# 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:
```shell
[{'class': 0,
'confidence': 0.8197850585,
'name': 'person',
'xmax': 1159.1403808594,
'xmin': 750.912902832,
'ymax': 711.2583007812,
'ymin': 44.0350036621},
{'class': 0,
'confidence': 0.5667674541,
'name': 'person',
'xmax': 1065.5523681641,
'xmin': 116.0448303223,
'ymax': 713.8904418945,
'ymin': 198.4603881836},
{'class': 27,
'confidence': 0.5661227107,
'name': 'tie',
'xmax': 516.7975463867,
'xmin': 416.6880187988,
'ymax': 717.0524902344,
'ymin': 429.2020568848}]
```
An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given in `example_request.py`
基于YOLOv7-plate和CRNN的车牌号检测识别项目用PyQt5构建了UI界面源码+模型+详细说明.zip （125个子文件） 
detect_rec_plate.cpp 31KB plate_rec.cpp 8KB onnx2trt.cpp 7KB utils.cpp 5KB preprocess.cu 4KB logging.h 17KB preprocess.h 373B utils.hpp 3KB
single_blue.jpg 1.81MB single_blue.jpg 1.81MB demo.jpg 1.23MB xue.jpg 999KB single_green.jpg 903KB hongkang1.jpg 571KB single_blue.jpg 475KB police.jpg 382KB 000000390555_AE.jpg 225KB xue.jpg 210KB 000000390555_YP.jpg 179KB police.jpg 116KB shi_lin_guan.jpg 105KB single_yellow.jpg 105KB single_yellow.jpg 85KB shi_lin_guan.jpg 47KB 14.jpg 34KB nongyong_double.jpg 34KB double_yellow.jpg 29KB README.md 1KB README.md 1KB README.md 1KB README.md 444B
tmp3514.png 2.06MB minghang.png 711KB minghang.png 692KB 1.png 26KB AP50_GMACS_val.png 25KB yolov7-lite-s.pt 2.33MB best.pt 2.32MB yolov7-lite-t.pt 727KB plate_rec_color.pth 733KB plate_rec.pth 674KB datasets.py 50KB train.py 35KB general.py 31KB yolo.py 30KB common.py 30KB common_ori.py 28KB test.py 22KB plots.py 22KB wandb_utils.py 16KB loss.py 13KB torch_utils.py 12KB ui_yolo.py 11KB detect.py 11KB yolov7_plate_onnx_infer.py 11KB metrics.py 9KB plateNet.py 8KB yolov7_detect_rec.py 8KB export.py 7KB autoanchor.py 7KB test_widerface.py 7KB hubconf.py 6KB experimental.py 5KB google_utils.py 5KB get_small_pic.py 4KB plate_rec.py 4KB activations.py 4KB ncnn_export.py 3KB resume.py 1KB restapi.py 1KB test.py 880B log_dataset.py 824B cv_puttext.py 818B read_image.py 580B double_plate_split_merge.py 476B example_request.py 312B __init__.py 6B __init__.py 6B __init__.py 6B __init__.py 6B datasets.cpython-37.pyc 36KB common.cpython-37.pyc 34KB general.cpython-37.pyc 24KB yolo.cpython-37.pyc 21KB plots.cpython-37.pyc 18KB torch_utils.cpython-37.pyc 11KB wandb_utils.cpython-37.pyc 11KB loss.cpython-37.pyc 8KB metrics.cpython-37.pyc 7KB autoanchor.cpython-37.pyc 6KB plateNet.cpython-37.pyc 6KB yolov7_detect_rec.cpython-37.pyc 6KB experimental.cpython-37.pyc 6KB plate_rec.cpython-37.pyc 3KB google_utils.cpython-37.pyc 3KB cv_puttext.cpython-37.pyc 925B double_plate_split_merge.cpython-37.pyc 699B __init__.cpython-37.pyc 163B __init__.cpython-37.pyc 150B __init__.cpython-37.pyc 149B共 125 条
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