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Merge pull request #6849 from alcinos:optflow_interface

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This commit is contained in:
Vadim Pisarevsky 2016-07-18 15:05:13 +00:00
commit d62b0bd363
9 changed files with 351 additions and 157 deletions
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11
modules/core/include/opencv2/core/ptr.inl.hpp
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@ -361,6 +361,17 @@ Ptr<T> makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5&
return Ptr<T>(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10));
}
template<typename T, typename A1, typename A2, typename A3, typename A4, typename A5, typename A6, typename A7, typename A8, typename A9, typename A10, typename A11>
Ptr<T> makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10, const A11& a11)
{
return Ptr<T>(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11));
}
template<typename T, typename A1, typename A2, typename A3, typename A4, typename A5, typename A6, typename A7, typename A8, typename A9, typename A10, typename A11, typename A12>
Ptr<T> makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10, const A11& a11, const A12& a12)
{
return Ptr<T>(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12));
}
} // namespace cv
//! @endcond

4
modules/cudaoptflow/src/farneback.cpp
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@ -93,7 +93,7 @@ namespace cv { namespace cuda { namespace device { namespace optflow_farneback
namespace
{
class FarnebackOpticalFlowImpl : public FarnebackOpticalFlow
class FarnebackOpticalFlowImpl : public cv::cuda::FarnebackOpticalFlow
{
public:
FarnebackOpticalFlowImpl(int numLevels, double pyrScale, bool fastPyramids, int winSize,
@ -459,7 +459,7 @@ namespace
}
}
Ptr<FarnebackOpticalFlow> cv::cuda::FarnebackOpticalFlow::create(int numLevels, double pyrScale, bool fastPyramids, int winSize,
Ptr<cv::cuda::FarnebackOpticalFlow> cv::cuda::FarnebackOpticalFlow::create(int numLevels, double pyrScale, bool fastPyramids, int winSize,
int numIters, int polyN, double polySigma, int flags)
{
return makePtr<FarnebackOpticalFlowImpl>(numLevels, pyrScale, fastPyramids, winSize,

12
modules/cudaoptflow/src/pyrlk.cpp
View File

@ -47,9 +47,9 @@ using namespace cv::cuda;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
Ptr<SparsePyrLKOpticalFlow> cv::cuda::SparsePyrLKOpticalFlow::create(Size, int, int, bool) { throw_no_cuda(); return Ptr<SparsePyrLKOpticalFlow>(); }
Ptr<cv::cuda::SparsePyrLKOpticalFlow> cv::cuda::SparsePyrLKOpticalFlow::create(Size, int, int, bool) { throw_no_cuda(); return Ptr<SparsePyrLKOpticalFlow>(); }
Ptr<DensePyrLKOpticalFlow> cv::cuda::DensePyrLKOpticalFlow::create(Size, int, int, bool) { throw_no_cuda(); return Ptr<DensePyrLKOpticalFlow>(); }
Ptr<cv::cuda::DensePyrLKOpticalFlow> cv::cuda::DensePyrLKOpticalFlow::create(Size, int, int, bool) { throw_no_cuda(); return Ptr<DensePyrLKOpticalFlow>(); }
#else /* !defined (HAVE_CUDA) */
@ -283,7 +283,7 @@ namespace
vPyr[idx].copyTo(v, stream);
}
class SparsePyrLKOpticalFlowImpl : public SparsePyrLKOpticalFlow, private PyrLKOpticalFlowBase
class SparsePyrLKOpticalFlowImpl : public cv::cuda::SparsePyrLKOpticalFlow, private PyrLKOpticalFlowBase
{
public:
SparsePyrLKOpticalFlowImpl(Size winSize, int maxLevel, int iters, bool useInitialFlow) :
@ -366,14 +366,14 @@ namespace
};
}
Ptr<SparsePyrLKOpticalFlow> cv::cuda::SparsePyrLKOpticalFlow::create(Size winSize, int maxLevel, int iters, bool useInitialFlow)
Ptr<cv::cuda::SparsePyrLKOpticalFlow> cv::cuda::SparsePyrLKOpticalFlow::create(Size winSize, int maxLevel, int iters, bool useInitialFlow)
{
return makePtr<SparsePyrLKOpticalFlowImpl>(winSize, maxLevel, iters, useInitialFlow);
}
Ptr<DensePyrLKOpticalFlow> cv::cuda::DensePyrLKOpticalFlow::create(Size winSize, int maxLevel, int iters, bool useInitialFlow)
Ptr<cv::cuda::DensePyrLKOpticalFlow> cv::cuda::DensePyrLKOpticalFlow::create(Size winSize, int maxLevel, int iters, bool useInitialFlow)
{
return makePtr<DensePyrLKOpticalFlowImpl>(winSize, maxLevel, iters, useInitialFlow);
}
#endif /* !defined (HAVE_CUDA) */
#endif /* !defined (HAVE_CUDA) */

