I often find myself wanting to collapse an n-dimensional matrix across one dimension using a custom function, and can't figure out if there is a concise incantation I can use to do this.
For example, when parsing an image, I often want to do something like this. (Note! Illustrative example only. I know about rgb2gray for this specific case.)
img = imread('whatever.jpg');
s = size(img);
for i=1:s(1)
for j=1:s(2)
bw_img(i,j) = mean(img(i,j,:));
end
end
I would love to express this as something like:
bw = on(color, 3, #mean);
or
bw(:,:,1) = mean(color);
Is there a short way to do this?
EDIT: Apparently mean already does this; I want to be able to do this for any function I've written. E.g.,
...
filtered_img(i,j) = reddish_tint(img(i,j,:));
...
where
function out = reddish_tint(in)
out = in(1) * 0.5 + in(2) * 0.25 + in(3) * 0.25;
end
Many basic MATLAB functions, like MEAN, MAX, MIN, SUM, etc., are designed to operate across a specific dimension:
bw = mean(img,3); %# Mean across dimension 3
You can also take advantage of the fact that MATLAB arithmetic operators are designed to operate in an element-wise fashion on matrices. For example, the operation in your function reddish_tint can be applied to all pixels of your image with this single line:
filtered_img = 0.5.*img(:,:,1)+0.25.*img(:,:,2)+0.25.*img(:,:,3);
To handle a more general case where you want to apply a function to an arbitrary dimension of an N-dimensional matrix, you will probably want to write your function such that it accepts an additional input argument for which dimension to operate over (like the above-mentioned MATLAB functions do) and then uses some simple logic (i.e. if-else statements) and element-wise matrix operations to apply its computations to the proper dimension of the matrix.
Although I would not suggest using it, there is a quick-and-dirty solution, but it's rather ugly and computationally more expensive. You can use the function NUM2CELL to collect values along a dimension of your array into cells of a cell array, then apply your function to each cell using the function CELLFUN:
cellArray = num2cell(img,3); %# Collect values in dimension 3 into cells
filtered_img = cellfun(#reddish_tint,cellArray); %# Apply function to each cell
I wrote a helper function called 'vecfun' that might be useful for this, if it's what you're trying to achieve?
link
You could use BSXFUN for at least some of your tasks. It performs an element-wise operation among two arrays by expanding the size 1 - dimensions to match the size in the other array. The 'reddish tint' function would become
reddish_image = bsxfun(#times,img,cat(3,0.5,0.25,0.25));
filtered_img = sum(reddish_image,3);
All the above statement requires in order to work is that the third dimension of img has size 1 or 3. Number and size of the other dimensions can be chosen freely.
If you are consistently trying to apply a function to a vector comprised by the 3 dimension in a block of images, I recommend using a pair reshapes, for instance:
Img = rand(480,640,3);
sz = size(Img);
output = reshape(myFavoriteFunction(reshape(Img,[prod(sz(1:2)),sz(3)])'),sz);
This way you can swap in any function that operates on matrices along their first dimension.
edit.
The above code will crash if you input an image which has only one layer: The function below can fix it.
function o = nLayerImage2MatrixOfPixels(i)
%function o = nLayerImage2MatrixOfPixels(i)
s = size(i);
if(length(s) == 2)
s3 = 1;
else
s3 = s(3);
end
o = reshape(i,[s(1)*s(2),s(3)])';
Well, if you are only concerned with multiplying vectors together you could just use the dot product, like this:
bw(:,:,1)*[0.3;0.2;0.5]
taking care that the shapes of your vectors conform.
Related
I've written code to smooth an image using a 3x3 averaging filter, however the output is strange, it is almost all black. Here's my code.
function [filtered_img] = average_filter(noisy_img)
[m,n] = size(noisy_img);
filtered_img = zeros(m,n);
for i = 1:m-2
for j = 1:n-2
sum = 0;
for k = i:i+2
for l = j:j+2
sum = sum+noisy_img(k,l);
end
end
filtered_img(i+1,j+1) = sum/9.0;
end
end
end
I call the function as follows:
img=imread('img.bmp');
filtered = average_filter(img);
imshow(uint8(filtered));
I can't see anything wrong in the code logic so far, I'd appreciate it if someone can spot the problem.
