MATLAB: How to detect different colors in an image to identify different pieces of fruit

fruitimage processingImage Processing Toolbox

I have this image:

I need to identify various types of fruit, including pears, apples, oranges and bananas.

Initially I started by recognizing them through the form, with the help of this code:

Im = imread('frame147.jpg'); figure,imshow(Im);title('Imagem Original');

I1 = rgb2gray(Im);

level=graythresh(I1); bw=im2bw(I1,level);

[I2, Num]=bwlabel(bw);

objetos=regionprops(I2); figure,imshow(bw);title('Binario da Imagem com box');

for n=1:size(objetos,1) rectangle('Position',objetos(n).BoundingBox,'EdgeColor','y','LineWidth',1); end

for n=1:size(find([objetos.Area]<100),2) s=find([objetos.Area]<100); d=round(objetos(s(n)).BoundingBox); bw(d(2):d(2)+d(4),d(1):d(1)+d(3))=0; end figure,imshow(bw);title('Objetos Definidos');

[I2, Ne]=bwlabel(bw);

objetos2=regionprops(I2,'ALL');

for n=1:size(objetos2,1) X=rectangle('Position',objetos2(n).BoundingBox,'EdgeColor','y','LineWidth',1); end

for n=1:size(find([objetos2.Area]>100),2) X1=objetos2(n).Centroid(1); X2=objetos2(n).Centroid(2); text('Position',[X1,X2],'String',n,'color','b') end fprintf('\n\nForam encontradas %d objetos:\n\n',size(objetos2,1))

tol1=0.9; % tolerancia circulos – oranges and aples tol2=0.2; % tolerancia triangulos – pear tol3=0.9; % tolerancia retangulos – banana

   for n=1:size(objetos2,1)
    for m=1:15
        dif(m)=(objetos2(n).Extrema(m)/objetos2(n).Extrema(m+1));
    end
                 % Circulos: oranges or apples
        if ((4*pi*(objetos2(n).Area))/((objetos2(n).Perimeter)^2)>tol1);
         fprintf('Peça nº %d: Círculo.\n',n);
         Area=(objetos2(n).Area)
                 % Triângulos: pears
        elseif ((sqrt((objetos2(n).Area/(sqrt(3)/4)))>=(objetos2(n).Perimeter/3)*(1-tol2))&&((sqrt((objetos2(n).Area/(sqrt(3)/4))))<=(objetos2(n).Perimeter/3)*(1+tol2))) 
        fprintf('Peça nº %d: Pêra.\n',n);
        Area=(objetos2(n).Area)
                %Retangulos: banana
        elseif (dif(3)==1 && dif(7)==1 && dif(9)==1 && dif(13)==1)   
            if  (objetos2(n).MinorAxisLength/objetos2(n).MajorAxisLength>=(tol3))
                 fprintf('Peça nº %d: banana.\n',n);
                 Area=(objetos2(n).Area)
                  % Not a fruit
             else fprintf('Peça nº %d: Não é uma peça de fruta.\n',n);
                  Area=(objetos2(n).Area)
            end
        end
   end

bananas have not yet been able to adapt a formula to recognize it

but in the case of apples and oranges the only way to distinguish them would be through color recognition, but I can not find a method of doing so.

Can someone help me?

Best Answer

Try this demo:

clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
workspace;  % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 16;
%===============================================================================
% Get the name of the image the user wants to use.
baseFileName = 'frame147.jpg';
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.
[rows, columns, numberOfColorChannels] = size(rgbImage)
% Display image.
subplot(2, 2, 1);
imshow(rgbImage, []);
axis on;
caption = sprintf('Original Color Image\n%s', baseFileName);
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
hp = impixelinfo(); % Set up status line to see values when you mouse over the image.
% Set up figure properties:
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0 0.05 1 0.95]);
% 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;
% Do color segmentation:
[BW, maskedRGBImage] = createMask(rgbImage);
% Display the image.


