added dual tvl1 optical flow gpu implementation

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

@@ -1982,6 +1982,95 @@ private:
 };
 
 
+// Implementation of the Zach, Pock and Bischof Dual TV-L1 Optical Flow method
+//
+// see reference:
+//   [1] C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow".
+//   [2] Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
+class CV_EXPORTS OpticalFlowDual_TVL1_GPU
+{
+public:
+    OpticalFlowDual_TVL1_GPU();
+
+    void operator ()(const GpuMat& I0, const GpuMat& I1, GpuMat& flowx, GpuMat& flowy);
+
+    void collectGarbage();
+
+    /**
+     * Time step of the numerical scheme.
+     */
+    double tau;
+
+    /**
+     * Weight parameter for the data term, attachment parameter.
+     * This is the most relevant parameter, which determines the smoothness of the output.
+     * The smaller this parameter is, the smoother the solutions we obtain.
+     * It depends on the range of motions of the images, so its value should be adapted to each image sequence.
+     */
+    double lambda;
+
+    /**
+     * Weight parameter for (u - v)^2, tightness parameter.
+     * It serves as a link between the attachment and the regularization terms.
+     * In theory, it should have a small value in order to maintain both parts in correspondence.
+     * The method is stable for a large range of values of this parameter.
+     */
+    double theta;
+
+    /**
+     * Number of scales used to create the pyramid of images.
+     */
+    int nscales;
+
+    /**
+     * Number of warpings per scale.
+     * Represents the number of times that I1(x+u0) and grad( I1(x+u0) ) are computed per scale.
+     * This is a parameter that assures the stability of the method.
+     * It also affects the running time, so it is a compromise between speed and accuracy.
+     */
+    int warps;
+
+    /**
+     * Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time.
+     * A small value will yield more accurate solutions at the expense of a slower convergence.
+     */
+    double epsilon;
+
+    /**
+     * Stopping criterion iterations number used in the numerical scheme.
+     */
+    int iterations;
+
+    bool useInitialFlow;
+
+private:
+    void procOneScale(const GpuMat& I0, const GpuMat& I1, GpuMat& u1, GpuMat& u2);
+
+    std::vector<GpuMat> I0s;
+    std::vector<GpuMat> I1s;
+    std::vector<GpuMat> u1s;
+    std::vector<GpuMat> u2s;
+
+    GpuMat I1x_buf;
+    GpuMat I1y_buf;
+
+    GpuMat I1w_buf;
+    GpuMat I1wx_buf;
+    GpuMat I1wy_buf;
+
+    GpuMat grad_buf;
+    GpuMat rho_c_buf;
+
+    GpuMat p11_buf;
+    GpuMat p12_buf;
+    GpuMat p21_buf;
+    GpuMat p22_buf;
+
+    GpuMat diff_buf;
+    GpuMat norm_buf;
+};
+
+
 //! Interpolate frames (images) using provided optical flow (displacement field).
 //! frame0   - frame 0 (32-bit floating point images, single channel)
 //! frame1   - frame 1 (the same type and size)

modules/gpu/perf/perf_video.cpp

@@ -394,6 +394,56 @@ PERF_TEST_P(ImagePair, Video_FarnebackOpticalFlow,
     }
 }
 
+//////////////////////////////////////////////////////
+// OpticalFlowDual_TVL1
+
+PERF_TEST_P(ImagePair, Video_OpticalFlowDual_TVL1,
+    Values<pair_string>(make_pair("gpu/opticalflow/frame0.png", "gpu/opticalflow/frame1.png")))
+{
+    declare.time(20);
