DetectLabelsOR와 함께 사용 AWS SDK CLI - AWS SDK코드 예제

AWS 문서 AWS SDK SDK 예제 GitHub 리포지토리에 더 많은 예제가 있습니다.

기계 번역으로 제공되는 번역입니다. 제공된 번역과 원본 영어의 내용이 상충하는 경우에는 영어 버전이 우선합니다.

DetectLabelsOR와 함께 사용 AWS SDK CLI

다음 코드 예제는 DetectLabels의 사용 방법을 보여 줍니다.

자세한 내용은 이미지에서 레이블 감지를 참조하세요.

.NET
AWS SDK for .NET
참고

더 많은 정보가 있습니다 GitHub. AWS 코드 예시 리포지토리에서 전체 예시를 찾고 설정 및 실행하는 방법을 배워보세요.

    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);
            }
        }
    }

컴퓨터에 저장된 이미지 파일에서 레이블을 감지합니다.

    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);
            }
        }
    }
C++
SDKC++의 경우
참고

더 많은 정보가 있습니다. GitHub AWS 코드 예시 리포지토리에서 전체 예시를 찾고 설정 및 실행하는 방법을 배워보세요.

//! 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();
}
CLI
AWS CLI

이미지에서 레이블을 감지하는 방법

다음 detect-labels 예시에서는 Amazon S3 버킷에 저장된 이미지에서 장면과 객체를 감지합니다.

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

출력:

{
    "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"
}

자세한 내용은 Amazon Rekognition 개발자 안내서의 이미지에서 레이블 감지를 참조하세요.

  • 자세한 API 내용은 AWS CLI 명령 DetectLabels참조를 참조하십시오.

Java
SDK자바 2.x의 경우
참고

더 많은 내용이 있습니다. GitHub AWS 코드 예시 리포지토리에서 전체 예시를 찾고 설정 및 실행하는 방법을 배워보세요.

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);
        }
    }
}
Kotlin
SDK코틀린의 경우
참고

더 많은 정보가 있습니다. GitHub AWS 코드 예시 리포지토리에서 전체 예시를 찾고 설정 및 실행하는 방법을 배워보세요.

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}")
        }
    }
}

  • 자세한 API AWS SDK내용은 Kotlin API 참조를 참조하세요 DetectLabels.

Python
SDK파이썬용 (보토3)
참고

더 많은 정보가 있습니다. GitHub AWS 코드 예시 리포지토리에서 전체 예시를 찾고 설정 및 실행하는 방법을 배워보세요.

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

  • 자세한 API AWS SDK내용은 Python (Boto3) API 참조를 참조하십시오 DetectLabels.