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

modules/gpu/include/opencv2/gpu/gpu.hpp

@@ -60,6 +60,10 @@
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
 #endif
 
+#if !defined(HAVE_TBB)
+    #define throw_notbb() CV_Error(CV_StsNotImplemented, "The library is compiled without TBB support")
+#endif
+
 namespace cv { namespace gpu {
 
 //////////////////////////////// CudaMem ////////////////////////////////
@@ -1824,6 +1828,14 @@ public:
     void sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts,
         GpuMat& status, GpuMat* err = 0);
 
+#if !defined(HAVE_TBB)
+    void sparse_multi(const GpuMat&, const GpuMat&, const GpuMat&, GpuMat&,
+        GpuMat&, Stream&, GpuMat*) {throw_notbb();}
+#else
+    void sparse_multi(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts,
+        GpuMat& status, Stream& stream, GpuMat* err = 0);
+#endif
+
     void dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err = 0);
 
     void releaseMemory();

modules/gpu/perf/perf_video.cpp

@@ -303,6 +303,88 @@ PERF_TEST_P(ImagePair_Gray_NPts_WinSz_Levels_Iters, Video_PyrLKOpticalFlowSparse
     }
 }
 
+//////////////////////////////////////////////////////
+// PyrLKOpticalFlowSparseMulti
+
+#ifdef HAVE_TBB
+
+DEF_PARAM_TEST(ImagePair_Gray_NPts_WinSz_Levels_Iters, pair_string, bool, int, int, int, int);
+
+PERF_TEST_P(ImagePair_Gray_NPts_WinSz_Levels_Iters, Video_PyrLKOpticalFlowSparseMulti,
+            Combine(Values<pair_string>(make_pair("gpu/opticalflow/frame0.png", "gpu/opticalflow/frame1.png")),
+                    Bool(),
+                    Values(8000),
+                    Values(21),
+                    Values(1, 3),
+                    Values(1, 30)))
+{
+    declare.time(20.0);
+
+    const pair_string imagePair = GET_PARAM(0);
+    const bool useGray = GET_PARAM(1);
+    const int points = GET_PARAM(2);
+    const int winSize = GET_PARAM(3);
+    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);
+    ASSERT_FALSE(frame0.empty());
+
+    const cv::Mat frame1 = readImage(imagePair.second, useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
+    ASSERT_FALSE(frame1.empty());
+
+    cv::Mat gray_frame;
+    if (useGray)
+        gray_frame = frame0;
+    else
+        cv::cvtColor(frame0, gray_frame, cv::COLOR_BGR2GRAY);
+
+    cv::Mat pts;
+    cv::goodFeaturesToTrack(gray_frame, pts, points, 0.01, 0.0);
+
+    if (PERF_RUN_GPU())
+    {
+        const cv::gpu::GpuMat d_pts(pts.reshape(2, 1));
+
+        cv::gpu::PyrLKOpticalFlow d_pyrLK;
+        d_pyrLK.winSize = cv::Size(winSize, winSize);
+        d_pyrLK.maxLevel = levels - 1;
+        d_pyrLK.iters = iters;
+
+        const cv::gpu::GpuMat d_frame0(frame0);
+        const cv::gpu::GpuMat d_frame1(frame1);
+        cv::gpu::GpuMat nextPts;
+        cv::gpu::GpuMat status;
+
+        cv::gpu::Stream stream;
+        TEST_CYCLE()
+        {
+            d_pyrLK.sparse_multi(d_frame0, d_frame1, d_pts, nextPts, status, stream);
+            stream.waitForCompletion();
+        }
+
+        GPU_SANITY_CHECK(nextPts);
+        GPU_SANITY_CHECK(status);
+    }
+    else
+    {
+        cv::Mat nextPts;
+        cv::Mat status;
+
+        TEST_CYCLE()
+        {
+            cv::calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts, status, cv::noArray(),
+                                     cv::Size(winSize, winSize), levels - 1,
+                                     cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, iters, 0.01));
+        }
+
+        CPU_SANITY_CHECK(nextPts);
+        CPU_SANITY_CHECK(status);
+    }
+}
+
+#endif // HAVE_TBB
+
 //////////////////////////////////////////////////////
 // PyrLKOpticalFlowDense
 

modules/gpu/src/cuda/pyrlk.cu

@@ -49,6 +49,8 @@
 #include "opencv2/gpu/device/vec_math.hpp"
 #include "opencv2/gpu/device/reduce.hpp"
 
+#include "opencv2/core/core.hpp"
+
 using namespace cv::gpu;
 using namespace cv::gpu::device;
 
