本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。

# 使用 AWS SDKs Amazon Rekognition 案例
<a name="service_code_examples_scenarios"></a>

下列程式碼範例示範如何在 Amazon Rekognition AWS SDKs 中實作常見案例。這些案例示範如何呼叫 Amazon Rekognition 中的多個函數，或與其他 AWS 服務結合，藉以完成特定任務。每個案例均包含完整原始碼的連結，您可在連結中找到如何設定和執行程式碼的相關指示。

案例的目標是獲得中等水平的經驗，協助您了解內容中的服務動作。

**Topics**
+ [建立集合並在其中尋找人臉](example_rekognition_Usage_FindFacesInCollection_section.md)
+ [建立無伺服器應用程式來管理相片](example_cross_PAM_section.md)
+ [偵測映像中的 PPE](example_cross_RekognitionPhotoAnalyzerPPE_section.md)
+ [偵測並顯示映像中的元素](example_rekognition_Usage_DetectAndDisplayImage_section.md)
+ [偵測映像中的人臉](example_cross_DetectFaces_section.md)
+ [偵測映像中的資訊](example_rekognition_VideoDetection_section.md)
+ [偵測映像中的物件](example_cross_RekognitionPhotoAnalyzer_section.md)
+ [偵測映像中的人物和物件](example_cross_RekognitionVideoDetection_section.md)
+ [儲存 EXIF 和其他映像資訊](example_cross_DetectLabels_section.md)

# 建置 Amazon Rekognition 集合，並使用 AWS SDK 在其中尋找臉部
<a name="example_rekognition_Usage_FindFacesInCollection_section"></a>

以下程式碼範例顯示做法：
+ 建立 Amazon Rekognition 集合。
+ 將映像新增到集合中並偵測其中的人臉。
+ 在集合中搜尋符合參考映像的人臉。
+ 刪除集合。

如需詳細資訊，請參閱[搜尋集合中的人臉](https://docs.aws.amazon.com/rekognition/latest/dg/collections.html)。

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

**適用於 Python 的 SDK (Boto3)**  
 GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples)中設定和執行。
建立包裝 Amazon Rekognition 函數的類別。  

```
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
```
使用包裝函式類別從一組映像建立人臉集合，然後搜尋集合中的人臉。  

```
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)
```

------

如需 AWS SDK 開發人員指南和程式碼範例的完整清單，請參閱 [搭配 AWS SDK 使用 Rekognition](sdk-general-information-section.md)。此主題也包含有關入門的資訊和舊版 SDK 的詳細資訊。

# 建立相片資產管理應用程式，讓使用者以標籤管理相片
<a name="example_cross_PAM_section"></a>

下列程式碼範例示範如何建立無伺服器應用程式，讓使用者以標籤管理相片。

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

**適用於 .NET 的 SDK**  
 顯示如何開發照片資產管理應用程式，以便使用 Amazon Rekognition 偵測圖片中的標籤，並將其儲存以供日後擷取。  
如需完整的原始碼和如何設定及執行的指示，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/cross-service/PhotoAssetManager) 上的完整範例。  
如要深入探索此範例的來源，請參閱 [AWS  社群](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app)上的文章。  

**此範例中使用的服務**
+ API Gateway
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS

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

**適用於 C\$1\$1 的 SDK**  
 顯示如何開發照片資產管理應用程式，以便使用 Amazon Rekognition 偵測圖片中的標籤，並將其儲存以供日後擷取。  
如需完整的原始碼和如何設定及執行的指示，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/cpp/example_code/cross-service/photo_asset_manager) 上的完整範例。  
如要深入探索此範例的來源，請參閱 [AWS  社群](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app)上的文章。  

**此範例中使用的服務**
+ API Gateway
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS

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

**適用於 Java 2.x 的 SDK**  
 顯示如何開發照片資產管理應用程式，以便使用 Amazon Rekognition 偵測圖片中的標籤，並將其儲存以供日後擷取。  
如需完整的原始碼和如何設定及執行的指示，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/pam_source_files) 上的完整範例。  
如要深入探索此範例的來源，請參閱 [AWS  社群](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app)上的文章。  

