release: OpenCV 4.5.1

Merge pull request #19187 from alalek:samples_python_3.9
Merge pull request #19186 from AsyaPronina:asyadev/fix_gframe_as_internal_data_for_gexecutor
2026-07-23 12:23:02 +04:00 · 2020-12-21 20:01:38 +00:00 · 2020-12-21 18:16:00 +00:00 · 2020-12-21 18:15:24 +00:00 · 2020-12-21 14:59:34 +00:00 · 2020-12-21 14:06:47 +03:00
897 changed files with 45278 additions and 7853 deletions
@@ -1,6 +1,6 @@
 name: arm64 build checks

-on: [pull_request]
+on: workflow_dispatch

 jobs:
  build:
@@ -27,7 +27,7 @@ if(CMAKE_COMPILER_IS_GNUCC)
    endif()
 endif()

-add_library(carotene_objs OBJECT
+add_library(carotene_objs OBJECT EXCLUDE_FROM_ALL
  ${carotene_headers}
  ${carotene_sources}
 )
@@ -41,4 +41,4 @@ if(WITH_NEON)
 endif()

 # we add dummy file to fix XCode build
-add_library(carotene STATIC EXCLUDE_FROM_ALL "$<TARGET_OBJECTS:carotene_objs>" "${CAROTENE_SOURCE_DIR}/dummy.cpp")
+add_library(carotene STATIC ${OPENCV_3RDPARTY_EXCLUDE_FROM_ALL} "$<TARGET_OBJECTS:carotene_objs>" "${CAROTENE_SOURCE_DIR}/dummy.cpp")
@@ -14,7 +14,7 @@ if(NOT DEFINED CPUFEATURES_SOURCES)
 endif()

 include_directories(${CPUFEATURES_INCLUDE_DIRS})
-add_library(${OPENCV_CPUFEATURES_TARGET_NAME} STATIC ${CPUFEATURES_SOURCES})
+add_library(${OPENCV_CPUFEATURES_TARGET_NAME} STATIC ${OPENCV_3RDPARTY_EXCLUDE_FROM_ALL} ${CPUFEATURES_SOURCES})

 set_target_properties(${OPENCV_CPUFEATURES_TARGET_NAME}
  PROPERTIES OUTPUT_NAME cpufeatures
@@ -29,7 +29,7 @@ if(ENABLE_SOLUTION_FOLDERS)
 endif()

 if(NOT BUILD_SHARED_LIBS)
-  ocv_install_target(${OPENCV_CPUFEATURES_TARGET_NAME} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev)
+  ocv_install_target(${OPENCV_CPUFEATURES_TARGET_NAME} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev OPTIONAL)
 endif()

 ocv_install_3rdparty_licenses(cpufeatures LICENSE README.md)
@@ -17,7 +17,7 @@ file(GLOB lib_hdrs ${IPP_IW_PATH}/include/*.h ${IPP_IW_PATH}/include/iw/*.h ${IP
 #         Define the library target:
 # ----------------------------------------------------------------------------------

-add_library(${IPP_IW_LIBRARY} STATIC ${lib_srcs} ${lib_hdrs})
+add_library(${IPP_IW_LIBRARY} STATIC ${OPENCV_3RDPARTY_EXCLUDE_FROM_ALL} ${lib_srcs} ${lib_hdrs})

 if(UNIX)
  if(CV_GCC OR CV_CLANG OR CV_ICC)
@@ -41,5 +41,5 @@ if(ENABLE_SOLUTION_FOLDERS)
 endif()

 if(NOT BUILD_SHARED_LIBS)
-  ocv_install_target(${IPP_IW_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev)
+  ocv_install_target(${IPP_IW_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev OPTIONAL)
 endif()
@@ -37,7 +37,7 @@ set(ITT_SRCS
    src/ittnotify/jitprofiling.c
 )

-add_library(${ITT_LIBRARY} STATIC ${ITT_SRCS} ${ITT_PUBLIC_HDRS} ${ITT_PRIVATE_HDRS})
+add_library(${ITT_LIBRARY} STATIC ${OPENCV_3RDPARTY_EXCLUDE_FROM_ALL} ${ITT_SRCS} ${ITT_PUBLIC_HDRS} ${ITT_PRIVATE_HDRS})

 if(NOT WIN32)
  if(HAVE_DL_LIBRARY)
@@ -60,7 +60,7 @@ if(ENABLE_SOLUTION_FOLDERS)
 endif()

 if(NOT BUILD_SHARED_LIBS)
-  ocv_install_target(${ITT_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev)
+  ocv_install_target(${ITT_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev OPTIONAL)
 endif()

 ocv_install_3rdparty_licenses(ittnotify src/ittnotify/LICENSE.BSD src/ittnotify/LICENSE.GPL)
@@ -17,7 +17,7 @@ file(GLOB lib_ext_hdrs jasper/*.h)
 #         Define the library target:
 # ----------------------------------------------------------------------------------

-add_library(${JASPER_LIBRARY} STATIC ${lib_srcs} ${lib_hdrs} ${lib_ext_hdrs})
+add_library(${JASPER_LIBRARY} STATIC ${OPENCV_3RDPARTY_EXCLUDE_FROM_ALL} ${lib_srcs} ${lib_hdrs} ${lib_ext_hdrs})

 if(WIN32 AND NOT MINGW)
  add_definitions(-DJAS_WIN_MSVC_BUILD)
@@ -46,7 +46,7 @@ if(ENABLE_SOLUTION_FOLDERS)
 endif()

 if(NOT BUILD_SHARED_LIBS)
-  ocv_install_target(${JASPER_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev)
+  ocv_install_target(${JASPER_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev OPTIONAL)
 endif()

 ocv_install_3rdparty_licenses(jasper LICENSE README copyright)
@@ -4,9 +4,9 @@ ocv_warnings_disable(CMAKE_C_FLAGS -Wunused-parameter -Wsign-compare -Wshorten-6

 set(VERSION_MAJOR 2)
 set(VERSION_MINOR 0)
-set(VERSION_REVISION 5)
+set(VERSION_REVISION 6)
 set(VERSION ${VERSION_MAJOR}.${VERSION_MINOR}.${VERSION_REVISION})
-set(LIBJPEG_TURBO_VERSION_NUMBER 2000005)
+set(LIBJPEG_TURBO_VERSION_NUMBER 2000006)

 string(TIMESTAMP BUILD "opencv-${OPENCV_VERSION}-libjpeg-turbo")
 if(CMAKE_BUILD_TYPE STREQUAL "Debug")
@@ -106,7 +106,7 @@ set(JPEG_SOURCES ${JPEG_SOURCES} jsimd_none.c)

 ocv_list_add_prefix(JPEG_SOURCES src/)

-add_library(${JPEG_LIBRARY} STATIC ${JPEG_SOURCES} ${SIMD_OBJS})
+add_library(${JPEG_LIBRARY} STATIC ${OPENCV_3RDPARTY_EXCLUDE_FROM_ALL} ${JPEG_SOURCES} ${SIMD_OBJS})

 set_target_properties(${JPEG_LIBRARY}
  PROPERTIES OUTPUT_NAME ${JPEG_LIBRARY}
@@ -121,7 +121,7 @@ if(ENABLE_SOLUTION_FOLDERS)
 endif()

 if(NOT BUILD_SHARED_LIBS)
-  ocv_install_target(${JPEG_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev)
+  ocv_install_target(${JPEG_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev OPTIONAL)
 endif()

 ocv_install_3rdparty_licenses(libjpeg-turbo README.md LICENSE.md README.ijg)
@@ -91,7 +91,7 @@ best of our understanding.
 The Modified (3-clause) BSD License
 ===================================

-Copyright (C)2009-2019 D. R. Commander.  All Rights Reserved.
+Copyright (C)2009-2020 D. R. Commander.  All Rights Reserved.
 Copyright (C)2015 Viktor Szathmáry.  All Rights Reserved.

 Redistribution and use in source and binary forms, with or without
@@ -223,12 +223,12 @@ https://www.iso.org/standard/54989.html and http://www.itu.int/rec/T-REC-T.871.
 A PDF file of the older JFIF 1.02 specification is available at
 http://www.w3.org/Graphics/JPEG/jfif3.pdf.

-The TIFF 6.0 file format specification can be obtained by FTP from
-ftp://ftp.sgi.com/graphics/tiff/TIFF6.ps.gz.  The JPEG incorporation scheme
-found in the TIFF 6.0 spec of 3-June-92 has a number of serious problems.
-IJG does not recommend use of the TIFF 6.0 design (TIFF Compression tag 6).
-Instead, we recommend the JPEG design proposed by TIFF Technical Note #2
-(Compression tag 7).  Copies of this Note can be obtained from
+The TIFF 6.0 file format specification can be obtained from
+http://mirrors.ctan.org/graphics/tiff/TIFF6.ps.gz.  The JPEG incorporation
+scheme found in the TIFF 6.0 spec of 3-June-92 has a number of serious
+problems.  IJG does not recommend use of the TIFF 6.0 design (TIFF Compression
+tag 6).  Instead, we recommend the JPEG design proposed by TIFF Technical Note
+#2 (Compression tag 7).  Copies of this Note can be obtained from
 http://www.ijg.org/files/.  It is expected that the next revision
 of the TIFF spec will replace the 6.0 JPEG design with the Note's design.
 Although IJG's own code does not support TIFF/JPEG, the free libtiff library
@@ -243,14 +243,8 @@ The most recent released version can always be found there in
 directory "files".

 The JPEG FAQ (Frequently Asked Questions) article is a source of some
-general information about JPEG.
-It is available on the World Wide Web at http://www.faqs.org/faqs/jpeg-faq/
-and other news.answers archive sites, including the official news.answers
-archive at rtfm.mit.edu: ftp://rtfm.mit.edu/pub/usenet/news.answers/jpeg-faq/.
-If you don't have Web or FTP access, send e-mail to mail-server@rtfm.mit.edu
-with body
-        send usenet/news.answers/jpeg-faq/part1
-        send usenet/news.answers/jpeg-faq/part2
+general information about JPEG.  It is available at
+http://www.faqs.org/faqs/jpeg-faq.


 FILE FORMAT COMPATIBILITY
@@ -2,7 +2,7 @@ Background
 ==========

 libjpeg-turbo is a JPEG image codec that uses SIMD instructions to accelerate
-baseline JPEG compression and decompression on x86, x86-64, ARM, PowerPC, and
+baseline JPEG compression and decompression on x86, x86-64, Arm, PowerPC, and
 MIPS systems, as well as progressive JPEG compression on x86 and x86-64
 systems.  On such systems, libjpeg-turbo is generally 2-6x as fast as libjpeg,
 all else being equal.  On other types of systems, libjpeg-turbo can still
@@ -179,8 +179,8 @@ supported and which aren't.

 NOTE:  As of this writing, extensive research has been conducted into the
 usefulness of DCT scaling as a means of data reduction and SmartScale as a
-means of quality improvement.  The reader is invited to peruse the research at
-<http://www.libjpeg-turbo.org/About/SmartScale> and draw his/her own conclusions,
+means of quality improvement.  Readers are invited to peruse the research at
+<http://www.libjpeg-turbo.org/About/SmartScale> and draw their own conclusions,
 but it is the general belief of our project that these features have not
 demonstrated sufficient usefulness to justify inclusion in libjpeg-turbo.

