mirror of
https://github.com/opencv/opencv.git
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Merge pull request #11060 from juanecito:2.4
* Thread-safe version of sparse function in cv::gpu::PyrLKOpticalFlow class. The new function name is sparse_multi * Thread-safe sparse function in cv::gpu::PyrLKOpticalFlow. Tests * Thread-safe sparse function in cv::gpu::PyrLKOpticalFlow class. Add intel_TBB conditional compilation
This commit is contained in:
Juan María Gómez López
committed by
Alexander Alekhin
Alexander Alekhin
parent
a32aec5ba6
commit
0239c195d8
@@ -49,6 +49,8 @@
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#include "opencv2/gpu/device/vec_math.hpp"
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#include "opencv2/gpu/device/reduce.hpp"
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#include "opencv2/core/core.hpp"
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using namespace cv::gpu;
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using namespace cv::gpu::device;
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@@ -60,12 +62,54 @@ namespace pyrlk
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__constant__ int c_halfWin_y;
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__constant__ int c_iters;
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texture<float, cudaTextureType2D, cudaReadModeElementType> tex_If(false, cudaFilterModeLinear, cudaAddressModeClamp);
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texture<float4, cudaTextureType2D, cudaReadModeElementType> tex_If4(false, cudaFilterModeLinear, cudaAddressModeClamp);
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texture<uchar, cudaTextureType2D, cudaReadModeElementType> tex_Ib(false, cudaFilterModePoint, cudaAddressModeClamp);
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#define CUDA_CONSTANTS(index) \
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__constant__ int c_winSize_x##index; \
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__constant__ int c_winSize_y##index; \
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__constant__ int c_halfWin_x##index; \
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__constant__ int c_halfWin_y##index; \
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__constant__ int c_iters##index;
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texture<float, cudaTextureType2D, cudaReadModeElementType> tex_Jf(false, cudaFilterModeLinear, cudaAddressModeClamp);
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texture<float4, cudaTextureType2D, cudaReadModeElementType> tex_Jf4(false, cudaFilterModeLinear, cudaAddressModeClamp);
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CUDA_CONSTANTS(0)
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CUDA_CONSTANTS(1)
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CUDA_CONSTANTS(2)
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CUDA_CONSTANTS(3)
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CUDA_CONSTANTS(4)
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template <int index> struct c_multi_winSize_x;
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template <int index> struct c_multi_winSize_y;
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template <int index> struct c_multi_halfWin_x;
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template <int index> struct c_multi_halfWin_y;
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template <int index> struct c_multi_iters;
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#define CUDA_CONSTANTS_ACCESSOR(index) \
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template <> struct c_multi_winSize_x<index> \
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{ static __device__ __forceinline__ int get(void){ return c_winSize_x##index;} }; \
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template <> struct c_multi_winSize_y<index> \
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{ static __device__ __forceinline__ int get(void){ return c_winSize_y##index;} }; \
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template <> struct c_multi_halfWin_x<index> \
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{ static __device__ __forceinline__ int get(void){ return c_halfWin_x##index;} }; \
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template <> struct c_multi_halfWin_y<index> \
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{ static __device__ __forceinline__ int get(void){ return c_halfWin_y##index;} }; \
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template <> struct c_multi_iters<index> \
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{ static __device__ __forceinline__ int get(void){ return c_iters##index;} };
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CUDA_CONSTANTS_ACCESSOR(0)
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CUDA_CONSTANTS_ACCESSOR(1)
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CUDA_CONSTANTS_ACCESSOR(2)
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CUDA_CONSTANTS_ACCESSOR(3)
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CUDA_CONSTANTS_ACCESSOR(4)
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texture<float, cudaTextureType2D, cudaReadModeElementType>
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tex_If(false, cudaFilterModeLinear, cudaAddressModeClamp);
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texture<float4, cudaTextureType2D, cudaReadModeElementType>
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tex_If4(false, cudaFilterModeLinear, cudaAddressModeClamp);
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texture<uchar, cudaTextureType2D, cudaReadModeElementType>
