added cuda support for chambolle parameter
This commit is contained in:
Ernest Galbrun
parent
afb9b9540c
commit
b66a13183e
@ -209,9 +209,9 @@ namespace tvl1flow
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__global__ void estimateUKernel(const PtrStepSzf I1wx, const PtrStepf I1wy,
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const PtrStepf grad, const PtrStepf rho_c,
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const PtrStepf p11, const PtrStepf p12, const PtrStepf p21, const PtrStepf p22,
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PtrStepf u1, PtrStepf u2, PtrStepf error,
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const float l_t, const float theta, const bool calcError)
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const PtrStepf p11, const PtrStepf p12, const PtrStepf p21, const PtrStepf p22, const PtrStepf p31, const PtrStepf p32,
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PtrStepf u1, PtrStepf u2, PtrStepf u3, PtrStepf error,
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const float l_t, const float theta, const float gamma, const bool calcError)
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{
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const int x = blockIdx.x * blockDim.x + threadIdx.x;
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const int y = blockIdx.y * blockDim.y + threadIdx.y;
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@ -223,66 +223,76 @@ namespace tvl1flow
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const float I1wyVal = I1wy(y, x);
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const float gradVal = grad(y, x);
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const float u1OldVal = u1(y, x);
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const float u2OldVal = u2(y, x);
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const float u2OldVal = u2(y, x);
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const float u3OldVal = u3(y, x);
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const float rho = rho_c(y, x) + (I1wxVal * u1OldVal + I1wyVal * u2OldVal);
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const float rho = rho_c(y, x) + (I1wxVal * u1OldVal + I1wyVal * u2OldVal + gamma * u3OldVal);
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// estimate the values of the variable (v1, v2) (thresholding operator TH)
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float d1 = 0.0f;
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float d2 = 0.0f;
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float d3 = 0.0f;
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if (rho < -l_t * gradVal)
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{
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d1 = l_t * I1wxVal;
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d2 = l_t * I1wyVal;
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d3 = l_t * gamma;
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}
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else if (rho > l_t * gradVal)
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{
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d1 = -l_t * I1wxVal;
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d2 = -l_t * I1wyVal;
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d3 = -l_t * gamma;
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}
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else if (gradVal > numeric_limits<float>::epsilon())
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{
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const float fi = -rho / gradVal;
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d1 = fi * I1wxVal;
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d2 = fi * I1wyVal;
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d3 = fi * gamma;
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}
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const float v1 = u1OldVal + d1;
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const float v2 = u2OldVal + d2;
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const float v2 = u2OldVal + d2;
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const float v3 = u3OldVal + d3;
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// compute the divergence of the dual variable (p1, p2)
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const float div_p1 = divergence(p11, p12, y, x);
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const float div_p2 = divergence(p21, p22, y, x);
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const float div_p2 = divergence(p21, p22, y, x);
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const float div_p3 = divergence(p31, p32, y, x);
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// estimate the values of the optical flow (u1, u2)
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const float u1NewVal = v1 + theta * div_p1;
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const float u2NewVal = v2 + theta * div_p2;
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const float u2NewVal = v2 + theta * div_p2;
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const float u3NewVal = v3 + theta * div_p3;
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u1(y, x) = u1NewVal;
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u2(y, x) = u2NewVal;
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u2(y, x) = u2NewVal;
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u3(y, x) = u3NewVal;
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if (calcError)
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{
