https://github.com/justsecret123/human-pose-estimation

A quick tutorial on multi-pose estimation with OpenCV, Tensorflow and MoveNet lightning.
https://github.com/justsecret123/human-pose-estimation

computer-vision deep-learning deep-neural-networks human-pose-estimation imageio ipython jupyter-notebook machine-learning matplotlib movenet movenet-lightning numpy opencv pose-estimation python tensorflow tensorflow-hub tutorial tutorials

Last synced: 24 days ago
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A quick tutorial on multi-pose estimation with OpenCV, Tensorflow and MoveNet lightning.

• Host: GitHub
• URL: https://github.com/justsecret123/human-pose-estimation
• Owner: Justsecret123
• License: gpl-3.0
• Created: 2022-02-17T10:13:40.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-05-08T10:30:51.000Z (over 2 years ago)
• Last Synced: 2024-10-12T11:21:54.856Z (24 days ago)
• Topics: computer-vision, deep-learning, deep-neural-networks, human-pose-estimation, imageio, ipython, jupyter-notebook, machine-learning, matplotlib, movenet, movenet-lightning, numpy, opencv, pose-estimation, python, tensorflow, tensorflow-hub, tutorial, tutorials
• Language: Jupyter Notebook
• Homepage:
• Size: 139 MB
• Stars: 4
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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README

        
# Human-pose-estimation ![Language_support](https://img.shields.io/pypi/pyversions/Tensorflow) ![Last_commit](https://img.shields.io/github/last-commit/JustSecret123/Human-pose-estimation) ![Workflow](https://img.shields.io/github/workflow/status/JustSecret123/Human-pose-estimation/Pylint/main) ![Tensorflow_version](https://img.shields.io/badge/Tensorflow%20version-2.6.2-orange) 

A quick tutorial on multi-pose estimation with OpenCV, Tensorflow and MoveNet lightning.

> **Also available on [Kaggle](https://www.kaggle.com/ibrahimserouis99/human-pose-estimation-with-movenet) 



  



# Motivation 

I found multipose estimation Notebooks and codes not so explicit or even understandable for pure beginners. Moreover, most of the available tutorials focus on single-pose estimation, with only one instance (human).  As a result, the idea of writing my own tutorial naturally came to me. After some research and a bit of styling, code cleaning, presentation...I finally made it public. 

# Model info 

- Model type : MoveNet 

- Pose estimation method : multipose, bottom-up

- Keypoint count : 17

# Results (example)

![Results](https://github.com/Justsecret123/Human-pose-estimation/blob/main/Screenshots/results.gif)

# How to use 

## Command line runner

- [Test script](/Scripts/movenet_inference.py)

- [Bat file for a sample test](/Scripts/test_inference.bat) : requires the model path to be *../Model/TFLite/lite-model_movenet_multipose_lightning_tflite_float16_1.tflite*. You can [download it directly from TFHub](https://tfhub.dev/google/lite-model/movenet/multipose/lightning/tflite/float16/1)

> Args :

![Command_line_args](/Screenshots/command_line_args.PNG)

## Notebook 

- [Kaggle](https://www.kaggle.com/ibrahimserouis99/human-pose-estimation-with-movenet)

> Requires Tensorflow Hub and a Kaggle environment. However, feel free to adapt to Notebook to your local setup

# Required packages

- Deep Learning and calculations : Tensorflow 2.x, NumPy, Tensorflow Docs

- Computer graphics/vision : OpenCV 

- Display : IPython, Matplotlib 

- Image/video writer : image io

# Acknowledgements 

- Tensorflow official tutorial