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I'm trying to covert this Matlab code to Scilab, but I have some problems.
N = 101;
L = 4*pi;
x = linspace(0,L,N);
% It has three data set; 1: past, 2: current, 3: future.
u = zeros(N,3);
s = 0.5;
% Gaussian Pulse
y = 2*exp(-(x-L/2).^2);
u(:,1) = y;
u(:,2) = y;
% Plot the initial condition.
handle_line = plot(x,u(:,2),'LineWidth',2);
axis([0,L,-2,2]);
xlabel('x'); ylabel('u');
title('Wave equation');
% Dirichet Boundary conditions
u(1,:) = 0;
u(end,:) = 0;
filename = 'wave.gif';
for ii=1:100
disp(['at ii= ', num2str(ii)]);
u(2:end-1,3) = s*(u(3:end,2)+u(1:end-2,2)) ...
+ 2*(1-s)*u(2:end-1,2) ...
- u(2:end-1,1);
u(:,1) = u(:,2);
u(:,2) = u(:,3);
handle_line.YData = u(:,2);
drawnow;
frame = getframe(gcf);
im = frame2im(frame);
[A,map] = rgb2ind(im,256);
if ii==1
imwrite(A,map,filename,'gif','LoopCount',Inf,'DelayTime',0.05);
else
imwrite(A,map,filename,'gif','WriteMode','append','DelayTime',0.05);
end
end
I get an error for this line:
handle_line = plot(x,u(:,2),'LineWidth',2);
Error states: Wrong number of output arguments
What should i change to fix it?
The line
axis([0,L,-2,2]);
has to be translated in Scilab to
set(gca(),"data_bounds",[0,L,-2,2]);
Try this out:
N = 101;
L = 4*pi;
x = linspace(0,L,N);
% It has three data set; 1: past, 2: current, 3: future.
u = zeros(N,3);
s = 0.5;
% Gaussian Pulse
y = 2*exp(-(x-L/2).^2);
u(:,1) = y;
u(:,2) = y;
% Define a standard plot range for x and y
x_range=[min(x) max(x)];
y_range=[-max(y) max(y)];
% Plot the initial condition.
plot(x,u(:,2),'LineWidth',2);
axis([0,L,-2,2]);
xlabel('x'); ylabel('u');
title('Wave equation');
% Dirichet Boundary conditions
u(1,:) = 0;
u(end,:) = 0;
filename = 'wave.gif';
for ii=1:100
disp(['at ii= ', num2str(ii)]);
u(2:end-1,3) = s*(u(3:end,2)+u(1:end-2,2)) ...
+ 2*(1-s)*u(2:end-1,2) ...
- u(2:end-1,1);
u(:,1) = u(:,2);
u(:,2) = u(:,3);
plot(x,u(:,2),'LineWidth',2);
axis([x_range y_range]);
frame = getframe(gcf);
im = frame2im(frame);
[A,map] = rgb2ind(im,256);
if ii==1
imwrite(A,map,filename,'gif','LoopCount',Inf,'DelayTime',0.05);
else
imwrite(A,map,filename,'gif','WriteMode','append','DelayTime',0.05);
end
end
I removed the output and added axis limit independently.
I want to find the corners of objects.
I tried the following code:
Vstats = regionprops(BW2,'Centroid','MajorAxisLength','MinorAxisLength',...
