jiti-meet/react/features/face-landmarks/functions.js

228 lines
6.2 KiB
JavaScript

// @flow
import { getLocalParticipant } from '../base/participants';
import { extractFqnFromPath } from '../dynamic-branding/functions.any';
import { DETECT_FACE, FACE_BOX_EVENT_TYPE, SEND_IMAGE_INTERVAL_MS } from './constants';
import logger from './logger';
let canvas;
let context;
if (typeof OffscreenCanvas === 'undefined') {
canvas = document.createElement('canvas');
context = canvas.getContext('2d');
}
/**
* Sends the face expression with its duration to all the other participants.
*
* @param {Object} conference - The current conference.
* @param {string} faceExpression - Face expression to be sent.
* @param {number} duration - The duration of the face expression in seconds.
* @returns {void}
*/
export function sendFaceExpressionToParticipants(
conference: Object,
faceExpression: string,
duration: number
): void {
try {
conference.sendEndpointMessage('', {
type: 'face_landmark',
faceExpression,
duration
});
} catch (err) {
logger.warn('Could not broadcast the face expression to the other participants', err);
}
}
/**
* Sends the face box to all the other participants.
*
* @param {Object} conference - The current conference.
* @param {Object} faceBox - Face box to be sent.
* @returns {void}
*/
export function sendFaceBoxToParticipants(
conference: Object,
faceBox: Object
): void {
try {
conference.sendEndpointMessage('', {
type: FACE_BOX_EVENT_TYPE,
faceBox
});
} catch (err) {
logger.warn('Could not broadcast the face box to the other participants', err);
}
}
/**
* Sends the face expression with its duration to xmpp server.
*
* @param {Object} conference - The current conference.
* @param {string} faceExpression - Face expression to be sent.
* @param {number} duration - The duration of the face expression in seconds.
* @returns {void}
*/
export function sendFaceExpressionToServer(
conference: Object,
faceExpression: string,
duration: number
): void {
try {
conference.sendFaceLandmarks({
faceExpression,
duration
});
} catch (err) {
logger.warn('Could not send the face expression to xmpp server', err);
}
}
/**
* Sends face expression to backend.
*
* @param {Object} state - Redux state.
* @returns {boolean} - True if sent, false otherwise.
*/
export async function sendFaceExpressionsWebhook(state: Object) {
const { webhookProxyUrl: url } = state['features/base/config'];
const { conference } = state['features/base/conference'];
const { jwt } = state['features/base/jwt'];
const { connection } = state['features/base/connection'];
const jid = connection.getJid();
const localParticipant = getLocalParticipant(state);
const { faceExpressionsBuffer } = state['features/face-landmarks'];
if (faceExpressionsBuffer.length === 0) {
return false;
}
const headers = {
...jwt ? { 'Authorization': `Bearer ${jwt}` } : {},
'Content-Type': 'application/json'
};
const reqBody = {
meetingFqn: extractFqnFromPath(),
sessionId: conference.sessionId,
submitted: Date.now(),
emotions: faceExpressionsBuffer,
participantId: localParticipant.jwtId,
participantName: localParticipant.name,
participantJid: jid
};
if (url) {
try {
const res = await fetch(`${url}/emotions`, {
method: 'POST',
headers,
body: JSON.stringify(reqBody)
});
if (res.ok) {
return true;
}
logger.error('Status error:', res.status);
} catch (err) {
logger.error('Could not send request', err);
}
}
return false;
}
/**
* Sends the image data a canvas from the track in the image capture to the face recognition worker.
*
* @param {Worker} worker - Face recognition worker.
* @param {Object} imageCapture - Image capture that contains the current track.
* @param {number} threshold - Movement threshold as percentage for sharing face coordinates.
* @returns {Promise<void>}
*/
export async function sendDataToWorker(
worker: Worker,
imageCapture: Object,
threshold: number = 10
): Promise<void> {
if (imageCapture === null || imageCapture === undefined) {
return;
}
let imageBitmap;
let image;
try {
imageBitmap = await imageCapture.grabFrame();
} catch (err) {
logger.warn(err);
return;
}
if (typeof OffscreenCanvas === 'undefined') {
canvas.width = imageBitmap.width;
canvas.height = imageBitmap.height;
context.drawImage(imageBitmap, 0, 0);
image = context.getImageData(0, 0, imageBitmap.width, imageBitmap.height);
} else {
image = imageBitmap;
}
worker.postMessage({
type: DETECT_FACE,
image,
threshold
});
imageBitmap.close();
}
/**
* Gets face box for a participant id.
*
* @param {string} id - The participant id.
* @param {Object} state - The redux state.
* @returns {Object}
*/
function getFaceBoxForId(id: string, state: Object) {
return state['features/face-landmarks'].faceBoxes[id];
}
/**
* Gets the video object position for a participant id.
*
* @param {Object} state - The redux state.
* @param {string} id - The participant id.
* @returns {string} - CSS object-position in the shape of '{horizontalPercentage}% {verticalPercentage}%'.
*/
export function getVideoObjectPosition(state: Object, id: string) {
const faceBox = getFaceBoxForId(id, state);
if (faceBox) {
const { right, width } = faceBox;
return `${right - (width / 2)}% 50%`;
}
return '50% 50%';
}
/**
* Gets the video object position for a participant id.
*
* @param {Object} state - The redux state.
* @returns {number} - Number of miliseconds for doing face detection.
*/
export function getDetectionInterval(state: Object) {
const { faceLandmarks } = state['features/base/config'];
return Math.max(faceLandmarks?.captureInterval || SEND_IMAGE_INTERVAL_MS);
}