added estimateTranslation3D to calib3d/ptsetreg
* added estimateTranslation3D; follows API and implementation structure for estimateAffine3D, but only allows for translation
* void variables in null function to suppress compiler warnings
* added test for estimateTranslation3D
* changed to Matx13d datatype for translation vector in ptsetreg and test; used short license in test
* removed iostream include
* calib3d: code cleanup
Image sharpness, as well as brightness, are a critical parameter for
accuracte camera calibration. For accessing these parameters for
filtering out problematic calibraiton images, this method calculates
edge profiles by traveling from black to white chessboard cell centers.
Based on this, the number of pixels is calculated required to transit
from black to white. This width of the transition area is a good
indication of how sharp the chessboard is imaged and should be below
~3.0 pixels.
Based on this also motion blur can be detectd by comparing sharpness in
vertical and horizontal direction. All unsharp images should be excluded
from calibration as they will corrupt the calibration result. The same
is true for overexposued images due to a none-linear sensor response.
This can be detected by looking at the average cell brightness of the
detected chessboard.
Lets the user choose the maximum number of iterations the robust
estimator runs for, similary to findHomography. This can significantly
improve performance (at a computational cost).
Changes:
* UMat for blur + rotate resulting in a speedup of around 2X on an i7
* support for boards larger than specified allowing to cover full FOV
* support for markers moving the origin into the center of the board
* increase detection accuracy
The main change is for supporting boards that are larger than the FOV of
the camera and have their origin in the board center. This allows
building OEM calibration targets similar to the one from intel real
sense utilizing corner points as close as possible to the image border.
This is a correction of the previously missleading documentation and a warning related to a common calibration failure described in issue 15992
* corrected incorrect description of failed calibration state.
see issue 15992
* calib3d: apply suggestions from code review by catree
Added type check for solvePnPGeneric | Issue: #16049
* Added type check
* Added checks before type fix
* Tests for 16049
* calib3d: update solvePnP regression check (16049)
Add checks for empty operands in Matrix expressions that don't check properly
* Starting to add checks for empty operands in Matrix expressions that
don't check properly.
* Adding checks and delcarations for checker functions
* Fix signatures and add checks for each class of Matrix Expr operation
* Make it catch the right exception
* Don't expose helper functions to public API
* calib3d: use normalized input in solvePnPGeneric()
* calib3d: java regression test for solvePnPGeneric
* calib3d: python regression test for solvePnPGeneric
(1/4) Revert "Correct image borders and principal point computation in cv::stereoRectify"
This reverts commit 93ff1fb2f2.
(2/4) Revert "fix calib3d changes in 6836 plus some others"
This reverts commit fa42a1cfc2.
(3/4) Revert "fix compiler warning"
This reverts commit b3d55489d3.
(4/4) Revert "add test for 6836"
This reverts commit d06b8c4ea9.
Clarify stereoRectify() doc
The function stereoRectify() takes as input a coordinate transform between two cameras. It is ambiguous how it goes. I clarified that it goes from the second camera to the first.
* Doc bugfix
The documentation page StereoBinaryBM and StereoBinarySGBM says that it returns a disparity that is scaled multiplied by 16. This scaling must be undone before calling reprojectImageTo3D, otherwise the results are wrong. The function reprojectImageTo3D() could do this scaling internally, maybe, but at least the documentation must explain that this has to be done.
* calib3d: update reprojectImageTo3D documentation
* calib3d: add StereoBM/StereoSGBM into notes list
* issue 5769 fixed: cv::stereoRectify fails if given inliers mask of type vector<uchar>
* issue5769 fix using reshape and add regression test
* regression test with outlier detection, testing vector and mat data
* Size comparision of wrong vector within CV_Assert in regression test corrected
* cleanup test code
