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

Added unsigned char support for 1, 3, 4 channel images.

modules/cudaoptflow/perf/perf_optflow.cpp

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

modules/cudaoptflow/src/cuda/pyrlk.cu

@@ -48,6 +48,8 @@
 #include "opencv2/core/cuda/limits.hpp"
 #include "opencv2/core/cuda/vec_math.hpp"
 #include "opencv2/core/cuda/reduce.hpp"
+#include "opencv2/core/cuda/filters.hpp"
+#include "opencv2/core/cuda/border_interpolate.hpp"
 
 using namespace cv::cuda;
 using namespace cv::cuda::device;
@@ -60,53 +62,240 @@ namespace pyrlk
     __constant__ int c_halfWin_y;
     __constant__ int c_iters;
 
+    texture<uchar, cudaTextureType2D, cudaReadModeNormalizedFloat> tex_I8U(false, cudaFilterModeLinear, cudaAddressModeClamp);
+    texture<uchar4, cudaTextureType2D, cudaReadModeNormalizedFloat> tex_I8UC4(false, cudaFilterModeLinear, cudaAddressModeClamp);
+
+    texture<ushort4, cudaTextureType2D, cudaReadModeNormalizedFloat> tex_I16UC4(false, cudaFilterModeLinear, cudaAddressModeClamp);
+
+
     texture<float, cudaTextureType2D, cudaReadModeElementType> tex_If(false, cudaFilterModeLinear, cudaAddressModeClamp);
     texture<float4, cudaTextureType2D, cudaReadModeElementType> tex_If4(false, cudaFilterModeLinear, cudaAddressModeClamp);
+
     texture<uchar, cudaTextureType2D, cudaReadModeElementType> tex_Ib(false, cudaFilterModePoint, cudaAddressModeClamp);
 
+    texture<uchar, cudaTextureType2D, cudaReadModeNormalizedFloat> tex_J8U(false, cudaFilterModeLinear, cudaAddressModeClamp);
+    texture<uchar4, cudaTextureType2D, cudaReadModeNormalizedFloat> tex_J8UC4(false, cudaFilterModeLinear, cudaAddressModeClamp);
+
+    texture<ushort4, cudaTextureType2D, cudaReadModeNormalizedFloat> tex_J16UC4(false, cudaFilterModeLinear, cudaAddressModeClamp);
+
+
     texture<float, cudaTextureType2D, cudaReadModeElementType> tex_Jf(false, cudaFilterModeLinear, cudaAddressModeClamp);
     texture<float4, cudaTextureType2D, cudaReadModeElementType> tex_Jf4(false, cudaFilterModeLinear, cudaAddressModeClamp);
 
-    template <int cn> struct Tex_I;
+
-    template <> struct Tex_I<1>
+    template <int cn, typename T> struct Tex_I
+    {
+        static __host__ __forceinline__ void bindTexture_(PtrStepSz<typename TypeVec<T, cn>::vec_type> I)
+        {
+            (void)I;
+        }
+    };
+
+    template <> struct Tex_I<1, uchar>
+    {
+        static __device__ __forceinline__ float read(float x, float y)
+        {
+            return tex2D(tex_I8U, x, y);
+        }
+        static __host__ __forceinline__ void bindTexture_(PtrStepSz<uchar>& I)
+        {
+            bindTexture(&tex_I8U, I);
+        }
+    };
+    template <> struct Tex_I<1, ushort>
+    {
+        static __device__ __forceinline__ float read(float x, float y)
+        {
+            return 0.0;
+        }
+        static __host__ __forceinline__ void bindTexture_(PtrStepSz<ushort>& I)
+        {
+            (void)I;
+        }
+    };
+    template <> struct Tex_I<1, int>
+    {
+        static __device__ __forceinline__ float read(float x, float y)
+        {
+            return 0.0;
+        }
+        static __host__ __forceinline__ void bindTexture_(PtrStepSz<int>& I)
+        {
+            (void)I;
+        }
+    };
+    template <> struct Tex_I<1, float>
     {
         static __device__ __forceinline__ float read(float x, float y)
         {
             return tex2D(tex_If, x, y);
         }
+        static __host__ __forceinline__ void bindTexture_(PtrStepSz<float>& I)
+        {
+            bindTexture(&tex_If, I);
+        }
     };
-    template <> struct Tex_I<4>
+    // ****************** 3 channel specializations ************************
+    template <> struct Tex_I<3, uchar>
+    {
+        static __device__ __forceinline__ float3 read(float x, float y)
+        {
+            return make_float3(0,0,0);
+        }
+        static __host__ __forceinline__ void bindTexture_(PtrStepSz<uchar3> I)
+        {
+            (void)I;
+        }
+    };
+    template <> struct Tex_I<3, ushort>
+    {
+        static __device__ __forceinline__ float3 read(float x, float y)
+        {
+            return make_float3(0, 0, 0);
+        }
+        static __host__ __forceinline__ void bindTexture_(PtrStepSz<ushort3> I)
+        {
+            (void)I;
+        }
+    };
+    template <> struct Tex_I<3, int>
+    {
+        static __device__ __forceinline__ float3 read(float x, float y)
+        {
+            return make_float3(0, 0, 0);
+        }
+        static __host__ __forceinline__ void bindTexture_(PtrStepSz<int3> I)
+        {
+            (void)I;
+        }
+    };
+    template <> struct Tex_I<3, float>
+    {
+        static __device__ __forceinline__ float3 read(float x, float y)
+        {
+            return make_float3(0, 0, 0);
+        }
+        static __host__ __forceinline__ void bindTexture_(PtrStepSz<float3> I)
+        {
+            (void)I;
+        }
+    };
+    // ****************** 4 channel specializations ************************
+
+    template <> struct Tex_I<4, uchar>
+    {
+        static __device__ __forceinline__ float4 read(float x, float y)
+        {
+            return tex2D(tex_I8UC4, x, y);
+        }
+        static __host__ __forceinline__ void bindTexture_(PtrStepSz<uchar4>& I)
+        {
+            bindTexture(&tex_I8UC4, I);
+        }
+    };
+    template <> struct Tex_I<4, ushort>
+    {
+        static __device__ __forceinline__ float4 read(float x, float y)
+        {
+            return tex2D(tex_I16UC4, x, y);
+        }
+        static __host__ __forceinline__ void bindTexture_(PtrStepSz<ushort4>& I)
+        {
+            bindTexture(&tex_I16UC4, I);
+        }
+    };
+    template <> struct Tex_I<4, float>
     {
         static __device__ __forceinline__ float4 read(float x, float y)
         {
             return tex2D(tex_If4, x, y);
         }
+        static __host__ __forceinline__ void bindTexture_(PtrStepSz<float4>& I)
+        {
+            bindTexture(&tex_If4, I);
+        }
     };
-
+    // ************* J  ***************
-    template <int cn> struct Tex_J;
+    template <int cn, typename T> struct Tex_J
-    template <> struct Tex_J<1>
+    {
+        static __host__ __forceinline__ void bindTexture_(PtrStepSz<typename TypeVec<T,cn>::vec_type>& J)
+        {
+            (void)J;
+        }
+    };
+    template <> struct Tex_J<1, uchar>
+    {
+        static __device__ __forceinline__ float read(float x, float y)
+        {
+            return tex2D(tex_J8U, x, y);
+        }
+        static __host__ __forceinline__ void bindTexture_(PtrStepSz<uchar>& J)
+        {
+            bindTexture(&tex_J8U, J);
+        }
+    };
+    template <> struct Tex_J<1, float>
     {
         static __device__ __forceinline__ float read(float x, float y)
         {
             return tex2D(tex_Jf, x, y);
         }
+        static __host__ __forceinline__ void bindTexture_(PtrStepSz<float>& J)
+        {
+            bindTexture(&tex_Jf, J);
+        }
     };
-    template <> struct Tex_J<4>
+    // ************* 4 channel specializations ***************
+    template <> struct Tex_J<4, uchar>
+    {
+        static __device__ __forceinline__ float4 read(float x, float y)
+        {
+            return tex2D(tex_J8UC4, x, y);
+        }
+        static __host__ __forceinline__ void bindTexture_(PtrStepSz<uchar4>& J)
+        {
+            bindTexture(&tex_J8UC4, J);
+        }
+    };
+    template <> struct Tex_J<4, ushort>
+    {
+        static __device__ __forceinline__ float4 read(float x, float y)
+        {
+            return tex2D(tex_J16UC4, x, y);
+        }
+        static __host__ __forceinline__ void bindTexture_(PtrStepSz<ushort4>& J)
+        {
+            bindTexture(&tex_J16UC4, J);
+        }
+    };
+    template <> struct Tex_J<4, float>
     {
         static __device__ __forceinline__ float4 read(float x, float y)
         {
             return tex2D(tex_Jf4, x, y);
         }
+        static __host__ __forceinline__ void bindTexture_(PtrStepSz<float4>& J)
+        {
+            bindTexture(&tex_Jf4, J);
+        }
     };
 
