Chiebot-Mirror
/
opencv_contrib
8
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

fixed bugs in WH transform, some code compression

Browse Source
pull/49/head
Bellaktris 11 years ago
parent 79020cf755
commit e92394d0aa
10 changed files with 262 additions and 167 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 2
      modules/ximpgroc/doc/ximpgroc/structured_edge_detection.rst
  2. 2
      modules/xphoto/CMakeLists.txt
  3. 20
      modules/xphoto/doc/inpainting/inpainting.rst
  4. 1
      modules/xphoto/doc/xphoto.rst
  5. 8
      modules/xphoto/samples/inpainting.cpp
  6. 103
      modules/xphoto/src/annf.hpp
  7. 136
      modules/xphoto/src/inpainting.cpp
  8. 46
      modules/xphoto/src/norm2.hpp
  9. 3
      modules/xphoto/src/photomontage.hpp
  10. 86
      modules/xphoto/src/whs.hpp

2
modules/ximpgroc/doc/ximpgroc/structured_edge_detection.rst
Unescape View File

@ -62,6 +62,6 @@ createStructuredEdgeDetection
:param model: model file name
.. [Dollar2013] Dollár P., Zitnick C. L., "Structured forests for fast edge detection",
.. [Dollar2013] P. Dollár, C. L. Zitnick, "Structured forests for fast edge detection",
IEEE International Conference on Computer Vision (ICCV), 2013,
pp. 1841-1848. `DOI <http://dx.doi.org/10.1109/ICCV.2013.231>`_

2
modules/xphoto/CMakeLists.txt
Unescape View File

@ -1,2 +1,2 @@
set(the_description "Addon to basic photo module")
ocv_define_module(xphoto opencv_core opencv_imgproc opencv_photo OPTIONAL opencv_photo opencv_highgui)
ocv_define_module(xphoto opencv_core opencv_imgproc OPTIONAL opencv_photo opencv_highgui opencv_photo)

20
modules/xphoto/doc/inpainting/inpainting.rst
Unescape View File

@ -0,0 +1,20 @@
Single image inpainting
***********************
.. highlight:: cpp
Inpainting
----------
.. ocv:function:: void inpaint(const Mat &src, const Mat &mask, Mat &dst, const int algorithmType)
The function implements different single-image inpainting algorithms.
:param src : source image, it could be of any type and any number of channels from 1 to 4. In case of 3- and 4-channels images the function expect them in CIELab colorspace or similar one, where first color component shows intensity, while second and third shows colors. Nonetheless you can try any colorspaces.
:param mask : mask (CV_8UC1), where non-zero pixels indicate valid image area, while zero pixels indicate area to be inpainted
:param dst : destination image
:param algorithmType : expected noise standard deviation
* INPAINT_SHIFTMAP: This algorithm searches for dominant correspondences (transformations) of image patches and tries to seamlessly fill-in the area to be inpainted using this transformations. Look in the original paper [He2012]_ for details.
.. [He2012] K. He, J. Sun., "Statistics of Patch Offsets for Image Completion",
IEEE European Conference on Computer Vision (ICCV), 2012,
pp. 16-29. `DOI <http://dx.doi.org/10.1007/978-3-642-33709-3_2>`_

1
modules/xphoto/doc/xphoto.rst
Unescape View File

@ -7,3 +7,4 @@ xphoto. Addon to basic photo modul
Color balance <colorbalance/whitebalance>
Denoising <denoising/denoising>
Inpainting <inpainting/inpainting>

8
modules/xphoto/samples/inpainting.cpp
Unescape View File

@ -46,6 +46,8 @@ int main( int argc, const char** argv )
return -1;
}
cv::cvtColor(src, src, CV_RGB2Lab);
cv::Mat mask = cv::imread(maskFilename, 0);
if ( mask.empty() )
{
@ -54,13 +56,13 @@ int main( int argc, const char** argv )
}
cv::Mat res(src.size(), src.type());
cv::inpaint( src, mask, res, cv::INPAINT_SHIFTMAP );
cv::cvtColor(res, res, CV_Lab2RGB);
if ( outFilename == "" )
{
cv::namedWindow("denoising result", 1);
cv::imshow("denoising result", res);
cv::namedWindow("inpainting result", 1);
cv::imshow("inpainting result", res);
cv::waitKey(0);
}

