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human
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autodetect inputSizes

pull/293/head
Vladimir Mandic 2021-03-11 10:26:14 -05:00
parent d5b6c676c9
commit 1b53cd4b6b
10 changed files with 27 additions and 60 deletions
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12
config.js
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@ -67,7 +67,6 @@ export default {
// (note: module is not loaded until it is required)
detector: {
modelPath: '../models/blazeface-back.json',
inputSize: 256, // fixed value
rotation: true, // use best-guess rotated face image or just box with rotation as-is
// false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees
// this parameter is not valid in nodejs
@ -91,19 +90,16 @@ export default {
mesh: {
enabled: true,
modelPath: '../models/facemesh.json',
inputSize: 192, // fixed value
},
iris: {
enabled: true,
modelPath: '../models/iris.json',
inputSize: 64, // fixed value
},
age: {
enabled: true,
modelPath: '../models/age-ssrnet-imdb.json',
inputSize: 64, // fixed value
modelPath: '../models/age.json',
skipFrames: 31, // how many frames to go without re-running the detector
// only used for video inputs
},
@ -112,14 +108,12 @@ export default {
enabled: true,
minConfidence: 0.1, // threshold for discarding a prediction
modelPath: '../models/gender.json', // can be 'gender' or 'gender-ssrnet-imdb'
inputSize: 64, // fixed value
skipFrames: 32, // how many frames to go without re-running the detector
// only used for video inputs
},
emotion: {
enabled: true,
inputSize: 64, // fixed value
minConfidence: 0.1, // threshold for discarding a prediction
skipFrames: 33, // how many frames to go without re-running the detector
modelPath: '../models/emotion.json',
@ -127,7 +121,6 @@ export default {
embedding: {
enabled: false,
inputSize: 112, // fixed value
modelPath: '../models/mobilefacenet.json',
},
},
@ -135,7 +128,6 @@ export default {
body: {
enabled: true,
modelPath: '../models/posenet.json', // can be 'posenet' or 'blazepose'
inputSize: 257, // fixed value, 257 for posenet and 256 for blazepose
maxDetections: 10, // maximum number of people detected in the input
// should be set to the minimum number for performance
// only valid for posenet as blazepose only detects single pose
@ -144,14 +136,12 @@ export default {
// only valid for posenet as blazepose only detects single pose
nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression
// only valid for posenet as blazepose only detects single pose
modelType: 'posenet-mobilenet', // can be 'posenet-mobilenet', 'posenet-resnet', 'blazepose'
},
hand: {
enabled: true,
rotation: false, // use best-guess rotated hand image or just box with rotation as-is
// false means higher performance, but incorrect finger mapping if hand is inverted
inputSize: 256, // fixed value
skipFrames: 12, // how many frames to go without re-running the hand bounding box detector
// only used for video inputs
// e.g., if model is running st 25 FPS, we can re-use existing bounding

17
demo/browser.js
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@ -3,20 +3,18 @@ import Human from '../src/human';
import Menu from './menu.js';
import GLBench from './gl-bench.js';
const userConfig = { backend: 'webgl' }; // add any user configuration overrides
// const userConfig = { backend: 'webgl' }; // add any user configuration overrides
/*
const userConfig = {
backend: 'wasm',
backend: 'webgl',
async: false,
warmup: 'none',
warmup: 'face',
videoOptimized: false,
face: { enabled: true, mesh: { enabled: false }, iris: { enabled: false }, age: { enabled: false }, gender: { enabled: false }, emotion: { enabled: false }, embedding: { enabled: false } },
face: { enabled: true, mesh: { enabled: false }, iris: { enabled: false }, age: { enabled: false }, gender: { enabled: false }, emotion: { enabled: false }, embedding: { enabled: true } },
hand: { enabled: false },
gesture: { enabled: false },
body: { enabled: false, modelType: 'blazepose', modelPath: '../models/blazepose.json' },
body: { enabled: false, modelPath: '../models/blazepose.json' },
};
*/
const human = new Human(userConfig);
@ -40,7 +38,7 @@ const ui = {
detectFPS: [], // internal, holds fps values for detection performance
drawFPS: [], // internal, holds fps values for draw performance
buffered: false, // experimental, should output be buffered between frames
drawWarmup: false, // debug only, should warmup image processing be displayed on startup
drawWarmup: true, // debug only, should warmup image processing be displayed on startup
drawThread: null, // internl, perform draw operations in a separate thread
detectThread: null, // internl, perform detect operations in a separate thread
framesDraw: 0, // internal, statistics on frames drawn
@ -104,9 +102,6 @@ async function drawResults(input) {
if (ui.drawFPS.length > ui.maxFPSframes) ui.drawFPS.shift();
lastDraw = performance.now();
// enable for continous performance monitoring
// console.log(result.performance);
// draw fps chart
await menu.process.updateChart('FPS', ui.detectFPS);

