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demo_curet.m
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demo_curet.m
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clear all;
close all;
addpath(genpath('classification_toolbox_4.0'))
rootpath = '/Users/ahmedtaha/Documents/MATLAB/Datasets/curetcol/sample'
patch_width = -1;patch_height = -1;
training_Dataset = [];
no_classes = 3; %% Should be 61. This is the number of classes you are going to used during the modeling and classification phase
no_training_classes = 2; %% Should be 20. This is the number of classes you are going to used during the texton dictionary phase
training_classes = [1,4,6,10,12,14,16,18,20,22,25,27,30,33,35,41,45,48,50,59];
total_images_per_class = 92; %% Total number of images per texture class
training_per_class = 46; %% Number of images used per texture to build histogram models
test_per_class = total_images_per_class-training_per_class; %% Number of images used per texture to be classified
%% Specify the filter to be used.
%filter_bank = makeLMfilters; %% to use LM Filter bank
%filter_bank = makeSfilters; %% to use S Filter bank
filter_bank = makeRFSfilters; %% to use RFS Filter bank
training_options = 'MR8'; %% To indicate that you are going to extract the MR-8 from RFS repos.
classification_options = 'MR8';
numOfFilters = size(filter_bank,3);
if strfind(training_options, 'MR8')
numOfFilters =8;
end
KNN = 1;
numClustersPerClass = 10;
NUM_BINS = numClustersPerClass * no_classes;
total_images = cell(total_images_per_class,no_training_classes);
for c=1:no_training_classes
i = training_classes(c);
folderPath = [rootpath sprintf('%02d',i) '/'];
filenames = [fuf(folderPath ,'detail')];
total_images(1:total_images_per_class,c) = filenames(1:total_images_per_class ,:);
end
params = {};
params.numOfFilters = numOfFilters;
params.numClustersPerClass = numClustersPerClass;
params.no_training_classes = no_training_classes;
params.patch_width = patch_width;
params.patch_height = patch_height;
params.filter_bank = filter_bank;
params.training_options = training_options;
params.total_images_per_class = total_images_per_class;
params.total_images = total_images;
[ training_class_centroid ] = build_texton_dictionary( params );
classes = 1:no_classes;
training_images = cell(training_per_class,no_classes);
test_images = cell(test_per_class,no_classes);
total_images = cell(total_images_per_class,no_classes);
for c=1:no_classes
i = classes(c);
folderPath = [rootpath sprintf('%02d',i) '/'];
filenames = [fuf(folderPath ,'detail')];
total_images(1:total_images_per_class,c) = filenames(1:total_images_per_class ,:);
%% Taking images by order
%training_images(1:training_per_class ,c) = filenames(1:training_per_class ,:);
%test_images(1:test_per_class,c) = filenames(training_per_class+1:total_images_per_class,:);
%% Alternative images
%training_images(1:training_per_class ,c) = filenames(1:2:total_images_per_class ,:);
%test_images(1:test_per_class,c) = filenames(2:2:total_images_per_class,:);
perm = randperm(total_images_per_class);
sel = perm(1:training_per_class);
%% Random Images
training_images(1:training_per_class ,c) = filenames(sel);
test_images(1:test_per_class,c) = filenames(setdiff(1:total_images_per_class,sel));
end
params = {};
params.patch_width = patch_width;
params.patch_height = patch_height;
params.filter_bank = filter_bank;
params.training_options = training_options;
params.training_images = training_images;
params.training_class_centroid = training_class_centroid;
params.NUM_BINS = NUM_BINS;
params.training_per_class = training_per_class;
params.no_classes = no_classes;
[ training_histogram,training_classes ] = build_histogram_models( params);
params = {};
params.patch_width = patch_width;
params.patch_height = patch_height;
params.filter_bank = filter_bank;
params.classification_options = classification_options;
params.training_class_centroid = training_class_centroid;
params.NUM_BINS = NUM_BINS;
params.no_classes = no_classes;
params.training_histogram = training_histogram;
params.training_classes = training_classes;
params.test_images = test_images;
params.test_per_class = test_per_class;
params.KNN = KNN;
[test_histogram,test_classes, accuracy ] = classify_images( params );
figure;imagesc(accuracy);
mean(diag(accuracy) * 100 / test_per_class)
per_class_accuracy = diag(accuracy) * 100 / test_per_class;