Assuming you're working with grayscal images, you should replace the inner two for loops with :
filtered_img(i+1,j+1) = mean2(noisy_img(i:i+2,j:j+2));
Does it change anything?
EDIT: don't forget to reconvert it to uint8!!
filtered_img = uint8(filtered_img);
Edit 2: the reason why it's not working in your code is because sum is saturating at 255, the upper limit of uint8. mean seems to prevent that from happening
another option:
f = #(x) mean(x(:));
filtered_img = nlfilter(noisy_img,[3 3],f);
img = imread('img.bmp');
filtered = imfilter(double(img), ones(3) / 9, 'replicate');
imshow(uint8(filtered));
Implement neighborhood operation of sum of product operation between an image and a filter of size 3x3, the filter should be averaging filter.
Then use the same function/code to compute Laplacian(2nd order derivative, prewitt and sobel operation(first order derivatives).
Use a simple 10*10 matrix to perform these operations
need matlab code
Tangentially to the question:
Especially for 5x5 or larger window you can consider averaging first in one direction and then in the other and you save some operations. So, point at 3 would be (P1+P2+P3+P4+P5). Point at 4 would be (P2+P3+P4+P5+P6). Divided by 5 in the end. So, point at 4 could be calculated as P3new + P6 - P2. Etc for point 5 and so on. Repeat the same procedure in other direction.
Make sure to divide first, then sum.
I would need to time this, but I believe it could work a bit faster for larger windows. It is sequential per line which might not seem the best, but you have many lines where you can work in parallel, so it shouldn't be a problem.
This first divide, then sum also prevents saturation if you have integers, so you might use the approach even in 3x3 case, as it is less wrong (though slower) to divide twice by 3 than once by 9. But note that you will always underestimate final value with that, so you might as well add a bit of bias (say all values +1 between the steps).
img=imread('camraman.tif');
nsy-img=imnoise(img,'salt&pepper',0.2);
imshow('nsy-img');
h=ones(3,3)/9;
avg=conv2(img,h,'same');
imshow(Unit8(avg));
I need to create an array filled within a range in Matlab
e.g.
from=2
to=6
increment=1
result
[2,3,4,5,6]
e.g.
from=15
to=25
increment=2
result
[15,17,19,21,23,25]
Obviously I can create a loop to perform this action from scratch but I wondering if there is a coincise and efficent way to do this with built-in matlab commands since seems a very common operation
EDIT
If I use linspace the operation is weird since the spacing between the points is (x2-x1)/(n-1).
This can be handled simply by the : operator in the following notation
array = from:increment:to
Note that the increment defaults to 1 if written with only one colon seperator
array = from:to
Example
array1 = 2:6 %Produces [2,3,4,5,6]
array2 = 15:2:25 %Produces [15,17,19,21,23,25]
I'm quite new to MatLab and this problem really drives me insane:
I have a huge array of 2 column and about 31,000 rows. One of the two columns depicts a spatial coordinate on a grid the other one a dependent parameter. What I want to do is the following:
I. I need to split the array into smaller parts defined by the spatial column; let's say the spatial coordinate are ranging from 0 to 500 - I now want arrays that give me the two column values for spatial coordinate 0-10, then 10-20 and so on. This would result in 50 arrays of unequal size that cover a spatial range from 0 to 500.
II. Secondly, I would need to calculate the average values of the resulting columns of every single array so that I obtain per array one 2-dimensional point.
III. Thirdly, I could plot these points and I would be super happy.
Sadly, I'm super confused since I miserably fail at step I. - Maybe there is even an easier way than to split the giant array in so many small arrays - who knows..
I would be really really happy for any suggestion.