**此範例中使用的服務**
+ API Gateway
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS

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

**適用於 JavaScript (v3) 的 SDK**  
 顯示如何開發照片資產管理應用程式，以便使用 Amazon Rekognition 偵測圖片中的標籤，並將其儲存以供日後擷取。  
如需完整的原始碼和如何設定及執行的指示，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/cross-services/photo-asset-manager) 上的完整範例。  
如要深入探索此範例的來源，請參閱 [AWS  社群](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app)上的文章。  

**此範例中使用的服務**
+ API Gateway
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS

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

**SDK for Kotlin**  
 顯示如何開發照片資產管理應用程式，以便使用 Amazon Rekognition 偵測圖片中的標籤，並將其儲存以供日後擷取。  
如需完整的原始碼和如何設定及執行的指示，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/usecases/creating_pam) 上的完整範例。  
如要深入探索此範例的來源，請參閱 [AWS  社群](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app)上的文章。  

**此範例中使用的服務**
+ API Gateway
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS

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

**適用於 PHP 的 SDK**  
 顯示如何開發照片資產管理應用程式，以便使用 Amazon Rekognition 偵測圖片中的標籤，並將其儲存以供日後擷取。  
如需完整的原始碼和如何設定及執行的指示，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/php/applications/photo_asset_manager) 上的完整範例。  
如要深入探索此範例的來源，請參閱 [AWS  社群](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app)上的文章。  

**此範例中使用的服務**
+ API Gateway
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS

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

**適用於 Rust 的 SDK**  
 顯示如何開發照片資產管理應用程式，以便使用 Amazon Rekognition 偵測圖片中的標籤，並將其儲存以供日後擷取。  
如需完整的原始碼和如何設定及執行的指示，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/rustv1/cross_service/photo_asset_management) 上的完整範例。  
如要深入探索此範例的來源，請參閱 [AWS  社群](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app)上的文章。  

**此範例中使用的服務**
+ API Gateway
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS

------

如需 AWS SDK 開發人員指南和程式碼範例的完整清單，請參閱 [搭配 AWS SDK 使用 Rekognition](sdk-general-information-section.md)。此主題也包含有關入門的資訊和舊版 SDK 的詳細資訊。

# 使用 AWS SDK 偵測 Amazon Rekognition 影像中的個人防護裝備
<a name="example_cross_RekognitionPhotoAnalyzerPPE_section"></a>

以下程式碼範例說明如何建置可使用 Amazon Rekognition 在映像中偵測個人防護裝備 (PPE) 的應用程式。

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

**適用於 Java 2.x 的 SDK **  
 示範如何建立 AWS Lambda 函數，以偵測具有個人防護設備的映像。  
 如需完整的原始碼和如何設定及執行的指示，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/creating_lambda_ppe) 上的完整範例。  

**此範例中使用的服務**
+ DynamoDB
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES

------

如需 AWS SDK 開發人員指南和程式碼範例的完整清單，請參閱 [搭配 AWS SDK 使用 Rekognition](sdk-general-information-section.md)。此主題也包含有關入門的資訊和舊版 SDK 的詳細資訊。

# 使用 AWS SDK 在映像中使用 Amazon Rekognition 偵測和顯示元素
<a name="example_rekognition_Usage_DetectAndDisplayImage_section"></a>

以下程式碼範例顯示做法：
+ 使用 Amazon Rekognition 偵測映像中的元素。
+ 顯示映像並在偵測到的元素周圍繪製邊界方框。

如需詳細資訊，請參閱[顯示邊界方框](https://docs.aws.amazon.com/rekognition/latest/dg/images-displaying-bounding-boxes.html)。

