- avoid overflow in own::Mat::total() and according tests, part 2

- avoid overflow in own::Mat::total() and according tests

Problem is that iter1 += tcols, iter2 += tcols are called without check.
row < trows - 1 check is processed after increment in "for" statements.

- replaced bitwise &= on bools, with explicit && to make KW happy

- replace "unsafe" memcpy with "safe" :) std::copy_n to make KW happy

- cache value, evaluate once
- better support for MINGW
- anything in 'cv' namespace
- test: dump number of active threads

- FFmpeg 3.4.7

* feature: Add video capture bitrate read-only property for FFMPEG backend

* test: For WIN32 property should be either expected or 0.

Added `IsOneOf` helper function, enabled only for _WIN32.

- Android Manager version is not increased (stuck on 3.49)

G-API: Zero-height mat is cause of crash

* Added check for zero-height Mat case

* Refactoring, added validate_input_arg func

* No bool function now

dnn(darknet-importer): add grouped convolutions, sigmoid, swish, scale_channels

* update darknet importer to support enetb0-yolo

* remove dropout (pr16438) and fix formatting

* add test for scale_channels

* disable batch testing for scale channels

* do not set LayerParams::name

* merge all activations into setActivation

* Add Tengine support .

* Modify printf to CV_LOG_WARNING

* a few minor fixes in the code

* Renew Tengine version

* Add header file for CV_LOG_WARNING

* Add #ifdef HAVE_TENGINE in tengine_graph_convolution.cpp

* remove trailing whitespace

* Remove trailing whitespace

* Modify for compile problem

* Modify some code style error

* remove whitespace

* Move some code style problem

* test

* add ios limit and build problem

* Modified as alalek suggested

* Add cmake 2.8 support

* modify cmake 3.5.1 problem

* test and set BUILD_ANDROID_PROJECTS OFF

* remove some compile error

* remove some extra code in tengine

* close test.

* Test again

* disable android.

* delete ndk version judgement

* Remove setenv() call . and add License information

* Set tengine default OFF. Close test .
Co-authored-by: Vadim Pisarevsky <vadim.pisarevsky@gmail.com>

Image sharpness, as well as brightness, are a critical parameter for
accuracte camera calibration. For accessing these parameters for
filtering out problematic calibraiton images, this method calculates
edge profiles by traveling from black to white chessboard cell centers.
Based on this, the number of pixels is calculated required to transit
from black to white. This width of the transition area is a good
indication of how sharp the chessboard is imaged and should be below
~3.0 pixels.

Based on this also motion blur can be detectd by comparing sharpness in
vertical and horizontal direction. All unsharp images should be excluded
from calibration as they will corrupt the calibration result. The same
is true for overexposued images due to a none-linear sensor response.
This can be detected by looking at the average cell brightness of the
detected chessboard.

* core(test): FP16 norm test

* core: norm()-FP16 disable OpenCL

* core(norm): fix 16f32f local buffer size