59 lines
1.8 KiB
TypeScript
59 lines
1.8 KiB
TypeScript
import { Rtsp, IStreamEventArgs } from "../src/rtsp/rtsp";
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import { nets } from "@vladmandic/face-api";
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import * as faceapi from "@vladmandic/face-api";
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import canvas from "canvas";
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import fs from "fs";
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import * as path from "path";
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import dotenv from "dotenv-extended";
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import { delay, getFaceDetectorOptions, saveFile } from "../src/common";
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require("@tensorflow/tfjs-node");
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const { Canvas, Image, ImageData } = canvas;
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//@ts-ignore
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faceapi.env.monkeyPatch({ Canvas, Image, ImageData });
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const main = async () => {
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dotenv.load({
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silent: false,
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errorOnMissing: true,
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});
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const modelDir = process.env.TRAINED_MODEL_DIR as string;
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const rtsp = new Rtsp("rtsp://brandon:asdf1234@192.168.1.229/live", {
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rate: 0.5,
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image: true,
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codec: "copy",
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});
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const faceDetectionNet = nets.ssdMobilenetv1;
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await faceDetectionNet.loadFromDisk(path.join(__dirname, "../weights"));
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await nets.faceLandmark68Net.loadFromDisk(path.join(__dirname, "../weights"));
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await nets.faceRecognitionNet.loadFromDisk(
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path.join(__dirname, "../weights")
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);
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const raw = fs.readFileSync(path.join(modelDir, "data.json"), "utf-8");
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const content = JSON.parse(raw);
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const matcher = faceapi.FaceMatcher.fromJSON(content);
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rtsp.dataEvent.push(async (sender: Rtsp, args: IStreamEventArgs) => {
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const input = ((await canvas.loadImage(args.data)) as unknown) as ImageData;
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const out = faceapi.createCanvasFromMedia(input);
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await saveFile(process.env.OUT_DIR as string, "image.jpg", args.data);
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const resultsQuery = await faceapi
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.detectAllFaces(out, getFaceDetectorOptions(faceDetectionNet))
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.withFaceLandmarks()
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.withFaceDescriptors();
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for (const res of resultsQuery) {
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const bestMatch = matcher.matchDescriptor(res.descriptor);
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console.log("Face Detected: " + bestMatch.label);
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}
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});
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rtsp.start();
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};
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main();
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