6
demo/node.js
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@ -18,12 +18,12 @@ const myConfig = {
detector: { modelPath: 'file://models/blazeface-back.json', enabled: true },
mesh: { modelPath: 'file://models/facemesh.json', enabled: true },
iris: { modelPath: 'file://models/iris.json', enabled: true },
age: { modelPath: 'file://models/age-ssrnet-imdb.json', enabled: true },
age: { modelPath: 'file://models/age.json', enabled: true },
gender: { modelPath: 'file://models/gender.json', enabled: true },
emotion: { modelPath: 'file://models/emotion.json', enabled: true },
},
// body: { modelPath: 'file://models/blazepose.json', modelType: 'blazepose', inputSize: 256, enabled: true },
body: { modelPath: 'file://models/posenet.json', modelType: 'posenet', inputSize: 257, enabled: true },
// body: { modelPath: 'file://models/blazepose.json', modelType: 'blazepose', enabled: true },
body: { modelPath: 'file://models/posenet.json', modelType: 'posenet', enabled: true },
hand: {
enabled: true,
detector: { modelPath: 'file://models/handdetect.json' },

2
package.json
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@ -68,7 +68,7 @@
"eslint-plugin-node": "^11.1.0",
"eslint-plugin-promise": "^4.3.1",
"rimraf": "^3.0.2",
"simple-git": "^2.36.1",
"simple-git": "^2.36.2",
"tslib": "^2.1.0",
"typescript": "^4.2.3"
}

12
src/age/age.ts
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@ -23,17 +23,7 @@ export async function predict(image, config) {
if (config.videoOptimized) skipped = 0;
else skipped = Number.MAX_SAFE_INTEGER;
return new Promise(async (resolve) => {
/*
const zoom = [0, 0]; // 0..1 meaning 0%..100%
const box = [[
(image.shape[1] * zoom[0]) / image.shape[1],
(image.shape[2] * zoom[1]) / image.shape[2],
(image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],
(image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],
]];
const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);
*/
const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);
const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
const enhance = tf.mul(resize, [255.0]);
tf.dispose(resize);

12
src/emotion/emotion.ts
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@ -27,17 +27,7 @@ export async function predict(image, config) {
if (config.videoOptimized) skipped = 0;
else skipped = Number.MAX_SAFE_INTEGER;
return new Promise(async (resolve) => {
/*
const zoom = [0, 0]; // 0..1 meaning 0%..100%
const box = [[
(image.shape[1] * zoom[0]) / image.shape[1],
(image.shape[2] * zoom[1]) / image.shape[2],
(image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],
(image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],
]];
const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);
*/
const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);
const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
const [red, green, blue] = tf.split(resize, 3, 3);
resize.dispose();
// weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html

2
src/gender/gender.ts
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@ -28,7 +28,7 @@ export async function predict(image, config) {
if (config.videoOptimized) skipped = 0;
else skipped = Number.MAX_SAFE_INTEGER;
return new Promise(async (resolve) => {
const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);
const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
let enhance;
if (alternative) {
enhance = tf.tidy(() => {

6
src/handpose/handdetector.ts
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@ -5,6 +5,7 @@ export class HandDetector {
model: any;
anchors: any;
anchorsTensor: any;
inputSize: number;
inputSizeTensor: any;
doubleInputSizeTensor: any;
@ -12,6 +13,7 @@ export class HandDetector {
this.model = model;
this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);
this.anchorsTensor = tf.tensor2d(this.anchors);
this.inputSize = inputSize;
this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);
this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);
}
@ -67,7 +69,7 @@ export class HandDetector {
async estimateHandBounds(input, config) {
const inputHeight = input.shape[1];
const inputWidth = input.shape[2];
const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));
const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));
const predictions = await this.getBoxes(image, config);
image.dispose();
const hands: Array<{}> = [];
@ -79,7 +81,7 @@ export class HandDetector {
const palmLandmarks = prediction.palmLandmarks.arraySync();
prediction.box.dispose();
prediction.palmLandmarks.dispose();
hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));
hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));
}
return hands;
}

4
src/handpose/handpose.ts
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@ -54,8 +54,8 @@ export async function load(config) {
config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,
config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,
]);
const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);
const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);
const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);
const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);
const handPose = new HandPose(handPipeline);
if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\/(.*)\./)[1]}`);
if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`);

14
src/human.ts
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@ -109,7 +109,7 @@ class Human {
age,
gender,
emotion,
body: this.config.body.modelType.startsWith('posenet') ? posenet : blazepose,
body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,
hand: handpose,
};
// include platform info
@ -186,8 +186,8 @@ class Human {
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.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),
this.models.posenet || (this.config.body.enabled && this.config.body.modelType.startsWith('posenet') ? posenet.load(this.config) : null),
this.models.posenet || (this.config.body.enabled && this.config.body.modelType.startsWith('blazepose') ? blazepose.load(this.config) : null),
this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? posenet.load(this.config) : null),
this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),
]);
} else {
if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);
@ -196,8 +196,8 @@ class Human {
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.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);
if (this.config.body.enabled && !this.models.posenet && this.config.body.modelType.startsWith('posenet')) this.models.posenet = await posenet.load(this.config);
if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelType.startsWith('blazepose')) this.models.blazepose = await blazepose.load(this.config);
if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);
if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);
}
if (this.#firstRun) {
@ -477,13 +477,13 @@ class Human {
// run body: can be posenet or blazepose
this.#analyze('Start Body:');
if (this.config.async) {
if (this.config.body.modelType.startsWith('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];
if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];
else bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];
if (this.#perf.body) delete this.#perf.body;
} else {
this.state = 'run:body';
timeStamp = now();
if (this.config.body.modelType.startsWith('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];
if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];
else bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];
this.#perf.body = Math.trunc(now() - timeStamp);
}