Optical Flow Algorithms#

Detailed Description#

Dense optical flow algorithms compute motion for each point:

Motion templates is alternative technique for detecting motion and computing its direction. See samples/motempl.py.

Functions reading and writing .flo files in “Middlebury” format, see: http://vision.middlebury.edu/flow/code/flow-code/README.txt

Classes#

Name

Description

class cv::optflow::DenseRLOFOpticalFlow

Fast dense optical flow computation based on robust local optical flow (RLOF) algorithms and sparse-to-dense interpolation scheme. View details

class cv::optflow::DualTVL1OpticalFlow

“Dual TV L1” Optical Flow Algorithm. View details

class cv::optflow::GPCDetails

View details

class cv::optflow::GPCForest< T >

View details

struct cv::optflow::GPCMatchingParams

Class encapsulating matching parameters. View details

struct cv::optflow::GPCPatchDescriptor

View details

struct cv::optflow::GPCPatchSample

View details

struct cv::optflow::GPCTrainingParams

Class encapsulating training parameters. View details

class cv::optflow::GPCTrainingSamples

Class encapsulating training samples. View details

class cv::optflow::GPCTree

Class for individual tree. View details

class cv::optflow::OpticalFlowPCAFlow

PCAFlow algorithm. View details

class cv::optflow::PCAPrior

This class can be used for imposing a learned prior on the resulting optical flow. Solution will be regularized according to this prior. You need to generate appropriate prior file with “learn_prior.py” script beforehand. View details

class cv::optflow::RLOFOpticalFlowParameter

This is used store and set up the parameters of the robust local optical flow (RLOF) algoritm. View details

class cv::optflow::SparseRLOFOpticalFlow

Class used for calculation sparse optical flow and feature tracking with robust local optical flow (RLOF) algorithms. View details

Enumerations#

enum cv::optflow::GPCDescType {
    cv::optflow::GPC_DESCRIPTOR_DCT = 0,
    cv::optflow::GPC_DESCRIPTOR_WHT
}

Descriptor types for the Global Patch Collider. View details

enum cv::optflow::InterpolationType {
    cv::optflow::INTERP_GEO = 0,
    cv::optflow::INTERP_EPIC = 1,
    cv::optflow::INTERP_RIC = 2
}

View details

enum cv::optflow::SolverType {
    cv::optflow::ST_STANDART = 0,
    cv::optflow::ST_BILINEAR = 1
}

View details

enum cv::optflow::SupportRegionType {
    cv::optflow::SR_FIXED = 0,
    cv::optflow::SR_CROSS = 1
}

View details

Functions#

Return

Name

Description

double

cv::calcGlobalOrientation(
InputArray orientation,
InputArray mask,
InputArray mhi,
double timestamp,
double duration )

Calculates a global motion orientation in a selected region.

void

cv::calcMotionGradient(
InputArray mhi,
OutputArray mask,
OutputArray orientation,
double delta1,
double delta2,
int apertureSize = 3 )

Calculates a gradient orientation of a motion history image.

void

cv::calcOpticalFlowDenseRLOF(
InputArray I0,
InputArray I1,
InputOutputArray flow,
Ptr< RLOFOpticalFlowParameter > rlofParam = Ptr< RLOFOpticalFlowParameter >(),
float forwardBackwardThreshold = 0,
Size gridStep = Size(6, 6),
InterpolationType interp_type = InterpolationType::INTERP_EPIC,
int epicK = 128,
float epicSigma = 0.05f,
float epicLambda = 100.f,
int ricSPSize = 15,
int ricSLICType = 100,
bool use_post_proc = true,
float fgsLambda = 500.0f,
float fgsSigma = 1.5f,
bool use_variational_refinement = false )

Fast dense optical flow computation based on robust local optical flow (RLOF) algorithms and sparse-to-dense interpolation scheme.

