View Source AWS.Rekognition (aws-elixir v0.13.3)
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 Image
DescribeCollection DetectFaces
DetectLabels DetectModerationLabels
DetectProtectiveEquipment DetectText
RecognizeCelebrities SearchFaces
amazon-rekognition-custom-labels
Amazon Rekognition Custom Labels
CreateProjectVersion DeleteDataset
DeleteProject DeleteProjectPolicy
DeleteProjectVersion DescribeDataset
DescribeProjects DescribeProjectVersions
DetectCustomLabels DistributeDatasetEntries
ListDatasetEntries ListDatasetLabels
ListProjectPolicies PutProjectPolicy
StartProjectVersion StopProjectVersion
amazon-rekognition-video-stored-video
Amazon Rekognition Video Stored Video
GetContentModeration GetFaceDetection
GetFaceSearch GetLabelDetection
GetPersonTracking GetSegmentDetection
GetTextDetection StartCelebrityRecognition
StartContentModeration StartFaceDetection
StartFaceSearch StartLabelDetection
StartPersonTracking StartSegmentDetection
amazon-rekognition-video-streaming-video
Amazon Rekognition Video Streaming Video
DeleteStreamProcessor DescribeStreamProcessor
Link to this section Summary
Functions
Compares a face in the source input image with each of the 100 largest faces detected in the target input image.
Copies a version of an Amazon Rekognition Custom Labels model from a source project to a destination project.
Creates a collection in an AWS Region.
Creates a new Amazon Rekognition Custom Labels dataset.
This API operation initiates a Face Liveness session.
Creates a new Amazon Rekognition Custom Labels project.
Creates a new version of a model and begins training.
Creates an Amazon Rekognition stream processor that you can use to detect and recognize faces or to detect labels in a streaming video.
Deletes the specified collection.
Deletes an existing Amazon Rekognition Custom Labels dataset.
Deletes faces from a collection.
Deletes an Amazon Rekognition Custom Labels project.
Deletes an existing project policy.
Deletes an Amazon Rekognition Custom Labels model.
Deletes the stream processor identified by Name
.
Describes the specified collection.
Describes an Amazon Rekognition Custom Labels dataset.
Lists and describes the versions of a model in an Amazon Rekognition Custom Labels project.
Gets information about your Amazon Rekognition Custom Labels projects.
Provides information about a stream processor created by
CreateStreamProcessor
.
Detects custom labels in a supplied image by using an Amazon Rekognition Custom Labels model.
Detects faces within an image that is provided as input.
Detects instances of real-world entities within an image (JPEG or PNG) provided as input.
Detects unsafe content in a specified JPEG or PNG format image.
Detects Personal Protective Equipment (PPE) worn by people detected in an image.
Detects text in the input image and converts it into machine-readable text.
Distributes the entries (images) in a training dataset across the training dataset and the test dataset for a project.
Gets the name and additional information about a celebrity based on their Amazon Rekognition ID.
Gets the celebrity recognition results for a Amazon Rekognition Video analysis
started by StartCelebrityRecognition
.
Gets the inappropriate, unwanted, or offensive content analysis results for a
Amazon Rekognition Video analysis started by StartContentModeration
.
Gets face detection results for a Amazon Rekognition Video analysis started by
StartFaceDetection
.
Retrieves the results of a specific Face Liveness session.
Gets the face search results for Amazon Rekognition Video face search started by
StartFaceSearch
.
Gets the label detection results of a Amazon Rekognition Video analysis started
by StartLabelDetection
.
Gets the path tracking results of a Amazon Rekognition Video analysis started by
StartPersonTracking
.
Gets the segment detection results of a Amazon Rekognition Video analysis
started by StartSegmentDetection
.
Gets the text detection results of a Amazon Rekognition Video analysis started
by StartTextDetection
.
Detects faces in the input image and adds them to the specified collection.
Returns list of collection IDs in your account.
Lists the entries (images) within a dataset.
Lists the labels in a dataset.
Returns metadata for faces in the specified collection.
Gets a list of the project policies attached to a project.