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const float n1 = (u1OldVal - u1NewVal) * (u1OldVal - u1NewVal);
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const float n2 = (u2OldVal - u2NewVal) * (u2OldVal - u2NewVal);
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error(y, x) = n1 + n2;
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const float n2 = (u2OldVal - u2NewVal) * (u2OldVal - u2NewVal);
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const float n3 = 0;// (u3OldVal - u3NewVal) * (u3OldVal - u3NewVal);
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error(y, x) = n1 + n2 + n3;
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}
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}
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void estimateU(PtrStepSzf I1wx, PtrStepSzf I1wy,
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PtrStepSzf grad, PtrStepSzf rho_c,
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PtrStepSzf p11, PtrStepSzf p12, PtrStepSzf p21, PtrStepSzf p22,
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PtrStepSzf u1, PtrStepSzf u2, PtrStepSzf error,
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float l_t, float theta, bool calcError)
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PtrStepSzf p11, PtrStepSzf p12, PtrStepSzf p21, PtrStepSzf p22, PtrStepSzf p31, PtrStepSzf p32,
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PtrStepSzf u1, PtrStepSzf u2, PtrStepSzf u3, PtrStepSzf error,
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float l_t, float theta, float gamma, bool calcError)
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{
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const dim3 block(32, 8);
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const dim3 grid(divUp(I1wx.cols, block.x), divUp(I1wx.rows, block.y));
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estimateUKernel<<<grid, block>>>(I1wx, I1wy, grad, rho_c, p11, p12, p21, p22, u1, u2, error, l_t, theta, calcError);
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estimateUKernel<<<grid, block>>>(I1wx, I1wy, grad, rho_c, p11, p12, p21, p22, p31, p32, u1, u2, u3, error, l_t, theta, gamma, calcError);
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cudaSafeCall( cudaGetLastError() );
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cudaSafeCall( cudaDeviceSynchronize() );
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@ -294,7 +304,8 @@ namespace tvl1flow
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namespace tvl1flow
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{
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__global__ void estimateDualVariablesKernel(const PtrStepSzf u1, const PtrStepf u2, PtrStepf p11, PtrStepf p12, PtrStepf p21, PtrStepf p22, const float taut)
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__global__ void estimateDualVariablesKernel(const PtrStepSzf u1, const PtrStepf u2, const PtrStepSzf u3,
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PtrStepf p11, PtrStepf p12, PtrStepf p21, PtrStepf p22, PtrStepf p31, PtrStepf p32, const float taut)
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{
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const int x = blockIdx.x * blockDim.x + threadIdx.x;
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const int y = blockIdx.y * blockDim.y + threadIdx.y;
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@ -308,24 +319,31 @@ namespace tvl1flow
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const float u2x = u2(y, ::min(x + 1, u1.cols - 1)) - u2(y, x);
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const float u2y = u2(::min(y + 1, u1.rows - 1), x) - u2(y, x);
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const float u3x = u3(y, ::min(x + 1, u1.cols - 1)) - u3(y, x);
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const float u3y = u3(::min(y + 1, u1.rows - 1), x) - u3(y, x);
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const float g1 = ::hypotf(u1x, u1y);
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const float g2 = ::hypotf(u2x, u2y);
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const float g2 = ::hypotf(u2x, u2y);
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const float g3 = ::hypotf(u3x, u3y);
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const float ng1 = 1.0f + taut * g1;
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const float ng2 = 1.0f + taut * g2;
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const float ng2 = 1.0f + taut * g2;
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const float ng3 = 1.0f + taut * g3;
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p11(y, x) = (p11(y, x) + taut * u1x) / ng1;
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p12(y, x) = (p12(y, x) + taut * u1y) / ng1;
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p21(y, x) = (p21(y, x) + taut * u2x) / ng2;
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p22(y, x) = (p22(y, x) + taut * u2y) / ng2;
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p22(y, x) = (p22(y, x) + taut * u2y) / ng2;
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p31(y, x) = (p31(y, x) + taut * u3x) / ng3;
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p32(y, x) = (p32(y, x) + taut * u3y) / ng3;
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}
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void estimateDualVariables(PtrStepSzf u1, PtrStepSzf u2, PtrStepSzf p11, PtrStepSzf p12, PtrStepSzf p21, PtrStepSzf p22, float taut)
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void estimateDualVariables(PtrStepSzf u1, PtrStepSzf u2, PtrStepSzf u3, PtrStepSzf p11, PtrStepSzf p12, PtrStepSzf p21, PtrStepSzf p22, PtrStepSzf p31, PtrStepSzf p32, float taut)
