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我正在使用 Apple 提供的以下示例应用程序来进行一些对象检测。

https://developer.apple.com/documentation/vision/tracking_multiple_objects_or_rectangles_in_video

我正在尝试将一张脸的图像粘贴到视频中绿色矩形的顶部。(视频下载链接:https ://drive.google.com/file/d/1aw5L-6uBMTxeuq378Y98dZcTh6N_Y2Pf/view?usp=sharing )

到目前为止,我能够非常一致地从视频中检测到绿色矩形,但是每当我尝试叠加图像时,帧就不会出现在视图中。

这是我到目前为止所尝试的:

TrackingImageView.swift中,我添加了一个名为的实例变量faceImage,并尝试通过将以下代码添加到draw函数底部来将其添加到屏幕上。

UIGraphicsBeginImageContextWithOptions(self.imageAreaRect.size, false, 0.0)

//        self.faceImage.draw(in: CGRect(origin: CGPoint.init(x: rect.minX, y: rect.minY), size: rect.size))
self.faceImage.draw(in: CGRect(x: previous.x, y: previous.y, width: polyRect.boundingBox.width, height: polyRect.boundingBox.height))
//        self.faceImage.draw(in: rect)
let newImage = UIGraphicsGetImageFromCurrentImageContext()
UIGraphicsEndImageContext()

self.image = newImage

然后TrackingViewController,在名为 的函数中func displayFrame(_ frame: CVPixelBuffer?, withAffineTransform transform: CGAffineTransform, rects: [TrackedPolyRect]?),我添加了以下几行。

self.trackingView.faceImage = UIImage(named: "dwight1")
self.trackingView.displayImage(rect: self.trackingView.polyRects[0].boundingBox)

更新,这是我尝试的另一种方法:

这是它在文档中所说的:Use the observation’s boundingBox to determine its location, so you can update your app or UI with the tracked object’s new location. Also use it to seed the next round of tracking.

所以在函数func performTracking(type: TrackedObjectType)VisionTrackerProcessor,我添加了这个:

delegate?.updateImage(observation.boundingBox)

TrackingViewController我添加了这个:

    func updateImage(_ rect: CGRect) {
        print(rect)
        self.faceImage.frame = rect
    }

faceImage是:

@IBOutlet weak var faceImage: UIImageView!

当我打印出要放置图像的矩形的 CGPoints 时,我得到以下输出:

