Metadata-Version: 2.1
Name: image-similarity-measures
Version: 0.2.2
Summary: Evaluation metrics to assess the similarity between two images.
Home-page: https://github.com/up42/image-similarity-measures
Author: UP42
Author-email: support@up42.com
License: MIT
Description: # Image Similarity Measures
        
        Implementation of eight evaluation metrics to access the similarity between two images. The eight metrics are as follows:
        
        <i><a href="https://en.wikipedia.org/wiki/Root-mean-square_deviation">Root mean square error (RMSE)</a></i>,
        <i><a href="https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio">Peak signal-to-noise ratio (PSNR)</a></i>,
        <i><a href="https://en.wikipedia.org/wiki/Structural_similarity">Structural Similarity Index (SSIM)</a></i>,
        <i><a href="https://www.tandfonline.com/doi/full/10.1080/22797254.2019.1628617">Information theoretic-based Statistic Similarity Measure (ISSM)</a></i>,
        <i><a href="https://www4.comp.polyu.edu.hk/~cslzhang/IQA/TIP_IQA_FSIM.pdf">Feature-based similarity index (FSIM)</a></i>,
        <i><a href="https://www.sciencedirect.com/science/article/abs/pii/S0924271618302636">Signal to reconstruction error ratio (SRE)</a></i>,
        <i><a href="https://ntrs.nasa.gov/citations/19940012238">Spectral angle mapper (SAM)</a></i>, and
        <i><a href="https://www.researchgate.net/publication/3342733_A_Universal_Image_Quality_Index">Universal image quality index (UIQ)</a></i>
        
        
        
        ## Instructions
        
        The following step-by-step instructions will guide you through installing this package and run evaluation using the command line tool.
        
        ### Install package
        ```bash
        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:
        ```bash
        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
        ```bash
        import image_similarity_measures
        from image_similarity_measures.quality_metrics import rmse, psnr
        ```
        
        ### Install package from source
        
        #### Clone the repository
        
        ```bash
        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](https://virtualenvwrapper.readthedocs.io/en/latest/):
        
        ```bash
        mkvirtualenv --python=$(which python3.7) similarity-measures
        ```
        
        Activate the new environment:
        
        ```bash
        workon similarity-measures
        ```
        
        Install the necessary Python libraries via:
        
        ```bash
        bash setup.sh
        ```
        
        ## Citation
        Please use the following for citation purposes of this codebase:
        
        <strong>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.</strong>
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Development Status :: 5 - Production/Stable
Requires-Python: >=3.6, <3.8
Description-Content-Type: text/markdown
