aws_rekognition
This is the Amazon Rekognition API reference.
Summary
Functions
-
compare_faces(Client, Input)
Compares a face in the source input image with each of the 100 largest faces detected in the target input image.
- compare_faces(Client, Input, Options)
-
create_collection(Client, Input)
Creates a collection in an AWS Region.
- create_collection(Client, Input, Options)
-
create_project(Client, Input)
Creates a new Amazon Rekognition Custom Labels project.
- create_project(Client, Input, Options)
-
create_project_version(Client, Input)
Creates a new version of a model and begins training.
- create_project_version(Client, Input, Options)
-
create_stream_processor(Client, Input)
Creates an Amazon Rekognition stream processor that you can use to detect and recognize faces in a streaming video.
- create_stream_processor(Client, Input, Options)
-
delete_collection(Client, Input)
Deletes the specified collection.
- delete_collection(Client, Input, Options)
-
delete_faces(Client, Input)
Deletes faces from a collection.
- delete_faces(Client, Input, Options)
-
delete_project(Client, Input)
Deletes an Amazon Rekognition Custom Labels project.
- delete_project(Client, Input, Options)
-
delete_project_version(Client, Input)
Deletes an Amazon Rekognition Custom Labels model.
- delete_project_version(Client, Input, Options)
-
delete_stream_processor(Client, Input)
Deletes the stream processor identified by
Name
. - delete_stream_processor(Client, Input, Options)
-
describe_collection(Client, Input)
Describes the specified collection.
- describe_collection(Client, Input, Options)
-
describe_project_versions(Client, Input)
Lists and describes the models in an Amazon Rekognition Custom Labels project.
- describe_project_versions(Client, Input, Options)
-
describe_projects(Client, Input)
Lists and gets information about your Amazon Rekognition Custom Labels projects.
- describe_projects(Client, Input, Options)
-
describe_stream_processor(Client, Input)
Provides information about a stream processor created by
CreateStreamProcessor
. - describe_stream_processor(Client, Input, Options)
-
detect_custom_labels(Client, Input)
Detects custom labels in a supplied image by using an Amazon Rekognition Custom Labels model.
- detect_custom_labels(Client, Input, Options)
-
detect_faces(Client, Input)
Detects faces within an image that is provided as input.
- detect_faces(Client, Input, Options)
-
detect_labels(Client, Input)
Detects instances of real-world entities within an image (JPEG or PNG) provided as input.
- detect_labels(Client, Input, Options)
-
detect_moderation_labels(Client, Input)
Detects unsafe content in a specified JPEG or PNG format image.
- detect_moderation_labels(Client, Input, Options)
-
detect_protective_equipment(Client, Input)
Detects Personal Protective Equipment (PPE) worn by people detected in an image.
- detect_protective_equipment(Client, Input, Options)
-
detect_text(Client, Input)
Detects text in the input image and converts it into machine-readable text.
- detect_text(Client, Input, Options)
-
get_celebrity_info(Client, Input)
Gets the name and additional information about a celebrity based on his or her Amazon Rekognition ID.
- get_celebrity_info(Client, Input, Options)
-
get_celebrity_recognition(Client, Input)
Gets the celebrity recognition results for a Amazon Rekognition Video analysis started by
StartCelebrityRecognition
. - get_celebrity_recognition(Client, Input, Options)
-
get_content_moderation(Client, Input)
Gets the unsafe content analysis results for a Amazon Rekognition Video analysis started by
StartContentModeration
. - get_content_moderation(Client, Input, Options)
-
get_face_detection(Client, Input)
Gets face detection results for a Amazon Rekognition Video analysis started by
StartFaceDetection
. - get_face_detection(Client, Input, Options)
-
get_face_search(Client, Input)
Gets the face search results for Amazon Rekognition Video face search started by
StartFaceSearch
. - get_face_search(Client, Input, Options)
-
get_label_detection(Client, Input)
Gets the label detection results of a Amazon Rekognition Video analysis started by
StartLabelDetection
. - get_label_detection(Client, Input, Options)
-
get_person_tracking(Client, Input)
Gets the path tracking results of a Amazon Rekognition Video analysis started by
StartPersonTracking
. - get_person_tracking(Client, Input, Options)
-
get_segment_detection(Client, Input)
Gets the segment detection results of a Amazon Rekognition Video analysis started by
StartSegmentDetection
. - get_segment_detection(Client, Input, Options)
-
get_text_detection(Client, Input)
Gets the text detection results of a Amazon Rekognition Video analysis started by
StartTextDetection
. - get_text_detection(Client, Input, Options)
-
index_faces(Client, Input)
Detects faces in the input image and adds them to the specified collection.
