Feature/region layer batch mode (#12249)

* Add batch mode for Darknet networks.

Swap variables in test_darknet.

Adapt reorg layer to batch mode.

Adapt region layer.

Add OpenCL implementation.

Remove trailing whitespace.

Bugifx reorg opencl implementation.

Fix bug in OpenCL reorg.

Fix modulo bug.

Fix bug.

Reorg openCL.

Restore reorg layer opencl code.

OpenCl fix.

Work on openCL reorg.

Remove whitespace.

Fix openCL region layer implementation.

Fix bug.

Fix softmax region opencl bug.

Fix opencl bug.

Fix openCL bug.

Update aff_trans.cpp

When the fullAffine parameter is set to false, the estimateRigidTransform function maybe return empty, then the _localAffineEstimate function will be called, but the bug in it will result in incorrect results.

core(libva): support YV12 too

Added to CPU path only.
OpenCL code path still expects NV12 only (according to Intel OpenCL extension)

cmake: allow to specify own libva paths

via CMake:
- `-DVA_LIBRARIES=/opt/intel/mediasdk/lib64/libva.so.2\;/opt/intel/mediasdk/lib64/libva-drm.so.2`

android: NDK17 support

tested with NDK 17b (17.1.4828580)

Enable more deep learning tests using Intel's Inference Engine backend

ts: don't pass NULL for std::string() constructor

openvino: use 2018R3 defines

experimental version++

OpenCV version++

OpenCV 3.4.3

OpenCV version '-openvino'

openvino: use 2018R3 defines

Fixed windows build with InferenceEngine

dnn: fix variance setting bug for PriorBoxLayer

- The size of second channel should be size[2] of output tensor,
- The Scalar should be {variance[0], variance[0], variance[0], variance[0]}
  for _variance.size() == 1 case.
Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com>

Fix lifetime of networks which are loaded from Model Optimizer IRs

Adds a small note describing BUILD_opencv_world (#12332)

* 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

Update opengl_interop.cpp

resolves #12307

java: fix LIST_GET macro

fix typo

Added option to fail on missing testdata

Fixed that object_detection.py does not work in python3.

cleanup: IPP Async (IPP_A)

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

imgcodecs: add null pointer check

Include preprocessing nodes to object detection TensorFlow networks (#12211)

* Include preprocessing nodes to object detection TensorFlow networks

* Enable more fusion

* faster_rcnn_resnet50_coco_2018_01_28 test

countNonZero function reworked to use wide universal intrinsics instead of SSE2 intrinsics

resolve #5788

imgcodecs(webp): multiple fixes

- don't reallocate passed 'img' (test fixed - must use IMREAD_UNCHANGED / IMREAD_ANYCOLOR)
- avoid memory DDOS
- avoid reading of whole file during header processing
- avoid data access after allocated buffer during header processing (missing checks)
- use WebPFree() to free allocated buffers (libwebp >= 0.5.0)
- drop unused & undefined `.close()` method
- added checks for channels >= 5 in encoder

ml: fix adjusting K in KNearest (#12358)

dnn(perf): fix and merge Convolution tests

- OpenCL tests didn't run any OpenCL kernels
- use real configuration from existed models (the first 100 cases)
- batch size = 1

dnn(test): use dnnBackendsAndTargets() param generator

Bit-exact resize reworked to use wide intrinsics (#12038)

* Bit-exact resize reworked to use wide intrinsics

* Reworked bit-exact resize row data loading

* Added bit-exact resize row data loaders for SIMD256 and SIMD512

* Fixed type punned pointer dereferencing warning

* Reworked loading of source data for SIMD256 and SIMD512 bit-exact resize

Bit-exact GaussianBlur reworked to use wide intrinsics (#12073)

* Bit-exact GaussianBlur reworked to use wide intrinsics

* Added v_mul_hi universal intrinsic

* Removed custom SSE2 branch from bit-exact GaussianBlur

* Removed loop unrolling for gaussianBlur horizontal smoothing

doc: fix English gramma in tutorial out-of-focus-deblur filter (#12214)

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

* Update out_of_focus_deblur_filter.markdown

slightly modified one sentence

doc: add new tutorial motion deblur filter (#12215)

* doc: add new tutorial motion deblur filter

* Update motion_deblur_filter.markdown

a few minor changes

Replace Slice layer to Crop in Faster-RCNN networks from Caffe

js: use generated list of OpenCV headers

- replaces hand-written list

imgcodecs(webp): use safe cast to size_t on Win32

* Put Version status back to -dev.

follow the common codestyle

Exclude some target engines.

