32 lines
1.6 KiB
JavaScript
32 lines
1.6 KiB
JavaScript
const fs = require('fs');
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const tf = require('@tensorflow/tfjs-node');
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const image = require('@canvas/image'); // @canvas/image can decode jpeg, png, webp
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const faceapi = require('../dist/face-api.node.js'); // this is equivalent to '@vladmandic/faceapi'
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const modelPath = 'model/';
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const imageFile = 'demo/sample1.jpg';
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const ssdOptions = { minConfidence: 0.1, maxResults: 10 };
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async function main() {
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const buffer = fs.readFileSync(imageFile); // read image from disk
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const canvas = await image.imageFromBuffer(buffer); // decode to canvas
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const imageData = image.getImageData(canvas); // read decoded image data from canvas
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console.log('image:', imageFile, canvas.width, canvas.height);
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const tensor = tf.tidy(() => { // create tensor from image data
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const data = tf.tensor(Array.from(imageData.data), [canvas.height, canvas.width, 4], 'int32'); // create rgba image tensor from flat array and flip to height x width
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const channels = tf.split(data, 4, 2); // split rgba to channels
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const rgb = tf.stack([channels[0], channels[1], channels[2]], 2); // stack channels back to rgb
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const reshape = tf.reshape(rgb, [1, canvas.height, canvas.width, 3]); // move extra dim from the end of tensor and use it as batch number instead
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return reshape;
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});
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console.log('tensor:', tensor.shape, tensor.size);
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await faceapi.nets.ssdMobilenetv1.loadFromDisk(modelPath); // load basic model only
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const optionsSSDMobileNet = new faceapi.SsdMobilenetv1Options(ssdOptions); // create options object
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const result = await faceapi.detectAllFaces(tensor, optionsSSDMobileNet); // run detection
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console.log('results:', result);
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}
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main();
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