[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
- fixed uninitialized memory access and memory leaks
- extracted several code blocks to separate functions
- updated part of algorithm to use cv::Mat instead of CvMat and IplImage
* Use `nth_element()` to find the median instead of `sort()` in `LMeDSPointSetRegistrator::run()`
* Improves performance of this part of LMedS from `n log(n)` to `n` by avoiding doing a full sort.
* Makes LMedS 2x faster for 100 points, 4x faster for 5,000 points in `EstimateAffine2D()`.
* LMedS is now never more than 2x slower than RANSAC and is faster in some cases.
I have seen that you can input a Mat_<float> on StereoBM, but the value seems the same as CV_16S.
I changed it so, only if you input a Mat_<float> it makes use of a previously truncated 4 bits, giving more resolution to Disparity Matrix. (The algorithm stays the same, it's not more precise).
If any other input Mat is given, it changes nothing.
All of these: (performance) Prefer prefix ++/-- operators for non-primitive types.
[modules/calib3d/src/fundam.cpp:1049] -> [modules/calib3d/src/fundam.cpp:1049]: (style) Same expression on both sides of '&&'.
Fix for inconsistent asserts in cv::fisheye::initUndistortRectifyMap() which prevents from passing empty matrices in debug build (which is allowed according to the code bellow the asserts and the docs).
With a test image set of 2800x1400 bytes on a Intel Core i7 5960X this improves runtime of MODE_HH with about 10%. (this particular replaced code segment is approx 3 times faster than the non-SSE2 variant). I was able to reduce runtime by 130 ms by this simple fix.
The second part of the SSE2 optimized part could probably be optimized further by using shift SSE2 operations, but I imagine this would improve performance 10-20 ms at best.
use the same approach like in fisheye calibration: instead of setting
masked out rows to zero, remove them from the equation system.
This way JtJ does not become singular and we can use the much faster LU
decomposition instead of SVD.
This results in a speedup of the Calibrate unit tests of 3x-10x.
IPP_VERSION_MAJOR * 100 + IPP_VERSION_MINOR*10 + IPP_VERSION_UPDATE
to manage changes between updates more easily.
IPP_DISABLE_BLOCK was added to ease tracking of disabled IPP functions;
New mode is approximately 2-3 times faster than MODE_SGBM
with minimal degradation in quality and uses universal
HAL intrinsics. A performance test was added. The accuracy
test was updated to support the new mode.
Spaced methods & functions more consistently, and started documenting
which members does each method access directly or through its callers
within RHO_HEST_REFC.
- Deleted "RefC" from names of external-interface functions.
- Renamed rhorefc.[cpp|hpp] to rho.[cpp|hpp]
- Introduced RHO_HEST base class, from which RHO_HEST_REFC inherits.
- rhoInit() currently only returns a Ptr<RHO_HEST_REFC>, but in the
future it will be allowed to return pointers to other derived classes,
depending on the values returned by cv::checkHardwareSupport().
Cholesky decomposition is stable; It is not necessary to carry it out
internally at double precision if the result will be truncated to single
precision when stored.
- Switched to the extremely fast, while simple and high-quality,
xorshift128+ PRNG algorithm by Sebastiano Vigna in "Further scramblings
of Marsaglia's xorshift generators. CoRR, abs/1402.6246, 2014" (2^128-1
period, passes BigCrush tests). Performance improved by 10% over
random().
- Added an API to allow seeding with a specified seed, rather than using
rand() or random(). This allows deterministic, reproducible results in
tests using our algorithm (although findHomography() does not yet
support passing an entropy source on its own end).
Implemented a damping-parameter choice strategy similar to that
described in http://www2.imm.dtu.dk/documents/ftp/tr99/tr05_99.pdf.
Removed a few debug statements.
Chosen a new starting lambda value, 0.01.
We now actually output the mask of inliers.
Replaced the complex rules OpenCV uses to select lambda with a naive but
fast heuristic. It's imperfect but produces good results. It is still
subject to the same problem as OpenCV - namely, on occasion LevMarq will
make a poor result even worse.
(performance) Possible inefficient checking for 'lmap' emptiness
(warning) Assignment of function parameter has no effect outside the function. Did you forget dereferencing it?
(style) Array index '<xxx>' is used before limits check
Implements the RHO algorithm as presented in
Paper: Bilaniuk, Olexa, Hamid Bazargani, and Robert Laganiere. "Fast
Target Recognition on Mobile Devices: Revisiting Gaussian Elimination
for the Estimation of Planar Homographies." In Computer Vision and
Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on, pp.
119-125. IEEE, 2014.
- Complete, heavily documented reference C implementation, as well as
temporarily disabled dirty SSE2 port.
- Enabled tests for RHO in test_homography; Currently these fail
presumably due to too-stringent accuracy requirements.
- Refinement and final refinement are not yet functional; Do not pass
their corresponding flags to RHO.
IPP can be switched on and off on runtime;
Optional implementation collector was added (switched off by default in CMake). Gathers data of implementation used in functions and report this info through performance TS;
TS modifications for implementations control;
