翻訳は機械翻訳により提供されています。提供された翻訳内容と英語版の間で齟齬、不一致または矛盾がある場合、英語版が優先します。

# SDK を使用した Amazon Rekognition のシナリオ AWS SDKs
<a name="service_code_examples_scenarios"></a>

次のコード例は、 AWS SDKs を使用して Amazon Rekognition で一般的なシナリオを実装する方法を示しています。これらのシナリオでは、Amazon Rekognition 内で複数の関数を呼び出したり、他の AWS のサービスと組み合わせたりすることで、特定のタスクを実行する方法を示しています。各シナリオには、完全なソースコードへのリンクが含まれており、そこからコードの設定方法と実行方法に関する手順を確認できます。

シナリオは、サービスアクションをコンテキストで理解するのに役立つ中級レベルの経験を対象としています。

**Topics**
+ [コレクションを構築し、その中に顔を検索します。](example_rekognition_Usage_FindFacesInCollection_section.md)
+ [サーバーレスアプリケーションを作成して写真の管理](example_cross_PAM_section.md)
+ [画像内の PPE を検出する](example_cross_RekognitionPhotoAnalyzerPPE_section.md)
+ [イメージ内の要素の検出と表示](example_rekognition_Usage_DetectAndDisplayImage_section.md)
+ [イメージ内の顔を検出します](example_cross_DetectFaces_section.md)
+ [ビデオ内の情報を検出する](example_rekognition_VideoDetection_section.md)
+ [画像内のオブジェクトを検出する](example_cross_RekognitionPhotoAnalyzer_section.md)
+ [動画内の人物や物体を検出する](example_cross_RekognitionVideoDetection_section.md)
+ [EXIF およびその他のイメージ情報を保存します](example_cross_DetectLabels_section.md)

# AWS SDK を使用して Amazon Rekognition コレクションを構築し、その中に顔を見つける
<a name="example_rekognition_Usage_FindFacesInCollection_section"></a>

次のコード例は、以下の操作方法を示しています。
+ Amazon Rekognition コレクションを作成します。
+ このコレクションにイメージを追加し、その中から顔を検出します。
+ 参照イメージに一致する顔をコレクション内で検索します。
+ コレクションを削除します。

詳細については、[コレクション内の顔を検索するには](https://docs.aws.amazon.com/rekognition/latest/dg/collections.html) を参照してください。

------
#### [ Python ]

**SDK for Python (Boto3)**  
 GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples)での設定と実行の方法を確認してください。
Amazon Rekognition 関数をラップするクラスを作成します。  

```
import logging
from pprint import pprint
import boto3
from botocore.exceptions import ClientError
from rekognition_objects import RekognitionFace
from rekognition_image_detection import RekognitionImage

logger = logging.getLogger(__name__)


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


    @classmethod
    def from_file(cls, image_file_name, rekognition_client, image_name=None):
        """
        Creates a RekognitionImage object from a local file.

        :param image_file_name: The file name of the image. The file is opened and its
                                bytes are read.
        :param rekognition_client: A Boto3 Rekognition client.
        :param image_name: The name of the image. If this is not specified, the
                           file name is used as the image name.
        :return: The RekognitionImage object, initialized with image bytes from the
                 file.
        """
        with open(image_file_name, "rb") as img_file:
            image = {"Bytes": img_file.read()}
        name = image_file_name if image_name is None else image_name
        return cls(image, name, rekognition_client)


class RekognitionCollectionManager:
    """
    Encapsulates Amazon Rekognition collection management functions.
    This class is a thin wrapper around parts of the Boto3 Amazon Rekognition API.
    """

    def __init__(self, rekognition_client):
        """
        Initializes the collection manager object.

        :param rekognition_client: A Boto3 Rekognition client.
        """
        self.rekognition_client = rekognition_client


    def create_collection(self, collection_id):
        """
        Creates an empty collection.

        :param collection_id: Text that identifies the collection.
        :return: The newly created collection.
        """
        try:
            response = self.rekognition_client.create_collection(
                CollectionId=collection_id
            )
            response["CollectionId"] = collection_id
            collection = RekognitionCollection(response, self.rekognition_client)
            logger.info("Created collection %s.", collection_id)
        except ClientError:
            logger.exception("Couldn't create collection %s.", collection_id)
            raise
        else:
            return collection


    def list_collections(self, max_results):
        """
        Lists collections for the current account.

        :param max_results: The maximum number of collections to return.
        :return: The list of collections for the current account.
        """
        try:
            response = self.rekognition_client.list_collections(MaxResults=max_results)
            collections = [
                RekognitionCollection({"CollectionId": col_id}, self.rekognition_client)
                for col_id in response["CollectionIds"]
            ]
        except ClientError:
            logger.exception("Couldn't list collections.")
            raise
        else:
            return collections



class RekognitionCollection:
    """
    Encapsulates an Amazon Rekognition collection. This class is a thin wrapper
    around parts of the Boto3 Amazon Rekognition API.
    """

    def __init__(self, collection, rekognition_client):
        """
        Initializes a collection object.

        :param collection: Collection data in the format returned by a call to
                           create_collection.
        :param rekognition_client: A Boto3 Rekognition client.
        """
        self.collection_id = collection["CollectionId"]
        self.collection_arn, self.face_count, self.created = self._unpack_collection(
            collection
        )
        self.rekognition_client = rekognition_client

    @staticmethod
    def _unpack_collection(collection):
        """
        Unpacks optional parts of a collection that can be returned by
        describe_collection.

        :param collection: The collection data.
        :return: A tuple of the data in the collection.
        """
        return (
            collection.get("CollectionArn"),
            collection.get("FaceCount", 0),
            collection.get("CreationTimestamp"),
        )


    def to_dict(self):
        """
        Renders parts of the collection data to a dict.

        :return: The collection data as a dict.
        """
        rendering = {
            "collection_id": self.collection_id,
            "collection_arn": self.collection_arn,
            "face_count": self.face_count,
            "created": self.created,
        }
        return rendering


    def describe_collection(self):
        """
        Gets data about the collection from the Amazon Rekognition service.

        :return: The collection rendered as a dict.
        """
        try:
            response = self.rekognition_client.describe_collection(
                CollectionId=self.collection_id
            )
            # Work around capitalization of Arn vs. ARN
            response["CollectionArn"] = response.get("CollectionARN")
            (
                self.collection_arn,
                self.face_count,
                self.created,
            ) = self._unpack_collection(response)
            logger.info("Got data for collection %s.", self.collection_id)
        except ClientError:
            logger.exception("Couldn't get data for collection %s.", self.collection_id)
            raise
        else:
            return self.to_dict()


    def delete_collection(self):
        """
        Deletes the collection.
        """
        try:
            self.rekognition_client.delete_collection(CollectionId=self.collection_id)
            logger.info("Deleted collection %s.", self.collection_id)
            self.collection_id = None
        except ClientError:
            logger.exception("Couldn't delete collection %s.", self.collection_id)
            raise


    def index_faces(self, image, max_faces):
        """
        Finds faces in the specified image, indexes them, and stores them in the
        collection.

        :param image: The image to index.
        :param max_faces: The maximum number of faces to index.
        :return: A tuple. The first element is a list of indexed faces.
                 The second element is a list of faces that couldn't be indexed.
        """
        try:
            response = self.rekognition_client.index_faces(
                CollectionId=self.collection_id,
                Image=image.image,
                ExternalImageId=image.image_name,
                MaxFaces=max_faces,
                DetectionAttributes=["ALL"],
            )
            indexed_faces = [
                RekognitionFace({**face["Face"], **face["FaceDetail"]})
                for face in response["FaceRecords"]
            ]
            unindexed_faces = [
                RekognitionFace(face["FaceDetail"])
                for face in response["UnindexedFaces"]
            ]
            logger.info(
                "Indexed %s faces in %s. Could not index %s faces.",
                len(indexed_faces),
                image.image_name,
                len(unindexed_faces),
            )
        except ClientError:
            logger.exception("Couldn't index faces in image %s.", image.image_name)
            raise
        else:
            return indexed_faces, unindexed_faces


    def list_faces(self, max_results):
        """
        Lists the faces currently indexed in the collection.

        :param max_results: The maximum number of faces to return.
        :return: The list of faces in the collection.
        """
        try:
            response = self.rekognition_client.list_faces(
                CollectionId=self.collection_id, MaxResults=max_results
            )
            faces = [RekognitionFace(face) for face in response["Faces"]]
            logger.info(
                "Found %s faces in collection %s.", len(faces), self.collection_id
            )
        except ClientError:
            logger.exception(
                "Couldn't list faces in collection %s.", self.collection_id
            )
            raise
        else:
            return faces


    def search_faces(self, face_id, threshold, max_faces):
        """
        Searches for faces in the collection that match another face from the
        collection.

        :param face_id: The ID of the face in the collection to search for.
        :param threshold: The match confidence must be greater than this value
                          for a face to be included in the results.
        :param max_faces: The maximum number of faces to return.
        :return: The list of matching faces found in the collection. This list does
                 not contain the face specified by `face_id`.
        """
        try:
            response = self.rekognition_client.search_faces(
                CollectionId=self.collection_id,
                FaceId=face_id,
                FaceMatchThreshold=threshold,
                MaxFaces=max_faces,
            )
            faces = [RekognitionFace(face["Face"]) for face in response["FaceMatches"]]
            logger.info(
                "Found %s faces in %s that match %s.",
                len(faces),
                self.collection_id,
                face_id,
            )
        except ClientError:
            logger.exception(
                "Couldn't search for faces in %s that match %s.",
                self.collection_id,
                face_id,
            )
            raise
        else:
            return faces


    def search_faces_by_image(self, image, threshold, max_faces):
        """
        Searches for faces in the collection that match the largest face in the
        reference image.

        :param image: The image that contains the reference face to search for.
        :param threshold: The match confidence must be greater than this value
                          for a face to be included in the results.
        :param max_faces: The maximum number of faces to return.
        :return: A tuple. The first element is the face found in the reference image.
                 The second element is the list of matching faces found in the
                 collection.
        """
        try:
            response = self.rekognition_client.search_faces_by_image(
                CollectionId=self.collection_id,
                Image=image.image,
                FaceMatchThreshold=threshold,
                MaxFaces=max_faces,
            )
            image_face = RekognitionFace(
                {
                    "BoundingBox": response["SearchedFaceBoundingBox"],
                    "Confidence": response["SearchedFaceConfidence"],
                }
            )
            collection_faces = [
                RekognitionFace(face["Face"]) for face in response["FaceMatches"]
            ]
            logger.info(
                "Found %s faces in the collection that match the largest "
                "face in %s.",
                len(collection_faces),
                image.image_name,
            )
        except ClientError:
            logger.exception(
                "Couldn't search for faces in %s that match %s.",
                self.collection_id,
                image.image_name,
            )
            raise
        else:
            return image_face, collection_faces


class RekognitionFace:
    """Encapsulates an Amazon Rekognition face."""

    def __init__(self, face, timestamp=None):
        """
        Initializes the face object.

        :param face: Face data, in the format returned by Amazon Rekognition
                     functions.
        :param timestamp: The time when the face was detected, if the face was
                          detected in a video.
        """
        self.bounding_box = face.get("BoundingBox")
        self.confidence = face.get("Confidence")
        self.landmarks = face.get("Landmarks")
        self.pose = face.get("Pose")
        self.quality = face.get("Quality")
        age_range = face.get("AgeRange")
        if age_range is not None:
            self.age_range = (age_range.get("Low"), age_range.get("High"))
        else:
            self.age_range = None
        self.smile = face.get("Smile", {}).get("Value")
        self.eyeglasses = face.get("Eyeglasses", {}).get("Value")
        self.sunglasses = face.get("Sunglasses", {}).get("Value")
        self.gender = face.get("Gender", {}).get("Value", None)
        self.beard = face.get("Beard", {}).get("Value")
        self.mustache = face.get("Mustache", {}).get("Value")
        self.eyes_open = face.get("EyesOpen", {}).get("Value")
        self.mouth_open = face.get("MouthOpen", {}).get("Value")
        self.emotions = [
            emo.get("Type")
            for emo in face.get("Emotions", [])
            if emo.get("Confidence", 0) > 50
        ]
        self.face_id = face.get("FaceId")
        self.image_id = face.get("ImageId")
        self.timestamp = timestamp

    def to_dict(self):
        """
        Renders some of the face data to a dict.

        :return: A dict that contains the face data.
        """
        rendering = {}
        if self.bounding_box is not None:
            rendering["bounding_box"] = self.bounding_box
        if self.age_range is not None:
            rendering["age"] = f"{self.age_range[0]} - {self.age_range[1]}"
        if self.gender is not None:
            rendering["gender"] = self.gender
        if self.emotions:
            rendering["emotions"] = self.emotions
        if self.face_id is not None:
            rendering["face_id"] = self.face_id
        if self.image_id is not None:
            rendering["image_id"] = self.image_id
        if self.timestamp is not None:
            rendering["timestamp"] = self.timestamp
        has = []
        if self.smile:
            has.append("smile")
        if self.eyeglasses:
            has.append("eyeglasses")
        if self.sunglasses:
            has.append("sunglasses")
        if self.beard:
            has.append("beard")
        if self.mustache:
            has.append("mustache")
        if self.eyes_open:
            has.append("open eyes")
        if self.mouth_open:
            has.append("open mouth")
        if has:
            rendering["has"] = has
        return rendering
```
ラッパークラスを使用して、一連のイメージから顔のコレクションを作成し、コレクション内の顔を検索します。  

```
def usage_demo():
    print("-" * 88)
    print("Welcome to the Amazon Rekognition face collection demo!")
    print("-" * 88)

    logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")

    rekognition_client = boto3.client("rekognition")
    images = [
        RekognitionImage.from_file(
            ".media/pexels-agung-pandit-wiguna-1128316.jpg",
            rekognition_client,
            image_name="sitting",
        ),
        RekognitionImage.from_file(
            ".media/pexels-agung-pandit-wiguna-1128317.jpg",
            rekognition_client,
            image_name="hopping",
        ),
        RekognitionImage.from_file(
            ".media/pexels-agung-pandit-wiguna-1128318.jpg",
            rekognition_client,
            image_name="biking",
        ),
    ]

    collection_mgr = RekognitionCollectionManager(rekognition_client)
    collection = collection_mgr.create_collection("doc-example-collection-demo")
    print(f"Created collection {collection.collection_id}:")
    pprint(collection.describe_collection())

    print("Indexing faces from three images:")
    for image in images:
        collection.index_faces(image, 10)
    print("Listing faces in collection:")
    faces = collection.list_faces(10)
    for face in faces:
        pprint(face.to_dict())
    input("Press Enter to continue.")

    print(
        f"Searching for faces in the collection that match the first face in the "
        f"list (Face ID: {faces[0].face_id}."
    )
    found_faces = collection.search_faces(faces[0].face_id, 80, 10)
    print(f"Found {len(found_faces)} matching faces.")
    for face in found_faces:
        pprint(face.to_dict())
    input("Press Enter to continue.")

    print(
        f"Searching for faces in the collection that match the largest face in "
        f"{images[0].image_name}."
    )
    image_face, match_faces = collection.search_faces_by_image(images[0], 80, 10)
    print(f"The largest face in {images[0].image_name} is:")
    pprint(image_face.to_dict())
    print(f"Found {len(match_faces)} matching faces.")
    for face in match_faces:
        pprint(face.to_dict())
    input("Press Enter to continue.")

    collection.delete_collection()
    print("Thanks for watching!")
    print("-" * 88)
```

