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added GoodFeaturesToTrackDetector_GPU and PyrLKOpticalFlow to gpu module

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This commit is contained in:
Vladislav Vinogradov
2012-02-13 12:57:27 +00:00
parent edc9d4f951
commit a10fed8fd1
11 changed files with 1913 additions and 6 deletions
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146
modules/gpu/src/cuda/gftt.cu Normal file
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/*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 materials 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 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.
//
// Copyright (c) 2010, Paul Furgale, Chi Hay Tong
//
// The original code was written by Paul Furgale and Chi Hay Tong
// and later optimized and prepared for integration into OpenCV by Itseez.
//
//M*/
#include <thrust/sort.h>
#include "opencv2/gpu/device/common.hpp"
#include "opencv2/gpu/device/utility.hpp"
namespace cv { namespace gpu { namespace device
{
namespace gfft
{
texture<float, cudaTextureType2D, cudaReadModeElementType> eigTex(0, cudaFilterModePoint, cudaAddressModeClamp);
__device__ uint g_counter = 0;
template <class Mask> __global__ void findCorners(float threshold, const Mask mask, float2* corners, uint max_count, int rows, int cols)
{
#if __CUDA_ARCH__ >= 110
const int j = blockIdx.x * blockDim.x + threadIdx.x;
const int i = blockIdx.y * blockDim.y + threadIdx.y;
if (i > 0 && i < rows - 1 && j > 0 && j < cols - 1 && mask(i, j))
{
float val = tex2D(eigTex, j, i);
if (val > threshold)
{
float maxVal = val;
maxVal = ::fmax(tex2D(eigTex, j - 1, i - 1), maxVal);
maxVal = ::fmax(tex2D(eigTex, j , i - 1), maxVal);
maxVal = ::fmax(tex2D(eigTex, j + 1, i - 1), maxVal);
maxVal = ::fmax(tex2D(eigTex, j - 1, i), maxVal);
maxVal = ::fmax(tex2D(eigTex, j + 1, i), maxVal);
maxVal = ::fmax(tex2D(eigTex, j - 1, i + 1), maxVal);
maxVal = ::fmax(tex2D(eigTex, j , i + 1), maxVal);
maxVal = ::fmax(tex2D(eigTex, j + 1, i + 1), maxVal);
if (val == maxVal)
{
const uint ind = atomicInc(&g_counter, (uint)(-1));
if (ind < max_count)
corners[ind] = make_float2(j, i);
}
}
}
#endif // __CUDA_ARCH__ >= 110
}
int findCorners_gpu(DevMem2Df eig, float threshold, DevMem2Db mask, float2* corners, int max_count)
{
void* counter_ptr;
cudaSafeCall( cudaGetSymbolAddress(&counter_ptr, g_counter) );
cudaSafeCall( cudaMemset(counter_ptr, 0, sizeof(uint)) );
bindTexture(&eigTex, eig);
dim3 block(16, 16);
dim3 grid(divUp(eig.cols, block.x), divUp(eig.rows, block.y));
if (mask.data)
findCorners<<<grid, block>>>(threshold, SingleMask(mask), corners, max_count, eig.rows, eig.cols);
else
findCorners<<<grid, block>>>(threshold, WithOutMask(), corners, max_count, eig.rows, eig.cols);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
uint count;
cudaSafeCall( cudaMemcpy(&count, counter_ptr, sizeof(uint), cudaMemcpyDeviceToHost) );
return min(count, max_count);
}
class EigGreater
{
public:
__device__ __forceinline__ bool operator()(float2 a, float2 b) const
{
return tex2D(eigTex, a.x, a.y) > tex2D(eigTex, b.x, b.y);
}
};
void sortCorners_gpu(DevMem2Df eig, float2* corners, int count)
{
bindTexture(&eigTex, eig);
thrust::device_ptr<float2> ptr(corners);
thrust::sort(ptr, ptr + count, EigGreater());
}
} // namespace optical_flow
}}}

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modules/gpu/src/cuda/pyrlk.cu Normal file
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/*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 materials 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 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.