Thank you,
Arne
First of all, since you wish a data structure of array of different size you will need to place them in a cell array so you could try something like this:
res = arrayfun(#(x)arr(arr(:,1)==x,:), unique(arr(:,1)), 'UniformOutput', 0);
The previous code return a cell array with the array splitted according its first column with #(x)arr(arr(:,1)==x,:) you are doing a function on x and arrayfun(function, ..., 'UniformOutput', 0) applies function to each element in the following arguments (taken a single value of each argument to evaluate the function) but you must notice that arr must be numeric so if not you should map your values to numeric values or use another way to select this values.
In the same way you could do
uo = 'UniformOutput';
res = arrayfun(#(x){arr(arr(:,1)==x,:), mean(arr(arr(:,1)==x,2))), unique(arr(:,1)), uo, 0);
You will probably want to flat the returning value, check the function cat, you could do:
res = cat(1,res{:})
Plot your data depends on their format, so I can't help if i don't know how the data are, but you could try to plot inside a loop over your 'res' variable or something similar.
Step I indeed comes with some difficulties. Once these are solved, I guess steps II and III can easily be solved. Let me make some suggestions for step I:
You first define the maximum value (maxValue = 500;) and the step size (stepSize = 10;). Now it is possible to iterate through all steps and create your new vectors.
for k=1:maxValue/stepSize
...
end
As every resulting array will have different dimensions, I suggest you save the vectors in a cell array:
Y = cell(maxValue/stepSize,1);
Use the find function to find the rows of the entries for each matrix. At each step k, the range of values of interest will be (k-1)*stepSize to k*stepSize.
row = find( (k-1)*stepSize <= X(:,1) & X(:,1) < k*stepSize );
You can now create the matrix for a stepk by
Y{k,1} = X(row,:);
Putting everything together you should be able to create the cell array Y containing your matrices and continue with the other tasks. You could also save the average of each value range in a second column of the cell array Y:
Y{k,2} = mean( Y{k,1}(:,2) );
I hope this helps you with your task. Note that these are only suggestions and there may be different (maybe more appropriate) ways to handle this.
Say I have an array stack of dimensions NxMxR. I would like to use logical indexing on a given dimension to update its value, without the use of a temporary variable.
Example using the temporary variable:
for k=1:R
temp=stack(:,:,k);
temp(temp<max(max(temp))/2)=NaN;
cropped(:,:,k)=temp;
end
Note that it would be trivial if the condition was the same for the entire array, but it varies along the R dimension. bsxfun would be appropriate if I just wanted to subtract the threshold value in a syntax like:
cropped = bsxfun(#minus, stack, max(max(stack, [], 3))/2);
I find the use of the temporary variable clumsy and can't find a proper way of defining the function for bsxfun to do what the loop does.
Does anyone has a suggestion?
stack(bsxfun(#minus, stack, max(max(stack))/2) < 0) = NaN;
Another bsxfun implementation -
t1 = reshape(stack,size(stack,1)*size(stack,2),[])
t1(bsxfun(#lt,t1,max(t1)/2))=NaN
cropped = reshape(t1,size(stack))
One more -
t1 = max(reshape(stack,size(stack,1)*size(stack,2),[]))
stack(bsxfun(#lt,stack,permute(t1/2,[3 1 2])))=NaN
And more that is dangerously close to Luis's answer -
stack(bsxfun(#lt,stack,max(max(stack)/2)))=NaN
Can anyone tell me what the basic idea behind varying rows or columns in a matrix with respect to the row/column number is in matlab? I've been trying to replace all the columns in a given matrix by
i=1:101;
V=ones(121,101);
V_t=1000*10.^((i-1)/20);
e=V_arr(1:121)';
V_arr=V; V_arr(:,i)=V_t*e;
I know that the error lies in trying to replace a number of columns with respect to all rows, and I've seen an alternative, simpler method using repmat, but I'd like to know if there's a method similar to the one above.
Thanks.
One thing you can do is to use matrix multiplication, i.e. a n-by-1 array multiplied by an 1-by-m array creates a n-by-m array.
For example
ii = 1:101; %# 1-by-101
V_t = 1000*10.^((i-1)/20);
ee = ones(121,1); %# 121-by-1
V_arr = ee * V_t;