up model card id

OCR-SDK.xcworkspace/xcuserdata/itsol.xcuserdatad/xcdebugger/Breakpoints_v2.xcbkptlist

-<?xml version="1.0" encoding="UTF-8"?>
-<Bucket
-   uuid = "B08B0591-1A45-4415-9C04-B83659FBFE35"
-   type = "0"
-   version = "2.0">
-</Bucket>

OCR-SDK/ModelDataHandler/SBKModelDataHandler.swift

@@ -35,7 +35,8 @@ typealias FileInfo = (name: String, extension: String)
 /// Information about the MobileNet model.
 enum MobileNet {
   static let modelInfo: FileInfo = (name: "liveness", extension: "tflite")
-  static let cardModel: FileInfo = (name: "valid_card_10102020", extension: "tflite")
+  static let cardModel: FileInfo = (name: "idcard15072021", extension: "tflite")
+  static let landMarkModel: FileInfo = (name: "face_detection_front", extension: "tflite")
 }
 
 /// This class handles all data preprocessing and makes calls to run inference on a given frame
@@ -75,7 +76,7 @@ class SBKModelDataHandler {
   /// labels files are successfully loaded from the app's main bundle. Default `threadCount` is 1.
     init?(modelFileInfo: FileInfo, threadCount: Int = 1) {
     let modelFilename = modelFileInfo.name
-
+        
     // Construct the path to the model file.
     let bundle = Bundle(for: SBKRecordFace.self)
     guard let modelPath = bundle.path(
@@ -86,7 +87,6 @@ class SBKModelDataHandler {
       return nil
     }
         
-    let delegate = MetalDelegate()
         
     // Specify the options for the `Interpreter`.
     self.threadCount = threadCount
@@ -94,7 +94,7 @@ class SBKModelDataHandler {
     options.threadCount = threadCount
     do {
       // Create the `Interpreter`.
-      interpreter = try Interpreter(modelPath: modelPath, options: options, delegates: [delegate])
+      interpreter = try Interpreter(modelPath: modelPath, options: options)
       // Allocate memory for the model's input `Tensor`s.
       try interpreter.allocateTensors()
     } catch let error {
@@ -142,87 +142,63 @@ class SBKModelDataHandler {
   // MARK: - Internal Methods
 
   /// Performs image preprocessing, invokes the `Interpreter`, and processes the inference results.
-  func runModel(onFrame pixelBuffer: CVPixelBuffer) -> [Float]? {
-    
-    let sourcePixelFormat = CVPixelBufferGetPixelFormatType(pixelBuffer)
-    assert(sourcePixelFormat == kCVPixelFormatType_32ARGB ||
-             sourcePixelFormat == kCVPixelFormatType_32BGRA ||
-               sourcePixelFormat == kCVPixelFormatType_32RGBA)
-
-
-    let imageChannels = 4
-    assert(imageChannels >= inputChannels)
-
-    // Crops the image to the biggest square in the center and scales it down to model dimensions.
-    let scaledSize = CGSize(width: inputWidth, height: inputHeight)
-    guard let thumbnailPixelBuffer = pixelBuffer.centerThumbnail(ofSize: scaledSize) else {
-      return nil
-    }
-
-    let interval: TimeInterval
-    let outputTensor: Tensor
-    do {
-      let inputTensor = try interpreter.input(at: 0)
-
-      // Remove the alpha component from the image buffer to get the RGB data.
-      guard let rgbData = rgbDataFromBuffer(
-        thumbnailPixelBuffer,
-        byteCount: batchSize * inputWidth * inputHeight * inputChannels,
-        isModelQuantized: inputTensor.dataType == .uInt8
-      ) else {
-        print("Failed to convert the image buffer to RGB data.")
+    func runModel(onFrame pixelBuffer: CVPixelBuffer) -> [Float]? {
+      
+      let sourcePixelFormat = CVPixelBufferGetPixelFormatType(pixelBuffer)
+      assert(sourcePixelFormat == kCVPixelFormatType_32ARGB ||
+               sourcePixelFormat == kCVPixelFormatType_32BGRA ||
+                 sourcePixelFormat == kCVPixelFormatType_32RGBA)
+
+      let imageChannels = 4
+      assert(imageChannels >= inputChannels)
+      let scaledSize = CGSize(width: inputWidth, height: inputHeight)
+      guard let thumbnailPixelBuffer = pixelBuffer.resized(to: scaledSize) else {
         return nil
       }
-
-        let imageCap = UIImage(data: rgbData)
-       // self.fromImage(image: imageCap!, datas: rgbData, imagesss: imageCap!)
-        
-      // Copy the RGB data to the input `Tensor`.
-      try interpreter.copy(rgbData, toInputAt: 0)
-
-      // Run inference by invoking the `Interpreter`.
-      let startDate = Date()
-      try interpreter.invoke()
-      interval = Date().timeIntervalSince(startDate) * 1000
-
-      // Get the output `Tensor` to process the inference results.
-      outputTensor = try interpreter.output(at: 0)
-    } catch let error {
-      print("Failed to invoke the interpreter with error: \(error.localizedDescription)")
-      return nil
-    }
-
-    let results: [Float]
-    switch outputTensor.dataType {
-    case .uInt8:
-      guard let quantization = outputTensor.quantizationParameters else {
-        print("No results returned because the quantization values for the output tensor are nil.")
+      let interval: TimeInterval
+      let outputTensor: Tensor
+      do {
+        let inputTensor = try interpreter.input(at: 0)
+        guard let rgbData = rgbDataFromBuffer(
+          thumbnailPixelBuffer,
+          byteCount: batchSize * inputWidth * inputHeight * inputChannels,
+          isModelQuantized: inputTensor.dataType == .uInt8
+        ) else {
+          print("Failed to convert the image buffer to RGB data.")
+          return nil
+        }
+        try interpreter.copy(rgbData, toInputAt: 0)
+        let startDate = Date()
+        try interpreter.invoke()
+        interval = Date().timeIntervalSince(startDate) * 1000
+        outputTensor = try interpreter.output(at: 0)
+      } catch let error {
+        print("Failed to invoke the interpreter with error: \(error.localizedDescription)")
         return nil
       }
-      let quantizedResults = [UInt8](outputTensor.data)
-      results = quantizedResults.map {
-        quantization.scale * Float(Int($0) - quantization.zeroPoint)
+
+      let results: [Float]
+      switch outputTensor.dataType {
+      case .uInt8:
+        guard let quantization = outputTensor.quantizationParameters else {
+          print("No results returned because the quantization values for the output tensor are nil.")
+          return nil
+        }
+        let quantizedResults = [UInt8](outputTensor.data)
+        results = quantizedResults.map {
+          quantization.scale * Float(Int($0) - quantization.zeroPoint)
+        }
+      case .float32:
+        results = [Float32](unsafeData: outputTensor.data) ?? []
+      default:
+        print("Output tensor data type \(outputTensor.dataType) is unsupported for this example app.")
+        return nil
       }
-    case .float32:
-      results = [Float32](unsafeData: outputTensor.data) ?? []
-    default:
-      print("Output tensor data type \(outputTensor.dataType) is unsupported for this example app.")
-      return nil
+
+      return results
     }
 
-    return results
-  }
 
-  /// Returns the RGB data representation of the given image buffer with the specified `byteCount`.
-  ///
-  /// - Parameters
-  ///   - buffer: The pixel buffer to convert to RGB data.
-  ///   - byteCount: The expected byte count for the RGB data calculated using the values that the
-  ///       model was trained on: `batchSize * imageWidth * imageHeight * componentsCount`.
-  ///   - isModelQuantized: Whether the model is quantized (i.e. fixed point values rather than
-  ///       floating point values).
-  /// - Returns: The RGB data representation of the image buffer or `nil` if the buffer could not be
-  ///     converted.
   private func rgbDataFromBuffer(
     _ buffer: CVPixelBuffer,
     byteCount: Int,
@@ -293,26 +269,12 @@ class SBKModelDataHandler {
 // MARK: - Extensions
 
 extension Data {
-  /// Creates a new buffer by copying the buffer pointer of the given array.
-  ///
-  /// - Warning: The given array's element type `T` must be trivial in that it can be copied bit
-  ///     for bit with no indirection or reference-counting operations; otherwise, reinterpreting
-  ///     data from the resulting buffer has undefined behavior.
-  /// - Parameter array: An array with elements of type `T`.
   init<T>(copyingBufferOf array: [T]) {
     self = array.withUnsafeBufferPointer(Data.init)
   }
 }
 
 extension Array {
-  /// Creates a new array from the bytes of the given unsafe data.
-  ///
-  /// - Warning: The array's `Element` type must be trivial in that it can be copied bit for bit
-  ///     with no indirection or reference-counting operations; otherwise, copying the raw bytes in
-  ///     the `unsafeData`'s buffer to a new array returns an unsafe copy.
-  /// - Note: Returns `nil` if `unsafeData.count` is not a multiple of
-  ///     `MemoryLayout<Element>.stride`.
-  /// - Parameter unsafeData: The data containing the bytes to turn into an array.
   init?(unsafeData: Data) {
     guard unsafeData.count % MemoryLayout<Element>.stride == 0 else { return nil }
     #if swift(>=5.0)
@@ -327,20 +289,3 @@ extension Array {
     #endif  // swift(>=5.0)
   }
 }
-
-extension UIImage {
-    func getPixelColor(pos: CGPoint, dataImage: Data, image: UIImage) -> UIColor {
-
-        let pixelData = image.cgImage!.dataProvider!.data
-        let data: UnsafePointer<UInt8> = CFDataGetBytePtr(pixelData)
-
-        let pixelInfo: Int = ((Int(image.size.width) * Int(pos.y)) + Int(pos.x)) * 4
-
-        let r = CGFloat(dataImage[pixelInfo]) / CGFloat(255.0)
-        let g = CGFloat(dataImage[pixelInfo+1]) / CGFloat(255.0)
-        let b = CGFloat(dataImage[pixelInfo+2]) / CGFloat(255.0)
-        let a = CGFloat(dataImage[pixelInfo+3]) / CGFloat(255.0)
-
-        return UIColor(red: r, green: g, blue: b, alpha: a)
-    }
-}

OCR-SDK/Models/AnchorOption.swift

+//
+//  AnchorOption.swift
+//  OCR-SDK
+//
+//  Created by annguyen on 12/03/2021.
+//  Copyright © 2021 itsol. All rights reserved.
+//
+
+import Foundation
+class AnchorOption {
+    init(inputSizeWidth: Int, inputSizeHeight: Int, minScale: Double, maxScale: Double, anchorOffsetX: Double, anchorOffsetY: Double, numLayers: Int, featureMapWidth: [Int], featureMapHeight: [Int], strides: [Int], aspectRatios: [Double], reduceBoxesInLowestLayer: Bool, interpolatedScaleAspectRatio: Double, fixedAnchorSize: Bool) {
+        self.inputSizeWidth = inputSizeWidth
+        self.inputSizeHeight = inputSizeHeight
+        self.minScale = minScale
+        self.maxScale = maxScale
+        self.anchorOffsetX = anchorOffsetX
+        self.anchorOffsetY = anchorOffsetY
+        self.numLayers = numLayers
+        self.featureMapWidth = featureMapWidth
+        self.featureMapHeight = featureMapHeight
+        self.strides = strides
+        self.aspectRatios = aspectRatios
+        self.reduceBoxesInLowestLayer = reduceBoxesInLowestLayer
+        self.interpolatedScaleAspectRatio = interpolatedScaleAspectRatio
+        self.fixedAnchorSize = fixedAnchorSize
+    }
+    
+    var inputSizeWidth: Int
+    var inputSizeHeight: Int
+    var minScale: Double
+    var maxScale: Double
+    var anchorOffsetX: Double
+    var anchorOffsetY: Double
+    var numLayers: Int
+    var featureMapWidth: [Int]
+    var featureMapHeight: [Int]
+    var strides: [Int]
+    var aspectRatios: [Double]
+    var reduceBoxesInLowestLayer: Bool
+    var interpolatedScaleAspectRatio: Double
+    var fixedAnchorSize: Bool
+    
+    func stridesSize() -> Int {
+        return strides.count
+      }
+
+    func featureMapHeightSize() -> Int {
+        return featureMapHeight.count
+      }
+
+    func featureMapWidthSize() -> Int {
+        return featureMapWidth.count
+      }
+}

OCR-SDK/Models/Detection.swift

+//
+//  Detection.swift
+//  OCR-SDK
+//
+//  Created by annguyen on 12/03/2021.
+//  Copyright © 2021 itsol. All rights reserved.
+//
+
+import Foundation
+struct Detection {
+    init(score: Double, xMin: Double, yMin: Double, width: Double, height: Double, classID: Int, landMark: [Landmark]) {
+        self.score = score
+        self.xMin = xMin
+        self.yMin = yMin
+        self.width = width
+        self.height = height
+        self.classID = classID
+        self.landMark = landMark
+    }
+    var score: Double
+    var xMin: Double
+    var yMin: Double
+    var width: Double
+    var height: Double
+    var classID: Int
+    var landMark: [Landmark]
+}

