1、引言
近年来随着对泊车辅助系统需求的快速增长，提出了多种车位定位的方法，这些方法大致可分为4类：基于用户界面的、基于设施的、基于空闲位的和基于车位线的方法。与其他方法相比，基于车位线的方法有以下优势：(1)可以与基于用户界面的方法结合使用来减少由于司机重复操作带来的不便，而这是基于用户界面方法的主要缺陷。(2)不同于基于空闲位的方法，它能更准确地定位停车位，因为其定位过程不依赖于相邻汽车的停放姿势而仅依赖于车位线。(3)它也可以有效地应用于倾斜车位的情况。由于传感器的局限性，运用超声波传感器基于空闲位的方法在倾斜车位的情况下会失效。(4)与应用双目或者运动声波的基于空闲位的方法相比，它通常花费少量时间。(5)它不需要额外的传感器，例如立体摄像头、扫描激光雷达或者短波雷达，而是运用后视摄像头。
基于车位线的方法可以分为半自动和全自动的方法。与全自动的方法相比，半自动的方法可能产生更可靠的结果，而且花费更少的计算资源，因为它有来自人的额外信息。
本文提出的方法可分为以下几个过程：对汽车前、后、左、右4个摄像头采集到的图像进行重建形成全景图像;图像预处理，包括直方图均衡化，划分感兴趣区域;拟合车位线，追踪车位线，输出结构化数据，然后在参数空间中利用车位线特征的先验信息对结果进行优化，最终得出识别结果。
2、检测步骤
图像预处理
通过加装在汽车车身前、后、左、右的 4 个鱼眼摄像头，同时采集车辆四周的影像，经过鱼眼图像矫正，鸟瞰变换和拼接后，形成一幅车辆 4 周的 360°全景俯视图。原图像不可避免地会受到光照、噪声等的影响，因为前期处理的质量直接影响到后期识别的效果，所以为消除图像中无关的信息，恢复有用的真实信息，增强有关信息的可检测性和最大限度地简化。
原图效果如下：
首先将原彩色转化为灰度图像，考虑到光照影响导致灰度分布不均，需要对灰度图像进行直方图均衡化。直方图均衡化的基本思想是对原始图像中的像素作某种映射变换，使变换后的图像灰度概率密度是均匀分布的，即变换后图像是一幅灰度级均匀分布的图像，这意味着图像灰度的动态范围得到了增加，从而可提高图像的对比度。但是传统的直方图均衡化中灰度变换函数运算与像素所处的位置无关，这种全局性处理的算法，具有算法简单、计算速度快等优点，但由于其对所有像素点都做同样的处理，忽略了图像的局部特征，这就导致经过直方图均衡的图像将丢失有用信息，给图像的去噪处理及边缘检测带来损失。因此本文采用对比度受限自适应直方图均衡法( CLAHE) ，通过限制局部直方图的高度来限制局部对比度的增强幅度，从而限制噪声的放大及局部对比度的过增强。本文对其做了改进，可对光照和阴影有很好得鲁棒性；ROI选取与滤波图如下：
接下来为了识别水平线，所以对水平方向做了sobel检测；
定位滑窗车位线的的搜寻起始点：1）首先，划分搜素区域，按照x轴方向将图像划分出感兴趣区域，对图像两个部分在x方向与y方向做直方图统计，定位峰值作为水平与竖直两条车位线的搜寻起点。2）搜寻过程：首先，设置搜寻窗口大小（width和height）；然后，以搜寻起始点作为当前搜寻的基点，并以当前基点为中心，做一个网格化搜寻，由初始位置x，width为手工设定，height为图片大小除以设置搜寻窗口数目计算得到，这里窗口数目为10；其次，对每个搜寻窗口分别做水平和垂直方向直方图统计，统计在搜索框区域内非零像素个数，并过滤掉非零像素数目小于50的框；最后，计算非零像素坐标的均值作为当前搜索框的中心，并对这些中心点做一个二阶的最小二乘法拟合，得到当前搜寻对应的车道线参数。3）更新搜寻基点：步骤2)中，多项式逼近后，会得到一个直线方程，这样就可以得到新的搜寻基点。
按Y方向直方图统计：
追踪车位线
滑窗的方法通常用于第一帧或者检测失败重新开始的检测，因为对计算资源浪费过多，检测时间长，本项目由于自动泊车时候连续帧图像之间相差不大，之后几帧的图像可以只对第一帧拟合的曲线周围检测，设置周围的边框，然后在该范围内寻找下一帧曲线的像素点从而拟合曲线。在最后处理每一帧图像时要设置标志位检测是否检测到拟合的曲线，检测到的话用search_from_previous方法，否则的话要用滑窗的方法重新寻找车道线。
输出结构化数据
当检测出水平与竖直方向的车位线之后，求出两线之间的交点，输出到融合决策进程进行控车。
代码部分后面上传至博客，随时关注哦。。。。
更新时间２０１９．０６２４
部分代码如下
//
// Created by lqx on 19-3-4.
