getiing roi of real time image through matlab and block processing.

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satnam singh on 20 Feb 2013

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hi there, i am in great problem. can any one please tell me how to select ROI in any real time image through webcam. i am trying to get the real time image of face of the Rubik's cube. i want to select the Roi and then divide the image of the Rubik's face into nine blocks. then i want to take the mean of the pixels in the particular block,and thus identify the color in each block. please tell me how to get the boundary of the face of the Rubik on the webcam and then then divide it into blocks. it is really annoying me......please any one help me........it will be really grateful...........please help....... hoping for the solution soon.............

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satnam singh on 25 Feb 2013

any one please ,having the explanation..

Image Analyst on 25 Feb 2013

It's just plotting the x and y values.

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Answer 1

Image Analyst on 21 Feb 2013

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If all your images will be so nicely illuminated with bright saturated colors and "face on" views of the cube, then it should be easy. Just look over some of the color segmentation tutorials in my File Exchange.

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Image Analyst on 7 Mar 2013

Edited: Image Analyst on 7 Mar 2013

Open in MATLAB Online

satname, I don't know why you don't understand. You simply adapt the demo to use different hue values like I said. All you had to do was to change 2 lines to change the hue threshold between 0.06 and 0.12:

    hueThresholdLow = 0.06;
    hueThresholdHigh = 0.12;

Here's the whole huge demo again with those values in there. Select the peppers demo image, which has some orange and you'll see it picks out the orange peppers, and not the red or yellow peppers.

