TUDelftHao / image-similarity-measures

:chart_with_upwards_trend: Implementation of eight evaluation metrics to access the similarity between two images. The eight metrics are as follows: RMSE, PSNR, SSIM, ISSM, FSIM, SRE, SAM, and UIQ.

Image Similarity Measures

Implementation of eight evaluation metrics to access the similarity between two images. The eight metrics are as follows:

• Root mean square error (RMSE),
• Peak signal-to-noise ratio (PSNR),
• Structural Similarity Index (SSIM),
• Feature-based similarity index (FSIM),
• Information theoretic-based Statistic Similarity Measure (ISSM),
• Signal to reconstruction error ratio (SRE),
• Spectral angle mapper (SAM), and
• Universal image quality index (UIQ)

Instructions

The following step-by-step instructions will guide you through installing this package and run evaluation using the command line tool.

Note: Supported python versions are 3.6, 3.7, 3.8.

Install package

pip install image-similarity-measures

Usage

Parameters

--org_img_path : Path to the original image.
--pred_img_path : Path to the predicted or disordered image which is created from the original image.
--metric= : Name of the evaluation metric. Default set to be psnr. It can be one of the following: psnr, ssim, issm, fsim.
--mode : Image format. Default set to be "tif". can be one of the following: "tif", or "png", or "jpg".
--write_to_file : The final result will be written to a file. Set to False if you don't want a final file.

Evaluation

For doing the evaluation, you can easily run the following command:

image-similarity-measures --org_img_path=path_to_first_img --pred_img_path=path_to_second_img --mode=tif

If you want to save the final result in a file you can add --write_to_file at then end of above command.

Note that images that are used for evaluation should be channel last.

Usage in python

import image_similarity_measures
from image_similarity_measures.quality_metrics import rmse, psnr

Install package from source

Clone the repository

git clone https://github.com/up42/image-similarity-measures.git
cd image-similarity-measures

Then navigate to the folder via cd image-similarity-measures.

Installing the required libraries

First create a new virtual environment called similarity-measures, for example by using virtualenvwrapper:

mkvirtualenv --python=$(which python3.7) similarity-measures

Activate the new environment:

workon similarity-measures

Install the necessary Python libraries via:

bash setup.sh

Citation

Please use the following for citation purposes of this codebase:

Müller, M. U., Ekhtiari, N., Almeida, R. M., and Rieke, C.: SUPER-RESOLUTION OF MULTISPECTRAL SATELLITE IMAGES USING CONVOLUTIONAL NEURAL NETWORKS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 33–40, https://doi.org/10.5194/isprs-annals-V-1-2020-33-2020, 2020.