Without proposed change, module throws build error regarding missing `opencv2/cudaimgproc.hpp`

Found via `codespell`

- removed tr1 usage (dropped in C++17)
- moved includes of vector/map/iostream/limits into ts.hpp
- require opencv_test + anonymous namespace (added compile check)
- fixed norm() usage (must be from cvtest::norm for checks) and other conflict functions
- added missing license headers

allows Stitcher to be used for scans from within python.
I had to use very strange notation because I couldn't export the `enum`
`Mode` making the Cpython generated code unable to compile.

```c++
class Stitcher {
public:
enum Mode
    {
        PANORAMA = 0,
        SCANS = 1,
    };
...
```

Also removed duplicate code from the `createStitcher` function making
use of the `Stitcher::create` function

Implementation of bit-exact resize. Internal calls to linear resize updated to use bit-exact version. (#9468)

If there are no OpenCL/UMat methods calls from application.

OpenCL subsystem is initialized:
- haveOpenCL() is called from application
- useOpenCL() is called from application
- access to OpenCL allocator: UMat is created (empty UMat is ignored) or UMat <-> Mat conversions are called

Don't call OpenCL functions if OPENCV_OPENCL_RUNTIME=disabled
(independent from OpenCL linkage type)

OpenCL SURF implementation is unstable

it will allow to build features2d even if flann module is not available

Fixing explicitly call download method for cuda::GpuMat object

https://sourceforge.net/p/predef/wiki/OperatingSystems

Remove unnecessary Non-ASCII characters from source code (#9075)

* Remove unnecessary Non-ASCII characters from source code

Remove unnecessary Non-ASCII characters and replace them with ASCII
characters

* Remove dashes in the @param statement

Remove dashes and place single space in the @param statement to keep
coding style

* misc: more fixes for non-ASCII symbols

* misc: fix non-ASCII symbol in CMake file

[GSOC] Speeding-up AKAZE, part #1 (#8869)

* ts: expand arguments before stringifications in CV_ENUM and CV_FLAGS

added protective macros to always force macro expansion of arguments. This allows using CV_ENUM and CV_FLAGS with macro arguments.

* feature2d: unify perf test

use the same test for all detectors/descriptors we have.

* added AKAZE tests

* features2d: extend perf tests

* add BRISK, KAZE, MSER
* run all extract tests on AKAZE keypoints, so that the test si more comparable for the speed of extraction

* feature2d: rework opencl perf tests

use the same configuration as cpu tests

* feature2d: fix descriptors allocation for AKAZE and KAZE

fix crash when descriptors are UMat

* feature2d: name enum to fix build with older gcc

* Revert "ts: expand arguments before stringifications in CV_ENUM and CV_FLAGS"

This reverts commit 19538cac1e45b0cec98190cf06a5ecb07d9b596e.

This wasn't a great idea after all. There is a lot of flags implemented as #define, that we don't want to expand.

* feature2d: fix expansion problems with CV_ENUM in perf

* expand arguments before passing them to CV_ENUM. This does not need modifications of CV_ENUM.
* added include guards to `perf_feature2d.hpp`

* feature2d: fix crash in AKAZE when using KAZE descriptors

* out-of-bound access in Get_MSURF_Descriptor_64
* this happened reliably when running on provided keypoints (not computed by the same instance)

* feature2d: added regression tests for AKAZE

* test with both MLDB and KAZE keypoints

* feature2d: do not compute keypoints orientation twice

* always compute keypoints orientation, when computing keypoints
* do not recompute keypoint orientation when computing descriptors

this allows to test detection and extraction separately

* features2d: fix crash in AKAZE

* out-of-bound reads near the image edge
* same as the bug in KAZE descriptors

* feature2d: refactor invariance testing

* split detectors and descriptors tests
* rewrite to google test to simplify debugging
* add tests for AKAZE and one test for ORB

* stitching: add tests with AKAZE feature finder

* added basic stitching cpu and ocl tests
* fix bug in AKAZE wrapper for stitching pipeline causing lots of
! OPENCV warning: getUMat()/getMat() call chain possible problem.
!                 Base object is dead, while nested/derived object is still alive or processed.
!                 Please check lifetime of UMat/Mat objects!

AffineBasedEstimator crashed when called with an existing CameraParameters.
This happens e.g. when using Stitcher in SCANS mode.
CameraraParameters is now cleared before any calculation is executed.

Stitcher will now make a working copy of the CameraParams object to avoid side effects when composing Panorama.
Makes it possible to estimate transform once and then compose multiple panoramas. Useful for setup with fixed cameras.

force other RNG seed

(2.4) Backports from master branch (#7854)

[GSOC] New camera model for stitching pipeline

* implement estimateAffine2D

estimates affine transformation using robust RANSAC method.

