2. Görüntü etiketleyiciyi yapılandırma ve çalıştırma
Bir görüntüdeki nesneleri etiketlemek için VisionImage nesnesini
ImageLabeler ürününün processImage() yöntemi.
İlk olarak ImageLabeler öğesinin bir örneğini alın.
Swift
letlabeler=ImageLabeler.imageLabeler()// Or, to set the minimum confidence required:// let options = ImageLabelerOptions()// options.confidenceThreshold = 0.7// let labeler = ImageLabeler.imageLabeler(options: options)
Objective-C
MLKImageLabeler*labeler=[MLKImageLabelerimageLabeler];// Or, to set the minimum confidence required:// MLKImageLabelerOptions *options =// [[MLKImageLabelerOptions alloc] init];// options.confidenceThreshold = 0.7;// MLKImageLabeler *labeler =// [MLKImageLabeler imageLabelerWithOptions:options];
Görüntü etiketleme başarılı olursa tamamlama işleyicisi
ImageLabel nesne. Her ImageLabel nesnesi, ilk kez
resim olarak etiketlenmiştir. Temel model 400'den fazla farklı etiketi destekler.
Her etiketin metin açıklamasını alabilir, desteklenen tüm etiketler arasında dizine
ve eşleşmenin güven puanı gibi
değişir. Örneğin:
Grafikleri üzerine bindirmek için görüntü etiketleyicinin çıkışını kullanırsanız
giriş görüntüsünü kullanın, önce ML Kit'ten sonucu alın ve ardından görüntüyü oluşturun
tek bir adımda yapabilirsiniz. Bu şekilde, öğeleri ekran yüzeyinde
işlenen her giriş çerçevesi için yalnızca bir kez. Bkz. updatePreviewOverlayViewWithLastFrame
ML Kit hızlı başlangıç örneğine göz atın.
[null,null,["Son güncelleme tarihi: 2025-08-29 UTC."],[[["\u003cp\u003eML Kit's image labeling API lets you identify objects in images using a pre-trained model that recognizes over 400 labels.\u003c/p\u003e\n"],["\u003cp\u003eTo use this API, you need to include the \u003ccode\u003eGoogleMLKit/ImageLabeling\u003c/code\u003e pod, create a \u003ccode\u003eVisionImage\u003c/code\u003e object from your image, and then process it with an \u003ccode\u003eImageLabeler\u003c/code\u003e instance.\u003c/p\u003e\n"],["\u003cp\u003eResults are provided as an array of \u003ccode\u003eImageLabel\u003c/code\u003e objects, each containing the label's text, confidence score, and index.\u003c/p\u003e\n"],["\u003cp\u003eFor real-time applications, leverage the synchronous \u003ccode\u003eresults(in:)\u003c/code\u003e API and manage video frame processing efficiently to maintain optimal frame rates.\u003c/p\u003e\n"]]],["ML Kit allows image labeling using a default model with 400+ labels. To begin, include the `GoogleMLKit/ImageLabeling` pod in your Podfile and open the `.xcworkspace` in Xcode. Input images are prepared using `VisionImage` objects, created from either `UIImage` or `CMSampleBuffer`. An `ImageLabeler` instance processes the image, returning `ImageLabel` objects with text, confidence, and index. For real-time performance, use the synchronous `results(in:)` API with video frames, managing the frame rate.\n"],null,["You can use ML Kit to label objects recognized in an image. The default model provided with\nML Kit supports 400+ different labels.\n\n\u003cbr /\u003e\n\n| **Note:** ML Kit iOS APIs only run on 64-bit devices. If you build your app with 32-bit support, check the device's architecture before using this API.\n\nTry it out\n\n- Play around with [the sample app](https://github.com/googlesamples/mlkit/tree/master/ios/quickstarts/vision) to see an example usage of this API.\n\nBefore you begin\n\n1. Include the following ML Kit pods in your Podfile: \n\n ```\n pod 'GoogleMLKit/ImageLabeling', '8.0.0'\n ```\n2. After you install or update your project's Pods, open your Xcode project using its `.xcworkspace`. ML Kit is supported in Xcode version 12.4 or greater.\n\nNow you are ready to label images.\n\n1. Prepare the input image\n\nCreate a [`VisionImage`](/ml-kit/reference/swift/mlkitvision/api/reference/Classes/VisionImage) object using a `UIImage` or a\n`CMSampleBuffer`.\n\nIf you use a `UIImage`, follow these steps:\n\n- Create a [`VisionImage`](/ml-kit/reference/swift/mlkitvision/api/reference/Classes/VisionImage) object with the `UIImage`. Make sure to specify the correct `.orientation`. \n\n Swift \n\n ```text\n let image = VisionImage(image: UIImage)\n visionImage.orientation = image.imageOrientation\n ```\n\n Objective-C \n\n ```objective-c\n MLKVisionImage *visionImage = [[MLKVisionImage alloc] initWithImage:image];\n visionImage.orientation = image.imageOrientation;\n ```\n\nIf you use a `CMSampleBuffer`, follow these steps:\n\n- Specify the orientation of the image data contained in the\n `CMSampleBuffer`.