Mapping points to and from a Hilbert curve - c++

I have been trying to write a function for the Hilbert curve map and inverse map. Fortunately there was another SE post on it, and the accepted answer was highly upvoted, and featured code based on a paper in a peer-reviewed academic journal.
Unfortunately, I played around with the code above and looked at the paper, and I'm not sure how to get this to work. What appears to be broken is that my code drawing the second half of a 2-bit 2-dimensional Hilbert Curve backwards. If you draw out the 2-d coordinates in the last column, you'll see the second half of the curve (position 8 and on) backwards.
I don't think I'm allowed to post the original C code, but the C++ version below is only lightly edited. A few things that are different in my code.
C code is less strict on types, so I had to use std::bitset
In addition to the bug mentioned by #PaulChernoch in the aforementioned SE post, the next for loop segfaults, too.
The paper represents one-dimensional coordinates weirdly. They call it the number's "Transpose." I wrote a function that produces a "Transpose" from a regular integer.
Another thing about this algorithm: it doesn't produce a map between unit intervals and unit hypercubes. Rather, it stretches the problem out and maps between intervals and cubes with unit spacing.
NB: HTranspose is the representation of H they use in the paper
H, HTranspose, mappedCoordinates
------------------------------------
0: (00, 00), (0, 0)
1: (00, 01), (1, 0)
2: (01, 00), (1, 1)
3: (01, 01), (0, 1)
4: (00, 10), (0, 2)
5: (00, 11), (0, 3)
6: (01, 10), (1, 3)
7: (01, 11), (1, 2)
8: (10, 00), (3, 2)
9: (10, 01), (3, 3)
10: (11, 00), (2, 3)
11: (11, 01), (2, 2)
12: (10, 10), (2, 1)
13: (10, 11), (3, 1)
14: (11, 10), (3, 0)
15: (11, 11), (2, 0)
Here's the code (in c++).
#include <array>
#include <bitset>
#include <iostream>
#include <cmath>
namespace hilbert {
/// The Hilbert index is expressed as an array of transposed bits.
///
/// Example: 5 bits for each of n=3 coordinates.
/// 15-bit Hilbert integer = A B C D E F G H I J K L M N O is stored
/// as its Transpose ^
/// X[0] = A D G J M X[2]| 7
/// X[1] = B E H K N <-------> | /X[1]
/// X[2] = C F I L O axes |/
/// high low 0------> X[0]
template<size_t num_bits, size_t num_dims>
std::array<std::bitset<num_bits>,num_dims> TransposeToAxes(std::array<std::bitset<num_bits>,num_dims> X)
{
using coord_t = std::bitset<num_bits>;
using coords_t = std::array<coord_t, num_dims>;
coord_t N = 2 << (num_bits-1);
// Gray decode by H ^ (H/2)
coord_t t = X[num_dims-1] >> 1;
for(size_t i = num_dims-1; i > 0; i-- ) // https://stackoverflow.com/a/10384110
X[i] ^= X[i-1];
X[0] ^= t;
// Undo excess work
for( coord_t Q = 2; Q != N; Q <<= 1 ) {
coord_t P = Q.to_ulong() - 1;
for( size_t i = num_dims-1; i > 0 ; i-- ){ // did the same stackoverflow thing
if( (X[i] & Q).any() )
X[0] ^= P;
else{
t = (X[0]^X[i]) & P;
X[0] ^= t;
X[i] ^= t;
}
}
}
return X;
}
template<size_t num_bits, size_t num_dims>
std::array<std::bitset<num_bits>,num_dims> AxesToTranspose(std::array<std::bitset<num_bits>, num_dims> X)
{
using coord_t = std::bitset<num_bits>;
using coords_t = std::array<coord_t, num_dims>;
coord_t M = 1 << (num_bits-1);
// Inverse undo
for( coord_t Q = M; Q.to_ulong() > 1; Q >>= 1 ) {
coord_t P = Q.to_ulong() - 1;
for(size_t i = 0; i < num_bits; i++ ){
if( (X[i] & Q).any() )
X[0] ^= P;
else{
coord_t t = (X[0]^X[i]) & P;
X[0] ^= t;
X[i] ^= t;
}
}
} // exchange
// Gray encode
for( size_t i = 1; i < num_bits; i++ )
X[i] ^= X[i-1];
coord_t t = 0;
for( coord_t Q = M; Q.to_ulong() > 1; Q >>= 1 ){
if( (X[num_dims-1] & Q).any() )
t ^= Q.to_ulong()-1;
}
for( size_t i = 0; i < num_bits; i++ )
X[i] ^= t;
return X;
}
template<size_t num_bits, size_t num_dims>
std::array<std::bitset<num_bits>,num_dims> makeHTranspose(unsigned int H)
{
using coord_t = std::bitset<num_bits>;
using coords_t = std::array<coord_t, num_dims>;
using big_coord_t = std::bitset<num_bits*num_dims>;
big_coord_t Hb = H;
coords_t X;
for(size_t dim = 0; dim < num_dims; ++dim){
coord_t tmp;
unsigned c = num_dims - 1;
for(size_t rbit = dim; rbit < num_bits*num_dims; rbit += num_dims){
tmp[c] =Hb[num_bits*num_dims - 1 - rbit];
c--;
}
X[dim] = tmp;
}
return X;
}
} //namespace hilbert
int main()
{
constexpr unsigned nb = 2;
constexpr unsigned nd = 2;
using coord_t = std::bitset<nb>;
using coords_t = std::array<coord_t,nd>;
std::cout << "NB: HTranspose is the representation of H they use in the paper\n";
std::cout << "H, HTranspose, mappedCoordinates \n";
std::cout << "------------------------------------\n";
for(unsigned H = 0; H < pow(2,nb*nd); ++H){
// H with the representation they use in the paper
coords_t weirdH = hilbert::makeHTranspose<nb,nd>(H);
std::cout << H << ": ("
<< weirdH[0] << ", "
<< weirdH[1] << "), ("
<< hilbert::TransposeToAxes<nb,nd>(weirdH)[0].to_ulong() << ", "
<< hilbert::TransposeToAxes<nb,nd>(weirdH)[1].to_ulong() << ")\n";
}
}
Some strange things I noticed about the other post:
In addition to the bug mentioned by #PaulChernoch in the above mentioned SE post, the next for loop segfaults, too.
Nobody is talking about how the paper doesn't provide a mapping between the unit interval and the unit cubes, but rather a mapping from integers to big cubes, and
I don't see any mention here about the weird representation the paper uses for unsigned integers "Transpose".
If you draw out the 2-d coordinates in the last column, you'll see the second half of the curve (position 8 and on) backwards.

"In addition to the bug mentioned by #PaulChernoch in the above mentioned SE post, the next for loop segfaults, too." Actually it was a bug in my code--I was having a hard time iterating over a container backwards. I started looking at myself after I realized there were other Python packages (e.g. this and this) that use the same underlying C code. Neither of them were complaining about the other for loop.
Second, there was a bug in my function above that generates the transpose, too. And third, the inverse function, AxesToTranspose confused number of bits with number of dimensions.