------

 AWS SDK 開発者ガイドとコード例の完全なリストについては、「」を参照してください[AWS SDK での Rekognition の使用](sdk-general-information-section.md)。このトピックには、使用開始方法に関する情報と、以前の SDK バージョンの詳細も含まれています。

# ユーザーがラベルを使用して写真を管理できる写真アセット管理アプリケーションの作成
<a name="example_cross_PAM_section"></a>

以下のコード例は、ユーザーがラベルを使用して写真を管理できるサーバーレスアプリケーションの作成方法を示しています。

------
#### [ .NET ]

**SDK for .NET**  
 Amazon Rekognition を使用して画像内のラベルを検出し、保存して後で取得できるようにする写真アセット管理アプリケーションの開発方法を示します。  
完全なソースコードと設定および実行の手順については、[GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/cross-service/PhotoAssetManager) で完全な例を参照してください。  
この例のソースについて詳しくは、[AWS  コミュニティ](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app)でブログ投稿を参照してください。  

**この例で使用されているサービス**
+ API ゲートウェイ
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS

------
#### [ C\$1\$1 ]

**SDK for C\$1\$1**  
 Amazon Rekognition を使用して画像内のラベルを検出し、保存して後で取得できるようにする写真アセット管理アプリケーションの開発方法を示します。  
完全なソースコードと設定および実行の手順については、[GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/cpp/example_code/cross-service/photo_asset_manager) で完全な例を参照してください。  
この例のソースについて詳しくは、[AWS  コミュニティ](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app)でブログ投稿を参照してください。  

**この例で使用されているサービス**
+ API ゲートウェイ
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS

------
#### [ Java ]

**SDK for Java 2.x**  
 Amazon Rekognition を使用して画像内のラベルを検出し、保存して後で取得できるようにする写真アセット管理アプリケーションの開発方法を示します。  
完全なソースコードと設定および実行の手順については、[GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/pam_source_files) で完全な例を参照してください。  
この例のソースについて詳しくは、[AWS  コミュニティ](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app)でブログ投稿を参照してください。  

**この例で使用されているサービス**
+ API ゲートウェイ
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS

------
#### [ JavaScript ]

**SDK for JavaScript (v3)**  
 Amazon Rekognition を使用して画像内のラベルを検出し、保存して後で取得できるようにする写真アセット管理アプリケーションの開発方法を示します。  
完全なソースコードと設定および実行の手順については、[GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/cross-services/photo-asset-manager) で完全な例を参照してください。  
この例のソースについて詳しくは、[AWS  コミュニティ](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app)でブログ投稿を参照してください。  

**この例で使用されているサービス**
+ API ゲートウェイ
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS

------
#### [ Kotlin ]

**SDK for Kotlin**  
 Amazon Rekognition を使用して画像内のラベルを検出し、保存して後で取得できるようにする写真アセット管理アプリケーションの開発方法を示します。  
完全なソースコードと設定および実行の手順については、[GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/usecases/creating_pam) で完全な例を参照してください。  
この例のソースについて詳しくは、[AWS  コミュニティ](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app)でブログ投稿を参照してください。  

**この例で使用されているサービス**
+ API ゲートウェイ
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS

------
#### [ PHP ]

**SDK for PHP**  
 Amazon Rekognition を使用して画像内のラベルを検出し、保存して後で取得できるようにする写真アセット管理アプリケーションの開発方法を示します。  
完全なソースコードと設定および実行の手順については、[GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/php/applications/photo_asset_manager) で完全な例を参照してください。  
この例のソースについて詳しくは、[AWS  コミュニティ](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app)でブログ投稿を参照してください。  

**この例で使用されているサービス**
+ API ゲートウェイ
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS

------
#### [ Rust ]

**SDK for Rust**  
 Amazon Rekognition を使用して画像内のラベルを検出し、保存して後で取得できるようにする写真アセット管理アプリケーションの開発方法を示します。  
完全なソースコードと設定および実行の手順については、[GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/rustv1/cross_service/photo_asset_management) で完全な例を参照してください。  
この例のソースについて詳しくは、[AWS  コミュニティ](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app)でブログ投稿を参照してください。  

**この例で使用されているサービス**
+ API ゲートウェイ
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS

------

 AWS SDK 開発者ガイドとコード例の完全なリストについては、「」を参照してください[AWS SDK での Rekognition の使用](sdk-general-information-section.md)。このトピックには、使用開始方法に関する情報と、以前の SDK バージョンの詳細も含まれています。

# AWS SDK を使用して Amazon Rekognition でイメージ内の PPE を検出する
<a name="example_cross_RekognitionPhotoAnalyzerPPE_section"></a>

次のコード例は、Amazon Rekognition を使用してイメージ内の個人用防護具 (PPE) を検出するアプリケーションを構築する方法を示しています。

------
#### [ Java ]

**SDK for Java 2.x**  
 個人用保護具を使用してイメージを検出する AWS Lambda 関数を作成する方法を示します。  
 完全なソースコードとセットアップおよび実行の手順については、[GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/creating_lambda_ppe) で完全な例を参照してください。  

**この例で使用されているサービス**
+ DynamoDB
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES

------

 AWS SDK 開発者ガイドとコード例の完全なリストについては、「」を参照してください[AWS SDK での Rekognition の使用](sdk-general-information-section.md)。このトピックには、使用開始方法に関する情報と、以前の SDK バージョンの詳細も含まれています。

# AWS SDK を使用して Amazon Rekognition でイメージ内の要素を検出して表示する
<a name="example_rekognition_Usage_DetectAndDisplayImage_section"></a>

次のコード例は、以下の操作方法を示しています。
+ Amazon Rekognition を使用してイメージから要素を検出します。
+ イメージを表示し、検出された要素の周囲に境界ボックスを描画します。

詳細については、[境界ボックスの表示](https://docs.aws.amazon.com/rekognition/latest/dg/images-displaying-bounding-boxes.html) を参照してください。

------
#### [ Python ]