111
modules/video/include/opencv2/video/tracking.hpp
View File

@ -397,6 +397,27 @@ public:
CV_WRAP virtual void collectGarbage() = 0;
};
/** @brief Base interface for sparse optical flow algorithms.
*/
class CV_EXPORTS_W SparseOpticalFlow : public Algorithm
{
public:
/** @brief Calculates a sparse optical flow.
@param prevImg First input image.
@param nextImg Second input image of the same size and the same type as prevImg.
@param prevPts Vector of 2D points for which the flow needs to be found.
@param nextPts Output vector of 2D points containing the calculated new positions of input features in the second image.
@param status Output status vector. Each element of the vector is set to 1 if the
flow for the corresponding features has been found. Otherwise, it is set to 0.
@param err Optional output vector that contains error response for each point (inverse confidence).
*/
CV_WRAP virtual void calc(InputArray prevImg, InputArray nextImg,
InputArray prevPts, InputOutputArray nextPts,
OutputArray status,
OutputArray err = cv::noArray()) = 0;
};
/** @brief "Dual TV L1" Optical Flow Algorithm.
The class implements the "Dual TV L1" optical flow algorithm described in @cite Zach2007 and
@ -502,12 +523,102 @@ public:
virtual int getMedianFiltering() const = 0;
/** @copybrief getMedianFiltering @see getMedianFiltering */
virtual void setMedianFiltering(int val) = 0;
/** @brief Creates instance of cv::DualTVL1OpticalFlow*/
static Ptr<DualTVL1OpticalFlow> create(
double tau = 0.25,
double lambda = 0.15,
double theta = 0.3,
int nscales = 5,
int warps = 5,
double epsilon = 0.01,
int innnerIterations = 30,
int outerIterations = 10,
double scaleStep = 0.8,
double gamma = 0.0,
int medianFiltering = 5,
bool useInitialFlow = false);
};
/** @brief Creates instance of cv::DenseOpticalFlow
*/
CV_EXPORTS_W Ptr<DualTVL1OpticalFlow> createOptFlow_DualTVL1();
/** @brief Class computing a dense optical flow using the Gunnar Farneback’s algorithm.
*/
class CV_EXPORTS_W FarnebackOpticalFlow : public DenseOpticalFlow
{
public:
virtual int getNumLevels() const = 0;
virtual void setNumLevels(int numLevels) = 0;
virtual double getPyrScale() const = 0;
virtual void setPyrScale(double pyrScale) = 0;
virtual bool getFastPyramids() const = 0;
virtual void setFastPyramids(bool fastPyramids) = 0;
virtual int getWinSize() const = 0;
virtual void setWinSize(int winSize) = 0;
virtual int getNumIters() const = 0;
virtual void setNumIters(int numIters) = 0;
virtual int getPolyN() const = 0;
virtual void setPolyN(int polyN) = 0;
virtual double getPolySigma() const = 0;
virtual void setPolySigma(double polySigma) = 0;
virtual int getFlags() const = 0;
virtual void setFlags(int flags) = 0;
static Ptr<FarnebackOpticalFlow> create(
int numLevels = 5,
double pyrScale = 0.5,
bool fastPyramids = false,
int winSize = 13,
int numIters = 10,
int polyN = 5,
double polySigma = 1.1,
int flags = 0);
};
/** @brief Class used for calculating a sparse optical flow.
The class can calculate an optical flow for a sparse feature set using the
iterative Lucas-Kanade method with pyramids.
@sa calcOpticalFlowPyrLK
*/
class CV_EXPORTS SparsePyrLKOpticalFlow : public SparseOpticalFlow
{
public:
virtual Size getWinSize() const = 0;
virtual void setWinSize(Size winSize) = 0;
virtual int getMaxLevel() const = 0;
virtual void setMaxLevel(int maxLevel) = 0;
virtual TermCriteria getTermCriteria() const = 0;
virtual void setTermCriteria(TermCriteria& crit) = 0;
virtual int getFlags() const = 0;
virtual void setFlags(int flags) = 0;
virtual double getMinEigThreshold() const = 0;
virtual void setMinEigThreshold(double minEigThreshold) = 0;
static Ptr<SparsePyrLKOpticalFlow> create(
Size winSize = Size(21, 21),
int maxLevel = 3, TermCriteria crit =
TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01),
int flags = 0,
double minEigThreshold = 1e-4);
};
//! @} video_track
} // cv

