Finding Values From Arrays

2 vues (au cours des 30 derniers jours)
rizwan
rizwan le 4 Avr 2015
Commenté : rizwan le 7 Avr 2015
Hi Experts,
I have the following lines of code
I =
I = imread(abc.jpg)
[idx,C] = adaptcluster_kmeans(I);
idx =
a = unique(idx); //Unique values from idx e.g 1 2 3
out = [a,histc(idx(:),a)];//Frequency of unique value
e.g
1 18221
2 28383
3 18080
[a,ix]= max(out(:,2)); //max unique value and its index
a = 28383 ix = 2
Now what i want to do is to use this 'ix' and extract the corresponding pixel values and store them in an excel file....
e.g ix = 2 in this case then i want to extract
Can any help how i can achieve this
Regards & Thanks

Réponse acceptée

Mahdiyar
Mahdiyar le 4 Avr 2015
Hi rizwan
You can simply use the "find" command line.
[row column] = find(idx == ix)
Regards
  3 commentaires
Mahdiyar
Mahdiyar le 5 Avr 2015
Hi
As I understood you just want to store the found pixel in the new variable in the exactly the same position (Row and Column). If so, you may follow the code below.
New_Variable = zeros(n, m); % n and m are the row and column of the matrix in which the original image is saved
for i=1:lenght(Row)
New_Variable(Row(i), Col(i)) = Original_Image(Row(i), Col(i));
end
I think now it works. Regards,
rizwan
rizwan le 7 Avr 2015
Thanks Can You please help me in my svm implementation
clc;
clear all;
%%%%%%%%%%%%%svm training%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
data = [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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 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 59 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255];
label = ['Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y';'Y'; 'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';'N';' N';'N';'N';'N';'N';'N';'N';];
species = cellstr(label);
groups = ismember(species,'Y');
SVMModel = svmtrain(data,label,'showplot',true,'kernel_function','rbf');
%%%%%%%%%%%%%svm training%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%svm Testing%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
load 'D:\MS\Research\Classification Model\Research Implementation\test.mat'; % loading test data i.e cp
[rI,cI] = size(cp);% size of test data
resultantImage = zeros(rI,cI); % image to store road / non road pixels
for i=1:rI %loop to generate a classified image
classes = svmclassify(SVMModel,cp(i),'Showplot', true); % passting test pixels and storing classifier restult in classes (Y or N)
index(cp(i))
if (classes == 'Y')
resultantImage(rI(i), cI(i)) = 1; % If classess is Y then store 1 at the coresponding location of cp
classes = 1;
else if (classes == 'N')
resultantImage(rI(i), cI(i)) = 0;
end
end
end
I m confused in the above loop,what i want to achieve in the loop is
1: Pass each pixel to classifier and store the results, it is working fine in code line classes=svmclassify(SVMModel,cp(i),'Showplot', true);
2: Now what i want to do is to mark the this pixel as 1 or 0 and store the result at the same location as it exists in cp
Please help in above two points.

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