changing bin size

This commit is contained in:
aj 2019-11-08 15:27:25 +00:00
parent e5302730b7
commit 1393656203
4 changed files with 3 additions and 188 deletions

1
.gitignore vendored
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dataset dataset
descriptors descriptors
*~

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%% 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;

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function F=extractGlobalColHist(img) function F=extractGlobalColHist(img)
divs = 15; divs = 4;
qimg = floor(img .* divs); qimg = floor(img .* divs);
bin = qimg(:,:,1) * divs^2 + qimg(:,:,2) * divs^1 + qimg(:,:,3); bin = qimg(:,:,1) * divs^2 + qimg(:,:,2) * divs^1 + qimg(:,:,3);
vals = reshape(bin, 1, size(bin, 1) * size(bin, 2)); 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; hist_values = histogram(vals, divs^3, 'Normalization', 'probability').Values;
F=hist_values; F=hist_values;

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