[+] Added Brox optical flow (implementation courtesy of Michael Smirnov)

pull/13383/head
Anton Obukhov 13 years ago
parent f838db92c7
commit 42c7aece36
  1. 1136
      modules/gpu/src/nvidia/NCVBroxOpticalFlow.cu
  2. 103
      modules/gpu/src/nvidia/NCVBroxOpticalFlow.hpp
  3. 949
      modules/gpu/src/nvidia/NPP_staging/NPP_staging.cu
  4. 249
      modules/gpu/src/nvidia/NPP_staging/NPP_staging.hpp
  5. 639
      samples/gpu/opticalflow_nvidia_api.cpp
  6. BIN
      samples/gpu/rubberwhale1.png
  7. BIN
      samples/gpu/rubberwhale2.png

File diff suppressed because it is too large Load Diff

@ -0,0 +1,103 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
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// If you do not agree to this license, do not download, install,
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//
//
// License Agreement
// For Open Source Computer Vision Library
//
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// or tort (including negligence or otherwise) arising in any way out of
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//
//M*/
////////////////////////////////////////////////////////////////////////////////
//
// NVIDIA CUDA implementation of Brox et al Optical Flow algorithm
//
// Algorithm is explained in the original paper:
// T. Brox, A. Bruhn, N. Papenberg, J. Weickert:
// High accuracy optical flow estimation based on a theory for warping.
// ECCV 2004.
//
// Implementation by Mikhail Smirnov
// email: msmirnov@nvidia.com, devsupport@nvidia.com
//
// Credits for help with the code to:
// Alexey Mendelenko, Anton Obukhov, and Alexander Kharlamov.
//
////////////////////////////////////////////////////////////////////////////////
#ifndef _ncv_optical_flow_h_
#define _ncv_optical_flow_h_
#include "NCV.hpp"
/// \brief Model and solver parameters
struct NCVBroxOpticalFlowDescriptor
{
/// flow smoothness
Ncv32f alpha;
/// gradient constancy importance
Ncv32f gamma;
/// pyramid scale factor
Ncv32f scale_factor;
/// number of lagged non-linearity iterations (inner loop)
Ncv32u number_of_inner_iterations;
/// number of warping iterations (number of pyramid levels)
Ncv32u number_of_outer_iterations;
/// number of linear system solver iterations
Ncv32u number_of_solver_iterations;
};
/////////////////////////////////////////////////////////////////////////////////////////
/// \brief Compute optical flow
///
/// Based on method by Brox et al [2004]
/// \param [in] desc model and solver parameters
/// \param [in] gpu_mem_allocator GPU memory allocator
/// \param [in] frame0 source frame
/// \param [in] frame1 frame to track
/// \param [out] u flow horizontal component (along \b x axis)
/// \param [out] v flow vertical component (along \b y axis)
/// \return computation status
/////////////////////////////////////////////////////////////////////////////////////////
NCV_EXPORTS
NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
INCVMemAllocator &gpu_mem_allocator,
const NCVMatrix<Ncv32f> &frame0,
const NCVMatrix<Ncv32f> &frame1,
NCVMatrix<Ncv32f> &u,
NCVMatrix<Ncv32f> &v,
cudaStream_t stream);
#endif

@ -1610,3 +1610,952 @@ NCVStatus nppsStCompact_32f_host(Ncv32f *h_src, Ncv32u srcLen,
{
return nppsStCompact_32u_host((Ncv32u *)h_src, srcLen, (Ncv32u *)h_dst, dstLen, *(Ncv32u *)&elemRemove);
}
//==============================================================================
//
// Filter.cu
//
//==============================================================================
texture <float, 1, cudaReadModeElementType> texSrc;
texture <float, 1, cudaReadModeElementType> texKernel;
__forceinline__ __device__ float getValueMirrorRow(const int rowOffset,
int i,
int w)
{
if (i < 0) i = 1 - i;
if (i >= w) i = w + w - i - 1;
return tex1Dfetch (texSrc, rowOffset + i);
}
__forceinline__ __device__ float getValueMirrorColumn(const int offset,
const int rowStep,
int j,
int h)
{
if (j < 0) j = 1 - j;
if (j >= h) j = h + h - j - 1;
return tex1Dfetch (texSrc, offset + j * rowStep);
}
__global__ void FilterRowBorderMirror_32f_C1R(Ncv32u srcStep,
Ncv32f *pDst,
NcvSize32u dstSize,
Ncv32u dstStep,
NcvRect32u roi,
Ncv32s nKernelSize,
Ncv32s nAnchor,
Ncv32f multiplier)
{
// position within ROI
const int ix = blockDim.x * blockIdx.x + threadIdx.x;
const int iy = blockDim.y * blockIdx.y + threadIdx.y;
if (ix >= roi.width || iy >= roi.height)
{
return;
}
const int p = nKernelSize - nAnchor - 1;
const int j = roi.y + iy;
const int rowOffset = j * srcStep + roi.x;
float sum = 0.0f;
for (int m = 0; m < nKernelSize; ++m)
{
sum += getValueMirrorRow (rowOffset, ix + m - p, roi.width)
* tex1Dfetch (texKernel, m);
}
pDst[iy * dstStep + ix] = sum * multiplier;
}
__global__ void FilterColumnBorderMirror_32f_C1R(Ncv32u srcStep,
Ncv32f *pDst,
NcvSize32u dstSize,
Ncv32u dstStep,
NcvRect32u roi,
Ncv32s nKernelSize,
Ncv32s nAnchor,
Ncv32f multiplier)
{
const int ix = blockDim.x * blockIdx.x + threadIdx.x;
const int iy = blockDim.y * blockIdx.y + threadIdx.y;
if (ix >= roi.width || iy >= roi.height)
{
return;
}
const int p = nKernelSize - nAnchor - 1;
const int i = roi.x + ix;
const int offset = i + roi.y * srcStep;
float sum = 0.0f;
for (int m = 0; m < nKernelSize; ++m)
{
sum += getValueMirrorColumn (offset, srcStep, iy + m - p, roi.height)
* tex1Dfetch (texKernel, m);
}
pDst[ix + iy * dstStep] = sum * multiplier;
}
NCVStatus nppiStFilterRowBorder_32f_C1R(const Ncv32f *pSrc,
NcvSize32u srcSize,
Ncv32u nSrcStep,
Ncv32f *pDst,
NcvSize32u dstSize,
Ncv32u nDstStep,
NcvRect32u oROI,
NppStBorderType borderType,
const Ncv32f *pKernel,
Ncv32s nKernelSize,
Ncv32s nAnchor,
Ncv32f multiplier)
{
ncvAssertReturn (pSrc != NULL &&
pDst != NULL &&
pKernel != NULL, NCV_NULL_PTR);
ncvAssertReturn (oROI.width > 0 && oROI.height > 0, NPPST_INVALID_ROI);
ncvAssertReturn (srcSize.width * sizeof (Ncv32f) <= nSrcStep &&
dstSize.width * sizeof (Ncv32f) <= nDstStep &&
oROI.width * sizeof (Ncv32f) <= nSrcStep &&
oROI.width * sizeof (Ncv32f) <= nDstStep &&
nSrcStep % sizeof (Ncv32f) == 0 &&
nDstStep % sizeof (Ncv32f) == 0, NPPST_INVALID_STEP);
Ncv32u srcStep = nSrcStep / sizeof (Ncv32f);
Ncv32u dstStep = nDstStep / sizeof (Ncv32f);
// adjust ROI size to be within source image
if (oROI.x + oROI.width > srcSize.width)
{
oROI.width = srcSize.width - oROI.x;
}
if (oROI.y + oROI.height > srcSize.height)
{
oROI.height = srcSize.height - oROI.y;
}
cudaChannelFormatDesc floatChannel = cudaCreateChannelDesc <float> ();
texSrc.normalized = false;
texKernel.normalized = false;