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tex_Ib(false, cudaFilterModePoint, cudaAddressModeClamp);
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texture<float, cudaTextureType2D, cudaReadModeElementType>
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tex_Jf(false, cudaFilterModeLinear, cudaAddressModeClamp);
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texture<float4, cudaTextureType2D, cudaReadModeElementType>
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tex_Jf4(false, cudaFilterModeLinear, cudaAddressModeClamp);
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template <int cn> struct Tex_I;
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template <> struct Tex_I<1>
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@@ -99,6 +143,57 @@ namespace pyrlk
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}
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};
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//--------------------------------------------------------------------------
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#define CUDA_DECL_TEX_MULTI(texname, type, filtermode) \
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texture<type, cudaTextureType2D, cudaReadModeElementType> \
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texname##_multi0(false, filtermode, cudaAddressModeClamp); \
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texture<type, cudaTextureType2D, cudaReadModeElementType> \
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texname##_multi1(false, filtermode, cudaAddressModeClamp); \
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texture<type, cudaTextureType2D, cudaReadModeElementType> \
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texname##_multi2(false, filtermode, cudaAddressModeClamp); \
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texture<type, cudaTextureType2D, cudaReadModeElementType> \
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texname##_multi3(false, filtermode, cudaAddressModeClamp); \
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texture<type, cudaTextureType2D, cudaReadModeElementType> \
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texname##_multi4(false, filtermode, cudaAddressModeClamp); \
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CUDA_DECL_TEX_MULTI(tex_If1, float, cudaFilterModeLinear)
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CUDA_DECL_TEX_MULTI(tex_If4, float4, cudaFilterModeLinear)
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CUDA_DECL_TEX_MULTI(tex_Ib1, uchar, cudaFilterModePoint)
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CUDA_DECL_TEX_MULTI(tex_Jf1, float, cudaFilterModeLinear)
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CUDA_DECL_TEX_MULTI(tex_Jf4, float4, cudaFilterModeLinear)
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template <int cn, int index> struct Tex_I_multi;
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template <int cn, int index> struct Tex_J_multi;
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template <int cn, int index> struct Tex_B_multi;
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#define CUDA_DECL_TEX_MULTI_ACCESS(accessorname, texname, cn, returntype) \
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template <> struct accessorname##_multi<cn, 0> \
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{ static __device__ __forceinline__ returntype read(float x, float y) \
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{ return tex2D(texname##cn##_multi0, x, y); } }; \
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template <> struct accessorname##_multi<cn, 1> \
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{ static __device__ __forceinline__ returntype read(float x, float y) \
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{ return tex2D(texname##cn##_multi1, x, y); } }; \
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template <> struct accessorname##_multi<cn, 2> \
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{ static __device__ __forceinline__ returntype read(float x, float y) \
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{ return tex2D(texname##cn##_multi2, x, y); } }; \
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template <> struct accessorname##_multi<cn, 3> \
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{ static __device__ __forceinline__ returntype read(float x, float y) \
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{ return tex2D(texname##cn##_multi3, x, y); } }; \
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template <> struct accessorname##_multi<cn, 4> \
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{ static __device__ __forceinline__ returntype read(float x, float y) \
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{ return tex2D(texname##cn##_multi4, x, y); } };
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CUDA_DECL_TEX_MULTI_ACCESS(Tex_I, tex_If, 1, float)
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CUDA_DECL_TEX_MULTI_ACCESS(Tex_I, tex_If, 4, float4)
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CUDA_DECL_TEX_MULTI_ACCESS(Tex_B, tex_Ib, 1, uchar)
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CUDA_DECL_TEX_MULTI_ACCESS(Tex_J, tex_Jf, 1, float)
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CUDA_DECL_TEX_MULTI_ACCESS(Tex_J, tex_Jf, 4, float4)
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//--------------------------------------------------------------------------
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__device__ __forceinline__ void accum(float& dst, float val)
