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Obscurra

Obscurra is a Python-based image processing tool designed to detect faces in images and apply a blur effect to anonymize them. The project aims to enhance privacy by automatically processing images to obscure identifiable facial features through a user-friendly graphical interface.

This repository contains the source code, documentation, and models used by the tool.

Features

  • Detects faces in images using Haar cascades.
  • Blurs detected faces to anonymize them.
  • Supports multiple image formats: JPG, JPEG, PNG, and WEBP.
  • Modular design for easy maintenance and extensibility.

Objectives

  • Develop a tool to detect and blur faces in images.
  • Provide a simple GUI for image processing.
  • Compile the application into a portable executable.

Timeline

Phase 1: Planning (January - February 2024)

  • Research: Investigated face detection models and image processing techniques.
  • Requirements: Defined project scope and objectives.

Phase 2: Development (March - June 2024)

  • Core Features: Implemented face detection using Haar Cascade and MTCNN.
  • GUI: Developed a Tkinter-based user interface.

Phase 3: Testing and Optimization (July 2024)

  • Testing: Identified and resolved bugs.
  • Optimization: Enhanced performance and accuracy.

Phase 4: Deployment (August 2024)

  • Executable: Packaged application using PyInstaller.
  • Documentation: Created user and developer guides.

Achievements

  • Integrated multiple face detection models.
  • Developed a portable executable with a comprehensive GUI.
  • Enhanced user experience with real-time image processing updates.

Challenges and Bugs

  • Model Integration: Ensured compatibility between different face detection models.
  • Performance: Required optimization for processing speed and accuracy.
  • GUI Usability: Improved interface design based on user feedback.

Methodology

The project followed an agile development process with iterative cycles of development, testing, and refinement, focusing on usability and performance.

Technologies Used

  • Python: Core language
  • Tkinter: GUI framework
  • OpenCV: Image processing
  • TensorFlow: MTCNN model
  • PyInstaller: Executable packaging

Project Structure

Obscurrra/
├── src/
│   ├── Obscurrra.py        # Main script
│   ├── icon.ico            # Application icon
│   ├── haarcascade_frontalface_default.xml # Frontal face detection model
│   ├── haarcascade_profileface.xml # Profile face detection model
│   ├── mtcnn_weights.npy   # MTCNN model weights
└── dist/                   # Compiled executable

User Guide

  1. Download the Executable: Obtain from the release section.
  2. Run the Application: Launch the GUI executable.
  3. Process Images:
    • Select input and output folders.
    • Choose detection models and set preferences.
    • Click "Start Processing."

Developer Guide

Setup

  1. Clone the Repository:

    git clone https://github.com/jakujobi/obscurrra.git
cd obscurrra

  2. Create a Virtual Environment and Install Dependencies:

    python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`
pip install -r requirements.txt

  3. Run the Application:

    python src/Obscurrra.py

Building the Executable

  1. Install PyInstaller:

    pip install pyinstaller

  2. Compile the Application:

    pyinstaller --onefile --windowed src/Obscurrra.py

Future Improvements

  • Explore advanced face detection models.
  • Add real-time video processing capabilities.
  • Enhance cross-platform compatibility.

Contributors

  • John Akujobi- Project Lead, UI Designer, Software Engineer

This documentation provides a comprehensive yet concise overview of the Obscurrra project, highlighting key features, challenges, and future directions for both users and developers.