Performing Image Semantic Segmentation on CamVid Data Unsing DeepLabV3+ Model with MobileNetV2 Backbone in MATLAB Environment

outputFolder = "C:\Anwar\CamVid\"

imgDir = fullfile(outputFolder,'train\');

imds = imageDatastore(imgDir);

I = readimage(imds, 200);

I = histeq(I);

imshow(I)

function labelIDs = camvidPixelLabelIDs()

% Return the label IDs corresponding to each class.

%

% The CamVid dataset has 32 classes. Group them into 11 classes following

% the original SegNet training methodology [1].

%

% The 11 classes are:

%   "Sky" "Building", "Pole", "Road", "Pavement", "Tree", "SignSymbol",

%   "Fence", "Car", "Pedestrian",  and "Bicyclist".

%

% CamVid pixel label IDs are provided as RGB color values. Group them into

% 11 classes and return them as a cell array of M-by-3 matrices. The

% original CamVid class names are listed alongside each RGB value. Note

% that the Other/Void class are excluded below.

labelIDs = { ...

    

    % "Sky"

    [

    128 128 128; ... % "Sky"

    ]

    

    % "Building" 

    [

    000 128 064; ... % "Bridge"

    128 000 000; ... % "Building"

    064 192 000; ... % "Wall"

    064 000 064; ... % "Tunnel"

    192 000 128; ... % "Archway"

    ]

    

    % "Pole"

    [

    192 192 128; ... % "Column_Pole"

    000 000 064; ... % "TrafficCone"

    ]

    

    % Road

    [

    128 064 128; ... % "Road"

    128 000 192; ... % "LaneMkgsDriv"

    192 000 064; ... % "LaneMkgsNonDriv"

    ]

    

    % "Pavement"

    [

    000 000 192; ... % "Sidewalk" 

    064 192 128; ... % "ParkingBlock"

    128 128 192; ... % "RoadShoulder"

    ]

        

    % "Tree"

    [

    128 128 000; ... % "Tree"

    192 192 000; ... % "VegetationMisc"

    ]

    

    % "SignSymbol"

    [

    192 128 128; ... % "SignSymbol"

    128 128 064; ... % "Misc_Text"

    000 064 064; ... % "TrafficLight"

    ]

    

    % "Fence"

    [

    064 064 128; ... % "Fence"

    ]

    

    % "Car"

    [

    064 000 128; ... % "Car"

    064 128 192; ... % "SUVPickupTruck"

    192 128 192; ... % "Truck_Bus"

    192 064 128; ... % "Train"

    128 064 064; ... % "OtherMoving"

    ]

    

    % "Pedestrian"

    [

    064 064 000; ... % "Pedestrian"

    192 128 064; ... % "Child"

    064 000 192; ... % "CartLuggagePram"

    064 128 064; ... % "Animal"

    ]

    

    % "Bicyclist"

    [

    000 128 192; ... % "Bicyclist"

    192 000 192; ... % "MotorcycleScooter"

    ]

    

    };

end

function classes = getClassNames()

classes = [

    "Sky"

    "Building"

    "Pole"

    "Road"

    "Pavement"

    "Tree"

    "SignSymbol"

    "Fence"

    "Car"

    "Pedestrian"

    "Bicyclist"

    ];

end

function pixelLabelColorbar(cmap, classNames)

% Add a colorbar to the current axis. The colorbar is formatted

% to display the class names with the color.

colormap(gca,cmap)

% Add colorbar to current figure.

c = colorbar('peer', gca);

% Use class names for tick marks.

c.TickLabels = classNames;

numClasses = size(cmap,1);

% Center tick labels.

c.Ticks = 1/(numClasses*2):1/numClasses:1;

% Remove tick mark.

c.TickLength = 0;

end

function cmap = camvidColorMap()

% Define the colormap used by CamVid dataset.

cmap = [

    128 128 128   % Sky

    128 0 0       % Building

    192 192 192   % Pole

    128 64 128    % Road

    60 40 222     % Pavement

    128 128 0     % Tree

    192 128 128   % SignSymbol

    64 64 128     % Fence

    64 0 128      % Car

    64 64 0       % Pedestrian

    0 128 192     % Bicyclist

    ];

% Normalize between [0 1].

cmap = cmap ./ 255;

end

function [imdsTrain, imdsVal, imdsTest, pxdsTrain, pxdsVal, pxdsTest] = partitionCamVidData(imds,pxds)

% Partition CamVid data by randomly selecting 60% of the data for training. The

% rest is used for testing.

