# Scenarios for Amazon Rekognition using AWS SDKs
<a name="service_code_examples_scenarios"></a>

The following code examples show you how to implement common scenarios in Amazon Rekognition with AWS SDKs. These scenarios show you how to accomplish specific tasks by calling multiple functions within Amazon Rekognition or combined with other AWS services. Each scenario includes a link to the complete source code, where you can find instructions on how to set up and run the code. 

Scenarios target an intermediate level of experience to help you understand service actions in context.

**Topics**
+ [Build a collection and find faces in it](example_rekognition_Usage_FindFacesInCollection_section.md)
+ [Create a serverless application to manage photos](example_cross_PAM_section.md)
+ [Detect PPE in images](example_cross_RekognitionPhotoAnalyzerPPE_section.md)
+ [Detect and display elements in images](example_rekognition_Usage_DetectAndDisplayImage_section.md)
+ [Detect faces in an image](example_cross_DetectFaces_section.md)
+ [Detect information in videos](example_rekognition_VideoDetection_section.md)
+ [Detect objects in images](example_cross_RekognitionPhotoAnalyzer_section.md)
+ [Detect people and objects in a video](example_cross_RekognitionVideoDetection_section.md)
+ [Save EXIF and other image information](example_cross_DetectLabels_section.md)

# Build an Amazon Rekognition collection and find faces in it using an AWS SDK
<a name="example_rekognition_Usage_FindFacesInCollection_section"></a>

The following code example shows how to:
+ Create an Amazon Rekognition collection.
+ Add images to the collection and detect faces in it.
+ Search the collection for faces that match a reference image.
+ Delete a collection.

For more information, see [Searching faces in a collection](https://docs.aws.amazon.com/rekognition/latest/dg/collections.html).

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

**SDK for Python (Boto3)**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples). 
Create classes that wrap Amazon Rekognition functions.  

```
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
```
Use the wrapper classes to build a collection of faces from a set of images and then search for faces in the collection.  

```
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)
```

------

For a complete list of AWS SDK developer guides and code examples, see [Using Rekognition with an AWS SDK](sdk-general-information-section.md). This topic also includes information about getting started and details about previous SDK versions.

# Create a photo asset management application that lets users manage photos using labels
<a name="example_cross_PAM_section"></a>

The following code examples show how to create a serverless application that lets users manage photos using labels.

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

**SDK for .NET**  
 Shows how to develop a photo asset management application that detects labels in images using Amazon Rekognition and stores them for later retrieval.   
For complete source code and instructions on how to set up and run, see the full example on [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/cross-service/PhotoAssetManager).  
For a deep dive into the origin of this example see the post on [AWS Community](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app).  

**Services used in this example**
+ API Gateway
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS

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

**SDK for C\$1\$1**  
 Shows how to develop a photo asset management application that detects labels in images using Amazon Rekognition and stores them for later retrieval.   
For complete source code and instructions on how to set up and run, see the full example on [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/cpp/example_code/cross-service/photo_asset_manager).  
For a deep dive into the origin of this example see the post on [AWS Community](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app).  

**Services used in this example**
+ API Gateway
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS

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

**SDK for Java 2.x**  
 Shows how to develop a photo asset management application that detects labels in images using Amazon Rekognition and stores them for later retrieval.   
For complete source code and instructions on how to set up and run, see the full example on [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/pam_source_files).  
For a deep dive into the origin of this example see the post on [AWS Community](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app).  

**Services used in this example**
+ API Gateway
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS

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

**SDK for JavaScript (v3)**  
 Shows how to develop a photo asset management application that detects labels in images using Amazon Rekognition and stores them for later retrieval.   
For complete source code and instructions on how to set up and run, see the full example on [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/cross-services/photo-asset-manager).  
For a deep dive into the origin of this example see the post on [AWS Community](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app).  

**Services used in this example**
+ API Gateway
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS

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

**SDK for Kotlin**  
 Shows how to develop a photo asset management application that detects labels in images using Amazon Rekognition and stores them for later retrieval.   
For complete source code and instructions on how to set up and run, see the full example on [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/usecases/creating_pam).  
For a deep dive into the origin of this example see the post on [AWS Community](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app).  

