[108] | 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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