Working on a little project to do with computer graphics. So far I have (I think) everything in order as the code below patches my world to the screen fine.
However, I have a final hurdle to jump: I would like the code to patch for different values of theta. In the code, it is set at 2*pi/4 but I would like to iterate and patch for every angle between 0:pi/4:2*pi. However, when I try to put the code in a for or while loop it doesn't seem to do what I expect, that is, to patch with one angle, then patch with another etc.
Really stuck I have tried a lot of stuff and now I'm just without any ideas. Would really appreciate any help or suggestions.
function world()
% Defining House Vertices
house_verts = [-5, 0, -5;
5, 0, -5;
5, 10, -5;
0,15,-5;
-5,10,-5;
-5,0,5;
5,0,5;
5,10,5;
0,15,5;
-5,10,5];
% Sorting out the homeogenous co-ordinates
ones = [1,1,1,1,1,1,1,1,1,1];
ones=transpose(ones);
house_verts = [house_verts, ones];
house_verts = transpose(house_verts);
% House faces
house_faces = [1,2,3,4,5;
2,7,8,3,3;
6,7,8,9,10;
1,6,10,5,5;
3,4,9,8,8;
4,5,10,9,9;
1,2,7,6,6];
world_pos = [];
% creating a street
street_vector = [1,0,1]; % the direction of the street
orthog_street_vector = [-1,0,1];
for i = 1:15
% current_pos1 and 2 will be the positions of the two houses
% opposite each other on the street
current_pos1 = 30*i*street_vector + 50*orthog_street_vector;
current_pos2 = 30*i*street_vector - 50*orthog_street_vector;
world_pos = [world_pos;current_pos1;current_pos2];
end
% initialising world vertices and faces
world_verts = [];
world_faces = [];
% Populating the street
for i =1:size(world_pos,1)
T = transmatrix(world_pos(i,:)); % a translation matrix
s = [1,1/2 + rand(),1];
S=scalmatrix(s); % a matrix for a random scaling of the height (y-coordinate)
Ry = rotymatrix(rand()*2*pi); % a matrix for a random rotation about the y-axis
A = T*Ry*S; % the compound transformation matrix to take the house into the world
obj_faces = size(world_verts,2) + house_faces; %increments the basic house faces to match the current object
obj_verts = A*house_verts;
world_verts = [world_verts, obj_verts]; % adds the vertices to the world
world_faces = [world_faces; obj_faces]; % adds the faces to the world
end
% initialising aligned vertices
align_verts = [];
% Aligning the vertices to the particular camera at angle theta
for elm = world_verts
x = 350 + 350*cos(2*pi/4);
z = 350 + 350*sin(2*pi/4);
y = 80;
u = [x,y,z];
v = [250,0,250];
d = v - u;
phiy = atan2(d(1),d(3));
phix = -atan2(d(2),sqrt(d(1)^2+d(3)^2));
T = transmatrix([-u(1),-u(2),-u(3)]);
Ry = rotymatrix(phiy);
Rx = rotxmatrix(phix);
A = Rx*Ry*T;
align_verts = [align_verts, A*elm];
end
% initialising projected vertices
proj_verts=[];
% Performing the projection
for elm = align_verts
proj = projmatrix(10);
projverts = proj*elm;
projverts = ((10/projverts(3))*projverts);
proj_verts = [proj_verts,projverts];
end
% Displaying the world
for i = 1:size(world_faces,1)
for j = 1:size(world_faces,2)
x(j) =proj_verts(1,world_faces(i,j));
z(j) = proj_verts(2,world_faces(i,j));
end
patch(x,z,'w')
end
end
function T = transmatrix(p)
T = [1 0 0 p(1) ; 0 1 0 p(2) ; 0 0 1 p(3) ; 0 0 0 1];
end
function S = scalmatrix(s)
S = [s(1) 0 0 0 ; 0 s(2) 0 0 ; 0 0 s(3) 0 ; 0 0 0 1];
end
function Ry = rotymatrix(theta)
Ry = [cos(theta), 0, -sin(theta),0;
0,1,0,0;
sin(theta),0,cos(theta),0;
0,0,0,1];
end
function Rx = rotxmatrix(phi)
Rx = [1, 0, 0, 0;
0, cos(phi), -sin(phi), 0;
0, sin(phi), cos(phi), 0;
0, 0, 0, 1];
end
function P = projmatrix(f)
P = [1,0,0,0
0,1,0,0
0,0,1,0
0,0,1/f,0];
end
Updated code: managed to get the loop to work but now there is some bug i don't understand when it does a full rotation again any help would be great.
