@anpanman/opencv_ts/tests/opencv/test_calib3d.ts

Experimental WIP TypeScript typings and OpenCV.js/wasm loader.

// 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.

import cv from '../../'

QUnit.module('Camera Calibration and 3D Reconstruction', {
  before: cv.loadOpenCV
})

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) {
  const srcPoints = cv.matFromArray(4, 1, cv.CV_32FC2, [
    56,
    65,
    368,
    52,
    28,
    387,
    389,
    390,
  ])
  const dstPoints = cv.matFromArray(4, 1, cv.CV_32FC2, [
    0,
    0,
    300,
    0,
    0,
    300,
    300,
    300,
  ])

  const mat = cv.findHomography(srcPoints, dstPoints)

  assert.true(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])
  const rMat0 = new cv.Mat()
  const 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.strictEqual(rMat0.data64F.length, 9)
  assert.true(Math.abs(rMat0.data64F[0] - 0.22) < 0.01)

  // 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.strictEqual(rvec1.data64F.length, 3)
  assert.true(Math.abs(rvec1.data64F[0] - 1) < 0.01)
})

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.true(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, 99, 56, 27, 66, 14, 254,
    212, 63, 103, 102, 102, 102, 102, 102, 182, 191,
    195, 252, 174, 22, 55, 97, 73, 64
  ])
})