| OpenCV 3.4.8 Open Source Computer Vision

Classes | Typedefs | Enumerations | Functions
Optical Flow Algorithms

## Classes
class | cv::optflow::DISOpticalFlow
| DIS optical flow algorithm. More...
class | cv::optflow::GPCDetails
class | cv::optflow::GPCForest< T >
struct | cv::optflow::GPCMatchingParams
| Class encapsulating matching parameters. More...
struct | cv::optflow::GPCPatchDescriptor
struct | cv::optflow::GPCPatchSample
struct | cv::optflow::GPCTrainingParams
| Class encapsulating training parameters. More...
class | cv::optflow::GPCTrainingSamples
| Class encapsulating training samples. More...
class | cv::optflow::GPCTree
| Class for individual tree. More...
class | cv::optflow::OpticalFlowPCAFlow
| PCAFlow algorithm. More...
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. More...
class | cv::optflow::VariationalRefinement
| Variational optical flow refinement. More...

## Typedefs
typedef std::vector< GPCPatchSample > | cv::optflow::GPCSamplesVector

## Enumerations
enum | cv::optflow::GPCDescType { cv::optflow::GPC_DESCRIPTOR_DCT = 0, cv::optflow::GPC_DESCRIPTOR_WHT }
| Descriptor types for the Global Patch Collider. More...

## Functions
double | cv::motempl::calcGlobalOrientation (InputArray orientation, InputArray mask, InputArray mhi, double timestamp, double duration)
| Calculates a global motion orientation in a selected region. More...
void | cv::motempl::calcMotionGradient (InputArray mhi, OutputArray mask, OutputArray orientation, double delta1, double delta2, int apertureSize=3)
| Calculates a gradient orientation of a motion history image. More...
void | cv::optflow::calcOpticalFlowSF (InputArray from, InputArray to, OutputArray flow, int layers, int averaging_block_size, int max_flow)
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)
| Calculate an optical flow using "SimpleFlow" algorithm. More...
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)
| Fast dense optical flow based on PyrLK sparse matches interpolation. More...
Ptr< DenseOpticalFlow > | cv::optflow::createOptFlow_DeepFlow ()
| DeepFlow optical flow algorithm implementation. More...
Ptr< DISOpticalFlow > | cv::optflow::createOptFlow_DIS (int preset=DISOpticalFlow::PRESET_FAST)
| Creates an instance of DISOpticalFlow. More...
Ptr< DenseOpticalFlow > | cv::optflow::createOptFlow_Farneback ()
| Additional interface to the Farneback's algorithm - calcOpticalFlowFarneback() More...
Ptr< DenseOpticalFlow > | cv::optflow::createOptFlow_PCAFlow ()
| Creates an instance of PCAFlow. More...
Ptr< DenseOpticalFlow > | cv::optflow::createOptFlow_SimpleFlow ()
| Additional interface to the SimpleFlow algorithm - calcOpticalFlowSF() More...
Ptr< DenseOpticalFlow > | cv::optflow::createOptFlow_SparseToDense ()
| Additional interface to the SparseToDenseFlow algorithm - calcOpticalFlowSparseToDense() More...
Ptr< VariationalRefinement > | cv::optflow::createVariationalFlowRefinement ()
| Creates an instance of VariationalRefinement. More...
void | cv::optflow::GPCForest< T >::findCorrespondences (InputArray imgFrom, InputArray imgTo, std::vector< std::pair< Point2i, Point2i > > &corr, const GPCMatchingParams params=GPCMatchingParams()) const
| Find correspondences between two images. More...
Mat | cv::optflow::readOpticalFlow (const String &path)
| Read a .flo file. More...
void | cv::motempl::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). More...
void | cv::motempl::updateMotionHistory (InputArray silhouette, InputOutputArray mhi, double timestamp, double duration)
| Updates the motion history image by a moving silhouette. More...
bool | cv::optflow::writeOpticalFlow (const String &path, InputArray flow)
| Write a .flo to disk. More...

## Detailed Description

Dense optical flow algorithms compute motion for each point:

- cv::optflow::calcOpticalFlowSF
- cv::optflow::createOptFlow_DeepFlow

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

- cv::motempl::updateMotionHistory
- cv::motempl::calcMotionGradient
- cv::motempl::calcGlobalOrientation
- cv::motempl::segmentMotion

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

- cv::optflow::readOpticalFlow
- cv::optflow::writeOpticalFlow

## 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 Python: cv.optflow.GPC_DESCRIPTOR_DCT | Better quality but slow.
GPC_DESCRIPTOR_WHT Python: cv.optflow.GPC_DESCRIPTOR_WHT | Worse quality but much faster.

## Function Documentation

## § calcGlobalOrientation()

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

Python:
| retval | = | cv.motempl.calcGlobalOrientation( | orientation, mask, mhi, timestamp, duration | )

`#include <opencv2/optflow/motempl.hpp>`

Calculates a global motion orientation in a selected region.

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

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 .

## § calcMotionGradient()

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

Python:
| mask, orientation | = | cv.motempl.calcMotionGradient( | mhi, delta1, delta2[, mask[, orientation[, apertureSize]]] | )

`#include <opencv2/optflow/motempl.hpp>`

Calculates a gradient orientation of a motion history image.

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.

