Metadata-Version: 2.1
Name: agentclpr
Version: 1.1.0
Summary: An easy-to-use Chinese license plate recognition system.
Home-page: https://github.com/AgentMaker/AgentCLPR
Author: jm12138
License: Apache License 2.0
Description: # AgentCLPR
        
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        ## 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())
                ```
        
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