changing bin size

.gitignore

@@ -1,2 +1,3 @@
 dataset
 descriptors
+*~

cvpr_visualsearch.m~

@@ -1,156 +0,0 @@
-%% 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=zeros([1, NIMG]);
-recall_values=zeros([1, NIMG]);
-
-correct_at_n=zeros([1, NIMG]);
-
-query_row = dst(1,:);
-query_category = query_row(1,3);
-for i=1:NIMG
-    
-    rows = dst(1:i, :);
-    
-    correct_results = 0;
-    incorrect_results = 0;
-    
-    if i > 1    
-        for n=1:i - 1
-            row = rows(n, :);
-            category = row(3);
-
-            if category == query_category
-                correct_results = correct_results + 1;
-            else
-                incorrect_results = incorrect_results + 1;
-            end
-
-        end
-    end
-    
-    % LAST ROW
-    row = rows(i, :);
-    category = row(3);
-
-    if category == query_category
-        correct_results = correct_results + 1;
-        correct_at_n(i) = 1;
-    else
-        incorrect_results = incorrect_results + 1;
-    end
-    
-    precision = correct_results / i;
-    recall = correct_results / CAT_HIST(1,query_category);
-    
-    precision_values(i) = precision;
-    recall_values(i) = recall;
-end
-
-
-%% 3.6) calculate AP
-for i = 1:NIMG
-    precision = precision_values(i);
-    i
-end
-
-
-%% 3.8) plot PR curve
-% plot(recall_values, precision_values);
-% title('PR Curve');
-% xlabel('Recall');
-% ylabel('Precision');
-
-
-%% 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;

extractGlobalColHist.m

@@ -1,42 +1,12 @@
 function F=extractGlobalColHist(img)
 
-divs = 15;
+divs = 4;
 
 qimg = floor(img .* divs);
 bin = qimg(:,:,1) * divs^2 + qimg(:,:,2) * divs^1 + qimg(:,:,3);
 
 vals = reshape(bin, 1, size(bin, 1) * size(bin, 2));
 
-% dimensions = size(img);
-% 
-% width = dimensions(2);
-% height = dimensions(1);
-% 
-% pixel_count = width * height;
-% 
-% bin_values = zeros([1, pixel_count]);
-% count = 1;
-% for i = 1:length(img(:, 1, 1))
-%     for j = 1:length(img(1, :, 1))
-%         red = img(i, j, 1);
-%         green = img(i, j, 2);
-%         blue = img(i, j, 3);
-%         
-%         red_bin = floor(red*divs);
-%         green_bin = floor(green*divs);
-%         blue_bin = floor(blue*divs);
-%         
-%         bin_value = red_bin * (divs^2) + green_bin * (divs^1) + blue_bin;
-%         
-%         bin_values(count) = bin_value;
-%         
-%         count = count + 1;
-%     end
-% end
-
-% hist_values = histogram(bin_values, (divs^3 - 1)).Values ./ pixel_count;
-% hist_values = histogram(bin_values, divs^3, 'Normalization', 'probability').Values;
-
 hist_values = histogram(vals, divs^3, 'Normalization', 'probability').Values;
 
 F=hist_values;

scratch.m

@@ -4,5 +4,5 @@ img = double(imread('dataset/Images/10_10_s.bmp'))./255;
 imshow(img);
 
 
-glo = extractGlobalColHist(img)
+glo = extractGlobalColHist(img);
 size(img);