All four correct functions (the two provided in the paper that do all the heavy lifting, and two more for converting between integers and "Transpose"s) are as follows:
.
/**
* #brief converts an integer in a transpose form to a position on the Hilbert Curve.
* Code is based off of John Skilling , "Programming the Hilbert curve",
* AIP Conference Proceedings 707, 381-387 (2004) https://doi.org/10.1063/1.1751381
* #file resamplers.h
* #tparam num_bits how "accurate/fine/squiggly" you want the Hilbert curve
* #tparam num_dims the number of dimensions the curve is in
* #param X an unsigned integer in a "Transpose" form.
* #return a position on the hilbert curve
*/
template<size_t num_bits, size_t num_dims>
std::array<std::bitset<num_bits>,num_dims> TransposeToAxes(std::array<std::bitset<num_bits>,num_dims> X)
{
using coord_t = std::bitset<num_bits>;
using coords_t = std::array<coord_t, num_dims>;
// Gray decode by H ^ (H/2)
coord_t t = X[num_dims-1] >> 1;
for(int i = num_dims-1; i > 0; i-- ) // https://stackoverflow.com/a/10384110
X[i] ^= X[i-1];
X[0] ^= t;
// Undo excess work
coord_t N = 2 << (num_bits-1);
for( coord_t Q = 2; Q != N; Q <<= 1 ) {
coord_t P = Q.to_ulong() - 1;
for( int i = num_dims - 1; i >= 0; i--){
if( (X[i] & Q).any() ){ // invert low bits of X[0]
X[0] ^= P;
} else{ // exchange low bits of X[i] and X[0]
t = (X[0]^X[i]) & P;
X[0] ^= t;
X[i] ^= t;
}
}
}
return X;
}
/**
* #brief converts a position on the Hilbert curve into an integer in a "transpose" form.
* Code is based off of John Skilling , "Programming the Hilbert curve",
* AIP Conference Proceedings 707, 381-387 (2004) https://doi.org/10.1063/1.1751381
* #file resamplers.h
* #tparam num_bits how "accurate/fine/squiggly" you want the Hilbert curve
* #tparam num_dims the number of dimensions the curve is in
* #param X a position on the hilbert curve (each dimension coordinate is in base 2)
* #return a position on the real line (in a "Transpose" form)
*/
template<size_t num_bits, size_t num_dims>
std::array<std::bitset<num_bits>,num_dims> AxesToTranspose(std::array<std::bitset<num_bits>, num_dims> X)
{
using coord_t = std::bitset<num_bits>;
using coords_t = std::array<coord_t, num_dims>;
// Inverse undo
coord_t M = 1 << (num_bits-1);
for( coord_t Q = M; Q.to_ulong() > 1; Q >>= 1 ) {
coord_t P = Q.to_ulong() - 1;
for(size_t i = 0; i < num_dims; i++ ){
if( (X[i] & Q).any() )
X[0] ^= P;
else{
coord_t t = (X[0]^X[i]) & P;
X[0] ^= t;
X[i] ^= t;
}
}
} // exchange
// Gray encode
for( size_t i = 1; i < num_dims; i++ )
X[i] ^= X[i-1];
coord_t t = 0;
for( coord_t Q = M; Q.to_ulong() > 1; Q >>= 1 ){
if( (X[num_dims-1] & Q).any() )
t ^= Q.to_ulong()-1;
}
for( size_t i = 0; i < num_dims; i++ )
X[i] ^= t;
return X;
}
/**
* #brief converts an integer on the positive integers into its "Transpose" representation..
* This code supplements the above two functions that are
* based off of John Skilling , "Programming the Hilbert curve",
* AIP Conference Proceedings 707, 381-387 (2004) https://doi.org/10.1063/1.1751381
* #file resamplers.h
* #tparam num_bits how "accurate/fine/squiggly" you want the Hilbert curve
* #tparam num_dims the number of dimensions the curve is in
* #param H a position on the hilbert curve (0,1,..,2^(num_dims * num_bits) )
* #return a position on the real line (in a "Transpose" form)
*/
template<size_t num_bits, size_t num_dims>
std::array<std::bitset<num_bits>,num_dims> makeHTranspose(unsigned int H)
{
using coord_t = std::bitset<num_bits>;
using coords_t = std::array<coord_t, num_dims>;
using big_coord_t = std::bitset<num_bits*num_dims>;
big_coord_t Hb = H;
coords_t X;
for(size_t dim = 0; dim < num_dims; ++dim){
coord_t dim_coord_tmp;
unsigned start_bit = num_bits*num_dims-1-dim;
unsigned int c = num_bits - 1;
for(int bit = start_bit; bit >= 0; bit -= num_dims){
dim_coord_tmp[c] = Hb[bit];
c--;
}
X[dim] = dim_coord_tmp;
}
return X;
}
/**
* #brief converts an integer in its "Transpose" representation into a positive integer..
* This code supplements two functions above that are
* based off of John Skilling , "Programming the Hilbert curve",
* AIP Conference Proceedings 707, 381-387 (2004) https://doi.org/10.1063/1.1751381
* #file resamplers.h
* #tparam num_bits how "accurate/fine/squiggly" you want the Hilbert curve
* #tparam num_dims the number of dimensions the curve is in
* #param Htrans a position on the real line (in a "Transpose" form)
* #return a position on the hilbert curve (0,1,..,2^(num_dims * num_bits) )
*/
template<size_t num_bits, size_t num_dims>
unsigned int makeH(std::array<std::bitset<num_bits>,num_dims> Htrans)
{
using coord_t = std::bitset<num_bits>;
using coords_t = std::array<coord_t, num_dims>;
using big_coord_t = std::bitset<num_bits*num_dims>;
big_coord_t H;
unsigned int which_dim = 0;
unsigned which_bit;
for(int i = num_bits*num_dims - 1; i >= 0; i--){
which_bit = i / num_dims;
H[i] = Htrans[which_dim][which_bit];
which_dim = (which_dim + 1) % num_dims;
}
return H.to_ulong();
}
I have some unit tests here as well.

Related

Time dependent 1D Schrodinger equation C++

I wrote the code in C++ which solves the time-dependent 1D Schrodinger equation for the anharmonic potential V = x^2/2 + lambda*x^4, using Thomas algorithm. My code is working and I animate the results in Mathematica, to check what is going on. I test the code against the known solution for the harmonic potential (I put lambda = 0), but the animation shows that abs(Psi) is changing with time, and I know that is not correct for the harmonic potential. Actually, I see that in one point it time it becomes constant, but before that is oscillating.
So I understand that I need to have constant magnitude of the wave function over the time interval, but I don't know how to do it, or where am I doing mistake.
Here is my code and the animation for 100 time steps and 100 points on the grid.
