This guide explains how to set up and run a multi-modal fusion technique for real-time pose estimation. It combines 2D and 3D images to predict all the joints in the human body, unlocking a wide range of applications in computer vision.

To begin, download the repository. You can do this by either cloning it using Git or downloading the files manually.

Run the following command in your terminal to clone the repository into your working directory:

git clone https://github.com/BadBoy0170/Pose_Estimation.git

Download the necessary files from the repository, including the required weights for pose estimation.

Using a dedicated Conda environment ensures that the dependencies are isolated and do not interfere with other projects.

1. Create the Conda environment using the provided environment.yml file:

   conda env create -f environment.yml

2. Activate the Conda environment:

   conda activate posefusion

After activating the Conda environment, navigate to the directory containing the project files:

cd Pose_Estimation

The pose estimation model requires pre-trained weights to perform inference.

1. Download the pose weights:
   Visit this link and download the yolov7-w6-pose.pt file.

2. Alternatively, download using the terminal (Ubuntu):

   wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6-pose.pt

3. Place the yolov7-w6-pose.pt file in the Pose_Estimation directory where other project files are located.

With everything set up, you can now run the pose estimation model. Below are different methods for running inference based on your requirements:

To use a webcam as the input source (source 0):

python run_pose.py --source 0

To perform pose estimation on a specific video file, provide the path to the video:

python run_pose.py --source [path_to_video]

If you have a GPU available, specify the GPU device (default 0) for faster processing:

python run_pose.py --source 0 --device 0

After running the commands, the model will process the input and display pose estimation results, including the keypoints detected on the human body.

By following this guide, you will have the multi-modal fusion pose estimation fully set up and running. This enables you to incorporate advanced pose estimation capabilities into your computer vision projects.