function [laneFound, ltPts, rtPts] = detect_lane(frame, laneCoeffMeans, laneCoeffStds) %从网络输出中，计算图像中左右车道点%坐标。相机坐标由加州理工学院的单相机模型描述。一个持久化对象mynet用于加载系列网络对象。在%第一次调用此函数时，将构造持久对象，并且% setup。当该函数随后被调用时，相同的对象将被%重用，以便对输入调用predict，从而避免重构和%重新加载网络对象。持久lanenet;if isempty(lanenet) lanenet = code . loaddeeplearningnetwork (' lanenet . loaddeeplearningnetwork ')席”、“lanenet”);end lanecoeffsNetworkOutput = lanenet。预测(permute(frame， [2 1 3]));通过反向归一化步骤恢复原始coeffs params = lanecoeffsNetworkOutput .* laneCoeffStds + laneCoeffMeans;isRightLaneFound = abs(params(6)) > 0.5; %c should be more than 0.5 for it to be a right lane isLeftLaneFound = abs(params(3)) > 0.5; vehicleXPoints = 3:30; %meters, ahead of the sensor ltPts = coder.nullcopy(zeros(28,2,'single')); rtPts = coder.nullcopy(zeros(28,2,'single')); if isRightLaneFound && isLeftLaneFound rtBoundary = params(4:6); rt_y = computeBoundaryModel(rtBoundary, vehicleXPoints); ltBoundary = params(1:3); lt_y = computeBoundaryModel(ltBoundary, vehicleXPoints); % Visualize lane boundaries of the ego vehicle tform = get_tformToImage; % map vehicle to image coordinates ltPts = tform.transformPointsInverse([vehicleXPoints', lt_y']); rtPts = tform.transformPointsInverse([vehicleXPoints', rt_y']); laneFound = true; else laneFound = false; end end function yWorld = computeBoundaryModel(model, xWorld) yWorld = polyval(model, xWorld); end function tform = get_tformToImage % Compute extrinsics based on camera setup yaw = 0; pitch = 14; % pitch of the camera in degrees roll = 0; translation = translationVector(yaw, pitch, roll); rotation = rotationMatrix(yaw, pitch, roll); % Construct a camera matrix focalLength = [309.4362, 344.2161]; principalPoint = [318.9034, 257.5352]; Skew = 0; camMatrix = [rotation; translation] * intrinsicMatrix(focalLength, ... Skew, principalPoint); % Turn camMatrix into 2-D homography tform2D = [camMatrix(1,:); camMatrix(2,:); camMatrix(4,:)]; % drop Z tform = projective2d(tform2D); tform = tform.invert(); end function translation = translationVector(yaw, pitch, roll) SensorLocation = [0 0]; Height = 2.1798; % mounting height in meters from the ground rotationMatrix = (... rotZ(yaw)*... % last rotation rotX(90-pitch)*... rotZ(roll)... % first rotation ); % Adjust for the SensorLocation by adding a translation sl = SensorLocation; translationInWorldUnits = [sl(2), sl(1), Height]; translation = translationInWorldUnits*rotationMatrix; end %------------------------------------------------------------------ % Rotation around X-axis function R = rotX(a) a = deg2rad(a); R = [... 1 0 0; 0 cos(a) -sin(a); 0 sin(a) cos(a)]; end %------------------------------------------------------------------ % Rotation around Y-axis function R = rotY(a) a = deg2rad(a); R = [... cos(a) 0 sin(a); 0 1 0; -sin(a) 0 cos(a)]; end %------------------------------------------------------------------ % Rotation around Z-axis function R = rotZ(a) a = deg2rad(a); R = [... cos(a) -sin(a) 0; sin(a) cos(a) 0; 0 0 1]; end %------------------------------------------------------------------ % Given the Yaw, Pitch, and Roll, determine the appropriate Euler angles % and the sequence in which they are applied to align the camera's % coordinate system with the vehicle coordinate system. The resulting % matrix is a Rotation matrix that together with the Translation vector % defines the extrinsic parameters of the camera. function rotation = rotationMatrix(yaw, pitch, roll) rotation = (... rotY(180)*... % last rotation: point Z up rotZ(-90)*... % X-Y swap rotZ(yaw)*... % point the camera forward rotX(90-pitch)*... % "un-pitch" rotZ(roll)... % 1st rotation: "un-roll" ); end function intrinsicMat = intrinsicMatrix(FocalLength, Skew, PrincipalPoint) intrinsicMat = ... [FocalLength(1) , 0 , 0; ... Skew , FocalLength(2) , 0; ... PrincipalPoint(1), PrincipalPoint(2), 1]; end

