diff --git a/modules/contrib/src/colormap.cpp b/modules/contrib/src/colormap.cpp index a5c84ed95c..eca78dc1ad 100644 --- a/modules/contrib/src/colormap.cpp +++ b/modules/contrib/src/colormap.cpp @@ -45,7 +45,7 @@ static void sortMatrixRowsByIndices(InputArray _src, InputArray _indices, Output vector indices = _indices.getMat(); _dst.create(src.rows, src.cols, src.type()); Mat dst = _dst.getMat(); - for(int idx = 0; idx < indices.size(); idx++) { + for(size_t idx = 0; idx < indices.size(); idx++) { Mat originalRow = src.row(indices[idx]); Mat sortedRow = dst.row(idx); originalRow.copyTo(sortedRow); diff --git a/modules/contrib/src/facerec.cpp b/modules/contrib/src/facerec.cpp index e614064ed3..d7a529d183 100644 --- a/modules/contrib/src/facerec.cpp +++ b/modules/contrib/src/facerec.cpp @@ -310,7 +310,7 @@ void Eigenfaces::train(InputArray src, InputArray _lbls) { // dimensionality of data //int d = data.cols; // assert there are as much samples as labels - if(n != labels.size()) + if((size_t)n != labels.size()) CV_Error(CV_StsBadArg, "The number of samples must equal the number of labels!"); // clip number of components to be valid if((_num_components <= 0) || (_num_components > n)) @@ -336,7 +336,7 @@ int Eigenfaces::predict(InputArray _src) const { Mat q = subspaceProject(_eigenvectors, _mean, src.reshape(1,1)); double minDist = DBL_MAX; int minClass = -1; - for(int sampleIdx = 0; sampleIdx < _projections.size(); sampleIdx++) { + for(size_t sampleIdx = 0; sampleIdx < _projections.size(); sampleIdx++) { double dist = norm(_projections[sampleIdx], q, NORM_L2); if(dist < minDist) { minDist = dist; @@ -381,7 +381,7 @@ void Fisherfaces::train(InputArray src, InputArray _lbls) { int N = data.rows; // number of samples //int D = data.cols; // dimension of samples // assert correct data alignment - if(labels.size() != N) + if(labels.size() != (size_t)N) CV_Error(CV_StsUnsupportedFormat, "Labels must be given as integer (CV_32SC1)."); // compute the Fisherfaces int C = remove_dups(labels).size(); // number of unique classes @@ -415,7 +415,7 @@ int Fisherfaces::predict(InputArray _src) const { // find 1-nearest neighbor double minDist = DBL_MAX; int minClass = -1; - for(int sampleIdx = 0; sampleIdx < _projections.size(); sampleIdx++) { + for(size_t sampleIdx = 0; sampleIdx < _projections.size(); sampleIdx++) { double dist = norm(_projections[sampleIdx], q, NORM_L2); if(dist < minDist) { minDist = dist; @@ -657,7 +657,7 @@ void LBPH::train(InputArray _src, InputArray _lbls) { // store given labels _labels = labels; // store the spatial histograms of the original data - for(int sampleIdx = 0; sampleIdx < src.size(); sampleIdx++) { + for(size_t sampleIdx = 0; sampleIdx < src.size(); sampleIdx++) { // calculate lbp image Mat lbp_image = elbp(src[sampleIdx], _radius, _neighbors); // get spatial histogram from this lbp image @@ -686,7 +686,7 @@ int LBPH::predict(InputArray _src) const { // find 1-nearest neighbor double minDist = DBL_MAX; int minClass = -1; - for(int sampleIdx = 0; sampleIdx < _histograms.size(); sampleIdx++) { + for(size_t sampleIdx = 0; sampleIdx < _histograms.size(); sampleIdx++) { double dist = compareHist(_histograms[sampleIdx], query, CV_COMP_CHISQR); if(dist < minDist) { minDist = dist; diff --git a/modules/contrib/src/lda.cpp b/modules/contrib/src/lda.cpp index 591fa755b7..44eb493093 100644 --- a/modules/contrib/src/lda.cpp +++ b/modules/contrib/src/lda.cpp @@ -80,7 +80,7 @@ void sortMatrixColumnsByIndices(InputArray _src, InputArray _indices, OutputArra vector indices = _indices.getMat(); _dst.create(src.rows, src.cols, src.type()); Mat dst = _dst.getMat(); - for(int idx = 0; idx < indices.size(); idx++) { + for(size_t idx = 0; idx < indices.size(); idx++) { Mat originalCol = src.col(indices[idx]); Mat sortedCol = dst.col(idx); originalCol.copyTo(sortedCol); @@ -169,7 +169,7 @@ Mat subspaceProject(InputArray _W, InputArray _mean, InputArray _src) int n = X.rows; int d = X.cols; // center the data if correct aligned sample mean is given - if(mean.total() == d) + if(mean.total() == (size_t)d) subtract(X, repeat(mean.reshape(1,1), n, 1), X); // finally calculate projection as Y = (X-mean)*W gemm(X, W, 1.0, Mat(), 0.0, Y); @@ -196,8 +196,8 @@ Mat subspaceReconstruct(InputArray _W, InputArray _mean, InputArray _src) gemm(Y, W, 1.0, - (d == mean.total()) ? repeat(mean.reshape(1,1), n, 1) : Mat(), - (d == mean.total()) ? 1.0 : 0.0, + ((size_t)d == mean.total()) ? repeat(mean.reshape(1,1), n, 1) : Mat(), + ((size_t)d == mean.total()) ? 1.0 : 0.0, X, GEMM_2_T); return X; @@ -296,7 +296,7 @@ private: double norm = 0.0; for (int i = 0; i < nn; i++) { - if (i < low | i > high) { + if (i < low || i > high) { d[i] = H[i][i]; e[i] = 0.0; } @@ -658,7 +658,7 @@ private: y = H[i + 1][i]; vr = (d[i] - p) * (d[i] - p) + e[i] * e[i] - q * q; vi = (d[i] - p) * 2.0 * q; - if (vr == 0.0 & vi == 0.0) { + if (vr == 0.0 && vi == 0.0) { vr = eps * norm * (std::abs(w) + std::abs(q) + std::abs(x) + std::abs(y) + std::abs(z)); } @@ -696,7 +696,7 @@ private: // Vectors of isolated roots for (int i = 0; i < nn; i++) { - if (i < low | i > high) { + if (i < low || i > high) { for (int j = i; j < nn; j++) { V[i][j] = H[i][j]; } @@ -946,9 +946,9 @@ void LDA::lda(InputArray _src, InputArray _lbls) { vector mapped_labels(labels.size()); vector num2label = remove_dups(labels); map label2num; - for (int i = 0; i < num2label.size(); i++) + for (size_t i = 0; i < num2label.size(); i++) label2num[num2label[i]] = i; - for (int i = 0; i < labels.size(); i++) + for (size_t i = 0; i < labels.size(); i++) mapped_labels[i] = label2num[labels[i]]; // get sample size, dimension int N = data.rows; @@ -956,7 +956,7 @@ void LDA::lda(InputArray _src, InputArray _lbls) { // number of unique labels int C = num2label.size(); // throw error if less labels, than samples - if (labels.size() != N) + if (labels.size() != (size_t)N) CV_Error(CV_StsBadArg, "Error: The number of samples must equal the number of labels."); // warn if within-classes scatter matrix becomes singular if (N < D)