**SDK for Python (Boto3)**  
 GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples)での設定と実行の方法を確認してください。
Amazon Rekognition 関数をラップするクラスを作成します。  

```
import logging
from pprint import pprint
import boto3
from botocore.exceptions import ClientError
import requests

from rekognition_objects import (
    RekognitionFace,
    RekognitionCelebrity,
    RekognitionLabel,
    RekognitionModerationLabel,
    RekognitionText,
    show_bounding_boxes,
    show_polygons,
)

logger = logging.getLogger(__name__)


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


    @classmethod
    def from_file(cls, image_file_name, rekognition_client, image_name=None):
        """
        Creates a RekognitionImage object from a local file.

        :param image_file_name: The file name of the image. The file is opened and its
                                bytes are read.
        :param rekognition_client: A Boto3 Rekognition client.
        :param image_name: The name of the image. If this is not specified, the
                           file name is used as the image name.
        :return: The RekognitionImage object, initialized with image bytes from the
                 file.
        """
        with open(image_file_name, "rb") as img_file:
            image = {"Bytes": img_file.read()}
        name = image_file_name if image_name is None else image_name
        return cls(image, name, rekognition_client)


    @classmethod
    def from_bucket(cls, s3_object, rekognition_client):
        """
        Creates a RekognitionImage object from an Amazon S3 object.

        :param s3_object: An Amazon S3 object that identifies the image. The image
                          is not retrieved until needed for a later call.
        :param rekognition_client: A Boto3 Rekognition client.
        :return: The RekognitionImage object, initialized with Amazon S3 object data.
        """
        image = {"S3Object": {"Bucket": s3_object.bucket_name, "Name": s3_object.key}}
        return cls(image, s3_object.key, rekognition_client)


    def detect_faces(self):
        """
        Detects faces in the image.

        :return: The list of faces found in the image.
        """
        try:
            response = self.rekognition_client.detect_faces(
                Image=self.image, Attributes=["ALL"]
            )
            faces = [RekognitionFace(face) for face in response["FaceDetails"]]
            logger.info("Detected %s faces.", len(faces))
        except ClientError:
            logger.exception("Couldn't detect faces in %s.", self.image_name)
            raise
        else:
            return faces


    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


    def recognize_celebrities(self):
        """
        Detects celebrities in the image.

        :return: A tuple. The first element is the list of celebrities found in
                 the image. The second element is the list of faces that were
                 detected but did not match any known celebrities.
        """
        try:
            response = self.rekognition_client.recognize_celebrities(Image=self.image)
            celebrities = [
                RekognitionCelebrity(celeb) for celeb in response["CelebrityFaces"]
            ]
            other_faces = [
                RekognitionFace(face) for face in response["UnrecognizedFaces"]
            ]
            logger.info(
                "Found %s celebrities and %s other faces in %s.",
                len(celebrities),
                len(other_faces),
                self.image_name,
            )
        except ClientError:
            logger.exception("Couldn't detect celebrities in %s.", self.image_name)
            raise
        else:
            return celebrities, other_faces



    def compare_faces(self, target_image, similarity):
        """
        Compares faces in the image with the largest face in the target image.

        :param target_image: The target image to compare against.
        :param similarity: Faces in the image must have a similarity value greater
                           than this value to be included in the results.
        :return: A tuple. The first element is the list of faces that match the
                 reference image. The second element is the list of faces that have
                 a similarity value below the specified threshold.
        """
        try:
            response = self.rekognition_client.compare_faces(
                SourceImage=self.image,
                TargetImage=target_image.image,
                SimilarityThreshold=similarity,
            )
            matches = [
                RekognitionFace(match["Face"]) for match in response["FaceMatches"]
            ]
            unmatches = [RekognitionFace(face) for face in response["UnmatchedFaces"]]
            logger.info(
                "Found %s matched faces and %s unmatched faces.",
                len(matches),
                len(unmatches),
            )
        except ClientError:
            logger.exception(
                "Couldn't match faces from %s to %s.",
                self.image_name,
                target_image.image_name,
            )
            raise
        else:
            return matches, unmatches


    def detect_moderation_labels(self):
        """
        Detects moderation labels in the image. Moderation labels identify content
        that may be inappropriate for some audiences.

        :return: The list of moderation labels found in the image.
        """
        try:
            response = self.rekognition_client.detect_moderation_labels(
                Image=self.image
            )
            labels = [
                RekognitionModerationLabel(label)
                for label in response["ModerationLabels"]
            ]
            logger.info(
                "Found %s moderation labels in %s.", len(labels), self.image_name
            )
        except ClientError:
            logger.exception(
                "Couldn't detect moderation labels in %s.", self.image_name
            )
            raise
        else:
            return labels


    def detect_text(self):
        """
        Detects text in the image.

        :return The list of text elements found in the image.
        """
        try:
            response = self.rekognition_client.detect_text(Image=self.image)
            texts = [RekognitionText(text) for text in response["TextDetections"]]
            logger.info("Found %s texts in %s.", len(texts), self.image_name)
        except ClientError:
            logger.exception("Couldn't detect text in %s.", self.image_name)
            raise
        else:
            return texts
```
境界ボックスとポリゴンを描画するヘルパー関数を作成します。  

```
import io
import logging
from PIL import Image, ImageDraw

logger = logging.getLogger(__name__)


def show_bounding_boxes(image_bytes, box_sets, colors):
    """
    Draws bounding boxes on an image and shows it with the default image viewer.

    :param image_bytes: The image to draw, as bytes.
    :param box_sets: A list of lists of bounding boxes to draw on the image.
    :param colors: A list of colors to use to draw the bounding boxes.
    """
    image = Image.open(io.BytesIO(image_bytes))
    draw = ImageDraw.Draw(image)
    for boxes, color in zip(box_sets, colors):
        for box in boxes:
            left = image.width * box["Left"]
            top = image.height * box["Top"]
            right = (image.width * box["Width"]) + left
            bottom = (image.height * box["Height"]) + top
            draw.rectangle([left, top, right, bottom], outline=color, width=3)
    image.show()



def show_polygons(image_bytes, polygons, color):
    """
    Draws polygons on an image and shows it with the default image viewer.

    :param image_bytes: The image to draw, as bytes.
    :param polygons: The list of polygons to draw on the image.
    :param color: The color to use to draw the polygons.
    """
    image = Image.open(io.BytesIO(image_bytes))
    draw = ImageDraw.Draw(image)
    for polygon in polygons:
        draw.polygon(
            [
                (image.width * point["X"], image.height * point["Y"])
                for point in polygon
            ],
            outline=color,
        )
    image.show()
```
Amazon Rekognition から返されたオブジェクトを解析するクラスを作成します。  

```
class RekognitionFace:
    """Encapsulates an Amazon Rekognition face."""

    def __init__(self, face, timestamp=None):
        """
        Initializes the face object.

        :param face: Face data, in the format returned by Amazon Rekognition
                     functions.
        :param timestamp: The time when the face was detected, if the face was
                          detected in a video.
        """
        self.bounding_box = face.get("BoundingBox")
        self.confidence = face.get("Confidence")
        self.landmarks = face.get("Landmarks")
        self.pose = face.get("Pose")
        self.quality = face.get("Quality")
        age_range = face.get("AgeRange")
        if age_range is not None:
            self.age_range = (age_range.get("Low"), age_range.get("High"))
        else:
            self.age_range = None
        self.smile = face.get("Smile", {}).get("Value")
        self.eyeglasses = face.get("Eyeglasses", {}).get("Value")
        self.sunglasses = face.get("Sunglasses", {}).get("Value")
        self.gender = face.get("Gender", {}).get("Value", None)
        self.beard = face.get("Beard", {}).get("Value")
        self.mustache = face.get("Mustache", {}).get("Value")
        self.eyes_open = face.get("EyesOpen", {}).get("Value")
        self.mouth_open = face.get("MouthOpen", {}).get("Value")
        self.emotions = [
            emo.get("Type")
            for emo in face.get("Emotions", [])
            if emo.get("Confidence", 0) > 50
        ]
        self.face_id = face.get("FaceId")
        self.image_id = face.get("ImageId")
        self.timestamp = timestamp

    def to_dict(self):
        """
        Renders some of the face data to a dict.

        :return: A dict that contains the face data.
        """
        rendering = {}
        if self.bounding_box is not None:
            rendering["bounding_box"] = self.bounding_box
        if self.age_range is not None:
            rendering["age"] = f"{self.age_range[0]} - {self.age_range[1]}"
        if self.gender is not None:
            rendering["gender"] = self.gender
        if self.emotions:
            rendering["emotions"] = self.emotions
        if self.face_id is not None:
            rendering["face_id"] = self.face_id
        if self.image_id is not None:
            rendering["image_id"] = self.image_id
        if self.timestamp is not None:
            rendering["timestamp"] = self.timestamp
        has = []
        if self.smile:
            has.append("smile")
        if self.eyeglasses:
            has.append("eyeglasses")
        if self.sunglasses:
            has.append("sunglasses")
        if self.beard:
            has.append("beard")
        if self.mustache:
            has.append("mustache")
        if self.eyes_open:
            has.append("open eyes")
        if self.mouth_open:
            has.append("open mouth")
        if has:
            rendering["has"] = has
        return rendering



class RekognitionCelebrity:
    """Encapsulates an Amazon Rekognition celebrity."""

    def __init__(self, celebrity, timestamp=None):
        """
        Initializes the celebrity object.

        :param celebrity: Celebrity data, in the format returned by Amazon Rekognition
                          functions.
        :param timestamp: The time when the celebrity was detected, if the celebrity
                          was detected in a video.
        """
        self.info_urls = celebrity.get("Urls")
        self.name = celebrity.get("Name")
        self.id = celebrity.get("Id")
        self.face = RekognitionFace(celebrity.get("Face"))
        self.confidence = celebrity.get("MatchConfidence")
        self.bounding_box = celebrity.get("BoundingBox")
        self.timestamp = timestamp

    def to_dict(self):
        """
        Renders some of the celebrity data to a dict.

        :return: A dict that contains the celebrity data.
        """
        rendering = self.face.to_dict()
        if self.name is not None:
            rendering["name"] = self.name
        if self.info_urls:
            rendering["info URLs"] = self.info_urls
        if self.timestamp is not None:
            rendering["timestamp"] = self.timestamp
        return rendering



class RekognitionPerson:
    """Encapsulates an Amazon Rekognition person."""

    def __init__(self, person, timestamp=None):
        """
        Initializes the person object.

        :param person: Person data, in the format returned by Amazon Rekognition
                       functions.
        :param timestamp: The time when the person was detected, if the person
                          was detected in a video.
        """
        self.index = person.get("Index")
        self.bounding_box = person.get("BoundingBox")
        face = person.get("Face")
        self.face = RekognitionFace(face) if face is not None else None
        self.timestamp = timestamp

    def to_dict(self):
        """
        Renders some of the person data to a dict.

        :return: A dict that contains the person data.
        """
        rendering = self.face.to_dict() if self.face is not None else {}
        if self.index is not None:
            rendering["index"] = self.index
        if self.bounding_box is not None:
            rendering["bounding_box"] = self.bounding_box
        if self.timestamp is not None:
            rendering["timestamp"] = self.timestamp
        return rendering



class RekognitionLabel:
    """Encapsulates an Amazon Rekognition label."""

    def __init__(self, label, timestamp=None):
        """
        Initializes the label object.

        :param label: Label data, in the format returned by Amazon Rekognition
                      functions.
        :param timestamp: The time when the label was detected, if the label
                          was detected in a video.
        """
        self.name = label.get("Name")
        self.confidence = label.get("Confidence")
        self.instances = label.get("Instances")
        self.parents = label.get("Parents")
        self.timestamp = timestamp

    def to_dict(self):
        """
        Renders some of the label data to a dict.

        :return: A dict that contains the label data.
        """
        rendering = {}
        if self.name is not None:
            rendering["name"] = self.name
        if self.timestamp is not None:
            rendering["timestamp"] = self.timestamp
        return rendering



class RekognitionModerationLabel:
    """Encapsulates an Amazon Rekognition moderation label."""

    def __init__(self, label, timestamp=None):
        """
        Initializes the moderation label object.

        :param label: Label data, in the format returned by Amazon Rekognition
                      functions.
        :param timestamp: The time when the moderation label was detected, if the
                          label was detected in a video.
        """
        self.name = label.get("Name")
        self.confidence = label.get("Confidence")
        self.parent_name = label.get("ParentName")
        self.timestamp = timestamp

    def to_dict(self):
        """
        Renders some of the moderation label data to a dict.

        :return: A dict that contains the moderation label data.
        """
        rendering = {}
        if self.name is not None:
            rendering["name"] = self.name
        if self.parent_name is not None:
            rendering["parent_name"] = self.parent_name
        if self.timestamp is not None:
            rendering["timestamp"] = self.timestamp
        return rendering



class RekognitionText:
    """Encapsulates an Amazon Rekognition text element."""

    def __init__(self, text_data):
        """
        Initializes the text object.

        :param text_data: Text data, in the format returned by Amazon Rekognition
                          functions.
        """
        self.text = text_data.get("DetectedText")
        self.kind = text_data.get("Type")
        self.id = text_data.get("Id")
        self.parent_id = text_data.get("ParentId")
        self.confidence = text_data.get("Confidence")
        self.geometry = text_data.get("Geometry")

    def to_dict(self):
        """
        Renders some of the text data to a dict.

        :return: A dict that contains the text data.
        """
        rendering = {}
        if self.text is not None:
            rendering["text"] = self.text
        if self.kind is not None:
            rendering["kind"] = self.kind
        if self.geometry is not None:
            rendering["polygon"] = self.geometry.get("Polygon")
        return rendering
```
ラッパークラスを使用して、イメージ内の要素を検出し、その境界ボックスを表示します。この例で使用されているイメージは GitHub で、指示やコードとともに確認できます。  

```
def usage_demo():
    print("-" * 88)
    print("Welcome to the Amazon Rekognition image detection demo!")
    print("-" * 88)

    logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
    rekognition_client = boto3.client("rekognition")
    street_scene_file_name = ".media/pexels-kaique-rocha-109919.jpg"
    celebrity_file_name = ".media/pexels-pixabay-53370.jpg"
    one_girl_url = "https://dhei5unw3vrsx.cloudfront.net/images/source3_resized.jpg"
    three_girls_url = "https://dhei5unw3vrsx.cloudfront.net/images/target3_resized.jpg"
    swimwear_object = boto3.resource("s3").Object(
        "console-sample-images-pdx", "yoga_swimwear.jpg"
    )
    book_file_name = ".media/pexels-christina-morillo-1181671.jpg"

    street_scene_image = RekognitionImage.from_file(
        street_scene_file_name, rekognition_client
    )
    print(f"Detecting faces in {street_scene_image.image_name}...")
    faces = street_scene_image.detect_faces()
    print(f"Found {len(faces)} faces, here are the first three.")
    for face in faces[:3]:
        pprint(face.to_dict())
    show_bounding_boxes(
        street_scene_image.image["Bytes"],
        [[face.bounding_box for face in faces]],
        ["aqua"],
    )
    input("Press Enter to continue.")

    print(f"Detecting labels in {street_scene_image.image_name}...")
    labels = street_scene_image.detect_labels(100)
    print(f"Found {len(labels)} labels.")
    for label in labels:
        pprint(label.to_dict())
    names = []
    box_sets = []
    colors = ["aqua", "red", "white", "blue", "yellow", "green"]
    for label in labels:
        if label.instances:
            names.append(label.name)
            box_sets.append([inst["BoundingBox"] for inst in label.instances])
    print(f"Showing bounding boxes for {names} in {colors[:len(names)]}.")
    show_bounding_boxes(
        street_scene_image.image["Bytes"], box_sets, colors[: len(names)]
    )
    input("Press Enter to continue.")

    celebrity_image = RekognitionImage.from_file(
        celebrity_file_name, rekognition_client
    )
    print(f"Detecting celebrities in {celebrity_image.image_name}...")
    celebs, others = celebrity_image.recognize_celebrities()
    print(f"Found {len(celebs)} celebrities.")
    for celeb in celebs:
        pprint(celeb.to_dict())
    show_bounding_boxes(
        celebrity_image.image["Bytes"],
        [[celeb.face.bounding_box for celeb in celebs]],
        ["aqua"],
    )
    input("Press Enter to continue.")

    girl_image_response = requests.get(one_girl_url)
    girl_image = RekognitionImage(
        {"Bytes": girl_image_response.content}, "one-girl", rekognition_client
    )
    group_image_response = requests.get(three_girls_url)
    group_image = RekognitionImage(
        {"Bytes": group_image_response.content}, "three-girls", rekognition_client
    )
    print("Comparing reference face to group of faces...")
    matches, unmatches = girl_image.compare_faces(group_image, 80)
    print(f"Found {len(matches)} face matching the reference face.")
    show_bounding_boxes(
        group_image.image["Bytes"],
        [[match.bounding_box for match in matches]],
        ["aqua"],
    )
    input("Press Enter to continue.")

    swimwear_image = RekognitionImage.from_bucket(swimwear_object, rekognition_client)
    print(f"Detecting suggestive content in {swimwear_object.key}...")
    labels = swimwear_image.detect_moderation_labels()
    print(f"Found {len(labels)} moderation labels.")
    for label in labels:
        pprint(label.to_dict())
    input("Press Enter to continue.")

    book_image = RekognitionImage.from_file(book_file_name, rekognition_client)
    print(f"Detecting text in {book_image.image_name}...")
    texts = book_image.detect_text()
    print(f"Found {len(texts)} text instances. Here are the first seven:")
    for text in texts[:7]:
        pprint(text.to_dict())
    show_polygons(
        book_image.image["Bytes"], [text.geometry["Polygon"] for text in texts], "aqua"
    )

    print("Thanks for watching!")
    print("-" * 88)
```

