fixes for the newly added gcc warning keys

modules/gpu/CMakeLists.txt

@@ -30,6 +30,7 @@ if (HAVE_CUDA)
   source_group("Src\\NVidia" FILES ${ncv_files})
   ocv_include_directories("src/nvidia" "src/nvidia/core" "src/nvidia/NPP_staging" ${CUDA_INCLUDE_DIRS})
   ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef -Wmissing-declarations /wd4211 /wd4201 /wd4100 /wd4505 /wd4408)
+  string(REPLACE "-Wsign-promo" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
 
   #set (CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-keep")
   #set (CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-Xcompiler;/EHsc-;")

modules/gpu/src/arithm.cpp

@@ -68,7 +68,7 @@ void cv::gpu::polarToCart(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, bool,
 void cv::gpu::gemm(const GpuMat& src1, const GpuMat& src2, double alpha, const GpuMat& src3, double beta, GpuMat& dst, int flags, Stream& stream)
 {
 #ifndef HAVE_CUBLAS
-
+    (void)src1; (void)src2; (void)alpha; (void)src3; (void)beta; (void)dst; (void)flags; (void)stream;
     CV_Error(CV_StsNotImplemented, "The library was build without CUBLAS");
 
 #else

modules/gpu/src/cuda/bf_knnmatch.cu

@@ -748,6 +748,7 @@ namespace cv { namespace gpu { namespace device
                               const DevMem2Db& trainIdx, const DevMem2Db& distance,
                               int cc, cudaStream_t stream)
         {
+            (void)cc;
             if (query.cols <= 64)
             {
                 matchUnrolledCached<16, 64, Dist>(query, train, mask, static_cast< DevMem2D_<int2> >(trainIdx), static_cast< DevMem2D_<float2> > (distance), stream);
@@ -779,6 +780,7 @@ namespace cv { namespace gpu { namespace device
                               const DevMem2Db& trainIdx, const DevMem2Db& imgIdx, const DevMem2Db& distance,
                               int cc, cudaStream_t stream)
         {
+            (void)cc;
             if (query.cols <= 64)
             {
                 matchUnrolledCached<16, 64, Dist>(query, trains, n, mask, static_cast< DevMem2D_<int2> >(trainIdx), static_cast< DevMem2D_<int2> >(imgIdx), static_cast< DevMem2D_<float2> > (distance), stream);
@@ -943,6 +945,7 @@ namespace cv { namespace gpu { namespace device
                                     const DevMem2Df& allDist,
                                     int cc, cudaStream_t stream)
         {
+            (void)cc;
             if (query.cols <= 64)
             {
                 calcDistanceUnrolled<16, 64, Dist>(query, train, mask, allDist, stream);

modules/gpu/src/cuda/bf_match.cu

@@ -567,6 +567,7 @@ namespace cv { namespace gpu { namespace device
                              const DevMem2Di& trainIdx, const DevMem2Df& distance,
                              int cc, cudaStream_t stream)
         {
+            (void)cc;
             if (query.cols <= 64)
             {
                 matchUnrolledCached<16, 64, Dist>(query, train, mask, trainIdx, distance, stream);
@@ -598,6 +599,7 @@ namespace cv { namespace gpu { namespace device
                              const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance,
                              int cc, cudaStream_t stream)
         {
+            (void)cc;
             if (query.cols <= 64)
             {
                 matchUnrolledCached<16, 64, Dist>(query, trains, n, mask, trainIdx, imgIdx, distance, stream);

modules/gpu/src/cuda/bf_radius_match.cu

@@ -281,6 +281,7 @@ namespace cv { namespace gpu { namespace device
                              const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
                              int cc, cudaStream_t stream)
         {
+            (void)cc;
             if (query.cols <= 64)
             {
                 matchUnrolled<16, 64, Dist>(query, train, maxDistance, mask, trainIdx, distance, nMatches, stream);
@@ -312,6 +313,7 @@ namespace cv { namespace gpu { namespace device
                              const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
                              int cc, cudaStream_t stream)
         {
+            (void)cc;
             if (query.cols <= 64)
             {
                 matchUnrolled<16, 64, Dist>(query, trains, n, maxDistance, masks, trainIdx, imgIdx, distance, nMatches, stream);

modules/gpu/src/cuda/hog.cu

@@ -619,6 +619,7 @@ namespace cv { namespace gpu { namespace device
         void compute_gradients_8UC4(int nbins, int height, int width, const DevMem2Db& img,
                                     float angle_scale, DevMem2Df grad, DevMem2Db qangle, bool correct_gamma)
         {
+            (void)nbins;
             const int nthreads = 256;
 
