fix missing addref() in ocl::Context::create(str)

backported fix from PR 18907

.github/workflows/arm64-build-checks.yml

@@ -1,6 +1,6 @@
 name: arm64 build checks
 
-on: workflow_dispatch
+on: [pull_request]
 
 jobs:
   build:

3rdparty/openexr/CMakeLists.txt

@@ -109,7 +109,6 @@ ocv_warnings_disable(CMAKE_CXX_FLAGS -Wshadow -Wunused -Wsign-compare -Wundef -W
                                      -Wmissing-prototypes  # gcc/clang
                                      -Wreorder
                                      -Wunused-result
-                                     -Wimplicit-const-int-float-conversion  # clang
 )
 if(CV_GCC AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 8.0)
   ocv_warnings_disable(CMAKE_CXX_FLAGS -Wclass-memaccess)

3rdparty/openjpeg/CMakeLists.txt

@@ -11,10 +11,6 @@ set(OPENJPEG_LIBRARY_NAME libopenjp2)
 
 project(openjpeg C)
 
-ocv_warnings_disable(CMAKE_C_FLAGS
-    -Wimplicit-const-int-float-conversion  # clang
-)
-
 #-----------------------------------------------------------------------------
 # OPENJPEG version number, useful for packaging and doxygen doc:
 set(OPENJPEG_VERSION_MAJOR 2)

3rdparty/protobuf/CMakeLists.txt

@@ -153,11 +153,6 @@ set_target_properties(libprotobuf
     ARCHIVE_OUTPUT_DIRECTORY ${3P_LIBRARY_OUTPUT_PATH}
     )
 
-if(ANDROID)
-  # https://github.com/opencv/opencv/issues/17282
-  target_link_libraries(libprotobuf INTERFACE "-landroid" "-llog")
-endif()
-
 get_protobuf_version(Protobuf_VERSION "${PROTOBUF_ROOT}/src")
 set(Protobuf_VERSION ${Protobuf_VERSION} CACHE INTERNAL "" FORCE)
 

CMakeLists.txt

@@ -17,7 +17,9 @@ endif()
 
 include(cmake/OpenCVMinDepVersions.cmake)
 
-if(CMAKE_SYSTEM_NAME MATCHES WindowsPhone OR CMAKE_SYSTEM_NAME MATCHES WindowsStore)
+if(CMAKE_GENERATOR MATCHES Xcode AND XCODE_VERSION VERSION_GREATER 4.3)
+  cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
+elseif(CMAKE_SYSTEM_NAME MATCHES WindowsPhone OR CMAKE_SYSTEM_NAME MATCHES WindowsStore)
   cmake_minimum_required(VERSION 3.1 FATAL_ERROR)
   #Required to resolve linker error issues due to incompatibility with CMake v3.0+ policies.
   #CMake fails to find _fseeko() which leads to subsequent linker error.
@@ -486,7 +488,7 @@ OCV_OPTION(OPENCV_ENABLE_MEMORY_SANITIZER "Better support for memory/address san
 OCV_OPTION(ENABLE_OMIT_FRAME_POINTER  "Enable -fomit-frame-pointer for GCC"                      ON   IF CV_GCC )
 OCV_OPTION(ENABLE_POWERPC             "Enable PowerPC for GCC"                                   ON   IF (CV_GCC AND CMAKE_SYSTEM_PROCESSOR MATCHES powerpc.*) )
 OCV_OPTION(ENABLE_FAST_MATH           "Enable compiler options for fast math optimizations on FP computations (not recommended)" OFF)
-if(NOT IOS AND (NOT ANDROID OR OPENCV_ANDROID_USE_LEGACY_FLAGS) AND CMAKE_CROSSCOMPILING)  # Use CPU_BASELINE instead
+if(NOT IOS AND (NOT ANDROID OR OPENCV_ANDROID_USE_LEGACY_FLAGS))  # Use CPU_BASELINE instead
 OCV_OPTION(ENABLE_NEON                "Enable NEON instructions"                                 (NEON OR ANDROID_ARM_NEON OR AARCH64) IF (CV_GCC OR CV_CLANG) AND (ARM OR AARCH64 OR IOS) )
 OCV_OPTION(ENABLE_VFPV3               "Enable VFPv3-D32 instructions"                            OFF  IF (CV_GCC OR CV_CLANG) AND (ARM OR AARCH64 OR IOS) )
 endif()

cmake/FindONNX.cmake

@@ -4,7 +4,7 @@ set(ONNXRT_ROOT_DIR "" CACHE PATH "ONNX Runtime install directory")
 
 # For now, check the old name ORT_INSTALL_DIR
 if(ORT_INSTALL_DIR AND NOT ONNXRT_ROOT_DIR)
-  set(ONNXRT_ROOT_DIR ${ORT_INSTALL_DIR})
+  set(ONNXRT_ROOT_DIR ORT_INSTALL_DIR)
 endif()
 
 if(ONNXRT_ROOT_DIR)

cmake/OpenCVCompilerOptions.cmake

@@ -122,6 +122,7 @@ if(CV_GCC OR CV_CLANG)
   endif()
   add_extra_compiler_option(-Wsign-promo)
   add_extra_compiler_option(-Wuninitialized)
+  add_extra_compiler_option(-Winit-self)
   if(CV_GCC AND (CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 6.0) AND (CMAKE_CXX_COMPILER_VERSION VERSION_LESS 7.0))
     add_extra_compiler_option(-Wno-psabi)
   endif()
@@ -152,7 +153,7 @@ if(CV_GCC OR CV_CLANG)
     if(CV_GCC AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 5.0)
       add_extra_compiler_option(-Wno-missing-field-initializers)  # GCC 4.x emits warnings about {}, fixed in GCC 5+
     endif()
-    if(CV_CLANG AND NOT CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang" AND NOT CMAKE_CXX_COMPILER_VERSION VERSION_LESS 10.0)
+    if(CV_CLANG AND NOT CMAKE_CXX_COMPILER_VERSION VERSION_LESS 10.0)
       add_extra_compiler_option(-Wno-deprecated-enum-enum-conversion)
       add_extra_compiler_option(-Wno-deprecated-anon-enum-enum-conversion)
     endif()

cmake/OpenCVFindLibsGrfmt.cmake

@@ -6,7 +6,6 @@
 if(BUILD_ZLIB)
   ocv_clear_vars(ZLIB_FOUND)
 else()
-  ocv_clear_internal_cache_vars(ZLIB_LIBRARY ZLIB_INCLUDE_DIR)
   find_package(ZLIB "${MIN_VER_ZLIB}")
   if(ZLIB_FOUND AND ANDROID)
     if(ZLIB_LIBRARIES MATCHES "/usr/(lib|lib32|lib64)/libz.so$")
@@ -16,12 +15,11 @@ else()
 endif()
 
 if(NOT ZLIB_FOUND)
-  ocv_clear_vars(ZLIB_LIBRARY ZLIB_LIBRARIES ZLIB_INCLUDE_DIR)
+  ocv_clear_vars(ZLIB_LIBRARY ZLIB_LIBRARIES ZLIB_INCLUDE_DIRS)
 
-  set(ZLIB_LIBRARY zlib CACHE INTERNAL "")
+  set(ZLIB_LIBRARY zlib)
   add_subdirectory("${OpenCV_SOURCE_DIR}/3rdparty/zlib")
-  set(ZLIB_INCLUDE_DIR "${${ZLIB_LIBRARY}_SOURCE_DIR}" "${${ZLIB_LIBRARY}_BINARY_DIR}" CACHE INTERNAL "")
-  set(ZLIB_INCLUDE_DIRS ${ZLIB_INCLUDE_DIR})
+  set(ZLIB_INCLUDE_DIRS "${${ZLIB_LIBRARY}_SOURCE_DIR}" "${${ZLIB_LIBRARY}_BINARY_DIR}")
   set(ZLIB_LIBRARIES ${ZLIB_LIBRARY})
 
   ocv_parse_header2(ZLIB "${${ZLIB_LIBRARY}_SOURCE_DIR}/zlib.h" ZLIB_VERSION)
@@ -32,25 +30,23 @@ if(WITH_JPEG)
   if(BUILD_JPEG)
     ocv_clear_vars(JPEG_FOUND)
   else()
-    ocv_clear_internal_cache_vars(JPEG_LIBRARY JPEG_INCLUDE_DIR)
     include(FindJPEG)
   endif()
 
   if(NOT JPEG_FOUND)
-    ocv_clear_vars(JPEG_LIBRARY JPEG_INCLUDE_DIR)
+    ocv_clear_vars(JPEG_LIBRARY JPEG_LIBRARIES JPEG_INCLUDE_DIR)
 
     if(NOT BUILD_JPEG_TURBO_DISABLE)
-      set(JPEG_LIBRARY libjpeg-turbo CACHE INTERNAL "")
+      set(JPEG_LIBRARY libjpeg-turbo)
       set(JPEG_LIBRARIES ${JPEG_LIBRARY})
       add_subdirectory("${OpenCV_SOURCE_DIR}/3rdparty/libjpeg-turbo")
-      set(JPEG_INCLUDE_DIR "${${JPEG_LIBRARY}_SOURCE_DIR}/src" CACHE INTERNAL "")
+      set(JPEG_INCLUDE_DIR "${${JPEG_LIBRARY}_SOURCE_DIR}/src")
     else()
-      set(JPEG_LIBRARY libjpeg CACHE INTERNAL "")
+      set(JPEG_LIBRARY libjpeg)
       set(JPEG_LIBRARIES ${JPEG_LIBRARY})
       add_subdirectory("${OpenCV_SOURCE_DIR}/3rdparty/libjpeg")
-      set(JPEG_INCLUDE_DIR "${${JPEG_LIBRARY}_SOURCE_DIR}" CACHE INTERNAL "")
+      set(JPEG_INCLUDE_DIR "${${JPEG_LIBRARY}_SOURCE_DIR}")
     endif()
-    set(JPEG_INCLUDE_DIRS "${JPEG_INCLUDE_DIR}")
   endif()
 
   macro(ocv_detect_jpeg_version header_file)
@@ -78,7 +74,6 @@ if(WITH_TIFF)
   if(BUILD_TIFF)
     ocv_clear_vars(TIFF_FOUND)
   else()
-    ocv_clear_internal_cache_vars(TIFF_LIBRARY TIFF_INCLUDE_DIR)
     include(FindTIFF)
     if(TIFF_FOUND)
       ocv_parse_header("${TIFF_INCLUDE_DIR}/tiff.h" TIFF_VERSION_LINES TIFF_VERSION_CLASSIC TIFF_VERSION_BIG TIFF_VERSION TIFF_BIGTIFF_VERSION)
@@ -88,10 +83,10 @@ if(WITH_TIFF)
   if(NOT TIFF_FOUND)
     ocv_clear_vars(TIFF_LIBRARY TIFF_LIBRARIES TIFF_INCLUDE_DIR)
 
-    set(TIFF_LIBRARY libtiff CACHE INTERNAL "")
+    set(TIFF_LIBRARY libtiff)
     set(TIFF_LIBRARIES ${TIFF_LIBRARY})
     add_subdirectory("${OpenCV_SOURCE_DIR}/3rdparty/libtiff")
-    set(TIFF_INCLUDE_DIR "${${TIFF_LIBRARY}_SOURCE_DIR}" "${${TIFF_LIBRARY}_BINARY_DIR}" CACHE INTERNAL "")
+    set(TIFF_INCLUDE_DIR "${${TIFF_LIBRARY}_SOURCE_DIR}" "${${TIFF_LIBRARY}_BINARY_DIR}")
     ocv_parse_header("${${TIFF_LIBRARY}_SOURCE_DIR}/tiff.h" TIFF_VERSION_LINES TIFF_VERSION_CLASSIC TIFF_VERSION_BIG TIFF_VERSION TIFF_BIGTIFF_VERSION)
   endif()
 
@@ -122,7 +117,6 @@ if(WITH_WEBP)
   if(BUILD_WEBP)
     ocv_clear_vars(WEBP_FOUND WEBP_LIBRARY WEBP_LIBRARIES WEBP_INCLUDE_DIR)
   else()
-    ocv_clear_internal_cache_vars(WEBP_LIBRARY WEBP_INCLUDE_DIR)
     include(cmake/OpenCVFindWebP.cmake)
     if(WEBP_FOUND)
       set(HAVE_WEBP 1)
@@ -134,12 +128,12 @@ endif()
 if(WITH_WEBP AND NOT WEBP_FOUND
     AND (NOT ANDROID OR HAVE_CPUFEATURES)
 )
-  ocv_clear_vars(WEBP_LIBRARY WEBP_INCLUDE_DIR)
-  set(WEBP_LIBRARY libwebp CACHE INTERNAL "")
+
+  set(WEBP_LIBRARY libwebp)
   set(WEBP_LIBRARIES ${WEBP_LIBRARY})
 
   add_subdirectory("${OpenCV_SOURCE_DIR}/3rdparty/libwebp")
-  set(WEBP_INCLUDE_DIR "${${WEBP_LIBRARY}_SOURCE_DIR}/src" CACHE INTERNAL "")
+  set(WEBP_INCLUDE_DIR "${${WEBP_LIBRARY}_SOURCE_DIR}/src")
   set(HAVE_WEBP 1)
 endif()
 
@@ -198,10 +192,10 @@ if(WITH_JASPER AND NOT HAVE_OPENJPEG)
   if(NOT JASPER_FOUND)
     ocv_clear_vars(JASPER_LIBRARY JASPER_LIBRARIES JASPER_INCLUDE_DIR)
 
-    set(JASPER_LIBRARY libjasper CACHE INTERNAL "")
+    set(JASPER_LIBRARY libjasper)
     set(JASPER_LIBRARIES ${JASPER_LIBRARY})
     add_subdirectory("${OpenCV_SOURCE_DIR}/3rdparty/libjasper")
-    set(JASPER_INCLUDE_DIR "${${JASPER_LIBRARY}_SOURCE_DIR}" CACHE INTERNAL "")
+    set(JASPER_INCLUDE_DIR "${${JASPER_LIBRARY}_SOURCE_DIR}")
   endif()
 
   set(HAVE_JASPER YES)
@@ -216,7 +210,6 @@ if(WITH_PNG)
   if(BUILD_PNG)
     ocv_clear_vars(PNG_FOUND)
   else()
-    ocv_clear_internal_cache_vars(PNG_LIBRARY PNG_INCLUDE_DIR)
     include(FindPNG)
     if(PNG_FOUND)
       include(CheckIncludeFile)
@@ -232,10 +225,10 @@ if(WITH_PNG)
   if(NOT PNG_FOUND)
     ocv_clear_vars(PNG_LIBRARY PNG_LIBRARIES PNG_INCLUDE_DIR PNG_PNG_INCLUDE_DIR HAVE_LIBPNG_PNG_H PNG_DEFINITIONS)
 
-    set(PNG_LIBRARY libpng CACHE INTERNAL "")
+    set(PNG_LIBRARY libpng)
     set(PNG_LIBRARIES ${PNG_LIBRARY})
     add_subdirectory("${OpenCV_SOURCE_DIR}/3rdparty/libpng")
-    set(PNG_INCLUDE_DIR "${${PNG_LIBRARY}_SOURCE_DIR}" CACHE INTERNAL "")
+    set(PNG_INCLUDE_DIR "${${PNG_LIBRARY}_SOURCE_DIR}")
     set(PNG_DEFINITIONS "")
     ocv_parse_header("${PNG_INCLUDE_DIR}/png.h" PNG_VERSION_LINES PNG_LIBPNG_VER_MAJOR PNG_LIBPNG_VER_MINOR PNG_LIBPNG_VER_RELEASE)
   endif()
@@ -248,7 +241,6 @@ endif()
 if(WITH_OPENEXR)
   ocv_clear_vars(HAVE_OPENEXR)
   if(NOT BUILD_OPENEXR)
-    ocv_clear_internal_cache_vars(OPENEXR_INCLUDE_PATHS OPENEXR_LIBRARIES OPENEXR_ILMIMF_LIBRARY OPENEXR_VERSION)
     include("${OpenCV_SOURCE_DIR}/cmake/OpenCVFindOpenEXR.cmake")
   endif()
 
@@ -278,7 +270,7 @@ if(WITH_GDAL)
     endif()
 endif()
 
-if(WITH_GDCM)
+if (WITH_GDCM)
   find_package(GDCM QUIET)
   if(NOT GDCM_FOUND)
     set(HAVE_GDCM NO)

cmake/OpenCVFindLibsPerf.cmake

@@ -51,10 +51,7 @@ endif(WITH_CUDA)
 
 # --- Eigen ---
 if(WITH_EIGEN AND NOT HAVE_EIGEN)
-  if((OPENCV_FORCE_EIGEN_FIND_PACKAGE_CONFIG
-      OR NOT (CMAKE_VERSION VERSION_LESS "3.0.0")  # Eigen3Targets.cmake required CMake 3.0.0+
-      ) AND NOT OPENCV_SKIP_EIGEN_FIND_PACKAGE_CONFIG
-  )
+  if(NOT OPENCV_SKIP_EIGEN_FIND_PACKAGE_CONFIG)
     find_package(Eigen3 CONFIG QUIET)  # Ceres 2.0.0 CMake scripts doesn't work with CMake's FindEigen3.cmake module (due to missing EIGEN3_VERSION_STRING)
   endif()
   if(NOT Eigen3_FOUND)

cmake/OpenCVFindMKL.cmake

@@ -3,14 +3,7 @@
 # installation/package
 #
 # Parameters:
-# MKL_ROOT_DIR / ENV{MKLROOT}
-# MKL_INCLUDE_DIR
-# MKL_LIBRARIES
-# MKL_USE_SINGLE_DYNAMIC_LIBRARY - use single dynamic library mkl_rt.lib / libmkl_rt.so
-# MKL_WITH_TBB / MKL_WITH_OPENMP
-#
-# Extra:
-# MKL_LIB_FIND_PATHS
+# MKL_WITH_TBB
 #
 # On return this will define:
 #
@@ -20,6 +13,12 @@
 # MKL_LIBRARIES     - MKL libraries that are used by OpenCV
 #
 
+macro (mkl_find_lib VAR NAME DIRS)
+    find_path(${VAR} ${NAME} ${DIRS} NO_DEFAULT_PATH)
+    set(${VAR} ${${VAR}}/${NAME})
+    unset(${VAR} CACHE)
+endmacro()
+
 macro(mkl_fail)
     set(HAVE_MKL OFF)
     set(MKL_ROOT_DIR "${MKL_ROOT_DIR}" CACHE PATH "Path to MKL directory")
@@ -40,50 +39,43 @@ macro(get_mkl_version VERSION_FILE)
     set(MKL_VERSION_STR "${MKL_VERSION_MAJOR}.${MKL_VERSION_MINOR}.${MKL_VERSION_UPDATE}" CACHE STRING "MKL version" FORCE)
 endmacro()
 
-OCV_OPTION(MKL_USE_SINGLE_DYNAMIC_LIBRARY "Use MKL Single Dynamic Library thorugh mkl_rt.lib / libmkl_rt.so" OFF)
-OCV_OPTION(MKL_WITH_TBB "Use MKL with TBB multithreading" OFF)#ON IF WITH_TBB)
-OCV_OPTION(MKL_WITH_OPENMP "Use MKL with OpenMP multithreading" OFF)#ON IF WITH_OPENMP)
 
-if(NOT MKL_ROOT_DIR AND DEFINED MKL_INCLUDE_DIR AND EXISTS "${MKL_INCLUDE_DIR}/mkl.h")
-  file(TO_CMAKE_PATH "${MKL_INCLUDE_DIR}" MKL_INCLUDE_DIR)
-  get_filename_component(MKL_ROOT_DIR "${MKL_INCLUDE_DIR}/.." ABSOLUTE)
-endif()
-if(NOT MKL_ROOT_DIR)
-  file(TO_CMAKE_PATH "${MKL_ROOT_DIR}" mkl_root_paths)
-  if(DEFINED ENV{MKLROOT})
-      file(TO_CMAKE_PATH "$ENV{MKLROOT}" path)
-      list(APPEND mkl_root_paths "${path}")
-  endif()
-
-  if(WITH_MKL AND NOT mkl_root_paths)
-    if(WIN32)
-      set(ProgramFilesx86 "ProgramFiles(x86)")
-      file(TO_CMAKE_PATH "$ENV{${ProgramFilesx86}}" path)
-      list(APPEND mkl_root_paths ${path}/IntelSWTools/compilers_and_libraries/windows/mkl)
-    endif()
-    if(UNIX)
-      list(APPEND mkl_root_paths "/opt/intel/mkl")
-    endif()
-  endif()
-
-  find_path(MKL_ROOT_DIR include/mkl.h PATHS ${mkl_root_paths})
+if(NOT DEFINED MKL_USE_MULTITHREAD)
+    OCV_OPTION(MKL_WITH_TBB "Use MKL with TBB multithreading" OFF)#ON IF WITH_TBB)
+    OCV_OPTION(MKL_WITH_OPENMP "Use MKL with OpenMP multithreading" OFF)#ON IF WITH_OPENMP)
 endif()
 
+#check current MKL_ROOT_DIR
 if(NOT MKL_ROOT_DIR OR NOT EXISTS "${MKL_ROOT_DIR}/include/mkl.h")
-  mkl_fail()
+    set(mkl_root_paths "${MKL_ROOT_DIR}")
+    if(DEFINED ENV{MKLROOT})
+        list(APPEND mkl_root_paths "$ENV{MKLROOT}")
+    endif()
+
+    if(WITH_MKL AND NOT mkl_root_paths)
+      if(WIN32)
+        set(ProgramFilesx86 "ProgramFiles(x86)")
+        list(APPEND mkl_root_paths $ENV{${ProgramFilesx86}}/IntelSWTools/compilers_and_libraries/windows/mkl)
+      endif()
+      if(UNIX)
+        list(APPEND mkl_root_paths "/opt/intel/mkl")
+      endif()
+    endif()
+
+    find_path(MKL_ROOT_DIR include/mkl.h PATHS ${mkl_root_paths})
 endif()
 
-set(MKL_INCLUDE_DIR "${MKL_ROOT_DIR}/include" CACHE PATH "Path to MKL include directory")
+set(MKL_INCLUDE_DIRS "${MKL_ROOT_DIR}/include" CACHE PATH "Path to MKL include directory")
 
 if(NOT MKL_ROOT_DIR
     OR NOT EXISTS "${MKL_ROOT_DIR}"
-    OR NOT EXISTS "${MKL_INCLUDE_DIR}"
-    OR NOT EXISTS "${MKL_INCLUDE_DIR}/mkl_version.h"
+    OR NOT EXISTS "${MKL_INCLUDE_DIRS}"
+    OR NOT EXISTS "${MKL_INCLUDE_DIRS}/mkl_version.h"
 )
-  mkl_fail()
+    mkl_fail()
 endif()
 
-get_mkl_version(${MKL_INCLUDE_DIR}/mkl_version.h)
+get_mkl_version(${MKL_INCLUDE_DIRS}/mkl_version.h)
 
 #determine arch
 if(CMAKE_CXX_SIZEOF_DATA_PTR EQUAL 8)
@@ -103,66 +95,52 @@ else()
     set(MKL_ARCH_SUFFIX "c")
 endif()
 
-set(mkl_lib_find_paths ${MKL_LIB_FIND_PATHS} ${MKL_ROOT_DIR}/lib)
-foreach(MKL_ARCH ${MKL_ARCH_LIST})
-  list(APPEND mkl_lib_find_paths
-    ${MKL_ROOT_DIR}/lib/${MKL_ARCH}
-    ${MKL_ROOT_DIR}/${MKL_ARCH}
-  )
-endforeach()
+if(MKL_VERSION_STR VERSION_GREATER "11.3.0" OR MKL_VERSION_STR VERSION_EQUAL "11.3.0")
+    set(mkl_lib_find_paths
+        ${MKL_ROOT_DIR}/lib)
+    foreach(MKL_ARCH ${MKL_ARCH_LIST})
+      list(APPEND mkl_lib_find_paths
+        ${MKL_ROOT_DIR}/lib/${MKL_ARCH}
+        ${MKL_ROOT_DIR}/../tbb/lib/${MKL_ARCH}
+        ${MKL_ROOT_DIR}/${MKL_ARCH})
+    endforeach()
 
