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human
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update node-webcam

pull/193/head
Vladimir Mandic 2021-08-13 18:47:37 -04:00
parent 7d3915cf2a
commit 26b3fa28cf
1 changed files with 42 additions and 39 deletions
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81
demo/nodejs/node-webcam.js
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@ -6,8 +6,7 @@
* Working version of fswebcam must be present on the system * Working version of fswebcam must be present on the system
*/ */
const util = require('util'); let initial = true; // remember if this is the first run to print additional details
const process = require('process');
const log = require('@vladmandic/pilogger'); const log = require('@vladmandic/pilogger');
// eslint-disable-next-line node/no-missing-require // eslint-disable-next-line node/no-missing-require
const nodeWebCam = require('node-webcam'); const nodeWebCam = require('node-webcam');
@ -18,6 +17,7 @@ const tf = require('@tensorflow/tfjs-node'); // or const tf = require('@tensorfl
const Human = require('../../dist/human.node.js').default; // or const Human = require('../dist/human.node-gpu.js').default; const Human = require('../../dist/human.node.js').default; // or const Human = require('../dist/human.node-gpu.js').default;
// options for node-webcam // options for node-webcam
const tempFile = 'webcam-snap'; // node-webcam requires writting snapshot to a file, recommended to use tmpfs to avoid excessive disk writes
const optionsCamera = { const optionsCamera = {
callbackReturn: 'buffer', // this means whatever `fswebcam` writes to disk, no additional processing so it's fastest 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 saveShots: false, // don't save processed frame to disk, note that temp file is still created by fswebcam thus recommendation for tmpfs
@ -31,35 +31,23 @@ const optionsHuman = {
}; };
const human = new Human(optionsHuman); const human = new Human(optionsHuman);
const results = []; function buffer2tensor(buffer) {
const list = util.promisify(camera.list); return human.tf.tidy(() => {
const capture = util.promisify(camera.capture); if (!buffer) return null;
const decode = human.tf.node.decodeImage(buffer, 3);
async function init() { let expand;
try { if (decode.shape[2] === 4) { // input is in rgba format, need to convert to rgb
const found = await list(); const channels = human.tf.split(decode, 4, 2); // tf.split(tensor, 4, 2); // split rgba to channels
log.data('Camera data:', found); const rgb = human.tf.stack([channels[0], channels[1], channels[2]], 2); // stack channels back to rgb and ignore alpha
} catch { 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
log.error('Could not access camera'); } else {
process.exit(1); expand = human.tf.expandDims(decode, 0); // inpur ia rgb so use as-is
} }
const cast = human.tf.cast(expand, 'float32');
return cast;
});
} }
const buffer2tensor = human.tf.tidy((buffer) => {
if (!buffer) return null;
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 detect() { async function detect() {
// trigger next frame every 5 sec // trigger next frame every 5 sec
// triggered here before actual capture and detection since we assume it will complete in less than 5sec // triggered here before actual capture and detection since we assume it will complete in less than 5sec
@ -67,21 +55,36 @@ async function detect() {
// 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 // 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(() => detect(), 5000); setTimeout(() => detect(), 5000);
const buffer = await capture(); // gets the (default) jpeg data from from webcam camera.capture(tempFile, (err, data) => { // gets the (default) jpeg data from from webcam
const tensor = buffer2tensor(buffer); // create tensor from image buffer if (err) {
if (tensor) { log.error('error capturing webcam:', err);
const res = await human.detect(tensor); // run detection } else {
const tensor = buffer2tensor(data); // create tensor from image buffer
// do whatever here with the res if (initial) log.data('input tensor:', tensor.shape);
// or just append it to results array that will contain all processed results over time // eslint-disable-next-line promise/no-promise-in-callback
results.push(res); human.detect(tensor).then((result) => {
} if (result && result.face && result.face.length > 0) {
for (let i = 0; i < result.face.length; i++) {
const face = result.face[i];
const emotion = face.emotion.reduce((prev, curr) => (prev.score > curr.score ? prev : curr));
log.data(`detected face: #${i} boxScore:${face.boxScore} faceScore:${face.faceScore} age:${face.age} genderScore:${face.genderScore} gender:${face.gender} emotionScore:${emotion.score} emotion:${emotion.emotion} iris:${face.iris}`);
}
} else {
log.data(' Face: N/A');
}
});
}
initial = false;
});
// alternatively to triggering every 5sec sec, simply trigger next frame as fast as possible // alternatively to triggering every 5sec sec, simply trigger next frame as fast as possible
// setImmediate(() => process()); // setImmediate(() => process());
} }
async function main() { async function main() {
await init(); camera.list((list) => {
log.data('detected camera:', list);
});
await human.load();
detect(); detect();
} }