#include <iostream>
#include <iomanip>
#include <cmath>
#include <vector>
#include <cstdlib>
#include <complex>
#include <fstream>
using namespace std;
// Mandatory parameters
const int L = 1; //length of domain in x direction
const int tmax = 10; //end time
const int nx = 100, nt = 100; //number of the grid points and time steps respectively
double lambda; //dictates the shape of the potential (we can use lambda = 0.0
// to test the code against the known solution for the harmonic
// oscillator)
complex<double> I(0.0, 1.0); //imaginary unit
// Derived parameters
double delta_x = 1. / (nx - 1);
//spacing between the grid points
double delta_t = 1. / (nt - 1);
//the time step
double r = delta_t / (delta_x * delta_x); //used to simplify expressions for
// the coefficients of the lhs and
// rhs of the matrix eqn
// Algorithm for solving the tridiagonal matrix system
vector<complex<double> > thomas_algorithm(vector<double>& a,
vector<complex<double> >& b,
vector<double>& c,
vector<complex<double> >& d)
{
// Temporary wave function
vector<complex<double> > y(nx + 1, 0.0);
// Modified matrix coefficients
vector<complex<double> > c_prime(nx + 1, 0.0);
vector<complex<double> > d_prime(nx + 1, 0.0);
// This updates the coefficients in the first row
c_prime[0] = c[0] / b[0];
d_prime[0] = d[0] / b[0];
// Create the c_prime and d_prime coefficients in the forward sweep
for (int i = 1; i < nx + 1; i++)
{
complex<double> m = 1.0 / (b[i] - a[i] * c_prime[i - 1]);
c_prime[i] = c[i] * m;
d_prime[i] = (d[i] - a[i] * d_prime[i - 1]) * m;
}
// This gives the value of the last equation in the system
y[nx] = d_prime[nx];
// This is the reverse sweep, used to update the solution vector
for (int i = nx - 1; i > 0; i--)
{
y[i] = d_prime[i] - c_prime[i] * y[i + 1];
}
return y;
}
void calc()
{
// First create the vectors to store the coefficients
vector<double> a(nx + 1, 1.0);
vector<complex<double> > b(nx + 1, 0.0);
vector<double> c(nx + 1, 1.0);
vector<complex<double> > d(nx + 1, 0.0);
vector<complex<double> > psi(nx + 1, 0.0);
vector<complex<double> > phi(nx + 1, 0.0);
vector<double> V(nx + 1, 0.0);
vector<double> x(nx + 1, 0);
vector<vector<complex<double> > > PSI(nt + 1,
vector<complex<double> >(nx + 1,
0.0));
vector<double> prob(nx + 1, 0);
// We don't have the first member of the left diagonal and the last member
// of the right diagonal
a[0] = 0.0;
c[nx] = 0.0;
for (int i = 0; i < nx + 1; i++)
{
x[i] = (-nx / 2) + i; // Values on the x axis
// Eigenfunction of the harmonic oscillator in the ground state
phi[i] = exp(-pow(x[i] * delta_x, 2) / 2) / (pow(M_PI, 0.25));
// Anharmonic potential
V[i] = pow(x[i] * delta_x, 2) / 2 + lambda * pow(x[i] * delta_x, 4);
// The main diagonal coefficients
b[i] = 2.0 * I / r - 2.0 + V[i] * delta_x * delta_x;
}
double sum0 = 0.0;
for (int i = 0; i < nx + 1; i++)
{
PSI[0][i] = phi[i]; // Initial condition for the wave function
sum0 += abs(pow(PSI[0][i], 2)); // Needed for the normalization
}
sum0 = sum0 * delta_x;
for (int i = 0; i < nx + 1; i++)
{
PSI[0][i] = PSI[0][i] / sqrt(sum0); // Normalization of the initial
// wave function
}
for (int j = 0; j < nt; j++)
{
PSI[j][0] = 0.0;
PSI[j][nx] = 0.0; // Boundary conditions for the wave function
d[0] = 0.0;
d[nx] = 0.0; // Boundary conditions for the rhs
// Fill in the current time step vector d representing the rhs
for (int i = 1; i < nx + 1; i++)
{
d[i] = PSI[j][i + 1]
+ (2.0 - 2.0 * I / r - V[i] * delta_x * delta_x) * PSI[j][i]
+ PSI[j][i - 1];
}
// Now solve the tridiagonal system
psi = thomas_algorithm(a, b, c, d);
for (int i = 1; i < nx; i++)
{
PSI[j + 1][i] = psi[i]; // Assign values to the wave function
}
for (int i = 0; i < nx + 1; i++)
{
// Probability density of the wave function in the next time step
prob[i] = abs(PSI[j + 1][i] * conj(PSI[j + 1][i]));
}
double sum = 0.0;
for (int i = 0; i < nx + 1; i++)
{
sum += prob[i] * delta_x;
}
for (int i = 0; i < nx + 1; i++)
{
// Normalization of the wave function in the next time step
PSI[j + 1][i] /= sqrt(sum);
}
}
// Opening files for writing the results
ofstream file_psi_re, file_psi_imag, file_psi_abs, file_potential,
file_phi0;
file_psi_re.open("psi_re.dat");
file_psi_imag.open("psi_imag.dat");
file_psi_abs.open("psi_abs.dat");
for (int i = 0; i < nx + 1; i++)
{
file_psi_re << fixed << x[i] << " ";
file_psi_imag << fixed << x[i] << " ";
file_psi_abs << fixed << x[i] << " ";
for (int j = 0; j < nt + 1; j++)
{
file_psi_re << fixed << setprecision(6) << PSI[j][i].real() << " ";
file_psi_imag << fixed << setprecision(6) << PSI[j][i].imag()
<< " ";
file_psi_abs << fixed << setprecision(6) << abs(PSI[j][i]) << " ";
}
file_psi_re << endl;
file_psi_imag << endl;
file_psi_abs << endl;
}
}
int main(int argc, char **argv)
{
calc();
return 0;
}
The black line is abs(psi), the red one is Im(psi) and the blue one is Re(psi).
(Bear in mind that my computational physics course was ten years ago now)
You say you are solving a time-dependent system, but I don't see any time-dependence (even if lambda != 0). In the Schrodinger Equation, if the potential function does not depend on time then the different equation is called separable because you can solve the time component and spatial component of the differential equation separately.
The general solution in that case is just the solution to the time-independent Schrodinger Equation multiplied by exp(-iE/h_bar). When you plot the magnitude of the probability that term just becomes 1 and so the probability doesn't change over time. In these cases people quite typically just ignore the time component altogether.
All this is to say that since your potential function doesn't depend on time then you aren't solving a time-dependent Schrodinger Equation. The Tridiagonal Matrix Algorithm can only be used to solve ordinary differential equations, whereas if your potential depended on time you would have a partial differential equation and would need a different method to solve it. Also as a result of that plotting the probability density over time is rarely interesting.
As for why your potential is not constant, numerical methods for finding eigenvalues and eigenvectors rarely produce the normalised eigenvectors naturally, so are you manually normalising your eigenvector before computing your probabilities?

How to fit a bounding ellipse around a set of 2D points

Given a set of 2d points (in Cartesian form), I need to find the minimum-area ellipse such that every point in the set lies either on or inside the ellipse.
