Structure

The pose_format package aims to help users with the pose data management and pose analysis.: The toolkit offers many functionalities, from reading and editing to visualizing and testing pose data. It provides a wide range of features for these tasks.

This section gives an brief overview of the main feature structure of the package and its functionalities.

Main Features

Reading and Manipulating Pose Data:
- pose files ensuring cross-compatibility between popular libraries such as NumPy, PyTorch, and TensorFlow.
- The loaded data presents multiple manipulation options including:
  - Normalizing pose data.
  - Agumentation of data.
  - Interpolation of data.
Visualization Capabilities:
- Methods to visualize raw and processed pose data using pose_format.pose_visualizer.PoseVisualizer module.
- Includes overlay functions for videos.
Package Organization and Components:
- Structured with submodules and subpackages serving the purposes:
  - pose_format.numpy package for NumPy interactions.
  - pose_format.tensorflow package for TensorFlow functionalities.
  - pose_format.torch package for PyTorch-related tools.
  - pose_format.third_party package for externals.
  - pose_format.utils package for utility tools.
Testing Suite:
- Tests for the reliability of the package and its setups/data can be found in tests package.

This section illustrates the content of the testing suite and the used data.