103
modules/xphoto/src/annf.hpp
Unescape View File

@ -76,15 +76,18 @@ private:
: main(_main), dimIdx(_dimIdx) {}
};
const int height, width;
const int leafNumber;
std::vector <cv::Vec <Tp, cn> > data;
std::vector <int> idx;
std::vector <cv::Point2i> nodes;
int getMaxSpreadN(const int left, const int right) const;
void operator =(const KDTree <Tp, cn> &) const {};
public:
std::vector <cv::Vec <Tp, cn> > data;
std::vector <int> idx;
std::vector <cv::Point2i> nodes;
void updateDist(const int leaf, const int &idx0, int &bestIdx, double &dist);
KDTree(const cv::Mat &data, const int leafNumber = 8);
~KDTree(){};
@ -109,15 +112,16 @@ getMaxSpreadN(const int _left, const int _right) const
template <typename Tp, int cn> KDTree <Tp, cn>::
KDTree(const cv::Mat &img, const int _leafNumber)
: leafNumber(_leafNumber)
: height(img.rows), width(img.cols),
leafNumber(_leafNumber)
///////////////////////////////////////////////////
{
for (int i = 0; i < img.rows; ++i)
for (int j = 0; j < img.cols; ++j)
data.push_back(img.template at<cv::Vec <Tp, cn> >(i, j));
std::copy( (cv::Vec <Tp, cn> *) img.data,
(cv::Vec <Tp, cn> *) img.data + img.total(),
std::back_inserter(data) );
generate_seq( std::back_inserter(idx), 0, int(data.size()) );
fill_n( std::back_inserter(nodes), int(data.size()), cv::Point2i(0, 0) );
std::fill_n( std::back_inserter(nodes),
int(data.size()), cv::Point2i(0, 0) );
std::stack <int> left, right;
left.push( 0 );
@ -131,59 +135,51 @@ KDTree(const cv::Mat &img, const int _leafNumber)
if ( _right - _left <= leafNumber)
{
for (int i = _left; i < _right; ++i)
{
nodes[idx[i]].x = _left;
nodes[idx[i]].y = _right;
}
nodes[idx[i]] = cv::Point2i(_left, _right);
continue;
}
std::vector <int>::iterator begIt = idx.begin();
int nth = _left + (_right - _left)/2;
std::nth_element(/**/ begIt + _left,
begIt + nth, begIt + _right,
KDTreeComparator( this,
getMaxSpreadN(_left, _right) ) /**/);
int dimIdx = getMaxSpreadN(_left, _right);
KDTreeComparator comp( this, dimIdx );
std::nth_element(/**/
idx.begin() + _left,
idx.begin() + nth,
idx.begin() + _right, comp
/**/);
left.push(_left); right.push(nth + 1);
left.push(nth + 1); right.push(_right);
}
}
/************************** ANNF search **************************/
template <typename Tp, int cn>
static void updateDist(const KDTree <Tp, cn> &kdTree, const cv::Point2i &I, const int height,
const int width, const int &currentIdx, int &bestIdx, double &dist)
template <typename Tp, int cn> void KDTree <Tp, cn>::
updateDist(const int leaf, const int &idx0, int &bestIdx, double &dist)
{
for (int k = I.x; k < I.y; ++k)
