Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Firebase ML Kit for iOS Developer
Search
Kajornsak Peerapathananont
October 07, 2018
Technology
90
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
Firebase ML Kit for iOS Developer
Firebase Dev Day 2018 @Bangkok, Thailand
Kajornsak Peerapathananont
October 07, 2018
More Decks by Kajornsak Peerapathananont
See All by Kajornsak Peerapathananont
Full-Stack Development with FlutterFire
kajornsakp
0
26
How to build native-experience with cross-platform
kajornsakp
0
27
Understanding your Android build
kajornsakp
0
55
iOSDevTH #21
kajornsakp
0
64
What's new in Flutter (Google I/O Extended Bangkok 22)
kajornsakp
0
94
Mobile Design System at scale
kajornsakp
0
150
What's new in Flutter 2020
kajornsakp
0
86
Mobile Machine Learning for All Skill Levels
kajornsakp
0
51
What's new in Flutter 1.9
kajornsakp
0
67
Other Decks in Technology
See All in Technology
Amplify Gen2でbackend.tsにCDKを定義する/しない事によるCDKの挙動の違いとユースケース
smt7174
1
420
公式ドキュメントの歩き方etc
coco_se
1
120
マルチアカウント環境でSecurity Hubの運用、その後どうなった? / SRE NEXT 2026 miniLT会
genda
0
110
カードゲーム作りが教えてくれた プロダクトオーナーシップ
moritamasami
0
110
シンガポールで登壇してきます
yama3133
0
250
ボーイスカウトルールでメモリやスキルを改善しよう
azukiazusa1
4
1.5k
企業でAWS Organizationsを動かすための組織設計の考え方
nrinetcom
PRO
1
110
「最後に責任を取るのはチーム」— 人間のPRレビューを最小化してアップデートしたメンタルモデル
jnishime_dresscode
0
920
人を動かすのは時間ではなく、納得感 〜新任EMが入社3ヶ月、組織を2回変えた話〜
kakehashi
PRO
3
270
Playwright × AI Agent でE2Eテストはどう変わるか AI駆動テストの可能性と実用検証の結果
taiga7543
1
420
ソニー銀行におけるビジネスアジリティ向上のためのクラウドシフト戦略
srenext
0
840
「守りたい体験」を渡すだけで E2E を生成させられるようになった話
hinac0
1
640
Featured
See All Featured
B2B Lead Gen: Tactics, Traps & Triumph
marketingsoph
0
170
Measuring & Analyzing Core Web Vitals
bluesmoon
9
880
Pawsitive SEO: Lessons from My Dog (and Many Mistakes) on Thriving as a Consultant in the Age of AI
davidcarrasco
0
190
AI Search: Implications for SEO and How to Move Forward - #ShenzhenSEOConference
aleyda
1
1.3k
<Decoding/> the Language of Devs - We Love SEO 2024
nikkihalliwell
1
280
Code Review Best Practice
trishagee
74
20k
Organizational Design Perspectives: An Ontology of Organizational Design Elements
kimpetersen
PRO
1
770
Intergalactic Javascript Robots from Outer Space
tanoku
273
27k
Unlocking the hidden potential of vector embeddings in international SEO
frankvandijk
0
870
The #1 spot is gone: here's how to win anyway
tamaranovitovic
3
1.1k
Groundhog Day: Seeking Process in Gaming for Health
codingconduct
0
250
DevOps and Value Stream Thinking: Enabling flow, efficiency and business value
helenjbeal
1
260
Transcript
ML Kit for iOS developers Kajornsak Peerapathananont Agoda
Machine Learning
#FirebaseDevDay
Google Lens
Smart Reply
On-device Machine Learning
#FirebaseDevDay Doable, but hard.
