Rekognition

Table of Contents

Client

class Rekognition.Client

A low-level client representing Amazon Rekognition:

import boto3

client = boto3.client('rekognition')

These are the available methods:

can_paginate(operation_name)

Check if an operation can be paginated.

Parameters
operation_name (string) -- The operation name. This is the same name as the method name on the client. For example, if the method name is create_foo, and you'd normally invoke the operation as client.create_foo(**kwargs), if the create_foo operation can be paginated, you can use the call client.get_paginator("create_foo").
Returns
True if the operation can be paginated, False otherwise.
compare_faces(**kwargs)

Compares a face in the source input image with each of the 100 largest faces detected in the target input image.

Note

If the source image contains multiple faces, the service detects the largest face and compares it with each face detected in the target image.

You pass the input and target images either as base64-encoded image bytes or as a references to images in an Amazon S3 bucket. If you use the Amazon CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.

In response, the operation returns an array of face matches ordered by similarity score in descending order. For each face match, the response provides a bounding box of the face, facial landmarks, pose details (pitch, role, and yaw), quality (brightness and sharpness), and confidence value (indicating the level of confidence that the bounding box contains a face). The response also provides a similarity score, which indicates how closely the faces match.

Note

By default, only faces with a similarity score of greater than or equal to 80% are returned in the response. You can change this value by specifying the SimilarityThreshold parameter.

CompareFaces also returns an array of faces that don't match the source image. For each face, it returns a bounding box, confidence value, landmarks, pose details, and quality. The response also returns information about the face in the source image, including the bounding box of the face and confidence value.

If the image doesn't contain Exif metadata, CompareFaces returns orientation information for the source and target images. Use these values to display the images with the correct image orientation.

If no faces are detected in the source or target images, CompareFaces returns an InvalidParameterException error.

Note

This is a stateless API operation. That is, data returned by this operation doesn't persist.

For an example, see faces-compare-images .

This operation requires permissions to perform the rekognition:CompareFaces action.

See also: AWS API Documentation

Request Syntax

response = client.compare_faces(
    SourceImage={
        'Bytes': b'bytes',
        'S3Object': {
            'Bucket': 'string',
            'Name': 'string',
            'Version': 'string'
        }
    },
    TargetImage={
        'Bytes': b'bytes',
        'S3Object': {
            'Bucket': 'string',
            'Name': 'string',
            'Version': 'string'
        }
    },
    SimilarityThreshold=...
)
Parameters
  • SourceImage (dict) --

    [REQUIRED]

    The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.

    • Bytes (bytes) --

      Blob of image bytes up to 5 MBs.

    • S3Object (dict) --

      Identifies an S3 object as the image source.

      • Bucket (string) --

        Name of the S3 bucket.

      • Name (string) --

        S3 object key name.

      • Version (string) --

        If the bucket is versioning enabled, you can specify the object version.

  • TargetImage (dict) --

    [REQUIRED]

    The target image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.

    • Bytes (bytes) --

      Blob of image bytes up to 5 MBs.

    • S3Object (dict) --

      Identifies an S3 object as the image source.

      • Bucket (string) --

        Name of the S3 bucket.

      • Name (string) --

        S3 object key name.

      • Version (string) --

        If the bucket is versioning enabled, you can specify the object version.

  • SimilarityThreshold (float) -- The minimum level of confidence in the face matches that a match must meet to be included in the FaceMatches array.
Return type

dict

Returns

Response Syntax

{
    'SourceImageFace': {
        'BoundingBox': {
            'Width': ...,
            'Height': ...,
            'Left': ...,
            'Top': ...
        },
        'Confidence': ...
    },
    'FaceMatches': [
        {
            'Similarity': ...,
            'Face': {
                'BoundingBox': {
                    'Width': ...,
                    'Height': ...,
                    'Left': ...,
                    'Top': ...
                },
                'Confidence': ...,
                'Landmarks': [
                    {
                        'Type': 'eyeLeft'|'eyeRight'|'nose'|'mouthLeft'|'mouthRight'|'leftEyeBrowLeft'|'leftEyeBrowRight'|'leftEyeBrowUp'|'rightEyeBrowLeft'|'rightEyeBrowRight'|'rightEyeBrowUp'|'leftEyeLeft'|'leftEyeRight'|'leftEyeUp'|'leftEyeDown'|'rightEyeLeft'|'rightEyeRight'|'rightEyeUp'|'rightEyeDown'|'noseLeft'|'noseRight'|'mouthUp'|'mouthDown'|'leftPupil'|'rightPupil',
                        'X': ...,
                        'Y': ...
                    },
                ],
                'Pose': {
                    'Roll': ...,
                    'Yaw': ...,
                    'Pitch': ...
                },
                'Quality': {
                    'Brightness': ...,
                    'Sharpness': ...
                }
            }
        },
    ],
    'UnmatchedFaces': [
        {
            'BoundingBox': {
                'Width': ...,
                'Height': ...,
                'Left': ...,
                'Top': ...
            },
            'Confidence': ...,
            'Landmarks': [
                {
                    'Type': 'eyeLeft'|'eyeRight'|'nose'|'mouthLeft'|'mouthRight'|'leftEyeBrowLeft'|'leftEyeBrowRight'|'leftEyeBrowUp'|'rightEyeBrowLeft'|'rightEyeBrowRight'|'rightEyeBrowUp'|'leftEyeLeft'|'leftEyeRight'|'leftEyeUp'|'leftEyeDown'|'rightEyeLeft'|'rightEyeRight'|'rightEyeUp'|'rightEyeDown'|'noseLeft'|'noseRight'|'mouthUp'|'mouthDown'|'leftPupil'|'rightPupil',
                    'X': ...,
                    'Y': ...
                },
            ],
            'Pose': {
                'Roll': ...,
                'Yaw': ...,
                'Pitch': ...
            },
            'Quality': {
                'Brightness': ...,
                'Sharpness': ...
            }
        },
    ],
    'SourceImageOrientationCorrection': 'ROTATE_0'|'ROTATE_90'|'ROTATE_180'|'ROTATE_270',
    'TargetImageOrientationCorrection': 'ROTATE_0'|'ROTATE_90'|'ROTATE_180'|'ROTATE_270'
}

Response Structure

  • (dict) --

    • SourceImageFace (dict) --

      The face in the source image that was used for comparison.

      • BoundingBox (dict) --

        Bounding box of the face.

        • Width (float) --

          Width of the bounding box as a ratio of the overall image width.

        • Height (float) --

          Height of the bounding box as a ratio of the overall image height.

        • Left (float) --

          Left coordinate of the bounding box as a ratio of overall image width.

        • Top (float) --

          Top coordinate of the bounding box as a ratio of overall image height.

      • Confidence (float) --

        Confidence level that the selected bounding box contains a face.

    • FaceMatches (list) --

      An array of faces in the target image that match the source image face. Each CompareFacesMatch object provides the bounding box, the confidence level that the bounding box contains a face, and the similarity score for the face in the bounding box and the face in the source image.

      • (dict) --

        Provides information about a face in a target image that matches the source image face analysed by CompareFaces . The Face property contains the bounding box of the face in the target image. The Similarity property is the confidence that the source image face matches the face in the bounding box.

        • Similarity (float) --

          Level of confidence that the faces match.

        • Face (dict) --

          Provides face metadata (bounding box and confidence that the bounding box actually contains a face).

          • BoundingBox (dict) --

            Bounding box of the face.

            • Width (float) --

              Width of the bounding box as a ratio of the overall image width.

            • Height (float) --

              Height of the bounding box as a ratio of the overall image height.

            • Left (float) --

              Left coordinate of the bounding box as a ratio of overall image width.

            • Top (float) --

              Top coordinate of the bounding box as a ratio of overall image height.

          • Confidence (float) --

            Level of confidence that what the bounding box contains is a face.

          • Landmarks (list) --

            An array of facial landmarks.

            • (dict) --

              Indicates the location of the landmark on the face.

              • Type (string) --

                Type of the landmark.

              • X (float) --

                x-coordinate from the top left of the landmark expressed as the ratio of the width of the image. For example, if the images is 700x200 and the x-coordinate of the landmark is at 350 pixels, this value is 0.5.

              • Y (float) --

                y-coordinate from the top left of the landmark expressed as the ratio of the height of the image. For example, if the images is 700x200 and the y-coordinate of the landmark is at 100 pixels, this value is 0.5.

          • Pose (dict) --

            Indicates the pose of the face as determined by its pitch, roll, and yaw.

            • Roll (float) --

              Value representing the face rotation on the roll axis.

            • Yaw (float) --

              Value representing the face rotation on the yaw axis.

            • Pitch (float) --

              Value representing the face rotation on the pitch axis.

          • Quality (dict) --

            Identifies face image brightness and sharpness.

            • Brightness (float) --

              Value representing brightness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a brighter face image.

            • Sharpness (float) --

              Value representing sharpness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a sharper face image.

    • UnmatchedFaces (list) --

      An array of faces in the target image that did not match the source image face.

      • (dict) --

        Provides face metadata for target image faces that are analysed by CompareFaces and RecognizeCelebrities .

        • BoundingBox (dict) --

          Bounding box of the face.

          • Width (float) --

            Width of the bounding box as a ratio of the overall image width.

          • Height (float) --

            Height of the bounding box as a ratio of the overall image height.

          • Left (float) --

            Left coordinate of the bounding box as a ratio of overall image width.

          • Top (float) --

            Top coordinate of the bounding box as a ratio of overall image height.

        • Confidence (float) --

          Level of confidence that what the bounding box contains is a face.

        • Landmarks (list) --

          An array of facial landmarks.

          • (dict) --

            Indicates the location of the landmark on the face.

            • Type (string) --

              Type of the landmark.

            • X (float) --

              x-coordinate from the top left of the landmark expressed as the ratio of the width of the image. For example, if the images is 700x200 and the x-coordinate of the landmark is at 350 pixels, this value is 0.5.

            • Y (float) --

              y-coordinate from the top left of the landmark expressed as the ratio of the height of the image. For example, if the images is 700x200 and the y-coordinate of the landmark is at 100 pixels, this value is 0.5.

        • Pose (dict) --

          Indicates the pose of the face as determined by its pitch, roll, and yaw.

          • Roll (float) --

            Value representing the face rotation on the roll axis.

          • Yaw (float) --

            Value representing the face rotation on the yaw axis.

          • Pitch (float) --

            Value representing the face rotation on the pitch axis.

        • Quality (dict) --

          Identifies face image brightness and sharpness.

          • Brightness (float) --

            Value representing brightness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a brighter face image.

          • Sharpness (float) --

            Value representing sharpness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a sharper face image.

    • SourceImageOrientationCorrection (string) --

      The orientation of the source image (counterclockwise direction). If your application displays the source image, you can use this value to correct image orientation. The bounding box coordinates returned in SourceImageFace represent the location of the face before the image orientation is corrected.

      Note

      If the source image is in .jpeg format, it might contain exchangeable image (Exif) metadata that includes the image's orientation. If the Exif metadata for the source image populates the orientation field, the value of OrientationCorrection is null and the SourceImageFace bounding box coordinates represent the location of the face after Exif metadata is used to correct the orientation. Images in .png format don't contain Exif metadata.

    • TargetImageOrientationCorrection (string) --

      The orientation of the target image (in counterclockwise direction). If your application displays the target image, you can use this value to correct the orientation of the image. The bounding box coordinates returned in FaceMatches and UnmatchedFaces represent face locations before the image orientation is corrected.

      Note

      If the target image is in .jpg format, it might contain Exif metadata that includes the orientation of the image. If the Exif metadata for the target image populates the orientation field, the value of OrientationCorrection is null and the bounding box coordinates in FaceMatches and UnmatchedFaces represent the location of the face after Exif metadata is used to correct the orientation. Images in .png format don't contain Exif metadata.

Examples

This operation compares the largest face detected in the source image with each face detected in the target image.

response = client.compare_faces(
    SimilarityThreshold=90,
    SourceImage={
        'S3Object': {
            'Bucket': 'mybucket',
            'Name': 'mysourceimage',
        },
    },
    TargetImage={
        'S3Object': {
            'Bucket': 'mybucket',
            'Name': 'mytargetimage',
        },
    },
)

print(response)

Expected Output:

{
    'FaceMatches': [
        {
            'Face': {
                'BoundingBox': {
                    'Height': 0.33481481671333313,
                    'Left': 0.31888890266418457,
                    'Top': 0.4933333396911621,
                    'Width': 0.25,
                },
                'Confidence': 99.9991226196289,
            },
            'Similarity': 100,
        },
    ],
    'SourceImageFace': {
        'BoundingBox': {
            'Height': 0.33481481671333313,
            'Left': 0.31888890266418457,
            'Top': 0.4933333396911621,
            'Width': 0.25,
        },
        'Confidence': 99.9991226196289,
    },
    'ResponseMetadata': {
        '...': '...',
    },
}
create_collection(**kwargs)

Creates a collection in an AWS Region. You can add faces to the collection using the operation.

For example, you might create collections, one for each of your application users. A user can then index faces using the IndexFaces operation and persist results in a specific collection. Then, a user can search the collection for faces in the user-specific container.

Note

Collection names are case-sensitive.

This operation requires permissions to perform the rekognition:CreateCollection action.

See also: AWS API Documentation

Request Syntax

response = client.create_collection(
    CollectionId='string'
)
Parameters
CollectionId (string) --

[REQUIRED]

ID for the collection that you are creating.

Return type
dict
Returns
Response Syntax
{
    'StatusCode': 123,
    'CollectionArn': 'string',
    'FaceModelVersion': 'string'
}

Response Structure

  • (dict) --
    • StatusCode (integer) --

      HTTP status code indicating the result of the operation.

    • CollectionArn (string) --

      Amazon Resource Name (ARN) of the collection. You can use this to manage permissions on your resources.

    • FaceModelVersion (string) --

      Version number of the face detection model associated with the collection you are creating.

Examples

This operation creates a Rekognition collection for storing image data.

response = client.create_collection(
    CollectionId='myphotos',
)

print(response)

Expected Output:

{
    'CollectionArn': 'aws:rekognition:us-west-2:123456789012:collection/myphotos',
    'StatusCode': 200,
    'ResponseMetadata': {
        '...': '...',
    },
}
create_stream_processor(**kwargs)

Creates an Amazon Rekognition stream processor that you can use to detect and recognize faces in a streaming video.

Rekognition Video is a consumer of live video from Amazon Kinesis Video Streams. Rekognition Video sends analysis results to Amazon Kinesis Data Streams.

You provide as input a Kinesis video stream (Input ) and a Kinesis data stream (Output ) stream. You also specify the face recognition criteria in Settings . For example, the collection containing faces that you want to recognize. Use Name to assign an identifier for the stream processor. You use Name to manage the stream processor. For example, you can start processing the source video by calling with the Name field.

After you have finished analyzing a streaming video, use to stop processing. You can delete the stream processor by calling .

See also: AWS API Documentation

Request Syntax

response = client.create_stream_processor(
    Input={
        'KinesisVideoStream': {
            'Arn': 'string'
        }
    },
    Output={
        'KinesisDataStream': {
            'Arn': 'string'
        }
    },
    Name='string',
    Settings={
        'FaceSearch': {
            'CollectionId': 'string',
            'FaceMatchThreshold': ...
        }
    },
    RoleArn='string'
)
Parameters
  • Input (dict) --

    [REQUIRED]

    Kinesis video stream stream that provides the source streaming video. If you are using the AWS CLI, the parameter name is StreamProcessorInput .

    • KinesisVideoStream (dict) --

      The Kinesis video stream input stream for the source streaming video.

      • Arn (string) --

        ARN of the Kinesis video stream stream that streams the source video.

  • Output (dict) --

    [REQUIRED]

    Kinesis data stream stream to which Rekognition Video puts the analysis results. If you are using the AWS CLI, the parameter name is StreamProcessorOutput .

    • KinesisDataStream (dict) --

      The Amazon Kinesis Data Streams stream to which the Amazon Rekognition stream processor streams the analysis results.

      • Arn (string) --

        ARN of the output Amazon Kinesis Data Streams stream.

  • Name (string) --

    [REQUIRED]

    An identifier you assign to the stream processor. You can use Name to manage the stream processor. For example, you can get the current status of the stream processor by calling . Name is idempotent.

  • Settings (dict) --

    [REQUIRED]

    Face recognition input parameters to be used by the stream processor. Includes the collection to use for face recognition and the face attributes to detect.

    • FaceSearch (dict) --

      Face search settings to use on a streaming video.

      • CollectionId (string) --

        The ID of a collection that contains faces that you want to search for.

      • FaceMatchThreshold (float) --

        Minimum face match confidence score that must be met to return a result for a recognized face. Default is 70. 0 is the lowest confidence. 100 is the highest confidence.

  • RoleArn (string) --

    [REQUIRED]

    ARN of the IAM role that allows access to the stream processor.

Return type

dict

Returns

Response Syntax

{
    'StreamProcessorArn': 'string'
}

Response Structure

  • (dict) --

    • StreamProcessorArn (string) --

      ARN for the newly create stream processor.

delete_collection(**kwargs)

Deletes the specified collection. Note that this operation removes all faces in the collection. For an example, see delete-collection-procedure .

This operation requires permissions to perform the rekognition:DeleteCollection action.

See also: AWS API Documentation

Request Syntax

response = client.delete_collection(
    CollectionId='string'
)
Parameters
CollectionId (string) --

[REQUIRED]

ID of the collection to delete.

Return type
dict
Returns
Response Syntax
{
    'StatusCode': 123
}

Response Structure

  • (dict) --
    • StatusCode (integer) --

      HTTP status code that indicates the result of the operation.

Examples

This operation deletes a Rekognition collection.

response = client.delete_collection(
    CollectionId='myphotos',
)

print(response)

Expected Output:

{
    'StatusCode': 200,
    'ResponseMetadata': {
        '...': '...',
    },
}
delete_faces(**kwargs)

Deletes faces from a collection. You specify a collection ID and an array of face IDs to remove from the collection.

This operation requires permissions to perform the rekognition:DeleteFaces action.

See also: AWS API Documentation

Request Syntax

response = client.delete_faces(
    CollectionId='string',
    FaceIds=[
        'string',
    ]
)
Parameters
  • CollectionId (string) --

    [REQUIRED]

    Collection from which to remove the specific faces.

  • FaceIds (list) --

    [REQUIRED]

    An array of face IDs to delete.

    • (string) --
Return type

dict

Returns

Response Syntax

{
    'DeletedFaces': [
        'string',
    ]
}

Response Structure

  • (dict) --

    • DeletedFaces (list) --

      An array of strings (face IDs) of the faces that were deleted.

      • (string) --

Examples

This operation deletes one or more faces from a Rekognition collection.

response = client.delete_faces(
    CollectionId='myphotos',
    FaceIds=[
        'ff43d742-0c13-5d16-a3e8-03d3f58e980b',
    ],
)

print(response)

Expected Output:

{
    'DeletedFaces': [
        'ff43d742-0c13-5d16-a3e8-03d3f58e980b',
    ],
    'ResponseMetadata': {
        '...': '...',
    },
}
delete_stream_processor(**kwargs)

Deletes the stream processor identified by Name . You assign the value for Name when you create the stream processor with . You might not be able to use the same name for a stream processor for a few seconds after calling DeleteStreamProcessor .

See also: AWS API Documentation

Request Syntax

response = client.delete_stream_processor(
    Name='string'
)
Parameters
Name (string) --

[REQUIRED]

The name of the stream processor you want to delete.

Return type
dict
Returns
Response Syntax
{}

Response Structure

  • (dict) --
describe_stream_processor(**kwargs)

Provides information about a stream processor created by . You can get information about the input and output streams, the input parameters for the face recognition being performed, and the current status of the stream processor.

See also: AWS API Documentation

Request Syntax

response = client.describe_stream_processor(
    Name='string'
)
Parameters
Name (string) --

[REQUIRED]

Name of the stream processor for which you want information.

