Table of Contents
Rekognition.Client¶A low-level client representing Amazon Rekognition
This is the API Reference for Amazon Rekognition Image , Amazon Rekognition Custom Labels , Amazon Rekognition Stored Video , Amazon Rekognition Streaming Video . It provides descriptions of actions, data types, common parameters, and common errors.
Amazon Rekognition Image
Amazon Rekognition Custom Labels
Amazon Rekognition Video Stored Video
Amazon Rekognition Video Streaming Video
import boto3
client = boto3.client('rekognition')
These are the available methods:
can_paginate()close()compare_faces()copy_project_version()create_collection()create_dataset()create_project()create_project_version()create_stream_processor()delete_collection()delete_dataset()delete_faces()delete_project()delete_project_policy()delete_project_version()delete_stream_processor()describe_collection()describe_dataset()describe_project_versions()describe_projects()describe_stream_processor()detect_custom_labels()detect_faces()detect_labels()detect_moderation_labels()detect_protective_equipment()detect_text()distribute_dataset_entries()get_celebrity_info()get_celebrity_recognition()get_content_moderation()get_face_detection()get_face_search()get_label_detection()get_paginator()get_person_tracking()get_segment_detection()get_text_detection()get_waiter()index_faces()list_collections()list_dataset_entries()list_dataset_labels()list_faces()list_project_policies()list_stream_processors()list_tags_for_resource()put_project_policy()recognize_celebrities()search_faces()search_faces_by_image()start_celebrity_recognition()start_content_moderation()start_face_detection()start_face_search()start_label_detection()start_person_tracking()start_project_version()start_segment_detection()start_stream_processor()start_text_detection()stop_project_version()stop_stream_processor()tag_resource()untag_resource()update_dataset_entries()update_stream_processor()can_paginate(operation_name)¶Check if an operation can be paginated.
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").True if the operation can be paginated,
False otherwise.close()¶Closes underlying endpoint connections.
compare_faces(**kwargs)¶Compares a face in the source input image with each of the 100 largest faces detected in the target input image.
If the source image contains multiple faces, the service detects the largest face and compares it with each face detected in the target image.
Note
CompareFaces uses machine learning algorithms, which are probabilistic. A false negative is an incorrect prediction that a face in the target image has a low similarity confidence score when compared to the face in the source image. To reduce the probability of false negatives, we recommend that you compare the target image against multiple source images. If you plan to use CompareFaces to make a decision that impacts an individual's rights, privacy, or access to services, we recommend that you pass the result to a human for review and further validation before taking action.
You pass the input and target images either as base64-encoded image bytes or as references to images in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes isn't supported. The image must be formatted as a PNG or JPEG 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, roll, 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 theSimilarityThresholdparameter.
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.
The QualityFilter input parameter allows you to filter out detected faces that don’t meet a required quality bar. The quality bar is based on a variety of common use cases. Use QualityFilter to set the quality bar by specifying LOW , MEDIUM , or HIGH . If you do not want to filter detected faces, specify NONE . The default value is NONE .
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 Comparing Faces in Images in the Amazon Rekognition Developer Guide.
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=...,
QualityFilter='NONE'|'AUTO'|'LOW'|'MEDIUM'|'HIGH'
)
[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.
If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.
Blob of image bytes up to 5 MBs.
Identifies an S3 object as the image source.
Name of the S3 bucket.
S3 object key name.
If the bucket is versioning enabled, you can specify the object version.
[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.
If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.
Blob of image bytes up to 5 MBs.
Identifies an S3 object as the image source.
Name of the S3 bucket.
S3 object key name.
If the bucket is versioning enabled, you can specify the object version.
FaceMatches array.A filter that specifies a quality bar for how much filtering is done to identify faces. Filtered faces aren't compared. If you specify AUTO , Amazon Rekognition chooses the quality bar. If you specify LOW , MEDIUM , or HIGH , filtering removes all faces that don’t meet the chosen quality bar. The quality bar is based on a variety of common use cases. Low-quality detections can occur for a number of reasons. Some examples are an object that's misidentified as a face, a face that's too blurry, or a face with a pose that's too extreme to use. If you specify NONE , no filtering is performed. The default value is NONE .
To use quality filtering, the collection you are using must be associated with version 3 of the face model or higher.
dict
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'|'upperJawlineLeft'|'midJawlineLeft'|'chinBottom'|'midJawlineRight'|'upperJawlineRight',
'X': ...,
'Y': ...
},
],
'Pose': {
'Roll': ...,
'Yaw': ...,
'Pitch': ...
},
'Quality': {
'Brightness': ...,
'Sharpness': ...
},
'Emotions': [
{
'Type': 'HAPPY'|'SAD'|'ANGRY'|'CONFUSED'|'DISGUSTED'|'SURPRISED'|'CALM'|'UNKNOWN'|'FEAR',
'Confidence': ...
},
],
'Smile': {
'Value': True|False,
'Confidence': ...
}
}
},
],
'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'|'upperJawlineLeft'|'midJawlineLeft'|'chinBottom'|'midJawlineRight'|'upperJawlineRight',
'X': ...,
'Y': ...
},
],
'Pose': {
'Roll': ...,
'Yaw': ...,
'Pitch': ...
},
'Quality': {
'Brightness': ...,
'Sharpness': ...
},
'Emotions': [
{
'Type': 'HAPPY'|'SAD'|'ANGRY'|'CONFUSED'|'DISGUSTED'|'SURPRISED'|'CALM'|'UNKNOWN'|'FEAR',
'Confidence': ...
},
],
'Smile': {
'Value': True|False,
'Confidence': ...
}
},
],
'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 analyzed 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 landmark.
X (float) --
The x-coordinate of the landmark expressed as a ratio of the width of the image. The x-coordinate is measured from the left-side of the image. For example, if the image is 700 pixels wide and the x-coordinate of the landmark is at 350 pixels, this value is 0.5.
Y (float) --
The y-coordinate of the landmark expressed as a ratio of the height of the image. The y-coordinate is measured from the top of the image. For example, if the image height is 200 pixels and the y-coordinate of the landmark is at 50 pixels, this value is 0.25.
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.
Emotions (list) --
The emotions that appear to be expressed on the face, and the confidence level in the determination. Valid values include "Happy", "Sad", "Angry", "Confused", "Disgusted", "Surprised", "Calm", "Unknown", and "Fear".
(dict) --
The emotions that appear to be expressed on the face, and the confidence level in the determination. The API is only making a determination of the physical appearance of a person's face. It is not a determination of the person’s internal emotional state and should not be used in such a way. For example, a person pretending to have a sad face might not be sad emotionally.
Type (string) --
Type of emotion detected.
Confidence (float) --
Level of confidence in the determination.
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.
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 analyzed 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 landmark.
X (float) --
The x-coordinate of the landmark expressed as a ratio of the width of the image. The x-coordinate is measured from the left-side of the image. For example, if the image is 700 pixels wide and the x-coordinate of the landmark is at 350 pixels, this value is 0.5.
Y (float) --
The y-coordinate of the landmark expressed as a ratio of the height of the image. The y-coordinate is measured from the top of the image. For example, if the image height is 200 pixels and the y-coordinate of the landmark is at 50 pixels, this value is 0.25.
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.
Emotions (list) --
The emotions that appear to be expressed on the face, and the confidence level in the determination. Valid values include "Happy", "Sad", "Angry", "Confused", "Disgusted", "Surprised", "Calm", "Unknown", and "Fear".
(dict) --
The emotions that appear to be expressed on the face, and the confidence level in the determination. The API is only making a determination of the physical appearance of a person's face. It is not a determination of the person’s internal emotional state and should not be used in such a way. For example, a person pretending to have a sad face might not be sad emotionally.
Type (string) --
Type of emotion detected.
Confidence (float) --
Level of confidence in the determination.
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.
SourceImageOrientationCorrection (string) --
The value of SourceImageOrientationCorrection is always null.
If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.
Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated.
TargetImageOrientationCorrection (string) --
The value of TargetImageOrientationCorrection is always null.
If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.
Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated.
Exceptions
Rekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.InvalidS3ObjectExceptionRekognition.Client.exceptions.ImageTooLargeExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.InvalidImageFormatExceptionExamples
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': {
'...': '...',
},
}
copy_project_version(**kwargs)¶Copies a version of an Amazon Rekognition Custom Labels model from a source project to a destination project. The source and destination projects can be in different AWS accounts but must be in the same AWS Region. You can't copy a model to another AWS service.
To copy a model version to a different AWS account, you need to create a resource-based policy known as a project policy . You attach the project policy to the source project by calling PutProjectPolicy . The project policy gives permission to copy the model version from a trusting AWS account to a trusted account.
For more information creating and attaching a project policy, see Attaching a project policy (SDK) in the Amazon Rekognition Custom Labels Developer Guide .
If you are copying a model version to a project in the same AWS account, you don't need to create a project policy.
Note
To copy a model, the destination project, source project, and source model version must already exist.
Copying a model version takes a while to complete. To get the current status, call DescribeProjectVersions and check the value of Status in the ProjectVersionDescription object. The copy operation has finished when the value of Status is COPYING_COMPLETED .
See also: AWS API Documentation
Request Syntax
response = client.copy_project_version(
SourceProjectArn='string',
SourceProjectVersionArn='string',
DestinationProjectArn='string',
VersionName='string',
OutputConfig={
'S3Bucket': 'string',
'S3KeyPrefix': 'string'
},
Tags={
'string': 'string'
},
KmsKeyId='string'
)
[REQUIRED]
The ARN of the source project in the trusting AWS account.
[REQUIRED]
The ARN of the model version in the source project that you want to copy to a destination project.
[REQUIRED]
The ARN of the project in the trusted AWS account that you want to copy the model version to.
[REQUIRED]
A name for the version of the model that's copied to the destination project.
[REQUIRED]
The S3 bucket and folder location where the training output for the source model version is placed.
The S3 bucket where training output is placed.
The prefix applied to the training output files.
The key-value tags to assign to the model version.
The identifier for your AWS Key Management Service key (AWS KMS key). You can supply the Amazon Resource Name (ARN) of your KMS key, the ID of your KMS key, an alias for your KMS key, or an alias ARN. The key is used to encrypt training results and manifest files written to the output Amazon S3 bucket (OutputConfig ).
If you choose to use your own KMS key, you need the following permissions on the KMS key.
If you don't specify a value for KmsKeyId , images copied into the service are encrypted using a key that AWS owns and manages.
dict
Response Syntax
{
'ProjectVersionArn': 'string'
}
Response Structure
(dict) --
ProjectVersionArn (string) --
The ARN of the copied model version in the destination project.
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.LimitExceededExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ServiceQuotaExceededExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.ResourceInUseExceptioncreate_collection(**kwargs)¶Creates a collection in an AWS Region. You can add faces to the collection using the IndexFaces 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.
When you create a collection, it is associated with the latest version of the face model version.
Note
Collection names are case-sensitive.
This operation requires permissions to perform the rekognition:CreateCollection action. If you want to tag your collection, you also require permission to perform the rekognition:TagResource operation.
See also: AWS API Documentation
Request Syntax
response = client.create_collection(
CollectionId='string',
Tags={
'string': 'string'
}
)
[REQUIRED]
ID for the collection that you are creating.
A set of tags (key-value pairs) that you want to attach to the collection.
dict
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.
Exceptions
Rekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.ResourceAlreadyExistsExceptionRekognition.Client.exceptions.ServiceQuotaExceededExceptionExamples
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_dataset(**kwargs)¶Creates a new Amazon Rekognition Custom Labels dataset. You can create a dataset by using an Amazon Sagemaker format manifest file or by copying an existing Amazon Rekognition Custom Labels dataset.
To create a training dataset for a project, specify train for the value of DatasetType . To create the test dataset for a project, specify test for the value of DatasetType .
The response from CreateDataset is the Amazon Resource Name (ARN) for the dataset. Creating a dataset takes a while to complete. Use DescribeDataset to check the current status. The dataset created successfully if the value of Status is CREATE_COMPLETE .
To check if any non-terminal errors occurred, call ListDatasetEntries and check for the presence of errors lists in the JSON Lines.
Dataset creation fails if a terminal error occurs (Status = CREATE_FAILED ). Currently, you can't access the terminal error information.
For more information, see Creating dataset in the Amazon Rekognition Custom Labels Developer Guide .
This operation requires permissions to perform the rekognition:CreateDataset action. If you want to copy an existing dataset, you also require permission to perform the rekognition:ListDatasetEntries action.
See also: AWS API Documentation
Request Syntax
response = client.create_dataset(
DatasetSource={
'GroundTruthManifest': {
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
},
'DatasetArn': 'string'
},
DatasetType='TRAIN'|'TEST',
ProjectArn='string'
)
The source files for the dataset. You can specify the ARN of an existing dataset or specify the Amazon S3 bucket location of an Amazon Sagemaker format manifest file. If you don't specify datasetSource , an empty dataset is created. To add labeled images to the dataset, You can use the console or call UpdateDatasetEntries .
The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.
Provides the S3 bucket name and object name.
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.
Name of the S3 bucket.
S3 object key name.
If the bucket is versioning enabled, you can specify the object version.
The ARN of an Amazon Rekognition Custom Labels dataset that you want to copy.
[REQUIRED]
The type of the dataset. Specify train to create a training dataset. Specify test to create a test dataset.
[REQUIRED]
The ARN of the Amazon Rekognition Custom Labels project to which you want to asssign the dataset.
dict
Response Syntax
{
'DatasetArn': 'string'
}
Response Structure
(dict) --
DatasetArn (string) --
The ARN of the created Amazon Rekognition Custom Labels dataset.
Exceptions
Rekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.LimitExceededExceptionRekognition.Client.exceptions.InvalidS3ObjectExceptionRekognition.Client.exceptions.ResourceAlreadyExistsExceptionRekognition.Client.exceptions.ResourceNotFoundExceptioncreate_project(**kwargs)¶Creates a new Amazon Rekognition Custom Labels project. A project is a group of resources (datasets, model versions) that you use to create and manage Amazon Rekognition Custom Labels models.
This operation requires permissions to perform the rekognition:CreateProject action.
See also: AWS API Documentation
Request Syntax
response = client.create_project(
ProjectName='string'
)
[REQUIRED]
The name of the project to create.
{
'ProjectArn': 'string'
}
Response Structure
The Amazon Resource Name (ARN) of the new project. You can use the ARN to configure IAM access to the project.
Exceptions
Rekognition.Client.exceptions.ResourceInUseExceptionRekognition.Client.exceptions.LimitExceededExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptioncreate_project_version(**kwargs)¶Creates a new version of a model and begins training. Models are managed as part of an Amazon Rekognition Custom Labels project. The response from CreateProjectVersion is an Amazon Resource Name (ARN) for the version of the model.
Training uses the training and test datasets associated with the project. For more information, see Creating training and test dataset in the Amazon Rekognition Custom Labels Developer Guide .
Note
You can train a model in a project that doesn't have associated datasets by specifying manifest files in the TrainingData and TestingData fields.
If you open the console after training a model with manifest files, Amazon Rekognition Custom Labels creates the datasets for you using the most recent manifest files. You can no longer train a model version for the project by specifying manifest files.
Instead of training with a project without associated datasets, we recommend that you use the manifest files to create training and test datasets for the project.
Training takes a while to complete. You can get the current status by calling DescribeProjectVersions . Training completed successfully if the value of the Status field is TRAINING_COMPLETED .
If training fails, see Debugging a failed model training in the Amazon Rekognition Custom Labels developer guide.
Once training has successfully completed, call DescribeProjectVersions to get the training results and evaluate the model. For more information, see Improving a trained Amazon Rekognition Custom Labels model in the Amazon Rekognition Custom Labels developers guide.
After evaluating the model, you start the model by calling StartProjectVersion .
This operation requires permissions to perform the rekognition:CreateProjectVersion action.
See also: AWS API Documentation
Request Syntax
response = client.create_project_version(
ProjectArn='string',
VersionName='string',
OutputConfig={
'S3Bucket': 'string',
'S3KeyPrefix': 'string'
},
TrainingData={
'Assets': [
{
'GroundTruthManifest': {
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
}
},
]
},
TestingData={
'Assets': [
{
'GroundTruthManifest': {
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
}
},
],
'AutoCreate': True|False
},
Tags={
'string': 'string'
},
KmsKeyId='string'
)
[REQUIRED]
The ARN of the Amazon Rekognition Custom Labels project that manages the model that you want to train.
[REQUIRED]
A name for the version of the model. This value must be unique.
[REQUIRED]
The Amazon S3 bucket location to store the results of training. The S3 bucket can be in any AWS account as long as the caller has s3:PutObject permissions on the S3 bucket.
The S3 bucket where training output is placed.
The prefix applied to the training output files.
Specifies an external manifest that the services uses to train the model. If you specify TrainingData you must also specify TestingData . The project must not have any associated datasets.
A Sagemaker GroundTruth manifest file that contains the training images (assets).
Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.
The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.
Provides the S3 bucket name and object name.
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.
Name of the S3 bucket.
S3 object key name.
If the bucket is versioning enabled, you can specify the object version.
Specifies an external manifest that the service uses to test the model. If you specify TestingData you must also specify TrainingData . The project must not have any associated datasets.
The assets used for testing.
Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.
The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.
Provides the S3 bucket name and object name.
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.
Name of the S3 bucket.
S3 object key name.
If the bucket is versioning enabled, you can specify the object version.
If specified, Amazon Rekognition Custom Labels temporarily splits the training dataset (80%) to create a test dataset (20%) for the training job. After training completes, the test dataset is not stored and the training dataset reverts to its previous size.
A set of tags (key-value pairs) that you want to attach to the model.
The identifier for your AWS Key Management Service key (AWS KMS key). You can supply the Amazon Resource Name (ARN) of your KMS key, the ID of your KMS key, an alias for your KMS key, or an alias ARN. The key is used to encrypt training and test images copied into the service for model training. Your source images are unaffected. The key is also used to encrypt training results and manifest files written to the output Amazon S3 bucket (OutputConfig ).
If you choose to use your own KMS key, you need the following permissions on the KMS key.
If you don't specify a value for KmsKeyId , images copied into the service are encrypted using a key that AWS owns and manages.
dict
Response Syntax
{
'ProjectVersionArn': 'string'
}
Response Structure
(dict) --
ProjectVersionArn (string) --
The ARN of the model version that was created. Use DescribeProjectVersion to get the current status of the training operation.
Exceptions
Rekognition.Client.exceptions.ResourceInUseExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.LimitExceededExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.ServiceQuotaExceededExceptioncreate_stream_processor(**kwargs)¶Creates an Amazon Rekognition stream processor that you can use to detect and recognize faces or to detect labels in a streaming video.
Amazon Rekognition Video is a consumer of live video from Amazon Kinesis Video Streams. There are two different settings for stream processors in Amazon Rekognition: detecting faces and detecting labels.
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. After you have finished analyzing a streaming video, use StopStreamProcessor to stop processing.Input ), Amazon S3 bucket information (Output ), and an Amazon SNS topic ARN (NotificationChannel ). You can also provide a KMS key ID to encrypt the data sent to your Amazon S3 bucket. You specify what you want to detect in ConnectedHomeSettings , such as people, packages and people, or pets, people, and packages. You can also specify where in the frame you want Amazon Rekognition to monitor with RegionsOfInterest . When you run the StartStreamProcessor operation on a label detection stream processor, you input start and stop information to determine the length of the processing time.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 StartStreamProcessor with the Name field.
This operation requires permissions to perform the rekognition:CreateStreamProcessor action. If you want to tag your stream processor, you also require permission to perform the rekognition:TagResource operation.
See also: AWS API Documentation
Request Syntax
response = client.create_stream_processor(
Input={
'KinesisVideoStream': {
'Arn': 'string'
}
},
Output={
'KinesisDataStream': {
'Arn': 'string'
},
'S3Destination': {
'Bucket': 'string',
'KeyPrefix': 'string'
}
},
Name='string',
Settings={
'FaceSearch': {
'CollectionId': 'string',
'FaceMatchThreshold': ...
},
'ConnectedHome': {
'Labels': [
'string',
],
'MinConfidence': ...
}
},
RoleArn='string',
Tags={
'string': 'string'
},
NotificationChannel={
'SNSTopicArn': 'string'
},
KmsKeyId='string',
RegionsOfInterest=[
{
'BoundingBox': {
'Width': ...,
'Height': ...,
'Left': ...,
'Top': ...
},
'Polygon': [
{
'X': ...,
'Y': ...
},
]
},
],
DataSharingPreference={
'OptIn': True|False
}
)
[REQUIRED]
Kinesis video stream stream that provides the source streaming video. If you are using the AWS CLI, the parameter name is StreamProcessorInput . This is required for both face search and label detection stream processors.
The Kinesis video stream input stream for the source streaming video.
ARN of the Kinesis video stream stream that streams the source video.
[REQUIRED]
Kinesis data stream stream or Amazon S3 bucket location to which Amazon Rekognition Video puts the analysis results. If you are using the AWS CLI, the parameter name is StreamProcessorOutput . This must be a S3Destination of an Amazon S3 bucket that you own for a label detection stream processor or a Kinesis data stream ARN for a face search stream processor.
The Amazon Kinesis Data Streams stream to which the Amazon Rekognition stream processor streams the analysis results.
ARN of the output Amazon Kinesis Data Streams stream.
The Amazon S3 bucket location to which Amazon Rekognition publishes the detailed inference results of a video analysis operation.
The name of the Amazon S3 bucket you want to associate with the streaming video project. You must be the owner of the Amazon S3 bucket.
The prefix value of the location within the bucket that you want the information to be published to. For more information, see Using prefixes .
[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 DescribeStreamProcessor . Name is idempotent. This is required for both face search and label detection stream processors.
[REQUIRED]
Input parameters used in a streaming video analyzed by a stream processor. You can use FaceSearch to recognize faces in a streaming video, or you can use ConnectedHome to detect labels.
Face search settings to use on a streaming video.
The ID of a collection that contains faces that you want to search for.
Minimum face match confidence score that must be met to return a result for a recognized face. The default is 80. 0 is the lowest confidence. 100 is the highest confidence. Values between 0 and 100 are accepted, and values lower than 80 are set to 80.
