want to calculate std_value and mean_value
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below is my coding but i dont what happend i dont get data
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img1 = imread('SCM Data.jpg');
img = (-0.18 / (-0.28 / (45.39 / double(img1) - 1)) + 1) * 5.3;
std_value = std2(img);
mean_value = mean2(img);
fprintf('standard deviation:%.2f\n', std_value);
fprintf('average value:%.2f\n', mean_value);
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![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1523076/image.jpeg)
by the way if i conducted calculate it will chage to
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1523081/image.jpeg)
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Accepted Answer
Star Strider
on 27 Oct 2023
There are 395449 ‘Inf’ values in ‘img’ (probably the result of the subtraction in the denominator of the transformation) so those are going to produce either Inf or NaN values in the mean and standard deviation. One way to deal with that is to assign all the Inf values to be NaN and then use the new fillmissing2 function (R2020a does not have it, and I am not certain what to suggest in its absence other than perhaps fillmissing and then hope for the best, since it seems to produce the same result) on each channel of ‘img’.
Then, do the statistics —
img1 = imread('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1523076/image.jpeg');
img = (-0.18 / (-0.28 / (45.39 / double(img1) - 1)) + 1) * 5.3;
size(img)
nans = nnz(isnan(img)) % Check 'NaN' Values
infs = nnz(isinf(img)) % Check 'Inf' Values
img(isinf(img)) = NaN; % Assign 'Inf' Values To 'NaN'
imgz = zeros(size(img));
for k = 1:size(img,3)
img(:,:,k) = fillmissing2(img(:,:,k), 'nearest');
imgz(:,:,k) = fillmissing(img(:,:,k), 'nearest');
end
std_value = std2(img);
mean_value = mean2(img);
fprintf('standard deviation:%.2f\n', std_value);
fprintf('average value:%.2f\n', mean_value);
std_value = std2(imgz);
mean_value = mean2(imgz);
fprintf('standard deviation:%.2f\n', std_value);
fprintf('average value:%.2f\n', mean_value);
This uses the provided image, that includes the borders and colorbar. (I did not crop it.) You will likely get different results from your actual image (that ideally should have been provided).
.
2 Comments
Star Strider
on 27 Oct 2023
Just use fillmissing. When I checked (included in my code), it gave the same result as fillmissing2.
img1 = imread('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1523076/image.jpeg');
img = (-0.18 / (-0.28 / (45.39 / double(img1) - 1)) + 1) * 5.3;
size(img)
nans = nnz(isnan(img)) % Check 'NaN' Values
infs = nnz(isinf(img)) % Check 'Inf' Values
img(isinf(img)) = NaN; % Assign 'Inf' Values To 'NaN'
for k = 1:size(img,3)
img(:,:,k) = fillmissing(img(:,:,k), 'nearest');
end
std_value = std2(img);
mean_value = mean2(img);
fprintf('standard deviation:%.2f\n', std_value);
fprintf('average value:%.2f\n', mean_value);
.
More Answers (1)
Sulaymon Eshkabilov
on 27 Oct 2023
Here is the correct code:
img1 = imread('ALPS.jpg');
figure
imshow(img1); title('Original Image')
std_value1 = std2(img1);
mean_value1 = mean2(img1);
fprintf('Original Image: \n')
fprintf('standard deviation:%.5f\n', std_value1);
fprintf('average value:%.5f\n', mean_value1);
img2 = (-0.18./(-0.28/(45.39./(img1)- 1))+1) * 5.3;
std_value2 = std2(img2);
mean_value2 = mean2(img2);
fprintf('Changed Image: \n')
fprintf('standard deviation:%.5f\n', std_value2);
fprintf('average value:%.5f\n', mean_value2);
figure
% See why std2 is giving '0'
imshow(img2), title('Changed Image')
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