@@ -60,12 +62,54 @@ namespace pyrlk
     __constant__ int c_halfWin_y;
     __constant__ int c_iters;
 
-    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<float, cudaTextureType2D, cudaReadModeElementType> tex_Jf(false, cudaFilterModeLinear, cudaAddressModeClamp);
-    texture<float4, cudaTextureType2D, cudaReadModeElementType> tex_Jf4(false, cudaFilterModeLinear, cudaAddressModeClamp);
+#define CUDA_CONSTANTS(index) \
+    __constant__ int c_winSize_x##index; \
+    __constant__ int c_winSize_y##index; \
+    __constant__ int c_halfWin_x##index; \
+    __constant__ int c_halfWin_y##index; \
+    __constant__ int c_iters##index;
+
+    CUDA_CONSTANTS(0)
+    CUDA_CONSTANTS(1)
+    CUDA_CONSTANTS(2)
+    CUDA_CONSTANTS(3)
+    CUDA_CONSTANTS(4)
+
+    template <int index> struct c_multi_winSize_x;
+    template <int index> struct c_multi_winSize_y;
+    template <int index> struct c_multi_halfWin_x;
+    template <int index> struct c_multi_halfWin_y;
+    template <int index> struct c_multi_iters;
+
+#define CUDA_CONSTANTS_ACCESSOR(index) \
+    template <> struct c_multi_winSize_x<index> \
+    { static __device__ __forceinline__ int get(void){ return c_winSize_x##index;} }; \
+    template <> struct c_multi_winSize_y<index> \
+    { static __device__ __forceinline__ int get(void){ return c_winSize_y##index;} }; \
+    template <> struct c_multi_halfWin_x<index> \
+    { static __device__ __forceinline__ int get(void){ return c_halfWin_x##index;} }; \
+    template <> struct c_multi_halfWin_y<index> \
+    { static __device__ __forceinline__ int get(void){ return c_halfWin_y##index;} }; \
+    template <> struct c_multi_iters<index> \
+    { static __device__ __forceinline__ int get(void){ return c_iters##index;} };
+
+    CUDA_CONSTANTS_ACCESSOR(0)
+    CUDA_CONSTANTS_ACCESSOR(1)
+    CUDA_CONSTANTS_ACCESSOR(2)
+    CUDA_CONSTANTS_ACCESSOR(3)
+    CUDA_CONSTANTS_ACCESSOR(4)
+
+    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<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>
@@ -99,6 +143,57 @@ namespace pyrlk
         }
     };
 
+    //--------------------------------------------------------------------------
+
+#define CUDA_DECL_TEX_MULTI(texname, type, filtermode) \
+    texture<type, cudaTextureType2D, cudaReadModeElementType> \
+            texname##_multi0(false, filtermode, cudaAddressModeClamp); \
+    texture<type, cudaTextureType2D, cudaReadModeElementType> \
+            texname##_multi1(false, filtermode, cudaAddressModeClamp); \
+    texture<type, cudaTextureType2D, cudaReadModeElementType> \
+            texname##_multi2(false, filtermode, cudaAddressModeClamp); \
+    texture<type, cudaTextureType2D, cudaReadModeElementType> \
+            texname##_multi3(false, filtermode, cudaAddressModeClamp); \
+    texture<type, cudaTextureType2D, cudaReadModeElementType> \
+            texname##_multi4(false, filtermode, cudaAddressModeClamp); \
+
+    CUDA_DECL_TEX_MULTI(tex_If1, float, cudaFilterModeLinear)
+    CUDA_DECL_TEX_MULTI(tex_If4, float4, cudaFilterModeLinear)
+    CUDA_DECL_TEX_MULTI(tex_Ib1, uchar, cudaFilterModePoint)
+    CUDA_DECL_TEX_MULTI(tex_Jf1, float, cudaFilterModeLinear)
+    CUDA_DECL_TEX_MULTI(tex_Jf4, float4, cudaFilterModeLinear)
+
+    template <int cn, int index> struct Tex_I_multi;
+    template <int cn, int index> struct Tex_J_multi;
+    template <int cn, int index> struct Tex_B_multi;
+
+#define CUDA_DECL_TEX_MULTI_ACCESS(accessorname, texname, cn, returntype) \
+    template <> struct accessorname##_multi<cn, 0> \
+    { static __device__ __forceinline__ returntype read(float x, float y) \
+        { return tex2D(texname##cn##_multi0, x, y); } }; \
+    template <> struct accessorname##_multi<cn, 1> \
+    { static __device__ __forceinline__ returntype read(float x, float y) \
+        { return tex2D(texname##cn##_multi1, x, y); } }; \
+    template <> struct accessorname##_multi<cn, 2> \
+    { static __device__ __forceinline__ returntype read(float x, float y) \
+        { return tex2D(texname##cn##_multi2, x, y); } }; \
+    template <> struct accessorname##_multi<cn, 3> \
+    { static __device__ __forceinline__ returntype read(float x, float y) \
+        { return tex2D(texname##cn##_multi3, x, y); } }; \
+    template <> struct accessorname##_multi<cn, 4> \
+    { static __device__ __forceinline__ returntype read(float x, float y) \
+        { return tex2D(texname##cn##_multi4, x, y); } };
+
+    CUDA_DECL_TEX_MULTI_ACCESS(Tex_I, tex_If, 1, float)
+    CUDA_DECL_TEX_MULTI_ACCESS(Tex_I, tex_If, 4, float4)
+
+    CUDA_DECL_TEX_MULTI_ACCESS(Tex_B, tex_Ib, 1, uchar)
+
+    CUDA_DECL_TEX_MULTI_ACCESS(Tex_J, tex_Jf, 1, float)
+    CUDA_DECL_TEX_MULTI_ACCESS(Tex_J, tex_Jf, 4, float4)
+
+    //--------------------------------------------------------------------------
+
     __device__ __forceinline__ void accum(float& dst, float val)
     {
         dst += val;
@@ -309,6 +404,200 @@ namespace pyrlk
         }
     }
 
