What is the difference between hist and imhist functions in Matlab? I have a matrix of color levels values loaded from image with imread and need to count entropy value of the image using histogram.
When using imhist the resulting matrix contains zeros in all places except the last one (lower-right) which contains some high value number (few thousands or so).
Because that output seems to be wrong, I have tried to use hist instead of imhist and the resulting values are much better, the matrix is fulfilled with correct-looking values instead of zeros.
However, according to the docs, imhist should be better in this case and hist should give weird results..
Unfortunately I am not good at Matlab, so I can not provide you with better problem description. I can add some other information in the future, though.
So I will try to better explain my problem..I have an image, for which I should count entropy and few other values (how much bytes it will take to save that image,..). I wrote this function and it works pretty well
function [entropy, bytes_image, bytes_coding] = entropy_single_pixels(im)
im = double(im);
histg = hist(im);
histg(histg==0) = [];
nzhist = histg ./ numel(im);
entropy = -sum(nzhist.*log2(nzhist));
bytes_image = (entropy*(numel(im))/8);
bytes_coding = 2*numel(unique(im));
fprintf('ENTROPY_VALUE:%s\n',num2str(entropy));
fprintf('BYTES_IMAGE:%s\n',num2str(bytes_image));
fprintf('BYTES_CODING:%s\n',num2str(bytes_coding));
end
Then I have to count the same, but I have to make "pairs" from pixels which are below each other. So I have only half the rows and the same count of columns. I need to express every unique pixel pair as a different number, so I multiplied the first one by 1000 and added the second one to it... Subsequently I need to actually apply the same function as in the first example, but that is the time, when I am getting weird numbers from the imhist function. When using hist, it seems to be OK, but I really don't think that behavior is correct, so that must be my error somewhere. I actually understand pretty good, to what I want to do, or at least I hope so, but unfortunately Matlab makes all that kind of hard for me :)
hist- compute histogram(count number of occurance of each pixel) in color image.........
imhist- compute histogram in two dimensional image.
Use im2double instead of double if you want to use imhist. The imhist function expects double or single-precision data to be in the [0,1] data range, which is why you see everything in the last bin of the histogram.
Related
I created binarized images by using the Otsu methode in Matlab and cut out parts of the resulting image using a function. Now i want to take a look at these images with the VolumeViewer command. I know the x,y and z dimensions of the resulting imgages. I currently run this code doing it(excluding the volumeViewerwhich happens after the loop):
files= {'C3\C3_000mal_550_539_527.raw';...
};
for i=1:numel(files)
Image = fopen(files{i},'r');
ImageData{i} = fread(Image,Inf,'uint16=>uint16');
ImageData{i} = reshape(ImageData{i},550,539,[]);
fclose(openedCrystalImage);
end
Using this code runs into the following error using reshape:
Error using reshape
Product of known dimensions, 296450, not divisible into total number of elements, 78114575.
I did the maths and 550*539=296450 and 296450 * 527=156229150: If we divide the last number by the number of elements it equals 2 and thus is divisible into the total number of elements. In my opinion the reshape function is not able to find the size of the last dimension or defines it as 1.
Defining the size of z also results in an error suggesting using the brackets [], so the function can find it.
Error using reshape
Number of elements must not change. Use [] as one of the size inputs to automatically calculate the appropriate size
for that dimension.
Now to the weird part. This code works for another set of images, with diffrent sizes of the x,y and z ranges. So don´t know where the issue lies to be frank. So i would really appreciate and Answer to my question
I figured it out. The error lies here:
ImageData{i} = fread(Image,Inf,'uint16=>uint16');
Apparently by saving them as .raw before it converts the image to an 8 bit file rather than 16 bits it had before. Therefore, my dimension is double the size of the number of elements. With this alteration it works:
ImageData{i} = fread(Image,Inf,'uint8=>uint8');
The reason i was able to look at the other pictures was that the z range was divisble by 2.
So the reshape function was not the problem but size of the integer data while creating the array for the variable ImageData.
P.S. I just started out programming so the accuracy in the answer should be taken with a grain of salt
I have this function below and I need to calculate the minimum and maximum of this function U, and also plotting the maximum and minimum value in 3D graph together with the function.
How can I write the code?
