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YOLO v5 object detection end-to-end with FastAPI, Celery, Redis, RabbitMQ and Containers

This repository show the code created to be as a "template" to deploy applications with containers using FastAPI, Celery, Redis and RabbitMQ.

As a demo application, it was build a API service using YOLO v5 to perform object detection.

Since Yolo is a deep model which may take some time to return results, we will use Celery, Redis and RabbitMQ to control the tasks in background.

  • FastAPI: high performance python framework for building APIs.
  • Celery: A Task Queue with focus on real-time processing, while also supporting task scheduling.
  • RabbitMQ: A message broker used to route messages between API and the workers from Celery.
  • Redis: An in-memory database to store results and process status from the tasks.

The image below ilustrate the data flow from all components.

Overview of the code

  • api/app.py: expose the endpoints and send the request task to celery.
  • celery_tasks/tasks.py: receive the task and send (enqueue) to workers process.
  • celery_tasks/yolo.py: code to initilize and expose a method receive a picture and return the predictions.

Services available

Endpoint Method Description
http://localhost:8000/api/process POST Send one or more pictures to be processed by Yolo. Return the task_id of each task.
http://localhost:8000/api/status/<task_id> GET Retrieve the status of a given task
http://localhost:8000/api/result/<task_id> GET Retrieve the results of a given task
http://localhost:8000/docs GET Documentation generated for each endpoint
http://localhost:15672 GET RabbitMQ monitor. User: guest Password: guest.
http://localhost GET Simple webapp to show how to use and display results from the API.

POST: /api/process

Input

Form with enctype=multipart/form-data and imagens in attribute files. See the example in Ajax.

var form_data = new FormData();
files = $('#input_file_form').prop('files')
for (i = 0; i < files.length; i++)
    form_data.append('files', $('#input_file_form').prop('files')[i]);

$.ajax({
    url: URL + '/api/process',
    type: "post",
    data: form_data,
    enctype: 'multipart/form-data',
    contentType: false,
    processData: false,
    cache: false,
}).done(function (jsondata, textStatus, jqXHR) {
    console.log(jsondata)

}).fail(function (jsondata, textStatus, jqXHR) {
    console.log(jsondata)

});

Output:

[
  {
    "task_id": "2b593c5c-3f0b-49c1-a145-ad613f4ecda5",
    "status": "PROCESSING",
    "url_result": "/api/result/2b593c5c-3f0b-49c1-a145-ad613f4ecda5"
  }
]

Using CURL

curl -X POST "http://localhost:8000/api/process" -H  "accept: application/json" -H  "Content-Type: multipart/form-data" -F "[email protected];type=image/jpeg"

GET: /api/status/<task_id>

Input

task_uid

Output:

{
  "task_id": "2b593c5c-3f0b-49c1-a145-ad613f4ecda5",
  "status": "PROCESSING",
  "result": ""
}

Using CURL

curl -X GET "http://localhost/api/status/ren123/"

GET: /api/results/<task_id>

Input

task_id

Output (if it is done):

{
  "task_id": "2b593c5c-3f0b-49c1-a145-ad613f4ecda5",
  "status": "SUCCESS",
  "result": {
    "file_name": "static/f5956eea.jpg",
    "bbox": [
      {
        "x": "0.4734227",
        "y": "0.63320345",
        "w": "0.76526415",
        "h": "0.7137341",
        "prob": "0.8920207",
        "class": "person"
      },
      {
        "x": "0.3752669",
        "y": "0.8009622",
        "w": "0.07171597",
        "h": "0.38714227",
        "prob": "0.89087594",
        "class": "tie"
      },
      {
        "x": "0.8268833",
        "y": "0.6996484",
        "w": "0.11784973",
        "h": "0.5577593",
        "prob": "0.28665677",
        "class": "tie"
      }
    ]
  }
}

If it is processing:

{
  "task_id": "2b593c5c-3f0b-49c1-a145-ad613f4ecda5",
  "status": "PROCESSING",
  "result": ""
}

Install

  1. Clone this repository
git clone https://github.com/renatoviolin/Deploying-YOLOv5-fastapi-celery-redis-rabbitmq.git
cd Deploying-YOLOv5-fastapi-celery-redis-rabbitmq
  1. Install docker. If you already have, create the container with the command:
docker-compose build
  1. Run all containers
docker-compose up

This will start:

  • rabbitmq: message broker
  • redis: in-memory database
  • worker: application logic (Yolo model, FastAPI and Celery)
  • webapp: demo application
  1. Perform some requests using the integrated Swagger UI. http://localhost:8000/docs

  1. Open the demo webapp. http://localhost/