Merge pull request #13168 from alalek:cmake_dnn_warnings

doc/Doxyfile.in

@@ -252,6 +252,7 @@ PREDEFINED             = __cplusplus=1 \
                          CV_SSE2=1 \
                          CV__DEBUG_NS_BEGIN= \
                          CV__DEBUG_NS_END= \
+                         CV_DEPRECATED_EXTERNAL= \
                          CV_DEPRECATED=
 EXPAND_AS_DEFINED      =
 SKIP_FUNCTION_MACROS   = YES

modules/core/include/opencv2/core/cvdef.h

@@ -363,6 +363,15 @@ Cv64suf;
 #  endif
 #endif
 
+#ifndef CV_DEPRECATED_EXTERNAL
+#  if defined(__OPENCV_BUILD)
+#    define CV_DEPRECATED_EXTERNAL /* nothing */
+#  else
+#    define CV_DEPRECATED_EXTERNAL CV_DEPRECATED
+#  endif
+#endif
+
+
 #ifndef CV_EXTERN_C
 #  ifdef __cplusplus
 #    define CV_EXTERN_C extern "C"

modules/core/src/matmul.cpp

@@ -1699,7 +1699,7 @@ transform_( const T* src, T* dst, const WT* m, int len, int scn, int dcn )
     }
 }
 
-#if CV_SIMD128
+#if CV_SIMD128 && !defined(__aarch64__)
 static inline void
 load3x3Matrix(const float* m, v_float32x4& m0, v_float32x4& m1, v_float32x4& m2, v_float32x4& m3)
 {
@@ -1708,7 +1708,9 @@ load3x3Matrix(const float* m, v_float32x4& m0, v_float32x4& m1, v_float32x4& m2,
     m2 = v_float32x4(m[2], m[6], m[10], 0);
     m3 = v_float32x4(m[3], m[7], m[11], 0);
 }
+#endif
 
+#if CV_SIMD128
 static inline v_int16x8
 v_matmulvec(const v_int16x8 &v0, const v_int16x8 &m0, const v_int16x8 &m1, const v_int16x8 &m2, const v_int32x4 &m3, const int BITS)
 {

modules/dnn/CMakeLists.txt

@@ -20,11 +20,6 @@ else()
   ocv_cmake_hook_append(INIT_MODULE_SOURCES_opencv_dnn "${CMAKE_CURRENT_LIST_DIR}/cmake/hooks/INIT_MODULE_SOURCES_opencv_dnn.cmake")
 endif()
 
-ocv_warnings_disable(CMAKE_CXX_FLAGS -Wno-shadow -Wno-parentheses -Wmaybe-uninitialized -Wsign-promo
-                                     -Wmissing-declarations -Wmissing-prototypes
-)
-ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4701 /wd4100)
-
 if(MSVC)
   add_definitions( -D_CRT_SECURE_NO_WARNINGS=1 )
   ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4244 /wd4267 /wd4018 /wd4355 /wd4800 /wd4251 /wd4996 /wd4146
@@ -33,12 +28,14 @@ if(MSVC)
   )
 else()
   ocv_warnings_disable(CMAKE_CXX_FLAGS -Wno-deprecated -Wmissing-prototypes -Wmissing-declarations -Wshadow
-                                       -Wunused-parameter -Wunused-local-typedefs -Wsign-compare -Wsign-promo
-                                       -Wundef -Wtautological-undefined-compare -Wignored-qualifiers -Wextra
-                                       -Wunused-function -Wunused-const-variable -Wdeprecated-declarations
+                                       -Wunused-parameter -Wsign-compare
   )
 endif()
 
+if(NOT HAVE_CXX11)
+  ocv_warnings_disable(CMAKE_CXX_FLAGS -Wno-undef)  # LANG_CXX11 from protobuf files
+endif()
+
 if(APPLE_FRAMEWORK)
   ocv_warnings_disable(CMAKE_CXX_FLAGS -Wshorten-64-to-32)
 endif()
@@ -55,8 +52,6 @@ add_definitions(-DHAVE_PROTOBUF=1)
 
