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Modified sparse pyrlk optical flow to allow input of an image pyramid which thus allows caching of image pyramids on successive calls.

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Added unsigned char support for 1, 3, 4 channel images.
This commit is contained in:
Dan Moodie 2015-12-29 10:48:14 -05:00
parent 8d79285d02
commit 66738d748f
7 changed files with 818 additions and 213 deletions
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14
modules/cudaoptflow/perf/perf_optflow.cpp
View File

@ -116,10 +116,10 @@ PERF_TEST_P(ImagePair_Gray_NPts_WinSz_Levels_Iters, PyrLKOpticalFlowSparse,
const int levels = GET_PARAM(4);
const int iters = GET_PARAM(5);
const cv::Mat frame0 = readImage(imagePair.first, useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
cv::Mat frame0 = readImage(imagePair.first, useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
ASSERT_FALSE(frame0.empty());
const cv::Mat frame1 = readImage(imagePair.second, useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
cv::Mat frame1 = readImage(imagePair.second, useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
ASSERT_FALSE(frame1.empty());
cv::Mat gray_frame;
@ -131,6 +131,14 @@ PERF_TEST_P(ImagePair_Gray_NPts_WinSz_Levels_Iters, PyrLKOpticalFlowSparse,
cv::Mat pts;
cv::goodFeaturesToTrack(gray_frame, pts, points, 0.01, 0.0);
frame0.convertTo(frame0, CV_32F);
frame1.convertTo(frame1, CV_32F);
if(!useGray)
{
cv::cvtColor(frame0, frame0, cv::COLOR_BGR2BGRA);
cv::cvtColor(frame1, frame1, cv::COLOR_BGR2BGRA);
}
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_pts(pts.reshape(2, 1));
@ -318,4 +326,4 @@ PERF_TEST_P(ImagePair, OpticalFlowDual_TVL1,
CPU_SANITY_CHECK(flow);
}
}
}

738
modules/cudaoptflow/src/cuda/pyrlk.cu
View File

@ -48,6 +48,8 @@
#include "opencv2/core/cuda/limits.hpp"
#include "opencv2/core/cuda/vec_math.hpp"
#include "opencv2/core/cuda/reduce.hpp"
#include "opencv2/core/cuda/filters.hpp"
#include "opencv2/core/cuda/border_interpolate.hpp"
using namespace cv::cuda;
using namespace cv::cuda::device;
@ -60,53 +62,240 @@ namespace pyrlk
__constant__ int c_halfWin_y;
__constant__ int c_iters;
texture<uchar, cudaTextureType2D, cudaReadModeNormalizedFloat> tex_I8U(false, cudaFilterModeLinear, cudaAddressModeClamp);
texture<uchar4, cudaTextureType2D, cudaReadModeNormalizedFloat> tex_I8UC4(false, cudaFilterModeLinear, cudaAddressModeClamp);
texture<ushort4, cudaTextureType2D, cudaReadModeNormalizedFloat> tex_I16UC4(false, cudaFilterModeLinear, cudaAddressModeClamp);
texture<float, cudaTextureType2D, cudaReadModeElementType> tex_If(false, cudaFilterModeLinear, cudaAddressModeClamp);
texture<float4, cudaTextureType2D, cudaReadModeElementType> tex_If4(false, cudaFilterModeLinear, cudaAddressModeClamp);
texture<uchar, cudaTextureType2D, cudaReadModeElementType> tex_Ib(false, cudaFilterModePoint, cudaAddressModeClamp);
texture<uchar, cudaTextureType2D, cudaReadModeNormalizedFloat> tex_J8U(false, cudaFilterModeLinear, cudaAddressModeClamp);
texture<uchar4, cudaTextureType2D, cudaReadModeNormalizedFloat> tex_J8UC4(false, cudaFilterModeLinear, cudaAddressModeClamp);
texture<ushort4, cudaTextureType2D, cudaReadModeNormalizedFloat> tex_J16UC4(false, cudaFilterModeLinear, cudaAddressModeClamp);
texture<float, cudaTextureType2D, cudaReadModeElementType> tex_Jf(false, cudaFilterModeLinear, cudaAddressModeClamp);
texture<float4, cudaTextureType2D, cudaReadModeElementType> tex_Jf4(false, cudaFilterModeLinear, cudaAddressModeClamp);
template <int cn> struct Tex_I;
template <> struct Tex_I<1>
template <int cn, typename T> struct Tex_I
{
static __host__ __forceinline__ void bindTexture_(PtrStepSz<typename TypeVec<T, cn>::vec_type> I)
{
(void)I;
}
};
template <> struct Tex_I<1, uchar>
{
static __device__ __forceinline__ float read(float x, float y)
{
return tex2D(tex_I8U, x, y);
}
static __host__ __forceinline__ void bindTexture_(PtrStepSz<uchar>& I)
{
bindTexture(&tex_I8U, I);
}
};
template <> struct Tex_I<1, ushort>
{
static __device__ __forceinline__ float read(float x, float y)
{
return 0.0;
}
static __host__ __forceinline__ void bindTexture_(PtrStepSz<ushort>& I)
{
(void)I;
}
};
template <> struct Tex_I<1, int>
{
static __device__ __forceinline__ float read(float x, float y)
{
return 0.0;
}
static __host__ __forceinline__ void bindTexture_(PtrStepSz<int>& I)
{
(void)I;
}
};
template <> struct Tex_I<1, float>
{
static __device__ __forceinline__ float read(float x, float y)
{
return tex2D(tex_If, x, y);
}
static __host__ __forceinline__ void bindTexture_(PtrStepSz<float>& I)
{
bindTexture(&tex_If, I);
}
};
template <> struct Tex_I<4>
// ****************** 3 channel specializations ************************
template <> struct Tex_I<3, uchar>
{
static __device__ __forceinline__ float3 read(float x, float y)
{
return make_float3(0,0,0);
}
static __host__ __forceinline__ void bindTexture_(PtrStepSz<uchar3> I)
{
(void)I;
}
};
template <> struct Tex_I<3, ushort>
{
static __device__ __forceinline__ float3 read(float x, float y)
{
return make_float3(0, 0, 0);
}
static __host__ __forceinline__ void bindTexture_(PtrStepSz<ushort3> I)
{
(void)I;
}
};
template <> struct Tex_I<3, int>
{
static __device__ __forceinline__ float3 read(float x, float y)
{
return make_float3(0, 0, 0);