I have found the solution in the form of pseudo-code on this site, but my attempt to implement the solution in C++ has been unsuccessful.
The following image illustrates graphically what the solution to my problem looks like:
In my attempt, I used the Eigen library for the various operations on matrices.
//The tolerance for error in fitting the ellipse
double tolerance = 0.2;
int n = 10; // number of points
int d = 2; // dimension
MatrixXd p = MatrixXd::Random(d,n); //Fill matrix with random points
MatrixXd q = p;
q.conservativeResize(p.rows() + 1, p.cols());
for(size_t i = 0; i < q.cols(); i++)
{
q(q.rows() - 1, i) = 1;
}
int count = 1;
double err = 1;
const double init_u = 1.0 / (double) n;
MatrixXd u = MatrixXd::Constant(n, 1, init_u);
while(err > tolerance)
{
MatrixXd Q_tr = q.transpose();
cout << "1 " << endl;
MatrixXd X = q * u.asDiagonal() * Q_tr;
cout << "1a " << endl;
MatrixXd M = (Q_tr * X.inverse() * q).asDiagonal();
cout << "1b " << endl;
int j_x, j_y;
double maximum = M.maxCoeff(&j_x, &j_y);
double step_size = (maximum - d - 1) / ((d + 1) * (maximum + 1));
MatrixXd new_u = (1 - step_size) * u;
new_u(j_x, 0) += step_size;
cout << "2 " << endl;
//Find err
MatrixXd u_diff = new_u - u;
for(size_t i = 0; i < u_diff.rows(); i++)
{
for(size_t j = 0; j < u_diff.cols(); j++)
u_diff(i, j) *= u_diff(i, j); // Square each element of the matrix
}
err = sqrt(u_diff.sum());
count++;
u = new_u;
}
cout << "3 " << endl;
MatrixXd U = u.asDiagonal();
MatrixXd A = (1.0 / (double) d) * (p * U * p.transpose() - (p * u) * (p * u).transpose()).inverse();
MatrixXd c = p * u;
The error occurs on the following line:
MatrixXd M = (Q_tr * X.inverse() * q).asDiagonal();
and it reads as follows:
run: /usr/include/eigen3/Eigen/src/Core/DenseBase.h:261: void Eigen::DenseBase<Derived>::resize(Eigen::Index, Eigen::Index) [with Derived = Eigen::Diagonal<Eigen::Matrix<double, -1, -1>, 0>; Eigen::Index = long int]: Assertion `rows == this->rows() && cols == this->cols() && "DenseBase::resize() does not actually allow to resize."' failed.
Aborted (core dumped)
Can someone please point out why this error is occurring or even better, give me some advice on how to fit an ellipse to a set of points using C++?
With Eigen, you can get the diagonal vector from a matrix with .diagonal(); you can treat a vector as a diagonal matrix with .asDiagonal(); but you cannot treat a dense matrix as a diagonal matrix. So that line should be
MatrixXd M = (Q_tr * X.inverse() * q).diagonal();

Is this C++ FFT function equivalent to "fft" matlab function?

I have always used the function "fft(x)" in matlab where "x" is a vector of complex numbers. I am looking for an easy to use function in C++ that would return complex numbers.
I have found this code : http://paulbourke.net/miscellaneous/dft/
If it is equivalent, how can I use it ? Thank you for your time !
/*
This computes an in-place complex-to-complex FFT
x and y are the real and imaginary arrays of 2^m points.
dir = 1 gives forward transform
dir = -1 gives reverse transform
*/
short FFT(short int dir,long m,double *x,double *y)
{
long n,i,i1,j,k,i2,l,l1,l2;
double c1,c2,tx,ty,t1,t2,u1,u2,z;
/* Calculate the number of points */
n = 1;
for (i=0;i<m;i++)
n *= 2;
/* Do the bit reversal */
i2 = n >> 1;
j = 0;
for (i=0;i<n-1;i++) {
if (i < j) {
tx = x[i];
ty = y[i];
x[i] = x[j];
y[i] = y[j];
x[j] = tx;
y[j] = ty;
}
k = i2;
while (k <= j) {
j -= k;
k >>= 1;
}
j += k;
}
/* Compute the FFT */
c1 = -1.0;
c2 = 0.0;
l2 = 1;
for (l=0;l<m;l++) {
l1 = l2;
l2 <<= 1;
u1 = 1.0;
u2 = 0.0;
for (j=0;j<l1;j++) {
for (i=j;i<n;i+=l2) {
i1 = i + l1;
t1 = u1 * x[i1] - u2 * y[i1];
t2 = u1 * y[i1] + u2 * x[i1];
x[i1] = x[i] - t1;
y[i1] = y[i] - t2;
x[i] += t1;
y[i] += t2;
}
z = u1 * c1 - u2 * c2;
u2 = u1 * c2 + u2 * c1;
u1 = z;
}
c2 = sqrt((1.0 - c1) / 2.0);
if (dir == 1)
c2 = -c2;
c1 = sqrt((1.0 + c1) / 2.0);
}
/* Scaling for forward transform */
if (dir == 1) {
for (i=0;i<n;i++) {
x[i] /= n;
y[i] /= n;
}
}
return(TRUE);
}
Alternative Suggestion:
I had same problem. I used fft library from fftw.
http://www.fftw.org/download.html
Its performance is similar to matlab.
The code looks fine at first glance. The original FFT is not a lot of code.
One feature of the FFT is that it is an inplace operation. Many higher level bindings effectively hide that fact.
So you put your real and imaginary parts into the x and y arrays. After the executing the function you read those same arrays for your result.
This particularly simple implementation will only work with powers of 2 as the original FFT. If you have inputs of lengths other than powers of 2 you could zero-pad your signal.
Google the book Numerical Recipes and fft (older versions are freely available) if you want to read up on the background of the FFT. The version in that book is different from other implementations in that you have to feed in the real and imaginary parts interleaved.
What I'm missing on the implementation that you are quoting is the use of pi or trigonometric functions. You'll have to try it out to compare against Matlab.

Confusion testing fftw3 - poisson equation 2d test

I am having trouble explaining/understanding the following phenomenon:
To test fftw3 i am using the 2d poisson test case:
laplacian(f(x,y)) = - g(x,y) with periodic boundary conditions.
After applying the fourier transform to the equation we obtain : F(kx,ky) = G(kx,ky) /(kx² + ky²) (1)
if i take g(x,y) = sin (x) + sin(y) , (x,y) \in [0,2 \pi] i have immediately f(x,y) = g(x,y)
which is what i am trying to obtain with the fft :
i compute G from g with a forward Fourier transform
From this i can compute the Fourier transform of f with (1).
Finally, i compute f with the backward Fourier transform (without forgetting to normalize by 1/(nx*ny)).
In practice, the results are pretty bad?
(For instance, the amplitude for N = 256 is twice the amplitude obtained with N = 512)
Even worse, if i try g(x,y) = sin(x)*sin(y) , the curve has not even the same form of the solution.
