函数[laneFound，ltPts，rtPts]=从网络输出中检测车道（帧，laneCoeffMeans，laneCoeffStds）%1，计算图像%坐标中的左右车道点。摄像机坐标由加州理工学院mono%摄像机型号描述#codegen%A持久对象mynet用于加载series网络对象。第一次调用此函数时，将构造持久对象并%setup。在以后调用该函数时，将%重用同一对象以调用predict on inputs，从而避免重建和%重新加载网络对象。宿存花被；如果isempty（lanenet）lanenet=coder.loadDeepLearningNetwork（'lanenet.mat'，'lanenet'）；end lanecoeffsNetworkOutput=lanenet.predict（permute（frame[2 1 3]）；%通过反转规范化步骤params=lanecoeffsNetworkOutput.*laneCoeffStds+lanecoeffsmeans恢复原始系数；isRightLaneFound=abs（参数（6））>0.5；%c应大于0.5，以使其成为右车道isLeftLaneFound=abs（参数（3））>0.5；车辆积分=3:30；%米，传感器前面ltPts=编码器。零拷贝（零（28,2，'single'）；rtPts=编码器.nullcopy（零（28,2，'single'）；如果isRightLaneFound和isLeftLaneFound rtBoundary=params（4:6）；rt_y=计算边界模型（rtBoundary，车辆点）；ltBoundary=参数（1:3）；lt_y=计算边界模型（ltBoundary，车辆点）；%可视化ego车辆的车道边界tform=获取\u tformToImage；%将车辆映射到图像坐标ltPts=t形式转换点相反（[vehicleXPoints'，lt_y']）；rtPts=t形式转换点相反（[vehicleXPoints'，rt_y']）；laneFound=true；else-laneFound=false；结束函数yWorld=computeBoundaryModel（model，xWorld）yWorld=polyval（model，xWorld）；结束函数tform=get_tformToImage%基于相机设置计算外部偏航=0；螺距=14；%摄像机的俯仰角（以度为单位）滚动=0；平移=平移向量（偏航、俯仰、横摇）；旋转=旋转矩阵（偏航、俯仰、横摇）；%构造一个摄像机矩阵聚焦长度=[309.4362344.2161]；主点=[318.9034257.5352]；歪斜=0；camMatrix=[旋转；平移]*内部矩阵（聚焦长度，…倾斜，主点）；%将camMatrix转换为二维单应性tform2D=[camMatrix（1，：）；camMatrix（2，：）；camMatrix（4，：）]；%下降Z t形式=投影2D（t或M2D）；tform=tform.invert（）；端部功能平移=平移向量（偏航、俯仰、滚转）传感器位置=[0]；高度=2.1798；%离地面旋转矩阵的安装高度（米）=（…旋转（偏航）*…%最后一次旋转旋转（90节）*…旋转（滚转）…%第一次旋转）；%通过添加平移sl=传感器位置来调整传感器位置；转换单位=[sl（2），sl（1），高度]；平移=平移世界单位*旋转矩阵；结束%-------------------------------------------------------------------------------------绕X轴旋转%函数R=rotX（a）a=deg2rad（a）；R=[…10 0；0 cos（a）-sin（a）；0 sin（a）cos（a）]；结束%-------------------------------------------------------------------------------------绕Y轴旋转%函数R=rotY（a）a=deg2rad（a）；R=[…cos（a）0sin（a）；0110；-sin（a）0cos（a）]；结束%-------------------------------------------------------------------------------------绕Z轴旋转%函数R=rotZ（a）a=deg2rad（a）；R=[…cos（a）-sin（a）0；sin（a）cos（a）0；01]；end%-------------------------------------------------------------------------------------%给定偏航、俯仰和横摇，确定适当的欧拉角%，以及应用它们将摄像机的%坐标系与车辆坐标系对齐的顺序。生成的%矩阵是一个旋转矩阵，它与平移向量%一起定义相机的外部参数。函数旋转=旋转矩阵（偏航、俯仰、滚转）旋转=（…旋转（180）*…%上次旋转：点Z向上旋转（-90）*…%X-Y交换旋转（偏航）*…%点相机向前旋转（90俯仰）*…%取消俯仰旋转（滚转）.%第一次旋转：“取消滚转”）；结束函数intrinsicMat=intrinsicMatrix（焦点长度、偏移、主点）intrinsicMat=。。。[FocalLength（1），0，0；…歪斜，FocalLength（2），0；…主点（1），主点（2），1]；终止

代码生成成功:查看报告

