交通标识检测c++代码实例及运行结果

#include<iostream>
#include<opencv2/opencv.hpp>
#define PI 3.1415926

using namespace std;
using namespace cv;

void RGB2HSV(double red, double green, double blue, double& hue, double& saturation, double& intensity)
{

double r, g, b;
double h, s, i;

double sum;
double minRGB, maxRGB;
double theta;

r = red / 255.0;
g = green / 255.0;
b = blue / 255.0;

minRGB = ((r<g) ? (r) : (g));
minRGB = (minRGB<b) ? (minRGB) : (b);

maxRGB = ((r>g) ? (r) : (g));
maxRGB = (maxRGB>b) ? (maxRGB) : (b);

sum = r + g + b;
i = sum / 3.0;

if (i<0.001 || maxRGB - minRGB<0.001)
{

h = 0.0;
s = 0.0;
}
else
{
s = 1.0 - 3.0*minRGB / sum;
theta = sqrt((r - g)*(r - g) + (r - b)*(g - b));
theta = acos((r - g + r - b)*0.5 / theta);
if (b <= g)
h = theta;
else
h = 2 * PI - theta;
if (s <= 0.01)
h = 0;
}

hue = (int)(h * 180 / PI);
saturation = (int)(s * 100);
intensity = (int)(i * 100);
}

void fillHole(const Mat srcBw, Mat &dstBw)
{
Size m_Size = srcBw.size();
Mat Temp = Mat::zeros(m_Size.height + 2, m_Size.width + 2, srcBw.type());
srcBw.copyTo(Temp(Range(1, m_Size.height + 1), Range(1, m_Size.width + 1)));

cv::floodFill(Temp, Point(0, 0), Scalar(255));

Mat cutImg;
Temp(Range(1, m_Size.height + 1), Range(1, m_Size.width + 1)).copyTo(cutImg);

dstBw = srcBw | (~cutImg);
}

int main()
{
Mat srcImg = imread("result\\1.jpg");
//resize(srcImg, srcImg, Size(204.8, 137.6));重置图片大小为什么检测不到
imshow("srcImg",srcImg);
Mat srcImgCopy;
srcImg.copyTo(srcImgCopy);

int width = srcImg.cols;//图像宽度
int height = srcImg.rows;//图像高度
double B = 0.0, G = 0.0, R = 0.0, H = 0.0, S = 0.0, V = 0.0;
Mat matRgb = Mat::zeros(srcImg.size(), CV_8UC1);
int x, y; //循环
for (y = 0; y<height; y++)
{
for (x = 0; x<width; x++)
{
// 获取BGR值
B = srcImg.at<Vec3b>(y, x)[0];
G = srcImg.at<Vec3b>(y, x)[1];
R = srcImg.at<Vec3b>(y, x)[2];
RGB2HSV(R, G, B, H, S, V);
//红色范围
if ((H >= 337 && H <= 360 || H >= 0 && H <= 10) && S >= 12 && S <= 100 && V>20 && V<99)
{
matRgb.at<uchar>(y, x) = 255;
}
}
}
blur(matRgb, matRgb, Size(3, 3));
Mat element = getStructuringElement(MORPH_ELLIPSE, Size(2 * 1 + 1, 2 * 1 + 1), Point(1, 1));
Mat element1 = getStructuringElement(MORPH_ELLIPSE, Size(2 * 3 + 1, 2 * 3 + 1), Point(3, 3));
erode(matRgb, matRgb, element);//腐蚀
imshow("erode", matRgb);
dilate(matRgb, matRgb, element1);//膨胀
imshow("dilate", matRgb);
fillHole(matRgb, matRgb);//填充
imshow("fillHole", matRgb);
Mat matRgbCopy;
matRgb.copyTo(matRgbCopy);
//cvtColor(srcImg, grayImg, CV_BGR2GRAY);
vector<vector<Point>>contours; //轮廓
vector<Vec4i> hierarchy;//分层
findContours(matRgb, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));//寻找轮廓
vector<vector<Point>> contours_poly(contours.size());  //近似后的轮廓点集
vector<Rect> boundRect(contours.size());  //包围点集的最小矩形vector
vector<Point2f> center(contours.size());  //包围点集的最小圆形vector
vector<float> radius(contours.size());  //包围点集的最小圆形半径vector
for (int i = 0; i < contours.size(); i++)
{
approxPolyDP(Mat(contours[i]), contours_poly[i], 3, true); //对多边形曲线做适当近似，contours_poly[i]是输出的近似点集
boundRect[i] = boundingRect(Mat(contours_poly[i])); //计算并返回包围轮廓点集的最小矩形
minEnclosingCircle(contours_poly[i], center[i], radius[i]);//计算并返回包围轮廓点集的最小圆形及其半径
}

Mat drawing = Mat::zeros(matRgb.size(), CV_8UC3);
//int count1 = 0;
for (int i = 0; i < contours.size(); i++)
{
Rect rect = boundRect[i];
//首先进行一定的限制，筛选出区域
//高宽比限制
float ratio = (float)rect.width / (float)rect.height;
//轮廓面积
float Area = (float)rect.width * (float)rect.height;
float dConArea = (float)contourArea(contours[i]);
float dConLen = (float)arcLength(contours[i], 1);
if (dConArea < 400)
continue;
if (ratio > 2 || ratio < 0.5)
continue;

//进行圆筛选，通过四块的缺失像素比较
Mat roiImage;
matRgbCopy(rect).copyTo(roiImage);
Mat temp;
srcImgCopy(rec
b3e5
t).copyTo(temp);
float C = (4 * PI*dConArea) / (dConLen*dConLen);
if (C < 0.4)//利用圆度初步对形状进行筛选
continue;
srcImgCopy(rect).copyTo(roiImage);
Scalar color = (0, 0, 255);//蓝色线画轮廓
drawContours(drawing, contours_poly, i, color, 1, 8, vector<Vec4i>(), 0, Point());
rectangle(drawing, boundRect[i].tl(), boundRect[i].br(), color, 2, 8, 0);
rectangle(srcImg, boundRect[i].tl(), boundRect[i].br(), color, 2, 8, 0);
imshow("1_1.jpg", srcImg);
imwrite("result\\1_1.jpg", srcImg);//保存最终的检测识别结果
}
waitKey(0);
//system("pause");
return 0;
}