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There are few algorithms to generate the points needed to render a sphere, but I couldn't not find a good algorithm nor to adapt a full sphere algorithm to generate the points for a part of the sphere.
Let's assume I have the min/max latitude, min/max longitude and the sphere radius. How do I generate a mesh to be able to render this part of the sphere?
I made hemisphere & part of hemisphere mesh. I edit songho Ahn's source code slightly and you can refer from following link :
http://www.songho.ca/opengl/gl_sphere.html
the main method of class Sphere are buildVertices*
You can get desired result from editing this method.
I made hemisphere like this :
void HemiSphere::buildVerticesSmooth()
{
...
float sectorStep = 2 *PI / sectorCount;
float stackStep = (PI / 2) / stackCount;
float sectorAngle, stackAngle;
for(int i = 0; i <= stackCount; ++i)
{
stackAngle = -PI / 2 + i * stackStep; // starting from -pi/2 to 0
xy = radius * cosf(stackAngle); // r * cos(u)
z = radius *( 1 + sinf(stackAngle) ); // r * sin(u)
...
and I also made semi-hemisphere which cut its floor so that it have even floor on the bottom. To do this, leave x y coordinate unchanged, just edit z coordinate to 0.
void HemiSphere::buildVerticesSmooth()
{
....
float sectorStep = 2 *PI / sectorCount;
float stackStep = (PI / 2) / stackCount;
float sectorAngle, stackAngle;
float d = sqrt(radius*radius - flat_radius*flat_radius)/radius;
for(int i = 0; i <= stackCount; ++i)
{
stackAngle = -PI / 2 + i * stackStep; // starting from -pi/2 to 0
xy = radius * cosf(stackAngle); // r * cos(u)
z = radius *( d + sinf(stackAngle) ); // r * sin(u)
if (z<0.0) z = 0.0;
....
Hope this helps.
I am implementing a hough transform probabilistic algorithm.
I got the conept of the algorithm, but there is a thing that I don't understand.
The code below is from OpenCV, I understand that the rest of lines, but why do they set numrho to be ((width * height)*2 + 1) / rho other than the distance from (0,0) to (width,height)?
int i, j;
float irho = 1 / rho;
CV_Assert( img.type() == CV_8UC1 );
const uchar* image = img.ptr();
int step = (int)img.step;
int width = img.cols;
int height = img.rows;
int numangle = cvRound((max_theta - min_theta) / theta);
int numrho = cvRound(((width + height) * 2 + 1) / rho);
This is a question about algorithm, there is no error messages to put
rho and theta are discretization steps, you can set them to any value you prefer.
"(width + height) * 2" is the perimeter (alias: "circumference" of your image) to be discretized
My question is not how to filter an image using the laplacian of gaussian (basically using filter2D with the relevant kernel etc.).
What I want to know is how I generate the NxN kernel.
I'll give an example showing how I generated a [Winsize x WinSize] Gaussian kernel in openCV.
In Matlab:
gaussianKernel = fspecial('gaussian', WinSize, sigma);
In openCV:
cv::Mat gaussianKernel = cv::getGaussianKernel(WinSize, sigma, CV_64F);
cv::mulTransposed(gaussianKernel,gaussianKernel,false);
Where sigma and WinSize are predefined.
I want to do the same for a Laplacian of Gaussian.
In Matlab:
LoGKernel = fspecial('log', WinSize, sigma);
How do I get the exact kernel in openCV (exact up to negligible numerical differences)?
I'm working on a specific application where I need the actual kernel values and simply finding another way of implementing LoG filtering by approximating Difference of gaussians is not what I'm after.
Thanks!