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{
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dst += val;
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@@ -309,6 +404,200 @@ namespace pyrlk
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}
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}
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#if defined(HAVE_TBB)
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template <int cn, int index, int PATCH_X, int PATCH_Y, bool calcErr>
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__global__ void sparseKernel_multi(const float2* prevPts, float2* nextPts, uchar* status, float* err, const int level, const int rows, const int cols)
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{
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#if __CUDA_ARCH__ <= 110
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const int BLOCK_SIZE = 128;
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#else
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const int BLOCK_SIZE = 256;
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#endif
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__shared__ float smem1[BLOCK_SIZE];
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__shared__ float smem2[BLOCK_SIZE];
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__shared__ float smem3[BLOCK_SIZE];
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const unsigned int tid = threadIdx.y * blockDim.x + threadIdx.x;
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float2 prevPt = prevPts[blockIdx.x];
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prevPt.x *= (1.0f / (1 << level));
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prevPt.y *= (1.0f / (1 << level));
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if (prevPt.x < 0 || prevPt.x >= cols || prevPt.y < 0 || prevPt.y >= rows)
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{
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if (tid == 0 && level == 0)
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status[blockIdx.x] = 0;
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return;
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}
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prevPt.x -= c_multi_halfWin_x<index>::get();
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prevPt.y -= c_multi_halfWin_y<index>::get();
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// extract the patch from the first image, compute covariation matrix of derivatives
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float A11 = 0;
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float A12 = 0;
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float A22 = 0;
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typedef typename TypeVec<float, cn>::vec_type work_type;
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work_type I_patch [PATCH_Y][PATCH_X];
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work_type dIdx_patch[PATCH_Y][PATCH_X];
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work_type dIdy_patch[PATCH_Y][PATCH_X];
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for (int yBase = threadIdx.y, i = 0; yBase < c_multi_winSize_y<index>::get(); yBase += blockDim.y, ++i)
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{
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for (int xBase = threadIdx.x, j = 0; xBase < c_multi_winSize_x<index>::get(); xBase += blockDim.x, ++j)
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{
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float x = prevPt.x + xBase + 0.5f;
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float y = prevPt.y + yBase + 0.5f;
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I_patch[i][j] = Tex_I_multi<cn,index>::read(x, y);
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// Sharr Deriv
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work_type dIdx = 3.0f * Tex_I_multi<cn,index>::read(x+1, y-1) + 10.0f * Tex_I_multi<cn,index>::read(x+1, y) + 3.0f * Tex_I_multi<cn,index>::read(x+1, y+1) -
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(3.0f * Tex_I_multi<cn,index>::read(x-1, y-1) + 10.0f * Tex_I_multi<cn,index>::read(x-1, y) + 3.0f * Tex_I_multi<cn,index>::read(x-1, y+1));
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work_type dIdy = 3.0f * Tex_I_multi<cn,index>::read(x-1, y+1) + 10.0f * Tex_I_multi<cn,index>::read(x, y+1) + 3.0f * Tex_I_multi<cn,index>::read(x+1, y+1) -
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(3.0f * Tex_I_multi<cn,index>::read(x-1, y-1) + 10.0f * Tex_I_multi<cn,index>::read(x, y-1) + 3.0f * Tex_I_multi<cn,index>::read(x+1, y-1));
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dIdx_patch[i][j] = dIdx;
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dIdy_patch[i][j] = dIdy;
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accum(A11, dIdx * dIdx);
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accum(A12, dIdx * dIdy);
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accum(A22, dIdy * dIdy);
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}
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}
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reduce<BLOCK_SIZE>(smem_tuple(smem1, smem2, smem3), thrust::tie(A11, A12, A22), tid, thrust::make_tuple(plus<float>(), plus<float>(), plus<float>()));
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#if __CUDA_ARCH__ >= 300
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if (tid == 0)
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{
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smem1[0] = A11;