+#if defined(HAVE_TBB)
+    template <int cn, int index, int PATCH_X, int PATCH_Y, bool calcErr>
+    __global__ void sparseKernel_multi(const float2* prevPts, float2* nextPts, uchar* status, float* err, const int level, const int rows, const int cols)
+    {
+    #if __CUDA_ARCH__ <= 110
+        const int BLOCK_SIZE = 128;
+    #else
+        const int BLOCK_SIZE = 256;
+    #endif
+
+        __shared__ float smem1[BLOCK_SIZE];
+        __shared__ float smem2[BLOCK_SIZE];
+        __shared__ float smem3[BLOCK_SIZE];
+
+        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_multi_halfWin_x<index>::get();
+        prevPt.y -= c_multi_halfWin_y<index>::get();
+
+        // 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_multi_winSize_y<index>::get(); yBase += blockDim.y, ++i)
+        {
+            for (int xBase = threadIdx.x, j = 0; xBase < c_multi_winSize_x<index>::get(); xBase += blockDim.x, ++j)
+            {
+                float x = prevPt.x + xBase + 0.5f;
+                float y = prevPt.y + yBase + 0.5f;
+
+                I_patch[i][j] = Tex_I_multi<cn,index>::read(x, y);
+
+                // Sharr Deriv
+
+                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) -
+                                 (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));
+
+                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) -
+                                (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));
+
+                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 (abs_(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_multi_halfWin_x<index>::get();
+        nextPt.y -= c_multi_halfWin_y<index>::get();
+
+        for (int k = 0; k < c_multi_iters<index>::get(); ++k)
+        {
+            if (nextPt.x < -c_multi_halfWin_x<index>::get() || nextPt.x >= cols || nextPt.y < -c_multi_halfWin_y<index>::get() || 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_multi_winSize_y<index>::get(); y += blockDim.y, ++i)
+            {
+                for (int x = threadIdx.x, j = 0; x < c_multi_winSize_x<index>::get(); x += blockDim.x, ++j)
+                {
+                    work_type I_val = I_patch[i][j];
+                    work_type J_val = Tex_J_multi<cn,index>::read(nextPt.x + x + 0.5f, nextPt.y + y + 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_multi_winSize_y<index>::get(); y += blockDim.y, ++i)
+            {
+                for (int x = threadIdx.x, j = 0; x < c_multi_winSize_x<index>::get(); x += blockDim.x, ++j)
+                {
+                    work_type I_val = I_patch[i][j];
+                    work_type J_val = Tex_J_multi<cn,index>::read(nextPt.x + x + 0.5f, nextPt.y + y + 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_multi_halfWin_x<index>::get();
+            nextPt.y += c_multi_halfWin_y<index>::get();
+
+            nextPts[blockIdx.x] = nextPt;
+
+            if (calcErr)
+                err[blockIdx.x] = static_cast<float>(errval) / (cn * c_multi_winSize_x<index>::get() * c_multi_winSize_y<index>::get());
+        }
+    }
+#endif // defined(HAVE_TBB)
+
     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)
@@ -326,6 +615,26 @@ namespace pyrlk
             cudaSafeCall( cudaDeviceSynchronize() );
     }
 