cudaBindTexture (0, texSrc, pSrc, floatChannel, srcSize.height * nSrcStep);
cudaBindTexture (0, texKernel, pKernel, floatChannel, nKernelSize * sizeof (Ncv32f));
dim3 ctaSize (32, 6);
dim3 gridSize ((oROI.width + ctaSize.x - 1) / ctaSize.x,
(oROI.height + ctaSize.y - 1) / ctaSize.y);
switch (borderType)
{
case nppStBorderNone:
return NPPST_ERROR;
case nppStBorderClamp:
return NPPST_ERROR;
case nppStBorderWrap:
return NPPST_ERROR;
case nppStBorderMirror:
FilterRowBorderMirror_32f_C1R <<<gridSize, ctaSize, 0, nppStGetActiveCUDAstream ()>>>
(srcStep, pDst, dstSize, dstStep, oROI, nKernelSize, nAnchor, multiplier);
break;
default:
return NPPST_ERROR;
}
return NPPST_SUCCESS;
}
NCVStatus nppiStFilterColumnBorder_32f_C1R(const Ncv32f *pSrc,
NcvSize32u srcSize,
Ncv32u nSrcStep,
Ncv32f *pDst,
NcvSize32u dstSize,
Ncv32u nDstStep,
NcvRect32u oROI,
NppStBorderType borderType,
const Ncv32f *pKernel,
Ncv32s nKernelSize,
Ncv32s nAnchor,
Ncv32f multiplier)
{
ncvAssertReturn (pSrc != NULL &&
pDst != NULL &&
pKernel != NULL, NCV_NULL_PTR);
ncvAssertReturn (oROI.width > 0 && oROI.height > 0, NPPST_INVALID_ROI);
ncvAssertReturn (srcSize.width * sizeof (Ncv32f) <= nSrcStep &&
dstSize.width * sizeof (Ncv32f) <= nDstStep &&
oROI.width * sizeof (Ncv32f) <= nSrcStep &&
oROI.width * sizeof (Ncv32f) <= nDstStep &&
nSrcStep % sizeof (Ncv32f) == 0 &&
nDstStep % sizeof (Ncv32f) == 0, NPPST_INVALID_STEP);
Ncv32u srcStep = nSrcStep / sizeof (Ncv32f);
Ncv32u dstStep = nDstStep / sizeof (Ncv32f);
// adjust ROI size to be within source image
if (oROI.x + oROI.width > srcSize.width)
{
oROI.width = srcSize.width - oROI.x;
}
if (oROI.y + oROI.height > srcSize.height)
{
oROI.height = srcSize.height - oROI.y;
}
cudaChannelFormatDesc floatChannel = cudaCreateChannelDesc <float> ();
texSrc.normalized = false;
texKernel.normalized = false;
cudaBindTexture (0, texSrc, pSrc, floatChannel, srcSize.height * nSrcStep);
cudaBindTexture (0, texKernel, pKernel, floatChannel, nKernelSize * sizeof (Ncv32f));
dim3 ctaSize (32, 6);
dim3 gridSize ((oROI.width + ctaSize.x - 1) / ctaSize.x,
(oROI.height + ctaSize.y - 1) / ctaSize.y);
switch (borderType)
{
case nppStBorderClamp:
return NPPST_ERROR;
case nppStBorderWrap:
return NPPST_ERROR;
case nppStBorderMirror:
FilterColumnBorderMirror_32f_C1R <<<gridSize, ctaSize, 0, nppStGetActiveCUDAstream ()>>>
(srcStep, pDst, dstSize, dstStep, oROI, nKernelSize, nAnchor, multiplier);
break;
default:
return NPPST_ERROR;
}
return NPPST_SUCCESS;
}
//==============================================================================
//
// FrameInterpolate.cu
//
//==============================================================================
inline Ncv32u iDivUp(Ncv32u num, Ncv32u denom)
{
return (num + denom - 1)/denom;
}
texture<float, 2, cudaReadModeElementType> tex_src1;
texture<float, 2, cudaReadModeElementType> tex_src0;
__global__ void BlendFramesKernel(const float *u, const float *v, // forward flow
const float *ur, const float *vr, // backward flow
const float *o0, const float *o1, // coverage masks
int w, int h, int s,
float theta, float *out)
{
const int ix = threadIdx.x + blockDim.x * blockIdx.x;
const int iy = threadIdx.y + blockDim.y * blockIdx.y;
const int pos = ix + s * iy;
if (ix >= w || iy >= h) return;
float _u = u[pos];
float _v = v[pos];
float _ur = ur[pos];
float _vr = vr[pos];
float x = (float)ix + 0.5f;
float y = (float)iy + 0.5f;
bool b0 = o0[pos] > 1e-4f;
bool b1 = o1[pos] > 1e-4f;
if (b0 && b1)
{
// pixel is visible on both frames
out[pos] = tex2D(tex_src0, x - _u * theta, y - _v * theta) * (1.0f - theta) +
tex2D(tex_src1, x + _u * (1.0f - theta), y + _v * (1.0f - theta)) * theta;
}
else if (b0)
{
// visible on the first frame only
out[pos] = tex2D(tex_src0, x - _u * theta, y - _v * theta);
}
else
{
// visible on the second frame only
out[pos] = tex2D(tex_src1, x - _ur * (1.0f - theta), y - _vr * (1.0f - theta));
}
}
NCVStatus BlendFrames(const Ncv32f *src0,
const Ncv32f *src1,
const Ncv32f *ufi,
const Ncv32f *vfi,
const Ncv32f *ubi,
const Ncv32f *vbi,
const Ncv32f *o1,
const Ncv32f *o2,
Ncv32u width,
Ncv32u height,
Ncv32u stride,
Ncv32f theta,
Ncv32f *out)
{
tex_src1.addressMode[0] = cudaAddressModeClamp;
tex_src1.addressMode[1] = cudaAddressModeClamp;
tex_src1.filterMode = cudaFilterModeLinear;
tex_src1.normalized = false;
tex_src0.addressMode[0] = cudaAddressModeClamp;
tex_src0.addressMode[1] = cudaAddressModeClamp;
tex_src0.filterMode = cudaFilterModeLinear;
tex_src0.normalized = false;
cudaChannelFormatDesc desc = cudaCreateChannelDesc <float> ();
const Ncv32u pitch = stride * sizeof (float);
ncvAssertCUDAReturn (cudaBindTexture2D (0, tex_src1, src1, desc, width, height, pitch), NPPST_TEXTURE_BIND_ERROR);
ncvAssertCUDAReturn (cudaBindTexture2D (0, tex_src0, src0, desc, width, height, pitch), NPPST_TEXTURE_BIND_ERROR);
dim3 threads (32, 4);
dim3 blocks (iDivUp (width, threads.x), iDivUp (height, threads.y));
BlendFramesKernel<<<blocks, threads, 0, nppStGetActiveCUDAstream ()>>>
(ufi, vfi, ubi, vbi, o1, o2, width, height, stride, theta, out);
ncvAssertCUDAReturn (cudaGetLastError (), NPPST_CUDA_KERNEL_EXECUTION_ERROR);
return NPPST_SUCCESS;
}
NCVStatus nppiStGetInterpolationBufferSize(NcvSize32u srcSize,
Ncv32u nStep,
Ncv32u *hpSize)
{
NCVStatus status = NPPST_ERROR;
status = nppiStVectorWarpGetBufferSize(srcSize, nStep, hpSize);
return status;
}
NCVStatus nppiStInterpolateFrames(const NppStInterpolationState *pState)
{
// check state validity
ncvAssertReturn (pState->pSrcFrame0 != 0 &&
pState->pSrcFrame1 != 0 &&
pState->pFU != 0 &&
pState->pFV != 0 &&
pState->pBU != 0 &&
pState->pBV != 0 &&
pState->pNewFrame != 0 &&
pState->ppBuffers[0] != 0 &&
pState->ppBuffers[1] != 0 &&
pState->ppBuffers[2] != 0 &&
pState->ppBuffers[3] != 0 &&
pState->ppBuffers[4] != 0 &&
pState->ppBuffers[5] != 0, NPPST_NULL_POINTER_ERROR);
ncvAssertReturn (pState->size.width > 0 &&
pState->size.height > 0, NPPST_ERROR);
ncvAssertReturn (pState->nStep >= pState->size.width * sizeof (Ncv32f) &&
pState->nStep > 0 &&
pState->nStep % sizeof (Ncv32f) == 0,
NPPST_INVALID_STEP);
// change notation
Ncv32f *cov0 = pState->ppBuffers[0];
Ncv32f *cov1 = pState->ppBuffers[1];
Ncv32f *fwdU = pState->ppBuffers[2]; // forward u
Ncv32f *fwdV = pState->ppBuffers[3]; // forward v
Ncv32f *bwdU = pState->ppBuffers[4]; // backward u
Ncv32f *bwdV = pState->ppBuffers[5]; // backward v
// warp flow
ncvAssertReturnNcvStat (
nppiStVectorWarp_PSF2x2_32f_C1 (pState->pFU,
pState->size,
pState->nStep,
pState->pFU,
