Merge pull request opencv#12147 from D-Alex:master

* add new chessboard detector

The chessboar detector is based on the paper.
Accurate Detection and Localization of Checkerboard Corners for
Calibration Alexander Duda, Udo Frese
British Machine Vision Conference, o.A., 2018.

It utilizes point symmetry of checkerboard corners in combination with a
localized Radon transform approximated by box filters to achieve high
performance even on large images. Here, tests have shown that the
ability to localize checkerboard corners is close to the theoretical
limit of 1/100 of a pixel while being considerably less sensitive
to image noise than standard methods.

* chessboard: add reference to bibtex file

* chessboard: add dependency to opencv_flann

* fix: test chesscorners. It is valid to return an empty list

In case no chessboard was detected it should be valid for the detector
to return an empty list.

For simplifcation, it should be allowed to return any number of corners
if they are flagged as not found.

* fix: opencv.bib remove empty lines

* fix: doc findChessboardCorners replace cvSize with cv::Size

* chessboard tests: factor out logic selecting detector

* chessboard: add unit test for findChessboardCorners2

This is includes a new chessboard generator which supports subpix
corners with high accuracy by wrapping an optimal chessboard using
wrapPerspective.

* fix: chessboard unit test - overwrite of default parameter flag of findCirclesGrid

* chessboard: remove trailing whitespace

* chessboard: fix debug drawing

* chessboard: fix some issues during code review

* chessboard: normalize asymmetric chessboard

* chessboard: fix float double warning

* remove trailing whitespace

* chessboards: fix compiler warnings

* chessboards: fix compiler warnings

* checkerboard: some performance improvements

* chessboard: remove NULL macros for language bindinges from internal headers

* chessboard: shorten license terms

* chessboard: remove unused internal method

* chessboard: set helper functions to static

* chessboard: fix normalizePoints1D using unshifted points

* chessboard: remove wrongly copied text

* chessboard: use CV_CheckTypeEQ macro

* chessboard: comment all NaN checks

* chessboard: use consistent color conversion

* chessboard: use CheckChannelEQ macro

* chessboard: assume gray color image for internal methods

* chessboard: use std::swap

* chessboard: use Mat.dataend

* chessboard: fix compiler warnings

* chessboard: replace some checks witch CV_CHECK macro

* chessboard: fix comparison function for partial sort

* chessboard: small cleanup

* chessboard: use short license header

* chessboard: rename findChessboard2 to findChessboardSB

* chessboard: fix type in unit test

Author: D-Alex

doc/opencv.bib

@@ -1016,17 +1016,22 @@ @INPROCEEDINGS{Ke17
   year = {2017},
   organization = {IEEE}
 }
-
 @ARTICLE{gonzalez,
   title={Digital Image Fundamentals, Digital Imaging Processing},
   author={Gonzalez, Rafael C and others},
   year={1987},
   publisher={Addison Wesley Publishing Company}
 }
-
 @ARTICLE{gruzman,
   title={Цифровая обработка изображений в информационных системах},
   author={Грузман, И.С. and Киричук, В.С. and Косых, В.П. and Перетягин, Г.И. and Спектор, А.А.},
   year={2000},
   publisher={Изд-во НГТУ Новосибирск}
 }
+@INPROCEEDINGS{duda2018,
+    title = {Accurate Detection and Localization of Checkerboard Corners for Calibration},
+    year = {2018},
+    booktitle = {29th British Machine Vision Conference. British Machine Vision Conference (BMVC-29), September 3-6, Newcastle, United Kingdom},
+    publisher = {BMVA Press},
+    author = {Alexander Duda and Udo Frese},
+}

modules/calib3d/CMakeLists.txt

@@ -1,2 +1,2 @@
 set(the_description "Camera Calibration and 3D Reconstruction")
-ocv_define_module(calib3d opencv_imgproc opencv_features2d WRAP java python)
+ocv_define_module(calib3d opencv_imgproc opencv_features2d opencv_flann WRAP java python)

modules/calib3d/doc/pics/checkerboard_radon.png

@@ -793,7 +793,7 @@ CV_EXPORTS_W Mat initCameraMatrix2D( InputArrayOfArrays objectPoints,
 
 @param image Source chessboard view. It must be an 8-bit grayscale or color image.
 @param patternSize Number of inner corners per a chessboard row and column
-( patternSize = cvSize(points_per_row,points_per_colum) = cvSize(columns,rows) ).
+( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).
 @param corners Output array of detected corners.
 @param flags Various operation flags that can be zero or a combination of the following values:
 -   **CALIB_CB_ADAPTIVE_THRESH** Use adaptive thresholding to convert the image to black
@@ -841,6 +841,34 @@ square grouping and ordering algorithm fails.
 CV_EXPORTS_W bool findChessboardCorners( InputArray image, Size patternSize, OutputArray corners,
                                          int flags = CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE );
 
+/** @brief Finds the positions of internal corners of the chessboard using a sector based approach.
+
+@param image Source chessboard view. It must be an 8-bit grayscale or color image.
+@param patternSize Number of inner corners per a chessboard row and column
+( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).
+@param corners Output array of detected corners.
+@param flags operation flags for future improvements
+
+The function is analog to findchessboardCorners but uses a localized radon
+transformation approximated by box filters being more robust to all sort of
+noise, faster on larger images and is able to directly return the sub-pixel
+position of the internal chessboard corners. The Method is based on the paper
+@cite duda2018 "Accurate Detection and Localization of Checkerboard Corners for
+Calibration" demonstrating that the returned sub-pixel positions are more
+accurate than the one returned by cornerSubPix allowing a precise camera
+calibration for demanding applications.
+
+@note The function requires a white boarder with roughly the same width as one
+of the checkerboard fields around the whole board to improve the detection in
+various environments. In addition, because of the localized radon
+transformation it is beneficial to use round corners for the field corners
+which are located on the outside of the board. The following figure illustrates
+a sample checkerboard optimized for the detection. However, any other checkerboard
+can be used as well.
+![Checkerboard](pics/checkerboard_radon.png)
+ */
+CV_EXPORTS_W bool findChessboardCornersSB(InputArray image,Size patternSize, OutputArray corners,int flags=0);
+
 //! finds subpixel-accurate positions of the chessboard corners
 CV_EXPORTS bool find4QuadCornerSubpix( InputArray img, InputOutputArray corners, Size region_size );