+
+    cv::Mat frame0 = readImage(GetParam().first, cv::IMREAD_GRAYSCALE);
+    ASSERT_FALSE(frame0.empty());
+
+    cv::Mat frame1 = readImage(GetParam().second, cv::IMREAD_GRAYSCALE);
+    ASSERT_FALSE(frame1.empty());
+
+    if (PERF_RUN_GPU())
+    {
+        cv::gpu::GpuMat d_frame0(frame0);
+        cv::gpu::GpuMat d_frame1(frame1);
+        cv::gpu::GpuMat d_flowx;
+        cv::gpu::GpuMat d_flowy;
+
+        cv::gpu::OpticalFlowDual_TVL1_GPU d_alg;
+
+        d_alg(d_frame0, d_frame1, d_flowx, d_flowy);
+
+        TEST_CYCLE()
+        {
+            d_alg(d_frame0, d_frame1, d_flowx, d_flowy);
+        }
+
+        GPU_SANITY_CHECK(d_flowx);
+        GPU_SANITY_CHECK(d_flowy);
+    }
+    else
+    {
+        cv::Mat flow;
+
+        cv::OpticalFlowDual_TVL1 alg;
+
+        alg(frame0, frame1, flow);
+
+        TEST_CYCLE()
+        {
+            alg(frame0, frame1, flow);
+        }
+
+        CPU_SANITY_CHECK(flow);
+    }
+}
+
 //////////////////////////////////////////////////////
 // FGDStatModel
 

modules/gpu/src/cuda/tvl1flow.cu

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or bpied warranties, including, but not limited to, the bpied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#if !defined CUDA_DISABLER
+
+#include "opencv2/gpu/device/common.hpp"
+#include "opencv2/gpu/device/border_interpolate.hpp"
+#include "opencv2/gpu/device/limits.hpp"
+
+using namespace cv::gpu;
+using namespace cv::gpu::device;
+
+////////////////////////////////////////////////////////////
+// centeredGradient
+
+namespace tvl1flow
+{
+    __global__ void centeredGradientKernel(const PtrStepSzf src, PtrStepf dx, PtrStepf dy)
+    {
+        const int x = blockIdx.x * blockDim.x + threadIdx.x;
+        const int y = blockIdx.y * blockDim.y + threadIdx.y;
+
+        if (x >= src.cols || y >= src.rows)
+            return;
+
+        dx(y, x) = 0.5f * (src(y, ::min(x + 1, src.cols - 1)) - src(y, ::max(x - 1, 0)));
+        dy(y, x) = 0.5f * (src(::min(y + 1, src.rows - 1), x) - src(::max(y - 1, 0), x));
+    }
+
+    void centeredGradient(PtrStepSzf src, PtrStepSzf dx, PtrStepSzf dy)
+    {
+        const dim3 block(32, 8);
+        const dim3 grid(divUp(src.cols, block.x), divUp(src.rows, block.y));
+
+        centeredGradientKernel<<<grid, block>>>(src, dx, dy);
+        cudaSafeCall( cudaGetLastError() );
+
+        cudaSafeCall( cudaDeviceSynchronize() );
+    }
+}
+
+////////////////////////////////////////////////////////////
+// warpBackward
+
+namespace tvl1flow
+{
+    static __device__ __forceinline__ float bicubicCoeff(float x_)
+    {
+        float x = fabsf(x_);
+        if (x <= 1.0f)
+        {
+            return x * x * (1.5f * x - 2.5f) + 1.0f;
+        }
+        else if (x < 2.0f)
+        {
+            return x * (x * (-0.5f * x + 2.5f) - 4.0f) + 2.0f;
+        }
+        else
+        {
+            return 0.0f;
+        }
+    }
+
+    texture<float, cudaTextureType2D, cudaReadModeElementType> tex_I1 (false, cudaFilterModePoint, cudaAddressModeClamp);
+    texture<float, cudaTextureType2D, cudaReadModeElementType> tex_I1x(false, cudaFilterModePoint, cudaAddressModeClamp);
+    texture<float, cudaTextureType2D, cudaReadModeElementType> tex_I1y(false, cudaFilterModePoint, cudaAddressModeClamp);
+
