HyperBlend Algae version 0.2.1a

This is the first release of HyperBlend Algae, which is forked from the original HyperBlend project at https://github.com/silmae/hyperblend and modified for photobioreactor design purposes. This fork and the orginal one has been developed at Spectral Laboratory of University of Jyväskylä by Kimmo Riihiaho (kimmo.a.riihiaho at jyu.fi). You can freely use and modify this software under terms of the MIT licence (see LICENCE file). If you use the software, please give us credit for our work by citing our published scientific papers (instructions below).

As a kind disclaimer: this code is brutally modified from the original using hard-coded values and function names that do not really make sense in the photobioreactor context (e.g., rendering the reactor scene is done by calling render_forest()). We do not expect to continue developing this fork in the future, but instead focus on making the original fork more accomodating for different simulation schemes. This fork exist mainly for purposes of reproducing the published results in the future if the need arises.

Table of contents

1. How to cite
2. Installing
3. Working principle
4. Usage

How to cite

If you find our work usefull in your project, please cite us:

The first HyperBlend paper

Riihiaho, K. A., Rossi, T., and Pölönen, I.: HYPERBLEND: SIMULATING SPECTRAL REFLECTANCE AND TRANSMITTANCE OF LEAF TISSUE WITH BLENDER, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 471–476, https://doi.org/10.5194/isprs-annals-V-3-2022-471-2022, 2022.

@Article{riihiaho22,
AUTHOR = {Riihiaho, K. A. and Rossi, T. and P\"ol\"onen, I.},
TITLE = {HYPERBLEND: SIMULATING SPECTRAL REFLECTANCE AND TRANSMITTANCE OF LEAF TISSUE WITH BLENDER},
JOURNAL = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
VOLUME = {V-3-2022},
YEAR = {2022},
PAGES = {471--476},
URL = {https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2022/471/2022/},
DOI = {10.5194/isprs-annals-V-3-2022-471-2022}
}

The second HyperBlend paper

Riihiaho, K. A., Lind, L., & Pölönen, I. (2023). HyperBlend leaf simulator: improvements on simulation speed, generalizability, and parameterization. Journal of Applied Remote Sensing, 17(3), 038505-038505.

@article{riihiaho2023hyperblend,
  title={HyperBlend leaf simulator: improvements on simulation speed, generalizability, and parameterization},
  author={Riihiaho, Kimmo A and Lind, Leevi and P{\"o}l{\"o}nen, Ilkka},
  journal={Journal of Applied Remote Sensing},
  volume={17},
  number={3},
  pages={038505--038505},
  year={2023},
  publisher={Society of Photo-Optical Instrumentation Engineers}
}

The third (this one) HyperBlend paper

Coming soonish...

Installing

Clone the repository to some location on your machine. Create a python environment by running conda env create -n hb --file hb_env.yml in yor anaconda command prompt when in project root directory. Use your favourite IDE for editing and running the code (developed using PyCharm). Command line build and run is untested, but it should work as well.

You will also need open-source 3D-modeling and rendering software Blender, which you can download and install from (blender.org). Tests in the paper were run with Blender 4.0.X. Other versions may or may not work, depending on the API changes made by Blender. Note that at least Blender 3.6 is slower by one order of magnitude in reder time of the algae scenes (tested with the validation scene). Change your Blender executable path to constants.py.

Working principle

For algae, this is kind of a leaf made out of algae. The paper calls this stage the slab simulation.

The measured (or simulated) reflectances and transmittances look like this:

wavelength [nm]	reflectance	transmittance
400	0.21435	0.26547
401	0.21431	0.26540
...	...	...

We call this the target. Reflectance and transmittance values represent the fraction of reflected and transmitted light so both values are separately bound to closed interval [0,1] and their sum cannot exceed 1.

We use a Blender scene with a rectangular box that represents a leaf (scene_leaf_material.blend). The material of the leaf has four adjustable parameters: absorption particle density, scattering particle density, scattering anisotropy, and mix factor. These control how the light is scattered and absorbed in the leaf material. HyperBlend will find the values that will produce the same reflectance transmittance spectrum that the target has.

For each wavelength in the target, we adjust the leaf material parameters until the modeled reflectance and transmittance match the target values. As of version 0.2.0, there are three different ways to do this:

1. Original optimization method (slow but accurate)
2. Surface fitting method approximates parameter spaces with analytical function surfaces
3. NN (neural network) does the same as Surface fitting but uses NN instead of analytical functions

NN is currently the default and recommended method as it is fast (200 x speedup compared to Original) and fairly accurate (error increased 2-4 times compared to Original). Surface fitting is as fast as NN but not as accurate.

Usage

The entry point of the software is __main__.py file. The experimetns run for the results in the paper can be found from the main method at __main__.py. Do not expect that all of them work without tinkering.