https://github.com/agentmaker/agentclpr

Chinese license plate recognition
https://github.com/agentmaker/agentclpr

Last synced: 10 months ago
JSON representation

Chinese license plate recognition

• Host: GitHub
• URL: https://github.com/agentmaker/agentclpr
• Owner: AgentMaker
• License: apache-2.0
• Created: 2021-09-28T08:38:47.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2021-11-13T05:14:06.000Z (over 4 years ago)
• Last Synced: 2024-12-01T04:41:38.987Z (over 1 year ago)
• Language: Python
• Homepage:
• Size: 9 MB
• Stars: 28
• Watchers: 1
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# AgentCLPR

![GitHub forks](https://img.shields.io/github/forks/AgentMaker/AgentCLPR)

![GitHub Repo stars](https://img.shields.io/github/stars/AgentMaker/AgentCLPR)

![Pypi Downloads](https://pepy.tech/badge/agentclpr)

![GitHub release (latest by date including pre-releases)](https://img.shields.io/github/v/release/AgentMaker/AgentCLPR?include_prereleases)

![GitHub](https://img.shields.io/github/license/AgentMaker/AgentCLPR) 

## 简介

* 一个基于 [ONNXRuntime](https://github.com/microsoft/onnxruntime)、[AgentOCR](https://github.com/AgentMaker/AgentOCR) 和 [License-Plate-Detector](https://github.com/zeusees/License-Plate-Detector) 项目开发的中国车牌检测识别系统。

## 车牌识别效果

* 支持多种车牌的检测和识别（其中单层车牌识别效果较好）：

    * 单层车牌：

        ![](https://img-blog.csdnimg.cn/e5801d1a4d394d8ba7b50bed4b0a6b55.png)

            [[[[373, 282], [69, 284], [73, 188], [377, 185]], ['苏E05EV8', 0.9923506379127502]]]

            [[[[393, 278], [318, 279], [318, 257], [393, 255]], ['VA30093', 0.7386096119880676]]]

            [[[[[487, 366], [359, 372], [361, 331], [488, 324]], ['皖K66666', 0.9409016370773315]]]]

            [[[[304, 500], [198, 498], [199, 467], [305, 468]], ['鲁QF02599', 0.995299220085144]]]

            [[[[309, 219], [162, 223], [160, 181], [306, 177]], ['使198476', 0.9938704371452332]]]

            [[[[957, 918], [772, 920], [771, 862], [956, 860]], ['陕A06725D', 0.9791222810745239]]]

    * 双层车牌：

    

        ![](https://ai-studio-static-online.cdn.bcebos.com/4e34243377b8461e90ae4a5b5b63577d2d1434b2811245a1a6ea86a76752f0e6)

            [[[[399, 298], [256, 301], [256, 232], [400, 230]], ['浙G66666', 0.8870148431461757]]]

            [[[[398, 308], [228, 305], [227, 227], [398, 230]], ['陕A00087', 0.9578166644088313]]]

            [[[[352, 234], [190, 244], [190, 171], [352, 161]], ['宁A66666', 0.9958433652812175]]]

        

## 快速使用

* 快速安装

    ```bash

    # 安装 AgentCLPR

    $ pip install agentclpr

    # 根据设备平台安装合适版本的 ONNXRuntime

    # CPU 版本（推荐非 win10 系统，无 CUDA 支持的设备安装）

    $ pip install onnxruntime

    # GPU 版本（推荐有 CUDA 支持的设备安装）

    $ pip install onnxruntime-gpu

    # DirectML 版本（推荐 win10 系统的设备安装，可实现通用的显卡加速）

    $ pip install onnxruntime-directml

    # 更多版本的安装详情请参考 ONNXRuntime 官网

    ```

* 简单调用：

    ```python

    # 导入 CLPSystem 模块

    from agentclpr import CLPSystem

    # 初始化车牌识别模型

    clp = CLPSystem()

    # 使用模型对图像进行车牌识别

    results = clp('test.jpg')

    ```

* 服务器部署：

    * 启动 AgentCLPR Server 服务

        ```shell

        $ agentclpr server

        ```

    * Python 调用

        ```python

        import cv2

        import json

        import base64

        import requests

        # 图片 Base64 编码

        def cv2_to_base64(image):

            data = cv2.imencode('.jpg', image)[1]

            image_base64 = base64.b64encode(data.tobytes()).decode('UTF-8')

            return image_base64

        # 读取图片

        image = cv2.imread('test.jpg')

        image_base64 = cv2_to_base64(image)

        # 构建请求数据

        data = {

            'image': image_base64

        }

        # 发送请求

        url = "http://127.0.0.1:5000/ocr"

        r = requests.post(url=url, data=json.dumps(data))

        # 打印预测结果

        print(r.json())

        ```

## Contact us

Email : [agentmaker@163.com]()


QQ Group : 1005109853