I have this program that finds the vanishing point for a given set of images. Is there a way to find the distance from the camera and the vanishing point?
Also once the vanishing point is found out, I manually need to find the X and Y coordinates using the tool provided in matlab. How can i code a snippet that writes all the X and Y coordinates into a text or excel file?
Also is there a better and simpler way to find the vanishing point in matlab?
Matlab Calling Function to find Vanishing Point:
clear all; close all;
dname = 'Height';
files = dir(dname);
files(1) = [];
files(1) = [];
for i=1:size(files, 1)
original = imread(fullfile(dname, files(i).name));
original = imresize(original,0.35);
im = im2double(rgb2gray(original));
[row, col] = findVanishingPoint(im);
imshow(original);hold;plot(col,row,'rx');
saveas(gcf,strcat('Height_Result',num2str(i)),'jpg');
close
end
The findVanishingPoint function:
function [row, col] = findVanishingPoint(im)
DEBUG = 0;
IM = fft2(im);
ROWS = size(IM,1); COLS = size(IM,2);
PERIOD = 2^floor(log2(COLS)-5)+2;
SIZE = floor(10*PERIOD/pi);
SIGMA = SIZE/9;
NORIENT = 72;
E = 8;
[C, S] = createGaborBank(SIZE, PERIOD, SIGMA, NORIENT, ROWS, COLS, E);
D = ones(ROWS, COLS);
AMAX = ifftshift(real(ifft2(C{1}.*IM)).^2+real(ifft2(S{1}.*IM))).^2;
for n=2:NORIENT
A = ifftshift(real(ifft2(C{n}.*IM)).^2+real(ifft2(S{n}.*IM))).^2;
D(find(A > AMAX)) = n;
AMAX = max(A, AMAX);
if (DEBUG==1)
colormap('hot');subplot(131);imagesc(real(A));subplot(132);imagesc(real(AMAX));colorbar;
subplot(133);imagesc(D);
pause
end
end
if (DEBUG==2)
figure('DoubleBuffer','on');
end
T = mean(AMAX(:))-3*std(AMAX(:));
VOTE = zeros(ROWS, COLS);
for row=round(1+SIZE/2):round(ROWS-SIZE/2)
for col=round(1+SIZE/2):round(COLS-SIZE/2)
if (AMAX(row,col) > T)
indices = lineBresenham(ROWS, COLS, col, row, D(row, col)*pi/NORIENT-pi/2);
VOTE(indices) = VOTE(indices)+AMAX(row,col);
end
end
if (DEBUG==2)
colormap('hot');imagesc(VOTE);pause;
end
end
if (DEBUG==2)
close
end
M=1;
[b index] = sort(-VOTE(:));
col = floor((index(1:M)-1) / ROWS)+1;
row = mod(index(1:M)-1, ROWS)+1;
col = round(mean(col));
row = round(mean(row));
The creatGaborBank function:
function [C, S] = createGaborBank(SIZE, PERIOD, SIGMA, NORIENT, ROWS, COLS, E)
if (length(NORIENT)==1)
orientations=[1:NORIENT];
else
orientations = NORIENT;
NORIENT = max(orientations);
end
for n=orientations
[C{n}, S{n}] = gabormask(SIZE, SIGMA, PERIOD, n*pi/NORIENT);
C{n} = fft2(padWithZeros(C{n}, ROWS, COLS));
S{n} = fft2(padWithZeros(S{n}, ROWS, COLS));
end
The gabormask function:
function [cmask, smask] = gabormask(Size, sigma, period, orient, E)
if nargin < 5; E = 8; end;
if nargin < 4; orient = 0; end;
if nargin < 3; period = []; end;
if nargin < 2; sigma = []; end;
if nargin < 1; Size = []; end;
if isempty(period) & isempty(sigma); sigma = 5; end;
if isempty(period); period = sigma*2*sqrt(2); end;
if isempty(sigma); sigma = period/(2*sqrt(2)); end;
