├── Image
    ├── v0.1_QT界面.jpg
    └── v0.2_QT界面.jpg
├── README.md
├── LaneLine-and-Sign-Detection
    ├── main.cpp
    ├── mythread.h
    ├── lanedetector.h
    ├── mainwindow.h
    ├── LaneLine-and-Sign-Detection.pro
    ├── mythread.cpp
    ├── mainwindow.ui
    ├── worker.cpp
    ├── mainwindow.cpp
    ├── lanedetector.cpp
    └── LaneLine-and-Sign-Detection.pro.user
├── CourseDesign
    ├── main.cpp
    ├── lanedetector.h
    ├── CourseDesign.pro
    ├── lanedetector.cpp
    └── CourseDesign.pro.user
└── LICENSE


/Image/v0.1_QT界面.jpg:
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https://raw.githubusercontent.com/TomLao/LaneLine-and-Sign-Detection/HEAD/Image/v0.1_QT界面.jpg


--------------------------------------------------------------------------------
/Image/v0.2_QT界面.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/TomLao/LaneLine-and-Sign-Detection/HEAD/Image/v0.2_QT界面.jpg


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/README.md:
--------------------------------------------------------------------------------
 1 | # Laneline-and-Sign-Detection
 2 | 车道线和路牌的简单识别，使用qt界面
 3 | 
 4 | 
 5 | **v0.2_QT界面效果图**
 6 | ![v0.1_QT界面.jpg](Image/v0.2_QT界面.jpg)
 7 | 
 8 | 
 9 | **V0.1_QT界面效果图**
10 | ![v0.1_QT界面.jpg](Image/v0.1_QT界面.jpg)
11 | 


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/LaneLine-and-Sign-Detection/main.cpp:
--------------------------------------------------------------------------------
 1 | #include "mainwindow.h"
 2 | #include <QApplication>
 3 | 
 4 | int main(int argc, char *argv[])
 5 | {
 6 |     QApplication a(argc, argv);
 7 |     MainWindow w;
 8 |     w.show();
 9 | 
10 |     return a.exec();
11 | }
12 | 


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/CourseDesign/main.cpp:
--------------------------------------------------------------------------------
 1 | #include <string>
 2 | #include <vector>
 3 | #include <opencv2/opencv.hpp>
 4 | #include "lanedetector.h"
 5 | #include <iostream>
 6 | using namespace std;
 7 | using namespace cv;
 8 | 
 9 | 
10 | 
11 | int main() {
12 | 
13 |        LaneDetector lanedetector;  // 创建类对象
14 |        lanedetector.doDetection("F:\\QT_code\\LiuWenguo_homework\\CourseDesign\\loadVideo1.mp4");
15 | }
16 | 
17 | 


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/LaneLine-and-Sign-Detection/mythread.h:
--------------------------------------------------------------------------------
 1 | #ifndef MYTHREAD_H
 2 | #define MYTHREAD_H
 3 | #include <QThread>
 4 | #include <QMutex>
 5 | #include <QMainWindow>
 6 | #include <opencv2/opencv.hpp>
 7 | 
 8 | using namespace cv;
 9 | 
10 | namespace Ui {
11 |     class MainWindow;
12 | }
13 | 
14 | class ThreadFromQThread : public QThread
15 | {
16 |     Q_OBJECT
17 | 
18 | signals:
19 |     void message(const QString& info);
20 |     void progress(int present);
21 | 
22 | public slots:
23 |     void stopImmediately();
24 |     void ThreadFromQThread::setVideoPath(std::string videoPath, Ui::MainWindow* &ui);
25 | 
26 | public:
27 |     ThreadFromQThread(QObject* par);
28 |     ~ThreadFromQThread();
29 |     void setSomething();
30 |     void getSomething();
31 |     void setRunCount(int count);
32 |     void run();
33 |     void doSomething();
34 | 
35 |     QMutex myPause;
36 | 
37 | private:
38 |     int m_runCount;
39 |     QMutex myLock;
40 |     bool myIsCanRun;
41 |     std::string videoPath;
42 |     Ui::MainWindow* &ui;
43 | };
44 | 
45 | 
46 | #endif // MYTHREAD_H
47 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2018 HezeLao
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/CourseDesign/lanedetector.h:
--------------------------------------------------------------------------------
 1 | #ifndef LANEDETECTOR_H
 2 | #define LANEDETECTOR_H
 3 | #include "iostream";
 4 | using namespace std;
 5 | using namespace cv;
 6 | class LaneDetector
 7 | {
 8 | private:
 9 |     double img_size;
10 |     double img_center;
11 |     bool left_flag = false;  // 告诉我们是否检测到车道的左边界
12 |     bool right_flag = false;  // 告诉我们是否检测到车道的右边界
13 |     Point right_b;  // 车道边界线方程的两个成员:
14 |     double right_m;  // y = m*x + b
15 |     Point left_b;  //
16 |     double left_m;  //
17 | 
18 | public:
19 |     void doDetection(String videoPath);             //开始检测
20 |     Mat deNoise(Mat inputImage);  // 对输入图像应用高斯模糊
21 |     Mat edgeDetector(Mat img_noise);  // 过滤图像，只获取边缘
22 |     Mat mask(Mat img_edges);  // 掩盖边缘图像，只关心ROI
23 |     void streetSign(Mat img_original);//路牌检测
24 |     vector<Vec4i> houghLines(Mat img_mask);  // 检测蒙面边缘图像中的霍夫线
25 |     vector<vector<Vec4i> > lineSeparation(vector<Vec4i> lines, Mat img_edges);  // Sprt通过直线的斜率检测出左右直线
26 |     vector<Point> regression(vector<vector<Vec4i> > left_right_lines, Mat inputImage);  //每条车道只能排一条线
27 |     string predictTurn();  //通过计算消失点的位置来确定车道是否在转弯
28 |     int plotLane(Mat inputImage, vector<Point> lane, string turn);  //绘制合成车道并在框架中进行转弯预测。
29 | };
30 | #endif // LANEDETECTOR_H
31 | 


--------------------------------------------------------------------------------
/LaneLine-and-Sign-Detection/lanedetector.h:
--------------------------------------------------------------------------------
 1 | #ifndef LANEDETECTOR_H
 2 | #define LANEDETECTOR_H
 3 | #include <string>
 4 | #include <vector>
 5 | #include <opencv2/opencv.hpp>
 6 | #include "iostream";
 7 | using namespace std;
 8 | using namespace cv;
 9 | class LaneDetector
10 | {
11 | private:
12 |     double img_size;
13 |     double img_center;
14 |     bool left_flag = false;  // 告诉我们是否检测到车道的左边界
15 |     bool right_flag = false;  // 告诉我们是否检测到车道的右边界
16 |     Point right_b;  // 车道边界线方程的两个成员:
17 |     double right_m;  // y = m*x + b
18 |     Point left_b;  //
19 |     double left_m;  //
20 | 
21 | public:
22 |     void LaneDetector::myDetector(Mat frame); //qt检测算法运行模块
23 |     void doDetection(String videoPath);             //开始检测
24 |     Mat deNoise(Mat inputImage);  // 对输入图像应用高斯模糊
25 |     Mat edgeDetector(Mat img_noise);  // 过滤图像，只获取边缘
26 |     Mat mask(Mat img_edges);  // 掩盖边缘图像，只关心ROI
27 |     bool streetSign(Mat img_original);//路牌检测
28 |     vector<Vec4i> houghLines(Mat img_mask);  // 检测蒙面边缘图像中的霍夫线
29 |     vector<vector<Vec4i> > lineSeparation(vector<Vec4i> lines, Mat img_edges);  // Sprt通过直线的斜率检测出左右直线
30 |     vector<Point> regression(vector<vector<Vec4i> > left_right_lines, Mat inputImage);  //每条车道只能排一条线
31 |     string predictTurn();  //通过计算消失点的位置来确定车道是否在转弯
32 |     void plotLane(Mat inputImage, vector<Point> lane);  //绘制合成车道并在框架中进行转弯预测。
33 | };
34 | #endif // LANEDETECTOR_H
35 | 


--------------------------------------------------------------------------------
/CourseDesign/CourseDesign.pro:
--------------------------------------------------------------------------------
 1 | 
 2 | ##D:\\app\\opencv\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_alt2.xml
 3 | 
 4 | #TEMPLATE = app
 5 | #CONFIG += console c++11
 6 | #CONFIG -= app_bundle
 7 | #CONFIG -= qt
 8 | 
 9 | #SOURCES += \
10 |         main.cpp
11 | QT       += core gui
12 | 
13 | greaterThan(QT_MAJOR_VERSION, 4): QT += widgets
14 | 
15 | TARGET = CourseDesign
16 | TEMPLATE = app
17 | 
18 | # The following define makes your compiler emit warnings if you use
19 | # any feature of Qt which has been marked as deprecated (the exact warnings
20 | # depend on your compiler). Please consult the documentation of the
21 | # deprecated API in order to know how to port your code away from it.
22 | DEFINES += QT_DEPRECATED_WARNINGS
23 | 
24 | # You can also make your code fail to compile if you use deprecated APIs.
25 | # In order to do so, uncomment the following line.
26 | # You can also select to disable deprecated APIs only up to a certain version of Qt.
27 | #DEFINES += QT_DISABLE_DEPRECATED_BEFORE=0x060000    # disables all the APIs deprecated before Qt 6.0.0
28 | 
29 | SOURCES += \
30 |     main.cpp \
31 |     lanedetector.cpp
32 | 
33 | INCLUDEPATH += D:\app\opencv\opencv\build\include \
34 |             D:\app\opencv\opencv\build\include\opencv \
35 |             D:\app\opencv\opencv\build\include\opencv2
36 | 
37 | LIBS += D:\app\opencv\opencv\build\x64\vc15\lib\opencv_world341d.lib
38 | 
39 | HEADERS += \
40 |     lanedetector.h
41 | 
42 | DISTFILES +=
43 | 
44 | 