+    __global__ void warpBackwardKernel(const PtrStepSzf I0, const PtrStepf u1, const PtrStepf u2, PtrStepf I1w, PtrStepf I1wx, PtrStepf I1wy, PtrStepf grad, PtrStepf rho)
+    {
+        const int x = blockIdx.x * blockDim.x + threadIdx.x;
+        const int y = blockIdx.y * blockDim.y + threadIdx.y;
+
+        if (x >= I0.cols || y >= I0.rows)
+            return;
+
+        const float u1Val = u1(y, x);
+        const float u2Val = u2(y, x);
+
+        const float wx = x + u1Val;
+        const float wy = y + u2Val;
+
+        const int xmin = ::ceilf(wx - 2.0f);
+        const int xmax = ::floorf(wx + 2.0f);
+
+        const int ymin = ::ceilf(wy - 2.0f);
+        const int ymax = ::floorf(wy + 2.0f);
+
+        float sum  = 0.0f;
+        float sumx = 0.0f;
+        float sumy = 0.0f;
+        float wsum = 0.0f;
+
+        for (int cy = ymin; cy <= ymax; ++cy)
+        {
+            for (int cx = xmin; cx <= xmax; ++cx)
+            {
+                const float w = bicubicCoeff(wx - cx) * bicubicCoeff(wy - cy);
+
+                sum  += w * tex2D(tex_I1 , cx, cy);
+                sumx += w * tex2D(tex_I1x, cx, cy);
+                sumy += w * tex2D(tex_I1y, cx, cy);
+
+                wsum += w;
+            }
+        }
+
+        const float coeff = 1.0f / wsum;
+
+        const float I1wVal  = sum  * coeff;
+        const float I1wxVal = sumx * coeff;
+        const float I1wyVal = sumy * coeff;
+
+        I1w(y, x)  = I1wVal;
+        I1wx(y, x) = I1wxVal;
+        I1wy(y, x) = I1wyVal;
+
+        const float Ix2 = I1wxVal * I1wxVal;
+        const float Iy2 = I1wyVal * I1wyVal;
+
+        // store the |Grad(I1)|^2
+        grad(y, x) = Ix2 + Iy2;
+
+        // compute the constant part of the rho function
+        const float I0Val = I0(y, x);
+        rho(y, x) = I1wVal - I1wxVal * u1Val - I1wyVal * u2Val - I0Val;
+    }
+
+    void warpBackward(PtrStepSzf I0, PtrStepSzf I1, PtrStepSzf I1x, PtrStepSzf I1y, PtrStepSzf u1, PtrStepSzf u2, PtrStepSzf I1w, PtrStepSzf I1wx, PtrStepSzf I1wy, PtrStepSzf grad, PtrStepSzf rho)
+    {
+        const dim3 block(32, 8);
+        const dim3 grid(divUp(I0.cols, block.x), divUp(I0.rows, block.y));
+
+        bindTexture(&tex_I1 , I1);
+        bindTexture(&tex_I1x, I1x);
+        bindTexture(&tex_I1y, I1y);
+
+        warpBackwardKernel<<<grid, block>>>(I0, u1, u2, I1w, I1wx, I1wy, grad, rho);
+        cudaSafeCall( cudaGetLastError() );
+
+        cudaSafeCall( cudaDeviceSynchronize() );
+    }
+}
+
+////////////////////////////////////////////////////////////
+// estimateU
+
+namespace tvl1flow
+{
+    __device__ float divergence(const PtrStepf& v1, const PtrStepf& v2, int y, int x)
+    {
+        if (x > 0 && y > 0)
+        {
+            const float v1x = v1(y, x) - v1(y, x - 1);
+            const float v2y = v2(y, x) - v2(y - 1, x);
+            return v1x + v2y;
+        }
+        else
+        {
+            if (y > 0)
+                return v1(y, 0) + v2(y, 0) - v2(y - 1, 0);
+            else
+            {
+                if (x > 0)
+                    return v1(0, x) - v1(0, x - 1) + v2(0, x);
+                else
+                    return v1(0, 0) + v2(0, 0);
+            }
+        }
+    }
+
+    __global__ void estimateUKernel(const PtrStepSzf I1wx, const PtrStepf I1wy,
