Cv2.getAffineTransform seems simple but its not...
The getAffine gets in second run perfectly logical result: the matrixindicates a simple translation on x. If we think the mt=A+b; A=[1,0,0,1] and b=[40,0]
But why is not the same in first run? It is a triangle transformation with +40 in x again...What is the difference???
[
The thing is, the matrix is true: it is actually [1,-1],[0,1],[40,0] but the e-17 e-17 make the number practicallly zero and the e+01 to 40, which was easy. I did not take care of the e thing :)
Related
I have two matrices S and T which have n columns and a row vector v of length n. By my construction, I know that S does not have any duplicates. What I'm looking for is a fast way to find out whether or not the row vector v appears as one of the rows of S. Currently I'm using the test
if min([sum(abs(S - repmat(f,size(S,1),1)),2);sum(abs(T - repmat(v,size(dS_new,1),1)),2)]) ~= 0 ....
When I first wrote it, I had a for loop testing each (I knew this would be slow, I was just making sure the whole thing worked first). I then changed this to defining a matrix diff by the two components above and then summing, but this was slightly slower than the above.
All the stuff I've found online says to use the function unique. However, this is very slow as it orders my matrix after. I don't need this, and it's a massively waste of time (it makes the process really slow). This is a bottleneck in my code -- taking nearly 90% of the run time. If anyone has any advice as how to speed this up, I'd be most appreciative!
I imagine there's a fairly straightforward way, but I'm not that experienced with Matlab (fairly, just not lots). I know how to use basic stuff, but not some of the more specialist functions.
Thanks!
To clarify following Sardar_Usama's comment, I want this to work for a matrix with any number of rows and a single vector. I'd forgotten to mention that the elements are all in the set {0,1,...,q-1}. I don't know whether that helps or not to make it faster!
You may want this:
ismember(v,S,'rows')
and replace arguments S and v to get indices of duplicates
ismember(S,v,'rows')
Or
for test if v is member of S:
any(all(bsxfun(#eq,S,v,2))
this returns logical indices of all duplicates
all(bsxfun(#eq,S,v),2)
I am trying to avoid the for loops and I have been reading through all the old posts there are about it but I am not able to solve my problem. I am new in MATLAB, so apologies for my ignorance.
The thing is that I have a 300x2 cell and in each one I have a 128x128x256 matrix. Each one is an image with 128x128 pixels and 256 channels per pixel. In the first column of the 300x2 cell I have my parallel intensity values and in the second one my perpendicular intensity values.
What I want to do is to take every pixel of every image (for each component) and sum the intensity values channel by channel.
The code I have is the following:
Image_par_channels=zeros(128,128,256);
Image_per_channels=zeros(128,128,256);
Image_tot_channels=zeros(128,128,256);
for a=1:128
for b=1:128
for j=1:256
for i=1:numfiles
Image_par_channels(a,b,j)=Image_par_channels(a,b,j)+Image_cell_par_per{i,1}(a,b,j);
Image_per_channels(a,b,j)=Image_per_channels(a,b,j)+Image_cell_par_per{i,2}(a,b,j);
end
Image_tot_channels(a,b,j)=Image_par_channels(a,b,j)+2*G*Image_per_channels(a,b,j);
end
end
end
I think I could speed it up introducing (:,:,j) instead of specifying a and b. But still a for loop. I am trying to use cellfun without any success due to my lack of expertise. Could you please give me a hand?
I would really appreciate it.
Many thanks and have a nice day!
Y
I believe you could do something like
Image_par_channels=zeros(128,128,256);
Image_per_channels=zeros(128,128,256);
Image_tot_channels=zeros(128,128,256);
for i=1:numfiles
Image_par_channels = Image_par_channels + Image_cell_par_per{i,1};
Image_per_channels = Image_per_channels + Image_cell_par_per{i,2};
end
Image_tot_channels = Image_par_channels + 2*G*Image_per_channels;
I haven't work with matlab in a long time, but I seem to recall you can do something like this. g is a constant.
EDIT:
Removed the +=. Incremental assignment is not an operator available in matlab. You should also note that Image_tot_channels can be build directly in the loop, if you don't need the other two variables later.
There is a [Q,R] = qr(A,0) function in Matlab, which, according to documentation, returns an "economy" version of qr-decomposition of A. norm(A-Q*R) returns ~1e-12 for my data set. Also Q'*Q should theoretically return I. In practice there are small nonzero elements above and below the diagonal (of the order of 1e-6 or so), as well as diagonal elements that are slightly greater than 1 (again, by 1e-6 or so). Is anyone aware of a way to control precision of qr(.,0), or quality(orthogonality) of resulting Q, either by specifying epsilon, or via the number of iterations ? The size of the data set makes qr(A) run out of memory so I have to use qr(A,0).