Return type
dict
Returns
Response Syntax
{
    'Name': 'string',
    'StreamProcessorArn': 'string',
    'Status': 'STOPPED'|'STARTING'|'RUNNING'|'FAILED'|'STOPPING',
    'StatusMessage': 'string',
    'CreationTimestamp': datetime(2015, 1, 1),
    'LastUpdateTimestamp': datetime(2015, 1, 1),
    'Input': {
        'KinesisVideoStream': {
            'Arn': 'string'
        }
    },
    'Output': {
        'KinesisDataStream': {
            'Arn': 'string'
        }
    },
    'RoleArn': 'string',
    'Settings': {
        'FaceSearch': {
            'CollectionId': 'string',
            'FaceMatchThreshold': ...
        }
    }
}

Response Structure

  • (dict) --
    • Name (string) --

      Name of the stream processor.

    • StreamProcessorArn (string) --

      ARN of the stream processor.

    • Status (string) --

      Current status of the stream processor.

    • StatusMessage (string) --

      Detailed status message about the stream processor.

    • CreationTimestamp (datetime) --

      Date and time the stream processor was created

    • LastUpdateTimestamp (datetime) --

      The time, in Unix format, the stream processor was last updated. For example, when the stream processor moves from a running state to a failed state, or when the user starts or stops the stream processor.

    • Input (dict) --

      Kinesis video stream that provides the source streaming video.

      • KinesisVideoStream (dict) --

        The Kinesis video stream input stream for the source streaming video.

        • Arn (string) --

          ARN of the Kinesis video stream stream that streams the source video.

    • Output (dict) --

      Kinesis data stream to which Rekognition Video puts the analysis results.

      • KinesisDataStream (dict) --

        The Amazon Kinesis Data Streams stream to which the Amazon Rekognition stream processor streams the analysis results.

        • Arn (string) --

          ARN of the output Amazon Kinesis Data Streams stream.

    • RoleArn (string) --

      ARN of the IAM role that allows access to the stream processor.

    • Settings (dict) --

      Face recognition input parameters that are being used by the stream processor. Includes the collection to use for face recognition and the face attributes to detect.

      • FaceSearch (dict) --

        Face search settings to use on a streaming video.

        • CollectionId (string) --

          The ID of a collection that contains faces that you want to search for.

        • FaceMatchThreshold (float) --

          Minimum face match confidence score that must be met to return a result for a recognized face. Default is 70. 0 is the lowest confidence. 100 is the highest confidence.

detect_faces(**kwargs)

Detects faces within an image that is provided as input.

DetectFaces detects the 100 largest faces in the image. For each face detected, the operation returns face details including a bounding box of the face, a confidence value (that the bounding box contains a face), and a fixed set of attributes such as facial landmarks (for example, coordinates of eye and mouth), gender, presence of beard, sunglasses, etc.

The face-detection algorithm is most effective on frontal faces. For non-frontal or obscured faces, the algorithm may not detect the faces or might detect faces with lower confidence.

You pass the input image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the Amazon CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.

Note

This is a stateless API operation. That is, the operation does not persist any data.

For an example, see procedure-detecting-faces-in-images .

This operation requires permissions to perform the rekognition:DetectFaces action.

See also: AWS API Documentation

Request Syntax

response = client.detect_faces(
    Image={
        'Bytes': b'bytes',
        'S3Object': {
            'Bucket': 'string',
            'Name': 'string',
            'Version': 'string'
        }
    },
    Attributes=[
        'DEFAULT'|'ALL',
    ]
)
Parameters
  • Image (dict) --

    [REQUIRED]

    The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.

    • Bytes (bytes) --

      Blob of image bytes up to 5 MBs.

    • S3Object (dict) --

      Identifies an S3 object as the image source.

      • Bucket (string) --

        Name of the S3 bucket.

      • Name (string) --

        S3 object key name.

      • Version (string) --

        If the bucket is versioning enabled, you can specify the object version.

  • Attributes (list) --

    An array of facial attributes you want to be returned. This can be the default list of attributes or all attributes. If you don't specify a value for Attributes or if you specify ["DEFAULT"] , the API returns the following subset of facial attributes: BoundingBox , Confidence , Pose , Quality and Landmarks . If you provide ["ALL"] , all facial attributes are returned but the operation will take longer to complete.

    If you provide both, ["ALL", "DEFAULT"] , the service uses a logical AND operator to determine which attributes to return (in this case, all attributes).

    • (string) --
Return type

dict

Returns

Response Syntax

{
    'FaceDetails': [
        {
            'BoundingBox': {
                'Width': ...,
                'Height': ...,
                'Left': ...,
                'Top': ...
            },
            'AgeRange': {
                'Low': 123,
                'High': 123
            },
            'Smile': {
                'Value': True|False,
                'Confidence': ...
            },
            'Eyeglasses': {
                'Value': True|False,
                'Confidence': ...
            },
            'Sunglasses': {
                'Value': True|False,
                'Confidence': ...
            },
            'Gender': {
                'Value': 'Male'|'Female',
                'Confidence': ...
            },
            'Beard': {
                'Value': True|False,
                'Confidence': ...
            },
            'Mustache': {
                'Value': True|False,
                'Confidence': ...
            },
            'EyesOpen': {
                'Value': True|False,
                'Confidence': ...
            },
            'MouthOpen': {
                'Value': True|False,
                'Confidence': ...
            },
            'Emotions': [
                {
                    'Type': 'HAPPY'|'SAD'|'ANGRY'|'CONFUSED'|'DISGUSTED'|'SURPRISED'|'CALM'|'UNKNOWN',
                    'Confidence': ...
                },
            ],
            'Landmarks': [
                {
                    'Type': 'eyeLeft'|'eyeRight'|'nose'|'mouthLeft'|'mouthRight'|'leftEyeBrowLeft'|'leftEyeBrowRight'|'leftEyeBrowUp'|'rightEyeBrowLeft'|'rightEyeBrowRight'|'rightEyeBrowUp'|'leftEyeLeft'|'leftEyeRight'|'leftEyeUp'|'leftEyeDown'|'rightEyeLeft'|'rightEyeRight'|'rightEyeUp'|'rightEyeDown'|'noseLeft'|'noseRight'|'mouthUp'|'mouthDown'|'leftPupil'|'rightPupil',
                    'X': ...,
                    'Y': ...
                },
            ],
            'Pose': {
                'Roll': ...,
                'Yaw': ...,
                'Pitch': ...
            },
            'Quality': {
                'Brightness': ...,
                'Sharpness': ...
            },
            'Confidence': ...
        },
    ],
    'OrientationCorrection': 'ROTATE_0'|'ROTATE_90'|'ROTATE_180'|'ROTATE_270'
}

Response Structure

  • (dict) --

    • FaceDetails (list) --

      Details of each face found in the image.

      • (dict) --

        Structure containing attributes of the face that the algorithm detected.

        • BoundingBox (dict) --

          Bounding box of the face.

          • Width (float) --

            Width of the bounding box as a ratio of the overall image width.

          • Height (float) --

            Height of the bounding box as a ratio of the overall image height.

          • Left (float) --

            Left coordinate of the bounding box as a ratio of overall image width.

          • Top (float) --

            Top coordinate of the bounding box as a ratio of overall image height.

        • AgeRange (dict) --

          The estimated age range, in years, for the face. Low represents the lowest estimated age and High represents the highest estimated age.

          • Low (integer) --

            The lowest estimated age.

          • High (integer) --

            The highest estimated age.

        • Smile (dict) --

          Indicates whether or not the face is smiling, and the confidence level in the determination.

          • Value (boolean) --

            Boolean value that indicates whether the face is smiling or not.

          • Confidence (float) --

            Level of confidence in the determination.

        • Eyeglasses (dict) --

          Indicates whether or not the face is wearing eye glasses, and the confidence level in the determination.

          • Value (boolean) --

            Boolean value that indicates whether the face is wearing eye glasses or not.

          • Confidence (float) --

            Level of confidence in the determination.

        • Sunglasses (dict) --

          Indicates whether or not the face is wearing sunglasses, and the confidence level in the determination.

          • Value (boolean) --

            Boolean value that indicates whether the face is wearing sunglasses or not.

          • Confidence (float) --

            Level of confidence in the determination.

        • Gender (dict) --

          Gender of the face and the confidence level in the determination.

          • Value (string) --

            Gender of the face.

          • Confidence (float) --

            Level of confidence in the determination.

        • Beard (dict) --

          Indicates whether or not the face has a beard, and the confidence level in the determination.

          • Value (boolean) --

            Boolean value that indicates whether the face has beard or not.

          • Confidence (float) --

            Level of confidence in the determination.

        • Mustache (dict) --

          Indicates whether or not the face has a mustache, and the confidence level in the determination.

          • Value (boolean) --

            Boolean value that indicates whether the face has mustache or not.

          • Confidence (float) --

            Level of confidence in the determination.

        • EyesOpen (dict) --

          Indicates whether or not the eyes on the face are open, and the confidence level in the determination.

          • Value (boolean) --

            Boolean value that indicates whether the eyes on the face are open.

          • Confidence (float) --

            Level of confidence in the determination.

        • MouthOpen (dict) --

          Indicates whether or not the mouth on the face is open, and the confidence level in the determination.

          • Value (boolean) --

            Boolean value that indicates whether the mouth on the face is open or not.

          • Confidence (float) --

            Level of confidence in the determination.

        • Emotions (list) --

          The emotions detected on the face, and the confidence level in the determination. For example, HAPPY, SAD, and ANGRY.

          • (dict) --

            The emotions detected on the face, and the confidence level in the determination. For example, HAPPY, SAD, and ANGRY.

            • Type (string) --

              Type of emotion detected.

            • Confidence (float) --

              Level of confidence in the determination.

        • Landmarks (list) --

          Indicates the location of landmarks on the face.

          • (dict) --

            Indicates the location of the landmark on the face.

            • Type (string) --

              Type of the landmark.

            • X (float) --

              x-coordinate from the top left of the landmark expressed as the ratio of the width of the image. For example, if the images is 700x200 and the x-coordinate of the landmark is at 350 pixels, this value is 0.5.

            • Y (float) --

              y-coordinate from the top left of the landmark expressed as the ratio of the height of the image. For example, if the images is 700x200 and the y-coordinate of the landmark is at 100 pixels, this value is 0.5.

        • Pose (dict) --

          Indicates the pose of the face as determined by its pitch, roll, and yaw.

          • Roll (float) --

            Value representing the face rotation on the roll axis.

          • Yaw (float) --

            Value representing the face rotation on the yaw axis.

          • Pitch (float) --

            Value representing the face rotation on the pitch axis.

        • Quality (dict) --

          Identifies image brightness and sharpness.

          • Brightness (float) --

            Value representing brightness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a brighter face image.

          • Sharpness (float) --

            Value representing sharpness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a sharper face image.

        • Confidence (float) --

          Confidence level that the bounding box contains a face (and not a different object such as a tree).

    • OrientationCorrection (string) --

      The orientation of the input image (counter-clockwise direction). If your application displays the image, you can use this value to correct image orientation. The bounding box coordinates returned in FaceDetails represent face locations before the image orientation is corrected.

      Note

      If the input image is in .jpeg format, it might contain exchangeable image (Exif) metadata that includes the image's orientation. If so, and the Exif metadata for the input image populates the orientation field, the value of OrientationCorrection is null and the FaceDetails bounding box coordinates represent face locations after Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.

Examples

This operation detects faces in an image stored in an AWS S3 bucket.

response = client.detect_faces(
    Image={
        'S3Object': {
            'Bucket': 'mybucket',
            'Name': 'myphoto',
        },
    },
)

print(response)

Expected Output:

{
    'FaceDetails': [
        {
            'BoundingBox': {
                'Height': 0.18000000715255737,
                'Left': 0.5555555820465088,
                'Top': 0.33666667342185974,
                'Width': 0.23999999463558197,
            },
            'Confidence': 100,
            'Landmarks': [
                {
                    'Type': 'EYE_LEFT',
                    'X': 0.6394737362861633,
                    'Y': 0.40819624066352844,
                },
                {
                    'Type': 'EYE_RIGHT',
                    'X': 0.7266660928726196,
                    'Y': 0.41039225459098816,
                },
                {
                    'Type': 'NOSE_LEFT',
                    'X': 0.6912462115287781,
                    'Y': 0.44240960478782654,
                },
                {
                    'Type': 'MOUTH_DOWN',
                    'X': 0.6306198239326477,
                    'Y': 0.46700039505958557,
                },
                {
                    'Type': 'MOUTH_UP',
                    'X': 0.7215608954429626,
                    'Y': 0.47114261984825134,
                },
            ],
            'Pose': {
                'Pitch': 4.050806522369385,
                'Roll': 0.9950747489929199,
                'Yaw': 13.693790435791016,
            },
            'Quality': {
                'Brightness': 37.60169982910156,
                'Sharpness': 80,
            },
        },
    ],
    'OrientationCorrection': 'ROTATE_0',
    'ResponseMetadata': {
        '...': '...',
    },
}
detect_labels(**kwargs)

Detects instances of real-world entities within an image (JPEG or PNG) provided as input. This includes objects like flower, tree, and table; events like wedding, graduation, and birthday party; and concepts like landscape, evening, and nature. For an example, see images-s3 .

Note

DetectLabels does not support the detection of activities. However, activity detection is supported for label detection in videos. For more information, see .

You pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the Amazon CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.

For each object, scene, and concept the API returns one or more labels. Each label provides the object name, and the level of confidence that the image contains the object. For example, suppose the input image has a lighthouse, the sea, and a rock. The response will include all three labels, one for each object.

{Name: lighthouse, Confidence: 98.4629}

{Name: rock,Confidence: 79.2097}

{Name: sea,Confidence: 75.061}

In the preceding example, the operation returns one label for each of the three objects. The operation can also return multiple labels for the same object in the image. For example, if the input image shows a flower (for example, a tulip), the operation might return the following three labels.

{Name: flower,Confidence: 99.0562}

{Name: plant,Confidence: 99.0562}

{Name: tulip,Confidence: 99.0562}

In this example, the detection algorithm more precisely identifies the flower as a tulip.

In response, the API returns an array of labels. In addition, the response also includes the orientation correction. Optionally, you can specify MinConfidence to control the confidence threshold for the labels returned. The default is 50%. You can also add the MaxLabels parameter to limit the number of labels returned.

Note

If the object detected is a person, the operation doesn't provide the same facial details that the DetectFaces operation provides.

This is a stateless API operation. That is, the operation does not persist any data.

This operation requires permissions to perform the rekognition:DetectLabels action.

See also: AWS API Documentation

Request Syntax

response = client.detect_labels(
    Image={
        'Bytes': b'bytes',
        'S3Object': {
            'Bucket': 'string',
            'Name': 'string',
            'Version': 'string'
        }
    },
    MaxLabels=123,
    MinConfidence=...
)
Parameters
  • Image (dict) --

    [REQUIRED]

    The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.

    • Bytes (bytes) --

      Blob of image bytes up to 5 MBs.

    • S3Object (dict) --

      Identifies an S3 object as the image source.

      • Bucket (string) --

        Name of the S3 bucket.

      • Name (string) --

        S3 object key name.

      • Version (string) --

        If the bucket is versioning enabled, you can specify the object version.

  • MaxLabels (integer) -- Maximum number of labels you want the service to return in the response. The service returns the specified number of highest confidence labels.
  • MinConfidence (float) --

    Specifies the minimum confidence level for the labels to return. Amazon Rekognition doesn't return any labels with confidence lower than this specified value.

    If MinConfidence is not specified, the operation returns labels with a confidence values greater than or equal to 50 percent.

Return type

dict

Returns

Response Syntax

{
    'Labels': [
        {
            'Name': 'string',
            'Confidence': ...
        },
    ],
    'OrientationCorrection': 'ROTATE_0'|'ROTATE_90'|'ROTATE_180'|'ROTATE_270'
}

Response Structure

  • (dict) --

    • Labels (list) --

      An array of labels for the real-world objects detected.

      • (dict) --

        Structure containing details about the detected label, including name, and level of confidence.

        • Name (string) --

          The name (label) of the object.

        • Confidence (float) --

          Level of confidence.

    • OrientationCorrection (string) --

      The orientation of the input image (counter-clockwise direction). If your application displays the image, you can use this value to correct the orientation. If Amazon Rekognition detects that the input image was rotated (for example, by 90 degrees), it first corrects the orientation before detecting the labels.

      Note

      If the input image Exif metadata populates the orientation field, Amazon Rekognition does not perform orientation correction and the value of OrientationCorrection will be null.

Examples

This operation detects labels in the supplied image

response = client.detect_labels(
    Image={
        'S3Object': {
            'Bucket': 'mybucket',
            'Name': 'myphoto',
        },
    },
    MaxLabels=123,
    MinConfidence=70,
)

print(response)

Expected Output:

{
    'Labels': [
        {
            'Confidence': 99.25072479248047,
            'Name': 'People',
        },
        {
            'Confidence': 99.25074005126953,
            'Name': 'Person',
        },
    ],
    'ResponseMetadata': {
        '...': '...',
    },
}
detect_moderation_labels(**kwargs)

Detects explicit or suggestive adult content in a specified JPEG or PNG format image. Use DetectModerationLabels to moderate images depending on your requirements. For example, you might want to filter images that contain nudity, but not images containing suggestive content.

To filter images, use the labels returned by DetectModerationLabels to determine which types of content are appropriate. For information about moderation labels, see moderation .

You pass the input image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the Amazon CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.

See also: AWS API Documentation

Request Syntax

response = client.detect_moderation_labels(
    Image={
        'Bytes': b'bytes',
        'S3Object': {
            'Bucket': 'string',
            'Name': 'string',
            'Version': 'string'
        }
    },
    MinConfidence=...
)
Parameters
  • Image (dict) --

    [REQUIRED]

    The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.

    • Bytes (bytes) --

      Blob of image bytes up to 5 MBs.

    • S3Object (dict) --

      Identifies an S3 object as the image source.

      • Bucket (string) --

        Name of the S3 bucket.

      • Name (string) --

        S3 object key name.

      • Version (string) --

        If the bucket is versioning enabled, you can specify the object version.

  • MinConfidence (float) --

    Specifies the minimum confidence level for the labels to return. Amazon Rekognition doesn't return any labels with a confidence level lower than this specified value.

    If you don't specify MinConfidence , the operation returns labels with confidence values greater than or equal to 50 percent.

Return type

dict

Returns

Response Syntax

{
    'ModerationLabels': [
        {
            'Confidence': ...,
            'Name': 'string',
            'ParentName': 'string'
        },
    ]
}

Response Structure

  • (dict) --

    • ModerationLabels (list) --

      Array of detected Moderation labels and the time, in millseconds from the start of the video, they were detected.

      • (dict) --

        Provides information about a single type of moderated content found in an image or video. Each type of moderated content has a label within a hierarchical taxonomy. For more information, see moderation .

        • Confidence (float) --

          Specifies the confidence that Amazon Rekognition has that the label has been correctly identified.

          If you don't specify the MinConfidence parameter in the call to DetectModerationLabels , the operation returns labels with a confidence value greater than or equal to 50 percent.

        • Name (string) --

          The label name for the type of content detected in the image.

        • ParentName (string) --

          The name for the parent label. Labels at the top-level of the hierarchy have the parent label "" .

detect_text(**kwargs)

Detects text in the input image and converts it into machine-readable text.

Pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, you must pass it as a reference to an image in an Amazon S3 bucket. For the AWS CLI, passing image bytes is not supported. The image must be either a .png or .jpeg formatted file.

The DetectText operation returns text in an array of elements, TextDetections . Each TextDetection element provides information about a single word or line of text that was detected in the image.

A word is one or more ISO basic latin script characters that are not separated by spaces. DetectText can detect up to 50 words in an image.

