Verwenden Sie DetectLabels mit einem AWS SDKoder CLI

Die folgenden Codebeispiele zeigen die VerwendungDetectLabels.

Weitere Informationen finden Sie unter Erkennen von Labels in einem Bild.

.NET

AWS SDK for .NET

Anmerkung

Es gibt noch mehr dazu GitHub. Das vollständige Beispiel und Informationen zur Einrichtung und Ausführung finden Sie in AWS Repository mit Codebeispielen.

    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);
            }
        }
    }

Erkennen Sie Labels in einer Bilddatei, die auf Ihrem Computer gespeichert ist.

    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);
            }
        }
    }

• APIEinzelheiten finden Sie DetectLabelsunter AWS SDK for .NET APIReferenz.

C++

SDKfür C++

Anmerkung

Es gibt noch mehr dazu GitHub. Das vollständige Beispiel und Informationen zur Einrichtung und Ausführung finden Sie in AWS Repository mit Codebeispielen.

//! 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();
}

• APIEinzelheiten finden Sie DetectLabelsunter AWS SDK for C++ APIReferenz.

CLI

AWS CLI

Um ein Etikett in einem Bild zu erkennen

Das folgende detect-labels Beispiel erkennt Szenen und Objekte in einem Bild, das in einem Amazon S3 S3-Bucket gespeichert ist.

aws rekognition detect-labels \
    --image '{"S3Object":{"Bucket":"bucket","Name":"image"}}'

Ausgabe:

{
    "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"
}

Weitere Informationen finden Sie unter Erkennen von Labels in einem Bild im Amazon Rekognition Developer Guide.

• APIEinzelheiten finden Sie unter DetectLabelsAWS CLI Befehlsreferenz.

Java

SDKfür Java 2.x

Anmerkung

Es gibt noch mehr dazu. GitHub Das vollständige Beispiel und Informationen zur Einrichtung und Ausführung finden Sie in AWS Repository mit Codebeispielen.

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);
        }
    }
}

• APIEinzelheiten finden Sie DetectLabelsunter AWS SDK for Java 2.x APIReferenz.

Kotlin

SDKfür Kotlin

Anmerkung

Es gibt noch mehr dazu. GitHub Das vollständige Beispiel und Informationen zur Einrichtung und Ausführung finden Sie in AWS Repository mit Codebeispielen.

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}")
        }
    }
}

• APIEinzelheiten finden Sie DetectLabelsunter AWS SDKals API Kotlin-Referenz.

Python

SDKfür Python (Boto3)

Anmerkung

Es gibt noch mehr dazu. GitHub Das vollständige Beispiel und Informationen zur Einrichtung und Ausführung finden Sie in AWS Repository mit Codebeispielen.

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

• APIEinzelheiten finden Sie DetectLabelsunter AWS SDKfür Python (Boto3) API -Referenz.

Für eine vollständige Liste von AWS SDKEntwicklerhandbücher und Codebeispiele finden Sie unterRekognition mit einem AWS SDK. Dieses Thema enthält auch Informationen zu den ersten Schritten und Details zu früheren SDK Versionen.