//
#include <stdlib.h>
#include "detectslot.h"
/*
* 用于sort()函数中降序排列
*/
bool comp(const ValueIndex &a,const ValueIndex &b)
{
return a.value>b.value;
}
bool comp1(const Point2f &a,const Point2f &b)
{
return a.y<b.y;
}
DetectParkingSlot::DetectParkingSlot():mSlotData("0x10014")
{}
DetectParkingSlot::~DetectParkingSlot() {
}
//4、直方图统计
int DetectParkingSlot::HistFromroi(cv::Mat img)
{
Mat hist_eq(img.rows,img.cols,CV_8UC1);
int sum_pix[256]={0};
int gray_sum=0;
int temp_gray[256];
int all_pix=img.rows*img.cols;
float n_per[256]={0.0};
for(int i=0;i<=img.rows;i++)
{
uchar* data=img.ptr<uchar>(i);
for (int j = 0; j <=img.cols ; ++j)
{
sum_pix[data[j]]++;
}
}
for (int k = 0; k <256 ; ++k)
{
gray_sum+=sum_pix[k];
temp_gray[k]=gray_sum;
}
//for (int l = 1; l <256 ; ++l)
// {
// n_per[l]=n_per[l]+n_per[l-1];
//}
//int maxnum=0;
int max_gray=0;
//20步滤波,获取最大差分值,找到边缘最大值位置的灰度值
for (int a = 20; a <256 ; ++a)
{
if(maxnum<temp_gray[a]-temp_gray[a-20])
{
maxnum=temp_gray[a]-temp_gray[a-20];
max_gray=a-10;
}
}
//imshow("eqhist",hist_eq);
return max_gray;
}
cv::Mat DetectParkingSlot::gray_binnary(cv::Mat img,int gray_value)
{
int tempgray=0;
Mat dst_img(img.rows,img.cols,CV_8UC1);
for (int i = 0; i <img.rows ; ++i)
{
for (int j = 0; j <img.cols ; ++j)
{
tempgray=img.at<uchar> (i,j);
if (max_gray>tempgray)
{
dst_img.at<uchar>(i,j)=0;
}
else
dst_img.at<uchar>(i,j)=tempgray-max_gray;
//dst_img.at<uchar>(i,j)=255;
}
}
//imshow("binnary",dst_img);
return dst_img;
}
int DetectParkingSlot::ProjectYdirect(cv::Mat img)
{
int temp[img.cols];
int maxpos;
//向Y轴投影,获取最大位置
for (int i = 0; i <img.cols; ++i)
{ int sumY=0;
for (int j = 0; j <img.rows ; ++j)
{
sumY+=img.at<uchar>(j,i);
}
//最大位置像素列平均
temp[i]=sumY/img.rows;
}
int tempgray=0;
int temp_sum=0;
int temp_sum_right=0;
//int tempgray_right=0;
int maxgray=0;
int maxgray_right=0;
int max_index=0;
int max_index_right=0;
//左半边图像获取最大位置与像素平均值
for (int k = 5; k <img.cols-1; ++k)
{
tempgray=(temp[k-1]+temp[k]+temp[k+1])/3;
if (tempgray>maxgray)
{
maxgray = tempgray;
max_index = k;
}
}
if(maxgray<20)
{
//cout<<"no deteced"<<endl;
maxgray=0;
return maxgray;
}
maxpos=max_index;
return maxpos;
}
/*
*寻找波峰
* matData：输入的一列数据
* minPeakDistance：峰值最小值
* minPeakHeight：峰值之间的最小距离
* peaks：峰值的位置和大小
*/
void DetectParkingSlot::findPeaks(Mat &matData,float minPeakDistance,float minPeakHeight,vector<ValueIndex> &peaks)
{
int row = matData.rows;
vector<int> Sign;
float diff;
for (int i = 1; i < row; i++)
{
/*相邻值做差：
*小于0，赋-1
*大于0，赋1
*等于0，赋0
*/
diff = matData.at<float>(i,0)-matData.at<float>(i-1,0);
if (diff > 0)
{
Sign.push_back(1);
}
else if (diff < 0)
{
Sign.push_back(-1);
}
else
{
Sign.push_back(0);
}
}
//再对Sign相邻位做差
//保存极大值
ValueIndex temp;
for (int j = 1; j < Sign.size(); j++)
{
int diff = Sign[j] - Sign[j - 1];
if (diff < 0)
{
if (matData.at<float>(j,0)>minPeakHeight)
{
//根据峰值最小高度进行筛选