(note that i must change the equation; i divide by two the laplacian in this case : (1) becomes F(kx,ky) = 2*G(kx,ky)/(kx²+ky²)
Here is the code:
/*
* fftw test -- double precision
*/
#include <iostream>
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <fftw3.h>
using namespace std;
int main()
{
int N = 128;
int i, j ;
double pi = 3.14159265359;
double *X, *Y ;
X = (double*) malloc(N*sizeof(double));
Y = (double*) malloc(N*sizeof(double));
fftw_complex *out1, *in2, *out2, *in1;
fftw_plan p1, p2;
double L = 2.*pi;
double dx = L/(N - 1);
in1 = (fftw_complex*) fftw_malloc(sizeof(fftw_complex)*(N*N) );
out2 = (fftw_complex*) fftw_malloc(sizeof(fftw_complex)*(N*N) );
out1 = (fftw_complex*) fftw_malloc(sizeof(fftw_complex)*(N*N) );
in2 = (fftw_complex*) fftw_malloc(sizeof(fftw_complex)*(N*N) );
p1 = fftw_plan_dft_2d(N, N, in1, out1, FFTW_FORWARD,FFTW_MEASURE );
p2 = fftw_plan_dft_2d(N, N, in2, out2, FFTW_BACKWARD,FFTW_MEASURE);
for(i = 0; i < N; i++){
X[i] = -pi + i*dx ;
for(j = 0; j < N; j++){
Y[j] = -pi + j*dx ;
in1[i*N + j][0] = sin(X[i]) + sin(Y[j]) ; // row major ordering
//in1[i*N + j][0] = sin(X[i]) * sin(Y[j]) ; // 2nd test case
in1[i*N + j][1] = 0 ;
}
}
fftw_execute(p1); // FFT forward
for ( i = 0; i < N; i++){ // f = g / ( kx² + ky² )
for( j = 0; j < N; j++){
in2[i*N + j][0] = out1[i*N + j][0]/ (i*i+j*j+1e-16);
in2[i*N + j][1] = out1[i*N + j][1]/ (i*i+j*j+1e-16);
//in2[i*N + j][0] = 2*out1[i*N + j][0]/ (i*i+j*j+1e-16); // 2nd test case
//in2[i*N + j][1] = 2*out1[i*N + j][1]/ (i*i+j*j+1e-16);
}
}
fftw_execute(p2); //FFT backward
// checking the results computed
double erl1 = 0.;
for ( i = 0; i < N; i++) {
for( j = 0; j < N; j++){
erl1 += fabs( in1[i*N + j][0] - out2[i*N + j][0]/N/N )*dx*dx;
cout<< i <<" "<< j<<" "<< sin(X[i])+sin(Y[j])<<" "<< out2[i*N+j][0]/N/N <<" "<< endl; // > output
}
}
cout<< erl1 << endl ; // L1 error
fftw_destroy_plan(p1);
fftw_destroy_plan(p2);
fftw_free(out1);
fftw_free(out2);
fftw_free(in1);
fftw_free(in2);
return 0;
}
I can't find any (more) mistakes in my code (i installed the fftw3 library last week) and i don't see a problem with the maths either but i don't think it's the fft's fault. Hence my predicament. I am all out of ideas and all out of google as well.
Any help solving this puzzle would be greatly appreciated.
note :
compiling : g++ test.cpp -lfftw3 -lm
executing : ./a.out > output
and i use gnuplot in order to plot the curves :
(in gnuplot ) splot "output" u 1:2:4 ( for the computed solution )
Here are some little points to be modified :
You need to account for all small frequencies, including the negative ones ! Index i corresponds to the frequency 2PI i/N but also to the frequency 2PI (i-N)/N. In the Fourier space, the end of the array matters as much as the beginning ! In our case, we keep the smallest frequency : it's 2PI i/N for the first half of the array, and 2PI(i-N)/N on the second half.
Of course, as Paul said, N-1 should be Nin double dx = L/(N - 1); => double dx = L/(N ); N-1 does not correspond to a continious periodic signal. It woud be hard to use it as a test case...
Scaling...I did it empirically
The result i obtain is closer to the expected one, for both cases. Here is the code :
/*
* fftw test -- double precision
*/
#include <iostream>
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <fftw3.h>
using namespace std;
int main()
{
int N = 128;
int i, j ;
double pi = 3.14159265359;
double *X, *Y ;
X = (double*) malloc(N*sizeof(double));
Y = (double*) malloc(N*sizeof(double));
fftw_complex *out1, *in2, *out2, *in1;
fftw_plan p1, p2;
double L = 2.*pi;
double dx = L/(N );
in1 = (fftw_complex*) fftw_malloc(sizeof(fftw_complex)*(N*N) );
out2 = (fftw_complex*) fftw_malloc(sizeof(fftw_complex)*(N*N) );
out1 = (fftw_complex*) fftw_malloc(sizeof(fftw_complex)*(N*N) );
in2 = (fftw_complex*) fftw_malloc(sizeof(fftw_complex)*(N*N) );
p1 = fftw_plan_dft_2d(N, N, in1, out1, FFTW_FORWARD,FFTW_MEASURE );
p2 = fftw_plan_dft_2d(N, N, in2, out2, FFTW_BACKWARD,FFTW_MEASURE);
for(i = 0; i < N; i++){
X[i] = -pi + i*dx ;
for(j = 0; j < N; j++){
Y[j] = -pi + j*dx ;
in1[i*N + j][0] = sin(X[i]) + sin(Y[j]) ; // row major ordering
// in1[i*N + j][0] = sin(X[i]) * sin(Y[j]) ; // 2nd test case
in1[i*N + j][1] = 0 ;
}
}
fftw_execute(p1); // FFT forward
for ( i = 0; i < N; i++){ // f = g / ( kx² + ky² )
for( j = 0; j < N; j++){
double fact=0;
in2[i*N + j][0]=0;
in2[i*N + j][1]=0;
if(2*i<N){
fact=((double)i*i);
}else{
fact=((double)(N-i)*(N-i));
}
if(2*j<N){
fact+=((double)j*j);
}else{
fact+=((double)(N-j)*(N-j));
}
if(fact!=0){
in2[i*N + j][0] = out1[i*N + j][0]/fact;
in2[i*N + j][1] = out1[i*N + j][1]/fact;
}else{
in2[i*N + j][0] = 0;
in2[i*N + j][1] = 0;
}
//in2[i*N + j][0] = out1[i*N + j][0];
//in2[i*N + j][1] = out1[i*N + j][1];
// in2[i*N + j][0] = out1[i*N + j][0]*(1.0/(i*i+1e-16)+1.0/(j*j+1e-16)+1.0/((N-i)*(N-i)+1e-16)+1.0/((N-j)*(N-j)+1e-16))*N*N;
// in2[i*N + j][1] = out1[i*N + j][1]*(1.0/(i*i+1e-16)+1.0/(j*j+1e-16)+1.0/((N-i)*(N-i)+1e-16)+1.0/((N-j)*(N-j)+1e-16))*N*N;
//in2[i*N + j][0] = 2*out1[i*N + j][0]/ (i*i+j*j+1e-16); // 2nd test case
//in2[i*N + j][1] = 2*out1[i*N + j][1]/ (i*i+j*j+1e-16);
}
}
fftw_execute(p2); //FFT backward
// checking the results computed
double erl1 = 0.;
for ( i = 0; i < N; i++) {
for( j = 0; j < N; j++){
erl1 += fabs( in1[i*N + j][0] - out2[i*N + j][0]/(N*N))*dx*dx;
cout<< i <<" "<< j<<" "<< sin(X[i])+sin(Y[j])<<" "<< out2[i*N+j][0]/(N*N) <<" "<< endl; // > output
// cout<< i <<" "<< j<<" "<< sin(X[i])*sin(Y[j])<<" "<< out2[i*N+j][0]/(N*N) <<" "<< endl; // > output
}
}
cout<< erl1 << endl ; // L1 error
fftw_destroy_plan(p1);
fftw_destroy_plan(p2);
fftw_free(out1);
fftw_free(out2);
fftw_free(in1);
fftw_free(in2);
return 0;
}
This code is far from being perfect, it is neither optimized nor beautiful. But it gives almost what is expected.
