Merge branch 4.x

modules/calib/include/opencv2/calib.hpp

@@ -424,6 +424,9 @@ f_{\text{mm}} = \frac{\text{sensor_size_in_mm}}{2 \times \tan{\frac{\text{fov}}{
 \f]
 This latter conversion can be useful when using a rendering software to mimic a physical camera device.
 
+@note
+    -    See also #calibrationMatrixValues
+
 <B> Additional references, notes </B><br>
 @note
     -   Many functions in this module take a camera intrinsic matrix as an input parameter. Although all
@@ -626,7 +629,8 @@ the board to make the detection more robust in various environments. Otherwise,
 border and the background is dark, the outer black squares cannot be segmented properly and so the
 square grouping and ordering algorithm fails.
 
-Use gen_pattern.py (@ref tutorial_camera_calibration_pattern) to create checkerboard.
+Use the `gen_pattern.py` Python script (@ref tutorial_camera_calibration_pattern)
+to create the desired checkerboard pattern.
  */
 CV_EXPORTS_W bool findChessboardCorners( InputArray image, Size patternSize, OutputArray corners,
                                          int flags = CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE );
@@ -684,8 +688,9 @@ 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.
 
-Use gen_pattern.py (@ref tutorial_camera_calibration_pattern) to create checkerboard.
-![Checkerboard](pics/checkerboard_radon.png)
+Use the `gen_pattern.py` Python script (@ref tutorial_camera_calibration_pattern)
+to create the corresponding checkerboard pattern:
+\image html pics/checkerboard_radon.png width=60%
  */
 CV_EXPORTS_AS(findChessboardCornersSBWithMeta)
 bool findChessboardCornersSB(InputArray image,Size patternSize, OutputArray corners,