+                              const PtrStepf grad, const PtrStepf rho_c,
+                              const PtrStepf p11, const PtrStepf p12, const PtrStepf p21, const PtrStepf p22,
+                              PtrStepf u1, PtrStepf u2, PtrStepf error,
+                              const float l_t, const float theta)
+    {
+        const int x = blockIdx.x * blockDim.x + threadIdx.x;
+        const int y = blockIdx.y * blockDim.y + threadIdx.y;
+
+        if (x >= I1wx.cols || y >= I1wx.rows)
+            return;
+
+        const float I1wxVal = I1wx(y, x);
+        const float I1wyVal = I1wy(y, x);
+        const float gradVal = grad(y, x);
+        const float u1OldVal = u1(y, x);
+        const float u2OldVal = u2(y, x);
+
+        const float rho = rho_c(y, x) + (I1wxVal * u1OldVal + I1wyVal * u2OldVal);
+
+        // estimate the values of the variable (v1, v2) (thresholding operator TH)
+
+        float d1 = 0.0f;
+        float d2 = 0.0f;
+
+        if (rho < -l_t * gradVal)
+        {
+            d1 = l_t * I1wxVal;
+            d2 = l_t * I1wyVal;
+        }
+        else if (rho > l_t * gradVal)
+        {
+            d1 = -l_t * I1wxVal;
+            d2 = -l_t * I1wyVal;
+        }
+        else if (gradVal > numeric_limits<float>::epsilon())
+        {
+            const float fi = -rho / gradVal;
+            d1 = fi * I1wxVal;
+            d2 = fi * I1wyVal;
+        }
+
+        const float v1 = u1OldVal + d1;
+        const float v2 = u2OldVal + d2;
+
+        // compute the divergence of the dual variable (p1, p2)
+
+        const float div_p1 = divergence(p11, p12, y, x);
+        const float div_p2 = divergence(p21, p22, y, x);
+
+        // estimate the values of the optical flow (u1, u2)
+
+        const float u1NewVal = v1 + theta * div_p1;
+        const float u2NewVal = v2 + theta * div_p2;
+
+        u1(y, x) = u1NewVal;
+        u2(y, x) = u2NewVal;
+
+        const float n1 = (u1OldVal - u1NewVal) * (u1OldVal - u1NewVal);
+        const float n2 = (u2OldVal - u2NewVal) * (u2OldVal - u2NewVal);
+        error(y, x) = n1 + n2;
+    }
+
+    void estimateU(PtrStepSzf I1wx, PtrStepSzf I1wy,
+                   PtrStepSzf grad, PtrStepSzf rho_c,
+                   PtrStepSzf p11, PtrStepSzf p12, PtrStepSzf p21, PtrStepSzf p22,
+                   PtrStepSzf u1, PtrStepSzf u2, PtrStepSzf error,
+                   float l_t, float theta)
+    {
+        const dim3 block(32, 8);
+        const dim3 grid(divUp(I1wx.cols, block.x), divUp(I1wx.rows, block.y));
+
+        estimateUKernel<<<grid, block>>>(I1wx, I1wy, grad, rho_c, p11, p12, p21, p22, u1, u2, error, l_t, theta);
+        cudaSafeCall( cudaGetLastError() );
+
+        cudaSafeCall( cudaDeviceSynchronize() );
+    }
+}
+
+////////////////////////////////////////////////////////////
+// estimateDualVariables
+
+namespace tvl1flow
+{
+    __global__ void estimateDualVariablesKernel(const PtrStepSzf u1, const PtrStepf u2, PtrStepf p11, PtrStepf p12, PtrStepf p21, PtrStepf p22, const float taut)
+    {
+        const int x = blockIdx.x * blockDim.x + threadIdx.x;
+        const int y = blockIdx.y * blockDim.y + threadIdx.y;
+
+        if (x >= u1.cols || y >= u1.rows)
+            return;
+
+        const float u1x = u1(y, ::min(x + 1, u1.cols - 1)) - u1(y, x);
