Fixed small warn and example error

modules/dnn/include/opencv2/dnn/blob.hpp

@@ -83,18 +83,18 @@ namespace dnn
         /** @brief returns size of corresponding dimension (axis)
         @param axis dimension index
         Python-like indexing is supported, so \p axis can be negative, i. e. -1 is last dimension.
-        @note Unlike ::xsize, if \p axis points to non-existing dimension then an error will be generated.
+        @note Unlike xsize(), if \p axis points to non-existing dimension then an error will be generated.
         */
         int size(int axis) const;
 
         /** @brief returns number of elements
         @param startAxis starting axis (inverse indexing can be used)
         @param endAxis ending (excluded) axis
-        @see ::canonicalAxis
+        @see canonicalAxis()
         */
         size_t total(int startAxis = 0, int endAxis = -1) const;
 
-        /** @brief converts axis index to canonical format (where 0 <= axis <= ::dims)
+        /** @brief converts axis index to canonical format (where 0 <= axis < dims())
         */
         int canonicalAxis(int axis) const;
 

modules/dnn/include/opencv2/dnn/dict.hpp

@@ -167,7 +167,7 @@ inline int64 DictValue::get<int64>(int idx) const
         fracpart = std::modf(doubleValue, &intpart);
         CV_Assert(fracpart == 0.0);
 
-        return doubleValue;
+        return (int64)doubleValue;
     }
     else
     {

modules/dnn/samples/classify_with_googlenet.cpp

@@ -4,6 +4,7 @@
 #include <opencv2/highgui.hpp>
 #include <algorithm>
 #include <fstream>
+#include <iostream>
 using namespace cv;
 using namespace cv::dnn;
 
@@ -12,7 +13,7 @@ typedef std::pair<int, double> ClassProb;
 ClassProb getMaxClass(Blob &probBlob, int sampleNum = 0)
 {
     int numClasses = (int)probBlob.total(1);
-    Mat probMat(1, numClasses, CV_32F, probBlob.ptr<float>(sampleNum));
+    Mat probMat(1, numClasses, CV_32F, probBlob.ptrf(sampleNum));
 
     double prob;
     Point probLoc;
@@ -21,7 +22,7 @@ ClassProb getMaxClass(Blob &probBlob, int sampleNum = 0)
     return std::make_pair(probLoc.x, prob);
 }
 
-std::vector<String> CLASES_NAMES;
+std::vector<String> CLASSES_NAMES;
 
 void initClassesNames()
 {
@@ -33,7 +34,7 @@ void initClassesNames()
     {
         std::getline(fp, name);
         if (name.length())
-            CLASES_NAMES.push_back( name.substr(name.find(' ')+1) );
+            CLASSES_NAMES.push_back( name.substr(name.find(' ')+1) );
     }
 
     fp.close();
@@ -63,7 +64,7 @@ int main(int argc, char **argv)
     ClassProb bc = getMaxClass(prob);
 
     initClassesNames();
-    std::string className = (bc.first < (int)CLASES_NAMES.size()) ? CLASES_NAMES[bc.first] : "unnamed";
+    std::string className = (bc.first < (int)CLASSES_NAMES.size()) ? CLASSES_NAMES[bc.first] : "unnamed";
 
     std::cout << "Best class:";
     std::cout << " #" << bc.first;

modules/dnn/src/layers/mvn_layer.cpp

@@ -48,8 +48,8 @@ void MVNLayer::forward(std::vector<Blob *> &inputs, std::vector<Blob> &outputs)
 
         int workSize[2];
         int splitDim = (acrossChannels) ? 1 : 2;
-        workSize[0] = inpBlob.total(0, splitDim);
+        workSize[0] = (int)inpBlob.total(0, splitDim);
-        workSize[1] = inpBlob.total(splitDim);
+        workSize[1] = (int)inpBlob.total(splitDim);
 
         Mat inpMat = inpBlob.getMatRef().reshape(1, 2, workSize);
         Mat outMat = outBlob.getMatRef().reshape(1, 2, workSize);

modules/dnn/src/torch/torch_importer.cpp

@@ -662,7 +662,7 @@ CV_EXPORTS Ptr<Importer> createTorchImporter(const String&, bool)
     return Ptr<Importer>();
 }
 
