This README is designed for the WahidSaeed/MSTeamBlurFilter repository. Since this project uses Python, OpenCV, and MediaPipe to replicate the background blurring effect seen in video conferencing apps, this template focuses on the technical setup and implementation.

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MS Teams Background Blur Filter

Ever wondered how Microsoft Teams or Zoom hides your messy room during a video call? This project explores the mechanics behind background blurring filters using Python, OpenCV, and MediaPipe.

Overview

This application uses machine learning to perform real-time Selfie Segmentation. By identifying the person in the video frame, we can separate the foreground from the background and apply a Gaussian blur only to the environment, keeping the user in sharp focus.

Built With

• Python: The core programming language.
• OpenCV: Used for real-time computer vision processing and image manipulation.
• MediaPipe: Specifically the "Selfie Segmentation" solution for high-fidelity background separation.

Features

• Real-time Processing: Fast enough to run on a standard webcam feed.
• Dynamic Backgrounds: Demonstrates how to either blur the current background or replace it with a static image (e.g., office.jpg).
• Precise Masking: Uses ML-based segmentation rather than simple color-keying.

Getting Started

Prerequisites

Ensure you have Python 3.x installed. You will need to install the following dependencies:

pip install opencv-python mediapipe

Installation

1. Clone the repository:

git clone https://github.com/WahidSaeed/MSTeamBlurFilter.git
cd MSTeamBlurFilter

2. Run the script:

python main.py

Project Structure

• main.py: The main script containing the webcam loop and MediaPipe segmentation logic.
• office.jpg: A sample background image for replacement testing.
• README.md: Project documentation.

How it Works

1. Capture: The webcam feed is captured frame-by-frame using OpenCV.
2. Segment: MediaPipe processes the frame to create a binary mask (identifying "person" vs. "background").
3. Blur: A Gaussian blur filter is applied to the original frame.
4. Combine: The mask is used to stitch the sharp user (foreground) back onto the blurred background.

📜 License

This project is open-source and available under the MIT License.

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**Maintained by Wahid Saeed**