基于OpenPose卷积神经网络的人体姿态识别及预警系统源码+部署教程文档+全部数据+训练好的模型（高分项目）.zip

# Flask REST API [REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s 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](https://pytorch.org/hub/ultralytics_yolov5/). ## Requirements [Flask](https://palletsprojects.com/p/flask/) is required. Install with: ```shell $ pip install Flask ``` ## Run After Flask installation run: ```shell $ python3 restapi.py --port 5000 ``` Then use [curl](https://curl.se/) to perform a request: ```shell $ curl -X POST -F image=@zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5s'` ``` The model inference results are returned as a JSON response: ```json [ { "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](https://docs.python-requests.org/en/master/) is given in `example_request.py`