generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Merged the trunk r8547:8574, r8587

Browse Source
This commit is contained in:
Andrey Kamaev
2012-06-15 08:36:35 +00:00
parent 4b1724aeb9
commit ab20da0f53
12 changed files with 133 additions and 33 deletions
Download Patch File Download Diff File Expand all files Collapse all files

32
modules/imgproc/doc/motion_analysis_and_object_tracking.rst
View File

@@ -161,34 +161,34 @@ Return value: detected phase shift (sub-pixel) between the two arrays.
The function performs the following equations
*
First it applies a Hanning window (see http://en.wikipedia.org/wiki/Hann\_function) to each image to remove possible edge effects. This window is cached until the array size changes to speed up processing time.
* First it applies a Hanning window (see http://en.wikipedia.org/wiki/Hann\_function) to each image to remove possible edge effects. This window is cached until the array size changes to speed up processing time.
*
Next it computes the forward DFTs of each source array:
.. math::
* Next it computes the forward DFTs of each source array:
.. math::
\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}
where
:math:`\mathcal{F}` is the forward DFT.
where
:math:`\mathcal{F}` is the forward DFT.
* It then computes the cross-power spectrum of each frequency domain array:
*
It then computes the cross-power spectrum of each frequency domain array:
.. math::
R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}
R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}
* Next the cross-correlation is converted back into the time domain via the inverse DFT:
*
Next the cross-correlation is converted back into the time domain via the inverse DFT:
.. math::
r = \mathcal{F}^{-1}\{R\}
*
Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to achieve sub-pixel accuracy.
r = \mathcal{F}^{-1}\{R\}
* Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to achieve sub-pixel accuracy.
.. math::
(\Delta x, \Delta y) = \texttt{weighted_centroid}\{\arg \max_{(x, y)}\{r\}\}
(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}
.. seealso::
:ocv:func:`dft`,

8
modules/imgproc/src/imgwarp.cpp
View File

@@ -1207,7 +1207,7 @@ struct DecimateAlpha
};
template<typename T, typename WT>
static void resizeArea_( const Mat& src, Mat& dst, const DecimateAlpha* xofs, int xofs_count )
static void resizeArea_( const Mat& src, Mat& dst, const DecimateAlpha* xofs, int xofs_count, double scale_y_)
{
Size ssize = src.size(), dsize = dst.size();
int cn = src.channels();
@@ -1215,7 +1215,7 @@ static void resizeArea_( const Mat& src, Mat& dst, const DecimateAlpha* xofs, in
AutoBuffer<WT> _buffer(dsize.width*2);
WT *buf = _buffer, *sum = buf + dsize.width;
int k, sy, dx, cur_dy = 0;
WT scale_y = (WT)ssize.height/dsize.height;
WT scale_y = (WT)scale_y_;
CV_Assert( cn <= 4 );
for( dx = 0; dx < dsize.width; dx++ )
@@ -1315,7 +1315,7 @@ typedef void (*ResizeAreaFastFunc)( const Mat& src, Mat& dst,
int scale_x, int scale_y );
typedef void (*ResizeAreaFunc)( const Mat& src, Mat& dst,
const DecimateAlpha* xofs, int xofs_count );
const DecimateAlpha* xofs, int xofs_count, double scale_y_);
}
@@ -1532,7 +1532,7 @@ void cv::resize( InputArray _src, OutputArray _dst, Size dsize,
}
}
func( src, dst, xofs, k );
func( src, dst, xofs, k ,scale_y);
return;
}