OCR-SDK/Models/EMSimilarity.swift

+//
+//  EMSimilarity.swift
+//  SwiftSim
+//
+//  Created by Evan Moss on 8/1/16.
+//  Copyright © 2016 Enterprising Technologies LLC. All rights reserved.
+//
+//  The MIT License (MIT)
+//
+//  Copyright (c) 2016 Evan Moss
+//
+//  Permission is hereby granted, free of charge, to any person obtaining a copy
+//  of this software and associated documentation files (the "Software"), to deal
+//  in the Software without restriction, including without limitation the rights
+//  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+//  copies of the Software, and to permit persons to whom the Software is
+//  furnished to do so, subject to the following conditions:
+//
+//  The above copyright notice and this permission notice shall be included in all
+//  copies or substantial portions of the Software.
+//
+//  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+//  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+//  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+//  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+//  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+//  OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+//  SOFTWARE.
+
+import Foundation
+
+enum EMSimilarityMode {
+    case Cosine
+    case Tanimoto
+    case Ochiai
+    case JaccardIndex
+    case JaccardDistance
+    case Dice
+    case Hamming
+}
+
+enum EMVectorSizeMismatchMode {
+    case Bail
+    case Truncate
+}
+
+class EMSimilarity {
+    /** Similarity metric mode **/
+    private var currentSimMode = [EMSimilarityMode.Cosine]
+    
+    /** Set the currentSimMode via push **/
+    func pushSimMode(mode: EMSimilarityMode) {
+        self.currentSimMode.append(mode)
+    }
+    
+    /** Pop the currentSimMode via pop if it won't make the stack empty **/
+    func popSimMode() {
+        if self.currentSimMode.count > 1 {
+            let _ = self.currentSimMode.popLast()
+        }
+    }
+    
+    /** Get the currently set similarity mode **/
+    func getCurrentSimMode() -> EMSimilarityMode? {
+        return self.currentSimMode.last
+    }
+    
+    /** Mismatch Mode **/
+    private var currentMismatchMode = [EMVectorSizeMismatchMode.Bail]
+    
+    /** Set the currentMismatcMode via push **/
+    func pushMismatchMode(mode: EMVectorSizeMismatchMode) {
+        self.currentMismatchMode.append(mode)
+    }
+    
+    /** Pop the currentMismatchMode via pop if it won't make the stack empty **/
+    func popMismatchMode() {
+        if self.currentMismatchMode.count > 1 {
+            let _ = self.currentMismatchMode.popLast()
+        }
+    }
+    
+    /** Get the currently set mistmatch mode **/
+    func getCurrentMismatchMode() -> EMVectorSizeMismatchMode? {
+        return self.currentMismatchMode.last
+    }
+    
+    /** Dot Product **/
+    private func dot(A: [Double], B: [Double]) -> Double {
+        var x: Double = 0
+        for i in 0...A.count-1 {
+            x += A[i] * B[i]
+        }
+        return x
+    }
+    
+    /** Vector Magnitude **/
+    private func magnitude(A: [Double]) -> Double {
+        var x: Double = 0
+        for elt in A {
+            x += elt * elt
+        }
+        return sqrt(x)
+    }
+    
+    /** Cosine similarity **/
+    private func cosineSim(A: [Double], B: [Double]) -> Double {
+        return dot(A: A, B: B) / (magnitude(A: A) * magnitude(A: B))
+    }
+    
+    /** Tanimoto similarity **/
+    private func tanimotoSim(A: [Double], B: [Double]) -> Double {
+        let Amag = magnitude(A: A)
+        let Bmag = magnitude(A: B)
+        let AdotB = dot(A: A, B: B)
+        return AdotB / (Amag * Amag + Bmag * Bmag - AdotB)
+    }
+    
+    /** Ochiai similarity **/
+    private func ochiaiSim(A: [Double], B: [Double]) -> Double {
+        let a = Set(A)
+        let b = Set(B)
+        
+        return Double(a.intersection(b).count) / sqrt(Double(a.count) * Double(b.count))
+    }
+    
+    /** Jaccard index **/
+    private func jaccardIndex(A: [Double], B: [Double]) -> Double {
+        let a = Set(A)
+        let b = Set(B)
+        
+        return Double(a.intersection(b).count) / Double(a.union(b).count)
+    }
+    
+    /** Jaccard distance **/
+    private func jaccardDist(A: [Double], B: [Double]) -> Double {
+        return 1.0 - jaccardIndex(A: A, B: B)
+    }
+    
+    /** Dice coeeficient **/
+    private func diceCoef(A: [Double], B: [Double]) -> Double {
+        let a = Set(A)
+        let b = Set(B)
+        
+        return 2.0 * Double(a.intersection(b).count) / (Double(a.count) + Double(b.count))
+    }
+    
+    /** Hamming distance **/
+    private func hammingDist(A: [Double], B: [Double]) -> Double {
+        var x: Double = 0
+        
+        if A.isEmpty {
+            return x
+        }
+        
+        for i in 0...A.count-1 {
+            if A[i] != B[i] {
+                x += 1
+            }
+        }
+        
+        return x
+    }
+    
+    private let encforceEqualVectorSizes: Set<EMSimilarityMode> = [.Cosine, .Tanimoto, .Hamming]
+    private let bailOnEmptyInput: Set<EMSimilarityMode> = [.Cosine, .Tanimoto, .Ochiai]
+    private let allowEmptyInputs: Set<EMSimilarityMode> = [.Hamming]
+    
+    /**
+     * Main compute mode
+     * Double types
+     * Returns the similarity results or -1.0 on caught error
+     */
+    func compute(A: [Double], B: [Double]) -> Double {
+        // get the mode
+        var mode = EMSimilarityMode.Cosine
+        if let _mode = self.getCurrentSimMode() {
+            mode = _mode
+        }
+        else {
+            return -1
+        }
+        
+        // are both vectors empty?
+        if A.isEmpty && B.isEmpty && !allowEmptyInputs.contains(mode) {
+            // divide by zero -> D.N.E.
+            return -1
+        }
+        
+        // is one of the vectors empty and would this case a divide by zero error?
+        if bailOnEmptyInput.contains(mode) && (A.isEmpty || B.isEmpty) {
+            return -1
+        }
+        
+        // look for vector size mismatch for modes in encforceEqualVectorSizes
+        if encforceEqualVectorSizes.contains(mode) && A.count != B.count {
+            if let mismatchMode = self.getCurrentMismatchMode() {
+                switch mismatchMode {
+                case .Bail:
+                    return -1
+                case .Truncate:
+                    let a = A.count < B.count ? A : B
+                    let _b = A.count < B.count ? B : A
+                    var b = [Double]()
+                    if a.count > 0 {
+                        for i in 0...a.count-1 {
+                            b.append(_b[i])
+                        }
+                    }
+                    return compute(A: a, B: b)
+                }
+            }
+            else {
+                return -1
+            }
+        }
+        
+        switch mode {
+        case .Cosine:
+            return cosineSim(A: A, B: B)
+        case .Tanimoto:
+            return tanimotoSim(A: A, B: B)
+        case .Ochiai:
+            return ochiaiSim(A: A, B: B)
+        case .JaccardIndex:
+            return jaccardIndex(A: A, B: B)
+        case .JaccardDistance:
+            return jaccardDist(A: A, B: B)
+        case .Dice:
+            return diceCoef(A: A, B: B)
+        case .Hamming:
+            return hammingDist(A: A, B: B)
+        }
+    }
+}

OCR-SDK/Models/LandMark.swift

+//
+//  LandMark.swift
+//  OCR-SDK
+//
+//  Created by annguyen on 12/03/2021.
+//  Copyright © 2021 itsol. All rights reserved.
+//
+
+import Foundation
+class Landmark{
+    init(x: Double, y: Double) {
+        self.x = x
+        self.y = y
+    }
+    
+    var x: Double
+    var y: Double
+}

OCR-SDK/Models/NormalizeOp.swift

+//
+//  NormalizeOp.swift
+//  movanai
+//
+//  Created by Nguyen Van An on 4/4/21.
+//
+
+import Foundation
+class NormalizeOp {
+    var x: Float = 0
+    var y: Float = 0
+    init(_ x: Float, _ y: Float ) {
+        self.x = x
+        self.y = y
+    }
+}

OCR-SDK/Models/OptionsFace.swift

+//
+//  OptionsFace.swift
+//  OCR-SDK
+//
+//  Created by annguyen on 12/03/2021.
+//  Copyright © 2021 itsol. All rights reserved.
+//
+
+import Foundation
+class OptionsFace {
+    init(numClasses: Int, numBoxes: Int, numCoords: Int, keypointCoordOffset: Int, ignoreClasses: [Int], scoreClippingThresh: Double, minScoreThresh: Double, numKeypoints: Int, numValuesPerKeypoint: Int, boxCoordOffset: Int, xScale: Double, yScale: Double, wScale: Double, hScale: Double, applyExponentialOnBoxSize: Bool, reverseOutputOrder: Bool, sigmoidScore: Bool, flipVertically: Bool) {
+        self.numClasses = numClasses
+        self.numBoxes = numBoxes
+        self.numCoords = numCoords
+        self.keypointCoordOffset = keypointCoordOffset
+        self.ignoreClasses = ignoreClasses
+        self.scoreClippingThresh = scoreClippingThresh
+        self.minScoreThresh = minScoreThresh
+        self.numKeypoints = numKeypoints
+        self.numValuesPerKeypoint = numValuesPerKeypoint
+        self.boxCoordOffset = boxCoordOffset
+        self.xScale = xScale
+        self.yScale = yScale
+        self.wScale = wScale
+        self.hScale = hScale
+        self.applyExponentialOnBoxSize = applyExponentialOnBoxSize
+        self.reverseOutputOrder = reverseOutputOrder
+        self.sigmoidScore = sigmoidScore
+        self.flipVertically = flipVertically
+    }
+    
+  var numClasses: Int
+    var numBoxes: Int
+    var numCoords: Int
+    var keypointCoordOffset : Int
+    var ignoreClasses: [Int]
+    var scoreClippingThresh: Double
+    var minScoreThresh: Double
+    var numKeypoints: Int
+    var numValuesPerKeypoint: Int
+    var boxCoordOffset: Int
+    var xScale: Double
+    var yScale: Double
+    var wScale : Double
+    var hScale: Double
+    var applyExponentialOnBoxSize: Bool
+    var reverseOutputOrder: Bool
+    var sigmoidScore: Bool
+    var flipVertically: Bool
+
+}
+
+class Anchor {
+    init(xCenter: Double, yCenter: Double, h: Double, w: Double) {
+        self.xCenter = xCenter
+        self.yCenter = yCenter
+        self.h = h
+        self.w = w
+    }
+    var xCenter: Double
+    var yCenter: Double
+    var h: Double
+    var w: Double
+}

OCR-SDK/UI/SBKShowDataPhoto/SBKResultCapture.swift

@@ -11,6 +11,7 @@ class SBKResultCapture: UIViewController {
     @IBOutlet weak var imgPhotoCard: UIImageView!
     public var imageData: Data?
     public var dataCrop: Data?
+    var cropZone: CGRect?
     @IBOutlet weak var btnNext: UIButton!
     @IBOutlet weak var btnClose: UIButton!
     @IBOutlet weak var lbDescription: UILabel!
@@ -61,7 +62,9 @@ class SBKResultCapture: UIViewController {
         if UIDevice.current.userInterfaceIdiom == .pad {
             cropImage = SBKValidateInput.shared.cropImageHorizontal(image: imageCap!, rect: CGRect(x: imageCap!.size.width * 1 / 10, y: imageCap!.size.height * 3 / 20, width: imageCap!.size.width * 8 / 10, height: imageCap!.size.height * 8 / 10), scale: 1.0)!.rotate(radians: .pi / 2)
         } else {
-            cropImage = self.cropImage(image: imageCap!, rect: CGRect(x: imageCap!.size.width / 20, y: imageCap!.size.height / 8 + imageCap!.size.height / 50, width: imageCap!.size.width * 18 / 20, height: imageCap!.size.width * 18 / 20 * 3 / 4 ), scale: scale)
+            if self.cropZone != nil {
+                cropImage = imageCap!.crop(rect: self.cropZone!, scale: 1.0)
+            }
         }
         
         dataCrop = cropImage!.pngData()
@@ -72,14 +75,7 @@ class SBKResultCapture: UIViewController {
         fatalError("init(coder:) has not been implemented")
     }
 
-    //Xử lý ảnh hiển thị
-    func cropImage(image: UIImage, rect: CGRect, scale: CGFloat) -> UIImage? {
-        UIGraphicsBeginImageContextWithOptions(CGSize(width: rect.width, height: rect.height), true, 0.0)
-        image.draw(at: CGPoint(x: -rect.origin.x , y: -rect.origin.y ))
-        let croppedImage = UIGraphicsGetImageFromCurrentImageContext()
-        UIGraphicsEndImageContext()
-        return croppedImage
-    }
+   
     
     func navigateToFace() {
         DispatchQueue.main.async {
@@ -153,27 +149,3 @@ class SBKResultCapture: UIViewController {
     }
     
 }
-
-extension UIImage {
-    func rotate(radians: Float) -> UIImage? {
-        var newSize = CGRect(origin: CGPoint.zero, size: self.size).applying(CGAffineTransform(rotationAngle: CGFloat(radians))).size
-        // Trim off the extremely small float value to prevent core graphics from rounding it up
-        newSize.width = floor(newSize.width)
-        newSize.height = floor(newSize.height)
-
-        UIGraphicsBeginImageContextWithOptions(newSize, false, self.scale)
-        let context = UIGraphicsGetCurrentContext()!
-
-        // Move origin to middle
-        context.translateBy(x: newSize.width/2, y: newSize.height/2)
-        // Rotate around middle
-        context.rotate(by: CGFloat(radians))
-        // Draw the image at its center
-        self.draw(in: CGRect(x: -self.size.width/2, y: -self.size.height/2, width: self.size.width, height: self.size.height))
-
-        let newImage = UIGraphicsGetImageFromCurrentImageContext()
-        UIGraphicsEndImageContext()
-
-        return newImage
-    }
-}

