/aspera_pose_estimation

Primary LanguageJupyter Notebook

📝 Table of Contents

🧐 About

The ASPERA Pose Estimation project leverages advanced computer vision techniques to tackle keypoints regression and pose estimation tasks. It utilizes the state-of-the-art Yolov8 model to deliver robust pose estimation solutions through keypoint detection.

Components

This repository contains :

  • assets/: files for README.md.
  • data/: folder that should contain the datasets in spv2-COCO format. They can be generated using the ASPERA_dataset_generation repository.
  • keypoints_regression/: folder that contains the scripts and results of ai-based model keypoints regression.
  • pose_estimation/: folder that contains the scripts and results for PnP solver pose estimation.
  • utils/: useful classes and functions.

🏁 Getting Started

Follow these steps to set up and start using the ASPERA Pose Estimation models:

Prerequisites

Make sure you have the following installed:

  • Git: For cloning the repository.
  • Python: Programming language used, along with pip for package management.
  • Virtual Environment: Recommended for managing Python dependencies.

Installing

Cloning the Repository

  1. Open your terminal.
  2. Navigate to the directory where you want to set up the project.
  3. Clone the repository using: 
    git clone https://github.com/your-repository/ASPERA_pose_estimation.git
  4. Navigate into the project directory: 
    cd ASPERA_pose_estimation

Python and Virtual Environment

Set up your Python environment as follows:

  1. Ensure Python and pip are installed. If not, download them from Python's official site.
  2. Create and activate a virtual environment:
    • Windows: 
      python -m venv venv
venv\Scripts\activate
    • macOS/Linux: 
      python -m venv venv
source venv/bin/activate
  3. Install dependencies: 
    pip install -r requirements.txt

🎈 Usage

Navigate to specific directories for detailed steps and usage:

  1. Keypoints Regression: Use the keypoints_regression/README.md to have more informations on keypoints regression process.
  2. Pose Estimation: Use the pose_estimation/README.md to have more informations on pose estimation process.