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ML Kit Introduction (for iOS)
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Elvis Lin
July 19, 2018
Programming
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ML Kit Introduction (for iOS)
Introduce the basic concept of ML Kit and how to use it in iOS development
Elvis Lin
July 19, 2018
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Transcript
ML Kit 使⽤用簡介 (iOS) Elvis Lin @Cocoahead Taipei 2018-07-19
關於我 • Elvis Lin • iOS 與 Android 永遠的初學者 •
Twitter: @elvismetaphor • Blog: https://blog.elvismetaphor.me
⼤大綱 • 什什麼是(我理理解的)機器學習 • 移動裝置上實作機器學習應⽤用的限制 • TensorFlow Lite 與 ML
Kit • 範例例
機器學習的應⽤用
機器學習 • 從資料中歸納出有⽤用的規則 • 訓練模型 • 使⽤用模型 • Mobile Application
Engineer 參參 與開發主要是在「使⽤用模型」 這個範圍
Data Result (Trained) Model
移動裝置上 實作機器學習應⽤用的限制 • 記憶體有限與儲存空間有限 • 計算能⼒力力不如⼤大型伺服器 • 電池容量量有限
移動裝置上 實作機器學習應⽤用的改良⽅方向 • 記憶體有限與儲存空間有限 —> 減少模型(Model)的體積 • 計算能⼒力力不如⼤大型伺服器 —> 降低演算法的複雜度
• 電池容量量有限 —> 降低演算法的複雜度
Google 推出的解決⽅方案 • TensorFlow Lite • ML Kit
Tensorflow Lite https://youtu.be/ByJnpbDd-zc
https://www.tensorflow.org/mobile/tflite/
轉換 Tensorflow 檔案的⼯工具 • Tensorflow converter • 轉成 Tensorflow Lite
格式 • Tensorflow-CoreML converter • 轉成 CoreML 格式 • https://github.com/tf-coreml/tf-coreml
ML Kit https://youtu.be/Z-dqGRSsaBs
Neural Networks API Metal
ML Kit • Cloud Vision API / Mobile Vision API
• Tensorflow Lite • 整合 Firebase,託管「客製化的模型」
ML Kit Base APIs • Image labeling • Text recognition
(OCR) • Face detection • Barcode scanning • Landmark detection • others……
託管客製化的模型 ⽬目前只⽀支援 Tensorflow Lite 格式
使⽤用 ML Kit
建立⼀一個 Firebase 專案
建立⼀一個 iOS app 然後下載設定檔 設定好 Bundle ID 下載 GoogleService-info.plist
新增 plist 檔案到專案 • 將 GoogleService-Info.plist 放到 <root>/<application_folder>/ 下
安裝 Firebase 函式庫 • 修改 Podfile,新增以下的內容 • cd <root> pod
install • 打改 <project_name>.xcworkspace pod 'Firebase/Core' pod 'Firebase/MLVision' pod 'Firebase/MLVisionTextModel' pod 'Firebase/MLVisionFaceModel' pod 'Firebase/MLVisionBarcodeModel' pod 'Firebase/MLVision' pod 'Firebase/MLVisionLabelModel'
掃描 Barcode (Local) let barcodeDetector: VisionBarcodeDetector = Vision.vision().barcodeDetector(options: options)
let visionImage = VisionImage(image: pickedImage) barcodeDetector.detect(in: visionImage) { (barcodes, error) in guard error == nil, let barcodes = barcodes, !barcodes.isEmpty else { self.dismiss(animated: true, completion: nil) self.resultView.text = "No Barcode Detected" return } for barcode in barcodes { // handle the detected barcode } }
第1步:初始化 Detector let barcodeDetector: VisionBarcodeDetector = Vision.vision().barcodeDetector(options: options) let
visionImage = VisionImage(image: pickedImage)
第2步:取得結果 barcodeDetector.detect(in: visionImage) { (barcodes, error) in guard error ==
nil, let barcodes = barcodes, !barcodes.isEmpty else { self.dismiss(animated: true, completion: nil) self.resultView.text = "No Barcode Detected" return } for barcode in barcodes { // handle the detected barcode } }
⽀支援的 Barcode 格式 • Code 128 (FORMAT_CODE_128) • Code 39
(FORMAT_CODE_39) • Code 93 (FORMAT_CODE_93) • Codabar (FORMAT_CODABAR) • EAN-13 (FORMAT_EAN_13) • EAN-8 (FORMAT_EAN_8) • ITF (FORMAT_ITF) • UPC-A (FORMAT_UPC_A) • UPC-E (FORMAT_UPC_E) •QR Code (FORMAT_QR_CODE) • PDF417 (FORMAT_PDF417) • Aztec (FORMAT_AZTEC) • Data Matrix (FORMAT_DATA_MATRIX)
辨識⽂文字 (Local) lazy var textDetector: VisionTextDetector = Vision.vision().textDetector() func
runTextRecognition(with image: UIImage) { let visionImage = VisionImage(image: image) textDetector.detect(in: visionImage) { (features, error) in if let error = error { print("Received error: \(error)") } self.processResult(from: features, error: error) } }
辨識⽂文字 (Cloud) Lazy var cloudTextDetector: VisionCloudTextDetector = Vision.vision().cloudTextDetector() func
runCloudTextRecognition(with image: UIImage) { let visionImage = VisionImage(image: image) cloudTextDetector.detect(in: visionImage) { (features, error) in if let error = error { print("Received error: \(error)") } self.processCloudResult(from: features, error: error) } }
補充資料 • ML Kit 簡介 (for Android) https://blog.elvismetaphor.me/ml-kit-fundamentals-for- android-6444e2db0fdb •
ML Kit 簡介 (for iOS) https://blog.elvismetaphor.me/ml-kit-fundamentals-for- ios-cb705044e69b
參參考資料 • https://youtu.be/Z-dqGRSsaBs • https://codelabs.developers.google.com/codelabs/mlkit-ios/ • https://github.com/firebase/quickstart-ios/tree/master/ mlvision • https://www.appcoda.com.tw/ml-kit/
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