if isempty(Size); Size = 2*round(2.575*sigma) + 1; end;
if length(Size) == 1
sx = Size-1; sy = sx;
elseif all(size(Size) == [1 2])
sy = Size(1)-1; sx = Size(2)-1;
else
error('Size must be scalar or 1-by-2 vector');
end;
hy = sy/2; hx = sx/2;
[x, y] = meshgrid(-hx:sx-hx, -hy:sy-hy);
omega = 2*pi/period;
cs = omega * cos(orient);
sn = omega * sin(orient);
k = -1/(E*sigma*sigma);
g = exp(k * (E*x.*x + y.*y));
xp = x * cs + y * sn;
cx = cos(xp);
cmask = g .* cx;
sx = sin(xp);
smask = g .* sx;
cmask = cmask - mean(cmask(:));
cmask = cmask/sum(abs(cmask(:)));
smask = smask - mean(smask(:));
smask = smask/sum(abs(smask(:)));
The padWithZeros function:
function out = padWithZeros(in, ROWS, COLS)
out = padarray(in,[floor((ROWS-size(in,1))/2) floor((COLS-size(in,2))/2)],0,'both');
if size(out,1) == ROWS-1
out = padarray(out,[1 0],0,'pre');
end
if size(out,2) == COLS-1
out = padarray(out,[0 1],0,'pre');
end
The findHorizonEdge function:
function row = findHorizon(im)
DEBUG = 2;
ROWS = size(im,1); COLS = size(im,2);
e = edge(im,'sobel', [], 'horizontal');
dd = sum(e, 2);
N=3;
row = 1;
M = 0;
for i=1+N:length(dd)-N
m = sum(dd(i-N:i+N));
if (m > M)
M = m;
row = i;
end
end
imshow(e);pause
The findHorizon function:
function row = findHorizon(im)
DEBUG = 2;
IM = fft2(im);
ROWS = size(IM,1); COLS = size(IM,2);
PERIOD = 2^floor(log2(COLS)-5)+2;
SIZE = floor(10*PERIOD/pi);
SIGMA = SIZE/9;
NORIENT = 72;
E = 16;
orientations = [NORIENT/2-10:NORIENT/2+10];
[C, S] = createGaborBank(SIZE, PERIOD, SIGMA, orientations, ROWS, COLS, E);
ASUM = zeros(ROWS, COLS);
for n=orientations
A = ifftshift(real(ifft2(C{n}.*IM)).^2+real(ifft2(S{n}.*IM))).^2;
ASUM = ASUM + A;
if (DEBUG==1)
colormap('hot');subplot(131);imagesc(real(A));subplot(132);imagesc(real(AMAX));colorbar;
pause
end
end
ASUM(1:round(1+SIZE/2), :)=0; ASUM(end-round(SIZE/2):end, :)=0;
ASUM(:,end-round(SIZE/2):end)=0; ASUM(:, 1:1+round(SIZE/2))=0;
dd = sum(ASUM, 2);
[temp, row] = sort(-dd);
row = round(mean(row(1:10)));
if (DEBUG == 2)
imagesc(ASUM);hold on;line([1:COLS],repmat(row,COLS));
pause
end
The lineImage function:
function v = lineimage(x0, y0, angle, s)
if (abs(tan(angle)) > 1e015)
a(1,:) = repmat(x0,s(1),1)';
a(2,:) = [1:s(1)];
elseif (abs(tan(angle)) < 1e-015)
a(2,:) = repmat(y0,s(2),1)';
a(1,:) = [1:s(2)];
else
k = tan(angle);
hiX = round((1-(s(1)-y0+1)+k*x0)/k);
loX = round((s(1)-(s(1)-y0+1)+k*x0)/k);
temp = max(loX, hiX);
loX = max(min(loX, hiX), 1);
hiX = min(s(2),temp);
a(1,:) = [loX:hiX];
a(2,:) = max(1, floor(s(1)-(k*a(1,:)+(s(1)-y0+1)-k*x0)));
end
v = (a(1,:)-1).*s(1)+a(2,:);
The lineVector function:
function [abscissa, ordinate] = linevector(x0, y0, angle, s)
if (rad2deg(angle) == 90)
abscissa = repmat(x0,s(1),1);
ordinate = [1:s(1)];
else
k = tan(angle);
hiX = round((1-(s(1)-y0+1)+k*x0)/k);
loX = round((s(1)-(s(1)-y0+1)+k*x0)/k);
temp = max(loX, hiX);
loX = max(min(loX, hiX), 1);