 #suppress warnings in autogenerated caffe.pb.* files
 ocv_warnings_disable(CMAKE_CXX_FLAGS
-    -Wunused-parameter -Wundef -Wignored-qualifiers -Wno-enum-compare
-    -Wdeprecated-declarations
     /wd4125 /wd4267 /wd4127 /wd4244 /wd4512 /wd4702
     /wd4456 /wd4510 /wd4610 /wd4800
     /wd4701 /wd4703                    # potentially uninitialized local/pointer variable 'value' used

modules/dnn/include/opencv2/dnn/all_layers.hpp

@@ -236,7 +236,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
         int type;
         Size kernel, stride;
         int pad_l, pad_t, pad_r, pad_b;
-        CV_DEPRECATED Size pad;
+        CV_DEPRECATED_EXTERNAL Size pad;
         bool globalPooling;
         bool computeMaxIdx;
         String padMode;
@@ -578,7 +578,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
     {
     public:
         float pnorm, epsilon;
-        CV_DEPRECATED bool acrossSpatial;
+        CV_DEPRECATED_EXTERNAL bool acrossSpatial;
 
         static Ptr<NormalizeBBoxLayer> create(const LayerParams& params);
     };

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

@@ -60,12 +60,13 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
 struct CV_EXPORTS_W DictValue
 {
     DictValue(const DictValue &r);
+    DictValue(bool i)           : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = i ? 1 : 0; }       //!< Constructs integer scalar
     DictValue(int64 i = 0)      : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = i; }       //!< Constructs integer scalar
-    CV_WRAP DictValue(int i)            : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = i; }       //!< Constructs integer scalar
+    CV_WRAP DictValue(int i)    : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = i; }       //!< Constructs integer scalar
     DictValue(unsigned p)       : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = p; }       //!< Constructs integer scalar
     CV_WRAP DictValue(double p)         : type(Param::REAL), pd(new AutoBuffer<double,1>) { (*pd)[0] = p; }     //!< Constructs floating point scalar
     CV_WRAP DictValue(const String &s)  : type(Param::STRING), ps(new AutoBuffer<String,1>) { (*ps)[0] = s; }   //!< Constructs string scalar
-    DictValue(const char *s)    : type(Param::STRING), ps(new AutoBuffer<String,1>) { (*ps)[0] = s; }   //!< @overload
+    DictValue(const char *s)            : type(Param::STRING), ps(new AutoBuffer<String,1>) { (*ps)[0] = s; }   //!< @overload
 
     template<typename TypeIter>
     static DictValue arrayInt(TypeIter begin, int size);    //!< Constructs integer array

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

@@ -186,7 +186,8 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
          * If this method is called after network has allocated all memory for input and output blobs
          * and before inferencing.
          */
-        CV_DEPRECATED virtual void finalize(const std::vector<Mat*> &input, std::vector<Mat> &output);
+        CV_DEPRECATED_EXTERNAL
+        virtual void finalize(const std::vector<Mat*> &input, std::vector<Mat> &output);
 
         /** @brief Computes and sets internal parameters according to inputs, outputs and blobs.
          *  @param[in]  inputs  vector of already allocated input blobs
@@ -203,7 +204,8 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
          *  @param[out] output allocated output blobs, which will store results of the computation.
          *  @param[out] internals allocated internal blobs
          */
-        CV_DEPRECATED virtual void forward(std::vector<Mat*> &input, std::vector<Mat> &output, std::vector<Mat> &internals);
+        CV_DEPRECATED_EXTERNAL
+        virtual void forward(std::vector<Mat*> &input, std::vector<Mat> &output, std::vector<Mat> &internals);
 
         /** @brief Given the @p input blobs, computes the output @p blobs.
          *  @param[in]  inputs  the input blobs.
@@ -223,7 +225,8 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
          * @overload
          * @deprecated Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead
          */
-        CV_DEPRECATED void finalize(const std::vector<Mat> &inputs, CV_OUT std::vector<Mat> &outputs);
+        CV_DEPRECATED_EXTERNAL
+        void finalize(const std::vector<Mat> &inputs, CV_OUT std::vector<Mat> &outputs);
 
