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242bb9a71b
fix:修复在20240627test4中的classifier.py的analyze_tomato函数中white_defect的函数忘记传递两个阈值量的错误；修复analyze_tomato函数中的叶片实例分割存在的问题，解决由于变量污染引起的分割错误；
1.7 KiB
1.7 KiB
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