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new human.compare api

pull/233/head
Vladimir Mandic 2021-11-07 10:03:33 -05:00
parent fc93c39201
commit 8a504215a6
24 changed files with 1109 additions and 780 deletions
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6
CHANGELOG.md
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@ -9,8 +9,12 @@
## Changelog
### **HEAD -> main** 2021/11/05 mandic00@live.com
### **HEAD -> main** 2021/11/07 mandic00@live.com
### **origin/main** 2021/11/06 mandic00@live.com
- new frame change detection algorithm
- add histogram equalization
- implement wasm missing ops
- performance and memory optimizations

25
TODO.md
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@ -42,18 +42,27 @@ MoveNet MultiPose model does not work with WASM backend due to missing F32 broad
### Pending release
New:
- New frame change detection algorithm used for cache determination
- New frame change detection algorithm used for [cache determination](https://vladmandic.github.io/human/typedoc/interfaces/Config.html#cacheSensitivity)
based on temporal input difference
- New optional input histogram equalization
auto-level input for optimal brightness/contrast via `config.filter.equalization`
- New built-in Tensorflow profiling [human.profile](https://vladmandic.github.io/human/typedoc/classes/Human.html#profile)
- New optional input histogram equalization [config.filter.equalization](https://vladmandic.github.io/human/)
auto-level input for optimal brightness/contrast
- New event-baseed interface [human.events](https://vladmandic.github.io/human/typedoc/classes/Human.html#events)
- New configuration validation [human.validate](https://vladmandic.github.io/human/typedoc/classes/Human.html#validate)
- New input compare function [human.compare](https://vladmandic.github.io/human/typedoc/classes/Human.html#compare)
this function is internally used by `human` to determine frame changes and cache validation
- New [custom built TFJS](https://github.com/vladmandic/tfjs) for bundled version
result is a pure module with reduced bundle size and include built-in support for all backends
note: **nobundle** and **node** versions link to standard `@tensorflow` packages
Changed:
- Supports all modules on all backends
- [Default configuration values](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) have been tuned for precision and performance
- Supports all built-in modules on all backends
via custom implementation of missing kernel ops
- Performance and precision improvements
**face**, **hand** and **gestures** modules
- Use custom built TFJS for bundled version
reduced bundle size and built-in support for all backends
`nobundle` and `node` versions link to standard `@tensorflow` packages
- **face**, **hand**
- **gestures** modules
- **face matching**
- Fix **ReactJS** compatibility
- Better precision using **WASM**
Previous issues due to math low-precision in WASM implementation

105
dist/human.esm-nobundle.js vendored
View File

@ -910,8 +910,8 @@ function GLImageFilter() {
this.get = function() {
return filterChain;
};
this.apply = function(image24) {
resize(image24.width, image24.height);
this.apply = function(image25) {
resize(image25.width, image25.height);
drawCount = 0;
if (!sourceTexture)
sourceTexture = gl.createTexture();
@ -920,7 +920,7 @@ function GLImageFilter() {
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image24);
gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image25);
for (let i = 0; i < filterChain.length; i++) {
lastInChain = i === filterChain.length - 1;
const f = filterChain[i];
@ -928,9 +928,9 @@ function GLImageFilter() {
}
return fxcanvas;
};
this.draw = function(image24) {
this.draw = function(image25) {
this.add("brightness", 0);
return this.apply(image24);
return this.apply(image25);
};
}
@ -1170,6 +1170,28 @@ async function skip(config3, input) {
}
return skipFrame;
}
async function compare(config3, input1, input2) {
const t = {};
if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {
if (!config3.debug)
log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape);
return 0;
}
if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {
if (!config3.debug)
log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape);
return 0;
}
t.input1 = tfjs_esm_exports.clone(input1);
t.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? tfjs_esm_exports.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tfjs_esm_exports.clone(input2);
t.diff = tfjs_esm_exports.sub(t.input1, t.input2);
t.squared = tfjs_esm_exports.mul(t.diff, t.diff);
t.sum = tfjs_esm_exports.sum(t.squared);
const diffSum = await t.sum.data();
const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;
tfjs_esm_exports.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);
return diffRelative;
}
// src/util/env.ts
var Env = class {
@ -1321,7 +1343,7 @@ async function load2(config3) {
log("cached model:", model2["modelUrl"]);
return model2;
}
async function predict(image24, config3, idx, count2) {
async function predict(image25, config3, idx, count2) {
var _a, _b;
if (!model2)
return null;
@ -1333,7 +1355,7 @@ async function predict(image24, config3, idx, count2) {
}
skipped2 = 0;
return new Promise(async (resolve) => {
const resize = tfjs_esm_exports.image.resizeBilinear(image24, [(model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[2] : 0, (model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[1] : 0], false);
const resize = tfjs_esm_exports.image.resizeBilinear(image25, [(model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[2] : 0, (model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[1] : 0], false);
const res = model2 == null ? void 0 : model2.execute(resize);
const num = (await res.data())[0];
cached[idx] = Math.round(100 * num) / 100;
@ -4647,10 +4669,10 @@ var scaleBoxCoordinates = (box4, factor) => {
const endPoint = [box4.endPoint[0] * factor[0], box4.endPoint[1] * factor[1]];
return { startPoint, endPoint, landmarks: box4.landmarks, confidence: box4.confidence };
};
var cutBoxFromImageAndResize = (box4, image24, cropSize) => {
const h = image24.shape[1];
const w = image24.shape[2];
const crop2 = tfjs_esm_exports.image.cropAndResize(image24, [[box4.startPoint[1] / h, box4.startPoint[0] / w, box4.endPoint[1] / h, box4.endPoint[0] / w]], [0], cropSize);
var cutBoxFromImageAndResize = (box4, image25, cropSize) => {
const h = image25.shape[1];
const w = image25.shape[2];
const crop2 = tfjs_esm_exports.image.cropAndResize(image25, [[box4.startPoint[1] / h, box4.startPoint[0] / w, box4.endPoint[1] / h, box4.endPoint[0] / w]], [0], cropSize);
const norm = tfjs_esm_exports.div(crop2, 255);
tfjs_esm_exports.dispose(crop2);
return norm;
@ -5298,7 +5320,7 @@ function max2d(inputs, minScore) {
return [0, 0, newScore];
});
}
async function predict4(image24, config3) {
async function predict4(image25, config3) {
const skipTime = (config3.body.skipTime || 0) > now() - lastTime4;
const skipFrame = skipped5 < (config3.body.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) {
@ -5311,7 +5333,7 @@ async function predict4(image24, config3) {
const tensor3 = tfjs_esm_exports.tidy(() => {
if (!(model5 == null ? void 0 : model5.inputs[0].shape))
return null;
const resize = tfjs_esm_exports.image.resizeBilinear(image24, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
const resize = tfjs_esm_exports.image.resizeBilinear(image25, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
const enhance3 = tfjs_esm_exports.mul(resize, 2);
const norm = enhance3.sub(1);
return norm;
@ -5338,8 +5360,8 @@ async function predict4(image24, config3) {
y2 / model5.inputs[0].shape[1]
],
position: [
Math.round(image24.shape[2] * x2 / model5.inputs[0].shape[2]),
Math.round(image24.shape[1] * y2 / model5.inputs[0].shape[1])
Math.round(image25.shape[2] * x2 / model5.inputs[0].shape[2]),
Math.round(image25.shape[1] * y2 / model5.inputs[0].shape[1])
]
});
}
@ -5399,7 +5421,7 @@ async function load6(config3) {
log("cached model:", model6["modelUrl"]);
return model6;
}
async function predict5(image24, config3, idx, count2) {
async function predict5(image25, config3, idx, count2) {
var _a, _b;
if (!model6)
return null;
@ -5416,7 +5438,7 @@ async function predict5(image24, config3, idx, count2) {
if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) {
const t = {};
const inputSize8 = (model6 == null ? void 0 : model6.inputs[0].shape) ? model6.inputs[0].shape[2] : 0;
t.resize = tfjs_esm_exports.image.resizeBilinear(image24, [inputSize8, inputSize8], false);
t.resize = tfjs_esm_exports.image.resizeBilinear(image25, [inputSize8, inputSize8], false);
[t.red, t.green, t.blue] = tfjs_esm_exports.split(t.resize, 3, 3);
t.redNorm = tfjs_esm_exports.mul(t.red, rgb[0]);
t.greenNorm = tfjs_esm_exports.mul(t.green, rgb[1]);
@ -5723,7 +5745,7 @@ function enhance2(input) {
tfjs_esm_exports.dispose(crop2);
return norm;
}
async function predict7(image24, config3, idx, count2) {
async function predict7(image25, config3, idx, count2) {
var _a, _b, _c, _d;
if (!model9)
return null;
@ -5743,7 +5765,7 @@ async function predict7(image24, config3, idx, count2) {
descriptor: []
};
if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) {
const enhanced = enhance2(image24);
const enhanced = enhance2(image25);
const resT = model9 == null ? void 0 : model9.execute(enhanced);
lastTime7 = now();
tfjs_esm_exports.dispose(enhanced);
@ -5784,16 +5806,16 @@ function getBoxCenter2(box4) {
box4.startPoint[1] + (box4.endPoint[1] - box4.startPoint[1]) / 2
];
}
function cutBoxFromImageAndResize2(box4, image24, cropSize) {
const h = image24.shape[1];
const w = image24.shape[2];
function cutBoxFromImageAndResize2(box4, image25, cropSize) {
const h = image25.shape[1];
const w = image25.shape[2];
const boxes = [[
box4.startPoint[1] / h,
box4.startPoint[0] / w,
box4.endPoint[1] / h,
box4.endPoint[0] / w
]];
return tfjs_esm_exports.image.cropAndResize(image24, boxes, [0], cropSize);
return tfjs_esm_exports.image.cropAndResize(image25, boxes, [0], cropSize);
}
function scaleBoxCoordinates2(box4, factor) {
const startPoint = [box4.startPoint[0] * factor[0], box4.startPoint[1] * factor[1]];
@ -8975,13 +8997,13 @@ var HandPipeline = class {
Math.trunc(coord[2])
]);
}
async estimateHands(image24, config3) {
async estimateHands(image25, config3) {
let useFreshBox = false;
let boxes;
const skipTime = (config3.hand.skipTime || 0) > now() - lastTime8;
const skipFrame = this.skipped < (config3.hand.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame) {
boxes = await this.handDetector.predict(image24, config3);
boxes = await this.handDetector.predict(image25, config3);
this.skipped = 0;
}
if (config3.skipAllowed)
@ -9000,8 +9022,8 @@ var HandPipeline = class {
if (config3.hand.landmarks) {
const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;
const palmCenter = getBoxCenter2(currentBox);
const palmCenterNormalized = [palmCenter[0] / image24.shape[2], palmCenter[1] / image24.shape[1]];
const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tfjs_esm_exports.image.rotateWithOffset(image24, angle, 0, palmCenterNormalized) : image24.clone();
const palmCenterNormalized = [palmCenter[0] / image25.shape[2], palmCenter[1] / image25.shape[1]];
const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tfjs_esm_exports.image.rotateWithOffset(image25, angle, 0, palmCenterNormalized) : image25.clone();
const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
const croppedInput = cutBoxFromImageAndResize2(newBox, rotatedImage, [this.inputSize, this.inputSize]);
@ -9842,11 +9864,11 @@ function bodyParts(body4) {
}
}
}
for (const [pair, compare] of relative) {
for (const [pair, compare2] of relative) {
const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]);
const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]);
const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare[0]);
const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare[1]);
const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]);
const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]);
if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo])
continue;
const distanceLeft = body4.keypoints[left] ? [
@ -9939,7 +9961,7 @@ async function load11(config3) {
inputSize7 = 256;
return model10;
}
async function parseSinglePose(res, config3, image24, inputBox) {
async function parseSinglePose(res, config3, image25, inputBox) {
const kpt4 = res[0][0];
const keypoints = [];
let score = 0;
@ -9955,15 +9977,15 @@ async function parseSinglePose(res, config3, image24, inputBox) {
part: kpt3[id],
positionRaw,
position: [
Math.round((image24.shape[2] || 0) * positionRaw[0]),
Math.round((image24.shape[1] || 0) * positionRaw[1])
Math.round((image25.shape[2] || 0) * positionRaw[0]),
Math.round((image25.shape[1] || 0) * positionRaw[1])
]
});
}
}
score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
const bodies = [];
const newBox = calc(keypoints.map((pt) => pt.position), [image24.shape[2], image24.shape[1]]);
const newBox = calc(keypoints.map((pt) => pt.position), [image25.shape[2], image25.shape[1]]);
const annotations2 = {};
for (const [name, indexes] of Object.entries(connected3)) {
const pt = [];
@ -9980,7 +10002,7 @@ async function parseSinglePose(res, config3, image24, inputBox) {
bodies.push(body4);
return bodies;
}
async function parseMultiPose(res, config3, image24, inputBox) {
async function parseMultiPose(res, config3, image25, inputBox) {
const bodies = [];
for (let id = 0; id < res[0].length; id++) {
const kpt4 = res[0][id];
@ -9998,11 +10020,11 @@ async function parseMultiPose(res, config3, image24, inputBox) {
part: kpt3[i],
score: Math.round(100 * score) / 100,
positionRaw,
position: [Math.round((image24.shape[2] || 0) * positionRaw[0]), Math.round((image24.shape[1] || 0) * positionRaw[1])]
position: [Math.round((image25.shape[2] || 0) * positionRaw[0]), Math.round((image25.shape[1] || 0) * positionRaw[1])]
});
}
}
const newBox = calc(keypoints.map((pt) => pt.position), [image24.shape[2], image24.shape[1]]);
const newBox = calc(keypoints.map((pt) => pt.position), [image25.shape[2], image25.shape[1]]);
const annotations2 = {};
for (const [name, indexes] of Object.entries(connected3)) {
const pt = [];
@ -10134,7 +10156,7 @@ async function process4(res, inputSize8, outputShape, config3) {
results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
return results;
}
async function predict11(image24, config3) {
async function predict11(image25, config3) {
const skipTime = (config3.object.skipTime || 0) > now() - lastTime10;
const skipFrame = skipped11 < (config3.object.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame && last5.length > 0) {
@ -10145,8 +10167,8 @@ async function predict11(image24, config3) {
if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense"))
return last5;
return new Promise(async (resolve) => {
const outputSize2 = [image24.shape[2], image24.shape[1]];
const resize = tfjs_esm_exports.image.resizeBilinear(image24, [model11.inputSize, model11.inputSize], false);
const outputSize2 = [image25.shape[2], image25.shape[1]];
const resize = tfjs_esm_exports.image.resizeBilinear(image25, [model11.inputSize, model11.inputSize], false);
const norm = tfjs_esm_exports.div(resize, 255);
const transpose = norm.transpose([0, 3, 1, 2]);
tfjs_esm_exports.dispose(norm);
@ -12862,6 +12884,9 @@ var Human = class {
enhance(input) {
return enhance2(input);
}
compare(firstImageTensor, secondImageTensor) {
return compare(this.config, firstImageTensor, secondImageTensor);
}
async init() {
await check(this, true);
await this.tf.ready();

