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authorStanislaw Halik <sthalik@misaki.pl>2019-01-08 09:10:33 +0100
committerStanislaw Halik <sthalik@misaki.pl>2019-01-16 07:48:48 +0100
commit9e0c82209c9dea0af3f3f16b88a606f5b8cec380 (patch)
treeadae01a431916f959cf3eaacbf812ee7fb716a1e
parent7e172957947179b1643caa5da1488daa3ca1c1f0 (diff)
tracker/pt: fix prototypes
-rw-r--r--tracker-pt/module/point_extractor.cpp4
-rw-r--r--tracker-pt/module/point_extractor.h2
2 files changed, 3 insertions, 3 deletions
diff --git a/tracker-pt/module/point_extractor.cpp b/tracker-pt/module/point_extractor.cpp
index 53f03fa9..6eaf0d2f 100644
--- a/tracker-pt/module/point_extractor.cpp
+++ b/tracker-pt/module/point_extractor.cpp
@@ -49,7 +49,7 @@ corresponding location is a good candidate for the extracted point.
The idea similar to the window scaling suggested in Berglund et al. "Fast, bias-free
algorithm for tracking single particles with variable size and shape." (2008).
*/
-static cv::Vec2d MeanShiftIteration(const cv::Mat &frame_gray, const vec2 &current_center, f filter_width)
+static vec2 MeanShiftIteration(const cv::Mat1b &frame_gray, const vec2 &current_center, f filter_width)
{
// Most amazingly this function runs faster with doubles than with floats.
const f s = 1 / filter_width;
@@ -109,7 +109,7 @@ void PointExtractor::ensure_buffers(const cv::Mat& frame)
}
}
-void PointExtractor::extract_single_channel(const cv::Mat& orig_frame, int idx, cv::Mat& dest)
+void PointExtractor::extract_single_channel(const cv::Mat& orig_frame, int idx, cv::Mat1b& dest)
{
ensure_channel_buffers(orig_frame);
diff --git a/tracker-pt/module/point_extractor.h b/tracker-pt/module/point_extractor.h
index 03b43489..06948ac7 100644
--- a/tracker-pt/module/point_extractor.h
+++ b/tracker-pt/module/point_extractor.h
@@ -48,7 +48,7 @@ private:
void ensure_channel_buffers(const cv::Mat& orig_frame);
void ensure_buffers(const cv::Mat& frame);
- void extract_single_channel(const cv::Mat& orig_frame, int idx, cv::Mat& dest);
+ void extract_single_channel(const cv::Mat& orig_frame, int idx, cv::Mat1b& dest);
void color_to_grayscale(const cv::Mat& frame, cv::Mat1b& output);
void threshold_image(const cv::Mat& frame_gray, cv::Mat1b& output);