-if(MKL_USE_SINGLE_DYNAMIC_LIBRARY AND NOT (MKL_VERSION_STR VERSION_LESS "10.3.0"))
+    set(mkl_lib_list "mkl_intel_${MKL_ARCH_SUFFIX}")
 
-  # https://software.intel.com/content/www/us/en/develop/articles/a-new-linking-model-single-dynamic-library-mkl_rt-since-intel-mkl-103.html
-  set(mkl_lib_list "mkl_rt")
-
-elseif(NOT (MKL_VERSION_STR VERSION_LESS "11.3.0"))
-
-  foreach(MKL_ARCH ${MKL_ARCH_LIST})
-    list(APPEND mkl_lib_find_paths
-      ${MKL_ROOT_DIR}/../tbb/lib/${MKL_ARCH}
-    )
-  endforeach()
-
-  set(mkl_lib_list "mkl_intel_${MKL_ARCH_SUFFIX}")
-
-  if(MKL_WITH_TBB)
-    list(APPEND mkl_lib_list mkl_tbb_thread tbb)
-  elseif(MKL_WITH_OPENMP)
-    if(MSVC)
-      list(APPEND mkl_lib_list mkl_intel_thread libiomp5md)
+    if(MKL_WITH_TBB)
+        list(APPEND mkl_lib_list mkl_tbb_thread tbb)
+    elseif(MKL_WITH_OPENMP)
+        if(MSVC)
+            list(APPEND mkl_lib_list mkl_intel_thread libiomp5md)
+        else()
+            list(APPEND mkl_lib_list mkl_gnu_thread)
+        endif()
     else()
-      list(APPEND mkl_lib_list mkl_gnu_thread)
+        list(APPEND mkl_lib_list mkl_sequential)
     endif()
-  else()
-    list(APPEND mkl_lib_list mkl_sequential)
-  endif()
 
-  list(APPEND mkl_lib_list mkl_core)
+    list(APPEND mkl_lib_list mkl_core)
 else()
-  message(STATUS "MKL version ${MKL_VERSION_STR} is not supported")
-  mkl_fail()
+    message(STATUS "MKL version ${MKL_VERSION_STR} is not supported")
+    mkl_fail()
 endif()
 
-if(NOT MKL_LIBRARIES)
-  set(MKL_LIBRARIES "")
-  foreach(lib ${mkl_lib_list})
-    set(lib_var_name MKL_LIBRARY_${lib})
-    find_library(${lib_var_name} NAMES ${lib} ${lib}_dll HINTS ${mkl_lib_find_paths})
-    mark_as_advanced(${lib_var_name})
-    if(NOT ${lib_var_name})
-      mkl_fail()
+set(MKL_LIBRARIES "")
+foreach(lib ${mkl_lib_list})
+    find_library(${lib} NAMES ${lib} ${lib}_dll HINTS ${mkl_lib_find_paths})
+    mark_as_advanced(${lib})
+    if(NOT ${lib})
+        mkl_fail()
     endif()
-    list(APPEND MKL_LIBRARIES ${${lib_var_name}})
-  endforeach()
-endif()
+    list(APPEND MKL_LIBRARIES ${${lib}})
+endforeach()
 
 message(STATUS "Found MKL ${MKL_VERSION_STR} at: ${MKL_ROOT_DIR}")
 set(HAVE_MKL ON)
 set(MKL_ROOT_DIR "${MKL_ROOT_DIR}" CACHE PATH "Path to MKL directory")
-set(MKL_INCLUDE_DIRS "${MKL_INCLUDE_DIR}")
-set(MKL_LIBRARIES "${MKL_LIBRARIES}")
-if(UNIX AND NOT MKL_USE_SINGLE_DYNAMIC_LIBRARY AND NOT MKL_LIBRARIES_DONT_HACK)
+set(MKL_INCLUDE_DIRS "${MKL_INCLUDE_DIRS}" CACHE PATH "Path to MKL include directory")
+set(MKL_LIBRARIES "${MKL_LIBRARIES}" CACHE STRING "MKL libraries")
+if(UNIX AND NOT MKL_LIBRARIES_DONT_HACK)
     #it's ugly but helps to avoid cyclic lib problem
     set(MKL_LIBRARIES ${MKL_LIBRARIES} ${MKL_LIBRARIES} ${MKL_LIBRARIES} "-lpthread" "-lm" "-ldl")
 endif()

cmake/OpenCVGenInfoPlist.cmake

@@ -2,11 +2,7 @@ set(OPENCV_APPLE_BUNDLE_NAME "OpenCV")
 set(OPENCV_APPLE_BUNDLE_ID "org.opencv")
 
 if(IOS)
-  if(MAC_CATALYST)
-    # Copy the iOS plist over to the OSX directory if building iOS library for Catalyst
-    configure_file("${OpenCV_SOURCE_DIR}/platforms/ios/Info.plist.in"
-                  "${CMAKE_BINARY_DIR}/osx/Info.plist")
-  elseif(APPLE_FRAMEWORK AND DYNAMIC_PLIST)
+  if (APPLE_FRAMEWORK AND DYNAMIC_PLIST)
     configure_file("${OpenCV_SOURCE_DIR}/platforms/ios/Info.Dynamic.plist.in"
                    "${CMAKE_BINARY_DIR}/ios/Info.plist")
   else()

cmake/OpenCVModule.cmake

@@ -98,6 +98,15 @@ macro(ocv_add_dependencies full_modname)
   endforeach()
   unset(__depsvar)
 
+  # hack for python
+  set(__python_idx)
+  list(FIND OPENCV_MODULE_${full_modname}_WRAPPERS "python" __python_idx)
+  if (NOT __python_idx EQUAL -1)
+    list(REMOVE_ITEM OPENCV_MODULE_${full_modname}_WRAPPERS "python")
+    list(APPEND OPENCV_MODULE_${full_modname}_WRAPPERS "python_bindings_generator" "python2" "python3")
+  endif()
+  unset(__python_idx)
+
   ocv_list_unique(OPENCV_MODULE_${full_modname}_REQ_DEPS)
   ocv_list_unique(OPENCV_MODULE_${full_modname}_OPT_DEPS)
   ocv_list_unique(OPENCV_MODULE_${full_modname}_PRIVATE_REQ_DEPS)
@@ -201,6 +210,11 @@ macro(ocv_add_module _name)
       set(OPENCV_MODULES_DISABLED_USER ${OPENCV_MODULES_DISABLED_USER} "${the_module}" CACHE INTERNAL "List of OpenCV modules explicitly disabled by user")
     endif()
 
+    # add reverse wrapper dependencies
+    foreach (wrapper ${OPENCV_MODULE_${the_module}_WRAPPERS})
+      ocv_add_dependencies(opencv_${wrapper} OPTIONAL ${the_module})
+    endforeach()
+
     # stop processing of current file
     ocv_cmake_hook(POST_ADD_MODULE)
     ocv_cmake_hook(POST_ADD_MODULE_${the_module})
@@ -487,21 +501,6 @@ function(__ocv_resolve_dependencies)
     endforeach()
   endif()
 
-  # add reverse wrapper dependencies (BINDINDS)
-  foreach(the_module ${OPENCV_MODULES_BUILD})
-    foreach (wrapper ${OPENCV_MODULE_${the_module}_WRAPPERS})
-      if(wrapper STREQUAL "python")  # hack for python (BINDINDS)
-        ocv_add_dependencies(opencv_python2 OPTIONAL ${the_module})
-        ocv_add_dependencies(opencv_python3 OPTIONAL ${the_module})
-      else()
-        ocv_add_dependencies(opencv_${wrapper} OPTIONAL ${the_module})
-      endif()
-      if(DEFINED OPENCV_MODULE_opencv_${wrapper}_bindings_generator_CLASS)
-        ocv_add_dependencies(opencv_${wrapper}_bindings_generator OPTIONAL ${the_module})
-      endif()
-    endforeach()
-  endforeach()
-
   # disable MODULES with unresolved dependencies
   set(has_changes ON)
   while(has_changes)

cmake/OpenCVUtils.cmake

@@ -8,20 +8,7 @@ include(CMakeParseArguments)
 function(ocv_cmake_dump_vars)
   set(OPENCV_SUPPRESS_DEPRECATIONS 1)  # suppress deprecation warnings from variable_watch() guards
   get_cmake_property(__variableNames VARIABLES)
-  cmake_parse_arguments(DUMP "FORCE" "TOFILE" "" ${ARGN})
-
-  # avoid generation of excessive logs with "--trace" or "--trace-expand" parameters
-  # Note: `-DCMAKE_TRACE_MODE=1` should be passed to CMake through command line. It is not a CMake buildin variable for now (2020-12)
-  #       Use `cmake . -UCMAKE_TRACE_MODE` to remove this variable from cache
-  if(CMAKE_TRACE_MODE AND NOT DUMP_FORCE)
-    if(DUMP_TOFILE)
-      file(WRITE ${CMAKE_BINARY_DIR}/${DUMP_TOFILE} "Skipped due to enabled CMAKE_TRACE_MODE")
-    else()
-      message(AUTHOR_WARNING "ocv_cmake_dump_vars() is skipped due to enabled CMAKE_TRACE_MODE")
-    endif()
-    return()
-  endif()
-
+  cmake_parse_arguments(DUMP "" "TOFILE" "" ${ARGN})
   set(regex "${DUMP_UNPARSED_ARGUMENTS}")
   string(TOLOWER "${regex}" regex_lower)
   set(__VARS "")
@@ -413,24 +400,6 @@ macro(ocv_clear_vars)
   endforeach()
 endmacro()
 
-
-# Clears passed variables with INTERNAL type from CMake cache
-macro(ocv_clear_internal_cache_vars)
-  foreach(_var ${ARGN})
-    get_property(_propertySet CACHE ${_var} PROPERTY TYPE SET)
-    if(_propertySet)
-      get_property(_type CACHE ${_var} PROPERTY TYPE)
-      if(_type STREQUAL "INTERNAL")
-        message("Cleaning INTERNAL cached variable: ${_var}")
-        unset(${_var} CACHE)
-      endif()
-    endif()
-  endforeach()
-  unset(_propertySet)
-  unset(_type)
-endmacro()
-
-
 set(OCV_COMPILER_FAIL_REGEX
     "argument .* is not valid"                  # GCC 9+ (including support of unicode quotes)
     "command[- ]line option .* is valid for .* but not for C\\+\\+" # GNU
@@ -1543,16 +1512,10 @@ function(ocv_add_library target)
 
     set(CMAKE_SHARED_LIBRARY_RUNTIME_C_FLAG 1)
 
-    if(IOS AND NOT MAC_CATALYST)
-      set(OPENCV_APPLE_INFO_PLIST "${CMAKE_BINARY_DIR}/ios/Info.plist")
-    else()
-      set(OPENCV_APPLE_INFO_PLIST "${CMAKE_BINARY_DIR}/osx/Info.plist")
-    endif()
-
     set_target_properties(${target} PROPERTIES
       FRAMEWORK TRUE
       MACOSX_FRAMEWORK_IDENTIFIER org.opencv
-      MACOSX_FRAMEWORK_INFO_PLIST ${OPENCV_APPLE_INFO_PLIST}
+      MACOSX_FRAMEWORK_INFO_PLIST ${CMAKE_BINARY_DIR}/ios/Info.plist
       # "current version" in semantic format in Mach-O binary file
       VERSION ${OPENCV_LIBVERSION}
       # "compatibility version" in semantic format in Mach-O binary file

cmake/platforms/OpenCV-Emscripten.cmake

@@ -1 +0,0 @@
-set(OPENCV_SKIP_LINK_AS_NEEDED 1)

cmake/templates/opencv_abi.xml.in

@@ -27,7 +27,6 @@
     opencv2/core/cuda*
     opencv2/core/opencl*
     opencv2/core/private*
-    opencv2/core/quaternion*
     opencv/cxeigen.hpp
     opencv2/core/eigen.hpp
     opencv2/flann/hdf5.h

doc/Doxyfile.in

@@ -39,6 +39,7 @@ ALIASES               += end_toggle="@htmlonly[block] </div> @endhtmlonly"
 ALIASES               += prev_tutorial{1}="**Prev  Tutorial:** \ref \1 \n"
 ALIASES               += next_tutorial{1}="**Next  Tutorial:** \ref \1 \n"
 ALIASES               += youtube{1}="@htmlonly[block]<div align='center'><iframe title='Video' width='560' height='349' src='https://www.youtube.com/embed/\1?rel=0' frameborder='0' align='middle' allowfullscreen></iframe></div>@endhtmlonly"
+TCL_SUBST              =
 OPTIMIZE_OUTPUT_FOR_C  = NO
 OPTIMIZE_OUTPUT_JAVA   = NO
 OPTIMIZE_FOR_FORTRAN   = NO

doc/js_tutorials/js_assets/js_dnn_example_helper.js

@@ -1,119 +0,0 @@
-getBlobFromImage = function(inputSize, mean, std, swapRB, image) {
-    let mat;
-    if (typeof(image) === 'string') {
-        mat = cv.imread(image);
-    } else {
-        mat = image;
-    }
-
-    let matC3 = new cv.Mat(mat.matSize[0], mat.matSize[1], cv.CV_8UC3);
-    cv.cvtColor(mat, matC3, cv.COLOR_RGBA2BGR);
-    let input = cv.blobFromImage(matC3, std, new cv.Size(inputSize[0], inputSize[1]),
-                                 new cv.Scalar(mean[0], mean[1], mean[2]), swapRB);
-
-    matC3.delete();
-    return input;
-}
-
-loadLables = async function(labelsUrl) {
-    let response = await fetch(labelsUrl);
-    let label = await response.text();
-    label = label.split('\n');
-    return label;
-}
-
-loadModel = async function(e) {
-    return new Promise((resolve) => {
-        let file = e.target.files[0];
-        let path = file.name;
-        let reader = new FileReader();
-        reader.readAsArrayBuffer(file);
-        reader.onload = function(ev) {
-            if (reader.readyState === 2) {
-                let buffer = reader.result;
-                let data = new Uint8Array(buffer);
-                cv.FS_createDataFile('/', path, data, true, false, false);
-                resolve(path);
-            }
-        }
-    });
-}
-
-getTopClasses = function(probs, labels, topK = 3) {
-    probs = Array.from(probs);
-    let indexes = probs.map((prob, index) => [prob, index]);
-    let sorted = indexes.sort((a, b) => {
-        if (a[0] === b[0]) {return 0;}
-        return a[0] < b[0] ? -1 : 1;
-    });
-    sorted.reverse();
-    let classes = [];
-    for (let i = 0; i < topK; ++i) {
-        let prob = sorted[i][0];
-        let index = sorted[i][1];
-        let c = {
-            label: labels[index],
-            prob: (prob * 100).toFixed(2)
-        }
-        classes.push(c);
-    }
-    return classes;
-}
-
-loadImageToCanvas = function(e, canvasId) {
-    let files = e.target.files;
-    let imgUrl = URL.createObjectURL(files[0]);
-    let canvas = document.getElementById(canvasId);
-    let ctx = canvas.getContext('2d');
-    let img = new Image();
-    img.crossOrigin = 'anonymous';
-    img.src = imgUrl;
-    img.onload = function() {
-        ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
-    };
-}
-
-drawInfoTable = async function(jsonUrl, divId) {
-    let response = await fetch(jsonUrl);
-    let json = await response.json();
-
-    let appendix = document.getElementById(divId);
-    for (key of Object.keys(json)) {
-        let h3 = document.createElement('h3');
-        h3.textContent = key + " model";
-        appendix.appendChild(h3);
-
-        let table = document.createElement('table');
-        let head_tr = document.createElement('tr');
-        for (head of Object.keys(json[key][0])) {
-            let th = document.createElement('th');
-            th.textContent = head;
-            th.style.border = "1px solid black";
-            head_tr.appendChild(th);
-        }
-        table.appendChild(head_tr)
-
-        for (model of json[key]) {
-            let tr = document.createElement('tr');
-            for (params of Object.keys(model)) {
-                let td = document.createElement('td');
-                td.style.border = "1px solid black";
-                if (params !== "modelUrl" && params !== "configUrl" && params !== "labelsUrl") {
-                    td.textContent = model[params];
-                    tr.appendChild(td);
-                } else {
-                    let a = document.createElement('a');
-                    let link = document.createTextNode('link');
-                    a.append(link);
-                    a.href = model[params];
-                    td.appendChild(a);
-                    tr.appendChild(td);
-                }
-            }
-            table.appendChild(tr);
-        }
-        table.style.width = "800px";
-        table.style.borderCollapse = "collapse";
-        appendix.appendChild(table);
-    }
-}

doc/js_tutorials/js_assets/js_image_classification.html

@@ -1,263 +0,0 @@
-<!DOCTYPE html>
-<html>
-
-<head>
-    <meta charset="utf-8">
-    <title>Image Classification Example</title>
-    <link href="js_example_style.css" rel="stylesheet" type="text/css" />
-</head>
-
-<body>
-<h2>Image Classification Example</h2>
-<p>
-    This tutorial shows you how to write an image classification example with OpenCV.js.<br>
-    To try the example you should click the <b>modelFile</b> button(and <b>configFile</b> button if needed) to upload inference model.
-    You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
-    Then You should change the parameters in the first code snippet according to the uploaded model.
-    Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
-</p>
-
-<div class="control"><button id="tryIt" disabled>Try it</button></div>
-<div>
-    <table cellpadding="0" cellspacing="0" width="0" border="0">
-        <tr>
-            <td>
-                <canvas id="canvasInput" width="400" height="400"></canvas>
-            </td>
-            <td>
-                <table style="visibility: hidden;" id="result">
-                    <thead>
-                        <tr>
-                            <th scope="col">#</th>
-                            <th scope="col" width=300>Label</th>
-                            <th scope="col">Probability</th>
-                        </tr>
-                    </thead>
-                    <tbody>
-                        <tr>
-                            <th scope="row">1</th>
-                            <td id="label0" align="center"></td>
-                            <td id="prob0" align="center"></td>
-                        </tr>
-                        <tr>
-                            <th scope="row">2</th>
-                            <td id="label1" align="center"></td>
-                            <td id="prob1" align="center"></td>
-                        </tr>
-                        <tr>
-                            <th scope="row">3</th>
-                            <td id="label2" align="center"></td>
-                            <td id="prob2" align="center"></td>
-                        </tr>
-                    </tbody>
-                </table>
-                <p id='status' align="left"></p>
-            </td>
-        </tr>
-        <tr>
-            <td>
-                <div class="caption">
-                    canvasInput <input type="file" id="fileInput" name="file" accept="image/*">
-                </div>
-            </td>
-            <td></td>
-        </tr>
-        <tr>
-            <td>
-                <div class="caption">
-                    modelFile <input type="file" id="modelFile">
-                </div>
-            </td>
-        </tr>
-        <tr>
-            <td>
-                <div class="caption">
-                    configFile <input type="file" id="configFile">
-                </div>
-            </td>
-        </tr>
-    </table>
-</div>
-
-<div>
-    <p class="err" id="errorMessage"></p>
-</div>
-
-<div>
-    <h3>Help function</h3>
-    <p>1.The parameters for model inference which you can modify to investigate more models.</p>
-    <textarea class="code" rows="13" cols="100" id="codeEditor" spellcheck="false"></textarea>
-    <p>2.Main loop in which will read the image from canvas and do inference once.</p>
-    <textarea class="code" rows="17" cols="100" id="codeEditor1" spellcheck="false"></textarea>
-    <p>3.Load labels from txt file and process it into an array.</p>
-    <textarea class="code" rows="7" cols="100" id="codeEditor2" spellcheck="false"></textarea>
-    <p>4.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
-    <textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
-    <p>5.Fetch model file and save to emscripten file system once click the input button.</p>
-    <textarea class="code" rows="17" cols="100" id="codeEditor4" spellcheck="false"></textarea>
-    <p>6.The post-processing, including softmax if needed and get the top classes from the output vector.</p>
-    <textarea class="code" rows="35" cols="100" id="codeEditor5" spellcheck="false"></textarea>
-</div>
-
-<div id="appendix">
-    <h2>Model Info:</h2>
-</div>
-
-<script src="utils.js" type="text/javascript"></script>
-<script src="js_dnn_example_helper.js" type="text/javascript"></script>
-
-<script id="codeSnippet" type="text/code-snippet">
-inputSize = [224,224];
-mean = [104, 117, 123];
-std = 1;
-swapRB = false;
-
-// record if need softmax function for post-processing
-needSoftmax = false;
-
-// url for label file, can from local or Internet
-labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/classification_classes_ILSVRC2012.txt";
-</script>
-
-<script id="codeSnippet1" type="text/code-snippet">
-main = async function() {
-    const labels = await loadLables(labelsUrl);
-    const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
-    let net = cv.readNet(configPath, modelPath);
-    net.setInput(input);
-    const start = performance.now();
-    const result = net.forward();
-    const time  = performance.now()-start;
-    const probs = softmax(result);
-    const classes = getTopClasses(probs, labels);
-
-    updateResult(classes, time);
-    input.delete();
-    net.delete();
-    result.delete();
-}
-</script>
-
-<script id="codeSnippet5" type="text/code-snippet">
-softmax = function(result) {
-    let arr = result.data32F;
-    if (needSoftmax) {
-        const maxNum = Math.max(...arr);
-        const expSum = arr.map((num) => Math.exp(num - maxNum)).reduce((a, b) => a + b);
-        return arr.map((value, index) => {
-            return Math.exp(value - maxNum) / expSum;
-        });
-    } else {
-        return arr;
-    }
-}
-</script>
-
-<script type="text/javascript">
-    let jsonUrl = "js_image_classification_model_info.json";
-    drawInfoTable(jsonUrl, 'appendix');
-
-    let utils = new Utils('errorMessage');
-    utils.loadCode('codeSnippet', 'codeEditor');
-    utils.loadCode('codeSnippet1', 'codeEditor1');
-
-    let loadLablesCode = 'loadLables = ' + loadLables.toString();
-    document.getElementById('codeEditor2').value = loadLablesCode;
-    let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
-    document.getElementById('codeEditor3').value = getBlobFromImageCode;
-    let loadModelCode = 'loadModel = ' + loadModel.toString();
-    document.getElementById('codeEditor4').value = loadModelCode;
-
-    utils.loadCode('codeSnippet5', 'codeEditor5');
-    let getTopClassesCode = 'getTopClasses = ' + getTopClasses.toString();
-    document.getElementById('codeEditor5').value  += '\n' + '\n' + getTopClassesCode;
-
-    let canvas = document.getElementById('canvasInput');
-    let ctx = canvas.getContext('2d');
-    let img = new Image();
-    img.crossOrigin = 'anonymous';
-    img.src = 'space_shuttle.jpg';
-    img.onload = function() {
-        ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
-    };
-
-    let tryIt = document.getElementById('tryIt');
-    tryIt.addEventListener('click', () => {
-        initStatus();
-        document.getElementById('status').innerHTML = 'Running function main()...';
-        utils.executeCode('codeEditor');
-        utils.executeCode('codeEditor1');
-        if (modelPath === "") {
-            document.getElementById('status').innerHTML = 'Runing failed.';
-            utils.printError('Please upload model file by clicking the button first.');
-        } else {
-            setTimeout(main, 1);
-        }
-    });
-
-    let fileInput = document.getElementById('fileInput');
-    fileInput.addEventListener('change', (e) => {
-        initStatus();
-        loadImageToCanvas(e, 'canvasInput');
-    });
-
-    let configPath = "";
-    let configFile = document.getElementById('configFile');
-    configFile.addEventListener('change', async (e) => {
-        initStatus();
-        configPath = await loadModel(e);
-        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
-    });
-
-    let modelPath = "";
-    let modelFile = document.getElementById('modelFile');
-    modelFile.addEventListener('change', async (e) => {
-        initStatus();
-        modelPath = await loadModel(e);
-        document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
-        configPath = "";
-        configFile.value = "";
-    });
-
-    utils.loadOpenCv(() => {
-        tryIt.removeAttribute('disabled');
-    });
-
-    var main = async function() {};
-    var softmax = function(result){};
-    var getTopClasses = function(mat, labels, topK = 3){};
-
-    utils.executeCode('codeEditor1');
-    utils.executeCode('codeEditor2');
-    utils.executeCode('codeEditor3');
-    utils.executeCode('codeEditor4');
-    utils.executeCode('codeEditor5');
-
-    function updateResult(classes, time) {
-        try{
-            classes.forEach((c,i) => {
-                let labelElement = document.getElementById('label'+i);
-                let probElement = document.getElementById('prob'+i);
-                labelElement.innerHTML = c.label;
-                probElement.innerHTML = c.prob + '%';
-            });
-            let result = document.getElementById('result');
-            result.style.visibility = 'visible';
-            document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
-                                                         <b>Inference time:</b> ${time.toFixed(2)} ms`;
-        } catch(e) {
-            console.log(e);
-        }
-    }
-
-    function initStatus() {
-        document.getElementById('status').innerHTML = '';
-        document.getElementById('result').style.visibility = 'hidden';
-        utils.clearError();
-    }
-
-</script>
-
-</body>
-
-</html>

