Merge remote-tracking branch 'refs/remotes/opencv/master' into FileStorageBase64DocsTests

# Conflicts:
#	modules/core/test/test_io.cpp

.github/ISSUE_TEMPLATE.md

@@ -1,6 +1,6 @@
 <!--
 If you have a question rather than reporting a bug please go to http://answers.opencv.org where you get much faster responses.
-If you need further assistance please read [How To Contribute](https://github.com/Itseez/opencv/wiki/How_to_contribute).
+If you need further assistance please read [How To Contribute](https://github.com/opencv/opencv/wiki/How_to_contribute).
 
 This is a template helping you to create an issue which can be processed as quickly as possible. This is the bug reporting section for the OpenCV library.
 -->

3rdparty/carotene/src/resize.cpp

@@ -432,7 +432,7 @@ inline void resizeAreaRounding(const Size2D &ssize, const Size2D &dsize,
                     vSum = vaddq_u16(vSum, vaddl_u8(vLane4.val[0], vLane4.val[1]));
                     vSum = vaddq_u16(vSum, vaddl_u8(vLane4.val[2], vLane4.val[3]));
 
-                    vst1_u8(dst_row + dj, areaDownsamplingDivision<opencv_like,4>(vSum));
+                    vst1_u8(dst_row + dj, (areaDownsamplingDivision<opencv_like,4>(vSum)));
                 }
 
                 for ( ; dj < dsize.width; ++dj, sj += 4)
@@ -706,7 +706,7 @@ inline void resizeAreaRounding(const Size2D &ssize, const Size2D &dsize,
                     uint16x8_t v_sum = vcombine_u16(vadd_u16(vget_low_u16(v_sum0), vget_high_u16(v_sum0)),
                                                     vadd_u16(vget_low_u16(v_sum1), vget_high_u16(v_sum1)));
 
-                    vst1_u8(dst_row + dj, areaDownsamplingDivision<opencv_like,4>(v_sum));
+                    vst1_u8(dst_row + dj, (areaDownsamplingDivision<opencv_like,4>(v_sum)));
                 }
 
                 for (size_t dwidth = dsize.width << 2; dj < dwidth; dj += 4, sj += 16)

3rdparty/ffmpeg/ffmpeg.cmake

@@ -1,11 +1,11 @@
-# Binary branch name: ffmpeg/master_20150703
+# Binary branch name: ffmpeg/master_20160715
-# Binaries were created for OpenCV: e379ea6ed60b0caad4d4e3eea096e9d850cb8c86
+# Binaries were created for OpenCV: 0e6aa189cb9a9642b0ae7983d301693516faad5d
-set(FFMPEG_BINARIES_COMMIT "8aeefc4efe3215de89d8c7e114ae6f7a6091b8eb")
+set(FFMPEG_BINARIES_COMMIT "7eef9080d3271c7547d303fa839a62e1124ff1e6")
-set(FFMPEG_FILE_HASH_BIN32 "89c783eee1c47bfc733f08334ec2e31c")
+set(FFMPEG_FILE_HASH_BIN32 "3bb2a8388af90adf6c762210e696400d")
-set(FFMPEG_FILE_HASH_BIN64 "35fe6ccdda6d7a04e9056b0d73b98e76")
+set(FFMPEG_FILE_HASH_BIN64 "ebcfc963f0a94f7e83d58d60eaf23849")
-set(FFMPEG_FILE_HASH_CMAKE "8606f947a780071f8fcce8cbf39ceef5")
+set(FFMPEG_FILE_HASH_CMAKE "f99941d10c1e87bf16b9055e8fc91ab2")
 
-set(FFMPEG_DOWNLOAD_URL ${OPENCV_FFMPEG_URL};$ENV{OPENCV_FFMPEG_URL};https://raw.githubusercontent.com/Itseez/opencv_3rdparty/${FFMPEG_BINARIES_COMMIT}/ffmpeg/)
+set(FFMPEG_DOWNLOAD_URL ${OPENCV_FFMPEG_URL};$ENV{OPENCV_FFMPEG_URL};https://raw.githubusercontent.com/opencv/opencv_3rdparty/${FFMPEG_BINARIES_COMMIT}/ffmpeg/)
 
 ocv_download(PACKAGE opencv_ffmpeg.dll
              HASH ${FFMPEG_FILE_HASH_BIN32}

3rdparty/ippicv/downloader.cmake

@@ -64,7 +64,7 @@ function(_icv_downloader)
       if(DEFINED ENV{OPENCV_ICV_URL})
         set(OPENCV_ICV_URL $ENV{OPENCV_ICV_URL})
       else()
-        set(OPENCV_ICV_URL "https://raw.githubusercontent.com/Itseez/opencv_3rdparty/${IPPICV_BINARIES_COMMIT}/ippicv")
+        set(OPENCV_ICV_URL "https://raw.githubusercontent.com/opencv/opencv_3rdparty/${IPPICV_BINARIES_COMMIT}/ippicv")
       endif()
     endif()
 

CMakeLists.txt

@@ -191,6 +191,7 @@ OCV_OPTION(WITH_OPENGL         "Include OpenGL support"                      OFF
 OCV_OPTION(WITH_OPENNI         "Include OpenNI support"                      OFF  IF (NOT ANDROID AND NOT IOS AND NOT WINRT) )
 OCV_OPTION(WITH_OPENNI2        "Include OpenNI2 support"                     OFF  IF (NOT ANDROID AND NOT IOS AND NOT WINRT) )
 OCV_OPTION(WITH_PNG            "Include PNG support"                         ON)
+OCV_OPTION(WITH_GDCM           "Include DICOM support"                       OFF)
 OCV_OPTION(WITH_PVAPI          "Include Prosilica GigE support"              OFF   IF (NOT ANDROID AND NOT IOS AND NOT WINRT) )
 OCV_OPTION(WITH_GIGEAPI        "Include Smartek GigE support"                OFF   IF (NOT ANDROID AND NOT IOS AND NOT WINRT) )
 OCV_OPTION(WITH_QT             "Build with Qt Backend support"               OFF  IF (NOT ANDROID AND NOT IOS AND NOT WINRT) )
@@ -1008,6 +1009,7 @@ if(WITH_PNG)
 else()
   status("    PNG:"        "NO")
 endif()
+
 if(WITH_TIFF)
   if(TIFF_VERSION_STRING AND TIFF_FOUND)
     status("    TIFF:"     "${TIFF_LIBRARY} (ver ${TIFF_VERSION} - ${TIFF_VERSION_STRING})")
@@ -1034,6 +1036,12 @@ else()
   status("    GDAL:"     "NO")
 endif()
 
+if(WITH_GDCM)
+  status("    GDCM:"   GDCM_FOUND THEN "YES (ver ${GDCM_VERSION})" ELSE "NO")
+else()
+  status("    GDCM:"     "NO")
+endif()
+
 # ========================== VIDEO IO ==========================
 status("")
 status("  Video I/O:")

CONTRIBUTING.md

@@ -1,3 +1,3 @@
 ## Contributing guidelines
 
-All guidelines for contributing to the OpenCV repository can be found at [`How to contribute guideline`](https://github.com/Itseez/opencv/wiki/How_to_contribute).
+All guidelines for contributing to the OpenCV repository can be found at [`How to contribute guideline`](https://github.com/opencv/opencv/wiki/How_to_contribute).

README.md

@@ -5,11 +5,11 @@
 * Homepage: <http://opencv.org>
 * Docs: <http://docs.opencv.org/master/>
 * Q&A forum: <http://answers.opencv.org>
-* Issue tracking: <https://github.com/Itseez/opencv/issues>
+* Issue tracking: <https://github.com/opencv/opencv/issues>
 
 #### Contributing
 
-Please read before starting work on a pull request: <https://github.com/Itseez/opencv/wiki/How_to_contribute>
+Please read before starting work on a pull request: <https://github.com/opencv/opencv/wiki/How_to_contribute>
 
 Summary of guidelines:
 

apps/annotation/opencv_annotation.cpp

@@ -224,28 +224,24 @@ vector<Rect> get_annotations(Mat input_image)
 
 int main( int argc, const char** argv )
 {
-    // If no arguments are given, then supply some information on how this tool works
+    // Use the cmdlineparser to process input arguments
-    if( argc == 1 ){
+    CommandLineParser parser(argc, argv,
-        cout << "Usage: " << argv[0] << endl;
+        "{ help h usage ? |      | show this message }"
-        cout << " -images <folder_location> [example - /data/testimages/]" << endl;
+        "{ images i       |      | (required) path to image folder [example - /data/testimages/] }"
-        cout << " -annotations <ouput_file> [example - /data/annotations.txt]" << endl;
+        "{ annotations a  |      | (required) path to annotations txt file [example - /data/annotations.txt] }"
-        cout << "TIP: Use absolute paths to avoid any problems with the software!" << endl;
+    );
-        return -1;
-    }
-
     // Read in the input arguments
-    string image_folder;
+    if (parser.has("help")){
-    string annotations_file;
+        parser.printMessage();
-    for(int i = 1; i < argc; ++i )
+        cerr << "TIP: Use absolute paths to avoid any problems with the software!" << endl;
-    {
+        return 0;
-        if( !strcmp( argv[i], "-images" ) )
+    }
-        {
+    string image_folder(parser.get<string>("images"));
-            image_folder = argv[++i];
+    string annotations_file(parser.get<string>("annotations"));
-        }
+    if (image_folder.empty() || annotations_file.empty()){
-        else if( !strcmp( argv[i], "-annotations" ) )
+        parser.printMessage();
-        {
+        cerr << "TIP: Use absolute paths to avoid any problems with the software!" << endl;
-            annotations_file = argv[++i];
+        return -1;
-        }
     }
 
     // Check if the folder actually exists

apps/createsamples/createsamples.cpp

@@ -76,6 +76,7 @@ int main( int argc, char* argv[] )
     double scale = 4.0;
     int width  = 24;
     int height = 24;
+    double maxscale = -1.0;
 
     srand((unsigned int)time(0));
 
@@ -92,9 +93,10 @@ int main( int argc, char* argv[] )
                 "  [-maxyangle <max_y_rotation_angle = %f>]\n"
                 "  [-maxzangle <max_z_rotation_angle = %f>]\n"
                 "  [-show [<scale = %f>]]\n"
-                "  [-w <sample_width = %d>]\n  [-h <sample_height = %d>]\n",
+                "  [-w <sample_width = %d>]\n  [-h <sample_height = %d>]\n"
+                "  [-maxscale <max sample scale = %f>]\n",
                 argv[0], num, bgcolor, bgthreshold, maxintensitydev,
-                maxxangle, maxyangle, maxzangle, scale, width, height );
+                maxxangle, maxyangle, maxzangle, scale, width, height, maxscale );
 
         return 0;
     }
@@ -172,6 +174,10 @@ int main( int argc, char* argv[] )
         {
             height = atoi( argv[++i] );
         }
+        else if( !strcmp( argv[i], "-maxscale" ) )
+        {
+            maxscale = atof( argv[++i] );
+        }
     }
 
     printf( "Info file name: %s\n", ((infoname == NULL) ?   nullname : infoname ) );
@@ -194,6 +200,7 @@ int main( int argc, char* argv[] )
     }
     printf( "Width: %d\n", width );
     printf( "Height: %d\n", height );
+    printf( "Max Scale: %g\n", maxscale);
 
     /* determine action */
     if( imagename && vecname )
@@ -213,7 +220,7 @@ int main( int argc, char* argv[] )
 
         cvCreateTestSamples( infoname, imagename, bgcolor, bgthreshold, bgfilename, num,
             invert, maxintensitydev,
-            maxxangle, maxyangle, maxzangle, showsamples, width, height );
+            maxxangle, maxyangle, maxzangle, showsamples, width, height, maxscale);
 
         printf( "Done\n" );
     }

apps/createsamples/utility.cpp

@@ -38,7 +38,6 @@
 // the use of this software, even if advised of the possibility of such damage.
 //
 //M*/
-
 #include <cstring>
 #include <ctime>
 
@@ -1308,7 +1307,7 @@ void cvCreateTestSamples( const char* infoname,
                           int invert, int maxintensitydev,
                           double maxxangle, double maxyangle, double maxzangle,
                           int showsamples,
-                          int winwidth, int winheight )
+                          int winwidth, int winheight, double maxscale )
 {
     CvSampleDistortionData data;
 
@@ -1337,7 +1336,6 @@ void cvCreateTestSamples( const char* infoname,
             int i;
             int x, y, width, height;
             float scale;
-            float maxscale;
             int inverse;
 
             if( showsamples )
@@ -1366,12 +1364,16 @@ void cvCreateTestSamples( const char* infoname,
             for( i = 0; i < count; i++ )
             {
                 icvGetNextFromBackgroundData( cvbgdata, cvbgreader );
-
+                if( maxscale < 0.0 )
-                maxscale = MIN( 0.7F * cvbgreader->src.cols / winwidth,
+                {
+                    maxscale = MIN( 0.7F * cvbgreader->src.cols / winwidth,
                                    0.7F * cvbgreader->src.rows / winheight );
+                }
+
                 if( maxscale < 1.0F ) continue;
 
-                scale = (maxscale - 1.0F) * rand() / RAND_MAX + 1.0F;
+                scale = ((float)maxscale - 1.0F) * rand() / RAND_MAX + 1.0F;
+
                 width = (int) (scale * winwidth);
                 height = (int) (scale * winheight);
                 x = (int) ((0.1+0.8 * rand()/RAND_MAX) * (cvbgreader->src.cols - width));

apps/createsamples/utility.hpp

@@ -86,7 +86,7 @@ void cvCreateTestSamples( const char* infoname,
                           int invert, int maxintensitydev,
                           double maxxangle, double maxyangle, double maxzangle,
                           int showsamples,
-                          int winwidth, int winheight );
+                          int winwidth, int winheight, double maxscale );
 
 /*
  * cvCreateTrainingSamplesFromInfo

apps/traincascade/cascadeclassifier.cpp

@@ -310,7 +310,8 @@ bool CvCascadeClassifier::updateTrainingSet( double minimumAcceptanceRatio, doub
     int proNumNeg = cvRound( ( ((double)numNeg) * ((double)posCount) ) / numPos ); // apply only a fraction of negative samples. double is required since overflow is possible
     int negCount = fillPassedSamples( posCount, proNumNeg, false, minimumAcceptanceRatio, negConsumed );
     if ( !negCount )
-        return false;
+        if ( !(negConsumed > 0 && ((double)negCount+1)/(double)negConsumed <= minimumAcceptanceRatio) )
+            return false;
 
     curNumSamples = posCount + negCount;
     acceptanceRatio = negConsumed == 0 ? 0 : ( (double)negCount/(double)(int64)negConsumed );

apps/visualisation/opencv_visualisation.cpp

@@ -49,13 +49,6 @@ understanding of the used features.
 USAGE:
 ./opencv_visualisation --model=<model.xml> --image=<ref.png> --data=<video output folder>
 
