D'autres AWS SDK exemples sont disponibles dans le GitHub dépôt AWS Doc SDK Examples
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À utiliser DetectLabels avec un AWS SDK ou CLI
Les exemples de code suivants montrent comment utiliserDetectLabels.
Pour plus d'informations, veuillez consulter Détection des étiquettes dans une image.
- .NET
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- AWS SDK for .NET
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Note
Il y en a plus sur GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le référentiel d’exemples de code AWS
. using System; using System.Threading.Tasks; using Amazon.Rekognition; using Amazon.Rekognition.Model; /// <summary> /// Uses the Amazon Rekognition Service to detect labels within an image /// stored in an Amazon Simple Storage Service (Amazon S3) bucket. /// </summary> public class DetectLabels { public static async Task Main() { string photo = "del_river_02092020_01.jpg"; // "input.jpg"; string bucket = "igsmiths3photos"; // "bucket"; var rekognitionClient = new AmazonRekognitionClient(); var detectlabelsRequest = new DetectLabelsRequest { Image = new Image() { S3Object = new S3Object() { Name = photo, Bucket = bucket, }, }, MaxLabels = 10, MinConfidence = 75F, }; try { DetectLabelsResponse detectLabelsResponse = await rekognitionClient.DetectLabelsAsync(detectlabelsRequest); Console.WriteLine("Detected labels for " + photo); foreach (Label label in detectLabelsResponse.Labels) { Console.WriteLine($"Name: {label.Name} Confidence: {label.Confidence}"); } } catch (Exception ex) { Console.WriteLine(ex.Message); } } }Détectez les étiquettes dans un fichier image stocké sur votre ordinateur.
using System; using System.IO; using System.Threading.Tasks; using Amazon.Rekognition; using Amazon.Rekognition.Model; /// <summary> /// Uses the Amazon Rekognition Service to detect labels within an image /// stored locally. /// </summary> public class DetectLabelsLocalFile { public static async Task Main() { string photo = "input.jpg"; var image = new Amazon.Rekognition.Model.Image(); try { using var fs = new FileStream(photo, FileMode.Open, FileAccess.Read); byte[] data = null; data = new byte[fs.Length]; fs.Read(data, 0, (int)fs.Length); image.Bytes = new MemoryStream(data); } catch (Exception) { Console.WriteLine("Failed to load file " + photo); return; } var rekognitionClient = new AmazonRekognitionClient(); var detectlabelsRequest = new DetectLabelsRequest { Image = image, MaxLabels = 10, MinConfidence = 77F, }; try { DetectLabelsResponse detectLabelsResponse = await rekognitionClient.DetectLabelsAsync(detectlabelsRequest); Console.WriteLine($"Detected labels for {photo}"); foreach (Label label in detectLabelsResponse.Labels) { Console.WriteLine($"{label.Name}: {label.Confidence}"); } } catch (Exception ex) { Console.WriteLine(ex.Message); } } }-
Pour API plus de détails, voir DetectLabelsla section AWS SDK for .NET APIRéférence.
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- C++
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- SDKpour C++
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Note
Il y en a plus sur GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le référentiel d’exemples de code AWS
. //! Detect instances of real-world entities within an image by using Amazon Rekognition /*! \param imageBucket: The Amazon Simple Storage Service (Amazon S3) bucket containing an image. \param imageKey: The Amazon S3 key of an image object. \param clientConfiguration: AWS client configuration. \return bool: Function succeeded. */ bool AwsDoc::Rekognition::detectLabels(const Aws::String &imageBucket, const Aws::String &imageKey, const Aws::Client::ClientConfiguration &clientConfiguration) { Aws::Rekognition::RekognitionClient rekognitionClient(clientConfiguration); Aws::Rekognition::Model::DetectLabelsRequest request; Aws::Rekognition::Model::S3Object s3Object; s3Object.SetBucket(imageBucket); s3Object.SetName(imageKey); Aws::Rekognition::Model::Image image; image.SetS3Object(s3Object); request.SetImage(image); const Aws::Rekognition::Model::DetectLabelsOutcome outcome = rekognitionClient.DetectLabels(request); if (outcome.IsSuccess()) { const Aws::Vector<Aws::Rekognition::Model::Label> &labels = outcome.GetResult().GetLabels(); if (labels.empty()) { std::cout << "No labels detected" << std::endl; } else { for (const Aws::Rekognition::Model::Label &label: labels) { std::cout << label.GetName() << ": " << label.GetConfidence() << std::endl; } } } else { std::cerr << "Error while detecting labels: '" << outcome.GetError().GetMessage() << "'" << std::endl; } return outcome.IsSuccess(); }-
Pour API plus de détails, voir DetectLabelsla section AWS SDK for C++ APIRéférence.
