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RF7.m
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RF7.m
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%Classic Iteration Wrapping Scheme
addpath(genpath('..\'))
%% Load image and GT
clear all;
add2='C:\Users\khoa\Dropbox\Database\optical flow\';
add3='C:\Users\khoa\Dropbox\Database\eval-data\';
addKITTI='C:\Users\MAI\Documents\KITI Flow\training\image_2\';
add='C:\Users\MAI\Dropbox\Database\other-data\GT\';
subPath = {'Venus', 'Dimetrodon', 'Hydrangea', 'RubberWhale',...
'Grove2', 'Grove3', 'Urban2', 'Urban3'};
% subPath = {'Army', 'Backyard', 'Basketball', 'Dumptruck',...
% 'Evergreen', 'Grove', 'Mequon', 'Schefflera','Teddy','Urban','Wooden','Yosemite'};
%% Parameters to run script
gt=1; % Load ground truth
save = 0; % Save image to file
for seq=8:8
subPath{seq}
img1c = imread([add subPath{seq} '\frame10.png']);
img2c = imread([add subPath{seq} '\frame11.png']);
%img1c = imread([addKITTI '\000000_10.png']);
%img2c = imread([addKITTI '\000000_11.png']);
if size(img1c,3) < 3
img1c = repmat(img1c,[1 1 3]);
img2c = repmat(img2c,[1 1 3]);
end
%img1c = imread('/home/mai/Dropbox/Database/other-data/GT/RubberWhale/frame10.png');
%img2c = imread('/home/mai/Dropbox/Database/other-data/GT/RubberWhale/frame11.png');
if(gt)
flowGT = readFlowFile([add subPath{seq} '\flow10.flo']);
% flowGT = readFlowFile('/home/mai/Dropbox/Database/other-data/GT/RubberWhale/flow10.flo');
tu = flowGT(:,:,1);
tv = flowGT(:,:,2);
% Set unknown values to nan
UNKNOWN_FLOW_THRESH = 1e9;
tu (tu>UNKNOWN_FLOW_THRESH) = NaN;
tv (tv>UNKNOWN_FLOW_THRESH) = NaN;
end
img1 = double(rgb2gray(img1c));
img2 = double(rgb2gray(img2c));
% img1 = denoise_LO(img1,[3 3],0.5,10);
% img2 = denoise_LO(img2,[3 3],0.5,10);
img1dc = double(img1c);
img2dc = double(img2c);
% img1=scale_image(img1,0,255);
% img2=scale_image(img2,0,255);
%[aa,img1] = structure_texture_decomposition_rof(img1);
%[ab,img2] = structure_texture_decomposition_rof(img2);
sz = [ size(img1,1),size(img1,2)];
%% Create pyramidal images
pyramid_spacing=(1/0.5);
%auto-detect pyramid levels
pyramid_levels = 1 + floor( log( min(sz(1), sz(2))/16) / log(pyramid_spacing) );
factor = sqrt(2); % sqrt(3)
smooth_sigma = sqrt(pyramid_spacing)/factor; % or sqrt(3) recommended by Manuel Werlberger
f = fspecial('gaussian', 2*round(1.5*smooth_sigma) +1, smooth_sigma);
%Create pyramidal images
pyramid_images1 = compute_image_pyramid(img1, f, pyramid_levels, 1/pyramid_spacing);