-    __device__ __forceinline__ void accum(float& dst, float val)
+    __device__ __forceinline__ void accum(float& dst, const float& val)
     {
         dst += val;
     }
-    __device__ __forceinline__ void accum(float& dst, const float4& val)
+    __device__ __forceinline__ void accum(float& dst, const float2& val)
+    {
+        dst += val.x + val.y;
+    }
+    __device__ __forceinline__ void accum(float& dst, const float3& val)
     {
         dst += val.x + val.y + val.z;
     }
+    __device__ __forceinline__ void accum(float& dst, const float4& val)
+    {
+        dst += val.x + val.y + val.z + val.w;
+    }
 
     __device__ __forceinline__ float abs_(float a)
     {
@@ -116,8 +305,46 @@ namespace pyrlk
     {
         return abs(a);
     }
+    __device__ __forceinline__ float2 abs_(const float2& a)
+    {
+        return abs(a);
+    }
+    __device__ __forceinline__ float3 abs_(const float3& a)
+    {
+        return abs(a);
+    }
 
-    template <int cn, int PATCH_X, int PATCH_Y, bool calcErr>
+
+    template<typename T> __device__ __forceinline__ typename TypeVec<float, 1>::vec_type ToFloat(const typename TypeVec<T, 1>::vec_type& other)
+    {
+        return other;
+    }
+    template<typename T> __device__ __forceinline__  typename TypeVec<float, 2>::vec_type ToFloat(const typename TypeVec<T, 2>::vec_type& other)
+    {
+        typename TypeVec<float, 2>::vec_type ret;
+        ret.x = other.x;
+        ret.y = other.y;
+        return ret;
+    }
+    template<typename T> __device__ __forceinline__  typename TypeVec<float, 3>::vec_type ToFloat(const typename TypeVec<T, 3>::vec_type& other)
+    {
+        typename TypeVec<float, 3>::vec_type ret;
+        ret.x = other.x;
+        ret.y = other.y;
+        ret.z = other.z;
+        return ret;
+    }
+    template<typename T> __device__ __forceinline__  typename TypeVec<float, 4>::vec_type ToFloat(const typename TypeVec<T, 4>::vec_type& other)
+    {
+        typename TypeVec<float, 4>::vec_type ret;
+        ret.x = other.x;
+        ret.y = other.y;
+        ret.z = other.z;
+        ret.w = other.w;
+        return ret;
+    }
+
+    template <int cn, int PATCH_X, int PATCH_Y, bool calcErr, typename T>
     __global__ void sparseKernel(const float2* prevPts, float2* nextPts, uchar* status, float* err, const int level, const int rows, const int cols)
     {
     #if __CUDA_ARCH__ <= 110
@@ -166,15 +393,15 @@ namespace pyrlk
                 float x = prevPt.x + xBase + 0.5f;
                 float y = prevPt.y + yBase + 0.5f;
 
-                I_patch[i][j] = Tex_I<cn>::read(x, y);
+                I_patch[i][j] = Tex_I<cn, T>::read(x, y);
 
                 // Sharr Deriv
 
-                work_type dIdx = 3.0f * Tex_I<cn>::read(x+1, y-1) + 10.0f * Tex_I<cn>::read(x+1, y) + 3.0f * Tex_I<cn>::read(x+1, y+1) -
+                work_type dIdx = 3.0f * Tex_I<cn,T>::read(x+1, y-1) + 10.0f * Tex_I<cn, T>::read(x+1, y) + 3.0f * Tex_I<cn,T>::read(x+1, y+1) -
-                                 (3.0f * Tex_I<cn>::read(x-1, y-1) + 10.0f * Tex_I<cn>::read(x-1, y) + 3.0f * Tex_I<cn>::read(x-1, y+1));
+                                 (3.0f * Tex_I<cn,T>::read(x-1, y-1) + 10.0f * Tex_I<cn, T>::read(x-1, y) + 3.0f * Tex_I<cn,T>::read(x-1, y+1));
 