for (int k = nodes[leaf].x; k < nodes[leaf].y; ++k)
{
int newIdx = kdTree.idx[k];
int y = idx0/width, ny = idx[k]/width;
int x = idx0%width, nx = idx[k]%width;
if (newIdx%width == width - 1)
if (abs(ny - y) + abs(nx - x) < 32)
continue;
if (newIdx/width == height - 1)
if (nx == width - 1 || ny == height - 1)
continue;
int dx = currentIdx%width - newIdx%width;
int dy = currentIdx/width - newIdx/width;
double ndist = norm2(data[idx0], data[idx[k]]);
if (abs(dx) + abs(dy) < 32)
continue;
double ndist = norm2(kdTree.data[newIdx],
kdTree.data[currentIdx]);
if (ndist < dist)
{
dist = ndist;
bestIdx = newIdx;
bestIdx = idx[k];
}
}
}
static void getANNF(const cv::Mat &img, std::vector <cv::Matx33f> &transforms,
/************************** ANNF search **************************/
static void dominantTransforms(const cv::Mat &img, std::vector <cv::Matx33f> &transforms,
const int nTransform, const int psize)
{
/** Walsh-Hadamard Transformation **/
@ -191,9 +187,14 @@ static void getANNF(const cv::Mat &img, std::vector <cv::Matx33f> &transforms,
std::vector <cv::Mat> channels;
cv::split(img, channels);
const int np[] = {16, 4, 4};
int cncase = std::max(img.channels() - 2, 0);
const int np[] = {cncase == 0 ? 12 : (cncase == 1 ? 16 : 10),
cncase == 0 ? 12 : (cncase == 1 ? 04 : 02),
cncase == 0 ? 00 : (cncase == 1 ? 04 : 02),
cncase == 0 ? 00 : (cncase == 1 ? 00 : 10)};
for (int i = 0; i < img.channels(); ++i)
getWHSeries(channels[i], channels[i], np[i], psize);
rgb2whs(channels[i], channels[i], np[i], psize);
cv::Mat whs; // Walsh-Hadamard series
cv::merge(channels, whs);
@ -209,21 +210,15 @@ static void getANNF(const cv::Mat &img, std::vector <cv::Matx33f> &transforms,
double dist = std::numeric_limits <double>::max();
int current = i*whs.cols + j;
cv::Point2i I = kdTree.nodes[i*whs.cols + j];
updateDist(kdTree, I, whs.rows, whs.cols, current, annf[i*whs.cols + j], dist);
if (i != 0)
{
int idx = annf[(i - 1)*whs.cols + j] + whs.cols;
cv::Point2i I = kdTree.nodes[idx];
updateDist(kdTree, I, whs.rows, whs.cols, current, annf[i*whs.cols + j], dist);
}
if (j != 0)
int dy[] = {0, 1, 0}, dx[] = {0, 0, 1};
for (int k = 0; k < sizeof(dy)/sizeof(int); ++k)
if (i - dy[k] >= 0 && j - dx[k] >= 0)
{
int idx = annf[i*whs.cols + (j - 1)] + 1;
cv::Point2i I = kdTree.nodes[idx];
updateDist(kdTree, I, whs.rows, whs.cols, current, annf[i*whs.cols + j], dist);
int neighbor = (i - dy[k])*whs.cols + (j - dx[k]);
int leafIdx = k == 0 ? neighbor :
annf[neighbor] + dy[k]*whs.cols + dx[k];
kdTree.updateDist(leafIdx, current,
annf[i*whs.cols + j], dist);
}
}