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/LaneLine-and-Sign-Detection/mainwindow.h:
--------------------------------------------------------------------------------
 1 | #ifndef MAINWINDOW_H
 2 | #define MAINWINDOW_H
 3 | 
 4 | #include <QMainWindow>
 5 | #include <QDir>
 6 | #include <QFileDevice>
 7 | #include <QLabel>
 8 | #include <QPainter>
 9 | #include <QTimer>
10 | #include <QDebug>
11 | #include <QSlider>
12 | #include <QSpinBox>
13 | #include <QFileDialog>
14 | #include <QCloseEvent>
15 | #include <QMessageBox>
16 | #include <opencv2/opencv.hpp>
17 | #include <opencv2/core/core.hpp>
18 | #include <opencv2/highgui/highgui.hpp>
19 | 
20 | using namespace cv;
21 | using namespace std;
22 | 
23 | namespace Ui {
24 | class MainWindow;
25 | }
26 | 
27 | class MainWindow : public QMainWindow
28 | {
29 |     Q_OBJECT
30 | 
31 | public:
32 |     explicit MainWindow(QWidget *parent = nullptr);
33 |     ~MainWindow();
34 | 
35 | 
36 | protected:
37 |     int MyRunner(bool isSign);
38 |     Mat start(Mat frame);
39 | 
40 | 
41 | private slots:
42 |     void on_pushButton_open_clicked();
43 |     void on_pushButton_start_line_clicked();
44 |     void on_pushButton_start_sign_clicked();
45 |     void on_pushButton_pause_clicked();
46 |     void on_pushButton_stop_clicked();
47 | 
48 | private:
49 |     Ui::MainWindow *ui;
50 |     Mat srcFrame;
51 |     string videoPath;
52 |     bool isOpen;                //是否选择并打开页面
53 |     bool isStart;               //是否开始检测了
54 |     bool isPause;               //是否暂停检测
55 |     bool isStop;                //是否结束检测
56 |     bool isLaneLine;            //是否检测到车道线
57 |     bool isSign;                //是否检测到标志
58 |     QTimer *timer;
59 |     QImage qimg, qsign;
60 | 
61 |     void closeEvent(QCloseEvent *enent);
62 | };
63 | 
64 | 
65 | #endif // MAINWINDOW_H
66 | 


--------------------------------------------------------------------------------
/LaneLine-and-Sign-Detection/LaneLine-and-Sign-Detection.pro:
--------------------------------------------------------------------------------
 1 | #-------------------------------------------------
 2 | #
 3 | # Project created by QtCreator 2018-12-24T19:33:27
 4 | #
 5 | #-------------------------------------------------
 6 | 
 7 | QT       += core gui
 8 | 
 9 | greaterThan(QT_MAJOR_VERSION, 4): QT += widgets
10 | 
11 | TARGET = LaneLine-and-Sign-Detection
12 | TEMPLATE = app
13 | 
14 | # The following define makes your compiler emit warnings if you use
15 | # any feature of Qt which has been marked as deprecated (the exact warnings
16 | # depend on your compiler). Please consult the documentation of the
17 | # deprecated API in order to know how to port your code away from it.
18 | DEFINES += QT_DEPRECATED_WARNINGS
19 | 
20 | # You can also make your code fail to compile if you use deprecated APIs.
21 | # In order to do so, uncomment the following line.
22 | # You can also select to disable deprecated APIs only up to a certain version of Qt.
23 | #DEFINES += QT_DISABLE_DEPRECATED_BEFORE=0x060000    # disables all the APIs deprecated before Qt 6.0.0
24 | 
25 | CONFIG += c++11
26 | 
27 | SOURCES += \
28 |         main.cpp \
29 |         mainwindow.cpp \
30 | #        worker.cpp
31 |     lanedetector.cpp
32 | 
33 | HEADERS += \
34 |         mainwindow.h \
35 |     lanedetector.h
36 | 
37 | FORMS += \
38 |         mainwindow.ui
39 | 
40 | # Default rules for deployment.
41 | qnx: target.path = /tmp/$${TARGET}/bin
42 | else: unix:!android: target.path = /opt/$${TARGET}/bin
43 | !isEmpty(target.path): INSTALLS += target
44 | 
45 | INCLUDEPATH += D:\Software\opencv\build\include \
46 |             D:\Software\opencv\build\include\opencv \
47 |             D:\Software\opencv\build\include\opencv2
48 | 
49 | LIBS += D:\Software\opencv\build\x64\vc14\lib\opencv_world343d.lib
50 | 


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/LaneLine-and-Sign-Detection/mythread.cpp:
--------------------------------------------------------------------------------
  1 | #include "mythread.h"
  2 | #include <QDebug>
  3 | 
  4 | ThreadFromQThread::ThreadFromQThread(QObject* par) : QThread(par)
  5 | ,m_runCount(20)
  6 | {
  7 | 
  8 | }
  9 | 
 10 | ThreadFromQThread::~ThreadFromQThread()
 11 | {
 12 |     qDebug() << "ThreadFromQThread::~ThreadFromQThread()";
 13 | }
 14 | 
 15 | 
 16 | /**
 17 |  * @brief ThreadFromQThread::stopImmediately 终止线程
 18 |  * @author HezeLao
 19 |  * @date 2018-12-28 15:40
 20 |  */
 21 | void ThreadFromQThread::stopImmediately(){
 22 |     QMutexLocker locker(&myLock);
 23 |     myIsCanRun = false;
 24 | }
 25 | 
 26 | void ThreadFromQThread::setSomething()
 27 | {
 28 |     msleep(500);
 29 |     QString str = QString("%1->%2,thread id:%3").arg(__FUNCTION__).arg(__FILE__).arg((int)QThread::currentThreadId());
 30 |     emit message(str);
 31 | }
 32 | 
 33 | void ThreadFromQThread::getSomething()
 34 | {
 35 |     msleep(500);
 36 |     emit message(QString("%1->%2,thread id:%3").arg(__FUNCTION__).arg(__FILE__).arg((int)QThread::currentThreadId()));
 37 | }
 38 | 
 39 | void ThreadFromQThread::setRunCount(int count)
 40 | {
 41 |     m_runCount = count;
 42 |     emit message(QString("%1->%2,thread id:%3").arg(__FUNCTION__).arg(__FILE__).arg((int)QThread::currentThreadId()));
 43 | }
 44 | 
 45 | 
 46 | 
 47 | void ThreadFromQThread::setVideoPath(std::string videoPath, Ui::MainWindow* &ui){
 48 |     this->videoPath = videoPath;
 49 |     this->ui = ui;
 50 | }
 51 | void ThreadFromQThread::doSomething()
 52 | {
 53 |     msleep(500);
 54 |     emit message(QString("%1->%2,thread id:%3").arg(__FUNCTION__).arg(__FILE__).arg((int)QThread::currentThreadId()));
 55 | }
 56 | 
 57 | void ThreadFromQThread::run()
 58 | {
 59 |     int count = 0;
 60 |     myIsCanRun = true;  //标记可以运行
 61 | //    QString str = QString("%1->%2,thread id:%3").arg(__FILE__).arg(__FUNCTION__).arg((int)QThread::currentThreadId());
 62 | //    emit message(str);
 63 | //    while(1)
 64 | //    {
 65 | //        sleep(1);
 66 | //        ++count;
 67 | //        emit progress(((float)count / m_runCount) * 100);
 68 | //        emit message(QString("ThreadFromQThread::run times:%1").arg(count));
 69 | //        doSomething();
 70 | //        if(m_runCount == count)
 71 | //        {
 72 | //            break;
 73 | //        }
 74 | //        QMutexLocker locker(&myLock);
 75 | 
 76 |         //获取帧数，不然会播放速度不对
 77 |     VideoCapture videoCapture(this->videoPath);
 78 |     Mat srcFrame;
 79 |         int length = videoCapture.get(CAP_PROP_FRAME_COUNT)/videoCapture.get(CAP_PROP_FPS);
 80 |         double FPS = videoCapture.get(CAP_PROP_FPS);
 81 |         Mat res;
 82 |         while(1){
 83 |             waitKey(1000.0 / FPS);
 84 |             this->myPause.lock();
 85 | 
 86 |             videoCapture>>srcFrame;
 87 | 
 88 | //            if(srcFrame.empty() || isStop == true){
 89 | //                ui->label_video->clear();   //视频结束或按下结束按钮，清理播放窗口，退出
 90 | //                videoCapture.release();
 91 | //                break;
 92 | //            }
 93 | 
 94 |             cv::resize(srcFrame, srcFrame, Size(576, 324), 0, 0, INTER_LINEAR);
 95 |             //TODO:从这里出发执行检测算法
 96 |             cvtColor(srcFrame, srcFrame, CV_BGR2RGB);
 97 | 
 98 |             QImage qimg = QImage((const uchar*)(res.data),srcFrame.cols,srcFrame.rows, QImage::Format_RGB888); //简单地转换一下为Image对象，rgbSwapped是为了显示效果色彩好一些。
 99 |     //        QPixmap fitpixmap = QPixmap::fromImage(qimg).scaled(576, 324, Qt::IgnoreAspectRatio, Qt::SmoothTransformation);  // 饱满填充
100 |     //        ui->label_video->setPixmap(fitpixmap);
101 |             ui->label_video->setPixmap(QPixmap::fromImage(qimg));
102 |             ui->label_video->show();
103 |             this->myPause.unlock();
104 |         }
105 |         videoCapture.release();
106 |         if(myIsCanRun == false){    //在每次循环判断是否可以运行，如果不想就退出循环
107 |             return;
108 |         }
109 | //    }
110 | }
111 | 
112 | 
113 | 