**Services used in this example**
+ API Gateway
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS

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

**SDK for PHP**  
 Shows how to develop a photo asset management application that detects labels in images using Amazon Rekognition and stores them for later retrieval.   
For complete source code and instructions on how to set up and run, see the full example on [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/php/applications/photo_asset_manager).  
For a deep dive into the origin of this example see the post on [AWS Community](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app).  

**Services used in this example**
+ API Gateway
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS

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

**SDK for Rust**  
 Shows how to develop a photo asset management application that detects labels in images using Amazon Rekognition and stores them for later retrieval.   
For complete source code and instructions on how to set up and run, see the full example on [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/rustv1/cross_service/photo_asset_management).  
For a deep dive into the origin of this example see the post on [AWS Community](https://community.aws/posts/cloud-journeys/01-serverless-image-recognition-app).  

**Services used in this example**
+ API Gateway
+ DynamoDB
+ Lambda
+ Amazon Rekognition
+ Amazon S3
+ Amazon SNS

------

For a complete list of AWS SDK developer guides and code examples, see [Using Rekognition with an AWS SDK](sdk-general-information-section.md). This topic also includes information about getting started and details about previous SDK versions.

# Detect PPE in images with Amazon Rekognition using an AWS SDK
<a name="example_cross_RekognitionPhotoAnalyzerPPE_section"></a>

The following code example shows how to build an app that uses Amazon Rekognition to detect Personal Protective Equipment (PPE) in images.

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

**SDK for Java 2.x**  
 Shows how to create an AWS Lambda function that detects images with Personal Protective Equipment.   
 For complete source code and instructions on how to set up and run, see the full example on [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/creating_lambda_ppe).   

**Services used in this example**
+ DynamoDB
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES

------

For a complete list of AWS SDK developer guides and code examples, see [Using Rekognition with an AWS SDK](sdk-general-information-section.md). This topic also includes information about getting started and details about previous SDK versions.

# Detect and display elements in images with Amazon Rekognition using an AWS SDK
<a name="example_rekognition_Usage_DetectAndDisplayImage_section"></a>

The following code example shows how to:
+ Detect elements in images by using Amazon Rekognition.
+ Display images and draw bounding boxes around detected elements.

For more information, see [Displaying bounding boxes](https://docs.aws.amazon.com/rekognition/latest/dg/images-displaying-bounding-boxes.html).

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

**SDK for Python (Boto3)**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples). 
Create classes to wrap Amazon Rekognition functions.  

```
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
```
Create helper functions to draw bounding boxes and polygons.  

```
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()
```
Create classes to parse objects returned by Amazon Rekognition.  

```
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
```
Use the wrapper classes to detect elements in images and display their bounding boxes. The images used in this example can be found on GitHub along with instructions and more code.  

```
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)
```

------

For a complete list of AWS SDK developer guides and code examples, see [Using Rekognition with an AWS SDK](sdk-general-information-section.md). This topic also includes information about getting started and details about previous SDK versions.

# Detect faces in an image using an AWS SDK
<a name="example_cross_DetectFaces_section"></a>

The following code example shows how to:
+ Save an image in an Amazon S3 bucket.
+ Use Amazon Rekognition to detect facial details, such as age range, gender, and emotion (such as smiling).
+ Display those details.

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

**SDK for Rust**  
 Save the image in an Amazon S3 bucket with an **uploads** prefix, use Amazon Rekognition to detect facial details, such as age range, gender, and emotion (smiling, etc.), and display those details.   
 For complete source code and instructions on how to set up and run, see the full example on [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/blob/main/rustv1/cross_service/detect_faces/src/main.rs).   

**Services used in this example**
+ Amazon Rekognition
+ Amazon S3

------

For a complete list of AWS SDK developer guides and code examples, see [Using Rekognition with an AWS SDK](sdk-general-information-section.md). This topic also includes information about getting started and details about previous SDK versions.

# Detect information in videos using Amazon Rekognition and the AWS SDK
<a name="example_rekognition_VideoDetection_section"></a>

The following code examples show how to:
+ Start Amazon Rekognition jobs to detect elements like people, objects, and text in videos.
+ Check job status until jobs finish.
+ Output the list of elements detected by each job.