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{
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const dim3 block(32, 8);
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const dim3 grid(divUp(u1.cols, block.x), divUp(u1.rows, block.y));
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estimateDualVariablesKernel<<<grid, block>>>(u1, u2, p11, p12, p21, p22, taut);
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estimateDualVariablesKernel<<<grid, block>>>(u1, u2, u3, p11, p12, p21, p22, p31, p32, taut);
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cudaSafeCall( cudaGetLastError() );
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cudaSafeCall( cudaDeviceSynchronize() );
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@ -64,6 +64,7 @@ cv::cuda::OpticalFlowDual_TVL1_CUDA::OpticalFlowDual_TVL1_CUDA()
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epsilon = 0.01;
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iterations = 300;
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scaleStep = 0.8;
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gamma = 0.0;
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useInitialFlow = false;
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}
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@ -80,6 +81,7 @@ void cv::cuda::OpticalFlowDual_TVL1_CUDA::operator ()(const GpuMat& I0, const Gp
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I1s.resize(nscales);
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u1s.resize(nscales);
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u2s.resize(nscales);
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u3s.resize(nscales);
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I0.convertTo(I0s[0], CV_32F, I0.depth() == CV_8U ? 1.0 : 255.0);
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I1.convertTo(I1s[0], CV_32F, I1.depth() == CV_8U ? 1.0 : 255.0);
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@ -92,6 +94,7 @@ void cv::cuda::OpticalFlowDual_TVL1_CUDA::operator ()(const GpuMat& I0, const Gp
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u1s[0] = flowx;
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u2s[0] = flowy;
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u3s[0].create(I0.size(), CV_32FC1);
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I1x_buf.create(I0.size(), CV_32FC1);
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I1y_buf.create(I0.size(), CV_32FC1);
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@ -106,7 +109,9 @@ void cv::cuda::OpticalFlowDual_TVL1_CUDA::operator ()(const GpuMat& I0, const Gp
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p11_buf.create(I0.size(), CV_32FC1);
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p12_buf.create(I0.size(), CV_32FC1);
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p21_buf.create(I0.size(), CV_32FC1);
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p22_buf.create(I0.size(), CV_32FC1);
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p22_buf.create(I0.size(), CV_32FC1);
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p31_buf.create(I0.size(), CV_32FC1);
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p32_buf.create(I0.size(), CV_32FC1);
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diff_buf.create(I0.size(), CV_32FC1);
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@ -134,7 +139,8 @@ void cv::cuda::OpticalFlowDual_TVL1_CUDA::operator ()(const GpuMat& I0, const Gp
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{
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u1s[s].create(I0s[s].size(), CV_32FC1);
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u2s[s].create(I0s[s].size(), CV_32FC1);
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}
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}
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u3s[s].create(I0s[s].size(), CV_32FC1);
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}
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if (!useInitialFlow)
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@ -142,12 +148,13 @@ void cv::cuda::OpticalFlowDual_TVL1_CUDA::operator ()(const GpuMat& I0, const Gp
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u1s[nscales-1].setTo(Scalar::all(0));
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u2s[nscales-1].setTo(Scalar::all(0));
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}
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u3s[nscales - 1].setTo(Scalar::all(0));
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// pyramidal structure for computing the optical flow
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for (int s = nscales - 1; s >= 0; --s)
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{
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// compute the optical flow at the current scale
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procOneScale(I0s[s], I1s[s], u1s[s], u2s[s]);
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procOneScale(I0s[s], I1s[s], u1s[s], u2s[s], u3s[s]);
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// if this was the last scale, finish now
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if (s == 0)
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@ -157,7 +164,8 @@ void cv::cuda::OpticalFlowDual_TVL1_CUDA::operator ()(const GpuMat& I0, const Gp
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// zoom the optical flow for the next finer scale
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cuda::resize(u1s[s], u1s[s - 1], I0s[s - 1].size());
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cuda::resize(u2s[s], u2s[s - 1], I0s[s - 1].size());