-LIMITS
-- Use an absolute path for the output folder to ensure the tool works
-- Only handles cascade classifier models
-- Handles stumps only for the moment
-- Needs a valid training/test sample window with the original model dimensions, passed as `ref.png`
-- Can handle HAAR and LBP features
-
 Created by: Puttemans Steven - April 2016
 *****************************************************************************************************/
 
@@ -79,6 +72,13 @@ struct rect_data{
     float weight;
 };
 
+static void printLimits(){
+    cerr << "Limits of the current interface:" << endl;
+    cerr << " - Only handles cascade classifier models, trained with the opencv_traincascade tool, containing stumps as decision trees [default settings]." << endl;
+    cerr << " - The image provided needs to be a sample window with the original model dimensions, passed to the --image parameter." << endl;
+    cerr << " - ONLY handles HAAR and LBP features." << endl;
+}
+
 int main( int argc, const char** argv )
 {
     CommandLineParser parser(argc, argv,
@@ -90,6 +90,7 @@ int main( int argc, const char** argv )
     // Read in the input arguments
     if (parser.has("help")){
         parser.printMessage();
+        printLimits();
         return 0;
     }
     string model(parser.get<string>("model"));
@@ -97,6 +98,7 @@ int main( int argc, const char** argv )
     string image_ref = (parser.get<string>("image"));
     if (model.empty() || image_ref.empty()){
         parser.printMessage();
+        printLimits();
         return -1;
     }
 

cmake/OpenCVCompilerOptions.cmake

@@ -146,8 +146,11 @@ if(CMAKE_COMPILER_IS_GNUCXX)
   elseif(X86 OR X86_64)
     add_extra_compiler_option(-mno-sse2)
   endif()
+  if(ARM)
+    add_extra_compiler_option("-mfp16-format=ieee")
+  endif(ARM)
   if(ENABLE_NEON)
-    add_extra_compiler_option("-mfpu=neon")
+    add_extra_compiler_option("-mfpu=neon-fp16")
   endif()
   if(ENABLE_VFPV3 AND NOT ENABLE_NEON)
     add_extra_compiler_option("-mfpu=vfpv3")
@@ -366,3 +369,18 @@ if(MSVC)
     ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4589) # Constructor of abstract class 'cv::ORB' ignores initializer for virtual base class 'cv::Algorithm'
   endif()
 endif()
+
+if(NOT OPENCV_FP16_DISABLE)
+  try_compile(__VALID_FP16
+    "${OpenCV_BINARY_DIR}"
+    "${OpenCV_SOURCE_DIR}/cmake/checks/fp16.cpp"
+    COMPILE_DEFINITIONS "-DCHECK_FP16"
+    OUTPUT_VARIABLE TRY_OUT
+    )
+  if(NOT __VALID_FP16)
+    message(STATUS "FP16: Compiler support is not available")
+  else()
+    message(STATUS "FP16: Compiler support is available")
+    set(HAVE_FP16 1)
+  endif()
+endif()

cmake/OpenCVDetectPython.cmake

@@ -75,10 +75,10 @@ if(NOT ${found})
 
     if(NOT ANDROID AND NOT APPLE_FRAMEWORK)
       ocv_check_environment_variables(${library_env} ${include_dir_env})
-      if(NOT ${${library_env}} EQUAL "")
+      if(NOT ${${library_env}} STREQUAL "")
           set(PYTHON_LIBRARY "${${library_env}}")
       endif()
-      if(NOT ${${include_dir_env}} EQUAL "")
+      if(NOT ${${include_dir_env}} STREQUAL "")
           set(PYTHON_INCLUDE_DIR "${${include_dir_env}}")
       endif()
 
@@ -162,10 +162,10 @@ if(NOT ${found})
           message(STATUS "Cannot probe for Python/Numpy support (because we are cross-compiling OpenCV)")
           message(STATUS "If you want to enable Python/Numpy support, set the following variables:")
           message(STATUS "  PYTHON2_INCLUDE_PATH")
-          message(STATUS "  PYTHON2_LIBRARIES")
+          message(STATUS "  PYTHON2_LIBRARIES (optional on Unix-like systems)")
           message(STATUS "  PYTHON2_NUMPY_INCLUDE_DIRS")
           message(STATUS "  PYTHON3_INCLUDE_PATH")
-          message(STATUS "  PYTHON3_LIBRARIES")
+          message(STATUS "  PYTHON3_LIBRARIES (optional on Unix-like systems)")
           message(STATUS "  PYTHON3_NUMPY_INCLUDE_DIRS")
         else()
           # Attempt to discover the NumPy include directory. If this succeeds, then build python API with NumPy

cmake/OpenCVFindLibsGrfmt.cmake

@@ -212,3 +212,15 @@ if(WITH_GDAL)
         ocv_include_directories(${GDAL_INCLUDE_DIR})
     endif()
 endif()
+
+if (WITH_GDCM)
+  find_package(GDCM)
+  if(NOT GDCM_FOUND)
+    set(HAVE_GDCM NO)
+    ocv_clear_vars(GDCM_VERSION GDCM_LIBRARIES)
+  else()
+    set(HAVE_GDCM YES)
+    # include(${GDCM_USE_FILE})
+    set(GDCM_LIBRARIES gdcmMSFF) # GDCM does not set this variable for some reason
+  endif()
+endif()

cmake/checks/fp16.cpp

@@ -0,0 +1,33 @@
+#include <stdio.h>
+
+#if defined __F16C__ || (defined _MSC_VER && _MSC_VER >= 1700)
+#include <immintrin.h>
+int test()
+{
+    const float src[] = { 0.0f, 0.0f, 0.0f, 0.0f };
+    short dst[8];
+    __m128 v_src = _mm_load_ps(src);
+    __m128i v_dst = _mm_cvtps_ph(v_src, 0);
+    _mm_storel_epi64((__m128i*)dst, v_dst);
+    return (int)dst[0];
+}
+#elif defined __GNUC__ && (defined __arm__ || defined __aarch64__)
+#include "arm_neon.h"
+int test()
+{
+    const float src[] = { 0.0f, 0.0f, 0.0f, 0.0f };
+    short dst[8];
+    float32x4_t v_src = *(float32x4_t*)src;
+    float16x4_t v_dst = vcvt_f16_f32(v_src);
+    *(float16x4_t*)dst = v_dst;
+    return (int)dst[0];
+}
+#else
+#error "FP16 is not supported"
+#endif
+
+int main()
+{
+  printf("%d\n", test());
+  return 0;
+}

cmake/templates/cvconfig.h.in

@@ -111,6 +111,9 @@
 /* libpng/png.h needs to be included */
 #cmakedefine HAVE_LIBPNG_PNG_H
 
+/* GDCM DICOM codec */
+#cmakedefine HAVE_GDCM
+
 /* V4L/V4L2 capturing support via libv4l */
 #cmakedefine HAVE_LIBV4L
 
@@ -200,3 +203,6 @@
 
 /* Lapack */
 #cmakedefine HAVE_LAPACK
+
+/* FP16 */
+#cmakedefine HAVE_FP16

doc/Doxyfile.in

@@ -150,7 +150,7 @@ BINARY_TOC             = NO
 TOC_EXPAND             = NO
 GENERATE_QHP           = @CMAKE_DOXYGEN_GENERATE_QHP@
 QCH_FILE               = ../opencv-@OPENCV_VERSION@.qch
-QHP_NAMESPACE          = org.itseez.opencv.@OPENCV_VERSION@
+QHP_NAMESPACE          = org.opencv.@OPENCV_VERSION@
 QHP_VIRTUAL_FOLDER     = opencv
 QHP_CUST_FILTER_NAME   =
 QHP_CUST_FILTER_ATTRS  =

doc/opencv.bib

@@ -750,7 +750,7 @@
   organization = {ACM}
 }
 @INPROCEEDINGS{Viola01,
-  author = {Viola, Paul and Jones, Michael},
+  author = {Viola, Paul and Jones, Michael J.},
   title = {Rapid object detection using a boosted cascade of simple features},
   booktitle = {Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on},
   year = {2001},
@@ -758,6 +758,16 @@
   volume = {1},
   organization = {IEEE}
 }
+@ARTICLE{Viola04,
+  author = {Viola, Paul and Jones, Michael J.},
+  title = {Robust real-time face detection},
+  journal = {International Journal of Computer Vision},
+  year = {2004},
+  volume = {57},
+  number = {2},
+  pages = {137--154},
+  publisher = {Kluwer Academic Publishers}
+}
 @INPROCEEDINGS{WJ10,
   author = {Xu, Wei and Mulligan, Jane},
   title = {Performance evaluation of color correction approaches for automatic multi-view image and video stitching},

doc/py_tutorials/py_calib3d/py_calibration/py_calibration.markdown

@@ -130,11 +130,11 @@ for fname in images:
     if ret == True:
         objpoints.append(objp)
 
-        cv2.cornerSubPix(gray,corners, (11,11), (-1,-1), criteria)
+        corners2=cv2.cornerSubPix(gray,corners, (11,11), (-1,-1), criteria)
         imgpoints.append(corners)
 
         # Draw and display the corners
-        cv2.drawChessboardCorners(img, (7,6), corners, ret)
+        cv2.drawChessboardCorners(img, (7,6), corners2, ret)
         cv2.imshow('img', img)
         cv2.waitKey(500)
 

doc/py_tutorials/py_calib3d/py_pose/py_pose.markdown

@@ -70,15 +70,15 @@ for fname in glob.glob('left*.jpg'):
         corners2 = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
 
         # Find the rotation and translation vectors.
-        rvecs, tvecs, inliers = cv2.solvePnPRansac(objp, corners2, mtx, dist)
+        ret,rvecs, tvecs, inliers = cv2.solvePnP(objp, corners2, mtx, dist)
 
         # project 3D points to image plane
         imgpts, jac = cv2.projectPoints(axis, rvecs, tvecs, mtx, dist)
 
         img = draw(img,corners2,imgpts)
         cv2.imshow('img',img)
-        k = cv2.waitKey(0) & 0xff
+        k = cv2.waitKey(0) & 0xFF
-        if k == 's':
+        if k == ord('s'):
             cv2.imwrite(fname[:6]+'.png', img)
 
 cv2.destroyAllWindows()

doc/py_tutorials/py_feature2d/py_brief/py_brief.markdown

@@ -49,7 +49,7 @@ BRIEF in OpenCV
 Below code shows the computation of BRIEF descriptors with the help of CenSurE detector. (CenSurE
 detector is called STAR detector in OpenCV)
 
-note, that you need [opencv contrib](https://github.com/Itseez/opencv_contrib)) to use this.
+note, that you need [opencv contrib](https://github.com/opencv/opencv_contrib)) to use this.
 @code{.py}
 import numpy as np
 import cv2

doc/py_tutorials/py_feature2d/py_features_meaning/py_features_meaning.markdown

@@ -22,61 +22,61 @@ Well, the questions and imaginations continue. But it all depends on the most ba
 you play jigsaw puzzles? How do you arrange lots of scrambled image pieces into a big single image?
 How can you stitch a lot of natural images to a single image?
 
-The answer is, we are looking for specific patterns or specific features which are unique, which can
+The answer is, we are looking for specific patterns or specific features which are unique, can
-be easily tracked, which can be easily compared. If we go for a definition of such a feature, we may
+be easily tracked and can be easily compared. If we go for a definition of such a feature, we may
-find it difficult to express it in words, but we know what are they. If some one asks you to point
+find it difficult to express it in words, but we know what they are. If someone asks you to point
 out one good feature which can be compared across several images, you can point out one. That is
-why, even small children can simply play these games. We search for these features in an image, we
+why even small children can simply play these games. We search for these features in an image,
-find them, we find the same features in other images, we align them. That's it. (In jigsaw puzzle,
+find them, look for the same features in other images and align them. That's it. (In jigsaw puzzle,
 we look more into continuity of different images). All these abilities are present in us inherently.
 
 So our one basic question expands to more in number, but becomes more specific. **What are these
-features?**. *(The answer should be understandable to a computer also.)*
+features?**. (The answer should be understandable also to a computer.)
 
-Well, it is difficult to say how humans find these features. It is already programmed in our brain.
+It is difficult to say how humans find these features. This is already programmed in our brain.
 But if we look deep into some pictures and search for different patterns, we will find something
 interesting. For example, take below image:
 
 ![image](images/feature_building.jpg)
 
-Image is very simple. At the top of image, six small image patches are given. Question for you is to
+The image is very simple. At the top of image, six small image patches are given. Question for you is to
-find the exact location of these patches in the original image. How many correct results you can
+find the exact location of these patches in the original image. How many correct results can you
-find ?
+find?
 
-A and B are flat surfaces, and they are spread in a lot of area. It is difficult to find the exact
+A and B are flat surfaces and they are spread over a lot of area. It is difficult to find the exact
 location of these patches.
 
-C and D are much more simpler. They are edges of the building. You can find an approximate location,
+C and D are much more simple. They are edges of the building. You can find an approximate location,
-but exact location is still difficult. It is because, along the edge, it is same everywhere. Normal
+but exact location is still difficult. This is because the pattern is same everywhere along the edge.
-to the edge, it is different. So edge is a much better feature compared to flat area, but not good
+At the edge, however, it is different. An edge is therefore better feature compared to flat area, but
-enough (It is good in jigsaw puzzle for comparing continuity of edges).
+not good enough (It is good in jigsaw puzzle for comparing continuity of edges).
 
-Finally, E and F are some corners of the building. And they can be easily found out. Because at
+Finally, E and F are some corners of the building. And they can be easily found. Because at the
-corners, wherever you move this patch, it will look different. So they can be considered as a good
+corners, wherever you move this patch, it will look different. So they can be considered as good
-feature. So now we move into more simpler (and widely used image) for better understanding.
+features. So now we move into simpler (and widely used image) for better understanding.
 
 ![image](images/feature_simple.png)
 
-Just like above, blue patch is flat area and difficult to find and track. Wherever you move the blue
+Just like above, the blue patch is flat area and difficult to find and track. Wherever you move the blue
-patch, it looks the same. For black patch, it is an edge. If you move it in vertical direction (i.e.
+patch it looks the same. The black patch has an edge. If you move it in vertical direction (i.e.
-along the gradient) it changes. Put along the edge (parallel to edge), it looks the same. And for
+along the gradient) it changes. Moved along the edge (parallel to edge), it looks the same. And for
 red patch, it is a corner. Wherever you move the patch, it looks different, means it is unique. So
 basically, corners are considered to be good features in an image. (Not just corners, in some cases
 blobs are considered good features).
 
