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Merge pull request #724 from jet47:tvl1-updates

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pull/787/merge
Andrey Kamaev 12 years ago committed by OpenCV Buildbot
parent 7ac0d86992 c89786bc2d
commit 54511b4198
6 changed files with 64 additions and 36 deletions
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  1. 2
      modules/gpu/include/opencv2/gpu.hpp
  2. 3
      modules/gpu/perf/perf_video.cpp
  3. 17
      modules/gpu/src/tvl1flow.cpp
  4. 7
      modules/gpu/test/test_optflow.cpp
  5. 2
      modules/video/perf/perf_tvl1optflow.cpp
  6. 45
      modules/video/src/tvl1flow.cpp

2
modules/gpu/include/opencv2/gpu.hpp
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@ -1810,6 +1810,8 @@ public:
*/ */
int iterations; int iterations;
double scaleStep;
bool useInitialFlow; bool useInitialFlow;
private: private:

3
modules/gpu/perf/perf_video.cpp
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@ -434,6 +434,9 @@ PERF_TEST_P(ImagePair, Video_OpticalFlowDual_TVL1,
cv::Mat flow; cv::Mat flow;
cv::Ptr<cv::DenseOpticalFlow> alg = cv::createOptFlow_DualTVL1(); cv::Ptr<cv::DenseOpticalFlow> alg = cv::createOptFlow_DualTVL1();
alg->set("medianFiltering", 1);
alg->set("innerIterations", 1);
alg->set("outerIterations", 300);
TEST_CYCLE() alg->calc(frame0, frame1, flow); TEST_CYCLE() alg->calc(frame0, frame1, flow);

17
modules/gpu/src/tvl1flow.cpp
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@ -63,6 +63,7 @@ cv::gpu::OpticalFlowDual_TVL1_GPU::OpticalFlowDual_TVL1_GPU()
warps = 5; warps = 5;
epsilon = 0.01; epsilon = 0.01;
iterations = 300; iterations = 300;
scaleStep = 0.8;
useInitialFlow = false; useInitialFlow = false;
} }
@ -112,8 +113,8 @@ void cv::gpu::OpticalFlowDual_TVL1_GPU::operator ()(const GpuMat& I0, const GpuM
// create the scales // create the scales
for (int s = 1; s < nscales; ++s) for (int s = 1; s < nscales; ++s)
{ {
gpu::pyrDown(I0s[s - 1], I0s[s]); gpu::resize(I0s[s-1], I0s[s], Size(), scaleStep, scaleStep);
gpu::pyrDown(I1s[s - 1], I1s[s]); gpu::resize(I1s[s-1], I1s[s], Size(), scaleStep, scaleStep);
if (I0s[s].cols < 16 || I0s[s].rows < 16) if (I0s[s].cols < 16 || I0s[s].rows < 16)
{ {
@ -123,11 +124,11 @@ void cv::gpu::OpticalFlowDual_TVL1_GPU::operator ()(const GpuMat& I0, const GpuM
if (useInitialFlow) if (useInitialFlow)
{ {
gpu::pyrDown(u1s[s - 1], u1s[s]); gpu::resize(u1s[s-1], u1s[s], Size(), scaleStep, scaleStep);
gpu::pyrDown(u2s[s - 1], u2s[s]); gpu::resize(u2s[s-1], u2s[s], Size(), scaleStep, scaleStep);
gpu::multiply(u1s[s], Scalar::all(0.5), u1s[s]); gpu::multiply(u1s[s], Scalar::all(scaleStep), u1s[s]);
gpu::multiply(u2s[s], Scalar::all(0.5), u2s[s]); gpu::multiply(u2s[s], Scalar::all(scaleStep), u2s[s]);
} }
else else
{ {
@ -159,8 +160,8 @@ void cv::gpu::OpticalFlowDual_TVL1_GPU::operator ()(const GpuMat& I0, const GpuM
gpu::resize(u2s[s], u2s[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 // scale the optical flow with the appropriate zoom factor
gpu::multiply(u1s[s - 1], Scalar::all(2), u1s[s - 1]); gpu::multiply(u1s[s - 1], Scalar::all(1/scaleStep), u1s[s - 1]);
gpu::multiply(u2s[s - 1], Scalar::all(2), u2s[s - 1]); gpu::multiply(u2s[s - 1], Scalar::all(1/scaleStep), u2s[s - 1]);
} }
} }