function world()
% Defining House Vertices
house_verts = [-5, 0, -5;
5, 0, -5;
5, 10, -5;
0,15,-5;
-5,10,-5;
-5,0,5;
5,0,5;
5,10,5;
0,15,5;
-5,10,5];
% Sorting out the homeogenous co-ordinates
ones = [1,1,1,1,1,1,1,1,1,1];
ones=transpose(ones);
house_verts = [house_verts, ones];
house_verts = transpose(house_verts);
% House faces
house_faces = [1,2,3,4,5;
2,7,8,3,3;
6,7,8,9,10;
1,6,10,5,5;
3,4,9,8,8;
4,5,10,9,9;
1,2,7,6,6];
world_pos = [];
% creating a street
street_vector = [1,0,1]; % the direction of the street
orthog_street_vector = [-1,0,1];
for i = 1:15
% current_pos1 and 2 will be the positions of the two houses
% opposite each other on the street
current_pos1 = 30*i*street_vector + 50*orthog_street_vector;
current_pos2 = 30*i*street_vector - 50*orthog_street_vector;
world_pos = [world_pos;current_pos1;current_pos2];
end
% initialising world vertices and faces
world_verts = [];
world_faces = [];
% Populating the street
for i =1:size(world_pos,1)
T = transmatrix(world_pos(i,:)); % a translation matrix
s = [1,1/2 + rand(),1];
S=scalmatrix(s); % a matrix for a random scaling of the height (y-coordinate)
Ry = rotymatrix(rand()*2*pi); % a matrix for a random rotation about the y-axis
A = T*Ry*S; % the compound transformation matrix to take the house into the world
obj_faces = size(world_verts,2) + house_faces; %increments the basic house faces to match the current object
obj_verts = A*house_verts;
world_verts = [world_verts, obj_verts]; % adds the vertices to the world
world_faces = [world_faces; obj_faces]; % adds the faces to the world
end
% initialising aligned vertices
align_verts = [];
% initialising projected vertices
proj_verts=[];
% Aligning the vertices to the particular camera at angle theta
theta = 0;
t = 0;
while t < 100
proj_verts=[];
align_verts = [];
for elm = world_verts
x = 300 + 300*cos(theta);
z = 300 + 300*sin(theta);
y = 80;
u = [x,y,z];
v = [200,0,200];
d = v - u;
phiy = atan2(d(1),d(3));
phix = -atan2(d(2),sqrt(d(1)^2+d(3)^2));
T = transmatrix([-u(1),-u(2),-u(3)]);
Ry = rotymatrix(phiy);
Rx = rotxmatrix(phix);
A = Rx*Ry*T;
align_verts = [align_verts, A*elm];
end
% Performing the projection
for elm = align_verts
proj = projmatrix(6);
projverts = proj*elm;
projverts = ((6/projverts(3))*projverts);
proj_verts = [proj_verts,projverts];
end
% Displaying the world
for i = 1:size(world_faces,1)
for j = 1:size(world_faces,2)
x(j) = proj_verts(1,world_faces(i,j));
z(j) = proj_verts(2,world_faces(i,j));
end
patch(x,z,'w')
pbaspect([1,1,1]); % adjusts the aspect ratio of the figure
end
title('Projected Space', 'fontsize', 16, 'interpreter', 'latex')
xlabel('$x$', 'fontsize', 16, 'interpreter', 'latex')
ylabel('$z$', 'fontsize', 16, 'interpreter', 'latex')
zlabel('$y$', 'fontsize', 16, 'interpreter', 'latex')
axis([-5,5,-5,2,0,5]) % sets the axes limits
view(0,89)
pause(0.0000001)
theta = theta + 0.01;
clf
end
end
function T = transmatrix(p)
T = [1 0 0 p(1) ; 0 1 0 p(2) ; 0 0 1 p(3) ; 0 0 0 1];
end
function S = scalmatrix(s)
S = [s(1) 0 0 0 ; 0 s(2) 0 0 ; 0 0 s(3) 0 ; 0 0 0 1];
end
function Ry = rotymatrix(theta)
Ry = [cos(theta), 0, -sin(theta),0;
0,1,0,0;
sin(theta),0,cos(theta),0;
0,0,0,1];
end
function Rx = rotxmatrix(phi)
Rx = [1, 0, 0, 0;
0, cos(phi), -sin(phi), 0;
0, sin(phi), cos(phi), 0;
0, 0, 0, 1];
end
function P = projmatrix(f)
P = [1,0,0,0
0,1,0,0
0,0,1,0
0,0,1/f,0];
end
Related
Having Trouble with this Error "Index in position 2 is invalid. Array indices must be positive integers or logical values." It only occurs when I enter certain values for the translation. Such as -25 x-direction and +50 in the y-direction. But it will work if I enter in any decimal values such as (12.75,-19.3). Please help me out.
Note: A is an imported image
if (delta_x == 0 && delta_y == 0) % Allows the Orginal Image to be displayed
figure(1)
image(0:ynew-1,0:xnew-1,A) % display a matrix as an image
axis equal
axis ij % put 0,0 in UL corner
axis tight
colormap(gray(L)) % use an 8-bit grayscale
xlabel('y')
ylabel('x')
title('Original Image')
return
end
%Translation Matrix 3x3
Trans = [1 0 delta_x; 0 1 delta_y; 0 0 1];
InverseTrans = (inv(Trans)); %Inverse Mapping Begins
%Allocate Space
E = zeros(xnew, ynew,k);
for i=1:xold
for j=1:yold
% matrix math assumes origin is (0,0) not (1,1)
vw=InverseTrans*[i; j; 1]; % vw will be [v; w; 1]
v = vw(1);
w = vw(2);
if (v < 0 || v >(xold-1)) % Testing for Out of Bounds
E(i,j) = 0;
elseif (w < 0 || w >(yold-1)) % Testing for out of Bounds
E(i,j) = 0;
else
%Takes the Real INTEGER Part
v_fix = fix(v);
w_fix = fix(w);
v_fix1 = v_fix +1;%Indexing
w_fix1 = w_fix +1;
%Change in the Values
dX = v - v_fix;
dY = w - w_fix;
%Indexing Purposes **This is where I'm getting my error**
D1(i,j) = A(v_fix1, w_fix1);
D2(i,j) = A(v_fix1 + 1, w_fix1);
D3(i,j) = A(v_fix1, w_fix1 + 1);
D4(i,j) = A(v_fix1 + 1, w_fix1 + 1);
end
end
end```
I need to create a binary mask. I have some coordinates and I make those coordinates and inside that region equal to 1, and background equal to zero.