 So now we answered our question, "what are these features?". But next question arises. How do we
-find them? Or how do we find the corners?. That also we answered in an intuitive way, i.e., look for
+find them? Or how do we find the corners?. We answered that in an intuitive way, i.e., look for
 the regions in images which have maximum variation when moved (by a small amount) in all regions
 around it. This would be projected into computer language in coming chapters. So finding these image
 features is called **Feature Detection**.
 
-So we found the features in image (Assume you did it). Once you found it, you should find the same
+We found the features in the images. Once you have found it, you should be able to find the same
-in the other images. What we do? We take a region around the feature, we explain it in our own
+in the other images. How is this done? We take a region around the feature, we explain it in our own
-words, like "upper part is blue sky, lower part is building region, on that building there are some
+words, like "upper part is blue sky, lower part is region from a building, on that building there is
-glasses etc" and you search for the same area in other images. Basically, you are describing the
+glass etc" and you search for the same area in the other images. Basically, you are describing the
-feature. Similar way, computer also should describe the region around the feature so that it can
+feature. Similarly, a computer also should describe the region around the feature so that it can
 find it in other images. So called description is called **Feature Description**. Once you have the
-features and its description, you can find same features in all images and align them, stitch them
+features and its description, you can find same features in all images and align them, stitch them together
 or do whatever you want.
 
 So in this module, we are looking to different algorithms in OpenCV to find features, describe them,

doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.markdown

@@ -104,7 +104,7 @@ greater than 0.8, they are rejected. It eliminaters around 90% of false matches
 
 So this is a summary of SIFT algorithm. For more details and understanding, reading the original
 paper is highly recommended. Remember one thing, this algorithm is patented. So this algorithm is
-included in [the opencv contrib repo](https://github.com/Itseez/opencv_contrib)
+included in [the opencv contrib repo](https://github.com/opencv/opencv_contrib)
 
 SIFT in OpenCV
 --------------

doc/py_tutorials/py_setup/py_intro/py_intro.markdown

@@ -58,7 +58,7 @@ OpenCV Needs You !!!
 Since OpenCV is an open source initiative, all are welcome to make contributions to the library,
 documentation, and tutorials. If you find any mistake in this tutorial (from a small spelling
 mistake to an egregious error in code or concept), feel free to correct it by cloning OpenCV in
-[GitHub](https://github.com/Itseez/opencv) and submitting a pull request. OpenCV developers will
+[GitHub](https://github.com/opencv/opencv) and submitting a pull request. OpenCV developers will
 check your pull request, give you important feedback and (once it passes the approval of the
 reviewer) it will be merged into OpenCV. You will then become an open source contributor :-)
 

doc/py_tutorials/py_setup/py_setup_in_fedora/py_setup_in_fedora.markdown

@@ -119,7 +119,7 @@ Or you can download latest source from OpenCV's github repo. (If you want to con
 choose this. It always keeps your OpenCV up-to-date). For that, you need to install **Git** first.
 @code{.sh}
 yum install git
-git clone https://github.com/Itseez/opencv.git
+git clone https://github.com/opencv/opencv.git
 @endcode
 It will create a folder OpenCV in home directory (or the directory you specify). The cloning may
 take some time depending upon your internet connection.

doc/py_tutorials/py_setup/py_setup_in_windows/py_setup_in_windows.markdown

@@ -76,7 +76,7 @@ Building OpenCV from source
 
 -#  Download OpenCV source. It can be from
     [Sourceforge](http://sourceforge.net/projects/opencvlibrary/) (for official release version) or
-    from [Github](https://github.com/Itseez/opencv) (for latest source).
+    from [Github](https://github.com/opencv/opencv) (for latest source).
 -#  Extract it to a folder, opencv and create a new folder build in it.
 -#  Open CMake-gui (*Start \> All Programs \> CMake-gui*)
 -#  Fill the fields as follows (see the image below):

doc/tutorials/calib3d/camera_calibration/camera_calibration.markdown

@@ -77,13 +77,13 @@ Source code
 
 You may also find the source code in the `samples/cpp/tutorial_code/calib3d/camera_calibration/`
 folder of the OpenCV source library or [download it from here
-](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/calib3d/camera_calibration/camera_calibration.cpp). The program has a
+](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/calib3d/camera_calibration/camera_calibration.cpp). The program has a
 single argument: the name of its configuration file. If none is given then it will try to open the
 one named "default.xml". [Here's a sample configuration file
-](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/calib3d/camera_calibration/in_VID5.xml) in XML format. In the
+](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/calib3d/camera_calibration/in_VID5.xml) in XML format. In the
 configuration file you may choose to use camera as an input, a video file or an image list. If you
 opt for the last one, you will need to create a configuration file where you enumerate the images to
-use. Here's [an example of this ](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/calib3d/camera_calibration/VID5.xml).
+use. Here's [an example of this ](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/calib3d/camera_calibration/VID5.xml).
 The important part to remember is that the images need to be specified using the absolute path or
 the relative one from your application's working directory. You may find all this in the samples
 directory mentioned above.

doc/tutorials/calib3d/interactive_calibration/interactive_calibration.markdown

@@ -92,7 +92,7 @@ QR faster than SVD, but potentially less precise
 -  *camera_resolution*: resolution of camera which is used for calibration
 
 **Note:** *charuco_dict*, *charuco_square_lenght* and *charuco_marker_size* are used for chAruco pattern generation
-(see Aruco module description for details: [Aruco tutorials](https://github.com/Itseez/opencv_contrib/tree/master/modules/aruco/tutorials))
+(see Aruco module description for details: [Aruco tutorials](https://github.com/opencv/opencv_contrib/tree/master/modules/aruco/tutorials))
 
 Default chAruco pattern:
 

doc/tutorials/core/basic_geometric_drawing/basic_geometric_drawing.markdown

@@ -47,7 +47,7 @@ Code
 ----
 
 -   This code is in your OpenCV sample folder. Otherwise you can grab it from
-    [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/core/Matrix/Drawing_1.cpp)
+    [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/core/Matrix/Drawing_1.cpp)
 
 Explanation
 -----------

doc/tutorials/core/discrete_fourier_transform/discrete_fourier_transform.markdown

@@ -15,7 +15,7 @@ Source code
 -----------
 
 You can [download this from here
-](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.cpp) or
+](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.cpp) or
 find it in the
 `samples/cpp/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.cpp` of the
 OpenCV source code library.
@@ -140,7 +140,7 @@ An application idea would be to determine the geometrical orientation present in
 example, let us find out if a text is horizontal or not? Looking at some text you'll notice that the
 text lines sort of form also horizontal lines and the letters form sort of vertical lines. These two
 main components of a text snippet may be also seen in case of the Fourier transform. Let us use
-[this horizontal ](https://github.com/Itseez/opencv/tree/master/samples/data/imageTextN.png) and [this rotated](https://github.com/Itseez/opencv/tree/master/samples/data/imageTextR.png)
+[this horizontal ](https://github.com/opencv/opencv/tree/master/samples/data/imageTextN.png) and [this rotated](https://github.com/opencv/opencv/tree/master/samples/data/imageTextR.png)
 image about a text.
 
 In case of the horizontal text:

doc/tutorials/core/file_input_output_with_xml_yml/file_input_output_with_xml_yml.markdown

@@ -16,7 +16,7 @@ Source code
 -----------
 
 You can [download this from here
-](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/core/file_input_output/file_input_output.cpp) or find it in the
+](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/core/file_input_output/file_input_output.cpp) or find it in the
 `samples/cpp/tutorial_code/core/file_input_output/file_input_output.cpp` of the OpenCV source code
 library.
 

doc/tutorials/core/how_to_scan_images/how_to_scan_images.markdown

@@ -51,7 +51,7 @@ three major ways of going through an image pixel by pixel. To make things a litt
 will make the scanning for each image using all of these methods, and print out how long it took.
 
 You can download the full source code [here
-](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/core/how_to_scan_images/how_to_scan_images.cpp) or look it up in
+](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/core/how_to_scan_images/how_to_scan_images.cpp) or look it up in
 the samples directory of OpenCV at the cpp tutorial code for the core section. Its basic usage is:
 @code{.bash}
 how_to_scan_images imageName.jpg intValueToReduce [G]

doc/tutorials/core/how_to_use_ippa_conversion/how_to_use_ippa_conversion.markdown

@@ -16,7 +16,7 @@ Code
 
 You may also find the source code in the
 `samples/cpp/tutorial_code/core/ippasync/ippasync_sample.cpp` file of the OpenCV source library or
-download it from [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/core/ippasync/ippasync_sample.cpp).
+download it from [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/core/ippasync/ippasync_sample.cpp).
 
 @include cpp/tutorial_code/core/ippasync/ippasync_sample.cpp
 

doc/tutorials/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.markdown

@@ -85,7 +85,7 @@ L = Mat(pI);
 A case study
 ------------
 
-Now that you have the basics done [here's](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp)
+Now that you have the basics done [here's](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp)
 an example that mixes the usage of the C interface with the C++ one. You will also find it in the
 sample directory of the OpenCV source code library at the
 `samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp` .
@@ -132,7 +132,7 @@ output:
 
 You may observe a runtime instance of this on the [YouTube
 here](https://www.youtube.com/watch?v=qckm-zvo31w) and you can [download the source code from here
-](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp)
+](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp)
 or find it in the
 `samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp`
 of the OpenCV source code library.

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

@@ -133,7 +133,7 @@ For example:
 ![](images/resultMatMaskFilter2D.png)
 
 You can download this source code from [here
-](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp) or look in the
+](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp) or look in the
 OpenCV source code libraries sample directory at
 `samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp`.
 

doc/tutorials/core/mat_the_basic_image_container/mat_the_basic_image_container.markdown

@@ -258,7 +258,7 @@ OpenCV offers support for output of other common OpenCV data structures too via
     ![](images/MatBasicContainerOut15.png)
 
 Most of the samples here have been included in a small console application. You can download it from
-[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/core/mat_the_basic_image_container/mat_the_basic_image_container.cpp)
+[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/core/mat_the_basic_image_container/mat_the_basic_image_container.cpp)
 or in the core section of the cpp samples.
 
 You can also find a quick video demonstration of this on

doc/tutorials/features2d/trackingmotion/corner_subpixeles/corner_subpixeles.markdown

@@ -16,7 +16,7 @@ Code
 ----
 
 This tutorial code's is shown lines below. You can also download it from
-[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/cornerSubPix_Demo.cpp)
+[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/cornerSubPix_Demo.cpp)
 @code{.cpp}
 #include "opencv2/highgui.hpp"
 #include "opencv2/imgproc.hpp"

doc/tutorials/features2d/trackingmotion/generic_corner_detector/generic_corner_detector.markdown

@@ -20,7 +20,7 @@ Code
 ----
 
 This tutorial code's is shown lines below. You can also download it from
-[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/cornerDetector_Demo.cpp)
+[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/cornerDetector_Demo.cpp)
 
 @include cpp/tutorial_code/TrackingMotion/cornerDetector_Demo.cpp
 

doc/tutorials/features2d/trackingmotion/good_features_to_track/good_features_to_track.markdown

@@ -15,7 +15,7 @@ Code
 ----
 
 This tutorial code's is shown lines below. You can also download it from
-[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/goodFeaturesToTrack_Demo.cpp)
+[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/goodFeaturesToTrack_Demo.cpp)
 @code{.cpp}
 #include "opencv2/highgui.hpp"
 #include "opencv2/imgproc.hpp"

doc/tutorials/features2d/trackingmotion/harris_detector/harris_detector.markdown

@@ -119,7 +119,7 @@ Code
 ----
 
 This tutorial code's is shown lines below. You can also download it from
-[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/cornerHarris_Demo.cpp)
+[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/cornerHarris_Demo.cpp)
 @code{.cpp}
 #include "opencv2/highgui.hpp"
 #include "opencv2/imgproc.hpp"

doc/tutorials/gpu/gpu-basics-similarity/gpu_basics_similarity.markdown

@@ -24,7 +24,7 @@ The source code
 
 You may also find the source code and these video file in the
 `samples/cpp/tutorial_code/gpu/gpu-basics-similarity/gpu-basics-similarity` folder of the OpenCV
-source library or download it from [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/gpu/gpu-basics-similarity/gpu-basics-similarity.cpp).
+source library or download it from [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/gpu/gpu-basics-similarity/gpu-basics-similarity.cpp).
 The full source code is quite long (due to the controlling of the application via the command line
 arguments and performance measurement). Therefore, to avoid cluttering up these sections with those
 you'll find here only the functions itself.

doc/tutorials/imgproc/erosion_dilatation/erosion_dilatation.markdown

@@ -60,7 +60,7 @@ Code
 ----
 
 This tutorial code's is shown lines below. You can also download it from
-[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Morphology_1.cpp)
+[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Morphology_1.cpp)
 @include samples/cpp/tutorial_code/ImgProc/Morphology_1.cpp
 
 Explanation

doc/tutorials/imgproc/gausian_median_blur_bilateral_filter/gausian_median_blur_bilateral_filter.markdown

@@ -94,7 +94,7 @@ Code
     -   Applies 4 different kinds of filters (explained in Theory) and show the filtered images
         sequentially
 -   **Downloadable code**: Click
-    [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Smoothing.cpp)
+    [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Smoothing.cpp)
 -   **Code at glance:**
 @code{.cpp}
 #include "opencv2/imgproc.hpp"

doc/tutorials/imgproc/histograms/back_projection/back_projection.markdown

@@ -70,13 +70,13 @@ Code
 -   **Downloadable code**:
 
     -#  Click
-        [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp)
+        [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp)
         for the basic version (explained in this tutorial).
     -#  For stuff slightly fancier (using H-S histograms and floodFill to define a mask for the
         skin area) you can check the [improved
-        demo](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo2.cpp)
+        demo](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo2.cpp)
     -#  ...or you can always check out the classical
-        [camshiftdemo](https://github.com/Itseez/opencv/tree/master/samples/cpp/camshiftdemo.cpp)
+        [camshiftdemo](https://github.com/opencv/opencv/tree/master/samples/cpp/camshiftdemo.cpp)
         in samples.
 
