Object Tracking#

Detailed Description#

Classes#

Name

Description

class cv::DenseOpticalFlow

View details

class cv::DISOpticalFlow

DIS optical flow algorithm. View details

struct cv::ECCParameters

struct ECCParameters is used by findTransformECCMultiScale View details

class cv::FarnebackOpticalFlow

Class computing a dense optical flow using the Gunnar Farneback’s algorithm. View details

class cv::KalmanFilter

Kalman filter class. View details

class cv::SparseOpticalFlow

Base interface for sparse optical flow algorithms. View details

class cv::SparsePyrLKOpticalFlow

Class used for calculating a sparse optical flow. View details

class cv::Tracker

Base abstract class for the long-term tracker. View details

class cv::TrackerDaSiamRPN

View details

class cv::TrackerMIL

The [17] algorithm trains a classifier in an online manner to separate the object from the background. View details

class cv::TrackerNano

the Nano tracker is a super lightweight dnn-based general object tracking. View details

class cv::TrackerVit

the VIT tracker is a super lightweight dnn-based general object tracking. View details

class cv::VariationalRefinement

Variational optical flow refinement. View details

Enumerations#

enum cv {
    OPTFLOW_USE_INITIAL_FLOW = 4,
    OPTFLOW_LK_GET_MIN_EIGENVALS = 8,
    OPTFLOW_FARNEBACK_GAUSSIAN = 256
}

View details

enum cv {
    MOTION_TRANSLATION = 0,
    MOTION_EUCLIDEAN = 1,
    MOTION_AFFINE = 2,
    MOTION_HOMOGRAPHY = 3
}

View details

Enumeration Type Documentation#

enum#

#include <opencv2/video/tracking.hpp>

Enumerator:

OPTFLOW_USE_INITIAL_FLOW

OPTFLOW_LK_GET_MIN_EIGENVALS

OPTFLOW_FARNEBACK_GAUSSIAN

enum#

#include <opencv2/video/tracking.hpp>

Enumerator:

MOTION_TRANSLATION

MOTION_EUCLIDEAN

MOTION_AFFINE

MOTION_HOMOGRAPHY

Function Documentation#

buildOpticalFlowPyramid()#

int cv::buildOpticalFlowPyramid(
InputArray img,
OutputArrayOfArrays pyramid,
Size winSize,
int maxLevel,
bool withDerivatives = true,
int pyrBorder = BORDER_REFLECT_101,
int derivBorder = BORDER_CONSTANT,
bool tryReuseInputImage = true )

#include <opencv2/video/tracking.hpp>

Python:

cv.buildOpticalFlowPyramid(img, winSize, maxLevel[, pyramid[, withDerivatives[, pyrBorder[, derivBorder[, tryReuseInputImage]]]]]) -> retval, pyramid

Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.

Parameters

  • img — 8-bit input image.

  • pyramid — output pyramid.

  • winSize — window size of optical flow algorithm. Must be not less than winSize argument of calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels.

  • maxLevel — 0-based maximal pyramid level number.

  • withDerivatives — set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.

  • pyrBorder — the border mode for pyramid layers.

  • derivBorder — the border mode for gradients.

  • tryReuseInputImage — put ROI of input image into the pyramid if possible. You can pass false to force data copying.

Returns

number of levels in constructed pyramid. Can be less than maxLevel.

calcOpticalFlowFarneback()#

void cv::calcOpticalFlowFarneback(
InputArray prev,
InputArray next,
InputOutputArray flow,
double pyr_scale,
int levels,
int winsize,
int iterations,
int poly_n,
double poly_sigma,
int flags )

#include <opencv2/video/tracking.hpp>

Python:

cv.calcOpticalFlowFarneback(prev, next, flow, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags) -> flow

Computes a dense optical flow using the Gunnar Farneback’s algorithm.