------

 AWS SDK 開発者ガイドとコード例の完全なリストについては、「」を参照してください[AWS SDK での Rekognition の使用](sdk-general-information-section.md)。このトピックには、使用開始方法に関する情報と、以前の SDK バージョンの詳細も含まれています。

# AWS SDK を使用してイメージ内の顔を検出する
<a name="example_cross_DetectFaces_section"></a>

次のコード例は、以下の操作方法を示しています。
+ イメージを Amazon S3 バケットに保存します。
+ Amazon Rekognition を使用して、年齢層、性別、感情 (笑顔など) などの顔の詳細を検出します。
+ これらの詳細を表示します。

------
#### [ Rust ]

**SDK for Rust**  
 **アップロード** プレフィックスを付け、Amazon S3 バケット内でイメージを保存し、Amazon Rekognition を使用して、年齢層、性別、感情 (笑顔など) などの顔の詳細を検出し、それらの詳細を表示します。  
 完全なソースコードとセットアップおよび実行の手順については、[GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/blob/main/rustv1/cross_service/detect_faces/src/main.rs) で完全な例を参照してください。  

**この例で使用されているサービス**
+ Amazon Rekognition
+ Amazon S3

------

 AWS SDK 開発者ガイドとコード例の完全なリストについては、「」を参照してください[AWS SDK での Rekognition の使用](sdk-general-information-section.md)。このトピックには、使用開始方法に関する情報と、以前の SDK バージョンの詳細も含まれています。

# Amazon Rekognition と AWS SDK を使用してビデオ内の情報を検出する
<a name="example_rekognition_VideoDetection_section"></a>

次のコード例は、以下を実行する方法を示しています。
+ Amazon Rekognition のジョブを開始し、人物、オブジェクト、テキストなどの要素を動画から検出します。
+ ジョブが完了するまでジョブのステータスを確認します。
+ 検出された要素のリストをジョブごとに出力します。

------
#### [ Java ]

