Wrapper for DeepLabCut to take inputs from our manual-marking GUI and outputs compatible with ACM. For productive application, please use the latest tag release.
By Arne Monsees.
This software utilizes DeepLabCut 2.1, which dependends on tensorflow 1.15. The version that is installed by conda requires CUDA 10.0 installed. See below for installation hints. If graphics support is not present, the software should automatically default to CPU computations, which will, however, be substantially and potentially unfeasibly slower.
- Install Anaconda
- Clone https://github.com/bbo-lab/ACM-dlcdetect.git
- Create conda environment
conda env create -f https://raw.githubusercontent.com/bbo-lab/ACM-dlcdetect/main/environment.yml - Navigate into the ACM-dlcdetect repository
- Install using
pip install .(Alternatively, set repository base directory as working directory when running.)
Later versions of DLC 2.1 are separated into the pip packages deeplabcut and deeplabcut[gui]. The former is installed by default, thus headless usage should be possible. The module also supports the switch --headless, which enables headless DLClight mode for earlier versions.
CUDA in the correct version is included in the conda environment and should run out of the box.
- Create a folder corresponding to your dataset (e.g.
~/data/YYYYMMDD_exp1). - Adjust and add to this folder the config file in
examples/. Especially, set path for manual labels and video files, and update frame ranges. - Enter conda environment with
conda activate bbo_acm_dlcdetect-gpu - Run with
python -m ACM-dlcdetect ~/data/YYYYMMDD_exp1
Note that the process will fail if the algorithm has already been (fully or partially) run on this folder. In this case, delete examples/data recursively.