}
static __host__ __forceinline__ void bindTexture_(PtrStepSz<int3> I)
{
(void)I;
}
};
template <> struct Tex_I<3, float>
{
static __device__ __forceinline__ float3 read(float x, float y)
{
return make_float3(0, 0, 0);
}
static __host__ __forceinline__ void bindTexture_(PtrStepSz<float3> I)
{
(void)I;
}
};
// ****************** 4 channel specializations ************************
template <> struct Tex_I<4, uchar>
{
static __device__ __forceinline__ float4 read(float x, float y)
{
return tex2D(tex_I8UC4, x, y);
}
static __host__ __forceinline__ void bindTexture_(PtrStepSz<uchar4>& I)
{
bindTexture(&tex_I8UC4, I);
}
};
template <> struct Tex_I<4, ushort>
{
static __device__ __forceinline__ float4 read(float x, float y)
{
return tex2D(tex_I16UC4, x, y);
}
static __host__ __forceinline__ void bindTexture_(PtrStepSz<ushort4>& I)
{
bindTexture(&tex_I16UC4, I);
}
};
template <> struct Tex_I<4, float>
{
static __device__ __forceinline__ float4 read(float x, float y)
{
return tex2D(tex_If4, x, y);
}
static __host__ __forceinline__ void bindTexture_(PtrStepSz<float4>& I)
{
bindTexture(&tex_If4, I);
}
};
template <int cn> struct Tex_J;
template <> struct Tex_J<1>
// ************* J ***************
template <int cn, typename T> struct Tex_J
{
static __host__ __forceinline__ void bindTexture_(PtrStepSz<typename TypeVec<T,cn>::vec_type>& J)
{
(void)J;
}
};
template <> struct Tex_J<1, uchar>
{
static __device__ __forceinline__ float read(float x, float y)
{
return tex2D(tex_J8U, x, y);
}
static __host__ __forceinline__ void bindTexture_(PtrStepSz<uchar>& J)
{
bindTexture(&tex_J8U, J);
}
};
template <> struct Tex_J<1, float>
{
static __device__ __forceinline__ float read(float x, float y)
{
return tex2D(tex_Jf, x, y);
}
static __host__ __forceinline__ void bindTexture_(PtrStepSz<float>& J)
{
bindTexture(&tex_Jf, J);
}
};
template <> struct Tex_J<4>
// ************* 4 channel specializations ***************
template <> struct Tex_J<4, uchar>
{
static __device__ __forceinline__ float4 read(float x, float y)
{
return tex2D(tex_J8UC4, x, y);
}
static __host__ __forceinline__ void bindTexture_(PtrStepSz<uchar4>& J)
{
bindTexture(&tex_J8UC4, J);
}
};
template <> struct Tex_J<4, ushort>
{
static __device__ __forceinline__ float4 read(float x, float y)
{
return tex2D(tex_J16UC4, x, y);
}
static __host__ __forceinline__ void bindTexture_(PtrStepSz<ushort4>& J)
{
bindTexture(&tex_J16UC4, J);
}
};
template <> struct Tex_J<4, float>
{
static __device__ __forceinline__ float4 read(float x, float y)
{
return tex2D(tex_Jf4, x, y);
}
static __host__ __forceinline__ void bindTexture_(PtrStepSz<float4>& J)
{
bindTexture(&tex_Jf4, J);
}
};
__device__ __forceinline__ void accum(float& dst, float val)
__device__ __forceinline__ void accum(float& dst, const float& val)
{
dst += val;
}
__device__ __forceinline__ void accum(float& dst, const float4& val)
__device__ __forceinline__ void accum(float& dst, const float2& val)
{
dst += val.x + val.y;
}
__device__ __forceinline__ void accum(float& dst, const float3& val)
{
dst += val.x + val.y + val.z;
}
__device__ __forceinline__ void accum(float& dst, const float4& val)
{
dst += val.x + val.y + val.z + val.w;
}
__device__ __forceinline__ float abs_(float a)
{
@ -116,8 +305,46 @@ namespace pyrlk
{
return abs(a);
}
__device__ __forceinline__ float2 abs_(const float2& a)
{
return abs(a);
}
__device__ __forceinline__ float3 abs_(const float3& a)
{
return abs(a);
}
template <int cn, int PATCH_X, int PATCH_Y, bool calcErr>
template<typename T> __device__ __forceinline__ typename TypeVec<float, 1>::vec_type ToFloat(const typename TypeVec<T, 1>::vec_type& other)
{
return other;
}
template<typename T> __device__ __forceinline__ typename TypeVec<float, 2>::vec_type ToFloat(const typename TypeVec<T, 2>::vec_type& other)
{
typename TypeVec<float, 2>::vec_type ret;
ret.x = other.x;
ret.y = other.y;
return ret;
}
template<typename T> __device__ __forceinline__ typename TypeVec<float, 3>::vec_type ToFloat(const typename TypeVec<T, 3>::vec_type& other)
{
typename TypeVec<float, 3>::vec_type ret;
ret.x = other.x;
ret.y = other.y;
ret.z = other.z;
return ret;
}
template<typename T> __device__ __forceinline__ typename TypeVec<float, 4>::vec_type ToFloat(const typename TypeVec<T, 4>::vec_type& other)
{
typename TypeVec<float, 4>::vec_type ret;
ret.x = other.x;
ret.y = other.y;
ret.z = other.z;
ret.w = other.w;
return ret;
}
template <int cn, int PATCH_X, int PATCH_Y, bool calcErr, typename T>
__global__ void sparseKernel(const float2* prevPts, float2* nextPts, uchar* status, float* err, const int level, const int rows, const int cols)
{
#if __CUDA_ARCH__ <= 110
@ -166,15 +393,15 @@ namespace pyrlk
float x = prevPt.x + xBase + 0.5f;
float y = prevPt.y + yBase + 0.5f;
I_patch[i][j] = Tex_I<cn>::read(x, y);
I_patch[i][j] = Tex_I<cn, T>::read(x, y);
// Sharr Deriv
work_type dIdx = 3.0f * Tex_I<cn>::read(x+1, y-1) + 10.0f * Tex_I<cn>::read(x+1, y) + 3.0f * Tex_I<cn>::read(x+1, y+1) -
(3.0f * Tex_I<cn>::read(x-1, y-1) + 10.0f * Tex_I<cn>::read(x-1, y) + 3.0f * Tex_I<cn>::read(x-1, y+1));
work_type dIdx = 3.0f * Tex_I<cn,T>::read(x+1, y-1) + 10.0f * Tex_I<cn, T>::read(x+1, y) + 3.0f * Tex_I<cn,T>::read(x+1, y+1) -
(3.0f * Tex_I<cn,T>::read(x-1, y-1) + 10.0f * Tex_I<cn, T>::read(x-1, y) + 3.0f * Tex_I<cn,T>::read(x-1, y+1));
