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added wrappers for BroxOpticalFlow and interpolateFrames

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This commit is contained in:
Vladislav Vinogradov 2011-10-17 13:12:39 +00:00
parent 87f3451ec6
commit b0536279eb
6 changed files with 986 additions and 216 deletions
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59
modules/gpu/include/opencv2/gpu/gpu.hpp
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@ -1485,6 +1485,65 @@ namespace cv
GpuMat maxPosBuffer;
};
////////////////////////////////// Optical Flow //////////////////////////////////////////
class CV_EXPORTS BroxOpticalFlow
{
public:
BroxOpticalFlow(float alpha_, float gamma_, float scale_factor_, int inner_iterations_, int outer_iterations_, int solver_iterations_) :
alpha(alpha_), gamma(gamma_), scale_factor(scale_factor_),
inner_iterations(inner_iterations_), outer_iterations(outer_iterations_), solver_iterations(solver_iterations_)
{
}
//! Compute optical flow
//! frame0 - source frame (supports only CV_32FC1 type)
//! frame1 - frame to track (with the same size and type as frame0)
//! u - flow horizontal component (along x axis)
//! v - flow vertical component (along y axis)
void operator ()(const GpuMat& frame0, const GpuMat& frame1, GpuMat& u, GpuMat& v, Stream& stream = Stream::Null());
//! flow smoothness
float alpha;
//! gradient constancy importance
float gamma;
//! pyramid scale factor
float scale_factor;
//! number of lagged non-linearity iterations (inner loop)
int inner_iterations;
//! number of warping iterations (number of pyramid levels)
int outer_iterations;
//! number of linear system solver iterations
int solver_iterations;
GpuMat buf;
};
//! Interpolate frames (images) using provided optical flow (displacement field).
//! frame0 - frame 0 (32-bit floating point images, single channel)
//! frame1 - frame 1 (the same type and size)
//! fu - forward horizontal displacement
//! fv - forward vertical displacement
//! bu - backward horizontal displacement
//! bv - backward vertical displacement
//! pos - new frame position
//! newFrame - new frame
//! buf - temporary buffer, will have width x 6*height size, CV_32FC1 type and contain 6 GpuMat;
//! occlusion masks 0, occlusion masks 1,
//! interpolated forward flow 0, interpolated forward flow 1,
//! interpolated backward flow 0, interpolated backward flow 1
//!
CV_EXPORTS void interpolateFrames(const GpuMat& frame0, const GpuMat& frame1,
const GpuMat& fu, const GpuMat& fv,
const GpuMat& bu, const GpuMat& bv,
float pos, GpuMat& newFrame, GpuMat& buf,
Stream& stream = Stream::Null());
}
//! Speckle filtering - filters small connected components on diparity image.

245
modules/gpu/src/nvidia/NCVBroxOpticalFlow.cu
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@ -65,76 +65,6 @@
#include "opencv2/gpu/device/utility.hpp"
////////////////////////////////////////////
template<typename _Tp> class shared_ptr
{
public:
shared_ptr() : obj(0), refcount(0) {}
shared_ptr(_Tp* _obj);
~shared_ptr() { release(); }
shared_ptr(const shared_ptr& ptr);
shared_ptr& operator = (const shared_ptr& ptr);
void addref() { if( refcount ) (*refcount)+=1; }
void release();
void delete_obj() { if( obj ) delete obj; }
_Tp* operator -> () { return obj; }
const _Tp* operator -> () const { return obj; }
operator _Tp* () { return obj; }
operator const _Tp*() const { return obj; }
protected:
_Tp* obj; //< the object pointer.
int* refcount; //< the associated reference counter
};
template<typename _Tp> inline shared_ptr<_Tp>::shared_ptr(_Tp* _obj) : obj(_obj)
{
if(obj)
{
refcount = new int;
*refcount = 1;
}
else
refcount = 0;
}
template<typename _Tp> inline void shared_ptr<_Tp>::release()
{
if( refcount)
{
*refcount -= 1;
if (*refcount == 0)
{
delete_obj();
delete refcount;
}
}
refcount = 0;
obj = 0;
}
template<typename _Tp> inline shared_ptr<_Tp>::shared_ptr(const shared_ptr<_Tp>& ptr)
{
obj = ptr.obj;
refcount = ptr.refcount;
addref();
}
template<typename _Tp> inline shared_ptr<_Tp>& shared_ptr<_Tp>::operator = (const shared_ptr<_Tp>& ptr)
{
int* _refcount = ptr.refcount;
if( _refcount )
*_refcount += 1;
release();
obj = ptr.obj;
refcount = _refcount;
return *this;
}
////////////////////////////////////////////
//using std::tr1::shared_ptr;
typedef NCVVectorAlloc<Ncv32f> FloatVector;
/////////////////////////////////////////////////////////////////////////////////////////
@ -738,6 +668,42 @@ void InitTextures()
initTexture1D(tex_numerator_v);
}
namespace
{
struct ImagePyramid
{
std::vector<FloatVector*> img0;
