Editing / adding Point Cloud 'Location'

Question

Raffaele Spielmann 2020-4-3

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https://ww2.mathworks.cn/matlabcentral/answers/515109-editing-adding-point-cloud-location

编辑： kevin harianto 2022-4-11

采纳的回答： Harsha Priya Daggubati

Hi there!

I am working with LiDAR data gahtered using an Ouster sensor and Matlab's "Computer Vision Toolbox".

I would like to plot a recorded point cloud as a "movie", i.e. showing the different frames one after the other.

Since the "VelodyneReader" does not work for Ouster data, I tried to edit the point cloud manually, i.e. I tried to merge different frames of a point cloud to one single point cloud. Therefore, I tried to access the 'Location' in the Point Cloud using 'ptCloud.Location'. However, the 'Location' is a read-only property.

Does any one know how to change the 'Location' property of a 'pointCloud' from read-only to something that I can edit?

Thanks a lot for your help!

Cheers,

Raffi

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Answer 1

Harsha Priya Daggubati 2020-4-6

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I guess it would be better if you can access the Location property and store it in a MATLAB variable, make desired modifications and create a new point-cloud object using the modified points.

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Raffaele Spielmann 2020-4-6

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Thanks for you answer.

I don't fully understand what you mean.

I am not trying to create multiple "Point Cloud" objects, I am just trying to put different frames of an Ouster LiDAR scan into one and the same "Point Cloud" object, but as different "layers" in the 'ptCloud.Location' place.

I think that is what is done by the VelodyneReader automatically, but since I am not using Velodyne but Ouster, I would like to do this manually.

I would like to load the different frames into single point clouds, then take each of this point clouds and put them as a different "layer" in the .Location place of a "Master" point cloud.

Something like:

% loading frames into individual Point Clouds
ptCloud1 = pointCloud(Frame1);
ptCloud2 = pointCloud(Frame2);
ptCloud3 = pointCloud(Frame3);
% ...
% Putting all the indiviudal point clouds into different .Locations of Master point cloud
ptCloud.Location(:,:,1)=ptCloud1.Location; %ptCloud = Master Point Cloud
ptCloud.Location(:,:,2)=ptCloud2.Location;
ptCloud.Location(:,:,3)=ptCloud3.Location;
% ...
% the last three lines do not work:
% ERROR: You cannot set the read-only property 'Location' of pointCloud.

If I get the point cloud "ptCloud" with all the different frames in the .Location places, I could take the "Master" point cloud and "play the movie" using pcplayer and view.

I hope this helps. Thank you so much for your help!

aws hawary 2020-4-13

Hello

I have a question if anyone could help me

I got Data LIDAR files ( pcap and csv)

I wanna to apply that data but the files should be compatible with MATLAB platform .. How can I read all the frames of ply file .. I read the total of pcap but I need the frames #separately for each timestamp

Raffaele Spielmann 2020-4-14

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Hi,

Just try loading the files as .csv as different frames into different point clouds:

Frame62 = readtable('Trial3_Gravel_Frame62.csv');
Frame68 = readtable('Trial3_Gravel_Frame68.csv');
Frame62n = Frame62(:, [1:3]); %creates table with columns 1:3 = xyz-coord.
Frame68n = Frame68(:, [1:3]);
Frame62n = table2array(Frame62n); %converts table to array
Frame68n = table2array(Frame68n);
%Creating Point Clouds with Intensity:
ptCloud62 = pointCloud(Frame62n(:, 1:3));

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Answer 2

Sudarsono Sianipar 2020-5-14

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Hello, i have trouble to moving pointcloud manually with editing .Location pointcloud. but enable, because "You cannot set the read-only property 'Location' of pointCloud.".

How to change that?

Thank You

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Walter Roberson 2020-5-14

Unfortunately it is set as readonly in the class definition toolbox/shared/pointclouds/+pointclouds/+internal/+pc/pointCloudImpl.m

In some modes, a kdtree is generated to search the point cloud; modifying the points would invalidate the kdtree.

