mirror of https://github.com/vladmandic/human
154 lines
5.4 KiB
TypeScript
154 lines
5.4 KiB
TypeScript
/**
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* Result interface definition for **Human** library
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*
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* Contains all possible detection results
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*/
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/** Face results
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* Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models
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* Some values may be null if specific model is not enabled
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*
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* Array of individual results with one object per detected face
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* Each result has:
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* - id: face number
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* - confidence: overal detection confidence value
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* - boxConfidence: face box detection confidence value
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* - faceConfidence: face keypoints detection confidence value
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* - box: face bounding box as array of [x, y, width, height], normalized to image resolution
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* - boxRaw: face bounding box as array of [x, y, width, height], normalized to range 0..1
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* - mesh: face keypoints as array of [x, y, z] points of face mesh, normalized to image resolution
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* - meshRaw: face keypoints as array of [x, y, z] points of face mesh, normalized to range 0..1
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* - annotations: annotated face keypoints as array of annotated face mesh points
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* - age: age as value
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* - gender: gender as value
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* - genderConfidence: gender detection confidence as value
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* - emotion: emotions as array of possible emotions with their individual scores
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* - embedding: facial descriptor as array of numerical elements
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* - iris: iris distance from current viewpoint as distance value
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* - rotation: face rotiation that contains both angles and matrix used for 3d transformations
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* - angle: face angle as object with values for roll, yaw and pitch angles
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* - matrix: 3d transofrmation matrix as array of numeric values
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* - tensor: face tensor as Tensor object which contains detected face
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*/
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export interface Face {
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id: number
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confidence: number,
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boxConfidence: number,
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faceConfidence: number,
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box: [number, number, number, number],
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boxRaw: [number, number, number, number],
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mesh: Array<[number, number, number]>
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meshRaw: Array<[number, number, number]>
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annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,
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age: number,
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gender: string,
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genderConfidence: number,
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emotion: Array<{ score: number, emotion: string }>,
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embedding: Array<number>,
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iris: number,
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rotation: {
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angle: { roll: number, yaw: number, pitch: number },
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matrix: [number, number, number, number, number, number, number, number, number],
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}
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tensor: any,
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}
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/** Body results
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*
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* Array of individual results with one object per detected body
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* Each results has:
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* - id:body id number
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* - score: overall detection score
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* - box: bounding box: x, y, width, height normalized to input image resolution
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* - boxRaw: bounding box: x, y, width, height normalized to 0..1
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* - keypoints: array of keypoints
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* - part: body part name
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* - position: body part position with x,y,z coordinates
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* - score: body part score value
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* - presence: body part presence value
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*/
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export interface Body {
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id: number,
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score: number,
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box?: [x: number, y: number, width: number, height: number],
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boxRaw?: [x: number, y: number, width: number, height: number],
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keypoints: Array<{
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part: string,
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position: { x: number, y: number, z: number },
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score: number,
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presence: number,
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}>
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}
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/** Hand results
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*
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* Array of individual results with one object per detected hand
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* Each result has:
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* - confidence as value
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* - box as array of [x, y, width, height], normalized to image resolution
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* - boxRaw as array of [x, y, width, height], normalized to range 0..1
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* - landmarks as array of [x, y, z] points of hand, normalized to image resolution
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* - annotations as array of annotated face landmark points
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*/
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export interface Hand {
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id: number,
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confidence: number,
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box: [number, number, number, number],
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boxRaw: [number, number, number, number],
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landmarks: Array<[number, number, number]>,
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// annotations: Array<{ part: string, points: Array<[number, number, number]> }>,
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// annotations: Annotations,
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annotations: Record<string, Array<{ part: string, points: Array<[number, number, number]> }>>,
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}
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/** Object results
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*
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* Array of individual results with one object per detected gesture
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* Each result has:
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* - score as value
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* - label as detected class name
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* - center as array of [x, y], normalized to image resolution
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* - centerRaw as array of [x, y], normalized to range 0..1
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* - box as array of [x, y, width, height], normalized to image resolution
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* - boxRaw as array of [x, y, width, height], normalized to range 0..1
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*/
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export interface Item {
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score: number,
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strideSize?: number,
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class: number,
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label: string,
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center?: number[],
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centerRaw?: number[],
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box: number[],
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boxRaw: number[],
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}
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/** Gesture results
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*
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* Array of individual results with one object per detected gesture
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* Each result has:
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* - part: part name and number where gesture was detected: face, iris, body, hand
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* - gesture: gesture detected
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*/
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export type Gesture =
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{ 'face': number, gesture: string }
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| { 'iris': number, gesture: string }
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| { 'body': number, gesture: string }
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| { 'hand': number, gesture: string }
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export interface Result {
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/** {@link Face}: detection & analysis results */
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face: Array<Face>,
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/** {@link Body}: detection & analysis results */
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body: Array<Body>,
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/** {@link Hand}: detection & analysis results */
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hand: Array<Hand>,
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/** {@link Gesture}: detection & analysis results */
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gesture: Array<Gesture>,
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/** {@link Object}: detection & analysis results */
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object: Array<Item>
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performance: { any },
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canvas: OffscreenCanvas | HTMLCanvasElement,
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}
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