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smem2[0] = A12;
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smem3[0] = A22;
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}
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#endif
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__syncthreads();
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A11 = smem1[0];
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A12 = smem2[0];
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A22 = smem3[0];
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float D = A11 * A22 - A12 * A12;
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if (abs_(D) < numeric_limits<float>::epsilon())
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{
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if (tid == 0 && level == 0)
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status[blockIdx.x] = 0;
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return;
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}
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D = 1.f / D;
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A11 *= D;
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A12 *= D;
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A22 *= D;
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float2 nextPt = nextPts[blockIdx.x];
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nextPt.x *= 2.f;
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nextPt.y *= 2.f;
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nextPt.x -= c_multi_halfWin_x<index>::get();
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nextPt.y -= c_multi_halfWin_y<index>::get();
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for (int k = 0; k < c_multi_iters<index>::get(); ++k)
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{
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if (nextPt.x < -c_multi_halfWin_x<index>::get() || nextPt.x >= cols || nextPt.y < -c_multi_halfWin_y<index>::get() || nextPt.y >= rows)
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{
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if (tid == 0 && level == 0)
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status[blockIdx.x] = 0;
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return;
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}
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float b1 = 0;
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float b2 = 0;
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for (int y = threadIdx.y, i = 0; y < c_multi_winSize_y<index>::get(); y += blockDim.y, ++i)
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{
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for (int x = threadIdx.x, j = 0; x < c_multi_winSize_x<index>::get(); x += blockDim.x, ++j)
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{
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work_type I_val = I_patch[i][j];
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work_type J_val = Tex_J_multi<cn,index>::read(nextPt.x + x + 0.5f, nextPt.y + y + 0.5f);
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work_type diff = (J_val - I_val) * 32.0f;
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accum(b1, diff * dIdx_patch[i][j]);
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accum(b2, diff * dIdy_patch[i][j]);
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}
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}
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reduce<BLOCK_SIZE>(smem_tuple(smem1, smem2), thrust::tie(b1, b2), tid, thrust::make_tuple(plus<float>(), plus<float>()));
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#if __CUDA_ARCH__ >= 300
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if (tid == 0)
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{
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smem1[0] = b1;
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smem2[0] = b2;
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}
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#endif
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__syncthreads();
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b1 = smem1[0];
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b2 = smem2[0];
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float2 delta;
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delta.x = A12 * b2 - A22 * b1;
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delta.y = A12 * b1 - A11 * b2;
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nextPt.x += delta.x;
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nextPt.y += delta.y;
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if (::fabs(delta.x) < 0.01f && ::fabs(delta.y) < 0.01f)
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break;
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}
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float errval = 0;
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if (calcErr)
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{
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for (int y = threadIdx.y, i = 0; y < c_multi_winSize_y<index>::get(); y += blockDim.y, ++i)
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{
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for (int x = threadIdx.x, j = 0; x < c_multi_winSize_x<index>::get(); x += blockDim.x, ++j)
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{
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work_type I_val = I_patch[i][j];
