Analysis tools for SLEAP-based plant root phenotyping.
Project description
sleap-roots
Analysis tools for SLEAP-based plant root phenotyping.
Installation
pip install sleap-roots
If you are using conda (recommended):
conda create -n sleap-roots python=3.11
conda activate sleap-roots
pip install sleap-roots
Usage
Detailed trait documentation per pipeline is available here: sleap-roots HackMD
DicotPipeline
1. Computing traits for a single plant:
import sleap_roots as sr
plant = sr.Series.load(
"tests/data/canola_7do/919QDUH.h5",
# Specify the names of the primary and lateral roots for trait calculation
primary_name="primary",
lateral_name="lateral"
)
pipeline = sr.DicotPipeline()
traits = pipeline.compute_plant_traits(plant, write_csv=True)
2. Computing traits for a batch of plants:
import sleap_roots as sr
plant_paths = sr.find_all_series("tests/data/soy_6do")
plants = [
sr.Series.load(
plant_path,
# Specify the names of the primary and lateral roots for trait calculation
primary_name="primary",
lateral_name="lateral",
) for plant_path in plant_paths]
pipeline = sr.DicotPipeline()
all_traits = pipeline.compute_batch_traits(plants, write_csv=True)
3. Computing individual traits:
import sleap_roots as sr
import numpy as np
import numpy as np
# Import utility for combining primary and lateral root points
from sleap_roots.points import get_all_pts_array
plant = sr.Series.load(
"tests/data/canola_7do/919QDUH.h5",
primary_name="primary",
lateral_name="lateral"
)
frame_index = 0
primary_pts = plant.get_primary_points(frame_index)
lateral_pts = plant.get_lateral_points(frame_index)
pts = get_all_pts_array(primary_pts, lateral_pts)
convex_hull = sr.convhull.get_convhull(pts)
YoungerMonocotPipeline
1. Computing traits for a single plant:
import sleap_roots as sr
plant = sr.Series.load(
"tests/data/rice_3do/0K9E8BI.h5",
primary_name="primary",
lateral_name="crown"
)
pipeline = sr.YoungerMonocotPipeline()
traits = pipeline.compute_plant_traits(plant, write_csv=True)
2. Computing traits for a batch of plants:
import sleap_roots as sr
plant_paths = sr.find_all_series("tests/data/rice_3do")
plants = [
sr.Series.load(
plant_path,
primary_name="primary",
lateral_name="crown"
) for plant_path in plant_paths]
pipeline = sr.YoungerMonocotPipeline()
all_traits = pipeline.compute_batch_traits(plants, write_csv=True)
3. Computing individual traits:
import sleap_roots as sr
import numpy as np
from sleap_roots.points import get_all_pts_array
plant = sr.Series.load(
"tests/data/rice_3do/0K9E8BI.h5",
primary_name="primary",
lateral_name="crown"
)
frame_index = 0
primary_pts = plant.get_primary_points(frame_index)
lateral_pts = plant.get_lateral_points(frame_index)
pts = get_all_pts_array(primary_pts, lateral_pts)
convex_hull = sr.convhull.get_convhull(pts)
Tutorials
Jupyter notebooks are located in this repo at sleap-roots/notebooks
.
To use them, activate your conda environment which includes JupyterLab (recommended):
conda activate sleap-roots
Clone this repository if you haven't already:
git clone https://github.com/talmolab/sleap-roots.git && cd sleap-roots
Then you can change directories to the location of the notebooks, and open Jupyter Lab:
cd notebooks
jupyter lab
Go through the commands in the notebooks to learn about each pipeline.
You can use the test data located at tests/data
or copy the notebooks elsewhere for use with your own data!
Development
For development, first clone the repository:
git clone https://github.com/talmolab/sleap-roots && cd sleap-roots
Then, to create a new conda environment and install the package in editable mode:
conda env create -f environment.yml
This will create a conda environment called sleap-roots
.
If you have an existing conda environment (such as where you installed SLEAP), you can just install in editable mode directly. First, activate your environment and then:
pip install -e ".[dev]"
Note: The [dev]
makes sure that the development-only dependencies are also
installed.
To start fresh, just delete the environment:
conda env remove -n sleap-roots
To run tests, first activate the environment:
conda activate sleap-roots
Then run pytest
with:
pytest tests
Acknowledgments
This repository was created by the Talmo Lab and Busch Lab at the Salk Institute for Biological Studies as part of the Harnessing Plants Initiative.
Contributors
- Elizabeth Berrigan
- Lin Wang
- Talmo Pereira
Citation
E.M. Berrigan, L. Wang, H. Carrillo, K. Echegoyen, M. Kappes, J. Torres, A. Ai-Perreira, E. McCoy, E. Shane, C.D. Copeland, L. Ragel,
C. Georgousakis, S. Lee, D. Reynolds, A. Talgo, J. Gonzalez, L. Zhang, A.B. Rajurkar, M. Ruiz, E. Daniels, L. Maree, S. Pariyar, W. Busch, T.D. Pereira.
"Fast and Efficient Root Phenotyping via Pose Estimation", Plant Phenomics 0: DOI:10.34133/plantphenomics.0175.
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