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a wrapper of DeepLabCut for its ease of use.

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A wrapper of DeepLabCut for its ease of use.

dlclib is supposed to work with almost all single-camera setups of all DLC versions.


It is better building your DeepLabCut environment first, before running:

$ pip install dlclib



A thin wrapper over the DeepLabCut inference network based on TensorFlow.

  • session = TFSession.from_config(cfg) will return a TFSession object using the specified config.yaml file of the DeepLabCut project.
  • session.get_pose(image) will return a numpy.ndarray corresponding to the estimation. for a batch-processing network, use a list of images, or a 4-D uint8 array whose axis 0 correspond to indexes of images.
  • session.open_writer(path) opens a CSVWriter at the specified path (see below).


A utility class that maps the output of TFSession to a dictionary. Note that the current implementation:

  • does not care anything about conversion speed.

  • currently only supports non-batch models.

  • mapper = PostureMapper.from_config(cfg) will return a PostureMapper object using the specified config.yaml file of the DeepLabCut project.

  • posedict = receives the output of TFSession.get_pose(), and returns a dictionary. posedict[part] is a named tuple that has x, y and prob attributes.


A simple class for writing the estimation into a CSV file. This class does support batch-processing networks.

  • writer = session.open_writer(path) from a TFSession object initializes the writer associated with the inference session.
  • For every video frame, you can call writer.push(frame). It automatically calls TFSession.get_pose() internally, and writes its outputs into the file.
  • Calling writer.flush() will flush the pended video frames (in case of batch modes; it does not wait for more frames and runs get_pose() to writes the estimation).
  • Calling writer.close() will flush its internal buffer, close the file and invalidates itself. You can keep using the originating TFSession object.

This class supports the context-manager model, so you can write:

with session.open_writer(path) as out:
    for frame in video_frames:


2020-2021 Keisuke Sehara, the MIT License

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