             dim3 bdim(nthreads, 1);
@@ -691,6 +692,7 @@ namespace cv { namespace gpu { namespace device
         void compute_gradients_8UC1(int nbins, int height, int width, const DevMem2Db& img,
                                     float angle_scale, DevMem2Df grad, DevMem2Db qangle, bool correct_gamma)
         {
+            (void)nbins;
             const int nthreads = 256;
 
             dim3 bdim(nthreads, 1);

modules/gpu/src/cuda/remap.cu

@@ -87,6 +87,9 @@ namespace cv { namespace gpu { namespace device
         {
             static void call(DevMem2D_<T> src, DevMem2D_<T> srcWhole, int xoff, int yoff, DevMem2Df mapx, DevMem2Df mapy, DevMem2D_<T> dst, const float* borderValue, int)
             {
+                (void)srcWhole;
+                (void)xoff;
+                (void)yoff;
                 typedef typename TypeVec<float, VecTraits<T>::cn>::vec_type work_type;
 
                 dim3 block(32, 8);

modules/gpu/src/cuda/resize.cu

@@ -131,6 +131,10 @@ namespace cv { namespace gpu { namespace device
         {
             static void call(DevMem2D_<T> src, DevMem2D_<T> srcWhole, int xoff, int yoff, float fx, float fy, DevMem2D_<T> dst)
             {
+                (void)srcWhole;
+                (void)xoff;
+                (void)yoff;
+
                 dim3 block(32, 8);
                 dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y));
 
@@ -219,6 +223,9 @@ namespace cv { namespace gpu { namespace device
         {
             static void call(DevMem2D_<T> src, DevMem2D_<T> srcWhole, int xoff, int yoff, float fx, float fy, DevMem2D_<T> dst, cudaStream_t stream)
             {
+                (void)srcWhole;
+                (void)xoff;
+                (void)yoff;
                 int iscale_x = round(fx);
                 int iscale_y = round(fy);
 

modules/gpu/src/cuda/warp.cu

@@ -158,6 +158,10 @@ namespace cv { namespace gpu { namespace device
         {
             static void call(DevMem2D_<T> src, DevMem2D_<T> srcWhole, int xoff, int yoff, DevMem2D_<T> dst, const float* borderValue, int)
             {
+                (void)xoff;
+                (void)yoff;
+                (void)srcWhole;
+
                 typedef typename TypeVec<float, VecTraits<T>::cn>::vec_type work_type;
 
                 dim3 block(32, 8);

modules/gpu/src/nvidia/NCVBroxOpticalFlow.cu

@@ -1136,7 +1136,7 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
                     ptrVNew->ptr(), dstSize, ns * sizeof (float), dstROI, 1.0f/scale_factor, 1.0f/scale_factor, nppStBicubic) );
 
                 ScaleVector(ptrVNew->ptr(), ptrVNew->ptr(), 1.0f/scale_factor, ns * nh, stream);
-                ncvAssertCUDALastErrorReturn(NCV_CUDA_ERROR);
+                ncvAssertCUDALastErrorReturn((int)NCV_CUDA_ERROR);
 
                 cv::gpu::device::swap<FloatVector*>(ptrU, ptrUNew);
                 cv::gpu::device::swap<FloatVector*>(ptrV, ptrVNew);
@@ -1145,17 +1145,17 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
         }
 
         // end of warping iterations
-        ncvAssertCUDAReturn(cudaStreamSynchronize(stream), NCV_CUDA_ERROR);
+        ncvAssertCUDAReturn(cudaStreamSynchronize(stream), (int)NCV_CUDA_ERROR);
 
         ncvAssertCUDAReturn( cudaMemcpy2DAsync
             (uOut.ptr(), uOut.pitch(), ptrU->ptr(),
-            kSourcePitch, kSourceWidth*sizeof(float), kSourceHeight, cudaMemcpyDeviceToDevice, stream), NCV_CUDA_ERROR );
+            kSourcePitch, kSourceWidth*sizeof(float), kSourceHeight, cudaMemcpyDeviceToDevice, stream), (int)NCV_CUDA_ERROR );
 
         ncvAssertCUDAReturn( cudaMemcpy2DAsync
             (vOut.ptr(), vOut.pitch(), ptrV->ptr(),
-            kSourcePitch, kSourceWidth*sizeof(float), kSourceHeight, cudaMemcpyDeviceToDevice, stream), NCV_CUDA_ERROR );
+            kSourcePitch, kSourceWidth*sizeof(float), kSourceHeight, cudaMemcpyDeviceToDevice, stream), (int)NCV_CUDA_ERROR );
 
