mirror/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2025-01-19 06:53:50 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Expose maxIters in findFundamentalMat

Browse Source 
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).
This commit is contained in:
Eduard Trulls 2020-02-04 07:28:07 +00:00 committed by Alexander Smorkalov
parent d2cc30e2a5
commit 7e637c134f
2 changed files with 18 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
modules/calib3d/include/opencv2/calib3d.hpp
View File

@ -1905,6 +1905,7 @@ point localization, image resolution, and the image noise.
@param confidence Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level @param confidence Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level
of confidence (probability) that the estimated matrix is correct. of confidence (probability) that the estimated matrix is correct.
@param mask @param mask
@param maxIters The maximum number of robust method iterations.
The epipolar geometry is described by the following equation: The epipolar geometry is described by the following equation:
@ -1938,6 +1939,11 @@ stereoRectifyUncalibrated to compute the rectification transformation. :
findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99); findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99);
@endcode @endcode
*/ */
CV_EXPORTS_W Mat findFundamentalMat( InputArray points1, InputArray points2,
int method, double ransacReprojThreshold, double confidence,
int maxIters, OutputArray mask = noArray() );
/** @overload */
CV_EXPORTS_W Mat findFundamentalMat( InputArray points1, InputArray points2, CV_EXPORTS_W Mat findFundamentalMat( InputArray points1, InputArray points2,
int method = FM_RANSAC, int method = FM_RANSAC,
double ransacReprojThreshold = 3., double confidence = 0.99, double ransacReprojThreshold = 3., double confidence = 0.99,

18
modules/calib3d/src/fundam.cpp
View File

@ -809,7 +809,7 @@ public:
cv::Mat cv::findFundamentalMat( InputArray _points1, InputArray _points2, cv::Mat cv::findFundamentalMat( InputArray _points1, InputArray _points2,
int method, double ransacReprojThreshold, double confidence, int method, double ransacReprojThreshold, double confidence,
OutputArray _mask ) int maxIters, OutputArray _mask )
{ {
CV_INSTRUMENT_REGION(); CV_INSTRUMENT_REGION();
@ -861,7 +861,7 @@ cv::Mat cv::findFundamentalMat( InputArray _points1, InputArray _points2,
confidence = 0.99; confidence = 0.99;
if( (method & ~3) == FM_RANSAC && npoints >= 15 ) if( (method & ~3) == FM_RANSAC && npoints >= 15 )
result = createRANSACPointSetRegistrator(cb, 7, ransacReprojThreshold, confidence)->run(m1, m2, F, _mask); result = createRANSACPointSetRegistrator(cb, 7, ransacReprojThreshold, confidence, maxIters)->run(m1, m2, F, _mask);
else else
result = createLMeDSPointSetRegistrator(cb, 7, confidence)->run(m1, m2, F, _mask); result = createLMeDSPointSetRegistrator(cb, 7, confidence)->run(m1, m2, F, _mask);
} }
@ -872,11 +872,17 @@ cv::Mat cv::findFundamentalMat( InputArray _points1, InputArray _points2,
return F; return F;
} }
cv::Mat cv::findFundamentalMat( InputArray _points1, InputArray _points2, cv::Mat cv::findFundamentalMat( cv::InputArray points1, cv::InputArray points2,
OutputArray _mask, int method, int method, double ransacReprojThreshold, double confidence,
double ransacReprojThreshold , double confidence) cv::OutputArray mask )
{ {
return cv::findFundamentalMat(_points1, _points2, method, ransacReprojThreshold, confidence, _mask); return cv::findFundamentalMat(points1, points2, method, ransacReprojThreshold, confidence, 1000, mask);
}
cv::Mat cv::findFundamentalMat( cv::InputArray points1, cv::InputArray points2, cv::OutputArray mask,
int method, double ransacReprojThreshold, double confidence )
{
return cv::findFundamentalMat(points1, points2, method, ransacReprojThreshold, confidence, 1000, mask);
} }