I have a point in 3d space (x,y,z). I want to move radially outward from that point discretely (say for r=1 and r=2). In the x,y plane I can simply move outward by stepping ((x+r cos(theta)), (y+r sin(theta)), z) with r = 1 or 2 and theta varying every, say 10 degrees.
However, I am unsure how to describe this movement if I want to have lines moving outward on a tilted plane and step my lines within this plane.
I thought it would be just using spherical coordinates. But if I'm drawing lines from a center point using (x=rho sin phi cos theta, y=..., z=...) won't that form a cone rather than a circle tilted on a plane?
P.S. Will be implementing this in MATLAB
You could first make the coordinates going outwards from P0 and then rotate the coordinates using a rotation matrix.
So you take points P for all R's and thetas, as MBo pointed out:
P = [ P0x + R * cos(theta); P0y + R * sin(theta); 0 ]
Then you make a rotation matrix that rotates the XY plane with the angles you want
If you multiply that with your coordinates you get the rotated coordinates. For example a 90 degree rotation about the Z axis for the point [1,0,0]:
However you probably want to rotate about the point P0 and not about the origin, then you have to make an affine matrix with the following translation:
tx = x- r00 * x - r01 * y - r02 * z
ty = y- r10 * x - r11 * y - r12 * z
tz = z- r20 * x - r21 * y - r22 * z
And then make an affine transformation matrix with T and R (designated as M in the figure, sorry):
In this figure Q are the old coordinates and Q' the new coordinates.
I had a similar problem and used this answer and adjusted it to your problem:
%input point and rotated plane
p0 = [10;10;10;1]; % the last entry is your homogeneous dimension
r0 = [45,45,45]; r0 = r0*pi/180;
%rotation to plane
Rx=[1 0 0 0;
0 cos(r0(1)) sin(r0(1)) 0;
0 -sin(r0(1)) cos(r0(1)) 0;
0 0 0 1];
Ry=[cos(r0(2)) 0 -sin(r0(2)) 0;
0 1 0 0;
sin(r0(2)) 0 cos(r0(2)) 0;
0 0 0 1];
Rz=[cos(r0(3)) sin(r0(3)) 0 0;
-sin(r0(3)) cos(r0(3)) 0 0;
0 0 1 0;
0 0 0 1];
R = Rz*Ry*Rx; A = R;
T = ( eye(3)-R(1:3,1:3) ) * p0(1:3); %calculate translation to rotate about the point P0
A(1:3,4) = T; % to rotate about the origin just leave out this line
%make coordinates for the points going outward from p0
nangles = 36; anglestep = 2*pi/nangles;
nradii = 2; radiistep = 1;
thetas = anglestep:anglestep:2*pi;
rs = radiistep:radiistep:nradii*radiistep;
npoints = nradii*nangles;
coordinates = zeros(4,npoints); curpoint = 0;
for itheta = 1:nangles; for iradius = 1:nradii;
curpoint = curpoint+1;
coordinates(:, curpoint) = p0+rs(iradius)*[cos(thetas(itheta));sin(thetas(itheta));0;0];
end; end
coordinates_tilted = A*coordinates; %rotate the coordinates to the new plane
Which results in this figure:
figure;
scatter3(coordinates_tilted(1,:),coordinates_tilted(2,:),coordinates_tilted(3,:), 'MarkerEdgeColor', 'green')
hold on
scatter3(coordinates(1,:),coordinates(2,:),coordinates(3,:), 'MarkerEdgeColor', 'red')
legend('tilted', 'original')
Or plot them as lines:
%or as lines
coorarray = reshape(coordinates, [4 nradii nangles]);
Xline = squeeze(coorarray(1,:,:));
Yline = squeeze(coorarray(2,:,:));
Zline = squeeze(coorarray(3,:,:));
coorarray_tilted = reshape(coordinates_tilted, [4 nradii nangles]);
Xline_tilted = squeeze(coorarray_tilted(1,:,:));
Yline_tilted = squeeze(coorarray_tilted(2,:,:));
Zline_tilted = squeeze(coorarray_tilted(3,:,:));
figure;
plot3(Xline,Yline,Zline, 'r');
hold on
plot3(Xline_tilted,Yline_tilted,Zline_tilted, 'g');
legend( 'original', 'tilted')
Does this answer your question? These are now points at all multiples of 36 degree angles at a distance of one and two from point P0 in the plane that is tilted 45 degrees on all axes around the point P0. If you need individual 'pixels' to designate your line (so integer coordinates) you can round the coordinates and that would be sort of a nearest neighbour approach:
coordinates_tilted_nearest = round(coordinates_tilted);
How is your tilted plane defined?
