├── disp2_cones.pgm ├── disp2_venus.pgm ├── disp1_laundry.png ├── NDTensorvoting.exe ├── disp2_sawtooth.pgm ├── calc_sparse_field.m ├── create_cached_vf.m ├── README.md ├── calc_refined_field.m ├── create_ball_tensorfield.m ├── find_features.m ├── convert_tensor_ev.m ├── calc_vote_ball.m ├── calc_vote_stick.m ├── create_stick_tensorfield.m ├── surf_input_img.txt ├── surf_output_img.txt ├── inpaint_image.m └── input_img.txt /disp2_cones.pgm: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/akhiljain93/inpainting/HEAD/disp2_cones.pgm -------------------------------------------------------------------------------- /disp2_venus.pgm: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/akhiljain93/inpainting/HEAD/disp2_venus.pgm -------------------------------------------------------------------------------- /disp1_laundry.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/akhiljain93/inpainting/HEAD/disp1_laundry.png -------------------------------------------------------------------------------- /NDTensorvoting.exe: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/akhiljain93/inpainting/HEAD/NDTensorvoting.exe -------------------------------------------------------------------------------- /disp2_sawtooth.pgm: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/akhiljain93/inpainting/HEAD/disp2_sawtooth.pgm -------------------------------------------------------------------------------- /calc_sparse_field.m: -------------------------------------------------------------------------------- 1 | function [ T ] = calc_sparse_field( image ) 2 | [h w] = size(image); 3 | T = zeros(h,w,2,2); 4 | 5 | [rows,cols] = find(image>0); 6 | 7 | n = size(rows,1); 8 | for i=1:n 9 | T(rows(i),cols(i),:,:) = [1,0;0,1]; 10 | end 11 | end -------------------------------------------------------------------------------- /create_cached_vf.m: -------------------------------------------------------------------------------- 1 | function [ out ] = create_cached_vf( sigma ) 2 | ws = floor( ceil(sqrt(-log(0.01)*sigma^2)*2) / 2 )*2 + 1; 3 | out = zeros(180,ws,ws,2,2); 4 | for i=1:180 5 | x = cos(pi/180*i); 6 | y = sin(pi/180*i); 7 | v = [x,y]; 8 | Fk = create_stick_tensorfield(v,sigma); 9 | out(i,:,:,:,:) = Fk; 10 | end 11 | 12 | end -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Inpainting 2 | ========== 3 | 4 | A MATLAB implementation of the revolutionary Kulkarni and Rajagopalan algorithm[1] for inpainting using tensor voting. 5 | 6 | The tensor voting framework was taken from [here](http://www.mathworks.in/matlabcentral/fileexchange/21051-tensor-voting-framework) and NDTensorVoting.exe was taken from [here](iris.usc.edu/Projects/tensorvt/tensorvt.html). 7 | 8 | [1] Kulkarni, Mandar, A. N. Rajagopalan, and Gerhard Rigoll. "Depth Inpainting with Tensor Voting using Local Geometry." In VISAPP (1), pp. 22-30. 2012. 9 | -------------------------------------------------------------------------------- /calc_refined_field.m: -------------------------------------------------------------------------------- 1 | function [ T ] = calc_refined_field( tf, im, sigma ) 2 | 3 | % Get votes for ball 4 | ball_vf = calc_vote_ball(tf,im,sigma); 5 | 6 | % Erase anything that's not in the original image 7 | [rows cols] = find(im==0); 8 | s = size(rows,1); 9 | 10 | for i=1:s 11 | ball_vf(rows(i),cols(i),1,1) = 0; 12 | ball_vf(rows(i),cols(i),1,2) = 0; 13 | ball_vf(rows(i),cols(i),2,1) = 0; 14 | ball_vf(rows(i),cols(i),2,2) = 0; 15 | end 16 | 17 | T = tf + ball_vf; 18 | end 19 | 20 | -------------------------------------------------------------------------------- /create_ball_tensorfield.m: -------------------------------------------------------------------------------- 1 | function [ T ] = create_ball_tensorfield( sigma ) 2 | %CREATE_BALL_TENSORFIELD creates a ball tensor field, sigma 3 | % determines the scale and size of the tensor field. 4 | % 5 | % Default for sigma is 18.25. 6 | 7 | if nargin < 1 8 | sigma = 18.25; 9 | end 10 | 11 | wsize = ceil(sqrt(-log(0.01)*sigma^2)*2); 12 | wsize = floor(wsize/2)*2+1; 13 | 14 | T = zeros(wsize,wsize,2,2); 15 | for theta = (0:1/32:1-1/32)*2*pi 16 | v = [cos(theta);sin(theta)]; 17 | B = create_stick_tensorfield(v,sigma); 18 | T = T + B; 19 | end 20 | T = T/32; 21 | end -------------------------------------------------------------------------------- /find_features.m: -------------------------------------------------------------------------------- 1 | function [ T ] = find_features( im, sigma ) 2 | %FIND_FEATURES returns the tensorfield after voting on the binary image im 3 | % using the sigma supplied. 4 | % 5 | % T = find_features(im,sigma) 6 | % 7 | % IM should be a binary (logical) image. 8 | % SIGMA should be the scale used in voting, i.e. 18.25. 9 | % 10 | % Returns a tensor field T 11 | % 12 | 13 | % Calculate cached voting field at various angles, this way we can save 14 | % a lot of time by preprocessing this data. 15 | cached_vtf = create_cached_vf(sigma); 16 | 17 | % normalize the gray scale image from 0 to 1 18 | im = double(im) / double(max(im(:))); 19 | 20 | % First step is to produce the initially encode the image 21 | % as sparse tensor tokens. 22 | sparse_tf = calc_sparse_field(im); 23 | 24 | % First run of tensor voting, use ball votes weighted by 25 | % the images grayscale. 26 | refined_tf = calc_refined_field(sparse_tf,im,sigma); 27 | 28 | % third run is to apply the stick tensor voting after 29 | % zero'ing out the e2(l2) components so that everything 30 | % is a stick vote. 31 | 32 | [e1,e2,l1,l2] = convert_tensor_ev(refined_tf); 33 | l2(:) = 0; 34 | zerol2_tf = convert_tensor_ev(e1,e2,l1,l2); 35 | 36 | 37 | T = calc_vote_stick(zerol2_tf,sigma,cached_vtf); 38 | end -------------------------------------------------------------------------------- /convert_tensor_ev.m: -------------------------------------------------------------------------------- 1 | function [ o1, o2, o3, o4 ] = convert_tensor_ev( i1, i2, i3, i4 ) 2 | %CONVERT_TENSOR_EV converts the tensor field to eigenvectors and 3 | % eigenvalues, vise versa. 4 | % 5 | % [E1,E2,L1,L2] = CONVERT_TENSOR_EV(TENSOR_FIELD) converts a 6 | % tensor field to eigenvectors and eigenvalues. 7 | % 8 | % TENSOR_FIELD = CONVERT_TENSOR_EV(E1,E2,L1,L2) converts 9 | % eigenvectors and eigenvalues to a tensor field. 10 | % 11 | % Example: 12 | % [e1,e2,l1,l2] = convert_tensor_ev(tensor_field) 13 | % 14 | % tensor_field = conver_tensor_ev(e1,e2,l1,l2); 15 | % 16 | if nargin==1 17 | K11 = i1(:,:,1,1); 18 | K12 = i1(:,:,1,2); 19 | K21 = i1(:,:,2,1); 20 | K22 = i1(:,:,2,2); 21 | 22 | [n,p] = size(K11); 23 | 24 | o1 = zeros(n,p,2); 25 | o2 = zeros(n,p,2); 26 | o3 = zeros(n,p); 27 | o4 = zeros(n,p); 28 | 29 | % trace/2 30 | t = (K11+K22)/2; 31 | 32 | a = K11 - t; 33 | b = K12; 34 | 35 | ab2 = sqrt(a.^2+b.^2); 36 | o3 = ab2 + t; 37 | o4 = -ab2 + t; 38 | 39 | theta = atan2( ab2-a, b ); 40 | 41 | o1(:,:,1) = cos(theta); 42 | o1(:,:,2) = sin(theta); 43 | o2(:,:,1) = -sin(theta); 44 | o2(:,:,2) = cos(theta); 45 | else 46 | o1 = zeros( [size(i3),2,2] ); 47 | o1(:,:,1,1) = i3.