An IQA toolbox with pure python and pytorch. Please refer to Awesome-Image-Quality-Assessment for a comprehensive survey of IQA methods and download links for IQA datasets.
This is a image quality assessment toolbox with pure python and pytorch. We provide reimplementation of many mainstream full reference (FR) and no reference (NR) metrics (results are calibrated with official matlab scripts if exist). With GPU acceleration, most of our implementations are much faster than Matlab. Please refer to the following documents for details:
📦 Model Cards | 🗃️ Dataset Cards | 🤗 Datasets | 📚 Documentation
- 🔥June, 2024. Add
arniqaand its variances trained on different datasets, refer to official repo here. Thanks for the contribution from Lorenzo Agnolucci 🤗. - Apr 24, 2024. Add
inception_scoreand console entry point withpyiqacommand. - Mar 11, 2024. Add
unique, refer to official repo here. Thanks for the contribution from Weixia Zhang 🤗. - 💥 Jan 31, 2024. Add
qalignfor both NR and IAA. It is our most powerful unified metric based on large vision-language models, and shows remarkable performance and robustness. Refer Q-Align for more details. Use it with the following codes:qalign = create_metric('qalign').cuda() quality_score = qalign(input, task_='quality') aesthetic_score = qalign(input, task_='aesthetic') - Jan 19, 2024. Add
wadiqam_frandwadiqam_nr. All implemented methods are usable now 🍻. - Dec 23, 2023. Add
liqeandliqe_mix. Thanks for the contribution from Weixia Zhang 🤗. - Oct 09, 2023. Add datasets: PIQ2023, GFIQA. Add metric
topiq_nr-face. We release example results on FFHQ here for reference. - More
# Install with pip
pip install pyiqa
# Install latest github version
pip uninstall pyiqa # if have older version installed already
pip install git+https://github.com/chaofengc/IQA-PyTorch.git
# Install with git clone
git clone https://github.com/chaofengc/IQA-PyTorch.git
cd IQA-PyTorch
pip install -r requirements.txt
python setup.py develop
You can simply use the package with commandline interface.
# list all available metrics
pyiqa -ls
# test with default settings
pyiqa [metric_name(s)] --target [image_path or dir] --ref [image_path or dir]
import pyiqa
import torch
# list all available metrics
print(pyiqa.list_models())
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
# create metric with default setting
iqa_metric = pyiqa.create_metric('lpips', device=device)
# check if lower better or higher better
print(iqa_metric.lower_better)
# example for iqa score inference
# Tensor inputs, img_tensor_x/y: (N, 3, H, W), RGB, 0 ~ 1
score_fr = iqa_metric(img_tensor_x, img_tensor_y)
# img path as inputs.
score_fr = iqa_metric('./ResultsCalibra/dist_dir/I03.bmp', './ResultsCalibra/ref_dir/I03.bmp')
# For FID metric, use directory or precomputed statistics as inputs
# refer to clean-fid for more details: https://github.com/GaParmar/clean-fid
fid_metric = pyiqa.create_metric('fid')
score = fid_metric('./ResultsCalibra/dist_dir/', './ResultsCalibra/ref_dir')
score = fid_metric('./ResultsCalibra/dist_dir/', dataset_name="FFHQ", dataset_res=1024, dataset_split="trainval70k")
Note that gradient propagation is disabled by default. Set as_loss=True to enable it as a loss function. Not all metrics support backpropagation, please refer to Model Cards and be sure that you are using it in a lower_better way.
lpips_loss = pyiqa.create_metric('lpips', device=device, as_loss=True)
ssim_loss = pyiqa.create_metric('ssimc', device=device, as_loss=True)
loss = 1 - ssim_loss(img_tensor_x, img_tensor_y) # ssim is not lower better
We also provide a flexible way to use custom settings and weights in case you want to retrain or fine-tune the models.
iqa_metric = pyiqa.create_metric('topiq_nr', device=device, **custom_opts)
# Note that if you train the model with this package, the weights will be saved in weight_dict['params']. Otherwise, please set weight_keys=None.
iqa_metric.load_weights('path/to/weights.pth', weight_keys='params')
Example test script with input directory/images and reference directory/images.
# example for FR metric with dirs
python inference_iqa.py -m LPIPS[or lpips] -i ./ResultsCalibra/dist_dir[dist_img] -r ./ResultsCalibra/ref_dir[ref_img]
# example for NR metric with single image
python inference_iqa.py -m brisque -i ./ResultsCalibra/dist_dir/I03.bmp
Please refer to the results calibration to verify the correctness of the python implementations compared with official scripts in matlab or python.