- index_faces(Client, Input, Options)
-
list_collections(Client, Input)
Returns list of collection IDs in your account.
- list_collections(Client, Input, Options)
-
list_faces(Client, Input)
Returns metadata for faces in the specified collection.
- list_faces(Client, Input, Options)
-
list_stream_processors(Client, Input)
Gets a list of stream processors that you have created with
CreateStreamProcessor
. - list_stream_processors(Client, Input, Options)
-
recognize_celebrities(Client, Input)
Returns an array of celebrities recognized in the input image.
- recognize_celebrities(Client, Input, Options)
-
search_faces(Client, Input)
For a given input face ID, searches for matching faces in the collection the face belongs to.
- search_faces(Client, Input, Options)
-
search_faces_by_image(Client, Input)
For a given input image, first detects the largest face in the image, and then searches the specified collection for matching faces.
- search_faces_by_image(Client, Input, Options)
-
start_celebrity_recognition(Client, Input)
Starts asynchronous recognition of celebrities in a stored video.
- start_celebrity_recognition(Client, Input, Options)
-
start_content_moderation(Client, Input)
Starts asynchronous detection of unsafe content in a stored video.
- start_content_moderation(Client, Input, Options)
-
start_face_detection(Client, Input)
Starts asynchronous detection of faces in a stored video.
- start_face_detection(Client, Input, Options)
-
start_face_search(Client, Input)
Starts the asynchronous search for faces in a collection that match the faces of persons detected in a stored video.
- start_face_search(Client, Input, Options)
-
start_label_detection(Client, Input)
Starts asynchronous detection of labels in a stored video.
- start_label_detection(Client, Input, Options)
-
start_person_tracking(Client, Input)
Starts the asynchronous tracking of a person's path in a stored video.
- start_person_tracking(Client, Input, Options)
-
start_project_version(Client, Input)
Starts the running of the version of a model.
- start_project_version(Client, Input, Options)
-
start_segment_detection(Client, Input)
Starts asynchronous detection of segment detection in a stored video.
- start_segment_detection(Client, Input, Options)
-
start_stream_processor(Client, Input)
Starts processing a stream processor.
- start_stream_processor(Client, Input, Options)
-
start_text_detection(Client, Input)
Starts asynchronous detection of text in a stored video.
- start_text_detection(Client, Input, Options)
-
stop_project_version(Client, Input)
Stops a running model.
- stop_project_version(Client, Input, Options)
-
stop_stream_processor(Client, Input)
Stops a running stream processor that was created by
CreateStreamProcessor
. - stop_stream_processor(Client, Input, Options)
Functions
compare_faces(Client, Input)
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.
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, role, and yaw), quality (brightness and sharpness), and confidence value (indicating the level of confidence that the bounding box contains a face). The response also provides a similarity score, which indicates how closely the faces match.
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 therekognition:CompareFaces
action.
compare_faces(Client, Input, Options)
create_collection(Client, Input)
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 therekognition:CreateCollection
action.
create_collection(Client, Input, Options)
create_project(Client, Input)
Creates a new Amazon Rekognition Custom Labels project.
A project is a logical grouping of resources (images, Labels, models) and operations (training, evaluation and detection).
This operation requires permissions to perform therekognition:CreateProject
action.
create_project(Client, Input, Options)
create_project_version(Client, Input)
Creates a new version of a model and begins training.
Models are managed as part of an Amazon Rekognition Custom Labels project.
You can specify one training dataset and one testing dataset. The response
from CreateProjectVersion
is an Amazon Resource Name (ARN) for the
version of the model.
Training takes a while to complete. You can get the current status by
calling DescribeProjectVersions
.
Once training has successfully completed, call DescribeProjectVersions
to get the training results and evaluate the model.
After evaluating the model, you start the model by calling
StartProjectVersion
.
rekognition:CreateProjectVersion
action.
create_project_version(Client, Input, Options)
create_stream_processor(Client, Input)
Creates an Amazon Rekognition stream processor that you can use to detect and recognize faces in a streaming video.