*i1(:,:,1).^2 + i4.*i2(:,:,1).^2; 48 | o1(:,:,1,2) = i3.*i1(:,:,1).*i1(:,:,2) + i4.*i2(:,:,1).*i2(:,:,2); 49 | o1(:,:,2,1) = o1(:,:,1,2); 50 | o1(:,:,2,2) = i3.*i1(:,:,2).^2 + i4.*i2(:,:,2).^2; 51 | end 52 | 53 | 54 | 55 | end -------------------------------------------------------------------------------- /calc_vote_ball.m: -------------------------------------------------------------------------------- 1 | function T = calc_vote_ball(T,im,sigma) 2 | 3 | Fk = create_ball_tensorfield(sigma); 4 | wsize = floor( ceil(sqrt(-log(0.01)*sigma^2)*2) / 2 )*2 + 1; 5 | wsize_half = (wsize-1)/2; 6 | 7 | % resize the tensor to make calculations easier. This gives us a margin 8 | % around the tensor that's as large as half the window of the voting 9 | % field so when we begin to multiple and add the tensors we dont have 10 | % to worry about trimming the voting field to avoid negative and 11 | % overflow array indices. 12 | Th = size(T,1); 13 | Tw = size(T,2); 14 | 15 | Tn = zeros(Th+wsize_half*2,Tw+wsize_half*2,2,2,'double'); 16 | Tn((wsize_half+1):(wsize_half+Th), (wsize_half+1):(wsize_half+Tw), :, :) = T(1:end,1:end,:,:); 17 | T = Tn; 18 | 19 | % perform eigen-decomposition, assign default tensor from estimate 20 | [e1,e2,l1,l2] = convert_tensor_ev(T); 21 | 22 | % Find everything that's above 0 23 | I = find ( l1 > 0 ); 24 | 25 | % perform ball voting 26 | d = waitbar(0,'Please wait, ball voting...'); 27 | 28 | % Loop through each stick found in the tensor T. 29 | a = 0; 30 | 31 | [u,v] = ind2sub(size(l1),I'); 32 | p = size(u,2); 33 | D = zeros(2,p,'double'); 34 | D(1,:) = u; 35 | D(2,:) = v; 36 | op = ceil(p*0.01); 37 | 38 | for s = D; 39 | a = a+1; 40 | 41 | if mod(a,op) == 0 42 | waitbar(a/p,d); 43 | end 44 | 45 | % the current intensity of the vote 46 | % Apply weights 47 | Zk = im(s(1)-wsize_half,s(2)-wsize_half)*Fk; 48 | 49 | % Calculate positions of window, add subsequent values back in. 50 | beginy = s(1)-wsize_half; 51 | endy = s(1)+wsize_half; 52 | beginx = s(2)-wsize_half; 53 | endx = s(2)+wsize_half; 54 | T(beginy:endy,beginx:endx,:,:) = T(beginy:endy,beginx:endx,:,:) + Zk; 55 | end 56 | 57 | close(d); 58 | % Trim the T of the margins we used. 59 | T = T((wsize_half+1):(wsize_half+Th), (wsize_half+1):(wsize_half+Tw), :, :); 60 | end -------------------------------------------------------------------------------- /calc_vote_stick.m: -------------------------------------------------------------------------------- 1 | function T = calc_vote_stick(T,sigma,cachedvf) 2 | %CALC_VOTE_STICK votes using stick tensors returning a new 3 | % tensor field. 4 | % 5 | % T = calc_vote_stick(M,sigma,cachedvf); 6 | % 7 | % M is the input tensor field, this should be an estimate. 8 | % sigma determines the scale of the tensor field. 9 | % cachedvf is a cached voting field produced by 10 | % create_cached_vf in order to speed up voting process. 11 | % 12 | 13 | if nargin<2 14 | sigma = 18.25; 15 | end 16 | 17 | wsize = floor( ceil(sqrt(-log(0.01)*sigma^2)*2) / 2 )*2 + 1; 18 | wsize_half = (wsize-1)/2; 19 | 20 | % resize the tensor to make calculations easier. This gives us a margin 21 | % around the tensor that's as large as half the window of the voting 22 | % field so when we begin to multiple and add the tensors we dont have 23 | % to worry about trimming the voting field to avoid negative and 24 | % overflow array indices. 25 | Th = size(T,1); 26 | Tw = size(T,2); 27 | 28 | Tn = zeros(Th+wsize_half*2,Tw+wsize_half*2,2,2,'double'); 29 | Tn((wsize_half+1):(wsize_half+Th), (wsize_half+1):(wsize_half+Tw), :, :) = T(1:end,1:end,:,:); 30 | T = Tn; 31 | 32 | % perform eigen-decomposition, assign default tensor from estimate 33 | [e1,e2,l1,l2] = convert_tensor_ev(T); 34 | 35 | % Find everything that's a stick vote. Prehaps use a threshold here? 36 | I = find ( l1-l2 > 0 ); 37 | 38 | % perform stick voting 39 | d = waitbar(0,'Please wait, stick voting...'); 40 | 41 | % Loop through each stick found in the tensor T. 42 | a = 0; 43 | 44 | [u,v] = ind2sub(size(l1),I'); 45 | p = size(u,2); 46 | D = zeros(2,p,'double'); 47 | D(1,:) = u; 48 | D(2,:) = v; 49 | op = ceil(p*0.01); 50 | 51 | for s = D; 52 | a = a+1; 53 | 54 | if mod(a,op) == 0 55 | waitbar(a/p,d); 56 | end 57 | 58 | % the direction is e1 with intensity l1-l2 59 | v = e1(s(1),s(2),:); 60 | 61 | if nargin < 6 62 | %v(:,:,1) = -v(:,:,1); 63 | v=v(:); 64 | Fk = create_stick_tensorfield([-v(2),v(1)],sigma); 65 | else 66 | angle = round(180/pi*atan(v(2)/v(1))); 67 | if angle < 1 68 | angle = angle + 180; 69 | end 70 | Fk = shiftdim(cachedvf(angle,:,:,:,:)); 71 | end 72 | 73 | % the current intensity of the vote 74 | % Apply weights 75 | Fk = (l1(s(1),s(2))-l2(s(1),s(2)))*Fk; 76 | 77 | % Calculate positions of window, add subsequent values back in. 78 | beginy = s(1)-wsize_half; 79 | endy = s(1)+wsize_half; 80 | beginx = s(2)-wsize_half; 81 | endx = s(2)+wsize_half; 82 | T(beginy:endy,beginx:endx,:,:) = T(beginy:endy,beginx:endx,:,:) + Fk; 83 | end 84 | 85 | close(d); 86 | % Trim the T of the margins we used. 87 | T = T((wsize_half+1):(wsize_half+Th), (wsize_half+1):(wsize_half+Tw), :, :); 88 | 89 | end -------------------------------------------------------------------------------- /create_stick_tensorfield.m: -------------------------------------------------------------------------------- 1 | function [ T ] = create_stick_tensorfield( uv, sigma ) 2 | %CREATE_STICK_TENSORFIELD Creates a second order tensor 3 | % field aligned along the unit vector provided and with 4 | % the scale of sigma. 5 | % 6 | % unit_vector describes the direction the field should go in 7 | % it's a 2 column, 1 row matrix where the first element is 8 | % the x axis, the second element is the y axis. Default is 9 | % [1,0], aligned on the x-axis. 10 | % 11 | % sigma describes the scale for the tensorfield, default is 12 | % 18.25. 13 | % 14 | % Ret urns T a MxMx2x2 tensor field, where M is the rectangular 15 | % size of the field. 16 | % 17 | % Example: 18 | % T = create_stick_tensorfield(unit_vector, sigma); 19 | % 20 | 21 | % Generate the defaults if they're not available. 22 | if nargin < 1 23 | uv = [1,0]; 24 | end 25 | if nargin < 2 26 | sigma = 18.25; 27 | end 28 | 29 | % Generate initial parameters used by the entire system 30 | 31 | % Calculate the window size from sigma using 32 | % equation 5.7 from Emerging Topics in Computer Vision 33 | % make the field odd, if it turns out to be even. 34 | ws = floor( ceil(sqrt(-log(0.01)*sigma^2)*2) / 2 )*2 + 1; 35 | whalf = (ws-1)/2; 36 | 37 | % Turn the unit vector into a rotation matrix 38 | rot = [uv(:),[-uv(2);uv(1)]]/norm(uv); 39 | btheta = atan2(uv(2),uv(1)); 40 | 41 | % Generate our theta's at each point in the 42 | % field, adjust by our base theta so we rotate 43 | % in funcion. 