RGBDimagegrains is a grain size image recognition software that integrates RGB-D images, PebblecountAuto, and Imagegrains. It primarily utilizes RGB-D images to detect large grain sizes in advance and replaces labels in the deep learning model. The goal is to enhance segmentation in images of non-uniform gravel riverbeds.
If you use software and/or data from here in your research, please cite the following works:
- Mair, D., Witz, G., Do Prado, A.H., Garefalakis, P. & Schlunegger, F. (2023) Automated detecting, segmenting and measuring of grains in images of fluvial sediments: The potential for large and precise data from specialist deep learning models and transfer learning. Earth Surface Processes and Landforms, 1–18. https://doi.org/10.1002/esp.5755.
- Stringer, C.A., Pachitariu, M., (2021). Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100–106. https://doi.org/10.1038/s41592-020-01018-x.
- Benjamin, Purinton., Bodo, Bookhagen., (2019). Introducing PebbleCounts: a grain-sizing tool for photo surveys of dynamic gravel-bed rivers. Earth Surface Dynamics 7, 859-877. https://doi.org/10.1002/esp.5782.
- Required software ( Python )
$git clone https://github.com/birard/RGBDimagegrains
$cd RGBDimagegrains
$conda env create -f environment.yml
$conda activate RGBDimagegrainsThe following is a practical example of the operation process for RGBDimagegrains and RGBDgrains: https://www.youtube.com/watch?v=i9PZDbwDekc.