Label detection settings to use on a streaming video. Defining the settings is required in the request parameter for CreateStreamProcessor . Including this setting in the CreateStreamProcessor request enables you to use the stream processor for label detection. You can then select what you want the stream processor to detect, such as people or pets. When the stream processor has started, one notification is sent for each object class specified. For example, if packages and pets are selected, one SNS notification is published the first time a package is detected and one SNS notification is published the first time a pet is detected, as well as an end-of-session summary.
Specifies what you want to detect in the video, such as people, packages, or pets. The current valid labels you can include in this list are: "PERSON", "PET", "PACKAGE", and "ALL".
The minimum confidence required to label an object in the video.
[REQUIRED]
The Amazon Resource Number (ARN) of the IAM role that allows access to the stream processor. The IAM role provides Rekognition read permissions for a Kinesis stream. It also provides write permissions to an Amazon S3 bucket and Amazon Simple Notification Service topic for a label detection stream processor. This is required for both face search and label detection stream processors.
A set of tags (key-value pairs) that you want to attach to the stream processor.
The Amazon Simple Notification Service topic to which Amazon Rekognition publishes the object detection results and completion status of a video analysis operation.
Amazon Rekognition publishes a notification the first time an object of interest or a person is detected in the video stream. For example, if Amazon Rekognition detects a person at second 2, a pet at second 4, and a person again at second 5, Amazon Rekognition sends 2 object class detected notifications, one for a person at second 2 and one for a pet at second 4.
Amazon Rekognition also publishes an an end-of-session notification with a summary when the stream processing session is complete.
The Amazon Resource Number (ARN) of the Amazon Amazon Simple Notification Service topic to which Amazon Rekognition posts the completion status.
Specifies locations in the frames where Amazon Rekognition checks for objects or people. You can specify up to 10 regions of interest, and each region has either a polygon or a bounding box. This is an optional parameter for label detection stream processors and should not be used to create a face search stream processor.
Specifies a location within the frame that Rekognition checks for objects of interest such as text, labels, or faces. It uses a BoundingBox or Polygon to set a region of the screen.
A word, face, or label is included in the region if it is more than half in that region. If there is more than one region, the word, face, or label is compared with all regions of the screen. Any object of interest that is more than half in a region is kept in the results.
The box representing a region of interest on screen.
Width of the bounding box as a ratio of the overall image width.
Height of the bounding box as a ratio of the overall image height.
Left coordinate of the bounding box as a ratio of overall image width.
Top coordinate of the bounding box as a ratio of overall image height.
Specifies a shape made up of up to 10 Point objects to define a region of interest.
The X and Y coordinates of a point on an image or video frame. The X and Y values are ratios of the overall image size or video resolution. For example, if an input image is 700x200 and the values are 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 makes up a Polygon . A Polygon is returned by DetectText and by DetectCustomLabels Polygon represents a fine-grained polygon around a detected item. For more information, see Geometry in the Amazon Rekognition Developer Guide.
The value of the X coordinate for a point on a Polygon .
The value of the Y coordinate for a point on a Polygon .
Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams.
If this option is set to true, you choose to share data with Rekognition to improve model performance.
dict
Response Syntax
{
'StreamProcessorArn': 'string'
}
Response Structure
(dict) --
StreamProcessorArn (string) --
Amazon Resource Number for the newly created stream processor.
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.LimitExceededExceptionRekognition.Client.exceptions.ResourceInUseExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.ServiceQuotaExceededExceptiondelete_collection(**kwargs)¶Deletes the specified collection. Note that this operation removes all faces in the collection. For an example, see Deleting a collection .
This operation requires permissions to perform the rekognition:DeleteCollection action.
See also: AWS API Documentation
Request Syntax
response = client.delete_collection(
CollectionId='string'
)
[REQUIRED]
ID of the collection to delete.
{
'StatusCode': 123
}
Response Structure
HTTP status code that indicates the result of the operation.
Exceptions
Rekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionExamples
This operation deletes a Rekognition collection.
response = client.delete_collection(
CollectionId='myphotos',
)
print(response)
Expected Output:
{
'StatusCode': 200,
'ResponseMetadata': {
'...': '...',
},
}
delete_dataset(**kwargs)¶Deletes an existing Amazon Rekognition Custom Labels dataset. Deleting a dataset might take while. Use DescribeDataset to check the current status. The dataset is still deleting if the value of Status is DELETE_IN_PROGRESS . If you try to access the dataset after it is deleted, you get a ResourceNotFoundException exception.
You can't delete a dataset while it is creating (Status = CREATE_IN_PROGRESS ) or if the dataset is updating (Status = UPDATE_IN_PROGRESS ).
This operation requires permissions to perform the rekognition:DeleteDataset action.
See also: AWS API Documentation
Request Syntax
response = client.delete_dataset(
DatasetArn='string'
)
[REQUIRED]
The ARN of the Amazon Rekognition Custom Labels dataset that you want to delete.
{}
Response Structure
Exceptions
Rekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.LimitExceededExceptionRekognition.Client.exceptions.ResourceInUseExceptionRekognition.Client.exceptions.ResourceNotFoundExceptiondelete_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',
]
)
[REQUIRED]
Collection from which to remove the specific faces.
[REQUIRED]
An array of face IDs to delete.
dict
Response Syntax
{
'DeletedFaces': [
'string',
]
}
Response Structure
(dict) --
DeletedFaces (list) --
An array of strings (face IDs) of the faces that were deleted.
Exceptions
Rekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionExamples
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_project(**kwargs)¶Deletes an Amazon Rekognition Custom Labels project. To delete a project you must first delete all models associated with the project. To delete a model, see DeleteProjectVersion .
DeleteProjectis an asynchronous operation. To check if the project is deleted, call DescribeProjects . The project is deleted when the project no longer appears in the response. Be aware that deleting a given project will also delete anyProjectPoliciesassociated with that project.
This operation requires permissions to perform the rekognition:DeleteProject action.
See also: AWS API Documentation
Request Syntax
response = client.delete_project(
ProjectArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the project that you want to delete.
{
'Status': 'CREATING'|'CREATED'|'DELETING'
}
Response Structure
The current status of the delete project operation.
Exceptions
Rekognition.Client.exceptions.ResourceInUseExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptiondelete_project_policy(**kwargs)¶Deletes an existing project policy.
To get a list of project policies attached to a project, call ListProjectPolicies . To attach a project policy to a project, call PutProjectPolicy .
See also: AWS API Documentation
Request Syntax
response = client.delete_project_policy(
ProjectArn='string',
PolicyName='string',
PolicyRevisionId='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the project that the project policy you want to delete is attached to.
[REQUIRED]
The name of the policy that you want to delete.
dict
Response Syntax
{}
Response Structure
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.InvalidPolicyRevisionIdExceptiondelete_project_version(**kwargs)¶Deletes an Amazon Rekognition Custom Labels model.
You can't delete a model if it is running or if it is training. To check the status of a model, use the Status field returned from DescribeProjectVersions . To stop a running model call StopProjectVersion . If the model is training, wait until it finishes.
This operation requires permissions to perform the rekognition:DeleteProjectVersion action.
See also: AWS API Documentation
Request Syntax
response = client.delete_project_version(
ProjectVersionArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the model version that you want to delete.
{
'Status': 'TRAINING_IN_PROGRESS'|'TRAINING_COMPLETED'|'TRAINING_FAILED'|'STARTING'|'RUNNING'|'FAILED'|'STOPPING'|'STOPPED'|'DELETING'|'COPYING_IN_PROGRESS'|'COPYING_COMPLETED'|'COPYING_FAILED'
}
Response Structure
The status of the deletion operation.
Exceptions
Rekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ResourceInUseExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptiondelete_stream_processor(**kwargs)¶Deletes the stream processor identified by Name . You assign the value for Name when you create the stream processor with CreateStreamProcessor . 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'
)
[REQUIRED]
The name of the stream processor you want to delete.
{}
Response Structure
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ResourceInUseExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptiondescribe_collection(**kwargs)¶Describes the specified collection. You can use DescribeCollection to get information, such as the number of faces indexed into a collection and the version of the model used by the collection for face detection.
For more information, see Describing a Collection in the Amazon Rekognition Developer Guide.
See also: AWS API Documentation
Request Syntax
response = client.describe_collection(
CollectionId='string'
)
[REQUIRED]
The ID of the collection to describe.
{
'FaceCount': 123,
'FaceModelVersion': 'string',
'CollectionARN': 'string',
'CreationTimestamp': datetime(2015, 1, 1)
}
Response Structure
The number of faces that are indexed into the collection. To index faces into a collection, use IndexFaces .
The version of the face model that's used by the collection for face detection.
For more information, see Model versioning in the Amazon Rekognition Developer Guide.
The Amazon Resource Name (ARN) of the collection.
The number of milliseconds since the Unix epoch time until the creation of the collection. The Unix epoch time is 00:00:00 Coordinated Universal Time (UTC), Thursday, 1 January 1970.
Exceptions
Rekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.ResourceNotFoundExceptiondescribe_dataset(**kwargs)¶Describes an Amazon Rekognition Custom Labels dataset. You can get information such as the current status of a dataset and statistics about the images and labels in a dataset.
This operation requires permissions to perform the rekognition:DescribeDataset action.
See also: AWS API Documentation
Request Syntax
response = client.describe_dataset(
DatasetArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the dataset that you want to describe.
{
'DatasetDescription': {
'CreationTimestamp': datetime(2015, 1, 1),
'LastUpdatedTimestamp': datetime(2015, 1, 1),
'Status': 'CREATE_IN_PROGRESS'|'CREATE_COMPLETE'|'CREATE_FAILED'|'UPDATE_IN_PROGRESS'|'UPDATE_COMPLETE'|'UPDATE_FAILED'|'DELETE_IN_PROGRESS',
'StatusMessage': 'string',
'StatusMessageCode': 'SUCCESS'|'SERVICE_ERROR'|'CLIENT_ERROR',
'DatasetStats': {
'LabeledEntries': 123,
'TotalEntries': 123,
'TotalLabels': 123,
'ErrorEntries': 123
}
}
}
Response Structure
The description for the dataset.
The Unix timestamp for the time and date that the dataset was created.
The Unix timestamp for the date and time that the dataset was last updated.
The status of the dataset.
The status message for the dataset.
The status message code for the dataset operation. If a service error occurs, try the API call again later. If a client error occurs, check the input parameters to the dataset API call that failed.
The status message code for the dataset.
The total number of images in the dataset that have labels.
The total number of images in the dataset.
The total number of labels declared in the dataset.
The total number of entries that contain at least one error.
Exceptions
Rekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.ResourceNotFoundExceptiondescribe_project_versions(**kwargs)¶Lists and describes the versions of a model in an Amazon Rekognition Custom Labels project. You can specify up to 10 model versions in ProjectVersionArns . If you don't specify a value, descriptions for all model versions in the project are returned.
This operation requires permissions to perform the rekognition:DescribeProjectVersions action.
See also: AWS API Documentation
Request Syntax
response = client.describe_project_versions(
ProjectArn='string',
VersionNames=[
'string',
],
NextToken='string',
MaxResults=123
)
[REQUIRED]
The Amazon Resource Name (ARN) of the project that contains the models you want to describe.
A list of model version names that you want to describe. You can add up to 10 model version names to the list. If you don't specify a value, all model descriptions are returned. A version name is part of a model (ProjectVersion) ARN. For example, my-model.2020-01-21T09.10.15 is the version name in the following ARN. arn:aws:rekognition:us-east-1:123456789012:project/getting-started/version/*my-model.2020-01-21T09.10.15* /1234567890123 .
dict
Response Syntax
{
'ProjectVersionDescriptions': [
{
'ProjectVersionArn': 'string',
'CreationTimestamp': datetime(2015, 1, 1),
'MinInferenceUnits': 123,
'Status': 'TRAINING_IN_PROGRESS'|'TRAINING_COMPLETED'|'TRAINING_FAILED'|'STARTING'|'RUNNING'|'FAILED'|'STOPPING'|'STOPPED'|'DELETING'|'COPYING_IN_PROGRESS'|'COPYING_COMPLETED'|'COPYING_FAILED',
'StatusMessage': 'string',
'BillableTrainingTimeInSeconds': 123,
'TrainingEndTimestamp': datetime(2015, 1, 1),
'OutputConfig': {
'S3Bucket': 'string',
'S3KeyPrefix': 'string'
},
'TrainingDataResult': {
'Input': {
'Assets': [
{
'GroundTruthManifest': {
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
}
},
]
},
'Output': {
'Assets': [
{
'GroundTruthManifest': {
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
}
},
]
},
'Validation': {
'Assets': [
{
'GroundTruthManifest': {
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
}
},
]
}
},
'TestingDataResult': {
'Input': {
'Assets': [
{
'GroundTruthManifest': {
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
}
},
],
'AutoCreate': True|False
},
'Output': {
'Assets': [
{
'GroundTruthManifest': {
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
}
},
],
'AutoCreate': True|False
},
'Validation': {
'Assets': [
{
'GroundTruthManifest': {
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
}
},
]
}
},
'EvaluationResult': {
'F1Score': ...,
'Summary': {
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
}
},
'ManifestSummary': {
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
},
'KmsKeyId': 'string',
'MaxInferenceUnits': 123,
'SourceProjectVersionArn': 'string'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
ProjectVersionDescriptions (list) --
A list of model descriptions. The list is sorted by the creation date and time of the model versions, latest to earliest.
(dict) --
A description of a version of an Amazon Rekognition Custom Labels model.
ProjectVersionArn (string) --
The Amazon Resource Name (ARN) of the model version.
CreationTimestamp (datetime) --
The Unix datetime for the date and time that training started.
MinInferenceUnits (integer) --
The minimum number of inference units used by the model. For more information, see StartProjectVersion .
Status (string) --
The current status of the model version.
StatusMessage (string) --
A descriptive message for an error or warning that occurred.
BillableTrainingTimeInSeconds (integer) --
The duration, in seconds, that you were billed for a successful training of the model version. This value is only returned if the model version has been successfully trained.
TrainingEndTimestamp (datetime) --
The Unix date and time that training of the model ended.
OutputConfig (dict) --
The location where training results are saved.
S3Bucket (string) --
The S3 bucket where training output is placed.
S3KeyPrefix (string) --
The prefix applied to the training output files.
TrainingDataResult (dict) --
Contains information about the training results.
Input (dict) --
The training assets that you supplied for training.
Assets (list) --
A Sagemaker GroundTruth manifest file that contains the training images (assets).
(dict) --
Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.
GroundTruthManifest (dict) --
The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.
S3Object (dict) --
Provides the S3 bucket name and object name.
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.
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.
Output (dict) --
The images (assets) that were actually trained by Amazon Rekognition Custom Labels.
Assets (list) --
A Sagemaker GroundTruth manifest file that contains the training images (assets).
(dict) --
Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.
GroundTruthManifest (dict) --
The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.
S3Object (dict) --
Provides the S3 bucket name and object name.
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.
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.
Validation (dict) --
The location of the data validation manifest. The data validation manifest is created for the training dataset during model training.
Assets (list) --
The assets that comprise the validation data.
(dict) --
Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.
GroundTruthManifest (dict) --
The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.
S3Object (dict) --
Provides the S3 bucket name and object name.
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.
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.
TestingDataResult (dict) --
Contains information about the testing results.
Input (dict) --
The testing dataset that was supplied for training.
Assets (list) --
The assets used for testing.
(dict) --
Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.
GroundTruthManifest (dict) --
The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.
S3Object (dict) --
Provides the S3 bucket name and object name.
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.
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.
AutoCreate (boolean) --
If specified, Amazon Rekognition Custom Labels temporarily splits the training dataset (80%) to create a test dataset (20%) for the training job. After training completes, the test dataset is not stored and the training dataset reverts to its previous size.
Output (dict) --
The subset of the dataset that was actually tested. Some images (assets) might not be tested due to file formatting and other issues.
Assets (list) --
The assets used for testing.
(dict) --
Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.
GroundTruthManifest (dict) --
The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.
S3Object (dict) --
Provides the S3 bucket name and object name.
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.
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.
AutoCreate (boolean) --
If specified, Amazon Rekognition Custom Labels temporarily splits the training dataset (80%) to create a test dataset (20%) for the training job. After training completes, the test dataset is not stored and the training dataset reverts to its previous size.
Validation (dict) --
The location of the data validation manifest. The data validation manifest is created for the test dataset during model training.
Assets (list) --
The assets that comprise the validation data.
(dict) --
Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.
GroundTruthManifest (dict) --
The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.
S3Object (dict) --
Provides the S3 bucket name and object name.
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.
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.
EvaluationResult (dict) --
The training results. EvaluationResult is only returned if training is successful.
F1Score (float) --
The F1 score for the evaluation of all labels. The F1 score metric evaluates the overall precision and recall performance of the model as a single value. A higher value indicates better precision and recall performance. A lower score indicates that precision, recall, or both are performing poorly.
Summary (dict) --
The S3 bucket that contains the training summary.
S3Object (dict) --
Provides the S3 bucket name and object name.
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.
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.
ManifestSummary (dict) --
The location of the summary manifest. The summary manifest provides aggregate data validation results for the training and test datasets.
S3Object (dict) --
Provides the S3 bucket name and object name.
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.
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.
KmsKeyId (string) --
The identifer for the AWS Key Management Service key (AWS KMS key) that was used to encrypt the model during training.
MaxInferenceUnits (integer) --
The maximum number of inference units Amazon Rekognition Custom Labels uses to auto-scale the model. For more information, see StartProjectVersion .
SourceProjectVersionArn (string) --
If the model version was copied from a different project, SourceProjectVersionArn contains the ARN of the source model version.
NextToken (string) --
If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.
Exceptions
Rekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.InvalidPaginationTokenExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptiondescribe_projects(**kwargs)¶Gets information about your Amazon Rekognition Custom Labels projects.
This operation requires permissions to perform the rekognition:DescribeProjects action.
See also: AWS API Documentation
Request Syntax
response = client.describe_projects(
NextToken='string',
MaxResults=123,
ProjectNames=[
'string',
]
)
A list of the projects that you want Amazon Rekognition Custom Labels to describe. If you don't specify a value, the response includes descriptions for all the projects in your AWS account.
dict
Response Syntax
{
'ProjectDescriptions': [
{
'ProjectArn': 'string',
'CreationTimestamp': datetime(2015, 1, 1),
'Status': 'CREATING'|'CREATED'|'DELETING',
'Datasets': [
{
'CreationTimestamp': datetime(2015, 1, 1),
'DatasetType': 'TRAIN'|'TEST',
'DatasetArn': 'string',
'Status': 'CREATE_IN_PROGRESS'|'CREATE_COMPLETE'|'CREATE_FAILED'|'UPDATE_IN_PROGRESS'|'UPDATE_COMPLETE'|'UPDATE_FAILED'|'DELETE_IN_PROGRESS',
'StatusMessage': 'string',
'StatusMessageCode': 'SUCCESS'|'SERVICE_ERROR'|'CLIENT_ERROR'
},
]
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
ProjectDescriptions (list) --
A list of project descriptions. The list is sorted by the date and time the projects are created.
(dict) --
A description of an Amazon Rekognition Custom Labels project. For more information, see DescribeProjects .
ProjectArn (string) --
The Amazon Resource Name (ARN) of the project.
CreationTimestamp (datetime) --
The Unix timestamp for the date and time that the project was created.
Status (string) --
The current status of the project.
Datasets (list) --
Information about the training and test datasets in the project.
(dict) --
Summary information for an Amazon Rekognition Custom Labels dataset. For more information, see ProjectDescription .
CreationTimestamp (datetime) --
The Unix timestamp for the date and time that the dataset was created.
DatasetType (string) --
The type of the dataset.
DatasetArn (string) --
The Amazon Resource Name (ARN) for the dataset.
Status (string) --
The status for the dataset.
StatusMessage (string) --
The status message for the dataset.
StatusMessageCode (string) --
The status message code for the dataset operation. If a service error occurs, try the API call again later. If a client error occurs, check the input parameters to the dataset API call that failed.
NextToken (string) --
If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.
Exceptions
Rekognition.Client.exceptions.InvalidPaginationTokenExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptiondescribe_stream_processor(**kwargs)¶Provides information about a stream processor created by CreateStreamProcessor . 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'
)
[REQUIRED]
Name of the stream processor for which you want information.
{
'Name': 'string',
'StreamProcessorArn': 'string',
'Status': 'STOPPED'|'STARTING'|'RUNNING'|'FAILED'|'STOPPING'|'UPDATING',
'StatusMessage': 'string',
'CreationTimestamp': datetime(2015, 1, 1),
'LastUpdateTimestamp': datetime(2015, 1, 1),
'Input': {
'KinesisVideoStream': {
'Arn': 'string'
}
},
'Output': {
'KinesisDataStream': {
'Arn': 'string'
},
'S3Destination': {
'Bucket': 'string',
'KeyPrefix': 'string'
}
},
'RoleArn': 'string',
'Settings': {
'FaceSearch': {
'CollectionId': 'string',
'FaceMatchThreshold': ...
},
'ConnectedHome': {
'Labels': [
'string',
],
'MinConfidence': ...
}
},
'NotificationChannel': {
'SNSTopicArn': 'string'
},
'KmsKeyId': 'string',
'RegionsOfInterest': [
{
'BoundingBox': {
'Width': ...,
'Height': ...,
'Left': ...,
'Top': ...
},
'Polygon': [
{
'X': ...,
'Y': ...
},
]
},
],
'DataSharingPreference': {
'OptIn': True|False
}
}
Response Structure
Name of the stream processor.
ARN of the stream processor.
Current status of the stream processor.
Detailed status message about the stream processor.
Date and time the stream processor was created
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.
Kinesis video stream that provides the source streaming video.
The Kinesis video stream input stream for the source streaming video.
ARN of the Kinesis video stream stream that streams the source video.
Kinesis data stream to which Amazon Rekognition Video puts the analysis results.
The Amazon Kinesis Data Streams stream to which the Amazon Rekognition stream processor streams the analysis results.
ARN of the output Amazon Kinesis Data Streams stream.
