takmin / YoloX_FastAPI

Simple WebAPI for YOLOX

YoloX + FastAPI

2024/03/15 MINAGAWAA Takuya

I created simple API for object detector YOLOX.

YOLOX code and a onnx model are from OpenCV Zoo:

https://github.com/opencv/opencv_zoo/tree/main/models/object_detection_yolox

Install

git and pip are required.

$ git clone https://github.com/takmin/YoloX_FastAPI.git
$ cd YoloX_FastAPI
$ pip install -r requirements.txt

Configuration

Edit configuration file "config.ini" for parameters for Yolo and log file.

• backend: What backend and target processors are:
  • 0: (default) OpenCV implementation + CPU
  • 1: CUDA + GPU (CUDA)
  • 2: CUDA + GPU (CUDA FP16)
  • 3: TIM-VX + NPU
  • 4: CANN + NPU
• onnxFile: File path to YOLOX trained model file (onnx format)
• confidence: Class confidence for YoloX
• nmsIoU: NMS IoU threshold for YoloX
• threshold: Object threshold for YoloX
• LogDirectory: Directory to store log files.
• ImageLogDirectory: Directory to store images. If comment out, no image is stored.

Start API

You launch api service as below:

$  uvicorn detect_api:app

To get object detection results using API, HTTP POST json like below:

{
  image: <base64 encoded image>,
  visualize: <"true" to get visualized result>
{

Then you will get the following response

{
  detected: [
    {
      "id":0,
      "label":"person",
      "score":0.8404238820075989,
      "position":[711,73,1008,879]
    },
    {
      "id":2,
      "label":"car",
      "score":0.6321859955787659,
      "position":[65,28,846,880]
    }
  ],
  visualize: <base64 encoded image>
}

"position" indicates coordinates of bounding box left-up and right-bottom corners: x0, y0, x1, y1.

You can test the API by accessing the following URL in your browser.

http://localhost:8000