doc/js_tutorials/js_assets/js_image_classification_model_info.json

@@ -1,65 +0,0 @@
-{
-    "caffe": [
-        {
-            "model": "alexnet",
-            "mean": "104, 117, 123",
-            "std": "1",
-            "swapRB": "false",
-            "needSoftmax": "false",
-            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/classification_classes_ILSVRC2012.txt",
-            "modelUrl": "http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel",
-            "configUrl": "https://raw.githubusercontent.com/BVLC/caffe/master/models/bvlc_alexnet/deploy.prototxt"
-        },
-        {
-            "model": "densenet",
-            "mean": "127.5, 127.5, 127.5",
-            "std": "0.007843",
-            "swapRB": "false",
-            "needSoftmax": "true",
-            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/classification_classes_ILSVRC2012.txt",
-            "modelUrl": "https://drive.google.com/open?id=0B7ubpZO7HnlCcHlfNmJkU2VPelE",
-            "configUrl": "https://raw.githubusercontent.com/shicai/DenseNet-Caffe/master/DenseNet_121.prototxt"
-        },
-        {
-            "model": "googlenet",
-            "mean": "104, 117, 123",
-            "std": "1",
-            "swapRB": "false",
-            "needSoftmax": "false",
-            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/classification_classes_ILSVRC2012.txt",
-            "modelUrl": "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel",
-            "configUrl": "https://raw.githubusercontent.com/BVLC/caffe/master/models/bvlc_googlenet/deploy.prototxt"
-        },
-        {
-            "model": "squeezenet",
-            "mean": "104, 117, 123",
-            "std": "1",
-            "swapRB": "false",
-            "needSoftmax": "false",
-            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/classification_classes_ILSVRC2012.txt",
-            "modelUrl": "https://raw.githubusercontent.com/forresti/SqueezeNet/master/SqueezeNet_v1.0/squeezenet_v1.0.caffemodel",
-            "configUrl": "https://raw.githubusercontent.com/forresti/SqueezeNet/master/SqueezeNet_v1.0/deploy.prototxt"
-        },
-        {
-            "model": "VGG",
-            "mean": "104, 117, 123",
-            "std": "1",
-            "swapRB": "false",
-            "needSoftmax": "false",
-            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/classification_classes_ILSVRC2012.txt",
-            "modelUrl": "http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_19_layers.caffemodel",
-            "configUrl": "https://gist.githubusercontent.com/ksimonyan/3785162f95cd2d5fee77/raw/f02f8769e64494bcd3d7e97d5d747ac275825721/VGG_ILSVRC_19_layers_deploy.prototxt"
-        }
-    ],
-    "tensorflow": [
-        {
-            "model": "inception",
-            "mean": "123, 117, 104",
-            "std": "1",
-            "swapRB": "true",
-            "needSoftmax": "false",
-            "labelsUrl": "https://raw.githubusercontent.com/petewarden/tf_ios_makefile_example/master/data/imagenet_comp_graph_label_strings.txt",
-            "modelUrl": "https://raw.githubusercontent.com/petewarden/tf_ios_makefile_example/master/data/tensorflow_inception_graph.pb"
-        }
-    ]
-}

doc/js_tutorials/js_assets/js_image_classification_with_camera.html

@@ -1,281 +0,0 @@
-<!DOCTYPE html>
-<html>
-
-<head>
-    <meta charset="utf-8">
-    <title>Image Classification Example with Camera</title>
-    <link href="js_example_style.css" rel="stylesheet" type="text/css" />
-</head>
-
-<body>
-<h2>Image Classification Example with Camera</h2>
-<p>
-    This tutorial shows you how to write an image classification example with camera.<br>
-    To try the example you should click the <b>modelFile</b> button(and <b>configFile</b> button if needed) to upload inference model.
-    You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
-    Then You should change the parameters in the first code snippet according to the uploaded model.
-    Finally click <b>Start/Stop</b> button to start or stop the camera capture.<br>
-</p>
-
-<div class="control"><button id="startAndStop" disabled>Start</button></div>
-<div>
-    <table cellpadding="0" cellspacing="0" width="0" border="0">
-        <tr>
-            <td>
-                <video id="videoInput" width="400" height="400"></video>
-            </td>
-            <td>
-                <table style="visibility: hidden;" id="result">
-                    <thead>
-                        <tr>
-                            <th scope="col">#</th>
-                            <th scope="col" width=300>Label</th>
-                            <th scope="col">Probability</th>
-                        </tr>
-                    </thead>
-                    <tbody>
-                        <tr>
-                            <th scope="row">1</th>
-                            <td id="label0" align="center"></td>
-                            <td id="prob0" align="center"></td>
-                        </tr>
-                        <tr>
-                            <th scope="row">2</th>
-                            <td id="label1" align="center"></td>
-                            <td id="prob1" align="center"></td>
-                        </tr>
-                        <tr>
-                            <th scope="row">3</th>
-                            <td id="label2" align="center"></td>
-                            <td id="prob2" align="center"></td>
-                        </tr>
-                    </tbody>
-                </table>
-                <p id='status' align="left"></p>
-            </td>
-        </tr>
-        <tr>
-            <td>
-                <div class="caption">
-                    videoInput
-                </div>
-            </td>
-            <td></td>
-        </tr>
-        <tr>
-            <td>
-                <div class="caption">
-                    modelFile <input type="file" id="modelFile">
-                </div>
-            </td>
-        </tr>
-        <tr>
-            <td>
-                <div class="caption">
-                    configFile <input type="file" id="configFile">
-                </div>
-            </td>
-        </tr>
-    </table>
-</div>
-
-<div>
-    <p class="err" id="errorMessage"></p>
-</div>
-
-<div>
-    <h3>Help function</h3>
-    <p>1.The parameters for model inference which you can modify to investigate more models.</p>
-    <textarea class="code" rows="13" cols="100" id="codeEditor" spellcheck="false"></textarea>
-    <p>2.The function to capture video from camera, and the main loop in which will do inference once.</p>
-    <textarea class="code" rows="35" cols="100" id="codeEditor1" spellcheck="false"></textarea>
-    <p>3.Load labels from txt file and process it into an array.</p>
-    <textarea class="code" rows="7" cols="100" id="codeEditor2" spellcheck="false"></textarea>
-    <p>4.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
-    <textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
-    <p>5.Fetch model file and save to emscripten file system once click the input button.</p>
-    <textarea class="code" rows="17" cols="100" id="codeEditor4" spellcheck="false"></textarea>
-    <p>6.The post-processing, including softmax if needed and get the top classes from the output vector.</p>
-    <textarea class="code" rows="35" cols="100" id="codeEditor5" spellcheck="false"></textarea>
-</div>
-
-<div id="appendix">
-    <h2>Model Info:</h2>
-</div>
-
-<script src="utils.js" type="text/javascript"></script>
-<script src="js_dnn_example_helper.js" type="text/javascript"></script>
-
-<script id="codeSnippet" type="text/code-snippet">
-inputSize = [224,224];
-mean = [104, 117, 123];
-std = 1;
-swapRB = false;
-
-// record if need softmax function for post-processing
-needSoftmax = false;
-
-// url for label file, can from local or Internet
-labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/classification_classes_ILSVRC2012.txt";
-</script>
-
-<script id="codeSnippet1" type="text/code-snippet">
-let frame = new cv.Mat(video.height, video.width, cv.CV_8UC4);
-let cap = new cv.VideoCapture(video);
-
-main = async function(frame) {
-    const labels = await loadLables(labelsUrl);
-    const input = getBlobFromImage(inputSize, mean, std, swapRB, frame);
-    let net = cv.readNet(configPath, modelPath);
-    net.setInput(input);
-    const start = performance.now();
-    const result = net.forward();
-    const time  = performance.now()-start;
-    const probs = softmax(result);
-    const classes = getTopClasses(probs, labels);
-
-    updateResult(classes, time);
-    setTimeout(processVideo, 0);
-    input.delete();
-    net.delete();
-    result.delete();
-}
-
-function processVideo() {
-    try {
-        if (!streaming) {
-            return;
-        }
-        cap.read(frame);
-        main(frame);
-    } catch (err) {
-        utils.printError(err);
-    }
-}
-
-setTimeout(processVideo, 0);
-</script>
-
-<script id="codeSnippet5" type="text/code-snippet">
-softmax = function(result) {
-    let arr = result.data32F;
-    if (needSoftmax) {
-        const maxNum = Math.max(...arr);
-        const expSum = arr.map((num) => Math.exp(num - maxNum)).reduce((a, b) => a + b);
-        return arr.map((value, index) => {
-            return Math.exp(value - maxNum) / expSum;
-        });
-    } else {
-        return arr;
-    }
-}
-</script>
-
-<script type="text/javascript">
-    let jsonUrl = "js_image_classification_model_info.json";
-    drawInfoTable(jsonUrl, 'appendix');
-
-    let utils = new Utils('errorMessage');
-    utils.loadCode('codeSnippet', 'codeEditor');
-    utils.loadCode('codeSnippet1', 'codeEditor1');
-
-    let loadLablesCode = 'loadLables = ' + loadLables.toString();
-    document.getElementById('codeEditor2').value = loadLablesCode;
-    let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
-    document.getElementById('codeEditor3').value = getBlobFromImageCode;
-    let loadModelCode = 'loadModel = ' + loadModel.toString();
-    document.getElementById('codeEditor4').value = loadModelCode;
-
-    utils.loadCode('codeSnippet5', 'codeEditor5');
-    let getTopClassesCode = 'getTopClasses = ' + getTopClasses.toString();
-    document.getElementById('codeEditor5').value  += '\n' + '\n' + getTopClassesCode;
-
-    let video = document.getElementById('videoInput');
-    let streaming = false;
-    let startAndStop = document.getElementById('startAndStop');
-    startAndStop.addEventListener('click', () => {
-        if (!streaming) {
-            utils.clearError();
-            utils.startCamera('qvga', onVideoStarted, 'videoInput');
-        } else {
-            utils.stopCamera();
-            onVideoStopped();
-        }
-    });
-
-    let configPath = "";
-    let configFile = document.getElementById('configFile');
-    configFile.addEventListener('change', async (e) => {
-        initStatus();
-        configPath = await loadModel(e);
-        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
-    });
-
-    let modelPath = "";
-    let modelFile = document.getElementById('modelFile');
-    modelFile.addEventListener('change', async (e) => {
-        initStatus();
-        modelPath = await loadModel(e);
-        document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
-        configPath = "";
-        configFile.value = "";
-    });
-
-    utils.loadOpenCv(() => {
-        startAndStop.removeAttribute('disabled');
-
-    });
-
-    var main = async function(frame) {};
-    var softmax = function(result){};
-    var getTopClasses = function(mat, labels, topK = 3){};
-
-    utils.executeCode('codeEditor1');
-    utils.executeCode('codeEditor2');
-    utils.executeCode('codeEditor3');
-    utils.executeCode('codeEditor4');
-    utils.executeCode('codeEditor5');
-
-    function onVideoStarted() {
-        streaming = true;
-        startAndStop.innerText = 'Stop';
-        videoInput.width = videoInput.videoWidth;
-        videoInput.height = videoInput.videoHeight;
-        utils.executeCode('codeEditor');
-        utils.executeCode('codeEditor1');
-    }
-
-    function onVideoStopped() {
-        streaming = false;
-        startAndStop.innerText = 'Start';
-        initStatus();
-    }
-
-    function updateResult(classes, time) {
-        try{
-            classes.forEach((c,i) => {
-                let labelElement = document.getElementById('label'+i);
-                let probElement = document.getElementById('prob'+i);
-                labelElement.innerHTML = c.label;
-                probElement.innerHTML = c.prob + '%';
-            });
-            let result = document.getElementById('result');
-            result.style.visibility = 'visible';
-            document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
-                                                         <b>Inference time:</b> ${time.toFixed(2)} ms`;
-        } catch(e) {
-            console.log(e);
-        }
-    }
-
-    function initStatus() {
-        document.getElementById('status').innerHTML = '';
-        document.getElementById('result').style.visibility = 'hidden';
-        utils.clearError();
-    }
-
-</script>
-
-</body>
-
-</html>

doc/js_tutorials/js_assets/js_object_detection.html

@@ -1,387 +0,0 @@
-<!DOCTYPE html>
-<html>
-
-<head>
-    <meta charset="utf-8">
-    <title>Object Detection Example</title>
-    <link href="js_example_style.css" rel="stylesheet" type="text/css" />
-</head>
-
-<body>
-<h2>Object Detection Example</h2>
-<p>
-    This tutorial shows you how to write an object detection example with OpenCV.js.<br>
-    To try the example you should click the <b>modelFile</b> button(and <b>configFile</b> button if needed) to upload inference model.
-    You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
-    Then You should change the parameters in the first code snippet according to the uploaded model.
-    Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
-</p>
-
-<div class="control"><button id="tryIt" disabled>Try it</button></div>
-<div>
-    <table cellpadding="0" cellspacing="0" width="0" border="0">
-        <tr>
-            <td>
-                <canvas id="canvasInput" width="400" height="400"></canvas>
-            </td>
-            <td>
-                <canvas id="canvasOutput" style="visibility: hidden;" width="400" height="400"></canvas>
-            </td>
-        </tr>
-        <tr>
-            <td>
-                <div class="caption">
-                    canvasInput <input type="file" id="fileInput" name="file" accept="image/*">
-                </div>
-            </td>
-            <td>
-                <p id='status' align="left"></p>
-            </td>
-        </tr>
-        <tr>
-            <td>
-                <div class="caption">
-                    modelFile <input type="file" id="modelFile" name="file">
-                </div>
-            </td>
-        </tr>
-        <tr>
-            <td>
-                <div class="caption">
-                    configFile <input type="file" id="configFile">
-                </div>
-            </td>
-        </tr>
-    </table>
-</div>
-
-<div>
-    <p class="err" id="errorMessage"></p>
-</div>
-
-<div>
-    <h3>Help function</h3>
-    <p>1.The parameters for model inference which you can modify to investigate more models.</p>
-    <textarea class="code" rows="15" cols="100" id="codeEditor" spellcheck="false"></textarea>
-    <p>2.Main loop in which will read the image from canvas and do inference once.</p>
-    <textarea class="code" rows="16" cols="100" id="codeEditor1" spellcheck="false"></textarea>
-    <p>3.Load labels from txt file and process it into an array.</p>
-    <textarea class="code" rows="7" cols="100" id="codeEditor2" spellcheck="false"></textarea>
-    <p>4.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
-    <textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
-    <p>5.Fetch model file and save to emscripten file system once click the input button.</p>
-    <textarea class="code" rows="17" cols="100" id="codeEditor4" spellcheck="false"></textarea>
-    <p>6.The post-processing, including get boxes from output and draw boxes into the image.</p>
-    <textarea class="code" rows="35" cols="100" id="codeEditor5" spellcheck="false"></textarea>
-</div>
-
-<div id="appendix">
-    <h2>Model Info:</h2>
-</div>
-
-<script src="utils.js" type="text/javascript"></script>
-<script src="js_dnn_example_helper.js" type="text/javascript"></script>
-
-<script id="codeSnippet" type="text/code-snippet">
-inputSize = [300, 300];
-mean = [127.5, 127.5, 127.5];
-std = 0.007843;
-swapRB = false;
-confThreshold = 0.5;
-nmsThreshold = 0.4;
-
-// The type of output, can be YOLO or SSD
-outType = "SSD";
-
-// url for label file, can from local or Internet
-labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/object_detection_classes_pascal_voc.txt";
-</script>
-
-<script id="codeSnippet1" type="text/code-snippet">
-main = async function() {
-    const labels = await loadLables(labelsUrl);
-    const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
-    let net = cv.readNet(configPath, modelPath);
-    net.setInput(input);
-    const start = performance.now();
-    const result = net.forward();
-    const time  = performance.now()-start;
-    const output = postProcess(result, labels);
-
-    updateResult(output, time);
-    input.delete();
-    net.delete();
-    result.delete();
-}
-</script>
-
-<script id="codeSnippet5" type="text/code-snippet">
-postProcess = function(result, labels) {
-    let canvasOutput = document.getElementById('canvasOutput');
-    const outputWidth = canvasOutput.width;
-    const outputHeight = canvasOutput.height;
-    const resultData = result.data32F;
-
-    // Get the boxes(with class and confidence) from the output
-    let boxes = [];
-    switch(outType) {
-        case "YOLO": {
-            const vecNum = result.matSize[0];
-            const vecLength = result.matSize[1];
-            const classNum = vecLength - 5;
-
-            for (let i = 0; i < vecNum; ++i) {
-                let vector = resultData.slice(i*vecLength, (i+1)*vecLength);
-                let scores = vector.slice(5, vecLength);
-                let classId = scores.indexOf(Math.max(...scores));
-                let confidence = scores[classId];
-                if (confidence > confThreshold) {
-                    let center_x = Math.round(vector[0] * outputWidth);
-                    let center_y = Math.round(vector[1] * outputHeight);
-                    let width = Math.round(vector[2] * outputWidth);
-                    let height = Math.round(vector[3] * outputHeight);
-                    let left = Math.round(center_x - width / 2);
-                    let top = Math.round(center_y - height / 2);
-
-                    let box = {
-                        scores: scores,
-                        classId: classId,
-                        confidence: confidence,
-                        bounding: [left, top, width, height],
-                        toDraw: true
-                    }
-                    boxes.push(box);
-                }
-            }
-
-            // NMS(Non Maximum Suppression) algorithm
-            let boxNum = boxes.length;
-            let tmp_boxes = [];
-            let sorted_boxes = [];
-            for (let c = 0; c < classNum; ++c) {
-                for (let i = 0; i < boxes.length; ++i) {
-                    tmp_boxes[i] = [boxes[i], i];
-                }
-                sorted_boxes = tmp_boxes.sort((a, b) => { return (b[0].scores[c] - a[0].scores[c]); });
-                for (let i = 0; i < boxNum; ++i) {
-                    if (sorted_boxes[i][0].scores[c] === 0) continue;
-                    else {
-                        for (let j = i + 1; j < boxNum; ++j) {
-                            if (IOU(sorted_boxes[i][0], sorted_boxes[j][0]) >= nmsThreshold) {
-                                boxes[sorted_boxes[j][1]].toDraw = false;
-                            }
-                        }
-                    }
-                }
-            }
-        } break;
-        case "SSD": {
-            const vecNum = result.matSize[2];
-            const vecLength = 7;
-
-            for (let i = 0; i < vecNum; ++i) {
-                let vector = resultData.slice(i*vecLength, (i+1)*vecLength);
-                let confidence = vector[2];
-                if (confidence > confThreshold) {
-                    let left, top, right, bottom, width, height;
-                    left = Math.round(vector[3]);
-                    top = Math.round(vector[4]);
-                    right = Math.round(vector[5]);
-                    bottom = Math.round(vector[6]);
-                    width = right - left + 1;
-                    height = bottom - top + 1;
-                    if (width <= 2 || height <= 2) {
-                        left = Math.round(vector[3] * outputWidth);
-                        top = Math.round(vector[4] * outputHeight);
-                        right = Math.round(vector[5] * outputWidth);
-                        bottom = Math.round(vector[6] * outputHeight);
-                        width = right - left + 1;
-                        height = bottom - top + 1;
-                    }
-                    let box = {
-                        classId: vector[1] - 1,
-                        confidence: confidence,
-                        bounding: [left, top, width, height],
-                        toDraw: true
-                    }
-                    boxes.push(box);
-                }
-            }
-        } break;
-        default:
-            console.error(`Unsupported output type ${outType}`)
-    }
-
-    // Draw the saved box into the image
-    let image = cv.imread("canvasInput");
-    let output = new cv.Mat(outputWidth, outputHeight, cv.CV_8UC3);
-    cv.cvtColor(image, output, cv.COLOR_RGBA2RGB);
-    let boxNum = boxes.length;
-    for (let i = 0; i < boxNum; ++i) {
-        if (boxes[i].toDraw) {
-            drawBox(boxes[i]);
-        }
-    }
-
-    return output;
-
-
-    // Calculate the IOU(Intersection over Union) of two boxes
-    function IOU(box1, box2) {
-        let bounding1 = box1.bounding;
-        let bounding2 = box2.bounding;
-        let s1 = bounding1[2] * bounding1[3];
-        let s2 = bounding2[2] * bounding2[3];
-
-        let left1 = bounding1[0];
-        let right1 = left1 + bounding1[2];
-        let left2 = bounding2[0];
-        let right2 = left2 + bounding2[2];
-        let overlapW = calOverlap([left1, right1], [left2, right2]);
-
-        let top1 = bounding2[1];
-        let bottom1 = top1 + bounding1[3];
-        let top2 = bounding2[1];
-        let bottom2 = top2 + bounding2[3];
-        let overlapH = calOverlap([top1, bottom1], [top2, bottom2]);
-
-        let overlapS = overlapW * overlapH;
-        return overlapS / (s1 + s2 + overlapS);
-    }
-
-    // Calculate the overlap range of two vector
-    function calOverlap(range1, range2) {
-        let min1 = range1[0];
-        let max1 = range1[1];
-        let min2 = range2[0];
-        let max2 = range2[1];
-
-        if (min2 > min1 && min2 < max1) {
-            return max1 - min2;
-        } else if (max2 > min1 && max2 < max1) {
-            return max2 - min1;
-        } else {
-            return 0;
-        }
-    }
-
-    // Draw one predict box into the origin image
-    function drawBox(box) {
-        let bounding = box.bounding;
-        let left = bounding[0];
-        let top = bounding[1];
-        let width = bounding[2];
-        let height = bounding[3];
-
-        cv.rectangle(output, new cv.Point(left, top), new cv.Point(left + width, top + height),
-                             new cv.Scalar(0, 255, 0));
-        cv.rectangle(output, new cv.Point(left, top), new cv.Point(left + width, top + 15),
-                             new cv.Scalar(255, 255, 255), cv.FILLED);
-        let text = `${labels[box.classId]}: ${box.confidence.toFixed(4)}`;
-        cv.putText(output, text, new cv.Point(left, top + 10), cv.FONT_HERSHEY_SIMPLEX, 0.3,
-                                 new cv.Scalar(0, 0, 0));
-    }
-}
-</script>
-
-<script type="text/javascript">
-    let jsonUrl = "js_object_detection_model_info.json";
-    drawInfoTable(jsonUrl, 'appendix');
-
-    let utils = new Utils('errorMessage');
-    utils.loadCode('codeSnippet', 'codeEditor');
-    utils.loadCode('codeSnippet1', 'codeEditor1');
-
-    let loadLablesCode = 'loadLables = ' + loadLables.toString();
-    document.getElementById('codeEditor2').value = loadLablesCode;
-    let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
-    document.getElementById('codeEditor3').value = getBlobFromImageCode;
-    let loadModelCode = 'loadModel = ' + loadModel.toString();
-    document.getElementById('codeEditor4').value = loadModelCode;
-
-    utils.loadCode('codeSnippet5', 'codeEditor5');
-
-    let canvas = document.getElementById('canvasInput');
-    let ctx = canvas.getContext('2d');
-    let img = new Image();
-    img.crossOrigin = 'anonymous';
-    img.src = 'lena.png';
-    img.onload = function() {
-        ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
-    };
-
-    let tryIt = document.getElementById('tryIt');
-    tryIt.addEventListener('click', () => {
-        initStatus();
-        document.getElementById('status').innerHTML = 'Running function main()...';
-        utils.executeCode('codeEditor');
-        utils.executeCode('codeEditor1');
-        if (modelPath === "") {
-            document.getElementById('status').innerHTML = 'Runing failed.';
-            utils.printError('Please upload model file by clicking the button first.');
-        } else {
-            setTimeout(main, 1);
-        }
-    });
-
-    let fileInput = document.getElementById('fileInput');
-    fileInput.addEventListener('change', (e) => {
-        initStatus();
-        loadImageToCanvas(e, 'canvasInput');
-    });
-
-    let configPath = "";
-    let configFile = document.getElementById('configFile');
-    configFile.addEventListener('change', async (e) => {
-        initStatus();
-        configPath = await loadModel(e);
-        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
-    });
-
-    let modelPath = "";
-    let modelFile = document.getElementById('modelFile');
-    modelFile.addEventListener('change', async (e) => {
-        initStatus();
-        modelPath = await loadModel(e);
-        document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
-        configPath = "";
-        configFile.value = "";
-    });
-
-    utils.loadOpenCv(() => {
-        tryIt.removeAttribute('disabled');
-    });
-
-    var main = async function() {};
-    var postProcess = function(result, labels) {};
-
-    utils.executeCode('codeEditor1');
-    utils.executeCode('codeEditor2');
-    utils.executeCode('codeEditor3');
-    utils.executeCode('codeEditor4');
-    utils.executeCode('codeEditor5');
-
-
-    function updateResult(output, time) {
-        try{
-            let canvasOutput = document.getElementById('canvasOutput');
-            canvasOutput.style.visibility = "visible";
-            cv.imshow('canvasOutput', output);
-            document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
-                                                           <b>Inference time:</b> ${time.toFixed(2)} ms`;
-        } catch(e) {
-            console.log(e);
-        }
-    }
-
-    function initStatus() {
-        document.getElementById('status').innerHTML = '';
-        document.getElementById('canvasOutput').style.visibility = "hidden";
-        utils.clearError();
-    }
-
-</script>
-
-</body>
-
-</html>

