Face Anonymizer
As part of my ongoing exploration into object detection, I developed a Face Anonymizer that blurs faces detected in images, videos, and live webcam feeds. This project leverages OpenCV and MediaPipe to ensure efficient and accurate face detection across various media formats.
Project Overview
Objective: To create a tool that automatically detects and blurs faces in different media, enhancing privacy in shared content.
Technologies Used:
OpenCV: Utilized for image and video processing tasks, including reading media files, accessing webcam feeds, and applying blurring effects.
MediaPipe: Employed for its robust face detection capabilities, providing real-time performance and high accuracy.
Understanding MediaPipe Face Detection
MediaPipe’s face detection solution is based on the BlazeFace model, a lightweight and efficient architecture optimized for mobile and real-time applications. BlazeFace employs a single-shot detector approach, predicting face locations and key landmarks in a single pass through the neural network. This design enables rapid detection with minimal computational overhead, making it suitable for applications requiring real-time processing. (MediaPipe)
The video demonstration showcases the Face Anonymizer in action. The tool effectively detects and blurs faces in real-time, ensuring privacy without compromising the overall content quality.
Implementation Highlights
Face Detection: MediaPipe processes each frame to detect faces, returning bounding box coordinates for each detected face.
Face Anonymization: OpenCV applies a Gaussian blur to the regions within the bounding boxes, effectively anonymizing the detected faces.
Output Generation: The processed frames are displayed in real-time for webcam feeds or saved as new media files for images and videos.
Access the Code
The complete code for this project is available on GitHub. Feel free to explore, use, or modify it for your own applications.
Acknowledgment:
This project was part of the Computer Vision in 30 Days Coding Challenge by Felipe, also known as Computer Vision Engineer on YouTube.