Hi,i am dealing with a project in which i have to detect the defected Rice grains from a sample of rice.I want to do segmentation to seprate the defected rice grains and then find the ratio of defect in the sample.kindly help me.

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Zeeshan Ahmed 2017-12-24

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https://ww2.mathworks.cn/matlabcentral/answers/374388-hi-i-am-dealing-with-a-project-in-which-i-have-to-detect-the-defected-rice-grains-from-a-sample-of-r

评论： Image Analyst 2022-2-9

采纳的回答： Image Analyst

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Answer 1

Image Analyst 2017-12-24

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/374388-hi-i-am-dealing-with-a-project-in-which-i-have-to-detect-the-defected-rice-grains-from-a-sample-of-r#answer_297567

在 MATLAB Online 中打开

BGCorrect_demo.m

Which ones are defective?

What I'd do is to first take a "blank shot" of just the white background, then divide that out to remove any dependencies of the non-uniform background illumination and shading. See attached background correction demo.

Then I'd threshold to find the rice grains.

binaryImage = grayImage < someThreshold;

You might want to call bwareafilt() or bwareopen() to get rid of small noise pixels

binaryImage = bwareaopen(binaryImage, 30);

Then I'd get the mean intensity of each grain.

props = regionprops(binaryImage, 'MeanIntensity');

Histogram the mean intensities to see their distribution

allIntensities = [props.MeanIntensity];
histogram(allIntensities);
grid on;
xlabel('Mean Intensity');
ylabel('Count');

Presumably you can determine defective grains based on their mean intensity. Give that code a try and come back if you need any more help.

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Image Analyst 2017-12-24

在 MATLAB Online 中打开

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 = 20;
% Check that user has the specified Toolbox installed and licensed.
hasLicenseForToolbox = license('test', 'image_toolbox');   % license('test','Statistics_toolbox'), license('test','Signal_toolbox')
if ~hasLicenseForToolbox
  % User does not have the toolbox installed, or if it is, there is no available license for it.
  % For example, there is a pool of 10 licenses and all 10 have been checked out by other people already.
  ver % List what toolboxes the user has licenses available for.
  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
%===============================================================================
% Read in gray scale demo image.
folder = pwd;
baseFileName = 'sample17.jpg';
% 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
rgbImage = imread(fullFileName);
% Display the image.
subplot(2, 2, 1);
imshow(rgbImage, []);
title('Original Image', 'FontSize', fontSize, 'Interpreter', 'None');
axis on;
hp = impixelinfo();
% 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(rgbImage);
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(rgbImage); 
  % 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 = rgbImage(:, :, 1); % Take red channel.
else
  grayImage = rgbImage; % It's already gray scale.
end
% Display the image.
hFig1 = figure;
subplot(2, 2, 1);
imshow(grayImage, []);
title('Gray Scale Image', 'FontSize', fontSize, 'Interpreter', 'None');
axis on;
%------------------------------------------------------------------------------
% Set up figure properties:
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0, 0.04, 1, 0.96]);
% 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;
% Display the histogram of the image.
subplot(2, 2, 2);
histogram(grayImage);
grid on;
title('Histogram of Gray Scale Image', 'FontSize', fontSize, 'Interpreter', 'None');
axis on;
% Get a binary image
binaryImage = grayImage < 136;
% Remove blobs touching the border
binaryImage = imclearborder(binaryImage);
% Only take blobs 500 pixels or larger.
binaryImage = bwareaopen(binaryImage, 500);
% Display the image.
subplot(2, 2, 3);
imshow(binaryImage, []);
title('Binary Image', 'FontSize', fontSize, 'Interpreter', 'None');
axis on;
% Measure areas
props = regionprops(binaryImage, grayImage, 'Area', 'MeanIntensity');
% Display the histogram of the areas.
subplot(2, 2, 4);
allAreas = sort([props.Area], 'descend');
histogram(allAreas);
grid on;
title('Histogram of Areas', 'FontSize', fontSize, 'Interpreter', 'None');
axis on;
% Display the histogram of the intensities.
hFig2 = figure;
subplot(2, 2, 1);
allIntensities = [props.MeanIntensity];
histogram(allIntensities);
grid on;
title('Histogram of Intensities', 'FontSize', fontSize, 'Interpreter', 'None');
axis on;
% Extract a binary image of just the dark ones.
darkBlobs = bwpropfilt(binaryImage, grayImage, 'MeanIntensity', [0, 110]);
% Display the image.
subplot(2, 2, 2);
imshow(darkBlobs, []);
title('Binary Image of Dark Blobs Only', 'FontSize', fontSize, 'Interpreter', 'None');
axis on;
% Use this to mask the image.
maskedImage = grayImage; % Initialize.
maskedImage(~darkBlobs) = 0;
% Display the image.
subplot(2, 2, 3);
imshow(maskedImage, []);
title('Masked Image of Dark Blobs Only', 'FontSize', fontSize, 'Interpreter', 'None');
axis on;

I have not done background correction. That's why the results are so bad. Again YOU NEED TO CORRECT FOR THE BACKGROUND GRADIENT! Please do that next, and then your results should be pretty good.

Ankit Rathore 2022-2-9

Have you got the answer zeeshan Ahmed. I need your help and i am wirking in a similar project. Please let me know how can i reach you.

Image Analyst 2022-2-9

@Ankit Rathore, yes, he did. He accepted this code as the Answer: https://www.mathworks.com/matlabcentral/answers/374388-hi-i-am-dealing-with-a-project-in-which-i-have-to-detect-the-defected-rice-grains-from-a-sample-of-r#comment_519087

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Hi,i am dealing with a project in which i have to detect the defected Rice grains from a sample of rice.I want to do segmentation to seprate the defected rice grains and then find the ratio of defect in the sample.kindly help me.

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Hi,i am dealing with a project in which i have to detect the defected Rice grains from a sample of rice.I want to do segmentation to seprate the defected rice grains and then find the ratio of defect in the sample.kindly help me.

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