get_label_detection ¶

def get_label_detection(
    self,
    job_id: "capo_rekognition.types.job_id.JobId",
    *,
    config_overrides: Optional[RekognitionClientConfig] = None,
    max_results: Optional["capo_rekognition.types.max_results.MaxResults"] = None,
    next_token: Optional[
        "capo_rekognition.types.pagination_token.PaginationToken"
    ] = None,
    sort_by: Optional[
        "capo_rekognition.types.label_detection_sort_by.LabelDetectionSortBy"
    ] = None,
    aggregate_by: Optional[
        "capo_rekognition.types.label_detection_aggregate_by.LabelDetectionAggregateBy"
    ] = None,
) -> (
    "capo_rekognition.types.get_label_detection_response.GetLabelDetectionResponse"
):
    """<p>Gets the label detection results of a Amazon Rekognition Video analysis started by <a>StartLabelDetection</a>. </p> <p>The label detection operation is started by a call to <a>StartLabelDetection</a> which returns a job identifier (<code>JobId</code>). When the label detection operation finishes, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to <code>StartlabelDetection</code>. </p> <p>To get the results of the label detection operation, first check that the status value published to the Amazon SNS topic is <code>SUCCEEDED</code>. If so, call <a>GetLabelDetection</a> and pass the job identifier (<code>JobId</code>) from the initial call to <code>StartLabelDetection</code>.</p> <p> <code>GetLabelDetection</code> returns an array of detected labels (<code>Labels</code>) sorted by the time the labels were detected. You can also sort by the label name by specifying <code>NAME</code> for the <code>SortBy</code> input parameter. If there is no <code>NAME</code> specified, the default sort is by timestamp.</p> <p>You can select how results are aggregated by using the <code>AggregateBy</code> input parameter. The default aggregation method is <code>TIMESTAMPS</code>. You can also aggregate by <code>SEGMENTS</code>, which aggregates all instances of labels detected in a given segment. </p> <p>The returned Labels array may include the following attributes:</p> <ul> <li> <p>Name - The name of the detected label.</p> </li> <li> <p>Confidence - The level of confidence in the label assigned to a detected object. </p> </li> <li> <p>Parents - The ancestor labels for a detected label. GetLabelDetection returns a hierarchical taxonomy of detected labels. For example, a detected car might be assigned the label car. The label car has two parent labels: Vehicle (its parent) and Transportation (its grandparent). The response includes the all ancestors for a label, where every ancestor is a unique label. In the previous example, Car, Vehicle, and Transportation are returned as unique labels in the response. </p> </li> <li> <p> Aliases - Possible Aliases for the label. </p> </li> <li> <p>Categories - The label categories that the detected label belongs to.</p> </li> <li> <p>BoundingBox — Bounding boxes are described for all instances of detected common object labels, returned in an array of Instance objects. An Instance object contains a BoundingBox object, describing the location of the label on the input image. It also includes the confidence for the accuracy of the detected bounding box.</p> </li> <li> <p>Timestamp - Time, in milliseconds from the start of the video, that the label was detected. For aggregation by <code>SEGMENTS</code>, the <code>StartTimestampMillis</code>, <code>EndTimestampMillis</code>, and <code>DurationMillis</code> structures are what define a segment. Although the “Timestamp” structure is still returned with each label, its value is set to be the same as <code>StartTimestampMillis</code>.</p> </li> </ul> <p>Timestamp and Bounding box information are returned for detected Instances, only if aggregation is done by <code>TIMESTAMPS</code>. If aggregating by <code>SEGMENTS</code>, information about detected instances isn’t returned. </p> <p>The version of the label model used for the detection is also returned.</p> <p> <b>Note <code>DominantColors</code> isn't returned for <code>Instances</code>, although it is shown as part of the response in the sample seen below.</b> </p> <p>Use <code>MaxResults</code> parameter to limit the number of labels returned. If there are more results than specified in <code>MaxResults</code>, the value of <code>NextToken</code> in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call <code>GetlabelDetection</code> and populate the <code>NextToken</code> request parameter with the token value returned from the previous call to <code>GetLabelDetection</code>.</p> <p>If you are retrieving results while using the Amazon Simple Notification Service, note that you will receive an "ERROR" notification if the job encounters an issue.</p>

    Args:
        job_id: <p>Job identifier for the label detection operation for which you want results returned. You get the job identifer from an initial call to <code>StartlabelDetection</code>.</p>
        max_results: <p>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.</p>
        next_token: <p>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. </p>
        sort_by: <p>Sort to use for elements in the <code>Labels</code> array. Use <code>TIMESTAMP</code> to sort array elements by the time labels are detected. Use <code>NAME</code> 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 <code>TIMESTAMP</code>.</p>
        aggregate_by: <p>Defines how to aggregate the returned results. Results can be aggregated by timestamps or segments.</p>

    Raises:
        capo_rekognition.errors.access_denied_exception.AccessDeniedException: <p>You are not authorized to perform the action.</p>
        capo_rekognition.errors.internal_server_error.InternalServerError: <p>Amazon Rekognition experienced a service issue. Try your call again.</p>
        capo_rekognition.errors.invalid_pagination_token_exception.InvalidPaginationTokenException: <p>Pagination token in the request is not valid.</p>
        capo_rekognition.errors.invalid_parameter_exception.InvalidParameterException: <p>Input parameter violated a constraint. Validate your parameter before calling the API operation again.</p>
        capo_rekognition.errors.provisioned_throughput_exceeded_exception.ProvisionedThroughputExceededException: <p>The number of requests exceeded your throughput limit. If you want to increase this limit, contact Amazon Rekognition.</p>
        capo_rekognition.errors.resource_not_found_exception.ResourceNotFoundException: <p>The resource specified in the request cannot be found.</p>
        capo_rekognition.errors.throttling_exception.ThrottlingException: <p>Amazon Rekognition is temporarily unable to process the request. Try your call again.</p>
        capo_rekognition.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_rekognition.types.get_label_detection_request.GetLabelDetectionRequest]",
    ) -> OperationResponse[
        "capo_rekognition.types.get_label_detection_response.GetLabelDetectionResponse"
    ]:
        import capo_rekognition._operations.rekognition_service.get_label_detection

        output, http_response = (
            capo_rekognition._operations.rekognition_service.get_label_detection.get_label_detection(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("GetLabelDetection")
    )
    input_: capo_rekognition.types.get_label_detection_request.GetLabelDetectionRequest = {
        "job_id": job_id
    }
    if max_results is not None:
        input_["max_results"] = max_results
    if next_token is not None:
        input_["next_token"] = next_token
    if sort_by is not None:
        input_["sort_by"] = sort_by
    if aggregate_by is not None:
        input_["aggregate_by"] = aggregate_by

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output