Amazon Rekognition Video is a consumer of live video from Amazon Kinesis Video Streams. Amazon Rekognition Video sends analysis results to Amazon Kinesis Data Streams.
You provide as input a Kinesis video stream (Input
) and a Kinesis data
stream (Output
) stream. You also specify the face recognition criteria
in Settings
. For example, the collection containing faces that you want
to recognize. Use Name
to assign an identifier for the stream processor.
You use Name
to manage the stream processor. For example, you can start
processing the source video by calling StartStreamProcessor
with the
Name
field.
StopStreamProcessor
to stop processing. You can delete the stream
processor by calling DeleteStreamProcessor
.
create_stream_processor(Client, Input, Options)
delete_collection(Client, Input)
Deletes the specified collection.
Note that this operation removes all faces in the collection. For an
example, see delete-collection-procedure
.
rekognition:DeleteCollection
action.
delete_collection(Client, Input, Options)
delete_faces(Client, Input)
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 therekognition:DeleteFaces
action.
delete_faces(Client, Input, Options)
delete_project(Client, Input)
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
.
rekognition:DeleteProject
action.
delete_project(Client, Input, Options)
delete_project_version(Client, Input)
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.
rekognition:DeleteProjectVersion
action.
delete_project_version(Client, Input, Options)
delete_stream_processor(Client, Input)
Deletes the stream processor identified by Name
.
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
.
delete_stream_processor(Client, Input, Options)
describe_collection(Client, Input)
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.
describe_collection(Client, Input, Options)
describe_project_versions(Client, Input)
Lists and describes the models 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 models are returned.
rekognition:DescribeProjectVersions
action.
describe_project_versions(Client, Input, Options)
describe_projects(Client, Input)
Lists and gets information about your Amazon Rekognition Custom Labels projects.
This operation requires permissions to perform therekognition:DescribeProjects
action.
describe_projects(Client, Input, Options)
describe_stream_processor(Client, Input)
Provides information about a stream processor created by
CreateStreamProcessor
.
describe_stream_processor(Client, Input, Options)
detect_custom_labels(Client, Input)
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
).
During training model calculates a threshold value that determines if a
prediction for a label is true. By default, DetectCustomLabels
doesn't
return labels whose confidence value is below the model's calculated
threshold value. To filter labels that are returned, specify a value for
MinConfidence
that is higher than the model's calculated threshold. You
can get the model's calculated threshold from the model's training results
shown in the Amazon Rekognition Custom Labels console. To get all labels,
regardless of confidence, specify a MinConfidence
value of 0.
You can also add the MaxResults
parameter to limit the number of labels
returned.
This is a stateless API operation. That is, the operation does not persist any data.
This operation requires permissions to perform therekognition:DetectCustomLabels
action.
detect_custom_labels(Client, Input, Options)
detect_faces(Client, Input)
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 therekognition:DetectFaces
action.
detect_faces(Client, Input, Options)
detect_labels(Client, Input)
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.
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.
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 therekognition:DetectLabels
action.
detect_labels(Client, Input, Options)
detect_moderation_labels(Client, Input)
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.detect_moderation_labels(Client, Input, Options)
detect_protective_equipment(Client, Input)
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 therekognition:DetectProtectiveEquipment
action.
detect_protective_equipment(Client, Input, Options)
detect_text(Client, Input)
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 ISO basic latin script characters that are not
separated by spaces. DetectText
can detect up to 50 words in an image.
A line is a string of equally spaced words. A line isn't necessarily a
complete sentence. For example, a driver's license number is detected as a
line. A line ends when there is no aligned text after it. Also, a line
ends when there is a large gap between words, relative to the length of
the words. This means, depending on the gap between words, Amazon
Rekognition may detect multiple lines in text aligned in the same
direction. Periods don't represent the end of a line. If a sentence spans
multiple lines, the DetectText
operation returns multiple lines.
To determine whether a TextDetection
element is a line of text or a
word, use the TextDetection
object Type
field.
To be detected, text must be within +/- 90 degrees orientation of the horizontal axis.
