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redo hand detection

pull/280/head
Vladimir Mandic 2020-11-04 01:11:24 -05:00
parent bfdcb301f4
commit 430a950112
6 changed files with 353 additions and 109 deletions
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1
demo/index.html
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@ -16,7 +16,6 @@
<!-- <script src="../assets/tf.es2017.js"></script> -->
<!-- <script src="../assets/tf-backend-wasm.es2017.js"></script> -->
<!-- <script src="../assets/tf-backend-webgpu.js"></script> -->
<!-- <script src='../assets/tfjs-vis.min.js'></script> -->
<script src="./browser.js" type="module"></script>
<style>
body { margin: 0; background: black; color: white; font-family: 'Segoe UI'; font-size: 16px; font-variant: small-caps; overflow-x: hidden; scrollbar-width: none; }

50
src/handpose/box.js
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@ -1,3 +1,19 @@
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const tf = require('@tensorflow/tfjs');
function getBoxSize(box) {
@ -6,35 +22,32 @@ function getBoxSize(box) {
Math.abs(box.endPoint[1] - box.startPoint[1]),
];
}
exports.getBoxSize = getBoxSize;
function getBoxCenter(box) {
return [
box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,
box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,
];
}
exports.getBoxCenter = getBoxCenter;
function cutBoxFromImageAndResize(box, image, cropSize) {
const h = image.shape[1];
const w = image.shape[2];
const boxes = [[
box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,
box.startPoint[1] / h,
box.startPoint[0] / w,
box.endPoint[1] / h,
box.endPoint[0] / w,
]];
return tf.image.cropAndResize(image, boxes, [0], cropSize);
}
exports.cutBoxFromImageAndResize = cutBoxFromImageAndResize;
function scaleBoxCoordinates(box, factor) {
const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
const palmLandmarks = box.palmLandmarks.map((coord) => [coord[0] * factor[0], coord[1] * factor[1]]);
const palmLandmarks = box.palmLandmarks.map((coord) => {
const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];
return scaledCoord;
});
return { startPoint, endPoint, palmLandmarks };
}
exports.scaleBoxCoordinates = scaleBoxCoordinates;
function enlargeBox(box, factor = 1.5) {
const center = getBoxCenter(box);
const size = getBoxSize(box);
@ -43,8 +56,6 @@ function enlargeBox(box, factor = 1.5) {
const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];
return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };
}
exports.enlargeBox = enlargeBox;
function squarifyBox(box) {
const centers = getBoxCenter(box);
const size = getBoxSize(box);
@ -54,15 +65,22 @@ function squarifyBox(box) {
const endPoint = [centers[0] + halfSize, centers[1] + halfSize];
return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };
}
exports.squarifyBox = squarifyBox;
function shiftBox(box, shiftFactor) {
const boxSize = [
box.endPoint[0] - box.startPoint[0], box.endPoint[1] - box.startPoint[1],
box.endPoint[0] - box.startPoint[0],
box.endPoint[1] - box.startPoint[1],
];
const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];
const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];
const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];
return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };
}
exports.shiftBox = shiftBox;
export {
cutBoxFromImageAndResize,
enlargeBox,
getBoxCenter,
getBoxSize,
scaleBoxCoordinates,
shiftBox,
squarifyBox,
};

103
src/handpose/handdetector.js
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@ -1,15 +1,32 @@
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const tf = require('@tensorflow/tfjs');
const bounding = require('./box');
const box = require('./box');
class HandDetector {
constructor(model, anchors, config) {
constructor(model, inputSize, anchorsAnnotated) {
this.model = model;
this.width = config.inputSize;
this.height = config.inputSize;
this.anchors = anchors.map((anchor) => [anchor.x_center, anchor.y_center]);
this.width = inputSize;
this.height = inputSize;
this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);
this.anchorsTensor = tf.tensor2d(this.anchors);
this.inputSizeTensor = tf.tensor1d([config.inputSize, config.inputSize]);
this.doubleInputSizeTensor = tf.tensor1d([config.inputSize * 2, config.inputSize * 2]);
this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);
this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);
}
normalizeBoxes(boxes) {
@ -31,59 +48,59 @@ class HandDetector {
});
}
async getBoundingBoxes(input) {
const batchedPrediction = this.model.predict(input);
async getBoundingBoxes(input, config) {
const normalizedInput = tf.tidy(() => tf.mul(tf.sub(input, 0.5), 2));
const batchedPrediction = this.model.predict(normalizedInput);
const prediction = batchedPrediction.squeeze();
// Regression score for each anchor point.
