-
Notifications
You must be signed in to change notification settings - Fork 6
/
Copy pathdemo_curet.m
128 lines (92 loc) · 4.22 KB
/
demo_curet.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
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;