subplot(2, 2, 2);
imshow(BW);
title('Color Segmentation', 'FontSize', fontSize, 'Interpreter', 'None');
axis on;
drawnow;
% Clean up by filling holes and taking the larger blobs, bigger than 1000 pixels.
fruitMask = imfill(BW, 'holes');
fruitMask = bwareaopen(fruitMask, 1000);
% Display the image.
subplot(2, 2, 3);
imshow(fruitMask);
title('Final Mask', 'FontSize', fontSize, 'Interpreter', 'None');
axis on;
drawnow;
% Mask the image using bsxfun() function to multiply the mask by each channel individually.
maskedRGBImage = bsxfun(@times, rgbImage, cast(fruitMask, 'like', rgbImage));
% Display the image.
subplot(2, 2, 4);
imshow(maskedRGBImage);
title('Final Masked Image', 'FontSize', fontSize, 'Interpreter', 'None');
axis on;
drawnow;
% Extract the individual red, green, and blue color channels.
redChannel = rgbImage(:, :, 1);
greenChannel = rgbImage(:, :, 2);
blueChannel = rgbImage(:, :, 3);
% Get the mean color for each blob.
propsR = regionprops(fruitMask, redChannel, 'MeanIntensity', 'Centroid');
propsG = regionprops(fruitMask, greenChannel, 'MeanIntensity');
propsB = regionprops(fruitMask, blueChannel, 'MeanIntensity');
redMeans = [propsR.MeanIntensity]
greenMeans = [propsG.MeanIntensity]
blueMeans = [propsB.MeanIntensity]
% Define reference color
orangeColor = [234, 150, 60];
pearColor = [180, 194, 62];
% Loop over blobs finding out which color is the shortest distance
hold on;
for k = 1 : length(propsR)
  thisColor = [redMeans(k), greenMeans(k), blueMeans(k)];
  distanceToOrange = sqrt((thisColor(1) - orangeColor(1)) .^ 2 + ...
    (thisColor(2) - orangeColor(2)) .^ 2 + ...
    (thisColor(3) - orangeColor(3)) .^ 2);
  distanceToPear = sqrt((thisColor(1) - pearColor(1)) .^ 2 + ...
    (thisColor(2) - pearColor(2)) .^ 2 + ...
    (thisColor(3) - pearColor(3)) .^ 2);
  fprintf('For blob #%d,\n    Distance to Orange = %f, distance to pear = %f.\n', distanceToOrange, distanceToPear);
  xCenter = propsR(k).Centroid(1);
  yCenter = propsR(k).Centroid(2);
  if distanceToOrange < distanceToPear
    text(xCenter, yCenter, 'Orange', 'Color', 'r', 'FontSize', fontSize, 'FontWeight', 'bold');
  else
    text(xCenter, yCenter, 'Pear', 'Color', 'r', 'FontSize', fontSize, 'FontWeight', 'bold');
  end
end
function [BW,maskedRGBImage] = createMask(RGB)
%createMask  Threshold RGB image using auto-generated code from colorThresholder app.
%  [BW,MASKEDRGBIMAGE] = createMask(RGB) thresholds image RGB using
%  auto-generated code from the colorThresholder app. The colorspace and
%  range for each channel of the colorspace were set within the app. The
%  segmentation mask is returned in BW, and a composite of the mask and
%  original RGB images is returned in maskedRGBImage.
% Auto-generated by colorThresholder app on 08-Jun-2018
%------------------------------------------------------
% Convert RGB image to chosen color space
I = rgb2hsv(RGB);
% Define thresholds for channel 1 based on histogram settings
channel1Min = 0.000;
channel1Max = 1.000;
% Define thresholds for channel 2 based on histogram settings
channel2Min = 0.338;
channel2Max = 1.000;
% Define thresholds for channel 3 based on histogram settings
channel3Min = 0.000;
channel3Max = 1.000;
% Create mask based on chosen histogram thresholds
sliderBW = (I(:,:,1) >= channel1Min ) & (I(:,:,1) <= channel1Max) & ...
  (I(:,:,2) >= channel2Min ) & (I(:,:,2) <= channel2Max) & ...
  (I(:,:,3) >= channel3Min ) & (I(:,:,3) <= channel3Max);
BW = sliderBW;
% Initialize output masked image based on input image.
maskedRGBImage = RGB;
% Set background pixels where BW is false to zero.
maskedRGBImage(repmat(~BW,[1 1 3])) = 0;
end

Related Solutions

MATLAB: Obtain average image threshold without the background

Try 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 = 22;
%--------------------------------------------------------------------------------------------------------

%    READ IN IMAGE
folder = pwd;
baseFileName = 'image.png';
% Get the full filename, with path prepended.
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
grayImage = imread(fullFileName);
% Get the dimensions of the image.
% numberOfColorChannels should be = 1 for a gray scale image, and 3 for an RGB color image.
[rows, columns, numberOfColorChannels] = size(grayImage);
if numberOfColorChannels > 1
	% It's not really gray scale like we expected - it's color.
	% Use weighted sum of ALL channels to create a gray scale image.
	grayImage = rgb2gray(grayImage);
	% ALTERNATE METHOD: Convert it to gray scale by taking only the green channel,
	% which in a typical snapshot will be the least noisy channel.
	% grayImage = grayImage(:, :, 2); % Take green channel.
end
% Display the image.
subplot(2, 2, 1);
imshow(grayImage, []);
title('Original Grayscale Image', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
hFig = gcf;
hFig.WindowState = 'maximized'; % May not work in earlier versions of MATLAB.
drawnow;
%--------------------------------------------------------------------------------------------------------
%    SEGMENTATION OF IMAGE
% Get a binary image of just the blob and not including the black background or the white surround.
mask = grayImage > 0 & grayImage < 255;  %imbinarize(grayImage);
subplot(2, 2, 2);
imshow(mask, []);
impixelinfo;
title('Initial Mask Image', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
% Display histogram of image but only the image within the mask
subplot(2, 3, 4);
histogram(grayImage(mask), 0:255);
grid on;
title('Histogram of gray image', 'FontSize', fontSize);
% Now get the threshold
thresh = 255 * graythresh(grayImage(mask))
% Put a red line there at the threshold
xline(thresh, 'Color', 'r', 'LineWidth', 2);
% Get a binary image of the bright stuff
brightStuff = grayImage >= thresh & mask;
subplot(2, 3, 5);
imshow(brightStuff, []);
impixelinfo;
title('Bright Pixels in Region', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
subplot(2, 3, 6);
darkStuff = ~brightStuff & mask;
imshow(darkStuff, []);
impixelinfo;
title('Dark Pixels in Region', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;

MATLAB: Purple colour extraction only

Try this:

mask = imdilate(redChannel>200, true(5)); %the mask

Best Answer

Related Solutions

MATLAB: Obtain average image threshold without the background

MATLAB: Purple colour extraction only

Related Question