Refactor formulas.

Refactor code.

* Remove unused variable.

* Remove inference engine check for yolov2.

* Alter darknet batch tests to test with two different images.

* Add yolov3 second image GT.

* Fix bug.

* Fix bug.

* Add second test.

* Remove comment.

* Add NMS on network level.

* Add helper files to dev.

* syntax fix.

* Fix OD sample.

Fix sample dnn object detection.

Fix NMS boxes bug.

remove trailing whitespace.

Remove debug function.

Change thresholds for opencl tests.

* Adapt score diff and iou diff.

* Alter iouDiffs.

* Add debug messages.

* Adapt iouDiff.

* Fix tests

Support MSVC 2013 (#12486)

* Added CV_CONSTEXPR macro

* Utilize CV_NOEXCEPT and CV_CONSTEXPR

* Provides some Ptr<> logical operators

* Add Squeezenet support in ONNX

* Add AlexNet support in ONNX

* Add Googlenet support in ONNX

* Add CaffeNet and RCNN support in ONNX

* Add VGG16 and VGG16 with batch normalization support in ONNX

* Add RCNN, ZFNet, ResNet18v1 and ResNet50v1 support in ONNX

* Add ResNet101_DUC_HDC

* Add Tiny Yolov2

* Add CNN_MNIST, MobileNetv2 and LResNet100 support in ONNX

* Add ONNX models for emotion recognition

* Add DenseNet121 support in ONNX

* Add Inception v1 support in ONNX

* Refactoring

* Fix tests

* Fix tests

* Skip unstable test

* Modify Reshape operation

* trying to fix the custom AVX2 builder test failures (false alarms)

* fixed compile error with CPU_BASELINE=AVX2 on x86; raised tolerance thresholds in a couple of tests

* fixed compile error with CPU_BASELINE=AVX2 on x86; raised tolerance thresholds in a couple of tests

* fixed compile error with CPU_BASELINE=AVX2 on x86; raised tolerance thresholds in a couple of tests

* seemingly disabled false alarm warning in surf.cpp; increased tolerance thresholds in the tests for SolvePnP and in DNN/ENet

* Remove a forward method in dnn::Layer

* Add a test

* Fix tests

* Mark multiple dnn::Layer::finalize methods as deprecated

* Replace back dnn's inputBlobs to vector of pointers

* Remove Layer::forward_fallback from CV_OCL_RUN scopes

- differ from 3.4 branch

* Remove TensorFlow and protobuf dependencies from object detection scripts

* Create text graphs for TensorFlow object detection networks from sample

Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com>

* Add HPX backend for OpenCV implementation
Adds hpx backend for cv::parallel_for_() calls respecting the nstripes chunking parameter. C++ code for the backend is added to modules/core/parallel.cpp. Also, the necessary changes to cmake files are introduced.
Backend can operate in 2 versions (selectable by cmake build option WITH_HPX_STARTSTOP): hpx (runtime always on) and hpx_startstop (start and stop the backend for each cv::parallel_for_() call)

* WIP: Conditionally include hpx_main.hpp to tests in core module
Header hpx_main.hpp is included to both core/perf/perf_main.cpp and core/test/test_main.cpp.
The changes to cmake files for linking hpx library to above mentioned test executalbles are proposed but have issues.

* Add coditional iclusion of hpx_main.hpp to cpp cpu modules

* Remove start/stop version of hpx backend

* Include preprocessing nodes to object detection TensorFlow networks

* Enable more fusion

* faster_rcnn_resnet50_coco_2018_01_28 test

- OpenCL tests didn't run any OpenCL kernels
- use real configuration from existed models (the first 100 cases)
- batch size = 1

- The size of second channel should be size[2] of output tensor,
- The Scalar should be {variance[0], variance[0], variance[0], variance[0]}
  for _variance.size() == 1 case.
Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com>

* Support Mask-RCNN from TensorFlow

* Fix a sample

Multi-argument CV_Assert() is accessible via CV_Assert_N() (with malformed messages).