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

**適用於 Python 的 SDK (Boto3)**  
 GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples)中設定和執行。
建立類來包裝 Amazon Rekognition 函數。  

```
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
```
建立輔助函數來繪製邊界框和多邊形。  

```
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()
```
建立類別以剖析 Amazon Rekognition 傳回的剖析物件。  

```
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
```
使用包裝函式類別來偵測映像中的元素，並顯示其邊界方框。在 GitHub 可以找到這個例子中映像的說明和更多程式碼。  

```
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)
```

------

如需 AWS SDK 開發人員指南和程式碼範例的完整清單，請參閱 [搭配 AWS SDK 使用 Rekognition](sdk-general-information-section.md)。此主題也包含有關入門的資訊和舊版 SDK 的詳細資訊。

# 使用 AWS SDK 偵測映像中的臉部
<a name="example_cross_DetectFaces_section"></a>

以下程式碼範例顯示做法：
+ 在 Amazon S3 儲存貯體儲存映像。
+ 使用 Amazon Rekognition 偵測面部細節，例如年齡範圍、性別和情感 (例如微笑)。
+ 顯示這些詳細資訊。

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

**適用於 Rust 的 SDK**  
 將映像儲存在 Amazon S3 儲存貯體中，並包含**上傳**字首，使用 Amazon Rekognition 偵測面部細節，例如年齡範圍、性別和情感 (微笑等)，並顯示這些詳細資訊。  
 如需完整的原始碼和如何設定及執行的指示，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/blob/main/rustv1/cross_service/detect_faces/src/main.rs) 上的完整範例。  

**此範例中使用的服務**
+ Amazon Rekognition
+ Amazon S3

------

如需 AWS SDK 開發人員指南和程式碼範例的完整清單，請參閱 [搭配 AWS SDK 使用 Rekognition](sdk-general-information-section.md)。此主題也包含有關入門的資訊和舊版 SDK 的詳細資訊。

# 使用 Amazon Rekognition 和 AWS SDK 偵測影片中的資訊
<a name="example_rekognition_VideoDetection_section"></a>

下列程式碼範例示範如何：
+ 啟動 Amazon Rekognition 任務，以偵測影片中的人物、物件和文字等元素。
+ 檢查工作狀態，直到工作完成。
+ 輸出每個工作偵測到的元素清單。

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

**適用於 Java 2.x 的 SDK **  
 GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples)中設定和執行。
從 Amazon S3 儲存貯體中的影片中取得名人結果。  

```
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);
        }
    }
}
```
通過標籤偵測操作，偵測影片中的標籤。  

```
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);
        }
    }
}
```
偵測存放於 Amazon S3 儲存貯體中的人臉。  

```
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);
        }
    }
}
```
偵測 Amazon S3 儲存貯體中存放影片中的不當或冒犯性內容。  

```
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);
        }
    }
}
```
偵測 Amazon S3 儲存貯體中存放影片中的技術提示區段和鏡頭偵測區段。  

```
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);
        }
    }
}
```
偵測 Amazon S3 儲存貯體中存放影片中所存的影片文字。  

```
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);
        }
    }
}
```
偵測 Amazon S3 儲存貯體中存放影片中所存的影片人物。  

```
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);
        }
    }
}
```
+ 如需 API 詳細資訊，請參閱《AWS SDK for Java 2.x API 參考》**中的下列主題。
  + [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 ]

**適用於 Kotlin 的 SDK**  
 GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples)中設定和執行。
偵測存放於 Amazon S3 儲存貯體中的人臉。  

```
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}")
        }
    }
}
```
偵測 Amazon S3 儲存貯體中存放影片中的不當或冒犯性內容。  

```
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)
        }
    }
}
```
+ 如需 API 詳細資訊，請參閱《AWS 適用於 Kotlin 的 SDK API 參考》**中的下列主題。
  + [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)

------

如需 AWS SDK 開發人員指南和程式碼範例的完整清單，請參閱 [搭配 AWS SDK 使用 Rekognition](sdk-general-information-section.md)。此主題也包含有關入門的資訊和舊版 SDK 的詳細資訊。

