基于C++实现的车道线检测识别系统源码+注释说明

#include<string> #include<vector> #include<iostream> #include"roadDetection.h" //#define __TEST__ #define __DEBUG__ using namespace std; using namespace cv; //调试时显示处理后的图片 void myShow(std::string str, Mat inputImage) { #ifdef __DEBUG__ imshow(str, inputImage); #endif } void testShow(std::string str, Mat inputImage) { #ifdef __TEST__ imshow(str, inputImage); #endif } //高斯去噪 void RoadDetection::deNoise(Mat inputImage, Mat& outputImage) { GaussianBlur(inputImage, outputImage, Size(3, 3), 0, 0); //blur(inputImage, outputImage, Size(7, 7)); //均值 //medianBlur(inputImage, outputImage, 7); //中值 //bilateralFilter(inputImage, outputImage, 25, 25 * 2, 25 / 2); //双边 } //闭运算 void RoadDetection::CloseOperation(Mat inputImage, Mat& outputImage) { Mat element = getStructuringElement(MORPH_RECT, Size(7, 7)); //进行形态学操作 //morphologyEx(inputImage, outputImage, MORPH_GRADIENT, element); //闭运算 morphologyEx(inputImage, outputImage, MORPH_CLOSE, element); //morphologyEx(inputImage, outputImage, MORPH_OPEN, element); //morphologyEx(inputImage, outputImage, MORPH_DILATE, element); //膨胀 //dilate(inputImage, outputImage, element); //腐蚀 //erode(inputImage, outputImage, element); } //设置ROI void RoadDetection::mask(Mat inputImage, Mat& outputImage) { Mat mask = Mat::zeros(inputImage.size(), inputImage.type()); Point pts[4] = { //Point(inputImage.cols / 4, inputImage.rows / 4), Point(0, inputImage.rows>>1), //Point(inputImage.cols /4, inputImage.rows >> 1), Point(0, inputImage.rows), Point(inputImage.cols, inputImage.rows), Point(inputImage.cols, inputImage.rows >>1) //Point(inputImage.cols/4*3, inputImage.rows >> 1) }; // Create a binary polygon mask fillConvexPoly(mask, pts, 4, Scalar(255, 255, 255)); // Multiply the edges image and the mask to get the output bitwise_and(inputImage, mask, outputImage); ////定义一个Mat类型并给其设定ROI区域 //Mat imageROI; ////方法一 //imageROI = inputImage(Rect(0, inputImage.rows >> 1, inputImage.cols, inputImage.rows>>1)); } Mat Histogram(Mat inputImage) { /// 分割成3个单通道图像 ( R, G 和 B ) vector<Mat> rgb_planes; split(inputImage, rgb_planes); /// 设定bin数目 int histSize = 255; /// 设定取值范围 ( R,G,B) ) float range[] = { 0, 255 }; const float* histRange = { range }; bool uniform = true; bool accumulate = false; Mat r_hist, g_hist, b_hist; /// 计算直方图: calcHist(&rgb_planes[0], 1, 0, Mat(), r_hist, 1, &histSize, &histRange, uniform, accumulate); //calcHist(&rgb_planes[1], 1, 0, Mat(), g_hist, 1, &histSize, &histRange, uniform, accumulate); //calcHist(&rgb_planes[2], 1, 0, Mat(), b_hist, 1, &histSize, &histRange, uniform, accumulate); // 创建直方图画布 int hist_w = 400; int hist_h = 400; int bin_w = cvRound((double)hist_w / histSize); Mat histImage(hist_w, hist_h, CV_8UC3, Scalar(0, 0, 0)); /// 将直方图归一化到范围 [ 0, histImage.rows ] normalize(r_hist, r_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat()); //normalize(g_hist, g_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat()); //normalize(b_hist, b_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat()); /// 在直方图画布上画出直方图 for (int i = 1; i < histSize; i++) { line(histImage, Point(bin_w*(i - 1), hist_h - cvRound(r_hist.at<float>(i - 1))), Point(bin_w*(i), hist_h - cvRound(r_hist.at<float>(i))), Scalar(255, 255, 255), 2, 8, 0); //line(histImage, Point(bin_w*(i - 1), hist_h - cvRound(g_hist.at<float>(i - 1))), // Point(bin_w*(i), hist_h - cvRound(g_hist.at<float>(i))), // Scalar(0, 255, 0), 2, 8, 0); //line(histImage, Point(bin_w*(i - 1), hist_h - cvRound(b_hist.at<float>(i - 1))), // Point(bin_w*(i), hist_h - cvRound(b_hist.at<float>(i))), // Scalar(255, 0, 0), 2, 8, 0); } return histImage; } //直方图均衡化 void RoadDetection::HistEqualize(Mat