temp.index=j;
temp.value=matData.at<float>(j,0);
peaks.push_back(temp);
}
}
}
if(minPeakDistance>0)
{
int i = 1;
while(i<peaks.size())
{
int sub = peaks[i].index - peaks[i-1].index;
if(sub < minPeakDistance)
{
peaks.erase(peaks.begin()+i);
}
else i ++;
}
}
}
/*
* 图像灰度化函数
*/
void DetectParkingSlot::rgbGray(Mat &srcImg,Mat &grayImg)
{
if(srcImg.empty())
{
cout<<"can not load image";
exit(1);
}
if (srcImg.channels()==1)
{
cout<<"please load a RGB image"<<endl;
exit(1);
}
for (int i = 0; i < srcImg.rows; i++)
{
Vec3b *dataColor = srcImg.ptr<Vec3b>(i);//彩色图
uchar *dataGray = grayImg.ptr<uchar>(i);//灰度图
for (int j = 0; j < srcImg.cols; j++)
{
if (srcImg.channels()==3)
{
dataGray[j]=0.5 * dataColor[j][1] + 0.5 * dataColor[j][2];
}
}
}
}
/*
* srcImage:经预处理后的灰度图像，数据类型为32F
* startLoction：为全局的按列求和后，强度最大值的位置
* step 滑窗步长
* windowWidth 滑窗的宽
* windowHeight 滑窗的高
* linePoint：按强度最大值，检测出垂直车位线上的点
*/
void DetectParkingSlot::VerticalProjectionW(Mat &srcImage,Point &startLoction,int step,int windowWidth,
int windowHeight, vector<Point> &linePoint)
{
int halfWidth=windowWidth/2;
int halfHeight=windowHeight/2;
//类似卷积后求图像大小
int windowsnum=(srcImage.rows-windowHeight)/step+1;
Mat windowimage;
Mat sumwindowcol=Mat::zeros(1,windowWidth,CV_32F);
double maxValue = 0;
Point maxloction;
linePoint.push_back(startLoction);
for (int i=0;i<windowsnum;i++)
{
if(linePoint.back().x < halfWidth)
{
Rect roi(0, step * i, windowWidth, windowHeight);
windowimage = srcImage(roi);
reduce(windowimage, sumwindowcol, 0, CV_REDUCE_SUM, CV_32F);
minMaxLoc(sumwindowcol, 0, &maxValue, 0, &maxloction);
if (maxValue > 5)
{
Point temp=Point(maxloction.x, step * i + halfHeight);
linePoint.push_back(temp);
}
}
else if(linePoint.back().x+halfWidth>srcImage.cols)
{
Rect roi(srcImage.cols-windowWidth,step*i,windowWidth,windowHeight);
windowimage=srcImage(roi);
reduce(windowimage,sumwindowcol,0,CV_REDUCE_SUM,CV_32F);
if (maxValue > 5)
{
Point temp=Point(srcImage.cols-windowWidth + maxloction.x, step * i + halfHeight);
linePoint.push_back(temp);
}
}
else
{
Rect roi(linePoint.back().x - halfWidth, step * i, windowWidth, windowHeight);
windowimage = srcImage(roi);
reduce(windowimage, sumwindowcol, 0, CV_REDUCE_SUM, CV_32F);
minMaxLoc(sumwindowcol, 0, &maxValue, 0, &maxloction);
if (maxValue > 5) {
Point temp=Point(linePoint.back().x - halfWidth + maxloction.x, step * i + halfHeight);
linePoint.push_back(temp);
}
}
}
linePoint.erase(linePoint.begin());
}
/*
* srcImage:经预处理后的灰度图像，数据类型为32F
* startLoction:全局的按列求和和按行求和后最大值的交点位置
* linePoint：按强度最大值，检测出水平车位线上的点
*/
int DetectParkingSlot::HorizonProjectionW(Mat &srcImage,Point &startLoction, int step,int windowWidth,
int windowHeight,vector<Point> &linePoint)
{
int halfWidth=windowWidth/2;
int halfHeight=windowHeight/2;
//类似卷积后求图像大小
int windowsnum=(srcImage.cols-startLoction.x-windowWidth)/step+1;
