View Source Evision.DISOpticalFlow (Evision v0.1.8)
Link to this section Summary
cv
Creates an instance of DISOpticalFlow
Creates an instance of DISOpticalFlow
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.
Maximum number of gradient descent iterations in the patch inverse search stage. Higher values may improve quality in some cases.
Size of an image patch for matching (in pixels). Normally, default 8x8 patches work well enough in most cases.
Stride between neighbor patches. Must be less than patch size. Lower values correspond to higher flow quality.
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.
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.
Weight of the smoothness term
Weight of the color constancy term
Weight of the gradient constancy term
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.
Positional Arguments
- val:
int
@copybrief getFinestScale @see getFinestScale
Positional Arguments
- val:
int
@copybrief getGradientDescentIterations @see getGradientDescentIterations
Positional Arguments
- val:
int
@copybrief getPatchSize @see getPatchSize
Positional Arguments
- val:
int
@copybrief getPatchStride @see getPatchStride
Positional Arguments
- val:
bool
@copybrief getUseMeanNormalization @see getUseMeanNormalization
Positional Arguments
- val:
bool
@copybrief getUseSpatialPropagation @see getUseSpatialPropagation
Positional Arguments
- val:
float
@copybrief getVariationalRefinementAlpha @see getVariationalRefinementAlpha
Positional Arguments
- val:
float
@copybrief getVariationalRefinementDelta @see getVariationalRefinementDelta
Positional Arguments
- val:
float
@copybrief getVariationalRefinementGamma @see getVariationalRefinementGamma
Positional Arguments
- val:
int
@copybrief getGradientDescentIterations @see getGradientDescentIterations
Functions
Raising version of create/1
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Link to this section cv
Creates an instance of DISOpticalFlow
Keyword Arguments
preset:
int
.one of PRESET_ULTRAFAST, PRESET_FAST and PRESET_MEDIUM
Python prototype (for reference):
create([, preset]) -> retval
Creates an instance of DISOpticalFlow
Keyword Arguments
preset:
int
.one of PRESET_ULTRAFAST, PRESET_FAST and PRESET_MEDIUM
Python prototype (for reference):
create([, preset]) -> retval
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.
@see setFinestScale
Python prototype (for reference):
getFinestScale() -> retval
Maximum number of gradient descent iterations in the patch inverse search stage. Higher values may improve quality in some cases.
@see setGradientDescentIterations
Python prototype (for reference):
getGradientDescentIterations() -> retval
Size of an image patch for matching (in pixels). Normally, default 8x8 patches work well enough in most cases.
@see setPatchSize
Python prototype (for reference):
getPatchSize() -> retval
Stride between neighbor patches. Must be less than patch size. Lower values correspond to higher flow quality.
@see setPatchStride
Python prototype (for reference):
getPatchStride() -> retval
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.
@see setUseMeanNormalization
Python prototype (for reference):
getUseMeanNormalization() -> retval
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.
@see setUseSpatialPropagation
Python prototype (for reference):
getUseSpatialPropagation() -> retval
Weight of the smoothness term
@see setVariationalRefinementAlpha
Python prototype (for reference):
getVariationalRefinementAlpha() -> retval
Weight of the color constancy term
@see setVariationalRefinementDelta
Python prototype (for reference):
getVariationalRefinementDelta() -> retval
Weight of the gradient constancy term
@see setVariationalRefinementGamma
Python prototype (for reference):
getVariationalRefinementGamma() -> retval
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.
@see setGradientDescentIterations
Python prototype (for reference):
getVariationalRefinementIterations() -> retval
Positional Arguments
- val:
int
@copybrief getFinestScale @see getFinestScale
Python prototype (for reference):
setFinestScale(val) -> None
Positional Arguments
- val:
int
@copybrief getGradientDescentIterations @see getGradientDescentIterations
Python prototype (for reference):
setGradientDescentIterations(val) -> None
Positional Arguments
- val:
int
@copybrief getPatchSize @see getPatchSize
Python prototype (for reference):
setPatchSize(val) -> None
Positional Arguments
- val:
int
@copybrief getPatchStride @see getPatchStride
Python prototype (for reference):
setPatchStride(val) -> None
Positional Arguments
- val:
bool
@copybrief getUseMeanNormalization @see getUseMeanNormalization
Python prototype (for reference):
setUseMeanNormalization(val) -> None
Positional Arguments
- val:
bool
@copybrief getUseSpatialPropagation @see getUseSpatialPropagation
Python prototype (for reference):
setUseSpatialPropagation(val) -> None
Positional Arguments
- val:
float
@copybrief getVariationalRefinementAlpha @see getVariationalRefinementAlpha
Python prototype (for reference):
setVariationalRefinementAlpha(val) -> None
Positional Arguments
- val:
float
@copybrief getVariationalRefinementDelta @see getVariationalRefinementDelta
Python prototype (for reference):
setVariationalRefinementDelta(val) -> None
Positional Arguments
- val:
float
@copybrief getVariationalRefinementGamma @see getVariationalRefinementGamma
Python prototype (for reference):
setVariationalRefinementGamma(val) -> None
Positional Arguments
- val:
int
@copybrief getGradientDescentIterations @see getGradientDescentIterations
Python prototype (for reference):
setVariationalRefinementIterations(val) -> None
Link to this section Functions
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