Testing1.m

%% Name: Rakshit Kothari
% Title: ACV project, Scene Classification
% Method: Scene classification using GMM and fixed SIFT features
% File details: This is the M file for matching SIFT features for each
% category. The output is fed into a neural network, which estimates the scene.
% To re-extract SIFT features,please run the M file named 'Reset_files.m'
%%
clear all
close all
clc

%% Setup for VL feat library
run VLFEATROOT/toolbox/vl_setup

%% Constants
load('Data.mat')

label_names=struct('Names',{'Sky' 'Water' 'Greens' 'Trees' 'Sand/Snow' 'Rocks/Mountains' 'Highway/Roads' 'Bldgs' 'Unclassified'});
% num=input('Please enter the image you want to classify');
h=fspecial('gaussian',5,sqrt(2));
trans_size=[128 128];
radius=8;
grid_k=5;

%% Algorithm
current=cd('Images_Training');
direc=dir('*.jpg');
num=1;
z=1;
while num<=150
    count_labels=zeros(1,9);
    scene_name=direc(num).name;
    scene_name=scene_name(1:(find(scene_name=='_')-1));
    img=double(imread(direc(num).name));
    cd(current)
    [X, Y, ~]=size(img);

    img_ycbcr=rgb2ycbcr(img);
    img_filtered=imfilter(img,h,'same');

    img_ycbcr_trans=rgb2ycbcr(img_filtered);
    img_ycbcr_trans=imresize(img_ycbcr_trans,trans_size);
    a=img_ycbcr_trans(:,:,2); %Cb frame
    trans_img(1,:)=a(:);
    a=img_ycbcr_trans(:,:,3); %Cr frame
    trans_img(2,:)=a(:);

    tic
    [label,model,~]=vbgm(trans_img,6);
    figure(1)
    spread(trans_img,label);
    toc
    r=reshape(label,trans_size);
    figure(2)
    imshow(r,[])
    means=model.m;
    reconstructed=zeros(X,Y);

    for i=1:X
        for j=1:Y
            temp(1,1)=img_ycbcr(i,j,2);
            temp(2,1)=img_ycbcr(i,j,3);
            temp=repmat(temp,1,length(means));
            temp=abs(means-temp);
            temp=sum(temp,1);
            [~,loc]=min(temp);
            reconstructed(i,j)=loc;
            clear temp
        end
    end

    figure(3)
    subplot(1,2,1)
    imshow(reconstructed,[])
    subplot(1,2,2)
    imshow(uint8(img),[])

    %% Extracting SIFT features
    [x,y]=form_grid(X,Y,grid_k);
    x=x(:)';
    y=y(:)';
    fc=[x;y;radius*ones(1,length(x));zeros(1,length(x))];
    img_sift=single(rgb2gray(uint8(img)));
    [f,d]=vl_sift(img_sift,'frames',fc,'orientations');
    %% SIFT matching
    for i=1:max(label)
        mask=reconstructed==i;
        if any(mask(:))
            [com]=find_com(mask);
            com_locs(i,:)=com;
            trip=complete_belong(f,mask,0.9,radius);
            g=f(:,trip);
            sift_vector=d(:,trip);
            if ~isempty(g)
                masked_img=img_sift.*mask; 
    %             figure(5)
    %             subplot(1,2,1)
    %             imshow(uint8(img),[])
    %             subplot(1,2,2)
    %             imshow(uint8(masked_img),[])
    %             h11 = vl_plotframe(g);
    %             h22 = vl_plotframe(g);
    %             set(h11,'color','k','linewidth',3);
    %             set(h22,'color','y','linewidth',2);
    %             drawnow
                count_matches=zeros(1,max(categories));
                for j=1:max(categories)
                    database=cell2mat(categories_sift_features(j,1));
                    if ~isempty(database)
                        [matches,scores]=vl_ubcmatch(sift_vector,database,1.2);
                        if ~isempty(matches)
                            matches=remove_repeated(matches);
                        end
                        count_matches(j)=length(matches)/length(g);
                        count_matches_check(i,j)={count_matches(j)};
                        count_scores(i,j)={scores};
                    end

                end
            end
        end
        if ~exist('count_matches','var') || isempty(count_matches)
            count_matches=[zeros(1,8) 1];
        end
        [~,loc]=max(count_matches);
        label_region(i)=loc;
        count_labels(loc)=count_labels(loc)+1;
    end
    figure(6)
    imshow(uint8(img),[])
    for i=1:max(label)
        x=com_locs(i,1);
        y=com_locs(i,2);
        text(x,y,label_names(label_region(i)).Names,'Color',[0 0 0],'EdgeColor',[0 0 0],'BackgroundColor',[1 1 1])
    end
    
testing_ip(:,z)=count_labels';
testing_op(z)=get_category(scene_name);
num=num+1;
z=z+1;
current=cd('Images_Training');
save('Testing.mat','testing_ip','testing_op')
end
cd(current)
save('Testing.mat','testing_ip','testing_op')