函数(laneFound、ltPts rtPts] = detect_lane(框架、laneCoeffMeans laneCoeffStds) %的网络输出,计算左和右车道点在图像%坐标。摄像机坐标被加州理工学院mono %相机模型。% # codegen %一系列持久对象mynet用于加载网络对象。在%第一次调用这个函数,构造持久对象和%设置。只有在函数被调用时,随后的时期,%重用相同的对象调用预测输入,从而避免网络重构和%重新加载对象。持久lanenet;如果isempty (lanenet) lanenet = coder.loadDeepLearningNetwork (lanenet。席”、“lanenet”);lanecoeffsNetworkOutput = lanenet结束。预测(排列(框架、[2 1 3]));%恢复原始多项式系数通过扭转规范化步骤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.cu MWNormLayer.o DeepLearningNetwork.h MWOutputLayer.cpp DeepLearningNetwork.o MWOutputLayer.hpp MWActivationFunctionType.hpp MWOutputLayer.o MWCNNLayer.cpp MWRNNParameterTypes.hpp MWCNNLayer.hpp MWReLULayer.cpp MWCNNLayer.o MWReLULayer.hpp MWCNNLayerImplBase.hpp MWReLULayer.o MWCUSOLVERUtils.cpp MWTargetNetworkImplBase.hpp MWCUSOLVERUtils.hpp MWTargetTypes.hpp MWCUSOLVERUtils.o MWTensor.hpp MWCudaDimUtility.hpp MWTensorBase.cpp MWCudaMemoryFunctions.hpp MWTensorBase.hpp MWCudnnCNNLayerImpl.cu MWTensorBase.o MWCudnnCNNLayerImpl.hpp _clang-format MWCudnnCNNLayerImpl.o buildInfo.mat MWCudnnCommonHeaders.hpp cnn_lanenet0_0_conv1_b.bin MWCudnnCustomLayerBase.cu 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

/ * 2016年版权MathWorks公司* / # include < stdio。h > # include < stdlib。h > # include < cuda。h > # include < opencv2 / opencv。hpp > # include < opencv2 / imgproc。hpp > # include < opencv2 /核心/核心。hpp > # include < opencv2 /核心/类型。hpp > # include < opencv2 / highgui。hpp > # include <列表> # include < cmath > #包括“detect_lane。使用名称空间的简历h”;空白readData(浮动*输入,垫源自,垫和im){大小尺寸(227227);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]); input[0*227*227+j]=(float)(im.data[j*3+2]); } } void addLane(float pts[28][2], Mat & im, int numPts) { std::vector iArray; for(int k=0; k> 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; }