work_type dIdy = 3.0f * Tex_I<cn>::read(x-1, y+1) + 10.0f * Tex_I<cn>::read(x, y+1) + 3.0f * Tex_I<cn>::read(x+1, y+1) -
(3.0f * Tex_I<cn>::read(x-1, y-1) + 10.0f * Tex_I<cn>::read(x, y-1) + 3.0f * Tex_I<cn>::read(x+1, y-1));
work_type dIdy = 3.0f * Tex_I<cn,T>::read(x-1, y+1) + 10.0f * Tex_I<cn, T>::read(x, y+1) + 3.0f * Tex_I<cn,T>::read(x+1, y+1) -
(3.0f * Tex_I<cn,T>::read(x-1, y-1) + 10.0f * Tex_I<cn, T>::read(x, y-1) + 3.0f * Tex_I<cn,T>::read(x+1, y-1));
dIdx_patch[i][j] = dIdx;
dIdy_patch[i][j] = dIdy;
@ -243,7 +470,7 @@ namespace pyrlk
for (int x = threadIdx.x, j = 0; x < c_winSize_x; x += blockDim.x, ++j)
{
work_type I_val = I_patch[i][j];
work_type J_val = Tex_J<cn>::read(nextPt.x + x + 0.5f, nextPt.y + y + 0.5f);
work_type J_val = Tex_J<cn, T>::read(nextPt.x + x + 0.5f, nextPt.y + y + 0.5f);
work_type diff = (J_val - I_val) * 32.0f;
@ -286,7 +513,7 @@ namespace pyrlk
for (int x = threadIdx.x, j = 0; x < c_winSize_x; x += blockDim.x, ++j)
{
work_type I_val = I_patch[i][j];
work_type J_val = Tex_J<cn>::read(nextPt.x + x + 0.5f, nextPt.y + y + 0.5f);
work_type J_val = Tex_J<cn, T>::read(nextPt.x + x + 0.5f, nextPt.y + y + 0.5f);
work_type diff = J_val - I_val;
@ -309,22 +536,352 @@ namespace pyrlk
}
}
template <int cn, int PATCH_X, int PATCH_Y>
void sparse_caller(int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, cudaStream_t stream)
// Kernel, uses non texture fetches
template <int PATCH_X, int PATCH_Y, bool calcErr, int cn, typename T, typename Ptr2D>
__global__ void sparseKernel_(Ptr2D I, Ptr2D J, const float2* prevPts, float2* nextPts, uchar* status, float* err, const int level, const int rows, const int cols)
{
dim3 grid(ptcount);
#if __CUDA_ARCH__ <= 110
const int BLOCK_SIZE = 128;
#else
const int BLOCK_SIZE = 256;
#endif
if (level == 0 && err)
sparseKernel<cn, PATCH_X, PATCH_Y, true><<<grid, block>>>(prevPts, nextPts, status, err, level, rows, cols);
else
sparseKernel<cn, PATCH_X, PATCH_Y, false><<<grid, block>>>(prevPts, nextPts, status, err, level, rows, cols);
__shared__ float smem1[BLOCK_SIZE];
__shared__ float smem2[BLOCK_SIZE];
__shared__ float smem3[BLOCK_SIZE];
cudaSafeCall( cudaGetLastError() );
const unsigned 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));
if (prevPt.x < 0 || prevPt.x >= cols || prevPt.y < 0 || prevPt.y >= rows)
{
if (tid == 0 && level == 0)
status[blockIdx.x] = 0;
return;
}
prevPt.x -= c_halfWin_x;
prevPt.y -= c_halfWin_y;
// extract the patch from the first image, compute covariation matrix of derivatives
float A11 = 0;
float A12 = 0;
float A22 = 0;
typedef typename TypeVec<float, cn>::vec_type work_type;
work_type I_patch[PATCH_Y][PATCH_X];
work_type dIdx_patch[PATCH_Y][PATCH_X];
work_type dIdy_patch[PATCH_Y][PATCH_X];
for (int yBase = threadIdx.y, i = 0; yBase < c_winSize_y; yBase += blockDim.y, ++i)
{
for (int xBase = threadIdx.x, j = 0; xBase < c_winSize_x; xBase += blockDim.x, ++j)
{
float x = prevPt.x + xBase + 0.5f;
float y = prevPt.y + yBase + 0.5f;
I_patch[i][j] = ToFloat<T>(I(y, x));
// Sharr Deriv
work_type dIdx = 3.0f * I(y - 1, x + 1) + 10.0f * I(y, x + 1) + 3.0f * I(y + 1, x + 1) -
(3.0f * I(y - 1, x - 1) + 10.0f * I(y, x - 1) + 3.0f * I(y + 1 , x - 1));
work_type dIdy = 3.0f * I(y + 1, x - 1) + 10.0f * I(y + 1, x) + 3.0f * I(y+1, x + 1) -
(3.0f * I(y - 1, x - 1) + 10.0f * I(y-1, x) + 3.0f * I(y - 1, x + 1));
dIdx_patch[i][j] = dIdx;
dIdy_patch[i][j] = dIdy;
accum(A11, dIdx * dIdx);
accum(A12, dIdx * dIdy);
accum(A22, dIdy * dIdy);
}
}
reduce<BLOCK_SIZE>(smem_tuple(smem1, smem2, smem3), thrust::tie(A11, A12, A22), tid, thrust::make_tuple(plus<float>(), plus<float>(), plus<float>()));
#if __CUDA_ARCH__ >= 300
if (tid == 0)
{
smem1[0] = A11;
smem2[0] = A12;
smem3[0] = A22;
}
#endif
__syncthreads();
A11 = smem1[0];
A12 = smem2[0];
A22 = smem3[0];
float D = A11 * A22 - A12 * A12;
if (D < numeric_limits<float>::epsilon())
{
if (tid == 0 && level == 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;
for (int k = 0; k < c_iters; ++k)
{
if (nextPt.x < -c_halfWin_x || nextPt.x >= cols || nextPt.y < -c_halfWin_y || nextPt.y >= rows)
{
if (tid == 0 && level == 0)
status[blockIdx.x] = 0;
return;
}
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; x += blockDim.x, ++j)
{
work_type I_val = I_patch[i][j];
work_type J_val = ToFloat<T>(J(nextPt.y + y + 0.5f, nextPt.x + x + 0.5f));
work_type diff = (J_val - I_val) * 32.0f;
accum(b1, diff * dIdx_patch[i][j]);
accum(b2, diff * dIdy_patch[i][j]);
}
}
reduce<BLOCK_SIZE>(smem_tuple(smem1, smem2), thrust::tie(b1, b2), tid, thrust::make_tuple(plus<float>(), plus<float>()));
#if __CUDA_ARCH__ >= 300
if (tid == 0)
{
smem1[0] = b1;
smem2[0] = b2;
}
#endif
__syncthreads();
b1 = smem1[0];
b2 = smem2[0];
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;
}
float errval = 0;
if (calcErr)
{
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; x += blockDim.x, ++j)
{
work_type I_val = I_patch[i][j];
work_type J_val = ToFloat<T>(J(nextPt.y + y + 0.5f, nextPt.x + x + 0.5f));
work_type diff = J_val - I_val;
accum(errval, abs_(diff));
}
}
reduce<BLOCK_SIZE>(smem1, errval, tid, plus<float>());
}
if (tid == 0)
{
nextPt.x += c_halfWin_x;
nextPt.y += c_halfWin_y;
nextPts[blockIdx.x] = nextPt;
if (calcErr)
err[blockIdx.x] = static_cast<float>(errval) / (3 * c_winSize_x * c_winSize_y);