-                work_type dIdy = 3.0f * Tex_I<cn>::read(x-1, y+1) + 10.0f * Tex_I<cn>::read(x, y+1) + 3.0f * Tex_I<cn>::read(x+1, y+1) -
+                work_type dIdy = 3.0f * Tex_I<cn,T>::read(x-1, y+1) + 10.0f * Tex_I<cn, T>::read(x, y+1) + 3.0f * Tex_I<cn,T>::read(x+1, y+1) -
-                                (3.0f * Tex_I<cn>::read(x-1, y-1) + 10.0f * Tex_I<cn>::read(x, y-1) + 3.0f * Tex_I<cn>::read(x+1, y-1));
+                                (3.0f * Tex_I<cn,T>::read(x-1, y-1) + 10.0f * Tex_I<cn, T>::read(x, y-1) + 3.0f * Tex_I<cn,T>::read(x+1, y-1));
 
                 dIdx_patch[i][j] = dIdx;
                 dIdy_patch[i][j] = dIdy;
@@ -243,7 +470,7 @@ namespace pyrlk
                 for (int x = threadIdx.x, j = 0; x < c_winSize_x; x += blockDim.x, ++j)
                 {
                     work_type I_val = I_patch[i][j];
-                    work_type J_val = Tex_J<cn>::read(nextPt.x + x + 0.5f, nextPt.y + y + 0.5f);
+                    work_type J_val = Tex_J<cn, T>::read(nextPt.x + x + 0.5f, nextPt.y + y + 0.5f);
 
                     work_type diff = (J_val - I_val) * 32.0f;
 
@@ -286,7 +513,7 @@ namespace pyrlk
                 for (int x = threadIdx.x, j = 0; x < c_winSize_x; x += blockDim.x, ++j)
                 {
                     work_type I_val = I_patch[i][j];
-                    work_type J_val = Tex_J<cn>::read(nextPt.x + x + 0.5f, nextPt.y + y + 0.5f);
+                    work_type J_val = Tex_J<cn, T>::read(nextPt.x + x + 0.5f, nextPt.y + y + 0.5f);
 
                     work_type diff = J_val - I_val;
 
@@ -309,22 +536,352 @@ namespace pyrlk
         }
     }
 
-    template <int cn, int PATCH_X, int PATCH_Y>
+    // Kernel, uses non texture fetches
-    void sparse_caller(int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
+    template <int PATCH_X, int PATCH_Y, bool calcErr, int cn, typename T, typename Ptr2D>
+    __global__ void sparseKernel_(Ptr2D I, Ptr2D J, const float2* prevPts, float2* nextPts, uchar* status, float* err, const int level, const int rows, const int cols)
+    {
+#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_halfWin_x;
+        prevPt.y -= c_halfWin_y;
+
+        // extract the patch from the first image, compute covariation matrix of derivatives
+
+        float A11 = 0;
+        float A12 = 0;
+        float A22 = 0;
+
+        typedef typename TypeVec<float, cn>::vec_type work_type;
+
+        work_type I_patch[PATCH_Y][PATCH_X];
+        work_type dIdx_patch[PATCH_Y][PATCH_X];
+        work_type dIdy_patch[PATCH_Y][PATCH_X];
+
+        for (int yBase = threadIdx.y, i = 0; yBase < c_winSize_y; yBase += blockDim.y, ++i)
+        {
+            for (int xBase = threadIdx.x, j = 0; xBase < c_winSize_x; xBase += blockDim.x, ++j)
+            {
+                float x = prevPt.x + xBase + 0.5f;
+                float y = prevPt.y + yBase + 0.5f;
+
+                I_patch[i][j] = ToFloat<T>(I(y, x));
+
+                // Sharr Deriv
+
+                work_type dIdx = 3.0f * I(y - 1, x + 1) + 10.0f * I(y, x + 1) + 3.0f * I(y + 1, x + 1) -
+                    (3.0f * I(y - 1, x - 1) + 10.0f * I(y, x - 1) + 3.0f * I(y + 1 , x - 1));
+
+                work_type dIdy = 3.0f * I(y + 1, x - 1) + 10.0f * I(y + 1, x) + 3.0f * I(y+1, x + 1) -
+                    (3.0f * I(y - 1, x - 1) + 10.0f * I(y-1, x) + 3.0f * I(y - 1, x + 1));
+
+                dIdx_patch[i][j] = dIdx;
+                dIdy_patch[i][j] = dIdy;
+
+                accum(A11, dIdx * dIdx);
+                accum(A12, dIdx * dIdy);
+                accum(A22, dIdy * dIdy);
+            }
+        }
+
+        reduce<BLOCK_SIZE>(smem_tuple(smem1, smem2, smem3), thrust::tie(A11, A12, A22), tid, thrust::make_tuple(plus<float>(), plus<float>(), plus<float>()));
+
+#if __CUDA_ARCH__ >= 300
+        if (tid == 0)
+        {
+            smem1[0] = A11;
+            smem2[0] = A12;
+            smem3[0] = A22;
+        }
+#endif
+
+        __syncthreads();
+
+        A11 = smem1[0];
+        A12 = smem2[0];
+        A22 = smem3[0];
+
+        float D = A11 * A22 - A12 * A12;
+
+        if (D < numeric_limits<float>::epsilon())
+        {
+            if (tid == 0 && level == 0)
+                status[blockIdx.x] = 0;
+
+            return;
+        }
+
+        D = 1.f / D;
+
+        A11 *= D;
+        A12 *= D;
+        A22 *= D;
+
+        float2 nextPt = nextPts[blockIdx.x];
+        nextPt.x *= 2.f;
+        nextPt.y *= 2.f;
+
+        nextPt.x -= c_halfWin_x;
+        nextPt.y -= c_halfWin_y;
+
+        for (int k = 0; k < c_iters; ++k)
+        {
+            if (nextPt.x < -c_halfWin_x || nextPt.x >= cols || nextPt.y < -c_halfWin_y || nextPt.y >= rows)
+            {
+                if (tid == 0 && level == 0)
+                    status[blockIdx.x] = 0;
+
+                return;
+            }
+
+            float b1 = 0;
+            float b2 = 0;
+
+            for (int y = threadIdx.y, i = 0; y < c_winSize_y; y += blockDim.y, ++i)
+            {
+                for (int x = threadIdx.x, j = 0; x < c_winSize_x; x += blockDim.x, ++j)
+                {
+                    work_type I_val = I_patch[i][j];
+                    work_type J_val = ToFloat<T>(J(nextPt.y + y + 0.5f, nextPt.x + x + 0.5f));
+
+                    work_type diff = (J_val - I_val) * 32.0f;
+
+                    accum(b1, diff * dIdx_patch[i][j]);
+                    accum(b2, diff * dIdy_patch[i][j]);
+                }
+            }
+
+            reduce<BLOCK_SIZE>(smem_tuple(smem1, smem2), thrust::tie(b1, b2), tid, thrust::make_tuple(plus<float>(), plus<float>()));
+
+#if __CUDA_ARCH__ >= 300
+            if (tid == 0)
+            {
+                smem1[0] = b1;
+                smem2[0] = b2;
+            }
+#endif
+
+            __syncthreads();
+
+            b1 = smem1[0];
+            b2 = smem2[0];
+
+            float2 delta;
+            delta.x = A12 * b2 - A22 * b1;
+            delta.y = A12 * b1 - A11 * b2;
+
+            nextPt.x += delta.x;
+            nextPt.y += delta.y;
+
+            if (::fabs(delta.x) < 0.01f && ::fabs(delta.y) < 0.01f)
+                break;
+        }
+
+        float errval = 0;
+        if (calcErr)
+        {
+            for (int y = threadIdx.y, i = 0; y < c_winSize_y; y += blockDim.y, ++i)
+            {
+                for (int x = threadIdx.x, j = 0; x < c_winSize_x; x += blockDim.x, ++j)
+                {
+                    work_type I_val = I_patch[i][j];
+                    work_type J_val = ToFloat<T>(J(nextPt.y + y + 0.5f, nextPt.x + x + 0.5f));
+
+                    work_type diff = J_val - I_val;
+
+                    accum(errval, abs_(diff));
+                }
+            }
+
+            reduce<BLOCK_SIZE>(smem1, errval, tid, plus<float>());
+        }
+
+        if (tid == 0)
+        {
+            nextPt.x += c_halfWin_x;
+            nextPt.y += c_halfWin_y;
+
+            nextPts[blockIdx.x] = nextPt;
+
+            if (calcErr)
+                err[blockIdx.x] = static_cast<float>(errval) / (3 * c_winSize_x * c_winSize_y);
+        }
+    } // __global__ void sparseKernel_
+
+
+    template <int cn, int PATCH_X, int PATCH_Y, typename T> class sparse_caller
+    {
+    public:
+        static void call(PtrStepSz<typename TypeVec<T, cn>::vec_type> I, PtrStepSz<typename TypeVec<T, cn>::vec_type> J, int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
             int level, dim3 block, cudaStream_t stream)
         {
             dim3 grid(ptcount);
-
+            (void)I;
+            (void)J;
             if (level == 0 && err)
-            sparseKernel<cn, PATCH_X, PATCH_Y, true><<<grid, block>>>(prevPts, nextPts, status, err, level, rows, cols);
+                sparseKernel<cn, PATCH_X, PATCH_Y, true, T> <<<grid, block, 0, stream >>>(prevPts, nextPts, status, err, level, rows, cols);
             else
-            sparseKernel<cn, PATCH_X, PATCH_Y, false><<<grid, block>>>(prevPts, nextPts, status, err, level, rows, cols);
+                sparseKernel<cn, PATCH_X, PATCH_Y, false, T> <<<grid, block, 0, stream >>>(prevPts, nextPts, status, err, level, rows, cols);
 
