车道检测和GPU编码器优化

SeriesNetwork属性:层:[23×1 nnet.cnn.layer.Layer] InputNames:{“数据”}OutputNames:{“输出”}

函数detectLanesInVideo (videoFile,净、laneCoeffMeans laneCoeffsStds) % detectLanesInVideo入口点函数的车道检测优化与GPU编码器例子% % % detectLanesInVideo (videoFile,净,laneCoeffMeans laneCoeffsStds)使用% VideoFileReader系统对象阅读从输入视频帧,调用% LaneNet网络对象的预测方法,并将检测到的%车道对输入视频。DeployableVideoPlayer系统对象用于%显示车道检测视频输出。% 2022年版权MathWorks公司% # codegen % %创建视频读者和视频播放器对象videoFReader = vision.VideoFileReader (videoFile);depVideoPlayer =愿景。DeployableVideoPlayer (Name =“GPU车道检测”);% %视频帧处理循环而~结束(videoFReader) videoFrame = videoFReader ();scaledFrame = 255。* (imresize (videoFrame (227 227)));[laneFound, ltPts rtPts] = laneNetPredict(净,scaledFrame,…laneCoeffMeans laneCoeffsStds); if(laneFound) pts = [reshape(ltPts',1,[]);reshape(rtPts',1,[])]; videoFrame = insertShape(videoFrame, 'Line', pts, 'LineWidth', 4); end depVideoPlayer(videoFrame); end end

函数[laneFound、ltPts rtPts] = laneNetPredict(净,框架,意味着,性病)% laneNetPredict预测车道标记输入图像帧使用% % %车道检测网络版权2017 - 2022 MathWorks, Inc . % # codegen %持久对象lanenet用于加载网络对象。%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 =编码器。loadDeepLearningNetwork(网络,“lanenet”);结束lanecoeffsNetworkOutput =预测(lanenet,框架);%恢复原始多项式系数通过逆转标准化的步骤。params = lanecoeffsNetworkOutput。*性病+意味着;% ' c '应该超过0.5是一个车道。 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

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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.bin cnn_lanenet0_0_fcLane1_w.bin