Rice grain chalky area

Hello I would like to know how we can calculate chalky/white area in a rice grain? Also i used following matlab code given on matworks site to calculate area of rice grain.Even after removing all small areas with less than 50 pixels it is showing grains with area 1 which is not visible on imshow.Please let me know the solution of these 2 problems. Thanks a lot I=imread('1_1.jpg'); imshow(I) background = imopen(I,strel('disk',15)); I2 = I - background; imshow(I2) %I2=rgb2gray(I2); I3 = imadjust(I2); figure imshow(I3); level = graythresh(I3); bw = im2bw(I3,level); bw = bwareaopen(bw, 30); figure imshow(bw) cc = bwconncomp(bw, 4) cc.NumObjects grain = false(size(bw)); grain(cc.PixelIdxList{50}) = true; figure imshow(grain); graindata = regionprops(cc, 'basic');

grain_areas = [graindata.Area]; [min_area, idx] = min(grain_areas); grain = false(size(bw)); grain(cc.PixelIdxList{idx}) = true; imshow(grain);

[Image Analyst]

There is nothing we can do until you read this and read this and attach your image with the green and brown frame icon.

[Evolution]

Thank you very much for the suggestion.It was careless of me just to copy paste the matlab code without formatting .

I would appreciate if you could give your feedback on the new uploaded question.

Answer 1

[Evolution]

Hello

I would like to know how we can calculate chalky/white area in a rice grain?

Also i used following matlab code given on Mathworks site to calculate area of rice grain scanned through a scanner.

Even after removing all small objects having area with less than 50 pixels it is showing grains with area 1 which is not visible on imshow.

Please let me know the solution of these 2 problems. Thank you very much in advance .

if true
I=imread('1_1.jpg');
imshow(I) 
background = imopen(I,strel('disk',15)); 
I2 = I - background; 
imshow(I2) 
I2=rgb2gray(I2); 
I3 = imadjust(I2); 
figure 
imshow(I3); 
level = graythresh(I3);
bw = im2bw(I3,level); 
bw = bwareaopen(bw, 30);
figure imshow(bw) 
cc = bwconncomp(bw, 4) 
cc.NumObjects grain = false(size(bw)); 
grain(cc.PixelIdxList{50}) = true; 
figure imshow(grain); 
graindata = regionprops(cc, 'basic');
grain_areas = [graindata.Area]; 
[min_area, idx] = min(grain_areas); 
grain = false(size(bw)); 
grain(cc.PixelIdxList{idx}) = true; 
imshow(grain);

end

[Evolution]

Any one please?

[Asha Deepthi Kothapalli]

May I know if this is solved sir/mam? I am currently working on the same but I get the same category according the percentage of chalkiness I have calculated for both chalky rice and standard rice. I took help from a website but couldn't find a way to solve it. Can you please help me out?

clear

close all

tic; % Start timer.

captionFontSize = 14;

%% Load the file

fullFileName = "archive\train\Chalky Rice\914.jpg";

%% Convert RGB2Grayscale

originalImage = imread(fullFileName);

originalImage = rgb2gray(originalImage);

subplot(2, 4, 1);

imshow(originalImage);

%% Explore the image/data using histogram:

[pixelCount, grayLevels] = imhist(originalImage);

subplot(2, 4, 2);

bar(pixelCount);

xlim([0 grayLevels(end)]); % Scale x axis manually.

grid on;

thresholdValue = 90; % Choose the threshold value in roder to mask out the background noise.

% Show the threshold as a vertical red bar on the histogram.

hold on;

maxYValue = ylim;

line([thresholdValue, thresholdValue], maxYValue, 'Color', 'r');

annotationText = sprintf('Thresholded at %d gray levels', thresholdValue);

text(double(thresholdValue + 5), double(0.5 * maxYValue(2)), annotationText, 'FontSize', 10, 'Color', [0 .5 0]);

text(double(thresholdValue - 70), double(0.94 * maxYValue(2)), 'Background', 'FontSize', 10, 'Color', [0 0 .5]);

text(double(thresholdValue + 50), double(0.94 * maxYValue(2)), 'Foreground', 'FontSize', 10, 'Color', [0 0 .5]);

%% Get and show binary image

binaryImage = originalImage > thresholdValue;

binaryImage = imfill(binaryImage, 'holes');

