/exercise-counting-with-YOLOv8

Pose estimation demo for exercise counting with YOLOv8

Primary LanguagePythonApache License 2.0Apache-2.0

Exercise Counter with YOLOv8 on NVIDIA Jetson

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This is a pose estimation demo application for exercise counting with YOLOv8 using YOLOv8-Pose model.

This has been tested and deployed on a reComputer Jetson J4011. However, you can use any NVIDIA Jetson device to deploy this demo.

Current only 3 different exercise types can be counted:

  • Squats
  • Pushups
  • Situps

However, I will keep updating this repo to add more exercises and also add the function of detecting the exercise type.

Introduction

The YOLOv8-Pose model can detect 17 key points in the human body, then select discriminative key-points based on the characteristics of the exercise. Calculate the angle between key-point lines, when the angle reaches a certain threshold, the target can be considered to have completed a certain action. By utilizing the above-mentioned mechanism, it is possible to achieve an interesting Exercise Counter Application.

Installation

  • Step 1: Flash JetPack OS to reComputer Jetson device (Refer to here).

  • Step 2: Access the terminal of Jetson device, install pip and upgrade it

sudo apt update
sudo apt install -y python3-pip
pip3 install --upgrade pip
  • Step 3: Clone the following repo
git clone https://github.com/ultralytics/ultralytics.git
  • Step 4: Open requirements.txt
cd ultralytics
vi requirements.txt
  • Step 5: Edit the following lines. Here you need to press i first to enter editing mode. Press ESC, then type :wq to save and quit
# torch>=1.7.0
# torchvision>=0.8.1

Note: torch and torchvision are excluded for now because they will be installed later.

  • Step 6: Install the necessary packages
pip3 install -e .
  • Step 7: If there is an error in numpy version, install the required version of numpy
pip3 install numpy==1.20.3

  • Step 8: Install PyTorch and Torchvision (Refer to here).

  • Step 9: Run the following command to make sure yolo is installed properly

yolo detect predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
  • Step 10: Clone exercise counter demo
git clone https://github.com/yuyoujiang/exercise-counting-with-YOLOv8.git

Prepare The Model File

YOLOv8-pose pretrained pose models are PyTorch models and you can directly use them for inferencing on the Jetson device. However, to have a better speed, you can convert the PyTorch models to TensorRT optimized models by following below instructions.

  • Step 1: Download model weights in PyTorch format (Refer to here).

  • Step 2: Execute the following command to convert this PyTorch model into a TensorRT model

# TensorRT FP32 export
yolo export model=yolov8s-pose.pt format=engine device=0

# TensorRT FP16 export
yolo export model=yolov8s-pose.pt format=engine half=True device=0

Tip: Click here to learn more about yolo export

  • Step 3: Prepare a video to be tested. Here we have included sample videos for you to test

Let's Run It!

To run the exercise counter, enter the following commands with the exercise_type as:

  • sit-up
  • pushup
  • squat

For video

python3 demo.py --sport <exercise_type> --model yolov8s-pose.pt --show True --input <path_to_your_video>

For webcam

python3 demo.py --sport <exercise_type> --model yolov8s-pose.pt --show True --input 0

result 00_00_00-00_00_30

References

https://github.com/ultralytics/
https://wiki.seeedstudio.com