_labels 166 modules/core/src/kmeans.cpp int *_labels, _labels 170 modules/core/src/kmeans.cpp labels(_labels), _labels 237 modules/core/src/kmeans.cpp Mat _labels, best_labels = _bestLabels.getMat(); _labels 244 modules/core/src/kmeans.cpp best_labels.copyTo(_labels); _labels 253 modules/core/src/kmeans.cpp _labels.create(best_labels.size(), best_labels.type()); _labels 255 modules/core/src/kmeans.cpp int* labels = _labels.ptr<int>(); _labels 451 modules/core/src/kmeans.cpp _labels.copyTo(best_labels); _labels 4221 modules/core/src/matrix.cpp cvKMeans2( const CvArr* _samples, int cluster_count, CvArr* _labels, _labels 4225 modules/core/src/matrix.cpp cv::Mat data = cv::cvarrToMat(_samples), labels = cv::cvarrToMat(_labels), centers; _labels 140 modules/cudalegacy/perf/perf_labeling.cpp cv::Mat _labels; _labels 190 modules/cudalegacy/perf/perf_labeling.cpp TEST_CYCLE() host(host._labels); _labels 192 modules/cudalegacy/perf/perf_labeling.cpp cv::Mat components = host._labels; _labels 135 modules/cudalegacy/test/test_labeling.cpp cv::Mat diff = gpu - _labels; _labels 141 modules/cudalegacy/test/test_labeling.cpp if ( (_labels.at<int>(j,i) == gpu.at<int>(j,i + 1)) && (diff.at<int>(j, i) != diff.at<int>(j,i + 1))) _labels 150 modules/cudalegacy/test/test_labeling.cpp cv::Mat _labels; _labels 180 modules/cudalegacy/test/test_labeling.cpp host(host._labels); _labels 367 modules/imgproc/src/connectedcomponents.cpp int cv::connectedComponents(InputArray _img, OutputArray _labels, int connectivity, int ltype){ _labels 369 modules/imgproc/src/connectedcomponents.cpp _labels.create(img.size(), CV_MAT_DEPTH(ltype)); _labels 370 modules/imgproc/src/connectedcomponents.cpp cv::Mat labels = _labels.getMat(); _labels 382 modules/imgproc/src/connectedcomponents.cpp int cv::connectedComponentsWithStats(InputArray _img, OutputArray _labels, OutputArray statsv, _labels 386 modules/imgproc/src/connectedcomponents.cpp _labels.create(img.size(), CV_MAT_DEPTH(ltype)); _labels 387 modules/imgproc/src/connectedcomponents.cpp cv::Mat labels = _labels.getMat(); _labels 233 modules/imgproc/src/distransform.cpp distanceTransformEx_5x5( const Mat& _src, Mat& _temp, Mat& _dist, Mat& _labels, const float* metrics ) _labels 246 modules/imgproc/src/distransform.cpp int* labels = _labels.ptr<int>(); _labels 250 modules/imgproc/src/distransform.cpp int lstep = (int)(_labels.step/sizeof(dist[0])); _labels 710 modules/imgproc/src/distransform.cpp void cv::distanceTransform( InputArray _src, OutputArray _dst, OutputArray _labels, _labels 714 modules/imgproc/src/distransform.cpp bool need_labels = _labels.needed(); _labels 725 modules/imgproc/src/distransform.cpp _labels.create(src.size(), CV_32S); _labels 726 modules/imgproc/src/distransform.cpp labels = _labels.getMat(); _labels 75 modules/ml/src/kdtree.cpp KDTree::KDTree(InputArray _points, InputArray _labels, bool _copyData) _labels 79 modules/ml/src/kdtree.cpp build(_points, _labels, _copyData); _labels 166 modules/ml/src/kdtree.cpp Mat _points = __points.getMat(), _labels = __labels.getMat(); _labels 187 modules/ml/src/kdtree.cpp if( !_labels.empty() ) _labels 189 modules/ml/src/kdtree.cpp int nlabels = _labels.checkVector(1, CV_32S, true); _labels 191 modules/ml/src/kdtree.cpp _labels_data = _labels.ptr<int>(); _labels 276 modules/ml/src/kdtree.cpp OutputArray _dist, OutputArray _labels) const _labels 411 modules/ml/src/kdtree.cpp if( _neighbors.needed() || _labels.needed() ) _labels 412 modules/ml/src/kdtree.cpp getPoints(Mat(K, 1, CV_32S, idx), _neighbors, _labels); _labels 421 modules/ml/src/kdtree.cpp OutputArray _labels ) const _labels 470 modules/ml/src/kdtree.cpp getPoints( idx, _neighbors, _labels ); _labels 474 modules/ml/src/kdtree.cpp void KDTree::getPoints(InputArray _idx, OutputArray _pts, OutputArray _labels) const _labels 487 modules/ml/src/kdtree.cpp _labels.release(); _labels 497 modules/ml/src/kdtree.cpp if(_labels.needed()) _labels 499 modules/ml/src/kdtree.cpp _labels.create(nidx, 1, CV_32S, -1, true); _labels 500 modules/ml/src/kdtree.cpp labelsmat = _labels.getMat(); _labels 60 modules/ml/src/kdtree.hpp CV_WRAP KDTree(InputArray points, InputArray _labels, _labels 110 modules/ml/src/lr.cpp double compute_cost(const Mat& _data, const Mat& _labels, const Mat& _init_theta); _labels 111 modules/ml/src/lr.cpp Mat compute_batch_gradient(const Mat& _data, const Mat& _labels, const Mat& _init_theta); _labels 112 modules/ml/src/lr.cpp Mat compute_mini_batch_gradient(const Mat& _data, const Mat& _labels, const Mat& _init_theta); _labels 307 modules/ml/src/lr.cpp double LogisticRegressionImpl::compute_cost(const Mat& _data, const Mat& _labels, const Mat& _init_theta) _labels 344 modules/ml/src/lr.cpp multiply(d_a, _labels, d_a); _labels 348 modules/ml/src/lr.cpp multiply(d_b, 1-_labels, d_b); _labels 356 modules/ml/src/lr.cpp Mat LogisticRegressionImpl::compute_batch_gradient(const Mat& _data, const Mat& _labels, const Mat& _init_theta) _labels 387 modules/ml/src/lr.cpp ccost = compute_cost(_data, _labels, theta_p); _labels 394 modules/ml/src/lr.cpp pcal_b = calc_sigmoid((_data*theta_p) - _labels); _labels 400 modules/ml/src/lr.cpp pcal_a = calc_sigmoid(_data*theta_p) - _labels; _labels 425 modules/ml/src/lr.cpp Mat LogisticRegressionImpl::compute_mini_batch_gradient(const Mat& _data, const Mat& _labels, const Mat& _init_theta) _labels 462 modules/ml/src/lr.cpp labels_l = _labels(Range(j,j+size_b),Range::all()); _labels 467 modules/ml/src/lr.cpp labels_l = _labels(Range(j, _labels.rows),Range::all());