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work_type J_val = Tex_J_multi<cn,index>::read(nextPt.x + x + 0.5f, nextPt.y + y + 0.5f);
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work_type diff = J_val - I_val;
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accum(errval, abs_(diff));
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}
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}
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reduce<BLOCK_SIZE>(smem1, errval, tid, plus<float>());
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}
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if (tid == 0)
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{
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nextPt.x += c_multi_halfWin_x<index>::get();
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nextPt.y += c_multi_halfWin_y<index>::get();
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nextPts[blockIdx.x] = nextPt;
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if (calcErr)
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err[blockIdx.x] = static_cast<float>(errval) / (cn * c_multi_winSize_x<index>::get() * c_multi_winSize_y<index>::get());
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}
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}
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#endif // defined(HAVE_TBB)
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template <int cn, int PATCH_X, int PATCH_Y>
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void sparse_caller(int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
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int level, dim3 block, cudaStream_t stream)
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@@ -326,6 +615,26 @@ namespace pyrlk
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cudaSafeCall( cudaDeviceSynchronize() );
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}
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#if defined(HAVE_TBB)
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template <int cn, int index, int PATCH_X, int PATCH_Y>
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void sparse_caller_multi(int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
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int level, dim3 block, cudaStream_t stream)
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{
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dim3 grid(ptcount);
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if (level == 0 && err)
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sparseKernel_multi<cn, index, PATCH_X, PATCH_Y, true><<<grid, block>>>(prevPts, nextPts, status, err, level, rows, cols);
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else
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sparseKernel_multi<cn, index, PATCH_X, PATCH_Y, false><<<grid, block>>>(prevPts, nextPts, status, err, level, rows, cols);
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cudaSafeCall( cudaGetLastError() );
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if (stream == 0)
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cudaSafeCall( cudaDeviceSynchronize() );
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}
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#endif // defined(HAVE_TBB)
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template <bool calcErr>
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__global__ void denseKernel(PtrStepf u, PtrStepf v, const PtrStepf prevU, const PtrStepf prevV, PtrStepf err, const int rows, const int cols)
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{
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@@ -484,6 +793,30 @@ namespace pyrlk
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cudaSafeCall( cudaMemcpyToSymbol(c_iters, &iters, sizeof(int)) );
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}
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#if defined(HAVE_TBB)
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void loadConstants_multi(int2 winSize, int iters, int index, cudaStream_t stream = 0)
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{
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int2 halfWin;
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#define COPY_TO_SYMBOL_CALL(index) \
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cudaSafeCall( cudaMemcpyToSymbolAsync(c_winSize_x##index, &winSize.x, sizeof(int), 0, cudaMemcpyHostToDevice, stream) ); \
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cudaSafeCall( cudaMemcpyToSymbolAsync(c_winSize_y##index, &winSize.y, sizeof(int), 0, cudaMemcpyHostToDevice, stream) ); \
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halfWin = make_int2((winSize.x - 1) / 2, (winSize.y - 1) / 2); \
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cudaSafeCall( cudaMemcpyToSymbolAsync(c_halfWin_x##index, &halfWin.x, sizeof(int), 0, cudaMemcpyHostToDevice, stream) ); \
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cudaSafeCall( cudaMemcpyToSymbolAsync(c_halfWin_y##index, &halfWin.y, sizeof(int), 0, cudaMemcpyHostToDevice, stream) ); \
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cudaSafeCall( cudaMemcpyToSymbolAsync(c_iters##index, &iters, sizeof(int), 0, cudaMemcpyHostToDevice, stream) );
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switch(index)
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{
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case 0: COPY_TO_SYMBOL_CALL(0) break;
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case 1: COPY_TO_SYMBOL_CALL(1) break;
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case 2: COPY_TO_SYMBOL_CALL(2) break;
