开源软件名称：

mobile-lpr

开源软件地址：

https://gitee.com/damone/mobile-lpr

开源软件介绍：

mobile-lpr

Mobile-LPR 是一个面向移动端的准商业级车牌识别库，以NCNN作为推理后端，使用DNN作为算法核心，支持多种车牌检测算法，支持车牌识别和车牌颜色识别。

Android Demo 见 example/android-example

特点

超轻量，核心库只依赖NCNN，并且对模型量化进行支持
多检测，支持SSD,MTCNN,LFFD等目标检测算法
精度高，LFFD目标检测在CCPD检测AP达到98.9，车牌识别达到99.95%，综合识别率超过99%
易使用，只需要10行代码即可完成车牌识别
易扩展，可快速扩展各类检测算法

算法流程

构建及安装

下载源码

git clone https://github.com/xiangweizeng/mobile-lpr.git

准备环境

安装opencv4.0及以上， freetype库
安装cmake3.0以上版本，支持c++11的c++编译器，如gcc-6.3

编译安装

mkdir buildcd buildcmake ..make install

使用及样例

1.使用MTCNN检测

代码样例

void test_mtcnn_plate(){    pr::fix_mtcnn_detector("../../models/float", pr::mtcnn_float_detector);    pr::PlateDetector detector = pr::IPlateDetector::create_plate_detector(pr::mtcnn_float_detector);    pr::fix_lpr_recognizer("../../models/float", pr::float_lpr_recognizer);    pr::LPRRecognizer lpr =  pr::float_lpr_recognizer.create_recognizer();    Mat img = imread("../../image/plate.png");    ncnn::Mat sample = ncnn::Mat::from_pixels(img.data, ncnn::Mat::PIXEL_BGR, img.cols, img.rows);    std::vector<pr::PlateInfo> objects;    detector->plate_detect(sample, objects);    lpr->decode_plate_infos(objects);    for (auto pi : objects)    {        cout << "plate_no: " << pi.plate_color << pi.plate_no << " box:" << pi.bbox.xmin << ","        << pi.bbox.ymin << "," << pi.bbox.xmax << "," << pi.bbox.ymax << "," << pi.bbox.score << endl;    }}

效果示例:

MTCNN车牌识别

2.使用LFFD检测

代码样例

void test_lffd_plate(){    pr::fix_lffd_detector("../../models/float", pr::lffd_float_detector);    pr::PlateDetector detector = pr::IPlateDetector::create_plate_detector(pr::lffd_float_detector);    pr::fix_lpr_recognizer("../../models/float", pr::float_lpr_recognizer);    pr::LPRRecognizer lpr =  pr::float_lpr_recognizer.create_recognizer();    Mat img = imread("../../image/plate.png");    ncnn::Mat sample = ncnn::Mat::from_pixels(img.data, ncnn::Mat::PIXEL_BGR, img.cols, img.rows);    std::vector<pr::PlateInfo> objects;    detector->plate_detect(sample, objects);    lpr->decode_plate_infos(objects);    for (auto pi : objects)    {        cout << "plate_no: " << pi.plate_color << pi.plate_no << " box:" << pi.bbox.xmin << ","             << pi.bbox.ymin << "," << pi.bbox.xmax << "," << pi.bbox.ymax << "," << pi.bbox.score << endl;    }}

效果示例:

LFFD车牌识别

3.使用SSD检测

代码样例

void test_ssd_plate(){    pr::fix_ssd_detector("../../models/float", pr::ssd_float_detector);    pr::PlateDetector detector = pr::IPlateDetector::create_plate_detector(pr::ssd_float_detector);    pr::fix_lpr_recognizer("../../models/float", pr::float_lpr_recognizer);    pr::LPRRecognizer lpr =  pr::float_lpr_recognizer.create_recognizer();    Mat img = imread("../../image/manys.jpeg");    ncnn::Mat sample = ncnn::Mat::from_pixels(img.data, ncnn::Mat::PIXEL_BGR, img.cols, img.rows);    std::vector<pr::PlateInfo> objects;    detector->plate_detect(sample, objects);    lpr->decode_plate_infos(objects);    for (auto pi : objects)    {        cout << "plate_no: " << pi.plate_color << pi.plate_no << " box:" << pi.bbox.xmin << ","             << pi.bbox.ymin << "," << pi.bbox.xmax << "," << pi.bbox.ymax << "," << pi.bbox.score << endl;    }}

效果示例:

SSD车牌识别

4.使用量化模型

代码样例

void test_quantize_mtcnn_plate(){    pr::fix_mtcnn_detector("../../models/quantize", pr::mtcnn_int8_detector);    pr::PlateDetector detector = pr::IPlateDetector::create_plate_detector(pr::mtcnn_int8_detector);    pr::fix_lpr_recognizer("../../models/quantize", pr::int8_lpr_recognizer);    pr::LPRRecognizer lpr =  pr::int8_lpr_recognizer.create_recognizer();    Mat img = imread("../../image/plate.png");    ncnn::Mat sample = ncnn::Mat::from_pixels(img.data, ncnn::Mat::PIXEL_BGR, img.cols, img.rows);    std::vector<pr::PlateInfo> objects;    detector->plate_detect(sample, objects);    lpr->decode_plate_infos(objects);    for (auto pi : objects)    {        cout << "plate_no: " << pi.plate_color << pi.plate_no << " box:" << pi.bbox.xmin << ","             << pi.bbox.ymin << "," << pi.bbox.xmax << "," << pi.bbox.ymax << "," << pi.bbox.score << endl;    }}