mirror of
https://github.com/opencv/opencv.git
synced 2024-12-12 15:19:11 +08:00
87 lines
2.5 KiB
JavaScript
87 lines
2.5 KiB
JavaScript
// This file is part of OpenCV project.
|
|
// It is subject to the license terms in the LICENSE file found in the top-level directory
|
|
// of this distribution and at http://opencv.org/license.html.
|
|
|
|
QUnit.module('Camera Calibration and 3D Reconstruction', {});
|
|
|
|
QUnit.test('constants', function(assert) {
|
|
assert.strictEqual(typeof cv.LMEDS, 'number');
|
|
assert.strictEqual(typeof cv.RANSAC, 'number');
|
|
assert.strictEqual(typeof cv.RHO, 'number');
|
|
});
|
|
|
|
QUnit.test('findHomography', function(assert) {
|
|
let srcPoints = cv.matFromArray(4, 1, cv.CV_32FC2, [
|
|
56,
|
|
65,
|
|
368,
|
|
52,
|
|
28,
|
|
387,
|
|
389,
|
|
390,
|
|
]);
|
|
let dstPoints = cv.matFromArray(4, 1, cv.CV_32FC2, [
|
|
0,
|
|
0,
|
|
300,
|
|
0,
|
|
0,
|
|
300,
|
|
300,
|
|
300,
|
|
]);
|
|
|
|
const mat = cv.findHomography(srcPoints, dstPoints);
|
|
|
|
assert.ok(mat instanceof cv.Mat);
|
|
});
|
|
|
|
QUnit.test('Rodrigues', function(assert) {
|
|
// Converts a rotation matrix to a rotation vector and vice versa
|
|
// data64F is the output array
|
|
const rvec0 = cv.matFromArray(1, 3, cv.CV_64F, [1,1,1]);
|
|
let rMat0 = new cv.Mat();
|
|
let rvec1 = new cv.Mat();
|
|
|
|
// Args: input Mat, output Mat. The function mutates the output Mat, so the function does not return anything.
|
|
// cv.Rodrigues (InputArray=src, OutputArray=dst, jacobian=0)
|
|
// https://docs.opencv.org/2.4/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html#void%20Rodrigues(InputArray%20src,%20OutputArray%20dst,%20OutputArray%20jacobian)
|
|
// vec to Mat, starting number is 3 long and each element is 1.
|
|
cv.Rodrigues(rvec0, rMat0);
|
|
|
|
assert.ok(rMat0.data64F.length == 9);
|
|
assert.ok(0.23 > rMat0.data64F[0] > 0.22);
|
|
|
|
// convert Mat to Vec, should be same as what we started with, 3 long and each item should be a 1.
|
|
cv.Rodrigues(rMat0, rvec1);
|
|
|
|
assert.ok(rvec1.data64F.length == 3);
|
|
assert.ok(1.01 > rvec1.data64F[0] > 0.9);
|
|
// Answer should be around 1: 0.9999999999999999
|
|
});
|
|
|
|
QUnit.test('estimateAffine2D', function(assert) {
|
|
const inputs = cv.matFromArray(4, 1, cv.CV_32FC2, [
|
|
1, 1,
|
|
80, 0,
|
|
0, 80,
|
|
80, 80
|
|
]);
|
|
const outputs = cv.matFromArray(4, 1, cv.CV_32FC2, [
|
|
21, 51,
|
|
70, 77,
|
|
40, 40,
|
|
10, 70
|
|
]);
|
|
const M = cv.estimateAffine2D(inputs, outputs);
|
|
assert.ok(M instanceof cv.Mat);
|
|
assert.deepEqual(Array.from(M.data), [
|
|
23, 55, 97, 126, 87, 139, 227, 63, 0, 0,
|
|
0, 0, 0, 0, 232, 191, 71, 246, 12, 68,
|
|
165, 35, 53, 64, 101, 56, 27, 66, 14, 254,
|
|
212, 63, 103, 102, 102, 102, 102, 102, 182, 191,
|
|
194, 252, 174, 22, 55, 97, 73, 64
|
|
]);
|
|
});
|