std::vector<FloatVector*> img1;
std::vector<Ncv32u> w;
std::vector<Ncv32u> h;
explicit ImagePyramid(int outer_iterations)
{
img0.reserve(outer_iterations);
img1.reserve(outer_iterations);
w.reserve(outer_iterations);
h.reserve(outer_iterations);
}
~ImagePyramid()
{
w.clear();
h.clear();
for (int i = img0.size() - 1; i >= 0; --i)
{
delete img1[i];
delete img0[i];
}
img0.clear();
img1.clear();
}
};
}
/////////////////////////////////////////////////////////////////////////////////////////
// MAIN FUNCTION
/////////////////////////////////////////////////////////////////////////////////////////
@ -766,14 +732,12 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
bool kSkipProcessing = gpu_mem_allocator.isCounting();
cudaDeviceProp device_props;
int cuda_device;
ncvAssertCUDAReturn(cudaGetDevice(&cuda_device), NCV_CUDA_ERROR);
cudaDeviceProp device_props;
ncvAssertCUDAReturn(cudaGetDeviceProperties(&device_props, cuda_device), NCV_CUDA_ERROR);
Ncv32u alignmentValue = gpu_mem_allocator.alignment ();
const Ncv32u kStrideAlignmentFloat = alignmentValue / sizeof(float);
@ -817,8 +781,7 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
// temporary storage
SAFE_VECTOR_DECL(device_buffer, gpu_mem_allocator,
alignUp(kSourceWidth, kStrideAlignmentFloat)
* alignUp(kSourceHeight, kStrideAlignmentFloat));
alignUp(kSourceWidth, kStrideAlignmentFloat) * alignUp(kSourceHeight, kStrideAlignmentFloat));
// image derivatives
SAFE_VECTOR_DECL(Ix, gpu_mem_allocator, kSizeInPixelsAligned);
@ -831,35 +794,31 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
// spatial derivative filter size
const int kDFilterSize = 5;
const float derivativeFilterHost[kDFilterSize] = {1.0f, -8.0f, 0.0f, 8.0f, -1.0f};
SAFE_VECTOR_DECL(derivativeFilter, gpu_mem_allocator, kDFilterSize);
ncvAssertCUDAReturn(
cudaMemcpy(derivativeFilter.ptr(),
derivativeFilterHost,
sizeof(float) * kDFilterSize,
cudaMemcpyHostToDevice),
NCV_CUDA_ERROR);
if (!kSkipProcessing)
{
const float derivativeFilterHost[kDFilterSize] = {1.0f, -8.0f, 0.0f, 8.0f, -1.0f};
ncvAssertCUDAReturn(cudaMemcpy(derivativeFilter.ptr(), derivativeFilterHost, sizeof(float) * kDFilterSize,
cudaMemcpyHostToDevice), NCV_CUDA_ERROR);
InitTextures();
}
//prepare image pyramid
std::vector< shared_ptr<FloatVector> > img0_pyramid;
std::vector< shared_ptr<FloatVector> > img1_pyramid;
std::vector<Ncv32u> w_pyramid;
std::vector<Ncv32u> h_pyramid;
ImagePyramid pyr(desc.number_of_outer_iterations);
cudaChannelFormatDesc channel_desc = cudaCreateChannelDesc<float>();
float scale = 1.0f;
//cuda arrays for frames
shared_ptr<FloatVector> I0(new FloatVector(gpu_mem_allocator, kSizeInPixelsAligned));
ncvAssertReturn(I0->isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
std::auto_ptr<FloatVector> pI0(new FloatVector(gpu_mem_allocator, kSizeInPixelsAligned));
ncvAssertReturn(pI0->isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
shared_ptr<FloatVector> I1(new FloatVector(gpu_mem_allocator, kSizeInPixelsAligned));
ncvAssertReturn(I1->isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
std::auto_ptr<FloatVector> pI1(new FloatVector(gpu_mem_allocator, kSizeInPixelsAligned));
ncvAssertReturn(pI1->isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
if (!kSkipProcessing)
{
@ -867,25 +826,29 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
size_t dst_width_in_bytes = alignUp(kSourceWidth, kStrideAlignmentFloat) * sizeof(float);
size_t src_width_in_bytes = kSourceWidth * sizeof(float);
size_t src_pitch_in_bytes = frame0.pitch();
ncvAssertCUDAReturn( cudaMemcpy2DAsync(I0->ptr(), dst_width_in_bytes, frame0.ptr(),
ncvAssertCUDAReturn( cudaMemcpy2DAsync(pI0->ptr(), dst_width_in_bytes, frame0.ptr(),
src_pitch_in_bytes, src_width_in_bytes, kSourceHeight, cudaMemcpyDeviceToDevice, stream), NCV_CUDA_ERROR );
ncvAssertCUDAReturn( cudaMemcpy2DAsync(I1->ptr(), dst_width_in_bytes, frame1.ptr(),
ncvAssertCUDAReturn( cudaMemcpy2DAsync(pI1->ptr(), dst_width_in_bytes, frame1.ptr(),
src_pitch_in_bytes, src_width_in_bytes, kSourceHeight, cudaMemcpyDeviceToDevice, stream), NCV_CUDA_ERROR );
}
//prepare pyramid
img0_pyramid.push_back(I0);
img1_pyramid.push_back(I1);
FloatVector* I0 = pI0.release();
FloatVector* I1 = pI1.release();
w_pyramid.push_back(kSourceWidth);
h_pyramid.push_back(kSourceHeight);
//prepare pyramid
pyr.img0.push_back(I0);
pyr.img1.push_back(I1);
pyr.w.push_back(kSourceWidth);
pyr.h.push_back(kSourceHeight);
scale *= scale_factor;
Ncv32u prev_level_width = kSourceWidth;
Ncv32u prev_level_height = kSourceHeight;
