type nonrec start_content_moderation_response = {job_id : job_id option;(*The identifier for the content analysis job. Use
*)JobIdto identify the job in a subsequent call toGetContentModeration.
}Smaws_Client_Rekognition.Typestype nonrec video_too_large_exception = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;}The file size or duration of the supplied media is too large. The maximum file size is 10GB. The maximum duration is 6 hours.
type nonrec u_long = Smaws_Lib.CoreTypes.Int64.ttype nonrec video_metadata = {color_range : video_color_range option;A description of the range of luminance values in a video, either LIMITED (16 to 235) or FULL (0 to 255).
*)frame_width : u_long option;Horizontal pixel dimension of the video.
*)frame_height : u_long option;Vertical pixel dimension of the video.
*)frame_rate : float_ option;Number of frames per second in the video.
*)format : string_ option;Format of the analyzed video. Possible values are MP4, MOV and AVI.
*)duration_millis : u_long option;Length of the video in milliseconds.
*)codec : string_ option;Type of compression used in the analyzed video.
*)}Information about a video that Amazon Rekognition analyzed. Videometadata is returned in every page of paginated responses from a Amazon Rekognition video operation.
type nonrec video_metadata_list = video_metadata listtype nonrec s3_object = {version : s3_object_version option;If the bucket is versioning enabled, you can specify the object version.
*)name : s3_object_name option;S3 object key name.
*)bucket : s3_bucket option;Name of the S3 bucket.
*)}Provides the S3 bucket name and object name.
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.
type nonrec video = {s3_object : s3_object option;The Amazon S3 bucket name and file name for the video.
*)}Video file stored in an Amazon S3 bucket. Amazon Rekognition video start operations such as StartLabelDetection use Video to specify a video for analysis. The supported file formats are .mp4, .mov and .avi.
type nonrec versions = {maximum : version option;The desired maximum version for the challenge.
*)minimum : version option;The desired minimum version for the challenge.
*)}Object specifying the acceptable range of challenge versions.
type nonrec version_names = version_name listThe S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.
Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.
type nonrec assets = asset listtype nonrec validation_data = {assets : assets option;The assets that comprise the validation data.
*)}Contains the Amazon S3 bucket location of the validation data for a model training job.
The validation data includes error information for individual JSON Lines in the dataset. For more information, see Debugging a Failed Model Training in the Amazon Rekognition Custom Labels Developer Guide.
You get the ValidationData object for the training dataset (TrainingDataResult) and the test dataset (TestingDataResult) by calling DescribeProjectVersions.
The assets array contains a single Asset object. The GroundTruthManifest field of the Asset object contains the S3 bucket location of the validation data.
type nonrec matched_user = {user_status : user_status option;The status of the user matched to a provided FaceID.
*)user_id : user_id option;A provided ID for the UserID. Unique within the collection.
*)}Contains metadata for a UserID matched with a given face.
type nonrec user_match = {user : matched_user option;Confidence in the match of this UserID with the input face.
*)similarity : percent option;Describes the UserID metadata.
*)}Provides UserID metadata along with the confidence in the match of this UserID with the input face.
type nonrec user_match_list = user_match listtype nonrec user = {user_status : user_status option;Communicates if the UserID has been updated with latest set of faces to be associated with the UserID.
*)user_id : user_id option;A provided ID for the User. Unique within the collection.
*)}Metadata of the user stored in a collection.
type nonrec user_list = user listtype nonrec user_face_id_list = face_id listtype nonrec urls = url listtype nonrec connected_home_labels = connected_home_label listtype nonrec connected_home_settings_for_update = {min_confidence : percent option;The minimum confidence required to label an object in the video.
*)labels : connected_home_labels option;Specifies what you want to detect in the video, such as people, packages, or pets. The current valid labels you can include in this list are: "PERSON", "PET", "PACKAGE", and "ALL".
*)}The label detection settings you want to use in your stream processor. This includes the labels you want the stream processor to detect and the minimum confidence level allowed to label objects.
type nonrec stream_processor_settings_for_update = {connected_home_for_update : connected_home_settings_for_update option;The label detection settings you want to use for your stream processor.
*)}The stream processor settings that you want to update. ConnectedHome settings can be updated to detect different labels with a different minimum confidence.
type nonrec bounding_box = {top : float_ option;Top coordinate of the bounding box as a ratio of overall image height.
*)left : float_ option;Left coordinate of the bounding box as a ratio of overall image width.
*)height : float_ option;Height of the bounding box as a ratio of the overall image height.
*)width : float_ option;Width of the bounding box as a ratio of the overall image width.
*)}Identifies the bounding box around the label, face, text, object of interest, or personal protective equipment. The left (x-coordinate) and top (y-coordinate) are coordinates representing the top and left sides of the bounding box. Note that the upper-left corner of the image is the origin (0,0).
The top and left values returned are ratios of the overall image size. For example, if the input image is 700x200 pixels, and the top-left coordinate of the bounding box is 350x50 pixels, the API returns a left value of 0.5 (350/700) and a top value of 0.25 (50/200).
The width and height values represent the dimensions of the bounding box as a ratio of the overall image dimension. For example, if the input image is 700x200 pixels, and the bounding box width is 70 pixels, the width returned is 0.1.
The bounding box coordinates can have negative values. For example, if Amazon Rekognition is able to detect a face that is at the image edge and is only partially visible, the service can return coordinates that are outside the image bounds and, depending on the image edge, you might get negative values or values greater than 1 for the left or top values.
type nonrec point = {y : float_ option;The value of the Y coordinate for a point on a Polygon.
x : float_ option;The value of the X coordinate for a point on a Polygon.
}The X and Y coordinates of a point on an image or video frame. The X and Y values are ratios of the overall image size or video resolution. For example, if an input image is 700x200 and the values are X=0.5 and Y=0.25, then the point is at the (350,50) pixel coordinate on the image.
An array of Point objects makes up a Polygon. A Polygon is returned by DetectText and by DetectCustomLabels Polygon represents a fine-grained polygon around a detected item. For more information, see Geometry in the Amazon Rekognition Developer Guide.
type nonrec polygon = point listtype nonrec region_of_interest = {polygon : polygon option;Specifies a shape made up of up to 10 Point objects to define a region of interest.
bounding_box : bounding_box option;The box representing a region of interest on screen.
*)}Specifies a location within the frame that Rekognition checks for objects of interest such as text, labels, or faces. It uses a BoundingBox or Polygon to set a region of the screen.
A word, face, or label is included in the region if it is more than half in that region. If there is more than one region, the word, face, or label is compared with all regions of the screen. Any object of interest that is more than half in a region is kept in the results.
type nonrec regions_of_interest = region_of_interest listtype nonrec stream_processor_data_sharing_preference = {opt_in : boolean_;If this option is set to true, you choose to share data with Rekognition to improve model performance.
*)}Allows you to opt in or opt out to share data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams.
type nonrec stream_processor_parameters_to_delete =
stream_processor_parameter_to_delete listtype nonrec update_stream_processor_request = {parameters_to_delete : stream_processor_parameters_to_delete option;A list of parameters you want to delete from the stream processor.
*)data_sharing_preference_for_update : stream_processor_data_sharing_preference
option;Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams.
*)regions_of_interest_for_update : regions_of_interest option;Specifies locations in the frames where Amazon Rekognition checks for objects or people. This is an optional parameter for label detection stream processors.
*)settings_for_update : stream_processor_settings_for_update option;The stream processor settings that you want to update. Label detection settings can be updated to detect different labels with a different minimum confidence.
*)name : stream_processor_name;Name of the stream processor that you want to update.
*)}type nonrec throttling_exception = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;}Amazon Rekognition is temporarily unable to process the request. Try your call again.
type nonrec resource_not_found_exception = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;}The resource specified in the request cannot be found.
type nonrec resource_in_use_exception = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;}The specified resource is already being used.
type nonrec provisioned_throughput_exceeded_exception = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;}The number of requests exceeded your throughput limit. If you want to increase this limit, contact Amazon Rekognition.
type nonrec invalid_parameter_exception = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;}Input parameter violated a constraint. Validate your parameter before calling the API operation again.
type nonrec internal_server_error = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;}Amazon Rekognition experienced a service issue. Try your call again.
type nonrec access_denied_exception = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;}You are not authorized to perform the action.
type nonrec dataset_changes = {ground_truth : ground_truth_blob;A Base64-encoded binary data object containing one or JSON lines that either update the dataset or are additions to the dataset. You change a dataset by calling UpdateDatasetEntries. If you are using an AWS SDK to call UpdateDatasetEntries, you don't need to encode Changes as the SDK encodes the data for you.
For example JSON lines, see Image-Level labels in manifest files and and Object localization in manifest files in the Amazon Rekognition Custom Labels Developer Guide.
*)}Describes updates or additions to a dataset. A Single update or addition is an entry (JSON Line) that provides information about a single image. To update an existing entry, you match the source-ref field of the update entry with the source-ref filed of the entry that you want to update. If the source-ref field doesn't match an existing entry, the entry is added to dataset as a new entry.
type nonrec update_dataset_entries_request = {changes : dataset_changes;The changes that you want to make to the dataset.
*)dataset_arn : dataset_arn;The Amazon Resource Name (ARN) of the dataset that you want to update.
*)}type nonrec limit_exceeded_exception = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;}An Amazon Rekognition service limit was exceeded. For example, if you start too many jobs concurrently, subsequent calls to start operations (ex: StartLabelDetection) will raise a LimitExceededException exception (HTTP status code: 400) until the number of concurrently running jobs is below the Amazon Rekognition service limit.
type nonrec tag_key_list = tag_key listtype nonrec untag_resource_request = {tag_keys : tag_key_list;A list of the tags that you want to remove.
*)resource_arn : resource_arn;Amazon Resource Name (ARN) of the model, collection, or stream processor that you want to remove the tags from.
*)}type nonrec unsuccessful_face_disassociation_reasons =
unsuccessful_face_disassociation_reason listtype nonrec unsuccessful_face_disassociation = {reasons : unsuccessful_face_disassociation_reasons option;The reason why the deletion was unsuccessful.
*)user_id : user_id option;A provided ID for the UserID. Unique within the collection.
*)face_id : face_id option;A unique identifier assigned to the face.
*)}Contains metadata like FaceId, UserID, and Reasons, for a face that was unsuccessfully disassociated.
type nonrec unsuccessful_face_disassociation_list =
unsuccessful_face_disassociation listtype nonrec unsuccessful_face_deletion_reasons =
unsuccessful_face_deletion_reason listtype nonrec unsuccessful_face_deletion = {reasons : unsuccessful_face_deletion_reasons option;The reason why the deletion was unsuccessful.
*)user_id : user_id option;A provided ID for the UserID. Unique within the collection.
*)face_id : face_id option;A unique identifier assigned to the face.
*)}Contains metadata like FaceId, UserID, and Reasons, for a face that was unsuccessfully deleted.
type nonrec unsuccessful_face_deletions_list = unsuccessful_face_deletion listtype nonrec unsuccessful_face_association_reasons =
unsuccessful_face_association_reason listtype nonrec unsuccessful_face_association = {reasons : unsuccessful_face_association_reasons option;The reason why the association was unsuccessful.
*)confidence : percent option;Match confidence with the UserID, provides information regarding if a face association was unsuccessful because it didn't meet UserMatchThreshold.
*)user_id : user_id option;A provided ID for the UserID. Unique within the collection.
*)face_id : face_id option;A unique identifier assigned to the face.
*)}Contains metadata like FaceId, UserID, and Reasons, for a face that was unsuccessfully associated.
type nonrec unsuccessful_face_association_list =
unsuccessful_face_association listtype nonrec age_range = {high : u_integer option;The highest estimated age.
*)low : u_integer option;The lowest estimated age.
*)}Structure containing the estimated age range, in years, for a face.
Amazon Rekognition estimates an age range for faces detected in the input image. Estimated age ranges can overlap. A face of a 5-year-old might have an estimated range of 4-6, while the face of a 6-year-old might have an estimated range of 4-8.
type nonrec smile = {confidence : percent option;Level of confidence in the determination.
*)value : boolean_ option;Boolean value that indicates whether the face is smiling or not.
*)}Indicates whether or not the face is smiling, and the confidence level in the determination.
type nonrec eyeglasses = {confidence : percent option;Level of confidence in the determination.
*)value : boolean_ option;Boolean value that indicates whether the face is wearing eye glasses or not.
*)}Indicates whether or not the face is wearing eye glasses, and the confidence level in the determination.
type nonrec sunglasses = {confidence : percent option;Level of confidence in the determination.
*)value : boolean_ option;Boolean value that indicates whether the face is wearing sunglasses or not.
*)}Indicates whether or not the face is wearing sunglasses, and the confidence level in the determination.
type nonrec gender = {confidence : percent option;Level of confidence in the prediction.
*)value : gender_type option;The predicted gender of the face.
*)}The predicted gender of a detected face.
Amazon Rekognition makes gender binary (male/female) predictions based on the physical appearance of a face in a particular image. This kind of prediction is not designed to categorize a person’s gender identity, and you shouldn't use Amazon Rekognition to make such a determination. For example, a male actor wearing a long-haired wig and earrings for a role might be predicted as female.
Using Amazon Rekognition to make gender binary predictions is best suited for use cases where aggregate gender distribution statistics need to be analyzed without identifying specific users. For example, the percentage of female users compared to male users on a social media platform.
We don't recommend using gender binary predictions to make decisions that impact an individual's rights, privacy, or access to services.
type nonrec beard = {confidence : percent option;Level of confidence in the determination.
*)value : boolean_ option;Boolean value that indicates whether the face has beard or not.
*)}Indicates whether or not the face has a beard, and the confidence level in the determination.
type nonrec mustache = {confidence : percent option;Level of confidence in the determination.
*)value : boolean_ option;Boolean value that indicates whether the face has mustache or not.
*)}Indicates whether or not the face has a mustache, and the confidence level in the determination.
type nonrec eye_open = {confidence : percent option;Level of confidence in the determination.
*)value : boolean_ option;Boolean value that indicates whether the eyes on the face are open.
*)}Indicates whether or not the eyes on the face are open, and the confidence level in the determination.
type nonrec mouth_open = {confidence : percent option;Level of confidence in the determination.
*)value : boolean_ option;Boolean value that indicates whether the mouth on the face is open or not.
*)}Indicates whether or not the mouth on the face is open, and the confidence level in the determination.
type nonrec emotion = {confidence : percent option;Level of confidence in the determination.
*)type_ : emotion_name option;Type of emotion detected.
*)}The API returns a prediction of an emotion based on a person's facial expressions, along with the confidence level for the predicted emotion. It is not a determination of the person’s internal emotional state and should not be used in such a way. For example, a person pretending to have a sad face might not be sad emotionally. The API is not intended to be used, and you may not use it, in a manner that violates the EU Artificial Intelligence Act or any other applicable law.
type nonrec emotions = emotion listtype nonrec landmark_type = | UpperJawlineRight| MidJawlineRight| ChinBottom| MidJawlineLeft| UpperJawlineLeft| RightPupil| LeftPupil| MouthDown| MouthUp| NoseRight| NoseLeft| RightEyeDown| RightEyeUp| RightEyeRight| RightEyeLeft| LeftEyeDown| LeftEyeUp| LeftEyeRight| LeftEyeLeft| RightEyeBrowUp| RightEyeBrowRight| RightEyeBrowLeft| LeftEyeBrowUp| LeftEyeBrowRight| LeftEyeBrowLeft| MouthRight| MouthLeft| Nose| EyeRight| EyeLefttype nonrec landmark = {y : float_ option;The y-coordinate of the landmark expressed as a ratio of the height of the image. The y-coordinate is measured from the top of the image. For example, if the image height is 200 pixels and the y-coordinate of the landmark is at 50 pixels, this value is 0.25.
*)x : float_ option;The x-coordinate of the landmark expressed as a ratio of the width of the image. The x-coordinate is measured from the left-side of the image. For example, if the image is 700 pixels wide and the x-coordinate of the landmark is at 350 pixels, this value is 0.5.
*)type_ : landmark_type option;Type of landmark.
*)}Indicates the location of the landmark on the face.
type nonrec landmarks = landmark listtype nonrec pose = {pitch : degree option;Value representing the face rotation on the pitch axis.
*)yaw : degree option;Value representing the face rotation on the yaw axis.
*)roll : degree option;Value representing the face rotation on the roll axis.
*)}Indicates the pose of the face as determined by its pitch, roll, and yaw.
type nonrec image_quality = {sharpness : float_ option;Value representing sharpness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a sharper face image.
*)brightness : float_ option;Value representing brightness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a brighter face image.
*)}Identifies face image brightness and sharpness.
type nonrec face_occluded = {confidence : percent option;The confidence that the service has detected the presence of a face occlusion.
*)value : boolean_ option;True if a detected face’s eyes, nose, and mouth are partially captured or if they are covered by masks, dark sunglasses, cell phones, hands, or other objects. False if common occurrences that do not impact face verification are detected, such as eye glasses, lightly tinted sunglasses, strands of hair, and others.
*)}FaceOccluded should return "true" with a high confidence score if a detected face’s eyes, nose, and mouth are partially captured or if they are covered by masks, dark sunglasses, cell phones, hands, or other objects. FaceOccluded should return "false" with a high confidence score if common occurrences that do not impact face verification are detected, such as eye glasses, lightly tinted sunglasses, strands of hair, and others.
You can use FaceOccluded to determine if an obstruction on a face negatively impacts using the image for face matching.
type nonrec eye_direction = {confidence : percent option;The confidence that the service has in its predicted eye direction.
*)pitch : degree option;Value representing eye direction on the pitch axis.
*)yaw : degree option;Value representing eye direction on the yaw axis.