void

cv::calcOpticalFlowSF(
InputArray from,
InputArray to,
OutputArray flow,
int layers,
int averaging_block_size,
int max_flow )

void

cv::calcOpticalFlowSF(
InputArray from,
InputArray to,
OutputArray flow,
int layers,
int averaging_block_size,
int max_flow,
double sigma_dist,
double sigma_color,
int postprocess_window,
double sigma_dist_fix,
double sigma_color_fix,
double occ_thr,
int upscale_averaging_radius,
double upscale_sigma_dist,
double upscale_sigma_color,
double speed_up_thr )

Calculate an optical flow using “SimpleFlow” algorithm.

void

cv::calcOpticalFlowSparseRLOF(
InputArray prevImg,
InputArray nextImg,
InputArray prevPts,
InputOutputArray nextPts,
OutputArray status,
OutputArray err,
Ptr< RLOFOpticalFlowParameter > rlofParam = Ptr< RLOFOpticalFlowParameter >(),
float forwardBackwardThreshold = 0 )

Calculates fast optical flow for a sparse feature set using the robust local optical flow (RLOF) similar to optflow::calcOpticalFlowPyrLK().

void

cv::calcOpticalFlowSparseToDense(
InputArray from,
InputArray to,
OutputArray flow,
int grid_step = 8,
int k = 128,
float sigma = 0.05f,
bool use_post_proc = true,
float fgs_lambda = 500.0f,
float fgs_sigma = 1.5f )

Fast dense optical flow based on PyrLK sparse matches interpolation.

Ptr< DenseOpticalFlow >

cv::createOptFlow_DeepFlow()

DeepFlow optical flow algorithm implementation.

Ptr< DenseOpticalFlow >

cv::createOptFlow_DenseRLOF()

Additional interface to the Dense RLOF algorithm - optflow::calcOpticalFlowDenseRLOF()

Ptr< DualTVL1OpticalFlow >

cv::createOptFlow_DualTVL1()

Creates instance of cv::DenseOpticalFlow.

Ptr< DenseOpticalFlow >

cv::createOptFlow_Farneback()

Additional interface to the Farneback’s algorithm - calcOpticalFlowFarneback()

Ptr< DenseOpticalFlow >

cv::createOptFlow_PCAFlow()

Creates an instance of PCAFlow.

Ptr< DenseOpticalFlow >

cv::createOptFlow_SimpleFlow()

Additional interface to the SimpleFlow algorithm - calcOpticalFlowSF()

Ptr< SparseOpticalFlow >

cv::createOptFlow_SparseRLOF()

Additional interface to the Sparse RLOF algorithm - optflow::calcOpticalFlowSparseRLOF()

Ptr< DenseOpticalFlow >

cv::createOptFlow_SparseToDense()

Additional interface to the SparseToDenseFlow algorithm - calcOpticalFlowSparseToDense()

void

cv::findCorrespondences(
InputArray imgFrom,
InputArray imgTo,
std::vector< std::pair< Point2i, Point2i > > & corr,
const GPCMatchingParams params = GPCMatchingParams() )

Find correspondences between two images.

void

cv::segmentMotion(
InputArray mhi,
OutputArray segmask,
std::vector< Rect > & boundingRects,
double timestamp,
double segThresh )

Splits a motion history image into a few parts corresponding to separate independent motions (for example, left hand, right hand).

void

cv::updateMotionHistory(
InputArray silhouette,
InputOutputArray mhi,
double timestamp,
double duration )

Updates the motion history image by a moving silhouette.

Typedef Documentation#

GPCSamplesVector#

typedef std::vector< GPCPatchSample > cv::optflow::GPCSamplesVector

#include <opencv2/optflow/sparse_matching_gpc.hpp>

Enumeration Type Documentation#

GPCDescType#

enum cv::optflow::GPCDescType

#include <opencv2/optflow/sparse_matching_gpc.hpp>

Descriptor types for the Global Patch Collider.

Enumerator:

GPC_DESCRIPTOR_DCT

Better quality but slow.

GPC_DESCRIPTOR_WHT

Worse quality but much faster.

InterpolationType#

enum cv::optflow::InterpolationType

#include <opencv2/optflow/rlofflow.hpp>

Enumerator:

INTERP_GEO

Fast geodesic interpolation, see [115]

INTERP_EPIC

Edge-preserving interpolation using ximgproc::EdgeAwareInterpolator, see [250],[115].

