changing bin size
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vendored
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dataset
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descriptors
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*~
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%% 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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ALLCATs=[];
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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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%identify photo category for PR calculation
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split_string = split(fname, '_');
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ALLCATs(filenum) = str2double(split_string(1));
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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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% get counts for each category for PR calculation
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CAT_HIST = histogram(ALLCATs).Values;
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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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category=ALLCATs(i);
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%% COMPARE FUNCTION
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thedst=compareEuclidean(query,candidate);
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dst=[dst ; [thedst i category]];
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end
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dst=sortrows(dst,1); % sort the results
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%% 3.5) Calculate PR
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precision_values=zeros([1, NIMG]);
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recall_values=zeros([1, NIMG]);
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correct_at_n=zeros([1, NIMG]);
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query_row = dst(1,:);
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query_category = query_row(1,3);
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for i=1:NIMG
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rows = dst(1:i, :);
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correct_results = 0;
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incorrect_results = 0;
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if i > 1
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for n=1:i - 1
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row = rows(n, :);
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category = row(3);
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if category == query_category
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correct_results = correct_results + 1;
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else
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incorrect_results = incorrect_results + 1;
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end
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end
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end
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% LAST ROW
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row = rows(i, :);
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category = row(3);
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if category == query_category
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correct_results = correct_results + 1;
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correct_at_n(i) = 1;
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else
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incorrect_results = incorrect_results + 1;
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end
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precision = correct_results / i;
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recall = correct_results / CAT_HIST(1,query_category);
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precision_values(i) = precision;
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recall_values(i) = recall;
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end
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%% 3.6) calculate AP
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for i = 1:NIMG
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precision = precision_values(i);
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i
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end
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%% 3.8) plot PR curve
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% plot(recall_values, precision_values);
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% title('PR Curve');
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% xlabel('Recall');
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% ylabel('Precision');
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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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@ -1,42 +1,12 @@
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function F=extractGlobalColHist(img)
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divs = 15;
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divs = 4;
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qimg = floor(img .* divs);
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bin = qimg(:,:,1) * divs^2 + qimg(:,:,2) * divs^1 + qimg(:,:,3);
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vals = reshape(bin, 1, size(bin, 1) * size(bin, 2));
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% dimensions = size(img);
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%
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% width = dimensions(2);
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% height = dimensions(1);
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%
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% pixel_count = width * height;
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%
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% bin_values = zeros([1, pixel_count]);
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% count = 1;
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% for i = 1:length(img(:, 1, 1))
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% for j = 1:length(img(1, :, 1))
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% red = img(i, j, 1);
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% green = img(i, j, 2);
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% blue = img(i, j, 3);
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%
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% red_bin = floor(red*divs);
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% green_bin = floor(green*divs);
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% blue_bin = floor(blue*divs);
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%
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% bin_value = red_bin * (divs^2) + green_bin * (divs^1) + blue_bin;
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%
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% bin_values(count) = bin_value;
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%
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% count = count + 1;
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% end
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% end
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% hist_values = histogram(bin_values, (divs^3 - 1)).Values ./ pixel_count;
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% hist_values = histogram(bin_values, divs^3, 'Normalization', 'probability').Values;
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hist_values = histogram(vals, divs^3, 'Normalization', 'probability').Values;
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F=hist_values;
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