4
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dist/human.esm.js vendored
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@ -1280,7 +1280,7 @@ var require_long = __commonJS({
};
LongPrototype.gte = LongPrototype.greaterThanOrEqual;
LongPrototype.ge = LongPrototype.greaterThanOrEqual;
LongPrototype.compare = function compare(other) {
LongPrototype.compare = function compare2(other) {
if (!isLong(other))
other = fromValue(other);
if (this.eq(other))
@ -71495,6 +71495,28 @@ async function skip(config3, input2) {
}
return skipFrame;
}
async function compare(config3, input1, input2) {
const t = {};
if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {
if (!config3.debug)
log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape);
return 0;
}
if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {
if (!config3.debug)
log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape);
return 0;
}
t.input1 = clone(input1);
t.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : clone(input2);
t.diff = sub(t.input1, t.input2);
t.squared = mul(t.diff, t.diff);
t.sum = sum2(t.squared);
const diffSum = await t.sum.data();
const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;
dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);
return diffRelative;
}
// src/util/env.ts
var Env = class {
@ -80167,11 +80189,11 @@ function bodyParts(body4) {
}
}
}
for (const [pair, compare] of relative) {
for (const [pair, compare2] of relative) {
const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]);
const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]);
const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare[0]);
const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare[1]);
const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]);
const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]);
if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo])
continue;
const distanceLeft = body4.keypoints[left] ? [
@ -83187,6 +83209,9 @@ var Human = class {
enhance(input2) {
return enhance2(input2);
}
compare(firstImageTensor, secondImageTensor) {
return compare(this.config, firstImageTensor, secondImageTensor);
}
async init() {
await check(this, true);
await this.tf.ready();

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dist/human.js vendored
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@ -1290,7 +1290,7 @@ var Human = (() => {
};
LongPrototype.gte = LongPrototype.greaterThanOrEqual;
LongPrototype.ge = LongPrototype.greaterThanOrEqual;
LongPrototype.compare = function compare(other) {
LongPrototype.compare = function compare2(other) {
if (!isLong(other))
other = fromValue(other);
if (this.eq(other))
@ -71505,6 +71505,28 @@ return a / b;`;
}
return skipFrame;
}
async function compare(config3, input1, input2) {
const t = {};
if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {
if (!config3.debug)
log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape);
return 0;
}
if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {
if (!config3.debug)
log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape);
return 0;
}
t.input1 = clone(input1);
t.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : clone(input2);
t.diff = sub(t.input1, t.input2);
t.squared = mul(t.diff, t.diff);
t.sum = sum2(t.squared);
const diffSum = await t.sum.data();
const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;
dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);
return diffRelative;
}
// src/util/env.ts
var Env = class {
@ -80177,11 +80199,11 @@ return a / b;`;
}
}
}
for (const [pair, compare] of relative) {
for (const [pair, compare2] of relative) {
const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]);
const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]);
const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare[0]);
const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare[1]);
const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]);
const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]);
if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo])
continue;
const distanceLeft = body4.keypoints[left] ? [
@ -83197,6 +83219,9 @@ lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/
enhance(input2) {
return enhance2(input2);
}
compare(firstImageTensor, secondImageTensor) {
return compare(this.config, firstImageTensor, secondImageTensor);
}
async init() {
await check(this, true);
await this.tf.ready();