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- CLI
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- AWS CLI
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Pour détecter une étiquette dans une image
L'
detect-labelsexemple suivant détecte des scènes et des objets dans une image stockée dans un compartiment Amazon S3.aws rekognition detect-labels \ --image '{"S3Object":{"Bucket":"bucket","Name":"image"}}'Sortie :
{ "Labels": [ { "Instances": [], "Confidence": 99.15271759033203, "Parents": [ { "Name": "Vehicle" }, { "Name": "Transportation" } ], "Name": "Automobile" }, { "Instances": [], "Confidence": 99.15271759033203, "Parents": [ { "Name": "Transportation" } ], "Name": "Vehicle" }, { "Instances": [], "Confidence": 99.15271759033203, "Parents": [], "Name": "Transportation" }, { "Instances": [ { "BoundingBox": { "Width": 0.10616336017847061, "Top": 0.5039216876029968, "Left": 0.0037978808395564556, "Height": 0.18528179824352264 }, "Confidence": 99.15271759033203 }, { "BoundingBox": { "Width": 0.2429988533258438, "Top": 0.5251884460449219, "Left": 0.7309805154800415, "Height": 0.21577216684818268 }, "Confidence": 99.1286392211914 }, { "BoundingBox": { "Width": 0.14233611524105072, "Top": 0.5333095788955688, "Left": 0.6494812965393066, "Height": 0.15528248250484467 }, "Confidence": 98.48368072509766 }, { "BoundingBox": { "Width": 0.11086395382881165, "Top": 0.5354844927787781, "Left": 0.10355594009160995, "Height": 0.10271988064050674 }, "Confidence": 96.45606231689453 }, { "BoundingBox": { "Width": 0.06254628300666809, "Top": 0.5573825240135193, "Left": 0.46083059906959534, "Height": 0.053911514580249786 }, "Confidence": 93.65448760986328 }, { "BoundingBox": { "Width": 0.10105438530445099, "Top": 0.534368634223938, "Left": 0.5743985772132874, "Height": 0.12226245552301407 }, "Confidence": 93.06217193603516 }, { "BoundingBox": { "Width": 0.056389667093753815, "Top": 0.5235804319381714, "Left": 0.9427769780158997, "Height": 0.17163699865341187 }, "Confidence": 92.6864013671875 }, { "BoundingBox": { "Width": 0.06003860384225845, "Top": 0.5441341400146484, "Left": 0.22409997880458832, "Height": 0.06737709045410156 }, "Confidence": 90.4227066040039 }, { "BoundingBox": { "Width": 0.02848697081208229, "Top": 0.5107086896896362, "Left": 0, "Height": 0.19150497019290924 }, "Confidence": 86.65286254882812 }, { "BoundingBox": { "Width": 0.04067881405353546, "Top": 0.5566273927688599, "Left": 0.316415935754776, "Height": 0.03428703173995018 }, "Confidence": 85.36471557617188 }, { "BoundingBox": { "Width": 0.043411049991846085, "Top": 0.5394920110702515, "Left": 0.18293385207653046, "Height": 0.0893595889210701 }, "Confidence": 82.21705627441406 }, { "BoundingBox": { "Width": 0.031183116137981415, "Top": 0.5579366683959961, "Left": 0.2853088080883026, "Height": 0.03989990055561066 }, "Confidence": 81.0157470703125 }, { "BoundingBox": { "Width": 0.031113790348172188, "Top": 