*)}Indicates the direction the eyes are gazing in (independent of the head pose) as determined by its pitch and yaw.
type nonrec face_detail = {eye_direction : eye_direction option;Indicates the direction the eyes are gazing in, as defined by pitch and yaw.
*)face_occluded : face_occluded option;FaceOccluded should return "true" with a high confidence score if a detected face’s eyes, nose, and mouth are partially captured or if they are covered by masks, dark sunglasses, cell phones, hands, or other objects. FaceOccluded should return "false" with a high confidence score if common occurrences that do not impact face verification are detected, such as eye glasses, lightly tinted sunglasses, strands of hair, and others.
confidence : percent option;Confidence level that the bounding box contains a face (and not a different object such as a tree). Default attribute.
*)quality : image_quality option;Identifies image brightness and sharpness. Default attribute.
*)pose : pose option;Indicates the pose of the face as determined by its pitch, roll, and yaw. Default attribute.
*)landmarks : landmarks option;Indicates the location of landmarks on the face. Default attribute.
*)emotions : emotions option;The emotions that appear to be expressed on the face, and the confidence level in the determination. The API is only making a determination of the physical appearance of a person's face. It is not a determination of the person’s internal emotional state and should not be used in such a way. For example, a person pretending to have a sad face might not be sad emotionally.
*)mouth_open : mouth_open option;Indicates whether or not the mouth on the face is open, and the confidence level in the determination.
*)eyes_open : eye_open option;Indicates whether or not the eyes on the face are open, and the confidence level in the determination.
*)mustache : mustache option;Indicates whether or not the face has a mustache, and the confidence level in the determination.
*)beard : beard option;Indicates whether or not the face has a beard, and the confidence level in the determination.
*)gender : gender option;The predicted gender of a detected face.
*)sunglasses : sunglasses option;Indicates whether or not the face is wearing sunglasses, and the confidence level in the determination.
*)eyeglasses : eyeglasses option;Indicates whether or not the face is wearing eye glasses, and the confidence level in the determination.
*)smile : smile option;Indicates whether or not the face is smiling, and the confidence level in the determination.
*)age_range : age_range option;The estimated age range, in years, for the face. Low represents the lowest estimated age and High represents the highest estimated age.
*)bounding_box : bounding_box option;Bounding box of the face. Default attribute.
*)}Structure containing attributes of the face that the algorithm detected.
A FaceDetail object contains either the default facial attributes or all facial attributes. The default attributes are BoundingBox, Confidence, Landmarks, Pose, and Quality.
GetFaceDetection is the only Amazon Rekognition Video stored video operation that can return a FaceDetail object with all attributes. To specify which attributes to return, use the FaceAttributes input parameter for StartFaceDetection. The following Amazon Rekognition Video operations return only the default attributes. The corresponding Start operations don't have a FaceAttributes input parameter:
The Amazon Rekognition Image DetectFaces and IndexFaces operations can return all facial attributes. To specify which attributes to return, use the Attributes input parameter for DetectFaces. For IndexFaces, use the DetectAttributes input parameter.
type nonrec unsearched_face_reasons = unsearched_face_reason listtype nonrec unsearched_face = {reasons : unsearched_face_reasons option;Reasons why a face wasn't used for Search.
*)face_details : face_detail option;}Face details inferred from the image but not used for search. The response attribute contains reasons for why a face wasn't used for Search.
type nonrec unsearched_faces_list = unsearched_face listtype nonrec reasons = reason listtype nonrec unindexed_face = {face_detail : face_detail option;The structure that contains attributes of a face that IndexFacesdetected, but didn't index.
reasons : reasons option;An array of reasons that specify why a face wasn't indexed.
MaxFaces input parameter for IndexFaces.}A face that IndexFaces detected, but didn't index. Use the Reasons response attribute to determine why a face wasn't indexed.
type nonrec unindexed_faces = unindexed_face listtype nonrec training_data = {assets : assets option;A manifest file that contains references to the training images and ground-truth annotations.
*)}The dataset used for training.
type nonrec training_data_result = {validation : validation_data option;A manifest that you supplied for training, with validation results for each line.
*)output : training_data option;Reference to images (assets) that were actually used during training with trained model predictions.
*)input : training_data option;The training data that you supplied.
*)}The data validation manifest created for the training dataset during model training.
type nonrec timestamp = Smaws_Lib.CoreTypes.Int64.ttype nonrec geometry = {polygon : polygon option;Within the bounding box, a fine-grained polygon around the detected item.
*)bounding_box : bounding_box option;An axis-aligned coarse representation of the detected item's location on the image.
*)}Information about where an object (DetectCustomLabels) or text (DetectText) is located on an image.
type nonrec text_detection = {geometry : geometry option;The location of the detected text on the image. Includes an axis aligned coarse bounding box surrounding the text and a finer grain polygon for more accurate spatial information.
*)confidence : percent option;The confidence that Amazon Rekognition has in the accuracy of the detected text and the accuracy of the geometry points around the detected text.
*)parent_id : u_integer option;The Parent identifier for the detected text identified by the value of ID. If the type of detected text is LINE, the value of ParentId is Null.
id : u_integer option;The identifier for the detected text. The identifier is only unique for a single call to DetectText.
type_ : text_types option;The type of text that was detected.
*)detected_text : string_ option;The word or line of text recognized by Amazon Rekognition.
*)}Information about a word or line of text detected by DetectText.
The DetectedText field contains the text that Amazon Rekognition detected in the image.
Every word and line has an identifier (Id). Each word belongs to a line and has a parent identifier (ParentId) that identifies the line of text in which the word appears. The word Id is also an index for the word within a line of words.
For more information, see Detecting text in the Amazon Rekognition Developer Guide.
type nonrec text_detection_result = {text_detection : text_detection option;Details about text detected in a video.
*)timestamp : timestamp option;The time, in milliseconds from the start of the video, that the text was detected. Note that Timestamp is not guaranteed to be accurate to the individual frame where the text first appears.
}Information about text detected in a video. Incudes the detected text, the time in milliseconds from the start of the video that the text was detected, and where it was detected on the screen.
type nonrec text_detection_results = text_detection_result listtype nonrec text_detection_list = text_detection listtype nonrec testing_data = {auto_create : boolean_ option;If specified, Rekognition splits training dataset to create a test dataset for the training job.
*)assets : assets option;The assets used for testing.
*)}The dataset used for testing. Optionally, if AutoCreate is set, Amazon Rekognition uses the training dataset to create a test dataset with a temporary split of the training dataset.
type nonrec testing_data_result = {validation : validation_data option;The location of the data validation manifest. The data validation manifest is created for the test dataset during model training.
*)output : testing_data option;The subset of the dataset that was actually tested. Some images (assets) might not be tested due to file formatting and other issues.
*)input : testing_data option;The testing dataset that was supplied for training.
*)}Sagemaker Groundtruth format manifest files for the input, output and validation datasets that are used and created during testing.
type nonrec technical_cue_segment = {confidence : segment_confidence option;The confidence that Amazon Rekognition Video has in the accuracy of the detected segment.
*)type_ : technical_cue_type option;The type of the technical cue.
*)}Information about a technical cue segment. For more information, see SegmentDetection.
type nonrec tag_resource_request = {resource_arn : resource_arn;Amazon Resource Name (ARN) of the model, collection, or stream processor that you want to assign the tags to.
*)}type nonrec service_quota_exceeded_exception = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;}The size of the collection exceeds the allowed limit. For more information, see Guidelines and quotas in Amazon Rekognition in the Amazon Rekognition Developer Guide.
The S3 bucket that contains the training summary. The training summary includes aggregated evaluation metrics for the entire testing dataset and metrics for each individual label.
You get the training summary S3 bucket location by calling DescribeProjectVersions.
type nonrec face_search_settings = {face_match_threshold : percent option;Minimum face match confidence score that must be met to return a result for a recognized face. The default is 80. 0 is the lowest confidence. 100 is the highest confidence. Values between 0 and 100 are accepted, and values lower than 80 are set to 80.
*)collection_id : collection_id option;The ID of a collection that contains faces that you want to search for.
*)}Input face recognition parameters for an Amazon Rekognition stream processor. Includes the collection to use for face recognition and the face attributes to detect. Defining the settings is required in the request parameter for CreateStreamProcessor.
type nonrec connected_home_settings = {min_confidence : percent option;The minimum confidence required to label an object in the video.
*)labels : connected_home_labels;Specifies what you want to detect in the video, such as people, packages, or pets. The current valid labels you can include in this list are: "PERSON", "PET", "PACKAGE", and "ALL".
*)}Label detection settings to use on a streaming video. Defining the settings is required in the request parameter for CreateStreamProcessor. Including this setting in the CreateStreamProcessor request enables you to use the stream processor for label detection. You can then select what you want the stream processor to detect, such as people or pets. When the stream processor has started, one notification is sent for each object class specified. For example, if packages and pets are selected, one SNS notification is published the first time a package is detected and one SNS notification is published the first time a pet is detected, as well as an end-of-session summary.
type nonrec stream_processor_settings = {connected_home : connected_home_settings option;face_search : face_search_settings option;Face search settings to use on a streaming video.
*)}Input parameters used in a streaming video analyzed by a Amazon Rekognition stream processor. You can use FaceSearch to recognize faces in a streaming video, or you can use ConnectedHome to detect labels.
type nonrec kinesis_data_stream = {arn : kinesis_data_arn option;ARN of the output Amazon Kinesis Data Streams stream.
*)}The Kinesis data stream Amazon Rekognition to which the analysis results of a Amazon Rekognition stream processor are streamed. For more information, see CreateStreamProcessor in the Amazon Rekognition Developer Guide.
type nonrec s3_destination = {key_prefix : s3_key_prefix option;The prefix value of the location within the bucket that you want the information to be published to. For more information, see Using prefixes.
*)bucket : s3_bucket option;The name of the Amazon S3 bucket you want to associate with the streaming video project. You must be the owner of the Amazon S3 bucket.
*)}The Amazon S3 bucket location to which Amazon Rekognition publishes the detailed inference results of a video analysis operation. These results include the name of the stream processor resource, the session ID of the stream processing session, and labeled timestamps and bounding boxes for detected labels.
type nonrec stream_processor_output = {s3_destination : s3_destination option;The Amazon S3 bucket location to which Amazon Rekognition publishes the detailed inference results of a video analysis operation.
*)kinesis_data_stream : kinesis_data_stream option;The Amazon Kinesis Data Streams stream to which the Amazon Rekognition stream processor streams the analysis results.
*)}Information about the Amazon Kinesis Data Streams stream to which a Amazon Rekognition Video stream processor streams the results of a video analysis. For more information, see CreateStreamProcessor in the Amazon Rekognition Developer Guide.
type nonrec stream_processor_notification_channel = {sns_topic_arn : sns_topic_arn;The Amazon Resource Number (ARN) of the Amazon Amazon Simple Notification Service topic to which Amazon Rekognition posts the completion status.
*)}The Amazon Simple Notification Service topic to which Amazon Rekognition publishes the object detection results and completion status of a video analysis operation.
Amazon Rekognition publishes a notification the first time an object of interest or a person is detected in the video stream. For example, if Amazon Rekognition detects a person at second 2, a pet at second 4, and a person again at second 5, Amazon Rekognition sends 2 object class detected notifications, one for a person at second 2 and one for a pet at second 4.
Amazon Rekognition also publishes an an end-of-session notification with a summary when the stream processing session is complete.
type nonrec stream_processor = {status : stream_processor_status option;Current status of the Amazon Rekognition stream processor.
*)name : stream_processor_name option;Name of the Amazon Rekognition stream processor.
*)}An object that recognizes faces or labels in a streaming video. An Amazon Rekognition stream processor is created by a call to CreateStreamProcessor. The request parameters for CreateStreamProcessor describe the Kinesis video stream source for the streaming video, face recognition parameters, and where to stream the analysis resullts.
type nonrec stream_processor_list = stream_processor listtype nonrec kinesis_video_stream = {arn : kinesis_video_arn option;ARN of the Kinesis video stream stream that streams the source video.
*)}Kinesis video stream stream that provides the source streaming video for a Amazon Rekognition Video stream processor. For more information, see CreateStreamProcessor in the Amazon Rekognition Developer Guide.
type nonrec stream_processor_input = {kinesis_video_stream : kinesis_video_stream option;The Kinesis video stream input stream for the source streaming video.
*)}Information about the source streaming video.
type nonrec max_duration_in_seconds_u_long = Smaws_Lib.CoreTypes.Int64.ttype nonrec stream_processing_stop_selector = {max_duration_in_seconds : max_duration_in_seconds_u_long option;Specifies the maximum amount of time in seconds that you want the stream to be processed. The largest amount of time is 2 minutes. The default is 10 seconds.
*)}Specifies when to stop processing the stream. You can specify a maximum amount of time to process the video.
type nonrec kinesis_video_stream_start_selector = {fragment_number : kinesis_video_stream_fragment_number option;The unique identifier of the fragment. This value monotonically increases based on the ingestion order.
*)producer_timestamp : u_long option;The timestamp from the producer corresponding to the fragment, in milliseconds, expressed in unix time format.
*)}Specifies the starting point in a Kinesis stream to start processing. You can use the producer timestamp or the fragment number. One of either producer timestamp or fragment number is required. If you use the producer timestamp, you must put the time in milliseconds. For more information about fragment numbers, see Fragment.
type nonrec stream_processing_start_selector = {kvs_stream_start_selector : kinesis_video_stream_start_selector option;Specifies the starting point in the stream to start processing. This can be done with a producer timestamp or a fragment number in a Kinesis stream.
*)}This is a required parameter for label detection stream processors and should not be used to start a face search stream processor.
type nonrec stop_stream_processor_request = {name : stream_processor_name;The name of a stream processor created by CreateStreamProcessor.
}type nonrec stop_project_version_response = {status : project_version_status option;The current status of the stop operation.
*)}type nonrec stop_project_version_request = {project_version_arn : project_version_arn;The Amazon Resource Name (ARN) of the model version that you want to stop.
This operation requires permissions to perform the rekognition:StopProjectVersion action.
}type nonrec start_text_detection_response = {job_id : job_id option;Identifier for the text detection job. Use JobId to identify the job in a subsequent call to GetTextDetection.
}type nonrec notification_channel = {role_arn : role_arn;The ARN of an IAM role that gives Amazon Rekognition publishing permissions to the Amazon SNS topic.
*)sns_topic_arn : sns_topic_arn;The Amazon SNS topic to which Amazon Rekognition posts the completion status.
*)}The Amazon Simple Notification Service topic to which Amazon Rekognition publishes the completion status of a video analysis operation. For more information, see Calling Amazon Rekognition Video operations. Note that the Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy to access the topic. For more information, see Giving access to multiple Amazon SNS topics.
type nonrec detection_filter = {min_bounding_box_width : bounding_box_width option;Sets the minimum width of the word bounding box. Words with bounding boxes widths lesser than this value will be excluded from the result. Value is relative to the video frame width.
*)min_bounding_box_height : bounding_box_height option;Sets the minimum height of the word bounding box. Words with bounding box heights lesser than this value will be excluded from the result. Value is relative to the video frame height.
*)min_confidence : percent option;Sets the confidence of word detection. Words with detection confidence below this will be excluded from the result. Values should be between 0 and 100. The default MinConfidence is 80.
*)}A set of parameters that allow you to filter out certain results from your returned results.
type nonrec start_text_detection_filters = {regions_of_interest : regions_of_interest option;Filter focusing on a certain area of the frame. Uses a BoundingBox object to set the region of the screen.
word_filter : detection_filter option;Filters focusing on qualities of the text, such as confidence or size.
*)}Set of optional parameters that let you set the criteria text must meet to be included in your response. WordFilter looks at a word's height, width and minimum confidence. RegionOfInterest lets you set a specific region of the screen to look for text in.
type nonrec start_text_detection_request = {filters : start_text_detection_filters option;Optional parameters that let you set criteria the text must meet to be included in your response.
*)job_tag : job_tag option;An identifier returned in the completion status published by your Amazon Simple Notification Service topic. For example, you can use JobTag to group related jobs and identify them in the completion notification.
notification_channel : notification_channel option;client_request_token : client_request_token option;Idempotent token used to identify the start request. If you use the same token with multiple StartTextDetection requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidentaly started more than once.
video : video;}type nonrec invalid_s3_object_exception = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;}Amazon Rekognition is unable to access the S3 object specified in the request.
type nonrec idempotent_parameter_mismatch_exception = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;}A ClientRequestToken input parameter was reused with an operation, but at least one of the other input parameters is different from the previous call to the operation.
type nonrec black_frame = {min_coverage_percentage : min_coverage_percentage option;The minimum percentage of pixels in a frame that need to have a luminance below the max_black_pixel_value for a frame to be considered a black frame. Luminance is calculated using the BT.709 matrix.
The default value is 99, which means at least 99% of all pixels in the frame are black pixels as per the MaxPixelThreshold set. You can reduce this value to allow more noise on the black frame.
max_pixel_threshold : max_pixel_threshold option;A threshold used to determine the maximum luminance value for a pixel to be considered black. In a full color range video, luminance values range from 0-255. A pixel value of 0 is pure black, and the most strict filter. The maximum black pixel value is computed as follows: max_black_pixel_value = minimum_luminance + MaxPixelThreshold *luminance_range.
For example, for a full range video with BlackPixelThreshold = 0.1, max_black_pixel_value is 0 + 0.1 * (255-0) = 25.5.
The default value of MaxPixelThreshold is 0.2, which maps to a max_black_pixel_value of 51 for a full range video. You can lower this threshold to be more strict on black levels.
