DIS optical flow algorithm. [詳解]

#include <tracking.hpp>

cv::DenseOpticalFlowを継承しています。

公開型
enum	{ PRESET_ULTRAFAST = 0 , PRESET_FAST = 1 , PRESET_MEDIUM = 2 }

公開メンバ関数
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. [詳解]

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. [詳解]

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. [詳解]

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. [詳解]

virtual CV_WRAP int	getPatchStride () const =0
	Stride between neighbor patches. Must be less than patch size. Lower values correspond to higher flow quality. [詳解]

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. [詳解]

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. [詳解]

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. [詳解]

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. [詳解]

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. [詳解]

virtual CV_WRAP float	getVariationalRefinementAlpha () const =0
	Weight of the smoothness term [詳解]

virtual CV_WRAP void	setVariationalRefinementAlpha (float val)=0
	Weight of the smoothness term [詳解]

virtual CV_WRAP float	getVariationalRefinementDelta () const =0
	Weight of the color constancy term [詳解]

virtual CV_WRAP void	setVariationalRefinementDelta (float val)=0
	Weight of the color constancy term [詳解]

virtual CV_WRAP float	getVariationalRefinementGamma () const =0
	Weight of the gradient constancy term [詳解]

virtual CV_WRAP void	setVariationalRefinementGamma (float val)=0
	Weight of the gradient constancy term [詳解]

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. [詳解]

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. [詳解]

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. [詳解]

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. [詳解]

基底クラス cv::DenseOpticalFlow に属する継承公開メンバ関数
virtual CV_WRAP void	calc (InputArray I0, InputArray I1, InputOutputArray flow)=0
	Calculates an optical flow. [詳解]

virtual CV_WRAP void	collectGarbage ()=0
	Releases all inner buffers. [詳解]

基底クラス cv::Algorithm に属する継承公開メンバ関数
virtual CV_WRAP void	clear ()
	Clears the algorithm state [詳解]

virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage [詳解]

CV_WRAP void	write (const Ptr< FileStorage > &fs, const String &name=String()) const
	simplified API for language bindings これはオーバーロードされたメンバ関数です。利便性のために用意されています。元の関数との違いは引き数のみです。

virtual CV_WRAP void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage [詳解]

virtual CV_WRAP bool	empty () const
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read [詳解]

virtual CV_WRAP void	save (const String &filename) const

virtual CV_WRAP String	getDefaultName () const

静的公開メンバ関数
static CV_WRAP Ptr< DISOpticalFlow >	create (int preset=DISOpticalFlow::PRESET_FAST)
	Creates an instance of DISOpticalFlow [詳解]

基底クラス cv::Algorithm に属する継承静的公開メンバ関数
template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node [詳解]

template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file [詳解]

template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String [詳解]

その他の継承メンバ
基底クラス cv::Algorithm に属する継承限定公開メンバ関数
void	writeFormat (FileStorage &fs) const

詳解

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).

関数詳解

◆ create()

static CV_WRAP Ptr< DISOpticalFlow > cv::DISOpticalFlow::create ( int preset = DISOpticalFlow::PRESET_FAST )

static

Creates an instance of DISOpticalFlow

引数

preset one of PRESET_ULTRAFAST, PRESET_FAST and PRESET_MEDIUM

◆ getFinestScale()

virtual CV_WRAP int cv::DISOpticalFlow::getFinestScale ( ) const

pure virtual

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.

参照: setFinestScale

◆ getGradientDescentIterations()

virtual CV_WRAP int cv::DISOpticalFlow::getGradientDescentIterations ( ) const

pure virtual

Maximum number of gradient descent iterations in the patch inverse search stage. Higher values may improve quality in some cases.

参照: setGradientDescentIterations

◆ getPatchSize()

virtual CV_WRAP int cv::DISOpticalFlow::getPatchSize ( ) const

pure virtual

Size of an image patch for matching (in pixels). Normally, default 8x8 patches work well enough in most cases.

参照: setPatchSize

◆ getPatchStride()

virtual CV_WRAP int cv::DISOpticalFlow::getPatchStride ( ) const

pure virtual

Stride between neighbor patches. Must be less than patch size. Lower values correspond to higher flow quality.

参照: setPatchStride

◆ getUseMeanNormalization()

virtual CV_WRAP bool cv::DISOpticalFlow::getUseMeanNormalization ( ) const

pure virtual

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.