The function finds an optical flow for each prev pixel using the [94] algorithm so that

\[ \texttt{prev} (y,x) \sim \texttt{next} ( y + \texttt{flow} (y,x)[1], x + \texttt{flow} (y,x)[0]) \]

Note

Some examples:

Parameters

  • prev — first 8-bit single-channel input image.

  • next — second input image of the same size and the same type as prev.

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

  • pyr_scale — parameter, specifying the image scale (<1) to build pyramids for each image; pyr_scale=0.5 means a classical pyramid, where each next layer is twice smaller than the previous one.

  • levels — number of pyramid layers including the initial image; levels=1 means that no extra layers are created and only the original images are used.

  • winsize — averaging window size; larger values increase the algorithm robustness to image noise and give more chances for fast motion detection, but yield more blurred motion field.

  • iterations — number of iterations the algorithm does at each pyramid level.

  • poly_n — size of the pixel neighborhood used to find polynomial expansion in each pixel; larger values mean that the image will be approximated with smoother surfaces, yielding more robust algorithm and more blurred motion field, typically poly_n =5 or 7.

  • poly_sigma — standard deviation of the Gaussian that is used to smooth derivatives used as a basis for the polynomial expansion; for poly_n=5, you can set poly_sigma=1.1, for poly_n=7, a good value would be poly_sigma=1.5.

  • flags — operation flags that can be a combination of the following:

    • OPTFLOW_USE_INITIAL_FLOW uses the input flow as an initial flow approximation.

    • OPTFLOW_FARNEBACK_GAUSSIAN uses the Gaussian \(\texttt{winsize}\times\texttt{winsize}\) filter instead of a box filter of the same size for optical flow estimation; usually, this option gives z more accurate flow than with a box filter, at the cost of lower speed; normally, winsize for a Gaussian window should be set to a larger value to achieve the same level of robustness.

calcOpticalFlowPyrLK()#

void cv::calcOpticalFlowPyrLK(
InputArray prevImg,
InputArray nextImg,
InputArray prevPts,
InputOutputArray nextPts,
OutputArray status,
OutputArray err,
Size winSize = Size(21, 21),
int maxLevel = 3,
TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01),
int flags = 0,
double minEigThreshold = 1e-4 )

#include <opencv2/video/tracking.hpp>

Python:

cv.calcOpticalFlowPyrLK(prevImg, nextImg, prevPts, nextPts[, status[, err[, winSize[, maxLevel[, criteria[, flags[, minEigThreshold]]]]]]]) -> nextPts, status, err

Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.

The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See [41] . The function is parallelized with the TBB library.

Note

Some examples:

Parameters

  • prevImg — first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.

  • nextImg — second input image or pyramid of the same size and the same type as prevImg.

  • 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_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input.

  • status — output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0.

  • err — output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn’t found then the error is not defined (use the status parameter to find such cases).

  • winSize — size of the search window at each pyramid level.

  • maxLevel — 0-based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than maxLevel.

  • criteria — parameter, specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon.

  • flags — operation flags:

    • OPTFLOW_USE_INITIAL_FLOW uses initial estimations, stored in nextPts; if the flag is not set, then prevPts is copied to nextPts and is considered the initial estimate.

    • OPTFLOW_LK_GET_MIN_EIGENVALS use minimum eigen values as an error measure (see minEigThreshold description); if the flag is not set, then L1 distance between patches around the original and a moved point, divided by number of pixels in a window, is used as a error measure.

  • minEigThreshold — the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [41]), divided by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding feature is filtered out and its flow is not processed, so it allows to remove bad points and get a performance boost.

CamShift()#

RotatedRect cv::CamShift(
InputArray probImage,
Rect & window,
TermCriteria criteria )

#include <opencv2/video/tracking.hpp>

Python:

cv.CamShift(probImage, window, criteria) -> retval, window

Finds an object center, size, and orientation.

See the OpenCV sample camshiftdemo.c that tracks colored objects.

Note

Parameters

  • probImage — Back projection of the object histogram. See calcBackProject.

  • window — Initial search window.