hiX = min(s(2),temp);
abscissa = [loX:hiX];
ordinate = k*abscissa+((s(1)-y0+1)-k*x0);
end
The lineBresenham function:
function [i] = lineBresenham(H,W,Sx,Sy,angle)
k = tan(angle);
if (angle == pi || angle == 0)
Ex = W;
Ey = Sy;
Sx = 1;
elseif (angle == pi/2)
Ey = 1;
i = (Sx-1)*H+[Ey:Sy];
return;
elseif k>0 & k < (Sy-1)/(W-Sx)
Ex = W;
Ey = round(Sy-tan(angle)*(Ex-Sx));
elseif k < 0 & abs(k) < (Sy-1)/(Sx-1)
Ex = 1;
Ey = round(Sy-tan(angle)*(Ex-Sx));
else
Ey = 1;
Ex = round((Sy-1)/tan(angle)+Sx);
end
Dx = Ex - Sx;
Dy = Ey - Sy;
iCoords=1;
if(abs(Dy) <= abs(Dx))
if(Ex >= Sx)
D = 2*Dy + Dx;
IncH = 2*Dy;
IncD = 2*(Dy + Dx);
X = Sx;
Y = Sy;
i(iCoords) = (Sx-1)*H+Sy;
iCoords = iCoords + 1;
while(X < Ex)
if(D >= 0)
D = D + IncH;
X = X + 1;
else
D = D + IncD;
X = X + 1;
Y = Y - 1;
end
i(iCoords) = (X-1)*H+Y;
iCoords = iCoords + 1;
end
else
D = -2*Dy + Dx;
IncH = -2*Dy;
IncD = 2*(-Dy + Dx);
X = Sx;
Y = Sy;
i(iCoords) = (Sx-1)*H+Sy;
iCoords = iCoords + 1;
while(X > Ex)
if(D <= 0)
D = D + IncH;
X = X - 1;
else
D = D + IncD;
X = X - 1;
Y = Y - 1;
end
i(iCoords) = (X-1)*H+Y;
iCoords = iCoords + 1;
end
end
else
Tmp = Ex;
Ex = Ey;
Ey = Tmp;
Tmp = Sx;
Sx = Sy;
Sy = Tmp;
Dx = Ex - Sx;
Dy = Ey - Sy;
if(Ex >= Sx)
D = 2*Dy + Dx;
IncH = 2*Dy;
IncD = 2*(Dy + Dx);
X = Sx;
Y = Sy;
i(iCoords) = (Sy-1)*H+Sx;
iCoords = iCoords + 1;
while(X < Ex)
if(D >= 0)
D = D + IncH;
X = X + 1;
else
D = D + IncD;
X = X + 1;
Y = Y - 1;
end
i(iCoords) = (Y-1)*H+X;
iCoords = iCoords + 1;
end
else
D = -2*Dy + Dx;
IncH = -2*Dy;
IncD = 2*(-Dy + Dx);
X = Sx;
Y = Sy;
i(iCoords) = (Sy-1)*H+Sx;
iCoords = iCoords + 1;
while(X > Ex)
if(D <= 0)
D = D + IncH;
X = X - 1;
else
D = D + IncD;
X = X - 1;
Y = Y - 1;
end
i(iCoords) = (Y-1)*H+X;
iCoords = iCoords + 1;
end
end
end
The vanishing point is at infinity hence the distance to the camera is of no use.
Use xlswrite or dlmwrite to write into excel or text file respectively.
Related
I am getting an error when I run this code for disc waves. The code is attached.
The Error is in line 137 and 292. Please help in resolving issue.
function waves
% WAVES Wave equation in one and two space dimensions.
% The two-dimensional domains include a pi-by-pi square, a unit disc,
% a three-quarter circular sector and the L-shaped union of three squares.
% The eigenfunctions of the square are sin(m*x)*sin(n*y). With polar
% coordinates, the eigenfunctions of the disc and the sector involve Bessel
% functions. The eigenfunctions of the L-shaped domain also involve
% Bessel functions and are computed by the MATLAB function membranetx.m.
% 2-D eigenvalues and eigenfunctions
m = 11; % Determines number of grid points
speed = 1;
bvals = [1; 0; 0; 0; 0];
t = 0;
while bvals(5) == 0
% Initialize figure
shg
clf reset
set(gcf,'doublebuffer','on','menubar','none','tag','', ...
'numbertitle','off','name','Waves','colormap',hot(64));
for k= 1:5
b(k) = uicontrol('style','toggle','value',bvals(k), ...
