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delete_stream_processor

delete_stream_processor(
    name: StreamProcessorName,
    *,
    config_overrides: Optional[RekognitionClientConfig] = None,
) -> DeleteStreamProcessorResponse

Service availability notice: Streaming Video and Bulk Image Analysis is no longer available to new customers. For more information, see Rekognition feature availability changes.

This change does not impact the availability of other Amazon Rekognition features.

Deletes the stream processor identified by Name. You assign the value for Name when you create the stream processor with CreateStreamProcessor. You might not be able to use the same name for a stream processor for a few seconds after calling DeleteStreamProcessor.

Parameters:

Raises:

Source code in docs_src/capo_rekognition/_services/rekognition.py
def delete_stream_processor(
    self,
    name: "capo_rekognition.types.stream_processor_name.StreamProcessorName",
    *,
    config_overrides: Optional[RekognitionClientConfig] = None,
) -> "capo_rekognition.types.delete_stream_processor_response.DeleteStreamProcessorResponse":
    """<important> <p>Service availability notice: Streaming Video and Bulk Image Analysis is no longer available to new customers. For more information, see <a href="https://docs.aws.amazon.com/rekognition/latest/dg/rekognition-availability-changes.html">Rekognition feature availability changes</a>.</p> <p> <b>This change does not impact the availability of other Amazon Rekognition features.</b> </p> </important> <p>Deletes the stream processor identified by <code>Name</code>. You assign the value for <code>Name</code> when you create the stream processor with <a>CreateStreamProcessor</a>. You might not be able to use the same name for a stream processor for a few seconds after calling <code>DeleteStreamProcessor</code>.</p>

    Args:
        name: <p>The name of the stream processor you want to delete.</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_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_in_use_exception.ResourceInUseException: <p>The specified resource is already being used.</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.delete_stream_processor_request.DeleteStreamProcessorRequest]",
    ) -> OperationResponse[
        "capo_rekognition.types.delete_stream_processor_response.DeleteStreamProcessorResponse"
    ]:
        import capo_rekognition._operations.rekognition_service.delete_stream_processor

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("DeleteStreamProcessor")
    )
    input_: capo_rekognition.types.delete_stream_processor_request.DeleteStreamProcessorRequest = {
        "name": name
    }

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