matlab编程3D立体视差源码程序分享

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问答对人有帮助，内容完整，我也想知道答案 0 基于matlab编程3D立体视差源码程序 0
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答案对人有帮助，有参考价值 0 %----------------------------------------------------------------- % function [dsp pixel_dsp segs labels] = total_stereo... % (i1,i2, hs,hr,M,mins, maxs, segs, labels) % % A first take at 3D from stereo. This function takes a stereo pAIr % (that should already be registered so the only difference is in the % 'x' dimension), and produces a 'disparity map.' The output here is % pixel disparity, which can be converted to actual distance from the % cameras if information about the camera geometry is known. % % The output here does show which objects are closer and 'segments' % by distance to camera. % % EXAMPLE: % i1 = imread('tsuL.jpg'); % i2 = imread('tsuR.jpg'); % [d p s l] = total_stereo(i1,i2,10,7,30,1,20); % % dsp = final disparity map % pixel_dsp = pixel disparities before final filtering % segs = segmented image from msseg % labels = labemap from msseg % % i1 = right image % i2 = left image % hs = spacial bandwidth (for msseg) (usually 10) % hr = range(color) bandwidth (for msseg) (usually 7) % M = minimum segment size (for msseg) (usually 30) % mins = minimum shift (usually 1) % maxs = maximum shift (depends on images) % segs = segments (if you have them pre-computed) % labels = labelmap (if you have it pre-computed) % % Algorithm adapted from: "Segment-Based Stereo Matching Using % Belief Propogation and Self-Adapting Dissimilarity Measure" by % Klaus, Sormann, and Karner. % % (The algorithm in the paper is better, and more complete. The % codes here are inspired by these guys, and parts are original) % % Coded by Shawn Lankton (http://www.shawnlankton.com) Dec. 2007 %----------------------------------------------------------------- function [dsp pixel_dsp segs labels] = total_stereo... (i1,i2, hs,hr,M,mins, maxs, segs, labels) web www.hslogic.com %QQ:1224848052 win_size = 5; %-- larger for less textured surfaces tolerance = 0; %-- larger for less textured surfaces [dimy dimx c] = size(i1); [xx yy] = meshgrid(1:size(i1,2),1:size(i1,1)); dsp = ones(size(i1,1),size(i1,2)); %--segment reference image if(nargin<9) [segs labels] = msseg(i1,hs,hr,M); %-- mean shift segmentation end %--determine pixel correspondence Right-to-Left [disparity1 mindiff1] = slide_images(i1,i2, mins, maxs, win_size); %--determine pixel correspondence Left-to-Right [disparity2 mindiff2] = slide_images(i2,i1, -mins, -maxs, win_size); disparity2 = abs(disparity2); %-- disprities will be negative %--create high-confidence disparity map pixel_dsp = winner_take_all(disparity1, mindiff1, disparity2, mindiff2); %--filter with segmented image for(i = 0:length(unique(labels))-1) lab_idx = find((labels == i)); inf_idx = find(labels == i & pixel_dsp dsp(lab_idx) = median(pixel_dsp(inf_idx)); end %--I think this looks cleaner, but it doesn't really serve a purpose pixel_dsp(pixel_dsp==inf)=NaN; %%----- HELPER FUNCTIONS %-- slides images across each other to get disparity estimate function [disparity mindiff] = slide_images(i1,i2,mins,maxs,win_size) [dimy,dimx,c] = size(i1); disparity = zeros(dimy,dimx); %-- init outputs mindiff = inf(dimy,dimx); w = 5; %-- weight of CSAD vs CGRAD hx = [-1 0 1]; hy = [-1 0 1]'; %-- gradient filter h = 1/win_size.^2ones(win_size); %-- averaging filter g1x = sum(imfilter(i1,hx).^2,3); %-- get gradient for each image g1y = sum(imfilter(i1,hy).^2,3); %-- used to compute CGRAD g2x = sum(imfilter(i2,hx).^2,3); g2y = sum(imfilter(i2,hy).^2,3); step = sign(maxs-mins); %-- adjusts to reverse slide for(i=mins:step:maxs) s = shift_image(i2,i); %-- shift image and derivs sx = shift_image(g2x,i); sy = shift_image(g2y,i); %--CSAD is Cost from Sum of Absolute Differences %--CGRAD is Cost from Gradient of Absolute Differences diffs = sum(abs(i1-s),3); %-- get CSAD and CGRAD CSAD = imfilter(diffs,h); gdiff = w (sum(abs(g1x-sx),3)+sum(abs(g1y-sy),3)); CGRAD = imfilter(gdiff,h); d = CSAD+CGRAD; %-- total 'difference' score idx = find(d disparity(idx) = i; % into correct place in image mindiff(idx) = d(idx); end %-- reconsiles two noisy disparity estimates function [pd] = winner_take_all(d1,m1,d2,m2,tolerance) if(~exist('tolerance','var')) tolerance = 0; end [dimy dimx] = size(d1); d3 = zeros(size(d1)); m3 = zeros(size(d1)); %-- scoot L-R disparity (this should make disprities line up % between the refernce image(left) and the other image(right) for(i=1:max(d2(:))) [yy xx] = find(d2==i); %-- get all disprities 'i' idx2 = sub2ind([dimy, dimx],yy,xx); xx = xx+i-1; %-- figure out new position xx(xx>dimx)=dimx; %-- check boundary idx3 = sub2ind([dimy dimx],yy,xx); d3(idx3)=d2(idx2); %-- move disparities and m3(idx3)=m2(idx2); %-- diffs to the right spot end %-- keep the best ones and mark the bad ones pd = d3; %-- start with shifted L-R idx = find(m1 pd(idx) = d1(idx); %-- use disp from R-L there diff(idx) = m1(idx); %-- use L-R mindiff's too idx = find(m1==m3); %-- find where its a tie pd(idx) = round(d1(idx)+d3(idx))/2; %-- split the difference pd(abs(d1-d3)>tolerance) = inf; %-- mark points that are % likley wrong %-- Shift an image function I = shift_image(I,shift) dimx = size(I,2); if(shift > 0) I(:,shift:dimx,:) = I(:,1:dimx-shift+1,:); I(:,1:shift-1,:) = 0; else if(shift<0) I(:,1:dimx+shift+1,:) = I(:,-shift:dimx,:); I(:,dimx+shift+1:dimx,:) = 0; end end

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