+        const float u1y = u1(::min(y + 1, u1.rows - 1), x) - u1(y, x);
+
+        const float u2x = u2(y, ::min(x + 1, u1.cols - 1)) - u2(y, x);
+        const float u2y = u2(::min(y + 1, u1.rows - 1), x) - u2(y, x);
+
+        const float g1 = ::hypotf(u1x, u1y);
+        const float g2 = ::hypotf(u2x, u2y);
+
+        const float ng1 = 1.0f + taut * g1;
+        const float ng2 = 1.0f + taut * g2;
+
+        p11(y, x) = (p11(y, x) + taut * u1x) / ng1;
+        p12(y, x) = (p12(y, x) + taut * u1y) / ng1;
+        p21(y, x) = (p21(y, x) + taut * u2x) / ng2;
+        p22(y, x) = (p22(y, x) + taut * u2y) / ng2;
+    }
+
+    void estimateDualVariables(PtrStepSzf u1, PtrStepSzf u2, PtrStepSzf p11, PtrStepSzf p12, PtrStepSzf p21, PtrStepSzf p22, float taut)
+    {
+        const dim3 block(32, 8);
+        const dim3 grid(divUp(u1.cols, block.x), divUp(u1.rows, block.y));
+
+        estimateDualVariablesKernel<<<grid, block>>>(u1, u2, p11, p12, p21, p22, taut);
+        cudaSafeCall( cudaGetLastError() );
+
+        cudaSafeCall( cudaDeviceSynchronize() );
+    }
+}
+
+#endif // !defined CUDA_DISABLER

modules/gpu/src/tvl1flow.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "precomp.hpp"
+
+#if !defined HAVE_CUDA || defined(CUDA_DISABLER)
+
+cv::gpu::OpticalFlowDual_TVL1_GPU::OpticalFlowDual_TVL1_GPU() { throw_nogpu(); }
+void cv::gpu::OpticalFlowDual_TVL1_GPU::operator ()(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&) { throw_nogpu(); }
+void cv::gpu::OpticalFlowDual_TVL1_GPU::collectGarbage() {}
+void cv::gpu::OpticalFlowDual_TVL1_GPU::procOneScale(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&) { throw_nogpu(); }
+
+#else
+
+using namespace std;
+using namespace cv;
+using namespace cv::gpu;
+
+cv::gpu::OpticalFlowDual_TVL1_GPU::OpticalFlowDual_TVL1_GPU()
+{
+    tau            = 0.25;
+    lambda         = 0.15;
+    theta          = 0.3;
+    nscales        = 5;
+    warps          = 5;
+    epsilon        = 0.01;
+    iterations     = 300;
+    useInitialFlow = false;
+}
+
+void cv::gpu::OpticalFlowDual_TVL1_GPU::operator ()(const GpuMat& I0, const GpuMat& I1, GpuMat& flowx, GpuMat& flowy)
+{
+    CV_Assert( I0.type() == CV_8UC1 || I0.type() == CV_32FC1 );
+    CV_Assert( I0.size() == I1.size() );
+    CV_Assert( I0.type() == I1.type() );
+    CV_Assert( !useInitialFlow || (flowx.size() == I0.size() && flowx.type() == CV_32FC1 && flowy.size() == flowx.size() && flowy.type() == flowx.type()) );
+    CV_Assert( nscales > 0 );
+
+    // allocate memory for the pyramid structure
+    I0s.resize(nscales);
+    I1s.resize(nscales);
+    u1s.resize(nscales);
+    u2s.resize(nscales);
+
+    I0.convertTo(I0s[0], CV_32F, I0.depth() == CV_8U ? 1.0 : 255.0);
+    I1.convertTo(I1s[0], CV_32F, I1.depth() == CV_8U ? 1.0 : 255.0);
+
+    if (!useInitialFlow)
+    {
+        flowx.create(I0.size(), CV_32FC1);
+        flowy.create(I0.size(), CV_32FC1);
+    }
+
+    u1s[0] = flowx;
+    u2s[0] = flowy;
+
+    I1x_buf.create(I0.size(), CV_32FC1);
+    I1y_buf.create(I0.size(), CV_32FC1);
+
+    I1w_buf.create(I0.size(), CV_32FC1);
+    I1wx_buf.create(I0.size(), CV_32FC1);
+    I1wy_buf.create(I0.size(), CV_32FC1);
+
+    grad_buf.create(I0.size(), CV_32FC1);
+    rho_c_buf.create(I0.size(), CV_32FC1);