OCR-SDK/UI/View/OverLayCardView.swift

+//
+//  OverLayView.swift
+//  OCR-SDK
+//
+//  Created by Nguyen Van An on 4/26/21.
+//  Copyright © 2021 itsol. All rights reserved.
+//
+
+
+import AVFoundation
+import UIKit
+class OverLayCardView: UIView {
+    @IBInspectable var previewWidth: CGFloat = 100
+    @IBInspectable var previewHeight: CGFloat = 100
+    @IBInspectable var borderLength : CGFloat = 10
+    @IBInspectable var borderPadding : CGFloat = 0
+    @IBInspectable var borderWidth : CGFloat = 2
+    @IBInspectable var borderColor : CGColor = UIColor.white.cgColor
+    @IBInspectable var marginTop : CGFloat = 0
+    @IBInspectable var marginLeft : CGFloat = 0
+    @IBInspectable var connerRadius : CGFloat = 8
+    
+    let vContainer: UIView = {
+        let view = UIView()
+        view.backgroundColor =  UIColor.black.withAlphaComponent(0.6)
+        return view
+    }()
+    let vContainer2: UIView = {
+        let view = UIView()
+        return view
+    }()
+    override init(frame: CGRect) {
+        super.init(frame: frame)
+        vContainer.frame = self.bounds
+        self.addSubview(vContainer)
+        vContainer2.frame = self.bounds
+        self.addSubview(vContainer2)
+    }
+    
+    required init?(coder: NSCoder) {
+        fatalError("init(coder:) has not been implemented")
+    }
+    func setLayer(){
+        var x = marginTop
+        var y = marginLeft
+        if marginTop == 0 {
+            x = self.center.x - previewWidth/2
+        }
+        if marginLeft == 0 {
+            y = self.center.y - previewHeight/2
+        }
+        //add to container dask
+        let maskLayer = CALayer()
+        maskLayer.frame = self.bounds
+        let circleLayer = CAShapeLayer()
+        circleLayer.frame = CGRect(x:0 , y:0,width: self.frame.size.width,height: self.frame.size.height)
+        let finalPath = UIBezierPath(roundedRect: CGRect(x:0 , y:0,width: self.frame.size.width,height: self.frame.size.height), cornerRadius: 0)
+        let rectPath = UIBezierPath(roundedRect: CGRect(x: x, y: y,width: previewWidth, height: previewHeight),  cornerRadius: connerRadius)
+        finalPath.append(rectPath.reversing())
+        circleLayer.path = finalPath.cgPath
+        maskLayer.addSublayer(circleLayer)
+        vContainer.layer.mask = maskLayer
+        let clearLayer = CALayer()
+        clearLayer.frame = vContainer2.bounds
+        
+        //draw border radius
+        let path = UIBezierPath(roundedRect: CGRect(x: x + borderPadding, y: y + borderPadding, width: previewWidth - borderPadding*2, height: previewHeight - borderPadding*2),  cornerRadius: connerRadius)
+        let shape = CAShapeLayer()
+        shape.path = path.cgPath
+        shape.strokeColor = borderColor
+        shape.lineWidth = borderWidth
+        shape.fillColor = UIColor.clear.cgColor
+        vContainer2.layer.addSublayer(shape)
+    }
+    
+    func setBorderColor(color: CGColor){
+        if borderColor != color {
+            vContainer2.layer.sublayers = nil
+            borderColor = color
+            var x = marginTop
+            var y = marginLeft
+            if marginTop == 0 {
+                x = self.center.x - previewWidth/2
+            }
+            if marginLeft == 0 {
+                y = self.center.y - previewHeight/2
+            }
+        //draw border radius
+        let path = UIBezierPath(roundedRect: CGRect(x: x + borderPadding, y: y + borderPadding, width: previewWidth - borderPadding*2, height: previewHeight - borderPadding*2),  cornerRadius: connerRadius)
+        let shape = CAShapeLayer()
+        shape.path = path.cgPath
+        shape.strokeColor = borderColor
+        shape.lineWidth = borderWidth
+        shape.fillColor = UIColor.clear.cgColor
+        vContainer2.layer.addSublayer(shape)
+        }
+    }
+    
+}
+

OCR-SDK/Utils/Extension/ExtCGImage.swift

+//
+//  ExtCGImage.swift
+//  OCR-SDK
+//
+//  Created by Nguyen Van An on 4/26/21.
+//  Copyright © 2021 itsol. All rights reserved.
+//
+
+import Foundation
+import UIKit
+import CoreGraphics
+import CoreImage
+import VideoToolbox
+extension CGImage{
+    func toCVPixelBuffer() -> CVPixelBuffer? {
+        let frameSize = CGSize(width: self.width, height: self.height)
+        var pixelBuffer:CVPixelBuffer? = nil
+        let status = CVPixelBufferCreate(kCFAllocatorDefault, Int(frameSize.width), Int(frameSize.height), kCVPixelFormatType_32BGRA , nil, &pixelBuffer)
+        if status != kCVReturnSuccess {
+            return nil
+        }
+        CVPixelBufferLockBaseAddress(pixelBuffer!, CVPixelBufferLockFlags.init(rawValue: 0))
+        let data = CVPixelBufferGetBaseAddress(pixelBuffer!)
+        let rgbColorSpace = CGColorSpaceCreateDeviceRGB()
+        let bitmapInfo = CGBitmapInfo(rawValue: CGBitmapInfo.byteOrder32Little.rawValue | CGImageAlphaInfo.premultipliedFirst.rawValue)
+        let context = CGContext(data: data, width: Int(frameSize.width), height: Int(frameSize.height), bitsPerComponent: 8, bytesPerRow: CVPixelBufferGetBytesPerRow(pixelBuffer!), space: rgbColorSpace, bitmapInfo: bitmapInfo.rawValue)
+        context?.draw(self, in: CGRect(x: 0, y: 0, width: self.width, height: self.height))
+        CVPixelBufferUnlockBaseAddress(pixelBuffer!, CVPixelBufferLockFlags(rawValue: 0))
+        return pixelBuffer
+    }
+    public static func create(pixelBuffer: CVPixelBuffer) -> CGImage? {
+       var cgImage: CGImage?
+       VTCreateCGImageFromCVPixelBuffer(pixelBuffer, options: nil, imageOut: &cgImage)
+       return cgImage
+     }
+}

OCR-SDK/Utils/Extension/ExtCIImage.swift

+//
+//  ExtCIImage.swift
+//  OCR-SDK
+//
+//  Created by Nguyen Van An on 4/26/21.
+//  Copyright © 2021 itsol. All rights reserved.
+//
+
+import Foundation
+import UIKit
+
+extension CIImage{
+    func toUIImage() -> UIImage {
+        let eaglContext = EAGLContext(api: .openGLES2)
+        let context:CIContext = CIContext(eaglContext: eaglContext!)
+        let cgImage:CGImage = context.createCGImage(self, from: self.extent)!
+        let image:UIImage = UIImage.init(cgImage: cgImage)
+        return image
+    }
+}

OCR-SDK/Utils/CVPixelBufferExtension.swift → OCR-SDK/Utils/Extension/ExtCVPixelBuffer.swift

-// Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
 import UIKit
 import Accelerate
 
 extension CVPixelBuffer {
-
-  /**
-   Returns thumbnail by cropping pixel buffer to biggest square and scaling the cropped image to
-   model dimensions.
-   */
   func centerThumbnail(ofSize size: CGSize ) -> CVPixelBuffer? {
-
     let imageWidth = CVPixelBufferGetWidth(self)
     let imageHeight = CVPixelBufferGetHeight(self)
     let pixelBufferType = CVPixelBufferGetPixelFormatType(self)
-
     assert(pixelBufferType == kCVPixelFormatType_32BGRA)
-
     let inputImageRowBytes = CVPixelBufferGetBytesPerRow(self)
     let imageChannels = 4
-
     let thumbnailSize = min(imageWidth, imageHeight)
     CVPixelBufferLockBaseAddress(self, CVPixelBufferLockFlags(rawValue: 0))
 
@@ -139,5 +115,76 @@ extension CVPixelBuffer {
 
     return pixelBuffer
   }
+    
+    func resized(to size: CGSize ) -> CVPixelBuffer? {
+
+      let imageWidth = CVPixelBufferGetWidth(self)
+      let imageHeight = CVPixelBufferGetHeight(self)
+
+      let pixelBufferType = CVPixelBufferGetPixelFormatType(self)
+
+      assert(pixelBufferType == kCVPixelFormatType_32BGRA ||
+             pixelBufferType == kCVPixelFormatType_32ARGB)
+
+      let inputImageRowBytes = CVPixelBufferGetBytesPerRow(self)
+      let imageChannels = 4
+
+      CVPixelBufferLockBaseAddress(self, CVPixelBufferLockFlags(rawValue: 0))
+
+      // Finds the biggest square in the pixel buffer and advances rows based on it.
+      guard let inputBaseAddress = CVPixelBufferGetBaseAddress(self) else {
+        return nil
+      }
+
+      // Gets vImage Buffer from input image
+      var inputVImageBuffer = vImage_Buffer(data: inputBaseAddress, height: UInt(imageHeight), width: UInt(imageWidth), rowBytes: inputImageRowBytes)
+
+      let scaledImageRowBytes = Int(size.width) * imageChannels
+      guard  let scaledImageBytes = malloc(Int(size.height) * scaledImageRowBytes) else {
+        return nil
+      }
 
+      // Allocates a vImage buffer for scaled image.
+      var scaledVImageBuffer = vImage_Buffer(data: scaledImageBytes, height: UInt(size.height), width: UInt(size.width), rowBytes: scaledImageRowBytes)
+
+      // Performs the scale operation on input image buffer and stores it in scaled image buffer.
+      let scaleError = vImageScale_ARGB8888(&inputVImageBuffer, &scaledVImageBuffer, nil, vImage_Flags(0))
+
+      CVPixelBufferUnlockBaseAddress(self, CVPixelBufferLockFlags(rawValue: 0))
+
+      guard scaleError == kvImageNoError else {
+        return nil
+      }
+
+      let releaseCallBack: CVPixelBufferReleaseBytesCallback = {mutablePointer, pointer in
+
+        if let pointer = pointer {
+          free(UnsafeMutableRawPointer(mutating: pointer))
+        }
+      }
+
+      var scaledPixelBuffer: CVPixelBuffer?
+      // Converts the scaled vImage buffer to CVPixelBuffer
+      let conversionStatus = CVPixelBufferCreateWithBytes(nil, Int(size.width), Int(size.height), pixelBufferType, scaledImageBytes, scaledImageRowBytes, releaseCallBack, nil, nil, &scaledPixelBuffer)
+
+      guard conversionStatus == kCVReturnSuccess else {
+
+        free(scaledImageBytes)
+        return nil
+      }
+
+      return scaledPixelBuffer
+    }
+    
+    func toUIImage() -> UIImage {
+        let ciimage : CIImage = CIImage(cvPixelBuffer: self)
+        let imageView : UIImage = ciimage.toUIImage()
+        return imageView
+    }
+    
+    func crop(rect: CGRect, scale: CGFloat) -> CVPixelBuffer? {
+        let imageCgi = CGImage.create(pixelBuffer: self)!
+        return imageCgi.cropping(to: rect)!.toCVPixelBuffer()
+    }
+   
 }

OCR-SDK/Utils/Extension/ExtUIImage.swift

+//
+//  ExtUIImage.swift
+//  OCR-SDK
+//
+//  Created by Nguyen Van An on 4/26/21.
+//  Copyright © 2021 itsol. All rights reserved.
+//
+
+import Foundation
+import UIKit
+
+extension UIImage {
+    //Xử lý ảnh hiển thị
+    func crop(rect: CGRect, scale: CGFloat) -> UIImage? {
+        UIGraphicsBeginImageContextWithOptions(CGSize(width: rect.width, height: rect.height), true, 0.0)
+        self.draw(at: CGPoint(x: -rect.minX / scale, y: -rect.minY / scale))
+        let croppedImage = UIGraphicsGetImageFromCurrentImageContext()
+        UIGraphicsEndImageContext()
+        return croppedImage
+    }
+    
+    
+    func getPixelColor(pos: CGPoint, dataImage: Data, image: UIImage) -> UIColor {
+        let pixelData = image.cgImage!.dataProvider!.data
+        let data: UnsafePointer<UInt8> = CFDataGetBytePtr(pixelData)
+        let pixelInfo: Int = ((Int(image.size.width) * Int(pos.y)) + Int(pos.x)) * 4
+        let r = CGFloat(dataImage[pixelInfo]) / CGFloat(255.0)
+        let g = CGFloat(dataImage[pixelInfo+1]) / CGFloat(255.0)
+        let b = CGFloat(dataImage[pixelInfo+2]) / CGFloat(255.0)
+        let a = CGFloat(dataImage[pixelInfo+3]) / CGFloat(255.0)
+
+        return UIColor(red: r, green: g, blue: b, alpha: a)
+    }
+    
+    func rotate(radians: Float) -> UIImage? {
+        var newSize = CGRect(origin: CGPoint.zero, size: self.size).applying(CGAffineTransform(rotationAngle: CGFloat(radians))).size
+        newSize.width = floor(newSize.width)
+        newSize.height = floor(newSize.height)
+        UIGraphicsBeginImageContextWithOptions(newSize, false, self.scale)
+        let context = UIGraphicsGetCurrentContext()!
+        context.translateBy(x: newSize.width/2, y: newSize.height/2)
+        context.rotate(by: CGFloat(radians))
+        self.draw(in: CGRect(x: -self.size.width/2, y: -self.size.height/2, width: self.size.width, height: self.size.height))
+        let newImage = UIGraphicsGetImageFromCurrentImageContext()
+        UIGraphicsEndImageContext()
+        return newImage
+    }
+    
+    func resize(targetSize: CGSize) -> UIImage {
+        let size = self.size
+        let widthRatio  = targetSize.width  / self.size.width
+        let heightRatio = targetSize.height / self.size.height
+        var newSize: CGSize
+        if(widthRatio > heightRatio) {
+            newSize = CGSize(width: size.width * heightRatio, height: size.height * heightRatio)
+        } else {
+            newSize = CGSize(width: size.width * widthRatio,  height: size.height * widthRatio)
+        }
+        let rect = CGRect(x: 0, y: 0, width: newSize.width, height: newSize.height)
+        UIGraphicsBeginImageContextWithOptions(newSize, false, 1.0)
+        self.draw(in: rect)
+        let newImage = UIGraphicsGetImageFromCurrentImageContext()
+        UIGraphicsEndImageContext()
+        return newImage!
+    }
+    
+    func toCVPixel() -> CVPixelBuffer {
+        let ciimage = CIImage(image: self)
+        let eaglContext = EAGLContext(api: .openGLES2)
+        let tmpcontext = CIContext(eaglContext: eaglContext!)
+        let cgimage = tmpcontext.createCGImage(ciimage!, from: ciimage!.extent)
+        return cgimage!.toCVPixelBuffer()!
+    }
+    
+}

OCR-SDK/Utils/ExtUiViewController.swift → OCR-SDK/Utils/Extension/ExtUiViewController.swift

@@ -37,4 +37,24 @@ extension UIViewController {
     func validateImage(image: Data) -> Bool {
         return true
     }
+    
+    func popupBackToCaptureCardFront() {
+        let alert = UIAlertController(title: "Confirm".localized(), message: "You will go back to front card photography.".localized(), preferredStyle: .alert)
+        
+        let actionClose = UIAlertAction(title: "No".localized(), style: .cancel, handler: nil)
+        alert.addAction(actionClose)
+        
+        let actionOk = UIAlertAction(title: "Yes".localized(), style: .default, handler: {
+            action in
+            for controller in self.navigationController!.viewControllers as Array {
+                if controller.isKind(of: SBKCaptureCardVC.self) {
+                    self.navigationController!.popToViewController(controller, animated: true)
+                    break
+                }
+            }
+        })
+        alert.addAction(actionOk)
+        
+        present(alert, animated: true, completion: nil)
+    }
 }

OCR-SDK/Utils/SBValidateInput.swift

@@ -57,9 +57,7 @@ class SBKValidateInput {
         guard (currentTimeMs - previousInferenceTimeMs) >= delayBetweenInferencesMs else { return .ERROR }
         previousInferenceTimeMs = currentTimeMs
         
-        // Pass the pixel buffer to TensorFlow Lite to perform inference.
         result = modelDataHandler?.runModel(onFrame: pixelBuffer)
-        
         if result == nil {
             return .ERROR
         }
@@ -72,7 +70,7 @@ class SBKValidateInput {
             return .IMAGE_BACK
         case 3:
             return .PASSPORT
-        case 4:
+        case 4,5,6,7,8,9:
             return .IMAGE_FAKE
         default:
             return .ERROR

Podfile

@@ -6,13 +6,45 @@ target 'OCR-SDK' do
   use_frameworks!
 
