start_segment_detection ¶

def start_segment_detection(
    self,
    video: "capo_rekognition.types.video.Video",
    segment_types: "capo_rekognition.types.segment_types.SegmentTypes",
    *,
    config_overrides: Optional[RekognitionClientConfig] = None,
    client_request_token: Optional[
        "capo_rekognition.types.client_request_token.ClientRequestToken"
    ] = None,
    notification_channel: Optional[
        "capo_rekognition.types.notification_channel.NotificationChannel"
    ] = None,
    job_tag: Optional["capo_rekognition.types.job_tag.JobTag"] = None,
    filters: Optional[
        "capo_rekognition.types.start_segment_detection_filters.StartSegmentDetectionFilters"
    ] = None,
) -> "capo_rekognition.types.start_segment_detection_response.StartSegmentDetectionResponse":
    """<p>Starts asynchronous detection of segment detection in a stored video.</p> <p>Amazon Rekognition Video can detect segments in a video stored in an Amazon S3 bucket. Use <a>Video</a> to specify the bucket name and the filename of the video. <code>StartSegmentDetection</code> returns a job identifier (<code>JobId</code>) which you use to get the results of the operation. When segment detection is finished, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic that you specify in <code>NotificationChannel</code>.</p> <p>You can use the <code>Filters</code> (<a>StartSegmentDetectionFilters</a>) input parameter to specify the minimum detection confidence returned in the response. Within <code>Filters</code>, use <code>ShotFilter</code> (<a>StartShotDetectionFilter</a>) to filter detected shots. Use <code>TechnicalCueFilter</code> (<a>StartTechnicalCueDetectionFilter</a>) to filter technical cues. </p> <p>To get the results of the segment detection operation, first check that the status value published to the Amazon SNS topic is <code>SUCCEEDED</code>. if so, call <a>GetSegmentDetection</a> and pass the job identifier (<code>JobId</code>) from the initial call to <code>StartSegmentDetection</code>. </p> <p>For more information, see Detecting video segments in stored video in the Amazon Rekognition Developer Guide.</p>

    Args:
        client_request_token: <p>Idempotent token used to identify the start request. If you use the same token with multiple <code>StartSegmentDetection</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidently started more than once. </p>
        notification_channel: <p>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 <i>AmazonRekognition</i> if you are using the AmazonRekognitionServiceRole permissions policy to access the topic.</p>
        job_tag: <p>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 <code>JobTag</code> to group related jobs and identify them in the completion notification.</p>
        filters: <p>Filters for technical cue or shot detection.</p>
        segment_types: <p>An array of segment types to detect in the video. Valid values are TECHNICAL_CUE and SHOT.</p>

    Raises:
        capo_rekognition.errors.access_denied_exception.AccessDeniedException: <p>You are not authorized to perform the action.</p>
        capo_rekognition.errors.idempotent_parameter_mismatch_exception.IdempotentParameterMismatchException: <p>A <code>ClientRequestToken</code> 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.</p>
        capo_rekognition.errors.internal_server_error.InternalServerError: <p>Amazon Rekognition experienced a service issue. Try your call again.</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.invalid_s3_object_exception.InvalidS3ObjectException: <p>Amazon Rekognition is unable to access the S3 object specified in the request.</p>
        capo_rekognition.errors.limit_exceeded_exception.LimitExceededException: <p>An Amazon Rekognition service limit was exceeded. For example, if you start too many jobs concurrently, subsequent calls to start operations (ex: <code>StartLabelDetection</code>) will raise a <code>LimitExceededException</code> exception (HTTP status code: 400) until the number of concurrently running jobs is below the Amazon Rekognition service limit. </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.throttling_exception.ThrottlingException: <p>Amazon Rekognition is temporarily unable to process the request. Try your call again.</p>
        capo_rekognition.errors.video_too_large_exception.VideoTooLargeException: <p>The file size or duration of the supplied media is too large. The maximum file size is 10GB. The maximum duration is 6 hours. </p>
        capo_rekognition.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_rekognition.types.start_segment_detection_request.StartSegmentDetectionRequest]",
    ) -> OperationResponse[
        "capo_rekognition.types.start_segment_detection_response.StartSegmentDetectionResponse"
    ]:
        import capo_rekognition._operations.rekognition_service.start_segment_detection

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("StartSegmentDetection")
    )
    input_: capo_rekognition.types.start_segment_detection_request.StartSegmentDetectionRequest = {
        "video": video,
        "segment_types": segment_types,
    }
    if client_request_token is not None:
        input_["client_request_token"] = client_request_token
    if notification_channel is not None:
        input_["notification_channel"] = notification_channel
    if job_tag is not None:
        input_["job_tag"] = job_tag
    if filters is not None:
        input_["filters"] = filters

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