'units','normal','position',[.15*k .01 .14 .05]);
end
set(b(1),'style','pop','string', ...
{'1-d','square','disc','sector'})
set(b(2),'string','modes/wave')
set(b(3),'string','slower')
set(b(4),'string','faster')
set(b(5),'string','close')
if bvals(3)==1
speed = speed/sqrt(2);
set(b(3),'value',0);
end
if bvals(4)==1
speed = speed*sqrt(2);
set(b(4),'value',0);
end
bvals = cell2mat(get(b,'value'));
region = bvals(1);
modes = bvals(2)==0;
if region == 1
% 1-D
x = (0:4*m)/(4*m)*pi;
orange = [1 1/3 0];
gray = get(gcf,'color');
if modes
% 1-D modes
for k = 1:4
subplot(2,2,k)
h(k) = plot(x,zeros(size(x)));
axis([0 pi -3/2 3/2])
set(h(k),'color',orange,'linewidth',3)
set(gca,'color',gray','xtick',[],'ytick',[])
end
delta = 0.005*speed;
bvs = bvals;
while all(bvs == bvals)
t = t + delta;
for k = 1:4
u = sin(k*t)*sin(k*x);
set(h(k),'ydata',u)
end
drawnow
bvs = cell2mat(get(b,'value'));
end
else
% 1-D wave
h = plot(x,zeros(size(x)));
axis([0 pi -9/4 9/4])
set(h,'color',orange,'linewidth',3)
set(gca,'color',gray','xtick',[],'ytick',[])
delta = 0.005*speed;
a = 1./(1:4);
bvs = bvals;
while all(bvs == bvals)
t = t + delta;
u = zeros(size(x));
for k = 1:4
u = u + a(k)*sin(k*t)*sin(k*x);
end
set(h,'ydata',u)
drawnow
bvs = cell2mat(get(b,'value'));
end
end
elseif region <= 5
switch region
case 2
% Square
x = (0:2*m)/(2*m)*pi;
y = x';
lambda = zeros(4,1);
V = cell(4,1);
k = 0;
for i = 1:2
for j = 1:2
k = k+1;
lambda(k) = i^2 + j^2;
V{k} = sin(i*y)*sin(j*x);
end
end
ax = [0 pi 0 pi -1.75 1.75];
case 3
% Disc, mu = zeros of J_0(r) and J_1(r)
mu = [bjzeros(0,2) bjzeros(1,2)];
[r,theta] = meshgrid((0:m)/m,(-m:m)/m*pi);
x = r.*cos(theta);
y = r.*sin(theta);
V = cell(4,1);
k = 0;
for j = 0:1
for i = 1:2
k = k+1;
if j == 0
V{k} = besselj(0,mu(k)*r);
else
V{k} = besselj(j,mu(k)*r).*sin(j*theta);
end
V{k} = V{k}/max(max(abs(V{k})));
end
end
lambda = mu.^2;
ax = [-1 1 -1 1 -1.75 1.75];
case 4
% Circular sector , mu = zeros of J_(2/3)(r) and J_(4/3)(r)
mu = [bjzeros(2/3,2) bjzeros(4/3,2)];
[r,theta] = meshgrid((0:m)/m,(3/4)*(0:2*m)/m*pi);
x = r.*cos(theta+pi);
y = r.*sin(theta+pi);
V = cell(4,1);
k = 0;
for j = 1:2
for i = 1:2
k = k+1;
alpha = 2*j/3;
V{k} = besselj(alpha,mu(k)*r).*sin(alpha*theta);
V{k} = V{k}/max(max(abs(V{k})));
end
end
lambda = mu.^2;
ax = [-1 1 -1 1 -1.75 1.75];
case 5\
% L-membrane
x = (-m:m)/m;
y = x';
lambda = zeros(4,1);
V = cell(4,1);
for k = 1:4
[L lambda(k)] = membranetx(k,m,9,9);
L(m+2:2*m+1,m+2:2*m+1) = NaN;
V{k} = rot90(L,-1);
end
ax = [-1 1 -1 1 -1.75 1.75];
end
if modes
% 2-D modes
p = [.02 .52 .02 .52];
q = [.52 .52 .02 .02];
for k = 1:4
axes('position',[p(k) q(k) .46 .46]);
h(k) = surf(x,y,zeros(size(V{k})));
axis(ax)
axis off
view(225,30);
caxis([-1.5 1]);
end
delta = .08*speed;
mu = sqrt(lambda(:));
bvs = bvals;
while all(bvs == bvals)
t = t + delta;
for k = 1:4
U = 1.5*sin(mu(k)*t)*V{k};
set(h(k),'zdata',U)
set(h(k),'cdata',U)
end
drawnow
bvs = cell2mat(get(b,'value'));
end
else
% 2-D wave
h = surf(x,y,zeros(size(V{1})));
axis(ax);
axis off
view(225,30);
caxis([-1.5 1]);
delta = .02*speed;
mu = sqrt(lambda(:));
a = 1.25./(1:4);
bvs = bvals;
while all(bvs == bvals)
t = t + delta;
U = zeros(size(V{1}));
for k = 1:4
U = U + a(k)*sin(mu(k)*t)*V{k};
end
set(h,'zdata',U)
set(h,'cdata',U)
drawnow
bvs = cell2mat(get(b,'value'));
end
end
elseif region == 6
figure
bizcard
set(b(1),'value',1)
end
% Retain uicontrol values
bvals = cell2mat(get(b,'value'));
end
close
% -------------------------------
function z = bjzeros(n,k)
% BJZEROS Zeros of the Bessel function.