The Amazon S3 bucket location to which Amazon Rekognition publishes the detailed inference results of a video analysis operation.
The name of the Amazon S3 bucket you want to associate with the streaming video project. You must be the owner of the Amazon S3 bucket.
The prefix value of the location within the bucket that you want the information to be published to. For more information, see Using prefixes .
ARN of the IAM role that allows access to the stream processor.
Input parameters used in a streaming video analyzed by a stream processor. You can use FaceSearch to recognize faces in a streaming video, or you can use ConnectedHome to detect labels.
Face search settings to use on a streaming video.
The ID of a collection that contains faces that you want to search for.
Minimum face match confidence score that must be met to return a result for a recognized face. The default is 80. 0 is the lowest confidence. 100 is the highest confidence. Values between 0 and 100 are accepted, and values lower than 80 are set to 80.
Label detection settings to use on a streaming video. Defining the settings is required in the request parameter for CreateStreamProcessor . Including this setting in the CreateStreamProcessor request enables you to use the stream processor for label detection. You can then select what you want the stream processor to detect, such as people or pets. When the stream processor has started, one notification is sent for each object class specified. For example, if packages and pets are selected, one SNS notification is published the first time a package is detected and one SNS notification is published the first time a pet is detected, as well as an end-of-session summary.
Specifies what you want to detect in the video, such as people, packages, or pets. The current valid labels you can include in this list are: "PERSON", "PET", "PACKAGE", and "ALL".
The minimum confidence required to label an object in the video.
The Amazon Simple Notification Service topic to which Amazon Rekognition publishes the object detection results and completion status of a video analysis operation.
Amazon Rekognition publishes a notification the first time an object of interest or a person is detected in the video stream. For example, if Amazon Rekognition detects a person at second 2, a pet at second 4, and a person again at second 5, Amazon Rekognition sends 2 object class detected notifications, one for a person at second 2 and one for a pet at second 4.
Amazon Rekognition also publishes an an end-of-session notification with a summary when the stream processing session is complete.
The Amazon Resource Number (ARN) of the Amazon Amazon Simple Notification Service topic to which Amazon Rekognition posts the completion status.
The identifier for your AWS Key Management Service key (AWS KMS key). This is an optional parameter for label detection stream processors.
Specifies locations in the frames where Amazon Rekognition checks for objects or people. This is an optional parameter for label detection stream processors.
Specifies a location within the frame that Rekognition checks for objects of interest such as text, labels, or faces. It uses a BoundingBox or Polygon to set a region of the screen.
A word, face, or label is included in the region if it is more than half in that region. If there is more than one region, the word, face, or label is compared with all regions of the screen. Any object of interest that is more than half in a region is kept in the results.
The box representing a region of interest on screen.
Width of the bounding box as a ratio of the overall image width.
Height of the bounding box as a ratio of the overall image height.
Left coordinate of the bounding box as a ratio of overall image width.
Top coordinate of the bounding box as a ratio of overall image height.
Specifies a shape made up of up to 10 Point objects to define a region of interest.
The X and Y coordinates of a point on an image or video frame. The X and Y values are ratios of the overall image size or video resolution. For example, if an input image is 700x200 and the values are 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 makes up a Polygon . A Polygon is returned by DetectText and by DetectCustomLabels Polygon represents a fine-grained polygon around a detected item. For more information, see Geometry in the Amazon Rekognition Developer Guide.
The value of the X coordinate for a point on a Polygon .
The value of the Y coordinate for a point on a Polygon .
Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams.
If this option is set to true, you choose to share data with Rekognition to improve model performance.
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptiondetect_custom_labels(**kwargs)¶Detects custom labels in a supplied image by using an Amazon Rekognition Custom Labels model.
You specify which version of a model version to use by using the ProjectVersionArn input parameter.
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 AWS 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 that the model version detects on an image, the API returns a (CustomLabel ) object in an array (CustomLabels ). Each CustomLabel object provides the label name (Name ), the level of confidence that the image contains the object (Confidence ), and object location information, if it exists, for the label on the image (Geometry ).
To filter labels that are returned, specify a value for MinConfidence . DetectCustomLabelsLabels only returns labels with a confidence that's higher than the specified value. The value of MinConfidence maps to the assumed threshold values created during training. For more information, see Assumed threshold in the Amazon Rekognition Custom Labels Developer Guide. Amazon Rekognition Custom Labels metrics expresses an assumed threshold as a floating point value between 0-1. The range of MinConfidence normalizes the threshold value to a percentage value (0-100). Confidence responses from DetectCustomLabels are also returned as a percentage. You can use MinConfidence to change the precision and recall or your model. For more information, see Analyzing an image in the Amazon Rekognition Custom Labels Developer Guide.
If you don't specify a value for MinConfidence , DetectCustomLabels returns labels based on the assumed threshold of each label.
This is a stateless API operation. That is, the operation does not persist any data.
This operation requires permissions to perform the rekognition:DetectCustomLabels action.
For more information, see Analyzing an image in the Amazon Rekognition Custom Labels Developer Guide.
See also: AWS API Documentation
Request Syntax
response = client.detect_custom_labels(
ProjectVersionArn='string',
Image={
'Bytes': b'bytes',
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
},
MaxResults=123,
MinConfidence=...
)
[REQUIRED]
The ARN of the model version that you want to use.
[REQUIRED]
Provides the input image either as bytes or an S3 object.
You pass image bytes to an Amazon Rekognition API operation by using the Bytes property. For example, you would use the Bytes property to pass an image loaded from a local file system. Image bytes passed by using the Bytes property must be base64-encoded. Your code may not need to encode image bytes if you are using an AWS SDK to call Amazon Rekognition API operations.
For more information, see Analyzing an Image Loaded from a Local File System in the Amazon Rekognition Developer Guide.
You pass images stored in an S3 bucket to an Amazon Rekognition API operation by using the S3Object property. Images stored in an S3 bucket do not need to be base64-encoded.
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes using the Bytes property is not supported. You must first upload the image to an Amazon S3 bucket and then call the operation using the S3Object property.
For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.
Blob of image bytes up to 5 MBs.
Identifies an S3 object as the image source.
Name of the S3 bucket.
S3 object key name.
If the bucket is versioning enabled, you can specify the object version.
DetectCustomLabels doesn't return any labels with a confidence value that's lower than this specified value. If you specify a value of 0, DetectCustomLabels returns all labels, regardless of the assumed threshold applied to each label. If you don't specify a value for MinConfidence , DetectCustomLabels returns labels based on the assumed threshold of each label.dict
Response Syntax
{
'CustomLabels': [
{
'Name': 'string',
'Confidence': ...,
'Geometry': {
'BoundingBox': {
'Width': ...,
'Height': ...,
'Left': ...,
'Top': ...
},
'Polygon': [
{
'X': ...,
'Y': ...
},
]
}
},
]
}
Response Structure
(dict) --
CustomLabels (list) --
An array of custom labels detected in the input image.
(dict) --
A custom label detected in an image by a call to DetectCustomLabels .
Name (string) --
The name of the custom label.
Confidence (float) --
The confidence that the model has in the detection of the custom label. The range is 0-100. A higher value indicates a higher confidence.
Geometry (dict) --
The location of the detected object on the image that corresponds to the custom label. Includes an axis aligned coarse bounding box surrounding the object and a finer grain polygon for more accurate spatial information.
BoundingBox (dict) --
An axis-aligned coarse representation of the detected item'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 item.
(dict) --
The X and Y coordinates of a point on an image or video frame. The X and Y values are ratios of the overall image size or video resolution. For example, if an input image is 700x200 and the values are 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 makes up a Polygon . A Polygon is returned by DetectText and by DetectCustomLabels Polygon represents a fine-grained polygon around a detected item. For more information, see Geometry in the Amazon Rekognition Developer Guide.
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 .
Exceptions
Rekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ResourceNotReadyExceptionRekognition.Client.exceptions.InvalidS3ObjectExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.ImageTooLargeExceptionRekognition.Client.exceptions.LimitExceededExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.InvalidImageFormatExceptiondetect_faces(**kwargs)¶Detects faces within an image that is provided as input.
DetectFacesdetects the 100 largest faces in the image. For each face detected, the operation returns face details. These details include 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), presence of beard, sunglasses, and so on.
The face-detection algorithm is most effective on frontal faces. For non-frontal or obscured faces, the algorithm might 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 AWS 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.
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',
]
)
[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.
If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.
Blob of image bytes up to 5 MBs.
Identifies an S3 object as the image source.
Name of the S3 bucket.
S3 object key name.
If the bucket is versioning enabled, you can specify the object version.
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 takes 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).
dict
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'|'FEAR',
'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'|'upperJawlineLeft'|'midJawlineLeft'|'chinBottom'|'midJawlineRight'|'upperJawlineRight',
'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.
A FaceDetail object contains either the default facial attributes or all facial attributes. The default attributes are BoundingBox , Confidence , Landmarks , Pose , and Quality .
GetFaceDetection is the only Amazon Rekognition Video stored video operation that can return a
FaceDetailobject with all attributes. To specify which attributes to return, use theFaceAttributesinput parameter for StartFaceDetection . The following Amazon Rekognition Video operations return only the default attributes. The corresponding Start operations don't have aFaceAttributesinput parameter.
The Amazon Rekognition Image DetectFaces and IndexFaces operations can return all facial attributes. To specify which attributes to return, use the Attributes input parameter for DetectFaces . For IndexFaces , use the DetectAttributes input parameter.
BoundingBox (dict) --
Bounding box of the face. Default attribute.
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) --
The predicted gender of a detected face.
Value (string) --
The predicted gender of the face.
Confidence (float) --
Level of confidence in the prediction.
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 that appear to be expressed on the face, and the confidence level in the determination. The API is only making a determination of the physical appearance of a person's face. It is not a determination of the person’s internal emotional state and should not be used in such a way. For example, a person pretending to have a sad face might not be sad emotionally.
(dict) --
The emotions that appear to be expressed on the face, and the confidence level in the determination. The API is only making a determination of the physical appearance of a person's face. It is not a determination of the person’s internal emotional state and should not be used in such a way. For example, a person pretending to have a sad face might not be sad emotionally.
Type (string) --
Type of emotion detected.
Confidence (float) --
Level of confidence in the determination.
Landmarks (list) --
Indicates the location of landmarks on the face. Default attribute.
(dict) --
Indicates the location of the landmark on the face.
Type (string) --
Type of landmark.
X (float) --
The x-coordinate of the landmark expressed as a ratio of the width of the image. The x-coordinate is measured from the left-side of the image. For example, if the image is 700 pixels wide and the x-coordinate of the landmark is at 350 pixels, this value is 0.5.
Y (float) --
The y-coordinate of the landmark expressed as a ratio of the height of the image. The y-coordinate is measured from the top of the image. For example, if the image height is 200 pixels and the y-coordinate of the landmark is at 50 pixels, this value is 0.25.
Pose (dict) --
Indicates the pose of the face as determined by its pitch, roll, and yaw. Default attribute.
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. Default attribute.
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). Default attribute.
OrientationCorrection (string) --
The value of OrientationCorrection is always null.
If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.
Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated.
Exceptions
Rekognition.Client.exceptions.InvalidS3ObjectExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.ImageTooLargeExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.InvalidImageFormatExceptionExamples
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': 'eyeLeft',
'X': 0.6394737362861633,
'Y': 0.40819624066352844,
},
{
'Type': 'eyeRight',
'X': 0.7266660928726196,
'Y': 0.41039225459098816,
},
{
'Type': 'eyeRight',
'X': 0.6912462115287781,
'Y': 0.44240960478782654,
},
{
'Type': 'mouthDown',
'X': 0.6306198239326477,
'Y': 0.46700039505958557,
},
{
'Type': 'mouthUp',
'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 Analyzing images stored in an Amazon S3 bucket in the Amazon Rekognition Developer Guide.
Note
DetectLabels does not support the detection of activities. However, activity detection is supported for label detection in videos. For more information, see StartLabelDetection in the Amazon Rekognition Developer Guide.
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 AWS 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 includes 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 55%. 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.
DetectLabels returns bounding boxes for instances of common object labels in an array of Instance objects. An Instance object contains a BoundingBox object, for the location of the label on the image. It also includes the confidence by which the bounding box was detected.
DetectLabels also returns a hierarchical taxonomy of detected labels. For example, a detected car might be assigned the label car . The label car has two parent labels: Vehicle (its parent) and Transportation (its grandparent). The response returns the entire list of ancestors for a label. Each ancestor is a unique label in the response. In the previous example, Car , Vehicle , and Transportation are returned as unique labels in the response.
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=...
)
[REQUIRED]
The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. Images stored in an S3 Bucket do not need to be base64-encoded.
If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.
Blob of image bytes up to 5 MBs.
Identifies an S3 object as the image source.
Name of the S3 bucket.
S3 object key name.
If the bucket is versioning enabled, you can specify the object version.
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 55 percent.
dict
Response Syntax
{
'Labels': [
{
'Name': 'string',
'Confidence': ...,
'Instances': [
{
'BoundingBox': {
'Width': ...,
'Height': ...,
'Left': ...,
'Top': ...
},
'Confidence': ...
},
],
'Parents': [
{
'Name': 'string'
},
]
},
],
'OrientationCorrection': 'ROTATE_0'|'ROTATE_90'|'ROTATE_180'|'ROTATE_270',
'LabelModelVersion': 'string'
}
Response Structure
(dict) --
Labels (list) --
An array of labels for the real-world objects detected.
(dict) --
Structure containing details about the detected label, including the name, detected instances, parent labels, and level of confidence.
Name (string) --
The name (label) of the object or scene.
Confidence (float) --
Level of confidence.
Instances (list) --
If Label represents an object, Instances contains the bounding boxes for each instance of the detected object. Bounding boxes are returned for common object labels such as people, cars, furniture, apparel or pets.
(dict) --
An instance of a label returned by Amazon Rekognition Image ( DetectLabels ) or by Amazon Rekognition Video ( GetLabelDetection ).
BoundingBox (dict) --
The position of the label instance 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.
Confidence (float) --
The confidence that Amazon Rekognition has in the accuracy of the bounding box.
Parents (list) --
The parent labels for a label. The response includes all ancestor labels.
(dict) --
A parent label for a label. A label can have 0, 1, or more parents.
Name (string) --
The name of the parent label.
OrientationCorrection (string) --
The value of OrientationCorrection is always null.
If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.
Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated.
LabelModelVersion (string) --
Version number of the label detection model that was used to detect labels.
Exceptions
Rekognition.Client.exceptions.InvalidS3ObjectExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.ImageTooLargeExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.InvalidImageFormatExceptionExamples
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 unsafe 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 Detecting Unsafe Content in the Amazon Rekognition Developer Guide.
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 AWS 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=...,
HumanLoopConfig={
'HumanLoopName': 'string',
'FlowDefinitionArn': 'string',
'DataAttributes': {
'ContentClassifiers': [
'FreeOfPersonallyIdentifiableInformation'|'FreeOfAdultContent',
]
}
}
)
[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.
If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.
Blob of image bytes up to 5 MBs.
Identifies an S3 object as the image source.
Name of the S3 bucket.
S3 object key name.
If the bucket is versioning enabled, you can specify the object version.
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.
Sets up the configuration for human evaluation, including the FlowDefinition the image will be sent to.
The name of the human review used for this image. This should be kept unique within a region.
The Amazon Resource Name (ARN) of the flow definition. You can create a flow definition by using the Amazon Sagemaker CreateFlowDefinition Operation.
Sets attributes of the input data.
Sets whether the input image is free of personally identifiable information.
dict
Response Syntax
{
'ModerationLabels': [
{
'Confidence': ...,
'Name': 'string',
'ParentName': 'string'
},
],
'ModerationModelVersion': 'string',
'HumanLoopActivationOutput': {
'HumanLoopArn': 'string',
'HumanLoopActivationReasons': [
'string',
],
'HumanLoopActivationConditionsEvaluationResults': 'string'
}
}
Response Structure
(dict) --
ModerationLabels (list) --
Array of detected Moderation labels and the time, in milliseconds from the start of the video, they were detected.
(dict) --
Provides information about a single type of inappropriate, unwanted, or offensive content found in an image or video. Each type of moderated content has a label within a hierarchical taxonomy. For more information, see Content moderation in the Amazon Rekognition Developer Guide.
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 unsafe 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 "" .
ModerationModelVersion (string) --
Version number of the moderation detection model that was used to detect unsafe content.
HumanLoopActivationOutput (dict) --
Shows the results of the human in the loop evaluation.
HumanLoopArn (string) --
The Amazon Resource Name (ARN) of the HumanLoop created.
HumanLoopActivationReasons (list) --
Shows if and why human review was needed.
HumanLoopActivationConditionsEvaluationResults (string) --
Shows the result of condition evaluations, including those conditions which activated a human review.
Exceptions
Rekognition.Client.exceptions.InvalidS3ObjectExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.ImageTooLargeExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.InvalidImageFormatExceptionRekognition.Client.exceptions.HumanLoopQuotaExceededExceptiondetect_protective_equipment(**kwargs)¶Detects Personal Protective Equipment (PPE) worn by people detected in an image. Amazon Rekognition can detect the following types of PPE.
You pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. The image must be either a PNG or JPG formatted file.
DetectProtectiveEquipmentdetects PPE worn by up to 15 persons detected in an image.
For each person detected in the image the API returns an array of body parts (face, head, left-hand, right-hand). For each body part, an array of detected items of PPE is returned, including an indicator of whether or not the PPE covers the body part. The API returns the confidence it has in each detection (person, PPE, body part and body part coverage). It also returns a bounding box ( BoundingBox ) for each detected person and each detected item of PPE.
You can optionally request a summary of detected PPE items with the SummarizationAttributes input parameter. The summary provides the following information.
This is a stateless API operation. That is, the operation does not persist any data.
This operation requires permissions to perform the rekognition:DetectProtectiveEquipment action.
See also: AWS API Documentation
Request Syntax
response = client.detect_protective_equipment(
Image={
'Bytes': b'bytes',
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
},
SummarizationAttributes={
'MinConfidence': ...,
'RequiredEquipmentTypes': [
'FACE_COVER'|'HAND_COVER'|'HEAD_COVER',
]
}
)
[REQUIRED]
The image in which you want to detect PPE on detected persons. The image can be passed as image bytes or you can reference an image stored in an Amazon S3 bucket.
Blob of image bytes up to 5 MBs.
Identifies an S3 object as the image source.
Name of the S3 bucket.
S3 object key name.
If the bucket is versioning enabled, you can specify the object version.
An array of PPE types that you want to summarize.
The minimum confidence level for which you want summary information. The confidence level applies to person detection, body part detection, equipment detection, and body part coverage. Amazon Rekognition doesn't return summary information with a confidence than this specified value. There isn't a default value.
Specify a MinConfidence value that is between 50-100% as DetectProtectiveEquipment returns predictions only where the detection confidence is between 50% - 100%. If you specify a value that is less than 50%, the results are the same specifying a value of 50%.
An array of personal protective equipment types for which you want summary information. If a person is detected wearing a required requipment type, the person's ID is added to the PersonsWithRequiredEquipment array field returned in ProtectiveEquipmentSummary by DetectProtectiveEquipment .
dict
Response Syntax
{
'ProtectiveEquipmentModelVersion': 'string',
'Persons': [
{
'BodyParts': [
{
'Name': 'FACE'|'HEAD'|'LEFT_HAND'|'RIGHT_HAND',
'Confidence': ...,
'EquipmentDetections': [
{
'BoundingBox': {
'Width': ...,
'Height': ...,
'Left': ...,
'Top': ...
},
'Confidence': ...,
'Type': 'FACE_COVER'|'HAND_COVER'|'HEAD_COVER',
'CoversBodyPart': {
'Confidence': ...,
'Value': True|False
}
},
]
},
],
'BoundingBox': {
'Width': ...,
'Height': ...,
'Left': ...,
'Top': ...
},
'Confidence': ...,
'Id': 123
},
],
'Summary': {
'PersonsWithRequiredEquipment': [
123,
],
'PersonsWithoutRequiredEquipment': [
123,
],
'PersonsIndeterminate': [
123,
]
}
}
Response Structure
(dict) --
ProtectiveEquipmentModelVersion (string) --
The version number of the PPE detection model used to detect PPE in the image.
Persons (list) --
An array of persons detected in the image (including persons not wearing PPE).
(dict) --
A person detected by a call to DetectProtectiveEquipment . The API returns all persons detected in the input image in an array of ProtectiveEquipmentPerson objects.
BodyParts (list) --
An array of body parts detected on a person's body (including body parts without PPE).
(dict) --
Information about a body part detected by DetectProtectiveEquipment that contains PPE. An array of ProtectiveEquipmentBodyPart objects is returned for each person detected by DetectProtectiveEquipment .
Name (string) --
The detected body part.
Confidence (float) --
The confidence that Amazon Rekognition has in the detection accuracy of the detected body part.
EquipmentDetections (list) --
An array of Personal Protective Equipment items detected around a body part.
(dict) --
Information about an item of Personal Protective Equipment (PPE) detected by DetectProtectiveEquipment . For more information, see DetectProtectiveEquipment .
BoundingBox (dict) --
A bounding box surrounding the item of detected PPE.
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) --
The confidence that Amazon Rekognition has that the bounding box (BoundingBox ) contains an item of PPE.
Type (string) --
The type of detected PPE.
CoversBodyPart (dict) --
Information about the body part covered by the detected PPE.
Confidence (float) --
The confidence that Amazon Rekognition has in the value of Value .
Value (boolean) --
True if the PPE covers the corresponding body part, otherwise false.
BoundingBox (dict) --
A 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.
Confidence (float) --
The confidence that Amazon Rekognition has that the bounding box contains a person.
Id (integer) --
The identifier for the detected person. The identifier is only unique for a single call to DetectProtectiveEquipment .
Summary (dict) --
Summary information for the types of PPE specified in the SummarizationAttributes input parameter.
PersonsWithRequiredEquipment (list) --
An array of IDs for persons who are wearing detected personal protective equipment.
PersonsWithoutRequiredEquipment (list) --
An array of IDs for persons who are not wearing all of the types of PPE specified in the RequiredEquipmentTypes field of the detected personal protective equipment.