@@ -287,12 +287,13 @@ following reasons:
  (and slightly faster) floating point IDCT algorithm introduced in libjpeg
  v8a as opposed to the algorithm used in libjpeg v6b.  It should be noted,
  however, that this algorithm basically brings the accuracy of the floating
-  point IDCT in line with the accuracy of the slow integer IDCT.  The floating
-  point DCT/IDCT algorithms are mainly a legacy feature, and they do not
-  produce significantly more accuracy than the slow integer algorithms (to put
-  numbers on this, the typical difference in PNSR between the two algorithms
-  is less than 0.10 dB, whereas changing the quality level by 1 in the upper
-  range of the quality scale is typically more like a 1.0 dB difference.)
+  point IDCT in line with the accuracy of the accurate integer IDCT.  The
+  floating point DCT/IDCT algorithms are mainly a legacy feature, and they do
+  not produce significantly more accuracy than the accurate integer algorithms
+  (to put numbers on this, the typical difference in PNSR between the two
+  algorithms is less than 0.10 dB, whereas changing the quality level by 1 in
+  the upper range of the quality scale is typically more like a 1.0 dB
+  difference.)

 - If the floating point algorithms in libjpeg-turbo are not implemented using
  SIMD instructions on a particular platform, then the accuracy of the
@@ -340,7 +341,7 @@ The algorithm used by the SIMD-accelerated quantization function cannot produce
 correct results whenever the fast integer forward DCT is used along with a JPEG
 quality of 98-100.  Thus, libjpeg-turbo must use the non-SIMD quantization
 function in those cases.  This causes performance to drop by as much as 40%.
-It is therefore strongly advised that you use the slow integer forward DCT
+It is therefore strongly advised that you use the accurate integer forward DCT
 whenever encoding images with a JPEG quality of 98 or higher.


@@ -34,10 +34,10 @@
 * memory footprint by 64k, which is important for some mobile applications
 * that create many isolated instances of libjpeg-turbo (web browsers, for
 * instance.)  This may improve performance on some mobile platforms as well.
- * This feature is enabled by default only on ARM processors, because some x86
+ * This feature is enabled by default only on Arm processors, because some x86
 * chips have a slow implementation of bsr, and the use of clz/bsr cannot be
 * shown to have a significant performance impact even on the x86 chips that
- * have a fast implementation of it.  When building for ARMv6, you can
+ * have a fast implementation of it.  When building for Armv6, you can
 * explicitly disable the use of clz/bsr by adding -mthumb to the compiler
 * flags (this defines __thumb__).
 */
@@ -1,8 +1,10 @@
 /*
 * jcinit.c
 *
+ * This file was part of the Independent JPEG Group's software:
 * Copyright (C) 1991-1997, Thomas G. Lane.
- * This file is part of the Independent JPEG Group's software.
+ * libjpeg-turbo Modifications:
+ * Copyright (C) 2020, D. R. Commander.
 * For conditions of distribution and use, see the accompanying README.ijg
 * file.
 *
@@ -19,6 +21,7 @@
 #define JPEG_INTERNALS
 #include "jinclude.h"
 #include "jpeglib.h"
+#include "jpegcomp.h"


 /*
@@ -43,10 +43,10 @@
 * memory footprint by 64k, which is important for some mobile applications
 * that create many isolated instances of libjpeg-turbo (web browsers, for
 * instance.)  This may improve performance on some mobile platforms as well.
- * This feature is enabled by default only on ARM processors, because some x86
+ * This feature is enabled by default only on Arm processors, because some x86
 * chips have a slow implementation of bsr, and the use of clz/bsr cannot be
 * shown to have a significant performance impact even on the x86 chips that
- * have a fast implementation of it.  When building for ARMv6, you can
+ * have a fast implementation of it.  When building for Armv6, you can
 * explicitly disable the use of clz/bsr by adding -mthumb to the compiler
 * flags (this defines __thumb__).
 */
@@ -4,8 +4,8 @@
 * This file was part of the Independent JPEG Group's software:
 * Copyright (C) 1995-1998, Thomas G. Lane.
 * Modified 2000-2009 by Guido Vollbeding.
- * It was modified by The libjpeg-turbo Project to include only code relevant
- * to libjpeg-turbo.
+ * libjpeg-turbo Modifications:
+ * Copyright (C) 2020, D. R. Commander.
 * For conditions of distribution and use, see the accompanying README.ijg
 * file.
 *
@@ -17,6 +17,7 @@
 #define JPEG_INTERNALS
 #include "jinclude.h"
 #include "jpeglib.h"
+#include "jpegcomp.h"


 /* Forward declarations */
@@ -4,7 +4,7 @@
 * This file was part of the Independent JPEG Group's software:
 * Copyright (C) 1994-1996, Thomas G. Lane.
 * libjpeg-turbo Modifications:
- * Copyright (C) 2010, 2015-2018, D. R. Commander.
+ * Copyright (C) 2010, 2015-2018, 2020, D. R. Commander.
 * Copyright (C) 2015, Google, Inc.
 * For conditions of distribution and use, see the accompanying README.ijg
 * file.
@@ -21,6 +21,8 @@
 #include "jinclude.h"
 #include "jdmainct.h"
 #include "jdcoefct.h"
+#include "jdmaster.h"
+#include "jdmerge.h"
 #include "jdsample.h"
 #include "jmemsys.h"

@@ -316,6 +318,8 @@ LOCAL(void)
 read_and_discard_scanlines(j_decompress_ptr cinfo, JDIMENSION num_lines)
 {
  JDIMENSION n;
+  my_master_ptr master = (my_master_ptr)cinfo->master;
+  JSAMPARRAY scanlines = NULL;
  void (*color_convert) (j_decompress_ptr cinfo, JSAMPIMAGE input_buf,
                         JDIMENSION input_row, JSAMPARRAY output_buf,
                         int num_rows) = NULL;
@@ -332,8 +336,13 @@ read_and_discard_scanlines(j_decompress_ptr cinfo, JDIMENSION num_lines)
    cinfo->cquantize->color_quantize = noop_quantize;
  }

+  if (master->using_merged_upsample && cinfo->max_v_samp_factor == 2) {
+    my_merged_upsample_ptr upsample = (my_merged_upsample_ptr)cinfo->upsample;
+    scanlines = &upsample->spare_row;
+  }
+
  for (n = 0; n < num_lines; n++)
-    jpeg_read_scanlines(cinfo, NULL, 1);
+    jpeg_read_scanlines(cinfo, scanlines, 1);

  if (color_convert)
    cinfo->cconvert->color_convert = color_convert;
@@ -353,6 +362,12 @@ increment_simple_rowgroup_ctr(j_decompress_ptr cinfo, JDIMENSION rows)
 {
  JDIMENSION rows_left;
  my_main_ptr main_ptr = (my_main_ptr)cinfo->main;
+  my_master_ptr master = (my_master_ptr)cinfo->master;
+
+  if (master->using_merged_upsample && cinfo->max_v_samp_factor == 2) {
+    read_and_discard_scanlines(cinfo, rows);
+    return;
+  }

  /* Increment the counter to the next row group after the skipped rows. */
  main_ptr->rowgroup_ctr += rows / cinfo->max_v_samp_factor;
@@ -382,21 +397,27 @@ jpeg_skip_scanlines(j_decompress_ptr cinfo, JDIMENSION num_lines)
 {
  my_main_ptr main_ptr = (my_main_ptr)cinfo->main;
  my_coef_ptr coef = (my_coef_ptr)cinfo->coef;
+  my_master_ptr master = (my_master_ptr)cinfo->master;
  my_upsample_ptr upsample = (my_upsample_ptr)cinfo->upsample;
  JDIMENSION i, x;
  int y;
  JDIMENSION lines_per_iMCU_row, lines_left_in_iMCU_row, lines_after_iMCU_row;
  JDIMENSION lines_to_skip, lines_to_read;

+  /* Two-pass color quantization is not supported. */
+  if (cinfo->quantize_colors && cinfo->two_pass_quantize)
+    ERREXIT(cinfo, JERR_NOTIMPL);
+
  if (cinfo->global_state != DSTATE_SCANNING)
    ERREXIT1(cinfo, JERR_BAD_STATE, cinfo->global_state);

  /* Do not skip past the bottom of the image. */
  if (cinfo->output_scanline + num_lines >= cinfo->output_height) {
+    num_lines = cinfo->output_height - cinfo->output_scanline;
    cinfo->output_scanline = cinfo->output_height;
    (*cinfo->inputctl->finish_input_pass) (cinfo);
    cinfo->inputctl->eoi_reached = TRUE;
-    return cinfo->output_height - cinfo->output_scanline;
+    return num_lines;
  }

  if (num_lines == 0)
@@ -445,8 +466,10 @@ jpeg_skip_scanlines(j_decompress_ptr cinfo, JDIMENSION num_lines)
    main_ptr->buffer_full = FALSE;
    main_ptr->rowgroup_ctr = 0;
    main_ptr->context_state = CTX_PREPARE_FOR_IMCU;
-    upsample->next_row_out = cinfo->max_v_samp_factor;
-    upsample->rows_to_go = cinfo->output_height - cinfo->output_scanline;
+    if (!master->using_merged_upsample) {
+      upsample->next_row_out = cinfo->max_v_samp_factor;
+      upsample->rows_to_go = cinfo->output_height - cinfo->output_scanline;
+    }
  }

  /* Skipping is much simpler when context rows are not required. */
@@ -458,8 +481,10 @@ jpeg_skip_scanlines(j_decompress_ptr cinfo, JDIMENSION num_lines)
      cinfo->output_scanline += lines_left_in_iMCU_row;
      main_ptr->buffer_full = FALSE;
      main_ptr->rowgroup_ctr = 0;
-      upsample->next_row_out = cinfo->max_v_samp_factor;
-      upsample->rows_to_go = cinfo->output_height - cinfo->output_scanline;
+      if (!master->using_merged_upsample) {
+        upsample->next_row_out = cinfo->max_v_samp_factor;
+        upsample->rows_to_go = cinfo->output_height - cinfo->output_scanline;
+      }
    }
  }

@@ -494,7 +519,8 @@ jpeg_skip_scanlines(j_decompress_ptr cinfo, JDIMENSION num_lines)
      cinfo->output_iMCU_row += lines_to_skip / lines_per_iMCU_row;
      increment_simple_rowgroup_ctr(cinfo, lines_to_read);
    }
-    upsample->rows_to_go = cinfo->output_height - cinfo->output_scanline;
+    if (!master->using_merged_upsample)
+      upsample->rows_to_go = cinfo->output_height - cinfo->output_scanline;
    return num_lines;
  }

@@ -535,7 +561,8 @@ jpeg_skip_scanlines(j_decompress_ptr cinfo, JDIMENSION num_lines)
   * bit odd, since "rows_to_go" seems to be redundantly keeping track of
   * output_scanline.
   */
-  upsample->rows_to_go = cinfo->output_height - cinfo->output_scanline;
+  if (!master->using_merged_upsample)
+    upsample->rows_to_go = cinfo->output_height - cinfo->output_scanline;