}
} // __global__ void sparseKernel_
template <int cn, int PATCH_X, int PATCH_Y, typename T> class sparse_caller
{
public:
static void call(PtrStepSz<typename TypeVec<T, cn>::vec_type> I, PtrStepSz<typename TypeVec<T, cn>::vec_type> J, int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, cudaStream_t stream)
{
dim3 grid(ptcount);
(void)I;
(void)J;
if (level == 0 && err)
sparseKernel<cn, PATCH_X, PATCH_Y, true, T> <<<grid, block, 0, stream >>>(prevPts, nextPts, status, err, level, rows, cols);
else
sparseKernel<cn, PATCH_X, PATCH_Y, false, T> <<<grid, block, 0, stream >>>(prevPts, nextPts, status, err, level, rows, cols);
cudaSafeCall(cudaGetLastError());
if (stream == 0)
cudaSafeCall(cudaDeviceSynchronize());
}
};
// Specialization to use non texture path because for some reason the texture path keeps failing accuracy tests
template<int PATCH_X, int PATCH_Y> class sparse_caller<1, PATCH_X, PATCH_Y, unsigned short>
{
public:
typedef typename TypeVec<unsigned short, 1>::vec_type work_type;
typedef PtrStepSz<work_type> Ptr2D;
typedef BrdConstant<work_type> BrdType;
typedef BorderReader<Ptr2D, BrdType> Reader;
typedef LinearFilter<Reader> Filter;
static void call(Ptr2D I, Ptr2D J, int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, cudaStream_t stream)
{
dim3 grid(ptcount);
if (level == 0 && err)
{
sparseKernel_<PATCH_X, PATCH_Y, true, 1, unsigned short> <<<grid, block, 0, stream >>>(
Filter(Reader(I, BrdType(rows, cols))),
Filter(Reader(J, BrdType(rows, cols))),
prevPts, nextPts, status, err, level, rows, cols);
}
else
{
sparseKernel_<PATCH_X, PATCH_Y, false, 1, unsigned short> <<<grid, block, 0, stream >>>(
Filter(Reader(I, BrdType(rows, cols))),
Filter(Reader(J, BrdType(rows, cols))),
prevPts, nextPts, status, err, level, rows, cols);
}
cudaSafeCall(cudaGetLastError());
if (stream == 0)
cudaSafeCall(cudaDeviceSynchronize());
}
};
// Specialization for int because the texture path keeps failing
template<int PATCH_X, int PATCH_Y> class sparse_caller<1, PATCH_X, PATCH_Y, int>
{
public:
typedef typename TypeVec<int, 1>::vec_type work_type;
typedef PtrStepSz<work_type> Ptr2D;
typedef BrdConstant<work_type> BrdType;
typedef BorderReader<Ptr2D, BrdType> Reader;
typedef LinearFilter<Reader> Filter;
static void call(Ptr2D I, Ptr2D J, int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, cudaStream_t stream)
{
dim3 grid(ptcount);
if (level == 0 && err)
{
sparseKernel_<PATCH_X, PATCH_Y, true, 1, int> <<<grid, block, 0, stream >>>(
Filter(Reader(I, BrdType(rows, cols))),
Filter(Reader(J, BrdType(rows, cols))),
prevPts, nextPts, status, err, level, rows, cols);
}
else
{
sparseKernel_<PATCH_X, PATCH_Y, false, 1, int> <<<grid, block, 0, stream >>>(
Filter(Reader(I, BrdType(rows, cols))),
Filter(Reader(J, BrdType(rows, cols))),
prevPts, nextPts, status, err, level, rows, cols);
}
cudaSafeCall(cudaGetLastError());
if (stream == 0)
cudaSafeCall(cudaDeviceSynchronize());
}
};
template<int PATCH_X, int PATCH_Y> class sparse_caller<4, PATCH_X, PATCH_Y, int>
{
public:
typedef typename TypeVec<int, 4>::vec_type work_type;
typedef PtrStepSz<work_type> Ptr2D;
typedef BrdConstant<work_type> BrdType;
typedef BorderReader<Ptr2D, BrdType> Reader;
typedef LinearFilter<Reader> Filter;
static void call(Ptr2D I, Ptr2D J, int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, cudaStream_t stream)
{
dim3 grid(ptcount);
if (level == 0 && err)
{
sparseKernel_<PATCH_X, PATCH_Y, true, 4, int> <<<grid, block, 0, stream >>>(
Filter(Reader(I, BrdType(rows, cols))),
Filter(Reader(J, BrdType(rows, cols))),
prevPts, nextPts, status, err, level, rows, cols);
}
else
{
sparseKernel_<PATCH_X, PATCH_Y, false, 4, int> <<<grid, block, 0, stream >>>(
Filter(Reader(I, BrdType(rows, cols))),
Filter(Reader(J, BrdType(rows, cols))),
prevPts, nextPts, status, err, level, rows, cols);
}
cudaSafeCall(cudaGetLastError());
if (stream == 0)
cudaSafeCall(cudaDeviceSynchronize());
}
};
using namespace cv::cuda::device;
template <int PATCH_X, int PATCH_Y, typename T> class sparse_caller<3, PATCH_X, PATCH_Y, T>
{
public:
typedef typename TypeVec<T, 3>::vec_type work_type;
typedef PtrStepSz<work_type> Ptr2D;
typedef BrdConstant<work_type> BrdType;
typedef BorderReader<Ptr2D, BrdType> Reader;
typedef LinearFilter<Reader> Filter;
static void call(Ptr2D I, Ptr2D J, int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, cudaStream_t stream)
{
dim3 grid(ptcount);
if (level == 0 && err)
{
sparseKernel_<PATCH_X, PATCH_Y, true, 3, T> <<<grid, block, 0, stream >>>(
Filter(Reader(I, BrdType(rows, cols))),
Filter(Reader(J, BrdType(rows, cols))),
prevPts, nextPts, status, err, level, rows, cols);
}
else
{
sparseKernel_<PATCH_X, PATCH_Y, false, 3, T> <<<grid, block, 0, stream >>>(
Filter(Reader(I, BrdType(rows, cols))),
Filter(Reader(J, BrdType(rows, cols))),
prevPts, nextPts, status, err, level, rows, cols);
}
cudaSafeCall(cudaGetLastError());
if (stream == 0)
cudaSafeCall(cudaDeviceSynchronize());
}
};
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
template <bool calcErr>
__global__ void denseKernel(PtrStepf u, PtrStepf v, const PtrStepf prevU, const PtrStepf prevV, PtrStepf err, const int rows, const int cols)
@ -484,77 +1041,72 @@ namespace pyrlk
cudaSafeCall( cudaMemcpyToSymbolAsync(c_iters, &iters, sizeof(int), 0, cudaMemcpyHostToDevice, stream) );
