# 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`
基于 YoloV5 实时目标检测的高效 Apex Legends Aimbot
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ProjectAim-ApexLegends-main.zip （86个子文件） 
ProjectAim-ApexLegends-main .vscode 
settings.json 69B weights apexSt1.pt 13.74MB main.py 8KB
testvideo.mp4 22.23MB utils __init__.py 0B google_utils.py 6KB loss.py 9KB flask_rest_api example_request.py 299B restapi.py 1KB README.md 2KB metrics.py 9KB aws __init__.py 0B userdata.sh 1KB mime.sh 780B resume.py 1KB autoanchor.py 7KB general.py 29KB wandb_logging __init__.py 0B log_dataset.py 800B __pycache__ wandb_utils.cpython-36.pyc 12KB __init__.cpython-36.pyc 134B wandb_utils.cpython-38.pyc 12KB __init__.cpython-38.pyc 142B wandb_utils.py 16KB activations.py 4KB google_app_engine Dockerfile 821B app.yaml 173B
additional_requirements.txt 105B plots.py 18KB datasets.py 44KB __pycache__ metrics.cpython-38.pyc 7KB torch_utils.cpython-38.pyc 11KB datasets.cpython-38.pyc 34KB plots.cpython-36.pyc 16KB general.cpython-36.pyc 23KB datasets.cpython-36.pyc 34KB torch_utils.cpython-36.pyc 11KB google_utils.cpython-38.pyc 4KB loss.cpython-38.pyc 6KB google_utils.cpython-36.pyc 4KB general.cpython-38.pyc 23KB __init__.cpython-36.pyc 120B loss.cpython-36.pyc 6KB autoanchor.cpython-38.pyc 6KB autoanchor.cpython-36.pyc 6KB metrics.cpython-36.pyc 7KB __init__.cpython-38.pyc 128B plots.cpython-38.pyc 16KB torch_utils.py 12KB
game.jpg 3.93MB tmpimg haha.jpg 7.91MB requirements.txt 677B models hub yolov5x6.yaml 2KB anchors.yaml 3KB yolov5-p2.yaml 2KB yolov5-panet.yaml 1KB yolov5s6.yaml 2KB yolov3.yaml 1KB yolov5-p6.yaml 2KB yolov5-p7.yaml 2KB yolov5l6.yaml 2KB yolov5m6.yaml 2KB yolov3-spp.yaml 1KB yolov3-tiny.yaml 1KB yolov5-fpn.yaml 1KB yolov5s-transformer.yaml 1KB __init__.py 0B export.py 7KB yolov5m.yaml 1KB yolov5s.yaml 1KB yolov5l.yaml 1KB common.py 16KB experimental.py 5KB __pycache__ experimental.cpython-38.pyc 6KB experimental.cpython-36.pyc 6KB yolo.cpython-36.pyc 11KB common.cpython-36.pyc 19KB __init__.cpython-36.pyc 121B common.cpython-38.pyc 18KB yolo.cpython-38.pyc 11KB __init__.cpython-38.pyc 129B yolov5x.yaml 1KB yolo.py 13KB detect.py 9KB __pycache__ detect.cpython-38.pyc 6KB README.md 1KB资源评论
