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2019-12-09 10:55:38 -0600	commented question	Can i find if a line connected component is strongly connected or not? You could write your own region growing method which would follow valid ("white") pixels until the method reaches a pixe
2018-02-05 01:40:49 -0600	commented question	Mock Camera Intrinsics What do you mean by oddly stretched? Isn't the "stretch" occuring simply due to the perspective distortion? If you don't
2018-01-26 09:04:23 -0600	received badge	● Editor (source)
2018-01-26 09:04:23 -0600	edited question	Question on the Random Forest Classifier implementation Question on the Random Forest Classifier implementation Hi, I am currently comparing OpenCV's RandomTrees Classifier wi
2018-01-26 08:58:54 -0600	asked a question	Question on the Random Forest Classifier implementation Question on the Random Forest Classifier implementation Hi, I am currently comparing OpenCV's RandomTrees Classifier wi
2018-01-26 08:16:25 -0600	answered a question	can I use stereoCalibrate with different image sizes? What about first calibrating the two cameras individually, and then in the second step using stereoCalibrate() only to r
2016-02-11 07:57:02 -0600	received badge	● Enthusiast
2016-02-08 15:34:08 -0600	commented question	RTrees training fails due to Assertion in calcDir OpenCV 2.4.11's regression forest seems to work fine with the same data and same settings.
2016-02-02 03:55:25 -0600	asked a question	RTrees training fails due to Assertion in calcDir Hi, I'm trying to train a random descision forest for regression but it fails due to an assertion in tree.cpp:1190 `OpenCV Error: Assertion failed ((int)_sleft.size() < n && (int)_sright.size() < n) in cv::ml::DTreesImpl::calcDir, file C:\builds\master_PackSlave-win64-vc12-shared\opencv\modules\ml\src\tree.cpp, line 1190 exception caught: C:\builds\master_PackSlave-win64-vc12-shared\opencv\modules\ml\src\tree.cpp:1190: error: (-215) (int)_sleft.size() < n && (int)_sright.size() < n in function cv::ml::DTreesImpl::calcDir` With smaller training sets the forest seems to work fine, but as soon as I use > 20000 features it fails during training. My current settings are: `Ptr<RTrees> rtrees = RTrees::create(); rtrees->setMaxDepth(10); rtrees->setMinSampleCount(10); rtrees->setRegressionAccuracy(0.1f); rtrees->setUseSurrogates(false); rtrees->setMaxCategories(16); rtrees->setPriors(Mat()); rtrees->setCalculateVarImportance(false); rtrees->setActiveVarCount(1); rtrees->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER,100, 0));` Has anyone else experienced this problem or knows the reason for the error? Thanks!