'Orientation');
u = [Vstats.Centroid];
VcX = u(1:2:end);
VcY = u(2:2:end);
[VcY id] = sort(VcY); % sorting regions by vertical position
VcX = VcX(id);
Vstats = Vstats(id); % permute according sort
Bv = Bv(id);
Vori = [Vstats.Orientation];
VRmaj = [Vstats.MajorAxisLength]/2;
VRmin = [Vstats.MinorAxisLength]/2;
% find corners of vertebrae
figure,imshow(BW2)
hold on
% C = corner(VER);
% plot(C(:,1), C(:,2), 'or');
C = cell(size(Bv));
Anterior = zeros(2*length(C),2);
Posterior = zeros(2*length(C),2);
for i = 1:length(C) % for each region
cx = VcX(i); % centroid coordinates
cy = VcY(i);
bx = Bv{i}(:,2); % edge points coordinates
by = Bv{i}(:,1);
ux = bx-cx; % move to the origin
uy = by-cy;
[t, r] = cart2pol(ux,uy); % translate in polar coodinates
t = t - deg2rad(Vori(i)); % unrotate
for k = 1:4 % find corners (look each quadrant)
fi = t( (t>=(k-3)*pi/2) & (t<=(k-2)*pi/2) );
ri = r( (t>=(k-3)*pi/2) & (t<=(k-2)*pi/2) );
[rp, ip] = max(ri); % find farthest point
tc(k) = fi(ip); % save coordinates
rc(k) = rp;
end
[xc,yc] = pol2cart(tc+1*deg2rad(Vori(i)) ,rc); % de-rotate, translate in cartesian
C{i}(:,1) = xc + cx; % return to previous place
C{i}(:,2) = yc + cy;
plot(C{i}([1,4],1),C{i}([1,4],2),'or',C{i}([2,3],1),C{i}([2,3],2),'og')
% save coordinates :
Anterior([2*i-1,2*i],:) = [C{i}([1,4],1), C{i}([1,4],2)];
Posterior([2*i-1,2*i],:) = [C{i}([2,3],1), C{i}([2,3],2)];
end
My input image is :
I got the following output image
The bottommost object in the image is not detected properly. How can I correct the code? It fails to work for a rotated image.
You can get all the points from the image, and use kmeans clustering and partition the points into 8 groups. Once partition is done, you have the points in and and you can pick what ever the points you want.
rgbImage = imread('your image') ;
%% crop out the unwanted white background from the image
grayImage = min(rgbImage, [], 3);
binaryImage = grayImage < 200;
binaryImage = bwareafilt(binaryImage, 1);
[rows, columns] = find(binaryImage);
row1 = min(rows);
row2 = max(rows);
col1 = min(columns);
col2 = max(columns);
% Crop
croppedImage = rgbImage(row1:row2, col1:col2, :);
I = rgb2gray(croppedImage) ;
%% Get the white regions
[y,x,val] = find(I) ;
%5 use kmeans clustering
[idx,C] = kmeans([x,y],8) ;
%%
figure
imshow(I) ;
hold on
for i = 1:8
xi = x(idx==i) ; yi = y(idx==i) ;
id1=convhull(xi,yi) ;
coor = [xi(id1) yi(id1)] ;
[id,c] = kmeans(coor,4) ;
plot(coor(:,1),coor(:,2),'r','linewidth',3) ;
plot(c(:,1),c(:,2),'*b')
end
Now we are able to capture the regions..the boundary/convex hull points are in hand. You can do what ever math you want with the points.
Did you solve the problem? I Looked into it and it seems that the rotation given by 'regionprops' seems to be off. To fix that I've prepared a quick solution: I've dilated the image to close the gaps, found 4 most distant peaks of each spine, and then validated if a peak is on the left, or on the right of the centerline (that I have obtained by extrapolating form sorted centroids). This method seems to work for this particular problem.
BW2 = rgb2gray(Image);
BW2 = imbinarize(BW2);
%dilate and erode will help to remove extra features of the vertebra
se = strel('disk',4,4);
BW2_dilate = imdilate(BW2,se);
BW2_erode = imerode(BW2_dilate,se);
sb = bwboundaries(BW2_erode);
figure
imshow(BW2)
hold on
centerLine = [];
corners = [];
for bone = 1:length(sb)
x0 = sb{bone}(:,2) - mean(sb{bone}(:,2));
y0 = sb{bone}(:,1) - mean(sb{bone}(:,1));
%save the position of the centroid
centerLine = [centerLine; [mean(sb{bone}(:,1)) mean(sb{bone}(:,2))]];
[th0,rho0] = cart2pol(x0,y0);
%make sure that the indexing starts at the dip, not at the corner
lowest_val = find(rho0==min(rho0));
rho1 = [rho0(lowest_val:end); rho0(1:lowest_val-1)];
th00 = [th0(lowest_val:end); th0(1:lowest_val-1)];
y1 = [y0(lowest_val:end); y0(1:lowest_val-1)];
x1 = [x0(lowest_val:end); x0(1:lowest_val-1)];
%detect corners, using smooth data to remove noise
[pks,locs] = findpeaks(smooth(rho1));
[pksS,idS] = sort(pks,'descend');
%4 most pronounced peaks are where the corners are
edgesFndCx = x1(locs(idS(1:4)));
edgesFndCy = y1(locs(idS(1:4)));
edgesFndCx = edgesFndCx + mean(sb{bone}(:,2));
edgesFndCy = edgesFndCy + mean(sb{bone}(:,1));
corners{bone} = [edgesFndCy edgesFndCx];
end
[~,idCL] = sort(centerLine(:,1),'descend');
centerLine = centerLine(idCL,:);
%extrapolate the spine centerline
yDatExt= 1:size(BW2_erode,1);
extrpLine = interp1(centerLine(:,1),centerLine(:,2),yDatExt,'spline','extrap');
plot(centerLine(:,2),centerLine(:,1),'r')
plot(extrpLine,yDatExt,'r')
%find edges to the left, and to the right of the centerline
for bone = 1:length(corners)
x0 = corners{bone}(:,2);
y0 = corners{bone}(:,1);
for crn = 1:4
xCompare = extrpLine(y0(crn));
if x0(crn) < xCompare
plot(x0(crn),y0(crn),'go','LineWidth',2)
else
plot(x0(crn),y0(crn),'ro','LineWidth',2)
end
end
end
Solution
I'm solving a system of ODEs using RK4. I'm generating a straight line plot that seems to be due to the fact that k3_1 is capped at -3.1445e+24. I don't understand why it is capped.