Bye,

Implementing Gaussian Blur - How to calculate convolution matrix (kernel)

My question is very close to this question: How do I gaussian blur an image without using any in-built gaussian functions?
The answer to this question is very good, but it doesn't give an example of actually calculating a real Gaussian filter kernel. The answer gives an arbitrary kernel and shows how to apply the filter using that kernel but not how to calculate a real kernel itself. I am trying to implement a Gaussian blur in C++ or Matlab from scratch, so I need to know how to calculate the kernel from scratch.
I'd appreciate it if someone could calculate a real Gaussian filter kernel using any small example image matrix.
You can create a Gaussian kernel from scratch as noted in MATLAB documentation of fspecial. Please read the Gaussian kernel creation formula in the algorithms part in that page and follow the code below. The code is to create an m-by-n matrix with sigma = 1.
m = 5; n = 5;
sigma = 1;
[h1, h2] = meshgrid(-(m-1)/2:(m-1)/2, -(n-1)/2:(n-1)/2);
hg = exp(- (h1.^2+h2.^2) / (2*sigma^2));
h = hg ./ sum(hg(:));
h =
0.0030 0.0133 0.0219 0.0133 0.0030
0.0133 0.0596 0.0983 0.0596 0.0133
0.0219 0.0983 0.1621 0.0983 0.0219
0.0133 0.0596 0.0983 0.0596 0.0133
0.0030 0.0133 0.0219 0.0133 0.0030
Observe that this can be done by the built-in fspecial as follows:
fspecial('gaussian', [m n], sigma)
ans =
0.0030 0.0133 0.0219 0.0133 0.0030
0.0133 0.0596 0.0983 0.0596 0.0133
0.0219 0.0983 0.1621 0.0983 0.0219
0.0133 0.0596 0.0983 0.0596 0.0133
0.0030 0.0133 0.0219 0.0133 0.0030
I think it is straightforward to implement this in any language you like.
EDIT: Let me also add the values of h1 and h2 for the given case, since you may be unfamiliar with meshgrid if you code in C++.
h1 =
-2 -1 0 1 2
-2 -1 0 1 2
-2 -1 0 1 2
-2 -1 0 1 2
-2 -1 0 1 2
h2 =
-2 -2 -2 -2 -2
-1 -1 -1 -1 -1
0 0 0 0 0
1 1 1 1 1
2 2 2 2 2
It's as simple as it sounds:
double sigma = 1;
int W = 5;
double kernel[W][W];
double mean = W/2;
double sum = 0.0; // For accumulating the kernel values
for (int x = 0; x < W; ++x)
for (int y = 0; y < W; ++y) {
kernel[x][y] = exp( -0.5 * (pow((x-mean)/sigma, 2.0) + pow((y-mean)/sigma,2.0)) )
/ (2 * M_PI * sigma * sigma);
// Accumulate the kernel values
sum += kernel[x][y];
}
// Normalize the kernel
for (int x = 0; x < W; ++x)
for (int y = 0; y < W; ++y)
kernel[x][y] /= sum;
To implement the gaussian blur you simply take the gaussian function and compute one value for each of the elements in your kernel.
Usually you want to assign the maximum weight to the central element in your kernel and values close to zero for the elements at the kernel borders.
This implies that the kernel should have an odd height (resp. width) to ensure that there actually is a central element.
To compute the actual kernel elements you may scale the gaussian bell to the kernel grid (choose an arbitrary e.g. sigma = 1 and an arbitrary range e.g. -2*sigma ... 2*sigma) and normalize it, s.t. the elements sum to one.
To achieve this, if you want to support arbitrary kernel sizes, you might want to adapt the sigma to the required kernel size.
Here's a C++ example:
#include <cmath>
#include <vector>
#include <iostream>
#include <iomanip>
double gaussian( double x, double mu, double sigma ) {
const double a = ( x - mu ) / sigma;
return std::exp( -0.5 * a * a );
}
typedef std::vector<double> kernel_row;
typedef std::vector<kernel_row> kernel_type;
kernel_type produce2dGaussianKernel (int kernelRadius) {
double sigma = kernelRadius/2.;
kernel_type kernel2d(2*kernelRadius+1, kernel_row(2*kernelRadius+1));
double sum = 0;
// compute values
for (int row = 0; row < kernel2d.size(); row++)
for (int col = 0; col < kernel2d[row].size(); col++) {
double x = gaussian(row, kernelRadius, sigma)
* gaussian(col, kernelRadius, sigma);
kernel2d[row][col] = x;
sum += x;
}
// normalize
for (int row = 0; row < kernel2d.size(); row++)
for (int col = 0; col < kernel2d[row].size(); col++)
kernel2d[row][col] /= sum;
return kernel2d;
}
int main() {
kernel_type kernel2d = produce2dGaussianKernel(3);
std::cout << std::setprecision(5) << std::fixed;
for (int row = 0; row < kernel2d.size(); row++) {
for (int col = 0; col < kernel2d[row].size(); col++)
std::cout << kernel2d[row][col] << ' ';
std::cout << '\n';
}
}
The output is:
$ g++ test.cc && ./a.out
0.00134 0.00408 0.00794 0.00992 0.00794 0.00408 0.00134
0.00408 0.01238 0.02412 0.03012 0.02412 0.01238 0.00408
0.00794 0.02412 0.04698 0.05867 0.04698 0.02412 0.00794
0.00992 0.03012 0.05867 0.07327 0.05867 0.03012 0.00992
0.00794 0.02412 0.04698 0.05867 0.04698 0.02412 0.00794
0.00408 0.01238 0.02412 0.03012 0.02412 0.01238 0.00408
0.00134 0.00408 0.00794 0.00992 0.00794 0.00408 0.00134
As a simplification you don't need to use a 2d-kernel. Easier to implement and also more efficient to compute is to use two orthogonal 1d-kernels. This is possible due to the associativity of this type of a linear convolution (linear separability).