105
dist/human.node-gpu.js vendored
View File

@ -919,8 +919,8 @@ function GLImageFilter() {
this.get = function() {
return filterChain;
};
this.apply = function(image24) {
resize(image24.width, image24.height);
this.apply = function(image25) {
resize(image25.width, image25.height);
drawCount = 0;
if (!sourceTexture)
sourceTexture = gl.createTexture();
@ -929,7 +929,7 @@ function GLImageFilter() {
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image24);
gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image25);
for (let i = 0; i < filterChain.length; i++) {
lastInChain = i === filterChain.length - 1;
const f = filterChain[i];
@ -937,9 +937,9 @@ function GLImageFilter() {
}
return fxcanvas;
};
this.draw = function(image24) {
this.draw = function(image25) {
this.add("brightness", 0);
return this.apply(image24);
return this.apply(image25);
};
}
@ -1180,6 +1180,28 @@ async function skip(config3, input) {
}
return skipFrame;
}
async function compare(config3, input1, input2) {
const t = {};
if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {
if (!config3.debug)
log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape);
return 0;
}
if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {
if (!config3.debug)
log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape);
return 0;
}
t.input1 = tf2.clone(input1);
t.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? tf2.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf2.clone(input2);
t.diff = tf2.sub(t.input1, t.input2);
t.squared = tf2.mul(t.diff, t.diff);
t.sum = tf2.sum(t.squared);
const diffSum = await t.sum.data();
const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;
tf2.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);
return diffRelative;
}
// src/util/env.ts
var Env = class {
@ -1339,7 +1361,7 @@ async function load2(config3) {
log("cached model:", model2["modelUrl"]);
return model2;
}
async function predict(image24, config3, idx, count2) {
async function predict(image25, config3, idx, count2) {
var _a, _b;
if (!model2)
return null;
@ -1351,7 +1373,7 @@ async function predict(image24, config3, idx, count2) {
}
skipped2 = 0;
return new Promise(async (resolve) => {
const resize = tf5.image.resizeBilinear(image24, [(model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[2] : 0, (model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[1] : 0], false);
const resize = tf5.image.resizeBilinear(image25, [(model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[2] : 0, (model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[1] : 0], false);
const res = model2 == null ? void 0 : model2.execute(resize);
const num = (await res.data())[0];
cached[idx] = Math.round(100 * num) / 100;
@ -4671,10 +4693,10 @@ var scaleBoxCoordinates = (box4, factor) => {
const endPoint = [box4.endPoint[0] * factor[0], box4.endPoint[1] * factor[1]];
return { startPoint, endPoint, landmarks: box4.landmarks, confidence: box4.confidence };
};
var cutBoxFromImageAndResize = (box4, image24, cropSize) => {
const h = image24.shape[1];
const w = image24.shape[2];
const crop2 = tf6.image.cropAndResize(image24, [[box4.startPoint[1] / h, box4.startPoint[0] / w, box4.endPoint[1] / h, box4.endPoint[0] / w]], [0], cropSize);
var cutBoxFromImageAndResize = (box4, image25, cropSize) => {
const h = image25.shape[1];
const w = image25.shape[2];
const crop2 = tf6.image.cropAndResize(image25, [[box4.startPoint[1] / h, box4.startPoint[0] / w, box4.endPoint[1] / h, box4.endPoint[0] / w]], [0], cropSize);
const norm = tf6.div(crop2, 255);
tf6.dispose(crop2);
return norm;
@ -5331,7 +5353,7 @@ function max2d(inputs, minScore) {
return [0, 0, newScore];
});
}
async function predict4(image24, config3) {
async function predict4(image25, config3) {
const skipTime = (config3.body.skipTime || 0) > now() - lastTime4;
const skipFrame = skipped5 < (config3.body.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) {
@ -5344,7 +5366,7 @@ async function predict4(image24, config3) {
const tensor3 = tf10.tidy(() => {
if (!(model5 == null ? void 0 : model5.inputs[0].shape))
return null;
const resize = tf10.image.resizeBilinear(image24, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
const resize = tf10.image.resizeBilinear(image25, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
const enhance3 = tf10.mul(resize, 2);
const norm = enhance3.sub(1);
return norm;
@ -5371,8 +5393,8 @@ async function predict4(image24, config3) {
y2 / model5.inputs[0].shape[1]
],
position: [
Math.round(image24.shape[2] * x2 / model5.inputs[0].shape[2]),
Math.round(image24.shape[1] * y2 / model5.inputs[0].shape[1])
Math.round(image25.shape[2] * x2 / model5.inputs[0].shape[2]),
Math.round(image25.shape[1] * y2 / model5.inputs[0].shape[1])
]
});
}
@ -5433,7 +5455,7 @@ async function load6(config3) {
log("cached model:", model6["modelUrl"]);
return model6;
}
async function predict5(image24, config3, idx, count2) {
async function predict5(image25, config3, idx, count2) {
var _a, _b;
if (!model6)
return null;
@ -5450,7 +5472,7 @@ async function predict5(image24, config3, idx, count2) {
if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) {
const t = {};
const inputSize8 = (model6 == null ? void 0 : model6.inputs[0].shape) ? model6.inputs[0].shape[2] : 0;
t.resize = tf11.image.resizeBilinear(image24, [inputSize8, inputSize8], false);
t.resize = tf11.image.resizeBilinear(image25, [inputSize8, inputSize8], false);
[t.red, t.green, t.blue] = tf11.split(t.resize, 3, 3);
t.redNorm = tf11.mul(t.red, rgb[0]);
t.greenNorm = tf11.mul(t.green, rgb[1]);
@ -5762,7 +5784,7 @@ function enhance2(input) {
tf14.dispose(crop2);
return norm;
}
async function predict7(image24, config3, idx, count2) {
async function predict7(image25, config3, idx, count2) {
var _a, _b, _c, _d;
if (!model9)
return null;
@ -5782,7 +5804,7 @@ async function predict7(image24, config3, idx, count2) {
descriptor: []
};
if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) {
const enhanced = enhance2(image24);
const enhanced = enhance2(image25);
const resT = model9 == null ? void 0 : model9.execute(enhanced);
lastTime7 = now();
tf14.dispose(enhanced);
@ -5830,16 +5852,16 @@ function getBoxCenter2(box4) {
box4.startPoint[1] + (box4.endPoint[1] - box4.startPoint[1]) / 2
];
}
function cutBoxFromImageAndResize2(box4, image24, cropSize) {
const h = image24.shape[1];
const w = image24.shape[2];
function cutBoxFromImageAndResize2(box4, image25, cropSize) {
const h = image25.shape[1];
const w = image25.shape[2];
const boxes = [[
box4.startPoint[1] / h,
box4.startPoint[0] / w,
box4.endPoint[1] / h,
box4.endPoint[0] / w
]];
return tf15.image.cropAndResize(image24, boxes, [0], cropSize);
return tf15.image.cropAndResize(image25, boxes, [0], cropSize);
}
function scaleBoxCoordinates2(box4, factor) {
const startPoint = [box4.startPoint[0] * factor[0], box4.startPoint[1] * factor[1]];
@ -9022,13 +9044,13 @@ var HandPipeline = class {
Math.trunc(coord[2])
]);
}
async estimateHands(image24, config3) {
async estimateHands(image25, config3) {
let useFreshBox = false;
let boxes;
const skipTime = (config3.hand.skipTime || 0) > now() - lastTime8;
const skipFrame = this.skipped < (config3.hand.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame) {
boxes = await this.handDetector.predict(image24, config3);
boxes = await this.handDetector.predict(image25, config3);
this.skipped = 0;
}
if (config3.skipAllowed)
@ -9047,8 +9069,8 @@ var HandPipeline = class {
if (config3.hand.landmarks) {
const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;
const palmCenter = getBoxCenter2(currentBox);
const palmCenterNormalized = [palmCenter[0] / image24.shape[2], palmCenter[1] / image24.shape[1]];
const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf17.image.rotateWithOffset(image24, angle, 0, palmCenterNormalized) : image24.clone();
const palmCenterNormalized = [palmCenter[0] / image25.shape[2], palmCenter[1] / image25.shape[1]];
const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf17.image.rotateWithOffset(image25, angle, 0, palmCenterNormalized) : image25.clone();
const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
const croppedInput = cutBoxFromImageAndResize2(newBox, rotatedImage, [this.inputSize, this.inputSize]);
@ -9894,11 +9916,11 @@ function bodyParts(body4) {
}
}
}
for (const [pair, compare] of relative) {
for (const [pair, compare2] of relative) {
const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]);
const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]);
const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare[0]);
const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare[1]);
const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]);
const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]);
if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo])
continue;
const distanceLeft = body4.keypoints[left] ? [
@ -9991,7 +10013,7 @@ async function load11(config3) {
inputSize7 = 256;
return model10;
}
async function parseSinglePose(res, config3, image24, inputBox) {
async function parseSinglePose(res, config3, image25, inputBox) {
const kpt4 = res[0][0];
const keypoints = [];
let score = 0;
@ -10007,15 +10029,15 @@ async function parseSinglePose(res, config3, image24, inputBox) {
part: kpt3[id],
positionRaw,
position: [
Math.round((image24.shape[2] || 0) * positionRaw[0]),
Math.round((image24.shape[1] || 0) * positionRaw[1])
Math.round((image25.shape[2] || 0) * positionRaw[0]),
Math.round((image25.shape[1] || 0) * positionRaw[1])
]
});
}
}
score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
const bodies = [];
const newBox = calc(keypoints.map((pt) => pt.position), [image24.shape[2], image24.shape[1]]);
const newBox = calc(keypoints.map((pt) => pt.position), [image25.shape[2], image25.shape[1]]);
const annotations2 = {};
for (const [name, indexes] of Object.entries(connected3)) {
const pt = [];
@ -10032,7 +10054,7 @@ async function parseSinglePose(res, config3, image24, inputBox) {
bodies.push(body4);
return bodies;
}
async function parseMultiPose(res, config3, image24, inputBox) {
async function parseMultiPose(res, config3, image25, inputBox) {
const bodies = [];
for (let id = 0; id < res[0].length; id++) {
const kpt4 = res[0][id];
@ -10050,11 +10072,11 @@ async function parseMultiPose(res, config3, image24, inputBox) {
part: kpt3[i],
score: Math.round(100 * score) / 100,
positionRaw,
position: [Math.round((image24.shape[2] || 0) * positionRaw[0]), Math.round((image24.shape[1] || 0) * positionRaw[1])]
position: [Math.round((image25.shape[2] || 0) * positionRaw[0]), Math.round((image25.shape[1] || 0) * positionRaw[1])]
});
}
}
const newBox = calc(keypoints.map((pt) => pt.position), [image24.shape[2], image24.shape[1]]);
const newBox = calc(keypoints.map((pt) => pt.position), [image25.shape[2], image25.shape[1]]);
const annotations2 = {};
for (const [name, indexes] of Object.entries(connected3)) {
const pt = [];
@ -10187,7 +10209,7 @@ async function process4(res, inputSize8, outputShape, config3) {
results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
return results;
}
async function predict11(image24, config3) {
async function predict11(image25, config3) {
const skipTime = (config3.object.skipTime || 0) > now() - lastTime10;
const skipFrame = skipped11 < (config3.object.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame && last5.length > 0) {
@ -10198,8 +10220,8 @@ async function predict11(image24, config3) {
if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense"))
return last5;
return new Promise(async (resolve) => {
const outputSize2 = [image24.shape[2], image24.shape[1]];
const resize = tf22.image.resizeBilinear(image24, [model11.inputSize, model11.inputSize], false);
const outputSize2 = [image25.shape[2], image25.shape[1]];
const resize = tf22.image.resizeBilinear(image25, [model11.inputSize, model11.inputSize], false);
const norm = tf22.div(resize, 255);
const transpose = norm.transpose([0, 3, 1, 2]);
tf22.dispose(norm);
@ -12924,6 +12946,9 @@ var Human = class {
enhance(input) {
return enhance2(input);
}
compare(firstImageTensor, secondImageTensor) {
return compare(this.config, firstImageTensor, secondImageTensor);
}
async init() {
await check(this, true);
await this.tf.ready();

