/document/1576816>_ FSIM 2011 Feature Similarity Index Measure <https://ieeexplore.ieee.org/document/5705575>_ IW-PSNR 2011 Information Weighted PSNR <https://ece.uwaterloo.ca/~z70wang/publications/IWSSIM.pdf>_ IW-SSIM 2011 Information Weighted SSIM <https://ece.uwaterloo.ca/~z70wang/publications/IWSSIM.pdf)>_ SR-SIM 2012 Spectral Residual Based Similarity <https://sse.tongji.edu.cn/linzhang/ICIP12/ICIP-SR-SIM.pdf>_ GMSD 2013 Gradient Magnitude Similarity Deviation <https://arxiv.org/abs/1308.3052>_ VSI 2014 Visual Saliency-induced Index <https://ieeexplore.ieee.org/document/6873260>_ - 2016 Content Score <https://arxiv.org/abs/1508.06576>_ - 2016 Style Score <https://arxiv.org/abs/1508.06576>_ HaarPSI 2016 Haar Perceptual Similarity Index <https://arxiv.org/abs/1607.06140>_ MDSI 2016 Mean Deviation Similarity Index <https://arxiv.org/abs/1608.07433>_ MS-GMSD 2017 Multi-Scale Gradient Magnitude Similiarity Deviation <https://ieeexplore.ieee.org/document/7952357>_ LPIPS 2018 Learned Perceptual Image Patch Similarity <https://arxiv.org/abs/1801.03924>_ PieAPP 2018 Perceptual Image-Error Assessment through Pairwise Preference <https://arxiv.org/abs/1806.02067>_ DISTS 2020 Deep Image Structure and Texture Similarity <https://arxiv.org/abs/2004.07728>_ =========== ====== ==========

No Reference ^^^^^^^^^^^^

=========== ====== ========== Acronym Year Metric =========== ====== ========== TV 1937 Total Variation <https://en.wikipedia.org/wiki/Total_variation>_ BRISQUE 2012 Blind/Referenceless Image Spatial Quality Evaluator <https://ieeexplore.ieee.org/document/6272356>_ =========== ====== ==========

Feature based ^^^^^^^^^^^^^

=========== ====== ========== Acronym Year Metric =========== ====== ========== IS 2016 Inception Score <https://arxiv.org/abs/1606.03498>_ FID 2017 Frechet Inception Distance <https://arxiv.org/abs/1706.08500>_ GS 2018 Geometry Score <https://arxiv.org/abs/1802.02664>_ KID 2018 Kernel Inception Distance <https://arxiv.org/abs/1801.01401>_ MSID 2019 Multi-Scale Intrinsic Distance <https://arxiv.org/abs/1905.11141>_ =========== ====== ==========

.. list-of-metrics-end

.. benchmark-section-start

Benchmark

As part of our library we provide code to benchmark all metrics on a set of common Mean Opinon Scores databases. Currently only TID2013_ and KADID10k_ are supported. You need to download them separately and provide path to images as an argument to the script.

Here is an example how to evaluate SSIM and MS-SSIM metrics on TID2013 dataset:

.. code-block:: bash

python3 tests/results_benchmark.py --dataset tid2013 --metrics SSIM MS-SSIM --path ~/datasets/tid2013 --batch_size 16

We report Spearman's Rank Correlation cCoefficient <https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient>_ (SRCC) and Kendall rank correlation coefficient <https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient>_ (KRCC). We do not report Pearson linear correlation coefficient <https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>_ (PLCC) as it's highly dependent on fitting method and is biased towards simple examples.

For metrics that can take greyscale or colour images, c means chromatic version.

=========== ================= ================================ ================= ================================ \ TID2013 KADID10k