doc/js_tutorials/js_assets/js_object_detection_model_info.json

@@ -1,39 +0,0 @@
-{
-    "caffe": [
-        {
-            "model": "mobilenet_SSD",
-            "inputSize": "300, 300",
-            "mean": "127.5, 127.5, 127.5",
-            "std": "0.007843",
-            "swapRB": "false",
-            "outType": "SSD",
-            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/object_detection_classes_pascal_voc.txt",
-            "modelUrl": "https://raw.githubusercontent.com/chuanqi305/MobileNet-SSD/master/mobilenet_iter_73000.caffemodel",
-            "configUrl": "https://raw.githubusercontent.com/chuanqi305/MobileNet-SSD/master/deploy.prototxt"
-        },
-        {
-            "model": "VGG_SSD",
-            "inputSize": "300, 300",
-            "mean": "104, 117, 123",
-            "std": "1",
-            "swapRB": "false",
-            "outType": "SSD",
-            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/object_detection_classes_pascal_voc.txt",
-            "modelUrl": "https://drive.google.com/uc?id=0BzKzrI_SkD1_WVVTSmQxU0dVRzA&export=download",
-            "configUrl": "https://drive.google.com/uc?id=0BzKzrI_SkD1_WVVTSmQxU0dVRzA&export=download"
-        }
-    ],
-    "darknet": [
-        {
-            "model": "yolov2_tiny",
-            "inputSize": "416, 416",
-            "mean": "0, 0, 0",
-            "std": "0.00392",
-            "swapRB": "false",
-            "outType": "YOLO",
-            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/object_detection_classes_yolov3.txt",
-            "modelUrl": "https://pjreddie.com/media/files/yolov2-tiny.weights",
-            "configUrl": "https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov2-tiny.cfg"
-        }
-    ]
-}

doc/js_tutorials/js_assets/js_object_detection_with_camera.html

@@ -1,402 +0,0 @@
-<!DOCTYPE html>
-<html>
-
-<head>
-    <meta charset="utf-8">
-    <title>Object Detection Example with Camera</title>
-    <link href="js_example_style.css" rel="stylesheet" type="text/css" />
-</head>
-
-<body>
-<h2>Object Detection Example with Camera </h2>
-<p>
-    This tutorial shows you how to write an object detection example with camera.<br>
-    To try the example you should click the <b>modelFile</b> button(and <b>configInput</b> button if needed) to upload inference model.
-    You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
-    Then You should change the parameters in the first code snippet according to the uploaded model.
-    Finally click <b>Start/Stop</b> button to start or stop the camera capture.<br>
-</p>
-
-<div class="control"><button id="startAndStop" disabled>Start</button></div>
-<div>
-    <table cellpadding="0" cellspacing="0" width="0" border="0">
-        <tr>
-            <td>
-                <video id="videoInput" width="400" height="400"></video>
-            </td>
-            <td>
-                <canvas id="canvasOutput" style="visibility: hidden;" width="400" height="400"></canvas>
-            </td>
-        </tr>
-        <tr>
-            <td>
-                <div class="caption">
-                    videoInput
-                </div>
-            </td>
-            <td>
-                <p id='status' align="left"></p>
-            </td>
-        </tr>
-        <tr>
-            <td>
-                <div class="caption">
-                    modelFile <input type="file" id="modelFile" name="file">
-                </div>
-            </td>
-        </tr>
-        <tr>
-            <td>
-                <div class="caption">
-                    configFile <input type="file" id="configFile">
-                </div>
-            </td>
-        </tr>
-    </table>
-</div>
-
-<div>
-    <p class="err" id="errorMessage"></p>
-</div>
-
-<div>
-    <h3>Help function</h3>
-    <p>1.The parameters for model inference which you can modify to investigate more models.</p>
-    <textarea class="code" rows="15" cols="100" id="codeEditor" spellcheck="false"></textarea>
-    <p>2.The function to capture video from camera, and the main loop in which will do inference once.</p>
-    <textarea class="code" rows="34" cols="100" id="codeEditor1" spellcheck="false"></textarea>
-    <p>3.Load labels from txt file and process it into an array.</p>
-    <textarea class="code" rows="7" cols="100" id="codeEditor2" spellcheck="false"></textarea>
-    <p>4.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
-    <textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
-    <p>5.Fetch model file and save to emscripten file system once click the input button.</p>
-    <textarea class="code" rows="17" cols="100" id="codeEditor4" spellcheck="false"></textarea>
-    <p>6.The post-processing, including get boxes from output and draw boxes into the image.</p>
-    <textarea class="code" rows="35" cols="100" id="codeEditor5" spellcheck="false"></textarea>
-</div>
-
-<div id="appendix">
-    <h2>Model Info:</h2>
-</div>
-
-<script src="utils.js" type="text/javascript"></script>
-<script src="js_dnn_example_helper.js" type="text/javascript"></script>
-
-<script id="codeSnippet" type="text/code-snippet">
-inputSize = [300, 300];
-mean = [127.5, 127.5, 127.5];
-std = 0.007843;
-swapRB = false;
-confThreshold = 0.5;
-nmsThreshold = 0.4;
-
-// the type of output, can be YOLO or SSD
-outType = "SSD";
-
-// url for label file, can from local or Internet
-labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/object_detection_classes_pascal_voc.txt";
-</script>
-
-<script id="codeSnippet1" type="text/code-snippet">
-let frame = new cv.Mat(videoInput.height, videoInput.width, cv.CV_8UC4);
-let cap = new cv.VideoCapture(videoInput);
-
-main = async function(frame) {
-    const labels = await loadLables(labelsUrl);
-    const input = getBlobFromImage(inputSize, mean, std, swapRB, frame);
-    let net = cv.readNet(configPath, modelPath);
-    net.setInput(input);
-    const start = performance.now();
-    const result = net.forward();
-    const time  = performance.now()-start;
-    const output = postProcess(result, labels, frame);
-
-    updateResult(output, time);
-    setTimeout(processVideo, 0);
-    input.delete();
-    net.delete();
-    result.delete();
-}
-
-function processVideo() {
-    try {
-        if (!streaming) {
-            return;
-        }
-        cap.read(frame);
-        main(frame);
-    } catch (err) {
-        utils.printError(err);
-    }
-}
-
-setTimeout(processVideo, 0);
-</script>
-
-<script id="codeSnippet5" type="text/code-snippet">
-postProcess = function(result, labels, frame) {
-    let canvasOutput = document.getElementById('canvasOutput');
-    const outputWidth = canvasOutput.width;
-    const outputHeight = canvasOutput.height;
-    const resultData = result.data32F;
-
-    // Get the boxes(with class and confidence) from the output
-    let boxes = [];
-    switch(outType) {
-        case "YOLO": {
-            const vecNum = result.matSize[0];
-            const vecLength = result.matSize[1];
-            const classNum = vecLength - 5;
-
-            for (let i = 0; i < vecNum; ++i) {
-                let vector = resultData.slice(i*vecLength, (i+1)*vecLength);
-                let scores = vector.slice(5, vecLength);
-                let classId = scores.indexOf(Math.max(...scores));
-                let confidence = scores[classId];
-                if (confidence > confThreshold) {
-                    let center_x = Math.round(vector[0] * outputWidth);
-                    let center_y = Math.round(vector[1] * outputHeight);
-                    let width = Math.round(vector[2] * outputWidth);
-                    let height = Math.round(vector[3] * outputHeight);
-                    let left = Math.round(center_x - width / 2);
-                    let top = Math.round(center_y - height / 2);
-
-                    let box = {
-                        scores: scores,
-                        classId: classId,
-                        confidence: confidence,
-                        bounding: [left, top, width, height],
-                        toDraw: true
-                    }
-                    boxes.push(box);
-                }
-            }
-
-            // NMS(Non Maximum Suppression) algorithm
-            let boxNum = boxes.length;
-            let tmp_boxes = [];
-            let sorted_boxes = [];
-            for (let c = 0; c < classNum; ++c) {
-                for (let i = 0; i < boxes.length; ++i) {
-                    tmp_boxes[i] = [boxes[i], i];
-                }
-                sorted_boxes = tmp_boxes.sort((a, b) => { return (b[0].scores[c] - a[0].scores[c]); });
-                for (let i = 0; i < boxNum; ++i) {
-                    if (sorted_boxes[i][0].scores[c] === 0) continue;
-                    else {
-                        for (let j = i + 1; j < boxNum; ++j) {
-                            if (IOU(sorted_boxes[i][0], sorted_boxes[j][0]) >= nmsThreshold) {
-                                boxes[sorted_boxes[j][1]].toDraw = false;
-                            }
-                        }
-                    }
-                }
-            }
-        } break;
-        case "SSD": {
-            const vecNum = result.matSize[2];
-            const vecLength = 7;
-
-            for (let i = 0; i < vecNum; ++i) {
-                let vector = resultData.slice(i*vecLength, (i+1)*vecLength);
-                let confidence = vector[2];
-                if (confidence > confThreshold) {
-                    let left, top, right, bottom, width, height;
-                    left = Math.round(vector[3]);
-                    top = Math.round(vector[4]);
-                    right = Math.round(vector[5]);
-                    bottom = Math.round(vector[6]);
-                    width = right - left + 1;
-                    height = bottom - top + 1;
-                    if (width <= 2 || height <= 2) {
-                        left = Math.round(vector[3] * outputWidth);
-                        top = Math.round(vector[4] * outputHeight);
-                        right = Math.round(vector[5] * outputWidth);
-                        bottom = Math.round(vector[6] * outputHeight);
-                        width = right - left + 1;
-                        height = bottom - top + 1;
-                    }
-                    let box = {
-                        classId: vector[1] - 1,
-                        confidence: confidence,
-                        bounding: [left, top, width, height],
-                        toDraw: true
-                    }
-                    boxes.push(box);
-                }
-            }
-        } break;
-        default:
-            console.error(`Unsupported output type ${outType}`)
-    }
-
-    // Draw the saved box into the image
-    let output = new cv.Mat(outputWidth, outputHeight, cv.CV_8UC3);
-    cv.cvtColor(frame, output, cv.COLOR_RGBA2RGB);
-    let boxNum = boxes.length;
-    for (let i = 0; i < boxNum; ++i) {
-        if (boxes[i].toDraw) {
-            drawBox(boxes[i]);
-        }
-    }
-
-    return output;
-
-
-    // Calculate the IOU(Intersection over Union) of two boxes
-    function IOU(box1, box2) {
-        let bounding1 = box1.bounding;
-        let bounding2 = box2.bounding;
-        let s1 = bounding1[2] * bounding1[3];
-        let s2 = bounding2[2] * bounding2[3];
-
-        let left1 = bounding1[0];
-        let right1 = left1 + bounding1[2];
-        let left2 = bounding2[0];
-        let right2 = left2 + bounding2[2];
-        let overlapW = calOverlap([left1, right1], [left2, right2]);
-
-        let top1 = bounding2[1];
-        let bottom1 = top1 + bounding1[3];
-        let top2 = bounding2[1];
-        let bottom2 = top2 + bounding2[3];
-        let overlapH = calOverlap([top1, bottom1], [top2, bottom2]);
-
-        let overlapS = overlapW * overlapH;
-        return overlapS / (s1 + s2 + overlapS);
-    }
-
-    // Calculate the overlap range of two vector
-    function calOverlap(range1, range2) {
-        let min1 = range1[0];
-        let max1 = range1[1];
-        let min2 = range2[0];
-        let max2 = range2[1];
-
-        if (min2 > min1 && min2 < max1) {
-            return max1 - min2;
-        } else if (max2 > min1 && max2 < max1) {
-            return max2 - min1;
-        } else {
-            return 0;
-        }
-    }
-
-    // Draw one predict box into the origin image
-    function drawBox(box) {
-        let bounding = box.bounding;
-        let left = bounding[0];
-        let top = bounding[1];
-        let width = bounding[2];
-        let height = bounding[3];
-
-        cv.rectangle(output, new cv.Point(left, top), new cv.Point(left + width, top + height),
-                             new cv.Scalar(0, 255, 0));
-        cv.rectangle(output, new cv.Point(left, top), new cv.Point(left + width, top + 15),
-                             new cv.Scalar(255, 255, 255), cv.FILLED);
-        let text = `${labels[box.classId]}: ${box.confidence.toFixed(4)}`;
-        cv.putText(output, text, new cv.Point(left, top + 10), cv.FONT_HERSHEY_SIMPLEX, 0.3,
-                                 new cv.Scalar(0, 0, 0));
-    }
-}
-</script>
-
-<script type="text/javascript">
-    let jsonUrl = "js_object_detection_model_info.json";
-    drawInfoTable(jsonUrl, 'appendix');
-
-    let utils = new Utils('errorMessage');
-    utils.loadCode('codeSnippet', 'codeEditor');
-    utils.loadCode('codeSnippet1', 'codeEditor1');
-
-    let loadLablesCode = 'loadLables = ' + loadLables.toString();
-    document.getElementById('codeEditor2').value = loadLablesCode;
-    let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
-    document.getElementById('codeEditor3').value = getBlobFromImageCode;
-    let loadModelCode = 'loadModel = ' + loadModel.toString();
-    document.getElementById('codeEditor4').value = loadModelCode;
-
-    utils.loadCode('codeSnippet5', 'codeEditor5');
-
-    let videoInput = document.getElementById('videoInput');
-    let streaming = false;
-    let startAndStop = document.getElementById('startAndStop');
-    startAndStop.addEventListener('click', () => {
-        if (!streaming) {
-            utils.clearError();
-            utils.startCamera('qvga', onVideoStarted, 'videoInput');
-        } else {
-            utils.stopCamera();
-            onVideoStopped();
-        }
-    });
-
-    let configPath = "";
-    let configFile = document.getElementById('configFile');
-    configFile.addEventListener('change', async (e) => {
-        initStatus();
-        configPath = await loadModel(e);
-        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
-    });
-
-    let modelPath = "";
-    let modelFile = document.getElementById('modelFile');
-    modelFile.addEventListener('change', async (e) => {
-        initStatus();
-        modelPath = await loadModel(e);
-        document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
-        configPath = "";
-        configFile.value = "";
-    });
-
-    utils.loadOpenCv(() => {
-        startAndStop.removeAttribute('disabled');
-    });
-
-    var main = async function(frame) {};
-    var postProcess = function(result, labels, frame) {};
-
-    utils.executeCode('codeEditor1');
-    utils.executeCode('codeEditor2');
-    utils.executeCode('codeEditor3');
-    utils.executeCode('codeEditor4');
-    utils.executeCode('codeEditor5');
-
-    function onVideoStarted() {
-        streaming = true;
-        startAndStop.innerText = 'Stop';
-        videoInput.width = videoInput.videoWidth;
-        videoInput.height = videoInput.videoHeight;
-        utils.executeCode('codeEditor');
-        utils.executeCode('codeEditor1');
-    }
-
-    function onVideoStopped() {
-        streaming = false;
-        startAndStop.innerText = 'Start';
-        initStatus();
-    }
-
-    function updateResult(output, time) {
-        try{
-            let canvasOutput = document.getElementById('canvasOutput');
-            canvasOutput.style.visibility = "visible";
-            cv.imshow('canvasOutput', output);
-            document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
-                                                           <b>Inference time:</b> ${time.toFixed(2)} ms`;
-        } catch(e) {
-            console.log(e);
-        }
-    }
-
-    function initStatus() {
-        document.getElementById('status').innerHTML = '';
-        document.getElementById('canvasOutput').style.visibility = "hidden";
-        utils.clearError();
-    }
-
-</script>
-
-</body>
-
-</html>

doc/js_tutorials/js_assets/js_pose_estimation.html

@@ -1,327 +0,0 @@
-<!DOCTYPE html>
-<html>
-
-<head>
-    <meta charset="utf-8">
-    <title>Pose Estimation Example</title>
-    <link href="js_example_style.css" rel="stylesheet" type="text/css" />
-</head>
-
-<body>
-<h2>Pose Estimation Example</h2>
-<p>
-    This tutorial shows you how to write an pose estimation example with OpenCV.js.<br>
-    To try the example you should click the <b>modelFile</b> button(and <b>configInput</b> button if needed) to upload inference model.
-    You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
-    Then You should change the parameters in the first code snippet according to the uploaded model.
-    Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
-</p>
-
-<div class="control"><button id="tryIt" disabled>Try it</button></div>
-<div>
-    <table cellpadding="0" cellspacing="0" width="0" border="0">
-        <tr>
-            <td>
-                <canvas id="canvasInput" width="400" height="250"></canvas>
-            </td>
-            <td>
-                <canvas id="canvasOutput" style="visibility: hidden;" width="400" height="250"></canvas>
-            </td>
-        </tr>
-        <tr>
-            <td>
-                <div class="caption">
-                    canvasInput <input type="file" id="fileInput" name="file" accept="image/*">
-                </div>
-            </td>
-            <td>
-                <p id='status' align="left"></p>
-            </td>
-        </tr>
-        <tr>
-            <td>
-                <div class="caption">
-                    modelFile <input type="file" id="modelFile" name="file">
-                </div>
-            </td>
-        </tr>
-        <tr>
-            <td>
-                <div class="caption">
-                    configFile <input type="file" id="configFile">
-                </div>
-            </td>
-        </tr>
-    </table>
-</div>
-
-<div>
-    <p class="err" id="errorMessage"></p>
-</div>
-
-<div>
-    <h3>Help function</h3>
-    <p>1.The parameters for model inference which you can modify to investigate more models.</p>
-    <textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false"></textarea>
-    <p>2.Main loop in which will read the image from canvas and do inference once.</p>
-    <textarea class="code" rows="15" cols="100" id="codeEditor1" spellcheck="false"></textarea>
-    <p>3.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
-    <textarea class="code" rows="17" cols="100" id="codeEditor2" spellcheck="false"></textarea>
-    <p>4.Fetch model file and save to emscripten file system once click the input button.</p>
-    <textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
-    <p>5.The pairs of keypoints of different dataset.</p>
-    <textarea class="code" rows="30" cols="100" id="codeEditor4" spellcheck="false"></textarea>
-    <p>6.The post-processing, including get the predicted points and draw lines into the image.</p>
-    <textarea class="code" rows="30" cols="100" id="codeEditor5" spellcheck="false"></textarea>
-</div>
-
-<div id="appendix">
-    <h2>Model Info:</h2>
-</div>
-
-<script src="utils.js" type="text/javascript"></script>
-<script src="js_dnn_example_helper.js" type="text/javascript"></script>
-
-<script id="codeSnippet" type="text/code-snippet">
-inputSize = [368, 368];
-mean = [0, 0, 0];
-std = 0.00392;
-swapRB = false;
-threshold = 0.1;
-
-// the pairs of keypoint, can be "COCO", "MPI" and "BODY_25"
-dataset = "COCO";
-</script>
-
-<script id="codeSnippet1" type="text/code-snippet">
-main = async function() {
-    const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
-    let net = cv.readNet(configPath, modelPath);
-    net.setInput(input);
-    const start = performance.now();
-    const result = net.forward();
-    const time  = performance.now()-start;
-    const output = postProcess(result);
-
-    updateResult(output, time);
-    input.delete();
-    net.delete();
-    result.delete();
-}
-</script>
-
-<script id="codeSnippet4" type="text/code-snippet">
-BODY_PARTS = {};
-POSE_PAIRS = [];
-
-if (dataset === 'COCO') {
-    BODY_PARTS = { "Nose": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,
-                   "LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip": 8, "RKnee": 9,
-                   "RAnkle": 10, "LHip": 11, "LKnee": 12, "LAnkle": 13, "REye": 14,
-                   "LEye": 15, "REar": 16, "LEar": 17, "Background": 18 };
-
-    POSE_PAIRS = [ ["Neck", "RShoulder"], ["Neck", "LShoulder"], ["RShoulder", "RElbow"],
-                   ["RElbow", "RWrist"], ["LShoulder", "LElbow"], ["LElbow", "LWrist"],
-                   ["Neck", "RHip"], ["RHip", "RKnee"], ["RKnee", "RAnkle"], ["Neck", "LHip"],
-                   ["LHip", "LKnee"], ["LKnee", "LAnkle"], ["Neck", "Nose"], ["Nose", "REye"],
-                   ["REye", "REar"], ["Nose", "LEye"], ["LEye", "LEar"] ]
-} else if (dataset === 'MPI') {
-    BODY_PARTS = { "Head": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,
-                   "LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip": 8, "RKnee": 9,
-                   "RAnkle": 10, "LHip": 11, "LKnee": 12, "LAnkle": 13, "Chest": 14,
-                   "Background": 15 }
-
-    POSE_PAIRS = [ ["Head", "Neck"], ["Neck", "RShoulder"], ["RShoulder", "RElbow"],
-                   ["RElbow", "RWrist"], ["Neck", "LShoulder"], ["LShoulder", "LElbow"],
-                   ["LElbow", "LWrist"], ["Neck", "Chest"], ["Chest", "RHip"], ["RHip", "RKnee"],
-                   ["RKnee", "RAnkle"], ["Chest", "LHip"], ["LHip", "LKnee"], ["LKnee", "LAnkle"] ]
-} else if (dataset === 'BODY_25') {
-    BODY_PARTS = { "Nose": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,
-                   "LShoulder": 5, "LElbow": 6, "LWrist": 7, "MidHip": 8, "RHip": 9,
-                   "RKnee": 10, "RAnkle": 11, "LHip": 12, "LKnee": 13, "LAnkle": 14,
-                   "REye": 15, "LEye": 16, "REar": 17, "LEar": 18, "LBigToe": 19,
-                   "LSmallToe": 20, "LHeel": 21, "RBigToe": 22, "RSmallToe": 23,
-                   "RHeel": 24, "Background": 25 }
-
-    POSE_PAIRS = [ ["Neck", "Nose"], ["Neck", "RShoulder"],
-                   ["Neck", "LShoulder"], ["RShoulder", "RElbow"],
-                   ["RElbow", "RWrist"], ["LShoulder", "LElbow"],
-                   ["LElbow", "LWrist"], ["Nose", "REye"],
-                   ["REye", "REar"], ["Neck", "LEye"],
-                   ["LEye", "LEar"], ["Neck", "MidHip"],
-                   ["MidHip", "RHip"], ["RHip", "RKnee"],
-                   ["RKnee", "RAnkle"], ["RAnkle", "RBigToe"],
-                   ["RBigToe", "RSmallToe"], ["RAnkle", "RHeel"],
-                   ["MidHip", "LHip"], ["LHip", "LKnee"],
-                   ["LKnee", "LAnkle"], ["LAnkle", "LBigToe"],
-                   ["LBigToe", "LSmallToe"], ["LAnkle", "LHeel"] ]
-}
-</script>
-
-<script id="codeSnippet5" type="text/code-snippet">
-postProcess = function(result) {
-    const resultData = result.data32F;
-    const matSize = result.matSize;
-    const size1 = matSize[1];
-    const size2 = matSize[2];
-    const size3 = matSize[3];
-    const mapSize = size2 * size3;
-
-    let canvasOutput = document.getElementById('canvasOutput');
-    const outputWidth = canvasOutput.width;
-    const outputHeight = canvasOutput.height;
-
-    let image = cv.imread("canvasInput");
-    let output = new cv.Mat(outputWidth, outputHeight, cv.CV_8UC3);
-    cv.cvtColor(image, output, cv.COLOR_RGBA2RGB);
-
-    // get position of keypoints from output
-    let points = [];
-    for (let i = 0; i < Object.keys(BODY_PARTS).length; ++i) {
-        heatMap = resultData.slice(i*mapSize, (i+1)*mapSize);
-
-        let maxIndex = 0;
-        let maxConf = heatMap[0];
-        for (index in heatMap) {
-            if (heatMap[index] > heatMap[maxIndex]) {
-                maxIndex = index;
-                maxConf = heatMap[index];
-            }
-        }
-
-        if (maxConf > threshold) {
-            indexX = maxIndex % size3;
-            indexY = maxIndex / size3;
-
-            x = outputWidth * indexX / size3;
-            y = outputHeight * indexY / size2;
-
-            points[i] = [Math.round(x), Math.round(y)];
-        }
-    }
-
-    // draw the points and lines into the image
-    for (pair of POSE_PAIRS) {
-        partFrom = pair[0];
-        partTo = pair[1];
-        idFrom = BODY_PARTS[partFrom];
-        idTo = BODY_PARTS[partTo];
-        pointFrom = points[idFrom];
-        pointTo = points[idTo];
-
-        if (points[idFrom] && points[idTo]) {
-            cv.line(output, new cv.Point(pointFrom[0], pointFrom[1]),
-                            new cv.Point(pointTo[0], pointTo[1]), new cv.Scalar(0, 255, 0), 3);
-            cv.ellipse(output, new cv.Point(pointFrom[0], pointFrom[1]), new cv.Size(3, 3), 0, 0, 360,
-                               new cv.Scalar(0, 0, 255), cv.FILLED);
-            cv.ellipse(output, new cv.Point(pointTo[0], pointTo[1]), new cv.Size(3, 3), 0, 0, 360,
-                               new cv.Scalar(0, 0, 255), cv.FILLED);
-        }
-    }
-
-    return output;
-}
-</script>
-
-<script type="text/javascript">
-    let jsonUrl = "js_pose_estimation_model_info.json";
-    drawInfoTable(jsonUrl, 'appendix');
-
-    let utils = new Utils('errorMessage');
-    utils.loadCode('codeSnippet', 'codeEditor');
-    utils.loadCode('codeSnippet1', 'codeEditor1');
-
-    let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
-    document.getElementById('codeEditor2').value = getBlobFromImageCode;
-    let loadModelCode = 'loadModel = ' + loadModel.toString();
-    document.getElementById('codeEditor3').value = loadModelCode;
-
-    utils.loadCode('codeSnippet4', 'codeEditor4');
-    utils.loadCode('codeSnippet5', 'codeEditor5');
-
-    let canvas = document.getElementById('canvasInput');
-    let ctx = canvas.getContext('2d');
-    let img = new Image();
-    img.crossOrigin = 'anonymous';
-    img.src = 'roi.jpg';
-    img.onload = function() {
-        ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
-    };
-
-    let tryIt = document.getElementById('tryIt');
-    tryIt.addEventListener('click', () => {
-        initStatus();
-        document.getElementById('status').innerHTML = 'Running function main()...';
-        utils.executeCode('codeEditor');
-        utils.executeCode('codeEditor1');
-        if (modelPath === "") {
-            document.getElementById('status').innerHTML = 'Runing failed.';
-            utils.printError('Please upload model file by clicking the button first.');
-        } else {
-            setTimeout(main, 1);
-        }
-    });
-
-    let fileInput = document.getElementById('fileInput');
-    fileInput.addEventListener('change', (e) => {
-        initStatus();
-        loadImageToCanvas(e, 'canvasInput');
-    });
-
-    let configPath = "";
-    let configFile = document.getElementById('configFile');
-    configFile.addEventListener('change', async (e) => {
-        initStatus();
-        configPath = await loadModel(e);
-        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
-    });
-
-    let modelPath = "";
-    let modelFile = document.getElementById('modelFile');
-    modelFile.addEventListener('change', async (e) => {
-        initStatus();
-        modelPath = await loadModel(e);
-        document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
-        configPath = "";
-        configFile.value = "";
-    });
-
-    utils.loadOpenCv(() => {
-        tryIt.removeAttribute('disabled');
-    });
-
-    var main = async function() {};
-    var postProcess = function(result) {};
-
-    utils.executeCode('codeEditor');
-    utils.executeCode('codeEditor1');
-    utils.executeCode('codeEditor2');
-    utils.executeCode('codeEditor3');
-    utils.executeCode('codeEditor4');
-    utils.executeCode('codeEditor5');
-
-    function updateResult(output, time) {
-        try{
-            let canvasOutput = document.getElementById('canvasOutput');
-            canvasOutput.style.visibility = "visible";
-            let resized = new cv.Mat(canvasOutput.width, canvasOutput.height, cv.CV_8UC4);
-            cv.resize(output, resized, new cv.Size(canvasOutput.width, canvasOutput.height));
-            cv.imshow('canvasOutput', resized);
-            document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
-                                                           <b>Inference time:</b> ${time.toFixed(2)} ms`;
-        } catch(e) {
-            console.log(e);
-        }
-    }
-
-    function initStatus() {
-        document.getElementById('status').innerHTML = '';
-        document.getElementById('canvasOutput').style.visibility = "hidden";
-        utils.clearError();
-    }
-
-</script>
-
-</body>
-
-</html>

