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match.m~
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match.m~
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% num = match(image1, image2)
%
% This function reads two images, finds their SIFT features, and
% displays lines connecting the matched keypoints. A match is accepted
% only if its distance is less than distRatio times the distance to the
% second closest match.
% It returns the number of matches displayed.
%
% Example: match('scene.pgm','book.pgm');
function [rez,nr] = match(image1, image2)
% Find SIFT keypoints for each image
[im1, des1, loc1] = sift(image1);
[im2, des2, loc2] = sift(image2);
% For efficiency in Matlab, it is cheaper to compute dot products between
% unit vectors rather than Euclidean distances. Note that the ratio of
% angles (acos of dot products of unit vectors) is a close approximation
% to the ratio of Euclidean distances for small angles.
%
% distRatio: Only keep matches in which the ratio of vector angles from the
% nearest to second nearest neighbor is less than distRatio.
distRatio = 0.6;
% For each descriptor in the first image, select its match to second image.
des2t = des2'; % Precompute matrix transpose
for i = 1 : size(des1,1)
dotprods = des1(i,:) * des2t; % Computes vector of dot products
[vals,indx] = sort(acos(dotprods)); % Take inverse cosine and sort results
% Check if nearest neighbor has angle less than distRatio times 2nd.
if (vals(1) < distRatio * vals(2))
match(i) = indx(1);
else
match(i) = 0;
end
end
% Create a new image showing the two images side by side.
im3 = appendimages(im1,im2);
% Show a figure with lines joining the accepted matches.
%figure('Position', [100 100 size(im3,2) size(im3,1)]);
%colormap('gray');
%imagesc(im3);
%hold on;
cols1 = size(im1,2);
rez = [];
for i = 1: size(des1,1)
if (match(i) > 0)
% line([loc1(i,2) loc2(match(i),2)+cols1], ...
% [loc1(i,1) loc2(match(i),1)], 'Color', 'c');
rez = [rez; loc1(i,2), loc1(i,1), loc2(match(i),2), loc2(match(i),1)];
end
end
%hold off;
num = sum(match > 0);
fprintf('Found %d matches.\n', num);
nr = size(des1,1) + size(des2,1);