}
void sparse1(PtrStepSzf I, PtrStepSzf J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, dim3 patch, cudaStream_t stream)
template<typename T, int cn> struct pyrLK_caller
{
typedef void (*func_t)(int rows, int cols, 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] =
static void sparse(PtrStepSz<typename TypeVec<T, cn>::vec_type> I, PtrStepSz<typename TypeVec<T, cn>::vec_type> J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, dim3 patch, cudaStream_t stream)
{
{sparse_caller<1, 1, 1>, sparse_caller<1, 2, 1>, sparse_caller<1, 3, 1>, sparse_caller<1, 4, 1>, sparse_caller<1, 5, 1>},
{sparse_caller<1, 1, 2>, sparse_caller<1, 2, 2>, sparse_caller<1, 3, 2>, sparse_caller<1, 4, 2>, sparse_caller<1, 5, 2>},
{sparse_caller<1, 1, 3>, sparse_caller<1, 2, 3>, sparse_caller<1, 3, 3>, sparse_caller<1, 4, 3>, sparse_caller<1, 5, 3>},
{sparse_caller<1, 1, 4>, sparse_caller<1, 2, 4>, sparse_caller<1, 3, 4>, sparse_caller<1, 4, 4>, sparse_caller<1, 5, 4>},
{sparse_caller<1, 1, 5>, sparse_caller<1, 2, 5>, sparse_caller<1, 3, 5>, sparse_caller<1, 4, 5>, sparse_caller<1, 5, 5>}
};
typedef void(*func_t)(PtrStepSz<typename TypeVec<T, cn>::vec_type> I, PtrStepSz<typename TypeVec<T, cn>::vec_type> J,
int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, cudaStream_t stream);
bindTexture(&tex_If, I);
bindTexture(&tex_Jf, J);
static const func_t funcs[5][5] =
{
{ sparse_caller<cn, 1, 1,T>::call, sparse_caller<cn, 2, 1,T>::call, sparse_caller<cn, 3, 1,T>::call, sparse_caller<cn, 4, 1,T>::call, sparse_caller<cn, 5, 1,T>::call },
{ sparse_caller<cn, 1, 2,T>::call, sparse_caller<cn, 2, 2,T>::call, sparse_caller<cn, 3, 2,T>::call, sparse_caller<cn, 4, 2,T>::call, sparse_caller<cn, 5, 2,T>::call },
{ sparse_caller<cn, 1, 3,T>::call, sparse_caller<cn, 2, 3,T>::call, sparse_caller<cn, 3, 3,T>::call, sparse_caller<cn, 4, 3,T>::call, sparse_caller<cn, 5, 3,T>::call },
{ sparse_caller<cn, 1, 4,T>::call, sparse_caller<cn, 2, 4,T>::call, sparse_caller<cn, 3, 4,T>::call, sparse_caller<cn, 4, 4,T>::call, sparse_caller<cn, 5, 4,T>::call },
{ sparse_caller<cn, 1, 5,T>::call, sparse_caller<cn, 2, 5,T>::call, sparse_caller<cn, 3, 5,T>::call, sparse_caller<cn, 4, 5,T>::call, sparse_caller<cn, 5, 5,T>::call }
};
funcs[patch.y - 1][patch.x - 1](I.rows, I.cols, prevPts, nextPts, status, err, ptcount,
level, block, stream);
}
Tex_I<cn, T>::bindTexture_(I);
Tex_J<cn, T>::bindTexture_(J);
void sparse4(PtrStepSz<float4> I, PtrStepSz<float4> J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, dim3 patch, cudaStream_t stream)
{
typedef void (*func_t)(int rows, int cols, 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] =
{
{sparse_caller<4, 1, 1>, sparse_caller<4, 2, 1>, sparse_caller<4, 3, 1>, sparse_caller<4, 4, 1>, sparse_caller<4, 5, 1>},
{sparse_caller<4, 1, 2>, sparse_caller<4, 2, 2>, sparse_caller<4, 3, 2>, sparse_caller<4, 4, 2>, sparse_caller<4, 5, 2>},
{sparse_caller<4, 1, 3>, sparse_caller<4, 2, 3>, sparse_caller<4, 3, 3>, sparse_caller<4, 4, 3>, sparse_caller<4, 5, 3>},
{sparse_caller<4, 1, 4>, sparse_caller<4, 2, 4>, sparse_caller<4, 3, 4>, sparse_caller<4, 4, 4>, sparse_caller<4, 5, 4>},
{sparse_caller<4, 1, 5>, sparse_caller<4, 2, 5>, sparse_caller<4, 3, 5>, sparse_caller<4, 4, 5>, sparse_caller<4, 5, 5>}
};
bindTexture(&tex_If4, I);
bindTexture(&tex_Jf4, J);
funcs[patch.y - 1][patch.x - 1](I.rows, I.cols, prevPts, nextPts, status, err, ptcount,
level, block, stream);
}
void dense(PtrStepSzb I, PtrStepSzf J, PtrStepSzf u, PtrStepSzf v, PtrStepSzf prevU, PtrStepSzf prevV, PtrStepSzf err, int2 winSize, cudaStream_t stream)
{
dim3 block(16, 16);
dim3 grid(divUp(I.cols, block.x), divUp(I.rows, block.y));
bindTexture(&tex_Ib, I);
bindTexture(&tex_Jf, J);
int2 halfWin = make_int2((winSize.x - 1) / 2, (winSize.y - 1) / 2);
const int patchWidth = block.x + 2 * halfWin.x;
const int patchHeight = block.y + 2 * halfWin.y;
size_t smem_size = 3 * patchWidth * patchHeight * sizeof(int);
if (err.data)
{
denseKernel<true><<<grid, block, smem_size, stream>>>(u, v, prevU, prevV, err, I.rows, I.cols);
cudaSafeCall( cudaGetLastError() );
funcs[patch.y - 1][patch.x - 1](I, J, I.rows, I.cols, prevPts, nextPts, status, err, ptcount,
level, block, stream);
}
else
static void dense(PtrStepSzb I, PtrStepSz<T> J, PtrStepSzf u, PtrStepSzf v, PtrStepSzf prevU, PtrStepSzf prevV, PtrStepSzf err, int2 winSize, cudaStream_t stream)
{
denseKernel<false><<<grid, block, smem_size, stream>>>(u, v, prevU, prevV, PtrStepf(), I.rows, I.cols);
cudaSafeCall( cudaGetLastError() );
}
dim3 block(16, 16);
dim3 grid(divUp(I.cols, block.x), divUp(I.rows, block.y));
Tex_I<1, uchar>::bindTexture_(I);
Tex_J<1, T>::bindTexture_(J);
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
int2 halfWin = make_int2((winSize.x - 1) / 2, (winSize.y - 1) / 2);
const int patchWidth = block.x + 2 * halfWin.x;
const int patchHeight = block.y + 2 * halfWin.y;
size_t smem_size = 3 * patchWidth * patchHeight * sizeof(int);
if (err.data)
{
denseKernel<true> << <grid, block, smem_size, stream >> >(u, v, prevU, prevV, err, I.rows, I.cols);
cudaSafeCall(cudaGetLastError());
}
else
{
denseKernel<false> << <grid, block, smem_size, stream >> >(u, v, prevU, prevV, PtrStepf(), I.rows, I.cols);
cudaSafeCall(cudaGetLastError());
}
if (stream == 0)
