An example program shows using cv::findHomography and cv::warpPerspective for image warping

OpenCV 3.3.0

OpenCV 3.3.0

OpenCV 3.3.0 for Intel Computer Vision SDK

- boundary checks
- div by zero guard
- fixed bilinear interpolation (another case)
- reduce memory allocation

- OpenCL kernels don't handle matrices properly. Assumptions are not checked.
- OpenCL/T-API integration is not correct.

- integer division => divUp()
- cast to 'int' => cvCeil()

cmake: fix __STDC macros for opencv_world build

[GSOC] Speeding-up AKAZE, part #3 (#9249)

* use finding of scale extremas from fast_akaze

* incorporade finding of extremas and subpixel refinement from Hideaki Suzuki's fast_akaze (https://github.com/h2suzuki/fast_akaze)
* use opencv parallel framework
* do not search for keypoints near the border, where we can't compute sensible descriptors (bugs fixed in ffd9ad99f4946e31508677dab09bddbecb82ae9f, 2c5389594bb560b62097de3602755ef97e60135f), but the descriptors were not 100% correct. this is a better solution

this version produces less keypoints with the same treshold. It is more effective in pruning similar keypoints (which do not bring any new information), so we have less keypoints, but with high quality. Accuracy is about the same.

* incorporate bugfix from upstream

* fix bug in subpixel refinement
* see commit db3dc22981e856ca8111f2f7fe57d9c2e0286efc in Pablo's repo

* rework finding of scale space extremas

* store just keypoints positions
* store positions in uchar mask for effective spatial search for neighbours
* construct keypoints structs at the very end

* lower inlier threshold in test

* win32 has lower accuracy

Results are not stable with srand()

Check for empty objects via .area() is not a good practice due overflows