How train my own svm in opencv using C++? -


i want train own svm features extracted images,so extract features sequence of images(720x576) wich contain persons. want use svm classify object detected such person or other object. piece of features.dat:

    [-0.00011304629, -0.0012236957, 0.00027119479, 0.0012647118, -0.0018265223, -0.018638615, 0.0098637585, 0.020142596, -0.0012514826, -0.0067296866, 0.0043024337, 0.0080097318, 0.0011584486, -0.00077929819, 0.0013905426, 0.00095518644, 0.0017369018, 0.0079498151, 0.0018530694, 0.0085842144, 0.15540655, 0.23714867, 0.15860459, 0.23714867, -0.028113011, 0.25352797, 0.268282, 0.279008, -0.037383422, 0.02249903, 0.042661294, 0.02340897, 0.0013400698, 0.0048319609, 0.0021884302, 0.0056850719, 0.37904194, -0.066282742, 0.41964364, 0.080511056, -0.23055254, 0.010807713, 0.46629098, 0.049539972, -0.041018508, 0.0058587855, 0.045177955, 0.0067843986, -0.00016448426, 0.0008450991, 0.0019716914, 0.0026907444, 0.053080089, -0.0070953579, 0.057075631, 0.0085162139, -0.03202837, 0.0012674005, 0.053649519, 0.0080802822, 9.4343457e-05, 0.00010923775, 0.0083377175, 0.0015355083;   -0.0024956728, -0.0024603403, 0.0044404925, 0.0040253471, 0.055309348, 0.006694437, 0.058669563, 0.008123802, -0.031681322, -0.0028714964, 0.056370165, 0.0060769469, -0.0010096794, 6.9015719e-06, 0.0067517483, 0.0011420506, 0.001166827, -0.00068250211, 0.0015904121, 0.0022194763, 0.37670693, 0.056508597, 0.40771538, 0.065799005, -0.22081745, -0.017952221, 0.47782964, 0.051509246, -0.037203062, -0.0030379333, 0.038590115, 0.0047737048, 0.0011766918, -0.01367875, 0.001783903, 0.014545292, 0.15056053, -0.25023517, 0.16026139, 0.25364164, -0.065814815, -0.27434519, 0.20718332, 0.29574585, -0.016427297, -0.033821814, 0.023312433, 0.034270149, -4.2133952e-06, 0.0015094492, 0.00026528162, 0.0015094492, 0.0021759982, 0.021547779, 0.0083950981, 0.021547779, -0.0033076329, 0.015553456, 0.0094896331, 0.015718656, -6.9612266e-05, 0.00065918162, 0.0017252946, 0.0017238797;

you need change image array row matrix first. 

mat img_mat = imread(imgname,0); // used 0 greyscale int ii = 0; // current column in training_mat (int = 0; i<img_mat.rows; i++) {     (int j = 0; j < img_mat.cols; j++) {         training_mat.at<float>(file_num,ii++) = img_mat.at<uchar>(i,j);     } }   cvsvmparams params; params.svm_type = cvsvm::c_svc; params.kernel_type = cvsvm::poly; params.gamma = 3; cvsvm svm; svm.train(training_mat, labels, mat(), mat(), params); svm.predict(img_mat_1d);

besides, image scale quite large (720*576 = 414720). can have preprocess before training model. example, using pca reduce dimension. more details,look @ using opencv , svm images


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