0.5504819750785828, "Left": 0.2580395042896271, "Height": 0.056484755128622055 }, "Confidence": 56.13441467285156 }, { "BoundingBox": { "Width": 0.08586374670267105, "Top": 0.5438792705535889, "Left": 0.5128012895584106, "Height": 0.08550430089235306 }, "Confidence": 52.37760925292969 } ], "Confidence": 99.15271759033203, "Parents": [ { "Name": "Vehicle" }, { "Name": "Transportation" } ], "Name": "Car" }, { "Instances": [], "Confidence": 98.9914321899414, "Parents": [], "Name": "Human" }, { "Instances": [ { "BoundingBox": { "Width": 0.19360728561878204, "Top": 0.35072067379951477, "Left": 0.43734854459762573, "Height": 0.2742200493812561 }, "Confidence": 98.9914321899414 }, { "BoundingBox": { "Width": 0.03801717236638069, "Top": 0.5010883808135986, "Left": 0.9155802130699158, "Height": 0.06597328186035156 }, "Confidence": 85.02790832519531 } ], "Confidence": 98.9914321899414, "Parents": [], "Name": "Person" }, { "Instances": [], "Confidence": 93.24951934814453, "Parents": [], "Name": "Machine" }, { "Instances": [ { "BoundingBox": { "Width": 0.03561960905790329, "Top": 0.6468243598937988, "Left": 0.7850857377052307, "Height": 0.08878646790981293 }, "Confidence": 93.24951934814453 }, { "BoundingBox": { "Width": 0.02217046171426773, "Top": 0.6149078607559204, "Left": 0.04757237061858177, "Height": 0.07136218994855881 }, "Confidence": 91.5025863647461 }, { "BoundingBox": { "Width": 0.016197510063648224, "Top": 0.6274210214614868, "Left": 0.6472989320755005, "Height": 0.04955997318029404 }, "Confidence": 85.14686584472656 }, { "BoundingBox": { "Width": 0.020207518711686134, "Top": 0.6348286867141724, "Left": 0.7295016646385193, "Height": 0.07059963047504425 }, "Confidence": 83.34547424316406 }, { "BoundingBox": { "Width": 0.020280985161662102, "Top": 0.6171894669532776, "Left": 0.08744934946298599, "Height": 0.05297485366463661 }, "Confidence": 79.9981460571289 }, { "BoundingBox": { "Width": 0.018318990245461464, "Top": 0.623889148235321, "Left": 0.6836880445480347, "Height": 0.06730121374130249 }, "Confidence": 78.87144470214844 }, { "BoundingBox": { "Width": 0.021310249343514442, "Top": 0.6167286038398743, "Left": 0.004064912907779217, "Height": 0.08317798376083374 }, "Confidence": 75.89361572265625 }, { "BoundingBox": { "Width": 0.03604431077837944, "Top": 0.7030032277107239, "Left": 0.9254803657531738, "Height": 0.04569442570209503 }, "Confidence": 64.402587890625 }, { "BoundingBox": { "Width": 0.009834849275648594, "Top": 0.5821820497512817, "Left": 0.28094568848609924, "Height": 0.01964157074689865 }, "Confidence": 62.79907989501953 }, { "BoundingBox": { "Width": 0.01475677452981472, "Top": 0.6137543320655823, "Left": 0.5950819253921509, "Height": 0.039063986390829086 }, "Confidence": 59.40483474731445 } ], "Confidence": 93.24951934814453, "Parents": [ { "Name": "Machine" } ], "Name": "Wheel" }, { "Instances": [], "Confidence": 92.61514282226562, "Parents": [], "Name": "Road" }, { "Instances": [], "Confidence": 