https://github.com/george-mountain/computer-vision-kafka-realtime-object-detection
Kafka Realtime Object Detection Using: Fastapi, Kafka, Yolo, Postgresql, Grafana
https://github.com/george-mountain/computer-vision-kafka-realtime-object-detection
computer-vision fastapi grafana kafka pgadmin4 postgresql yolo yolov8
Last synced: 7 months ago
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Kafka Realtime Object Detection Using: Fastapi, Kafka, Yolo, Postgresql, Grafana
- Host: GitHub
- URL: https://github.com/george-mountain/computer-vision-kafka-realtime-object-detection
- Owner: george-mountain
- Created: 2024-04-23T02:40:48.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-24T09:04:26.000Z (over 1 year ago)
- Last Synced: 2025-01-21T06:11:33.076Z (9 months ago)
- Topics: computer-vision, fastapi, grafana, kafka, pgadmin4, postgresql, yolo, yolov8
- Language: Python
- Homepage:
- Size: 10.7 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## 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](https://docs.docker.com/get-docker/)
### Getting Started
1. Fork or Clone the repository:
To clone the repository:
```shell
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:
```shell
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.
```shell
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](http://localhost:8000/docs)
2. Grafana URL:
[http://127.0.0.1:3000](http://127.0.0.1:3000)
3. pgAdmin URL:
[http://127.0.0.1:5080](http://127.0.0.1:5080)
Feel free to explore and use the application as needed!