112
modules/video/src/lkpyramid.cpp
View File

@ -837,10 +837,11 @@ int cv::buildOpticalFlowPyramid(InputArray _img, OutputArrayOfArrays pyramid, Si
return maxLevel;
}
#ifdef HAVE_OPENCL
namespace cv
{
class PyrLKOpticalFlow
namespace
{
class SparsePyrLKOpticalFlowImpl : public SparsePyrLKOpticalFlow
{
struct dim3
{
@ -848,17 +849,40 @@ namespace cv
dim3() : x(0), y(0), z(0) { }
};
public:
PyrLKOpticalFlow()
SparsePyrLKOpticalFlowImpl(Size winSize_ = Size(21,21),
int maxLevel_ = 3,
TermCriteria criteria_ = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01),
int flags_ = 0,
double minEigThreshold_ = 1e-4) :
winSize(winSize_), maxLevel(maxLevel_), criteria(criteria_), flags(flags_), minEigThreshold(minEigThreshold_)
#ifdef HAVE_OPENCL
, iters(criteria_.maxCount), derivLambda(criteria_.epsilon), useInitialFlow(0 != (flags_ & OPTFLOW_LK_GET_MIN_EIGENVALS)), waveSize(0)
#endif
{
winSize = Size(21, 21);
maxLevel = 3;
iters = 30;
derivLambda = 0.5;
useInitialFlow = false;
waveSize = 0;
}
virtual Size getWinSize() const {return winSize;}
virtual void setWinSize(Size winSize_){winSize = winSize_;}
virtual int getMaxLevel() const {return maxLevel;}
virtual void setMaxLevel(int maxLevel_){maxLevel = maxLevel_;}
virtual TermCriteria getTermCriteria() const {return criteria;}
virtual void setTermCriteria(TermCriteria& crit_){criteria=crit_;}
virtual int getFlags() const {return flags; }
virtual void setFlags(int flags_){flags=flags_;}
virtual double getMinEigThreshold() const {return minEigThreshold;}
virtual void setMinEigThreshold(double minEigThreshold_){minEigThreshold=minEigThreshold_;}
virtual void calc(InputArray prevImg, InputArray nextImg,
InputArray prevPts, InputOutputArray nextPts,
OutputArray status,
OutputArray err = cv::noArray());
private:
#ifdef HAVE_OPENCL
bool checkParam()
{
iters = std::min(std::max(iters, 0), 100);
@ -930,14 +954,17 @@ namespace cv
}
return true;
}
#endif
Size winSize;
int maxLevel;
TermCriteria criteria;
int flags;
double minEigThreshold;
#ifdef HAVE_OPENCL
int iters;
double derivLambda;
bool useInitialFlow;
private:
int waveSize;
bool initWaveSize()
{
@ -1017,15 +1044,11 @@ namespace cv
{
return (cv::ocl::Device::TYPE_CPU == cv::ocl::Device::getDefault().type());
}
};
static bool ocl_calcOpticalFlowPyrLK(InputArray _prevImg, InputArray _nextImg,
InputArray _prevPts, InputOutputArray _nextPts,
OutputArray _status, OutputArray _err,
Size winSize, int maxLevel,
TermCriteria criteria,
int flags/*, double minEigThreshold*/ )
bool ocl_calcOpticalFlowPyrLK(InputArray _prevImg, InputArray _nextImg,
InputArray _prevPts, InputOutputArray _nextPts,
OutputArray _status, OutputArray _err)
{
if (0 != (OPTFLOW_LK_GET_MIN_EIGENVALS & flags))
return false;
@ -1045,7 +1068,6 @@ namespace cv
if ((1 != _prevPts.size().height) && (1 != _prevPts.size().width))
return false;
