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Fixed Android build warnings

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This commit is contained in:
Andrey Kamaev 2012-03-27 06:16:13 +00:00
parent 6412e17df6
commit 4a996111ea
3 changed files with 17 additions and 17 deletions
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2
modules/contrib/src/colormap.cpp
View File

@ -45,7 +45,7 @@ static void sortMatrixRowsByIndices(InputArray _src, InputArray _indices, Output
vector<int> 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);

12
modules/contrib/src/facerec.cpp
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@ -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;

20
modules/contrib/src/lda.cpp
View File

@ -80,7 +80,7 @@ void sortMatrixColumnsByIndices(InputArray _src, InputArray _indices, OutputArra
vector<int> 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<int> mapped_labels(labels.size());
vector<int> num2label = remove_dups(labels);
map<int, int> 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)