A line is a string of equally spaced words. A line isn't necessarily a complete sentence. For example, a driver's license number is detected as a line. A line ends when there is no aligned text after it. Also, a line ends when there is a large gap between words, relative to the length of the words. This means, depending on the gap between words, Amazon Rekognition may detect multiple lines in text aligned in the same direction. Periods don't represent the end of a line. If a sentence spans multiple lines, the DetectText operation returns multiple lines.

To determine whether a TextDetection element is a line of text or a word, use the TextDetection object Type field.

To be detected, text must be within +/- 30 degrees orientation of the horizontal axis.

For more information, see text-detection .

See also: AWS API Documentation

Request Syntax

response = client.detect_text(
    Image={
        'Bytes': b'bytes',
        'S3Object': {
            'Bucket': 'string',
            'Name': 'string',
            'Version': 'string'
        }
    }
)
Parameters
Image (dict) --

[REQUIRED]

The input image as base64-encoded bytes or an Amazon S3 object. If you use the AWS CLI to call Amazon Rekognition operations, you can't pass image bytes.

  • Bytes (bytes) --

    Blob of image bytes up to 5 MBs.

  • S3Object (dict) --

    Identifies an S3 object as the image source.

    • Bucket (string) --

      Name of the S3 bucket.

    • Name (string) --

      S3 object key name.

    • Version (string) --

      If the bucket is versioning enabled, you can specify the object version.

Return type
dict
Returns
Response Syntax
{
    'TextDetections': [
        {
            'DetectedText': 'string',
            'Type': 'LINE'|'WORD',
            'Id': 123,
            'ParentId': 123,
            'Confidence': ...,
            'Geometry': {
                'BoundingBox': {
                    'Width': ...,
                    'Height': ...,
                    'Left': ...,
                    'Top': ...
                },
                'Polygon': [
                    {
                        'X': ...,
                        'Y': ...
                    },
                ]
            }
        },
    ]
}

Response Structure

  • (dict) --
    • TextDetections (list) --

      An array of text that was detected in the input image.

      • (dict) --

        Information about a word or line of text detected by .

        The DetectedText field contains the text that Amazon Rekognition detected in the image.

        Every word and line has an identifier (Id ). Each word belongs to a line and has a parent identifier (ParentId ) that identifies the line of text in which the word appears. The word Id is also an index for the word within a line of words.

        For more information, see text-detection .

        • DetectedText (string) --

          The word or line of text recognized by Amazon Rekognition.

        • Type (string) --

          The type of text that was detected.

        • Id (integer) --

          The identifier for the detected text. The identifier is only unique for a single call to DetectText .

        • ParentId (integer) --

          The Parent identifier for the detected text identified by the value of ID . If the type of detected text is LINE , the value of ParentId is Null .

        • Confidence (float) --

          The confidence that Amazon Rekognition has in the accuracy of the detected text and the accuracy of the geometry points around the detected text.

        • Geometry (dict) --

          The location of the detected text on the image. Includes an axis aligned coarse bounding box surrounding the text and a finer grain polygon for more accurate spatial information.

          • BoundingBox (dict) --

            An axis-aligned coarse representation of the detected text's location on the image.

            • Width (float) --

              Width of the bounding box as a ratio of the overall image width.

            • Height (float) --

              Height of the bounding box as a ratio of the overall image height.

            • Left (float) --

              Left coordinate of the bounding box as a ratio of overall image width.

            • Top (float) --

              Top coordinate of the bounding box as a ratio of overall image height.

          • Polygon (list) --

            Within the bounding box, a fine-grained polygon around the detected text.

            • (dict) --

              The X and Y coordinates of a point on an image. The X and Y values returned are ratios of the overall image size. For example, if the input image is 700x200 and the operation returns X=0.5 and Y=0.25, then the point is at the (350,50) pixel coordinate on the image.

              An array of Point objects, Polygon , is returned by . Polygon represents a fine-grained polygon around detected text. For more information, see .

              • X (float) --

                The value of the X coordinate for a point on a Polygon .

              • Y (float) --

                The value of the Y coordinate for a point on a Polygon .

generate_presigned_url(ClientMethod, Params=None, ExpiresIn=3600, HttpMethod=None)

Generate a presigned url given a client, its method, and arguments

Parameters
  • ClientMethod (string) -- The client method to presign for
  • Params (dict) -- The parameters normally passed to ClientMethod.
  • ExpiresIn (int) -- The number of seconds the presigned url is valid for. By default it expires in an hour (3600 seconds)
  • HttpMethod (string) -- The http method to use on the generated url. By default, the http method is whatever is used in the method's model.
Returns

The presigned url

get_celebrity_info(**kwargs)

Gets the name and additional information about a celebrity based on his or her Rekognition ID. The additional information is returned as an array of URLs. If there is no additional information about the celebrity, this list is empty. For more information, see get-celebrity-info-procedure .

This operation requires permissions to perform the rekognition:GetCelebrityInfo action.

See also: AWS API Documentation

Request Syntax

response = client.get_celebrity_info(
    Id='string'
)
Parameters
Id (string) --

[REQUIRED]

The ID for the celebrity. You get the celebrity ID from a call to the operation, which recognizes celebrities in an image.

Return type
dict
Returns
Response Syntax
{
    'Urls': [
        'string',
    ],
    'Name': 'string'
}

Response Structure

  • (dict) --
    • Urls (list) --

      An array of URLs pointing to additional celebrity information.

      • (string) --
    • Name (string) --

      The name of the celebrity.

get_celebrity_recognition(**kwargs)

Gets the celebrity recognition results for a Rekognition Video analysis started by .

Celebrity recognition in a video is an asynchronous operation. Analysis is started by a call to which returns a job identifier (JobId ). When the celebrity recognition operation finishes, Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartCelebrityRecognition . To get the results of the celebrity recognition analysis, first check that the status value published to the Amazon SNS topic is SUCCEEDED . If so, call GetCelebrityDetection and pass the job identifier (JobId ) from the initial call to StartCelebrityDetection . For more information, see video .

GetCelebrityRecognition returns detected celebrities and the time(s) they are detected in an array (Celebrities ) of objects. Each CelebrityRecognition contains information about the celebrity in a object and the time, Timestamp , the celebrity was detected.

By default, the Celebrities array is sorted by time (milliseconds from the start of the video). You can also sort the array by celebrity by specifying the value ID in the SortBy input parameter.

The CelebrityDetail object includes the celebrity identifer and additional information urls. If you don't store the additional information urls, you can get them later by calling with the celebrity identifer.

No information is returned for faces not recognized as celebrities.

Use MaxResults parameter to limit the number of labels returned. If there are more results than specified in MaxResults , the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetCelebrityDetection and populate the NextToken request parameter with the token value returned from the previous call to GetCelebrityRecognition .

See also: AWS API Documentation

Request Syntax

response = client.get_celebrity_recognition(
    JobId='string',
    MaxResults=123,
    NextToken='string',
    SortBy='ID'|'TIMESTAMP'
)
Parameters
  • JobId (string) --

    [REQUIRED]

    Job identifier for the required celebrity recognition analysis. You can get the job identifer from a call to StartCelebrityRecognition .

  • MaxResults (integer) -- Maximum number of celebrities you want Rekognition Video to return in the response. The default is 1000.
  • NextToken (string) -- If the previous response was incomplete (because there is more recognized celebrities to retrieve), Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of celebrities.
  • SortBy (string) -- Sort to use for celebrities returned in Celebrities field. Specify ID to sort by the celebrity identifier, specify TIMESTAMP to sort by the time the celebrity was recognized.
Return type

dict

Returns

Response Syntax

{
    'JobStatus': 'IN_PROGRESS'|'SUCCEEDED'|'FAILED',
    'StatusMessage': 'string',
    'VideoMetadata': {
        'Codec': 'string',
        'DurationMillis': 123,
        'Format': 'string',
        'FrameRate': ...,
        'FrameHeight': 123,
        'FrameWidth': 123
    },
    'NextToken': 'string',
    'Celebrities': [
        {
            'Timestamp': 123,
            'Celebrity': {
                'Urls': [
                    'string',
                ],
                'Name': 'string',
                'Id': 'string',
                'Confidence': ...,
                'BoundingBox': {
                    'Width': ...,
                    'Height': ...,
                    'Left': ...,
                    'Top': ...
                },
                'Face': {
                    'BoundingBox': {
                        'Width': ...,
                        'Height': ...,
                        'Left': ...,
                        'Top': ...
                    },
                    'AgeRange': {
                        'Low': 123,
                        'High': 123
                    },
                    'Smile': {
                        'Value': True|False,
                        'Confidence': ...
                    },
                    'Eyeglasses': {
                        'Value': True|False,
                        'Confidence': ...
                    },
                    'Sunglasses': {
                        'Value': True|False,
                        'Confidence': ...
                    },
                    'Gender': {
                        'Value': 'Male'|'Female',
                        'Confidence': ...
                    },
                    'Beard': {
                        'Value': True|False,
                        'Confidence': ...
                    },
                    'Mustache': {
                        'Value': True|False,
                        'Confidence': ...
                    },
                    'EyesOpen': {
                        'Value': True|False,
                        'Confidence': ...
                    },
                    'MouthOpen': {
                        'Value': True|False,
                        'Confidence': ...
                    },
                    'Emotions': [
                        {
                            'Type': 'HAPPY'|'SAD'|'ANGRY'|'CONFUSED'|'DISGUSTED'|'SURPRISED'|'CALM'|'UNKNOWN',
                            'Confidence': ...
                        },
                    ],
                    'Landmarks': [
                        {
                            'Type': 'eyeLeft'|'eyeRight'|'nose'|'mouthLeft'|'mouthRight'|'leftEyeBrowLeft'|'leftEyeBrowRight'|'leftEyeBrowUp'|'rightEyeBrowLeft'|'rightEyeBrowRight'|'rightEyeBrowUp'|'leftEyeLeft'|'leftEyeRight'|'leftEyeUp'|'leftEyeDown'|'rightEyeLeft'|'rightEyeRight'|'rightEyeUp'|'rightEyeDown'|'noseLeft'|'noseRight'|'mouthUp'|'mouthDown'|'leftPupil'|'rightPupil',
                            'X': ...,
                            'Y': ...
                        },
                    ],
                    'Pose': {
                        'Roll': ...,
                        'Yaw': ...,
                        'Pitch': ...
                    },
                    'Quality': {
                        'Brightness': ...,
                        'Sharpness': ...
                    },
                    'Confidence': ...
                }
            }
        },
    ]
}

Response Structure

  • (dict) --

    • JobStatus (string) --

      The current status of the celebrity recognition job.

    • StatusMessage (string) --

      If the job fails, StatusMessage provides a descriptive error message.

    • VideoMetadata (dict) --

      Information about a video that Rekognition Video analyzed. Videometadata is returned in every page of paginated responses from a Rekognition Video operation.

      • Codec (string) --

        Type of compression used in the analyzed video.

      • DurationMillis (integer) --

        Length of the video in milliseconds.

      • Format (string) --

        Format of the analyzed video. Possible values are MP4, MOV and AVI.

      • FrameRate (float) --

        Number of frames per second in the video.

      • FrameHeight (integer) --

        Vertical pixel dimension of the video.

      • FrameWidth (integer) --

        Horizontal pixel dimension of the video.

    • NextToken (string) --

      If the response is truncated, Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of celebrities.

    • Celebrities (list) --

      Array of celebrities recognized in the video.

      • (dict) --

        Information about a detected celebrity and the time the celebrity was detected in a stored video. For more information, see .

        • Timestamp (integer) --

          The time, in milliseconds from the start of the video, that the celebrity was recognized.

        • Celebrity (dict) --

          Information about a recognized celebrity.

          • Urls (list) --

            An array of URLs pointing to additional celebrity information.

            • (string) --
          • Name (string) --

            The name of the celebrity.

          • Id (string) --

            The unique identifier for the celebrity.

          • Confidence (float) --

            The confidence, in percentage, that Amazon Rekognition has that the recognized face is the celebrity.

          • BoundingBox (dict) --

            Bounding box around the body of a celebrity.

            • Width (float) --

              Width of the bounding box as a ratio of the overall image width.

            • Height (float) --

              Height of the bounding box as a ratio of the overall image height.

            • Left (float) --

              Left coordinate of the bounding box as a ratio of overall image width.

            • Top (float) --

              Top coordinate of the bounding box as a ratio of overall image height.

          • Face (dict) --

            Face details for the recognized celebrity.

            • BoundingBox (dict) --

              Bounding box of the face.

              • Width (float) --

                Width of the bounding box as a ratio of the overall image width.

              • Height (float) --

                Height of the bounding box as a ratio of the overall image height.

              • Left (float) --

                Left coordinate of the bounding box as a ratio of overall image width.

              • Top (float) --

                Top coordinate of the bounding box as a ratio of overall image height.

            • AgeRange (dict) --

              The estimated age range, in years, for the face. Low represents the lowest estimated age and High represents the highest estimated age.

              • Low (integer) --

                The lowest estimated age.

              • High (integer) --

                The highest estimated age.

            • Smile (dict) --

              Indicates whether or not the face is smiling, and the confidence level in the determination.

              • Value (boolean) --

                Boolean value that indicates whether the face is smiling or not.

              • Confidence (float) --

                Level of confidence in the determination.

            • Eyeglasses (dict) --

              Indicates whether or not the face is wearing eye glasses, and the confidence level in the determination.

              • Value (boolean) --

                Boolean value that indicates whether the face is wearing eye glasses or not.

              • Confidence (float) --

                Level of confidence in the determination.

            • Sunglasses (dict) --

              Indicates whether or not the face is wearing sunglasses, and the confidence level in the determination.

              • Value (boolean) --

                Boolean value that indicates whether the face is wearing sunglasses or not.

              • Confidence (float) --

                Level of confidence in the determination.

            • Gender (dict) --

              Gender of the face and the confidence level in the determination.

              • Value (string) --

                Gender of the face.

              • Confidence (float) --

                Level of confidence in the determination.

            • Beard (dict) --

              Indicates whether or not the face has a beard, and the confidence level in the determination.

              • Value (boolean) --

                Boolean value that indicates whether the face has beard or not.

              • Confidence (float) --

                Level of confidence in the determination.

            • Mustache (dict) --

              Indicates whether or not the face has a mustache, and the confidence level in the determination.

              • Value (boolean) --

                Boolean value that indicates whether the face has mustache or not.

              • Confidence (float) --

                Level of confidence in the determination.

            • EyesOpen (dict) --

              Indicates whether or not the eyes on the face are open, and the confidence level in the determination.

              • Value (boolean) --

                Boolean value that indicates whether the eyes on the face are open.

              • Confidence (float) --

                Level of confidence in the determination.

            • MouthOpen (dict) --

              Indicates whether or not the mouth on the face is open, and the confidence level in the determination.

              • Value (boolean) --

                Boolean value that indicates whether the mouth on the face is open or not.

              • Confidence (float) --

                Level of confidence in the determination.

            • Emotions (list) --

              The emotions detected on the face, and the confidence level in the determination. For example, HAPPY, SAD, and ANGRY.

              • (dict) --

                The emotions detected on the face, and the confidence level in the determination. For example, HAPPY, SAD, and ANGRY.

                • Type (string) --

                  Type of emotion detected.

                • Confidence (float) --

                  Level of confidence in the determination.

            • Landmarks (list) --

              Indicates the location of landmarks on the face.

              • (dict) --

                Indicates the location of the landmark on the face.

                • Type (string) --

                  Type of the landmark.

                • X (float) --

                  x-coordinate from the top left of the landmark expressed as the ratio of the width of the image. For example, if the images is 700x200 and the x-coordinate of the landmark is at 350 pixels, this value is 0.5.

                • Y (float) --

                  y-coordinate from the top left of the landmark expressed as the ratio of the height of the image. For example, if the images is 700x200 and the y-coordinate of the landmark is at 100 pixels, this value is 0.5.

            • Pose (dict) --

              Indicates the pose of the face as determined by its pitch, roll, and yaw.

              • Roll (float) --

                Value representing the face rotation on the roll axis.

              • Yaw (float) --

                Value representing the face rotation on the yaw axis.

              • Pitch (float) --

                Value representing the face rotation on the pitch axis.

            • Quality (dict) --

              Identifies image brightness and sharpness.

              • Brightness (float) --

                Value representing brightness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a brighter face image.

              • Sharpness (float) --

                Value representing sharpness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a sharper face image.

            • Confidence (float) --

              Confidence level that the bounding box contains a face (and not a different object such as a tree).

get_content_moderation(**kwargs)

Gets the content moderation analysis results for a Rekognition Video analysis started by .

Content moderation analysis of a video is an asynchronous operation. You start analysis by calling . which returns a job identifier (JobId ). When analysis finishes, Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartContentModeration . To get the results of the content moderation analysis, first check that the status value published to the Amazon SNS topic is SUCCEEDED . If so, call GetCelebrityDetection and pass the job identifier (JobId ) from the initial call to StartCelebrityDetection . For more information, see video .

GetContentModeration returns detected content moderation labels, and the time they are detected, in an array, ModerationLabels , of objects.

By default, the moderated labels are returned sorted by time, in milliseconds from the start of the video. You can also sort them by moderated label by specifying NAME for the SortBy input parameter.

Since video analysis can return a large number of results, use the MaxResults parameter to limit the number of labels returned in a single call to GetContentModeration . If there are more results than specified in MaxResults , the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetContentModeration and populate the NextToken request parameter with the value of NextToken returned from the previous call to GetContentModeration .

For more information, see moderation .

See also: AWS API Documentation

Request Syntax

response = client.get_content_moderation(
    JobId='string',
    MaxResults=123,
    NextToken='string',
    SortBy='NAME'|'TIMESTAMP'
)
Parameters
  • JobId (string) --

    [REQUIRED]

    The identifier for the content moderation job. Use JobId to identify the job in a subsequent call to GetContentModeration .

  • MaxResults (integer) -- Maximum number of content moderation labels to return. The default is 1000.
  • NextToken (string) -- If the previous response was incomplete (because there is more data to retrieve), Amazon Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of content moderation labels.
  • SortBy (string) -- Sort to use for elements in the ModerationLabelDetections array. Use TIMESTAMP to sort array elements by the time labels are detected. Use NAME to alphabetically group elements for a label together. Within each label group, the array element are sorted by detection confidence. The default sort is by TIMESTAMP .
Return type

dict

Returns

Response Syntax

{
    'JobStatus': 'IN_PROGRESS'|'SUCCEEDED'|'FAILED',
    'StatusMessage': 'string',
    'VideoMetadata': {
        'Codec': 'string',
        'DurationMillis': 123,
        'Format': 'string',
        'FrameRate': ...,
        'FrameHeight': 123,
        'FrameWidth': 123
    },
    'ModerationLabels': [
        {
            'Timestamp': 123,
            'ModerationLabel': {
                'Confidence': ...,
                'Name': 'string',
                'ParentName': 'string'
            }
        },
    ],
    'NextToken': 'string'
}

Response Structure

  • (dict) --

    • JobStatus (string) --

      The current status of the content moderation job.

    • StatusMessage (string) --

      If the job fails, StatusMessage provides a descriptive error message.

    • VideoMetadata (dict) --

      Information about a video that Amazon Rekognition analyzed. Videometadata is returned in every page of paginated responses from GetContentModeration .

      • Codec (string) --

        Type of compression used in the analyzed video.

      • DurationMillis (integer) --

        Length of the video in milliseconds.

      • Format (string) --

        Format of the analyzed video. Possible values are MP4, MOV and AVI.

      • FrameRate (float) --

        Number of frames per second in the video.

      • FrameHeight (integer) --

        Vertical pixel dimension of the video.

      • FrameWidth (integer) --

        Horizontal pixel dimension of the video.

    • ModerationLabels (list) --

      The detected moderation labels and the time(s) they were detected.

      • (dict) --

        Information about a moderation label detection in a stored video.

        • Timestamp (integer) --

          Time, in milliseconds from the beginning of the video, that the moderation label was detected.

        • ModerationLabel (dict) --

          The moderation label detected by in the stored video.

          • Confidence (float) --

            Specifies the confidence that Amazon Rekognition has that the label has been correctly identified.