//
// Copyright (c) 2010, Paul Furgale, Chi Hay Tong
//
// The original code was written by Paul Furgale and Chi Hay Tong
// and later optimized and prepared for integration into OpenCV by Itseez.
//
//M*/
#include "opencv2/gpu/device/common.hpp"
#include "opencv2/gpu/device/utility.hpp"
#include "opencv2/gpu/device/functional.hpp"
#include "opencv2/gpu/device/limits.hpp"
namespace cv { namespace gpu { namespace device
{
namespace pyrlk
{
__constant__ int c_cn;
__constant__ float c_minEigThreshold;
__constant__ int c_winSize_x;
__constant__ int c_winSize_y;
__constant__ int c_winSize_x_cn;
__constant__ int c_halfWin_x;
__constant__ int c_halfWin_y;
__constant__ int c_iters;
void loadConstants(int cn, float minEigThreshold, int2 winSize, int iters)
{
int2 halfWin = make_int2((winSize.x - 1) / 2, (winSize.y - 1) / 2);
cudaSafeCall( cudaMemcpyToSymbol(c_cn, &cn, sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(c_minEigThreshold, &minEigThreshold, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_winSize_x, &winSize.x, sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(c_winSize_y, &winSize.y, sizeof(int)) );
winSize.x *= cn;
cudaSafeCall( cudaMemcpyToSymbol(c_winSize_x_cn, &winSize.x, sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(c_halfWin_x, &halfWin.x, sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(c_halfWin_y, &halfWin.y, sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(c_iters, &iters, sizeof(int)) );
}
__global__ void calcSharrDeriv_vertical(const PtrStepb src, PtrStep<short> dx_buf, PtrStep<short> dy_buf, int rows, int colsn)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (y < rows && x < colsn)
{
const uchar src_val0 = src(y > 0 ? y - 1 : 1, x);
const uchar src_val1 = src(y, x);
const uchar src_val2 = src(y < rows - 1 ? y + 1 : rows - 2, x);
dx_buf(y, x) = (src_val0 + src_val2) * 3 + src_val1 * 10;
dy_buf(y, x) = src_val2 - src_val0;
}
}
__global__ void calcSharrDeriv_horizontal(const PtrStep<short> dx_buf, const PtrStep<short> dy_buf, PtrStep<short> dIdx, PtrStep<short> dIdy, int rows, int cols)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
const int colsn = cols * c_cn;
if (y < rows && x < colsn)
{
const short* dx_buf_row = dx_buf.ptr(y);
const short* dy_buf_row = dy_buf.ptr(y);
const int xr = x + c_cn < colsn ? x + c_cn : (cols - 2) * c_cn + x + c_cn - colsn;
const int xl = x - c_cn >= 0 ? x - c_cn : c_cn + x;
dIdx(y, x) = dx_buf_row[xr] - dx_buf_row[xl];
dIdy(y, x) = (dy_buf_row[xr] + dy_buf_row[xl]) * 3 + dy_buf_row[x] * 10;
}
}
void calcSharrDeriv_gpu(DevMem2Db src, DevMem2D_<short> dx_buf, DevMem2D_<short> dy_buf, DevMem2D_<short> dIdx, DevMem2D_<short> dIdy, int cn,
cudaStream_t stream)
{
dim3 block(32, 8);
dim3 grid(divUp(src.cols * cn, block.x), divUp(src.rows, block.y));
calcSharrDeriv_vertical<<<grid, block, 0, stream>>>(src, dx_buf, dy_buf, src.rows, src.cols * cn);