7
modules/gpu/test/test_optflow.cpp
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@ -435,13 +435,16 @@ GPU_TEST_P(OpticalFlowDual_TVL1, Accuracy)
d_alg(loadMat(frame0, useRoi), loadMat(frame1, useRoi), d_flowx, d_flowy); d_alg(loadMat(frame0, useRoi), loadMat(frame1, useRoi), d_flowx, d_flowy);
cv::Ptr<cv::DenseOpticalFlow> alg = cv::createOptFlow_DualTVL1(); cv::Ptr<cv::DenseOpticalFlow> alg = cv::createOptFlow_DualTVL1();
alg->set("medianFiltering", 1);
alg->set("innerIterations", 1);
alg->set("outerIterations", d_alg.iterations);
cv::Mat flow; cv::Mat flow;
alg->calc(frame0, frame1, flow); alg->calc(frame0, frame1, flow);
cv::Mat gold[2]; cv::Mat gold[2];
cv::split(flow, gold); cv::split(flow, gold);
EXPECT_MAT_SIMILAR(gold[0], d_flowx, 3e-3); EXPECT_MAT_SIMILAR(gold[0], d_flowx, 4e-3);
EXPECT_MAT_SIMILAR(gold[1], d_flowy, 3e-3); EXPECT_MAT_SIMILAR(gold[1], d_flowy, 4e-3);
} }
INSTANTIATE_TEST_CASE_P(GPU_Video, OpticalFlowDual_TVL1, testing::Combine( INSTANTIATE_TEST_CASE_P(GPU_Video, OpticalFlowDual_TVL1, testing::Combine(

2
modules/video/perf/perf_tvl1optflow.cpp
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@ -26,5 +26,5 @@ PERF_TEST_P(ImagePair, OpticalFlowDual_TVL1, testing::Values(impair("cv/optflow/
TEST_CYCLE_N(10) tvl1->calc(frame1, frame2, flow); TEST_CYCLE_N(10) tvl1->calc(frame1, frame2, flow);
SANITY_CHECK(flow, 0.5); SANITY_CHECK(flow, 0.8);
} }