    

% Set initial random state for example reproducibility.

rng(0); 

numFiles = numpartitions(imds);

shuffledIndices = randperm(numFiles);

% Use 60% of the images for training.

numTrain = round(0.60 * numFiles);

trainingIdx = shuffledIndices(1:numTrain);

% Use 20% of the images for validation

numVal = round(0.20 * numFiles);

valIdx = shuffledIndices(numTrain+1:numTrain+numVal);

% Use the rest for testing.

testIdx = shuffledIndices(numTrain+numVal+1:end);

% Create image datastores for training and test.

imdsTrain = subset(imds,trainingIdx);

imdsVal = subset(imds,valIdx);

imdsTest = subset(imds,testIdx);

% Create pixel label datastores for training and test.

pxdsTrain = subset(pxds,trainingIdx);

pxdsVal = subset(pxds,valIdx);

pxdsTest = subset(pxds,testIdx);

end

function data = augmentImageAndLabel(data, xTrans, yTrans)

% Augment images and pixel label images using random reflection and

% translation.

for i = 1:size(data,1)

    

    tform = randomAffine2d(...

        XReflection=true,...

        XTranslation=xTrans, ...

        YTranslation=yTrans);

    

    % Center the view at the center of image in the output space while

    % allowing translation to move the output image out of view.

    rout = affineOutputView(size(data{i,1}), tform, BoundsStyle='centerOutput');

    

    % Warp the image and pixel labels using the same transform.

    data{i,1} = imwarp(data{i,1}, tform, OutputView=rout);

    data{i,2} = imwarp(data{i,2}, tform, OutputView=rout);

    

end

end

function loss = modelLoss(Y,T,classWeights)

    weights = dlarray(classWeights,"C");

    mask = ~isnan(T);

    T(isnan(T)) = 0;

    loss = crossentropy(Y,T,weights,Mask=mask,NormalizationFactor="mask-included");

end

classes = getClassNames()

imageSize = [720 960 3];

numClasses = 11;

net = deeplabv3plus(imageSize, numClasses, 'mobilenetv2');

I = imread("C:\Anwar\CamVid\train\0001TP_009210.png");

inputSize = net.Layers(1).InputSize;

I = imresize(I,inputSize(1:2));

C = semanticseg(I,net);

cmap = camvidColorMap;

B = labeloverlay(I,C,Colormap=cmap,Transparency=0.4);

figure

imshow(B)

pixelLabelColorbar(cmap, classes);

labelIDs = camvidPixelLabelIDs();

labelDir = fullfile(outputFolder,"train_labels");

pxds = pixelLabelDatastore(labelDir,classes,labelIDs);

C = readimage(pxds,200);

cmap = camvidColorMap;

B = labeloverlay(I,C,ColorMap=cmap);

imshow(B)

pixelLabelColorbar(cmap,classes);

tbl = countEachLabel(pxds)

frequency = tbl.PixelCount/sum(tbl.PixelCount);

bar(1:numel(classes),frequency)

xticks(1:numel(classes)) 

xticklabels(tbl.Name)

xtickangle(45)

ylabel("Frequency")

valdir = fullfile(outputFolder, 'val\')

imdsVal = imageDatastore(valdir)

vallabelDir = fullfile(outputFolder,"val_labels\");

pxdsVal = pixelLabelDatastore(vallabelDir,classes,labelIDs);

dsTrain = combine(imds, pxds)

dsVal = combine(imdsVal, pxdsVal)

xTrans = [-10 10];

yTrans = [-10 10];

dsTrain = transform(dsTrain, @(data)augmentImageAndLabel(data,xTrans,yTrans));

imageSize = [720 960 3];

numClasses = numel(classes);

net = deeplabv3plus(imageSize, numClasses, 'mobilenetv2');

imageFreq = tbl.PixelCount ./ tbl.ImagePixelCount;

classWeights = median(imageFreq) ./ imageFreq;

function iou = computeIoU(YTrue, YPred)

