OpenCV Object Detection

Environment Setup

Windows

To set up your environment on Windows, follow these steps:

python -m venv venv
.\venv\Scripts\activate
pip install -r requirements.txt

Linux and macOS

For Linux and macOS, use these commands:

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Once you've completed these steps, your environment will be ready for different detection using the mediapipe! For face detection and face mesh landmarks, check out the Face Detection Directory. For hand detection and related projects, visit the Hand Detection Directory. For pose detection, refer to the Pose Detection Directory.

MediaPipe

MediaPipe is an open-source framework developed by Google for building real-time multimedia processing pipelines. It provides a set of pre-built components and tools that can be used to create complex multimedia applications, such as real-time object detection, face detection and tracking, hand tracking, and pose estimation.

MediaPipe uses a modular architecture that allows developers to build custom pipelines by combining pre-built components, such as video and audio processing modules, with custom application logic. The framework provides a high-level API for accessing its components and tools, making it easy for developers to integrate MediaPipe into their applications.

MediaPipe supports a wide range of platforms, including desktop, mobile, and embedded devices. It also provides cross-platform support for a variety of programming languages, including C++, Python, and Java.

The framework has been used in a wide range of applications, such as augmented reality, virtual reality, and video analytics. It is well-known for its accuracy and performance in real-time applications, and it has been widely adopted by developers and researchers in the field of computer vision and multimedia processing.

Use Cases of mediapipe

1. Face detection and tracking: MediaPipe can detect and track human faces in real-time video streams. It uses a combination of feature extraction and machine learning techniques to detect faces and track them across frames.

2. Hand tracking: MediaPipe can track hand movements in real-time video streams. It uses a deep learning-based model to detect and track the position of hands and fingers in 3D space.

3. Pose estimation: MediaPipe can estimate the pose of humans in real-time video streams. It uses a deep learning-based model to detect and track the position and orientation of human body parts, such as the head, torso, arms, and legs.

Contributing

Contributions are welcome! If you find any issues or want to add new features, feel free to submit a pull request.

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