Gets a list of stream processors that you have created with
CreateStreamProcessor
.
Returns a list of tags in an Amazon Rekognition collection, stream processor, or Custom Labels model.
Attaches a project policy to a Amazon Rekognition Custom Labels project in a trusting AWS account.
Returns an array of celebrities recognized in the input image.
For a given input face ID, searches for matching faces in the collection the face belongs to.
For a given input image, first detects the largest face in the image, and then searches the specified collection for matching faces.
Starts asynchronous recognition of celebrities in a stored video.
Starts asynchronous detection of inappropriate, unwanted, or offensive content in a stored video.
Starts asynchronous detection of faces in a stored video.
Starts the asynchronous search for faces in a collection that match the faces of persons detected in a stored video.
Starts asynchronous detection of labels in a stored video.
Starts the asynchronous tracking of a person's path in a stored video.
Starts the running of the version of a model.
Starts asynchronous detection of segment detection in a stored video.
Starts processing a stream processor.
Starts asynchronous detection of text in a stored video.
Stops a running model.
Stops a running stream processor that was created by CreateStreamProcessor
.
Adds one or more key-value tags to an Amazon Rekognition collection, stream processor, or Custom Labels model.
Removes one or more tags from an Amazon Rekognition collection, stream processor, or Custom Labels model.
Adds or updates one or more entries (images) in a dataset.
Allows you to update a stream processor.
Link to this section Functions
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.
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.
By default, only faces with a similarity score of greater than or equal to 80%
are returned in the response. You can change this value by specifying the
SimilarityThreshold
parameter.
CompareFaces
also returns an array of faces that don't match the source image.
For each face, it returns a bounding box, confidence value, landmarks, pose
details, and quality. The response also returns information about the face in
the source image, including the bounding box of the face and confidence value.
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.
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.
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.
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
.
This operation requires permissions to perform the
rekognition:CopyProjectVersion
action.
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.
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.
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.
This API operation initiates a Face Liveness session.
It returns a SessionId
, which you can use to start streaming Face Liveness
video and get the results for a Face Liveness session. You can use the
OutputConfig
option in the Settings parameter to provide an Amazon S3 bucket
location. The Amazon S3 bucket stores reference images and audit images. You can
use AuditImagesLimit
to limit of audit images returned. This number is between
0 and 4. By default, it is set to 0. The limit is best effort and based on the
duration of the selfie-video.
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.
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.
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.
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.
If you are creating a stream processor for detecting faces, you provide as input a Kinesis video stream (
Input
) and a Kinesis data stream (Output
) stream for receiving the output. You must use theFaceSearch
option inSettings
, specifying the collection that contains the faces you want to recognize. After you have finished analyzing a streaming video, useStopStreamProcessor
to stop processing.If you are creating a stream processor to detect labels, you provide as input a Kinesis video stream (
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 by using theConnectedHome
option in settings, and selecting one of the following:PERSON
,PET
,PACKAGE
,ALL
You can also specify where in the frame you want Amazon Rekognition to monitor withRegionsOfInterest
. When you run theStartStreamProcessor
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.
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.
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.
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.
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
.
DeleteProject
is 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 any ProjectPolicies
associated with that project.
This operation requires permissions to perform the rekognition:DeleteProject
action.
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
.
This operation requires permissions to perform the
rekognition:DeleteProjectPolicy
action.
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.
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
.
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.
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.
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.
Gets information about your Amazon Rekognition Custom Labels projects.
This operation requires permissions to perform the
rekognition:DescribeProjects
action.
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.
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.
Detects faces within an image that is provided as input.
DetectFaces
detects the 100 largest faces in the image. For each face
detected, the operation returns face details. 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.
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.
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.
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.
optional-parameters
Optional Parameters
You can specify one or both of the GENERAL_LABELS
and IMAGE_PROPERTIES
feature types when calling the DetectLabels API. Including GENERAL_LABELS
will
ensure the response includes the labels detected in the input image, while
including IMAGE_PROPERTIES
will ensure the response includes information about
the image quality and color.
