* G-API: First steps with tutorial
* G-API Tutorial: First iteration
* G-API port of anisotropic image segmentation tutorial;
* Currently works via OpenCV only;
* Some new kernels have been required.
* G-API Tutorial: added chapters on execution code, inspection, and profiling
* G-API Tutorial: make Fluid kernel headers public
For some reason, these headers were not moved to the public
headers subtree during the initial development. Somehow it even
worked for the existing workloads.
* G-API Tutorial: Fix a couple of issues found during the work
* Introduced Phase & Sqrt kernels, OCV & Fluid versions
* Extended GKernelPackage to allow kernel removal & policies on include()
All the above stuff needs to be tested, tests will be added later
* G-API Tutorial: added chapter on running Fluid backend
* G-API Tutorial: fix a number of issues in the text
* G-API Tutorial - some final updates
- Fixed post-merge issues after Sobel kernel renaming;
- Simplified G-API code a little bit;
- Put a conclusion note in text.
* G-API Tutorial - fix build issues in test/perf targets
Public headers were refactored but tests suites were not updated in time
* G-API Tutorial: Added tests & reference docs on new kernels
* Phase
* Sqrt
* G-API Tutorial: added link to the tutorial from the main module doc
* G-API Tutorial: Added tests on new GKernelPackage functionality
* G-API Tutorial: Extended InRange tests to cover 32F
* G-API Tutorial: Misc fixes
* Avoid building examples when gapi module is not there
* Added a volatile API disclaimer to G-API root documentation page
* G-API Tutorial: Fix perf tests build issue
This change came from master where Fluid kernels are still used
incorrectly.
* G-API Tutorial: Fixed channels support in Sqrt/Phase fluid kernels
Extended tests to cover this case
* G-API Tutorial: Fix text problems found on team review
cap_libv4l depends on an external library (libv4l) yet is still larger
(1966 loc vs 1822 loc).
It was initially introduced copy pasting cap_v4l in order to offload
various color conversions to libv4l.
However nowadays we handle most of the needed color conversions inside
OpenCV. Our own implementation is better tested and (probably) also
better performing. (as it can optionally leverage IPP/ OpenCL)
Currently cap_v4l is better maintained and generally the code is in
better shape. There is however an API
difference in getting unconverted frames:
* on cap_libv4l one need to set `CV_CAP_MODE_GRAY=1` or
`CV_CAP_MODE_YUYV=1`
* on cap_v4l one needs to set `CV_CAP_PROP_CONVERT_RGB=0`
the latter is more flexible though as it also allows accessing undecoded
JPEG images.
fixes#4563
[evolution] Stitching for OpenCV 4.0
* stitching: wrap Stitcher::create for bindings
* provide method for consistent stitcher usage across languages
* samples: add python stitching sample
* port cpp stitching sample to python
* stitching: consolidate Stitcher create methods
* remove Stitcher::createDefault, it returns Stitcher, not Ptr<Stitcher> -> inconsistent API
* deprecate cv::createStitcher and cv::createStitcherScans in favor of Stitcher::create
* stitching: avoid anonymous enum in Stitcher
* ORIG_RESOL should be double
* add documentatiton
* stitching: improve documentation in Stitcher
* stitching: expose estimator in Stitcher
* remove ABI hack
* stitching: drop try_use_gpu flag
* OCL will be used automatically through T-API in OCL-enable paths
* CUDA won't be used unless user sets CUDA-enabled classes manually
* stitching: drop FeaturesFinder
* use Feature2D instead of FeaturesFinder
* interoperability with features2d module
* detach from dependency on xfeatures2d
* features2d: fix compute and detect to work with UMat vectors
* correctly pass UMats as UMats to allow OCL paths
* support vector of UMats as output arg
* stitching: use nearest interpolation for resizing masks
* fix warnings
* Support for Matx read/write by FileStorage
* Only empty filestorage read now produces default Matx. Split Matx IO test into smaller units. Test checks for exception thrown if reading a Mat into a Matx of different size.