if(windowsnum<2)
{
return 0;
}
Mat windowimage;
Mat sumwindowrow=Mat::zeros(windowHeight,1,CV_32F);
double maxValue = 0;
Point maxloction;
linePoint.push_back(startLoction);
//暂时没有考虑垂直线极其靠近边缘的情况，即startloction.y-100<0
for(int i=0;i<windowsnum;i++)
{
if(linePoint.back().y-halfHeight<0)
{
Rect roi(startLoction.x+step*i,0,windowWidth,windowHeight);
windowimage=srcImage(roi);
reduce(windowimage, sumwindowrow, 1, CV_REDUCE_SUM,CV_32F);
minMaxLoc(sumwindowrow,0,&maxValue, 0, &maxloction);
if (maxValue>20)
{
Point temp=Point(startLoction.x+step*i+halfWidth,maxloction.y);
linePoint.push_back(temp);
}
}
else if(linePoint.back().y+halfHeight>srcImage.rows)
{
Rect roi(startLoction.x+step*i,srcImage.rows-windowHeight,windowWidth,windowHeight);
windowimage=srcImage(roi);
reduce(windowimage, sumwindowrow, 1, CV_REDUCE_SUM,CV_32F);
minMaxLoc(sumwindowrow,0,&maxValue, 0, &maxloction);
if (maxValue>20)
{
Point temp=Point(startLoction.x+step*i+halfWidth,srcImage.rows-windowHeight+maxloction.y);
linePoint.push_back(temp);
}
}
else
{
Rect roi(startLoction.x+step*i,linePoint.back().y-halfHeight,windowWidth,windowHeight);
windowimage=srcImage(roi);
reduce(windowimage, sumwindowrow, 1, CV_REDUCE_SUM,CV_32F);
minMaxLoc(sumwindowrow,0,&maxValue, 0, &maxloction);
if (maxValue>0)
{
Point temp=Point(startLoction.x+step*i+halfWidth,linePoint.back().y-halfHeight+maxloction.y);
linePoint.push_back(temp);
}
}
}
linePoint.erase(linePoint.begin());
return 1;
}
/*
* lineA 拟合出的垂直车位线的参数
* lineB 拟合出的水平车位线的参数
*/
Point2f DetectParkingSlot::getCrossPoint(Vec4f &lineA, Vec4f &lineB)
{
Point2f crossPoint;
if(lineA[1]==1)
{
double kb = lineB[1]/lineB[0];
crossPoint.x=lineA[2];
crossPoint.y=kb*(crossPoint.x-lineB[2])+lineB[3];
}
else{
double ka,kb;
ka = lineA[1]/lineA[0]; //求出LineA斜率
kb = lineB[1]/lineB[0]; //求出LineB斜率
crossPoint.x = (ka*lineA[2] - lineA[3] - kb*lineB[2] + lineB[3]) / (ka - kb);
crossPoint.y = (ka*kb*(lineA[2] - lineB[2]) + ka*lineB[3] - kb*lineA[3]) / (ka - kb);
}
return crossPoint;
}
/*
* 检测车位线函数
* srcImage：原始图像
* srcRoi：在原图像上截取的感兴趣区域
* frangiRoi：frangi滤波后去掉四周的白色区域
* parkingSpacePoint：检测出的车位线交点的位置，顺序为按照y轴从小到大。
*/
int DetectParkingSlot::detecSlot(Mat &srcImage,Rect &srcRoi,Slot_Point &parkingSlotPoint)
{
Mat roi_image0=srcImage(srcRoi);
Mat gray(roi_image0.rows,roi_image0.cols,CV_8UC1);
rgbGray(roi_image0,gray);
Point roistart(srcRoi.x,srcRoi.y);
Size roisize(srcRoi.width,srcRoi.height);
//分别按列累加和按行累加
//获取最大合适值
int maxvalue=HistFromroi(gray);
Mat grad_y,abs_grad_y;
Mat gray_image=gray_binnary(gray,maxvalue);
imshow("gray",gray_image);
Sobel(gray_image, grad_y,CV_16S,0, 1,3, 1, 1, BORDER_DEFAULT);
convertScaleAbs(grad_y,abs_grad_y);
imshow("y向soble", abs_grad_y);
// 按行投影到Ｙ轴
Mat sumcol=Mat::zeros(1,roisize.width,CV_32F);
Mat sumrow=Mat::zeros(roisize.height,1,CV_32F);
reduce(gray_image, sumcol, 0, CV_REDUCE_SUM,CV_32F);