PersonsIndeterminate (list) --
An array of IDs for persons where it was not possible to determine if they are wearing personal protective equipment.
Exceptions
Rekognition.Client.exceptions.InvalidS3ObjectExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.ImageTooLargeExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.InvalidImageFormatExceptiondetect_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 TextDetection 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 script characters that are not separated by spaces. DetectText can detect up to 100 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 +/- 90 degrees orientation of the horizontal axis.
For more information, see Detecting text in the Amazon Rekognition Developer Guide.
See also: AWS API Documentation
Request Syntax
response = client.detect_text(
Image={
'Bytes': b'bytes',
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
},
Filters={
'WordFilter': {
'MinConfidence': ...,
'MinBoundingBoxHeight': ...,
'MinBoundingBoxWidth': ...
},
'RegionsOfInterest': [
{
'BoundingBox': {
'Width': ...,
'Height': ...,
'Left': ...,
'Top': ...
},
'Polygon': [
{
'X': ...,
'Y': ...
},
]
},
]
}
)
[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.
If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.
Blob of image bytes up to 5 MBs.
Identifies an S3 object as the image source.
Name of the S3 bucket.
S3 object key name.
If the bucket is versioning enabled, you can specify the object version.
Optional parameters that let you set the criteria that the text must meet to be included in your response.
A set of parameters that allow you to filter out certain results from your returned results.
Sets the confidence of word detection. Words with detection confidence below this will be excluded from the result. Values should be between 0 and 100. The default MinConfidence is 80.
Sets the minimum height of the word bounding box. Words with bounding box heights lesser than this value will be excluded from the result. Value is relative to the video frame height.
Sets the minimum width of the word bounding box. Words with bounding boxes widths lesser than this value will be excluded from the result. Value is relative to the video frame width.
A Filter focusing on a certain area of the image. Uses a BoundingBox object to set the region of the image.
Specifies a location within the frame that Rekognition checks for objects of interest such as text, labels, or faces. It uses a BoundingBox or Polygon to set a region of the screen.
A word, face, or label is included in the region if it is more than half in that region. If there is more than one region, the word, face, or label is compared with all regions of the screen. Any object of interest that is more than half in a region is kept in the results.
The box representing a region of interest on screen.
Width of the bounding box as a ratio of the overall image width.
Height of the bounding box as a ratio of the overall image height.
Left coordinate of the bounding box as a ratio of overall image width.
Top coordinate of the bounding box as a ratio of overall image height.
Specifies a shape made up of up to 10 Point objects to define a region of interest.
The X and Y coordinates of a point on an image or video frame. The X and Y values are ratios of the overall image size or video resolution. For example, if an input image is 700x200 and the values are 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 makes up a Polygon . A Polygon is returned by DetectText and by DetectCustomLabels Polygon represents a fine-grained polygon around a detected item. For more information, see Geometry in the Amazon Rekognition Developer Guide.
The value of the X coordinate for a point on a Polygon .
The value of the Y coordinate for a point on a Polygon .
dict
Response Syntax
{
'TextDetections': [
{
'DetectedText': 'string',
'Type': 'LINE'|'WORD',
'Id': 123,
'ParentId': 123,
'Confidence': ...,
'Geometry': {
'BoundingBox': {
'Width': ...,
'Height': ...,
'Left': ...,
'Top': ...
},
'Polygon': [
{
'X': ...,
'Y': ...
},
]
}
},
],
'TextModelVersion': 'string'
}
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 DetectText .
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 Detecting text in the Amazon Rekognition Developer Guide.
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 item'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 item.
(dict) --
The X and Y coordinates of a point on an image or video frame. The X and Y values are ratios of the overall image size or video resolution. For example, if an input image is 700x200 and the values are 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 makes up a Polygon . A Polygon is returned by DetectText and by DetectCustomLabels Polygon represents a fine-grained polygon around a detected item. For more information, see Geometry in the Amazon Rekognition Developer Guide.
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 .
TextModelVersion (string) --
The model version used to detect text.
Exceptions
Rekognition.Client.exceptions.InvalidS3ObjectExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.ImageTooLargeExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.InvalidImageFormatExceptiondistribute_dataset_entries(**kwargs)¶Distributes the entries (images) in a training dataset across the training dataset and the test dataset for a project. DistributeDatasetEntries moves 20% of the training dataset images to the test dataset. An entry is a JSON Line that describes an image.
You supply the Amazon Resource Names (ARN) of a project's training dataset and test dataset. The training dataset must contain the images that you want to split. The test dataset must be empty. The datasets must belong to the same project. To create training and test datasets for a project, call CreateDataset .
Distributing a dataset takes a while to complete. To check the status call DescribeDataset . The operation is complete when the Status field for the training dataset and the test dataset is UPDATE_COMPLETE . If the dataset split fails, the value of Status is UPDATE_FAILED .
This operation requires permissions to perform the rekognition:DistributeDatasetEntries action.
See also: AWS API Documentation
Request Syntax
response = client.distribute_dataset_entries(
Datasets=[
{
'Arn': 'string'
},
]
)
[REQUIRED]
The ARNS for the training dataset and test dataset that you want to use. The datasets must belong to the same project. The test dataset must be empty.
A training dataset or a test dataset used in a dataset distribution operation. For more information, see DistributeDatasetEntries .
The Amazon Resource Name (ARN) of the dataset that you want to use.
{}
Response Structure
Exceptions
Rekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.ResourceNotReadyExceptionget_celebrity_info(**kwargs)¶Gets the name and additional information about a celebrity based on their Amazon 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 Getting information about a celebrity in the Amazon Rekognition Developer Guide.
This operation requires permissions to perform the rekognition:GetCelebrityInfo action.
See also: AWS API Documentation
Request Syntax
response = client.get_celebrity_info(
Id='string'
)
[REQUIRED]
The ID for the celebrity. You get the celebrity ID from a call to the RecognizeCelebrities operation, which recognizes celebrities in an image.
{
'Urls': [
'string',
],
'Name': 'string',
'KnownGender': {
'Type': 'Male'|'Female'|'Nonbinary'|'Unlisted'
}
}
Response Structure
An array of URLs pointing to additional celebrity information.
The name of the celebrity.
Retrieves the known gender for the celebrity.
A string value of the KnownGender info about the Celebrity.
Exceptions
Rekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionget_celebrity_recognition(**kwargs)¶Gets the celebrity recognition results for a Amazon Rekognition Video analysis started by StartCelebrityRecognition .
Celebrity recognition in a video is an asynchronous operation. Analysis is started by a call to StartCelebrityRecognition which returns a job identifier (JobId ).
When the celebrity recognition operation finishes, Amazon 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 Working With Stored Videos in the Amazon Rekognition Developer Guide.
GetCelebrityRecognitionreturns detected celebrities and the time(s) they are detected in an array (Celebrities) of CelebrityRecognition objects. EachCelebrityRecognitioncontains information about the celebrity in a CelebrityDetail object and the time,Timestamp, the celebrity was detected. This CelebrityDetail object stores information about the detected celebrity's face attributes, a face bounding box, known gender, the celebrity's name, and a confidence estimate.
Note
GetCelebrityRecognition only returns the default facial attributes (BoundingBox , Confidence , Landmarks , Pose , and Quality ). The BoundingBox field only applies to the detected face instance. The other facial attributes listed in the Face object of the following response syntax are not returned. For more information, see FaceDetail in the Amazon Rekognition Developer Guide.
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 GetCelebrityInfo 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'
)
[REQUIRED]
Job identifier for the required celebrity recognition analysis. You can get the job identifer from a call to StartCelebrityRecognition .
Celebrities field. Specify ID to sort by the celebrity identifier, specify TIMESTAMP to sort by the time the celebrity was recognized.dict
Response Syntax
{
'JobStatus': 'IN_PROGRESS'|'SUCCEEDED'|'FAILED',
'StatusMessage': 'string',
'VideoMetadata': {
'Codec': 'string',
'DurationMillis': 123,
'Format': 'string',
'FrameRate': ...,
'FrameHeight': 123,
'FrameWidth': 123,
'ColorRange': 'FULL'|'LIMITED'
},
'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'|'FEAR',
'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'|'upperJawlineLeft'|'midJawlineLeft'|'chinBottom'|'midJawlineRight'|'upperJawlineRight',
'X': ...,
'Y': ...
},
],
'Pose': {
'Roll': ...,
'Yaw': ...,
'Pitch': ...
},
'Quality': {
'Brightness': ...,
'Sharpness': ...
},
'Confidence': ...
},
'KnownGender': {
'Type': 'Male'|'Female'|'Nonbinary'|'Unlisted'
}
}
},
]
}
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 Amazon 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.
ColorRange (string) --
A description of the range of luminance values in a video, either LIMITED (16 to 235) or FULL (0 to 255).
NextToken (string) --
If the response is truncated, Amazon 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 GetCelebrityRecognition in the Amazon Rekognition Developer Guide.
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.
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. Default attribute.
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) --
The predicted gender of a detected face.
Value (string) --
The predicted gender of the face.
Confidence (float) --
Level of confidence in the prediction.
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 that appear to be expressed on the face, and the confidence level in the determination. The API is only making a determination of the physical appearance of a person's face. It is not a determination of the person’s internal emotional state and should not be used in such a way. For example, a person pretending to have a sad face might not be sad emotionally.
(dict) --
The emotions that appear to be expressed on the face, and the confidence level in the determination. The API is only making a determination of the physical appearance of a person's face. It is not a determination of the person’s internal emotional state and should not be used in such a way. For example, a person pretending to have a sad face might not be sad emotionally.
Type (string) --
Type of emotion detected.
Confidence (float) --
Level of confidence in the determination.
Landmarks (list) --
Indicates the location of landmarks on the face. Default attribute.
(dict) --
Indicates the location of the landmark on the face.
Type (string) --
Type of landmark.
X (float) --
The x-coordinate of the landmark expressed as a ratio of the width of the image. The x-coordinate is measured from the left-side of the image. For example, if the image is 700 pixels wide and the x-coordinate of the landmark is at 350 pixels, this value is 0.5.
Y (float) --
The y-coordinate of the landmark expressed as a ratio of the height of the image. The y-coordinate is measured from the top of the image. For example, if the image height is 200 pixels and the y-coordinate of the landmark is at 50 pixels, this value is 0.25.
Pose (dict) --
Indicates the pose of the face as determined by its pitch, roll, and yaw. Default attribute.
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. Default attribute.
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). Default attribute.
KnownGender (dict) --
Retrieves the known gender for the celebrity.
Type (string) --
A string value of the KnownGender info about the Celebrity.
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.InvalidPaginationTokenExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ThrottlingExceptionget_content_moderation(**kwargs)¶Gets the inappropriate, unwanted, or offensive content analysis results for a Amazon Rekognition Video analysis started by StartContentModeration . For a list of moderation labels in Amazon Rekognition, see Using the image and video moderation APIs .
Amazon Rekognition Video inappropriate or offensive content detection in a stored video is an asynchronous operation. You start analysis by calling StartContentModeration which returns a job identifier (JobId ). When analysis finishes, Amazon 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 analysis, first check that the status value published to the Amazon SNS topic is SUCCEEDED . If so, call GetContentModeration and pass the job identifier (JobId ) from the initial call to StartContentModeration .
For more information, see Working with Stored Videos in the Amazon Rekognition Devlopers Guide.
GetContentModerationreturns detected inappropriate, unwanted, or offensive content moderation labels, and the time they are detected, in an array,ModerationLabels, of ContentModerationDetection 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 moderating content in the Amazon Rekognition Developer Guide.
See also: AWS API Documentation
Request Syntax
response = client.get_content_moderation(
JobId='string',
MaxResults=123,
NextToken='string',
SortBy='NAME'|'TIMESTAMP'
)
[REQUIRED]
The identifier for the inappropriate, unwanted, or offensive content moderation job. Use JobId to identify the job in a subsequent call to GetContentModeration .
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 .dict
Response Syntax
{
'JobStatus': 'IN_PROGRESS'|'SUCCEEDED'|'FAILED',
'StatusMessage': 'string',
'VideoMetadata': {
'Codec': 'string',
'DurationMillis': 123,
'Format': 'string',
'FrameRate': ...,
'FrameHeight': 123,
'FrameWidth': 123,
'ColorRange': 'FULL'|'LIMITED'
},
'ModerationLabels': [
{
'Timestamp': 123,
'ModerationLabel': {
'Confidence': ...,
'Name': 'string',
'ParentName': 'string'
}
},
],
'NextToken': 'string',
'ModerationModelVersion': 'string'
}
Response Structure
(dict) --
JobStatus (string) --
The current status of the content moderation analysis 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.
ColorRange (string) --
A description of the range of luminance values in a video, either LIMITED (16 to 235) or FULL (0 to 255).
ModerationLabels (list) --
The detected inappropriate, unwanted, or offensive content moderation labels and the time(s) they were detected.
(dict) --
Information about an inappropriate, unwanted, or offensive content label detection in a stored video.
Timestamp (integer) --
Time, in milliseconds from the beginning of the video, that the content moderation label was detected.
ModerationLabel (dict) --
The content 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 unsafe 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, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of content moderation labels.
ModerationModelVersion (string) --
Version number of the moderation detection model that was used to detect inappropriate, unwanted, or offensive content.
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.InvalidPaginationTokenExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ThrottlingExceptionget_face_detection(**kwargs)¶Gets face detection results for a Amazon Rekognition Video analysis started by StartFaceDetection .
Face detection with Amazon Rekognition Video is an asynchronous operation. You start face detection by calling StartFaceDetection which returns a job identifier (JobId ). When the face detection operation finishes, Amazon 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 GetFaceDetection and pass the job identifier (JobId ) from the initial call to StartFaceDetection .
GetFaceDetectionreturns 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'
)
[REQUIRED]
Unique identifier for the face detection job. The JobId is returned from StartFaceDetection .
dict
Response Syntax
{
'JobStatus': 'IN_PROGRESS'|'SUCCEEDED'|'FAILED',
'StatusMessage': 'string',
'VideoMetadata': {
'Codec': 'string',
'DurationMillis': 123,
'Format': 'string',
'FrameRate': ...,
'FrameHeight': 123,
'FrameWidth': 123,
'ColorRange': 'FULL'|'LIMITED'
},
'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'|'FEAR',
'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'|'upperJawlineLeft'|'midJawlineLeft'|'chinBottom'|'midJawlineRight'|'upperJawlineRight',
'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 Amazon 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.
ColorRange (string) --
A description of the range of luminance values in a video, either LIMITED (16 to 235) or FULL (0 to 255).
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. Default attribute.
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) --
The predicted gender of a detected face.
Value (string) --
The predicted gender of the face.
Confidence (float) --
Level of confidence in the prediction.
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 that appear to be expressed on the face, and the confidence level in the determination. The API is only making a determination of the physical appearance of a person's face. It is not a determination of the person’s internal emotional state and should not be used in such a way. For example, a person pretending to have a sad face might not be sad emotionally.
(dict) --
The emotions that appear to be expressed on the face, and the confidence level in the determination. The API is only making a determination of the physical appearance of a person's face. It is not a determination of the person’s internal emotional state and should not be used in such a way. For example, a person pretending to have a sad face might not be sad emotionally.
Type (string) --
Type of emotion detected.
Confidence (float) --
Level of confidence in the determination.
Landmarks (list) --
Indicates the location of landmarks on the face. Default attribute.
(dict) --
Indicates the location of the landmark on the face.
Type (string) --
Type of landmark.
X (float) --
The x-coordinate of the landmark expressed as a ratio of the width of the image. The x-coordinate is measured from the left-side of the image. For example, if the image is 700 pixels wide and the x-coordinate of the landmark is at 350 pixels, this value is 0.5.
Y (float) --
The y-coordinate of the landmark expressed as a ratio of the height of the image. The y-coordinate is measured from the top of the image. For example, if the image height is 200 pixels and the y-coordinate of the landmark is at 50 pixels, this value is 0.25.
Pose (dict) --
Indicates the pose of the face as determined by its pitch, roll, and yaw. Default attribute.
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. Default attribute.
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). Default attribute.
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.InvalidPaginationTokenExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ThrottlingExceptionget_face_search(**kwargs)¶Gets the face search results for Amazon Rekognition Video face search started by StartFaceSearch . 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 StartFaceSearch which returns a job identifier (JobId ). When the search operation finishes, Amazon 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 Searching Faces in a Collection in the Amazon Rekognition Developer Guide.
The search results are retured in an array, Persons , of PersonMatch objects. Each``PersonMatch`` element contains details about the matching faces in the input collection, person information (facial attributes, bounding boxes, and person identifer) for the matched person, and the time the person was matched in the video.
Note
GetFaceSearch only returns the default facial attributes (BoundingBox , Confidence , Landmarks , Pose , and Quality ). The other facial attributes listed in the Face object of the following response syntax are not returned. For more information, see FaceDetail in the Amazon Rekognition Developer Guide.
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'
)
[REQUIRED]
The job identifer for the search request. You get the job identifier from an initial call to StartFaceSearch .
TIMESTAMP to group faces by the time that they are recognized. Use INDEX to sort by recognized faces.dict
Response Syntax
{
'JobStatus': 'IN_PROGRESS'|'SUCCEEDED'|'FAILED',
'StatusMessage': 'string',
'NextToken': 'string',
'VideoMetadata': {
'Codec': 'string',
'DurationMillis': 123,
'Format': 'string',
'FrameRate': ...,
'FrameHeight': 123,
'FrameWidth': 123,
'ColorRange': 'FULL'|'LIMITED'
},
'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'|'FEAR',
'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'|'upperJawlineLeft'|'midJawlineLeft'|'chinBottom'|'midJawlineRight'|'upperJawlineRight',
'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': ...,
'IndexFacesModelVersion': 'string'
}
},
]
},
]
}
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, Amazon 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 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.
ColorRange (string) --
A description of the range of luminance values in a video, either LIMITED (16 to 235) or FULL (0 to 255).
Persons (list) --
An array of persons, PersonMatch , 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 an Amazon Rekognition collection. Includes information about the faces in the Amazon Rekognition collection ( FaceMatch ), information about the person ( PersonDetail ), and the time stamp for when the person was detected in a video. An array of PersonMatch objects is returned by GetFaceSearch .
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. Default attribute.
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) --
The predicted gender of a detected face.
Value (string) --
The predicted gender of the face.
Confidence (float) --
Level of confidence in the prediction.
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 that appear to be expressed on the face, and the confidence level in the determination. The API is only making a determination of the physical appearance of a person's face. It is not a determination of the person’s internal emotional state and should not be used in such a way. For example, a person pretending to have a sad face might not be sad emotionally.
(dict) --
The emotions that appear to be expressed on the face, and the confidence level in the determination. The API is only making a determination of the physical appearance of a person's face. It is not a determination of the person’s internal emotional state and should not be used in such a way. For example, a person pretending to have a sad face might not be sad emotionally.
Type (string) --
Type of emotion detected.
Confidence (float) --
Level of confidence in the determination.
Landmarks (list) --
Indicates the location of landmarks on the face. Default attribute.
(dict) --
Indicates the location of the landmark on the face.
Type (string) --
Type of landmark.
X (float) --
The x-coordinate of the landmark expressed as a ratio of the width of the image. The x-coordinate is measured from the left-side of the image. For example, if the image is 700 pixels wide and the x-coordinate of the landmark is at 350 pixels, this value is 0.5.
Y (float) --
The y-coordinate of the landmark expressed as a ratio of the height of the image. The y-coordinate is measured from the top of the image. For example, if the image height is 200 pixels and the y-coordinate of the landmark is at 50 pixels, this value is 0.25.
Pose (dict) --
Indicates the pose of the face as determined by its pitch, roll, and yaw. Default attribute.
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. Default attribute.
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). Default attribute.
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).
IndexFacesModelVersion (string) --
The version of the face detect and storage model that was used when indexing the face vector.
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.InvalidPaginationTokenExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ThrottlingExceptionget_label_detection(**kwargs)¶Gets the label detection results of a Amazon Rekognition Video analysis started by StartLabelDetection .
The label detection operation is started by a call to StartLabelDetection 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 GetLabelDetection and pass the job identifier (JobId ) from the initial call to StartLabelDetection .
GetLabelDetectionreturns an array of detected labels (Labels) sorted by the time the labels were detected. You can also sort by the label name by specifyingNAMEfor theSortByinput 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.
The returned labels also include bounding box information for common objects, a hierarchical taxonomy of detected labels, and the version of the label model used for detection.
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'
)
[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 .
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 .dict
Response Syntax
{
'JobStatus': 'IN_PROGRESS'|'SUCCEEDED'|'FAILED',
'StatusMessage': 'string',
'VideoMetadata': {
'Codec': 'string',
'DurationMillis': 123,
'Format': 'string',
'FrameRate': ...,
'FrameHeight': 123,
'FrameWidth': 123,
'ColorRange': 'FULL'|'LIMITED'
},
'NextToken': 'string',
'Labels': [
{
'Timestamp': 123,
'Label': {
'Name': 'string',
'Confidence': ...,
'Instances': [
{
'BoundingBox': {
'Width': ...,
'Height': ...,
'Left': ...,
'Top': ...
},
'Confidence': ...
},
],
'Parents': [
{
'Name': 'string'
},
]
}
},
],
'LabelModelVersion': 'string'
}
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 Amazon 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.
ColorRange (string) --
A description of the range of luminance values in a video, either LIMITED (16 to 235) or FULL (0 to 255).
NextToken (string) --
If the response is truncated, Amazon 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 or scene.
Confidence (float) --
Level of confidence.
Instances (list) --
If Label represents an object, Instances contains the bounding boxes for each instance of the detected object. Bounding boxes are returned for common object labels such as people, cars, furniture, apparel or pets.
(dict) --
An instance of a label returned by Amazon Rekognition Image ( DetectLabels ) or by Amazon Rekognition Video ( GetLabelDetection ).
BoundingBox (dict) --
The position of the label instance 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.
Confidence (float) --
The confidence that Amazon Rekognition has in the accuracy of the bounding box.
Parents (list) --
The parent labels for a label. The response includes all ancestor labels.