         /** @brief
          * @overload

modules/dnn/perf/perf_net.cpp

@@ -175,8 +175,7 @@ PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow)
 PERF_TEST_P_(DNNTestNetwork, DenseNet_121)
 {
     if (backend == DNN_BACKEND_HALIDE ||
-        backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL_FP16 ||
-                                                    target == DNN_TARGET_MYRIAD))
+        (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)))
         throw SkipTestException("");
     processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", "",
                Mat(cv::Size(224, 224), CV_32FC3));
@@ -185,7 +184,7 @@ PERF_TEST_P_(DNNTestNetwork, DenseNet_121)
 PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_coco)
 {
     if (backend == DNN_BACKEND_HALIDE ||
-        backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+        (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
         throw SkipTestException("");
     processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt", "",
                Mat(cv::Size(368, 368), CV_32FC3));
@@ -194,7 +193,7 @@ PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_coco)
 PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_mpi)
 {
     if (backend == DNN_BACKEND_HALIDE ||
-        backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+        (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
         throw SkipTestException("");
     processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt", "",
                Mat(cv::Size(368, 368), CV_32FC3));
@@ -203,7 +202,7 @@ PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_mpi)
 PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
 {
     if (backend == DNN_BACKEND_HALIDE ||
-        backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+        (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
         throw SkipTestException("");
     // The same .caffemodel but modified .prototxt
     // See https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/pose/poseParameters.cpp
@@ -230,7 +229,7 @@ PERF_TEST_P_(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
 PERF_TEST_P_(DNNTestNetwork, YOLOv3)
 {
     if (backend == DNN_BACKEND_HALIDE ||
-        backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+        (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
         throw SkipTestException("");
     Mat sample = imread(findDataFile("dnn/dog416.png", false));
     Mat inp;
@@ -241,7 +240,7 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv3)
 PERF_TEST_P_(DNNTestNetwork, EAST_text_detection)
 {
     if (backend == DNN_BACKEND_HALIDE ||
-        backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+        (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
         throw SkipTestException("");
     processNet("dnn/frozen_east_text_detection.pb", "", "", Mat(cv::Size(320, 320), CV_32FC3));
 }

modules/dnn/src/caffe/caffe_io.cpp

@@ -404,7 +404,7 @@ bool UpgradeV0LayerParameter(V1LayerParameter* v0_layer_connection_,
               PoolingParameter_PoolMethod_STOCHASTIC);
           break;
         default:
-          LOG(ERROR) << "Unknown pool method " << pool;
+          LOG(ERROR) << "Unknown pool method " << (int)pool;
           is_fully_compatible = false;
         }
       } else {
@@ -863,7 +863,7 @@ bool UpgradeV1LayerParameter(V1LayerParameter* v1_layer_param_,
     while (layer_param->param_size() <= i) { layer_param->add_param(); }
     layer_param->mutable_param(i)->set_name(v1_layer_param.param(i));
   }
-  ParamSpec_DimCheckMode mode;
+  ParamSpec_DimCheckMode mode = ParamSpec_DimCheckMode_STRICT;
   for (int i = 0; i < v1_layer_param.blob_share_mode_size(); ++i) {
     while (layer_param->param_size() <= i) { layer_param->add_param(); }
     switch (v1_layer_param.blob_share_mode(i)) {
@@ -875,8 +875,8 @@ bool UpgradeV1LayerParameter(V1LayerParameter* v1_layer_param_,
       break;
     default:
       LOG(FATAL) << "Unknown blob_share_mode: "
-                 << v1_layer_param.blob_share_mode(i);
-      break;
+                 << (int)v1_layer_param.blob_share_mode(i);
+      CV_Error_(Error::StsError, ("Unknown blob_share_mode: %d", (int)v1_layer_param.blob_share_mode(i)));
     }
     layer_param->mutable_param(i)->set_share_mode(mode);
   }
@@ -1102,12 +1102,12 @@ const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type) {
   case V1LayerParameter_LayerType_THRESHOLD:
     return "Threshold";
   default:
-    LOG(FATAL) << "Unknown V1LayerParameter layer type: " << type;
+    LOG(FATAL) << "Unknown V1LayerParameter layer type: " << (int)type;
     return "";
   }
 }
 
-const int kProtoReadBytesLimit = INT_MAX;  // Max size of 2 GB minus 1 byte.
+static const int kProtoReadBytesLimit = INT_MAX;  // Max size of 2 GB minus 1 byte.
 
 bool ReadProtoFromBinary(ZeroCopyInputStream* input, Message *proto) {
     CodedInputStream coded_input(input);

modules/dnn/src/dnn.cpp

@@ -352,7 +352,7 @@ struct LayerPin
 
     bool operator<(const LayerPin &r) const
     {
-        return lid < r.lid || lid == r.lid && oid < r.oid;
+        return lid < r.lid || (lid == r.lid && oid < r.oid);
     }
 
     bool operator ==(const LayerPin &r) const
@@ -427,7 +427,7 @@ struct DataLayer : public Layer
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE && inputsData.size() == 1;
+               (backendId == DNN_BACKEND_INFERENCE_ENGINE && inputsData.size() == 1);
     }
 
     void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
@@ -1690,8 +1690,8 @@ struct Net::Impl
 
     void fuseLayers(const std::vector<LayerPin>& blobsToKeep_)
     {
-        if( !fusion || preferableBackend != DNN_BACKEND_OPENCV &&
-                       preferableBackend != DNN_BACKEND_INFERENCE_ENGINE)
+        if( !fusion || (preferableBackend != DNN_BACKEND_OPENCV &&
+                        preferableBackend != DNN_BACKEND_INFERENCE_ENGINE))
             return;
 