  • criteria — Stop criteria for the underlying meanShift. returns (in old interfaces) Number of iterations CAMSHIFT took to converge The function implements the CAMSHIFT object tracking algorithm [43] . First, it finds an object center using meanShift and then adjusts the window size and finds the optimal rotation. The function returns the rotated rectangle structure that includes the object position, size, and orientation. The next position of the search window can be obtained with RotatedRect::boundingRect()

computeECC()#

double cv::computeECC(
InputArray templateImage,
InputArray inputImage,
InputArray inputMask = noArray() )

#include <opencv2/video/tracking.hpp>

Python:

cv.computeECC(templateImage, inputImage[, inputMask]) -> retval

Computes the Enhanced Correlation Coefficient (ECC) value between two images.

The Enhanced Correlation Coefficient (ECC) is a normalized measure of similarity between two images [91]. The result lies in the range [-1, 1], where 1 corresponds to perfect similarity (modulo affine shift and scale), 0 indicates no correlation, and -1 indicates perfect negative correlation.

For single-channel images, the ECC is defined as:

\[ \mathrm{ECC}(I, T) = \frac{\sum_{x} (I(x) - \mu_I)(T(x) - \mu_T)} {\sqrt{\sum_{x} (I(x) - \mu_I)^2} \cdot \sqrt{\sum_{x} (T(x) - \mu_T)^2}} \]

For multi-channel images (e.g., 3-channel RGB), the formula generalizes to:

\[ \mathrm{ECC}(I, T) = \frac{\sum_{x} \sum_{c=1}^{C} (I_c(x) - \mu_{I_c})(T_c(x) - \mu_{T_c})} {\sqrt{\sum_{x} \sum_{c=1}^{C} (I_c(x) - \mu_{I_c})^2} \cdot \sqrt{\sum_{x} \sum_{c=1}^{C} (T_c(x) - \mu_{T_c})^2}} \]

Where:

  • \(I_c(x), T_c(x)\) are the values of channel \(c\) at spatial location \(x\),

  • \(\mu_{I_c}, \mu_{T_c}\) are the mean values of channel \(c\) over the masked region (if provided),

  • \(C\) is the number of channels (only 1 and 3 are currently supported),

  • The sums run over all pixels \(x\) in the image domain (optionally restricted by mask).

See also

findTransformECC

Parameters

  • templateImage — Input template image; must have either 1 or 3 channels and be of type CV_8U, CV_16U, CV_32F, or CV_64F.

  • inputImage — Input image to be compared with the template; must have the same type and number of channels as templateImage.

  • inputMask — Optional single-channel mask to specify the valid region of interest in inputImage and templateImage.

Returns

The ECC similarity coefficient in the range [-1, 1].

estimateRigidTransform()#

Mat cv::estimateRigidTransform(
InputArray src,
InputArray dst,
bool fullAffine )

#include <opencv2/video/tracking.hpp>

Computes an optimal affine transformation between two 2D point sets.

The function finds an optimal affine transform [A|b] (a 2 x 3 floating-point matrix) that approximates best the affine transformation between: Two point sets Two raster images. In this case, the function first finds some features in the src image and finds the corresponding features in dst image. After that, the problem is reduced to the first case. In case of point sets, the problem is formulated as follows: you need to find a 2x2 matrix A and 2x1 vector b so that:

\[ [A^*|b^*] = arg \min _{[A|b]} \sum _i \| \texttt{dst}[i] - A { \texttt{src}[i]}^T - b \| ^2 \]

where src[i] and dst[i] are the i-th points in src and dst, respectively \([A|b]\) can be either arbitrary (when fullAffine=true ) or have a form of

\[\begin{split} \begin{bmatrix} a_{11} & a_{12} & b_1 \\ -a_{12} & a_{11} & b_2 \end{bmatrix} \end{split}\]

when fullAffine=false.

Deprecated

Use cv::estimateAffine2D, cv::estimateAffinePartial2D instead. If you are using this function with images, extract points using cv::calcOpticalFlowPyrLK and then use the estimation functions.

