function [f inlierIdx] = ransac1( x,y,ransacCoef,funcFindF,funcDist )
%[f inlierIdx] = ransac1( x,y,ransacCoef,funcFindF,funcDist )
% Use RANdom SAmple Consensus to find a fit from X to Y.
% X is M*n matrix including n points with dim M, Y is N*n;
% The fit, f, and the indices of inliers, are returned.
%
% RANSACCOEF is a struct with following fields:
% minPtNum,iterNum,thDist,thInlrRatio
% MINPTNUM is the minimum number of points with whom can we
% find a fit. For line fitting, it's 2. For homography, it's 4.
% ITERNUM is the number of iteration, THDIST is the inlier
% distance threshold and ROUND(THINLRRATIO*n) is the inlier number threshold.
%
% FUNCFINDF is a func handle, f1 = funcFindF(x1,y1)
% x1 is M*n1 and y1 is N*n1, n1 >= ransacCoef.minPtNum
% f1 can be of any type.
% FUNCDIST is a func handle, d = funcDist(f,x1,y1)
% It uses f returned by FUNCFINDF, and return the distance
% between f and the points, d is 1*n1.
% For line fitting, it should calculate the dist between the line and the
% points [x1;y1]; for homography, it should project x1 to y2 then
% calculate the dist between y1 and y2.
minPtNum = ransacCoef.minPtNum;
iterNum = ransacCoef.iterNum;
thInlrRatio = ransacCoef.thInlrRatio;
thDist = ransacCoef.thDist;
ptNum = size(x,2);
thInlr = round(thInlrRatio*ptNum);
inlrNum = zeros(1,iterNum);
fLib = cell(1,iterNum);
for p = 1:iterNum
% 1. fit using random points
sampleIdx = randIndex(ptNum,minPtNum);
f1 = funcFindF(x(:,sampleIdx),y(:,sampleIdx));
% 2. count the inliers, if more than thInlr, refit; else iterate
dist = funcDist(f1,x,y);
inlier1 = find(dist < thDist);
inlrNum(p) = length(inlier1);
if length(inlier1) < thInlr, continue; end
fLib{p} = funcFindF(x(:,inlier1),y(:,inlier1));
end
% 3. choose the coef with the most inliers
[~,idx] = max(inlrNum);
f = fLib{idx};
dist = funcDist(f,x,y);
inlierIdx = find(dist < thDist);
end
评论0