* uses RANSAC framework in calib3d
* includes accuracy test
* uses SVD decomposition for solving 3 point equation

* implement estimateAffinePartial2D

estimates limited affine transformation

* includes accuracy test

* stitching: add affine matcher

initial version of matcher that estimates affine transformation

* stitching: added affine transform estimator

initial version of estimator that simply chain transformations in homogeneous coordinates

* calib3d: rename estimateAffine3D test

test Calib3d_EstimateAffineTransform rename to Calib3d_EstimateAffine3D. This is more descriptive and prevents confusion with estimateAffine2D tests.

* added perf test for estimateAffine functions

tests both estimateAffine2D and estimateAffinePartial2D

* calib3d: compare error in square in estimateAffine2D

* incorporates fix from #6768

* rerun affine estimation on inliers

* stitching: new API for parallel feature finding

due to ABI breakage new functionality is added to `FeaturesFinder2`, `SurfFeaturesFinder2` and `OrbFeaturesFinder2`

* stitching: add tests for parallel feature find API

* perf test (about linear speed up)
* accuracy test compares results with serial version

* stitching: use dynamic_cast to overcome ABI issues

adding parallel API to FeaturesFinder breaks ABI. This commit uses dynamic_cast and hardcodes thread-safe finders to avoid breaking ABI.

This should be replaced by proper method similar to FeaturesMatcher on next ABI break.

* use estimateAffinePartial2D in AffineBestOf2NearestMatcher

* add constructor to AffineBestOf2NearestMatcher

* allows to choose between full affine transform and partial affine transform. Other params are the as for BestOf2NearestMatcher
* added protected field

* samples: stitching_detailed support affine estimator and matcher

* added new flags to choose matcher and estimator

* stitching: rework affine matcher

represent transformation in homogeneous coordinates

affine matcher: remove duplicite code
rework flow to get rid of duplicite code

affine matcher: do not center points to (0, 0)
it is not needed for affine model. it should not affect estimation in any way.

affine matcher: remove unneeded cv namespacing

* stitching: add stub bundle adjuster

* adds stub bundle adjuster that does nothing
* can be used in place of standard bundle adjusters to omit bundle adjusting step

* samples: stitching detailed, support no budle adjust

* uses new NoBundleAdjuster

* added affine warper

* uses R to get whole affine transformation and propagates rotation and translation to plane warper

* add affine warper factory class

* affine warper: compensate transformation

* samples: stitching_detailed add support for affine warper

* add Stitcher::create method

this method follows similar constructor methods and returns smart pointer. This allows constructing Stitcher according to OpenCV guidelines.

* supports multiple stitcher configurations (PANORAMA and SCANS) for convenient setup
* returns cv::Ptr

* stitcher: dynamicaly determine correct estimator

we need to use affine estimator for affine matcher

* preserves ABI (but add hints for ABI 4)
* uses dynamic_cast hack to inject correct estimator

* sample stitching: add support for multiple modes

shows how to use different configurations of stitcher easily (panorama stitching and scans affine model)

* stitcher: find features in parallel

use new FeatureFinder API to find features in parallel. Parallelized using TBB.

* stitching: disable parallel feature finding for OCL

it does not bring much speedup to run features finder in parallel when OpenCL is enabled, because finder needs to wait for OCL device.

Also, currently ORB is not thread-safe when OCL is enabled.

* stitching: move matcher tests

move matchers tests perf_stich.cpp -> perf_matchers.cpp

* stitching: add affine stiching integration test

test basic affine stitching (SCANS mode of stitcher) with images that have only translation between them

* enable surf for stitching tests

stitching.b12 test was failing with surf

investigated the issue, surf is producing good result. Transformation is only slightly different from ORB, so that resulting pano does not exactly match ORB's result. That caused sanity check to fail.

* added size checks similar to other tests
* sanity check will be applied only for ORB

* stitching: fix wrong estimator choice

if case was exactly wrong, estimators were chosen wrong

added logging for estimated transformation

* enable surf for matchers stitching tests

* enable SURF
* rework sanity checking. Check estimated transform instead of matches. Est. transform should be more stable and comparable between SURF and ORB.
* remove regression checking for VectorFeatures tests. It has a lot if data andtest is the same as previous except it test different vector size for performance, so sanity checking does not add any value here. Added basic sanity asserts instead.

* stitching tests: allow relative error for transform

* allows .01 relative error for estimated homography sanity check in stitching matchers tests
* fix VS warning

stitching tests: increase relative error

increase relative error to make it pass on all platforms (results are still good).

stitching test: allow bigger relative error

transformation can differ in small values (with small absolute difference, but large relative difference). transformation output still looks usable for all platforms. This difference affects only mac and windows, linux passes fine with small difference.