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/LaneLine-and-Sign-Detection/mainwindow.ui:
--------------------------------------------------------------------------------
  1 | <?xml version="1.0" encoding="UTF-8"?>
  2 | <ui version="4.0">
  3 |  <class>MainWindow</class>
  4 |  <widget class="QMainWindow" name="MainWindow">
  5 |   <property name="geometry">
  6 |    <rect>
  7 |     <x>0</x>
  8 |     <y>0</y>
  9 |     <width>808</width>
 10 |     <height>605</height>
 11 |    </rect>
 12 |   </property>
 13 |   <property name="windowTitle">
 14 |    <string>MainWindow</string>
 15 |   </property>
 16 |   <widget class="QWidget" name="centralWidget">
 17 |    <widget class="QLabel" name="label_video">
 18 |     <property name="geometry">
 19 |      <rect>
 20 |       <x>190</x>
 21 |       <y>30</y>
 22 |       <width>576</width>
 23 |       <height>324</height>
 24 |      </rect>
 25 |     </property>
 26 |     <property name="text">
 27 |      <string>点击打开-选择视频</string>
 28 |     </property>
 29 |    </widget>
 30 |    <widget class="QPushButton" name="pushButton_start_line">
 31 |     <property name="geometry">
 32 |      <rect>
 33 |       <x>40</x>
 34 |       <y>90</y>
 35 |       <width>111</width>
 36 |       <height>41</height>
 37 |      </rect>
 38 |     </property>
 39 |     <property name="text">
 40 |      <string>车道线检测</string>
 41 |     </property>
 42 |    </widget>
 43 |    <widget class="QPushButton" name="pushButton_open">
 44 |     <property name="geometry">
 45 |      <rect>
 46 |       <x>40</x>
 47 |       <y>30</y>
 48 |       <width>111</width>
 49 |       <height>41</height>
 50 |      </rect>
 51 |     </property>
 52 |     <property name="text">
 53 |      <string>打开</string>
 54 |     </property>
 55 |    </widget>
 56 |    <widget class="QPushButton" name="pushButton_pause">
 57 |     <property name="geometry">
 58 |      <rect>
 59 |       <x>40</x>
 60 |       <y>210</y>
 61 |       <width>111</width>
 62 |       <height>41</height>
 63 |      </rect>
 64 |     </property>
 65 |     <property name="text">
 66 |      <string>暂停</string>
 67 |     </property>
 68 |    </widget>
 69 |    <widget class="QPushButton" name="pushButton_stop">
 70 |     <property name="geometry">
 71 |      <rect>
 72 |       <x>40</x>
 73 |       <y>270</y>
 74 |       <width>111</width>
 75 |       <height>41</height>
 76 |      </rect>
 77 |     </property>
 78 |     <property name="text">
 79 |      <string>结束</string>
 80 |     </property>
 81 |    </widget>
 82 |    <widget class="QLabel" name="label_sign_1">
 83 |     <property name="geometry">
 84 |      <rect>
 85 |       <x>120</x>
 86 |       <y>370</y>
 87 |       <width>211</width>
 88 |       <height>121</height>
 89 |      </rect>
 90 |     </property>
 91 |     <property name="text">
 92 |      <string>路牌1</string>
 93 |     </property>
 94 |    </widget>
 95 |    <widget class="QPushButton" name="pushButton_start_sign">
 96 |     <property name="geometry">
 97 |      <rect>
 98 |       <x>40</x>
 99 |       <y>150</y>
100 |       <width>111</width>
101 |       <height>41</height>
102 |      </rect>
103 |     </property>
104 |     <property name="text">
105 |      <string>路牌检测</string>
106 |     </property>
107 |    </widget>
108 |    <widget class="QLabel" name="label_sign_2">
109 |     <property name="geometry">
110 |      <rect>
111 |       <x>420</x>
112 |       <y>370</y>
113 |       <width>211</width>
114 |       <height>121</height>
115 |      </rect>
116 |     </property>
117 |     <property name="text">
118 |      <string>路牌2</string>
119 |     </property>
120 |    </widget>
121 |   </widget>
122 |   <widget class="QMenuBar" name="menuBar">
123 |    <property name="geometry">
124 |     <rect>
125 |      <x>0</x>
126 |      <y>0</y>
127 |      <width>808</width>
128 |      <height>25</height>
129 |     </rect>
130 |    </property>
131 |    <widget class="QMenu" name="menu">
132 |     <property name="title">
133 |      <string>车道线和路牌识别</string>
134 |     </property>
135 |    </widget>
136 |    <addaction name="menu"/>
137 |   </widget>
138 |   <widget class="QToolBar" name="mainToolBar">
139 |    <attribute name="toolBarArea">
140 |     <enum>TopToolBarArea</enum>
141 |    </attribute>
142 |    <attribute name="toolBarBreak">
143 |     <bool>false</bool>
144 |    </attribute>
145 |   </widget>
146 |   <widget class="QStatusBar" name="statusBar"/>
147 |  </widget>
148 |  <layoutdefault spacing="6" margin="11"/>
149 |  <resources/>
150 |  <connections/>
151 | </ui>
152 | 


--------------------------------------------------------------------------------
/LaneLine-and-Sign-Detection/worker.cpp:
--------------------------------------------------------------------------------
  1 | //#include "mainwindow.h"
  2 | 
  3 | #include <opencv2/opencv.hpp>
  4 | #include <opencv2/core/core.hpp>
  5 | #include <opencv2/highgui/highgui.hpp>
  6 | #include <vector>
  7 | 
  8 | using namespace cv;
  9 | using namespace std;
 10 | 
 11 | //转为hls颜色空间
 12 | Mat convertHls(Mat image) {
 13 |     Mat converted;
 14 |     cvtColor(image, converted, CV_RGB2HLS);
 15 |     return converted;
 16 | }
 17 | 
 18 | //根据hls颜色空间选取白黄车道线
 19 | Mat selectWhiteYellow(Mat image) {
 20 |     Mat converted = convertHls(image);
 21 |     Mat mask, maskWhite, maskYellow;
 22 |     //取白色和黄色mask
 23 |     inRange(converted, Scalar(0, 200, 0), Scalar(255, 255, 255), maskWhite);
 24 |     inRange(converted, Scalar(10, 0, 100), Scalar(40, 255, 255), maskYellow);
 25 |     //合并
 26 |     bitwise_or(maskWhite, maskYellow, mask);
 27 |     bitwise_and(image, image, mask);
 28 |     return image;
 29 | }
 30 | 
 31 | //灰度化
 32 | Mat convertGrayScale(Mat image) {
 33 |     Mat converted;
 34 |     cvtColor(image, converted, CV_RGB2GRAY);
 35 |     return converted;
 36 | }
 37 | 
 38 | //高斯模糊
 39 | Mat smoothing(Mat image, int kernelSize) {
 40 |     Mat smoothed;
 41 |     GaussianBlur(image, smoothed, Size(kernelSize, kernelSize), 0);
 42 |     return smoothed;
 43 | }
 44 | 
 45 | //canny边缘检测
 46 | Mat detectEdge(Mat image, int lowThreshold, int highThreshold) {
 47 |     Mat edge;
 48 |     Canny(image, edge, lowThreshold, highThreshold);
 49 |     return edge;
 50 | }
 51 | 
 52 | //选取车道线，区域过滤，去除噪声
 53 | Mat selectRegion(Mat image) {
 54 |     float rows = image.rows, cols = image.cols;
 55 |     Point rootPoints[1][4];
 56 |     rootPoints[0][0] = Point(cols * 0.1, rows * 0.95);
 57 |     rootPoints[0][1] = Point(cols * 0.4, rows * 0.6);
 58 |     rootPoints[0][2] = Point(cols * 0.6, rows * 0.6);
 59 |     rootPoints[0][3] = Point(cols * 0.9, rows * 0.95);
 60 |     const Point *ppt[1] = {rootPoints[0]};
 61 |     Mat mask = image.clone();
 62 |     int npt[] = {255};
 63 |     fillPoly(mask, ppt, npt, 1, Scalar(255, 255, 255));
 64 |     return mask;
 65 | }
 66 | 
 67 | //Hough线变换
 68 | vector<Vec4i> houghLine(Mat image) {
 69 |     vector<Vec4i> lines;
 70 |     HoughLinesP(image, lines, 1, CV_PI / 180, 20, 20, 300);
 71 |     return lines;
 72 | }
 73 | 
 74 | //在原图上画线
 75 | Mat drawLines(Mat image, vector<Vec4i> lines, Scalar color, int thickness, bool makeCopy) {
 76 |     Mat copy;
 77 |     if (makeCopy) {
 78 |         image.copyTo(copy);
 79 |     }
 80 |     for (size_t i = 0; i < lines.size(); i++) {
 81 |         line(copy, Point(lines[i][0], lines[i][1]), Point(lines[i][2], lines[i][3]), color, thickness);
 82 |     }
 83 |     return copy;
 84 | }
 85 | 
 86 | //主控函数
 87 | Mat start(Mat frame) {
 88 |     int gaussianKernelSize = 15;
 89 |     int lowThreshold = 50;
 90 |     int highThreshold = 150;
 91 | 
 92 | //    Mat converted = convertHls(frame);
 93 | //    imshow("converted", converted);
 94 | //    Mat whiteYellowLines = selectWhiteYellow(converted);
 95 | //    imshow("whiteYellowLines", whiteYellowLines);
 96 | //    Mat gray = convertGrayScale(whiteYellowLines);
 97 | //    imshow("gray", gray);
 98 | //    Mat smoothed = smoothing(gray, gaussianKernelSize);
 99 | //    imshow("smoothed", smoothed);
100 | //    Mat edge = detectEdge(smoothed, lowThreshold, highThreshold);
101 | //    imshow("edge", edge);
102 | //    Mat roi = selectRegion(edge);
103 | //    imshow("roi", roi);
104 | //    vector<Vec4i> lines = houghLine(roi);
105 | //    Mat lineImage = drawLines(roi, lines, Scalar(255, 0, 0), 3, true);
106 | //    imshow("lineImage", lineImage);
107 | //    return lineImage;
108 | 
109 |     Mat res;
110 |     cvtColor(frame, res, COLOR_BGR2HLS);
111 | 
112 |     Mat mask, maskWhite, maskYellow;
113 | 
114 |     //取白色和黄色mask
115 |     inRange(res, Scalar(0, 200, 0), Scalar(255, 255, 255), maskWhite);
116 |     inRange(res, Scalar(10, 0, 100), Scalar(40, 255, 255), maskYellow);
117 |     //合并
118 |     bitwise_or(maskWhite, maskYellow, mask);
119 |     bitwise_and(frame, frame, res, mask);
120 | 
121 |     cvtColor(res, res, CV_RGB2GRAY);
122 | 
123 |     GaussianBlur(res, res, Size(gaussianKernelSize, gaussianKernelSize), 0);
124 | 
125 |     Canny(res, res, lowThreshold, highThreshold);
126 | 
127 |     int rows = frame.rows, cols = frame.cols;
128 |     Point rootPoints[1][4];
129 |     cout<<rows<<"  "<<cols<<endl;
130 |     rootPoints[0][0] = Point(cols * 0.1, rows * 0.95);
131 |     rootPoints[0][1] = Point(cols * 0.4, rows * 0.6);
132 |     rootPoints[0][2] = Point(cols * 0.6, rows * 0.6);
133 |     rootPoints[0][3] = Point(cols * 0.9, rows * 0.95);
134 |     const Point *ppt[1] = {rootPoints[0]};
135 |     int npt[] = {50};
136 |     mask = Mat::zeros(res.size(), CV_8UC1); //CV_8UC1 新建单颜色通道
137 |     fillPoly(mask, ppt, npt, 1, 255, 8);
138 | 
139 |     bitwise_and(res, mask, res);
140 |     imshow("res", res);
141 | //    waitKey(0);
142 | //    return res;
143 |     vector<Vec4i> lines;
144 |     HoughLinesP(res, lines, 1, CV_PI / 180, 20, 20, 300);
145 |     Mat copy = frame.clone();
146 |     for( size_t i = 0; i < lines.size(); i++ )
147 |     {
148 |         Vec4i l = lines[i];
149 |         line(copy , Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0,0,255), 3, CV_AA);
150 |     }
151 | 
152 | 
153 | //    imshow("copy", copy);
154 | //    waitKey(0);
155 |     return copy;
156 | 
157 | }
158 | 
159 | int main() {
160 |     String path = "FinalProject/test_video/solidYellowLeft.mp4";
161 |     VideoCapture videoCapture(path);
162 |     double FPS = videoCapture.get(CAP_PROP_FPS);
163 |     Mat frame;
164 |     while (1) {
165 |         videoCapture >> frame;
166 |         if (frame.empty())
167 |             break;
168 |         Mat res = start(frame);
169 |         imshow("detect", res);
170 |         if (waitKey(1000.0 / FPS) == 27)
171 |             break;
172 |     }
173 | //    Mat image = imread(path);
174 | //    Mat res = start(image);
175 | //    imshow("detect", res);
176 | //    waitKey(0);
177 | }