**SDK for Java 2.x**  
 GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples)での設定と実行の方法を確認してください。
Amazon S3 バケット内のビデオから有名人の結果を取得します。  

```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartCelebrityRecognitionResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.CelebrityRecognitionSortBy;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.CelebrityRecognition;
import software.amazon.awssdk.services.rekognition.model.CelebrityDetail;
import software.amazon.awssdk.services.rekognition.model.StartCelebrityRecognitionRequest;
import software.amazon.awssdk.services.rekognition.model.GetCelebrityRecognitionRequest;
import software.amazon.awssdk.services.rekognition.model.GetCelebrityRecognitionResponse;
import java.util.List;

/**
 * To run this code example, ensure that you perform the Prerequisites as stated
 * in the Amazon Rekognition Guide:
 * https://docs.aws.amazon.com/rekognition/latest/dg/video-analyzing-with-sqs.html
 *
 * Also, ensure that set up your development environment, including your
 * credentials.
 *
 * For information, see this documentation topic:
 *
 * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
 */

public class VideoCelebrityDetection {
    private static String startJobId = "";

    public static void main(String[] args) {
        final String usage = """

                Usage:    <bucket> <video> <topicArn> <roleArn>

                Where:
                   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s
                   video - The name of video (for example, people.mp4).\s
                   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s
                   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s
                """;

        if (args.length != 4) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucket = args[0];
        String video = args[1];
        String topicArn = args[2];
        String roleArn = args[3];
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        NotificationChannel channel = NotificationChannel.builder()
                .snsTopicArn(topicArn)
                .roleArn(roleArn)
                .build();

        startCelebrityDetection(rekClient, channel, bucket, video);
        getCelebrityDetectionResults(rekClient);
        System.out.println("This example is done!");
        rekClient.close();
    }

    public static void startCelebrityDetection(RekognitionClient rekClient,
            NotificationChannel channel,
            String bucket,
            String video) {
        try {
            S3Object s3Obj = S3Object.builder()
                    .bucket(bucket)
                    .name(video)
                    .build();

            Video vidOb = Video.builder()
                    .s3Object(s3Obj)
                    .build();

            StartCelebrityRecognitionRequest recognitionRequest = StartCelebrityRecognitionRequest.builder()
                    .jobTag("Celebrities")
                    .notificationChannel(channel)
                    .video(vidOb)
                    .build();

            StartCelebrityRecognitionResponse startCelebrityRecognitionResult = rekClient
                    .startCelebrityRecognition(recognitionRequest);
            startJobId = startCelebrityRecognitionResult.jobId();

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }

    public static void getCelebrityDetectionResults(RekognitionClient rekClient) {
        try {
            String paginationToken = null;
            GetCelebrityRecognitionResponse recognitionResponse = null;
            boolean finished = false;
            String status;
            int yy = 0;

            do {
                if (recognitionResponse != null)
                    paginationToken = recognitionResponse.nextToken();

                GetCelebrityRecognitionRequest recognitionRequest = GetCelebrityRecognitionRequest.builder()
                        .jobId(startJobId)
                        .nextToken(paginationToken)
                        .sortBy(CelebrityRecognitionSortBy.TIMESTAMP)
                        .maxResults(10)
                        .build();

                // Wait until the job succeeds
                while (!finished) {
                    recognitionResponse = rekClient.getCelebrityRecognition(recognitionRequest);
                    status = recognitionResponse.jobStatusAsString();

                    if (status.compareTo("SUCCEEDED") == 0)
                        finished = true;
                    else {
                        System.out.println(yy + " status is: " + status);
                        Thread.sleep(1000);
                    }
                    yy++;
                }

                finished = false;

                // Proceed when the job is done - otherwise VideoMetadata is null.
                VideoMetadata videoMetaData = recognitionResponse.videoMetadata();
                System.out.println("Format: " + videoMetaData.format());
                System.out.println("Codec: " + videoMetaData.codec());
                System.out.println("Duration: " + videoMetaData.durationMillis());
                System.out.println("FrameRate: " + videoMetaData.frameRate());
                System.out.println("Job");

                List<CelebrityRecognition> celebs = recognitionResponse.celebrities();
                for (CelebrityRecognition celeb : celebs) {
                    long seconds = celeb.timestamp() / 1000;
                    System.out.print("Sec: " + seconds + " ");
                    CelebrityDetail details = celeb.celebrity();
                    System.out.println("Name: " + details.name());
                    System.out.println("Id: " + details.id());
                    System.out.println();
                }

            } while (recognitionResponse.nextToken() != null);

        } catch (RekognitionException | InterruptedException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}
```
ラベル検出オペレーションによって、ビデオ内のラベルを検出します。  

```
import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.databind.JsonMappingException;
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.StartLabelDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartLabelDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.GetLabelDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.GetLabelDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.LabelDetectionSortBy;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.LabelDetection;
import software.amazon.awssdk.services.rekognition.model.Label;
import software.amazon.awssdk.services.rekognition.model.Instance;
import software.amazon.awssdk.services.rekognition.model.Parent;
import software.amazon.awssdk.services.sqs.SqsClient;
import software.amazon.awssdk.services.sqs.model.Message;
import software.amazon.awssdk.services.sqs.model.ReceiveMessageRequest;
import software.amazon.awssdk.services.sqs.model.DeleteMessageRequest;
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 VideoDetect {
    private static String startJobId = "";

    public static void main(String[] args) {
        final String usage = """

                Usage:    <bucket> <video> <queueUrl> <topicArn> <roleArn>

                Where:
                   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s
                   video - The name of the video (for example, people.mp4).\s
                   queueUrl- The URL of a SQS queue.\s
                   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s
                   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s
                """;

        if (args.length != 5) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucket = args[0];
        String video = args[1];
        String queueUrl = args[2];
        String topicArn = args[3];
        String roleArn = args[4];
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        SqsClient sqs = SqsClient.builder()
                .region(Region.US_EAST_1)
                .build();

        NotificationChannel channel = NotificationChannel.builder()
                .snsTopicArn(topicArn)
                .roleArn(roleArn)
                .build();

        startLabels(rekClient, channel, bucket, video);
        getLabelJob(rekClient, sqs, queueUrl);
        System.out.println("This example is done!");
        sqs.close();
        rekClient.close();
    }

    public static void startLabels(RekognitionClient rekClient,
            NotificationChannel channel,
            String bucket,
            String video) {
        try {
            S3Object s3Obj = S3Object.builder()
                    .bucket(bucket)
                    .name(video)
                    .build();

            Video vidOb = Video.builder()
                    .s3Object(s3Obj)
                    .build();

            StartLabelDetectionRequest labelDetectionRequest = StartLabelDetectionRequest.builder()
                    .jobTag("DetectingLabels")
                    .notificationChannel(channel)
                    .video(vidOb)
                    .minConfidence(50F)
                    .build();

            StartLabelDetectionResponse labelDetectionResponse = rekClient.startLabelDetection(labelDetectionRequest);
            startJobId = labelDetectionResponse.jobId();

            boolean ans = true;
            String status = "";
            int yy = 0;
            while (ans) {

                GetLabelDetectionRequest detectionRequest = GetLabelDetectionRequest.builder()
                        .jobId(startJobId)
                        .maxResults(10)
                        .build();

                GetLabelDetectionResponse result = rekClient.getLabelDetection(detectionRequest);
                status = result.jobStatusAsString();

                if (status.compareTo("SUCCEEDED") == 0)
                    ans = false;
                else
                    System.out.println(yy + " status is: " + status);

                Thread.sleep(1000);
                yy++;
            }

            System.out.println(startJobId + " status is: " + status);

        } catch (RekognitionException | InterruptedException e) {
            e.getMessage();
            System.exit(1);
        }
    }

    public static void getLabelJob(RekognitionClient rekClient, SqsClient sqs, String queueUrl) {
        List<Message> messages;
        ReceiveMessageRequest messageRequest = ReceiveMessageRequest.builder()
                .queueUrl(queueUrl)
                .build();

        try {
            messages = sqs.receiveMessage(messageRequest).messages();

            if (!messages.isEmpty()) {
                for (Message message : messages) {
                    String notification = message.body();

                    // Get the status and job id from the notification
                    ObjectMapper mapper = new ObjectMapper();
                    JsonNode jsonMessageTree = mapper.readTree(notification);
                    JsonNode messageBodyText = jsonMessageTree.get("Message");
                    ObjectMapper operationResultMapper = new ObjectMapper();
                    JsonNode jsonResultTree = operationResultMapper.readTree(messageBodyText.textValue());
                    JsonNode operationJobId = jsonResultTree.get("JobId");
                    JsonNode operationStatus = jsonResultTree.get("Status");
                    System.out.println("Job found in JSON is " + operationJobId);

                    DeleteMessageRequest deleteMessageRequest = DeleteMessageRequest.builder()
                            .queueUrl(queueUrl)
                            .build();

                    String jobId = operationJobId.textValue();
                    if (startJobId.compareTo(jobId) == 0) {
                        System.out.println("Job id: " + operationJobId);
                        System.out.println("Status : " + operationStatus.toString());

                        if (operationStatus.asText().equals("SUCCEEDED"))
                            getResultsLabels(rekClient);
                        else
                            System.out.println("Video analysis failed");

                        sqs.deleteMessage(deleteMessageRequest);
                    } else {
                        System.out.println("Job received was not job " + startJobId);
                        sqs.deleteMessage(deleteMessageRequest);
                    }
                }
            }

        } catch (RekognitionException e) {
            e.getMessage();
            System.exit(1);
        } catch (JsonMappingException e) {
            e.printStackTrace();
        } catch (JsonProcessingException e) {
            e.printStackTrace();
        }
    }

    // Gets the job results by calling GetLabelDetection
    private static void getResultsLabels(RekognitionClient rekClient) {

        int maxResults = 10;
        String paginationToken = null;
        GetLabelDetectionResponse labelDetectionResult = null;

        try {
            do {
                if (labelDetectionResult != null)
                    paginationToken = labelDetectionResult.nextToken();

                GetLabelDetectionRequest labelDetectionRequest = GetLabelDetectionRequest.builder()
                        .jobId(startJobId)
                        .sortBy(LabelDetectionSortBy.TIMESTAMP)
                        .maxResults(maxResults)
                        .nextToken(paginationToken)
                        .build();

                labelDetectionResult = rekClient.getLabelDetection(labelDetectionRequest);
                VideoMetadata videoMetaData = labelDetectionResult.videoMetadata();
                System.out.println("Format: " + videoMetaData.format());
                System.out.println("Codec: " + videoMetaData.codec());
                System.out.println("Duration: " + videoMetaData.durationMillis());
                System.out.println("FrameRate: " + videoMetaData.frameRate());

                List<LabelDetection> detectedLabels = labelDetectionResult.labels();
                for (LabelDetection detectedLabel : detectedLabels) {
                    long seconds = detectedLabel.timestamp();
                    Label label = detectedLabel.label();
                    System.out.println("Millisecond: " + seconds + " ");

                    System.out.println("   Label:" + label.name());
                    System.out.println("   Confidence:" + detectedLabel.label().confidence().toString());

                    List<Instance> instances = label.instances();
                    System.out.println("   Instances of " + label.name());

                    if (instances.isEmpty()) {
                        System.out.println("        " + "None");
                    } else {
                        for (Instance instance : instances) {
                            System.out.println("        Confidence: " + instance.confidence().toString());
                            System.out.println("        Bounding box: " + instance.boundingBox().toString());
                        }
                    }
                    System.out.println("   Parent labels for " + label.name() + ":");
                    List<Parent> parents = label.parents();

                    if (parents.isEmpty()) {
                        System.out.println("        None");
                    } else {
                        for (Parent parent : parents) {
                            System.out.println("   " + parent.name());
                        }
                    }
                    System.out.println();
                }
            } while (labelDetectionResult != null && labelDetectionResult.nextToken() != null);

        } catch (RekognitionException e) {
            e.getMessage();
            System.exit(1);
        }
    }
}
```
Amazon S3 バケットに保存されたビデオ内の顔を検出する  

```
import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.databind.JsonMappingException;
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.StartLabelDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartLabelDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.GetLabelDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.GetLabelDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.LabelDetectionSortBy;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.LabelDetection;
import software.amazon.awssdk.services.rekognition.model.Label;
import software.amazon.awssdk.services.rekognition.model.Instance;
import software.amazon.awssdk.services.rekognition.model.Parent;
import software.amazon.awssdk.services.sqs.SqsClient;
import software.amazon.awssdk.services.sqs.model.Message;
import software.amazon.awssdk.services.sqs.model.ReceiveMessageRequest;
import software.amazon.awssdk.services.sqs.model.DeleteMessageRequest;
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 VideoDetect {
    private static String startJobId = "";

    public static void main(String[] args) {
        final String usage = """

                Usage:    <bucket> <video> <queueUrl> <topicArn> <roleArn>

                Where:
                   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s
                   video - The name of the video (for example, people.mp4).\s
                   queueUrl- The URL of a SQS queue.\s
                   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s
                   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s
                """;

        if (args.length != 5) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucket = args[0];
        String video = args[1];
        String queueUrl = args[2];
        String topicArn = args[3];
        String roleArn = args[4];
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        SqsClient sqs = SqsClient.builder()
                .region(Region.US_EAST_1)
                .build();

        NotificationChannel channel = NotificationChannel.builder()
                .snsTopicArn(topicArn)
                .roleArn(roleArn)
                .build();

        startLabels(rekClient, channel, bucket, video);
        getLabelJob(rekClient, sqs, queueUrl);
        System.out.println("This example is done!");
        sqs.close();
        rekClient.close();
    }

    public static void startLabels(RekognitionClient rekClient,
            NotificationChannel channel,
            String bucket,
            String video) {
        try {
            S3Object s3Obj = S3Object.builder()
                    .bucket(bucket)
                    .name(video)
                    .build();

            Video vidOb = Video.builder()
                    .s3Object(s3Obj)
                    .build();

            StartLabelDetectionRequest labelDetectionRequest = StartLabelDetectionRequest.builder()
                    .jobTag("DetectingLabels")
                    .notificationChannel(channel)
                    .video(vidOb)
                    .minConfidence(50F)
                    .build();

            StartLabelDetectionResponse labelDetectionResponse = rekClient.startLabelDetection(labelDetectionRequest);
            startJobId = labelDetectionResponse.jobId();

            boolean ans = true;
            String status = "";
            int yy = 0;
            while (ans) {

                GetLabelDetectionRequest detectionRequest = GetLabelDetectionRequest.builder()
                        .jobId(startJobId)
                        .maxResults(10)
                        .build();

                GetLabelDetectionResponse result = rekClient.getLabelDetection(detectionRequest);
                status = result.jobStatusAsString();

                if (status.compareTo("SUCCEEDED") == 0)
                    ans = false;
                else
                    System.out.println(yy + " status is: " + status);

                Thread.sleep(1000);
                yy++;
            }

            System.out.println(startJobId + " status is: " + status);

        } catch (RekognitionException | InterruptedException e) {
            e.getMessage();
            System.exit(1);
        }
    }

    public static void getLabelJob(RekognitionClient rekClient, SqsClient sqs, String queueUrl) {
        List<Message> messages;
        ReceiveMessageRequest messageRequest = ReceiveMessageRequest.builder()
                .queueUrl(queueUrl)
                .build();

        try {
            messages = sqs.receiveMessage(messageRequest).messages();

            if (!messages.isEmpty()) {
                for (Message message : messages) {
                    String notification = message.body();

                    // Get the status and job id from the notification
                    ObjectMapper mapper = new ObjectMapper();
                    JsonNode jsonMessageTree = mapper.readTree(notification);
                    JsonNode messageBodyText = jsonMessageTree.get("Message");
                    ObjectMapper operationResultMapper = new ObjectMapper();
                    JsonNode jsonResultTree = operationResultMapper.readTree(messageBodyText.textValue());
                    JsonNode operationJobId = jsonResultTree.get("JobId");
                    JsonNode operationStatus = jsonResultTree.get("Status");
                    System.out.println("Job found in JSON is " + operationJobId);

                    DeleteMessageRequest deleteMessageRequest = DeleteMessageRequest.builder()
                            .queueUrl(queueUrl)
                            .build();

                    String jobId = operationJobId.textValue();
                    if (startJobId.compareTo(jobId) == 0) {
                        System.out.println("Job id: " + operationJobId);
                        System.out.println("Status : " + operationStatus.toString());

                        if (operationStatus.asText().equals("SUCCEEDED"))
                            getResultsLabels(rekClient);
                        else
                            System.out.println("Video analysis failed");

                        sqs.deleteMessage(deleteMessageRequest);
                    } else {
                        System.out.println("Job received was not job " + startJobId);
                        sqs.deleteMessage(deleteMessageRequest);
                    }
                }
            }

        } catch (RekognitionException e) {
            e.getMessage();
            System.exit(1);
        } catch (JsonMappingException e) {
            e.printStackTrace();
        } catch (JsonProcessingException e) {
            e.printStackTrace();
        }
    }

    // Gets the job results by calling GetLabelDetection
    private static void getResultsLabels(RekognitionClient rekClient) {

        int maxResults = 10;
        String paginationToken = null;
        GetLabelDetectionResponse labelDetectionResult = null;

        try {
            do {
                if (labelDetectionResult != null)
                    paginationToken = labelDetectionResult.nextToken();

                GetLabelDetectionRequest labelDetectionRequest = GetLabelDetectionRequest.builder()
                        .jobId(startJobId)
                        .sortBy(LabelDetectionSortBy.TIMESTAMP)
                        .maxResults(maxResults)
                        .nextToken(paginationToken)
                        .build();

                labelDetectionResult = rekClient.getLabelDetection(labelDetectionRequest);
                VideoMetadata videoMetaData = labelDetectionResult.videoMetadata();
                System.out.println("Format: " + videoMetaData.format());
                System.out.println("Codec: " + videoMetaData.codec());
                System.out.println("Duration: " + videoMetaData.durationMillis());
                System.out.println("FrameRate: " + videoMetaData.frameRate());

                List<LabelDetection> detectedLabels = labelDetectionResult.labels();
                for (LabelDetection detectedLabel : detectedLabels) {
                    long seconds = detectedLabel.timestamp();
                    Label label = detectedLabel.label();
                    System.out.println("Millisecond: " + seconds + " ");

                    System.out.println("   Label:" + label.name());
                    System.out.println("   Confidence:" + detectedLabel.label().confidence().toString());

                    List<Instance> instances = label.instances();
                    System.out.println("   Instances of " + label.name());

                    if (instances.isEmpty()) {
                        System.out.println("        " + "None");
                    } else {
                        for (Instance instance : instances) {
                            System.out.println("        Confidence: " + instance.confidence().toString());
                            System.out.println("        Bounding box: " + instance.boundingBox().toString());
                        }
                    }
                    System.out.println("   Parent labels for " + label.name() + ":");