-        ncvAssertCUDAReturn(cudaStreamSynchronize(stream), NCV_CUDA_ERROR);
+        ncvAssertCUDAReturn(cudaStreamSynchronize(stream), (int)NCV_CUDA_ERROR);
     }
 
     return NCV_SUCCESS;

modules/gpu/src/nvidia/NCVHaarObjectDetection.cu

@@ -687,6 +687,7 @@ struct applyHaarClassifierAnchorParallelFunctor
     template<class TList>
     void call(TList tl)
     {
+        (void)tl;
         applyHaarClassifierAnchorParallel <
             Loki::TL::TypeAt<TList, 0>::Result::value,
             Loki::TL::TypeAt<TList, 1>::Result::value,
@@ -796,6 +797,7 @@ struct applyHaarClassifierClassifierParallelFunctor
     template<class TList>
     void call(TList tl)
     {
+        (void)tl;
         applyHaarClassifierClassifierParallel <
             Loki::TL::TypeAt<TList, 0>::Result::value,
             Loki::TL::TypeAt<TList, 1>::Result::value,
@@ -876,6 +878,7 @@ struct initializeMaskVectorFunctor
     template<class TList>
     void call(TList tl)
     {
+        (void)tl;
         initializeMaskVector <
             Loki::TL::TypeAt<TList, 0>::Result::value,
             Loki::TL::TypeAt<TList, 1>::Result::value >

modules/gpu/src/nvidia/core/NCV.cu

@@ -854,6 +854,7 @@ static NCVStatus drawRectsWrapperDevice(T *d_dst,
                                         T color,
                                         cudaStream_t cuStream)
 {
+    (void)cuStream;
     ncvAssertReturn(d_dst != NULL && d_rects != NULL, NCV_NULL_PTR);
     ncvAssertReturn(dstWidth > 0 && dstHeight > 0, NCV_DIMENSIONS_INVALID);
     ncvAssertReturn(dstStride >= dstWidth, NCV_INVALID_STEP);

modules/gpu/src/nvidia/core/NCV.hpp

@@ -802,7 +802,7 @@ public:
         return ncvStat;
     }
 
-    T &at(Ncv32u x, Ncv32u y) const
+    T& at(Ncv32u x, Ncv32u y) const
     {
         NcvBool bOutRange = (x >= this->_width || y >= this->_height);
         ncvAssertPrintCheck(!bOutRange, "Error addressing matrix at [" << x << ", " << y << "]");

modules/gpu/src/nvidia/core/NCVRuntimeTemplates.hpp

@@ -211,6 +211,7 @@ namespace NCVRuntimeTemplateBool
 
         static void call(Func &functor, std::vector<int> &templateParams)
         {
+            (void)templateParams;
             functor.call(TList());
         }
     };

modules/gpu/src/opencv2/gpu/device/filters.hpp

@@ -55,7 +55,12 @@ namespace cv { namespace gpu { namespace device
         typedef typename Ptr2D::elem_type elem_type;
         typedef float index_type;
 
-        explicit __host__ __device__ __forceinline__ PointFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) : src(src_) {}
+        explicit __host__ __device__ __forceinline__ PointFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f)
+        : src(src_)
+        {
+            (void)fx;
+            (void)fy;
+        }
 
         __device__ __forceinline__ elem_type operator ()(float y, float x) const
         {
@@ -70,8 +75,12 @@ namespace cv { namespace gpu { namespace device
         typedef typename Ptr2D::elem_type elem_type;
         typedef float index_type;
 
-        explicit __host__ __device__ __forceinline__ LinearFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) : src(src_) {}
-
+        explicit __host__ __device__ __forceinline__ LinearFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f)
+        : src(src_)
+        {
+            (void)fx;
+            (void)fy;
+        }
         __device__ __forceinline__ elem_type operator ()(float y, float x) const
         {
             typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
@@ -107,7 +116,12 @@ namespace cv { namespace gpu { namespace device
         typedef float index_type;
         typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
 
-        explicit __host__ __device__ __forceinline__ CubicFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) : src(src_) {}
+        explicit __host__ __device__ __forceinline__ CubicFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) 
+        : src(src_)
+        {
+            (void)fx;
+            (void)fy;
+        }
 
         static __device__ __forceinline__ float bicubicCoeff(float x_)
         {

modules/gpu/src/opencv2/gpu/device/functional.hpp

@@ -470,7 +470,7 @@ namespace cv { namespace gpu { namespace device
 
     template <typename T> struct thresh_trunc_func : unary_function<T, T>
     {
-        explicit __host__ __device__ __forceinline__ thresh_trunc_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {}
+        explicit __host__ __device__ __forceinline__ thresh_trunc_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {(void)maxVal_;}
 