105
dist/human.node-wasm.js vendored
View File

@ -920,8 +920,8 @@ function GLImageFilter() {
this.get = function() {
return filterChain;
};
this.apply = function(image24) {
resize(image24.width, image24.height);
this.apply = function(image25) {
resize(image25.width, image25.height);
drawCount = 0;
if (!sourceTexture)
sourceTexture = gl.createTexture();
@ -930,7 +930,7 @@ function GLImageFilter() {
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image24);
gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image25);
for (let i = 0; i < filterChain.length; i++) {
lastInChain = i === filterChain.length - 1;
const f = filterChain[i];
@ -938,9 +938,9 @@ function GLImageFilter() {
}
return fxcanvas;
};
this.draw = function(image24) {
this.draw = function(image25) {
this.add("brightness", 0);
return this.apply(image24);
return this.apply(image25);
};
}
@ -1181,6 +1181,28 @@ async function skip(config3, input) {
}
return skipFrame;
}
async function compare(config3, input1, input2) {
const t = {};
if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {
if (!config3.debug)
log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape);
return 0;
}
if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {
if (!config3.debug)
log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape);
return 0;
}
t.input1 = tf2.clone(input1);
t.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? tf2.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf2.clone(input2);
t.diff = tf2.sub(t.input1, t.input2);
t.squared = tf2.mul(t.diff, t.diff);
t.sum = tf2.sum(t.squared);
const diffSum = await t.sum.data();
const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;
tf2.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);
return diffRelative;
}
// src/util/env.ts
var Env = class {
@ -1340,7 +1362,7 @@ async function load2(config3) {
log("cached model:", model2["modelUrl"]);
return model2;
}
async function predict(image24, config3, idx, count2) {
async function predict(image25, config3, idx, count2) {
var _a, _b;
if (!model2)
return null;
@ -1352,7 +1374,7 @@ async function predict(image24, config3, idx, count2) {
}
skipped2 = 0;
return new Promise(async (resolve) => {
const resize = tf5.image.resizeBilinear(image24, [(model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[2] : 0, (model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[1] : 0], false);
const resize = tf5.image.resizeBilinear(image25, [(model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[2] : 0, (model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[1] : 0], false);
const res = model2 == null ? void 0 : model2.execute(resize);
const num = (await res.data())[0];
cached[idx] = Math.round(100 * num) / 100;
@ -4672,10 +4694,10 @@ var scaleBoxCoordinates = (box4, factor) => {
const endPoint = [box4.endPoint[0] * factor[0], box4.endPoint[1] * factor[1]];
return { startPoint, endPoint, landmarks: box4.landmarks, confidence: box4.confidence };
};
var cutBoxFromImageAndResize = (box4, image24, cropSize) => {
const h = image24.shape[1];
const w = image24.shape[2];
const crop2 = tf6.image.cropAndResize(image24, [[box4.startPoint[1] / h, box4.startPoint[0] / w, box4.endPoint[1] / h, box4.endPoint[0] / w]], [0], cropSize);
var cutBoxFromImageAndResize = (box4, image25, cropSize) => {
const h = image25.shape[1];
const w = image25.shape[2];
const crop2 = tf6.image.cropAndResize(image25, [[box4.startPoint[1] / h, box4.startPoint[0] / w, box4.endPoint[1] / h, box4.endPoint[0] / w]], [0], cropSize);
const norm = tf6.div(crop2, 255);
tf6.dispose(crop2);
return norm;
@ -5332,7 +5354,7 @@ function max2d(inputs, minScore) {
return [0, 0, newScore];
});
}
async function predict4(image24, config3) {
async function predict4(image25, config3) {
const skipTime = (config3.body.skipTime || 0) > now() - lastTime4;
const skipFrame = skipped5 < (config3.body.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) {
@ -5345,7 +5367,7 @@ async function predict4(image24, config3) {
const tensor3 = tf10.tidy(() => {
if (!(model5 == null ? void 0 : model5.inputs[0].shape))
return null;
const resize = tf10.image.resizeBilinear(image24, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
const resize = tf10.image.resizeBilinear(image25, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
const enhance3 = tf10.mul(resize, 2);
const norm = enhance3.sub(1);
return norm;
@ -5372,8 +5394,8 @@ async function predict4(image24, config3) {
y2 / model5.inputs[0].shape[1]
],
position: [
Math.round(image24.shape[2] * x2 / model5.inputs[0].shape[2]),
Math.round(image24.shape[1] * y2 / model5.inputs[0].shape[1])
Math.round(image25.shape[2] * x2 / model5.inputs[0].shape[2]),
Math.round(image25.shape[1] * y2 / model5.inputs[0].shape[1])
]
});
}
@ -5434,7 +5456,7 @@ async function load6(config3) {
log("cached model:", model6["modelUrl"]);
return model6;
}
async function predict5(image24, config3, idx, count2) {
async function predict5(image25, config3, idx, count2) {
var _a, _b;
if (!model6)
return null;
@ -5451,7 +5473,7 @@ async function predict5(image24, config3, idx, count2) {
if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) {
const t = {};
const inputSize8 = (model6 == null ? void 0 : model6.inputs[0].shape) ? model6.inputs[0].shape[2] : 0;
t.resize = tf11.image.resizeBilinear(image24, [inputSize8, inputSize8], false);
t.resize = tf11.image.resizeBilinear(image25, [inputSize8, inputSize8], false);
[t.red, t.green, t.blue] = tf11.split(t.resize, 3, 3);
t.redNorm = tf11.mul(t.red, rgb[0]);
t.greenNorm = tf11.mul(t.green, rgb[1]);
@ -5763,7 +5785,7 @@ function enhance2(input) {
tf14.dispose(crop2);
return norm;
}
async function predict7(image24, config3, idx, count2) {
async function predict7(image25, config3, idx, count2) {
var _a, _b, _c, _d;
if (!model9)
return null;
@ -5783,7 +5805,7 @@ async function predict7(image24, config3, idx, count2) {
descriptor: []
};
if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) {
const enhanced = enhance2(image24);
const enhanced = enhance2(image25);
const resT = model9 == null ? void 0 : model9.execute(enhanced);
lastTime7 = now();
tf14.dispose(enhanced);
@ -5831,16 +5853,16 @@ function getBoxCenter2(box4) {
box4.startPoint[1] + (box4.endPoint[1] - box4.startPoint[1]) / 2
];
}
function cutBoxFromImageAndResize2(box4, image24, cropSize) {
const h = image24.shape[1];
const w = image24.shape[2];
function cutBoxFromImageAndResize2(box4, image25, cropSize) {
const h = image25.shape[1];
const w = image25.shape[2];
const boxes = [[
box4.startPoint[1] / h,
box4.startPoint[0] / w,
box4.endPoint[1] / h,
box4.endPoint[0] / w
]];
return tf15.image.cropAndResize(image24, boxes, [0], cropSize);
return tf15.image.cropAndResize(image25, boxes, [0], cropSize);
}
function scaleBoxCoordinates2(box4, factor) {
const startPoint = [box4.startPoint[0] * factor[0], box4.startPoint[1] * factor[1]];
@ -9023,13 +9045,13 @@ var HandPipeline = class {
Math.trunc(coord[2])
]);
}
async estimateHands(image24, config3) {
async estimateHands(image25, config3) {
let useFreshBox = false;
let boxes;
const skipTime = (config3.hand.skipTime || 0) > now() - lastTime8;
const skipFrame = this.skipped < (config3.hand.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame) {
boxes = await this.handDetector.predict(image24, config3);
boxes = await this.handDetector.predict(image25, config3);
this.skipped = 0;
}
if (config3.skipAllowed)
@ -9048,8 +9070,8 @@ var HandPipeline = class {
if (config3.hand.landmarks) {
const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;
const palmCenter = getBoxCenter2(currentBox);
const palmCenterNormalized = [palmCenter[0] / image24.shape[2], palmCenter[1] / image24.shape[1]];
const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf17.image.rotateWithOffset(image24, angle, 0, palmCenterNormalized) : image24.clone();
const palmCenterNormalized = [palmCenter[0] / image25.shape[2], palmCenter[1] / image25.shape[1]];
const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf17.image.rotateWithOffset(image25, angle, 0, palmCenterNormalized) : image25.clone();
const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
const croppedInput = cutBoxFromImageAndResize2(newBox, rotatedImage, [this.inputSize, this.inputSize]);
@ -9895,11 +9917,11 @@ function bodyParts(body4) {
}
}
}
for (const [pair, compare] of relative) {
for (const [pair, compare2] of relative) {
const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]);
const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]);
const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare[0]);
const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare[1]);
const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]);
const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]);
if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo])
continue;
const distanceLeft = body4.keypoints[left] ? [
@ -9992,7 +10014,7 @@ async function load11(config3) {
inputSize7 = 256;
return model10;
}
async function parseSinglePose(res, config3, image24, inputBox) {
async function parseSinglePose(res, config3, image25, inputBox) {
const kpt4 = res[0][0];
const keypoints = [];
let score = 0;
@ -10008,15 +10030,15 @@ async function parseSinglePose(res, config3, image24, inputBox) {
part: kpt3[id],
positionRaw,
position: [
Math.round((image24.shape[2] || 0) * positionRaw[0]),
Math.round((image24.shape[1] || 0) * positionRaw[1])
Math.round((image25.shape[2] || 0) * positionRaw[0]),
Math.round((image25.shape[1] || 0) * positionRaw[1])
]
});
}
}
score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
const bodies = [];
const newBox = calc(keypoints.map((pt) => pt.position), [image24.shape[2], image24.shape[1]]);
const newBox = calc(keypoints.map((pt) => pt.position), [image25.shape[2], image25.shape[1]]);
const annotations2 = {};
for (const [name, indexes] of Object.entries(connected3)) {
const pt = [];
@ -10033,7 +10055,7 @@ async function parseSinglePose(res, config3, image24, inputBox) {
bodies.push(body4);
return bodies;
}
async function parseMultiPose(res, config3, image24, inputBox) {
async function parseMultiPose(res, config3, image25, inputBox) {
const bodies = [];
for (let id = 0; id < res[0].length; id++) {
const kpt4 = res[0][id];
@ -10051,11 +10073,11 @@ async function parseMultiPose(res, config3, image24, inputBox) {
part: kpt3[i],
score: Math.round(100 * score) / 100,
positionRaw,
position: [Math.round((image24.shape[2] || 0) * positionRaw[0]), Math.round((image24.shape[1] || 0) * positionRaw[1])]
position: [Math.round((image25.shape[2] || 0) * positionRaw[0]), Math.round((image25.shape[1] || 0) * positionRaw[1])]
});
}
}
const newBox = calc(keypoints.map((pt) => pt.position), [image24.shape[2], image24.shape[1]]);
const newBox = calc(keypoints.map((pt) => pt.position), [image25.shape[2], image25.shape[1]]);
const annotations2 = {};
for (const [name, indexes] of Object.entries(connected3)) {
const pt = [];
@ -10188,7 +10210,7 @@ async function process4(res, inputSize8, outputShape, config3) {
results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
return results;
}
async function predict11(image24, config3) {
async function predict11(image25, config3) {
const skipTime = (config3.object.skipTime || 0) > now() - lastTime10;
const skipFrame = skipped11 < (config3.object.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame && last5.length > 0) {
@ -10199,8 +10221,8 @@ async function predict11(image24, config3) {
if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense"))
return last5;
return new Promise(async (resolve) => {
const outputSize2 = [image24.shape[2], image24.shape[1]];
const resize = tf22.image.resizeBilinear(image24, [model11.inputSize, model11.inputSize], false);
const outputSize2 = [image25.shape[2], image25.shape[1]];
const resize = tf22.image.resizeBilinear(image25, [model11.inputSize, model11.inputSize], false);
const norm = tf22.div(resize, 255);
const transpose = norm.transpose([0, 3, 1, 2]);
tf22.dispose(norm);
@ -12925,6 +12947,9 @@ var Human = class {
enhance(input) {
return enhance2(input);
}
compare(firstImageTensor, secondImageTensor) {
return compare(this.config, firstImageTensor, secondImageTensor);
}
async init() {
await check(this, true);
await this.tf.ready();

