get_text_detection async ¶

async def get_text_detection(
    self,
    job_id: "capo_rekognition.types.job_id.JobId",
    *,
    config_overrides: Optional[AsyncRekognitionClientConfig] = None,
    max_results: Optional["capo_rekognition.types.max_results.MaxResults"] = None,
    next_token: Optional[
        "capo_rekognition.types.pagination_token.PaginationToken"
    ] = None,
) -> "capo_rekognition.types.get_text_detection_response.GetTextDetectionResponse":
    """<p>Gets the text detection results of a Amazon Rekognition Video analysis started by <a>StartTextDetection</a>.</p> <p>Text detection with Amazon Rekognition Video is an asynchronous operation. You start text detection by calling <a>StartTextDetection</a> which returns a job identifier (<code>JobId</code>) When the text detection operation finishes, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to <code>StartTextDetection</code>. To get the results of the text detection operation, first check that the status value published to the Amazon SNS topic is <code>SUCCEEDED</code>. if so, call <code>GetTextDetection</code> and pass the job identifier (<code>JobId</code>) from the initial call of <code>StartLabelDetection</code>.</p> <p> <code>GetTextDetection</code> returns an array of detected text (<code>TextDetections</code>) sorted by the time the text was detected, up to 100 words per frame of video.</p> <p>Each element of the array includes the detected text, the precentage confidence in the acuracy of the detected text, the time the text was detected, bounding box information for where the text was located, and unique identifiers for words and their lines.</p> <p>Use MaxResults parameter to limit the number of text detections 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>GetTextDetection</code> and populate the <code>NextToken</code> request parameter with the token value returned from the previous call to <code>GetTextDetection</code>.</p>

    Args:
        job_id: <p>Job identifier for the text detection operation for which you want results returned. You get the job identifer from an initial call to <code>StartTextDetection</code>.</p>
        max_results: <p>Maximum number of results to return per paginated call. The largest value you can specify 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 text.</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.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_rekognition.types.get_text_detection_request.GetTextDetectionRequest]",
    ) -> AsyncOperationResponse[
        "capo_rekognition.types.get_text_detection_response.GetTextDetectionResponse"
    ]:
        import capo_rekognition._operations.rekognition_service.get_text_detection

        (
            output,
            http_response,
        ) = await capo_rekognition._operations.rekognition_service.get_text_detection.async_get_text_detection(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("GetTextDetection")
    )
    input_: capo_rekognition.types.get_text_detection_request.GetTextDetectionRequest = {
        "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

    response = await aexecute_pipeline(
        AsyncOperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    await response.response.aclose()
    return response.output