computationalplantscience/3d_beanroot_pipeline repository overview

⁠3D_bean_root_model_traits_measurement

Function: Compute 3D root traits from 3D bean root model

RGB image captured for two bean roots interaction in a meshed box, Designed by William Alexander Lavoy⁠ @ [email protected]⁠,

Segmentation of above RGB image for building 3D root models, from AI_U2net_color_clustering (SMART branch)⁠

Computed 3D root point cloud model from DIRT/3D⁠

Visualization of computed root structure along with 3D point cloud model

⁠Input

3D root models (*.ply) in Polygon File Format or the Stanford Triangle Format.

computed from Computational-Plant-Science / 3D_model_reconstruction_demo⁠

⁠Output

trait.xlsx Excel format, contains traits results

⁠Requirements

Docker⁠ is required to run this project in a Linux environment.

Install Docker Engine (https://docs.docker.com/engine/install/⁠)

⁠Usage

We suggest to run the pipeline inside a docker container,

The Docker container allows you to package up your application(s) and deliver them to the cloud without any dependencies. It is a portable computing environment. It contains everything an application needs to run, from binaries to dependencies to configuration files.

There are two ways to run the pipeline inside a docker container,

One was is to build a docker based on the docker recipe file inside the GitHub repository. In our case, please follow step 1 and step 3.

Antoher way is to download prebuild docker image directly from Docker hub. In our case, please follow step 2 and step 3.

1. Build docker image on your PC under linux environment

git clone https://github.com/Computational-Plant-Science/3D_beanroot_phenotyping_pipeline.git

docker build -t 3d-model-traits -f Dockerfile .

1. Download prebuild docker image directly from Docker hub, without building docker image on your local PC

docker pull computationalplantscience/3d_beanroot_pipeline

1. Run the pipeline inside the docker container

link your test 3D model path (e.g. '/home/test/test.ply', $path_to_your_3D_model = /home/test, $your_3D_model_name.ply = test.ply)to the /srv/test/ path inside the docker container

docker run -v /$path_to_your_3D_model:/srv/test -it 3d-model-traits

or 

docker run -v /$path_to_your_3D_model:/srv/test -it computationalplantscience/3d_beanroot_pipeline

1. Run the pipeline inside the container

python3 /opt/code/pipeline.py -i /srv/test/$your_3D_model_name.ply -o /srv/test/$result/ 

Reference:

Shenglan Du, Roderik Lindenbergh, Hugo Ledoux, Jantien Stoter, and Liangliang Nan. AdTree: Accurate, Detailed, and Automatic Modelling of Laser-Scanned Trees. Remote Sensing. 2019, 11(18), 2074.

@article{du2019adtree, title={AdTree: Accurate, detailed, and automatic modelling of laser-scanned trees}, author={Du, Shenglan and Lindenbergh, Roderik and Ledoux, Hugo and Stoter, Jantien and Nan, Liangliang}, journal={Remote Sensing}, volume={11}, number={18}, pages={2074}, year={2019} }

⁠Author

Suxing Liu ([email protected]⁠), William Alexander Lavoy([email protected]⁠), Wesley Paul Bonelli([email protected]⁠), Alexander Bucksch([email protected]⁠)

⁠Other contributions

Docker container was maintained and deployed to PlantIT⁠ by Wes Bonelli ([email protected]⁠).

⁠License

GNU Public License