while((prev_level_width > 15) && (prev_level_height > 15) && (static_cast<Ncv32u>(img0_pyramid.size()) < desc.number_of_outer_iterations))
while((prev_level_width > 15) && (prev_level_height > 15) && (static_cast<Ncv32u>(pyr.img0.size()) < desc.number_of_outer_iterations))
{
//current resolution
Ncv32u level_width = static_cast<Ncv32u>(ceilf(kSourceWidth * scale));
@ -897,16 +860,16 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
Ncv32u prev_level_pitch = alignUp(prev_level_width, kStrideAlignmentFloat) * sizeof(float);
shared_ptr<FloatVector> level_frame0(new FloatVector(gpu_mem_allocator, buffer_size));
std::auto_ptr<FloatVector> level_frame0(new FloatVector(gpu_mem_allocator, buffer_size));
ncvAssertReturn(level_frame0->isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
shared_ptr<FloatVector> level_frame1(new FloatVector(gpu_mem_allocator, buffer_size));
std::auto_ptr<FloatVector> level_frame1(new FloatVector(gpu_mem_allocator, buffer_size));
ncvAssertReturn(level_frame1->isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
ncvAssertCUDAReturn(cudaStreamSynchronize(stream), NCV_CUDA_ERROR);
if (!kSkipProcessing)
{
ncvAssertCUDAReturn(cudaStreamSynchronize(stream), NCV_CUDA_ERROR);
NcvSize32u srcSize (prev_level_width, prev_level_height);
NcvSize32u dstSize (level_width, level_height);
NcvRect32u srcROI (0, 0, prev_level_width, prev_level_height);
@ -921,20 +884,20 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
level_frame1->ptr(), dstSize, level_width_aligned * sizeof (float), dstROI, scale_factor, scale_factor, nppStSupersample);
}
//store pointers
img0_pyramid.push_back(level_frame0);
img1_pyramid.push_back(level_frame1);
I0 = level_frame0.release();
I1 = level_frame1.release();
w_pyramid.push_back(level_width);
h_pyramid.push_back(level_height);
//store pointers
pyr.img0.push_back(I0);
pyr.img1.push_back(I1);
pyr.w.push_back(level_width);
pyr.h.push_back(level_height);
scale *= scale_factor;
prev_level_width = level_width;
prev_level_height = level_height;
I0 = level_frame0;
I1 = level_frame1;
}
if (!kSkipProcessing)
@ -944,26 +907,26 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
ncvAssertCUDAReturn(cudaMemsetAsync(v.ptr(), 0, kSizeInPixelsAligned * sizeof(float), stream), NCV_CUDA_ERROR);
//select images with lowest resolution
size_t pitch = alignUp(w_pyramid.back(), kStrideAlignmentFloat) * sizeof(float);
ncvAssertCUDAReturn(cudaBindTexture2D(0, tex_I0, img0_pyramid.back()->ptr(), channel_desc, w_pyramid.back(), h_pyramid.back(), pitch), NCV_CUDA_ERROR);
ncvAssertCUDAReturn(cudaBindTexture2D(0, tex_I1, img1_pyramid.back()->ptr(), channel_desc, w_pyramid.back(), h_pyramid.back(), pitch), NCV_CUDA_ERROR);
size_t pitch = alignUp(pyr.w.back(), kStrideAlignmentFloat) * sizeof(float);
ncvAssertCUDAReturn(cudaBindTexture2D(0, tex_I0, pyr.img0.back()->ptr(), channel_desc, pyr.w.back(), pyr.h.back(), pitch), NCV_CUDA_ERROR);
ncvAssertCUDAReturn(cudaBindTexture2D(0, tex_I1, pyr.img1.back()->ptr(), channel_desc, pyr.w.back(), pyr.h.back(), pitch), NCV_CUDA_ERROR);
ncvAssertCUDAReturn(cudaStreamSynchronize(stream), NCV_CUDA_ERROR);
}
FloatVector* ptrU = &u;
FloatVector* ptrV = &v;
FloatVector* ptrUNew = &u_new;
FloatVector* ptrVNew = &v_new;
std::vector< shared_ptr<FloatVector> >::const_reverse_iterator img0Iter = img0_pyramid.rbegin();
std::vector< shared_ptr<FloatVector> >::const_reverse_iterator img1Iter = img1_pyramid.rbegin();
std::vector<FloatVector*>::const_reverse_iterator img0Iter = pyr.img0.rbegin();
std::vector<FloatVector*>::const_reverse_iterator img1Iter = pyr.img1.rbegin();
//outer loop
//warping fixed point iteration
while(!w_pyramid.empty())
while(!pyr.w.empty())
{
//current grid dimensions
const Ncv32u kLevelWidth = w_pyramid.back();
const Ncv32u kLevelHeight = h_pyramid.back();
const Ncv32u kLevelWidth = pyr.w.back();
const Ncv32u kLevelHeight = pyr.h.back();
const Ncv32u kLevelStride = alignUp(kLevelWidth, kStrideAlignmentFloat);
//size of current image in bytes
@ -972,12 +935,9 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
//number of points at current resolution
const int kLevelSizeInPixels = kLevelStride * kLevelHeight;
if (!kSkipProcessing)
{
//initial guess for du and dv
ncvAssertCUDAReturn(cudaMemsetAsync(du.ptr(), 0, kLevelSizeInBytes, stream), NCV_CUDA_ERROR);
ncvAssertCUDAReturn(cudaMemsetAsync(dv.ptr(), 0, kLevelSizeInBytes, stream), NCV_CUDA_ERROR);
}
//texture format descriptor
cudaChannelFormatDesc channel_desc = cudaCreateChannelDesc<float>();
@ -988,18 +948,15 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
++img0Iter;
