Function: Compute 3D root traits from 3D root model for field-grown maize roots
Example of computed root structure v.s. 3D root point cloud model
3D root models (*.ply) in Polygon File Format or the Stanford Triangle Format.
computed from Computational-Plant-Science / 3D_model_reconstruction_demo (https://github.com/Computational-Plant-Science/3D_model_reconstruction_demo)
trait.xlsx Excel format, contains 18 traits results
Docker is required to run this project in a Linux environment.
Install Docker Engine (https://docs.docker.com/engine/install/)
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.
- Build docker image on your PC under linux environment
git clone https://github.com/Computational-Plant-Science/3D_model_traits_demo.git
docker build -t 3d-model-traits -f Dockerfile .- Download prebuild docker image directly from Docker hub, without building docker image on your local PC
docker pull computationalplantscience/3d-model-traits- 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-model-traits
- Run the pipeline inside the container
python3 pipeline.py -p /srv/test/ -m $your_3D_model_name.ply
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} }
Suxing Liu (suxingliu@gmail.com), Wesley Paul Bonelli(wbonelli@uga.edu), Alexander Bucksch
Docker container was maintained and deployed to PlantIT by Wes Bonelli (wbonelli@uga.edu).
GNU Public License