You can generate it manually, using formula
LoG(x,y) = (1/(pi*sigma^4)) * (1 - (x^2+y^2)/(sigma^2))* (e ^ (- (x^2 + y^2) / 2sigma^2)
http://homepages.inf.ed.ac.uk/rbf/HIPR2/log.htm
cv::Mat kernel(WinSize,WinSize,CV_64F);
int rows = kernel.rows;
int cols = kernel.cols;
double halfSize = (double) WinSize / 2.0;
for (size_t i=0; i<rows;i++)
for (size_t j=0; j<cols;j++)
{
double x = (double)j - halfSize;
double y = (double)i - halfSize;
kernel.at<double>(j,i) = (1.0 /(M_PI*pow(sigma,4))) * (1 - (x*x+y*y)/(sigma*sigma))* (pow(2.718281828, - (x*x + y*y) / 2*sigma*sigma));
}
If function above is not OK, you can simply rewrite matlab version of fspecial:
case 'log' % Laplacian of Gaussian
% first calculate Gaussian
siz = (p2-1)/2;
std2 = p3^2;
[x,y] = meshgrid(-siz(2):siz(2),-siz(1):siz(1));
arg = -(x.*x + y.*y)/(2*std2);
h = exp(arg);
h(h<eps*max(h(:))) = 0;
sumh = sum(h(:));
if sumh ~= 0,
h = h/sumh;
end;
% now calculate Laplacian
h1 = h.*(x.*x + y.*y - 2*std2)/(std2^2);
h = h1 - sum(h1(:))/prod(p2); % make the filter sum to zero
I want to thank old-ufo for nudging me in the correct direction.
I was hoping I won't have to reinvent the wheel by doing a quick matlab-->openCV conversion but guess this is the best solution I have for a quick solution.
NOTE - I did this for square kernels only (easy to modify otherwise, but I have no need for that so...).
Maybe this can be written in a more elegant form but is a quick job I did so I can carry on with more pressing matters.
From main function:
int WinSize(7); int sigma(1); // can be changed to other odd-sized WinSize and different sigma values
cv::Mat h = fspecialLoG(WinSize,sigma);
And the actual function is:
// return NxN (square kernel) of Laplacian of Gaussian as is returned by Matlab's: fspecial(Winsize,sigma)
cv::Mat fspecialLoG(int WinSize, double sigma){
// I wrote this only for square kernels as I have no need for kernels that aren't square
cv::Mat xx (WinSize,WinSize,CV_64F);
for (int i=0;i<WinSize;i++){
for (int j=0;j<WinSize;j++){
xx.at<double>(j,i) = (i-(WinSize-1)/2)*(i-(WinSize-1)/2);
}
}
cv::Mat yy;
cv::transpose(xx,yy);
cv::Mat arg = -(xx+yy)/(2*pow(sigma,2));
cv::Mat h (WinSize,WinSize,CV_64F);
for (int i=0;i<WinSize;i++){
for (int j=0;j<WinSize;j++){
h.at<double>(j,i) = pow(exp(1),(arg.at<double>(j,i)));
}
}
double minimalVal, maximalVal;
minMaxLoc(h, &minimalVal, &maximalVal);
cv::Mat tempMask = (h>DBL_EPSILON*maximalVal)/255;
tempMask.convertTo(tempMask,h.type());
cv::multiply(tempMask,h,h);
if (cv::sum(h)[0]!=0){h=h/cv::sum(h)[0];}
cv::Mat h1 = (xx+yy-2*(pow(sigma,2))/(pow(sigma,4));
cv::multiply(h,h1,h1);
h = h1 - cv::sum(h1)[0]/(WinSize*WinSize);
return h;
}
There is some difference between your function and the matlab version:
http://br1.einfach.org/tmp/log-matlab-vs-opencv.png.
Above is matlab fspecial('log', 31, 6) and below is the result of your function with the same parameters. Somehow the hat is more 'bent' - is this intended and what is the effect of this in later processing?