(dict) --
A parent label for a label. A label can have 0, 1, or more parents.
Name (string) --
The name of the parent label.
LabelModelVersion (string) --
Version number of the label detection model that was used to detect labels.
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.InvalidPaginationTokenExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ThrottlingExceptionget_paginator(operation_name)¶Create a paginator for an operation.
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").client.can_paginate method to
check if an operation is pageable.get_person_tracking(**kwargs)¶Gets the path tracking results of a Amazon Rekognition Video analysis started by StartPersonTracking .
The person path tracking operation is started by a call to StartPersonTracking which returns a job identifier (JobId ). When the operation finishes, Amazon 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 path tracking operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED . If so, call GetPersonTracking and pass the job identifier (JobId ) from the initial call to StartPersonTracking .
GetPersonTrackingreturns an array,Persons, of tracked persons and the time(s) their paths were tracked in the video.
Note
GetPersonTrackingonly returns the default facial attributes (BoundingBox,Confidence,Landmarks,Pose, andQuality). The other facial attributes listed in theFaceobject of the following response syntax are not returned.
For more information, see FaceDetail in the Amazon Rekognition Developer Guide.
By default, the array is sorted by the time(s) a person's path 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'
)
[REQUIRED]
The identifier for a job that tracks persons in a video. You get the JobId from a call to StartPersonTracking .
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 .dict
Response Syntax
{
'JobStatus': 'IN_PROGRESS'|'SUCCEEDED'|'FAILED',
'StatusMessage': 'string',
'VideoMetadata': {
'Codec': 'string',
'DurationMillis': 123,
'Format': 'string',
'FrameRate': ...,
'FrameHeight': 123,
'FrameWidth': 123,
'ColorRange': 'FULL'|'LIMITED'
},
'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'|'FEAR',
'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'|'upperJawlineLeft'|'midJawlineLeft'|'chinBottom'|'midJawlineRight'|'upperJawlineRight',
'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 Amazon 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.
ColorRange (string) --
A description of the range of luminance values in a video, either LIMITED (16 to 235) or FULL (0 to 255).
NextToken (string) --
If the response is truncated, Amazon 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 time(s) their path was tracked throughout the video. An array element will exist for each time a person's path is tracked.
(dict) --
Details and path tracking information for a single time a person's path is tracked in a video. Amazon Rekognition operations that track people's paths return an array of PersonDetection objects with elements for each time a person's path is tracked in a video.
For more information, see GetPersonTracking in the Amazon Rekognition Developer Guide.
Timestamp (integer) --
The time, in milliseconds from the start of the video, that the person's path was tracked.
Person (dict) --
Details about a person whose path was 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. Default attribute.
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) --
The predicted gender of a detected face.
Value (string) --
The predicted gender of the face.
Confidence (float) --
Level of confidence in the prediction.
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 that appear to be expressed on the face, and the confidence level in the determination. The API is only making a determination of the physical appearance of a person's face. It is not a determination of the person’s internal emotional state and should not be used in such a way. For example, a person pretending to have a sad face might not be sad emotionally.
(dict) --
The emotions that appear to be expressed on the face, and the confidence level in the determination. The API is only making a determination of the physical appearance of a person's face. It is not a determination of the person’s internal emotional state and should not be used in such a way. For example, a person pretending to have a sad face might not be sad emotionally.
Type (string) --
Type of emotion detected.
Confidence (float) --
Level of confidence in the determination.
Landmarks (list) --
Indicates the location of landmarks on the face. Default attribute.
(dict) --
Indicates the location of the landmark on the face.
Type (string) --
Type of landmark.
X (float) --
The x-coordinate of the landmark expressed as a ratio of the width of the image. The x-coordinate is measured from the left-side of the image. For example, if the image is 700 pixels wide and the x-coordinate of the landmark is at 350 pixels, this value is 0.5.
Y (float) --
The y-coordinate of the landmark expressed as a ratio of the height of the image. The y-coordinate is measured from the top of the image. For example, if the image height is 200 pixels and the y-coordinate of the landmark is at 50 pixels, this value is 0.25.
Pose (dict) --
Indicates the pose of the face as determined by its pitch, roll, and yaw. Default attribute.
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. Default attribute.
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). Default attribute.
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.InvalidPaginationTokenExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ThrottlingExceptionget_segment_detection(**kwargs)¶Gets the segment detection results of a Amazon Rekognition Video analysis started by StartSegmentDetection .
Segment detection with Amazon Rekognition Video is an asynchronous operation. You start segment detection by calling StartSegmentDetection which returns a job identifier (JobId ). When the segment detection operation finishes, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartSegmentDetection . To get the results of the segment detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED . if so, call GetSegmentDetection and pass the job identifier (JobId ) from the initial call of StartSegmentDetection .
GetSegmentDetectionreturns detected segments in an array (Segments) of SegmentDetection objects.Segmentsis sorted by the segment types specified in theSegmentTypesinput parameter ofStartSegmentDetection. Each element of the array includes the detected segment, the precentage confidence in the acuracy of the detected segment, the type of the segment, and the frame in which the segment was detected.
Use SelectedSegmentTypes to find out the type of segment detection requested in the call to StartSegmentDetection .
Use the MaxResults parameter to limit the number of segment detections 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 GetSegmentDetection and populate the NextToken request parameter with the token value returned from the previous call to GetSegmentDetection .
For more information, see Detecting video segments in stored video in the Amazon Rekognition Developer Guide.
See also: AWS API Documentation
Request Syntax
response = client.get_segment_detection(
JobId='string',
MaxResults=123,
NextToken='string'
)
[REQUIRED]
Job identifier for the text detection operation for which you want results returned. You get the job identifer from an initial call to StartSegmentDetection .
dict
Response Syntax
{
'JobStatus': 'IN_PROGRESS'|'SUCCEEDED'|'FAILED',
'StatusMessage': 'string',
'VideoMetadata': [
{
'Codec': 'string',
'DurationMillis': 123,
'Format': 'string',
'FrameRate': ...,
'FrameHeight': 123,
'FrameWidth': 123,
'ColorRange': 'FULL'|'LIMITED'
},
],
'AudioMetadata': [
{
'Codec': 'string',
'DurationMillis': 123,
'SampleRate': 123,
'NumberOfChannels': 123
},
],
'NextToken': 'string',
'Segments': [
{
'Type': 'TECHNICAL_CUE'|'SHOT',
'StartTimestampMillis': 123,
'EndTimestampMillis': 123,
'DurationMillis': 123,
'StartTimecodeSMPTE': 'string',
'EndTimecodeSMPTE': 'string',
'DurationSMPTE': 'string',
'TechnicalCueSegment': {
'Type': 'ColorBars'|'EndCredits'|'BlackFrames'|'OpeningCredits'|'StudioLogo'|'Slate'|'Content',
'Confidence': ...
},
'ShotSegment': {
'Index': 123,
'Confidence': ...
},
'StartFrameNumber': 123,
'EndFrameNumber': 123,
'DurationFrames': 123
},
],
'SelectedSegmentTypes': [
{
'Type': 'TECHNICAL_CUE'|'SHOT',
'ModelVersion': 'string'
},
]
}
Response Structure
(dict) --
JobStatus (string) --
Current status of the segment detection job.
StatusMessage (string) --
If the job fails, StatusMessage provides a descriptive error message.
VideoMetadata (list) --
Currently, Amazon Rekognition Video returns a single object in the VideoMetadata array. The object contains information about the video stream in the input file that Amazon Rekognition Video chose to analyze. The VideoMetadata object includes the video codec, video format and other information. Video metadata is returned in each page of information returned by GetSegmentDetection .
(dict) --
Information about a video that Amazon Rekognition 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.
ColorRange (string) --
A description of the range of luminance values in a video, either LIMITED (16 to 235) or FULL (0 to 255).
AudioMetadata (list) --
An array of objects. There can be multiple audio streams. Each AudioMetadata object contains metadata for a single audio stream. Audio information in an AudioMetadata objects includes the audio codec, the number of audio channels, the duration of the audio stream, and the sample rate. Audio metadata is returned in each page of information returned by GetSegmentDetection .
(dict) --
Metadata information about an audio stream. An array of AudioMetadata objects for the audio streams found in a stored video is returned by GetSegmentDetection .
Codec (string) --
The audio codec used to encode or decode the audio stream.
DurationMillis (integer) --
The duration of the audio stream in milliseconds.
SampleRate (integer) --
The sample rate for the audio stream.
NumberOfChannels (integer) --
The number of audio channels in the segment.
NextToken (string) --
If the previous response was incomplete (because there are more labels to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of text.
Segments (list) --
An array of segments detected in a video. The array is sorted by the segment types (TECHNICAL_CUE or SHOT) specified in the SegmentTypes input parameter of StartSegmentDetection . Within each segment type the array is sorted by timestamp values.
(dict) --
A technical cue or shot detection segment detected in a video. An array of SegmentDetection objects containing all segments detected in a stored video is returned by GetSegmentDetection .
Type (string) --
The type of the segment. Valid values are TECHNICAL_CUE and SHOT .
StartTimestampMillis (integer) --
The start time of the detected segment in milliseconds from the start of the video. This value is rounded down. For example, if the actual timestamp is 100.6667 milliseconds, Amazon Rekognition Video returns a value of 100 millis.
EndTimestampMillis (integer) --
The end time of the detected segment, in milliseconds, from the start of the video. This value is rounded down.
DurationMillis (integer) --
The duration of the detected segment in milliseconds.
StartTimecodeSMPTE (string) --
The frame-accurate SMPTE timecode, from the start of a video, for the start of a detected segment. StartTimecode is in HH:MM:SS:fr format (and ;fr for drop frame-rates).
EndTimecodeSMPTE (string) --
The frame-accurate SMPTE timecode, from the start of a video, for the end of a detected segment. EndTimecode is in HH:MM:SS:fr format (and ;fr for drop frame-rates).
DurationSMPTE (string) --
The duration of the timecode for the detected segment in SMPTE format.
TechnicalCueSegment (dict) --
If the segment is a technical cue, contains information about the technical cue.
Type (string) --
The type of the technical cue.
Confidence (float) --
The confidence that Amazon Rekognition Video has in the accuracy of the detected segment.
ShotSegment (dict) --
If the segment is a shot detection, contains information about the shot detection.
Index (integer) --
An Identifier for a shot detection segment detected in a video.
Confidence (float) --
The confidence that Amazon Rekognition Video has in the accuracy of the detected segment.
StartFrameNumber (integer) --
The frame number of the start of a video segment, using a frame index that starts with 0.
EndFrameNumber (integer) --
The frame number at the end of a video segment, using a frame index that starts with 0.
DurationFrames (integer) --
The duration of a video segment, expressed in frames.
SelectedSegmentTypes (list) --
An array containing the segment types requested in the call to StartSegmentDetection .
(dict) --
Information about the type of a segment requested in a call to StartSegmentDetection . An array of SegmentTypeInfo objects is returned by the response from GetSegmentDetection .
Type (string) --
The type of a segment (technical cue or shot detection).
ModelVersion (string) --
The version of the model used to detect segments.
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.InvalidPaginationTokenExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ThrottlingExceptionget_text_detection(**kwargs)¶Gets the text detection results of a Amazon Rekognition Video analysis started by StartTextDetection .
Text detection with Amazon Rekognition Video is an asynchronous operation. You start text detection by calling StartTextDetection which returns a job identifier (JobId ) When the text detection operation finishes, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartTextDetection . To get the results of the text detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED . if so, call GetTextDetection and pass the job identifier (JobId ) from the initial call of StartLabelDetection .
GetTextDetectionreturns an array of detected text (TextDetections) sorted by the time the text was detected, up to 50 words per frame of video.
Each element of the array includes the detected text, the precentage confidence in the acuracy of the detected text, the time the text was detected, bounding box information for where the text was located, and unique identifiers for words and their lines.
Use MaxResults parameter to limit the number of text detections 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 GetTextDetection and populate the NextToken request parameter with the token value returned from the previous call to GetTextDetection .
See also: AWS API Documentation
Request Syntax
response = client.get_text_detection(
JobId='string',
MaxResults=123,
NextToken='string'
)
[REQUIRED]
Job identifier for the text detection operation for which you want results returned. You get the job identifer from an initial call to StartTextDetection .
dict
Response Syntax
{
'JobStatus': 'IN_PROGRESS'|'SUCCEEDED'|'FAILED',
'StatusMessage': 'string',
'VideoMetadata': {
'Codec': 'string',
'DurationMillis': 123,
'Format': 'string',
'FrameRate': ...,
'FrameHeight': 123,
'FrameWidth': 123,
'ColorRange': 'FULL'|'LIMITED'
},
'TextDetections': [
{
'Timestamp': 123,
'TextDetection': {
'DetectedText': 'string',
'Type': 'LINE'|'WORD',
'Id': 123,
'ParentId': 123,
'Confidence': ...,
'Geometry': {
'BoundingBox': {
'Width': ...,
'Height': ...,
'Left': ...,
'Top': ...
},
'Polygon': [
{
'X': ...,
'Y': ...
},
]
}
}
},
],
'NextToken': 'string',
'TextModelVersion': 'string'
}
Response Structure
(dict) --
JobStatus (string) --
Current status of the text detection 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 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.
ColorRange (string) --
A description of the range of luminance values in a video, either LIMITED (16 to 235) or FULL (0 to 255).
TextDetections (list) --
An array of text detected in the video. Each element contains the detected text, the time in milliseconds from the start of the video that the text was detected, and where it was detected on the screen.
(dict) --
Information about text detected in a video. Incudes the detected text, the time in milliseconds from the start of the video that the text was detected, and where it was detected on the screen.
Timestamp (integer) --
The time, in milliseconds from the start of the video, that the text was detected.
TextDetection (dict) --
Details about text detected in a video.
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 item'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 item.
(dict) --
The X and Y coordinates of a point on an image or video frame. The X and Y values are ratios of the overall image size or video resolution. For example, if an input image is 700x200 and the values are 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 makes up a Polygon . A Polygon is returned by DetectText and by DetectCustomLabels Polygon represents a fine-grained polygon around a detected item. For more information, see Geometry in the Amazon Rekognition Developer Guide.
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 .
NextToken (string) --
If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of text.
TextModelVersion (string) --
Version number of the text detection model that was used to detect text.
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.InvalidPaginationTokenExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ThrottlingExceptionget_waiter(waiter_name)¶Returns an object that can wait for some condition.
index_faces(**kwargs)¶Detects faces in the input image and adds them to the specified collection.
Amazon Rekognition doesn't save the actual faces that are detected. Instead, the underlying detection algorithm first detects the faces in the input image. For each face, the algorithm extracts facial features into a feature vector, and stores it in the backend database. Amazon Rekognition uses feature vectors when it performs face match and search operations using the SearchFaces and SearchFacesByImage operations.
For more information, see Adding faces to a collection in the Amazon Rekognition Developer Guide.
To get the number of faces in a collection, call DescribeCollection .
If you're 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.
If you're using version 4 or later of the face model, image orientation information is not returned in the OrientationCorrection field.
To determine which version of the model you're using, call DescribeCollection and supply the collection ID. You can also get the model version from the value of FaceModelVersion in the response from IndexFaces
For more information, see Model Versioning in the Amazon Rekognition Developer Guide.
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 ListFaces 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.
You can specify the maximum number of faces to index with the MaxFaces input parameter. This is useful when you want to index the largest faces in an image and don't want to index smaller faces, such as those belonging to people standing in the background.
The QualityFilter input parameter allows you to filter out detected faces that don’t meet a required quality bar. The quality bar is based on a variety of common use cases. By default, IndexFaces chooses the quality bar that's used to filter faces. You can also explicitly choose the quality bar. Use QualityFilter , to set the quality bar by specifying LOW , MEDIUM , or HIGH . If you do not want to filter detected faces, specify NONE .
Note
To use quality filtering, you need a collection associated with version 3 of the face model or higher. To get the version of the face model associated with a collection, call DescribeCollection .
Information about faces detected in an image, but not indexed, is returned in an array of UnindexedFace objects, UnindexedFaces . Faces aren't indexed for reasons such as:
MaxFaces request parameter.In response, the IndexFaces operation returns an array of metadata for all detected faces, FaceRecords . This includes:
BoundingBox , of the detected face.Confidence , which indicates the confidence that the bounding box contains a face.FaceId , assigned by the service for each face that's detected and stored.ImageId , assigned by the service for the input image.If you request all facial attributes (by using the detectionAttributes parameter), Amazon Rekognition returns detailed facial attributes, such as facial landmarks (for example, location of eye and mouth) and other facial attributes. 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 AWS CLI to call Amazon Rekognition operations, passing image bytes isn't supported. The image must be formatted as a PNG or JPEG 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',
],
MaxFaces=123,
QualityFilter='NONE'|'AUTO'|'LOW'|'MEDIUM'|'HIGH'
)
[REQUIRED]
The ID of an existing collection to which you want to add the faces that are detected in the input images.
[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 isn't supported.
If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.
Blob of image bytes up to 5 MBs.
Identifies an S3 object as the image source.
Name of the S3 bucket.
S3 object key name.
If the bucket is versioning enabled, you can specify the object version.
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 takes 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).
The maximum number of faces to index. The value of MaxFaces must be greater than or equal to 1. IndexFaces returns no more than 100 detected faces in an image, even if you specify a larger value for MaxFaces .
If IndexFaces detects more faces than the value of MaxFaces , the faces with the lowest quality are filtered out first. If there are still more faces than the value of MaxFaces , the faces with the smallest bounding boxes are filtered out (up to the number that's needed to satisfy the value of MaxFaces ). Information about the unindexed faces is available in the UnindexedFaces array.
The faces that are returned by IndexFaces are sorted by the largest face bounding box size to the smallest size, in descending order.
MaxFacescan be used with a collection associated with any version of the face model.
A filter that specifies a quality bar for how much filtering is done to identify faces. Filtered faces aren't indexed. If you specify AUTO , Amazon Rekognition chooses the quality bar. If you specify LOW , MEDIUM , or HIGH , filtering removes all faces that don’t meet the chosen quality bar. The default value is AUTO . The quality bar is based on a variety of common use cases. Low-quality detections can occur for a number of reasons. Some examples are an object that's misidentified as a face, a face that's too blurry, or a face with a pose that's too extreme to use. If you specify NONE , no filtering is performed.
To use quality filtering, the collection you are using must be associated with version 3 of the face model or higher.
dict
Response Syntax
{
'FaceRecords': [
{
'Face': {
'FaceId': 'string',
'BoundingBox': {
'Width': ...,
'Height': ...,
'Left': ...,
'Top': ...
},
'ImageId': 'string',
'ExternalImageId': 'string',
'Confidence': ...,
'IndexFacesModelVersion': 'string'
},
'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'|'FEAR',
'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'|'upperJawlineLeft'|'midJawlineLeft'|'chinBottom'|'midJawlineRight'|'upperJawlineRight',
'X': ...,
'Y': ...
},
],
'Pose': {
'Roll': ...,
'Yaw': ...,
'Pitch': ...
},
'Quality': {
'Brightness': ...,
'Sharpness': ...
},
'Confidence': ...
}
},
],
'OrientationCorrection': 'ROTATE_0'|'ROTATE_90'|'ROTATE_180'|'ROTATE_270',
'FaceModelVersion': 'string',
'UnindexedFaces': [
{
'Reasons': [
'EXCEEDS_MAX_FACES'|'EXTREME_POSE'|'LOW_BRIGHTNESS'|'LOW_SHARPNESS'|'LOW_CONFIDENCE'|'SMALL_BOUNDING_BOX'|'LOW_FACE_QUALITY',
],
'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'|'FEAR',
'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'|'upperJawlineLeft'|'midJawlineLeft'|'chinBottom'|'midJawlineRight'|'upperJawlineRight',
'X': ...,
'Y': ...
},
],
'Pose': {
'Roll': ...,
'Yaw': ...,
'Pitch': ...
},
'Quality': {
'Brightness': ...,
'Sharpness': ...
},
'Confidence': ...
}
},
]
}
Response Structure
(dict) --
FaceRecords (list) --
An array of faces detected and added to the collection. For more information, see Searching Faces in a Collection in the Amazon Rekognition Developer Guide.
(dict) --
Object containing both the face metadata (stored in the backend 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).
IndexFacesModelVersion (string) --
The version of the face detect and storage model that was used when indexing the face vector.
FaceDetail (dict) --
Structure containing attributes of the face that the algorithm detected.
BoundingBox (dict) --
Bounding box of the face. Default attribute.
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) --
The predicted gender of a detected face.
Value (string) --
The predicted gender of the face.
Confidence (float) --
Level of confidence in the prediction.
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 that appear to be expressed on the face, and the confidence level in the determination. The API is only making a determination of the physical appearance of a person's face. It is not a determination of the person’s internal emotional state and should not be used in such a way. For example, a person pretending to have a sad face might not be sad emotionally.
(dict) --
The emotions that appear to be expressed on the face, and the confidence level in the determination. The API is only making a determination of the physical appearance of a person's face. It is not a determination of the person’s internal emotional state and should not be used in such a way. For example, a person pretending to have a sad face might not be sad emotionally.
Type (string) --
Type of emotion detected.
Confidence (float) --
Level of confidence in the determination.
Landmarks (list) --
Indicates the location of landmarks on the face. Default attribute.
(dict) --
Indicates the location of the landmark on the face.
Type (string) --
Type of landmark.
X (float) --
The x-coordinate of the landmark expressed as a ratio of the width of the image. The x-coordinate is measured from the left-side of the image. For example, if the image is 700 pixels wide and the x-coordinate of the landmark is at 350 pixels, this value is 0.5.
Y (float) --
The y-coordinate of the landmark expressed as a ratio of the height of the image. The y-coordinate is measured from the top of the image. For example, if the image height is 200 pixels and the y-coordinate of the landmark is at 50 pixels, this value is 0.25.
Pose (dict) --
Indicates the pose of the face as determined by its pitch, roll, and yaw. Default attribute.
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. Default attribute.
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). Default attribute.
OrientationCorrection (string) --
If your collection is associated with a face detection model that's later than version 3.0, the value of OrientationCorrection is always null and no orientation information is returned.