++img1Iter;
if (!kSkipProcessing)
{
ncvAssertCUDAReturn(cudaBindTexture2D(0, tex_I0, I0->ptr(), channel_desc, kLevelWidth, kLevelHeight, kLevelStride*sizeof(float)), NCV_CUDA_ERROR);
ncvAssertCUDAReturn(cudaBindTexture2D(0, tex_I1, I1->ptr(), channel_desc, kLevelWidth, kLevelHeight, kLevelStride*sizeof(float)), NCV_CUDA_ERROR);
}
//compute derivatives
dim3 dBlocks(iDivUp(kLevelWidth, 32), iDivUp(kLevelHeight, 6));
dim3 dThreads(32, 6);
const int kPitchTex = kLevelStride * sizeof(float);
if (!kSkipProcessing)
{
NcvSize32u srcSize(kLevelWidth, kLevelHeight);
Ncv32u nSrcStep = kLevelStride * sizeof(float);
NcvRect32u oROI(0, 0, kLevelWidth, kLevelHeight);
@ -1031,10 +988,7 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
// Ixy
nppiStFilterRowBorder_32f_C1R (Iy.ptr(), srcSize, nSrcStep, Ixy.ptr(), srcSize, nSrcStep, oROI,
nppStBorderMirror, derivativeFilter.ptr(), kDFilterSize, kDFilterSize/2, 1.0f/12.0f);
}
if (!kSkipProcessing)
{
ncvAssertCUDAReturn(cudaBindTexture2D(0, tex_Ix, Ix.ptr(), channel_desc, kLevelWidth, kLevelHeight, kPitchTex), NCV_CUDA_ERROR);
ncvAssertCUDAReturn(cudaBindTexture2D(0, tex_Ixx, Ixx.ptr(), channel_desc, kLevelWidth, kLevelHeight, kPitchTex), NCV_CUDA_ERROR);
ncvAssertCUDAReturn(cudaBindTexture2D(0, tex_Ix0, Ix0.ptr(), channel_desc, kLevelWidth, kLevelHeight, kPitchTex), NCV_CUDA_ERROR);
@ -1049,8 +1003,6 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
// flow increments
ncvAssertCUDAReturn(cudaBindTexture(0, tex_du, du.ptr(), channel_desc, kLevelSizeInBytes), NCV_CUDA_ERROR);
ncvAssertCUDAReturn(cudaBindTexture(0, tex_dv, dv.ptr(), channel_desc, kLevelSizeInBytes), NCV_CUDA_ERROR);
}
dim3 psor_blocks(iDivUp(kLevelWidth, PSOR_TILE_WIDTH), iDivUp(kLevelHeight, PSOR_TILE_HEIGHT));
dim3 psor_threads(PSOR_TILE_WIDTH, PSOR_TILE_HEIGHT);
@ -1064,8 +1016,6 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
for (Ncv32u current_inner_iteration = 0; current_inner_iteration < desc.number_of_inner_iterations; ++current_inner_iteration)
{
//compute coefficients
if (!kSkipProcessing)
{
prepare_sor_stage_1_tex<<<psor_blocks, psor_threads, 0, stream>>>
(diffusivity_x.ptr(),
diffusivity_y.ptr(),
@ -1101,13 +1051,12 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
ncvAssertCUDAReturn(cudaBindTexture(0, tex_inv_denominator_u, denom_u.ptr(), channel_desc, kLevelSizeInBytes), NCV_CUDA_ERROR);
ncvAssertCUDAReturn(cudaBindTexture(0, tex_inv_denominator_v, denom_v.ptr(), channel_desc, kLevelSizeInBytes), NCV_CUDA_ERROR);
}
//solve linear system
for (Ncv32u solver_iteration = 0; solver_iteration < desc.number_of_solver_iterations; ++solver_iteration)
{
float omega = 1.99f;
if (!kSkipProcessing)
{
ncvAssertCUDAReturn(cudaBindTexture(0, tex_du, du.ptr(), channel_desc, kLevelSizeInBytes), NCV_CUDA_ERROR);
ncvAssertCUDAReturn(cudaBindTexture(0, tex_dv, dv.ptr(), channel_desc, kLevelSizeInBytes), NCV_CUDA_ERROR);
@ -1139,7 +1088,6 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
kLevelWidth,
kLevelHeight,
kLevelStride);
}
ncvAssertCUDAReturn(cudaBindTexture(0, tex_du, du.ptr(), channel_desc, kLevelSizeInBytes), NCV_CUDA_ERROR);
ncvAssertCUDAReturn(cudaBindTexture(0, tex_dv, dv.ptr(), channel_desc, kLevelSizeInBytes), NCV_CUDA_ERROR);
@ -1147,25 +1095,22 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
}// end of inner loop
//update u and v
if (!kSkipProcessing)
{
add(ptrU->ptr(), du.ptr(), kLevelSizeInPixels, stream);
add(ptrV->ptr(), dv.ptr(), kLevelSizeInPixels, stream);
}
//prolongate using texture
w_pyramid.pop_back();
h_pyramid.pop_back();
if (!w_pyramid.empty())
pyr.w.pop_back();
pyr.h.pop_back();
if (!pyr.w.empty())
{
//compute new image size
Ncv32u nw = w_pyramid.back();
Ncv32u nh = h_pyramid.back();
Ncv32u nw = pyr.w.back();
Ncv32u nh = pyr.h.back();
Ncv32u ns = alignUp(nw, kStrideAlignmentFloat);
dim3 p_blocks(iDivUp(nw, 32), iDivUp(nh, 8));
dim3 p_threads(32, 8);
if (!kSkipProcessing)
{
NcvSize32u srcSize (kLevelWidth, kLevelHeight);
NcvSize32u dstSize (nw, nh);
NcvRect32u srcROI (0, 0, kLevelWidth, kLevelHeight);
@ -1180,7 +1125,6 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
ptrVNew->ptr(), dstSize, ns * sizeof (float), dstROI, 1.0f/scale_factor, 1.0f/scale_factor, nppStBicubic);
ScaleVector(ptrVNew->ptr(), ptrVNew->ptr(), 1.0f/scale_factor, ns * nh, stream);
}
cv::gpu::device::swap<FloatVector*>(ptrU, ptrUNew);
cv::gpu::device::swap<FloatVector*>(ptrV, ptrVNew);
@ -1201,6 +1145,7 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
ncvAssertCUDAReturn(cudaGetLastError(), NCV_CUDA_ERROR);
