* 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.
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.
Lets the user choose the maximum number of iterations the robust
estimator runs for, similary to findHomography. This can significantly
improve performance (at a computational cost).
