Readme.md

Advanced Computer Vision Surveillance System (Ongoing Project)

This project is an advanced computer vision surveillance system that allows for real-time monitoring, face recognition, and location tracking. The system includes features for managing cameras and persons, and displays the last known locations of identified persons on a map.

Table of Contents

• Features
• Tech Stack
• Setup
• Usage
• API Endpoints
• License

Features

• Real-time Camera Feed: Stream video from multiple cameras.
• Face Recognition: Identify known persons using face recognition.
• Location Tracking: Track the last known locations of identified persons.
• Person Management: Add, edit, and delete person details.
• Camera Management: Add, edit, and delete camera details.
• Location Map: Display the last known locations of identified persons on a map with detailed information.

Tech Stack

• Frontend: React, Tailwind CSS
• Backend: FastAPI
• Database: SQLite
• Computer Vision: OpenCV, YOLO, face_recognition
• Deployment: Docker, Azure

Setup

Prerequisites

• Node.js
• Python 3.7+
• Docker (optional for containerized deployment)

Backend Setup

1. Clone the repository:

   git clone https://github.com/yourusername/surveillance-system.git
   cd surveillance-system

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required Python packages:

   pip install -r requirements.txt

4. Download YOLO configuration and weights files:

   • Download yolov3.cfg from: https://github.com/pjreddie/darknet/blob/master/cfg/yolov3.cfg
   • Download yolov3.weights from: https://pjreddie.com/media/files/yolov3.weights
   • Download coco.names from: https://github.com/pjreddie/darknet/blob/master/data/coco.names

   Place these files in a directory named backend/app/yolo inside the project root directory.

5. Run the FastAPI server:

   uvicorn app.main:app --reload

Frontend Setup

1. Navigate to the frontend directory:

   cd frontend

2. Install the required npm packages:

   npm install

3. Start the React development server:

   npm start

Docker Setup (Optional)

1. Build the Docker image:

   docker build -t surveillance-system .

2. Run the Docker container:

   docker run -p 8000:8000 surveillance-system

Usage

1. Access the application: Open your browser and navigate to http://localhost:3000 to access the frontend.

2. Navigate to the Dashboard: The dashboard displays the real-time camera feed and the location map.

3. Manage Persons and Cameras: Navigate to the "Manage" section from the navbar to add, edit, or delete persons and cameras.

API Endpoints

Persons

• GET /persons: Get a list of all persons.
• POST /persons: Add a new person.
• DELETE /persons/{person_id}: Delete a person by ID.

Cameras

• GET /cameras: Get a list of all cameras.
• POST /cameras: Add a new camera.
• DELETE /cameras/{camera_id}: Delete a camera by ID.

Locations

• GET /locations: Get a list of all locations.
• GET /locations/person/{person_id}: Get locations for a specific person.

Some Images

License

This project is licensed under the MIT License.