            If you don't specify the MinConfidence parameter in the call to DetectModerationLabels , the operation returns labels with a confidence value greater than or equal to 50 percent.

          • Name (string) --

            The label name for the type of content detected in the image.

          • ParentName (string) --

            The name for the parent label. Labels at the top-level of the hierarchy have the parent label "" .

    • NextToken (string) --

      If the response is truncated, Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of moderation labels.

get_face_detection(**kwargs)

Gets face detection results for a Rekognition Video analysis started by .

Face detection with Rekognition Video is an asynchronous operation. You start face detection by calling which returns a job identifier (JobId ). When the face detection operation finishes, Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartFaceDetection . To get the results of the face detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED . If so, call and pass the job identifier (JobId ) from the initial call to StartFaceDetection .

GetFaceDetection returns an array of detected faces (Faces ) sorted by the time the faces were detected.

Use MaxResults parameter to limit the number of labels returned. If there are more results than specified in MaxResults , the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetFaceDetection and populate the NextToken request parameter with the token value returned from the previous call to GetFaceDetection .

See also: AWS API Documentation

Request Syntax

response = client.get_face_detection(
    JobId='string',
    MaxResults=123,
    NextToken='string'
)
Parameters
  • JobId (string) --

    [REQUIRED]

    Unique identifier for the face detection job. The JobId is returned from StartFaceDetection .

  • MaxResults (integer) -- Maximum number of detected faces to return. The default is 1000.
  • NextToken (string) -- If the previous response was incomplete (because there are more faces to retrieve), Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of faces.
Return type

dict

Returns

Response Syntax

{
    'JobStatus': 'IN_PROGRESS'|'SUCCEEDED'|'FAILED',
    'StatusMessage': 'string',
    'VideoMetadata': {
        'Codec': 'string',
        'DurationMillis': 123,
        'Format': 'string',
        'FrameRate': ...,
        'FrameHeight': 123,
        'FrameWidth': 123
    },
    'NextToken': 'string',
    'Faces': [
        {
            'Timestamp': 123,
            'Face': {
                'BoundingBox': {
                    'Width': ...,
                    'Height': ...,
                    'Left': ...,
                    'Top': ...
                },
                'AgeRange': {
                    'Low': 123,
                    'High': 123
                },
                'Smile': {
                    'Value': True|False,
                    'Confidence': ...
                },
                'Eyeglasses': {
                    'Value': True|False,
                    'Confidence': ...
                },
                'Sunglasses': {
                    'Value': True|False,
                    'Confidence': ...
                },
                'Gender': {
                    'Value': 'Male'|'Female',
                    'Confidence': ...
                },
                'Beard': {
                    'Value': True|False,
                    'Confidence': ...
                },
                'Mustache': {
                    'Value': True|False,
                    'Confidence': ...
                },
                'EyesOpen': {
                    'Value': True|False,
                    'Confidence': ...
                },
                'MouthOpen': {
                    'Value': True|False,
                    'Confidence': ...
                },
                'Emotions': [
                    {
                        'Type': 'HAPPY'|'SAD'|'ANGRY'|'CONFUSED'|'DISGUSTED'|'SURPRISED'|'CALM'|'UNKNOWN',
                        'Confidence': ...
                    },
                ],
                'Landmarks': [
                    {
                        'Type': 'eyeLeft'|'eyeRight'|'nose'|'mouthLeft'|'mouthRight'|'leftEyeBrowLeft'|'leftEyeBrowRight'|'leftEyeBrowUp'|'rightEyeBrowLeft'|'rightEyeBrowRight'|'rightEyeBrowUp'|'leftEyeLeft'|'leftEyeRight'|'leftEyeUp'|'leftEyeDown'|'rightEyeLeft'|'rightEyeRight'|'rightEyeUp'|'rightEyeDown'|'noseLeft'|'noseRight'|'mouthUp'|'mouthDown'|'leftPupil'|'rightPupil',
                        'X': ...,
                        'Y': ...
                    },
                ],
                'Pose': {
                    'Roll': ...,
                    'Yaw': ...,
                    'Pitch': ...
                },
                'Quality': {
                    'Brightness': ...,
                    'Sharpness': ...
                },
                'Confidence': ...
            }
        },
    ]
}

Response Structure

  • (dict) --

    • JobStatus (string) --

      The current status of the face detection job.

    • StatusMessage (string) --

      If the job fails, StatusMessage provides a descriptive error message.

    • VideoMetadata (dict) --

      Information about a video that Rekognition Video analyzed. Videometadata is returned in every page of paginated responses from a Amazon Rekognition video operation.

      • Codec (string) --

        Type of compression used in the analyzed video.

      • DurationMillis (integer) --

        Length of the video in milliseconds.

      • Format (string) --

        Format of the analyzed video. Possible values are MP4, MOV and AVI.

      • FrameRate (float) --

        Number of frames per second in the video.

      • FrameHeight (integer) --

        Vertical pixel dimension of the video.

      • FrameWidth (integer) --

        Horizontal pixel dimension of the video.

    • NextToken (string) --

      If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of faces.

    • Faces (list) --

      An array of faces detected in the video. Each element contains a detected face's details and the time, in milliseconds from the start of the video, the face was detected.

      • (dict) --

        Information about a face detected in a video analysis request and the time the face was detected in the video.

        • Timestamp (integer) --

          Time, in milliseconds from the start of the video, that the face was detected.

        • Face (dict) --

          The face properties for the detected face.

          • BoundingBox (dict) --

            Bounding box of the face.

            • Width (float) --

              Width of the bounding box as a ratio of the overall image width.

            • Height (float) --

              Height of the bounding box as a ratio of the overall image height.

            • Left (float) --

              Left coordinate of the bounding box as a ratio of overall image width.

            • Top (float) --

              Top coordinate of the bounding box as a ratio of overall image height.

          • AgeRange (dict) --

            The estimated age range, in years, for the face. Low represents the lowest estimated age and High represents the highest estimated age.

            • Low (integer) --

              The lowest estimated age.

            • High (integer) --

              The highest estimated age.

          • Smile (dict) --

            Indicates whether or not the face is smiling, and the confidence level in the determination.

            • Value (boolean) --

              Boolean value that indicates whether the face is smiling or not.

            • Confidence (float) --

              Level of confidence in the determination.

          • Eyeglasses (dict) --

            Indicates whether or not the face is wearing eye glasses, and the confidence level in the determination.

            • Value (boolean) --

              Boolean value that indicates whether the face is wearing eye glasses or not.

            • Confidence (float) --

              Level of confidence in the determination.

          • Sunglasses (dict) --

            Indicates whether or not the face is wearing sunglasses, and the confidence level in the determination.

            • Value (boolean) --

              Boolean value that indicates whether the face is wearing sunglasses or not.

            • Confidence (float) --

              Level of confidence in the determination.

          • Gender (dict) --

            Gender of the face and the confidence level in the determination.

            • Value (string) --

              Gender of the face.

            • Confidence (float) --

              Level of confidence in the determination.

          • Beard (dict) --

            Indicates whether or not the face has a beard, and the confidence level in the determination.

            • Value (boolean) --

              Boolean value that indicates whether the face has beard or not.

            • Confidence (float) --

              Level of confidence in the determination.

          • Mustache (dict) --

            Indicates whether or not the face has a mustache, and the confidence level in the determination.

            • Value (boolean) --

              Boolean value that indicates whether the face has mustache or not.

            • Confidence (float) --

              Level of confidence in the determination.

          • EyesOpen (dict) --

            Indicates whether or not the eyes on the face are open, and the confidence level in the determination.

            • Value (boolean) --

              Boolean value that indicates whether the eyes on the face are open.

            • Confidence (float) --

              Level of confidence in the determination.

          • MouthOpen (dict) --

            Indicates whether or not the mouth on the face is open, and the confidence level in the determination.

            • Value (boolean) --

              Boolean value that indicates whether the mouth on the face is open or not.

            • Confidence (float) --

              Level of confidence in the determination.

          • Emotions (list) --

            The emotions detected on the face, and the confidence level in the determination. For example, HAPPY, SAD, and ANGRY.

            • (dict) --

              The emotions detected on the face, and the confidence level in the determination. For example, HAPPY, SAD, and ANGRY.

              • Type (string) --

                Type of emotion detected.

              • Confidence (float) --

                Level of confidence in the determination.

          • Landmarks (list) --

            Indicates the location of landmarks on the face.

            • (dict) --

              Indicates the location of the landmark on the face.

              • Type (string) --

                Type of the landmark.

              • X (float) --

                x-coordinate from the top left of the landmark expressed as the ratio of the width of the image. For example, if the images is 700x200 and the x-coordinate of the landmark is at 350 pixels, this value is 0.5.

              • Y (float) --

                y-coordinate from the top left of the landmark expressed as the ratio of the height of the image. For example, if the images is 700x200 and the y-coordinate of the landmark is at 100 pixels, this value is 0.5.

          • Pose (dict) --

            Indicates the pose of the face as determined by its pitch, roll, and yaw.

            • Roll (float) --

              Value representing the face rotation on the roll axis.

            • Yaw (float) --

              Value representing the face rotation on the yaw axis.

            • Pitch (float) --

              Value representing the face rotation on the pitch axis.

          • Quality (dict) --

            Identifies image brightness and sharpness.

            • Brightness (float) --

              Value representing brightness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a brighter face image.

            • Sharpness (float) --

              Value representing sharpness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a sharper face image.

          • Confidence (float) --

            Confidence level that the bounding box contains a face (and not a different object such as a tree).

Gets the face search results for Rekognition Video face search started by . The search returns faces in a collection that match the faces of persons detected in a video. It also includes the time(s) that faces are matched in the video.

Face search in a video is an asynchronous operation. You start face search by calling to which returns a job identifier (JobId ). When the search operation finishes, Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartFaceSearch . To get the search results, first check that the status value published to the Amazon SNS topic is SUCCEEDED . If so, call GetFaceSearch and pass the job identifier (JobId ) from the initial call to StartFaceSearch . For more information, see collections .

The search results are retured in an array, Persons , of objects. Each``PersonMatch`` element contains details about the matching faces in the input collection, person information for the matched person, and the time the person was matched in the video.

By default, the Persons array is sorted by the time, in milliseconds from the start of the video, persons are matched. You can also sort by persons by specifying INDEX for the SORTBY input parameter.

See also: AWS API Documentation

Request Syntax

response = client.get_face_search(
    JobId='string',
    MaxResults=123,
    NextToken='string',
    SortBy='INDEX'|'TIMESTAMP'
)
Parameters
  • JobId (string) --

    [REQUIRED]

    The job identifer for the search request. You get the job identifier from an initial call to StartFaceSearch .

  • MaxResults (integer) -- Maximum number of search results you want Rekognition Video to return in the response. The default is 1000.
  • NextToken (string) -- If the previous response was incomplete (because there is more search results to retrieve), Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of search results.
  • SortBy (string) -- Sort to use for grouping faces in the response. Use TIMESTAMP to group faces by the time that they are recognized. Use INDEX to sort by recognized faces.
Return type

dict

Returns

Response Syntax

{
    'JobStatus': 'IN_PROGRESS'|'SUCCEEDED'|'FAILED',
    'StatusMessage': 'string',
    'NextToken': 'string',
    'VideoMetadata': {
        'Codec': 'string',
        'DurationMillis': 123,
        'Format': 'string',
        'FrameRate': ...,
        'FrameHeight': 123,
        'FrameWidth': 123
    },
    'Persons': [
        {
            'Timestamp': 123,
            'Person': {
                'Index': 123,
                'BoundingBox': {
                    'Width': ...,
                    'Height': ...,
                    'Left': ...,
                    'Top': ...
                },
                'Face': {
                    'BoundingBox': {
                        'Width': ...,
                        'Height': ...,
                        'Left': ...,
                        'Top': ...
                    },
                    'AgeRange': {
                        'Low': 123,
                        'High': 123
                    },
                    'Smile': {
                        'Value': True|False,
                        'Confidence': ...
                    },
                    'Eyeglasses': {
                        'Value': True|False,
                        'Confidence': ...
                    },
                    'Sunglasses': {
                        'Value': True|False,
                        'Confidence': ...
                    },
                    'Gender': {
                        'Value': 'Male'|'Female',
                        'Confidence': ...
                    },
                    'Beard': {
                        'Value': True|False,
                        'Confidence': ...
                    },
                    'Mustache': {
                        'Value': True|False,
                        'Confidence': ...
                    },
                    'EyesOpen': {
                        'Value': True|False,
                        'Confidence': ...
                    },
                    'MouthOpen': {
                        'Value': True|False,
                        'Confidence': ...
                    },
                    'Emotions': [
                        {
                            'Type': 'HAPPY'|'SAD'|'ANGRY'|'CONFUSED'|'DISGUSTED'|'SURPRISED'|'CALM'|'UNKNOWN',
                            'Confidence': ...
                        },
                    ],
                    'Landmarks': [
                        {
                            'Type': 'eyeLeft'|'eyeRight'|'nose'|'mouthLeft'|'mouthRight'|'leftEyeBrowLeft'|'leftEyeBrowRight'|'leftEyeBrowUp'|'rightEyeBrowLeft'|'rightEyeBrowRight'|'rightEyeBrowUp'|'leftEyeLeft'|'leftEyeRight'|'leftEyeUp'|'leftEyeDown'|'rightEyeLeft'|'rightEyeRight'|'rightEyeUp'|'rightEyeDown'|'noseLeft'|'noseRight'|'mouthUp'|'mouthDown'|'leftPupil'|'rightPupil',
                            'X': ...,
                            'Y': ...
                        },
                    ],
                    'Pose': {
                        'Roll': ...,
                        'Yaw': ...,
                        'Pitch': ...
                    },
                    'Quality': {
                        'Brightness': ...,
                        'Sharpness': ...
                    },
                    'Confidence': ...
                }
            },
            'FaceMatches': [
                {
                    'Similarity': ...,
                    'Face': {
                        'FaceId': 'string',
                        'BoundingBox': {
                            'Width': ...,
                            'Height': ...,
                            'Left': ...,
                            'Top': ...
                        },
                        'ImageId': 'string',
                        'ExternalImageId': 'string',
                        'Confidence': ...
                    }
                },
            ]
        },
    ]
}

Response Structure

  • (dict) --

    • JobStatus (string) --

      The current status of the face search job.

    • StatusMessage (string) --

      If the job fails, StatusMessage provides a descriptive error message.

    • NextToken (string) --

      If the response is truncated, Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of search results.

    • VideoMetadata (dict) --

      Information about a video that Amazon Rekognition analyzed. Videometadata is returned in every page of paginated responses from a Rekognition Video operation.

      • Codec (string) --

        Type of compression used in the analyzed video.

      • DurationMillis (integer) --

        Length of the video in milliseconds.

      • Format (string) --

        Format of the analyzed video. Possible values are MP4, MOV and AVI.

      • FrameRate (float) --

        Number of frames per second in the video.

      • FrameHeight (integer) --

        Vertical pixel dimension of the video.

      • FrameWidth (integer) --

        Horizontal pixel dimension of the video.

    • Persons (list) --

      An array of persons, , in the video whose face(s) match the face(s) in an Amazon Rekognition collection. It also includes time information for when persons are matched in the video. You specify the input collection in an initial call to StartFaceSearch . Each Persons element includes a time the person was matched, face match details (FaceMatches ) for matching faces in the collection, and person information (Person ) for the matched person.

      • (dict) --

        Information about a person whose face matches a face(s) in a Amazon Rekognition collection. Includes information about the faces in the Amazon Rekognition collection (,information about the person ( PersonDetail ) and the timestamp for when the person was detected in a video. An array of PersonMatch objects is returned by .

        • Timestamp (integer) --

          The time, in milliseconds from the beginning of the video, that the person was matched in the video.

        • Person (dict) --

          Information about the matched person.

          • Index (integer) --

            Identifier for the person detected person within a video. Use to keep track of the person throughout the video. The identifier is not stored by Amazon Rekognition.

          • BoundingBox (dict) --

            Bounding box around the detected person.

            • Width (float) --

              Width of the bounding box as a ratio of the overall image width.

            • Height (float) --

              Height of the bounding box as a ratio of the overall image height.

            • Left (float) --

              Left coordinate of the bounding box as a ratio of overall image width.

            • Top (float) --

              Top coordinate of the bounding box as a ratio of overall image height.

          • Face (dict) --

            Face details for the detected person.

            • BoundingBox (dict) --

              Bounding box of the face.

              • Width (float) --

                Width of the bounding box as a ratio of the overall image width.

              • Height (float) --

                Height of the bounding box as a ratio of the overall image height.

              • Left (float) --

                Left coordinate of the bounding box as a ratio of overall image width.

              • Top (float) --

                Top coordinate of the bounding box as a ratio of overall image height.

            • AgeRange (dict) --

              The estimated age range, in years, for the face. Low represents the lowest estimated age and High represents the highest estimated age.

              • Low (integer) --

                The lowest estimated age.

              • High (integer) --

                The highest estimated age.

            • Smile (dict) --

              Indicates whether or not the face is smiling, and the confidence level in the determination.

              • Value (boolean) --

                Boolean value that indicates whether the face is smiling or not.

              • Confidence (float) --

                Level of confidence in the determination.

            • Eyeglasses (dict) --

              Indicates whether or not the face is wearing eye glasses, and the confidence level in the determination.

              • Value (boolean) --

                Boolean value that indicates whether the face is wearing eye glasses or not.

              • Confidence (float) --

                Level of confidence in the determination.

            • Sunglasses (dict) --

              Indicates whether or not the face is wearing sunglasses, and the confidence level in the determination.

              • Value (boolean) --

                Boolean value that indicates whether the face is wearing sunglasses or not.

              • Confidence (float) --

                Level of confidence in the determination.

            • Gender (dict) --

              Gender of the face and the confidence level in the determination.

              • Value (string) --

                Gender of the face.

              • Confidence (float) --

                Level of confidence in the determination.

            • Beard (dict) --

              Indicates whether or not the face has a beard, and the confidence level in the determination.

              • Value (boolean) --

                Boolean value that indicates whether the face has beard or not.

              • Confidence (float) --

                Level of confidence in the determination.

            • Mustache (dict) --

              Indicates whether or not the face has a mustache, and the confidence level in the determination.

              • Value (boolean) --

                Boolean value that indicates whether the face has mustache or not.

              • Confidence (float) --

                Level of confidence in the determination.

            • EyesOpen (dict) --

              Indicates whether or not the eyes on the face are open, and the confidence level in the determination.

              • Value (boolean) --

                Boolean value that indicates whether the eyes on the face are open.

              • Confidence (float) --

                Level of confidence in the determination.

            • MouthOpen (dict) --

              Indicates whether or not the mouth on the face is open, and the confidence level in the determination.

              • Value (boolean) --

                Boolean value that indicates whether the mouth on the face is open or not.

              • Confidence (float) --

                Level of confidence in the determination.

            • Emotions (list) --

              The emotions detected on the face, and the confidence level in the determination. For example, HAPPY, SAD, and ANGRY.

              • (dict) --

                The emotions detected on the face, and the confidence level in the determination. For example, HAPPY, SAD, and ANGRY.

                • Type (string) --

                  Type of emotion detected.

                • Confidence (float) --

                  Level of confidence in the determination.

            • Landmarks (list) --

              Indicates the location of landmarks on the face.

              • (dict) --

                Indicates the location of the landmark on the face.

                • Type (string) --

                  Type of the landmark.

                • X (float) --

                  x-coordinate from the top left of the landmark expressed as the ratio of the width of the image. For example, if the images is 700x200 and the x-coordinate of the landmark is at 350 pixels, this value is 0.5.

                • Y (float) --

                  y-coordinate from the top left of the landmark expressed as the ratio of the height of the image. For example, if the images is 700x200 and the y-coordinate of the landmark is at 100 pixels, this value is 0.5.

            • Pose (dict) --

              Indicates the pose of the face as determined by its pitch, roll, and yaw.

