我一直在尝试使用xcode快速创建一个简单的相机图像识别应用程序，以允许用户拍照。然后将照片输入到已经训练好的coreML模型中，并将具有预测精度的输出输出到标签。
我搜索了多个网站，而我只能找到
之类的教程。https://medium.freecodecamp.org/ios-coreml-vision-image-recognition-3619cf319d0b
允许实时识别图像。我不希望它是实时的，而只是允许某人拍照。我想知道如何以非实时输入的方式转换此代码：
import UIKit
import AVFoundation
import Vision
class ViewController: UIViewController, AVCaptureVideoDataOutputSampleBufferDelegate {
let label: UILabel = {
let label = UILabel()
label.textColor = .white
label.translatesAutoresizingMaskIntoConstraints = false
label.text = "Label"
label.font = label.font.withSize(30)
return label
}()
override func viewDidLoad() {
super.viewDidLoad()
// establish the capture session and add the label
setupCaptureSession()
view.addSubview(label)
setupLabel()
// Do any additional setup after loading the view, typically from a nib.
}
func setupCaptureSession() {
// create a new capture session
let captureSession = AVCaptureSession()
// find the available cameras
let availableDevices = AVCaptureDevice.DiscoverySession(deviceTypes: [.builtInWideAngleCamera], mediaType: AVMediaType.video, position: .back).devices
do {
// select a camera
if let captureDevice = availableDevices.first {
captureSession.addInput(try AVCaptureDeviceInput(device: captureDevice))
}
} catch {
// print an error if the camera is not available
print(error.localizedDescription)
}
// setup the video output to the screen and add output to our capture session
let captureOutput = AVCaptureVideoDataOutput()
captureSession.addOutput(captureOutput)
let previewLayer = AVCaptureVideoPreviewLayer(session: captureSession)
previewLayer.frame = view.frame
view.layer.addSublayer(previewLayer)
// buffer the video and start the capture session
captureOutput.setSampleBufferDelegate(self, queue: DispatchQueue(label: "videoQueue"))
captureSession.startRunning()
}
func captureOutput(_ output: AVCaptureOutput, didOutput sampleBuffer: CMSampleBuffer, from connection: AVCaptureConnection) {
// load our CoreML Pokedex model
guard let model = try? VNCoreMLModel(for: aslModel().model) else { return }
// run an inference with CoreML
let request = VNCoreMLRequest(model: model) { (finishedRequest, error) in
// grab the inference results
guard let results = finishedRequest.results as? [VNClassificationObservation] else { return }
// grab the highest confidence result
guard let Observation = results.first else { return }
// create the label text components
let predclass = "\(Observation.identifier)"
let predconfidence = String(format: "%.02f%", Observation.confidence * 100)
// set the label text
DispatchQueue.main.async(execute: {
self.label.text = "\(predclass) \(predconfidence)"
})
}
// create a Core Video pixel buffer which is an image buffer that holds pixels in main memory
// Applications generating frames, compressing or decompressing video, or using Core Image
// can all make use of Core Video pixel buffers
guard let pixelBuffer: CVPixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer) else { return }
// execute the request
try? VNImageRequestHandler(cvPixelBuffer: pixelBuffer, options: [:]).perform([request])
}
func setupLabel() {
// constrain the label in the center
label.centerXAnchor.constraint(equalTo: view.centerXAnchor).isActive = true
// constrain the the label to 50 pixels from the bottom
label.bottomAnchor.constraint(equalTo: view.bottomAnchor, constant: -50).isActive = true
}
override func didReceiveMemoryWarning() {
super.didReceiveMemoryWarning()
// Dispose of any resources that can be recreated.
}
}
现在就像在输入实时图像输入之前所述。