lpr（license plate recognition）

构建及安装

源码准备

包括deps，include，models，src文件夹

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 build
cd build
cmake ..
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;
    }
}

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;
    }
}

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;
    }
}

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;
    }
}

LPR （License Plate Recognition）

LPR is a quasi-commercial grade license plate recognition library for mobile, with NCNN as the inference backend, using DNN as the core of the algorithm, supporting a variety of license plate detection algorithms, supporting license plate recognition and license plate color recognition.

Features

Ultra-lightweight, the core library relies only on NCNN and supports model quantization
Multi-detection, support SSD, MTCNN, LFFD and other target detection algorithms
High accuracy, LFFD target detection reaches 98.9 in CCPD detection AP, license plate recognition reaches 99.95%, comprehensive recognition rate over 99%
Easy to use, only need 10 lines of code to complete license plate recognition
Easy to expand, can quickly expand all kinds of detection algorithms

Algorithms Flow

Build and Installation

Source code preparation

incloud deps，include，models，src Folders

Environment Preparation

opencv4.0 and above, freetype
cmake3.0 and above, c++ compiler that supports c++11, such as gcc-6.3

Compilation and installation

mkdir build
cd build
cmake ..
make install

Usage and samples

1.Detection with MTCNN

Sample Code

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;
    }
}

Result Example:

2.Detection with MTCNN

Sample Code

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;
    }
}

Results Example:

3.Detection with SSD

Sample Code

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;
    }
}

Results Example:

4.Detection with quantitative models

Sample Code

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;
    }
}

Results Example:

Follow-Up works

Add better algorithm support
Optimize the model, support more license plate types, currently support common license plate recognition
Optimize the model, higher accuracy
Performance evaluation

Reference

light-LPR
NCNN
LFFD
CCPD Chinese license plate dataset, reaching 2 million samples
HyperLPR

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lpr（license plate recognition）

构建及安装

使用及样例

LPR （License Plate Recognition）

Features

Algorithms Flow

Build and Installation

Usage and samples

Follow-Up works

Reference

About

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lpr（license plate recognition）

构建及安装

使用及样例

LPR （License Plate Recognition）

Features

Algorithms Flow

Build and Installation

Usage and samples

Follow-Up works

Reference

About

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