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human
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Code Issues Packages Projects Releases Wiki Activity

fix serious performance bug around skipframes

pull/50/head
Vladimir Mandic 2020-11-17 17:42:44 -05:00
parent 3c0dde0d6b
commit 0b7bc69421
30 changed files with 3436 additions and 3240 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
demo/browser.js
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@ -331,7 +331,7 @@ async function processImage(input) {
// just initialize everything and call main function // just initialize everything and call main function
async function detectVideo() { async function detectVideo() {
human.config.videoOptimized = true; userConfig.videoOptimized = true;
document.getElementById('samples-container').style.display = 'none'; document.getElementById('samples-container').style.display = 'none';
document.getElementById('canvas').style.display = 'block'; document.getElementById('canvas').style.display = 'block';
const video = document.getElementById('video'); const video = document.getElementById('video');
@ -353,7 +353,7 @@ async function detectVideo() {
// just initialize everything and call main function // just initialize everything and call main function
async function detectSampleImages() { async function detectSampleImages() {
document.getElementById('play').style.display = 'none'; document.getElementById('play').style.display = 'none';
human.config.videoOptimized = false; userConfig.videoOptimized = false;
const size = 12 + Math.trunc(12 * ui.columns * window.innerWidth / document.body.clientWidth); const size = 12 + Math.trunc(12 * ui.columns * window.innerWidth / document.body.clientWidth);
ui.baseFont = ui.baseFontProto.replace(/{size}/, `${size}px`); ui.baseFont = ui.baseFontProto.replace(/{size}/, `${size}px`);
ui.baseLineHeight = ui.baseLineHeightProto * ui.columns; ui.baseLineHeight = ui.baseLineHeightProto * ui.columns;

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@ -5,7 +5,7 @@
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}, },
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{ {
"path": "dist/human.esm.js" "path": "dist/human.esm.js"
@ -30,7 +30,7 @@
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@ -38,29 +38,29 @@
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} }
} }

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@ -5,7 +5,7 @@
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@ -159,7 +159,7 @@
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@ -179,7 +179,7 @@
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@ -196,7 +196,7 @@
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@ -244,7 +244,7 @@
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@ -255,7 +255,7 @@
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@ -269,7 +269,7 @@
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@ -290,7 +290,7 @@
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{ {
"path": "dist/tfjs.esm.js" "path": "dist/tfjs.esm.js"
@ -357,7 +357,7 @@
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@ -366,112 +366,112 @@
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@ -5,7 +5,7 @@
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@ -159,7 +159,7 @@
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@ -5,7 +5,7 @@
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@ -159,7 +159,7 @@
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@ -179,7 +179,7 @@
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1
server/build.js
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@ -10,6 +10,7 @@ let es;
// common configuration // common configuration
const common = { const common = {
minifyWhitespace: true, minifyWhitespace: true,
minifySyntax: true,
bundle: true, bundle: true,
sourcemap: true, sourcemap: true,
logLevel: 'error', logLevel: 'error',

14
src/face/blazeface.js
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@ -67,9 +67,9 @@ function scaleBoxFromPrediction(face, scaleFactor) {
class BlazeFaceModel { class BlazeFaceModel {
constructor(model, config) { constructor(model, config) {
this.blazeFaceModel = model; this.blazeFaceModel = model;
this.width = config.detector.inputSize; this.width = config.face.detector.inputSize;
this.height = config.detector.inputSize; this.height = config.face.detector.inputSize;
this.anchorsData = generateAnchors(config.detector.inputSize); this.anchorsData = generateAnchors(config.face.detector.inputSize);
this.anchors = tf.tensor2d(this.anchorsData); this.anchors = tf.tensor2d(this.anchorsData);
this.inputSize = tf.tensor1d([this.width, this.height]); this.inputSize = tf.tensor1d([this.width, this.height]);
this.config = config; this.config = config;
@ -100,7 +100,7 @@ class BlazeFaceModel {
const scoresOut = tf.sigmoid(logits).squeeze(); const scoresOut = tf.sigmoid(logits).squeeze();
return [prediction, decodedBounds, scoresOut]; return [prediction, decodedBounds, scoresOut];
}); });
const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.detector.maxFaces, this.config.detector.iouThreshold, this.config.detector.scoreThreshold); const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);
const boxIndices = boxIndicesTensor.arraySync(); const boxIndices = boxIndicesTensor.arraySync();
boxIndicesTensor.dispose(); boxIndicesTensor.dispose();
const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1])); const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));
@ -115,7 +115,7 @@ class BlazeFaceModel {
for (const i in boundingBoxes) { for (const i in boundingBoxes) {
const boxIndex = boxIndices[i]; const boxIndex = boxIndices[i];
const confidence = scoresVal[boxIndex]; const confidence = scoresVal[boxIndex];
if (confidence > this.config.detector.minConfidence) { if (confidence > this.config.face.detector.minConfidence) {
const box = createBox(boundingBoxes[i]); const box = createBox(boundingBoxes[i]);
const anchor = this.anchorsData[boxIndex]; const anchor = this.anchorsData[boxIndex];
const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1])); const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));
@ -164,10 +164,10 @@ class BlazeFaceModel {
} }
async function load(config) { async function load(config) {
const blazeface = await tf.loadGraphModel(config.detector.modelPath, { fromTFHub: config.detector.modelPath.includes('tfhub.dev') }); const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });
const model = new BlazeFaceModel(blazeface, config); const model = new BlazeFaceModel(blazeface, config);
// eslint-disable-next-line no-console // eslint-disable-next-line no-console
console.log(`Human: load model: ${config.detector.modelPath.match(/\/(.*)\./)[1]}`); console.log(`Human: load model: ${config.face.detector.modelPath.match(/\/(.*)\./)[1]}`);
return model; return model;
} }