[x,y]=meshgrid(0:0.1:pi,0:0.1:2*pi);% x-theta,y-phi
a=90.7;b=36.2;c=12.9;
E=1.44;
U=-E.^2*(a.*sin(x).^2.*cos(y).^2+b.*sin(x).^2.*sin(y).^2+c.*cos(x).^2);
meshc(x,y,U)
xlabel('\theta')
ylabel('\Phi ')
zlabel('U')
I tired this way to find max but I don't know if i did it correct
max(U,[],1) %max row-wise
max(U,[],2) %max column-wise
and for the minimum it didn't work the same idea, also I didn't get the exact value of the maximum
As stated above, to simply find the maximum/minimum of your sampled function, use m = min(U(:)); M = max(U(:)). To be able to plot them, what you are missing are the coordinates that give you those values. For this, you will need the second output of the min/max functions, which gives you the index where the extreme happens.
A possible implementation (possibly not the best one) would be (might not work perfectly, I don't have matlab at hand):
[Ms,I] = max(U,[],1); %row-wise maximum and their indexes
[M,j] = max(Ms); %maximum among all rows and its index
Now i = I(j) is the location of the maximum. You can finally do plot3(x(i,j),y(i,j),U(i,j),'ro') to plot a big red circle in the maximums location, or whatever you like.
Note: I might have it backwards and it might be x(j,i), and so on. Just check. Of course you can do the same thing for min().
EDIT: I just remembered the ind2sub function , which solves all your problems. Following the syntax used above:
[M,ind] = max(U(:));
[i,j] = ind2sub(size(U),ind)
The rest holds the unchanged.
You can simply use something like
max(max(U))
this will find the maximum for your 2D matrix.
For the minimum you just have to replace max with min.
In Matlab, is it possible to measure local variation of a signal across an entire signal without using for loops? I.e., can I implement the following:
window_length = <something>
for n = 1:(length_of_signal - window_length/2)
global_variance(n) = var(my_signal(1:window_length))
end
in a vectorized format?
If you have the image processing toolbox, you can use STDFILT:
global_std = stdfilt(my_signal(:),ones(window_length,1));
% square to get the variance
global_variance = global_std.^2;
You could create a 2D array where each row is shifted one w.r.t. to the row above, and with the number of rows equal to the window width; then computing the variance is trivial. This doesn't require any toolboxes. Not sure if it's much faster than the for loop though:
longSignal = repmat(mySignal(:), [1 window_length+1]);
longSignal = reshape(longSignal(1:((length_of_signal+1)*window_length)), [length_of_signal+1, window_length])';
global_variance = sum(longSignal.*longSignal, 2);
global_variance = global_variance(1:length_of_signal-window_length));
Note that the second column is shifted down by one relative to the one above - this means that when we have the blocks of data on which we want to operate in rows, so I take the transpose. After that, the sum operator will sum over the first dimension, which gives you a row vector with the results you want. However, there is a bit of wrapping of data going on, so we have to limit to the number of "good" values.
I don't have matlab handy right now (I'm at home), so I was unable to test the above - but I think the general idea should work. It's vectorized - I can't guarantee it's fast...
Check the "moving window standard deviation" function at Matlab Central. Your code would be:
movingstd(my_signal, window_length, 'forward').^2
There's also moving variance code, but it seems to be broken.
The idea is to use filter function.
I have a csv file which contains data like below:[1st row is header]
Element,State,Time
Water,Solid,1
Water,Solid,2
Water,Solid,3
Water,Solid,4
Water,Solid,5
Water,Solid,2
Water,Solid,3
Water,Solid,4
Water,Solid,5
Water,Solid,6
Water,Solid,7
Water,Solid,8
Water,Solid,7
Water,Solid,6
Water,Solid,5
Water,Solid,4
Water,Solid,3
The similar pattern is repeated for State: "Solid" replaced with Liquid and Gas.
And moreover the Element "Water" can be replaced by some other element too.
Time as Integer's are in seconds (to simplify) but can be any real number.
Additionally there might by some comment line starting with # in between the file.
Problem Statement: I want to eliminate the first dip in Time values and smooth out using some quadratic or cubic or polynomial interpolation [please notice the first change from 5->2 --->8. I want to replace these numbers to intermediate values giving a gradual/smooth increase from 5--->8].
And I wish this to be done for all the combinations of Elements and States.
