% Read the image img = imread('tablets.jpeg'); % Replace 'path_to_image.jpg' with the actual filename % Convert the image to grayscale grayImg = rgb2gray(img); % Apply additional preprocessing (e.g., Gaussian blur and adaptive thresholding) blurredImg = imgaussfilt(grayImg, 2); % Adjust the standard deviation as needed bwImg = imbinarize(blurredImg, 'adaptive', 'ForegroundPolarity', 'dark', 'Sensitivity', 0.5); % Apply edge detection to find boundaries edges = edge(bwImg, 'Canny'); % Perform morphological operations to enhance object features se = strel('disk', 5); dilatedEdges = imdilate(edges, se); % Find connected components in the binary image connectedComponents = bwlabel(dilatedEdges); % Calculate properties of connected components stats = regionprops(connectedComponents, 'Area', 'BoundingBox'); % Initialize a counter for missing pills missingPillsCount = 0; % Define a threshold area for considering a region as a pill thresholdArea = 50; % Adjust based on your specific case % Plot the original image figure; imshow(img); hold on; % Iterate through each connected component for i = 1:length(stats) % Check if the area of the component is below the threshold if stats(i).Area < thresholdArea % Draw a bounding box around the detected pill rectangle('Position', stats(i).BoundingBox, 'EdgeColor', 'r', 'LineWidth', 2); % Increment the counter for missing pills missingPillsCount = missingPillsCount + 1; end end % Display the results disp(['Number of Missing Pills: ' num2str(missingPillsCount)]); can you please make the output to detect the missing pill to make a rectangle around the missing one