             cudaSafeCall(cudaGetLastError());
 
             if (stream == 0)
                 cudaSafeCall(cudaDeviceSynchronize());
         }
+    };
+    // Specialization to use non texture path because for some reason the texture path keeps failing accuracy tests
+    template<int PATCH_X, int PATCH_Y> class sparse_caller<1, PATCH_X, PATCH_Y, unsigned short>
+    {
+    public:
+        typedef typename TypeVec<unsigned short, 1>::vec_type work_type;
+        typedef PtrStepSz<work_type> Ptr2D;
+        typedef BrdConstant<work_type> BrdType;
+        typedef BorderReader<Ptr2D, BrdType> Reader;
+        typedef LinearFilter<Reader> Filter;
+        static void call(Ptr2D I, Ptr2D J, int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
+            int level, dim3 block, cudaStream_t stream)
+        {
+            dim3 grid(ptcount);
+            if (level == 0 && err)
+            {
+                sparseKernel_<PATCH_X, PATCH_Y, true, 1, unsigned short> <<<grid, block, 0, stream >>>(
+                    Filter(Reader(I, BrdType(rows, cols))),
+                    Filter(Reader(J, BrdType(rows, cols))),
+                    prevPts, nextPts, status, err, level, rows, cols);
+            }
+            else
+            {
+                sparseKernel_<PATCH_X, PATCH_Y, false, 1, unsigned short> <<<grid, block, 0, stream >>>(
+                    Filter(Reader(I, BrdType(rows, cols))),
+                    Filter(Reader(J, BrdType(rows, cols))),
+                    prevPts, nextPts, status, err, level, rows, cols);
+            }
+            cudaSafeCall(cudaGetLastError());
+
+            if (stream == 0)
+                cudaSafeCall(cudaDeviceSynchronize());
+        }
+    };
+    // Specialization for int because the texture path keeps failing
+    template<int PATCH_X, int PATCH_Y> class sparse_caller<1, PATCH_X, PATCH_Y, int>
+    {
+    public:
+        typedef typename TypeVec<int, 1>::vec_type work_type;
+        typedef PtrStepSz<work_type> Ptr2D;
+        typedef BrdConstant<work_type> BrdType;
+        typedef BorderReader<Ptr2D, BrdType> Reader;
+        typedef LinearFilter<Reader> Filter;
+        static void call(Ptr2D I, Ptr2D J, int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
+            int level, dim3 block, cudaStream_t stream)
+        {
+            dim3 grid(ptcount);
+            if (level == 0 && err)
+            {
+                sparseKernel_<PATCH_X, PATCH_Y, true, 1, int> <<<grid, block, 0, stream >>>(
+                    Filter(Reader(I, BrdType(rows, cols))),
+                    Filter(Reader(J, BrdType(rows, cols))),
+                    prevPts, nextPts, status, err, level, rows, cols);
+            }
+            else
+            {
+                sparseKernel_<PATCH_X, PATCH_Y, false, 1, int> <<<grid, block, 0, stream >>>(
+                    Filter(Reader(I, BrdType(rows, cols))),
+                    Filter(Reader(J, BrdType(rows, cols))),
+                    prevPts, nextPts, status, err, level, rows, cols);
+            }
+            cudaSafeCall(cudaGetLastError());
+
+            if (stream == 0)
+                cudaSafeCall(cudaDeviceSynchronize());
+        }
+    };
+    template<int PATCH_X, int PATCH_Y> class sparse_caller<4, PATCH_X, PATCH_Y, int>
+    {
+    public:
+        typedef typename TypeVec<int, 4>::vec_type work_type;
+        typedef PtrStepSz<work_type> Ptr2D;
+        typedef BrdConstant<work_type> BrdType;
+        typedef BorderReader<Ptr2D, BrdType> Reader;
+        typedef LinearFilter<Reader> Filter;
+        static void call(Ptr2D I, Ptr2D J, int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
+            int level, dim3 block, cudaStream_t stream)
+        {
+            dim3 grid(ptcount);
+            if (level == 0 && err)
+            {
+                sparseKernel_<PATCH_X, PATCH_Y, true, 4, int> <<<grid, block, 0, stream >>>(
+                    Filter(Reader(I, BrdType(rows, cols))),
+                    Filter(Reader(J, BrdType(rows, cols))),
+                    prevPts, nextPts, status, err, level, rows, cols);
+            }
+            else
+            {
+                sparseKernel_<PATCH_X, PATCH_Y, false, 4, int> <<<grid, block, 0, stream >>>(
+                    Filter(Reader(I, BrdType(rows, cols))),
+                    Filter(Reader(J, BrdType(rows, cols))),
+                    prevPts, nextPts, status, err, level, rows, cols);
+            }
+            cudaSafeCall(cudaGetLastError());
+
+            if (stream == 0)
+                cudaSafeCall(cudaDeviceSynchronize());
+        }
+    };
+    using namespace cv::cuda::device;
+    template <int PATCH_X, int PATCH_Y, typename T> class sparse_caller<3, PATCH_X, PATCH_Y, T>
+    {
+    public:
+        typedef typename TypeVec<T, 3>::vec_type work_type;
+        typedef PtrStepSz<work_type> Ptr2D;
+        typedef BrdConstant<work_type> BrdType;
+        typedef BorderReader<Ptr2D, BrdType> Reader;
+        typedef LinearFilter<Reader> Filter;
+        static void call(Ptr2D I, Ptr2D J, int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
+            int level, dim3 block, cudaStream_t stream)
+        {
+            dim3 grid(ptcount);
+            if (level == 0 && err)
+            {
+                sparseKernel_<PATCH_X, PATCH_Y, true, 3, T> <<<grid, block, 0, stream >>>(
+                    Filter(Reader(I, BrdType(rows, cols))),
+                    Filter(Reader(J, BrdType(rows, cols))),
+                    prevPts, nextPts, status, err, level, rows, cols);
+            }
+            else
+            {
+                sparseKernel_<PATCH_X, PATCH_Y, false, 3, T> <<<grid, block, 0, stream >>>(
+                    Filter(Reader(I, BrdType(rows, cols))),
+                    Filter(Reader(J, BrdType(rows, cols))),
+                    prevPts, nextPts, status, err, level, rows, cols);
+            }
+            cudaSafeCall(cudaGetLastError());
+
+            if (stream == 0)
+                cudaSafeCall(cudaDeviceSynchronize());
+        }
+    };
+
 
