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* Update README.md * More info * Runnable code * typos * Add link * typo
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Original file line number | Diff line number | Diff line change |
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const express = require('express') | ||
const multer = require('multer') | ||
const jpeg = require('jpeg-js') | ||
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const tf = require('@tensorflow/tfjs-node') | ||
const nsfw = require('../../dist') | ||
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const app = express() | ||
const upload = multer() | ||
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let _model | ||
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const convert = async (img) => { | ||
// Decoded image in UInt8 Byte array | ||
const image = await jpeg.decode(img, true) | ||
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const numChannels = 3 | ||
const numPixels = image.width * image.height | ||
const values = new Int32Array(numPixels * numChannels) | ||
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for (let i = 0; i < numPixels; i++) | ||
for (let c = 0; c < numChannels; ++c) | ||
values[i * numChannels + c] = image.data[i * 4 + c] | ||
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return tf.tensor3d(values, [image.height, image.width, numChannels], 'int32') | ||
} | ||
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app.post('/nsfw', upload.single("image"), async (req, res) => { | ||
if (!req.file) | ||
res.status(400).send("Missing image multipart/form-data") | ||
else { | ||
const image = await convert(req.file.buffer) | ||
const predictions = await _model.classify(image) | ||
res.json(predictions) | ||
} | ||
}) | ||
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const load_model = async () => { | ||
_model = await nsfw.load() | ||
} | ||
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// Keep the model in memory, make sure it's loaded only once | ||
load_model().then(() => app.listen(8080)) |