If your collection is associated with a face detection model that's version 3.0 or earlier, the following applies:
OrientationCorrection is null.Bounding box information is returned in the FaceRecords array. You can get the version of the face detection model by calling DescribeCollection .
FaceModelVersion (string) --
The version number of the face detection model that's associated with the input collection (CollectionId ).
UnindexedFaces (list) --
An array of faces that were detected in the image but weren't indexed. They weren't indexed because the quality filter identified them as low quality, or the MaxFaces request parameter filtered them out. To use the quality filter, you specify the QualityFilter request parameter.
(dict) --
A face that IndexFaces detected, but didn't index. Use the Reasons response attribute to determine why a face wasn't indexed.
Reasons (list) --
An array of reasons that specify why a face wasn't indexed.
MaxFaces input parameter for IndexFaces .FaceDetail (dict) --
The structure that contains attributes of a face that IndexFaces detected, but didn't index.
BoundingBox (dict) --
Bounding box of the face. Default attribute.
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) --
The predicted gender of a detected face.
Value (string) --
The predicted gender of the face.
Confidence (float) --
Level of confidence in the prediction.
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 that appear to be expressed on the face, and the confidence level in the determination. The API is only making a determination of the physical appearance of a person's face. It is not a determination of the person’s internal emotional state and should not be used in such a way. For example, a person pretending to have a sad face might not be sad emotionally.
(dict) --
The emotions that appear to be expressed on the face, and the confidence level in the determination. The API is only making a determination of the physical appearance of a person's face. It is not a determination of the person’s internal emotional state and should not be used in such a way. For example, a person pretending to have a sad face might not be sad emotionally.
Type (string) --
Type of emotion detected.
Confidence (float) --
Level of confidence in the determination.
Landmarks (list) --
Indicates the location of landmarks on the face. Default attribute.
(dict) --
Indicates the location of the landmark on the face.
Type (string) --
Type of landmark.
X (float) --
The x-coordinate of the landmark expressed as a ratio of the width of the image. The x-coordinate is measured from the left-side of the image. For example, if the image is 700 pixels wide and the x-coordinate of the landmark is at 350 pixels, this value is 0.5.
Y (float) --
The y-coordinate of the landmark expressed as a ratio of the height of the image. The y-coordinate is measured from the top of the image. For example, if the image height is 200 pixels and the y-coordinate of the landmark is at 50 pixels, this value is 0.25.
Pose (dict) --
Indicates the pose of the face as determined by its pitch, roll, and yaw. Default attribute.
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. Default attribute.
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). Default attribute.
Exceptions
Rekognition.Client.exceptions.InvalidS3ObjectExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.ImageTooLargeExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.InvalidImageFormatExceptionRekognition.Client.exceptions.ServiceQuotaExceededExceptionExamples
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': 'eyeLeft',
'X': 0.3976764678955078,
'Y': 0.6248345971107483,
},
{
'Type': 'eyeRight',
'X': 0.4810936450958252,
'Y': 0.6317117214202881,
},
{
'Type': 'noseLeft',
'X': 0.41986238956451416,
'Y': 0.7111940383911133,
},
{
'Type': 'mouthDown',
'X': 0.40525302290916443,
'Y': 0.7497701048851013,
},
{
'Type': 'mouthUp',
'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': 'eyeLeft',
'X': 0.6006892323493958,
'Y': 0.290842205286026,
},
{
'Type': 'eyeRight',
'X': 0.6808141469955444,
'Y': 0.29609042406082153,
},
{
'Type': 'noseLeft',
'X': 0.6395332217216492,
'Y': 0.3522595763206482,
},
{
'Type': 'mouthDown',
'X': 0.5892083048820496,
'Y': 0.38689887523651123,
},
{
'Type': 'mouthUp',
'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 Listing collections in the Amazon Rekognition Developer Guide.
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
)
dict
Response Syntax
{
'CollectionIds': [
'string',
],
'NextToken': 'string',
'FaceModelVersions': [
'string',
]
}
Response Structure
(dict) --
CollectionIds (list) --
An array of collection IDs.
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] .
Exceptions
Rekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.InvalidPaginationTokenExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionExamples
This operation returns a list of Rekognition collections.
response = client.list_collections(
)
print(response)
Expected Output:
{
'CollectionIds': [
'myphotos',
],
'ResponseMetadata': {
'...': '...',
},
}
list_dataset_entries(**kwargs)¶Lists the entries (images) within a dataset. An entry is a JSON Line that contains the information for a single image, including the image location, assigned labels, and object location bounding boxes. For more information, see Creating a manifest file .
JSON Lines in the response include information about non-terminal errors found in the dataset. Non terminal errors are reported in errors lists within each JSON Line. The same information is reported in the training and testing validation result manifests that Amazon Rekognition Custom Labels creates during model training.
You can filter the response in variety of ways, such as choosing which labels to return and returning JSON Lines created after a specific date.
This operation requires permissions to perform the rekognition:ListDatasetEntries action.
See also: AWS API Documentation
Request Syntax
response = client.list_dataset_entries(
DatasetArn='string',
ContainsLabels=[
'string',
],
Labeled=True|False,
SourceRefContains='string',
HasErrors=True|False,
NextToken='string',
MaxResults=123
)
[REQUIRED]
The Amazon Resource Name (ARN) for the dataset that you want to use.
Specifies a label filter for the response. The response includes an entry only if one or more of the labels in ContainsLabels exist in the entry.
true to get only the JSON Lines where the image is labeled. Specify false to get only the JSON Lines where the image isn't labeled. If you don't specify Labeled , ListDatasetEntries returns JSON Lines for labeled and unlabeled images.ListDatasetEntries only returns JSON Lines where the value of SourceRefContains is part of the source-ref field. The source-ref field contains the Amazon S3 location of the image. You can use SouceRefContains for tasks such as getting the JSON Line for a single image, or gettting JSON Lines for all images within a specific folder.True to only include entries that have errors.dict
Response Syntax
{
'DatasetEntries': [
'string',
],
'NextToken': 'string'
}
Response Structure
(dict) --
DatasetEntries (list) --
A list of entries (images) in the dataset.
NextToken (string) --
If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.
Exceptions
Rekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.ResourceInUseExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.InvalidPaginationTokenExceptionRekognition.Client.exceptions.ResourceNotReadyExceptionlist_dataset_labels(**kwargs)¶Lists the labels in a dataset. Amazon Rekognition Custom Labels uses labels to describe images. For more information, see Labeling images .
Lists the labels in a dataset. Amazon Rekognition Custom Labels uses labels to describe images. For more information, see Labeling images in the Amazon Rekognition Custom Labels Developer Guide .
See also: AWS API Documentation
Request Syntax
response = client.list_dataset_labels(
DatasetArn='string',
NextToken='string',
MaxResults=123
)
[REQUIRED]
The Amazon Resource Name (ARN) of the dataset that you want to use.
dict
Response Syntax
{
'DatasetLabelDescriptions': [
{
'LabelName': 'string',
'LabelStats': {
'EntryCount': 123,
'BoundingBoxCount': 123
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
DatasetLabelDescriptions (list) --
A list of the labels in the dataset.
(dict) --
Describes a dataset label. For more information, see ListDatasetLabels .
LabelName (string) --
The name of the label.
LabelStats (dict) --
Statistics about the label.
EntryCount (integer) --
The total number of images that use the label.
BoundingBoxCount (integer) --
The total number of images that have the label assigned to a bounding box.
NextToken (string) --
If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.
Exceptions
Rekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.ResourceInUseExceptionRekognition.Client.exceptions.InvalidPaginationTokenExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ResourceNotReadyExceptionlist_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 Listing Faces in a Collection in the Amazon Rekognition Developer Guide.
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
)
[REQUIRED]
ID of the collection from which to list the faces.
dict
Response Syntax
{
'Faces': [
{
'FaceId': 'string',
'BoundingBox': {
'Width': ...,
'Height': ...,
'Left': ...,
'Top': ...
},
'ImageId': 'string',
'ExternalImageId': 'string',
'Confidence': ...,
'IndexFacesModelVersion': 'string'
},
],
'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).
IndexFacesModelVersion (string) --
The version of the face detect and storage model that was used when indexing the face vector.
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 ).
Exceptions
Rekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.InvalidPaginationTokenExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionExamples
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_project_policies(**kwargs)¶Gets a list of the project policies attached to a project.
To attach a project policy to a project, call PutProjectPolicy . To remove a project policy from a project, call DeleteProjectPolicy .
See also: AWS API Documentation
Request Syntax
response = client.list_project_policies(
ProjectArn='string',
NextToken='string',
MaxResults=123
)
[REQUIRED]
The ARN of the project for which you want to list the project policies.
dict
Response Syntax
{
'ProjectPolicies': [
{
'ProjectArn': 'string',
'PolicyName': 'string',
'PolicyRevisionId': 'string',
'PolicyDocument': 'string',
'CreationTimestamp': datetime(2015, 1, 1),
'LastUpdatedTimestamp': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
ProjectPolicies (list) --
A list of project policies attached to the project.
(dict) --
Describes a project policy in the response from ListProjectPolicies .
ProjectArn (string) --
The Amazon Resource Name (ARN) of the project to which the project policy is attached.
PolicyName (string) --
The name of the project policy.
PolicyRevisionId (string) --
The revision ID of the project policy.
PolicyDocument (string) --
The JSON document for the project policy.
CreationTimestamp (datetime) --
The Unix datetime for the creation of the project policy.
LastUpdatedTimestamp (datetime) --
The Unix datetime for when the project policy was last updated.
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 project policies.
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.InvalidPaginationTokenExceptionlist_stream_processors(**kwargs)¶Gets a list of stream processors that you have created with CreateStreamProcessor .
See also: AWS API Documentation
Request Syntax
response = client.list_stream_processors(
NextToken='string',
MaxResults=123
)
dict
Response Syntax
{
'NextToken': 'string',
'StreamProcessors': [
{
'Name': 'string',
'Status': 'STOPPED'|'STARTING'|'RUNNING'|'FAILED'|'STOPPING'|'UPDATING'
},
]
}
Response Structure
(dict) --
NextToken (string) --
If the response is truncated, Amazon 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 or labels in a streaming video. An Amazon Rekognition stream processor is created by a call to CreateStreamProcessor . 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.
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.InvalidPaginationTokenExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionReturns a list of tags in an Amazon Rekognition collection, stream processor, or Custom Labels model.
This operation requires permissions to perform the rekognition:ListTagsForResource action.
See also: AWS API Documentation
Request Syntax
response = client.list_tags_for_resource(
ResourceArn='string'
)
[REQUIRED]
Amazon Resource Name (ARN) of the model, collection, or stream processor that contains the tags that you want a list of.
{
'Tags': {
'string': 'string'
}
}
Response Structure
A list of key-value tags assigned to the resource.
Exceptions
Rekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionput_project_policy(**kwargs)¶Attaches a project policy to a Amazon Rekognition Custom Labels project in a trusting AWS account. A project policy specifies that a trusted AWS account can copy a model version from a trusting AWS account to a project in the trusted AWS account. To copy a model version you use the CopyProjectVersion operation.
For more information about the format of a project policy document, see Attaching a project policy (SDK) in the Amazon Rekognition Custom Labels Developer Guide .
The response from PutProjectPolicy is a revision ID for the project policy. You can attach multiple project policies to a project. You can also update an existing project policy by specifying the policy revision ID of the existing policy.
To remove a project policy from a project, call DeleteProjectPolicy . To get a list of project policies attached to a project, call ListProjectPolicies .
You copy a model version by calling CopyProjectVersion .
See also: AWS API Documentation
Request Syntax
response = client.put_project_policy(
ProjectArn='string',
PolicyName='string',
PolicyRevisionId='string',
PolicyDocument='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the project that the project policy is attached to.
[REQUIRED]
A name for the policy.
PolicyRevisionId and then deletes the previous version of the policy.[REQUIRED]
A resource policy to add to the model. The policy is a JSON structure that contains one or more statements that define the policy. The policy must follow the IAM syntax. For more information about the contents of a JSON policy document, see IAM JSON policy reference .
dict
Response Syntax
{
'PolicyRevisionId': 'string'
}
Response Structure
(dict) --
PolicyRevisionId (string) --
The ID of the project policy.
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.InvalidPolicyRevisionIdExceptionRekognition.Client.exceptions.MalformedPolicyDocumentExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ResourceAlreadyExistsExceptionRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ServiceQuotaExceededExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.LimitExceededExceptionrecognize_celebrities(**kwargs)¶Returns an array of celebrities recognized in the input image. For more information, see Recognizing celebrities in the Amazon Rekognition Developer Guide.
RecognizeCelebritiesreturns the 64 largest faces in the image. It lists the recognized celebrities in theCelebrityFacesarray and any unrecognized faces in theUnrecognizedFacesarray.RecognizeCelebritiesdoesn't return celebrities whose faces aren't among the largest 64 faces in the image.
For each celebrity recognized, 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.
Amazon Rekognition doesn't 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 GetCelebrityInfo 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 AWS 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 Recognizing celebrities in an image in the Amazon Rekognition Developer Guide.
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'
}
}
)
[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.
If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.
Blob of image bytes up to 5 MBs.
Identifies an S3 object as the image source.
Name of the S3 bucket.
S3 object key name.
If the bucket is versioning enabled, you can specify the object version.
{
'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'|'upperJawlineLeft'|'midJawlineLeft'|'chinBottom'|'midJawlineRight'|'upperJawlineRight',
'X': ...,
'Y': ...
},
],
'Pose': {
'Roll': ...,
'Yaw': ...,
'Pitch': ...
},
'Quality': {
'Brightness': ...,
'Sharpness': ...
},
'Emotions': [
{
'Type': 'HAPPY'|'SAD'|'ANGRY'|'CONFUSED'|'DISGUSTED'|'SURPRISED'|'CALM'|'UNKNOWN'|'FEAR',
'Confidence': ...
},
],
'Smile': {
'Value': True|False,
'Confidence': ...
}
},
'MatchConfidence': ...,
'KnownGender': {
'Type': 'Male'|'Female'|'Nonbinary'|'Unlisted'
}
},
],
'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'|'upperJawlineLeft'|'midJawlineLeft'|'chinBottom'|'midJawlineRight'|'upperJawlineRight',
'X': ...,
'Y': ...
},
],
'Pose': {
'Roll': ...,
'Yaw': ...,
'Pitch': ...
},
'Quality': {
'Brightness': ...,
'Sharpness': ...
},
'Emotions': [
{
'Type': 'HAPPY'|'SAD'|'ANGRY'|'CONFUSED'|'DISGUSTED'|'SURPRISED'|'CALM'|'UNKNOWN'|'FEAR',
'Confidence': ...
},
],
'Smile': {
'Value': True|False,
'Confidence': ...
}
},
],
'OrientationCorrection': 'ROTATE_0'|'ROTATE_90'|'ROTATE_180'|'ROTATE_270'
}
Response Structure
Details about each celebrity found in the image. Amazon Rekognition can detect a maximum of 64 celebrities in an image. Each celebrity object includes the following attributes: Face , Confidence , Emotions , Landmarks , Pose , Quality , Smile , Id , KnownGender , MatchConfidence , Name , Urls .
Provides information about a celebrity recognized by the RecognizeCelebrities operation.
An array of URLs pointing to additional information about the celebrity. If there is no additional information about the celebrity, this list is empty.
The name of the celebrity.
A unique identifier for the celebrity.
Provides information about the celebrity's face, such as its location on the image.
Bounding box of the face.
Width of the bounding box as a ratio of the overall image width.
Height of the bounding box as a ratio of the overall image height.
Left coordinate of the bounding box as a ratio of overall image width.
Top coordinate of the bounding box as a ratio of overall image height.
Level of confidence that what the bounding box contains is a face.
An array of facial landmarks.
Indicates the location of the landmark on the face.
Type of landmark.
The x-coordinate of the landmark expressed as a ratio of the width of the image. The x-coordinate is measured from the left-side of the image. For example, if the image is 700 pixels wide and the x-coordinate of the landmark is at 350 pixels, this value is 0.5.
The y-coordinate of the landmark expressed as a ratio of the height of the image. The y-coordinate is measured from the top of the image. For example, if the image height is 200 pixels and the y-coordinate of the landmark is at 50 pixels, this value is 0.25.
Indicates the pose of the face as determined by its pitch, roll, and yaw.
Value representing the face rotation on the roll axis.
Value representing the face rotation on the yaw axis.
Value representing the face rotation on the pitch axis.
Identifies face image brightness and sharpness.
Value representing brightness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a brighter face image.
Value representing sharpness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a sharper face image.
The emotions that appear to be expressed on the face, and the confidence level in the determination. Valid values include "Happy", "Sad", "Angry", "Confused", "Disgusted", "Surprised", "Calm", "Unknown", and "Fear".
The emotions that appear to be expressed on the face, and the confidence level in the determination. The API is only making a determination of the physical appearance of a person's face. It is not a determination of the person’s internal emotional state and should not be used in such a way. For example, a person pretending to have a sad face might not be sad emotionally.
Type of emotion detected.
Level of confidence in the determination.
Indicates whether or not the face is smiling, and the confidence level in the determination.
Boolean value that indicates whether the face is smiling or not.
Level of confidence in the determination.
The confidence, in percentage, that Amazon Rekognition has that the recognized face is the celebrity.
The known gender identity for the celebrity that matches the provided ID. The known gender identity can be Male, Female, Nonbinary, or Unlisted.
A string value of the KnownGender info about the Celebrity.
Details about each unrecognized face in the image.
Provides face metadata for target image faces that are analyzed by CompareFaces and RecognizeCelebrities .
Bounding box of the face.
Width of the bounding box as a ratio of the overall image width.
Height of the bounding box as a ratio of the overall image height.
Left coordinate of the bounding box as a ratio of overall image width.
Top coordinate of the bounding box as a ratio of overall image height.
Level of confidence that what the bounding box contains is a face.
An array of facial landmarks.
Indicates the location of the landmark on the face.
Type of landmark.
The x-coordinate of the landmark expressed as a ratio of the width of the image. The x-coordinate is measured from the left-side of the image. For example, if the image is 700 pixels wide and the x-coordinate of the landmark is at 350 pixels, this value is 0.5.
The y-coordinate of the landmark expressed as a ratio of the height of the image. The y-coordinate is measured from the top of the image. For example, if the image height is 200 pixels and the y-coordinate of the landmark is at 50 pixels, this value is 0.25.
Indicates the pose of the face as determined by its pitch, roll, and yaw.
Value representing the face rotation on the roll axis.
Value representing the face rotation on the yaw axis.
Value representing the face rotation on the pitch axis.
Identifies face image brightness and sharpness.
Value representing brightness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a brighter face image.
Value representing sharpness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a sharper face image.
The emotions that appear to be expressed on the face, and the confidence level in the determination. Valid values include "Happy", "Sad", "Angry", "Confused", "Disgusted", "Surprised", "Calm", "Unknown", and "Fear".
The emotions that appear to be expressed on the face, and the confidence level in the determination. The API is only making a determination of the physical appearance of a person's face. It is not a determination of the person’s internal emotional state and should not be used in such a way. For example, a person pretending to have a sad face might not be sad emotionally.
Type of emotion detected.
Level of confidence in the determination.
Indicates whether or not the face is smiling, and the confidence level in the determination.
Boolean value that indicates whether the face is smiling or not.
Level of confidence in the determination.
Note
Support for estimating image orientation using the the OrientationCorrection field has ceased as of August 2021. Any returned values for this field included in an API response will always be NULL.
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. 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.
Exceptions
Rekognition.Client.exceptions.InvalidS3ObjectExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.InvalidImageFormatExceptionRekognition.Client.exceptions.ImageTooLargeExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.InvalidImageFormatExceptionsearch_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 Searching for a face using its face ID in the Amazon Rekognition Developer Guide.
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=...
)
[REQUIRED]
ID of the collection the face belongs to.
[REQUIRED]
ID of a face to find matches for in the collection.
dict
Response Syntax
{
'SearchedFaceId': 'string',
'FaceMatches': [
{
'Similarity': ...,
'Face': {
'FaceId': 'string',
'BoundingBox': {
'Width': ...,
'Height': ...,
'Left': ...,
'Top': ...
},
'ImageId': 'string',
'ExternalImageId': 'string',
'Confidence': ...,
'IndexFacesModelVersion': 'string'
}
},
],
'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).
IndexFacesModelVersion (string) --
The version of the face detect and storage model that was used when indexing the face vector.
FaceModelVersion (string) --
Version number of the face detection model associated with the input collection (CollectionId ).
Exceptions
Rekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionExamples
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 IndexFaces operation, and then use the face IDs returned in subsequent calls to the SearchFaces 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 AWS 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.
If no faces are detected in the input image, SearchFacesByImage returns an InvalidParameterException error.
For an example, Searching for a Face Using an Image in the Amazon Rekognition Developer Guide.
The QualityFilter input parameter allows you to filter out detected faces that don’t meet a required quality bar. The quality bar is based on a variety of common use cases. Use QualityFilter to set the quality bar for filtering by specifying LOW , MEDIUM , or HIGH . If you do not want to filter detected faces, specify NONE . The default value is NONE .
Note
To use quality filtering, you need a collection associated with version 3 of the face model or higher. To get the version of the face model associated with a collection, call DescribeCollection .
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=...,
QualityFilter='NONE'|'AUTO'|'LOW'|'MEDIUM'|'HIGH'
)
[REQUIRED]
ID of the collection to search.
[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.
If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.
Blob of image bytes up to 5 MBs.
Identifies an S3 object as the image source.
Name of the S3 bucket.
S3 object key name.
If the bucket is versioning enabled, you can specify the object version.
A filter that specifies a quality bar for how much filtering is done to identify faces. Filtered faces aren't searched for in the collection. If you specify AUTO , Amazon Rekognition chooses the quality bar. If you specify LOW , MEDIUM , or HIGH , filtering removes all faces that don’t meet the chosen quality bar. The quality bar is based on a variety of common use cases. Low-quality detections can occur for a number of reasons. Some examples are an object that's misidentified as a face, a face that's too blurry, or a face with a pose that's too extreme to use. If you specify NONE , no filtering is performed. The default value is NONE .
