MATLAB: How to make hsv converted image two dimensional

hsvMATLABtwo dimensional

I have converted an image to hsv image…now i need to find the edges by using canny.But while doing i am getting an error…

Error using ==> iptcheckinput Function EDGE expected its first input, I, to be two-dimensional.

Error in ==> edge>parse_inputs at 541 iptcheckinput(I,{'numeric','logical'},{'nonsparse','2d'},mfilename,'I',1);

Error in ==> edge at 197 [a,method,thresh,sigma,thinning,H,kx,ky] = parse_inputs(varargin{:});

Error in ==> mergealgo at 37 im_canny = edge(inputgray, 'canny');

how to make hsv image two dimensional?

Best Answer

You have to decide for one channel, e.g.:

RGB = rand(100, 200, 3);
HSV = rgb2hsv(RGB);
im_canny = edge(HSV(:, :, 1), 'canny');

Depending on your problem the H, S or V channel is best to recognize edges.

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MATLAB: How to separate the whole lip image into upper and lower part using code

Do you mean like this:

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 = 15;
%===============================================================================

% Get the name of the image the user wants to use.
baseFileName = 'originalimage.jpg';
% Get the full filename, with path prepended.
folder = pwd
fullFileName = fullfile(folder, baseFileName);
% Check if file exists.
if ~exist(fullFileName, 'file')
  % The file doesn't exist -- didn't find it there in that folder.
  % Check the entire search path (other folders) for the file by stripping off the folder.
  fullFileNameOnSearchPath = baseFileName; % No path this time.
  if ~exist(fullFileNameOnSearchPath, 'file')
    % Still didn't find it.  Alert user.
    errorMessage = sprintf('Error: %s does not exist in the search path folders.', fullFileName);
    uiwait(warndlg(errorMessage));
    return;
  end
end
%===============================================================================
% Read in demo image.
rgbImage = imread(fullFileName);
% Get the dimensions of the image.
[imageRows, imageColumns, numberOfColorChannels] = size(rgbImage);
% Display the original image.
subplot(3, 3, 1);
imshow(rgbImage, []);
axis on;
caption = sprintf('Original Color Image, %s', baseFileName);
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
% 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;
hp = impixelinfo(); % Set up status line to see values when you mouse over the image.
% Crop the image
rgbImage = rgbImage(29:391, 87:762, :);
% Convert to HSV color space so we can segment the saturation channel.
hsvImage = rgb2hsv(rgbImage);
sImage = hsvImage(:, :, 2);
% Display the image.





subplot(3, 3, 2);
imshow(sImage, []);
axis on;
caption = sprintf('Saturation Image');
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
drawnow;
% Display the histogram
subplot(3, 3, 3);
histogram(sImage);
grid on;
title('Histogram of Saturation Image', 'FontSize', fontSize);
% Threshold the image
saturationThreshold = 0.15;
mask = sImage > saturationThreshold;
% Fill holes.
mask = imfill(mask, 'holes');
% Extract the two largest blobs only.
mask = bwareafilt(mask, 2);
% Put a red line at the threshold.
line([saturationThreshold, saturationThreshold], ylim, 'Color', 'r', 'LineWidth', 3);
% Display the image.
subplot(3, 3, 4);
imshow(mask, []);
axis on;
caption = sprintf('Mask Image');
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
drawnow;
% Give each blob an ID number.
labeledImage = bwlabel(mask);
% Get the top and bottom mask
topMask = ismember(labeledImage, 1);
bottomMask = ismember(labeledImage, 2);
% Display the image.
subplot(3, 3, 5);
imshow(topMask, []);
axis on;
caption = sprintf('Top Mask Image');
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
drawnow;
% Display the image.
subplot(3, 3, 6);
imshow(bottomMask, []);
axis on;
caption = sprintf('Bottom Mask Image');
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
drawnow;
% Mask the original RGB image and display.
% Mask the image using bsxfun() function
topMaskedRgbImage = bsxfun(@times, rgbImage, cast(topMask, 'like', rgbImage));
bottomMaskedRgbImage = bsxfun(@times, rgbImage, cast(bottomMask, 'like', rgbImage));
% Display the image.
subplot(3, 3, 7);
imshow(topMaskedRgbImage, []);
axis on;
caption = sprintf('Top Mask Image');
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
drawnow;
% Display the image.
subplot(3, 3, 8);
imshow(bottomMaskedRgbImage, []);
axis on;
caption = sprintf('Bottom Mask Image');
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
drawnow;

MATLAB: How to correction hue values of image

You need to snap an image of a color standard with known color values. The usual standard is the x-rite Color Checker Chart. Then you have known XYZ values. You can use least squares to derive a transform to convert your RGB values, of the color chips on a picture of that chart that you have taken, into XYZ values. Then you can use analytical formulas to convert XYZ into LAB or other calibrated color spaces.

Best Answer

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MATLAB: How to separate the whole lip image into upper and lower part using code

MATLAB: How to correction hue values of image

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