136
modules/xphoto/src/inpainting.cpp
Unescape View File

@ -57,6 +57,9 @@
#include "opencv2/core/types.hpp"
#include "opencv2/core/types_c.h"
#include "opencv2/highgui.hpp"
namespace xphotoInternal
{
# include "photomontage.hpp"
@ -66,22 +69,18 @@ namespace xphotoInternal
namespace cv
{
template <typename Tp, unsigned int cn>
static void shiftMapInpaint(const Mat &src, const Mat &mask, Mat &dst)
static void shiftMapInpaint(const Mat &src, const Mat &mask, Mat &dst,
const int nTransform = 60, const int psize = 8)
{
const int nTransform = 60; // number of dominant transforms for stitching
const int psize = 8; // single ANNF patch size
/** Preparing input **/
cv::Mat img;
cvtColor(src, img, CV_RGB2Lab);
src.convertTo( img, CV_32F );
img.setTo(0, 255 - mask);
img.convertTo( img, CV_32F );
/** ANNF computation **/
std::vector <Matx33f> transforms( nTransform );
xphotoInternal::getANNF(img, transforms,
nTransform, psize);
xphotoInternal::dominantTransforms(img,
transforms, nTransform, psize);
/** Warping **/
std::vector <Mat> images( nTransform + 1 ); // source image transformed with transforms
@ -109,8 +108,7 @@ namespace cv
.assignResImage(photomontageResult);
/** Writing result **/
photomontageResult.convertTo( photomontageResult, dst.type() );
cvtColor(photomontageResult, dst, CV_Lab2RGB);
photomontageResult.convertTo( dst, dst.type() );
}
template <typename Tp, unsigned int cn>
@ -143,66 +141,90 @@ namespace cv
switch ( src.type() )
{
//case CV_8UC1:
// inpaint <uchar, 1>( src, mask, dst, algorithmType );
// break;
//case CV_8UC2:
// inpaint <uchar, 2>( src, mask, dst, algorithmType );
// break;
case CV_8SC1:
inpaint <char, 1>( src, mask, dst, algorithmType );
break;
case CV_8SC2:
inpaint <char, 2>( src, mask, dst, algorithmType );
break;
case CV_8SC3:
inpaint <char, 3>( src, mask, dst, algorithmType );
break;
case CV_8SC4:
inpaint <char, 4>( src, mask, dst, algorithmType );
break;
case CV_8UC1:
inpaint <uchar, 1>( src, mask, dst, algorithmType );
break;
case CV_8UC2:
inpaint <uchar, 2>( src, mask, dst, algorithmType );
break;
case CV_8UC3:
inpaint <uchar, 3>( src, mask, dst, algorithmType );
break;
//case CV_8UC4:
// inpaint <uchar, 4>( src, mask, dst, algorithmType );
// break;
//case CV_16SC1:
// inpaint <short, 1>( src, mask, dst, algorithmType );
// break;
//case CV_16SC2:
// inpaint <short, 2>( src, mask, dst, algorithmType );
// break;
case CV_8UC4:
inpaint <uchar, 4>( src, mask, dst, algorithmType );
break;
case CV_16SC1:
inpaint <short, 1>( src, mask, dst, algorithmType );
break;
case CV_16SC2:
inpaint <short, 2>( src, mask, dst, algorithmType );
break;
case CV_16SC3:
inpaint <short, 3>( src, mask, dst, algorithmType );
break;
//case CV_16SC4:
// inpaint <short, 4>( src, mask, dst, algorithmType );
// break;
//case CV_32SC1:
// inpaint <int, 1>( src, mask, dst, algorithmType );
// break;
//case CV_32SC2:
// inpaint <int, 2>( src, mask, dst, algorithmType );
// break;
case CV_16SC4:
inpaint <short, 4>( src, mask, dst, algorithmType );
break;
case CV_16UC1:
inpaint <ushort, 1>( src, mask, dst, algorithmType );
break;
case CV_16UC2:
inpaint <ushort, 2>( src, mask, dst, algorithmType );
break;
case CV_16UC3:
inpaint <ushort, 3>( src, mask, dst, algorithmType );
break;
case CV_16UC4:
inpaint <ushort, 4>( src, mask, dst, algorithmType );
break;
case CV_32SC1:
inpaint <int, 1>( src, mask, dst, algorithmType );
break;
case CV_32SC2:
inpaint <int, 2>( src, mask, dst, algorithmType );
break;
case CV_32SC3:
inpaint <int, 3>( src, mask, dst, algorithmType );
break;
//case CV_32SC4:
// inpaint <int, 4>( src, mask, dst, algorithmType );
// break;
//case CV_32FC1:
// inpaint <float, 1>( src, mask, dst, algorithmType );
// break;
//case CV_32FC2:
// inpaint <float, 2>( src, mask, dst, algorithmType );
// break;
case CV_32SC4:
inpaint <int, 4>( src, mask, dst, algorithmType );
break;
case CV_32FC1:
inpaint <float, 1>( src, mask, dst, algorithmType );
break;
case CV_32FC2:
inpaint <float, 2>( src, mask, dst, algorithmType );
break;
case CV_32FC3:
inpaint <float, 3>( src, mask, dst, algorithmType );
break;
//case CV_32FC4:
// inpaint <float, 4>( src, mask, dst, algorithmType );
// break;
//case CV_64FC1:
// inpaint <double, 1>( src, mask, dst, algorithmType );
// break;
//case CV_64FC2:
// inpaint <double, 2>( src, mask, dst, algorithmType );
// break;
case CV_32FC4:
inpaint <float, 4>( src, mask, dst, algorithmType );
break;
case CV_64FC1:
inpaint <double, 1>( src, mask, dst, algorithmType );
break;
case CV_64FC2:
inpaint <double, 2>( src, mask, dst, algorithmType );
break;
case CV_64FC3:
inpaint <double, 3>( src, mask, dst, algorithmType );
break;
//case CV_64FC4:
// inpaint <double, 4>( src, mask, dst, algorithmType );
// break;
case CV_64FC4:
inpaint <double, 4>( src, mask, dst, algorithmType );
break;
default:
CV_Error_( CV_StsNotImplemented,
("Unsupported source image format (=%d)",

