v0.1.9-move models to inside truecolorhsi folder

move models to inside truecolorhsi folder

v0.1.8-add init files to each sub folder

add init files to each sub folder

v0.1.7 - fix bugs for importing models in WBsRGB

fix bugs for importing models in WBsRGB

v0.1.6

add opencv to the dependency list.

v0.1.5-Fix bug in white patch visualization

Fix the bug in white patch visualization

v0.1.4 - MSI and Enhanced White Balance and Visualization Features

Release Notes for TrueColorHSI

New Features

• Added gray_world and white patch white balance algorithms.
• Integrated multispectral image processing.
• Added chromaticity_diagram code and plots.
• Improved the logic regarding the saveimages flag.

Enhancements

• Updated README with explanations of parameters.
• Improved the display of examples.
• Added new visualization code for biomedical data with percentile stretch.
• Integrated a notebook for testing on the HeiPorSPECTRAL dataset.

Bug Fixes

• Fixed image paths in README.
• Fixed bugs in the visualization import list.

Contributions

• Added logo by Felix0911.

For more details on the commits, you can check the commit history.

v0.1.3.1-update the cache to v4 in setup.py

Merge pull request #4 from fz-rit/feature/bio_vis

update the uses versions in the steps of the publish.yml

v0.1.2 - adapt code for HeiPorSPECTRAL

• add visualization code for biomedical data
• improve the vanilla visualization by adding percentile stretch.
• add a notebook for testing the code on the HeiPorSPECTRAL dataset: https://heiporspectral.org/

v0.1.1 - Update Example Image Paths and Improve GitHub Actions Workflow

TrueColorHSI v0.1.1

This minor update addresses improvements in the package's functionality and the publishing process.

Key Changes:

• Updated Image Paths: The paths to example images have been corrected to ensure they are properly referenced in the documentation.
• Improved GitHub Actions Workflow: The publish.yml workflow has been updated to streamline the package publishing process, ensuring better compatibility and automation for PyPI releases.

Installation:

To install the latest version of TrueColorHSI, run:

pip install TrueColorHSI==0.1.1

Notes:

• This release does not introduce new features but ensures that the package is easier to use and deploy.
• Users upgrading to this version will benefit from corrected image paths and an improved publishing process.

v0.1.0 - Initial Release of TrueColorHSI: A More Natural Visualization of Hyperspectral Data

TrueColorHSI v0.1.0

This is the initial release of the TrueColorHSI package, designed to improve the visualization of hyperspectral images. Unlike traditional methods that often use only a few spectral bands, resulting in distorted or incomplete representations, TrueColorHSI leverages colorimetric science, standard illuminants, and standard observers to generate vivid and accurate color images from the entire visible spectrum.

Key Features:

• True Color Visualization: Integrates the full visible spectrum, creating more natural and accurate color representations of hyperspectral data.
• Colorimetric Science: Uses scientifically validated methods to replicate how the human eye perceives color, making the images more intuitive.
• Standard Illuminants & Observers: Applies standard illuminants and observers to ensure that images reflect real-world viewing conditions.
• Improved Data Interpretation: Provides clearer, more understandable visualizations that make exploring hyperspectral data easier.

Installation:

You can install TrueColorHSI via pip:

pip install TrueColorHSI

Usage:

from truecolorhsi.visualization import vanilla_visualization, colorimetric_visualization

hsi_header_file = "path/to/the/header/file"
vanilla_display_images = vanilla_visualization(header_file)
colorimetric_display_images = colorimetric_visualization(header_file, visualize=True, saveimages=True)

Notes:

• This is the first official release, featuring the foundational tools for accurate hyperspectral image visualization.
• The package provides methods that help translate complex hyperspectral data into intuitive, true-to-life images that are easier to interpret and analyze.