Update drone

.drone.yml

@@ -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]

.env.schema

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

README.md

@@ -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

package-lock.json

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+          "version": "3.0.0",
+          "resolved": "https://registry.npmjs.org/array-flatten/-/array-flatten-3.0.0.tgz",
+          "integrity": "sha512-zPMVc3ZYlGLNk4mpK1NzP2wg0ml9t7fUgDsayR5Y5rSzxQilzR9FGu/EH2jQOcKSAeAfWeylyW8juy3OkWRvNA=="
+        },
+        "decompress-response": {
+          "version": "6.0.0",
+          "resolved": "https://registry.npmjs.org/decompress-response/-/decompress-response-6.0.0.tgz",
+          "integrity": "sha512-aW35yZM6Bb/4oJlZncMH2LCoZtJXTRxES17vE3hoRiowU2kWHaJKFkSBDnDR+cm9J+9QhXmREyIfv0pji9ejCQ==",
+          "requires": {
+            "mimic-response": "^3.1.0"
+          }
+        },
+        "mimic-response": {
+          "version": "3.1.0",
+          "resolved": "https://registry.npmjs.org/mimic-response/-/mimic-response-3.1.0.tgz",
+          "integrity": "sha512-z0yWI+4FDrrweS8Zmt4Ej5HdJmky15+L2e6Wgn3+iK5fWzb6T3fhNFq2+MeTRb064c6Wr4N/wv0DzQTjNzHNGQ=="
+        },
+        "semver": {
+          "version": "7.3.4",
+          "resolved": "https://registry.npmjs.org/semver/-/semver-7.3.4.tgz",
+          "integrity": "sha512-tCfb2WLjqFAtXn4KEdxIhalnRtoKFN7nAwj0B3ZXCbQloV2tq5eDbcTmT68JJD3nRJq24/XgxtQKFIpQdtvmVw==",
+          "requires": {
+            "lru-cache": "^6.0.0"
+          }
+        },
+        "simple-get": {
+          "version": "4.0.0",
+          "resolved": "https://registry.npmjs.org/simple-get/-/simple-get-4.0.0.tgz",
+          "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",

package.json

@@ -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": {

scripts/detectFromImage.ts

@@ -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();

scripts/train.ts

@@ -1,100 +1,16 @@
-import * as faceapi from "@vladmandic/face-api";
-import canvas from "canvas";
-import fs, { lstatSync } from "fs";
-import * as path from "path";
-import { LabeledFaceDescriptors, TNetInput } from "@vladmandic/face-api";
-import * as mime from "mime-types";
 import dotenv from "dotenv-extended";
-import { getFaceDetectorOptions } from "../src/common";
+import { Trainer } from "../src/trainer";
-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 inputDir = process.env.REF_IMAGE_DIR as string;
-  const outDir = process.env.TRAINED_MODEL_DIR as string;
 
-  const faceDetectionNet = faceapi.nets.ssdMobilenetv1;
+  const trainer = new Trainer(
-  await faceDetectionNet.loadFromDisk(path.join(__dirname, "../weights"));
+    process.env.REF_IMAGE_DIR as string,
-  await faceapi.nets.faceLandmark68Net.loadFromDisk(
+    process.env.TRAINED_MODEL_DIR as string
-    path.join(__dirname, "../weights")
-  );
-  await faceapi.nets.faceRecognitionNet.loadFromDisk(
-    path.join(__dirname, "../weights")
-  );
-
-  const options = getFaceDetectorOptions(faceDetectionNet);
-
-  const dirs = fs.readdirSync(inputDir);
-
-  const refs: Array<LabeledFaceDescriptors> = [];
-  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
-            .detectSingleFace(referenceImage as TNetInput, options)
-            .withFaceLandmarks()
-            .withFaceDescriptor();
-          if (!descriptor || !descriptor.descriptor) {
-            throw new Error("No face found");
-          }
-
-          const faceDescriptors = [descriptor.descriptor];
-          return new faceapi.LabeledFaceDescriptors(dir, faceDescriptors);
-        } catch (err) {
-          console.log(
-            "An error occurred loading image at path: " +
-              path.join(inputDir, dir, file)
-          );
-        }
-        return undefined;
-      })
-    );
-
-    if (referenceResults) {
-      refs.push(
-        ...(referenceResults.filter((e) => e) as LabeledFaceDescriptors[])
-      );
-    }
-  }
-
-  const faceMatcher = new faceapi.FaceMatcher(refs);
-
-  fs.writeFile(
-    path.join(outDir, "data.json"),
-    JSON.stringify(faceMatcher.toJSON()),
-    "utf8",
-    (err) => {
-      if (err) {
-        console.log(`An error occurred while writing data model to file`);
-      }
-
-      console.log(`Successfully wrote data model to file`);
-    }
   );
+  await trainer.train(true);
 };
 
 main();

src/common.ts

@@ -6,13 +6,25 @@ import fs from "fs";
 export const minConfidence = 0.4;
 
