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Remove unused code that required tensorflow

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This commit is contained in:
Manav Rathi 2024-04-11 12:29:06 +05:30
parent da5edc610f
commit 485a1e0be4
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1 changed files with 2 additions and 91 deletions
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93
web/apps/photos/src/services/machineLearning/mobileFaceNetEmbeddingService.ts
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@ -1,11 +1,7 @@
import log from "@/next/log";
import * as tf from "@tensorflow/tfjs-core";
import {
MOBILEFACENET_EMBEDDING_SIZE,
MOBILEFACENET_FACE_SIZE,
} from "constants/mlConfig";
// import { TFLiteModel } from "@tensorflow/tfjs-tflite";
// import PQueue from "p-queue";
import {
FaceEmbedding,
FaceEmbeddingMethod,
@ -18,12 +14,6 @@ import {
// import { env } from "onnxruntime-web";
const ort: any = {};
import {
clamp,
getPixelBilinear,
normalizePixelBetweenMinus1And1,
} from "utils/image";
// TODO(MR): onnx-yolo
// env.wasm.wasmPaths = "/js/onnx/";
class MobileFaceNetEmbeddingService implements FaceEmbeddingService {
@ -33,12 +23,12 @@ class MobileFaceNetEmbeddingService implements FaceEmbeddingService {
public method: Versioned<FaceEmbeddingMethod>;
public faceSize: number;
public constructor(faceSize: number = MOBILEFACENET_FACE_SIZE) {
public constructor() {
this.method = {
value: "MobileFaceNet",
version: 2,
};
this.faceSize = faceSize;
this.faceSize = MOBILEFACENET_FACE_SIZE;
// TODO: set timeout
}
@ -73,85 +63,6 @@ class MobileFaceNetEmbeddingService implements FaceEmbeddingService {
return this.onnxInferenceSession;
}
private preprocessImageBitmapToFloat32(
imageBitmap: ImageBitmap,
requiredWidth: number = this.faceSize,
requiredHeight: number = this.faceSize,
maintainAspectRatio: boolean = true,
normFunction: (
pixelValue: number,
) => number = normalizePixelBetweenMinus1And1,
) {
// Create an OffscreenCanvas and set its size
const offscreenCanvas = new OffscreenCanvas(
imageBitmap.width,
imageBitmap.height,
);
const ctx = offscreenCanvas.getContext("2d");
ctx.drawImage(imageBitmap, 0, 0, imageBitmap.width, imageBitmap.height);
const imageData = ctx.getImageData(
0,
0,
imageBitmap.width,
imageBitmap.height,
);
const pixelData = imageData.data;
let scaleW = requiredWidth / imageBitmap.width;
let scaleH = requiredHeight / imageBitmap.height;
if (maintainAspectRatio) {
const scale = Math.min(
requiredWidth / imageBitmap.width,
requiredHeight / imageBitmap.height,
);
scaleW = scale;
scaleH = scale;
}
const scaledWidth = clamp(
Math.round(imageBitmap.width * scaleW),
0,
requiredWidth,
);
const scaledHeight = clamp(
Math.round(imageBitmap.height * scaleH),
0,
requiredHeight,
);
const processedImage = new Float32Array(
1 * requiredWidth * requiredHeight * 3,
);
log.info("loaded mobileFaceNetModel: ", tf.getBackend());
// Populate the Float32Array with normalized pixel values
for (let h = 0; h < requiredHeight; h++) {
for (let w = 0; w < requiredWidth; w++) {
let pixel: {
r: number;
g: number;
b: number;
};
if (w >= scaledWidth || h >= scaledHeight) {
pixel = { r: 114, g: 114, b: 114 };
} else {
pixel = getPixelBilinear(
w / scaleW,
h / scaleH,
pixelData,
imageBitmap.width,
imageBitmap.height,
);
}
const pixelIndex = 3 * (h * requiredWidth + w);
processedImage[pixelIndex] = normFunction(pixel.r);
processedImage[pixelIndex + 1] = normFunction(pixel.g);
processedImage[pixelIndex + 2] = normFunction(pixel.b);
}
}
return processedImage;
}
public async getFaceEmbeddings(
faceData: Float32Array,
): Promise<Array<FaceEmbedding>> {