TrueColorHSI 0.1.0

A package for accurate and vivid visualization of hyperspectral images

TrueColorHSI

Overview

Traditional methods for visualizing (previewing) hyperspectral images often use only a few selected spectral bands, which can lead to incomplete or distorted images that don't reflect how we truly see the world. These approaches also overlook how our eyes naturally perceive color.

TrueColorHSI solves this by using colorimetric science, standard illuminants, and standard observers to integrate the entire visible spectrum. This results in vivid, accurate images that better represent hyperspectral data and are easier for users to understand, providing a more intuitive and natural way to explore the information.

Setup Instructions

# Clone the repo and cd to the folder.
git clone https://github.com/fz-rit/TrueColorHSI.git
cd TrueColorHSI

# Create a new conda environment with Python 3.9 (not the latest python), 
# since it's relatively more compatible to different packages as of 11/14/2024.
conda create -n truecolorhsi_env python=3.9
conda activate truecolorhsi_env

# Install the dependencies with pip
pip install spectral matplotlib scipy scikit-image pysptools
pip install colour-science


# [Optional] If you want to download the test dataset. Login to huggingface
pip install huggingface_hub
huggingface-cli login # To login, create and add your token according to the guide

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Files description

File/Folder	Description
Accessories.py	Tool function of getting the spectral power distribution of illuminant.
true_color_of_hyperspectral_image.py	The true color visualization script.

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Usage

1. Clone the repo and setup the environment;
2. [Optional: if you want to run the test dataset] Clone the test dataset to a specific folder:

git clone fz-rit-hf/rit-cis-hyperspectral-Symeon # you may need to request for access. 

3. Adjust the input paths in lines 220-222 in the true_color_of_hyperspectral_image.py file. Then run the script:

python true_color_of_hyperspectral_image.py

Example results

Figure 1. Visualization from appximated RGB bands (traditional method).

Figure 2. Visualization from colorimetric conversion (our method).

Figure 3. True color visualization using different standard illuminants (D50, D65, D75). Adjusting the chosen illuminant allows for tuning the color temperature.

Figure 4. The spectral power distribution of the D65 illuminant and the CIE xyz curves.

Citation

If you find this repository useful in your research, please consider the following citation.

@article{amiri2024colorimetric,
  title={Colorimetric characterization of multispectral imaging systems for visualization of historical artifacts},
  author={Amiri, Morteza Maali and Messinger, David W and Hanneken, Todd R},
  journal={Journal of Cultural Heritage},
  volume={68},
  pages={136--148},
  year={2024},
  publisher={Elsevier}
}