．MWReLULayer。o . .MWReLULayerImpl。cu .gitignore MWReLULayerImpl.hpp DeepLearningNetwork.cu MWReLULayerImpl.o DeepLearningNetwork.h MWTargetNetworkImpl.cu DeepLearningNetwork.o MWTargetNetworkImpl.hpp MWCNNLayer.cpp MWTargetNetworkImpl.o MWCNNLayer.hpp MWTensor.hpp MWCNNLayer.o MWTensorBase.cpp MWCNNLayerImpl.cu MWTensorBase.hpp MWCNNLayerImpl.hpp MWTensorBase.o MWCNNLayerImpl.o _clang-format MWCUSOLVERUtils.cpp buildInfo.mat MWCUSOLVERUtils.hpp cnn_lanenet0_0_conv1_b.bin MWCUSOLVERUtils.o cnn_lanenet0_0_conv1_w.bin MWCudaDimUtility.hpp cnn_lanenet0_0_conv2_b.bin MWCustomLayerForCuDNN.cpp cnn_lanenet0_0_conv2_w.bin MWCustomLayerForCuDNN.hpp cnn_lanenet0_0_conv3_b.bin MWCustomLayerForCuDNN.o cnn_lanenet0_0_conv3_w.bin MWElementwiseAffineLayer.cpp cnn_lanenet0_0_conv4_b.bin MWElementwiseAffineLayer.hpp cnn_lanenet0_0_conv4_w.bin MWElementwiseAffineLayer.o cnn_lanenet0_0_conv5_b.bin MWElementwiseAffineLayerImpl.cu cnn_lanenet0_0_conv5_w.bin MWElementwiseAffineLayerImpl.hpp cnn_lanenet0_0_data_offset.bin MWElementwiseAffineLayerImpl.o cnn_lanenet0_0_data_scale.bin MWElementwiseAffineLayerImplKernel.cu cnn_lanenet0_0_fc6_b.bin MWElementwiseAffineLayerImplKernel.o cnn_lanenet0_0_fc6_w.bin MWFCLayer.cpp cnn_lanenet0_0_fcLane1_b.bin MWFCLayer.hpp cnn_lanenet0_0_fcLane1_w.bin MWFCLayer.o cnn_lanenet0_0_fcLane2_b.bin MWFCLayerImpl.cu cnn_lanenet0_0_fcLane2_w.bin MWFCLayerImpl.hpp cnn_lanenet0_0_responseNames.txt MWFCLayerImpl.o codeInfo.mat MWFusedConvReLULayer.cpp codedescriptor.dmr MWFusedConvReLULayer.hpp compileInfo.mat MWFusedConvReLULayer.o defines.txt MWFusedConvReLULayerImpl.cu detect_lane.a MWFusedConvReLULayerImpl.hpp detect_lane.cu MWFusedConvReLULayerImpl.o detect_lane.h MWInputLayer.cpp detect_lane.o MWInputLayer.hpp detect_lane_data.cu MWInputLayer.o detect_lane_data.h MWInputLayerImpl.hpp detect_lane_data.o MWKernelHeaders.hpp detect_lane_initialize.cu MWMaxPoolingLayer.cpp detect_lane_initialize.h MWMaxPoolingLayer.hpp detect_lane_initialize.o MWMaxPoolingLayer.o detect_lane_internal_types.h MWMaxPoolingLayerImpl.cu detect_lane_rtw.mk MWMaxPoolingLayerImpl.hpp detect_lane_terminate.cu MWMaxPoolingLayerImpl.o detect_lane_terminate.h MWNormLayer.cpp detect_lane_terminate.o MWNormLayer.hpp detect_lane_types.h MWNormLayer.o examples MWNormLayerImpl.cu gpu_codegen_info.mat MWNormLayerImpl.hpp html MWNormLayerImpl.o interface MWOutputLayer.cpp mean.bin MWOutputLayer.hpp predict.cu MWOutputLayer.o predict.h MWOutputLayerImpl.cu predict.o MWOutputLayerImpl.hpp rtw_proj.tmw MWOutputLayerImpl.o rtwtypes.h MWReLULayer.cpp MWReLULayer.hpp

/* Copyright 2016 The MathWorks, Inc. */ #include  #include  #include  #include  #include  #include  #include  #include  #include  #include < detect_lane.h" using namespace cv;void readData(float *input, mat&orig, Mat& im) {Size Size(227,227); / /输入数据调整(源自,im,大小,0,0,INTER_LINEAR);(int j = 0; < 227 * 227; j + +) {/ / BGR RGB输入[2 * 227 * 227 + j] =(浮动)(im.data [j * 3 + 0]);输入(1 * 227 * 227 + j] =(浮动)(im.data [j * 3 + 1]);输入[0 * 227 * 227 + j] =(浮动)(im.data [j * 3 + 2]);}} void addLane(float pts[28][2]， Mat & im, int numPts) {std::vector iArray;for (int k = 0;k < numPts;k + +) {iArray.push_back (Point2f (pts [k] [0], pts [k] [1])); } Mat curve(iArray, true); curve.convertTo(curve, CV_32S); //adapt type for polylines polylines(im, curve, false, CV_RGB(255,255,0), 2, LINE_AA); } void writeData(float *outputBuffer, Mat & im, int N, double means[6], double stds[6]) { // get lane coordinates boolean_T laneFound = 0; float ltPts[56]; float rtPts[56]; detect_lane(outputBuffer, means, stds, &laneFound, ltPts, rtPts); if (!laneFound) { return; } float ltPtsM[28][2]; float rtPtsM[28][2]; for(int k=0; k<28; k++) { ltPtsM[k][0] = ltPts[k]; ltPtsM[k][1] = ltPts[k+28]; rtPtsM[k][0] = rtPts[k]; rtPtsM[k][1] = rtPts[k+28]; } addLane(ltPtsM, im, 28); addLane(rtPtsM, im, 28); } void readMeanAndStds(const char* filename, double means[6], double stds[6]) { FILE* pFile = fopen(filename, "rb"); if (pFile==NULL) { fputs ("File error",stderr); return; } // obtain file size fseek (pFile , 0 , SEEK_END); long lSize = ftell(pFile); rewind(pFile); double* buffer = (double*)malloc(lSize); size_t result = fread(buffer,sizeof(double),lSize,pFile); if (result*sizeof(double) != lSize) { fputs ("Reading error",stderr); return; } for (int k = 0 ; k < 6; k++) { means[k] = buffer[k]; stds[k] = buffer[k+6]; } free(buffer); } // Main function int main(int argc, char* argv[]) { float *inputBuffer = (float*)calloc(sizeof(float),227*227*3); float *outputBuffer = (float*)calloc(sizeof(float),6); if ((inputBuffer == NULL) || (outputBuffer == NULL)) { printf("ERROR: Input/Output buffers could not be allocated!\n"); exit(-1); } // get ground truth mean and std double means[6]; double stds[6]; readMeanAndStds("mean.bin", means, stds); if (argc < 2) { printf("Pass in input video file name as argument\n"); return -1; } VideoCapture cap(argv[1]); if (!cap.isOpened()) { printf("Could not open the video capture device.\n"); return -1; } cudaEvent_t start, stop; float fps = 0; cudaEventCreate(&start); cudaEventCreate(&stop); Mat orig, im; namedWindow("Lane detection demo",WINDOW_NORMAL); while(true) { cudaEventRecord(start); cap >> orig; if (orig.empty()) break; readData(inputBuffer, orig, im); writeData(inputBuffer, orig, 6, means, stds); cudaEventRecord(stop); cudaEventSynchronize(stop); char strbuf[50]; float milliseconds = -1.0; cudaEventElapsedTime(&milliseconds, start, stop); fps = fps*.9+1000.0/milliseconds*.1; sprintf (strbuf, "%.2f FPS", fps); putText(orig, strbuf, Point(200,30), FONT_HERSHEY_DUPLEX, 1, CV_RGB(0,0,0), 2); imshow("Lane detection demo", orig); if( waitKey(50)%256 == 27 ) break; // stop capturing by pressing ESC */ } destroyWindow("Lane detection demo"); free(inputBuffer); free(outputBuffer); return 0; }