   # Pods for OCR-SDK
-  pod 'TensorFlowLiteSwift'
+  pod 'TensorFlowLiteSwift', '~> 0.0.1-nightly'
   #pod 'GoogleMobileVision/FaceDetector'
   #pod 'GTMSessionFetcher'
 end
 
+#post_install do |installer|
+#  installer.pods_project.build_configurations.each do |config|
+#    config.build_settings["EXCLUDED_ARCHS[sdk=iphonesimulator*]"] = "arm64"
+#    config.build_settings["BITCODE_GENERATION_MODE"] = "bitcode"
+#  end
+#end
+#bitcode enable
 post_install do |installer|
-  installer.pods_project.build_configurations.each do |config|
-    config.build_settings["EXCLUDED_ARCHS[sdk=iphonesimulator*]"] = "arm64"
+  installer.pods_project.targets.each do |target|
+    target.build_configurations.each do |config|
+
+      # set valid architecture
+      config.build_settings['VALID_ARCHS'] = 'arm64 armv7 armv7s i386 x86_64'
+
+      # build active architecture only (Debug build all)
+      config.build_settings['ONLY_ACTIVE_ARCH'] = 'NO'
+
+      config.build_settings['ENABLE_BITCODE'] = 'YES'
+
+      if config.name == 'Release' || config.name == 'Pro'
+          config.build_settings['BITCODE_GENERATION_MODE'] = 'bitcode'
+      else # Debug
+          config.build_settings['BITCODE_GENERATION_MODE'] = 'marker'
+      end
+
+      cflags = config.build_settings['OTHER_CFLAGS'] || ['$(inherited)']
+
+      if config.name == 'Release' || config.name == 'Pro'
+          cflags << '-fembed-bitcode'
+      else # Debug
+          cflags << '-fembed-bitcode-marker'
+      end
+
+      config.build_settings['OTHER_CFLAGS'] = cflags
+    end
   end
 end

Podfile.lock

 PODS:
-  - TensorFlowLiteC (2.2.0)
-  - TensorFlowLiteSwift (2.2.0):
-    - TensorFlowLiteC (= 2.2.0)
+  - TensorFlowLiteC (0.0.1-nightly.20211102):
+    - TensorFlowLiteC/Core (= 0.0.1-nightly.20211102)
+  - TensorFlowLiteC/Core (0.0.1-nightly.20211102)
+  - TensorFlowLiteSwift (0.0.1-nightly.20211102):
+    - TensorFlowLiteSwift/Core (= 0.0.1-nightly.20211102)
+  - TensorFlowLiteSwift/Core (0.0.1-nightly.20211102):
+    - TensorFlowLiteC (= 0.0.1-nightly.20211102)
 
 DEPENDENCIES:
-  - TensorFlowLiteSwift
+  - TensorFlowLiteSwift (~> 0.0.1-nightly)
 
 SPEC REPOS:
   trunk:
@@ -12,9 +16,9 @@ SPEC REPOS:
     - TensorFlowLiteSwift
 
 SPEC CHECKSUMS:
-  TensorFlowLiteC: b3ab9e867b0b71052ca102a32a786555b330b02e
-  TensorFlowLiteSwift: 2dd5e9c895e1819501f0fba3d8b69a536bda6c65
+  TensorFlowLiteC: 671507d2d839e633f3dc7ab464677d28857a37b6
+  TensorFlowLiteSwift: 039777297cdea077fcdd2f8a0f000c1d651ac45f
 
-PODFILE CHECKSUM: a8990648dc4761bcfc73655f0e8e51e3109f0e4f
+PODFILE CHECKSUM: 5d1bc9d5125d5fec48a2110d5d6596947a9bac74
 
-COCOAPODS: 1.10.1
+COCOAPODS: 1.10.2

Pods/Manifest.lock

 PODS:
-  - TensorFlowLiteC (2.2.0)
-  - TensorFlowLiteSwift (2.2.0):
-    - TensorFlowLiteC (= 2.2.0)
+  - TensorFlowLiteC (0.0.1-nightly.20211102):
+    - TensorFlowLiteC/Core (= 0.0.1-nightly.20211102)
+  - TensorFlowLiteC/Core (0.0.1-nightly.20211102)
+  - TensorFlowLiteSwift (0.0.1-nightly.20211102):
+    - TensorFlowLiteSwift/Core (= 0.0.1-nightly.20211102)
+  - TensorFlowLiteSwift/Core (0.0.1-nightly.20211102):
+    - TensorFlowLiteC (= 0.0.1-nightly.20211102)
 
 DEPENDENCIES:
-  - TensorFlowLiteSwift
+  - TensorFlowLiteSwift (~> 0.0.1-nightly)
 
 SPEC REPOS:
   trunk:
@@ -12,9 +16,9 @@ SPEC REPOS:
     - TensorFlowLiteSwift
 
 SPEC CHECKSUMS:
-  TensorFlowLiteC: b3ab9e867b0b71052ca102a32a786555b330b02e
-  TensorFlowLiteSwift: 2dd5e9c895e1819501f0fba3d8b69a536bda6c65
+  TensorFlowLiteC: 671507d2d839e633f3dc7ab464677d28857a37b6
+  TensorFlowLiteSwift: 039777297cdea077fcdd2f8a0f000c1d651ac45f
 
-PODFILE CHECKSUM: a8990648dc4761bcfc73655f0e8e51e3109f0e4f
+PODFILE CHECKSUM: 5d1bc9d5125d5fec48a2110d5d6596947a9bac74
 
-COCOAPODS: 1.10.1
+COCOAPODS: 1.10.2

Pods/Pods.xcodeproj/xcuserdata/vu.xcuserdatad/xcschemes/Pods-OCR-SDK.xcscheme → Pods/Pods.xcodeproj/xcuserdata/ad.xcuserdatad/xcschemes/Pods-OCR-SDK.xcscheme

 <?xml version="1.0" encoding="UTF-8"?>
 <Scheme
-   LastUpgradeVersion = "1100"
+   LastUpgradeVersion = "1240"
    version = "1.3">
    <BuildAction
       parallelizeBuildables = "YES"

Pods/Pods.xcodeproj/xcuserdata/itsol.xcuserdatad/xcschemes/Pods-OCR-SDK.xcscheme → Pods/Pods.xcodeproj/xcuserdata/ad.xcuserdatad/xcschemes/TensorFlowLiteC.xcscheme

 <?xml version="1.0" encoding="UTF-8"?>
 <Scheme
-   LastUpgradeVersion = "1100"
+   LastUpgradeVersion = "1240"
    version = "1.3">
    <BuildAction
       parallelizeBuildables = "YES"
@@ -14,9 +14,9 @@
             buildForAnalyzing = "YES">
             <BuildableReference
                BuildableIdentifier = "primary"
-               BlueprintIdentifier = "DE1F4D51AD94C30627575AEE202FD099"
-               BuildableName = "Pods_OCR_SDK.framework"
-               BlueprintName = "Pods-OCR-SDK"
+               BlueprintIdentifier = "AC559E53E13B6FBEF4F5CC310A73AFE6"
+               BuildableName = "TensorFlowLiteC"
+               BlueprintName = "TensorFlowLiteC"
                ReferencedContainer = "container:Pods.xcodeproj">
             </BuildableReference>
          </BuildActionEntry>

Pods/Pods.xcodeproj/xcuserdata/vu.xcuserdatad/xcschemes/TensorFlowLiteSwift.xcscheme → Pods/Pods.xcodeproj/xcuserdata/ad.xcuserdatad/xcschemes/TensorFlowLiteSwift.xcscheme

 <?xml version="1.0" encoding="UTF-8"?>
 <Scheme
-   LastUpgradeVersion = "1100"
+   LastUpgradeVersion = "1240"
    version = "1.3">
    <BuildAction
       parallelizeBuildables = "YES"

Pods/Pods.xcodeproj/xcuserdata/itsol.xcuserdatad/xcschemes/TensorFlowLiteC.xcscheme

-<?xml version="1.0" encoding="UTF-8"?>
-<Scheme
-   LastUpgradeVersion = "1100"
-   version = "1.3">
-   <BuildAction
-      parallelizeBuildables = "YES"
-      buildImplicitDependencies = "YES">
-      <BuildActionEntries>
-         <BuildActionEntry
-            buildForAnalyzing = "YES"
-            buildForTesting = "YES"
-            buildForRunning = "YES"
-            buildForProfiling = "YES"
-            buildForArchiving = "YES">
-            <BuildableReference
-               BuildableIdentifier = "primary"
-               BlueprintIdentifier = "AC559E53E13B6FBEF4F5CC310A73AFE6"
-               BuildableName = "TensorFlowLiteC"
-               BlueprintName = "TensorFlowLiteC"
-               ReferencedContainer = "container:Pods.xcodeproj">
-            </BuildableReference>
-         </BuildActionEntry>
-      </BuildActionEntries>
-   </BuildAction>
-   <TestAction
-      selectedDebuggerIdentifier = "Xcode.DebuggerFoundation.Debugger.LLDB"
-      selectedLauncherIdentifier = "Xcode.DebuggerFoundation.Launcher.LLDB"
-      shouldUseLaunchSchemeArgsEnv = "YES"
-      buildConfiguration = "Debug">
-      <AdditionalOptions>
-      </AdditionalOptions>
-   </TestAction>
-   <LaunchAction
-      selectedDebuggerIdentifier = "Xcode.DebuggerFoundation.Debugger.LLDB"
-      selectedLauncherIdentifier = "Xcode.DebuggerFoundation.Launcher.LLDB"
-      launchStyle = "0"
-      useCustomWorkingDirectory = "NO"
-      ignoresPersistentStateOnLaunch = "NO"
-      debugDocumentVersioning = "YES"
-      debugServiceExtension = "internal"
-      buildConfiguration = "Debug"
-      allowLocationSimulation = "YES">
-      <AdditionalOptions>
-      </AdditionalOptions>
-   </LaunchAction>
-   <ProfileAction
-      savedToolIdentifier = ""
-      useCustomWorkingDirectory = "NO"
-      debugDocumentVersioning = "YES"
-      buildConfiguration = "Release"
-      shouldUseLaunchSchemeArgsEnv = "YES">
-   </ProfileAction>
-   <AnalyzeAction
-      buildConfiguration = "Debug">
-   </AnalyzeAction>
-   <ArchiveAction
-      buildConfiguration = "Release"
-      revealArchiveInOrganizer = "YES">
-   </ArchiveAction>
-</Scheme>

Pods/Pods.xcodeproj/xcuserdata/itsol.xcuserdatad/xcschemes/TensorFlowLiteSwift.xcscheme

-<?xml version="1.0" encoding="UTF-8"?>
-<Scheme
-   LastUpgradeVersion = "1100"
-   version = "1.3">
-   <BuildAction
-      parallelizeBuildables = "YES"
-      buildImplicitDependencies = "YES">
-      <BuildActionEntries>
-         <BuildActionEntry
-            buildForTesting = "YES"
-            buildForRunning = "YES"
-            buildForProfiling = "YES"
-            buildForArchiving = "YES"
-            buildForAnalyzing = "YES">
-            <BuildableReference
-               BuildableIdentifier = "primary"
-               BlueprintIdentifier = "10418167F619D6DA72BADAD10F9EC02B"
-               BuildableName = "TensorFlowLite.framework"
-               BlueprintName = "TensorFlowLiteSwift"
-               ReferencedContainer = "container:Pods.xcodeproj">
-            </BuildableReference>
-         </BuildActionEntry>
-      </BuildActionEntries>
-   </BuildAction>
-   <TestAction
-      buildConfiguration = "Debug"
-      selectedDebuggerIdentifier = "Xcode.DebuggerFoundation.Debugger.LLDB"
-      selectedLauncherIdentifier = "Xcode.DebuggerFoundation.Launcher.LLDB"
-      shouldUseLaunchSchemeArgsEnv = "YES">
-      <Testables>
-      </Testables>
-   </TestAction>
-   <LaunchAction
-      buildConfiguration = "Debug"
-      selectedDebuggerIdentifier = "Xcode.DebuggerFoundation.Debugger.LLDB"
-      selectedLauncherIdentifier = "Xcode.DebuggerFoundation.Launcher.LLDB"
-      launchStyle = "0"
-      useCustomWorkingDirectory = "NO"
-      ignoresPersistentStateOnLaunch = "NO"
-      debugDocumentVersioning = "YES"
-      debugServiceExtension = "internal"
-      allowLocationSimulation = "YES">
-   </LaunchAction>
-   <ProfileAction
-      buildConfiguration = "Release"
-      shouldUseLaunchSchemeArgsEnv = "YES"
-      savedToolIdentifier = ""
-      useCustomWorkingDirectory = "NO"
-      debugDocumentVersioning = "YES">
-   </ProfileAction>
-   <AnalyzeAction
-      buildConfiguration = "Debug">
-   </AnalyzeAction>
-   <ArchiveAction
-      buildConfiguration = "Release"
-      revealArchiveInOrganizer = "YES">
-   </ArchiveAction>
-</Scheme>