44 | [X,Y] = meshgrid(-whalf:1:whalf,whalf:-1:-whalf); 45 | Z = rot'*[X(:),Y(:)]'; 46 | X = reshape( Z(1,:),ws,ws); 47 | Y = reshape( Z(2,:),ws,ws); 48 | theta = atan2(Y,X); 49 | 50 | % Generate the tensor field direction aligned with the normal 51 | Tb = reshape([theta,theta,theta,theta],ws,ws,2,2); 52 | T1 = -sin(2*Tb+btheta); 53 | T2 = cos(2*Tb+btheta); 54 | T3 = T1; 55 | T4 = T2; 56 | T1(:,:,2,1:2) = 1; 57 | T2(:,:,1:2,1) = 1; 58 | T3(:,:,1:2,2) = 1; 59 | T4(:,:,1,1:2) = 1; 60 | T = T1.*T2.*T3.*T4; 61 | 62 | 63 | % Generate the attenuation field, taken from Equation 64 | % 5.2 in Emerging Topics in Computer Vision. Note our 65 | % thetas must be symmetric over the Y axis for the arc 66 | % length to be correct so there's a bit of a coordinate 67 | % translation. 68 | theta = abs(theta); 69 | theta(theta>pi/2) = pi - theta(theta>pi/2); 70 | theta = 4*theta; 71 | 72 | s = zeros(ws,ws); 73 | k = zeros(ws,ws); 74 | % Calculate the attenuation field. 75 | l = sqrt(X.^2+Y.^2); 76 | c = (-16*log2(0.1)*(sigma-1))/pi^2; 77 | s(l~=0 & theta~=0) = (theta(l~=0 & theta~=0).*l(l~=0 & theta~=0))./sin(theta(l~=0 & theta~=0)); 78 | s(l==0 | theta==0) = l(l==0 | theta==0); 79 | k(l~=0) = 2*sin(theta(l~=0))./l(l~=0); 80 | DF = exp(-((s.^2+c*(k.^2))/sigma^2)); 81 | DF(theta>pi/2) = 0; 82 | % Generate the final tensor field 83 | T = T.*reshape([DF,DF,DF,DF],ws,ws,2,2); 84 | 85 | end -------------------------------------------------------------------------------- /surf_input_img.txt: -------------------------------------------------------------------------------- 1 | 3 2 | 214.000000 119.000000 136.000000 0.000000 3 | 213.000000 120.000000 136.000000 0.000000 4 | 216.000000 120.000000 137.000000 0.000000 5 | 217.000000 120.000000 137.000000 0.000000 6 | 218.000000 120.000000 137.000000 0.000000 7 | 214.000000 121.000000 137.000000 0.000000 8 | 215.000000 121.000000 137.000000 0.000000 9 | 216.000000 121.000000 137.000000 0.000000 10 | 217.000000 121.000000 137.000000 0.000000 11 | 212.000000 122.000000 137.000000 0.000000 12 | 213.000000 122.000000 137.000000 0.000000 13 | 214.000000 122.000000 137.000000 0.000000 14 | 215.000000 122.000000 137.000000 0.000000 15 | 216.000000 122.000000 137.000000 0.000000 16 | 210.000000 123.000000 137.000000 0.000000 17 | 211.000000 123.000000 137.000000 0.000000 18 | 212.000000 123.000000 137.000000 0.000000 19 | 213.000000 123.000000 137.000000 0.000000 20 | 214.000000 123.000000 137.000000 0.000000 21 | 215.000000 123.000000 137.000000 0.000000 22 | 226.000000 123.000000 138.000000 0.000000 23 | 227.000000 123.000000 138.000000 0.000000 24 | 228.000000 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0.000000 67 | 209.000000 128.000000 138.000000 0.000000 68 | 210.000000 128.000000 138.000000 0.000000 69 | 211.000000 128.000000 138.000000 0.000000 70 | 212.000000 128.000000 138.000000 0.000000 71 | 213.000000 128.000000 138.000000 0.000000 72 | 214.000000 128.000000 138.000000 0.000000 73 | 209.000000 129.000000 138.000000 0.000000 74 | 210.000000 129.000000 138.000000 0.000000 75 | 211.000000 129.000000 138.000000 0.000000 76 | 212.000000 129.000000 138.000000 0.000000 77 | 213.000000 129.000000 138.000000 0.000000 78 | -------------------------------------------------------------------------------- /surf_output_img.txt: -------------------------------------------------------------------------------- 1 | 3 2 | 214.000000 119.000000 136.000000 1 36.570488 28.098274 8.770864 -0.147613 -0.292208 0.944894 0.979288 0.090682 0.181029 -0.138583 0.952046 0.272770 3 | 213.000000 120.000000 136.000000 1 40.890858 29.307686 12.137609 -0.171610 -0.323920 0.930390 0.983732 -0.005428 0.179559 -0.053112 0.946069 0.319583 4 | 216.000000 120.000000 137.000000 1 39.221603 30.025406 9.796360 0.084768 0.140805 -0.986402 0.777830 0.609358 0.153828 0.622732 -0.780292 -0.057868 5 | 217.000000 120.000000 137.000000 1 37.253700 29.591614 8.008383 0.078492 0.157241 -0.984436 0.657608 0.734004 0.169673 0.749260 -0.660691 -0.045789 6 | 218.000000 120.000000 137.000000 1 34.847637 28.248072 6.789739 -0.069897 -0.174931 0.982096 -0.530975 -0.826928 -0.185083 0.844500 -0.534405 -0.035084 7 | 214.000000 121.000000 137.000000 1 45.371700 31.897387 14.581139 -0.070960 -0.160504 0.984481 0.885719 0.443793 0.136194 0.458766 -0.881638 -0.110670 8 | 215.000000 121.000000 137.000000 1 44.501076 32.780933 12.448576 0.083334 0.176296 -0.980803 0.778203 0.603267 0.174555 -0.622459 0.777810 0.086921 9 | 216.000000 121.000000 137.000000 1 42.820862 32.938091 10.418024 -0.076180 -0.183055 0.980147 0.683120 0.706476 0.185038 0.726322 -0.683654 -0.071229 10 | 217.000000 121.000000 137.000000 1 40.529346 31.440302 9.506927 0.052652 0.180218 -0.982216 0.557250 0.810897 0.178655 0.828674 -0.556747 -0.057731 11 | 212.000000 122.000000 137.000000 1 48.521198 29.462305 19.926195 -0.041693 -0.166013 0.985242 0.943025 0.319255 0.093701 0.330099 -0.933014 -0.143244 12 | 213.000000 122.000000 137.000000 1 49.095852 32.407421 17.571989 -0.063345 -0.186071 0.980492 0.853545 0.499008 0.149842 0.517154 -0.846386 -0.127211 13 | 214.000000 122.000000 137.000000 1 48.743763 34.325981 15.295779 -0.078034 -0.200994 0.976480 0.797638 0.574992 0.182095 0.598068 -0.793086 -0.115451 14 | 215.000000 122.000000 137.000000 1 47.509380 34.785107 13.613636 0.070674 0.209404 -0.975272 0.724951 0.660794 0.194416 -0.685165 0.720764 0.105106 15 | 216.000000 122.000000 137.000000 1 45.535126 33.786915 12.616903 -0.040635 -0.201742 0.978595 0.619101 0.763658 0.183140 0.784259 -0.613291 -0.093867 16 | 210.000000 123.000000 137.000000 1 47.178158 26.183664 21.722395 -0.050465 -0.183521 0.981720 0.612244 -0.782295 -0.114769 -0.789057 -0.595260 -0.151838 17 | 211.000000 123.000000 137.000000 1 49.593395 27.204407 23.210644 -0.067634 -0.201796 0.977090 -0.778861 -0.601374 -0.178113 0.623539 -0.773063 -0.116497 18 | 212.000000 123.000000 137.000000 1 51.024414 31.538523 20.479029 -0.080607 -0.218302 0.972547 0.790455 0.580386 0.195791 -0.607193 0.784536 0.125774 19 | 213.000000 123.000000 137.000000 1 51.402241 34.116028 18.538170 -0.082808 -0.230181 0.969618 0.775440 0.596259 0.207772 0.625968 -0.769086 -0.129117 20 | 214.000000 123.000000 137.000000 1 50.727859 35.250195 17.124809 -0.076303 -0.238227 0.968208 0.746035 0.630599 0.213952 0.661520 -0.738642 -0.129609 21 | 215.000000 123.000000 137.000000 1 49.098724 34.735355 16.494381 -0.042083 -0.238882 0.970136 0.682370 0.702385 0.202552 0.729795 -0.670515 -0.133448 22 | 226.000000 123.000000 138.000000 1 18.464088 12.616968 5.900572 -0.031857 -0.012583 0.999413 -0.041724 -0.999032 -0.013908 -0.998621 0.042143 -0.031301 23 | 227.000000 123.000000 138.000000 1 16.421692 11.600090 4.856550 -0.026726 -0.016143 0.999512 -0.240576 -0.970378 -0.022106 -0.970262 0.241050 -0.022050 24 | 228.000000 123.000000 138.000000 1 14.518381 11.207151 3.333700 -0.022345 -0.019592 0.999558 -0.326145 -0.944967 -0.025813 -0.945055 0.326578 -0.014726 25 | 209.000000 124.000000 137.000000 1 45.496136 28.020403 18.205637 -0.080549 -0.227749 0.970382 0.235205 -0.950396 -0.203535 0.968602 0.211844 0.130121 26 | 210.000000 124.000000 137.000000 1 48.649990 27.146540 22.536787 -0.098067 -0.237148 0.966511 -0.209831 -0.944432 -0.253021 0.972807 -0.227617 0.042857 27 | 211.000000 