egirlskey/packages/backend/src/services/detect-sensitive.ts

49 lines
1.3 KiB
TypeScript
Raw Normal View History

import * as fs from 'node:fs';
import { fileURLToPath } from 'node:url';
import { dirname } from 'node:path';
import * as nsfw from 'nsfwjs';
import si from 'systeminformation';
const _filename = fileURLToPath(import.meta.url);
const _dirname = dirname(_filename);
const REQUIRED_CPU_FLAGS = ['avx2', 'fma'];
let isSupportedCpu: undefined | boolean = undefined;
let model: nsfw.NSFWJS;
export async function detectSensitive(path: string): Promise<nsfw.predictionType[] | null> {
try {
if (isSupportedCpu === undefined) {
const cpuFlags = await getCpuFlags();
isSupportedCpu = REQUIRED_CPU_FLAGS.every(required => cpuFlags.includes(required));
}
if (!isSupportedCpu) {
console.error('This CPU cannot use TensorFlow.');
return null;
}
const tf = await import('@tensorflow/tfjs-node');
if (model == null) model = await nsfw.load(`file://${_dirname}/../../nsfw-model/`, { size: 299 });
const buffer = await fs.promises.readFile(path);
const image = await tf.node.decodeImage(buffer, 3) as any;
try {
const predictions = await model.classify(image);
return predictions;
} finally {
image.dispose();
}
} catch (err) {
console.error(err);
return null;
}
}
async function getCpuFlags(): Promise<string[]> {
const str = await si.cpuFlags();
return str.split(/\s+/);
}