 -   **Code at glance:**

doc/tutorials/imgproc/histograms/histogram_calculation/histogram_calculation.markdown

@@ -66,7 +66,7 @@ Code
     -   Calculate the Histogram of each 1-channel plane by calling the function @ref cv::calcHist
     -   Plot the three histograms in a window
 -   **Downloadable code**: Click
-    [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/calcHist_Demo.cpp)
+    [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/calcHist_Demo.cpp)
 -   **Code at glance:**
     @include samples/cpp/tutorial_code/Histograms_Matching/calcHist_Demo.cpp
 

doc/tutorials/imgproc/histograms/histogram_comparison/histogram_comparison.markdown

@@ -44,7 +44,7 @@ Code
         histogram of the lower half base image and with the same base image histogram.
     -   Display the numerical matching parameters obtained.
 -   **Downloadable code**: Click
-    [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/compareHist_Demo.cpp)
+    [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/compareHist_Demo.cpp)
 -   **Code at glance:**
 
 @include cpp/tutorial_code/Histograms_Matching/compareHist_Demo.cpp

doc/tutorials/imgproc/histograms/histogram_equalization/histogram_equalization.markdown

@@ -62,7 +62,7 @@ Code
     -   Equalize the Histogram by using the OpenCV function @ref cv::equalizeHist
     -   Display the source and equalized images in a window.
 -   **Downloadable code**: Click
-    [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/EqualizeHist_Demo.cpp)
+    [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/EqualizeHist_Demo.cpp)
 -   **Code at glance:**
     @include samples/cpp/tutorial_code/Histograms_Matching/EqualizeHist_Demo.cpp
 

doc/tutorials/imgproc/histograms/template_matching/template_matching.markdown

@@ -19,6 +19,10 @@ Theory
 Template matching is a technique for finding areas of an image that match (are similar) to a
 template image (patch).
 
+While the patch must be a rectangle it may be that not all of the
+rectangle is relevant.  In such a case, a mask can be used to isolate the portion of the patch
+that should be used to find the match.
+
 ### How does it work?
 
 -   We need two primary components:
@@ -51,6 +55,30 @@ template image (patch).
 -   In practice, we use the function @ref cv::minMaxLoc to locate the highest value (or lower,
     depending of the type of matching method) in the *R* matrix.
 
+### How does the mask work?
+- If masking is needed for the match, three components are required:
+
+    -#  **Source image (I):** The image in which we expect to find a match to the template image
+    -#  **Template image (T):** The patch image which will be compared to the template image
+    -#  **Mask image (M):** The mask, a grayscale image that masks the template
+
+
+-   Only two matching methods currently accept a mask: CV_TM_SQDIFF and CV_TM_CCORR_NORMED (see
+    below for explanation of all the matching methods available in opencv).
+
+
+-   The mask must have the same dimensions as the template
+
+
+-   The mask should have a CV_8U or CV_32F depth and the same number of channels
+    as the template image. In CV_8U case, the mask values are treated as binary,
+    i.e. zero and non-zero. In CV_32F case, the values should fall into [0..1]
+    range and the template pixels will be multiplied by the corresponding mask pixel
+    values. Since the input images in the sample have the CV_8UC3 type, the mask
+    is also read as color image.
+
+    ![](images/Template_Matching_Mask_Example.jpg)
+
 ### Which are the matching methods available in OpenCV?
 
 Good question. OpenCV implements Template matching in the function @ref cv::matchTemplate . The
@@ -88,15 +116,16 @@ Code
 ----
 
 -   **What does this program do?**
-    -   Loads an input image and a image patch (*template*)
+    -   Loads an input image, an image patch (*template*), and optionally a mask
     -   Perform a template matching procedure by using the OpenCV function @ref cv::matchTemplate
         with any of the 6 matching methods described before. The user can choose the method by
-        entering its selection in the Trackbar.
+        entering its selection in the Trackbar.  If a mask is supplied, it will only be used for
+        the methods that support masking
     -   Normalize the output of the matching procedure
     -   Localize the location with higher matching probability
     -   Draw a rectangle around the area corresponding to the highest match
 -   **Downloadable code**: Click
-    [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp)
+    [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp)
 -   **Code at glance:**
     @include samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp
 
@@ -113,10 +142,14 @@ Explanation
     int match_method;
     int max_Trackbar = 5;
     @endcode
--#  Load the source image and template:
+-#  Load the source image, template, and optionally, if supported for the matching method, a mask:
     @code{.cpp}
-    img = imread( argv[1], 1 );
+     bool method_accepts_mask = (CV_TM_SQDIFF == match_method || match_method == CV_TM_CCORR_NORMED);
-    templ = imread( argv[2], 1 );
+  if (use_mask && method_accepts_mask)
+    { matchTemplate( img, templ, result, match_method, mask); }
+  else
+    { matchTemplate( img, templ, result, match_method); }
+
     @endcode
 -#  Create the windows to show the results:
     @code{.cpp}
@@ -150,10 +183,14 @@ Explanation
     @endcode
 -#  Perform the template matching operation:
     @code{.cpp}
-    matchTemplate( img, templ, result, match_method );
+    bool method_accepts_mask = (CV_TM_SQDIFF == match_method || match_method == CV_TM_CCORR_NORMED);
+    if (use_mask && method_accepts_mask)
+        { matchTemplate( img, templ, result, match_method, mask); }
+    else
+        { matchTemplate( img, templ, result, match_method); }
     @endcode
-    the arguments are naturally the input image **I**, the template **T**, the result **R** and the
+    the arguments are naturally the input image **I**, the template **T**, the result **R**, the
-    match_method (given by the Trackbar)
+    match_method (given by the Trackbar), and optionally the mask image **M**
 
 -#  We normalize the results:
     @code{.cpp}

doc/tutorials/imgproc/imgtrans/canny_detector/canny_detector.markdown

@@ -74,7 +74,7 @@ Code
     -   Applies the mask obtained on the original image and display it in a window.
 
 -#  The tutorial code's is shown lines below. You can also download it from
-    [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp)
+    [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp)
     @include samples/cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp
 
 Explanation

doc/tutorials/imgproc/imgtrans/copyMakeBorder/copyMakeBorder.markdown

@@ -46,7 +46,7 @@ Code
     -   The program finishes when the user presses 'ESC'
 
 -#  The tutorial code's is shown lines below. You can also download it from
-    [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/copyMakeBorder_demo.cpp)
+    [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/copyMakeBorder_demo.cpp)
     @include samples/cpp/tutorial_code/ImgTrans/copyMakeBorder_demo.cpp
 
 Explanation

doc/tutorials/imgproc/imgtrans/distance_transformation/distance_transform.markdown

@@ -17,7 +17,7 @@ Code
 ----
 
 This tutorial code's is shown lines below. You can also download it from
-    [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp).
+    [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp).
 @include samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp
 
 Explanation / Result

doc/tutorials/imgproc/imgtrans/filter_2d/filter_2d.markdown

@@ -62,7 +62,7 @@ Code
     -   The filter output (with each kernel) will be shown during 500 milliseconds
 
 -#  The tutorial code's is shown lines below. You can also download it from
-    [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/filter2D_demo.cpp)
+    [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/filter2D_demo.cpp)
 @code{.cpp}
 #include "opencv2/imgproc.hpp"
 #include "opencv2/highgui.hpp"

doc/tutorials/imgproc/imgtrans/hough_circle/hough_circle.markdown

@@ -39,9 +39,9 @@ Code
     -   Applies the *Hough Circle Transform* to the blurred image .
     -   Display the detected circle in a window.
 
--#  The sample code that we will explain can be downloaded from [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/houghcircles.cpp).
+-#  The sample code that we will explain can be downloaded from [here](https://github.com/opencv/opencv/tree/master/samples/cpp/houghcircles.cpp).
     A slightly fancier version (which shows trackbars for
-    changing the threshold values) can be found [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/HoughCircle_Demo.cpp).
+    changing the threshold values) can be found [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/HoughCircle_Demo.cpp).
     @include samples/cpp/houghcircles.cpp
 
 Explanation

doc/tutorials/imgproc/imgtrans/hough_lines/hough_lines.markdown

@@ -95,9 +95,9 @@ Code
     -   Applies either a *Standard Hough Line Transform* or a *Probabilistic Line Transform*.
     -   Display the original image and the detected line in two windows.
 
--#  The sample code that we will explain can be downloaded from [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/houghlines.cpp). A slightly fancier version
+-#  The sample code that we will explain can be downloaded from [here](https://github.com/opencv/opencv/tree/master/samples/cpp/houghlines.cpp). A slightly fancier version
     (which shows both Hough standard and probabilistic with trackbars for changing the threshold
-    values) can be found [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/HoughLines_Demo.cpp).
+    values) can be found [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/HoughLines_Demo.cpp).
     @include samples/cpp/houghlines.cpp
 
 Explanation

doc/tutorials/imgproc/imgtrans/laplace_operator/laplace_operator.markdown

@@ -51,7 +51,7 @@ Code
     -   Display the result in a window
 
 -#  The tutorial code's is shown lines below. You can also download it from
-    [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp)
+    [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp)
     @include samples/cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp
 
 Explanation

doc/tutorials/imgproc/imgtrans/remap/remap.markdown

@@ -52,7 +52,7 @@ Code
     -   Wait for the user to exit the program
 
 -#  The tutorial code's is shown lines below. You can also download it from
-    [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/Remap_Demo.cpp)
+    [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/Remap_Demo.cpp)
     @include samples/cpp/tutorial_code/ImgTrans/Remap_Demo.cpp
 
 Explanation

doc/tutorials/imgproc/imgtrans/sobel_derivatives/sobel_derivatives.markdown

@@ -108,7 +108,7 @@ Code
         bright on a darker background.
 
 -#  The tutorial code's is shown lines below. You can also download it from
-    [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp)
+    [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp)
     @include samples/cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp
 
 Explanation

doc/tutorials/imgproc/imgtrans/warp_affine/warp_affine.markdown

@@ -89,7 +89,7 @@ Code
     -   Waits until the user exits the program
 
 -#  The tutorial code's is shown lines below. You can also download it from
-    [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/Geometric_Transforms_Demo.cpp)
+    [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/Geometric_Transforms_Demo.cpp)
     @include samples/cpp/tutorial_code/ImgTrans/Geometric_Transforms_Demo.cpp
 
 Explanation

doc/tutorials/imgproc/morph_lines_detection/moprh_lines_detection.md

@@ -48,7 +48,7 @@ A structuring element can have many common shapes, such as lines, diamonds, disk
 Code
 ----
 
-This tutorial code's is shown lines below. You can also download it from [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Morphology_3.cpp).
+This tutorial code's is shown lines below. You can also download it from [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Morphology_3.cpp).
 @include samples/cpp/tutorial_code/ImgProc/Morphology_3.cpp
 
 Explanation / Result

doc/tutorials/imgproc/opening_closing_hats/opening_closing_hats.markdown

@@ -80,7 +80,7 @@ Code
 ----
 
 This tutorial code's is shown lines below. You can also download it from
-[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Morphology_2.cpp)
+[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Morphology_2.cpp)
 @code{.cpp}
 #include "opencv2/imgproc.hpp"
 #include "opencv2/highgui.hpp"

doc/tutorials/imgproc/pyramids/pyramids.markdown

@@ -66,7 +66,7 @@ Code
 ----
 
 This tutorial code's is shown lines below. You can also download it from
-[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Pyramids.cpp)
+[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Pyramids.cpp)
 
 @include samples/cpp/tutorial_code/ImgProc/Pyramids.cpp
 

doc/tutorials/imgproc/shapedescriptors/bounding_rects_circles/bounding_rects_circles.markdown

@@ -16,7 +16,7 @@ Code
 ----
 
 This tutorial code's is shown lines below. You can also download it from
-[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/generalContours_demo1.cpp)
+[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/generalContours_demo1.cpp)
 @include samples/cpp/tutorial_code/ShapeDescriptors/generalContours_demo1.cpp
 
 Explanation

doc/tutorials/imgproc/shapedescriptors/bounding_rotated_ellipses/bounding_rotated_ellipses.markdown

@@ -16,7 +16,7 @@ Code
 ----
 
 This tutorial code's is shown lines below. You can also download it from
-[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/generalContours_demo2.cpp)
+[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/generalContours_demo2.cpp)
 @include samples/cpp/tutorial_code/ShapeDescriptors/generalContours_demo2.cpp
 
 Explanation

doc/tutorials/imgproc/shapedescriptors/find_contours/find_contours.markdown

@@ -16,7 +16,7 @@ Code
 ----
 
 This tutorial code's is shown lines below. You can also download it from
-[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/findContours_demo.cpp)
+[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/findContours_demo.cpp)
 @include samples/cpp/tutorial_code/ShapeDescriptors/findContours_demo.cpp
 
 Explanation

doc/tutorials/imgproc/shapedescriptors/hull/hull.markdown

@@ -15,7 +15,7 @@ Code
 ----
 
 This tutorial code's is shown lines below. You can also download it from
-[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/hull_demo.cpp)
+[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/hull_demo.cpp)
 
 @include samples/cpp/tutorial_code/ShapeDescriptors/hull_demo.cpp
 

doc/tutorials/imgproc/shapedescriptors/moments/moments.markdown

@@ -17,7 +17,7 @@ Code
 ----
 
 This tutorial code's is shown lines below. You can also download it from
-[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/moments_demo.cpp)
+[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/moments_demo.cpp)
 @include samples/cpp/tutorial_code/ShapeDescriptors/moments_demo.cpp
 
 Explanation

doc/tutorials/imgproc/shapedescriptors/point_polygon_test/point_polygon_test.markdown

@@ -15,7 +15,7 @@ Code
 ----
 
 This tutorial code's is shown lines below. You can also download it from
-[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/pointPolygonTest_demo.cpp)
+[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/pointPolygonTest_demo.cpp)
 @include samples/cpp/tutorial_code/ShapeDescriptors/pointPolygonTest_demo.cpp
 
 Explanation

doc/tutorials/imgproc/threshold/threshold.markdown

@@ -97,7 +97,7 @@ Code
 ----
 
 The tutorial code's is shown lines below. You can also download it from
-[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Threshold.cpp)
+[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Threshold.cpp)
 @include samples/cpp/tutorial_code/ImgProc/Threshold.cpp
 
 Explanation

doc/tutorials/imgproc/threshold_inRange/threshold_inRange.markdown

@@ -19,7 +19,7 @@ Code
 ----
 
 The tutorial code's is shown lines below. You can also download it from
-[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp)
+[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp)
 @include samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp
 
 Explanation

doc/tutorials/introduction/android_binary_package/android_ocl_intro.markdown

@@ -17,7 +17,7 @@ If you need help with anything of the above, you may refer to our @ref tutorial_
 This tutorial also assumes you have an Android operated device with OpenCL enabled.
 
 The related source code is located within OpenCV samples at
-[opencv/samples/android/tutorial-4-opencl](https://github.com/Itseez/opencv/tree/master/samples/android/tutorial-4-opencl/) directory.
+[opencv/samples/android/tutorial-4-opencl](https://github.com/opencv/opencv/tree/master/samples/android/tutorial-4-opencl/) directory.
 
