% Testing on New Data (capsule endoscopy images)

% Developer: Tonmoy Ghosh (tghosh@crimson.ua.edu)

clear; close all; clc;

% load trained network

data = load('CEtrainedSegNet.mat');

net = data.net;

labelIDs = PixelLabelIDs();

cmap = CEColorMap;

classes = [

    "Bleeding"

    "Non_Bleeding"

    "Background"

    ];

imdsTest = imageDatastore('testImage.png'); % image location and name

pxdsTest = pixelLabelDatastore('testLabel.png',classes,labelIDs);

tic

I = read(imdsTest);

I = imresize(I,[256 256]);

C = semanticseg(I, net);

%Display the results.

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

figure

imshow(B)

pixelLabelColorbar(cmap, classes);

L = read(pxdsTest);

expectedResult = imresize(L{1},[256 256],'nearest');

actual = uint8(C);

expected = uint8(expectedResult);

figure;

imshowpair(actual, expected)

iou = jaccard(C, expectedResult);

table(classes,iou)

%Evaluate Trained Network

pxdsResults = semanticseg(imdsTest,net,'WriteLocation',tempdir,'Verbose',false);

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

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