function RK4system_MNModel()
parsec = 3.08*10^18;
r_1 = 8.5*1000.0*parsec; % in cm
z_1 = 0.0; % in cm also
theta_1 = 0.0;
grav = 6.6720*10^-8;
amsun = 1.989*10^33; % in grams
amg = 1.5d11*amsun; % in grams
gm = grav*amg; % constant
q = 0.9; % axial ratio
u_1 = 130.0; % in cm/sec
w_1 = 95*10^4.0; % in cm/sec
v = 180*10^4.0; % in cm/sec
vcirc = sqrt(gm/r_1); % circular speed (constant)
nsteps = 50000;
deltat = 5.0*10^11; % in seconds
angmom = r_1*v; % these are the same
angmom2 = angmom^2.0;
e = -gm/r_1+u_1*u_1/2.0+angmom2/(2.0*r_1*r_1);
time=0.0;
for i=1:nsteps
k3_1 = deltat*u_1 %%%%% THIS LINE
k4_1 = deltat*(-gm*r_1/((r_1^2.0+(1+sqrt(1+z_1^2.0))^2.0)^1.5) + angmom2/(r_1^3.0)); % u'=-dphi_dr+lz^2/(r^3.0) with lz=vi*ri this gives deltau
k5_1 = deltat*(angmom/(r_1^2.0)); % theta'=lz/r^2 this gives deltatheta
k6_1 = deltat*w_1;
k7_1 = deltat*(-gm*z_1*(1+sqrt(1+z_1^2.0))/(sqrt(1+z_1^2.0)*(r_1^2.0+(1+sqrt(1+z_1^2.0))^2.0)^1.5));
r_2 = r_1+k3_1/2.0;
u_2 = u_1+k4_1/2.0;
theta_2 = theta_1+k5_1/2.0;
z_2 = z_1 + k6_1/2.0;
w_2 = w_1 + k7_1/2.0;
k3_2 = deltat*u_2;
k4_2 = deltat*(-gm*r_2/((r_2^2.0+(1+sqrt(1+z_2^2.0))^2.0)^1.5)+angmom2/(r_2^3.0));
k5_2 = deltat*(angmom/(r_2^2.0)); % theta'=lz/r^2 =====> deltatheta
k6_2 = deltat*w_2;
k7_2 = deltat*(-gm*z_2*(1+sqrt(1+z_2^2.0))/(sqrt(1+z_2^2.0)*(r_2^2.0+(1+sqrt(1+z_2^2.0))^2.0)^1.5));
r_3 = r_1+k3_2/2.0;
u_3 = u_1+k4_2/2.0;
theta_3 = theta_1+k5_2/2.0;
z_3 = z_1 + k6_2/2.0;
w_3 = w_1 + k7_2/2.0;
k3_3 = deltat*u_3; % r'=u
k4_3 = deltat*(-gm*r_3/((r_3^2.0+(1+sqrt(1+z_3^2.0))^2.0)^1.5)+angmom2/(r_3^3.0));% u'=-dphi_dr+lz^2/(r^3.0)
k5_3 = deltat*(angmom/(r_3^2.0)); % theta'=lz/r^2
k6_3 = deltat*w_3;
k7_3 = deltat*(-gm*z_3*(1+sqrt(1+z_3^2.0))/(sqrt(1+z_3^2.0)*(r_3^2.0+(1+sqrt(1+z_3^2.0))^2.0)^1.5));
r_4 = r_1+k3_2;
u_4 = u_1+k4_2;
theta_4 = theta_1+k5_2;
z_4 = z_1 + k6_2;
w_4 = w_1 + k7_2;
k3_4 = deltat*u_4; % r'=u
k4_4 = deltat*(-gm*r_4/((r_4^2.0+(1+sqrt(1+z_4^2.0))^2.0)^1.5)+angmom2/(r_4^3.0)); % u'=-dphi_dr+lz^2/(r^3.0)
k5_4 = deltat*(angmom/(r_4^2.0)); % theta'=lz/r^2
k6_4 = deltat*w_4;