For convenience, we upload all related datasets to huggingface. Here are example codes to download datasets from huggingface:
Caution
we only collect the datasets for academic, research, and educational purposes. It is important for the users to adhere to the usage guidelines, licensing terms, and conditions set forth by the original creators or owners of each dataset.
import os
from huggingface_hub import snapshot_download
save_dir = './datasets'
os.makedirs(save_dir, exist_ok=True)
filename = "meta_info.tgz"
snapshot_download("chaofengc/IQA-Toolbox-Datasets", repo_type="dataset", local_dir=save_dir, allow_patterns=filename, local_dir_use_symlinks=False)
os.system(f"tar -xzvf {save_dir}/{filename} -C {save_dir}")
Examples to specific dataset options can be found in ./options/default_dataset_opt.yml. Details of the dataloader inferface and meta information files can be found in Dataset Preparation
We use official models for evaluation if available. Otherwise, we use the following settings to train and evaluate different models for simplicity and consistency:
| Metric Type | Train | Test | Results |
|---|---|---|---|
| FR | KADID-10k | CSIQ, LIVE, TID2008, TID2013 | FR benchmark results |
| NR | KonIQ-10k | LIVEC, KonIQ-10k (official split), TID2013, SPAQ | NR benchmark results |
| Aesthetic IQA | AVA | AVA (official split) | IAA benchmark results |
Results are calculated with:
- PLCC without any correction. Although test time value correction is common in IQA papers, we want to use the original value in our benchmark.
- Full image single input. We do not use multi-patch testing unless necessary.
Basically, we use the largest existing datasets for training, and cross dataset evaluation performance for fair comparison. The following models do not provide official weights, and are retrained by our scripts:
| Metric Type | Reproduced Models |
|---|---|
| FR | wadiqam_fr |
| NR | cnniqa, dbcnn, hyperiqa, wadiqam_nr |
| Aesthetic IQA | nima, nima-vgg16-ava |
Note
- Due to optimized training process, performance of some retrained approaches may be different with original paper.
- Results of all retrained models by ours are normalized to [0, 1] and change to higher better for convenience.
- Results of KonIQ-10k, AVA are both tested with official split.
- NIMA is only applicable to AVA dataset now. We use
inception_resnet_v2for defaultnima. - MUSIQ is not included in the IAA benchmark because we do not have train/split information of the official model.
Here is an example script to get performance benchmark on different datasets:
# NOTE: this script will test ALL specified metrics on ALL specified datasets
# Test default metrics on default datasets
python benchmark_results.py -m psnr ssim -d csiq tid2013 tid2008
# Test with your own options
python benchmark_results.py -m psnr --data_opt options/example_benchmark_data_opts.yml
python benchmark_results.py --metric_opt options/example_benchmark_metric_opts.yml tid2013 tid2008
python benchmark_results.py --metric_opt options/example_benchmark_metric_opts.yml --data_opt options/example_benchmark_data_opts.yml
Example to train DBCNN on LIVEChallenge dataset
# train for single experiment
python pyiqa/train.py -opt options/train/DBCNN/train_DBCNN.yml
# train N splits for small datasets
python pyiqa/train_nsplits.py -opt options/train/DBCNN/train_DBCNN.yml
Example for distributed training
torchrun --nproc_per_node=2 --master_port=4321 pyiqa/train.py -opt options/train/CLIPIQA/train_CLIPIQA_koniq10k.yml --launcher pytorch
Any contributions to this repository are greatly appreciated. Please follow the contribution instructions for contribution guidance.
This work is licensed under a NTU S-Lab License and Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
If you find our codes helpful to your research, please consider to use the following citation:
@misc{pyiqa,
title={{IQA-PyTorch}: PyTorch Toolbox for Image Quality Assessment},
author={Chaofeng Chen and Jiadi Mo},
year={2022},
howpublished = "[Online]. Available: \url{https://github.com/chaofengc/IQA-PyTorch}"
}
Please also consider to cite our works on image quality assessment if it is useful to you:
@article{chen2023topiq,
title={TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment},
author={Chen, Chaofeng and Mo, Jiadi and Hou, Jingwen and Wu, Haoning and Liao, Liang and Sun, Wenxiu and Yan, Qiong and Lin, Weisi},
journal={arXiv preprint arXiv:2308.03060},
year={2023}
}
@article{wu2023qalign,
title={Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels},
author={Wu, Haoning and Zhang, Zicheng and Zhang, Weixia and Chen, Chaofeng and Li, Chunyi and Liao, Liang and Wang, Annan and Zhang, Erli and Sun, Wenxiu and Yan, Qiong and Min, Xiongkuo and Zhai, Guangtai and Lin, Weisi},
journal={arXiv preprint arXiv:2312.17090},
year={2023},
institution={Nanyang Technological University and Shanghai Jiao Tong University and Sensetime Research},
note={Equal Contribution by Wu, Haoning and Zhang, Zicheng. Project Lead by Wu, Haoning. Corresponding Authors: Zhai, Guangtai and Lin, Weisi.}
}
The code architecture is borrowed from BasicSR. Several implementations are taken from: IQA-optimization, Image-Quality-Assessment-Toolbox, piq, piqa, clean-fid
We also thanks the following public repositories: MUSIQ, DBCNN, NIMA, HyperIQA, CNNIQA, WaDIQaM, PieAPP, paq2piq, MANIQA
If you have any questions, please email chaofenghust@gmail.com