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The app is built using modern Python practices and follows PEP8 standards.\n\n---\n\n## Table of Contents\n\n  - [About the Project](#-about-the-project)\n    - [What is the Haar Cascade Classifier?](#-what-is-the-haar-cascade-classifier)\n      - [How It Works:](#-how-it-works)\n  - [Features](#-features)\n  - [Getting Started](#-getting-started)\n    - [Prerequisites](#-prerequisites)\n    - [Folder Structure](#-folder-structure)\n  - [Installation](#-installation)\n  - [Running Script](#-running-script)\n  - [Expectations When Running This App](#-expectations-when-running-this-app)\n  - [Demo](#-demo)\n  - [Acknowledgments](#-acknowledgments)\n  - [License](#-license)\n  - [Notes](#-notes)\n\n---\n\n## About the Project\n\nThis project is a simple yet powerful face detection tool that allows users to upload images and detect faces using the **Haar Cascade classifier**. It's built with Python, OpenCV, and Streamlit.\n\n### What is the Haar Cascade Classifier?\n\nThe **Haar Cascade** is a machine learning object detection algorithm developed by Paul Viola and Michael Jones. It's widely used for detecting objects in images, such as:\n\n- Faces\n- Eyes\n- Smile\n- Vehicles\n\n#### How It Works:\n1. **Haar Features**: The algorithm uses simple rectangular features to detect patterns in images.\n2. **Cascade of Classifiers**: A series of classifiers is trained, each one filtering out non-matching regions.\n3. + **Real-Time Detection**: The algorithm is fast and efficient, making it ideal for real-time applications.\n\nThe `haarcascade_frontalface_default.xml` file used in this app is a pre-trained model for detecting frontal faces.\n\n---\n\n## Features\n\n- Upload images in `.jpg`, `.jpeg`, or `.png` format\n- Face detection using the Haar Cascade classifier\n- Display of detected faces with bounding boxes\n- Full-width image display in Streamlit\n\n---\n\n## Getting Started\n\n### Prerequisites\nMake sure you have the following installed:\n\n```bash\npip install streamlit opencv-python numpy\n```\n\n### Folder Structure\n\nEnsure the following structure exists in your project root:\n\n```\nproject/\n│\n├── cascade-model/\n│   └── haarcascade_frontalface_default.xml\n├── app.py        # This file (you're reading now)\n└── README.md      # This file\n```\n\n---\n\n## Installation\n\n1. Clone or download the project files.\n2. Place the `haarcascade_frontalface_default.xml` file in the `cascade-model/` directory.\n3. Run the app using Streamlit.\n\n---\n\n## Running Script\n\nRun the following command in your terminal:\n\n```bash\nstreamlit run app.py\n```\n\n---\n\n## Expectations When Running This App\n\n- The user should upload images in `.jpg`, `.jpeg`, or `.png` format.\n- If the file is not valid, an error message will be displayed.\n- The app will display detected faces with blue rectangles around them.\n\n---\n\n## Demo\n\nHere's what the app does:\n\n1. User uploads a face image.\n2. The app loads the image and detects faces using the Haar Cascade classifier.\n3. Bounding boxes are drawn around detected faces.\n4. The processed image is displayed in the browser.\n\n\n\nhttps://github.com/user-attachments/assets/e4c9f9fe-e6b7-499f-8222-cd2e85fdda1d\n\n\n\n\n---\n\n## Acknowledgments\n\n- [OpenCV](https://opencv.org) — For the Haar Cascade algorithm\n- [Streamlit](https://streamlit.io) — For building the web app\n\n---\n\n## License\n\nThis project is licensed under the **MIT License** — see the [LICENSE](LICENSE) file for details.\n\n---\n\n## Notes\n\n- The `haarcascade_frontalface_default.xml` file is required for face detection. You can download it from [OpenCV's GitHub](https://github.com/opencv/opencv/tree/master/data/hcascades).\n- This app is designed to be lightweight and easy to deploy using Streamlit.\n\n---\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpabs-code%2Fface-detection-using-haar-cascade-classifier","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpabs-code%2Fface-detection-using-haar-cascade-classifier","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpabs-code%2Fface-detection-using-haar-cascade-classifier/lists"}