% Demo macro to very, very simple color detection in 
% HSV (Hue, Saturation, Value) color space.
% Requires the Image Processing Toolbox.  Developed under MATLAB R2010a.
%
function SimpleColorDetectionByHue()
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.
% (The "cd" line of code below is from Brett Shoelson of The Mathworks.)
if(~isdeployed)
  cd(fileparts(which(mfilename))); % From Brett
end
ver % Display user's toolboxes in their command window.
% Introduce the demo, and ask user if they want to continue or exit.
message = sprintf('This demo will illustrate very simple orange color detection\nin HSV color space.\nIt requires the Image Processing Toolbox.\nDo you wish to continue?');
reply = questdlg(message, 'Run Demo?', 'OK','Cancel', 'OK');
if strcmpi(reply, 'Cancel')
  % User canceled so exit.
  return;
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 = 16;
  figure;
  % Maximize the figure. 
  set(gcf, 'units','normalized','outerposition',[0 0 1 1]); 
  % 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.
  %   fullImageFileName = 'peppers.png';
    message = sprintf('Which demo image do you want to use?');
    selectedImage = questdlg(message, 'Which Demo Image?', 'Onions', 'Peppers', 'Kids', 'Onions');
    if strcmp(selectedImage, 'Onions')
      fullImageFileName = 'onion.png';
    elseif strcmp(selectedImage, 'Peppers')
      fullImageFileName = 'peppers.png';
    else
      fullImageFileName = 'kids.tif';
    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 = 'C:\Program Files\MATLAB\R2010a\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);
    selectedImage = 'My own image'; % Need for the if threshold selection statement later.
  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);
    uiwait(msgbox(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 RGB image to HSV
  hsvImage = rgb2hsv(rgbImage);
  % Extract out the H, S, and V images individually
  hImage = hsvImage(:,:,1);
  sImage = hsvImage(:,:,2);
  vImage = hsvImage(:,:,3);
%   [lowThreshold highThreshold lastThresholdedBand] = threshold(.06, .12, hImage);
  % Display them.
  subplot(3, 4, 2);
  imshow(hImage);
  title('Hue Image', 'FontSize', fontSize);
  subplot(3, 4, 3);
  imshow(sImage);
  title('Saturation Image', 'FontSize', fontSize);
  subplot(3, 4, 4);
  imshow(vImage);
  title('Value Image', 'FontSize', fontSize);
  message = sprintf('These are the individual HSV color bands.\nNow we will compute the image histograms.');
  reply = questdlg(message, 'Continue with Demo?', 'OK','Cancel', 'OK');
  if strcmpi(reply, 'Cancel')
    % User canceled so exit.
    return;
  end
  % Compute and plot the histogram of the "hue" band.
  hHuePlot = subplot(3, 4, 6); 
  [hueCounts, hueBinValues] = imhist(hImage); 
  maxHueBinValue = find(hueCounts > 0, 1, 'last'); 
  maxCountHue = max(hueCounts); 
  bar(hueBinValues, hueCounts, 'r'); 
  grid on; 
  xlabel('Hue Value'); 
  ylabel('Pixel Count'); 
  title('Histogram of Hue Image', 'FontSize', fontSize);
  % Compute and plot the histogram of the "saturation" band.
  hSaturationPlot = subplot(3, 4, 7); 
  [saturationCounts, saturationBinValues] = imhist(sImage); 
  maxSaturationBinValue = find(saturationCounts > 0, 1, 'last'); 
  maxCountSaturation = max(saturationCounts); 
  bar(saturationBinValues, saturationCounts, 'g', 'BarWidth', 0.95); 
  grid on; 
  xlabel('Saturation Value'); 
  ylabel('Pixel Count'); 
  title('Histogram of Saturation Image', 'FontSize', fontSize);
  % Compute and plot the histogram of the "value" band.
  hValuePlot = subplot(3, 4, 8); 
  [valueCounts, valueBinValues] = imhist(vImage); 
  maxValueBinValue = find(valueCounts > 0, 1, 'last'); 
  maxCountValue = max(valueCounts); 
  bar(valueBinValues, valueCounts, 'b'); 
  grid on; 
  xlabel('Value Value'); 
  ylabel('Pixel Count'); 
  title('Histogram of Value Image', 'FontSize', fontSize);
  % Set all axes to be the same width and height.
  % This makes it easier to compare them.
  maxCount = max([maxCountHue,  maxCountSaturation, maxCountValue]); 
  axis([hHuePlot hSaturationPlot hValuePlot], [0 1 0 maxCount]); 
  % Plot all 3 histograms in one plot.
  subplot(3, 4, 5); 
  plot(hueBinValues, hueCounts, 'r', 'LineWidth', 2); 
  grid on; 
  xlabel('Values'); 
  ylabel('Pixel Count'); 
  hold on; 
  plot(saturationBinValues, saturationCounts, 'g', 'LineWidth', 2); 
  plot(valueBinValues, valueCounts, 'b', 'LineWidth', 2); 
  title('Histogram of All Bands', 'FontSize', fontSize); 
  maxGrayLevel = max([maxHueBinValue, maxSaturationBinValue, maxValueBinValue]); 
  % Make x-axis to just the max gray level on the bright end. 
  xlim([0 1]); 
  % Now select thresholds for the 3 color bands.
  message = sprintf('Now we will select some color threshold ranges\nand display them over the histograms.');
  reply = questdlg(message, 'Continue with Demo?', 'OK','Cancel', 'OK');
  if strcmpi(reply, 'Cancel')
    % User canceled so exit.
    return;
  end
  % Assign the low and high thresholds for each color band.
  if strcmpi(reply2, 'My Own') || strcmpi(selectedImage, 'Kids') > 0
    % Take a guess at the values that might work for the user's image.
    hueThresholdLow = 0;
    hueThresholdHigh = graythresh(hImage);
    saturationThresholdLow = graythresh(sImage);
    saturationThresholdHigh = 1.0;
    valueThresholdLow = graythresh(vImage);
    valueThresholdHigh = 1.0;
  else
    % Use values that I know work for the onions and peppers demo images.
    hueThresholdLow = 0.06;
    hueThresholdHigh = 0.12;
    saturationThresholdLow = 0.4;
    saturationThresholdHigh = 1;
    valueThresholdLow = 0.8;
    valueThresholdHigh = 1.0;
  end
  % Show the thresholds as vertical red bars on the histograms.
  PlaceThresholdBars(6, hueThresholdLow, hueThresholdHigh);
  PlaceThresholdBars(7, saturationThresholdLow, saturationThresholdHigh);
  PlaceThresholdBars(8, valueThresholdLow, valueThresholdHigh);
  message = sprintf('Next we will apply each color band threshold range to its respective color band.');
  reply = questdlg(message, 'Continue with Demo?', 'OK','Cancel', 'OK');
  if strcmpi(reply, 'Cancel')