105
dist/human.node.js vendored
View File

@ -919,8 +919,8 @@ function GLImageFilter() {
this.get = function() {
return filterChain;
};
this.apply = function(image24) {
resize(image24.width, image24.height);
this.apply = function(image25) {
resize(image25.width, image25.height);
drawCount = 0;
if (!sourceTexture)
sourceTexture = gl.createTexture();
@ -929,7 +929,7 @@ function GLImageFilter() {
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image24);
gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image25);
for (let i = 0; i < filterChain.length; i++) {
lastInChain = i === filterChain.length - 1;
const f = filterChain[i];
@ -937,9 +937,9 @@ function GLImageFilter() {
}
return fxcanvas;
};
this.draw = function(image24) {
this.draw = function(image25) {
this.add("brightness", 0);
return this.apply(image24);
return this.apply(image25);
};
}
@ -1180,6 +1180,28 @@ async function skip(config3, input) {
}
return skipFrame;
}
async function compare(config3, input1, input2) {
const t = {};
if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {
if (!config3.debug)
log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape);
return 0;
}
if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {
if (!config3.debug)
log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape);
return 0;
}
t.input1 = tf2.clone(input1);
t.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? tf2.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf2.clone(input2);
t.diff = tf2.sub(t.input1, t.input2);
t.squared = tf2.mul(t.diff, t.diff);
t.sum = tf2.sum(t.squared);
const diffSum = await t.sum.data();
const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;
tf2.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);
return diffRelative;
}
// src/util/env.ts
var Env = class {
@ -1339,7 +1361,7 @@ async function load2(config3) {
log("cached model:", model2["modelUrl"]);
return model2;
}
async function predict(image24, config3, idx, count2) {
async function predict(image25, config3, idx, count2) {
var _a, _b;
if (!model2)
return null;
@ -1351,7 +1373,7 @@ async function predict(image24, config3, idx, count2) {
}
skipped2 = 0;
return new Promise(async (resolve) => {
const resize = tf5.image.resizeBilinear(image24, [(model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[2] : 0, (model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[1] : 0], false);
const resize = tf5.image.resizeBilinear(image25, [(model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[2] : 0, (model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[1] : 0], false);
const res = model2 == null ? void 0 : model2.execute(resize);
const num = (await res.data())[0];
cached[idx] = Math.round(100 * num) / 100;
@ -4671,10 +4693,10 @@ var scaleBoxCoordinates = (box4, factor) => {
const endPoint = [box4.endPoint[0] * factor[0], box4.endPoint[1] * factor[1]];
return { startPoint, endPoint, landmarks: box4.landmarks, confidence: box4.confidence };
};
var cutBoxFromImageAndResize = (box4, image24, cropSize) => {
const h = image24.shape[1];
const w = image24.shape[2];
const crop2 = tf6.image.cropAndResize(image24, [[box4.startPoint[1] / h, box4.startPoint[0] / w, box4.endPoint[1] / h, box4.endPoint[0] / w]], [0], cropSize);
var cutBoxFromImageAndResize = (box4, image25, cropSize) => {
const h = image25.shape[1];
const w = image25.shape[2];
const crop2 = tf6.image.cropAndResize(image25, [[box4.startPoint[1] / h, box4.startPoint[0] / w, box4.endPoint[1] / h, box4.endPoint[0] / w]], [0], cropSize);
const norm = tf6.div(crop2, 255);
tf6.dispose(crop2);
return norm;
@ -5331,7 +5353,7 @@ function max2d(inputs, minScore) {
return [0, 0, newScore];
});
}
async function predict4(image24, config3) {
async function predict4(image25, config3) {
const skipTime = (config3.body.skipTime || 0) > now() - lastTime4;
const skipFrame = skipped5 < (config3.body.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) {
@ -5344,7 +5366,7 @@ async function predict4(image24, config3) {
const tensor3 = tf10.tidy(() => {
if (!(model5 == null ? void 0 : model5.inputs[0].shape))
return null;
const resize = tf10.image.resizeBilinear(image24, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
const resize = tf10.image.resizeBilinear(image25, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
const enhance3 = tf10.mul(resize, 2);
const norm = enhance3.sub(1);
return norm;
@ -5371,8 +5393,8 @@ async function predict4(image24, config3) {
y2 / model5.inputs[0].shape[1]
],
position: [
Math.round(image24.shape[2] * x2 / model5.inputs[0].shape[2]),
Math.round(image24.shape[1] * y2 / model5.inputs[0].shape[1])
Math.round(image25.shape[2] * x2 / model5.inputs[0].shape[2]),
Math.round(image25.shape[1] * y2 / model5.inputs[0].shape[1])
]
});
}
@ -5433,7 +5455,7 @@ async function load6(config3) {
log("cached model:", model6["modelUrl"]);
return model6;
}
async function predict5(image24, config3, idx, count2) {
async function predict5(image25, config3, idx, count2) {
var _a, _b;
if (!model6)
return null;
@ -5450,7 +5472,7 @@ async function predict5(image24, config3, idx, count2) {
if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) {
const t = {};
const inputSize8 = (model6 == null ? void 0 : model6.inputs[0].shape) ? model6.inputs[0].shape[2] : 0;
t.resize = tf11.image.resizeBilinear(image24, [inputSize8, inputSize8], false);
t.resize = tf11.image.resizeBilinear(image25, [inputSize8, inputSize8], false);
[t.red, t.green, t.blue] = tf11.split(t.resize, 3, 3);
t.redNorm = tf11.mul(t.red, rgb[0]);
t.greenNorm = tf11.mul(t.green, rgb[1]);
@ -5762,7 +5784,7 @@ function enhance2(input) {
tf14.dispose(crop2);
return norm;
}
async function predict7(image24, config3, idx, count2) {
async function predict7(image25, config3, idx, count2) {
var _a, _b, _c, _d;
if (!model9)
return null;
@ -5782,7 +5804,7 @@ async function predict7(image24, config3, idx, count2) {
descriptor: []
};
if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) {
const enhanced = enhance2(image24);
const enhanced = enhance2(image25);
const resT = model9 == null ? void 0 : model9.execute(enhanced);
lastTime7 = now();
tf14.dispose(enhanced);
@ -5830,16 +5852,16 @@ function getBoxCenter2(box4) {
box4.startPoint[1] + (box4.endPoint[1] - box4.startPoint[1]) / 2
];
}
function cutBoxFromImageAndResize2(box4, image24, cropSize) {
const h = image24.shape[1];
const w = image24.shape[2];
function cutBoxFromImageAndResize2(box4, image25, cropSize) {
const h = image25.shape[1];
const w = image25.shape[2];
const boxes = [[
box4.startPoint[1] / h,
box4.startPoint[0] / w,
box4.endPoint[1] / h,
box4.endPoint[0] / w
]];
return tf15.image.cropAndResize(image24, boxes, [0], cropSize);
return tf15.image.cropAndResize(image25, boxes, [0], cropSize);
}
function scaleBoxCoordinates2(box4, factor) {
const startPoint = [box4.startPoint[0] * factor[0], box4.startPoint[1] * factor[1]];
@ -9022,13 +9044,13 @@ var HandPipeline = class {
Math.trunc(coord[2])
]);
}
async estimateHands(image24, config3) {
async estimateHands(image25, config3) {
let useFreshBox = false;
let boxes;
const skipTime = (config3.hand.skipTime || 0) > now() - lastTime8;
const skipFrame = this.skipped < (config3.hand.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame) {
boxes = await this.handDetector.predict(image24, config3);
boxes = await this.handDetector.predict(image25, config3);
this.skipped = 0;
}
if (config3.skipAllowed)
@ -9047,8 +9069,8 @@ var HandPipeline = class {
if (config3.hand.landmarks) {
const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;
const palmCenter = getBoxCenter2(currentBox);
const palmCenterNormalized = [palmCenter[0] / image24.shape[2], palmCenter[1] / image24.shape[1]];
const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf17.image.rotateWithOffset(image24, angle, 0, palmCenterNormalized) : image24.clone();
const palmCenterNormalized = [palmCenter[0] / image25.shape[2], palmCenter[1] / image25.shape[1]];
const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf17.image.rotateWithOffset(image25, angle, 0, palmCenterNormalized) : image25.clone();
const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
const croppedInput = cutBoxFromImageAndResize2(newBox, rotatedImage, [this.inputSize, this.inputSize]);
@ -9894,11 +9916,11 @@ function bodyParts(body4) {
}
}
}
for (const [pair, compare] of relative) {
for (const [pair, compare2] of relative) {
const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]);
const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]);
const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare[0]);
const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare[1]);
const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]);
const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]);
if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo])
continue;
const distanceLeft = body4.keypoints[left] ? [
@ -9991,7 +10013,7 @@ async function load11(config3) {
inputSize7 = 256;
return model10;
}
async function parseSinglePose(res, config3, image24, inputBox) {
async function parseSinglePose(res, config3, image25, inputBox) {
const kpt4 = res[0][0];
const keypoints = [];
let score = 0;
@ -10007,15 +10029,15 @@ async function parseSinglePose(res, config3, image24, inputBox) {
part: kpt3[id],
positionRaw,
position: [
Math.round((image24.shape[2] || 0) * positionRaw[0]),
Math.round((image24.shape[1] || 0) * positionRaw[1])
Math.round((image25.shape[2] || 0) * positionRaw[0]),
Math.round((image25.shape[1] || 0) * positionRaw[1])
]
});
}
}
score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
const bodies = [];
const newBox = calc(keypoints.map((pt) => pt.position), [image24.shape[2], image24.shape[1]]);
const newBox = calc(keypoints.map((pt) => pt.position), [image25.shape[2], image25.shape[1]]);
const annotations2 = {};
for (const [name, indexes] of Object.entries(connected3)) {
const pt = [];
@ -10032,7 +10054,7 @@ async function parseSinglePose(res, config3, image24, inputBox) {
bodies.push(body4);
return bodies;
}
async function parseMultiPose(res, config3, image24, inputBox) {
async function parseMultiPose(res, config3, image25, inputBox) {
const bodies = [];
for (let id = 0; id < res[0].length; id++) {
const kpt4 = res[0][id];
@ -10050,11 +10072,11 @@ async function parseMultiPose(res, config3, image24, inputBox) {
part: kpt3[i],
score: Math.round(100 * score) / 100,
positionRaw,
position: [Math.round((image24.shape[2] || 0) * positionRaw[0]), Math.round((image24.shape[1] || 0) * positionRaw[1])]
position: [Math.round((image25.shape[2] || 0) * positionRaw[0]), Math.round((image25.shape[1] || 0) * positionRaw[1])]
});
}
}
const newBox = calc(keypoints.map((pt) => pt.position), [image24.shape[2], image24.shape[1]]);
const newBox = calc(keypoints.map((pt) => pt.position), [image25.shape[2], image25.shape[1]]);
const annotations2 = {};
for (const [name, indexes] of Object.entries(connected3)) {
const pt = [];
@ -10187,7 +10209,7 @@ async function process4(res, inputSize8, outputShape, config3) {
results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
return results;
}
async function predict11(image24, config3) {
async function predict11(image25, config3) {
const skipTime = (config3.object.skipTime || 0) > now() - lastTime10;
const skipFrame = skipped11 < (config3.object.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame && last5.length > 0) {
@ -10198,8 +10220,8 @@ async function predict11(image24, config3) {
if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense"))
return last5;
return new Promise(async (resolve) => {
const outputSize2 = [image24.shape[2], image24.shape[1]];
const resize = tf22.image.resizeBilinear(image24, [model11.inputSize, model11.inputSize], false);
const outputSize2 = [image25.shape[2], image25.shape[1]];
const resize = tf22.image.resizeBilinear(image25, [model11.inputSize, model11.inputSize], false);
const norm = tf22.div(resize, 255);
const transpose = norm.transpose([0, 3, 1, 2]);
tf22.dispose(norm);
@ -12924,6 +12946,9 @@ var Human = class {
enhance(input) {
return enhance2(input);
}
compare(firstImageTensor, secondImageTensor) {
return compare(this.config, firstImageTensor, secondImageTensor);
}
async init() {
await check(this, true);
await this.tf.ready();