Define it with base point P0 and two perpendicular unit vectors U and V. It is not hard to get this representation from any other. For example, if normal vector of your plane have angles ax, ay, az with axes OX, OY, OZ respectively, it's normalized form is N = (nx, ny, nz) = (Cos(ax), Cos(ay), Cos(az)). You can choose arbitrary vector U (lying in the plane) as described here, and find V vector as vector product V = U x N
Then needed points are:
P = P0 + U * R * Cos(Theta) + V * R * Sin(Theta)
Related
"Particles are centered at [0 0]. There are (free) particles moving around the center. Color interpolation is adopted to color the colors of the free particles.
t begins at 0. The particle position is randomly generated within a region in a uniform manner. A point p is inside the region iff (if only if), 50>=||p|| >= 10. Here, ||p|| is the distance between p and the origin [0 0]. Initialize the velocity of each particle so that it is orthogonal to the vector (p-[0 0]).
Velocity normalization: Make the velocity of the particle p orthogonal to the vector p – [0 0].
Assume p = [x y]. Then the velocity is
A) s * [y -x] /||p|| or
B) s * [ -y x]/||p||
Set s to 20. If you adopt the same rule (A or B) to set the velocities of all the particles, all the particles rotate in the same direction.
Color interpolation: The color of a particle depends on the distance, d, of the particle to the origin [0 0].
"
This is an old midterm question from a class. Here is my answer:
t = 0;
p = zeros(1,2); v = [0,0];
dt = 0.05;
M = 10000; m= 1;
tmax = 100;
figure, hold on
d = 0.025;
cp0 = [ 0 0 1]; %blue
cp1 = [ 1 0 0]; % red
n=50; s=20;
clf;
for i = 1:n
for j = 1:n
p([i,j]) = [10 + (50-10) .* rand(1,1),(10 + (50-10) .* rand(1,1))];
end
end
while t < tmax
for k= 1:n
for l = 1:n
F = -(p([k,l])./norm(p)).*(m*M./(1+p([k,l]).*p([k,l])));
a = F/m;
v = v + a.*dt;
v = s.*[-k,l]./norm(p);
d = norm([0,0] - p([k,l]))
p([k,l]) = p([k,l]) + v.*dt;
vD = min(1, d/50);
particle_color = cp0 + vD*(cp1-cp0); %color interpolation
end
clf;
plot(p,'o','MarkerEdgeColor',particle_color);
axis([-80 80 -80 80]); hold on
pause(0.05);
t = t + dt;
end
end
In my code, why are the particles not moving in circular motion?
I've been trying to generate points along the ring of a 2D disc (both translated and rotated) in 3D space using only the disc's position and normal.