Here is what I have done, but the problem is ROI located not in the correct position and located on the right bottom of the image. I appreciate if someone could point me to the right direction.
function [X,Y, BW] = Create_mask(X,Y,im)
X = round(X);
Y = round(Y);
X ( X < 1 ) = 1;
Y ( Y < 1 ) = 1;
BW = im > 255;
for p = 1:length(X)
BW(Y(p),X(p)) = 1;
end
for n = 0:(1/round(sqrt((X(end)-X(1))^2 + (Y(end)-Y(1))^2 ))):1
xn = round(X(1) +(X(end) - X(1))*n);
yn = round(Y(1) +(Y(end) - Y(1))*n);
BW(yn,xn) = 1;
end
se = strel('disk',10);
BW = imclose(BW,se);
BW = imdilate(BW,se);
BW = imfill(BW,'holes');
im( im < 255 ) = 0;
im = imclose(im,se);
BW = BW * 255;
BW = im2uint8(BW);
% BW = imresize(BW, [256 256],'nearest');
figure;
imshow(BW);
% close all;
end
Here is the output the function:
I was expecting to be similar to this image. This is not the exact solution but it shows my expectation.
X and Y coordinates are attached here, The first col is X and the second Y.
you can do this by calling function inpolygon, try this
function mask=createmask(x,y, cmin, cmax, dx)
if(nargin<3)
cmin=min([x(:) y(:)]);
end
if(nargin<4)
cmax=max([x(:) y(:)]);
end
if(nargin<5)
dx=(cmax-cmin)/100;
end
if(length(dx)==1)
dx=[dx dx];
end
[xi,yi]=meshgrid(cmin(1):dx(1):cmax(1),cmin(2):dx(2):cmax(2));
mask=reshape(inpolygon(xi(:),yi(:),x(:),y(:)), size(xi));
to test
xv = [0 3 3 0 0 NaN 1 1 2 2 1];
yv = [0 0 3 3 0 NaN 1 2 2 1 1];
mask=createmask(xv,yv, [-1 -1], [4 4]);
imagesc(mask)
I want to move a red star marker along the spiral trajectory with an equal distance of 5 units between the red star points on its circumference like in the below image.
vertspacing = 10;
horzspacing = 10;
thetamax = 10*pi;
% Calculation of (x,y) - underlying archimedean spiral.
b = vertspacing/2/pi;
theta = 0:0.01:thetamax;
x = b*theta.*cos(theta)+50;
y = b*theta.*sin(theta)+50;
% Calculation of equidistant (xi,yi) points on spiral.
smax = 0.5*b*thetamax.*thetamax;
s = 0:horzspacing:smax;
thetai = sqrt(2*s/b);
xi = b*thetai.*cos(thetai);
yi = b*thetai.*sin(thetai);
plot(x,y,'b-');
hold on
I want to get a figure that looks like the following:
This is my code for the circle trajectory:
% Initialization steps.
format long g;
format compact;
fontSize = 20;
r1 = 50;
r2 = 35;
r3= 20;
xc = 50;
yc = 50;
% Since arclength = radius * (angle in radians),
% (angle in radians) = arclength / radius = 5 / radius.
deltaAngle1 = 5 / r1;
deltaAngle2 = 5 / r2;
deltaAngle3 = 5 / r3;
theta1 = 0 : deltaAngle1 : (2 * pi);
theta2 = 0 : deltaAngle2 : (2 * pi);
theta3 = 0 : deltaAngle3 : (2 * pi);
x1 = r1*cos(theta1) + xc;
y1 = r1*sin(theta1) + yc;
x2 = r2*cos(theta2) + xc;
y2 = r2*sin(theta2) + yc;
x3 = r3*cos(theta3) + xc;
y3 = r3*sin(theta3) + yc;
plot(x1,y1,'color',[1 0.5 0])
hold on
plot(x2,y2,'color',[1 0.5 0])
hold on
plot(x3,y3,'color',[1 0.5 0])
hold on
% Connecting Line:
plot([70 100], [50 50],'color',[1 0.5 0])
% Set up figure properties:
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0, 0, 1, 1]);
drawnow;
axis square;
for i = 1 : length(theta1)
plot(x1(i),y1(i),'r*')
pause(0.1)
end
for i = 1 : length(theta2)
plot(x2(i),y2(i),'r*')
pause(0.1)
end
for i = 1 : length(theta3)
plot(x3(i),y3(i),'r*')
pause(0.1)
end
I can't think of a way to compute distance along a spiral, so I'm approximating it with circles, in hopes that it will still be useful.
My solution relies on the InterX function from FEX, to find the intersection of circles with the spiral. I am providing an animation so it is easier to understand.
