交通标识牌检测及识别c++代码实例及运行结果 （可自行在网上下载图片测试）
运行环境：vs2013+opencv2.4.9+win10ios
数据来源于GTSRBc++
效果不是很理想（预处理方法、检测用的rgb2hsv、圆度检测，、参数，总之改变程序中不少东西能够尝试提升准确率），但检测及识别的道路是打通了app
c++代码测试
#include<iostream>
#include<opencv2/opencv.hpp>
#include<string>
#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;
//return ;
}
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()
{
char path[512];
CvSVM classifier;//载入分类器
classifier.load("E:\\vs2013\\opencv_code\\GTSRBtrafficSign\\train\\train.xml");//路径
for (int k = 0; k<10; k++)//k为测试图片数量
{
sprintf_s(path, "E:\\vs2013\\opencv_code\\GTSRBtrafficSign\\extractAndPredict\\image\\%d.jpg",k+1);
cout << path << endl;
Mat src = imread(path);
Mat copy;
src.copyTo(copy);
int width = src.cols; //图像宽度
int height = src.rows; //图像高度
//色彩分割
double B = 0.0, G = 0.0, R = 0.0, H = 0.0, S = 0.0, V = 0.0;
Mat matRgb = Mat::zeros(src.size(), CV_8UC1);
Mat Mat_rgb_copy;//一个暂存单元
int x, y;
for (y = 0; y<height; y++)
{
for (x = 0; x<width; x++)
{
B = src.at<Vec3b>(y, x)[0];
G = src.at<Vec3b>(y, x)[1];
R = src.at<Vec3b>(y, x)[2];
RGB2HSV(R, G, B, H, S, V);
//红色：337-360
if ((H >= 337 && H <= 360 || H >= 0 && H <= 10) && S >= 12 && S <= 100 && V>20 && V<99)
{
matRgb.at<uchar>(y, x) = 255;
}
}
}
//imshow("hsi",Mat_rgb);
//imshow("Mat_rgb",Mat_rgb);
medianBlur(matRgb, matRgb, 3);
//imshow("medianBlur", Mat_rgb);
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", Mat_rgb);
dilate(matRgb, matRgb, element1);//膨胀
//imshow("dilate", Mat_rgb);
fillHole(matRgb, matRgb);//填充
//imshow("fillHole", Mat_rgb);
matRgb.copyTo(Mat_rgb_copy);
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));
/// 多边形逼近轮廓 + 获取矩形和圆形边界框
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);//对多边形曲线作适当近似，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];
//cout << rect<<endl;
//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，经过四块的缺失像素比较
Mat roiImage;
Mat_rgb_copy(rect).copyTo(roiImage);
//imshow("roiImage",roiImage);
//imshow("test",roiImage);
Mat temp;
copy(rect).copyTo(temp);
//imshow("test2",temp);//显示从场景图中提取出的标识，留着。
copy(rect).copyTo(roiImage);
//*********svm*********
Mat temp2 = Mat::zeros(temp.size(), CV_8UC1);
cvtColor(temp, temp2, CV_BGR2GRAY);
//resize(temp2, temp2, Size(48, 48));
resize(temp2, temp2, Size(30, 30));//30*30=900
temp2 = temp2.reshape(0, 1);
temp2.convertTo(temp2, CV_32F);
cout << temp2.size() << endl;
int result = (int)classifier.predict(temp2) - 1;//svm预测
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(src, boundRect[i].tl(), boundRect[i].br(), color, 2, 8, 0);
//putText(src, labelname[result], cvPoint(boundRect[i].x, boundRect[i].y - 10), 1, 1, CV_RGB(255, 0, 0), 2);//红色字体注释
//circle( drawing, center[i], (int)radius[i], color, 2, 8, 0 );
count1++;
//sprintf_s(path, "E:\\vs2013\\opencv_code\\GTSRBtrafficSign\\extractAndPredict\\image\\result/%d_%d.jpg", k, count1);
sprintf_s(path, "E:\\vs2013\\opencv_code\\GTSRBtrafficSign\\extractAndPredict\\image\\%d_%d.jpg",k+1, count1);
imwrite(path, src);//保存最终的检测识别结果
}
}
system("pause");
waitKey(0);
return 0;
}
运行结果字体