         CV_TRACE_FUNCTION();

modules/dnn/src/layers/batch_norm_layer.cpp

@@ -151,8 +151,8 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_HALIDE && haveHalide() ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
+               (backendId == DNN_BACKEND_HALIDE && haveHalide()) ||
+               (backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
     }
 
 #ifdef HAVE_OPENCL

modules/dnn/src/layers/blank_layer.cpp

@@ -57,7 +57,7 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
+               (backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,

modules/dnn/src/layers/concat_layer.cpp

@@ -104,8 +104,8 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1 && !padding ||  // By channels
-               backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && !padding;
+               (backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1 && !padding) ||  // By channels
+               (backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && !padding);
     }
 
     class ChannelConcatInvoker : public ParallelLoopBody

modules/dnn/src/layers/crop_layer.cpp

@@ -68,7 +68,7 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE && crop_ranges.size() == 4;
+               (backendId == DNN_BACKEND_INFERENCE_ENGINE && crop_ranges.size() == 4);
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,

modules/dnn/src/layers/detection_output_layer.cpp

@@ -198,7 +198,7 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE && !_locPredTransposed && _bboxesNormalized && !_clip;
+               (backendId == DNN_BACKEND_INFERENCE_ENGINE && !_locPredTransposed && _bboxesNormalized && !_clip);
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,

modules/dnn/src/layers/eltwise_layer.cpp

@@ -98,7 +98,7 @@ public:
     {
         return backendId == DNN_BACKEND_OPENCV ||
                backendId == DNN_BACKEND_HALIDE ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE && (op != SUM || coeffs.empty());
+               (backendId == DNN_BACKEND_INFERENCE_ENGINE && (op != SUM || coeffs.empty()));
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,

modules/dnn/src/layers/flatten_layer.cpp

@@ -65,7 +65,7 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
+               (backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,

modules/dnn/src/layers/fully_connected_layer.cpp

@@ -123,8 +123,8 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1 ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && axis == 1;
+               (backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1) ||
+               (backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && axis == 1);
     }
 
     virtual bool setActivation(const Ptr<ActivationLayer>& layer) CV_OVERRIDE

modules/dnn/src/layers/lrn_layer.cpp

@@ -92,7 +92,7 @@ public:
     {
         return backendId == DNN_BACKEND_OPENCV ||
                backendId == DNN_BACKEND_HALIDE ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE && (preferableTarget != DNN_TARGET_MYRIAD || type == CHANNEL_NRM);
+               (backendId == DNN_BACKEND_INFERENCE_ENGINE && (preferableTarget != DNN_TARGET_MYRIAD || type == CHANNEL_NRM));
     }
 
 #ifdef HAVE_OPENCL

modules/dnn/src/layers/max_unpooling_layer.cpp

@@ -35,8 +35,7 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_HALIDE && haveHalide() &&
-               !poolPad.width && !poolPad.height;
+               (backendId == DNN_BACKEND_HALIDE && haveHalide() && !poolPad.width && !poolPad.height);
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,

modules/dnn/src/layers/padding_layer.cpp

@@ -91,7 +91,7 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_HALIDE && haveHalide() && dstRanges.size() == 4;
+               (backendId == DNN_BACKEND_HALIDE && haveHalide() && dstRanges.size() == 4);
     }
 
     void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE

modules/dnn/src/layers/permute_layer.cpp

@@ -105,7 +105,7 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
+               (backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,

modules/dnn/src/layers/pooling_layer.cpp

@@ -154,8 +154,8 @@ public:
         }
         else
             return backendId == DNN_BACKEND_OPENCV ||
-                   backendId == DNN_BACKEND_HALIDE && haveHalide() &&
-                   (type == MAX || type == AVE && !pad_t && !pad_l && !pad_b && !pad_r);
+                   (backendId == DNN_BACKEND_HALIDE && haveHalide() &&
+                   (type == MAX || (type == AVE && !pad_t && !pad_l && !pad_b && !pad_r)));
     }
 
