sfm_fundamentalFromCorrespondences7PointRobust

sfmfunctionOpenCV 5.0.0

import { sfm_fundamentalFromCorrespondences7PointRobust } from '@banou/opencv-wasm'

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

ARGUMENTSx1, x2, max_error, F

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FUNCTIONsfm_fundamentalFromCorrespondences7PointRobust

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RETURN TYPEnumber

Call structure. A void return can still write to destination arguments. The parameter descriptions define inputs, outputs and ownership.

Estimate robustly the fundamental matrix between two dataset of 2D point (image coords space).

sfm_fundamentalFromCorrespondences7PointRobust(x1: Mat, x2: Mat, max_error: number, F: Mat, inliers: Mat, outliers_probability: number): number;

2 available overloads

sfm_fundamentalFromCorrespondences7PointRobust(x1: Mat, x2: Mat, max_error: number, F: Mat, inliers: Mat): number;

sfm_fundamentalFromCorrespondences7PointRobust(x1: Mat, x2: Mat, max_error: number, F: Mat, inliers: Mat, outliers_probability: number): number;

x1

Input 2xN Array of 2D points in view 1.

x2

Input 2xN Array of 2D points in view 2.

max_error

maximum error (in pixels).

F

Output destination, filled by the native operation. Output 3x3 fundamental matrix such that x_2^T F x_1=0.

inliers

Output destination, filled by the native operation. Output 1xN vector that contains the indexes of the detected inliers.

outliers_probability

outliers probability (in ]0,1[). The number of iterations is controlled using the following equation: k = \frac{log(1-p)}{log(1.0 - w^n )} where k, w and n are the number of iterations, the inliers ratio and minimun number of selected independent samples. The more this value is high, the less the function selects ramdom samples.

The fundamental solver relies on the 7 point solution. Returns the best error (in pixels), associated to the solution F.

Returns

The number result.

Upstream declaration ↗

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