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% function [xs,ys,gx,gy,par,threshold,mag,mage,g,FIe,FIo,mago] = quadedgep(I,par,threshold);
% Input:
% I = image
% par = vector for 4 parameters
% [number of filter orientations, number of scale, filter size, elongation]
% To use default values, put 0.
% threshold = threshold on edge strength
% Output:
% [x,y,gx,gy] = locations and gradients of an ordered list of edgels
% x,y could be horizontal or vertical or 45 between pixel sites
% but it is guaranteed that there [floor(y) + (floor(x)-1)*nr]
% is ordered and unique. In other words, each edgel has a unique pixel id.
% par = actual par used
% threshold = actual threshold used
% mag = edge magnitude
% mage = phase map
% g = gradient map at each pixel
% [FIe,FIo] = odd and even filter outputs
% mago = odd filter output of optimum orientation
% Stella X. Yu, 2001
% This is the multi scale version of the filtering
% For the moment the parameters are defined by default. See line 34
function [x,y,gx,gy,par,threshold,mag_s,mage,g,FIe,FIo,mago] = quadedgep2(I,par,data,threshold);
if nargin<4 | isempty(threshold),
threshold = 0.1;
end
[r,c] = size(I);
def_par = [4,30,3];
display_on = 1;
% take care of parameters, any missing value is substituted by a default value
if nargin<2 | isempty(par),
par = def_par;
end
% par(end+1:4)=0;
% par = par(:);
% j = (par>0);
% have_value = [ j, 1-j ];
% j = 1; n_filter = have_value(j,:) * [par(j); def_par(j)];
% j = 2; n_scale = have_value(j,:) * [par(j); def_par(j)];
% j = 3; winsz = have_value(j,:) * [par(j); def_par(j)];
% j = 4; enlong = have_value(j,:) * [par(j); def_par(j)];
n_ori = par(1); %if it doesn't work, par<-def_par
winsz = par(2);
enlong = par(3);
% always make filter size an odd number so that the results will not be skewed
j = winsz/2;
if not(j > fix(j) + 0.1),
winsz = winsz + 1;
end
% filter the image with quadrature filters
if (isempty(data.W.scales))
error ('no scales entered');
end
n_scale=length(data.W.scales);
filter_scales=data.W.scales;
%
% if strcmp(data.dataWpp.mode,'multiscale')
% n_scale=size((data.dataWpp.scales),2);
% filter_scales=data.dataWpp.scales;
% else
% filter_scales=data.dataWpp.scales(1);
% n_scale=1;
% end
% if n_scale>0&&strcmp(data.dataWpp.mode,'multiscale')
% if (~isempty(data.dataWpp.scales))
% filter_scales=data.dataWpp.scales;
% else
% filter_scales=zeros(1,n_scale);
%
% for i=1:n_scale,
% filter_scales(i)=(sqrt(2))^(i-1);
% end
% data.dataWpp.scales=filter_scales;
% end
% else filter_scale=1;
% data.dataWpp.scales=filter_scales;
% end
%
% %%%%%%% juste pour que ca tourne
% if ~strcmp(data.dataWpp.mode,'multiscale')
% filter_scales=data.dataWpp.scales(1);
% n_scale=4;
% end
% %%%%%%%%%%%%
FBo = make_filterbank_odd2(n_ori,filter_scales,winsz,enlong);
FBe = make_filterbank_even2(n_ori,filter_scales,winsz,enlong);
n = ceil(winsz/2);
f = [fliplr(I(:,2:n+1)), I, fliplr(I(:,c-n:c-1))];
f = [flipud(f(2:n+1,:)); f; flipud(f(r-n:r-1,:))];
FIo = fft_filt_2(f,FBo,1);
FIo = FIo(n+[1:r],n+[1:c],:);
FIe = fft_filt_2(f,FBe,1);
FIe = FIe(n+[1:r],n+[1:c],:);
% compute the orientation energy and recover a smooth edge map
% pick up the maximum energy across scale and orientation
% even filter's output: as it is the second derivative, zero cross localize the edge
% odd filter's output: orientation
[nr,nc,nb] = size(FIe);
FIe = reshape(FIe, nr,nc,n_ori,length(filter_scales));
FIo = reshape(FIo, nr,nc,n_ori,length(filter_scales));
mag_s = zeros(nr,nc,n_scale);
mag_a = zeros(nr,nc,n_ori);
mage = zeros(nr,nc,n_scale);
mago = zeros(nr,nc,n_scale);
mage = zeros(nr,nc,n_scale);
mago = zeros(nr,nc,n_scale);
for i = 1:n_scale,
mag_s(:,:,i) = sqrt(sum(FIo(:,:,:,i).^2,3)+sum(FIe(:,:,:,i).^2,3));
mag_a = sqrt(FIo(:,:,:,i).^2+FIe(:,:,:,i).^2);
[tmp,max_id] = max(mag_a,[],3);
base_size = nr * nc;
id = [1:base_size]';
mage(:,:,i) = reshape(FIe(id+(max_id(:)-1)*base_size+(i-1)*base_size*n_ori),[nr,nc]);
mago(:,:,i) = reshape(FIo(id+(max_id(:)-1)*base_size+(i-1)*base_size*n_ori),[nr,nc]);
mage(:,:,i) = (mage(:,:,i)>0) - (mage(:,:,i)<0);
if display_on,
ori_incr=pi/n_ori; % to convert jshi's coords to conventional image xy
ori_offset=ori_incr/2;
theta = ori_offset+([1:n_ori]-1)*ori_incr; % orientation detectors
% [gx,gy] are image gradient in image xy coords, winner take all
ori = theta(max_id);
ori = ori .* (mago(:,:,i)>0) + (ori + pi).*(mago(:,:,i)<0);
gy{i} = mag_s(:,:,i) .* cos(ori);
gx{i} = -mag_s(:,:,i) .* sin(ori);
g = cat(3,gx{i},gy{i});
% phase map: edges are where the phase changes
mag_th = max(max(mag_s(:,:,i))) * threshold;
eg = (mag_s(:,:,i)>mag_th);
h = eg & [(mage(2:r,:,i) ~= mage(1:r-1,:,i)); zeros(1,nc)];
v = eg & [(mage(:,2:c,i) ~= mage(:,1:c-1,i)), zeros(nr,1)];
[y{i},x{i}] = find(h | v);
k = y{i} + (x{i}-1) * nr;
h = h(k);
v = v(k);
y{i} = y{i} + h * 0.5; % i
x{i} = x{i} + v * 0.5; % j
t = h + v * nr;
gx{i} = g(k) + g(k+t);
k = k + (nr * nc);
gy{i} = g(k) + g(k+t);
% figure(1);
% clf;
% imagesc(I);colormap(gray);
% hold on;
% quiver(x,y,gx,gy); hold off;
% title(sprintf('scale = %d, press return',i));
% pause;
0;
else
x = [];
y = [];
gx = [];
gy =[];
g= [];
end
end
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