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Answer 3

kevin harianto 2022-4-4

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I am also trying to edit the point cloud location as well however I am getting an issue with left and right side having different number of elements. Do you have any clue in what it may be?

classdef LidarSemanticSegmentation < lidar.labeler.AutomationAlgorithm
    % LidarSemanticSegmentation Automation algorithm performs semantic
    % segmentation in the point cloud.
    %   LidarSemanticSegmentation is an automation algorithm for segmenting
    %   a point cloud using SqueezeSegV2 semantic segmentation network
    %   which is trained on Pandaset data set.
    %
    %   See also lidarLabeler, groundTruthLabeler
    %   lidar.labeler.AutomationAlgorithm.
    %   Copyright 2021 The MathWorks, Inc.
    % ----------------------------------------------------------------------
    % Step 1: Define the required properties describing the algorithm. This
    % includes Name, Description, and UserDirections.
    properties(Constant)
        % Name Algorithm Name
        %   Character vector specifying the name of the algorithm.
        Name = 'Lidar Semantic Segmentation';
        % Description Algorithm Description
        %   Character vector specifying the short description of the algorithm.
        Description = 'Segment the point cloud using SqueezeSegV2 network.';
        % UserDirections Algorithm Usage Directions
        %   Cell array of character vectors specifying directions for
        %   algorithm users to follow to use the algorithm.
        UserDirections = {['ROI Label Definition Selection: select one of ' ...
            'the ROI definitions to be labeled'], ...
            'Run: Press RUN to run the automation algorithm. ', ...
            ['Review and Modify: Review automated labels over the interval ', ...
            'using playback controls. Modify/delete/add ROIs that were not ' ...
            'satisfactorily automated at this stage. If the results are ' ...
            'satisfactory, click Accept to accept the automated labels.'], ...
            ['Accept/Cancel: If the results of automation are satisfactory, ' ...
            'click Accept to accept all automated labels and return to ' ...
            'manual labeling. If the results of automation are not ' ...
            'satisfactory, click Cancel to return to manual labeling ' ...
            'without saving the automated labels.']};
    end
    % ---------------------------------------------------------------------
    % Step 2: Define properties you want to use during the algorithm
    % execution.
    properties
        % AllCategories
        % AllCategories holds the default 'unlabelled', 'Vegetation',
        % 'Ground', 'Road', 'RoadMarkings', 'SideWalk', 'Car', 'Truck',
        % 'OtherVehicle', 'Pedestrian', 'RoadBarriers', 'Signs',
        % 'Buildings' categorical types.
        AllCategories = {'unlabelled'};
        % PretrainedNetwork
        %   PretrainedNetwork saves the pretrained SqueezeSegV2 network.
        PretrainedNetwork
    end
    %----------------------------------------------------------------------
    % Note: this method needs to be included for lidarLabeler app to
    % recognize it as using pointcloud
    methods (Static)
        % This method is static to allow the apps to call it and check the
        % signal type before instantiation. When users refresh the
        % algorithm list, we can quickly check and discard algorithms for
        % any signal that is not support in a given app.
        function isValid = checkSignalType(signalType)
            isValid = (signalType == vision.labeler.loading.SignalType.PointCloud);
        end
    end
    %----------------------------------------------------------------------
    % Step 3: Define methods used for setting up the algorithm.
    methods
        function isValid = checkLabelDefinition(algObj, labelDef)
            % Only Voxel ROI label definitions are valid for the Lidar
            %  semantic segmentation algorithm.
            isValid = labelDef.Type == lidarLabelType.Voxel;
            if isValid
                algObj.AllCategories{end+1} = labelDef.Name;
            end
        end
        function isReady = checkSetup(algObj)
            % Is there one selected ROI Label definition to automate.
            isReady = ~isempty(algObj.SelectedLabelDefinitions);
        end
    end
    %----------------------------------------------------------------------
    % Step 4: Specify algorithm execution. This controls what happens when
    %         the user presses RUN. Algorithm execution proceeds by first
    %         executing initialize on the first frame, followed by run on
    %         every frame, and terminate on the last frame.
    methods
        function initialize(algObj,~)
            % Load the pretrained SqueezeSegV2 semantic segmentation network.
            outputFolder = fullfile(tempdir, 'Pandaset');
            pretrainedSqueezeSeg = load(fullfile(outputFolder,'trainedSqueezeSegV2PandasetNet.mat'));
            % Store the network in the 'PretrainedNetwork' property of this object.
            algObj.PretrainedNetwork = pretrainedSqueezeSeg.net;
        end
        function autoLabels = run(algObj, pointCloud)
            % Setup categorical matrix with categories including
            % 'Vegetation', 'Ground', 'Road', 'RoadMarkings', 'SideWalk',
            % 'Car', 'Truck', 'OtherVehicle', 'Pedestrian', 'RoadBarriers',
            % and 'Signs'.
            autoLabels = categorical(zeros(size(pointCloud.Location,1), size(pointCloud.Location,2)), ...
                0:12,algObj.AllCategories);
            %A = zeros(10000,10000);
            %filling in the minimum required resolution
            % to meet the neural network's specification.
  %(first iteration failed)   pointCloud.Location = zeros(65,1856,5);
            %Due to an error we must append the various point cloud data
            %first.
           Location =  zeros(64,1856,5);
            %next we can add in the ptCloud locations
       %     Location(:,:,1) = pointCloud.Location;
         %   Location = zeros(65,1856,5);
         Location(:) = [pointCloud.Location]
            %
            ptCloud=pointCloud(Location);
            %This will also be applied to the pointCloud Intensity levels
            % as these are also analyzed by the machine learning algorithm.
  %(Pushed aside for later modifications)          pointCloud.Intensity = zeros(64,1865);
           