size_t npoints = _prevPts.total();
bool useInitialFlow = (0 != (flags & OPTFLOW_USE_INITIAL_FLOW));
if (useInitialFlow)
{
if (_nextPts.empty() || _nextPts.type() != CV_32FC2 || (!_prevPts.isContinuous()))
@ -1060,14 +1082,7 @@ namespace cv
_nextPts.create(_prevPts.size(), _prevPts.type());
}
PyrLKOpticalFlow opticalFlow;
opticalFlow.winSize = winSize;
opticalFlow.maxLevel = maxLevel;
opticalFlow.iters = criteria.maxCount;
opticalFlow.derivLambda = criteria.epsilon;
opticalFlow.useInitialFlow = useInitialFlow;
if (!opticalFlow.checkParam())
if (!checkParam())
return false;
UMat umatErr;
@ -1082,28 +1097,19 @@ namespace cv
_status.create((int)npoints, 1, CV_8UC1);
UMat umatNextPts = _nextPts.getUMat();
UMat umatStatus = _status.getUMat();
return opticalFlow.sparse(_prevImg.getUMat(), _nextImg.getUMat(), _prevPts.getUMat(), umatNextPts, umatStatus, umatErr);
return sparse(_prevImg.getUMat(), _nextImg.getUMat(), _prevPts.getUMat(), umatNextPts, umatStatus, umatErr);
}
#endif
};
#endif
void cv::calcOpticalFlowPyrLK( InputArray _prevImg, InputArray _nextImg,
void SparsePyrLKOpticalFlowImpl::calc( InputArray _prevImg, InputArray _nextImg,
InputArray _prevPts, InputOutputArray _nextPts,
OutputArray _status, OutputArray _err,
Size winSize, int maxLevel,
TermCriteria criteria,
int flags, double minEigThreshold )
OutputArray _status, OutputArray _err)
{
#ifdef HAVE_OPENCL
bool use_opencl = ocl::useOpenCL() &&
(_prevImg.isUMat() || _nextImg.isUMat()) &&
ocl::Image2D::isFormatSupported(CV_32F, 1, false);
if ( use_opencl && ocl_calcOpticalFlowPyrLK(_prevImg, _nextImg, _prevPts, _nextPts, _status, _err, winSize, maxLevel, criteria, flags/*, minEigThreshold*/))
{
CV_IMPL_ADD(CV_IMPL_OCL);
return;
}
#endif
CV_OCL_RUN(ocl::useOpenCL() &&
(_prevImg.isUMat() || _nextImg.isUMat()) &&
ocl::Image2D::isFormatSupported(CV_32F, 1, false),
ocl_calcOpticalFlowPyrLK(_prevImg, _nextImg, _prevPts, _nextPts, _status, _err))
Mat prevPtsMat = _prevPts.getMat();
const int derivDepth = DataType<cv::detail::deriv_type>::depth;
@ -1262,6 +1268,22 @@ void cv::calcOpticalFlowPyrLK( InputArray _prevImg, InputArray _nextImg,
}
}
} // namespace
} // namespace cv
cv::Ptr<cv::SparsePyrLKOpticalFlow> cv::SparsePyrLKOpticalFlow::create(Size winSize, int maxLevel, TermCriteria crit, int flags, double minEigThreshold){
return makePtr<SparsePyrLKOpticalFlowImpl>(winSize,maxLevel,crit,flags,minEigThreshold);
}
void cv::calcOpticalFlowPyrLK( InputArray _prevImg, InputArray _nextImg,
InputArray _prevPts, InputOutputArray _nextPts,
OutputArray _status, OutputArray _err,
Size winSize, int maxLevel,
TermCriteria criteria,
int flags, double minEigThreshold )
{
Ptr<cv::SparsePyrLKOpticalFlow> optflow = cv::SparsePyrLKOpticalFlow::create(winSize,maxLevel,criteria,flags,minEigThreshold);
optflow->calc(_prevImg,_nextImg,_prevPts,_nextPts,_status,_err);
}
namespace cv
{