+
+    p11_buf.create(I0.size(), CV_32FC1);
+    p12_buf.create(I0.size(), CV_32FC1);
+    p21_buf.create(I0.size(), CV_32FC1);
+    p22_buf.create(I0.size(), CV_32FC1);
+
+    diff_buf.create(I0.size(), CV_32FC1);
+
+    // create the scales
+    for (int s = 1; s < nscales; ++s)
+    {
+        gpu::pyrDown(I0s[s - 1], I0s[s]);
+        gpu::pyrDown(I1s[s - 1], I1s[s]);
+
+        if (I0s[s].cols < 16 || I0s[s].rows < 16)
+        {
+            nscales = s;
+            break;
+        }
+
+        if (useInitialFlow)
+        {
+            gpu::pyrDown(u1s[s - 1], u1s[s]);
+            gpu::pyrDown(u2s[s - 1], u2s[s]);
+
+            gpu::multiply(u1s[s], Scalar::all(0.5), u1s[s]);
+            gpu::multiply(u2s[s], Scalar::all(0.5), u2s[s]);
+        }
+    }
+
+    // pyramidal structure for computing the optical flow
+    for (int s = nscales - 1; s >= 0; --s)
+    {
+        // compute the optical flow at the current scale
+        procOneScale(I0s[s], I1s[s], u1s[s], u2s[s]);
+
+        // if this was the last scale, finish now
+        if (s == 0)
+            break;
+
+        // otherwise, upsample the optical flow
+
+        // zoom the optical flow for the next finer scale
+        gpu::resize(u1s[s], u1s[s - 1], I0s[s - 1].size());
+        gpu::resize(u2s[s], u2s[s - 1], I0s[s - 1].size());
+
+        // scale the optical flow with the appropriate zoom factor
+        gpu::multiply(u1s[s - 1], Scalar::all(2), u1s[s - 1]);
+        gpu::multiply(u2s[s - 1], Scalar::all(2), u2s[s - 1]);
+    }
+}
+
+namespace tvl1flow
+{
+    void centeredGradient(PtrStepSzf src, PtrStepSzf dx, PtrStepSzf dy);
+    void warpBackward(PtrStepSzf I0, PtrStepSzf I1, PtrStepSzf I1x, PtrStepSzf I1y, PtrStepSzf u1, PtrStepSzf u2, PtrStepSzf I1w, PtrStepSzf I1wx, PtrStepSzf I1wy, PtrStepSzf grad, PtrStepSzf rho);
+    void estimateU(PtrStepSzf I1wx, PtrStepSzf I1wy,
+                   PtrStepSzf grad, PtrStepSzf rho_c,
+                   PtrStepSzf p11, PtrStepSzf p12, PtrStepSzf p21, PtrStepSzf p22,
+                   PtrStepSzf u1, PtrStepSzf u2, PtrStepSzf error,
+                   float l_t, float theta);
+    void estimateDualVariables(PtrStepSzf u1, PtrStepSzf u2, PtrStepSzf p11, PtrStepSzf p12, PtrStepSzf p21, PtrStepSzf p22, float taut);
+}
+
+void cv::gpu::OpticalFlowDual_TVL1_GPU::procOneScale(const GpuMat& I0, const GpuMat& I1, GpuMat& u1, GpuMat& u2)
+{
+    using namespace tvl1flow;
+
+    const double scaledEpsilon = epsilon * epsilon * I0.size().area();
+
+    CV_DbgAssert( I1.size() == I0.size() );
+    CV_DbgAssert( I1.type() == I0.type() );
+    CV_DbgAssert( u1.empty() || u1.size() == I0.size() );
+    CV_DbgAssert( u2.size() == u1.size() );
+
+    if (u1.empty())
+    {
+        u1.create(I0.size(), CV_32FC1);
+        u1.setTo(Scalar::all(0));
+
+        u2.create(I0.size(), CV_32FC1);
+        u2.setTo(Scalar::all(0));
+    }
+
+    GpuMat I1x = I1x_buf(Rect(0, 0, I0.cols, I0.rows));
+    GpuMat I1y = I1y_buf(Rect(0, 0, I0.cols, I0.rows));
+    centeredGradient(I1, I1x, I1y);
+
+    GpuMat I1w = I1w_buf(Rect(0, 0, I0.cols, I0.rows));
+    GpuMat I1wx = I1wx_buf(Rect(0, 0, I0.cols, I0.rows));
+    GpuMat I1wy = I1wy_buf(Rect(0, 0, I0.cols, I0.rows));
+
+    GpuMat grad = grad_buf(Rect(0, 0, I0.cols, I0.rows));
