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Using the Node SDK

The Dragoneye Node.js SDK simplifies integrating with our APIs in your JavaScript/TypeScript projects. This guide covers installation, example usage, type definitions, and available endpoints.

Installation​

Install the SDK using npm:

npm install dragoneye-node

Quick Start​

Once installed, you can call the classifier using your desired model:

import { Dragoneye } from "dragoneye-node";

const dragoneyeClient = new Dragoneye({
  apiKey: "<YOUR_ACCESS_TOKEN>",
});

const media = await Dragoneye.Video.fromFilePath("example.mp4");

const results = await dragoneyeClient.classification.predictVideo(
  media,
  "dragoneye/animals"
);

console.log(results.predictions);

Types and Endpoints​

Types​

TaxonID​

A TaxonID is a unique numeric identifier for each taxon (e.g., category or trait).

export type TaxonID = number & { readonly brand: unique symbol };

Use the createTaxonID helper function to safely create TaxonID values:

export function createTaxonID(taxonId: number): TaxonID;

TaxonType​

Defines whether a taxon is a "category" or a "trait".

NormalizedBbox​

Represents the location of an object in an image. It is an array of four numbers: [x_min, y_min, x_max, y_max].

TaxonPrediction​

Describes a predicted taxon returned by the API. Predictions may include children to represent hierarchies.

export type TaxonPrediction = {
  id: TaxonID;
  type: TaxonType;
  name: string;
  displayName: string;
  score?: number;
  children: TaxonPrediction[];
};

ClassificationTraitRootPrediction​

Contains predictions for traits related to a detected object.

export interface ClassificationTraitRootPrediction {
  id: TaxonID;
  name: string;
  displayName: string;
  taxons: TaxonPrediction[];
}

ClassificationObjectPrediction​

Represents a predicted object in an image.

export interface ClassificationObjectPrediction {
  normalizedBbox: NormalizedBbox;
  category: TaxonPrediction;
  traits: ClassificationTraitRootPrediction[];
}

ClassificationPredictImageResponse​

Response structure for image predictions.

export interface ClassificationPredictImageResponse {
  predictions: ClassificationObjectPrediction[];
  prediction_task_uuid?: string;
}

ClassificationPredictVideoResponse​

Response structure for video predictions.

export interface ClassificationPredictVideoResponse {
  predictions: Record<number, ClassificationObjectPrediction[]>; // keyed by frame
  prediction_task_uuid?: string;
}

PredictionTaskStatusResponse​

Represents the status of an async prediction task.

export interface PredictionTaskStatusResponse {
  prediction_task_uuid: string;
  status: string; // e.g. "predicted", "failed"
}

Endpoints​

predict_image (Image Classification)​

Performs a classification prediction on a single image.

Arguments:

  • media: A Media object wrapping an image.
  • modelName: The name of the model to use.

Response:

  • Returns a ClassificationPredictImageResponse.

predict_video (Video Classification)​

Performs a classification prediction on a video.

Arguments:

  • media: A Media object wrapping a video.
  • modelName: The model name to use.
  • framesPerSecond: Number of frames per second to sample.

Response:

  • Returns a ClassificationPredictVideoResponse.

get_status (Prediction Task Status)​

Checks the status of an in-progress prediction task.

Arguments:

  • predictionTaskUuid: The UUID of the prediction task.

Response:

  • Returns a PredictionTaskStatusResponse.

get_results (Retrieve Prediction Results)​

Fetches the results of a completed prediction task.

Arguments:

  • predictionTaskUuid: The UUID of the prediction task.
  • predictionType: "image" or "video".

Response:

  • If "image" → ClassificationPredictImageResponse.
  • If "video" → ClassificationPredictVideoResponse.

Notes​

  • All methods are asynchronous and return Promises.
  • For images, always use predict_image. For videos, use predict_video. Passing the wrong media type will throw an IncorrectMediaTypeError.
  • Predictions run as tasks: the SDK automatically begins the task, uploads media, initiates prediction, polls for completion, and retrieves results.