Image Quality Evaluation Metrics
Implementation of some common full reference image quality metrics. The repo is built based on full reference image quality metrics such as L1, L2, PSNR, SSIM, LPIPS. and feature-level quality metrics such as FID, IS. It can be used for evaluating image denoising, colorization, inpainting, deraining, dehazing etc. where we have access to ground truth.
The goal of this repo is to provide a common evaluation script for image evaluation tasks. It contains some commonly used image quality metrics for image evaluation (e.g., L1, L2, SSIM, PSNR, LPIPS, FID, IS).
Pull requests and corrections/suggestions will be cordially appreciated.
Inception Score is not correct. I will check and confirm. Other metrics are ok!
- Images are scaled to [0,1]. If you need to change the data range, please make sure to change the data range in SSIM and PSNR.
- Number of generated images and ground truth images have to be exactly same.
- I have resized the images to be (
256,256). You can change the resolution based on your needs.
- Please make sure that all the images (generated and ground_truth images) are in the corresponding folders.
- PyTorch (
- Python (
- PyTorch Image Quality (PIQ) (
$ pip install piq)
How to use
config.yaml as per your need.
- Options in
dataset_name– Name of the dataset (e.g. Places, DIV2K etc. Used for saving dataset name in csv file.). Default
dataset_with_subfolders– Set to
Trueif your dataset has sub-folders containing images. Default – False
multiple_evaluation– Whether you want sequential evaluation ro single evaluation. Please refer to the folder structure for this.
dataset_format– Whether you are providing flists or just path to the image folders. Default – image.
model_name– Name of the model. Used for saving metrics values in the CSV. Default – Own.
generated_image_path– Path to your generated images.
ground_truth_image_path– Path to your ground truth images.
batch_size– batch size you want to use. Default – 4.
image_shape– Shape of the image. Both generated image and ground truth images will be resized to this width. Default – [256, 256, 3].
threads– Threads to be used for multi-processing Default – 4.
random_crop– If you want random cropped image, instead of resized. Currently not implemented.
save_results– If you want to save the results in csv files. Saved to
resultsfolder. Default – True.
save_type– csv or npz. npz is not implemented yet.
Single or multiple evaluation
# ================= Single structure ===================# |- root | |- image_1 | |- image_2 | | - ..... |- gt | |- image_1 | |- image_2 | | - ..... For multiple_evaluation, I assumed the file system like this: # ================= structure 1 ===================# |- root | |- file_10_20 | |- image_1 | |- image_2 | | - ..... | |- file_20_30 | |- image_1 | |- image_2 | | - ..... |- gt | |- image_1 | |- image_2 | | - ..... or nested structure like this.... # ================= structure 2 ===================# |- root | |- 01_cond | |- cond_10_20 | |- image_1 | |- image_2 | | - ..... | |- 02_cond | |- cond_10_20 | |- image_1 | |- image_2 | | - ..... |- gt | |- image_1 | |- image_2 | | - .....
- implementation of the framework
- primary check for errors
- Sequential evaluation (i.e. folder1,folder2, folder3… vs ground_truth, useful for denoising, inpainting etc.)
Thanks to PhotoSynthesis Team for the wonderful implementation of the metrics. Please cite accordingly if you use PIQ for the evaluation.