INTERP_RIC

SLIC based robust interpolation using ximgproc::RICInterpolator, see [148].

SolverType#

enum cv::optflow::SolverType

#include <opencv2/optflow/rlofflow.hpp>

Enumerator:

ST_STANDART

Apply standard iterative refinement

ST_BILINEAR

Apply optimized iterative refinement based bilinear equation solutions as described in [265]

SupportRegionType#

enum cv::optflow::SupportRegionType

#include <opencv2/optflow/rlofflow.hpp>

Enumerator:

SR_FIXED

Apply a constant support region

SR_CROSS

Apply a adaptive support region obtained by cross-based segmentation as described in [266]

Function Documentation#

calcGlobalOrientation()#

double cv::motempl::calcGlobalOrientation(
InputArray orientation,
InputArray mask,
InputArray mhi,
double timestamp,
double duration )

#include <opencv2/optflow/motempl.hpp>

Calculates a global motion orientation in a selected region.

The function calculates an average motion direction in the selected region and returns the angle between 0 degrees and 360 degrees. The average direction is computed from the weighted orientation histogram, where a recent motion has a larger weight and the motion occurred in the past has a smaller weight, as recorded in mhi .

Parameters

  • orientation — Motion gradient orientation image calculated by the function calcMotionGradient

  • mask — Mask image. It may be a conjunction of a valid gradient mask, also calculated by calcMotionGradient , and the mask of a region whose direction needs to be calculated.

  • mhi — Motion history image calculated by updateMotionHistory .

  • timestamp — Timestamp passed to updateMotionHistory .

  • duration — Maximum duration of a motion track in milliseconds, passed to updateMotionHistory

calcMotionGradient()#

void cv::motempl::calcMotionGradient(
InputArray mhi,
OutputArray mask,
OutputArray orientation,
double delta1,
double delta2,
int apertureSize = 3 )

#include <opencv2/optflow/motempl.hpp>

Calculates a gradient orientation of a motion history image.

The function calculates a gradient orientation at each pixel \((x, y)\) as:

\[ \texttt{orientation} (x,y)= \arctan{\frac{d\texttt{mhi}/dy}{d\texttt{mhi}/dx}} \]

In fact, fastAtan2 and phase are used so that the computed angle is measured in degrees and covers the full range 0..360. Also, the mask is filled to indicate pixels where the computed angle is valid.

Note

Parameters

  • mhi — Motion history single-channel floating-point image.

  • mask — Output mask image that has the type CV_8UC1 and the same size as mhi . Its non-zero elements mark pixels where the motion gradient data is correct.

  • orientation — Output motion gradient orientation image that has the same type and the same size as mhi . Each pixel of the image is a motion orientation, from 0 to 360 degrees.

  • delta1 — Minimal (or maximal) allowed difference between mhi values within a pixel neighborhood.

  • delta2 — Maximal (or minimal) allowed difference between mhi values within a pixel neighborhood. That is, the function finds the minimum ( \(m(x,y)\) ) and maximum ( \(M(x,y)\) ) mhi values over \(3 \times 3\) neighborhood of each pixel and marks the motion orientation at \((x, y)\) as valid only if

    \[ \min ( \texttt{delta1} , \texttt{delta2} ) \le M(x,y)-m(x,y) \le \max ( \texttt{delta1} , \texttt{delta2} ). \]

  • apertureSize — Aperture size of the Sobel operator.

calcOpticalFlowDenseRLOF()#

void cv::optflow::calcOpticalFlowDenseRLOF(
InputArray I0,
InputArray I1,
InputOutputArray flow,
Ptr< RLOFOpticalFlowParameter > rlofParam = Ptr< RLOFOpticalFlowParameter >(),
float forwardBackwardThreshold = 0,
Size gridStep = Size(6, 6),
InterpolationType interp_type = InterpolationType::INTERP_EPIC,
int epicK = 128,
float epicSigma = 0.05f,
float epicLambda = 100.f,
int ricSPSize = 15,
int ricSLICType = 100,
bool use_post_proc = true,
float fgsLambda = 500.0f,
float fgsSigma = 1.5f,
bool use_variational_refinement = false )

#include <opencv2/optflow/rlofflow.hpp>

Fast dense optical flow computation based on robust local optical flow (RLOF) algorithms and sparse-to-dense interpolation scheme.

