1 | // eigenface.c, by Robin Hewitt, 2007 |
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2 | // |
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3 | // Example program showing how to implement eigenface with OpenCV |
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4 | |
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5 | // Usage: |
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6 | // |
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7 | // First, you need some face images. I used the ORL face database. |
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8 | // You can download it for free at |
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9 | // www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html |
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10 | // |
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11 | // List the training and test face images you want to use in the |
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12 | // input files train.txt and test.txt. (Example input files are provided |
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13 | // in the download.) To use these input files exactly as provided, unzip |
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14 | // the ORL face database, and place train.txt, test.txt, and eigenface.exe |
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15 | // at the root of the unzipped database. |
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16 | // |
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17 | // To run the learning phase of eigenface, enter |
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18 | // eigenface train |
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19 | // at the command prompt. To run the recognition phase, enter |
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20 | // eigenface test |
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21 | |
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22 | |
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23 | #include <stdio.h> |
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24 | #include <string.h> |
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25 | #include "cv.h" |
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26 | #include "cvaux.h" |
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27 | #include "highgui.h" |
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28 | #include "eigenface.h" |
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29 | |
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30 | //// Global variables |
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31 | IplImage ** faceImgArr = 0; // array of face images |
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32 | CvMat * personNumTruthMat = 0; // array of person numbers |
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33 | int nTrainFaces = 0; // the number of training images |
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34 | int nEigens = 0; // the number of eigenvalues |
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35 | IplImage * pAvgTrainImg = 0; // the average image |
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36 | IplImage ** eigenVectArr = 0; // eigenvectors |
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37 | CvMat * eigenValMat = 0; // eigenvalues |
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38 | CvMat * projectedTrainFaceMat = 0; // projected training faces |
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39 | |
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40 | IplImage ** eigenPics = 0; // eigenvectors |
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41 | char eig_name[20]; |
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42 | |
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43 | //// Function prototypes |
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44 | void learn(); |
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45 | void recognize(); |
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46 | void doPCA(); |
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47 | void storeTrainingData(); |
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48 | int loadTrainingData(CvMat ** pTrainPersonNumMat); |
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49 | int findNearestNeighbor(float * projectedTestFace); |
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50 | int loadFaceImgArray(char * filename); |
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51 | void convertToUImage(IplImage *fImg, IplImage *uImg); |
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52 | |
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53 | ////////////////////////////////// |
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54 | // learn() |
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55 | // |
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56 | void learn(char * filename) |
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57 | { |
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58 | int i, offset; |
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59 | |
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60 | // load training data |
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61 | nTrainFaces = loadFaceImgArray(filename); |
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62 | if( nTrainFaces < 2 ) |
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63 | { |
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64 | fprintf(stderr, |
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65 | "Need 2 or more training faces\n" |
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66 | "Input file contains only %d\n", nTrainFaces); |
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67 | return; |
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68 | } |
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69 | |
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70 | // do PCA on the training faces |
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71 | doPCA(); |
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72 | |
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73 | // project the training images onto the PCA subspace |
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74 | projectedTrainFaceMat = cvCreateMat( nTrainFaces, nEigens, CV_32FC1 ); |
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75 | offset = projectedTrainFaceMat->step / sizeof(float); |
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76 | for(i=0; i<nTrainFaces; i++) |
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77 | { |
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78 | //int offset = i * nEigens; |
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79 | cvEigenDecomposite( |
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80 | faceImgArr[i], |
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81 | nEigens, |
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82 | eigenVectArr, |
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83 | 0, 0, |
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84 | pAvgTrainImg, |
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85 | //projectedTrainFaceMat->data.fl + i*nEigens); |
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86 | projectedTrainFaceMat->data.fl + i*offset); |
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87 | //~ calcDecomp( |
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88 | //~ faceImgArr[i], |
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89 | //~ nEigens, |
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90 | //~ eigenVectArr, |
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91 | //~ pAvgTrainImg, |
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92 | //~ //projectedTrainFaceMat->data.fl + i*nEigens); |
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93 | //~ projectedTrainFaceMat->data.fl + i*offset); |
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94 | } |