You may also want to see this section of the corresponding wikipedia article.
Here's the same in Python (in the hope someone might find it useful):
from math import exp
def gaussian(x, mu, sigma):
return exp( -(((x-mu)/(sigma))**2)/2.0 )
#kernel_height, kernel_width = 7, 7
kernel_radius = 3 # for an 7x7 filter
sigma = kernel_radius/2. # for [-2*sigma, 2*sigma]
# compute the actual kernel elements
hkernel = [gaussian(x, kernel_radius, sigma) for x in range(2*kernel_radius+1)]
vkernel = [x for x in hkernel]
kernel2d = [[xh*xv for xh in hkernel] for xv in vkernel]
# normalize the kernel elements
kernelsum = sum([sum(row) for row in kernel2d])
kernel2d = [[x/kernelsum for x in row] for row in kernel2d]
for line in kernel2d:
print ["%.3f" % x for x in line]
produces the kernel:
['0.001', '0.004', '0.008', '0.010', '0.008', '0.004', '0.001']
['0.004', '0.012', '0.024', '0.030', '0.024', '0.012', '0.004']
['0.008', '0.024', '0.047', '0.059', '0.047', '0.024', '0.008']
['0.010', '0.030', '0.059', '0.073', '0.059', '0.030', '0.010']
['0.008', '0.024', '0.047', '0.059', '0.047', '0.024', '0.008']
['0.004', '0.012', '0.024', '0.030', '0.024', '0.012', '0.004']
['0.001', '0.004', '0.008', '0.010', '0.008', '0.004', '0.001']
OK, a late answer but in case of...
Using the #moooeeeep answer, but with numpy;
import numpy as np
radius = 3
sigma = radius/2.
k = np.arange(2*radius +1)
row = np.exp( -(((k - radius)/(sigma))**2)/2.)
col = row.transpose()
out = np.outer(row, col)
out = out/np.sum(out)
for line in out:
print(["%.3f" % x for x in line])
Just a bit less of lines.
Gaussian blur in python using PIL image library. For more info read this: http://blog.ivank.net/fastest-gaussian-blur.html
from PIL import Image
import math
# img = Image.open('input.jpg').convert('L')
# r = radiuss
def gauss_blur(img, r):
imgData = list(img.getdata())
bluredImg = Image.new(img.mode, img.size)
bluredImgData = list(bluredImg.getdata())
rs = int(math.ceil(r * 2.57))
for i in range(0, img.height):
for j in range(0, img.width):
val = 0
wsum = 0
for iy in range(i - rs, i + rs + 1):
for ix in range(j - rs, j + rs + 1):
x = min(img.width - 1, max(0, ix))
y = min(img.height - 1, max(0, iy))
dsq = (ix - j) * (ix - j) + (iy - i) * (iy - i)
weight = math.exp(-dsq / (2 * r * r)) / (math.pi * 2 * r * r)
val += imgData[y * img.width + x] * weight
wsum += weight
bluredImgData[i * img.width + j] = round(val / wsum)
bluredImg.putdata(bluredImgData)
return bluredImg
// my_test.cpp : Defines the entry point for the console application.
//
#include "stdafx.h"
#include <cmath>
#include <vector>
#include <iostream>
#include <iomanip>
#include <string>
//https://stackoverflow.com/questions/8204645/implementing-gaussian-blur-how-to-calculate-convolution-matrix-kernel
//https://docs.opencv.org/2.4/modules/imgproc/doc/filtering.html#getgaussiankernel
//http://dev.theomader.com/gaussian-kernel-calculator/
double gaussian(double x, double mu, double sigma) {
const double a = (x - mu) / sigma;
return std::exp(-0.5 * a * a);
}
typedef std::vector<double> kernel_row;
typedef std::vector<kernel_row> kernel_type;
kernel_type produce2dGaussianKernel(int kernelRadius, double sigma) {
kernel_type kernel2d(2 * kernelRadius + 1, kernel_row(2 * kernelRadius + 1));
double sum = 0;
// compute values
for (int row = 0; row < kernel2d.size(); row++)
for (int col = 0; col < kernel2d[row].size(); col++) {
double x = gaussian(row, kernelRadius, sigma)
* gaussian(col, kernelRadius, sigma);
kernel2d[row][col] = x;
sum += x;
}
// normalize
for (int row = 0; row < kernel2d.size(); row++)
for (int col = 0; col < kernel2d[row].size(); col++)
kernel2d[row][col] /= sum;
return kernel2d;
}
char* gMatChar[10] = {
" ",
" ",
" ",
" ",
" ",
" ",
" ",
" ",
" ",
" "
};
static int countSpace(float aValue)
{
int count = 0;
int value = (int)aValue;
while (value > 9)
{
count++;
value /= 10;
}
return count;
}
int main() {
while (1)
{
char str1[80]; // window size
char str2[80]; // sigma
char str3[80]; // coefficient
int space;
int i, ch;
printf("\n-----------------------------------------------------------------------------\n");
printf("Start generate Gaussian matrix\n");
printf("-----------------------------------------------------------------------------\n");
// input window size
printf("\nPlease enter window size (from 3 to 10) It should be odd (ksize/mod 2 = 1 ) and positive: Exit enter q \n");
for (i = 0; (i < 80) && ((ch = getchar()) != EOF)
&& (ch != '\n'); i++)
{
str1[i] = (char)ch;
}
// Terminate string with a null character
str1[i] = '\0';
if (str1[0] == 'q')
{
break;
}
int input1 = atoi(str1);
int window_size = input1 / 2;
printf("Input window_size was: %d\n", input1);
// input sigma
printf("Please enter sigma. Use default press Enter . Exit enter q \n");
str2[0] = '0';
for (i = 0; (i < 80) && ((ch = getchar()) != EOF)
&& (ch != '\n'); i++)
{
str2[i] = (char)ch;
}
// Terminate string with a null character
str2[i] = '\0';
if (str2[0] == 'q')
{
break;
}
float input2 = atof(str2);
float sigma;
if (input2 == 0)
{
// Open-CV sigma � Gaussian standard deviation. If it is non-positive, it is computed from ksize as sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8 .