% z = bjzeros(n,k) is the first k zeros of besselj(n,x)
% delta must be chosen so that the linear search can take
% steps as large as possible without skipping any zeros.
% delta is approx bjzero(0,2)-bjzero(0,1)
delta = .99*pi;
Jsubn = inline('besselj(n,x)''x','n');
a = n+1;
fa = besselj(n,a);
z = zeros(1,k);
j = 0;
while j < k
b = a + delta;
fb = besselj(n,b);
if sign(fb) ~= sign(fa)
j = j+1;
z(j) = fzerotx(Jsubn,[a b],n);
end
a = b;
fa = fb;
end
I have written a code that reads an image and does scaling of 2 units in x-axis direction. Scaling matrix is filled by values that are read from a text file.
Scaling Matrix looks like
2 0 0
0 1 0
0 0 1
Original Image
Transformed Image (Scaling of 2 units in X-direction)
Code
file = importdata('transform_c.txt');
fileData = file.data;
image = imread('mecca06.pgm');
[row, col] = size(image);
scalingMatrix = zeros(3,3);
scalingMatrix(1,1) = fileData(2);
scalingMatrix(1,2) = fileData(3);
scalingMatrix(1,3) = fileData(4);
scalingMatrix(2,1) = fileData(5);
scalingMatrix(2,2) = fileData(6);
scalingMatrix(2,3) = fileData(7);
scalingMatrix(3,1) = fileData(8);
scalingMatrix(3,2) = fileData(9);
scalingMatrix(3,3) = fileData(10);
m1Inverse = inv(scalingMatrix);
outputImage = applyTransformation(image, row, col, m1Inverse);
figure
imshow(outputImage);
function outImage = applyTransformation(image, row, col, m1Inverse)
points = zeros(3,1);
for i=1:row
for j=1:col
points(1,1) = i;
points(2,1) = j;
points(3,1) = 1;
m2 = m1Inverse * points;
x = m2(1,1);
y = m2(2,1);
xlb = floor(x);
ylb = floor(y);
if(xlb <= 0)
xlb = 1;
end
if(xlb > row)
xlb = row;
end
if(ylb <= 0)
ylb = 1;
end
if(ylb > col)
ylb = col;
end
xub = xlb+1;
yub = ylb+1;
if(xub <= 0)
xub = 1;
end
if(xub > row)
xub = row;
end
if(yub <= 0)
yub = 1;
end
if(yub > col)
yub = col;
end
exub = xub-x;
eyub = yub-y;
exlb = x-xlb;
eylb = y-ylb;
outImage(i,j) = (exub*eyub*image(xlb,ylb))+(exlb*eyub*image(xub,ylb))+(exub*eylb*image(xlb,yub))+(exlb*eylb*image(xub,yub));
end
end
end
My question is how can i modify the above code to get uncropped image ?
I want to get following image
Try to see if this code produces the result you need:
img = imread('aSxLS.png');
scale = [
2 0 0
0 1 0
0 0 1
];
tform = maketform('affine',inv(scale));
img_tform = imtransform(img,tform,'bilinear');
figure();
imshow(img);
figure();
imshow(img_tform);
I changed the script by adding text instead of an image.