I've been using the following code to generate points and have been testing it in Matlab (but will be utilising this in c#) to check that the points are generated correctly, however it doesn't seem to be generating points correctly.
numPoints = 25;
radius = 1;
pos = [1; 2; 3];
dir = normc([3; 4; 6]); % normalised
function [pointsT, points] = GenerateDiscPoints(numPoints, radius, pos, dir)
points = zeros(numPoints, 3);
pointsT = zeros(numPoints, 3);
% Angle between points
angle = 2 * pi / numPoints;
for i = 1:numPoints+1
% Current point angle
theta = angle * i;
% Generate point in flat disc (y is vertical axis in Unity)
x = radius * cos(theta) + pos(1);
y = 0 + pos(2);
z = radius * sin(theta) + pos(3);
% Save points
points(i, 1) = x;
points(i, 2) = y;
points(i, 3) = z;
% Calculate t value to translate points
t = (dir(1) * pos(1) - dir(1) * x + dir(2) * pos(2) - dir(2) * y + dir(3) * pos(3) - dir(3) * z) / (dir(1)*dir(1) + dir(2)*dir(2) + dir(3)*dir(3));
% Translate points to correct location
xT = x + t*dir(1);
yT = y + t*dir(2);
zT = z + t*dir(3);
% Save translated points
pointsT(i, 1) = xT;
pointsT(i, 2) = yT;
pointsT(i, 3) = zT;
end
% Plot
figure;
hold all;
grid on;
scatter3(points(:,1), points(:,2), points(:,3), 25, 'r');
scatter3(pointsT(:,1), pointsT(:,2), pointsT(:,3), 25, 'g');
p3 = line([pos(1) pos(1)+dir(1)], [pos(2) pos(2)+dir(2)], [pos(3) pos(3)+dir(3)]);
set(p3, 'Color', 'blue');
end
The blue line is the normal of the disc, the red points are the points before being translated, and the green points are the points after being translated. To my eye it appears that the translated points don't seem to be generating in a disc that has the normal specified.
What's wrong with my current algorithm? What would a better way to do this be?
A simple linear translation along the direction of dir is insufficient - you'll end up with the projection of the circle on the plane at pos with normal dir, i.e. an ellipse.
You could either:
Use quaternions to construct a rotation matrix to re-orientate your generated circle to dir.
Creating this quaternion: https://stackoverflow.com/a/1171995/8204776
Converting it to a matrix: https://stackoverflow.com/a/1556470/8204776
or
Construct an orthogonal basis at pos where one axis is dir.
Easy way to do this:
Check if the X-axis is parallel to dir - ideally do abs(dot(dir, X)) < 0.8 (assuming both are normalized), so they are not too close to each other
If (1) is true then take dir2 = Y, else dir2 = X.
To create the first basis vector, A = normalize(cross(dir, dir2)).
To create the second, B = cross(dir, A).
Now you can generate points at each value of theta by pos + radius * (A * cos(theta) + B * sin(theta)) (in vector notation).
I'm looking for a simple way for creating a random unit vector constrained by a conical region. The origin is always the [0,0,0].
My solution up to now:
function v = GetRandomVectorInsideCone(coneDir,coneAngleDegree)
coneDir = normc(coneDir);
ang = coneAngleDegree + 1;
while ang > coneAngleDegree
v = [randn(1); randn(1); randn(1)];
v = v + coneDir;
v = normc(v);
ang = atan2(norm(cross(v,coneDir)), dot(v,coneDir))*180/pi;
end
My code loops until the random generated unit vector is inside the defined cone. Is there a better way to do that?
Resultant image from test code bellow
Resultant frequency distribution using Ahmed Fasih code (in comments).
I wonder how to get a rectangular or normal distribution.