--------------------------------------------------------------------------------
/LaneLine-and-Sign-Detection/mainwindow.cpp:
--------------------------------------------------------------------------------
  1 | #include "mainwindow.h"
  2 | #include "lanedetector.h"
  3 | #include "ui_mainwindow.h"
  4 | 
  5 | MainWindow::MainWindow(QWidget *parent) :
  6 |     QMainWindow(parent),
  7 |     ui(new Ui::MainWindow)
  8 | {
  9 |     ui->setupUi(this);
 10 | 
 11 |     QString title = QString::fromLocal8Bit("HezeLao-RuiweiXie-车道线识别和路牌识别");
 12 |     setWindowTitle(title);
 13 | 
 14 |     //初始化
 15 |     videoPath = "";
 16 |     isOpen = false;
 17 |     isStart = false;
 18 |     isPause = false;
 19 |     isStop = false;
 20 |     isLaneLine = true;
 21 |     isSign = true;
 22 | }
 23 | 
 24 | MainWindow::~MainWindow()
 25 | {
 26 |     delete ui;
 27 | }
 28 | 
 29 | 
 30 | /**
 31 |  * @brief MainWindow::MyRunner 检测控制函数
 32 |  * @author HezeLao
 33 |  * @date 2018-12-28 23:24
 34 |  */
 35 | int MainWindow::MyRunner(bool isSign){
 36 |     isStart = true;
 37 | 
 38 |     VideoCapture videoCapture(videoPath);
 39 |     if(!videoCapture.isOpened()){
 40 |         QMessageBox::critical(NULL, "critical", QString::fromLocal8Bit("视频打开失败"), QMessageBox::Yes);
 41 |         isStart = false;
 42 |         isOpen = false;
 43 |         return -1;
 44 |     }
 45 | 
 46 |     //获取帧数，不然会播放速度不对
 47 |     int length = videoCapture.get(CAP_PROP_FRAME_COUNT)/videoCapture.get(CAP_PROP_FPS);
 48 |     double FPS = videoCapture.get(CAP_PROP_FPS);
 49 |     Mat res;
 50 |     bool haveSign;
 51 |     int countShow=1;
 52 |     LaneDetector lanedetector;  // 创建类对象
 53 |     while(1){
 54 | //        waitKey(1000.0 / FPS);
 55 |         waitKey(5);
 56 |         if(isPause == true) //暂停检测
 57 |             continue;
 58 | 
 59 |         videoCapture>>srcFrame;
 60 | 
 61 |         if(srcFrame.empty() || isStop == true){
 62 |             ui->label_video->clear();   //视频结束或按下结束按钮，清理播放窗口，退出
 63 |             ui->label_sign_1->clear();
 64 |             ui->label_sign_2->clear();
 65 |             videoCapture.release();
 66 |             break;
 67 |         }
 68 | 
 69 |         cv::resize(srcFrame, srcFrame, Size(576, 324), 0, 0, INTER_LINEAR);
 70 |         cvtColor(srcFrame, srcFrame, COLOR_BGR2RGB);
 71 | 
 72 |         //TODO:从这里出发执行检测算法，分别调用路牌检测和车道线检测
 73 |         if(isSign){
 74 |             haveSign = lanedetector.streetSign(srcFrame);
 75 |             qimg = QImage((const uchar*)(srcFrame.data),srcFrame.cols,srcFrame.rows, QImage::Format_RGB888); //简单地转换一下为Image对象，rgbSwapped是为了显示效果色彩好一些。
 76 |             ui->label_video->setPixmap(QPixmap::fromImage(qimg));
 77 |             ui->label_video->show();
 78 |             if(haveSign){
 79 |                 qsign = QImage("cut.jpg");  //读取截取的路牌
 80 |                 if(countShow%50==0){
 81 |                     ui->label_sign_1->setPixmap(QPixmap::fromImage(qsign));
 82 |                     ui->label_sign_1->show();
 83 |                 }
 84 |                 else if(countShow%50==25){
 85 |                     ui->label_sign_2->setPixmap(QPixmap::fromImage(qsign));
 86 |                     ui->label_sign_2->show();
 87 |                 }
 88 |                 countShow++;
 89 |             }
 90 |         }else{
 91 |             lanedetector.myDetector(srcFrame);
 92 |             qimg = QImage((const uchar*)(srcFrame.data),srcFrame.cols,srcFrame.rows, QImage::Format_RGB888); //简单地转换一下为Image对象，rgbSwapped是为了显示效果色彩好一些。
 93 |             ui->label_video->setPixmap(QPixmap::fromImage(qimg));
 94 |             ui->label_video->show();
 95 |         }
 96 | 
 97 | 
 98 |     }
 99 |     videoCapture.release();
100 |     return 0;
101 | }
102 | 
103 | 
104 | /**
105 |  * @brief MainWindow::on_pushButton_open_clicked 点击选择文件
106 |  * @author HezeLao
107 |  * @date 2018-12-28 21:50
108 |  */
109 | void MainWindow::on_pushButton_open_clicked(){
110 |     cout << "debug:开始选择文件事件" << endl;
111 |     auto fileName = QFileDialog::getOpenFileName(nullptr, "Open Video!", QDir::currentPath(), "video files(*.avi;*.mp4;*.wmv;*.flv;);;all files(*.*)");
112 |     if(fileName.isEmpty()){
113 |         cout << "debug:打开文件失败" << endl;
114 |     }
115 |     else{
116 |         videoPath = fileName.toStdString();
117 |         isOpen = true;
118 |         cout << "debug:打开文件成功" << endl;
119 |         cout << "debug:路径是:" << videoPath << endl;
120 |     }
121 |     cout << "debug:结束选择文件事件" << endl;
122 | }
123 | 
124 | 
125 | /**
126 |  * @brief MainWindow::on_pushButton_start_line_clicked 点击开始检测车道线
127 |  * @author HezeLao
128 |  * @date 2018-12-28 21:50
129 |  */
130 | void MainWindow::on_pushButton_start_line_clicked(){
131 |     //正在运行检测中
132 |     if(isStart == true && isPause == false){
133 |         cout << "debug:正在检测中" << endl;
134 |         return;
135 |     }
136 | 
137 |     //从暂停恢复检测
138 |     if(isPause == true){
139 |         isPause = false;
140 |         cout << "debug:从暂停恢复检测" << endl;
141 |         return;
142 |     }
143 | 
144 |     //初次点击开始，检查是否选择目标视频
145 |     if(isOpen == true){
146 |         if(!MyRunner(false)){
147 |             //成功开始检测
148 |             cout << "debug:成功开始检测" << endl;
149 |             isOpen = false;
150 |             isStart = false;
151 |             isPause = false;
152 |             isStop = false;
153 |         }
154 |         else{
155 |             //检测失败
156 |             cout << "debug:检测失败" << endl;
157 |         }
158 |     }
159 |     else{
160 |         cout << "debug:未选择文件" << endl;
161 |         QMessageBox box(QMessageBox::Warning,"warning",QString::fromLocal8Bit("请先选择视频文件"));
162 |         box.setStandardButtons (QMessageBox::Ok|QMessageBox::Cancel);
163 |         box.setButtonText (QMessageBox::Ok,QString::fromLocal8Bit("打开文件"));
164 |         box.setButtonText (QMessageBox::Cancel,QString::fromLocal8Bit("取消"));
165 |         auto select = box.exec ();
166 |         if(select == QMessageBox::Ok){
167 |             on_pushButton_open_clicked();
168 |         }
169 |         return;
170 |     }
171 | 
172 |     cout << "debug:结束检测事件" << endl;
173 | }
174 | 
175 | 
176 | /**
177 |  * @brief MainWindow::on_pushButton_sign_clicked 点击开始检测
178 |  * @author HezeLao
179 |  * @date 2018-12-28 21:50
180 |  */
181 | void MainWindow::on_pushButton_start_sign_clicked(){
182 |     //正在运行检测中
183 |     if(isStart == true && isPause == false){
184 |         cout << "debug:正在检测中" << endl;
185 |         return;
186 |     }
187 | 
188 |     //从暂停恢复检测
189 |     if(isPause == true){
190 |         isPause = false;
191 |         cout << "debug:从暂停恢复检测" << endl;
192 |         return;
193 |     }
194 | 
195 |     //初次点击开始，检查是否选择目标视频
196 |     if(isOpen == true){
197 |         if(!MyRunner(true)){
198 |             //成功开始检测
199 |             cout << "debug:成功开始检测" << endl;
200 |             isOpen = false;
201 |             isStart = false;
202 |             isPause = false;
203 |             isStop = false;
204 |         }
205 |         else{
206 |             //检测失败
207 |             cout << "debug:检测失败" << endl;
208 |         }
209 |     }
210 |     else{
211 |         cout << "debug:未选择文件" << endl;
212 |         QMessageBox box(QMessageBox::Warning,"warning",QString::fromLocal8Bit("请先选择视频文件"));
213 |         box.setStandardButtons (QMessageBox::Ok|QMessageBox::Cancel);
214 |         box.setButtonText (QMessageBox::Ok,QString::fromLocal8Bit("打开文件"));
215 |         box.setButtonText (QMessageBox::Cancel,QString::fromLocal8Bit("取消"));
216 |         auto select = box.exec ();
217 |         if(select == QMessageBox::Ok){
218 |             on_pushButton_open_clicked();
219 |         }
220 |         return;
221 |     }
222 | 
223 |     cout << "debug:结束检测事件" << endl;
224 | }
225 | 
226 | 
227 | /**
228 |  * @brief MainWindow::on_pushButton_pause_clicked 点击暂停
229 |  * @author HezeLao
230 |  * @date 2018-12-28 21:50
231 |  */
232 | void MainWindow::on_pushButton_pause_clicked(){
233 |     //在已经开始检测的条件下可以暂停
234 |     if(isStart == true){
235 |         isPause = true;
236 |         cout << "debug:已暂停" << endl;
237 |         return;
238 |     }
239 |     else{
240 |         //未在运行检测，无法暂停
241 |         QMessageBox::warning(NULL, "warning", QString::fromLocal8Bit("未在运行检测"), QMessageBox::Yes);
242 |         cout << "debug:未在运行检测，无法暂停" << endl;
243 |     }
244 | }
245 | 
246 | 
247 | /**
248 |  * @brief MainWindow::on_pushButton_stop_clicked 点击结束
249 |  * @author HezeLao
250 |  * @date 2018-12-28 21:50
251 |  */
252 | void MainWindow::on_pushButton_stop_clicked(){
253 |     //未在运行检测，无法结束
254 |     if(isStart == false){
255 |         QMessageBox::warning(NULL, "warning", QString::fromLocal8Bit("未在运行检测"), QMessageBox::Yes);
256 |         cout << "debug:未在运行检测，无法结束" << endl;
257 |     }
258 |     else{
259 |         //执行结束事件
260 |         isOpen = false;
261 |         isStart = false;
262 |         isPause = false;
263 |         isStop = true;
264 |         cout << "debug:执行结束事件" << endl;
265 |     }
266 | }
267 | 
268 | 
269 | /**
270 |  * @brief MainWindow::closeEvent 关闭窗口事件
271 |  * @param event 事件
272 |  * @author HezeLao
273 |  * @date 2018-12-28 22:20
274 |  */
275 | void MainWindow::closeEvent(QCloseEvent *event){
276 |     auto isExit = QMessageBox::information(this, "Tips", QString::fromLocal8Bit("确认退出？"), QMessageBox::Yes | QMessageBox::No);
277 |     if(isExit == QMessageBox::Yes)
278 |         event->accept();
279 |     else
280 |         event->ignore();
281 | }
282 | 