 Preface
 -------
@@ -244,7 +244,7 @@ As you can see, inheritors for `Camera` and `Camera2` APIs should implement the
 @endcode
 
 Let's leave the details of their implementation beyond of this tutorial, please refer the
-[source code](https://github.com/Itseez/opencv/tree/master/samples/android/tutorial-4-opencl/) to see them.
+[source code](https://github.com/opencv/opencv/tree/master/samples/android/tutorial-4-opencl/) to see them.
 
 Preview Frames modification
 ---------------------------

doc/tutorials/introduction/clojure_dev_intro/clojure_dev_intro.markdown

@@ -40,7 +40,7 @@ I'm assuming you already installed [xcode](https://developer.apple.com/xcode/),
 @code{.bash}
 cd ~/
 mkdir opt
-git clone https://github.com/Itseez/opencv.git
+git clone https://github.com/opencv/opencv.git
 cd opencv
 git checkout 2.4
 mkdir build

doc/tutorials/introduction/crosscompilation/arm_crosscompile_with_cmake.markdown

@@ -33,7 +33,7 @@ Getting OpenCV Source Code
 --------------------------
 
 You can use the latest stable OpenCV version available in *sourceforge* or you can grab the latest
-snapshot from our [Git repository](https://github.com/Itseez/opencv.git).
+snapshot from our [Git repository](https://github.com/opencv/opencv.git).
 
 ### Getting the Latest Stable OpenCV Version
 
@@ -42,12 +42,12 @@ snapshot from our [Git repository](https://github.com/Itseez/opencv.git).
 
 ### Getting the Cutting-edge OpenCV from the Git Repository
 
-Launch Git client and clone [OpenCV repository](http://github.com/itseez/opencv)
+Launch Git client and clone [OpenCV repository](http://github.com/opencv/opencv)
 
 In Linux it can be achieved with the following command in Terminal:
 @code{.bash}
 cd ~/<my_working _directory>
-git clone https://github.com/Itseez/opencv.git
+git clone https://github.com/opencv/opencv.git
 @endcode
 
 Building OpenCV

doc/tutorials/introduction/desktop_java/java_dev_intro.markdown

@@ -36,7 +36,7 @@ from the [OpenCV SourceForge repository](http://sourceforge.net/projects/opencvl
 sources.
 
 Another option to get OpenCV sources is to clone [OpenCV git
-repository](https://github.com/Itseez/opencv/). In order to build OpenCV with Java bindings you need
+repository](https://github.com/opencv/opencv/). In order to build OpenCV with Java bindings you need
 JDK (Java Development Kit) (we recommend [Oracle/Sun JDK 6 or
 7](http://www.oracle.com/technetwork/java/javase/downloads/)), [Apache Ant](http://ant.apache.org/)
 and Python v2.6 or higher to be installed.
@@ -45,7 +45,7 @@ and Python v2.6 or higher to be installed.
 
 Let's build OpenCV:
 @code{.bash}
-git clone git://github.com/Itseez/opencv.git
+git clone git://github.com/opencv/opencv.git
 cd opencv
 git checkout 2.4
 mkdir build

doc/tutorials/introduction/display_image/display_image.markdown

@@ -14,7 +14,7 @@ Source Code
 -----------
 
 Download the source code from
-[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/introduction/display_image/display_image.cpp).
+[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/introduction/display_image/display_image.cpp).
 
 @include cpp/tutorial_code/introduction/display_image/display_image.cpp
 

doc/tutorials/introduction/ios_install/ios_install.markdown

@@ -9,13 +9,13 @@ Required Packages
 
 ### Getting the Cutting-edge OpenCV from Git Repository
 
-Launch GIT client and clone OpenCV repository from [here](http://github.com/itseez/opencv)
+Launch GIT client and clone OpenCV repository from [here](http://github.com/opencv/opencv)
 
 In MacOS it can be done using the following command in Terminal:
 
 @code{.bash}
 cd ~/<my_working _directory>
-git clone https://github.com/Itseez/opencv.git
+git clone https://github.com/opencv/opencv.git
 @endcode
 
 Building OpenCV from Source, using CMake and Command Line

doc/tutorials/introduction/linux_install/linux_install.markdown

@@ -16,6 +16,7 @@ Required Packages
 -   [optional] libtbb2 libtbb-dev
 -   [optional] libdc1394 2.x
 -   [optional] libjpeg-dev, libpng-dev, libtiff-dev, libjasper-dev, libdc1394-22-dev
+-   [optional] CUDA Toolkit 6.5 or higher
 
 The packages can be installed using a terminal and the following commands or by using Synaptic
 Manager:
@@ -28,7 +29,7 @@ Getting OpenCV Source Code
 --------------------------
 
 You can use the latest stable OpenCV version or you can grab the latest snapshot from our [Git
-repository](https://github.com/Itseez/opencv.git).
+repository](https://github.com/opencv/opencv.git).
 
 ### Getting the Latest Stable OpenCV Version
 
@@ -37,14 +38,14 @@ repository](https://github.com/Itseez/opencv.git).
 
 ### Getting the Cutting-edge OpenCV from the Git Repository
 
-Launch Git client and clone [OpenCV repository](http://github.com/itseez/opencv). If you need
+Launch Git client and clone [OpenCV repository](http://github.com/opencv/opencv). If you need
-modules from [OpenCV contrib repository](http://github.com/itseez/opencv_contrib) then clone it too.
+modules from [OpenCV contrib repository](http://github.com/opencv/opencv_contrib) then clone it too.
 
 For example
 @code{.bash}
 cd ~/<my_working_directory>
-git clone https://github.com/Itseez/opencv.git
+git clone https://github.com/opencv/opencv.git
-git clone https://github.com/Itseez/opencv_contrib.git
+git clone https://github.com/opencv/opencv_contrib.git
 @endcode
 Building OpenCV from Source Using CMake
 ---------------------------------------
@@ -114,11 +115,11 @@ Building OpenCV from Source Using CMake
 -#  [optional] Running tests
 
     -   Get the required test data from [OpenCV extra
-        repository](https://github.com/Itseez/opencv_extra).
+        repository](https://github.com/opencv/opencv_extra).
 
     For example
     @code{.bash}
-    git clone https://github.com/Itseez/opencv_extra.git
+    git clone https://github.com/opencv/opencv_extra.git
     @endcode
     -   set OPENCV_TEST_DATA_PATH environment variable to \<path to opencv_extra/testdata\>.
     -   execute tests from build directory.

doc/tutorials/introduction/transition_guide/transition_guide.markdown

@@ -12,7 +12,7 @@ OpenCV 3.0 introduced many new algorithms and features comparing to version 2.4.
 This section describes most notable changes in general, all details and examples of transition actions are in the next part of the document.
 
 ##### Contrib repository
-<https://github.com/Itseez/opencv_contrib>
+<https://github.com/opencv/opencv_contrib>
 
 This is a place for all new, experimental and non-free algorithms. It does not receive so much attention from the support team comparing to main repository, but the community makes an effort to keep it in a good shape.
 

doc/tutorials/introduction/windows_install/windows_install.markdown

@@ -43,7 +43,7 @@ These videos above are long-obsolete and contain inaccurate information. Be care
 solutions described in those videos are no longer supported and may even break your install.
 
 If you are building your own libraries you can take the source files from our [Git
-repository](https://github.com/Itseez/opencv.git).
+repository](https://github.com/opencv/opencv.git).
 
 Building the OpenCV library from scratch requires a couple of tools installed beforehand:
 
@@ -114,7 +114,7 @@ libraries). If you do not need the support for some of these you can just freely
     you're doing -- it's OK.
     -#  Clone the repository to the selected directory. After clicking *Clone* button, a window will
         appear where you can select from what repository you want to download source files
-        (<https://github.com/Itseez/opencv.git>) and to what directory (`D:/OpenCV`).
+        (<https://github.com/opencv/opencv.git>) and to what directory (`D:/OpenCV`).
     -#  Push the OK button and be patient as the repository is quite a heavy download. It will take
         some time depending on your Internet connection.
 

doc/tutorials/introduction/windows_visual_studio_Opencv/windows_visual_studio_Opencv.markdown

@@ -189,7 +189,7 @@ Test it!
 --------
 
 Now to try this out download our little test [source code
-](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/introduction/windows_visual_studio_Opencv/introduction_windows_vs.cpp)
+](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/introduction/windows_visual_studio_Opencv/introduction_windows_vs.cpp)
 or get it from the sample code folder of the OpenCV sources. Add this to your project and build it.
 Here's its content:
 
@@ -205,7 +205,7 @@ the *IDE* the console window will not close once finished. It will wait for a ke
 This is important to remember when you code inside the code open and save commands. You're resources
 will be saved ( and queried for at opening!!!) relatively to your working directory. This is unless
 you give a full, explicit path as parameter for the I/O functions. In the code above we open [this
-OpenCV logo](https://github.com/Itseez/opencv/tree/master/samples/data/opencv-logo.png). Before starting up the application make sure you place
+OpenCV logo](https://github.com/opencv/opencv/tree/master/samples/data/opencv-logo.png). Before starting up the application make sure you place
 the image file in your current working directory. Modify the image file name inside the code to try
 it out on other images too. Run it and voil á:
 

doc/tutorials/ml/introduction_to_pca/introduction_to_pca.markdown

@@ -92,10 +92,10 @@ Source Code
 -----------
 
 This tutorial code's is shown lines below. You can also download it from
-    [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp).
+    [here](https://github.com/opencv/tree/master/samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp).
 @include cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp
 
-@note Another example using PCA for dimensionality reduction while maintaining an amount of variance can be found at [opencv_source_code/samples/cpp/pca.cpp](https://github.com/Itseez/opencv/tree/master/samples/cpp/pca.cpp)
+@note Another example using PCA for dimensionality reduction while maintaining an amount of variance can be found at [opencv_source_code/samples/cpp/pca.cpp](https://github.com/opencv/tree/master/samples/cpp/pca.cpp)
 
 Explanation
 -----------

doc/tutorials/ml/non_linear_svms/non_linear_svms.markdown

@@ -87,7 +87,7 @@ Source Code
 -----------
 
 You may also find the source code in `samples/cpp/tutorial_code/ml/non_linear_svms` folder of the OpenCV source library or
-[download it from here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp).
+[download it from here](https://github.com/opencv/tree/master/samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp).
 
 @note The following code has been implemented with OpenCV 3.0 classes and functions. An equivalent version of the code
 using OpenCV 2.4 can be found in [this page.](http://docs.opencv.org/2.4/doc/tutorials/ml/non_linear_svms/non_linear_svms.html#nonlinearsvms)

doc/tutorials/objdetect/cascade_classifier/cascade_classifier.markdown

@@ -18,9 +18,9 @@ Code
 ----
 
 This tutorial code's is shown lines below. You can also download it from
-[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/objectDetection/objectDetection.cpp)
+[here](https://github.com/opencv/tree/master/samples/cpp/tutorial_code/objectDetection/objectDetection.cpp)
 . The second version (using LBP for face detection) can be [found
-here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/objectDetection/objectDetection2.cpp)
+here](https://github.com/opencv/tree/master/samples/cpp/tutorial_code/objectDetection/objectDetection2.cpp)
 @code{.cpp}
 #include "opencv2/objdetect.hpp"
 #include "opencv2/highgui.hpp"

doc/tutorials/objdetect/traincascade.markdown

@@ -6,7 +6,7 @@ Introduction
 
 The work with a cascade classifier inlcudes two major stages: training and detection. Detection
 stage is described in a documentation of objdetect module of general OpenCV documentation.
-Documentation gives some basic information about cascade classifier. Current guide is describing how
+This documentation gives some basic information about cascade classifier. Current guide is describing how
 to train a cascade classifier: preparation of the training data and running the training application.
 
 ### Important notes
@@ -14,7 +14,7 @@ to train a cascade classifier: preparation of the training data and running the
 There are two applications in OpenCV to train cascade classifier: opencv_haartraining and
 opencv_traincascade. opencv_traincascade is a newer version, written in C++ in accordance to
 OpenCV 2.x API. But the main difference between this two applications is that opencv_traincascade
-supports both Haar @cite Viola01 and @cite Liao2007 (Local Binary Patterns) features. LBP features
+supports both Haar @cite Viola01 and LBP (Local Binary Patterns) @cite Liao2007 features. LBP features
 are integer in contrast to Haar features, so both training and detection with LBP are several times
 faster then with Haar features. Regarding the LBP and Haar detection quality, it depends on
 training: the quality of training dataset first of all and training parameters too. It's possible to
@@ -152,7 +152,7 @@ Command line arguments:
     Height (in pixels) of the output samples.
 
 For following procedure is used to create a sample object instance: The source image is rotated
-randomly around all three axes. The chosen angle is limited my -max?angle. Then pixels having the
+randomly around all three axes. The chosen angle is limited by -maxxangle, -maxyangle and maxzangle. Then pixels having the
 intensity from [bg_color-bg_color_threshold; bg_color+bg_color_threshold] range are
 interpreted as transparent. White noise is added to the intensities of the foreground. If the -inv
 key is specified then foreground pixel intensities are inverted. If -randinv key is specified then
@@ -288,12 +288,12 @@ Command line arguments of opencv_traincascade application grouped by purposes:
     -   -minHitRate \<min_hit_rate\>
 
         Minimal desired hit rate for each stage of the classifier. Overall hit rate may be estimated
-        as (min_hit_rate\^number_of_stages).
+        as (min_hit_rate ^ number_of_stages), @cite Viola04 §4.1.
 
     -   -maxFalseAlarmRate \<max_false_alarm_rate\>
 
         Maximal desired false alarm rate for each stage of the classifier. Overall false alarm rate
-        may be estimated as (max_false_alarm_rate\^number_of_stages).
+        may be estimated as (max_false_alarm_rate ^ number_of_stages), @cite Viola04 §4.1.
 
     -   -weightTrimRate \<weight_trim_rate\>
 
@@ -326,4 +326,4 @@ in cascade.xml file in the folder, which was passed as -data parameter. Other fi
 are created for the case of interrupted training, so you may delete them after completion of
 training.
 
-Training is finished and you can test you cascade classifier!
+Training is finished and you can test your cascade classifier!

doc/tutorials/video/background_subtraction/background_subtraction.markdown

@@ -45,7 +45,7 @@ Two different methods are used to generate two foreground masks:
 -#  @ref cv::BackgroundSubtractorMOG2
 
 The results as well as the input data are shown on the screen.
-The source file can be downloaded [here ](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/video/bg_sub.cpp).
+The source file can be downloaded [here ](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/video/bg_sub.cpp).
 
 @include samples/cpp/tutorial_code/video/bg_sub.cpp
 

doc/tutorials/videoio/intelperc.markdown

@@ -78,5 +78,5 @@ there are two flags that should be used to set/get property of the needed genera
     flag value is assumed by default if neither of the two possible values of the property is set.
 
