};
+// 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)
}
//////////////////////////////////////////////////////
+// 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
DEF_PARAM_TEST_1(Video, string);
--- /dev/null
+/*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
--- /dev/null
+/*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)
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
projects / profile / ivi / opencv.git / commitdiff