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基于matlab编程3D立体视差源码程序

热度 10已有 3827 次阅读| 2020-11-12 23:17 |系统分类:芯片设计

%-----------------------------------------------------------------

% 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<inf);

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.^2*ones(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<mindiff); %-- put corresponding disarity

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<m3); %-- find where m1 is better

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

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