233
modules/video/src/optflowgf.cpp
View File

@ -583,39 +583,63 @@ FarnebackUpdateFlow_GaussianBlur( const Mat& _R0, const Mat& _R1,
}
#ifdef HAVE_OPENCL
namespace cv
{
class FarnebackOpticalFlow
namespace
{
class FarnebackOpticalFlowImpl : public FarnebackOpticalFlow
{
public:
FarnebackOpticalFlow()
FarnebackOpticalFlowImpl(int numLevels=5, double pyrScale=0.5, bool fastPyramids=false, int winSize=13,
int numIters=10, int polyN=5, double polySigma=1.1, int flags=0) :
numLevels_(numLevels), pyrScale_(pyrScale), fastPyramids_(fastPyramids), winSize_(winSize),
numIters_(numIters), polyN_(polyN), polySigma_(polySigma), flags_(flags)
{
numLevels = 5;
pyrScale = 0.5;
fastPyramids = false;
winSize = 13;
numIters = 10;
polyN = 5;
polySigma = 1.1;
flags = 0;
}
int numLevels;
double pyrScale;
bool fastPyramids;
int winSize;
int numIters;
int polyN;
double polySigma;
int flags;
virtual int getNumLevels() const { return numLevels_; }
virtual void setNumLevels(int numLevels) { numLevels_ = numLevels; }
virtual double getPyrScale() const { return pyrScale_; }
virtual void setPyrScale(double pyrScale) { pyrScale_ = pyrScale; }
virtual bool getFastPyramids() const { return fastPyramids_; }
virtual void setFastPyramids(bool fastPyramids) { fastPyramids_ = fastPyramids; }
virtual int getWinSize() const { return winSize_; }
virtual void setWinSize(int winSize) { winSize_ = winSize; }
virtual int getNumIters() const { return numIters_; }
virtual void setNumIters(int numIters) { numIters_ = numIters; }
virtual int getPolyN() const { return polyN_; }
virtual void setPolyN(int polyN) { polyN_ = polyN; }
virtual double getPolySigma() const { return polySigma_; }
virtual void setPolySigma(double polySigma) { polySigma_ = polySigma; }
virtual int getFlags() const { return flags_; }
virtual void setFlags(int flags) { flags_ = flags; }
virtual void calc(InputArray I0, InputArray I1, InputOutputArray flow);
private:
int numLevels_;
double pyrScale_;
bool fastPyramids_;
int winSize_;
int numIters_;
int polyN_;
double polySigma_;
int flags_;
#ifdef HAVE_OPENCL
bool operator ()(const UMat &frame0, const UMat &frame1, UMat &flowx, UMat &flowy)
{
CV_Assert(frame0.channels() == 1 && frame1.channels() == 1);
CV_Assert(frame0.size() == frame1.size());
CV_Assert(polyN == 5 || polyN == 7);
CV_Assert(!fastPyramids || std::abs(pyrScale - 0.5) < 1e-6);
CV_Assert(polyN_ == 5 || polyN_ == 7);
CV_Assert(!fastPyramids_ || std::abs(pyrScale_ - 0.5) < 1e-6);
const int min_size = 32;
@ -630,9 +654,9 @@ public:
// Crop unnecessary levels
double scale = 1;
int numLevelsCropped = 0;
for (; numLevelsCropped < numLevels; numLevelsCropped++)
for (; numLevelsCropped < numLevels_; numLevelsCropped++)
{
scale *= pyrScale;
scale *= pyrScale_;
if (size.width*scale < min_size || size.height*scale < min_size)
break;
}
@ -640,7 +664,7 @@ public:
frame0.convertTo(frames_[0], CV_32F);
frame1.convertTo(frames_[1], CV_32F);
if (fastPyramids)
if (fastPyramids_)
{
// Build Gaussian pyramids using pyrDown()
pyramid0_.resize(numLevelsCropped + 1);
@ -654,13 +678,13 @@ public:
}
}
setPolynomialExpansionConsts(polyN, polySigma);
setPolynomialExpansionConsts(polyN_, polySigma_);
for (int k = numLevelsCropped; k >= 0; k--)
{
scale = 1;
for (int i = 0; i < k; i++)
scale *= pyrScale;
scale *= pyrScale_;
double sigma = (1./scale - 1) * 0.5;
int smoothSize = cvRound(sigma*5) | 1;
@ -669,7 +693,7 @@ public:
int width = cvRound(size.width*scale);
int height = cvRound(size.height*scale);
if (fastPyramids)
if (fastPyramids_)
{
width = pyramid0_[k].cols;
height = pyramid0_[k].rows;
@ -688,7 +712,7 @@ public:
if (prevFlowX.empty())
{
if (flags & cv::OPTFLOW_USE_INITIAL_FLOW)
if (flags_ & cv::OPTFLOW_USE_INITIAL_FLOW)
{
resize(flowx0, curFlowX, Size(width, height), 0, 0, INTER_LINEAR);
resize(flowy0, curFlowY, Size(width, height), 0, 0, INTER_LINEAR);
@ -705,8 +729,8 @@ public:
{
resize(prevFlowX, curFlowX, Size(width, height), 0, 0, INTER_LINEAR);