The code (tested on R2017a):
function [x,y,xi,yi] = q44916610(doPlot)
%% Input handling:
if nargin < 1 || isempty(doPlot)
doPlot = false;
end
%% Initialization:
origin = [50,50];
vertspacing = 10;
thetamax = 5*(2*pi);
%% Calculation of (x,y) - underlying archimedean spiral.
b = vertspacing/(2*pi);
theta = 0:0.01:thetamax;
x = b*theta.*cos(theta) + origin(1);
y = b*theta.*sin(theta) + origin(2);
%% Calculation of equidistant (xi,yi) points on spiral.
DST = 5; cRes = 360;
numPts = ceil(vertspacing*thetamax); % Preallocation
[xi,yi] = deal(NaN(numPts,1));
if doPlot && isHG2() % Plots are only enabled if the MATLAB version is new enough.
figure(); plot(x,y,'b-'); hold on; axis equal; grid on; grid minor;
hAx = gca; hAx.XLim = [-5 105]; hAx.YLim = [-5 105];
hP = plot(xi,yi,'r*');
else
hP = struct('XData',xi,'YData',yi);
end
hP.XData(1) = origin(1); hP.YData(1) = origin(2);
for ind = 2:numPts
P = InterX([x;y], makeCircle([hP.XData(ind-1),hP.YData(ind-1)],DST/2,cRes));
[~,I] = max(abs(P(1,:)-origin(1)+1i*(P(2,:)-origin(2))));
if doPlot, pause(0.1); end
hP.XData(ind) = P(1,I); hP.YData(ind) = P(2,I);
if doPlot, pause(0.1); delete(hAx.Children(1)); end
end
xi = hP.XData(~isnan(hP.XData)); yi = hP.YData(~isnan(hP.YData));
%% Nested function(s):
function [XY] = makeCircle(cnt, R, nPts)
P = (cnt(1)+1i*cnt(2))+R*exp(linspace(0,1,nPts)*pi*2i);
if doPlot, plot(P,'Color',lines(1)); end
XY = [real(P); imag(P)];
end
end
%% Local function(s):
function tf = isHG2()
try
tf = ~verLessThan('MATLAB', '8.4');
catch
tf = false;
end
end
function P = InterX(L1,varargin)
% DOCUMENTATION REMOVED. For a full version go to:
% https://www.mathworks.com/matlabcentral/fileexchange/22441-curve-intersections
narginchk(1,2);
if nargin == 1
L2 = L1; hF = #lt; %...Avoid the inclusion of common points
else
L2 = varargin{1}; hF = #le;
end
%...Preliminary stuff
x1 = L1(1,:)'; x2 = L2(1,:);
y1 = L1(2,:)'; y2 = L2(2,:);
dx1 = diff(x1); dy1 = diff(y1);
dx2 = diff(x2); dy2 = diff(y2);
%...Determine 'signed distances'
S1 = dx1.*y1(1:end-1) - dy1.*x1(1:end-1);
S2 = dx2.*y2(1:end-1) - dy2.*x2(1:end-1);
C1 = feval(hF,D(bsxfun(#times,dx1,y2)-bsxfun(#times,dy1,x2),S1),0);
C2 = feval(hF,D((bsxfun(#times,y1,dx2)-bsxfun(#times,x1,dy2))',S2'),0)';
%...Obtain the segments where an intersection is expected
[i,j] = find(C1 & C2);
if isempty(i), P = zeros(2,0); return; end
%...Transpose and prepare for output
i=i'; dx2=dx2'; dy2=dy2'; S2 = S2';
L = dy2(j).*dx1(i) - dy1(i).*dx2(j);
i = i(L~=0); j=j(L~=0); L=L(L~=0); %...Avoid divisions by 0
%...Solve system of eqs to get the common points
P = unique([dx2(j).*S1(i) - dx1(i).*S2(j), ...
dy2(j).*S1(i) - dy1(i).*S2(j)]./[L L],'rows')';
function u = D(x,y)
u = bsxfun(#minus,x(:,1:end-1),y).*bsxfun(#minus,x(:,2:end),y);
end
end
Result:
Note that in the animation above, the diameter of the circle (and hence the distance between the red points) is 10 and not 5.
Forum
I've got a set of data that apparently forms an ellipse in 3D space (not an ellipsoid, but a curve in 3D).
Being inspired by following thread http://au.mathworks.com/matlabcentral/newsreader/view_thread/65773
and with the help from someone ,I manage to get the optimization code running and outputs a set of best parameters x (vector). However, when I try to use this x to replicate the ellipse,the outcomes is a strange straight line in the space. I have been stacked on this for days., still don't know what went wrong....pretty much devastated... I hope someone can shed some light on this. The Mathematica formulations for the ellipse follows the same as in the above thread ,where
The 3D-ellipse is given by: (x;y;z) = (z1;z2;z3) + R(alpha,beta,gamma).(acos(phi); b*sin(phi);0)
where:
* z is the translation vector.
* R is the rotation matrix (using Euler angles, we first rotate alpha rad around the x-axis, then beta rad around the y-axis and finally gamma rad around the z-axis again).
* a is the long axis of the ellipse
* b is the short axis of the ellipse.