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

**SDK for Java 2.x**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples). 
Get celebrity results from a video located in an Amazon S3 bucket.  

```
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);
        }
    }
}
```
Detect labels in a video by a label detection operation.  

```
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);
        }
    }
}
```
Detect faces in a video stored in an Amazon S3 bucket.  

```
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);
        }
    }
}
```
Detect inappropriate or offensive content in a video stored in an Amazon S3 bucket.  

```
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);
        }
    }
}
```
Detect technical cue segments and shot detection segments in a video stored in an Amazon S3 bucket.  

```
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);
        }
    }
}
```
Detect text in a video stored in a video stored in an Amazon S3 bucket.  

```
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);
        }
    }
}
```
Detect people in a video stored in a video stored in an Amazon S3 bucket.  

```
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);
        }
    }
}
```
+ For API details, see the following topics in *AWS SDK for Java 2.x API Reference*.
  + [GetCelebrityRecognition](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/GetCelebrityRecognition)
  + [GetContentModeration](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/GetContentModeration)
  + [GetLabelDetection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/GetLabelDetection)
  + [GetPersonTracking](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/GetPersonTracking)
  + [GetSegmentDetection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/GetSegmentDetection)
  + [GetTextDetection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/GetTextDetection)
  + [StartCelebrityRecognition](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/StartCelebrityRecognition)
  + [StartContentModeration](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/StartContentModeration)
  + [StartLabelDetection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/StartLabelDetection)
  + [StartPersonTracking](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/StartPersonTracking)
  + [StartSegmentDetection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/StartSegmentDetection)
  + [StartTextDetection](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/StartTextDetection)

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

**SDK for Kotlin**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples). 
Detect faces in a video stored in an Amazon S3 bucket.  

```
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}")
        }
    }
}
```
Detect inappropriate or offensive content in a video stored in an Amazon S3 bucket.  

```
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)
        }
    }
}
```
+ For API details, see the following topics in *AWS SDK for Kotlin API reference*.
  + [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)

------

For a complete list of AWS SDK developer guides and code examples, see [Using Rekognition with an AWS SDK](sdk-general-information-section.md). This topic also includes information about getting started and details about previous SDK versions.

# Detect objects in images with Amazon Rekognition using an AWS SDK
<a name="example_cross_RekognitionPhotoAnalyzer_section"></a>

The following code examples show how to build an app that uses Amazon Rekognition to detect objects by category in images.

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

**SDK for .NET**  
 Shows how to use Amazon Rekognition .NET API to create an app that uses Amazon Rekognition to identify objects by category in images located in an Amazon Simple Storage Service (Amazon S3) bucket. The app sends the admin an email notification with the results using Amazon Simple Email Service (Amazon SES).   
 For complete source code and instructions on how to set up and run, see the full example on [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/cross-service/PhotoAnalyzerApp).   

**Services used in this example**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES

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

**SDK for Java 2.x**  
 Shows how to use Amazon Rekognition Java API to create an app that uses Amazon Rekognition to identify objects by category in images located in an Amazon Simple Storage Service (Amazon S3) bucket. The app sends the admin an email notification with the results using Amazon Simple Email Service (Amazon SES).   
 For complete source code and instructions on how to set up and run, see the full example on [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/creating_photo_analyzer_app).   

**Services used in this example**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES

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

**SDK for JavaScript (v3)**  
 Shows how to use Amazon Rekognition with the AWS SDK for JavaScript to create an app that uses Amazon Rekognition to identify objects by category in images located in an Amazon Simple Storage Service (Amazon S3) bucket. The app sends the admin an email notification with the results using Amazon Simple Email Service (Amazon SES).   
Learn how to:  
+ Create an unauthenticated user using Amazon Cognito.
+ Analyze images for objects using Amazon Rekognition.
+ Verify an email address for Amazon SES.
+ Send an email notification using Amazon SES.
 For complete source code and instructions on how to set up and run, see the full example on [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/cross-services/photo_analyzer).   

**Services used in this example**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES

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

**SDK for Kotlin**  
 Shows how to use Amazon Rekognition Kotlin API to create an app that uses Amazon Rekognition to identify objects by category in images located in an Amazon Simple Storage Service (Amazon S3) bucket. The app sends the admin an email notification with the results using Amazon Simple Email Service (Amazon SES).   
 For complete source code and instructions on how to set up and run, see the full example on [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/usecases/creating_photo_analyzer_app).   