Is this possible through some sort of coding in Matlab etc ?
Any Pointers will be helpful !!
Thanks in advance :)
You can use the interp1 function for 1D-interpolation. The syntax is
yi = interp1(x,y,xi,method)
where x are your original coordinates, y are your original values, xi are the coordinates at which you want the values to be interpolated at and yi are the interpolated values. method can be 'spline' (cubic spline interpolation), 'pchip' (piece-wise Hermite), 'cubic' (cubic polynomial) and others (see the documentation for details).
You have alot of options here, it really depends on the nature of your data, but I would start of with a simple moving average (MA) filter (which replaces each data point with the average of the neighboring data points), and see were that takes me. It's easy to implement, and fine-tuning the MA-span a couple of times on some sample data is usually enough.
http://www.mathworks.se/help/curvefit/smoothing-data.html
I would not try to fit a polynomial to the entire data set unless I really needed to compress it, (but to do so you can use the polyfit function).
I am trying to put my dataset into the MATLAB [ranked,weights] = relieff(X,Ylogical,10, 'categoricalx', 'on') function to rank the importance of my predictor features. The dataset<double n*m> has n observations and m discrete (i.e. categorical) features. It happens that each observation (row) in my dataset has at least one NaN value. These NaNs represent unobserved, i.e. missing or null, predictor values in the dataset. (There is no corruption in the dataset, it is just incomplete.)
relieff() uses this function below to remove any rows that contain a NaN:
function [X,Y] = removeNaNs(X,Y)
% Remove observations with missing data
NaNidx = bsxfun(#or,isnan(Y),any(isnan(X),2));
X(NaNidx,:) = [];
Y(NaNidx,:) = [];
This is not ideal, especially for my case, since it leaves me with X=[] and Y=[] (i.e. no observations!)
In this case:
1) Would replacing all NaN's with a random value, e.g. 99999, help? By doing this, I am introducing a new feature state for all the predictor features so I guess it is not ideal.
2) or is replacing NaNs with the mode of the corresponding feature column vector (as below) statistically more sound? (I am not vectorising for clarity's sake)
function [matrixdata] = replaceNaNswithModes(matrixdata)
for i=1: size(matrixdata,2)
cv= matrixdata(:,i);
modevalue= mode(cv);
cv(find(isnan(cv))) = modevalue;
matrixdata(:,i) = cv;
end
3) Or any other sensible way that would make sense for "categorical" data?
P.S: This link gives possible ways to handle missing data.
I suggest to use a table instead of a matrix.
Then you have functions such as ismissing (for the entire table), and isundefined to deal with missing values for categorical variables.
T = array2table(matrix);
T = standardizeMissing(T); % NaN is standard for double but this
% can be useful for other data type
var1 = categorical(T.var1);
missing = isundefined(var1);
T = T(missing,:); % removes lines with NaN
matrix = table2array(T);
For a start both solutiona (1) and (2) do not help you handle your data more properly, since NaN is in fact a labelling that is handled appropriately by Matlab; warnings will be issued. What you should do is:
Handle the NaNs per case
Use try catch blocks
NaN is like a number, and there is nothing bad about it. Even is you divide by NaN matlab will treat it properly and give you a NaN.
If you still want to replace them, then you will need an assumption that holds. For example, if your data is engine speeds in a timeseries that have been input by the engine operator, but some time instances have not been specified then there are more than one ways to handle the NaN that will appear in the matrix.
Replace with 0s
Replace with the previous value
Replace with the next value
Replace with the average of the previous and the next value
and many more.
As you can see your problem is ill-posed, and depends on the predictor and the data source.
In case of categorical data, e.g. three categories {0,1,2} and supposing NaN occurs in Y.
for k=1:size(Y,2)
[ id ]=isnan(Y(:,k);
m(k)=median(Y(~id),k);
Y(id,k)=round(m(k));
end
I feel really bad that I had to write a for-loop but I cannot see any other way. As you can see I made a number of assumptions, by using median and round. You may want to use a threshold depending on you knowledge about the data.
I think the answer to this has been given by gd047 in dimension-reduction-in-categorical-data-with-missing-values:
I am going to look into this, if anyone has any other suggestions or particular MatLab implementations, it would be great to hear.
You can take a look at this page http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html the firs a1a, it says transforming categorical into binary. Could possibly work. (: