基于python+YOLOv7-plate和CRNN的车牌检测识别系统+PyQt构建UI界面+源码+开发文档+训练素材+视频演示

# 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`