82 lines
2.7 KiB
Matlab
82 lines
2.7 KiB
Matlab
%% EEE3032 - Computer Vision and Pattern Recognition (ee3.cvpr)
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%%
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%% cvpr_visualsearch.m
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%% Skeleton code provided as part of the coursework assessment
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%%
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%% This code will load in all descriptors pre-computed (by the
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%% function cvpr_computedescriptors) from the images in the MSRCv2 dataset.
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%%
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%% It will pick a descriptor at random and compare all other descriptors to
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%% it - by calling cvpr_compare. In doing so it will rank the images by
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%% similarity to the randomly picked descriptor. Note that initially the
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%% function cvpr_compare returns a random number - you need to code it
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%% so that it returns the Euclidean distance or some other distance metric
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%% between the two descriptors it is passed.
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%%
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%% (c) John Collomosse 2010 (J.Collomosse@surrey.ac.uk)
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%% Centre for Vision Speech and Signal Processing (CVSSP)
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%% University of Surrey, United Kingdom
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close all;
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clear all;
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%% Edit the following line to the folder you unzipped the MSRCv2 dataset to
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DATASET_FOLDER = 'dataset';
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%% Folder that holds the results...
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DESCRIPTOR_FOLDER = 'descriptors';
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%% and within that folder, another folder to hold the descriptors
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%% we are interested in working with
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DESCRIPTOR_SUBFOLDER='globalRGBhisto';
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%% 1) Load all the descriptors into "ALLFEAT"
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%% each row of ALLFEAT is a descriptor (is an image)
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ALLFEAT=[];
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ALLFILES=cell(1,0);
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ctr=1;
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allfiles=dir (fullfile([DATASET_FOLDER,'/Images/*.bmp']));
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for filenum=1:length(allfiles)
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fname=allfiles(filenum).name;
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imgfname_full=([DATASET_FOLDER,'/Images/',fname]);
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img=double(imread(imgfname_full))./255;
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thesefeat=[];
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featfile=[DESCRIPTOR_FOLDER,'/',DESCRIPTOR_SUBFOLDER,'/',fname(1:end-4),'.mat'];%replace .bmp with .mat
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load(featfile,'F');
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ALLFILES{ctr}=imgfname_full;
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ALLFEAT=[ALLFEAT ; F];
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ctr=ctr+1;
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end
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%% 2) Pick an image at random to be the query
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NIMG=size(ALLFEAT,1); % number of images in collection
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queryimg=floor(rand()*NIMG); % index of a random image
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%% 3) Compute the distance of image to the query
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dst=[];
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for i=1:NIMG
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candidate=ALLFEAT(i,:);
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query=ALLFEAT(queryimg,:);
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thedst=cvpr_compare(query,candidate);
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dst=[dst ; [thedst i]];
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end
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dst=sortrows(dst,1); % sort the results
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%% 4) Visualise the results
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%% These may be a little hard to see using imgshow
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%% If you have access, try using imshow(outdisplay) or imagesc(outdisplay)
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SHOW=15; % Show top 15 results
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dst=dst(1:SHOW,:);
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outdisplay=[];
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for i=1:size(dst,1)
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img=imread(ALLFILES{dst(i,2)});
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img=img(1:2:end,1:2:end,:); % make image a quarter size
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img=img(1:81,:,:); % crop image to uniform size vertically (some MSVC images are different heights)
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outdisplay=[outdisplay img];
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end
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imgshow(outdisplay);
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axis off;
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