cudaSafeCall( cudaGetLastError() );
calcSharrDeriv_horizontal<<<grid, block, 0, stream>>>(dx_buf, dy_buf, dIdx, dIdy, src.rows, src.cols);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
#define W_BITS 14
#define W_BITS1 14
#define CV_DESCALE(x, n) (((x) + (1 << ((n)-1))) >> (n))
__device__ int linearFilter(const PtrStepb& src, float2 pt, int x, int y)
{
int2 ipt;
ipt.x = __float2int_rd(pt.x);
ipt.y = __float2int_rd(pt.y);
float a = pt.x - ipt.x;
float b = pt.y - ipt.y;
int iw00 = __float2int_rn((1.0f - a) * (1.0f - b) * (1 << W_BITS));
int iw01 = __float2int_rn(a * (1.0f - b) * (1 << W_BITS));
int iw10 = __float2int_rn((1.0f - a) * b * (1 << W_BITS));
int iw11 = (1 << W_BITS) - iw00 - iw01 - iw10;
const uchar* src_row = src.ptr(ipt.y + y) + ipt.x * c_cn;
const uchar* src_row1 = src.ptr(ipt.y + y + 1) + ipt.x * c_cn;
return CV_DESCALE(src_row[x] * iw00 + src_row[x + c_cn] * iw01 + src_row1[x] * iw10 + src_row1[x + c_cn] * iw11, W_BITS1 - 5);
}
__device__ int linearFilter(const PtrStep<short>& src, float2 pt, int x, int y)
{
int2 ipt;
ipt.x = __float2int_rd(pt.x);
ipt.y = __float2int_rd(pt.y);
float a = pt.x - ipt.x;
float b = pt.y - ipt.y;
int iw00 = __float2int_rn((1.0f - a) * (1.0f - b) * (1 << W_BITS));
int iw01 = __float2int_rn(a * (1.0f - b) * (1 << W_BITS));
int iw10 = __float2int_rn((1.0f - a) * b * (1 << W_BITS));
int iw11 = (1 << W_BITS) - iw00 - iw01 - iw10;
const short* src_row = src.ptr(ipt.y + y) + ipt.x * c_cn;
const short* src_row1 = src.ptr(ipt.y + y + 1) + ipt.x * c_cn;
return CV_DESCALE(src_row[x] * iw00 + src_row[x + c_cn] * iw01 + src_row1[x] * iw10 + src_row1[x + c_cn] * iw11, W_BITS1);
}
__device__ void reduce(float& val1, float& val2, float& val3, float* smem1, float* smem2, float* smem3, int tid)
{
smem1[tid] = val1;
smem2[tid] = val2;
smem3[tid] = val3;
__syncthreads();
if (tid < 128)
{
smem1[tid] = val1 += smem1[tid + 128];
smem2[tid] = val2 += smem2[tid + 128];
smem3[tid] = val3 += smem3[tid + 128];
}
__syncthreads();
if (tid < 64)
{
smem1[tid] = val1 += smem1[tid + 64];
smem2[tid] = val2 += smem2[tid + 64];
smem3[tid] = val3 += smem3[tid + 64];
}
__syncthreads();
if (tid < 32)
{
volatile float* vmem1 = smem1;
volatile float* vmem2 = smem2;
volatile float* vmem3 = smem3;
vmem1[tid] = val1 += vmem1[tid + 32];
vmem2[tid] = val2 += vmem2[tid + 32];
vmem3[tid] = val3 += vmem3[tid + 32];
vmem1[tid] = val1 += vmem1[tid + 16];
vmem2[tid] = val2 += vmem2[tid + 16];
vmem3[tid] = val3 += vmem3[tid + 16];
vmem1[tid] = val1 += vmem1[tid + 8];
vmem2[tid] = val2 += vmem2[tid + 8];
vmem3[tid] = val3 += vmem3[tid + 8];
vmem1[tid] = val1 += vmem1[tid + 4];
vmem2[tid] = val2 += vmem2[tid + 4];
vmem3[tid] = val3 += vmem3[tid + 4];
vmem1[tid] = val1 += vmem1[tid + 2];
vmem2[tid] = val2 += vmem2[tid + 2];
vmem3[tid] = val3 += vmem3[tid + 2];