 #ifdef HAVE_OPENCL
@@ -341,8 +341,8 @@ public:
                       src.isContinuous(), dst.isContinuous(),
                       src.type() == CV_32F, src.type() == dst.type(),
                       src.dims == 4, dst.dims == 4,
-                      ((poolingType == ROI || poolingType == PSROI) && dst.size[0] ==rois.size[0] || src.size[0] == dst.size[0]),
-                       poolingType == PSROI || src.size[1] == dst.size[1],
+                      (((poolingType == ROI || poolingType == PSROI) && dst.size[0] == rois.size[0]) || src.size[0] == dst.size[0]),
+                      poolingType == PSROI || src.size[1] == dst.size[1],
                       (mask.empty() || (mask.type() == src.type() && mask.size == dst.size)));
 
             PoolingInvoker p;

modules/dnn/src/layers/prior_box_layer.cpp

@@ -271,7 +271,7 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
+               (backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,

modules/dnn/src/layers/proposal_layer.cpp

@@ -87,7 +87,7 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE && preferableTarget != DNN_TARGET_MYRIAD;
+               (backendId == DNN_BACKEND_INFERENCE_ENGINE && preferableTarget != DNN_TARGET_MYRIAD);
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,

modules/dnn/src/layers/recurrent_layers.cpp

@@ -175,7 +175,7 @@ public:
                          std::vector<MatShape> &outputs,
                          std::vector<MatShape> &internals) const CV_OVERRIDE
     {
-        CV_Assert(!usePeephole && blobs.size() == 3 || usePeephole && blobs.size() == 6);
+        CV_Assert((!usePeephole && blobs.size() == 3) || (usePeephole && blobs.size() == 6));
         CV_Assert(inputs.size() == 1);
         const MatShape& inp0 = inputs[0];
 
@@ -221,7 +221,7 @@ public:
         std::vector<Mat> input;
         inputs_arr.getMatVector(input);
 
-        CV_Assert(!usePeephole && blobs.size() == 3 || usePeephole && blobs.size() == 6);
+        CV_Assert((!usePeephole && blobs.size() == 3) || (usePeephole && blobs.size() == 6));
         CV_Assert(input.size() == 1);
         const Mat& inp0 = input[0];
 

modules/dnn/src/layers/reshape_layer.cpp

@@ -178,7 +178,7 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
+               (backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,

modules/dnn/src/layers/scale_layer.cpp

@@ -45,13 +45,13 @@ public:
         std::vector<Mat> inputs;
         inputs_arr.getMatVector(inputs);
         hasWeights = blobs.size() == 2 || (blobs.size() == 1 && !hasBias);
-        CV_Assert(inputs.size() == 2 && blobs.empty() || blobs.size() == (int)hasWeights + (int)hasBias);
+        CV_Assert((inputs.size() == 2 && blobs.empty()) || blobs.size() == (int)hasWeights + (int)hasBias);
     }
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE && axis == 1;
+               (backendId == DNN_BACKEND_INFERENCE_ENGINE && axis == 1);
     }
 
     void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE

modules/dnn/src/layers/slice_layer.cpp

@@ -111,7 +111,7 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE && sliceRanges.size() == 1 && sliceRanges[0].size() == 4;
+               (backendId == DNN_BACKEND_INFERENCE_ENGINE && sliceRanges.size() == 1 && sliceRanges[0].size() == 4);
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,

modules/dnn/src/layers/softmax_layer.cpp

@@ -89,8 +89,8 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_HALIDE && haveHalide() && axisRaw == 1 ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && !logSoftMax;
+               (backendId == DNN_BACKEND_HALIDE && haveHalide() && axisRaw == 1) ||
+               (backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && !logSoftMax);
     }
 
