版本记录

前言

目前世界上科技界的所有大佬一致认为人工智能是下一代科技革命，苹果作为科技界的巨头，当然也会紧跟新的科技革命的步伐，其中ios API 就新出了一个框架Core ML。ML是Machine Learning的缩写，也就是机器学习，这正是现在很火的一个技术，它也是人工智能最核心的内容。感兴趣的可以看我写的下面几篇。
1. Core ML框架详细解析（一） —— Core ML基本概览
2. Core ML框架详细解析（二） —— 获取模型并集成到APP中
3. Core ML框架详细解析（三） —— 利用Vision和Core ML对图像进行分类
4. Core ML框架详细解析（四） —— 将训练模型转化为Core ML
5. Core ML框架详细解析（五） —— 一个Core ML简单示例（一）
6. Core ML框架详细解析（六） —— 一个Core ML简单示例（二）
7. Core ML框架详细解析（七） —— 减少Core ML应用程序的大小（一）
8. Core ML框架详细解析（八） —— 在用户设备上下载和编译模型（一）
9. Core ML框架详细解析（九） —— 用一系列输入进行预测（一）
10. Core ML框架详细解析（十） —— 集成自定义图层（一）
11. Core ML框架详细解析（十一） —— 创建自定义图层（一）
12. Core ML框架详细解析（十二） —— 用scikit-learn开始机器学习（一）
13. Core ML框架详细解析（十三） —— 使用Keras和Core ML开始机器学习（一）
14. Core ML框架详细解析（十四） —— 使用Keras和Core ML开始机器学习（二）
15. Core ML框架详细解析（十五） —— 机器学习：分类（一）
16. Core ML框架详细解析（十六） —— 人工智能和IBM Watson Services（一）
17. Core ML框架详细解析（十七） —— Core ML 和 Vision简单示例（一）
18. Core ML框架详细解析（十八） —— 基于Core ML 和 Vision的设备上的训练（一）
19. Core ML框架详细解析（十九） —— 基于Core ML 和 Vision的设备上的训练（二）
20. Core ML框架详细解析（二十） —— 在iOS设备上使用Style Transfer创建一个自定义图像滤波器（一）

源码

1. Swift

首先看下工程组织结构

下面就是正文了

1. AppMain.swift

import SwiftUI

@main
struct AppMain: App {
  var body: some Scene {
    WindowGroup {
      ContentView()
    }
  }
}

2. ContentView.swift

import SwiftUI

struct AlertMessage: Identifiable {
  let id = UUID()
  var title: Text
  var message: Text
  var actionButton: Alert.Button?
  var cancelButton: Alert.Button = .default(Text("OK"))
}

struct PickerInfo: Identifiable {
  let id = UUID()
  let picker: PickerView
}

struct ContentView: View {
  @State private var image: UIImage?
  @State private var styleImage: UIImage?
  @State private var stylizedImage: UIImage?
  @State private var processing = false
  @State private var showAlertMessage: AlertMessage?
  @State private var showImagePicker: PickerInfo?

  var body: some View {
    VStack {
      Text("PETRA")
        .font(.title)
      Spacer()
      Button(action: {
        if self.stylizedImage != nil {
          self.showAlertMessage = .init(
            title: Text("Choose new image?"),
            message: Text("This will clear the existing image!"),
            actionButton: .destructive(
              Text("Continue")) {
                self.stylizedImage = nil
                self.image = nil
                self.showImagePicker = PickerInfo(picker: PickerView(selectedImage: self.$image))
            },
            cancelButton: .cancel(Text("Cancel")))
        } else {
          self.showImagePicker = PickerInfo(picker: PickerView(selectedImage: self.$image))
        }
      }, label: {
        if let anImage = self.stylizedImage ?? self.image {
          Image(uiImage: anImage)
            .resizable()
            .scaledToFit()
            .aspectRatio(contentMode: ContentMode.fit)
            .border(.blue, width: 3)
        } else {
          Text("Choose a Pet Image")
            .font(.callout)
            .foregroundColor(.blue)
            .padding()
            .cornerRadius(10)
            .border(.blue, width: 3)
        }
      })
      Spacer()
      Text("Choose Style to Apply")
      Button(action: {
        self.showImagePicker = PickerInfo(picker: PickerView(selectedImage: self.$styleImage))
      }, label: {
        Image(uiImage: styleImage ?? UIImage(named: Constants.Path.presetStyle1) ?? UIImage())
          .resizable()
          .frame(width: 100, height: 100, alignment: .center)
          .scaledToFit()
          .aspectRatio(contentMode: ContentMode.fit)
          .cornerRadius(10)
          .border(.blue, width: 3)
      })
      Button(action: {
        guard let petImage = image, let styleImage = styleImage ?? UIImage(named: Constants.Path.presetStyle1) else {
          self.showAlertMessage = .init(
            title: Text("Error"),
            message: Text("You need to choose a Pet photo before applying the style!"),
            actionButton: nil,
            cancelButton: .default(Text("OK")))
          return
        }
        if !self.processing {
          self.processing = true
          MLStyleTransferHelper.shared.applyStyle(styleImage, on: petImage) { stylizedImage in
            processing = false
            self.stylizedImage = stylizedImage
          }
        }
      }, label: {
        Text(self.processing ? "Processing..." : "Apply Style!")
          .padding(EdgeInsets.init(top: 4, leading: 8, bottom: 4, trailing: 8))
          .font(.callout)
          .background(.blue)
          .foregroundColor(.white)
          .cornerRadius(8)
      })
      .padding()
    }
    .sheet(item: self.$showImagePicker) { pickerInfo in
      return pickerInfo.picker
    }
    .alert(item: self.$showAlertMessage) { alertMessage in
      if let actionButton = alertMessage.actionButton {
        return Alert(
          title: alertMessage.title,
          message: alertMessage.message,
          primaryButton: actionButton,
          secondaryButton: alertMessage.cancelButton)
      } else {
        return Alert(
          title: alertMessage.title,
          message: alertMessage.message,
          dismissButton: alertMessage.cancelButton)
      }
    }
  }
}