 // TinyFaceDetectorOptions
-export const inputSize = 408;
+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.SsdMobilenetv1Options({
-    : new faceapi.TinyFaceDetectorOptions({ inputSize, scoreThreshold });
+        minConfidence: options?.confidence ?? minConfidence,
+      })
+    : new faceapi.TinyFaceDetectorOptions({
+        inputSize: options?.inputSize ?? inputSize,
+        scoreThreshold: options?.scoreThreshold ?? scoreThreshold,
+      });
 };
 
 export const saveFile = async (

src/config.ts

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

src/homeLocationPlatform.ts

@@ -10,17 +10,12 @@ import {
 import { IConfig, isConfig } from "./config";
 import * as faceapi from "@vladmandic/face-api";
 import canvas from "canvas";
-import fs, { lstatSync } from "fs";
+import fs from "fs";
 import * as path from "path";
 import { nets } from "@vladmandic/face-api";
-import {
+import { FaceMatcher } from "@vladmandic/face-api";
-  LabeledFaceDescriptors,
-  TNetInput,
-  FaceMatcher,
-} from "@vladmandic/face-api";
-import * as mime from "mime-types";
 import { Monitor } from "./monitor/monitor";
-import { getFaceDetectorOptions } from "./common";
+import { Trainer } from "./trainer";
 require("@tensorflow/tfjs-node");
 
 const { Canvas, Image, ImageData } = canvas;
@@ -82,17 +77,21 @@ export class HomeLocationPlatform implements DynamicPlatformPlugin {
    * must not be registered again to prevent "duplicate UUID" errors.
    */
   public async discoverDevices() {
+    const faceDetectionNet = nets.ssdMobilenetv1;
+    await faceDetectionNet.loadFromDisk(this.config.weightDirectory);
+    await nets.faceLandmark68Net.loadFromDisk(this.config.weightDirectory);
+    await nets.faceRecognitionNet.loadFromDisk(this.config.weightDirectory);
+
     //Train facial recognition model
     let faceMatcher: FaceMatcher;
     if (this.config.trainOnStartup) {
-      faceMatcher = await this.trainModels();
+      const trainer = new Trainer(
+        this.config.refImageDirectory,
+        this.config.trainedModelDirectory,
+        this.config.confidence
+      );
+      faceMatcher = await trainer.train(true);
     } else {
-      const faceDetectionNet = nets.ssdMobilenetv1;
-
-      await faceDetectionNet.loadFromDisk(this.config.weightDirectory);
-      await nets.faceLandmark68Net.loadFromDisk(this.config.weightDirectory);
-      await nets.faceRecognitionNet.loadFromDisk(this.config.weightDirectory);
-
       const raw = fs.readFileSync(
         path.join(this.config.trainedModelDirectory, "data.json"),
         "utf-8"
@@ -142,88 +141,4 @@ export class HomeLocationPlatform implements DynamicPlatformPlugin {
       }
     }
   }
-
-  private async trainModels(): Promise<FaceMatcher> {
-    const faceDetectionNet = faceapi.nets.ssdMobilenetv1;
-    await faceDetectionNet.loadFromDisk(this.config.weightDirectory);
-    await faceapi.nets.faceLandmark68Net.loadFromDisk(
-      this.config.weightDirectory
-    );
-    await faceapi.nets.faceRecognitionNet.loadFromDisk(
-      this.config.weightDirectory
-    );
-
-    const options = getFaceDetectorOptions(faceDetectionNet);
-
-    const dirs = fs.readdirSync(this.config.refImageDirectory);
-
-    const refs: Array<LabeledFaceDescriptors> = [];
-    for (const dir of dirs) {
-      if (
-        !lstatSync(path.join(this.config.refImageDirectory, dir)).isDirectory()
-      ) {
-        continue;
-      }
-      const files = fs.readdirSync(
-        path.join(this.config.refImageDirectory, dir)
-      );
-      let referenceResults = await Promise.all(
-        files.map(async (file: string) => {
-          const mimeType = mime.contentType(
-            path.extname(path.join(this.config.refImageDirectory, dir, file))
-          );
-          if (!mimeType || !mimeType.startsWith("image")) {
-            return;
-          }
-          this.log.info(path.join(this.config.refImageDirectory, dir, file));
-
-          try {
-            const referenceImage = (await canvas.loadImage(
-              path.join(this.config.refImageDirectory, dir, file)
-            )) as unknown;
-
-            const descriptor = await faceapi
-              .detectSingleFace(referenceImage as TNetInput, options)
-              .withFaceLandmarks()
-              .withFaceDescriptor();
-            if (!descriptor || !descriptor.descriptor) {
-              throw new Error("No face found");
-            }
-
-            const faceDescriptors = [descriptor.descriptor];
-            return new faceapi.LabeledFaceDescriptors(dir, faceDescriptors);
-          } catch (err) {
-            this.log.info(
-              "An error occurred loading image at path: " +
-                path.join(this.config.refImageDirectory, dir, file)
-            );
-          }
-          return undefined;
-        })
-      );
-
-      if (referenceResults) {
-        refs.push(
-          ...(referenceResults.filter((e) => e) as LabeledFaceDescriptors[])
-        );
-      }
-    }
-
-    const faceMatcher = new faceapi.FaceMatcher(refs);
-
-    fs.writeFile(
-      path.join(this.config.trainedModelDirectory, "data.json"),
-      JSON.stringify(faceMatcher.toJSON()),
-      "utf8",
-      (err) => {
-        if (err) {
-          this.log.info(`An error occurred while writing data model to file`);
-        }
-
-        this.log.info(`Successfully wrote data model to file`);
-      }
-    );
-
-    return faceMatcher;
-  }
 }