              • Roll (float) --

                Value representing the face rotation on the roll axis.

              • Yaw (float) --

                Value representing the face rotation on the yaw axis.

              • Pitch (float) --

                Value representing the face rotation on the pitch axis.

            • Quality (dict) --

              Identifies image brightness and sharpness.

              • Brightness (float) --

                Value representing brightness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a brighter face image.

              • Sharpness (float) --

                Value representing sharpness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a sharper face image.

            • Confidence (float) --

              Confidence level that the bounding box contains a face (and not a different object such as a tree).

        • FaceMatches (list) --

          Information about the faces in the input collection that match the face of a person in the video.

          • (dict) --

            Provides face metadata. In addition, it also provides the confidence in the match of this face with the input face.

            • Similarity (float) --

              Confidence in the match of this face with the input face.

            • Face (dict) --

              Describes the face properties such as the bounding box, face ID, image ID of the source image, and external image ID that you assigned.

              • FaceId (string) --

                Unique identifier that Amazon Rekognition assigns to the face.

              • BoundingBox (dict) --

                Bounding box of the face.

                • Width (float) --

                  Width of the bounding box as a ratio of the overall image width.

                • Height (float) --

                  Height of the bounding box as a ratio of the overall image height.

                • Left (float) --

                  Left coordinate of the bounding box as a ratio of overall image width.

                • Top (float) --

                  Top coordinate of the bounding box as a ratio of overall image height.

              • ImageId (string) --

                Unique identifier that Amazon Rekognition assigns to the input image.

              • ExternalImageId (string) --

                Identifier that you assign to all the faces in the input image.

              • Confidence (float) --

                Confidence level that the bounding box contains a face (and not a different object such as a tree).

get_label_detection(**kwargs)

Gets the label detection results of a Rekognition Video analysis started by .

The label detection operation is started by a call to which returns a job identifier (JobId ). When the label detection operation finishes, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartlabelDetection . To get the results of the label detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED . If so, call and pass the job identifier (JobId ) from the initial call to StartLabelDetection .

GetLabelDetection returns an array of detected labels (Labels ) sorted by the time the labels were detected. You can also sort by the label name by specifying NAME for the SortBy input parameter.

The labels returned include the label name, the percentage confidence in the accuracy of the detected label, and the time the label was detected in the video.

Use MaxResults parameter to limit the number of labels returned. If there are more results than specified in MaxResults , the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetlabelDetection and populate the NextToken request parameter with the token value returned from the previous call to GetLabelDetection .

See also: AWS API Documentation

Request Syntax

response = client.get_label_detection(
    JobId='string',
    MaxResults=123,
    NextToken='string',
    SortBy='NAME'|'TIMESTAMP'
)
Parameters
  • JobId (string) --

    [REQUIRED]

    Job identifier for the label detection operation for which you want results returned. You get the job identifer from an initial call to StartlabelDetection .

  • MaxResults (integer) -- Maximum number of labels you want Amazon Rekognition to return in the response. The default is 1000.
  • NextToken (string) -- If the previous response was incomplete (because there are more labels to retrieve), Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of labels.
  • SortBy (string) -- Sort to use for elements in the Labels array. Use TIMESTAMP to sort array elements by the time labels are detected. Use NAME to alphabetically group elements for a label together. Within each label group, the array element are sorted by detection confidence. The default sort is by TIMESTAMP .
Return type

dict

Returns

Response Syntax

{
    'JobStatus': 'IN_PROGRESS'|'SUCCEEDED'|'FAILED',
    'StatusMessage': 'string',
    'VideoMetadata': {
        'Codec': 'string',
        'DurationMillis': 123,
        'Format': 'string',
        'FrameRate': ...,
        'FrameHeight': 123,
        'FrameWidth': 123
    },
    'NextToken': 'string',
    'Labels': [
        {
            'Timestamp': 123,
            'Label': {
                'Name': 'string',
                'Confidence': ...
            }
        },
    ]
}

Response Structure

  • (dict) --

    • JobStatus (string) --

      The current status of the label detection job.

    • StatusMessage (string) --

      If the job fails, StatusMessage provides a descriptive error message.

    • VideoMetadata (dict) --

      Information about a video that Rekognition Video analyzed. Videometadata is returned in every page of paginated responses from a Amazon Rekognition video operation.

      • Codec (string) --

        Type of compression used in the analyzed video.

      • DurationMillis (integer) --

        Length of the video in milliseconds.

      • Format (string) --

        Format of the analyzed video. Possible values are MP4, MOV and AVI.

      • FrameRate (float) --

        Number of frames per second in the video.

      • FrameHeight (integer) --

        Vertical pixel dimension of the video.

      • FrameWidth (integer) --

        Horizontal pixel dimension of the video.

    • NextToken (string) --

      If the response is truncated, Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of labels.

    • Labels (list) --

      An array of labels detected in the video. Each element contains the detected label and the time, in milliseconds from the start of the video, that the label was detected.

      • (dict) --

        Information about a label detected in a video analysis request and the time the label was detected in the video.

        • Timestamp (integer) --

          Time, in milliseconds from the start of the video, that the label was detected.

        • Label (dict) --

          Details about the detected label.

          • Name (string) --

            The name (label) of the object.

          • Confidence (float) --

            Level of confidence.

get_paginator(operation_name)

Create a paginator for an operation.

Parameters
operation_name (string) -- The operation name. This is the same name as the method name on the client. For example, if the method name is create_foo, and you'd normally invoke the operation as client.create_foo(**kwargs), if the create_foo operation can be paginated, you can use the call client.get_paginator("create_foo").
Raises OperationNotPageableError
Raised if the operation is not pageable. You can use the client.can_paginate method to check if an operation is pageable.
Return type
L{botocore.paginate.Paginator}
Returns
A paginator object.
get_person_tracking(**kwargs)

Gets the person tracking results of a Rekognition Video analysis started by .

The person detection operation is started by a call to StartPersonTracking which returns a job identifier (JobId ). When the person detection operation finishes, Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartPersonTracking .

To get the results of the person tracking operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED . If so, call and pass the job identifier (JobId ) from the initial call to StartPersonTracking .

GetPersonTracking returns an array, Persons , of tracked persons and the time(s) they were tracked in the video.

By default, the array is sorted by the time(s) a person is tracked in the video. You can sort by tracked persons by specifying INDEX for the SortBy input parameter.

Use the MaxResults parameter to limit the number of items returned. If there are more results than specified in MaxResults , the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetPersonTracking and populate the NextToken request parameter with the token value returned from the previous call to GetPersonTracking .

See also: AWS API Documentation

Request Syntax

response = client.get_person_tracking(
    JobId='string',
    MaxResults=123,
    NextToken='string',
    SortBy='INDEX'|'TIMESTAMP'
)
Parameters
  • JobId (string) --

    [REQUIRED]

    The identifier for a job that tracks persons in a video. You get the JobId from a call to StartPersonTracking .

  • MaxResults (integer) -- Maximum number of tracked persons to return. The default is 1000.
  • NextToken (string) -- If the previous response was incomplete (because there are more persons to retrieve), Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of persons.
  • SortBy (string) -- Sort to use for elements in the Persons array. Use TIMESTAMP to sort array elements by the time persons are detected. Use INDEX to sort by the tracked persons. If you sort by INDEX , the array elements for each person are sorted by detection confidence. The default sort is by TIMESTAMP .
Return type

dict

Returns

Response Syntax

{
    'JobStatus': 'IN_PROGRESS'|'SUCCEEDED'|'FAILED',
    'StatusMessage': 'string',
    'VideoMetadata': {
        'Codec': 'string',
        'DurationMillis': 123,
        'Format': 'string',
        'FrameRate': ...,
        'FrameHeight': 123,
        'FrameWidth': 123
    },
    'NextToken': 'string',
    'Persons': [
        {
            'Timestamp': 123,
            'Person': {
                'Index': 123,
                'BoundingBox': {
                    'Width': ...,
                    'Height': ...,
                    'Left': ...,
                    'Top': ...
                },
                'Face': {
                    'BoundingBox': {
                        'Width': ...,
                        'Height': ...,
                        'Left': ...,
                        'Top': ...
                    },
                    'AgeRange': {
                        'Low': 123,
                        'High': 123
                    },
                    'Smile': {
                        'Value': True|False,
                        'Confidence': ...
                    },
                    'Eyeglasses': {
                        'Value': True|False,
                        'Confidence': ...
                    },
                    'Sunglasses': {
                        'Value': True|False,
                        'Confidence': ...
                    },
                    'Gender': {
                        'Value': 'Male'|'Female',
                        'Confidence': ...
                    },
                    'Beard': {
                        'Value': True|False,
                        'Confidence': ...
                    },
                    'Mustache': {
                        'Value': True|False,
                        'Confidence': ...
                    },
                    'EyesOpen': {
                        'Value': True|False,
                        'Confidence': ...
                    },
                    'MouthOpen': {
                        'Value': True|False,
                        'Confidence': ...
                    },
                    'Emotions': [
                        {
                            'Type': 'HAPPY'|'SAD'|'ANGRY'|'CONFUSED'|'DISGUSTED'|'SURPRISED'|'CALM'|'UNKNOWN',
                            'Confidence': ...
                        },
                    ],
                    'Landmarks': [
                        {
                            'Type': 'eyeLeft'|'eyeRight'|'nose'|'mouthLeft'|'mouthRight'|'leftEyeBrowLeft'|'leftEyeBrowRight'|'leftEyeBrowUp'|'rightEyeBrowLeft'|'rightEyeBrowRight'|'rightEyeBrowUp'|'leftEyeLeft'|'leftEyeRight'|'leftEyeUp'|'leftEyeDown'|'rightEyeLeft'|'rightEyeRight'|'rightEyeUp'|'rightEyeDown'|'noseLeft'|'noseRight'|'mouthUp'|'mouthDown'|'leftPupil'|'rightPupil',
                            'X': ...,
                            'Y': ...
                        },
                    ],
                    'Pose': {
                        'Roll': ...,
                        'Yaw': ...,
                        'Pitch': ...
                    },
                    'Quality': {
                        'Brightness': ...,
                        'Sharpness': ...
                    },
                    'Confidence': ...
                }
            }
        },
    ]
}

Response Structure

  • (dict) --

    • JobStatus (string) --

      The current status of the person tracking job.

    • StatusMessage (string) --

      If the job fails, StatusMessage provides a descriptive error message.

    • VideoMetadata (dict) --

      Information about a video that Rekognition Video analyzed. Videometadata is returned in every page of paginated responses from a Rekognition Video operation.

      • Codec (string) --

        Type of compression used in the analyzed video.

      • DurationMillis (integer) --

        Length of the video in milliseconds.

      • Format (string) --

        Format of the analyzed video. Possible values are MP4, MOV and AVI.

      • FrameRate (float) --

        Number of frames per second in the video.

      • FrameHeight (integer) --

        Vertical pixel dimension of the video.

      • FrameWidth (integer) --

        Horizontal pixel dimension of the video.

    • NextToken (string) --

      If the response is truncated, Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of persons.

    • Persons (list) --

      An array of the persons detected in the video and the times they are tracked throughout the video. An array element will exist for each time the person is tracked.

      • (dict) --

        Details and tracking information for a single time a person is tracked in a video. Amazon Rekognition operations that track persons return an array of PersonDetection objects with elements for each time a person is tracked in a video. For more information, see .

        • Timestamp (integer) --

          The time, in milliseconds from the start of the video, that the person was tracked.

        • Person (dict) --

          Details about a person tracked in a video.

          • Index (integer) --

            Identifier for the person detected person within a video. Use to keep track of the person throughout the video. The identifier is not stored by Amazon Rekognition.

          • BoundingBox (dict) --

            Bounding box around the detected person.

            • Width (float) --

              Width of the bounding box as a ratio of the overall image width.

            • Height (float) --

              Height of the bounding box as a ratio of the overall image height.

            • Left (float) --

              Left coordinate of the bounding box as a ratio of overall image width.

            • Top (float) --

              Top coordinate of the bounding box as a ratio of overall image height.

          • Face (dict) --

            Face details for the detected person.

            • BoundingBox (dict) --

              Bounding box of the face.

              • Width (float) --

                Width of the bounding box as a ratio of the overall image width.

              • Height (float) --

                Height of the bounding box as a ratio of the overall image height.

              • Left (float) --

                Left coordinate of the bounding box as a ratio of overall image width.

              • Top (float) --

                Top coordinate of the bounding box as a ratio of overall image height.

            • AgeRange (dict) --

              The estimated age range, in years, for the face. Low represents the lowest estimated age and High represents the highest estimated age.

              • Low (integer) --

                The lowest estimated age.

              • High (integer) --

                The highest estimated age.

            • Smile (dict) --

              Indicates whether or not the face is smiling, and the confidence level in the determination.

              • Value (boolean) --

                Boolean value that indicates whether the face is smiling or not.

              • Confidence (float) --

                Level of confidence in the determination.

            • Eyeglasses (dict) --

              Indicates whether or not the face is wearing eye glasses, and the confidence level in the determination.

              • Value (boolean) --

                Boolean value that indicates whether the face is wearing eye glasses or not.

              • Confidence (float) --

                Level of confidence in the determination.

            • Sunglasses (dict) --

              Indicates whether or not the face is wearing sunglasses, and the confidence level in the determination.

              • Value (boolean) --

                Boolean value that indicates whether the face is wearing sunglasses or not.

              • Confidence (float) --

                Level of confidence in the determination.

            • Gender (dict) --

              Gender of the face and the confidence level in the determination.

              • Value (string) --

                Gender of the face.

              • Confidence (float) --

                Level of confidence in the determination.

            • Beard (dict) --

              Indicates whether or not the face has a beard, and the confidence level in the determination.

              • Value (boolean) --

                Boolean value that indicates whether the face has beard or not.

              • Confidence (float) --

                Level of confidence in the determination.

            • Mustache (dict) --

              Indicates whether or not the face has a mustache, and the confidence level in the determination.

              • Value (boolean) --

                Boolean value that indicates whether the face has mustache or not.

              • Confidence (float) --

                Level of confidence in the determination.

            • EyesOpen (dict) --

              Indicates whether or not the eyes on the face are open, and the confidence level in the determination.

              • Value (boolean) --

                Boolean value that indicates whether the eyes on the face are open.

              • Confidence (float) --

                Level of confidence in the determination.

            • MouthOpen (dict) --

              Indicates whether or not the mouth on the face is open, and the confidence level in the determination.

              • Value (boolean) --

                Boolean value that indicates whether the mouth on the face is open or not.

              • Confidence (float) --

                Level of confidence in the determination.

            • Emotions (list) --

              The emotions detected on the face, and the confidence level in the determination. For example, HAPPY, SAD, and ANGRY.

              • (dict) --

                The emotions detected on the face, and the confidence level in the determination. For example, HAPPY, SAD, and ANGRY.

                • Type (string) --

                  Type of emotion detected.

                • Confidence (float) --

                  Level of confidence in the determination.

            • Landmarks (list) --

              Indicates the location of landmarks on the face.

              • (dict) --

                Indicates the location of the landmark on the face.

                • Type (string) --

                  Type of the landmark.

                • X (float) --

                  x-coordinate from the top left of the landmark expressed as the ratio of the width of the image. For example, if the images is 700x200 and the x-coordinate of the landmark is at 350 pixels, this value is 0.5.

                • Y (float) --

                  y-coordinate from the top left of the landmark expressed as the ratio of the height of the image. For example, if the images is 700x200 and the y-coordinate of the landmark is at 100 pixels, this value is 0.5.

            • Pose (dict) --

              Indicates the pose of the face as determined by its pitch, roll, and yaw.

              • Roll (float) --

                Value representing the face rotation on the roll axis.

              • Yaw (float) --

                Value representing the face rotation on the yaw axis.

              • Pitch (float) --

                Value representing the face rotation on the pitch axis.

            • Quality (dict) --

              Identifies image brightness and sharpness.

              • Brightness (float) --

                Value representing brightness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a brighter face image.

              • Sharpness (float) --

                Value representing sharpness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a sharper face image.

            • Confidence (float) --

              Confidence level that the bounding box contains a face (and not a different object such as a tree).

get_waiter(waiter_name)
index_faces(**kwargs)

Detects faces in the input image and adds them to the specified collection.

Amazon Rekognition does not save the actual faces detected. Instead, the underlying detection algorithm first detects the faces in the input image, and for each face extracts facial features into a feature vector, and stores it in the back-end database. Amazon Rekognition uses feature vectors when performing face match and search operations using the and operations.

If you are using version 1.0 of the face detection model, IndexFaces indexes the 15 largest faces in the input image. Later versions of the face detection model index the 100 largest faces in the input image. To determine which version of the model you are using, check the the value of FaceModelVersion in the response from IndexFaces . For more information, see face-detection-model .

If you provide the optional ExternalImageID for the input image you provided, Amazon Rekognition associates this ID with all faces that it detects. When you call the operation, the response returns the external ID. You can use this external image ID to create a client-side index to associate the faces with each image. You can then use the index to find all faces in an image.

In response, the operation returns an array of metadata for all detected faces. This includes, the bounding box of the detected face, confidence value (indicating the bounding box contains a face), a face ID assigned by the service for each face that is detected and stored, and an image ID assigned by the service for the input image. If you request all facial attributes (using the detectionAttributes parameter, Amazon Rekognition returns detailed facial attributes such as facial landmarks (for example, location of eye and mount) and other facial attributes such gender. If you provide the same image, specify the same collection, and use the same external ID in the IndexFaces operation, Amazon Rekognition doesn't save duplicate face metadata.

The input image is passed either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the Amazon CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.

This operation requires permissions to perform the rekognition:IndexFaces action.

See also: AWS API Documentation

Request Syntax

response = client.index_faces(
    CollectionId='string',
    Image={
        'Bytes': b'bytes',
        'S3Object': {
            'Bucket': 'string',
            'Name': 'string',
            'Version': 'string'
        }
    },
    ExternalImageId='string',
    DetectionAttributes=[
        'DEFAULT'|'ALL',
    ]
)
Parameters
  • CollectionId (string) --

    [REQUIRED]

    The ID of an existing collection to which you want to add the faces that are detected in the input images.

  • Image (dict) --

    [REQUIRED]

    The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.

    • Bytes (bytes) --

      Blob of image bytes up to 5 MBs.

    • S3Object (dict) --

      Identifies an S3 object as the image source.

      • Bucket (string) --

        Name of the S3 bucket.

      • Name (string) --

        S3 object key name.

      • Version (string) --

        If the bucket is versioning enabled, you can specify the object version.

  • ExternalImageId (string) -- ID you want to assign to all the faces detected in the image.
  • DetectionAttributes (list) --

    An array of facial attributes that you want to be returned. This can be the default list of attributes or all attributes. If you don't specify a value for Attributes or if you specify ["DEFAULT"] , the API returns the following subset of facial attributes: BoundingBox , Confidence , Pose , Quality and Landmarks . If you provide ["ALL"] , all facial attributes are returned but the operation will take longer to complete.

    If you provide both, ["ALL", "DEFAULT"] , the service uses a logical AND operator to determine which attributes to return (in this case, all attributes).