pState->pFV,
pState->nStep,
cov0,
pState->pos,
fwdU) );
ncvAssertReturnNcvStat (
nppiStVectorWarp_PSF2x2_32f_C1 (pState->pFV,
pState->size,
pState->nStep,
pState->pFU,
pState->pFV,
pState->nStep,
cov0,
pState->pos,
fwdV) );
// warp backward flow
ncvAssertReturnNcvStat (
nppiStVectorWarp_PSF2x2_32f_C1 (pState->pBU,
pState->size,
pState->nStep,
pState->pBU,
pState->pBV,
pState->nStep,
cov1,
1.0f - pState->pos,
bwdU) );
ncvAssertReturnNcvStat (
nppiStVectorWarp_PSF2x2_32f_C1 (pState->pBV,
pState->size,
pState->nStep,
pState->pBU,
pState->pBV,
pState->nStep,
cov1,
1.0f - pState->pos,
bwdU) );
// interpolate frame
ncvAssertReturnNcvStat (
BlendFrames (pState->pSrcFrame0,
pState->pSrcFrame1,
fwdU,
fwdV,
bwdU,
bwdV,
cov0,
cov1,
pState->size.width,
pState->size.height,
pState->nStep / sizeof (Ncv32f),
pState->pos,
pState->pNewFrame) );
return NPPST_SUCCESS;
}
//==============================================================================
//
// VectorWarpFrame.cu
//
//==============================================================================
#if __CUDA_ARCH__ < 200
// FP32 atomic add
static __forceinline__ __device__ float _atomicAdd(float *addr, float val)
{
float old = *addr, assumed;
do {
assumed = old;
old = int_as_float(__iAtomicCAS((int*)addr,
float_as_int(assumed),
float_as_int(val+assumed)));
} while( assumed!=old );
return old;
}
#else
#define _atomicAdd atomicAdd
#endif
__global__ void ForwardWarpKernel_PSF2x2(const float *u,
const float *v,
const float *src,
const int w,
const int h,
const int flow_stride,
const int image_stride,
const float time_scale,
float *normalization_factor,
float *dst)
{
int j = threadIdx.x + blockDim.x * blockIdx.x;
int i = threadIdx.y + blockDim.y * blockIdx.y;
if (i >= h || j >= w) return;
int flow_row_offset = i * flow_stride;
int image_row_offset = i * image_stride;
//bottom left corner of a target pixel
float cx = u[flow_row_offset + j] * time_scale + (float)j + 1.0f;
float cy = v[flow_row_offset + j] * time_scale + (float)i + 1.0f;
// pixel containing bottom left corner
float px;
float py;
float dx = modff (cx, &px);
float dy = modff (cy, &py);
// target pixel integer coords
int tx;
int ty;
tx = (int) px;
ty = (int) py;
float value = src[image_row_offset + j];
float weight;
// fill pixel containing bottom right corner
if (!((tx >= w) || (tx < 0) || (ty >= h) || (ty < 0)))
{
weight = dx * dy;
_atomicAdd (dst + ty * image_stride + tx, value * weight);
_atomicAdd (normalization_factor + ty * image_stride + tx, weight);
}
// fill pixel containing bottom left corner
tx -= 1;
if (!((tx >= w) || (tx < 0) || (ty >= h) || (ty < 0)))
{
weight = (1.0f - dx) * dy;
_atomicAdd (dst + ty * image_stride + tx, value * weight);
_atomicAdd (normalization_factor + ty * image_stride + tx, weight);
}
// fill pixel containing upper left corner
ty -= 1;
if (!((tx >= w) || (tx < 0) || (ty >= h) || (ty < 0)))
{
weight = (1.0f - dx) * (1.0f - dy);
_atomicAdd (dst + ty * image_stride + tx, value * weight);
_atomicAdd (normalization_factor + ty * image_stride + tx, weight);
}
// fill pixel containing upper right corner
tx += 1;
if (!((tx >= w) || (tx < 0) || (ty >= h) || (ty < 0)))
{
weight = dx * (1.0f - dy);
_atomicAdd (dst + ty * image_stride + tx, value * weight);
_atomicAdd (normalization_factor + ty * image_stride + tx, weight);
}
}
__global__ void ForwardWarpKernel_PSF1x1(const float *u,
const float *v,
const float *src,
const int w,
const int h,
const int flow_stride,
const int image_stride,
const float time_scale,
float *dst)
{
int j = threadIdx.x + blockDim.x * blockIdx.x;
int i = threadIdx.y + blockDim.y * blockIdx.y;
if (i >= h || j >= w) return;
int flow_row_offset = i * flow_stride;
int image_row_offset = i * image_stride;
float u_ = u[flow_row_offset + j];
float v_ = v[flow_row_offset + j];
//bottom left corner of target pixel
float cx = u_ * time_scale + (float)j + 1.0f;
float cy = v_ * time_scale + (float)i + 1.0f;
// pixel containing bottom left corner
int tx = __float2int_rn (cx);
int ty = __float2int_rn (cy);
float value = src[image_row_offset + j];
// fill pixel
if (!((tx >= w) || (tx < 0) || (ty >= h) || (ty < 0)))
{
_atomicAdd (dst + ty * image_stride + tx, value);
}
}
__global__ void NormalizeKernel(const float *normalization_factor, int w, int h, int s, float *image)
{
int i = threadIdx.y + blockDim.y * blockIdx.y;
int j = threadIdx.x + blockDim.x * blockIdx.x;
if (i >= h || j >= w) return;
const int pos = i * s + j;
float scale = normalization_factor[pos];
float invScale = (scale == 0.0f) ? 1.0f : (1.0f / scale);
image[pos] *= invScale;
}
__global__ void MemsetKernel(const float value, int w, int h, float *image)
{
int i = threadIdx.y + blockDim.y * blockIdx.y;
int j = threadIdx.x + blockDim.x * blockIdx.x;
if (i >= h || j >= w) return;
const int pos = i * w + j;
image[pos] = value;
}
NCVStatus nppiStVectorWarpGetBufferSize (NcvSize32u srcSize, Ncv32u nSrcStep, Ncv32u *hpSize)
{
ncvAssertReturn (hpSize != NULL, NPPST_NULL_POINTER_ERROR);
ncvAssertReturn (srcSize.width * sizeof (Ncv32f) <= nSrcStep,
NPPST_INVALID_STEP);
*hpSize = nSrcStep * srcSize.height;
return NPPST_SUCCESS;
}
// does not require normalization
NCVStatus nppiStVectorWarp_PSF1x1_32f_C1(const Ncv32f *pSrc,
NcvSize32u srcSize,
Ncv32u nSrcStep,
const Ncv32f *pU,
const Ncv32f *pV,
Ncv32u nVFStep,
Ncv32f timeScale,
Ncv32f *pDst)
{
ncvAssertReturn (pSrc != NULL &&
pU != NULL &&
pV != NULL &&
pDst != NULL, NPPST_NULL_POINTER_ERROR);
ncvAssertReturn (srcSize.width * sizeof (Ncv32f) <= nSrcStep &&
srcSize.width * sizeof (Ncv32f) <= nVFStep,
NPPST_INVALID_STEP);
Ncv32u srcStep = nSrcStep / sizeof (Ncv32f);
Ncv32u vfStep = nVFStep / sizeof (Ncv32f);
dim3 ctaSize (32, 6);
dim3 gridSize (iDivUp (srcSize.width, ctaSize.x), iDivUp (srcSize.height, ctaSize.y));
ForwardWarpKernel_PSF1x1 <<<gridSize, ctaSize, 0, nppStGetActiveCUDAstream()>>>
(pU, pV, pSrc, srcSize.width, srcSize.height, vfStep, srcStep, timeScale, pDst);
return NPPST_SUCCESS;
}
NCVStatus nppiStVectorWarp_PSF2x2_32f_C1(const Ncv32f *pSrc,
NcvSize32u srcSize,
Ncv32u nSrcStep,
const Ncv32f *pU,
const Ncv32f *pV,
Ncv32u nVFStep,
Ncv32f *pBuffer,
Ncv32f timeScale,
Ncv32f *pDst)
{
ncvAssertReturn (pSrc != NULL &&
pU != NULL &&
pV != NULL &&
pDst != NULL &&
pBuffer != NULL, NPPST_NULL_POINTER_ERROR);
ncvAssertReturn (srcSize.width * sizeof (Ncv32f) <= nSrcStep &&
srcSize.width * sizeof (Ncv32f) <= nVFStep, NPPST_INVALID_STEP);
Ncv32u srcStep = nSrcStep / sizeof (Ncv32f);
Ncv32u vfStep = nVFStep / sizeof(Ncv32f);