I want to wave was vertically downwards. I know that a little editing but I tried different options and it did not work.
http://jsfiddle.net/7ynn4/3/
var options = {
period:100,
squeeze:0,
wavelength:40,
amplitude:30,
shading:300,
fps:30
}
var ca = document.getElementById('canvas');
var ctx = ca.getContext('2d');
ctx.canvas.width = 400;
ctx.canvas.height = 150;
ctx.font = 'bold 45pt Arial';
ctx.textAlign = 'center';
ctx.fillStyle = 'blue';
ctx.fillText('Hello World', 170, 60);
w = canvas.width,
h = canvas.height,
od = ctx.getImageData( 0, 0, w, h ).data;
setInterval(function() {
var id = ctx.getImageData( 0, 0, w, h ),
d = id.data,
now = ( new Date() )/options.period,
y,
x,
lastO,
shade,
sq = ( y - h/2 ) * options.squeeze,
px,
pct,
o,
y2,
opx;
for ( y = 0; y < h; y += 1 ) {
lastO = 0;
shade = 0;
sq = ( y - h/2 ) * options.squeeze;
for ( x = 0; x < w; x += 1 ) {
px = ( y * w + x ) * 4;
pct = x/w;
o = Math.sin( x/options.wavelength - now ) * options.amplitude * pct;
y2 = y + ( o + sq * pct ) << 0;
opx = ( y2 * w + x ) * 4;
shade = (o-lastO) * options.shading;
d[px ] = od[opx ]+shade;
d[px+1] = od[opx+1]+shade;
d[px+2] = od[opx+2]+shade;
d[px+3] = od[opx+3];
lastO = o;
}
}
ctx.putImageData( id, 0, 0 );
},
1000/options.fps
);
You just flip the values around so that x is affected instead of y -
... vars cut, but replace y2 with x2 ...
/// reversed from here
for (x = 0; x < w; x += 1) {
lastO = 0;
shade = 0;
sq = (x - w * 0.5) * options.squeeze;
for (y = 0; y < h; y += 1) {
px = (y * w + x) * 4;
pct = y / h;
o = Math.sin(y/options.wavelength-now) * options.amplitude * pct;
/// the important one: you might need to compensate here (-5)
x2 = x - 5 + (o + sq * pct) | 0;
opx = (x2 + y * w) * 4;
shade = (o - lastO) * options.shading;
d[px] = od[opx] + shade;
d[px + 1] = od[opx + 1] + shade;
d[px + 2] = od[opx + 2] + shade;
d[px + 3] = od[opx + 3];
lastO = o;
}
}
ctx.putImageData(id, 0, 0);
MODIFIED FIDDLE HERE
I am using the following lines of code for edge detection using canny edge detector :
I=imread('bradd.tif');
figure,imshow(I);
IDtemp = im2double(I);
[r c]=size(I);
ID(r,c) = 0;
IDx(r,c) = 0;
IDfil(r,c) = 0;
IDxx(r,c) = 0;
IDy(r,c) = 0;
IDyy(r,c) = 0;
mod(r,c) = 0;
for i= 1 : r+4
for j = 1:c+4
if(i<=2 || j<=2 || i>=r+3 || j>=c+3)
ID(i,j) = 0;
else
ID(i,j) = IDtemp(i-2,j-2);
end;
end
end
%figure,imshow(ID);
filter=[2 4 5 4 2;4 9 12 9 4;5 12 15 12 5;4 9 12 9 4;2 4 5 4 2];
for i=1:5
for j=1:5
filter(i,j)=filter(i,j)/159;
end
end
%figure,imshow(filter);
for v = 3 : r
for u = 3 : c
sum = 0;
for i = -2 : 2
for j = -2 : 2
sum = sum + (ID(u+i, v+j) * filter(i+3, j+3));
end
end
IDx(u,v) = sum;
end
end
%figure,imshow(IDx);
IDxtemp = IDx;
for i= 1 : r+2
for j = 1:c+2
if(i<=1 || j<=1 || i>=r || j>=c)
IDfil(i,j) = 0;