124.000000 137.000000 1 50.873196 30.070196 22.276251 -0.112647 -0.253673 0.960708 -0.646890 -0.715173 -0.264691 0.754218 -0.651289 -0.083536 28 | 212.000000 124.000000 137.000000 1 51.962959 33.190834 20.984335 -0.112962 -0.272993 0.955361 -0.715319 -0.644995 -0.268886 0.689607 -0.713762 -0.122417 29 | 213.000000 124.000000 137.000000 1 51.914768 34.872196 20.181242 -0.088089 -0.271323 0.958448 0.732199 0.634733 0.246979 -0.675370 0.723531 0.142750 30 | 214.000000 124.000000 137.000000 1 50.752338 35.001854 20.020933 -0.050414 -0.256320 0.965277 0.710201 0.670336 0.215093 0.702192 -0.696384 -0.148244 31 | 215.000000 124.000000 138.000000 1 49.942970 32.998310 19.780554 -0.204667 -0.268615 0.941253 -0.530775 -0.777502 -0.337296 0.822429 -0.568627 0.016555 32 | 216.000000 124.000000 138.000000 1 48.422737 32.484303 17.193138 -0.171541 -0.256274 0.951261 -0.308765 -0.902941 -0.298936 0.935541 -0.344996 0.075763 33 | 225.000000 124.000000 138.000000 1 20.939583 17.047007 3.960938 -0.032077 -0.040968 0.998645 -0.056612 -0.997481 -0.042738 -0.997881 0.057907 -0.029677 34 | 226.000000 124.000000 138.000000 1 18.702440 14.421594 4.328103 -0.028012 -0.028179 0.999210 -0.125027 -0.991654 -0.031471 -0.991758 0.125810 -0.024255 35 | 227.000000 124.000000 138.000000 1 16.633106 12.685114 3.980898 -0.021920 -0.020313 0.999553 -0.131745 -0.991016 -0.023029 -0.991041 0.132191 -0.019047 36 | 228.000000 124.000000 138.000000 1 14.708074 12.018509 2.712325 -0.014729 -0.016261 0.999759 -0.049390 -0.998635 -0.016970 -0.998671 0.049628 -0.013906 37 | 229.000000 124.000000 138.000000 1 12.905512 11.646065 1.274319 -0.009287 -0.017531 0.999803 0.015211 -0.999733 -0.017389 -0.999841 -0.015046 -0.009552 38 | 208.000000 125.000000 137.000000 1 42.206558 29.743059 13.138981 0.124098 0.280500 -0.951798 0.079663 -0.958933 -0.272216 0.989067 0.042042 0.141347 39 | 209.000000 125.000000 137.000000 1 45.767529 28.691317 18.543715 -0.144776 -0.289031 0.946309 -0.071202 -0.950863 -0.301315 0.986899 -0.111002 0.117082 40 | 210.000000 125.000000 137.000000 1 48.508095 29.242638 21.768190 -0.151252 -0.289301 0.945213 -0.412070 -0.850715 -0.326317 0.898510 -0.438850 0.009460 41 | 211.000000 125.000000 137.000000 1 50.159863 31.667587 22.436060 -0.154828 -0.285860 0.945681 -0.654804 -0.687076 -0.314894 0.739770 -0.667990 -0.080804 42 | 212.000000 125.000000 138.000000 1 51.084290 34.050507 21.940817 -0.081369 -0.233446 0.968959 -0.723613 -0.654707 -0.218500 0.685393 -0.718931 -0.115652 43 | 213.000000 125.000000 138.000000 1 52.107285 34.171265 21.417971 -0.116496 -0.246437 0.962132 -0.701325 -0.665524 -0.255382 0.703258 -0.704518 -0.095302 44 | 214.000000 125.000000 138.000000 1 52.015579 33.117180 21.149641 -0.141042 -0.250496 0.957789 -0.615150 -0.735854 -0.283038 0.775692 -0.629104 -0.050306 45 | 215.000000 125.000000 138.000000 1 50.875202 31.614496 20.522989 -0.135057 -0.233036 0.963044 -0.409316 -0.872018 -0.268412 0.902341 -0.430440 0.022387 46 | 216.000000 125.000000 138.000000 1 48.712582 31.924358 17.542414 -0.105178 -0.216834 0.970526 -0.096683 -0.969086 -0.226990 0.989742 -0.117708 0.080962 47 | 224.000000 125.000000 138.000000 1 23.089630 20.017874 3.124012 -0.039764 -0.095579 0.994627 -0.102384 -0.989786 -0.099207 0.993950 -0.105778 0.029572 48 | 225.000000 125.000000 138.000000 1 20.697136 16.831202 3.915867 -0.033482 -0.053958 0.997982 -0.146372 -0.987510 -0.058303 -0.988663 0.148029 -0.025166 49 | 226.000000 125.000000 138.000000 1 18.490513 13.662980 4.868968 -0.025620 -0.026443 0.999322 -0.109113 -0.993604 -0.029089 -0.993699 0.109785 -0.022571 50 | 227.000000 125.000000 138.000000 1 16.437407 11.331352 5.136469 -0.019354 -0.011991 0.999741 0.062276 -0.998001 -0.010765 -0.997871 -0.062051 -0.020062 51 | 209.000000 126.000000 138.000000 1 44.933956 29.792086 17.689846 0.058463 -0.214628 0.974945 0.396954 0.901085 0.174565 0.915975 -0.376802 -0.137877 52 | 210.000000 126.000000 138.000000 1 47.890171 31.137913 19.679134 0.012333 -0.223208 0.974693 0.574287 0.799548 0.175833 0.818561 -0.557585 -0.138047 53 | 211.000000 126.000000 138.000000 1 50.242088 32.436398 20.536808 -0.038338 -0.223598 0.973927 0.678675 0.709540 0.189614 0.733438 -0.668249 -0.124548 54 | 212.000000 126.000000 138.000000 1 51.801754 32.854542 21.065372 -0.070244 -0.209274 0.975331 -0.735771 -0.649349 -0.192320 0.673578 -0.731129 -0.108365 55 | 213.000000 126.000000 138.000000 1 52.279930 32.077450 21.771749 -0.084533 -0.199167 0.976313 -0.733956 -0.650242 -0.196198 0.673916 -0.733156 -0.091213 56 | 214.000000 126.000000 138.000000 1 51.706612 30.295561 22.525526 -0.090775 -0.191954 0.977197 -0.622101 -0.755308 -0.206157 0.777657 -0.626629 -0.050852 57 | 215.000000 126.000000 138.000000 1 50.158783 29.068846 21.878794 -0.079702 -0.181299 0.980193 -0.159522 -0.968328 -0.192075 0.983972 -0.171671 0.048256 58 | 223.000000 126.000000 138.000000 1 24.898357 20.497215 4.457143 0.040455 0.093517 -0.994795 -0.082737 -0.991878 -0.096608 -0.995750 0.086215 -0.032389 59 | 208.000000 127.000000 138.000000 1 40.816513 30.294865 11.299822 -0.027981 0.187814 -0.981806 0.377830 0.911314 0.163561 0.925452 -0.366379 -0.096461 60 | 209.000000 127.000000 138.000000 1 44.335274 30.648870 14.784456 0.013768 -0.180749 0.983433 0.534321 0.832655 0.145556 0.845169 -0.523465 -0.108042 61 | 210.000000 127.000000 138.000000 1 47.234108 31.106241 17.377518 -0.007723 -0.179588 0.983712 0.655531 0.741965 0.140601 0.755129 -0.645939 -0.111996 62 | 211.000000 127.000000 138.000000 1 49.341419 31.314688 19.256454 -0.030390 -0.174265 0.984230 0.743767 0.653882 0.138740 0.667747 -0.736254 -0.109741 63 | 212.000000 127.000000 138.000000 1 50.527618 30.794456 20.829594 -0.045906 -0.162940 0.985568 0.811263 0.569596 0.131957 0.582876 -0.805613 -0.106039 64 | 213.000000 127.000000 138.000000 1 50.711460 29.190098 22.449633 -0.052835 -0.151355 0.987066 0.860719 0.494279 0.121864 0.506331 -0.856025 -0.104159 65 | 214.000000 127.000000 138.000000 1 49.922775 26.118687 24.566982 -0.052161 -0.141855 0.988512 0.914625 0.390613 0.104317 -0.400924 0.909559 0.109369 66 | 208.000000 128.000000 138.000000 1 38.985844 30.670090 8.771598 0.008934 0.158238 -0.987361 0.529141 0.837081 0.138941 0.848487 -0.523694 -0.076252 67 | 209.000000 128.000000 138.000000 1 42.323891 30.538399 12.399837 -0.007453 -0.142076 0.989828 0.646361 0.754591 0.113178 0.762995 -0.640630 -0.086208 68 | 210.000000 128.000000 138.000000 1 45.036392 30.508179 15.225885 -0.014958 -0.136523 0.990524 0.748306 0.655519 0.101650 0.663185 -0.742735 -0.092356 69 | 211.000000 128.000000 138.000000 1 46.935596 29.920103 17.734407 -0.022174 -0.129969 0.991270 0.840748 0.534089 0.088833 0.540972 -0.835378 -0.097428 70 | 212.000000 128.000000 138.000000 1 47.941807 28.920717 19.716852 -0.026734 -0.121164 0.992272 0.934936 0.348295 0.067719 0.353809 -0.929521 -0.103969 71 | 213.000000 128.000000 138.000000 1 47.974926 27.737438 20.876457 -0.027818 -0.112874 0.993220 0.999077 0.029384 0.031322 0.032720 -0.993174 -0.111953 72 | 214.000000 