  /* Always skip the requested number of lines. */
  return num_lines;
@@ -6,7 +6,7 @@
 * libjpeg-turbo Modifications:
 * Copyright 2009 Pierre Ossman <ossman@cendio.se> for Cendio AB
 * Copyright (C) 2010, 2015-2016, D. R. Commander.
- * Copyright (C) 2015, Google, Inc.
+ * Copyright (C) 2015, 2020, Google, Inc.
 * For conditions of distribution and use, see the accompanying README.ijg
 * file.
 *
@@ -495,11 +495,13 @@ decompress_smooth_data(j_decompress_ptr cinfo, JSAMPIMAGE output_buf)
      if (first_row && block_row == 0)
        prev_block_row = buffer_ptr;
      else
-        prev_block_row = buffer[block_row - 1];
+        prev_block_row = buffer[block_row - 1] +
+                         cinfo->master->first_MCU_col[ci];
      if (last_row && block_row == block_rows - 1)
        next_block_row = buffer_ptr;
      else
-        next_block_row = buffer[block_row + 1];
+        next_block_row = buffer[block_row + 1] +
+                         cinfo->master->first_MCU_col[ci];
      /* We fetch the surrounding DC values using a sliding-register approach.
       * Initialize all nine here so as to do the right thing on narrow pics.
       */
@@ -571,11 +571,10 @@ ycck_cmyk_convert(j_decompress_ptr cinfo, JSAMPIMAGE input_buf,
 * RGB565 conversion
 */

-#define PACK_SHORT_565_LE(r, g, b)  ((((r) << 8) & 0xF800) | \
-                                     (((g) << 3) & 0x7E0) | ((b) >> 3))
-#define PACK_SHORT_565_BE(r, g, b)  (((r) & 0xF8) | ((g) >> 5) | \
-                                     (((g) << 11) & 0xE000) | \
-                                     (((b) << 5) & 0x1F00))
+#define PACK_SHORT_565_LE(r, g, b) \
+  ((((r) << 8) & 0xF800) | (((g) << 3) & 0x7E0) | ((b) >> 3))
+#define PACK_SHORT_565_BE(r, g, b) \
+  (((r) & 0xF8) | ((g) >> 5) | (((g) << 11) & 0xE000) | (((b) << 5) & 0x1F00))

 #define PACK_TWO_PIXELS_LE(l, r)    ((r << 16) | l)
 #define PACK_TWO_PIXELS_BE(l, r)    ((l << 16) | r)
@@ -5,7 +5,7 @@
 * Copyright (C) 1994-1996, Thomas G. Lane.
 * libjpeg-turbo Modifications:
 * Copyright 2009 Pierre Ossman <ossman@cendio.se> for Cendio AB
- * Copyright (C) 2009, 2011, 2014-2015, D. R. Commander.
+ * Copyright (C) 2009, 2011, 2014-2015, 2020, D. R. Commander.
 * Copyright (C) 2013, Linaro Limited.
 * For conditions of distribution and use, see the accompanying README.ijg
 * file.
@@ -40,41 +40,13 @@
 #define JPEG_INTERNALS
 #include "jinclude.h"
 #include "jpeglib.h"
+#include "jdmerge.h"
 #include "jsimd.h"
 #include "jconfigint.h"

 #ifdef UPSAMPLE_MERGING_SUPPORTED


-/* Private subobject */
-
-typedef struct {
-  struct jpeg_upsampler pub;    /* public fields */
-
-  /* Pointer to routine to do actual upsampling/conversion of one row group */
-  void (*upmethod) (j_decompress_ptr cinfo, JSAMPIMAGE input_buf,
-                    JDIMENSION in_row_group_ctr, JSAMPARRAY output_buf);
-
-  /* Private state for YCC->RGB conversion */
-  int *Cr_r_tab;                /* => table for Cr to R conversion */
-  int *Cb_b_tab;                /* => table for Cb to B conversion */
-  JLONG *Cr_g_tab;              /* => table for Cr to G conversion */
-  JLONG *Cb_g_tab;              /* => table for Cb to G conversion */
-
-  /* For 2:1 vertical sampling, we produce two output rows at a time.
-   * We need a "spare" row buffer to hold the second output row if the
-   * application provides just a one-row buffer; we also use the spare
-   * to discard the dummy last row if the image height is odd.
-   */
-  JSAMPROW spare_row;
-  boolean spare_full;           /* T if spare buffer is occupied */
-
-  JDIMENSION out_row_width;     /* samples per output row */
-  JDIMENSION rows_to_go;        /* counts rows remaining in image */
-} my_upsampler;
-
-typedef my_upsampler *my_upsample_ptr;
-
 #define SCALEBITS       16      /* speediest right-shift on some machines */
 #define ONE_HALF        ((JLONG)1 << (SCALEBITS - 1))
 #define FIX(x)          ((JLONG)((x) * (1L << SCALEBITS) + 0.5))
@@ -189,7 +161,7 @@ typedef my_upsampler *my_upsample_ptr;
 LOCAL(void)
 build_ycc_rgb_table(j_decompress_ptr cinfo)
 {
-  my_upsample_ptr upsample = (my_upsample_ptr)cinfo->upsample;
+  my_merged_upsample_ptr upsample = (my_merged_upsample_ptr)cinfo->upsample;
  int i;
  JLONG x;
  SHIFT_TEMPS
@@ -232,7 +204,7 @@ build_ycc_rgb_table(j_decompress_ptr cinfo)
 METHODDEF(void)
 start_pass_merged_upsample(j_decompress_ptr cinfo)
 {
-  my_upsample_ptr upsample = (my_upsample_ptr)cinfo->upsample;
+  my_merged_upsample_ptr upsample = (my_merged_upsample_ptr)cinfo->upsample;

  /* Mark the spare buffer empty */
  upsample->spare_full = FALSE;
@@ -254,7 +226,7 @@ merged_2v_upsample(j_decompress_ptr cinfo, JSAMPIMAGE input_buf,
                   JDIMENSION *out_row_ctr, JDIMENSION out_rows_avail)
 /* 2:1 vertical sampling case: may need a spare row. */
 {
-  my_upsample_ptr upsample = (my_upsample_ptr)cinfo->upsample;
+  my_merged_upsample_ptr upsample = (my_merged_upsample_ptr)cinfo->upsample;
  JSAMPROW work_ptrs[2];
  JDIMENSION num_rows;          /* number of rows returned to caller */

@@ -305,7 +277,7 @@ merged_1v_upsample(j_decompress_ptr cinfo, JSAMPIMAGE input_buf,
                   JDIMENSION *out_row_ctr, JDIMENSION out_rows_avail)
 /* 1:1 vertical sampling case: much easier, never need a spare row. */
 {
-  my_upsample_ptr upsample = (my_upsample_ptr)cinfo->upsample;
+  my_merged_upsample_ptr upsample = (my_merged_upsample_ptr)cinfo->upsample;

  /* Just do the upsampling. */
  (*upsample->upmethod) (cinfo, input_buf, *in_row_group_ctr,
@@ -420,11 +392,10 @@ h2v2_merged_upsample(j_decompress_ptr cinfo, JSAMPIMAGE input_buf,
 * RGB565 conversion
 */

-#define PACK_SHORT_565_LE(r, g, b)  ((((r) << 8) & 0xF800) | \
-                                     (((g) << 3) & 0x7E0) | ((b) >> 3))
-#define PACK_SHORT_565_BE(r, g, b)  (((r) & 0xF8) | ((g) >> 5) | \
-                                     (((g) << 11) & 0xE000) | \
-                                     (((b) << 5) & 0x1F00))
+#define PACK_SHORT_565_LE(r, g, b) \
+  ((((r) << 8) & 0xF800) | (((g) << 3) & 0x7E0) | ((b) >> 3))
+#define PACK_SHORT_565_BE(r, g, b) \
+  (((r) & 0xF8) | ((g) >> 5) | (((g) << 11) & 0xE000) | (((b) << 5) & 0x1F00))

 #define PACK_TWO_PIXELS_LE(l, r)    ((r << 16) | l)
 #define PACK_TWO_PIXELS_BE(l, r)    ((l << 16) | r)
@@ -566,11 +537,11 @@ h2v2_merged_upsample_565D(j_decompress_ptr cinfo, JSAMPIMAGE input_buf,
 GLOBAL(void)
 jinit_merged_upsampler(j_decompress_ptr cinfo)
 {
-  my_upsample_ptr upsample;
+  my_merged_upsample_ptr upsample;

