%% 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);
ALLCATs=[];
ctr=1;
allfiles=dir (fullfile([DATASET_FOLDER,'/Images/*.bmp']));
for filenum=1:length(allfiles)
    fname=allfiles(filenum).name;
    
    %identify photo category for PR calculation
    split_string = split(fname, '_');
    ALLCATs(filenum) = str2double(split_string(1));
    
    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

% get counts for each category for PR calculation
CAT_HIST = histogram(ALLCATs).Values;

%% 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,:);
    
    category=ALLCATs(i);
    
    %% COMPARE FUNCTION
    thedst=compareEuclidean(query,candidate);
    dst=[dst ; [thedst i category]];
end
dst=sortrows(dst,1);  % sort the results

%% 3.5) Calculate PR
precision_values=[];
recall_values=[];
query_row = dst(1,:);
query_category = query_row(1,3);
for i=1:NIMG
    
    rows = dst(1:i, :);
    
    correct_results = 0;
    incorrect_results = 0;
    
    for n=1:i
        row = rows(n, :);
        category = row(3);
        
        if category == query_category
            correct_results = correct_results + 1;
        else
            incorrect_results = incorrect_results + 1;
        end
        
    end
    
    precision = correct_results / i;
    recall = correct_results / CAT_HIST(1,query_category);
    
    precision_values(i) = precision;
    recall_values(i) = recall;
end

% plot PR curve
plot(recall_values, precision_values);
title('PR Curve');
xlabel('Recall');
ylabel('Precision');

% for i=1:NIMG
%     [i, " -> p: ", precision_values(i), "r: ", recall_values(i)]
% end

%% 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)
   img=imread(ALLFILES{dst(i,2)});
   img=img(1:2:end,1:2:end,:); % make image a quarter size
   img=img(1:81,:,:); % crop image to uniform size vertically (some MSVC images are different heights)
   outdisplay=[outdisplay img];
end
% imgshow(outdisplay);
% axis off;