Error using reshape, To RESHAPE the number of elements must not change?

This code was written by Stephen:

a = [0.75  0.23  -2.86
0.75  0.47  3.87
0.75  0.64  2.78
2.45  0.23  4.56
2.45  0.47  0.89
2.45  0.64  2.74
3.23  0.23  2.10
3.23  0.47  -3.65
3.23  0.64  -5.89]
x = reshape(a(:,1),3,3);
y = reshape(a(:,2),3,3);
z = reshape(a(:,3),3,3);
surf(x,y,z, 'FaceColor','interp')

The code above works, but when I added four more point (each row is a point) the reshape gets an error saying:

"Error using reshape

To RESHAPE the number of elements must not change.

Error in Code_Version_1 (line 16)

x = reshape(a(:,1),3,3); "

Code below gets the error.

a = [0.75  0.23  -2.86
0.75  0.47  3.87
0.75  0.64  2.78
2.45  0.23  4.56
2.45  0.47  0.89
2.45  0.64  2.74
3.23  0.23  2.10
3.23  0.47  -3.65
3.23  0.64  -5.89
0  0  0
4  0  0
4  1  0
0  1  0]
x = reshape(a(:,1),3,3);
y = reshape(a(:,2),3,3);
z = reshape(a(:,3),3,3);
surf(x,y,z, 'FaceColor','interp')

I believe it is because the dimensions of the matrix is changing(from 9x3 to 13x3), but I do not know how to alter the code in order to accommodate for the change in the new dimensions of the matrix.

Answer 1

[dpb]

Well, since 13 is prime, you're stuck, there is no factor of 13 other than 1 and itself.

By symmetry to the initial, it looks like you need multiples of three and I note that for the initial data the first column value is repeated that many times--altho I've not tried to dig into what Stephen was doing here, I'd venture that is probably an unstated assumption for the method to work.

But, the general syntax with a proper set of input data (namely length divisible by N) would be

reshape(x,N,[])

where the [] tells Matlab to use length(x)/N as the second dimension.

Now whether that works for the intended purpose here is another question altho I would venture it would if the additional points follow the same pattern as the initial data set.

[kevin harianto]

I am also trying to reshape an array to 64, 1856, 3 however it i am getting the same error. I have also tried permut but to no luck.

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.
           
           
            %next we can add in the ptCloud locations
       %     Location(:,:,1) = pointCloud.Location;
         %   Location = zeros(65,1856,5);
         % adding the additional elements to the array.
         appendingArray = zeros(64,1856,3);
         Location = [pointCloud];



         %We now have to convert the location 1D array to 3D array
         % permute(reshape(TheList, 300, 300, 400), [2 1 3]).
        % reshape(Location, 64, 1856, 3);

         Location = permute(reshape(Location, 3, 1856, 64), [3 2 1]);


            Location =[Location, appendingArray];
            %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);
           
  pointCloud=Location;

            % 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

[dpb]

Far too much code and none of it of any real help -- but, if the reshape operation fails, that implies the input array isn't of conforming size. The (one and only) solution is to fix that problem.