doc/js_tutorials/js_assets/js_pose_estimation_model_info.json

@@ -1,34 +0,0 @@
-{
-    "caffe": [
-        {
-            "model": "body_25",
-            "inputSize": "368, 368",
-            "mean": "0, 0, 0",
-            "std": "0.00392",
-            "swapRB": "false",
-            "dataset": "BODY_25",
-            "modelUrl": "http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/body_25/pose_iter_584000.caffemodel",
-            "configUrl": "https://raw.githubusercontent.com/CMU-Perceptual-Computing-Lab/openpose/master/models/pose/body_25/pose_deploy.prototxt"
-        },
-        {
-            "model": "coco",
-            "inputSize": "368, 368",
-            "mean": "0, 0, 0",
-            "std": "0.00392",
-            "swapRB": "false",
-            "dataset": "COCO",
-            "modelUrl": "http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/coco/pose_iter_440000.caffemodel",
-            "configUrl": "https://raw.githubusercontent.com/CMU-Perceptual-Computing-Lab/openpose/master/models/pose/coco/pose_deploy_linevec.prototxt"
-        },
-        {
-            "model": "mpi",
-            "inputSize": "368, 368",
-            "mean": "0, 0, 0",
-            "std": "0.00392",
-            "swapRB": "false",
-            "dataset": "MPI",
-            "modelUrl": "http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/mpi/pose_iter_160000.caffemodel",
-            "configUrl": "https://raw.githubusercontent.com/CMU-Perceptual-Computing-Lab/openpose/master/models/pose/mpi/pose_deploy_linevec.prototxt"
-        }
-    ]
-}

doc/js_tutorials/js_assets/js_semantic_segmentation.html

@@ -1,243 +0,0 @@
-<!DOCTYPE html>
-<html>
-
-<head>
-    <meta charset="utf-8">
-    <title>Semantic Segmentation Example</title>
-    <link href="js_example_style.css" rel="stylesheet" type="text/css" />
-</head>
-
-<body>
-<h2>Semantic Segmentation Example</h2>
-<p>
-    This tutorial shows you how to write an semantic segmentation example with OpenCV.js.<br>
-    To try the example you should click the <b>modelFile</b> button(and <b>configInput</b> button if needed) to upload inference model.
-    You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
-    Then You should change the parameters in the first code snippet according to the uploaded model.
-    Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
-</p>
-
-<div class="control"><button id="tryIt" disabled>Try it</button></div>
-<div>
-    <table cellpadding="0" cellspacing="0" width="0" border="0">
-        <tr>
-            <td>
-                <canvas id="canvasInput" width="400" height="400"></canvas>
-            </td>
-            <td>
-                <canvas id="canvasOutput" style="visibility: hidden;" width="400" height="400"></canvas>
-            </td>
-        </tr>
-        <tr>
-            <td>
-                <div class="caption">
-                    canvasInput <input type="file" id="fileInput" name="file" accept="image/*">
-                </div>
-            </td>
-            <td>
-                <p id='status' align="left"></p>
-            </td>
-        </tr>
-        <tr>
-            <td>
-                <div class="caption">
-                    modelFile <input type="file" id="modelFile" name="file">
-                </div>
-            </td>
-        </tr>
-        <tr>
-            <td>
-                <div class="caption">
-                    configFile <input type="file" id="configFile">
-                </div>
-            </td>
-        </tr>
-    </table>
-</div>
-
-<div>
-    <p class="err" id="errorMessage"></p>
-</div>
-
-<div>
-    <h3>Help function</h3>
-    <p>1.The parameters for model inference which you can modify to investigate more models.</p>
-    <textarea class="code" rows="5" cols="100" id="codeEditor" spellcheck="false"></textarea>
-    <p>2.Main loop in which will read the image from canvas and do inference once.</p>
-    <textarea class="code" rows="16" cols="100" id="codeEditor1" spellcheck="false"></textarea>
-    <p>3.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
-    <textarea class="code" rows="17" cols="100" id="codeEditor2" spellcheck="false"></textarea>
-    <p>4.Fetch model file and save to emscripten file system once click the input button.</p>
-    <textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
-    <p>5.The post-processing, including gengerate colors for different classes and argmax to get the classes for each pixel.</p>
-    <textarea class="code" rows="34" cols="100" id="codeEditor4" spellcheck="false"></textarea>
-</div>
-
-<div id="appendix">
-    <h2>Model Info:</h2>
-</div>
-
-<script src="utils.js" type="text/javascript"></script>
-<script src="js_dnn_example_helper.js" type="text/javascript"></script>
-
-<script id="codeSnippet" type="text/code-snippet">
-inputSize = [513, 513];
-mean = [127.5, 127.5, 127.5];
-std = 0.007843;
-swapRB = false;
-</script>
-
-<script id="codeSnippet1" type="text/code-snippet">
-main = async function() {
-    const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
-    let net = cv.readNet(configPath, modelPath);
-    net.setInput(input);
-    const start = performance.now();
-    const result = net.forward();
-    const time  = performance.now()-start;
-    const colors = generateColors(result);
-    const output = argmax(result, colors);
-
-    updateResult(output, time);
-    input.delete();
-    net.delete();
-    result.delete();
-}
-</script>
-
-<script id="codeSnippet4" type="text/code-snippet">
-generateColors = function(result) {
-    const numClasses = result.matSize[1];
-    let colors = [0,0,0];
-    while(colors.length < numClasses*3){
-        colors.push(Math.round((Math.random()*255 + colors[colors.length-3]) / 2));
-    }
-    return colors;
-}
-
-argmax = function(result, colors) {
-    const C = result.matSize[1];
-    const H = result.matSize[2];
-    const W = result.matSize[3];
-    const resultData = result.data32F;
-    const imgSize = H*W;
-
-    let classId = [];
-    for (i = 0; i<imgSize; ++i) {
-        let id = 0;
-        for (j = 0; j < C; ++j) {
-            if (resultData[j*imgSize+i] > resultData[id*imgSize+i]) {
-                id = j;
-            }
-        }
-        classId.push(colors[id*3]);
-        classId.push(colors[id*3+1]);
-        classId.push(colors[id*3+2]);
-        classId.push(255);
-    }
-
-    output = cv.matFromArray(H,W,cv.CV_8UC4,classId);
-    return output;
-}
-</script>
-
-<script type="text/javascript">
-    let jsonUrl = "js_semantic_segmentation_model_info.json";
-    drawInfoTable(jsonUrl, 'appendix');
-
-    let utils = new Utils('errorMessage');
-    utils.loadCode('codeSnippet', 'codeEditor');
-    utils.loadCode('codeSnippet1', 'codeEditor1');
-
-    let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
-    document.getElementById('codeEditor2').value = getBlobFromImageCode;
-    let loadModelCode = 'loadModel = ' + loadModel.toString();
-    document.getElementById('codeEditor3').value = loadModelCode;
-
-    utils.loadCode('codeSnippet4', 'codeEditor4');
-
-    let canvas = document.getElementById('canvasInput');
-    let ctx = canvas.getContext('2d');
-    let img = new Image();
-    img.crossOrigin = 'anonymous';
-    img.src = 'roi.jpg';
-    img.onload = function() {
-        ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
-    };
-
-    let tryIt = document.getElementById('tryIt');
-    tryIt.addEventListener('click', () => {
-        initStatus();
-        document.getElementById('status').innerHTML = 'Running function main()...';
-        utils.executeCode('codeEditor');
-        utils.executeCode('codeEditor1');
-        if (modelPath === "") {
-            document.getElementById('status').innerHTML = 'Runing failed.';
-            utils.printError('Please upload model file by clicking the button first.');
-        } else {
-            setTimeout(main, 1);
-        }
-    });
-
-    let fileInput = document.getElementById('fileInput');
-    fileInput.addEventListener('change', (e) => {
-        initStatus();
-        loadImageToCanvas(e, 'canvasInput');
-    });
-
-    let configPath = "";
-    let configFile = document.getElementById('configFile');
-    configFile.addEventListener('change', async (e) => {
-        initStatus();
-        configPath = await loadModel(e);
-        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
-    });
-
-    let modelPath = "";
-    let modelFile = document.getElementById('modelFile');
-    modelFile.addEventListener('change', async (e) => {
-        initStatus();
-        modelPath = await loadModel(e);
-        document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
-        configPath = "";
-        configFile.value = "";
-    });
-
-    utils.loadOpenCv(() => {
-        tryIt.removeAttribute('disabled');
-    });
-
-    var main = async function() {};
-    var generateColors = function(result) {};
-    var argmax = function(result, colors) {};
-
-    utils.executeCode('codeEditor1');
-    utils.executeCode('codeEditor2');
-    utils.executeCode('codeEditor3');
-    utils.executeCode('codeEditor4');
-
-    function updateResult(output, time) {
-        try{
-            let canvasOutput = document.getElementById('canvasOutput');
-            canvasOutput.style.visibility = "visible";
-            let resized = new cv.Mat(canvasOutput.width, canvasOutput.height, cv.CV_8UC4);
-            cv.resize(output, resized, new cv.Size(canvasOutput.width, canvasOutput.height));
-            cv.imshow('canvasOutput', resized);
-            document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
-                                                           <b>Inference time:</b> ${time.toFixed(2)} ms`;
-        } catch(e) {
-            console.log(e);
-        }
-    }
-
-    function initStatus() {
-        document.getElementById('status').innerHTML = '';
-        document.getElementById('canvasOutput').style.visibility = "hidden";
-        utils.clearError();
-    }
-
-</script>
-
-</body>
-
-</html>

doc/js_tutorials/js_assets/js_semantic_segmentation_model_info.json

@@ -1,12 +0,0 @@
-{
-    "tensorflow": [
-        {
-            "model": "deeplabv3",
-            "inputSize": "513, 513",
-            "mean": "127.5, 127.5, 127.5",
-            "std": "0.007843",
-            "swapRB": "false",
-            "modelUrl": "https://drive.google.com/uc?id=1v-hfGenaE9tiGOzo5qdgMNG_gqQ5-Xn4&export=download"
-        }
-    ]
-}

doc/js_tutorials/js_assets/js_style_transfer.html

@@ -1,228 +0,0 @@
-<!DOCTYPE html>
-<html>
-
-<head>
-    <meta charset="utf-8">
-    <title>Style Transfer Example</title>
-    <link href="js_example_style.css" rel="stylesheet" type="text/css" />
-</head>
-
-<body>
-<h2>Style Transfer Example</h2>
-<p>
-    This tutorial shows you how to write an style transfer example with OpenCV.js.<br>
-    To try the example you should click the <b>modelFile</b> button(and <b>configFile</b> button if needed) to upload inference model.
-    You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
-    Then You should change the parameters in the first code snippet according to the uploaded model.
-    Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
-</p>
-
-<div class="control"><button id="tryIt" disabled>Try it</button></div>
-<div>
-    <table cellpadding="0" cellspacing="0" width="0" border="0">
-        <tr>
-            <td>
-                <canvas id="canvasInput" width="400" height="400"></canvas>
-            </td>
-            <td>
-                <canvas id="canvasOutput" style="visibility: hidden;" width="400" height="400"></canvas>
-            </td>
-        </tr>
-        <tr>
-            <td>
-                <div class="caption">
-                    canvasInput <input type="file" id="fileInput" name="file" accept="image/*">
-                </div>
-            </td>
-            <td>
-                <p id='status' align="left"></p>
-            </td>
-        </tr>
-        <tr>
-            <td>
-                <div class="caption">
-                    modelFile <input type="file" id="modelFile" name="file">
-                </div>
-            </td>
-        </tr>
-        <tr>
-            <td>
-                <div class="caption">
-                    configFile <input type="file" id="configFile">
-                </div>
-            </td>
-        </tr>
-    </table>
-</div>
-
-<div>
-    <p class="err" id="errorMessage"></p>
-</div>
-
-<div>
-    <h3>Help function</h3>
-    <p>1.The parameters for model inference which you can modify to investigate more models.</p>
-    <textarea class="code" rows="5" cols="100" id="codeEditor" spellcheck="false"></textarea>
-    <p>2.Main loop in which will read the image from canvas and do inference once.</p>
-    <textarea class="code" rows="15" cols="100" id="codeEditor1" spellcheck="false"></textarea>
-    <p>3.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
-    <textarea class="code" rows="17" cols="100" id="codeEditor2" spellcheck="false"></textarea>
-    <p>4.Fetch model file and save to emscripten file system once click the input button.</p>
-    <textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
-    <p>5.The post-processing, including scaling and reordering.</p>
-    <textarea class="code" rows="21" cols="100" id="codeEditor4" spellcheck="false"></textarea>
-</div>
-
-<div id="appendix">
-    <h2>Model Info:</h2>
-</div>
-
-<script src="utils.js" type="text/javascript"></script>
-<script src="js_dnn_example_helper.js" type="text/javascript"></script>
-
-<script id="codeSnippet" type="text/code-snippet">
-inputSize = [224, 224];
-mean = [104, 117, 123];
-std = 1;
-swapRB = false;
-</script>
-
-<script id="codeSnippet1" type="text/code-snippet">
-main = async function() {
-    const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
-    let net = cv.readNet(configPath, modelPath);
-    net.setInput(input);
-    const start = performance.now();
-    const result = net.forward();
-    const time  = performance.now()-start;
-    const output = postProcess(result);
-
-    updateResult(output, time);
-    input.delete();
-    net.delete();
-    result.delete();
-}
-</script>
-
-<script id="codeSnippet4" type="text/code-snippet">
-postProcess = function(result) {
-    const resultData = result.data32F;
-    const C = result.matSize[1];
-    const H = result.matSize[2];
-    const W = result.matSize[3];
-    const mean = [104, 117, 123];
-
-    let normData = [];
-    for (let h = 0; h < H; ++h) {
-        for (let w = 0; w < W; ++w) {
-            for (let c = 0; c < C; ++c) {
-                normData.push(resultData[c*H*W + h*W + w] + mean[c]);
-            }
-            normData.push(255);
-        }
-    }
-
-    let output = new cv.matFromArray(H, W, cv.CV_8UC4, normData);
-    return output;
-}
-</script>
-
-<script type="text/javascript">
-    let jsonUrl = "js_style_transfer_model_info.json";
-    drawInfoTable(jsonUrl, 'appendix');
-
-    let utils = new Utils('errorMessage');
-    utils.loadCode('codeSnippet', 'codeEditor');
-    utils.loadCode('codeSnippet1', 'codeEditor1');
-
-    let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
-    document.getElementById('codeEditor2').value = getBlobFromImageCode;
-    let loadModelCode = 'loadModel = ' + loadModel.toString();
-    document.getElementById('codeEditor3').value = loadModelCode;
-
-    utils.loadCode('codeSnippet4', 'codeEditor4');
-
-    let canvas = document.getElementById('canvasInput');
-    let ctx = canvas.getContext('2d');
-    let img = new Image();
-    img.crossOrigin = 'anonymous';
-    img.src = 'lena.png';
-    img.onload = function() {
-        ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
-    };
-
-    let tryIt = document.getElementById('tryIt');
-    tryIt.addEventListener('click', () => {
-        initStatus();
-        document.getElementById('status').innerHTML = 'Running function main()...';
-        utils.executeCode('codeEditor');
-        utils.executeCode('codeEditor1');
-        if (modelPath === "") {
-            document.getElementById('status').innerHTML = 'Runing failed.';
-            utils.printError('Please upload model file by clicking the button first.');
-        } else {
-            setTimeout(main, 1);
-        }
-    });
-
-    let fileInput = document.getElementById('fileInput');
-    fileInput.addEventListener('change', (e) => {
-        initStatus();
-        loadImageToCanvas(e, 'canvasInput');
-    });
-
-    let configPath = "";
-    let configFile = document.getElementById('configFile');
-    configFile.addEventListener('change', async (e) => {
-        initStatus();
-        configPath = await loadModel(e);
-        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
-    });
-
-    let modelPath = "";
-    let modelFile = document.getElementById('modelFile');
-    modelFile.addEventListener('change', async (e) => {
-        initStatus();
-        modelPath = await loadModel(e);
-        document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
-        configPath = "";
-        configFile.value = "";
-    });
-
-    utils.loadOpenCv(() => {
-        tryIt.removeAttribute('disabled');
-    });
-
-    var main = async function() {};
-    var postProcess = function(result) {};
-
-    utils.executeCode('codeEditor1');
-    utils.executeCode('codeEditor2');
-    utils.executeCode('codeEditor3');
-    utils.executeCode('codeEditor4');
-
-    function updateResult(output, time) {
-        try{
-            let canvasOutput = document.getElementById('canvasOutput');
-            canvasOutput.style.visibility = "visible";
-            let resized = new cv.Mat(canvasOutput.width, canvasOutput.height, cv.CV_8UC4);
-            cv.resize(output, resized, new cv.Size(canvasOutput.width, canvasOutput.height));
-            cv.imshow('canvasOutput', resized);
-            document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
-                                                           <b>Inference time:</b> ${time.toFixed(2)} ms`;
-        } catch(e) {
-            console.log(e);
-        }
-    }
-
-    function initStatus() {
-        document.getElementById('status').innerHTML = '';
-        document.getElementById('canvasOutput').style.visibility = "hidden";
-        utils.clearError();
-    }
-
-</script>
-
-</body>
-
-</html>

doc/js_tutorials/js_assets/js_style_transfer_model_info.json

@@ -1,76 +0,0 @@
-{
-    "torch": [
-        {
-            "model": "candy.t7",
-            "inputSize": "224, 224",
-            "mean": "104, 117, 123",
-            "std": "1",
-            "swapRB": "false",
-            "modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//instance_norm/candy.t7"
-        },
-        {
-            "model": "composition_vii.t7",
-            "inputSize": "224, 224",
-            "mean": "104, 117, 123",
-            "std": "1",
-            "swapRB": "false",
-            "modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//eccv16/composition_vii.t7"
-        },
-        {
-            "model": "feathers.t7",
-            "inputSize": "224, 224",
-            "mean": "104, 117, 123",
-            "std": "1",
-            "swapRB": "false",
-            "modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//instance_norm/feathers.t7"
-        },
-        {
-            "model": "la_muse.t7",
-            "inputSize": "224, 224",
-            "mean": "104, 117, 123",
-            "std": "1",
-            "swapRB": "false",
-            "modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//instance_norm/la_muse.t7"
-        },
-        {
-            "model": "mosaic.t7",
-            "inputSize": "224, 224",
-            "mean": "104, 117, 123",
-            "std": "1",
-            "swapRB": "false",
-            "modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//instance_norm/mosaic.t7"
-        },
-        {
-            "model": "starry_night.t7",
-            "inputSize": "224, 224",
-            "mean": "104, 117, 123",
-            "std": "1",
-            "swapRB": "false",
-            "modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//eccv16/starry_night.t7"
-        },
-        {
-            "model": "the_scream.t7",
-            "inputSize": "224, 224",
-            "mean": "104, 117, 123",
-            "std": "1",
-            "swapRB": "false",
-            "modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//instance_norm/the_scream.t7"
-        },
-        {
-            "model": "the_wave.t7",
-            "inputSize": "224, 224",
-            "mean": "104, 117, 123",
-            "std": "1",
-            "swapRB": "false",
-            "modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//eccv16/the_wave.t7"
-        },
-        {
-            "model": "udnie.t7",
-            "inputSize": "224, 224",
-            "mean": "104, 117, 123",
-            "std": "1",
-            "swapRB": "false",
-            "modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//instance_norm/udnie.t7"
-        }
-    ]
-}

doc/js_tutorials/js_assets/utils.js

@@ -7,7 +7,7 @@ function Utils(errorOutputId) { // eslint-disable-line no-unused-vars
         let script = document.createElement('script');
         script.setAttribute('async', '');
         script.setAttribute('type', 'text/javascript');
-        script.addEventListener('load', async () => {
+        script.addEventListener('load', () => {
             if (cv.getBuildInformation)
             {
                 console.log(cv.getBuildInformation());
@@ -16,15 +16,9 @@ function Utils(errorOutputId) { // eslint-disable-line no-unused-vars
             else
             {
                 // WASM
-                if (cv instanceof Promise) {
-                    cv = await cv;
+                cv['onRuntimeInitialized']=()=>{
                     console.log(cv.getBuildInformation());
                     onloadCallback();
-                } else {
-                    cv['onRuntimeInitialized']=()=>{
-                        console.log(cv.getBuildInformation());
-                        onloadCallback();
-                    }
                 }
             }
         });

doc/js_tutorials/js_dnn/js_image_classification/js_image_classification.markdown

@@ -1,13 +0,0 @@
-Image Classification Example {#tutorial_js_image_classification}
-=======================================
-
-Goal
-----
-
-- In this tutorial you will learn how to use OpenCV.js dnn module for image classification.
-
-\htmlonly
-<iframe src="../../js_image_classification.html" width="100%"
-        onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
-</iframe>
-\endhtmlonly

doc/js_tutorials/js_dnn/js_image_classification/js_image_classification_with_camera.markdown