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case 3: COPY_TO_SYMBOL_CALL(3) break;
|
||||
case 4: COPY_TO_SYMBOL_CALL(4) break;
|
||||
default: CV_Error(CV_StsBadArg, "invalid execution line index"); break;
|
||||
}
|
||||
}
|
||||
#endif // defined(HAVE_TBB)
|
||||
|
||||
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)
|
||||
{
|
||||
@@ -528,6 +861,161 @@ namespace pyrlk
|
||||
level, block, stream);
|
||||
}
|
||||
|
||||
#if defined(HAVE_TBB)
|
||||
void sparse1_multi(PtrStepSzf I, PtrStepSzf J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
|
||||
int level, dim3 block, dim3 patch, cudaStream_t stream, int index)
|
||||
{
|
||||
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][5] =
|
||||
{
|
||||
{ // index 0
|
||||
{sparse_caller_multi<1, 0, 1, 1>, sparse_caller_multi<1, 0, 2, 1>, sparse_caller_multi<1, 0, 3, 1>, sparse_caller_multi<1, 0, 4, 1>, sparse_caller_multi<1, 0, 5, 1>},
|
||||
{sparse_caller_multi<1, 0, 1, 2>, sparse_caller_multi<1, 0, 2, 2>, sparse_caller_multi<1, 0, 3, 2>, sparse_caller_multi<1, 0, 4, 2>, sparse_caller_multi<1, 0, 5, 2>},
|
||||
{sparse_caller_multi<1, 0, 1, 3>, sparse_caller_multi<1, 0, 2, 3>, sparse_caller_multi<1, 0, 3, 3>, sparse_caller_multi<1, 0, 4, 3>, sparse_caller_multi<1, 0, 5, 3>},
|
||||
{sparse_caller_multi<1, 0, 1, 4>, sparse_caller_multi<1, 0, 2, 4>, sparse_caller_multi<1, 0, 3, 4>, sparse_caller_multi<1, 0, 4, 4>, sparse_caller_multi<1, 0, 5, 4>},
|
||||
{sparse_caller_multi<1, 0, 1, 5>, sparse_caller_multi<1, 0, 2, 5>, sparse_caller_multi<1, 0, 3, 5>, sparse_caller_multi<1, 0, 4, 5>, sparse_caller_multi<1, 0, 5, 5>}
|
||||
},
|
||||
{ // index 1
|
||||
{sparse_caller_multi<1, 1, 1, 1>, sparse_caller_multi<1, 1, 2, 1>, sparse_caller_multi<1, 1, 3, 1>, sparse_caller_multi<1, 1, 4, 1>, sparse_caller_multi<1, 1, 5, 1>},
|
||||
{sparse_caller_multi<1, 1, 1, 2>, sparse_caller_multi<1, 1, 2, 2>, sparse_caller_multi<1, 1, 3, 2>, sparse_caller_multi<1, 1, 4, 2>, sparse_caller_multi<1, 1, 5, 2>},
|
||||
{sparse_caller_multi<1, 1, 1, 3>, sparse_caller_multi<1, 1, 2, 3>, sparse_caller_multi<1, 1, 3, 3>, sparse_caller_multi<1, 1, 4, 3>, sparse_caller_multi<1, 1, 5, 3>},
|
||||
{sparse_caller_multi<1, 1, 1, 4>, sparse_caller_multi<1, 1, 2, 4>, sparse_caller_multi<1, 1, 3, 4>, sparse_caller_multi<1, 1, 4, 4>, sparse_caller_multi<1, 1, 5, 4>},
|
||||
{sparse_caller_multi<1, 1, 1, 5>, sparse_caller_multi<1, 1, 2, 5>, sparse_caller_multi<1, 1, 3, 5>, sparse_caller_multi<1, 1, 4, 5>, sparse_caller_multi<1, 1, 5, 5>}
|
||||
},
|
||||
{ // index 2
|
||||
{sparse_caller_multi<1, 2, 1, 1>, sparse_caller_multi<1, 2, 2, 1>, sparse_caller_multi<1, 2, 3, 1>, sparse_caller_multi<1, 2, 4, 1>, sparse_caller_multi<1, 2, 5, 1>},
|
||||
{sparse_caller_multi<1, 2, 1, 2>, sparse_caller_multi<1, 2, 2, 2>, sparse_caller_multi<1, 2, 3, 2>, sparse_caller_multi<1, 2, 4, 2>, sparse_caller_multi<1, 2, 5, 2>},
|
||||
{sparse_caller_multi<1, 2, 1, 3>, sparse_caller_multi<1, 2, 2, 3>, sparse_caller_multi<1, 2, 3, 3>, sparse_caller_multi<1, 2, 4, 3>, sparse_caller_multi<1, 2, 5, 3>},
|
||||
{sparse_caller_multi<1, 2, 1, 4>, sparse_caller_multi<1, 2, 2, 4>, sparse_caller_multi<1, 2, 3, 4>, sparse_caller_multi<1, 2, 4, 4>, sparse_caller_multi<1, 2, 5, 4>},
|
||||
{sparse_caller_multi<1, 2, 1, 5>, sparse_caller_multi<1, 2, 2, 5>, sparse_caller_multi<1, 2, 3, 5>, sparse_caller_multi<1, 2, 4, 5>, sparse_caller_multi<1, 2, 5, 5>}
|
||||
},
|
||||
{ // index 3
|
||||
{sparse_caller_multi<1, 3, 1, 1>, sparse_caller_multi<1, 3, 2, 1>, sparse_caller_multi<1, 3, 3, 1>, sparse_caller_multi<1, 3, 4, 1>, sparse_caller_multi<1, 3, 5, 1>},
|
||||
{sparse_caller_multi<1, 3, 1, 2>, sparse_caller_multi<1, 3, 2, 2>, sparse_caller_multi<1, 3, 3, 2>, sparse_caller_multi<1, 3, 4, 2>, sparse_caller_multi<1, 3, 5, 2>},
|
||||
{sparse_caller_multi<1, 3, 1, 3>, sparse_caller_multi<1, 3, 2, 3>, sparse_caller_multi<1, 3, 3, 3>, sparse_caller_multi<1, 3, 4, 3>, sparse_caller_multi<1, 3, 5, 3>},
|
||||
{sparse_caller_multi<1, 3, 1, 4>, sparse_caller_multi<1, 3, 2, 4>, sparse_caller_multi<1, 3, 3, 4>, sparse_caller_multi<1, 3, 4, 4>, sparse_caller_multi<1, 3, 5, 4>},
|
||||
{sparse_caller_multi<1, 3, 1, 5>, sparse_caller_multi<1, 3, 2, 5>, sparse_caller_multi<1, 3, 3, 5>, sparse_caller_multi<1, 3, 4, 5>, sparse_caller_multi<1, 3, 5, 5>}
|
||||
},
|
||||
{ // index 4
|
||||
{sparse_caller_multi<1, 4, 1, 1>, sparse_caller_multi<1, 4, 2, 1>, sparse_caller_multi<1, 4, 3, 1>, sparse_caller_multi<1, 4, 4, 1>, sparse_caller_multi<1, 4, 5, 1>},
|
||||
{sparse_caller_multi<1, 4, 1, 2>, sparse_caller_multi<1, 4, 2, 2>, sparse_caller_multi<1, 4, 3, 2>, sparse_caller_multi<1, 4, 4, 2>, sparse_caller_multi<1, 4, 5, 2>},
|
||||
{sparse_caller_multi<1, 4, 1, 3>, sparse_caller_multi<1, 4, 2, 3>, sparse_caller_multi<1, 4, 3, 3>, sparse_caller_multi<1, 4, 4, 3>, sparse_caller_multi<1, 4, 5, 3>},
|
||||
{sparse_caller_multi<1, 4, 1, 4>, sparse_caller_multi<1, 4, 2, 4>, sparse_caller_multi<1, 4, 3, 4>, sparse_caller_multi<1, 4, 4, 4>, sparse_caller_multi<1, 4, 5, 4>},
|
||||
{sparse_caller_multi<1, 4, 1, 5>, sparse_caller_multi<1, 4, 2, 5>, sparse_caller_multi<1, 4, 3, 5>, sparse_caller_multi<1, 4, 4, 5>, sparse_caller_multi<1, 4, 5, 5>}
|
||||
}
|
||||
};
|
||||
|
||||
switch(index)
|
||||
{
|
||||
case 0:
|
||||
bindTexture(&tex_If1_multi0, I);
|
||||
bindTexture(&tex_Jf1_multi0, J);
|
||||
break;
|
||||
case 1:
|
||||
bindTexture(&tex_If1_multi1, I);
|
||||
bindTexture(&tex_Jf1_multi1, J);
|
||||
break;
|
||||
case 2:
|
||||
bindTexture(&tex_If1_multi2, I);
|
||||
bindTexture(&tex_Jf1_multi2, J);
|
||||
break;
|
||||
case 3:
|
||||
bindTexture(&tex_If1_multi3, I);
|
||||
bindTexture(&tex_Jf1_multi3, J);