13
src/face/facemesh.js
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@ -6,11 +6,10 @@ import * as coords from './coords.js';
class MediaPipeFaceMesh { class MediaPipeFaceMesh {
constructor(blazeFace, blazeMeshModel, irisModel, config) { constructor(blazeFace, blazeMeshModel, irisModel, config) {
this.pipeline = new pipe.Pipeline(blazeFace, blazeMeshModel, irisModel, config); this.pipeline = new pipe.Pipeline(blazeFace, blazeMeshModel, irisModel, config);
if (config) this.config = config; this.config = config;
} }
async estimateFaces(input, config) { async estimateFaces(input, config) {
if (config) this.config = config;
const predictions = await this.pipeline.predict(input, config); const predictions = await this.pipeline.predict(input, config);
const results = []; const results = [];
for (const prediction of (predictions || [])) { for (const prediction of (predictions || [])) {
@ -20,7 +19,7 @@ class MediaPipeFaceMesh {
const annotations = {}; const annotations = {};
if (mesh && mesh.length > 0) { if (mesh && mesh.length > 0) {
for (const key in coords.MESH_ANNOTATIONS) { for (const key in coords.MESH_ANNOTATIONS) {
if (this.config.iris.enabled || key.includes('Iris') === false) { if (config.face.iris.enabled || key.includes('Iris') === false) {
annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]); annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);
} }
} }
@ -42,14 +41,14 @@ class MediaPipeFaceMesh {
async function load(config) { async function load(config) {
const models = await Promise.all([ const models = await Promise.all([
blazeface.load(config), blazeface.load(config),
tf.loadGraphModel(config.mesh.modelPath, { fromTFHub: config.mesh.modelPath.includes('tfhub.dev') }), tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }),
tf.loadGraphModel(config.iris.modelPath, { fromTFHub: config.iris.modelPath.includes('tfhub.dev') }), tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }),
]); ]);
const faceMesh = new MediaPipeFaceMesh(models[0], models[1], models[2], config); const faceMesh = new MediaPipeFaceMesh(models[0], models[1], models[2], config);
// eslint-disable-next-line no-console // eslint-disable-next-line no-console
console.log(`Human: load model: ${config.mesh.modelPath.match(/\/(.*)\./)[1]}`); console.log(`Human: load model: ${config.face.mesh.modelPath.match(/\/(.*)\./)[1]}`);
// eslint-disable-next-line no-console // eslint-disable-next-line no-console
console.log(`Human: load model: ${config.iris.modelPath.match(/\/(.*)\./)[1]}`); console.log(`Human: load model: ${config.face.iris.modelPath.match(/\/(.*)\./)[1]}`);
return faceMesh; return faceMesh;
} }