*)}A filter that allows you to control the black frame detection by specifying the black levels and pixel coverage of black pixels in a frame. As videos can come from multiple sources, formats, and time periods, they may contain different standards and varying noise levels for black frames that need to be accounted for. For more information, see StartSegmentDetection.
type nonrec start_technical_cue_detection_filter = {black_frame : black_frame option;A filter that allows you to control the black frame detection by specifying the black levels and pixel coverage of black pixels in a frame. Videos can come from multiple sources, formats, and time periods, with different standards and varying noise levels for black frames that need to be accounted for.
*)min_segment_confidence : segment_confidence option;Specifies the minimum confidence that Amazon Rekognition Video must have in order to return a detected segment. Confidence represents how certain Amazon Rekognition is that a segment is correctly identified. 0 is the lowest confidence. 100 is the highest confidence. Amazon Rekognition Video doesn't return any segments with a confidence level lower than this specified value.
If you don't specify MinSegmentConfidence, GetSegmentDetection returns segments with confidence values greater than or equal to 50 percent.
}Filters for the technical segments returned by GetSegmentDetection. For more information, see StartSegmentDetectionFilters.
type nonrec start_stream_processor_response = {session_id : start_stream_processor_session_id option;A unique identifier for the stream processing session.
*)}type nonrec start_stream_processor_request = {stop_selector : stream_processing_stop_selector option;Specifies when to stop processing the stream. You can specify a maximum amount of time to process the video.
This is a required parameter for label detection stream processors and should not be used to start a face search stream processor.
*)start_selector : stream_processing_start_selector option;Specifies the starting point in the Kinesis stream to start processing. You can use the producer timestamp or the fragment number. If you use the producer timestamp, you must put the time in milliseconds. For more information about fragment numbers, see Fragment.
This is a required parameter for label detection stream processors and should not be used to start a face search stream processor.
*)name : stream_processor_name;The name of the stream processor to start processing.
*)}type nonrec start_shot_detection_filter = {min_segment_confidence : segment_confidence option;Specifies the minimum confidence that Amazon Rekognition Video must have in order to return a detected segment. Confidence represents how certain Amazon Rekognition is that a segment is correctly identified. 0 is the lowest confidence. 100 is the highest confidence. Amazon Rekognition Video doesn't return any segments with a confidence level lower than this specified value.
If you don't specify MinSegmentConfidence, the GetSegmentDetection returns segments with confidence values greater than or equal to 50 percent.
}Filters for the shot detection segments returned by GetSegmentDetection. For more information, see StartSegmentDetectionFilters.
type nonrec start_segment_detection_response = {job_id : job_id option;Unique identifier for the segment detection job. The JobId is returned from StartSegmentDetection.
}type nonrec start_segment_detection_filters = {shot_filter : start_shot_detection_filter option;Filters that are specific to shot detections.
*)technical_cue_filter : start_technical_cue_detection_filter option;Filters that are specific to technical cues.
*)}Filters applied to the technical cue or shot detection segments. For more information, see StartSegmentDetection.
type nonrec segment_types = segment_type listtype nonrec start_segment_detection_request = {segment_types : segment_types;An array of segment types to detect in the video. Valid values are TECHNICAL_CUE and SHOT.
*)filters : start_segment_detection_filters option;Filters for technical cue or shot detection.
*)job_tag : job_tag option;An identifier you specify that's returned in the completion notification that's published to your Amazon Simple Notification Service topic. For example, you can use JobTag to group related jobs and identify them in the completion notification.
notification_channel : notification_channel option;The ARN of the Amazon SNS topic to which you want Amazon Rekognition Video to publish the completion status of the segment detection operation. Note that the Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy to access the topic.
*)client_request_token : client_request_token option;Idempotent token used to identify the start request. If you use the same token with multiple StartSegmentDetection requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.
video : video;}type nonrec start_project_version_response = {status : project_version_status option;The current running status of the model.
*)}type nonrec start_project_version_request = {max_inference_units : inference_units option;The maximum number of inference units to use for auto-scaling the model. If you don't specify a value, Amazon Rekognition Custom Labels doesn't auto-scale the model.
*)min_inference_units : inference_units;The minimum number of inference units to use. A single inference unit represents 1 hour of processing.
Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use.
*)project_version_arn : project_version_arn;The Amazon Resource Name(ARN) of the model version that you want to start.
*)}type nonrec start_person_tracking_response = {job_id : job_id option;The identifier for the person detection job. Use JobId to identify the job in a subsequent call to GetPersonTracking.
}type nonrec start_person_tracking_request = {job_tag : job_tag option;An identifier you specify that's returned in the completion notification that's published to your Amazon Simple Notification Service topic. For example, you can use JobTag to group related jobs and identify them in the completion notification.
notification_channel : notification_channel option;The Amazon SNS topic ARN you want Amazon Rekognition Video to publish the completion status of the people detection operation to. The Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy.
*)client_request_token : client_request_token option;Idempotent token used to identify the start request. If you use the same token with multiple StartPersonTracking requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.
video : video;The video in which you want to detect people. The video must be stored in an Amazon S3 bucket.
*)}type nonrec start_media_analysis_job_response = {job_id : media_analysis_job_id;Identifier for the created job.
*)}type nonrec media_analysis_detect_moderation_labels_config = {project_version : project_version_id option;Specifies the custom moderation model to be used during the label detection job. If not provided the pre-trained model is used.
*)min_confidence : percent option;Specifies the minimum confidence level for the moderation labels to return. Amazon Rekognition doesn't return any labels with a confidence level lower than this specified value.
*)}Configuration for Moderation Labels Detection.
type nonrec media_analysis_operations_config = {detect_moderation_labels : media_analysis_detect_moderation_labels_config
option;Contains configuration options for a DetectModerationLabels job.
*)}Configuration options for a media analysis job. Configuration is operation-specific.
Contains input information for a media analysis job.
type nonrec media_analysis_output_config = {s3_key_prefix : media_analysis_s3_key_prefix option;Specifies the Amazon S3 key prefix that comes after the name of the bucket you have designated for storage.
*)s3_bucket : s3_bucket;Specifies the Amazon S3 bucket to contain the output of the media analysis job.
*)}Output configuration provided in the job creation request.
type nonrec start_media_analysis_job_request = {kms_key_id : kms_key_id option;The identifier of customer managed AWS KMS key (name or ARN). The key is used to encrypt images copied into the service. The key is also used to encrypt results and manifest files written to the output Amazon S3 bucket.
*)output_config : media_analysis_output_config;The Amazon S3 bucket location to store the results.
*)input : media_analysis_input;Input data to be analyzed by the job.
*)operations_config : media_analysis_operations_config;Configuration options for the media analysis job to be created.
*)job_name : media_analysis_job_name option;The name of the job. Does not have to be unique.
*)client_request_token : client_request_token option;Idempotency token used to prevent the accidental creation of duplicate versions. If you use the same token with multiple StartMediaAnalysisJobRequest requests, the same response is returned. Use ClientRequestToken to prevent the same request from being processed more than once.
}type nonrec resource_not_ready_exception = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;}The requested resource isn't ready. For example, this exception occurs when you call DetectCustomLabels with a model version that isn't deployed.
type nonrec invalid_manifest_exception = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;}Indicates that a provided manifest file is empty or larger than the allowed limit.
type nonrec start_label_detection_response = {job_id : job_id option;The identifier for the label detection job. Use JobId to identify the job in a subsequent call to GetLabelDetection.
}type nonrec label_detection_feature_list = label_detection_feature_name listtype nonrec general_labels_filter_list = general_labels_filter_value listtype nonrec general_labels_settings = {label_category_exclusion_filters : general_labels_filter_list option;The label categories that should be excluded from the return from DetectLabels.
*)label_category_inclusion_filters : general_labels_filter_list option;The label categories that should be included in the return from DetectLabels.
*)label_exclusion_filters : general_labels_filter_list option;The labels that should be excluded from the return from DetectLabels.
*)label_inclusion_filters : general_labels_filter_list option;The labels that should be included in the return from DetectLabels.
*)}Contains filters for the object labels returned by DetectLabels. Filters can be inclusive, exclusive, or a combination of both and can be applied to individual labels or entire label categories. To see a list of label categories, see Detecting Labels.
Contains the specified filters that should be applied to a list of returned GENERAL_LABELS.
type nonrec start_label_detection_request = {settings : label_detection_settings option;The settings for a StartLabelDetection request.Contains the specified parameters for the label detection request of an asynchronous label analysis operation. Settings can include filters for GENERAL_LABELS.
*)features : label_detection_feature_list option;The features to return after video analysis. You can specify that GENERAL_LABELS are returned.
*)job_tag : job_tag option;An identifier you specify that's returned in the completion notification that's published to your Amazon Simple Notification Service topic. For example, you can use JobTag to group related jobs and identify them in the completion notification.
notification_channel : notification_channel option;The Amazon SNS topic ARN you want Amazon Rekognition Video to publish the completion status of the label detection operation to. The Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy.
*)min_confidence : percent option;Specifies the minimum confidence that Amazon Rekognition Video must have in order to return a detected label. Confidence represents how certain Amazon Rekognition is that a label is correctly identified.0 is the lowest confidence. 100 is the highest confidence. Amazon Rekognition Video doesn't return any labels with a confidence level lower than this specified value.
If you don't specify MinConfidence, the operation returns labels and bounding boxes (if detected) with confidence values greater than or equal to 50 percent.
client_request_token : client_request_token option;Idempotent token used to identify the start request. If you use the same token with multiple StartLabelDetection requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.
video : video;The video in which you want to detect labels. The video must be stored in an Amazon S3 bucket.
*)}type nonrec start_face_search_response = {job_id : job_id option;The identifier for the search job. Use JobId to identify the job in a subsequent call to GetFaceSearch.
}type nonrec start_face_search_request = {job_tag : job_tag option;An identifier you specify that's returned in the completion notification that's published to your Amazon Simple Notification Service topic. For example, you can use JobTag to group related jobs and identify them in the completion notification.
notification_channel : notification_channel option;The ARN of the Amazon SNS topic to which you want Amazon Rekognition Video to publish the completion status of the search. The Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy to access the topic.
*)collection_id : collection_id;ID of the collection that contains the faces you want to search for.
*)face_match_threshold : percent option;The minimum confidence in the person match to return. For example, don't return any matches where confidence in matches is less than 70%. The default value is 80%.
*)client_request_token : client_request_token option;Idempotent token used to identify the start request. If you use the same token with multiple StartFaceSearch requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.
video : video;The video you want to search. The video must be stored in an Amazon S3 bucket.
*)}type nonrec start_face_detection_response = {job_id : job_id option;The identifier for the face detection job. Use JobId to identify the job in a subsequent call to GetFaceDetection.
}type nonrec start_face_detection_request = {job_tag : job_tag option;An identifier you specify that's returned in the completion notification that's published to your Amazon Simple Notification Service topic. For example, you can use JobTag to group related jobs and identify them in the completion notification.
face_attributes : face_attributes option;The face attributes you want returned.
DEFAULT - The following subset of facial attributes are returned: BoundingBox, Confidence, Pose, Quality and Landmarks.
ALL - All facial attributes are returned.
notification_channel : notification_channel option;The ARN of the Amazon SNS topic to which you want Amazon Rekognition Video to publish the completion status of the face detection operation. The Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy.
*)client_request_token : client_request_token option;Idempotent token used to identify the start request. If you use the same token with multiple StartFaceDetection requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.
video : video;The video in which you want to detect faces. The video must be stored in an Amazon S3 bucket.
*)}type nonrec start_content_moderation_response = {job_id : job_id option;The identifier for the content analysis job. Use JobId to identify the job in a subsequent call to GetContentModeration.
}type nonrec start_content_moderation_request = {job_tag : job_tag option;An identifier you specify that's returned in the completion notification that's published to your Amazon Simple Notification Service topic. For example, you can use JobTag to group related jobs and identify them in the completion notification.
notification_channel : notification_channel option;The Amazon SNS topic ARN that you want Amazon Rekognition Video to publish the completion status of the content analysis to. The Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy to access the topic.
*)client_request_token : client_request_token option;Idempotent token used to identify the start request. If you use the same token with multiple StartContentModeration requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.
min_confidence : percent option;Specifies the minimum confidence that Amazon Rekognition must have in order to return a moderated content label. Confidence represents how certain Amazon Rekognition is that the moderated content is correctly identified. 0 is the lowest confidence. 100 is the highest confidence. Amazon Rekognition doesn't return any moderated content labels with a confidence level lower than this specified value. If you don't specify MinConfidence, GetContentModeration returns labels with confidence values greater than or equal to 50 percent.
video : video;The video in which you want to detect inappropriate, unwanted, or offensive content. The video must be stored in an Amazon S3 bucket.
*)}type nonrec start_celebrity_recognition_response = {job_id : job_id option;The identifier for the celebrity recognition analysis job. Use JobId to identify the job in a subsequent call to GetCelebrityRecognition.
}type nonrec start_celebrity_recognition_request = {job_tag : job_tag option;An identifier you specify that's returned in the completion notification that's published to your Amazon Simple Notification Service topic. For example, you can use JobTag to group related jobs and identify them in the completion notification.
notification_channel : notification_channel option;The Amazon SNS topic ARN that you want Amazon Rekognition Video to publish the completion status of the celebrity recognition analysis to. The Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy.
*)client_request_token : client_request_token option;Idempotent token used to identify the start request. If you use the same token with multiple StartCelebrityRecognition requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.
video : video;The video in which you want to recognize celebrities. The video must be stored in an Amazon S3 bucket.
*)}type nonrec shot_segment = {confidence : segment_confidence option;The confidence that Amazon Rekognition Video has in the accuracy of the detected segment.
*)index : u_long option;An Identifier for a shot detection segment detected in a video.
*)}Information about a shot detection segment detected in a video. For more information, see SegmentDetection.
type nonrec session_not_found_exception = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;}Occurs when a given sessionId is not found.
type nonrec segment_type_info = {model_version : string_ option;The version of the model used to detect segments.
*)type_ : segment_type option;The type of a segment (technical cue or shot detection).
*)}Information about the type of a segment requested in a call to StartSegmentDetection. An array of SegmentTypeInfo objects is returned by the response from GetSegmentDetection.
type nonrec segment_types_info = segment_type_info listtype nonrec segment_detection = {duration_frames : u_long option;The duration of a video segment, expressed in frames.
*)end_frame_number : u_long option;The frame number at the end of a video segment, using a frame index that starts with 0.
*)start_frame_number : u_long option;The frame number of the start of a video segment, using a frame index that starts with 0.
*)shot_segment : shot_segment option;If the segment is a shot detection, contains information about the shot detection.
*)technical_cue_segment : technical_cue_segment option;If the segment is a technical cue, contains information about the technical cue.
*)duration_smpt_e : timecode option;The duration of the timecode for the detected segment in SMPTE format.
*)end_timecode_smpt_e : timecode option;The frame-accurate SMPTE timecode, from the start of a video, for the end of a detected segment. EndTimecode is in HH:MM:SS:fr format (and ;fr for drop frame-rates).
start_timecode_smpt_e : timecode option;The frame-accurate SMPTE timecode, from the start of a video, for the start of a detected segment. StartTimecode is in HH:MM:SS:fr format (and ;fr for drop frame-rates).
duration_millis : u_long option;The duration of the detected segment in milliseconds.
*)end_timestamp_millis : timestamp option;The end time of the detected segment, in milliseconds, from the start of the video. This value is rounded down.
*)start_timestamp_millis : timestamp option;The start time of the detected segment in milliseconds from the start of the video. This value is rounded down. For example, if the actual timestamp is 100.6667 milliseconds, Amazon Rekognition Video returns a value of 100 millis.
*)type_ : segment_type option;The type of the segment. Valid values are TECHNICAL_CUE and SHOT.
}A technical cue or shot detection segment detected in a video. An array of SegmentDetection objects containing all segments detected in a stored video is returned by GetSegmentDetection.
type nonrec segment_detections = segment_detection listtype nonrec searched_user = {user_id : user_id option;A provided ID for the UserID. Unique within the collection.
*)}Contains metadata about a User searched for within a collection.
Contains data regarding the input face used for a search.
Provides face metadata such as FaceId, BoundingBox, Confidence of the input face used for search.
type nonrec search_users_response = {searched_user : searched_user option;Contains the ID of the UserID that was used to search for matches in a collection.
*)searched_face : searched_face option;Contains the ID of a face that was used to search for matches in a collection.
*)face_model_version : string_ option;Version number of the face detection model associated with the input CollectionId.
*)user_matches : user_match_list option;An array of UserMatch objects that matched the input face along with the confidence in the match. Array will be empty if there are no matches.
*)}type nonrec search_users_request = {max_users : max_user_results option;Maximum number of identities to return.
*)user_match_threshold : percent option;Optional value that specifies the minimum confidence in the matched UserID to return. Default value of 80.
*)face_id : face_id option;ID for the existing face.
*)user_id : user_id option;ID for the existing User.
*)collection_id : collection_id;The ID of an existing collection containing the UserID, used with a UserId or FaceId. If a FaceId is provided, UserId isn’t required to be present in the Collection.
*)}type nonrec search_users_by_image_response = {unsearched_faces : unsearched_faces_list option;List of UnsearchedFace objects. Contains the face details infered from the specified image but not used for search. Contains reasons that describe why a face wasn't used for Search.
*)searched_face : searched_face_details option;A list of FaceDetail objects containing the BoundingBox for the largest face in image, as well as the confidence in the bounding box, that was searched for matches. If no valid face is detected in the image the response will contain no SearchedFace object.
*)face_model_version : string_ option;Version number of the face detection model associated with the input collection CollectionId.