参照: setUseMeanNormalization

◆ getUseSpatialPropagation()

virtual CV_WRAP bool cv::DISOpticalFlow::getUseSpatialPropagation ( ) const

pure virtual

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.

参照: setUseSpatialPropagation

◆ getVariationalRefinementAlpha()

virtual CV_WRAP float cv::DISOpticalFlow::getVariationalRefinementAlpha ( ) const

pure virtual

Weight of the smoothness term

参照: setVariationalRefinementAlpha

◆ getVariationalRefinementDelta()

virtual CV_WRAP float cv::DISOpticalFlow::getVariationalRefinementDelta ( ) const

pure virtual

Weight of the color constancy term

参照: setVariationalRefinementDelta

◆ getVariationalRefinementGamma()

virtual CV_WRAP float cv::DISOpticalFlow::getVariationalRefinementGamma ( ) const

pure virtual

Weight of the gradient constancy term

参照: setVariationalRefinementGamma

◆ getVariationalRefinementIterations()

virtual CV_WRAP int cv::DISOpticalFlow::getVariationalRefinementIterations ( ) const

pure virtual

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.

参照: setGradientDescentIterations

◆ setFinestScale()

virtual CV_WRAP void cv::DISOpticalFlow::setFinestScale ( int val )

pure virtual

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.

参照: getFinestScale

◆ setGradientDescentIterations()

virtual CV_WRAP void cv::DISOpticalFlow::setGradientDescentIterations ( int val )

pure virtual

Maximum number of gradient descent iterations in the patch inverse search stage. Higher values may improve quality in some cases.

参照: getGradientDescentIterations

◆ setPatchSize()

virtual CV_WRAP void cv::DISOpticalFlow::setPatchSize ( int val )

pure virtual

Size of an image patch for matching (in pixels). Normally, default 8x8 patches work well enough in most cases.

参照: getPatchSize

◆ setPatchStride()

virtual CV_WRAP void cv::DISOpticalFlow::setPatchStride ( int val )

pure virtual

Stride between neighbor patches. Must be less than patch size. Lower values correspond to higher flow quality.

参照: getPatchStride

◆ setUseMeanNormalization()

virtual CV_WRAP void cv::DISOpticalFlow::setUseMeanNormalization ( bool val )

pure virtual

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.

参照: getUseMeanNormalization

◆ setUseSpatialPropagation()

virtual CV_WRAP void cv::DISOpticalFlow::setUseSpatialPropagation ( bool val )

pure virtual

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.

参照: getUseSpatialPropagation

◆ setVariationalRefinementAlpha()

virtual CV_WRAP void cv::DISOpticalFlow::setVariationalRefinementAlpha ( float val )

pure virtual

Weight of the smoothness term

参照: getVariationalRefinementAlpha

◆ setVariationalRefinementDelta()

virtual CV_WRAP void cv::DISOpticalFlow::setVariationalRefinementDelta ( float val )

pure virtual

Weight of the color constancy term

参照: getVariationalRefinementDelta

◆ setVariationalRefinementGamma()

virtual CV_WRAP void cv::DISOpticalFlow::setVariationalRefinementGamma ( float val )

pure virtual

Weight of the gradient constancy term

参照: getVariationalRefinementGamma

◆ setVariationalRefinementIterations()

virtual CV_WRAP void cv::DISOpticalFlow::setVariationalRefinementIterations ( int val )

pure virtual

Maximum number of gradient descent iterations in the patch inverse search stage. Higher values may improve quality in some cases.

参照: getGradientDescentIterations

このクラス詳解は次のファイルから抽出されました:

video/tracking.hpp

公開型

公開メンバ関数

静的公開メンバ関数

その他の継承メンバ

詳解

関数詳解

◆ create()

◆ getFinestScale()

◆ getGradientDescentIterations()

◆ getPatchSize()

◆ getPatchStride()

◆ getUseMeanNormalization()

◆ getUseSpatialPropagation()

◆ getVariationalRefinementAlpha()

◆ getVariationalRefinementDelta()

◆ getVariationalRefinementGamma()

◆ getVariationalRefinementIterations()

◆ setFinestScale()

◆ setGradientDescentIterations()

◆ setPatchSize()

◆ setPatchStride()

◆ setUseMeanNormalization()

◆ setUseSpatialPropagation()

◆ setVariationalRefinementAlpha()

◆ setVariationalRefinementDelta()

◆ setVariationalRefinementGamma()

◆ setVariationalRefinementIterations()