4 Commits

Author SHA1 Message Date
c75e6d341d Update drone
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continuous-integration/drone/push Build is failing
2022-09-06 08:53:38 -05:00
1ea24b222e Adding drone yml
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continuous-integration/drone/push Build is failing
2021-06-02 22:09:58 -04:00
5722c40429 Adding Readme 2020-12-20 11:35:39 -05:00
a57b576962 Wip 2020-12-19 17:09:39 -05:00
12 changed files with 559 additions and 21 deletions

41
.drone.yml Normal file
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@ -0,0 +1,41 @@
kind: pipeline
type: docker
name: default
clone:
disable: true
steps:
- name: clone
image: alpine/git
commands:
- git clone https://gitea.watsonlabs.net/watsonb8/homebridge-face-location.git .
- git checkout $DRONE_COMMIT
- name: build
image: node
commands:
- npm install
- npm run build
- name: publish
image: plugins/npm:1.0.0
settings:
username: admin
password:
from_secret: npm_password
email: brandon@watsonlabs.net
registry: "http://linuxhost.me:4873/"
when:
event:
- tag
notify:
image: drillster/drone-email
host: smtp.watsonlabs.net
username: srvGitea
password:
from_secret: smtp_password
from: drone@watsonlabs.net
when:
status: [failure]

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@ -1,3 +1,4 @@
REF_IMAGE_DIR=
TRAINED_MODEL_DIR=
OUT_DIR=
CONFIDENCE=

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@ -1,2 +1,77 @@
# homebridge-face-location
Homebridge plugin providing real time location tracking via facial recognition.
## Installation
1. Download FFMPEG [here](https://ffmpeg.org/download.html)
> Note: FFMPEG is required in order to run homebridge-face-location
1. Clone the repository by running `git clone ssh://git@thebword.ddns.net:3122/watsonb8/homebridge-face-location.git`
1. Run `npm install` to install required modules
1. Run `npm run build` to build the module
1. Run `npm link` to link this instance to your global homebridge instance
## Configuration
```
{
"platform": "HomeLocation",
"name": "HomeLocation",
"refImageDirectory": "/path/to/reference/images/",
"trainedModelDirectory": "/path/to/trainedModel/data.json/",
"weightDirectory": "/path/to/trained/weights/",
"outputDirectory": "/path/to/desired/output/image/directory",
"trainOnStartup": false,
"rate": 1,
"detectionTimeout": 90000,
"watchdogTimeout": 10000,
"debug": false,
"writeOutput": true,
"rooms": [
{
"name": "Kitchen",
"rtspConnectionStrings": [
"rtsp://username:password@example.com"
]
},
{
"name": "LivingRoom",
"rtspConnectionStrings": [
"rtsp://username:password@example.com"
]
}
]
}
```
#### Platform
- `refImageDirectory`: The location of the images used to train the facial recognition matcher
> NOTE: This directory should only contain sub directories labeled with the name of the person to be matched with their corresponding images in the labeled folder. Any labels found in this directory will be used in face tracking
- `trainedModelDirectory`: The location of the trained `data.json` file if it exists. This is also the location where training data will be placed if `trainOnStartup` is true
- `weightDirectory`: The location of the pre-trained weight files used for face detection
- `outputDirectory`: The directory to be used if debug output images are wanted
- `trainOnStartup`: If true, a trained model will be created and persisted in the `trainedModelDirectory` during startup
- `rate`: The rate at which collect images from rtsp stream in fps
- `detectionTimeout`: The number of milliseconds to wait after a person is detected before returing to the undetected state
- `watchdogTimeout`: The number of milliseconds to wait before restarting the rtsp stream when a new image has not been recieved
- `debug`: If true, additional debug logging is displayed in the console
- `writeOutput`: If true, an image will be written to disk for every frame received from the rtsp stream
#### Rooms
- `name`: The name of the room in which to detect
- `rtspConnectionStrings`: A list of camera rtsp connection strings to associate with the room

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"integrity": "sha512-ZalZGexYr3TA0SwySsr5HlgOOinS4Jsa8YB2GJ6lUNAazyAu4KG/VmzMTwAt2YVXzzVj8QmefmAonZIK2BSGcQ==",
"requires": {
"decompress-response": "^6.0.0",
"once": "^1.3.1",
"simple-concat": "^1.0.0"
}
}
}
},
"shebang-command": {
"version": "2.0.0",
"resolved": "https://registry.npmjs.org/shebang-command/-/shebang-command-2.0.0.tgz",
@ -1602,6 +1841,14 @@
"simple-concat": "^1.0.0"
}
},
"simple-swizzle": {
"version": "0.2.2",
"resolved": "https://registry.npmjs.org/simple-swizzle/-/simple-swizzle-0.2.2.tgz",
"integrity": "sha1-pNprY1/8zMoz9w0Xy5JZLeleVXo=",
"requires": {
"is-arrayish": "^0.3.1"
}
},
"source-map": {
"version": "0.6.1",
"resolved": "https://registry.npmjs.org/source-map/-/source-map-0.6.1.tgz",
@ -1696,6 +1943,41 @@
"yallist": "^3.0.3"
}
},
"tar-fs": {
"version": "2.1.1",
"resolved": "https://registry.npmjs.org/tar-fs/-/tar-fs-2.1.1.tgz",
"integrity": "sha512-V0r2Y9scmbDRLCNex/+hYzvp/zyYjvFbHPNgVTKfQvVrb6guiE/fxP+XblDNR011utopbkex2nM4dHNV6GDsng==",
"requires": {
"chownr": "^1.1.1",
"mkdirp-classic": "^0.5.2",
"pump": "^3.0.0",
"tar-stream": "^2.1.4"
}
},
"tar-stream": {
"version": "2.1.4",
"resolved": "https://registry.npmjs.org/tar-stream/-/tar-stream-2.1.4.tgz",
"integrity": "sha512-o3pS2zlG4gxr67GmFYBLlq+dM8gyRGUOvsrHclSkvtVtQbjV0s/+ZE8OpICbaj8clrX3tjeHngYGP7rweaBnuw==",
"requires": {
"bl": "^4.0.3",
"end-of-stream": "^1.4.1",
"fs-constants": "^1.0.0",
"inherits": "^2.0.3",
"readable-stream": "^3.1.1"
},
"dependencies": {
"readable-stream": {
"version": "3.6.0",
"resolved": "https://registry.npmjs.org/readable-stream/-/readable-stream-3.6.0.tgz",
"integrity": "sha512-BViHy7LKeTz4oNnkcLJ+lVSL6vpiFeX6/d3oSH8zCW7UxP2onchk+vTGB143xuFjHS3deTgkKoXXymXqymiIdA==",
"requires": {
"inherits": "^2.0.3",
"string_decoder": "^1.1.1",
"util-deprecate": "^1.0.1"
}
}
}
},
"through2": {
"version": "2.0.5",
"resolved": "https://registry.npmjs.org/through2/-/through2-2.0.5.tgz",
@ -1738,6 +2020,14 @@
"tslib": "^1.9.3"
}
},
"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",

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": {

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();

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 (

View File

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

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 {

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}`
);
}
};

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

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
);
}
})