 For more information please refer to the example of usage
-[intelperc_capture.cpp](https://github.com/Itseez/opencv/tree/master/samples/cpp/intelperc_capture.cpp)
+[intelperc_capture.cpp](https://github.com/opencv/tree/master/samples/cpp/intelperc_capture.cpp)
 in opencv/samples/cpp folder.

doc/tutorials/videoio/kinect_openni.markdown

@@ -134,5 +134,5 @@ property. The following properties of cameras available through OpenNI interface
     -   CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_REGISTRATION
 
 For more information please refer to the example of usage
-[openni_capture.cpp](https://github.com/Itseez/opencv/tree/master/samples/cpp/openni_capture.cpp) in
+[openni_capture.cpp](https://github.com/opencv/tree/master/samples/cpp/openni_capture.cpp) in
 opencv/samples/cpp folder.

doc/tutorials/videoio/video-input-psnr-ssim/video_input_psnr_ssim.markdown

@@ -20,8 +20,8 @@ As a test case where to show off these using OpenCV I've created a small program
 video files and performs a similarity check between them. This is something you could use to check
 just how well a new video compressing algorithms works. Let there be a reference (original) video
 like [this small Megamind clip
-](https://github.com/Itseez/opencv/tree/master/samples/data/Megamind.avi) and [a compressed
+](https://github.com/opencv/opencv/tree/master/samples/data/Megamind.avi) and [a compressed
-version of it ](https://github.com/Itseez/opencv/tree/master/samples/data/Megamind_bugy.avi).
+version of it ](https://github.com/opencv/opencv/tree/master/samples/data/Megamind_bugy.avi).
 You may also find the source code and these video file in the
 `samples/data` folder of the OpenCV source library.
 

doc/tutorials/videoio/video-write/video_write.markdown

@@ -31,7 +31,7 @@ The source code
 
 You may also find the source code and these video file in the
 `samples/cpp/tutorial_code/videoio/video-write/` folder of the OpenCV source library or [download it
-from here ](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/videoio/video-write/video-write.cpp).
+from here ](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/videoio/video-write/video-write.cpp).
 
 @include cpp/tutorial_code/videoio/video-write/video-write.cpp
 

doc/tutorials/viz/creating_widgets/creating_widgets.markdown

@@ -12,7 +12,7 @@ In this tutorial you will learn how to
 Code
 ----
 
-You can download the code from [here ](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/viz/creating_widgets.cpp).
+You can download the code from [here ](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/viz/creating_widgets.cpp).
 @include samples/cpp/tutorial_code/viz/creating_widgets.cpp
 
 Explanation

doc/tutorials/viz/launching_viz/launching_viz.markdown

@@ -14,7 +14,7 @@ In this tutorial you will learn how to
 Code
 ----
 
-You can download the code from [here ](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/viz/launching_viz.cpp).
+You can download the code from [here ](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/viz/launching_viz.cpp).
 @include samples/cpp/tutorial_code/viz/launching_viz.cpp
 
 Explanation

doc/tutorials/viz/transformations/transformations.markdown

@@ -13,7 +13,7 @@ In this tutorial you will learn how to
 Code
 ----
 
-You can download the code from [here ](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/viz/transformations.cpp).
+You can download the code from [here ](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/viz/transformations.cpp).
 @include samples/cpp/tutorial_code/viz/transformations.cpp
 
 Explanation

doc/tutorials/viz/widget_pose/widget_pose.markdown

@@ -13,7 +13,7 @@ In this tutorial you will learn how to
 Code
 ----
 
-You can download the code from [here ](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/viz/widget_pose.cpp).
+You can download the code from [here ](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/viz/widget_pose.cpp).
 @include samples/cpp/tutorial_code/viz/widget_pose.cpp
 
 Explanation

modules/calib3d/src/calibinit.cpp

@@ -59,23 +59,27 @@
 
 \************************************************************************************/
 
+/************************************************************************************\
+  This version adds a new and improved variant of chessboard corner detection
+  that works better in poor lighting condition. It is based on work from
+  Oliver Schreer and Stefano Masneri. This method works faster than the previous
+  one and reverts back to the older method in case no chessboard detection is
+  possible. Overall performance improves also because now the method avoids
+  performing the same computation multiple times when not necessary.
+
+\************************************************************************************/
+
 #include "precomp.hpp"
 #include "opencv2/imgproc/imgproc_c.h"
 #include "opencv2/calib3d/calib3d_c.h"
 #include "circlesgrid.hpp"
 #include <stdarg.h>
+#include <vector>
 
 //#define ENABLE_TRIM_COL_ROW
 
 //#define DEBUG_CHESSBOARD
-#ifdef DEBUG_CHESSBOARD
+
-#  include "opencv2/opencv_modules.hpp"
-#  ifdef HAVE_OPENCV_HIGHGUI
-#    include "opencv2/highgui.hpp"
-#  else
-#    undef DEBUG_CHESSBOARD
-#  endif
-#endif
 #ifdef DEBUG_CHESSBOARD
 static int PRINTF( const char* fmt, ... )
 {
@@ -191,38 +195,204 @@ static void icvRemoveQuadFromGroup(CvCBQuad **quads, int count, CvCBQuad *q0);
 
 static int icvCheckBoardMonotony( CvPoint2D32f* corners, CvSize pattern_size );
 
-#if 0
+int cvCheckChessboardBinary(IplImage* src, CvSize size);
-static void
+
-icvCalcAffineTranf2D32f(CvPoint2D32f* pts1, CvPoint2D32f* pts2, int count, CvMat* affine_trans)
+/***************************************************************************************************/
+//COMPUTE INTENSITY HISTOGRAM OF INPUT IMAGE
+static int icvGetIntensityHistogram( unsigned char* pucImage, int iSizeCols, int iSizeRows, std::vector<int>& piHist );
+//SMOOTH HISTOGRAM USING WINDOW OF SIZE 2*iWidth+1
+static int icvSmoothHistogram( const std::vector<int>& piHist, std::vector<int>& piHistSmooth, int iWidth );
+//COMPUTE FAST HISTOGRAM GRADIENT
+static int icvGradientOfHistogram( const std::vector<int>& piHist, std::vector<int>& piHistGrad );
+//PERFORM SMART IMAGE THRESHOLDING BASED ON ANALYSIS OF INTENSTY HISTOGRAM
+static bool icvBinarizationHistogramBased( unsigned char* pucImg, int iCols, int iRows );
+/***************************************************************************************************/
+int icvGetIntensityHistogram( unsigned char* pucImage, int iSizeCols, int iSizeRows, std::vector<int>& piHist )
 {
-    int i, j;
+  int iVal;
-    int real_count = 0;
+
-    for( j = 0; j < count; j++ )
+  // sum up all pixel in row direction and divide by number of columns
+  for ( int j=0; j<iSizeRows; j++ )
+  {
+    for ( int i=0; i<iSizeCols; i++ )
     {
-        if( pts1[j].x >= 0 ) real_count++;
+      iVal = (int)pucImage[j*iSizeCols+i];
+      piHist[iVal]++;
     }
-    if(real_count < 3) return;
+  }
-    cv::Ptr<CvMat> xy = cvCreateMat( 2*real_count, 6, CV_32FC1 );
+  return 0;
-    cv::Ptr<CvMat> uv = cvCreateMat( 2*real_count, 1, CV_32FC1 );
+}
-    //estimate affine transfromation
+/***************************************************************************************************/
-    for( i = 0, j = 0; j < count; j++ )
+int icvSmoothHistogram( const std::vector<int>& piHist, std::vector<int>& piHistSmooth, int iWidth )
+{
+  int iIdx;
+  for ( int i=0; i<256; i++)
+  {
+    int iSmooth = 0;
+    for ( int ii=-iWidth; ii<=iWidth; ii++)
     {
-        if( pts1[j].x >= 0 )
+      iIdx = i+ii;
-        {
+      if (iIdx > 0 && iIdx < 256)
-            CV_MAT_ELEM( *xy, float, i*2+1, 2 ) = CV_MAT_ELEM( *xy, float, i*2, 0 ) = pts2[j].x;
+      {
-            CV_MAT_ELEM( *xy, float, i*2+1, 3 ) = CV_MAT_ELEM( *xy, float, i*2, 1 ) = pts2[j].y;
+        iSmooth += piHist[iIdx];
-            CV_MAT_ELEM( *xy, float, i*2, 2 ) = CV_MAT_ELEM( *xy, float, i*2, 3 ) = CV_MAT_ELEM( *xy, float, i*2, 5 ) = \
+      }
-                CV_MAT_ELEM( *xy, float, i*2+1, 0 ) = CV_MAT_ELEM( *xy, float, i*2+1, 1 ) = CV_MAT_ELEM( *xy, float, i*2+1, 4 ) = 0;
+    }
-            CV_MAT_ELEM( *xy, float, i*2, 4 ) = CV_MAT_ELEM( *xy, float, i*2+1, 5 ) = 1;
+    piHistSmooth[i] = iSmooth/(2*iWidth+1);
-            CV_MAT_ELEM( *uv, float, i*2, 0 ) = pts1[j].x;
+  }
-            CV_MAT_ELEM( *uv, float, i*2+1, 0 ) = pts1[j].y;
+  return 0;
-            i++;
+}
-        }
+/***************************************************************************************************/
+int icvGradientOfHistogram( const std::vector<int>& piHist, std::vector<int>& piHistGrad )
+{
+  piHistGrad[0] = 0;
+  for ( int i=1; i<255; i++)
+  {
+    piHistGrad[i] = piHist[i-1] - piHist[i+1];
+    if ( abs(piHistGrad[i]) < 100 )
+    {
+      if ( piHistGrad[i-1] == 0)
+        piHistGrad[i] = -100;
+      else
+        piHistGrad[i] = piHistGrad[i-1];
+    }
+  }
+  return 0;
+}
+/***************************************************************************************************/
+bool icvBinarizationHistogramBased( unsigned char* pucImg, int iCols, int iRows )
+{
+  int iMaxPix = iCols*iRows;
+  int iMaxPix1 = iMaxPix/100;
+  const int iNumBins = 256;
+  std::vector<int> piHistIntensity(iNumBins, 0);
+  std::vector<int> piHistSmooth(iNumBins, 0);
+  std::vector<int> piHistGrad(iNumBins, 0);
+  std::vector<int> piAccumSum(iNumBins, 0);
+  std::vector<int> piMaxPos(20, 0);
+  int iThresh = 0;
+  int iIdx;
+  int iWidth = 1;
+
+  icvGetIntensityHistogram( pucImg, iCols, iRows, piHistIntensity );
+
+  // get accumulated sum starting from bright
+  piAccumSum[iNumBins-1] = piHistIntensity[iNumBins-1];
+  for ( int i=iNumBins-2; i>=0; i-- )
+  {
+    piAccumSum[i] = piHistIntensity[i] + piAccumSum[i+1];
+  }
+
+  // first smooth the distribution
+  icvSmoothHistogram( piHistIntensity, piHistSmooth, iWidth );
+
+  // compute gradient
+  icvGradientOfHistogram( piHistSmooth, piHistGrad );
+
+  // check for zeros
+  int iCntMaxima = 0;
+  for ( int i=iNumBins-2; (i>2) && (iCntMaxima<20); i--)
+  {
+    if ( (piHistGrad[i-1] < 0) && (piHistGrad[i] > 0) )
+    {
+      piMaxPos[iCntMaxima] = i;
+      iCntMaxima++;
+    }
+  }
+
+  iIdx = 0;
+  int iSumAroundMax = 0;
+  for ( int i=0; i<iCntMaxima; i++ )
+  {
+    iIdx = piMaxPos[i];
+    iSumAroundMax = piHistSmooth[iIdx-1] + piHistSmooth[iIdx] + piHistSmooth[iIdx+1];
+    if ( iSumAroundMax < iMaxPix1 && iIdx < 64 )
+    {
+      for ( int j=i; j<iCntMaxima-1; j++ )
+      {
+        piMaxPos[j] = piMaxPos[j+1];
+      }
+      iCntMaxima--;
+      i--;
+    }
+  }
+  if ( iCntMaxima == 1)
+  {
+    iThresh = piMaxPos[0]/2;
+  }
+  else if ( iCntMaxima == 2)
+  {
+    iThresh = (piMaxPos[0] + piMaxPos[1])/2;
+  }
+  else // iCntMaxima >= 3
+  {
+    // CHECKING THRESHOLD FOR WHITE
+    int iIdxAccSum = 0, iAccum = 0;
+    for (int i=iNumBins-1; i>0; i--)
+    {
+      iAccum += piHistIntensity[i];
+      // iMaxPix/18 is about 5,5%, minimum required number of pixels required for white part of chessboard
+      if ( iAccum > (iMaxPix/18) )
+      {
+        iIdxAccSum = i;
+        break;
+      }
     }
 
-    cvSolve( xy, uv, affine_trans, CV_SVD );
+    int iIdxBGMax = 0;
+    int iBrightMax = piMaxPos[0];
+    // printf("iBrightMax = %d\n", iBrightMax);
+    for ( int n=0; n<iCntMaxima-1; n++)
+    {
+      iIdxBGMax = n+1;
+      if ( piMaxPos[n] < iIdxAccSum )
+      {
+        break;
+      }
+      iBrightMax = piMaxPos[n];
+    }
+
+    // CHECKING THRESHOLD FOR BLACK
+    int iMaxVal = piHistIntensity[piMaxPos[iIdxBGMax]];
+
+    //IF TOO CLOSE TO 255, jump to next maximum
+    if ( piMaxPos[iIdxBGMax] >= 250 && iIdxBGMax < iCntMaxima )
+    {
+      iIdxBGMax++;
+      iMaxVal = piHistIntensity[piMaxPos[iIdxBGMax]];
+    }
+
+    for ( int n=iIdxBGMax + 1; n<iCntMaxima; n++)
+    {
+      if ( piHistIntensity[piMaxPos[n]] >= iMaxVal )
+      {
+        iMaxVal = piHistIntensity[piMaxPos[n]];
+        iIdxBGMax = n;
+      }
+    }
+
+    //SETTING THRESHOLD FOR BINARIZATION
+    int iDist2 = (iBrightMax - piMaxPos[iIdxBGMax])/2;
+    iThresh = iBrightMax - iDist2;
+    PRINTF("THRESHOLD SELECTED = %d, BRIGHTMAX = %d, DARKMAX = %d\n", iThresh, iBrightMax, piMaxPos[iIdxBGMax]);
+  }
+
+
+  if ( iThresh > 0 )
+  {
+    for ( int jj=0; jj<iRows; jj++)
+    {
+      for ( int ii=0; ii<iCols; ii++)
+      {
+        if ( pucImg[jj*iCols+ii]< iThresh )
+          pucImg[jj*iCols+ii] = 0;
+        else
+          pucImg[jj*iCols+ii] = 255;
+      }
+    }
+  }
+
+  return true;
 }
-#endif
 