-  upsample = (my_upsample_ptr)
+  upsample = (my_merged_upsample_ptr)
    (*cinfo->mem->alloc_small) ((j_common_ptr)cinfo, JPOOL_IMAGE,
-                                sizeof(my_upsampler));
+                                sizeof(my_merged_upsampler));
  cinfo->upsample = (struct jpeg_upsampler *)upsample;
  upsample->pub.start_pass = start_pass_merged_upsample;
  upsample->pub.need_context_rows = FALSE;
@@ -0,0 +1,47 @@
+/*
+ * jdmerge.h
+ *
+ * This file was part of the Independent JPEG Group's software:
+ * Copyright (C) 1994-1996, Thomas G. Lane.
+ * libjpeg-turbo Modifications:
+ * Copyright (C) 2020, D. R. Commander.
+ * For conditions of distribution and use, see the accompanying README.ijg
+ * file.
+ */
+
+#define JPEG_INTERNALS
+#include "jpeglib.h"
+
+#ifdef UPSAMPLE_MERGING_SUPPORTED
+
+
+/* Private subobject */
+
+typedef struct {
+  struct jpeg_upsampler pub;    /* public fields */
+
+  /* Pointer to routine to do actual upsampling/conversion of one row group */
+  void (*upmethod) (j_decompress_ptr cinfo, JSAMPIMAGE input_buf,
+                    JDIMENSION in_row_group_ctr, JSAMPARRAY output_buf);
+
+  /* Private state for YCC->RGB conversion */
+  int *Cr_r_tab;                /* => table for Cr to R conversion */
+  int *Cb_b_tab;                /* => table for Cb to B conversion */
+  JLONG *Cr_g_tab;              /* => table for Cr to G conversion */
+  JLONG *Cb_g_tab;              /* => table for Cb to G conversion */
+
+  /* For 2:1 vertical sampling, we produce two output rows at a time.
+   * We need a "spare" row buffer to hold the second output row if the
+   * application provides just a one-row buffer; we also use the spare
+   * to discard the dummy last row if the image height is odd.
+   */
+  JSAMPROW spare_row;
+  boolean spare_full;           /* T if spare buffer is occupied */
+
+  JDIMENSION out_row_width;     /* samples per output row */
+  JDIMENSION rows_to_go;        /* counts rows remaining in image */
+} my_merged_upsampler;
+
+typedef my_merged_upsampler *my_merged_upsample_ptr;
+
+#endif /* UPSAMPLE_MERGING_SUPPORTED */
@@ -5,7 +5,7 @@
 * Copyright (C) 1994-1996, Thomas G. Lane.
 * libjpeg-turbo Modifications:
 * Copyright (C) 2013, Linaro Limited.
- * Copyright (C) 2014-2015, 2018, D. R. Commander.
+ * Copyright (C) 2014-2015, 2018, 2020, D. R. Commander.
 * For conditions of distribution and use, see the accompanying README.ijg
 * file.
 *
@@ -19,7 +19,7 @@ h2v1_merged_upsample_565_internal(j_decompress_ptr cinfo, JSAMPIMAGE input_buf,
                                  JDIMENSION in_row_group_ctr,
                                  JSAMPARRAY output_buf)
 {
-  my_upsample_ptr upsample = (my_upsample_ptr)cinfo->upsample;
+  my_merged_upsample_ptr upsample = (my_merged_upsample_ptr)cinfo->upsample;
  register int y, cred, cgreen, cblue;
  int cb, cr;
  register JSAMPROW outptr;
@@ -90,7 +90,7 @@ h2v1_merged_upsample_565D_internal(j_decompress_ptr cinfo,
                                   JDIMENSION in_row_group_ctr,
                                   JSAMPARRAY output_buf)
 {
-  my_upsample_ptr upsample = (my_upsample_ptr)cinfo->upsample;
+  my_merged_upsample_ptr upsample = (my_merged_upsample_ptr)cinfo->upsample;
  register int y, cred, cgreen, cblue;
  int cb, cr;
  register JSAMPROW outptr;
@@ -163,7 +163,7 @@ h2v2_merged_upsample_565_internal(j_decompress_ptr cinfo, JSAMPIMAGE input_buf,
                                  JDIMENSION in_row_group_ctr,
                                  JSAMPARRAY output_buf)
 {
-  my_upsample_ptr upsample = (my_upsample_ptr)cinfo->upsample;
+  my_merged_upsample_ptr upsample = (my_merged_upsample_ptr)cinfo->upsample;
  register int y, cred, cgreen, cblue;
  int cb, cr;
  register JSAMPROW outptr0, outptr1;
@@ -259,7 +259,7 @@ h2v2_merged_upsample_565D_internal(j_decompress_ptr cinfo,
                                   JDIMENSION in_row_group_ctr,
                                   JSAMPARRAY output_buf)
 {
-  my_upsample_ptr upsample = (my_upsample_ptr)cinfo->upsample;
+  my_merged_upsample_ptr upsample = (my_merged_upsample_ptr)cinfo->upsample;
  register int y, cred, cgreen, cblue;
  int cb, cr;
  register JSAMPROW outptr0, outptr1;
@@ -4,7 +4,7 @@
 * This file was part of the Independent JPEG Group's software:
 * Copyright (C) 1994-1996, Thomas G. Lane.
 * libjpeg-turbo Modifications:
- * Copyright (C) 2011, 2015, D. R. Commander.
+ * Copyright (C) 2011, 2015, 2020, D. R. Commander.
 * For conditions of distribution and use, see the accompanying README.ijg
 * file.
 *
@@ -25,7 +25,7 @@ h2v1_merged_upsample_internal(j_decompress_ptr cinfo, JSAMPIMAGE input_buf,
                              JDIMENSION in_row_group_ctr,
                              JSAMPARRAY output_buf)
 {
-  my_upsample_ptr upsample = (my_upsample_ptr)cinfo->upsample;
+  my_merged_upsample_ptr upsample = (my_merged_upsample_ptr)cinfo->upsample;
  register int y, cred, cgreen, cblue;
  int cb, cr;
  register JSAMPROW outptr;
@@ -97,7 +97,7 @@ h2v2_merged_upsample_internal(j_decompress_ptr cinfo, JSAMPIMAGE input_buf,
                              JDIMENSION in_row_group_ctr,
                              JSAMPARRAY output_buf)
 {
-  my_upsample_ptr upsample = (my_upsample_ptr)cinfo->upsample;
+  my_merged_upsample_ptr upsample = (my_merged_upsample_ptr)cinfo->upsample;
  register int y, cred, cgreen, cblue;
  int cb, cr;
  register JSAMPROW outptr0, outptr1;
@@ -3,8 +3,8 @@
 *
 * This file was part of the Independent JPEG Group's software:
 * Copyright (C) 1995-1997, Thomas G. Lane.
- * It was modified by The libjpeg-turbo Project to include only code relevant
- * to libjpeg-turbo.
+ * libjpeg-turbo Modifications:
+ * Copyright (C) 2020, D. R. Commander.
 * For conditions of distribution and use, see the accompanying README.ijg
 * file.
 *
@@ -16,6 +16,7 @@
 #define JPEG_INTERNALS
 #include "jinclude.h"
 #include "jpeglib.h"
+#include "jpegcomp.h"


 /* Forward declarations */
@@ -4,11 +4,11 @@
 * This file was part of the Independent JPEG Group's software:
 * Copyright (C) 1991-1996, Thomas G. Lane.
 * libjpeg-turbo Modifications:
- * Copyright (C) 2015, D. R. Commander.
+ * Copyright (C) 2015, 2020, D. R. Commander.
 * For conditions of distribution and use, see the accompanying README.ijg
 * file.
 *
- * This file contains a slow-but-accurate integer implementation of the
+ * This file contains a slower but more accurate integer implementation of the
 * forward DCT (Discrete Cosine Transform).
 *
 * A 2-D DCT can be done by 1-D DCT on each row followed by 1-D DCT
@@ -5,11 +5,11 @@
 * Copyright (C) 1991-1998, Thomas G. Lane.
 * Modification developed 2002-2009 by Guido Vollbeding.
 * libjpeg-turbo Modifications:
- * Copyright (C) 2015, D. R. Commander.
+ * Copyright (C) 2015, 2020, D. R. Commander.
 * For conditions of distribution and use, see the accompanying README.ijg
 * file.
 *
- * This file contains a slow-but-accurate integer implementation of the
+ * This file contains a slower but more accurate integer implementation of the
 * inverse DCT (Discrete Cosine Transform).  In the IJG code, this routine
 * must also perform dequantization of the input coefficients.
 *
@@ -5,7 +5,7 @@
 * Copyright (C) 1991-1997, Thomas G. Lane.
 * Modified 1997-2009 by Guido Vollbeding.
 * libjpeg-turbo Modifications:
- * Copyright (C) 2009, 2011, 2014-2015, 2018, D. R. Commander.
+ * Copyright (C) 2009, 2011, 2014-2015, 2018, 2020, D. R. Commander.
 * For conditions of distribution and use, see the accompanying README.ijg
 * file.
 *
@@ -273,9 +273,9 @@ typedef int boolean;

 /* Capability options common to encoder and decoder: */

-#define DCT_ISLOW_SUPPORTED     /* slow but accurate integer algorithm */
-#define DCT_IFAST_SUPPORTED     /* faster, less accurate integer method */
-#define DCT_FLOAT_SUPPORTED     /* floating-point: accurate, fast on fast HW */
+#define DCT_ISLOW_SUPPORTED     /* accurate integer method */
+#define DCT_IFAST_SUPPORTED     /* less accurate int method [legacy feature] */
+#define DCT_FLOAT_SUPPORTED     /* floating-point method [legacy feature] */

 /* Encoder capability options: */

@@ -1,7 +1,7 @@
 /*
 * jpegcomp.h
 *
- * Copyright (C) 2010, D. R. Commander.
+ * Copyright (C) 2010, 2020, D. R. Commander.
 * For conditions of distribution and use, see the accompanying README.ijg
 * file.
 *
@@ -19,6 +19,7 @@
 #define _min_DCT_v_scaled_size  min_DCT_v_scaled_size
 #define _jpeg_width  jpeg_width
 #define _jpeg_height  jpeg_height
+#define JERR_ARITH_NOTIMPL  JERR_NOT_COMPILED
 #else
 #define _DCT_scaled_size  DCT_scaled_size
 #define _DCT_h_scaled_size  DCT_scaled_size
@@ -5,7 +5,7 @@
 * Copyright (C) 1991-1998, Thomas G. Lane.
 * Modified 2002-2009 by Guido Vollbeding.
 * libjpeg-turbo Modifications:
- * Copyright (C) 2009-2011, 2013-2014, 2016-2017, D. R. Commander.
+ * Copyright (C) 2009-2011, 2013-2014, 2016-2017, 2020, D. R. Commander.
 * Copyright (C) 2015, Google, Inc.
 * For conditions of distribution and use, see the accompanying README.ijg
 * file.
@@ -244,9 +244,9 @@ typedef enum {
 /* DCT/IDCT algorithm options. */

 typedef enum {
-  JDCT_ISLOW,             /* slow but accurate integer algorithm */
-  JDCT_IFAST,             /* faster, less accurate integer method */
-  JDCT_FLOAT              /* floating-point: accurate, fast on fast HW */
+  JDCT_ISLOW,             /* accurate integer method */
+  JDCT_IFAST,             /* less accurate integer method [legacy feature] */
+  JDCT_FLOAT              /* floating-point method [legacy feature] */
 } J_DCT_METHOD;

 #ifndef JDCT_DEFAULT            /* may be overridden in jconfig.h */
@@ -4,7 +4,7 @@
 * This file was part of the Independent JPEG Group's software:
 * Copyright (C) 1991-1996, Thomas G. Lane.
 * libjpeg-turbo Modifications:
- * Copyright (C) 2009, 2014-2015, D. R. Commander.
+ * Copyright (C) 2009, 2014-2015, 2020, D. R. Commander.
 * For conditions of distribution and use, see the accompanying README.ijg
 * file.
 *
@@ -1145,7 +1145,7 @@ start_pass_2_quant(j_decompress_ptr cinfo, boolean is_pre_scan)
  int i;

  /* Only F-S dithering or no dithering is supported. */
-  /* If user asks for ordered dither, give him F-S. */
+  /* If user asks for ordered dither, give them F-S. */
  if (cinfo->dither_mode != JDITHER_NONE)
    cinfo->dither_mode = JDITHER_FS;

@@ -1263,7 +1263,7 @@ jinit_2pass_quantizer(j_decompress_ptr cinfo)
    cquantize->sv_colormap = NULL;

  /* Only F-S dithering or no dithering is supported. */
-  /* If user asks for ordered dither, give him F-S. */
+  /* If user asks for ordered dither, give them F-S. */
  if (cinfo->dither_mode != JDITHER_NONE)
    cinfo->dither_mode = JDITHER_FS;

@@ -30,23 +30,25 @@
 * NOTE: It is our convention to place the authors in the following order:
 * - libjpeg-turbo authors (2009-) in descending order of the date of their
 *   most recent contribution to the project, then in ascending order of the
- *   date of their first contribution to the project
+ *   date of their first contribution to the project, then in alphabetical
+ *   order
 * - Upstream authors in descending order of the date of the first inclusion of
 *   their code
 */

 #define JCOPYRIGHT \
  "Copyright (C) 2009-2020 D. R. Commander\n" \
-  "Copyright (C) 2011-2016 Siarhei Siamashka\n" \
+  "Copyright (C) 2015, 2020 Google, Inc.\n" \
+  "Copyright (C) 2019 Arm Limited\n" \
  "Copyright (C) 2015-2016, 2018 Matthieu Darbois\n" \
+  "Copyright (C) 2011-2016 Siarhei Siamashka\n" \
  "Copyright (C) 2015 Intel Corporation\n" \
-  "Copyright (C) 2015 Google, Inc.\n" \
+  "Copyright (C) 2013-2014 Linaro Limited\n" \
  "Copyright (C) 2013-2014 MIPS Technologies, Inc.\n" \
-  "Copyright (C) 2013 Linaro Limited\n" \
+  "Copyright (C) 2009, 2012 Pierre Ossman for Cendio AB\n" \
  "Copyright (C) 2009-2011 Nokia Corporation and/or its subsidiary(-ies)\n" \
-  "Copyright (C) 2009 Pierre Ossman for Cendio AB\n" \
  "Copyright (C) 1999-2006 MIYASAKA Masaru\n" \
-  "Copyright (C) 1991-2016 Thomas G. Lane, Guido Vollbeding"
+  "Copyright (C) 1991-2017 Thomas G. Lane, Guido Vollbeding"