#FirebaseDevDay
#FirebaseDevDay Get Image Image Classification Transform Interpret Get Result
#FirebaseDevDay Transform unsigned char *sourceBaseAddr = (unsigned char *)(CVPixelBufferGetBaseAddress(pixelBuffer)); int
image_height; unsigned char *sourceStartAddr; if (fullHeight <= image_width) { image_height = fullHeight; sourceStartAddr = sourceBaseAddr; } else { image_height = image_width; const int marginY = ((fullHeight - image_width) / 2); sourceStartAddr = (sourceBaseAddr + (marginY * sourceRowBytes)); } const int image_channels = 4; assert(image_channels >= wanted_input_channels); tensorflow::Tensor image_tensor( tensorflow::DT_FLOAT, tensorflow::TensorShape( {1, wanted_input_height, wanted_input_width, wanted_input_channels})); auto image_tensor_mapped = image_tensor.tensor<float, 4>(); tensorflow::uint8 *in = sourceStartAddr; float *out = image_tensor_mapped.data(); for (int y = 0; y < wanted_input_height; ++y) { float *out_row = out + (y * wanted_input_width * wanted_input_channels); for (int x = 0; x < wanted_input_width; ++x) { const int in_x = (y * image_width) / wanted_input_width; const int in_y = (x * image_height) / wanted_input_height; tensorflow::uint8 *in_pixel = in + (in_y * image_width * image_channels) + (in_x * image_channels); float *out_pixel = out_row + (x * wanted_input_channels); for (int c = 0; c < wanted_input_channels; ++c) { out_pixel[c] = (in_pixel[c] - input_mean) / input_std; } } }
#FirebaseDevDay Interpret if (tf_session.get()) { std::vector<tensorflow::Tensor> outputs; tensorflow::Status run_status =
tf_session->Run( {{input_layer_name, image_tensor}}, {output_layer_name}, {}, &outputs); if (!run_status.ok()) { LOG(ERROR) << "Running model failed:" << run_status; } else { tensorflow::Tensor *output = &outputs[0]; auto predictions = output->flat<float>(); NSMutableDictionary *newValues = [NSMutableDictionary dictionary]; for (int index = 0; index < predictions.size(); index += 1) { const float predictionValue = predictions(index); if (predictionValue > 0.05f) { std::string label = labels[index % predictions.size()]; NSString *labelObject = [NSString stringWithUTF8String:label.c_str()]; NSNumber *valueObject = [NSNumber numberWithFloat:predictionValue]; [newValues setObject:valueObject forKey:labelObject]; } } dispatch_async(dispatch_get_main_queue(), ^(void) { [self setPredictionValues:newValues]; }); } }
None
#FirebaseDevDay
#FirebaseDevDay Real-world Common Use Cases
#FirebaseDevDay FIRVisionImage | VisionImage NS_SWIFT_NAME(VisionImage) @interface FIRVisionImage : NSObject @property(nonatomic,
nullable) FIRVisionImageMetadata *metadata; - (instancetype)initWithImage:(UIImage *)image NS_DESIGNATED_INITIALIZER; - (instancetype)initWithBuffer:(CMSampleBufferRef)sampleBuffer NS_DESIGNATED_INITIALIZER; - (instancetype)init NS_UNAVAILABLE; @end
Text Recognition - On-device - On-cloud
#FirebaseDevDay https://firebase.google.com/docs/ml-kit/recognize-text
#FirebaseDevDay FIRVisionText | VisionText NS_SWIFT_NAME(VisionText) @interface FIRVisionText : NSObject @property(nonatomic,
readonly) NSString *text; @property(nonatomic, readonly) NSArray<FIRVisionTextBlock *> *blocks; - (instancetype)init NS_UNAVAILABLE; @end
#FirebaseDevDay On-device Usage let textRecognizer = vision.onDeviceTextRecognizer() textRecognizer.process(visionImage) { (text,
error) in guard let text = text else { return } // do something with your text }
#FirebaseDevDay On-cloud Usage let textRecognizer = vision.cloudTextRecognize() textRecognizer.process(visionImage) { (text,
error) in guard let text = text else { return } // do something with your text }
Image Labeling - On-device (400+ labels) - On-cloud (10,000+ labels)
#FirebaseDevDay https://firebase.google.com/docs/ml-kit/label-images
#FirebaseDevDay FIRVisionLabel | VisionLabel NS_SWIFT_NAME(VisionLabel) @interface FIRVisionLabel : NSObject @property(nonatomic,
readonly) CGRect frame; @property(nonatomic, readonly) float confidence; @property(nonatomic, copy, readonly) NSString *entityID; @property(nonatomic, copy, readonly) NSString *label; @end
#FirebaseDevDay On-device Usage let labelDetector = vision.labelDetector() labelDetector.detect(in: visionImage) {
(labels, error) in guard let error == nill, let labels = labels, !labels.isEmpty else { return } // do something with your labels }
#FirebaseDevDay On-cloud Usage let labelDetector = vision.cloudLabelDetector() labelDetector.detect(in: visionImage) {
(labels, error) in guard let error == nill, let labels = labels, !labels.isEmpty else { return } // do something with your labels }
Face detection - On-device
#FirebaseDevDay
#FirebaseDevDay FIRVisionFace | VisionFace NS_SWIFT_NAME(VisionFace) @interface FIRVisionFace : NSObject @property(nonatomic,
readonly) CGRect frame; @property(nonatomic, readonly) BOOL hasTrackingID; @property(nonatomic, readonly) NSInteger trackingID; @property(nonatomic, readonly) BOOL hasHeadEulerAngleY; @property(nonatomic, readonly) CGFloat headEulerAngleY; @property(nonatomic, readonly) BOOL hasHeadEulerAngleZ; @property(nonatomic, readonly) CGFloat headEulerAngleZ; @property(nonatomic, readonly) BOOL hasSmilingProbability; @property(nonatomic, readonly) CGFloat smilingProbability; @property(nonatomic, readonly) BOOL hasLeftEyeOpenProbability; @property(nonatomic, readonly) CGFloat leftEyeOpenProbability; @property(nonatomic, readonly) BOOL hasRightEyeOpenProbability; @property(nonatomic, readonly) CGFloat rightEyeOpenProbability; - (instancetype)init NS_UNAVAILABLE; - (nullable FIRVisionFaceLandmark *)landmarkOfType:(FIRFaceLandmarkType)type; #ifdef ENABLE_FACE_CONTOUR - (nullable FIRVisionFaceContour *)contourOfType:(FIRFaceContourType)type; #endif // ENABLE_FACE_CONTOUR @end
#FirebaseDevDay On-device Usage let faceDetector = vision.faceDetector() faceDetector.detect(in: visionImage) {
(faces, error) in guard let error == nill, let faces = faces, !faces.isEmpty else { return } // do something with your faces }
#FirebaseDevDay Face Contour?
