G-API: Tutorial: Face beautification algorithm implementation
* Introduce a tutorial on face beautification algorithm
- small typo issue in render_ocv.cpp
* Addressing comments rgarnov smirnov-alexey
* G-API-NG/Docs: Added a tutorial page on interactive face detection sample
- Introduced a "--ser" option to run the pipeline serially for
benchmarking purposes
- Reorganized sample code to better fit the documentation;
- Fixed a couple of issues (mainly typos) in the public headers
* G-API-NG/Docs: Reflected meta-less compilation in new G-API tutorial
* G-API-NG/Docs: Addressed review comments on Face Analytics Pipeline example
* core: disable invalid constructors in C API by default
- C API objects will lose their default initializers through constructors
* samples: stop using of C API
* Changes disparity image to float representation
Signed-off-by: Connor James Smith <cjs.connor.smith@gmail.com>
* samples: update disparity multiplier handling in stereo_match.cpp
I think it would help to change all 3 of the the input file arguments to be "positional" for consistency with the other tutorials. This also simplifies the command line input to run this tutorial by reducing typing, and helpfully prints the "usage" info if any of the 3 required inputs are missing.
I'm new to OpenCV and working through the tutorials. I kept getting runtime errors with this one until I realized that the arguments weren't positional, and I was missing the "--input1", "--input2, "--input3" flags preceding the filenames. All of the previous tutorials had required filenames as positional arguments and didn't require this.
The original code would require each input to be specified like this:
./compareHist_Demo --input1 filename1 --input2 filename2 --input3 filename3
But with this change, the above command is simplified to:
./compareHist_Demo filename1 filename2 filename3
This avoids a confusing runtime error to make things simpler for newcomers like me :)
The usage function states that the default for match_conf is
0.65 if the default SURF feature finder is used, and 0.3 for
orbs. Indeed, if --feature orbs is used, match_conf is set
to 0.3f. This is a NOP, because the real default is also set
to 0.3f. Change it to 0.65f when SURF is in play.
* G-API: Doxygen documentatation for Async API
* G-API: Doxygen documentatation for Async API
- renamed local variable (reading parameter async) async ->
asyncNumReq in object_detection DNN sample
to avoid Doxygen erroneous linking the sample to cv::gapi::wip::async
documentation
Description:
Moved NVIDIA_Optical_flow sample app and comparison app to
opencv_contrib branch. Added CUDA_CUDA_LIBRARY in CMakeLists.txt for
resolving linker errors.
G-API: Kernel package design (#13851)
* Remove cv::unite_policy from API
* Add check that all id in kernel package are unique
* Refactor checker id procedure
* Remove cv::gapi::GLookupOrder from API
* Implement cv::gapi::use_only
* Fix samples
* Fix docs
* Fix comments to review
* Remove unite_policy
* Fix GKernelPackage::backends()
* Fix comments to review
* Fix all_unique
* Fix comments to review
* Fix comments to review
* Remove out of date tests
Extend optical flow tutorial (#14314)
* extend python optical flow tutorial with cpp example code and add it to general tutorial directory
* remove unused parameters, fix comparison between signed and unsigned int
* fix hsv range problem
* switch to samples::findFile for sample file location
* switch to command line parameter for path
* remove old tutorial as in 14393
* minor fixes
Extend meanshift tutorial (#14393)
* copy original tutorial and python code
* add cpp code, fix python code
* add camshift cpp code, fix bug in meanshift code
* add description to ToC page
* fix shadowing previous local declaration
* fix grammar: with -> within
* docs: remove content of old py_meanshift tutorial, add link
* docs: replace meanshift tutorial subpage in Python tutorials
* switch to ref to fix wrong breadcrumb navigation
* switch to cmdline for path as in #14314
* Apply suggestions from code review
* order programming languages alphabetically
Grammatical errors for help() in detect_mser.cpp (#14122)
* Grammatical errors for help() statement
Corrected spelling of "synthetic" and added grammatical clarification for keys to press to change view or use mouse.
* Adjustment of superfluous spaces
* Created python version of the code for the anisotropic image segmentation tutorial. Created python/cpp toggles for the markdown file.
* fix doxygen warnings
Many of the Android samples rely on an options menu to work properly
but, at least on newer devices, the menu is permanently hidden by the
Android theme "Theme.NoTitleBar.Fullscreen", which means that most
of the examples were dysfunctional.
Improve stitching detailed (#13584)
* Added block size getter/setters
* Added a bunch of new features to the stitching_detailed sample
* Do not required XFEATURES2D for default use
* Add support for akaze features in stitching_detailed
* Improved sample logs
* Python wrapper for detail
* hide pyrotationwrapper
* copy code in pyopencv_rotationwarper.hpp
* move ImageFeatures MatchInfo and CameraParams in core/misc/
* add python test for detail
* move test_detail in test_stitching
* rename
The load() function returns a new object, and as such does not use the one it is called on.
This commit updates the uses of model.load in this program so it will work as intended and not throw an error.
* 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
[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
* moved DIS optical flow from opencv_contrib to opencv, moved TVL1 from opencv to opencv_contrib
* fixed compile warning
* TVL1 optical flow example moved to opencv_contrib
More accurate pinhole camera calibration with imperfect planar target (#12772)
43 commits:
* Add derivatives with respect to object points
Add an output parameter to calculate derivatives of image points with
respect to 3D coordinates of object points. The output jacobian matrix
is a 2Nx3N matrix where N is the number of points.
This commit introduces incompatibility to old function signature.
* Set zero for dpdo matrix before using
dpdo is a sparse matrix with only non-zero value close to major
diagonal. Set it to zero because only elements near major diagonal are
computed.
