Evaluation metrics to assess the similarity between two images.
Project description
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), Information theoretic-based Statistic Similarity Measure (ISSM), Feature-based similarity index (FSIM), 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.
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.
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