index.js

const express = require('express')
const multer = require('multer')
const jpeg = require('jpeg-js')

const tf = require('@tensorflow/tfjs-node')
const nsfw = require('nsfwjs')

const app = express()
const sharp = require('sharp');
const upload = multer({
  fileFilter: function (req, file, cb) {
    if (
      file.mimetype !== 'image/png' &&
      file.mimetype !== 'image/jpg' &&
      file.mimetype !== 'image/jpeg'
    ) {
      cb(null, false);
    } else {
      cb(null, true);
    }
  }
});

let _model

const convert = async (img) => {
  // Decoded image in UInt8 Byte array
  const image = await jpeg.decode(img, true)

  const numChannels = 3
  const numPixels = image.width * image.height
  const values = new Int32Array(numPixels * numChannels)

  for (let i = 0; i < numPixels; i++)
    for (let c = 0; c < numChannels; ++c)
      values[i * numChannels + c] = image.data[i * 4 + c]

  return tf.tensor3d(values, [image.height, image.width, numChannels], 'int32')
}

app.post('/api', upload.single('image'), async (req, res) => {
  if (!req.file) res.status(400).send('Missing image multipart/form-data')
  else {
    try {
      const data = await sharp(req.file.buffer)
      .jpeg()
      .toBuffer()
      const image = await convert(data)
      const predictions = await _model.classify(image)
      image.dispose()
      res.json(predictions)
    } catch (error) {
      res.json(400).send('错误请求！');
    }
  }
})

app.get('/index', async (req, res)=> {
  res.send('23124124');
})

const load_model = async () => {
  _model = await nsfw.load()
}

// Keep the model in memory, make sure it's loaded only once
load_model().then(() => app.listen(3027))