    % Convert predictions to binary mask (assuming single class for simplicity)

    YPred = YPred > 0.5; % Threshold predictions

    intersection = sum((YPred & YTrue), 'all');

    union = sum((YPred | YTrue), 'all');

    

    if union == 0

        iou = 1; % Perfect match (or no objects in both)

    else

        iou = intersection / union;

    end

end

options = trainingOptions("sgdm", ...

    LearnRateSchedule="piecewise", ...

    LearnRateDropPeriod=6, ...

    LearnRateDropFactor=0.1, ...

    Momentum=0.9, ...

    InitialLearnRate=1e-2, ...

    L2Regularization=0.005, ...

    ValidationData=dsVal, ...

    MaxEpochs=18, ...

    MiniBatchSize=4, ...

    Shuffle="every-epoch", ...

    CheckpointPath="C:\Anwar\CamVid\", ...

    VerboseFrequency=10, ...

    ValidationPatience=4, ...

    Metrics = { "accuracy", @computeIoU }, ...

    Plots="training-progress");  % Enable training graphs

doTraining = true;

if doTraining

    [net,info] = trainnet(dsTrain,net,@(Y,T) modelLoss(Y,T,classWeights),options);

end

testimgDir = fullfile(outputFolder,'test\');

imdsTest = imageDatastore(testimgDir);

testlabelDir = fullfile(outputFolder,"test_labels\");

pxdsTest = pixelLabelDatastore(testlabelDir,classes,labelIDs);

I = readimage(imdsTest,35);

C = semanticseg(I,net,Classes=classes);

B = labeloverlay(I,C,Colormap=cmap,Transparency=0.4);

imshow(B)

pixelLabelColorbar(cmap, classes);

expectedResult = readimage(pxdsTest,35);

actual = uint8(C);

expected = uint8(expectedResult);

imshowpair(actual, expected)

iou = jaccard(C,expectedResult);

table(classes,iou)

pxdsResults = semanticseg(imdsTest,net, ...

    Classes=classes, ...

    MiniBatchSize=4, ...

    WriteLocation=tempdir, ...

    Verbose=false);

metrics = evaluateSemanticSegmentation(pxdsResults,pxdsTest,Verbose=false);

metrics.DataSetMetrics

metrics.ClassMetrics

testData = combine(imdsTest, pxdsTest)

function meanIoU = computeTestmIoU(net, testData, classes)

    % Initialize IoU computation

    numClasses = numel(classes);

    intersection = zeros(numClasses, 1);

    union = zeros(numClasses, 1);

    

    % Reset the datastore before looping

    reset(testData);

    % Loop through test data

    while hasdata(testData)

        % Read a batch (image, ground truth mask)

        data = read(testData);

        img = data{1};  % Test image

        trueMask = data{2};  % Ground truth mask

        

        % Get network prediction

        predMask = semanticseg(img, net);

        % Compute IoU for each class

        for c = 1:numClasses

            % Get binary masks for class c

            predClass = (predMask == classNames(c));

            trueClass = (trueMask == classNames(c));

            % Compute intersection and union

            intersection(c) = intersection(c) + sum(predClass(:) & trueClass(:));

            union(c) = union(c) + sum(predClass(:) | trueClass(:));

        end

    end

    

    % Compute IoU for each class and take mean

    IoU = intersection ./ (union + eps); % Avoid division by zero

    meanIoU = mean(IoU, 'omitnan');

    

    % Display result

    fprintf('Overall Test mIoU: %.4f\n', meanIoU);

end

testmIoU = computeTestmIoU(net, testData, classes);