 #ifdef HAVE_OPENCL

modules/dnn/src/ocl4dnn/src/ocl4dnn_conv_spatial.cpp

@@ -638,7 +638,7 @@ void OCL4DNNConvSpatial<Dtype>::generateKey()
                << "p" << pad_w_ << "x" << pad_h_ << "_"
                << "num" << num_ << "_"
                << "M" << M_ << "_"
-               << "activ" << fused_activ_ << "_"
+               << "activ" << (int)fused_activ_ << "_"
                << "eltwise" << fused_eltwise_ << "_"
                << precision;
 

modules/dnn/src/op_inf_engine.cpp

@@ -559,7 +559,7 @@ bool InfEngineBackendLayer::getMemoryShapes(const std::vector<MatShape> &inputs,
 bool InfEngineBackendLayer::supportBackend(int backendId)
 {
     return backendId == DNN_BACKEND_DEFAULT ||
-           backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
+           (backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
 }
 
 void InfEngineBackendLayer::forward(InputArrayOfArrays inputs, OutputArrayOfArrays outputs,

modules/dnn/src/tensorflow/tf_importer.cpp

@@ -156,6 +156,7 @@ void blobFromTensor(const tensorflow::TensorProto &tensor, Mat &dstBlob)
     }
 }
 
+#if 0
 void printList(const tensorflow::AttrValue::ListValue &val)
 {
     std::cout << "(";
@@ -235,6 +236,7 @@ void printLayerAttr(const tensorflow::NodeDef &layer)
         std::cout << std::endl;
     }
 }
+#endif
 
 bool hasLayerAttr(const tensorflow::NodeDef &layer, const std::string &name)
 {

modules/dnn/src/tensorflow/tf_io.cpp

@@ -37,8 +37,6 @@ using namespace tensorflow;
 using namespace ::google::protobuf;
 using namespace ::google::protobuf::io;
 
-const int kProtoReadBytesLimit = INT_MAX;  // Max size of 2 GB minus 1 byte.
-
 void ReadTFNetParamsFromBinaryFileOrDie(const char* param_file,
                                         tensorflow::GraphDef* param) {
     CHECK(ReadProtoFromBinaryFile(param_file, param))

modules/dnn/test/test_backends.cpp

@@ -194,7 +194,7 @@ TEST_P(DNNTestNetwork, SSD_VGG16)
 TEST_P(DNNTestNetwork, OpenPose_pose_coco)
 {
     if (backend == DNN_BACKEND_HALIDE ||
-        backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+        (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
         throw SkipTestException("");
     processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt",
                Size(368, 368));
@@ -203,7 +203,7 @@ TEST_P(DNNTestNetwork, OpenPose_pose_coco)
 TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
 {
     if (backend == DNN_BACKEND_HALIDE ||
-        backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+        (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
         throw SkipTestException("");
     processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt",
                Size(368, 368));
@@ -212,7 +212,7 @@ TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
 TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
 {
     if (backend == DNN_BACKEND_HALIDE ||
-        backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+        (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
         throw SkipTestException("");
     // The same .caffemodel but modified .prototxt
     // See https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/pose/poseParameters.cpp

modules/dnn/test/test_common.hpp

@@ -56,7 +56,7 @@ static inline void PrintTo(const cv::dnn::Backend& v, std::ostream* os)
     case DNN_BACKEND_INFERENCE_ENGINE: *os << "DLIE"; return;
     case DNN_BACKEND_OPENCV: *os << "OCV"; return;
     } // don't use "default:" to emit compiler warnings
-    *os << "DNN_BACKEND_UNKNOWN(" << v << ")";
+    *os << "DNN_BACKEND_UNKNOWN(" << (int)v << ")";
 }
 
 static inline void PrintTo(const cv::dnn::Target& v, std::ostream* os)
@@ -67,7 +67,7 @@ static inline void PrintTo(const cv::dnn::Target& v, std::ostream* os)
     case DNN_TARGET_OPENCL_FP16: *os << "OCL_FP16"; return;
     case DNN_TARGET_MYRIAD: *os << "MYRIAD"; return;
     } // don't use "default:" to emit compiler warnings
-    *os << "DNN_TARGET_UNKNOWN(" << v << ")";
+    *os << "DNN_TARGET_UNKNOWN(" << (int)v << ")";
 }
 
 using opencv_test::tuple;
@@ -235,7 +235,8 @@ namespace opencv_test {
 
 using namespace cv::dnn;
 
-static testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargets(
+static inline
+testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargets(
         bool withInferenceEngine = true,
         bool withHalide = false,
         bool withCpuOCV = true
@@ -283,4 +284,103 @@ static testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAnd
 
