phenomate-core 0.3.1

Add your description here

phenomate-core

Overview

phenomate-core is a Python package for processing Phenomate sensor binaries into appropriate outputs. The Phenomate platform collects data from the following sensors

• JAI RGB camera
• IMU - INS401
• Lidar (2D)
• Hyperspectral Camera

And it packs the data (typically) into Protobuffer messages as the sensors collect it. This package unpacks and and possibly transforms the data from the protobuffer files, ready for further processing.

Installation

Clone the repository and install dependencies:

git clone https://github.com/yourusername/phenomate-core.git
cd phenomate-core
make install

Installing libjpeg-turbo - Oak-d

Please see the official page for installing libjpeg-turbo for your operating system.

Installing Sickscan - 2D Lidar

The conversion code for the 2D LIDAR has the required Python code as part of this repository. If the code needs updating then it can be built from the GitHub repository:

mkdir -p ./sick_scan_ws
cd ./sick_scan_ws

git clone -b master https://github.com/SICKAG/sick_scan_xd.git

mkdir -p ./build
pushd ./build
rm -rf ./*
export ROS_VERSION=0

# specify optimisation level: -DO=0 (compiler flags -g -O0), -DO=1 (for compiler flags -O1) or -DO=2
# Install to local directory uising CMAKE_INSTALL_PREFIX=
cmake -DCMAKE_INSTALL_PREFIX=~/local -DROS_VERSION=0 -DLDMRS=0 -DSCANSEGMENT_XD=0 -G "Unix Makefiles" ../sick_scan_xd
make -j4
make -j4 install  # install locally
popd

# The output Python code can be found in:
# ~/local/include/sick_scan_xd/sick_scan_xd.py
# and can be copied to phenomate-core/phenomate_core/preprocessing/lidar

Usage

Example usage for extracting and saving images:

from phenomate_core import JaiPreprocessor

preproc = JaiPreprocessor(path="path/to/data.bin")
preproc.extract()
preproc.save(path="output_dir")

Development

• Python 3.11+
• Uses ruff and mypy for linting and type checking
• Protobuf files should be compiled with protoc as needed

uv pip install protobuf
make compile-pb

Project Updating version numbers

Version numbers follow the standard pattern of: MAJOR.MINOR.PATCH and the project has been configured to use the Python libray bump-my-version to help automate the change of version numbers that are used in the files within the project.

The following proceedures outline its use:

Make sure mump-my-version is installed

uv pip install  bump-my-version
# add to pyproject.toml 
uv add --dev bump-my-version

This tool uses the file .bumpmyversion.toml for configuring what files get modified.

N.B. If files are added to the project that use an explicit version number, then add the files to .bumpmyversion.toml along with the rules.

Use the tool as follows:

1. make sure the current version in .bumpmyversion.toml is correct e.g.

current_version = "0.3.1"

Set the bumpwhat value and run the bump-my-version command:

# uv run bump-my-version -h

export bumpwhat=major | minor | patch
uv run bump-my-version bump $bumpwhat

Post bump version tasks

After a version update the package can be published to PyPi:

rm -fr ./dist
uv build
uv publish # requires a token from PyPi - see .pypirc file

Now setup the Phenomate project repository telling it about the new version -

1. Edit pyproject.toml and change the "phenomate-core>=X.Y.Z" dependency to the latest version.
2. Then run:

uv lock

N.B. If installing into the Docker application, first comment out the local installation path in pyproject.toml

#[tool.uv.sources]
# phenomate-core = { path = "../phenomate-core" }
# appm = { path = "../appn-project-manager" }

and then rebuild the the docker container:

docker compose up -d --force-recreate --build celery_worker

If not installing using Docker, just reinstall the new package into the uv virtual environment:

make install-local-phenomate-core  # this runs uv pip install ${LOCAL_APPM}

Contributing

Contributions are welcome! Please open issues or pull requests for bug fixes, features, or improvements.