+    GpuMat rho_c = rho_c_buf(Rect(0, 0, I0.cols, I0.rows));
+
+    GpuMat p11 = p11_buf(Rect(0, 0, I0.cols, I0.rows));
+    GpuMat p12 = p12_buf(Rect(0, 0, I0.cols, I0.rows));
+    GpuMat p21 = p21_buf(Rect(0, 0, I0.cols, I0.rows));
+    GpuMat p22 = p22_buf(Rect(0, 0, I0.cols, I0.rows));
+    p11.setTo(Scalar::all(0));
+    p12.setTo(Scalar::all(0));
+    p21.setTo(Scalar::all(0));
+    p22.setTo(Scalar::all(0));
+
+    GpuMat diff = diff_buf(Rect(0, 0, I0.cols, I0.rows));
+
+    const float l_t = static_cast<float>(lambda * theta);
+    const float taut = static_cast<float>(tau / theta);
+
+    for (int warpings = 0; warpings < warps; ++warpings)
+    {
+        warpBackward(I0, I1, I1x, I1y, u1, u2, I1w, I1wx, I1wy, grad, rho_c);
+
+        double error = numeric_limits<double>::max();
+        for (int n = 0; error > scaledEpsilon && n < iterations; ++n)
+        {
+            estimateU(I1wx, I1wy, grad, rho_c, p11, p12, p21, p22, u1, u2, diff, l_t, static_cast<float>(theta));
+
+            error = gpu::sum(diff, norm_buf)[0];
+
+            estimateDualVariables(u1, u2, p11, p12, p21, p22, taut);
+        }
+    }
+}
+
+void cv::gpu::OpticalFlowDual_TVL1_GPU::collectGarbage()
+{
+    I0s.clear();
+    I1s.clear();
+    u1s.clear();
+    u2s.clear();
+
+    I1x_buf.release();
+    I1y_buf.release();
+
+    I1w_buf.release();
+    I1wx_buf.release();
+    I1wy_buf.release();
+
+    grad_buf.release();
+    rho_c_buf.release();
+
+    p11_buf.release();
+    p12_buf.release();
+    p21_buf.release();
+    p22_buf.release();
+
+    diff_buf.release();
+    norm_buf.release();
+}
+
+#endif // !defined HAVE_CUDA || defined(CUDA_DISABLER)

modules/gpu/test/test_optflow.cpp

@@ -401,4 +401,48 @@ INSTANTIATE_TEST_CASE_P(GPU_Video, FarnebackOpticalFlow, testing::Combine(
     testing::Values(FarnebackOptFlowFlags(0), FarnebackOptFlowFlags(cv::OPTFLOW_FARNEBACK_GAUSSIAN)),
     testing::Values(UseInitFlow(false), UseInitFlow(true))));
 
+//////////////////////////////////////////////////////
+// OpticalFlowDual_TVL1
+
+PARAM_TEST_CASE(OpticalFlowDual_TVL1, cv::gpu::DeviceInfo, UseRoi)
+{
+    cv::gpu::DeviceInfo devInfo;
+    bool useRoi;
+
+    virtual void SetUp()
+    {
+        devInfo = GET_PARAM(0);
+        useRoi = GET_PARAM(1);
+
+        cv::gpu::setDevice(devInfo.deviceID());
+    }
+};
+
+GPU_TEST_P(OpticalFlowDual_TVL1, Accuracy)
+{
+    cv::Mat frame0 = readImage("opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
+    ASSERT_FALSE(frame0.empty());
+
+    cv::Mat frame1 = readImage("opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
+    ASSERT_FALSE(frame1.empty());
+
+    cv::gpu::OpticalFlowDual_TVL1_GPU d_alg;
+    cv::gpu::GpuMat d_flowx = createMat(frame0.size(), CV_32FC1, useRoi);
+    cv::gpu::GpuMat d_flowy = createMat(frame0.size(), CV_32FC1, useRoi);
+    d_alg(loadMat(frame0, useRoi), loadMat(frame1, useRoi), d_flowx, d_flowy);
+
+    cv::OpticalFlowDual_TVL1 alg;
+    cv::Mat flow;
+    alg(frame0, frame1, flow);
+    cv::Mat gold[2];
+    cv::split(flow, gold);
+
+    EXPECT_MAT_SIMILAR(gold[0], d_flowx, 3e-3);
+    EXPECT_MAT_SIMILAR(gold[1], d_flowy, 3e-3);
+}
+
+INSTANTIATE_TEST_CASE_P(GPU_Video, OpticalFlowDual_TVL1, testing::Combine(
+    ALL_DEVICES,
+    WHOLE_SUBMAT));
+
 #endif // HAVE_CUDA