struct ContentView_Previews: PreviewProvider {
  static var previews: some View {
    ContentView()
  }
}

3. ImagePicker.swift

import Foundation
import SwiftUI
import UIKit

struct PickerView: UIViewControllerRepresentable {
  @Binding var selectedImage: UIImage?
  @Environment(\.presentationMode) private var presentationMode
  func makeUIViewController(context: Context) -> UIImagePickerController {
    let imagePicker = UIImagePickerController()
    imagePicker.sourceType = .photoLibrary
    imagePicker.delegate = context.coordinator
    return imagePicker
  }
  func makeCoordinator() -> Coordinator {
    Coordinator { image in
      self.selectedImage = image
      self.presentationMode.wrappedValue.dismiss()
    }
  }
  func updateUIViewController(_ uiViewController: UIImagePickerController, context: Context) {
  }
  // Coordinator -
  final class Coordinator: NSObject, UIImagePickerControllerDelegate, UINavigationControllerDelegate {
    private let onComplete: (UIImage?) -> Void
    init(withCompletion onComplete: @escaping (UIImage?) -> Void) {
      self.onComplete = onComplete
    }
    func imagePickerController(_ picker: UIImagePickerController, didFinishPickingMediaWithInfo info: [UIImagePickerController.InfoKey: Any]) {
      if let image = info[.originalImage] as? UIImage {
        self.onComplete(image.upOrientationImage())
      }
    }
    func imagePickerControllerDidCancel(_ picker: UIImagePickerController) {
      self.onComplete(nil)
    }
  }
}

4. MLStyleTransferHelper.swift

import Foundation
import SwiftUI
import UIKit

struct MLStyleTransferHelper {
  static var shared = MLStyleTransferHelper()
  private var trainedModelPath: URL?
  mutating func applyStyle(_ styleImg: UIImage, on petImage: UIImage, onCompletion: @escaping (UIImage?) -> Void) {
    let sessionID = UUID()
    let sessionDir = Constants.Path.sessionDir.appendingPathComponent(sessionID.uuidString, isDirectory: true)
    debugPrint("Starting session in directory: \(sessionDir)")
    let petImagePath = Constants.Path.documentsDir.appendingPathComponent("MyPetImage.jpeg")
    let styleImagePath = Constants.Path.documentsDir.appendingPathComponent("StyleImage.jpeg")
    guard
      let petImageURL = petImage.saveImage(path: petImagePath),
      let styleImageURL = styleImg.saveImage(path: styleImagePath)
    else {
      debugPrint("Error Saving the image to disk.")
      return onCompletion(nil)
    }
    do {
      try FileManager.default.createDirectory(at: sessionDir, withIntermediateDirectories: true)
    } catch {
      debugPrint("Error creating directory: \(error.localizedDescription)")
      return onCompletion(nil)
    }
    // 1
    MLModelTrainer.trainModel(using: styleImageURL, validationImage: petImageURL, sessionDir: sessionDir) { modelPath in
      guard
        let aModelPath = modelPath
      else {
        debugPrint("Error creating the ML model.")
        return onCompletion(nil)
      }
      // 2
      MLPredictor.predictUsingModel(aModelPath, inputImage: petImage) { stylizedImage in
        onCompletion(stylizedImage)
      }
    }
  }
}