To use quality filtering, the collection you are using must be associated with version 3 of the face model or higher.
dict
Response Syntax
{
'SearchedFaceBoundingBox': {
'Width': ...,
'Height': ...,
'Left': ...,
'Top': ...
},
'SearchedFaceConfidence': ...,
'FaceMatches': [
{
'Similarity': ...,
'Face': {
'FaceId': 'string',
'BoundingBox': {
'Width': ...,
'Height': ...,
'Left': ...,
'Top': ...
},
'ImageId': 'string',
'ExternalImageId': 'string',
'Confidence': ...,
'IndexFacesModelVersion': 'string'
}
},
],
'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).
IndexFacesModelVersion (string) --
The version of the face detect and storage model that was used when indexing the face vector.
FaceModelVersion (string) --
Version number of the face detection model associated with the input collection (CollectionId ).
Exceptions
Rekognition.Client.exceptions.InvalidS3ObjectExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.ImageTooLargeExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.InvalidImageFormatExceptionExamples
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.
Amazon 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, Amazon 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 GetCelebrityRecognition and pass the job identifier (JobId ) from the initial call to StartCelebrityRecognition .
For more information, see Recognizing celebrities in the Amazon Rekognition Developer Guide.
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'
)
[REQUIRED]
The video in which you want to recognize celebrities. The video must be stored in an Amazon S3 bucket.
The Amazon S3 bucket name and file name for the video.
Name of the S3 bucket.
S3 object key name.
If the bucket is versioning enabled, you can specify the object version.
StartCelebrityRecognition requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.The Amazon SNS topic ARN that you want Amazon Rekognition Video to publish the completion status of the celebrity recognition analysis to. The Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy.
The Amazon SNS topic to which Amazon Rekognition posts the completion status.
The ARN of an IAM role that gives Amazon Rekognition publishing permissions to the Amazon SNS topic.
JobTag to group related jobs and identify them in the completion notification.dict
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 .
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.IdempotentParameterMismatchExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.InvalidS3ObjectExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.VideoTooLargeExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.LimitExceededExceptionRekognition.Client.exceptions.ThrottlingExceptionstart_content_moderation(**kwargs)¶Starts asynchronous detection of inappropriate, unwanted, or offensive content in a stored video. For a list of moderation labels in Amazon Rekognition, see Using the image and video moderation APIs .
Amazon 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 analysis is finished, Amazon 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 analysis, first check that the status value published to the Amazon SNS topic is SUCCEEDED . If so, call GetContentModeration and pass the job identifier (JobId ) from the initial call to StartContentModeration .
For more information, see Moderating content in the Amazon Rekognition Developer Guide.
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'
)
[REQUIRED]
The video in which you want to detect inappropriate, unwanted, or offensive content. The video must be stored in an Amazon S3 bucket.
The Amazon S3 bucket name and file name for the video.
Name of the S3 bucket.
S3 object key name.
If the bucket is versioning enabled, you can specify the object version.
MinConfidence , GetContentModeration returns labels with confidence values greater than or equal to 50 percent.StartContentModeration requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.The Amazon SNS topic ARN that you want Amazon Rekognition Video to publish the completion status of the content analysis to. The Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy to access the topic.
The Amazon SNS topic to which Amazon Rekognition posts the completion status.
The ARN of an IAM role that gives Amazon Rekognition publishing permissions to the Amazon SNS topic.
JobTag to group related jobs and identify them in the completion notification.dict
Response Syntax
{
'JobId': 'string'
}
Response Structure
(dict) --
JobId (string) --
The identifier for the content analysis job. Use JobId to identify the job in a subsequent call to GetContentModeration .
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.IdempotentParameterMismatchExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.InvalidS3ObjectExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.VideoTooLargeExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.LimitExceededExceptionRekognition.Client.exceptions.ThrottlingExceptionstart_face_detection(**kwargs)¶Starts asynchronous detection of faces in a stored video.
Amazon 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, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic that you specify in NotificationChannel . 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 GetFaceDetection and pass the job identifier (JobId ) from the initial call to StartFaceDetection .
For more information, see Detecting faces in a stored video in the Amazon Rekognition Developer Guide.
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'
)
[REQUIRED]
The video in which you want to detect faces. The video must be stored in an Amazon S3 bucket.
The Amazon S3 bucket name and file name for the video.
Name of the S3 bucket.
S3 object key name.
If the bucket is versioning enabled, you can specify the object version.
StartFaceDetection requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.The ARN of the Amazon SNS topic to which you want Amazon Rekognition Video to publish the completion status of the face detection operation. The Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy.
The Amazon SNS topic to which Amazon Rekognition posts the completion status.
The ARN of an IAM role that gives Amazon Rekognition publishing permissions to the Amazon SNS topic.
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 to group related jobs and identify them in the completion notification.dict
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 .
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.IdempotentParameterMismatchExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.InvalidS3ObjectExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.VideoTooLargeExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.LimitExceededExceptionRekognition.Client.exceptions.ThrottlingExceptionstart_face_search(**kwargs)¶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, Amazon 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 GetFaceSearch and pass the job identifier (JobId ) from the initial call to StartFaceSearch . For more information, see Searching stored videos for faces .
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'
)
[REQUIRED]
The video you want to search. The video must be stored in an Amazon S3 bucket.
The Amazon S3 bucket name and file name for the video.
Name of the S3 bucket.
S3 object key name.
If the bucket is versioning enabled, you can specify the object version.
StartFaceSearch requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.[REQUIRED]
ID of the collection that contains the faces you want to search for.
The ARN of the Amazon SNS topic to which you want Amazon Rekognition Video to publish the completion status of the search. The Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy to access the topic.
The Amazon SNS topic to which Amazon Rekognition posts the completion status.
The ARN of an IAM role that gives Amazon Rekognition publishing permissions to the Amazon SNS topic.
JobTag to group related jobs and identify them in the completion notification.dict
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 .
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.IdempotentParameterMismatchExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.InvalidS3ObjectExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.VideoTooLargeExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.LimitExceededExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ThrottlingExceptionstart_label_detection(**kwargs)¶Starts asynchronous detection of labels in a stored video.
Amazon 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, Amazon 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 GetLabelDetection 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'
)
[REQUIRED]
The video in which you want to detect labels. The video must be stored in an Amazon S3 bucket.
The Amazon S3 bucket name and file name for the video.
Name of the S3 bucket.
S3 object key name.
If the bucket is versioning enabled, you can specify the object version.
StartLabelDetection requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.Specifies the minimum confidence that Amazon 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. Amazon 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.
The Amazon SNS topic ARN you want Amazon Rekognition Video to publish the completion status of the label detection operation to. The Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy.
The Amazon SNS topic to which Amazon Rekognition posts the completion status.
The ARN of an IAM role that gives Amazon Rekognition publishing permissions to the Amazon SNS topic.
JobTag to group related jobs and identify them in the completion notification.dict
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 .
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.IdempotentParameterMismatchExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.InvalidS3ObjectExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.VideoTooLargeExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.LimitExceededExceptionRekognition.Client.exceptions.ThrottlingExceptionstart_person_tracking(**kwargs)¶Starts the asynchronous tracking of a person's path in a stored video.
Amazon Rekognition Video can track the path of people 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 GetPersonTracking 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'
)
[REQUIRED]
The video in which you want to detect people. The video must be stored in an Amazon S3 bucket.
The Amazon S3 bucket name and file name for the video.
Name of the S3 bucket.
S3 object key name.
If the bucket is versioning enabled, you can specify the object version.
StartPersonTracking requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.The Amazon SNS topic ARN you want Amazon Rekognition Video to publish the completion status of the people detection operation to. The Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy.
The Amazon SNS topic to which Amazon Rekognition posts the completion status.
The ARN of an IAM role that gives Amazon Rekognition publishing permissions to the Amazon SNS topic.
JobTag to group related jobs and identify them in the completion notification.dict
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 .
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.IdempotentParameterMismatchExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.InvalidS3ObjectExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.VideoTooLargeExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.LimitExceededExceptionRekognition.Client.exceptions.ThrottlingExceptionstart_project_version(**kwargs)¶Starts the running of the version of a model. Starting a model takes a while to complete. To check the current state of the model, use DescribeProjectVersions .
Once the model is running, you can detect custom labels in new images by calling DetectCustomLabels .
Note
You are charged for the amount of time that the model is running. To stop a running model, call StopProjectVersion .
For more information, see Running a trained Amazon Rekognition Custom Labels model in the Amazon Rekognition Custom Labels Guide.
This operation requires permissions to perform the rekognition:StartProjectVersion action.
See also: AWS API Documentation
Request Syntax
response = client.start_project_version(
ProjectVersionArn='string',
MinInferenceUnits=123,
MaxInferenceUnits=123
)
[REQUIRED]
The Amazon Resource Name(ARN) of the model version that you want to start.
[REQUIRED]
The minimum number of inference units to use. A single inference unit represents 1 hour of processing.
For information about the number of transactions per second (TPS) that an inference unit can support, see Running a trained Amazon Rekognition Custom Labels model in the Amazon Rekognition Custom Labels Guide.
Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use.
dict
Response Syntax
{
'Status': 'TRAINING_IN_PROGRESS'|'TRAINING_COMPLETED'|'TRAINING_FAILED'|'STARTING'|'RUNNING'|'FAILED'|'STOPPING'|'STOPPED'|'DELETING'|'COPYING_IN_PROGRESS'|'COPYING_COMPLETED'|'COPYING_FAILED'
}
Response Structure
(dict) --
Status (string) --
The current running status of the model.
Exceptions
Rekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ResourceInUseExceptionRekognition.Client.exceptions.LimitExceededExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionstart_segment_detection(**kwargs)¶Starts asynchronous detection of segment detection in a stored video.
Amazon Rekognition Video can detect segments in a video stored in an Amazon S3 bucket. Use Video to specify the bucket name and the filename of the video. StartSegmentDetection returns a job identifier (JobId ) which you use to get the results of the operation. When segment detection is finished, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic that you specify in NotificationChannel .
You can use the Filters ( StartSegmentDetectionFilters ) input parameter to specify the minimum detection confidence returned in the response. Within Filters , use ShotFilter ( StartShotDetectionFilter ) to filter detected shots. Use TechnicalCueFilter ( StartTechnicalCueDetectionFilter ) to filter technical cues.
To get the results of the segment detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED . if so, call GetSegmentDetection and pass the job identifier (JobId ) from the initial call to StartSegmentDetection .
For more information, see Detecting video segments in stored video in the Amazon Rekognition Developer Guide.
See also: AWS API Documentation
Request Syntax
response = client.start_segment_detection(
Video={
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
},
ClientRequestToken='string',
NotificationChannel={
'SNSTopicArn': 'string',
'RoleArn': 'string'
},
JobTag='string',
Filters={
'TechnicalCueFilter': {
'MinSegmentConfidence': ...,
'BlackFrame': {
'MaxPixelThreshold': ...,
'MinCoveragePercentage': ...
}
},
'ShotFilter': {
'MinSegmentConfidence': ...
}
},
SegmentTypes=[
'TECHNICAL_CUE'|'SHOT',
]
)
[REQUIRED]
Video file stored in an Amazon S3 bucket. Amazon Rekognition video start operations such as StartLabelDetection use Video to specify a video for analysis. The supported file formats are .mp4, .mov and .avi.
The Amazon S3 bucket name and file name for the video.
Name of the S3 bucket.
S3 object key name.
If the bucket is versioning enabled, you can specify the object version.
StartSegmentDetection requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.The ARN of the Amazon SNS topic to which you want Amazon Rekognition Video to publish the completion status of the segment detection operation. Note that the Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy to access the topic.
The Amazon SNS topic to which Amazon Rekognition posts the completion status.
The ARN of an IAM role that gives Amazon Rekognition publishing permissions to the Amazon SNS topic.
JobTag to group related jobs and identify them in the completion notification.Filters for technical cue or shot detection.
Filters that are specific to technical cues.
Specifies the minimum confidence that Amazon Rekognition Video must have in order to return a detected segment. Confidence represents how certain Amazon Rekognition is that a segment is correctly identified. 0 is the lowest confidence. 100 is the highest confidence. Amazon Rekognition Video doesn't return any segments with a confidence level lower than this specified value.
If you don't specify MinSegmentConfidence , GetSegmentDetection returns segments with confidence values greater than or equal to 50 percent.
A filter that allows you to control the black frame detection by specifying the black levels and pixel coverage of black pixels in a frame. Videos can come from multiple sources, formats, and time periods, with different standards and varying noise levels for black frames that need to be accounted for.
A threshold used to determine the maximum luminance value for a pixel to be considered black. In a full color range video, luminance values range from 0-255. A pixel value of 0 is pure black, and the most strict filter. The maximum black pixel value is computed as follows: max_black_pixel_value = minimum_luminance + MaxPixelThreshold *luminance_range.
For example, for a full range video with BlackPixelThreshold = 0.1, max_black_pixel_value is 0 + 0.1 * (255-0) = 25.5.
The default value of MaxPixelThreshold is 0.2, which maps to a max_black_pixel_value of 51 for a full range video. You can lower this threshold to be more strict on black levels.
The minimum percentage of pixels in a frame that need to have a luminance below the max_black_pixel_value for a frame to be considered a black frame. Luminance is calculated using the BT.709 matrix.
The default value is 99, which means at least 99% of all pixels in the frame are black pixels as per the MaxPixelThreshold set. You can reduce this value to allow more noise on the black frame.
Filters that are specific to shot detections.
Specifies the minimum confidence that Amazon Rekognition Video must have in order to return a detected segment. Confidence represents how certain Amazon Rekognition is that a segment is correctly identified. 0 is the lowest confidence. 100 is the highest confidence. Amazon Rekognition Video doesn't return any segments with a confidence level lower than this specified value.
If you don't specify MinSegmentConfidence , the GetSegmentDetection returns segments with confidence values greater than or equal to 50 percent.
[REQUIRED]
An array of segment types to detect in the video. Valid values are TECHNICAL_CUE and SHOT.
dict
Response Syntax
{
'JobId': 'string'
}
Response Structure
(dict) --
JobId (string) --
Unique identifier for the segment detection job. The JobId is returned from StartSegmentDetection .
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.IdempotentParameterMismatchExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.InvalidS3ObjectExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.VideoTooLargeExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.LimitExceededExceptionRekognition.Client.exceptions.ThrottlingExceptionstart_stream_processor(**kwargs)¶Starts processing a stream processor. You create a stream processor by calling CreateStreamProcessor . To tell StartStreamProcessor which stream processor to start, use the value of the Name field specified in the call to CreateStreamProcessor .
If you are using a label detection stream processor to detect labels, you need to provide a Start selector and a Stop selector to determine the length of the stream processing time.
See also: AWS API Documentation
Request Syntax
response = client.start_stream_processor(
Name='string',
StartSelector={
'KVSStreamStartSelector': {
'ProducerTimestamp': 123,
'FragmentNumber': 'string'
}
},
StopSelector={
'MaxDurationInSeconds': 123
}
)
[REQUIRED]
The name of the stream processor to start processing.
Specifies the starting point in the Kinesis stream to start processing. You can use the producer timestamp or the fragment number. For more information, see Fragment .
This is a required parameter for label detection stream processors and should not be used to start a face search stream processor.
Specifies the starting point in the stream to start processing. This can be done with a timestamp or a fragment number in a Kinesis stream.
The timestamp from the producer corresponding to the fragment.
The unique identifier of the fragment. This value monotonically increases based on the ingestion order.
Specifies when to stop processing the stream. You can specify a maximum amount of time to process the video.
This is a required parameter for label detection stream processors and should not be used to start a face search stream processor.
Specifies the maximum amount of time in seconds that you want the stream to be processed. The largest amount of time is 2 minutes. The default is 10 seconds.
dict
Response Syntax
{
'SessionId': 'string'
}
Response Structure
(dict) --
SessionId (string) --
A unique identifier for the stream processing session.
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ResourceInUseExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionstart_text_detection(**kwargs)¶Starts asynchronous detection of text in a stored video.
Amazon Rekognition Video can detect text in a video stored in an Amazon S3 bucket. Use Video to specify the bucket name and the filename of the video. StartTextDetection returns a job identifier (JobId ) which you use to get the results of the operation. When text detection is finished, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic that you specify in NotificationChannel .
To get the results of the text detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED . if so, call GetTextDetection and pass the job identifier (JobId ) from the initial call to StartTextDetection .
See also: AWS API Documentation
Request Syntax
response = client.start_text_detection(
Video={
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
},
ClientRequestToken='string',
NotificationChannel={
'SNSTopicArn': 'string',
'RoleArn': 'string'
},
JobTag='string',
Filters={
'WordFilter': {
'MinConfidence': ...,
'MinBoundingBoxHeight': ...,
'MinBoundingBoxWidth': ...
},
'RegionsOfInterest': [
{
'BoundingBox': {
'Width': ...,
'Height': ...,
'Left': ...,
'Top': ...
},
'Polygon': [
{
'X': ...,
'Y': ...
},
]
},
]
}
)
[REQUIRED]
Video file stored in an Amazon S3 bucket. Amazon Rekognition video start operations such as StartLabelDetection use Video to specify a video for analysis. The supported file formats are .mp4, .mov and .avi.
The Amazon S3 bucket name and file name for the video.
Name of the S3 bucket.
S3 object key name.
If the bucket is versioning enabled, you can specify the object version.
StartTextDetection requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidentaly started more than once.The Amazon Simple Notification Service topic to which Amazon Rekognition publishes the completion status of a video analysis operation. For more information, see Calling Amazon Rekognition Video operations . Note that the Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy to access the topic. For more information, see Giving access to multiple Amazon SNS topics .
The Amazon SNS topic to which Amazon Rekognition posts the completion status.
The ARN of an IAM role that gives Amazon Rekognition publishing permissions to the Amazon SNS topic.
JobTag to group related jobs and identify them in the completion notification.Optional parameters that let you set criteria the text must meet to be included in your response.
Filters focusing on qualities of the text, such as confidence or size.
Sets the confidence of word detection. Words with detection confidence below this will be excluded from the result. Values should be between 0 and 100. The default MinConfidence is 80.
Sets the minimum height of the word bounding box. Words with bounding box heights lesser than this value will be excluded from the result. Value is relative to the video frame height.
Sets the minimum width of the word bounding box. Words with bounding boxes widths lesser than this value will be excluded from the result. Value is relative to the video frame width.
Filter focusing on a certain area of the frame. Uses a BoundingBox object to set the region of the screen.
Specifies a location within the frame that Rekognition checks for objects of interest such as text, labels, or faces. It uses a BoundingBox or Polygon to set a region of the screen.
A word, face, or label is included in the region if it is more than half in that region. If there is more than one region, the word, face, or label is compared with all regions of the screen. Any object of interest that is more than half in a region is kept in the results.
The box representing a region of interest on screen.
Width of the bounding box as a ratio of the overall image width.
Height of the bounding box as a ratio of the overall image height.
Left coordinate of the bounding box as a ratio of overall image width.
Top coordinate of the bounding box as a ratio of overall image height.
Specifies a shape made up of up to 10 Point objects to define a region of interest.
The X and Y coordinates of a point on an image or video frame. The X and Y values are ratios of the overall image size or video resolution. For example, if an input image is 700x200 and the values are 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 makes up a Polygon . A Polygon is returned by DetectText and by DetectCustomLabels Polygon represents a fine-grained polygon around a detected item. For more information, see Geometry in the Amazon Rekognition Developer Guide.
The value of the X coordinate for a point on a Polygon .
The value of the Y coordinate for a point on a Polygon .
dict
Response Syntax
{
'JobId': 'string'
}
Response Structure
(dict) --
JobId (string) --
Identifier for the text detection job. Use JobId to identify the job in a subsequent call to GetTextDetection .
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.IdempotentParameterMismatchExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.InvalidS3ObjectExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.VideoTooLargeExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.LimitExceededExceptionRekognition.Client.exceptions.ThrottlingExceptionstop_project_version(**kwargs)¶Stops a running model. The operation might take a while to complete. To check the current status, call DescribeProjectVersions .
See also: AWS API Documentation
Request Syntax
response = client.stop_project_version(
ProjectVersionArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the model version that you want to delete.
This operation requires permissions to perform the rekognition:StopProjectVersion action.
{
'Status': 'TRAINING_IN_PROGRESS'|'TRAINING_COMPLETED'|'TRAINING_FAILED'|'STARTING'|'RUNNING'|'FAILED'|'STOPPING'|'STOPPED'|'DELETING'|'COPYING_IN_PROGRESS'|'COPYING_COMPLETED'|'COPYING_FAILED'
}
Response Structure
The current status of the stop operation.
Exceptions
Rekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ResourceInUseExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionstop_stream_processor(**kwargs)¶Stops a running stream processor that was created by CreateStreamProcessor .
See also: AWS API Documentation
Request Syntax
response = client.stop_stream_processor(
Name='string'
)
[REQUIRED]
The name of a stream processor created by CreateStreamProcessor .
{}
Response Structure
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ResourceInUseExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptiontag_resource(**kwargs)¶Adds one or more key-value tags to an Amazon Rekognition collection, stream processor, or Custom Labels model. For more information, see Tagging AWS Resources .
This operation requires permissions to perform the rekognition:TagResource action.
See also: AWS API Documentation
Request Syntax
response = client.tag_resource(
ResourceArn='string',
Tags={
'string': 'string'
}
)
[REQUIRED]
Amazon Resource Name (ARN) of the model, collection, or stream processor that you want to assign the tags to.
[REQUIRED]
The key-value tags to assign to the resource.
dict
Response Syntax
{}
Response Structure
Exceptions
Rekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.ServiceQuotaExceededExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionuntag_resource(**kwargs)¶Removes one or more tags from an Amazon Rekognition collection, stream processor, or Custom Labels model.
This operation requires permissions to perform the rekognition:UntagResource action.
See also: AWS API Documentation
Request Syntax
response = client.untag_resource(
ResourceArn='string',
TagKeys=[
'string',
]
)
[REQUIRED]
Amazon Resource Name (ARN) of the model, collection, or stream processor that you want to remove the tags from.