@@ -1,15 +0,0 @@
-Image Classification Example with Camera {#tutorial_js_image_classification_with_camera}
-=======================================
-
-Goal
-----
-
-- In this tutorial you will learn how to use OpenCV.js dnn module for image classification example with camera.
-
-@note  If you don't know how to capture video from camera, please review @ref tutorial_js_video_display.
-
-\htmlonly
-<iframe src="../../js_image_classification_with_camera.html" width="100%"
-        onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
-</iframe>
-\endhtmlonly

doc/js_tutorials/js_dnn/js_object_detection/js_object_detection.markdown

@@ -1,13 +0,0 @@
-Object Detection Example {#tutorial_js_object_detection}
-=======================================
-
-Goal
-----
-
-- In this tutorial you will learn how to use OpenCV.js dnn module for object detection.
-
-\htmlonly
-<iframe src="../../js_object_detection.html" width="100%"
-        onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
-</iframe>
-\endhtmlonly

doc/js_tutorials/js_dnn/js_object_detection/js_object_detection_with_camera.markdown

@@ -1,13 +0,0 @@
-Object Detection Example with Camera{#tutorial_js_object_detection_with_camera}
-=======================================
-
-Goal
-----
-
-- In this tutorial you will learn how to use OpenCV.js dnn module for object detection with camera.
-
-\htmlonly
-<iframe src="../../js_object_detection_with_camera.html" width="100%"
-        onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
-</iframe>
-\endhtmlonly

doc/js_tutorials/js_dnn/js_pose_estimation/js_pose_estimation.markdown

@@ -1,13 +0,0 @@
-Pose Estimation Example {#tutorial_js_pose_estimation}
-=======================================
-
-Goal
-----
-
-- In this tutorial you will learn how to use OpenCV.js dnn module for pose estimation.
-
-\htmlonly
-<iframe src="../../js_pose_estimation.html" width="100%"
-        onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
-</iframe>
-\endhtmlonly

doc/js_tutorials/js_dnn/js_semantic_segmentation/js_semantic_segmentation.markdown

@@ -1,13 +0,0 @@
-Semantic Segmentation Example {#tutorial_js_semantic_segmentation}
-=======================================
-
-Goal
-----
-
-- In this tutorial you will learn how to use OpenCV.js dnn module for semantic segmentation.
-
-\htmlonly
-<iframe src="../../js_semantic_segmentation.html" width="100%"
-        onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
-</iframe>
-\endhtmlonly

doc/js_tutorials/js_dnn/js_style_transfer/js_style_transfer.markdown

@@ -1,13 +0,0 @@
-Style Transfer Example {#tutorial_js_style_transfer}
-=======================================
-
-Goal
-----
-
-- In this tutorial you will learn how to use OpenCV.js dnn module for style transfer.
-
-\htmlonly
-<iframe src="../../js_style_transfer.html" width="100%"
-        onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
-</iframe>
-\endhtmlonly

doc/js_tutorials/js_dnn/js_table_of_contents_dnn.markdown

@@ -1,30 +0,0 @@
-Deep Neural Networks (dnn module) {#tutorial_js_table_of_contents_dnn}
-============
-
--   @subpage tutorial_js_image_classification
-
-    Image classification example
-
--   @subpage tutorial_js_image_classification_with_camera
-
-    Image classification example with camera
-
--   @subpage tutorial_js_object_detection
-
-    Object detection example
-
--   @subpage tutorial_js_object_detection_with_camera
-
-    Object detection example with camera
-
--   @subpage tutorial_js_semantic_segmentation
-
-    Semantic segmentation example
-
--   @subpage tutorial_js_style_transfer
-
-    Style transfer example
-
--   @subpage tutorial_js_pose_estimation
-
-    Pose estimation example

doc/js_tutorials/js_setup/js_intro/js_intro.markdown

@@ -13,7 +13,7 @@ OpenCV.js: OpenCV for the JavaScript programmer
 
 Web is the most ubiquitous open computing platform. With HTML5 standards implemented in every browser, web applications are able to render online video with HTML5 video tags, capture webcam video via WebRTC API, and access each pixel of a video frame via canvas API. With abundance of available multimedia content, web developers are in need of a wide array of image and vision processing algorithms in JavaScript to build innovative applications. This requirement is even more essential for emerging applications on the web, such as Web Virtual Reality (WebVR) and Augmented Reality (WebAR). All of these use cases demand efficient implementations of computation-intensive vision kernels on web.
 
-[Emscripten](https://emscripten.org/) is an LLVM-to-JavaScript compiler. It takes LLVM bitcode - which can be generated from C/C++ using clang, and compiles that into asm.js or WebAssembly that can execute directly inside the web browsers. .  Asm.js is a highly optimizable, low-level subset of JavaScript. Asm.js enables ahead-of-time compilation and optimization in JavaScript engine that provide near-to-native execution speed. WebAssembly is a new portable, size- and load-time-efficient binary format suitable for compilation to the web. WebAssembly aims to execute at native speed. WebAssembly is currently being designed as an open standard by W3C.
+[Emscripten](http://kripken.github.io/emscripten-site) is an LLVM-to-JavaScript compiler. It takes LLVM bitcode - which can be generated from C/C++ using clang, and compiles that into asm.js or WebAssembly that can execute directly inside the web browsers. .  Asm.js is a highly optimizable, low-level subset of JavaScript. Asm.js enables ahead-of-time compilation and optimization in JavaScript engine that provide near-to-native execution speed. WebAssembly is a new portable, size- and load-time-efficient binary format suitable for compilation to the web. WebAssembly aims to execute at native speed. WebAssembly is currently being designed as an open standard by W3C.
 
 OpenCV.js is a JavaScript binding for selected subset of OpenCV functions for the web platform. It allows emerging web applications with multimedia processing to benefit from the wide variety of vision functions available in OpenCV. OpenCV.js leverages Emscripten to compile OpenCV functions into asm.js or WebAssembly targets, and provides a JavaScript APIs for web application to access them. The future versions of the library will take advantage of acceleration APIs that are available on the Web such as SIMD and multi-threaded execution.
 
@@ -42,4 +42,4 @@ Below is the list of contributors of OpenCV.js bindings and tutorials.
 -  Gang Song (GSoC student, Shanghai Jiao Tong University)
 -  Wenyao Gan (Student intern, Shanghai Jiao Tong University)
 -  Mohammad Reza Haghighat (Project initiator & sponsor, Intel Corporation)
--  Ningxin Hu (Students' supervisor, Intel Corporation)
+-  Ningxin Hu (Students' supervisor, Intel Corporation)

doc/js_tutorials/js_setup/js_setup/js_setup.markdown

@@ -7,12 +7,12 @@ You don't have to build your own copy if you simply want to start using it. Refe
 Installing Emscripten
 -----------------------------
 
-[Emscripten](https://github.com/emscripten-core/emscripten) is an LLVM-to-JavaScript compiler. We will use Emscripten to build OpenCV.js.
+[Emscripten](https://github.com/kripken/emscripten) is an LLVM-to-JavaScript compiler. We will use Emscripten to build OpenCV.js.
 
 @note
 While this describes installation of required tools from scratch, there's a section below also describing an alternative procedure to perform the same build using docker containers which is often easier.
 
-To Install Emscripten, follow instructions of [Emscripten SDK](https://emscripten.org/docs/getting_started/downloads.html).
+To Install Emscripten, follow instructions of [Emscripten SDK](https://kripken.github.io/emscripten-site/docs/getting_started/downloads.html).
 
 For example:
 @code{.bash}
@@ -21,29 +21,24 @@ For example:
 ./emsdk activate latest
 @endcode
 
+@note
+To compile to [WebAssembly](http://webassembly.org), you need to install and activate [Binaryen](https://github.com/WebAssembly/binaryen) with the `emsdk` command. Please refer to [Developer's Guide](http://webassembly.org/getting-started/developers-guide/) for more details.
 
-After install, ensure the `EMSDK` environment is setup correctly.
+After install, ensure the `EMSCRIPTEN` environment is setup correctly.
 
 For example:
 @code{.bash}
 source ./emsdk_env.sh
-echo ${EMSDK}
+echo ${EMSCRIPTEN}
 @endcode
 
-Modern versions of Emscripten requires to use `emcmake` / `emmake` launchers:
-
-@code{.bash}
-emcmake sh -c 'echo ${EMSCRIPTEN}'
-@endcode
-
-
-The version 2.0.10 of emscripten is verified for latest WebAssembly. Please check the version of Emscripten to use the newest features of WebAssembly.
+The version 1.39.16 of emscripten is verified for latest WebAssembly. Please check the version of emscripten to use the newest features of WebAssembly.
 
 For example:
 @code{.bash}
 ./emsdk update
-./emsdk install 2.0.10
-./emsdk activate 2.0.10
+./emsdk install 1.39.16
+./emsdk activate 1.39.16
 @endcode
 
 Obtaining OpenCV Source Code
@@ -76,7 +71,8 @@ Building OpenCV.js from Source
 
     For example, to build in `build_js` directory:
     @code{.bash}
-    emcmake python ./opencv/platforms/js/build_js.py build_js
+    cd opencv
+    python ./platforms/js/build_js.py build_js
     @endcode
 
     @note
@@ -86,14 +82,14 @@ Building OpenCV.js from Source
 
     For example, to build wasm version in `build_wasm` directory:
     @code{.bash}
-    emcmake python ./opencv/platforms/js/build_js.py build_wasm --build_wasm
+    python ./platforms/js/build_js.py build_wasm --build_wasm
     @endcode
 
 -#  [Optional] To build the OpenCV.js loader, append `--build_loader`.
 
     For example:
     @code{.bash}
-    emcmake python ./opencv/platforms/js/build_js.py build_js --build_loader
+    python ./platforms/js/build_js.py build_js --build_loader
     @endcode
 
     @note
@@ -118,7 +114,7 @@ Building OpenCV.js from Source
 
     For example:
     @code{.bash}
-    emcmake python ./opencv/platforms/js/build_js.py build_js --build_doc
+    python ./platforms/js/build_js.py build_js --build_doc
     @endcode
 
     @note
@@ -128,21 +124,7 @@ Building OpenCV.js from Source
 
     For example:
     @code{.bash}
-    emcmake python ./opencv/platforms/js/build_js.py build_js --build_test
-    @endcode
-
--#  [optional] To enable OpenCV contrib modules append `--cmake_option="-DOPENCV_EXTRA_MODULES_PATH=/path/to/opencv_contrib/modules/"`
-
-    For example:
-    @code{.bash}
-    python ./platforms/js/build_js.py build_js --cmake_option="-DOPENCV_EXTRA_MODULES_PATH=opencv_contrib/modules"
-    @endcode
-
--#  [optional] To enable OpenCV contrib modules append `--cmake_option="-DOPENCV_EXTRA_MODULES_PATH=/path/to/opencv_contrib/modules/"`
-
-    For example:
-    @code{.bash}
-    python ./platforms/js/build_js.py build_js --cmake_option="-DOPENCV_EXTRA_MODULES_PATH=opencv_contrib/modules"
+    python ./platforms/js/build_js.py build_js --build_test
     @endcode
 
 Running OpenCV.js Tests
@@ -204,7 +186,7 @@ node tests.js
 
     For example:
     @code{.bash}
-    emcmake python ./opencv/platforms/js/build_js.py build_js --build_wasm --threads
+    python ./platforms/js/build_js.py build_js --build_wasm --threads
     @endcode
 
     The default threads number is the logic core number of your device. You can use `cv.parallel_pthreads_set_threads_num(number)` to set threads number by yourself and use `cv.parallel_pthreads_get_threads_num()` to get the current threads number.
@@ -216,7 +198,7 @@ node tests.js
 
     For example:
     @code{.bash}
-    emcmake python ./opencv/platforms/js/build_js.py build_js --build_wasm --simd
+    python ./platforms/js/build_js.py build_js --build_wasm --simd
     @endcode
 
     The simd optimization is experimental as wasm simd is still in development.
@@ -240,7 +222,7 @@ node tests.js
 
     For example:
     @code{.bash}
-    emcmake python ./opencv/platforms/js/build_js.py build_js --build_wasm --simd --build_wasm_intrin_test
+    python ./platforms/js/build_js.py build_js --build_wasm --simd --build_wasm_intrin_test
     @endcode
 
     For wasm intrinsics tests, you can use the following function to test all the cases:
@@ -268,7 +250,7 @@ node tests.js
 
     For example:
     @code{.bash}
-    emcmake python ./opencv/platforms/js/build_js.py build_js --build_perf
+    python ./platforms/js/build_js.py build_js --build_perf
     @endcode
 
     To run performance tests, launch a local web server in \<build_dir\>/bin folder. For example, node http-server which serves on `localhost:8080`.
@@ -289,25 +271,25 @@ Building OpenCV.js with Docker
 
 Alternatively, the same build can be can be accomplished using [docker](https://www.docker.com/) containers which is often easier and more reliable, particularly in non linux systems. You only need to install [docker](https://www.docker.com/) on your system and use a popular container that provides a clean well tested environment for emscripten builds like this, that already has latest versions of all the necessary tools installed.
 
-So, make sure [docker](https://www.docker.com/) is installed in your system and running. The following shell script should work in Linux and MacOS:
+So, make sure [docker](https://www.docker.com/) is installed in your system and running. The following shell script should work in linux and MacOS:
 
 @code{.bash}
 git clone https://github.com/opencv/opencv.git
 cd opencv
-docker run --rm -v $(pwd):/src -u $(id -u):$(id -g) emscripten/emsdk emcmake python3 ./dev/platforms/js/build_js.py build_js
+docker run --rm --workdir /code -v "$PWD":/code "trzeci/emscripten:latest" python ./platforms/js/build_js.py build
 @endcode
 
 In Windows use the following PowerShell command:
 
 @code{.bash}
-docker run --rm --workdir /src -v "$(get-location):/src" "emscripten/emsdk" emcmake python3 ./dev/platforms/js/build_js.py build_js
+docker run --rm --workdir /code -v "$(get-location):/code" "trzeci/emscripten:latest" python ./platforms/js/build_js.py build
 @endcode
 
 @warning
-The example uses latest version of emscripten. If the build fails you should try a version that is known to work fine which is `2.0.10` using the following command:
+The example uses latest version of emscripten. If the build fails you should try a version that is known to work fine which is `1.38.32` using the following command:
 
 @code{.bash}
-docker run --rm -v $(pwd):/src -u $(id -u):$(id -g) emscripten/emsdk:2.0.10 emcmake python3 ./dev/platforms/js/build_js.py build_js
+docker run --rm --workdir /code -v "$PWD":/code "trzeci/emscripten:sdk-tag-1.38.32-64bit" python ./platforms/js/build_js.py build
 @endcode
 
 ### Building the documentation with Docker
@@ -315,11 +297,10 @@ docker run --rm -v $(pwd):/src -u $(id -u):$(id -g) emscripten/emsdk:2.0.10 emcm
 To build the documentation `doxygen` needs to be installed. Create a file named `Dockerfile` with the following content:
 
 ```
-FROM emscripten/emsdk:2.0.10
+FROM trzeci/emscripten:sdk-tag-1.38.32-64bit
 
-RUN apt-get update \
-  && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends doxygen \
-  && rm -rf /var/lib/apt/lists/*
+RUN apt-get update -y
+RUN apt-get install -y doxygen
 ```
 
 Then we build the docker image and name it `opencv-js-doc` with the following command (that needs to be run only once):
@@ -331,5 +312,5 @@ docker build . -t opencv-js-doc
 Now run the build command again, this time using the new image and passing `--build_doc`:
 
 @code{.bash}
-docker run --rm -v $(pwd):/src -u $(id -u):$(id -g) "opencv-js-doc" emcmake python3 ./dev/platforms/js/build_js.py build_js --build_doc
+docker run --rm --workdir /code -v "$PWD":/code "opencv-js-doc" python ./platforms/js/build_js.py build --build_doc
 @endcode

doc/js_tutorials/js_setup/js_usage/js_usage.markdown

@@ -4,7 +4,7 @@ Using OpenCV.js {#tutorial_js_usage}
 Steps
 -----
 
-In this tutorial, you will learn how to include and start to use `opencv.js` inside a web page. You can get a copy of `opencv.js` from `opencv-{VERSION_NUMBER}-docs.zip` in each [release](https://github.com/opencv/opencv/releases), or simply download the prebuilt script from the online documentations at "https://docs.opencv.org/{VERSION_NUMBER}/opencv.js" (For example, [https://docs.opencv.org/3.4.0/opencv.js](https://docs.opencv.org/3.4.0/opencv.js). Use `master` if you want the latest build). You can also build your own copy by following the tutorial on Build Opencv.js.
+In this tutorial, you will learn how to include and start to use `opencv.js` inside a web page. You can get a copy of `opencv.js` from `opencv-{VERSION_NUMBER}-docs.zip` in each [release](https://github.com/opencv/opencv/releases), or simply download the prebuilt script from the online documentations at "https://docs.opencv.org/{VERISON_NUMBER}/opencv.js" (For example, [https://docs.opencv.org/3.4.0/opencv.js](https://docs.opencv.org/3.4.0/opencv.js). Use `master` if you want the latest build). You can also build your own copy by following the tutorial on Build Opencv.js.
 
 ### Create a web page
 
@@ -129,7 +129,7 @@ function onOpenCvReady() {
 </html>
 @endcode
 
-@note You have to call delete method of cv.Mat to free memory allocated in Emscripten's heap. Please refer to [Memory management of Emscripten](https://emscripten.org/docs/porting/connecting_cpp_and_javascript/embind.html#memory-management) for details.
+@note You have to call delete method of cv.Mat to free memory allocated in Emscripten's heap. Please refer to [Memory management of Emscripten](https://kripken.github.io/emscripten-site/docs/porting/connecting_cpp_and_javascript/embind.html#memory-management) for details.
 
