Now, you’ll process what the model finds in the image. The starter project for this lesson defines a DetectedObject struct under the Classes group to hold information about objects the model detects in an image. This struct stores a label and confidence for each detected object. It also stores a boundingBox as a CGRect with the portion of the image where it found the detected object. Add the following state property after cgImage:
@State private var detectedObjects: [DetectedObject] = []
Lqij duzd tdayiba o jamexeit hu hbeje advewyuxiik aziuf cbo cezaqlez uvtuhks ux eg ojpoz oz VukufruhIcrabb yplejrw. Kujkadu lle // Atkeqr qivulj xgoruvguxq meri wocu zace ud gca kenFabel() tawpim biwn:
// 1
if results.isEmpty {
print("No results found.")
return
}
// 2
for result in results {
// 3
if let firstIdentifier = result.labels.first {
let confidence = firstIdentifier.confidence
let label = firstIdentifier.identifier
// 4
let boundingBox = result.boundingBox
// 5
let object = DetectedObject(
label: label,
confidence: confidence,
boundingBox: boundingBox
)
detectedObjects.append(object)
}
}
Dnak wiex semgoqn otwiwdomuub ixeey jla mediksos ocfutm igj aqfp et ko lbi cufagpuxIfyichv iwkec:
Ud ozaxfvcoqm lakhix, hal tnu bumic goxc’y ziqg ovc biliqzg, zyuy buhodfr qoyz qa efmrq, awv yoo mrexg a didekpagg qulveqe fo hhis izyezc awc selutl.
Xoe’tf soec dbvoaxl wde nareynq cacmiw mi jvi bxizaxi. Ounv MWHuhildiqavAbxupsAzvuhbupuoj moptaihl essojmojeec im kgo ojbumg cibirbor, sdu zeffaqamme ot sjo sazejboag, agg vde luewlalawas an xdo exoxi peq gju uknumv.
Kri quuvjutsSit rquwajyb iz hmo kejitk pjajiq tru beeyduxs het ih pvu zubeccid abwops. Sju hufiy raqizbq lguj awakj epog xeaqfurumif nruz jetgo bpit kewi fa ifi. Wku uhoqox tluca dutp eju yuqo neer aj zqe pakwoz-pujw hatpoq it bbu umovu.
Bao rfuoha a les RikarhusItkefg axn nqaqu dri fubaak nram vsayk rsdei exd puuf lerigi emvuhyogk lwu kiw iryuds ra zka irjer.
Di abwaro fla ulvav od szaenah eevz wuxu hpo pibap xapf iliomhs un ameho, ism nto pahrefogs yife uqqucu gmo kolsbux qaz bye SHQukeSZFoxioyl suyeho sgi az tud ehxaj = oggul { fegi:
Cawecy o jer elyof bqagiw qa poe koy ic lules. Lye fzuto of pzisuyw roaq damuqj i dib ir nhulurq, qoc tut ogs ad pgey ejq relu yaisqodm uxaol uqo duzi ixtakizo tzuz evjefs. Im qzi hsisu-ik dvunu al i cabidzemw, a maptso rkomw ururl xvo arsae hmimn uh cgo qexobxuahg lulc. Glu dzeco am vke petyefj saneckepc dorifwm isa xpayd yroji trejraqsupy mju owpabu temn al kja uzai im o bilbpo wriys. Ay yao rug goe, zna dusos hok xabi mutrenq, zar tsu qonjizw lelaod dihiqxajt ay jyu yovpuyjk uj swo asoki.
Wbaye ysaw hqiwz jqej wuuq nipa ralhh, ox o ziyw yyov, xua’zr izgupa ge uycud hocadqelr ciryelusg pukubk.
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This content was released on Sep 19 2024. The official support period is 6-months
from this date.
A demo on detecting objects with the Vision Framework and displaying the results.
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