128.000000 138.000000 1 47.101307 27.602890 20.055109 -0.022025 -0.108564 0.993846 0.865851 -0.499053 -0.035326 -0.499817 -0.859744 -0.104992 73 | 209.000000 129.000000 138.000000 1 39.395824 30.637627 9.174129 0.022754 0.108484 -0.993838 0.765779 0.637180 0.087085 0.642701 -0.763042 -0.068576 74 | 210.000000 129.000000 138.000000 1 41.865730 30.098234 12.237217 -0.018225 -0.099899 0.994831 0.850362 0.521789 0.067975 0.525882 -0.847205 -0.075441 75 | 211.000000 129.000000 138.000000 1 43.555439 29.301548 14.748000 -0.015722 -0.093440 0.995501 0.936072 0.348587 0.047503 0.351458 -0.932607 -0.081986 76 | 212.000000 129.000000 138.000000 1 44.400555 28.810873 16.082094 -0.013020 -0.087577 0.996073 0.995725 0.089965 0.020926 0.091445 -0.992087 -0.086031 77 | 213.000000 129.000000 138.000000 1 44.356506 29.139170 15.684551 -0.008821 -0.083115 0.996501 0.978273 -0.207141 -0.008617 -0.207132 -0.974774 -0.083136 78 | -------------------------------------------------------------------------------- /inpaint_image.m: -------------------------------------------------------------------------------- 1 | clear all; 2 | close all; 3 | clc; 4 | 5 | delete('input_img.txt'); 6 | delete('output_img.txt'); 7 | delete('surf_input_img.txt'); 8 | delete('surf_output_img.txt'); 9 | 10 | input_image=imread('disp2_cones.pgm'); 11 | input_gray=edge(input_image,'canny'); 12 | input_image=double(input_image); 13 | figure,imshow(input_gray,[]); 14 | 15 | plane_pts_threshold=50; 16 | thresh=2; 17 | window=15; 18 | threshold=30; 19 | 20 | original_img=input_image; 21 | s=size(input_image); 22 | 23 | [BW,xi,yi] = roipoly(uint8(input_image)); 24 | BW1=1-BW; 25 | 26 | tic; 27 | 28 | [black_row, black_col]=find(BW); 29 | 30 | for i=1:length(black_row) 31 | input_image(black_row(i),black_col(i))=0; 32 | input_gray(black_row(i),black_col(i))=0; 33 | end 34 | 35 | selected_area=BW.*255; 36 | size_missing_area=length(find(selected_area)); 37 | 38 | damged_img=input_image; 39 | 40 | [row_miss, col_miss]=find(selected_area); 41 | 42 | min_row=min(black_row); 43 | min_col=min(black_col); 44 | max_row=max(black_row); 45 | max_col=max(black_col); 46 | 47 | chk_mat=input_gray(min_row-window:max_row+window,min_col-window:max_col+window); 48 | 49 | if(length(find(chk_mat))>10) 50 | test_mat=zeros(s(1),s(2)); 51 | img=zeros(s(1),s(2)); 52 | selected_area=zeros(s(1),s(2)); 53 | 54 | test_mat(min_row-window:max_row+window,min_col-window:max_col+window)=chk_mat; 55 | selected_area(min_row-window:max_row+window,min_col-window:max_col+window)=1; 56 | 57 | figure,imshow(test_mat,[]) 58 | 59 | edge_cnt=1; 60 | edge_cnter=0; 61 | 62 | seg_rgn=zeros(s(1),s(2),10); 63 | seg_rgn1=zeros(s(1),s(2),10); 64 | 65 | %%%%%%%%%%%TENSOR VOTING BASED REFINING%%%%%%%%%%%%%%%%% 66 | sigma=3; 67 | eta=7; 68 | T = find_features(test_mat,sigma); 69 | [e1,e2,l1,l2] = convert_tensor_ev(T); 70 | cur_sal=l1-l2; 71 | 72 | clear r; 73 | clear c; 74 | 75 | [r, c]=find(cur_sal=pp2) 130 | for index=1:length(img_row) 131 | A(:,index)=[curve_pts(1,index)^2 curve_pts(1,index) 1]; 132 | b(index)=[curve_pts(2,index)]; 133 | w(index)=cur_sal(curve_pts(1,index),curve_pts(2,index)); 134 | end 135 | 136 | else 137 | for index=1:length(img_row) 138 | A(:,index)=[curve_pts(2,index)^2 curve_pts(2,index) 1]; 139 | b(index)=[curve_pts(1,index)]; 140 | w(index)=cur_sal(curve_pts(1,index),curve_pts(2,index)); 141 | end 142 | end 143 | 144 | p=lscov(A',b',w); 145 | o=1; 146 | seg=zeros(s(1),s(2)); 147 | seg1=zeros(s(1),s(2)); 148 | 149 | if(var(curve_pts(1,:))>=var(curve_pts(2,:))) 150 | for i=1:s(1) 151 | for j=1:s(2) 152 | coef=j-p(1)*i^2-p(2)*i-p(3); 153 | if(coef>=0) 154 | seg(i,j)=5; 155 | else 156 | seg(i,j)=10; 157 | end 158 | end 159 | end 160 | else 161 | for i=1:s(1) 162 | for j=1:s(2) 163 | coef=i-p(1)*j^2-p(2)*j-p(3); 164 | if(coef>=0) 165 | seg(i,j)=5; 166 | else 167 | seg(i,j)=10; 168 | end 169 | end 170 | end 171 | end 172 | 173 | seg1=edge(seg,'canny'); 174 | 175 | img1=zeros(s(1),s(2)); 176 | img2=zeros(s(1),s(2)); 177 | img3=zeros(s(1),s(2)); 178 | img4=zeros(s(1),s(2)); 179 | 180 | for index=1:length(row) 181 | img1(row(index),col(index))=255; 182 | end 183 | 184 | for index=1:length(row1) 185 | img2(row1(index),col1(index))=255; 186 | end 187 | 188 | img3=img1.*seg1; 189 | img4=img2.*seg1; 190 | 191 | match_cnt(mm)=length(find(img3))*length(find(img4)); 192 | matched_imgs(:,:,mm)=seg1; 193 | 194 | clear r; 195 | clear c; 196 | 197 | mm=mm+1; 198 | 199 | for index=1:length(row1) 200 | img(row1(index),col1(index))=0; 201 | end 202 | 203 | o=1; 204 | 205 | clear row1; 206 | clear col1; 207 | clear A; 208 | clear nx; 209 | clear ny; 210 | clear img_row; 211 | clear img_col; 212 | clear T; 213 | clear p; 214 | clear curve_pts; 215 | clear T; 216 | clear l1; 217 | clear l2; 218 | clear z; 219 | clear r; 220 | clear c; 221 | clear seg1; 222 | end 223 | 224 | length(find(test_mat)) 225 | 226 | if(mm>1) 227 | min_err=max(match_cnt); 228 | else 229 | min_err=threshold+1; 230 | end 231 | 232 | if(min_err<=threshold | mm==1 | number_of_edges==1) 233 | [cur_row cur_col]=find(edge_map==ptr); 234 | rre=zeros(s(1),s(2)); 235 | 236 | for tt=1:length(cur_row) 237 | rre(cur_row(tt),cur_col(tt))=255; 238 | end 239 | 240 | rre=bwmorph(rre,'thin','inf'); 241 | 242 | [lin_row lin_col]=find(rre); 243 | 244 | curve_pts(:,1)=[lin_row(1);lin_col(1)]; 245 | curve_pts(:,2)=[lin_row(round(length(lin_row)/2));lin_col(round(length(lin_row)/2))]; 246 | 247 | slope=(curve_pts(2,2)-curve_pts(2,1))/(curve_pts(1,2)-curve_pts(1,1)); 248 | intercept=curve_pts(2,1)-slope*curve_pts(1,1); 249 | 250 | seg=zeros(s(1),s(2)); 251 | seg1=zeros(s(1),s(2)); 252 | 253 | for i=1:s(1) 254 | for j=1:s(2) 255 | coef=j-slope*i-intercept; 256 | if(coef>=0) 257 | seg(i,j)=5; 258 | else 259 | seg(i,j)=10; 260 | end 261 | end 262 | end 263 | 264 | seg1=edge(seg,'canny'); 265 | conn_rgn=seg1.*selected_area; 266 | input_gray=input_gray+conn_rgn; 267 | figure,imshow(input_gray); 268 | %%%%%%%%%%%%%%% 269 | clear curve_pts; 270 | 271 | [con_row con_col]=find(conn_rgn); 272 | 273 | if(length(con_row)>0) 274 | edge_cnter=edge_cnter+1; 275 | ptr=edge_cnter; 276 | 277 | for index=1:length(con_row) 278 | curve_pts(:,index)=[con_row(index);con_col(index)]; 279 | end 280 | 281 | %%%%%%%%%%%%%% polynimial fit %%%%%%%%%% 282 | if(var(curve_pts(1,:))>=var(curve_pts(2,:))) 283 | p=polyfit(curve_pts(1,:),curve_pts(2,:),2); 284 | 285 | for i=1:length(row_miss) 286 | coef=col_miss(i)-p(1)*row_miss(i)^2-p(2)*row_miss(i)-p(3); 287 | if(coef>=0) 288 | seg_rgn(row_miss(i),col_miss(i),ptr)=edge_cnt; 289 | else 290 | seg_rgn(row_miss(i),col_miss(i),ptr)=edge_cnt+1; 291 | end 292 | end 293 | 294 | o=1; 295 | 296 | for i=min_row-window:max_row+window 297 | for j=min_col-window:max_col+window 298 | coef=j-p(1)*i^2-p(2)*i-p(3); 299 | if(coef>=0) 300 | seg_rgn1(i,j,ptr)=edge_cnt; 301 | else 302 | seg_rgn1(i,j,ptr)=edge_cnt+1; 303 | end 304 | end 305 | end 306 | 307 | else 308 | p=polyfit(curve_pts(2,:),curve_pts(1,:),2); 309 | 310 | for i=1:length(row_miss) 311 | coef=row_miss(i)-p(1)*col_miss(i)^2-p(2)*col_miss(i)-p(3); 312 | if(coef>=0) 313 | seg_rgn(row_miss(i),col_miss(i),ptr)=edge_cnt; 314 | else 315 | seg_rgn(row_miss(i),col_miss(i),ptr)=edge_cnt+1; 316 | end 317 | end 318 | 319 | o=1; 320 | 321 | for i=min_row-window:max_row+window 322 | for j=min_col-window:max_col+window 323 | coef=i-p(1)*j^2-p(2)*j-p(3); 324 | if(coef>=0) 325 | seg_rgn1(i,j,ptr)=edge_cnt; 326 | else 327 | seg_rgn1(i,j,ptr)=edge_cnt+1; 328 | end 329 | end 330 | end 331 | end 332 | end 333 | 334 | for index=1:length(cur_row) 335 | test_mat(cur_row(index),cur_col(index))=0; 336 | end 337 | 338 | clear lin_row; 339 | clear lin_col; 340 | clear cur_row; 341 | clear cur_col; 342 | clear con_row; 343 | clear con_col; 344 | clear curve_pts; 345 | 346 | break; 347 | end 348 | 349 | for match_no=1:length(match_cnt) 350 | if(match_cnt(match_no)==min_err) 351 | break; 352 | end 353 | end 354 | 355 | seg1=matched_imgs(:,:,match_no); 356 | conn_rgn=seg1.