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．MWMaxPoolingLayer。o . .MWNormLayer.cpp .gitignore MWNormLayer.hpp DeepLearningNetwork。铜MWNormLayer。o DeepLearningNetwork.h MWOutputLayer.cpp DeepLearningNetwork.ho MWOutputLayer.hpp MWActivationFunctionType.hpp MWOutputLayer.hpp MWOutputLayer.hppo MWCNNLayer.cpp MWRNNParameterTypes.hpp MWCNNLayer.hpp MWReLULayer.cpp MWCNNLayer。o MWReLULayer.hpp MWCNNLayerImplBase.hpp MWReLULayer.hppo MWCUSOLVERUtils.cpp MWTargetNetworkImplBase.hpp MWCUSOLVERUtils.hpp MWTargetTypes.hpp MWCUSOLVERUtils.cpp MWTargetNetworkImplBase.hpp MWCUSOLVERUtils.hppo MWTensorBase.hpp mwcudadimulti .hpp MWTensorBase.cpp MWCudaMemoryFunctions.hpp MWTensorBase.hpp MWCudnnCNNLayerImpl. hpp MWCudaMemoryFunctions.hpp MWTensorBase.hpp MWCudnnCNNLayerImpl。铜MWTensorBase。MWCudnnCNNLayerImpl.hpp _clang-format MWCudnnCNNLayerImpl. oo buildInfo。MWCudnnCommonHeaders.hpp cnn_lanenet0_0_conv1_b.bin MWCudnnCustomLayerBase. mat MWCudnnCommonHeaders.hppcu cnn_lanenet0_0_conv1_w.bin MWCudnnCustomLayerBase.hpp cnn_lanenet0_0_conv2_b.bin MWCudnnCustomLayerBase.o cnn_lanenet0_0_conv2_w.bin MWCudnnElementwiseAffineLayerImpl.cu cnn_lanenet0_0_conv3_b.bin MWCudnnElementwiseAffineLayerImpl.hpp cnn_lanenet0_0_conv3_w.bin MWCudnnElementwiseAffineLayerImpl.o cnn_lanenet0_0_conv4_b.bin MWCudnnFCLayerImpl.cu cnn_lanenet0_0_conv4_w.bin MWCudnnFCLayerImpl.hpp cnn_lanenet0_0_conv5_b.bin MWCudnnFCLayerImpl.o cnn_lanenet0_0_conv5_w.bin MWCudnnFusedConvActivationLayerImpl.cu cnn_lanenet0_0_data_offset.bin MWCudnnFusedConvActivationLayerImpl.hpp cnn_lanenet0_0_data_scale.bin MWCudnnFusedConvActivationLayerImpl.o cnn_lanenet0_0_fc6_b.bin MWCudnnInputLayerImpl.hpp cnn_lanenet0_0_fc6_w.bin MWCudnnLayerImplFactory.cu cnn_lanenet0_0_fcLane1_b.bin MWCudnnLayerImplFactory.hpp cnn_lanenet0_0_fcLane1_w.bin MWCudnnLayerImplFactory.o cnn_lanenet0_0_fcLane2_b.bin MWCudnnMaxPoolingLayerImpl.cu cnn_lanenet0_0_fcLane2_w.bin MWCudnnMaxPoolingLayerImpl.hpp cnn_lanenet0_0_responseNames.txt MWCudnnMaxPoolingLayerImpl.o codeInfo.mat MWCudnnNormLayerImpl.cu codedescriptor.dmr MWCudnnNormLayerImpl.hpp compileInfo.mat MWCudnnNormLayerImpl.o detect_lane.a MWCudnnOutputLayerImpl.cu detect_lane.cu MWCudnnOutputLayerImpl.hpp detect_lane.h MWCudnnOutputLayerImpl.o detect_lane.o MWCudnnReLULayerImpl.cu detect_lane_data.cu MWCudnnReLULayerImpl.hpp detect_lane_data.h MWCudnnReLULayerImpl.o detect_lane_data.o MWCudnnTargetNetworkImpl.cu detect_lane_initialize.cu MWCudnnTargetNetworkImpl.hpp detect_lane_initialize.h MWCudnnTargetNetworkImpl.o detect_lane_initialize.o MWElementwiseAffineLayer.cpp detect_lane_internal_types.h MWElementwiseAffineLayer.hpp detect_lane_rtw.mk MWElementwiseAffineLayer.o detect_lane_terminate.cu MWElementwiseAffineLayerImplKernel.cu detect_lane_terminate.h MWElementwiseAffineLayerImplKernel.o detect_lane_terminate.o MWFCLayer.cpp detect_lane_types.h MWFCLayer.hpp examples MWFCLayer.o gpu_codegen_info.mat MWFusedConvActivationLayer.cpp html MWFusedConvActivationLayer.hpp interface MWFusedConvActivationLayer.o networkParamsInfo_lanenet0_0.bin MWInputLayer.cpp predict.cu MWInputLayer.hpp predict.h MWInputLayer.o predict.o MWKernelHeaders.hpp rtw_proj.tmw MWLayerImplFactory.hpp rtwtypes.h MWMaxPoolingLayer.cpp shared_layers_export_macros.hpp MWMaxPoolingLayer.hpp

/*版权2016 The MathWorks, Inc. */ #include  #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);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; }