* Add jacobian columns to projectPoints()
The output jacobian matrix of derivatives with respect to coordinates of
3D object points are added. This might break callers who assume the
columns of jacobian matrix.
* Adapt test code to updated project functions
The test cases for projectPoints() and cvProjectPoints2() are updated to
fit new function signatures.
* Add accuracy test code for dpdo
* Add badarg test for dpdo
* Add new enum item for new calibration method
CALIB_RELEASE_OBJECT is used to whether to release 3D coordinates of
object points. The method was proposed in: K. H. Strobl and G. Hirzinger.
"More Accurate Pinhole Camera Calibration with Imperfect Planar Target".
In Proceedings of the IEEE International Conference on Computer Vision
(ICCV 2011), 1st IEEE Workshop on Challenges and Opportunities in Robot
Perception, Barcelona, Spain, pp. 1068-1075, November 2011.
* Add releasing object method into internal function
It's a simple extension of the standard calibration scheme. We choose to
fix the first and last object point and a user-selected fixed point.
* Add interfaces for extended calibration method
* Refine document for calibrateCamera()
When releasing object points, only the z coordinates of the
objectPoints[0].back is fixed.
* Add link to strobl2011iccv paper
* Improve documentation for calibrateCamera()
* Add implementations of wrapping calibrateCamera()
* Add checking for params of new calibration method
If input parameters are not qualified, then fall back to standard
calibration method.
* Add camera calibration method of releasing object
The current implementation is equal to or better than
https://github.com/xoox/calibrel
* Update doc for CALIB_RELEASE_OBJECT
CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with
potentially less precise and less stable in some rare cases.
* Add RELEASE_OBJECT calibration to tutorial code
To select the calibration method of releasing object points, a command
line parameter `-d=<number>` should be provided.
* Update tutorial doc for camera_calibration
If the method of releasing object points is merged into OpenCV. It will
be expected to be firstly released in 4.1, I think.
* Reduce epsilon for cornerSubPix()
Epsilon of 0.1 is a bigger one. Preciser corner positions are required
with calibration method of releasing object.
* Refine camera calibration tutorial
The hypothesis coordinates are used to indicate which distance must be
measured between two specified object points.
* Update sample calibration code method selection
Similar to camera_calibration tutorial application, a command line
argument `-dt=<number>` is used to select the calibration method.
* Add guard to flags of cvCalibrateCamera2()
cvCalibrateCamera2() doesn't accept CALIB_RELEASE_OBJECT unless overload
interface is added in the future.
* Simplify fallback when iFixedPoint is out of range
* Refactor projectPoints() to keep compatibilities
* Fix arg string "Bad rvecs header"
* Read calibration flags from test data files
Instead of being hard coded into source file, the calibration flags will
be read from test data files.
opencv_extra/testdata/cv/cameracalibration/calib?.dat must be sync with
the test code.
* Add new C interface of cvCalibrateCamera4()
With this new added C interface, the extended calibration method with
CALIB_RELEASE_OBJECT can be called by C API.
* Add regression test of extended calibration method
It has been tested with new added test data in xoox:calib-release-object
branch of opencv_extra.
* Fix assertion in test_cameracalibration.cpp
The total number of refined 3D object coordinates is checked.
* Add checker for iFixedPoint in cvCalibrateCamera4
If iFixedPoint is out of rational range, fall back to standard method.
* Fix documentation for overloaded calibrateCamera()
* Remove calibration flag of CALIB_RELEASE_OBJECT
The method selection is based on the range of the index of fixed point.
For minus values, standard calibration method will be chosen. Values in
a rational range will make the object-releasing calibration method
selected.
* Use new interfaces instead of function overload
Existing interfaces are preserved and new interfaces are added. Since
most part of the code base are shared, calibrateCamera() is now a
wrapper function of calibrateCameraRO().
* Fix exported name of calibrateCameraRO()
* Update documentation for calibrateCameraRO()
The circumstances where this method is mostly helpful are described.
* Add note on the rigidity of the calibration target
* Update documentation for calibrateCameraRO()
It is clarified that iFixedPoint is used as a switch to select
calibration method. If input data are not qualified, exceptions will be
thrown instead of fallback scheme.
* Clarify iFixedPoint as switch and remove fallback
iFixedPoint is now used as a switch for calibration method selection. No
fallback scheme is utilized anymore. If the input data are not
qualified, exceptions will be thrown.
* Add badarg test for object-releasing method
* Fix document format of sample list
List items of same level should be indented the same way. Otherwise they
will be formatted as nested lists by Doxygen.
* Add brief intro for objectPoints and imagePoints
* Sync tutorial to sample calibration code
* Update tutorial compatibility version to 4.0
- accepts script parameter (allows drag & drop from 'explorer')
- use script dir instead of current dir (can launch samples from 'samples/dnn')
- added 'pause' to show error messages (about missing numpy) instead of instant closing
* Remove a forward method in dnn::Layer
* Add a test
* Fix tests
* Mark multiple dnn::Layer::finalize methods as deprecated
* Replace back dnn's inputBlobs to vector of pointers
* Remove Layer::forward_fallback from CV_OCL_RUN scopes
In this tutorial you will learn:
- what is a degradation image model
- what is a PSF of an out-of-focus image
- how to restore a blurred image
- what is the Wiener filter
Mser sample improvments (#12032)
* Fixed bug in detect_mser sample
Wrong number of colors used to generate the synthetic images
* Formatting improvements
* Using safer casts
* Improved readability of legend generation
* Various readability fixes in detect_mser sample
* Include ELA example
* Include ELA example
* Include ELA example
* Include ELA example
* Include ELA example
* Include ELA example
* Include ELA example
* Include ELA example
* Include ELA example
* Include ELA example
* Include ELA example
* did some code cleanup
* fixed compile error