dim3 ctaSize(32, 6);
dim3 gridSize (iDivUp (srcSize.width, ctaSize.x), iDivUp (srcSize.height, ctaSize.y));
MemsetKernel <<<gridSize, ctaSize, 0, nppStGetActiveCUDAstream()>>>
(0, srcSize.width, srcSize.height, pBuffer);
ForwardWarpKernel_PSF2x2 <<<gridSize, ctaSize, 0, nppStGetActiveCUDAstream()>>>
(pU, pV, pSrc, srcSize.width, srcSize.height, vfStep, srcStep, timeScale, pBuffer, pDst);
NormalizeKernel <<<gridSize, ctaSize, 0, nppStGetActiveCUDAstream()>>>
(pBuffer, srcSize.width, srcSize.height, srcStep, pDst);
return NPPST_SUCCESS;
}
//==============================================================================
//
// Resize.cu
//
//==============================================================================
texture <float, 2, cudaReadModeElementType> texSrc2D;
__forceinline__
__device__ float processLine(int spos,
float xmin,
float xmax,
int ixmin,
int ixmax,
float fxmin,
float cxmax)
{
// first element
float wsum = 1.0f - xmin + fxmin;
float sum = tex1Dfetch(texSrc, spos) * (1.0f - xmin + fxmin);
spos++;
for (int ix = ixmin + 1; ix < ixmax; ++ix)
{
sum += tex1Dfetch(texSrc, spos);
spos++;
wsum += 1.0f;
}
sum += tex1Dfetch(texSrc, spos) * (cxmax - xmax);
wsum += cxmax - xmax;
return sum / wsum;
}
__global__ void resizeSuperSample_32f(NcvSize32u srcSize,
Ncv32u srcStep,
NcvRect32u srcROI,
Ncv32f *dst,
NcvSize32u dstSize,
Ncv32u dstStep,
NcvRect32u dstROI,
Ncv32f scaleX,
Ncv32f scaleY)
{
// position within dst ROI
const int ix = blockIdx.x * blockDim.x + threadIdx.x;
const int iy = blockIdx.y * blockDim.y + threadIdx.y;
if (ix >= dstROI.width || iy >= dstROI.height)
{
return;
}
float rw = (float) srcROI.width;
float rh = (float) srcROI.height;
// source position
float x = scaleX * (float) ix;
float y = scaleY * (float) iy;
// x sampling range
float xBegin = fmax (x - scaleX, 0.0f);
float xEnd = fmin (x + scaleX, rw - 1.0f);
// y sampling range
float yBegin = fmax (y - scaleY, 0.0f);
float yEnd = fmin (y + scaleY, rh - 1.0f);
// x range of source samples
float floorXBegin = floorf (xBegin);
float ceilXEnd = ceilf (xEnd);
int iXBegin = srcROI.x + (int) floorXBegin;
int iXEnd = srcROI.x + (int) ceilXEnd;
// y range of source samples
float floorYBegin = floorf (yBegin);
float ceilYEnd = ceilf (yEnd);
int iYBegin = srcROI.y + (int) floorYBegin;
int iYEnd = srcROI.y + (int) ceilYEnd;
// first row
int pos = iYBegin * srcStep + iXBegin;
float wsum = 1.0f - yBegin + floorYBegin;
float sum = processLine (pos, xBegin, xEnd, iXBegin, iXEnd, floorXBegin,
ceilXEnd) * (1.0f - yBegin + floorYBegin);
pos += srcStep;
for (int iy = iYBegin + 1; iy < iYEnd; ++iy)
{
sum += processLine (pos, xBegin, xEnd, iXBegin, iXEnd, floorXBegin,
ceilXEnd);
pos += srcStep;
wsum += 1.0f;
}
sum += processLine (pos, xBegin, xEnd, iXBegin, iXEnd, floorXBegin,
ceilXEnd) * (ceilYEnd - yEnd);
wsum += ceilYEnd - yEnd;
sum /= wsum;
dst[(ix + dstROI.x) + (iy + dstROI.y) * dstStep] = sum;
}
// bicubic interpolation
__forceinline__
__device__ float bicubicCoeff(float x_)
{
float x = fabsf(x_);
if (x <= 1.0f)
{
return x * x * (1.5f * x - 2.5f) + 1.0f;
}
else if (x < 2.0f)
{
return x * (x * (-0.5f * x + 2.5f) - 4.0f) + 2.0f;
}
else
{
return 0.0f;
}
}
__global__ void resizeBicubic(NcvSize32u srcSize,
NcvRect32u srcROI,
NcvSize32u dstSize,
Ncv32u dstStep,
Ncv32f *dst,
NcvRect32u dstROI,
Ncv32f scaleX,
Ncv32f scaleY)
{
const int ix = blockIdx.x * blockDim.x + threadIdx.x;
const int iy = blockIdx.y * blockDim.y + threadIdx.y;
if (ix >= dstROI.width || iy >= dstROI.height)
{
return;
}
const float dx = 1.0f / srcROI.width;
const float dy = 1.0f / srcROI.height;
float rx = (float) srcROI.x;
float ry = (float) srcROI.y;
float rw = (float) srcROI.width;
float rh = (float) srcROI.height;
float x = scaleX * (float) ix;
float y = scaleY * (float) iy;
// sampling range
// border mode is clamp
float xmin = fmax (ceilf (x - 2.0f), 0.0f);
float xmax = fmin (floorf (x + 2.0f), rw - 1.0f);
float ymin = fmax (ceilf (y - 2.0f), 0.0f);
float ymax = fmin (floorf (y + 2.0f), rh - 1.0f);
// shift data window to match ROI
rx += 0.5f;
ry += 0.5f;
x += rx;
y += ry;
xmin += rx;
xmax += rx;
ymin += ry;
ymax += ry;
float sum = 0.0f;
float wsum = 0.0f;
for (float cy = ymin; cy <= ymax; cy += 1.0f)
{
for (float cx = xmin; cx <= xmax; cx += 1.0f)
{
float xDist = x - cx;
float yDist = y - cy;
float wx = bicubicCoeff (xDist);
float wy = bicubicCoeff (yDist);
wx *= wy;
sum += wx * tex2D (texSrc2D, cx * dx, cy * dy);
wsum += wx;
}
}
dst[(ix + dstROI.x)+ (iy + dstROI.y) * dstStep] = sum / wsum;
}
NCVStatus nppiStResize_32f_C1R(const Ncv32f *pSrc,
NcvSize32u srcSize,
Ncv32u nSrcStep,
NcvRect32u srcROI,
Ncv32f *pDst,
NcvSize32u dstSize,
Ncv32u nDstStep,
NcvRect32u dstROI,
Ncv32f xFactor,
Ncv32f yFactor,
NppStInterpMode interpolation)
{
NCVStatus status = NPPST_SUCCESS;
ncvAssertReturn (pSrc != NULL && pDst != NULL, NPPST_NULL_POINTER_ERROR);
ncvAssertReturn (xFactor != 0.0 && yFactor != 0.0, NPPST_INVALID_SCALE);
ncvAssertReturn (nSrcStep >= sizeof (Ncv32f) * (Ncv32u) srcSize.width &&
nDstStep >= sizeof (Ncv32f) * (Ncv32f) dstSize.width,
NPPST_INVALID_STEP);
Ncv32u srcStep = nSrcStep / sizeof (Ncv32f);
Ncv32u dstStep = nDstStep / sizeof (Ncv32f);
// TODO: preprocess ROI to prevent out of bounds access
if (interpolation == nppStSupersample)
{
// bind texture
cudaBindTexture (0, texSrc, pSrc, srcSize.height * nSrcStep);
// invoke kernel
dim3 ctaSize (32, 6);
dim3 gridSize ((dstROI.width + ctaSize.x - 1) / ctaSize.x,
(dstROI.height + ctaSize.y - 1) / ctaSize.y);
resizeSuperSample_32f <<<gridSize, ctaSize, 0, nppStGetActiveCUDAstream ()>>>
(srcSize, srcStep, srcROI, pDst, dstSize, dstStep, dstROI, 1.0f / xFactor, 1.0f / yFactor);
}
else if (interpolation == nppStBicubic)
{
texSrc2D.addressMode[0] = cudaAddressModeMirror;
texSrc2D.addressMode[1] = cudaAddressModeMirror;
texSrc2D.normalized = true;
cudaChannelFormatDesc desc = cudaCreateChannelDesc <float> ();
cudaBindTexture2D (0, texSrc2D, pSrc, desc, srcSize.width, srcSize.height,
nSrcStep);
dim3 ctaSize (32, 6);
dim3 gridSize ((dstSize.width + ctaSize.x - 1) / ctaSize.x,
(dstSize.height + ctaSize.y - 1) / ctaSize.y);
resizeBicubic <<<gridSize, ctaSize, 0, nppStGetActiveCUDAstream ()>>>
(srcSize, srcROI, dstSize, dstStep, pDst, dstROI, 1.0f / xFactor, 1.0f / yFactor);
}
else
{
status = NPPST_ERROR;
}
return status;
}

@ -84,6 +84,255 @@ cudaStream_t nppStSetActiveCUDAstream(cudaStream_t cudaStream);
*/
/** Border type
*
* Filtering operations assume that each pixel has a neighborhood of pixels.
