8d06a6475e
* chore: 著作権とライセンスについての情報を各ファイルに追加する * chore: Add the SPDX information to each file Add copyright and licensing information as defined in version 3.0 of the REUSE Specification. * tweak format --------- Co-authored-by: syuilo <Syuilotan@yahoo.co.jp> * chore: Add SPDX-License-Identifier [skip ci] * add missing SPDX-License-Identifier * remove unused file --------- Co-authored-by: Shun Sakai <sorairolake@protonmail.ch> Co-authored-by: syuilo <Syuilotan@yahoo.co.jp> Co-authored-by: Chocolate Pie <106949016+chocolate-pie@users.noreply.github.com>
77 lines
1.9 KiB
TypeScript
77 lines
1.9 KiB
TypeScript
/*
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* SPDX-FileCopyrightText: syuilo and other misskey contributors
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* SPDX-License-Identifier: AGPL-3.0-only
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*/
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import * as fs from 'node:fs';
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import { fileURLToPath } from 'node:url';
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import { dirname } from 'node:path';
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import { Inject, Injectable } from '@nestjs/common';
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import * as nsfw from 'nsfwjs';
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import si from 'systeminformation';
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import { Mutex } from 'async-mutex';
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import type { Config } from '@/config.js';
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import { DI } from '@/di-symbols.js';
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import { bindThis } from '@/decorators.js';
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const _filename = fileURLToPath(import.meta.url);
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const _dirname = dirname(_filename);
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const REQUIRED_CPU_FLAGS = ['avx2', 'fma'];
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let isSupportedCpu: undefined | boolean = undefined;
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@Injectable()
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export class AiService {
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private model: nsfw.NSFWJS;
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private modelLoadMutex: Mutex = new Mutex();
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constructor(
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@Inject(DI.config)
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private config: Config,
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) {
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}
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@bindThis
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public async detectSensitive(path: string): Promise<nsfw.predictionType[] | null> {
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try {
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if (isSupportedCpu === undefined) {
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const cpuFlags = await this.getCpuFlags();
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isSupportedCpu = REQUIRED_CPU_FLAGS.every(required => cpuFlags.includes(required));
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}
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if (!isSupportedCpu) {
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console.error('This CPU cannot use TensorFlow.');
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return null;
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}
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const tf = await import('@tensorflow/tfjs-node');
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if (this.model == null) {
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await this.modelLoadMutex.runExclusive(async () => {
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if (this.model == null) {
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this.model = await nsfw.load(`file://${_dirname}/../../nsfw-model/`, { size: 299 });
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}
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});
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}
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const buffer = await fs.promises.readFile(path);
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const image = await tf.node.decodeImage(buffer, 3) as any;
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try {
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const predictions = await this.model.classify(image);
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return predictions;
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} finally {
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image.dispose();
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}
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} catch (err) {
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console.error(err);
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return null;
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}
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}
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@bindThis
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private async getCpuFlags(): Promise<string[]> {
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const str = await si.cpuFlags();
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return str.split(/\s+/);
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}
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}
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