else
IDfil(i,j) = IDxtemp(i-1,j-1);
end;
end
end
%figure,imshow(IDfil);
Mx = [-1 0 1; -2 0 2; -1 0 1]; % Sobel Mask in X-Direction
My = [-1 -2 -1; 0 0 0; 1 2 1]; % Sobel Mask in Y-Direction
for u = 2:r
for v = 2:c
sum1 = 0;
for i=-1:1
for j=-1:1
sum1 = sum1 + IDfil(u + i, v + j)* Mx(i + 2,j + 2);
end
end
IDxx(u,v) = sum1;
end;
end
%figure,imshow(IDxx);
for u = 2:r
for v = 2:c
sum2 = 0;
for i=-1:1
for j=-1:1
sum2 = sum2 + IDfil(u + i, v + j)* My(i + 2,j + 2);
end
end
IDyy(u,v) = sum2;
end
end
%figure,imshow(IDyy);
for u = 1:r
for v = 1:c
mod(u,v) = sqrt(IDxx(u,v)^2 + IDyy(u,v)^2) ;
%mod(u,v) = sqrt(IDxx(u,v)^2 + IDyy(u,v)^2);
end
end
%figure,imshow(mod);
modtemp = mod;
for i= 1 : r+2
for j = 1:c+2
if(i<=1 || j<=1 || i>=r || j>=c)
mod(i,j) = 0;
else
mod(i,j) = modtemp(i-1,j-1);
end;
end
end
%figure,imshow(mod);
theta(u,v) = 0;
supimg(u,v) = 0;
ntheta(u,v) = 0;
for u = 2 : r
for v = 2 : c
theta(u,v) = atand(IDyy(u,v)/IDxx(u,v));
if ((theta(u,v) > 0 ) && (theta(u,v) < 22.5) || (theta(u,v) > 157.5) && (theta(u,v) < -157.5))
ntheta(u,v) = 0;
end
if ((theta(u,v) > 22.5) && (theta(u,v) < 67.5) || (theta(u,v) < -112.5) && (theta(u,v) > -157.5))
ntheta(u,v) = 45;
end
if ((theta(u,v) > 67.5 && theta(u,v) < 112.5) || (theta(u,v) < -67.5 && theta(u,v) > 112.5))
ntheta(u,v) = 90;
end
if ((theta(u,v) > 112.5 && theta(u,v) <= 157.5) || (theta(u,v) < -22.5 && theta(u,v) > -67.5))
ntheta(u,v) = 135;
end
if (ntheta(u,v) == 0)
if (mod(u, v) > mod(u, v-1) && mod(u, v) > mod(u, v+1))
supimg(u,v) = mod(u,v);
else supimg(u,v) = 0;
end
end
if (ntheta(u,v) == 45)
if (mod(u, v) > mod(u+1, v-1) && mod(u, v) > mod(u-1, v+1))
supimg(u,v) = mod(u,v);
else supimg(u,v) = 0;
end
end
if (ntheta(u,v) == 90)
if (mod(u, v) > mod(u-1, v) && mod(u, v) > mod(u+1, v))
supimg(u,v) = mod(u,v);
else supimg(u,v) = 0;
end
end
if (ntheta(u,v) == 135)
if (mod(u, v) > mod(u-1, v-1) && mod(u, v) > mod(u+1, v+1))
supimg(u,v) = mod(u,v);
else supimg(u,v) = 0;
end
end
end
end
%figure,imshow(ntheta);
th = 0.2;
tl = 0.1;
resimg(u,v)= 0;
for u = 2 : r-1
for v = 2 : c-1
if(supimg(u,v) > th)
resimg(u,v) = 1;
else
if(supimg(u,v) >= tl && supimg(u,v) <= th )
resimg(u,v) = 1;
else
if (supimg(u,v) < tl)
resimg(u,v) = 0;
end
end
end
if (supimg(u-1,v-1) > th || supimg(u,v-1) > th || supimg(u+1,v-1) > th || supimg(u+1,v) > th || supimg(u+1,v+1) > th || supimg(u,v+1) > th || supimg(u-1,v+1) > th || supimg(u-1,v) > th)
resimg(u,v) = 1;
else
resimg(u,v) = 0;
end
end
end
figure,imshow(supimg);
figure,imshow(resimg);
However, for some of the images it is working fine, while for others it is showing the following error :
Index exceeds matrix dimensions.
Error in canny_edge (line 45)
sum = sum + (ID(u+i, v+j) * filter(i+3, j+3));
Can someone help me sort out this problem ??