45
modules/video/src/tvl1flow.cpp
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@ -95,8 +95,11 @@ protected:
int nscales; int nscales;
int warps; int warps;
double epsilon; double epsilon;
int iterations; int innerIterations;
int outerIterations;
bool useInitialFlow; bool useInitialFlow;
double scaleStep;
int medianFiltering;
private: private:
void procOneScale(const Mat_<float>& I0, const Mat_<float>& I1, Mat_<float>& u1, Mat_<float>& u2); void procOneScale(const Mat_<float>& I0, const Mat_<float>& I1, Mat_<float>& u1, Mat_<float>& u2);
@ -144,8 +147,11 @@ OpticalFlowDual_TVL1::OpticalFlowDual_TVL1()
nscales = 5; nscales = 5;
warps = 5; warps = 5;
epsilon = 0.01; epsilon = 0.01;
iterations = 300; innerIterations = 30;
outerIterations = 10;
useInitialFlow = false; useInitialFlow = false;
medianFiltering = 5;
scaleStep = 0.8;
} }
void OpticalFlowDual_TVL1::calc(InputArray _I0, InputArray _I1, InputOutputArray _flow) void OpticalFlowDual_TVL1::calc(InputArray _I0, InputArray _I1, InputOutputArray _flow)
@ -209,8 +215,8 @@ void OpticalFlowDual_TVL1::calc(InputArray _I0, InputArray _I1, InputOutputArray
// create the scales // create the scales
for (int s = 1; s < nscales; ++s) for (int s = 1; s < nscales; ++s)
{ {
pyrDown(I0s[s - 1], I0s[s]); resize(I0s[s-1], I0s[s], Size(), scaleStep, scaleStep);
pyrDown(I1s[s - 1], I1s[s]); resize(I1s[s-1], I1s[s], Size(), scaleStep, scaleStep);
if (I0s[s].cols < 16 || I0s[s].rows < 16) if (I0s[s].cols < 16 || I0s[s].rows < 16)
{ {
@ -220,11 +226,11 @@ void OpticalFlowDual_TVL1::calc(InputArray _I0, InputArray _I1, InputOutputArray
if (useInitialFlow) if (useInitialFlow)
{ {
pyrDown(u1s[s - 1], u1s[s]); resize(u1s[s-1], u1s[s], Size(), scaleStep, scaleStep);
pyrDown(u2s[s - 1], u2s[s]); resize(u2s[s-1], u2s[s], Size(), scaleStep, scaleStep);
multiply(u1s[s], Scalar::all(0.5), u1s[s]); multiply(u1s[s], Scalar::all(scaleStep), u1s[s]);
multiply(u2s[s], Scalar::all(0.5), u2s[s]); multiply(u2s[s], Scalar::all(scaleStep), u2s[s]);
} }
else else
{ {
@ -256,8 +262,8 @@ void OpticalFlowDual_TVL1::calc(InputArray _I0, InputArray _I1, InputOutputArray
resize(u2s[s], u2s[s - 1], I0s[s - 1].size()); resize(u2s[s], u2s[s - 1], I0s[s - 1].size());
// scale the optical flow with the appropriate zoom factor // scale the optical flow with the appropriate zoom factor
multiply(u1s[s - 1], Scalar::all(2), u1s[s - 1]); multiply(u1s[s - 1], Scalar::all(1/scaleStep), u1s[s - 1]);
multiply(u2s[s - 1], Scalar::all(2), u2s[s - 1]); multiply(u2s[s - 1], Scalar::all(1/scaleStep), u2s[s - 1]);
} }
Mat uxy[] = {u1s[0], u2s[0]}; Mat uxy[] = {u1s[0], u2s[0]};
@ -853,7 +859,13 @@ void OpticalFlowDual_TVL1::procOneScale(const Mat_<float>& I0, const Mat_<float>
calcGradRho(I0, I1w, I1wx, I1wy, u1, u2, grad, rho_c); calcGradRho(I0, I1w, I1wx, I1wy, u1, u2, grad, rho_c);
float error = std::numeric_limits<float>::max(); float error = std::numeric_limits<float>::max();
for (int n = 0; error > scaledEpsilon && n < iterations; ++n) for (int n_outer = 0; error > scaledEpsilon && n_outer < outerIterations; ++n_outer)
{
if (medianFiltering > 1) {
cv::medianBlur(u1, u1, medianFiltering);
cv::medianBlur(u2, u2, medianFiltering);
}
for (int n_inner = 0; error > scaledEpsilon && n_inner < innerIterations; ++n_inner)
{ {
// estimate the values of the variable (v1, v2) (thresholding operator TH) // estimate the values of the variable (v1, v2) (thresholding operator TH)
estimateV(I1wx, I1wy, u1, u2, grad, rho_c, v1, v2, l_t); estimateV(I1wx, I1wy, u1, u2, grad, rho_c, v1, v2, l_t);
@ -874,6 +886,7 @@ void OpticalFlowDual_TVL1::procOneScale(const Mat_<float>& I0, const Mat_<float>
} }
} }
} }
}
void OpticalFlowDual_TVL1::collectGarbage() void OpticalFlowDual_TVL1::collectGarbage()
{ {
@ -923,10 +936,16 @@ CV_INIT_ALGORITHM(OpticalFlowDual_TVL1, "DenseOpticalFlow.DualTVL1",
"Number of scales used to create the pyramid of images"); "Number of scales used to create the pyramid of images");
obj.info()->addParam(obj, "warps", obj.warps, false, 0, 0, obj.info()->addParam(obj, "warps", obj.warps, false, 0, 0,
"Number of warpings per scale"); "Number of warpings per scale");
obj.info()->addParam(obj, "medianFiltering", obj.medianFiltering, false, 0, 0,
"Median filter kernel size (1 = no filter) (3 or 5)");
obj.info()->addParam(obj, "scaleStep", obj.scaleStep, false, 0, 0,
"Step between scales (<1)");
obj.info()->addParam(obj, "epsilon", obj.epsilon, false, 0, 0, obj.info()->addParam(obj, "epsilon", obj.epsilon, false, 0, 0,
"Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time"); "Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time");
obj.info()->addParam(obj, "iterations", obj.iterations, false, 0, 0, obj.info()->addParam(obj, "innerIterations", obj.innerIterations, false, 0, 0,
"Stopping criterion iterations number used in the numerical scheme"); "inner iterations (between outlier filtering) used in the numerical scheme");
obj.info()->addParam(obj, "outerIterations", obj.outerIterations, false, 0, 0,
"outer iterations (number of inner loops) used in the numerical scheme");
obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow)); obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow));
} // namespace } // namespace

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