+#if defined(HAVE_TBB)
+    template <int cn, int index, int PATCH_X, int PATCH_Y>
+    void sparse_caller_multi(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_multi<cn, index, PATCH_X, PATCH_Y, true><<<grid, block>>>(prevPts, nextPts, status, err, level, rows, cols);
+        else
+            sparseKernel_multi<cn, index, PATCH_X, PATCH_Y, false><<<grid, block>>>(prevPts, nextPts, status, err, level, rows, cols);
+
+        cudaSafeCall( cudaGetLastError() );
+
+        if (stream == 0)
+            cudaSafeCall( cudaDeviceSynchronize() );
+    }
+
+#endif // defined(HAVE_TBB)
+
     template <bool calcErr>
     __global__ void denseKernel(PtrStepf u, PtrStepf v, const PtrStepf prevU, const PtrStepf prevV, PtrStepf err, const int rows, const int cols)
     {
@@ -484,6 +793,30 @@ namespace pyrlk
         cudaSafeCall( cudaMemcpyToSymbol(c_iters, &iters, sizeof(int)) );
     }
 
+#if defined(HAVE_TBB)
+    void loadConstants_multi(int2 winSize, int iters, int index, cudaStream_t stream = 0)
+    {
+        int2 halfWin;
+#define COPY_TO_SYMBOL_CALL(index) \
+        cudaSafeCall( cudaMemcpyToSymbolAsync(c_winSize_x##index, &winSize.x, sizeof(int), 0, cudaMemcpyHostToDevice, stream) ); \
+        cudaSafeCall( cudaMemcpyToSymbolAsync(c_winSize_y##index, &winSize.y, sizeof(int), 0, cudaMemcpyHostToDevice, stream) ); \
+        halfWin = make_int2((winSize.x - 1) / 2, (winSize.y - 1) / 2); \
+        cudaSafeCall( cudaMemcpyToSymbolAsync(c_halfWin_x##index, &halfWin.x, sizeof(int), 0, cudaMemcpyHostToDevice, stream) ); \
+        cudaSafeCall( cudaMemcpyToSymbolAsync(c_halfWin_y##index, &halfWin.y, sizeof(int), 0, cudaMemcpyHostToDevice, stream) ); \
+        cudaSafeCall( cudaMemcpyToSymbolAsync(c_iters##index, &iters, sizeof(int), 0, cudaMemcpyHostToDevice, stream) );
+
+        switch(index)
+        {
+            case 0: COPY_TO_SYMBOL_CALL(0) break;
+            case 1: COPY_TO_SYMBOL_CALL(1) break;
+            case 2: COPY_TO_SYMBOL_CALL(2) break;
+            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);

modules/gpu/src/pyrlk.cpp

@@ -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);

modules/gpu/test/test_optflow.cpp

@@ -44,6 +44,10 @@
 
 #ifdef HAVE_CUDA
 
+#ifdef HAVE_TBB
+#include <tbb/tbb.h>
+#endif
+
 using namespace cvtest;
 
 //////////////////////////////////////////////////////
@@ -322,6 +326,134 @@ GPU_TEST_P(PyrLKOpticalFlow, Sparse)
     ASSERT_LE(bad_ratio, 0.01);
 }
 
