Skip to content

Latest commit

 

History

History
69 lines (41 loc) · 1.83 KB

README.md

File metadata and controls

69 lines (41 loc) · 1.83 KB
Raw

Image Inference API with FastAPI and YOLO

This project provides a simple API for image inference using FastAPI and the YOLO (You Only Look Once) model for object detection.

Installation

To get started, you'll need to install the required packages. You can do this using pip:

pip install fastapi[all] ultralytics opencv-python

Usage

Once you have the necessary packages installed, you can run the FastAPI application using Uvicorn:

uvicorn main:app --reload

This will start the development server on http://127.0.0.1:8000.

API Endpoints

POST /inference-image

Infer objects from the uploaded image and get the inferenced image.

  • Input:
    • file: The image file you want to infer.

image

  • Output:
    • The inferenced image file.

image

Example

Here is an example of how you might call the API using curl:

curl -X 'POST' \
  'http://127.0.0.1:8000/inference-image' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'file=@your_image.png;type=image/png'

Replace your_image.png with the path to the image you want to infer.

Implementation Details

main.py

This file initializes the FastAPI app and defines the API endpoint for image inference. It saves the uploaded image, performs inference using the model defined in predictor.py, and returns the inferenced image.

predictor.py

This file defines the ImageInferencer class which uses the YOLO model from the Ultralytics library to perform inference on the input image. The results are plotted on the image and saved to a new file.

Contributing

If you'd like to contribute, please fork the repository and use a feature branch. Pull requests are warmly welcome.

License

MIT