c = [1;1;1]; angs = arrayfun(#(i) subspace(c, GetRandomVectorInsideCone(c, 30)), 1:1e5) * 180/pi; figure(); hist(angs, 50);
Testing code:
clearvars; clc; close all;
coneDir = [randn(1); randn(1); randn(1)];
coneDir = [0 0 1]';
coneDir = normc(coneDir);
coneAngle = 45;
N = 1000;
vAngles = zeros(N,1);
vs = zeros(3,N);
for i=1:N
vs(:,i) = GetRandomVectorInsideCone(coneDir,coneAngle);
vAngles(i) = subspace(vs(:,i),coneDir)*180/pi;
end
maxAngle = max(vAngles);
minAngle = min(vAngles);
meanAngle = mean(vAngles);
AngleStd = std(vAngles);
fprintf('v angle\n');
fprintf('Direction: [%.3f %.3f %.3f]^T. Angle: %.2fº\n',coneDir,coneAngle);
fprintf('Min: %.2fº. Max: %.2fº\n',minAngle,maxAngle);
fprintf('Mean: %.2fº\n',meanAngle);
fprintf('Standard Dev: %.2fº\n',AngleStd);
%% Plot
figure;
grid on;
rotate3d on;
axis equal;
axis vis3d;
axis tight;
hold on;
xlabel('X'); ylabel('Y'); zlabel('Z');
% Plot all vectors
p1 = [0 0 0]';
for i=1:N
p2 = vs(:,i);
plot3ex(p1,p2);
end
% Trying to plot the limiting cone, but no success here :(
% k = [0 1];
% [X,Y,Z] = cylinder([0 1 0]');
% testsubject = surf(X,Y,Z);
% set(testsubject,'FaceAlpha',0.5)
% N = 50;
% r = linspace(0, 1, N);
% [X,Y,Z] = cylinder(r, N);
%
% h = surf(X, Y, Z);
%
% rotate(h, [1 1 0], 90);
plot3ex.m:
function p = plot3ex(varargin)
% Plots a line from each p1 to each p2.
% Inputs:
% p1 3xN
% p2 3xN
% args plot3 configuration string
% NOTE: p1 and p2 number of points can range from 1 to N
% but if the number of points are different, one must be 1!
% PVB 2016
p1 = varargin{1};
p2 = varargin{2};
extraArgs = varargin(3:end);
N1 = size(p1,2);
N2 = size(p2,2);
N = N1;
if N1 == 1 && N2 > 1
N = N2;
elseif N1 > 1 && N2 == 1
N = N1
elseif N1 ~= N2
error('if size(p1,2) ~= size(p1,2): size(p1,2) and/or size(p1,2) must be 1 !');
end
for i=1:N
i1 = i;
i2 = i;
if i > N1
i1 = N1;
end
if i > N2
i2 = N2;
end
x = [p1(1,i1) p2(1,i2)];
y = [p1(2,i1) p2(2,i2)];
z = [p1(3,i1) p2(3,i2)];
p = plot3(x,y,z,extraArgs{:});
end
Here’s the solution. It’s based on the wonderful answer at https://math.stackexchange.com/a/205589/81266. I found this answer by googling “random points on spherical cap”, after I learned on Mathworld that a spherical cap is this cut of a 3-sphere with a plane.
Here’s the function:
function r = randSphericalCap(coneAngleDegree, coneDir, N, RNG)
if ~exist('coneDir', 'var') || isempty(coneDir)
coneDir = [0;0;1];
end
if ~exist('N', 'var') || isempty(N)
N = 1;
end
if ~exist('RNG', 'var') || isempty(RNG)
RNG = RandStream.getGlobalStream();
end
coneAngle = coneAngleDegree * pi/180;
% Generate points on the spherical cap around the north pole [1].
% [1] See https://math.stackexchange.com/a/205589/81266
z = RNG.rand(1, N) * (1 - cos(coneAngle)) + cos(coneAngle);
phi = RNG.rand(1, N) * 2 * pi;
x = sqrt(1-z.^2).*cos(phi);
y = sqrt(1-z.^2).*sin(phi);
% If the spherical cap is centered around the north pole, we're done.
if all(coneDir(:) == [0;0;1])
r = [x; y; z];
return;
end
% Find the rotation axis `u` and rotation angle `rot` [1]
u = normc(cross([0;0;1], normc(coneDir)));
rot = acos(dot(normc(coneDir), [0;0;1]));
% Convert rotation axis and angle to 3x3 rotation matrix [2]
% [2] See https://en.wikipedia.org/wiki/Rotation_matrix#Rotation_matrix_from_axis_and_angle
crossMatrix = #(x,y,z) [0 -z y; z 0 -x; -y x 0];
R = cos(rot) * eye(3) + sin(rot) * crossMatrix(u(1), u(2), u(3)) + (1-cos(rot))*(u * u');
% Rotate [x; y; z] from north pole to `coneDir`.
r = R * [x; y; z];
end
function y = normc(x)
y = bsxfun(#rdivide, x, sqrt(sum(x.^2)));
end
This code just implements joriki’s answer on math.stackexchange, filling in all the details that joriki omitted.
