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Successfully training models based on a collection of images

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watsonb8 2020-11-08 20:57:57 -05:00
parent e1902a362e
commit fda68e7144
7 changed files with 176 additions and 113 deletions
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2
.env.schema Normal file
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@ -0,0 +1,2 @@
REF_IMAGE_DIR=
TRAINED_MODEL_DIR=

1
.gitignore vendored
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@ -110,3 +110,4 @@ dist
images/*
bin
out
trainedModels/*

4
.vscode/tasks.json vendored
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@ -7,9 +7,7 @@
"label": "build",
"type": "typescript",
"tsconfig": "tsconfig.json",
"problemMatcher": [
"$tsc"
]
"problemMatcher": ["$tsc"]
}
]
}

130
package-lock.json generated
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@ -92,6 +92,11 @@
"tar": "^4.4.6"
}
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@ -390,6 +403,16 @@
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@ -459,40 +498,6 @@
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},
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"requires": {
"@tensorflow/tfjs-core": "1.7.0",
"tslib": "^1.11.1"
},
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"requires": {
"@types/offscreencanvas": "~2019.3.0",
"@types/seedrandom": "2.4.27",
"@types/webgl-ext": "0.0.30",
"@types/webgl2": "0.0.4",
"node-fetch": "~2.1.2",
"seedrandom": "2.4.3"
}
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"find-up": {
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@ -525,6 +530,14 @@
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@ -803,6 +821,11 @@
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@ -914,6 +937,11 @@
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@ -1020,6 +1048,19 @@
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@ -1133,16 +1174,19 @@
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8
package.json
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@ -5,8 +5,9 @@
"main": "index.ts",
"scripts": {
"test": "echo \"Error: no test specified\" && exit 1",
"build": "tsc --build && npm run copy-files",
"copy-files": "copyfiles -u 1 resources/* bin/resources"
"build": "npm run copy-files && tsc --build",
"copy-files": "copyfiles -u 1 -s weights/* bin/weights",
"train": "npx ts-node ./scripts/train.ts"
},
"repository": {
"type": "git",
@ -21,9 +22,12 @@
"dependencies": {
"@tensorflow/tfjs": "^2.6.0",
"@tensorflow/tfjs-node": "^2.6.0",
"@types/mime-types": "^2.1.0",
"@vladmandic/face-api": "^0.8.8",
"canvas": "^2.6.1",
"copyfiles": "^2.4.0",
"dotenv-extended": "^2.9.0",
"mime-types": "^2.1.27",
"rtsp-stream": "file:../rtsp-stream",
"ts-node": "^9.0.0",
"typescript": "^4.0.5"

