How to identify boundaries of shapes in an image using the change in color

Question

Yash 2013-4-27

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/73741-how-to-identify-boundaries-of-shapes-in-an-image-using-the-change-in-color

评论： ankita taparia 2018-12-11

Hi I am new to Matlab, I have got to identify boundaries of irregular shapes in Image using the change in color, Please guide me how to get info from image into a matrix

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Answer 1

Image Analyst 2013-4-28

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/73741-how-to-identify-boundaries-of-shapes-in-an-image-using-the-change-in-color#answer_83735

If you want the color edges, then the most sensible thing is to measure Delta E - the color difference - between a pixel and its neighbors. I just gave the algorithm a few days ago here: http://www.mathworks.com/matlabcentral/answers/73635#answer_83593 An edge will have a high delta E while non-edges will have low delta E. I now have a demo script of this. Let me know if you want me to post it.

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Image Analyst 2013-4-28

在 MATLAB Online 中打开

OK, here it is. Please "Accept" the answer if it answered your question.

% Demo to computer color contrast using the CIE Color Difference Formula (Delta E).
% Has the effect of finding color edges (high contrast regions),
% and "uniform" regions (low contrast regions).
% The RGB image is converted to LAB color space and then the
% mean color in a localized window is computed (i.e. the local average image).
% The Delta E (the color difference in LAB color space) is then calculated
% for every pixel in the image between that pixel's color and the average
% LAB color of the window region.  The user can then specify a number that
% says how close to that color would they like to be.  The software will
% then find all pixels within that specified Delta E of the color each pixel.
%
function LocalDeltaE()
clc;  % Clear command window.
clear;  % Delete all variables.
close all;  % Close all figure windows except those created by imtool.
% imtool close all;  % Close all figure windows created by imtool.
workspace;  % Make sure the workspace panel is showing.
% Change the current folder to the folder of this m-file.
if(~isdeployed)
  cd(fileparts(which(mfilename)));
end
try
  % Check that user has the Image Processing Toolbox installed.
  hasIPT = license('test', 'image_toolbox');
  if ~hasIPT
    % User does not have the toolbox installed.
    message = sprintf('Sorry, but you do not seem to have the Image Processing Toolbox.\nDo you want to try to continue anyway?');
    reply = questdlg(message, 'Toolbox missing', 'Yes', 'No', 'Yes');
    if strcmpi(reply, 'No')
      % User said No, so exit.
      return;
    end
  end
  
  % Continue with the demo.  Do some initialization stuff.
  close all;
  fontSize = 14;
  figure;
  defaultThresholdAnswer = {'7'};  % Suggest this number to the user to threshold the contrast.
  % Maximize the figure.
  set(gcf, 'Position', get(0, 'ScreenSize'));
  set(gcf,'name','Local Color Contrast Demo','numbertitle','off')
  
  % Change the current folder to the folder of this m-file.
  % (The line of code below is from Brett Shoelson of The Mathworks.)
  if(~isdeployed)
    cd(fileparts(which(mfilename)));
  end
  
  % Ask user if they want to use a demo image or their own image.
  message = sprintf('Do you want use a standard demo image,\nOr pick one of your own?');
  reply2 = questdlg(message, 'Which Image?', 'Demo','My Own', 'Demo');
  % Open an image.
  if strcmpi(reply2, 'Demo')
    % Read standard MATLAB demo image.
    message = sprintf('Which demo image do you want to use?');
    selectedImage = questdlg(message, 'Which Demo Image?', 'Onions', 'Peppers', 'Trees', 'Peppers');
    if strcmp(selectedImage, 'Onions')
      fullImageFileName = 'onion.png';
      defaultThresholdAnswer = {'5'};  % Suggest this number to the user.
    elseif strcmp(selectedImage, 'Peppers')
      fullImageFileName = 'peppers.png';
      defaultThresholdAnswer = {'8'};  % Suggest this number to the user.
    else
      fullImageFileName = 'trees.tif';
      defaultThresholdAnswer = {'20'};  % Suggest this number to the user.
    end
  else
    % They want to pick their own.
    % Change default directory to the one containing the standard demo images for the MATLAB Image Processing Toolbox.
    originalFolder = pwd;
    folder = fullfile(matlabroot, '\toolbox\images\imdemos');
    if ~exist(folder, 'dir')
      folder = pwd;
    end
    cd(folder);
    % Browse for the image file.
    [baseFileName, folder] = uigetfile('*.*', 'Specify an image file');
    fullImageFileName = fullfile(folder, baseFileName);
    % Set current folder back to the original one.
    cd(originalFolder);
  end
  
