nfeatures_.setTo(cv::Scalar::all(0));
- boxFilter_ = cv::gpu::createBoxFilter_GPU(CV_8UC1, CV_8UC1, cv::Size(smoothingRadius, smoothingRadius));
+ if (smoothingRadius > 0)
+ boxFilter_ = cv::gpu::createBoxFilter_GPU(CV_8UC1, CV_8UC1, cv::Size(smoothingRadius, smoothingRadius));
loadConstants(frameSize_.width, frameSize_.height, minVal_, maxVal_, quantizationLevels, backgroundPrior, decisionThreshold, maxFeatures, numInitializationFrames);
}
initialize(frame.size(), 0.0f, frame.depth() == CV_8U ? 255.0f : frame.depth() == CV_16U ? std::numeric_limits<ushort>::max() : 1.0f);
fgmask.create(frameSize_, CV_8UC1);
+ if (stream)
+ stream.enqueueMemSet(fgmask, cv::Scalar::all(0));
+ else
+ fgmask.setTo(cv::Scalar::all(0));
funcs[frame.depth()][frame.channels() - 1](frame, fgmask, colors_, weights_, nfeatures_, frameNum_, learningRate, cv::gpu::StreamAccessor::getStream(stream));
// medianBlur
- boxFilter_->apply(fgmask, buf_, cv::Rect(0,0,-1,-1), stream);
- int minCount = (smoothingRadius * smoothingRadius + 1) / 2;
- double thresh = 255.0 * minCount / (smoothingRadius * smoothingRadius);
- cv::gpu::threshold(buf_, fgmask, thresh, 255.0, cv::THRESH_BINARY, stream);
+ if (smoothingRadius > 0)
+ {
+ boxFilter_->apply(fgmask, buf_, cv::Rect(0,0,-1,-1), stream);
+ int minCount = (smoothingRadius * smoothingRadius + 1) / 2;
+ double thresh = 255.0 * minCount / (smoothingRadius * smoothingRadius);
+ cv::gpu::threshold(buf_, fgmask, thresh, 255.0, cv::THRESH_BINARY, stream);
+ }
// keep track of how many frames we have processed
++frameNum_;
int nfeatures = nfeatures_(y, x);
- bool isForeground = false;
-
- if (frameNum > c_numInitializationFrames)
+ if (frameNum >= c_numInitializationFrames)
{
// typical operation
+
const float weight = findFeature(newFeatureColor, colors_, weights_, x, y, nfeatures);
// see Godbehere, Matsukawa, Goldberg (2012) for reasoning behind this implementation of Bayes rule
const float posterior = (weight * c_backgroundPrior) / (weight * c_backgroundPrior + (1.0f - weight) * (1.0f - c_backgroundPrior));
- isForeground = ((1.0f - posterior) > c_decisionThreshold);
- }
-
- fgmask(y, x) = (uchar)(-isForeground);
+ const bool isForeground = ((1.0f - posterior) > c_decisionThreshold);
+ fgmask(y, x) = (uchar)(-isForeground);
- if (frameNum <= c_numInitializationFrames + 1)
- {
- // training-mode update
-
- insertFeature(newFeatureColor, 1.0f, colors_, weights_, x, y, nfeatures);
-
- if (frameNum == c_numInitializationFrames + 1)
- normalizeHistogram(weights_, x, y, nfeatures);
- }
- else
- {
// update histogram.
for (int i = 0, fy = y; i < nfeatures; ++i, fy += c_height)
nfeatures_(y, x) = nfeatures;
}
}
+ else
+ {
+ // training-mode update
+
+ insertFeature(newFeatureColor, 1.0f, colors_, weights_, x, y, nfeatures);
+
+ if (frameNum == c_numInitializationFrames - 1)
+ normalizeHistogram(weights_, x, y, nfeatures);
+ }
}
template <typename SrcT>