resize(prevFlowY, curFlowY, Size(width, height), 0, 0, INTER_LINEAR);
multiply(1./pyrScale, curFlowX, curFlowX);
multiply(1./pyrScale, curFlowY, curFlowY);
multiply(1./pyrScale_, curFlowX, curFlowX);
multiply(1./pyrScale_, curFlowY, curFlowY);
}
UMat M = allocMatFromBuf(5*height, width, CV_32F, M_);
@ -717,7 +741,7 @@ public:
allocMatFromBuf(5*height, width, CV_32F, R_[1])
};
if (fastPyramids)
if (fastPyramids_)
{
if (!polynomialExpansionOcl(pyramid0_[k], R[0]))
return false;
@ -752,18 +776,18 @@ public:
if (!updateMatricesOcl(curFlowX, curFlowY, R[0], R[1], M))
return false;
if (flags & OPTFLOW_FARNEBACK_GAUSSIAN)
setGaussianBlurKernel(winSize, winSize/2*0.3f);
for (int i = 0; i < numIters; i++)
if (flags_ & OPTFLOW_FARNEBACK_GAUSSIAN)
setGaussianBlurKernel(winSize_, winSize_/2*0.3f);
for (int i = 0; i < numIters_; i++)
{
if (flags & OPTFLOW_FARNEBACK_GAUSSIAN)
if (flags_ & OPTFLOW_FARNEBACK_GAUSSIAN)
{
if (!updateFlow_gaussianBlur(R[0], R[1], curFlowX, curFlowY, M, bufM, winSize, i < numIters-1))
if (!updateFlow_gaussianBlur(R[0], R[1], curFlowX, curFlowY, M, bufM, winSize_, i < numIters_-1))
return false;
}
else
{
if (!updateFlow_boxFilter(R[0], R[1], curFlowX, curFlowY, M, bufM, winSize, i < numIters-1))
if (!updateFlow_boxFilter(R[0], R[1], curFlowX, curFlowY, M, bufM, winSize_, i < numIters_-1))
return false;
}
}
@ -776,7 +800,9 @@ public:
flowy = curFlowY;
return true;
}
virtual void collectGarbage(){
releaseMemory();
}
void releaseMemory()
{
frames_[0].release();
@ -898,15 +924,15 @@ private:
#else
size_t localsize[2] = { 256, 1};
#endif
size_t globalsize[2] = { DIVUP((size_t)src.cols, localsize[0] - 2*polyN) * localsize[0], (size_t)src.rows};
size_t globalsize[2] = { DIVUP((size_t)src.cols, localsize[0] - 2*polyN_) * localsize[0], (size_t)src.rows};
#if 0
const cv::ocl::Device &device = cv::ocl::Device::getDefault();
bool useDouble = (0 != device.doubleFPConfig());
cv::String build_options = cv::format("-D polyN=%d -D USE_DOUBLE=%d", polyN, useDouble ? 1 : 0);
cv::String build_options = cv::format("-D polyN=%d -D USE_DOUBLE=%d", polyN_, useDouble ? 1 : 0);
#else
cv::String build_options = cv::format("-D polyN=%d", polyN);
cv::String build_options = cv::format("-D polyN=%d", polyN_);
#endif
ocl::Kernel kernel;
if (!kernel.create("polynomialExpansion", cv::ocl::video::optical_flow_farneback_oclsrc, build_options))
@ -1036,60 +1062,43 @@ private:
return false;
return true;
}
bool calc_ocl( InputArray _prev0, InputArray _next0,
InputOutputArray _flow0)
{
if ((5 != polyN_) && (7 != polyN_))
return false;
if (_next0.size() != _prev0.size())
return false;
int typePrev = _prev0.type();
int typeNext = _next0.type();
if ((1 != CV_MAT_CN(typePrev)) || (1 != CV_MAT_CN(typeNext)))
return false;
std::vector<UMat> flowar;
if (!_flow0.empty())
split(_flow0, flowar);
else
{
flowar.push_back(UMat());
flowar.push_back(UMat());
}
if(!this->operator()(_prev0.getUMat(), _next0.getUMat(), flowar[0], flowar[1])){
return false;
}
merge(flowar, _flow0);
return true;
}
#else // HAVE_OPENCL
virtual void collectGarbage(){}
#endif
};
static bool ocl_calcOpticalFlowFarneback( InputArray _prev0, InputArray _next0,
InputOutputArray _flow0, double pyr_scale, int levels, int winsize,
int iterations, int poly_n, double poly_sigma, int flags )
void FarnebackOpticalFlowImpl::calc(InputArray _prev0, InputArray _next0,
InputOutputArray _flow0)
{
if ((5 != poly_n) && (7 != poly_n))
return false;
if (_next0.size() != _prev0.size())
return false;
int typePrev = _prev0.type();
int typeNext = _next0.type();
if ((1 != CV_MAT_CN(typePrev)) || (1 != CV_MAT_CN(typeNext)))
return false;
FarnebackOpticalFlow opticalFlow;
opticalFlow.numLevels = levels;
opticalFlow.pyrScale = pyr_scale;
opticalFlow.fastPyramids= false;
opticalFlow.winSize = winsize;
opticalFlow.numIters = iterations;
opticalFlow.polyN = poly_n;
opticalFlow.polySigma = poly_sigma;
opticalFlow.flags = flags;
std::vector<UMat> flowar;
