I have to find specific parts of an image , should I mention the size of those parts to be detected?

this is the output, I just want to detect the white cotton and the green bolls from the whole pic, also i need to validate the size so that it could predict the objects of that particular size, help me where I am going wrong? used the below code

clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
clear;  % Erase all existing variables. Or clearvars if you want.
workspace;  % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 14;
% Read in image
img=imread("F:\E\Evaluation\cotton bulb\Average\(20).JPG");
subplot(3, 3, 1);
imshow(img);
title('Original Color Image', 'FontSize', fontSize);
% Set up figure properties:
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0 0 1 1]);
% Get rid of tool bar and pulldown menus that are along top of figure.
set(gcf, 'Toolbar', 'none', 'Menu', 'none');
% Give a name to the title bar.
set(gcf, 'Name', 'Demo by ImageAnalyst', 'NumberTitle', 'Off') 
drawnow;
grayImage = min( img, [], 3 );
subplot(3, 3, 2);
imshow(grayImage, []);
title('Gray Scale Image', 'FontSize', fontSize, 'Interpreter', 'None');
% Let's compute and display the histogram.
[pixelCount, grayLevels] = imhist(grayImage);
subplot(3, 3, 3); 
bar(grayLevels, pixelCount); % Plot it as a bar chart.
grid on;
title('Histogram of original image', 'FontSize', fontSize, 'Interpreter', 'None');
xlabel('Gray Level', 'FontSize', fontSize);
ylabel('Pixel Count', 'FontSize', fontSize);
xlim([0 grayLevels(end)]); % Scale x axis manually.
% Threshold and invert
% BW = ~im2bw( grayImage, .5 );
BW = grayImage < 170;
subplot(3, 3, 4);
imshow(BW);
title('Initial Binary Image', 'FontSize', fontSize);
% Get rid of border.
BW = imclearborder(BW);
% Get rid of small blobs
BW = bwareaopen(BW, 400);
subplot(3, 3, 5);
imshow(BW);
% Identify individual blobs by seeing which pixels are connected to each other.
% Each group of connected pixels will be given a label, a number, to identify it and distinguish it from the other blobs.
% Do connected components labeling with either bwlabel() or bwconncomp().
labeledImage = bwlabel(BW, 8);     % Label each blob so we can make measurements of it
% labeledImage is an integer-valued image where all pixels in the blobs have values of 1, or 2, or 3, or ... etc.
subplot(3, 3, 6);
imshow(labeledImage, []);  % Show the gray scale image.
title('Labeled Image, from bwlabel()', 'FontSize', fontSize);
% Let's assign each blob a different color to visually show the user the distinct blobs.
coloredLabels = label2rgb (labeledImage, 'hsv', 'k', 'shuffle'); % pseudo random color labels
% coloredLabels is an RGB image.  We could have applied a colormap instead (but only with R2014b and later)
subplot(3, 3, 7);
imshow(coloredLabels);
axis image; % Make sure image is not artificially stretched because of screen's aspect ratio.
caption = sprintf('Pseudo colored labels, from label2rgb().\nBlobs are numbered from top to bottom, then from left to right.');
title(caption, 'FontSize', fontSize);
% Get all the blob properties.
blobMeasurements = regionprops(labeledImage, 'Area', 'Perimeter', 'EulerNumber');
numberOfBlobs = size(blobMeasurements, 1);
allPerimeters = [blobMeasurements.Perimeter];
allAreas = [blobMeasurements.Area]
allEuler = [blobMeasurements.EulerNumber]; % Num regions - num holes in region.
circularities = allPerimeters .^ 2 ./ (4*pi*allAreas)
% Find blobs with circularities less than 2 and with no holes insdie them.
circularBlobIndexes = find(circularities < 2)
% Extract circular blobs
circlesImage = ismember(labeledImage, circularBlobIndexes);
subplot(3, 3, 8);
imshow(circlesImage);
axis image; % Make sure image is not artificially stretched because of screen's aspect ratio.
caption = sprintf('Only the circular blobs.');
title(caption, 'FontSize', fontSize);
% Remeasure with the new, final binary image composed of only circles
labeledImage = bwlabel(circlesImage);
blobMeasurements = regionprops(labeledImage, 'EquivDiameter');
allDiameters = [blobMeasurements.EquivDiameter]

Answer 1

[Hitesh]

Hi Aiman Zara,

Your code mostly looks correct, but you need to use color-based segmentation for detecting specific parts of an image such as white cotton and green bolls in MATLAB. Follow the below steps for the color based segmentation.

• Convert to HSV: This color space is useful for color-based segmentation.
• Thresholding: Define color thresholds for the objects you want to detect.
• Remove Small Objects: Use "bwareaopen" to filter out small noise.

You need to adjust the thresholds based on your collection of images.

• Threshold Values: You need to adjust the HSV threshold values based on lighting conditions and other factors specific to your collection of images.
• Size Threshold: Adjust the size threshold in "bwareaopen" to fit your needs.

Kindly refer to the following revised code:

% Read the image
img = imread('freshcotton.jpg');

% Convert the image to the HSV color space
hsvImg = rgb2hsv(img);

% Define thresholds for white cotton
whiteMask = (hsvImg(:,:,3) > 0.8) & (hsvImg(:,:,2) < 0.2);

% Define thresholds for green bolls
greenMask = (hsvImg(:,:,1) > 0.25) & (hsvImg(:,:,1) < 0.45) & (hsvImg(:,:,2) > 0.4);

% Remove small objects from the masks
whiteMask = bwareaopen(whiteMask, 50); % Adjust the size threshold as needed
greenMask = bwareaopen(greenMask, 50); % Adjust the size threshold as needed

% Display the results
figure;
subplot(1,3,1), imshow(img), title('Original Image');
subplot(1,3,2), imshow(whiteMask), title('Detected White Cotton');
subplot(1,3,3), imshow(greenMask), title('Detected Green Bolls');

For more information regarding the "bwareaopen", kindly refer to the following MATLAB documentation:

https://www.mathworks.com/help/images/ref/bwareaopen.html