codegenYOLOv8Seg.m

% Read test image.
I = imread(fullfile('data','inputTeam.jpg'));

% Get classnames of COCO dataset.
classNames = helper.getCOCOClassNames;
numClasses = size(classNames,1);

% Replace 'yolov8n' with other values in 'yolov8SegPredict.m' to generate code for
% other YOLO v8 variants.
modelName = 'yolov8n';

% Display yolov8SegPredict function.
type('yolov8SegPredict.m');

% Generate MATLAB code.
cfg = coder.config('mex');
cfg.TargetLang = 'C++';
% % 'cudnn' and 'none' are also supported.
cfg.DeepLearningConfig = coder.DeepLearningConfig(TargetLibrary = 'mkldnn');
inputArgs = {I,coder.Constant(numClasses)};
codegen -config cfg yolov8SegPredict -args inputArgs -report

% Perform detection using pretrained model.
[masks,labelIds,scores,bboxes] = yolov8SegPredict_mex(I,numClasses);

% Map labelIds back to labels.
labels = classNames(labelIds);

% Visualize detection results.
Idisp = insertObjectAnnotation(I,"rectangle",bboxes,labels);
numMasks = size(masks,3);
overlayedImage = insertObjectMask(Idisp,masks,MaskColor=lines(numMasks));
figure;imshow(overlayedImage);