% Display the binary image.

subplot(2, 4, 3);

imshow(binaryImage);

title('Binary Image', 'FontSize', captionFontSize);

%% Maskout the background noise

MaskedImage = originalImage;

MaskedImage(~binaryImage) = 0;

subplot(2, 4, 4);

imshow(MaskedImage);

title('Masked Image', 'FontSize', captionFontSize);

%% Get the centroid, mean intensity, permieter of the whole seed etc

blobMeasurements = regionprops(binaryImage, originalImage, 'all');

%% Calculate the chalkiness by first exploring the histogram

[pixelCount_1, grayLevels_1] = imhist(MaskedImage);

subplot(2, 4, 5);

bar(pixelCount_1);

xlim([0 grayLevels_1(end)]); % Scale x axis manually.

grid on;

thresholdValue_1 = 180; %% Choose the threshold that segments the chalkiness

binary_MaskedImage = MaskedImage > thresholdValue_1;

binary_MaskedImage = imfill(binary_MaskedImage, 'holes');

% % % Show the threshold as a vertical red bar on the histogram.

hold on;

maxYValue_1 = ylim;

line([thresholdValue_1, thresholdValue_1], maxYValue_1, 'Color', 'r');

annotationText = sprintf('Thresholded at %d gray levels', thresholdValue_1);

text(double(thresholdValue_1 + 5), double(0.5 * maxYValue_1(2)), annotationText, 'FontSize', 10, 'Color', [0 .5 0]);

text(double(thresholdValue_1 - 70), double(0.94 * maxYValue_1(2)), 'Background', 'FontSize', 10, 'Color', [0 0 .5]);

text(double(thresholdValue_1 + 50), double(0.94 * maxYValue_1(2)), 'Foreground', 'FontSize', 10, 'Color', [0 0 .5]);

%% Display the binary Masked image.

subplot(2, 4, 6);

imshow(binary_MaskedImage);

title('Binary Masked Image', 'FontSize', captionFontSize);

%% Get the centroid, mean intensity, permieter of the chlky area

blobMeasurements_subblobs = regionprops(binary_MaskedImage, originalImage, 'all');

% Plot the borders of all the seeds on the original grayscale image using the coordinates returned by bwboundaries.

subplot(2, 4, 7);

imshow(originalImage);

axis image; % Make sure image is not artificially stretched because of screen's aspect ratio.

hold on;

boundaries = bwboundaries(binaryImage);

numberOfBoundaries = size(boundaries, 1);

for k = 1 : numberOfBoundaries

thisBoundary = boundaries{k};

plot(thisBoundary(:,2), thisBoundary(:,1), 'g', 'LineWidth', 2);

end

boundaries_Masked = bwboundaries(binary_MaskedImage);

numberOfBoundaries_Masked = size(boundaries_Masked, 1);

for k = 1 : numberOfBoundaries_Masked

thisBoundary_Masked = boundaries_Masked{k};

plot(thisBoundary_Masked(:,2), thisBoundary_Masked(:,1), 'b', 'LineWidth', 2);

end

hold off;

%% Overlay the chalky area on the original image

subplot(2, 4, 8);

imshow(labeloverlay(originalImage,binary_MaskedImage))

title('Chalkiness')

%% Chalkiness

Sum_Subblobs_Perimeter = sum([blobMeasurements_subblobs(1:end).Perimeter]);

Sum_Blobs_Perimeter = sum([blobMeasurements(1:end).Perimeter]);

Percentage_Chalkiness=(Sum_Subblobs_Perimeter/Sum_Blobs_Perimeter)*100;

if Percentage_Chalkiness<10

fprintf('<strong> This belongs to category 1 </strong>\n')

elseif (Percentage_Chalkiness>10)&&(Percentage_Chalkiness<25)

fprintf('<strong> This belongs to category 2 </strong>\n')

elseif (Percentage_Chalkiness>25)&&(Percentage_Chalkiness<50)

fprintf('<strong> This belongs to category 3 </strong>\n')

elseif (Percentage_Chalkiness>50)&&(Percentage_Chalkiness<75)

fprintf('<strong> This belongs to category 4 </strong>\n')

elseif (Percentage_Chalkiness>75)&&(Percentage_Chalkiness<100)

fprintf('<strong> This belongs to category 5 </strong>\n')

end

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