Here’s a script that shows how to use it.
clearvars
coneDir = [1;1;1];
coneAngleDegree = 30;
N = 1e4;
sol = randSphericalCap(coneAngleDegree, coneDir, N);
figure;plot3(sol(1,:), sol(2,:), sol(3,:), 'b.', 0,0,0,'rx');
grid
xlabel('x'); ylabel('y'); zlabel('z')
legend('random points','origin','location','best')
title('Final random points on spherical cap')
Here is a 3D plot of 10'000 points from the 30° spherical cap centered around the [1; 1; 1] vector:
Here’s 120° spherical cap:
Now, if you look at the histogram of the angles between these random points at the coneDir = [1;1;1], you will see that the distribution is skewed. Here’s the distribution:
Code to generate this:
normc = #(x) bsxfun(#rdivide, x, sqrt(sum(x.^2)));
mysubspace = #(a,b) real(acos(sum(bsxfun(#times, normc(a), normc(b)))));
angs = arrayfun(#(i) mysubspace(coneDir, sol(:,i)), 1:N) * 180/pi;
nBins = 16;
[n, edges] = histcounts(angs, nBins);
centers = diff(edges(1:2))*[0:(length(n)-1)] + mean(edges(1:2));
figure('color','white');
bar(centers, n);
xlabel('Angle (degrees)')
ylabel('Frequency')
title(sprintf('Histogram of angles between coneDir and random points: %d deg', coneAngleDegree))
Well, this makes sense! If you generate points from the 120° spherical cap around coneDir, of course the 1° cap is going to have very few of those samples, whereas the strip between the 10° and 11° caps will have far more points. So what we want to do is normalize the number of points at a given angle by the surface area of the spherical cap at that angle.
Here’s a function that gives us the surface area of the spherical cap with radius R and angle in radians theta (equation 16 on Mathworld’s spherical cap article):
rThetaToH = #(R, theta) R * (1 - cos(theta));
rThetaToS = #(R, theta) 2 * pi * R * rThetaToH(R, theta);
Then, we can normalize the histogram count for each bin (n above) by the difference in surface area of the spherical caps at the bin’s edges:
figure('color','white');
bar(centers, n ./ diff(rThetaToS(1, edges * pi/180)))
The figure:
This tells us “the number of random vectors divided by the surface area of the spherical segment between histogram bin edges”. This is uniform!
(N.B. If you do this normalized histogram for the vectors generated by your original code, using rejection sampling, the same holds: the normalized histogram is uniform. It’s just that rejection sampling is expensive compared to this.)
(N.B. 2: note that the naive way of picking random points on a sphere—by first generating azimuth/elevation angles and then converting these spherical coordinates to Cartesian coordinates—is no good because it bunches points near the poles: Mathworld, example, example 2. One way to pick points on the entire sphere is sampling from the 3D normal distribution: that way you won’t get bunching near poles. So I believe that your original technique is perfectly appropriate, giving you nice, evenly-distributed points on the sphere without any bunching. This algorithm described above also does the “right thing” and should avoid bunching. Carefully evaluate any proposed algorithms to ensure that the bunching-near-poles problem is avoided.)