Here is my target function for optimization (ellipsefit.m)
function [merit]= ellipsefit(x, vmatrix) % x is the initial parameters, vmatrix stores the datapoints
load vmatrix.txt % In vmatrix, the data are stored: N rows x 3 columns
a = x(1);
b = x(2);c
alpha = x(3);
beta = x(4);
gamma = x(5);
z = [x(6),x(7),x(8)];
t = z'
[dim1, dim2]=size(vmatrix);
% construction of rotation matrix R(alpha,beta,gamma)
R1 = [cos(alpha), sin(alpha), 0; -sin(alpha), cos(alpha), 0; 0, 0, 1];v
R2 = [1, 0, 0; 0, cos(beta), sin(beta); 0, -sin(beta), cos(beta)];
R3 = [cos(gamma), sin(gamma), 0; -sin(gamma), cos(gamma), 0; 0, 0, 1];
R = R3*R2*R1;
% first compute vector phi (in the length of the data) by minimizing for every
% point in the data set the distance of this point to the ellipse
% (with its initial parameters a,b,alpha,beta,gamma, z1,z2,z3 held fixed) with respect to phi.
for i=1:dim1
point=vmatrix(i,:)';
dist=#(phi)sum((R*[a*cos(phi); b*sin(phi); 0]+t-point)).^2;
phi(i)=fminbnd(dist,0,2*pi);
end
v = [a*cos(phi); b*sin(phi); zeros(size(phi))];
P = R*v;
%The targetfunction is: g = (xi1,xi2,xi3)' -(z1,z2,z3)'-R(alpha,beta,gamma)(a cos(phi), b sin(phi),0)'
% Construction of distance function
merit = [vmatrix(:,1)-z(1)-P(1),vmatrix(:,2)-z(2)-P(2),vmatrix(:,3)-z(3)-P(3)];
merit = sqrt(sum(sum(merit.^2)))
end
here is the main function for parameters initialization and call for opts (xfit.m)
function [y] = xfit (x)
x= [1 1 1 1 1 1 1 1] % initial parameters
[x] = fminsearch(#ellipsefit,x) % set the distance minimum as the target function
y=x
end
code to reconstruct the ellipse in scatter points (ellipsescatter.txt)
x= [0.655,0.876,1.449,2.248,1.024,0.201,-0.11,0.002] % values obtained according to above routines
a = x(1);
b = x(2);
alpha = x(3);
beta = x(4);
gamma = x(5);
z = [x(6),x(7),x(8)];
R1 = [cos(alpha), sin(alpha), 0; -sin(alpha), cos(alpha), 0; 0, 0, 1];
R2 = [1, 0, 0; 0, cos(beta), sin(beta); 0, -sin(beta), cos(beta)];
R3 = [cos(gamma), sin(gamma), 0; -sin(gamma), cos(gamma), 0; 0, 0, 1];
R = R3*R2*R1;
phi=linspace(0,2*pi,100)
v = [a*cos(phi); b*sin(phi); zeros(size(phi))];
P = R*v;
u = P'
and last the data points (vmatrix)
0.002037965 0.004225765 0.002020202
0.005766671 0.007269193 0.004040404
0.010004802 0.00995638 0.006060606
0.014444336 0.012502725 0.008080808
0.019083408 0.014909533 0.01010101
0.023967745 0.017144827 0.012121212
0.03019849 0.01969697 0.014591289
0.038857283 0.022727273 0.017839321
0.045443501 0.024730475 0.02020202
0.051213405 0.026346492 0.022222222
0.061038174 0.028787879 0.02555121
0.069408829 0.030575164 0.028282828
0.075785861 0.031818182 0.030321465
0.088818543 0.033954681 0.034343434
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0.23715887 0.034848485 0.071748051
0.251787024 0.033009003 0.074747475
0.26196429 0.031542949 0.076767677
0.278510276 0.028787879 0.079919236
0.294365342 0.025757576 0.082799669
0.306221108 0.023197784 0.084848485
0.31843759 0.020305704 0.086868687
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0.342989936 0.013636364 0.090622484
0.352806191 0.010606061 0.091993214
0.36201461 0.007575758 0.093211986
0.376385537 0.002386324 0.094949495
0.386214665 -0.001515152 0.096012
0.396173756 -0.005800677 0.096969697
0.406365393 -0.010606061 0.097799682
0.417897899 -0.016666667 0.098516141
0.428059375 -0.022727273 0.098889844
0.436894505 -0.028787879 0.09893196
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0.45074522 -0.040909091 0.098061305
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0.457867157 -0.05 0.096591789
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0.461974832 -0.059090909 0.094368708
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0.449335762 -0.088184223 0.080808081
0.445185841 -0.090933095 0.078787879
0.440695103 -0.093443633 0.076767677
0.435904796 -0.095744683 0.074747475
0.429042582 -0.098484848 0.072052312
0.419877272 -0.101489369 0.068686869
0.41402731 -0.103049401 0.066666667
0.407719192 -0.104545455 0.064554798
0.395265308 -0.106881864 0.060606061
0.388611992 -0.107880111 0.058585859
0.374697979 -0.10945186 0.054545455
0.360058411 -0.11051623 0.050505051
0.352443612 -0.11084211 0.048484848
0.336646801 -0.111097219 0.044444444
0.320085063 -0.110817414 0.04040404
0.31150078 -0.110465333 0.038383838
0.293673303 -0.109300395 0.034343434
0.275417637 -0.107575758 0.030396076
0.265228963 -0.106361993 0.028282828
0.251914589 -0.104545455 0.025603647
0.234385536 -0.101745907 0.022222222
0.223443994 -0.099745394 0.02020202
0.212154519 -0.097501571 0.018181818
0.20046153 -0.09497557 0.016161616
0.188298809 -0.092121085 0.014141414
0.17558878 -0.088883868 0.012121212
0.162241674 -0.085201142 0.01010101
0.148154337 -0.081000773 0.008080808
0.136529019 -0.077272727 0.006507255
0.127611912 -0.074242424 0.005361311
0.116762238 -0.070350086 0.004040404
0.103195122 -0.065151515 0.002507114
0.095734279 -0.062121212 0.001725236
0.081719652 -0.056060606 0.000388246
0 0 0
This answer is not a direct fit in 3D, it instead involves first a rotation of the data so that the plane of the points coincides with the xy plane, then a fit to the data in 2D.