            % Convert the input point cloud to five channel image.
            I = helperPointCloudToImage(pointCloud);
            % Predict the segmentation result.
            predictedResult = semanticseg(I, algObj.PretrainedNetwork);
            autoLabels(:) = predictedResult;
            %using this area we would be able to continuously update the latest file on
            % sending the output towards the CAN Network or atleast ensure that the
            % item is obtainable
            % This area would work the best.
            %first we must
        end
    end
end
function helperDisplayLabelOverlaidPointCloud(I,predictedResult)
    % helperDisplayLabelOverlaidPointCloud Overlay labels over point cloud object.
    %  helperDisplayLabelOverlaidPointCloud(I,predictedResult)
    %  displays the overlaid pointCloud object. I is the 5 channels organized
    %  input image. predictedResult contains pixel labels.
    ptCloud = pointCloud(I(:,:,1:3),Intensity = I(:,:,4));
    cmap = helperPandasetColorMap;
    B = ...
        labeloverlay(uint8(ptCloud.Intensity),predictedResult,Colormap = cmap,Transparency = 0.4);
    pc = pointCloud(ptCloud.Location,Color = B);
    ax = pcshow(pc);
    set(ax,XLim = [-70 70],YLim = [-70 70])
    zoom(ax,3.5)
end
function cmap = helperPandasetColorMap
    cmap = [[30 30 30];  % Unlabeled
        [0 255 0];       % Vegetation
        [255 150 255];   % Ground
        [237 117 32];    % Road
        [255 0 0];       % Road Markings
        [90 30 150];     % Sidewalk
        [255 255 30];    % Car
        [245 150 100];   % Truck
        [150 60 30];     % Other Vehicle
        [255 255 0];     % Pedestrian
        [0 200 255];     % Road Barriers
        [170 100 150];   % Signs
        [255 0 255]];    % Building
    cmap = cmap./255;
end
function image = helperPointCloudToImage(ptcloud)
% helperPointCloudToImage converts the point cloud to 5 channel image
image = ptcloud.Location;
image(:,:,5) = ptcloud.Intensity;
rangeData = iComputeRangeData(image(:,:,1),image(:,:,2),image(:,:,3));
image(:,:,4) = rangeData;
index = isnan(image);
image(index) = 0;
end
function rangeData = iComputeRangeData(xChannel,yChannel,zChannel)
rangeData = sqrt(xChannel.*xChannel+yChannel.*yChannel+zChannel.*zChannel);
end