Landmark recognition - On-cloud
#FirebaseDevDay
#FirebaseDevDay FIRVisionCloudLandmark | VisionCloudLandmark NS_SWIFT_NAME(VisionCloudLandmark) @interface FIRVisionCloudLandmark : NSObject @property(nonatomic,
copy, readonly, nullable) NSString *entityId; @property(nonatomic, copy, readonly, nullable) NSString *landmark; @property(nonatomic, readonly, nullable) NSNumber *confidence; @property(nonatomic, readonly) CGRect frame; @property(nonatomic, readonly, nullable) NSArray<FIRVisionLatitudeLongitude *> *locations; - (instancetype)init NS_UNAVAILABLE; @end
#FirebaseDevDay On-cloud Usage let landmarkDetector = vision.cloudLandmarkDetector() landmarkDetector.detect(in: visionImage) {
(landmarks, error) in guard let error == nill, let landmarks = landmarks, !landmarks.isEmpty else { return } // do something with your landmarks }
Barcode scanning - On-device
#FirebaseDevDay https://firebase.google.com/docs/ml-kit/label-images
#FirebaseDevDay FIRVisionBarcode | VisionBarcode NS_SWIFT_NAME(VisionBarcode) @interface FIRVisionBarcode : NSObject @property(nonatomic,
readonly) CGRect frame; @property(nonatomic, readonly, nullable) NSString *rawValue; @property(nonatomic, readonly, nullable) NSString *displayValue; @property(nonatomic, readonly) FIRVisionBarcodeFormat format; @property(nonatomic, readonly, nullable) NSArray<NSValue *> *cornerPoints; @property(nonatomic, readonly) FIRVisionBarcodeValueType valueType; @property(nonatomic, readonly, nullable) FIRVisionBarcodeEmail *email; @property(nonatomic, readonly, nullable) FIRVisionBarcodePhone *phone; @property(nonatomic, readonly, nullable) FIRVisionBarcodeSMS *sms; @property(nonatomic, readonly, nullable) FIRVisionBarcodeURLBookmark *URL; @property(nonatomic, readonly, nullable) FIRVisionBarcodeWiFi *wifi; @property(nonatomic, readonly, nullable) FIRVisionBarcodeGeoPoint *geoPoint; @property(nonatomic, readonly, nullable) FIRVisionBarcodeContactInfo *contactInfo; @property(nonatomic, readonly, nullable) FIRVisionBarcodeCalendarEvent *calendarEvent; @property(nonatomic, readonly, nullable) FIRVisionBarcodeDriverLicense *driverLicense; - (instancetype)init NS_UNAVAILABLE; @end
#FirebaseDevDay FIRVisionBarcodeCalendarEvent | VisionBarcodeCalendarEvent NS_SWIFT_NAME(VisionBarcodeCalendarEvent) @interface FIRVisionBarcodeCalendarEvent : NSObject @property(nonatomic,
readonly, nullable) NSString *eventDescription; @property(nonatomic, readonly, nullable) NSString *location; @property(nonatomic, readonly, nullable) NSString *organizer; @property(nonatomic, readonly, nullable) NSString *status; @property(nonatomic, readonly, nullable) NSString *summary; @property(nonatomic, readonly, nullable) NSDate *start; @property(nonatomic, readonly, nullable) NSDate *end; - (instancetype)init NS_UNAVAILABLE; @end
#FirebaseDevDay On-device Usage let barcodeDetector = vision.barcodeDetector() barcodeDetector.detect(in: visionImage) {
(barcodes, error) in guard let error == nill, let barcodes = barcodes, !barcodes.isEmpty else { return } // do something with your barcodes }
Custom model - Tensorflow Lite
#FirebaseDevDay let conditions = ModelDownloadConditions(isWiFiRequired: true, canDownloadInBackground: true) let cloudModelSource
= CloudModelSource( modelName: "my_cloud_model", enableModelUpdates: true, initialConditions: conditions, updateConditions: conditions ) let registrationSuccessful = ModelManager.modelManager().register(cloudModelSource)
Demo
Thank You! #FirebaseDevDay Helpful resources fb.com/FirebaseThailand fb.com/groups/FirebaseDevTH medium.com/FirebaseThailand Kajornsak Peerapathananont