Pods/Pods.xcodeproj/xcuserdata/nguyenanh.xcuserdatad/xcschemes/xcschememanagement.plist

-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-	<key>SchemeUserState</key>
-	<dict>
-		<key>Pods-OCR-SDK.xcscheme_^#shared#^_</key>
-		<dict>
-			<key>orderHint</key>
-			<integer>3</integer>
-		</dict>
-		<key>TensorFlowLiteC.xcscheme_^#shared#^_</key>
-		<dict>
-			<key>orderHint</key>
-			<integer>1</integer>
-		</dict>
-		<key>TensorFlowLiteSwift.xcscheme_^#shared#^_</key>
-		<dict>
-			<key>orderHint</key>
-			<integer>2</integer>
-		</dict>
-	</dict>
-</dict>
-</plist>

Pods/Pods.xcodeproj/xcuserdata/nguyenvanan.xcuserdatad/xcschemes/xcschememanagement.plist

-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-	<key>SchemeUserState</key>
-	<dict>
-		<key>Pods-OCR-SDK.xcscheme_^#shared#^_</key>
-		<dict>
-			<key>orderHint</key>
-			<integer>2</integer>
-		</dict>
-		<key>TensorFlowLiteC.xcscheme_^#shared#^_</key>
-		<dict>
-			<key>orderHint</key>
-			<integer>1</integer>
-		</dict>
-		<key>TensorFlowLiteSwift.xcscheme_^#shared#^_</key>
-		<dict>
-			<key>orderHint</key>
-			<integer>3</integer>
-		</dict>
-	</dict>
-</dict>
-</plist>

Pods/Pods.xcodeproj/xcuserdata/vu.xcuserdatad/xcschemes/TensorFlowLiteC.xcscheme

-<?xml version="1.0" encoding="UTF-8"?>
-<Scheme
-   LastUpgradeVersion = "1100"
-   version = "1.3">
-   <BuildAction
-      parallelizeBuildables = "YES"
-      buildImplicitDependencies = "YES">
-      <BuildActionEntries>
-         <BuildActionEntry
-            buildForAnalyzing = "YES"
-            buildForTesting = "YES"
-            buildForRunning = "YES"
-            buildForProfiling = "YES"
-            buildForArchiving = "YES">
-            <BuildableReference
-               BuildableIdentifier = "primary"
-               BlueprintIdentifier = "AC559E53E13B6FBEF4F5CC310A73AFE6"
-               BuildableName = "TensorFlowLiteC"
-               BlueprintName = "TensorFlowLiteC"
-               ReferencedContainer = "container:Pods.xcodeproj">
-            </BuildableReference>
-         </BuildActionEntry>
-      </BuildActionEntries>
-   </BuildAction>
-   <TestAction
-      selectedDebuggerIdentifier = "Xcode.DebuggerFoundation.Debugger.LLDB"
-      selectedLauncherIdentifier = "Xcode.DebuggerFoundation.Launcher.LLDB"
-      shouldUseLaunchSchemeArgsEnv = "YES"
-      buildConfiguration = "Debug">
-      <AdditionalOptions>
-      </AdditionalOptions>
-   </TestAction>
-   <LaunchAction
-      selectedDebuggerIdentifier = "Xcode.DebuggerFoundation.Debugger.LLDB"
-      selectedLauncherIdentifier = "Xcode.DebuggerFoundation.Launcher.LLDB"
-      launchStyle = "0"
-      useCustomWorkingDirectory = "NO"
-      ignoresPersistentStateOnLaunch = "NO"
-      debugDocumentVersioning = "YES"
-      debugServiceExtension = "internal"
-      buildConfiguration = "Debug"
-      allowLocationSimulation = "YES">
-      <AdditionalOptions>
-      </AdditionalOptions>
-   </LaunchAction>
-   <ProfileAction
-      savedToolIdentifier = ""
-      useCustomWorkingDirectory = "NO"
-      debugDocumentVersioning = "YES"
-      buildConfiguration = "Release"
-      shouldUseLaunchSchemeArgsEnv = "YES">
-   </ProfileAction>
-   <AnalyzeAction
-      buildConfiguration = "Debug">
-   </AnalyzeAction>
-   <ArchiveAction
-      buildConfiguration = "Release"
-      revealArchiveInOrganizer = "YES">
-   </ArchiveAction>
-</Scheme>

Pods/Pods.xcodeproj/xcuserdata/vu.xcuserdatad/xcschemes/xcschememanagement.plist

-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-	<key>SchemeUserState</key>
-	<dict>
-		<key>Pods-OCR-SDK.xcscheme</key>
-		<dict>
-			<key>isShown</key>
-			<false/>
-		</dict>
-		<key>TensorFlowLiteC.xcscheme</key>
-		<dict>
-			<key>isShown</key>
-			<false/>
-		</dict>
-		<key>TensorFlowLiteSwift.xcscheme</key>
-		<dict>
-			<key>isShown</key>
-			<false/>
-		</dict>
-	</dict>
-	<key>SuppressBuildableAutocreation</key>
-	<dict/>
-</dict>
-</plist>

Pods/Target Support Files/Pods-OCR-SDK/Pods-OCR-SDK-acknowledgements.markdown

@@ -3,8 +3,6 @@ This application makes use of the following third party libraries:
 
 ## TensorFlowLiteSwift
 
-Copyright 2019 The TensorFlow Authors.  All rights reserved.
-
                                  Apache License
                            Version 2.0, January 2004
                         http://www.apache.org/licenses/
@@ -207,4 +205,87 @@ Copyright 2019 The TensorFlow Authors.  All rights reserved.
    See the License for the specific language governing permissions and
    limitations under the License.
 
+------------------
+Files: third_party/compute_library/...
+
+MIT License
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+------------------
+Files: ACKNOWLEDGEMENTS
+LICENSE
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+   ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+   WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+   DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+   ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+   (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+   LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+   ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+   (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+   SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+------------------
+Files: third_party/hexagon
+
+Copyright (c) 2016-2019, The Linux Foundation. All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted (subject to the limitations in the
+disclaimer below) provided that the following conditions are met:
+
+   * Redistributions of source code must retain the above copyright
+     notice, this list of conditions and the following disclaimer.
+
+   * Redistributions in binary form must reproduce the above
+     copyright notice, this list of conditions and the following
+     disclaimer in the documentation and/or other materials provided
+     with the distribution.
+
+   * Neither the name of The Linux Foundation nor the names of its
+     contributors may be used to endorse or promote products derived
+     from this software without specific prior written permission.
+
+NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE
+GRANTED BY THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT
+HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED
+WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
+MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
+ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
+GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER
+IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
+OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
+IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
 Generated by CocoaPods - https://cocoapods.org

Pods/Target Support Files/Pods-OCR-SDK/Pods-OCR-SDK-acknowledgements.plist

@@ -14,9 +14,7 @@
 		</dict>
 		<dict>
 			<key>FooterText</key>
-			<string>Copyright 2019 The TensorFlow Authors.  All rights reserved.
-
-                                 Apache License
+			<string>                                 Apache License
                            Version 2.0, January 2004
                         http://www.apache.org/licenses/
 
@@ -217,6 +215,89 @@
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    See the License for the specific language governing permissions and
    limitations under the License.
+
+------------------
+Files: third_party/compute_library/...
+
+MIT License
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+------------------
+Files: ACKNOWLEDGEMENTS
+LICENSE
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+   ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+   WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+   DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+   ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+   (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+   LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+   ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+   (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+   SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+------------------
+Files: third_party/hexagon
+
+Copyright (c) 2016-2019, The Linux Foundation. All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted (subject to the limitations in the
+disclaimer below) provided that the following conditions are met:
+
+   * Redistributions of source code must retain the above copyright
+     notice, this list of conditions and the following disclaimer.
+
+   * Redistributions in binary form must reproduce the above
+     copyright notice, this list of conditions and the following
+     disclaimer in the documentation and/or other materials provided
+     with the distribution.
+
+   * Neither the name of The Linux Foundation nor the names of its
+     contributors may be used to endorse or promote products derived
+     from this software without specific prior written permission.
+
+NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE
+GRANTED BY THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT
+HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED
+WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
+MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
+ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
+GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER
+IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
+OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
+IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 </string>
 			<key>License</key>
 			<string>Apache</string>

Pods/Target Support Files/Pods-OCR-SDK/Pods-OCR-SDK-resources-Debug-input-files.xcfilelist

-${PODS_ROOT}/Target Support Files/Pods-OCR-SDK/Pods-OCR-SDK-resources.sh
-${PODS_CONFIGURATION_BUILD_DIR}/GoogleMobileVision/GoogleMVFaceDetectorResources.bundle
\ No newline at end of file

Pods/Target Support Files/Pods-OCR-SDK/Pods-OCR-SDK-resources-Debug-output-files.xcfilelist

-${TARGET_BUILD_DIR}/${UNLOCALIZED_RESOURCES_FOLDER_PATH}/GoogleMVFaceDetectorResources.bundle
\ No newline at end of file

Pods/Target Support Files/Pods-OCR-SDK/Pods-OCR-SDK-resources-Release-input-files.xcfilelist

-${PODS_ROOT}/Target Support Files/Pods-OCR-SDK/Pods-OCR-SDK-resources.sh
-${PODS_CONFIGURATION_BUILD_DIR}/GoogleMobileVision/GoogleMVFaceDetectorResources.bundle
\ No newline at end of file

Pods/Target Support Files/Pods-OCR-SDK/Pods-OCR-SDK-resources-Release-output-files.xcfilelist

-${TARGET_BUILD_DIR}/${UNLOCALIZED_RESOURCES_FOLDER_PATH}/GoogleMVFaceDetectorResources.bundle
\ No newline at end of file

