}
}
-void GridAdaptedFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
+namespace {
+class GridAdaptedFeatureDetectorInvoker
{
- keypoints.reserve(maxTotalKeypoints);
+private:
+ int gridRows_, gridCols_;
+ int maxPerCell_;
+ vector<KeyPoint>& keypoints_;
+ const Mat& image_;
+ const Mat& mask_;
+ const Ptr<FeatureDetector>& detector_;
+#ifdef HAVE_TBB
+ tbb::mutex* kptLock_;
+#endif
+
+public:
+
+ GridAdaptedFeatureDetectorInvoker(const Ptr<FeatureDetector>& detector, const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints, int maxPerCell, int gridRows, int gridCols
+#ifdef HAVE_TBB
+ , tbb::mutex* kptLock
+#endif
+ ) : gridRows_(gridRows), gridCols_(gridCols), maxPerCell_(maxPerCell),
+ keypoints_(keypoints), image_(image), mask_(mask), detector_(detector)
+#ifdef HAVE_TBB
+ , kptLock_(kptLock)
+#endif
+ {
+ }
- int maxPerCell = maxTotalKeypoints / (gridRows * gridCols);
- for( int i = 0; i < gridRows; ++i )
+ void operator() (const BlockedRange& range) const
{
- Range row_range((i*image.rows)/gridRows, ((i+1)*image.rows)/gridRows);
- for( int j = 0; j < gridCols; ++j )
+ for (int i = range.begin(); i < range.end(); ++i)
{
- Range col_range((j*image.cols)/gridCols, ((j+1)*image.cols)/gridCols);
- Mat sub_image = image(row_range, col_range);
+ int celly = i / gridCols_;
+ int cellx = i - celly * gridCols_;
+
+ Range row_range((celly*image_.rows)/gridRows_, ((celly+1)*image_.rows)/gridRows_);
+ Range col_range((cellx*image_.cols)/gridCols_, ((cellx+1)*image_.cols)/gridCols_);
+
+ Mat sub_image = image_(row_range, col_range);
Mat sub_mask;
- if( !mask.empty() )
- sub_mask = mask(row_range, col_range);
+ if (!mask_.empty()) sub_mask = mask_(row_range, col_range);
vector<KeyPoint> sub_keypoints;
- detector->detect( sub_image, sub_keypoints, sub_mask );
- keepStrongest( maxPerCell, sub_keypoints );
+ sub_keypoints.reserve(maxPerCell_);
+
+ detector_->detect( sub_image, sub_keypoints, sub_mask );
+ keepStrongest( maxPerCell_, sub_keypoints );
+
std::vector<cv::KeyPoint>::iterator it = sub_keypoints.begin(),
end = sub_keypoints.end();
for( ; it != end; ++it )
it->pt.x += col_range.start;
it->pt.y += row_range.start;
}
-
- keypoints.insert( keypoints.end(), sub_keypoints.begin(), sub_keypoints.end() );
+#ifdef HAVE_TBB
+ tbb::mutex::scoped_lock join_keypoints(*kptLock_);
+#endif
+ keypoints_.insert( keypoints_.end(), sub_keypoints.begin(), sub_keypoints.end() );
}
}
+};
+} // namepace
+
+void GridAdaptedFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
+{
+ if (image.empty() || maxTotalKeypoints < gridRows * gridCols)
+ {
+ keypoints.clear();
+ return;
+ }
+ keypoints.reserve(maxTotalKeypoints);
+ int maxPerCell = maxTotalKeypoints / (gridRows * gridCols);
+
+#ifdef HAVE_TBB
+ tbb::mutex kptLock;
+ cv::parallel_for(cv::BlockedRange(0, gridRows * gridCols),
+ GridAdaptedFeatureDetectorInvoker(detector, image, mask, keypoints, maxPerCell, gridRows, gridCols, &kptLock));
+#else
+ GridAdaptedFeatureDetectorInvoker(detector, image, mask, keypoints, maxPerCell, gridRows, gridCols)(cv::BlockedRange(0, gridRows * gridCols));
+#endif
}
/*