92.37877655029297, "Parents": [ { "Name": "Person" } ], "Name": "Sport" }, { "Instances": [], "Confidence": 92.37877655029297, "Parents": [ { "Name": "Person" } ], "Name": "Sports" }, { "Instances": [ { "BoundingBox": { "Width": 0.12326609343290329, "Top": 0.6332163214683533, "Left": 0.44815489649772644, "Height": 0.058117982000112534 }, "Confidence": 92.37877655029297 } ], "Confidence": 92.37877655029297, "Parents": [ { "Name": "Person" }, { "Name": "Sport" } ], "Name": "Skateboard" }, { "Instances": [], "Confidence": 90.62931060791016, "Parents": [ { "Name": "Person" } ], "Name": "Pedestrian" }, { "Instances": [], "Confidence": 88.81334686279297, "Parents": [], "Name": "Asphalt" }, { "Instances": [], "Confidence": 88.81334686279297, "Parents": [], "Name": "Tarmac" }, { "Instances": [], "Confidence": 88.23201751708984, "Parents": [], "Name": "Path" }, { "Instances": [], "Confidence": 80.26520538330078, "Parents": [], "Name": "Urban" }, { "Instances": [], "Confidence": 80.26520538330078, "Parents": [ { "Name": "Building" }, { "Name": "Urban" } ], "Name": "Town" }, { "Instances": [], "Confidence": 80.26520538330078, "Parents": [], "Name": "Building" }, { "Instances": [], "Confidence": 80.26520538330078, "Parents": [ { "Name": "Building" }, { "Name": "Urban" } ], "Name": "City" }, { "Instances": [], "Confidence": 78.37934875488281, "Parents": [ { "Name": "Car" }, { "Name": "Vehicle" }, { "Name": "Transportation" } ], "Name": "Parking Lot" }, { "Instances": [], "Confidence": 78.37934875488281, "Parents": [ { "Name": "Car" }, { "Name": "Vehicle" }, { "Name": "Transportation" } ], "Name": "Parking" }, { "Instances": [], "Confidence": 74.37590026855469, "Parents": [ { "Name": "Building" }, { "Name": "Urban" }, { "Name": "City" } ], "Name": "Downtown" }, { "Instances": [], "Confidence": 69.84622955322266, "Parents": [ { "Name": "Road" } ], "Name": "Intersection" }, { "Instances": [], "Confidence": 57.68518829345703, "Parents": [ { "Name": "Sports Car" }, { "Name": "Car" }, { "Name": "Vehicle" }, { "Name": "Transportation" } ], "Name": "Coupe" }, { "Instances": [], "Confidence": 57.68518829345703, "Parents": [ { "Name": "Car" }, { "Name": "Vehicle" }, { "Name": "Transportation" } ], "Name": "Sports Car" }, { "Instances": [], "Confidence": 56.59492111206055, "Parents": [ { "Name": "Path" } ], "Name": "Sidewalk" }, { "Instances": [], "Confidence": 56.59492111206055, "Parents": [ { "Name": "Path" } ], "Name": "Pavement" }, { "Instances": [], "Confidence": 55.58770751953125, "Parents": [ { "Name": "Building" }, { "Name": "Urban" } ], "Name": "Neighborhood" } ], "LabelModelVersion": "2.0" }Pour plus d'informations, consultez la section Détection d'étiquettes dans une image dans le manuel Amazon Rekognition Developer Guide.
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Pour API plus de détails, voir DetectLabels
la section Référence des AWS CLI commandes.