+#ifdef HAVE_TBB
+
+struct Sparse_Multi_Functor
+{
+    explicit Sparse_Multi_Functor(const cv::Mat& in_frame0, const cv::Mat& in_frame1,
+                                  const cv::Mat& in_pts_mat,
+                                  cv::gpu::GpuMat* in_d_pts,
+                                  cv::gpu::GpuMat* in_d_nextPts,
+                                  cv::gpu::GpuMat* in_d_status,
+                                  cv::gpu::Stream* in_streams):
+                                          m_frame0(in_frame0), m_frame1(in_frame1),
+                                          m_pts_mat(in_pts_mat),
+                                          m_d_pts(in_d_pts), m_d_nextPts(in_d_nextPts),
+                                          m_d_status(in_d_status), m_streams(in_streams){}
+
+    void operator()( const tbb::blocked_range<size_t>& r ) const
+    {
+        for( size_t i = r.begin(); i != r.end(); ++i )
+        {
+            m_d_pts[i].upload(m_pts_mat);
+            cv::gpu::PyrLKOpticalFlow pyrLK;
+            pyrLK.sparse_multi(loadMat(m_frame0), loadMat(m_frame1), m_d_pts[i],
+                               m_d_nextPts[i], m_d_status[i], m_streams[i]);
+            m_streams[i].waitForCompletion();
+        }
+    }
+
+    const cv::Mat& m_frame0;
+    const cv::Mat& m_frame1;
+    const cv::Mat& m_pts_mat;
+    cv::gpu::GpuMat* m_d_pts;
+    cv::gpu::GpuMat* m_d_nextPts;
+    cv::gpu::GpuMat* m_d_status;
+    cv::gpu::Stream* m_streams;
+};
+
+
+GPU_TEST_P(PyrLKOpticalFlow, Sparse_Multi)
+{
+    cv::Mat frame0 = readImage("opticalflow/frame0.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
+    ASSERT_FALSE(frame0.empty());
+
+    cv::Mat frame1 = readImage("opticalflow/frame1.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
+    ASSERT_FALSE(frame1.empty());
+
+    cv::Mat gray_frame;
+    if (useGray)
+        gray_frame = frame0;
+    else
+        cv::cvtColor(frame0, gray_frame, cv::COLOR_BGR2GRAY);
+
+    std::vector<cv::Point2f> pts;
+    cv::goodFeaturesToTrack(gray_frame, pts, 1000, 0.01, 0.0);
+
+    //--------------------------------------------------------------------------
+    // GPU
+    const unsigned int NB_EXEC_LINES = 27;
+
+    cv::gpu::GpuMat d_pts[NB_EXEC_LINES];
+    cv::gpu::GpuMat d_nextPts[NB_EXEC_LINES];
+    cv::gpu::GpuMat d_status[NB_EXEC_LINES];
+    cv::gpu::Stream streams[NB_EXEC_LINES];
+
+    cv::Mat pts_mat(1, (int) pts.size(), CV_32FC2, (void*) &pts[0]);
+
+    tbb::parallel_for(tbb::blocked_range<size_t>(0, NB_EXEC_LINES),
+                      Sparse_Multi_Functor(frame0, frame1, pts_mat,
+                                           d_pts, d_nextPts, d_status, streams));
+
+    std::vector<cv::Point2f> nextPts[NB_EXEC_LINES];
+    std::vector<unsigned char> status[NB_EXEC_LINES];
+
+    for (unsigned int i = 0; i < NB_EXEC_LINES; ++i)
+    {
+        nextPts[i].resize(d_nextPts[i].cols);
+        cv::Mat nextPts_mat(1, d_nextPts[i].cols, CV_32FC2, (void*) &(nextPts[i][0]));
+        d_nextPts[i].download(nextPts_mat);
+
+        status[i].resize(d_status[i].cols);
+        cv::Mat status_mat(1, d_status[i].cols, CV_8UC1, (void*) &(status[i][0]));
+        d_status[i].download(status_mat);
+    }
+
+    //--------------------------------------------------------------------------
+    // CPU
+    std::vector<cv::Point2f> nextPts_gold;
+    std::vector<unsigned char> status_gold;
+    cv::calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts_gold, status_gold, cv::noArray());
+
+    //--------------------------------------------------------------------------
+    // CHECKS
+    for (unsigned int uiI = 0; uiI < NB_EXEC_LINES; ++uiI)
+    {
+        ASSERT_EQ(nextPts_gold.size(), nextPts[uiI].size());
+        ASSERT_EQ(status_gold.size(), status[uiI].size());
+    }
+
+    size_t mistmatch = 0;
+    for (unsigned int uiI = 0; uiI < NB_EXEC_LINES; ++uiI)
+    {
+        for (size_t i = 0; i < nextPts[uiI].size(); ++i)
+        {
+            cv::Point2i a = nextPts[uiI][i];
+            cv::Point2i b = nextPts_gold[i];
+
+            if (status[uiI][i] != status_gold[i])
+            {
+                ++mistmatch;
+                continue;
+            }
+
+            if (status[uiI][i])
+            {
+                bool eq = std::abs(a.x - b.x) <= 1 && std::abs(a.y - b.y) <= 1;
+
+                if (!eq)
+                    ++mistmatch;
+            }
+        }
+    }
+
+    double bad_ratio = static_cast<double>(mistmatch) / (nextPts[0].size() * NB_EXEC_LINES);
+
+    ASSERT_LE(bad_ratio, 0.01);
+}
+
+#endif // HAVE_TBB
+
 INSTANTIATE_TEST_CASE_P(GPU_Video, PyrLKOpticalFlow, testing::Combine(
     ALL_DEVICES,
     testing::Values(UseGray(true), UseGray(false))));