Kafka Realtime Object Detection - Fastapi, Kafka, Yolo, Postgresql, Grafana

Introduction

This project is a Kafka-based realtime object detection system. It allows you to process and analyze data in real time using Kafka streams, computer visions, Fastapi and Grafana.

Prerequisites

Before running the application, make sure you have the following prerequisites installed:

• Docker: Installation Guide

Getting Started

1. Fork or Clone the repository: To clone the repository:

   git clone https://github.com/george-mountain/Computer-Vision-Kafka-Realtime-Object-Detection.git

2. Create a new file named .env in the project root directory and copy the contents of .env-sample to this .env file. Modify the credentials in the .env file, such as the PostgreSQL credentials, if needed.

3. Build and run the application using Docker:

   docker-compose up --build -d

4. To see the detection and processing in realtime from the terminal, run the docker command below after running the command on step 3 above.

   docker-compose up 

   Alternatively, if you have a Makefile in your PC, you can use the following commands:

   • make build to build the Docker containers
   • make up-v to run the Docker containers

Usage

After running the application, you can access the following endpoints and URLs:

1. FastAPI Endpoints Documentation: http://localhost:8000/docs

2. Grafana URL: http://127.0.0.1:3000

3. pgAdmin URL: http://127.0.0.1:5080

Feel free to explore and use the application as needed!