pyramid_images2 = compute_image_pyramid(img2, f, pyramid_levels, 1/pyramid_spacing);
pyramid_images1c = compute_image_pyramid(img1dc, f, pyramid_levels, 1/pyramid_spacing);
pyramid_images2c = compute_image_pyramid(img2dc, f, pyramid_levels, 1/pyramid_spacing);
%%
median_filter_size = [5 5];
H_min = size(pyramid_images2{pyramid_levels},1);
W_min = size(pyramid_images2{pyramid_levels},2);
uvklt=zeros(H_min,W_min,2);
w = ones(H_min,W_min)*(1/(H_min*W_min));
nb_wrap=[10,10,10,10,10];
%nb_wrap=[1,1,1,1,1];
w_size=[5,5,5,5,5];
%w_size=[15,11,9,7,5];
v_size=[5,5,5,5,5];
for lvl=pyramid_levels:-1:1
tic
%fprintf('Pyr lvl: %d \n',lvl)
pyr_image1 = pyramid_images1{lvl};
pyr_image2 = pyramid_images2{lvl};
pyr_image1c = pyramid_images1c{lvl};
pyr_image2c = pyramid_images2c{lvl};
H = size(pyr_image2, 1);
W = size(pyr_image2, 2);
[x,y] = meshgrid(1:W,1:H);
if gt
tuv = resample_flow(cat(3,tu,tv),[H W]);
tup=tuv(:,:,1);
tvp=tuv(:,:,2);
end
%
% figure(1);
% subplot(2,2,1)
% imshow(pyr_image1/255)
% subplot(2,2,2)
% imshow(pyr_image2/255)
% subplot(2,2,3)
% imshow(pyr_image1c/255)
% subplot(2,2,4)
% imshow(pyr_image2c/255)
% figure(2)
% imshow(tup);
%
uvklt = resample_flow(uvklt,[H W]);
uv = uvklt;
% DO SOMETHING ? PROPAGATE FLOW ?
clear warpImc warpIm;
clear ux uxB uxG uxR eigx;
clear vx vxB vxG vxR;
%taux= makemtrx_prop(H,W,pyr_image1c);
maxh = w_size(lvl);
maxw =w_size(lvl);
res_prog=zeros(H,W);
res_pre=zeros(H,W,2);
for k=1:nb_wrap(lvl)
%% Warping Image
[Ix,Iy,It,warpImc]=partial_derivation(pyr_image1c,pyr_image2c,uvklt,1);
% [x,y] = meshgrid(1:W,1:H);
% x2 = x + uvklt(:,:,1);
% y2 = y + uvklt(:,:,2);
% idxx = max(1,min(W,x2));
% idxy = max(1,min(H,y2));
%
% warpImc(:,:,1) = interp2(pyr_image2c(:,:,1),idxx,idxy,'cubic');%linear,spline,nearest
% warpImc(:,:,2) = interp2(pyr_image2c(:,:,2),idxx,idxy,'cubic');%linear,spline,nearest
% warpImc(:,:,3) = interp2(pyr_image2c(:,:,3),idxx,idxy,'cubic');%linear,spline,nearest
% warpImc=0.5*pyr_image1c+0.5*warpImc;
taux =makemtrx_prop5(H,W,warpImc);
%% KLT Flow
%fprintf('KLT ... ')
[ures,vres,minEig,iD]=LKT_res_color(Ix,Iy,It,maxh,maxw);
% if lvl ==1
% [umod,vmod, conv,conv_eig] = eval_uv2( cat(3,ures,vres),iD,minEig,v_size(lvl),Ix,Iy,It );
% % [ux,vx]=KLTIM_double(maxh,maxw,IIxx,IIxy,IIyy,IIxt,IIyt);
% % ev = minev(Ixx,Ixy,Iyy,maxh,maxw);
% % [uresklt, vresklt, perti,weightp]=eval_resuv(ux,vx,ev,Ix,Iy,It,maxh,maxw);
% %
[umod,vmod]=fusion_distance(cat(3,ures,vres),Ix,Iy,It,v_size(lvl));
[uref , vref,w]=likelihood_flow4c(taux,minEig,iD,uvklt(:,:,1),uvklt(:,:,2),umod,vmod,lvl,k,subPath{seq});
