% ---------------------------------------------------------------------- % 步骤1:定义所需的属性描述算法,%,包括名称、描述和UserDirections。properties(Constant) % Name算法名称%指定算法名称的字符向量。Name = '多信号车辆检测器';% Description算法描述%字符向量，指定算法的简短描述。Description =['使用ACF车辆检测器检测车辆'…'图像和估计他们在点云。'];% UserDirections算法使用方向%字符向量的单元格数组，指定算法用户要遵循的方向。UserDirections ={['选择一个矩形ROI标签'…'标签对象为Vehicle。'],……['点击设置并打开激光雷达摄像机校准'… 'Parameters tab, load the cameraIntrinsics and rigid3d ' ... 'objects from the workspace.'], ... ['Specify additional parameters under Settings.'], ... ['Click Run to detect vehicles in each image and point cloud.'], ... ['Review automated labels manually. You can modify, delete ', ... 'and add new labels.'], ... ['If you are not satisfied with the results, click Undo ' ... 'Run. Click Settings to modify algorithm settings and click ', ... 'Run again.'] ... ['When you are satisfied with the results, click Accept and ', ... 'return to manual labeling.']}; end

% --------------------------------------------------------------------- % 第二步:定义属性被用来管理算法执行。properties % SelectedLabelName选中标签名称%选中标签的名称。算法检测到的车辆将被赋给这个变量名。%检测器检测器%预先训练的车辆检测器，一个类别为% acfobject检测器的对象。Detector % VehicleModelName车辆检测器型号%预训练车辆检测器型号。VehicleModelName =“众目睽睽”;%重叠阈值重叠阈值%用于消除重叠边界框的阈值。边界框重叠比例分母'RatioType'被设置为% 'Min'。OverlapThreshold = 0.45;% ScoreThreshold分类评分阈值%用于拒绝检测分数低的检测的阈值。 ScoreThreshold = 20; % ConfigureDetector Detection configuration flag % Boolean value that determines whether the detector is % configured using monoCamera sensor. ConfigureDetector = false; % SensorObj monoCamera sensor % Monocular camera sensor object, monoCamera, used to configure % the detector. A configured detector runs faster and can % potentially result in better detections. SensorObj = []; % SensorStr monoCamera sensor variable name % Character vector specifying the monoCamera object variable name % used to configure the detector. SensorStr = ''; % VehicleWidth Vehicle width % Vehicle width used to configure the detector, specified as % [minWidth, maxWidth], which describes the approximate width of the % object in world units. VehicleWidth = [1.5 2.5]; % VehicleLength Vehicle length % Vehicle length used to configure the detector, specified as % [minLength, maxLength] vector, which describes the approximate % length of the object in world units. VehicleLength = []; % IntrinsicsObj Camera intrinsics % cameraIntrinsics object, which represents a projective % transformation from camera to image coordinates. IntrinsicsObj = []; % IntrinsicsStr cameraIntrinsics variable name % cameraIntrinsics object variable name. IntrinsicsStr = ''; % ExtrinsicsObj Camera-to-lidar rigid transformation % rigid3d object representing the 3-D rigid geometric transformation % from the camera to the lidar. ExtrinsicsObj = []; % ExtrinsicsStr rigid3d variable name % Camera-to-lidar rigid3d object variable name. ExtrinsicsStr = ''; % ClusterThreshold Clustering threshold for two adjacent points % Threshold specifying the maximum distance between two adjacent points % for those points to belong to the same cluster. ClusterThreshold = 1; end

function success = 金宝appsupportsMultisignalAutomation(~) %支持MultiSignal。成功= true;结束

只有视频/图像序列和点云信号数据%有效。isValid = any(signalType == vision.label .loading. signalType . image) &&…任何(signalType = = vision.labeler.loading.SignalType.PointCloud);结束

只有矩形/长方体的ROI标签定义对%车辆检测器有效。isValid = (labelDef。= = labelType类型。长方体||labelDef.Type == labelType.Rectangle); end

是否有一个选择的ROI标签定义要自动化?isReady = ~ isempty (algObj.SelectedLabelDefinitions);结束

函数SettingsDialog（Algobj）％调用对话框输入摄像机内在机构和％摄像机到LIDAR刚性转换和选择％预磨损模型的选项，重叠阈值，检测得分％阈值和聚类阈值。可选地，输入％校准的单纸传感器以配置检测器。MultiSignalvehicledEtectorsettings（Algobj）;结束

函数initialize(algObj， ~) %存储选定标签定义的名称。使用这个%名称来标记检测到的车辆。algObj。选择edLabelName = algObj.SelectedLabelDefinitions.Name; % Initialize the vehicle detector with a pretrained model. algObj.Detector = vehicleDetectorACF(algObj.VehicleModelName); end