                    List<Parent> parents = label.parents();

                    if (parents.isEmpty()) {
                        System.out.println("        None");
                    } else {
                        for (Parent parent : parents) {
                            System.out.println("   " + parent.name());
                        }
                    }
                    System.out.println();
                }
            } while (labelDetectionResult != null && labelDetectionResult.nextToken() != null);

        } catch (RekognitionException e) {
            e.getMessage();
            System.exit(1);
        }
    }
}
```
Amazon S3 バケットに保存されたビデオ内の不適切なコンテンツや攻撃的なコンテンツを検出します。  

```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartContentModerationRequest;
import software.amazon.awssdk.services.rekognition.model.StartContentModerationResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.GetContentModerationResponse;
import software.amazon.awssdk.services.rekognition.model.GetContentModerationRequest;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.ContentModerationDetection;
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 VideoDetectInappropriate {
    private static String startJobId = "";

    public static void main(String[] args) {

        final String usage = """

                Usage:    <bucket> <video> <topicArn> <roleArn>

                Where:
                   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s
                   video - The name of video (for example, people.mp4).\s
                   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s
                   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s
                """;

        if (args.length != 4) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucket = args[0];
        String video = args[1];
        String topicArn = args[2];
        String roleArn = args[3];
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        NotificationChannel channel = NotificationChannel.builder()
                .snsTopicArn(topicArn)
                .roleArn(roleArn)
                .build();

        startModerationDetection(rekClient, channel, bucket, video);
        getModResults(rekClient);
        System.out.println("This example is done!");
        rekClient.close();
    }

    public static void startModerationDetection(RekognitionClient rekClient,
            NotificationChannel channel,
            String bucket,
            String video) {

        try {
            S3Object s3Obj = S3Object.builder()
                    .bucket(bucket)
                    .name(video)
                    .build();

            Video vidOb = Video.builder()
                    .s3Object(s3Obj)
                    .build();

            StartContentModerationRequest modDetectionRequest = StartContentModerationRequest.builder()
                    .jobTag("Moderation")
                    .notificationChannel(channel)
                    .video(vidOb)
                    .build();

            StartContentModerationResponse startModDetectionResult = rekClient
                    .startContentModeration(modDetectionRequest);
            startJobId = startModDetectionResult.jobId();

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }

    public static void getModResults(RekognitionClient rekClient) {
        try {
            String paginationToken = null;
            GetContentModerationResponse modDetectionResponse = null;
            boolean finished = false;
            String status;
            int yy = 0;

            do {
                if (modDetectionResponse != null)
                    paginationToken = modDetectionResponse.nextToken();

                GetContentModerationRequest modRequest = GetContentModerationRequest.builder()
                        .jobId(startJobId)
                        .nextToken(paginationToken)
                        .maxResults(10)
                        .build();

                // Wait until the job succeeds.
                while (!finished) {
                    modDetectionResponse = rekClient.getContentModeration(modRequest);
                    status = modDetectionResponse.jobStatusAsString();

                    if (status.compareTo("SUCCEEDED") == 0)
                        finished = true;
                    else {
                        System.out.println(yy + " status is: " + status);
                        Thread.sleep(1000);
                    }
                    yy++;
                }

                finished = false;

                // Proceed when the job is done - otherwise VideoMetadata is null.
                VideoMetadata videoMetaData = modDetectionResponse.videoMetadata();
                System.out.println("Format: " + videoMetaData.format());
                System.out.println("Codec: " + videoMetaData.codec());
                System.out.println("Duration: " + videoMetaData.durationMillis());
                System.out.println("FrameRate: " + videoMetaData.frameRate());
                System.out.println("Job");

                List<ContentModerationDetection> mods = modDetectionResponse.moderationLabels();
                for (ContentModerationDetection mod : mods) {
                    long seconds = mod.timestamp() / 1000;
                    System.out.print("Mod label: " + seconds + " ");
                    System.out.println(mod.moderationLabel().toString());
                    System.out.println();
                }

            } while (modDetectionResponse != null && modDetectionResponse.nextToken() != null);

        } catch (RekognitionException | InterruptedException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}
```
Amazon S3 バケットに保存されているビデオ内のテクニカルキューセグメントおよびショット検出セグメントを検出します。  

```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartShotDetectionFilter;
import software.amazon.awssdk.services.rekognition.model.StartTechnicalCueDetectionFilter;
import software.amazon.awssdk.services.rekognition.model.StartSegmentDetectionFilters;
import software.amazon.awssdk.services.rekognition.model.StartSegmentDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.StartSegmentDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.GetSegmentDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.GetSegmentDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.SegmentDetection;
import software.amazon.awssdk.services.rekognition.model.TechnicalCueSegment;
import software.amazon.awssdk.services.rekognition.model.ShotSegment;
import software.amazon.awssdk.services.rekognition.model.SegmentType;
import software.amazon.awssdk.services.sqs.SqsClient;
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 VideoDetectSegment {
    private static String startJobId = "";

    public static void main(String[] args) {
        final String usage = """

                Usage:    <bucket> <video> <topicArn> <roleArn>

                Where:
                   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s
                   video - The name of video (for example, people.mp4).\s
                   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s
                   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s
                """;

        if (args.length != 4) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucket = args[0];
        String video = args[1];
        String topicArn = args[2];
        String roleArn = args[3];

        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        SqsClient sqs = SqsClient.builder()
                .region(Region.US_EAST_1)
                .build();

        NotificationChannel channel = NotificationChannel.builder()
                .snsTopicArn(topicArn)
                .roleArn(roleArn)
                .build();

        startSegmentDetection(rekClient, channel, bucket, video);
        getSegmentResults(rekClient);
        System.out.println("This example is done!");
        sqs.close();
        rekClient.close();
    }

    public static void startSegmentDetection(RekognitionClient rekClient,
            NotificationChannel channel,
            String bucket,
            String video) {
        try {
            S3Object s3Obj = S3Object.builder()
                    .bucket(bucket)
                    .name(video)
                    .build();

            Video vidOb = Video.builder()
                    .s3Object(s3Obj)
                    .build();

            StartShotDetectionFilter cueDetectionFilter = StartShotDetectionFilter.builder()
                    .minSegmentConfidence(60F)
                    .build();

            StartTechnicalCueDetectionFilter technicalCueDetectionFilter = StartTechnicalCueDetectionFilter.builder()
                    .minSegmentConfidence(60F)
                    .build();

            StartSegmentDetectionFilters filters = StartSegmentDetectionFilters.builder()
                    .shotFilter(cueDetectionFilter)
                    .technicalCueFilter(technicalCueDetectionFilter)
                    .build();

            StartSegmentDetectionRequest segDetectionRequest = StartSegmentDetectionRequest.builder()
                    .jobTag("DetectingLabels")
                    .notificationChannel(channel)
                    .segmentTypes(SegmentType.TECHNICAL_CUE, SegmentType.SHOT)
                    .video(vidOb)
                    .filters(filters)
                    .build();

            StartSegmentDetectionResponse segDetectionResponse = rekClient.startSegmentDetection(segDetectionRequest);
            startJobId = segDetectionResponse.jobId();

        } catch (RekognitionException e) {
            e.getMessage();
            System.exit(1);
        }
    }

    public static void getSegmentResults(RekognitionClient rekClient) {
        try {
            String paginationToken = null;
            GetSegmentDetectionResponse segDetectionResponse = null;
            boolean finished = false;
            String status;
            int yy = 0;

            do {
                if (segDetectionResponse != null)
                    paginationToken = segDetectionResponse.nextToken();

                GetSegmentDetectionRequest recognitionRequest = GetSegmentDetectionRequest.builder()
                        .jobId(startJobId)
                        .nextToken(paginationToken)
                        .maxResults(10)
                        .build();

                // Wait until the job succeeds.
                while (!finished) {
                    segDetectionResponse = rekClient.getSegmentDetection(recognitionRequest);
                    status = segDetectionResponse.jobStatusAsString();

                    if (status.compareTo("SUCCEEDED") == 0)
                        finished = true;
                    else {
                        System.out.println(yy + " status is: " + status);
                        Thread.sleep(1000);
                    }
                    yy++;
                }
                finished = false;

                // Proceed when the job is done - otherwise VideoMetadata is null.
                List<VideoMetadata> videoMetaData = segDetectionResponse.videoMetadata();
                for (VideoMetadata metaData : videoMetaData) {
                    System.out.println("Format: " + metaData.format());
                    System.out.println("Codec: " + metaData.codec());
                    System.out.println("Duration: " + metaData.durationMillis());
                    System.out.println("FrameRate: " + metaData.frameRate());
                    System.out.println("Job");
                }

                List<SegmentDetection> detectedSegments = segDetectionResponse.segments();
                for (SegmentDetection detectedSegment : detectedSegments) {
                    String type = detectedSegment.type().toString();
                    if (type.contains(SegmentType.TECHNICAL_CUE.toString())) {
                        System.out.println("Technical Cue");
                        TechnicalCueSegment segmentCue = detectedSegment.technicalCueSegment();
                        System.out.println("\tType: " + segmentCue.type());
                        System.out.println("\tConfidence: " + segmentCue.confidence().toString());
                    }

                    if (type.contains(SegmentType.SHOT.toString())) {
                        System.out.println("Shot");
                        ShotSegment segmentShot = detectedSegment.shotSegment();
                        System.out.println("\tIndex " + segmentShot.index());
                        System.out.println("\tConfidence: " + segmentShot.confidence().toString());
                    }

                    long seconds = detectedSegment.durationMillis();
                    System.out.println("\tDuration : " + seconds + " milliseconds");
                    System.out.println("\tStart time code: " + detectedSegment.startTimecodeSMPTE());
                    System.out.println("\tEnd time code: " + detectedSegment.endTimecodeSMPTE());
                    System.out.println("\tDuration time code: " + detectedSegment.durationSMPTE());
                    System.out.println();
                }

            } while (segDetectionResponse != null && segDetectionResponse.nextToken() != null);

        } catch (RekognitionException | InterruptedException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}
```
Amazon S3 バケットに保存されたビデオに保存されたビデオ内のテキストを検出します。  

```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartTextDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.StartTextDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.GetTextDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.GetTextDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.TextDetectionResult;
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 VideoDetectText {
    private static String startJobId = "";

    public static void main(String[] args) {
        final String usage = """

                Usage:    <bucket> <video> <topicArn> <roleArn>

                Where:
                   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s
                   video - The name of video (for example, people.mp4).\s
                   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s
                   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s
                """;

        if (args.length != 4) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucket = args[0];
        String video = args[1];
        String topicArn = args[2];
        String roleArn = args[3];

        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        NotificationChannel channel = NotificationChannel.builder()
                .snsTopicArn(topicArn)
                .roleArn(roleArn)
                .build();

        startTextLabels(rekClient, channel, bucket, video);
        getTextResults(rekClient);
        System.out.println("This example is done!");
        rekClient.close();
    }

    public static void startTextLabels(RekognitionClient rekClient,
            NotificationChannel channel,
            String bucket,
            String video) {
        try {
            S3Object s3Obj = S3Object.builder()
                    .bucket(bucket)
                    .name(video)
                    .build();

            Video vidOb = Video.builder()
                    .s3Object(s3Obj)
                    .build();

            StartTextDetectionRequest labelDetectionRequest = StartTextDetectionRequest.builder()
                    .jobTag("DetectingLabels")
                    .notificationChannel(channel)
                    .video(vidOb)
                    .build();

            StartTextDetectionResponse labelDetectionResponse = rekClient.startTextDetection(labelDetectionRequest);
            startJobId = labelDetectionResponse.jobId();

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }

    public static void getTextResults(RekognitionClient rekClient) {
        try {
            String paginationToken = null;
            GetTextDetectionResponse textDetectionResponse = null;
            boolean finished = false;
            String status;
            int yy = 0;

            do {
                if (textDetectionResponse != null)
                    paginationToken = textDetectionResponse.nextToken();

                GetTextDetectionRequest recognitionRequest = GetTextDetectionRequest.builder()
                        .jobId(startJobId)
                        .nextToken(paginationToken)
                        .maxResults(10)
                        .build();

                // Wait until the job succeeds.
                while (!finished) {
                    textDetectionResponse = rekClient.getTextDetection(recognitionRequest);
                    status = textDetectionResponse.jobStatusAsString();

                    if (status.compareTo("SUCCEEDED") == 0)
                        finished = true;
                    else {
                        System.out.println(yy + " status is: " + status);
                        Thread.sleep(1000);
                    }
                    yy++;
                }

                finished = false;

                // Proceed when the job is done - otherwise VideoMetadata is null.
                VideoMetadata videoMetaData = textDetectionResponse.videoMetadata();
                System.out.println("Format: " + videoMetaData.format());
                System.out.println("Codec: " + videoMetaData.codec());
                System.out.println("Duration: " + videoMetaData.durationMillis());
                System.out.println("FrameRate: " + videoMetaData.frameRate());
                System.out.println("Job");

                List<TextDetectionResult> labels = textDetectionResponse.textDetections();
                for (TextDetectionResult detectedText : labels) {
                    System.out.println("Confidence: " + detectedText.textDetection().confidence().toString());
                    System.out.println("Id : " + detectedText.textDetection().id());
                    System.out.println("Parent Id: " + detectedText.textDetection().parentId());
                    System.out.println("Type: " + detectedText.textDetection().type());
                    System.out.println("Text: " + detectedText.textDetection().detectedText());
                    System.out.println();
                }

            } while (textDetectionResponse != null && textDetectionResponse.nextToken() != null);

        } catch (RekognitionException | InterruptedException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}
```
Amazon S3 バケットに保存されたビデオに保存されたビデオ内の人物を検出します。  

```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.StartPersonTrackingRequest;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartPersonTrackingResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.GetPersonTrackingResponse;
import software.amazon.awssdk.services.rekognition.model.GetPersonTrackingRequest;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.PersonDetection;
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 VideoPersonDetection {
    private static String startJobId = "";

    public static void main(String[] args) {

        final String usage = """

                Usage:    <bucket> <video> <topicArn> <roleArn>

                Where:
                   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s
                   video - The name of video (for example, people.mp4).\s
                   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s
                   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s
                """;

        if (args.length != 4) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucket = args[0];
        String video = args[1];
        String topicArn = args[2];
        String roleArn = args[3];
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        NotificationChannel channel = NotificationChannel.builder()
                .snsTopicArn(topicArn)
                .roleArn(roleArn)
                .build();

        startPersonLabels(rekClient, channel, bucket, video);
        getPersonDetectionResults(rekClient);
        System.out.println("This example is done!");
        rekClient.close();
    }

    public static void startPersonLabels(RekognitionClient rekClient,
            NotificationChannel channel,
            String bucket,
            String video) {
        try {
            S3Object s3Obj = S3Object.builder()
                    .bucket(bucket)
                    .name(video)
                    .build();

            Video vidOb = Video.builder()
                    .s3Object(s3Obj)
                    .build();

            StartPersonTrackingRequest personTrackingRequest = StartPersonTrackingRequest.builder()
                    .jobTag("DetectingLabels")
                    .video(vidOb)
                    .notificationChannel(channel)
                    .build();

            StartPersonTrackingResponse labelDetectionResponse = rekClient.startPersonTracking(personTrackingRequest);
            startJobId = labelDetectionResponse.jobId();

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }

    public static void getPersonDetectionResults(RekognitionClient rekClient) {
        try {
            String paginationToken = null;
            GetPersonTrackingResponse personTrackingResult = null;
            boolean finished = false;
            String status;
            int yy = 0;

            do {
                if (personTrackingResult != null)
                    paginationToken = personTrackingResult.nextToken();

                GetPersonTrackingRequest recognitionRequest = GetPersonTrackingRequest.builder()
                        .jobId(startJobId)
                        .nextToken(paginationToken)
                        .maxResults(10)
                        .build();

                // Wait until the job succeeds
                while (!finished) {

                    personTrackingResult = rekClient.getPersonTracking(recognitionRequest);
                    status = personTrackingResult.jobStatusAsString();

                    if (status.compareTo("SUCCEEDED") == 0)
                        finished = true;
                    else {
                        System.out.println(yy + " status is: " + status);
                        Thread.sleep(1000);
                    }
                    yy++;
                }

                finished = false;

                // Proceed when the job is done - otherwise VideoMetadata is null.
                VideoMetadata videoMetaData = personTrackingResult.videoMetadata();

                System.out.println("Format: " + videoMetaData.format());
                System.out.println("Codec: " + videoMetaData.codec());
                System.out.println("Duration: " + videoMetaData.durationMillis());
                System.out.println("FrameRate: " + videoMetaData.frameRate());
                System.out.println("Job");

                List<PersonDetection> detectedPersons = personTrackingResult.persons();
                for (PersonDetection detectedPerson : detectedPersons) {
                    long seconds = detectedPerson.timestamp() / 1000;
                    System.out.print("Sec: " + seconds + " ");
                    System.out.println("Person Identifier: " + detectedPerson.person().index());
                    System.out.println();
                }

            } while (personTrackingResult != null && personTrackingResult.nextToken() != null);

        } catch (RekognitionException | InterruptedException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}
```
+ API の詳細については、「*AWS SDK for Java 2.x API リファレンス*」の以下のトピックを参照してください。
  + [GetCelebrityRecognition](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/GetCelebrityRecognition)
  + [GetContentModeration](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/GetContentModeration)
  + [GetLabelDetection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/GetLabelDetection)
  + [GetPersonTracking](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/GetPersonTracking)
  + [GetSegmentDetection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/GetSegmentDetection)
  + [GetTextDetection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/GetTextDetection)
  + [StartCelebrityRecognition](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/StartCelebrityRecognition)
  + [StartContentModeration](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/StartContentModeration)
  + [StartLabelDetection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/StartLabelDetection)
  + [StartPersonTracking](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/StartPersonTracking)
  + [StartSegmentDetection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/StartSegmentDetection)
  + [StartTextDetection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/StartTextDetection)