20
src/face/facepipeline.js
View File

@ -45,9 +45,9 @@ class Pipeline {
this.boundingBoxDetector = boundingBoxDetector; this.boundingBoxDetector = boundingBoxDetector;
this.meshDetector = meshDetector; this.meshDetector = meshDetector;
this.irisModel = irisModel; this.irisModel = irisModel;
this.meshWidth = config.mesh.inputSize; this.meshWidth = config.face.mesh.inputSize;
this.meshHeight = config.mesh.inputSize; this.meshHeight = config.face.mesh.inputSize;
this.irisSize = config.iris.inputSize; this.irisSize = config.face.iris.inputSize;
this.irisEnlarge = 2.3; this.irisEnlarge = 2.3;
this.skipped = 1000; this.skipped = 1000;
this.detectedFaces = 0; this.detectedFaces = 0;
@ -134,14 +134,14 @@ class Pipeline {
let useFreshBox = false; let useFreshBox = false;
// run new detector every skipFrames unless we only want box to start with // run new detector every skipFrames unless we only want box to start with
let detector; let detector;
if ((this.skipped > config.detector.skipFrames) || !config.mesh.enabled || !config.videoOptimized) { if ((this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {
detector = await this.boundingBoxDetector.getBoundingBoxes(input); detector = await this.boundingBoxDetector.getBoundingBoxes(input);
// don't reset on test image // don't reset on test image
if ((input.shape[1] !== 255) && (input.shape[2] !== 255)) this.skipped = 0; if ((input.shape[1] !== 255) && (input.shape[2] !== 255)) this.skipped = 0;
} }
// if detector result count doesn't match current working set, use it to reset current working set // if detector result count doesn't match current working set, use it to reset current working set
if (detector && detector.boxes && (detector.boxes.length > 0) && (!config.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.detector.maxFaces))) { if (detector && detector.boxes && (detector.boxes.length > 0) && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {
this.storedBoxes = []; this.storedBoxes = [];
this.detectedFaces = 0; this.detectedFaces = 0;
for (const possible of detector.boxes) { for (const possible of detector.boxes) {
@ -173,7 +173,7 @@ class Pipeline {
}); });
} }
// console.log(this.skipped, config.detector.skipFrames, this.detectedFaces, config.detector.maxFaces, detector?.boxes.length, this.storedBoxes.length); // console.log(this.skipped, config.face.detector.skipFrames, this.detectedFaces, config.face.detector.maxFaces, detector?.boxes.length, this.storedBoxes.length);
let results = tf.tidy(() => this.storedBoxes.map((box, i) => { let results = tf.tidy(() => this.storedBoxes.map((box, i) => {
let angle = 0; let angle = 0;
@ -193,10 +193,10 @@ class Pipeline {
rotationMatrix = util.buildRotationMatrix(-angle, faceCenter); rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);
} }
const face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255); const face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);
const outputFace = config.detector.rotation ? tf.image.rotateWithOffset(face, angle) : face; const outputFace = config.face.detector.rotation ? tf.image.rotateWithOffset(face, angle) : face;
// if we're not going to produce mesh, don't spend time with further processing // if we're not going to produce mesh, don't spend time with further processing
if (!config.mesh.enabled) { if (!config.face.mesh.enabled) {
const prediction = { const prediction = {
coords: null, coords: null,
box, box,
@ -211,13 +211,13 @@ class Pipeline {
const [, confidence, contourCoords] = this.meshDetector.predict(face); const [, confidence, contourCoords] = this.meshDetector.predict(face);
const confidenceVal = confidence.dataSync()[0]; const confidenceVal = confidence.dataSync()[0];
confidence.dispose(); confidence.dispose();
if (confidenceVal < config.detector.minConfidence) { if (confidenceVal < config.face.detector.minConfidence) {
contourCoords.dispose(); contourCoords.dispose();
return null; return null;
} }
const coordsReshaped = tf.reshape(contourCoords, [-1, 3]); const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);
let rawCoords = coordsReshaped.arraySync(); let rawCoords = coordsReshaped.arraySync();
if (config.iris.enabled) { if (config.face.iris.enabled) {
const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true); const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);
const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]); const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);
const eyePredictions = (this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]))); const eyePredictions = (this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop])));

