Chinese license plate recognition

Related tags

Deep LearningAgentCLPR
Overview

AgentCLPR

GitHub forks GitHub Repo stars Pypi Downloads GitHub release (latest by date including pre-releases) GitHub

简介

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：

        [[[[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：

        [[[[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

    # 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：

    # 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

      $ agentclpr server

    • Python 调用

      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 : [email protected]
QQ Group : 1005109853

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