it is better to use spherical coordinates and convert it to cartesian coordinates:
coneDirtheta = rand(1) * 2 * pi;
coneDirphi = rand(1) * pi;
coneAngle = 45;
N = 1000;
%perfom transformation preventing concentration of points around the pole
rpolar = acos(cos(coneAngle/2*pi/180) + (1-cos(coneAngle/2*pi/180)) * rand(N, 1));
thetapolar = rand(N,1) * 2 * pi;
x0 = rpolar .* cos(thetapolar);
y0 = rpolar .* sin(thetapolar);
theta = coneDirtheta + x0;
phi = coneDirphi + y0;
r = rand(N, 1);
x = r .* cos(theta) .* sin(phi);
y = r .* sin(theta) .* sin(phi);
z = r .* cos(phi);
scatter3(x,y,z)
if all points should be of length 1 set r = ones(N,1);
Edit:
since intersection of cone with sphere forms a circle first we create random points inside a circle with raduis of (45 / 2) in polar coordinates and as #Ahmed Fasih commented to prevent concentration of points near the pole we should first transform this random points, then convert polar to cartesian 2D coordinates to form x0 and y0
we can use x0 and y0 as phi & theta angle of spherical coordinates and add coneDirtheta & coneDirphi as offsets to these coordinates.
then convert spherical to cartesian 3D coordinates
I am trying to create N random pairs of points (N = 50) of a given distances, inside a 500 meters hexagon. The distance D created by using (dmax - dmin).*rand(N,1) + dmin, with dmin = 10 and dmax = 100 in Matlab. I understant that the first I have to generate a set of points ([x1 y1]) that have at least distance D from the main hexagon border, then generate the second set of points ([x2 y2]) that have exact distance D from the first set. But sometime I got the problem with the second point outside of hexagon, because if the first position on the hexagol border and plus Ddisance, then the second position is outside of hexagon (I mean that I want to generate random pair position inside of hexagol). Could anybody help me in generating this kind of scenario and fix the problem? Thanks.
For example as
R = 500; % hexagol radius
N = 50; % number pair positions
d_min = 10; % minimum distance
d_max = 100; % maximum distance
D = (d_max - d_min).*rand(N,1) + d_min; % randomly distance
X = [0,0]; % hexagol center
j=0;
while j < N
j=j+1;
theta(j)=2*pi*rand(1,1);
u= rand()+ rand();
if u < 1
r(j) = R * u;
else
r(j) = R * (2 - u);
end
% to create the first position
x1(j)=r(j)*cos(theta(j)) + X(1,1); % first x positions
y1(j)=r(j)*sin(theta(j)) + X(1,2); % first y positions
end
% to create the second position
x2(j) = x1(j) + D(j); % second x positions
y2(j) = y1(j) + D(j); % second y positions
This is quite like your other question and its solution is almost the same, but it needs a little more math. Let’s focus on one pair of points. There still are two steps:
Step 1: Find a random point that is inside the hexagon, and has distance d from its border.
Step 2: Find another point that has distance d from first point.
Main problem is step 1. We can say that a points that has distance d form a hexagon with radius r, is actually inside a hexagon with radius r-d. Then we just need to find a random point that lays on a hexagon!
Polar Formula of Hexagons:
I want to solve this problem in polar space, so I have to formulate hexagons in this space. Remember circle formula in polar space:
The formula of a hexagon in polar space is pretty much like its circumscribe circle, except that the radius of the hexagon differs at every t (angle). Let’s call this changing radius r2. So, if we find the function R2 that returns r2 for all ts then we can write polar formula for hexagon:
This image demonstrates parameters of the problem:
The key parameter here is α. Now we need a function Alpha that returns α for all ts:
Now we have all points on border of the hexagon in polar space:
r = 500;
T = linspace(0, 2*pi, 181);
Alpha = #(t) pi/2-abs(rem(t, pi/3)-(pi/6));
R2 = #(t) r*cos(pi/6)./sin(Alpha(t));
X = R2(T).*cos(T);
Y = R2(T).*sin(T);
hold on
plot(X, Y, '.b');
plot((r).*cos(T), (r).*sin(T), '.r')
Polar Formula of a Regular Polygon:
Before I go on I’d like to generalize Alpha and R2 functions to cover all regular polygons:
Alpha = #(t) pi/2-abs(rem(t, 2*pi/(n))-(pi/(n)));
R2 = #(t) r*cos(pi/n)./sin(Alpha(t));
Where n is the number of edges of the polygon.