10
src/config.ts
View File

@ -184,7 +184,7 @@ export interface GestureConfig {
* Contains all configurable parameters
* @typedef Config
*
* Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L292)
* Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)
*/
export interface Config {
/** Backend used for TFJS operations
@ -196,16 +196,19 @@ export interface Config {
backend: '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu',
/** Path to *.wasm files if backend is set to `wasm`
*
* default: auto-detects to link to CDN `jsdelivr` when running in browser
*/
wasmPath: string,
/** Print debug statements to console
*
* default: `true`
*/
debug: boolean,
/** Perform model loading and inference concurrently or sequentially
*
* default: `true`
*/
async: boolean,
@ -213,6 +216,7 @@ export interface Config {
/** What to use for `human.warmup()`
* - warmup pre-initializes all models for faster inference but can take significant time on startup
* - used by `webgl`, `humangl` and `webgpu` backends
*
* default: `full`
*/
warmup: 'none' | 'face' | 'full' | 'body',
@ -220,6 +224,7 @@ export interface Config {
/** Base model path (typically starting with file://, http:// or https://) for all models
* - individual modelPath values are relative to this path
*
* default: `../models/` for browsers and `file://models/` for nodejs
*/
modelBasePath: string,
@ -227,6 +232,7 @@ export interface Config {
/** Cache sensitivity
* - values 0..1 where 0.01 means reset cache if input changed more than 1%
* - set to 0 to disable caching
*
* default: 0.7
*/
cacheSensitivity: number;
@ -259,7 +265,7 @@ export interface Config {
segmentation: Partial<SegmentationConfig>,
}
/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L253) */
/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */
const config: Config = {
backend: '',
modelBasePath: '',

14
src/human.ts
View File

@ -68,7 +68,7 @@ export class Human {
version: string;
/** Current configuration
* - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L250)
* - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)
*/
config: Config;
@ -267,6 +267,18 @@ export class Human {
return faceres.enhance(input);
}
/** Compare two input tensors for pixel simmilarity
* - use `human.image` to process any valid input and get a tensor that can be used for compare
* - when passing manually generated tensors:
* - both input tensors must be in format [1, height, width, 3]
* - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor
* @returns {number}
* - return value is pixel similarity score normalized by input resolution and rgb channels
*/
compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number> {
return image.compare(this.config, firstImageTensor, secondImageTensor);
}
/** Explicit backend initialization
* - Normally done implicitly during initial load phase
* - Call to explictly register and initialize TFJS backend without any other operations

21
src/image/image.ts
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@ -272,3 +272,24 @@ export async function skip(config, input: Tensor) {
}
return skipFrame;
}
export async function compare(config, input1: Tensor, input2: Tensor): Promise<number> {
const t: Record<string, Tensor> = {};
if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {
if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);
return 0;
}
if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {
if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);
return 0;
}
t.input1 = tf.clone(input1);
t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);
t.diff = tf.sub(t.input1, t.input2);
t.squared = tf.mul(t.diff, t.diff);
t.sum = tf.sum(t.squared);
const diffSum = await t.sum.data();
const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;
tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);
return diffRelative;
}

27
test/README.md
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@ -6,11 +6,10 @@ Not required for normal funcioning of library
### NodeJS using TensorFlow library
- Image filters are disabled due to lack of Canvas and WeBGL access
- Image filters are disabled due to lack of Canvas and WebGL access
- Face rotation is disabled for `NodeJS` platform:
`Kernel 'RotateWithOffset' not registered for backend 'tensorflow'`
<https://github.com/tensorflow/tfjs/issues/4606>
Work has recently been completed and will likely be included in TFJS 3.9.0
### NodeJS using WASM
@ -18,27 +17,11 @@ Not required for normal funcioning of library
See <https://github.com/tensorflow/tfjs/issues/4927>
- Image filters are disabled due to lack of Canvas and WeBGL access
- Only supported input is Tensor due to missing image decoders
- Warmup returns null and is marked as failed
Missing image decode in `tfjs-core`
- Fails on object detection:
`Kernel 'SparseToDense' not registered for backend 'wasm'`
<https://github.com/tensorflow/tfjs/issues/4824>
<br>
## Manual Tests
## Browser Tests
### Browser using WebGL backend
- Chrome/Edge: All Passing
- Firefox: WebWorkers not supported due to missing support for OffscreenCanvas
- Safari: Limited Testing
### Browser using WASM backend
- Chrome/Edge: All Passing
- Firefox: WebWorkers not supported due to missing support for OffscreenCanvas
- Safari: Limited Testing
- Fails on object detection:
`Kernel 'SparseToDense' not registered for backend 'wasm'`
<https://github.com/tensorflow/tfjs/issues/4824>
- Chrome/Edge: **All Passing**
- Firefox: WebWorkers not supported due to missing support for `OffscreenCanvas`
- Safari: **Limited Testing**