ncvAssertCUDAReturn(cudaStreamSynchronize(stream), NCV_CUDA_ERROR);
}
return NCV_SUCCESS;
}

198
modules/gpu/src/optical_flow.cpp Normal file
View File

@ -0,0 +1,198 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other GpuMaterials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or bpied warranties, including, but not limited to, the bpied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
using namespace cv;
using namespace cv::gpu;
using namespace std;
#if !defined (HAVE_CUDA)
void cv::gpu::BroxOpticalFlow::operator ()(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::interpolateFrames(const GpuMat&, const GpuMat&, const GpuMat&, const GpuMat&, const GpuMat&, const GpuMat&, float, GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
#else
namespace
{
size_t getBufSize(const NCVBroxOpticalFlowDescriptor& desc, const NCVMatrix<Ncv32f>& frame0, const NCVMatrix<Ncv32f>& frame1,
NCVMatrix<Ncv32f>& u, NCVMatrix<Ncv32f>& v, const cudaDeviceProp& devProp)
{
NCVMemStackAllocator gpuCounter(static_cast<Ncv32u>(devProp.textureAlignment));
CV_Assert(gpuCounter.isInitialized());
NCVStatus ncvStat = NCVBroxOpticalFlow(desc, gpuCounter, frame0, frame1, u, v, 0);
CV_Assert(ncvStat == NCV_SUCCESS);
return gpuCounter.maxSize();
}
}
namespace
{
void outputHandler(const char* msg)
{
CV_Error(CV_GpuApiCallError, msg);
}
}
void cv::gpu::BroxOpticalFlow::operator ()(const GpuMat& frame0, const GpuMat& frame1, GpuMat& u, GpuMat& v, Stream& s)
{
ncvSetDebugOutputHandler(outputHandler);
CV_Assert(frame0.type() == CV_32FC1);
CV_Assert(frame1.size() == frame0.size() && frame1.type() == frame0.type());
u.create(frame0.size(), CV_32FC1);
v.create(frame0.size(), CV_32FC1);
cudaDeviceProp devProp;
cudaSafeCall( cudaGetDeviceProperties(&devProp, getDevice()) );
NCVBroxOpticalFlowDescriptor desc;
desc.alpha = alpha;
desc.gamma = gamma;
desc.scale_factor = scale_factor;
desc.number_of_inner_iterations = inner_iterations;
desc.number_of_outer_iterations = outer_iterations;
desc.number_of_solver_iterations = solver_iterations;
NCVMemSegment frame0MemSeg;
frame0MemSeg.begin.memtype = NCVMemoryTypeDevice;
frame0MemSeg.begin.ptr = const_cast<uchar*>(frame0.data);
frame0MemSeg.size = frame0.step * frame0.rows;
NCVMemSegment frame1MemSeg;
frame1MemSeg.begin.memtype = NCVMemoryTypeDevice;
frame1MemSeg.begin.ptr = const_cast<uchar*>(frame1.data);
frame1MemSeg.size = frame1.step * frame1.rows;
NCVMemSegment uMemSeg;
uMemSeg.begin.memtype = NCVMemoryTypeDevice;
uMemSeg.begin.ptr = u.ptr();
uMemSeg.size = u.step * u.rows;
NCVMemSegment vMemSeg;
vMemSeg.begin.memtype = NCVMemoryTypeDevice;
vMemSeg.begin.ptr = v.ptr();
vMemSeg.size = v.step * v.rows;
NCVMatrixReuse<Ncv32f> frame0Mat(frame0MemSeg, devProp.textureAlignment, frame0.cols, frame0.rows, frame0.step);
NCVMatrixReuse<Ncv32f> frame1Mat(frame1MemSeg, devProp.textureAlignment, frame1.cols, frame1.rows, frame1.step);
NCVMatrixReuse<Ncv32f> uMat(uMemSeg, devProp.textureAlignment, u.cols, u.rows, u.step);
NCVMatrixReuse<Ncv32f> vMat(vMemSeg, devProp.textureAlignment, v.cols, v.rows, v.step);
cudaStream_t stream = StreamAccessor::getStream(s);
size_t bufSize = getBufSize(desc, frame0Mat, frame1Mat, uMat, vMat, devProp);
ensureSizeIsEnough(1, bufSize, CV_8UC1, buf);
NCVMemStackAllocator gpuAllocator(NCVMemoryTypeDevice, bufSize, static_cast<Ncv32u>(devProp.textureAlignment), buf.ptr());
CV_Assert(gpuAllocator.isInitialized());
NCVStatus ncvStat = NCVBroxOpticalFlow(desc, gpuAllocator, frame0Mat, frame1Mat, uMat, vMat, stream);
CV_Assert(ncvStat == NCV_SUCCESS);
}
void cv::gpu::interpolateFrames(const GpuMat& frame0, const GpuMat& frame1, const GpuMat& fu, const GpuMat& fv, const GpuMat& bu, const GpuMat& bv,
float pos, GpuMat& newFrame, GpuMat& buf, Stream& s)
{
CV_Assert(frame0.type() == CV_32FC1);
CV_Assert(frame1.size() == frame0.size() && frame1.type() == frame0.type());
CV_Assert(fu.size() == frame0.size() && fu.type() == frame0.type());
CV_Assert(fv.size() == frame0.size() && fv.type() == frame0.type());
CV_Assert(bu.size() == frame0.size() && bu.type() == frame0.type());
CV_Assert(bv.size() == frame0.size() && bv.type() == frame0.type());
newFrame.create(frame0.size(), frame0.type());
buf.create(6 * frame0.rows, frame0.cols, CV_32FC1);
buf.setTo(Scalar::all(0));
// occlusion masks
GpuMat occ0 = buf.rowRange(0 * frame0.rows, 1 * frame0.rows);
GpuMat occ1 = buf.rowRange(1 * frame0.rows, 2 * frame0.rows);
// interpolated forward flow
GpuMat fui = buf.rowRange(2 * frame0.rows, 3 * frame0.rows);
GpuMat fvi = buf.rowRange(3 * frame0.rows, 4 * frame0.rows);