*)user_matches : user_match_list option;An array of UserID objects that matched the input face, along with the confidence in the match. The returned structure will be empty if there are no matches. Returned if the SearchUsersByImageResponse action is successful.
*)}type nonrec image = {s3_object : s3_object option;Identifies an S3 object as the image source.
*)bytes : image_blob option;Blob of image bytes up to 5 MBs. Note that the maximum image size you can pass to DetectCustomLabels is 4MB.
}Provides the input image either as bytes or an S3 object.
You pass image bytes to an Amazon Rekognition API operation by using the Bytes property. For example, you would use the Bytes property to pass an image loaded from a local file system. Image bytes passed by using the Bytes property must be base64-encoded. Your code may not need to encode image bytes if you are using an AWS SDK to call Amazon Rekognition API operations.
For more information, see Analyzing an Image Loaded from a Local File System in the Amazon Rekognition Developer Guide.
You pass images stored in an S3 bucket to an Amazon Rekognition API operation by using the S3Object property. Images stored in an S3 bucket do not need to be base64-encoded.
The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.
If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes using the Bytes property is not supported. You must first upload the image to an Amazon S3 bucket and then call the operation using the S3Object property.
For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.
type nonrec search_users_by_image_request = {quality_filter : quality_filter option;A filter that specifies a quality bar for how much filtering is done to identify faces. Filtered faces aren't searched for in the collection. The default value is NONE.
*)max_users : max_user_results option;Maximum number of UserIDs to return.
*)user_match_threshold : percent option;Specifies the minimum confidence in the UserID match to return. Default value is 80.
*)image : image;collection_id : collection_id;The ID of an existing collection containing the UserID.
*)}type nonrec invalid_image_format_exception = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;}The provided image format is not supported.
type nonrec image_too_large_exception = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;}The input image size exceeds the allowed limit. If you are calling DetectProtectiveEquipment, the image size or resolution exceeds the allowed limit. For more information, see Guidelines and quotas in Amazon Rekognition in the Amazon Rekognition Developer Guide.
type nonrec face = {user_id : user_id option;Unique identifier assigned to the user.
*)index_faces_model_version : index_faces_model_version option;The version of the face detect and storage model that was used when indexing the face vector.
*)confidence : percent option;Confidence level that the bounding box contains a face (and not a different object such as a tree).
*)external_image_id : external_image_id option;Identifier that you assign to all the faces in the input image.
*)image_id : image_id option;Unique identifier that Amazon Rekognition assigns to the input image.
*)bounding_box : bounding_box option;Bounding box of the face.
*)face_id : face_id option;Unique identifier that Amazon Rekognition assigns to the face.
*)}Describes the face properties such as the bounding box, face ID, image ID of the input image, and external image ID that you assigned.
type nonrec face_match = {face : face option;Describes the face properties such as the bounding box, face ID, image ID of the source image, and external image ID that you assigned.
*)similarity : percent option;Confidence in the match of this face with the input face.
*)}Provides face metadata. In addition, it also provides the confidence in the match of this face with the input face.
type nonrec face_match_list = face_match listtype nonrec search_faces_response = {face_model_version : string_ option;Version number of the face detection model associated with the input collection (CollectionId).
face_matches : face_match_list option;An array of faces that matched the input face, along with the confidence in the match.
*)searched_face_id : face_id option;ID of the face that was searched for matches in a collection.
*)}type nonrec search_faces_request = {face_match_threshold : percent option;Optional value specifying the minimum confidence in the face match to return. For example, don't return any matches where confidence in matches is less than 70%. The default value is 80%.
*)max_faces : max_faces option;Maximum number of faces to return. The operation returns the maximum number of faces with the highest confidence in the match.
*)face_id : face_id;ID of a face to find matches for in the collection.
*)collection_id : collection_id;ID of the collection the face belongs to.
*)}type nonrec search_faces_by_image_response = {face_model_version : string_ option;Version number of the face detection model associated with the input collection (CollectionId).
face_matches : face_match_list option;An array of faces that match the input face, along with the confidence in the match.
*)searched_face_confidence : percent option;The level of confidence that the searchedFaceBoundingBox, contains a face.
searched_face_bounding_box : bounding_box option;The bounding box around the face in the input image that Amazon Rekognition used for the search.
*)}type nonrec search_faces_by_image_request = {quality_filter : quality_filter option;A filter that specifies a quality bar for how much filtering is done to identify faces. Filtered faces aren't searched for in the collection. If you specify AUTO, Amazon Rekognition chooses the quality bar. If you specify LOW, MEDIUM, or HIGH, filtering removes all faces that don’t meet the chosen quality bar. The quality bar is based on a variety of common use cases. Low-quality detections can occur for a number of reasons. Some examples are an object that's misidentified as a face, a face that's too blurry, or a face with a pose that's too extreme to use. If you specify NONE, no filtering is performed. The default value is NONE.
To use quality filtering, the collection you are using must be associated with version 3 of the face model or higher.
*)face_match_threshold : percent option;(Optional) Specifies the minimum confidence in the face match to return. For example, don't return any matches where confidence in matches is less than 70%. The default value is 80%.
*)max_faces : max_faces option;Maximum number of faces to return. The operation returns the maximum number of faces with the highest confidence in the match.
*)image : image;The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.
If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.
collection_id : collection_id;ID of the collection to search.
*)}type nonrec resource_already_exists_exception = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;}A resource with the specified ID already exists.
type nonrec compared_face = {smile : smile option;Indicates whether or not the face is smiling, and the confidence level in the determination.
*)emotions : emotions option;The emotions that appear to be expressed on the face, and the confidence level in the determination. Valid values include "Happy", "Sad", "Angry", "Confused", "Disgusted", "Surprised", "Calm", "Unknown", and "Fear".
*)quality : image_quality option;Identifies face image brightness and sharpness.
*)pose : pose option;Indicates the pose of the face as determined by its pitch, roll, and yaw.
*)landmarks : landmarks option;An array of facial landmarks.
*)confidence : percent option;Level of confidence that what the bounding box contains is a face.
*)bounding_box : bounding_box option;Bounding box of the face.
*)}Provides face metadata for target image faces that are analyzed by CompareFaces and RecognizeCelebrities.
A list of enum string of possible gender values that Celebrity returns.
type nonrec known_gender = {type_ : known_gender_type option;A string value of the KnownGender info about the Celebrity.
*)}The known gender identity for the celebrity that matches the provided ID. The known gender identity can be Male, Female, Nonbinary, or Unlisted.
type nonrec celebrity = {known_gender : known_gender option;match_confidence : percent option;The confidence, in percentage, that Amazon Rekognition has that the recognized face is the celebrity.
*)face : compared_face option;Provides information about the celebrity's face, such as its location on the image.
*)id : rekognition_unique_id option;A unique identifier for the celebrity.
*)name : string_ option;The name of the celebrity.
*)urls : urls option;An array of URLs pointing to additional information about the celebrity. If there is no additional information about the celebrity, this list is empty.
*)}Provides information about a celebrity recognized by the RecognizeCelebrities operation.
type nonrec celebrity_list = celebrity listtype nonrec compared_face_list = compared_face listtype nonrec recognize_celebrities_response = {orientation_correction : orientation_correction option;Support for estimating image orientation using the the OrientationCorrection field has ceased as of August 2021. Any returned values for this field included in an API response will always be NULL.
The orientation of the input image (counterclockwise direction). If your application displays the image, you can use this value to correct the orientation. The bounding box coordinates returned in CelebrityFaces and UnrecognizedFaces represent face locations before the image orientation is corrected.
If the input image is in .jpeg format, it might contain exchangeable image (Exif) metadata that includes the image's orientation. If so, and the Exif metadata for the input image populates the orientation field, the value of OrientationCorrection is null. The CelebrityFaces and UnrecognizedFaces bounding box coordinates represent face locations after Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.
unrecognized_faces : compared_face_list option;Details about each unrecognized face in the image.
*)celebrity_faces : celebrity_list option;Details about each celebrity found in the image. Amazon Rekognition can detect a maximum of 64 celebrities in an image. Each celebrity object includes the following attributes: Face, Confidence, Emotions, Landmarks, Pose, Quality, Smile, Id, KnownGender, MatchConfidence, Name, Urls.
}type nonrec recognize_celebrities_request = {image : image;The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.
If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.
}type nonrec malformed_policy_document_exception = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;}The format of the project policy document that you supplied to PutProjectPolicy is incorrect.
type nonrec invalid_policy_revision_id_exception = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;}The supplied revision id for the project policy is invalid.
type nonrec put_project_policy_response = {policy_revision_id : project_policy_revision_id option;The ID of the project policy.
*)}type nonrec put_project_policy_request = {policy_document : project_policy_document;A resource policy to add to the model. The policy is a JSON structure that contains one or more statements that define the policy. The policy must follow the IAM syntax. For more information about the contents of a JSON policy document, see IAM JSON policy reference.
*)policy_revision_id : project_policy_revision_id option;The revision ID for the Project Policy. Each time you modify a policy, Amazon Rekognition Custom Labels generates and assigns a new PolicyRevisionId and then deletes the previous version of the policy.
policy_name : project_policy_name;A name for the policy.
*)project_arn : project_arn;The Amazon Resource Name (ARN) of the project that the project policy is attached to.
*)}type nonrec invalid_pagination_token_exception = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;}Pagination token in the request is not valid.
type nonrec list_users_response = {next_token : pagination_token option;A pagination token to be used with the subsequent request if the response is truncated.
*)users : user_list option;List of UsersID associated with the specified collection.
*)}type nonrec list_users_request = {next_token : pagination_token option;Pagingation token to receive the next set of UsersID.
*)max_results : max_user_results option;Maximum number of UsersID to return.
*)collection_id : collection_id;The ID of an existing collection.
*)}type nonrec list_stream_processors_response = {stream_processors : stream_processor_list option;List of stream processors that you have created.
*)next_token : pagination_token option;If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of stream processors.
*)}type nonrec list_stream_processors_request = {max_results : max_results option;Maximum number of stream processors you want Amazon Rekognition Video to return in the response. The default is 1000.
*)next_token : pagination_token option;If the previous response was incomplete (because there are more stream processors to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of stream processors.
*)}type nonrec project_policy = {last_updated_timestamp : date_time option;The Unix datetime for when the project policy was last updated.
*)creation_timestamp : date_time option;The Unix datetime for the creation of the project policy.
*)policy_document : project_policy_document option;The JSON document for the project policy.
*)policy_revision_id : project_policy_revision_id option;The revision ID of the project policy.
*)policy_name : project_policy_name option;The name of the project policy.
*)project_arn : project_arn option;The Amazon Resource Name (ARN) of the project to which the project policy is attached.
*)}Describes a project policy in the response from ListProjectPolicies.
type nonrec project_policies = project_policy listtype nonrec list_project_policies_response = {next_token : extended_pagination_token option;If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of project policies.
*)project_policies : project_policies option;A list of project policies attached to the project.
*)}type nonrec list_project_policies_request = {max_results : list_project_policies_page_size option;The maximum number of results to return per paginated call. The largest value you can specify is 5. If you specify a value greater than 5, a ValidationException error occurs. The default value is 5.
*)next_token : extended_pagination_token option;If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.
*)project_arn : project_arn;The ARN of the project for which you want to list the project policies.
*)}type nonrec media_analysis_job_failure_details = {message : string_ option;Human readable error message.
*)code : media_analysis_job_failure_code option;Error code for the failed job.
*)}Details about the error that resulted in failure of the job.
type nonrec media_analysis_model_versions = {moderation : string_ option;The Moderation base model version.
*)}Object containing information about the model versions of selected features in a given job.
type nonrec media_analysis_results = {model_versions : media_analysis_model_versions option;Information about the model versions for the features selected in a given job.
*)s3_object : s3_object option;}Contains the results for a media analysis job created with StartMediaAnalysisJob.
Summary that provides statistics on input manifest and errors identified in the input manifest.
type nonrec media_analysis_job_description = {manifest_summary : media_analysis_manifest_summary option;Provides statistics on input manifest and errors identified in the input manifest.
*)results : media_analysis_results option;Output manifest that contains prediction results.
*)kms_key_id : kms_key_id option;KMS Key that was provided in the creation request.
*)output_config : media_analysis_output_config;Output configuration that was provided in the creation request.
*)input : media_analysis_input;Reference to the input manifest that was provided in the job creation request.
*)completion_timestamp : date_time option;The Unix date and time when the job finished.
*)creation_timestamp : date_time;The Unix date and time when the job was started.
*)failure_details : media_analysis_job_failure_details option;Details about the error that resulted in failure of the job.
*)status : media_analysis_job_status;The status of the media analysis job being retrieved.
*)operations_config : media_analysis_operations_config;Operation configurations that were provided during job creation.
*)job_name : media_analysis_job_name option;The name of a media analysis job.
*)job_id : media_analysis_job_id;The identifier for a media analysis job.
*)}Description for a media analysis job.
type nonrec media_analysis_job_descriptions =
media_analysis_job_description listtype nonrec list_media_analysis_jobs_response = {media_analysis_jobs : media_analysis_job_descriptions;Contains a list of all media analysis jobs.
*)next_token : extended_pagination_token option;Pagination token, if the previous response was incomplete.
*)}type nonrec list_media_analysis_jobs_request = {max_results : list_media_analysis_jobs_page_size option;The maximum number of results to return per paginated call. The largest value user can specify is 100. If user specifies a value greater than 100, an InvalidParameterException error occurs. The default value is 100.
next_token : extended_pagination_token option;Pagination token, if the previous response was incomplete.
*)}type nonrec face_list = face listtype nonrec list_faces_response = {face_model_version : string_ option;Version number of the face detection model associated with the input collection (CollectionId).
next_token : string_ option;If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of faces.
*)faces : face_list option;An array of Face objects.
}type nonrec face_id_list = face_id listtype nonrec list_faces_request = {face_ids : face_id_list option;An array of face IDs to filter results with when listing faces in a collection.
*)user_id : user_id option;An array of user IDs to filter results with when listing faces in a collection.
*)max_results : page_size option;Maximum number of faces to return.
*)next_token : pagination_token option;If the previous response was incomplete (because there is more data to retrieve), Amazon Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of faces.
*)collection_id : collection_id;ID of the collection from which to list the faces.
*)}type nonrec dataset_label_stats = {bounding_box_count : u_integer option;The total number of images that have the label assigned to a bounding box.
*)entry_count : u_integer option;The total number of images that use the label.
*)}Statistics about a label used in a dataset. For more information, see DatasetLabelDescription.
type nonrec dataset_label_description = {label_stats : dataset_label_stats option;Statistics about the label.
*)label_name : dataset_label option;The name of the label.
*)}Describes a dataset label. For more information, see ListDatasetLabels.
type nonrec dataset_label_descriptions = dataset_label_description listtype nonrec list_dataset_labels_response = {next_token : extended_pagination_token option;If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.
*)dataset_label_descriptions : dataset_label_descriptions option;A list of the labels in the dataset.
*)}type nonrec list_dataset_labels_request = {max_results : list_dataset_labels_page_size option;The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100.
*)next_token : extended_pagination_token option;If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.
*)dataset_arn : dataset_arn;The Amazon Resource Name (ARN) of the dataset that you want to use.
*)}type nonrec dataset_entries = dataset_entry listtype nonrec list_dataset_entries_response = {next_token : extended_pagination_token option;If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.
*)dataset_entries : dataset_entries option;A list of entries (images) in the dataset.
*)}type nonrec dataset_labels = dataset_label listtype nonrec list_dataset_entries_request = {max_results : list_dataset_entries_page_size option;The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100.
*)next_token : extended_pagination_token option;If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.
*)has_errors : has_errors option;Specifies an error filter for the response. Specify True to only include entries that have errors.
source_ref_contains : query_string option;If specified, ListDatasetEntries only returns JSON Lines where the value of SourceRefContains is part of the source-ref field. The source-ref field contains the Amazon S3 location of the image. You can use SouceRefContains for tasks such as getting the JSON Line for a single image, or gettting JSON Lines for all images within a specific folder.
labeled : is_labeled option;Specify true to get only the JSON Lines where the image is labeled. Specify false to get only the JSON Lines where the image isn't labeled. If you don't specify Labeled, ListDatasetEntries returns JSON Lines for labeled and unlabeled images.
contains_labels : dataset_labels option;Specifies a label filter for the response. The response includes an entry only if one or more of the labels in ContainsLabels exist in the entry.
dataset_arn : dataset_arn;The Amazon Resource Name (ARN) for the dataset that you want to use.
*)}type nonrec collection_id_list = collection_id listtype nonrec face_model_version_list = string_ listtype nonrec list_collections_response = {face_model_versions : face_model_version_list option;Version numbers of the face detection models associated with the collections in the array CollectionIds. For example, the value of FaceModelVersions[2] is the version number for the face detection model used by the collection in CollectionId[2].
next_token : pagination_token option;If the result is truncated, the response provides a NextToken that you can use in the subsequent request to fetch the next set of collection IDs.
collection_ids : collection_id_list option;An array of collection IDs.
*)}type nonrec list_collections_request = {max_results : page_size option;Maximum number of collection IDs to return.
*)next_token : pagination_token option;Pagination token from the previous response.
*)}type nonrec face_record = {face_detail : face_detail option;Structure containing attributes of the face that the algorithm detected.
*)face : face option;Describes the face properties such as the bounding box, face ID, image ID of the input image, and external image ID that you assigned.
*)}Object containing both the face metadata (stored in the backend database), and facial attributes that are detected but aren't stored in the database.
type nonrec face_record_list = face_record listtype nonrec index_faces_response = {unindexed_faces : unindexed_faces option;An array of faces that were detected in the image but weren't indexed. They weren't indexed because the quality filter identified them as low quality, or the MaxFaces request parameter filtered them out. To use the quality filter, you specify the QualityFilter request parameter.
face_model_version : string_ option;The version number of the face detection model that's associated with the input collection (CollectionId).
orientation_correction : orientation_correction option;If your collection is associated with a face detection model that's later than version 3.0, the value of OrientationCorrection is always null and no orientation information is returned.