 #define JCOPYRIGHT_SHORT \
  "Copyright (C) 1991-2020 The libjpeg-turbo Project and many others"
@@ -19,7 +19,7 @@ endif()
 #         Define the library target:
 # ----------------------------------------------------------------------------------

-add_library(${JPEG_LIBRARY} STATIC ${lib_srcs} ${lib_hdrs})
+add_library(${JPEG_LIBRARY} STATIC ${OPENCV_3RDPARTY_EXCLUDE_FROM_ALL} ${lib_srcs} ${lib_hdrs})

 if(CV_GCC OR CV_CLANG)
  set_source_files_properties(jcdctmgr.c PROPERTIES COMPILE_FLAGS "-O1")
@@ -42,7 +42,7 @@ if(ENABLE_SOLUTION_FOLDERS)
 endif()

 if(NOT BUILD_SHARED_LIBS)
-  ocv_install_target(${JPEG_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev)
+  ocv_install_target(${JPEG_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev OPTIONAL)
 endif()

 ocv_install_3rdparty_licenses(libjpeg README)
@@ -74,7 +74,7 @@ if(MSVC)
  add_definitions(-D_CRT_SECURE_NO_DEPRECATE)
 endif(MSVC)

-add_library(${PNG_LIBRARY} STATIC ${lib_srcs} ${lib_hdrs})
+add_library(${PNG_LIBRARY} STATIC ${OPENCV_3RDPARTY_EXCLUDE_FROM_ALL} ${lib_srcs} ${lib_hdrs})
 target_link_libraries(${PNG_LIBRARY} ${ZLIB_LIBRARIES})

 ocv_warnings_disable(CMAKE_C_FLAGS -Wundef -Wcast-align -Wimplicit-fallthrough -Wunused-parameter -Wsign-compare)
@@ -92,7 +92,7 @@ if(ENABLE_SOLUTION_FOLDERS)
 endif()

 if(NOT BUILD_SHARED_LIBS)
-  ocv_install_target(${PNG_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev)
+  ocv_install_target(${PNG_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev OPTIONAL)
 endif()

 ocv_install_3rdparty_licenses(libpng LICENSE README)
@@ -462,7 +462,7 @@ ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4456 /wd4457 /wd4312) # vs2015

 ocv_warnings_disable(CMAKE_C_FLAGS /wd4267 /wd4244 /wd4018 /wd4311 /wd4312)

-add_library(${TIFF_LIBRARY} STATIC ${lib_srcs})
+add_library(${TIFF_LIBRARY} STATIC ${OPENCV_3RDPARTY_EXCLUDE_FROM_ALL} ${lib_srcs})
 target_link_libraries(${TIFF_LIBRARY} ${ZLIB_LIBRARIES})

 set_target_properties(${TIFF_LIBRARY}
@@ -479,7 +479,7 @@ if(ENABLE_SOLUTION_FOLDERS)
 endif()

 if(NOT BUILD_SHARED_LIBS)
-  ocv_install_target(${TIFF_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev)
+  ocv_install_target(${TIFF_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev OPTIONAL)
 endif()

 ocv_install_3rdparty_licenses(libtiff COPYRIGHT)
@@ -34,7 +34,7 @@ endif()

 add_definitions(-DWEBP_USE_THREAD)

-add_library(${WEBP_LIBRARY} STATIC ${lib_srcs} ${lib_hdrs})
+add_library(${WEBP_LIBRARY} STATIC ${OPENCV_3RDPARTY_EXCLUDE_FROM_ALL} ${lib_srcs} ${lib_hdrs})
 if(ANDROID)
  target_link_libraries(${WEBP_LIBRARY} ${CPUFEATURES_LIBRARIES})
 endif()
@@ -59,6 +59,6 @@ if(ENABLE_SOLUTION_FOLDERS)
 endif()

 if(NOT BUILD_SHARED_LIBS)
-  ocv_install_target(${WEBP_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev)
+  ocv_install_target(${WEBP_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev OPTIONAL)
 endif()

@@ -109,6 +109,7 @@ 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)
@@ -125,7 +126,7 @@ if(MSVC AND CV_ICC)
  set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /Qrestrict")
 endif()

-add_library(IlmImf STATIC ${lib_hdrs} ${lib_srcs})
+add_library(IlmImf STATIC ${OPENCV_3RDPARTY_EXCLUDE_FROM_ALL} ${lib_hdrs} ${lib_srcs})
 target_link_libraries(IlmImf ${ZLIB_LIBRARIES})

 set_target_properties(IlmImf
@@ -142,7 +143,7 @@ if(ENABLE_SOLUTION_FOLDERS)
 endif()

 if(NOT BUILD_SHARED_LIBS)
-  ocv_install_target(IlmImf EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev)
+  ocv_install_target(IlmImf EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev OPTIONAL)
 endif()

 ocv_install_3rdparty_licenses(openexr LICENSE AUTHORS.ilmbase AUTHORS.openexr)
@@ -11,6 +11,10 @@ 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)
@@ -140,7 +140,8 @@ append_if_exist(Protobuf_SRCS
  ${PROTOBUF_ROOT}/src/google/protobuf/wrappers.pb.cc
 )

-add_library(libprotobuf STATIC ${Protobuf_SRCS})
+include_directories(BEFORE "${PROTOBUF_ROOT}/src")  # ensure using if own headers: https://github.com/opencv/opencv/issues/13328
+add_library(libprotobuf STATIC ${OPENCV_3RDPARTY_EXCLUDE_FROM_ALL} ${Protobuf_SRCS})
 target_include_directories(libprotobuf SYSTEM PUBLIC $<BUILD_INTERFACE:${PROTOBUF_ROOT}/src>)
 set_target_properties(libprotobuf
    PROPERTIES
@@ -152,11 +153,16 @@ 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)

 if(NOT BUILD_SHARED_LIBS)
-  ocv_install_target(libprotobuf EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev)
+  ocv_install_target(libprotobuf EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev OPTIONAL)
 endif()

 ocv_install_3rdparty_licenses(protobuf LICENSE README.md)
@@ -8,7 +8,7 @@ ocv_include_directories(${CURR_INCLUDE_DIR})
 file(GLOB_RECURSE quirc_headers RELATIVE "${CMAKE_CURRENT_LIST_DIR}" "include/*.h")
 file(GLOB_RECURSE quirc_sources RELATIVE "${CMAKE_CURRENT_LIST_DIR}" "src/*.c")

-add_library(${PROJECT_NAME} STATIC ${quirc_headers} ${quirc_sources})
+add_library(${PROJECT_NAME} STATIC ${OPENCV_3RDPARTY_EXCLUDE_FROM_ALL} ${quirc_headers} ${quirc_sources})
 ocv_warnings_disable(CMAKE_C_FLAGS -Wunused-variable -Wshadow)

 set_target_properties(${PROJECT_NAME}
@@ -24,7 +24,7 @@ if(ENABLE_SOLUTION_FOLDERS)
 endif()

 if(NOT BUILD_SHARED_LIBS)
-  ocv_install_target(${PROJECT_NAME} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev)
+  ocv_install_target(${PROJECT_NAME} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev OPTIONAL)
 endif()

 ocv_install_3rdparty_licenses(${PROJECT_NAME} LICENSE)
@@ -108,7 +108,7 @@ set(tbb_version_file "version_string.ver")
 configure_file("${CMAKE_CURRENT_SOURCE_DIR}/${tbb_version_file}.cmakein" "${CMAKE_CURRENT_BINARY_DIR}/${tbb_version_file}" @ONLY)
 list(APPEND TBB_SOURCE_FILES "${CMAKE_CURRENT_BINARY_DIR}/${tbb_version_file}")

-add_library(tbb ${TBB_SOURCE_FILES})
+add_library(tbb ${OPENCV_3RDPARTY_EXCLUDE_FROM_ALL} ${TBB_SOURCE_FILES})
 target_compile_definitions(tbb PUBLIC
    TBB_USE_GCC_BUILTINS=1
    __TBB_GCC_BUILTIN_ATOMICS_PRESENT=1
@@ -165,6 +165,7 @@ ocv_install_target(tbb EXPORT OpenCVModules
    RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} COMPONENT libs
    LIBRARY DESTINATION ${OPENCV_LIB_INSTALL_PATH} COMPONENT libs
    ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev
+    OPTIONAL
    )

 ocv_install_3rdparty_licenses(tbb "${tbb_src_dir}/LICENSE" "${tbb_src_dir}/README")
@@ -76,7 +76,7 @@ set(ZLIB_SRCS
    zutil.c
 )

-add_library(${ZLIB_LIBRARY} STATIC ${ZLIB_SRCS} ${ZLIB_PUBLIC_HDRS} ${ZLIB_PRIVATE_HDRS})
+add_library(${ZLIB_LIBRARY} STATIC ${OPENCV_3RDPARTY_EXCLUDE_FROM_ALL} ${ZLIB_SRCS} ${ZLIB_PUBLIC_HDRS} ${ZLIB_PRIVATE_HDRS})
 set_target_properties(${ZLIB_LIBRARY} PROPERTIES DEFINE_SYMBOL ZLIB_DLL)

 ocv_warnings_disable(CMAKE_C_FLAGS -Wshorten-64-to-32 -Wattributes -Wstrict-prototypes -Wmissing-prototypes -Wmissing-declarations -Wshift-negative-value
@@ -17,9 +17,7 @@ endif()

 include(cmake/OpenCVMinDepVersions.cmake)

-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)
+if(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.
@@ -367,6 +365,9 @@ OCV_OPTION(WITH_MSMF_DXVA "Enable hardware acceleration in Media Foundation back
 OCV_OPTION(WITH_XIMEA "Include XIMEA cameras support" OFF
  VISIBLE_IF NOT ANDROID AND NOT WINRT
  VERIFY HAVE_XIMEA)
+OCV_OPTION(WITH_UEYE "Include UEYE camera support" OFF
+  VISIBLE_IF NOT ANDROID AND NOT APPLE AND NOT WINRT
+  VERIFY HAVE_UEYE)
 OCV_OPTION(WITH_XINE "Include Xine support (GPL)" OFF
  VISIBLE_IF UNIX AND NOT APPLE AND NOT ANDROID
  VERIFY HAVE_XINE)
@@ -439,6 +440,9 @@ OCV_OPTION(WITH_ANDROID_MEDIANDK "Use Android Media NDK for Video I/O (Android)"
 OCV_OPTION(WITH_TENGINE "Include Arm Inference Tengine support" OFF
  VISIBLE_IF (ARM OR AARCH64) AND (UNIX OR ANDROID) AND NOT IOS
  VERIFY HAVE_TENGINE)
+OCV_OPTION(WITH_ONNX "Include Microsoft ONNX Runtime support" OFF
+  VISIBLE_IF TRUE
+  VERIFY HAVE_ONNX)