// interpolated backward flow
GpuMat bui = buf.rowRange(4 * frame0.rows, 5 * frame0.rows);
GpuMat bvi = buf.rowRange(5 * frame0.rows, 6 * frame0.rows);
size_t step = frame0.step;
CV_Assert(frame1.step == step && fu.step == step && fv.step == step && bu.step == step && bv.step == step && newFrame.step == step && buf.step == step);
cudaStream_t stream = StreamAccessor::getStream(s);
NppStStreamHandler h(stream);
NppStInterpolationState state;
state.size = NcvSize32u(frame0.cols, frame0.rows);
state.nStep = static_cast<Ncv32u>(step);
state.pSrcFrame0 = const_cast<Ncv32f*>(frame0.ptr<Ncv32f>());
state.pSrcFrame1 = const_cast<Ncv32f*>(frame1.ptr<Ncv32f>());
state.pFU = const_cast<Ncv32f*>(fu.ptr<Ncv32f>());
state.pFV = const_cast<Ncv32f*>(fv.ptr<Ncv32f>());
state.pBU = const_cast<Ncv32f*>(bu.ptr<Ncv32f>());
state.pBV = const_cast<Ncv32f*>(bv.ptr<Ncv32f>());
state.pos = pos;
state.pNewFrame = newFrame.ptr<Ncv32f>();
state.ppBuffers[0] = occ0.ptr<Ncv32f>();
state.ppBuffers[1] = occ1.ptr<Ncv32f>();
state.ppBuffers[2] = fui.ptr<Ncv32f>();
state.ppBuffers[3] = fvi.ptr<Ncv32f>();
state.ppBuffers[4] = bui.ptr<Ncv32f>();
state.ppBuffers[5] = bvi.ptr<Ncv32f>();
nppSafeCall( nppiStInterpolateFrames(&state) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
#endif /* HAVE_CUDA */

1
modules/gpu/src/precomp.hpp
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@ -76,6 +76,7 @@
#include "nvidia/core/NCV.hpp"
#include "nvidia/NPP_staging/NPP_staging.hpp"
#include "nvidia/NCVHaarObjectDetection.hpp"
#include "nvidia/NCVBroxOpticalFlow.hpp"
#define CUDART_MINIMUM_REQUIRED_VERSION 4000
#define NPP_MINIMUM_REQUIRED_VERSION 4000

227
modules/gpu/test/test_video.cpp Normal file
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@ -0,0 +1,227 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
#ifdef HAVE_CUDA
//#define DUMP
struct BroxOpticalFlow : testing::TestWithParam< cv::gpu::DeviceInfo >
{
cv::gpu::DeviceInfo devInfo;
cv::Mat frame0;
cv::Mat frame1;
cv::Mat u_gold;
cv::Mat v_gold;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
frame0 = readImage("opticalflow/frame0.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame0.empty());
frame0.convertTo(frame0, CV_32F, 1.0 / 255.0);
frame1 = readImage("opticalflow/frame1.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame1.empty());
frame1.convertTo(frame1, CV_32F, 1.0 / 255.0);
#ifndef DUMP
std::ifstream f((std::string(cvtest::TS::ptr()->get_data_path()) + "opticalflow/opticalflow_gold.bin").c_str(), std::ios_base::binary);
int rows, cols;
f.read((char*)&rows, sizeof(rows));
f.read((char*)&cols, sizeof(cols));
u_gold.create(rows, cols, CV_32FC1);
for (int i = 0; i < u_gold.rows; ++i)
f.read((char*)u_gold.ptr(i), u_gold.cols * sizeof(float));
v_gold.create(rows, cols, CV_32FC1);
for (int i = 0; i < v_gold.rows; ++i)
f.read((char*)v_gold.ptr(i), v_gold.cols * sizeof(float));
#endif
}
};
TEST_P(BroxOpticalFlow, Regression)
{
PRINT_PARAM(devInfo);
cv::Mat u;
cv::Mat v;
cv::gpu::BroxOpticalFlow d_flow(0.197f /*alpha*/, 50.0f /*gamma*/, 0.8f /*scale_factor*/,
10 /*inner_iterations*/, 77 /*outer_iterations*/, 10 /*solver_iterations*/);
ASSERT_NO_THROW(
cv::gpu::GpuMat d_u;
cv::gpu::GpuMat d_v;
d_flow(cv::gpu::GpuMat(frame0), cv::gpu::GpuMat(frame1), d_u, d_v);
d_u.download(u);
d_v.download(v);
d_flow.buf.release();
);
#ifndef DUMP
EXPECT_MAT_NEAR(u_gold, u, 0);
EXPECT_MAT_NEAR(v_gold, v, 0);
#else
std::ofstream f((std::string(cvtest::TS::ptr()->get_data_path()) + "opticalflow/opticalflow_gold.bin").c_str(), std::ios_base::binary);
f.write((char*)&u.rows, sizeof(u.rows));
f.write((char*)&u.cols, sizeof(u.cols));
for (int i = 0; i < u.rows; ++i)
f.write((char*)u.ptr(i), u.cols * sizeof(float));
for (int i = 0; i < v.rows; ++i)
f.write((char*)v.ptr(i), v.cols * sizeof(float));
#endif
}
INSTANTIATE_TEST_CASE_P(Video, BroxOpticalFlow, testing::ValuesIn(devices()));
struct InterpolateFrames : testing::TestWithParam< cv::gpu::DeviceInfo >
{
cv::gpu::DeviceInfo devInfo;
cv::Mat frame0;
cv::Mat frame1;
cv::Mat newFrame_gold;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
frame0 = readImage("opticalflow/frame0.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame0.empty());
frame0.convertTo(frame0, CV_32F, 1.0 / 255.0);
frame1 = readImage("opticalflow/frame1.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame1.empty());
frame1.convertTo(frame1, CV_32F, 1.0 / 255.0);
#ifndef DUMP
std::ifstream f((std::string(cvtest::TS::ptr()->get_data_path()) + "opticalflow/interpolate_frames_gold.bin").c_str(), std::ios_base::binary);
int rows, cols;