const scores = tf.tidy(() => tf.sigmoid(tf.slice(prediction, [0, 0], [-1, 1])).squeeze());
// Bounding box for each anchor point.
const rawBoxes = tf.slice(prediction, [0, 1], [-1, 4]);
const boxes = this.normalizeBoxes(rawBoxes);
const boxesWithHandsTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.maxHands, this.iouThreshold, this.scoreThreshold);
const boxesWithHandsTensor = tf.image.nonMaxSuppression(boxes, scores, config.maxHands, config.iouThreshold, config.scoreThreshold);
const boxesWithHands = boxesWithHandsTensor.arraySync();
const detectedHands = tf.tidy(() => {
const detectedBoxes = [];
for (const i in boxesWithHands) {
const boxIndex = boxesWithHands[i];
const toDispose = [
normalizedInput,
batchedPrediction,
boxesWithHandsTensor,
prediction,
boxes,
rawBoxes,
scores,
];
if (boxesWithHands.length === 0) {
toDispose.forEach((tensor) => tensor.dispose());
return null;
}
const hands = [];
for (const boxIndex of boxesWithHands) {
const matchingBox = tf.slice(boxes, [boxIndex, 0], [1, -1]);
const rawPalmLandmarks = tf.slice(prediction, [boxIndex, 5], [1, 14]);
const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, boxIndex).reshape([-1, 2]));
detectedBoxes.push({ boxes: matchingBox, palmLandmarks });
rawPalmLandmarks.dispose();
hands.push({ boxes: matchingBox, palmLandmarks });
}
return detectedBoxes;
});
[batchedPrediction, boxesWithHandsTensor, prediction, boxes, rawBoxes, scores].forEach((tensor) => tensor.dispose());
return detectedHands;
toDispose.forEach((tensor) => tensor.dispose());
return hands;
}
/**
* Returns a Box identifying the bounding box of a hand within the image.
* Returns null if there is no hand in the image.
*
* @param input The image to classify.
*/
async estimateHandBounds(input, config) {
this.iouThreshold = config.iouThreshold;
this.scoreThreshold = config.scoreThreshold;
this.maxHands = config.maxHands;
const resized = input.resizeBilinear([this.width, this.height]);
const divided = resized.mul([1 / 127.5]);
const image = divided.sub(0.5);
resized.dispose();
divided.dispose();
const predictions = await this.getBoundingBoxes(image);
const inputHeight = input.shape[1];
const inputWidth = input.shape[2];
const image = tf.tidy(() => input.resizeBilinear([this.width, this.height]).div(255));
const predictions = await this.getBoundingBoxes(image, config);
if (!predictions || predictions.length === 0) {
image.dispose();
if (!predictions || (predictions.length === 0)) return null;
return null;
}
const hands = [];
for (const i in predictions) {
const prediction = predictions[i];
const boundingBoxes = prediction.boxes.dataSync();
const startPoint = [boundingBoxes[0], boundingBoxes[1]];
const endPoint = [boundingBoxes[2], boundingBoxes[3]];
for (const prediction of predictions) {
const boundingBoxes = prediction.boxes.arraySync();
const startPoint = boundingBoxes[0].slice(0, 2);
const endPoint = boundingBoxes[0].slice(2, 4);
const palmLandmarks = prediction.palmLandmarks.arraySync();
image.dispose();
prediction.boxes.dispose();
prediction.palmLandmarks.dispose();
hands.push(bounding.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks }, [input.shape[2] / this.width, input.shape[1] / this.height]));
hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks }, [inputWidth / this.width, inputHeight / this.height]));
}
return hands;
}

184
src/handpose/handpipeline.js Normal file
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@ -0,0 +1,184 @@
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const tf = require('@tensorflow/tfjs');
const box = require('./box');
const util = require('./util');
const UPDATE_REGION_OF_INTEREST_IOU_THRESHOLD = 0.8;
const PALM_BOX_SHIFT_VECTOR = [0, -0.4];
const PALM_BOX_ENLARGE_FACTOR = 3;
const HAND_BOX_SHIFT_VECTOR = [0, -0.1];