\n\n To get the image orientation: \n\n Swift \n\n ```gdscript\n func imageOrientation(\n deviceOrientation: UIDeviceOrientation,\n cameraPosition: AVCaptureDevice.Position\n ) -\u003e UIImage.Orientation {\n switch deviceOrientation {\n case .portrait:\n return cameraPosition == .front ? .leftMirrored : .right\n case .landscapeLeft:\n return cameraPosition == .front ? .downMirrored : .up\n case .portraitUpsideDown:\n return cameraPosition == .front ? .rightMirrored : .left\n case .landscapeRight:\n return cameraPosition == .front ? .upMirrored : .down\n case .faceDown, .faceUp, .unknown:\n return .up\n }\n }\n \n ```\n\n Objective-C \n\n ```css+lasso\n - (UIImageOrientation)\n imageOrientationFromDeviceOrientation:(UIDeviceOrientation)deviceOrientation\n cameraPosition:(AVCaptureDevicePosition)cameraPosition {\n switch (deviceOrientation) {\n case UIDeviceOrientationPortrait:\n return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationLeftMirrored\n : UIImageOrientationRight;\n\n case UIDeviceOrientationLandscapeLeft:\n return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationDownMirrored\n : UIImageOrientationUp;\n case UIDeviceOrientationPortraitUpsideDown:\n return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationRightMirrored\n : UIImageOrientationLeft;\n case UIDeviceOrientationLandscapeRight:\n return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationUpMirrored\n : UIImageOrientationDown;\n case UIDeviceOrientationUnknown:\n case UIDeviceOrientationFaceUp:\n case UIDeviceOrientationFaceDown:\n return UIImageOrientationUp;\n }\n }\n \n ```\n- Create a [`VisionImage`](/ml-kit/reference/swift/mlkitvision/api/reference/Classes/VisionImage) object using the `CMSampleBuffer` object and orientation: \n\n Swift \n\n ```povray\n let image = VisionImage(buffer: sampleBuffer)\n image.orientation = imageOrientation(\n deviceOrientation: UIDevice.current.orientation,\n cameraPosition: cameraPosition)\n ```\n\n Objective-C \n\n ```objective-c\n MLKVisionImage *image = [[MLKVisionImage alloc] initWithBuffer:sampleBuffer];\n image.orientation =\n [self imageOrientationFromDeviceOrientation:UIDevice.currentDevice.orientation\n cameraPosition:cameraPosition];\n ```\n\n2. Configure and run the image labeler To label objects in an image, pass the `VisionImage` object to the `ImageLabeler`'s `processImage()` method.\n\n\u003cbr /\u003e\n\n1. First, get an instance of `ImageLabeler`.\n\nSwift \n\n```swift\nlet labeler = ImageLabeler.imageLabeler()\n\n// Or, to set the minimum confidence required:\n// let options = ImageLabelerOptions()\n// options.confidenceThreshold = 0.7\n// let labeler = ImageLabeler.imageLabeler(options: options)\n```\n\nObjective-C \n\n```objective-c\nMLKImageLabeler *labeler = [MLKImageLabeler imageLabeler];\n\n// Or, to set the minimum confidence required:\n// MLKImageLabelerOptions *options =\n// [[MLKImageLabelerOptions alloc] init];\n// options.confidenceThreshold = 0.7;\n// MLKImageLabeler *labeler =\n// [MLKImageLabeler imageLabelerWithOptions:options];\n```\n\n1. Then, pass the image to the `processImage()` method:\n\nSwift \n\n```swift\nlabeler.process(image) { labels, error in\n guard error == nil, let labels = labels else { return }\n\n // Task succeeded.\n // ...\n}\n```\n\nObjective-C \n\n```objective-c\n[labeler processImage:image\ncompletion:^(NSArray *_Nullable labels,\n NSError *_Nullable error) {\n if (error != nil) { return; }\n\n // Task succeeded.\n // ...\n}];\n```\n\n3. Get information about labeled objects\n\nIf image labeling succeeds, the completion handler receives an array of\n`ImageLabel` objects. Each `ImageLabel` object represents something that was\nlabeled in the image. The base model supports [400+ different labels](/ml-kit/vision/image-labeling/label-map).\nYou can get each label's text description, index among all labels supported by\nthe model, and the confidence score of the match. For example: \n\nSwift \n\n```swift\nfor label in labels {\n let labelText = label.text\n let confidence = label.confidence\n let index = label.index\n}\n```\n\nObjective-C \n\n```objective-c\nfor (MLKImageLabel *label in labels) {\n NSString *labelText = label.text;\n float confidence = label.confidence;\n NSInteger index = label.index;\n}\n```\n\nTips to improve real-time performance\n\nIf you want to label images in a real-time application, follow these\nguidelines to achieve the best framerates:\n\n- For processing video frames, use the `results(in:)` synchronous API of the image labeler. Call this method from the [`AVCaptureVideoDataOutputSampleBufferDelegate`](https://developer.apple.com/documentation/avfoundation/avcapturevideodataoutputsamplebufferdelegate)'s [`captureOutput(_, didOutput:from:)`](https://developer.apple.com/documentation/avfoundation/avcapturevideodataoutputsamplebufferdelegate/1385775-captureoutput) function to synchronously get results from the given video frame. Keep [`AVCaptureVideoDataOutput`](https://developer.apple.com/documentation/avfoundation/avcapturevideodataoutput)'s [`alwaysDiscardsLateVideoFrames`](https://developer.apple.com/documentation/avfoundation/avcapturevideodataoutput/1385780-alwaysdiscardslatevideoframes) as `true` to throttle calls to the image labeler. If a new video frame becomes available while the image labeler is running, it will be dropped.\n- If you use the output of the image labeler to overlay graphics on the input image, first get the result from ML Kit, then render the image and overlay in a single step. By doing so, you render to the display surface only once for each processed input frame. See the [updatePreviewOverlayViewWithLastFrame](https://github.com/googlesamples/mlkit/blob/master/ios/quickstarts/vision/VisionExample/CameraViewController.swift) in the ML Kit quickstart sample for an example."]]
