TrueColorHSI 0.1.2

A package for accurate and vivid visualization of hyperspectral images

TrueColorHSI

Overview

Traditional hyperspectral visualization methods convert images to RGB by averaging bands into fixed ranges corresponding to blue, green, and red. While practical, this method oversimplifies the data and may result in a loss of important details and nuances.

TrueColorHSI takes a more sophisticated approach by using colorimetric science to process the entire visible spectrum, delivering vivid, perceptually accurate images. Additionally, it offers users the flexibility to adjust the illuminant (D50, D55, D65, D75), enhancing the interpretation of hyperspectral data under different lighting conditions.

Updated Comparison Table: Traditional Method vs. TrueColorHSI

Aspect	Traditional Method	✨ TrueColorHSI ✨
Spectral Band Usage	⚙️ Fixed RGB ranges (Blue, Green, Red)	🌈 Full visible spectrum utilization
Color Basis	⚙️ Based on peak wavelengths (~470, ~545, ~680 nm)	🌈 Colorimetric science-based
Color Accuracy	⚠️ Approximate color reproduction	✅ Highly accurate color representation
Visualization Quality	⚠️ Simplified, may lose details	✅ Vivid and detailed output
User Experience	⚙️ Limited user control	✅ Tunable illuminants (D50, D55, D65, D75)

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Installation:

You can install TrueColorHSI via pip:

pip install TrueColorHSI

Usage:

from truecolorhsi.visualization import vanilla_visualization, colorimetric_visualization
from pathlib import Path
input_path = Path("path/to/the/input/file")
vanilla_display_images = vanilla_visualization(input_path)
colorimetric_display_images = colorimetric_visualization(input_path, visualize=True, saveimages=True)

Supportted data format:

• Symeon-Cultural-Heritage ENVI HSI data, which comes with a .hdr file along with the data file (prefered)
• Heidelberg Porcine HyperSPECTRAL Imaging Dataset - a binary file ends with .dat
  • For more details about tests, checkout the note book test_visualization_bio.ipynb

Notes:

• The package provides methods that help translate complex hyperspectral data into intuitive, true-to-life images that are easier to interpret and analyze.

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}
}