When using GENERAL_LABELS
and/or IMAGE_PROPERTIES
you can provide filtering
criteria to the Settings parameter. You can filter with sets of individual
labels or with label categories. You can specify inclusive filters, exclusive
filters, or a combination of inclusive and exclusive filters. For more
information on filtering see Detecting Labels in an Image.
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. The default and upper limit is 1000
labels.
response-elements
Response Elements
For each object, scene, and concept the API returns one or more labels. The API returns the following types of information about labels:
Name - The name of the detected label.
Confidence - The level of confidence in the label assigned to a detected object.
Parents - The ancestor labels for a detected label. DetectLabels 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 includes the all ancestors for a label, where every ancestor is a unique label. In the previous example, Car, Vehicle, and Transportation are returned as unique labels in the response.
Aliases - Possible Aliases for the label.
Categories - The label categories that the detected label belongs to.
BoundingBox — Bounding boxes are described for all instances of detected common object labels, returned in an array of Instance objects. An Instance object contains a BoundingBox object, describing the location of the label on the input image. It also includes the confidence for the accuracy of the detected bounding box.
The API returns the following information regarding the image, as part of the ImageProperties structure:
Quality - Information about the Sharpness, Brightness, and Contrast of the input image, scored between 0 to 100. Image quality is returned for the entire image, as well as the background and the foreground.
Dominant Color - An array of the dominant colors in the image.
Foreground - Information about the sharpness, brightness, and dominant colors of the input image’s foreground.
Background - Information about the sharpness, brightness, and dominant colors of the input image’s background.
The list of returned labels will include at least one label for every detected object, along with information about that label. In the following example, suppose the input image has a lighthouse, the sea, and a rock. The response includes all three labels, one for each object, as well as the confidence in the label:
{Name: lighthouse, Confidence: 98.4629}
{Name: rock,Confidence: 79.2097}
{Name: sea,Confidence: 75.061}
The list of labels can include multiple labels for the same object. 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.
If the object detected is a person, the operation doesn't provide the same
facial details that the DetectFaces
operation provides.
This is a stateless API operation that doesn't return any data.
This operation requires permissions to perform the rekognition:DetectLabels
action.
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.
Detects Personal Protective Equipment (PPE) worn by people detected in an image.
Amazon Rekognition can detect the following types of PPE.
Face cover
Hand cover
Head cover
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.
DetectProtectiveEquipment
detects 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.
The persons detected as wearing all of the types of PPE that you specify.
The persons detected as not wearing all of the types PPE that you specify.
The persons detected where PPE adornment could not be determined.
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.
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.
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.
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.
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.
GetCelebrityRecognition
returns detected celebrities and the time(s) they are
detected in an array (Celebrities
) of CelebrityRecognition
objects. Each
CelebrityRecognition
contains 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.
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
.
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.
GetContentModeration
returns 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.
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
.
GetFaceDetection
returns an array of detected faces (Faces
) sorted by the
time the faces were detected.
Use MaxResults parameter to limit the number of labels returned. If there are
more results than specified in MaxResults
, the value of NextToken
in the
operation response contains a pagination token for getting the next set of
results. To get the next page of results, call GetFaceDetection
and populate
the NextToken
request parameter with the token value returned from the
previous call to GetFaceDetection
.
Retrieves the results of a specific Face Liveness session.
It requires the sessionId
as input, which was created using
CreateFaceLivenessSession
. Returns the corresponding Face Liveness confidence
score, a reference image that includes a face bounding box, and audit images
that also contain face bounding boxes. The Face Liveness confidence score ranges
from 0 to 100. The reference image can optionally be returned.
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.
EachPersonMatch
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.
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.
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
.
GetLabelDetection
returns an array of detected labels (Labels
) sorted by the
time the labels were detected. You can also sort by the label name by specifying
NAME
for the SortBy
input parameter. If there is no NAME
specified, the
default sort is by timestamp.
You can select how results are aggregated by using the AggregateBy
input
parameter. The default aggregation method is TIMESTAMPS
. You can also
aggregate by SEGMENTS
, which aggregates all instances of labels detected in a
given segment.