* stitching: add tests for affine matcher

uses s1, s2 images. added also new sanity data.

* stitching tests: use different data for matchers tests

this data should yeild more stable transformation (it has much more matches, especially for surf). Sanity data regenerated.

* stitching test: rework tests for matchers

* separated rotation and translations as they are different by scale.
* use appropriate absolute error for them separately. (relative error does not work for values near zero.)

* stitching: fix affine warper compensation

calculation of rotation and translation extracted for plane warper was wrong

* stitching test: enable surf for opencl integration tests

* enable SURF with correct guard (HAVE_OPENCV_XFEATURES2D)
* add OPENCL guard and correct namespace as usual for opencl tests

* stitching: add ocl accuracy test for affine warper

test consistent results with ocl on and off

* stitching: add affine warper ocl perf test

add affine warper to existing warper perf tests. Added new sanity data.

* stitching: do not overwrite inliers in affine matcher

* estimation is run second time on inliers only, inliers produces in second run will not be therefore correct for all matches

* calib3d: add Levenberg–Marquardt refining to estimateAffine2D* functions

this adds affine Levenberg–Marquardt refining to estimateAffine2D functions similar to what is done in findHomography.

implements Levenberg–Marquardt refinig for both full affine and partial affine transformations.

* stitching: remove reestimation step in affine matcher

reestimation step is not needed. estimateAffine2D* functions are running their own reestimation on inliers using the Levenberg-Marquardt algorithm, which is better than simply rerunning RANSAC on inliers.

* implement partial affine bundle adjuster

bundle adjuster that expect affine transform with 4DOF. Refines parameters for all cameras together.

stitching: fix bug in BundleAdjusterAffinePartial

* use the invers properly
* use static buffer for invers to speed it up

* samples: add affine bundle adjuster option to stitching_detailed

* add support for using affine bundle adjuster with 4DOF
* improve logging of initial intristics

* sttiching: add affine bundle adjuster test

* fix build warnings

* stitching: increase limit on sanity check

prevents spurious test failures on mac. values are still pretty fine.

* stitching: set affine bundle adjuster for SCANS mode

* fix bug with AffineBestOf2NearestMatcher (we want to select affine partial mode)
* select right bundle adjuster

* stitching: increase error bound for matcher tests

* this prevents failure on mac. tranformation is still ok.

* stitching: implement affine bundle adjuster

* implements affine bundle adjuster that is using full affine transform
* existing test case modified to test both affinePartial an full affine bundle adjuster

* add stitching tutorial

* show basic usage of stitching api (Stitcher class)

* stitching: add more integration test for affine stitching

* added new datasets to existing testcase
* removed unused include

* calib3d: move `haveCollinearPoints` to common header

* added comment to make that this also checks too close points

* calib3d: redone checkSubset for estimateAffine* callback

* use common function to check collinearity
* this also ensures that point will not be too close to each other

* calib3d: change estimateAffine* functions API

* more similar to `findHomography`, `findFundamentalMat`, `findEssentialMat` and similar
* follows standard recommended semantic INPUTS, OUTPUTS, FLAGS
* allows to disable refining
* supported LMEDS robust method (tests yet to come) along with RANSAC
* extended docs with some tips

* calib3d: rewrite estimateAffine2D test

* rewrite in googletest style
* parametrize to test both robust methods (RANSAC and LMEDS)
* get rid of boilerplate

* calib3d: rework estimateAffinePartial2D test

* rework in googletest style
* add testing for LMEDS

* calib3d: rework estimateAffine*2D perf test

* test for LMEDS speed
* test with/without Levenberg-Marquart
* remove sanity checking (this is covered by accuracy tests)

* calib3d: improve estimateAffine*2D tests

* test transformations in loop
* improves test by testing more potential transformations

* calib3d: rewrite kernels for estimateAffine*2D functions

* use analytical solution instead of SVD
* this version is faster especially for smaller amount of points

* calib3d: tune up perf of estimateAffine*2D functions

* avoid copying inliers
* avoid converting input points if not necessary
* check only `from` point for collinearity, as `to` does not affect stability of transform

* tutorials: add commands examples to stitching tutorials

* add some examples how to run stitcher sample code
* mention stitching_detailed.cpp

* calib3d: change computeError for estimateAffine*2D

* do error computing in floats instead of doubles

this have required precision + we were storing the result in float anyway. This make code faster and allows auto-vectorization by smart compilers.

* documentation: mention estimateAffine*2D function

* refer to new functions on appropriate places
* prefer estimateAffine*2D over estimateRigidTransform

* stitching: add camera models documentations

* mention camera models in module documentation to give user a better overview and reduce confusion