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:
在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)
中VisionTrackerProcessor
,我添加了这个:
delegate?.updateImage(observation.boundingBox)
在TrackingViewController
我添加了这个:
func updateImage(_ rect: CGRect) {
print(rect)
self.faceImage.frame = rect
}
这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!