C++ OpenCV汽车检测障碍物与测距
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运行代码如下：
#include
#include
#include
#include
#include
#include
#include
#pragma comment(lib, "Winmm.lib")
using namespace std;
using namespace cv;
//全局变量定义
bool a(false);
int i = 1;
int main()
{
//打开视频文件：其实就是建立一个VideoCapture结构
VideoCapture capture("i.avi");
//检测是否正常打开:成功打开时，isOpened返回ture
if (!capture.isOpened())
cout << "fail to open!" << endl;
//获取整个帧数
long totalFrameNumber = capture.get(CV_CAP_PROP_FRAME_COUNT);
cout << "整个视频共" << totalFrameNumber << "帧" << endl;
//设置开始帧()
long frameToStart = 0;
capture.set(CV_CAP_PROP_POS_FRAMES, frameToStart);
cout << "从第" << frameToStart << "帧开始读" << endl;
//设置结束帧
int frameToStop = totalFrameNumber;
if (frameToStop < frameToStart)
{
cout << "结束帧小于开始帧，程序错误，即将退出！" << endl;
return -1;
}
else
{
cout << "结束帧为：第" << frameToStop << "帧" << endl;
}
//获取帧率
double rate = capture.get(CV_CAP_PROP_FPS);
cout << "帧率为:" << rate << endl;
//定义一个用来控制读取视频循环结束的变量
bool stop = false;
//承载每一帧的图像
Mat frame;
Mat grayImage;
Mat binaryImage;
Mat reverseBinaryImage;
Mat imgROI;
Mat drawing;
//显示每一帧的窗口
// namedWindow("frame");
//两帧间的间隔时间:
int delay = 5000;
//利用while循环读取帧
//currentFrame是在循环体中控制读取到指定的帧后循环结束的变量
long currentFrame = frameToStart;
while (!stop)
{
//读取下一帧
if (!capture.read(frame))
{
cout << "读取失败" << endl;
return -1;
}
if (currentFrame % 30 == 0)
{
cout << "正在读取第" << currentFrame << "帧" << endl;
//imshow("frame",frame);
waitKey(delay);
/****ROI****/
if (!a)
{
cout << "图像的宽度：" << frame.size().width << ",图像的高度：" << frame.size().height << endl;
a = true;
}
Mat mask = Mat::zeros(frame.size()); // 八位单通道
//梯形ROI
//vector(向量): C++中的一种数据结构,确切的说是一个类.它相当于一个动态的数组 相当于存放点的坐标的容器
vector> contour; //vector>。外层vector的size代表了图像中轮廓的个数，里面vector的size代表了轮廓上点的个数。
vector pts;
pts.push_back(Point(480, 200));
pts.push_back(Point(620, 200));
pts.push_back(Point(970, 490));
pts.push_back(Point(115, 490));
//push_back 在数组的最后添加一个数据 即将四个坐标点依次存入数组
contour.push_back();
drawContours(mask, contour, 0, Scalar::all(255), -1);
frame.copyTo(imgROI, mask); // 将 frame 的位于 mask 中的部分，拷贝到 imgROI 中。
//imshow("ROI",imgROI);
//去噪
//blur(imgROI,grayImage,Size(3, 3));
medianBlur(imgROI, grayImage, 3);
//灰度处理
cvtColor(grayImage, grayImage, CV_BGR2GRAY);
threshold(grayImage, binaryImage, 90, 255, CV_THRESH_BINARY); //转换为二值图
//imshow("二值化", binaryImage);
//开运算和形态学梯度
Mat element = getStructuringElement(MORPH_RECT, Size(15, 15));
morphologyEx(binaryImage, binaryImage, MORPH_OPEN, element);
Mat element1 = getStructuringElement(MORPH_RECT, Size(2, 2));
morphologyEx(binaryImage, binaryImage, MORPH_GRADIENT, element1);
//namedWindow("预处理");
//imshow("预处理", binaryImage);
/*轮廓提取*/
vector< vector< Point> > contours;
findContours(binaryImage, contours, CV_RETR_EXTERNAL, CV_RETR_TREE);
vector >::iterator itc = contours.begin();
while (itc != contours.end())
{
if (itc->size() < 100)
{
itc = contours.erase(itc); //删除轮廓
}
else
{
++itc;
}
}
drawContours(binaryImage, contours, -1, Scalar(255), 1);
imshow("结果图", binaryImage);
//计算轮廓的面积
for (int i = 0; i < (int)contours.size(); i++)
{
double g_dConArea = contourArea(contours[i], false);
cout << "【无障碍区域轮廓的面积为：】" << g_dConArea << endl;
if (g_dConArea < 95724)//报警
{
double distance;
distance = g_dConArea / 1000;
//printf("%f,%f,%f\n", distance);
char buf[100];
printf("离前车距离：");
printf("%f\2", distance);
printf("米");
//printf(buf,"%f,%f,%f\n", distance);
MessageBeep(MB_OK);
cout << "\n前方有障碍物！\n" << endl;
MessageBox(0, TEXT("车距过近，请注意安全！"), TEXT("车距预警"), MB_OK);
//MessageBox(0, TEXT("离前车距离："), buf, MB_OK);
}
}
if (currentFrame > frameToStop)
{
stop = true;
}
}
currentFrame++;
}
//关闭视频文件
capture.release();
waitKey(0);
return 0;
}
运行结果：
