search_users ¶

def search_users(
    self,
    collection_id: "capo_rekognition.types.collection_id.CollectionId",
    *,
    config_overrides: Optional[RekognitionClientConfig] = None,
    user_id: Optional["capo_rekognition.types.user_id.UserId"] = None,
    face_id: Optional["capo_rekognition.types.face_id.FaceId"] = None,
    user_match_threshold: Optional["capo_rekognition.types.percent.Percent"] = None,
    max_users: Optional[
        "capo_rekognition.types.max_user_results.MaxUserResults"
    ] = None,
) -> "capo_rekognition.types.search_users_response.SearchUsersResponse":
    """<p>Searches for UserIDs within a collection based on a <code>FaceId</code> or <code>UserId</code>. This API can be used to find the closest UserID (with a highest similarity) to associate a face. The request must be provided with either <code>FaceId</code> or <code>UserId</code>. The operation returns an array of UserID that match the <code>FaceId</code> or <code>UserId</code>, ordered by similarity score with the highest similarity first. </p>

    Args:
        collection_id: <p>The ID of an existing collection containing the UserID, used with a UserId or FaceId. If a FaceId is provided, UserId isn’t required to be present in the Collection.</p>
        user_id: <p>ID for the existing User.</p>
        face_id: <p>ID for the existing face.</p>
        user_match_threshold: <p>Optional value that specifies the minimum confidence in the matched UserID to return. Default value of 80.</p>
        max_users: <p>Maximum number of identities to return.</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_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.

    Examples:
        SearchUsers
        Searches for UserIDs within a collection based on a FaceId or UserId.

        >>> client.search_users(collection_id='MyCollection', user_id='DemoUser', max_users=2, user_match_threshold=70)
    """

    def _handler(
        req: "OperationRequest[capo_rekognition.types.search_users_request.SearchUsersRequest]",
    ) -> OperationResponse[
        "capo_rekognition.types.search_users_response.SearchUsersResponse"
    ]:
        import capo_rekognition._operations.rekognition_service.search_users

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("SearchUsers")
    )
    input_: capo_rekognition.types.search_users_request.SearchUsersRequest = {
        "collection_id": collection_id
    }
    if user_id is not None:
        input_["user_id"] = user_id
    if face_id is not None:
        input_["face_id"] = face_id
    if user_match_threshold is not None:
        input_["user_match_threshold"] = user_match_threshold
    if max_users is not None:
        input_["max_users"] = max_users

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