    • (string) --
Return type

dict

Returns

Response Syntax

{
    'FaceRecords': [
        {
            'Face': {
                'FaceId': 'string',
                'BoundingBox': {
                    'Width': ...,
                    'Height': ...,
                    'Left': ...,
                    'Top': ...
                },
                'ImageId': 'string',
                'ExternalImageId': 'string',
                'Confidence': ...
            },
            'FaceDetail': {
                'BoundingBox': {
                    'Width': ...,
                    'Height': ...,
                    'Left': ...,
                    'Top': ...
                },
                'AgeRange': {
                    'Low': 123,
                    'High': 123
                },
                'Smile': {
                    'Value': True|False,
                    'Confidence': ...
                },
                'Eyeglasses': {
                    'Value': True|False,
                    'Confidence': ...
                },
                'Sunglasses': {
                    'Value': True|False,
                    'Confidence': ...
                },
                'Gender': {
                    'Value': 'Male'|'Female',
                    'Confidence': ...
                },
                'Beard': {
                    'Value': True|False,
                    'Confidence': ...
                },
                'Mustache': {
                    'Value': True|False,
                    'Confidence': ...
                },
                'EyesOpen': {
                    'Value': True|False,
                    'Confidence': ...
                },
                'MouthOpen': {
                    'Value': True|False,
                    'Confidence': ...
                },
                'Emotions': [
                    {
                        'Type': 'HAPPY'|'SAD'|'ANGRY'|'CONFUSED'|'DISGUSTED'|'SURPRISED'|'CALM'|'UNKNOWN',
                        'Confidence': ...
                    },
                ],
                'Landmarks': [
                    {
                        'Type': 'eyeLeft'|'eyeRight'|'nose'|'mouthLeft'|'mouthRight'|'leftEyeBrowLeft'|'leftEyeBrowRight'|'leftEyeBrowUp'|'rightEyeBrowLeft'|'rightEyeBrowRight'|'rightEyeBrowUp'|'leftEyeLeft'|'leftEyeRight'|'leftEyeUp'|'leftEyeDown'|'rightEyeLeft'|'rightEyeRight'|'rightEyeUp'|'rightEyeDown'|'noseLeft'|'noseRight'|'mouthUp'|'mouthDown'|'leftPupil'|'rightPupil',
                        'X': ...,
                        'Y': ...
                    },
                ],
                'Pose': {
                    'Roll': ...,
                    'Yaw': ...,
                    'Pitch': ...
                },
                'Quality': {
                    'Brightness': ...,
                    'Sharpness': ...
                },
                'Confidence': ...
            }
        },
    ],
    'OrientationCorrection': 'ROTATE_0'|'ROTATE_90'|'ROTATE_180'|'ROTATE_270',
    'FaceModelVersion': 'string'
}

Response Structure

  • (dict) --

    • FaceRecords (list) --

      An array of faces detected and added to the collection. For more information, see collections-index-faces .

      • (dict) --

        Object containing both the face metadata (stored in the back-end database) and facial attributes that are detected but aren't stored in the database.

        • Face (dict) --

          Describes the face properties such as the bounding box, face ID, image ID of the input image, and external image ID that you assigned.

          • FaceId (string) --

            Unique identifier that Amazon Rekognition assigns to the face.

          • BoundingBox (dict) --

            Bounding box of the face.

            • Width (float) --

              Width of the bounding box as a ratio of the overall image width.

            • Height (float) --

              Height of the bounding box as a ratio of the overall image height.

            • Left (float) --

              Left coordinate of the bounding box as a ratio of overall image width.

            • Top (float) --

              Top coordinate of the bounding box as a ratio of overall image height.

          • ImageId (string) --

            Unique identifier that Amazon Rekognition assigns to the input image.

          • ExternalImageId (string) --

            Identifier that you assign to all the faces in the input image.

          • Confidence (float) --

            Confidence level that the bounding box contains a face (and not a different object such as a tree).

        • FaceDetail (dict) --

          Structure containing attributes of the face that the algorithm detected.

          • BoundingBox (dict) --

            Bounding box of the face.

            • Width (float) --

              Width of the bounding box as a ratio of the overall image width.

            • Height (float) --

              Height of the bounding box as a ratio of the overall image height.

            • Left (float) --

              Left coordinate of the bounding box as a ratio of overall image width.

            • Top (float) --

              Top coordinate of the bounding box as a ratio of overall image height.

          • AgeRange (dict) --

            The estimated age range, in years, for the face. Low represents the lowest estimated age and High represents the highest estimated age.

            • Low (integer) --

              The lowest estimated age.

            • High (integer) --

              The highest estimated age.

          • Smile (dict) --

            Indicates whether or not the face is smiling, and the confidence level in the determination.

            • Value (boolean) --

              Boolean value that indicates whether the face is smiling or not.

            • Confidence (float) --

              Level of confidence in the determination.

          • Eyeglasses (dict) --

            Indicates whether or not the face is wearing eye glasses, and the confidence level in the determination.

            • Value (boolean) --

              Boolean value that indicates whether the face is wearing eye glasses or not.

            • Confidence (float) --

              Level of confidence in the determination.

          • Sunglasses (dict) --

            Indicates whether or not the face is wearing sunglasses, and the confidence level in the determination.

            • Value (boolean) --

              Boolean value that indicates whether the face is wearing sunglasses or not.

            • Confidence (float) --

              Level of confidence in the determination.

          • Gender (dict) --

            Gender of the face and the confidence level in the determination.

            • Value (string) --

              Gender of the face.

            • Confidence (float) --

              Level of confidence in the determination.

          • Beard (dict) --

            Indicates whether or not the face has a beard, and the confidence level in the determination.

            • Value (boolean) --

              Boolean value that indicates whether the face has beard or not.

            • Confidence (float) --

              Level of confidence in the determination.

          • Mustache (dict) --

            Indicates whether or not the face has a mustache, and the confidence level in the determination.

            • Value (boolean) --

              Boolean value that indicates whether the face has mustache or not.

            • Confidence (float) --

              Level of confidence in the determination.

          • EyesOpen (dict) --

            Indicates whether or not the eyes on the face are open, and the confidence level in the determination.

            • Value (boolean) --

              Boolean value that indicates whether the eyes on the face are open.

            • Confidence (float) --

              Level of confidence in the determination.

          • MouthOpen (dict) --

            Indicates whether or not the mouth on the face is open, and the confidence level in the determination.

            • Value (boolean) --

              Boolean value that indicates whether the mouth on the face is open or not.

            • Confidence (float) --

              Level of confidence in the determination.

          • Emotions (list) --

            The emotions detected on the face, and the confidence level in the determination. For example, HAPPY, SAD, and ANGRY.

            • (dict) --

              The emotions detected on the face, and the confidence level in the determination. For example, HAPPY, SAD, and ANGRY.

              • Type (string) --

                Type of emotion detected.

              • Confidence (float) --

                Level of confidence in the determination.

          • Landmarks (list) --

            Indicates the location of landmarks on the face.

            • (dict) --

              Indicates the location of the landmark on the face.

              • Type (string) --

                Type of the landmark.

              • X (float) --

                x-coordinate from the top left of the landmark expressed as the ratio of the width of the image. For example, if the images is 700x200 and the x-coordinate of the landmark is at 350 pixels, this value is 0.5.

              • Y (float) --

                y-coordinate from the top left of the landmark expressed as the ratio of the height of the image. For example, if the images is 700x200 and the y-coordinate of the landmark is at 100 pixels, this value is 0.5.

          • Pose (dict) --

            Indicates the pose of the face as determined by its pitch, roll, and yaw.

            • Roll (float) --

              Value representing the face rotation on the roll axis.

            • Yaw (float) --

              Value representing the face rotation on the yaw axis.

            • Pitch (float) --

              Value representing the face rotation on the pitch axis.

          • Quality (dict) --

            Identifies image brightness and sharpness.

            • Brightness (float) --

              Value representing brightness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a brighter face image.

            • Sharpness (float) --

              Value representing sharpness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a sharper face image.

          • Confidence (float) --

            Confidence level that the bounding box contains a face (and not a different object such as a tree).

    • OrientationCorrection (string) --

      The orientation of the input image (counterclockwise direction). If your application displays the image, you can use this value to correct image orientation. The bounding box coordinates returned in FaceRecords represent face locations before the image orientation is corrected.

      Note

      If the input image is in jpeg format, it might contain exchangeable image (Exif) metadata. If so, and the Exif metadata populates the orientation field, the value of OrientationCorrection is null and the bounding box coordinates in FaceRecords represent face locations after Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.

    • FaceModelVersion (string) --

      Version number of the face detection model associated with the input collection (CollectionId ).

Examples

This operation detects faces in an image and adds them to the specified Rekognition collection.

response = client.index_faces(
    CollectionId='myphotos',
    DetectionAttributes=[
    ],
    ExternalImageId='myphotoid',
    Image={
        'S3Object': {
            'Bucket': 'mybucket',
            'Name': 'myphoto',
        },
    },
)

print(response)

Expected Output:

{
    'FaceRecords': [
        {
            'Face': {
                'BoundingBox': {
                    'Height': 0.33481481671333313,
                    'Left': 0.31888890266418457,
                    'Top': 0.4933333396911621,
                    'Width': 0.25,
                },
                'Confidence': 99.9991226196289,
                'FaceId': 'ff43d742-0c13-5d16-a3e8-03d3f58e980b',
                'ImageId': '465f4e93-763e-51d0-b030-b9667a2d94b1',
            },
            'FaceDetail': {
                'BoundingBox': {
                    'Height': 0.33481481671333313,
                    'Left': 0.31888890266418457,
                    'Top': 0.4933333396911621,
                    'Width': 0.25,
                },
                'Confidence': 99.9991226196289,
                'Landmarks': [
                    {
                        'Type': 'EYE_LEFT',
                        'X': 0.3976764678955078,
                        'Y': 0.6248345971107483,
                    },
                    {
                        'Type': 'EYE_RIGHT',
                        'X': 0.4810936450958252,
                        'Y': 0.6317117214202881,
                    },
                    {
                        'Type': 'NOSE_LEFT',
                        'X': 0.41986238956451416,
                        'Y': 0.7111940383911133,
                    },
                    {
                        'Type': 'MOUTH_DOWN',
                        'X': 0.40525302290916443,
                        'Y': 0.7497701048851013,
                    },
                    {
                        'Type': 'MOUTH_UP',
                        'X': 0.4753248989582062,
                        'Y': 0.7558549642562866,
                    },
                ],
                'Pose': {
                    'Pitch': -9.713645935058594,
                    'Roll': 4.707281112670898,
                    'Yaw': -24.438663482666016,
                },
                'Quality': {
                    'Brightness': 29.23358917236328,
                    'Sharpness': 80,
                },
            },
        },
        {
            'Face': {
                'BoundingBox': {
                    'Height': 0.32592591643333435,
                    'Left': 0.5144444704055786,
                    'Top': 0.15111111104488373,
                    'Width': 0.24444444477558136,
                },
                'Confidence': 99.99950408935547,
                'FaceId': '8be04dba-4e58-520d-850e-9eae4af70eb2',
                'ImageId': '465f4e93-763e-51d0-b030-b9667a2d94b1',
            },
            'FaceDetail': {
                'BoundingBox': {
                    'Height': 0.32592591643333435,
                    'Left': 0.5144444704055786,
                    'Top': 0.15111111104488373,
                    'Width': 0.24444444477558136,
                },
                'Confidence': 99.99950408935547,
                'Landmarks': [
                    {
                        'Type': 'EYE_LEFT',
                        'X': 0.6006892323493958,
                        'Y': 0.290842205286026,
                    },
                    {
                        'Type': 'EYE_RIGHT',
                        'X': 0.6808141469955444,
                        'Y': 0.29609042406082153,
                    },
                    {
                        'Type': 'NOSE_LEFT',
                        'X': 0.6395332217216492,
                        'Y': 0.3522595763206482,
                    },
                    {
                        'Type': 'MOUTH_DOWN',
                        'X': 0.5892083048820496,
                        'Y': 0.38689887523651123,
                    },
                    {
                        'Type': 'MOUTH_UP',
                        'X': 0.674560010433197,
                        'Y': 0.394125759601593,
                    },
                ],
                'Pose': {
                    'Pitch': -4.683138370513916,
                    'Roll': 2.1029529571533203,
                    'Yaw': 6.716655254364014,
                },
                'Quality': {
                    'Brightness': 34.951698303222656,
                    'Sharpness': 160,
                },
            },
        },
    ],
    'OrientationCorrection': 'ROTATE_0',
    'ResponseMetadata': {
        '...': '...',
    },
}
list_collections(**kwargs)

Returns list of collection IDs in your account. If the result is truncated, the response also provides a NextToken that you can use in the subsequent request to fetch the next set of collection IDs.

For an example, see list-collection-procedure .

This operation requires permissions to perform the rekognition:ListCollections action.

See also: AWS API Documentation

Request Syntax

response = client.list_collections(
    NextToken='string',
    MaxResults=123
)
Parameters
  • NextToken (string) -- Pagination token from the previous response.
  • MaxResults (integer) -- Maximum number of collection IDs to return.
Return type

dict

Returns

Response Syntax

{
    'CollectionIds': [
        'string',
    ],
    'NextToken': 'string',
    'FaceModelVersions': [
        'string',
    ]
}

Response Structure

  • (dict) --

    • CollectionIds (list) --

      An array of collection IDs.

      • (string) --
    • NextToken (string) --

      If the result is truncated, the response provides a NextToken that you can use in the subsequent request to fetch the next set of collection IDs.

    • FaceModelVersions (list) --

      Version numbers of the face detection models associated with the collections in the array CollectionIds . For example, the value of FaceModelVersions[2] is the version number for the face detection model used by the collection in CollectionId[2] .

      • (string) --

Examples

This operation returns a list of Rekognition collections.

response = client.list_collections(
)

print(response)

Expected Output:

{
    'CollectionIds': [
        'myphotos',
    ],
    'ResponseMetadata': {
        '...': '...',
    },
}
list_faces(**kwargs)

Returns metadata for faces in the specified collection. This metadata includes information such as the bounding box coordinates, the confidence (that the bounding box contains a face), and face ID. For an example, see list-faces-in-collection-procedure .

This operation requires permissions to perform the rekognition:ListFaces action.

See also: AWS API Documentation

Request Syntax

response = client.list_faces(
    CollectionId='string',
    NextToken='string',
    MaxResults=123
)
Parameters
  • CollectionId (string) --

    [REQUIRED]

    ID of the collection from which to list the faces.

  • NextToken (string) -- If the previous response was incomplete (because there is more data to retrieve), Amazon Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of faces.
  • MaxResults (integer) -- Maximum number of faces to return.
Return type

dict

Returns

Response Syntax

{
    'Faces': [
        {
            'FaceId': 'string',
            'BoundingBox': {
                'Width': ...,
                'Height': ...,
                'Left': ...,
                'Top': ...
            },
            'ImageId': 'string',
            'ExternalImageId': 'string',
            'Confidence': ...
        },
    ],
    'NextToken': 'string',
    'FaceModelVersion': 'string'
}

Response Structure

  • (dict) --

    • Faces (list) --

      An array of Face objects.

      • (dict) --

        Describes the face properties such as the bounding box, face ID, image ID of the input image, and external image ID that you assigned.

        • FaceId (string) --

          Unique identifier that Amazon Rekognition assigns to the face.

        • BoundingBox (dict) --

          Bounding box of the face.

          • Width (float) --

            Width of the bounding box as a ratio of the overall image width.

          • Height (float) --

            Height of the bounding box as a ratio of the overall image height.

          • Left (float) --

            Left coordinate of the bounding box as a ratio of overall image width.

          • Top (float) --

            Top coordinate of the bounding box as a ratio of overall image height.

        • ImageId (string) --

          Unique identifier that Amazon Rekognition assigns to the input image.

        • ExternalImageId (string) --

          Identifier that you assign to all the faces in the input image.

        • Confidence (float) --

          Confidence level that the bounding box contains a face (and not a different object such as a tree).

    • NextToken (string) --

      If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of faces.

    • FaceModelVersion (string) --

      Version number of the face detection model associated with the input collection (CollectionId ).

Examples

This operation lists the faces in a Rekognition collection.

response = client.list_faces(
    CollectionId='myphotos',
    MaxResults=20,
)

print(response)

Expected Output:

{
    'Faces': [
        {
            'BoundingBox': {
                'Height': 0.18000000715255737,
                'Left': 0.5555559992790222,
                'Top': 0.336667001247406,
                'Width': 0.23999999463558197,
            },
            'Confidence': 100,
            'FaceId': '1c62e8b5-69a7-5b7d-b3cd-db4338a8a7e7',
            'ImageId': '147fdf82-7a71-52cf-819b-e786c7b9746e',
        },
        {
            'BoundingBox': {
                'Height': 0.16555599868297577,
                'Left': 0.30963000655174255,
                'Top': 0.7066670060157776,
                'Width': 0.22074100375175476,
            },
            'Confidence': 100,
            'FaceId': '29a75abe-397b-5101-ba4f-706783b2246c',
            'ImageId': '147fdf82-7a71-52cf-819b-e786c7b9746e',
        },
        {
            'BoundingBox': {
                'Height': 0.3234420120716095,
                'Left': 0.3233329951763153,
                'Top': 0.5,
                'Width': 0.24222199618816376,
            },
            'Confidence': 99.99829864501953,
            'FaceId': '38271d79-7bc2-5efb-b752-398a8d575b85',
            'ImageId': 'd5631190-d039-54e4-b267-abd22c8647c5',
        },
        {
            'BoundingBox': {
                'Height': 0.03555560111999512,
                'Left': 0.37388700246810913,
                'Top': 0.2477779984474182,
                'Width': 0.04747769981622696,
            },
            'Confidence': 99.99210357666016,
            'FaceId': '3b01bef0-c883-5654-ba42-d5ad28b720b3',
            'ImageId': '812d9f04-86f9-54fc-9275-8d0dcbcb6784',
        },
        {
            'BoundingBox': {
                'Height': 0.05333330109715462,
                'Left': 0.2937690019607544,
                'Top': 0.35666701197624207,
                'Width': 0.07121659815311432,
            },
            'Confidence': 99.99919891357422,
            'FaceId': '4839a608-49d0-566c-8301-509d71b534d1',
            'ImageId': '812d9f04-86f9-54fc-9275-8d0dcbcb6784',
        },
        {
            'BoundingBox': {
                'Height': 0.3249259889125824,
                'Left': 0.5155559778213501,
                'Top': 0.1513350009918213,
                'Width': 0.24333299696445465,
            },
            'Confidence': 99.99949645996094,
            'FaceId': '70008e50-75e4-55d0-8e80-363fb73b3a14',
            'ImageId': 'd5631190-d039-54e4-b267-abd22c8647c5',
        },
        {
            'BoundingBox': {
                'Height': 0.03777780011296272,
                'Left': 0.7002969980239868,
                'Top': 0.18777799606323242,
                'Width': 0.05044509842991829,
            },
            'Confidence': 99.92639923095703,
            'FaceId': '7f5f88ed-d684-5a88-b0df-01e4a521552b',
            'ImageId': '812d9f04-86f9-54fc-9275-8d0dcbcb6784',
        },
        {
            'BoundingBox': {
                'Height': 0.05555560067296028,
                'Left': 0.13946600258350372,
                'Top': 0.46333301067352295,
                'Width': 0.07270029932260513,
            },
            'Confidence': 99.99469757080078,
            'FaceId': '895b4e2c-81de-5902-a4bd-d1792bda00b2',
            'ImageId': '812d9f04-86f9-54fc-9275-8d0dcbcb6784',
        },
        {
            'BoundingBox': {
                'Height': 0.3259260058403015,
                'Left': 0.5144439935684204,
                'Top': 0.15111100673675537,
                'Width': 0.24444399774074554,
            },
            'Confidence': 99.99949645996094,
            'FaceId': '8be04dba-4e58-520d-850e-9eae4af70eb2',
            'ImageId': '465f4e93-763e-51d0-b030-b9667a2d94b1',
        },
        {
            'BoundingBox': {
                'Height': 0.18888899683952332,
                'Left': 0.3783380091190338,
                'Top': 0.2355560064315796,
                'Width': 0.25222599506378174,
            },
            'Confidence': 99.9999008178711,
            'FaceId': '908544ad-edc3-59df-8faf-6a87cc256cf5',
            'ImageId': '3c731605-d772-541a-a5e7-0375dbc68a07',
        },
        {
            'BoundingBox': {
                'Height': 0.33481499552726746,
                'Left': 0.31888899207115173,
                'Top': 0.49333301186561584,
                'Width': 0.25,
            },
            'Confidence': 99.99909973144531,
            'FaceId': 'ff43d742-0c13-5d16-a3e8-03d3f58e980b',
            'ImageId': '465f4e93-763e-51d0-b030-b9667a2d94b1',
        },
    ],
    'ResponseMetadata': {
        '...': '...',
    },
}
list_stream_processors(**kwargs)

Gets a list of stream processors that you have created with .

