The armadillo print() method prints the imaginary value as the negative of its true value when transposing the column vector to a row vector. Is this a bug, or am I missing something?
Code:
#include <iostream>
#include <armadillo>
int
main()
{
arma::Col<std::complex<float>> v1 = {{1.0, 1.5}, {2.0,2.5}, {3.0,3.5}, {4.0,4.5}};
v1.t().print("v1':");
v1.print("v1:");
}
Output:
v1':
(+1.000e+00,-1.500e+00) (+2.000e+00,-2.500e+00) (+3.000e+00,-3.500e+00) (+4.000e+00,-4.500e+00)
v1:
(+1.000e+00,+1.500e+00)
(+2.000e+00,+2.500e+00)
(+3.000e+00,+3.500e+00)
(+4.000e+00,+4.500e+00)
The comment by #hbrerkere explains that the default transpose function is a conjugate transpose which flips the signs of imaginary values. Use strans() instead of trans() to perform simple transpose.
Related
I'd like to plot the following piecewise function with Fourier series in wxMaxima:
for given values of constants.
Here's my current input in wxMaxima:
a_1(t):=A_0+sum(A_n*cos(n*ω*(t-t_0))+B_n*sin(n*ω*(t-t_0)), n, 1, N);
a_2(t):=A_0;
a(t):=if(is(t>=t_0)) then a_1(t) else a_2(t);
N=2$
ω=31.416$
t_0=-0.1614$
A_0=0$
A_1=0.227$
B_1=0$
A_2=0.413$
B_2=0$
plot2d([a(t)], [t,0,0.5])$
Unfortunately, it doesn't work. I get the expression evaluates to non-numeric value everywhere in plotting range error. What can I do to make it work? Is it possible to plot this function in wxMaxima?
UPDATE: It works with modifications suggested by Robert Dodier:
a_1(t):=A[0]+sum(A[n]*cos(n*ω*(t-t_0))+B[n]*sin(n*ω*(t-t_0)), n, 1, N);
a_2(t):=A[0];
a(t):=if t>=t_0 then a_1(t) else a_2(t);
N:2$
ω:31.416$
t_0:-0.1614$
A[0]:0$
A[1]:0.227$
B[1]:0$
A[2]:0.413$
B[2]:0$
wxplot2d([a(t)], [t,0,0.5], [ylabel,"a"])$
I was wondering does anyone know / where I could find the calculations or formulas used for OpenCV's Normalize function on a distance map given the beta value 0 and alpha value 1, with the normalization type NORM_MINMAX:
normalize(distanceT, distanceT, 0, 1, NORM_MINMAX);
I cannot find any information in the documentation so I have been looking at articles such as:
http://en.wikipedia.org/wiki/Normalization_%28image_processing%29
but doing the calculations do not yield the same results as the OpenCV's algorithm.
Many thanks.
The formula is:
out_val=(in_val-min_val)/(max_val-min_val);
in_val - input matrix element value;
out_val - output matrix element value;
min_val - minimal element in input matrix;
max_val - maximal element in input matrix.
I want to mutiply 2 with each element of vec3 in opencv as we do in Matlab simplt by ".*". I searched alot but didn't find any command is their any command for this or not in opencv? thanks in advance for any help.
This answer would suggest you can just use the * assignment operator in C++.
If you are using Java I don't think this is possible, you can only multiply a Mat by another Mat.
So you would need to create a new Mat instance of the same size and type, initialised with the scalar value you want to multiply by.
You can easily create a funcion to do this:
public Mat multiplyScalar(Mat m, double i)
{
return m = m.mul(new Mat((int)m.size().height, (int)m.size().width, m.type(), new Scalar(i)));
}
Then x = multiplyScalar(x, 5); will multiply each element by 5.
can any one help me about how to get the absolute value of a complex matrix.the matrix contains real value in one channel and imaginary value in another one channel.please help me
if s possible means give me some example.
Thanks in advance
Arangarajan
Let's assume you have 2 components: X and Y, two matrices of the same size and type. In your case it can be real/im values.