    % User canceled so exit.
    return;
  end
  % Now apply each color band's particular thresholds to the color band
  hueMask = (hImage >= hueThresholdLow) & (hImage <= hueThresholdHigh);
  saturationMask = (sImage >= saturationThresholdLow) & (sImage <= saturationThresholdHigh);
  valueMask = (vImage >= valueThresholdLow) & (vImage <= valueThresholdHigh);
  % Display the thresholded binary images.
  fontSize = 16;
  subplot(3, 4, 10);
  imshow(hueMask, []);
  title('=   Hue Mask', 'FontSize', fontSize);
  subplot(3, 4, 11);
  imshow(saturationMask, []);
  title('&   Saturation Mask', 'FontSize', fontSize);
  subplot(3, 4, 12);
  imshow(valueMask, []);
  title('&   Value Mask', 'FontSize', fontSize);
  % Combine the masks to find where all 3 are "true."
  % Then we will have the mask of only the red parts of the image.
  orangeObjectsMask = uint8(hueMask & saturationMask & valueMask);
  subplot(3, 4, 9);
  imshow(orangeObjectsMask, []);
  caption = sprintf('Mask of Only\nThe Orange Objects');
  title(caption, 'FontSize', fontSize);
  % Tell user that we're going to filter out small objects.
  smallestAcceptableArea = 100; % Keep areas only if they're bigger than this.
  message = sprintf('Note the small regions in the image in the lower left.\nNext we will eliminate regions smaller than %d pixels.', smallestAcceptableArea);
  reply = questdlg(message, 'Continue with Demo?', 'OK','Cancel', 'OK');
  if strcmpi(reply, 'Cancel')
    % User canceled so exit.
    return;
  end
  % Open up a new figure, since the existing one is full.
  figure;  
  % Maximize the figure. 
  set(gcf, 'units','normalized','outerposition',[0 0 1 1]);
  % Get rid of small objects.  Note: bwareaopen returns a logical.
  orangeObjectsMask = uint8(bwareaopen(orangeObjectsMask, smallestAcceptableArea));
  subplot(3, 3, 1);
  imshow(orangeObjectsMask, []);
  fontSize = 13;
  caption = sprintf('bwareaopen() removed objects\nsmaller than %d pixels', smallestAcceptableArea);
  title(caption, 'FontSize', fontSize);
  % Smooth the border using a morphological closing operation, imclose().
  structuringElement = strel('disk', 4);
  orangeObjectsMask = imclose(orangeObjectsMask, structuringElement);
  subplot(3, 3, 2);
  imshow(orangeObjectsMask, []);
  fontSize = 16;
  title('Border smoothed', 'FontSize', fontSize);
  % Fill in any holes in the regions, since they are most likely red also.
  orangeObjectsMask = uint8(imfill(orangeObjectsMask, 'holes'));
  subplot(3, 3, 3);
  imshow(orangeObjectsMask, []);
  title('Regions Filled', 'FontSize', fontSize);
  message = sprintf('This is the filled, size-filtered mask.\nNext we will apply this mask to the original RGB image.');
  reply = questdlg(message, 'Continue with Demo?', 'OK','Cancel', 'OK');
  if strcmpi(reply, 'Cancel')
    % User canceled so exit.
    return;
  end
  % You can only multiply integers if they are of the same type.
  % (orangeObjectsMask is a logical array.)
  % We need to convert the type of orangeObjectsMask to the same data type as hImage.
  orangeObjectsMask = cast(orangeObjectsMask, class(rgbImage)); 
  % Use the orange object mask to mask out the orange-only portions of the rgb image.
  maskedImageR = orangeObjectsMask .* rgbImage(:,:,1);
  maskedImageG = orangeObjectsMask .* rgbImage(:,:,2);
  maskedImageB = orangeObjectsMask .* rgbImage(:,:,3);
  % Show the masked off red image.
  subplot(3, 3, 4);
  imshow(maskedImageR);
  title('Masked Red Image', 'FontSize', fontSize);
  % Show the masked off saturation image.
  subplot(3, 3, 5);
  imshow(maskedImageG);
  title('Masked Green Image', 'FontSize', fontSize);
  % Show the masked off value image.
  subplot(3, 3, 6);
  imshow(maskedImageB);
  title('Masked Blue Image', 'FontSize', fontSize);
  % Concatenate the masked color bands to form the rgb image.
  maskedRGBImage = cat(3, maskedImageR, maskedImageG, maskedImageB);
  % Show the masked off, original image.
  subplot(3, 3, 8);
  imshow(maskedRGBImage);
  fontSize = 13;
  caption = sprintf('Masked Original Image\nShowing Only the orange Objects');
  title(caption, 'FontSize', fontSize);
  % Show the original image next to it.
  subplot(3, 3, 7);
  imshow(rgbImage);
  title('The Original Image (Again)', 'FontSize', fontSize);
  % Measure the mean HSV and area of all the detected blobs.
  [meanHSV, areas, numberOfBlobs] = MeasureBlobs(orangeObjectsMask, hImage, sImage, vImage);
  if numberOfBlobs > 0
    fprintf(1, '\n----------------------------------------------\n');
    fprintf(1, 'Blob #, Area in Pixels, Mean H, Mean S, Mean V\n');
    fprintf(1, '----------------------------------------------\n');
    for blobNumber = 1 : numberOfBlobs
      fprintf(1, '#%5d, %14d, %6.2f, %6.2f, %6.2f\n', blobNumber, areas(blobNumber), ...
        meanHSV(blobNumber, 1), meanHSV(blobNumber, 2), meanHSV(blobNumber, 3));
    end
  else
    % Alert user that no orange blobs were found.
    message = sprintf('No orange blobs were found in the image:\n%s', fullImageFileName);
    fprintf(1, '\n%s\n', message);
    uiwait(msgbox(message));
  end
  subplot(3, 3, 9);
  ShowCredits();
  message = sprintf('Done!\n\nThe demo has finished.\n\nLook the MATLAB command window for\nthe area and color measurements of the %d regions.', numberOfBlobs);
  msgbox(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 ---------------------------------
%----------------------------------------------------------------------------
function [meanHSV, areas, numberOfBlobs] = MeasureBlobs(maskImage, hImage, sImage, vImage)
  [labeledImage numberOfBlobs] = bwlabel(maskImage, 8);     % Label each blob so we can make measurements of it
  if numberOfBlobs == 0
    % Didn't detect any orange blobs in this image.
    meanHSV = [0 0 0];
    areas = 0;
    return;
  end
  % Get all the blob properties.  Can only pass in originalImage in version R2008a and later.
  blobMeasurementsHue = regionprops(labeledImage, hImage, 'area', 'MeanIntensity');   
  blobMeasurementsSat = regionprops(labeledImage, sImage, 'area', 'MeanIntensity');   
  blobMeasurementsValue = regionprops(labeledImage, vImage, 'area', 'MeanIntensity');   
  