 # OpenCV build components
 # ===================================================
@@ -482,7 +486,7 @@ 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))  # Use CPU_BASELINE instead
+if(NOT IOS AND (NOT ANDROID OR OPENCV_ANDROID_USE_LEGACY_FLAGS) AND CMAKE_CROSSCOMPILING)  # 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()
@@ -775,6 +779,11 @@ if(WITH_QUIRC)
  add_subdirectory(3rdparty/quirc)
  set(HAVE_QUIRC TRUE)
 endif()
+
+if(WITH_ONNX)
+  include(cmake/FindONNX.cmake)
+endif()
+
 # ----------------------------------------------------------------------------
 # OpenCV HAL
 # ----------------------------------------------------------------------------
@@ -1372,6 +1381,10 @@ if(WITH_XIMEA OR HAVE_XIMEA)
  status("    XIMEA:" HAVE_XIMEA THEN YES ELSE NO)
 endif()

+if(WITH_UEYE OR HAVE_UEYE)
+  status("    uEye:" HAVE_UEYE THEN YES ELSE NO)
+endif()
+
 if(WITH_XINE OR HAVE_XINE)
  status("    Xine:"           HAVE_XINE           THEN "YES (ver ${XINE_VERSION})"     ELSE NO)
 endif()
@@ -1556,6 +1569,15 @@ if(WITH_OPENCL OR HAVE_OPENCL)
  endif()
 endif()

+if(WITH_ONNX OR HAVE_ONNX)
+  status("")
+  status("  ONNX:"     HAVE_ONNX THEN "YES" ELSE "NO")
+  if(HAVE_ONNX)
+    status("    Include path:"  ONNX_INCLUDE_DIR THEN "${ONNX_INCLUDE_DIR}" ELSE "NO")
+    status("    Link libraries:" ONNX_LIBRARIES THEN "${ONNX_LIBRARIES}" ELSE "NO")
+  endif()
+endif()
+
 # ========================== python ==========================
 if(BUILD_opencv_python2)
  status("")
@@ -32,7 +32,7 @@ bool calib::parametersController::loadFromFile(const std::string &inputFileName)

    if(!reader.isOpened()) {
        std::cerr << "Warning: Unable to open " << inputFileName <<
-                     " Applicatioin stated with default advanced parameters" << std::endl;
+                     " Application started with default advanced parameters" << std::endl;
        return true;
    }

@@ -0,0 +1,36 @@
+ocv_clear_vars(HAVE_ONNX)
+
+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})
+endif()
+
+if(ONNXRT_ROOT_DIR)
+  find_library(ORT_LIB onnxruntime
+    ${ONNXRT_ROOT_DIR}/lib
+    CMAKE_FIND_ROOT_PATH_BOTH)
+  find_path(ORT_INCLUDE onnxruntime_cxx_api.h
+    ${ONNXRT_ROOT_DIR}/include/onnxruntime/core/session
+    CMAKE_FIND_ROOT_PATH_BOTH)
+endif()
+
+if(ORT_LIB AND ORT_INCLUDE)
+  set(HAVE_ONNX TRUE)
+  # For CMake output only
+  set(ONNX_LIBRARIES "${ORT_LIB}" CACHE STRING "ONNX Runtime libraries")
+  set(ONNX_INCLUDE_DIR "${ORT_INCLUDE}" CACHE STRING "ONNX Runtime include path")
+
+  # Link target with associated interface headers
+  set(ONNX_LIBRARY "onnxruntime" CACHE STRING "ONNX Link Target")
+  ocv_add_library(${ONNX_LIBRARY} SHARED IMPORTED)
+  set_target_properties(${ONNX_LIBRARY} PROPERTIES
+                        INTERFACE_INCLUDE_DIRECTORIES ${ORT_INCLUDE}
+                        IMPORTED_LOCATION ${ORT_LIB}
+                        IMPORTED_IMPLIB ${ORT_LIB})
+endif()
+
+if(NOT HAVE_ONNX)
+  ocv_clear_vars(HAVE_ONNX ORT_LIB ORT_INCLUDE_DIR)
+endif()
@@ -122,7 +122,6 @@ 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()
@@ -153,7 +152,7 @@ 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_VERSION VERSION_LESS 10.0)
+    if(CV_CLANG AND NOT CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang" 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()
@@ -129,9 +129,9 @@ endif()

 if(INF_ENGINE_TARGET)
  if(NOT INF_ENGINE_RELEASE)
-    message(WARNING "InferenceEngine version has not been set, 2020.4 will be used by default. Set INF_ENGINE_RELEASE variable if you experience build errors.")
+    message(WARNING "InferenceEngine version has not been set, 2021.2 will be used by default. Set INF_ENGINE_RELEASE variable if you experience build errors.")
  endif()
-  set(INF_ENGINE_RELEASE "2020040000" CACHE STRING "Force IE version, should be in form YYYYAABBCC (e.g. 2020.1.0.2 -> 2020010002)")
+  set(INF_ENGINE_RELEASE "2021020000" CACHE STRING "Force IE version, should be in form YYYYAABBCC (e.g. 2020.1.0.2 -> 2020010002)")
  set_target_properties(${INF_ENGINE_TARGET} PROPERTIES
    INTERFACE_COMPILE_DEFINITIONS "HAVE_INF_ENGINE=1;INF_ENGINE_RELEASE=${INF_ENGINE_RELEASE}"
  )
@@ -6,6 +6,7 @@
 if(BUILD_ZLIB)
  ocv_clear_vars(ZLIB_FOUND)
 else()
+  ocv_clear_internal_cache_vars(ZLIB_LIBRARY ZLIB_INCLUDE_DIR)
  find_package(ZLIB "${MIN_VER_ZLIB}")
  if(ZLIB_FOUND AND ANDROID)
    if(ZLIB_LIBRARIES MATCHES "/usr/(lib|lib32|lib64)/libz.so$")
@@ -15,11 +16,12 @@ else()
 endif()

 if(NOT ZLIB_FOUND)
-  ocv_clear_vars(ZLIB_LIBRARY ZLIB_LIBRARIES ZLIB_INCLUDE_DIRS)
+  ocv_clear_vars(ZLIB_LIBRARY ZLIB_LIBRARIES ZLIB_INCLUDE_DIR)

-  set(ZLIB_LIBRARY zlib)
+  set(ZLIB_LIBRARY zlib CACHE INTERNAL "")
  add_subdirectory("${OpenCV_SOURCE_DIR}/3rdparty/zlib")
-  set(ZLIB_INCLUDE_DIRS "${${ZLIB_LIBRARY}_SOURCE_DIR}" "${${ZLIB_LIBRARY}_BINARY_DIR}")
+  set(ZLIB_INCLUDE_DIR "${${ZLIB_LIBRARY}_SOURCE_DIR}" "${${ZLIB_LIBRARY}_BINARY_DIR}" CACHE INTERNAL "")
+  set(ZLIB_INCLUDE_DIRS ${ZLIB_INCLUDE_DIR})
  set(ZLIB_LIBRARIES ${ZLIB_LIBRARY})

  ocv_parse_header2(ZLIB "${${ZLIB_LIBRARY}_SOURCE_DIR}/zlib.h" ZLIB_VERSION)
@@ -30,23 +32,25 @@ 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_LIBRARIES JPEG_INCLUDE_DIR)
+    ocv_clear_vars(JPEG_LIBRARY JPEG_INCLUDE_DIR)

    if(NOT BUILD_JPEG_TURBO_DISABLE)
-      set(JPEG_LIBRARY libjpeg-turbo)
+      set(JPEG_LIBRARY libjpeg-turbo CACHE INTERNAL "")
      set(JPEG_LIBRARIES ${JPEG_LIBRARY})
      add_subdirectory("${OpenCV_SOURCE_DIR}/3rdparty/libjpeg-turbo")
-      set(JPEG_INCLUDE_DIR "${${JPEG_LIBRARY}_SOURCE_DIR}/src")
+      set(JPEG_INCLUDE_DIR "${${JPEG_LIBRARY}_SOURCE_DIR}/src" CACHE INTERNAL "")
    else()
-      set(JPEG_LIBRARY libjpeg)
+      set(JPEG_LIBRARY libjpeg CACHE INTERNAL "")
      set(JPEG_LIBRARIES ${JPEG_LIBRARY})
      add_subdirectory("${OpenCV_SOURCE_DIR}/3rdparty/libjpeg")
-      set(JPEG_INCLUDE_DIR "${${JPEG_LIBRARY}_SOURCE_DIR}")
+      set(JPEG_INCLUDE_DIR "${${JPEG_LIBRARY}_SOURCE_DIR}" CACHE INTERNAL "")
    endif()
+    set(JPEG_INCLUDE_DIRS "${JPEG_INCLUDE_DIR}")
  endif()

  macro(ocv_detect_jpeg_version header_file)
@@ -74,6 +78,7 @@ 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)
@@ -83,10 +88,10 @@ if(WITH_TIFF)
  if(NOT TIFF_FOUND)
    ocv_clear_vars(TIFF_LIBRARY TIFF_LIBRARIES TIFF_INCLUDE_DIR)

-    set(TIFF_LIBRARY libtiff)
+    set(TIFF_LIBRARY libtiff CACHE INTERNAL "")
    set(TIFF_LIBRARIES ${TIFF_LIBRARY})
    add_subdirectory("${OpenCV_SOURCE_DIR}/3rdparty/libtiff")
-    set(TIFF_INCLUDE_DIR "${${TIFF_LIBRARY}_SOURCE_DIR}" "${${TIFF_LIBRARY}_BINARY_DIR}")
+    set(TIFF_INCLUDE_DIR "${${TIFF_LIBRARY}_SOURCE_DIR}" "${${TIFF_LIBRARY}_BINARY_DIR}" CACHE INTERNAL "")
    ocv_parse_header("${${TIFF_LIBRARY}_SOURCE_DIR}/tiff.h" TIFF_VERSION_LINES TIFF_VERSION_CLASSIC TIFF_VERSION_BIG TIFF_VERSION TIFF_BIGTIFF_VERSION)
  endif()

@@ -117,6 +122,7 @@ 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)
@@ -128,12 +134,12 @@ endif()
 if(WITH_WEBP AND NOT WEBP_FOUND
    AND (NOT ANDROID OR HAVE_CPUFEATURES)
 )
-
-  set(WEBP_LIBRARY libwebp)
+  ocv_clear_vars(WEBP_LIBRARY WEBP_INCLUDE_DIR)
+  set(WEBP_LIBRARY libwebp CACHE INTERNAL "")
  set(WEBP_LIBRARIES ${WEBP_LIBRARY})

  add_subdirectory("${OpenCV_SOURCE_DIR}/3rdparty/libwebp")
-  set(WEBP_INCLUDE_DIR "${${WEBP_LIBRARY}_SOURCE_DIR}/src")
+  set(WEBP_INCLUDE_DIR "${${WEBP_LIBRARY}_SOURCE_DIR}/src" CACHE INTERNAL "")
  set(HAVE_WEBP 1)
 endif()