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
  - (Python) An example on how to perform a motion template technique can be found at opencv_source_code/samples/python2/motempl.py

## § calcOpticalFlowSF() [1/2]

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

Python:
| flow | = | cv.optflow.calcOpticalFlowSF( | from, to, layers, averaging_block_size, max_flow[, flow] | )
| flow | = | cv.optflow.calcOpticalFlowSF( | from, to, layers, averaging_block_size, max_flow, sigma_dist, sigma_color, postprocess_window, sigma_dist_fix, sigma_color_fix, occ_thr, upscale_averaging_radius, upscale_sigma_dist, upscale_sigma_color, speed_up_thr[, 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() [2/2]

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
| ) |  |

Python:
| flow | = | cv.optflow.calcOpticalFlowSF( | from, to, layers, averaging_block_size, max_flow[, flow] | )
| flow | = | cv.optflow.calcOpticalFlowSF( | from, to, layers, averaging_block_size, max_flow, sigma_dist, sigma_color, postprocess_window, sigma_dist_fix, sigma_color_fix, occ_thr, upscale_averaging_radius, upscale_sigma_dist, upscale_sigma_color, speed_up_thr[, flow] | )

`#include <opencv2/optflow.hpp>`

Calculate an optical flow using "SimpleFlow" algorithm.

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

See [195] . 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

## § 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`
| ) |  |

Python:
| flow | = | cv.optflow.calcOpticalFlowSparseToDense( | from, to[, flow[, grid_step[, k[, sigma[, use_post_proc[, fgs_lambda[, fgs_sigma]]]]]]] | )

`#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 | ( |  | ) |

Python:
| retval | = | cv.optflow.createOptFlow_DeepFlow( |  | )

`#include <opencv2/optflow.hpp>`

DeepFlow optical flow algorithm implementation.

The class implements the DeepFlow optical flow algorithm described in [222] . 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_DIS()

Ptr<DISOpticalFlow> cv::optflow::createOptFlow_DIS | ( | int | preset = `DISOpticalFlow::PRESET_FAST` | ) |

Python:
| retval | = | cv.optflow.createOptFlow_DIS( | [, preset] | )

`#include <opencv2/optflow.hpp>`

Creates an instance of DISOpticalFlow.

Parameters

  preset | one of PRESET_ULTRAFAST, PRESET_FAST and PRESET_MEDIUM

## § createOptFlow_Farneback()

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

Python:
| retval | = | cv.optflow.createOptFlow_Farneback( |  | )

`#include <opencv2/optflow.hpp>`

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

## § createOptFlow_PCAFlow()

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

Python:
| retval | = | cv.optflow.createOptFlow_PCAFlow( |  | )

`#include <opencv2/optflow/pcaflow.hpp>`

Creates an instance of PCAFlow.

## § createOptFlow_SimpleFlow()

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

Python:
| retval | = | cv.optflow.createOptFlow_SimpleFlow( |  | )

`#include <opencv2/optflow.hpp>`

Additional interface to the SimpleFlow algorithm - calcOpticalFlowSF()

## § createOptFlow_SparseToDense()

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

Python:
| retval | = | cv.optflow.createOptFlow_SparseToDense( |  | )

`#include <opencv2/optflow.hpp>`

Additional interface to the SparseToDenseFlow algorithm - calcOpticalFlowSparseToDense()

## § createVariationalFlowRefinement()

Ptr<VariationalRefinement> cv::optflow::createVariationalFlowRefinement | ( |  | ) |

Python:
| retval | = | cv.optflow.createVariationalFlowRefinement( |  | )

`#include <opencv2/optflow.hpp>`

Creates an instance of VariationalRefinement.

## § findCorrespondences()

template<int T>

void cv::optflow::GPCForest< T >::findCorrespondences | ( | InputArray | imgFrom,
|  | InputArray | imgTo,
|  | std::vector< std::pair< Point2i, Point2i > > & | corr,
|  | const GPCMatchingParams | params = `GPCMatchingParams()`
| ) |  | const

`#include <opencv2/optflow/sparse_matching_gpc.hpp>`

Find correspondences between two images.

Parameters

  [in] | imgFrom | First image in a sequence.
  [in] | imgTo | Second image in a sequence.
  [out] | corr | Output vector with pairs of corresponding points.
  [in] | params | Additional matching parameters for fine-tuning.

## § readOpticalFlow()

Mat cv::optflow::readOpticalFlow | ( | const String & | path | ) |

Python:
| retval | = | cv.optflow.readOpticalFlow( | path | )

`#include <opencv2/optflow.hpp>`

Read a .flo file.

Parameters

  path | Path to the file to be loaded

The function readOpticalFlow loads a flow field from a file and returns it as a single matrix. Resulting Mat has a type CV_32FC2 - floating-point, 2-channel. First channel corresponds to the flow in the horizontal direction (u), second - vertical (v).

## § segmentMotion()

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

Python:
| segmask, boundingRects | = | cv.motempl.segmentMotion( | mhi, timestamp, segThresh[, segmask] | )

`#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).

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.

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.

## § updateMotionHistory()

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

Python:
| mhi | = | cv.motempl.updateMotionHistory( | silhouette, mhi, timestamp, duration | )

`#include <opencv2/optflow/motempl.hpp>`

Updates the motion history image by a moving silhouette.

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 .

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 [45] and [25] .

## § writeOpticalFlow()

bool cv::optflow::writeOpticalFlow | ( | const String & | path,
|  | InputArray | flow
| ) |  |

Python:
| retval | = | cv.optflow.writeOpticalFlow( | path, flow | )

`#include <opencv2/optflow.hpp>`

Write a .flo to disk.

Parameters

  path | Path to the file to be written
  flow | Flow field to be stored

The function stores a flow field in a file, returns true on success, false otherwise. The flow field must be a 2-channel, floating-point matrix (CV_32FC2). First channel corresponds to the flow in the horizontal direction (u), second - vertical (v).