mirror of https://github.com/vladmandic/human
66 lines
3.0 KiB
JavaScript
66 lines
3.0 KiB
JavaScript
/*
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Unsupported sample of using external utility fswebcam to capture screenshot from attached webcam in regular intervals and process it using Human
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*/
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const util = require('util');
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const log = require('@vladmandic/pilogger');
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const nodeWebCam = require('node-webcam');
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// for NodeJS, `tfjs-node` or `tfjs-node-gpu` should be loaded before using Human
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// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars
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const tf = require('@tensorflow/tfjs-node'); // or const tf = require('@tensorflow/tfjs-node-gpu');
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// load specific version of Human library that matches TensorFlow mode
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const Human = require('../dist/human.node.js').default; // or const Human = require('../dist/human.node-gpu.js').default;
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// options for node-webcam
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const optionsCamera = {
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callbackReturn: 'buffer', // this means whatever `fswebcam` writes to disk, no additional processing so it's fastest
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saveShots: false, // don't save processed frame to disk, note that temp file is still created by fswebcam thus recommendation for tmpfs
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};
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// options for human
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const optionsHuman = {
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backend: 'tensorflow',
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modelBasePath: 'file://node_modules/@vladmandic/human/models/',
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};
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const camera = nodeWebCam.create(optionsCamera);
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const capture = util.promisify(camera.capture);
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const human = new Human(optionsHuman);
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const results = [];
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const buffer2tensor = human.tf.tidy((buffer) => {
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const decode = human.tf.node.decodeImage(buffer, 3);
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let expand;
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if (decode.shape[2] === 4) { // input is in rgba format, need to convert to rgb
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const channels = human.tf.split(decode, 4, 2); // tf.split(tensor, 4, 2); // split rgba to channels
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const rgb = human.tf.stack([channels[0], channels[1], channels[2]], 2); // stack channels back to rgb and ignore alpha
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expand = human.tf.reshape(rgb, [1, decode.shape[0], decode.shape[1], 3]); // move extra dim from the end of tensor and use it as batch number instead
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} else {
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expand = human.tf.expandDims(decode, 0); // inpur ia rgb so use as-is
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}
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const cast = human.tf.cast(expand, 'float32');
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return cast;
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});
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async function process() {
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// trigger next frame every 5 sec
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// triggered here before actual capture and detection since we assume it will complete in less than 5sec
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// so it's as close as possible to real 5sec and not 5sec + detection time
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// if there is a chance of race scenario where detection takes longer than loop trigger, then trigger should be at the end of the function instead
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setTimeout(() => process(), 5000);
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const buffer = await capture(); // gets the (default) jpeg data from from webcam
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const tensor = buffer2tensor(buffer); // create tensor from image buffer
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const res = await human.detect(tensor); // run detection
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// do whatever here with the res
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// or just append it to results array that will contain all processed results over time
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results.push(res);
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// alternatively to triggering every 5sec sec, simply trigger next frame as fast as possible
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// setImmediate(() => process());
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}
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log.header();
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process();
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