--------------------------------------------------------------------------------
/LaneLine-and-Sign-Detection/lanedetector.cpp:
--------------------------------------------------------------------------------
  1 | /******************************************************
  2 | author: XiuRuiwei
  3 | co-author: LaoHeze
  4 | Date: 2019-1-2
  5 | 
  6 | 所有构成LaneDetector类的一部分的函数。
  7 | 类将以RGB图像作为输入并输出相同的RGB图像，但是
  8 | 带有检测到的车道图和转弯预测。
  9 | ********************************************************/
 10 | #include "lanedetector.h"
 11 | using namespace std;
 12 | 
 13 | 
 14 | /**
 15 |  * @brief LaneDetector::detector qt检测算法运行模块
 16 |  * @param frame 结果缩放的原始图像
 17 |  * @author HezeLao
 18 |  */
 19 | void LaneDetector::myDetector(Mat frame){
 20 |     Mat img_denoise;
 21 |     Mat img_edges;
 22 |     Mat img_mask;
 23 |     Mat img_lines;
 24 |     vector<cv::Vec4i> lines;
 25 |     vector<std::vector<cv::Vec4i> > left_right_lines;
 26 |     vector<cv::Point> lane;
 27 | 
 28 |     img_denoise = deNoise(frame);           // 使用高斯滤波器去噪图像
 29 |     img_edges = edgeDetector(img_denoise);  // 检测图像中的边缘
 30 |     img_mask = mask(img_edges);             // 对图像进行掩码，只得到ROI
 31 |     lines = houghLines(img_mask);           //霍夫变换
 32 | 
 33 |     if (!lines.empty()) {
 34 |         left_right_lines = lineSeparation(lines, img_edges);// 把线分成左行和右行
 35 |         lane = regression(left_right_lines, frame);         // 应用回归法，使每条泳道的每一侧只能得到一条直线
 36 |         plotLane(frame, lane);  // 画出车道线
 37 |     }
 38 | }
 39 | 
 40 | 
 41 | /**
 42 |  * @brief LaneDetector::streetSign 运用HSV颜色空间进行路牌检测
 43 |  * @param img_original
 44 |  * @author XieRuiwei LaoHeze
 45 |  */
 46 | bool LaneDetector::streetSign(Mat img_original){
 47 |     Mat hsv, roi;
 48 |     cvtColor(img_original, hsv, COLOR_RGB2HSV); //直接转换#HSV颜色空间
 49 | 
 50 |     //颜色筛选
 51 |     Mat mask1;
 52 |     inRange(hsv, Scalar(155,60,60), Scalar(180,255,255), mask1);    //红色
 53 |     Mat mask2;
 54 |     inRange(hsv, Scalar(97,80,45), Scalar(124,255,255), mask2);     //蓝色
 55 |     Mat mask = mask1 + mask2;
 56 |     GaussianBlur(mask, mask,Size(5, 5), 3, 3);              //高斯模糊
 57 | 
 58 |     Mat binary;
 59 |     threshold(mask, binary,127, 255, CV_THRESH_BINARY);     //二值化
 60 | 
 61 |     //定义核，形态学闭运算
 62 |     Mat closed, kernel = getStructuringElement(MORPH_RECT,Size(21,7));
 63 |     morphologyEx(binary,closed, MORPH_CLOSE, kernel);
 64 | 
 65 |     Mat img_erode,img_dilate;
 66 |     erode(closed,img_erode, NULL, Point(-1, -1), 4);        //腐蚀
 67 |     dilate(img_erode,img_dilate, NULL, Point(-1, -1), 4);   //膨胀
 68 | 
 69 |     vector<vector<Point>> contours;
 70 |     vector<Vec4i> hierarchy;        //分层
 71 |     findContours(img_dilate, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));   //寻找轮廓
 72 | 
 73 |     for (int i = 0; i < contours.size(); i++){
 74 |         double area = contourArea(Mat(contours[i]),false);
 75 |         if(area > 0.055*576*324 || area <0.0012*576*324)
 76 |             continue;
 77 | 
 78 |         //变换为旋转矩阵
 79 |         RotatedRect rect=minAreaRect(Mat(contours[i]));     //最小化矩形区域
 80 |         //定义一个存储以上四个点的坐标的变量
 81 |         Point2f fourPoint2f[4];
 82 |         //将rectPoint变量中存储的坐标值放到 fourPoint的数组中
 83 |         rect.points(fourPoint2f);
 84 |         double x,y,width,height;
 85 |         x = y = width = height = 0;
 86 |         x = fourPoint2f[1].x;
 87 |         y = fourPoint2f[1].y;
 88 |         width = fabs(fourPoint2f[0].x - fourPoint2f[3].x);
 89 |         height = fabs(fourPoint2f[0].y - fourPoint2f[1].y);
 90 | 
 91 |         if(width/height>1.2 && width/height<10.0){          //根据矩形的长宽比  圈出标志牌
 92 |             //根据得到的四个点的坐标  绘制矩形
 93 |             Scalar color = (0, 0, 255);                     //蓝色线画轮廓
 94 |             for (int i = 0; i < 3; i++) {
 95 |                 line(img_original, fourPoint2f[i], fourPoint2f[i + 1], color, 3);
 96 |             }
 97 |             line(img_original, fourPoint2f[3], fourPoint2f[0], color, 3);
 98 | 
 99 |             //显示裁剪后的图片
100 |             if(x>50 && y>50 && width >50 && height >30){
101 |                 roi.release();
102 |                 roi = img_original(Rect(x,y,width,height)).clone();
103 |                 //imshow("裁剪后的图片",roi);
104 |                 cvtColor(roi, roi, COLOR_BGR2RGB);
105 |                 imwrite("cut.jpg", roi);
106 |                 return true;
107 |             }
108 |         }
109 |     }
110 |     return false;
111 | }
112 | 
113 | 
114 | /**
115 |  *@brief 对输入图像进行高斯滤波去噪
116 |  * @param inputImage是一个视频的帧，其中
117 |  * @param lane将被检测到
118 |  * @return 模糊和去噪图像
119 |  */
120 | Mat LaneDetector::deNoise(Mat inputImage) {
121 |     Mat output;
122 |     GaussianBlur(inputImage, output, Size(3, 3), 0, 0);
123 |     return output;
124 | }
125 | 
126 | 
127 | /**
128 |  * @brief 通过过滤图像检测模糊帧中的所有边缘
129 |  * @param img_noise是之前模糊的帧
130 |  * @return 只有边缘用白色表示的二进制图像
131 |  */
132 | Mat LaneDetector::edgeDetector(Mat img_noise) {
133 |     Mat output;
134 |     Mat kernel;
135 |     Point anchor;
136 | 
137 |     cvtColor(img_noise, output, COLOR_RGB2GRAY);        //灰度化
138 |     threshold(output, output, 140, 255, THRESH_BINARY); //二值化
139 | 
140 |     //创建内核 kernel[-1 0 1]，这个内核是基于，Mathworks的车道偏离警告系统
141 |     anchor = Point(-1, -1);
142 |     kernel = Mat(1, 3, CV_32F);
143 |     kernel.at<float>(0, 0) = -1;
144 |     kernel.at<float>(0, 1) = 0;
145 |     kernel.at<float>(0, 2) = 1;
146 | 
147 |     // 对二值图像进行滤波以获得边缘
148 |     filter2D(output, output, -1, kernel, anchor, 0, BORDER_DEFAULT);
149 |     return output;
150 | }
151 | 
152 | 
153 | /**
154 |  * @brief 蒙版边缘图像，对图像进行掩码，只检测到构成车道一部分的边缘
155 |  * @param img_edge是前一个函数的边缘图像
156 |  * @return 二进制图像，只有需要的边缘具有代表性d
157 |  */
158 | Mat LaneDetector::mask(Mat img_edges) {
159 |     Mat output;
160 |     Mat mask = Mat::zeros(img_edges.size(), img_edges.type());
161 |     Point pts[4] = {
162 |         //非老师给的视频参数   loadVideo.mp4
163 |         Point(80,368),    //左下角
164 |         Point(120,280),   //左上角
165 |         Point(600,280),
166 |         Point(800,368)    //右下角
167 | 
168 |         //loadVideo1.mp4 2.MP4
169 |         //Point(-120,368),    //左下角
170 |         //Point(50,250),   //左上角
171 |         //Point(290,250),
172 |         //Point(450,368)    //右下角