src/monitor/monitor.ts

@@ -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(
-        this._logger.info(`Face Detected: ${bestMatch.label} in room ${room}`);
+        `Face Detected with ${
-      }
+          res.detection.score * 100
+        }% accuracy and a distance of ${bestMatch.distance}: ${
+          bestMatch.label
+        } in room ${room}`
+      );
     }
   };
 
@@ -208,7 +229,9 @@ export class Monitor {
 
   private onWatchdogTimeout = async (stream: IStream, roomName: string) => {
     this._logger.info(
-      `[${stream.connectionString}] Watchdog timeout: restarting stream`
+      `[${this.getRedactedConnectionString(
+        stream.connectionString
+      )}] Watchdog timeout: restarting stream`
     );
 
     //Close and remove old stream

src/rtsp/rtsp.ts

@@ -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

src/trainer.ts

@@ -0,0 +1,117 @@
+import * as faceapi from "@vladmandic/face-api";
+import canvas from "canvas";
+import fs, { lstatSync } from "fs";
+import * as path from "path";
+import { LabeledFaceDescriptors, TNetInput } from "@vladmandic/face-api";
+import * as mime from "mime-types";
+import { getFaceDetectorOptions } from "./common";
+require("@tensorflow/tfjs-node");
+
+const { Canvas, Image, ImageData } = canvas;
+//@ts-ignore
+faceapi.env.monkeyPatch({ Canvas, Image, ImageData });
+
+export class Trainer {
+  constructor(
+    private _refImageDir: string,
+    private _trainedModelDir: string,
+    private _confidence?: number
+  ) {}
+
+  public async train(writeToDisk: boolean): Promise<faceapi.FaceMatcher> {
+    const faceDetectionNet = faceapi.nets.ssdMobilenetv1;
+    await faceDetectionNet.loadFromDisk(path.join(__dirname, "../weights"));
+    await faceapi.nets.faceLandmark68Net.loadFromDisk(
+      path.join(__dirname, "../weights")
+    );
+    await faceapi.nets.faceRecognitionNet.loadFromDisk(
+      path.join(__dirname, "../weights")
+    );
+
+    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, []);
+      try {
+        await this.getLabeledFaceDescriptorFromDir(
+          path.join(this._refImageDir, dir),
+          descriptor,
+          options
+        );
+      } catch (err) {
+        console.log(err);
+      }
+
+      if (descriptor) {
+        refs.push(descriptor);
+      }
+    }
+
+    const faceMatcher = new faceapi.FaceMatcher(refs);
+
+    if (writeToDisk) {
+      fs.writeFile(
+        path.join(this._trainedModelDir, "data.json"),
+        JSON.stringify(faceMatcher.toJSON()),
+        "utf8",
+        (err) => {
+          if (err) {
+            console.log(`An error occurred while writing data model to file`);
+          }
+
+          console.log(`Successfully wrote data model to file`);
+        }
+      );
+    }
+
+    return faceMatcher;
+  }
+
+  private getLabeledFaceDescriptorFromDir = async (
+    dir: string,
+    labeldFaceDescriptors: LabeledFaceDescriptors,
+    options: faceapi.TinyFaceDetectorOptions | faceapi.SsdMobilenetv1Options
+  ): Promise<void> => {
+    if (!lstatSync(dir).isDirectory()) {
+      return;
+    }
+    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;
+        }
+        console.log(path.join(dir, file));
+
+        try {
+          const referenceImage = (await canvas.loadImage(
+            path.join(dir, file)
+          )) as unknown;
+
+          const descriptor = await faceapi
+            .detectSingleFace(referenceImage as TNetInput, options)
+            .withFaceLandmarks()
+            .withFaceDescriptor();
+          if (!descriptor || !descriptor.descriptor) {
+            throw new Error("No face found");
+          }
+
+          labeldFaceDescriptors.descriptors.push(descriptor.descriptor);
+        } catch (err) {
+          console.log(
+            "An error occurred loading image at " +
+              path.join(dir, file) +
+              ": " +
+              err.message
+          );
+        }
+      })
+    );
+  };
+}