249 lines
6.9 KiB
Matlab
249 lines
6.9 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='avgRGB';
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DESCRIPTOR_SUBFOLDER='globalRGBhisto';
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% DESCRIPTOR_SUBFOLDER='spatialColour';
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% DESCRIPTOR_SUBFOLDER='spatialTexture';
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% DESCRIPTOR_SUBFOLDER='spatialColourTexture';
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CATEGORIES = ["Farm Animal"
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"Tree"
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"Building"
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"Plane"
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"Cow"
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"Face"
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"Car"
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"Bike"
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"Sheep"
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"Flower"
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"Sign"
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"Bird"
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"Book Shelf"
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"Bench"
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"Cat"
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"Dog"
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"Road"
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"Water Features"
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"Human Figures"
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"Coast"
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];
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QUERY_INDEXES=[301 358 384 436 447 476 509 537 572 5 61 80 97 127 179 181 217 266 276 333];
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% 1_10 2_16 3_12 4_4 5_15 6_14 7_17 8_15 9_1 10_14 11_8 12_26 13_10 14_10
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% 15_8 16_10 17_16 18_5 19_15 20_12
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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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CAT_TOTAL = length(CAT_HIST);
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NIMG=size(ALLFEAT,1); % number of images in collection
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confusion_matrix = zeros(CAT_TOTAL);
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all_precision = [];
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all_recall = [];
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AP_values = zeros([1, CAT_TOTAL]);
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for iteration=1:CAT_TOTAL
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%% 2) Pick an image at random to be the query
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queryimg=QUERY_INDEXES(iteration); % 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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%% 4) Calculate PR
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precision_values=zeros([1, NIMG-1]);
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recall_values=zeros([1, NIMG-1]);
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correct_at_n=zeros([1, NIMG-1]);
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query_row = dst(1,:);
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query_category = query_row(1,3);
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if query_category ~= iteration
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dst
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end
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fprintf('category was %s\n', CATEGORIES(query_category))
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dst = dst(2:NIMG, :);
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%calculate PR for each n
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for i=1:size(dst, 1)
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% NIMG-1 and j iterator variable is in order to skip calculating for query image
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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 == iteration
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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 == iteration
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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,iteration) - 1);
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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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%% 5) calculate AP
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average_precision = sum(precision_values .* correct_at_n) / CAT_HIST(1,iteration);
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AP_values(iteration) = average_precision;
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all_precision = [all_precision ; precision_values];
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all_recall = [all_recall ; recall_values];
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%% 6) plot PR curve
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% figure(1)
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% plot(recall_values, precision_values,'LineWidth',1.5);
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% hold on;
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% title('Global Colour Histogram PR (n=20)');
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% xlabel('Recall');
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% ylabel('Precision');
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% xlim([0 1]);
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% ylim([0 1]);
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%% 7) 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=25; % Show top 25 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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%populate confusion matrix
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confusion_matrix(query_category, dst(i,3)) = confusion_matrix(query_category, dst(i,3)) + 1;
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end
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% figure(3)
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% imgshow(outdisplay);
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% axis off;
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end
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%% Plot average PR curve
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figure(4)
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mean_precision = mean(all_precision);
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mean_recall = mean(all_recall);
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plot(mean_recall, mean_precision,'LineWidth',5);
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title('Global Colour Histogram Average PR (n=5)');
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xlabel('Average Recall');
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ylabel('Average Precision');
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xlim([0 1]);
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ylim([0 1]);
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% normalise confusion matrix
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norm_confusion_matrix = confusion_matrix ./ sum(confusion_matrix, 'all');
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%% 8 Calculate MAP
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% figure(4)
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% histogram(AP_values);
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% title('Average Precision Distribution');
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% ylabel('Count');
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% xlabel('Average Precision');
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% xlim([0, 1]);
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MAP = mean(AP_values)
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AP_sd = std(AP_values)
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% figure(2)
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% plot(1:CAT_TOTAL, AP_values);
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% title('Average Precision Per Run');
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% xlabel('Run');
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% ylabel('Average Precision');
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