* moved BLAS/LAPACK detection scripts from opencv_contrib/dnn to the main repository.
* trying to fix the bug with undefined symbols sgesdd_ and dgesdd_
* removed extra whitespaces; disabled LAPACK on IOS
* OpenVX HAL updated to use generic OpenVX wrappers
* vxErr class from OpenVX HAL replaced with ivx::WrapperError
* reduced usage of vxImage class from OpenVX HAL replaced with ivx::Image
* vxImage class rewritten as ivx::Image subclass that calls swapHandle prior release
* Fix OpenVX HAL build
* Fix for review comments
Added OpenVX based processing to FAST (#7720)
* added wrapper for OVX FAST & fixes to IVX wrappers
* fixed type checks in wrappers, array downloading code simplified
* rewritten for new macro use
OpenVX pyrDown wrappers (#7793)
* wrappers for vx_pyramid added
* initial version of pyrDown() wrapper added
* disabled for Khronos
* rewritten for new macro use; border mode added to node
OpenVX optical flow PyrLK wrappers added (#7774)
* wrappers for vx_pyramid added
* initial version of Optical Flow PyrLK wrappers added
* array downloading code simplified
* disabled due to bad accuracy; fixed bugs, e.g. vendor-specific ones
* rewritten for new macro use
Currently, to select a submatrix of a N-dimensional matrix, it requires
two lines of code while only one line of code is required if using a 2D
array.
I added functionality to be able to select an N-dim submatrix using a
vector list instead of a Range pointer. This allows initializer lists to
be used for a one-line selection.
This allows for an N-dimensional array to be setup in one line instead of two when using C++11 initializer lists. cv::Mat(3, {zDim, yDim, xDim}, ...) can be used instead of having to create an int pointer to hold the size array.
the current camera model is only valid up to 180° FOV for larger FOV the
undistort loop does not converge.
Clip values so we still get plausible results for super fisheye images >
180°.
Add new 5x5 gaussian blur kernel for CV_8UC1 format,
it is 50% ~ 70% faster than current ocl kernel in the perf test.
Signed-off-by: Li Peng <peng.li@intel.com>
Add support image save parameters in cv::VideoWriter.
This change will become available setting same parameters as
cv::imwrite() to cv::VideoWriter::set( cv::IMWRITE_*, value ).
Add new OpenCL kernels for bicubic interploation, it is 20% faster
than current warp image kernel with bicubic interploation.
Signed-off-by: Li Peng <peng.li@intel.com>
Add new ocl kernels for warpAffine and warpPerspective,
The average performance improvemnt is about 30%. The new
ocl kernels require CV_8UC1 format and support nearest
neighbor and bilinear interpolation.
Signed-off-by: Li Peng <peng.li@intel.com>
* restore Google Test 1.7.0 (get patch)
* ts: update Google Test to 1.8.0 release
https://github.com/google/googletest
* ts: re-apply OpenCV patch for gtest
* ts: fixes for gtest 1.8.0
* ts: workaround MSVS2015 problem in gtest
ExifReader::getExif may enter infinite loop with jpeg image which have no EOI.
For example, bytesToSkip may be set to 0 and fseek seems like fseek(f, -2 , SEEK_CUR) for image that end with RST7(FF D7) instead of EOI.
This ocl kernel is 46%~171% faster than current laplacian 3x3
ocl kernel in the perf test, with image format "CV_8UC1".
Signed-off-by: Li Peng <peng.li@intel.com>
Change contour test images to be very wide (#7464)
* Change contour test images to be very wide (#7409, #7458)
Unfortunately, slows down the tests.
* Decrease the number of contour test cases, in order to (at least partially) offset the test run duration increase caused by making the test images wider
* Don't test with very wide images on 32-bit architectures
Maximum depth limit var was added to the instrumentation structure;
Trace names output console output fix: improper tree formatting could happen;
Output in case of error was added;
Custom regions improvements;
Improved timing and weight calculation for parallel regions; New TC (threads counter) value to indicate how many different threads accessed particular node;
parallel_for, warnings fixes and ReturnAddress code from Alexander Alekhin;
This ocl kernel is for 3x3 kernel size and CV_8UC1 format
It is 115% ~ 300% faster than current ocl path in perf test
python ./modules/ts/misc/run.py -t imgproc --gtest_filter=OCL_GaussianBlurFixture*
Signed-off-by: Li Peng <peng.li@intel.com>
This kernel is for CV_8UC1 format and 3x3 kernel size,
It is about 33% ~ 55% faster than current ocl kernel with below perf test
python ./modules/ts/misc/run.py -t imgproc --gtest_filter=OCL_ErodeFixture*
python ./modules/ts/misc/run.py -t imgproc --gtest_filter=OCL_DilateFixture*
Also add accuracy test cases for this kernel, the test command is
./bin/opencv_test_imgproc --gtest_filter=OCL_Filter/MorphFilter3x3*
Signed-off-by: Li Peng <peng.li@intel.com>
Fixes#7603, which was caused by OCVViewPort::icvmouseProcessing
not being declared as virtual, and hence was not overriden by
DefaultViewPort::icvmouseProcessing (which does the inverse
coordinate mapping).
modules/objectdetect/src/detection_based_tracker.cpp: made unique_lock<mutex> local to each function
samples/cpp/dbt_face_detection.cpp: fixed warnings on loop in Visual Studio
* use hasSIMD128 rather than calling checkHardwareSupport
* add SIMD check in spartialgradient.cpp
* add SIMD check in stereosgbm.cpp
* add SIMD check in canny.cpp
checks whether the window exists and is visible. On QT closing a window
merley hides it, so the common hack for checking whether a window exists
exists = cv2.getWindowProperty(.., 0) >= 0
does not work.
The optimization is for CV_8UC1 format and 3x3 box filter,
it is 15%~87% faster than current ocl kernel with below perf test
./modules/ts/misc/run.py -t imgproc --gtest_filter=OCL_BlurFixture*
Also add test cases for this ocl kernel.
Signed-off-by: Li Peng <peng.li@intel.com>
A bug in ICC improperly identified the first parameter as "void*"
rather than the proper "volatile long*". This is scheduled to be
fixed in ICC in a future release.
This patch casts only to a "long*" to preserve backwards compatibility
with the ICC 16 and ICC 17 releases.
In YAML 1.0 the colon is mandatory. See http://yaml.org/spec/1.0/#id2558635.
This also allows prior releases to read YAML files created with the current version.
[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
Aravis several updates
* Fix adressing camera with id=0
* Aravis buffer property control & status added
* Modify of autoexposure algorith, ream frame ID from aravis + new properites
* Change of macro name
* VideoCapture now returns no frame on camera disconnecion
* Allow aravis-0.4 usage, proper camera object release.