% input: data, a N x 3 array with one set of Cartesian coords per row
% remove the center of mass
CM = mean(data);
datap = data - ones(size(data,1),1)*CM;
% now rotate all points into the xy plane ...
% start by finding the plane:
[u s v]=svd(datap);
% rotate the data into the principal axes frame:
datap = datap*v;
% fit the equation for an ellipse to the rotated points
x= [0.25 0.07 0.037 0 0]'; % initial parameters
options=1;
xopt = fmins(#fellipse,x,options,[],datap) % set the distance minimum as the target function
This is the function fellipse, based on the function provided:
function [merit]= fellipse(x,data) % x is the initial parameters, data stores the datapoints
a = x(1);
b = x(2);
alpha = x(3);
z = x(4:5);
R = [cos(alpha), sin(alpha), 0; -sin(alpha), cos(alpha), 0; 0, 0, 1];
data = data*R;
merit = 0;
[dim1, dim2]=size(data);
for i=1:dim1
dist=#(phi)sum( ( [a*cos(phi);b*sin(phi)] + z - data(i,1:2)').^2 );
phi=fminbnd(dist,0,2*pi);
merit = merit+dist(phi);
end
end
Also, note again that this can be turned into a fit directly in 3D, but this answer is just as good if you can assume the data points lie approx. in a 2D plane. The current solution is likely much more efficient then a solution in 3D with additional parameters.
Hopefully the code is self-explanatory. I recommend looking at the link included in the OP, it explains the purpose of the loop over phi.
And this is how you can inspect the result of the fit:
a = xopt(1);
b = xopt(2);
alpha = xopt(3);
z = [xopt(4:5) ; 0]';
phi = linspace(0,2*pi,100)';
simdat = [a*cos(phi) b*sin(phi) zeros(size(phi))];
R = [cos(alpha), -sin(alpha), 0; sin(alpha), cos(alpha), 0; 0, 0, 1];
simdat = simdat*R + ones(size(simdat,1), 1)*z ;
figure, hold on
plot3(datap(:,1),datap(:,2),datap(:,3),'o')
plot3(simdat(:,1),simdat(:,2),zeros(size(simdat,1),1),'r-')
Edit
The following is a 3D approach. It does not appear to be very robust as it is quite sensitive to the choice of starting parameters. Some improvements may be necessary.
CM = mean(data);
datap = data - ones(size(data,1),1)*CM;
xopt = [ 0.07 0.25 1 -0.408976 0.610120 0 0 0]';
options=1;
xopt = fmins(#fellipse3d,xopt,options,[],datap) % set the distance minimum as the target function
The function fellipse3d is
function [merit]= fellipse3d(x,data) % x is the initial parameters, data stores the datapoints
a = abs(x(1));
b = abs(x(2));
alpha = x(3);
beta = x(4);
gamma = x(5);
z = x(6:8)';
[dim1, dim2]=size(data);
R1 = [cos(alpha), sin(alpha), 0; -sin(alpha), cos(alpha), 0; 0, 0, 1];
R2 = [1, 0, 0; 0, cos(beta), sin(beta); 0, -sin(beta), cos(beta)];
R3 = [cos(gamma), sin(gamma), 0; -sin(gamma), cos(gamma), 0; 0, 0, 1];
R = R3*R2*R1;
data = (data - z(ones(dim1,1),:))*R;
merit = 0;
for i=1:dim1
dist=#(phi)sum( ([a*cos(phi);b*sin(phi);0] - data(i,:)').^2 );
phi=fminbnd(dist,0,2*pi);
merit = merit+dist(phi);
end
end
You can visualize the results with
a = xopt(1);
b = xopt(2);
alpha = -xopt(3);
beta = -xopt(4);
gamma = -xopt(5);
z = xopt(6:8)' + CM;
dim1 = 100;
phi = linspace(0,2*pi,dim1)';
simdat = [a*cos(phi) b*sin(phi) zeros(size(phi))];
R1 = [cos(alpha), sin(alpha), 0; ...
-sin(alpha), cos(alpha), 0; ...
0, 0, 1];
R2 = [1, 0, 0; ...
0, cos(beta), sin(beta); ...
0, -sin(beta), cos(beta)];
R3 = [cos(gamma), sin(gamma), 0; ...
-sin(gamma), cos(gamma), 0; ...
