Not detecting multiple surgical tools

% Clear Everything

clear all;
clc;
close all;


%% Automating Region of Interest (ROI) for many pictures of Scalpels

% Load Scalpel Images and Data
% location_scalpel = 'G:\My Drive\Surgical Helper\surgicaltooltestdata\Scalpel\*.jpg';       %  folder in which your images exists
% location_scalpel = 'G:\My Drive\Surgical Helper\Surgical-Dataset\Images\Scalpel\Alone\*.jpg';

% imds_scalpel = imageDatastore(location_scalpel);         %  Creates a datastore for all images in your folder
% readall(imds_scalpel);                       % Read all of the images
% numFiles = numpartitions(imds_scalpel);     % Find number of files


% Load Scalpel and Curved Mayo Scissor Images and Data
location_scalpel_scissor = 'G:\My Drive\Surgical Helper\surgicaltooltestdata\Curved Mayo Scissor and Scalpel\*.jpg';
imds_scalpel_scissor = imageDatastore(location_scalpel_scissor);         %  Creates a datastore for all images in your folder
readall(imds_scalpel_scissor);                       % Read all of the images
numFiles = numpartitions(imds_scalpel_scissor);     % Find number of files

% Create Data Source

% imageFiles = imds_scalpel.Files;
imageFiles = imds_scalpel_scissor.Files;
dataSource = groundTruthDataSource(imageFiles);



% Create labelData from Scalpel Truth

scalpelTruth = cell(88, 1); % preallocate

for i = 1:44
    % Load Image to define the ROI of
    ROI_Image = readimage(imds_scalpel_scissor,i);

    % Object detection with BRISK Features of the image
    points = detectBRISKFeatures(ROI_Image);
    % points = detectMinEigenFeatures(ROI_Image);

    % Use min/max to determine bounding rectangle
    min_x = min(points.Location(:,1));
    max_x = max(points.Location(:,1));
    min_y = min(points.Location(:,2));
    max_y = max(points.Location(:,2));

    % Bounding rectangle coordinates for Scalpel AKA Scalpel Truth
    scalpelTruth{i}= [min_x min_y (max_x-min_x) (max_y-min_y)];

%     figure(i);
%     imshow(ROI_Image); hold on;
%     plot(points.selectStrongest(20));
%     hold on
%     % Use rectangle to draw bounding rectangle ROI
%     rectangle('Position',scalpelTruth(i,:), 'EdgeColor','red','LineWidth', 0.75 );
end

scissorTruth = cell(88, 1); % preallocate

for i = 45:88
    % Load Image to define the ROI of
    ROI_Image = readimage(imds_scalpel_scissor,i);

    % Object detection with BRISK Features of the image
    points = detectBRISKFeatures(ROI_Image);
    % points = detectMinEigenFeatures(ROI_Image);

    % Use min/max to determine bounding rectangle
    min_x = min(points.Location(:,1));
    max_x = max(points.Location(:,1));
    min_y = min(points.Location(:,2));
    max_y = max(points.Location(:,2));

    % Bounding rectangle coordinates for Curved Mayo Scissor AKA Scissor Truth
    scissorTruth{i}= [min_x min_y (max_x-min_x) (max_y-min_y)];

%     figure(i);
%     imshow(ROI_Image); hold on;
%     plot(points.selectStrongest(20));
%     hold on
%     % Use rectangle to draw bounding rectangle ROI
%     rectangle('Position',scissorTruth1(i-44,:), 'EdgeColor','red','LineWidth', 0.75 );
end

labelNames = {'Scalpel';'Curved_Mayo_Scissor'};
labelData = table(scalpelTruth,scissorTruth,'VariableNames',labelNames)

% Create Label Definition
ldc = labelDefinitionCreator();
addLabel(ldc,'Scalpel',labelType.Rectangle);
addLabel(ldc,'Curved_Mayo_Scissor',labelType.Rectangle);
labelDefs = create(ldc);


% Create your gTruth
gTruth = groundTruth(dataSource,labelDefs,labelData)


% Manual Gtruth
% load('GroundTruth_Scalpel_Scissors.mat')

%% Train R-CNN Surgical Tool Detector

% Set network training options to use mini-batch size of 32 to reduce GPU
% memory usage. Lower the InitialLearnRate to reduce the rate at which
% network parameters are changed. This is beneficial when fine-tuning a
% pre-trained network and prevents the network from changing too rapidly.

options = trainingOptions('sgdm', ...
  'MiniBatchSize', 32, ...
  'InitialLearnRate', 1e-6, ...
  'MaxEpochs', 10);


% Train the R-CNN detector. Training can take a few minutes to complete.

trainingdata = [gTruth.DataSource.Source, gTruth.LabelData];
network=alexnet;

layers = network.Layers;

numClassesPlusBackground = numel(labelNames)+1;

layers = [ ...
    imageInputLayer([28 28 1])
    convolution2dLayer(5,20)
    fullyConnectedLayer(numClassesPlusBackground);
    softmaxLayer()
    classificationLayer()];


% rcnn = trainRCNNObjectDetector(trainingdata, network, options, 'NegativeOverlapRange', [0 0.3])
rcnn = trainRCNNObjectDetector(trainingdata, network, options, 'NumStrongestRegions', 2000)


% Test the R-CNN detector on a test image. Test Image of Scalpel
img = imread('bisturi9TEST.jpg');

[bboxes, scores, labels] = detect(rcnn, img, 'MiniBatchSize', 32);

% Display strongest detection result.
[scores, idx] = max(scores);

bboxes = bboxes(idx, :);
annotation = sprintf('%s: (Confidence = %f)', labels(idx), scores);

detectedImg = insertObjectAnnotation(img, 'rectangle', bboxes, annotation);

figure(1);
imshow(detectedImg)
title('BRISK Feature Object Detection and AlexNet CNN');

% Test image 2 Curved Mayo Scissor
img2 = imread('tesouracurva4TEST.jpg');

[bboxes, scores, labels] = detect(rcnn, img2, 'MiniBatchSize', 32);

% Display strongest detection result.
[scores, idx] = max(scores);

bboxes = bboxes(idx, :);
annotation = sprintf('%s: (Confidence = %f)', labels(idx), scores);

detectedImg2 = insertObjectAnnotation(img2, 'rectangle', bboxes, annotation);

figure(2);
imshow(detectedImg2)
title('BRISK Feature Object Detection and AlexNet CNN');

% Test image 3 Curved Mayo Scissor
img3 = imread('scalpel_scissors_combined.jpg');

[bboxes, scores, labels] = detect(rcnn, img3);

% Display strongest detection result.
[scores, idx] = max(scores);

bboxes = bboxes(idx, :);
annotation = sprintf('%s: (Confidence = %f)', labels(idx), scores);

detectedImg3 = insertObjectAnnotation(img3, 'rectangle', bboxes, annotation);

figure(3);
imshow(detectedImg3)
title('BRISK Feature Object Detection and AlexNet CNN');



%%