sigma = 0.3*((input1 - 1)*0.5 - 1) + 0.8;
}
else
{
sigma = input2;
}
printf("Input sigma was: %f\n", sigma);
// input Coefficient K
printf("Please enter Coefficient K. Use default press Enter . Exit enter q \n");
str3[0] = '0';
for (i = 0; (i < 80) && ((ch = getchar()) != EOF)
&& (ch != '\n'); i++)
{
str3[i] = (char)ch;
}
// Terminate string with a null character
str3[i] = '\0';
if (str3[0] == 'q')
{
break;
}
int input3 = atoi(str3);
int cK;
if (input3 == 0)
{
cK = 1;
}
else
{
cK = input3;
}
float sum_f = 0;
float temp_f;
int sum = 0;
int temp;
printf("Input Coefficient K was: %d\n", cK);
printf("\nwindow size=%d | Sigma = %f Coefficient K = %d\n\n\n", input1, sigma, cK);
kernel_type kernel2d = produce2dGaussianKernel(window_size, sigma);
std::cout << std::setprecision(input1) << std::fixed;
for (int row = 0; row < kernel2d.size(); row++) {
for (int col = 0; col < kernel2d[row].size(); col++)
{
temp_f = cK* kernel2d[row][col];
sum_f += temp_f;
space = countSpace(temp_f);
std::cout << gMatChar[space] << temp_f << ' ';
}
std::cout << '\n';
}
printf("\n Sum array = %f | delta = %f", sum_f, sum_f - cK);
// rounding
printf("\nRecommend use round(): window size=%d | Sigma = %f Coefficient K = %d\n\n\n", input1, sigma, cK);
sum = 0;
std::cout << std::setprecision(0) << std::fixed;
for (int row = 0; row < kernel2d.size(); row++) {
for (int col = 0; col < kernel2d[row].size(); col++)
{
temp = round(cK* kernel2d[row][col]);
sum += temp;
space = countSpace((float)temp);
std::cout << gMatChar[space] << temp << ' ';
}
std::cout << '\n';
}
printf("\n Sum array = %d | delta = %d", sum, sum - cK);
// recommented
sum_f = 0;
int cK_d = 1 / kernel2d[0][0];
cK_d = cK_d / 2 * 2;
printf("\nRecommend: window size=%d | Sigma = %f Coefficient K = %d\n\n\n", input1, sigma, cK_d);
std::cout << std::setprecision(input1) << std::fixed;
for (int row = 0; row < kernel2d.size(); row++) {
for (int col = 0; col < kernel2d[row].size(); col++)
{
temp_f = cK_d* kernel2d[row][col];
sum_f += temp_f;
space = countSpace(temp_f);
std::cout << gMatChar[space] << temp_f << ' ';
}
std::cout << '\n';
}
printf("\n Sum array = %f | delta = %f", sum_f, sum_f - cK_d);
// rounding
printf("\nRecommend use round(): window size=%d | Sigma = %f Coefficient K = %d\n\n\n", input1, sigma, cK_d);
sum = 0;
std::cout << std::setprecision(0) << std::fixed;
for (int row = 0; row < kernel2d.size(); row++) {
for (int col = 0; col < kernel2d[row].size(); col++)
{
temp = round(cK_d* kernel2d[row][col]);
sum += temp;
space = countSpace((float)temp);
std::cout << gMatChar[space] << temp << ' ';
}
std::cout << '\n';
}
printf("\n Sum array = %d | delta = %d", sum, sum - cK_d);
}
}
function kernel = gauss_kernel(m, n, sigma)
% Generating Gauss Kernel
x = -(m-1)/2 : (m-1)/2;
y = -(n-1)/2 : (n-1)/2;
for i = 1:m
for j = 1:n
xx(i,j) = x(i);
yy(i,j) = y(j);
end
end
kernel = exp(-(xx.*xx + yy.*yy)/(2*sigma*sigma));
% Normalize the kernel
kernel = kernel/sum(kernel(:));
% Corresponding function in MATLAB
% fspecial('gaussian', [m n], sigma)
Here's a calculation in C#, which does not take single samples from the gaussian (or another kernel) function, but it calculates a large number of samples in a small grid and integrates the samples in the desired number of sections.
The calculation is for 1D, but it may easily be extended to 2D.
This calculation uses some other functions, which I did not add here, but I have added the function signatures so that you will know what they do.
This calculation produces the following discrete values for the limits +/- 3 (sum areaSum of integral is 0.997300):
kernel size: normalized kernel values, rounded to 6 decimals
3: 0.157731, 0.684538, 0.157731
5: 0.034674, 0.238968, 0.452716, 0.238968, 0.034674
7: 0.014752, 0.083434, 0.235482, 0.332663, 0.235482, 0.083434, 0.014752
This calculation produces the following discrete values for the limits +/- 2 (sum areaSum of integral is 0.954500):
kernel size: normalized kernel values, rounded to 6 decimals
3: 0.240694, 0.518612, 0.240694
5: 0.096720, 0.240449, 0.325661, 0.240449, 0.096720
7: 0.056379, 0.124798, 0.201012, 0.235624, 0.201012, 0.124798, 0.056379
Code:
using System.Linq;
private static void Main ()
{
int positionCount = 1024; // Number of samples in the range 0..1.
double positionStepSize = 1.0 / positionCount;
double limit = 3; // The calculation range of the kernel. +/- 3 is sufficient for gaussian.
int sectionCount = 3; // The number of elements in the kernel. May be 1, 3, 5, 7, ... (n*2+1)
// calculate the x positions for each kernel value to calculate.
double[] positions = CreateSeries (-limit, positionStepSize, (int)(limit * 2 * positionCount + 1));
// calculate the gaussian function value for each position
double[] values = positions.Select (pos => Gaussian (pos)).ToArray ();
// split the values into equal-sized sections and calculate the integral of each section.
double[] areas = IntegrateInSections (values, positionStepSize, sectionCount);
double areaSum = areas.Sum ();
// normalize to 1
double[] areas1 = areas.Select (a => a / areaSum).ToArray ();
double area1Sum = areas1.Sum (); // just to check it's 1 now
}
///-------------------------------------------------------------------
/// <summary>
/// Create a series of <paramref name="i_count"/> numbers, starting at <paramref name="i_start"/> and increasing by <paramref name="i_stepSize"/>.
/// </summary>
/// <param name="i_start">The start value of the series.</param>
/// <param name="i_stepSize">The step size between values in the series.</param>
/// <param name="i_count">The number of elements in the series.</param>
///-------------------------------------------------------------------
public static double[] CreateSeries (double i_start,
double i_stepSize,
int i_count)
{ ... }
private static readonly double s_gaussian_Divisor = Math.Sqrt (Math.PI * 2.0);
/// ------------------------------------------------------------------
/// <summary>
/// Calculate the value for the given position in a Gaussian kernel.
/// </summary>
/// <param name="i_position"> The position in the kernel for which the value will be calculated. </param>
/// <param name="i_bandwidth"> The width factor of the kernel. </param>
/// <returns> The value for the given position in a Gaussian kernel. </returns>
/// ------------------------------------------------------------------
public static double Gaussian (double i_position,
double i_bandwidth = 1)
{
double position = i_position / i_bandwidth;
return Math.Pow (Math.E, -0.5 * position * position) / s_gaussian_Divisor / i_bandwidth;
}
/// ------------------------------------------------------------------
/// <summary>
/// Calculate the integrals in the given number of sections of all given values with the given distance between the values.
/// </summary>
/// <param name="i_values"> The values for which the integral will be calculated. </param>
/// <param name="i_distance"> The distance between the values. </param>
/// <param name="i_sectionCount"> The number of sections in the integration. </param>
/// ------------------------------------------------------------------
public static double[] IntegrateInSections (IReadOnlyCollection<double> i_values,
double i_distance,
int i_sectionCount)
{ ... }