173 | 
174 |         //loadVideo2.mp4 1.MP4
175 |         //Point(-60,368),    //左下角
176 |         //Point(80,220),   //左上角
177 |         //Point(450,220),
178 |         //Point(600,368)    //右下角
179 | 
180 |         //loadVideo3.mp4 4.mp4
181 |         //Point(-90,368),    //左下角
182 |         //Point(40,235),   //左上角
183 |         //Point(350,235),
184 |         //Point(650,368)    //右下角
185 |     };
186 | 
187 |     fillConvexPoly(mask, pts, 4, Scalar(255, 0, 0));    //掩模填充
188 |     bitwise_and(img_edges, mask, output);   //取并集
189 | 
190 |     return output;
191 | }
192 | 
193 | 
194 | /**
195 |  * @brief 获取所有蒙面图像中的线段，这些线段将成为车道边界的一部分
196 |  * @param img_mask是前一个函数的屏蔽二进制映像
197 |  * @return 包含图像中所有检测到的线的向量
198 |  */
199 | vector<Vec4i> LaneDetector::houghLines(Mat img_mask) {
200 |     vector<Vec4i> line;
201 |     HoughLinesP(img_mask, line, 1, CV_PI / 180, 20, 20, 30);    // 和是通过反复试验得到的
202 |     return line;
203 | }
204 | 
205 | 
206 | // 对左右行进行排序
207 | /**
208 |  * @brief 按坡度对所有检测到的霍夫线进行排序。
209 |  * @brief 这些线可以分为左右两种
210 |  * @brief 关于他们的坡度符号和大致位置
211 |  * @param 线是包含所有检测到的线的向量
212 |  * @param img_edge用于确定图像中心
213 |  输出是一个向量(2)，它包含所有分类行
214 |  */
215 | vector<vector<Vec4i> > LaneDetector::lineSeparation(vector<Vec4i> lines, Mat img_edges) {
216 |     vector<vector<Vec4i> > output(2);
217 |     size_t j = 0;
218 |     Point ini;
219 |     Point fini;
220 |     double slope_thresh = 0.3;
221 |     vector<double> slopes;
222 |     vector<Vec4i> selected_lines;
223 |     vector<Vec4i> right_lines, left_lines;
224 | 
225 |     // 计算所有被测直线的斜率
226 |     for (auto i : lines) {
227 |         ini = Point(i[0], i[1]);
228 |         fini = Point(i[2], i[3]);
229 | 
230 |         // 基本代数:斜率  slope = (y1 - y0)/(x1 - x0)
231 |         double slope = (static_cast<double>(fini.y) - static_cast<double>(ini.y)) / (static_cast<double>(fini.x) - static_cast<double>(ini.x) + 0.00001);
232 | 
233 |         //如果坡度太水平，就放弃这条线
234 |         //如果没有，保存它们和它们各自的斜率
235 |         if (abs(slope) > slope_thresh) {
236 |             slopes.push_back(slope);
237 |             selected_lines.push_back(i);
238 |         }
239 |     }
240 | 
241 |     // 把线分成左右两行
242 |     img_center = static_cast<double>((img_edges.cols / 2));
243 |     while (j < selected_lines.size()) {
244 |         ini = Point(selected_lines[j][0], selected_lines[j][1]);
245 |         fini = Point(selected_lines[j][2], selected_lines[j][3]);
246 | 
247 |         // 将线分为左边或右边的条件
248 |         if (slopes[j] > 0 && fini.x > img_center && ini.x > img_center) {
249 |             right_lines.push_back(selected_lines[j]);
250 |             right_flag = true;
251 |         }
252 |         else if (slopes[j] < 0 && fini.x < img_center && ini.x < img_center) {
253 |             left_lines.push_back(selected_lines[j]);
254 |             left_flag = true;
255 |         }
256 |         j++;
257 |     }
258 | 
259 |     output[0] = right_lines;
260 |     output[1] = left_lines;
261 | 
262 |     return output;
263 | }
264 | 
265 | // 左右线的回归420/5000
266 | /**
267 |  * @brief 回归采用所有分类线段的起始点和结束点，用最小二乘法拟合出一条新的线段。
268 |  * @brief 左右两边都做了。
269 |  * @param left_right_lines是lineSeparation函数的输出
270 |  * @param inputImage用于选择行结束的位置
271 |  * @return 输出包含两个车道边界线的起始点和终点
272 |  */
273 | vector<Point> LaneDetector::regression(vector<vector<Vec4i> > left_right_lines, Mat inputImage) {
274 |     vector<Point> output(4);
275 |     Point ini;
276 |     Point fini;
277 |     Point ini2;
278 |     Point fini2;
279 |     Vec4d right_line;
280 |     Vec4d left_line;
281 |     vector<Point> right_pts;
282 |     vector<Point> left_pts;
283 | 
284 |     // 如果检测到右行，请使用该行的所有初始化和最终点匹配该行
285 |     if (right_flag == true) {
286 |         for (auto i : left_right_lines[0]) {
287 |             ini = Point(i[0], i[1]);
288 |             fini = Point(i[2], i[3]);
289 | 
290 |             right_pts.push_back(ini);
291 |             right_pts.push_back(fini);
292 |         }
293 | 
294 |         if (right_pts.size() > 0) {
295 |             // 这里形成了一条直线
296 |             fitLine(right_pts, right_line, CV_DIST_L2, 0, 0.01, 0.01);
297 |             right_m = right_line[1] / right_line[0];
298 |             right_b = Point(right_line[2], right_line[3]);
299 |         }
300 |     }
301 | 
302 |     // 如果检测到左行，使用该行的所有init和final点拟合一行
303 |     if (left_flag == true) {
304 |         for (auto j : left_right_lines[1]) {
305 |             ini2 = Point(j[0], j[1]);
306 |             fini2 = Point(j[2], j[3]);
307 | 
308 |             left_pts.push_back(ini2);
309 |             left_pts.push_back(fini2);
310 |         }
311 |         if (left_pts.size() > 0) {
312 |             // 左线在这里形成
313 |             fitLine(left_pts, left_line, CV_DIST_L2, 0, 0.01, 0.01);
314 |             left_m = left_line[1] / left_line[0];
315 |             left_b = Point(left_line[2], left_line[3]);
316 |         }
317 |     }
318 | 
319 |     // 首先求出了直线的斜率和偏置点，应用直线方程求出了直线点
320 |     int ini_y = inputImage.rows;
321 |     int fin_y = 250;
322 | 
323 |     double right_ini_x = ((ini_y - right_b.y) / right_m) + right_b.x;
324 |     double right_fin_x = ((fin_y - right_b.y) / right_m) + right_b.x;
325 | 
326 |     double left_ini_x = ((ini_y - left_b.y) / left_m) + left_b.x;
327 |     double left_fin_x = ((fin_y - left_b.y) / left_m) + left_b.x;
328 | 
329 |     output[0] = Point(right_ini_x, ini_y);
330 |     output[1] = Point(right_fin_x, fin_y);
331 |     output[2] = Point(left_ini_x, ini_y);
332 |     output[3] = Point(left_fin_x, fin_y);
333 | 
334 |     return output;
335 | }
336 | 
337 | 
338 | //绘制结果
339 | /**
340 |  * @brief该函数绘制车道两侧、转弯预测信息和覆盖车道边界内区域的透明多边形
341 |  * @param inputImage是原始捕获帧
342 |  * @param lane是包含两条直线信息的向量
343 |  * @param turn是包含转弯信息的输出字符串
344 |  */
345 | void LaneDetector::plotLane(Mat inputImage, vector<Point> lane) {
346 |     vector<Point> poly_points;
347 |     Mat output;
348 | 
349 |     // 创建透明多边形，以便更好地显示车道
350 |     inputImage.copyTo(output);
351 |     poly_points.push_back(lane[2]);
352 |     poly_points.push_back(lane[0]);
353 |     poly_points.push_back(lane[1]);
354 |     poly_points.push_back(lane[3]);
355 |     fillConvexPoly(output, poly_points, Scalar(255, 0, 0), CV_AA, 0);
356 |     addWeighted(output, 0.3, inputImage, 1.0 - 0.3, 0, inputImage);
357 | }
358 | 