Answer:
Now we can generate pairs of points just like what we did for the circle problem:
r = 500; n = 6;
a = 10; b = 50;
N = 100;
D = (b - a).*rand(N,1) + a;
Alpha = #(t) pi/2-abs(rem(t, 2*pi/(n))-(pi/(n)));
R2 = #(t) r*cos(pi/n)./sin(Alpha(t));
T1 = rand(N, 1) * 2 * pi;
RT1 = rand(N, 1) .* (R2(T1)-D);
X1 = RT1.*cos(T1);
Y1 = RT1.*sin(T1);
T2 = rand(N, 1) * 2 * pi;
X2 = X1+D.*cos(T2);
Y2 = Y1+D.*sin(T2);
Rotating the polygon:
For rotating the polygon we just need to update the Alpha function:
t0 = pi/8;
Alpha = #(t) pi/2-abs(rem(t+t0, 2*pi/(n))-(pi/(n)));
This is a test for n=7, N=50000 and t0=pi/10:
I am trying to transform an image using a 3D transformation matrix and assuming my camera is orthonormal.
I am defining my homography using the plane-induced homography formula H=R-t*n'/d (with d=Inf so H=R) as given in Hartley and Zisserman Chapter 13.
What I am confused about is when I use a rather modest rotation, the image seems to be distorting much more than I expect (I'm sure I'm not confounding radians and degrees).
What could be going wrong here?
I've attached my code and example output.
n = [0;0;-1];
d = Inf;
im = imread('cameraman.tif');
rotations = [0 0.01 0.1 1 10];
for ind = 1:length(rotations)
theta = rotations(ind)*pi/180;
R = [ 1 0 0 ;
0 cos(theta) -sin(theta);
0 sin(theta) cos(theta)];
t = [0;0;0];
H = R-t*n'/d;
tform = maketform('projective',H');
imT = imtransform(im,tform);
subplot(1,5,ind) ;
imshow(imT)
title(['Rot=' num2str(rotations(ind)) 'deg']);
axis square
end
The formula H = R-t*n'/d has one assumption which is not met in your case:
This formula implies that you are using pinhole camera model with focal length=1
But in your case, for your camera to be more real and for your code to work, you should set the focal length to some positive number much greater than 1. (focal length is the distance from your camera center to the image plane)
To do this you can define a calibration matrix K which handles the focal length. You just need to change your formula to
H=K R inv(K) - 1/d K t n' inv(K)
in which K is a 3-by-3 identity matrix whose two first elements along the diagonal are set to the focal length (e.g. f=300). The formula can be easily derived if you assume a projective camera.
Below is the corrected version of your code, in which the angles make sense.
n = [0;0;-1];
d = Inf;
im = imread('cameraman.tif');
rotations = [0 0.01 0.1 30 60];
for ind = 1:length(rotations)
theta = rotations(ind)*pi/180;
R = [ 1 0 0 ;
0 cos(theta) -sin(theta);
0 sin(theta) cos(theta)];
t = [0;0;0];
K=[300 0 0;
0 300 0;
0 0 1];
H=K*R/K-1/d*K*t*n'/K;
tform = maketform('projective',H');
imT = imtransform(im,tform);
subplot(1,5,ind) ;
imshow(imT)
title(['Rot=' num2str(rotations(ind)) 'deg']);
axis square
end
You can see the result in the image below:
You can also rotate the image around its center. For it to happen you should set the image plane origin to the center of the image which I think is not possible with that method of matlab (maketform).
You can use the method below instead.
imT=imagehomog(im,H','c');
Note that if you use this method, you'll have to change some settings in n, d, t and R to get the appropriate result.