I can create a kernel very similar to the matlab one with these functions, which just directly reflect the LoG formula:
float LoG(int x, int y, float sigma) {
float xy = (pow(x, 2) + pow(y, 2)) / (2 * pow(sigma, 2));
return -1.0 / (M_PI * pow(sigma, 4)) * (1.0 - xy) * exp(-xy);
}
static Mat LOGkernel(int size, float sigma) {
Mat kernel(size, size, CV_32F);
int halfsize = size / 2;
for (int x = -halfsize; x <= halfsize; ++x) {
for (int y = -halfsize; y <= halfsize; ++y) {
kernel.at<float>(x+halfsize,y+halfsize) = LoG(x, y, sigma);
}
}
return kernel;
}
Here's a NumPy version that is directly translated from the fspecial function in MATLAB.
import numpy as np
import sys
def get_log_kernel(siz, std):
x = y = np.linspace(-siz, siz, 2*siz+1)
x, y = np.meshgrid(x, y)
arg = -(x**2 + y**2) / (2*std**2)
h = np.exp(arg)
h[h < sys.float_info.epsilon * h.max()] = 0
h = h/h.sum() if h.sum() != 0 else h
h1 = h*(x**2 + y**2 - 2*std**2) / (std**4)
return h1 - h1.mean()
The code below is the exact equivalent to fspecial('log', p2, p3):
def fspecial_log(p2, std):
siz = int((p2-1)/2)
x = y = np.linspace(-siz, siz, 2*siz+1)
x, y = np.meshgrid(x, y)
arg = -(x**2 + y**2) / (2*std**2)
h = np.exp(arg)
h[h < sys.float_info.epsilon * h.max()] = 0
h = h/h.sum() if h.sum() != 0 else h
h1 = h*(x**2 + y**2 - 2*std**2) / (std**4)
return h1 - h1.mean()
I wrote exact Implementation of Matlab fspecial function in OpenCV
function:
Mat C_fspecial_LOG(double* kernel_size,double sigma)
{
double size[2]={ (kernel_size[0]-1)/2 , (kernel_size[1]-1)/2};
double std = sigma;
const double eps = 2.2204e-16;
cv::Mat kernel(kernel_size[0],kernel_size[1],CV_64FC1,0.0);
int row=0,col=0;
for (double y = -size[0]; y <= size[0]; ++y,++row)
{
col=0;
for (double x = -size[1]; x <= size[1]; ++x,++col)
{
kernel.at<double>(row,col)=exp( -( pow(x,2) + pow(y,2) ) /(2*pow(std,2)));
}
}
double MaxValue;
cv::minMaxLoc(kernel,nullptr,&MaxValue,nullptr,nullptr);
Mat condition=~(kernel < eps*MaxValue)/255;
condition.convertTo(condition,CV_64FC1);
kernel = kernel.mul(condition);
cv::Scalar SUM = cv::sum(kernel);
if(SUM[0]!=0)
{
kernel /= SUM[0];
}
return kernel;
}
usage of this function :
double kernel_size[2] = {4,4}; // kernel size set to 4x4
double sigma = 2.1;
Mat kernel = C_fspecial_LOG(kernel_size,sigma);
compare OpenCV result with Matlab:
opencv result:
[0.04918466596701741, 0.06170341496034986, 0.06170341496034986, 0.04918466596701741;
0.06170341496034986, 0.07740850411228289, 0.07740850411228289, 0.06170341496034986;
0.06170341496034986, 0.07740850411228289, 0.07740850411228289, 0.06170341496034986;
0.04918466596701741, 0.06170341496034986, 0.06170341496034986, 0.04918466596701741]
Matlab result for fspecial('gaussian', 4, 2.1) :
0.0492 0.0617 0.0617 0.0492
0.0617 0.0774 0.0774 0.0617
0.0617 0.0774 0.0774 0.0617
0.0492 0.0617 0.0617 0.0492
Just for the sake of reference, here is a Python implementation which creates the LoG filter kernel to detect blobs of a pre-defined radius in pixels.
def create_log_filter_kernel(r_in_px: float):
"""
Creates a LoG filter-kernel to detect blobs of a given radius r_in_px.
\[
LoG(x,y) = \frac{-1}{\pi\sigma^4}\left(1 - \frac{x^2 + y^2}{2\sigma^2}\right)e^{\frac{-(x^2+y^2)}{2\sigma^2}}
\]
Look for maxima if blob is black, minima if blob is white.