--------------------------------------------------------------------------------
/CourseDesign/lanedetector.cpp:
--------------------------------------------------------------------------------
  1 | /***
  2 | 所有构成LaneDetector类的一部分的函数。
  3 | 类将以RGB图像作为输入并输出相同的RGB图像，但是
  4 | 带有检测到的车道图和转弯预测。
  5 | */
  6 | #include <string>
  7 | #include <vector>
  8 | #include <opencv2/opencv.hpp>
  9 | #include "lanedetector.h"
 10 | #include "iostream"
 11 | using namespace std;
 12 | 
 13 | //运行检测代码块
 14 | void LaneDetector::doDetection(String videoPath){
 15 |     // The input argument is the location of the video
 16 |     cv::VideoCapture cap(videoPath);
 17 |     if (!cap.isOpened())
 18 |         return ;
 19 | 
 20 | 
 21 |     Mat frame;
 22 |     Mat img_denoise;
 23 |     Mat img_edges;
 24 |     Mat img_mask;
 25 |     Mat img_lines;
 26 |     vector<cv::Vec4i> lines;
 27 |     vector<std::vector<cv::Vec4i> > left_right_lines;
 28 |     vector<cv::Point> lane;
 29 |     string turn;
 30 |     int flag_plot = -1;
 31 | 
 32 |     // 主要算法开始。遍历视频的每一帧
 33 |     while (true) {
 34 |         // Capture frame
 35 |         if (!cap.read(frame))
 36 |             break;
 37 |         if(waitKey(1)==27)
 38 |             break;
 39 | 
 40 |         //图像缩小
 41 |         resize(frame,frame,Size(576,324),0,0,INTER_LINEAR);
 42 | 
 43 |         //交通标志检测及显示
 44 |         streetSign(frame);
 45 | 
 46 |         // 使用高斯滤波器去噪图像
 47 |         img_denoise = deNoise(frame);
 48 |         imshow("高斯滤波器去噪图像",frame);
 49 | 
 50 |         // 检测图像中的边缘
 51 |         img_edges = edgeDetector(img_denoise);
 52 |         imshow("检测图像中的边缘",img_edges);
 53 | 
 54 |         // 对图像进行掩码，只得到ROI
 55 |         img_mask = mask(img_edges);
 56 |         imshow("对图像进行掩码，只得到ROI",img_mask);
 57 | 
 58 |         // 获取裁剪图像中的霍夫线
 59 |         lines = houghLines(img_mask);
 60 |      //   imshow("裁剪图像中的霍夫线",lines);
 61 | 
 62 |         if (!lines.empty())
 63 |         {
 64 |             // 把线分成左行和右行
 65 |             left_right_lines = lineSeparation(lines, img_edges);
 66 | 
 67 |             // 应用回归法，使每条泳道的每一侧只能得到一条直线
 68 |             lane = regression(left_right_lines, frame);
 69 | 
 70 |             // 通过确定直线的消失点来预测转弯
 71 |             turn = predictTurn();
 72 | 
 73 |             // 情节车道检测
 74 |             flag_plot = plotLane(frame, lane, turn);
 75 |         }
 76 |         else {
 77 |             flag_plot = -1;
 78 |         }
 79 |     }
 80 | 
 81 |     return ;
 82 | }
 83 | 
 84 | 
 85 | //路牌检测
 86 | void LaneDetector::streetSign(Mat img_original){
 87 |     Mat hsv;
 88 |     cvtColor(img_original, hsv, CV_BGR2HSV); //直接转换#HSV颜色空间
 89 | //    imshow("颜色空间",hsv);
 90 | 
 91 | 
 92 |     //掩码
 93 |     Mat mask1;
 94 |     inRange(hsv, Scalar(155,60,60), Scalar(180,255,255), mask1);
 95 |     Mat mask2;
 96 |     inRange(hsv, Scalar(97,80,45), Scalar(124,255,255), mask2);
 97 |     Mat mask = mask1 + mask2;
 98 |     GaussianBlur(mask, mask,Size(5, 5), 3, 3);//高斯模糊
 99 | //    imshow("mask",mask);
100 | 
101 |     //二值化
102 |     Mat binary;
103 |     threshold(mask, binary,127, 255, CV_THRESH_BINARY);//二值化
104 | //    imshow("二值化",binary);
105 | 
106 |     //定义核
107 |     Mat kernel = getStructuringElement(MORPH_RECT,Size(21,7));
108 |     //进行形态学操作
109 |     Mat closed;
110 |     morphologyEx(binary,closed, MORPH_CLOSE, kernel);
111 | //    imshow("进行形态学操作",closed);
112 | 
113 |     Mat img_erode,img_dilate;
114 |     erode(closed,img_erode, NULL, Point(-1, -1), 4);//腐蚀
115 |     dilate(img_erode,img_dilate, NULL, Point(-1, -1), 4);//膨胀
116 | //    imshow("腐蚀-膨胀",img_dilate);
117 | 
118 | 
119 |     vector<vector<Point>> contours;
120 |     vector<Vec4i> hierarchy;        //分层
121 |     findContours(img_dilate, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));//寻找轮廓
122 | //    cout << contours.size() << endl;
123 | 
124 | 
125 |     for (int i = 0; i < contours.size(); i++){
126 |         double area = contourArea(Mat(contours[i]),false);
127 |         if(area > 0.055*576*324 || area <0.0012*576*324)
128 |             continue;
129 | 
130 |         //变换为旋转矩阵
131 |         RotatedRect rect=minAreaRect(Mat(contours[i]));
132 |         //定义一个存储以上四个点的坐标的变量
133 |         Point2f fourPoint2f[4];
134 |         //将rectPoint变量中存储的坐标值放到 fourPoint的数组中
135 |         rect.points(fourPoint2f);
136 |         double x,y,width,height;
137 |         x = y = width = height = 0;
138 |         x = fourPoint2f[1].x;
139 |         y = fourPoint2f[1].y;
140 |         width = fabs(fourPoint2f[0].x - fourPoint2f[3].x);
141 |         height = fabs(fourPoint2f[0].y - fourPoint2f[1].y);
142 | 
143 | 
144 |         if(width/height>1.2 && width/height<2.5){  //根据矩形的长宽比  圈出标志牌
145 |             //根据得到的四个点的坐标  绘制矩形
146 |             Scalar color = (0, 0, 255);//蓝色线画轮廓
147 |             for (int i = 0; i < 3; i++) {
148 |                 line(img_original, fourPoint2f[i], fourPoint2f[i + 1], color, 3);
149 |             }
150 |             line(img_original, fourPoint2f[3], fourPoint2f[0], color, 3);
151 | 
152 |             //显示裁剪后的图片
153 |             if(x>50 && y>50 && width >50 && height >20){
154 |                 Mat roi = img_original(Rect(x,y,width,height));
155 |                 imshow("裁剪后的图片",roi);
156 |             }
157 |         }
158 |     }
159 | }
160 | 
161 | // 图像模糊
162 | /**
163 | *@brief对输入图像进行高斯滤波去噪
164 | *@param inputImage是一个视频的帧，其中
165 | *@param lane将被检测到
166 | *@return模糊和去噪图像
167 | */
168 | Mat LaneDetector::deNoise(Mat inputImage) {
169 |     Mat output;
170 |     GaussianBlur(inputImage, output, Size(3, 3), 0, 0);
171 |     return output;
172 | }
173 | 
174 | // 边缘检测
175 | /**
176 | *@brief通过过滤图像检测模糊帧中的所有边缘
177 | *@param img_noise是之前模糊的帧
178 | *@return只有边缘用白色表示的二进制图像
179 | */
180 | Mat LaneDetector::edgeDetector(Mat img_noise) {
181 |     Mat output;
182 |     Mat kernel;
183 |     Point anchor;
184 | 
185 |     // 将图像从RGB转换为灰色
186 |     cvtColor(img_noise, output, COLOR_RGB2GRAY);
187 |     // Binarize灰度图像
188 |     threshold(output, output, 140, 255, THRESH_BINARY);
189 | 
190 |     //创建内核 kernel[-1 0 1]
191 |     //这个内核是基于
192 |     // Mathworks的车道偏离警告系统
193 |     anchor = Point(-1, -1);
194 |     kernel = Mat(1, 3, CV_32F);
195 |     kernel.at<float>(0, 0) = -1;
196 |     kernel.at<float>(0, 1) = 0;
197 |     kernel.at<float>(0, 2) = 1;
198 | 
199 |     // 对二值图像进行滤波以获得边缘
200 |     filter2D(output, output, -1, kernel, anchor, 0, BORDER_DEFAULT);
201 | //    imshow("output", output);
202 |     return output;
203 | }
204 | 
205 | // 蒙版边缘图像
206 | /**
207 | *@brief对图像进行掩码，只检测到构成车道一部分的边缘
208 | *@param img_edge是前一个函数的边缘图像
209 | *@return二进制图像，只有需要的边缘具有代表性d
210 | */
211 | Mat LaneDetector::mask(Mat img_edges) {
212 |     Mat output;
213 |     Mat mask = Mat::zeros(img_edges.size(), img_edges.type());
214 |     Point pts[4] = {
215 | //        //非刘文果给的视频参数   loadVideo.mp4
216 | //        Point(80,368),    //左下角
217 | //        Point(120,280),   //左上角
218 | //        Point(600,280),
219 | //        Point(800,368)    //右下角
220 | 
221 |         //刘文果给的视频参数   loadVideo1.mp4
222 |          Point(-120,368),    //左下角
223 |          Point(50,250),   //左上角
224 |          Point(290,250),
225 |          Point(450,368)    //右下角
226 | 
227 | //        //刘文果给的视频参数   loadVideo2.mp4
228 | //        Point(-60,368),    //左下角
229 | //        Point(80,220),   //左上角
230 | //        Point(450,220),
231 | //        Point(600,368)    //右下角
232 | 
233 | //        //刘文果给的视频参数   loadVideo3.mp4
