Merge pull request #1784 from mshabunin:printf

modules/face/src/eigen_faces.cpp

@@ -68,7 +68,7 @@ void Eigenfaces::train(InputArrayOfArrays _src, InputArray _local_labels) {
     if(_src.total() > 1) {
         for(int i = 1; i < static_cast<int>(_src.total()); i++) {
             if(_src.getMat(i-1).total() != _src.getMat(i).total()) {
-                String error_message = format("In the Eigenfaces method all input samples (training images) must be of equal size! Expected %d pixels, but was %d pixels.", _src.getMat(i-1).total(), _src.getMat(i).total());
+                String error_message = format("In the Eigenfaces method all input samples (training images) must be of equal size! Expected %zu pixels, but was %zu pixels.", _src.getMat(i-1).total(), _src.getMat(i).total());
                 CV_Error(Error::StsUnsupportedFormat, error_message);
             }
         }
@@ -82,7 +82,7 @@ void Eigenfaces::train(InputArrayOfArrays _src, InputArray _local_labels) {
    int n = data.rows;
     // assert there are as much samples as labels
     if(static_cast<int>(labels.total()) != n) {
-        String error_message = format("The number of samples (src) must equal the number of labels (labels)! len(src)=%d, len(labels)=%d.", n, labels.total());
+        String error_message = format("The number of samples (src) must equal the number of labels (labels)! len(src)=%d, len(labels)=%zu.", n, labels.total());
         CV_Error(Error::StsBadArg, error_message);
     }
     // clear existing model data
@@ -117,7 +117,7 @@ void Eigenfaces::predict(InputArray _src, Ptr<PredictCollector> collector) const
         CV_Error(Error::StsError, error_message);
     } else if(_eigenvectors.rows != static_cast<int>(src.total())) {
         // check data alignment just for clearer exception messages
-        String error_message = format("Wrong input image size. Reason: Training and Test images must be of equal size! Expected an image with %d elements, but got %d.", _eigenvectors.rows, src.total());
+        String error_message = format("Wrong input image size. Reason: Training and Test images must be of equal size! Expected an image with %d elements, but got %zu.", _eigenvectors.rows, src.total());
         CV_Error(Error::StsBadArg, error_message);
     }
     // project into PCA subspace

modules/face/src/face_utils.hpp

@@ -28,7 +28,7 @@ inline Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double
     for(unsigned int i = 0; i < n; i++) {
         // make sure data can be reshaped, throw exception if not!
         if(src.getMat(i).total() != d) {
-            String error_message = format("Wrong number of elements in matrix #%d! Expected %d was %d.", i, d, src.getMat(i).total());
+            String error_message = format("Wrong number of elements in matrix #%u! Expected %zu was %zu.", i, d, src.getMat(i).total());
             CV_Error(Error::StsBadArg, error_message);
         }
         // get a hold of the current row