     template <bool calcErr>
     __global__ void denseKernel(PtrStepf u, PtrStepf v, const PtrStepf prevU, const PtrStepf prevV, PtrStepf err, const int rows, const int cols)
@@ -484,57 +1041,36 @@ namespace pyrlk
         cudaSafeCall( cudaMemcpyToSymbolAsync(c_iters, &iters, sizeof(int), 0, cudaMemcpyHostToDevice, stream) );
     }
 
-    void sparse1(PtrStepSzf I, PtrStepSzf J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
+    template<typename T, int cn> struct pyrLK_caller
+    {
+        static void sparse(PtrStepSz<typename TypeVec<T, cn>::vec_type> I, PtrStepSz<typename TypeVec<T, cn>::vec_type> J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
             int level, dim3 block, dim3 patch, cudaStream_t stream)
         {
-        typedef void (*func_t)(int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
+            typedef void(*func_t)(PtrStepSz<typename TypeVec<T, cn>::vec_type> I, PtrStepSz<typename TypeVec<T, cn>::vec_type> J,
+                int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
                 int level, dim3 block, cudaStream_t stream);
 
             static const func_t funcs[5][5] =
             {
-            {sparse_caller<1, 1, 1>, sparse_caller<1, 2, 1>, sparse_caller<1, 3, 1>, sparse_caller<1, 4, 1>, sparse_caller<1, 5, 1>},
+                { sparse_caller<cn, 1, 1,T>::call, sparse_caller<cn, 2, 1,T>::call, sparse_caller<cn, 3, 1,T>::call, sparse_caller<cn, 4, 1,T>::call, sparse_caller<cn, 5, 1,T>::call },
-            {sparse_caller<1, 1, 2>, sparse_caller<1, 2, 2>, sparse_caller<1, 3, 2>, sparse_caller<1, 4, 2>, sparse_caller<1, 5, 2>},
+                { sparse_caller<cn, 1, 2,T>::call, sparse_caller<cn, 2, 2,T>::call, sparse_caller<cn, 3, 2,T>::call, sparse_caller<cn, 4, 2,T>::call, sparse_caller<cn, 5, 2,T>::call },
-            {sparse_caller<1, 1, 3>, sparse_caller<1, 2, 3>, sparse_caller<1, 3, 3>, sparse_caller<1, 4, 3>, sparse_caller<1, 5, 3>},
+                { sparse_caller<cn, 1, 3,T>::call, sparse_caller<cn, 2, 3,T>::call, sparse_caller<cn, 3, 3,T>::call, sparse_caller<cn, 4, 3,T>::call, sparse_caller<cn, 5, 3,T>::call },
-            {sparse_caller<1, 1, 4>, sparse_caller<1, 2, 4>, sparse_caller<1, 3, 4>, sparse_caller<1, 4, 4>, sparse_caller<1, 5, 4>},
+                { sparse_caller<cn, 1, 4,T>::call, sparse_caller<cn, 2, 4,T>::call, sparse_caller<cn, 3, 4,T>::call, sparse_caller<cn, 4, 4,T>::call, sparse_caller<cn, 5, 4,T>::call },
-            {sparse_caller<1, 1, 5>, sparse_caller<1, 2, 5>, sparse_caller<1, 3, 5>, sparse_caller<1, 4, 5>, sparse_caller<1, 5, 5>}
+                { sparse_caller<cn, 1, 5,T>::call, sparse_caller<cn, 2, 5,T>::call, sparse_caller<cn, 3, 5,T>::call, sparse_caller<cn, 4, 5,T>::call, sparse_caller<cn, 5, 5,T>::call }
             };
 
-        bindTexture(&tex_If, I);
+            Tex_I<cn, T>::bindTexture_(I);
-        bindTexture(&tex_Jf, J);
+            Tex_J<cn, T>::bindTexture_(J);
 