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95 | |
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96 | // store the recognition data as an xml file |
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97 | storeTrainingData(); |
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98 | } |
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99 | |
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100 | |
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101 | ////////////////////////////////// |
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102 | // recognize() |
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103 | // |
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104 | void recognize(IplImage *faceImg) |
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105 | { |
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106 | int i, nTestFaces = 0; // the number of test images |
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107 | CvMat * trainPersonNumMat = 0; // the person numbers during training |
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108 | float * projectedTestFace = 0; |
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109 | int iNearest, nearest, truth; |
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110 | |
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111 | // load test images and ground truth for person number |
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112 | //nTestFaces = loadFaceImgArray("test.txt"); |
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113 | //printf("%d test faces loaded\n", nTestFaces); |
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114 | |
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115 | // load the saved training data |
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116 | if( !loadTrainingData( &trainPersonNumMat ) ) return; |
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117 | |
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118 | // project the test images onto the PCA subspace |
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119 | projectedTestFace = (float *)cvAlloc( nEigens*sizeof(float) ); |
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120 | |
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121 | // project the test image onto the PCA subspace |
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122 | cvEigenDecomposite( |
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123 | faceImg, |
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124 | nEigens, |
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125 | eigenVectArr, |
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126 | 0, 0, |
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127 | pAvgTrainImg, |
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128 | projectedTestFace); |
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129 | //~ calcDecomp( |
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130 | //~ faceImg, |
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131 | //~ nEigens, |
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132 | //~ eigenVectArr, |
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133 | //~ pAvgTrainImg, |
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134 | //~ projectedTestFace); |
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135 | |
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136 | iNearest = findNearestNeighbor(projectedTestFace); |
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137 | nearest = trainPersonNumMat->data.i[iNearest]; |
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138 | |
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139 | printf("nearest = %d\n", nearest); |
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140 | } |
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141 | |
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142 | |
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143 | ////////////////////////////////// |
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144 | // loadTrainingData() |
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145 | // |
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146 | int loadTrainingData(CvMat ** pTrainPersonNumMat) |
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147 | { |
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148 | CvFileStorage * fileStorage; |
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149 | int i; |
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150 | |
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151 | // create a file-storage interface |
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152 | fileStorage = cvOpenFileStorage( "facedata.xml", 0, CV_STORAGE_READ ); |
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153 | if( !fileStorage ) |
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154 | { |
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155 | fprintf(stderr, "Can't open facedata.xml\n"); |
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156 | return 0; |
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157 | } |
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158 | |
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159 | nEigens = cvReadIntByName(fileStorage, 0, "nEigens", 0); |
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160 | nTrainFaces = cvReadIntByName(fileStorage, 0, "nTrainFaces", 0); |
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161 | *pTrainPersonNumMat = (CvMat *)cvReadByName(fileStorage, 0, "trainPersonNumMat", 0); |
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162 | eigenValMat = (CvMat *)cvReadByName(fileStorage, 0, "eigenValMat", 0); |
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163 | projectedTrainFaceMat = (CvMat *)cvReadByName(fileStorage, 0, "projectedTrainFaceMat", 0); |
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164 | pAvgTrainImg = (IplImage *)cvReadByName(fileStorage, 0, "avgTrainImg", 0); |
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165 | eigenVectArr = (IplImage **)cvAlloc(nTrainFaces*sizeof(IplImage *)); |
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166 | for(i=0; i<nEigens; i++) |
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167 | { |
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168 | char varname[200]; |
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169 | sprintf( varname, "eigenVect_%d", i ); |
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170 | eigenVectArr[i] = (IplImage *)cvReadByName(fileStorage, 0, varname, 0); |
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171 | } |
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172 | |
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173 | // release the file-storage interface |
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174 | cvReleaseFileStorage( &fileStorage ); |
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175 | |
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176 | return 1; |
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177 | } |
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178 | |
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179 | |
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180 | ////////////////////////////////// |
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181 | // storeTrainingData() |
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182 | // |
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183 | void storeTrainingData() |
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184 | { |
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185 | CvFileStorage * fileStorage; |
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186 | int i; |
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187 | |
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188 | // create a file-storage interface |
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189 | fileStorage = cvOpenFileStorage( "facedata.xml", 0, CV_STORAGE_WRITE ); |