See also

estimateAffine2D, estimateAffinePartial2D, getAffineTransform, getPerspectiveTransform, findHomography

Parameters

  • src — First input 2D point set stored in std::vector or Mat, or an image stored in Mat.

  • dst — Second input 2D point set of the same size and the same type as A, or another image.

  • fullAffine — If true, the function finds an optimal affine transformation with no additional restrictions (6 degrees of freedom). Otherwise, the class of transformations to choose from is limited to combinations of translation, rotation, and uniform scaling (4 degrees of freedom).

findTransformECC()#

double cv::findTransformECC(
InputArray templateImage,
InputArray inputImage,
InputOutputArray warpMatrix,
int motionType,
TermCriteria criteria,
InputArray inputMask,
int gaussFiltSize )

#include <opencv2/video/tracking.hpp>

Python:

cv.findTransformECC(templateImage, inputImage, warpMatrix, motionType, criteria, inputMask, gaussFiltSize) -> retval, warpMatrix

cv.findTransformECC(templateImage, inputImage, warpMatrix[, motionType[, criteria[, inputMask]]]) -> retval, warpMatrix

Finds the geometric transform (warp) between two images in terms of the ECC criterion [91] .

The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion ([91]), that is

\[ \texttt{warpMatrix} = \arg\max_{W} \texttt{ECC}(\texttt{templateImage}(x,y),\texttt{inputImage}(x',y')) \]

where

\[\begin{split} \begin{bmatrix} x' \\ y' \end{bmatrix} = W \cdot \begin{bmatrix} x \\ y \\ 1 \end{bmatrix} \end{split}\]

(the equation holds with homogeneous coordinates for homography). It returns the final enhanced correlation coefficient, that is the correlation coefficient between the template image and the final warped input image. When a \(3\times 3\) matrix is given with motionType =0, 1 or 2, the third row is ignored.

Unlike findHomography and estimateRigidTransform, the function findTransformECC implements an area-based alignment that builds on intensity similarities. In essence, the function updates the initial transformation that roughly aligns the images. If this information is missing, the identity warp (unity matrix) is used as an initialization. Note that if images undergo strong displacements/rotations, an initial transformation that roughly aligns the images is necessary (e.g., a simple euclidean/similarity transform that allows for the images showing the same image content approximately). Use inverse warping in the second image to take an image close to the first one, i.e. use the flag WARP_INVERSE_MAP with warpAffine or warpPerspective. See also the OpenCV sample image_alignment.cpp that demonstrates the use of the function. Note that the function throws an exception if algorithm does not converges.

See also

computeECC, estimateAffine2D, estimateAffinePartial2D, findHomography

Parameters

  • templateImage — 1 or 3 channel template image; CV_8U, CV_16U, CV_32F, CV_64F type.

  • inputImage — input image which should be warped with the final warpMatrix in order to provide an image similar to templateImage, same type as templateImage.

  • warpMatrix — floating-point \(2\times 3\) or \(3\times 3\) mapping matrix (warp).

  • motionType — parameter, specifying the type of motion:

    • MOTION_TRANSLATION sets a translational motion model; warpMatrix is \(2\times 3\) with the first \(2\times 2\) part being the unity matrix and the rest two parameters being estimated.

    • MOTION_EUCLIDEAN sets a Euclidean (rigid) transformation as motion model; three parameters are estimated; warpMatrix is \(2\times 3\).

    • MOTION_AFFINE sets an affine motion model (DEFAULT); six parameters are estimated; warpMatrix is \(2\times 3\).

    • MOTION_HOMOGRAPHY sets a homography as a motion model; eight parameters are estimated;warpMatrix is \(3\times 3\).

  • criteria — parameter, specifying the termination criteria of the ECC algorithm; criteria.epsilon defines the threshold of the increment in the correlation coefficient between two iterations (a negative criteria.epsilon makes criteria.maxcount the only termination criterion). Default values are shown in the declaration above.