  % Check to see that the image exists.  (Mainly to check on the demo images.)
  if ~exist(fullImageFileName, 'file')
    message = sprintf('This file does not exist:\n%s', fullImageFileName);
    WarnUser(message);
    return;
  end
  
  % Read in image into an array.
  [rgbImage, storedColorMap] = imread(fullImageFileName);
  [rows, columns, numberOfColorBands] = size(rgbImage);
  % If it's monochrome (indexed), convert it to color.
  % Check to see if it's an 8-bit image needed later for scaling).
  if strcmpi(class(rgbImage), 'uint8')
    % Flag for 256 gray levels.
    eightBit = true;
  else
    eightBit = false;
  end
  if numberOfColorBands == 1
    if isempty(storedColorMap)
      % Just a simple gray level image, not indexed with a stored color map.
      % Create a 3D true color image where we copy the monochrome image into all 3 (R, G, & B) color planes.
      rgbImage = cat(3, rgbImage, rgbImage, rgbImage);
    else
      % It's an indexed image.
      rgbImage = ind2rgb(rgbImage, storedColorMap);
      % ind2rgb() will convert it to double and normalize it to the range 0-1.
      % Convert back to uint8 in the range 0-255, if needed.
      if eightBit
        rgbImage = uint8(255 * rgbImage);
      end
    end
  end
  
  % Display the original image.
  subplot(3, 4, 1);
  imshow(rgbImage);
  drawnow; % Make it display immediately.
  if numberOfColorBands > 1
    title('Original Color Image', 'FontSize', fontSize);
  else
    caption = sprintf('Original Indexed Image\n(converted to true color with its stored colormap)');
    title(caption, 'FontSize', fontSize);
  end
  
  % Convert image from RGB colorspace to lab color space.
  cform = makecform('srgb2lab');
  lab_Image = applycform(im2double(rgbImage),cform);
  
  % Extract out the color bands from the original image
  % into 3 separate 2D arrays, one for each color component.
  LChannel = lab_Image(:, :, 1);
  aChannel = lab_Image(:, :, 2);
  bChannel = lab_Image(:, :, 3);
  
  % Display the lab images.
  subplot(3, 4, 2);
  imshow(LChannel, []);
  title('L Channel (Overall Lightness)', 'FontSize', fontSize);
  subplot(3, 4, 3);
  imshow(aChannel, []);
  title('a Channel (redness to greenness)', 'FontSize', fontSize);
  subplot(3, 4, 4);
  imshow(bChannel, []);
  title('b Channel (yellowness to blueness)', 'FontSize', fontSize);
  
  % Find out the square window size where wants to average the colors.
  prompt = {sprintf('What square window size do you want to take the average color within? ')};
  dialogTitle = 'Enter the window size';
  numberOfLines = 1;
  defaultAnswer = {'5'};  % Default window width.
  response = inputdlg(prompt, dialogTitle, numberOfLines, defaultAnswer);
  % Update tolerance with user's response.
  windowSize = str2double(cell2mat(response));
  
  % Get the average lab color value, localized within that window.
  [LMean, aMean, bMean] = GetMeanLABValues(LChannel, aChannel, bChannel, windowSize);
  
  % Get the elta colors for each LAB color channel.
  deltaL = LMean - LChannel;
  deltaa = aMean - aChannel;
  deltab = bMean - bChannel;
  
  % Create the Delta E image.
  % This is an image that represents the color difference.
  % Delta E is the square root of the sum of the squares of the delta images.
  deltaE = sqrt(deltaL .^ 2 + deltaa .^ 2 + deltab .^ 2);
  
  % Display the masked Delta E image - the delta E within the masked region only.
  subplot(3, 4, 5);
  imshow(deltaE, []);
  caption = sprintf('Delta E between original color image\nand the mean color within the local window region.');
  title(caption, 'FontSize', fontSize);
  
  % Plot the histogram of the Delta E color difference image.
  caption = sprintf('Histogram of the Delta E Image\n');
  PlotHistogram(deltaE, [3 4 6], caption);
  
  message = sprintf('Regions dissilimar in color to the local color\nwill be bright in the Delta E image.\nLow contrast regions will be dark.');
  msgboxw(message);
  
  % Find out how close the user wants to match the colors.
  prompt = {sprintf('First, examine the histogram.\nThen find pixels within this Delta E that you want to threshold at:')};
  dialogTitle = 'Enter Delta E to threshold at';
  numberOfLines = 1;
  % defaultThresholdAnswer was determined when they read in the demo image.
  response = inputdlg(prompt, dialogTitle, numberOfLines, defaultThresholdAnswer);
  % Update tolerance with user's response.
  thresholdValue = str2double(cell2mat(response));
  
  % Let them interactively select the threshold with the threshold() m-file.
  % (Note: This is a separate function in a separate file in my File Exchange.)
  % http://www.mathworks.com/matlabcentral/fileexchange/29372-thresholding-an-image
  %   threshold(deltaE);
  
  % Place a vertical bar at the threshold location.
  handleToSubPlot8 = subplot(3, 4, 6);  % Get the handle to the plot.
  PlaceVerticalBarOnPlot(handleToSubPlot8, thresholdValue, [0 .5 0]);  % Put a vertical red line there.
  