 Try it
 ------
@@ -137,4 +137,4 @@ Try it
 <iframe src="../../js_setup_usage.html" width="100%"
         onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
 </iframe>
-\endhtmlonly
+\endhtmlonly

doc/js_tutorials/js_tutorials.markdown

@@ -26,7 +26,3 @@ OpenCV.js Tutorials {#tutorial_js_root}
 
     In this section you
     will object detection techniques like face detection etc.
-
--   @subpage tutorial_js_table_of_contents_dnn
-
-    These tutorials show how to use dnn module in JavaScript

doc/opencv.bib

@@ -1261,26 +1261,3 @@
   pages={281--305},
   year={1987}
 }
-@inproceedings{liao2020real,
-  author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang},
-  title={Real-time Scene Text Detection with Differentiable Binarization},
-  booktitle={Proc. AAAI},
-  year={2020}
-}
-@article{shi2016end,
-  title={An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition},
-  author={Shi, Baoguang and Bai, Xiang and Yao, Cong},
-  journal={IEEE transactions on pattern analysis and machine intelligence},
-  volume={39},
-  number={11},
-  pages={2298--2304},
-  year={2016},
-  publisher={IEEE}
-}
-@inproceedings{zhou2017east,
-  title={East: an efficient and accurate scene text detector},
-  author={Zhou, Xinyu and Yao, Cong and Wen, He and Wang, Yuzhi and Zhou, Shuchang and He, Weiran and Liang, Jiajun},
-  booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
-  pages={5551--5560},
-  year={2017}
-}

doc/py_tutorials/py_calib3d/py_calibration/py_calibration.markdown

@@ -209,7 +209,7 @@ find the average error, we calculate the arithmetical mean of the errors calcula
 calibration images.
 @code{.py}
 mean_error = 0
-for i in range(len(objpoints)):
+for i in xrange(len(objpoints)):
     imgpoints2, _ = cv.projectPoints(objpoints[i], rvecs[i], tvecs[i], mtx, dist)
     error = cv.norm(imgpoints[i], imgpoints2, cv.NORM_L2)/len(imgpoints2)
     mean_error += error

doc/py_tutorials/py_calib3d/py_epipolar_geometry/py_epipolar_geometry.markdown

@@ -79,7 +79,7 @@ from matplotlib import pyplot as plt
 img1 = cv.imread('myleft.jpg',0)  #queryimage # left image
 img2 = cv.imread('myright.jpg',0) #trainimage # right image
 
-sift = cv.SIFT_create()
+sift = cv.SIFT()
 
 # find the keypoints and descriptors with SIFT
 kp1, des1 = sift.detectAndCompute(img1,None)
@@ -93,12 +93,14 @@ search_params = dict(checks=50)
 flann = cv.FlannBasedMatcher(index_params,search_params)
 matches = flann.knnMatch(des1,des2,k=2)
 
+good = []
 pts1 = []
 pts2 = []
 
 # ratio test as per Lowe's paper
 for i,(m,n) in enumerate(matches):
     if m.distance < 0.8*n.distance:
+        good.append(m)
         pts2.append(kp2[m.trainIdx].pt)
         pts1.append(kp1[m.queryIdx].pt)
 @endcode

doc/tools/scan_tutorials.py

@@ -1,96 +0,0 @@
-#!/usr/bin/env python
-
-from pathlib import Path
-import re
-
-# Tasks
-# 1. Find all tutorials
-# 2. Generate tree (@subpage)
-# 3. Check prev/next nodes
-
-class Tutorial(object):
-    def __init__(self, path):
-        self.path = path
-        self.title = None # doxygen title
-        self.children = [] # ordered titles
-        self.prev = None
-        self.next = None
-        with open(path, "rt") as f:
-            self.parse(f)
-
-    def parse(self, f):
-        rx_title = re.compile(r"\{#(\w+)\}")
-        rx_subpage = re.compile(r"@subpage\s+(\w+)")
-        rx_prev = re.compile(r"@prev_tutorial\{(\w+)\}")
-        rx_next = re.compile(r"@next_tutorial\{(\w+)\}")
-        for line in f:
-            if self.title is None:
-                m = rx_title.search(line)
-                if m:
-                    self.title = m.group(1)
-                    continue
-            if self.prev is None:
-                m = rx_prev.search(line)
-                if m:
-                    self.prev = m.group(1)
-                    continue
-            if self.next is None:
-                m = rx_next.search(line)
-                if m:
-                    self.next = m.group(1)
-                    continue
-            m = rx_subpage.search(line)
-            if m:
-                self.children.append(m.group(1))
-                continue
-
-    def verify_prev_next(self, storage):
-        res = True
-
-        if self.title is None:
-            print("[W] No title")
-            res = False
-
-        prev = None
-        for one in self.children:
-            c = storage[one]
-            if c.prev is not None and c.prev != prev:
-                print("[W] Wrong prev_tutorial: expected {} / actual {}".format(c.prev, prev))
-                res = False
-            prev = c.title
-
-        next = None
-        for one in reversed(self.children):
-            c = storage[one]
-            if c.next is not None and c.next != next:
-                print("[W] Wrong next_tutorial: expected {} / actual {}".format(c.next, next))
-                res = False
-            next = c.title
-
-        if len(self.children) == 0 and self.prev is None and self.next is None:
-            print("[W] No prev and next tutorials")
-            res = False
-
-        return res
-
-if __name__ == "__main__":
-
-    p = Path('tutorials')
-    print("Looking for tutorials in: '{}'".format(p))
-
-    all_tutorials = dict()
-    for f in p.glob('**/*'):
-        if f.suffix.lower() in ('.markdown', '.md'):
-            t = Tutorial(f)
-            all_tutorials[t.title] = t
-
-    res = 0
-    print("Found: {}".format(len(all_tutorials)))
-    print("------")
-    for title, t in all_tutorials.items():
-        if not t.verify_prev_next(all_tutorials):
-            print("[E] Verification failed: {}".format(t.path))
-            print("------")
-            res = 1
-
-    exit(res)

doc/tutorials/app/_old/table_of_content_highgui.markdown

@@ -1,4 +0,0 @@
-High Level GUI and Media (highgui module) {#tutorial_table_of_content_highgui}
-=========================================
-
-Content has been moved to this page: @ref tutorial_table_of_content_app

doc/tutorials/app/_old/table_of_content_imgcodecs.markdown

@@ -1,4 +0,0 @@
-Image Input and Output (imgcodecs module) {#tutorial_table_of_content_imgcodecs}
-=========================================
-
-Content has been moved to this page: @ref tutorial_table_of_content_app

doc/tutorials/app/_old/table_of_content_videoio.markdown

@@ -1,4 +0,0 @@
-Video Input and Output (videoio module) {#tutorial_table_of_content_videoio}
-=========================================
-
-Content has been moved to this page: @ref tutorial_table_of_content_app

doc/tutorials/app/orbbec_astra.markdown

@@ -1,165 +0,0 @@
-Using Orbbec Astra 3D cameras {#tutorial_orbbec_astra}
-======================================================
-
-@tableofcontents
-
-@prev_tutorial{tutorial_kinect_openni}
-@next_tutorial{tutorial_intelperc}
-
-
-### Introduction
-
-This tutorial is devoted to the Astra Series of Orbbec 3D cameras (https://orbbec3d.com/product-astra-pro/).
-That cameras have a depth sensor in addition to a common color sensor. The depth sensors can be read using
-the open source OpenNI API with @ref cv::VideoCapture class. The video stream is provided through the regular
-camera interface.
-
-### Installation Instructions
-
-In order to use a depth sensor with OpenCV you should do the following steps:
-
--#  Download the latest version of Orbbec OpenNI SDK (from here <https://orbbec3d.com/develop/>).
-    Unzip the archive, choose the build according to your operating system and follow installation
-    steps provided in the Readme file. For instance, if you use 64bit GNU/Linux run:
-    @code{.bash}
-    $ cd Linux/OpenNI-Linux-x64-2.3.0.63/
-    $ sudo ./install.sh
-    @endcode
-    When you are done with the installation, make sure to replug your device for udev rules to take
-    effect. The camera should now work as a general camera device. Note that your current user should
-    belong to group `video` to have access to the camera. Also, make sure to source `OpenNIDevEnvironment` file:
-    @code{.bash}
-    $ source OpenNIDevEnvironment
-    @endcode
-
--#  Run the following commands to verify that OpenNI library and header files can be found. You should see
-    something similar in your terminal:
-    @code{.bash}
-    $ echo $OPENNI2_INCLUDE
-    /home/user/OpenNI_2.3.0.63/Linux/OpenNI-Linux-x64-2.3.0.63/Include
-    $ echo $OPENNI2_REDIST
-    /home/user/OpenNI_2.3.0.63/Linux/OpenNI-Linux-x64-2.3.0.63/Redist
-    @endcode
-    If the above two variables are empty, then you need to source `OpenNIDevEnvironment` again. Now you can
-    configure OpenCV with OpenNI support enabled by setting the `WITH_OPENNI2` flag in CMake.
-    You may also like to enable the `BUILD_EXAMPLES` flag to get a code sample working with your Astra camera.
-    Run the following commands in the directory containing OpenCV source code to enable OpenNI support:
-    @code{.bash}
-    $ mkdir build
-    $ cd build
-    $ cmake -DWITH_OPENNI2=ON ..
-    @endcode
-    If the OpenNI library is found, OpenCV will be built with OpenNI2 support. You can see the status of OpenNI2
-    support in the CMake log:
-    @code{.text}
-    --   Video I/O:
-    --     DC1394:                      YES (2.2.6)
-    --     FFMPEG:                      YES
-    --       avcodec:                   YES (58.91.100)
-    --       avformat:                  YES (58.45.100)
-    --       avutil:                    YES (56.51.100)
-    --       swscale:                   YES (5.7.100)
-    --       avresample:                NO
-    --     GStreamer:                   YES (1.18.1)
-    --     OpenNI2:                     YES (2.3.0)
-    --     v4l/v4l2:                    YES (linux/videodev2.h)
-    @endcode
-
--#  Build OpenCV:
-    @code{.bash}
-    $ make
-    @endcode
-
-### Code
-
-The Astra Pro camera has two sensors -- a depth sensor and a color sensor. The depth sensors
-can be read using the OpenNI interface with @ref cv::VideoCapture class. The video stream is
-not available through OpenNI API and is only provided through the regular camera interface.
-So, to get both depth and color frames, two @ref cv::VideoCapture objects should be created:
-
-@snippetlineno samples/cpp/tutorial_code/videoio/orbbec_astra/orbbec_astra.cpp Open streams
-
-The first object will use the Video4Linux2 interface to access the color sensor. The second one
-is using OpenNI2 API to retrieve depth data.
-
-Before using the created VideoCapture objects you may want to set up stream parameters by setting
-objects' properties. The most important parameters are frame width, frame height and fps.
-For this example, we’ll configure width and height of both streams to VGA resolution as that’s
-the maximum resolution available for both sensors and we’d like both stream parameters to be the same:
-
-@snippetlineno samples/cpp/tutorial_code/videoio/orbbec_astra/orbbec_astra.cpp Setup streams
-
-For setting and getting some property of sensor data generators use @ref cv::VideoCapture::set and
-@ref cv::VideoCapture::get methods respectively, e.g. :
-
-@snippetlineno samples/cpp/tutorial_code/videoio/orbbec_astra/orbbec_astra.cpp Get properties
-
-The following properties of cameras available through OpenNI interfaces are supported for the depth
-generator:
-
--   @ref cv::CAP_PROP_FRAME_WIDTH -- Frame width in pixels.
--   @ref cv::CAP_PROP_FRAME_HEIGHT -- Frame height in pixels.
--   @ref cv::CAP_PROP_FPS -- Frame rate in FPS.
--   @ref cv::CAP_PROP_OPENNI_REGISTRATION -- Flag that registers the remapping depth map to image map
-    by changing the depth generator's viewpoint (if the flag is "on") or sets this view point to
-    its normal one (if the flag is "off"). The registration process’ resulting images are
-    pixel-aligned, which means that every pixel in the image is aligned to a pixel in the depth
-    image.
--   @ref cv::CAP_PROP_OPENNI2_MIRROR -- Flag to enable or disable mirroring for this stream. Set to 0
-    to disable mirroring
-
-    Next properties are available for getting only:
-
--   @ref cv::CAP_PROP_OPENNI_FRAME_MAX_DEPTH -- A maximum supported depth of the camera in mm.
--   @ref cv::CAP_PROP_OPENNI_BASELINE -- Baseline value in mm.
-
-After the VideoCapture objects are set up you can start reading frames from them.
-
-@note
-    OpenCV's VideoCapture provides synchronous API, so you have to grab frames in a new thread
-    to avoid one stream blocking while another stream is being read. VideoCapture is not a
-    thread-safe class, so you need to be careful to avoid any possible deadlocks or data races.
-
-As there are two video sources that should be read simultaneously, it’s necessary to create two
-threads to avoid blocking. Example implementation that gets frames from each sensor in a new thread
-and stores them in a list along with their timestamps:
-
-@snippetlineno samples/cpp/tutorial_code/videoio/orbbec_astra/orbbec_astra.cpp Read streams
-
-VideoCapture can retrieve the following data:
-
--#  data given from the depth generator:
-    -   @ref cv::CAP_OPENNI_DEPTH_MAP - depth values in mm (CV_16UC1)
-    -   @ref cv::CAP_OPENNI_POINT_CLOUD_MAP - XYZ in meters (CV_32FC3)
-    -   @ref cv::CAP_OPENNI_DISPARITY_MAP - disparity in pixels (CV_8UC1)
-    -   @ref cv::CAP_OPENNI_DISPARITY_MAP_32F - disparity in pixels (CV_32FC1)
-    -   @ref cv::CAP_OPENNI_VALID_DEPTH_MASK - mask of valid pixels (not occluded, not shaded, etc.)
-        (CV_8UC1)
-
--#  data given from the color sensor is a regular BGR image (CV_8UC3).
-
-When new data are available a reading thread notifies the main thread using a condition variable.
-A frame is stored in the ordered list -- the first frame is the latest one. As depth and color frames
-are read from independent sources two video streams may become out of sync even when both streams
-are set up for the same frame rate. A post-synchronization procedure can be applied to the streams
-to combine depth and color frames into pairs. The sample code below demonstrates this procedure:
-
-@snippetlineno samples/cpp/tutorial_code/videoio/orbbec_astra/orbbec_astra.cpp Pair frames
-
-In the code snippet above the execution is blocked until there are some frames in both frame lists.
-When there are new frames, their timestamps are being checked -- if they differ more than a half of
-the frame period then one of the frames is dropped. If timestamps are close enough, then two frames
-are paired. Now, we have two frames: one containing color information and another one -- depth information.
-In the example above retrieved frames are simply shown with cv::imshow function, but you can insert
-any other processing code here.
-
-In the sample images below you can see the color frame and the depth frame representing the same scene.
-Looking at the color frame it's hard to distinguish plant leaves from leaves painted on a wall,
-but the depth data makes it easy.
-
-![Color frame](images/astra_color.jpg)
-![Depth frame](images/astra_depth.png)
-
-The complete implementation can be found in
-[orbbec_astra.cpp](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/videoio/orbbec_astra/orbbec_astra.cpp)
-in `samples/cpp/tutorial_code/videoio` directory.

doc/tutorials/app/table_of_content_app.markdown

@@ -1,10 +0,0 @@
-Application utils (highgui, imgcodecs, videoio modules) {#tutorial_table_of_content_app}
-=======================================================
-
--   @subpage tutorial_trackbar
--   @subpage tutorial_raster_io_gdal
--   @subpage tutorial_video_input_psnr_ssim
--   @subpage tutorial_video_write
--   @subpage tutorial_kinect_openni
--   @subpage tutorial_orbbec_astra
--   @subpage tutorial_intelperc

doc/tutorials/calib3d/camera_calibration/camera_calibration.markdown

@@ -1,16 +1,9 @@
 Camera calibration With OpenCV {#tutorial_camera_calibration}
 ==============================
 
-@tableofcontents
-
 @prev_tutorial{tutorial_camera_calibration_square_chess}
 @next_tutorial{tutorial_real_time_pose}
 
-|    |    |
-| -: | :- |
-| Original author | Bernát Gábor |
-| Compatibility | OpenCV >= 4.0 |
-
 
 Cameras have been around for a long-long time. However, with the introduction of the cheap *pinhole*
 cameras in the late 20th century, they became a common occurrence in our everyday life.

doc/tutorials/calib3d/camera_calibration_pattern/camera_calibration_pattern.markdown

@@ -1,15 +1,8 @@
 Create calibration pattern {#tutorial_camera_calibration_pattern}
 =========================================
 
-@tableofcontents
-
 @next_tutorial{tutorial_camera_calibration_square_chess}
 
-|    |    |
-| -: | :- |
-| Original author | Laurent Berger |
-| Compatibility | OpenCV >= 3.0 |
-
 
 The goal of this tutorial is to learn how to create calibration pattern.
 

doc/tutorials/calib3d/camera_calibration_square_chess/camera_calibration_square_chess.markdown

@@ -1,16 +1,9 @@
 Camera calibration with square chessboard {#tutorial_camera_calibration_square_chess}
 =========================================
 
-@tableofcontents
-
 @prev_tutorial{tutorial_camera_calibration_pattern}
 @next_tutorial{tutorial_camera_calibration}
 
-|    |    |
-| -: | :- |
-| Original author | Victor Eruhimov |
-| Compatibility | OpenCV >= 4.0 |
-
 
 The goal of this tutorial is to learn how to calibrate a camera given a set of chessboard images.
 

doc/tutorials/calib3d/interactive_calibration/interactive_calibration.markdown

@@ -1,15 +1,8 @@
 Interactive camera calibration application {#tutorial_interactive_calibration}
 ==============================
 
-@tableofcontents
-
 @prev_tutorial{tutorial_real_time_pose}
 
-|    |    |
-| -: | :- |
-| Original author | Vladislav Sovrasov |
-| Compatibility | OpenCV >= 3.1 |
-
 
 According to classical calibration technique user must collect all data first and when run @ref cv::calibrateCamera function
 to obtain camera parameters. If average re-projection error is huge or if estimated parameters seems to be wrong, process of

doc/tutorials/calib3d/real_time_pose/real_time_pose.markdown

@@ -1,16 +1,9 @@
 Real Time pose estimation of a textured object {#tutorial_real_time_pose}
 ==============================================
 
-@tableofcontents
-
 @prev_tutorial{tutorial_camera_calibration}
 @next_tutorial{tutorial_interactive_calibration}
 
-|    |    |
-| -: | :- |
-| Original author | Edgar Riba |
-| Compatibility | OpenCV >= 3.0 |
-
 
 Nowadays, augmented reality is one of the top research topic in computer vision and robotics fields.
 The most elemental problem in augmented reality is the estimation of the camera pose respect of an

doc/tutorials/calib3d/table_of_content_calib3d.markdown

@@ -1,8 +1,58 @@
 Camera calibration and 3D reconstruction (calib3d module) {#tutorial_table_of_content_calib3d}
 ==========================================================
 
+Although we get most of our images in a 2D format they do come from a 3D world. Here you will learn how to find out 3D world information from 2D images.
+
 -   @subpage tutorial_camera_calibration_pattern
+
+    *Languages:* Python
+
+    *Compatibility:* \> OpenCV 2.0
+
+    *Author:* Laurent Berger
+
+    You will learn how to create some calibration pattern.
+
 -   @subpage tutorial_camera_calibration_square_chess
+
+    *Languages:* C++
+
+    *Compatibility:* \> OpenCV 2.0
+
+    *Author:* Victor Eruhimov
+
+    You will use some chessboard images to calibrate your camera.
+
 -   @subpage tutorial_camera_calibration
+
+    *Languages:* C++
+
+    *Compatibility:* \> OpenCV 4.0
+
+    *Author:* Bernát Gábor
+
+    Camera calibration by using either the chessboard, circle or the asymmetrical circle
+    pattern. Get the images either from a camera attached, a video file or from an image
+    collection.
+
 -   @subpage tutorial_real_time_pose
+
+    *Languages:* C++
+
+    *Compatibility:* \> OpenCV 2.0
+
+    *Author:* Edgar Riba
+
+    Real time pose estimation of a textured object using ORB features, FlannBased matcher, PnP
+    approach plus Ransac and Linear Kalman Filter to reject possible bad poses.
+
 -   @subpage tutorial_interactive_calibration
+
+    *Compatibility:* \> OpenCV 3.1
+
+    *Author:* Vladislav Sovrasov
+
+    Camera calibration by using either the chessboard, chAruco, asymmetrical circle or dual asymmetrical circle
+    pattern. Calibration process is continuous, so you can see results after each new pattern shot.
+    As an output you get average reprojection error, intrinsic camera parameters, distortion coefficients and
+     confidence intervals for all of evaluated variables.

doc/tutorials/core/adding_images/adding_images.markdown

@@ -1,17 +1,9 @@
 Adding (blending) two images using OpenCV {#tutorial_adding_images}
 =========================================
 
-@tableofcontents
-
 @prev_tutorial{tutorial_mat_operations}
 @next_tutorial{tutorial_basic_linear_transform}
 
-|    |    |
-| -: | :- |
-| Original author | Ana Huamán |
-| Compatibility | OpenCV >= 3.0 |
-
-We will learn how to blend two images!
 Goal
 ----
 

doc/tutorials/core/basic_linear_transform/basic_linear_transform.markdown

@@ -1,16 +1,9 @@
 Changing the contrast and brightness of an image! {#tutorial_basic_linear_transform}
 =================================================
 
-@tableofcontents
-
 @prev_tutorial{tutorial_adding_images}
 @next_tutorial{tutorial_discrete_fourier_transform}
 
-|    |    |
-| -: | :- |
-| Original author | Ana Huamán |
-| Compatibility | OpenCV >= 3.0 |
-
 Goal
 ----
 

doc/tutorials/core/discrete_fourier_transform/discrete_fourier_transform.markdown

@@ -1,16 +1,9 @@
 Discrete Fourier Transform {#tutorial_discrete_fourier_transform}
 ==========================
 
-@tableofcontents
-
 @prev_tutorial{tutorial_basic_linear_transform}
 @next_tutorial{tutorial_file_input_output_with_xml_yml}
 
-|    |    |
-| -: | :- |
-| Original author | Bernát Gábor |
-| Compatibility | OpenCV >= 3.0 |
-
 Goal
 ----
 

doc/tutorials/core/file_input_output_with_xml_yml/file_input_output_with_xml_yml.markdown

@@ -1,16 +1,9 @@
 File Input and Output using XML and YAML files {#tutorial_file_input_output_with_xml_yml}
 ==============================================
 
-@tableofcontents
-
 @prev_tutorial{tutorial_discrete_fourier_transform}
 @next_tutorial{tutorial_how_to_use_OpenCV_parallel_for_}
 
-|    |    |
-| -: | :- |
-| Original author | Bernát Gábor |
-| Compatibility | OpenCV >= 3.0 |
-
 Goal
 ----
 

doc/tutorials/core/how_to_scan_images/how_to_scan_images.markdown

@@ -1,16 +1,9 @@
 How to scan images, lookup tables and time measurement with OpenCV {#tutorial_how_to_scan_images}
 ==================================================================
 
-@tableofcontents
-
 @prev_tutorial{tutorial_mat_the_basic_image_container}
 @next_tutorial{tutorial_mat_mask_operations}
 
-|    |    |
-| -: | :- |
-| Original author | Bernát Gábor |
-| Compatibility | OpenCV >= 3.0 |
-
 Goal
 ----
 

doc/tutorials/core/how_to_use_OpenCV_parallel_for_/how_to_use_OpenCV_parallel_for_.markdown

@@ -1,14 +1,8 @@
 How to use the OpenCV parallel_for_ to parallelize your code {#tutorial_how_to_use_OpenCV_parallel_for_}
 ==================================================================
 
-@tableofcontents
-
 @prev_tutorial{tutorial_file_input_output_with_xml_yml}
 
-|    |    |
-| -: | :- |
-| Compatibility | OpenCV >= 3.0 |
-
 Goal
 ----
 
@@ -38,7 +32,7 @@ automatically available with the platform (e.g. APPLE GCD) but chances are that
 have access to a parallel framework either directly or by enabling the option in CMake and rebuild the library.
 
 The second (weak) precondition is more related to the task you want to achieve as not all computations
-are suitable / can be adapted to be run in a parallel way. To remain simple, tasks that can be split
+are suitable / can be adatapted to be run in a parallel way. To remain simple, tasks that can be split
 into multiple elementary operations with no memory dependency (no possible race condition) are easily
 parallelizable. Computer vision processing are often easily parallelizable as most of the time the processing of
 one pixel does not depend to the state of other pixels.

doc/tutorials/core/mat-mask-operations/mat_mask_operations.markdown

@@ -1,16 +1,9 @@
 Mask operations on matrices {#tutorial_mat_mask_operations}
 ===========================
 
-@tableofcontents
-
 @prev_tutorial{tutorial_how_to_scan_images}
 @next_tutorial{tutorial_mat_operations}
 
-|    |    |
-| -: | :- |
-| Original author | Bernát Gábor |
-| Compatibility | OpenCV >= 3.0 |
-
 Mask operations on matrices are quite simple. The idea is that we recalculate each pixel's value in
 an image according to a mask matrix (also known as kernel). This mask holds values that will adjust
 how much influence neighboring pixels (and the current pixel) have on the new pixel value. From a

doc/tutorials/core/mat_operations.markdown

@@ -1,15 +1,9 @@
 Operations with images {#tutorial_mat_operations}
 ======================
 
-@tableofcontents
-
 @prev_tutorial{tutorial_mat_mask_operations}
 @next_tutorial{tutorial_adding_images}
 
-|    |    |
-| -: | :- |
-| Compatibility | OpenCV >= 3.0 |
-
 Input/Output
 ------------
 

doc/tutorials/core/mat_the_basic_image_container/mat_the_basic_image_container.markdown

@@ -1,15 +1,8 @@
 Mat - The Basic Image Container {#tutorial_mat_the_basic_image_container}
 ===============================
 
-@tableofcontents
-
 @next_tutorial{tutorial_how_to_scan_images}
 
-|    |    |
-| -: | :- |
-| Original author | Bernát Gábor |
-| Compatibility | OpenCV >= 3.0 |
-
 Goal
 ----
 

doc/tutorials/core/table_of_content_core.markdown

@@ -1,12 +1,97 @@
 The Core Functionality (core module) {#tutorial_table_of_content_core}
 =====================================
 
+Here you will learn the about the basic building blocks of the library. A must read and know for
+understanding how to manipulate the images on a pixel level.
+
 -   @subpage tutorial_mat_the_basic_image_container
+
+    *Languages:* C++
+
+    *Compatibility:* \> OpenCV 2.0
+
+    *Author:* Bernát Gábor
+
+    You will learn how to store images in the memory and how to print out their content to the
+    console.
+
 -   @subpage tutorial_how_to_scan_images
+
+    *Languages:* C++
+
+    *Compatibility:* \> OpenCV 2.0
+
+    *Author:* Bernát Gábor
+
+    You'll find out how to scan images (go through each of the image pixels) with OpenCV.
+    Bonus: time measurement with OpenCV.
+
+
 -   @subpage tutorial_mat_mask_operations
+
+    *Languages:* C++, Java, Python
+
+    *Compatibility:* \> OpenCV 2.0
+
+    *Author:* Bernát Gábor
+
+    You'll find out how to scan images with neighbor access and use the @ref cv::filter2D
+    function to apply kernel filters on images.
+
 -   @subpage tutorial_mat_operations
+
+    *Languages:* C++, Java, Python
+
+    *Compatibility:* \> OpenCV 2.0
+
+    Reading/writing images from file, accessing pixels, primitive operations, visualizing images.
+
 -   @subpage tutorial_adding_images
+
+    *Languages:* C++, Java, Python
+
+    *Compatibility:* \> OpenCV 2.0
+
+    *Author:* Ana Huamán
+
+    We will learn how to blend two images!
+
 -   @subpage tutorial_basic_linear_transform
+
+    *Languages:* C++, Java, Python
+
+    *Compatibility:* \> OpenCV 2.0
+
+    *Author:* Ana Huamán
+
+    We will learn how to change our image appearance!
+
 -   @subpage tutorial_discrete_fourier_transform
+
+    *Languages:* C++, Java, Python
+
+    *Compatibility:* \> OpenCV 2.0
+
+    *Author:* Bernát Gábor
+
+    You will see how and why use the Discrete Fourier transformation with OpenCV.
+
+
 -   @subpage tutorial_file_input_output_with_xml_yml
+
+    *Languages:* C++, Python
+
+    *Compatibility:* \> OpenCV 2.0
+
+    *Author:* Bernát Gábor
+
+    You will see how to use the @ref cv::FileStorage data structure of OpenCV to write and read
+    data to XML or YAML file format.
+
 -   @subpage tutorial_how_to_use_OpenCV_parallel_for_
+
+    *Languages:* C++
+
+    *Compatibility:* \>= OpenCV 2.4.3
+
+    You will see how to use the OpenCV parallel_for_ to easily parallelize your code.

doc/tutorials/dnn/dnn_OCR/dnn_OCR.markdown

@@ -1,14 +1,6 @@
 # How to run custom OCR model {#tutorial_dnn_OCR}
 
-@tableofcontents
-
 @prev_tutorial{tutorial_dnn_custom_layers}
-@next_tutorial{tutorial_dnn_text_spotting}
-
-|    |    |
-| -: | :- |
-| Original author | Zihao Mu |
-| Compatibility | OpenCV >= 4.3 |
 
 ## Introduction
 
@@ -51,4 +43,4 @@ The input of text recognition model is the output of the text detection model, w
 
 DenseNet_CTC has the smallest parameters and best FPS, and it is suitable for edge devices, which are very sensitive to the cost of calculation. If you have limited computing resources and want to achieve better accuracy, VGG_CTC is a good choice.
 