@@ -192,10 +198,10 @@ 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)
+    set(JASPER_LIBRARY libjasper CACHE INTERNAL "")
    set(JASPER_LIBRARIES ${JASPER_LIBRARY})
    add_subdirectory("${OpenCV_SOURCE_DIR}/3rdparty/libjasper")
-    set(JASPER_INCLUDE_DIR "${${JASPER_LIBRARY}_SOURCE_DIR}")
+    set(JASPER_INCLUDE_DIR "${${JASPER_LIBRARY}_SOURCE_DIR}" CACHE INTERNAL "")
  endif()

  set(HAVE_JASPER YES)
@@ -210,6 +216,7 @@ 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)
@@ -225,10 +232,10 @@ 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)
+    set(PNG_LIBRARY libpng CACHE INTERNAL "")
    set(PNG_LIBRARIES ${PNG_LIBRARY})
    add_subdirectory("${OpenCV_SOURCE_DIR}/3rdparty/libpng")
-    set(PNG_INCLUDE_DIR "${${PNG_LIBRARY}_SOURCE_DIR}")
+    set(PNG_INCLUDE_DIR "${${PNG_LIBRARY}_SOURCE_DIR}" CACHE INTERNAL "")
    set(PNG_DEFINITIONS "")
    ocv_parse_header("${PNG_INCLUDE_DIR}/png.h" PNG_VERSION_LINES PNG_LIBPNG_VER_MAJOR PNG_LIBPNG_VER_MINOR PNG_LIBPNG_VER_RELEASE)
  endif()
@@ -241,6 +248,7 @@ endif()
 if(WITH_OPENEXR)
  ocv_clear_vars(HAVE_OPENEXR)
  if(NOT BUILD_OPENEXR)
+    ocv_clear_internal_cache_vars(OPENEXR_INCLUDE_PATHS OPENEXR_LIBRARIES OPENEXR_ILMIMF_LIBRARY OPENEXR_VERSION)
    include("${OpenCV_SOURCE_DIR}/cmake/OpenCVFindOpenEXR.cmake")
  endif()

@@ -270,7 +278,7 @@ if(WITH_GDAL)
    endif()
 endif()

-if (WITH_GDCM)
+if(WITH_GDCM)
  find_package(GDCM QUIET)
  if(NOT GDCM_FOUND)
    set(HAVE_GDCM NO)
@@ -51,7 +51,15 @@ endif(WITH_CUDA)

 # --- Eigen ---
 if(WITH_EIGEN AND NOT HAVE_EIGEN)
-  find_package(Eigen3 QUIET)
+  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
+  )
+    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)
+    find_package(Eigen3 QUIET)
+  endif()

  if(Eigen3_FOUND)
    if(TARGET Eigen3::Eigen)
@@ -3,7 +3,14 @@
 # installation/package
 #
 # Parameters:
-# MKL_WITH_TBB
+# 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
 #
 # On return this will define:
 #
@@ -13,12 +20,6 @@
 # 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 "${MKL_ROOT_DIR}" CACHE PATH "Path to MKL directory")
@@ -39,43 +40,50 @@ macro(get_mkl_version VERSION_FILE)
    set(MKL_VERSION_STR "${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 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)
+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})
 endif()

-#check current MKL_ROOT_DIR
 if(NOT MKL_ROOT_DIR OR NOT EXISTS "${MKL_ROOT_DIR}/include/mkl.h")
-    set(mkl_root_paths "${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})
+  mkl_fail()
 endif()

-set(MKL_INCLUDE_DIRS "${MKL_ROOT_DIR}/include" CACHE PATH "Path to MKL include directory")
+set(MKL_INCLUDE_DIR "${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_DIRS}"
-    OR NOT EXISTS "${MKL_INCLUDE_DIRS}/mkl_version.h"
+    OR NOT EXISTS "${MKL_INCLUDE_DIR}"
+    OR NOT EXISTS "${MKL_INCLUDE_DIR}/mkl_version.h"
 )
-    mkl_fail()
+  mkl_fail()
 endif()

-get_mkl_version(${MKL_INCLUDE_DIRS}/mkl_version.h)
+get_mkl_version(${MKL_INCLUDE_DIR}/mkl_version.h)

 #determine arch
 if(CMAKE_CXX_SIZEOF_DATA_PTR EQUAL 8)
@@ -95,52 +103,66 @@ else()
    set(MKL_ARCH_SUFFIX "c")
 endif()

-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()
+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()

-    set(mkl_lib_list "mkl_intel_${MKL_ARCH_SUFFIX}")
+if(MKL_USE_SINGLE_DYNAMIC_LIBRARY AND NOT (MKL_VERSION_STR VERSION_LESS "10.3.0"))

-    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()
+  # 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)
    else()
-        list(APPEND mkl_lib_list mkl_sequential)
+      list(APPEND mkl_lib_list mkl_gnu_thread)
    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()

-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()
+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()
    endif()
-    list(APPEND MKL_LIBRARIES ${${lib}})
-endforeach()
+    list(APPEND MKL_LIBRARIES ${${lib_var_name}})
+  endforeach()
+endif()

 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 "${MKL_INCLUDE_DIRS}" CACHE PATH "Path to MKL include directory")
-set(MKL_LIBRARIES "${MKL_LIBRARIES}" CACHE STRING "MKL libraries")
-if(UNIX AND NOT MKL_LIBRARIES_DONT_HACK)
+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)
    #it's ugly but helps to avoid cyclic lib problem
    set(MKL_LIBRARIES ${MKL_LIBRARIES} ${MKL_LIBRARIES} ${MKL_LIBRARIES} "-lpthread" "-lm" "-ldl")
 endif()
@@ -57,7 +57,7 @@ SET(Open_BLAS_INCLUDE_SEARCH_PATHS
 )

 SET(Open_BLAS_LIB_SEARCH_PATHS
-        $ENV{OpenBLAS}cd
+        $ENV{OpenBLAS}
        $ENV{OpenBLAS}/lib
        $ENV{OpenBLAS_HOME}
        $ENV{OpenBLAS_HOME}/lib
@@ -2,7 +2,11 @@ set(OPENCV_APPLE_BUNDLE_NAME "OpenCV")
 set(OPENCV_APPLE_BUNDLE_ID "org.opencv")

 if(IOS)
-  if (APPLE_FRAMEWORK AND DYNAMIC_PLIST)
+  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)
    configure_file("${OpenCV_SOURCE_DIR}/platforms/ios/Info.Dynamic.plist.in"
                   "${CMAKE_BINARY_DIR}/ios/Info.plist")
  else()
@@ -98,15 +98,6 @@ 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)
@@ -210,11 +201,6 @@ 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})
@@ -501,6 +487,21 @@ 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)
@@ -8,7 +8,20 @@ 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 "" "TOFILE" "" ${ARGN})
+  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()
+
  set(regex "${DUMP_UNPARSED_ARGUMENTS}")
  string(TOLOWER "${regex}" regex_lower)
  set(__VARS "")
@@ -400,6 +413,24 @@ 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
@@ -1512,10 +1543,16 @@ 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 ${CMAKE_BINARY_DIR}/ios/Info.plist
+      MACOSX_FRAMEWORK_INFO_PLIST ${OPENCV_APPLE_INFO_PLIST}
      # "current version" in semantic format in Mach-O binary file
      VERSION ${OPENCV_LIBVERSION}
      # "compatibility version" in semantic format in Mach-O binary file
@@ -1881,3 +1918,9 @@ function(ocv_update_file filepath content)
    file(WRITE "${filepath}" "${content}")
  endif()
 endfunction()
+
+if(NOT BUILD_SHARED_LIBS AND (CMAKE_VERSION VERSION_LESS "3.14.0"))
+  ocv_update(OPENCV_3RDPARTY_EXCLUDE_FROM_ALL "")  # avoid CMake warnings: https://gitlab.kitware.com/cmake/cmake/-/issues/18938
+else()
+  ocv_update(OPENCV_3RDPARTY_EXCLUDE_FROM_ALL "EXCLUDE_FROM_ALL")
+endif()
@@ -0,0 +1 @@
+set(OPENCV_SKIP_LINK_AS_NEEDED 1)
@@ -27,11 +27,13 @@
    opencv2/core/cuda*
    opencv2/core/opencl*
    opencv2/core/private*
+    opencv2/core/quaternion*
    opencv/cxeigen.hpp
    opencv2/core/eigen.hpp
    opencv2/flann/hdf5.h
    opencv2/imgcodecs/imgcodecs_c.h
    opencv2/imgcodecs/ios.h
+    opencv2/imgcodecs/macosx.h
    opencv2/videoio/videoio_c.h
    opencv2/videoio/cap_ios.h
    opencv2/xobjdetect/private.hpp
@@ -145,9 +145,23 @@ if(DOXYGEN_FOUND)
  set(tutorial_js_path "${CMAKE_CURRENT_SOURCE_DIR}/js_tutorials")
  set(example_path "${CMAKE_SOURCE_DIR}/samples")

+  set(doxygen_image_path
+      ${CMAKE_CURRENT_SOURCE_DIR}/images
+      ${paths_doc}
+      ${tutorial_path}
+      ${tutorial_py_path}
+      ${tutorial_js_path}
+      ${paths_tutorial}
+      #${OpenCV_SOURCE_DIR}/samples/data         # TODO: need to resolve ambiguous conflicts first
+      ${OpenCV_SOURCE_DIR}
+      ${OpenCV_SOURCE_DIR}/modules               # <opencv>/modules
+      ${OPENCV_EXTRA_MODULES_PATH}               # <opencv_contrib>/modules
+      ${OPENCV_DOCS_EXTRA_IMAGE_PATH}            # custom variable for user modules
+  )
+
  # set export variables
  string(REPLACE ";" " \\\n" CMAKE_DOXYGEN_INPUT_LIST "${rootfile} ; ${faqfile} ; ${paths_include} ; ${paths_hal_interface} ; ${paths_doc} ; ${tutorial_path} ; ${tutorial_py_path} ; ${tutorial_js_path} ; ${paths_tutorial} ; ${tutorial_contrib_root}")
-  string(REPLACE ";" " \\\n" CMAKE_DOXYGEN_IMAGE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/images ; ${paths_doc} ; ${tutorial_path} ; ${tutorial_py_path} ; ${tutorial_js_path} ; ${paths_tutorial}")
+  string(REPLACE ";" " \\\n" CMAKE_DOXYGEN_IMAGE_PATH "${doxygen_image_path}")
  string(REPLACE ";" " \\\n" CMAKE_DOXYGEN_EXCLUDE_LIST "${CMAKE_DOXYGEN_EXCLUDE_LIST}")
  string(REPLACE ";" " " CMAKE_DOXYGEN_ENABLED_SECTIONS "${CMAKE_DOXYGEN_ENABLED_SECTIONS}")
  # TODO: remove paths_doc from EXAMPLE_PATH after face module tutorials/samples moved to separate folders
@@ -39,7 +39,6 @@ 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
@@ -0,0 +1,119 @@
+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);
+    }
+}
@@ -0,0 +1,263 @@
+<!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>
@@ -0,0 +1,65 @@
+{
+    "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"
+        }
+    ]
+}
@@ -0,0 +1,281 @@
+<!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>
@@ -0,0 +1,387 @@
+<!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>
@@ -0,0 +1,39 @@
+{
+    "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"
+        }
+    ]
+}
@@ -0,0 +1,402 @@
+<!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>
@@ -0,0 +1,327 @@
+<!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>
@@ -0,0 +1,34 @@
+{
+    "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"
+        }
+    ]
+}
@@ -0,0 +1,243 @@
+<!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>
@@ -0,0 +1,12 @@
+{
+    "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"
+        }
+    ]
+}
@@ -0,0 +1,228 @@
+<!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>
@@ -0,0 +1,76 @@
+{
+    "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"
+        }
+    ]
+}
@@ -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', () => {
+        script.addEventListener('load', async () => {
            if (cv.getBuildInformation)
            {
                console.log(cv.getBuildInformation());
@@ -16,9 +16,15 @@ function Utils(errorOutputId) { // eslint-disable-line no-unused-vars
            else
            {
                // WASM
-                cv['onRuntimeInitialized']=()=>{
+                if (cv instanceof Promise) {
+                    cv = await cv;
                    console.log(cv.getBuildInformation());
                    onloadCallback();
+                } else {
+                    cv['onRuntimeInitialized']=()=>{
+                        console.log(cv.getBuildInformation());
+                        onloadCallback();
+                    }
                }
            }
        });
@@ -0,0 +1,13 @@
+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
@@ -0,0 +1,15 @@
+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
@@ -0,0 +1,13 @@
+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
@@ -0,0 +1,13 @@
+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
@@ -0,0 +1,13 @@
+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
@@ -0,0 +1,13 @@
+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
@@ -0,0 +1,13 @@
+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
@@ -0,0 +1,30 @@
+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
@@ -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](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.
+[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.

