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)
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.
pip install image-similarity-measures
--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.
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.
import image_similarity_measures
from image_similarity_measures.quality_metrics import rmse, psnr
git clone https://github.com/up42/image-similarity-measures.git
cd image-similarity-measures
Then navigate to the folder via cd image-similarity-measures
.
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
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.