vmem1[tid] = val1 += vmem1[tid + 1];
vmem2[tid] = val2 += vmem2[tid + 1];
vmem3[tid] = val3 += vmem3[tid + 1];
}
}
__device__ void reduce(float& val1, float& val2, float* smem1, float* smem2, int tid)
{
smem1[tid] = val1;
smem2[tid] = val2;
__syncthreads();
if (tid < 128)
{
smem1[tid] = val1 += smem1[tid + 128];
smem2[tid] = val2 += smem2[tid + 128];
}
__syncthreads();
if (tid < 64)
{
smem1[tid] = val1 += smem1[tid + 64];
smem2[tid] = val2 += smem2[tid + 64];
}
__syncthreads();
if (tid < 32)
{
volatile float* vmem1 = smem1;
volatile float* vmem2 = smem2;
vmem1[tid] = val1 += vmem1[tid + 32];
vmem2[tid] = val2 += vmem2[tid + 32];
vmem1[tid] = val1 += vmem1[tid + 16];
vmem2[tid] = val2 += vmem2[tid + 16];
vmem1[tid] = val1 += vmem1[tid + 8];
vmem2[tid] = val2 += vmem2[tid + 8];
vmem1[tid] = val1 += vmem1[tid + 4];
vmem2[tid] = val2 += vmem2[tid + 4];
vmem1[tid] = val1 += vmem1[tid + 2];
vmem2[tid] = val2 += vmem2[tid + 2];
vmem1[tid] = val1 += vmem1[tid + 1];
vmem2[tid] = val2 += vmem2[tid + 1];
}
}
#define SCALE (1.0f / (1 << 20))
template <int PATCH_X, int PATCH_Y, bool calcErr>
__global__ void lkSparse(const PtrStepb I, const PtrStepb J, const PtrStep<short> dIdx, const PtrStep<short> dIdy,
const float2* prevPts, float2* nextPts, uchar* status, float* err, const int level, const int rows, const int cols)
{
__shared__ float smem1[256];
__shared__ float smem2[256];
__shared__ float smem3[256];
const int tid = threadIdx.y * blockDim.x + threadIdx.x;
float2 prevPt = prevPts[blockIdx.x];
prevPt.x *= (1.0f / (1 << level));
prevPt.y *= (1.0f / (1 << level));
prevPt.x -= c_halfWin_x;
prevPt.y -= c_halfWin_y;
if (prevPt.x < -c_winSize_x || prevPt.x >= cols || prevPt.y < -c_winSize_y || prevPt.y >= rows)
{
if (level == 0 && tid == 0)
{
status[blockIdx.x] = 0;
if (calcErr)
err[blockIdx.x] = 0;
}
return;
}
// extract the patch from the first image, compute covariation matrix of derivatives
float A11 = 0;
float A12 = 0;
float A22 = 0;
int I_patch[PATCH_Y][PATCH_X];
int dIdx_patch[PATCH_Y][PATCH_X];
int dIdy_patch[PATCH_Y][PATCH_X];
for (int y = threadIdx.y, i = 0; y < c_winSize_y; y += blockDim.y, ++i)
{
for (int x = threadIdx.x, j = 0; x < c_winSize_x_cn; x += blockDim.x, ++j)
{
I_patch[i][j] = linearFilter(I, prevPt, x, y);
int ixval = linearFilter(dIdx, prevPt, x, y);
int iyval = linearFilter(dIdy, prevPt, x, y);
dIdx_patch[i][j] = ixval;
dIdy_patch[i][j] = iyval;
A11 += ixval * ixval;
A12 += ixval * iyval;
A22 += iyval * iyval;
}
}
reduce(A11, A12, A22, smem1, smem2, smem3, tid);
__syncthreads();
A11 = smem1[0];
A12 = smem2[0];
A22 = smem3[0];
A11 *= SCALE;
A12 *= SCALE;
A22 *= SCALE;
{
float D = A11 * A22 - A12 * A12;
float minEig = (A22 + A11 - ::sqrtf((A11 - A22) * (A11 - A22) + 4.f * A12 * A12)) / (2 * c_winSize_x * c_winSize_y);
if (calcErr && tid == 0)
err[blockIdx.x] = minEig;