 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)
@@ -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/kripken/emscripten) is an LLVM-to-JavaScript compiler. We will use Emscripten to build OpenCV.js.
+[Emscripten](https://github.com/emscripten-core/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://kripken.github.io/emscripten-site/docs/getting_started/downloads.html).
+To Install Emscripten, follow instructions of [Emscripten SDK](https://emscripten.org/docs/getting_started/downloads.html).

 For example:
@code{.bash}
@@ -21,15 +21,29 @@ 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 `EMSCRIPTEN` environment is setup correctly.
+After install, ensure the `EMSDK` environment is setup correctly.

 For example:
@code{.bash}
 source ./emsdk_env.sh
-echo ${EMSCRIPTEN}
+echo ${EMSDK}
+@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.
+
+For example:
+@code{.bash}
+./emsdk update
+./emsdk install 2.0.10
+./emsdk activate 2.0.10
@endcode

 Obtaining OpenCV Source Code
@@ -62,8 +76,7 @@ Building OpenCV.js from Source

    For example, to build in `build_js` directory:
    @code{.bash}
-    cd opencv
-    python ./platforms/js/build_js.py build_js
+    emcmake python ./opencv/platforms/js/build_js.py build_js
    @endcode

    @note
@@ -73,14 +86,39 @@ Building OpenCV.js from Source

    For example, to build wasm version in `build_wasm` directory:
    @code{.bash}
-    python ./platforms/js/build_js.py build_wasm --build_wasm
+    emcmake python ./opencv/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
+    @endcode
+
+    @note
+    The loader is implemented as a js file in the path `<opencv_js_dir>/bin/loader.js`. The loader utilizes the [WebAssembly Feature Detection](https://github.com/GoogleChromeLabs/wasm-feature-detect) to detect the features of the broswer and load corresponding OpenCV.js automatically. To use it, you need to use the UMD version of [WebAssembly Feature Detection](https://github.com/GoogleChromeLabs/wasm-feature-detect) and introduce the `loader.js` in your Web application.
+
+    Example Code:
+    @code{.javascipt}
+    // Set paths configuration
+    let pathsConfig = {
+        wasm: "../../build_wasm/opencv.js",
+        threads: "../../build_mt/opencv.js",
+        simd: "../../build_simd/opencv.js",
+        threadsSimd: "../../build_mtSIMD/opencv.js",
+    }
+
+    // Load OpenCV.js and use the pathsConfiguration and main function as the params.
+    loadOpenCV(pathsConfig, main);
+    @endcode
+
+
 -#  [optional] To build documents, append `--build_doc` option.

    For example:
    @code{.bash}
-    python ./platforms/js/build_js.py build_js --build_doc
+    emcmake python ./opencv/platforms/js/build_js.py build_js --build_doc
    @endcode

    @note
@@ -90,7 +128,21 @@ Building OpenCV.js from Source

    For example:
    @code{.bash}
-    python ./platforms/js/build_js.py build_js --build_test
+    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"
    @endcode

 Running OpenCV.js Tests
@@ -152,7 +204,7 @@ node tests.js

    For example:
    @code{.bash}
-    python ./platforms/js/build_js.py build_js --build_wasm --threads
+    emcmake python ./opencv/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.
@@ -164,7 +216,7 @@ node tests.js

    For example:
    @code{.bash}
-    python ./platforms/js/build_js.py build_js --build_wasm --simd
+    emcmake python ./opencv/platforms/js/build_js.py build_js --build_wasm --simd
    @endcode

    The simd optimization is experimental as wasm simd is still in development.
@@ -188,7 +240,7 @@ node tests.js

    For example:
    @code{.bash}
-    python ./platforms/js/build_js.py build_js --build_wasm --simd --build_wasm_intrin_test
+    emcmake python ./opencv/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:
@@ -216,7 +268,7 @@ node tests.js

    For example:
    @code{.bash}
-    python ./platforms/js/build_js.py build_js --build_perf
+    emcmake python ./opencv/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`.
@@ -237,25 +289,25 @@ 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 --workdir /code -v "$PWD":/code "trzeci/emscripten:latest" python ./platforms/js/build_js.py build
+docker run --rm -v $(pwd):/src -u $(id -u):$(id -g) emscripten/emsdk emcmake python3 ./dev/platforms/js/build_js.py build_js
@endcode

 In Windows use the following PowerShell command:

@code{.bash}
-docker run --rm --workdir /code -v "$(get-location):/code" "trzeci/emscripten:latest" python ./platforms/js/build_js.py build
+docker run --rm --workdir /src -v "$(get-location):/src" "emscripten/emsdk" emcmake python3 ./dev/platforms/js/build_js.py build_js
@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 `1.38.32` 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 `2.0.10` using the following command:

@code{.bash}
-docker run --rm --workdir /code -v "$PWD":/code "trzeci/emscripten:sdk-tag-1.38.32-64bit" python ./platforms/js/build_js.py build
+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
@endcode

 ### Building the documentation with Docker
@@ -263,10 +315,11 @@ docker run --rm --workdir /code -v "$PWD":/code "trzeci/emscripten:sdk-tag-1.38.
 To build the documentation `doxygen` needs to be installed. Create a file named `Dockerfile` with the following content:

 ```
-FROM trzeci/emscripten:sdk-tag-1.38.32-64bit
+FROM emscripten/emsdk:2.0.10

-RUN apt-get update -y
-RUN apt-get install -y doxygen
+RUN apt-get update \
+  && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends doxygen \
+  && rm -rf /var/lib/apt/lists/*
 ```

 Then we build the docker image and name it `opencv-js-doc` with the following command (that needs to be run only once):
@@ -278,5 +331,5 @@ 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 --workdir /code -v "$PWD":/code "opencv-js-doc" python ./platforms/js/build_js.py build --build_doc
+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
@endcode
@@ -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/{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.
+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.

 ### 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://kripken.github.io/emscripten-site/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://emscripten.org/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
@@ -26,3 +26,7 @@ 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
@@ -1261,3 +1261,26 @@
  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}
+}
@@ -92,11 +92,11 @@ def main():
                        dest="square_size", type=float)
    parser.add_argument("-R", "--radius_rate", help="circles_radius = square_size/radius_rate", default="5.0",
                        action="store", dest="radius_rate", type=float)
-    parser.add_argument("-w", "--page_width", help="page width in units", default="216", action="store",
+    parser.add_argument("-w", "--page_width", help="page width in units", default=argparse.SUPPRESS, action="store",
                        dest="page_width", type=float)
-    parser.add_argument("-h", "--page_height", help="page height in units", default="279", action="store",
-                        dest="page_width", type=float)
-    parser.add_argument("-a", "--page_size", help="page size, supersedes -h -w arguments", default="A4", action="store",
+    parser.add_argument("-h", "--page_height", help="page height in units", default=argparse.SUPPRESS, action="store",
+                        dest="page_height", type=float)
+    parser.add_argument("-a", "--page_size", help="page size, superseded if -h and -w are set", default="A4", action="store",
                        dest="page_size", choices=["A0", "A1", "A2", "A3", "A4", "A5"])
    args = parser.parse_args()

@@ -111,12 +111,16 @@ def main():
    units = args.units
    square_size = args.square_size
    radius_rate = args.radius_rate
-    page_size = args.page_size
-    # page size dict (ISO standard, mm) for easy lookup. format - size: [width, height]
-    page_sizes = {"A0": [840, 1188], "A1": [594, 840], "A2": [420, 594], "A3": [297, 420], "A4": [210, 297],
-                  "A5": [148, 210]}
-    page_width = page_sizes[page_size.upper()][0]
-    page_height = page_sizes[page_size.upper()][1]
+    if 'page_width' and 'page_height' in args:
+        page_width = args.page_width
+        page_height = args.page_height
+    else:
+        page_size = args.page_size
+        # page size dict (ISO standard, mm) for easy lookup. format - size: [width, height]
+        page_sizes = {"A0": [840, 1188], "A1": [594, 840], "A2": [420, 594], "A3": [297, 420], "A4": [210, 297],
+                      "A5": [148, 210]}
+        page_width = page_sizes[page_size][0]
+        page_height = page_sizes[page_size][1]
    pm = PatternMaker(columns, rows, output, units, square_size, radius_rate, page_width, page_height)
    # dict for easy lookup of pattern type
    mp = {"circles": pm.make_circles_pattern, "acircles": pm.make_acircles_pattern,
@@ -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 xrange(len(objpoints)):
+for i in range(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
@@ -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()
+sift = cv.SIFT_create()

 # find the keypoints and descriptors with SIFT
 kp1, des1 = sift.detectAndCompute(img1,None)
@@ -93,14 +93,12 @@ 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
@@ -48,7 +48,7 @@ titles = ['Original Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']
 images = [img, thresh1, thresh2, thresh3, thresh4, thresh5]

 for i in xrange(6):
-    plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')
+    plt.subplot(2,3,i+1),plt.imshow(images[i],'gray',vmin=0,vmax=255)
    plt.title(titles[i])
    plt.xticks([]),plt.yticks([])

@@ -0,0 +1,96 @@
+#!/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)
@@ -0,0 +1,4 @@
+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
@@ -0,0 +1,4 @@
+Image Input and Output (imgcodecs module) {#tutorial_table_of_content_imgcodecs}
+=========================================
+
+Content has been moved to this page: @ref tutorial_table_of_content_app
@@ -0,0 +1,4 @@
+Video Input and Output (videoio module) {#tutorial_table_of_content_videoio}
+=========================================
+
+Content has been moved to this page: @ref tutorial_table_of_content_app
--- a/Show More
+++ b/Show More