CoreML 简单使用

   今天简单使用了下CoreML ， 我的这个模型功能主要是 打开摄像头，然后对准物体，会自动帮我们识别摄像头中的物体，并且给我们大概的百分比值

   代码如下：

    @IBAction func startClick(_ sender: Any) {
        startFlag = !startFlag
        if  startFlag {
            startCaptureVideo()
            startButton.setTitle("stop", for: .normal)
        }else {
            startButton.setTitle("start", for: .normal)
            stop()
        }
    
    }
    
 
    //1、初始化CaptureSeason
    lazy var captureSession:AVCaptureSession? = {
　　　　//1.1 实例化
        let captureSession =  AVCaptureSession()
        captureSession.sessionPreset = .photo
　　    //1.2 获取默认设备
        guard  let captureDevice = AVCaptureDevice.default(for: .video) else {
            return nil
        }
        
        guard  let captureDeviceInput =  try? AVCaptureDeviceInput(device: captureDevice) else {
            return nil
        }
        
        captureSession.addInput(captureDeviceInput)
        
        let previewLayer = AVCaptureVideoPreviewLayer(session: captureSession)
        view.layer.addSublayer(previewLayer)
        previewLayer.frame = view.frame
        
        let dataOutput = AVCaptureVideoDataOutput()
        dataOutput.setSampleBufferDelegate(self, queue: DispatchQueue(label: "kingboVision"))
        captureSession.addOutput(dataOutput)
        return captureSession
    }()
    
    func startCaptureVideo(){
        captureSession?.startRunning()
    }
    
    func stop() {
        captureSession?.stopRunning()
    }
    
    
    func captureOutput(_ output: AVCaptureOutput, didOutput sampleBuffer: CMSampleBuffer, from connection: AVCaptureConnection) {
        print("Camera was safe ")
        guard let pixelBuffer:CVPixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer) else
        {
            print("nil object ")
            return
        }
        
        guard  let model = try? VNCoreMLModel(for: SqueezeNet().model) else {return}
        
        let request = VNCoreMLRequest(model: model) { [unowned self](finishRequest, error) in
            //
            guard let results = finishRequest.results as? [VNClassificationObservation]  else {
                return
            }
            guard let first = results.first else {
                return
            }
            DispatchQueue.main.async {
                self.descriptionLabel.text = "(first.identifier): (first.confidence * 100)%,"
            }
        }
        print("start to VN")
        
        try? VNImageRequestHandler(cvPixelBuffer: pixelBuffer, options: [:]).perform([request])
    }

分为以下几步操作

1、我们工程inf.plist 文件中添加摄像头权限说明

  Privacy - Camera Usage Description     相机权限设置

 Privacy - Microphone Usage Description  麦克风权限设置

2、头文件引用

import AVKit
import Vision

3、准备模型

模型准备我这里是使用App 官网上的SqueezeNet模型

4、将模型拖动到工程里面即可

5、代码编写部分

 5.1 初始化AVCaptureSession

       let captureSession =  AVCaptureSession()
        captureSession.sessionPreset = .photo

 5.2 获取当前iPhone 设备控制，当作输入数据来源

guard  let captureDevice = AVCaptureDevice.default(for: .video) else {
            return nil
        }

   captureSession.addInput(captureDeviceInput)

 5.3 设置输出数据

//设置输出数据处理对象
   let dataOutput = AVCaptureVideoDataOutput()
   dataOutput.setSampleBufferDelegate(self, queue: DispatchQueue(label: "kingboVision"))
   captureSession.addOutput(dataOutput)

  AVCaptureVideoDataOutput 设置代理，处理数据流图片数据，因此当前ViewController需要实现AVCaptureVideoDataOutputSampleBufferDelegate 的代理，如下

   
class ViewController: UIViewController ,AVCaptureVideoDataOutputSampleBufferDelegate{
......

func captureOutput(_ output: AVCaptureOutput, didOutput sampleBuffer: CMSampleBuffer, from connection: AVCaptureConnection) {
        //将视频数据转成CVPixBuffer
        guard let pixelBuffer:CVPixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer) else
        {
            print("nil object ")
            return
        }
        // 这里SquzzedNet是我从苹果中直接获取的模型
        guard  let model = try? VNCoreMLModel(for: SqueezeNet().model) else {return}
        
        let request = VNCoreMLRequest(model: model) { [unowned self](finishRequest, error) in
            //处理数据分析进行业务处理
            guard let results = finishRequest.results as? [VNClassificationObservation]  else {
                return
            }
            guard let first = results.first else {
                return
            }
            DispatchQueue.main.async {
                self.descriptionLabel.text = "(first.identifier): (first.confidence * 100)%,"
            }
        }
        print("start to VN")
        
        try? VNImageRequestHandler(cvPixelBuffer: pixelBuffer, options: [:]).perform([request])
    }
     .....
}

5.4 将当前的摄像头显示在指定的View 上，（我们这里使用当前VC 的view)

  let previewLayer = AVCaptureVideoPreviewLayer(session: captureSession)
        view.layer.addSublayer(previewLayer)
        previewLayer.frame = view.frame

效果如下：