if (minEig < c_minEigThreshold || D < numeric_limits<float>::epsilon())
{
if (level == 0 && tid == 0)
status[blockIdx.x] = 0;
return;
}
D = 1.f / D;
A11 *= D;
A12 *= D;
A22 *= D;
}
float2 nextPt = nextPts[blockIdx.x];
nextPt.x *= 2.f;
nextPt.y *= 2.f;
nextPt.x -= c_halfWin_x;
nextPt.y -= c_halfWin_y;
bool status_ = true;
for (int k = 0; k < c_iters; ++k)
{
if (nextPt.x < -c_winSize_x || nextPt.x >= cols || nextPt.y < -c_winSize_y || nextPt.y >= rows)
{
status_ = false;
break;
}
float b1 = 0;
float b2 = 0;
for (int y = threadIdx.y, i = 0; y < c_winSize_y; y += blockDim.y, ++i)
{
for (int x = threadIdx.x, j = 0; x < c_winSize_x_cn; x += blockDim.x, ++j)
{
int diff = linearFilter(J, nextPt, x, y) - I_patch[i][j];
b1 += diff * dIdx_patch[i][j];
b2 += diff * dIdy_patch[i][j];
}
}
reduce(b1, b2, smem1, smem2, tid);
__syncthreads();
b1 = smem1[0];
b2 = smem2[0];
b1 *= SCALE;
b2 *= SCALE;
float2 delta;
delta.x = A12 * b2 - A22 * b1;
delta.y = A12 * b1 - A11 * b2;
nextPt.x += delta.x;
nextPt.y += delta.y;
if (::fabs(delta.x) < 0.01f && ::fabs(delta.y) < 0.01f)
break;
}
if (tid == 0)
{
nextPt.x += c_halfWin_x;
nextPt.y += c_halfWin_y;
nextPts[blockIdx.x] = nextPt;
status[blockIdx.x] = status_;
}
}
template <int PATCH_X, int PATCH_Y>
void lkSparse_caller(DevMem2Db I, DevMem2Db J, DevMem2D_<short> dIdx, DevMem2D_<short> dIdy,
const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, cudaStream_t stream)
{
dim3 grid(ptcount);
if (err)
{
cudaSafeCall( cudaFuncSetCacheConfig(lkSparse<PATCH_X, PATCH_Y, true>, cudaFuncCachePreferL1) );
lkSparse<PATCH_X, PATCH_Y, true><<<grid, block>>>(I, J, dIdx, dIdy,
prevPts, nextPts, status, err, level, I.rows, I.cols);
}
else
{
cudaSafeCall( cudaFuncSetCacheConfig(lkSparse<PATCH_X, PATCH_Y, false>, cudaFuncCachePreferL1) );
lkSparse<PATCH_X, PATCH_Y, false><<<grid, block>>>(I, J, dIdx, dIdy,
prevPts, nextPts, status, err, level, I.rows, I.cols);
}
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
void lkSparse_gpu(DevMem2Db I, DevMem2Db J, DevMem2D_<short> dIdx, DevMem2D_<short> dIdy,
const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, dim3 patch, cudaStream_t stream)
{
typedef void (*func_t)(DevMem2Db I, DevMem2Db J, DevMem2D_<short> dIdx, DevMem2D_<short> dIdy,
const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, cudaStream_t stream);
static const func_t funcs[5][5] =
{
{lkSparse_caller<1, 1>, lkSparse_caller<2, 1>, lkSparse_caller<3, 1>, lkSparse_caller<4, 1>, lkSparse_caller<5, 1>},
{lkSparse_caller<1, 2>, lkSparse_caller<2, 2>, lkSparse_caller<3, 2>, lkSparse_caller<4, 2>, lkSparse_caller<5, 2>},
{lkSparse_caller<1, 3>, lkSparse_caller<2, 3>, lkSparse_caller<3, 3>, lkSparse_caller<4, 3>, lkSparse_caller<5, 3>},
{lkSparse_caller<1, 4>, lkSparse_caller<2, 4>, lkSparse_caller<3, 4>, lkSparse_caller<4, 4>, lkSparse_caller<5, 4>},