50
test/build.log
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@ -1,25 +1,25 @@
2021-11-06 10:12:23 INFO:  @vladmandic/human version 2.5.0
2021-11-06 10:12:23 INFO:  User: vlado Platform: linux Arch: x64 Node: v17.0.1
2021-11-06 10:12:23 INFO:  Application: {"name":"@vladmandic/human","version":"2.5.0"}
2021-11-06 10:12:23 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true}
2021-11-06 10:12:23 INFO:  Toolchain: {"build":"0.6.3","esbuild":"0.13.12","typescript":"4.4.4","typedoc":"0.22.7","eslint":"8.2.0"}
2021-11-06 10:12:23 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]}
2021-11-06 10:12:23 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]}
2021-11-06 10:12:23 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":1275}
2021-11-06 10:12:23 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":56,"inputBytes":520786,"outputBytes":440096}
2021-11-06 10:12:23 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":1283}
2021-11-06 10:12:23 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":56,"inputBytes":520794,"outputBytes":440100}
2021-11-06 10:12:23 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":149,"outputBytes":1350}
2021-11-06 10:12:23 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":56,"inputBytes":520861,"outputBytes":440172}
2021-11-06 10:12:23 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1063,"outputBytes":1652}
2021-11-06 10:12:23 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2326,"outputBytes":912}
2021-11-06 10:12:23 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":56,"inputBytes":520423,"outputBytes":441983}
2021-11-06 10:12:23 STATE: Compile: {"name":"tfjs/browser/esm/custom","format":"esm","platform":"browser","input":"tfjs/tf-custom.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2562175,"outputBytes":2497378}
2021-11-06 10:12:24 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":56,"inputBytes":3016889,"outputBytes":3096312}
2021-11-06 10:12:24 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":56,"inputBytes":3016889,"outputBytes":2945011}
2021-11-06 10:12:43 STATE: Typings: {"input":"src/human.ts","output":"types","files":49}
2021-11-06 10:12:49 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":48,"generated":true}
2021-11-06 10:12:49 STATE: Compile: {"name":"demo/browser","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":5851,"outputBytes":3818}
2021-11-06 10:13:25 STATE: Lint: {"locations":["*.json","src/**/*.ts","test/**/*.js","demo/**/*.js"],"files":90,"errors":0,"warnings":0}
2021-11-06 10:13:26 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"}
2021-11-06 10:13:26 INFO:  Done...
2021-11-07 09:49:34 INFO:  @vladmandic/human version 2.5.0
2021-11-07 09:49:34 INFO:  User: vlado Platform: linux Arch: x64 Node: v17.0.1
2021-11-07 09:49:34 INFO:  Application: {"name":"@vladmandic/human","version":"2.5.0"}
2021-11-07 09:49:34 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true}
2021-11-07 09:49:34 INFO:  Toolchain: {"build":"0.6.3","esbuild":"0.13.12","typescript":"4.4.4","typedoc":"0.22.7","eslint":"8.2.0"}
2021-11-07 09:49:34 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]}
2021-11-07 09:49:34 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]}
2021-11-07 09:49:34 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":1275}
2021-11-07 09:49:34 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":56,"inputBytes":522624,"outputBytes":441314}
2021-11-07 09:49:34 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":1283}
2021-11-07 09:49:34 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":56,"inputBytes":522632,"outputBytes":441318}
2021-11-07 09:49:34 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":149,"outputBytes":1350}
2021-11-07 09:49:34 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":56,"inputBytes":522699,"outputBytes":441390}
2021-11-07 09:49:34 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1063,"outputBytes":1652}
2021-11-07 09:49:34 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2326,"outputBytes":912}
2021-11-07 09:49:34 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":56,"inputBytes":522261,"outputBytes":443292}
2021-11-07 09:49:35 STATE: Compile: {"name":"tfjs/browser/esm/custom","format":"esm","platform":"browser","input":"tfjs/tf-custom.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2562175,"outputBytes":2497378}
2021-11-07 09:49:35 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":56,"inputBytes":3018727,"outputBytes":3097554}
2021-11-07 09:49:35 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":56,"inputBytes":3018727,"outputBytes":2946203}
2021-11-07 09:49:55 STATE: Typings: {"input":"src/human.ts","output":"types","files":49}
2021-11-07 09:50:02 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":48,"generated":true}
2021-11-07 09:50:02 STATE: Compile: {"name":"demo/browser","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":5851,"outputBytes":3818}
2021-11-07 09:50:38 STATE: Lint: {"locations":["*.json","src/**/*.ts","test/**/*.js","demo/**/*.js"],"files":90,"errors":0,"warnings":0}
2021-11-07 09:50:39 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"}
2021-11-07 09:50:39 INFO:  Done...

48
test/test-main.js
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@ -135,12 +135,55 @@ async function testDetect(human, input, title, checkLeak = true) {
}
return detect;
}
const evt = { image: 0, detect: 0, warmup: 0 };
async function events(event) {
log('state', 'event:', event);
evt[event]++;
}
const verify = (state, ...messages) => {
if (state) log('state', 'passed:', ...messages);
else log('error', 'failed:', ...messages);
};
async function verifyDetails(human) {
const res = await testDetect(human, 'samples/in/ai-body.jpg', 'default');
verify(res.face.length === 1, 'details face length', res.face.length);
for (const face of res.face) {
verify(face.score > 0.9 && face.boxScore > 0.9 && face.faceScore > 0.9, 'details face score', face.score, face.boxScore, face.faceScore);
verify(face.age > 29 && face.age < 30 && face.gender === 'female' && face.genderScore > 0.9 && face.iris > 70 && face.iris < 80, 'details face age/gender', face.age, face.gender, face.genderScore, face.iris);
verify(face.box.length === 4 && face.boxRaw.length === 4 && face.mesh.length === 478 && face.meshRaw.length === 478 && face.embedding.length === 1024, 'details face arrays', face.box.length, face.mesh.length, face.embedding.length);
verify(face.emotion.length === 3 && face.emotion[0].score > 0.5 && face.emotion[0].emotion === 'angry', 'details face emotion', face.emotion.length, face.emotion[0]);
}
verify(res.body.length === 1, 'details body length', res.body.length);
for (const body of res.body) {
verify(body.score > 0.9 && body.box.length === 4 && body.boxRaw.length === 4 && body.keypoints.length === 17 && Object.keys(body.annotations).length === 6, 'details body', body.score, body.keypoints.length, Object.keys(body.annotations).length);
}
verify(res.hand.length === 1, 'details hand length', res.hand.length);
for (const hand of res.hand) {
verify(hand.score > 0.5 && hand.boxScore > 0.5 && hand.fingerScore > 0.5 && hand.box.length === 4 && hand.boxRaw.length === 4 && hand.label === 'point', 'details hand', hand.boxScore, hand.fingerScore, hand.label);
verify(hand.keypoints.length === 21 && Object.keys(hand.landmarks).length === 5 && Object.keys(hand.annotations).length === 6, 'details hand arrays', hand.keypoints.length, Object.keys(hand.landmarks).length, Object.keys(hand.annotations).length);
}
verify(res.gesture.length === 5, 'details gesture length', res.gesture.length);
verify(res.gesture[0].gesture === 'facing right', 'details gesture first', res.gesture[0]);
verify(res.object.length === 1, 'details object length', res.object.length);
for (const obj of res.object) {
verify(obj.score > 0.7 && obj.label === 'person' && obj.box.length === 4 && obj.boxRaw.length === 4, 'details object', obj.score, obj.label);
}
}
async function verifyCompare(human) {
const t1 = await getImage(human, 'samples/in/ai-face.jpg');
const t2 = await getImage(human, 'samples/in/ai-body.jpg');
const n1 = await human.compare(t1, t1);
const n2 = await human.compare(t1, t2);
const n3 = await human.compare(t2, t1);
const n4 = await human.compare(t2, t2);
verify(n1 === 0 && n4 === 0 && Math.round(n2) === Math.round(n3) && n2 > 20 && n2 < 30, 'image compare', n1, n2);
human.tf.dispose([t1, t2]);
}
async function test(Human, inputConfig) {
config = inputConfig;
fetch = (await import('node-fetch')).default;
@ -202,6 +245,8 @@ async function test(Human, inputConfig) {
gestures: res.gesture,
});
await verifyDetails(human);
// test default config async
log('info', 'test default');
human.reset();
@ -357,6 +402,9 @@ async function test(Human, inputConfig) {
if (tensors1 === tensors2 && tensors1 === tensors3 && tensors2 === tensors3) log('state', 'passeed: equal usage');
else log('error', 'failed: equal usage', tensors1, tensors2, tensors3);
// validate cache compare algorithm
await verifyCompare(human);
// tests end
const t1 = process.hrtime.bigint();

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typedoc/classes/Human.html
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2
typedoc/index.html
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@ -75,7 +75,7 @@
<li>Can be TFJS that is bundled with <code>Human</code> or a manually imported TFJS library</li>
</ul>
</div><dl class="tsd-comment-tags"><dt>external</dt><dd><p><a href="https://js.tensorflow.org/api/latest/">API</a></p>
</dd></dl></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Variables</h2><section class="tsd-panel tsd-member tsd-kind-variable"><a id="defaults" class="tsd-anchor"></a><h3>defaults</h3><div class="tsd-signature tsd-kind-icon">defaults<span class="tsd-signature-symbol">:</span> <a href="interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol"> = ...</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L263">src/config.ts:263</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
</dd></dl></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Variables</h2><section class="tsd-panel tsd-member tsd-kind-variable"><a id="defaults" class="tsd-anchor"></a><h3>defaults</h3><div class="tsd-signature tsd-kind-icon">defaults<span class="tsd-signature-symbol">:</span> <a href="interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol"> = ...</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L269">src/config.ts:269</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
<ul>
<li><a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L253">See all default Config values...</a></li>
</ul>