 } // namespace
 
+
+namespace opencv_test {
+using namespace cv::dnn;
+
+static inline
+testing::internal::ParamGenerator<Target> availableDnnTargets()
+{
+    static std::vector<Target> targets;
+    if (targets.empty())
+    {
+        targets.push_back(DNN_TARGET_CPU);
+#ifdef HAVE_OPENCL
+        if (cv::ocl::useOpenCL())
+            targets.push_back(DNN_TARGET_OPENCL);
+#endif
+    }
+    return testing::ValuesIn(targets);
+}
+
+class DNNTestLayer : public TestWithParam<tuple<Backend, Target> >
+{
+public:
+    dnn::Backend backend;
+    dnn::Target target;
+    double default_l1, default_lInf;
+
+    DNNTestLayer()
+    {
+        backend = (dnn::Backend)(int)get<0>(GetParam());
+        target = (dnn::Target)(int)get<1>(GetParam());
+        getDefaultThresholds(backend, target, &default_l1, &default_lInf);
+    }
+
+   static void getDefaultThresholds(int backend, int target, double* l1, double* lInf)
+   {
+       if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
+       {
+           *l1 = 4e-3;
+           *lInf = 2e-2;
+       }
+       else
+       {
+           *l1 = 1e-5;
+           *lInf = 1e-4;
+       }
+   }
+
+   static void checkBackend(int backend, int target, Mat* inp = 0, Mat* ref = 0)
+   {
+       if (backend == DNN_BACKEND_OPENCV && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
+       {
+#ifdef HAVE_OPENCL
+           if (!cv::ocl::useOpenCL())
+#endif
+           {
+               throw SkipTestException("OpenCL is not available/disabled in OpenCV");
+           }
+       }
+       if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+       {
+           if (!checkMyriadTarget())
+           {
+               throw SkipTestException("Myriad is not available/disabled in OpenCV");
+           }
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
+           if (inp && ref && inp->size[0] != 1)
+           {
+               // Myriad plugin supports only batch size 1. Slice a single sample.
+               if (inp->size[0] == ref->size[0])
+               {
+                   std::vector<cv::Range> range(inp->dims, Range::all());
+                   range[0] = Range(0, 1);
+                   *inp = inp->operator()(range);
+
+                   range = std::vector<cv::Range>(ref->dims, Range::all());
+                   range[0] = Range(0, 1);
+                   *ref = ref->operator()(range);
+               }
+               else
+                   throw SkipTestException("Myriad plugin supports only batch size 1");
+           }
+#else
+           if (inp && ref && inp->dims == 4 && ref->dims == 4 &&
+               inp->size[0] != 1 && inp->size[0] != ref->size[0])
+               throw SkipTestException("Inconsistent batch size of input and output blobs for Myriad plugin");
+
+#endif
+       }
+   }
+
+protected:
+    void checkBackend(Mat* inp = 0, Mat* ref = 0)
+    {
+        checkBackend(backend, target, inp, ref);
+    }
+};
+
+} // namespace
+
 #endif

modules/dnn/test/test_layers.cpp

@@ -558,7 +558,9 @@ TEST_P(Test_Caffe_layers, FasterRCNN_Proposal)
         normAssert(outs[i].rowRange(0, numDets), ref);
 
         if (numDets < outs[i].size[0])
+        {
             EXPECT_EQ(countNonZero(outs[i].rowRange(numDets, outs[i].size[0])), 0);
+        }
     }
 }
 

modules/dnn/test/test_misc.cpp

@@ -140,9 +140,9 @@ TEST(LayerFactory, custom_layers)
         net.setPreferableBackend(DNN_BACKEND_OPENCV);
         Mat output = net.forward();
 
-        if (i == 0)      EXPECT_EQ(output.at<float>(0), 1);
-        else if (i == 1) EXPECT_EQ(output.at<float>(0), 2);
-        else if (i == 2) EXPECT_EQ(output.at<float>(0), 1);
+        if (i == 0)      { EXPECT_EQ(output.at<float>(0), 1); }
+        else if (i == 1) { EXPECT_EQ(output.at<float>(0), 2); }
+        else if (i == 2) { EXPECT_EQ(output.at<float>(0), 1); }
     }
     LayerFactory::unregisterLayer("CustomType");
 }

modules/dnn/test/test_onnx_importer.cpp

@@ -118,8 +118,8 @@ TEST_P(Test_ONNX_layers, Transpose)
 