-CV_EXPORTS Blob readTorchMat(const String &filename, bool isBinary)
+CV_EXPORTS Blob readTorchMat(const String&, bool)
 {
     CV_Error(Error::StsNotImplemented, "Module was build without Torch importer");
     return Blob();

modules/dnn/test/cnpy.cpp

@@ -9,10 +9,6 @@
 #include<cstring>
 #include<iomanip>
 
-#ifdef __GNUC__
-#  pragma GCC diagnostic ignored "-Wunused-but-set-variable"
-#endif
-
 char cnpy::BigEndianTest() {
     union
     {
@@ -73,7 +69,7 @@ void cnpy::parse_npy_header(FILE* fp, unsigned int& word_size, unsigned int*& sh
     if(res != 11)
         throw std::runtime_error("parse_npy_header: failed fread");
     std::string header = fgets(buffer,256,fp);
-    assert(header[header.size()-1] == '\n');
+    cnpy_assert(header[header.size()-1] == '\n');
 
     size_t loc1, loc2;
 
@@ -99,7 +95,7 @@ void cnpy::parse_npy_header(FILE* fp, unsigned int& word_size, unsigned int*& sh
     //not sure when this applies except for byte array
     loc1 = header.find("descr")+9;
     bool littleEndian = (header[loc1] == '<' || header[loc1] == '|' ? true : false);
-    assert(littleEndian);
+    cnpy_assert(littleEndian);
 
     //char type = header[loc1+1];
     //assert(type == map_type(T));
@@ -126,10 +122,10 @@ void cnpy::parse_zip_footer(FILE* fp, unsigned short& nrecs, unsigned int& globa
     global_header_offset = *(unsigned int*) &footer[16];
     comment_len = *(unsigned short*) &footer[20];
 
-    assert(disk_no == 0);
+    cnpy_assert(disk_no == 0);
-    assert(disk_start == 0);
+    cnpy_assert(disk_start == 0);
-    assert(nrecs_on_disk == nrecs);
+    cnpy_assert(nrecs_on_disk == nrecs);
-    assert(comment_len == 0);
+    cnpy_assert(comment_len == 0);
 }
 
 cnpy::NpyArray load_the_npy_file(FILE* fp) {
@@ -156,7 +152,7 @@ cnpy::npz_t cnpy::npz_load(std::string fname) {
     FILE* fp = fopen(fname.c_str(),"rb");
 
     if(!fp) printf("npz_load: Error! Unable to open file %s!\n",fname.c_str());
-    assert(fp);
+    cnpy_assert(fp);
 
     cnpy::npz_t arrays;
 

modules/dnn/test/cnpy.h

@@ -18,6 +18,12 @@
 #include<zlib.h>
 #endif
 
+#ifndef NDEBUG
+#define cnpy_assert(expression) assert(expression)
+#else
+#define cnpy_assert(expression) ((void)(expression))
+#endif
+
 namespace cnpy {
 
     struct NpyArray {
@@ -76,21 +82,21 @@ namespace cnpy {
             unsigned int* tmp_shape = 0;
             bool fortran_order;
             parse_npy_header(fp,word_size,tmp_shape,tmp_dims,fortran_order);
-            assert(!fortran_order);
+            cnpy_assert(!fortran_order);
 
             if(word_size != sizeof(T)) {
                 std::cout<<"libnpy error: "<<fname<<" has word size "<<word_size<<" but npy_save appending data sized "<<sizeof(T)<<"\n";
-                assert( word_size == sizeof(T) );
+                cnpy_assert( word_size == sizeof(T) );
             }
             if(tmp_dims != ndims) {
                 std::cout<<"libnpy error: npy_save attempting to append misdimensioned data to "<<fname<<"\n";
-                assert(tmp_dims == ndims);
+                cnpy_assert(tmp_dims == ndims);
             }
 
             for(unsigned i = 1; i < ndims; i++) {
                 if(shape[i] != tmp_shape[i]) {
                     std::cout<<"libnpy error: npy_save attempting to append misshaped data to "<<fname<<"\n";
-                    assert(shape[i] == tmp_shape[i]);
+                    cnpy_assert(shape[i] == tmp_shape[i]);
                 }
             }
             tmp_shape[0] += shape[0];