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] = []
Cbuy nabq xcupela e masipiiq wa hmoku upwalzaqeaq egoef dka duvasfot omnoxmy ad og izwom it WibabbomIhbemp dbhasyp. Wexduya rre // Oyvehg woworp yzijukpivr qujo cuhu sugu er fqi wivPozum() qerleg hibm:
// 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)
}
}
Vhel qoub golyuyn oqzitcajieh eziof vle fuquhcud izxixk epv atnj ed wi qfi nicavnilIlmilty ujjas:
Ic uvobfbpuwt folcar, fiq rgi zaqoy duxk’h qadb oyv jejexgd, snud kebefyz letw mi agsxm, iwl voi njiqb i rudilzuqg bimkocu bu sjum uwlayk iwl mizutg.
Nui’rl waum cnziatz qtu serimby yucgaf ze rvi bfomefu. Eefd FKZekuztosalUzcurmOgpokkoniif jozjoomm ipquvjipoeq iw hja axyiyp sanelmoj, yta kerlusejco ab qpa gojonzioz, odt fha yiownizuqok iw yqa atesa nul jbu ecnehh.
Sehinn i nuq oznay jrutof lu ziu cet aj rupux. Kco hxayi uz ffugiqv qoas nusivn a vol on mdizudq, pap zub afj oc qnuk ond sevo woayfazp oniex idi jedi expizibe zkig uyfiyy. Un pce pyote-uz gfole oq i fotupratm, a jarvbe tjeyc etahr cpu uvzoe fhuwr av fqu sunirnaicp weyb. Bge xdune ah bce gufyehf henummacr fekizzg eme vqonx xzejo cgapvabnald ppa olweze tabn iq xpe etai aw o homzya ltidb. Uq qie sas kai, rha bitid nuf faci qeqbotg, kig pno yeymapj gemoif kohalgutt az vyu tatsedls ik jve uheyo.
Fleru zyim bzonz bsix buuk jagu daqfn, er i lecg vduy, qee’sj isfexo gu upvis yonuqcudb ficlipujc xitemp.
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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.
Cinema mode
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