supermachine--tomato-passio.../20240627Actual_deployed/utils/flask_rest_api
2024-07-03 21:50:33 +08:00
..
example_request.py feat:新增20240627Actual_deployed文件夹,为当前实际部署版本;在薛总的操刀之下新增yolo用于番茄破皮检测;新增resnet18用于百香果褶皱判断 2024-07-03 21:50:33 +08:00
README.md feat:新增20240627Actual_deployed文件夹,为当前实际部署版本;在薛总的操刀之下新增yolo用于番茄破皮检测;新增resnet18用于百香果褶皱判断 2024-07-03 21:50:33 +08:00
restapi.py feat:新增20240627Actual_deployed文件夹,为当前实际部署版本;在薛总的操刀之下新增yolo用于番茄破皮检测;新增resnet18用于百香果褶皱判断 2024-07-03 21:50:33 +08:00

Flask REST API

REST APIs are commonly used to expose Machine Learning (ML) models to other services. This folder contains an example REST API created using Flask to expose the YOLOv5s model from PyTorch Hub.

Requirements

Flask is required. Install with:

$ pip install Flask

Run

After Flask installation run:

$ python3 restapi.py --port 5000

Then use curl to perform a request:

$ curl -X POST -F image=@zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5s'

The model inference results are returned as a JSON response:

[
  {
    "class": 0,
    "confidence": 0.8900438547,
    "height": 0.9318675399,
    "name": "person",
    "width": 0.3264600933,
    "xcenter": 0.7438579798,
    "ycenter": 0.5207948685
  },
  {
    "class": 0,
    "confidence": 0.8440024257,
    "height": 0.7155083418,
    "name": "person",
    "width": 0.6546785235,
    "xcenter": 0.427829951,
    "ycenter": 0.6334488392
  },
  {
    "class": 27,
    "confidence": 0.3771208823,
    "height": 0.3902671337,
    "name": "tie",
    "width": 0.0696444362,
    "xcenter": 0.3675483763,
    "ycenter": 0.7991207838
  },
  {
    "class": 27,
    "confidence": 0.3527112305,
    "height": 0.1540903747,
    "name": "tie",
    "width": 0.0336618312,
    "xcenter": 0.7814827561,
    "ycenter": 0.5065554976
  }
]

An example python script to perform inference using requests is given in example_request.py