  meanHSV = zeros(numberOfBlobs, 3);  % One row for each blob.  One column for each color.
  meanHSV(:,1) = [blobMeasurementsHue.MeanIntensity]';
  meanHSV(:,2) = [blobMeasurementsSat.MeanIntensity]';
  meanHSV(:,3) = [blobMeasurementsValue.MeanIntensity]';
  
  % Now assign the areas.
  areas = zeros(numberOfBlobs, 3);  % One row for each blob.  One column for each color.
  areas(:,1) = [blobMeasurementsHue.Area]';
  areas(:,2) = [blobMeasurementsSat.Area]';
  areas(:,3) = [blobMeasurementsValue.Area]';
  return; % from MeasureBlobs()
  
  
%----------------------------------------------------------------------------
% Function to show the low and high threshold bars on the histogram plots.
function PlaceThresholdBars(plotNumber, lowThresh, highThresh)
  % Show the thresholds as vertical red bars on the histograms.
  subplot(3, 4, plotNumber); 
  hold on;
  maxYValue = ylim;
  maxXValue = xlim;
  hStemLines = stem([lowThresh highThresh], [maxYValue(2) maxYValue(2)], 'r');
  children = get(hStemLines, 'children');
  set(children(2),'visible', 'off');
  % Place a text label on the bar chart showing the threshold.
  fontSizeThresh = 14;
  annotationTextL = sprintf('%d', lowThresh);
  annotationTextH = sprintf('%d', highThresh);
  % For text(), the x and y need to be of the data class "double" so let's cast both to double.
  text(double(lowThresh + 5), double(0.85 * maxYValue(2)), annotationTextL, 'FontSize', fontSizeThresh, 'Color', [0 .5 0], 'FontWeight', 'Bold');
  text(double(highThresh + 5), double(0.85 * maxYValue(2)), annotationTextH, 'FontSize', fontSizeThresh, 'Color', [0 .5 0], 'FontWeight', 'Bold');
  
  % Show the range as arrows.
  % Can't get it to work, with either gca or gcf.
%   annotation(gca, 'arrow', [lowThresh/maxXValue(2) highThresh/maxXValue(2)],[0.7 0.7]);
  return; % from PlaceThresholdBars()
%----------------------------------------------------------------------------
% Display the MATLAB logo.
function ShowCredits()
%   xpklein;
%   surf(peaks(30));
  logoFig = subplot(3,3,9);
  caption = sprintf('A MATLAB Demo');
  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);
  return; % from ShowCredits()

satnam singh on 15 Mar 2013

any one there having the answer ..please reply.....

satnam singh on 16 Mar 2013

any one there please answer....

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getiing roi of real time image through matlab and block processing.

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getiing roi of real time image through matlab and block processing.

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