# 使用 AWS SDK 透過 Amazon Rekognition 偵測映像中的物件
<a name="example_cross_RekognitionPhotoAnalyzer_section"></a>

下列程式碼範例說明如何建置可使用 Amazon Rekognition 按類別偵測映像中物件的應用程式。

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

**適用於 .NET 的 SDK**  
 說明如何使用 Amazon Rekognition .NET API 建立應用程式，該應用程式可使用 Amazon Rekognition 對 Amazon Simple Storage Service (Amazon S3) 儲存貯體中的映像按類別識別物件。此應用程式可使用 Amazon Simple Email Service (Amazon SES) 向管理員傳送包含結果的電子郵件通知。  
 如需完整的原始碼和如何設定及執行的指示，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/cross-service/PhotoAnalyzerApp) 上的完整範例。  

**此範例中使用的服務**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES

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

**適用於 Java 2.x 的 SDK **  
 說明如何使用 Amazon Rekognition Java API 建立應用程式，該應用程式可使用 Amazon Rekognition 對 Amazon Simple Storage Service (Amazon S3) 儲存貯體中的映像按類別識別物件。此應用程式可使用 Amazon Simple Email Service (Amazon SES) 向管理員傳送包含結果的電子郵件通知。  
 如需完整的原始碼和如何設定及執行的指示，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/creating_photo_analyzer_app) 上的完整範例。  

**此範例中使用的服務**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES

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

**適用於 JavaScript (v3) 的 SDK**  
 示範如何搭配 使用 Amazon Rekognition 適用於 JavaScript 的 AWS SDK ，以建立使用 Amazon Rekognition 的應用程式，在位於 Amazon Simple Storage Service (Amazon S3) 儲存貯體的影像中依類別識別物件。此應用程式可使用 Amazon Simple Email Service (Amazon SES) 向管理員傳送包含結果的電子郵件通知。  
了解如何：  
+ 使用 Amazon Cognito 建立未經身分驗證的使用者。
+ 使用 Amazon Rekognition 分析映像中的物件。
+ 驗證 Amazon SES 的電子郵件地址。
+ 使用 Amazon SES 傳送電子郵件通知。
 如需完整的原始碼和如何設定及執行的指示，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/cross-services/photo_analyzer) 上的完整範例。  

**此範例中使用的服務**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES

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

**適用於 Kotlin 的 SDK **  
 展示如何使用 Amazon Rekognition Kotlin API 建立應用程式，該應用程式使用 Amazon Rekognition 對位於 Amazon Simple Storage Service (Amazon S3) 儲存貯體中的映像按類別識別物件。此應用程式可使用 Amazon Simple Email Service (Amazon SES) 向管理員傳送包含結果的電子郵件通知。  
 如需完整的原始碼和如何設定及執行的指示，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/usecases/creating_photo_analyzer_app) 上的完整範例。  

**此範例中使用的服務**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES

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

**適用於 Python 的 SDK (Boto3)**  
 說明如何使用 適用於 Python (Boto3) 的 AWS SDK 來建立可讓您執行下列動作的 Web 應用程式：  
+ 將相片上傳到 Amazon Simple Storage Service (Amazon S3) 儲存貯體。
+ 使用 Amazon Rekognition 分析和標籤照片。
+ 使用 Amazon Simple Email Service (Amazon SES) 傳送映像分析的電子郵件報告。
 此範例包含兩個主要組件：一個使用 React 內建 JavaScript 編寫的網頁，以及一個使用 Flask-RESTful 內建 Python 編寫的 REST 服務。  
您可以使用 React 網頁執行以下操作：  
+ 顯示儲存於 S3 儲存貯體中的映像的清單。
+ 將映像從您的電腦上傳至 S3 儲存貯體。
+ 顯示識別映像中偵測到的專案的映像和標籤。
+ 取得 S3 儲存貯體中所有映像的報告，並傳送報告的電子郵件。
該網頁呼叫 REST 服務。該服務將請求發送到 AWS 來執行下列動作：  
+ 取得並篩選 S3 儲存貯體中的映像的清單。
+ 將相片上傳至 S3 儲存貯體。
+ 使用 Amazon Rekognition 分析個別照片，並取得標識照片中偵測到的專案的標籤清單。
+ 分析 S3 儲存貯體中的所有相片，然後使用 Amazon SES 傳送報告的電子郵件。
 如需完整的原始碼和如何設定及執行的指示，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/photo_analyzer) 上的完整範例。  

