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| virtual CV_WRAP int | getFinestScale () const =0 |
| | Finest level of the Gaussian pyramid on which the flow is computed (zero level corresponds to the original image resolution). The final flow is obtained by bilinear upscaling.
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| virtual CV_WRAP void | setFinestScale (int val)=0 |
| | Finest level of the Gaussian pyramid on which the flow is computed (zero level corresponds to the original image resolution). The final flow is obtained by bilinear upscaling.
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| virtual CV_WRAP int | getPatchSize () const =0 |
| | Size of an image patch for matching (in pixels). Normally, default 8x8 patches work well enough in most cases.
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| virtual CV_WRAP void | setPatchSize (int val)=0 |
| | Size of an image patch for matching (in pixels). Normally, default 8x8 patches work well enough in most cases.
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| virtual CV_WRAP int | getPatchStride () const =0 |
| | Stride between neighbor patches. Must be less than patch size. Lower values correspond to higher flow quality.
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| virtual CV_WRAP void | setPatchStride (int val)=0 |
| | Stride between neighbor patches. Must be less than patch size. Lower values correspond to higher flow quality.
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| virtual CV_WRAP int | getGradientDescentIterations () const =0 |
| | Maximum number of gradient descent iterations in the patch inverse search stage. Higher values may improve quality in some cases.
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| virtual CV_WRAP void | setGradientDescentIterations (int val)=0 |
| | Maximum number of gradient descent iterations in the patch inverse search stage. Higher values may improve quality in some cases.
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| virtual CV_WRAP int | getVariationalRefinementIterations () const =0 |
| | Number of fixed point iterations of variational refinement per scale. Set to zero to disable variational refinement completely. Higher values will typically result in more smooth and high-quality flow.
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| virtual CV_WRAP void | setVariationalRefinementIterations (int val)=0 |
| | Maximum number of gradient descent iterations in the patch inverse search stage. Higher values may improve quality in some cases.
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| virtual CV_WRAP float | getVariationalRefinementAlpha () const =0 |
| | Weight of the smoothness term.
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| virtual CV_WRAP void | setVariationalRefinementAlpha (float val)=0 |
| | Weight of the smoothness term.
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| virtual CV_WRAP float | getVariationalRefinementDelta () const =0 |
| | Weight of the color constancy term.
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| virtual CV_WRAP void | setVariationalRefinementDelta (float val)=0 |
| | Weight of the color constancy term.
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| virtual CV_WRAP float | getVariationalRefinementGamma () const =0 |
| | Weight of the gradient constancy term.
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| virtual CV_WRAP void | setVariationalRefinementGamma (float val)=0 |
| | Weight of the gradient constancy term.
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| virtual CV_WRAP float | getVariationalRefinementEpsilon () const =0 |
| | Norm value shift for robust penalizer.
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| virtual CV_WRAP void | setVariationalRefinementEpsilon (float val)=0 |
| | Norm value shift for robust penalizer.
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| virtual CV_WRAP bool | getUseMeanNormalization () const =0 |
| | 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 illumination variations. Turn it off if you are certain that your sequence doesn't contain any changes in illumination.
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| virtual CV_WRAP void | setUseMeanNormalization (bool val)=0 |
| | 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 illumination variations. Turn it off if you are certain that your sequence doesn't contain any changes in illumination.
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| virtual CV_WRAP bool | getUseSpatialPropagation () const =0 |
| | Whether to use spatial propagation of good optical flow vectors. This option is turned on by default, as it tends to work better on average and can sometimes help recover from major errors introduced by the coarse-to-fine scheme employed by the DIS optical flow algorithm. Turning this option off can make the output flow field a bit smoother, however.
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| virtual CV_WRAP void | setUseSpatialPropagation (bool val)=0 |
| | Whether to use spatial propagation of good optical flow vectors. This option is turned on by default, as it tends to work better on average and can sometimes help recover from major errors introduced by the coarse-to-fine scheme employed by the DIS optical flow algorithm. Turning this option off can make the output flow field a bit smoother, however.
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| virtual CV_WRAP void | calc (InputArray I0, InputArray I1, InputOutputArray flow)=0 |
| | Calculates an optical flow.
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| virtual CV_WRAP void | collectGarbage ()=0 |
| | Releases all inner buffers.
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| | Algorithm () |
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| virtual | ~Algorithm () |
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| virtual CV_WRAP void | clear () |
| | Clears the algorithm state.
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| virtual CV_WRAP void | write (FileStorage &fs) const |
| | Stores algorithm parameters in a file storage.
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| CV_WRAP void | write (FileStorage &fs, const String &name) const |
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| void | write (const Ptr< FileStorage > &fs, const String &name=String()) const |
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| virtual CV_WRAP void | read (const FileNode &fn) |
| | Reads algorithm parameters from a file storage.
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| virtual CV_WRAP bool | empty () const |
| | Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
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| virtual CV_WRAP void | save (const String &filename) const |
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| virtual CV_WRAP String | getDefaultName () const |
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DIS optical flow algorithm.
This class implements the Dense Inverse Search (DIS) optical flow algorithm. More details about the algorithm can be found at [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 (getUseSpatialPropagation), as well as an option to utilize an initial flow approximation passed to calc (which is, essentially, temporal propagation, if the previous frame's flow field is passed).
Public Member Functions inherited from cv::DenseOpticalFlow