------
#### [ Kotlin ]

**SDK for Kotlin**  
 GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples)での設定と実行の方法を確認してください。
Amazon S3 バケットに保存されたビデオ内の顔を検出する  

```
suspend fun startFaceDetection(
    channelVal: NotificationChannel?,
    bucketVal: String,
    videoVal: String,
) {
    val s3Obj =
        S3Object {
            bucket = bucketVal
            name = videoVal
        }
    val vidOb =
        Video {
            s3Object = s3Obj
        }

    val request =
        StartFaceDetectionRequest {
            jobTag = "Faces"
            faceAttributes = FaceAttributes.All
            notificationChannel = channelVal
            video = vidOb
        }

    RekognitionClient.fromEnvironment { region = "us-east-1" }.use { rekClient ->
        val startLabelDetectionResult = rekClient.startFaceDetection(request)
        startJobId = startLabelDetectionResult.jobId.toString()
    }
}

suspend fun getFaceResults() {
    var finished = false
    var status: String
    var yy = 0
    RekognitionClient.fromEnvironment { region = "us-east-1" }.use { rekClient ->
        var response: GetFaceDetectionResponse? = null

        val recognitionRequest =
            GetFaceDetectionRequest {
                jobId = startJobId
                maxResults = 10
            }

        // Wait until the job succeeds.
        while (!finished) {
            response = rekClient.getFaceDetection(recognitionRequest)
            status = response.jobStatus.toString()
            if (status.compareTo("Succeeded") == 0) {
                finished = true
            } else {
                println("$yy status is: $status")
                delay(1000)
            }
            yy++
        }

        // Proceed when the job is done - otherwise VideoMetadata is null.
        val videoMetaData = response?.videoMetadata
        println("Format: ${videoMetaData?.format}")
        println("Codec: ${videoMetaData?.codec}")
        println("Duration: ${videoMetaData?.durationMillis}")
        println("FrameRate: ${videoMetaData?.frameRate}")

        // Show face information.
        response?.faces?.forEach { face ->
            println("Age: ${face.face?.ageRange}")
            println("Face: ${face.face?.beard}")
            println("Eye glasses: ${face?.face?.eyeglasses}")
            println("Mustache: ${face.face?.mustache}")
            println("Smile: ${face.face?.smile}")
        }
    }
}
```
Amazon S3 バケットに保存されたビデオ内の不適切なコンテンツや攻撃的なコンテンツを検出します。  

```
suspend fun startModerationDetection(
    channel: NotificationChannel?,
    bucketVal: String?,
    videoVal: String?,
) {
    val s3Obj =
        S3Object {
            bucket = bucketVal
            name = videoVal
        }
    val vidOb =
        Video {
            s3Object = s3Obj
        }
    val request =
        StartContentModerationRequest {
            jobTag = "Moderation"
            notificationChannel = channel
            video = vidOb
        }

    RekognitionClient.fromEnvironment { region = "us-east-1" }.use { rekClient ->
        val startModDetectionResult = rekClient.startContentModeration(request)
        startJobId = startModDetectionResult.jobId.toString()
    }
}

suspend fun getModResults() {
    var finished = false
    var status: String
    var yy = 0
    RekognitionClient { region = "us-east-1" }.use { rekClient ->
        var modDetectionResponse: GetContentModerationResponse? = null

        val modRequest =
            GetContentModerationRequest {
                jobId = startJobId
                maxResults = 10
            }

        // Wait until the job succeeds.
        while (!finished) {
            modDetectionResponse = rekClient.getContentModeration(modRequest)
            status = modDetectionResponse.jobStatus.toString()
            if (status.compareTo("Succeeded") == 0) {
                finished = true
            } else {
                println("$yy status is: $status")
                delay(1000)
            }
            yy++
        }

        // Proceed when the job is done - otherwise VideoMetadata is null.
        val videoMetaData = modDetectionResponse?.videoMetadata
        println("Format: ${videoMetaData?.format}")
        println("Codec: ${videoMetaData?.codec}")
        println("Duration: ${videoMetaData?.durationMillis}")
        println("FrameRate: ${videoMetaData?.frameRate}")

        modDetectionResponse?.moderationLabels?.forEach { mod ->
            val seconds: Long = mod.timestamp / 1000
            print("Mod label: $seconds ")
            println(mod.moderationLabel)
        }
    }
}
```
+ API の詳細については、「*AWS SDK for Kotlin API リファレンス*」の以下のトピックを参照してください。
  + [GetCelebrityRecognition](https://sdk.amazonaws.com/kotlin/api/latest/index.html)
  + [GetContentModeration](https://sdk.amazonaws.com/kotlin/api/latest/index.html)
  + [GetLabelDetection](https://sdk.amazonaws.com/kotlin/api/latest/index.html)
  + [GetPersonTracking](https://sdk.amazonaws.com/kotlin/api/latest/index.html)
  + [GetSegmentDetection](https://sdk.amazonaws.com/kotlin/api/latest/index.html)
  + [GetTextDetection](https://sdk.amazonaws.com/kotlin/api/latest/index.html)
  + [StartCelebrityRecognition](https://sdk.amazonaws.com/kotlin/api/latest/index.html)
  + [StartContentModeration](https://sdk.amazonaws.com/kotlin/api/latest/index.html)
  + [StartLabelDetection](https://sdk.amazonaws.com/kotlin/api/latest/index.html)
  + [StartPersonTracking](https://sdk.amazonaws.com/kotlin/api/latest/index.html)
  + [StartSegmentDetection](https://sdk.amazonaws.com/kotlin/api/latest/index.html)
  + [StartTextDetection](https://sdk.amazonaws.com/kotlin/api/latest/index.html)

