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