Adding of docs headers for layers list, fixing of layer factory macroses

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

@@ -49,9 +49,28 @@ namespace dnn
 {
 //! @addtogroup dnn
 //! @{
-//!
-//! @defgroup LayerList Partial List of Implemented Layers
-//! @{
+
+/** @defgroup dnnLayerList Partial List of Implemented Layers
+  @{
+  This subsection of dnn module contains information about bult-in layers and their descriptions.
+
+  Classes listed here, in fact, provides C++ API for creating intances of bult-in layers.
+  In addition to this way of layers instantiation, there is a more common factory API (see @ref dnnLayerFactory), it allows to create layers dynamically (by name) and register new ones.
+  You can use both API, but factory API is less convinient for native C++ programming and basically designed for use inside importers (see @ref Importer, @ref createCaffeImporter(), @ref createTorchImporter()).
+
+  Bult-in layers partially reproduce functionality of corresponding Caffe and Torch7 layers.
+  In partuclar, the following layers and Caffe @ref Importer were tested to reproduce <a href="http://caffe.berkeleyvision.org/tutorial/layers.html">Caffe</a> functionality:
+  - Convolution
+  - Deconvolution
+  - Pooling
+  - InnerProduct
+  - TanH, ReLU, Sigmoid, BNLL, Power, AbsVal
+  - Softmax
+  - Reshape, Flatten, Slice, Split
+  - LRN
+  - MVN
+  - Dropout (since it does nothing on forward pass -))
+*/
 
     //! LSTM recurrent layer
     class LSTMLayer : public Layer

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

@@ -66,8 +66,10 @@ namespace dnn //! This namespace is used for dnn module functionlaity.
      * It includes dictionary with scalar params (which can be readed by using Dict interface),
      * blob params #blobs and optional meta information: #name and #type of layer instance.
     */
-    struct CV_EXPORTS LayerParams : public Dict
+    class CV_EXPORTS LayerParams : public Dict
     {
+    public:
+        //TODO: Add ability to name blob params
         std::vector<Blob> blobs; //!< List of learned parameters stored as blobs.
 
         String name; //!< Name of the layer instance (optional, can be used internal purposes).
@@ -77,10 +79,12 @@ namespace dnn //! This namespace is used for dnn module functionlaity.
     /** @brief This interface class allows to build new Layers - are building blocks of networks.
      *
      * Each class, derived from Layer, must implement allocate() methods to declare own outputs and forward() to compute outputs.
-     * Also before using the new layer into networks you must register your layer by using one of @ref LayerFactoryModule "LayerFactory" macros.
+     * Also before using the new layer into networks you must register your layer by using one of @ref dnnLayerFactory "LayerFactory" macros.
      */
-    struct CV_EXPORTS Layer
+    class CV_EXPORTS Layer
     {
+    public:
+
         //! List of learned parameters must be stored here to allow read them by using Net::getParam().
         std::vector<Blob> blobs;
 
@@ -267,10 +271,10 @@ namespace dnn //! This namespace is used for dnn module functionlaity.
      *  @param isBinary specifies whether the network was serialized in ascii mode or binary.
      *  @returns Pointer to the created importer, NULL in failure cases.
      *
-     *  @warning Torch7 importer is experimental now, you need explicitly set CMake opencv_dnn_BUILD_TORCH_IMPORTER flag to compile its.
+     *  @warning Torch7 importer is experimental now, you need explicitly set CMake `opencv_dnn_BUILD_TORCH_IMPORTER` flag to compile its.
      *
-     *  @note Ascii mode of Torch serializer is more preferable, because binary mode extensively use long type of C language,
-     *  which has different bit-length on different systems.
+     *  @note Ascii mode of Torch serializer is more preferable, because binary mode extensively use `long` type of C language,
+     *  which has various bit-length on different systems.
      *
      * The loading file must contain serialized <a href="https://github.com/torch/nn/blob/master/doc/module.md">nn.Module</a> object
      * with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors.

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

@@ -50,7 +50,7 @@ namespace dnn
 //! @addtogroup dnn
 //! @{
 //!
-//! @defgroup LayerFactoryModule Utilities for new layers registration
+//! @defgroup dnnLayerFactory Utilities for New Layers Registration
 //! @{
 
 /** @brief %Layer factory allows to create instances of registered layers. */
@@ -86,7 +86,7 @@ private:
 *   @details This macros must be placed inside the function code.
 */
 #define REG_RUNTIME_LAYER_FUNC(type, constuctorFunc) \
-    LayerFactory::registerLayer(#type, constuctorFunc);
+    cv::dnn::LayerFactory::registerLayer(#type, constuctorFunc);
 
 /** @brief Registers layer class in runtime.
  *  @param type string, containing type name of the layer.
@@ -94,7 +94,7 @@ private:
  *  @details This macros must be placed inside the function code.
  */
 #define REG_RUNTIME_LAYER_CLASS(type, class) \
-    LayerFactory::registerLayer(#type, _layerDynamicRegisterer<class>);
+    cv::dnn::LayerFactory::registerLayer(#type, _layerDynamicRegisterer<class>);
 
 /** @brief Registers layer constructor on module load time.
 *   @param type string, containing type name of the layer.
@@ -102,7 +102,7 @@ private:
 *   @details This macros must be placed outside the function code.
 */
 #define REG_STATIC_LAYER_FUNC(type, constuctorFunc) \
-static _LayerStaticRegisterer __LayerStaticRegisterer_##type(#type, constuctorFunc);
+static cv::dnn::_LayerStaticRegisterer __LayerStaticRegisterer_##type(#type, constuctorFunc);
 
 /** @brief Registers layer class on module load time.
  *  @param type string, containing type name of the layer.
@@ -126,9 +126,10 @@ Ptr<Layer> _layerDynamicRegisterer(LayerParams &params)
 }
 
 //allows automatically register created layer on module load time
-struct _LayerStaticRegisterer
+class _LayerStaticRegisterer
 {
     String type;
+public:
 
     _LayerStaticRegisterer(const String &type, LayerFactory::Constuctor constuctor)
     {