const HAND_BOX_ENLARGE_FACTOR = 1.65;
const PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];
const PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;
const PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;
class HandPipeline {
constructor(boundingBoxDetector, meshDetector, inputSize) {
this.boundingBoxDetector = boundingBoxDetector;
this.meshDetector = meshDetector;
this.inputSize = inputSize;
this.regionsOfInterest = [];
this.runsWithoutHandDetector = 0;
this.maxHandsNumber = 1;
this.skipFrames = 0;
}
getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {
const rotatedPalmLandmarks = palmLandmarks.map((coord) => {
const homogeneousCoordinate = [...coord, 1];
return util.rotatePoint(homogeneousCoordinate, rotationMatrix);
});
const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);
return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);
}
getBoxForHandLandmarks(landmarks) {
const boundingBox = this.calculateLandmarksBoundingBox(landmarks);
const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);
const palmLandmarks = [];
for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {
palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));
}
boxAroundHand.palmLandmarks = palmLandmarks;
return boxAroundHand;
}
transformRawCoords(rawCoords, box2, angle, rotationMatrix) {
const boxSize = box.getBoxSize(box2);
const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize];
const coordsScaled = rawCoords.map((coord) => [
scaleFactor[0] * (coord[0] - this.inputSize / 2),
scaleFactor[1] * (coord[1] - this.inputSize / 2),
coord[2],
]);
const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);
const coordsRotated = coordsScaled.map((coord) => {
const rotated = util.rotatePoint(coord, coordsRotationMatrix);
return [...rotated, coord[2]];
});
const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);
const boxCenter = [...box.getBoxCenter(box2), 1];
const originalBoxCenter = [
util.dot(boxCenter, inverseRotationMatrix[0]),
util.dot(boxCenter, inverseRotationMatrix[1]),
];
return coordsRotated.map((coord) => [
coord[0] + originalBoxCenter[0],
coord[1] + originalBoxCenter[1],
coord[2],
]);
}
async estimateHands(image, config) {
this.skipFrames = config.skipFrames;
const useFreshBox = this.shouldUpdateRegionsOfInterest();
if (useFreshBox) {
const boundingBoxPredictions = await this.boundingBoxDetector.estimateHandBounds(image, config);
this.regionsOfInterest = [];
if (!boundingBoxPredictions || boundingBoxPredictions.length === 0) {
image.dispose();
return null;
}
for (const boundingBoxPrediction of boundingBoxPredictions) {
this.regionsOfInterest.push(boundingBoxPrediction);
}
this.runsWithoutHandDetector = 0;
} else {
this.runsWithoutHandDetector++;
}
const hands = [];
for (const i in this.regionsOfInterest) {
const currentBox = this.regionsOfInterest[i];
if (!currentBox) continue;
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 palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];
const rotatedImage = tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized);
const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);
const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);
const handImage = croppedInput.div(255);
croppedInput.dispose();
rotatedImage.dispose();
const prediction = this.meshDetector.predict(handImage);
const [confidence, keypoints] = prediction;
handImage.dispose();
const confidenceValue = confidence.dataSync()[0];
confidence.dispose();
if (confidenceValue < config.minConfidence) {
keypoints.dispose();
this.regionsOfInterest[i] = null;
return null;
}
const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);
const rawCoords = keypointsReshaped.arraySync();
keypoints.dispose();
keypointsReshaped.dispose();