if (!_flow0.empty())
split(_flow0, flowar);
else
{
flowar.push_back(UMat());
flowar.push_back(UMat());
}
if (!opticalFlow(_prev0.getUMat(), _next0.getUMat(), flowar[0], flowar[1]))
return false;
merge(flowar, _flow0);
return true;
}
}
#endif // HAVE_OPENCL
void cv::calcOpticalFlowFarneback( InputArray _prev0, InputArray _next0,
InputOutputArray _flow0, double pyr_scale, int levels, int winsize,
int iterations, int poly_n, double poly_sigma, int flags )
{
#ifdef HAVE_OPENCL
bool use_opencl = ocl::useOpenCL() && _flow0.isUMat();
if( use_opencl && ocl_calcOpticalFlowFarneback(_prev0, _next0, _flow0, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags))
{
CV_IMPL_ADD(CV_IMPL_OCL);
return;
}
#endif
CV_OCL_RUN(_flow0.isUMat() &&
ocl::Image2D::isFormatSupported(CV_32F, 1, false),
calc_ocl(_prev0,_next0,_flow0))
Mat prev0 = _prev0.getMat(), next0 = _next0.getMat();
const int min_size = 32;
const Mat* img[2] = { &prev0, &next0 };
@ -1097,15 +1106,16 @@ void cv::calcOpticalFlowFarneback( InputArray _prev0, InputArray _next0,
int i, k;
double scale;
Mat prevFlow, flow, fimg;
int levels = numLevels_;
CV_Assert( prev0.size() == next0.size() && prev0.channels() == next0.channels() &&
prev0.channels() == 1 && pyr_scale < 1 );
prev0.channels() == 1 && pyrScale_ < 1 );
_flow0.create( prev0.size(), CV_32FC2 );
Mat flow0 = _flow0.getMat();
for( k = 0, scale = 1; k < levels; k++ )
{
scale *= pyr_scale;
scale *= pyrScale_;
if( prev0.cols*scale < min_size || prev0.rows*scale < min_size )
break;
}
@ -1115,7 +1125,7 @@ void cv::calcOpticalFlowFarneback( InputArray _prev0, InputArray _next0,
for( k = levels; k >= 0; k-- )
{
for( i = 0, scale = 1; i < k; i++ )
scale *= pyr_scale;
scale *= pyrScale_;
double sigma = (1./scale-1)*0.5;
int smooth_sz = cvRound(sigma*5)|1;
@ -1131,7 +1141,7 @@ void cv::calcOpticalFlowFarneback( InputArray _prev0, InputArray _next0,
if( prevFlow.empty() )
{
if( flags & OPTFLOW_USE_INITIAL_FLOW )
if( flags_ & OPTFLOW_USE_INITIAL_FLOW )
{
resize( flow0, flow, Size(width, height), 0, 0, INTER_AREA );
flow *= scale;
@ -1142,7 +1152,7 @@ void cv::calcOpticalFlowFarneback( InputArray _prev0, InputArray _next0,
else
{
resize( prevFlow, flow, Size(width, height), 0, 0, INTER_LINEAR );
flow *= 1./pyr_scale;
flow *= 1./pyrScale_;
}
Mat R[2], I, M;
@ -1151,19 +1161,38 @@ void cv::calcOpticalFlowFarneback( InputArray _prev0, InputArray _next0,
img[i]->convertTo(fimg, CV_32F);
GaussianBlur(fimg, fimg, Size(smooth_sz, smooth_sz), sigma, sigma);
resize( fimg, I, Size(width, height), INTER_LINEAR );
FarnebackPolyExp( I, R[i], poly_n, poly_sigma );
FarnebackPolyExp( I, R[i], polyN_, polySigma_ );
}
FarnebackUpdateMatrices( R[0], R[1], flow, M, 0, flow.rows );
for( i = 0; i < iterations; i++ )
for( i = 0; i < numIters_; i++ )
{
if( flags & OPTFLOW_FARNEBACK_GAUSSIAN )
FarnebackUpdateFlow_GaussianBlur( R[0], R[1], flow, M, winsize, i < iterations - 1 );
if( flags_ & OPTFLOW_FARNEBACK_GAUSSIAN )
FarnebackUpdateFlow_GaussianBlur( R[0], R[1], flow, M, winSize_, i < numIters_ - 1 );
else
FarnebackUpdateFlow_Blur( R[0], R[1], flow, M, winsize, i < iterations - 1 );
FarnebackUpdateFlow_Blur( R[0], R[1], flow, M, winSize_, i < numIters_ - 1 );
}
prevFlow = flow;
}
}
} // namespace
} // namespace cv
void cv::calcOpticalFlowFarneback( InputArray _prev0, InputArray _next0,
InputOutputArray _flow0, double pyr_scale, int levels, int winsize,
int iterations, int poly_n, double poly_sigma, int flags )
{
Ptr<cv::FarnebackOpticalFlow> optflow;
optflow = makePtr<FarnebackOpticalFlowImpl>(levels,pyr_scale,false,winsize,iterations,poly_n,poly_sigma,flags);
optflow->calc(_prev0,_next0,_flow0);
}
cv::Ptr<cv::FarnebackOpticalFlow> cv::FarnebackOpticalFlow::create(int numLevels, double pyrScale, bool fastPyramids, int winSize,
int numIters, int polyN, double polySigma, int flags)
{
return makePtr<FarnebackOpticalFlowImpl>(numLevels, pyrScale, fastPyramids, winSize,
numIters, polyN, polySigma, flags);
}