If your collection is associated with a face detection model that's version 3.0 or earlier, the following applies:
OrientationCorrection is null.Bounding box information is returned in the FaceRecords array. You can get the version of the face detection model by calling DescribeCollection.
face_records : face_record_list option;An array of faces detected and added to the collection. For more information, see Searching Faces in a Collection in the Amazon Rekognition Developer Guide.
*)}type nonrec attributes = attribute listtype nonrec index_faces_request = {quality_filter : quality_filter option;A filter that specifies a quality bar for how much filtering is done to identify faces. Filtered faces aren't indexed. If you specify AUTO, Amazon Rekognition chooses the quality bar. If you specify LOW, MEDIUM, or HIGH, filtering removes all faces that don’t meet the chosen quality bar. The default value is AUTO. The quality bar is based on a variety of common use cases. Low-quality detections can occur for a number of reasons. Some examples are an object that's misidentified as a face, a face that's too blurry, or a face with a pose that's too extreme to use. If you specify NONE, no filtering is performed.
To use quality filtering, the collection you are using must be associated with version 3 of the face model or higher.
*)max_faces : max_faces_to_index option;The maximum number of faces to index. The value of MaxFaces must be greater than or equal to 1. IndexFaces returns no more than 100 detected faces in an image, even if you specify a larger value for MaxFaces.
If IndexFaces detects more faces than the value of MaxFaces, the faces with the lowest quality are filtered out first. If there are still more faces than the value of MaxFaces, the faces with the smallest bounding boxes are filtered out (up to the number that's needed to satisfy the value of MaxFaces). Information about the unindexed faces is available in the UnindexedFaces array.
The faces that are returned by IndexFaces are sorted by the largest face bounding box size to the smallest size, in descending order.
MaxFaces can be used with a collection associated with any version of the face model.
detection_attributes : attributes option;An array of facial attributes you want to be returned. A DEFAULT subset of facial attributes - BoundingBox, Confidence, Pose, Quality, and Landmarks - will always be returned. You can request for specific facial attributes (in addition to the default list) - by using ["DEFAULT", "FACE_OCCLUDED"] or just ["FACE_OCCLUDED"]. You can request for all facial attributes by using ["ALL"]. Requesting more attributes may increase response time.
If you provide both, ["ALL", "DEFAULT"], the service uses a logical AND operator to determine which attributes to return (in this case, all attributes).
external_image_id : external_image_id option;The ID you want to assign to all the faces detected in the image.
*)image : image;The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes isn't supported.
If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.
collection_id : collection_id;The ID of an existing collection to which you want to add the faces that are detected in the input images.
*)}type nonrec get_text_detection_response = {job_tag : job_tag option;A job identifier specified in the call to StartTextDetection and returned in the job completion notification sent to your Amazon Simple Notification Service topic.
*)video : video option;job_id : job_id option;Job identifier for the text detection operation for which you want to obtain results. The job identifer is returned by an initial call to StartTextDetection.
*)text_model_version : string_ option;Version number of the text detection model that was used to detect text.
*)next_token : pagination_token option;If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of text.
*)text_detections : text_detection_results option;An array of text detected in the video. Each element contains the detected text, the time in milliseconds from the start of the video that the text was detected, and where it was detected on the screen.
*)video_metadata : video_metadata option;status_message : status_message option;If the job fails, StatusMessage provides a descriptive error message.
job_status : video_job_status option;Current status of the text detection job.
*)}type nonrec get_text_detection_request = {next_token : pagination_token option;If the previous response was incomplete (because there are more labels to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of text.
*)max_results : max_results option;Maximum number of results to return per paginated call. The largest value you can specify is 1000.
*)job_id : job_id;Job identifier for the text detection operation for which you want results returned. You get the job identifer from an initial call to StartTextDetection.
}type nonrec audio_metadata = {number_of_channels : u_long option;The number of audio channels in the segment.
*)sample_rate : u_long option;The sample rate for the audio stream.
*)duration_millis : u_long option;The duration of the audio stream in milliseconds.
*)codec : string_ option;The audio codec used to encode or decode the audio stream.
*)}Metadata information about an audio stream. An array of AudioMetadata objects for the audio streams found in a stored video is returned by GetSegmentDetection.
type nonrec audio_metadata_list = audio_metadata listtype nonrec get_segment_detection_response = {job_tag : job_tag option;A job identifier specified in the call to StartSegmentDetection and returned in the job completion notification sent to your Amazon Simple Notification Service topic.
*)video : video option;job_id : job_id option;Job identifier for the segment detection operation for which you want to obtain results. The job identifer is returned by an initial call to StartSegmentDetection.
*)selected_segment_types : segment_types_info option;An array containing the segment types requested in the call to StartSegmentDetection.
segments : segment_detections option;An array of segments detected in a video. The array is sorted by the segment types (TECHNICAL_CUE or SHOT) specified in the SegmentTypes input parameter of StartSegmentDetection. Within each segment type the array is sorted by timestamp values.
next_token : pagination_token option;If the previous response was incomplete (because there are more labels to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of text.
*)audio_metadata : audio_metadata_list option;An array of objects. There can be multiple audio streams. Each AudioMetadata object contains metadata for a single audio stream. Audio information in an AudioMetadata objects includes the audio codec, the number of audio channels, the duration of the audio stream, and the sample rate. Audio metadata is returned in each page of information returned by GetSegmentDetection.
video_metadata : video_metadata_list option;Currently, Amazon Rekognition Video returns a single object in the VideoMetadata array. The object contains information about the video stream in the input file that Amazon Rekognition Video chose to analyze. The VideoMetadata object includes the video codec, video format and other information. Video metadata is returned in each page of information returned by GetSegmentDetection.
status_message : status_message option;If the job fails, StatusMessage provides a descriptive error message.
job_status : video_job_status option;Current status of the segment detection job.
*)}type nonrec get_segment_detection_request = {next_token : pagination_token option;If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of text.
*)max_results : max_results option;Maximum number of results to return per paginated call. The largest value you can specify is 1000.
*)job_id : job_id;Job identifier for the text detection operation for which you want results returned. You get the job identifer from an initial call to StartSegmentDetection.
}type nonrec person_index = Smaws_Lib.CoreTypes.Int64.ttype nonrec person_detail = {face : face_detail option;Face details for the detected person.
*)bounding_box : bounding_box option;Bounding box around the detected person.
*)index : person_index option;Identifier for the person detected person within a video. Use to keep track of the person throughout the video. The identifier is not stored by Amazon Rekognition.
*)}Details about a person detected in a video analysis request.
type nonrec person_detection = {person : person_detail option;Details about a person whose path was tracked in a video.
*)timestamp : timestamp option;The time, in milliseconds from the start of the video, that the person's path was tracked. Note that Timestamp is not guaranteed to be accurate to the individual frame where the person's path first appears.
}Details and path tracking information for a single time a person's path is tracked in a video. Amazon Rekognition operations that track people's paths return an array of PersonDetection objects with elements for each time a person's path is tracked in a video.
For more information, see GetPersonTracking in the Amazon Rekognition Developer Guide.
type nonrec person_detections = person_detection listtype nonrec get_person_tracking_response = {job_tag : job_tag option;A job identifier specified in the call to StartCelebrityRecognition and returned in the job completion notification sent to your Amazon Simple Notification Service topic.
*)video : video option;job_id : job_id option;Job identifier for the person tracking operation for which you want to obtain results. The job identifer is returned by an initial call to StartPersonTracking.
*)persons : person_detections option;An array of the persons detected in the video and the time(s) their path was tracked throughout the video. An array element will exist for each time a person's path is tracked.
*)next_token : pagination_token option;If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of persons.
*)video_metadata : video_metadata option;Information about a video that Amazon Rekognition Video analyzed. Videometadata is returned in every page of paginated responses from a Amazon Rekognition Video operation.
status_message : status_message option;If the job fails, StatusMessage provides a descriptive error message.
job_status : video_job_status option;The current status of the person tracking job.
*)}type nonrec get_person_tracking_request = {sort_by : person_tracking_sort_by option;Sort to use for elements in the Persons array. Use TIMESTAMP to sort array elements by the time persons are detected. Use INDEX to sort by the tracked persons. If you sort by INDEX, the array elements for each person are sorted by detection confidence. The default sort is by TIMESTAMP.
next_token : pagination_token option;If the previous response was incomplete (because there are more persons to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of persons.
*)max_results : max_results option;Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.
*)job_id : job_id;The identifier for a job that tracks persons in a video. You get the JobId from a call to StartPersonTracking.
}type nonrec get_media_analysis_job_response = {manifest_summary : media_analysis_manifest_summary option;The summary manifest provides statistics on input manifest and errors identified in the input manifest.
*)results : media_analysis_results option;Output manifest that contains prediction results.
*)kms_key_id : kms_key_id option;KMS Key that was provided in the creation request.
*)output_config : media_analysis_output_config;Output configuration that was provided in the creation request.
*)input : media_analysis_input;Reference to the input manifest that was provided in the job creation request.
*)completion_timestamp : date_time option;The Unix date and time when the job finished.
*)creation_timestamp : date_time;The Unix date and time when the job was started.
*)failure_details : media_analysis_job_failure_details option;Details about the error that resulted in failure of the job.
*)status : media_analysis_job_status;The current status of the media analysis job.
*)operations_config : media_analysis_operations_config;Operation configurations that were provided during job creation.
*)job_name : media_analysis_job_name option;The name of the media analysis job.
*)job_id : media_analysis_job_id;The identifier for the media analysis job.
*)}type nonrec get_media_analysis_job_request = {job_id : media_analysis_job_id;Unique identifier for the media analysis job for which you want to retrieve results.
*)}type nonrec dominant_color = {pixel_percent : percent option;The percentage of image pixels that have a given dominant color.
*)simplified_color : string_ option;One of 12 simplified color names applied to a dominant color.
*)css_color : string_ option;The CSS color name of a dominant color.
*)hex_code : string_ option;The Hex code equivalent of the RGB values for a dominant color.
*)green : u_integer option;The Green RGB value for a dominant color.
*)blue : u_integer option;The Blue RGB value for a dominant color.
*)red : u_integer option;The Red RGB value for a dominant color.
*)}A description of the dominant colors in an image.
type nonrec dominant_colors = dominant_color listtype nonrec instance = {dominant_colors : dominant_colors option;The dominant colors found in an individual instance of a label.
*)confidence : percent option;The confidence that Amazon Rekognition has in the accuracy of the bounding box.
*)bounding_box : bounding_box option;The position of the label instance on the image.
*)}An instance of a label returned by Amazon Rekognition Image (DetectLabels) or by Amazon Rekognition Video (GetLabelDetection).
type nonrec instances = instance listA parent label for a label. A label can have 0, 1, or more parents.
type nonrec parents = parent listA potential alias of for a given label.
type nonrec label_aliases = label_alias listtype nonrec label_category = {name : string_ option;The name of a category that applies to a given label.
*)}The category that applies to a given label.
type nonrec label_categories = label_category listtype nonrec label = {categories : label_categories option;A list of the categories associated with a given label.
*)aliases : label_aliases option;A list of potential aliases for a given label.
*)parents : parents option;The parent labels for a label. The response includes all ancestor labels.
*)instances : instances option;If Label represents an object, Instances contains the bounding boxes for each instance of the detected object. Bounding boxes are returned for common object labels such as people, cars, furniture, apparel or pets.
confidence : percent option;Level of confidence.
*)name : string_ option;The name (label) of the object or scene.
*)}Structure containing details about the detected label, including the name, detected instances, parent labels, and level of confidence.
type nonrec label_detection = {duration_millis : u_long option;The time duration of a segment in milliseconds, I.e. time elapsed from StartTimestampMillis to EndTimestampMillis.
*)end_timestamp_millis : u_long option;The time in milliseconds defining the end of the timeline segment containing a continuously detected label.
*)start_timestamp_millis : u_long option;The time in milliseconds defining the start of the timeline segment containing a continuously detected label.
*)label : label option;Details about the detected label.
*)timestamp : timestamp option;Time, in milliseconds from the start of the video, that the label was detected. Note that Timestamp is not guaranteed to be accurate to the individual frame where the label first appears.
}Information about a label detected in a video analysis request and the time the label was detected in the video.
type nonrec label_detections = label_detection listtype nonrec get_label_detection_request_metadata = {aggregate_by : label_detection_aggregate_by option;The aggregation method chosen for a GetLabelDetection request.
*)sort_by : label_detection_sort_by option;The sorting method chosen for a GetLabelDetection request.
*)}Contains metadata about a label detection request, including the SortBy and AggregateBy options.
type nonrec get_label_detection_response = {get_request_metadata : get_label_detection_request_metadata option;Information about the paramters used when getting a response. Includes information on aggregation and sorting methods.
*)job_tag : job_tag option;A job identifier specified in the call to StartLabelDetection and returned in the job completion notification sent to your Amazon Simple Notification Service topic.
*)video : video option;job_id : job_id option;Job identifier for the label detection operation for which you want to obtain results. The job identifer is returned by an initial call to StartLabelDetection.
*)label_model_version : string_ option;Version number of the label detection model that was used to detect labels.
*)labels : label_detections option;An array of labels detected in the video. Each element contains the detected label and the time, in milliseconds from the start of the video, that the label was detected.
*)next_token : pagination_token option;If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of labels.
*)video_metadata : video_metadata option;Information about a video that Amazon Rekognition Video analyzed. Videometadata is returned in every page of paginated responses from a Amazon Rekognition video operation.
status_message : status_message option;If the job fails, StatusMessage provides a descriptive error message.
job_status : video_job_status option;The current status of the label detection job.
*)}type nonrec get_label_detection_request = {aggregate_by : label_detection_aggregate_by option;Defines how to aggregate the returned results. Results can be aggregated by timestamps or segments.
*)sort_by : label_detection_sort_by option;Sort to use for elements in the Labels array. Use TIMESTAMP to sort array elements by the time labels are detected. Use NAME to alphabetically group elements for a label together. Within each label group, the array element are sorted by detection confidence. The default sort is by TIMESTAMP.
next_token : pagination_token option;If the previous response was incomplete (because there are more labels to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of labels.
*)max_results : max_results option;Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.
*)job_id : job_id;Job identifier for the label detection operation for which you want results returned. You get the job identifer from an initial call to StartlabelDetection.
}type nonrec person_match = {face_matches : face_match_list option;Information about the faces in the input collection that match the face of a person in the video.
*)person : person_detail option;Information about the matched person.
*)timestamp : timestamp option;The time, in milliseconds from the beginning of the video, that the person was matched in the video.
*)}Information about a person whose face matches a face(s) in an Amazon Rekognition collection. Includes information about the faces in the Amazon Rekognition collection (FaceMatch), information about the person (PersonDetail), and the time stamp for when the person was detected in a video. An array of PersonMatch objects is returned by GetFaceSearch.
type nonrec person_matches = person_match listtype nonrec get_face_search_response = {job_tag : job_tag option;A job identifier specified in the call to StartFaceSearch and returned in the job completion notification sent to your Amazon Simple Notification Service topic.
*)video : video option;job_id : job_id option;Job identifier for the face search operation for which you want to obtain results. The job identifer is returned by an initial call to StartFaceSearch.
*)persons : person_matches option;An array of persons, PersonMatch, in the video whose face(s) match the face(s) in an Amazon Rekognition collection. It also includes time information for when persons are matched in the video. You specify the input collection in an initial call to StartFaceSearch. Each Persons element includes a time the person was matched, face match details (FaceMatches) for matching faces in the collection, and person information (Person) for the matched person.
video_metadata : video_metadata option;Information about a video that Amazon Rekognition analyzed. Videometadata is returned in every page of paginated responses from a Amazon Rekognition Video operation.
next_token : pagination_token option;If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of search results.
*)status_message : status_message option;If the job fails, StatusMessage provides a descriptive error message.
job_status : video_job_status option;The current status of the face search job.
*)}type nonrec get_face_search_request = {sort_by : face_search_sort_by option;Sort to use for grouping faces in the response. Use TIMESTAMP to group faces by the time that they are recognized. Use INDEX to sort by recognized faces.
next_token : pagination_token option;If the previous response was incomplete (because there is more search results to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of search results.
*)max_results : max_results option;Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.
*)job_id : job_id;The job identifer for the search request. You get the job identifier from an initial call to StartFaceSearch.
}type nonrec audit_image = {bounding_box : bounding_box option;s3_object : s3_object option;bytes : liveness_image_blob option;The Base64-encoded bytes representing an image selected from the Face Liveness video and returned for audit purposes.
*)}An image that is picked from the Face Liveness video and returned for audit trail purposes, returned as Base64-encoded bytes.
type nonrec audit_images = audit_image listtype nonrec challenge = {version : version;The version of the challenge being used for the Face Liveness session.
*)type_ : challenge_type;The type of the challenge being used for the Face Liveness session.
*)}Describes the type and version of the challenge being used for the Face Liveness session.
type nonrec get_face_liveness_session_results_response = {challenge : challenge option;Contains information regarding the challenge type used for the Face Liveness check.
*)audit_images : audit_images option;A set of images from the Face Liveness video that can be used for audit purposes. It includes a bounding box of the face and the Base64-encoded bytes that return an image. If the CreateFaceLivenessSession request included an OutputConfig argument, the image will be uploaded to an S3Object specified in the output configuration. If no Amazon S3 bucket is defined, raw bytes are sent instead.