f.read((char*)&rows, sizeof(rows));
f.read((char*)&cols, sizeof(cols));
newFrame_gold.create(rows, cols, CV_32FC1);
for (int i = 0; i < newFrame_gold.rows; ++i)
f.read((char*)newFrame_gold.ptr(i), newFrame_gold.cols * sizeof(float));
#endif
}
};
TEST_P(InterpolateFrames, Regression)
{
PRINT_PARAM(devInfo);
cv::Mat newFrame;
cv::gpu::BroxOpticalFlow d_flow(0.197f /*alpha*/, 50.0f /*gamma*/, 0.8f /*scale_factor*/,
10 /*inner_iterations*/, 77 /*outer_iterations*/, 10 /*solver_iterations*/);
ASSERT_NO_THROW(
cv::gpu::GpuMat d_frame0(frame0);
cv::gpu::GpuMat d_frame1(frame1);
cv::gpu::GpuMat d_fu;
cv::gpu::GpuMat d_fv;
cv::gpu::GpuMat d_bu;
cv::gpu::GpuMat d_bv;
cv::gpu::GpuMat d_newFrame;
cv::gpu::GpuMat d_buf;
d_flow(d_frame0, d_frame1, d_fu, d_fv);
d_flow(d_frame1, d_frame0, d_bu, d_bv);
cv::gpu::interpolateFrames(d_frame0, d_frame1, d_fu, d_fv, d_bu, d_bv, 0.5f, d_newFrame, d_buf);
d_newFrame.download(newFrame);
d_flow.buf.release();
);
#ifndef DUMP
EXPECT_MAT_NEAR(newFrame_gold, newFrame, 1e-4);
#else
std::ofstream f((std::string(cvtest::TS::ptr()->get_data_path()) + "opticalflow/interpolate_frames_gold.bin").c_str(), std::ios_base::binary);
f.write((char*)&newFrame.rows, sizeof(newFrame.rows));
f.write((char*)&newFrame.cols, sizeof(newFrame.cols));
for (int i = 0; i < newFrame.rows; ++i)
f.write((char*)newFrame.ptr(i), newFrame.cols * sizeof(float));
#endif
}
INSTANTIATE_TEST_CASE_P(Video, InterpolateFrames, testing::ValuesIn(devices()));
#endif

340
samples/gpu/optical_flow.cpp Normal file
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@ -0,0 +1,340 @@
#include <iostream>
#include <iomanip>
#include <string>
#include "cvconfig.h"
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/gpu/gpu.hpp"
#ifdef HAVE_CUDA
#include "NPP_staging/NPP_staging.hpp"
#endif
using namespace std;
using namespace cv;
using namespace cv::gpu;
#if !defined(HAVE_CUDA)
int main(int argc, const char* argv[])
{
cout << "Please compile the library with CUDA support" << endl;
return -1;
}
#else
#define PARAM_INPUT "--input"
#define PARAM_SCALE "--scale"
#define PARAM_ALPHA "--alpha"
#define PARAM_GAMMA "--gamma"
#define PARAM_INNER "--inner"
#define PARAM_OUTER "--outer"
#define PARAM_SOLVER "--solver"
#define PARAM_TIME_STEP "--time-step"
#define PARAM_HELP "--help"
void printHelp()
{
cout << "Usage help:\n";
cout << setiosflags(ios::left);
cout << "\t" << setw(15) << PARAM_ALPHA << " - set alpha\n";
cout << "\t" << setw(15) << PARAM_GAMMA << " - set gamma\n";
cout << "\t" << setw(15) << PARAM_INNER << " - set number of inner iterations\n";
cout << "\t" << setw(15) << PARAM_INPUT << " - specify input file names (2 image files)\n";
cout << "\t" << setw(15) << PARAM_OUTER << " - set number of outer iterations\n";
cout << "\t" << setw(15) << PARAM_SCALE << " - set pyramid scale factor\n";
cout << "\t" << setw(15) << PARAM_SOLVER << " - set number of basic solver iterations\n";
cout << "\t" << setw(15) << PARAM_TIME_STEP << " - set frame interpolation time step\n";
cout << "\t" << setw(15) << PARAM_HELP << " - display this help message\n";
}
int processCommandLine(int argc, const char* argv[], float& timeStep, string& frame0Name, string& frame1Name, BroxOpticalFlow& flow)
{
timeStep = 0.25f;
for (int iarg = 1; iarg < argc; ++iarg)
{
if (strcmp(argv[iarg], PARAM_INPUT) == 0)
{
if (iarg + 2 < argc)
{
frame0Name = argv[++iarg];
frame1Name = argv[++iarg];
}
else
return -1;
}
else if(strcmp(argv[iarg], PARAM_SCALE) == 0)
{
if (iarg + 1 < argc)
flow.scale_factor = static_cast<float>(atof(argv[++iarg]));
else
return -1;
}
else if(strcmp(argv[iarg], PARAM_ALPHA) == 0)
{
if (iarg + 1 < argc)
flow.alpha = static_cast<float>(atof(argv[++iarg]));
else
return -1;
}
else if(strcmp(argv[iarg], PARAM_GAMMA) == 0)
{
if (iarg + 1 < argc)
flow.gamma = static_cast<float>(atof(argv[++iarg]));
else
return -1;
}
else if(strcmp(argv[iarg], PARAM_INNER) == 0)
{
if (iarg + 1 < argc)
flow.inner_iterations = atoi(argv[++iarg]);
else
return -1;
}
else if(strcmp(argv[iarg], PARAM_OUTER) == 0)
{
if (iarg + 1 < argc)
flow.outer_iterations = atoi(argv[++iarg]);
else
return -1;
}
else if(strcmp(argv[iarg], PARAM_SOLVER) == 0)
{
if (iarg + 1 < argc)
flow.solver_iterations = atoi(argv[++iarg]);
else
return -1;
}
else if(strcmp(argv[iarg], PARAM_TIME_STEP) == 0)
{
if (iarg + 1 < argc)
timeStep = static_cast<float>(atof(argv[++iarg]));
else
return -1;
}
else if(strcmp(argv[iarg], PARAM_HELP) == 0)
{
printHelp();
return 0;
}
}
return 0;
}
template <typename T> inline T clamp (T x, T a, T b)
{
return ((x) > (a) ? ((x) < (b) ? (x) : (b)) : (a));
