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Make rescaling flexible and add median filtering

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Previously the pyramid was done with a rescaling factor of 2 (implied by the
use of pyrDown). This often leads to inferior results compared to a scale step
of e.g. 0.8 (a factor of 2 is obviously faster). This commit makes the scale
step configurable and uses a resonable default value.

The other change in this commit is that median filtering is added. This is not
described in this paper but it is done in the author's implementation. (See
e.g. "Secrets of optical flow estimation and their principles", Sun et al.,
CVPR 2010) This serves as periodic outlier removal during optimization, leading
to smoother flow fields while preserving motion edges. This includes splitting
the optimization loop into two loops.
This commit is contained in:
Stefan Walk 2013-03-22 13:07:02 +01:00 committed by Vladislav Vinogradov
parent 7ac0d86992
commit 6062601c4d
1 changed files with 44 additions and 25 deletions
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69
modules/video/src/tvl1flow.cpp
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@ -95,8 +95,11 @@ protected:
int nscales;
int warps;
double epsilon;
int iterations;
int innerIterations;
int outerIterations;
bool useInitialFlow;
double scaleStep;
int medianFiltering;
private:
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;
warps = 5;
epsilon = 0.01;
iterations = 300;
innerIterations = 30;
outerIterations = 10;
useInitialFlow = false;
medianFiltering = 5;
scaleStep = 0.8;
}
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
for (int s = 1; s < nscales; ++s)
{
pyrDown(I0s[s - 1], I0s[s]);
pyrDown(I1s[s - 1], I1s[s]);
resize(I0s[s-1], I0s[s], Size(), scaleStep, scaleStep);
resize(I1s[s-1], I1s[s], Size(), scaleStep, scaleStep);
if (I0s[s].cols < 16 || I0s[s].rows < 16)
{
@ -220,11 +226,11 @@ void OpticalFlowDual_TVL1::calc(InputArray _I0, InputArray _I1, InputOutputArray
if (useInitialFlow)
{
pyrDown(u1s[s - 1], u1s[s]);
pyrDown(u2s[s - 1], u2s[s]);
resize(u1s[s-1], u1s[s], Size(), scaleStep, scaleStep);
resize(u2s[s-1], u2s[s], Size(), scaleStep, scaleStep);
multiply(u1s[s], Scalar::all(0.5), u1s[s]);
multiply(u2s[s], Scalar::all(0.5), u2s[s]);
multiply(u1s[s], Scalar::all(scaleStep), u1s[s]);
multiply(u2s[s], Scalar::all(scaleStep), u2s[s]);
}
else
{
@ -256,8 +262,8 @@ void OpticalFlowDual_TVL1::calc(InputArray _I0, InputArray _I1, InputOutputArray
resize(u2s[s], u2s[s - 1], I0s[s - 1].size());
// scale the optical flow with the appropriate zoom factor
multiply(u1s[s - 1], Scalar::all(2), u1s[s - 1]);
multiply(u2s[s - 1], Scalar::all(2), u2s[s - 1]);
multiply(u1s[s - 1], Scalar::all(1/scaleStep), u1s[s - 1]);
multiply(u2s[s - 1], Scalar::all(1/scaleStep), u2s[s - 1]);
}
Mat uxy[] = {u1s[0], u2s[0]};
@ -853,24 +859,31 @@ void OpticalFlowDual_TVL1::procOneScale(const Mat_<float>& I0, const Mat_<float>
calcGradRho(I0, I1w, I1wx, I1wy, u1, u2, grad, rho_c);
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)
{
// estimate the values of the variable (v1, v2) (thresholding operator TH)
estimateV(I1wx, I1wy, u1, u2, grad, rho_c, v1, v2, l_t);
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)
estimateV(I1wx, I1wy, u1, u2, grad, rho_c, v1, v2, l_t);
// compute the divergence of the dual variable (p1, p2)
divergence(p11, p12, div_p1);
divergence(p21, p22, div_p2);
// compute the divergence of the dual variable (p1, p2)
divergence(p11, p12, div_p1);
divergence(p21, p22, div_p2);
// estimate the values of the optical flow (u1, u2)
error = estimateU(v1, v2, div_p1, div_p2, u1, u2, static_cast<float>(theta));
// estimate the values of the optical flow (u1, u2)
error = estimateU(v1, v2, div_p1, div_p2, u1, u2, static_cast<float>(theta));
// compute the gradient of the optical flow (Du1, Du2)
forwardGradient(u1, u1x, u1y);
forwardGradient(u2, u2x, u2y);
// compute the gradient of the optical flow (Du1, Du2)
forwardGradient(u1, u1x, u1y);
forwardGradient(u2, u2x, u2y);
// estimate the values of the dual variable (p1, p2)
estimateDualVariables(u1x, u1y, u2x, u2y, p11, p12, p21, p22, taut);
// estimate the values of the dual variable (p1, p2)
estimateDualVariables(u1x, u1y, u2x, u2y, p11, p12, p21, p22, taut);
}
}
}
}
@ -923,10 +936,16 @@ CV_INIT_ALGORITHM(OpticalFlowDual_TVL1, "DenseOpticalFlow.DualTVL1",
"Number of scales used to create the pyramid of images");
obj.info()->addParam(obj, "warps", obj.warps, false, 0, 0,
"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,
"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,
"Stopping criterion iterations number used in the numerical scheme");
obj.info()->addParam(obj, "innerIterations", obj.innerIterations, false, 0, 0,
"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));
} // namespace