See also: AWS API Documentation

Request Syntax

response = client.list_stream_processors(
    NextToken='string',
    MaxResults=123
)
Parameters
  • NextToken (string) -- If the previous response was incomplete (because there are more stream processors to retrieve), Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of stream processors.
  • MaxResults (integer) -- Maximum number of stream processors you want Rekognition Video to return in the response. The default is 1000.
Return type

dict

Returns

Response Syntax

{
    'NextToken': 'string',
    'StreamProcessors': [
        {
            'Name': 'string',
            'Status': 'STOPPED'|'STARTING'|'RUNNING'|'FAILED'|'STOPPING'
        },
    ]
}

Response Structure

  • (dict) --

    • NextToken (string) --

      If the response is truncated, Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of stream processors.

    • StreamProcessors (list) --

      List of stream processors that you have created.

      • (dict) --

        An object that recognizes faces in a streaming video. An Amazon Rekognition stream processor is created by a call to . The request parameters for CreateStreamProcessor describe the Kinesis video stream source for the streaming video, face recognition parameters, and where to stream the analysis resullts.

        • Name (string) --

          Name of the Amazon Rekognition stream processor.

        • Status (string) --

          Current status of the Amazon Rekognition stream processor.

recognize_celebrities(**kwargs)

Returns an array of celebrities recognized in the input image. For more information, see celebrities .

RecognizeCelebrities returns the 100 largest faces in the image. It lists recognized celebrities in the CelebrityFaces array and unrecognized faces in the UnrecognizedFaces array. RecognizeCelebrities doesn't return celebrities whose faces are not amongst the largest 100 faces in the image.

For each celebrity recognized, the RecognizeCelebrities returns a Celebrity object. The Celebrity object contains the celebrity name, ID, URL links to additional information, match confidence, and a ComparedFace object that you can use to locate the celebrity's face on the image.

Rekognition does not retain information about which images a celebrity has been recognized in. Your application must store this information and use the Celebrity ID property as a unique identifier for the celebrity. If you don't store the celebrity name or additional information URLs returned by RecognizeCelebrities , you will need the ID to identify the celebrity in a call to the operation.

You pass the imput image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the Amazon CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.

For an example, see celebrities-procedure-image .

This operation requires permissions to perform the rekognition:RecognizeCelebrities operation.

See also: AWS API Documentation

Request Syntax

response = client.recognize_celebrities(
    Image={
        'Bytes': b'bytes',
        'S3Object': {
            'Bucket': 'string',
            'Name': 'string',
            'Version': 'string'
        }
    }
)
Parameters
Image (dict) --

[REQUIRED]

The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.

  • Bytes (bytes) --

    Blob of image bytes up to 5 MBs.

  • S3Object (dict) --

    Identifies an S3 object as the image source.

    • Bucket (string) --

      Name of the S3 bucket.

    • Name (string) --

      S3 object key name.

    • Version (string) --

      If the bucket is versioning enabled, you can specify the object version.

Return type
dict
Returns
Response Syntax
{
    'CelebrityFaces': [
        {
            'Urls': [
                'string',
            ],
            'Name': 'string',
            'Id': 'string',
            'Face': {
                'BoundingBox': {
                    'Width': ...,
                    'Height': ...,
                    'Left': ...,
                    'Top': ...
                },
                'Confidence': ...,
                'Landmarks': [
                    {
                        'Type': 'eyeLeft'|'eyeRight'|'nose'|'mouthLeft'|'mouthRight'|'leftEyeBrowLeft'|'leftEyeBrowRight'|'leftEyeBrowUp'|'rightEyeBrowLeft'|'rightEyeBrowRight'|'rightEyeBrowUp'|'leftEyeLeft'|'leftEyeRight'|'leftEyeUp'|'leftEyeDown'|'rightEyeLeft'|'rightEyeRight'|'rightEyeUp'|'rightEyeDown'|'noseLeft'|'noseRight'|'mouthUp'|'mouthDown'|'leftPupil'|'rightPupil',
                        'X': ...,
                        'Y': ...
                    },
                ],
                'Pose': {
                    'Roll': ...,
                    'Yaw': ...,
                    'Pitch': ...
                },
                'Quality': {
                    'Brightness': ...,
                    'Sharpness': ...
                }
            },
            'MatchConfidence': ...
        },
    ],
    'UnrecognizedFaces': [
        {
            'BoundingBox': {
                'Width': ...,
                'Height': ...,
                'Left': ...,
                'Top': ...
            },
            'Confidence': ...,
            'Landmarks': [
                {
                    'Type': 'eyeLeft'|'eyeRight'|'nose'|'mouthLeft'|'mouthRight'|'leftEyeBrowLeft'|'leftEyeBrowRight'|'leftEyeBrowUp'|'rightEyeBrowLeft'|'rightEyeBrowRight'|'rightEyeBrowUp'|'leftEyeLeft'|'leftEyeRight'|'leftEyeUp'|'leftEyeDown'|'rightEyeLeft'|'rightEyeRight'|'rightEyeUp'|'rightEyeDown'|'noseLeft'|'noseRight'|'mouthUp'|'mouthDown'|'leftPupil'|'rightPupil',
                    'X': ...,
                    'Y': ...
                },
            ],
            'Pose': {
                'Roll': ...,
                'Yaw': ...,
                'Pitch': ...
            },
            'Quality': {
                'Brightness': ...,
                'Sharpness': ...
            }
        },
    ],
    'OrientationCorrection': 'ROTATE_0'|'ROTATE_90'|'ROTATE_180'|'ROTATE_270'
}

Response Structure

  • (dict) --
    • CelebrityFaces (list) --

      Details about each celebrity found in the image. Amazon Rekognition can detect a maximum of 15 celebrities in an image.

      • (dict) --

        Provides information about a celebrity recognized by the operation.

        • Urls (list) --

          An array of URLs pointing to additional information about the celebrity. If there is no additional information about the celebrity, this list is empty.

          • (string) --
        • Name (string) --

          The name of the celebrity.

        • Id (string) --

          A unique identifier for the celebrity.

        • Face (dict) --

          Provides information about the celebrity's face, such as its location on the image.

          • BoundingBox (dict) --

            Bounding box of the face.

            • Width (float) --

              Width of the bounding box as a ratio of the overall image width.

            • Height (float) --

              Height of the bounding box as a ratio of the overall image height.

            • Left (float) --

              Left coordinate of the bounding box as a ratio of overall image width.

            • Top (float) --

              Top coordinate of the bounding box as a ratio of overall image height.

          • Confidence (float) --

            Level of confidence that what the bounding box contains is a face.

          • Landmarks (list) --

            An array of facial landmarks.

            • (dict) --

              Indicates the location of the landmark on the face.

              • Type (string) --

                Type of the landmark.

              • X (float) --

                x-coordinate from the top left of the landmark expressed as the ratio of the width of the image. For example, if the images is 700x200 and the x-coordinate of the landmark is at 350 pixels, this value is 0.5.

              • Y (float) --

                y-coordinate from the top left of the landmark expressed as the ratio of the height of the image. For example, if the images is 700x200 and the y-coordinate of the landmark is at 100 pixels, this value is 0.5.

          • Pose (dict) --

            Indicates the pose of the face as determined by its pitch, roll, and yaw.

            • Roll (float) --

              Value representing the face rotation on the roll axis.

            • Yaw (float) --

              Value representing the face rotation on the yaw axis.

            • Pitch (float) --

              Value representing the face rotation on the pitch axis.

          • Quality (dict) --

            Identifies face image brightness and sharpness.

            • Brightness (float) --

              Value representing brightness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a brighter face image.

            • Sharpness (float) --

              Value representing sharpness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a sharper face image.

        • MatchConfidence (float) --

          The confidence, in percentage, that Rekognition has that the recognized face is the celebrity.

    • UnrecognizedFaces (list) --

      Details about each unrecognized face in the image.

      • (dict) --

        Provides face metadata for target image faces that are analysed by CompareFaces and RecognizeCelebrities .

        • BoundingBox (dict) --

          Bounding box of the face.

          • Width (float) --

            Width of the bounding box as a ratio of the overall image width.

          • Height (float) --

            Height of the bounding box as a ratio of the overall image height.

          • Left (float) --

            Left coordinate of the bounding box as a ratio of overall image width.

          • Top (float) --

            Top coordinate of the bounding box as a ratio of overall image height.

        • Confidence (float) --

          Level of confidence that what the bounding box contains is a face.

        • Landmarks (list) --

          An array of facial landmarks.

          • (dict) --

            Indicates the location of the landmark on the face.

            • Type (string) --

              Type of the landmark.

            • X (float) --

              x-coordinate from the top left of the landmark expressed as the ratio of the width of the image. For example, if the images is 700x200 and the x-coordinate of the landmark is at 350 pixels, this value is 0.5.

            • Y (float) --

              y-coordinate from the top left of the landmark expressed as the ratio of the height of the image. For example, if the images is 700x200 and the y-coordinate of the landmark is at 100 pixels, this value is 0.5.

        • Pose (dict) --

          Indicates the pose of the face as determined by its pitch, roll, and yaw.

          • Roll (float) --

            Value representing the face rotation on the roll axis.

          • Yaw (float) --

            Value representing the face rotation on the yaw axis.

          • Pitch (float) --

            Value representing the face rotation on the pitch axis.

        • Quality (dict) --

          Identifies face image brightness and sharpness.

          • Brightness (float) --

            Value representing brightness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a brighter face image.

          • Sharpness (float) --

            Value representing sharpness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a sharper face image.

    • OrientationCorrection (string) --

      The orientation of the input image (counterclockwise direction). If your application displays the image, you can use this value to correct the orientation. The bounding box coordinates returned in CelebrityFaces and UnrecognizedFaces represent face locations before the image orientation is corrected.

      Note

      If the input image is in .jpeg format, it might contain exchangeable image (Exif) metadata that includes the image's orientation. If so, and the Exif metadata for the input image populates the orientation field, the value of OrientationCorrection is null and the CelebrityFaces and UnrecognizedFaces bounding box coordinates represent face locations after Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.

search_faces(**kwargs)

For a given input face ID, searches for matching faces in the collection the face belongs to. You get a face ID when you add a face to the collection using the IndexFaces operation. The operation compares the features of the input face with faces in the specified collection.

Note

You can also search faces without indexing faces by using the SearchFacesByImage operation.

The operation response returns an array of faces that match, ordered by similarity score with the highest similarity first. More specifically, it is an array of metadata for each face match that is found. Along with the metadata, the response also includes a confidence value for each face match, indicating the confidence that the specific face matches the input face.

For an example, see search-face-with-id-procedure .

This operation requires permissions to perform the rekognition:SearchFaces action.

See also: AWS API Documentation

Request Syntax

response = client.search_faces(
    CollectionId='string',
    FaceId='string',
    MaxFaces=123,
    FaceMatchThreshold=...
)
Parameters
  • CollectionId (string) --

    [REQUIRED]

    ID of the collection the face belongs to.

  • FaceId (string) --

    [REQUIRED]

    ID of a face to find matches for in the collection.

  • MaxFaces (integer) -- Maximum number of faces to return. The operation returns the maximum number of faces with the highest confidence in the match.
  • FaceMatchThreshold (float) -- Optional value specifying the minimum confidence in the face match to return. For example, don't return any matches where confidence in matches is less than 70%.
Return type

dict

Returns

Response Syntax

{
    'SearchedFaceId': 'string',
    'FaceMatches': [
        {
            'Similarity': ...,
            'Face': {
                'FaceId': 'string',
                'BoundingBox': {
                    'Width': ...,
                    'Height': ...,
                    'Left': ...,
                    'Top': ...
                },
                'ImageId': 'string',
                'ExternalImageId': 'string',
                'Confidence': ...
            }
        },
    ],
    'FaceModelVersion': 'string'
}

Response Structure

  • (dict) --

    • SearchedFaceId (string) --

      ID of the face that was searched for matches in a collection.

    • FaceMatches (list) --

      An array of faces that matched the input face, along with the confidence in the match.

      • (dict) --

        Provides face metadata. In addition, it also provides the confidence in the match of this face with the input face.

        • Similarity (float) --

          Confidence in the match of this face with the input face.

        • Face (dict) --

          Describes the face properties such as the bounding box, face ID, image ID of the source image, and external image ID that you assigned.

          • FaceId (string) --

            Unique identifier that Amazon Rekognition assigns to the face.

          • BoundingBox (dict) --

            Bounding box of the face.

            • Width (float) --

              Width of the bounding box as a ratio of the overall image width.

            • Height (float) --

              Height of the bounding box as a ratio of the overall image height.

            • Left (float) --

              Left coordinate of the bounding box as a ratio of overall image width.

            • Top (float) --

              Top coordinate of the bounding box as a ratio of overall image height.

          • ImageId (string) --

            Unique identifier that Amazon Rekognition assigns to the input image.

          • ExternalImageId (string) --

            Identifier that you assign to all the faces in the input image.

          • Confidence (float) --

            Confidence level that the bounding box contains a face (and not a different object such as a tree).

    • FaceModelVersion (string) --

      Version number of the face detection model associated with the input collection (CollectionId ).

Examples

This operation searches for matching faces in the collection the supplied face belongs to.

response = client.search_faces(
    CollectionId='myphotos',
    FaceId='70008e50-75e4-55d0-8e80-363fb73b3a14',
    FaceMatchThreshold=90,
    MaxFaces=10,
)

print(response)

Expected Output:

{
    'FaceMatches': [
        {
            'Face': {
                'BoundingBox': {
                    'Height': 0.3259260058403015,
                    'Left': 0.5144439935684204,
                    'Top': 0.15111100673675537,
                    'Width': 0.24444399774074554,
                },
                'Confidence': 99.99949645996094,
                'FaceId': '8be04dba-4e58-520d-850e-9eae4af70eb2',
                'ImageId': '465f4e93-763e-51d0-b030-b9667a2d94b1',
            },
            'Similarity': 99.97222137451172,
        },
        {
            'Face': {
                'BoundingBox': {
                    'Height': 0.16555599868297577,
                    'Left': 0.30963000655174255,
                    'Top': 0.7066670060157776,
                    'Width': 0.22074100375175476,
                },
                'Confidence': 100,
                'FaceId': '29a75abe-397b-5101-ba4f-706783b2246c',
                'ImageId': '147fdf82-7a71-52cf-819b-e786c7b9746e',
            },
            'Similarity': 97.04154968261719,
        },
        {
            'Face': {
                'BoundingBox': {
                    'Height': 0.18888899683952332,
                    'Left': 0.3783380091190338,
                    'Top': 0.2355560064315796,
                    'Width': 0.25222599506378174,
                },
                'Confidence': 99.9999008178711,
                'FaceId': '908544ad-edc3-59df-8faf-6a87cc256cf5',
                'ImageId': '3c731605-d772-541a-a5e7-0375dbc68a07',
            },
            'Similarity': 95.94520568847656,
        },
    ],
    'SearchedFaceId': '70008e50-75e4-55d0-8e80-363fb73b3a14',
    'ResponseMetadata': {
        '...': '...',
    },
}
search_faces_by_image(**kwargs)

For a given input image, first detects the largest face in the image, and then searches the specified collection for matching faces. The operation compares the features of the input face with faces in the specified collection.

Note

To search for all faces in an input image, you might first call the operation, and then use the face IDs returned in subsequent calls to the operation.

You can also call the DetectFaces operation and use the bounding boxes in the response to make face crops, which then you can pass in to the SearchFacesByImage operation.

You pass the input image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the Amazon CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.

The response returns an array of faces that match, ordered by similarity score with the highest similarity first. More specifically, it is an array of metadata for each face match found. Along with the metadata, the response also includes a similarity indicating how similar the face is to the input face. In the response, the operation also returns the bounding box (and a confidence level that the bounding box contains a face) of the face that Amazon Rekognition used for the input image.

For an example, see search-face-with-image-procedure .

This operation requires permissions to perform the rekognition:SearchFacesByImage action.

See also: AWS API Documentation

Request Syntax

response = client.search_faces_by_image(
    CollectionId='string',
    Image={
        'Bytes': b'bytes',
        'S3Object': {
            'Bucket': 'string',
            'Name': 'string',
            'Version': 'string'
        }
    },
    MaxFaces=123,
    FaceMatchThreshold=...
)
Parameters
  • CollectionId (string) --

    [REQUIRED]

    ID of the collection to search.

  • Image (dict) --

    [REQUIRED]

    The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.

    • Bytes (bytes) --

      Blob of image bytes up to 5 MBs.

    • S3Object (dict) --

      Identifies an S3 object as the image source.

      • Bucket (string) --

        Name of the S3 bucket.

      • Name (string) --

        S3 object key name.

      • Version (string) --

        If the bucket is versioning enabled, you can specify the object version.

  • MaxFaces (integer) -- Maximum number of faces to return. The operation returns the maximum number of faces with the highest confidence in the match.
  • FaceMatchThreshold (float) -- (Optional) Specifies the minimum confidence in the face match to return. For example, don't return any matches where confidence in matches is less than 70%.
Return type

dict

Returns

Response Syntax

{
    'SearchedFaceBoundingBox': {
        'Width': ...,
        'Height': ...,
        'Left': ...,
        'Top': ...
    },
    'SearchedFaceConfidence': ...,
    'FaceMatches': [
        {
            'Similarity': ...,
            'Face': {
                'FaceId': 'string',
                'BoundingBox': {
                    'Width': ...,
                    'Height': ...,
                    'Left': ...,
                    'Top': ...
                },
                'ImageId': 'string',
                'ExternalImageId': 'string',
                'Confidence': ...
            }
        },
    ],
    'FaceModelVersion': 'string'
}

Response Structure

  • (dict) --

    • SearchedFaceBoundingBox (dict) --

      The bounding box around the face in the input image that Amazon Rekognition used for the search.

      • Width (float) --

        Width of the bounding box as a ratio of the overall image width.

      • Height (float) --

        Height of the bounding box as a ratio of the overall image height.

      • Left (float) --

        Left coordinate of the bounding box as a ratio of overall image width.

      • Top (float) --

        Top coordinate of the bounding box as a ratio of overall image height.

    • SearchedFaceConfidence (float) --

      The level of confidence that the searchedFaceBoundingBox , contains a face.

    • FaceMatches (list) --

      An array of faces that match the input face, along with the confidence in the match.

      • (dict) --

        Provides face metadata. In addition, it also provides the confidence in the match of this face with the input face.

        • Similarity (float) --

          Confidence in the match of this face with the input face.

        • Face (dict) --

          Describes the face properties such as the bounding box, face ID, image ID of the source image, and external image ID that you assigned.

          • FaceId (string) --

            Unique identifier that Amazon Rekognition assigns to the face.

          • BoundingBox (dict) --

            Bounding box of the face.

            • Width (float) --

              Width of the bounding box as a ratio of the overall image width.

            • Height (float) --

              Height of the bounding box as a ratio of the overall image height.

            • Left (float) --

              Left coordinate of the bounding box as a ratio of overall image width.

            • Top (float) --

              Top coordinate of the bounding box as a ratio of overall image height.

          • ImageId (string) --

            Unique identifier that Amazon Rekognition assigns to the input image.

          • ExternalImageId (string) --

            Identifier that you assign to all the faces in the input image.

          • Confidence (float) --

            Confidence level that the bounding box contains a face (and not a different object such as a tree).

    • FaceModelVersion (string) --

      Version number of the face detection model associated with the input collection (CollectionId ).