Pods/Target Support Files/Pods-OCR-SDK/Pods-OCR-SDK-resources.sh

-#!/bin/sh
-set -e
-set -u
-set -o pipefail
-
-function on_error {
-  echo "$(realpath -mq "${0}"):$1: error: Unexpected failure"
-}
-trap 'on_error $LINENO' ERR
-
-if [ -z ${UNLOCALIZED_RESOURCES_FOLDER_PATH+x} ]; then
-  # If UNLOCALIZED_RESOURCES_FOLDER_PATH is not set, then there's nowhere for us to copy
-  # resources to, so exit 0 (signalling the script phase was successful).
-  exit 0
-fi
-
-mkdir -p "${TARGET_BUILD_DIR}/${UNLOCALIZED_RESOURCES_FOLDER_PATH}"
-
-RESOURCES_TO_COPY=${PODS_ROOT}/resources-to-copy-${TARGETNAME}.txt
-> "$RESOURCES_TO_COPY"
-
-XCASSET_FILES=()
-
-# This protects against multiple targets copying the same framework dependency at the same time. The solution
-# was originally proposed here: https://lists.samba.org/archive/rsync/2008-February/020158.html
-RSYNC_PROTECT_TMP_FILES=(--filter "P .*.??????")
-
-case "${TARGETED_DEVICE_FAMILY:-}" in
-  1,2)
-    TARGET_DEVICE_ARGS="--target-device ipad --target-device iphone"
-    ;;
-  1)
-    TARGET_DEVICE_ARGS="--target-device iphone"
-    ;;
-  2)
-    TARGET_DEVICE_ARGS="--target-device ipad"
-    ;;
-  3)
-    TARGET_DEVICE_ARGS="--target-device tv"
-    ;;
-  4)
-    TARGET_DEVICE_ARGS="--target-device watch"
-    ;;
-  *)
-    TARGET_DEVICE_ARGS="--target-device mac"
-    ;;
-esac
-
-install_resource()
-{
-  if [[ "$1" = /* ]] ; then
-    RESOURCE_PATH="$1"
-  else
-    RESOURCE_PATH="${PODS_ROOT}/$1"
-  fi
-  if [[ ! -e "$RESOURCE_PATH" ]] ; then
-    cat << EOM
-error: Resource "$RESOURCE_PATH" not found. Run 'pod install' to update the copy resources script.
-EOM
-    exit 1
-  fi
-  case $RESOURCE_PATH in
-    *.storyboard)
-      echo "ibtool --reference-external-strings-file --errors --warnings --notices --minimum-deployment-target ${!DEPLOYMENT_TARGET_SETTING_NAME} --output-format human-readable-text --compile ${TARGET_BUILD_DIR}/${UNLOCALIZED_RESOURCES_FOLDER_PATH}/`basename \"$RESOURCE_PATH\" .storyboard`.storyboardc $RESOURCE_PATH --sdk ${SDKROOT} ${TARGET_DEVICE_ARGS}" || true
-      ibtool --reference-external-strings-file --errors --warnings --notices --minimum-deployment-target ${!DEPLOYMENT_TARGET_SETTING_NAME} --output-format human-readable-text --compile "${TARGET_BUILD_DIR}/${UNLOCALIZED_RESOURCES_FOLDER_PATH}/`basename \"$RESOURCE_PATH\" .storyboard`.storyboardc" "$RESOURCE_PATH" --sdk "${SDKROOT}" ${TARGET_DEVICE_ARGS}
-      ;;
-    *.xib)
-      echo "ibtool --reference-external-strings-file --errors --warnings --notices --minimum-deployment-target ${!DEPLOYMENT_TARGET_SETTING_NAME} --output-format human-readable-text --compile ${TARGET_BUILD_DIR}/${UNLOCALIZED_RESOURCES_FOLDER_PATH}/`basename \"$RESOURCE_PATH\" .xib`.nib $RESOURCE_PATH --sdk ${SDKROOT} ${TARGET_DEVICE_ARGS}" || true
-      ibtool --reference-external-strings-file --errors --warnings --notices --minimum-deployment-target ${!DEPLOYMENT_TARGET_SETTING_NAME} --output-format human-readable-text --compile "${TARGET_BUILD_DIR}/${UNLOCALIZED_RESOURCES_FOLDER_PATH}/`basename \"$RESOURCE_PATH\" .xib`.nib" "$RESOURCE_PATH" --sdk "${SDKROOT}" ${TARGET_DEVICE_ARGS}
-      ;;
-    *.framework)
-      echo "mkdir -p ${TARGET_BUILD_DIR}/${FRAMEWORKS_FOLDER_PATH}" || true
-      mkdir -p "${TARGET_BUILD_DIR}/${FRAMEWORKS_FOLDER_PATH}"
-      echo "rsync --delete -av "${RSYNC_PROTECT_TMP_FILES[@]}" $RESOURCE_PATH ${TARGET_BUILD_DIR}/${FRAMEWORKS_FOLDER_PATH}" || true
-      rsync --delete -av "${RSYNC_PROTECT_TMP_FILES[@]}" "$RESOURCE_PATH" "${TARGET_BUILD_DIR}/${FRAMEWORKS_FOLDER_PATH}"
-      ;;
-    *.xcdatamodel)
-      echo "xcrun momc \"$RESOURCE_PATH\" \"${TARGET_BUILD_DIR}/${UNLOCALIZED_RESOURCES_FOLDER_PATH}/`basename "$RESOURCE_PATH"`.mom\"" || true
-      xcrun momc "$RESOURCE_PATH" "${TARGET_BUILD_DIR}/${UNLOCALIZED_RESOURCES_FOLDER_PATH}/`basename "$RESOURCE_PATH" .xcdatamodel`.mom"
-      ;;
-    *.xcdatamodeld)
-      echo "xcrun momc \"$RESOURCE_PATH\" \"${TARGET_BUILD_DIR}/${UNLOCALIZED_RESOURCES_FOLDER_PATH}/`basename "$RESOURCE_PATH" .xcdatamodeld`.momd\"" || true
-      xcrun momc "$RESOURCE_PATH" "${TARGET_BUILD_DIR}/${UNLOCALIZED_RESOURCES_FOLDER_PATH}/`basename "$RESOURCE_PATH" .xcdatamodeld`.momd"
-      ;;
-    *.xcmappingmodel)
-      echo "xcrun mapc \"$RESOURCE_PATH\" \"${TARGET_BUILD_DIR}/${UNLOCALIZED_RESOURCES_FOLDER_PATH}/`basename "$RESOURCE_PATH" .xcmappingmodel`.cdm\"" || true
-      xcrun mapc "$RESOURCE_PATH" "${TARGET_BUILD_DIR}/${UNLOCALIZED_RESOURCES_FOLDER_PATH}/`basename "$RESOURCE_PATH" .xcmappingmodel`.cdm"
-      ;;
-    *.xcassets)
-      ABSOLUTE_XCASSET_FILE="$RESOURCE_PATH"
-      XCASSET_FILES+=("$ABSOLUTE_XCASSET_FILE")
-      ;;
-    *)
-      echo "$RESOURCE_PATH" || true
-      echo "$RESOURCE_PATH" >> "$RESOURCES_TO_COPY"
-      ;;
-  esac
-}
-if [[ "$CONFIGURATION" == "Debug" ]]; then
-  install_resource "${PODS_CONFIGURATION_BUILD_DIR}/GoogleMobileVision/GoogleMVFaceDetectorResources.bundle"
-fi
-if [[ "$CONFIGURATION" == "Release" ]]; then
-  install_resource "${PODS_CONFIGURATION_BUILD_DIR}/GoogleMobileVision/GoogleMVFaceDetectorResources.bundle"
-fi
-
-mkdir -p "${TARGET_BUILD_DIR}/${UNLOCALIZED_RESOURCES_FOLDER_PATH}"
-rsync -avr --copy-links --no-relative --exclude '*/.svn/*' --files-from="$RESOURCES_TO_COPY" / "${TARGET_BUILD_DIR}/${UNLOCALIZED_RESOURCES_FOLDER_PATH}"
-if [[ "${ACTION}" == "install" ]] && [[ "${SKIP_INSTALL}" == "NO" ]]; then
-  mkdir -p "${INSTALL_DIR}/${UNLOCALIZED_RESOURCES_FOLDER_PATH}"
-  rsync -avr --copy-links --no-relative --exclude '*/.svn/*' --files-from="$RESOURCES_TO_COPY" / "${INSTALL_DIR}/${UNLOCALIZED_RESOURCES_FOLDER_PATH}"
-fi
-rm -f "$RESOURCES_TO_COPY"
-
-if [[ -n "${WRAPPER_EXTENSION}" ]] && [ "`xcrun --find actool`" ] && [ -n "${XCASSET_FILES:-}" ]
-then
-  # Find all other xcassets (this unfortunately includes those of path pods and other targets).
-  OTHER_XCASSETS=$(find -L "$PWD" -iname "*.xcassets" -type d)
-  while read line; do
-    if [[ $line != "${PODS_ROOT}*" ]]; then
-      XCASSET_FILES+=("$line")
-    fi
-  done <<<"$OTHER_XCASSETS"
-
-  if [ -z ${ASSETCATALOG_COMPILER_APPICON_NAME+x} ]; then
-    printf "%s\0" "${XCASSET_FILES[@]}" | xargs -0 xcrun actool --output-format human-readable-text --notices --warnings --platform "${PLATFORM_NAME}" --minimum-deployment-target "${!DEPLOYMENT_TARGET_SETTING_NAME}" ${TARGET_DEVICE_ARGS} --compress-pngs --compile "${BUILT_PRODUCTS_DIR}/${UNLOCALIZED_RESOURCES_FOLDER_PATH}"
-  else
-    printf "%s\0" "${XCASSET_FILES[@]}" | xargs -0 xcrun actool --output-format human-readable-text --notices --warnings --platform "${PLATFORM_NAME}" --minimum-deployment-target "${!DEPLOYMENT_TARGET_SETTING_NAME}" ${TARGET_DEVICE_ARGS} --compress-pngs --compile "${BUILT_PRODUCTS_DIR}/${UNLOCALIZED_RESOURCES_FOLDER_PATH}" --app-icon "${ASSETCATALOG_COMPILER_APPICON_NAME}" --output-partial-info-plist "${TARGET_TEMP_DIR}/assetcatalog_generated_info_cocoapods.plist"
-  fi
-fi

Pods/Target Support Files/TensorFlowLiteSwift/TensorFlowLiteSwift-Info.plist

@@ -15,7 +15,7 @@
   <key>CFBundlePackageType</key>
   <string>FMWK</string>
   <key>CFBundleShortVersionString</key>
-  <string>2.2.0</string>
+  <string>0.0.1</string>
   <key>CFBundleSignature</key>
   <string>????</string>
   <key>CFBundleVersion</key>

Pods/TensorFlowLiteC/Frameworks/TensorFlowLiteC.framework/Headers/TensorFlowLiteC.h

+#import "builtin_ops.h"
 #import "c_api.h"
+#import "c_api_experimental.h"
 #import "common.h"
-#import "metal_delegate.h"
+#import "xnnpack_delegate.h"
+#import "c_api_types.h"

Pods/TensorFlowLiteC/Frameworks/TensorFlowLiteC.framework/Headers/builtin_ops.h

+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_BUILTIN_OPS_H_
+#define TENSORFLOW_LITE_BUILTIN_OPS_H_
+
+// DO NOT EDIT MANUALLY: This file is automatically generated by
+// `schema/builtin_ops_header/generator.cc`.
+
+#ifdef __cplusplus
+extern "C" {
+#endif  // __cplusplus
+
+// The enum for builtin operators.
+// Note: CUSTOM, DELEGATE, and PLACEHOLDER_FOR_GREATER_OP_CODES are 3 special
+// ops which are not real built-in ops.
+typedef enum {
+  kTfLiteBuiltinAdd = 0,
+  kTfLiteBuiltinAveragePool2d = 1,
+  kTfLiteBuiltinConcatenation = 2,
+  kTfLiteBuiltinConv2d = 3,
+  kTfLiteBuiltinDepthwiseConv2d = 4,
+  kTfLiteBuiltinDepthToSpace = 5,
+  kTfLiteBuiltinDequantize = 6,
+  kTfLiteBuiltinEmbeddingLookup = 7,
+  kTfLiteBuiltinFloor = 8,
+  kTfLiteBuiltinFullyConnected = 9,
+  kTfLiteBuiltinHashtableLookup = 10,
+  kTfLiteBuiltinL2Normalization = 11,
+  kTfLiteBuiltinL2Pool2d = 12,
+  kTfLiteBuiltinLocalResponseNormalization = 13,
+  kTfLiteBuiltinLogistic = 14,
+  kTfLiteBuiltinLshProjection = 15,
+  kTfLiteBuiltinLstm = 16,
+  kTfLiteBuiltinMaxPool2d = 17,
+  kTfLiteBuiltinMul = 18,
+  kTfLiteBuiltinRelu = 19,
+  kTfLiteBuiltinReluN1To1 = 20,
+  kTfLiteBuiltinRelu6 = 21,
+  kTfLiteBuiltinReshape = 22,
+  kTfLiteBuiltinResizeBilinear = 23,
+  kTfLiteBuiltinRnn = 24,
+  kTfLiteBuiltinSoftmax = 25,
+  kTfLiteBuiltinSpaceToDepth = 26,
+  kTfLiteBuiltinSvdf = 27,
+  kTfLiteBuiltinTanh = 28,
+  kTfLiteBuiltinConcatEmbeddings = 29,
+  kTfLiteBuiltinSkipGram = 30,
+  kTfLiteBuiltinCall = 31,
+  kTfLiteBuiltinCustom = 32,
+  kTfLiteBuiltinEmbeddingLookupSparse = 33,
+  kTfLiteBuiltinPad = 34,
+  kTfLiteBuiltinUnidirectionalSequenceRnn = 35,
+  kTfLiteBuiltinGather = 36,
+  kTfLiteBuiltinBatchToSpaceNd = 37,
+  kTfLiteBuiltinSpaceToBatchNd = 38,
+  kTfLiteBuiltinTranspose = 39,
+  kTfLiteBuiltinMean = 40,
+  kTfLiteBuiltinSub = 41,
+  kTfLiteBuiltinDiv = 42,
+  kTfLiteBuiltinSqueeze = 43,
+  kTfLiteBuiltinUnidirectionalSequenceLstm = 44,
+  kTfLiteBuiltinStridedSlice = 45,
+  kTfLiteBuiltinBidirectionalSequenceRnn = 46,
+  kTfLiteBuiltinExp = 47,
+  kTfLiteBuiltinTopkV2 = 48,
+  kTfLiteBuiltinSplit = 49,
+  kTfLiteBuiltinLogSoftmax = 50,
+  kTfLiteBuiltinDelegate = 51,
+  kTfLiteBuiltinBidirectionalSequenceLstm = 52,
+  kTfLiteBuiltinCast = 53,
+  kTfLiteBuiltinPrelu = 54,
+  kTfLiteBuiltinMaximum = 55,
+  kTfLiteBuiltinArgMax = 56,
+  kTfLiteBuiltinMinimum = 57,
+  kTfLiteBuiltinLess = 58,
+  kTfLiteBuiltinNeg = 59,
+  kTfLiteBuiltinPadv2 = 60,
+  kTfLiteBuiltinGreater = 61,
+  kTfLiteBuiltinGreaterEqual = 62,
+  kTfLiteBuiltinLessEqual = 63,
+  kTfLiteBuiltinSelect = 64,
+  kTfLiteBuiltinSlice = 65,
+  kTfLiteBuiltinSin = 66,
+  kTfLiteBuiltinTransposeConv = 67,
+  kTfLiteBuiltinSparseToDense = 68,
+  kTfLiteBuiltinTile = 69,
+  kTfLiteBuiltinExpandDims = 70,
+  kTfLiteBuiltinEqual = 71,
+  kTfLiteBuiltinNotEqual = 72,
+  kTfLiteBuiltinLog = 73,
+  kTfLiteBuiltinSum = 74,
+  kTfLiteBuiltinSqrt = 75,
+  kTfLiteBuiltinRsqrt = 76,
+  kTfLiteBuiltinShape = 77,
+  kTfLiteBuiltinPow = 78,
+  kTfLiteBuiltinArgMin = 79,
+  kTfLiteBuiltinFakeQuant = 80,
+  kTfLiteBuiltinReduceProd = 81,
+  kTfLiteBuiltinReduceMax = 82,
+  kTfLiteBuiltinPack = 83,
+  kTfLiteBuiltinLogicalOr = 84,
+  kTfLiteBuiltinOneHot = 85,
+  kTfLiteBuiltinLogicalAnd = 86,
+  kTfLiteBuiltinLogicalNot = 87,
+  kTfLiteBuiltinUnpack = 88,
+  kTfLiteBuiltinReduceMin = 89,
+  kTfLiteBuiltinFloorDiv = 90,
+  kTfLiteBuiltinReduceAny = 91,
+  kTfLiteBuiltinSquare = 92,
+  kTfLiteBuiltinZerosLike = 93,
+  kTfLiteBuiltinFill = 94,
+  kTfLiteBuiltinFloorMod = 95,
+  kTfLiteBuiltinRange = 96,
+  kTfLiteBuiltinResizeNearestNeighbor = 97,
+  kTfLiteBuiltinLeakyRelu = 98,
+  kTfLiteBuiltinSquaredDifference = 99,
+  kTfLiteBuiltinMirrorPad = 100,
+  kTfLiteBuiltinAbs = 101,
+  kTfLiteBuiltinSplitV = 102,
+  kTfLiteBuiltinUnique = 103,
+  kTfLiteBuiltinCeil = 104,
+  kTfLiteBuiltinReverseV2 = 105,
+  kTfLiteBuiltinAddN = 106,
+  kTfLiteBuiltinGatherNd = 107,
+  kTfLiteBuiltinCos = 108,
+  kTfLiteBuiltinWhere = 109,
+  kTfLiteBuiltinRank = 110,
+  kTfLiteBuiltinElu = 111,
+  kTfLiteBuiltinReverseSequence = 112,
+  kTfLiteBuiltinMatrixDiag = 113,
+  kTfLiteBuiltinQuantize = 114,
+  kTfLiteBuiltinMatrixSetDiag = 115,
+  kTfLiteBuiltinRound = 116,
+  kTfLiteBuiltinHardSwish = 117,
+  kTfLiteBuiltinIf = 118,
+  kTfLiteBuiltinWhile = 119,
+  kTfLiteBuiltinNonMaxSuppressionV4 = 120,
+  kTfLiteBuiltinNonMaxSuppressionV5 = 121,
+  kTfLiteBuiltinScatterNd = 122,
+  kTfLiteBuiltinSelectV2 = 123,
+  kTfLiteBuiltinDensify = 124,
+  kTfLiteBuiltinSegmentSum = 125,
+  kTfLiteBuiltinBatchMatmul = 126,
+  kTfLiteBuiltinPlaceholderForGreaterOpCodes = 127,
+  kTfLiteBuiltinCumsum = 128,
+  kTfLiteBuiltinCallOnce = 129,
+  kTfLiteBuiltinBroadcastTo = 130,
+  kTfLiteBuiltinRfft2d = 131,
+  kTfLiteBuiltinConv3d = 132,
+  kTfLiteBuiltinImag = 133,
+  kTfLiteBuiltinReal = 134,
+  kTfLiteBuiltinComplexAbs = 135,
+  kTfLiteBuiltinHashtable = 136,
+  kTfLiteBuiltinHashtableFind = 137,
+  kTfLiteBuiltinHashtableImport = 138,
+  kTfLiteBuiltinHashtableSize = 139,
+  kTfLiteBuiltinReduceAll = 140,
+  kTfLiteBuiltinConv3dTranspose = 141,
+  kTfLiteBuiltinVarHandle = 142,
+  kTfLiteBuiltinReadVariable = 143,
+  kTfLiteBuiltinAssignVariable = 144,
+  kTfLiteBuiltinBroadcastArgs = 145,
+  kTfLiteBuiltinRandomStandardNormal = 146,
+  kTfLiteBuiltinBucketize = 147,
+  kTfLiteBuiltinRandomUniform = 148,
+} TfLiteBuiltinOperator;
+
+#ifdef __cplusplus
+}  // extern "C"
+#endif  // __cplusplus
+#endif  // TENSORFLOW_LITE_BUILTIN_OPS_H_

Pods/TensorFlowLiteC/Frameworks/TensorFlowLiteC.framework/Headers/c_api.h

@@ -17,8 +17,9 @@ limitations under the License.
 