The RLOF is a fast local optical flow approach described in [264] [265] [266] and [267] similar to the pyramidal iterative Lucas-Kanade method as proposed by [41]. More details and experiments can be found in the following thesis [268]. The implementation is derived from optflow::calcOpticalFlowPyrLK().

The sparse-to-dense interpolation scheme allows for fast computation of dense optical flow using RLOF (see [115]). For this scheme the following steps are applied:

  1. motion vector seeded at a regular sampled grid are computed. The sparsity of this grid can be configured with setGridStep

  2. (optinally) errornous motion vectors are filter based on the forward backward confidence. The threshold can be configured with setForwardBackward. The filter is only applied if the threshold >0 but than the runtime is doubled due to the estimation of the backward flow.

  3. Vector field interpolation is applied to the motion vector set to obtain a dense vector field.

Parameters have been described in [264], [265], [266], [267]. For the RLOF configuration see optflow::RLOFOpticalFlowParameter for further details.

Note

If the grid size is set to (1,1) and the forward backward threshold <= 0 that the dense optical flow field is purely computed with the RLOF.

SIMD parallelization is only available when compiling with SSE4.1.

Note that in output, if no correspondences are found between I0 and I1, the flow is set to 0.

See also

optflow::DenseRLOFOpticalFlow, optflow::RLOFOpticalFlowParameter

Parameters

calcOpticalFlowSF()#

void cv::optflow::calcOpticalFlowSF(
InputArray from,
InputArray to,
OutputArray flow,
int layers,
int averaging_block_size,
int max_flow )

#include <opencv2/optflow.hpp>

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

calcOpticalFlowSF()#

void cv::optflow::calcOpticalFlowSF(
InputArray from,
InputArray to,
OutputArray flow,
int layers,
int averaging_block_size,
int max_flow,
double sigma_dist,
double sigma_color,
int postprocess_window,
double sigma_dist_fix,
double sigma_color_fix,
double occ_thr,
int upscale_averaging_radius,
double upscale_sigma_dist,
double upscale_sigma_color,
double speed_up_thr )

#include <opencv2/optflow.hpp>

Calculate an optical flow using “SimpleFlow” algorithm.

See [298] . And site of project - http://graphics.berkeley.edu/papers/Tao-SAN-2012-05/.

Note

  • An example using the simpleFlow algorithm can be found at samples/simpleflow_demo.cpp

Parameters

  • from — First 8-bit 3-channel image.

  • to — Second 8-bit 3-channel image of the same size as prev

  • flow — computed flow image that has the same size as prev and type CV_32FC2

  • layers — Number of layers

  • averaging_block_size — Size of block through which we sum up when calculate cost function for pixel

  • max_flow — maximal flow that we search at each level

  • sigma_dist — vector smooth spatial sigma parameter

  • sigma_color — vector smooth color sigma parameter

  • postprocess_window — window size for postprocess cross bilateral filter

  • sigma_dist_fix — spatial sigma for postprocess cross bilateralf filter

  • sigma_color_fix — color sigma for postprocess cross bilateral filter

  • occ_thr — threshold for detecting occlusions

  • upscale_averaging_radius — window size for bilateral upscale operation

  • upscale_sigma_dist — spatial sigma for bilateral upscale operation

  • upscale_sigma_color — color sigma for bilateral upscale operation

  • speed_up_thr — threshold to detect point with irregular flow - where flow should be recalculated after upscale

calcOpticalFlowSparseRLOF()#

void cv::optflow::calcOpticalFlowSparseRLOF(
InputArray prevImg,
InputArray nextImg,
InputArray prevPts,
InputOutputArray nextPts,
OutputArray status,
OutputArray err,
Ptr< RLOFOpticalFlowParameter > rlofParam = Ptr< RLOFOpticalFlowParameter >(),
float forwardBackwardThreshold = 0 )

#include <opencv2/optflow/rlofflow.hpp>

Calculates fast optical flow for a sparse feature set using the robust local optical flow (RLOF) similar to optflow::calcOpticalFlowPyrLK().

