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
* Fix for Homogenous precision #14242:
- moved scale computation to an inline function
- use std::numeric_limits<float>::epsilon() instead of != 0.0
* Fix for Homogenous precision #14242:
- fixed warnings for type conversion
* Fix for Homogenous precision #14242:
- use float epsilon() for truncation of doubles
objectPoints and imagePoints are not checked whether they're empty and
cause checkVector() to fail, thus result in a wrong error message.
Fixes: https://github.com/opencv/opencv/issues/6002
During the cluster-based detection of circle grids, the detected circle
pattern has to be mapped to 3D-points. When doing this the width (i.e.
more circles) and height (i.e. less circles) of the pattern need to
be identified in image coordinates.
Until now this was done by assuming that the shorter side in image
coordinates (length in pixels) corresponds to the height in 3D.
This assumption does not hold if we look at the pattern from
a perspective where the projection of the width is shorter
than the projection of the height. This in turn lead to misdetections in
although the circle pattern was clearly visible.
Instead count how many circles have been detected along two edges of the
projected quadrangle and use the one with more circles as width and the
one with less as height.