Updated result from CoreML to be a struct and not a tuple

Lumina/Lumina/Camera/LuminaCamera.swift

@@ -14,7 +14,7 @@ protocol LuminaCameraDelegate: class {
     func stillImageCaptured(camera: LuminaCamera, image: UIImage, livePhotoURL: URL?, depthData: Any?)
     func videoFrameCaptured(camera: LuminaCamera, frame: UIImage)
     @available (iOS 11.0, *)
-    func videoFrameCaptured(camera: LuminaCamera, frame: UIImage, predictedObjects: [([LuminaPrediction]?, Any.Type)]?)
+    func videoFrameCaptured(camera: LuminaCamera, frame: UIImage, predictedObjects: [LuminaRecognitionResult]?)
     func depthDataCaptured(camera: LuminaCamera, depthData: Any)
     func videoRecordingCaptured(camera: LuminaCamera, videoURL: URL)
     func finishedFocus(camera: LuminaCamera)

Lumina/Lumina/Camera/LuminaObjectRecognizer.swift

@@ -16,6 +16,16 @@ public struct LuminaPrediction {
     public var name: String
     /// The numeric value of the confidence of the prediction, out of 1.0
     public var confidence: Float
+    /// The unique identifier associated with this prediction, as determined by the Vision framework
+    public var UUID: UUID
+}
+
+/// An object that represents a collection of predictions that Lumina detects, along with their associated types
+public struct LuminaRecognitionResult {
+    /// The collection of predictions in a given result, as predicted by Lumina
+    public var predictions: [LuminaPrediction]?
+    /// The type of MLModel that made the predictions, best resolved as a String
+    public var type: Any.Type
 }
 
 @available(iOS 11.0, *)
@@ -27,8 +37,12 @@ final class LuminaObjectRecognizer: NSObject {
         self.modelPairs = modelPairs
     }
 
-    func recognize(from image: UIImage, completion: @escaping ([([LuminaPrediction]?, Any.Type)]) -> Void) {
-        var recognitionResults = [([LuminaPrediction]?, Any.Type)]()
+    func recognize(from image: UIImage, completion: @escaping ([LuminaRecognitionResult]?) -> Void) {
+        guard let coreImage = image.cgImage else {
+            completion(nil)
+            return
+        }
+        var recognitionResults = [LuminaRecognitionResult]()
         let recognitionGroup = DispatchGroup()
         for modelPair in modelPairs {
             recognitionGroup.enter()
@@ -38,18 +52,14 @@ final class LuminaObjectRecognizer: NSObject {
             }
             let request = VNCoreMLRequest(model: visionModel) { request, error in
                 if error != nil || request.results == nil {
-                    recognitionResults.append((nil, modelPair.1))
+                    recognitionResults.append(LuminaRecognitionResult(predictions: nil, type: modelPair.1))
                     recognitionGroup.leave()
                 } else if let results = request.results {
                     let mappedResults = self.mapResults(results)
-                    recognitionResults.append((mappedResults, modelPair.1))
+                    recognitionResults.append(LuminaRecognitionResult(predictions: mappedResults, type: modelPair.1))
                     recognitionGroup.leave()
                 }
             }
-            guard let coreImage = image.cgImage else {
-                recognitionGroup.leave()
-                continue
-            }
             let handler = VNImageRequestHandler(cgImage: coreImage)
             do {
                 try handler.perform([request])
@@ -67,7 +77,7 @@ final class LuminaObjectRecognizer: NSObject {
         var results = [LuminaPrediction]()
         for object in objects {
             if let object = object as? VNClassificationObservation {
-                results.append(LuminaPrediction(name: object.identifier, confidence: object.confidence))
+                results.append(LuminaPrediction(name: object.identifier, confidence: object.confidence, UUID: object.uuid))
             }
         }
         return results.sorted(by: {