-        funcs[patch.y - 1][patch.x - 1](I.rows, I.cols, prevPts, nextPts, status, err, ptcount,
+            funcs[patch.y - 1][patch.x - 1](I, J, I.rows, I.cols, prevPts, nextPts, status, err, ptcount,
                 level, block, stream);
         }
-
+        static void dense(PtrStepSzb I, PtrStepSz<T> J, PtrStepSzf u, PtrStepSzf v, PtrStepSzf prevU, PtrStepSzf prevV, PtrStepSzf err, int2 winSize, cudaStream_t stream)
-    void sparse4(PtrStepSz<float4> I, PtrStepSz<float4> J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
-                 int level, dim3 block, dim3 patch, cudaStream_t stream)
-    {
-        typedef void (*func_t)(int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
-                               int level, dim3 block, cudaStream_t stream);
-
-        static const func_t funcs[5][5] =
-        {
-            {sparse_caller<4, 1, 1>, sparse_caller<4, 2, 1>, sparse_caller<4, 3, 1>, sparse_caller<4, 4, 1>, sparse_caller<4, 5, 1>},
-            {sparse_caller<4, 1, 2>, sparse_caller<4, 2, 2>, sparse_caller<4, 3, 2>, sparse_caller<4, 4, 2>, sparse_caller<4, 5, 2>},
-            {sparse_caller<4, 1, 3>, sparse_caller<4, 2, 3>, sparse_caller<4, 3, 3>, sparse_caller<4, 4, 3>, sparse_caller<4, 5, 3>},
-            {sparse_caller<4, 1, 4>, sparse_caller<4, 2, 4>, sparse_caller<4, 3, 4>, sparse_caller<4, 4, 4>, sparse_caller<4, 5, 4>},
-            {sparse_caller<4, 1, 5>, sparse_caller<4, 2, 5>, sparse_caller<4, 3, 5>, sparse_caller<4, 4, 5>, sparse_caller<4, 5, 5>}
-        };
-
-        bindTexture(&tex_If4, I);
-        bindTexture(&tex_Jf4, J);
-
-        funcs[patch.y - 1][patch.x - 1](I.rows, I.cols, prevPts, nextPts, status, err, ptcount,
-            level, block, stream);
-    }
-
-    void dense(PtrStepSzb I, PtrStepSzf J, PtrStepSzf u, PtrStepSzf v, PtrStepSzf prevU, PtrStepSzf prevV, PtrStepSzf err, int2 winSize, cudaStream_t stream)
         {
             dim3 block(16, 16);
             dim3 grid(divUp(I.cols, block.x), divUp(I.rows, block.y));
-
+            Tex_I<1, uchar>::bindTexture_(I);
-        bindTexture(&tex_Ib, I);
+            Tex_J<1, T>::bindTexture_(J);
-        bindTexture(&tex_Jf, J);
 
             int2 halfWin = make_int2((winSize.x - 1) / 2, (winSize.y - 1) / 2);
             const int patchWidth = block.x + 2 * halfWin.x;
@@ -555,6 +1091,22 @@ namespace pyrlk
             if (stream == 0)
                 cudaSafeCall(cudaDeviceSynchronize());
         }
+    };
+
+    template class pyrLK_caller<unsigned char,1>;
+    template class pyrLK_caller<unsigned short,1>;
+    template class pyrLK_caller<int,1>;
+    template class pyrLK_caller<float,1>;
+
+    template class pyrLK_caller<unsigned char, 3>;
+    template class pyrLK_caller<unsigned short, 3>;
+    template class pyrLK_caller<int, 3>;
+    template class pyrLK_caller<float, 3>;
+
+    template class pyrLK_caller<unsigned char, 4>;
+    template class pyrLK_caller<unsigned short, 4>;
+    template class pyrLK_caller<int, 4>;
+    template class pyrLK_caller<float, 4>;
 }
 
 #endif /* CUDA_DISABLER */

modules/cudaoptflow/src/precomp.hpp

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

modules/cudaoptflow/src/pyrlk.cpp

@@ -56,14 +56,20 @@ Ptr<DensePyrLKOpticalFlow> cv::cuda::DensePyrLKOpticalFlow::create(Size, int, in
 namespace pyrlk
 {
     void loadConstants(int2 winSize, int iters, cudaStream_t stream);
-
+    template<typename T, int cn> struct pyrLK_caller
-    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);
+        static void sparse(PtrStepSz<typename device::TypeVec<T, cn>::vec_type> I, PtrStepSz<typename device::TypeVec<T, cn>::vec_type> J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
-    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);
 
-    void dense(PtrStepSzb I, PtrStepSzf J, PtrStepSzf u, PtrStepSzf v, PtrStepSzf prevU, PtrStepSzf prevV,
+        static void dense(PtrStepSzb I, PtrStepSzf J, PtrStepSzf u, PtrStepSzf v, PtrStepSzf prevU, PtrStepSzf prevV,
             PtrStepSzf err, int2 winSize, cudaStream_t stream);
+    };
+
+    template<typename T, int cn> void dispatcher(GpuMat I, GpuMat J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
+        int level, dim3 block, dim3 patch, cudaStream_t stream)
+    {
+        pyrLK_caller<T, cn>::sparse(I, J, prevPts, nextPts, status, err, ptcount, level, block, patch, stream);
+    }
 }
 
 namespace
@@ -76,6 +82,9 @@ namespace
         void sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts,
             GpuMat& status, GpuMat* err, Stream& stream);
 
+        void sparse(std::vector<GpuMat>& prevPyr, std::vector<GpuMat>& nextPyr, const GpuMat& prevPts, GpuMat& nextPts,
+            GpuMat& status, GpuMat* err, Stream& stream);
+
         void dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, Stream& stream);
 
     protected:
@@ -83,8 +92,9 @@ namespace
         int maxLevel_;
         int iters_;
         bool useInitialFlow_;
-
+        void buildImagePyramid(const GpuMat& prevImg, std::vector<GpuMat>& prevPyr, const GpuMat& nextImg, std::vector<GpuMat>& nextPyr, Stream stream);
     private:
+        friend class SparsePyrLKOpticalFlowImpl;
         std::vector<GpuMat> prevPyr_;
         std::vector<GpuMat> nextPyr_;
     };
@@ -113,26 +123,32 @@ namespace
         block.z = patch.z = 1;
     }
 
-    void PyrLKOpticalFlowBase::sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts, GpuMat& status, GpuMat* err, Stream& stream)
+    void PyrLKOpticalFlowBase::buildImagePyramid(const GpuMat& prevImg, std::vector<GpuMat>& prevPyr, const GpuMat& nextImg, std::vector<GpuMat>& nextPyr, Stream stream)
     {
-        if (prevPts.empty())
+        prevPyr.resize(maxLevel_ + 1);
+        nextPyr.resize(maxLevel_ + 1);
+
+        int cn = prevImg.channels();
+
+        CV_Assert(cn == 1 || cn == 3 || cn == 4);
+
+        prevPyr[0] = prevImg;
+        nextPyr[0] = nextImg;
+
+        for (int level = 1; level <= maxLevel_; ++level)
         {
-            nextPts.release();
+            cuda::pyrDown(prevPyr[level - 1], prevPyr[level], stream);
-            status.release();
+            cuda::pyrDown(nextPyr[level - 1], nextPyr[level], stream);
-            if (err) err->release();
-            return;
         }
-
+    }
-        dim3 block, patch;
+    void PyrLKOpticalFlowBase::sparse(std::vector<GpuMat>& prevPyr, std::vector<GpuMat>& nextPyr, const GpuMat& prevPts, GpuMat& nextPts,
-        calcPatchSize(winSize_, block, patch);
+        GpuMat& status, GpuMat* err, Stream& stream)
-
+    {
-        CV_Assert( prevImg.channels() == 1 || prevImg.channels() == 3 || prevImg.channels() == 4 );
+        CV_Assert(prevPyr.size() && nextPyr.size() && "Pyramid needs to at least contain the original matrix as the first element");
-        CV_Assert( prevImg.size() == nextImg.size() && prevImg.type() == nextImg.type() );
+        CV_Assert(prevPyr[0].size() == nextPyr[0].size());
+        CV_Assert(prevPts.rows == 1 && prevPts.type() == CV_32FC2);
         CV_Assert(maxLevel_ >= 0);
         CV_Assert(winSize_.width > 2 && winSize_.height > 2);
-        CV_Assert( patch.x > 0 && patch.x < 6 && patch.y > 0 && patch.y < 6 );
-        CV_Assert( prevPts.rows == 1 && prevPts.type() == CV_32FC2 );
-
         if (useInitialFlow_)
             CV_Assert(nextPts.size() == prevPts.size() && nextPts.type() == prevPts.type());
         else
@@ -142,66 +158,70 @@ namespace
         GpuMat temp2 = nextPts.reshape(1);
         cuda::multiply(temp1, Scalar::all(1.0 / (1 << maxLevel_) / 2.0), temp2, 1, -1, stream);
 