  • inputMask — An optional single channel mask to indicate valid values of inputImage.

  • gaussFiltSize — An optional value indicating size of gaussian blur filter; (DEFAULT: 5)

findTransformECC()#

double cv::findTransformECC(
InputArray templateImage,
InputArray inputImage,
InputOutputArray warpMatrix,
int motionType = MOTION_AFFINE,
TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 0.001),
InputArray inputMask = noArray() )

#include <opencv2/video/tracking.hpp>

Python:

cv.findTransformECC(templateImage, inputImage, warpMatrix, motionType, criteria, inputMask, gaussFiltSize) -> retval, warpMatrix

cv.findTransformECC(templateImage, inputImage, warpMatrix[, motionType[, criteria[, inputMask]]]) -> retval, warpMatrix

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

findTransformECCMultiScale()#

double cv::findTransformECCMultiScale(
InputArray reference,
InputArray sample,
InputOutputArray warpMatrix,
const ECCParameters & eccParams = ECCParameters(),
InputArray referenceMask = noArray(),
InputArray sampleMask = noArray() )

#include <opencv2/video/tracking.hpp>

Python:

cv.findTransformECCMultiScale(reference, sample, warpMatrix[, eccParams[, referenceMask[, sampleMask]]]) -> retval, warpMatrix

Finds the geometric transform (warp) between two images in terms of the ECC criterion [91]. Uses pyramids.

The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion ([91]), that is

\[ \texttt{warpMatrix} = \arg\max_{W} \texttt{ECC}(\texttt{templateImage}(x,y),\texttt{inputImage}(x',y')) \]

where

\[\begin{split} \begin{bmatrix} x' \\ y' \end{bmatrix} = W \cdot \begin{bmatrix} x \\ y \\ 1 \end{bmatrix} \end{split}\]

(the equation holds with homogeneous coordinates for homography). It returns the final enhanced correlation coefficient, that is the correlation coefficient between the template image and the final warped input image. When a \(3\times 3\) matrix is given with motionType =0, 1 or 2, the third row is ignored.

Unlike findHomography and estimateRigidTransform, the function findTransformECCMultiScale implements an area-based alignment that builds on intensity similarities. In essence, the function updates the initial transformation that roughly aligns the images. If this information is missing, the identity warp (unity matrix) is used as an initialization. Note that if images undergo strong displacements/rotations, an initial transformation that roughly aligns the images is necessary (e.g., a simple euclidean/similarity transform that allows for the images showing the same image content approximately). Use inverse warping in the second image to take an image close to the first one, i.e. use the flag WARP_INVERSE_MAP with warpAffine or warpPerspective. See also the OpenCV sample image_alignment.cpp that demonstrates the use of the function. Note that the function throws an exception if algorithm does not converges. Unlike findTransformECC, the findTransformECCMultiScale uses pyramids, making function more stable and able to handle correctly more sophisticated cases.

See also

computeECC, estimateAffine2D, estimateAffinePartial2D, findHomography

Parameters

  • reference — Single channel reference image; CV_8U, CV_16U, CV_32F, CV_64F type.

  • sample — sample image which should be warped with the final warpMatrix in order to provide an image similar to reference, same type as reference.

  • warpMatrix — floating-point \(2\times 3\) or \(3\times 3\) mapping matrix (warp).

  • eccParams — List of the algorithm parameters. See ECCParameters for details.

  • referenceMask — An optional single channel mask to indicate valid values of reference.

  • sampleMask — An optional single channel mask to indicate valid values of sample.

findTransformECCWithMask()#

double cv::findTransformECCWithMask(
InputArray templateImage,
InputArray inputImage,
InputArray templateMask,
InputArray inputMask,
InputOutputArray warpMatrix,
int motionType = MOTION_AFFINE,
TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 1e-6),
int gaussFiltSize = 5 )

#include <opencv2/video/tracking.hpp>

Python:

cv.findTransformECCWithMask(templateImage, inputImage, templateMask, inputMask, warpMatrix[, motionType[, criteria[, gaussFiltSize]]]) -> retval, warpMatrix

Finds the geometric transform (warp) between two images in terms of the ECC criterion [91] using validity masks for both the template and the input images.