8
src/hand/handdetector.js
View File

@ -55,14 +55,14 @@ class HandDetector {
const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]); const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);
const boxes = this.normalizeBoxes(rawBoxes); const boxes = this.normalizeBoxes(rawBoxes);
rawBoxes.dispose(); rawBoxes.dispose();
const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.maxHands, config.iouThreshold, config.scoreThreshold); const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);
const filtered = filteredT.arraySync(); const filtered = filteredT.arraySync();
scores.dispose(); scores.dispose();
filteredT.dispose(); filteredT.dispose();
const hands = []; const hands = [];
for (const boxIndex of filtered) { for (const boxIndex of filtered) {
if (scoresVal[boxIndex] >= config.minConfidence) { if (scoresVal[boxIndex] >= config.hand.minConfidence) {
const matchingBox = tf.slice(boxes, [boxIndex, 0], [1, -1]); const matchingBox = tf.slice(boxes, [boxIndex, 0], [1, -1]);
const rawPalmLandmarks = tf.slice(predictions, [boxIndex, 5], [1, 14]); const rawPalmLandmarks = tf.slice(predictions, [boxIndex, 5], [1, 14]);
const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, boxIndex).reshape([-1, 2])); const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, boxIndex).reshape([-1, 2]));
@ -78,7 +78,7 @@ class HandDetector {
async estimateHandBounds(input, config) { async estimateHandBounds(input, config) {
const inputHeight = input.shape[1]; const inputHeight = input.shape[1];
const inputWidth = input.shape[2]; const inputWidth = input.shape[2];
const image = tf.tidy(() => input.resizeBilinear([config.inputSize, config.inputSize]).div(127.5).sub(1)); const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));
const predictions = await this.getBoxes(image, config); const predictions = await this.getBoxes(image, config);
image.dispose(); image.dispose();
if (!predictions || predictions.length === 0) return null; if (!predictions || predictions.length === 0) return null;
@ -90,7 +90,7 @@ class HandDetector {
const palmLandmarks = prediction.palmLandmarks.arraySync(); const palmLandmarks = prediction.palmLandmarks.arraySync();
prediction.box.dispose(); prediction.box.dispose();
prediction.palmLandmarks.dispose(); prediction.palmLandmarks.dispose();
hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.inputSize, inputHeight / config.inputSize])); hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));
} }
return hands; return hands;
} }

10
src/hand/handpipeline.js
View File

@ -89,27 +89,27 @@ class HandPipeline {
// run new detector every skipFrames unless we only want box to start with // run new detector every skipFrames unless we only want box to start with
let boxes; let boxes;
if ((this.skipped > config.skipFrames) || !config.landmarks || !config.videoOptimized) { if ((this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {
boxes = await this.boxDetector.estimateHandBounds(image, config); boxes = await this.boxDetector.estimateHandBounds(image, config);
// don't reset on test image // don't reset on test image
if ((image.shape[1] !== 255) && (image.shape[2] !== 255)) this.skipped = 0; if ((image.shape[1] !== 255) && (image.shape[2] !== 255)) this.skipped = 0;
} }
// if detector result count doesn't match current working set, use it to reset current working set // if detector result count doesn't match current working set, use it to reset current working set
if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.maxHands) || !config.landmarks)) { if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {
this.storedBoxes = []; this.storedBoxes = [];
this.detectedHands = 0; this.detectedHands = 0;
for (const possible of boxes) this.storedBoxes.push(possible); for (const possible of boxes) this.storedBoxes.push(possible);
if (this.storedBoxes.length > 0) useFreshBox = true; if (this.storedBoxes.length > 0) useFreshBox = true;
} }
const hands = []; const hands = [];
// console.log(`skipped: ${this.skipped} max: ${config.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`); // console.log(`skipped: ${this.skipped} max: ${config.hand.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);
// go through working set of boxes // go through working set of boxes
for (const i in this.storedBoxes) { for (const i in this.storedBoxes) {
const currentBox = this.storedBoxes[i]; const currentBox = this.storedBoxes[i];
if (!currentBox) continue; if (!currentBox) continue;
if (config.landmarks) { if (config.hand.landmarks) {
const angle = util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]); const angle = util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]);
const palmCenter = box.getBoxCenter(currentBox); const palmCenter = box.getBoxCenter(currentBox);
const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]]; const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];
@ -124,7 +124,7 @@ class HandPipeline {
handImage.dispose(); handImage.dispose();
const confidenceValue = confidence.dataSync()[0]; const confidenceValue = confidence.dataSync()[0];
confidence.dispose(); confidence.dispose();
if (confidenceValue >= config.minConfidence) { if (confidenceValue >= config.hand.minConfidence) {
const keypointsReshaped = tf.reshape(keypoints, [-1, 3]); const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);
const rawCoords = keypointsReshaped.arraySync(); const rawCoords = keypointsReshaped.arraySync();
keypoints.dispose(); keypoints.dispose();

