Merge pull request #29220 from omrope79:doc_optimizations_v4

[FOLLOW UP] : Documentation optimizations for the new Sphinx structure #29220

### Pull Request Readiness Checklist

This PR serves as a follow-up to the new documentation system introduced in [#29206](https://github.com/opencv/opencv/pull/29206)
Co-authored by: @abhishek-gola @kirtijindal14 @Akansha-977 @Prasadayus @varun-jaiswal17

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake

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

@@ -57,8 +57,8 @@ CV__DNN_INLINE_NS_BEGIN
   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 convenient for native C++ programming and basically designed for use inside importers (see @ref readNetFromTensorflow()).
 
-  Built-in layers partially reproduce functionality of corresponding ONNX, TensorFlow and Caffe layers.
-  In particular, the following layers and Caffe importer were tested to reproduce <a href="http://caffe.berkeleyvision.org/tutorial/layers.html">Caffe</a> functionality:
+  Built-in layers reproduce the functionality of the corresponding ONNX and TensorFlow operators.
+  The following layers are among the core building blocks used to assemble imported networks:
   - Convolution
   - Deconvolution
   - Pooling

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

@@ -126,7 +126,6 @@ CV__DNN_INLINE_NS_BEGIN
         DNN_MODEL_ONNX = 1, //!< ONNX model
         DNN_MODEL_TF = 2, //!< TF model
         DNN_MODEL_TFLITE = 3, //!< TFLite model
-        DNN_MODEL_CAFFE = 4, //!< Caffe model
     };
 
     CV_EXPORTS std::string modelFormatToString(ModelFormat modelFormat);

modules/dnn/perf/perf_net.cpp

@@ -145,8 +145,7 @@ PERF_TEST_P_(DNNTestNetwork, SSD)
 {
     applyTestTag(CV_TEST_TAG_DEBUG_VERYLONG);
 
-    // The Caffe-SSD specific handling lives in the new engine importer only;
-    // the classic importer can no longer load this model.
+    // SSD_VGG16's specialized preprocessing is handled by the new engine importer only.
     auto engine_forced = static_cast<dnn::EngineType>(
         utils::getConfigurationParameterSizeT("OPENCV_FORCE_DNN_ENGINE", dnn::ENGINE_AUTO));
     if (engine_forced == dnn::ENGINE_CLASSIC)
@@ -190,9 +189,7 @@ PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
 
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_HDDL))
         throw SkipTestException("");
-    // The same .caffemodel but modified .prototxt
     // See https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/pose/poseParameters.cpp
-    // processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi_faster_4_stages.prototxt", cv::Size(368, 368));
     processNet("dnn/onnx/models/openpose_pose_mpi.onnx", "", cv::Size(368, 368));
 }
 

modules/dnn/src/net_impl2.cpp

@@ -263,8 +263,7 @@ std::string modelFormatToString(ModelFormat modelFormat)
     return
         modelFormat == DNN_MODEL_ONNX ? "ONNX" :
         modelFormat == DNN_MODEL_TF ? "TF" :
-        modelFormat == DNN_MODEL_TFLITE ? "TFLite" :
-        modelFormat == DNN_MODEL_CAFFE ? "Caffe" : "Unknown/Generic";
+        modelFormat == DNN_MODEL_TFLITE ? "TFLite" : "Unknown/Generic";
 }
 
 std::string argKindToString(ArgKind kind)

modules/java/jar/CMakeLists.txt

@@ -87,15 +87,7 @@ install(FILES ${OPENCV_JAR_FILE} OPTIONAL DESTINATION ${OPENCV_JAR_INSTALL_PATH}
 
 add_dependencies(${the_module} ${the_module}_jar)
 
-# Javadoc generation can be disabled independently of BUILD_DOCS so the C++
-# Doxygen / Sphinx docs still build when the Java bindings carry Doxygen-style
-# tags (e.g. @retval, @remarks) that JDK doclint rejects as fatal errors.
-# Default ON for parity with upstream; when OFF the `doxygen_javadoc` target is
-# never created and doc/CMakeLists.txt's `if(TARGET doxygen_javadoc)` guard
-# drops it from the doxygen_cpp dependency chain automatically.
-option(BUILD_JAVADOC "Generate Javadoc as part of the documentation build" ON)
-
-if(BUILD_DOCS AND BUILD_JAVADOC)
+if(BUILD_DOCS)
   if(OPENCV_JAVA_SDK_BUILD_TYPE STREQUAL "ANT")
     add_custom_command(OUTPUT "${OPENCV_DEPHELPER}/${the_module}doc"
       COMMAND ${ANT_EXECUTABLE} -noinput -k javadoc
@@ -142,4 +134,4 @@ if(BUILD_DOCS AND BUILD_JAVADOC)
   add_dependencies(opencv_docs ${the_module}doc)
 else()
   unset(CMAKE_DOXYGEN_JAVADOC_NODE CACHE)
-endif()
+endif()