This function extends findTransformECC() by adding a mask for the template image. The Enhanced Correlation Coefficient is evaluated only over pixels that are valid in both images: on each iteration inputMask is warped into the template frame and combined with templateMask, and only the intersection of these masks contributes to the objective function.

See also

findTransformECC, computeECC, estimateAffine2D, estimateAffinePartial2D, findHomography

Parameters

  • templateImage — 1 or 3 channel template image; CV_8U, CV_16U, CV_32F, CV_64F type.

  • inputImage — input image which should be warped with the final warpMatrix in order to provide an image similar to templateImage, same type as templateImage.

  • templateMask — single-channel 8-bit mask for templateImage indicating valid pixels to be used in the alignment. Must have the same size as templateImage.

  • inputMask — single-channel 8-bit mask for inputImage indicating valid pixels before warping. Must have the same size as inputImage.

  • warpMatrix — floating-point \(2\times 3\) or \(3\times 3\) mapping matrix (warp).

  • motionType — parameter, specifying the type of motion:

    • MOTION_TRANSLATION sets a translational motion model; warpMatrix is \(2\times 3\) with the first \(2\times 2\) part being the unity matrix and the rest two parameters being estimated.

    • MOTION_EUCLIDEAN sets a Euclidean (rigid) transformation as motion model; three parameters are estimated; warpMatrix is \(2\times 3\).

    • MOTION_AFFINE sets an affine motion model (DEFAULT); six parameters are estimated; warpMatrix is \(2\times 3\).

    • MOTION_HOMOGRAPHY sets a homography as a motion model; eight parameters are estimated; warpMatrix is \(3\times 3\).

  • criteria — parameter, specifying the termination criteria of the ECC algorithm; criteria.epsilon defines the threshold of the increment in the correlation coefficient between two iterations (a negative criteria.epsilon makes criteria.maxcount the only termination criterion). Default values are shown in the declaration above.

  • gaussFiltSize — size of the Gaussian blur filter used for smoothing images and masks before computing the alignment (DEFAULT: 5).

meanShift()#

int cv::meanShift(
InputArray probImage,
Rect & window,
TermCriteria criteria )

#include <opencv2/video/tracking.hpp>

Python:

cv.meanShift(probImage, window, criteria) -> retval, window

Finds an object on a back projection image.

Parameters

  • probImage — Back projection of the object histogram. See calcBackProject for details.

  • window — Initial search window.

  • criteria — Stop criteria for the iterative search algorithm. returns : Number of iterations CAMSHIFT took to converge. The function implements the iterative object search algorithm. It takes the input back projection of an object and the initial position. The mass center in window of the back projection image is computed and the search window center shifts to the mass center. The procedure is repeated until the specified number of iterations criteria.maxCount is done or until the window center shifts by less than criteria.epsilon. The algorithm is used inside CamShift and, unlike CamShift , the search window size or orientation do not change during the search. You can simply pass the output of calcBackProject to this function. But better results can be obtained if you pre-filter the back projection and remove the noise. For example, you can do this by retrieving connected components with findContours , throwing away contours with small area ( contourArea ), and rendering the remaining contours with drawContours.

readOpticalFlow()#

Mat cv::readOpticalFlow(const String & path)

#include <opencv2/video/tracking.hpp>

Python:

cv.readOpticalFlow(path) -> retval

Read a .flo file.

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).

Parameters

  • path — Path to the file to be loaded

writeOpticalFlow()#

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

#include <opencv2/video/tracking.hpp>

Python:

cv.writeOpticalFlow(path, flow) -> retval

Write a .flo to disk.

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).

Parameters

  • path — Path to the file to be written

  • flow — Flow field to be stored