The RLOF is a fast local optical flow approach described in [264] [265] [266] and [267] similar to the pyramidal iterative Lucas-Kanade method as proposed by [41]. More details and experiments can be found in the following thesis [268]. The implementation is derived from optflow::calcOpticalFlowPyrLK().

Note

SIMD parallelization is only available when compiling with SSE4.1.

Parameters have been described in [264], [265], [266] and [267]. For the RLOF configuration see optflow::RLOFOpticalFlowParameter for further details.

Parameters

  • prevImg — first 8-bit input image. If The cross-based RLOF is used (by selecting optflow::RLOFOpticalFlowParameter::supportRegionType = SupportRegionType::SR_CROSS) image has to be a 8-bit 3 channel image.

  • nextImg — second 8-bit input image. If The cross-based RLOF is used (by selecting optflow::RLOFOpticalFlowParameter::supportRegionType = SupportRegionType::SR_CROSS) image has to be a 8-bit 3 channel image.

  • prevPts — vector of 2D points for which the flow needs to be found; point coordinates must be single-precision floating-point numbers.

  • nextPts — output vector of 2D points (with single-precision floating-point coordinates) containing the calculated new positions of input features in the second image; when optflow::RLOFOpticalFlowParameter::useInitialFlow variable is true the vector must have the same size as in the input and contain the initialization point correspondences.

  • status — output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has passed the forward backward check.

  • err — output vector of errors; each element of the vector is set to the forward backward error for the corresponding feature.

  • rlofParam — see optflow::RLOFOpticalFlowParameter

  • forwardBackwardThreshold — Threshold for the forward backward confidence check. If forewardBackwardThreshold <=0 the forward

calcOpticalFlowSparseToDense()#

void cv::optflow::calcOpticalFlowSparseToDense(
InputArray from,
InputArray to,
OutputArray flow,
int grid_step = 8,
int k = 128,
float sigma = 0.05f,
bool use_post_proc = true,
float fgs_lambda = 500.0f,
float fgs_sigma = 1.5f )

#include <opencv2/optflow.hpp>

Fast dense optical flow based on PyrLK sparse matches interpolation.

Parameters

  • from — first 8-bit 3-channel or 1-channel image.

  • to — second 8-bit 3-channel or 1-channel image of the same size as from

  • flow — computed flow image that has the same size as from and CV_32FC2 type

  • grid_step — stride used in sparse match computation. Lower values usually result in higher quality but slow down the algorithm.

  • k — number of nearest-neighbor matches considered, when fitting a locally affine model. Lower values can make the algorithm noticeably faster at the cost of some quality degradation.

  • sigma — parameter defining how fast the weights decrease in the locally-weighted affine fitting. Higher values can help preserve fine details, lower values can help to get rid of the noise in the output flow.

  • use_post_proc — defines whether the ximgproc::fastGlobalSmootherFilter() is used for post-processing after interpolation

  • fgs_lambda — see the respective parameter of the ximgproc::fastGlobalSmootherFilter()

  • fgs_sigma — see the respective parameter of the ximgproc::fastGlobalSmootherFilter()

createOptFlow_DeepFlow()#

Ptr< DenseOpticalFlow > cv::optflow::createOptFlow_DeepFlow()

#include <opencv2/optflow.hpp>

DeepFlow optical flow algorithm implementation.

The class implements the DeepFlow optical flow algorithm described in [331] . See also http://lear.inrialpes.fr/src/deepmatching/ . Parameters - class fields - that may be modified after creating a class instance:

  • member float alpha Smoothness assumption weight

  • member float delta Color constancy assumption weight

  • member float gamma Gradient constancy weight

  • member float sigma Gaussian smoothing parameter

  • member int minSize Minimal dimension of an image in the pyramid (next, smaller images in the pyramid are generated until one of the dimensions reaches this size)

  • member float downscaleFactor Scaling factor in the image pyramid (must be < 1)

  • member int fixedPointIterations How many iterations on each level of the pyramid

