- cv::at => cv::Mat::at()
- removed unnecessary @ref

More accurate pinhole camera calibration with imperfect planar target (#12772)
43 commits:

* Add derivatives with respect to object points

Add an output parameter to calculate derivatives of image points with
respect to 3D coordinates of object points. The output jacobian matrix
is a 2Nx3N matrix where N is the number of points.

This commit introduces incompatibility to old function signature.

* Set zero for dpdo matrix before using

dpdo is a sparse matrix with only non-zero value close to major
diagonal. Set it to zero because only elements near major diagonal are
computed.

* Add jacobian columns to projectPoints()

The output jacobian matrix of derivatives with respect to coordinates of
3D object points are added. This might break callers who assume the
columns of jacobian matrix.

* Adapt test code to updated project functions

The test cases for projectPoints() and cvProjectPoints2() are updated to
fit new function signatures.

* Add accuracy test code for dpdo

* Add badarg test for dpdo

* Add new enum item for new calibration method

CALIB_RELEASE_OBJECT is used to whether to release 3D coordinates of
object points. The method was proposed in: K. H. Strobl and G. Hirzinger.
"More Accurate Pinhole Camera Calibration with Imperfect Planar Target".
In Proceedings of the IEEE International Conference on Computer Vision
(ICCV 2011), 1st IEEE Workshop on Challenges and Opportunities in Robot
Perception, Barcelona, Spain, pp. 1068-1075, November 2011.

* Add releasing object method into internal function

It's a simple extension of the standard calibration scheme. We choose to
fix the first and last object point and a user-selected fixed point.

* Add interfaces for extended calibration method

* Refine document for calibrateCamera()

When releasing object points, only the z coordinates of the
objectPoints[0].back is fixed.

* Add link to strobl2011iccv paper

* Improve documentation for calibrateCamera()

* Add implementations of wrapping calibrateCamera()

* Add checking for params of new calibration method

If input parameters are not qualified, then fall back to standard
calibration method.

* Add camera calibration method of releasing object

The current implementation is equal to or better than
https://github.com/xoox/calibrel

* Update doc for CALIB_RELEASE_OBJECT

CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with
potentially less precise and less stable in some rare cases.

* Add RELEASE_OBJECT calibration to tutorial code

To select the calibration method of releasing object points, a command
line parameter `-d=<number>` should be provided.

* Update tutorial doc for camera_calibration

If the method of releasing object points is merged into OpenCV. It will
be expected to be firstly released in 4.1, I think.

* Reduce epsilon for cornerSubPix()

Epsilon of 0.1 is a bigger one. Preciser corner positions are required
with calibration method of releasing object.

* Refine camera calibration tutorial

The hypothesis coordinates are used to indicate which distance must be
measured between two specified object points.

* Update sample calibration code method selection

Similar to camera_calibration tutorial application, a command line
argument `-dt=<number>` is used to select the calibration method.

* Add guard to flags of cvCalibrateCamera2()

cvCalibrateCamera2() doesn't accept CALIB_RELEASE_OBJECT unless overload
interface is added in the future.

* Simplify fallback when iFixedPoint is out of range

* Refactor projectPoints() to keep compatibilities

* Fix arg string "Bad rvecs header"

* Read calibration flags from test data files

Instead of being hard coded into source file, the calibration flags will
be read from test data files.
opencv_extra/testdata/cv/cameracalibration/calib?.dat must be sync with
the test code.

* Add new C interface of cvCalibrateCamera4()

With this new added C interface, the extended calibration method with
CALIB_RELEASE_OBJECT can be called by C API.

* Add regression test of extended calibration method

It has been tested with new added test data in xoox:calib-release-object
branch of opencv_extra.

* Fix assertion in test_cameracalibration.cpp

The total number of refined 3D object coordinates is checked.

* Add checker for iFixedPoint in cvCalibrateCamera4

If iFixedPoint is out of rational range, fall back to standard method.

* Fix documentation for overloaded calibrateCamera()

* Remove calibration flag of CALIB_RELEASE_OBJECT

The method selection is based on the range of the index of fixed point.
For minus values, standard calibration method will be chosen.  Values in
a rational range will make the object-releasing calibration method
selected.

* Use new interfaces instead of function overload

Existing interfaces are preserved and new interfaces are added. Since
most part of the code base are shared, calibrateCamera() is now a
wrapper function of calibrateCameraRO().

* Fix exported name of calibrateCameraRO()

* Update documentation for calibrateCameraRO()

The circumstances where this method is mostly helpful are described.

* Add note on the rigidity of the calibration target

* Update documentation for calibrateCameraRO()

It is clarified that iFixedPoint is used as a switch to select
calibration method. If input data are not qualified, exceptions will be
thrown instead of fallback scheme.

* Clarify iFixedPoint as switch and remove fallback

iFixedPoint is now used as a switch for calibration method selection. No
fallback scheme is utilized anymore. If the input data are not
qualified, exceptions will be thrown.

* Add badarg test for object-releasing method

* Fix document format of sample list

List items of same level should be indented the same way. Otherwise they
will be formatted as nested lists by Doxygen.

* Add brief intro for objectPoints and imagePoints

* Sync tutorial to sample calibration code

* Update tutorial compatibility version to 4.0

find innercircle of contour by using pointPolygonTest: (#12206)

* doc: add new tutorial motion deblur filter

* Update motion_deblur_filter.markdown

a few minor changes

* doc: fix English gramma in tutorial out-of-focus-deblur filter

* Update out_of_focus_deblur_filter.markdown

slightly modified one sentence

except header file with conversion routines (will be removed in OpenCV 4.0)

* Added a mall note describing BUILD_opencv_world cmake option to the Installation in Windows tutorial.

* Made slight changes in BUILD_opencv_world documentation.

* Update windows_install.markdown

improved grammar

OpenCV 3.4.3

Found via `codespell -q 3 -I ../opencv-whitelist.txt --skip="./3rdparty"`

In this tutorial you will learn:
- what is a degradation image model
- what is a PSF of an out-of-focus image
- how to restore a blurred image
- what is the Wiener filter

      - Changing the contrast and brightness of an image!
      - Operations with images

Add Java and Python code for AKAZE local features matching tutorial. Fix incorrect uses of Mat.mul() in Java code.

Uniform Lowe's ratio test in the code.

Add Java and Python code for Image Segmentation with Distance Transform and Watershed Algorithm tutorial. Use more Pythonic code.

Add Java and Python code for the following imgproc tutorials: Finding contours in your image, Convex Hull, Creating Bounding boxes and circles for contours, Creating Bounding rotated boxes and ellipses for contours, Image Moments, Point Polygon Test.

OpenCV 3.4.2

Add LD_PRELOAD instruction to be able to run Java code that calls OpenCV functions that use the Intel MKL library.

Other links:
- https://raw.githubusercontent.com/opencv/opencv/master
- https://github.com/opencv/opencv/blob/master

Add Java and Python code for the following features2d tutorials: Harris corner detector, Shi-Tomasi corner detector, Creating your own corner detector, Detecting corners location in subpixels, Feature Detection, Feature Description, Feature Matching with FLANN, Features2D + Homography to find a known object. Use Lowe's ratio test to filter the matches.

Add Java and Python code for the following imgproc tutorials: Affine Transformations, Histogram Equalization, Histogram Calculation, Histogram Comparison, Back Projection.

Add Java and Python code for the following imgproc tutorials: Canny, Remap, threshold and threshold inRange. Use HSV colorspace instead of RGB for inRange threshold tutorial.