:param r_in_px:
:return: filter kernel
"""
# sigma from radius: LoG has zero-crossing at $1 - \frac{x^2 + y^2}{2\sigma^2} = 0$
# i.e. r^2 = 2\sigma^2$ and thus $sigma = r / \sqrt{2}$
sigma = r_in_px/np.sqrt(2)
# ksize such that filter covers $3\sigma$
ksize = int(np.round(sigma*3))*2 + 1
# setup filter
xgv = np.arange(0, ksize) - ksize / 2
ygv = np.arange(0, ksize) - ksize / 2
x, y = np.meshgrid(xgv, ygv)
kernel = -1 / (np.pi * sigma**4) * (1 - (x**2 + y**2) / (2*sigma**2)) * np.exp(-(x**2 + y**2) / (2 * sigma**2))
#normalize to sum zero (does not change zero crossing, I tried it out for r < 100)
kernel -= np.sum(kernel) / ksize**2
#this is important: normalize such that positive/negative parts are comparable over different scales
kernel /= np.sum(kernel[kernel>0])
return kernel
I have an ellipse, defined by Center Point, radiusX and radiusY, and I have a Point. I want to find the point on the ellipse that is closest to the given point. In the illustration below, that would be S1.
Now I already have code, but there is a logical error somewhere in it, and I seem to be unable to find it. I broke the problem down to the following code example:
#include <vector>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <math.h>
using namespace std;
void dostuff();
int main()
{
dostuff();
return 0;
}
typedef std::vector<cv::Point> vectorOfCvPoints;
void dostuff()
{
const double ellipseCenterX = 250;
const double ellipseCenterY = 250;
const double ellipseRadiusX = 150;
const double ellipseRadiusY = 100;
vectorOfCvPoints datapoints;
for (int i = 0; i < 360; i+=5)
{
double angle = i / 180.0 * CV_PI;
double x = ellipseRadiusX * cos(angle);
double y = ellipseRadiusY * sin(angle);
x *= 1.4;
y *= 1.4;
x += ellipseCenterX;
y += ellipseCenterY;
datapoints.push_back(cv::Point(x,y));
}
cv::Mat drawing = cv::Mat::zeros( 500, 500, CV_8UC1 );
for (int i = 0; i < datapoints.size(); i++)
{
const cv::Point & curPoint = datapoints[i];
const double curPointX = curPoint.x;
const double curPointY = curPoint.y * -1; //transform from image coordinates to geometric coordinates
double angleToEllipseCenter = atan2(curPointY - ellipseCenterY * -1, curPointX - ellipseCenterX); //ellipseCenterY * -1 for transformation to geometric coords (from image coords)
double nearestEllipseX = ellipseCenterX + ellipseRadiusX * cos(angleToEllipseCenter);
double nearestEllipseY = ellipseCenterY * -1 + ellipseRadiusY * sin(angleToEllipseCenter); //ellipseCenterY * -1 for transformation to geometric coords (from image coords)
cv::Point center(ellipseCenterX, ellipseCenterY);
cv::Size axes(ellipseRadiusX, ellipseRadiusY);
cv::ellipse(drawing, center, axes, 0, 0, 360, cv::Scalar(255));
cv::line(drawing, curPoint, cv::Point(nearestEllipseX,nearestEllipseY*-1), cv::Scalar(180));
}
cv::namedWindow( "ellipse", CV_WINDOW_AUTOSIZE );
cv::imshow( "ellipse", drawing );
cv::waitKey(0);
}
It produces the following image:
You can see that it actually finds "near" points on the ellipse, but it are not the "nearest" points. What I intentionally want is this: (excuse my poor drawing)
would you extent the lines in the last image, they would cross the center of the ellipse, but this is not the case for the lines in the previous image.
I hope you get the picture. Can anyone tell me what I am doing wrong?
Consider a bounding circle around the given point (c, d), which passes through the nearest point on the ellipse. From the diagram it is clear that the closest point is such that a line drawn from it to the given point must be perpendicular to the shared tangent of the ellipse and circle. Any other points would be outside the circle and so must be further away from the given point.