函数autolabels =运行（algobj，i）％autolabels长度的单元阵列与％信号的数量相同。Autolabels = Cell（大小（I，1），1）;％获取图​​像和PointCloud帧的索引。如果是（i {1,1}，“pointcloud”）pcidx = 1;imidx = 2;否则imidx = 1;pcidx = 2;终端％检测图像帧上的边界框。SelectentBboxes =侦探（Algobj，i {imidx，1}）;点云帧上的％估计边界框。 if ~isempty(selectedBboxes) % Store labels from the image. imageLabels = struct('Type', labelType.Rectangle, ... 'Name', algObj.SelectedLabelDefinitions.Name, ... 'Position', selectedBboxes); autoLabels{imIdx, 1} = imageLabels; % Remove the ground plane for the point cloud. groundPtsIndex = segmentGroundFromLidarData(I{pcIdx,1}, ... "ElevationAngleDelta", 15, "InitialElevationAngle", 10); nonGroundPts = select(I{pcIdx,1}, ~groundPtsIndex); % Predict 3-D bounding boxes. pcBboxes = bboxCameraToLidar(selectedBboxes, nonGroundPts, algObj.IntrinsicsObj, ... algObj.ExtrinsicsObj, "ClusterThreshold", algObj.ClusterThreshold); % Store labels from the point cloud. if(~isempty(pcBboxes)) pcLabels = struct('Type', labelType.Cuboid,... 'Name', algObj.SelectedLabelDefinitions.Name,... 'Position', pcBboxes); autoLabels{pcIdx, 1} = pcLabels; else autoLabels{pcIdx, 1} = {}; end else autoLabels{imIdx, 1} = {}; autoLabels{pcIdx, 1} = {}; end end

函数终止（〜）结束

mkdir(“+视觉/ +贴标签机”);拷贝文件(fullfile (matlabroot“例子”,“驾驶”,“主要”,“MultiSignalVehicleDetector.m”),…“+视觉/ +贴标签机”);

imageURL = ' //www.tatmou.com金宝app/supportfiles/lidar/data/WPI_ImageData.tar.gz ';imageDataFolder = fullfile(tempdir， 'WPI_ImageData'，filesep);imageDataTarFile = imageDataFolder + "WPI_ImageData.tar.gz";if ~exist(imageDataFolder，'dir') mkdir(imageDataFolder) end if ~exist(imageDataTarFile， 'file') disp('正在下载WPI镜像驱动数据(225mb)…');websave (imageDataTarFile imageURL);解压(imageDataTarFile imageDataFolder);end %检查是否下载了image tar.gz文件，但没有解压。如果存在(fullfile(imageDataFolder，'imageData')，'dir') untar(imageDataTarFile, imageDataFolder)结束

%创建新文件夹并复制图像。imDataFolder = imageDataFolder + "imageDataSequence";如果~存在(imDataFolder“dir”)mkdir (imDataFolder);End for I = 920: 940 filename = strcat(num2str(I，'%06.0f')，'.jpg');源= fullfile (imageDataFolder imageData,文件名);目的地= fullfile (imageDataFolder, imageDataSequence,文件名);拷贝文件(来源、目的地)

lidarURL = ' //www.tatmou.com金宝app/supportfiles/lidar/data/WPI_LidarData.tar.gz ';lidarDataFolder = fullfile (tempdir WPI_LidarData, filesep);lidarDataTarFile = lidarDataFolder + "WPI_LidarData.tar.gz";if ~exist(lidarDataFolder) mkdir(lidarDataFolder) end if ~exist(lidarDataTarFile， 'file') disp('正在下载WPI Lidar驱动数据(760 MB)…');websave (lidarDataTarFile lidarURL);解压(lidarDataTarFile lidarDataFolder);end %检查lidar tar.gz文件是否已下载，但未解压。如果存在~ (fullfile (lidarDataFolder WPI_LidarData.mat),“文件”)解压(lidarDataTarFile lidarDataFolder);结束

%加载下载的激光雷达数据到工作区。负载(fullfile (lidarDataFolder WPI_LidarData.mat),“lidarData”);lidarData =重塑(lidarData、大小(lidarData, 2), 1);%创建新文件夹并将激光雷达数据写入PCD文件。pcdDataFolder = lidarDataFolder + "lidarDataSequence";如果存在(pcdDataFolder， 'dir') mkdir(fullfile(lidarDataFolder，'lidarDataSequence'));end disp('保存WPI激光雷达驱动数据到PCD文件…');for i = 920:940 filename = strcat(fullfile(lidarDataFolder，'lidarDataSequence'，filesep)，…num2str(我,“% 06.0 f”)、“.pcd”);我pcwrite (lidarData{},文件名); end

data =负载(fullfile (toolboxdir(激光雷达),‘lidardata’,‘lcc’,' bboxGT.mat '));cameraParams = data.cameraParams;camToLidar = data.camToLidar;

imageDir = fullfile(tempdir， 'WPI_ImageData'， 'imageDataSequence');pointCloudDir = fullfile(tempdir， 'WPI_LidarData'， 'lidarDataSequence');groundTruthLabeler