46
modules/xphoto/src/norm2.hpp
Unescape View File

@ -40,24 +40,52 @@
#ifndef __NORM2_HPP__
#define __NORM2_HPP__
static inline int sqr(uchar x) { return x*x; }
/************************ General template *************************/
template <typename Tp> static inline Tp sqr(Tp x) { return x*x; }
template <typename Tp, int cn> static inline Tp sqr( cv::Vec<Tp, cn> x) { return x.dot(x); }
template <typename Tp> static inline Tp norm2(const Tp &a, const Tp &b) { return sqr(a - b); }
template <typename Tp, int cn> static inline
Tp norm2(const cv::Vec <Tp, cn> &a, const cv::Vec<Tp, cn> &b) { return sqr(a - b); }
template <int cn> static inline int sqr( cv::Vec<uchar, cn> x) { return x.dot(x); }
static inline int norm2(const uchar &a, const uchar &b) { return sqr(a - b); }
/******************* uchar, char, ushort, uint *********************/
static inline int norm2(const uchar &a, const uchar &b) { return sqr(int(a) - int(b)); }
template <int cn> static inline
int norm2(const cv::Vec <uchar, cn> &a, const cv::Vec<uchar, cn> &b) { return sqr(a - b); }
int norm2(const cv::Vec <uchar, cn> &a, const cv::Vec<uchar, cn> &b)
{
return sqr( cv::Vec<int, cn>(a) - cv::Vec<int, cn>(b) );
}
static inline int norm2(const char &a, const char &b) { return sqr(int(a) - int(b)); }
template <int cn> static inline
int norm2(const cv::Vec <char, cn> &a, const cv::Vec<char, cn> &b)
{
return sqr( cv::Vec<int, cn>(a) - cv::Vec<int, cn>(b) );
}
template <typename Tp> static inline Tp sqr(Tp x) { return x*x; }
static inline short norm2(const ushort &a, const ushort &b) { return sqr <short>(short(a) - short(b)); }
template <typename Tp, int cn> static inline Tp sqr( cv::Vec<Tp, cn> x) { return x.dot(x); }
template <int cn> static inline
short norm2(const cv::Vec <ushort, cn> &a, const cv::Vec<ushort, cn> &b)
{
return sqr( cv::Vec<short, cn>(a) - cv::Vec<short, cn>(b) );
}
template <typename Tp> static inline Tp norm2(const Tp &a, const Tp &b) { return sqr(a - b); }
static inline int norm2(const uint &a, const uint &b) { return sqr(int(a) - int(b)); }
template <int cn> static inline
int norm2(const cv::Vec <uint, cn> &a, const cv::Vec<uint, cn> &b)
{
return sqr( cv::Vec<int, cn>(a) - cv::Vec<int, cn>(b) );
}
template <typename Tp, int cn> static inline
Tp norm2(const cv::Vec <Tp, cn> &a, const cv::Vec<Tp, cn> &b) { return sqr(a - b); }
#endif /* __NORM2_HPP__ */

3
modules/xphoto/src/photomontage.hpp
Unescape View File

@ -93,7 +93,8 @@ private:
protected:
virtual double dist(const Tp &l1p1, const Tp &l1p2, const Tp &l2p1, const Tp &l2p2);
virtual void setWeights(GCGraph <double> &graph, const cv::Point &pA, const cv::Point &pB, const int lA, const int lB, const int lX);
virtual void setWeights(GCGraph <double> &graph, const cv::Point &pA,
const cv::Point &pB, const int lA, const int lB, const int lX);
public:
Photomontage(const std::vector <cv::Mat> &images, const std::vector <cv::Mat> &masks);

