AI/ML based behaviour classification of pigeon coordinate data.
Clone the reposity and install it as an editable install:
pip install -e .
This will install Winkie in the currently active Python environment and makes it usable from other projects.
Refer to the individual documentation sections at https://kiview.github.io/winkie/ to learn how to use it.
Examples how to preprocess data as generated by DLC can be found in _preprocessing.ipynb. + It further uses the label config format as specified in labels_20210405.csv. The raw data as generated by DLC can also be found online.
The _preprocessing.ipynb notebook creates a single HDF file, containing the data for all tracked videos, including their labels. It is subsequently used in the machine learning steps and is also accessible online.
Examples how to integrate the provided module with different machine learning libraries such as scikit-learn or tsai can be found in the jupyter notebooks prefixed by _paper.
This project uses nbdev for development. A regular workflow involves running the following commands in order:
nbdev_build_lib
nbdev_test_nbs
nbdev_build_docsWe also provide a convenient wrapper script:
./nbdev_build.sh
The development environment can be created using pipenv.
A corresponding Pipfile is provided, check the Pipenv documentation on how to use it.
There are a couple of tools pre-installed, such as Jupyter, etc..
Besides installing the development environment, the actual dependencies are managed by nbdev through Python's setuptools and defined in the settings.ini file, under the requirements key.
In order to install them into your environment (ideally the environment created by Pipenv), just use the regular setuptools facilities:
pip install -e .
In case of questions or problems, please feel free to create an corresponding GitHub issue to get in contact with the maintainers.
Copyright 2021 Neslihan Wittek, Kevin Wittek
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.