105
scripts/train.ts
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@ -1,20 +1,24 @@
import * as faceapi from "@vladmandic/face-api";
import canvas from "canvas";
import fs from "fs";
import fs, { lstatSync } from "fs";
import * as path from "path";
import { TNetInput } from "@vladmandic/face-api";
import { LabeledFaceDescriptors, TNetInput } from "@vladmandic/face-api";
import * as mime from "mime-types";
import dotenv from "dotenv-extended";
require("@tensorflow/tfjs-node");
const { Canvas, Image, ImageData } = canvas;
//@ts-ignore
faceapi.env.monkeyPatch({ Canvas, Image, ImageData });
const REFERENCE_IMAGE =
"/Users/brandonwatson/Documents/Git/Gitea/homebridge-face-location/images/brandon/IMG_1958.jpg";
const QUERY_IMAGE =
"/Users/brandonwatson/Documents/Git/Gitea/homebridge-face-location/images/brandon/IMG_0001.JPG";
const main = async () => {
dotenv.load({
silent: false,
errorOnMissing: true,
});
const inputDir = process.env.REF_IMAGE_DIR as string;
const outDir = process.env.TRAINED_MODEL_DIR as string;
const faceDetectionNet = faceapi.nets.ssdMobilenetv1;
await faceDetectionNet.loadFromDisk(path.join(__dirname, "../weights"));
await faceapi.nets.faceLandmark68Net.loadFromDisk(
@ -24,41 +28,66 @@ const main = async () => {
path.join(__dirname, "../weights")
);
const referenceImage = (await canvas.loadImage(REFERENCE_IMAGE)) as unknown;
const queryImage = (await canvas.loadImage(QUERY_IMAGE)) as unknown;
const options = getFaceDetectorOptions(faceDetectionNet);
const resultsRef = await faceapi
const dirs = fs.readdirSync(inputDir);
for (const dir of dirs) {
if (!lstatSync(path.join(inputDir, dir)).isDirectory()) {
continue;
}
const files = fs.readdirSync(path.join(inputDir, dir));
let referenceResults = await Promise.all(
files.map(async (file: string) => {
const mimeType = mime.contentType(
path.extname(path.join(inputDir, dir, file))
);
if (!mimeType || !mimeType.startsWith("image")) {
return;
}
console.log(path.join(inputDir, dir, file));
try {
const referenceImage = (await canvas.loadImage(
path.join(inputDir, dir, file)
)) as unknown;
const descriptor = await faceapi
.detectAllFaces(referenceImage as TNetInput, options)
.withFaceLandmarks()
.withFaceDescriptors();
const resultsQuery = await faceapi
.detectAllFaces(queryImage as TNetInput, options)
.withFaceLandmarks()
.withFaceDescriptors();
return descriptor.length > 0 ? descriptor : undefined;
} catch (err) {
console.log(
"An error occurred loading image at path: " +
path.join(inputDir, dir, file)
);
}
return undefined;
})
);
const faceMatcher = new faceapi.FaceMatcher(resultsRef);
const items = [];
for (const item of referenceResults) {
if (item) {
items.push(...item);
}
}
const faceMatcher = new faceapi.FaceMatcher(items);
fs.writeFile(
path.join(outDir, dir + ".json"),
JSON.stringify(faceMatcher.toJSON()),
"utf8",
(err) => {
if (err) {
console.log(`An error occurred while writing ${dir} model to file`);
}
const labels = faceMatcher.labeledDescriptors.map((ld) => ld.label);
const refDrawBoxes = resultsRef
.map((res) => res.detection.box)
.map((box, i) => new faceapi.draw.DrawBox(box, { label: labels[i] }));
const outRef = faceapi.createCanvasFromMedia(referenceImage as ImageData);
refDrawBoxes.forEach((drawBox) => drawBox.draw(outRef));
saveFile("referenceImage.jpg", (outRef as any).toBuffer("image/jpeg"));
const queryDrawBoxes = resultsQuery.map((res) => {
const bestMatch = faceMatcher.findBestMatch(res.descriptor);
return new faceapi.draw.DrawBox(res.detection.box, {
label: bestMatch.toString(),
});
});
const outQuery = faceapi.createCanvasFromMedia(queryImage as ImageData);
queryDrawBoxes.forEach((drawBox) => drawBox.draw(outQuery));
saveFile("queryImage.jpg", (outQuery as any).toBuffer("image/jpeg"));
console.log("done, saved results to out/queryImage.jpg");
console.log(`Successfully wrote ${dir} model to file`);
}
);
}
};
// SsdMobilenetv1Options
@ -76,12 +105,4 @@ function getFaceDetectorOptions(net: faceapi.NeuralNetwork<any>) {
const baseDir = path.resolve(__dirname, "../out");
function saveFile(fileName: string, buf: Buffer) {
if (!fs.existsSync(baseDir)) {
fs.mkdirSync(baseDir);
}
fs.writeFileSync(path.resolve(baseDir, fileName), buf);
}
main();

15
src/index.ts
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@ -1,26 +1,11 @@
import { Rtsp } from "rtsp-stream/lib";
// import nodejs bindings to native tensorflow,
// not required, but will speed up things drastically (python required)
import * as faceapi from "face-api.js";
// implements nodejs wrappers for HTMLCanvasElement, HTMLImageElement, ImageData
const canvas = require("canvas");
// patch nodejs environment, we need to provide an implementation of
// HTMLCanvasElement and HTMLImageElement
const { Canvas, Image, ImageData } = canvas;
faceapi.env.monkeyPatch({ Canvas, Image, ImageData });
const main = async () => {
const rtsp = new Rtsp("rtsp://brandon:asdf1234@192.168.1.229/live", {
rate: 10,
});
await faceapi.nets.ssdMobilenetv1.loadFromDisk("./resources");
rtsp.on("data", async (data: Buffer) => {
const input = await canvas.loadImage(data);
const detections = await faceapi.detectAllFaces(input);
console.log();
process.exit(0);
});