ans = 23×1带有图层的图层数组:1的数据图像输入227×227×3图片2的zerocenter正常化conv1卷积96年11×11×3旋转步[4 4]和填充[0 0 0 0]3‘relu1 ReLU ReLU 4 norm1的横通道正常化横通道正常化与5频道/元素5“pool1”马克斯池3×3马克斯池步(2 - 2)和填充[0 0 0 0]6 conv2卷积256 5×5×48旋转步[1]和填充(2 2 2 2)7的relu2 ReLU ReLU 8 norm2横通道正常化横渠道规范化与5频道/元素9“pool2”马克斯池3×3马克斯池步(2 - 2)和填充[0 0 0 0]10 conv3卷积384 3×3×256旋转步[1]和填充[1 1 1 1]11的relu3 ReLU ReLU 12 conv4卷积384 3×3×192旋转步[1]和填充[1 1 1 1]13的relu4 ReLU ReLU 14 conv5卷积256 3×3×192旋转步[1]和填充[1 1 1 1]15 ' relu5 ReLU ReLU 16“pool5”马克斯池3×3马克斯池stride [2 2] and padding [0 0 0 0] 17 'fc6' Fully Connected 4096 Fully Connected layer 18 'relu6' ReLU ReLU 19 'drop6' Dropout 50% Dropout 20 ' fclain1 ' Fully Connected 16 Fully Connected layer 21 ' fclain1relu ' ReLU ReLU 22 ' fclain2 ' Fully Connected 6 Fully Connected layer 23 'output' Regression输出均方误差与'leftLane_a'， 'leftLane_b'和4个其他响应

function [laneFound, ltPts, rtPts] = detect_lane(frame, laneCoeffMeans, laneCoeffStds) %从网络输出，计算%图像坐标中的左右车道点。摄像机坐标由caltech % mono摄像机模型描述。持久对象mynet用于加载系列网络对象。第一次调用此函数时，将构造持久对象并设置。当该函数随后被调用时，相同的对象将被重用%以调用输入上的predict，从而避免重构和重新加载% network对象。持久lanenet;如果isempty(lanenet) lanenet = code . loaddeeplearningnetwork (' lanenet . lang = ')席”、“lanenet”);end lanecoeffsNetworkOutput = lanenet。预测(排列(帧，[2 1 3]));通过反转归一化步骤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

代码生成成功:要查看报告，请打开('codegen/lib/detect_lane/html/report.mldatx')。

．cnn_lanenet0_0_conv4_w.bin . .cnn_lanenet0_0_conv5_b.bin .gitignore cnn_lanenet0_0_conv5_w.bin DeepLearningNetwork. bincu cnn_lanenet0_0_data_offset.bin DeepLearningNetwork.h cnn_lanenet0_0_data_scale.bin DeepLearningNetwork.ho cnn_lanenet0_0_fc6_b.bin MWCNNLayerImpl. ocu cnn_lanenet0_0_fc6_w.bin mwcnnnlayerimpl .hpp cnn_lanenet0_0_fcle1_b .bin MWCNNLayerImpl.hppo cnn_lanenet0_0_fcle1_w .bin MWCudaDimUtility. ocu cnn_lanenet0_0_fclen2_b .bin mwcudadimuity .hpp cnn_lanenet0_0_fclen2_w .bin MWCustomLayerForCuDNN.cpp cnn_lanenet0_0_responseNames.txt MWCustomLayerForCuDNN.hpp codeInfo. txt垫MWCustomLayerForCuDNN。o codedescriptor。dmr MWElementwiseAffineLayer.cpp compileInfo. cpp编译信息。MWElementwiseAffineLayer.hpp definitions .txt MWElementwiseAffineLayer.hpp定义。o detect_lane。MWElementwiseAffineLayerImpl。铜detect_lane。cu MWElementwiseAffineLayerImpl.hpp detect_lane.h MWElementwiseAffineLayerImpl. cu MWElementwiseAffineLayerImpl.hpp detect_lane.ho detect_lane。o MWElementwiseAffineLayerImplKernel.cu detect_lane_data.cu MWElementwiseAffineLayerImplKernel.o detect_lane_data.h MWFusedConvReLULayer.cpp detect_lane_data.o MWFusedConvReLULayer.hpp detect_lane_initialize.cu MWFusedConvReLULayer.o detect_lane_initialize.h MWFusedConvReLULayerImpl.cu detect_lane_initialize.o MWFusedConvReLULayerImpl.hpp detect_lane_ref.rsp MWFusedConvReLULayerImpl.o detect_lane_rtw.mk MWKernelHeaders.hpp detect_lane_terminate.cu MWTargetNetworkImpl.cu detect_lane_terminate.h MWTargetNetworkImpl.hpp detect_lane_terminate.o MWTargetNetworkImpl.o detect_lane_types.h buildInfo.mat examples cnn_api.cpp gpu_codegen_info.mat cnn_api.hpp html cnn_api.o interface cnn_lanenet0_0_conv1_b.bin mean.bin cnn_lanenet0_0_conv1_w.bin predict.cu cnn_lanenet0_0_conv2_b.bin predict.h cnn_lanenet0_0_conv2_w.bin predict.o cnn_lanenet0_0_conv3_b.bin rtw_proj.tmw cnn_lanenet0_0_conv3_w.bin rtwtypes.h cnn_lanenet0_0_conv4_b.bin

/*版权2016 The MathWorks, Inc. */ #include  #include  #include  #include  #include  #include  #include  #include  #include  #include " detect_lang .h" using namespace cv;void readData(float *input, Mat& trans, 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; }