Merge branch 4.x

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

@@ -262,7 +262,7 @@ CV__DNN_INLINE_NS_BEGIN
     {
     public:
         int input_zp, output_zp;
-        float output_sc;
+        float input_sc, output_sc;
         static Ptr<BaseConvolutionLayer> create(const LayerParams& params);
     };
 
@@ -322,9 +322,24 @@ CV__DNN_INLINE_NS_BEGIN
     {
     public:
         int input_zp, output_zp;
+        float input_sc, output_sc;
         static Ptr<PoolingLayerInt8> create(const LayerParams& params);
     };
 
+    class CV_EXPORTS ReduceLayer : public Layer
+    {
+    public:
+        int reduceType;
+        std::vector<size_t> reduceDims;
+        static Ptr<ReduceLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS ReduceLayerInt8 : public ReduceLayer
+    {
+    public:
+        static Ptr<ReduceLayerInt8> create(const LayerParams& params);
+    };
+
     class CV_EXPORTS SoftmaxLayer : public Layer
     {
     public:
@@ -351,7 +366,8 @@ CV__DNN_INLINE_NS_BEGIN
     class CV_EXPORTS InnerProductLayerInt8 : public InnerProductLayer
     {
     public:
-        int output_zp;
+        int input_zp, output_zp;
+        float input_sc, output_sc;
         static Ptr<InnerProductLayerInt8> create(const LayerParams& params);
     };
 
@@ -778,6 +794,26 @@ CV__DNN_INLINE_NS_BEGIN
         static Ptr<ActivationLayerInt8> create(const LayerParams &params);
     };
 
+    class CV_EXPORTS SignLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<SignLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS ShrinkLayer : public ActivationLayer
+    {
+    public:
+        float bias;
+        float lambd;
+        static Ptr<ShrinkLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS ReciprocalLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<ReciprocalLayer> create(const LayerParams &params);
+    };
+
     /* Layers used in semantic segmentation */
 
     class CV_EXPORTS CropLayer : public Layer

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

@@ -75,6 +75,7 @@ CV__DNN_INLINE_NS_BEGIN
         DNN_BACKEND_VKCOM,
         DNN_BACKEND_CUDA,
         DNN_BACKEND_WEBNN,
+        DNN_BACKEND_TIMVX,
 #ifdef __OPENCV_BUILD
         DNN_BACKEND_INFERENCE_ENGINE_NGRAPH = 1000000,     // internal - use DNN_BACKEND_INFERENCE_ENGINE + setInferenceEngineBackendType()
         DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019,      // internal - use DNN_BACKEND_INFERENCE_ENGINE + setInferenceEngineBackendType()
@@ -95,7 +96,8 @@ CV__DNN_INLINE_NS_BEGIN
         DNN_TARGET_FPGA,  //!< FPGA device with CPU fallbacks using Inference Engine's Heterogeneous plugin.
         DNN_TARGET_CUDA,
         DNN_TARGET_CUDA_FP16,
-        DNN_TARGET_HDDL
+        DNN_TARGET_HDDL,
+        DNN_TARGET_NPU,
     };
 
     CV_EXPORTS std::vector< std::pair<Backend, Target> > getAvailableBackends();
@@ -321,6 +323,19 @@ CV__DNN_INLINE_NS_BEGIN
             const std::vector<Ptr<BackendWrapper>>& outputs
         );
 
+        /**
+         * @brief Returns a TimVX backend node
+         *
+         * @param   timVxInfo  void pointer to CSLContext object
+         * @param   inputsWrapper   layer inputs
+         * @param   outputsWrapper  layer outputs
+         * @param   isLast if the node is the last one of the TimVX Graph.
+         */
+        virtual Ptr<BackendNode> initTimVX(void* timVxInfo,
+                                           const std::vector<Ptr<BackendWrapper> > &inputsWrapper,
+                                           const std::vector<Ptr<BackendWrapper> > &outputsWrapper,
+                                           bool isLast);
+
        /**
         * @brief Automatic Halide scheduling based on layer hyper-parameters.
         * @param[in] node Backend node with Halide functions.
@@ -389,7 +404,7 @@ CV__DNN_INLINE_NS_BEGIN
 
 
         /**
-         * @brief "Deattaches" all the layers, attached to particular layer.
+         * @brief "Detaches" all the layers, attached to particular layer.
          */
         virtual void unsetAttached();
 
@@ -1310,6 +1325,9 @@ CV__DNN_INLINE_NS_BEGIN
      class CV_EXPORTS_W_SIMPLE ClassificationModel : public Model
      {
      public:
+         CV_DEPRECATED_EXTERNAL  // avoid using in C++ code, will be moved to "protected" (need to fix bindings first)
+         ClassificationModel();
+
          /**
           * @brief Create classification model from network represented in one of the supported formats.
           * An order of @p model and @p config arguments does not matter.
@@ -1324,6 +1342,24 @@ CV__DNN_INLINE_NS_BEGIN
           */
          CV_WRAP ClassificationModel(const Net& network);
 
+         /**
+          * @brief Set enable/disable softmax post processing option.
+          *
+          * If this option is true, softmax is applied after forward inference within the classify() function
+          * to convert the confidences range to [0.0-1.0].
+          * This function allows you to toggle this behavior.
+          * Please turn true when not contain softmax layer in model.
+          * @param[in] enable Set enable softmax post processing within the classify() function.
+          */
+         CV_WRAP ClassificationModel& setEnableSoftmaxPostProcessing(bool enable);
+
+         /**
+          * @brief Get enable/disable softmax post processing option.
+          *
+          * This option defaults to false, softmax post processing is not applied within the classify() function.
+          */
+         CV_WRAP bool getEnableSoftmaxPostProcessing() const;
+
          /** @brief Given the @p input frame, create input blob, run net and return top-1 prediction.
           *  @param[in]  frame  The input image.
           */
@@ -1558,7 +1594,7 @@ public:
      * - top-right
      * - bottom-right
      *
-     * Use cv::getPerspectiveTransform function to retrive image region without perspective transformations.
+     * Use cv::getPerspectiveTransform function to retrieve image region without perspective transformations.
      *
      * @note If DL model doesn't support that kind of output then result may be derived from detectTextRectangles() output.
      *