* The following structure describes possible ways to define non-existent pixels.
*/
enum NppStBorderType
{
nppStBorderNone = 0, ///< There is no need to define additional pixels, image is extended already
nppStBorderClamp = 1, ///< Clamp out of range position to borders
nppStBorderWrap = 2, ///< Wrap out of range position. Image becomes periodic.
nppStBorderMirror = 3 ///< reflect out of range position across borders
};
/**
* Filter types for image resizing
*/
enum NppStInterpMode
{
nppStSupersample, ///< Supersampling. For downscaling only
nppStBicubic ///< Bicubic convolution filter, a = -0.5 (cubic Hermite spline)
};
/** Frame interpolation state
*
* This structure holds parameters required for frame interpolation.
* Forward displacement field is a per-pixel mapping from frame 0 to frame 1.
* Backward displacement field is a per-pixel mapping from frame 1 to frame 0.
*/
struct NppStInterpolationState
{
NcvSize32u size; ///< frame size
Ncv32u nStep; ///< pitch
Ncv32f pos; ///< new frame position
Ncv32f *pSrcFrame0; ///< frame 0
Ncv32f *pSrcFrame1; ///< frame 1
Ncv32f *pFU; ///< forward horizontal displacement
Ncv32f *pFV; ///< forward vertical displacement
Ncv32f *pBU; ///< backward horizontal displacement
Ncv32f *pBV; ///< backward vertical displacement
Ncv32f *pNewFrame; ///< new frame
Ncv32f *ppBuffers[6]; ///< temporary buffers
};
/** Size of a buffer required for interpolation.
*
* Requires several such buffers. See \see NppStInterpolationState.
*
* \param srcSize [IN] Frame size (both frames must be of the same size)
* \param nStep [IN] Frame line step
* \param hpSize [OUT] Where to store computed size (host memory)
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppiStGetInterpolationBufferSize(NcvSize32u srcSize,
Ncv32u nStep,
Ncv32u *hpSize);
/** Interpolate frames (images) using provided optical flow (displacement field).
* 32-bit floating point images, single channel
*
* \param pState [IN] structure containing all required parameters (host memory)
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppiStInterpolateFrames(const NppStInterpolationState *pState);
/** Row linear filter. 32-bit floating point image, single channel
*
* Apply horizontal linear filter
*
* \param pSrc [IN] Source image pointer (CUDA device memory)
* \param srcSize [IN] Source image size
* \param nSrcStep [IN] Source image line step
* \param pDst [OUT] Destination image pointer (CUDA device memory)
* \param dstSize [OUT] Destination image size
* \param oROI [IN] Region of interest in the source image
* \param borderType [IN] Type of border
* \param pKernel [IN] Pointer to row kernel values (CUDA device memory)
* \param nKernelSize [IN] Size of the kernel in pixels
* \param nAnchor [IN] The kernel row alignment with respect to the position of the input pixel
* \param multiplier [IN] Value by which the computed result is multiplied
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppiStFilterRowBorder_32f_C1R(const Ncv32f *pSrc,
NcvSize32u srcSize,
Ncv32u nSrcStep,
Ncv32f *pDst,
NcvSize32u dstSize,
Ncv32u nDstStep,
NcvRect32u oROI,
NppStBorderType borderType,
const Ncv32f *pKernel,
Ncv32s nKernelSize,
Ncv32s nAnchor,
Ncv32f multiplier);
/** Column linear filter. 32-bit floating point image, single channel
*
* Apply vertical linear filter
*
* \param pSrc [IN] Source image pointer (CUDA device memory)
* \param srcSize [IN] Source image size
* \param nSrcStep [IN] Source image line step
* \param pDst [OUT] Destination image pointer (CUDA device memory)
* \param dstSize [OUT] Destination image size
* \param oROI [IN] Region of interest in the source image
* \param borderType [IN] Type of border
* \param pKernel [IN] Pointer to column kernel values (CUDA device memory)
* \param nKernelSize [IN] Size of the kernel in pixels
* \param nAnchor [IN] The kernel column alignment with respect to the position of the input pixel
* \param multiplier [IN] Value by which the computed result is multiplied
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppiStFilterColumnBorder_32f_C1R(const Ncv32f *pSrc,
NcvSize32u srcSize,
Ncv32u nSrcStep,
Ncv32f *pDst,
NcvSize32u dstSize,
Ncv32u nDstStep,
NcvRect32u oROI,
NppStBorderType borderType,
const Ncv32f *pKernel,
Ncv32s nKernelSize,
Ncv32s nAnchor,
Ncv32f multiplier);
/** Size of buffer required for vector image warping.
*
* \param srcSize [IN] Source image size
* \param nStep [IN] Source image line step
* \param hpSize [OUT] Where to store computed size (host memory)
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppiStVectorWarpGetBufferSize(NcvSize32u srcSize,
Ncv32u nSrcStep,
Ncv32u *hpSize);
/** Warp image using provided 2D vector field and 1x1 point spread function.
* 32-bit floating point image, single channel
*
* During warping pixels from the source image may fall between pixels of the destination image.
* PSF (point spread function) describes how the source image pixel affects pixels of the destination.
* For 1x1 PSF only single pixel with the largest intersection is affected (similar to nearest interpolation).
*
* Destination image size and line step must be the same as the source image size and line step
*
* \param pSrc [IN] Source image pointer (CUDA device memory)
* \param srcSize [IN] Source image size
* \param nSrcStep [IN] Source image line step
* \param pU [IN] Pointer to horizontal displacement field (CUDA device memory)
* \param pV [IN] Pointer to vertical displacement field (CUDA device memory)
* \param nVFStep [IN] Displacement field line step
* \param timeScale [IN] Value by which displacement field will be scaled for warping
* \param pDst [OUT] Destination image pointer (CUDA device memory)
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppiStVectorWarp_PSF1x1_32f_C1(const Ncv32f *pSrc,
NcvSize32u srcSize,
Ncv32u nSrcStep,
const Ncv32f *pU,
const Ncv32f *pV,
Ncv32u nVFStep,
Ncv32f timeScale,
Ncv32f *pDst);
/** Warp image using provided 2D vector field and 2x2 point spread function.
* 32-bit floating point image, single channel
*
* During warping pixels from the source image may fall between pixels of the destination image.
* PSF (point spread function) describes how the source image pixel affects pixels of the destination.
* For 2x2 PSF all four intersected pixels will be affected.
*
* Destination image size and line step must be the same as the source image size and line step
*
* \param pSrc [IN] Source image pointer (CUDA device memory)
* \param srcSize [IN] Source image size
* \param nSrcStep [IN] Source image line step
* \param pU [IN] Pointer to horizontal displacement field (CUDA device memory)
* \param pV [IN] Pointer to vertical displacement field (CUDA device memory)
* \param nVFStep [IN] Displacement field line step
* \param timeScale [IN] Value by which displacement field will be scaled for warping
* \param pDst [OUT] Destination image pointer (CUDA device memory)
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppiStVectorWarp_PSF2x2_32f_C1(const Ncv32f *pSrc,
NcvSize32u srcSize,
Ncv32u nSrcStep,
const Ncv32f *pU,
const Ncv32f *pV,
Ncv32u nVFStep,
Ncv32f *pBuffer,
Ncv32f timeScale,
Ncv32f *pDst);
/** Resize. 32-bit floating point image, single channel
*
* Resizes image using specified filter (interpolation type)
*
* \param pSrc [IN] Source image pointer (CUDA device memory)
* \param srcSize [IN] Source image size
* \param nSrcStep [IN] Source image line step
* \param srcROI [IN] Source image region of interest
* \param pDst [OUT] Destination image pointer (CUDA device memory)
* \param dstSize [IN] Destination image size
* \param nDstStep [IN] Destination image line step
* \param dstROI [IN] Destination image region of interest
* \param xFactor [IN] Row scale factor
* \param yFactor [IN] Column scale factor
* \param interpolation [IN] Interpolation type
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppiStResize_32f_C1R(const Ncv32f *pSrc,
NcvSize32u srcSize,
Ncv32u nSrcStep,
NcvRect32u srcROI,
Ncv32f *pDst,
NcvSize32u dstSize,
Ncv32u nDstStep,
NcvRect32u dstROI,
Ncv32f xFactor,
Ncv32f yFactor,
NppStInterpMode interpolation);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 32-bit unsigned pixels, single channel.
