Create index.js

index.js

@@ -0,0 +1,67 @@
+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))