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Merge pull request #745 from sbokov:DISflow_improvement

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Maksim Shabunin
2016-08-10 22:17:08 +00:00
parent 65e2efdb7f 406d944ec7
commit 73459049a3
3 changed files with 192 additions and 22 deletions
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9
modules/optflow/include/opencv2/optflow.hpp
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@@ -170,7 +170,7 @@ procedure can be found in @cite Brox2004
class CV_EXPORTS_W VariationalRefinement : public DenseOpticalFlow
{
public:
/** @brief calc function overload to handle separate horizontal (u) and vertical (v) flow components
/** @brief @ref calc function overload to handle separate horizontal (u) and vertical (v) flow components
(to avoid extra splits/merges) */
CV_WRAP virtual void calcUV(InputArray I0, InputArray I1, InputOutputArray flow_u, InputOutputArray flow_v) = 0;
@@ -258,6 +258,11 @@ This class implements the Dense Inverse Search (DIS) optical flow algorithm. Mor
details about the algorithm can be found at @cite Kroeger2016 . Includes three presets with preselected
parameters to provide reasonable trade-off between speed and quality. However, even the slowest preset is
still relatively fast, use DeepFlow if you need better quality and don't care about speed.
This implementation includes several additional features compared to the algorithm described in the paper,
including spatial propagation of flow vectors (@ref getUseSpatialPropagation), as well as an option to
utilize an initial flow approximation passed to @ref calc (which is, essentially, temporal propagation,
if the previous frame's flow field is passed).
*/
class CV_EXPORTS_W DISOpticalFlow : public DenseOpticalFlow
{
@@ -326,7 +331,7 @@ public:
/** @brief Whether to use mean-normalization of patches when computing patch distance. It is turned on
by default as it typically provides a noticeable quality boost because of increased robustness to
illumanition variations. Turn it off if you are certain that your sequence does't contain any changes
illumination variations. Turn it off if you are certain that your sequence doesn't contain any changes
in illumination.
@see setUseMeanNormalization */
CV_WRAP virtual bool getUseMeanNormalization() const = 0;