--- a
+++ b/SemanticSegmentation.m
@@ -0,0 +1,156 @@
+%% This code train and test semantic segmentation of Capsule endoscopy images
+% required MATLAB 2018
+% Developer: Tonmoy Ghosh (tghosh@crimson.ua.edu)
+
+% Load Images
+% Use |imageDatastore| to load images. The |imageDatastore| enables you 
+% to efficiently load a large collection of images on disk.
+%%
+clear; clc; close all;
+%imgDir = fullfile('bleeding images')
+imgDir = '/Users/tonmoyghosh/OneDrive - The University of Alabama/Paper with Code/Semantic Segmentation Based Bleeding Zone Detection/Dataset/bleeding';
+imds = imageDatastore(imgDir);
+%% 
+% Display one of the images.
+
+I = readimage(imds, 1);
+I = histeq(I);
+figure
+imshow(I)
+%% Load Pixel-Labeled Images
+classes = [
+    "Bleeding"
+    "Non_Bleeding"
+    "Background"
+    ];
+labelIDs = PixelLabelIDs();
+%% 
+% Use the classes and label IDs to create the |pixelLabelDatastore|:
+
+%labelDir = fullfile('labels');
+labelDir = '/Users/tonmoyghosh/OneDrive - The University of Alabama/Paper with Code/Semantic Segmentation Based Bleeding Zone Detection/Dataset/labels';
+pxds = pixelLabelDatastore(labelDir,classes,labelIDs);
+% Read and display one of the pixel-labeled images by overlaying it on top 
+% of an image.
+
+C = readimage(pxds, 1);
+
+
+cmap = CEColorMap;
+B = labeloverlay(I,C,'ColorMap',cmap);
+
+figure
+imshow(B)
+pixelLabelColorbar(cmap,classes);
+
+%%
+%analize the data statistics
+tbl = countEachLabel(pxds)
+
+
+%Visualize the pixel counts by class.
+
+frequency = tbl.PixelCount/sum(tbl.PixelCount);
+
+figure
+bar(1:numel(classes),frequency)
+xticks(1:numel(classes))
+xticklabels(tbl.Name)
+xtickangle(45)
+ylabel('Frequency')
+
+%%
+%Resize CamVid Data
+imageFolder = fullfile('imagesReszed',filesep);
+imds = resizeCEImages(imds,imageFolder);
+
+labelFolder = fullfile('labelsResized',filesep);
+pxds = resizeCEPixelLabels(pxds,labelFolder);
+
+
+
+%%
+%Prepare Training and Test Sets
+[imdsTrain, imdsTest, pxdsTrain, pxdsTest] = partitionCEData(imds,pxds);
+
+numTrainingImages = numel(imdsTrain.Files)
+numTestingImages = numel(imdsTest.Files)
+
+%Create the network
+imageSize = [256 256 3];
+numClasses = numel(classes);
+%lgraph = segnetLayers(imageSize,numClasses,'vgg16');
+
+
+%%
+
+%Balance Classes Using Class Weighting
+% imageFreq = tbl.PixelCount ./ tbl.ImagePixelCount;
+% classWeights = median(imageFreq) ./ imageFreq
+% 
+% pxLayer = pixelClassificationLayer('Name','labels','ClassNames', tbl.Name, 'ClassWeights', classWeights)
+% 
+% 
+% lgraph = removeLayers(lgraph, 'pixelLabels');
+% lgraph = addLayers(lgraph, pxLayer);
+% lgraph = connectLayers(lgraph, 'softmax' ,'labels');
+
+% load saved network architecture
+load lgraph
+
+%Select Training Options
+options = trainingOptions('sgdm', ...
+    'Momentum', 0.9, ...
+    'InitialLearnRate', 1e-3, ...
+    'L2Regularization', 0.0005, ...
+    'MaxEpochs', 100, ...
+    'MiniBatchSize', 3, ...
+    'Shuffle', 'every-epoch', ...
+    'VerboseFrequency', 2);
+
+
+
+%%
+
+%Data Augmentation
+augmenter = imageDataAugmenter('RandXReflection',true,...
+    'RandXTranslation', [-10 10], 'RandYTranslation',[-10 10]);
+
+
+%Start Training
+datasource = pixelLabelImageSource(imdsTrain,pxdsTrain, ...
+                    'DataAugmentation',augmenter);
+
+doTraining = false;
+if doTraining
+    [net, info] = trainNetwork(datasource,lgraph,options);
+else
+    data = load('CEtrainedSegNet.mat');
+    net = data.net;
+end
+
+%%
+%Test Network on One Image
+tic
+I = read(imdsTest);
+C = semanticseg(I, net);
+
+%Display the results.
+B = labeloverlay(I, C, 'Colormap', cmap, 'Transparency',0.4);
+figure
+imshow(B)
+pixelLabelColorbar(cmap, classes);
+
+expectedResult = read(pxdsTest);
+actual = uint8(C);
+expected = uint8(expectedResult{1});
+imshowpair(actual, expected)
+
+iou = jaccard(C, expectedResult{1});
+table(classes,iou)
+
+%Evaluate Trained Network
+pxdsResults = semanticseg(imdsTest,net,'WriteLocation',tempdir,'Verbose',false);
+metrics = evaluateSemanticSegmentation(pxdsResults,pxdsTest,'Verbose',false);
+
+toc