*

@ -0,0 +1,639 @@
#if _MSC_VER >= 1400
#pragma warning( disable : 4201 4408 4127 4100)
#endif
#include <iostream>
#include <iomanip>
#include <memory>
#include <exception>
#include <ctime>
#include "cvconfig.h"
#include <iostream>
#include <iomanip>
#include "opencv2/opencv.hpp"
#include "opencv2/gpu/gpu.hpp"
#ifdef HAVE_CUDA
#include "NPP_staging/NPP_staging.hpp"
#include "NCVBroxOpticalFlow.hpp"
#endif
#if !defined(HAVE_CUDA)
int main( int argc, const char** argv )
{
cout << "Please compile the library with CUDA support" << endl;
return -1;
}
#else
using std::tr1::shared_ptr;
#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"
shared_ptr<INCVMemAllocator> g_pGPUMemAllocator;
shared_ptr<INCVMemAllocator> g_pHostMemAllocator;
class RgbToMonochrome
{
public:
float operator ()(unsigned char b, unsigned char g, unsigned char r)
{
float _r = static_cast<float>(r)/255.0f;
float _g = static_cast<float>(g)/255.0f;
float _b = static_cast<float>(b)/255.0f;
return (_r + _g + _b)/3.0f;
}
};
class RgbToR
{
public:
float operator ()(unsigned char b, unsigned char g, unsigned char r)
{
return static_cast<float>(r)/255.0f;
}
};
class RgbToG
{
public:
float operator ()(unsigned char b, unsigned char g, unsigned char r)
{
return static_cast<float>(g)/255.0f;
}
};
class RgbToB
{
public:
float operator ()(unsigned char b, unsigned char g, unsigned char r)
{
return static_cast<float>(b)/255.0f;
}
};
template<class T>
NCVStatus CopyData(IplImage *image, shared_ptr<NCVMatrixAlloc<Ncv32f>> &dst)
{
dst = shared_ptr<NCVMatrixAlloc<Ncv32f>> (new NCVMatrixAlloc<Ncv32f> (*g_pHostMemAllocator, image->width, image->height));
ncvAssertReturn (dst->isMemAllocated (), NCV_ALLOCATOR_BAD_ALLOC);
unsigned char *row = reinterpret_cast<unsigned char*> (image->imageData);
T convert;
for (int i = 0; i < image->height; ++i)
{
for (int j = 0; j < image->width; ++j)
{
if (image->nChannels < 3)
{
dst->ptr ()[j + i*dst->stride ()] = static_cast<float> (*(row + j*image->nChannels))/255.0f;
}
else
{
unsigned char *color = row + j * image->nChannels;
dst->ptr ()[j +i*dst->stride ()] = convert (color[0], color[1], color[2]);
}
}
row += image->widthStep;
}
return NCV_SUCCESS;
}
template<class T>
NCVStatus CopyData(const IplImage *image, const NCVMatrixAlloc<Ncv32f> &dst)
{
unsigned char *row = reinterpret_cast<unsigned char*> (image->imageData);
T convert;
for (int i = 0; i < image->height; ++i)
{
for (int j = 0; j < image->width; ++j)
{
if (image->nChannels < 3)
{
dst.ptr ()[j + i*dst.stride ()] = static_cast<float>(*(row + j*image->nChannels))/255.0f;
}
else
{
unsigned char *color = row + j * image->nChannels;
dst.ptr ()[j +i*dst.stride()] = convert (color[0], color[1], color[2]);
}
}
row += image->widthStep;
}
return NCV_SUCCESS;
}
NCVStatus LoadImages (const char *frame0Name,
const char *frame1Name,
int &width,
int &height,
shared_ptr<NCVMatrixAlloc<Ncv32f>> &src,
shared_ptr<NCVMatrixAlloc<Ncv32f>> &dst,
IplImage *&firstFrame,
IplImage *&lastFrame)
{
IplImage *image;
image = cvLoadImage (frame0Name);
if (image == 0)
{
std::cout << "Could not open '" << frame0Name << "'\n";
return NCV_FILE_ERROR;
}
firstFrame = image;
// copy data to src
ncvAssertReturnNcvStat (CopyData<RgbToMonochrome> (image, src));
IplImage *image2;
image2 = cvLoadImage (frame1Name);
if (image2 == 0)
{
std::cout << "Could not open '" << frame1Name << "'\n";
return NCV_FILE_ERROR;
}
lastFrame = image2;
ncvAssertReturnNcvStat (CopyData<RgbToMonochrome> (image2, dst));
width = image->width;
height = image->height;
return NCV_SUCCESS;
}
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);
}
NCVStatus ShowFlow (NCVMatrixAlloc<Ncv32f> &u, NCVMatrixAlloc<Ncv32f> &v, const char *name)
{
IplImage *flowField;
NCVMatrixAlloc<Ncv32f> host_u(*g_pHostMemAllocator, u.width(), u.height());
ncvAssertReturn(host_u.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
NCVMatrixAlloc<Ncv32f> host_v (*g_pHostMemAllocator, u.width (), u.height ());
ncvAssertReturn (host_v.isMemAllocated (), NCV_ALLOCATOR_BAD_ALLOC);
ncvAssertReturnNcvStat (u.copySolid (host_u, 0));
ncvAssertReturnNcvStat (v.copySolid (host_v, 0));
float *ptr_u = host_u.ptr ();
float *ptr_v = host_v.ptr ();
float maxDisplacement = 1.0f;
for (Ncv32u i = 0; i < u.height (); ++i)
{
for (Ncv32u j = 0; j < u.width (); ++j)
{
float d = std::max ( fabsf(*ptr_u), fabsf(*ptr_v) );
if (d > maxDisplacement) maxDisplacement = d;
++ptr_u;
++ptr_v;
}
ptr_u += u.stride () - u.width ();
ptr_v += v.stride () - v.width ();
}
CvSize image_size = cvSize (u.width (), u.height ());
flowField = cvCreateImage (image_size, IPL_DEPTH_8U, 4);
if (flowField == 0) return NCV_NULL_PTR;
unsigned char *row = reinterpret_cast<unsigned char *> (flowField->imageData);
ptr_u = host_u.ptr();
ptr_v = host_v.ptr();
for (int i = 0; i < flowField->height; ++i)
{
for (int j = 0; j < flowField->width; ++j)
{
(row + j * flowField->nChannels)[0] = 0;
(row + j * flowField->nChannels)[1] = static_cast<unsigned char> (MapValue (-(*ptr_v), -maxDisplacement, maxDisplacement, 0.0f, 255.0f));
(row + j * flowField->nChannels)[2] = static_cast<unsigned char> (MapValue (*ptr_u , -maxDisplacement, maxDisplacement, 0.0f, 255.0f));
(row + j * flowField->nChannels)[3] = 255;
++ptr_u;
++ptr_v;
}
row += flowField->widthStep;
ptr_u += u.stride () - u.width ();
ptr_v += v.stride () - v.width ();
}
cvShowImage (name, flowField);
return NCV_SUCCESS;
}
IplImage *CreateImage (NCVMatrixAlloc<Ncv32f> &h_r, NCVMatrixAlloc<Ncv32f> &h_g, NCVMatrixAlloc<Ncv32f> &h_b)
{
CvSize imageSize = cvSize (h_r.width (), h_r.height ());
IplImage *image = cvCreateImage (imageSize, IPL_DEPTH_8U, 4);
if (image == 0) return 0;
unsigned char *row = reinterpret_cast<unsigned char*> (image->imageData);
for (int i = 0; i < image->height; ++i)
{
for (int j = 0; j < image->width; ++j)
{
int offset = j * image->nChannels;
int pos = i * h_r.stride () + j;
row[offset + 0] = static_cast<unsigned char> (h_b.ptr ()[pos] * 255.0f);