So the point you are looking for is not the intersection between the line and the ellipse, but the point (x, y) in the diagram.
Gradient of tangent:
Gradient of line:
Condition for perpedicular lines - product of gradients = -1:
When rearranged and substituted into the equation of your ellipse...
...this will give two nasty quartic (4th-degree polynomial) equations in terms of either x or y. AFAIK there are no general analytical (exact algebraic) methods to solve them. You could try an iterative method - look up the Newton-Raphson iterative root-finding algorithm.
Take a look at this very good paper on the subject:
http://www.spaceroots.org/documents/distance/distance-to-ellipse.pdf
Sorry for the incomplete answer - I totally blame the laws of mathematics and nature...
EDIT: oops, i seem to have a and b the wrong way round in the diagram xD
There is a relatively simple numerical method with better convergence than Newtons Method. I have a blog post about why it works http://wet-robots.ghost.io/simple-method-for-distance-to-ellipse/
This implementation works without any trig functions:
def solve(semi_major, semi_minor, p):
px = abs(p[0])
py = abs(p[1])
tx = 0.707
ty = 0.707
a = semi_major
b = semi_minor
for x in range(0, 3):
x = a * tx
y = b * ty
ex = (a*a - b*b) * tx**3 / a
ey = (b*b - a*a) * ty**3 / b
rx = x - ex
ry = y - ey
qx = px - ex
qy = py - ey
r = math.hypot(ry, rx)
q = math.hypot(qy, qx)
tx = min(1, max(0, (qx * r / q + ex) / a))
ty = min(1, max(0, (qy * r / q + ey) / b))
t = math.hypot(ty, tx)
tx /= t
ty /= t
return (math.copysign(a * tx, p[0]), math.copysign(b * ty, p[1]))
Credit to Adrian Stephens for the Trig-Free Optimization.
Here is the code translated to C# implemented from this paper to solve for the ellipse:
http://www.geometrictools.com/Documentation/DistancePointEllipseEllipsoid.pdf
Note that this code is untested - if you find any errors let me know.
//Pseudocode for robustly computing the closest ellipse point and distance to a query point. It
//is required that e0 >= e1 > 0, y0 >= 0, and y1 >= 0.
//e0,e1 = ellipse dimension 0 and 1, where 0 is greater and both are positive.
//y0,y1 = initial point on ellipse axis (center of ellipse is 0,0)
//x0,x1 = intersection point
double GetRoot ( double r0 , double z0 , double z1 , double g )
{
double n0 = r0*z0;
double s0 = z1 - 1;
double s1 = ( g < 0 ? 0 : Math.Sqrt(n0*n0+z1*z1) - 1 ) ;
double s = 0;
for ( int i = 0; i < maxIter; ++i ){
s = ( s0 + s1 ) / 2 ;
if ( s == s0 || s == s1 ) {break; }
double ratio0 = n0 /( s + r0 );
double ratio1 = z1 /( s + 1 );
g = ratio0*ratio0 + ratio1*ratio1 - 1 ;
if (g > 0) {s0 = s;} else if (g < 0) {s1 = s ;} else {break ;}
}
return s;
}
double DistancePointEllipse( double e0 , double e1 , double y0 , double y1 , out double x0 , out double x1)
{
double distance;
if ( y1 > 0){
if ( y0 > 0){
double z0 = y0 / e0;
double z1 = y1 / e1;
double g = z0*z0+z1*z1 - 1;
if ( g != 0){
double r0 = (e0/e1)*(e0/e1);
double sbar = GetRoot(r0 , z0 , z1 , g);
x0 = r0 * y0 /( sbar + r0 );
x1 = y1 /( sbar + 1 );
distance = Math.Sqrt( (x0-y0)*(x0-y0) + (x1-y1)*(x1-y1) );
}else{
x0 = y0;
x1 = y1;
distance = 0;
}
}
else // y0 == 0
x0 = 0 ; x1 = e1 ; distance = Math.Abs( y1 - e1 );
}else{ // y1 == 0
double numer0 = e0*y0 , denom0 = e0*e0 - e1*e1;
if ( numer0 < denom0 ){
double xde0 = numer0/denom0;
x0 = e0*xde0 ; x1 = e1*Math.Sqrt(1 - xde0*xde0 );
distance = Math.Sqrt( (x0-y0)*(x0-y0) + x1*x1 );
}else{
x0 = e0;
x1 = 0;
distance = Math.Abs( y0 - e0 );
}
}
return distance;
}
The following python code implements the equations described at "Distance from a Point to an Ellipse" and uses newton's method to find the roots and from that the closest point on the ellipse to the point.
