$ conda create -n pyannote python=3.6 anaconda
$ source activate pyannote

$ git clone https://github.com/davisking/dlib-models.git
$ bunzip2 dlib-models/dlib_face_recognition_resnet_model_v1.dat.bz2
$ bunzip2 dlib-models/shape_predictor_68_face_landmarks.dat.bz2

$ pip install jupyter
$ jupyter notebook --notebook-dir="demo2/doc"

Video structure

The standard pipeline for is the following:

    shot boundary detection ==> shot threading ==> segmentation into scenes

Usage:
  pynote-structure.py shot [options] <video> <output.json>
  pynote-structure.py thread [options] <video> <shot.json> <output.json>
  pynote-structure.py scene [options] <video> <thread.json> <output.json>
  pynote-structure.py (-h | --help)
  pynote-structure.py --version

Options:
  --height=<n_pixels>    Resize video frame to height <n_pixels> [default: 50].
  --window=<n_seconds>   Apply median filtering on <n_seconds> window [default: 2.0].
  --threshold=<value>    Set threshold to <value> [default: 1.0].
  --min-match=<n_match>  Set minimum number of matches to <n_match> [default: 20].
  --lookahead=<n_shots>  Look at up to <n_shots> following shots [default: 24].
  -h --help              Show this screen.
  --version              Show version.
  --verbose              Show progress.

Face detection and tracking

The standard pipeline is the following

      face tracking => feature extraction => face clustering

Usage:
  pynote-face track [options] <video> <shot.json> <tracking>
  pynote-face extract [options] <video> <tracking> <landmark_model> <embedding_model> <landmarks> <embeddings>
  pynote-face demo [options] <video> <tracking> <output>
  pynote-face (-h | --help)
  pynote-face --version

General options:

  -h --help                 Show this screen.
  --version                 Show version.
  --verbose                 Show processing progress.

Face tracking options (track):

  <video>                   Path to video file.
  <shot.json>               Path to shot segmentation result file.
  <tracking>                Path to tracking result file.

  --min-size=<ratio>        Approximate size (in video height ratio) of the
                            smallest face that should be detected. Default is
                            to try and detect any object [default: 0.0].
  --every=<seconds>         Only apply detection every <seconds> seconds.
                            Default is to process every frame [default: 0.0].
  --min-overlap=<ratio>     Associates face with tracker if overlap is greater
                            than <ratio> [default: 0.5].
  --min-confidence=<float>  Reset trackers with confidence lower than <float>
                            [default: 10.].
  --max-gap=<float>         Bridge gaps with duration shorter than <float>
                            [default: 1.].

Feature extraction options (features):

  <video>                   Path to video file.
  <tracking>                Path to tracking result file.
  <landmark_model>          Path to dlib facial landmark detection model.
  <embedding_model>         Path to dlib feature extraction model.
  <landmarks>               Path to facial landmarks detection result file.
  <embeddings>              Path to feature extraction result file.

Visualization options (demo):

  <video>                   Path to video file.
  <tracking>                Path to tracking result file.
  <output>                  Path to demo video file.

  --height=<pixels>         Height of demo video file [default: 400].
  --from=<sec>              Encode demo from <sec> seconds [default: 0].
  --until=<sec>             Encode demo until <sec> seconds.
  --shift=<sec>             Shift result files by <sec> seconds [default: 0].
  --landmark=<path>         Path to facial landmarks detection result file.
  --label=<path>            Path to track identification result file.