P6: Full Body Detection with OpenCV and MediaPipe

8d ago (edited) in 🥇 Wins

Ever thought about creating body pose-based apps or designing a system to control your screen using only gestures? Look no further—MediaPipe and Python make it possible!

In this project, I explore the fundamentals of body pose detection, facial landmark estimation, and hand pose tracking using MediaPipe, a versatile library that simplifies these tasks. With just a webcam and a few lines of code, you can dive into the world of gesture-based interaction.

The core of this system is MediaPipe Holistic, a cutting-edge model that leverages deep learning to accurately detect keypoints for the face, hands, and body. This capability opens up endless possibilities for prototyping innovative use cases, such as:

• Touchless gesture controls for devices and applications.

• Human sentiment analysis by understanding body language and expressions.

• Developing custom tools like an exercise counter for fitness tracking.

🔧 Tools and Libraries Used

• Python 3: The backbone of the project, providing an intuitive and flexible platform for scripting and development.

• OpenCV: A powerful open-source library for real-time computer vision tasks. It enables seamless video capture and frame processing, which are essential for this project.

• MediaPipe: Google’s robust framework for building multimodal machine learning pipelines. In this project, it handles real-time detection of facial landmarks, hand poses, and body keypoints with remarkable accuracy.

📋 Project Workflow

1. Install and Import Libraries: Begin by setting up Python and installing required libraries such as MediaPipe and OpenCV.

2. Get Real-Time Webcam Feed: Capture live video feed for real-time detections.

3. Make Detections from Feed: Process video frames and analyze data for keypoint detection.

4. Detect Facial Landmarks: Identify and track facial features such as eyes, nose, and mouth.

5. Detect Hand Poses: Use MediaPipe to recognize hand gestures and keypoints.

6. Detect Body Poses: Map key body points for full-body pose detection.

7. Customize Landmark Colors and Styling: Enhance visuals by changing colors and styles of the detected landmarks.

✨ Let’s Collaborate!

I’m always looking to improve my projects and learn from others in the community. 😊

Do you have suggestions for enhancing this system? Or maybe an exciting use case idea? Share your thoughts in the comments below—I’d love to hear them and explore more possibilities together!

🔗 Explore the project here: https://github.com/iamramzan/P7-Full-Body-Detection-with-OpenCV-and-MediaPipe

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