         __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
         {
@@ -487,7 +487,7 @@ namespace cv { namespace gpu { namespace device
 
     template <typename T> struct thresh_to_zero_func : unary_function<T, T>
     {
-        explicit __host__ __device__ __forceinline__ thresh_to_zero_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {}
+        explicit __host__ __device__ __forceinline__ thresh_to_zero_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {(void)maxVal_;}
 
         __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
         {
@@ -503,7 +503,7 @@ namespace cv { namespace gpu { namespace device
 
     template <typename T> struct thresh_to_zero_inv_func : unary_function<T, T>
     {
-        explicit __host__ __device__ __forceinline__ thresh_to_zero_inv_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {}
+        explicit __host__ __device__ __forceinline__ thresh_to_zero_inv_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {(void)maxVal_;}
 
         __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
         {

modules/gpu/test/nvidia/TestHypothesesFilter.cpp

@@ -13,14 +13,14 @@
 #include "NCVHaarObjectDetection.hpp"
 
 
-TestHypothesesFilter::TestHypothesesFilter(std::string testName, NCVTestSourceProvider<Ncv32u> &src,
-                                           Ncv32u numDstRects, Ncv32u minNeighbors, Ncv32f eps)
+TestHypothesesFilter::TestHypothesesFilter(std::string testName, NCVTestSourceProvider<Ncv32u> &src_,
+                                           Ncv32u numDstRects_, Ncv32u minNeighbors_, Ncv32f eps_)
     :
     NCVTestProvider(testName),
-    src(src),
-    numDstRects(numDstRects),
-    minNeighbors(minNeighbors),
-    eps(eps)
+    src(src_),
+    numDstRects(numDstRects_),
+    minNeighbors(minNeighbors_),
+    eps(eps_)
 {
 }
 
@@ -124,8 +124,8 @@ bool TestHypothesesFilter::process()
     //shuffle
     for (Ncv32u i=0; i<this->numDstRects*srcSlotSize-1; i++)
     {
-        Ncv32u randVal = h_random32u.ptr()[randCnt++]; randCnt = randCnt % h_random32u.length();
-        Ncv32u secondSwap = randVal % (this->numDstRects*srcSlotSize-1 - i);
+        Ncv32u randValLocal = h_random32u.ptr()[randCnt++]; randCnt = randCnt % h_random32u.length();
+        Ncv32u secondSwap = randValLocal % (this->numDstRects*srcSlotSize-1 - i);
         NcvRect32u tmp = h_vecSrc.ptr()[i + secondSwap];
         h_vecSrc.ptr()[i + secondSwap] = h_vecSrc.ptr()[i];
         h_vecSrc.ptr()[i] = tmp;

modules/gpu/test/nvidia/TestResize.cpp

@@ -15,15 +15,15 @@
 
 
 template <class T>
-TestResize<T>::TestResize(std::string testName, NCVTestSourceProvider<T> &src,
-                          Ncv32u width, Ncv32u height, Ncv32u scaleFactor, NcvBool bTextureCache)
+TestResize<T>::TestResize(std::string testName, NCVTestSourceProvider<T> &src_,
+                          Ncv32u width_, Ncv32u height_, Ncv32u scaleFactor_, NcvBool bTextureCache_)
     :
     NCVTestProvider(testName),
-    src(src),
-    width(width),
-    height(height),
-    scaleFactor(scaleFactor),
-    bTextureCache(bTextureCache)
+    src(src_),
+    width(width_),
+    height(height_),
+    scaleFactor(scaleFactor_),
+    bTextureCache(bTextureCache_)
 {
 }
 

modules/gpu/test/nvidia/main_nvidia.cpp

@@ -248,6 +248,7 @@ void generateHaarLoaderTests(NCVAutoTestLister &testLister)
 void generateHaarApplicationTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<Ncv8u> &src,
                                   Ncv32u maxWidth, Ncv32u maxHeight)
 {
+    (void)maxHeight;
     for (Ncv32u i=20; i<512; i+=11)
     {
         for (Ncv32u j=20; j<128; j+=5)
@@ -268,11 +269,12 @@ void generateHaarApplicationTests(NCVAutoTestLister &testLister, NCVTestSourcePr
 
 static void devNullOutput(const std::string& msg)
 {
+    (void)msg;
 }
 
 bool nvidia_NPPST_Integral_Image(const std::string& test_data_path, OutputLevel outputLevel)
 {
-    path = test_data_path;
+    path = test_data_path.c_str();
     ncvSetDebugOutputHandler(devNullOutput);
 