k7_4 = deltat*(-gm*z_4*(1+sqrt(1+z_4^2.0))/(sqrt(1+z_4^2.0)*(r_4^2.0+(1+sqrt(1+z_4^2.0))^2.0)^1.5));
r_1 = r_1+(k3_1+2.0*k3_2+2.0*k3_3+k3_4)/6.0; % New value of R for next step
u_1 = u_1+(k4_1+2.0*k4_2+2.0*k4_3+k4_4)/6.0; % New value of U for next step
theta_1 = theta_1+(k5_1+2.0*k5_2+2.0*k5_3+k5_4)/6.0; % New value of theta
z_1 = z_1+(k6_1+2.0*k6_2+2.0*k6_3+k6_4)/6.0;
w_1 = w_1+(k7_1+2.0*k7_2+2.0*k7_3+k7_4)/6.0;
e = -gm/r_1+u_1*u_1/2.0+angmom2/(2.0*r_1*r_1); % energy
ecc = (1.0+(2.0*e*angmom2)/(gm^2.0))^0.5; % eccentricity
x(i) = r_1*cos(theta_1)/(1000.0*parsec); % X for plotting orbit
y(i) = r_1*sin(theta_1)/(1000.0*parsec); % Y for plotting orbit
time = time+deltat;
r(i) = r_1;
z(i) = z_1;
time1(i)= time;
end
Note that the anomally occurs on the indicated line.
It's not k3_1 that's capped, it's the calculation of u_1 that returns a value of -3.1445e+24 / deltat (deltat is constant).
u_1 is calculated in the line:
u_1 = u_1+(k4_1+2.0*k4_2+2.0*k4_3+k4_4)/6.0;
After the first iteration, this returns:
u_1(1) = 6.500e+13 % Hard coded before the loop
u_1(2) = -1.432966614767040e+04 % Calculated using the equation above
u_1(3) = -2.878934017859105e+04 % Calculated using the equation above
u_1(4) = -4.324903004768405e+04
Based on the equation u_1(n+1) = u_1(n) + du it looks like du represents a relatively small difference. The difference between the two first values is very large, so I'm assuming it is this calculation that's incorrect.
If you find that that calculation is correct, then your error is in one of these lines:
k4_1 = deltat*(-gm*r_1/((r_1^2.0+(1+sqrt(1+z_1^2.0))^2.0)^1.5)+angmom2/(r_1^3.0)); % u'=-dphi_dr+lz^2/(r^3.0) with lz=vi*ri this gives delta
k4_2 = deltat*(-gm*r_2/((r_2^2.0+(1+sqrt(1+z_2^2.0))^2.0)^1.5)+angmom2/(r_2^3.0));
k4_3 = deltat*(-gm*r_3/((r_3^2.0+(1+sqrt(1+z_3^2.0))^2.0)^1.5)+angmom2/(r_3^3.0));% u'=-dphi_dr+lz^2/(r^3.0)
k4_4 = deltat*(-gm*r_4/((r_4^2.0+(1+sqrt(1+z_4^2.0))^2.0)^1.5)+angmom2/(r_4^3.0)); % u'=-dphi_dr+lz^2/(r^3.0)
You have to have the calfem toolbox to be able to run this code, so I'm hoping some people do!