cudaSafeCall(cudaDeviceSynchronize());
}
};
template class pyrLK_caller<unsigned char,1>;
template class pyrLK_caller<unsigned short,1>;
template class pyrLK_caller<int,1>;
template class pyrLK_caller<float,1>;
template class pyrLK_caller<unsigned char, 3>;
template class pyrLK_caller<unsigned short, 3>;
template class pyrLK_caller<int, 3>;
template class pyrLK_caller<float, 3>;
template class pyrLK_caller<unsigned char, 4>;
template class pyrLK_caller<unsigned short, 4>;
template class pyrLK_caller<int, 4>;
template class pyrLK_caller<float, 4>;
}
#endif /* CUDA_DISABLER */
#endif /* CUDA_DISABLER */

2
modules/cudaoptflow/src/precomp.hpp
View File

@ -52,7 +52,7 @@
#include "opencv2/video.hpp"
#include "opencv2/core/private.cuda.hpp"
#include "opencv2/core/cuda/vec_traits.hpp"
#include "opencv2/opencv_modules.hpp"
#ifdef HAVE_OPENCV_CUDALEGACY

203
modules/cudaoptflow/src/pyrlk.cpp
View File

@ -56,14 +56,20 @@ Ptr<DensePyrLKOpticalFlow> cv::cuda::DensePyrLKOpticalFlow::create(Size, int, in
namespace pyrlk
{
void loadConstants(int2 winSize, int iters, cudaStream_t stream);
template<typename T, int cn> struct pyrLK_caller
{
static void sparse(PtrStepSz<typename device::TypeVec<T, cn>::vec_type> I, PtrStepSz<typename device::TypeVec<T, cn>::vec_type> J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, dim3 patch, cudaStream_t stream);
void sparse1(PtrStepSzf I, PtrStepSzf J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, dim3 patch, cudaStream_t stream);
void sparse4(PtrStepSz<float4> I, PtrStepSz<float4> J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, dim3 patch, cudaStream_t stream);
static void dense(PtrStepSzb I, PtrStepSzf J, PtrStepSzf u, PtrStepSzf v, PtrStepSzf prevU, PtrStepSzf prevV,
PtrStepSzf err, int2 winSize, cudaStream_t stream);
};
void dense(PtrStepSzb I, PtrStepSzf J, PtrStepSzf u, PtrStepSzf v, PtrStepSzf prevU, PtrStepSzf prevV,
PtrStepSzf err, int2 winSize, cudaStream_t stream);
template<typename T, int cn> void dispatcher(GpuMat I, GpuMat J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, dim3 patch, cudaStream_t stream)
{
pyrLK_caller<T, cn>::sparse(I, J, prevPts, nextPts, status, err, ptcount, level, block, patch, stream);
}
}
namespace
@ -76,6 +82,9 @@ namespace
void sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts,
GpuMat& status, GpuMat* err, Stream& stream);
void sparse(std::vector<GpuMat>& prevPyr, std::vector<GpuMat>& nextPyr, const GpuMat& prevPts, GpuMat& nextPts,
GpuMat& status, GpuMat* err, Stream& stream);
void dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, Stream& stream);
protected:
@ -83,8 +92,9 @@ namespace
int maxLevel_;
int iters_;
bool useInitialFlow_;
void buildImagePyramid(const GpuMat& prevImg, std::vector<GpuMat>& prevPyr, const GpuMat& nextImg, std::vector<GpuMat>& nextPyr, Stream stream);
private:
friend class SparsePyrLKOpticalFlowImpl;
std::vector<GpuMat> prevPyr_;
std::vector<GpuMat> nextPyr_;
};
@ -113,6 +123,88 @@ namespace
block.z = patch.z = 1;
}
void PyrLKOpticalFlowBase::buildImagePyramid(const GpuMat& prevImg, std::vector<GpuMat>& prevPyr, const GpuMat& nextImg, std::vector<GpuMat>& nextPyr, Stream stream)
{
prevPyr.resize(maxLevel_ + 1);
nextPyr.resize(maxLevel_ + 1);
int cn = prevImg.channels();
CV_Assert(cn == 1 || cn == 3 || cn == 4);
prevPyr[0] = prevImg;
nextPyr[0] = nextImg;
for (int level = 1; level <= maxLevel_; ++level)
{
cuda::pyrDown(prevPyr[level - 1], prevPyr[level], stream);
cuda::pyrDown(nextPyr[level - 1], nextPyr[level], stream);
}
}
void PyrLKOpticalFlowBase::sparse(std::vector<GpuMat>& prevPyr, std::vector<GpuMat>& nextPyr, const GpuMat& prevPts, GpuMat& nextPts,
GpuMat& status, GpuMat* err, Stream& stream)
{
CV_Assert(prevPyr.size() && nextPyr.size() && "Pyramid needs to at least contain the original matrix as the first element");
CV_Assert(prevPyr[0].size() == nextPyr[0].size());
CV_Assert(prevPts.rows == 1 && prevPts.type() == CV_32FC2);
CV_Assert(maxLevel_ >= 0);
CV_Assert(winSize_.width > 2 && winSize_.height > 2);
if (useInitialFlow_)
CV_Assert(nextPts.size() == prevPts.size() && nextPts.type() == prevPts.type());
else
ensureSizeIsEnough(1, prevPts.cols, prevPts.type(), nextPts);
GpuMat temp1 = (useInitialFlow_ ? nextPts : prevPts).reshape(1);
GpuMat temp2 = nextPts.reshape(1);
cuda::multiply(temp1, Scalar::all(1.0 / (1 << maxLevel_) / 2.0), temp2, 1, -1, stream);
ensureSizeIsEnough(1, prevPts.cols, CV_8UC1, status);
status.setTo(Scalar::all(1), stream);
if (err)
ensureSizeIsEnough(1, prevPts.cols, CV_32FC1, *err);
if (prevPyr.size() != size_t(maxLevel_ + 1) || nextPyr.size() != size_t(maxLevel_ + 1))
{
buildImagePyramid(prevPyr[0], prevPyr, nextPyr[0], nextPyr, stream);
}
dim3 block, patch;
calcPatchSize(winSize_, block, patch);
CV_Assert(patch.x > 0 && patch.x < 6 && patch.y > 0 && patch.y < 6);
pyrlk::loadConstants(make_int2(winSize_.width, winSize_.height), iters_, StreamAccessor::getStream(stream));
const int cn = prevPyr[0].channels();
const int type = prevPyr[0].depth();
typedef void(*func_t)(GpuMat I, GpuMat J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, dim3 patch, cudaStream_t stream);
// Current int datatype is disabled due to pyrDown not implementing it
// while ushort does work, it has significantly worse performance, and thus doesn't pass accuracy tests.
