recoverPose1

geometryfunctionOpenCV 5.0.0

import { recoverPose1 } from '@banou/opencv-wasm'

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

ARGUMENTSE, points1, points2, cameraMatrix

FUNCTIONrecoverPose1

RETURN TYPEnumber

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

Recovers the relative camera rotation and the translation from an estimated essential matrix and the corresponding points in two images, using chirality check. Returns the number of inliers that pass the check.

    // Example. Estimation of fundamental matrix using the RANSAC algorithm
    int point_count = 100;
    vector<Point2f> points1(point_count);
    vector<Point2f> points2(point_count);

    // initialize the points here ...
    for( int i = 0; i < point_count; i++ )
    {
        points1[i] = ...;
        points2[i] = ...;
    }

    // cametra matrix with both focal lengths = 1, and principal point = (0, 0)
    Mat cameraMatrix = Mat::eye(3, 3, CV_64F);

    Mat E, R, t, mask;

    E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask);
    recoverPose(E, points1, points2, cameraMatrix, R, t, mask);

recoverPose1(E: Mat, points1: Mat, points2: Mat, cameraMatrix: Mat, R: Mat, t: Mat, mask: Mat): number;

2 available overloads

recoverPose1(E: Mat, points1: Mat, points2: Mat, cameraMatrix: Mat, R: Mat, t: Mat): number;

recoverPose1(E: Mat, points1: Mat, points2: Mat, cameraMatrix: Mat, R: Mat, t: Mat, mask: Mat): number;

E: The input essential matrix.
points1: Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2: Array of the second image points of the same size and format as points1 .
cameraMatrix: Camera intrinsic matrix \cameramatrix{A} . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix.
R: Output destination, filled by the native operation. Output rotation matrix. Together with the translation vector, this matrix makes up a tuple that performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Note that, in general, t can not be used for this tuple, see the parameter described below.
t: Output destination, filled by the native operation. Output translation vector. This vector is obtained by decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
mask: Input/output value, modified by the native operation. Input/output mask for inliers in points1 and points2. If it is not empty, then it marks inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the chirality check.

This function decomposes an essential matrix using decomposeEssentialMat and then verifies possible pose hypotheses by doing chirality check. The chirality check means that the triangulated 3D points should have positive depth. Some details can be found in [Nister03].

This function can be used to process the output E and mask from findEssentialMat. In this scenario, points1 and points2 are the same input for #findEssentialMat :

Returns

The number result.

Upstream declaration ↗

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