matlab-基于pointNet的三维点云目标分类识别matlab仿真,输出混淆矩阵验证识别率-源码

clc; clear; close all; warning off; addpath(genpath(pwd)); rng('default') %**************************************************************************** %更多关于matlab和fpga的搜索“fpga和matlab”的CSDN博客: %matlab/FPGA项目开发合作 %https://blog.csdn.net/ccsss22?type=blog %**************************************************************************** dsTrain = PtCloudClassificationDatastore('train'); dsVal = PtCloudClassificationDatastore('test'); ptCloud = pcread('Chair.ply'); label = 'Chair'; figure;pcshow(ptCloud) xlabel("X");ylabel("Y");zlabel("Z");title(label) dsLabelCounts = transform(dsTrain,@(data){data{2} data{1}.Count}); labelCounts = readall(dsLabelCounts); labels = vertcat(labelCounts{:,1}); counts = vertcat(labelCounts{:,2}); figure;histogram(labels);title('class distribution') rng(0) [G,classes] = findgroups(labels); numObservations = splitapply(@numel,labels,G); desiredNumObservationsPerClass = max(numObservations); filesOverSample=[]; for i=1:numel(classes) if i==1 targetFiles = {dsTrain.Files{1:numObservations(i)}}; else targetFiles = {dsTrain.Files{numObservations(i-1)+1:sum(numObservations(1:i))}}; end % Randomly replicate the point clouds belonging to the infrequent classes files = randReplicateFiles(targetFiles,desiredNumObservationsPerClass); filesOverSample = vertcat(filesOverSample,files'); end dsTrain.Files=filesOverSample; dsTrain.Files = dsTrain.Files(randperm(length(dsTrain.Files))); dsTrain.MiniBatchSize = 32; dsVal.MiniBatchSize = dsTrain.MiniBatchSize; dsTrain = transform(dsTrain,@augmentPointCloud); data = preview(dsTrain); ptCloud = data{1,1}; label = data{1,2}; figure;pcshow(ptCloud.Location,[0 0 1],"MarkerSize",40,"VerticalAxisDir","down") xlabel("X");ylabel("Y");zlabel("Z");title(label) minPointCount = splitapply(@min,counts,G); maxPointCount = splitapply(@max,counts,G); meanPointCount = splitapply(@(x)round(mean(x)),counts,G); stats = table(classes,numObservations,minPointCount,maxPointCount,meanPointCount) numPoints = 1000; dsTrain = transform(dsTrain,@(data)selectPoints(data,numPoints)); dsVal = transform(dsVal,@(data)selectPoints(data,numPoints)); dsTrain = transform(dsTrain,@preprocessPointCloud); dsVal = transform(dsVal,@preprocessPointCloud); data = preview(dsTrain); figure;pcshow(data{1,1},[0 0 1],"MarkerSize",40,"VerticalAxisDir","down"); xlabel("X");ylabel("Y");zlabel("Z");title(data{1,2}) inputChannelSize = 3; hiddenChannelSize1 = [64,128]; hiddenChannelSize2 = 256; [parameters.InputTransform, state.InputTransform] = initializeTransform(inputChannelSize,hiddenChannelSize1,hiddenChannelSize2); inputChannelSize = 3; hiddenChannelSize = [64 64]; [parameters.SharedMLP1,state.SharedMLP1] = initializeSharedMLP(inputChannelSize,hiddenChannelSize); inputChannelSize = 64; hiddenChannelSize1 = [64,128]; hiddenChannelSize2 = 256; [parameters.FeatureTransform, state.FeatureTransform] = initializeTransform(inputChannelSize,hiddenChannelSize,hiddenChannelSize2); inputChannelSize = 64; hiddenChannelSize = 64; [parameters.SharedMLP2,state.SharedMLP2] = initializeSharedMLP(inputChannelSize,hiddenChannelSize); inputChannelSize = 64; hiddenChannelSize = [512,256]; numClasses = numel(classes); [parameters.ClassificationMLP, state.ClassificationMLP] = initializeClassificationMLP(inputChannelSize,hiddenChannelSize,numClasses); numEpochs = 60; learnRate = 0.001; l2Regularization = 0.1; learnRateDropPeriod = 15; learnRateDropFactor = 0.5; gradientDecayFactor = 0.9; squaredGradientDecayFactor = 0.999; avgGradients = []; avgSquaredGradients = []; [lossPlotter, trainAccPlotter,valAccPlotter] = initializeTrainingProgressPlot; % Number of classes numClasses = numel(classes); % Initialize the iterations iteration = 0; % To calculate the time for training start = tic; % Loop over the epochs for epoch = 1:numEpochs % Reset training and validation datastores. reset(dsTrain); reset(dsVal); % Iterate through data set. while hasdata(dsTrain) % if no data to read, exit the loop to start the next epoch iteration = iteration + 1; % Read data. data = read(dsTrain); % Create batch. [XTrain,YTrain] = batchData(data,classes); % Evaluate the model gradients and loss using dlfeval and the % modelGradients function. [gradients, loss, state, acc] = dlfeval(@modelGradients,XTrain,YTrain,parameters,state); % L2 regularization. gradients = dlupdate(@(g,p) g + l2Regularization*p,gradients,parameters); % Update the network parameters using the Adam optimizer. [parameters, avgGradients, avgSquaredGradients] = adamupdate(parameters, gradients, ... avgGradients, avgSquaredGradients, iteration,learnRate,gradientDecayFactor, squaredGradientDecayFactor); % Update the training progress. D = duration(0,0,toc(start),"Format","hh:mm:ss"); title(lossPlotter.Parent,"Epoch: " + epoch + ", Elapsed: " + string(D)) addpoints(lossPlotter,iteration,double(gather(extractdata(loss)))) addpoints(trainAccPlotter,iteration,acc); drawnow end % Create confusion matrix cmat = sparse(numClasses,numClasses); % Classify the validation data to monitor the tranining process while hasdata(dsVal) data = read(dsVal); % Get the next batch of data. [XVal,YVal] = batchData(data,classes);% Create batch. % Compute label predictions. isTrainingVal = 0; %Set at zero for validation data YPred = pointnetClassifier(XVal,parameters,state,isTrainingVal); % Choose prediction with highest score as the class label for % XTest. [~,YValLabel] = max(YVal,[],1); [~,YPredLabel] = max(YPred,[],1); cmat = aggreateConfusionMetric(cmat,YValLabel,YPredLabel);% Update the confusion matrix end % Update training progress plot with average classification accuracy. acc = sum(diag(cmat))./sum(cmat,"all"); addpoints(valAccPlotter,iteration,acc); % Update the learning rate if mod(epoch,learnRateDropPeriod) == 0 learnRate = learnRate * learnRateDropFactor; end reset(dsTrain); % Reset the training data since all the training data were already read % Shuffle the data at every epoch dsTrain.UnderlyingDatastore.Files = dsTrain.UnderlyingDatastore.Files(randperm(length(dsTrain.UnderlyingDatastore.Files))); reset(dsVal); end cmat = sparse(numClasses,numClasses); % Prepare sparse-double variable to do like zeros(2,2) reset(dsVal); % Reset the validation data data = readall(dsVal); % Read all validation data [XVal,YVal] = batchData(data,classes); % Create batch. % Classify the validation data using the helper function pointnetClassifier YPred = pointnetClassifier(XVal,parameters,state,isTrainingVal); % Choose prediction with highest score as the class label for % XTest. [~,YValLabel] = max(YVal,[],1); [~,YPredLabel] = max(YPred,[],1); % Collect confusion metrics. cmat = aggreateConfusionMetric(cmat,YValLabel,YPredLabel); figure;chart = confusionchart(cmat,classes); acc = sum(diag(cmat))./sum(cmat,"all")