const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);
const nextBoundingBox = this.getBoxForHandLandmarks(coords);
this.updateRegionsOfInterest(nextBoundingBox, i);
const result = {
landmarks: coords,
handInViewConfidence: confidenceValue,
boundingBox: {
topLeft: nextBoundingBox.startPoint,
bottomRight: nextBoundingBox.endPoint,
},
};
hands.push(result);
}
return hands;
}
// eslint-disable-next-line class-methods-use-this
calculateLandmarksBoundingBox(landmarks) {
const xs = landmarks.map((d) => d[0]);
const ys = landmarks.map((d) => d[1]);
const startPoint = [Math.min(...xs), Math.min(...ys)];
const endPoint = [Math.max(...xs), Math.max(...ys)];
return { startPoint, endPoint };
}
updateRegionsOfInterest(newBox, i) {
const previousBox = this.regionsOfInterest[i];
let iou = 0;
if (previousBox != null && previousBox.startPoint != null) {
const [boxStartX, boxStartY] = newBox.startPoint;
const [boxEndX, boxEndY] = newBox.endPoint;
const [previousBoxStartX, previousBoxStartY] = previousBox.startPoint;
const [previousBoxEndX, previousBoxEndY] = previousBox.endPoint;
const xStartMax = Math.max(boxStartX, previousBoxStartX);
const yStartMax = Math.max(boxStartY, previousBoxStartY);
const xEndMin = Math.min(boxEndX, previousBoxEndX);
const yEndMin = Math.min(boxEndY, previousBoxEndY);
const intersection = (xEndMin - xStartMax) * (yEndMin - yStartMax);
const boxArea = (boxEndX - boxStartX) * (boxEndY - boxStartY);
const previousBoxArea = (previousBoxEndX - previousBoxStartX) * (previousBoxEndY - boxStartY);
iou = intersection / (boxArea + previousBoxArea - intersection);
}
this.regionsOfInterest[i] = iou > UPDATE_REGION_OF_INTEREST_IOU_THRESHOLD ? previousBox : newBox;
}
shouldUpdateRegionsOfInterest() {
console.log(this.regionsOfInterest.length, this.runsWithoutHandDetector, !this.regionsOfInterest || (this.regionsOfInterest.length === 0) || (this.runsWithoutHandDetector >= this.skipFrames));
return !this.regionsOfInterest || (this.regionsOfInterest.length === 0) || (this.runsWithoutHandDetector >= this.skipFrames);
}
}
exports.HandPipeline = HandPipeline;

61
src/handpose/handpose.js
View File

@ -1,30 +1,54 @@
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const tf = require('@tensorflow/tfjs');
const hand = require('./handdetector');
const keypoints = require('./keypoints');
const pipe = require('./pipeline');
const anchors = require('./anchors.js');
const handdetector = require('./handdetector');
const pipeline = require('./handpipeline');
const anchors = require('./anchors');
const MESH_ANNOTATIONS = {
thumb: [1, 2, 3, 4],
indexFinger: [5, 6, 7, 8],
middleFinger: [9, 10, 11, 12],
ringFinger: [13, 14, 15, 16],
pinky: [17, 18, 19, 20],
palmBase: [0],
};
class HandPose {
constructor(pipeline) {
this.pipeline = pipeline;
constructor(pipe) {
this.pipeline = pipe;
}
static getAnnotations() {
return MESH_ANNOTATIONS;
}
async estimateHands(input, config) {
this.skipFrames = config.skipFrames;
this.detectionConfidence = config.minConfidence;
this.maxHands = config.maxHands;
const predictions = await this.pipeline.estimateHands(input, config);
if (!predictions) return [];
const hands = [];
if (!predictions) return hands;
for (const prediction of predictions) {
if (!prediction) return [];
const annotations = {};
for (const key of Object.keys(keypoints.MESH_ANNOTATIONS)) {
annotations[key] = keypoints.MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);
for (const key of Object.keys(MESH_ANNOTATIONS)) {
annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);
}
hands.push({
confidence: prediction.confidence || 0,
box: prediction.box ? [prediction.box.topLeft[0], prediction.box.topLeft[1], prediction.box.bottomRight[0] - prediction.box.topLeft[0], prediction.box.bottomRight[1] - prediction.box.topLeft[1]] : 0,