+
         ensureSizeIsEnough(1, prevPts.cols, CV_8UC1, status);
         status.setTo(Scalar::all(1), stream);
 
         if (err)
             ensureSizeIsEnough(1, prevPts.cols, CV_32FC1, *err);
 
-        // build the image pyramids.
+        if (prevPyr.size() != size_t(maxLevel_ + 1) || nextPyr.size() != size_t(maxLevel_ + 1))
-
-        BufferPool pool(stream);
-
-        prevPyr_.resize(maxLevel_ + 1);
-        nextPyr_.resize(maxLevel_ + 1);
-
-        int cn = prevImg.channels();
-
-        if (cn == 1 || cn == 4)
         {
-            prevImg.convertTo(prevPyr_[0], CV_32F, stream);
+            buildImagePyramid(prevPyr[0], prevPyr, nextPyr[0], nextPyr, stream);
-            nextImg.convertTo(nextPyr_[0], CV_32F, stream);
-        }
-        else
-        {
-            GpuMat buf = pool.getBuffer(prevImg.size(), CV_MAKE_TYPE(prevImg.depth(), 4));
-
-            cuda::cvtColor(prevImg, buf, COLOR_BGR2BGRA, 0, stream);
-            buf.convertTo(prevPyr_[0], CV_32F, stream);
-
-            cuda::cvtColor(nextImg, buf, COLOR_BGR2BGRA, 0, stream);
-            buf.convertTo(nextPyr_[0], CV_32F, stream);
-        }
-
-        for (int level = 1; level <= maxLevel_; ++level)
-        {
-            cuda::pyrDown(prevPyr_[level - 1], prevPyr_[level], stream);
-            cuda::pyrDown(nextPyr_[level - 1], nextPyr_[level], stream);
         }
 
+        dim3 block, patch;
+        calcPatchSize(winSize_, block, patch);
+        CV_Assert(patch.x > 0 && patch.x < 6 && patch.y > 0 && patch.y < 6);
         pyrlk::loadConstants(make_int2(winSize_.width, winSize_.height), iters_, StreamAccessor::getStream(stream));
 
+        const int cn = prevPyr[0].channels();
+        const int type = prevPyr[0].depth();
+
+        typedef void(*func_t)(GpuMat I, GpuMat J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
+            int level, dim3 block, dim3 patch, cudaStream_t stream);
+
+        // Current int datatype is disabled due to pyrDown not implementing it
+        // while ushort does work, it has significantly worse performance, and thus doesn't pass accuracy tests.
+        static const func_t funcs[6][4] =
+        {
+          {   pyrlk::dispatcher<uchar, 1>     , /*pyrlk::dispatcher<uchar, 2>*/ 0, pyrlk::dispatcher<uchar, 3>     ,   pyrlk::dispatcher<uchar, 4>    },
+          { /*pyrlk::dispatcher<char, 1>*/   0, /*pyrlk::dispatcher<char, 2>*/  0, /*pyrlk::dispatcher<char, 3>*/  0, /*pyrlk::dispatcher<char, 4>*/ 0 },
+          { pyrlk::dispatcher<ushort, 1>      , /*pyrlk::dispatcher<ushort, 2>*/0, pyrlk::dispatcher<ushort, 3>     ,   pyrlk::dispatcher<ushort, 4>   },
+          { /*pyrlk::dispatcher<short, 1>*/  0, /*pyrlk::dispatcher<short, 2>*/ 0, /*pyrlk::dispatcher<short, 3>*/ 0, /*pyrlk::dispatcher<short, 4>*/0 },
+          {   pyrlk::dispatcher<int, 1>       , /*pyrlk::dispatcher<int, 2>*/   0, pyrlk::dispatcher<int, 3>        ,   pyrlk::dispatcher<int, 4>      },
+          {   pyrlk::dispatcher<float, 1>     , /*pyrlk::dispatcher<float, 2>*/ 0, pyrlk::dispatcher<float, 3>      ,   pyrlk::dispatcher<float, 4>    }
+        };
+
+        func_t func = funcs[type][cn-1];
+        CV_Assert(func != NULL && "Datatype not implemented");
         for (int level = maxLevel_; level >= 0; level--)
         {
-            if (cn == 1)
+            func(prevPyr[level], nextPyr[level],
-            {
-                pyrlk::sparse1(prevPyr_[level], nextPyr_[level],
                 prevPts.ptr<float2>(), nextPts.ptr<float2>(),
-                               status.ptr(),
+                status.ptr(), level == 0 && err ? err->ptr<float>() : 0,
-                               level == 0 && err ? err->ptr<float>() : 0, prevPts.cols,
+                prevPts.cols, level, block, patch,
-                               level, block, patch,
-                               StreamAccessor::getStream(stream));
-            }
-            else
-            {
-                pyrlk::sparse4(prevPyr_[level], nextPyr_[level],
-                               prevPts.ptr<float2>(), nextPts.ptr<float2>(),
-                               status.ptr(),
-                               level == 0 && err ? err->ptr<float>() : 0, prevPts.cols,
-                               level, block, patch,
                 StreamAccessor::getStream(stream));
         }
     }
+
+    void PyrLKOpticalFlowBase::sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts, GpuMat& status, GpuMat* err, Stream& stream)
+    {
+        if (prevPts.empty())
+        {
+            nextPts.release();
+            status.release();
+            if (err) err->release();
+            return;
+        }
+        CV_Assert( prevImg.channels() == 1 || prevImg.channels() == 3 || prevImg.channels() == 4 );
+        CV_Assert( prevImg.size() == nextImg.size() && prevImg.type() == nextImg.type() );
+
+        // build the image pyramids.
+        buildImagePyramid(prevImg, prevPyr_, nextImg, nextPyr_, stream);
+
+        sparse(prevPyr_, nextPyr_, prevPts, nextPts, status, err, stream);
+
     }
 
     void PyrLKOpticalFlowBase::dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, Stream& stream)
@@ -250,7 +270,7 @@ namespace
         {
             int idx2 = (idx + 1) & 1;
 