*)reference_image : audit_image option;A high-quality image from the Face Liveness video that can be used for face comparison or search. It includes a bounding box of the face and the Base64-encoded bytes that return an image. If the CreateFaceLivenessSession request included an OutputConfig argument, the image will be uploaded to an S3Object specified in the output configuration. In case the reference image is not returned, it's recommended to retry the Liveness check.
*)confidence : percent option;Probabalistic confidence score for if the person in the given video was live, represented as a float value between 0 to 100.
*)status : liveness_session_status;Represents a status corresponding to the state of the session. Possible statuses are: CREATED, IN_PROGRESS, SUCCEEDED, FAILED, EXPIRED.
*)session_id : liveness_session_id;The sessionId for which this request was called.
*)}type nonrec get_face_liveness_session_results_request = {session_id : liveness_session_id;A unique 128-bit UUID. This is used to uniquely identify the session and also acts as an idempotency token for all operations associated with the session.
*)}type nonrec face_detection = {face : face_detail option;The face properties for the detected face.
*)timestamp : timestamp option;Time, in milliseconds from the start of the video, that the face was detected. Note that Timestamp is not guaranteed to be accurate to the individual frame where the face first appears.
}Information about a face detected in a video analysis request and the time the face was detected in the video.
type nonrec face_detections = face_detection listtype nonrec get_face_detection_response = {job_tag : job_tag option;A job identifier specified in the call to StartFaceDetection and returned in the job completion notification sent to your Amazon Simple Notification Service topic.
*)video : video option;job_id : job_id option;Job identifier for the face detection operation for which you want to obtain results. The job identifer is returned by an initial call to StartFaceDetection.
*)faces : face_detections option;An array of faces detected in the video. Each element contains a detected face's details and the time, in milliseconds from the start of the video, the face was detected.
*)next_token : pagination_token option;If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of faces.
*)video_metadata : video_metadata option;Information about a video that Amazon Rekognition Video analyzed. Videometadata is returned in every page of paginated responses from a Amazon Rekognition video operation.
status_message : status_message option;If the job fails, StatusMessage provides a descriptive error message.
job_status : video_job_status option;The current status of the face detection job.
*)}type nonrec get_face_detection_request = {next_token : pagination_token option;If the previous response was incomplete (because there are more faces to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of faces.
*)max_results : max_results option;Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.
*)job_id : job_id;Unique identifier for the face detection job. The JobId is returned from StartFaceDetection.
}type nonrec moderation_label = {taxonomy_level : u_integer option;The level of the moderation label with regard to its taxonomy, from 1 to 3.
*)parent_name : string_ option;The name for the parent label. Labels at the top level of the hierarchy have the parent label "".
name : string_ option;The label name for the type of unsafe content detected in the image.
*)confidence : percent option;Specifies the confidence that Amazon Rekognition has that the label has been correctly identified.
If you don't specify the MinConfidence parameter in the call to DetectModerationLabels, the operation returns labels with a confidence value greater than or equal to 50 percent.
}Provides information about a single type of inappropriate, unwanted, or offensive content found in an image or video. Each type of moderated content has a label within a hierarchical taxonomy. For more information, see Content moderation in the Amazon Rekognition Developer Guide.
type nonrec content_type = {name : string_ option;The name of the label
*)confidence : percent option;The confidence level of the label given
*)}Contains information regarding the confidence and name of a detected content type.
type nonrec content_types = content_type listtype nonrec content_moderation_detection = {content_types : content_types option;A list of predicted results for the type of content an image contains. For example, the image content might be from animation, sports, or a video game.
*)duration_millis : u_long option;The time duration of a segment in milliseconds, I.e. time elapsed from StartTimestampMillis to EndTimestampMillis.
*)end_timestamp_millis : u_long option;The time in milliseconds defining the end of the timeline segment containing a continuously detected moderation label.
*)start_timestamp_millis : u_long option;The time in milliseconds defining the start of the timeline segment containing a continuously detected moderation label.
*)moderation_label : moderation_label option;The content moderation label detected by in the stored video.
*)timestamp : timestamp option;Time, in milliseconds from the beginning of the video, that the content moderation label was detected. Note that Timestamp is not guaranteed to be accurate to the individual frame where the moderated content first appears.
}Information about an inappropriate, unwanted, or offensive content label detection in a stored video.
type nonrec content_moderation_detections = content_moderation_detection listtype nonrec get_content_moderation_request_metadata = {aggregate_by : content_moderation_aggregate_by option;The aggregation method chosen for a GetContentModeration request.
*)sort_by : content_moderation_sort_by option;The sorting method chosen for a GetContentModeration request.
*)}Contains metadata about a content moderation request, including the SortBy and AggregateBy options.
type nonrec get_content_moderation_response = {get_request_metadata : get_content_moderation_request_metadata option;Information about the paramters used when getting a response. Includes information on aggregation and sorting methods.
*)job_tag : job_tag option;A job identifier specified in the call to StartContentModeration and returned in the job completion notification sent to your Amazon Simple Notification Service topic.
*)video : video option;job_id : job_id option;Job identifier for the content moderation operation for which you want to obtain results. The job identifer is returned by an initial call to StartContentModeration.
*)moderation_model_version : string_ option;Version number of the moderation detection model that was used to detect inappropriate, unwanted, or offensive content.
*)next_token : pagination_token option;If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of content moderation labels.
*)moderation_labels : content_moderation_detections option;The detected inappropriate, unwanted, or offensive content moderation labels and the time(s) they were detected.
*)video_metadata : video_metadata option;Information about a video that Amazon Rekognition analyzed. Videometadata is returned in every page of paginated responses from GetContentModeration.
status_message : status_message option;If the job fails, StatusMessage provides a descriptive error message.
job_status : video_job_status option;The current status of the content moderation analysis job.
*)}type nonrec get_content_moderation_request = {aggregate_by : content_moderation_aggregate_by option;Defines how to aggregate results of the StartContentModeration request. Default aggregation option is TIMESTAMPS. SEGMENTS mode aggregates moderation labels over time.
*)sort_by : content_moderation_sort_by option;Sort to use for elements in the ModerationLabelDetections array. Use TIMESTAMP to sort array elements by the time labels are detected. Use NAME to alphabetically group elements for a label together. Within each label group, the array element are sorted by detection confidence. The default sort is by TIMESTAMP.
next_token : pagination_token option;If the previous response was incomplete (because there is more data to retrieve), Amazon Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of content moderation labels.
*)max_results : max_results option;Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.
*)job_id : job_id;The identifier for the inappropriate, unwanted, or offensive content moderation job. Use JobId to identify the job in a subsequent call to GetContentModeration.
}type nonrec celebrity_detail = {known_gender : known_gender option;Retrieves the known gender for the celebrity.
*)face : face_detail option;Face details for the recognized celebrity.
*)bounding_box : bounding_box option;Bounding box around the body of a celebrity.
*)confidence : percent option;The confidence, in percentage, that Amazon Rekognition has that the recognized face is the celebrity.
*)id : rekognition_unique_id option;The unique identifier for the celebrity.
*)name : string_ option;The name of the celebrity.
*)urls : urls option;An array of URLs pointing to additional celebrity information.
*)}Information about a recognized celebrity.
type nonrec celebrity_recognition = {celebrity : celebrity_detail option;Information about a recognized celebrity.
*)timestamp : timestamp option;The time, in milliseconds from the start of the video, that the celebrity was recognized. Note that Timestamp is not guaranteed to be accurate to the individual frame where the celebrity first appears.
}Information about a detected celebrity and the time the celebrity was detected in a stored video. For more information, see GetCelebrityRecognition in the Amazon Rekognition Developer Guide.
type nonrec celebrity_recognitions = celebrity_recognition listtype nonrec get_celebrity_recognition_response = {job_tag : job_tag option;A job identifier specified in the call to StartCelebrityRecognition and returned in the job completion notification sent to your Amazon Simple Notification Service topic.
*)video : video option;job_id : job_id option;Job identifier for the celebrity recognition operation for which you want to obtain results. The job identifer is returned by an initial call to StartCelebrityRecognition.
*)celebrities : celebrity_recognitions option;Array of celebrities recognized in the video.
*)next_token : pagination_token option;If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of celebrities.
*)video_metadata : video_metadata option;Information about a video that Amazon Rekognition Video analyzed. Videometadata is returned in every page of paginated responses from a Amazon Rekognition Video operation.
status_message : status_message option;If the job fails, StatusMessage provides a descriptive error message.
job_status : video_job_status option;The current status of the celebrity recognition job.
*)}type nonrec get_celebrity_recognition_request = {sort_by : celebrity_recognition_sort_by option;Sort to use for celebrities returned in Celebrities field. Specify ID to sort by the celebrity identifier, specify TIMESTAMP to sort by the time the celebrity was recognized.
next_token : pagination_token option;If the previous response was incomplete (because there is more recognized celebrities to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of celebrities.
*)max_results : max_results option;Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.
*)job_id : job_id;Job identifier for the required celebrity recognition analysis. You can get the job identifer from a call to StartCelebrityRecognition.
}type nonrec get_celebrity_info_response = {known_gender : known_gender option;Retrieves the known gender for the celebrity.
*)name : string_ option;The name of the celebrity.
*)urls : urls option;An array of URLs pointing to additional celebrity information.
*)}type nonrec get_celebrity_info_request = {id : rekognition_unique_id;The ID for the celebrity. You get the celebrity ID from a call to the RecognizeCelebrities operation, which recognizes celebrities in an image.
}type nonrec distribute_dataset = {arn : dataset_arn;The Amazon Resource Name (ARN) of the dataset that you want to use.
*)}A training dataset or a test dataset used in a dataset distribution operation. For more information, see DistributeDatasetEntries.
type nonrec distribute_dataset_metadata_list = distribute_dataset listtype nonrec distribute_dataset_entries_request = {datasets : distribute_dataset_metadata_list;The ARNS for the training dataset and test dataset that you want to use. The datasets must belong to the same project. The test dataset must be empty.
*)}type nonrec conflict_exception = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;}A User with the same Id already exists within the collection, or the update or deletion of the User caused an inconsistent state. **
type nonrec disassociated_face = {face_id : face_id option;Unique identifier assigned to the face.
*)}Provides face metadata for the faces that are disassociated from a specific UserID.
type nonrec disassociated_faces_list = disassociated_face listtype nonrec disassociate_faces_response = {user_status : user_status option;The status of an update made to a User. Reflects if the User has been updated for every requested change.
*)unsuccessful_face_disassociations : unsuccessful_face_disassociation_list
option;An array of UnsuccessfulDisassociation objects containing FaceIds that are not successfully associated, along with the reasons for the failure to associate. Returned if the DisassociateFaces action is successful.
*)disassociated_faces : disassociated_faces_list option;An array of DissociatedFace objects containing FaceIds that are successfully disassociated with the UserID is returned. Returned if the DisassociatedFaces action is successful.
*)}type nonrec disassociate_faces_request = {face_ids : user_face_id_list;An array of face IDs to disassociate from the UserID.
*)client_request_token : client_request_token option;Idempotent token used to identify the request to DisassociateFaces. If you use the same token with multiple DisassociateFaces requests, the same response is returned. Use ClientRequestToken to prevent the same request from being processed more than once.
user_id : user_id;ID for the existing UserID.
*)collection_id : collection_id;The ID of an existing collection containing the UserID.
*)}type nonrec detect_text_response = {text_model_version : string_ option;The model version used to detect text.
*)text_detections : text_detection_list option;An array of text that was detected in the input image.
*)}type nonrec detect_text_filters = {regions_of_interest : regions_of_interest option;A Filter focusing on a certain area of the image. Uses a BoundingBox object to set the region of the image.
word_filter : detection_filter option;}A set of optional parameters that you can use to set the criteria that the text must meet to be included in your response. WordFilter looks at a word’s height, width, and minimum confidence. RegionOfInterest lets you set a specific region of the image to look for text in.
type nonrec detect_text_request = {filters : detect_text_filters option;Optional parameters that let you set the criteria that the text must meet to be included in your response.
*)image : image;The input image as base64-encoded bytes or an Amazon S3 object. If you use the AWS CLI to call Amazon Rekognition operations, you can't pass image bytes.
If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.
}type nonrec covers_body_part = {value : boolean_ option;True if the PPE covers the corresponding body part, otherwise false.
*)confidence : percent option;The confidence that Amazon Rekognition has in the value of Value.
}Information about an item of Personal Protective Equipment covering a corresponding body part. For more information, see DetectProtectiveEquipment.
type nonrec equipment_detection = {covers_body_part : covers_body_part option;Information about the body part covered by the detected PPE.
*)type_ : protective_equipment_type option;The type of detected PPE.
*)confidence : percent option;The confidence that Amazon Rekognition has that the bounding box (BoundingBox) contains an item of PPE.
bounding_box : bounding_box option;A bounding box surrounding the item of detected PPE.
*)}Information about an item of Personal Protective Equipment (PPE) detected by DetectProtectiveEquipment. For more information, see DetectProtectiveEquipment.
type nonrec equipment_detections = equipment_detection listtype nonrec protective_equipment_body_part = {equipment_detections : equipment_detections option;An array of Personal Protective Equipment items detected around a body part.
*)confidence : percent option;The confidence that Amazon Rekognition has in the detection accuracy of the detected body part.
*)name : body_part option;The detected body part.
*)}Information about a body part detected by DetectProtectiveEquipment that contains PPE. An array of ProtectiveEquipmentBodyPart objects is returned for each person detected by DetectProtectiveEquipment.
type nonrec body_parts = protective_equipment_body_part listtype nonrec protective_equipment_person = {id : u_integer option;The identifier for the detected person. The identifier is only unique for a single call to DetectProtectiveEquipment.
confidence : percent option;The confidence that Amazon Rekognition has that the bounding box contains a person.
*)bounding_box : bounding_box option;A bounding box around the detected person.
*)body_parts : body_parts option;An array of body parts detected on a person's body (including body parts without PPE).
*)}A person detected by a call to DetectProtectiveEquipment. The API returns all persons detected in the input image in an array of ProtectiveEquipmentPerson objects.
type nonrec protective_equipment_persons = protective_equipment_person listtype nonrec protective_equipment_person_ids = u_integer listtype nonrec protective_equipment_summary = {persons_indeterminate : protective_equipment_person_ids option;An array of IDs for persons where it was not possible to determine if they are wearing personal protective equipment.
*)persons_without_required_equipment : protective_equipment_person_ids option;An array of IDs for persons who are not wearing all of the types of PPE specified in the RequiredEquipmentTypes field of the detected personal protective equipment.
persons_with_required_equipment : protective_equipment_person_ids option;An array of IDs for persons who are wearing detected personal protective equipment.
*)}Summary information for required items of personal protective equipment (PPE) detected on persons by a call to DetectProtectiveEquipment. You specify the required type of PPE in the SummarizationAttributes (ProtectiveEquipmentSummarizationAttributes) input parameter. The summary includes which persons were detected wearing the required personal protective equipment (PersonsWithRequiredEquipment), which persons were detected as not wearing the required PPE (PersonsWithoutRequiredEquipment), and the persons in which a determination could not be made (PersonsIndeterminate).
To get a total for each category, use the size of the field array. For example, to find out how many people were detected as wearing the specified PPE, use the size of the PersonsWithRequiredEquipment array. If you want to find out more about a person, such as the location (BoundingBox) of the person on the image, use the person ID in each array element. Each person ID matches the ID field of a ProtectiveEquipmentPerson object returned in the Persons array by DetectProtectiveEquipment.
type nonrec detect_protective_equipment_response = {summary : protective_equipment_summary option;Summary information for the types of PPE specified in the SummarizationAttributes input parameter.
persons : protective_equipment_persons option;An array of persons detected in the image (including persons not wearing PPE).
*)protective_equipment_model_version : string_ option;The version number of the PPE detection model used to detect PPE in the image.
*)}type nonrec protective_equipment_types = protective_equipment_type listtype nonrec protective_equipment_summarization_attributes = {required_equipment_types : protective_equipment_types;An array of personal protective equipment types for which you want summary information. If a person is detected wearing a required requipment type, the person's ID is added to the PersonsWithRequiredEquipment array field returned in ProtectiveEquipmentSummary by DetectProtectiveEquipment.
min_confidence : percent;The minimum confidence level for which you want summary information. The confidence level applies to person detection, body part detection, equipment detection, and body part coverage. Amazon Rekognition doesn't return summary information with a confidence than this specified value. There isn't a default value.
Specify a MinConfidence value that is between 50-100% as DetectProtectiveEquipment returns predictions only where the detection confidence is between 50% - 100%. If you specify a value that is less than 50%, the results are the same specifying a value of 50%.
}Specifies summary attributes to return from a call to DetectProtectiveEquipment. You can specify which types of PPE to summarize. You can also specify a minimum confidence value for detections. Summary information is returned in the Summary (ProtectiveEquipmentSummary) field of the response from DetectProtectiveEquipment. The summary includes which persons in an image were detected wearing the requested types of person protective equipment (PPE), which persons were detected as not wearing PPE, and the persons in which a determination could not be made. For more information, see ProtectiveEquipmentSummary.
type nonrec detect_protective_equipment_request = {summarization_attributes : protective_equipment_summarization_attributes option;An array of PPE types that you want to summarize.
*)image : image;The image in which you want to detect PPE on detected persons. The image can be passed as image bytes or you can reference an image stored in an Amazon S3 bucket.
*)}type nonrec human_loop_quota_exceeded_exception = {logref : string_ option;A universally unique identifier (UUID) for the request.
*)code : string_ option;message : string_ option;service_code : string_ option;The service code.
*)quota_code : string_ option;The quota code.
*)resource_type : string_ option;The resource type.