21
modules/video/src/tvl1flow.cpp
View File

@ -89,6 +89,17 @@ namespace {
class OpticalFlowDual_TVL1 : public DualTVL1OpticalFlow
{
public:
OpticalFlowDual_TVL1(double tau_, double lambda_, double theta_, int nscales_, int warps_,
double epsilon_, int innerIterations_, int outerIterations_,
double scaleStep_, double gamma_, int medianFiltering_,
bool useInitialFlow_) :
tau(tau_), lambda(lambda_), theta(theta_), gamma(gamma_), nscales(nscales_),
warps(warps_), epsilon(epsilon_), innerIterations(innerIterations_),
outerIterations(outerIterations_), useInitialFlow(useInitialFlow_),
scaleStep(scaleStep_), medianFiltering(medianFiltering_)
{
}
OpticalFlowDual_TVL1();
void calc(InputArray I0, InputArray I1, InputOutputArray flow);
@ -1450,3 +1461,13 @@ Ptr<DualTVL1OpticalFlow> cv::createOptFlow_DualTVL1()
{
return makePtr<OpticalFlowDual_TVL1>();
}
Ptr<DualTVL1OpticalFlow> cv::DualTVL1OpticalFlow::create(
double tau, double lambda, double theta, int nscales, int warps,
double epsilon, int innerIterations, int outerIterations, double scaleStep,
double gamma, int medianFilter, bool useInitialFlow)
{
return makePtr<OpticalFlowDual_TVL1>(tau, lambda, theta, nscales, warps,
epsilon, innerIterations, outerIterations,
scaleStep, gamma, medianFilter, useInitialFlow);
}

2
modules/video/test/test_tvl1optflow.cpp
View File

@ -154,7 +154,7 @@ TEST(Video_calcOpticalFlowDual_TVL1, Regression)
ASSERT_FALSE(frame2.empty());
Mat_<Point2f> flow;
Ptr<DenseOpticalFlow> tvl1 = createOptFlow_DualTVL1();
Ptr<DualTVL1OpticalFlow> tvl1 = cv::DualTVL1OpticalFlow::create();
tvl1->calc(frame1, frame2, flow);

2
samples/cpp/tvl1_optical_flow.cpp
View File

@ -185,7 +185,7 @@ int main(int argc, const char* argv[])
}
Mat_<Point2f> flow;
Ptr<DenseOpticalFlow> tvl1 = createOptFlow_DualTVL1();
Ptr<DualTVL1OpticalFlow> tvl1 = cv::DualTVL1OpticalFlow::create();
const double start = (double)getTickCount();
tvl1->calc(frame0, frame1, flow);