234 | //         Point(-90,368),    //左下角
235 | //         Point(40,235),   //左上角
236 | //         Point(350,235),
237 | //         Point(650,368)    //右下角
238 |     };
239 | 
240 |     // 创建一个二元多边形掩模
241 |     fillConvexPoly(mask, pts, 4, Scalar(255, 0, 0));
242 |     // 将边缘图像和蒙版相乘得到输出
243 |     bitwise_and(img_edges, mask, output);
244 | 
245 |     imshow("mask",mask);
246 |     return output;
247 | }
248 | 
249 | // HOUGH LINES
250 | /**
251 | *@brief获取所有蒙面图像中的线段，这些线段将成为车道边界的一部分
252 | *@param img_mask是前一个函数的屏蔽二进制映像
253 | *@return包含图像中所有检测到的线的向量
254 | */
255 | vector<Vec4i> LaneDetector::houghLines(Mat img_mask) {
256 |     vector<Vec4i> line;
257 |     // 和是通过反复试验得到的
258 |     HoughLinesP(img_mask, line, 1, CV_PI / 180, 20, 20, 30);
259 |     return line;
260 | }
261 | 
262 | // 对左右行进行排序
263 | /**
264 | *@brief按坡度对所有检测到的霍夫线进行排序。
265 | *@brief这些线可以分为左右两种
266 | *@brief关于他们的坡度符号和大致位置
267 | *@param线是包含所有检测到的线的向量
268 | *@param img_edge用于确定图像中心
269 | 输出是一个向量(2)，它包含所有分类行
270 | */
271 | vector<vector<Vec4i> > LaneDetector::lineSeparation(vector<Vec4i> lines, Mat img_edges) {
272 |     vector<vector<Vec4i> > output(2);
273 |     size_t j = 0;
274 |     Point ini;
275 |     Point fini;
276 |     double slope_thresh = 0.3;
277 |     vector<double> slopes;
278 |     vector<Vec4i> selected_lines;
279 |     vector<Vec4i> right_lines, left_lines;
280 | 
281 |     // 计算所有被测直线的斜率
282 |     for (auto i : lines) {
283 |         ini = Point(i[0], i[1]);
284 |         fini = Point(i[2], i[3]);
285 | 
286 |         // 基本代数:斜率  slope = (y1 - y0)/(x1 - x0)
287 |         double slope = (static_cast<double>(fini.y) - static_cast<double>(ini.y)) / (static_cast<double>(fini.x) - static_cast<double>(ini.x) + 0.00001);
288 | 
289 |         //如果坡度太水平，就放弃这条线
290 |         //如果没有，保存它们和它们各自的斜率
291 |         if (abs(slope) > slope_thresh) {
292 |             slopes.push_back(slope);
293 |             selected_lines.push_back(i);
294 |         }
295 |     }
296 | 
297 |     // 把线分成左右两行
298 |     img_center = static_cast<double>((img_edges.cols / 2));
299 |     while (j < selected_lines.size()) {
300 |         ini = Point(selected_lines[j][0], selected_lines[j][1]);
301 |         fini = Point(selected_lines[j][2], selected_lines[j][3]);
302 | 
303 |         // 将线分为左边或右边的条件
304 |         if (slopes[j] > 0 && fini.x > img_center && ini.x > img_center) {
305 |             right_lines.push_back(selected_lines[j]);
306 |             right_flag = true;
307 |         }
308 |         else if (slopes[j] < 0 && fini.x < img_center && ini.x < img_center) {
309 |             left_lines.push_back(selected_lines[j]);
310 |             left_flag = true;
311 |         }
312 |         j++;
313 |     }
314 | 
315 |     output[0] = right_lines;
316 |     output[1] = left_lines;
317 | 
318 |     return output;
319 | }
320 | 
321 | // 左右线的回归
322 | /**
323 | 
324 | 420/5000
325 | *@brief回归采用所有分类线段的起始点和结束点，用最小二乘法拟合出一条新的线段。
326 | *@brief左右两边都做了。
327 | *@param left_right_lines是lineSeparation函数的输出
328 | *@param inputImage用于选择行结束的位置
329 | *@return输出包含两个车道边界线的起始点和终点
330 | */
331 | vector<Point> LaneDetector::regression(vector<vector<Vec4i> > left_right_lines, Mat inputImage) {
332 |     vector<Point> output(4);
333 |     Point ini;
334 |     Point fini;
335 |     Point ini2;
336 |     Point fini2;
337 |     Vec4d right_line;
338 |     Vec4d left_line;
339 |     vector<Point> right_pts;
340 |     vector<Point> left_pts;
341 | 
342 |     // 如果检测到右行，请使用该行的所有初始化和最终点匹配该行
343 |     if (right_flag == true) {
344 |         for (auto i : left_right_lines[0]) {
345 |             ini = Point(i[0], i[1]);
346 |             fini = Point(i[2], i[3]);
347 | 
348 |             right_pts.push_back(ini);
349 |             right_pts.push_back(fini);
350 |         }
351 | 
352 |         if (right_pts.size() > 0) {
353 |             // 这里形成了一条直线
354 |             fitLine(right_pts, right_line, CV_DIST_L2, 0, 0.01, 0.01);
355 |             right_m = right_line[1] / right_line[0];
356 |             right_b = Point(right_line[2], right_line[3]);
357 |         }
358 |     }
359 | 
360 |     // 如果检测到左行，使用该行的所有init和final点拟合一行
361 |     if (left_flag == true) {
362 |         for (auto j : left_right_lines[1]) {
363 |             ini2 = Point(j[0], j[1]);
364 |             fini2 = Point(j[2], j[3]);
365 | 
366 |             left_pts.push_back(ini2);
367 |             left_pts.push_back(fini2);
368 |         }
369 |         if (left_pts.size() > 0) {
370 |             // 左线在这里形成
371 |             fitLine(left_pts, left_line, CV_DIST_L2, 0, 0.01, 0.01);
372 |             left_m = left_line[1] / left_line[0];
373 |             left_b = Point(left_line[2], left_line[3]);
374 |         }
375 |     }
376 | 
377 |     // 首先求出了直线的斜率和偏置点，应用直线方程求出了直线点
378 |     int ini_y = inputImage.rows;
379 |     int fin_y = 250;
380 | 
381 |     double right_ini_x = ((ini_y - right_b.y) / right_m) + right_b.x;
382 |     double right_fin_x = ((fin_y - right_b.y) / right_m) + right_b.x;
383 | 
384 |     double left_ini_x = ((ini_y - left_b.y) / left_m) + left_b.x;
385 |     double left_fin_x = ((fin_y - left_b.y) / left_m) + left_b.x;
386 | 
387 |     output[0] = Point(right_ini_x, ini_y);
388 |     output[1] = Point(right_fin_x, fin_y);
389 |     output[2] = Point(left_ini_x, ini_y);
390 |     output[3] = Point(left_fin_x, fin_y);
391 | 
392 |     return output;
393 | }
394 | 
395 | // 预测
396 | /**
397 | *@brief预测车道是左转、右转还是直走
398 | *@brief通过查看消失点相对于图像中心的位置来完成
399 | *@return字符串，表示是否有左或右转弯或道路是否笔直
400 | */
401 | string LaneDetector::predictTurn() {
402 |     string output;
403 |     double vanish_x;
404 |     double thr_vp = 10;
405 | 
406 |     // 消失点是两个车道边界线相交的点
407 |     vanish_x = static_cast<double>(((right_m*right_b.x) - (left_m*left_b.x) - right_b.y + left_b.y) / (right_m - left_m));
408 | 
409 |     // 消失点的位置决定了道路转弯的位置
410 |     if (vanish_x < (img_center - thr_vp))
411 |         output = "Left Turn";
412 |     else if (vanish_x >(img_center + thr_vp))
413 |         output = "Right Turn";
414 |     else if (vanish_x >= (img_center - thr_vp) && vanish_x <= (img_center + thr_vp))
415 |         output = "Straight";
416 | 
417 |     return output;
418 | }
419 | 
420 | //绘制结果
421 | /**
422 | *@brief该函数绘制车道两侧、转弯预测信息和覆盖车道边界内区域的透明多边形
423 | *@param inputImage是原始捕获帧
424 | *@param lane是包含两条直线信息的向量
425 | *@param turn是包含转弯信息的输出字符串
426 | *@return函数返回0
427 | */
428 | int LaneDetector::plotLane(Mat inputImage, vector<Point> lane, string turn) {
429 |     vector<Point> poly_points;
430 |     Mat output;
431 | 
432 |     // 创建透明多边形，以便更好地显示车道
433 |     inputImage.copyTo(output);
434 |     poly_points.push_back(lane[2]);
435 |     poly_points.push_back(lane[0]);
436 |     poly_points.push_back(lane[1]);
437 |     poly_points.push_back(lane[3]);
438 |     fillConvexPoly(output, poly_points, Scalar(255, 0, 0), CV_AA, 0);
439 |     addWeighted(output, 0.3, inputImage, 1.0 - 0.3, 0, inputImage);
440 | 
441 |     // 绘制车道边界的两条线
442 |  /*   line(inputImage, lane[0], lane[1], Scalar(0, 0, 255), 5, CV_AA);
443 |     line(inputImage, lane[2], lane[3], Scalar(0, 0, 255), 5, CV_AA);*/
444 | 
445 |     // 绘制转弯信息
446 | //    putText(inputImage, turn, Point(50, 90), FONT_HERSHEY_COMPLEX_SMALL, 3, cvScalar(0, 255, 0), 1, CV_AA);
447 | 
448 |     // 显示最终输出图像
449 |     namedWindow("Lane", CV_WINDOW_AUTOSIZE);
450 |     imshow("Lane", inputImage);
451 |     return 0;
452 | }
453 | 


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1053 | 


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