*)}The number of in-progress human reviews you have has exceeded the number allowed.
type nonrec moderation_labels = moderation_label listtype nonrec human_loop_activation_reasons = human_loop_activation_reason listtype nonrec human_loop_activation_output = {human_loop_activation_conditions_evaluation_results : synthesized_json_human_loop_activation_conditions_evaluation_results
option;Shows the result of condition evaluations, including those conditions which activated a human review.
*)human_loop_activation_reasons : human_loop_activation_reasons option;Shows if and why human review was needed.
*)human_loop_arn : human_loop_arn option;The Amazon Resource Name (ARN) of the HumanLoop created.
*)}Shows the results of the human in the loop evaluation. If there is no HumanLoopArn, the input did not trigger human review.
type nonrec detect_moderation_labels_response = {content_types : content_types option;A list of predicted results for the type of content an image contains. For example, the image content might be from animation, sports, or a video game.
*)project_version : project_version_id option;Identifier of the custom adapter that was used during inference. If during inference the adapter was EXPIRED, then the parameter will not be returned, indicating that a base moderation detection project version was used.
*)human_loop_activation_output : human_loop_activation_output option;Shows the results of the human in the loop evaluation.
*)moderation_model_version : string_ option;Version number of the base moderation detection model that was used to detect unsafe content.
*)moderation_labels : moderation_labels option;Array of detected Moderation labels. For video operations, this includes the time, in milliseconds from the start of the video, they were detected.
*)}type nonrec content_classifiers = content_classifier listtype nonrec human_loop_data_attributes = {content_classifiers : content_classifiers option;Sets whether the input image is free of personally identifiable information.
*)}Allows you to set attributes of the image. Currently, you can declare an image as free of personally identifiable information.
type nonrec human_loop_config = {data_attributes : human_loop_data_attributes option;Sets attributes of the input data.
*)flow_definition_arn : flow_definition_arn;The Amazon Resource Name (ARN) of the flow definition. You can create a flow definition by using the Amazon Sagemaker CreateFlowDefinition Operation.
*)human_loop_name : human_loop_name;The name of the human review used for this image. This should be kept unique within a region.
*)}Sets up the flow definition the image will be sent to if one of the conditions is met. You can also set certain attributes of the image before review.
type nonrec detect_moderation_labels_request = {project_version : project_version_id option;Identifier for the custom adapter. Expects the ProjectVersionArn as a value. Use the CreateProject or CreateProjectVersion APIs to create a custom adapter.
*)human_loop_config : human_loop_config option;Sets up the configuration for human evaluation, including the FlowDefinition the image will be sent to.
*)min_confidence : percent option;Specifies the minimum confidence level for the labels to return. Amazon Rekognition doesn't return any labels with a confidence level lower than this specified value.
If you don't specify MinConfidence, the operation returns labels with confidence values greater than or equal to 50 percent.
image : image;The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.
If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.
}type nonrec labels = label listtype nonrec detect_labels_image_quality = {contrast : float_ option;The contrast of an image provided for label detection.
*)sharpness : float_ option;The sharpness of an image provided for label detection.
*)brightness : float_ option;The brightness of an image provided for label detection.
*)}The quality of an image provided for label detection, with regard to brightness, sharpness, and contrast.
type nonrec detect_labels_image_foreground = {dominant_colors : dominant_colors option;The dominant colors found in the foreground of an image, defined with RGB values, CSS color name, simplified color name, and PixelPercentage (the percentage of image pixels that have a particular color).
*)quality : detect_labels_image_quality option;The quality of the image foreground as defined by brightness and sharpness.
*)}The foreground of the image with regard to image quality and dominant colors.
type nonrec detect_labels_image_background = {dominant_colors : dominant_colors option;The dominant colors found in the background of an image, defined with RGB values, CSS color name, simplified color name, and PixelPercentage (the percentage of image pixels that have a particular color).
*)quality : detect_labels_image_quality option;The quality of the image background as defined by brightness and sharpness.
*)}The background of the image with regard to image quality and dominant colors.
type nonrec detect_labels_image_properties = {background : detect_labels_image_background option;Information about the properties of an image’s background, including the background’s quality and dominant colors, including the quality and dominant colors of the image.
*)foreground : detect_labels_image_foreground option;Information about the properties of an image’s foreground, including the foreground’s quality and dominant colors, including the quality and dominant colors of the image.
*)dominant_colors : dominant_colors option;Information about the dominant colors found in an image, described with RGB values, CSS color name, simplified color name, and PixelPercentage (the percentage of image pixels that have a particular color).
*)quality : detect_labels_image_quality option;Information about the quality of the image foreground as defined by brightness, sharpness, and contrast. The higher the value the greater the brightness, sharpness, and contrast respectively.
*)}Information about the quality and dominant colors of an input image. Quality and color information is returned for the entire image, foreground, and background.
type nonrec detect_labels_response = {image_properties : detect_labels_image_properties option;Information about the properties of the input image, such as brightness, sharpness, contrast, and dominant colors.
*)label_model_version : string_ option;Version number of the label detection model that was used to detect labels.
*)orientation_correction : orientation_correction option;The value of OrientationCorrection is always null.
If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.
Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated.
*)labels : labels option;An array of labels for the real-world objects detected.
*)}type nonrec detect_labels_feature_list = detect_labels_feature_name listtype nonrec detect_labels_image_properties_settings = {max_dominant_colors : detect_labels_max_dominant_colors option;The maximum number of dominant colors to return when detecting labels in an image. The default value is 10.
*)}Settings for the IMAGE_PROPERTIES feature type.
type nonrec detect_labels_settings = {image_properties : detect_labels_image_properties_settings option;Contains the chosen number of maximum dominant colors in an image.
*)general_labels : general_labels_settings option;Contains the specified filters for GENERAL_LABELS.
*)}Settings for the DetectLabels request. Settings can include filters for both GENERAL_LABELS and IMAGE_PROPERTIES. GENERAL_LABELS filters can be inclusive or exclusive and applied to individual labels or label categories. IMAGE_PROPERTIES filters allow specification of a maximum number of dominant colors.
type nonrec detect_labels_request = {settings : detect_labels_settings option;A list of the filters to be applied to returned detected labels and image properties. Specified filters can be inclusive, exclusive, or a combination of both. Filters can be used for individual labels or label categories. The exact label names or label categories must be supplied. For a full list of labels and label categories, see Detecting labels.
*)features : detect_labels_feature_list option;A list of the types of analysis to perform. Specifying GENERAL_LABELS uses the label detection feature, while specifying IMAGE_PROPERTIES returns information regarding image color and quality. If no option is specified GENERAL_LABELS is used by default.
*)min_confidence : percent option;Specifies the minimum confidence level for the labels to return. Amazon Rekognition doesn't return any labels with confidence lower than this specified value.
If MinConfidence is not specified, the operation returns labels with a confidence values greater than or equal to 55 percent. Only valid when GENERAL_LABELS is specified as a feature type in the Feature input parameter.
max_labels : u_integer option;Maximum number of labels you want the service to return in the response. The service returns the specified number of highest confidence labels. Only valid when GENERAL_LABELS is specified as a feature type in the Feature input parameter.
*)image : image;The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. Images stored in an S3 Bucket do not need to be base64-encoded.
If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.
}type nonrec face_detail_list = face_detail listtype nonrec detect_faces_response = {orientation_correction : orientation_correction option;The value of OrientationCorrection is always null.
If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.
Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated.
*)face_details : face_detail_list option;Details of each face found in the image.
*)}type nonrec detect_faces_request = {attributes : attributes option;An array of facial attributes you want to be returned. A DEFAULT subset of facial attributes - BoundingBox, Confidence, Pose, Quality, and Landmarks - will always be returned. You can request for specific facial attributes (in addition to the default list) - by using ["DEFAULT", "FACE_OCCLUDED"] or just ["FACE_OCCLUDED"]. You can request for all facial attributes by using ["ALL"]. Requesting more attributes may increase response time.
If you provide both, ["ALL", "DEFAULT"], the service uses a logical "AND" operator to determine which attributes to return (in this case, all attributes).
Note that while the FaceOccluded and EyeDirection attributes are supported when using DetectFaces, they aren't supported when analyzing videos with StartFaceDetection and GetFaceDetection.
image : image;The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.
If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.
}type nonrec custom_label = {geometry : geometry option;The location of the detected object on the image that corresponds to the custom label. Includes an axis aligned coarse bounding box surrounding the object and a finer grain polygon for more accurate spatial information.
*)confidence : percent option;The confidence that the model has in the detection of the custom label. The range is 0-100. A higher value indicates a higher confidence.
*)name : string_ option;The name of the custom label.
*)}A custom label detected in an image by a call to DetectCustomLabels.
type nonrec custom_labels = custom_label listtype nonrec detect_custom_labels_response = {custom_labels : custom_labels option;An array of custom labels detected in the input image.
*)}type nonrec detect_custom_labels_request = {min_confidence : percent option;Specifies the minimum confidence level for the labels to return. DetectCustomLabels doesn't return any labels with a confidence value that's lower than this specified value. If you specify a value of 0, DetectCustomLabels returns all labels, regardless of the assumed threshold applied to each label. If you don't specify a value for MinConfidence, DetectCustomLabels returns labels based on the assumed threshold of each label.
max_results : u_integer option;Maximum number of results you want the service to return in the response. The service returns the specified number of highest confidence labels ranked from highest confidence to lowest.
*)image : image;project_version_arn : project_version_arn;The ARN of the model version that you want to use. Only models associated with Custom Labels projects accepted by the operation. If a provided ARN refers to a model version associated with a project for a different feature type, then an InvalidParameterException is returned.
*)}type nonrec describe_stream_processor_response = {data_sharing_preference : stream_processor_data_sharing_preference option;Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams.
*)regions_of_interest : regions_of_interest option;Specifies locations in the frames where Amazon Rekognition checks for objects or people. This is an optional parameter for label detection stream processors.
*)kms_key_id : kms_key_id option;The identifier for your AWS Key Management Service key (AWS KMS key). This is an optional parameter for label detection stream processors.
*)notification_channel : stream_processor_notification_channel option;settings : stream_processor_settings option;Input parameters used in a streaming video analyzed by a stream processor. You can use FaceSearch to recognize faces in a streaming video, or you can use ConnectedHome to detect labels.
role_arn : role_arn option;ARN of the IAM role that allows access to the stream processor.
*)output : stream_processor_output option;Kinesis data stream to which Amazon Rekognition Video puts the analysis results.
*)input : stream_processor_input option;Kinesis video stream that provides the source streaming video.
*)last_update_timestamp : date_time option;The time, in Unix format, the stream processor was last updated. For example, when the stream processor moves from a running state to a failed state, or when the user starts or stops the stream processor.
*)creation_timestamp : date_time option;Date and time the stream processor was created
*)status_message : string_ option;Detailed status message about the stream processor.
*)status : stream_processor_status option;Current status of the stream processor.
*)stream_processor_arn : stream_processor_arn option;ARN of the stream processor.
*)name : stream_processor_name option;Name of the stream processor.
*)}type nonrec describe_stream_processor_request = {name : stream_processor_name;Name of the stream processor for which you want information.
*)}type nonrec output_config = {s3_key_prefix : s3_key_prefix option;The prefix applied to the training output files.
*)s3_bucket : s3_bucket option;The S3 bucket where training output is placed.
*)}The S3 bucket and folder location where training output is placed.
type nonrec evaluation_result = {summary : summary option;The S3 bucket that contains the training summary.
*)f1_score : float_ option;The F1 score for the evaluation of all labels. The F1 score metric evaluates the overall precision and recall performance of the model as a single value. A higher value indicates better precision and recall performance. A lower score indicates that precision, recall, or both are performing poorly.
*)}The evaluation results for the training of a model.
type nonrec customization_feature_content_moderation_config = {confidence_threshold : percent option;The confidence level you plan to use to identify if unsafe content is present during inference.
*)}Configuration options for Content Moderation training.
type nonrec customization_feature_config = {content_moderation : customization_feature_content_moderation_config option;Configuration options for Custom Moderation training.
*)}Feature specific configuration for the training job. Configuration provided for the job must match the feature type parameter associated with project. If configuration and feature type do not match an InvalidParameterException is returned.
type nonrec project_version_description = {feature_config : customization_feature_config option;Feature specific configuration that was applied during training.
*)base_model_version : string_ option;The base detection model version used to create the project version.
*)feature : customization_feature option;The feature that was customized.
*)version_description : version_description option;A user-provided description of the project version.
*)source_project_version_arn : project_version_arn option;If the model version was copied from a different project, SourceProjectVersionArn contains the ARN of the source model version.
max_inference_units : inference_units option;The maximum number of inference units Amazon Rekognition uses to auto-scale the model. Applies only to Custom Labels projects. For more information, see StartProjectVersion.
kms_key_id : kms_key_id option;The identifer for the AWS Key Management Service key (AWS KMS key) that was used to encrypt the model during training.
*)manifest_summary : ground_truth_manifest option;The location of the summary manifest. The summary manifest provides aggregate data validation results for the training and test datasets.
*)evaluation_result : evaluation_result option;The training results. EvaluationResult is only returned if training is successful.
testing_data_result : testing_data_result option;Contains information about the testing results.
*)training_data_result : training_data_result option;Contains information about the training results.
*)output_config : output_config option;The location where training results are saved.
*)training_end_timestamp : date_time option;The Unix date and time that training of the model ended.
*)billable_training_time_in_seconds : u_long option;The duration, in seconds, that you were billed for a successful training of the model version. This value is only returned if the model version has been successfully trained.
*)status_message : status_message option;A descriptive message for an error or warning that occurred.
*)status : project_version_status option;The current status of the model version.
*)min_inference_units : inference_units option;The minimum number of inference units used by the model. Applies only to Custom Labels projects. For more information, see StartProjectVersion.
creation_timestamp : date_time option;The Unix datetime for the date and time that training started.
*)project_version_arn : project_version_arn option;The Amazon Resource Name (ARN) of the project version.
*)}A description of a version of a Amazon Rekognition project version.
type nonrec project_version_descriptions = project_version_description listtype nonrec describe_project_versions_response = {next_token : extended_pagination_token option;If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.
*)project_version_descriptions : project_version_descriptions option;A list of project version descriptions. The list is sorted by the creation date and time of the project versions, latest to earliest.
*)}type nonrec describe_project_versions_request = {max_results : project_versions_page_size option;The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100.
*)next_token : extended_pagination_token option;If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.
*)version_names : version_names option;A list of model or project version names that you want to describe. You can add up to 10 model or project version names to the list. If you don't specify a value, all project version descriptions are returned. A version name is part of a project version ARN. For example, my-model.2020-01-21T09.10.15 is the version name in the following ARN.
arn:aws:rekognition:us-east-1:123456789012:project/getting-started/version/{i my-model.2020-01-21T09.10.15}/1234567890123.
*)project_arn : project_arn;The Amazon Resource Name (ARN) of the project that contains the model/adapter you want to describe.
*)}type nonrec dataset_metadata = {status_message_code : dataset_status_message_code option;The status message code for the dataset operation. If a service error occurs, try the API call again later. If a client error occurs, check the input parameters to the dataset API call that failed.
*)status_message : status_message option;The status message for the dataset.
*)status : dataset_status option;The status for the dataset.
*)dataset_arn : dataset_arn option;The Amazon Resource Name (ARN) for the dataset.
*)dataset_type : dataset_type option;The type of the dataset.
*)creation_timestamp : date_time option;The Unix timestamp for the date and time that the dataset was created.
*)}Summary information for an Amazon Rekognition Custom Labels dataset. For more information, see ProjectDescription.
type nonrec dataset_metadata_list = dataset_metadata listtype nonrec project_description = {auto_update : project_auto_update option;Indicates whether automatic retraining will be attempted for the versions of the project. Applies only to adapters.
*)feature : customization_feature option;Specifies the project that is being customized.
*)datasets : dataset_metadata_list option;Information about the training and test datasets in the project.
*)status : project_status option;The current status of the project.
*)creation_timestamp : date_time option;The Unix timestamp for the date and time that the project was created.
*)project_arn : project_arn option;The Amazon Resource Name (ARN) of the project.
*)}A description of an Amazon Rekognition Custom Labels project. For more information, see DescribeProjects.
type nonrec project_descriptions = project_description listtype nonrec describe_projects_response = {next_token : extended_pagination_token option;If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.
*)project_descriptions : project_descriptions option;A list of project descriptions. The list is sorted by the date and time the projects are created.
*)}type nonrec project_names = project_name listtype nonrec customization_features = customization_feature listtype nonrec describe_projects_request = {features : customization_features option;Specifies the type of customization to filter projects by. If no value is specified, CUSTOM_LABELS is used as a default.
*)project_names : project_names option;A list of the projects that you want Rekognition to describe. If you don't specify a value, the response includes descriptions for all the projects in your AWS account.
*)max_results : projects_page_size option;The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100.
*)next_token : extended_pagination_token option;If the previous response was incomplete (because there is more results to retrieve), Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.
*)}type nonrec dataset_stats = {error_entries : u_integer option;The total number of entries that contain at least one error.
*)total_labels : u_integer option;The total number of labels declared in the dataset.
*)total_entries : u_integer option;The total number of images in the dataset.
*)labeled_entries : u_integer option;The total number of images in the dataset that have labels.
*)}Provides statistics about a dataset. For more information, see DescribeDataset.
type nonrec dataset_description = {dataset_stats : dataset_stats option;The status message code for the dataset.
*)status_message_code : dataset_status_message_code option;The status message code for the dataset operation. If a service error occurs, try the API call again later. If a client error occurs, check the input parameters to the dataset API call that failed.
*)status_message : status_message option;The status message for the dataset.
