pocketpose 1.0.0a1.post0

An inference library for real-time human pose estimation.

PocketPose

Developed by Saif Khan

 

📘Documentation | 🛠️Installation | 👀Model Zoo | 📜Papers | 🆕Update News | 🤔Reporting Issues |

Introduction

PocketPose is an open-source library that facilitates fast and efficient pose estimation on mobile and edge devices. It supports TensorFlow Lite (TFLite) models and provides a collection of pre-trained models ready to use out of the box.

Features

• High-performance pose estimation on mobile devices
• Support for TensorFlow Lite models
• Collection of pre-trained models for various use-cases
• Easy-to-use API for custom model deployment

Installation

PocketPose can be installed from source:

$ git clone
$ cd pocket-pose
$ pip install .

Please refer to the installation guide for detailed installation instructions.

Quick Start

Running pose estimation on an image is as simple as:

from pocketpose import PoseInferencer

# Define an input image
image_path = "path/to/image.jpg"  # JPEG or PNG

# Load a model
pose_estimator = PoseEstimator(model_name="model-alias")

# Perform pose estimation
keypoints = pose_estimator.infer(image_path)

For more detailed usage and a list of all available models, check out our usage guide.

License

PocketPose is released under the MIT License.