modules/face/src/fisher_faces.cpp

@@ -77,7 +77,7 @@ void Fisherfaces::train(InputArrayOfArrays src, InputArray _lbls) {
     if(src.total() > 1) {
         for(int i = 1; i < static_cast<int>(src.total()); i++) {
             if(src.getMat(i-1).total() != src.getMat(i).total()) {
-                String error_message = format("In the Fisherfaces method all input samples (training images) must be of equal size! Expected %d pixels, but was %d pixels.", src.getMat(i-1).total(), src.getMat(i).total());
+                String error_message = format("In the Fisherfaces method all input samples (training images) must be of equal size! Expected %zu pixels, but was %zu pixels.", src.getMat(i-1).total(), src.getMat(i).total());
                 CV_Error(Error::StsUnsupportedFormat, error_message);
             }
         }
@@ -89,10 +89,10 @@ void Fisherfaces::train(InputArrayOfArrays src, InputArray _lbls) {
     int N = data.rows;
     // make sure labels are passed in correct shape
     if(labels.total() != (size_t) N) {
-        String error_message = format("The number of samples (src) must equal the number of labels (labels)! len(src)=%d, len(labels)=%d.", N, labels.total());
+        String error_message = format("The number of samples (src) must equal the number of labels (labels)! len(src)=%d, len(labels)=%zu.", N, labels.total());
         CV_Error(Error::StsBadArg, error_message);
     } else if(labels.rows != 1 && labels.cols != 1) {
-        String error_message = format("Expected the labels in a matrix with one row or column! Given dimensions are rows=%s, cols=%d.", labels.rows, labels.cols);
+        String error_message = format("Expected the labels in a matrix with one row or column! Given dimensions are rows=%d, cols=%d.", labels.rows, labels.cols);
        CV_Error(Error::StsBadArg, error_message);
     }
     // clear existing model data
@@ -136,7 +136,7 @@ void Fisherfaces::predict(InputArray _src, Ptr<PredictCollector> collector) cons
         String error_message = "This Fisherfaces model is not computed yet. Did you call Fisherfaces::train?";
         CV_Error(Error::StsBadArg, error_message);
     } else if(src.total() != (size_t) _eigenvectors.rows) {
-        String error_message = format("Wrong input image size. Reason: Training and Test images must be of equal size! Expected an image with %d elements, but got %d.", _eigenvectors.rows, src.total());
+        String error_message = format("Wrong input image size. Reason: Training and Test images must be of equal size! Expected an image with %d elements, but got %zu.", _eigenvectors.rows, src.total());
         CV_Error(Error::StsBadArg, error_message);
     }
     // project into LDA subspace

modules/face/src/lbph_faces.cpp

@@ -371,7 +371,7 @@ void LBPH::train(InputArrayOfArrays _in_src, InputArray _in_labels, bool preserv
     Mat labels = _in_labels.getMat();
     // check if data is well- aligned
     if(labels.total() != src.size()) {
-        String error_message = format("The number of samples (src) must equal the number of labels (labels). Was len(samples)=%d, len(labels)=%d.", src.size(), _labels.total());
+        String error_message = format("The number of samples (src) must equal the number of labels (labels). Was len(samples)=%zu, len(labels)=%zu.", src.size(), _labels.total());
         CV_Error(Error::StsBadArg, error_message);
     }
     // if this model should be trained without preserving old data, delete old model data

modules/hdf/src/hdf5.cpp

@@ -257,7 +257,7 @@ inline int HDF5Impl::GetCVtype( hid_t h5Type ) const
     else if ( H5Tequal( h5Type, H5T_NATIVE_INT    ) )
       cvType = CV_32S;
     else
-      CV_Error_(Error::StsInternal, ("Unknown H5Type: %d.", h5Type));
+      CV_Error_(Error::StsInternal, ("Unknown H5Type: %lld.", (long long)h5Type));
 
     return cvType;
 }

modules/xfeatures2d/src/pct_signatures/pct_clusterizer.cpp

@@ -126,7 +126,7 @@ namespace cv
 
                     if ((int)(this->mInitSeedIndexes.size()) > samples.rows)
                     {
-                        CV_Error_(Error::StsBadArg, ("Number of seeds %d must be less or equal to the number of samples %d.",
+                        CV_Error_(Error::StsBadArg, ("Number of seeds %zu must be less or equal to the number of samples %d.",
                             mInitSeedIndexes.size(), samples.rows));
                     }
 

modules/xfeatures2d/src/pct_signatures/pct_sampler.cpp

@@ -157,7 +157,7 @@ namespace cv
                     if (weights.size() != mWeights.size())
                     {
                         CV_Error_(Error::StsUnmatchedSizes,
-                            ("Invalid weights dimension %d (max %d)", weights.size(), mWeights.size()));
+                            ("Invalid weights dimension %zu (max %zu)", weights.size(), mWeights.size()));
                     }
                     else
                     {
@@ -178,7 +178,7 @@ namespace cv
                     if (translations.size() != mTranslations.size())
                     {
                         CV_Error_(Error::StsUnmatchedSizes,
-                            ("Invalid translations dimension %d (max %d)", translations.size(), mTranslations.size()));
+                            ("Invalid translations dimension %zu (max %zu)", translations.size(), mTranslations.size()));
                     }
                     else
                     {