层:[23×1 nnet.cnn.layer.Layer] InputNames: {'data'} OutputNames: {'output'}

% % detectLanesInVideo(videoFile,net,laneCoeffMeans,laneCoeffsStds)使用% VideoFileReader系统对象从输入视频中读取帧，调用LaneNet网络对象的预测方法，并在输入视频上绘制检测到的% lanes。DeployableVideoPlayer系统对象用于显示检测到的通道视频输出。%版权所有The MathWorks, Inc. %#codegen %%创建视频阅读器和视频播放器对象videoreader = vision.VideoFileReader(videoFile);depVideoPlayer = vision.DeployableVideoPlayer(Name='Lane Detection on GPU'); %% Video Frame Processing Loop while ~isDone(videoFReader) videoFrame = videoFReader(); scaledFrame = 255.*(imresize(videoFrame,[227 227])); [laneFound,ltPts,rtPts] = laneNetPredict(net,scaledFrame, ... laneCoeffMeans,laneCoeffsStds); if(laneFound) pts = [reshape(ltPts',1,[]);reshape(rtPts',1,[])]; videoFrame = insertShape(videoFrame, 'Line', pts, 'LineWidth', 4); end depVideoPlayer(videoFrame); end end

function [laneFound,ltPts,rtPts] = laneNetPredict(net,frame,means,stds) % laneNetPredict使用%车道检测网络% %预测输入图像帧上的车道标记。版权所有the MathWorks, Inc. %#codegen %一个持久对象lanenet用于加载网络对象。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%被重用以在输入上调用predict，从而避免重构和%重新加载网络对象。持久lanenet;如果isempty(lanenet) lanenet = coder。loadDeepLearningNetwork(网络,“lanenet”);end lanecoeffsNetworkOutput = predict(lanenet,frame);通过反转归一化步骤恢复原始系数。。params = lanecoeffsNetworkOutput .* stds + means;% 'c'应该大于0.5才能成为lane。 isRightLaneFound = abs(params(6)) > 0.5; isLeftLaneFound = abs(params(3)) > 0.5; % From the networks output, compute left and right lane points in the image % coordinates. vehicleXPoints = 3:30; 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 %% Helper Functions % Compute boundary model. function yWorld = computeBoundaryModel(model, xWorld) yWorld = polyval(model, xWorld); end % Compute extrinsics. function tform = get_tformToImage %The camera coordinates are described by the caltech mono % camera model. 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 % Translate to image co-ordinates. 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 % Intrinsic matrix computation. 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:查看报告

cnn_lanenet0_0_conv1_b.bin cnn_lanenet0_0_conv3_b.bin cnn_lanenet0_0_conv5_b.bin cnn_lanenet0_0_fc6_b.bin cnn_lanenet0_0_fcLane2_b.bin cnn_lanenet0_0_conv1_w.bin cnn_lanenet0_0_conv3_w.bin cnn_lanenet0_0_conv5_w.bin cnn_lanenet0_0_fc6_w.bin cnn_lanenet0_0_fcLane2_w.bin cnn_lanenet0_0_conv2_b.bin cnn_lanenet0_0_conv4_b.bin cnn_lanenet0_0_data_offset.bin cnn_lanenet0_0_fcLane1_b.bin networkParamsInfo_lanenet0_0.bin cnn_lanenet0_0_conv2_w.bin cnn_lanenet0_0_conv4_w.bin cnn_lanenet0_0_data_scale.bincnn_lanenet0_0_fcLane1_w.bin