static const func_t funcs[6][4] =
{
{ pyrlk::dispatcher<uchar, 1> , /*pyrlk::dispatcher<uchar, 2>*/ 0, pyrlk::dispatcher<uchar, 3> , pyrlk::dispatcher<uchar, 4> },
{ /*pyrlk::dispatcher<char, 1>*/ 0, /*pyrlk::dispatcher<char, 2>*/ 0, /*pyrlk::dispatcher<char, 3>*/ 0, /*pyrlk::dispatcher<char, 4>*/ 0 },
{ pyrlk::dispatcher<ushort, 1> , /*pyrlk::dispatcher<ushort, 2>*/0, pyrlk::dispatcher<ushort, 3> , pyrlk::dispatcher<ushort, 4> },
{ /*pyrlk::dispatcher<short, 1>*/ 0, /*pyrlk::dispatcher<short, 2>*/ 0, /*pyrlk::dispatcher<short, 3>*/ 0, /*pyrlk::dispatcher<short, 4>*/0 },
{ pyrlk::dispatcher<int, 1> , /*pyrlk::dispatcher<int, 2>*/ 0, pyrlk::dispatcher<int, 3> , pyrlk::dispatcher<int, 4> },
{ pyrlk::dispatcher<float, 1> , /*pyrlk::dispatcher<float, 2>*/ 0, pyrlk::dispatcher<float, 3> , pyrlk::dispatcher<float, 4> }
};
func_t func = funcs[type][cn-1];
CV_Assert(func != NULL && "Datatype not implemented");
for (int level = maxLevel_; level >= 0; level--)
{
func(prevPyr[level], nextPyr[level],
prevPts.ptr<float2>(), nextPts.ptr<float2>(),
status.ptr(), level == 0 && err ? err->ptr<float>() : 0,
prevPts.cols, level, block, patch,
StreamAccessor::getStream(stream));
}
}
void PyrLKOpticalFlowBase::sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts, GpuMat& status, GpuMat* err, Stream& stream)
{
if (prevPts.empty())
@ -122,86 +214,14 @@ namespace
if (err) err->release();
return;
}
dim3 block, patch;
calcPatchSize(winSize_, block, patch);
CV_Assert( prevImg.channels() == 1 || prevImg.channels() == 3 || prevImg.channels() == 4 );
CV_Assert( prevImg.size() == nextImg.size() && prevImg.type() == nextImg.type() );
CV_Assert( maxLevel_ >= 0 );
CV_Assert( winSize_.width > 2 && winSize_.height > 2 );
CV_Assert( patch.x > 0 && patch.x < 6 && patch.y > 0 && patch.y < 6 );
CV_Assert( prevPts.rows == 1 && prevPts.type() == CV_32FC2 );
if (useInitialFlow_)
CV_Assert( nextPts.size() == prevPts.size() && nextPts.type() == prevPts.type() );
else
ensureSizeIsEnough(1, prevPts.cols, prevPts.type(), nextPts);
GpuMat temp1 = (useInitialFlow_ ? nextPts : prevPts).reshape(1);
GpuMat temp2 = nextPts.reshape(1);
cuda::multiply(temp1, Scalar::all(1.0 / (1 << maxLevel_) / 2.0), temp2, 1, -1, stream);
ensureSizeIsEnough(1, prevPts.cols, CV_8UC1, status);
status.setTo(Scalar::all(1), stream);
if (err)
ensureSizeIsEnough(1, prevPts.cols, CV_32FC1, *err);
// build the image pyramids.
buildImagePyramid(prevImg, prevPyr_, nextImg, nextPyr_, stream);
BufferPool pool(stream);
sparse(prevPyr_, nextPyr_, prevPts, nextPts, status, err, stream);
prevPyr_.resize(maxLevel_ + 1);
nextPyr_.resize(maxLevel_ + 1);
int cn = prevImg.channels();
if (cn == 1 || cn == 4)
{
prevImg.convertTo(prevPyr_[0], CV_32F, stream);
nextImg.convertTo(nextPyr_[0], CV_32F, stream);
}
else
{
GpuMat buf = pool.getBuffer(prevImg.size(), CV_MAKE_TYPE(prevImg.depth(), 4));
cuda::cvtColor(prevImg, buf, COLOR_BGR2BGRA, 0, stream);
buf.convertTo(prevPyr_[0], CV_32F, stream);
cuda::cvtColor(nextImg, buf, COLOR_BGR2BGRA, 0, stream);
buf.convertTo(nextPyr_[0], CV_32F, stream);
}
for (int level = 1; level <= maxLevel_; ++level)
{
cuda::pyrDown(prevPyr_[level - 1], prevPyr_[level], stream);
cuda::pyrDown(nextPyr_[level - 1], nextPyr_[level], stream);
}
pyrlk::loadConstants(make_int2(winSize_.width, winSize_.height), iters_, StreamAccessor::getStream(stream));
for (int level = maxLevel_; level >= 0; level--)
{
if (cn == 1)
{
pyrlk::sparse1(prevPyr_[level], nextPyr_[level],
prevPts.ptr<float2>(), nextPts.ptr<float2>(),
status.ptr(),
level == 0 && err ? err->ptr<float>() : 0, prevPts.cols,
level, block, patch,
StreamAccessor::getStream(stream));
}
else
{
pyrlk::sparse4(prevPyr_[level], nextPyr_[level],
prevPts.ptr<float2>(), nextPts.ptr<float2>(),
status.ptr(),
level == 0 && err ? err->ptr<float>() : 0, prevPts.cols,
level, block, patch,
StreamAccessor::getStream(stream));
}
}
}
void PyrLKOpticalFlowBase::dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, Stream& stream)
@ -250,7 +270,7 @@ namespace
{
int idx2 = (idx + 1) & 1;
pyrlk::dense(prevPyr_[level], nextPyr_[level],
pyrlk::pyrLK_caller<float,1>::dense(prevPyr_[level], nextPyr_[level],
uPyr[idx], vPyr[idx], uPyr[idx2], vPyr[idx2],
PtrStepSzf(), winSize2i,
StreamAccessor::getStream(stream));
@ -289,14 +309,23 @@ namespace
OutputArray _err,
Stream& stream)
{
const GpuMat prevImg = _prevImg.getGpuMat();
const GpuMat nextImg = _nextImg.getGpuMat();
const GpuMat prevPts = _prevPts.getGpuMat();
GpuMat& nextPts = _nextPts.getGpuMatRef();
GpuMat& status = _status.getGpuMatRef();
GpuMat* err = _err.needed() ? &(_err.getGpuMatRef()) : NULL;
sparse(prevImg, nextImg, prevPts, nextPts, status, err, stream);
if (_prevImg.kind() == _InputArray::STD_VECTOR_CUDA_GPU_MAT && _prevImg.kind() == _InputArray::STD_VECTOR_CUDA_GPU_MAT)
{
std::vector<GpuMat> prevPyr, nextPyr;
_prevImg.getGpuMatVector(prevPyr);
_nextImg.getGpuMatVector(nextPyr);
sparse(prevPyr, nextPyr, prevPts, nextPts, status, err, stream);
}
else
{
const GpuMat prevImg = _prevImg.getGpuMat();
const GpuMat nextImg = _nextImg.getGpuMat();
sparse(prevImg, nextImg, prevPts, nextPts, status, err, stream);
}
}
};
@ -347,4 +376,4 @@ Ptr<DensePyrLKOpticalFlow> cv::cuda::DensePyrLKOpticalFlow::create(Size winSize,
return makePtr<DensePyrLKOpticalFlowImpl>(winSize, maxLevel, iters, useInitialFlow);
}
#endif /* !defined (HAVE_CUDA) */
#endif /* !defined (HAVE_CUDA) */

62
modules/cudaoptflow/test/test_optflow.cpp
View File

@ -167,33 +167,34 @@ INSTANTIATE_TEST_CASE_P(CUDA_OptFlow, BroxOpticalFlow, ALL_DEVICES);
namespace