Thanks and Regards.
Your loop ranges are in the wrong order leading to the error. If you modify your loop ranges to this
for u = 3 : r
for v = 3 : c
sum = 0;
for i = -2 : 2
for j = -2 : 2
sum = sum + (ID(u+i, v+j) * filter(i+3, j+3));
end
end
IDx(u,v) = sum;
end
end
the problem is solved.
My guess is that the code worked only for square images with c==r.
Note you are not making use of Matlab's vectorization capability, which allows you to shorten the first steps to:
ID = [zeros(2,c+4) ; [zeros(r,2) IDtemp zeros(r,2)]; zeros(2,c+4)];
filter=[2 4 5 4 2;4 9 12 9 4;5 12 15 12 5;4 9 12 9 4;2 4 5 4 2];
filter=filter/159;
for u = 1 : r
for v = 1 : c
IDx(u,v) = sum(reshape(ID(u+[0:4], v+[0:4]).* filter,25,1));
end
end
and this last loop can also be collapsed further but that might make readability an issue.
(edit) The loop can (for instance) be replaced with
IDx = conv2(ID, filter,'same');
I'm using matlab to implement a multilayer neural network. In the code I represent
the value of each node AS netValue{k}
the weight between layer k and k + 1 AS weight{k}
etc.
Since these data is three-dimensional, I have to use cell to hold a 2-D matrix to enable matrix multiply.
So it becomes really really slow to train the model, which I expect to have resulted from the usage of cell.
Can anyone tell me how to accelerate this code? Thanks
clc;
close all;
clear all;
input = [-2 : 0.4 : 2;-2:0.4:2];
ican = 4;
depth = 4; % total layer - 1, by convension
[featureNum , sampleNum] = size(input);
levelNum(1) = featureNum;
levelNum(2) = 5;
levelNum(3) = 5;
levelNum(4) = 5;
levelNum(5) = 2;
weight = cell(0);
for k = 1 : depth
weight{k} = rand(levelNum(k+1), levelNum(k)) - 2 * rand(levelNum(k+1) , levelNum(k));
threshold{k} = rand(levelNum(k+1) , 1) - 2 * rand(levelNum(k+1) , 1);
end
runCount = 0;
sumMSE = 1; % init MSE
minError = 1e-5;
afa = 0.1; % step of "gradient ascendence"
% training loop
while(runCount < 100000 & sumMSE > minError)
sumMSE = 0; % sum of MSE
for i = 1 : sampleNum % sample loop
netValue{1} = input(:,i);
for k = 2 : depth
netValue{k} = weight{k-1} * netValue{k-1} + threshold{k-1}; %calculate each layer
netValue{k} = 1 ./ (1 + exp(-netValue{k})); %apply logistic function
end
netValue{depth+1} = weight{depth} * netValue{depth} + threshold{depth}; %output layer
e = 1 + sin((pi / 4) * ican * netValue{1}) - netValue{depth + 1}; %calc error
assistS{depth} = diag(ones(size(netValue{depth+1})));
s{depth} = -2 * assistS{depth} * e;
for k = depth - 1 : -1 : 1
assistS{k} = diag((1-netValue{k+1}).*netValue{k+1});
s{k} = assistS{k} * weight{k+1}' * s{k+1};
end
for k = 1 : depth
weight{k} = weight{k} - afa * s{k} * netValue{k}';
threshold{k} = threshold{k} - afa * s{k};
end
sumMSE = sumMSE + e' * e;
end
sumMSE = sqrt(sumMSE) / sampleNum;
runCount = runCount + 1;
end
x = [-2 : 0.1 : 2;-2:0.1:2];
y = zeros(size(x));
z = 1 + sin((pi / 4) * ican .* x);
% test
for i = 1 : length(x)
netValue{1} = x(:,i);
for k = 2 : depth
netValue{k} = weight{k-1} * netValue{k-1} + threshold{k-1};
netValue{k} = 1 ./ ( 1 + exp(-netValue{k}));
end
y(:, i) = weight{depth} * netValue{depth} + threshold{depth};
end
plot(x(1,:) , y(1,:) , 'r');
hold on;
plot(x(1,:) , z(1,:) , 'g');
hold off;
Have you used the profiler to find out what functions are actually slowing down your code? It shows what lines take the most time to execute.