supermachine--tomato-passio.../utils/flask_rest_api
2024-07-22 11:53:49 +08:00
..
example_request.py 新增actual-deployment分支,为现场实际部署版本,当前部署版本为20240713版,存在几个问题未修改(lab色彩空间函数缺少阈值参数)、番茄叶片分割模型存在输入变量污染。以上问题已于家中发现并修改,但现场设备并未修改,胜哥将于20240723赶赴泉州,顺便修改。 2024-07-22 11:53:49 +08:00
README.md 新增actual-deployment分支,为现场实际部署版本,当前部署版本为20240713版,存在几个问题未修改(lab色彩空间函数缺少阈值参数)、番茄叶片分割模型存在输入变量污染。以上问题已于家中发现并修改,但现场设备并未修改,胜哥将于20240723赶赴泉州,顺便修改。 2024-07-22 11:53:49 +08:00
restapi.py 新增actual-deployment分支,为现场实际部署版本,当前部署版本为20240713版,存在几个问题未修改(lab色彩空间函数缺少阈值参数)、番茄叶片分割模型存在输入变量污染。以上问题已于家中发现并修改,但现场设备并未修改,胜哥将于20240723赶赴泉州,顺便修改。 2024-07-22 11:53:49 +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