------

 AWS SDK 開発者ガイドとコード例の完全なリストについては、「」を参照してください[AWS SDK での Rekognition の使用](sdk-general-information-section.md)。このトピックには、使用開始方法に関する情報と、以前の SDK バージョンの詳細も含まれています。

# AWS SDK を使用して Amazon Rekognition でイメージ内のオブジェクトを検出する
<a name="example_cross_RekognitionPhotoAnalyzer_section"></a>

次のコード例は、Amazon Rekognition を使用して画像内からカテゴリ別にオブジェクトを検出するアプリケーションを構築する方法を示しています。

------
#### [ .NET ]

**SDK for .NET**  
 Amazon Simple Storage Service (Amazon S3) バケットにあるイメージ内から、Amazon Rekognition を使用してカテゴリ別にオブジェクトを識別するアプリケーションを、Amazon Rekognition .NET API を使用して作成する方法を示します。アプリケーションは、Amazon Simple Email Service (Amazon SES) を使用して、結果を記載した E メール通知を管理者に送信します。  
 完全なソースコードとセットアップおよび実行の手順については、[GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/cross-service/PhotoAnalyzerApp) で完全な例を参照してください。  

**この例で使用されているサービス**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES

------
#### [ Java ]

**SDK for Java 2.x**  
 Amazon Rekognition Java API を使用して、Amazon Rekognition を使用し、　Amazon Simple Storage Service (Amazon S3) バケットにあるイメージ内でカテゴリ別にオブジェクトを識別するアプリケーションを作成する方法について説明します。アプリケーションは Amazon Simple Email Service (Amazon SES) を使用して、結果を含む E メール通知を管理者に送信します。  
 完全なソースコードとセットアップおよび実行の手順については、[GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/creating_photo_analyzer_app) で完全な例を参照してください。  

**この例で使用されているサービス**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES

------
#### [ JavaScript ]

**SDK for JavaScript (v3)**  
 で Amazon Rekognition を使用して、Amazon Rekognition を使用して Amazon Simple Storage Service (Amazon S3) バケットにあるイメージ内のオブジェクトをカテゴリ別に識別するアプリケーション AWS SDK for JavaScript を作成する方法を示します。アプリケーションは Amazon Simple Email Service (Amazon SES) を使用して、結果を含む E メール通知を管理者に送信します。  
以下ではその方法を説明しています。  
+ Amazon Cognito を使用して認証されていないユーザーを作成します。
+ Amazon Rekognition を使用して、オブジェクトのイメージを分析します。
+ Amazon SES の E メールアドレスを検証します。
+ Amazon SES を使用して、E メール通知を送信します。
 完全なソースコードとセットアップおよび実行の手順については、[GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/cross-services/photo_analyzer) で完全な例を参照してください。  

**この例で使用されているサービス**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES

------
#### [ Kotlin ]

**SDK for Kotlin**  
 Amazon Simple Storage Service (Amazon S3) バケットにある画像内からカテゴリ別にオブジェクトを Amazon Rekognition を使用して識別するアプリケーションを、Amazon Rekognition Kotlin API を使用して作成する方法を示します。アプリケーションは、Amazon Simple Email Service (Amazon SES) を使用して、結果を記載した E メール通知を管理者に送信します。  
 完全なソースコードとセットアップおよび実行の手順については、[GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/usecases/creating_photo_analyzer_app) で完全な例を参照してください。  

**この例で使用されているサービス**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES

------
#### [ Python ]

**SDK for Python (Boto3)**  
 を使用して AWS SDK for Python (Boto3) 、以下を可能にするウェブアプリケーションを作成する方法を示します。  
+ Amazon Simple Storage Service (Amazon S3) バケットに、写真をアップロードします。
+ Amazon Rekognition を使用して、写真を分析およびラベル付けします。
+ Amazon Simple Email Service (Amazon SES) を使用して、イメージ分析の E メールレポートを送信します。
 この例には、React で構築された JavaScript で記述されたウェブページと、Flask-RESTful で構築された Python で記述された REST サービスの 2 つの主要なコンポーネントが含まれています。  
React ウェブページを使用すると、次のことができます。  
+ S3 バケットに保存されているイメージのリストを表示します。
+ イメージを S3 バケットにアップロードします。
+ イメージ内で検出された項目を識別するイメージとラベルを表示します。
+ S3 バケット内のすべてのイメージのレポートを取得し、レポートの E メールを送信します。
ウェブページが REST サービスを呼び出します。サービスはリクエストを AWS に送信して、以下のアクションを実行します。  
+ S3 バケット内のイメージのリストを取得し、フィルタリングします。
+ Amazon S3 バケットに写真をアップロードします。
+ Amazon Rekognition を使用して個々の写真を分析し、写真で検出された項目を識別するラベルのリストを取得します。
+ S3 バケット内のすべての写真を分析し、Amazon SES を使用してレポートを E メールで送信します。
 完全なソースコードとセットアップおよび実行の手順については、[GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/photo_analyzer) で完全な例を参照してください。  

**この例で使用されているサービス**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES

------

 AWS SDK 開発者ガイドとコード例の完全なリストについては、「」を参照してください[AWS SDK での Rekognition の使用](sdk-general-information-section.md)。このトピックには、使用開始方法に関する情報と、以前の SDK バージョンの詳細も含まれています。

# AWS SDK を使用して Amazon Rekognition でビデオ内の人物とオブジェクトを検出する
<a name="example_cross_RekognitionVideoDetection_section"></a>

次のコード例は、Amazon Rekognition で動画内の人やオブジェクトを検出する方法を示します。

------
#### [ Java ]

**SDK for Java 2.x**  
 Amazon Rekognition Java API を使用し、Amazon Simple Storage Service (Amazon S3) バケットにあるビデオ内で顔やオブジェクトを検出するアプリケーションを作成する方法について説明します。アプリケーションは Amazon Simple Email Service (Amazon SES) を使用して、結果を含む E メール通知を管理者に送信します。  
 完全なソースコードとセットアップおよび実行の手順については、[GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/video_analyzer_application) で完全な例を参照してください。  

**この例で使用されているサービス**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES
+ Amazon SNS
+ Amazon SQS

------
#### [ Python ]

**SDK for Python (Boto3)**  
 Amazon Rekognition を使用して、非同期検出ジョブを開始して、動画内の顔、オブジェクト、人を検出します。この例では、ジョブが完了し、Amazon Simple Queue Service (Amazon SQS) キューをトピックにサブスクライブしたときに、Amazon Simple Notification Service (Amazon SNS) トピックに通知するように Amazon Rekognition を設定します。キューがジョブに関するメッセージを受信すると、ジョブが取得され、結果が出力されます。  
 この例は GitHub で最もよく見られます。完全なソースコードとセットアップおよび実行の手順については、[GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition) で完全な例を参照してください。  

**この例で使用されているサービス**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES
+ Amazon SNS
+ Amazon SQS

------

 AWS SDK 開発者ガイドとコード例の完全なリストについては、「」を参照してください[AWS SDK での Rekognition の使用](sdk-general-information-section.md)。このトピックには、使用開始方法に関する情報と、以前の SDK バージョンの詳細も含まれています。

# AWS SDK を使用して EXIF およびその他のイメージ情報を保存する
<a name="example_cross_DetectLabels_section"></a>

次のコード例は、以下の操作方法を示しています。
+ JPG、JPEG、または PNG ファイルから EXIF 情報を取得します。
+ Amazon S3 バケットにイメージファイルをアップロードします。
+ Amazon Rekognition を使用して、ファイル内の 3 つの上位属性 (ラベル) を特定します。
+ EXIF およびラベル情報を、リージョンの Amazon DynamoDB テーブルに追加します。

------
#### [ Rust ]

**SDK for Rust**  
 JPG、JPEG、または PNG ファイルから EXIF 情報を取得し、イメージファイルを Amazon S3 バケットにアップロードし、Amazon Rekognition を使用してファイル内の 3 つの上位属性 (Amazon Rekognition の*ラベル*) を特定し、リージョンの Amazon DynamoDB テーブルに EXIF およびラベル情報を追加します。  
 完全なソースコードとセットアップおよび実行の手順については、[GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/blob/main/rustv1/cross_service/detect_labels/src/main.rs) で完全な例を参照してください。  

**この例で使用されているサービス**
+ DynamoDB
+ Amazon Rekognition
+ Amazon S3

------

 AWS SDK 開発者ガイドとコード例の完全なリストについては、「」を参照してください[AWS SDK での Rekognition の使用](sdk-general-information-section.md)。このトピックには、使用開始方法に関する情報と、以前の SDK バージョンの詳細も含まれています。