3D_model_traits_measurement

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

Input

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)

Output

trait.xlsx Excel format, contains 18 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_model_traits_demo.git

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

2. Download prebuild docker image directly from Docker hub, without building docker image on your local PC 
```shell
docker pull computationalplantscience/3d-model-traits

3. 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

4. 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}
}

# Author
Suxing Liu (suxingliu@gmail.com), Wesley Paul Bonelli(wbonelli@uga.edu), Alexander Bucksch

## Other contributions

Docker container was maintained and deployed to [PlantIT](https://portnoy.cyverse.org) by Wes Bonelli (wbonelli@uga.edu).

# License
GNU Public License