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// //////////////////////////////////////////////////////////////////////////////////////
// Author: Sajjad Taheri, University of California, Irvine. sajjadt[at]uci[dot]edu
//
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// Copyright (c) 2015 The Regents of the University of California (Regents)
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import cv from '../../'

QUnit.module('Object Detection', {
  before: cv.loadOpenCV
})

QUnit.test('groupRectangles', function (assert) {
  const rectList = new cv.RectVector()
  const weights = new cv.IntVector()
  const groupThreshold = 1
  const eps = 0.2

  const rect1 = new cv.Rect(1, 2, 3, 4)
  const rect2 = new cv.Rect(1, 4, 2, 3)

  rectList.push_back(rect1)
  rectList.push_back(rect2)

  cv.groupRectangles(rectList, weights, groupThreshold, eps)

  assert.false(false, 'TODO')

  rectList.delete()
  weights.delete()
})

QUnit.test('CascadeClassifier', (assert) => {
  const classifier = new cv.CascadeClassifier()
  const modelPath = (typeof process !== 'undefined')
    ? (require('path').join(__dirname, 'haarcascade_frontalface_default.xml'))
    : '/haarcascade_frontalface_default.xml'

  // TODO: I can't get it to load... maybe Windows path issue?
  assert.true(true, 'TODO')
  return
  assert.true(classifier.load(modelPath))


  const image = cv.Mat.eye({ height: 10, width: 10 }, cv.CV_8UC3)
  const objects = new cv.RectVector()
  const numDetections = new cv.IntVector()
  const scaleFactor = 1.1
  const minNeighbors = 3
  const flags = 0
  const minSize = { height: 0, width: 0 }
  const maxSize = { height: 10, width: 10 }
  
  assert.strictEqual(classifier.empty(), true)
  try {
    classifier.detectMultiScale2(image, objects, numDetections, scaleFactor,
      minNeighbors, flags, minSize, maxSize)
  } catch (e) {
    console.log('e', e)
    console.log('ex', cv.exceptionFromPtr(e))
  }

  // test default parameters
  classifier.detectMultiScale2(image, objects, numDetections, scaleFactor,
    minNeighbors, flags, minSize)
  classifier.detectMultiScale2(image, objects, numDetections, scaleFactor,
    minNeighbors, flags)
  classifier.detectMultiScale2(image, objects, numDetections, scaleFactor,
    minNeighbors)
  classifier.detectMultiScale2(image, objects, numDetections, scaleFactor)

  classifier.delete()
  objects.delete()
  numDetections.delete()
})

QUnit.test('HOGDescriptor', (assert) => {
  const hog = new cv.HOGDescriptor()
  const mat = new cv.Mat({ height: 10, width: 10 }, cv.CV_8UC1)
  const descriptors = new cv.FloatVector()
  const locations = new cv.PointVector()

  assert.strictEqual(hog.winSize.height, 128)
  assert.strictEqual(hog.winSize.width, 64)
  assert.strictEqual(hog.nbins, 9)
  assert.strictEqual(hog.derivAperture, 1)
  assert.strictEqual(hog.winSigma, -1)
  assert.strictEqual(hog.histogramNormType, 0)
  assert.strictEqual(hog.nlevels, 64)

  // TODO: why is this ok when nlevels is normally a readonly property?
  // TODO: nlevels has to be explicitly handled in the tsgen
  hog.nlevels = 32
  assert.strictEqual(hog.nlevels, 32)

  hog.delete()
  mat.delete()
  descriptors.delete()
  locations.delete()
})