Jaap/refactor dlclive3 #143
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Instead using a single_animal parameter for PytorchRunner, which defaults to True, single_animal mode will be inferred from the models metadata configuration. This is useful for cases when you want to safely leave out the single_animal paramter, e.g. when running a multi-animal model in DeepLabCut-live-GUI, just passing a model configuration suffices.
When the detector does not detect any crops (with a supra-threshold confidence), no pose-detection is applied and and a zero-vector is returned for the pose-prediction. This solves DeepLabCut#137 and copies the already existing intended behavior of returning zeros for empty pose-predictions in single animals.
The config is validated immediately upon reading an exported model file. For invalid model configurations / exported model files, the user sees a warning before running pose estimations or model_initialization.
Current standards for python packaging. Allows for modern tools like uv and others (in general more tool-agnostic). Also makes it more interoperable with DeepLabCut-live-GUI.
re-installation can easily done via pip install -u, uv sync or poetry install as the user prefers. Building and publishing should be reserved for maintainers only.
Added a more robust downloading function in `utils.py` that handles errors more gracefully. The download is skipped if the file already exists. In previous implementation, the benchmarking video was downloaded to the home directory, which in windows could result in permission issues when reading the file. This is now changed to the check_install directory. OpenCV was silently failing to read the video file, resulting in a frame count of zero. Now a ValueError is raised when the video cannot be read.
…bility; e.g. with CI)
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Im in favor of updating the pyproject.toml file, I have since switched from poetry to pdm after some bad experiences with the devs anyway, but can we do it in a separate PR? |
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Requested to merge with this branch in particular since it is the most up-to-date. This PR addresses the following features:
Dependencies + interoperability with DeepLabCut-live-GUI
Fast-validation + early-failing
minor bugfix