 CV_IMPL
 int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
@@ -232,6 +402,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
     int found = 0;
     CvCBQuad *quads = 0, **quad_group = 0;
     CvCBCorner *corners = 0, **corner_group = 0;
+    IplImage* cImgSeg = 0;
 
     try
     {
@@ -239,14 +410,14 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
     const int min_dilations = 0;
     const int max_dilations = 7;
     cv::Ptr<CvMat> norm_img, thresh_img;
-#ifdef DEBUG_CHESSBOARD
-    cv::Ptr<IplImage> dbg_img;
-    cv::Ptr<IplImage> dbg1_img;
-    cv::Ptr<IplImage> dbg2_img;
-#endif
     cv::Ptr<CvMemStorage> storage;
 
     CvMat stub, *img = (CvMat*)arr;
+    cImgSeg = cvCreateImage(cvGetSize(img), IPL_DEPTH_8U, 1 );
+    memcpy( cImgSeg->imageData, cvPtr1D( img, 0), img->rows*img->cols );
+
+    CvMat stub2, *thresh_img_new;
+    thresh_img_new = cvGetMat( cImgSeg, &stub2, 0, 0 );
 
     int expected_corners_num = (pattern_size.width/2+1)*(pattern_size.height/2+1);
 
@@ -255,7 +426,6 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
     if( out_corner_count )
         *out_corner_count = 0;
 
-    IplImage _img;
     int quad_count = 0, group_idx = 0, dilations = 0;
 
     img = cvGetMat( img, &stub );
@@ -273,12 +443,6 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
     storage.reset(cvCreateMemStorage(0));
     thresh_img.reset(cvCreateMat( img->rows, img->cols, CV_8UC1 ));
 
-#ifdef DEBUG_CHESSBOARD
-    dbg_img = cvCreateImage(cvGetSize(img), IPL_DEPTH_8U, 3 );
-    dbg1_img = cvCreateImage(cvGetSize(img), IPL_DEPTH_8U, 3 );
-    dbg2_img = cvCreateImage(cvGetSize(img), IPL_DEPTH_8U, 3 );
-#endif
-
     if( CV_MAT_CN(img->type) != 1 || (flags & CV_CALIB_CB_NORMALIZE_IMAGE) )
     {
         // equalize the input image histogram -
@@ -300,11 +464,19 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
 
     if( flags & CV_CALIB_CB_FAST_CHECK)
     {
-        cvGetImage(img, &_img);
+        //perform new method for checking chessboard using a binary image.
-        int check_chessboard_result = cvCheckChessboard(&_img, pattern_size);
+        //image is binarised using a threshold dependent on the image histogram
-        if(check_chessboard_result <= 0)
+        icvBinarizationHistogramBased( (unsigned char*) cImgSeg->imageData, cImgSeg->width, cImgSeg->height );
+        int check_chessboard_result = cvCheckChessboardBinary(cImgSeg, pattern_size);
+        if(check_chessboard_result <= 0) //fall back to the old method
         {
-            return 0;
+            IplImage _img;
+            cvGetImage(img, &_img);
+            check_chessboard_result = cvCheckChessboard(&_img, pattern_size);
+            if(check_chessboard_result <= 0)
+            {
+                return 0;
+            }
         }
     }
 
@@ -312,201 +484,238 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
     // This is necessary because some squares simply do not separate properly with a single dilation.  However,
     // we want to use the minimum number of dilations possible since dilations cause the squares to become smaller,
     // making it difficult to detect smaller squares.
-    for( k = 0; k < 6; k++ )
+    for( dilations = min_dilations; dilations <= max_dilations; dilations++ )
     {
+      if (found)
+        break;      // already found it
+
+      cvFree(&quads);
+      cvFree(&corners);
+
+      int max_quad_buf_size = 0;
+
+      //USE BINARY IMAGE COMPUTED USING icvBinarizationHistogramBased METHOD
+      cvDilate( thresh_img_new, thresh_img_new, 0, 1 );
+
+      // So we can find rectangles that go to the edge, we draw a white line around the image edge.
+      // Otherwise FindContours will miss those clipped rectangle contours.
+      // The border color will be the image mean, because otherwise we risk screwing up filters like cvSmooth()...
+      cvRectangle( thresh_img_new, cvPoint(0,0), cvPoint(thresh_img_new->cols-1, thresh_img_new->rows-1), CV_RGB(255,255,255), 3, 8);
+      quad_count = icvGenerateQuads( &quads, &corners, storage, thresh_img_new, flags, &max_quad_buf_size );
+      PRINTF("Quad count: %d/%d\n", quad_count, expected_corners_num);
+
+      if( quad_count <= 0 )
+      {
+        continue;
+      }
+
+      // Find quad's neighbors
+      icvFindQuadNeighbors( quads, quad_count );
+
+      // allocate extra for adding in icvOrderFoundQuads
+      cvFree(&quad_group);
+      cvFree(&corner_group);
+      quad_group = (CvCBQuad**)cvAlloc( sizeof(quad_group[0]) * max_quad_buf_size);
+      corner_group = (CvCBCorner**)cvAlloc( sizeof(corner_group[0]) * max_quad_buf_size * 4 );
+
+      for( group_idx = 0; ; group_idx++ )
+      {
+        int count = 0;
+        count = icvFindConnectedQuads( quads, quad_count, quad_group, group_idx, storage );
+
+        int icount = count;
+        if( count == 0 )
+            break;
+
+        // order the quad corners globally
+        // maybe delete or add some
+        PRINTF("Starting ordering of inner quads\n");
+        count = icvOrderFoundConnectedQuads(count, quad_group, &quad_count, &quads, &corners, pattern_size, max_quad_buf_size, storage );
+        PRINTF("Orig count: %d  After ordering: %d\n", icount, count);
+
+        if (count == 0)
+            continue; // haven't found inner quads
+
+        // If count is more than it should be, this will remove those quads
+        // which cause maximum deviation from a nice square pattern.
+        count = icvCleanFoundConnectedQuads( count, quad_group, pattern_size );
+        PRINTF("Connected group: %d  orig count: %d cleaned: %d\n", group_idx, icount, count);
+
+        count = icvCheckQuadGroup( quad_group, count, corner_group, pattern_size );
+        PRINTF("Connected group: %d  count: %d  cleaned: %d\n", group_idx, icount, count);
+
+        int n = count > 0 ? pattern_size.width * pattern_size.height : -count;
+        n = MIN( n, pattern_size.width * pattern_size.height );
+        float sum_dist = 0;
+        int total = 0;
+
+        for(int i = 0; i < n; i++ )
+        {
+          int ni = 0;
+          float avgi = corner_group[i]->meanDist(&ni);
+          sum_dist += avgi*ni;
+          total += ni;
+        }
+        prev_sqr_size = cvRound(sum_dist/MAX(total, 1));
+
+        if( count > 0 || (out_corner_count && -count > *out_corner_count) )
+        {
+          // copy corners to output array
+          for(int i = 0; i < n; i++ )
+              out_corners[i] = corner_group[i]->pt;
+
+          if( out_corner_count )
+              *out_corner_count = n;
+
+          if( count == pattern_size.width*pattern_size.height &&
+              icvCheckBoardMonotony( out_corners, pattern_size ))
+          {
+              found = 1;
+              break;
+          }
+        }
+      }
+    }//dilations
+
+    PRINTF("Chessboard detection result 0: %d\n", found);
+
+    // revert to old, slower, method if detection failed
+    if (!found)
+    {
+      PRINTF("Fallback to old algorithm\n");
+      // empiric threshold level
+      // thresholding performed here and not inside the cycle to save processing time
+      int thresh_level;
+      if ( !(flags & CV_CALIB_CB_ADAPTIVE_THRESH) )
+      {
+          double mean = cvAvg( img ).val[0];
+          thresh_level = cvRound( mean - 10 );
+          thresh_level = MAX( thresh_level, 10 );
+          cvThreshold( img, thresh_img, thresh_level, 255, CV_THRESH_BINARY );
+      }
+      for( k = 0; k < 6; k++ )
+      {
         int max_quad_buf_size = 0;
         for( dilations = min_dilations; dilations <= max_dilations; dilations++ )
         {
-            if (found)
+          if (found)
-                break;      // already found it
+            break;      // already found it
 
-            cvFree(&quads);
+          cvFree(&quads);
-            cvFree(&corners);
+          cvFree(&corners);
 
-            /*if( k == 1 )
+          // convert the input grayscale image to binary (black-n-white)
+          if( flags & CV_CALIB_CB_ADAPTIVE_THRESH )
+          {
+              int block_size = cvRound(prev_sqr_size == 0 ?
+                  MIN(img->cols,img->rows)*(k%2 == 0 ? 0.2 : 0.1): prev_sqr_size*2)|1;
+
+              // convert to binary
+              cvAdaptiveThreshold( img, thresh_img, 255,
+                  CV_ADAPTIVE_THRESH_MEAN_C, CV_THRESH_BINARY, block_size, (k/2)*5 );
+              if (dilations > 0)
+                  cvDilate( thresh_img, thresh_img, 0, dilations-1 );
+          }
+          //if flag CV_CALIB_CB_ADAPTIVE_THRESH is not set it doesn't make sense
+          //to iterate over k
+          else
+          {
+            k = 6;
+            cvDilate( thresh_img, thresh_img, 0, 1 );
+          }
+
+          // So we can find rectangles that go to the edge, we draw a white line around the image edge.
+          // Otherwise FindContours will miss those clipped rectangle contours.
+          // The border color will be the image mean, because otherwise we risk screwing up filters like cvSmooth()...
+          cvRectangle( thresh_img, cvPoint(0,0), cvPoint(thresh_img->cols-1,
+             thresh_img->rows-1), CV_RGB(255,255,255), 3, 8);
+
+          quad_count = icvGenerateQuads( &quads, &corners, storage, thresh_img, flags, &max_quad_buf_size);
+          PRINTF("Quad count: %d/%d\n", quad_count, expected_corners_num);
+
+          if( quad_count <= 0 )
+          {
+            continue;
+          }
+
+          // Find quad's neighbors
+          icvFindQuadNeighbors( quads, quad_count );
+
+          // allocate extra for adding in icvOrderFoundQuads
+          cvFree(&quad_group);
+          cvFree(&corner_group);
+          quad_group = (CvCBQuad**)cvAlloc( sizeof(quad_group[0]) * max_quad_buf_size);
+          corner_group = (CvCBCorner**)cvAlloc( sizeof(corner_group[0]) * max_quad_buf_size * 4 );
+
+          for( group_idx = 0; ; group_idx++ )
+          {
+            int count = 0;
+            count = icvFindConnectedQuads( quads, quad_count, quad_group, group_idx, storage );
+
+            int icount = count;
+            if( count == 0 )
+              break;
+
+            // order the quad corners globally
+            // maybe delete or add some
+            PRINTF("Starting ordering of inner quads\n");
+            count = icvOrderFoundConnectedQuads(count, quad_group, &quad_count, &quads, &corners, pattern_size, max_quad_buf_size, storage );
+
+            PRINTF("Orig count: %d  After ordering: %d\n", icount, count);
+
+            if (count == 0)
+              continue;       // haven't found inner quads
+
+
+            // If count is more than it should be, this will remove those quads
+            // which cause maximum deviation from a nice square pattern.
+            count = icvCleanFoundConnectedQuads( count, quad_group, pattern_size );
+            PRINTF("Connected group: %d  orig count: %d cleaned: %d\n", group_idx, icount, count);
+
+            count = icvCheckQuadGroup( quad_group, count, corner_group, pattern_size );
+            PRINTF("Connected group: %d  count: %d  cleaned: %d\n", group_idx, icount, count);
+
+            int n = count > 0 ? pattern_size.width * pattern_size.height : -count;
+            n = MIN( n, pattern_size.width * pattern_size.height );
+            float sum_dist = 0;
+            int total = 0;
+
+            for(int i = 0; i < n; i++ )
             {
-                //Pattern was not found using binarization
+              int ni = 0;
-                // Run multi-level quads extraction
+              float avgi = corner_group[i]->meanDist(&ni);
-                // In case one-level binarization did not give enough number of quads
+              sum_dist += avgi*ni;
-                CV_CALL( quad_count = icvGenerateQuadsEx( &quads, &corners, storage, img, thresh_img, dilations, flags ));
+              total += ni;
-                PRINTF("EX quad count: %d/%d\n", quad_count, expected_corners_num);
             }
-            else*/
+            prev_sqr_size = cvRound(sum_dist/MAX(total, 1));
+
+            if( count > 0 || (out_corner_count && -count > *out_corner_count) )
             {
-                // convert the input grayscale image to binary (black-n-white)
+              // copy corners to output array
-                if( flags & CV_CALIB_CB_ADAPTIVE_THRESH )
+              for(int i = 0; i < n; i++ )
-                {
+                out_corners[i] = corner_group[i]->pt;
-                    int block_size = cvRound(prev_sqr_size == 0 ?
-                        MIN(img->cols,img->rows)*(k%2 == 0 ? 0.2 : 0.1): prev_sqr_size*2)|1;
 
-                    // convert to binary
+              if( out_corner_count )
-                    cvAdaptiveThreshold( img, thresh_img, 255,
+                *out_corner_count = n;
-                        CV_ADAPTIVE_THRESH_MEAN_C, CV_THRESH_BINARY, block_size, (k/2)*5 );
-                    if (dilations > 0)
-                        cvDilate( thresh_img, thresh_img, 0, dilations-1 );
-                }
-                else
-                {
-                    // Make dilation before the thresholding.
-                    // It splits chessboard corners
-                    //cvDilate( img, thresh_img, 0, 1 );
 