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190 | |
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191 | // store all the data |
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192 | cvWriteInt( fileStorage, "nEigens", nEigens ); |
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193 | cvWriteInt( fileStorage, "nTrainFaces", nTrainFaces ); |
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194 | cvWrite(fileStorage, "trainPersonNumMat", personNumTruthMat, cvAttrList(0,0)); |
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195 | cvWrite(fileStorage, "eigenValMat", eigenValMat, cvAttrList(0,0)); |
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196 | cvWrite(fileStorage, "projectedTrainFaceMat", projectedTrainFaceMat, cvAttrList(0,0)); |
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197 | cvWrite(fileStorage, "avgTrainImg", pAvgTrainImg, cvAttrList(0,0)); |
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198 | for(i=0; i<nEigens; i++) |
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199 | { |
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200 | char varname[200]; |
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201 | sprintf( varname, "eigenVect_%d", i ); |
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202 | cvWrite(fileStorage, varname, eigenVectArr[i], cvAttrList(0,0)); |
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203 | } |
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204 | |
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205 | // release the file-storage interface |
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206 | cvReleaseFileStorage( &fileStorage ); |
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207 | } |
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208 | |
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209 | |
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210 | ////////////////////////////////// |
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211 | // findNearestNeighbor() |
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212 | // |
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213 | int findNearestNeighbor(float * projectedTestFace) |
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214 | { |
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215 | //double leastDistSq = 1e12; |
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216 | double leastDistSq = DBL_MAX; |
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217 | int i, iTrain, iNearest = 0; |
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218 | |
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219 | for(iTrain=0; iTrain<nTrainFaces; iTrain++) |
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220 | { |
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221 | double distSq=0; |
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222 | |
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223 | for(i=0; i<nEigens; i++) |
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224 | { |
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225 | float d_i = |
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226 | projectedTestFace[i] - |
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227 | projectedTrainFaceMat->data.fl[iTrain*nEigens + i]; |
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228 | distSq += d_i*d_i / eigenValMat->data.fl[i]; // Mahalanobis |
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229 | //distSq += d_i*d_i; // Euclidean |
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230 | } |
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231 | |
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232 | if(distSq < leastDistSq) |
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233 | { |
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234 | leastDistSq = distSq; |
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235 | iNearest = iTrain; |
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236 | } |
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237 | } |
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238 | |
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239 | return iNearest; |
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240 | } |
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241 | |
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242 | |
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243 | ////////////////////////////////// |
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244 | // doPCA() |
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245 | // |
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246 | void doPCA() |
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247 | { |
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248 | int i; |
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249 | CvTermCriteria calcLimit; |
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250 | CvSize faceImgSize; |
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251 | |
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252 | // set the number of eigenvalues to use |
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253 | nEigens = nTrainFaces-1; |
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254 | |
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255 | // allocate the eigenvector images |
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256 | faceImgSize.width = faceImgArr[0]->width; |
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257 | faceImgSize.height = faceImgArr[0]->height; |
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258 | eigenVectArr = (IplImage**)cvAlloc(sizeof(IplImage*) * nEigens); |
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259 | eigenPics = (IplImage**)cvAlloc(sizeof(IplImage*) * nEigens); |
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260 | for(i=0; i<nEigens; i++) { |
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261 | eigenVectArr[i] = cvCreateImage(faceImgSize, IPL_DEPTH_32F, 1); |
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262 | eigenPics[i] = cvCreateImage(faceImgSize, IPL_DEPTH_8U, 1); |
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263 | } |
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264 | |
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265 | // allocate the eigenvalue array |
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266 | eigenValMat = cvCreateMat( 1, nEigens, CV_32FC1 ); |
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267 | |
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268 | // allocate the averaged image |
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269 | pAvgTrainImg = cvCreateImage(faceImgSize, IPL_DEPTH_32F, 1); |
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270 | |
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271 | // set the PCA termination criterion |
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272 | calcLimit = cvTermCriteria( CV_TERMCRIT_ITER, nEigens, 1); |
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273 | |
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274 | // compute average image, eigenvalues, and eigenvectors |
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275 | //~ cvCalcEigenObjects( |
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276 | //~ nTrainFaces, |
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277 | //~ (void*)faceImgArr, |
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278 | //~ (void*)eigenVectArr, |
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279 | //~ CV_EIGOBJ_NO_CALLBACK, |
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280 | //~ 0, |
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281 | //~ 0, |
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282 | //~ &calcLimit, |
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283 | //~ pAvgTrainImg, |
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284 | //~ eigenValMat->data.fl); |