 TEST_P(Test_ONNX_layers, Multiplication)
 {
-    if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16 ||
-        backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+    if ((backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) ||
+        (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
         throw SkipTestException("");
     testONNXModels("mul");
 }
@@ -296,7 +296,7 @@ TEST_P(Test_ONNX_nets, ResNet101_DUC_HDC)
 TEST_P(Test_ONNX_nets, TinyYolov2)
 {
     if (cvtest::skipUnstableTests ||
-        backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) {
+        (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))) {
         throw SkipTestException("");
     }
     // output range: [-11; 8]

modules/dnn/test/test_precomp.hpp

@@ -49,100 +49,4 @@
 #include "opencv2/dnn.hpp"
 #include "test_common.hpp"
 
-namespace opencv_test {
-using namespace cv::dnn;
-
-static testing::internal::ParamGenerator<Target> availableDnnTargets()
-{
-    static std::vector<Target> targets;
-    if (targets.empty())
-    {
-        targets.push_back(DNN_TARGET_CPU);
-#ifdef HAVE_OPENCL
-        if (cv::ocl::useOpenCL())
-            targets.push_back(DNN_TARGET_OPENCL);
-#endif
-    }
-    return testing::ValuesIn(targets);
-}
-
-class DNNTestLayer : public TestWithParam<tuple<Backend, Target> >
-{
-public:
-    dnn::Backend backend;
-    dnn::Target target;
-    double default_l1, default_lInf;
-
-    DNNTestLayer()
-    {
-        backend = (dnn::Backend)(int)get<0>(GetParam());
-        target = (dnn::Target)(int)get<1>(GetParam());
-        getDefaultThresholds(backend, target, &default_l1, &default_lInf);
-    }
-
-   static void getDefaultThresholds(int backend, int target, double* l1, double* lInf)
-   {
-       if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
-       {
-           *l1 = 4e-3;
-           *lInf = 2e-2;
-       }
-       else
-       {
-           *l1 = 1e-5;
-           *lInf = 1e-4;
-       }
-   }
-
-   static void checkBackend(int backend, int target, Mat* inp = 0, Mat* ref = 0)
-   {
-       if (backend == DNN_BACKEND_OPENCV && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
-       {
-#ifdef HAVE_OPENCL
-           if (!cv::ocl::useOpenCL())
-#endif
-           {
-               throw SkipTestException("OpenCL is not available/disabled in OpenCV");
-           }
-       }
-       if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
-       {
-           if (!checkMyriadTarget())
-           {
-               throw SkipTestException("Myriad is not available/disabled in OpenCV");
-           }
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
-           if (inp && ref && inp->size[0] != 1)
-           {
-               // Myriad plugin supports only batch size 1. Slice a single sample.
-               if (inp->size[0] == ref->size[0])
-               {
-                   std::vector<cv::Range> range(inp->dims, Range::all());
-                   range[0] = Range(0, 1);
-                   *inp = inp->operator()(range);
-
-                   range = std::vector<cv::Range>(ref->dims, Range::all());
-                   range[0] = Range(0, 1);
-                   *ref = ref->operator()(range);
-               }
-               else
-                   throw SkipTestException("Myriad plugin supports only batch size 1");
-           }
-#else
-           if (inp && ref && inp->dims == 4 && ref->dims == 4 &&
-               inp->size[0] != 1 && inp->size[0] != ref->size[0])
-               throw SkipTestException("Inconsistent batch size of input and output blobs for Myriad plugin");
-
-#endif
-       }
-   }
-
-protected:
-    void checkBackend(Mat* inp = 0, Mat* ref = 0)
-    {
-        checkBackend(backend, target, inp, ref);
-    }
-};
-
-} // namespace
 #endif

modules/dnn/test/test_tf_importer.cpp

@@ -101,7 +101,9 @@ public:
 
             string dataConfig;
             if (hasText)
+            {
                 ASSERT_TRUE(readFileInMemory(netConfig, dataConfig));
+            }
 
             net = readNetFromTensorflow(dataModel.c_str(), dataModel.size(),
                                         dataConfig.c_str(), dataConfig.size());

modules/python/src2/hdr_parser.py

@@ -423,7 +423,7 @@ class CppHeaderParser(object):
         # it means class methods, not instance methods
         decl_str = self.batch_replace(decl_str, [("static inline", ""), ("inline", ""),\
             ("CV_EXPORTS_W", ""), ("CV_EXPORTS", ""), ("CV_CDECL", ""), ("CV_WRAP ", " "), ("CV_INLINE", ""),
-            ("CV_DEPRECATED", "")]).strip()
+            ("CV_DEPRECATED", ""), ("CV_DEPRECATED_EXTERNAL", "")]).strip()
 
 
         if decl_str.strip().startswith('virtual'):