Unfortunately, as can be seen from the example, it seems to only be accurate outside the ellipse. Within the ellipse weird things happen.
from math import sin, cos, atan2, pi, fabs
def ellipe_tan_dot(rx, ry, px, py, theta):
'''Dot product of the equation of the line formed by the point
with another point on the ellipse's boundary and the tangent of the ellipse
at that point on the boundary.
'''
return ((rx ** 2 - ry ** 2) * cos(theta) * sin(theta) -
px * rx * sin(theta) + py * ry * cos(theta))
def ellipe_tan_dot_derivative(rx, ry, px, py, theta):
'''The derivative of ellipe_tan_dot.
'''
return ((rx ** 2 - ry ** 2) * (cos(theta) ** 2 - sin(theta) ** 2) -
px * rx * cos(theta) - py * ry * sin(theta))
def estimate_distance(x, y, rx, ry, x0=0, y0=0, angle=0, error=1e-5):
'''Given a point (x, y), and an ellipse with major - minor axis (rx, ry),
its center at (x0, y0), and with a counter clockwise rotation of
`angle` degrees, will return the distance between the ellipse and the
closest point on the ellipses boundary.
'''
x -= x0
y -= y0
if angle:
# rotate the points onto an ellipse whose rx, and ry lay on the x, y
# axis
angle = -pi / 180. * angle
x, y = x * cos(angle) - y * sin(angle), x * sin(angle) + y * cos(angle)
theta = atan2(rx * y, ry * x)
while fabs(ellipe_tan_dot(rx, ry, x, y, theta)) > error:
theta -= ellipe_tan_dot(
rx, ry, x, y, theta) / \
ellipe_tan_dot_derivative(rx, ry, x, y, theta)
px, py = rx * cos(theta), ry * sin(theta)
return ((x - px) ** 2 + (y - py) ** 2) ** .5
Here's an example:
rx, ry = 12, 35 # major, minor ellipse axis
x0 = y0 = 50 # center point of the ellipse
angle = 45 # ellipse's rotation counter clockwise
sx, sy = s = 100, 100 # size of the canvas background
dist = np.zeros(s)
for x in range(sx):
for y in range(sy):
dist[x, y] = estimate_distance(x, y, rx, ry, x0, y0, angle)
plt.imshow(dist.T, extent=(0, sx, 0, sy), origin="lower")
plt.colorbar()
ax = plt.gca()
ellipse = Ellipse(xy=(x0, y0), width=2 * rx, height=2 * ry, angle=angle,
edgecolor='r', fc='None', linestyle='dashed')
ax.add_patch(ellipse)
plt.show()
Which generates an ellipse and the distance from the boundary of the ellipse as a heat map. As can be seen, at the boundary the distance is zero (deep blue).
