pose-format 0.0.1

Library for viewing, augmenting, and handling .pose files

Pose Format

This repository aims to include a complete toolkit for working with poses. It includes a new file format with Python and Javascript readers and writers, in hope to make usage simple.

The File Format

The format supports any type of poses, arbitrary number of people, and arbitrary number of frames (for videos).

The main idea is having a header with instructions on how many points exists, where, and how to connect them.

The binary spec can be found in format/spec.md.

Python Usage

To load a .pose file, use the PoseReader class:

buffer = open("file.pose", "rb").read()
p = Pose.read(buffer)

By default, it uses NumPy for the data, but you can also use torch and tensorflow by writing:

p = Pose.read(buffer, TorchPoseBody)

Data Normalization

To normalize all of the data to be in the same scale, we can normalize every pose by a constant feature of their body. For example, for people we can use the the average span of their shoulders throughout the video to be a constant width.

p.normalize(
    dist_p1=("pose_keypoints_2d", 2),  # RShoulder
    dist_p2=("pose_keypoints_2d", 5),  # LShoulder
)

Data Augmentation

p.augment2d(rotation_std=0.2, shear_std=0.2, scale_std=0.2)

Data Interpolation

To change the frame rate of a video, using data interpolation, use the interpolate_fps method which gets a new fps and a interpolation kind.

p.interpolate_fps(24, kind='cubic')
p.interpolate_fps(24, kind='linear')

Testing

Use bazel to run tests

bazel test :all --test_output=errors

Local install

pip install -e /home/nlp/amit/PhD/PoseFormat/