Examples

This operation searches for faces in a Rekognition collection that match the largest face in an S3 bucket stored image.

response = client.search_faces_by_image(
    CollectionId='myphotos',
    FaceMatchThreshold=95,
    Image={
        'S3Object': {
            'Bucket': 'mybucket',
            'Name': 'myphoto',
        },
    },
    MaxFaces=5,
)

print(response)

Expected Output:

{
    'FaceMatches': [
        {
            'Face': {
                'BoundingBox': {
                    'Height': 0.3234420120716095,
                    'Left': 0.3233329951763153,
                    'Top': 0.5,
                    'Width': 0.24222199618816376,
                },
                'Confidence': 99.99829864501953,
                'FaceId': '38271d79-7bc2-5efb-b752-398a8d575b85',
                'ImageId': 'd5631190-d039-54e4-b267-abd22c8647c5',
            },
            'Similarity': 99.97036743164062,
        },
    ],
    'SearchedFaceBoundingBox': {
        'Height': 0.33481481671333313,
        'Left': 0.31888890266418457,
        'Top': 0.4933333396911621,
        'Width': 0.25,
    },
    'SearchedFaceConfidence': 99.9991226196289,
    'ResponseMetadata': {
        '...': '...',
    },
}
start_celebrity_recognition(**kwargs)

Starts asynchronous recognition of celebrities in a stored video.

Rekognition Video can detect celebrities in a video must be stored in an Amazon S3 bucket. Use Video to specify the bucket name and the filename of the video. StartCelebrityRecognition returns a job identifier (JobId ) which you use to get the results of the analysis. When celebrity recognition analysis is finished, Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic that you specify in NotificationChannel . To get the results of the celebrity recognition analysis, first check that the status value published to the Amazon SNS topic is SUCCEEDED . If so, call and pass the job identifier (JobId ) from the initial call to StartCelebrityRecognition . For more information, see celebrities .

See also: AWS API Documentation

Request Syntax

response = client.start_celebrity_recognition(
    Video={
        'S3Object': {
            'Bucket': 'string',
            'Name': 'string',
            'Version': 'string'
        }
    },
    ClientRequestToken='string',
    NotificationChannel={
        'SNSTopicArn': 'string',
        'RoleArn': 'string'
    },
    JobTag='string'
)
Parameters
  • Video (dict) --

    [REQUIRED]

    The video in which you want to recognize celebrities. The video must be stored in an Amazon S3 bucket.

    • S3Object (dict) --

      The Amazon S3 bucket name and file name for the video.

      • Bucket (string) --

        Name of the S3 bucket.

      • Name (string) --

        S3 object key name.

      • Version (string) --

        If the bucket is versioning enabled, you can specify the object version.

  • ClientRequestToken (string) -- Idempotent token used to identify the start request. If you use the same token with multiple StartCelebrityRecognition requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.
  • NotificationChannel (dict) --

    The Amazon SNS topic ARN that you want Rekognition Video to publish the completion status of the celebrity recognition analysis to.

    • SNSTopicArn (string) -- [REQUIRED]

      The Amazon SNS topic to which Amazon Rekognition to posts the completion status.

    • RoleArn (string) -- [REQUIRED]

      The ARN of an IAM role that gives Amazon Rekognition publishing permissions to the Amazon SNS topic.

  • JobTag (string) -- Unique identifier you specify to identify the job in the completion status published to the Amazon Simple Notification Service topic.
Return type

dict

Returns

Response Syntax

{
    'JobId': 'string'
}

Response Structure

  • (dict) --

    • JobId (string) --

      The identifier for the celebrity recognition analysis job. Use JobId to identify the job in a subsequent call to GetCelebrityRecognition .

start_content_moderation(**kwargs)

Starts asynchronous detection of explicit or suggestive adult content in a stored video.

Rekognition Video can moderate content in a video stored in an Amazon S3 bucket. Use Video to specify the bucket name and the filename of the video. StartContentModeration returns a job identifier (JobId ) which you use to get the results of the analysis. When content moderation analysis is finished, Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic that you specify in NotificationChannel .

To get the results of the content moderation analysis, first check that the status value published to the Amazon SNS topic is SUCCEEDED . If so, call and pass the job identifier (JobId ) from the initial call to StartContentModeration . For more information, see moderation .

See also: AWS API Documentation

Request Syntax

response = client.start_content_moderation(
    Video={
        'S3Object': {
            'Bucket': 'string',
            'Name': 'string',
            'Version': 'string'
        }
    },
    MinConfidence=...,
    ClientRequestToken='string',
    NotificationChannel={
        'SNSTopicArn': 'string',
        'RoleArn': 'string'
    },
    JobTag='string'
)
Parameters
  • Video (dict) --

    [REQUIRED]

    The video in which you want to moderate content. The video must be stored in an Amazon S3 bucket.

    • S3Object (dict) --

      The Amazon S3 bucket name and file name for the video.

      • Bucket (string) --

        Name of the S3 bucket.

      • Name (string) --

        S3 object key name.

      • Version (string) --

        If the bucket is versioning enabled, you can specify the object version.

  • MinConfidence (float) -- Specifies the minimum confidence that Amazon Rekognition must have in order to return a moderated content label. Confidence represents how certain Amazon Rekognition is that the moderated content is correctly identified. 0 is the lowest confidence. 100 is the highest confidence. Amazon Rekognition doesn't return any moderated content labels with a confidence level lower than this specified value.
  • ClientRequestToken (string) -- Idempotent token used to identify the start request. If you use the same token with multiple StartContentModeration requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.
  • NotificationChannel (dict) --

    The Amazon SNS topic ARN that you want Rekognition Video to publish the completion status of the content moderation analysis to.

    • SNSTopicArn (string) -- [REQUIRED]

      The Amazon SNS topic to which Amazon Rekognition to posts the completion status.

    • RoleArn (string) -- [REQUIRED]

      The ARN of an IAM role that gives Amazon Rekognition publishing permissions to the Amazon SNS topic.

  • JobTag (string) -- Unique identifier you specify to identify the job in the completion status published to the Amazon Simple Notification Service topic.
Return type

dict

Returns

Response Syntax

{
    'JobId': 'string'
}

Response Structure

  • (dict) --

    • JobId (string) --

      The identifier for the content moderation analysis job. Use JobId to identify the job in a subsequent call to GetContentModeration .

start_face_detection(**kwargs)

Starts asynchronous detection of faces in a stored video.

Rekognition Video can detect faces in a video stored in an Amazon S3 bucket. Use Video to specify the bucket name and the filename of the video. StartFaceDetection returns a job identifier (JobId ) that you use to get the results of the operation. When face detection is finished, Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic that you specify in NotificationChannel . To get the results of the label detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED . If so, call and pass the job identifier (JobId ) from the initial call to StartFaceDetection . For more information, see faces-video .

See also: AWS API Documentation

Request Syntax

response = client.start_face_detection(
    Video={
        'S3Object': {
            'Bucket': 'string',
            'Name': 'string',
            'Version': 'string'
        }
    },
    ClientRequestToken='string',
    NotificationChannel={
        'SNSTopicArn': 'string',
        'RoleArn': 'string'
    },
    FaceAttributes='DEFAULT'|'ALL',
    JobTag='string'
)
Parameters
  • Video (dict) --

    [REQUIRED]

    The video in which you want to detect faces. The video must be stored in an Amazon S3 bucket.

    • S3Object (dict) --

      The Amazon S3 bucket name and file name for the video.

      • Bucket (string) --

        Name of the S3 bucket.

      • Name (string) --

        S3 object key name.

      • Version (string) --

        If the bucket is versioning enabled, you can specify the object version.

  • ClientRequestToken (string) -- Idempotent token used to identify the start request. If you use the same token with multiple StartFaceDetection requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.
  • NotificationChannel (dict) --

    The ARN of the Amazon SNS topic to which you want Rekognition Video to publish the completion status of the face detection operation.

    • SNSTopicArn (string) -- [REQUIRED]

      The Amazon SNS topic to which Amazon Rekognition to posts the completion status.

    • RoleArn (string) -- [REQUIRED]

      The ARN of an IAM role that gives Amazon Rekognition publishing permissions to the Amazon SNS topic.

  • FaceAttributes (string) --

    The face attributes you want returned.

    DEFAULT - The following subset of facial attributes are returned: BoundingBox, Confidence, Pose, Quality and Landmarks.

    ALL - All facial attributes are returned.

  • JobTag (string) -- Unique identifier you specify to identify the job in the completion status published to the Amazon Simple Notification Service topic.
Return type

dict

Returns

Response Syntax

{
    'JobId': 'string'
}

Response Structure

  • (dict) --

    • JobId (string) --

      The identifier for the face detection job. Use JobId to identify the job in a subsequent call to GetFaceDetection .

Starts the asynchronous search for faces in a collection that match the faces of persons detected in a stored video.

The video must be stored in an Amazon S3 bucket. Use Video to specify the bucket name and the filename of the video. StartFaceSearch returns a job identifier (JobId ) which you use to get the search results once the search has completed. When searching is finished, Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic that you specify in NotificationChannel . To get the search results, first check that the status value published to the Amazon SNS topic is SUCCEEDED . If so, call and pass the job identifier (JobId ) from the initial call to StartFaceSearch . For more information, see collections-search-person .

See also: AWS API Documentation

Request Syntax

response = client.start_face_search(
    Video={
        'S3Object': {
            'Bucket': 'string',
            'Name': 'string',
            'Version': 'string'
        }
    },
    ClientRequestToken='string',
    FaceMatchThreshold=...,
    CollectionId='string',
    NotificationChannel={
        'SNSTopicArn': 'string',
        'RoleArn': 'string'
    },
    JobTag='string'
)
Parameters
  • Video (dict) --

    [REQUIRED]

    The video you want to search. The video must be stored in an Amazon S3 bucket.

    • S3Object (dict) --

      The Amazon S3 bucket name and file name for the video.

      • Bucket (string) --

        Name of the S3 bucket.

      • Name (string) --

        S3 object key name.

      • Version (string) --

        If the bucket is versioning enabled, you can specify the object version.

  • ClientRequestToken (string) -- Idempotent token used to identify the start request. If you use the same token with multiple StartFaceSearch requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.
  • FaceMatchThreshold (float) -- The minimum confidence in the person match to return. For example, don't return any matches where confidence in matches is less than 70%.
  • CollectionId (string) --

    [REQUIRED]

    ID of the collection that contains the faces you want to search for.

  • NotificationChannel (dict) --

    The ARN of the Amazon SNS topic to which you want Rekognition Video to publish the completion status of the search.

    • SNSTopicArn (string) -- [REQUIRED]

      The Amazon SNS topic to which Amazon Rekognition to posts the completion status.

    • RoleArn (string) -- [REQUIRED]

      The ARN of an IAM role that gives Amazon Rekognition publishing permissions to the Amazon SNS topic.

  • JobTag (string) -- Unique identifier you specify to identify the job in the completion status published to the Amazon Simple Notification Service topic.
Return type

dict

Returns

Response Syntax

{
    'JobId': 'string'
}

Response Structure

  • (dict) --

    • JobId (string) --

      The identifier for the search job. Use JobId to identify the job in a subsequent call to GetFaceSearch .

start_label_detection(**kwargs)

Starts asynchronous detection of labels in a stored video.

Rekognition Video can detect labels in a video. Labels are instances of real-world entities. This includes objects like flower, tree, and table; events like wedding, graduation, and birthday party; concepts like landscape, evening, and nature; and activities like a person getting out of a car or a person skiing.

The video must be stored in an Amazon S3 bucket. Use Video to specify the bucket name and the filename of the video. StartLabelDetection returns a job identifier (JobId ) which you use to get the results of the operation. When label detection is finished, Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic that you specify in NotificationChannel .

To get the results of the label detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED . If so, call and pass the job identifier (JobId ) from the initial call to StartLabelDetection .

See also: AWS API Documentation

Request Syntax

response = client.start_label_detection(
    Video={
        'S3Object': {
            'Bucket': 'string',
            'Name': 'string',
            'Version': 'string'
        }
    },
    ClientRequestToken='string',
    MinConfidence=...,
    NotificationChannel={
        'SNSTopicArn': 'string',
        'RoleArn': 'string'
    },
    JobTag='string'
)
Parameters
  • Video (dict) --

    [REQUIRED]

    The video in which you want to detect labels. The video must be stored in an Amazon S3 bucket.

    • S3Object (dict) --

      The Amazon S3 bucket name and file name for the video.

      • Bucket (string) --

        Name of the S3 bucket.

      • Name (string) --

        S3 object key name.

      • Version (string) --

        If the bucket is versioning enabled, you can specify the object version.

  • ClientRequestToken (string) -- Idempotent token used to identify the start request. If you use the same token with multiple StartLabelDetection requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.
  • MinConfidence (float) --

    Specifies the minimum confidence that Rekognition Video must have in order to return a detected label. Confidence represents how certain Amazon Rekognition is that a label is correctly identified.0 is the lowest confidence. 100 is the highest confidence. Rekognition Video doesn't return any labels with a confidence level lower than this specified value.

    If you don't specify MinConfidence , the operation returns labels with confidence values greater than or equal to 50 percent.

  • NotificationChannel (dict) --

    The Amazon SNS topic ARN you want Rekognition Video to publish the completion status of the label detection operation to.

    • SNSTopicArn (string) -- [REQUIRED]

      The Amazon SNS topic to which Amazon Rekognition to posts the completion status.

    • RoleArn (string) -- [REQUIRED]

      The ARN of an IAM role that gives Amazon Rekognition publishing permissions to the Amazon SNS topic.

  • JobTag (string) -- Unique identifier you specify to identify the job in the completion status published to the Amazon Simple Notification Service topic.
Return type

dict

Returns

Response Syntax

{
    'JobId': 'string'
}

Response Structure

  • (dict) --

    • JobId (string) --

      The identifier for the label detection job. Use JobId to identify the job in a subsequent call to GetLabelDetection .

start_person_tracking(**kwargs)

Starts the asynchronous tracking of persons in a stored video.

Rekognition Video can track persons in a video stored in an Amazon S3 bucket. Use Video to specify the bucket name and the filename of the video. StartPersonTracking returns a job identifier (JobId ) which you use to get the results of the operation. When label detection is finished, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic that you specify in NotificationChannel .

To get the results of the person detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED . If so, call and pass the job identifier (JobId ) from the initial call to StartPersonTracking .

See also: AWS API Documentation

Request Syntax

response = client.start_person_tracking(
    Video={
        'S3Object': {
            'Bucket': 'string',
            'Name': 'string',
            'Version': 'string'
        }
    },
    ClientRequestToken='string',
    NotificationChannel={
        'SNSTopicArn': 'string',
        'RoleArn': 'string'
    },
    JobTag='string'
)
Parameters
  • Video (dict) --

    [REQUIRED]

    The video in which you want to detect people. The video must be stored in an Amazon S3 bucket.

    • S3Object (dict) --

      The Amazon S3 bucket name and file name for the video.

      • Bucket (string) --

        Name of the S3 bucket.

      • Name (string) --

        S3 object key name.

      • Version (string) --

        If the bucket is versioning enabled, you can specify the object version.

  • ClientRequestToken (string) -- Idempotent token used to identify the start request. If you use the same token with multiple StartPersonTracking requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.
  • NotificationChannel (dict) --

    The Amazon SNS topic ARN you want Rekognition Video to publish the completion status of the people detection operation to.

    • SNSTopicArn (string) -- [REQUIRED]

      The Amazon SNS topic to which Amazon Rekognition to posts the completion status.

    • RoleArn (string) -- [REQUIRED]

      The ARN of an IAM role that gives Amazon Rekognition publishing permissions to the Amazon SNS topic.

  • JobTag (string) -- Unique identifier you specify to identify the job in the completion status published to the Amazon Simple Notification Service topic.
Return type

dict

Returns

Response Syntax

{
    'JobId': 'string'
}

Response Structure

  • (dict) --

    • JobId (string) --

      The identifier for the person detection job. Use JobId to identify the job in a subsequent call to GetPersonTracking .

start_stream_processor(**kwargs)

Starts processing a stream processor. You create a stream processor by calling . To tell StartStreamProcessor which stream processor to start, use the value of the Name field specified in the call to CreateStreamProcessor .

See also: AWS API Documentation

Request Syntax

response = client.start_stream_processor(
    Name='string'
)
Parameters
Name (string) --

[REQUIRED]

The name of the stream processor to start processing.

Return type
dict
Returns
Response Syntax
{}

Response Structure

  • (dict) --
stop_stream_processor(**kwargs)

Stops a running stream processor that was created by .

See also: AWS API Documentation

Request Syntax

response = client.stop_stream_processor(
    Name='string'
)
Parameters
Name (string) --

[REQUIRED]

The name of a stream processor created by .

Return type
dict
Returns
Response Syntax
{}

Response Structure

  • (dict) --

Paginators

The available paginators are:

class Rekognition.Paginator.ListCollections
paginator = client.get_paginator('list_collections')
paginate(**kwargs)

Creates an iterator that will paginate through responses from Rekognition.Client.list_collections().

See also: AWS API Documentation

Request Syntax

response_iterator = paginator.paginate(
    PaginationConfig={
        'MaxItems': 123,
        'PageSize': 123,
        'StartingToken': 'string'
    }
)
Parameters
PaginationConfig (dict) --

A dictionary that provides parameters to control pagination.

  • MaxItems (integer) --

    The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.

  • PageSize (integer) --

    The size of each page.

  • StartingToken (string) --

    A token to specify where to start paginating. This is the NextToken from a previous response.

Return type
dict
Returns
Response Syntax
{
    'CollectionIds': [
        'string',
    ],
    'FaceModelVersions': [
        'string',
    ]
}

Response Structure

  • (dict) --
    • CollectionIds (list) --

      An array of collection IDs.

      • (string) --
    • FaceModelVersions (list) --

      Version numbers of the face detection models associated with the collections in the array CollectionIds . For example, the value of FaceModelVersions[2] is the version number for the face detection model used by the collection in CollectionId[2] .

      • (string) --
class Rekognition.Paginator.ListFaces
paginator = client.get_paginator('list_faces')
paginate(**kwargs)

Creates an iterator that will paginate through responses from Rekognition.Client.list_faces().

See also: AWS API Documentation

Request Syntax

response_iterator = paginator.paginate(
    CollectionId='string',
    PaginationConfig={
        'MaxItems': 123,
        'PageSize': 123,
        'StartingToken': 'string'
    }
)
Parameters
  • CollectionId (string) --

    [REQUIRED]

    ID of the collection from which to list the faces.

  • PaginationConfig (dict) --

    A dictionary that provides parameters to control pagination.

    • MaxItems (integer) --

      The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.

    • PageSize (integer) --

      The size of each page.

    • StartingToken (string) --

      A token to specify where to start paginating. This is the NextToken from a previous response.

Return type

dict

Returns

Response Syntax

{
    'Faces': [
        {
            'FaceId': 'string',
            'BoundingBox': {
                'Width': ...,
                'Height': ...,
                'Left': ...,
                'Top': ...
            },
            'ImageId': 'string',
            'ExternalImageId': 'string',
            'Confidence': ...
        },
    ],
    'FaceModelVersion': 'string'
}

Response Structure

  • (dict) --

    • Faces (list) --

      An array of Face objects.

      • (dict) --

        Describes the face properties such as the bounding box, face ID, image ID of the input image, and external image ID that you assigned.

        • FaceId (string) --

          Unique identifier that Amazon Rekognition assigns to the face.

        • BoundingBox (dict) --

          Bounding box of the face.

          • Width (float) --

            Width of the bounding box as a ratio of the overall image width.

          • Height (float) --

            Height of the bounding box as a ratio of the overall image height.

          • Left (float) --

            Left coordinate of the bounding box as a ratio of overall image width.

          • Top (float) --

            Top coordinate of the bounding box as a ratio of overall image height.

        • ImageId (string) --

          Unique identifier that Amazon Rekognition assigns to the input image.

        • ExternalImageId (string) --

          Identifier that you assign to all the faces in the input image.

        • Confidence (float) --

          Confidence level that the bounding box contains a face (and not a different object such as a tree).

    • FaceModelVersion (string) --

      Version number of the face detection model associated with the input collection (CollectionId ).