% end
% ures(ures>1)=1;
% ures(ures<-1)=-1;
% vres(vres>1)=1;
% vres(vres<-1)=-1;
%
% uref = uvklt(:,:,1)+ures;
% vref = uvklt(:,:,2)+vres;
for l=1:1
uvklt(:,:,1) = medfilt2(uref, median_filter_size, 'symmetric');
uvklt(:,:,2) = medfilt2(vref, median_filter_size, 'symmetric');
uvklt(:,:,1) = medfilt2(uvklt(:,:,1), median_filter_size, 'symmetric');
uvklt(:,:,2) = medfilt2(uvklt(:,:,2), median_filter_size, 'symmetric');
end
% uv_res= uvklt2-uvklt;
% uvklt=uvklt2;
% if (k>1)
% dump=abs(uv_res)-abs(res_pre);
% end
% minu =min(min(min(uvklt(:,:,1))),min(tup(:)));
% maxu =max(max(max(uvklt(:,:,1))),max(tup(:)));
% minv =min(min(min(uvklt(:,:,2))),min(tvp(:)));
% maxv =max(max(max(uvklt(:,:,2))),max(tvp(:)));
% figure(11)
% surf(tup)
% axis([1 W 1 H minu maxu]);
% figure(12)
% surf(uvklt(:,:,1))
% axis([1 W 1 H minu maxu]);
% figure(13)
% surf(tvp)
% axis([1 W 1 H minv maxv]);
% figure(14)
% surf(uvklt(:,:,2))
% axis([1 W 1 H minv maxv]);
%res_pre=uv_res;
end
if gt
minu =min(min(min(uvklt(:,:,1))),min(tup(:)));
maxu =max(max(max(uvklt(:,:,1))),max(tup(:)));
minv =min(min(min(uvklt(:,:,2))),min(tvp(:)));
maxv =max(max(max(uvklt(:,:,2))),max(tvp(:)));
figure(11)
surf(tup)
axis([1 W 1 H minu maxu]);
figure(12)
surf(uvklt(:,:,1))
axis([1 W 1 H minu maxu]);
figure(13)
surf(tvp)
axis([1 W 1 H minv maxv]);
figure(14)
surf(uvklt(:,:,2))
axis([1 W 1 H minv maxv]);
else
minu =min(min(uvklt(:,:,1)));
maxu =max(max(uvklt(:,:,1)));
minv =min(min(uvklt(:,:,2)));
maxv =max(max(uvklt(:,:,2)));
figure(12)
surf(uvklt(:,:,1))
axis([1 W 1 H minu maxu]);
figure(14)
surf(uvklt(:,:,2))
axis([1 W 1 H minv maxv]);
end
toc
end
imgflowcolor = uint8(flowToColor(uvklt));
if gt
[aae stdae aepe] = flowAngErr(tu, tv, uvklt(:,:,1), uvklt(:,:,2), 0); % ignore 0 boundary pixels
fprintf('\nAAE %3.3f average EPE %3.3f \n', aae, aepe);
imgflowGT = uint8(flowToColor(flowGT));
figure(2)
subplot(1,2,1)
imshow(imgflowcolor);
subplot(1,2,2)
imshow(imgflowGT);
end
figure(5)
imshow(imgflowcolor);
%print(['C:\Users\Khoa\Dropbox\Database\RF7\' subPath{seq} '_rf7b'],'-dpng','-r0')
%analyse_res(uvklt,w,tup,tvp,subPath{seq});
%print(['C:\New\RF\submit\' subPath{seq} '_res'],'-dpng','-r0')
%save(['C:\New\RF\submit\' subPath{seq} '_analyze.mat'],'uvklt','w','tu','tv')
%save(['C:\New\RF\submit\' subPath{seq} '_submit.mat'],'uvklt')
%save(['C:\New\RF\analyze\' subPath{seq} '_uvklt.mat'],'uvklt','w','tu','tv')
%save(['C:\Users\Khoa\Dropbox\Database\RF7\' subPath{seq} 'uvklt_rf7b.mat'],'uvklt')
end