Given an ellipse E in parametric form and a point P
the square of the distance between P and E(t) is
The minimum must satisfy
Using the trigonometric identities
and substituting
yields the following quartic equation:
Here's an example C function that solves the quartic directly and computes sin(t) and cos(t) for the nearest point on the ellipse:
void nearest(double a, double b, double x, double y, double *ecos_ret, double *esin_ret) {
double ax = fabs(a*x);
double by = fabs(b*y);
double r = b*b - a*a;
double c, d;
int switched = 0;
if (ax <= by) {
if (by == 0) {
if (r >= 0) { *ecos_ret = 1; *esin_ret = 0; }
else { *ecos_ret = 0; *esin_ret = 1; }
return;
}
c = (ax - r) / by;
d = (ax + r) / by;
} else {
c = (by + r) / ax;
d = (by - r) / ax;
switched = 1;
}
double cc = c*c;
double D0 = 12*(c*d + 1); // *-4
double D1 = 54*(d*d - cc); // *4
double D = D1*D1 + D0*D0*D0; // *16
double St;
if (D < 0) {
double t = sqrt(-D0); // *2
double phi = acos(D1 / (t*t*t));
St = 2*t*cos((1.0/3)*phi); // *2
} else {
double Q = cbrt(D1 + sqrt(D)); // *2
St = Q - D0 / Q; // *2
}
double p = 3*cc; // *-2
double SS = (1.0/3)*(p + St); // *4
double S = sqrt(SS); // *2
double q = 2*cc*c + 4*d; // *2
double l = sqrt(p - SS + q / S) - S - c; // *2
double ll = l*l; // *4
double ll4 = ll + 4; // *4
double esin = (4*l) / ll4;
double ecos = (4 - ll) / ll4;
if (switched) {
double t = esin;
esin = ecos;
ecos = t;
}
*ecos_ret = copysign(ecos, a*x);
*esin_ret = copysign(esin, b*y);
}
Try it online!
You just need to calculate the intersection of the line [P1,P0] to your elipse which is S1.
If the line equeation is:
and the elipse equesion is:
than the values of S1 will be:
Now you just need to calculate the distance between S1 to P1 , the formula (for A,B points) is:
I've solved the distance issue via focal points.
For every point on the ellipse
r1 + r2 = 2*a0
where
r1 - Euclidean distance from the given point to focal point 1
r2 - Euclidean distance from the given point to focal point 2
a0 - semimajor axis length
I can also calculate the r1 and r2 for any given point which gives me another ellipse that this point lies on that is concentric to the given ellipse. So the distance is
d = Abs((r1 + r2) / 2 - a0)
As propposed by user3235832
you shall solve quartic equation to find the normal to the ellipse (https://www.mathpages.com/home/kmath505/kmath505.htm). With good initial value only few iterations are needed (I use it myself). As an initial value I use S1 from your picture.
The fastest method I guess is
http://wwwf.imperial.ac.uk/~rn/distance2ellipse.pdf
Which has been mentioned also by Matt but as he found out the method doesn't work very well inside of ellipse.
The problem is the theta initialization.
I proposed an stable initialization:
Find the intersection of ellipse and horizontal line passing the point.
Find the other intersection using vertical line.
Choose the one that is closer the point.
Calculate the initial angle based on that point.
I got good results with no issue inside and outside:
As you can see in the following image it just iterated about 3 times to reach 1e-8. Close to axis it is 1 iteration.
The C++ code is here:
double initialAngle(double a, double b, double x, double y) {
auto abs_x = fabs(x);
auto abs_y = fabs(y);
bool isOutside = false;
if (abs_x > a || abs_y > b) isOutside = true;
double xd, yd;
if (!isOutside) {
xd = sqrt((1.0 - y * y / (b * b)) * (a * a));
if (abs_x > xd)
isOutside = true;
else {
yd = sqrt((1.0 - x * x / (a * a)) * (b * b));
if (abs_y > yd)
isOutside = true;
}
}
double t;
if (isOutside)
t = atan2(a * y, b * x); //The point is outside of ellipse
else {
//The point is inside
if (xd < yd) {
if (x < 0) xd = -xd;
t = atan2(y, xd);
}
else {
if (y < 0) yd = -yd;
t = atan2(yd, x);
}
}
return t;
}
double distanceToElipse(double a, double b, double x, double y, int maxIter = 10, double maxError = 1e-5) {
//std::cout <<"p="<< x << "," << y << std::endl;
auto a2mb2 = a * a - b * b;
double t = initialAngle(a, b, x, y);
auto ct = cos(t);
auto st = sin(t);
int i;
double err;
for (i = 0; i < maxIter; i++) {
auto f = a2mb2 * ct * st - x * a * st + y * b * ct;
auto fp = a2mb2 * (ct * ct - st * st) - x * a * ct - y * b * st;
auto t2 = t - f / fp;
err = fabs(t2 - t);
//std::cout << i + 1 << " " << err << std::endl;
t = t2;
ct = cos(t);
st = sin(t);
if (err < maxError) break;
}
auto dx = a * ct - x;
auto dy = b * st - y;
//std::cout << a * ct << "," << b * st << std::endl;
return sqrt(dx * dx + dy * dy);
}
First, see:
https://math.stackexchange.com/questions/105180/positioning-a-widget-involving-intersection-of-line-and-a-circle
I have an algorithm that solves for the height of an object given a circle and an offset.