I've written some code to try and show the displacements, from applied udls, and I've used
uu = extract(Edof,UU);
but they aren't showing on my figure, can anyone figure out why?
addpath ([cd, '/fem']);
% Constants
E = 180e9;
AB = 39.7e2; IB = 4413e4;
AC = 110e2; IC = 9449e4;
q1 = -15e3; w1 = -10e3; w2 = 5e3;
LX1 = 5; LX2 = 10; LX3 = 15; LX4 = 20;
LY1 = 5; LY2 = 8; LY3 = 9;
% Specify Node Coordinates
nodes = [ 0, 0;
0, LY1;
LX1, LY2;
LX2, LY1;
LX2, LY3;
LX3, LY2;
LX4, LY1;
LX4, 0];
% Specify Elements Nodes
conn = [1,2;
2,3;
2,4;
3,4;
3,5;
4,5;
4,6;
4,7;
5,6;
6,7;
7,8];
Nelem = length(conn);
% Initialise Structural Force/Stiffness Matrices
KK = zeros(8*3);
FF = zeros(8*3,1);
% Define Column and Beam Properties
Cprop = [E, AC, IC];
Bprop = [E, AB, IB];
% Initialise system properties
Edof = zeros(Nelem, 1+6);
Ex = zeros(Nelem, 2);
Ey = zeros(Nelem, 2);
Eq = zeros(Nelem, 2);
Eprop = zeros(Nelem,3);
for ii = 1:Nelem
% Look up nodes of element
node1 = conn(ii,1); node2 = conn(ii,2);
% Work out dof based on node number
dof1 = node1*3 + [-2,-1,0];
dof2 = node2*3 + [-2,-1,0];
% Assign to Edof
Edof(ii,:) = [ii, dof1, dof2];
% Look up coordinate based on node
x1 = nodes(node1,1); y1=nodes(node1,2);
x2 = nodes(node2,1); y2=nodes(node2,2);
% Assign to Ex and Ey
Ex(ii,:) = [x1,x2];
Ey(ii,:) = [y1,y2];
% Decide if the element is column or not
if ii==1||11;
Eprop(ii,:) = Cprop;
elseif ii==2;
Eprop(ii,:) = Bprop;
Eq(ii,:) = [0.515*w1, 0.857*w1];
elseif ii==5;
Eprop(ii,:) = Bprop;
Eq(ii,:) = [0.196*w1, 0.98*w1];
elseif ii==3||8;
Eprop(ii,:) = Bprop;
Eq(ii,:) = [0, q1];
elseif ii==9;
Eprop(ii,:) = Bprop;
Eq(ii,:) = [0.196*w2, 0.98*w2];
elseif ii==10;
Eprop(ii,:) = Bprop;
Eq(ii,:) = [0.515*w1, 0.857*w1];
else
Eprop(ii,:) = Bprop;
end
% assemble system
[KE,FE] = beam2e(Ex(ii,:), Ey(ii,:), Eprop(ii,:), Eq(ii,:));
% Combine structural Stiffness forces
[KK,FF] = assem(Edof(ii,:), KK, KE, FF,FE);
end
% Apply Boundary Conditions
bc = [1,0; 2,0; 3,0; 22,0; 23,0; 24,0];
% Solve System
[UU,RR] = solveq(KK,FF,bc);
figure(1)
% Undisplaced Shape
eldraw2(Ex,Ey,[1,1,1])
% Extract Local Displacements
uu = extract(Edof,UU);
% Plot Displaced Shape
scale = 1e2;
eldisp2(Ex,Ey, uu, [1,2,1], scale);
% Add Scale Bar for 10mm
pltscalb2(scale,[1e-2,6,6],[2]);
% Tidy up Graph
axis([-2,22,0,10])
axis equal
xlabel('X, m'); ylabel('Y, m')
title('Simple Portal Frame - Displacement')
I'm trying to produce some computer generated holograms by using MATLAB. I used equally spaced mesh grid to initialize the spatial grid, and I got the following image
This pattern is sort of what I need except the center region. The fringe should be sharp but blurred. I think it might be the problem of the mesh grid. I tried generate a grid in polar coordinates and the map it into Cartesian coordinates by using MATLAB's pol2cart function. Unfortunately, it doesn't work as well. One may suggest that using fine grids. It doesn't work too. I think if I can generate a spiral mesh grid, perhaps the problem is solvable. In addition, the number of the spiral arms could, in general, be arbitrary, could anyone give me a hint on this?
