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Using TensorFlow.js with MobileNet models for image classification on Node.js
{
"name": "tf-js",
"version": "1.0.0",
"main": "script.js",
"license": "MIT",
"dependencies": {
"@tensorflow-models/mobilenet": "^0.2.2",
"@tensorflow/tfjs": "^0.12.3",
"@tensorflow/tfjs-node": "^0.1.9",
"jpeg-js": "^0.3.4"
}
}
const tf = require('@tensorflow/tfjs')
const mobilenet = require('@tensorflow-models/mobilenet');
require('@tensorflow/tfjs-node')
const fs = require('fs');
const jpeg = require('jpeg-js');
const NUMBER_OF_CHANNELS = 3
const readImage = path => {
const buf = fs.readFileSync(path)
const pixels = jpeg.decode(buf, true)
return pixels
}
const imageByteArray = (image, numChannels) => {
const pixels = image.data
const numPixels = image.width * image.height;
const values = new Int32Array(numPixels * numChannels);
for (let i = 0; i < numPixels; i++) {
for (let channel = 0; channel < numChannels; ++channel) {
values[i * numChannels + channel] = pixels[i * 4 + channel];
}
}
return values
}
const imageToInput = (image, numChannels) => {
const values = imageByteArray(image, numChannels)
const outShape = [image.height, image.width, numChannels];
const input = tf.tensor3d(values, outShape, 'int32');
return input
}
const loadModel = async path => {
const mn = new mobilenet.MobileNet(1, 1);
mn.path = `file://${path}`
await mn.load()
return mn
}
const classify = async (model, path) => {
const image = readImage(path)
const input = imageToInput(image, NUMBER_OF_CHANNELS)
const mn_model = await loadModel(model)
const predictions = await mn_model.classify(input)
console.log('classification results:', predictions)
}
if (process.argv.length !== 4) throw new Error('incorrect arguments: node script.js <MODEL> <IMAGE_FILE>')
classify(process.argv[2], process.argv[3])
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