It sort of works but the height is always off:
Here is the formula:
and here is a sketch of what it is supposed to do:
And here is sample output from the application:
In the formula, offset = 10 and widthRatio is 3. This is why it is (1 / 10) because (3 * 3) + 1 = 10.
The problem, as you can see is the height of the blue rectangle is not correct. I set the bottom left offsets to be the desired offset (in this case 10) so you can see the bottom left corner is correct. The top right corner is wrong because from the top right corner, I should only have to go 10 pixels until I touch the circle.
The code I use to set the size and location is:
void DataWidgetsHandler::resize( int w, int h )
{
int tabSz = getProportions()->getTableSize() * getProportions()->getScale();
int r = tabSz / 2;
agui::Point tabCenter = agui::Point(
w * getProportions()->getTableOffset().getX(),
h * getProportions()->getTableOffset().getY());
float widthRatio = 3.0f;
int offset = 10;
int height = solveHeight(offset,widthRatio,tabCenter.getX(),tabCenter.getY(),r);
int width = height * widthRatio;
int borderMargin = height;
m_frame->setLocation(offset,
h - height - offset);
m_frame->setSize(width,height);
m_borderLayout->setBorderMargins(0,0,borderMargin,borderMargin);
}
I can assert that the table radius and table center location are correct.
This is my implementation of the formula:
int DataWidgetsHandler::solveHeight( int offset, float widthRatio, float h, float k, float r ) const
{
float denom = (widthRatio * widthRatio) + 1.0f;
float rSq = denom * r * r;
float eq = widthRatio * offset - offset - offset + h - (widthRatio * k);
eq *= eq;
return (1.0f / denom) *
((widthRatio * h) + k - offset - (widthRatio * (offset + offset)) - sqrt(rSq - eq) );
}
It uses the quadratic formula to find what the height should be so that the distance between the top right of the rectangle, bottom left, amd top left are = offset.
Is there something wrong with the formula or implementation? The problem is the height is never long enough.
Thanks
Well, here's my solution, which looks to resemble your solveHeight function. There might be some arithmetic errors in the below, but the method is sound.
You can think in terms of matching the coordinates at the point of the circle across
from the rectangle (P).
Let o_x,o_y be the lower left corner offset distances, w and h be the
height of the rectangle, w_r be the width ratio, dx be the desired
distance between the top right hand corner of the rectangle and the
circle (moving horizontally), c_x and c_y the coordinates of the
circle's centre, theta the angle, and r the circle radius.
Labelling it is half the work! Simply write down the coordinates of the point P:
P_x = o_x + w + dx = c_x + r cos(theta)
P_y = o_y + h = c_y + r sin(theta)
and we know w = w_r * h.
To simplify the arithmetic, let's collect some of the constant terms, and let X = o_x + dx - c_x and Y = o_y - c_y. Then we have
X + w_r * h = r cos(theta)
Y + h = r sin(theta)
Squaring and summing gives a quadratic in h:
(w_r^2 + 1) * h^2 + 2 (X*w_r + Y) h + (X^2+Y^2-r^2) == 0
If you compare this with your effective quadratic, then as long as we made different mistakes :-), you might be able to figure out what's going on.
To be explicit: we can solve this using the quadratic formula, setting
a = (w_r^2 + 1)
b = 2 (X*w_r + Y)
c = (X^2+Y^2-r^2)