0, 0, 1];
R = R1*R2*R3;
simdat = simdat*R + z(ones(dim1,1),:);
figure, hold on
plot3(data(:,1),data(:,2),data(:,3),'o')
plot3(simdat(:,1),simdat(:,2),simdat(:,3),'r-')
I have to create an algorithm with Matlab that, with a image of a hand, can know the form of the hand by the number of raised fingers and the presence or absence of the thumb. So far, the algorithm is almost complete but I don't know what more I can do that could find the peaks that represents the fingers. We tried a lot of things but nothing works. The idea is to find when there is a sudden increasement but as the pixels are never completely aligned, nothing that we tried worked. Someone has any idea? Here is the code so far.
The image that he is reading is this one:
To know if the finger is relevant or not, we already have an idea that might work... but we need to find the fingers first.
clear all
close all
image=imread('mao2.jpg');
YCBCR = rgb2ycbcr(image);
image=YCBCR;
cb = image(:,:,2);
cr = image(:,:,3);
imagek(:,1) = cb(:);
imagek(:,2) = cr(:);
imagek = double(imagek);
[IDX, C] = kmeans(imagek, 2, 'EmptyAction', 'singleton');
s=size(image);
IDX= uint8(IDX);
C2=round(C);
imageNew = zeros(s(1),s(2));
temp = reshape(IDX, [s(1) s(2)]);
for i = 1 : 1 : s(1)
for j = 1 : 1 : s(2)
imageNew(i,j,:) = C2(temp(i,j));
end
end
imageNew=uint8(imageNew);
[m,n]=size(imageNew);
for i=1:1:m
for j = 1:1:n
if(imageNew(i,j)>=127)
pretobranco(i,j)=0;
else
pretobranco(i,j)=1;
end
end
end
I2=imfill(pretobranco);
imshow(I2);
imwrite(I2, 'mao1trab.jpg');
[m,n]=size(I2);
B=edge(I2);
figure
imshow(B);
hold on;
stats=regionprops(I2,'BoundingBox');
rect=rectangle('position', [stats(1).BoundingBox(1), stats(1).BoundingBox(2), stats(1).BoundingBox(3), stats(1).BoundingBox(4)], 'EdgeColor', 'r');
stats(1).BoundingBox(1)
stats(1).BoundingBox(2)
stats(1).BoundingBox(3)
stats(1).BoundingBox(4)
figure
Bound = B( stats(1).BoundingBox(2): stats(1).BoundingBox(2)+stats(1).BoundingBox(4)-1, stats(1).BoundingBox(1):stats(1).BoundingBox(1)+stats(1).BoundingBox(3)-1);
imshow(Bound)
y1 = round(stats(1).BoundingBox(2))
y2 = round(stats(1).BoundingBox(2)+stats(1).BoundingBox(4)-1)
x1 = round(stats(1).BoundingBox(1))
x2 = round(stats(1).BoundingBox(1)+stats(1).BoundingBox(3)-1)
% Bounding box contida em imagem[M, N].
[M,N] = size(Bound)
vertical=0;
horizontal=0;
if M > N
vertical = 1 %imagem vertical
else
horizontal = 1 %imagem horizontal
end
%Find thumb
MaoLeft = 0;
MaoRight = 0;
nPixelsBrancos = 0;
if vertical==1
for i = x1:1:x2
for j= y1:1:y2
if I2(j,i) == 1
nPixelsBrancos = nPixelsBrancos + 1; %Numero de pixels da mão
end
end
end
for i=x1:1:x1+30
for j=y1:1:y2
if I2(j,i) == 1
MaoLeft = MaoLeft + 1; %Number of pixels of the hand between the 30 first colums
end
end
end
for i=x2-30:1:x2
for j=y1:1:y2
if I2(j,1) == 1
MaoRight = MaoRight + 1; %Number of pixels of the hand between the 30 last colums
end
end
end
TaxaBrancoLeft = MaoLeft/nPixelsBrancos
TaxaBrancoRight = MaoRight/nPixelsBrancos
if TaxaBrancoLeft <= (7/100)
if TaxaBrancoRight <= (7/100)
Thumb = 0 %Thumb in both borders is defined as no Thumb.
else
ThumbEsquerdo = 1 %Thumb on left
end
end
if TaxaBrancoRight <= (7/100) && TaxaBrancoLeft >= (7/100)
ThumbDireito = 1 %Thumb on right
end
end
if horizontal==1
for i = x1:1:x2
for j= y1:y2
if I2(i,j) == 1
nPixelsBrancos = nPixelsBrancos + 1; %Numero de pixels da mão
end
end
end
for i=x1:1:x2
for j=y1:1:y1+30
if I2(i,j) == 1
MaoLeft = MaoLeft + 1; %Numero de pixels da mão entre as 30 primeiras colunas
end
end
end
for i=x1:1:x2
for j=y2-30:1:y2
if I2(j,1) == 1
MaoRight = MaoRight + 1; %Numero de pixels da mão entre as 30 ultimas colunas
end
end
end
TaxaBrancoLeft = MaoLeft/nPixelsBrancos
TaxaBrancoRight = MaoRight/nPixelsBrancos
if TaxaBrancoLeft <= (7/100)
if TaxaBrancoRight <= (7/100)
Thumb = 0 %Polegar nas duas bordas. Definimos como sem polegar.
