ml5.js TypeScript definitions draft

ml5.d.ts

// Not quite there yet, but better than nothing
// TODO: JSDoc, based on website documentation
// TODO: Interface/class types for each of these ML algorithm instances
// TODO: Break this apart into multiple files for readability
// TODO: Test autocomplete in a vanilla JS project
// TODO: Test autocomplete in a TypeScript project

import { MediaElement } from "p5";

export as namespace ml5;

type PoseNetOptions = {
  detectionType: "single" | "multiple";
  flipHorizontal: boolean;
  imageScaleFactor: number;
  maxPoseDetections: number;
  minConfidence: number;
  multiplier: number;
  nmsRadius: number;
  outputStride: number;
  scoreThreshold: number;
};

type ImageClassifierOptions = {
  alpha: number;
  topk: number;
  version: number;
};

type YoloOptions = {
  classProbThreshold: number;
  filterBoxesThreshold: number;
  IOUThreshold: number;
};

type SketchRnnModel =
  | "alarm_clock"
  | "ambulance"
  | "angel"
  | "ant"
  | "antyoga"
  | "backpack"
  | "barn"
  | "basket"
  | "bear"
  | "bee"
  | "beeflower"
  | "bicycle"
  | "bird"
  | "book"
  | "brain"
  | "bridge"
  | "bulldozer"
  | "bus"
  | "butterfly"
  | "cactus"
  | "calendar"
  | "castle"
  | "cat"
  | "catbus"
  | "catpig"
  | "chair"
  | "couch"
  | "crab"
  | "crabchair"
  | "crabrabbitfacepig"
  | "cruise_ship"
  | "diving_board"
  | "dog"
  | "dogbunny"
  | "dolphin"
  | "duck"
  | "elephant"
  | "elephantpig"
  | "eye"
  | "face"
  | "fan"
  | "fire_hydrant"
  | "firetruck"
  | "flamingo"
  | "flower"
  | "floweryoga"
  | "frog"
  | "frogsofa"
  | "garden"
  | "hand"
  | "hedgeberry"
  | "hedgehog"
  | "helicopter"
  | "kangaroo"
  | "key"
  | "lantern"
  | "lighthouse"
  | "lion"
  | "lionsheep"
  | "lobster"
  | "map"
  | "mermaid"
  | "monapassport"
  | "monkey"
  | "mosquito"
  | "octopus"
  | "owl"
  | "paintbrush"
  | "palm_tree"
  | "parrot"
  | "passport"
  | "peas"
  | "penguin"
  | "pig"
  | "pigsheep"
  | "pineapple"
  | "pool"
  | "postcard"
  | "power_outlet"
  | "rabbit"
  | "rabbitturtle"
  | "radio"
  | "radioface"
  | "rain"
  | "rhinoceros"
  | "rifle"
  | "roller_coaster"
  | "sandwich"
  | "scorpion"
  | "sea_turtle"
  | "sheep"
  | "skull"
  | "snail"
  | "snowflake"
  | "speedboat"
  | "spider"
  | "squirrel"
  | "steak"
  | "stove"
  | "strawberry"
  | "swan"
  | "swing_set"
  | "the_mona_lisa"
  | "tiger"
  | "toothbrush"
  | "toothpaste"
  | "tractor"
  | "trombone"
  | "truck"
  | "whale"
  | "windmill"
  | "yoga"
  | "yogabicycle"
  | "everything";

export function imageClassifier(
  model: "MobileNet" | "Darknet" | "Darknet-tiny" | string,
  callback?: (error: any, result: any) => void
): undefined | Promise<any>;

export function imageClassifier(
  model: "MobileNet" | "Darknet" | "Darknet-tiny" | string,
  options?: ImageClassifierOptions,
  callback?: (error: any, result: any) => void
): undefined | Promise<any>;

export function imageClassifier(
  model: "MobileNet" | "Darknet" | "Darknet-tiny" | string,
  video?: MediaElement | HTMLVideoElement,
  options?: ImageClassifierOptions,
  callback?: (error: any, result: any) => void
): undefined | Promise<any>;

export function poseNet(
  video?: MediaElement | HTMLVideoElement,
  type?: "single" | "multiple",
  callback?: (error: any, result: any) => void
): undefined | Promise<any>;

export function poseNet(
  video?: MediaElement | HTMLVideoElement,
  options?: PoseNetOptions,
  callback?: (error: any, result: any) => void
): undefined | Promise<any>;

export function poseNet(
  callback?: (error: any, result: any) => void,
  options?: PoseNetOptions
): undefined | Promise<any>;

export function bodyPix(
  video?: MediaElement | HTMLVideoElement,
  type?: "single" | "multiple",
  callback?: (error: any, result: any) => void,
  options?: any
): undefined | Promise<any>;

export function uNet(
  model: string,
  callback?: (error: any, result: any) => void
): undefined | Promise<any>;

export function styleTransfer(
  model: string,
  callback?: (error: any, result: any) => void
): undefined | Promise<any>;

export function styleTransfer(
  model: string,
  video?: MediaElement | HTMLVideoElement,
  callback?: (error: any, result: any) => void
): undefined | Promise<any>;

export function pix2pix(
  model: string,
  callback?: (error: any, result: any) => void
): undefined | Promise<any>;

export function CVAE(
  model: string,
  callback?: (error: any, result: any) => void
): undefined | Promise<any>;

export function DCGAN(
  modelPath: string,
  callback?: (error: any, result: any) => void
): undefined | Promise<any>;

export function SketchRNN(
  model: string,
  callback?: (error: any, result: any) => void
): undefined | Promise<any>;

export function YOLO(
  video?: MediaElement | HTMLVideoElement,
  options?: YoloOptions,
  callback?: (error: any, result: any) => void
): undefined | Promise<any>;

export function YOLO(
  options?: YoloOptions,
  callback?: (error: any, result: any) => void
): undefined | Promise<any>;

export function soundClassifier(
  model: string,
  options?: { probabilityThreshold: number },
  callback?: (error: any, result: any) => void
): undefined | Promise<any>;

export function pitchDetection(
  model: "CREPE",
  audioContext: AudioContext,
  stream: MediaStream,
  callback?: (error: any, result: any) => void
): undefined | Promise<any>;

export function charRNN(
  model: string,
  callback?: (error: any, result: any) => void
): undefined | Promise<any>;

export function sentiment(
  model: string,
  callback?: (error: any, result: any) => void
): undefined | Promise<any>;

export function word2vec(
  model: string,
  callback?: (error: any, result: any) => void
): undefined | Promise<any>;

export function SketchRNN(
  model: SketchRnnModel | string,
  callback?: (error: any, result: any) => void
): undefined | Promise<any>;

Thank you so much for this draft. It isn't everything but it's enough for starter projects with my students, who I've trained to love the autocomplete in VS Code!

Awesome work! Could you add NeuralNetwork to the declaration?

Thanks for this! is very helpful.

thanks a lot, it's really helpful for me, definitely

where is .setBackend() ?