12
src/hand/handpose.js
View File

@ -69,16 +69,16 @@ exports.HandPose = HandPose;
async function load(config) { async function load(config) {
const [handDetectorModel, handPoseModel] = await Promise.all([ const [handDetectorModel, handPoseModel] = await Promise.all([
tf.loadGraphModel(config.detector.modelPath, { fromTFHub: config.detector.modelPath.includes('tfhub.dev') }), tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }),
tf.loadGraphModel(config.skeleton.modelPath, { fromTFHub: config.skeleton.modelPath.includes('tfhub.dev') }), tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }),
]); ]);
const detector = new handdetector.HandDetector(handDetectorModel, config.inputSize, anchors.anchors); const detector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);
const pipe = new pipeline.HandPipeline(detector, handPoseModel, config.inputSize); const pipe = new pipeline.HandPipeline(detector, handPoseModel, config.hand.inputSize);
const handpose = new HandPose(pipe); const handpose = new HandPose(pipe);
// eslint-disable-next-line no-console // eslint-disable-next-line no-console
console.log(`Human: load model: ${config.detector.modelPath.match(/\/(.*)\./)[1]}`); console.log(`Human: load model: ${config.hand.detector.modelPath.match(/\/(.*)\./)[1]}`);
// eslint-disable-next-line no-console // eslint-disable-next-line no-console
console.log(`Human: load model: ${config.skeleton.modelPath.match(/\/(.*)\./)[1]}`); console.log(`Human: load model: ${config.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`);
return handpose; return handpose;
} }
exports.load = load; exports.load = load;

14
src/human.js
View File

@ -132,22 +132,22 @@ class Human {
this.models.posenet, this.models.posenet,
this.models.handpose, this.models.handpose,
] = await Promise.all([ ] = await Promise.all([
this.models.facemesh || (this.config.face.enabled ? facemesh.load(this.config.face) : null), this.models.facemesh || (this.config.face.enabled ? facemesh.load(this.config) : null),
this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null), this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),
this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null), this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),
this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null), this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),
this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null), this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),
this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null), this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),
this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config.hand) : null), this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),
]); ]);
} else { } else {
if (this.config.face.enabled && !this.models.facemesh) this.models.facemesh = await facemesh.load(this.config.face); if (this.config.face.enabled && !this.models.facemesh) this.models.facemesh = await facemesh.load(this.config);
if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config); if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);
if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config); if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);
if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config); if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);
if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config); if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);
if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config); if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);
if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config.hand); if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);
} }
const current = Math.trunc(now() - timeStamp); const current = Math.trunc(now() - timeStamp);
if (current > (this.perf.load || 0)) this.perf.load = current; if (current > (this.perf.load || 0)) this.perf.load = current;
@ -207,7 +207,7 @@ class Human {
const faceRes = []; const faceRes = [];
this.state = 'run:face'; this.state = 'run:face';
timeStamp = now(); timeStamp = now();
const faces = await this.models.facemesh.estimateFaces(input, this.config.face); const faces = await this.models.facemesh.estimateFaces(input, this.config);
this.perf.face = Math.trunc(now() - timeStamp); this.perf.face = Math.trunc(now() - timeStamp);
for (const face of faces) { for (const face of faces) {
this.analyze('Get Face'); this.analyze('Get Face');
@ -378,12 +378,12 @@ class Human {
// run handpose // run handpose
this.analyze('Start Hand:'); this.analyze('Start Hand:');
if (this.config.async) { if (this.config.async) {
handRes = this.config.hand.enabled ? this.models.handpose.estimateHands(process.tensor, this.config.hand) : []; handRes = this.config.hand.enabled ? this.models.handpose.estimateHands(process.tensor, this.config) : [];
if (this.perf.hand) delete this.perf.hand; if (this.perf.hand) delete this.perf.hand;
} else { } else {
this.state = 'run:hand'; this.state = 'run:hand';
timeStamp = now(); timeStamp = now();
handRes = this.config.hand.enabled ? await this.models.handpose.estimateHands(process.tensor, this.config.hand) : []; handRes = this.config.hand.enabled ? await this.models.handpose.estimateHands(process.tensor, this.config) : [];
this.perf.hand = Math.trunc(now() - timeStamp); this.perf.hand = Math.trunc(now() - timeStamp);
} }
// this.analyze('End Hand:'); // this.analyze('End Hand:');