 #include <stdarg.h>
 #include <stdint.h>
+#include <stdlib.h>
 
-#include "common.h"
+#include "c_api_types.h"  // IWYU pragma: export
 
 // --------------------------------------------------------------------------
 /// C API for TensorFlow Lite.
@@ -66,34 +67,39 @@ limitations under the License.
 /// TfLiteInterpreterOptionsDelete(options);
 /// TfLiteModelDelete(model);
 
-#ifdef SWIG
-#define TFL_CAPI_EXPORT
-#else
-#if defined(_WIN32)
-#ifdef TFL_COMPILE_LIBRARY
-#define TFL_CAPI_EXPORT __declspec(dllexport)
-#else
-#define TFL_CAPI_EXPORT __declspec(dllimport)
-#endif  // TFL_COMPILE_LIBRARY
-#else
-#define TFL_CAPI_EXPORT __attribute__((visibility("default")))
-#endif  // _WIN32
-#endif  // SWIG
-
 #ifdef __cplusplus
 extern "C" {
 #endif  // __cplusplus
 
 // --------------------------------------------------------------------------
-// TfLiteVersion returns a string describing version information of the
-// TensorFlow Lite library. TensorFlow Lite uses semantic versioning.
-TFL_CAPI_EXPORT extern const char* TfLiteVersion(void);
+// Opaque types used by the C API.
 
-// --------------------------------------------------------------------------
 // TfLiteModel wraps a loaded TensorFlow Lite model.
 typedef struct TfLiteModel TfLiteModel;
 
+// TfLiteInterpreterOptions allows customized interpreter configuration.
+typedef struct TfLiteInterpreterOptions TfLiteInterpreterOptions;
+
+// Allows delegation of nodes to alternative backends.
+typedef struct TfLiteDelegate TfLiteDelegate;
+
+// TfLiteInterpreter provides inference from a provided model.
+typedef struct TfLiteInterpreter TfLiteInterpreter;
+
+// A tensor in the interpreter system which is a wrapper around a buffer of
+// data including a dimensionality (or NULL if not currently defined).
+typedef struct TfLiteTensor TfLiteTensor;
+
+// --------------------------------------------------------------------------
+// TfLiteVersion returns a string describing version information of the
+// TensorFlow Lite library. TensorFlow Lite uses semantic versioning.
+TFL_CAPI_EXPORT extern const char* TfLiteVersion(void);
+
 // Returns a model from the provided buffer, or null on failure.
+//
+// NOTE: The caller retains ownership of the `model_data` and should ensure that
+// the lifetime of the `model_data` must be at least as long as the lifetime
+// of the `TfLiteModel`.
 TFL_CAPI_EXPORT extern TfLiteModel* TfLiteModelCreate(const void* model_data,
                                                       size_t model_size);
 
@@ -104,10 +110,6 @@ TFL_CAPI_EXPORT extern TfLiteModel* TfLiteModelCreateFromFile(
 // Destroys the model instance.
 TFL_CAPI_EXPORT extern void TfLiteModelDelete(TfLiteModel* model);
 
-// --------------------------------------------------------------------------
-// TfLiteInterpreterOptions allows customized interpreter configuration.
-typedef struct TfLiteInterpreterOptions TfLiteInterpreterOptions;
-
 // Returns a new interpreter options instances.
 TFL_CAPI_EXPORT extern TfLiteInterpreterOptions*
 TfLiteInterpreterOptionsCreate();
@@ -134,17 +136,13 @@ TFL_CAPI_EXPORT extern void TfLiteInterpreterOptionsAddDelegate(
 //
 // * `reporter` takes the provided `user_data` object, as well as a C-style
 //   format string and arg list (see also vprintf).
-// * `user_data` is optional. If provided, it is owned by the client and must
+// * `user_data` is optional. If non-null, it is owned by the client and must
 //   remain valid for the duration of the interpreter lifetime.
 TFL_CAPI_EXPORT extern void TfLiteInterpreterOptionsSetErrorReporter(
     TfLiteInterpreterOptions* options,
     void (*reporter)(void* user_data, const char* format, va_list args),
     void* user_data);
 
-// --------------------------------------------------------------------------
-// TfLiteInterpreter provides inference from a provided model.
-typedef struct TfLiteInterpreter TfLiteInterpreter;
-
 // Returns a new interpreter using the provided model and options, or null on
 // failure.
 //
@@ -176,7 +174,11 @@ TFL_CAPI_EXPORT extern TfLiteTensor* TfLiteInterpreterGetInputTensor(
 //
 // NOTE: After a resize, the client *must* explicitly allocate tensors before
 // attempting to access the resized tensor data or invoke the interpreter.
+//
 // REQUIRES: 0 <= input_index < TfLiteInterpreterGetInputTensorCount(tensor)
+//
+// This function makes a copy of the input dimensions, so the client can safely
+// deallocate `input_dims` immediately after this function returns.
 TFL_CAPI_EXPORT extern TfLiteStatus TfLiteInterpreterResizeInputTensor(
     TfLiteInterpreter* interpreter, int32_t input_index, const int* input_dims,
     int32_t input_dims_size);
@@ -191,9 +193,34 @@ TFL_CAPI_EXPORT extern TfLiteStatus TfLiteInterpreterAllocateTensors(
 
 // Runs inference for the loaded graph.
 //
+// Before calling this function, the caller should first invoke
+// TfLiteInterpreterAllocateTensors() and should also set the values for the
+// input tensors.  After successfully calling this function, the values for the
+// output tensors will be set.
+//
 // NOTE: It is possible that the interpreter is not in a ready state to
-// evaluate (e.g., if a ResizeInputTensor() has been performed without a call to
+// evaluate (e.g., if AllocateTensors() hasn't been called, or if a
+// ResizeInputTensor() has been performed without a subsequent call to
 // AllocateTensors()).
+//
+//   If the (experimental!) delegate fallback option was enabled in the
+//   interpreter options, then the interpreter will automatically fall back to
+//   not using any delegates if execution with delegates fails. For details, see
+//   TfLiteInterpreterOptionsSetEnableDelegateFallback in c_api_experimental.h.
+//
+// Returns one of the following status codes:
+//  - kTfLiteOk: Success. Output is valid.
+//  - kTfLiteDelegateError: Execution with delegates failed, due to a problem
+//    with the delegate(s). If fallback was not enabled, output is invalid.
+//    If fallback was enabled, this return value indicates that fallback
+//    succeeded, the output is valid, and all delegates previously applied to
+//    the interpreter have been undone.
+//  - kTfLiteApplicationError: Same as for kTfLiteDelegateError, except that
+//    the problem was not with the delegate itself, but rather was
+//    due to an incompatibility between the delegate(s) and the
+//    interpreter or model.
+//  - kTfLiteError: Unexpected/runtime failure. Output is invalid.
+
 TFL_CAPI_EXPORT extern TfLiteStatus TfLiteInterpreterInvoke(
     TfLiteInterpreter* interpreter);
 
@@ -202,7 +229,7 @@ TFL_CAPI_EXPORT extern int32_t TfLiteInterpreterGetOutputTensorCount(
     const TfLiteInterpreter* interpreter);
 
 // Returns the tensor associated with the output index.
-// REQUIRES: 0 <= input_index < TfLiteInterpreterGetOutputTensorCount(tensor)
+// REQUIRES: 0 <= output_index < TfLiteInterpreterGetOutputTensorCount(tensor)
 //
 // NOTE: The shape and underlying data buffer for output tensors may be not
 // be available until after the output tensor has been both sized and allocated.

Pods/TensorFlowLiteC/Frameworks/TensorFlowLiteC.framework/Headers/c_api_experimental.h

+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#ifndef TENSORFLOW_LITE_C_C_API_EXPERIMENTAL_H_
+#define TENSORFLOW_LITE_C_C_API_EXPERIMENTAL_H_
+
+#include "builtin_ops.h"
+#include "c_api.h"
+#include "common.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif  // __cplusplus
+
+/// Resets all variable tensors to zero.
+///
+/// WARNING: This is an experimental API and subject to change.
+TFL_CAPI_EXPORT extern TfLiteStatus TfLiteInterpreterResetVariableTensors(
+    TfLiteInterpreter* interpreter);
+
+/// Adds an op registration for a builtin operator.
+///
+/// Op registrations are used to map ops referenced in the flatbuffer model
+/// to executable function pointers (`TfLiteRegistration`s).
+///
+/// NOTE: The interpreter will make a shallow copy of `registration` internally,
+/// so the caller should ensure that its contents (function pointers, etc...)
+/// remain valid for the duration of the interpreter's lifetime. A common
+/// practice is making the provided `TfLiteRegistration` instance static.
+///
+/// Code that uses this function should NOT call
+/// `TfLiteInterpreterOptionsSetOpResolver` on the same options object.
+///
+/// WARNING: This is an experimental API and subject to change.
+TFL_CAPI_EXPORT void TfLiteInterpreterOptionsAddBuiltinOp(
+    TfLiteInterpreterOptions* options, TfLiteBuiltinOperator op,
+    const TfLiteRegistration* registration, int32_t min_version,
+    int32_t max_version);
+
+/// Adds an op registration for a custom operator.
+///
+/// Op registrations are used to map ops referenced in the flatbuffer model
+/// to executable function pointers (`TfLiteRegistration`s).
+///
+/// NOTE: The interpreter will make a shallow copy of `registration` internally,
+/// so the caller should ensure that its contents (function pointers, etc...)
+/// remain valid for the duration of any created interpreter's lifetime. A
+/// common practice is making the provided `TfLiteRegistration` instance static.
+///
+/// The lifetime of the string pointed to by `name` must be at least as long
+/// as the lifetime of the `TfLiteInterpreterOptions`.
+///
+/// Code that uses this function should NOT call
+/// `TfLiteInterpreterOptionsSetOpResolver` on the same options object.
+///
+/// WARNING: This is an experimental API and subject to change.
+TFL_CAPI_EXPORT void TfLiteInterpreterOptionsAddCustomOp(
+    TfLiteInterpreterOptions* options, const char* name,
+    const TfLiteRegistration* registration, int32_t min_version,
+    int32_t max_version);
+
+/// Registers callbacks for resolving builtin or custom operators.
+///
+/// The `TfLiteInterpreterOptionsSetOpResolver` function provides an alternative
+/// method for registering builtin ops and/or custom ops, by providing operator
+/// resolver callbacks.  Unlike using `TfLiteInterpreterOptionsAddBuiltinOp`
+/// and/or `TfLiteInterpreterOptionsAddAddCustomOp`, these let you register all
+/// the operators in a single call.
+///
+/// Code that uses this function should NOT call
+/// `TfLiteInterpreterOptionsAddBuiltin` or
+/// `TfLiteInterpreterOptionsAddCustomOp` on the same options object.
+///
+/// If `op_resolver_user_data` is non-null, its lifetime must be at least as
+/// long as the lifetime of the `TfLiteInterpreterOptions`.
+///
+/// WARNING: This is an experimental API and subject to change.
+void TfLiteInterpreterOptionsSetOpResolver(
+    TfLiteInterpreterOptions* options,
+    const TfLiteRegistration* (*find_builtin_op)(void* user_data,
+                                                 TfLiteBuiltinOperator op,
+                                                 int version),
+    const TfLiteRegistration* (*find_custom_op)(void* user_data,
+                                                const char* custom_op,
+                                                int version),
+    void* op_resolver_user_data);
+
+/// Returns a new interpreter using the provided model and options, or null on
+/// failure, where the model uses only the operators explicitly added to the
+/// options.  This is the same as `TFLiteInterpreterCreate` from `c_api.h`,
+/// except that the only operators that are supported are the ones registered
+/// in `options` via calls to `TfLiteInterpreterOptionsSetOpResolver`,
+/// `TfLiteInterpreterOptionsAddBuiltinOp`, and/or
+/// `TfLiteInterpreterOptionsAddCustomOp`.
+///
+/// * `model` must be a valid model instance. The caller retains ownership of
+///   the object, and can destroy it immediately after creating the interpreter;
+///   the interpreter will maintain its own reference to the underlying model
+///   data.
+/// * `options` should not be null. The caller retains ownership of the object,
+///   and can safely destroy it immediately after creating the interpreter.
+///
+/// NOTE: The client *must* explicitly allocate tensors before attempting to
+/// access input tensor data or invoke the interpreter.
+///
+/// WARNING: This is an experimental API and subject to change.
+TFL_CAPI_EXPORT extern TfLiteInterpreter*
+TfLiteInterpreterCreateWithSelectedOps(const TfLiteModel* model,
+                                       const TfLiteInterpreterOptions* options);
+
+/// Enable or disable the NN API delegate for the interpreter (true to enable).
+///
+/// WARNING: This is an experimental API and subject to change.
+TFL_CAPI_EXPORT extern void TfLiteInterpreterOptionsSetUseNNAPI(
+    TfLiteInterpreterOptions* options, bool enable);
+
+/// Enable or disable CPU fallback for the interpreter (true to enable).
+/// If enabled, TfLiteInterpreterInvoke will do automatic fallback from
+/// executing with delegate(s) to regular execution without delegates
+/// (i.e. on CPU).
+///
+/// Allowing the fallback is suitable only if both of the following hold:
+/// - The caller is known not to cache pointers to tensor data across
+///   TfLiteInterpreterInvoke calls.
+/// - The model is not stateful (no variables, no LSTMs) or the state isn't
+///   needed between batches.
+///
+/// When delegate fallback is enabled, TfLiteInterpreterInvoke will
+/// behave as follows:
+///   If one or more delegates were set in the interpreter options
+///   (see TfLiteInterpreterOptionsAddDelegate),
+///   AND inference fails,
+///   then the interpreter will fall back to not using any delegates.
+///   In that case, the previously applied delegate(s) will be automatically
+///   undone, and an attempt will be made to return the interpreter to an
+///   invokable state, which may invalidate previous tensor addresses,
+///   and the inference will be attempted again, using input tensors with
+///   the same value as previously set.
+///
+/// WARNING: This is an experimental API and subject to change.
+TFL_CAPI_EXPORT extern void TfLiteInterpreterOptionsSetEnableDelegateFallback(
+    TfLiteInterpreterOptions* options, bool enable);
+
+// Set if buffer handle output is allowed.
+//
+/// When using hardware delegation, Interpreter will make the data of output
+/// tensors available in `tensor->data` by default. If the application can
+/// consume the buffer handle directly (e.g. reading output from OpenGL
+/// texture), it can set this flag to false, so Interpreter won't copy the
+/// data from buffer handle to CPU memory. WARNING: This is an experimental
+/// API and subject to change.
+TFL_CAPI_EXPORT extern void TfLiteSetAllowBufferHandleOutput(
+    const TfLiteInterpreter* interpreter, bool allow_buffer_handle_output);
+
+/// Allow a delegate to look at the graph and modify the graph to handle
+/// parts of the graph themselves. After this is called, the graph may
+/// contain new nodes that replace 1 more nodes.
+/// 'delegate' must outlive the interpreter.
+/// Use `TfLiteInterpreterOptionsAddDelegate` instead of this unless
+/// absolutely required.
+/// Returns one of the following three status codes:
+/// 1. kTfLiteOk: Success.
+/// 2. kTfLiteDelegateError: Delegation failed due to an error in the
+/// delegate. The Interpreter has been restored to its pre-delegation state.
+/// NOTE: This undoes all delegates previously applied to the Interpreter.
+/// 3. kTfLiteError: Unexpected/runtime failure.
+/// WARNING: This is an experimental API and subject to change.
+TFL_CAPI_EXPORT extern TfLiteStatus TfLiteInterpreterModifyGraphWithDelegate(
+    const TfLiteInterpreter* interpreter, TfLiteDelegate* delegate);
+
+/// Returns the tensor index corresponding to the input tensor
+///
+/// WARNING: This is an experimental API and subject to change.
+TFL_CAPI_EXPORT extern int32_t TfLiteInterpreterGetInputTensorIndex(
+    const TfLiteInterpreter* interpreter, int32_t input_index);
+
+/// Returns the tensor index corresponding to the output tensor
+///
+/// WARNING: This is an experimental API and subject to change.
+TFL_CAPI_EXPORT extern int32_t TfLiteInterpreterGetOutputTensorIndex(
+    const TfLiteInterpreter* interpreter, int32_t output_index);
+
+#ifdef __cplusplus
+}  // extern "C"
+#endif  // __cplusplus
+
+#endif  // TENSORFLOW_LITE_C_C_API_EXPERIMENTAL_H_

