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watsonb8 2020-12-19 17:09:39 -05:00
parent cf2c20b6e0
commit a57b576962
10 changed files with 443 additions and 21 deletions
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3
.env.schema
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@ -1,3 +1,4 @@
REF_IMAGE_DIR=
TRAINED_MODEL_DIR=
OUT_DIR=
OUT_DIR=
CONFIDENCE=

295
package-lock.json generated
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},
"tunnel-agent": {
"version": "0.6.0",
"resolved": "https://registry.npmjs.org/tunnel-agent/-/tunnel-agent-0.6.0.tgz",
"integrity": "sha1-J6XeoGs2sEoKmWZ3SykIaPD8QP0=",
"requires": {
"safe-buffer": "^5.0.1"
}
},
"tweetnacl": {
"version": "1.0.3",
"resolved": "https://registry.npmjs.org/tweetnacl/-/tweetnacl-1.0.3.tgz",
@ -1808,6 +2098,11 @@
"integrity": "sha1-2e8H3Od7mQK4o6j6SzHD4/fm6Ho=",
"dev": true
},
"which-pm-runs": {
"version": "1.0.0",
"resolved": "https://registry.npmjs.org/which-pm-runs/-/which-pm-runs-1.0.0.tgz",
"integrity": "sha1-Zws6+8VS4LVd9rd4DKdGFfI60cs="
},
"which-typed-array": {
"version": "1.1.2",
"resolved": "https://registry.npmjs.org/which-typed-array/-/which-typed-array-1.1.2.tgz",

2
package.json
View File

@ -27,10 +27,12 @@
"@tensorflow/tfjs": "^2.6.0",
"@tensorflow/tfjs-node": "^2.6.0",
"@types/mime-types": "^2.1.0",
"@types/sharp": "^0.26.1",
"@vladmandic/face-api": "^0.8.8",
"canvas": "^2.6.1",
"dotenv-extended": "^2.9.0",
"mime-types": "^2.1.27",
"sharp": "^0.26.3",
"tsyringe": "^4.4.0"
},
"devDependencies": {

74
scripts/detectFromImage.ts Normal file
View File

@ -0,0 +1,74 @@
import { nets } from "@vladmandic/face-api";
import * as faceapi from "@vladmandic/face-api";
import canvas from "canvas";
import fs from "fs";
import * as path from "path";
import dotenv from "dotenv-extended";
import { getFaceDetectorOptions } from "../src/common";
import * as mime from "mime-types";
require("@tensorflow/tfjs-node");
const { Canvas, Image, ImageData } = canvas;
//@ts-ignore
faceapi.env.monkeyPatch({ Canvas, Image, ImageData });
const main = async () => {
dotenv.load({
silent: false,
errorOnMissing: true,
});
const modelDir = process.env.TRAINED_MODEL_DIR as string;
const faceDetectionNet = nets.ssdMobilenetv1;
await faceDetectionNet.loadFromDisk(path.join(__dirname, "../weights"));
await nets.faceLandmark68Net.loadFromDisk(path.join(__dirname, "../weights"));
await nets.faceRecognitionNet.loadFromDisk(
path.join(__dirname, "../weights")
);
const raw = fs.readFileSync(path.join(modelDir, "data.json"), "utf-8");
const content = JSON.parse(raw);
const matcher = faceapi.FaceMatcher.fromJSON(content);
let dir = path.join(process.env.OUT_DIR as string);
const files = fs.readdirSync(dir);
await Promise.all(
files.map(async (file: string) => {
const mimeType = mime.contentType(path.extname(path.join(dir, file)));
if (!mimeType || !mimeType.startsWith("image")) {
return;
}
const imgData = fs.readFileSync(path.join(dir, file));
const input = ((await canvas.loadImage(imgData)) as unknown) as ImageData;
const out = faceapi.createCanvasFromMedia(input);
const resultsQuery = await faceapi
.detectAllFaces(out, getFaceDetectorOptions(faceDetectionNet))
.withFaceLandmarks()
.withFaceDescriptors();
if (resultsQuery.length > 0) {
for (const res of resultsQuery) {
const bestMatch = matcher.findBestMatch(res.descriptor);
console.log(
`Face Detected with ${
res.detection.score * 100
}% accuracy and a distance of ${bestMatch.distance}: ${
bestMatch.label
} in file ${path.join(dir, file)}`
);
}
} else {
console.log(
`No faces detected in file ${path.join(
process.env.REF_IMAGE_DIR as string,
"aline",
file
)}`
);
}
})
);
};
main();

18
src/common.ts
View File

@ -9,10 +9,22 @@ export const minConfidence = 0.4;
export const inputSize = 416;
export const scoreThreshold = 0.5;
export const getFaceDetectorOptions = (net: faceapi.NeuralNetwork<any>) => {
export const getFaceDetectorOptions = (
net: faceapi.NeuralNetwork<any>,
options?: {
confidence?: number;
inputSize?: number;
scoreThreshold?: number;
}
) => {
return net === faceapi.nets.ssdMobilenetv1
? new faceapi.SsdMobilenetv1Options({ minConfidence })
: new faceapi.TinyFaceDetectorOptions({ inputSize, scoreThreshold });
? new faceapi.SsdMobilenetv1Options({
minConfidence: options?.confidence ?? minConfidence,
})
: new faceapi.TinyFaceDetectorOptions({
inputSize: options?.inputSize ?? inputSize,
scoreThreshold: options?.scoreThreshold ?? scoreThreshold,
});
};
export const saveFile = async (

