import type { MetadataBearer as __MetadataBearer } from "@smithy/types"; import type { DetectCustomLabelsRequest, DetectCustomLabelsResponse } from "../models/models_0"; /** * @public */ export type { __MetadataBearer }; /** * @public * * The input for {@link DetectCustomLabelsCommand}. */ export interface DetectCustomLabelsCommandInput extends DetectCustomLabelsRequest { } /** * @public * * The output of {@link DetectCustomLabelsCommand}. */ export interface DetectCustomLabelsCommandOutput extends DetectCustomLabelsResponse, __MetadataBearer { } declare const DetectCustomLabelsCommand_base: { new (input: DetectCustomLabelsCommandInput): import("@smithy/core/client").CommandImpl; new (input: DetectCustomLabelsCommandInput): import("@smithy/core/client").CommandImpl; getEndpointParameterInstructions(): import("@smithy/types").EndpointParameterInstructions; }; /** * *

This operation applies only to Amazon Rekognition Custom Labels.

* *

Detects custom labels in a supplied image by using an Amazon Rekognition Custom Labels model.

*

You specify which version of a model version to use by using the ProjectVersionArn input * parameter.

*

You pass the input image as base64-encoded image bytes or as a reference to an image in * an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing * image bytes is not supported. The image must be either a PNG or JPEG formatted file.

*

For each object that the model version detects on an image, the API returns a * (CustomLabel) object in an array (CustomLabels). * Each CustomLabel object provides the label name (Name), the level * of confidence that the image contains the object (Confidence), and * object location information, if it exists, for the label on the image (Geometry).

*

To filter labels that are returned, specify a value for MinConfidence. * DetectCustomLabelsLabels only returns labels with a confidence that's higher than * the specified value. * * The value of MinConfidence maps to the assumed threshold values * created during training. For more information, see Assumed threshold * in the Amazon Rekognition Custom Labels Developer Guide. * Amazon Rekognition Custom Labels metrics expresses an assumed threshold as a floating point value between 0-1. The range of * MinConfidence normalizes the threshold value to a percentage value (0-100). Confidence * responses from DetectCustomLabels are also returned as a percentage. * You can use MinConfidence to change the precision and recall or your model. * For more information, see * Analyzing an image in the Amazon Rekognition Custom Labels Developer Guide.

*

If you don't specify a value for MinConfidence, DetectCustomLabels * returns labels based on the assumed threshold of each label.

*

This is a stateless API operation. That is, the operation does not persist any * data.

*

This operation requires permissions to perform the * rekognition:DetectCustomLabels action.

*

For more information, see * Analyzing an image in the Amazon Rekognition Custom Labels Developer Guide.

* @example * Use a bare-bones client and the command you need to make an API call. * ```javascript * import { RekognitionClient, DetectCustomLabelsCommand } from "@aws-sdk/client-rekognition"; // ES Modules import * // const { RekognitionClient, DetectCustomLabelsCommand } = require("@aws-sdk/client-rekognition"); // CommonJS import * // import type { RekognitionClientConfig } from "@aws-sdk/client-rekognition"; * const config = {}; // type is RekognitionClientConfig * const client = new RekognitionClient(config); * const input = { // DetectCustomLabelsRequest * ProjectVersionArn: "STRING_VALUE", // required * Image: { // Image * Bytes: new Uint8Array(), // e.g. Buffer.from("") or new TextEncoder().encode("") * S3Object: { // S3Object * Bucket: "STRING_VALUE", * Name: "STRING_VALUE", * Version: "STRING_VALUE", * }, * }, * MaxResults: Number("int"), * MinConfidence: Number("float"), * }; * const command = new DetectCustomLabelsCommand(input); * const response = await client.send(command); * // { // DetectCustomLabelsResponse * // CustomLabels: [ // CustomLabels * // { // CustomLabel * // Name: "STRING_VALUE", * // Confidence: Number("float"), * // Geometry: { // Geometry * // BoundingBox: { // BoundingBox * // Width: Number("float"), * // Height: Number("float"), * // Left: Number("float"), * // Top: Number("float"), * // }, * // Polygon: [ // Polygon * // { // Point * // X: Number("float"), * // Y: Number("float"), * // }, * // ], * // }, * // }, * // ], * // }; * * ``` * * @param DetectCustomLabelsCommandInput - {@link DetectCustomLabelsCommandInput} * @returns {@link DetectCustomLabelsCommandOutput} * @see {@link DetectCustomLabelsCommandInput} for command's `input` shape. * @see {@link DetectCustomLabelsCommandOutput} for command's `response` shape. * @see {@link RekognitionClientResolvedConfig | config} for RekognitionClient's `config` shape. * * @throws {@link AccessDeniedException} (client fault) *