86
modules/xphoto/src/whs.hpp
Unescape View File

@ -51,69 +51,95 @@ static inline int hl(int x)
return res;
}
static inline int rp2(int x)
{
int res = 1;
while (res < x)
res <<= 1;
return res;
}
template <typename ForwardIterator>
static void generate_snake(ForwardIterator snake, const int n)
{
cv::Point previous;
if (n > 0)
{
previous = cv::Point(0, 0);
*snake = previous;
}
for (int k = 1, num = 1; num <= n; ++k)
{
const cv::Point2i dv[] = { cv::Point2i( !(k&1), (k&1) ),
cv::Point2i( -(k&1), -!(k&1) ) };
*snake = previous = previous - dv[1];
++num;
for (int i = 0; i < 2; ++i)
for (int j = 0; j < k && num < n; ++j)
{
*snake = previous = previous + dv[i];
++num;
}
}
}
static void nextProjection(std::vector <cv::Mat> &projections, const cv::Point &A,
const cv::Point &B, const int psize)
{
int xsign = (A.x != B.x)*(hl(A.x&B.x) + (B.x > A.x))&1;
int ysign = (A.y != B.y)*(hl(A.y&B.y) + (B.y > A.y))&1;
bool plusToMinusUpdate = std::max(xsign, ysign);
bool plusToMinusUpdate = xsign || ysign;
int dx = (A.x != B.x) << hl(psize - 1) - hl(A.x ^ B.x);
int dy = (A.y != B.y) << hl(psize - 1) - hl(A.y ^ B.y);
int dx = (A.x != B.x) << ( hl(psize - 1) - hl(A.x ^ B.x) );
int dy = (A.y != B.y) << ( hl(psize - 1) - hl(A.y ^ B.y) );
cv::Mat proj = projections[projections.size() - 1];
cv::Mat nproj( proj.size(), proj.type(), cv::Scalar::all(0) );
cv::Mat proj = projections[projections.size() - 1],
nproj = -proj.clone();
for (int i = dy; i < nproj.rows; ++i)
{
float *vCurrent = proj.template ptr<float>(i);
float *vxCurrent = proj.template ptr<float>(i - dy);
float *vxNext = nproj.template ptr<float>(i - dy);
float *vNext = nproj.template ptr<float>(i);
float *vxCurrent = proj.template ptr<float>(i - dy);
if (plusToMinusUpdate)
for (int j = dx; j < nproj.cols; ++j)
vNext[j] = -vxNext[j - dx] + vCurrent[j] - vxCurrent[j - dx];
vNext[j] += vxCurrent[j - dx] - vxNext[j - dx];
else
for (int j = dx; j < nproj.cols; ++j)
vNext[j] = +vxNext[j - dx] + vCurrent[j] + vxCurrent[j - dx];
vNext[j] -= vxCurrent[j - dx] - vxNext[j - dx];
}
projections.push_back(nproj);
}
static void getWHSeries(const cv::Mat &src, cv::Mat &dst, const int nProjections, const int psize)
static void rgb2whs(const cv::Mat &src, cv::Mat &dst, const int nProjections, const int psize)
{
CV_Assert(nProjections <= psize*psize && src.type() == CV_32FC1);
CV_Assert( hl(psize) == 1 );
const int npsize = rp2(psize);
std::vector <cv::Mat> projections;
cv::Mat proj;
cv::boxFilter(src, proj, CV_32F, cv::Size(psize, psize),
cv::Mat img, proj;
cv::copyMakeBorder(src, img, npsize, npsize, npsize, npsize,
cv::BORDER_CONSTANT, 0);
cv::boxFilter(img, proj, CV_32F, cv::Size(npsize, npsize),
cv::Point(-1, -1), true, cv::BORDER_REFLECT);
projections.push_back(proj);
std::vector <cv::Point2i> snake_idx( 1, cv::Point2i(0, 0) );
for (int k = 1, num = 1; k < psize && num <= nProjections; ++k)
{
const cv::Point2i dv[] = { cv::Point2i( !(k&1), (k&1) ),
cv::Point2i( -(k&1), -!(k&1) ) };
snake_idx.push_back(snake_idx[num++ - 1] - dv[1]);
for (int i = 0; i < k && num < nProjections; ++i)
snake_idx.push_back(snake_idx[num++ - 1] + dv[0]);
for (int i = 0; i < k && num < nProjections; ++i)
snake_idx.push_back(snake_idx[num++ - 1] + dv[1]);
}
std::vector <cv::Point2i> snake_idx;
generate_snake(std::back_inserter(snake_idx), nProjections);
for (int i = 1; i < nProjections; ++i)
nextProjection(projections, snake_idx[i - 1],
snake_idx[i], psize);
snake_idx[i], npsize);
cv::merge(projections, dst);
cv::merge(projections, img);
img(cv::Rect(npsize, npsize, src.cols, src.rows)).copyTo(dst);
}
#endif /* __WHS_HPP__ */

Write Preview
Loading…
Cancel
Save