[REQUIRED]
A list of the tags that you want to remove.
dict
Response Syntax
{}
Response Structure
Exceptions
Rekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionupdate_dataset_entries(**kwargs)¶Adds or updates one or more entries (images) in a dataset. An entry is a JSON Line which contains the information for a single image, including the image location, assigned labels, and object location bounding boxes. For more information, see Image-Level labels in manifest files and Object localization in manifest files in the Amazon Rekognition Custom Labels Developer Guide .
If the source-ref field in the JSON line references an existing image, the existing image in the dataset is updated. If source-ref field doesn't reference an existing image, the image is added as a new image to the dataset.
You specify the changes that you want to make in the Changes input parameter. There isn't a limit to the number JSON Lines that you can change, but the size of Changes must be less than 5MB.
UpdateDatasetEntriesreturns immediatly, but the dataset update might take a while to complete. Use DescribeDataset to check the current status. The dataset updated successfully if the value ofStatusisUPDATE_COMPLETE.
To check if any non-terminal errors occured, call ListDatasetEntries and check for the presence of errors lists in the JSON Lines.
Dataset update fails if a terminal error occurs (Status = UPDATE_FAILED ). Currently, you can't access the terminal error information from the Amazon Rekognition Custom Labels SDK.
This operation requires permissions to perform the rekognition:UpdateDatasetEntries action.
See also: AWS API Documentation
Request Syntax
response = client.update_dataset_entries(
DatasetArn='string',
Changes={
'GroundTruth': b'bytes'
}
)
[REQUIRED]
The Amazon Resource Name (ARN) of the dataset that you want to update.
[REQUIRED]
The changes that you want to make to the dataset.
A Base64-encoded binary data object containing one or JSON lines that either update the dataset or are additions to the dataset. You change a dataset by calling UpdateDatasetEntries . If you are using an AWS SDK to call UpdateDatasetEntries , you don't need to encode Changes as the SDK encodes the data for you.
For example JSON lines, see Image-Level labels in manifest files and and Object localization in manifest files in the Amazon Rekognition Custom Labels Developer Guide .
dict
Response Syntax
{}
Response Structure
Exceptions
Rekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.LimitExceededExceptionRekognition.Client.exceptions.ResourceInUseExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionupdate_stream_processor(**kwargs)¶Allows you to update a stream processor. You can change some settings and regions of interest and delete certain parameters.
See also: AWS API Documentation
Request Syntax
response = client.update_stream_processor(
Name='string',
SettingsForUpdate={
'ConnectedHomeForUpdate': {
'Labels': [
'string',
],
'MinConfidence': ...
}
},
RegionsOfInterestForUpdate=[
{
'BoundingBox': {
'Width': ...,
'Height': ...,
'Left': ...,
'Top': ...
},
'Polygon': [
{
'X': ...,
'Y': ...
},
]
},
],
DataSharingPreferenceForUpdate={
'OptIn': True|False
},
ParametersToDelete=[
'ConnectedHomeMinConfidence'|'RegionsOfInterest',
]
)
[REQUIRED]
Name of the stream processor that you want to update.
The stream processor settings that you want to update. Label detection settings can be updated to detect different labels with a different minimum confidence.
The label detection settings you want to use for your stream processor.
Specifies what you want to detect in the video, such as people, packages, or pets. The current valid labels you can include in this list are: "PERSON", "PET", "PACKAGE", and "ALL".
The minimum confidence required to label an object in the video.
Specifies locations in the frames where Amazon Rekognition checks for objects or people. This is an optional parameter for label detection stream processors.
Specifies a location within the frame that Rekognition checks for objects of interest such as text, labels, or faces. It uses a BoundingBox or Polygon to set a region of the screen.
A word, face, or label is included in the region if it is more than half in that region. If there is more than one region, the word, face, or label is compared with all regions of the screen. Any object of interest that is more than half in a region is kept in the results.
The box representing a region of interest on screen.
Width of the bounding box as a ratio of the overall image width.
Height of the bounding box as a ratio of the overall image height.
Left coordinate of the bounding box as a ratio of overall image width.
Top coordinate of the bounding box as a ratio of overall image height.
Specifies a shape made up of up to 10 Point objects to define a region of interest.
The X and Y coordinates of a point on an image or video frame. The X and Y values are ratios of the overall image size or video resolution. For example, if an input image is 700x200 and the values are 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 makes up a Polygon . A Polygon is returned by DetectText and by DetectCustomLabels Polygon represents a fine-grained polygon around a detected item. For more information, see Geometry in the Amazon Rekognition Developer Guide.
The value of the X coordinate for a point on a Polygon .
The value of the Y coordinate for a point on a Polygon .
Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams.
If this option is set to true, you choose to share data with Rekognition to improve model performance.
A list of parameters you want to delete from the stream processor.
dict
Response Syntax
{}
Response Structure
Exceptions
Rekognition.Client.exceptions.AccessDeniedExceptionRekognition.Client.exceptions.InternalServerErrorRekognition.Client.exceptions.ThrottlingExceptionRekognition.Client.exceptions.InvalidParameterExceptionRekognition.Client.exceptions.ResourceNotFoundExceptionRekognition.Client.exceptions.ProvisionedThroughputExceededExceptionThe available paginators are:
Rekognition.Paginator.DescribeProjectVersionsRekognition.Paginator.DescribeProjectsRekognition.Paginator.ListCollectionsRekognition.Paginator.ListDatasetEntriesRekognition.Paginator.ListDatasetLabelsRekognition.Paginator.ListFacesRekognition.Paginator.ListProjectPoliciesRekognition.Paginator.ListStreamProcessorsRekognition.Paginator.DescribeProjectVersions¶paginator = client.get_paginator('describe_project_versions')
paginate(**kwargs)¶Creates an iterator that will paginate through responses from Rekognition.Client.describe_project_versions().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
ProjectArn='string',
VersionNames=[
'string',
],
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
[REQUIRED]
The Amazon Resource Name (ARN) of the project that contains the models you want to describe.
A list of model version names that you want to describe. You can add up to 10 model version names to the list. If you don't specify a value, all model descriptions are returned. A version name is part of a model (ProjectVersion) ARN. For example, my-model.2020-01-21T09.10.15 is the version name in the following ARN. arn:aws:rekognition:us-east-1:123456789012:project/getting-started/version/*my-model.2020-01-21T09.10.15* /1234567890123 .
A dictionary that provides parameters to control pagination.
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.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'ProjectVersionDescriptions': [
{
'ProjectVersionArn': 'string',
'CreationTimestamp': datetime(2015, 1, 1),
'MinInferenceUnits': 123,
'Status': 'TRAINING_IN_PROGRESS'|'TRAINING_COMPLETED'|'TRAINING_FAILED'|'STARTING'|'RUNNING'|'FAILED'|'STOPPING'|'STOPPED'|'DELETING'|'COPYING_IN_PROGRESS'|'COPYING_COMPLETED'|'COPYING_FAILED',
'StatusMessage': 'string',
'BillableTrainingTimeInSeconds': 123,
'TrainingEndTimestamp': datetime(2015, 1, 1),
'OutputConfig': {
'S3Bucket': 'string',
'S3KeyPrefix': 'string'
},
'TrainingDataResult': {
'Input': {
'Assets': [
{
'GroundTruthManifest': {
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
}
},
]
},
'Output': {
'Assets': [
{
'GroundTruthManifest': {
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
}
},
]
},
'Validation': {
'Assets': [
{
'GroundTruthManifest': {
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
}
},
]
}
},
'TestingDataResult': {
'Input': {
'Assets': [
{
'GroundTruthManifest': {
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
}
},
],
'AutoCreate': True|False
},
'Output': {
'Assets': [
{
'GroundTruthManifest': {
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
}
},
],
'AutoCreate': True|False
},
'Validation': {
'Assets': [
{
'GroundTruthManifest': {
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
}
},
]
}
},
'EvaluationResult': {
'F1Score': ...,
'Summary': {
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
}
},
'ManifestSummary': {
'S3Object': {
'Bucket': 'string',
'Name': 'string',
'Version': 'string'
}
},
'KmsKeyId': 'string',
'MaxInferenceUnits': 123,
'SourceProjectVersionArn': 'string'
},
],
}
Response Structure
(dict) --
ProjectVersionDescriptions (list) --
A list of model descriptions. The list is sorted by the creation date and time of the model versions, latest to earliest.
(dict) --
A description of a version of an Amazon Rekognition Custom Labels model.
ProjectVersionArn (string) --
The Amazon Resource Name (ARN) of the model version.
CreationTimestamp (datetime) --
The Unix datetime for the date and time that training started.
MinInferenceUnits (integer) --
The minimum number of inference units used by the model. For more information, see StartProjectVersion .
Status (string) --
The current status of the model version.
StatusMessage (string) --
A descriptive message for an error or warning that occurred.
BillableTrainingTimeInSeconds (integer) --
The duration, in seconds, that you were billed for a successful training of the model version. This value is only returned if the model version has been successfully trained.
TrainingEndTimestamp (datetime) --
The Unix date and time that training of the model ended.
OutputConfig (dict) --
The location where training results are saved.
S3Bucket (string) --
The S3 bucket where training output is placed.
S3KeyPrefix (string) --
The prefix applied to the training output files.
TrainingDataResult (dict) --
Contains information about the training results.
Input (dict) --
The training assets that you supplied for training.
Assets (list) --
A Sagemaker GroundTruth manifest file that contains the training images (assets).
(dict) --
Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.
GroundTruthManifest (dict) --
The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.
S3Object (dict) --
Provides the S3 bucket name and object name.
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.
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.
Output (dict) --
The images (assets) that were actually trained by Amazon Rekognition Custom Labels.
Assets (list) --
A Sagemaker GroundTruth manifest file that contains the training images (assets).
(dict) --
Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.
GroundTruthManifest (dict) --
The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.
S3Object (dict) --
Provides the S3 bucket name and object name.
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.
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.
Validation (dict) --
The location of the data validation manifest. The data validation manifest is created for the training dataset during model training.
Assets (list) --
The assets that comprise the validation data.
(dict) --
Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.
GroundTruthManifest (dict) --
The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.
S3Object (dict) --
Provides the S3 bucket name and object name.
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.
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.
TestingDataResult (dict) --
Contains information about the testing results.
Input (dict) --
The testing dataset that was supplied for training.
Assets (list) --
The assets used for testing.
(dict) --
Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.
GroundTruthManifest (dict) --
The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.
S3Object (dict) --
Provides the S3 bucket name and object name.
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.
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.
AutoCreate (boolean) --
If specified, Amazon Rekognition Custom Labels temporarily splits the training dataset (80%) to create a test dataset (20%) for the training job. After training completes, the test dataset is not stored and the training dataset reverts to its previous size.
Output (dict) --
The subset of the dataset that was actually tested. Some images (assets) might not be tested due to file formatting and other issues.
Assets (list) --
The assets used for testing.
(dict) --
Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.
GroundTruthManifest (dict) --
The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.
S3Object (dict) --
Provides the S3 bucket name and object name.
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.
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.
AutoCreate (boolean) --
If specified, Amazon Rekognition Custom Labels temporarily splits the training dataset (80%) to create a test dataset (20%) for the training job. After training completes, the test dataset is not stored and the training dataset reverts to its previous size.
Validation (dict) --
The location of the data validation manifest. The data validation manifest is created for the test dataset during model training.
Assets (list) --
The assets that comprise the validation data.
(dict) --
Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.
GroundTruthManifest (dict) --
The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.
S3Object (dict) --
Provides the S3 bucket name and object name.
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.
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.
EvaluationResult (dict) --
The training results. EvaluationResult is only returned if training is successful.
F1Score (float) --
The F1 score for the evaluation of all labels. The F1 score metric evaluates the overall precision and recall performance of the model as a single value. A higher value indicates better precision and recall performance. A lower score indicates that precision, recall, or both are performing poorly.
Summary (dict) --
The S3 bucket that contains the training summary.
S3Object (dict) --
Provides the S3 bucket name and object name.
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.
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.
ManifestSummary (dict) --
The location of the summary manifest. The summary manifest provides aggregate data validation results for the training and test datasets.
S3Object (dict) --
Provides the S3 bucket name and object name.
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.
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.
KmsKeyId (string) --
The identifer for the AWS Key Management Service key (AWS KMS key) that was used to encrypt the model during training.
MaxInferenceUnits (integer) --
The maximum number of inference units Amazon Rekognition Custom Labels uses to auto-scale the model. For more information, see StartProjectVersion .
SourceProjectVersionArn (string) --
If the model version was copied from a different project, SourceProjectVersionArn contains the ARN of the source model version.
Rekognition.Paginator.DescribeProjects¶paginator = client.get_paginator('describe_projects')
paginate(**kwargs)¶Creates an iterator that will paginate through responses from Rekognition.Client.describe_projects().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
ProjectNames=[
'string',
],
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A list of the projects that you want Amazon Rekognition Custom Labels to describe. If you don't specify a value, the response includes descriptions for all the projects in your AWS account.
A dictionary that provides parameters to control pagination.
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.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'ProjectDescriptions': [
{
'ProjectArn': 'string',
'CreationTimestamp': datetime(2015, 1, 1),
'Status': 'CREATING'|'CREATED'|'DELETING',
'Datasets': [
{
'CreationTimestamp': datetime(2015, 1, 1),
'DatasetType': 'TRAIN'|'TEST',
'DatasetArn': 'string',
'Status': 'CREATE_IN_PROGRESS'|'CREATE_COMPLETE'|'CREATE_FAILED'|'UPDATE_IN_PROGRESS'|'UPDATE_COMPLETE'|'UPDATE_FAILED'|'DELETE_IN_PROGRESS',
'StatusMessage': 'string',
'StatusMessageCode': 'SUCCESS'|'SERVICE_ERROR'|'CLIENT_ERROR'
},
]
},
],
}
Response Structure
(dict) --
ProjectDescriptions (list) --
A list of project descriptions. The list is sorted by the date and time the projects are created.
(dict) --
A description of an Amazon Rekognition Custom Labels project. For more information, see DescribeProjects .
ProjectArn (string) --
The Amazon Resource Name (ARN) of the project.
CreationTimestamp (datetime) --
The Unix timestamp for the date and time that the project was created.
Status (string) --
The current status of the project.
Datasets (list) --
Information about the training and test datasets in the project.
(dict) --
Summary information for an Amazon Rekognition Custom Labels dataset. For more information, see ProjectDescription .
CreationTimestamp (datetime) --
The Unix timestamp for the date and time that the dataset was created.
DatasetType (string) --
The type of the dataset.
DatasetArn (string) --
The Amazon Resource Name (ARN) for the dataset.
Status (string) --
The status for the dataset.
StatusMessage (string) --
The status message for the dataset.
StatusMessageCode (string) --
The status message code for the dataset operation. If a service error occurs, try the API call again later. If a client error occurs, check the input parameters to the dataset API call that failed.
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'
}
)
A dictionary that provides parameters to control pagination.
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.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
{
'CollectionIds': [
'string',
],
'FaceModelVersions': [
'string',
]
}
Response Structure
An array of collection IDs.
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] .
Rekognition.Paginator.ListDatasetEntries¶paginator = client.get_paginator('list_dataset_entries')
paginate(**kwargs)¶Creates an iterator that will paginate through responses from Rekognition.Client.list_dataset_entries().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
DatasetArn='string',
ContainsLabels=[
'string',
],
Labeled=True|False,
SourceRefContains='string',
HasErrors=True|False,
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
[REQUIRED]
The Amazon Resource Name (ARN) for the dataset that you want to use.
Specifies a label filter for the response. The response includes an entry only if one or more of the labels in ContainsLabels exist in the entry.
true to get only the JSON Lines where the image is labeled. Specify false to get only the JSON Lines where the image isn't labeled. If you don't specify Labeled , ListDatasetEntries returns JSON Lines for labeled and unlabeled images.ListDatasetEntries only returns JSON Lines where the value of SourceRefContains is part of the source-ref field. The source-ref field contains the Amazon S3 location of the image. You can use SouceRefContains for tasks such as getting the JSON Line for a single image, or gettting JSON Lines for all images within a specific folder.True to only include entries that have errors.A dictionary that provides parameters to control pagination.
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.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'DatasetEntries': [
'string',
],
}
Response Structure
(dict) --
DatasetEntries (list) --
A list of entries (images) in the dataset.
Rekognition.Paginator.ListDatasetLabels¶paginator = client.get_paginator('list_dataset_labels')
paginate(**kwargs)¶Creates an iterator that will paginate through responses from Rekognition.Client.list_dataset_labels().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
DatasetArn='string',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
[REQUIRED]
The Amazon Resource Name (ARN) of the dataset that you want to use.
A dictionary that provides parameters to control pagination.
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.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'DatasetLabelDescriptions': [
{
'LabelName': 'string',
'LabelStats': {
'EntryCount': 123,
'BoundingBoxCount': 123
}
},
],
}
Response Structure
(dict) --
DatasetLabelDescriptions (list) --
A list of the labels in the dataset.
(dict) --
Describes a dataset label. For more information, see ListDatasetLabels .
LabelName (string) --
The name of the label.
LabelStats (dict) --
Statistics about the label.
EntryCount (integer) --
The total number of images that use the label.
BoundingBoxCount (integer) --
The total number of images that have the label assigned to a bounding box.
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'
}
)
[REQUIRED]
ID of the collection from which to list the faces.
A dictionary that provides parameters to control pagination.
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.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'Faces': [
{
'FaceId': 'string',
'BoundingBox': {
'Width': ...,
'Height': ...,
'Left': ...,
'Top': ...
},
'ImageId': 'string',
'ExternalImageId': 'string',
'Confidence': ...,
'IndexFacesModelVersion': '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).
IndexFacesModelVersion (string) --
The version of the face detect and storage model that was used when indexing the face vector.
FaceModelVersion (string) --
Version number of the face detection model associated with the input collection (CollectionId ).
Rekognition.Paginator.ListProjectPolicies¶paginator = client.get_paginator('list_project_policies')
paginate(**kwargs)¶Creates an iterator that will paginate through responses from Rekognition.Client.list_project_policies().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
ProjectArn='string',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
[REQUIRED]
The ARN of the project for which you want to list the project policies.
A dictionary that provides parameters to control pagination.
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.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'ProjectPolicies': [
{
'ProjectArn': 'string',
'PolicyName': 'string',
'PolicyRevisionId': 'string',
'PolicyDocument': 'string',
'CreationTimestamp': datetime(2015, 1, 1),
'LastUpdatedTimestamp': datetime(2015, 1, 1)
},
],
}
Response Structure
(dict) --
ProjectPolicies (list) --
A list of project policies attached to the project.
(dict) --
Describes a project policy in the response from ListProjectPolicies .
ProjectArn (string) --
The Amazon Resource Name (ARN) of the project to which the project policy is attached.
PolicyName (string) --
The name of the project policy.
PolicyRevisionId (string) --
The revision ID of the project policy.
PolicyDocument (string) --
The JSON document for the project policy.
CreationTimestamp (datetime) --
The Unix datetime for the creation of the project policy.
LastUpdatedTimestamp (datetime) --
The Unix datetime for when the project policy was last updated.
Rekognition.Paginator.ListStreamProcessors¶paginator = client.get_paginator('list_stream_processors')
paginate(**kwargs)¶Creates an iterator that will paginate through responses from Rekognition.Client.list_stream_processors().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
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.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
{
'StreamProcessors': [
{
'Name': 'string',
'Status': 'STOPPED'|'STARTING'|'RUNNING'|'FAILED'|'STOPPING'|'UPDATING'
},
]
}
Response Structure
List of stream processors that you have created.
An object that recognizes faces or labels in a streaming video. An Amazon Rekognition stream processor is created by a call to CreateStreamProcessor . 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 of the Amazon Rekognition stream processor.
Current status of the Amazon Rekognition stream processor.
The available waiters are:
Rekognition.Waiter.ProjectVersionRunning¶waiter = client.get_waiter('project_version_running')
wait(**kwargs)¶Polls Rekognition.Client.describe_project_versions() every 30 seconds until a successful state is reached. An error is returned after 40 failed checks.
See also: AWS API Documentation
Request Syntax
waiter.wait(
ProjectArn='string',
VersionNames=[
'string',
],
NextToken='string',
MaxResults=123,
WaiterConfig={
'Delay': 123,
'MaxAttempts': 123
}
)
[REQUIRED]
The Amazon Resource Name (ARN) of the project that contains the models you want to describe.
A list of model version names that you want to describe. You can add up to 10 model version names to the list. If you don't specify a value, all model descriptions are returned. A version name is part of a model (ProjectVersion) ARN. For example, my-model.2020-01-21T09.10.15 is the version name in the following ARN. arn:aws:rekognition:us-east-1:123456789012:project/getting-started/version/*my-model.2020-01-21T09.10.15* /1234567890123 .
A dictionary that provides parameters to control waiting behavior.
The amount of time in seconds to wait between attempts. Default: 30
The maximum number of attempts to be made. Default: 40
None
Rekognition.Waiter.ProjectVersionTrainingCompleted¶waiter = client.get_waiter('project_version_training_completed')
wait(**kwargs)¶Polls Rekognition.Client.describe_project_versions() every 120 seconds until a successful state is reached. An error is returned after 360 failed checks.
See also: AWS API Documentation
Request Syntax
waiter.wait(
ProjectArn='string',
VersionNames=[
'string',
],
NextToken='string',
MaxResults=123,
WaiterConfig={
'Delay': 123,
'MaxAttempts': 123
}
)
[REQUIRED]
The Amazon Resource Name (ARN) of the project that contains the models you want to describe.
A list of model version names that you want to describe. You can add up to 10 model version names to the list. If you don't specify a value, all model descriptions are returned. A version name is part of a model (ProjectVersion) ARN. For example, my-model.2020-01-21T09.10.15 is the version name in the following ARN. arn:aws:rekognition:us-east-1:123456789012:project/getting-started/version/*my-model.2020-01-21T09.10.15* /1234567890123 .
A dictionary that provides parameters to control waiting behavior.
The amount of time in seconds to wait between attempts. Default: 120
The maximum number of attempts to be made. Default: 360
None