  % Find pixels within that delta E.
  binaryImage = deltaE >= thresholdValue;
  % Display high contrast mask.
  subplot(3, 4, 7);
  imshow(binaryImage, []);
  caption = sprintf('Mask of High Delta Es (Delta E >= %.1f)', thresholdValue);
  title(caption, 'FontSize', fontSize);
  % Display low contrast mask.
  subplot(3, 4, 8);
  imshow(~binaryImage, []);
  caption = sprintf('Mask of Low Delta Es (Delta E <= %.1f)', thresholdValue);
  title(caption, 'FontSize', fontSize);
  
  % Mask the image with the high contrast colors and extract those pixels.
  highContrastPixelImage = bsxfun(@times, rgbImage, cast(binaryImage, class(rgbImage)));
  subplot(3, 4, 11);
  imshow(highContrastPixelImage);
  caption = sprintf('High Contrast Pixel Colors (Delta E <= %.1f)', thresholdValue);
  title(caption, 'FontSize', fontSize);
  
  % Mask the image with the low contast colors and extract those pixels.
  lowContrastPixelsImage = bsxfun(@times, rgbImage, cast(~binaryImage, class(rgbImage)));
  subplot(3, 4, 12);
  imshow(lowContrastPixelsImage);
  caption = sprintf('Low Contrast Pixel Colors (Delta E > %.1f)', thresholdValue);
  title(caption, 'FontSize', fontSize);
  
  % Display credits: the MATLAB logo and my name.
  ShowCredits(); % Display logo in plot position #12.
  
  % Alert user that the demo has finished.
  message = sprintf('Done!\n\nThe demo has finished.');
  msgboxw(message);
  
catch ME
  errorMessage = sprintf('Error running this m-file:\n%s\n\nThe error message is:\n%s', ...
    mfilename('fullpath'), ME.message);
  errordlg(errorMessage);
end
return; % from SimpleColorDetection()
% ---------- End of main function ---------------------------------
%----------------------------------------------------------------------------
% Display the MATLAB logo.
function ShowCredits()
try
  logoFig = subplot(3, 4, 9);
  caption = sprintf('A MATLAB Demo\nby Image Analyst');
  text(0.5,1.15, caption, 'Color','r', 'FontSize', 18, 'FontWeight','b', 'HorizontalAlignment', 'Center') ;
  positionOfLowerRightPlot = get(logoFig, 'position');
  L = 40*membrane(1,25);
  logoax = axes('CameraPosition', [-193.4013 -265.1546  220.4819],...
    'CameraTarget',[26 26 10], ...
    'CameraUpVector',[0 0 1], ...
    'CameraViewAngle',9.5, ...
    'DataAspectRatio', [1 1 .9],...
    'Position', positionOfLowerRightPlot, ...
    'Visible','off', ...
    'XLim',[1 51], ...
    'YLim',[1 51], ...
    'ZLim',[-13 40], ...
    'parent',gcf);
  s = surface(L, ...
    'EdgeColor','none', ...
    'FaceColor',[0.9 0.2 0.2], ...
    'FaceLighting','phong', ...
    'AmbientStrength',0.3, ...
    'DiffuseStrength',0.6, ...
    'Clipping','off',...
    'BackFaceLighting','lit', ...
    'SpecularStrength',1.1, ...
    'SpecularColorReflectance',1, ...
    'SpecularExponent',7, ...
    'Tag','TheMathWorksLogo', ...
    'parent',logoax);
  l1 = light('Position',[40 100 20], ...
    'Style','local', ...
    'Color',[0 0.8 0.8], ...
    'parent',logoax);
  l2 = light('Position',[.5 -1 .4], ...
    'Color',[0.8 0.8 0], ...
    'parent',logoax);
  
