list_dataset_entries ¶

def list_dataset_entries(
    self,
    dataset_arn: "capo_rekognition.types.dataset_arn.DatasetArn",
    *,
    config_overrides: Optional[RekognitionClientConfig] = None,
    contains_labels: Optional[
        "capo_rekognition.types.dataset_labels.DatasetLabels"
    ] = None,
    labeled: Optional["capo_rekognition.types.is_labeled.IsLabeled"] = None,
    source_ref_contains: Optional[
        "capo_rekognition.types.query_string.QueryString"
    ] = None,
    has_errors: Optional["capo_rekognition.types.has_errors.HasErrors"] = None,
    next_token: Optional[
        "capo_rekognition.types.extended_pagination_token.ExtendedPaginationToken"
    ] = None,
    max_results: Optional[
        "capo_rekognition.types.list_dataset_entries_page_size.ListDatasetEntriesPageSize"
    ] = None,
) -> "capo_rekognition.types.list_dataset_entries_response.ListDatasetEntriesResponse":
    """<note> <p>This operation applies only to Amazon Rekognition Custom Labels.</p> </note> <p> Lists the entries (images) within a dataset. An entry is a JSON Line that contains the information for a single image, including the image location, assigned labels, and object location bounding boxes. For more information, see <a href="https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-manifest-files.html">Creating a manifest file</a>.</p> <p>JSON Lines in the response include information about non-terminal errors found in the dataset. Non terminal errors are reported in <code>errors</code> lists within each JSON Line. The same information is reported in the training and testing validation result manifests that Amazon Rekognition Custom Labels creates during model training. </p> <p>You can filter the response in variety of ways, such as choosing which labels to return and returning JSON Lines created after a specific date. </p> <p>This operation requires permissions to perform the <code>rekognition:ListDatasetEntries</code> action.</p>

    Args:
        dataset_arn: <p> The Amazon Resource Name (ARN) for the dataset that you want to use. </p>
        contains_labels: <p>Specifies a label filter for the response. The response includes an entry only if one or more of the labels in <code>ContainsLabels</code> exist in the entry. </p>
        labeled: <p> Specify <code>true</code> to get only the JSON Lines where the image is labeled. Specify <code>false</code> to get only the JSON Lines where the image isn't labeled. If you don't specify <code>Labeled</code>, <code>ListDatasetEntries</code> returns JSON Lines for labeled and unlabeled images. </p>
        source_ref_contains: <p>If specified, <code>ListDatasetEntries</code> only returns JSON Lines where the value of <code>SourceRefContains</code> is part of the <code>source-ref</code> field. The <code>source-ref</code> field contains the Amazon S3 location of the image. You can use <code>SouceRefContains</code> for tasks such as getting the JSON Line for a single image, or gettting JSON Lines for all images within a specific folder.</p>
        has_errors: <p>Specifies an error filter for the response. Specify <code>True</code> to only include entries that have errors. </p>
        next_token: <p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>
        max_results: <p>The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100. </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_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.resource_not_ready_exception.ResourceNotReadyException: <p>The requested resource isn't ready. For example, this exception occurs when you call <code>DetectCustomLabels</code> with a model version that isn't deployed. </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.

    Examples:
        To list the entries in an Amazon Rekognition Custom Labels dataset
        Lists the JSON line entries in an Amazon Rekognition Custom Labels dataset.

        >>> client.list_dataset_entries(dataset_arn='arn:aws:rekognition:us-east-1:111122223333:project/my-proj-2/dataset/train/1690564858106', contains_labels=['camellia'], labeled=True, source_ref_contains='camellia4.jpg', has_errors=True, next_token='', max_results=100)
    """

    def _handler(
        req: "OperationRequest[capo_rekognition.types.list_dataset_entries_request.ListDatasetEntriesRequest]",
    ) -> OperationResponse[
        "capo_rekognition.types.list_dataset_entries_response.ListDatasetEntriesResponse"
    ]:
        import capo_rekognition._operations.rekognition_service.list_dataset_entries

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("ListDatasetEntries")
    )
    input_: capo_rekognition.types.list_dataset_entries_request.ListDatasetEntriesRequest = {
        "dataset_arn": dataset_arn
    }
    if contains_labels is not None:
        input_["contains_labels"] = contains_labels
    if labeled is not None:
        input_["labeled"] = labeled
    if source_ref_contains is not None:
        input_["source_ref_contains"] = source_ref_contains
    if has_errors is not None:
        input_["has_errors"] = has_errors
    if next_token is not None:
        input_["next_token"] = next_token
    if max_results is not None:
        input_["max_results"] = max_results

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