image-similarity-measures 0.3.5

Evaluation metrics to assess the similarity between two images.

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, and 3.9.

Install package

pip install image-similarity-measures

Usage

Parameters

  --org_img_path FILE   Path to original input image
  --pred_img_path FILE  Path to predicted image
  --metric METRIC       select an evaluation metric (fsim, issm, psnr, rmse,
                        sam, sre, ssim, uiq, all) (can be repeated)

Evaluation

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

image-similarity-measures --org_img_path=a.tif --pred_img_path=b.tif

The results are printed in machine-readable JSON, so you can redirect the output of the command into a file.

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.