Description
System information (version)
- OpenCV => 4.4.0
- Operating System / Platform => Debian
- Compiler => gcc 7.3.0
Detailed description
Recently I'm working on some background subtractor algorithm using cuda. I first test the algorithm on opencv+opencv_contrib 3.4.4, the result is nice and stable. But when I deploy it on opencv+opencv_contrib 4.4.0, the subtractor gives more difference areas than 3.4.4.
After some search I found out:
-
There is an issue Multiple MOG2_GPU objects share the same parameter values opencv#5296, which report a bug about the subtractor, "Multiple MOG2_GPU objects share the same parameter values".
-
After 4 years, there is a PR resolves the issue: cuda_mog2_issue_5296 opencv#16090, which mainly modifed mog2.hpp and mog2.cpp, the new code moved the parameters to Constants struct instead of global.
-
In the new code of 16090, I found the bug:
void setVarMin(double varMin) CV_OVERRIDE { constantsHost_.varMin_ = ::fminf((float)varMin, constantsHost_.varMax_); }void setVarMax(double varMax) CV_OVERRIDE { constantsHost_.varMax_ = ::fmaxf(constantsHost_.varMin_, (float)varMax); }The previous two funtions are called directly in constructor MOG2Impl::MOG2Impl. But when the fminf or fmaxf is called, varMin_ and varMax_ may still be undefined. For example, when setVarMin is called, varMin_ may be -99999999 or some strange number, and fminf may keep the number inside the object.
- I am not an expert of background subtractor algorithm, I just found the bug based on understanding of C++ language.Here is a solution:
varMin_ and varMax_ will be uploaded to GPU in MOG2Impl::initialize. So I move the fminf and fmaxf operations into MOG2Impl::initialize, before cudaMemcpyAsync. Then everything works fine, the result is same with 3.4.4, at least I can not see any difference with my eyes. Code is modified like below:
void setVarMin(double varMin) CV_OVERRIDE { constantsHost_.varMin_ = (float)varMin; }void setVarMax(double varMax) CV_OVERRIDE { constantsHost_.varMax_ = (float)varMax; }void MOG2ImplFixed::initialize(cv::Size frameSize, int frameType, Stream& stream)
{
using namespace cv::cuda::device::mog2;
CV_Assert(frameType == CV_8UC1 || frameType == CV_8UC3 || frameType == CV_8UC4);
frameSize_ = frameSize;
frameType_ = frameType;
nframes_ = 0;
const int ch = CV_MAT_CN(frameType);
const int work_ch = ch;
// for each gaussian mixture of each pixel bg model we store ...
// the mixture weight (w),
// the mean (nchannels values) and
// the covariance
weight_.create(frameSize.height * getNMixtures(), frameSize_.width, CV_32FC1);
variance_.create(frameSize.height * getNMixtures(), frameSize_.width, CV_32FC1);
mean_.create(frameSize.height * getNMixtures(), frameSize_.width, CV_32FC(work_ch));
// make the array for keeping track of the used modes per pixel - all zeros at
// start
bgmodelUsedModes_.create(frameSize_, CV_8UC1);
bgmodelUsedModes_.setTo(Scalar::all(0));
float real_varMin = ::fminf(constantsHost_.varMin_, constantsHost_.varMax_);
float real_varMax = ::fmaxf(constantsHost_.varMin_, constantsHost_.varMax_);
constantsHost_.varMin_ = real_varMin;
constantsHost_.varMax_ = real_varMax;
cudaSafeCall(cudaMemcpyAsync(constantsDevice_, &constantsHost_, sizeof(Constants),
cudaMemcpyHostToDevice, StreamAccessor::getStream(stream)));
}