{lkSparse_caller<1, 5>, lkSparse_caller<2, 5>, lkSparse_caller<3, 5>, lkSparse_caller<4, 5>, lkSparse_caller<5, 5>}
};
funcs[patch.y - 1][patch.x - 1](I, J, dIdx, dIdy,
prevPts, nextPts, status, err, ptcount,
level, block, stream);
}
template <bool calcErr>
__global__ void lkDense(const PtrStepb I, const PtrStepb J, const PtrStep<short> dIdx, const PtrStep<short> dIdy,
PtrStepf u, PtrStepf v, PtrStepf err, const int rows, const int cols)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x >= cols || y >= rows)
return;
// extract the patch from the first image, compute covariation matrix of derivatives
float A11 = 0;
float A12 = 0;
float A22 = 0;
for (int i = 0; i < c_winSize_y; ++i)
{
for (int j = 0; j < c_winSize_x; ++j)
{
int ixval = dIdx(y - c_halfWin_y + i, x - c_halfWin_x + j);
int iyval = dIdy(y - c_halfWin_y + i, x - c_halfWin_x + j);
A11 += ixval * ixval;
A12 += ixval * iyval;
A22 += iyval * iyval;
}
}
A11 *= SCALE;
A12 *= SCALE;
A22 *= SCALE;
{
float D = A11 * A22 - A12 * A12;
float minEig = (A22 + A11 - ::sqrtf((A11 - A22) * (A11 - A22) + 4.f * A12 * A12)) / (2 * c_winSize_x * c_winSize_y);
if (calcErr)
err(y, x) = minEig;
if (minEig < c_minEigThreshold || D < numeric_limits<float>::epsilon())
return;
D = 1.f / D;
A11 *= D;
A12 *= D;
A22 *= D;
}
float2 nextPt;
nextPt.x = x - c_halfWin_x + u(y, x);
nextPt.y = y - c_halfWin_y + v(y, x);
for (int k = 0; k < c_iters; ++k)
{
if (nextPt.x < -c_winSize_x || nextPt.x >= cols || nextPt.y < -c_winSize_y || nextPt.y >= rows)
break;
float b1 = 0;
float b2 = 0;
for (int i = 0; i < c_winSize_y; ++i)
{
for (int j = 0; j < c_winSize_x; ++j)
{
int I_val = I(y - c_halfWin_y + i, x - c_halfWin_x + j);
int diff = linearFilter(J, nextPt, j, i) - CV_DESCALE(I_val * (1 << W_BITS), W_BITS1 - 5);
b1 += diff * dIdx(y - c_halfWin_y + i, x - c_halfWin_x + j);
b2 += diff * dIdy(y - c_halfWin_y + i, x - c_halfWin_x + j);
}
}
b1 *= SCALE;
b2 *= SCALE;
float2 delta;
delta.x = A12 * b2 - A22 * b1;
delta.y = A12 * b1 - A11 * b2;
nextPt.x += delta.x;
nextPt.y += delta.y;
if (::fabs(delta.x) < 0.01f && ::fabs(delta.y) < 0.01f)
break;
}
u(y, x) = nextPt.x - x + c_halfWin_x;
v(y, x) = nextPt.y - y + c_halfWin_y;
}
void lkDense_gpu(DevMem2Db I, DevMem2Db J, DevMem2D_<short> dIdx, DevMem2D_<short> dIdy,
DevMem2Df u, DevMem2Df v, DevMem2Df* err, cudaStream_t stream)
{
dim3 block(32, 8);
dim3 grid(divUp(I.cols, block.x), divUp(I.rows, block.y));
if (err)
{
cudaSafeCall( cudaFuncSetCacheConfig(lkDense<true>, cudaFuncCachePreferL1) );
lkDense<true><<<grid, block, 0, stream>>>(I, J, dIdx, dIdy, u, v, *err, I.rows, I.cols);
cudaSafeCall( cudaGetLastError() );
}
else
{
cudaSafeCall( cudaFuncSetCacheConfig(lkDense<false>, cudaFuncCachePreferL1) );
lkDense<false><<<grid, block, 0, stream>>>(I, J, dIdx, dIdy, u, v, PtrStepf(), I.rows, I.cols);
cudaSafeCall( cudaGetLastError() );
}
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
}
}}}