reduce(abs_grad_y, sumrow, 1, CV_REDUCE_SUM,CV_32F);
//垂直车位线粗略检测，累加强度最大值的位置
double maxValue_x = 0;
Point maxloction_x;
minMaxLoc(sumcol,0,&maxValue_x, 0, &maxloction_x);
//垂直车位线精确检测，滑窗检测
vector<Point>linepointcol;
VerticalProjectionW(gray_image,maxloction_x,50,50,50,linepointcol);
if(linepointcol.size()<2)
{
//cout<<"未检测到车位线"<<endl;
namedWindow("image",WINDOW_NORMAL);
imshow("image",srcImage);
return 0;
}
//将检测出的点转换到原图上
for(int i=0;i<linepointcol.size();i++)
{
linepointcol[i]=linepointcol[i]+roistart;
}
Vec4f line_paracol;
fitLine(linepointcol, line_paracol, cv::DIST_L2, 0, 1e-2, 1e-2);
//画出检测出的点和拟合出的垂直线
if(line_paracol[0]>0.001)
{
Point point1;
point1.x = line_paracol[2];
point1.y = line_paracol[3];
double k1 = line_paracol[1] / line_paracol[0];
//计算直线的端点(y = k(x - x0) + y0)
Point point11, point12;
point11.x = 0;
point11.y = k1 * ( point11.x - point1.x) + point1.y;
point12.x = srcImage.cols-1;
point12.y = k1 * (point12.x - point1.x) + point1.y;
cout<<"the point11.y is "<<point11.y<<endl;
cout<<"the point12.y is "<<point12.y<<endl;
cv::line(srcImage, point11, point12, cv::Scalar(0, 255, 0), 2,CV_AA);
for (int i = 0; i < linepointcol.size(); i++)
{
circle(srcImage, linepointcol[i], 3, cv::Scalar(0, 0, 255), 2, 8, 0);
}
}
else
{
cv::line(srcImage, Point(line_paracol[2],0), Point(line_paracol[2],srcImage.rows), cv::Scalar(0, 255, 0), 2,CV_AA);
for (int i = 0; i < linepointcol.size(); i++)
{
circle(srcImage, linepointcol[i], 3, cv::Scalar(0, 0, 255), 2, 8, 0);
}
}
//通过求sumrow前两个最大的波峰，粗略找到水平车位线的位置
//将灰度累加强度值归一化到0～255
double maxValue_y=0;
double minValue_y=0;
minMaxLoc(sumrow,&minValue_y, &maxValue_y, 0, 0);
sumrow=(sumrow-minValue_y)*255/(maxValue_y-minValue_y);
vector<ValueIndex>peaks;
findPeaks(sumrow,100,100,peaks); //设置峰值的最小阈值为100
sort(peaks.begin(), peaks.end(),comp);//将峰值降序排列
Point startloction; //水平滑窗开始的位置
vector<Point2f>parkingSpacePoint;
if(peaks.size()>1)
{
for(int i=0;i<2;i++)
{
startloction=Point(maxloction_x.x,peaks[i].index);
vector<Point>linepointrow0;
//cout<<startloction<<endl;
HorizonProjectionW(abs_grad_y,startloction,20,40,30,linepointrow0);
Point2f crossPoint0;
Vec4f line_pararow0;
if (linepointrow0.size()>1)
{
//将所有点转换到原图坐标中
for(int i=0;i<linepointrow0.size();i++)
{
linepointrow0[i]=linepointrow0[i]+roistart;
}
//将检测出的点画到原图上
for (int i = 0; i < linepointrow0.size(); i++)
{
circle(srcImage, linepointrow0[i], 3, cv::Scalar(0, 0, 255), 2, 8, 0);
}
fitLine(linepointrow0, line_pararow0, cv::DIST_L2, 0, 1e-2, 1e-2);
//求交点
crossPoint0=getCrossPoint(line_paracol,line_pararow0);
//画出拟合的水平线
Point point2;
point2.x = line_pararow0[2];
point2.y = line_pararow0[3];
double k2 = line_pararow0[1] / line_pararow0[0];
//计算直线的终点(y = k(x - x0) + y0),起点为交点
Point point21;
point21.x = srcImage.cols-1;
point21.y = k2 * (point21.x - point2.x) + point2.y;