confidence: prediction.handInViewConfidence,
box: prediction.boundingBox ? [prediction.boundingBox.topLeft[0], prediction.boundingBox.topLeft[1], prediction.boundingBox.bottomRight[0] - prediction.boundingBox.topLeft[0], prediction.boundingBox.bottomRight[1] - prediction.boundingBox.topLeft[1]] : 0,
landmarks: prediction.landmarks,
annotations,
});
@ -35,13 +59,14 @@ class HandPose {
exports.HandPose = HandPose;
async function load(config) {
// maxContinuousChecks = Infinity, detectionConfidence = 0.8, iouThreshold = 0.3, scoreThreshold = 0.5
const [handDetectorModel, handPoseModel] = await Promise.all([
tf.loadGraphModel(config.detector.modelPath, { fromTFHub: config.detector.modelPath.includes('tfhub.dev') }),
tf.loadGraphModel(config.skeleton.modelPath, { fromTFHub: config.skeleton.modelPath.includes('tfhub.dev') }),
]);
const detector = new hand.HandDetector(handDetectorModel, anchors.anchors, config);
const pipeline = new pipe.HandPipeline(detector, handPoseModel, config);
const handpose = new HandPose(pipeline);
const detector = new handdetector.HandDetector(handDetectorModel, config.inputSize, anchors.anchors);
const pipe = new pipeline.HandPipeline(detector, handPoseModel, config.inputSize);
const handpose = new HandPose(pipe);
return handpose;
}
exports.load = load;

53
src/human.js
View File

@ -167,6 +167,7 @@ class Human {
this.log('Changing WebGL: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);
tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);
}
tf.ENV.set('WEBGL_CPU_FORWARD', true);
await tf.ready();
}
}
@ -284,32 +285,6 @@ class Human {
perf.image = Math.trunc(now() - timeStamp);
const imageTensor = image.tensor;
// run posenet
if (this.config.async) {
poseRes = this.config.body.enabled ? this.models.posenet.estimatePoses(imageTensor, this.config.body) : [];
} else {
this.state = 'run:body';
timeStamp = now();
this.analyze('Start PoseNet');
poseRes = this.config.body.enabled ? await this.models.posenet.estimatePoses(imageTensor, this.config.body) : [];
this.analyze('End PoseNet:');
perf.body = Math.trunc(now() - timeStamp);
}
// run handpose
if (this.config.async) {
handRes = this.config.hand.enabled ? this.models.handpose.estimateHands(imageTensor, this.config.hand) : [];
} else {
this.state = 'run:hand';
timeStamp = now();
this.analyze('Start HandPose:');
handRes = this.config.hand.enabled ? await this.models.handpose.estimateHands(imageTensor, this.config.hand) : [];
this.analyze('End HandPose:');
perf.hand = Math.trunc(now() - timeStamp);
}
if (this.config.async) [poseRes, handRes] = await Promise.all([poseRes, handRes]);
// run facemesh, includes blazeface and iris
const faceRes = [];
if (this.config.face.enabled) {
@ -357,6 +332,32 @@ class Human {
}
}
// run posenet
if (this.config.async) {
poseRes = this.config.body.enabled ? this.models.posenet.estimatePoses(imageTensor, this.config.body) : [];
} else {
this.state = 'run:body';
timeStamp = now();
this.analyze('Start PoseNet');
poseRes = this.config.body.enabled ? await this.models.posenet.estimatePoses(imageTensor, this.config.body) : [];
this.analyze('End PoseNet:');
perf.body = Math.trunc(now() - timeStamp);
}
// run handpose
if (this.config.async) {
handRes = this.config.hand.enabled ? this.models.handpose.estimateHands(imageTensor, this.config.hand) : [];
} else {
this.state = 'run:hand';
timeStamp = now();
this.analyze('Start HandPose:');
handRes = this.config.hand.enabled ? await this.models.handpose.estimateHands(imageTensor, this.config.hand) : [];
this.analyze('End HandPose:');
perf.hand = Math.trunc(now() - timeStamp);
}
if (this.config.async) [poseRes, handRes] = await Promise.all([poseRes, handRes]);
imageTensor.dispose();
this.state = 'idle';