5. MLModelTrainer.swift

import Foundation
import CreateML
import Combine

enum MLModelTrainer {
  private static var subscriptions = Set<AnyCancellable>()
  static func trainModel(using styleImage: URL, validationImage: URL, sessionDir: URL, onCompletion: @escaping (URL?) -> Void) {
    // 1
    let dataSource = MLStyleTransfer.DataSource.images(
      styleImage: styleImage,
      contentDirectory: Constants.Path.trainingImagesDir ?? Bundle.main.bundleURL,
      processingOption: nil)
    // 2
    let sessionParams = MLTrainingSessionParameters(
      sessionDirectory: sessionDir,
      reportInterval: Constants.MLSession.reportInterval,
      checkpointInterval: Constants.MLSession.checkpointInterval,
      iterations: Constants.MLSession.iterations)
    // 3
    let modelParams = MLStyleTransfer.ModelParameters(
      algorithm: .cnn,
      validation: .content(validationImage),
      maxIterations: Constants.MLModelParam.maxIterations,
      textelDensity: Constants.MLModelParam.styleDensity,
      styleStrength: Constants.MLModelParam.styleStrength)
    // 4
    guard let job = try? MLStyleTransfer.train(
      trainingData: dataSource,
      parameters: modelParams,
      sessionParameters: sessionParams) else {
      onCompletion(nil)
      return
    }
    // 5
    let modelPath = sessionDir.appendingPathComponent(Constants.Path.modelFileName)
    job.result.sink(receiveCompletion: { result in
      debugPrint(result)
    }, receiveValue: { model in
      do {
        try model.write(to: modelPath)
        onCompletion(modelPath)
        return
      } catch {
        debugPrint("Error saving ML Model: \(error.localizedDescription)")
      }
      onCompletion(nil)
    })
    .store(in: &subscriptions)
  }
}

6. MLPredictor.swift

import Foundation
import UIKit
import Vision
import CoreML

enum MLPredictor {
  static func predictUsingModel(_ modelPath: URL, inputImage: UIImage, onCompletion: @escaping (UIImage?) -> Void) {
    // 1
    guard
      let compiledModel = try? MLModel.compileModel(at: modelPath),
      let mlModel = try? MLModel.init(contentsOf: compiledModel)
    else {
      debugPrint("Error reading the ML Model")
      return onCompletion(nil)
    }
    // 2
    let imageOptions: [MLFeatureValue.ImageOption: Any] = [
      .cropAndScale: VNImageCropAndScaleOption.centerCrop.rawValue
    ]
    guard
      let cgImage = inputImage.cgImage,
      let imageConstraint = mlModel.modelDescription.inputDescriptionsByName["image"]?.imageConstraint,
      let inputImg = try? MLFeatureValue(cgImage: cgImage, constraint: imageConstraint, options: imageOptions),
      let inputImage = try? MLDictionaryFeatureProvider(dictionary: ["image": inputImg])
    else {
      return onCompletion(nil)
    }
    // 3
    guard
      let stylizedImage = try? mlModel.prediction(from: inputImage),
      let imgBuffer = stylizedImage.featureValue(for: "stylizedImage")?.imageBufferValue
    else {
      return onCompletion(nil)
    }
    let stylizedUIImage = UIImage(withCVImageBuffer: imgBuffer)
    return onCompletion(stylizedUIImage)
  }
}

7. Constants.swift

import Foundation

enum Constants {
  enum Path {
    static let trainingImagesDir = Bundle.main.resourceURL?.appendingPathComponent("TrainingData")
    static var documentsDir: URL = {
      return FileManager.default.urls(for: .documentDirectory, in: .userDomainMask)[0]
    }()
    static let sessionDir = documentsDir.appendingPathComponent("Session", isDirectory: true)
    static let modelFileName = "StyleTransfer.mlmodel"
    static let presetStyle1 = "PresetStyle_1"
  }
  enum MLSession {
    static var iterations = 100
    static var reportInterval = 50
    static var checkpointInterval = 25
  }
  enum MLModelParam {
    static var maxIterations = 200
    static var styleDensity = 128 // Multiples of 4
    static var styleStrength = 5 // Range 1 to 10
  }
}

8. UIImage+Utilities.swift

import Foundation
import UIKit
import VisionKit

extension UIImage {
  func saveImage(path: URL) -> URL? {
    guard
      let data = self.jpegData(compressionQuality: 0.8),
      (try? data.write(to: path)) != nil
    else {
      return nil
    }
    return path
  }
  convenience init?(withCVImageBuffer cvImageBuffer: CVImageBuffer) {
    let ciImage = CIImage(cvImageBuffer: cvImageBuffer)
    let context = CIContext.init(options: nil)
    guard
      let cgImage = context.createCGImage(ciImage, from: ciImage.extent)
    else {
      return nil
    }
    self.init(cgImage: cgImage)
  }
  func upOrientationImage() -> UIImage? {
    switch imageOrientation {
    case .up:
      return self
    default:
      UIGraphicsBeginImageContextWithOptions(size, false, scale)
      draw(in: CGRect(origin: .zero, size: size))
      let result = UIGraphicsGetImageFromCurrentImageContext()
      UIGraphicsEndImageContext()
      return result
    }
  }
}

后记

本篇主要讲述了在iOS设备上使用Style Transfer创建一个自定义图像滤波器，感兴趣的给个赞或者关注~~~