一个面向移动端的准商业级车牌识别库

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。

特点

算法流程

构建及安装

1. 下载源码

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

2. 准备环境

3. 编译安装

mkdir buildcd buildcmake ..make install

使用及样例

1.使用MTCNN检测

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;    }}

2.使用LFFD检测

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;    }}

3.使用SSD检测

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;    }}

4.使用量化模型

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;    }}

后续工作