skeleton-refinement 0.1.1

Skeletonization of Plant Point Cloud Data Using Stochastic Optimization Framework.

Skeleton Refinement

The documentation of the Plant Imager project can be found here: https://docs.romi-project.eu/plant_imager/

The API documentation of the skeleton_refinement library can be found here: https://romi.github.io/skeleton_refinement/

About

This library is intended to provide the implementation of a skeleton refinement method published here:

Chaudhury A. and Godin C. (2020) Skeletonization of Plant Point Cloud Data Using Stochastic Optimization Framework. Front. Plant Sci. 11:773. DOI: 10.3389/fpls.2020.00773.

This is a part of the implementation of the stochastic registration algorithm based on the following paper: Myronenko A. and Song X. (2010) Point set registration: Coherent Point drift. IEEE Transactions on Pattern Analysis and Machine Intelligence. 32 (2): 2262-2275. DOI: 10.1109/TPAMI.2010.46

Installation

We strongly advise to create isolated environments to install the ROMI libraries.

We often use conda as an environment and python package manager. If you do not yet have miniconda3 installed on your system, have a look here.

The skeleton_refinement package is available from the romi-eu channel.

Existing conda environment

To install the skeleton_refinement conda package in an existing environment, first activate it, then proceed as follows:

conda install skeleton_refinement -c romi-eu

New conda environment

To install the skeleton_refinement conda package in a new environment, here named romi, proceed as follows:

conda create -n romi skeleton_refinement -c romi-eu

Installation from sources

To install this library, simply clone the repo and use pip to install it and the required dependencies. Again, we strongly advise to create a conda environment.

All this can be done as follows:

git clone https://github.com/romi/skeleton_refinement.git
cd skeleton_refinement
conda create -n skeleton_refinement 'python =3.10'
conda activate skeleton_refinement  # do not forget to activate your environment!
python -m pip install -e .  # install the sources

Note that the -e option is to install the skeleton_refinement sources in "developer mode". That is, if you make changes to the source code of skeleton_refinement you will not have to pip install it again.

Usage

Example dataset

First, we download an example dataset from Zenodo, named real_plant_analyzed, to play with:

wget https://zenodo.org/records/10379172/files/real_plant_analyzed.zip
unzip real_plant_analyzed.zip -d /tmp

It contains:

• a plant point cloud under PointCloud_1_0_1_0_10_0_7ee836e5a9/PointCloud.ply
• a plant skeleton under CurveSkeleton__TriangleMesh_0393cb5708/CurveSkeleton.json
• a plant tree graph under TreeGraph__False_CurveSkeleton_c304a2cc71/TreeGraph.p

CLI

You may use the refine_skeleton CLI to refine a given skeleton using the original point cloud:

export DATA_PATH="/tmp/real_plant_analyzed"
refine_skeleton \
  ${DATA_PATH}/PointCloud_1_0_1_0_10_0_7ee836e5a9/PointCloud.ply \
  ${DATA_PATH}/CurveSkeleton__TriangleMesh_0393cb5708/CurveSkeleton.json \
  ${DATA_PATH}/optimized_skeleton.txt

Python API

Here is a minimal example how to use the skeleton_refinement library in Python:

from skeleton_refinement.stochastic_registration import perform_registration
from skeleton_refinement.io import load_json, load_ply

pcd = load_ply("/tmp/real_plant_analyzed/PointCloud_1_0_1_0_10_0_7ee836e5a9/PointCloud.ply")
skel = load_json("/tmp/real_plant_analyzed/CurveSkeleton__TriangleMesh_0393cb5708/CurveSkeleton.json", "points")
# Perform stochastic optimization
refined_skel = perform_registration(pcd, skel)

import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
ax.scatter(*pcd.T, marker='.', color='black')
ax.scatter(*skel.T, marker='o', color='r')
ax.scatter(*refined_skel.T, marker='o', color='b')
ax.set_aspect('equal')
plt.show()

A detailed documentation of the Python API is available here: https://romi.github.io/skeleton_refinement/reference.html

Developers & contributors

Unitary tests

Some tests are defined in the tests directory. We use nose2 to call them as follows:

nose2 -v -C

Notes:

• the configuratiomkdocs.ymln file used by nose2 is unittests.cfg
• the -C option generate a coverage report, as defined by the .coveragerc file.
• this requires the nose2 & coverage packages listed in the requirements.txt file.

You first have to install the library from sources as explained here.

Conda packaging

Start by installing the required conda-build & anaconda-client conda packages in the base environment as follows:

conda install -n base conda-build anaconda-client

Build a conda package

To build the romitask conda package, from the root directory of the repository and the base conda environment, run:

conda build conda/recipe/ -c conda-forge --user romi-eu

If you are struggling with some of the modifications you made to the recipe, notably when using environment variables or Jinja2 stuffs, you can always render the recipe with:

conda render conda/recipe/

The official documentation for conda-render can be found here.

Upload a conda package

To upload the built package, you need a valid account (here romi-eu) on anaconda.org & to log ONCE with anaconda login, then:

anaconda upload ~/miniconda3/conda-bld/linux-64/skeleton_refinement*.tar.bz2 --user romi-eu

Clean builds

To clean the source and build intermediates:

conda build purge

To clean ALL the built packages & build environments:

conda build purge-all