Average pixel color intensity of ROI

For the attached image I am able to find pixel intensity of bowls one by one by selecting one bowl at a time. This is hectic, can u help to find pixel intensity seperatly of two bowls but in one go without seperating it into two images. Similarly I want to find the pixel intensity of two bowls in its hsv image.

Answer 1

[Image Analyst]

What do you mean by "intensity"? Do you mean the values in the V image?

You can use regionprops().

[Apra Gupta]

Intensity I mean every pixel repersent a color. I used the command mean2(RGB).

I studied about regionprops(), but could not use it.

[Image Analyst]

See my Image Segmentation Tutorial

Image Analyst's File Exchange

[Apra Gupta]

Hi, I tried to run blob's demo for my images, but i not able to get BW image for any threshold, as there is no much difference between background and bowls and even between two bowls.

Basically it is not detecting only two bowls from whole image.

I need my program to detect two bowls from whole Image, and find the mean color intensity of each bowl seperatly.

[Image Analyst]

I already gave you code to mask it, remember? That's how you got the images you attached to your original image. Why are you not using that code?

[Apra Gupta]

Hi Sir, I tried using those masked images also to find the mean. But I am getting mean of whole image.

If I use regionprops it doesn't identify two bowls, rather lot many centroids. Conneccomp command is not able to identify one bowl whole as 1 component and 2nd bowl as another.

So i am seprating each image as attached and then finding mean of whole image as pixel color intensity of black background is zero so it will affect the average.

Can u provide any other easy approach?

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 = 18;

RGB = imread('visi 10ml.png');

subplot(2, 2, 1);

imshow (RGB)

title('Original Image', 'fontSize', fontSize);

[mask, maskedRgbImage] = segmentImage(RGB);

subplot(2, 2, 2);

imshow(mask)

title('Mask Image', 'fontSize', fontSize);

subplot(2, 2, 3);

imshow(maskedRgbImage)

title('Masked RGB Image', 'fontSize', fontSize);

fprintf('Done with %s\n', mfilename);

mean = mean(maskedRgbImage(:));

disp(mean)

hp = impixelinfo();

% To set location of the status bar

hp.Position = [0.2, 0.4, 0.03, 0.4]; % xLeft, yBottom, width, height, all normalized 0-1.

function [BW,maskedImage] = segmentImage(RGB)

%segmentImage Segment image using auto-generated code from imageSegmenter app

% [BW,MASKEDIMAGE] = segmentImage(RGB) segments image RGB using

% auto-generated code from the imageSegmenter app. The final segmentation

% is returned in BW, and a masked image is returned in MASKEDIMAGE.

% Auto-generated by imageSegmenter app on 23-Sep-2021

%----------------------------------------------------

% Convert RGB image into L*a*b* color space.

X = rgb2lab(RGB);

% Graph cut

foregroundInd = [3210315 3210318 3210322 3210325 3210327 3210330 3210332 3210337 3210339 3210344 3210347 3210351 3210354 3210363 3210366 3210371 3210373 3210378 3210383 3210388 3210392 3210395 3210397 3210404 3210407 3210414 3216056 3216058 3216172 3216174 3219881 3220013 3223701 3223705 3223708 3223713 3223850 3223855 3229608 3229613 3233273 3233450 3233454 3239205 3239207 3239210 3239212 3242851 3243051 3243056 3243059 3248585 3248592 3248807 3248809 3252646 3252651 3252653 3256241 3256485 3256487 3265814 3266067 3266070 3271823 3275655 3275662 3279214 3279496 3284952 3285247 3285249 3285252 3289084 3294834 3294836 3294841 3298350 3304426 3304431 3307930 3308265 3308270 3317505 3317722 3317742 3317763 3317773 3317857 3317860 3321330 3321335 3321535 3327363 3327445 3327447 3330900 3330903 3331286 3334727 3334732 3334942 3335036 3335121 3340466 3340473 3340475 3344290 3344298 3344510 3344623 3344703 3344705 3350456 3353856 3353858 3353863 3353866 3354208 3354288 3354290 3354293 3357681 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backgroundInd = [19340 19343 19345 19348 28920 28935 32774 48085 48088 48107 57692 61519 61531 74945 84528 88362 88367 111354 117102 117141 118444 118453 118461 118473 118497 118514 118528 118538 118550 118557 128147 130514 131812 131844 140094 140157 141380 145164 145195 145398 153506 154983 159358 160468 160733 182244 182386 183404 183740 186242 195829 199686 200571 200996 209068 209299 209336 215117 215868 222822 222844 222854 223998 228225 238199 238211 238399 238421 238447 238457 238771 242178 242200 242303 242530 242586 251644 251656 251746 251903 252745 255513 255534 255542 255563 255889 265339 270375 278777 284600 285324 293364 294127 297966 297969 310604 321728 350470 362328 367714 396454 402560 435129 446266 473444 506009 511400 523251 546238 569218 593773 601780 624767 630515 649333 653505 670742 678073 710642 712889 758866 779613 797178 843155 867742 875727 909226 909245 909303 921709 945253 955871 958113 968260 972413 990682 1018474 1023252 1024842 1050076 1066029 1069236 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L = superpixels(X,24625,'IsInputLab',true);

% Convert L*a*b* range to [0 1]

scaledX = prepLab(X);

BW = lazysnapping(scaledX,L,foregroundInd,backgroundInd);

% Create masked image.

maskedImage = RGB;

maskedImage(repmat(~BW,[1 1 3])) = 0;

end

function out = prepLab(in)

% Convert L*a*b* image to range [0,1]

out = in;

out(:,:,1) = in(:,:,1) / 100; % L range is [0 100].

out(:,:,2) = (in(:,:,2) + 86.1827) / 184.4170; % a* range is [-86.1827,98.2343].

out(:,:,3) = (in(:,:,3) + 107.8602) / 202.3382; % b* range is [-107.8602,94.4780].

end