Acronym SRCC / KRCC (PIQ) SRCC / KRCC SRCC / KRCC (PIQ) SRCC / KRCC =========== ================= ================================ ================= ================================ PSNR 0.6869 / 0.4958 0.687 0.496 TID2013_ 0.6757 / 0.4876 - / - SSIM 0.7201 / 0.5271 0.637 / 0.464 TID2013_ 0.7242 / 0.5370 0.718 / 0.532 KADID10k_ MS-SSIM 0.7983 / 0.5965 0.787 / 0.608 TID2013_ 0.8020 / 0.6088 0.802 / 0.609 KADID10k_ VIFp 0.6102 / 0.4579 0.610 / 0.457 TID2013_ 0.6500 / 0.4770 0.650 / 0.477 KADID10k_ FSIM 0.8015 / 0.6289 0.801 / 0.630 TID2013_ 0.8294 / 0.6390 0.829 / 0.639 KADID10k_ FSIMc 0.8509 / 0.6665 0.851 / 0.667 TID2013_ 0.8537 / 0.6650 0.854 / 0.665 KADID10k_ IW-PSNR - / - 0.6913 / - Eval2019_ - / - - / - IW-SSIM - / - 0.7779 / 0.5977 Eval2019_ - / - - / - SR-SIM - / - 0.8076 / 0.6406 Eval2019_ - / - 0.839 / 0.652 KADID10k_ SR-SIMc - / - - / - - / - - / - GMSD 0.8038 / 0.6334 0.8030 / 0.6352 MS-GMSD_ 0.8474 / 0.6640 0.847 / 0.664 KADID10k_ VSI 0.8949 / 0.7159 0.8965 / 0.7183 Eval2019_ 0.8780 / 0.6899 0.861 / 0.678 KADID10k_ Content 0.7049 / 0.5173 - / - 0.7237 / 0.5326 - / - Style 0.5384 / 0.3720 - / - 0.6470 / 0.4646 - / - HaarPSI 0.8732 / 0.6923 0.8732 / 0.6923 HaarPSI_ 0.8849 / 0.6995 0.885 / 0.699 KADID10k_ MDSI 0.8899 / 0.7123 0.8899 / 0.7123 MDSI_ 0.8853 / 0.7023 0.885 / 0.702 KADID10k_ MS-GMSD 0.8121 / 0.6455 0.8139 / 0.6467 MS-GMSD_ 0.8523 / 0.6692 - / - MS-GMSDc 0.8875 / 0.7105 0.687 / 0.496 MS-GMSD_ 0.8697 / 0.6831 - / - LPIPS-VGG 0.6696 / 0.4970 0.670 / 0.497 DISTS_ 0.7201 / 0.5313 - / - PieAPP 0.8355 / 0.6495 0.875 / 0.710 DISTS_ 0.8655 / 0.6758 - / - DISTS 0.8051 / 0.6133 0.830 / 0.639 DISTS_ 0.8749 / 0.6947 - / - =========== ================= ================================ ================= ================================

.. _TID2013: http://www.ponomarenko.info/tid2013.htm .. _KADID10k: http://database.mmsp-kn.de/kadid-10k-database.html .. _Eval2019: https://ieeexplore.ieee.org/abstract/document/8847307/ .. _MDSI: https://arxiv.org/abs/1608.07433 .. _MS-GMSD: https://ieeexplore.ieee.org/document/7952357 .. _DISTS: https://arxiv.org/abs/2004.07728 .. _HaarPSI: https://arxiv.org/abs/1607.06140

.. benchmark-section-end

.. assertions-section-start

Assertions

In PIQ we use assertions to raise meaningful messages when some component doesn't receive an input of the expected type. This makes prototyping and debugging easier, but it might hurt the performance. To disable all checks, use the Python -O flag: python -O your_script.py

.. assertions-section-end

Roadmap

See the open issues <https://github.com/photosynthesis-team/piq/issues>_ for a list of proposed features and known issues.

Contributing

If you would like to help develop this library, you'll find more information in our contribution guide <CONTRIBUTING.rst>_.

.. citation-section-start

Citation

If you use PIQ in your project, please, cite it as follows.

.. code-block:: tex

@misc{piq, title={{PyTorch Image Quality}: Metrics and Measure for Image Quality Assessment}, url={https://github.com/photosynthesis-team/piq}, note={Open-source software available at https://github.com/photosynthesis-team/piq}, author={Sergey Kastryulin and Dzhamil Zakirov and Denis Prokopenko}, year={2019}, }

.. citation-section-end

.. contacts-section-start

Contacts

Sergey Kastryulin - @snk4tr <https://github.com/snk4tr>_ - [email protected]

Djamil Zakirov - @zakajd <https://github.com/zakajd>_ - [email protected]

Denis Prokopenko - @denproc <https://github.com/denproc>_ - [email protected]

.. contacts-section-end