-CRNN_VGG_BiLSTM_CTC is suitable for scenarios that require high recognition accuracy.
+CRNN_VGG_BiLSTM_CTC is suitable for scenarios that require high recognition accuracy.

doc/tutorials/dnn/dnn_android/dnn_android.markdown

@@ -1,15 +1,8 @@
 # How to run deep networks on Android device {#tutorial_dnn_android}
 
-@tableofcontents
-
 @prev_tutorial{tutorial_dnn_halide_scheduling}
 @next_tutorial{tutorial_dnn_yolo}
 
-|    |    |
-| -: | :- |
-| Original author | Dmitry Kurtaev |
-| Compatibility | OpenCV >= 3.3 |
-
 ## Introduction
 In this tutorial you'll know how to run deep learning networks on Android device
 using OpenCV deep learning module.

doc/tutorials/dnn/dnn_custom_layers/dnn_custom_layers.md

@@ -1,15 +1,8 @@
 # Custom deep learning layers support {#tutorial_dnn_custom_layers}
 
-@tableofcontents
-
 @prev_tutorial{tutorial_dnn_javascript}
 @next_tutorial{tutorial_dnn_OCR}
 
-|    |    |
-| -: | :- |
-| Original author | Dmitry Kurtaev |
-| Compatibility | OpenCV >= 3.4.1 |
-
 ## Introduction
 Deep learning is a fast growing area. The new approaches to build neural networks
 usually introduce new types of layers. They could be modifications of existing

doc/tutorials/dnn/dnn_googlenet/dnn_googlenet.markdown

@@ -1,15 +1,8 @@
 Load Caffe framework models  {#tutorial_dnn_googlenet}
 ===========================
 
-@tableofcontents
-
 @next_tutorial{tutorial_dnn_halide}
 
-|    |    |
-| -: | :- |
-| Original author | Vitaliy Lyudvichenko |
-| Compatibility | OpenCV >= 3.3 |
-
 Introduction
 ------------
 

doc/tutorials/dnn/dnn_halide/dnn_halide.markdown

@@ -1,15 +1,8 @@
 # How to enable Halide backend for improve efficiency  {#tutorial_dnn_halide}
 
-@tableofcontents
-
 @prev_tutorial{tutorial_dnn_googlenet}
 @next_tutorial{tutorial_dnn_halide_scheduling}
 
-|    |    |
-| -: | :- |
-| Original author | Dmitry Kurtaev |
-| Compatibility | OpenCV >= 3.3 |
-
 ## Introduction
 This tutorial guidelines how to run your models in OpenCV deep learning module
 using Halide language backend. Halide is an open-source project that let us

doc/tutorials/dnn/dnn_halide_scheduling/dnn_halide_scheduling.markdown

@@ -1,15 +1,8 @@
 # How to schedule your network for Halide backend {#tutorial_dnn_halide_scheduling}
 
-@tableofcontents
-
 @prev_tutorial{tutorial_dnn_halide}
 @next_tutorial{tutorial_dnn_android}
 
-|    |    |
-| -: | :- |
-| Original author | Dmitry Kurtaev |
-| Compatibility | OpenCV >= 3.3 |
-
 ## Introduction
 Halide code is the same for every device we use. But for achieving the satisfied
 efficiency we should schedule computations properly. In this tutorial we describe

doc/tutorials/dnn/dnn_javascript/dnn_javascript.markdown

@@ -1,15 +1,8 @@
 # How to run deep networks in browser {#tutorial_dnn_javascript}
 
-@tableofcontents
-
 @prev_tutorial{tutorial_dnn_yolo}
 @next_tutorial{tutorial_dnn_custom_layers}
 
-|    |    |
-| -: | :- |
-| Original author | Dmitry Kurtaev |
-| Compatibility | OpenCV >= 3.3.1 |
-
 ## Introduction
 This tutorial will show us how to run deep learning models using OpenCV.js right
 in a browser. Tutorial refers a sample of face detection and face recognition

doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown

@@ -1,323 +0,0 @@
-# High Level API: TextDetectionModel and TextRecognitionModel {#tutorial_dnn_text_spotting}
-
-@tableofcontents
-
-@prev_tutorial{tutorial_dnn_OCR}
-
-|    |    |
-| -: | :- |
-| Original author | Wenqing Zhang |
-| Compatibility | OpenCV >= 4.5 |
-
-## Introduction
-In this tutorial, we will introduce the APIs for TextRecognitionModel and TextDetectionModel in detail.
-
----
-#### TextRecognitionModel:
-
-In the current version, @ref cv::dnn::TextRecognitionModel only supports CNN+RNN+CTC based algorithms,
-and the greedy decoding method for CTC is provided.
-For more information, please refer to the [original paper](https://arxiv.org/abs/1507.05717)
-
-Before recognition, you should `setVocabulary` and `setDecodeType`.
-- "CTC-greedy", the output of the text recognition model should be a probability matrix.
-    The shape should be `(T, B, Dim)`, where
-    - `T` is the sequence length
-    - `B` is the batch size (only support `B=1` in inference)
-    - and `Dim` is the length of vocabulary +1('Blank' of CTC is at the index=0 of Dim).
-
-@ref cv::dnn::TextRecognitionModel::recognize() is the main function for text recognition.
-- The input image should be a cropped text image or an image with `roiRects`
-- Other decoding methods may supported in the future
-
----
-
-#### TextDetectionModel:
-
-@ref cv::dnn::TextDetectionModel API provides these methods for text detection:
-- cv::dnn::TextDetectionModel::detect() returns the results in std::vector<std::vector<Point>> (4-points quadrangles)
-- cv::dnn::TextDetectionModel::detectTextRectangles() returns the results in std::vector<cv::RotatedRect> (RBOX-like)
-
-In the current version, @ref cv::dnn::TextDetectionModel supports these algorithms:
-- use @ref cv::dnn::TextDetectionModel_DB with "DB" models
-- and use @ref cv::dnn::TextDetectionModel_EAST with "EAST" models
-
-The following provided pretrained models are variants of DB (w/o deformable convolution),
-and the performance can be referred to the Table.1 in the [paper]((https://arxiv.org/abs/1911.08947)).
-For more information, please refer to the [official code](https://github.com/MhLiao/DB)
-
----
-
-You can train your own model with more data, and convert it into ONNX format.
-We encourage you to add new algorithms to these APIs.
-
-
-## Pretrained Models
-
-#### TextRecognitionModel:
-
-```
-crnn.onnx:
-url: https://drive.google.com/uc?export=dowload&id=1ooaLR-rkTl8jdpGy1DoQs0-X0lQsB6Fj
-sha: 270d92c9ccb670ada2459a25977e8deeaf8380d3,
-alphabet_36.txt: https://drive.google.com/uc?export=dowload&id=1oPOYx5rQRp8L6XQciUwmwhMCfX0KyO4b
-parameter setting: -rgb=0;
-description: The classification number of this model is 36 (0~9 + a~z).
-             The training dataset is MJSynth.
-
-crnn_cs.onnx:
-url: https://drive.google.com/uc?export=dowload&id=12diBsVJrS9ZEl6BNUiRp9s0xPALBS7kt
-sha: a641e9c57a5147546f7a2dbea4fd322b47197cd5
-alphabet_94.txt: https://drive.google.com/uc?export=dowload&id=1oKXxXKusquimp7XY1mFvj9nwLzldVgBR
-parameter setting: -rgb=1;
-description: The classification number of this model is 94 (0~9 + a~z + A~Z + punctuations).
-             The training datasets are MJsynth and SynthText.
-
-crnn_cs_CN.onnx:
-url: https://drive.google.com/uc?export=dowload&id=1is4eYEUKH7HR7Gl37Sw4WPXx6Ir8oQEG
-sha: 3940942b85761c7f240494cf662dcbf05dc00d14
-alphabet_3944.txt: https://drive.google.com/uc?export=dowload&id=18IZUUdNzJ44heWTndDO6NNfIpJMmN-ul
-parameter setting: -rgb=1;
-description: The classification number of this model is 3944 (0~9 + a~z + A~Z + Chinese characters + special characters).
-             The training dataset is ReCTS (https://rrc.cvc.uab.es/?ch=12).
-```
-
-More models can be found in [here](https://drive.google.com/drive/folders/1cTbQ3nuZG-EKWak6emD_s8_hHXWz7lAr?usp=sharing),
-which are taken from [clovaai](https://github.com/clovaai/deep-text-recognition-benchmark).
-You can train more models by [CRNN](https://github.com/meijieru/crnn.pytorch), and convert models by `torch.onnx.export`.
-
-#### TextDetectionModel:
-
-```
-- DB_IC15_resnet50.onnx:
-url: https://drive.google.com/uc?export=dowload&id=17_ABp79PlFt9yPCxSaarVc_DKTmrSGGf
-sha: bef233c28947ef6ec8c663d20a2b326302421fa3
-recommended parameter setting: -inputHeight=736, -inputWidth=1280;
-description: This model is trained on ICDAR2015, so it can only detect English text instances.
-
-- DB_IC15_resnet18.onnx:
-url: https://drive.google.com/uc?export=dowload&id=1sZszH3pEt8hliyBlTmB-iulxHP1dCQWV
-sha: 19543ce09b2efd35f49705c235cc46d0e22df30b
-recommended parameter setting: -inputHeight=736, -inputWidth=1280;
-description: This model is trained on ICDAR2015, so it can only detect English text instances.
-
-- DB_TD500_resnet50.onnx:
-url: https://drive.google.com/uc?export=dowload&id=19YWhArrNccaoSza0CfkXlA8im4-lAGsR
-sha: 1b4dd21a6baa5e3523156776970895bd3db6960a
-recommended parameter setting: -inputHeight=736, -inputWidth=736;
-description: This model is trained on MSRA-TD500, so it can detect both English and Chinese text instances.
-
-- DB_TD500_resnet18.onnx:
-url: https://drive.google.com/uc?export=dowload&id=1vY_KsDZZZb_svd5RT6pjyI8BS1nPbBSX
-sha: 8a3700bdc13e00336a815fc7afff5dcc1ce08546
-recommended parameter setting: -inputHeight=736, -inputWidth=736;
-description: This model is trained on MSRA-TD500, so it can detect both English and Chinese text instances.
-
-```
-
-We will release more models of DB [here](https://drive.google.com/drive/folders/1qzNCHfUJOS0NEUOIKn69eCtxdlNPpWbq?usp=sharing) in the future.
-
-```
-- EAST:
-Download link: https://www.dropbox.com/s/r2ingd0l3zt8hxs/frozen_east_text_detection.tar.gz?dl=1
-This model is based on https://github.com/argman/EAST
-```
-
-## Images for Testing
-
-```
-Text Recognition:
-url: https://drive.google.com/uc?export=dowload&id=1nMcEy68zDNpIlqAn6xCk_kYcUTIeSOtN
-sha: 89205612ce8dd2251effa16609342b69bff67ca3
-
-Text Detection:
-url: https://drive.google.com/uc?export=dowload&id=149tAhIcvfCYeyufRoZ9tmc2mZDKE_XrF
-sha: ced3c03fb7f8d9608169a913acf7e7b93e07109b
-```
-
-## Example for Text Recognition
-
-Step1. Loading images and models with a vocabulary
-
-```cpp
-    // Load a cropped text line image
-    // you can find cropped images for testing in "Images for Testing"
-    int rgb = IMREAD_COLOR; // This should be changed according to the model input requirement.
-    Mat image = imread("path/to/text_rec_test.png", rgb);
-
-    // Load models weights
-    TextRecognitionModel model("path/to/crnn_cs.onnx");
-
-    // The decoding method
-    // more methods will be supported in future
-    model.setDecodeType("CTC-greedy");
-
-    // Load vocabulary
-    // vocabulary should be changed according to the text recognition model
-    std::ifstream vocFile;
-    vocFile.open("path/to/alphabet_94.txt");
-    CV_Assert(vocFile.is_open());
-    String vocLine;
-    std::vector<String> vocabulary;
-    while (std::getline(vocFile, vocLine)) {
-        vocabulary.push_back(vocLine);
-    }
-    model.setVocabulary(vocabulary);
-```
-
-Step2. Setting Parameters
-
-```cpp
-    // Normalization parameters
-    double scale = 1.0 / 127.5;
-    Scalar mean = Scalar(127.5, 127.5, 127.5);
-
-    // The input shape
-    Size inputSize = Size(100, 32);
-
-    model.setInputParams(scale, inputSize, mean);
-```
-Step3. Inference
-```cpp
-    std::string recognitionResult = recognizer.recognize(image);
-    std::cout << "'" << recognitionResult << "'" << std::endl;
-```
-
-Input image:
-
-![Picture example](text_rec_test.png)
-
-Output:
-```
-'welcome'
-```
-
-
-## Example for Text Detection
-
-Step1. Loading images and models
-```cpp
-    // Load an image
-    // you can find some images for testing in "Images for Testing"
-    Mat frame = imread("/path/to/text_det_test.png");
-```
-
-Step2.a Setting Parameters (DB)
-```cpp
-    // Load model weights
-    TextDetectionModel_DB model("/path/to/DB_TD500_resnet50.onnx");
-
-    // Post-processing parameters
-    float binThresh = 0.3;
-    float polyThresh = 0.5;
-    uint maxCandidates = 200;
-    double unclipRatio = 2.0;
-    model.setBinaryThreshold(binThresh)
-         .setPolygonThreshold(polyThresh)
-         .setMaxCandidates(maxCandidates)
-         .setUnclipRatio(unclipRatio)
-    ;
-
-    // Normalization parameters
-    double scale = 1.0 / 255.0;
-    Scalar mean = Scalar(122.67891434, 116.66876762, 104.00698793);
-
-    // The input shape
-    Size inputSize = Size(736, 736);
-
-    model.setInputParams(scale, inputSize, mean);
-```
-
-Step2.b Setting Parameters (EAST)
-```cpp
-    TextDetectionModel_EAST model("EAST.pb");
-
-    float confThreshold = 0.5;
-    float nmsThreshold = 0.4;
-    model.setConfidenceThreshold(confThresh)
-         .setNMSThreshold(nmsThresh)
-    ;
-
-    double detScale = 1.0;
-    Size detInputSize = Size(320, 320);
-    Scalar detMean = Scalar(123.68, 116.78, 103.94);
-    bool swapRB = true;
-    model.setInputParams(detScale, detInputSize, detMean, swapRB);
-```
-
-
-Step3. Inference
-```cpp
-    std::vector<std::vector<Point>> detResults;
-    model.detect(detResults);
-
-    // Visualization
-    polylines(frame, results, true, Scalar(0, 255, 0), 2);
-    imshow("Text Detection", image);
-    waitKey();
-```
-
-Output:
-
-![Picture example](text_det_test_results.jpg)
-
-## Example for Text Spotting
-
-After following the steps above, it is easy to get the detection results of an input image.
-Then, you can do transformation and crop text images for recognition.
-For more information, please refer to **Detailed Sample**
-```cpp
-    // Transform and Crop
-    Mat cropped;
-    fourPointsTransform(recInput, vertices, cropped);
-
-    String recResult = recognizer.recognize(cropped);
-```
-
-Output Examples:
-
-![Picture example](detect_test1.jpg)
-
-![Picture example](detect_test2.jpg)
-
-## Source Code
-The [source code](https://github.com/opencv/opencv/blob/master/modules/dnn/src/model.cpp)
-of these APIs can be found in the DNN module.
-
-## Detailed Sample
-For more information, please refer to:
-- [samples/dnn/scene_text_recognition.cpp](https://github.com/opencv/opencv/blob/master/samples/dnn/scene_text_recognition.cpp)
-- [samples/dnn/scene_text_detection.cpp](https://github.com/opencv/opencv/blob/master/samples/dnn/scene_text_detection.cpp)
-- [samples/dnn/text_detection.cpp](https://github.com/opencv/opencv/blob/master/samples/dnn/text_detection.cpp)
-- [samples/dnn/scene_text_spotting.cpp](https://github.com/opencv/opencv/blob/master/samples/dnn/scene_text_spotting.cpp)
-
-#### Test with an image
-Examples:
-```bash
-example_dnn_scene_text_recognition -mp=path/to/crnn_cs.onnx -i=path/to/an/image -rgb=1 -vp=/path/to/alphabet_94.txt
-example_dnn_scene_text_detection -mp=path/to/DB_TD500_resnet50.onnx -i=path/to/an/image -ih=736 -iw=736
-example_dnn_scene_text_spotting -dmp=path/to/DB_IC15_resnet50.onnx -rmp=path/to/crnn_cs.onnx -i=path/to/an/image -iw=1280 -ih=736 -rgb=1 -vp=/path/to/alphabet_94.txt
-example_dnn_text_detection -dmp=path/to/EAST.pb -rmp=path/to/crnn_cs.onnx -i=path/to/an/image -rgb=1 -vp=path/to/alphabet_94.txt
-```
-
-#### Test on public datasets
-Text Recognition:
-
-The download link for testing images can be found in the **Images for Testing**
-
-
-Examples:
-```bash
-example_dnn_scene_text_recognition -mp=path/to/crnn.onnx -e=true -edp=path/to/evaluation_data_rec -vp=/path/to/alphabet_36.txt -rgb=0
-example_dnn_scene_text_recognition -mp=path/to/crnn_cs.onnx -e=true -edp=path/to/evaluation_data_rec -vp=/path/to/alphabet_94.txt -rgb=1
-```
-
-Text Detection:
-
-The download links for testing images can be found in the **Images for Testing**
-
-Examples:
-```bash
-example_dnn_scene_text_detection -mp=path/to/DB_TD500_resnet50.onnx -e=true -edp=path/to/evaluation_data_det/TD500 -ih=736 -iw=736
-example_dnn_scene_text_detection -mp=path/to/DB_IC15_resnet50.onnx -e=true -edp=path/to/evaluation_data_det/IC15 -ih=736 -iw=1280
-```

doc/tutorials/dnn/dnn_yolo/dnn_yolo.markdown

@@ -1,16 +1,9 @@
 YOLO DNNs  {#tutorial_dnn_yolo}
 ===============================
 
-@tableofcontents
-
 @prev_tutorial{tutorial_dnn_android}
 @next_tutorial{tutorial_dnn_javascript}
 
-|    |    |
-| -: | :- |
-| Original author | Alessandro de Oliveira Faria |
-| Compatibility | OpenCV >= 3.3.1 |
-
 Introduction
 ------------
 

doc/tutorials/dnn/table_of_content_dnn.markdown

@@ -2,11 +2,81 @@ Deep Neural Networks (dnn module) {#tutorial_table_of_content_dnn}
 =====================================
 
 -   @subpage tutorial_dnn_googlenet
+
+    *Languages:* C++
+
+    *Compatibility:* \> OpenCV 3.3
+
+    *Author:* Vitaliy Lyudvichenko
+
+    In this tutorial you will learn how to use opencv_dnn module for image classification by using GoogLeNet trained network from Caffe model zoo.
+
 -   @subpage tutorial_dnn_halide
+
+    *Languages:* Halide
+
+    *Compatibility:* \> OpenCV 3.3
+
+    *Author:* Dmitry Kurtaev
+
+    This tutorial guidelines how to run your models in OpenCV deep learning module using Halide language backend.
+
 -   @subpage tutorial_dnn_halide_scheduling
+
+    *Languages:* Halide
+
+    *Compatibility:* \> OpenCV 3.3
+
+    *Author:* Dmitry Kurtaev
+
+    In this tutorial we describe the ways to schedule your networks using Halide backend in OpenCV deep learning module.
+
 -   @subpage tutorial_dnn_android
+
+    *Languages:* Java
+
+    *Compatibility:* \> OpenCV 3.3
+
+    *Author:* Dmitry Kurtaev
+
+    This tutorial will show you how to run deep learning model using OpenCV on Android device.
+
 -   @subpage tutorial_dnn_yolo
+
+    *Languages:* C++, Python
+
+    *Compatibility:* \> OpenCV 3.3.1
+
+    *Author:* Alessandro de Oliveira Faria
+
+    In this tutorial you will learn how to use opencv_dnn module using yolo_object_detection with device capture, video file or image.
+
 -   @subpage tutorial_dnn_javascript
+
+    *Languages:* JavaScript
+
+    *Compatibility:* \> OpenCV 3.3.1
+
+    *Author:* Dmitry Kurtaev
+
+    In this tutorial we'll run deep learning models in browser using OpenCV.js.
+
 -   @subpage tutorial_dnn_custom_layers
+
+    *Languages:* C++, Python
+
+    *Compatibility:* \> OpenCV 3.4.1
+
+    *Author:* Dmitry Kurtaev
+
+    How to define custom layers to import networks.
+
 -   @subpage tutorial_dnn_OCR
--   @subpage tutorial_dnn_text_spotting
+
+    *Languages:* C++
+
+    *Compatibility:* \> OpenCV 4.3
+
+    *Author:* Zihao Mu
+
+    In this tutorial you will learn how to use opencv_dnn module using custom OCR models.

doc/tutorials/features2d/akaze_matching/akaze_matching.markdown

@@ -1,16 +1,9 @@
 AKAZE local features matching {#tutorial_akaze_matching}
 =============================
 
-@tableofcontents
-
 @prev_tutorial{tutorial_detection_of_planar_objects}
 @next_tutorial{tutorial_akaze_tracking}
 
-|    |    |
-| -: | :- |
-| Original author | Fedor Morozov |
-| Compatibility | OpenCV >= 3.0 |
-
 Introduction
 ------------
 

doc/tutorials/features2d/akaze_tracking/akaze_tracking.markdown

@@ -1,16 +1,9 @@
 AKAZE and ORB planar tracking {#tutorial_akaze_tracking}
 =============================
 
-@tableofcontents
-
 @prev_tutorial{tutorial_akaze_matching}
 @next_tutorial{tutorial_homography}
 
-|    |    |
-| -: | :- |
-| Original author | Fedor Morozov |
-| Compatibility | OpenCV >= 3.0 |
-
 Introduction
 ------------