inputImage, Mat& outputImage) { cvtColor(inputImage, inputImage, CV_RGB2GRAY); testShow("cvtColor", inputImage); testShow("均衡化前直方图", Histogram(inputImage)); vector<Mat> splitBGR(inputImage.channels()); split(inputImage, splitBGR); for (int i = 0; i < inputImage.channels(); ++i) { equalizeHist(splitBGR[i], splitBGR[i]); } Mat mergeImg; merge(splitBGR, mergeImg); mergeImg.copyTo(outputImage); testShow("均衡化后直方图", Histogram(outputImage)); //outputImage.convertTo(outputImage, -1, 1, 20); //testShow("convertTo", outputImage); } //亮度特征 void RoadDetection::Brightness(Mat inputImage, Mat& outputImage) { //cvtColor(inputImage, inputImage, CV_RGB2GRAY); //testShow("cvtColor", inputImage); //Mat tmp1,tmp2; //threshold(inputImage, tmp1, 140, 255, THRESH_TOZERO); //threshold(inputImage, tmp1, 245, 255, THRESH_BINARY); //testShow("250", tmp1); //threshold(inputImage, tmp2, 200, 255, THRESH_BINARY); //testShow("200", tmp2); //bitwise_and(tmp1, tmp2, outputImage); //addWeighted(tmp1, 1, tmp2, 1, 0.0, outputImage); threshold(inputImage, outputImage, 250, 255, THRESH_BINARY); } //颜色阈值 void RoadDetection::colorThreshold(Mat inputImage, Mat& outputImage) { //cvtColor(inputImage, outputImage, CV_RGB2GRAY); //threshold(inputImage, outputImage, 140, 255, THRESH_BINARY); Mat bgr,hsv, mask,result; testShow("input", inputImage); inputImage.convertTo(bgr, CV_32FC3, 1.0 / 255, 0); testShow("bgr", bgr); cvtColor(bgr, hsv, COLOR_BGR2HSV); testShow("COLOR_BGR2HSV", hsv); inRange(hsv, Scalar(0, 0, (float)(230)/255), Scalar(90, (float)255/255, (float)(255)/255), mask); //inRange(hsv, Scalar(0, 0, (float)(150) / 255), Scalar(90, (float)255 / 255, (float)(255) / 255), mask); testShow("inRange", mask); //只保留 //result = Mat::zeros(bgr.size(), CV_32FC3); //for (int r = 0; r < bgr.rows; r++) //{ // for (int c = 0; c < bgr.cols; c++) // { // if (mask.at<uchar>(r, c) == 255) // { // result.at<Vec3f>(r, c) = bgr.at<Vec3f>(r, c); // } // } //} result = mask; result.convertTo(result, CV_8UC3, 255.0, 0); outputImage = result.clone(); } //边缘检测 void RoadDetection::edgeDetector(Mat inputImage, Mat& outputImage) { //testShow("threshold", outputImage); Canny(inputImage, outputImage, 150, 100, 3); //Canny边缘检测算子 //Laplacian(inputImage, outputImage, CV_16S, 3, 1, 0, BORDER_DEFAULT); //拉普拉斯算子 //convertScaleAbs(outputImage, outputImage); //Soble边缘检测算子 //Mat grad_x, grad_y; //Mat abs_grad_x, abs_grad_y; ////【3】求 X方向梯度 //Sobel(inputImage, grad_x, CV_16S, 1, 0, 3, 1, 1, BORDER_DEFAULT); //convertScaleAbs(grad_x, abs_grad_x); //imshow("【效果图】 X方向Sobel", abs_grad_x); ////【4】求Y方向梯度 //Sobel(inputImage, grad_y, CV_16S, 0, 1, 3, 1, 1, BORDER_DEFAULT); //convertScaleAbs(grad_y, abs_grad_y); //imshow("【效果图】Y方向Sobel", abs_grad_y); ////【5】合并梯度(近似) //addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0, outputImage); //imshow("【效果图】整体方向Sobel", outputImage); } //霍夫线概率变换 vector<Vec4i> RoadDetection::houghLines(Mat inputImage) { vector<Vec4i> line; HoughLinesP(inputImage, line, 1, CV_PI / 180, 20, 20, 30); return line; } //画线 void drawDetectedLines(Mat &image, vector<Vec4i> lines, Scalar color = Scalar(255, 255, 255)) { vector<Vec4i>::const_iterator it2 = lines.begin(); while (it2 != lines.end()) { Point pt1((*it2)[0], (*it2)[1]); Point pt2((*it2)[2], (*it2)[3]); line(image, pt1, pt2, color); ++it2; } } //selected_lines void RoadDetection::lineSeparation(const vector<Vec4i>& lines, vector<std::vector<cv::Vec4i> >& output) { size_t j = 0; Point ini; Point fini; double slope_thresh = 0.3; vector<double> slopes; vector<Vec4i> selected_lines; vector<Vec4i> right_lines, left_lines; // 根据斜率筛选 for (auto i : lines) { ini = cv::Point(i[0], i[1]); fini = cv::Point(i[2], i[3]);