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cuda::resize(u2s[s], u2s[s - 1], I0s[s - 1].size());
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cuda::resize(u3s[s], u3s[s - 1], I0s[s - 1].size());
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// scale the optical flow with the appropriate zoom factor
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cuda::multiply(u1s[s - 1], Scalar::all(1/scaleStep), u1s[s - 1]);
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@ -171,13 +179,13 @@ namespace tvl1flow
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void warpBackward(PtrStepSzf I0, PtrStepSzf I1, PtrStepSzf I1x, PtrStepSzf I1y, PtrStepSzf u1, PtrStepSzf u2, PtrStepSzf I1w, PtrStepSzf I1wx, PtrStepSzf I1wy, PtrStepSzf grad, PtrStepSzf rho);
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void estimateU(PtrStepSzf I1wx, PtrStepSzf I1wy,
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PtrStepSzf grad, PtrStepSzf rho_c,
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PtrStepSzf p11, PtrStepSzf p12, PtrStepSzf p21, PtrStepSzf p22,
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PtrStepSzf u1, PtrStepSzf u2, PtrStepSzf error,
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float l_t, float theta, bool calcError);
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void estimateDualVariables(PtrStepSzf u1, PtrStepSzf u2, PtrStepSzf p11, PtrStepSzf p12, PtrStepSzf p21, PtrStepSzf p22, float taut);
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PtrStepSzf p11, PtrStepSzf p12, PtrStepSzf p21, PtrStepSzf p22, PtrStepSzf p31, PtrStepSzf p32,
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PtrStepSzf u1, PtrStepSzf u2, PtrStepSzf u3, PtrStepSzf error,
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float l_t, float theta, float gamma, bool calcError);
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void estimateDualVariables(PtrStepSzf u1, PtrStepSzf u2, PtrStepSzf u3, PtrStepSzf p11, PtrStepSzf p12, PtrStepSzf p21, PtrStepSzf p22, PtrStepSzf p31, PtrStepSzf p32, float taut);
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}
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void cv::cuda::OpticalFlowDual_TVL1_CUDA::procOneScale(const GpuMat& I0, const GpuMat& I1, GpuMat& u1, GpuMat& u2)
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void cv::cuda::OpticalFlowDual_TVL1_CUDA::procOneScale(const GpuMat& I0, const GpuMat& I1, GpuMat& u1, GpuMat& u2, GpuMat& u3)
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{
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using namespace tvl1flow;
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@ -202,11 +210,15 @@ void cv::cuda::OpticalFlowDual_TVL1_CUDA::procOneScale(const GpuMat& I0, const G
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GpuMat p11 = p11_buf(Rect(0, 0, I0.cols, I0.rows));
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GpuMat p12 = p12_buf(Rect(0, 0, I0.cols, I0.rows));
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GpuMat p21 = p21_buf(Rect(0, 0, I0.cols, I0.rows));
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GpuMat p22 = p22_buf(Rect(0, 0, I0.cols, I0.rows));
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GpuMat p22 = p22_buf(Rect(0, 0, I0.cols, I0.rows));
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GpuMat p31 = p31_buf(Rect(0, 0, I0.cols, I0.rows));
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GpuMat p32 = p32_buf(Rect(0, 0, I0.cols, I0.rows));
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p11.setTo(Scalar::all(0));
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p12.setTo(Scalar::all(0));
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p21.setTo(Scalar::all(0));
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p22.setTo(Scalar::all(0));
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p22.setTo(Scalar::all(0));
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p31.setTo(Scalar::all(0));
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p32.setTo(Scalar::all(0));
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GpuMat diff = diff_buf(Rect(0, 0, I0.cols, I0.rows));
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@ -224,7 +236,7 @@ void cv::cuda::OpticalFlowDual_TVL1_CUDA::procOneScale(const GpuMat& I0, const G
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// some tweaks to make sum operation less frequently
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bool calcError = (epsilon > 0) && (n & 0x1) && (prevError < scaledEpsilon);
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estimateU(I1wx, I1wy, grad, rho_c, p11, p12, p21, p22, u1, u2, diff, l_t, static_cast<float>(theta), calcError);
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estimateU(I1wx, I1wy, grad, rho_c, p11, p12, p21, p22, p31, p32, u1, u2, u3, diff, l_t, gamma, static_cast<float>(theta), calcError);
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if (calcError)
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{
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@ -237,7 +249,7 @@ void cv::cuda::OpticalFlowDual_TVL1_CUDA::procOneScale(const GpuMat& I0, const G
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prevError -= scaledEpsilon;
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}
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estimateDualVariables(u1, u2, p11, p12, p21, p22, taut);
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estimateDualVariables(u1, u2, u3, p11, p12, p21, p22, p31, p32, taut);
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}
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}
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}
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