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

Mser sample improvments (#12032)

* Fixed bug in detect_mser sample
Wrong number of colors used to generate the synthetic images

* Formatting improvements

* Using safer casts

* Improved readability of legend generation

* Various readability fixes in detect_mser sample

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

Not all backends support -1 index.

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.

- show capturing information: width / height / fps
- show average FPS for cap.read()+imshow() via cv::getTickCount()
- optional frame processing code path

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.

instead of an ad-hoc implementation of it

* Custom deep learning layers support

* Stack custom deep learning layers

* Rewrite polar transformations

- A new wrapPolar function encapsulate both linear and semi-log remap
- Destination size is a parameter or calculated automatically to keep objects size between remapping
- linearPolar and logPolar has been deprecated

* Fix build warning and error in accuracy test

* Fix function name to warpPolar

* Explicitly specify the mapping mode, so we retain all the parameters as non-optional.

Introduces WarpPolarMode enum to specify the mapping mode in flags

* resolves performance warning on windows build

* removed duplicated logPolar and linearPolar implementations

In line 104 `if ( full_neg_lst[i].cols >= box.width && full_neg_lst[i].rows >= box.height )` removed '=' as it causes divide By Zero Error in line 106 and 107 `box.x = rand() % ( full_neg_lst[i].cols - size_x );` when  full_neg_lst[i].cols = size_x or full_neg_lst[i].rows - size_y

- CV_RGB() macro is moved into opencv2/imgproc.hpp from imgproc_c.h
- samples/cpp/filestorage_base64.cpp is dropped

also add comments explaining the change

- bgfg_segm
- peopledetect
- opencv_version
- dnn/colorization
- tapi/opencl_custom_kernel
- tapi/dense_optical_flow (renamed tvl1_optical_flow)

- bgfg_segm
- peopledetect
- opencv_version
- dnn/colorization
- tapi/opencl_custom_kernel
- tapi/dense_optical_flow (renamed tvl1_optical_flow)