row[offset + 1] = static_cast<unsigned char> (h_g.ptr ()[pos] * 255.0f);
row[offset + 2] = static_cast<unsigned char> (h_r.ptr ()[pos] * 255.0f);
row[offset + 3] = 255;
}
row += image->widthStep;
}
return image;
}
void PrintHelp ()
{
std::cout << "Usage help:\n";
std::cout << std::setiosflags(std::ios::left);
std::cout << "\t" << std::setw(15) << PARAM_ALPHA << " - set alpha\n";
std::cout << "\t" << std::setw(15) << PARAM_GAMMA << " - set gamma\n";
std::cout << "\t" << std::setw(15) << PARAM_INNER << " - set number of inner iterations\n";
std::cout << "\t" << std::setw(15) << PARAM_INPUT << " - specify input file names (2 image files)\n";
std::cout << "\t" << std::setw(15) << PARAM_OUTER << " - set number of outer iterations\n";
std::cout << "\t" << std::setw(15) << PARAM_SCALE << " - set pyramid scale factor\n";
std::cout << "\t" << std::setw(15) << PARAM_SOLVER << " - set number of basic solver iterations\n";
std::cout << "\t" << std::setw(15) << PARAM_TIME_STEP << " - set frame interpolation time step\n";
std::cout << "\t" << std::setw(15) << PARAM_HELP << " - display this help message\n";
}
int ProcessCommandLine(int argc, char **argv,
Ncv32f &timeStep,
char *&frame0Name,
char *&frame1Name,
NCVBroxOpticalFlowDescriptor &desc)
{
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)
desc.scale_factor = static_cast<Ncv32f>(atof(argv[++iarg]));
else
return -1;
}
else if(strcmp(argv[iarg], PARAM_ALPHA) == 0)
{
if (iarg + 1 < argc)
desc.alpha = static_cast<Ncv32f>(atof(argv[++iarg]));
else
return -1;
}
else if(strcmp(argv[iarg], PARAM_GAMMA) == 0)
{
if (iarg + 1 < argc)
desc.gamma = static_cast<Ncv32f>(atof(argv[++iarg]));
else
return -1;
}
else if(strcmp(argv[iarg], PARAM_INNER) == 0)
{
if (iarg + 1 < argc)
desc.number_of_inner_iterations = static_cast<Ncv32u>(atoi(argv[++iarg]));
else
return -1;
}
else if(strcmp(argv[iarg], PARAM_OUTER) == 0)
{
if (iarg + 1 < argc)
desc.number_of_outer_iterations = static_cast<Ncv32u>(atoi(argv[++iarg]));
else
return -1;
}
else if(strcmp(argv[iarg], PARAM_SOLVER) == 0)
{
if (iarg + 1 < argc)
desc.number_of_solver_iterations = static_cast<Ncv32u>(atoi(argv[++iarg]));
else
return -1;
}
else if(strcmp(argv[iarg], PARAM_TIME_STEP) == 0)
{
if (iarg + 1 < argc)
timeStep = static_cast<Ncv32f>(atof(argv[++iarg]));
else
return -1;
}
else if(strcmp(argv[iarg], PARAM_HELP) == 0)
{
PrintHelp ();
return 0;
}
}
return 0;
}
int main(int argc, char **argv)
{
char *frame0Name = 0, *frame1Name = 0;
Ncv32f timeStep = 0.01f;
NCVBroxOpticalFlowDescriptor desc;
desc.alpha = 0.197f;
desc.gamma = 50.0f;
desc.number_of_inner_iterations = 10;
desc.number_of_outer_iterations = 77;
desc.number_of_solver_iterations = 10;
desc.scale_factor = 0.8f;
int result = ProcessCommandLine (argc, argv, timeStep, frame0Name, frame1Name, desc);
if (argc == 1 || result)
{
PrintHelp();
return result;
}
std::cout << "OpenCV / NVIDIA Computer Vision\n";
std::cout << "Optical Flow Demo: Frame Interpolation\n";
std::cout << "=========================================\n";
std::cout << "Press:\n ESC to quit\n 'a' to move to the previous frame\n 's' to move to the next frame\n";
int devId;
ncvAssertCUDAReturn(cudaGetDevice(&devId), -1);
cudaDeviceProp devProp;
ncvAssertCUDAReturn(cudaGetDeviceProperties(&devProp, devId), -1);
std::cout << "Using GPU: " << devId << "(" << devProp.name <<
"), arch=" << devProp.major << "." << devProp.minor << std::endl;
g_pGPUMemAllocator = shared_ptr<INCVMemAllocator> (new NCVMemNativeAllocator (NCVMemoryTypeDevice, devProp.textureAlignment));
ncvAssertPrintReturn (g_pGPUMemAllocator->isInitialized (), "Device memory allocator isn't initialized", -1);
g_pHostMemAllocator = shared_ptr<INCVMemAllocator> (new NCVMemNativeAllocator (NCVMemoryTypeHostPageable, devProp.textureAlignment));
ncvAssertPrintReturn (g_pHostMemAllocator->isInitialized (), "Host memory allocator isn't initialized", -1);
int width, height;
shared_ptr<NCVMatrixAlloc<Ncv32f>> src_host;
shared_ptr<NCVMatrixAlloc<Ncv32f>> dst_host;
IplImage *firstFrame, *lastFrame;
if (frame0Name != 0 && frame1Name != 0)
{
ncvAssertReturnNcvStat (LoadImages (frame0Name, frame1Name, width, height, src_host, dst_host, firstFrame, lastFrame));
}
else
{
ncvAssertReturnNcvStat (LoadImages ("frame10.bmp", "frame11.bmp", width, height, src_host, dst_host, firstFrame, lastFrame));
}
shared_ptr<NCVMatrixAlloc<Ncv32f>> src (new NCVMatrixAlloc<Ncv32f> (*g_pGPUMemAllocator, src_host->width (), src_host->height ()));
ncvAssertReturn(src->isMemAllocated(), -1);
shared_ptr<NCVMatrixAlloc<Ncv32f>> dst (new NCVMatrixAlloc<Ncv32f> (*g_pGPUMemAllocator, src_host->width (), src_host->height ()));
ncvAssertReturn (dst->isMemAllocated (), -1);
ncvAssertReturnNcvStat (src_host->copySolid ( *src, 0 ));
ncvAssertReturnNcvStat (dst_host->copySolid ( *dst, 0 ));
#if defined SAFE_MAT_DECL
#undef SAFE_MAT_DECL
#endif
#define SAFE_MAT_DECL(name, allocator, sx, sy) \
NCVMatrixAlloc<Ncv32f> name(*allocator, sx, sy);\
ncvAssertReturn(name##.isMemAllocated(), -1);
SAFE_MAT_DECL (u, g_pGPUMemAllocator, width, height);
SAFE_MAT_DECL (v, g_pGPUMemAllocator, width, height);
SAFE_MAT_DECL (uBck, g_pGPUMemAllocator, width, height);
SAFE_MAT_DECL (vBck, g_pGPUMemAllocator, width, height);
SAFE_MAT_DECL (h_r, g_pHostMemAllocator, width, height);
SAFE_MAT_DECL (h_g, g_pHostMemAllocator, width, height);
SAFE_MAT_DECL (h_b, g_pHostMemAllocator, width, height);
std::cout << "Estimating optical flow\nForward...\n";
if (NCV_SUCCESS != NCVBroxOpticalFlow (desc, *g_pGPUMemAllocator, *src, *dst, u, v, 0))
{
std::cout << "Failed\n";
return -1;
}
std::cout << "Backward...\n";
if (NCV_SUCCESS != NCVBroxOpticalFlow (desc, *g_pGPUMemAllocator, *dst, *src, uBck, vBck, 0))
{
std::cout << "Failed\n";
return -1;
}
// matrix for temporary data
SAFE_MAT_DECL (d_temp, g_pGPUMemAllocator, width, height);
// first frame color components (GPU memory)
SAFE_MAT_DECL (d_r, g_pGPUMemAllocator, width, height);
SAFE_MAT_DECL (d_g, g_pGPUMemAllocator, width, height);