     NCVAutoTestLister testListerII("NPPST Integral Image", outputLevel);
@@ -374,6 +376,7 @@ bool nvidia_NCV_Vector_Operations(const std::string& test_data_path, OutputLevel
     generateVectorTests(testListerVectorOperations, testSrcRandom_32u, 4096*4096);
 
     return testListerVectorOperations.invoke();
+
 }
 
 bool nvidia_NCV_Haar_Cascade_Loader(const std::string& test_data_path, OutputLevel outputLevel)

modules/gpu/test/test_nvidia.cpp

@@ -58,15 +58,15 @@ struct NVidiaTest : TestWithParam<cv::gpu::DeviceInfo>
 {
     cv::gpu::DeviceInfo devInfo;
 
-    std::string path;
+    std::string _path;
 
     virtual void SetUp()
     {
         devInfo = GetParam();
 
         cv::gpu::setDevice(devInfo.deviceID());
-
-        path = std::string(TS::ptr()->get_data_path()) + "haarcascade/";
+        _path = TS::ptr()->get_data_path().c_str();
+        _path = _path + "haarcascade/";
     }
 };
 
@@ -84,63 +84,63 @@ OutputLevel nvidiaTestOutputLevel = OutputLevelCompact;
 
 TEST_P(NPPST, SquaredIntegral)
 {
-    bool res = nvidia_NPPST_Squared_Integral_Image(path, nvidiaTestOutputLevel);
+    bool res = nvidia_NPPST_Squared_Integral_Image(_path, nvidiaTestOutputLevel);
 
     ASSERT_TRUE(res);
 }
 
 TEST_P(NPPST, RectStdDev)
 {
-    bool res = nvidia_NPPST_RectStdDev(path, nvidiaTestOutputLevel);
+    bool res = nvidia_NPPST_RectStdDev(_path, nvidiaTestOutputLevel);
 
     ASSERT_TRUE(res);
 }
 
 TEST_P(NPPST, Resize)
 {
-    bool res = nvidia_NPPST_Resize(path, nvidiaTestOutputLevel);
+    bool res = nvidia_NPPST_Resize(_path, nvidiaTestOutputLevel);
 
     ASSERT_TRUE(res);
 }
 
 TEST_P(NPPST, VectorOperations)
 {
-    bool res = nvidia_NPPST_Vector_Operations(path, nvidiaTestOutputLevel);
+    bool res = nvidia_NPPST_Vector_Operations(_path, nvidiaTestOutputLevel);
 
     ASSERT_TRUE(res);
 }
 
 TEST_P(NPPST, Transpose)
 {
-    bool res = nvidia_NPPST_Transpose(path, nvidiaTestOutputLevel);
+    bool res = nvidia_NPPST_Transpose(_path, nvidiaTestOutputLevel);
 
     ASSERT_TRUE(res);
 }
 
 TEST_P(NCV, VectorOperations)
 {
-    bool res = nvidia_NCV_Vector_Operations(path, nvidiaTestOutputLevel);
+    bool res = nvidia_NCV_Vector_Operations(_path, nvidiaTestOutputLevel);
 
     ASSERT_TRUE(res);
 }
 
 TEST_P(NCV, HaarCascadeLoader)
 {
-    bool res = nvidia_NCV_Haar_Cascade_Loader(path, nvidiaTestOutputLevel);
+    bool res = nvidia_NCV_Haar_Cascade_Loader(_path, nvidiaTestOutputLevel);
 
     ASSERT_TRUE(res);
 }
 
 TEST_P(NCV, HaarCascadeApplication)
 {
-    bool res = nvidia_NCV_Haar_Cascade_Application(path, nvidiaTestOutputLevel);
+    bool res = nvidia_NCV_Haar_Cascade_Application(_path, nvidiaTestOutputLevel);
 
     ASSERT_TRUE(res);
 }
 
 TEST_P(NCV, HypothesesFiltration)
 {
-    bool res = nvidia_NCV_Hypotheses_Filtration(path, nvidiaTestOutputLevel);
+    bool res = nvidia_NCV_Hypotheses_Filtration(_path, nvidiaTestOutputLevel);
 
     ASSERT_TRUE(res);
 }
@@ -148,7 +148,7 @@ TEST_P(NCV, HypothesesFiltration)
 TEST_P(NCV, Visualization)
 {
     // this functionality doesn't used in gpu module
-    bool res = nvidia_NCV_Visualization(path, nvidiaTestOutputLevel);
+    bool res = nvidia_NCV_Visualization(_path, nvidiaTestOutputLevel);
 
     ASSERT_TRUE(res);
 }