danigc98 / turingapi Goto Github PK

This is a FastAPI project that provides an endpoint for image detection using a YOLOv4 model. It accepts image uploads and returns bounding boxes for detected objects.

Before running the project, ensure you have the necessary dependencies installed. You can install them using pip:

pip install -r requirements.txt

To run the project, use the following command:

uvicorn main:app --host 0.0.0.0 --port 8000 --reload

This will start the FastAPI application and make it accessible at http://localhost:8000. You can explore the API and its functionality using FastAPI's built-in Swagger UI. Simply visit http://localhost:8000/docs in your browser to access the API documentation.

POST /detect

This endpoint allows you to upload an image and receive bounding boxes for detected objects. You can also specify a detection threshold (default is 0.25).

Request:
    Upload an image file (jpg or png format).
    Optionally, provide the threshold query parameter (0.0 to 1.0) to adjust the detection threshold.

Response:
    The response contains the number of detections and a list of detected objects with their bounding box coordinates and labels.

Example:

curl -X 'POST' \
  'http://0.0.0.0/detect' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F '[email protected];type=image/jpeg'
  

You can also run the project using Docker. To do so, first build the Docker image:

docker build -t fastapi-image-detection .

Then run the container:

docker run -p 8000:8000 fastapi-image-detection