// n rows, m cols, type float; we assume the following matrices are filled
cv::Mat X(n,m,CV_32F);
cv::Mat Y(n,m,CV_32F);
You can compute the absolute value of each complex number like this:
// create a new matrix for storage
cv::Mat A(n,m,CV_32F,cv::Scalar(0.0));
for(int i=0;i<n;i++){
// pointer to row(i) values
const float* rowi_x = X.ptr<float>(i);
const float* rowi_y = Y.ptr<float>(i);
float* rowi_a = A.ptr<float>(i);
for(int j=0;j<=m;j++){
rowi_a[j] = sqrt(rowi_x[j]*rowi_x[j]+rowi_y[j]*rowi_y[j]);
}
}
If you look in the OpenCV phasecorr.cpp module, there's a function called magSpectrums that does this already and will handle conjugate symmetry-packed DFT results too. I don't think it's exposed by the header file, but it's easy enough to copy it. If you care about speed, make sure you compile with any available SIMD options turned on too because they can make a big difference with this calculation.
Currently, I'm working on a project in medical engineering. I have a big image with several sub-images of the cell, so my first task is to divide the image.
I thought about the next thing:
Convert the image into binary
doing a projection of the brightness pixels into the x-axis so I can see where there are gaps between brightnesses values and then divide the image.
The problem comes when I try to reach the second part. My idea is using a vector as the projection and sum all the brightnesses values all along one column, so the position number 0 of the vector is the sum of all the brightnesses values that are in the first column of the image, the same until I reach the last column, so at the end I have the projection.
This is how I have tried:
void calculo(cv::Mat &result,cv::Mat &binary){ //result=the sum,binary the imag.
int i,j;
for (i=0;i<=binary.rows;i++){
for(j=0;j<=binary.cols;j++){
cv::Scalar intensity= binaria.at<uchar>(j,i);
result.at<uchar>(i,i)=result.at<uchar>(i,i)+intensity.val[0];
}
cv::Scalar intensity2= result.at<uchar>(i,i);
cout<< "content" "\n"<< intensity2.val[0] << endl;
}
}
When executing this code, I have a violation error. Another problem is that I cannot create a matrix with one unique row, so...I don't know what could I do.
Any ideas?! Thanks!
At the end, it does not work, I need to sum all the pixels in one COLUMN. I did:
cv::Mat suma(cv::Mat& matrix){
int i;
cv::Mat output(1,matrix.cols,CV_64F);
for (i=0;i<=matrix.cols;i++){
output.at<double>(0,i)=norm(matrix.col(i),1);
}
return output;
}
but It gave me a mistake:
Assertion failed (0 <= colRange.start && colRange.start <= colRange.end && colRange.end <= m.cols) in Mat, file /home/usuario/OpenCV-2.2.0/modules/core/src/matrix.cpp, line 276
I dont know, any idea would be helpful, anyway many thanks mevatron, you really left me in the way.
If you just want the sum of the binary image, you could simply take the L1-norm. Like so:
Mat binaryVectorSum(const Mat& binary)
{
Mat output(1, binary.rows, CV_64F);
for(int i = 0; i < binary.rows; i++)
{
output.at<double>(0, i) = norm(binary.row(i), NORM_L1);
}
return output;
}
I'm at work, so I can't test it out, but that should get you close.
EDIT : Got home. Tested it. It works. :) One caveat...this function works if your binary matrix is truly binary (i.e., 0's and 1's). You may need to scale the norm output with the maximum value if the binary matrix is say 0's and 255's.
EDIT : If you don't have using namespace cv; in your .cpp file, then you'll need to declare the namespace to use NORM_L1 like this cv::NORM_L1.
Have you considered transposing the matrix before you call the function? Like this:
sumCols = binaryVectorSum(binary.t());
vs.
sumRows = binaryVectorSum(binary);
EDIT : A bug with my code :)
I changed:
Mat output(1, binary.cols, CV_64F);
to
Mat output(1, binary.rows, CV_64F);
My test case was a square matrix, so that bug didn't get found...
Hope that is helpful!