* Remove isIntel check from deep learning layers

* Remove fp16->fp32 fallbacks where it's not necessary

* Fix Kernel::run to prevent localsize > globalsize

Support asymmetric padding in pooling layer (#12519)

* Add Inception_V1 support in ONNX

* Add asymmetric padding in OpenCL and Inference engine

* Refactoring

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

* 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

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

* Include preprocessing nodes to object detection TensorFlow networks

* Enable more fusion

* faster_rcnn_resnet50_coco_2018_01_28 test

- 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).

Enable Torch layers tests with Intel's Inference Engine backend.

set global size with real output size, also optimize

max pooling index computation if necessary.
Signed-off-by: Li Peng <peng.li@intel.com>

* optimize ocl kernel enqueue in fc layer
Signed-off-by: Li Peng <peng.li@intel.com>

* use CV_LOG_INFO in convolution auto tuning
Signed-off-by: Li Peng <peng.li@intel.com>

* update convolution IDLF kernel

extend parameter tuning range, also cleanup
ocl kernel implementation
Signed-off-by: Li Peng <peng.li@intel.com>

* update in-memory convolution cache config

fp16 and fp32 cache config are stored separately
Signed-off-by: Li Peng <peng.li@intel.com>

dnn: Fix output mismatch when forward dnn model contain [depthwise conv(group=1) + bn + prelu]  (#11649)

* this can make sure [depthwise conv(group=1) + bn + prelu] output not shift

* add TEST to show the output mismatch in [DWconv+Prelu]

* fix typo

* change loading image to init cvMat directly

* build runtime model, without loading external model

* remove whitespace

* change way to create a cvmat

* add bias_term, add target output

* fix [dwconv + prelu] value mismatch when no optimizations

* fix Test error when change output channels

* add parametric test

* change num_output to group value

* change conv code and change test back