在iOS11上推出了CoreML和架構在CoreML之上的Vision, 這樣為CNN（卷積神經網路）在iOS裝置上的應用鋪平了道路。

將CoreML模型載入到App

讓你的App整合CoreML模型非常簡單， 將模型檔案(*.mlmodel）拖進工程即可. 在Xcode中可以看到此模型的描述.

Xcode可以為此模型檔案自動生成一個可以被使用的物件, 此預測人年齡的CNN的自動生成程式碼如下(Swift)

//
// AgeNet.swift
//
// This file was automatically generated and should not be edited.
//

import
 
 CoreML


/// Model Prediction Input Type
@available(OSX 13.0, iOS 11.0, tvOS 11.0, watchOS 4.0, *)
class AgeNetInput : MLFeatureProvider {

    /// An image with a face. as color (kCVPixelFormatType_32BGRA) image buffer, 227 pixels wide by 227 pixels high
    var data: CVPixelBuffer

    var featureNames: Set<String> {
        get {
            return
 
 ["data"]
        }
    }

    func featureValue(for featureName: String) -> MLFeatureValue? {
        if (featureName == "data") {
            return MLFeatureValue(pixelBuffer: data)
        }
        return nil
    }

    init(data: CVPixelBuffer) {
        self.data = data
    }
}


/// Model Prediction Output Type
 

@available(OSX 13.0, iOS 11.0, tvOS 11.0, watchOS 4.0, *)
class AgeNetOutput : MLFeatureProvider {

    /// The probabilities for each age, for the given input. as dictionary of strings to doubles
    let prob: [String : Double]

    /// The most likely age, for the given input. as string value
    let classLabel: String

    var featureNames: Set<String> {
        get {
            return ["prob", "classLabel"]
        }
    }

    func featureValue(for featureName: String) -> MLFeatureValue? {
        if (featureName == "prob") {
            return try! MLFeatureValue(dictionary: prob as [NSObject : NSNumber])
        }
        if (featureName == "classLabel") {
            return MLFeatureValue(string: classLabel)
        }
        return nil
    }

    init(prob: [String : Double], classLabel: String) {
        self.prob = prob
        self.classLabel = classLabel
    }
}


/// Class for model loading and prediction
@available(OSX 13.0, iOS 11.0, tvOS 11.0, watchOS 4.0, *)
class AgeNet {
    var model: MLModel

    /**
        Construct a model with explicit path to mlmodel file
        - parameters:
           - url: the file url of the model
           - throws: an NSError object that describes the problem
    */
    init(contentsOf url: URL) throws {
        self.model = try MLModel(contentsOf: url)
    }

    /// Construct a model that automatically loads the model from the app's bundle
    convenience init() {
        let bundle = Bundle(for: AgeNet.self)
        let assetPath = bundle.url(forResource: "AgeNet", withExtension:"mlmodelc")
        try! self.init(contentsOf: assetPath!)
    }

    /**
        Make a prediction using the structured interface
        - parameters:
           - input: the input to the prediction as AgeNetInput
        - throws: an NSError object that describes the problem
        - returns: the result of the prediction as AgeNetOutput
    */
    func prediction(input: AgeNetInput) throws -> AgeNetOutput {
        let outFeatures = try model.prediction(from: input)
        let result = AgeNetOutput(prob: outFeatures.featureValue(for: "prob")!.dictionaryValue as! [String : Double], classLabel: outFeatures.featureValue(for: "classLabel")!.stringValue)
        return result
    }

    /**
        Make a prediction using the convenience interface
        - parameters:
            - data: An image with a face. as color (kCVPixelFormatType_32BGRA) image buffer, 227 pixels wide by 227 pixels high
        - throws: an NSError object that describes the problem
        - returns: the result of the prediction as AgeNetOutput
    */
    func prediction(data: CVPixelBuffer) throws -> AgeNetOutput {
        let input_ = AgeNetInput(data: data)
        return try self.prediction(input: input_)
    }
}

載入CNN， 並且建立分析請求(Image Analysis Request)

let ageModel = AgeNet()

func setupVision() {
        guard let vnAgeModel = try? VNCoreMLModel(for: ageModel.model) else {
            NSLog("Load age model fail")
            return
        }
        ageRequest = VNCoreMLRequest(model: vnAgeModel, completionHandler: { (request : VNRequest, error : Error? ) in
            //NSLog("VNCoreML Request complete")

            if let observations = request.results as? [VNClassificationObservation] {
                if( observations.count > 1  && observations[0].confidence > 0.5 ){
                    DispatchQueue.main.async {
                        self.mInfo.text = "Your age is " + observations[0].identifier + "/" + String(observations[0].confidence)
                    }
                }
            }
            return
        })
        ageRequest?.imageCropAndScaleOption = .scaleFit  
     }

執行分析

func predict(pixelBuffer : CVPixelBuffer) {
        let handler = VNImageRequestHandler(cvPixelBuffer: pixelBuffer)
        try? handler.perform([ageRequest])

        let genderHandler = VNImageRequestHandler(cvPixelBuffer: pixelBuffer)
        try? genderHandler.perform([genderRequest])
    }

Reference