catch ME
  errorMessage = sprintf('Error running ShowCredits().\n\nThe error message is:\n%s', ...
    ME.message);
  errordlg(errorMessage);
end
return; % from ShowCredits()
%-----------------------------------------------------------------------------
% Get the average lab color within the window region.
% Get new images where each pixel of the output image is the average
% of the pixels in a square window, of width windowSize,
% around the input pixel (including the central pixel).
function [LMean, aMean, bMean] = GetMeanLABValues(LChannel, aChannel, bChannel, windowSize)
try
  kernel = ones(windowSize, windowSize) / windowSize^2;
  LMean = conv2(LChannel, kernel, 'same'); % Average of only the pixels within the windowed area.
  aMean = conv2(aChannel, kernel, 'same'); % Average of only the pixels within the windowed area.
  bMean = conv2(bChannel, kernel, 'same'); % Average of only the pixels within the windowed area.
catch ME
  errorMessage = sprintf('Error running GetMeanLABValues:\n\n\nThe error message is:\n%s', ...
    ME.message);
  WarnUser(errorMessage);
end
return; % from GetMeanLABValues
%==========================================================================================================================
function WarnUser(warningMessage)
uiwait(warndlg(warningMessage));
return; % from WarnUser()
%==========================================================================================================================
function msgboxw(message)
uiwait(msgbox(message));
return; % from msgboxw()
%==========================================================================================================================
% Plots the histograms of the pixels in both the masked region and the entire image.
function PlotHistogram(doubleImage, plotNumber, caption)
try
  fontSize = 14;
  numberOfBins = 50;
  subplot(plotNumber(1), plotNumber(2), plotNumber(3));
  
  % Find out where the edges of the histogram bins should be.
  maxValue = max(doubleImage(:));
  histBinEdges = linspace(0, maxValue, numberOfBins);
  
  % Get the histogram of the entire image into 100 bins.
  pixelCount = histc(doubleImage(:), histBinEdges);
  
  % Plot the  histogram of the entire image.
  bar(histBinEdges, pixelCount, 'BarWidth', 1);
  title(caption, 'FontSize', fontSize);
  
  % Scale x axis manually.
  xlim([0 histBinEdges(end)]);
catch ME
  errorMessage = sprintf('Error running PlotHistogram:\n\n\nThe error message is:\n%s', ...
    ME.message);
  WarnUser(errorMessage);
end
return; % from PlotHistogram
%=====================================================================
% Shows vertical lines going up from the X axis to the curve on the plot.
function lineHandle = PlaceVerticalBarOnPlot(handleToPlot, x, lineColor)
try
  % If the plot is visible, plot the line.
  if get(handleToPlot, 'visible')
    axes(handleToPlot);  % makes existing axes handles.axesPlot the current axes.
    % Make sure x location is in the valid range along the horizontal X axis.
    XRange = get(handleToPlot, 'XLim');
    maxXValue = XRange(2);
    if x > maxXValue
      x = maxXValue;
    end
    % Erase the old line.
    %hOldBar=findobj('type', 'hggroup');
    %delete(hOldBar);
    % Draw a vertical line at the X location.
    hold on;
    yLimits = ylim;
    lineHandle = line([x x], [yLimits(1) yLimits(2)], 'Color', lineColor, 'LineWidth', 3);
    hold off;
  end
catch ME
  errorMessage = sprintf('Error running PlaceVerticalBarOnPlot:\n\n\nThe error message is:\n%s', ...
    ME.message);
  WarnUser(errorMessage);
end
return;  % End of PlaceVerticalBarOnPlot

ankita taparia 2018-12-11

Mean shift filter is not giving satisfactory result. And DEM code is giving errors which I am not able to fix. Please help.

ankita taparia 2018-12-11

Also, I tried Sobel filter and getting decent results with it..only if i could be suggested one or two ilters like sobel(not canny) so that i could compare and proceed further.

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Answer 2

Ahmed A. Selman 2013-4-27

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链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/73741-how-to-identify-boundaries-of-shapes-in-an-image-using-the-change-in-color#answer_83646

There are few files in the file-exchange http://www.mathworks.com/matlabcentral/fileexchange/ so do some search there, you might find what you need.

Or, you can convert the colored image to binary one, then use the function (edge).

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Yash 2013-4-27

I have searched but couldn't find useful sol

Ahmed A. Selman 2013-4-27

See this file:

http://www.mathworks.com/matlabcentral/fileexchange/28114-fast-edges-of-a-color-image-actual-color-not-converting-to-grayscale

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Answer 3

Yash 2013-5-2

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/73741-how-to-identify-boundaries-of-shapes-in-an-image-using-the-change-in-color#answer_84150

Thanks for the script, I think this will definitely be helpful to me. I need to construct a geometry through this So I need to get the Coordinates of the boundaries created in the image as well as the colour code to know about the orientation. Please help me how can I convert the output that I have got to a matrix containing the required information.

Regards yash