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285 | calcEigenFaces( |
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286 | nTrainFaces, |
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287 | faceImgArr, |
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288 | eigenVectArr, |
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289 | nEigens, |
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290 | pAvgTrainImg, |
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291 | eigenValMat->data.fl); |
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292 | |
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293 | cvNormalize(eigenValMat, eigenValMat, 1, 0, CV_L1, 0); |
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294 | |
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295 | for(i=0; i<nEigens; i++) |
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296 | { |
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297 | convertToUImage(eigenVectArr[i], eigenPics[i]); |
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298 | sprintf(eig_name,"eigen_%d.jpg", i); |
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299 | cvSaveImage(eig_name,eigenPics[i]); |
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300 | } |
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301 | } |
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302 | |
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303 | |
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304 | ////////////////////////////////// |
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305 | // loadFaceImgArray() |
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306 | // |
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307 | int loadFaceImgArray(char * filename) |
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308 | { |
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309 | FILE * imgListFile = 0; |
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310 | char imgFilename[512]; |
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311 | int iFace, nFaces=0; |
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312 | |
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313 | |
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314 | // open the input file |
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315 | if( !(imgListFile = fopen(filename, "r")) ) |
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316 | { |
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317 | fprintf(stderr, "Can\'t open file %s\n", filename); |
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318 | return 0; |
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319 | } |
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320 | |
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321 | // count the number of faces |
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322 | while( fgets(imgFilename, 512, imgListFile) ) ++nFaces; |
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323 | rewind(imgListFile); |
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324 | |
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325 | // allocate the face-image array and person number matrix |
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326 | faceImgArr = (IplImage **)cvAlloc( nFaces*sizeof(IplImage *) ); |
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327 | personNumTruthMat = cvCreateMat( 1, nFaces, CV_32SC1 ); |
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328 | |
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329 | // store the face images in an array |
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330 | for(iFace=0; iFace<nFaces; iFace++) |
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331 | { |
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332 | // read person number and name of image file |
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333 | fscanf(imgListFile, |
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334 | "%d %s", personNumTruthMat->data.i+iFace, imgFilename); |
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335 | // load the face image |
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336 | faceImgArr[iFace] = cvLoadImage(imgFilename, CV_LOAD_IMAGE_GRAYSCALE); |
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337 | |
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338 | if( !faceImgArr[iFace] ) |
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339 | { |
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340 | fprintf(stderr, "Can\'t load image from %s\n", imgFilename); |
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341 | return 0; |
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342 | } |
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343 | } |
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344 | |
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345 | fclose(imgListFile); |
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346 | |
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347 | return nFaces; |
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348 | } |
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349 | |
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350 | void convertToUImage(IplImage *fImg, IplImage *uImg) { |
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351 | int i; |
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352 | float *bf; |
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353 | uchar *bu; |
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354 | CvSize size; |
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355 | |
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356 | cvGetImageRawData(fImg, (uchar**)&bf, NULL, &size); |
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357 | cvGetImageRawData(uImg, (uchar**)&bu, NULL, NULL); |
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358 | |
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359 | // Find the Maximum and Minimum of the pixel values |
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360 | float max, min; |
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361 | max = min = 0.0; |
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362 | for(i=0; i< size.width * size.height; i++) { |
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363 | if(max < bf[i]) |
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364 | max = bf[i]; |
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365 | if(min > bf[i]) |
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366 | min = bf[i]; |
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367 | } |
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368 | |
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369 | // Normalize the eigenface values between 0 and 255 |
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370 | for(i = 0; i< size.width * size.height; i++) { |
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371 | bu[i] = (uchar)(( 255 * (( bf[i] - min)/ (max- min)) )); |
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372 | } |
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373 | } |
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374 | |
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375 | ////////////////////////////////// |
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376 | // printUsage() |
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377 | // |
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378 | void printUsage() |
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379 | { |
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380 | printf("Usage: eigenface <command>\n", |
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381 | " Valid commands are\n" |
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382 | " train\n" |
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383 | " test\n"); |
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384 | } |
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