*)status : dataset_status option;The status of the dataset.
*)last_updated_timestamp : date_time option;The Unix timestamp for the date and time that the dataset was last updated.
*)creation_timestamp : date_time option;The Unix timestamp for the time and date that the dataset was created.
*)}A description for a dataset. For more information, see DescribeDataset.
The status fields Status, StatusMessage, and StatusMessageCode reflect the last operation on the dataset.
type nonrec describe_dataset_response = {dataset_description : dataset_description option;The description for the dataset.
*)}type nonrec describe_dataset_request = {dataset_arn : dataset_arn;The Amazon Resource Name (ARN) of the dataset that you want to describe.
*)}type nonrec describe_collection_response = {user_count : u_long option;The number of UserIDs assigned to the specified colleciton.
*)creation_timestamp : date_time option;The number of milliseconds since the Unix epoch time until the creation of the collection. The Unix epoch time is 00:00:00 Coordinated Universal Time (UTC), Thursday, 1 January 1970.
*)collection_ar_n : string_ option;The Amazon Resource Name (ARN) of the collection.
*)face_model_version : string_ option;The version of the face model that's used by the collection for face detection.
For more information, see Model versioning in the Amazon Rekognition Developer Guide.
*)face_count : u_long option;The number of faces that are indexed into the collection. To index faces into a collection, use IndexFaces.
}type nonrec describe_collection_request = {collection_id : collection_id;The ID of the collection to describe.
*)}type nonrec delete_user_request = {client_request_token : client_request_token option;Idempotent token used to identify the request to DeleteUser. If you use the same token with multiple DeleteUser requests, the same response is returned. Use ClientRequestToken to prevent the same request from being processed more than once.
user_id : user_id;ID for the UserID to be deleted.
*)collection_id : collection_id;The ID of an existing collection from which the UserID needs to be deleted.
*)}type nonrec delete_stream_processor_request = {name : stream_processor_name;The name of the stream processor you want to delete.
*)}type nonrec delete_project_version_response = {status : project_version_status option;The status of the deletion operation.
*)}type nonrec delete_project_version_request = {project_version_arn : project_version_arn;The Amazon Resource Name (ARN) of the project version that you want to delete.
*)}type nonrec delete_project_policy_request = {policy_revision_id : project_policy_revision_id option;The ID of the project policy revision that you want to delete.
*)policy_name : project_policy_name;The name of the policy that you want to delete.
*)project_arn : project_arn;The Amazon Resource Name (ARN) of the project that the project policy you want to delete is attached to.
*)}type nonrec delete_project_response = {status : project_status option;The current status of the delete project operation.
*)}type nonrec delete_project_request = {project_arn : project_arn;The Amazon Resource Name (ARN) of the project that you want to delete.
*)}type nonrec delete_faces_response = {unsuccessful_face_deletions : unsuccessful_face_deletions_list option;An array of any faces that weren't deleted.
*)deleted_faces : face_id_list option;An array of strings (face IDs) of the faces that were deleted.
*)}type nonrec delete_faces_request = {face_ids : face_id_list;An array of face IDs to delete.
*)collection_id : collection_id;Collection from which to remove the specific faces.
*)}type nonrec delete_dataset_request = {dataset_arn : dataset_arn;The ARN of the Amazon Rekognition Custom Labels dataset that you want to delete.
*)}type nonrec delete_collection_response = {status_code : u_integer option;HTTP status code that indicates the result of the operation.
*)}type nonrec delete_collection_request = {collection_id : collection_id;ID of the collection to delete.
*)}type nonrec create_user_request = {client_request_token : client_request_token option;Idempotent token used to identify the request to CreateUser. If you use the same token with multiple CreateUser requests, the same response is returned. Use ClientRequestToken to prevent the same request from being processed more than once.
user_id : user_id;ID for the UserID to be created. This ID needs to be unique within the collection.
*)collection_id : collection_id;The ID of an existing collection to which the new UserID needs to be created.
*)}type nonrec create_stream_processor_response = {stream_processor_arn : stream_processor_arn option;Amazon Resource Number for the newly created stream processor.
*)}type nonrec create_stream_processor_request = {data_sharing_preference : stream_processor_data_sharing_preference option;Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams.
*)regions_of_interest : regions_of_interest option;Specifies locations in the frames where Amazon Rekognition checks for objects or people. You can specify up to 10 regions of interest, and each region has either a polygon or a bounding box. This is an optional parameter for label detection stream processors and should not be used to create a face search stream processor.
*)kms_key_id : kms_key_id option;The identifier for your AWS Key Management Service key (AWS KMS key). This is an optional parameter for label detection stream processors and should not be used to create a face search stream processor. You can supply the Amazon Resource Name (ARN) of your KMS key, the ID of your KMS key, an alias for your KMS key, or an alias ARN. The key is used to encrypt results and data published to your Amazon S3 bucket, which includes image frames and hero images. Your source images are unaffected.
*)notification_channel : stream_processor_notification_channel option;role_arn : role_arn;The Amazon Resource Number (ARN) of the IAM role that allows access to the stream processor. The IAM role provides Rekognition read permissions for a Kinesis stream. It also provides write permissions to an Amazon S3 bucket and Amazon Simple Notification Service topic for a label detection stream processor. This is required for both face search and label detection stream processors.
*)settings : stream_processor_settings;Input parameters used in a streaming video analyzed by a stream processor. You can use FaceSearch to recognize faces in a streaming video, or you can use ConnectedHome to detect labels.
name : stream_processor_name;An identifier you assign to the stream processor. You can use Name to manage the stream processor. For example, you can get the current status of the stream processor by calling DescribeStreamProcessor. Name is idempotent. This is required for both face search and label detection stream processors.
output : stream_processor_output;Kinesis data stream stream or Amazon S3 bucket location to which Amazon Rekognition Video puts the analysis results. If you are using the AWS CLI, the parameter name is StreamProcessorOutput. This must be a S3Destination of an Amazon S3 bucket that you own for a label detection stream processor or a Kinesis data stream ARN for a face search stream processor.
input : stream_processor_input;Kinesis video stream stream that provides the source streaming video. If you are using the AWS CLI, the parameter name is StreamProcessorInput. This is required for both face search and label detection stream processors.
}type nonrec create_project_version_response = {project_version_arn : project_version_arn option;The ARN of the model or the project version that was created. Use DescribeProjectVersion to get the current status of the training operation.
}type nonrec create_project_version_request = {feature_config : customization_feature_config option;Feature-specific configuration of the training job. If the job configuration does not match the feature type associated with the project, an InvalidParameterException is returned.
*)version_description : version_description option;A description applied to the project version being created.
*)kms_key_id : kms_key_id option;The identifier for your AWS Key Management Service key (AWS KMS key). You can supply the Amazon Resource Name (ARN) of your KMS key, the ID of your KMS key, an alias for your KMS key, or an alias ARN. The key is used to encrypt training images, test images, and manifest files copied into the service for the project version. Your source images are unaffected. The key is also used to encrypt training results and manifest files written to the output Amazon S3 bucket (OutputConfig).
If you choose to use your own KMS key, you need the following permissions on the KMS key.
If you don't specify a value for KmsKeyId, images copied into the service are encrypted using a key that AWS owns and manages.
testing_data : testing_data option;Specifies an external manifest that the service uses to test the project version. If you specify TestingData you must also specify TrainingData. The project must not have any associated datasets.
training_data : training_data option;Specifies an external manifest that the services uses to train the project version. If you specify TrainingData you must also specify TestingData. The project must not have any associated datasets.
output_config : output_config;The Amazon S3 bucket location to store the results of training. The bucket can be any S3 bucket in your AWS account. You need s3:PutObject permission on the bucket.
version_name : version_name;A name for the version of the project version. This value must be unique.
*)project_arn : project_arn;The ARN of the Amazon Rekognition project that will manage the project version you want to train.
*)}type nonrec create_project_response = {project_arn : project_arn option;The Amazon Resource Name (ARN) of the new project. You can use the ARN to configure IAM access to the project.
*)}type nonrec create_project_request = {auto_update : project_auto_update option;Specifies whether automatic retraining should be attempted for the versions of the project. Automatic retraining is done as a best effort. Required argument for Content Moderation. Applicable only to adapters.
*)feature : customization_feature option;Specifies feature that is being customized. If no value is provided CUSTOM_LABELS is used as a default.
*)project_name : project_name;The name of the project to create.
*)}type nonrec create_face_liveness_session_response = {session_id : liveness_session_id;A unique 128-bit UUID identifying a Face Liveness session. A new sessionID must be used for every Face Liveness check. If a given sessionID is used for subsequent Face Liveness checks, the checks will fail. Additionally, a SessionId expires 3 minutes after it's sent, making all Liveness data associated with the session (e.g., sessionID, reference image, audit images, etc.) unavailable.
*)}type nonrec liveness_output_config = {s3_key_prefix : liveness_s3_key_prefix option;The prefix prepended to the output files for the Face Liveness session results.
*)s3_bucket : s3_bucket;The path to an AWS Amazon S3 bucket used to store Face Liveness session results.
*)}Contains settings that specify the location of an Amazon S3 bucket used to store the output of a Face Liveness session. Note that the S3 bucket must be located in the caller's AWS account and in the same region as the Face Liveness end-point. Additionally, the Amazon S3 object keys are auto-generated by the Face Liveness system.
type nonrec challenge_preference = {versions : versions option;The version of the challenges that have been selected for the Face Liveness session.
*)type_ : challenge_type;The types of challenges that have been selected for the Face Liveness session.
*)}An ordered list of preferred challenge type and versions.
type nonrec challenge_preferences = challenge_preference listtype nonrec create_face_liveness_session_request_settings = {challenge_preferences : challenge_preferences option;Indicates preferred challenge types and versions for the Face Liveness session to be created.
*)audit_images_limit : audit_images_limit option;Number of audit images to be returned back. Takes an integer between 0-4. Any integer less than 0 will return 0, any integer above 4 will return 4 images in the response. By default, it is set to 0. The limit is best effort and is based on the actual duration of the selfie-video.
*)output_config : liveness_output_config option;Can specify the location of an Amazon S3 bucket, where reference and audit images will be stored. Note that the Amazon S3 bucket must be located in the caller's AWS account and in the same region as the Face Liveness end-point. Additionally, the Amazon S3 object keys are auto-generated by the Face Liveness system. Requires that the caller has the s3:PutObject permission on the Amazon S3 bucket.
}A session settings object. It contains settings for the operation to be performed. It accepts arguments for OutputConfig and AuditImagesLimit.
type nonrec create_face_liveness_session_request = {client_request_token : client_request_token option;Idempotent token is used to recognize the Face Liveness request. If the same token is used with multiple CreateFaceLivenessSession requests, the same session is returned. This token is employed to avoid unintentionally creating the same session multiple times.
settings : create_face_liveness_session_request_settings option;A session settings object. It contains settings for the operation to be performed. For Face Liveness, it accepts OutputConfig and AuditImagesLimit.
kms_key_id : kms_key_id option;The identifier for your AWS Key Management Service key (AWS KMS key). Used to encrypt audit images and reference images.
*)}type nonrec create_dataset_response = {dataset_arn : dataset_arn option;The ARN of the created Amazon Rekognition Custom Labels dataset.
*)}type nonrec dataset_source = {dataset_arn : dataset_arn option;The ARN of an Amazon Rekognition Custom Labels dataset that you want to copy.
*)ground_truth_manifest : ground_truth_manifest option;}The source that Amazon Rekognition Custom Labels uses to create a dataset. To use an Amazon Sagemaker format manifest file, specify the S3 bucket location in the GroundTruthManifest field. The S3 bucket must be in your AWS account. To create a copy of an existing dataset, specify the Amazon Resource Name (ARN) of an existing dataset in DatasetArn.
You need to specify a value for DatasetArn or GroundTruthManifest, but not both. if you supply both values, or if you don't specify any values, an InvalidParameterException exception occurs.
For more information, see CreateDataset.
type nonrec create_dataset_request = {project_arn : project_arn;The ARN of the Amazon Rekognition Custom Labels project to which you want to asssign the dataset.
*)dataset_type : dataset_type;The type of the dataset. Specify TRAIN to create a training dataset. Specify TEST to create a test dataset.
dataset_source : dataset_source option;The source files for the dataset. You can specify the ARN of an existing dataset or specify the Amazon S3 bucket location of an Amazon Sagemaker format manifest file. If you don't specify datasetSource, an empty dataset is created. To add labeled images to the dataset, You can use the console or call UpdateDatasetEntries.
}type nonrec create_collection_response = {face_model_version : string_ option;Version number of the face detection model associated with the collection you are creating.
*)collection_arn : string_ option;Amazon Resource Name (ARN) of the collection. You can use this to manage permissions on your resources.
*)status_code : u_integer option;HTTP status code indicating the result of the operation.
*)}type nonrec create_collection_request = {collection_id : collection_id;ID for the collection that you are creating.
*)}type nonrec copy_project_version_response = {project_version_arn : project_version_arn option;The ARN of the copied model version in the destination project.
*)}type nonrec copy_project_version_request = {kms_key_id : kms_key_id option;The identifier for your AWS Key Management Service key (AWS KMS key). You can supply the Amazon Resource Name (ARN) of your KMS key, the ID of your KMS key, an alias for your KMS key, or an alias ARN. The key is used to encrypt training results and manifest files written to the output Amazon S3 bucket (OutputConfig).
If you choose to use your own KMS key, you need the following permissions on the KMS key.
If you don't specify a value for KmsKeyId, images copied into the service are encrypted using a key that AWS owns and manages.
output_config : output_config;The S3 bucket and folder location where the training output for the source model version is placed.
*)version_name : version_name;A name for the version of the model that's copied to the destination project.
*)destination_project_arn : project_arn;The ARN of the project in the trusted AWS account that you want to copy the model version to.
*)source_project_version_arn : project_version_arn;The ARN of the model version in the source project that you want to copy to a destination project.
*)source_project_arn : project_arn;The ARN of the source project in the trusting AWS account.
*)}type nonrec compared_source_image_face = {confidence : percent option;Confidence level that the selected bounding box contains a face.
*)bounding_box : bounding_box option;Bounding box of the face.
*)}Type that describes the face Amazon Rekognition chose to compare with the faces in the target. This contains a bounding box for the selected face and confidence level that the bounding box contains a face. Note that Amazon Rekognition selects the largest face in the source image for this comparison.
type nonrec compare_faces_match = {face : compared_face option;Provides face metadata (bounding box and confidence that the bounding box actually contains a face).
*)similarity : percent option;Level of confidence that the faces match.
*)}Provides information about a face in a target image that matches the source image face analyzed by CompareFaces. The Face property contains the bounding box of the face in the target image. The Similarity property is the confidence that the source image face matches the face in the bounding box.
type nonrec compare_faces_match_list = compare_faces_match listtype nonrec compare_faces_unmatch_list = compared_face listtype nonrec compare_faces_response = {target_image_orientation_correction : orientation_correction option;The value of TargetImageOrientationCorrection is always null.
If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.
Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated.
*)source_image_orientation_correction : orientation_correction option;The value of SourceImageOrientationCorrection is always null.
If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.
Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated.
*)unmatched_faces : compare_faces_unmatch_list option;An array of faces in the target image that did not match the source image face.
*)face_matches : compare_faces_match_list option;An array of faces in the target image that match the source image face. Each CompareFacesMatch object provides the bounding box, the confidence level that the bounding box contains a face, and the similarity score for the face in the bounding box and the face in the source image.
source_image_face : compared_source_image_face option;The face in the source image that was used for comparison.
*)}type nonrec compare_faces_request = {quality_filter : quality_filter option;A filter that specifies a quality bar for how much filtering is done to identify faces. Filtered faces aren't compared. If you specify AUTO, Amazon Rekognition chooses the quality bar. If you specify LOW, MEDIUM, or HIGH, filtering removes all faces that don’t meet the chosen quality bar. The quality bar is based on a variety of common use cases. Low-quality detections can occur for a number of reasons. Some examples are an object that's misidentified as a face, a face that's too blurry, or a face with a pose that's too extreme to use. If you specify NONE, no filtering is performed. The default value is NONE.
To use quality filtering, the collection you are using must be associated with version 3 of the face model or higher.
*)similarity_threshold : percent option;The minimum level of confidence in the face matches that a match must meet to be included in the FaceMatches array.
target_image : image;The target image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.
If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.
source_image : image;The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.
If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.
}type nonrec associated_face = {face_id : face_id option;Unique identifier assigned to the face.
*)}Provides face metadata for the faces that are associated to a specific UserID.
type nonrec associated_faces_list = associated_face listtype nonrec associate_faces_response = {user_status : user_status option;The status of an update made to a UserID. Reflects if the UserID has been updated for every requested change.
*)unsuccessful_face_associations : unsuccessful_face_association_list option;An array of UnsuccessfulAssociation objects containing FaceIDs that are not successfully associated along with the reasons. Returned if the AssociateFaces action is successful.
*)associated_faces : associated_faces_list option;An array of AssociatedFace objects containing FaceIDs that have been successfully associated with the UserID. Returned if the AssociateFaces action is successful.
*)}type nonrec associate_faces_request = {client_request_token : client_request_token option;Idempotent token used to identify the request to AssociateFaces. If you use the same token with multiple AssociateFaces requests, the same response is returned. Use ClientRequestToken to prevent the same request from being processed more than once.
user_match_threshold : percent option;An optional value specifying the minimum confidence in the UserID match to return. The default value is 75.
*)face_ids : user_face_id_list;An array of FaceIDs to associate with the UserID.
*)user_id : user_id;The ID for the existing UserID.
*)collection_id : collection_id;The ID of an existing collection containing the UserID.
*)}