-            pyrlk::dense(prevPyr_[level], nextPyr_[level],
+            pyrlk::pyrLK_caller<float,1>::dense(prevPyr_[level], nextPyr_[level],
                          uPyr[idx], vPyr[idx], uPyr[idx2], vPyr[idx2],
                          PtrStepSzf(), winSize2i,
                          StreamAccessor::getStream(stream));
@@ -289,15 +309,24 @@ namespace
                           OutputArray _err,
                           Stream& stream)
         {
-            const GpuMat prevImg = _prevImg.getGpuMat();
-            const GpuMat nextImg = _nextImg.getGpuMat();
             const GpuMat prevPts = _prevPts.getGpuMat();
             GpuMat& nextPts = _nextPts.getGpuMatRef();
             GpuMat& status = _status.getGpuMatRef();
             GpuMat* err = _err.needed() ? &(_err.getGpuMatRef()) : NULL;
-
+            if (_prevImg.kind() == _InputArray::STD_VECTOR_CUDA_GPU_MAT && _prevImg.kind() == _InputArray::STD_VECTOR_CUDA_GPU_MAT)
+            {
+                std::vector<GpuMat> prevPyr, nextPyr;
+                _prevImg.getGpuMatVector(prevPyr);
+                _nextImg.getGpuMatVector(nextPyr);
+                sparse(prevPyr, nextPyr, prevPts, nextPts, status, err, stream);
+            }
+            else
+            {
+                const GpuMat prevImg = _prevImg.getGpuMat();
+                const GpuMat nextImg = _nextImg.getGpuMat();
                 sparse(prevImg, nextImg, prevPts, nextPts, status, err, stream);
             }
+        }
     };
 
     class DensePyrLKOpticalFlowImpl : public DensePyrLKOpticalFlow, private PyrLKOpticalFlowBase

modules/cudaoptflow/test/test_optflow.cpp

@@ -167,33 +167,34 @@ INSTANTIATE_TEST_CASE_P(CUDA_OptFlow, BroxOpticalFlow, ALL_DEVICES);
 
 namespace
 {
-    IMPLEMENT_PARAM_CLASS(UseGray, bool)
+    IMPLEMENT_PARAM_CLASS(Chan, int)
+    IMPLEMENT_PARAM_CLASS(DataType, int)
 }
 
-PARAM_TEST_CASE(PyrLKOpticalFlow, cv::cuda::DeviceInfo, UseGray)
+PARAM_TEST_CASE(PyrLKOpticalFlow, cv::cuda::DeviceInfo, Chan, DataType)
 {
     cv::cuda::DeviceInfo devInfo;
-    bool useGray;
+    int channels;
-
+    int dataType;
     virtual void SetUp()
     {
         devInfo = GET_PARAM(0);
-        useGray = GET_PARAM(1);
+        channels = GET_PARAM(1);
-
+        dataType = GET_PARAM(2);
         cv::cuda::setDevice(devInfo.deviceID());
     }
 };
 
 CUDA_TEST_P(PyrLKOpticalFlow, Sparse)
 {
-    cv::Mat frame0 = readImage("opticalflow/frame0.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
+    cv::Mat frame0 = readImage("opticalflow/frame0.png", channels == 1 ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
     ASSERT_FALSE(frame0.empty());
 
-    cv::Mat frame1 = readImage("opticalflow/frame1.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
+    cv::Mat frame1 = readImage("opticalflow/frame1.png", channels == 1 ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
     ASSERT_FALSE(frame1.empty());
 
     cv::Mat gray_frame;
-    if (useGray)
+    if (channels == 1)
         gray_frame = frame0;
     else
         cv::cvtColor(frame0, gray_frame, cv::COLOR_BGR2GRAY);
@@ -208,9 +209,23 @@ CUDA_TEST_P(PyrLKOpticalFlow, Sparse)
     cv::Ptr<cv::cuda::SparsePyrLKOpticalFlow> pyrLK =
             cv::cuda::SparsePyrLKOpticalFlow::create();
 
+    std::vector<cv::Point2f> nextPts_gold;
+    std::vector<unsigned char> status_gold;
+    cv::calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts_gold, status_gold, cv::noArray());
+
+
     cv::cuda::GpuMat d_nextPts;
     cv::cuda::GpuMat d_status;
-    pyrLK->calc(loadMat(frame0), loadMat(frame1), d_pts, d_nextPts, d_status);
+    cv::Mat converted0, converted1;
+    if(channels == 4)
+    {
+        cv::cvtColor(frame0, frame0, cv::COLOR_BGR2BGRA);
+        cv::cvtColor(frame1, frame1, cv::COLOR_BGR2BGRA);
+    }
+    frame0.convertTo(converted0, dataType);
+    frame1.convertTo(converted1, dataType);
+
+    pyrLK->calc(loadMat(converted0), loadMat(converted1), d_pts, d_nextPts, d_status);
 
     std::vector<cv::Point2f> nextPts(d_nextPts.cols);
     cv::Mat nextPts_mat(1, d_nextPts.cols, CV_32FC2, (void*)&nextPts[0]);
@@ -220,10 +235,6 @@ CUDA_TEST_P(PyrLKOpticalFlow, Sparse)
     cv::Mat status_mat(1, d_status.cols, CV_8UC1, (void*)&status[0]);
     d_status.download(status_mat);
 
-    std::vector<cv::Point2f> nextPts_gold;
-    std::vector<unsigned char> status_gold;
-    cv::calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts_gold, status_gold, cv::noArray());
-
     ASSERT_EQ(nextPts_gold.size(), nextPts.size());
     ASSERT_EQ(status_gold.size(), status.size());
 
@@ -251,11 +262,16 @@ CUDA_TEST_P(PyrLKOpticalFlow, Sparse)
     double bad_ratio = static_cast<double>(mistmatch) / nextPts.size();
 
     ASSERT_LE(bad_ratio, 0.01);
+
+
 }
 
 INSTANTIATE_TEST_CASE_P(CUDA_OptFlow, PyrLKOpticalFlow, testing::Combine(
     ALL_DEVICES,
-    testing::Values(UseGray(true), UseGray(false))));
+    testing::Values(Chan(1), Chan(3), Chan(4)),
+    testing::Values(DataType(CV_8U), DataType(CV_16U), DataType(CV_32S), DataType(CV_32F))));
+
+
 
 //////////////////////////////////////////////////////
 // FarnebackOpticalFlow

modules/cudawarping/src/cuda/pyr_down.cu

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

modules/cudawarping/src/pyramids.cpp

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