For more information, see DetectText in the Amazon Rekognition Developer Guide.detect_text(Client, Input, Options)
get_celebrity_info(Client, Input)
Gets the name and additional information about a celebrity based on his or her 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 Recognizing Celebrities in an Image in the Amazon Rekognition Developer Guide.
This operation requires permissions to perform therekognition:GetCelebrityInfo
action.
get_celebrity_info(Client, Input, Options)
get_celebrity_recognition(Client, Input)
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.
GetCelebrityRecognition
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 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 inMaxResults
, 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
.
get_celebrity_recognition(Client, Input, Options)
get_content_moderation(Client, Input)
Gets the unsafe content analysis results for a Amazon Rekognition
Video analysis started by StartContentModeration
.
Unsafe content analysis of a 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 unsafe 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 unsafe content 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
.
get_content_moderation(Client, Input, Options)
get_face_detection(Client, Input)
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.
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
.
get_face_detection(Client, Input, Options)
get_face_search(Client, Input)
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.
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.
get_face_search(Client, Input, Options)
get_label_detection(Client, Input)
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.
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 inMaxResults
, 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
.
get_label_detection(Client, Input, Options)
get_person_tracking(Client, Input)
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.
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
.
get_person_tracking(Client, Input, Options)
get_segment_detection(Client, Input)
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
.
get_segment_detection(Client, Input, Options)
get_text_detection(Client, Input)
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 inMaxResults
, 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
.
get_text_detection(Client, Input, Options)
index_faces(Client, Input)
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 therekognition:IndexFaces
action.
index_faces(Client, Input, Options)
list_collections(Client, Input)
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 therekognition:ListCollections
action.
list_collections(Client, Input, Options)
list_faces(Client, Input)
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 therekognition:ListFaces
action.
list_faces(Client, Input, Options)
list_stream_processors(Client, Input)
Gets a list of stream processors that you have created with
CreateStreamProcessor
.
list_stream_processors(Client, Input, Options)
recognize_celebrities(Client, Input)
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
recognized celebrities in the CelebrityFaces
array and 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 therekognition:RecognizeCelebrities
operation.
recognize_celebrities(Client, Input, Options)
search_faces(Client, Input)
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 therekognition:SearchFaces
action.
search_faces(Client, Input, Options)
search_faces_by_image(Client, Input)
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.
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
.
rekognition:SearchFacesByImage
action.
search_faces_by_image(Client, Input, Options)
start_celebrity_recognition(Client, Input)
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
.
start_celebrity_recognition(Client, Input, Options)
start_content_moderation(Client, Input)
Starts asynchronous detection of unsafe content in a stored video.
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 unsafe 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 unsafe 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
.
start_content_moderation(Client, Input, Options)
start_face_detection(Client, Input)
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
.
start_face_detection(Client, Input, Options)
start_face_search(Client, Input)
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. UseVideo
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 procedure-person-search-videos
.
start_face_search(Client, Input, Options)
start_label_detection(Client, Input)
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
.
SUCCEEDED
. If so, call
GetLabelDetection
and pass the job identifier (JobId
) from the initial
call to StartLabelDetection
.
start_label_detection(Client, Input, Options)
start_person_tracking(Client, Input)
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
.
SUCCEEDED
. If so, call
GetPersonTracking
and pass the job identifier (JobId
) from the initial
call to StartPersonTracking
.
start_person_tracking(Client, Input, Options)
start_project_version(Client, Input)
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
.
rekognition:StartProjectVersion
action.
start_project_version(Client, Input, Options)
start_segment_detection(Client, Input)
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
.
start_segment_detection(Client, Input, Options)
start_stream_processor(Client, Input)
Starts processing a stream processor.
You create a stream processor by callingCreateStreamProcessor
. To tell
StartStreamProcessor
which stream processor to start, use the value of
the Name
field specified in the call to CreateStreamProcessor
.
start_stream_processor(Client, Input, Options)
start_text_detection(Client, Input)
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
.
SUCCEEDED
. if so, call
GetTextDetection
and pass the job identifier (JobId
) from the initial
call to StartTextDetection
.
start_text_detection(Client, Input, Options)
stop_project_version(Client, Input)
Stops a running model.
The operation might take a while to complete. To check the current status, callDescribeProjectVersions
.
stop_project_version(Client, Input, Options)
stop_stream_processor(Client, Input)
Stops a running stream processor that was created by
CreateStreamProcessor
.