SAFE_MAT_DECL (d_b, g_pGPUMemAllocator, width, height);
// second frame color components (GPU memory)
SAFE_MAT_DECL (d_rt, g_pGPUMemAllocator, width, height);
SAFE_MAT_DECL (d_gt, g_pGPUMemAllocator, width, height);
SAFE_MAT_DECL (d_bt, g_pGPUMemAllocator, width, height);
// intermediate frame color components (GPU memory)
SAFE_MAT_DECL (d_rNew, g_pGPUMemAllocator, width, height);
SAFE_MAT_DECL (d_gNew, g_pGPUMemAllocator, width, height);
SAFE_MAT_DECL (d_bNew, g_pGPUMemAllocator, width, height);
// interpolated forward flow
SAFE_MAT_DECL (ui, g_pGPUMemAllocator, width, height);
SAFE_MAT_DECL (vi, g_pGPUMemAllocator, width, height);
// interpolated backward flow
SAFE_MAT_DECL (ubi, g_pGPUMemAllocator, width, height);
SAFE_MAT_DECL (vbi, g_pGPUMemAllocator, width, height);
// occlusion masks
SAFE_MAT_DECL (occ0, g_pGPUMemAllocator, width, height);
SAFE_MAT_DECL (occ1, g_pGPUMemAllocator, width, height);
// prepare color components on host and copy them to device memory
ncvAssertReturnNcvStat (CopyData<RgbToR> (firstFrame, h_r));
ncvAssertReturnNcvStat (CopyData<RgbToG> (firstFrame, h_g));
ncvAssertReturnNcvStat (CopyData<RgbToB> (firstFrame, h_b));
ncvAssertReturnNcvStat (h_r.copySolid ( d_r, 0 ));
ncvAssertReturnNcvStat (h_g.copySolid ( d_g, 0 ));
ncvAssertReturnNcvStat (h_b.copySolid ( d_b, 0 ));
ncvAssertReturnNcvStat (CopyData<RgbToR> (lastFrame, h_r));
ncvAssertReturnNcvStat (CopyData<RgbToG> (lastFrame, h_g));
ncvAssertReturnNcvStat (CopyData<RgbToB> (lastFrame, h_b));
ncvAssertReturnNcvStat (h_r.copySolid ( d_rt, 0 ));
ncvAssertReturnNcvStat (h_g.copySolid ( d_gt, 0 ));
ncvAssertReturnNcvStat (h_b.copySolid ( d_bt, 0 ));
std::cout << "Interpolating...\n";
std::cout.precision (4);
std::vector<IplImage*> frames;
frames.push_back (firstFrame);
// compute interpolated frames
for (Ncv32f timePos = timeStep; timePos < 1.0f; timePos += timeStep)
{
ncvAssertCUDAReturn (cudaMemset (ui.ptr (), 0, ui.pitch () * ui.height ()), NCV_CUDA_ERROR);
ncvAssertCUDAReturn (cudaMemset (vi.ptr (), 0, vi.pitch () * vi.height ()), NCV_CUDA_ERROR);
ncvAssertCUDAReturn (cudaMemset (ubi.ptr (), 0, ubi.pitch () * ubi.height ()), NCV_CUDA_ERROR);
ncvAssertCUDAReturn (cudaMemset (vbi.ptr (), 0, vbi.pitch () * vbi.height ()), NCV_CUDA_ERROR);
ncvAssertCUDAReturn (cudaMemset (occ0.ptr (), 0, occ0.pitch () * occ0.height ()), NCV_CUDA_ERROR);
ncvAssertCUDAReturn (cudaMemset (occ1.ptr (), 0, occ1.pitch () * occ1.height ()), NCV_CUDA_ERROR);
NppStInterpolationState state;
// interpolation state should be filled once except pSrcFrame0, pSrcFrame1, and pNewFrame
// we will only need to reset buffers content to 0 since interpolator doesn't do this itself
state.size = NcvSize32u (width, height);
state.nStep = d_r.pitch ();
state.pSrcFrame0 = d_r.ptr ();
state.pSrcFrame1 = d_rt.ptr ();
state.pFU = u.ptr ();
state.pFV = v.ptr ();
state.pBU = uBck.ptr ();
state.pBV = vBck.ptr ();
state.pos = timePos;
state.pNewFrame = d_rNew.ptr ();
state.ppBuffers[0] = occ0.ptr ();
state.ppBuffers[1] = occ1.ptr ();
state.ppBuffers[2] = ui.ptr ();
state.ppBuffers[3] = vi.ptr ();
state.ppBuffers[4] = ubi.ptr ();
state.ppBuffers[5] = vbi.ptr ();
// interpolate red channel
nppiStInterpolateFrames (&state);
// reset buffers
ncvAssertCUDAReturn (cudaMemset (ui.ptr (), 0, ui.pitch () * ui.height ()), NCV_CUDA_ERROR);
ncvAssertCUDAReturn (cudaMemset (vi.ptr (), 0, vi.pitch () * vi.height ()), NCV_CUDA_ERROR);
ncvAssertCUDAReturn (cudaMemset (ubi.ptr (), 0, ubi.pitch () * ubi.height ()), NCV_CUDA_ERROR);
ncvAssertCUDAReturn (cudaMemset (vbi.ptr (), 0, vbi.pitch () * vbi.height ()), NCV_CUDA_ERROR);
ncvAssertCUDAReturn (cudaMemset (occ0.ptr (), 0, occ0.pitch () * occ0.height ()), NCV_CUDA_ERROR);
ncvAssertCUDAReturn (cudaMemset (occ1.ptr (), 0, occ1.pitch () * occ1.height ()), NCV_CUDA_ERROR);
// interpolate green channel
state.pSrcFrame0 = d_g.ptr ();
state.pSrcFrame1 = d_gt.ptr ();
state.pNewFrame = d_gNew.ptr ();
nppiStInterpolateFrames (&state);
// reset buffers
ncvAssertCUDAReturn (cudaMemset (ui.ptr (), 0, ui.pitch () * ui.height ()), NCV_CUDA_ERROR);
ncvAssertCUDAReturn (cudaMemset (vi.ptr (), 0, vi.pitch () * vi.height ()), NCV_CUDA_ERROR);
ncvAssertCUDAReturn (cudaMemset (ubi.ptr (), 0, ubi.pitch () * ubi.height ()), NCV_CUDA_ERROR);
ncvAssertCUDAReturn (cudaMemset (vbi.ptr (), 0, vbi.pitch () * vbi.height ()), NCV_CUDA_ERROR);
ncvAssertCUDAReturn (cudaMemset (occ0.ptr (), 0, occ0.pitch () * occ0.height ()), NCV_CUDA_ERROR);
ncvAssertCUDAReturn (cudaMemset (occ1.ptr (), 0, occ1.pitch () * occ1.height ()), NCV_CUDA_ERROR);
// interpolate blue channel
state.pSrcFrame0 = d_b.ptr ();
state.pSrcFrame1 = d_bt.ptr ();
state.pNewFrame = d_bNew.ptr ();
nppiStInterpolateFrames (&state);
// copy to host memory
ncvAssertReturnNcvStat (d_rNew.copySolid (h_r, 0));
ncvAssertReturnNcvStat (d_gNew.copySolid (h_g, 0));
ncvAssertReturnNcvStat (d_bNew.copySolid (h_b, 0));
// convert to IplImage
IplImage *newFrame = CreateImage (h_r, h_g, h_b);
if (newFrame == 0)
{
std::cout << "Could not create new frame in host memory\n";
break;
}
frames.push_back (newFrame);
std::cout << timePos * 100.0f << "%\r";
}
std::cout << std::setw (5) << "100%\n";
frames.push_back (lastFrame);
Ncv32u currentFrame;
currentFrame = 0;
ShowFlow (u, v, "Forward flow");
ShowFlow (uBck, vBck, "Backward flow");
cvShowImage ("Interpolated frame", frames[currentFrame]);
bool qPressed = false;
while ( !qPressed )
{
int key = toupper (cvWaitKey (10));
switch (key)
{
case 27:
qPressed = true;
break;
case 'A':
if (currentFrame > 0) --currentFrame;
cvShowImage ("Interpolated frame", frames[currentFrame]);
break;
case 'S':
if (currentFrame < frames.size()-1) ++currentFrame;
cvShowImage ("Interpolated frame", frames[currentFrame]);
break;
}
}
cvDestroyAllWindows ();
std::vector<IplImage*>::iterator iter;
for (iter = frames.begin (); iter != frames.end (); ++iter)
{
cvReleaseImage (&(*iter));
}
return 0;
}
#endif

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