I've attached the code (My final projects are not exactly the same, but it has a similar problem).
clc; clear all; close all;
%% initialization
tic
lambda = 1.55e-6;
k0 = 2*pi/lambda;
c0 = 3e8;
eta0 = 377;
scale = 0.25e-6;
NELEMENTS = 1600;
GoldenRatio = (1+sqrt(5))/2;
g = 2*pi*(1-1/GoldenRatio);
pntsrc = zeros(NELEMENTS, 3);
phisrc = zeros(NELEMENTS, 1);
for idxe = 1:NELEMENTS
pntsrc(idxe, :) = scale*sqrt(idxe)*[cos(idxe*g), sin(idxe*g), 0];
phisrc(idxe) = angle(-sin(idxe*g)+1i*cos(idxe*g));
end
phisrc = 3*phisrc/2; % 3 arms (topological charge ell=3)
%% post processing
sigma = 1;
polfilter = [0, 0, 1i*sigma; 0, 0, -1; -1i*sigma, 1, 0]; % cp filter
xboundl = -100e-6; xboundu = 100e-6;
yboundl = -100e-6; yboundu = 100e-6;
xf = linspace(xboundl, xboundu, 100);
yf = linspace(yboundl, yboundu, 100);
zf = -400e-6;
[pntobsx, pntobsy] = meshgrid(xf, yf);
% how to generate a right mesh grid such that we can generate a decent result?
pntobs = [pntobsx(:), pntobsy(:), zf*ones(size(pntobsx(:)))];
% arbitrary mesh may result in "wrong" results
NPNTOBS = size(pntobs, 1);
nxp = length(xf);
nyp = length(yf);
%% observation
Eobs = zeros(NPNTOBS, 3);
matlabpool open local 12
parfor nobs = 1:NPNTOBS
rp = pntobs(nobs, :);
Erad = [0; 0; 0];
for idx = 1:NELEMENTS
rs = pntsrc(idx, :);
p = exp(sigma*1i*2*phisrc(idx))*[1 -sigma*1i 0]/2; % simplified here
u = rp - rs;
r = sqrt(u(1)^2+u(2)^2+u(3)^2); %norm(u);
u = u/r; % unit vector
ut = [u(2)*p(3)-u(3)*p(2),...
u(3)*p(1)-u(1)*p(3), ...
u(1)*p(2)-u(2)*p(1)]; % cross product: u cross p
Erad = Erad + ... % u cross p cross u, do not use the built-in func
c0*k0^2/4/pi*exp(1i*k0*r)/r*eta0*...
[ut(2)*u(3)-ut(3)*u(2);...
ut(3)*u(1)-ut(1)*u(3); ...
ut(1)*u(2)-ut(2)*u(1)];
end
Eobs(nobs, :) = Erad; % filter neglected here
end
matlabpool close
Eobs = Eobs/max(max(sum(abs(Eobs), 2))); % normailized
%% source, gaussian beam
E0 = 1;
w0 = 80e-6;
theta = 0; % may be titled
RotateX = [1, 0, 0; ...
0, cosd(theta), -sind(theta); ...
0, sind(theta), cosd(theta)];
Esrc = zeros(NPNTOBS, 3);
for nobs = 1:NPNTOBS
rp = RotateX*[pntobs(nobs, 1:2).'; 0];
z = rp(3);
r = sqrt(sum(abs(rp(1:2)).^2));
zR = pi*w0^2/lambda;
wz = w0*sqrt(1+z^2/zR^2);
Rz = z^2+zR^2;
zetaz = atan(z/zR);
gaussian = E0*w0/wz*exp(-r^2/wz^2-1i*k0*z-1i*k0*0*r^2/Rz/2+1i*zetaz);% ...
Esrc(nobs, :) = (polfilter*gaussian*[1; -1i; 0]).'/sqrt(2)/2;
end
Esrc = [Esrc(:, 2), Esrc(:, 3), Esrc(:, 1)];
Esrc = Esrc/max(max(sum(abs(Esrc), 2))); % normailized
toc
%% visualization
fringe = Eobs + Esrc; % I'll have a different formula in my code
normEsrc = reshape(sum(abs(Esrc).^2, 2), [nyp nxp]);
normEobs = reshape(sum(abs(Eobs).^2, 2), [nyp nxp]);
normFringe = reshape(sum(abs(fringe).^2, 2), [nyp nxp]);
close all;
xf0 = linspace(xboundl, xboundu, 500);
yf0 = linspace(yboundl, yboundu, 500);
[xfi, yfi] = meshgrid(xf0, yf0);
data = interp2(xf, yf, normFringe, xfi, yfi);
figure; surf(xfi, yfi, data,'edgecolor','none');
% tri = delaunay(xfi, yfi); trisurf(tri, xfi, yfi, data, 'edgecolor','none');
xlim([xboundl, xboundu])
ylim([yboundl, yboundu])
% colorbar
view(0,90)
colormap(hot)
axis equal
axis off
title('fringe thereo. ', ...