{
IMPLEMENT_PARAM_CLASS(UseGray, bool)
IMPLEMENT_PARAM_CLASS(Chan, int)
IMPLEMENT_PARAM_CLASS(DataType, int)
}
PARAM_TEST_CASE(PyrLKOpticalFlow, cv::cuda::DeviceInfo, UseGray)
PARAM_TEST_CASE(PyrLKOpticalFlow, cv::cuda::DeviceInfo, Chan, DataType)
{
cv::cuda::DeviceInfo devInfo;
bool useGray;
int channels;
int dataType;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
useGray = GET_PARAM(1);
channels = GET_PARAM(1);
dataType = GET_PARAM(2);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(PyrLKOpticalFlow, Sparse)
{
cv::Mat frame0 = readImage("opticalflow/frame0.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
cv::Mat frame0 = readImage("opticalflow/frame0.png", channels == 1 ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
ASSERT_FALSE(frame0.empty());
cv::Mat frame1 = readImage("opticalflow/frame1.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
cv::Mat frame1 = readImage("opticalflow/frame1.png", channels == 1 ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
ASSERT_FALSE(frame1.empty());
cv::Mat gray_frame;
if (useGray)
if (channels == 1)
gray_frame = frame0;
else
cv::cvtColor(frame0, gray_frame, cv::COLOR_BGR2GRAY);
@ -208,22 +209,32 @@ CUDA_TEST_P(PyrLKOpticalFlow, Sparse)
cv::Ptr<cv::cuda::SparsePyrLKOpticalFlow> pyrLK =
cv::cuda::SparsePyrLKOpticalFlow::create();
cv::cuda::GpuMat d_nextPts;
cv::cuda::GpuMat d_status;
pyrLK->calc(loadMat(frame0), loadMat(frame1), d_pts, d_nextPts, d_status);
std::vector<cv::Point2f> nextPts(d_nextPts.cols);
cv::Mat nextPts_mat(1, d_nextPts.cols, CV_32FC2, (void*) &nextPts[0]);
d_nextPts.download(nextPts_mat);
std::vector<unsigned char> status(d_status.cols);
cv::Mat status_mat(1, d_status.cols, CV_8UC1, (void*) &status[0]);
d_status.download(status_mat);
std::vector<cv::Point2f> nextPts_gold;
std::vector<unsigned char> status_gold;
cv::calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts_gold, status_gold, cv::noArray());
cv::cuda::GpuMat d_nextPts;
cv::cuda::GpuMat d_status;
cv::Mat converted0, converted1;
if(channels == 4)
{
cv::cvtColor(frame0, frame0, cv::COLOR_BGR2BGRA);
cv::cvtColor(frame1, frame1, cv::COLOR_BGR2BGRA);
}
frame0.convertTo(converted0, dataType);
frame1.convertTo(converted1, dataType);
pyrLK->calc(loadMat(converted0), loadMat(converted1), d_pts, d_nextPts, d_status);
std::vector<cv::Point2f> nextPts(d_nextPts.cols);
cv::Mat nextPts_mat(1, d_nextPts.cols, CV_32FC2, (void*)&nextPts[0]);
d_nextPts.download(nextPts_mat);
std::vector<unsigned char> status(d_status.cols);
cv::Mat status_mat(1, d_status.cols, CV_8UC1, (void*)&status[0]);
d_status.download(status_mat);
ASSERT_EQ(nextPts_gold.size(), nextPts.size());
ASSERT_EQ(status_gold.size(), status.size());
@ -251,11 +262,16 @@ CUDA_TEST_P(PyrLKOpticalFlow, Sparse)
double bad_ratio = static_cast<double>(mistmatch) / nextPts.size();
ASSERT_LE(bad_ratio, 0.01);
}
INSTANTIATE_TEST_CASE_P(CUDA_OptFlow, PyrLKOpticalFlow, testing::Combine(
ALL_DEVICES,
testing::Values(UseGray(true), UseGray(false))));
testing::Values(Chan(1), Chan(3), Chan(4)),
testing::Values(DataType(CV_8U), DataType(CV_16U), DataType(CV_32S), DataType(CV_32F))));
//////////////////////////////////////////////////////
// FarnebackOpticalFlow
@ -385,4 +401,4 @@ INSTANTIATE_TEST_CASE_P(CUDA_OptFlow, OpticalFlowDual_TVL1, testing::Combine(
ALL_DEVICES,
testing::Values(Gamma(0.0), Gamma(1.0))));
#endif // HAVE_CUDA
#endif // HAVE_CUDA

8
modules/cudawarping/src/cuda/pyr_down.cu
View File

@ -212,10 +212,10 @@ namespace cv { namespace cuda { namespace device
template void pyrDown_gpu<short3>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
template void pyrDown_gpu<short4>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
//template void pyrDown_gpu<int>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
template void pyrDown_gpu<int>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
//template void pyrDown_gpu<int2>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
//template void pyrDown_gpu<int3>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
//template void pyrDown_gpu<int4>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
template void pyrDown_gpu<int3>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
template void pyrDown_gpu<int4>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
template void pyrDown_gpu<float>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
//template void pyrDown_gpu<float2>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
@ -225,4 +225,4 @@ namespace cv { namespace cuda { namespace device
}}} // namespace cv { namespace cuda { namespace cudev
#endif /* CUDA_DISABLER */
#endif /* CUDA_DISABLER */

4
modules/cudawarping/src/pyramids.cpp
View File

@ -74,7 +74,7 @@ void cv::cuda::pyrDown(InputArray _src, OutputArray _dst, Stream& stream)
{0 /*pyrDown_gpu<schar>*/, 0 /*pyrDown_gpu<schar2>*/ , 0 /*pyrDown_gpu<schar3>*/, 0 /*pyrDown_gpu<schar4>*/},
{pyrDown_gpu<ushort> , 0 /*pyrDown_gpu<ushort2>*/, pyrDown_gpu<ushort3> , pyrDown_gpu<ushort4> },
{pyrDown_gpu<short> , 0 /*pyrDown_gpu<short2>*/ , pyrDown_gpu<short3> , pyrDown_gpu<short4> },
{0 /*pyrDown_gpu<int>*/ , 0 /*pyrDown_gpu<int2>*/ , 0 /*pyrDown_gpu<int3>*/ , 0 /*pyrDown_gpu<int4>*/ },
{pyrDown_gpu<int> , 0 /*pyrDown_gpu<int2>*/ , pyrDown_gpu<int3> , pyrDown_gpu<int4> },
{pyrDown_gpu<float> , 0 /*pyrDown_gpu<float2>*/ , pyrDown_gpu<float3> , pyrDown_gpu<float4> }
};
@ -131,4 +131,4 @@ void cv::cuda::pyrUp(InputArray _src, OutputArray _dst, Stream& stream)
func(src, dst, StreamAccessor::getStream(stream));
}
#endif
#endif