|
||||
break;
|
||||
case 4:
|
||||
bindTexture(&tex_If1_multi4, I);
|
||||
bindTexture(&tex_Jf1_multi4, J);
|
||||
break;
|
||||
default:
|
||||
CV_Error(CV_StsBadArg, "invalid execution line index");
|
||||
break;
|
||||
}
|
||||
|
||||
funcs[index][patch.y - 1][patch.x - 1](I.rows, I.cols, prevPts, nextPts, status, err, ptcount,
|
||||
level, block, stream);
|
||||
}
|
||||
|
||||
void sparse4_multi(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, int index)
|
||||
{
|
||||
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][5] =
|
||||
{
|
||||
{ // index 0
|
||||
{sparse_caller_multi<4, 0, 1, 1>, sparse_caller_multi<4, 0, 2, 1>, sparse_caller_multi<4, 0, 3, 1>, sparse_caller_multi<4, 0, 4, 1>, sparse_caller_multi<4, 0, 5, 1>},
|
||||
{sparse_caller_multi<4, 0, 1, 2>, sparse_caller_multi<4, 0, 2, 2>, sparse_caller_multi<4, 0, 3, 2>, sparse_caller_multi<4, 0, 4, 2>, sparse_caller_multi<4, 0, 5, 2>},
|
||||
{sparse_caller_multi<4, 0, 1, 3>, sparse_caller_multi<4, 0, 2, 3>, sparse_caller_multi<4, 0, 3, 3>, sparse_caller_multi<4, 0, 4, 3>, sparse_caller_multi<4, 0, 5, 3>},
|
||||
{sparse_caller_multi<4, 0, 1, 4>, sparse_caller_multi<4, 0, 2, 4>, sparse_caller_multi<4, 0, 3, 4>, sparse_caller_multi<4, 0, 4, 4>, sparse_caller_multi<4, 0, 5, 4>},
|
||||
{sparse_caller_multi<4, 0, 1, 5>, sparse_caller_multi<4, 0, 2, 5>, sparse_caller_multi<4, 0, 3, 5>, sparse_caller_multi<4, 0, 4, 5>, sparse_caller_multi<4, 0, 5, 5>}
|
||||
},
|
||||
{ // index 1
|
||||
{sparse_caller_multi<4, 1, 1, 1>, sparse_caller_multi<4, 1, 2, 1>, sparse_caller_multi<4, 1, 3, 1>, sparse_caller_multi<4, 1, 4, 1>, sparse_caller_multi<4, 1, 5, 1>},
|
||||
{sparse_caller_multi<4, 1, 1, 2>, sparse_caller_multi<4, 1, 2, 2>, sparse_caller_multi<4, 1, 3, 2>, sparse_caller_multi<4, 1, 4, 2>, sparse_caller_multi<4, 1, 5, 2>},
|
||||
{sparse_caller_multi<4, 1, 1, 3>, sparse_caller_multi<4, 1, 2, 3>, sparse_caller_multi<4, 1, 3, 3>, sparse_caller_multi<4, 1, 4, 3>, sparse_caller_multi<4, 1, 5, 3>},
|
||||
{sparse_caller_multi<4, 1, 1, 4>, sparse_caller_multi<4, 1, 2, 4>, sparse_caller_multi<4, 1, 3, 4>, sparse_caller_multi<4, 1, 4, 4>, sparse_caller_multi<4, 1, 5, 4>},
|
||||
{sparse_caller_multi<4, 1, 1, 5>, sparse_caller_multi<4, 1, 2, 5>, sparse_caller_multi<4, 1, 3, 5>, sparse_caller_multi<4, 1, 4, 5>, sparse_caller_multi<4, 1, 5, 5>}
|
||||
},
|
||||
{ // index 2
|
||||
{sparse_caller_multi<4, 2, 1, 1>, sparse_caller_multi<4, 2, 2, 1>, sparse_caller_multi<4, 2, 3, 1>, sparse_caller_multi<4, 2, 4, 1>, sparse_caller_multi<4, 2, 5, 1>},
|
||||
{sparse_caller_multi<4, 2, 1, 2>, sparse_caller_multi<4, 2, 2, 2>, sparse_caller_multi<4, 2, 3, 2>, sparse_caller_multi<4, 2, 4, 2>, sparse_caller_multi<4, 2, 5, 2>},
|
||||
{sparse_caller_multi<4, 2, 1, 3>, sparse_caller_multi<4, 2, 2, 3>, sparse_caller_multi<4, 2, 3, 3>, sparse_caller_multi<4, 2, 4, 3>, sparse_caller_multi<4, 2, 5, 3>},
|
||||
{sparse_caller_multi<4, 2, 1, 4>, sparse_caller_multi<4, 2, 2, 4>, sparse_caller_multi<4, 2, 3, 4>, sparse_caller_multi<4, 2, 4, 4>, sparse_caller_multi<4, 2, 5, 4>},
|
||||
{sparse_caller_multi<4, 2, 1, 5>, sparse_caller_multi<4, 2, 2, 5>, sparse_caller_multi<4, 2, 3, 5>, sparse_caller_multi<4, 2, 4, 5>, sparse_caller_multi<4, 2, 5, 5>}
|
||||
},
|
||||
{ // index 3
|
||||
{sparse_caller_multi<4, 3, 1, 1>, sparse_caller_multi<4, 3, 2, 1>, sparse_caller_multi<4, 3, 3, 1>, sparse_caller_multi<4, 3, 4, 1>, sparse_caller_multi<4, 3, 5, 1>},
|
||||
{sparse_caller_multi<4, 3, 1, 2>, sparse_caller_multi<4, 3, 2, 2>, sparse_caller_multi<4, 3, 3, 2>, sparse_caller_multi<4, 3, 4, 2>, sparse_caller_multi<4, 3, 5, 2>},
|
||||
{sparse_caller_multi<4, 3, 1, 3>, sparse_caller_multi<4, 3, 2, 3>, sparse_caller_multi<4, 3, 3, 3>, sparse_caller_multi<4, 3, 4, 3>, sparse_caller_multi<4, 3, 5, 3>},
|
||||
{sparse_caller_multi<4, 3, 1, 4>, sparse_caller_multi<4, 3, 2, 4>, sparse_caller_multi<4, 3, 3, 4>, sparse_caller_multi<4, 3, 4, 4>, sparse_caller_multi<4, 3, 5, 4>},
|
||||
{sparse_caller_multi<4, 3, 1, 5>, sparse_caller_multi<4, 3, 2, 5>, sparse_caller_multi<4, 3, 3, 5>, sparse_caller_multi<4, 3, 4, 5>, sparse_caller_multi<4, 3, 5, 5>}
|
||||
},
|
||||
{ // index 4
|
||||
{sparse_caller_multi<4, 4, 1, 1>, sparse_caller_multi<4, 4, 2, 1>, sparse_caller_multi<4, 4, 3, 1>, sparse_caller_multi<4, 4, 4, 1>, sparse_caller_multi<4, 4, 5, 1>},
|
||||
{sparse_caller_multi<4, 4, 1, 2>, sparse_caller_multi<4, 4, 2, 2>, sparse_caller_multi<4, 4, 3, 2>, sparse_caller_multi<4, 4, 4, 2>, sparse_caller_multi<4, 4, 5, 2>},
|
||||
{sparse_caller_multi<4, 4, 1, 3>, sparse_caller_multi<4, 4, 2, 3>, sparse_caller_multi<4, 4, 3, 3>, sparse_caller_multi<4, 4, 4, 3>, sparse_caller_multi<4, 4, 5, 3>},
|
||||
{sparse_caller_multi<4, 4, 1, 4>, sparse_caller_multi<4, 4, 2, 4>, sparse_caller_multi<4, 4, 3, 4>, sparse_caller_multi<4, 4, 4, 4>, sparse_caller_multi<4, 4, 5, 4>},
|
||||
{sparse_caller_multi<4, 4, 1, 5>, sparse_caller_multi<4, 4, 2, 5>, sparse_caller_multi<4, 4, 3, 5>, sparse_caller_multi<4, 4, 4, 5>, sparse_caller_multi<4, 4, 5, 5>}
|
||||
}
|
||||
};
|
||||
|
||||
switch(index)
|
||||
{
|
||||
case 0:
|
||||
bindTexture(&tex_If4_multi0, I);
|
||||
bindTexture(&tex_Jf4_multi0, J);
|
||||
break;
|
||||
case 1:
|
||||
bindTexture(&tex_If4_multi1, I);
|
||||
bindTexture(&tex_Jf4_multi1, J);
|
||||
break;
|
||||
case 2:
|
||||
bindTexture(&tex_If4_multi2, I);
|
||||
bindTexture(&tex_Jf4_multi2, J);
|
||||
break;
|
||||
case 3:
|
||||
bindTexture(&tex_If4_multi3, I);
|
||||
bindTexture(&tex_Jf4_multi3, J);
|
||||
break;
|
||||
case 4:
|
||||
bindTexture(&tex_If4_multi4, I);
|
||||
bindTexture(&tex_Jf4_multi4, J);
|
||||
break;
|
||||
default:
|
||||
CV_Error(CV_StsBadArg, "invalid execution line index");
|