*selected_area; 357 | 358 | [row col]=find(edge_map==match_options(match_no)); 359 | 360 | for index=1:length(row) 361 | test_mat(row(index),col(index))=0; 362 | end 363 | 364 | [row col]=find(edge_map==ptr); 365 | 366 | for index=1:length(row) 367 | test_mat(row(index),col(index))=0; 368 | end 369 | 370 | matx=seg1.*test_mat; 371 | 372 | clear r; 373 | clear c; 374 | [r c]=find(matx); 375 | 376 | for i=1:length(r) 377 | test_mat(r(i),c(i))=0; 378 | end 379 | 380 | test_mat=bwareaopen(test_mat,2); 381 | o=1; 382 | 383 | input_gray=input_gray+conn_rgn; 384 | figure,imshow(input_gray); 385 | 386 | length(find(test_mat)) 387 | oo=1; 388 | 389 | clear row; 390 | clear col; 391 | clear match_cnt; 392 | clear egde_map; 393 | clear curve_pts; 394 | clear matched_imgs; 395 | 396 | [row col]=find(conn_rgn); 397 | 398 | if(length(row)>0) 399 | edge_cnter=edge_cnter+1; 400 | ptr=edge_cnter; 401 | offset=round(length(row)/5); 402 | 403 | for index=1:length(row) 404 | curve_pts(:,index)=[row(index);col(index)]; 405 | end 406 | 407 | %%%%%%%%%%%%%% polynimial fit %%%%%%%%%% 408 | if(var(curve_pts(1,:))>=var(curve_pts(2,:))) 409 | p=polyfit(curve_pts(1,:),curve_pts(2,:),2); 410 | 411 | for i=1:length(row_miss) 412 | coef=col_miss(i)-p(1)*row_miss(i)^2-p(2)*row_miss(i)-p(3); 413 | if(coef>=0) 414 | seg_rgn(row_miss(i),col_miss(i),ptr)=edge_cnt; 415 | else 416 | seg_rgn(row_miss(i),col_miss(i),ptr)=edge_cnt+1; 417 | end 418 | end 419 | 420 | o=1; 421 | 422 | for i=min_row-window:max_row+window 423 | for j=min_col-window:max_col+window 424 | coef=j-p(1)*i^2-p(2)*i-p(3); 425 | if(coef>=0) 426 | seg_rgn1(i,j,ptr)=edge_cnt; 427 | else 428 | seg_rgn1(i,j,ptr)=edge_cnt+1; 429 | end 430 | end 431 | end 432 | 433 | else 434 | p=polyfit(curve_pts(2,:),curve_pts(1,:),2); 435 | 436 | for i=1:length(row_miss) 437 | coef=row_miss(i)-p(1)*col_miss(i)^2-p(2)*col_miss(i)-p(3); 438 | if(coef>=0) 439 | seg_rgn(row_miss(i),col_miss(i),ptr)=edge_cnt; 440 | else 441 | seg_rgn(row_miss(i),col_miss(i),ptr)=edge_cnt+1; 442 | end 443 | end 444 | 445 | o=1; 446 | 447 | for i=min_row-window:max_row+window 448 | for j=min_col-window:max_col+window 449 | coef=i-p(1)*j^2-p(2)*j-p(3); 450 | if(coef>=0) 451 | seg_rgn1(i,j,ptr)=edge_cnt; 452 | else 453 | seg_rgn1(i,j,ptr)=edge_cnt+1; 454 | end 455 | end 456 | end 457 | end 458 | end 459 | %%%%%%%%%%%% End of polynomial fit %%%%%%%%%%%%%%%%%%%%%% 460 | o=1; 461 | edge_cnt=edge_cnt+2; 462 | 463 | clear p; 464 | clear curve_pts; 465 | clear row; 466 | clear col; 467 | clear conn_rgn; 468 | clear seg1; 469 | end 470 | end 471 | 472 | o=1; 473 | 474 | seg_rgn_1=zeros(s(1),s(2),edge_cnter); 475 | seg_rgn_2=zeros(s(1),s(2),edge_cnter); 476 | 477 | seg_rgn_1=seg_rgn(:,:,1:edge_cnter); 478 | seg_rgn_2=seg_rgn1(:,:,1:edge_cnter); 479 | 480 | clear seg_rgn; 481 | clear seg_rgn1; 482 | 483 | seg_rgn=seg_rgn_1; 484 | seg_rgn1=seg_rgn_2; 485 | 486 | %%%%%%%%%%%%%%%%% Finding all present segment combinations%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 487 | [row_comb, col_comb]=find(seg_rgn(:,:,1)); 488 | index=1; 489 | for i=1:length(row_comb) 490 | if(index>1) 491 | comb_vect=zeros(edge_cnter,1); 492 | comb_vect(:)=seg_rgn(row_comb(i),col_comb(i),:); 493 | 494 | for j=1:index-1 495 | comb_match_arr(j)=norm(comb_mat(:,j)-comb_vect); 496 | end 497 | 498 | if(length(find(comb_match_arr==0))>0) 499 | continue; 500 | else 501 | comb_mat(:,index)=comb_vect; 502 | index=index+1; 503 | end 504 | end 505 | 506 | if(index==1) 507 | comb_mat(:,index)=seg_rgn(row_comb(i),col_comb(i),:); 508 | index=index+1; 509 | end 510 | end 511 | 512 | numb_of_comb=size(comb_mat); 513 | o=1; 514 | 515 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 516 | 517 | clear row_miss; 518 | clear col_miss; 519 | [row_miss col_miss]=find(seg_rgn1(:,:,1)); 520 | 521 | ee1=zeros(s(1),s(2)); 522 | for i=1:length(row_miss) 523 | ee1(row_miss(i),col_miss(i))=255; 524 | end 525 | 526 | for cnt=1:numb_of_comb(2) 527 | img_vect=zeros(1,edge_cnter); 528 | img_vect(:)=comb_mat(:,cnt); 529 | 530 | depth=0; 531 | ind=0; 532 | for ii=1:length(row_miss) 533 | img_vect1=zeros(1,edge_cnter); 534 | img_vect1(:)=seg_rgn1(row_miss(ii),col_miss(ii),:); 535 | 536 | if(norm(img_vect-img_vect1)<0.1 & input_image(row_miss(ii),col_miss(ii))>0) 537 | depth=depth+input_image(row_miss(ii),col_miss(ii)); 538 | ind=ind+1; 539 | end 540 | end 541 | 542 | avg_depth=depth/ind; 543 | avg_depth_arr(cnt)=avg_depth; 544 | end 545 | 546 | o=1; 547 | 548 | for cnt=1:numb_of_comb(2) 549 | img_vect=zeros(1,edge_cnter); 550 | img_vect1=zeros(1,edge_cnter); 551 | [row_seg col_seg]=find(seg_rgn(:,:,1)); 552 | mm=max(avg_depth_arr); 553 | 554 | for kk=1:numb_of_comb(2) 555 | if(avg_depth_arr(kk)==mm) 556 | break; 557 | end 558 | end 559 | 560 | img_vect(:)=comb_mat(:,kk); 561 | avg_depth_arr(kk)=0; 562 | 563 | img=zeros(s(1),s(2)); 564 | img1=zeros(s(1),s(2)); 565 | img2=zeros(s(1),s(2)); 566 | 567 | ind=1; 568 | ind1=1; 569 | for ii=1:length(row_miss) 570 | img_vect1(:)=seg_rgn1(row_miss(ii),col_miss(ii),:); 571 | img1(row_miss(ii),col_miss(ii))=255; 572 | 573 | if(norm(img_vect-img_vect1)<0.1 & input_image(row_miss(ii),col_miss(ii))>0) 574 | r_rgn(ind)=row_miss(ii); 575 | c_rgn(ind)=col_miss(ii); 576 | img(row_miss(ii),col_miss(ii))=255; 577 | ind=ind+1; 578 | end 579 | 580 | if(norm(img_vect-img_vect1)<0.1 & input_image(row_miss(ii),col_miss(ii))==0) 581 | miss_r_rgn(ind1)=row_miss(ii); 582 | miss_c_rgn(ind1)=col_miss(ii); 583 | img2(row_miss(ii),col_miss(ii))=255; 584 | ind1=ind1+1; 585 | end 586 | end 587 | 588 | o=1; 589 | 590 | if(ind>1) 591 | Y=zeros(s(1),s(2)); 592 | index=1; 593 | for k=1:length(r_rgn) 594 | class(:,index)=[r_rgn(k); c_rgn(k); input_image(r_rgn(k),c_rgn(k))]; 595 | Y(r_rgn(k),c_rgn(k))=255; 596 | index=index+1; 597 | end 598 | 599 | size_class=size(class); 600 | data_dim=size_class(1); 601 | figure,imshow(Y,[]); 602 | 603 | assign_class=cat(1,class,zeros(1,size_class(2))); 604 | T=zeros(s(1),s(2)); 605 | ind=1; 606 | thresh=20; 607 | cntr=1; 608 | loop_cnt=1; 609 | 610 | while(1) 611 | loop_cnt=loop_cnt+1; 