That method can be found at: https://github.com/covarep/covarep/blob/master/external/voicebox/imagehomog.m
The result of the program with imagehomog and some changes in n, d, t , and R is shown below which seems more real.
New settings are:
n = [0 0 1]';
d = 2;
t = [1 0 0]';
R = [cos(theta), 0, sin(theta);
0, 1, 0;
-sin(theta), 0, cos(theta)];
Hmm... I'm not 100% percent on this stuff, but it was an interesting question and relevant to my work, so I thought I'd play around and give it a shot.
EDIT: I tried this once using no built-ins. That was my original answer. Then I realized that you could do it your way pretty easily:
The easy answer to your question is to use the correct rotation matrix about the z-axis:
R = [cos(theta) -sin(theta) 0;
sin(theta) cos(theta) 0;
0 0 1];
Here's another way to do it (my original answer):
I'm going to share what I did; hopefully this is useful to you. I only did it in 2D (though that should be easy to expand to 3D). Note that if you want to rotate the image in plane, you will need to use a different rotation matrix that you have currently coded. You need to rotate about the Z-axis.
I did not use those matlab built-ins.
I referred to http://en.wikipedia.org/wiki/Rotation_matrix for some info.
im = double(imread('cameraman.tif')); % must be double for interpn
[x y] = ndgrid(1:size(im,1), 1:size(im,2));
rotation = 10;
theta = rotation*pi/180;
% calculate rotation matrix
R = [ cos(theta) -sin(theta);
sin(theta) cos(theta)]; % just 2D case
% calculate new positions of image indicies
tmp = R*[x(:)' ; y(:)']; % 2 by numel(im)
xi = reshape(tmp(1,:),size(x)); % new x-indicies
yi = reshape(tmp(2,:),size(y)); % new y-indicies
imrot = interpn(x,y,im,xi,yi); % interpolate from old->new indicies
imagesc(imrot);
My own question now is: "How do you change the origin about which you are rotating the image? Clearly, I'm rotating about (0,0), the top left corner.
EDIT 2 In response to the asker's comment, I've tried again.
This time I fixed a couple of things. Now I'm using the same transformation matrix (about x) as in the original question.
I rotated about the center of the image by redoing the way i do the ndgrids (put 0,0,0) in the center of the image. I also decided to show 3 planes of the image. This was not in the original question. The middle plane is the plane of interest. To get just the middle plane, you can leave out the zero-padding and redefine the 3rd ndgrid option to be just 1 instead of -1:1.
im = double(imread('cameraman.tif')); % must be double for interpn
im = padarray(im, [0 0 1],'both');
[x y z] = ndgrid(-floor(size(im,1)/2):floor(size(im,1)/2)-1, ...
-floor(size(im,2)/2):floor(size(im,2)/2)-1,...
-1:1);
rotation = 1;
theta = rotation*pi/180;
% calculate rotation matrix
R = [ 1 0 0 ;
0 cos(theta) -sin(theta);
0 sin(theta) cos(theta)];
% calculate new positions of image indicies
tmp = R*[x(:)'; y(:)'; z(:)']; % 2 by numel(im)
xi = reshape(tmp(1,:),size(x)); % new x-indicies
yi = reshape(tmp(2,:),size(y)); % new y-indicies
zi = reshape(tmp(3,:),size(z));
imrot = interpn(x,y,z,im,xi,yi,zi); % interpolate from old->new indicies
figure;
subplot(3,1,1);imagesc(imrot(:,:,1)); axis image; axis off;
subplot(3,1,2);imagesc(imrot(:,:,2)); axis image; axis off;
subplot(3,1,3);imagesc(imrot(:,:,3)); axis image; axis off;
You are performing rotations around the x-axis: in your matrix, the 1st component (x) is left unchanged by the rotation matrix. This is confirmed by the perspective deformations from your examples.
The actual amount of deformation will then depend on the distance between the camera and the image plane (or more accurately on its value relative to the focal length of the camera). It can be important when the cameraman image plane is located near the camera.