cuvis.r

cuvis.r provides R bindings to the Cubert CUVIS SDK for reading, calibrating, and exporting hyperspectral data from Cubert cameras.

This is the R equivalent of cuvis.python.

Prerequisites

Install the Cubert CUVIS SDK (>= 3.4.0) from cloud.cubert-gmbh.de
Set the CUVIS_SDK environment variable to the install directory
(Optional) Download sample data from cloud.cubert-gmbh.de

Installation

# install.packages("remotes")
# remotes::install_github("cttir/cuvis.r")
install.packages("cuvis.r", repos = c('https://cttir.r-universe.dev', 'https://cloud.r-project.org'))

Quick Start

library(cuvis.r)

cuvis_init()

# Load a session file
session <- cuvis_session("path/to/measurement.cu3s")
print(session)

# Extract the data cube
mesu <- cuvis_get_measurement(session, 1)
ctx <- cuvis_processing_context(session)
cuvis_reprocess(ctx, mesu, mode = "raw")
cube <- cuvis_get_cube(mesu)
dim(cube)                        # [rows, cols, bands]
attr(cube, "wavelengths")        # wavelengths in nm

# Calibrate to reflectance
cuvis_reprocess(ctx, mesu, mode = "reflectance")
ref_cube <- cuvis_get_cube(mesu)

# Export to ENVI
cuvis_export_envi(mesu, "output/")

cuvis_shutdown()

Full Calibration Workflow

cuvis_init()

session <- cuvis_session("measurement.cu3s")
mesu <- cuvis_get_measurement(session, 1)

# Load separate dark/white reference files
dark_session <- cuvis_session("dark.cu3s")
white_session <- cuvis_session("white.cu3s")

ctx <- cuvis_processing_context(session)
cuvis_set_reference(ctx, cuvis_get_measurement(dark_session, 1), "dark")
cuvis_set_reference(ctx, cuvis_get_measurement(white_session, 1), "white")

cuvis_reprocess(ctx, mesu, mode = "reflectance")
ref_cube <- cuvis_get_cube(mesu)

cuvis_shutdown()

API Reference

Function	Description
`cuvis_init()`	Initialize the CUVIS SDK
`cuvis_shutdown()`	Release SDK resources
`cuvis_available()`	Check if SDK is linked
`cuvis_session(path)`	Load a `.cu3s` session file
`cuvis_get_measurement(session, i)`	Extract measurement by index
`cuvis_get_metadata(mesu)`	Get measurement metadata
`cuvis_get_cube(mesu)`	Extract 3D data array
`cuvis_get_wavelengths(mesu)`	Get wavelength vector
`cuvis_processing_context(session)`	Create calibration context
`cuvis_set_reference(ctx, mesu, type)`	Set dark/white reference
`cuvis_reprocess(ctx, mesu, mode)`	Reprocess to target level
`cuvis_export_envi(mesu, dir)`	Export to ENVI format
`cuvis_export_tiff(mesu, dir)`	Export to TIFF format
`cuvis_export_session(mesu, dir)`	Export to .cu3s format

Integration with hyperspectR

cuvis.r is designed as the I/O backend for hyperspectR:

library(hyperspectR)

# hyperspectR uses cuvis.r internally for .cu3s files
cube <- hs_read_cubert("measurement.cu3s")

License

Apache License 2.0

Citation

If you use this software, please cite it as:

Heller, R. (2026). cuvis.r: R bindings for the Cubert CUVIS hyperspectral SDK (Version 0.1.0) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.21889930

DOI: 10.5281/zenodo.21889930

Use of LLM tools

Where applicable, LLM tools were used for narrowly defined, non-authorial tasks: copyediting, prose refinement, Markdown/LaTeX formatting, boilerplate scaffolding (such as CI configurations and build scripts), and code refactoring. Tools included Chat AI, the KISSKI (GWDG) service, and self-hosted Mistral Small (24B, Apache-2.0) served through a local vLLM architecture on an Ubuntu Linux workstation with Intel processors and NVIDIA GPUs. The self-hosted model ran locally, with no data sent to third parties.

All scientific claims, methodological choices, analyses, interpretations and conclusions remain the authors' own. Where applicable, LLM-generated material must be reviewed and revised before inclusion, and references must be verified against their DOI, arXiv ID or ISBN.

Local software setup: vLLM with Python/PyTorch, plus R and RStudio.