'fontsize', 18)
I didn't read your code because it is too long to do such a simple thing. I wrote mine and here is the result:
the code is
%spiral.m
function val = spiral(x,y)
r = sqrt( x*x + y*y);
a = atan2(y,x)*2+r;
x = r*cos(a);
y = r*sin(a);
val = exp(-x*x*y*y);
val = 1/(1+exp(-1000*(val)));
endfunction
%show.m
n=300;
l = 7;
A = zeros(n);
for i=1:n
for j=1:n
A(i,j) = spiral( 2*(i/n-0.5)*l,2*(j/n-0.5)*l);
end
end
imshow(A) %don't know if imshow is in matlab. I used octave.
the key for the sharpnes is line
val = 1/(1+exp(-1000*(val)));
It is logistic function. The number 1000 defines how sharp your image will be. So lower it for more blurry image or higher it for sharper.
I hope this answers your question ;)
Edit: It is real fun to play with. Here is another spiral:
function val = spiral(x,y)
s= 0.5;
r = sqrt( x*x + y*y);
a = atan2(y,x)*2+r*r*r;
x = r*cos(a);
y = r*sin(a);
val = 0;
if (abs(x)<s )
val = s-abs(x);
endif
if(abs(y)<s)
val =max(s-abs(y),val);
endif
%val = 1/(1+exp(-1*(val)));
endfunction
Edit2: Fun, fun, fun! Here the arms do not get thinner.
function val = spiral(x,y)
s= 0.1;
r = sqrt( x*x + y*y);
a = atan2(y,x)*2+r*r; % h
x = r*cos(a);
y = r*sin(a);
val = 0;
s = s*exp(r);
if (abs(x)<s )
val = s-abs(x);
endif
if(abs(y)<s)
val =max(s-abs(y),val);
endif
val = val/s;
val = 1/(1+exp(-10*(val)));
endfunction
Damn your question I really need to study for my exam, arghhh!
Edit3:
I vectorised the code and it runs much faster.
%spiral.m
function val = spiral(x,y)
s= 2;
r = sqrt( x.*x + y.*y);
a = atan2(y,x)*8+exp(r);
x = r.*cos(a);
y = r.*sin(a);
val = 0;
s = s.*exp(-0.1*r);
val = r;
val = (abs(x)<s ).*(s-abs(x));
val = val./s;
% val = 1./(1.+exp(-1*(val)));
endfunction
%show.m
n=1000;
l = 3;
A = zeros(n);
[X,Y] = meshgrid(-l:2*l/n:l);
A = spiral(X,Y);
imshow(A)
Sorry, can't post figures. But this might help. I wrote it for experiments with amplitude spatial modulators...
R=70; % radius of curvature of fresnel lens (in pixel units)
A=0; % oblique incidence by linear grating (1=oblique 0=collinear)
B=1; % expanding by fresnel lens (1=yes 0=no)
L=7; % topological charge
Lambda=30; % linear grating fringe spacing (in pixels)
aspect=1/2; % fraction of fringe period that is white/clear
xsize=1024; % resolution (xres x yres number data pts calculated)
ysize=768; %
% define the X and Y ranges (defined to skip zero)
xvec = linspace(-xsize/2, xsize/2, xsize); % list of x values
yvec = linspace(-ysize/2, ysize/2, ysize); % list of y values
% define the meshes - matrices linear in one dimension
[xmesh, ymesh] = meshgrid(xvec, yvec);
% calculate the individual phase components
vortexPh = atan2(ymesh,xmesh); % the vortex phase
linPh = -2*pi*ymesh; % a phase of linear grating
radialPh = (xmesh.^2+ymesh.^2); % a phase of defocus
% combine the phases with appropriate scales (phases are additive)
% the 'pi' at the end causes inversion of the pattern
Ph = L*vortexPh + A*linPh/Lambda + B*radialPh/R^2;
% transmittance function (the real part of exp(I*Ph))
T = cos(Ph);
% the binary version
binT = T > cos(pi*aspect);
% plot the pattern
% imagesc(binT)
imagesc(T)
colormap(gray)