||||
break;
|
||||
}
|
||||
|
||||
funcs[index][patch.y - 1][patch.x - 1](I.rows, I.cols, prevPts, nextPts, status, err, ptcount,
|
||||
level, block, stream);
|
||||
}
|
||||
|
||||
#endif // defined(HAVE_TBB)
|
||||
|
||||
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);
|
||||
|
||||
+148
-1
@@ -42,6 +42,11 @@
|
||||
|
||||
#include "precomp.hpp"
|
||||
|
||||
#ifdef HAVE_TBB
|
||||
#include <tbb/compat/condition_variable>
|
||||
#include <tbb/mutex.h>
|
||||
#endif
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
using namespace cv::gpu;
|
||||
@@ -64,6 +69,22 @@ namespace pyrlk
|
||||
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 = 0);
|
||||
|
||||
|
||||
#if !defined(HAVE_TBB)
|
||||
void loadConstants_multi(int2, int, int, cudaStream_t) { throw_notbb(); }
|
||||
void sparse1_multi(PtrStepSzf, PtrStepSzf, const float2*, float2*, uchar*, float*, int,
|
||||
int, dim3, dim3, cudaStream_t, int) { throw_notbb(); }
|
||||
void sparse4_multi(PtrStepSz<float4>, PtrStepSz<float4>, const float2*, float2*, uchar*, float*, int,
|
||||
int, dim3, dim3, cudaStream_t, int) { throw_notbb(); }
|
||||
#else
|
||||
void loadConstants_multi(int2 winSize, int iters, int index = 0, cudaStream_t stream = 0);
|
||||
|
||||
void sparse1_multi(PtrStepSzf I, PtrStepSzf J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
|
||||
int level, dim3 block, dim3 patch, cudaStream_t stream = 0, int index = 0);
|
||||
void sparse4_multi(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 = 0, int index = 0);
|
||||
#endif
|
||||
|
||||
void dense(PtrStepSzb I, PtrStepSzf J, PtrStepSzf u, PtrStepSzf v, PtrStepSzf prevU, PtrStepSzf prevV,
|
||||
PtrStepSzf err, int2 winSize, cudaStream_t stream = 0);
|
||||
}
|
||||
@@ -98,7 +119,9 @@ namespace
|
||||
}
|
||||
}
|
||||
|
||||
void cv::gpu::PyrLKOpticalFlow::sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts, GpuMat& status, GpuMat* err)
|
||||
void cv::gpu::PyrLKOpticalFlow::sparse(const GpuMat& prevImg,
|
||||
const GpuMat& nextImg, const GpuMat& prevPts,
|
||||
GpuMat& nextPts, GpuMat& status, GpuMat* err)
|
||||
{
|
||||
if (prevPts.empty())
|
||||
{
|
||||
@@ -181,6 +204,130 @@ void cv::gpu::PyrLKOpticalFlow::sparse(const GpuMat& prevImg, const GpuMat& next
|
||||
}
|
||||
}
|
||||
|
||||
#ifdef HAVE_TBB
|
||||
//--------------------------------------------------------------------------
|
||||
// Multi-threading support
|
||||
|
||||
static bool index_vector_use[5] = {true, true, true, true, true}; // all free
|
||||
static tbb::mutex s_PyrLKOpticalFlow_Mutex;
|
||||
static condition_variable s_PyrLKOpticalFlow_ConditionVariable;
|
||||
|
||||
void cv::gpu::PyrLKOpticalFlow::sparse_multi(const GpuMat& prevImg,
|
||||
const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts,
|
||||
GpuMat& status, Stream& stream, GpuMat* err)
|
||||
{
|
||||
if (prevPts.empty())
|
||||
{
|
||||
nextPts.release();
|
||||
status.release();
|
||||
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() == CV_32FC2);
|
||||
else
|
||||
ensureSizeIsEnough(1, prevPts.cols, prevPts.type(), nextPts);
|
||||
|
||||
GpuMat temp1 = (useInitialFlow ? nextPts : prevPts).reshape(1);
|
||||
GpuMat temp2 = nextPts.reshape(1);
|
||||
multiply(temp1, Scalar::all(1.0 / (1 << maxLevel) / 2.0), temp2);
|
||||
|
||||
ensureSizeIsEnough(1, prevPts.cols, CV_8UC1, status);
|
||||
status.setTo(Scalar::all(1));
|
||||
|
||||
if (err)
|
||||
ensureSizeIsEnough(1, prevPts.cols, CV_32FC1, *err);
|
||||
|
||||
// build the image pyramids.
|
||||
|
||||
prevPyr_.resize(maxLevel + 1);
|
||||
nextPyr_.resize(maxLevel + 1);
|
||||
|
||||
int cn = prevImg.channels();
|
||||
|
||||
if (cn == 1 || cn == 4)
|
||||
{
|
||||
prevImg.convertTo(prevPyr_[0], CV_32F);
|
||||
nextImg.convertTo(nextPyr_[0], CV_32F);
|
||||
}
|
||||
else
|
||||
{
|
||||
buf_.resize(1);
|
||||
|
||||
cvtColor(prevImg, buf_[0], COLOR_BGR2BGRA);
|
||||
buf_[0].convertTo(prevPyr_[0], CV_32F);
|
||||
|
||||
cvtColor(nextImg, buf_[0], COLOR_BGR2BGRA);
|
||||
buf_[0].convertTo(nextPyr_[0], CV_32F);
|
||||
}
|
||||
|
||||
for (int level = 1; level <= maxLevel; ++level)
|
||||
{
|
||||
pyrDown(prevPyr_[level - 1], prevPyr_[level]);
|
||||
pyrDown(nextPyr_[level - 1], nextPyr_[level]);
|
||||
}
|
||||
|
||||
//--------------------------------------------------------------------------
|
||||
// Multithreading support
|
||||
int index = -1;
|
||||
|
||||
do
|
||||
{
|
||||
unique_lock<tbb::mutex> ul(s_PyrLKOpticalFlow_Mutex);
|
||||
for (unsigned int uiI = 0; uiI < 5; ++uiI)
|
||||
{
|
||||
if (index_vector_use[uiI])
|
||||
{
|
||||
index = uiI;
|
||||
index_vector_use[uiI] = false;
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (index < 0)
|
||||
s_PyrLKOpticalFlow_ConditionVariable.wait(ul);
|
||||
|
||||
ul.unlock();
|
||||
}while (index < 0);
|
||||
|
||||
//--------------------------------------------------------------------------
|
||||
|
||||
pyrlk::loadConstants_multi(make_int2(winSize.width, winSize.height), iters, index);
|
||||
|
||||
for (int level = maxLevel; level >= 0; level--)
|
||||
{
|
||||
if (cn == 1)
|
||||
{
|
||||
pyrlk::sparse1_multi(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), index);
|
||||
}
|
||||
else
|
||||
{
|
||||
pyrlk::sparse4_multi(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), index);
|
||||
}
|
||||
}
|
||||
|
||||
unique_lock<tbb::mutex> ul(s_PyrLKOpticalFlow_Mutex);
|
||||
index_vector_use[index] = true;
|
||||
s_PyrLKOpticalFlow_ConditionVariable.notify_one();
|
||||
}
|
||||
#endif
|
||||
|
||||
void cv::gpu::PyrLKOpticalFlow::dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err)
|
||||
{
|
||||
CV_Assert(prevImg.type() == CV_8UC1);
|
||||
|
||||
Reference in New Issue
Block a user