MATLAB: K-means for a grayscale image

grayscale clusteringImage Processing Toolboxk-meansStatistics and Machine Learning Toolbox

I've tried the below code to cluster the grayscale image,

I = imread('sym_059.tif');
I = im2double(I);
c = kmeans(I, 3);
p = reshape(c, size(I));

executing this code, I am getting error as follows "Error using reshape-To RESHAPE the number of elements must not change." How can I debug this.? Help appreciated.

Best Answer

kmeans returns a vector of cluster indices, one index per row of input. You are trying to reshape that as if it had as many entries as the number of pixels in your image.

Possibly you want to try

c = kmeans(I(:), 3);

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C is your cneter.

Read about kmeans. You can your center along with the function. The output is random..the classe indices change for every run.

MATLAB: Crop dark area and display the face only

Convert to grayscale and then threshold and find the bounding box

clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
imtool close all;  % Close all imtool figures.
clear;  % Erase all existing variables. Or clearvars if you want.
workspace;  % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 20;
% Read in the color image.
folder = pwd;
baseFileName = 'm2.bmp';
% Get the full filename, with path prepended.
fullFileName = fullfile(folder, baseFileName);
if ~exist(fullFileName, 'file')
  % Didn't find it there.  Check the search path for it.
  fullFileName = baseFileName; % No path this time.
  if ~exist(fullFileName, 'file')
    % Still didn't find it.  Alert user.
    errorMessage = sprintf('Error: %s does not exist.', fullFileName);
    uiwait(warndlg(errorMessage));
    return;
  end
end
rgbImage = imread(fullFileName);
% Get the dimensions of the image.  numberOfColorBands should be = 3.
[rows, columns, numberOfColorBands] = size(rgbImage);
% Display the original color image.
subplot(2, 3, 1);
imshow(rgbImage, []);
axis on;
title('Original Color Image', 'FontSize', fontSize);
drawnow;
% Enlarge figure to full screen.
set(gcf, 'units','normalized','outerposition',[0 0 1 1]);
% Histogram the red channel, but there is a huge spike at 255 so let's not count those.
subplot(2, 3, 2);
grayImage = rgb2gray(rgbImage);
imshow(grayImage, []);
title('Gray Scale version', 'FontSize', fontSize);
subplot(2, 3, 3);
histogram(grayImage(grayImage<255))
grid on;
title('Histogram of Gray Scale Image', 'FontSize', fontSize);
someThresholdValue = 150;
binaryImage = grayImage > someThresholdValue;
% Take largest blob
binaryImage = bwareafilt(binaryImage, 1);
subplot(2, 3, 4);
imshow(binaryImage);
grid on;
title('Binary Image', 'FontSize', fontSize);
% Label the binary image.
labeledImage = bwlabel(binaryImage);
props = regionprops(labeledImage, 'BoundingBox');
bbox = props.BoundingBox
croppedImageRGB = imcrop(rgbImage, bbox);
subplot(2, 3, 5);
imshow(croppedImageRGB);
grid on;
title('Cropped RGB Image', 'FontSize', fontSize);
% Gray scale version of cropped image
croppedImageGray = imcrop(grayImage, bbox);
subplot(2, 3, 6);
imshow(croppedImageGray);
grid on;
title('Cropped Gray Scale Image', 'FontSize', fontSize);

Best Answer

Related Solutions

MATLAB: How can the center be determined by the Kmeans method

MATLAB: Crop dark area and display the face only

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