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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。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

/ * 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; }