(0.45066666666666666, 0.5595238095238095, 0.09599999999999997, 0.16666666666666663)
(0.4521519184112549, 0.5643428802490235, 0.09600000381469731, 0.16666666666666663)
(0.4546553611755371, 0.5875609927707248, 0.09555779099464418, 0.16589893764919705)
(0.4543778896331787, 0.5984047359890408, 0.09505770206451414, 0.1650307231479221)
(0.454343843460083, 0.6052030351426866, 0.09476101398468023, 0.16451564364963112)
(0.45296874046325686, 0.6065650092230903, 0.09457258582115169, 0.16418851216634112)
(0.4510493755340576, 0.6057157728407118, 0.09507998228073117, 0.1650694105360243)
(0.4481017589569092, 0.5987161000569662, 0.09499880075454714, 0.16492846806844075)
(0.44568862915039065, 0.5735456678602431, 0.09511266946792607, 0.16512615415785048)
(0.4434205532073975, 0.5485235426161025, 0.09506692290306096, 0.16504673428005645)
(0.4413131237030029, 0.5238201141357421, 0.09566491246223452, 0.1660849147372776)
(0.4388014316558838, 0.5072469923231336, 0.09601176977157588, 0.1666870964898003)
(0.4374812602996826, 0.4967741224500868, 0.09586981534957884, 0.16644064585367835)
(0.43827009201049805, 0.48819330003526473, 0.09551617503166199, 0.1658266809251574)
(0.44115781784057617, 0.4852377573649089, 0.09499365091323853, 0.1649195247226291)
(0.4417849540710449, 0.4845396253797743, 0.0949023962020874, 0.1647610982259115)
(0.4476351737976074, 0.49016346401638455, 0.09391363859176638, 0.16304450564914275)
(0.4497058391571045, 0.49209620157877604, 0.09434010386466984, 0.16378489600287544)
(0.4514862060546875, 0.49223976135253905, 0.09459822773933413, 0.16423302756415475)
(0.454580020904541, 0.4904879252115885, 0.0949873864650726, 0.16490865283542205)
(0.4566154479980469, 0.48613760206434464, 0.09480695724487309, 0.16459540261162653)
(0.45992450714111327, 0.47563196818033854, 0.09525291323661805, 0.1653696378072103)
(0.464534330368042, 0.46896955702039933, 0.09566755294799806, 0.1660895029703776)
(0.4682444095611572, 0.4513437059190538, 0.09700422883033755, 0.16841011047363275)
(0.4709425926208496, 0.438845952351888, 0.09843692183494568, 0.17089743084377712)
(0.47597203254699705, 0.4264893849690755, 0.10058027505874634, 0.17461851967705622)
(0.48175721168518065, 0.42467672559950087, 0.10141149759292606, 0.1760616196526421)
(0.483599328994751, 0.44046991136338975, 0.10279589891433716, 0.17846510145399308)
(0.4847916603088379, 0.44517923990885416, 0.10338790416717525, 0.17949288686116532)
(0.4889643669128418, 0.45437651740180124, 0.09983686804771424, 0.17332788043551978)
(0.49118928909301757, 0.4580091264512804, 0.09644789695739747, 0.16744425031873916)
(0.4905869483947754, 0.45951224433051213, 0.09397981166839603, 0.16315938101874455)
(0.4874621868133545, 0.45792486402723526, 0.09055853486061094, 0.15721967485215932)
(0.48279714584350586, 0.4531046549479167, 0.08872739672660823, 0.1540406121148004)
(0.4783169269561768, 0.4456812964545356, 0.0860174298286438, 0.1493358188205295)
(0.4728221893310547, 0.44693773057725694, 0.084199583530426, 0.14617982440524635)
(0.471103572845459, 0.4579927232530382, 0.08219499588012691, 0.14269964430067272)
(0.4676462173461914, 0.47325596279568144, 0.08054903745651243, 0.1398420651753744)
(0.463164234161377, 0.4803483327229818, 0.07916470766067507, 0.13743872112698025)
(0.4597337245941162, 0.4865601857503255, 0.07723031044006345, 0.1340803888108995)
(0.4575923442840576, 0.4861404842800564, 0.07577759623527525, 0.13155832290649416)
(0.456453275680542, 0.48211678398980035, 0.0741972386837006, 0.12881464428371853)
(0.45630569458007814, 0.47852266099717883, 0.0741972386837006, 0.12881464428371853)
(0.45930023193359376, 0.4749870724148221, 0.0741972386837006, 0.12881464428371847)
(0.4619853973388672, 0.460075675116645, 0.0741972386837006, 0.12881464428371853)
(0.4647641658782959, 0.44653006659613714, 0.0741972386837006, 0.12881464428371858)
(0.46242194175720214, 0.43739403618706596, 0.07220322489738468, 0.1253528171115451)
(0.4625579357147217, 0.41982913547092016, 0.07062785029411311, 0.12261778513590493)
(0.46608676910400393, 0.4134985182020399, 0.06866733431816097, 0.11921412150065108)
(0.46996197700500486, 0.41352043151855467, 0.0672459602355957, 0.11674645741780598)
(0.4733128547668457, 0.42267172071668835, 0.06592562794685364, 0.11445420583089194)
(0.4805797576904297, 0.4420909881591797, 0.06590123176574703, 0.11441185209486215)
(0.48854408264160154, 0.46238810221354165, 0.06529000997543333, 0.11335069868299696)
(0.4921866416931152, 0.47235264248318143, 0.06412824392318728, 0.11133375167846682)
(0.4948731899261475, 0.481452645195855, 0.06294543147087095, 0.10928025775485567)
(0.49323139190673826, 0.48434698316786023, 0.06219365000724797, 0.10797508027818464)
(0.4935962200164795, 0.47917471991644967, 0.061773008108139016, 0.10724479887220595)
(0.49112601280212403, 0.4626174502902561, 0.06177300810813907, 0.107244798872206)
(0.48893303871154786, 0.4498925950792101, 0.06069326996803287, 0.10537025663587785)
(0.4902684688568115, 0.45128373040093317, 0.06060827970504756, 0.10522270202636719)
(0.4870577812194824, 0.45470954047309026, 0.06060827970504756, 0.10522270202636724)
(0.45066666666666666, 0.5595238095238095, 0.09599999999999997, 0.16666666666666663)
(0.45066666666666666, 0.5595238095238095, 0.09599999999999997, 0.16666666666666663)

将图像叠加在我检测到的对象之上的任何帮助都会令人惊叹。谢谢!

4

1 回答 1

1

您是否意识到您从 Vision 框架获得的坐标是归一化的(在 0 和 1 之间)?您将不得不转换它们以适合您的视图的大小。

此外,据我记得,视觉坐标从左下角开始(与 UIKit 相反,从左上角开始),所以你可能也必须垂直翻转它们(这里不是 100% 确定)。

编辑:我看到您有可用videoReader.affineTransform的,您可以尝试使用该转换修改您的 CGRects。

于 2018-09-04T06:14:40.883 回答