left and right sides have a different number of elements
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william Smith
am 7 Apr. 2019
Beantwortet: kevin harianto
am 4 Apr. 2022
Getting error with this line, please advise,
Thanks!
dA(i)=(rate_a(i).*F.*Q)-(rate_b(i).*F.*L)-(rate_c(i).*F);
left and right sides have a different number of elements
3 Kommentare
Akzeptierte Antwort
Star Strider
am 7 Apr. 2019
My guess is that ‘A’, ‘B’ and ‘C’ are vectors, or you would not be subscripting them.
You need to subscript them here as well:
dA(i)=(rate_a(i).*A.*B)-(rate_b(i).*A.*C)-(rate_c(i).*A);
dB(i)=(rate_b(i).*A.*C)-(rate_a(i).*A.*B)-(rate_d(i).*B);
dC(i)=rate_d(i).*B;
or you will continue to have a dimension mismatch.
4 Kommentare
Star Strider
am 7 Apr. 2019
I don’t have the rest of your code.
Use your original while loop:
i=1;
while (i)<100 && dA(i)<= T && dA(i)>1
dA(i)=(rate_a(i).*A.*B)-(rate_b(i).*A.*C)-(rate_c(i).*A);
dB(i)=(rate_b(i).*A.*C)-(rate_a(i).*A.*B)-(rate_d(i).*B);
dC(i)=rate_d(i).*B;
dAdt(i)=x.*A(i); %where A is set as zeros vector
dBdt(i)=y.*B(i);%where B is set as zeros vector
dCdt(i)=z.*C(i);%where C is set as zeros vector
A(i+1)=A(i)+i.*(dAdt(i)+dAdt(i+1))/2;
B(i+1)=B(i)+i.*(dBdt(i)+dBdt(i+1))/2;
C(i+1)=C(i)+i.*(dCdt(i)+dCdt(i+1))/2;
i=i+1;
end
and then just do this:
iv = 1:numel(C);
so the plot call is then:
plot(iv, C)
That should work, if ‘C’ is a vector.
Weitere Antworten (1)
kevin harianto
am 4 Apr. 2022
I also have the same problem with it being from location(:) = [pointCloud.Location];
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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