An IQA toolbox with pure python and pytorch. Please refer to Awesome-Image-Quality-Assessment for a comprehensive survey of IQA methods, as well as 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. Below are details of supported methods and datasets in this project.
Supported methods and datasets:
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[1] This method use distorted image as reference. Please refer to the paper for details.
[2] Currently, only naive random forest regression is implemented and does not support backward.
- Sep 1, 2022. 1) Add pretrained models for MANIQA and AHIQ. 2) Add dataset interface for pieapp and PIPAL.
- June 3, 2022. Add FID metric. See clean-fid for more details.
- March 11, 2022. Add pretrained DBCNN, NIMA, and official model of PieAPP, paq2piq.
- More
- ⬜ Add pretrained models on different datasets.
- Ubuntu >= 18.04
- Python >= 3.8
- Pytorch >= 1.8.1
- CUDA >= 10.1 (if use GPU)
# Install with pip
pip install pyiqa
# Install latest github version
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
import pyiqa
import torch
# list all available metrics
print(pyiqa.list_models())
# create metric with default setting
iqa_metric = pyiqa.create_metric('lpips', device=torch.device('cuda'))
# Note that gradient propagation is disabled by default. set as_loss=True to enable it as a loss function.
iqa_loss = pyiqa.create_metric('lpips', device=torch.device('cuda'), as_loss=True)
# create metric with custom setting
iqa_metric = pyiqa.create_metric('psnr', test_y_channel=True, color_space='ycbcr').to(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)
score_nr = iqa_metric(img_tensor_x)
# 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")
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
- You only need to unzip downloaded datasets from official website without any extra operation. And then make soft links of these dataset folder under
datasets/folder. Download links are provided in Awesome-Image-Quality-Assessment. - We provide common interface to load these datasets with the prepared meta information files and train/val/test split files, which can be downloaded from download_link and extract them to
datasets/folder.
You may also use the following commands:
mkdir datasets && cd datasets
# make soft links of your dataset
ln -sf your/dataset/path datasetname
# download meta info files and train split files
wget https://github.com/chaofengc/IQA-PyTorch/releases/download/v0.1-weights/data_info_files.tgz
tar -xvf data_info_files.tgz
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
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
Please refer to the results calibration to verify the correctness of the python implementations compared with official scripts in matlab or python.
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
Please refer to FR benchmark results and NR benchmark results for benchmark performances of some metrics.
We report PLCC/SRCC here.
| Methods | CSIQ | TID2008 | TID2013 | LIVE | LIVEM | LIVEC |
|---|---|---|---|---|---|---|
| DBCNN | 0.8965/0.9086 | 0.8322/0.8463 | 0.7985/0.8320 | 0.9418/0.9308 | 0.9461/0.9371 | 0.8375/0.8530 |
| Methods | Dataset | Kon10k | LIVEC | SPAQ | AVA | Link(pth) |
|---|
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.
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