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- Java
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- SDKpour Java 2.x
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Note
Il y en a plus sur GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le référentiel d’exemples de code AWS
. import software.amazon.awssdk.core.SdkBytes; import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.rekognition.RekognitionClient; import software.amazon.awssdk.services.rekognition.model.Image; import software.amazon.awssdk.services.rekognition.model.DetectLabelsRequest; import software.amazon.awssdk.services.rekognition.model.DetectLabelsResponse; import software.amazon.awssdk.services.rekognition.model.Label; import software.amazon.awssdk.services.rekognition.model.RekognitionException; import java.io.FileInputStream; import java.io.FileNotFoundException; import java.io.InputStream; import java.util.List; /** * Before running this Java V2 code example, set up your development * environment, including your credentials. * * For more information, see the following documentation topic: * * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html */ public class DetectLabels { public static void main(String[] args) { final String usage = """ Usage: <sourceImage> Where: sourceImage - The path to the image (for example, C:\\AWS\\pic1.png).\s """; if (args.length != 1) { System.out.println(usage); System.exit(1); } String sourceImage = args[0]; Region region = Region.US_EAST_1; RekognitionClient rekClient = RekognitionClient.builder() .region(region) .build(); detectImageLabels(rekClient, sourceImage); rekClient.close(); } public static void detectImageLabels(RekognitionClient rekClient, String sourceImage) { try { InputStream sourceStream = new FileInputStream(sourceImage); SdkBytes sourceBytes = SdkBytes.fromInputStream(sourceStream); // Create an Image object for the source image. Image souImage = Image.builder() .bytes(sourceBytes) .build(); DetectLabelsRequest detectLabelsRequest = DetectLabelsRequest.builder() .image(souImage) .maxLabels(10) .build(); DetectLabelsResponse labelsResponse = rekClient.detectLabels(detectLabelsRequest); List<Label> labels = labelsResponse.labels(); System.out.println("Detected labels for the given photo"); for (Label label : labels) { System.out.println(label.name() + ": " + label.confidence().toString()); } } catch (RekognitionException | FileNotFoundException e) { System.out.println(e.getMessage()); System.exit(1); } } }-
Pour API plus de détails, voir DetectLabelsla section AWS SDK for Java 2.x APIRéférence.
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- Kotlin
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- SDKpour Kotlin
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Note
Il y en a plus sur GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le référentiel d’exemples de code AWS
. suspend fun detectImageLabels(sourceImage: String) { val souImage = Image { bytes = (File(sourceImage).readBytes()) } val request = DetectLabelsRequest { image = souImage maxLabels = 10 } RekognitionClient { region = "us-east-1" }.use { rekClient -> val response = rekClient.detectLabels(request) response.labels?.forEach { label -> println("${label.name} : ${label.confidence}") } } }-
Pour API plus de détails, voir DetectLabels
la APIréférence AWS SDK à Kotlin.
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- Python
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- SDKpour Python (Boto3)
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Note
Il y en a plus sur GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le référentiel d’exemples de code AWS
. class RekognitionImage: """ Encapsulates an Amazon Rekognition image. This class is a thin wrapper around parts of the Boto3 Amazon Rekognition API. """ def __init__(self, image, image_name, rekognition_client): """ Initializes the image object. :param image: Data that defines the image, either the image bytes or an Amazon S3 bucket and object key. :param image_name: The name of the image. :param rekognition_client: A Boto3 Rekognition client. """ self.image = image self.image_name = image_name self.rekognition_client = rekognition_client def detect_labels(self, max_labels): """ Detects labels in the image. Labels are objects and people. :param max_labels: The maximum number of labels to return. :return: The list of labels detected in the image. """ try: response = self.rekognition_client.detect_labels( Image=self.image, MaxLabels=max_labels ) labels = [RekognitionLabel(label) for label in response["Labels"]] logger.info("Found %s labels in %s.", len(labels), self.image_name) except ClientError: logger.info("Couldn't detect labels in %s.", self.image_name) raise else: return labels-
Pour API plus de détails, reportez-vous DetectLabelsà la section AWS SDKpour la référence Python (Boto3). API
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DetectFaces
DetectModerationLabels