You are not authorized to perform the action.

* * @throws {@link ImageTooLargeException} (client fault) *

The input image size exceeds the allowed limit. If you are calling * DetectProtectiveEquipment, the image size or resolution exceeds the allowed limit. For more * information, see Guidelines and quotas in Amazon Rekognition in the Amazon Rekognition Developer Guide. *

* * @throws {@link InternalServerError} (server fault) *

Amazon Rekognition experienced a service issue. Try your call again.

* * @throws {@link InvalidImageFormatException} (client fault) *

The provided image format is not supported.

* * @throws {@link InvalidParameterException} (client fault) *

Input parameter violated a constraint. Validate your parameter before calling the API * operation again.

* * @throws {@link InvalidS3ObjectException} (client fault) *

Amazon Rekognition is unable to access the S3 object specified in the request.

* * @throws {@link LimitExceededException} (client fault) *

An Amazon Rekognition service limit was exceeded. For example, if you start too many jobs * concurrently, subsequent calls to start operations (ex: * StartLabelDetection) will raise a LimitExceededException * exception (HTTP status code: 400) until the number of concurrently running jobs is below * the Amazon Rekognition service limit.

* * @throws {@link ProvisionedThroughputExceededException} (client fault) *

The number of requests exceeded your throughput limit. If you want to increase this * limit, contact Amazon Rekognition.

* * @throws {@link ResourceNotFoundException} (client fault) *

The resource specified in the request cannot be found.

* * @throws {@link ResourceNotReadyException} (client fault) *

The requested resource isn't ready. For example, * this exception occurs when you call DetectCustomLabels with a * model version that isn't deployed.

* * @throws {@link ThrottlingException} (server fault) *

Amazon Rekognition is temporarily unable to process the request. Try your call again.

* * @throws {@link RekognitionServiceException} *

Base exception class for all service exceptions from Rekognition service.

* * * @example To detect custom labels in an image with an Amazon Rekognition Custom Labels model * ```javascript * // Detects custom labels in an image with an Amazon Rekognition Custom Labels model * const input = { * Image: { * S3Object: { * Bucket: "custom-labels-console-us-east-1-1111111111", * Name: "assets/flowers_1_test_dataset/camellia4.jpg" * } * }, * MaxResults: 100, * MinConfidence: 50, * ProjectVersionArn: "arn:aws:rekognition:us-east-1:111122223333:project/my-project/version/my-project.2023-07-31T11.49.37/1690829378219" * }; * const command = new DetectCustomLabelsCommand(input); * const response = await client.send(command); * /* response is * { * CustomLabels: [ * { * Confidence: 67.56399536132812, * Name: "with_leaves" * }, * { * Confidence: 50.65699768066406, * Name: "without_leaves" * } * ] * } * *\/ * ``` * * @public */ export declare class DetectCustomLabelsCommand extends DetectCustomLabelsCommand_base { /** @internal type navigation helper, not in runtime. */ protected static __types: { api: { input: DetectCustomLabelsRequest; output: DetectCustomLabelsResponse; }; sdk: { input: DetectCustomLabelsCommandInput; output: DetectCustomLabelsCommandOutput; }; }; }