}
template <typename T> inline T mapValue(T x, T a, T b, T c, T d)
{
x = clamp(x, a, b);
return c + (d - c) * (x - a) / (b - a);
}
void getFlowField(const Mat& u, const Mat& v, Mat& flowField)
{
float maxDisplacement = 1.0f;
for (int i = 0; i < u.rows; ++i)
{
const float* ptr_u = u.ptr<float>(i);
const float* ptr_v = v.ptr<float>(i);
for (int j = 0; j < u.cols; ++j)
{
float d = max(fabsf(ptr_u[j]), fabsf(ptr_v[j]));
if (d > maxDisplacement)
maxDisplacement = d;
}
}
flowField.create(u.size(), CV_8UC4);
for (int i = 0; i < flowField.rows; ++i)
{
const float* ptr_u = u.ptr<float>(i);
const float* ptr_v = v.ptr<float>(i);
Vec4b* row = flowField.ptr<Vec4b>(i);
for (int j = 0; j < flowField.cols; ++j)
{
row[j][0] = 0;
row[j][1] = static_cast<unsigned char> (mapValue (-ptr_v[j], -maxDisplacement, maxDisplacement, 0.0f, 255.0f));
row[j][2] = static_cast<unsigned char> (mapValue ( ptr_u[j], -maxDisplacement, maxDisplacement, 0.0f, 255.0f));
row[j][3] = 255;
}
}
}
int main(int argc, const char* argv[])
{
string frame0Name, frame1Name;
float timeStep = 0.01f;
BroxOpticalFlow d_flow(0.197f /*alpha*/, 50.0f /*gamma*/, 0.8f /*scale_factor*/,
10 /*inner_iterations*/, 77 /*outer_iterations*/, 10 /*solver_iterations*/);
int result = processCommandLine(argc, argv, timeStep, frame0Name, frame1Name, d_flow);
if (argc == 1 || result)
{
printHelp();
return result;
}
if (frame0Name.empty() || frame1Name.empty())
{
cout << "Missing input file names\n";
return -1;
}
Mat frame0Color = imread(frame0Name);
Mat frame1Color = imread(frame1Name);
if (frame0Color.empty() || frame1Color.empty())
{
cout << "Can't load input images\n";
return -1;
}
cout << "OpenCV / NVIDIA Computer Vision\n";
cout << "Optical Flow Demo: Frame Interpolation\n";
cout << "=========================================\n";
cout << "Press:\n ESC to quit\n 'a' to move to the previous frame\n 's' to move to the next frame\n";
frame0Color.convertTo(frame0Color, CV_32F, 1.0 / 255.0);
frame1Color.convertTo(frame1Color, CV_32F, 1.0 / 255.0);
Mat frame0Gray, frame1Gray;
cvtColor(frame0Color, frame0Gray, COLOR_BGR2GRAY);
cvtColor(frame1Color, frame1Gray, COLOR_BGR2GRAY);
GpuMat d_frame0(frame0Gray);
GpuMat d_frame1(frame1Gray);
Mat fu, fv;
Mat bu, bv;
GpuMat d_fu, d_fv;
GpuMat d_bu, d_bv;
cout << "Estimating optical flow\nForward...\n";
d_flow(d_frame0, d_frame1, d_fu, d_fv);
d_flow(d_frame1, d_frame0, d_bu, d_bv);
d_fu.download(fu);
d_fv.download(fv);
d_bu.download(bu);
d_bv.download(bv);
// first frame color components (GPU memory)
GpuMat d_b, d_g, d_r;
// second frame color components (GPU memory)
GpuMat d_bt, d_gt, d_rt;
// prepare color components on host and copy them to device memory
Mat channels[3];
cv::split(frame0Color, channels);
d_b.upload(channels[0]);
d_g.upload(channels[1]);
d_r.upload(channels[2]);
cv::split(frame1Color, channels);
d_bt.upload(channels[0]);
d_gt.upload(channels[1]);
d_rt.upload(channels[2]);
cout << "Interpolating...\n";
cout.precision (4);
// temporary buffer
GpuMat d_buf;
// intermediate frame color components (GPU memory)
GpuMat d_rNew, d_gNew, d_bNew;
GpuMat d_newFrame;
vector<Mat> frames;
frames.reserve(1.0f / timeStep + 2);
frames.push_back(frame0Color);
// compute interpolated frames
for (float timePos = timeStep; timePos < 1.0f; timePos += timeStep)
{
// interpolate blue channel
interpolateFrames(d_b, d_bt, d_fu, d_fv, d_bu, d_bv, timePos, d_bNew, d_buf);
// interpolate green channel
interpolateFrames(d_g, d_gt, d_fu, d_fv, d_bu, d_bv, timePos, d_gNew, d_buf);
// interpolate red channel
interpolateFrames(d_r, d_rt, d_fu, d_fv, d_bu, d_bv, timePos, d_rNew, d_buf);
GpuMat channels[] = {d_bNew, d_gNew, d_rNew};
merge(channels, 3, d_newFrame);
Mat newFrame;
d_newFrame.download(newFrame);
frames.push_back(newFrame);
cout << timePos * 100.0f << "%\r";
}
cout << setw (5) << "100%\n";
frames.push_back(frame1Color);
int currentFrame;
currentFrame = 0;
Mat flowFieldForward;
Mat flowFieldBackward;
getFlowField(fu, fv, flowFieldForward);
getFlowField(bu, bv, flowFieldBackward);
imshow("Forward flow", flowFieldForward);
imshow("Backward flow", flowFieldBackward);
imshow("Interpolated frame", frames[currentFrame]);
bool qPressed = false;
while (!qPressed)
{
int key = toupper(waitKey(10));
switch (key)
{
case 27:
qPressed = true;
break;
case 'A':
if (currentFrame > 0)
--currentFrame;
imshow("Interpolated frame", frames[currentFrame]);
break;
case 'S':
if (currentFrame < frames.size() - 1)
++currentFrame;
imshow("Interpolated frame", frames[currentFrame]);
break;
}
}
return 0;
}
#endif