Pods/TensorFlowLiteC/Frameworks/TensorFlowLiteC.framework/Headers/c_api_types.h

+/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// This file declares types used by the pure C inference API defined in c_api.h,
+// some of which are also used in the C++ and C kernel and interpreter APIs.
+
+#ifndef TENSORFLOW_LITE_C_C_API_TYPES_H_
+#define TENSORFLOW_LITE_C_C_API_TYPES_H_
+
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+// Define TFL_CAPI_EXPORT macro to export a function properly with a shared
+// library.
+#ifdef SWIG
+#define TFL_CAPI_EXPORT
+#elif defined(TFL_STATIC_LIBRARY_BUILD)
+#define TFL_CAPI_EXPORT
+#else  // not definded TFL_STATIC_LIBRARY_BUILD
+#if defined(_WIN32)
+#ifdef TFL_COMPILE_LIBRARY
+#define TFL_CAPI_EXPORT __declspec(dllexport)
+#else
+#define TFL_CAPI_EXPORT __declspec(dllimport)
+#endif  // TFL_COMPILE_LIBRARY
+#else
+#define TFL_CAPI_EXPORT __attribute__((visibility("default")))
+#endif  // _WIN32
+#endif  // SWIG
+
+// Note that new error status values may be added in future in order to
+// indicate more fine-grained internal states, therefore, applications should
+// not rely on status values being members of the enum.
+typedef enum TfLiteStatus {
+  kTfLiteOk = 0,
+
+  // Generally referring to an error in the runtime (i.e. interpreter)
+  kTfLiteError = 1,
+
+  // Generally referring to an error from a TfLiteDelegate itself.
+  kTfLiteDelegateError = 2,
+
+  // Generally referring to an error in applying a delegate due to
+  // incompatibility between runtime and delegate, e.g., this error is returned
+  // when trying to apply a TF Lite delegate onto a model graph that's already
+  // immutable.
+  kTfLiteApplicationError = 3,
+
+  // Generally referring to serialized delegate data not being found.
+  // See tflite::delegates::Serialization.
+  kTfLiteDelegateDataNotFound = 4,
+
+  // Generally referring to data-writing issues in delegate serialization.
+  // See tflite::delegates::Serialization.
+  kTfLiteDelegateDataWriteError = 5,
+
+  // Generally referring to data-reading issues in delegate serialization.
+  // See tflite::delegates::Serialization.
+  kTfLiteDelegateDataReadError = 6,
+
+  // Generally referring to issues when the TF Lite model has ops that cannot be
+  // resolved at runtime. This could happen when the specific op is not
+  // registered or built with the TF Lite framework.
+  kTfLiteUnresolvedOps = 7,
+} TfLiteStatus;
+
+// Types supported by tensor
+typedef enum {
+  kTfLiteNoType = 0,
+  kTfLiteFloat32 = 1,
+  kTfLiteInt32 = 2,
+  kTfLiteUInt8 = 3,
+  kTfLiteInt64 = 4,
+  kTfLiteString = 5,
+  kTfLiteBool = 6,
+  kTfLiteInt16 = 7,
+  kTfLiteComplex64 = 8,
+  kTfLiteInt8 = 9,
+  kTfLiteFloat16 = 10,
+  kTfLiteFloat64 = 11,
+  kTfLiteComplex128 = 12,
+  kTfLiteUInt64 = 13,
+  kTfLiteResource = 14,
+  kTfLiteVariant = 15,
+  kTfLiteUInt32 = 16,
+} TfLiteType;
+
+// Legacy. Will be deprecated in favor of TfLiteAffineQuantization.
+// If per-layer quantization is specified this field will still be populated in
+// addition to TfLiteAffineQuantization.
+// Parameters for asymmetric quantization. Quantized values can be converted
+// back to float using:
+//     real_value = scale * (quantized_value - zero_point)
+typedef struct TfLiteQuantizationParams {
+  float scale;
+  int32_t zero_point;
+} TfLiteQuantizationParams;
+
+#ifdef __cplusplus
+}  // extern C
+#endif
+#endif  // TENSORFLOW_LITE_C_C_API_TYPES_H_

Pods/TensorFlowLiteC/Frameworks/TensorFlowLiteC.framework/Headers/metal_delegate.h → Pods/TensorFlowLiteC/Frameworks/TensorFlowLiteC.framework/Headers/xnnpack_delegate.h

-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -13,66 +13,43 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef TENSORFLOW_LITE_DELEGATES_GPU_METAL_DELEGATE_H_
-#define TENSORFLOW_LITE_DELEGATES_GPU_METAL_DELEGATE_H_
+#ifndef TENSORFLOW_LITE_DELEGATES_XNNPACK_XNNPACK_DELEGATE_H_
+#define TENSORFLOW_LITE_DELEGATES_XNNPACK_XNNPACK_DELEGATE_H_
 
-#ifdef SWIG
-#define TFL_CAPI_EXPORT
-#else
-#if defined(_WIN32)
-#ifdef TFL_COMPILE_LIBRARY
-#define TFL_CAPI_EXPORT __declspec(dllexport)
-#else
-#define TFL_CAPI_EXPORT __declspec(dllimport)
-#endif  // TFL_COMPILE_LIBRARY
-#else
-#define TFL_CAPI_EXPORT __attribute__((visibility("default")))
-#endif  // _WIN32
-#endif  // SWIG
+#include "common.h"
 
 #ifdef __cplusplus
 extern "C" {
-#else
-// For "C" 'bool' is not built-in type.
-#include <stdbool.h>
 #endif  // __cplusplus
 
-typedef struct TfLiteDelegate TfLiteDelegate;
-
-typedef enum {
-  // waitUntilCompleted
-  TFLGpuDelegateWaitTypePassive,
-  // Minimize latency. It uses active spinning instead of mutex and consumes
-  // additional CPU resources.
-  TFLGpuDelegateWaitTypeActive,
-  // Useful when the output is used with GPU pipeline then or if external
-  // command encoder is set.
-  TFLGpuDelegateWaitTypeDoNotWait,
-  // Tries to avoid GPU sleep mode.
-  TFLGpuDelegateWaitTypeAggressive,
-} TFLGpuDelegateWaitType;
-
-// Creates a new delegate instance that need to be destroyed with
-// DeleteFlowDelegate when delegate is no longer used by tflite.
 typedef struct {
-  // Allows to quantify tensors, downcast values, process in float16 etc.
-  bool allow_precision_loss;
-  TFLGpuDelegateWaitType wait_type;
-} TFLGpuDelegateOptions;
+  // Number of threads to use in the thread pool.
+  // 0 or negative value means no thread pool used.
+  int32_t num_threads;
+} TfLiteXNNPackDelegateOptions;
+
+// Returns a structure with the default XNNPack delegate options.
+TFL_CAPI_EXPORT TfLiteXNNPackDelegateOptions
+TfLiteXNNPackDelegateOptionsDefault();
 
 // Creates a new delegate instance that need to be destroyed with
-// `TFLDeleteTfLiteGpuDelegate` when delegate is no longer used by TFLite.
+// `TfLiteXNNPackDelegateDelete` when delegate is no longer used by TFLite.
 // When `options` is set to `nullptr`, the following default values are used:
-// .precision_loss_allowed = false,
-// .wait_type = kPassive,
-TFL_CAPI_EXPORT extern TfLiteDelegate* TFLGpuDelegateCreate(
-    const TFLGpuDelegateOptions* options);
+TFL_CAPI_EXPORT TfLiteDelegate* TfLiteXNNPackDelegateCreate(
+    const TfLiteXNNPackDelegateOptions* options);
+
+// Returns the pthreadpool_t object used for parallelization in XNNPACK.
+// Can return NULL if the XNNPack delegate is single-threaded.
+//
+// WARNING: This API is experimental and subject to change.
+TFL_CAPI_EXPORT void* TfLiteXNNPackDelegateGetThreadPool(
+    TfLiteDelegate* delegate);
 
-// Destroys a delegate created with `TFLGpuDelegateCreate` call.
-TFL_CAPI_EXPORT extern void TFLGpuDelegateDelete(TfLiteDelegate* delegate);
+// Destroys a delegate created with `TfLiteXNNPackDelegateCreate` call.
+TFL_CAPI_EXPORT void TfLiteXNNPackDelegateDelete(TfLiteDelegate* delegate);
 
 #ifdef __cplusplus
-}  // extern "C"
+}
 #endif  // __cplusplus
 
-#endif  // TENSORFLOW_LITE_DELEGATES_GPU_METAL_DELEGATE_H_
+#endif  // TENSORFLOW_LITE_DELEGATES_XNNPACK_XNNPACK_DELEGATE_H_

Pods/TensorFlowLiteC/Frameworks/TensorFlowLiteC.framework/Modules/module.modulemap

@@ -2,5 +2,4 @@ framework module TensorFlowLiteC {
   umbrella header "TensorFlowLiteC.h"
   export *
   module * { export * }
-  link framework "Metal"
 }

Pods/TensorFlowLiteSwift/tensorflow/lite/experimental/swift/Sources/InterpreterError.swift → Pods/TensorFlowLiteSwift/tensorflow/lite/swift/Sources/InterpreterError.swift

@@ -44,7 +44,7 @@ extension InterpreterError: LocalizedError {
     case .failedToCreateInterpreter:
       return "Failed to create the interpreter."
     case .failedToResizeInputTensor(let index):
-      return "Failed to resize input tesnor at index \(index)."
+      return "Failed to resize input tensor at index \(index)."
     case .failedToCopyDataToInputTensor:
       return "Failed to copy data to input tensor."
     case .failedToAllocateTensors:

Pods/TensorFlowLiteSwift/tensorflow/lite/experimental/swift/Sources/Tensor.swift → Pods/TensorFlowLiteSwift/tensorflow/lite/swift/Sources/Tensor.swift

@@ -73,6 +73,8 @@ extension Tensor {
     case float16
     /// A 32-bit single precision floating point.
     case float32
+    /// A 64-bit double precision floating point.
+    case float64
 
     /// Creates a new instance from the given `TfLiteType` or `nil` if the data type is unsupported
     /// or could not be determined because there was an error.
@@ -94,6 +96,8 @@ extension Tensor {
         self = .float16
       case kTfLiteFloat32:
         self = .float32
+      case kTfLiteFloat64:
+        self = .float64
       case kTfLiteNoType:
         fallthrough
       default:

yarn.lock

+# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
+# yarn lockfile v1
+
+