**Services used in this example**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES

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#### [ Python ]

**SDK for Python (Boto3)**  
 Shows you how to use the AWS SDK for Python (Boto3) to create a web application that lets you do the following:   
+ Upload photos to an Amazon Simple Storage Service (Amazon S3) bucket.
+ Use Amazon Rekognition to analyze and label the photos.
+ Use Amazon Simple Email Service (Amazon SES) to send email reports of image analysis.
 This example contains two main components: a webpage written in JavaScript that is built with React, and a REST service written in Python that is built with Flask-RESTful.   
You can use the React webpage to:  
+ Display a list of images that are stored in your S3 bucket.
+ Upload images from your computer to your S3 bucket.
+ Display images and labels that identify items that are detected in the image.
+ Get a report of all images in your S3 bucket and send an email of the report.
The webpage calls the REST service. The service sends requests to AWS to perform the following actions:   
+ Get and filter the list of images in your S3 bucket.
+ Upload photos to your S3 bucket.
+ Use Amazon Rekognition to analyze individual photos and get a list of labels that identify items that are detected in the photo.
+ Analyze all photos in your S3 bucket and use Amazon SES to email a report.
 For complete source code and instructions on how to set up and run, see the full example on [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/photo_analyzer).   

**Services used in this example**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES

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For a complete list of AWS SDK developer guides and code examples, see [Using Rekognition with an AWS SDK](sdk-general-information-section.md). This topic also includes information about getting started and details about previous SDK versions.

# Detect people and objects in a video with Amazon Rekognition using an AWS SDK
<a name="example_cross_RekognitionVideoDetection_section"></a>

The following code examples show how to detect people and objects in a video with Amazon Rekognition.

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#### [ Java ]

**SDK for Java 2.x**  
 Shows how to use Amazon Rekognition Java API to create an app to detect faces and objects in videos located in an Amazon Simple Storage Service (Amazon S3) bucket. The app sends the admin an email notification with the results using Amazon Simple Email Service (Amazon SES).   
 For complete source code and instructions on how to set up and run, see the full example on [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/video_analyzer_application).   

**Services used in this example**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES
+ Amazon SNS
+ Amazon SQS

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#### [ Python ]

**SDK for Python (Boto3)**  
 Use Amazon Rekognition to detect faces, objects, and people in videos by starting asynchronous detection jobs. This example also configures Amazon Rekognition to notify an Amazon Simple Notification Service (Amazon SNS) topic when jobs complete and subscribes an Amazon Simple Queue Service (Amazon SQS) queue to the topic. When the queue receives a message about a job, the job is retrieved and the results are output.   
 This example is best viewed on GitHub. For complete source code and instructions on how to set up and run, see the full example on [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition).   

**Services used in this example**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES
+ Amazon SNS
+ Amazon SQS

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For a complete list of AWS SDK developer guides and code examples, see [Using Rekognition with an AWS SDK](sdk-general-information-section.md). This topic also includes information about getting started and details about previous SDK versions.

# Save EXIF and other image information using an AWS SDK
<a name="example_cross_DetectLabels_section"></a>

The following code example shows how to:
+ Get EXIF information from a a JPG, JPEG, or PNG file.
+ Upload the image file to an Amazon S3 bucket.
+ Use Amazon Rekognition to identify the three top attributes (labels) in the file.
+ Add the EXIF and label information to an Amazon DynamoDB table in the Region.

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#### [ Rust ]

**SDK for Rust**  
 Get EXIF information from a JPG, JPEG, or PNG file, upload the image file to an Amazon S3 bucket, use Amazon Rekognition to identify the three top attributes (*labels* in Amazon Rekognition) in the file, and add the EXIF and label information to a Amazon DynamoDB table in the Region.   
 For complete source code and instructions on how to set up and run, see the full example on [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/blob/main/rustv1/cross_service/detect_labels/src/main.rs).   

**Services used in this example**
+ DynamoDB
+ Amazon Rekognition
+ Amazon S3

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For a complete list of AWS SDK developer guides and code examples, see [Using Rekognition with an AWS SDK](sdk-general-information-section.md). This topic also includes information about getting started and details about previous SDK versions.