612 | if(loop_cnt>200) 613 | break; 614 | end 615 | 616 | ww=find(assign_class((data_dim+1),:)==0); 617 | length(ww); 618 | 619 | if(length(ww)<10) 620 | break; 621 | end 622 | 623 | dp_vect=assign_class(1:3,ww(1)); 624 | ptr=1; 625 | 626 | for j=1:size_class(2) 627 | eul_dist=norm(dp_vect-assign_class(1:3,j)); 628 | 629 | if(eul_dist<=thresh & assign_class((data_dim+1),j)==0) 630 | test_class(:,ptr)=assign_class(1:3,j); 631 | arr_ind(ptr)=j; 632 | ptr=ptr+1; 633 | end 634 | end 635 | o=1; 636 | 637 | size_test_class=size(test_class); 638 | 639 | if(size_test_class(2)<50) 640 | for j=1:size_test_class(2) 641 | Y(test_class(1,j),test_class(2,j))=0; 642 | assign_class(4,arr_ind(j))=100; 643 | end 644 | 645 | if(length(find(Y))plane_pts_threshold) 667 | new_class=test_class; 668 | new_class=cat(1,test_class,zeros(1,size_test_class(2))); 669 | fid = fopen('surf_input_img.txt', 'wt'); 670 | fprintf(fid, '%d \n',data_dim); 671 | fprintf(fid, '%f %f %f %f \n', new_class); 672 | fclose(fid); 673 | 674 | status = dos('NDTensorvoting.exe surf_input_img.txt surf_output_img.txt -scale 80'); 675 | input_array=textread('surf_output_img.txt','%f'); 676 | 677 | %%%%%%%%%%%%%%%% eigen values %%%%%%%%%%%%%%%%%%%%%%%%%%% 678 | eig_val_mat=zeros(size_test_class(1),size_test_class(2)); 679 | ptr=1; 680 | index=2; 681 | 682 | while(index<=length(input_array)) 683 | index=index+data_dim+1; 684 | k=1; 685 | for j=index:(index+data_dim-1) 686 | eig_val_mat(k,ptr)=input_array(index); 687 | k=k+1; 688 | index=index+1; 689 | end 690 | ptr=ptr+1; 691 | index=index+data_dim^2; 692 | end 693 | 694 | tensor_sizes=sum(eig_val_mat(1:data_dim,:)); 695 | %%%%%%%%%%%%%%%%%%% for getting all the eigen vectors %%%%%%%%%%% 696 | index=2; 697 | ptr=1; 698 | 699 | while(index<=length(input_array)) 700 | index=index+2*data_dim+1; 701 | for k=1:data_dim 702 | eig_vect_mat(:,ptr)=input_array(index:index+data_dim-1); 703 | ptr=ptr+1; 704 | index=index+data_dim; 705 | end 706 | end 707 | 708 | size_vect=size(eig_vect_mat); 709 | %%%%%%%%%%%% for getting eigen vectors with highest eig val %%%%%% 710 | index=1; 711 | for i=1:data_dim:size_vect(2) 712 | highest_eig_vect(:,index)=eig_vect_mat(:,i); 713 | index=index+1; 714 | end 715 | 716 | index=1; 717 | for i=2:data_dim:size_vect(2) 718 | highest_eig_vect1(:,index)=eig_vect_mat(:,i); 719 | index=index+1; 720 | end 721 | 722 | index=1; 723 | for i=3:data_dim:size_vect(2) 724 | highest_eig_vect2(:,index)=eig_vect_mat(:,i); 725 | index=index+1; 726 | end 727 | 728 | size_highest_vect=size(highest_eig_vect); 729 | 730 | ev=zeros(data_dim,data_dim); 731 | for i=1:size_highest_vect(2) 732 | vect1=zeros(data_dim,1); 733 | vect1(:)=highest_eig_vect(:,i); 734 | vect2=zeros(data_dim,1); 735 | vect2(:)=highest_eig_vect1(:,i); 736 | vect3=zeros(data_dim,1); 737 | vect3(:)=highest_eig_vect2(:,i); 738 | 739 | ev=ev+vect1*vect1'; 740 | end 741 | 742 | ev=ev/size_highest_vect(2); 743 | [t1 t2]=eigs(ev); 744 | eigen_val=[t2(1,1) t2(2,2) t2(3,3)]; 745 | mm=max(eigen_val); 746 | 747 | for i=1:length(eigen_val) 748 | if(eigen_val(i)==mm) 749 | break; 750 | end 751 | end 752 | 753 | normal_dir=[t1(1,i); t1(2,i); t1(3,i)]; 754 | 755 | for ii=1:size_highest_vect(2) 756 | dot_prod_op(ii)=sum(normal_dir.*highest_eig_vect(:,ii)); 757 | end 758 | 759 | mm=max(dot_prod_op); 760 | for ii=1:size_highest_vect(2) 761 | if(dot_prod_op(ii)==mm) 762 | break; 763 | end 764 | end 765 | 766 | plane_pt=[test_class(1,ii);test_class(2,ii);test_class(3,ii)]; 767 | d=-1*(normal_dir(1)*plane_pt(1)+normal_dir(2)*plane_pt(2)+normal_dir(3)*plane_pt(3)); 768 | local_plane_info(:,cntr)=[normal_dir(1);normal_dir(2);normal_dir(3);d]; 769 | 770 | for i=1:size_class(2) 771 | fact=normal_dir(1)*class(1,i)+normal_dir(2)*class(2,i)+normal_dir(3)*class(3,i)+d; 772 | if(abs(fact)<1) 773 | assign_class(4,i)=cntr; 774 | Y(class(1,i),class(2,i))=0; 775 | end 776 | end 777 | 778 | cntr=cntr+1; 779 | figure,imshow(Y,[]); 780 | 781 | if(length(find(Y))0) 842 | class(:,index)=[row_miss(ii);col_miss(ii);input_image(row_miss(ii),col_miss(ii))]; 843 | sigma_class(:,new_ind)=[row_miss(ii);col_miss(ii);input_image(row_miss(ii),col_miss(ii))]; 844 | index=index+1; 845 | new_ind=new_ind+1; 846 | ee1(row_miss(ii),col_miss(ii))=255; 847 | end 848 | end 849 | 850 | min_pt=[min(sigma_class(1,:)) min(sigma_class(2,:)) min(sigma_class(3,:))]; 851 | max_pt=[max(sigma_class(1,:)) max(sigma_class(2,:)) max(sigma_class(3,:))]; 852 | 853 | sigma=round(1*norm(min_pt-max_pt)) 854 | new_class=class; 855 | 856 | size_class=size(class); 857 | data_dim=size_class(1); 858 | new_class=cat(1,class,zeros(1,size_class(2))); 859 | 860 | fid = fopen('input_img.txt', 'wt'); 861 | fprintf(fid, '%d \n',data_dim); 862 | fprintf(fid, '%f %f %f %f \n', new_class); 863 | fclose(fid); 864 | 865 | status = dos('NDTensorvoting.exe input_img.txt output_img.txt -scale 400'); 866 | input_array=textread('output_img.txt','%f'); 867 | 868 | %%%%%%%%%%%%%%%% eigen values %%%%%%%%%%%%%%%%%%%%%%%%%%%55 869 | eig_val_mat=zeros(size_class(1),size_class(2)); 870 | ptr=1; 871 | index=2; 872 | 873 | while(index<=length(input_array)) 874 | index=index+data_dim+1; 875 | k=1; 876 | 877 | for j=index:(index+data_dim-1) 878 | eig_val_mat(k,ptr)=input_array(index); 879 | k=k+1; 880 | index=index+1; 881 | end 882 | 883 | ptr=ptr+1; 884 | index=index+data_dim^2; 885 | end 886 | 887 | surf_sal=eig_val_mat(1,:)-eig_val_mat(2,:); 888 | 889 | for i=1:(cntr-1):class_lim 890 | arr=surf_sal(i:i+(cntr-1)-1); 891 | mm=max(arr); 892 | 893 | for ff=1:(cntr-1) 894 | if(arr(ff)==mm) 895 | break; 896 | end 897 | end 898 | input_image(class(1,i),class(2,i))=class(3,i+(ff-1)); 899 | end 900 | 901 | figure,imshow(uint8(input_image)) 902 | pause(0.5); 903 | 904 | clear r_rgn; 905 | clear c_rgn; 906 | clear miss_r_rgn; 907 | clear miss_c_rgn; 908 | clear highest_eig_vect; 909 | clear highest_eig_vect1; 910 | clear highest_eig_vect2; 911 | clear input_array; 912 | clear eig_vect_mat; 913 | clear eig_val_mat; 914 | clear class; 915 | clear new_class; 916 | clear row_seg; 917 | clear col_seg; 918 | clear dot_prod_op; 919 | clear assign_class; 920 | clear input_array; 921 | clear local_plane_info; 922 | clear normal_dir; 923 | clear d; 924 | 925 | rr=ones(s(1),s(2)); 926 | rr=abs(seg_rgn(:,:,1)-(input_image+seg_rgn(:,:,1))); 927 | [zero_row, zero_col]=find(rr==0); 928 | 929 | length(zero_row) 930 | end 931 | 932 | o=1; 933 | 934 | if(length(zero_row)==0) 935 | break; 936 | end 937 | end 938 | else 939 | test_mat=zeros(s(1),s(2)); 940 | window=15; 941 | plane_pts_threshold=150; 942 | 943 | test_mat(round(min(yi))-window:round(max(yi))+window,round(min(xi))-window:round(max(xi))+window)=255; 944 | data_mat=input_image(round(min(yi))-window:round(max(yi))+window,round(min(xi))-window:round(max(xi))+window); 945 | [row_miss col_miss]=find(test_mat); 946 | 947 | ind=1; 948 | ind1=1; 949 | for ii=1:length(row_miss) 950 | img1(row_miss(ii),col_miss(ii))=255; 951 | if(input_image(row_miss(ii),col_miss(ii))>0) 952 | r_rgn(ind)=row_miss(ii); 953 | c_rgn(ind)=col_miss(ii); 954 | img(row_miss(ii),col_miss(ii))=255; 955 | ind=ind+1; 956 | end 957 | end 958 | 959 | Y=zeros(s(1),s(2)); 960 | index=1; 961 | for k=1:length(r_rgn) 962 | class(:,index)=[r_rgn(k); c_rgn(k); input_image(r_rgn(k),c_rgn(k))]; 963 | Y(r_rgn(k),c_rgn(k))=255; 964 | index=index+1; 965 | end 966 | 967 | size_class=size(class); 968 | data_dim=size_class(1); 969 | 970 | figure,imshow(Y,[]); 971 | 972 | assign_class=cat(1,class,zeros(1,size_class(2))); 973 | T=zeros(s(1),s(2)); 974 | ind=1; 975 | thresh=20; 976 | cntr=1; 977 | loop_cnt=1; 978 | while(1) 979 | loop_cnt=loop_cnt+1; 980 | if(loop_cnt>50) 981 | break; 982 | end 983 | ww=find(assign_class((data_dim+1),:)==0); 984 | length(ww); 985 | 986 | if(length(ww)<10) 987 | break; 988 | end 989 | 990 | dp_vect=assign_class(1:3,ww(1)); 991 | ptr=1; 992 | 993 | for j=1:size_class(2) 994 | eul_dist=norm(dp_vect-assign_class(1:3,j)); 995 | if(eul_dist<=thresh & assign_class((data_dim+1),j)==0) 996 | test_class(:,ptr)=assign_class(1:3,j); 997 | arr_ind(ptr)=j; 998 | ptr=ptr+1; 999 | end 1000 | end 1001 | 1002 | o=1; 1003 | 1004 | size_test_class=size(test_class); 1005 | if(size_test_class(2)<50) 1006 | for j=1:size_test_class(2) 1007 | Y(test_class(1,j),test_class(2,j))=0; 1008 | assign_class(4,arr_ind(j))=100; 1009 | end 1010 | 1011 | if(length(find(Y))plane_pts_threshold) 1033 | new_class=test_class; 1034 | new_class=cat(1,test_class,zeros(1,size_test_class(2))); 1035 | 1036 | fid = fopen('surf_input_img.txt', 'wt'); 1037 | fprintf(fid, '%d \n',data_dim); 1038 | fprintf(fid, '%f %f %f %f \n', new_class); 1039 | fclose(fid); 1040 | 1041 | status = dos('NDTensorvoting.exe surf_input_img.txt surf_output_img.txt -scale 80'); 1042 | input_array=textread('surf_output_img.txt','%f'); 1043 | 1044 | %%%%%%%%%%%%%%%% eigen values %%%%%%%%%%%%%%%%%%%%%%%%%%%55 1045 | eig_val_mat=zeros(size_test_class(1),size_test_class(2)); 1046 | ptr=1; 1047 | index=2; 1048 | 1049 | while(index<=length(input_array)) 1050 | index=index+data_dim+1; 1051 | k=1; 1052 | 1053 | for j=index:(index+data_dim-1) 1054 | eig_val_mat(k,ptr)=input_array(index); 1055 | k=k+1; 1056 | index=index+1; 1057 | end 1058 | 1059 | ptr=ptr+1; 1060 | index=index+data_dim^2; 1061 | end 1062 | 1063 | tensor_sizes=sum(eig_val_mat(1:data_dim,:)); 1064 | index=2; 1065 | ptr=1; 1066 | 1067 | while(index<=length(input_array)) 1068 | index=index+2*data_dim+1; 1069 | 1070 | for k=1:data_dim 1071 | eig_vect_mat(:,ptr)=input_array(index:index+data_dim-1); 1072 | ptr=ptr+1; 1073 | index=index+data_dim; 1074 | end 1075 | end 1076 | 1077 | size_vect=size(eig_vect_mat); 1078 | 1079 | %%%%%%%%%%%% for getting eigen vectors with highest eig val %%%%%% 1080 | index=1; 1081 | for i=1:data_dim:size_vect(2) 1082 | highest_eig_vect(:,index)=eig_vect_mat(:,i); 1083 | index=index+1; 1084 | end 1085 | 1086 | index=1; 1087 | for i=2:data_dim:size_vect(2) 1088 | highest_eig_vect1(:,index)=eig_vect_mat(:,i); 1089 | index=index+1; 1090 | end 1091 | 1092 | index=1; 1093 | for i=3:data_dim:size_vect(2) 1094 | highest_eig_vect2(:,index)=eig_vect_mat(:,i); 1095 | index=index+1; 1096 | end 1097 | 1098 | size_highest_vect=size(highest_eig_vect); 1099 | 1100 | ev=zeros(data_dim,data_dim); 1101 | for i=1:size_highest_vect(2) 1102 | vect1=zeros(data_dim,1); 1103 | vect1(:)=highest_eig_vect(:,i); 1104 | vect2=zeros(data_dim,1); 1105 | vect2(:)=highest_eig_vect1(:,i); 1106 | vect3=zeros(data_dim,1); 1107 | vect3(:)=highest_eig_vect2(:,i); 1108 | ev=ev+vect1*vect1'; 1109 | end 1110 | 1111 | ev=ev/size_highest_vect(2); 1112 | [t1 t2]=eigs(ev); 1113 | eigen_val=[t2(1,1) t2(2,2) t2(3,3)]; 1114 | mm=max(eigen_val); 1115 | 1116 | for i=1:length(eigen_val) 1117 | if(eigen_val(i)==mm) 1118 | break; 1119 | end 1120 | end 1121 | 1122 | normal_dir=[t1(1,i); t1(2,i); t1(3,i)]; 1123 | for ii=1:size_highest_vect(2) 1124 | dot_prod_op(ii)=sum(normal_dir.*highest_eig_vect(:,ii)); 1125 | end 1126 | 1127 | mm=max(dot_prod_op); 1128 | for ii=1:size_highest_vect(2) 1129 | if(dot_prod_op(ii)==mm) 1130 | break; 1131 | end 1132 | end 1133 | 1134 | plane_pt=[test_class(1,ii);test_class(2,ii);test_class(3,ii)]; 1135 | d=-1*(normal_dir(1)*plane_pt(1)+normal_dir(2)*plane_pt(2)+normal_dir(3)*plane_pt(3)); 1136 | local_plane_info(:,cntr)=[normal_dir(1);normal_dir(2);normal_dir(3);d]; 1137 | 1138 | for i=1:size_class(2) 1139 | fact=normal_dir(1)*class(1,i)+normal_dir(2)*class(2,i)+normal_dir(3)*class(3,i)+d; 1140 | if(abs(fact)<1.5) 1141 | assign_class(4,i)=cntr; 1142 | Y(class(1,i),class(2,i))=0; 1143 | end 1144 | end 1145 | 1146 | cntr=cntr+1; 1147 | figure,imshow(Y,[]); 1148 | 1149 | if(length(find(Y))0) 1205 | class(:,index)=[row_miss(ii);col_miss(ii);input_image(row_miss(ii),col_miss(ii))]; 1206 | sigma_class(:,new_ind)=[row_miss(ii);col_miss(ii);input_image(row_miss(ii),col_miss(ii))]; 1207 | index=index+1; 1208 | new_ind=new_ind+1; 1209 | end 1210 | end 1211 | 1212 | min_pt=[min(sigma_class(1,:)) min(sigma_class(2,:)) min(sigma_class(3,:))]; 1213 | max_pt=[max(sigma_class(1,:)) max(sigma_class(2,:)) max(sigma_class(3,:))]; 1214 | sigma=round(1.8*norm(min_pt-max_pt)) 1215 | 1216 | new_class=class; 1217 | size_class=size(class); 1218 | data_dim=size_class(1); 1219 | new_class=cat(1,class,zeros(1,size_class(2))); 1220 | 1221 | fid = fopen('input_img.txt', 'wt'); 1222 | fprintf(fid, '%d \n',data_dim); 1223 | fprintf(fid, '%f %f %f %f \n', new_class); 1224 | fclose(fid); 1225 | 1226 | status = dos('NDTensorvoting.exe input_img.txt output_img.txt -scale sigma'); 1227 | input_array=textread('output_img.txt','%f'); 1228 | 1229 | %%%%%%%%%%%%%%%% eigen values %%%%%%%%%%%%%%%%%%%%%%%%%%%55 1230 | eig_val_mat=zeros(size_class(1),size_class(2)); 1231 | ptr=1; 1232 | index=2; 1233 | 1234 | while(index<=length(input_array)) 1235 | index=index+data_dim+1; 1236 | k=1; 1237 | 1238 | for j=index:(index+data_dim-1) 1239 | eig_val_mat(k,ptr)=input_array(index); 1240 | k=k+1; 1241 | index=index+1; 1242 | end 1243 | 1244 | ptr=ptr+1; 1245 | index=index+data_dim^2; 1246 | end 1247 | 1248 | surf_sal=eig_val_mat(1,:)-eig_val_mat(2,:); 1249 | 1250 | for i=1:(cntr-1):class_lim 1251 | arr=surf_sal(i:i+(cntr-1)-1); 1252 | mm=max(arr); 1253 | 1254 | for ff=1:(cntr-1) 1255 | if(arr(ff)==mm) 1256 | break; 1257 | end 1258 | end 1259 | 1260 | input_image(class(1,i),class(2,i))=class(3,i+(ff-1)); 1261 | end 1262 | 1263 | clear row; 1264 | clear col; 1265 | 1266 | [row, col]=find(original_img==0); 1267 | 1268 | for i=1:length(row) 1269 | input_image(row(i),col(i))=0; 1270 | end 1271 | 1272 | figure,imshow(uint8(input_image)) 1273 | 1274 | o=1; 1275 | end 1276 | toc; -------------------------------------------------------------------------------- /input_img.txt: -------------------------------------------------------------------------------- 1 | 3 2 | 258.000000 103.000000 175.402136 0.000000 3 | 258.000000 103.000000 141.851405 0.000000 4 | 258.000000 103.000000 146.300694 0.000000 5 | 258.000000 103.000000 151.624334 0.000000 6 | 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