From 01ce588a9edccbbc24cfb09b12ccde1b0b2df369 Mon Sep 17 00:00:00 2001 From: aj Date: Wed, 6 Nov 2019 15:22:15 +0000 Subject: [PATCH] initial commit with random descriptors and skeleton report --- .gitignore | 1 + cvpr_compare.m | 10 ++ cvpr_computedescriptors.m | 38 +++++++ cvpr_visualsearch.m | 81 +++++++++++++++ descriptors/globalRGBhisto/10_10_s.mat | Bin 0 -> 430 bytes descriptors/globalRGBhisto/10_11_s.mat | Bin 0 -> 431 bytes descriptors/globalRGBhisto/10_12_s.mat | Bin 0 -> 430 bytes descriptors/globalRGBhisto/10_13_s.mat | Bin 0 -> 432 bytes descriptors/globalRGBhisto/10_14_s.mat | Bin 0 -> 430 bytes descriptors/globalRGBhisto/10_15_s.mat | Bin 0 -> 429 bytes descriptors/globalRGBhisto/10_16_s.mat | Bin 0 -> 431 bytes descriptors/globalRGBhisto/10_17_s.mat | Bin 0 -> 429 bytes descriptors/globalRGBhisto/10_18_s.mat | Bin 0 -> 430 bytes descriptors/globalRGBhisto/10_19_s.mat | Bin 0 -> 430 bytes descriptors/globalRGBhisto/10_1_s.mat | Bin 0 -> 430 bytes 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+++ b/cvpr_compare.m @@ -0,0 +1,10 @@ +function dst=cvpr_compare(F1, F2) + +% This function should compare F1 to F2 - i.e. compute the distance +% between the two descriptors + +% For now it just returns a random number + +dst=rand(); + +return; diff --git a/cvpr_computedescriptors.m b/cvpr_computedescriptors.m new file mode 100644 index 0000000..36738f6 --- /dev/null +++ b/cvpr_computedescriptors.m @@ -0,0 +1,38 @@ +%% EEE3032 - Computer Vision and Pattern Recognition (ee3.cvpr) +%% +%% cvpr_computedescriptors.m +%% Skeleton code provided as part of the coursework assessment +%% This code will iterate through every image in the MSRCv2 dataset +%% and call a function 'extractRandom' to extract a descriptor from the +%% image. Currently that function returns just a random vector so should +%% be changed as part of the coursework exercise. +%% +%% (c) John Collomosse 2010 (J.Collomosse@surrey.ac.uk) +%% Centre for Vision Speech and Signal Processing (CVSSP) +%% University of Surrey, United Kingdom + +close all; +clear all; + +%% Edit the following line to the folder you unzipped the MSRCv2 dataset to +DATASET_FOLDER = 'dataset'; + +%% Create a folder to hold the results... +OUT_FOLDER = 'descriptors'; +%% and within that folder, create another folder to hold these descriptors +%% the idea is all your descriptors are in individual folders - within +%% the folder specified as 'OUT_FOLDER'. +OUT_SUBFOLDER='globalRGBhisto'; + +allfiles=dir (fullfile([DATASET_FOLDER,'/Images/*.bmp'])); +for filenum=1:length(allfiles) + fname=allfiles(filenum).name; + fprintf('Processing file %d/%d - %s\n',filenum,length(allfiles),fname); + tic; + imgfname_full=([DATASET_FOLDER,'/Images/',fname]); + img=double(imread(imgfname_full))./255; + fout=[OUT_FOLDER,'/',OUT_SUBFOLDER,'/',fname(1:end-4),'.mat'];%replace .bmp with .mat + F=extractRandom(img); + save(fout,'F'); + toc +end diff --git a/cvpr_visualsearch.m b/cvpr_visualsearch.m new file mode 100644 index 0000000..22872c3 --- /dev/null +++ b/cvpr_visualsearch.m @@ -0,0 +1,81 @@ +%% EEE3032 - Computer Vision and Pattern Recognition (ee3.cvpr) +%% +%% cvpr_visualsearch.m +%% Skeleton code provided as part of the coursework assessment +%% +%% This code will load in all descriptors pre-computed (by the +%% function cvpr_computedescriptors) from the images in the MSRCv2 dataset. +%% +%% It will pick a descriptor at random and compare all other descriptors to +%% it - by calling cvpr_compare. In doing so it will rank the images by +%% similarity to the randomly picked descriptor. Note that initially the +%% function cvpr_compare returns a random number - you need to code it +%% so that it returns the Euclidean distance or some other distance metric +%% between the two descriptors it is passed. +%% +%% (c) John Collomosse 2010 (J.Collomosse@surrey.ac.uk) +%% Centre for Vision Speech and Signal Processing (CVSSP) +%% University of Surrey, United Kingdom + +close all; +clear all; + +%% Edit the following line to the folder you unzipped the MSRCv2 dataset to +DATASET_FOLDER = 'dataset'; + +%% Folder that holds the results... +DESCRIPTOR_FOLDER = 'descriptors'; +%% and within that folder, another folder to hold the descriptors +%% we are interested in working with +DESCRIPTOR_SUBFOLDER='globalRGBhisto'; + + +%% 1) Load all the descriptors into "ALLFEAT" +%% each row of ALLFEAT is a descriptor (is an image) + +ALLFEAT=[]; +ALLFILES=cell(1,0); +ctr=1; +allfiles=dir (fullfile([DATASET_FOLDER,'/Images/*.bmp'])); +for filenum=1:length(allfiles) + fname=allfiles(filenum).name; + imgfname_full=([DATASET_FOLDER,'/Images/',fname]); + img=double(imread(imgfname_full))./255; + thesefeat=[]; + featfile=[DESCRIPTOR_FOLDER,'/',DESCRIPTOR_SUBFOLDER,'/',fname(1:end-4),'.mat'];%replace .bmp with .mat + load(featfile,'F'); + ALLFILES{ctr}=imgfname_full; + ALLFEAT=[ALLFEAT ; F]; + ctr=ctr+1; +end + +%% 2) Pick an image at random to be the query +NIMG=size(ALLFEAT,1); % number of images in collection +queryimg=floor(rand()*NIMG); % index of a random image + + +%% 3) Compute the distance of image to the query +dst=[]; +for i=1:NIMG + candidate=ALLFEAT(i,:); + query=ALLFEAT(queryimg,:); + thedst=cvpr_compare(query,candidate); + dst=[dst ; [thedst i]]; +end +dst=sortrows(dst,1); % sort the results + +%% 4) Visualise the results +%% These may be a little hard to see using imgshow +%% If you have access, try using imshow(outdisplay) or imagesc(outdisplay) + +SHOW=15; % Show top 15 results +dst=dst(1:SHOW,:); +outdisplay=[]; +for i=1:size(dst,1) + 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