cv::line(srcImage, crossPoint0, point21, cv::Scalar(0, 255, 0), 2,CV_AA);
}
else
{
if(line_paracol[0]>0.001)
{
double k=line_paracol[1]/line_paracol[0];
double b=line_paracol[3]-k*line_paracol[2];
crossPoint0.y=peaks[i].index+roistart.y; //转换到原图坐标
crossPoint0.x=(crossPoint0.y-b)/k;
//画出垂直于垂直车位线的水平线
double k1 = 1/k;
Point point21;
point21.x = srcImage.cols-1;
point21.y = k1 * (point21.x - crossPoint0.x) + crossPoint0.y;
cv::line(srcImage, crossPoint0, point21, cv::Scalar(0, 255, 0), 2,CV_AA);
}
else
{
crossPoint0.y=peaks[i].index+roistart.y; //转换到原图坐标
crossPoint0.x=line_paracol[2];
//画出垂直于垂直车位线的水平线
line(srcImage, crossPoint0, Point(srcImage.cols-1,crossPoint0.y), cv::Scalar(0, 255, 0), 2,CV_AA);
}
}
parkingSpacePoint.push_back(crossPoint0);
}
}
//只有一个波峰的时候，暂时考虑寻找水平车位线
else if(peaks.size()==1)
{
startloction=Point(maxloction_x.x,peaks[0].index);
vector<Point>linepointrow0;
HorizonProjectionW(gray_image,startloction,50,50,50,linepointrow0);
Point2f crossPoint0;
Vec4f line_pararow0;
if (linepointrow0.size()>1)
{
//将所有点转换到原图坐标中
for(int i=0;i<linepointrow0.size();i++)
{
linepointrow0[i]=linepointrow0[i]+roistart;
}
//将检测出的点画到原图上
for (int i = 0; i < linepointrow0.size(); i++)
{
circle(srcImage, linepointrow0[i], 5, cv::Scalar(0, 0, 255), 2, 8, 0);
}
fitLine(linepointrow0, line_pararow0, cv::DIST_L2, 0, 1e-2, 1e-2);
//求交点
crossPoint0=getCrossPoint(line_paracol,line_pararow0);
//画出拟合的水平线
Point point2;
point2.x = line_pararow0[2];
point2.y = line_pararow0[3];
double k2 = line_pararow0[1] / line_pararow0[0];
//计算直线的终点(y = k(x - x0) + y0),起点为交点
Point point21;
point21.x = srcImage.cols-1;
point21.y = k2 * (point21.x - point2.x) + point2.y;
cv::line(srcImage, crossPoint0, point21, cv::Scalar(0, 255, 0), 2,CV_AA);
}
else
{
double k=line_paracol[1]/line_paracol[0];
double b=line_paracol[3]-k*line_paracol[2];
crossPoint0.y=peaks[0].index+roistart.y;
crossPoint0.x=(crossPoint0.y-b)/k;
//画出垂直于垂直车位线的水平线
double k1 = 1/k;
Point point21;
point21.x = srcImage.cols-1;
point21.y = k1 * (point21.x - crossPoint0.x) + crossPoint0.y;
cv::line(srcImage, crossPoint0, point21, cv::Scalar(0, 255, 0), 2,CV_AA);
}
parkingSpacePoint.push_back(crossPoint0);
}
else
{
//cout<<"未检测到车位线"<<endl;
namedWindow("image",WINDOW_NORMAL);
imshow("image",srcImage);
return 0;
}
if(parkingSpacePoint.size()>0)
{
SlotPoint temp;
sort(parkingSpacePoint.begin(),parkingSpacePoint.end(),comp1);
for (int i = 0; i < parkingSpacePoint.size(); i++)
{
temp.x=int(parkingSpacePoint[i].x);
temp.y=int(parkingSpacePoint[i].y);
parkingSlotPoint.push_back(temp);
circle(srcImage, parkingSpacePoint[i], 2, cv::Scalar(0, 255, 255), 2, 8, 0);
}
}
//向共享内存传送数据
if (!mSlotData.sendData(parkingSlotPoint))
{
std::cerr << "RadarAnalysis send esr to share memory buf error !!" << std::endl;
}
namedWindow("image",WINDOW_NORMAL);
imshow("image",srcImage);
return 1;
}