#OpenCV #ComputerVision #AI #GenerativeAI #MediaPipe #Python
This project showcases real-time human pose detection using the MoveNet Lightning model, one of the fastest deep learning models for pose estimation. It leverages Python, TensorFlow Lite, OpenCV, and NumPy to capture live webcam footage, preprocess frames, and detect keypoints like joints in real time. The system renders skeletal connections dynamically, making it ideal for interactive applications such as fitness tracking, gaming, and gesture control.
🔧 Technologies and Tools Used
• Python 3: A versatile programming language for building AI applications.
• NumPy: For efficient numerical computations during data preprocessing.
• Matplotlib: For visualization and debugging pose estimation outputs.
• OpenCV: To capture video frames, render detections, and display results in real-time.
• TensorFlow Lite: A lightweight version of TensorFlow optimized for running machine learning models on edge devices.
📋 Step-by-Step Workflow
1. Install MoveNet for Python
• Set up the TensorFlow Lite interpreter for the MoveNet Lightning model.
• Ensure necessary Python libraries (OpenCV, NumPy, TensorFlow) are installed.
2. Load the TensorFlow Lite Model
• Use the TFLite interpreter to load the pre-trained MoveNet Lightning model.
• Allocate tensors for efficient handling of input and output data during inference.
3. Preprocess Webcam Frames
• Capture live frames using OpenCV and resize them to the required 192x192 dimensions.
• Normalize pixel values for optimal model performance.
4. Perform Pose Detection
• Feed preprocessed frames into the MoveNet model.
• Extract keypoints (e.g., joints like elbows, knees, shoulders) with confidence scores.
5. Render Pose Estimation Results
• Overlay detected keypoints on the original video feed using OpenCV.
• Connect keypoints with lines to visualize the human skeletal structure dynamically.
6. Real-Time Execution
• Continuously process and display frames, achieving real-time pose estimation and rendering with minimal latency.
✨ Engage and Collaborate!
This project is just the beginning! Whether you're curious about AI-powered pose estimation or have ideas for further applications (e.g., dance analysis, sports coaching, AR/VR interactions), let's discuss and collaborate.
📩 Share your feedback, suggestions, or questions in the comments. If you'd like to connect for deeper insights or potential collaborations, feel free to message me directly. Together, we can push the boundaries of AI-driven innovations!
🔗 Explore the project here: https://github.com/iamramzan/P4-Real-...