When I try the non- economy setting, I actually get comparable results for A-Q*R. Even for a tiny matrix containing small numbers as shown here:
A = magic(20);
[Q, R] = qr(A); %Result does not change when using qr(A,0)
norm(A-Q*R)
As such I don't believe the 'economy' is the problem as confirmed by #horchler in the comments, but that you have just ran into the limits of how accurate calculations can be done with data of type 'double'.
Even if you change the accuracy somehow, you will always be dealing with an approximation, so perhaps the first thing to consider here is whether you really need greater accuracy than you already have. If you need more accuracy there may always be a way, but I doubt whether it will be a straightforward one.
There is one thing I do not like on Matlab: It tries sometimes to be too smart. For instance, if I have a negative square root like
a = -1; sqrt(a)
Matlab does not throw an error but switches silently to complex numbers. The same happens for negative logarithms. This can lead to hard to find errors in a more complicated algorithm.
A similar problem is that Matlab "solves" silently non quadratic linear systems like in the following example:
A=eye(3,2); b=ones(3,1); x = A \ b
Obviously x does not satisfy A*x==b (It solves a least square problem instead).
Is there any possibility to turn that "features" off, or at least let Matlab print a warning message in this cases? That would really helps a lot in many situations.
I don't think there is anything like "being smart" in your examples. The square root of a negative number is complex. Similarly, the left-division operator is defined in Matlab as calculating the pseudoinverse for non-square inputs.
If you have an application that should not return complex numbers (beware of floating point errors!), then you can use isreal to test for that. If you do not want the left division operator to calculate the pseudoinverse, test for whether A is square.
Alternatively, if for some reason you are really unable to do input validation, you can overload both sqrt and \ to only work on positive numbers, and to not calculate the pseudoinverse.
You need to understand all of the implications of what you're writing and make sure that you use the right functions if you're going to guarantee good code. For example:
For the first case, use realsqrt instead
For the second case, use inv(A) * b instead
Or alternatively, include the appropriate checks before/after you call the built-in functions. If you need to do this every time, then you can always write your own functions.
Keeping simple, take a matrix of ones i.e.
U_iso = ones(72,37)
and some parameters
ThDeg = 0:5:180;
dtheta = 5*pi/180;
dphi = 5*pi/180;
Th = ThDeg*pi/180;
Now the code is
omega_iso = 0;
for i = 1:72
for j=1:37
omega_iso = omega_iso + U_iso(i,j)*sin(Th(j))*dphi*dtheta;
end
end
and
D_iso = (4 * pi)/omega_iso
This code is fine. It take a matrix with dimension 72*37. The loop is an approximation of the integral which is further divided by 4pi to get ONE value of directivity of antenna.
Now this code gives one value which will be around 1.002.
My problem is I dont need 1 value. I need a 72*37 matrix as my answer where the above integral approximation is implemented on each cell of the 72 * 37 matrix. and thus the Directviity 'D' also results in a matrix of same size with each cell giving the same value.
So all we have to do is instead of getting 1 value, we need value at each cell.
Can anyone please help.
You talk about creating a result that is a function essentially of the elements of U. However, in no place is that code dependent on the elements of U. Look carefully at what you have written. While you do use the variable U_iso, never is any element of U employed anywhere in that code as you have written it.
So while you talk about defining this for a matrix U, that definition is meaningless. So far, it appears that a call to repmat at the very end would create a matrix of the desired size, and clearly that is not what you are looking for.
Perhaps you tried to make the problem simple for ease of explanation. But what you did was to over-simplify, not leaving us with something that even made any sense. Please explain your problem more clearly and show code that is consistent with your explanation, for a better answer than I can provide so far.
(Note: One option MIGHT be to use arrayfun. Or the answer to this question might be more trivial, using simple vectorized operations. I cannot know at this point.)
EDIT:
Your question is still unanswerable. This loop creates a single scalar result, essentially summing over the entire array. You don't say what you mean for the integral to be computed for each element of U_iso, since you are already summing over the entire array. Please learn to be accurate in your questions, otherwise we are just guessing as to what you mean.
My best guess at the moment is that you might wish to compute a cumulative integral, in two dimensions. cumtrapz can help you there, IF that is your goal. But I'm not sure it is your goal, since your explanation is so incomplete.
You say that you wish to get the same value in each cell of the result. If that is what you wish, then a call to repmat at the end will do what you wish.