I understand you have a query regarding an algorithm to recognizes colours in different halves of an image and identifies any large black spots. You can achieve the same by following the below steps:
- Start by loading the image into MATLAB.
imagePath = 'your_image.jpg'; % Replace with your image file path
img = imread(imagePath);
- Split the image into two halves.
[rows, cols, ~] = size(img);
% Split the image into two halves
leftHalf = img(:, 1:floor(cols/2), :);
rightHalf = img(:, floor(cols/2)+1:end, :);
- Analyse each half to identify the predominant colours.
leftHSV = rgb2hsv(leftHalf);
rightHSV = rgb2hsv(rightHalf);
% Define thresholds for beige and blue in HSV
beigeThreshold = [0.1 0.1 0.5; 0.2 0.3 1]; % Adjust these values based on your image
blueThreshold = [0.55 0.4 0.2; 0.75 1 1];
% Detect beige in the left half
beigeMaskLeft = (leftHSV(:,:,1) >= beigeThreshold(1,1) & leftHSV(:,:,1) <= beigeThreshold(2,1)) & ...
(leftHSV(:,:,2) >= beigeThreshold(1,2) & leftHSV(:,:,2) <= beigeThreshold(2,2)) & ...
(leftHSV(:,:,3) >= beigeThreshold(1,3) & leftHSV(:,:,3) <= beigeThreshold(2,3));
% Detect blue in the right half
blueMaskRight = (rightHSV(:,:,1) >= blueThreshold(1,1) & rightHSV(:,:,1) <= blueThreshold(2,1)) & ...
(rightHSV(:,:,2) >= blueThreshold(1,2) & rightHSV(:,:,2) <= blueThreshold(2,2)) & ...
(rightHSV(:,:,3) >= blueThreshold(1,3) & rightHSV(:,:,3) <= blueThreshold(2,3));
% Calculate the proportion of each color
beigeProportion = sum(beigeMaskLeft(:)) / numel(beigeMaskLeft);
blueProportion = sum(blueMaskRight(:)) / numel(blueMaskRight);
- Check for any large black spots in the image.
blackThreshold = 0.1;
% Create a mask for black areas
blackMask = (img(:,:,1) < blackThreshold) & (img(:,:,2) < blackThreshold) & (img(:,:,3) < blackThreshold);
% Calculate the proportion of black areas
blackProportion = sum(blackMask(:)) / numel(blackMask);
For given image the output is given below:
For better understanding of color recognition using HSV, refer to the given documentation:
Hope that Helps!
