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fixed number of training mode operation

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This commit is contained in:
Vladislav Vinogradov 2012-08-09 12:37:35 +04:00
parent 9b16563f44
commit 55f8310cac
1 changed files with 16 additions and 18 deletions
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34
modules/video/src/bgfg_gmg.cpp
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@ -199,7 +199,7 @@ namespace
public: public:
GMG_LoopBody(const cv::Mat& frame, const cv::Mat& fgmask, const cv::Mat_<int>& nfeatures, const cv::Mat_<int>& colors, const cv::Mat_<float>& weights, GMG_LoopBody(const cv::Mat& frame, const cv::Mat& fgmask, const cv::Mat_<int>& nfeatures, const cv::Mat_<int>& colors, const cv::Mat_<float>& weights,
int maxFeatures, double learningRate, int numInitializationFrames, int quantizationLevels, double backgroundPrior, double decisionThreshold, int maxFeatures, double learningRate, int numInitializationFrames, int quantizationLevels, double backgroundPrior, double decisionThreshold,
double maxVal, double minVal, size_t frameNum) : double maxVal, double minVal, int frameNum) :
frame_(frame), fgmask_(fgmask), nfeatures_(nfeatures), colors_(colors), weights_(weights), frame_(frame), fgmask_(fgmask), nfeatures_(nfeatures), colors_(colors), weights_(weights),
maxFeatures_(maxFeatures), learningRate_(learningRate), numInitializationFrames_(numInitializationFrames), maxFeatures_(maxFeatures), learningRate_(learningRate), numInitializationFrames_(numInitializationFrames),
quantizationLevels_(quantizationLevels), backgroundPrior_(backgroundPrior), decisionThreshold_(decisionThreshold), quantizationLevels_(quantizationLevels), backgroundPrior_(backgroundPrior), decisionThreshold_(decisionThreshold),
@ -227,7 +227,7 @@ namespace
double maxVal_; double maxVal_;
double minVal_; double minVal_;
size_t frameNum_; int frameNum_;
}; };
void GMG_LoopBody::operator() (const cv::Range& range) const void GMG_LoopBody::operator() (const cv::Range& range) const
@ -262,7 +262,7 @@ namespace
bool isForeground = false; bool isForeground = false;
if (frameNum_ > numInitializationFrames_) if (frameNum_ >= numInitializationFrames_)
{ {
// typical operation // typical operation
@ -272,21 +272,7 @@ namespace
const double posterior = (weight * backgroundPrior_) / (weight * backgroundPrior_ + (1.0 - weight) * (1.0 - backgroundPrior_)); const double posterior = (weight * backgroundPrior_) / (weight * backgroundPrior_ + (1.0 - weight) * (1.0 - backgroundPrior_));
isForeground = ((1.0 - posterior) > decisionThreshold_); isForeground = ((1.0 - posterior) > decisionThreshold_);
}
fgmask_row[x] = (uchar)(-isForeground);
if (frameNum_ <= numInitializationFrames_ + 1)
{
// training-mode update
insertFeature(newFeatureColor, 1.0f, colors, weights, nfeatures, maxFeatures_);
if (frameNum_ == numInitializationFrames_ + 1)
normalizeHistogram(weights, nfeatures);
}
else
{
// update histogram. // update histogram.
for (int i = 0; i < nfeatures; ++i) for (int i = 0; i < nfeatures; ++i)
@ -295,10 +281,22 @@ namespace
bool inserted = insertFeature(newFeatureColor, learningRate_, colors, weights, nfeatures, maxFeatures_); bool inserted = insertFeature(newFeatureColor, learningRate_, colors, weights, nfeatures, maxFeatures_);
if (inserted) if (inserted)
{
normalizeHistogram(weights, nfeatures);
nfeatures_row[x] = nfeatures;
}
}
else
{
// training-mode update
insertFeature(newFeatureColor, 1.0f, colors, weights, nfeatures, maxFeatures_);
if (frameNum_ == numInitializationFrames_ - 1)
normalizeHistogram(weights, nfeatures); normalizeHistogram(weights, nfeatures);
} }
nfeatures_row[x] = nfeatures; fgmask_row[x] = (uchar)(-isForeground);
} }
} }
} }