Lumina/Lumina/UI/Extensions/Delegates/LuminaCameraDelegateExtension.swift

@@ -10,6 +10,10 @@ import Foundation
 import CoreML
 
 extension LuminaViewController: LuminaCameraDelegate {
+    func videoFrameCaptured(camera: LuminaCamera, frame: UIImage, predictedObjects: [LuminaRecognitionResult]?) {
+        delegate?.streamed(videoFrame: frame, with: predictedObjects, from: self)
+    }
+
     func videoRecordingCaptured(camera: LuminaCamera, videoURL: URL) {
         delegate?.captured(videoAt: videoURL, from: self)
     }
@@ -29,10 +33,6 @@ extension LuminaViewController: LuminaCameraDelegate {
         delegate?.streamed(videoFrame: frame, from: self)
     }
 
-    func videoFrameCaptured(camera: LuminaCamera, frame: UIImage, predictedObjects: [([LuminaPrediction]?, Any.Type)]?) {
-        delegate?.streamed(videoFrame: frame, with: predictedObjects, from: self)
-    }
-
     func detected(camera: LuminaCamera, metadata: [Any]) {
         delegate?.detected(metadata: metadata, from: self)
     }

Lumina/Lumina/UI/Extensions/Delegates/LuminaDelegate.swift

@@ -42,9 +42,9 @@ public protocol LuminaDelegate: class {
     /// - Warning: The other method for passing video frames back via a delegate will not be triggered in the presence of a CoreML model
     /// - Parameters:
     ///   - videoFrame: the frame captured by Lumina
-    ///   - predictions: an array of tuples, each containing the predictions made by a model used with Lumina, and its type, for matching against when parsing results.
+    ///   - predictions: an array of objects typed LuminaRecognitionResult, containing the predictions made by a model used with Lumina, and its type, for matching against when parsing results.
     ///   - controller: the instance of Lumina that is streaming the frames
-    func streamed(videoFrame: UIImage, with predictions: [([LuminaPrediction]?, Any.Type)]?, from controller: LuminaViewController)
+    func streamed(videoFrame: UIImage, with predictions: [LuminaRecognitionResult]?, from controller: LuminaViewController)
 
     /// Triggered whenever streamDepthData is set to true on Lumina, and streams depth data detected in the form of AVDepthData
     ///

Lumina/Lumina/Util/Info.plist

@@ -15,7 +15,7 @@
 	<key>CFBundlePackageType</key>
 	<string>FMWK</string>
 	<key>CFBundleShortVersionString</key>
-	<string>1.0.1</string>
+	<string>1.1.0</string>
 	<key>CFBundleVersion</key>
 	<string>$(CURRENT_PROJECT_VERSION)</string>
 	<key>NSPrincipalClass</key>

LuminaSample/LuminaSample/Info.plist

@@ -15,7 +15,7 @@
 	<key>CFBundlePackageType</key>
 	<string>APPL</string>
 	<key>CFBundleShortVersionString</key>
-	<string>1.0.1</string>
+	<string>1.1.0</string>
 	<key>CFBundleVersion</key>
 	<string>1</string>
 	<key>LSRequiresIPhoneOS</key>

LuminaSample/LuminaSample/ViewController.swift

@@ -98,20 +98,20 @@ extension ViewController { //MARK: IBActions
 }
 
 extension ViewController: LuminaDelegate {
-    func streamed(videoFrame: UIImage, with predictions: [([LuminaPrediction]?, Any.Type)]?, from controller: LuminaViewController) {
+    func streamed(videoFrame: UIImage, with predictions: [LuminaRecognitionResult]?, from controller: LuminaViewController) {
         if #available(iOS 11.0, *) {
             guard let predicted = predictions else {
                 return
             }
             var resultString = String()
             for prediction in predicted {
-                guard let values = prediction.0 else {
+                guard let values = prediction.predictions else {
                     continue
                 }
                 guard let bestPrediction = values.first else {
                     continue
                 }
-                resultString.append("\(String(describing: prediction.1)): \(bestPrediction.name)" + "\r\n")
+                resultString.append("\(String(describing: prediction.type)): \(bestPrediction.name)" + "\r\n")
             }
             controller.textPrompt = resultString
         } else {