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kevin harianto 2022-4-8

编辑：kevin harianto 2022-4-8

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So i have been trying out several ways in modification of the location however for some reason all im getting is a consistency error in cat: -previously it didnt work-

tempPtCloud = cat(2, tempPtCloud.Location, zeros(10000, size(tempPtCloud.Location,2)));

my code:

% Load the pretrained network.
outputFolder = fullfile(tempdir,"Pandaset");
load(fullfile(outputFolder,"trainedSqueezeSegV2PandasetNet.mat"),"net");
% Read the point cloud.
%----------------------------Note if we have access to hardware resources
% we would run the function to start loading the files in.
%F = parfeval(backgroundPool, @hardwareManipulation);
%to pause if a must we can do
%cancel(f) however this action is planned to be in continuous cycle while
%the automation class properties is doing the activities
%-----------------------------
%Next we shall change the outputfolder to face towards the data_2  saved/created by
%the hardwware manipulation function
outputFolderNew=fullfile("data_2");
ptCloud = pcread(fullfile(outputFolderNew,"0000000000.pcd"));
%Due to the ptCloud being a different data type
tempPtCloud = ptCloud;
%ptCloud being read only we can not add the third dimension
%-------------from suggestion
tempPtCloud = cat(2, tempPtCloud.Location, zeros(10000, size(tempPtCloud.Location,2)));

Walter Roberson 2022-4-10

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If you are sure that the new size is larger than the old size, then skip that cat(2) and instead do

tempPtCloud(DesiredNumberOfRows, DesiredNumberOfColumns) = 0;

This will extend the tempPtCloud array to have that many rows and columns, filling all of the holes with 0 (the 0 used as fill is automatic and independent of the 0 being assigned in that statement.)

kevin harianto 2022-4-11

编辑：kevin harianto 2022-4-11

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I am only trying to extend the tempPtCloud.Location array which why i was trying to add in the tmpPtCloud.Location matrixes for expanding -currently at 123398x3 and move to atleast 64x1856x3-. when I try your method it said unable to perform assignment because type double is not convertible to pointCloud. error at tempPtCloud(64, 1856) = 0; and even when i tried to change it to tempPtCloud(64, 1856, 3) = 0;

%These additional values should allow the raw pcd file matric to meet the
%resolutions demand
% Since we are only trying to influence the resolution
% (pandaSet Ideal resolution being 64x1856x3)
% and not the actual
% value's representation we will only be adding in the values
% However because the raw pcd file is single matrix, we shall be adding
% in additional dimensions.
tempPtCloud = ptCloud;
%ptCloud being read only we can not add the third dimension
%-------------from suggestion(issue)
tempPtCloud(64, 1856) = 0;
tempPtCloud = cat(2, tempPtCloud.Location, zeros(10000, size(tempPtCloud.Location,2)));
%----------------
B = tempPtCloud.Location;
B(:,3) = tempPtCloud.Location(:,2); %copy second column to move to third
for n = 2: size(ptCloud.Location)
    B(:,n)= tempPtCloud.Location(:,n-1);
end
%We can do the above twice to move the 2nd column to the third, and first
%to the second then add in the zeros to the first and replace.
B(:,2) = tempPtCloud.Location(:,1); %copy first column to move to second
for n = 2: size(ptCloud.Location)
    B(:,n)= tempPtCloud.Location(:,n-1);
end
%for the first vertices we will be substituting with the zeros
B(:,1) = zeros(64);
%next replace the pointCloud with the desire parts:
% (In terms of replaceing the pointCloud with the desired parts this shall be implemented in
% the semantic segmentation script as well)
%using the location variable B which should contain the 3 Dimensions in its
%rightful format we shall now modify the temporary pointCloud for
%manipulation
tempPtCloud = pointCloud(B(:, 1:3));