-                    // empiric threshold level
+              if( count == pattern_size.width*pattern_size.height && icvCheckBoardMonotony( out_corners, pattern_size ))
-                    double mean = cvAvg( img ).val[0];
+              {
-                    int thresh_level = cvRound( mean - 10 );
+                found = 1;
-                    thresh_level = MAX( thresh_level, 10 );
+                break;
-
+              }
-                    cvThreshold( img, thresh_img, thresh_level, 255, CV_THRESH_BINARY );
-                    cvDilate( thresh_img, thresh_img, 0, dilations );
-                }
-
-#ifdef DEBUG_CHESSBOARD
-                cvCvtColor(thresh_img,dbg_img,CV_GRAY2BGR);
-#endif
-
-                // So we can find rectangles that go to the edge, we draw a white line around the image edge.
-                // Otherwise FindContours will miss those clipped rectangle contours.
-                // The border color will be the image mean, because otherwise we risk screwing up filters like cvSmooth()...
-                cvRectangle( thresh_img, cvPoint(0,0), cvPoint(thresh_img->cols-1,
-                    thresh_img->rows-1), CV_RGB(255,255,255), 3, 8);
-
-                quad_count = icvGenerateQuads( &quads, &corners, storage, thresh_img, flags, &max_quad_buf_size);
-
-                PRINTF("Quad count: %d/%d\n", quad_count, expected_corners_num);
-            }
-
-
-#ifdef DEBUG_CHESSBOARD
-            cvCopy(dbg_img, dbg1_img);
-            cvNamedWindow("all_quads", 1);
-            // copy corners to temp array
-            for(int i = 0; i < quad_count; i++ )
-            {
-                for (int k=0; k<4; k++)
-                {
-                    CvPoint2D32f pt1, pt2;
-                    CvScalar color = CV_RGB(30,255,30);
-                    pt1 = quads[i].corners[k]->pt;
-                    pt2 = quads[i].corners[(k+1)%4]->pt;
-                    pt2.x = (pt1.x + pt2.x)/2;
-                    pt2.y = (pt1.y + pt2.y)/2;
-                    if (k>0)
-                        color = CV_RGB(200,200,0);
-                    cvLine( dbg1_img, cvPointFrom32f(pt1), cvPointFrom32f(pt2), color, 3, 8);
-                }
-            }
-
-
-            cvShowImage("all_quads", (IplImage*)dbg1_img);
-            cvWaitKey();
-#endif
-
-            if( quad_count <= 0 )
-                continue;
-
-            // Find quad's neighbors
-            icvFindQuadNeighbors( quads, quad_count );
-
-            // allocate extra for adding in icvOrderFoundQuads
-            cvFree(&quad_group);
-            cvFree(&corner_group);
-            quad_group = (CvCBQuad**)cvAlloc( sizeof(quad_group[0]) * max_quad_buf_size);
-            corner_group = (CvCBCorner**)cvAlloc( sizeof(corner_group[0]) * max_quad_buf_size * 4 );
-
-            for( group_idx = 0; ; group_idx++ )
-            {
-                int count = 0;
-                count = icvFindConnectedQuads( quads, quad_count, quad_group, group_idx, storage );
-
-                int icount = count;
-                if( count == 0 )
-                    break;
-
-                // order the quad corners globally
-                // maybe delete or add some
-                PRINTF("Starting ordering of inner quads\n");
-                count = icvOrderFoundConnectedQuads(count, quad_group, &quad_count, &quads, &corners,
-                    pattern_size, max_quad_buf_size, storage );
-                PRINTF("Orig count: %d  After ordering: %d\n", icount, count);
-
-
-#ifdef DEBUG_CHESSBOARD
-                cvCopy(dbg_img,dbg2_img);
-                cvNamedWindow("connected_group", 1);
-                // copy corners to temp array
-                for(int i = 0; i < quad_count; i++ )
-                {
-                    if (quads[i].group_idx == group_idx)
-                        for (int k=0; k<4; k++)
-                        {
-                            CvPoint2D32f pt1, pt2;
-                            CvScalar color = CV_RGB(30,255,30);
-                            if (quads[i].ordered)
-                                color = CV_RGB(255,30,30);
-                            pt1 = quads[i].corners[k]->pt;
-                            pt2 = quads[i].corners[(k+1)%4]->pt;
-                            pt2.x = (pt1.x + pt2.x)/2;
-                            pt2.y = (pt1.y + pt2.y)/2;
-                            if (k>0)
-                                color = CV_RGB(200,200,0);
-                            cvLine( dbg2_img, cvPointFrom32f(pt1), cvPointFrom32f(pt2), color, 3, 8);
-                        }
-                }
-                cvShowImage("connected_group", (IplImage*)dbg2_img);
-                cvWaitKey();
-#endif
-
-                if (count == 0)
-                    continue;       // haven't found inner quads
-
-
-                // If count is more than it should be, this will remove those quads
-                // which cause maximum deviation from a nice square pattern.
-                count = icvCleanFoundConnectedQuads( count, quad_group, pattern_size );
-                PRINTF("Connected group: %d  orig count: %d cleaned: %d\n", group_idx, icount, count);
-
-                count = icvCheckQuadGroup( quad_group, count, corner_group, pattern_size );
-                PRINTF("Connected group: %d  count: %d  cleaned: %d\n", group_idx, icount, count);
-
-                {
-                int n = count > 0 ? pattern_size.width * pattern_size.height : -count;
-                n = MIN( n, pattern_size.width * pattern_size.height );
-                float sum_dist = 0;
-                int total = 0;
-
-                for(int i = 0; i < n; i++ )
-                {
-                    int ni = 0;
-                    float avgi = corner_group[i]->meanDist(&ni);
-                    sum_dist += avgi*ni;
-                    total += ni;
-                }
-                prev_sqr_size = cvRound(sum_dist/MAX(total, 1));
-
-                if( count > 0 || (out_corner_count && -count > *out_corner_count) )
-                {
-                    // copy corners to output array
-                    for(int i = 0; i < n; i++ )
-                        out_corners[i] = corner_group[i]->pt;
-
-                    if( out_corner_count )
-                        *out_corner_count = n;
-
-                    if( count == pattern_size.width*pattern_size.height &&
-                        icvCheckBoardMonotony( out_corners, pattern_size ))
-                    {
-                        found = 1;
-                        break;
-                    }
-                }
-                }
             }
+          }
         }//dilations
-    }//
+      }// for k = 0 -> 6
+    }
+
+    PRINTF("Chessboard detection result 1: %d\n", found);
 
     if( found )
         found = icvCheckBoardMonotony( out_corners, pattern_size );
 
+    PRINTF("Chessboard detection result 2: %d\n", found);
+
     // check that none of the found corners is too close to the image boundary
     if( found )
     {
@@ -521,36 +730,38 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
         found = k == pattern_size.width*pattern_size.height;
     }
 
-    if( found && pattern_size.height % 2 == 0 && pattern_size.width % 2 == 0 )
+    PRINTF("Chessboard detection result 3: %d\n", found);
+
+    if( found )
     {
+      if ( pattern_size.height % 2 == 0 && pattern_size.width % 2 == 0 )
+      {
         int last_row = (pattern_size.height-1)*pattern_size.width;
         double dy0 = out_corners[last_row].y - out_corners[0].y;
         if( dy0 < 0 )
         {
-            int n = pattern_size.width*pattern_size.height;
+          int n = pattern_size.width*pattern_size.height;
-            for(int i = 0; i < n/2; i++ )
+          for(int i = 0; i < n/2; i++ )
-            {
+          {
-                CvPoint2D32f temp;
+            CvPoint2D32f temp;
-                CV_SWAP(out_corners[i], out_corners[n-i-1], temp);
+            CV_SWAP(out_corners[i], out_corners[n-i-1], temp);
-            }
+          }
         }
-    }
+      }
-
+      cv::Ptr<CvMat> gray;
-    if( found )
+      if( CV_MAT_CN(img->type) != 1 )
-    {
+      {
-        cv::Ptr<CvMat> gray;
+        gray.reset(cvCreateMat(img->rows, img->cols, CV_8UC1));
-        if( CV_MAT_CN(img->type) != 1 )
+        cvCvtColor(img, gray, CV_BGR2GRAY);
-        {
+      }
-            gray.reset(cvCreateMat(img->rows, img->cols, CV_8UC1));
+      else
-            cvCvtColor(img, gray, CV_BGR2GRAY);
+      {
-        }
+        gray.reset(cvCloneMat(img));
-        else
+      }
-        {
+      int wsize = 2;
-            gray.reset(cvCloneMat(img));
+      cvFindCornerSubPix( gray, out_corners, pattern_size.width*pattern_size.height,
-        }
+                          cvSize(wsize, wsize), cvSize(-1,-1),
-        int wsize = 2;
+                          cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 15, 0.1));
-        cvFindCornerSubPix( gray, out_corners, pattern_size.width*pattern_size.height,
-            cvSize(wsize, wsize), cvSize(-1,-1), cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 15, 0.1));
     }
     }
     catch(...)
@@ -559,6 +770,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
         cvFree(&corners);
         cvFree(&quad_group);
         cvFree(&corner_group);
+        cvFree(&cImgSeg);
         throw;
     }
 
@@ -566,6 +778,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
     cvFree(&corners);
     cvFree(&quad_group);
     cvFree(&corner_group);
+    cvFree(&cImgSeg);
     return found;
 }
 

modules/calib3d/src/calibration.cpp

@@ -2345,8 +2345,8 @@ void cvStereoRectify( const CvMat* _cameraMatrix1, const CvMat* _cameraMatrix2,
         for( i = 0; i < 4; i++ )
         {
             int j = (i<2) ? 0 : 1;
-            _pts[i].x = (float)((i % 2)*(nx-1));
+            _pts[i].x = (float)((i % 2)*(nx));
-            _pts[i].y = (float)(j*(ny-1));
+            _pts[i].y = (float)(j*(ny));
         }
         cvUndistortPoints( &pts, &pts, A, Dk, 0, 0 );
         cvConvertPointsHomogeneous( &pts, &pts_3 );
@@ -2360,8 +2360,8 @@ void cvStereoRectify( const CvMat* _cameraMatrix1, const CvMat* _cameraMatrix2,
         _a_tmp[1][2]=0.0;
         cvProjectPoints2( &pts_3, k == 0 ? _R1 : _R2, &Z, &A_tmp, 0, &pts );
         CvScalar avg = cvAvg(&pts);
-        cc_new[k].x = (nx-1)/2 - avg.val[0];
+        cc_new[k].x = (nx)/2 - avg.val[0];
-        cc_new[k].y = (ny-1)/2 - avg.val[1];
+        cc_new[k].y = (ny)/2 - avg.val[1];
     }
 
     // vertical focal length must be the same for both images to keep the epipolar constraint

modules/calib3d/src/checkchessboard.cpp

@@ -57,6 +57,8 @@
 #  endif
 #endif
 
+int cvCheckChessboardBinary(IplImage* src, CvSize size);
+
 static void icvGetQuadrangleHypotheses(CvSeq* contours, std::vector<std::pair<float, int> >& quads, int class_id)
 {
     const float min_aspect_ratio = 0.3f;
@@ -205,3 +207,97 @@ int cvCheckChessboard(IplImage* src, CvSize size)
 
     return result;
 }
+
+// does a fast check if a chessboard is in the input image. This is a workaround to
+// a problem of cvFindChessboardCorners being slow on images with no chessboard
+// - src: input binary image
+// - size: chessboard size
+// Returns 1 if a chessboard can be in this image and findChessboardCorners should be called,
+// 0 if there is no chessboard, -1 in case of error
+int cvCheckChessboardBinary(IplImage* src, CvSize size)
+{
+    if(src->nChannels > 1)
+    {
+        cvError(CV_BadNumChannels, "cvCheckChessboard", "supports single-channel images only",
+                __FILE__, __LINE__);
+    }
+
+    if(src->depth != 8)
+    {
+        cvError(CV_BadDepth, "cvCheckChessboard", "supports depth=8 images only",
+                __FILE__, __LINE__);
+    }
+
+    CvMemStorage* storage = cvCreateMemStorage();
+
+    IplImage* white = cvCloneImage(src);
+    IplImage* black = cvCloneImage(src);
+    IplImage* thresh = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1);
+
+    int result = 0;
+
+    for ( int erosion_count = 0; erosion_count <= 3; erosion_count++ )
+    {
+      if ( 1 == result )
+        break;
+
+      if ( 0 != erosion_count ) // first iteration keeps original images
+      {
+        cvErode(white, white, NULL, 1);
+        cvDilate(black, black, NULL, 1);
+      }
+
+      cvThreshold(white, thresh, 128, 255, CV_THRESH_BINARY);
+
+      CvSeq* first = 0;
+      std::vector<std::pair<float, int> > quads;
+      cvFindContours(thresh, storage, &first, sizeof(CvContour), CV_RETR_CCOMP);
+      icvGetQuadrangleHypotheses(first, quads, 1);
+
+      cvThreshold(black, thresh, 128, 255, CV_THRESH_BINARY_INV);
+      cvFindContours(thresh, storage, &first, sizeof(CvContour), CV_RETR_CCOMP);
+      icvGetQuadrangleHypotheses(first, quads, 0);
+
+      const size_t min_quads_count = size.width*size.height/2;
+      std::sort(quads.begin(), quads.end(), less_pred);
+
+      // now check if there are many hypotheses with similar sizes
+      // do this by floodfill-style algorithm
+      const float size_rel_dev = 0.4f;
+
+      for(size_t i = 0; i < quads.size(); i++)
+      {
+          size_t j = i + 1;
+          for(; j < quads.size(); j++)
+          {
+              if(quads[j].first/quads[i].first > 1.0f + size_rel_dev)
+              {
+                  break;
+              }
+          }
+
+          if(j + 1 > min_quads_count + i)
+          {
+              // check the number of black and white squares
+              std::vector<int> counts;
+              countClasses(quads, i, j, counts);
+              const int black_count = cvRound(ceil(size.width/2.0)*ceil(size.height/2.0));
+              const int white_count = cvRound(floor(size.width/2.0)*floor(size.height/2.0));
+              if(counts[0] < black_count*0.75 ||
+                 counts[1] < white_count*0.75)
+              {
+                continue;
+              }
+              result = 1;
+              break;
+          }
+      }
+    }
+
+    cvReleaseImage(&thresh);
+    cvReleaseImage(&white);
+    cvReleaseImage(&black);
+    cvReleaseMemStorage(&storage);
+
+    return result;
+}

modules/calib3d/src/solvepnp.cpp

@@ -158,7 +158,7 @@ public:
           rvec(_rvec), tvec(_tvec) {}
 
     /* Pre: True */
-    /* Post: compute _model with given points an return number of found models */
+    /* Post: compute _model with given points and return number of found models */
     int runKernel( InputArray _m1, InputArray _m2, OutputArray _model ) const
     {
         Mat opoints = _m1.getMat(), ipoints = _m2.getMat();

modules/calib3d/test/test_chesscorners_timing.cpp

@@ -113,11 +113,7 @@ void CV_ChessboardDetectorTimingTest::run( int start_from )
         if( img2.empty() )
         {
             ts->printf( cvtest::TS::LOG, "one of chessboard images can't be read: %s\n", filename.c_str() );
-            if( max_idx == 1 )
+            code = cvtest::TS::FAIL_MISSING_TEST_DATA;
-            {
-                code = cvtest::TS::FAIL_MISSING_TEST_DATA;
-                goto _exit_;
-            }
             continue;
         }