else
ThumbEsquerdo = 1 %Polegar na borda esquerda
end
end
if TaxaBrancoRight <= (7/100) && TaxaBrancoLeft >= (7/100)
ThumbDireito = 1 %Polegar na borda direita
end
end
figure
imshow(I2);
%detecção da centroid
Ibw = im2bw(I2);
Ilabel = bwlabel(Ibw);
stat = regionprops(Ilabel,'centroid');
figure
imshow(I2); hold on;
for x = 1: numel(stat)
plot(stat(x).Centroid(1),stat(x).Centroid(2),'ro');
end
centroid = [stat(x).Centroid(1) stat(x).Centroid(2)] %coordenadas x e y da centroid
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Seemed like an interesting problem, so I gave it a shot. Basically you start with a Sobel filter to find the edges in your image (after slight denoising). Then clean up the resulting lines, use them to separate regions within your binary mask of the hand, use a watershed transform to find the wrist, some distance transforms to find other landmarks, then remove the palm. What you're left with is separate regions for each finger and thumb. You can count those regions easily enough or find which way they are pointing, or whatever you'd like.
imgURL = 'https://encrypted-tbn2.gstatic.com/imgs?q=tbn:ANd9GcRQsqJtlrOnSbJNTnj35Z0uG9BXsecX2AXn1vV0YDKodq-zSuqnnQ';
imgIn=imread(imgURL);
gaussfilt = fspecial('gaussian', 3, .5); % Blur starting image
blurImg = imfilter(double(img(:,:,1)), gaussfilt);
edgeImg = edge(blurImg, 'sobel'); % Use Sobel edge filter to pick out contours of hand + fingers
% Clean up contours
edgeImg = bwmorph(edgeImg, 'close', 1);
edgeImg = bwmorph(edgeImg, 'thin', Inf);
% Clean up rogue spots in corners
edgeImg([2 end-1], 2) = 0;
edgeImg([2 end-1], end-1) = 0;
% Extend lines to edge of image (correct for 'close' operation above
edgeImg([1 end],:) = edgeImg([2 end-1],:);
edgeImg(:, [1 end]) = edgeImg(:, [2 end-1]);
% Remove all but the longest line
regs = regionprops(edgeImg, 'Area', 'PixelIdxList');
regs(vertcat(regs.Area) ~= max(vertcat(regs.Area))) = [];
lineImg = false(size(edgeImg, 1), size(edgeImg, 2));
lineImg(regs.PixelIdxList) = 1;
fillImg = edgeImg;
% Close in wrist
if any(fillImg(1,:))
fillImg(1,:) = 1;
end
if any(fillImg(end,:))
fillImg(end,:) = 1;
end
if any(fillImg(:,1))
fillImg(:,1) = 1;
end
if any(fillImg(:,end))
fillImg(:,end) = 1;
end
fillImg = imfill(fillImg, 'holes');
fillImg([1 end], :) = 0;
fillImg(:, [1 end]) = 0;
fillImg([1 end],:) = fillImg([2 end-1],:);
fillImg(:, [1 end]) = fillImg(:, [2 end-1]);
% Start segmenting out hand + fingers
handBin = fillImg;
% Set lines in above image to 0 to separate closely-spaced fingers
handBin(lineImg) = 0;
% Erode these lines to make fingers a bit more separate
handBin = bwmorph(handBin, 'erode', 1);
% Segment out just hand (remove wrist)
distImg = bwdist(~handBin);
[cDx, cDy] = find(distImg == max(distImg(:)));
midWrist = distImg;
midWrist = max(midWrist(:)) - midWrist;
midWrist(distImg == 0) = Inf;
wristWatershed = watershed(imerode(midWrist, strel('disk', 10)));
whichRegion = wristWatershed(cDx, cDy);
handBin(wristWatershed ~= whichRegion) = 0;
regs = regionprops(handBin, 'Area', 'PixelIdxList');
regs(vertcat(regs.Area) ~= max(vertcat(regs.Area))) = [];
handOnly = zeros(size(handBin, 1), size(handBin, 2));
handOnly(regs.PixelIdxList) = 1;
% Find radius of circle around palm centroid that excludes wrist and splits
% fingers into separate regions.
% This is estimated as D = 1/3 * [(Centroid->Fingertip) + 2*(Centroid->Wrist)]
% Find Centroid-> Wrist distance
dist2w = wristWatershed ~= whichRegion;
dist2w = bwdist(dist2w);
distToWrist = dist2w(cDx, cDy);
% Find Centroid-> Fingertip distance
dist2FE = zeros(size(handOnly, 1), size(handOnly, 2));
dist2FE(cDx, cDy) = 1;
dist2FE = bwdist(dist2FE).*handOnly;
distToFingerEnd = max(dist2FE(:));
circRad = mean([distToFingerEnd, distToWrist, distToWrist]); % Estimage circle diameter
% Draw circle
X = bsxfun(#plus,(1:size(handOnly, 1))',zeros(1,size(handOnly, 2)));
Y = bsxfun(#plus,(1:size(handOnly, 2)),zeros(size(handOnly, 1),1));
B = sqrt(sum(bsxfun(#minus,cat(3,X,Y),reshape([cDx, cDy],1,1,[])).^2,3))<=circRad;
% Cut out binary mask within circle
handOnly(B) = 0;
% Label separate regions, where each now corresponds to a separate digit
fingerCount = bwlabel(handOnly);
% Display overlay image
figure()
imshow(imgIn)
hold on
overlayImg = imshow(label2rgb(fingerCount, 'jet', 'k'));
set(overlayImg, 'AlphaData', 0.5);
hold off
Results:
http://imgur.com/ySn1fPy