The returned Labels array may include the following attributes:
Name - The name of the detected label.
Confidence - The level of confidence in the label assigned to a detected object.
Parents - The ancestor labels for a detected label. GetLabelDetection 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 includes the all ancestors for a label, where every ancestor is a unique label. In the previous example, Car, Vehicle, and Transportation are returned as unique labels in the response.
Aliases - Possible Aliases for the label.
Categories - The label categories that the detected label belongs to.
BoundingBox — Bounding boxes are described for all instances of detected common object labels, returned in an array of Instance objects. An Instance object contains a BoundingBox object, describing the location of the label on the input image. It also includes the confidence for the accuracy of the detected bounding box.
Timestamp - Time, in milliseconds from the start of the video, that the label was detected. For aggregation by
SEGMENTS
, theStartTimestampMillis
,EndTimestampMillis
, andDurationMillis
structures are what define a segment. Although the “Timestamp” structure is still returned with each label, its value is set to be the same asStartTimestampMillis
.
Timestamp and Bounding box information are returned for detected Instances, only
if aggregation is done by TIMESTAMPS
. If aggregating by SEGMENTS
,
information about detected instances isn’t returned.
The version of the label model used for the detection is also returned.
note-dominantcolors-isn-t-returned-for-instances-although-it-is-shown-as
Note DominantColors
isn't returned for Instances
, although it is shown as
part of the response in the sample seen below.
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
.
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
.
GetPersonTracking
returns an array, Persons
, of tracked persons and the
time(s) their paths were tracked in the video.
GetPersonTracking
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 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
.
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
.
GetSegmentDetection
returns detected segments in an array (Segments
) of
SegmentDetection
objects. Segments
is sorted by the segment types specified
in the SegmentTypes
input parameter of StartSegmentDetection
. 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.
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
.
GetTextDetection
returns 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
.
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
.
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:
The number of faces detected exceeds the value of the
MaxFaces
request parameter.The face is too small compared to the image dimensions.
The face is too blurry.
The image is too dark.
The face has an extreme pose.
The face doesn’t have enough detail to be suitable for face search.
In response, the IndexFaces
operation returns an array of metadata for all
detected faces, FaceRecords
. This includes:
The bounding box,
BoundingBox
, of the detected face.A confidence value,
Confidence
, which indicates the confidence that the bounding box contains a face.A face ID,
FaceId
, assigned by the service for each face that's detected and stored.An image ID,
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.
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.
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.
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.
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.
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
.
This operation requires permissions to perform the
rekognition:ListProjectPolicies
action.
Gets a list of stream processors that you have created with
CreateStreamProcessor
.
Returns 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.
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
.
This operation requires permissions to perform the
rekognition:PutProjectPolicy
action.
Returns an array of celebrities recognized in the input image.
For more information, see Recognizing celebrities in the Amazon Rekognition Developer Guide.
RecognizeCelebrities
returns the 64 largest faces in the image. It lists the
recognized celebrities in the CelebrityFaces
array and any unrecognized faces
in the UnrecognizedFaces
array. RecognizeCelebrities
doesn'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.
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.
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.
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.
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
.
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.
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.
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.
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.
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.
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
.
Optional Parameters
StartLabelDetection
has the GENERAL_LABELS
Feature applied by default. This
feature allows you to provide filtering criteria to the Settings
parameter.
You can filter with sets of individual labels or with label categories. You can
specify inclusive filters, exclusive filters, or a combination of inclusive and
exclusive filters. For more information on filtering, see Detecting labels in a video.
You can specify MinConfidence
to control the confidence threshold for the
labels returned. The default is 50.
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
.
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
.
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.
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.
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.
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
.
Stops a running model.
The operation might take a while to complete. To check the current status, call
DescribeProjectVersions
.
This operation requires permissions to perform the
rekognition:StopProjectVersion
action.
Stops a running stream processor that was created by CreateStreamProcessor
.
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.
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.
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.
UpdateDatasetEntries
returns 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 of Status
is UPDATE_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.
Allows you to update a stream processor.
You can change some settings and regions of interest and delete certain parameters.