**此範例中使用的服務**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES

------

如需 AWS SDK 開發人員指南和程式碼範例的完整清單，請參閱 [搭配 AWS SDK 使用 Rekognition](sdk-general-information-section.md)。此主題也包含有關入門的資訊和舊版 SDK 的詳細資訊。

# 使用 AWS SDK 透過 Amazon Rekognition 偵測影片中的人物和物件
<a name="example_cross_RekognitionVideoDetection_section"></a>

下列程式碼範例示範如何使用 Amazon Rekognition 偵測映像中的人物和物件。

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

**適用於 Java 2.x 的 SDK **  
 示範如何使用 Amazon Rekognition Java API 來建立應用程式，以偵測位於 Amazon Simple Storage Service (Amazon S3) 儲存貯體的映像中的人臉和物件。此應用程式可使用 Amazon Simple Email Service (Amazon SES) 向管理員傳送包含結果的電子郵件通知。  
 如需完整的原始碼和如何設定及執行的指示，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/video_analyzer_application) 上的完整範例。  

**此範例中使用的服務**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES
+ Amazon SNS
+ Amazon SQS

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

**適用於 Python (Boto3) 的 SDK**  
 使用 Amazon Rekognition 透過啟動非同步偵測任務來偵測映像中的人臉、物件和人物。此範例也會設定 Amazon Rekognition 以在任務完成時通知 Amazon Simple Notification Service (Amazon SNS) 主題，並訂閱 Amazon Simple Queue Service (Amazon SQS) 佇列到該主題。當佇列收到有關任務的訊息時，會擷取任務並輸出結果。  
 這個範例在 GitHub 上的檢視效果最佳。如需完整的原始碼和如何設定及執行的指示，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition) 上的完整範例。  

**此範例中使用的服務**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES
+ Amazon SNS
+ Amazon SQS

------

如需 AWS SDK 開發人員指南和程式碼範例的完整清單，請參閱 [搭配 AWS SDK 使用 Rekognition](sdk-general-information-section.md)。此主題也包含有關入門的資訊和舊版 SDK 的詳細資訊。

# 使用 AWS SDK 儲存 EXIF 和其他影像資訊
<a name="example_cross_DetectLabels_section"></a>

以下程式碼範例顯示做法：
+ 從 JPG、JPEG 或 PNG 檔案中取得 EXIF 資訊。
+ 將映像檔案上傳至 Amazon S3 儲存貯體。
+ 使用 Amazon Rekognition 識別檔案中的三個主要屬性 (標籤)。
+ 將 EXIF 和標籤資訊新增至區域中的 Amazon DynamoDB 資料表。

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

**適用於 Rust 的 SDK**  
 從 JPG、JPEG 或 PNG 檔案獲取 EXIF 資訊，將映像檔案上傳至 Amazon S3 儲存貯體，使用 Amazon Rekognition 識別三個主要屬性 (Amazon Rekognition 中的*標籤*)，然後將 EXIF 和標籤資訊新增至區域中的 Amazon DynamoDB 資料表中。  
 如需完整的原始碼和如何設定及執行的指示，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/blob/main/rustv1/cross_service/detect_labels/src/main.rs) 上的完整範例。  

**此範例中使用的服務**
+ DynamoDB
+ Amazon Rekognition
+ Amazon S3

------

如需 AWS SDK 開發人員指南和程式碼範例的完整清單，請參閱 [搭配 AWS SDK 使用 Rekognition](sdk-general-information-section.md)。此主題也包含有關入門的資訊和舊版 SDK 的詳細資訊。