  • member int sorIterations Iterations of Succesive Over-Relaxation (solver)

  • member float omega Relaxation factor in SOR

createOptFlow_DenseRLOF()#

Ptr< DenseOpticalFlow > cv::optflow::createOptFlow_DenseRLOF()

#include <opencv2/optflow/rlofflow.hpp>

Additional interface to the Dense RLOF algorithm - optflow::calcOpticalFlowDenseRLOF()

createOptFlow_DualTVL1()#

Ptr< DualTVL1OpticalFlow > cv::optflow::createOptFlow_DualTVL1()

#include <opencv2/optflow.hpp>

Creates instance of cv::DenseOpticalFlow.

createOptFlow_Farneback()#

Ptr< DenseOpticalFlow > cv::optflow::createOptFlow_Farneback()

#include <opencv2/optflow.hpp>

Additional interface to the Farneback’s algorithm - calcOpticalFlowFarneback()

createOptFlow_PCAFlow()#

Ptr< DenseOpticalFlow > cv::optflow::createOptFlow_PCAFlow()

#include <opencv2/optflow/pcaflow.hpp>

Creates an instance of PCAFlow.

createOptFlow_SimpleFlow()#

Ptr< DenseOpticalFlow > cv::optflow::createOptFlow_SimpleFlow()

#include <opencv2/optflow.hpp>

Additional interface to the SimpleFlow algorithm - calcOpticalFlowSF()

createOptFlow_SparseRLOF()#

Ptr< SparseOpticalFlow > cv::optflow::createOptFlow_SparseRLOF()

#include <opencv2/optflow/rlofflow.hpp>

Additional interface to the Sparse RLOF algorithm - optflow::calcOpticalFlowSparseRLOF()

createOptFlow_SparseToDense()#

Ptr< DenseOpticalFlow > cv::optflow::createOptFlow_SparseToDense()

#include <opencv2/optflow.hpp>

Additional interface to the SparseToDenseFlow algorithm - calcOpticalFlowSparseToDense()

segmentMotion()#

void cv::motempl::segmentMotion(
InputArray mhi,
OutputArray segmask,
std::vector< Rect > & boundingRects,
double timestamp,
double segThresh )

#include <opencv2/optflow/motempl.hpp>

Splits a motion history image into a few parts corresponding to separate independent motions (for example, left hand, right hand).

The function finds all of the motion segments and marks them in segmask with individual values (1,2,…). It also computes a vector with ROIs of motion connected components. After that the motion direction for every component can be calculated with calcGlobalOrientation using the extracted mask of the particular component.

Parameters

  • mhi — Motion history image.

  • segmask — Image where the found mask should be stored, single-channel, 32-bit floating-point.

  • boundingRects — Vector containing ROIs of motion connected components.

  • timestamp — Current time in milliseconds or other units.

  • segThresh — Segmentation threshold that is recommended to be equal to the interval between motion history “steps” or greater.

updateMotionHistory()#

void cv::motempl::updateMotionHistory(
InputArray silhouette,
InputOutputArray mhi,
double timestamp,
double duration )

#include <opencv2/optflow/motempl.hpp>

Updates the motion history image by a moving silhouette.

The function updates the motion history image as follows:

\[ \texttt{mhi} (x,y)= \forkthree{\texttt{timestamp}}{if \(\texttt{silhouette}(x,y) \ne 0\)}{0}{if \(\texttt{silhouette}(x,y) = 0\) and \(\texttt{mhi} < (\texttt{timestamp} - \texttt{duration})\)}{\texttt{mhi}(x,y)}{otherwise} \]

That is, MHI pixels where the motion occurs are set to the current timestamp , while the pixels where the motion happened last time a long time ago are cleared.

The function, together with calcMotionGradient and calcGlobalOrientation , implements a motion templates technique described in [74] and [44] .

Parameters

  • silhouette — Silhouette mask that has non-zero pixels where the motion occurs.

  • mhi — Motion history image that is updated by the function (single-channel, 32-bit floating-point).

  • timestamp — Current time in milliseconds or other units.

  • duration — Maximal duration of the motion track in the same units as timestamp .