get_content_moderation ¶

def get_content_moderation(
    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.content_moderation_sort_by.ContentModerationSortBy"
    ] = None,
    aggregate_by: Optional[
        "capo_rekognition.types.content_moderation_aggregate_by.ContentModerationAggregateBy"
    ] = None,
) -> "capo_rekognition.types.get_content_moderation_response.GetContentModerationResponse":
    """<p>Gets the inappropriate, unwanted, or offensive content analysis results for a Amazon Rekognition Video analysis started by <a>StartContentModeration</a>. For a list of moderation labels in Amazon Rekognition, see <a href="https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html#moderation-api">Using the image and video moderation APIs</a>.</p> <p>Amazon Rekognition Video inappropriate or offensive content detection in a stored video is an asynchronous operation. You start analysis by calling <a>StartContentModeration</a> which returns a job identifier (<code>JobId</code>). When analysis finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to <code>StartContentModeration</code>. To get the results of the content analysis, first check that the status value published to the Amazon SNS topic is <code>SUCCEEDED</code>. If so, call <code>GetContentModeration</code> and pass the job identifier (<code>JobId</code>) from the initial call to <code>StartContentModeration</code>. </p> <p>For more information, see Working with Stored Videos in the Amazon Rekognition Devlopers Guide.</p> <p> <code>GetContentModeration</code> returns detected inappropriate, unwanted, or offensive content moderation labels, and the time they are detected, in an array, <code>ModerationLabels</code>, of <a>ContentModerationDetection</a> objects. </p> <p>By default, the moderated labels are returned sorted by time, in milliseconds from the start of the video. You can also sort them by moderated label by specifying <code>NAME</code> for the <code>SortBy</code> input parameter. </p> <p>Since video analysis can return a large number of results, use the <code>MaxResults</code> parameter to limit the number of labels returned in a single call to <code>GetContentModeration</code>. 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>GetContentModeration</code> and populate the <code>NextToken</code> request parameter with the value of <code>NextToken</code> returned from the previous call to <code>GetContentModeration</code>.</p> <p>For more information, see moderating content in the Amazon Rekognition Developer Guide.</p>

    Args:
        job_id: <p>The identifier for the inappropriate, unwanted, or offensive content moderation job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetContentModeration</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 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.</p>
        sort_by: <p>Sort to use for elements in the <code>ModerationLabelDetections</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 results of the StartContentModeration request. Default aggregation option is TIMESTAMPS. SEGMENTS mode aggregates moderation labels over time.</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_content_moderation_request.GetContentModerationRequest]",
    ) -> OperationResponse[
        "capo_rekognition.types.get_content_moderation_response.GetContentModerationResponse"
    ]:
        import capo_rekognition._operations.rekognition_service.get_content_moderation

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("GetContentModeration")
    )
    input_: capo_rekognition.types.get_content_moderation_request.GetContentModerationRequest = {
        "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