1
src/config.ts
View File

@ -12,6 +12,7 @@ export interface IConfig extends PlatformConfig {
debug?: boolean;
writeOutput?: boolean;
rate?: number;
confidence?: number;
}
export interface IRoom {

3
src/homeLocationPlatform.ts
View File

@ -87,7 +87,8 @@ export class HomeLocationPlatform implements DynamicPlatformPlugin {
if (this.config.trainOnStartup) {
const trainer = new Trainer(
this.config.refImageDirectory,
this.config.trainedModelDirectory
this.config.trainedModelDirectory,
this.config.confidence
);
faceMatcher = await trainer.train(true);
} else {

35
src/monitor/monitor.ts
View File

@ -16,6 +16,7 @@ import { Event } from "../events";
import { IConfig } from "../config";
import { MonitorState } from "./monitorState";
import { IStream } from "./stream";
import sharp from "sharp";
const { Canvas, Image, ImageData } = canvas;
const defaultWatchDog = 30000;
@ -143,20 +144,36 @@ export class Monitor {
this._config.watchdogTimeout ?? 30000
);
const regularizedImgData = await sharp(args.data)
.modulate({ brightness: 3 })
.sharpen()
.toBuffer();
//Detect faces in image
const input = ((await canvas.loadImage(args.data)) as unknown) as ImageData;
const input = ((await canvas.loadImage(
regularizedImgData
)) as unknown) as ImageData;
const out = faceapi.createCanvasFromMedia(input);
const resultsQuery = await faceapi
.detectAllFaces(out, getFaceDetectorOptions(this._faceDetectionNet))
.detectAllFaces(
out,
getFaceDetectorOptions(this._faceDetectionNet, {
confidence: this._config.confidence,
})
)
.withFaceLandmarks()
.withFaceDescriptors();
//Write to output image
if (this._config.writeOutput) {
await saveFile(this._config.outputDirectory, room + ".jpg", args.data);
await saveFile(
this._config.outputDirectory,
room + ".jpg",
regularizedImgData
);
}
for (const res of resultsQuery) {
const bestMatch = this._matcher.matchDescriptor(res.descriptor);
const bestMatch = this._matcher.findBestMatch(res.descriptor);
const old = this._state[bestMatch.label];
this._state[bestMatch.label] = room;
this._stateChangedEvent.fire(this, {
@ -165,9 +182,13 @@ export class Monitor {
label: bestMatch.label,
});
if (this._config.debug) {
this._logger.info(`Face Detected: ${bestMatch.label} in room ${room}`);
}
this._logger.info(
`Face Detected with ${
res.detection.score * 100
}% accuracy and a distance of ${bestMatch.distance}: ${
bestMatch.label
} in room ${room}`
);
}
};

1
src/rtsp/rtsp.ts
View File

@ -78,6 +78,7 @@ export class Rtsp {
const argStrings = [
`-rtsp_transport tcp`,
`-i ${this._connecteionString}`,
`-qscale:v 1`,
`-r ${this._options.rate ?? 10}`,
`-vf mpdecimate,setpts=N/FRAME_RATE/TB`,
this._options.image

32
src/trainer.ts
View File

@ -12,7 +12,11 @@ const { Canvas, Image, ImageData } = canvas;
faceapi.env.monkeyPatch({ Canvas, Image, ImageData });
export class Trainer {
constructor(private _refImageDir: string, private _trainedModelDir: string) {}
constructor(
private _refImageDir: string,
private _trainedModelDir: string,
private _confidence?: number
) {}
public async train(writeToDisk: boolean): Promise<faceapi.FaceMatcher> {
const faceDetectionNet = faceapi.nets.ssdMobilenetv1;
@ -24,18 +28,25 @@ export class Trainer {
path.join(__dirname, "../weights")
);
const options = getFaceDetectorOptions(faceDetectionNet);
const options = getFaceDetectorOptions(faceDetectionNet, {
confidence: this._confidence,
});
const dirs = fs.readdirSync(this._refImageDir);
const refs = [];
for (const dir of dirs) {
const descriptor = new LabeledFaceDescriptors(dir, []);
await this.getLabeledFaceDescriptorFromDir(
path.join(this._refImageDir, dir),
descriptor,
options
);
try {
await this.getLabeledFaceDescriptorFromDir(
path.join(this._refImageDir, dir),
descriptor,
options
);
} catch (err) {
console.log(err);
}
if (descriptor) {
refs.push(descriptor);
}
@ -93,8 +104,11 @@ export class Trainer {
labeldFaceDescriptors.descriptors.push(descriptor.descriptor);
} catch (err) {
throw new Error(
"An error occurred loading image at path: " + path.join(dir, file)
console.log(
"An error occurred loading image at " +
path.join(dir, file) +
": " +
err.message
);
}
})