calcOpticalFlowPyrLK

Motion and trackingfunctionOpenCV 5.0.0

import { calcOpticalFlowPyrLK } from '@banou/opencv-wasm'

Use after await initOpenCV(). See the initialization and named imports guide.

Explore visually: Lucas-Kanade optical flow →

Follow selected points by finding the small displacement that best aligns each local patch.

VISUAL WALKTHROUGHMotion

REFERENCE INPUTTwo consecutive frames

The object moves right and upward; most of the background remains still.

→

RESULTSparse point tracks

Only selected points receive tracks. A real call also returns status and error; a plausible vector alone does not prove a valid track.

Illustrative example. The stages explain the method; they are not a live OpenCV execution.

example: samples/cpp/lkdemo.cpp An example using the Lucas-Kanade optical flow algorithm example: samples/python/snippets/lk_track.py An example using the Lucas-Kanade optical flow algorithm in python Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.

Note: Some examples:

• An example using the Lucas-Kanade optical flow algorithm can be found at opencv_source_code/samples/cpp/lkdemo.cpp
• (Python) An example using the Lucas-Kanade optical flow algorithm can be found at opencv_source_code/samples/python/lk_track.py
• (Python) An example using the Lucas-Kanade tracker for homography matching can be found at opencv_source_code/samples/python/lk_homography.py

calcOpticalFlowPyrLK(prevImg: Mat, nextImg: Mat, prevPts: Mat, nextPts: Mat, status: Mat, err: Mat, winSize: Size, maxLevel: number, criteria: TermCriteria, flags: number, minEigThreshold: number): void;

6 available overloads

calcOpticalFlowPyrLK(prevImg: Mat, nextImg: Mat, prevPts: Mat, nextPts: Mat, status: Mat, err: Mat): void;

calcOpticalFlowPyrLK(prevImg: Mat, nextImg: Mat, prevPts: Mat, nextPts: Mat, status: Mat, err: Mat, winSize: Size): void;

calcOpticalFlowPyrLK(prevImg: Mat, nextImg: Mat, prevPts: Mat, nextPts: Mat, status: Mat, err: Mat, winSize: Size, maxLevel: number): void;

calcOpticalFlowPyrLK(prevImg: Mat, nextImg: Mat, prevPts: Mat, nextPts: Mat, status: Mat, err: Mat, winSize: Size, maxLevel: number, criteria: TermCriteria): void;

calcOpticalFlowPyrLK(prevImg: Mat, nextImg: Mat, prevPts: Mat, nextPts: Mat, status: Mat, err: Mat, winSize: Size, maxLevel: number, criteria: TermCriteria, flags: number): void;

calcOpticalFlowPyrLK(prevImg: Mat, nextImg: Mat, prevPts: Mat, nextPts: Mat, status: Mat, err: Mat, winSize: Size, maxLevel: number, criteria: TermCriteria, flags: number, minEigThreshold: number): void;

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

Input/output value, modified by the native operation. 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 destination, filled by the native operation. 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 destination, filled by the native operation. 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 [Bouguet00]), 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.

The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

Upstream declaration ↗

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