54
typedoc/interfaces/Config.html
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@ -1,9 +1,9 @@
<!DOCTYPE html><html class="default no-js"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Config | @vladmandic/human - v2.5.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.5.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.5.0</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.5.0</a></li><li><a href="Config.html">Config</a></li></ul><h1>Interface Config</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
<p>Configuration interface definition for <strong>Human</strong> library</p>
</div><div><p>Contains all configurable parameters</p>
</div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">Config</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#async" class="tsd-kind-icon">async</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#backend" class="tsd-kind-icon">backend</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#body" class="tsd-kind-icon">body</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#cacheSensitivity" class="tsd-kind-icon">cache<wbr/>Sensitivity</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#deallocate" class="tsd-kind-icon">deallocate</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#debug" class="tsd-kind-icon">debug</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#face" class="tsd-kind-icon">face</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#filter" class="tsd-kind-icon">filter</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#gesture" class="tsd-kind-icon">gesture</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#hand" class="tsd-kind-icon">hand</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#modelBasePath" class="tsd-kind-icon">model<wbr/>Base<wbr/>Path</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#object" class="tsd-kind-icon">object</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#segmentation" class="tsd-kind-icon">segmentation</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#skipAllowed" class="tsd-kind-icon">skip<wbr/>Allowed</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#warmup" class="tsd-kind-icon">warmup</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#wasmPath" class="tsd-kind-icon">wasm<wbr/>Path</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="async" class="tsd-anchor"></a><h3>async</h3><div class="tsd-signature tsd-kind-icon">async<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L211">src/config.ts:211</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
<p>Perform model loading and inference concurrently or sequentially
default: <code>true</code></p>
</div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">Config</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#async" class="tsd-kind-icon">async</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#backend" class="tsd-kind-icon">backend</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#body" class="tsd-kind-icon">body</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#cacheSensitivity" class="tsd-kind-icon">cache<wbr/>Sensitivity</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#deallocate" class="tsd-kind-icon">deallocate</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#debug" class="tsd-kind-icon">debug</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#face" class="tsd-kind-icon">face</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#filter" class="tsd-kind-icon">filter</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#gesture" class="tsd-kind-icon">gesture</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#hand" class="tsd-kind-icon">hand</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#modelBasePath" class="tsd-kind-icon">model<wbr/>Base<wbr/>Path</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#object" class="tsd-kind-icon">object</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#segmentation" class="tsd-kind-icon">segmentation</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#skipAllowed" class="tsd-kind-icon">skip<wbr/>Allowed</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#warmup" class="tsd-kind-icon">warmup</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#wasmPath" class="tsd-kind-icon">wasm<wbr/>Path</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="async" class="tsd-anchor"></a><h3>async</h3><div class="tsd-signature tsd-kind-icon">async<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L214">src/config.ts:214</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
<p>Perform model loading and inference concurrently or sequentially</p>
</div><div><p>default: <code>true</code></p>
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="backend" class="tsd-anchor"></a><h3>backend</h3><div class="tsd-signature tsd-kind-icon">backend<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">&quot;&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;cpu&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;wasm&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;webgl&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;humangl&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;tensorflow&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;webgpu&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L196">src/config.ts:196</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
<p>Backend used for TFJS operations
valid build-in backends are:</p>
@ -12,48 +12,48 @@ valid build-in backends are:</p>
<li>NodeJS: <code>cpu</code>, <code>wasm</code>, <code>tensorflow</code>
default: <code>humangl</code> for browser and <code>tensorflow</code> for nodejs</li>
</ul>
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="body" class="tsd-anchor"></a><h3>body</h3><div class="tsd-signature tsd-kind-icon">body<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="BodyConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">BodyConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L250">src/config.ts:250</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="body" class="tsd-anchor"></a><h3>body</h3><div class="tsd-signature tsd-kind-icon">body<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="BodyConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">BodyConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L256">src/config.ts:256</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
<p><a href="BodyConfig.html">BodyConfig</a></p>
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="cacheSensitivity" class="tsd-anchor"></a><h3>cache<wbr/>Sensitivity</h3><div class="tsd-signature tsd-kind-icon">cache<wbr/>Sensitivity<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L232">src/config.ts:232</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="cacheSensitivity" class="tsd-anchor"></a><h3>cache<wbr/>Sensitivity</h3><div class="tsd-signature tsd-kind-icon">cache<wbr/>Sensitivity<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L238">src/config.ts:238</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
<p>Cache sensitivity</p>
<ul>
<li>values 0..1 where 0.01 means reset cache if input changed more than 1%</li>
<li>set to 0 to disable caching
default: 0.7</li>
<li>set to 0 to disable caching</li>
</ul>
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="deallocate" class="tsd-anchor"></a><h3>deallocate</h3><div class="tsd-signature tsd-kind-icon">deallocate<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L235">src/config.ts:235</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
</div><div><p>default: 0.7</p>
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="deallocate" class="tsd-anchor"></a><h3>deallocate</h3><div class="tsd-signature tsd-kind-icon">deallocate<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L241">src/config.ts:241</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
<p>Perform immediate garbage collection on deallocated tensors instead of caching them</p>
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="debug" class="tsd-anchor"></a><h3>debug</h3><div class="tsd-signature tsd-kind-icon">debug<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L206">src/config.ts:206</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
<p>Print debug statements to console
default: <code>true</code></p>
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="face" class="tsd-anchor"></a><h3>face</h3><div class="tsd-signature tsd-kind-icon">face<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="FaceConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L247">src/config.ts:247</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="debug" class="tsd-anchor"></a><h3>debug</h3><div class="tsd-signature tsd-kind-icon">debug<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L208">src/config.ts:208</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
<p>Print debug statements to console</p>
</div><div><p>default: <code>true</code></p>
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="face" class="tsd-anchor"></a><h3>face</h3><div class="tsd-signature tsd-kind-icon">face<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="FaceConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L253">src/config.ts:253</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
<p><a href="FaceConfig.html">FaceConfig</a></p>
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="filter" class="tsd-anchor"></a><h3>filter</h3><div class="tsd-signature tsd-kind-icon">filter<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="FilterConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FilterConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L241">src/config.ts:241</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="filter" class="tsd-anchor"></a><h3>filter</h3><div class="tsd-signature tsd-kind-icon">filter<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="FilterConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FilterConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L247">src/config.ts:247</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
<p><a href="FilterConfig.html">FilterConfig</a></p>
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="gesture" class="tsd-anchor"></a><h3>gesture</h3><div class="tsd-signature tsd-kind-icon">gesture<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="GestureConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">GestureConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L244">src/config.ts:244</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="gesture" class="tsd-anchor"></a><h3>gesture</h3><div class="tsd-signature tsd-kind-icon">gesture<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="GestureConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">GestureConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L250">src/config.ts:250</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
<p><a href="GestureConfig.html">GestureConfig</a></p>
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="hand" class="tsd-anchor"></a><h3>hand</h3><div class="tsd-signature tsd-kind-icon">hand<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="HandConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">HandConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L253">src/config.ts:253</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="hand" class="tsd-anchor"></a><h3>hand</h3><div class="tsd-signature tsd-kind-icon">hand<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="HandConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">HandConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L259">src/config.ts:259</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
<p><a href="HandConfig.html">HandConfig</a></p>
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="modelBasePath" class="tsd-anchor"></a><h3>model<wbr/>Base<wbr/>Path</h3><div class="tsd-signature tsd-kind-icon">model<wbr/>Base<wbr/>Path<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L225">src/config.ts:225</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="modelBasePath" class="tsd-anchor"></a><h3>model<wbr/>Base<wbr/>Path</h3><div class="tsd-signature tsd-kind-icon">model<wbr/>Base<wbr/>Path<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L230">src/config.ts:230</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
<p>Base model path (typically starting with file://, http:// or https://) for all models</p>
<ul>
<li>individual modelPath values are relative to this path
default: <code>../models/</code> for browsers and <code>file://models/</code> for nodejs</li>
<li>individual modelPath values are relative to this path</li>
</ul>
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="object" class="tsd-anchor"></a><h3>object</h3><div class="tsd-signature tsd-kind-icon">object<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="ObjectConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">ObjectConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L256">src/config.ts:256</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
</div><div><p>default: <code>../models/</code> for browsers and <code>file://models/</code> for nodejs</p>
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="object" class="tsd-anchor"></a><h3>object</h3><div class="tsd-signature tsd-kind-icon">object<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="ObjectConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">ObjectConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L262">src/config.ts:262</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
<p><a href="ObjectConfig.html">ObjectConfig</a></p>
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="segmentation" class="tsd-anchor"></a><h3>segmentation</h3><div class="tsd-signature tsd-kind-icon">segmentation<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="SegmentationConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">SegmentationConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L259">src/config.ts:259</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="segmentation" class="tsd-anchor"></a><h3>segmentation</h3><div class="tsd-signature tsd-kind-icon">segmentation<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="SegmentationConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">SegmentationConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L265">src/config.ts:265</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
<p><a href="SegmentationConfig.html">SegmentationConfig</a></p>
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="skipAllowed" class="tsd-anchor"></a><h3>skip<wbr/>Allowed</h3><div class="tsd-signature tsd-kind-icon">skip<wbr/>Allowed<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L238">src/config.ts:238</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="skipAllowed" class="tsd-anchor"></a><h3>skip<wbr/>Allowed</h3><div class="tsd-signature tsd-kind-icon">skip<wbr/>Allowed<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L244">src/config.ts:244</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
<p>Internal Variable</p>
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="warmup" class="tsd-anchor"></a><h3>warmup</h3><div class="tsd-signature tsd-kind-icon">warmup<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">&quot;face&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;body&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;none&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;full&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L218">src/config.ts:218</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="warmup" class="tsd-anchor"></a><h3>warmup</h3><div class="tsd-signature tsd-kind-icon">warmup<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">&quot;face&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;body&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;none&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;full&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L222">src/config.ts:222</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
<p>What to use for <code>human.warmup()</code></p>
<ul>
<li>warmup pre-initializes all models for faster inference but can take significant time on startup</li>
<li>used by <code>webgl</code>, <code>humangl</code> and <code>webgpu</code> backends
default: <code>full</code></li>
<li>used by <code>webgl</code>, <code>humangl</code> and <code>webgpu</code> backends</li>
</ul>
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="wasmPath" class="tsd-anchor"></a><h3>wasm<wbr/>Path</h3><div class="tsd-signature tsd-kind-icon">wasm<wbr/>Path<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L201">src/config.ts:201</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
<p>Path to *.wasm files if backend is set to <code>wasm</code>
default: auto-detects to link to CDN <code>jsdelivr</code> when running in browser</p>
</div><div><p>default: <code>full</code></p>
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="wasmPath" class="tsd-anchor"></a><h3>wasm<wbr/>Path</h3><div class="tsd-signature tsd-kind-icon">wasm<wbr/>Path<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L202">src/config.ts:202</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
<p>Path to *.wasm files if backend is set to <code>wasm</code></p>
</div><div><p>default: auto-detects to link to CDN <code>jsdelivr</code> when running in browser</p>
</div></div></section></section></div><div class="col-4 col-menu menu-sticky-wrap menu-highlight"><nav class="tsd-navigation primary"><ul><li class=""><a href="../index.html">Exports</a></li><li class=" tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li></ul></nav><nav class="tsd-navigation secondary menu-sticky"><ul><li class="current tsd-kind-interface"><a href="Config.html" class="tsd-kind-icon">Config</a><ul><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#async" class="tsd-kind-icon">async</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#backend" class="tsd-kind-icon">backend</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#body" class="tsd-kind-icon">body</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#cacheSensitivity" class="tsd-kind-icon">cache<wbr/>Sensitivity</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#deallocate" class="tsd-kind-icon">deallocate</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#debug" class="tsd-kind-icon">debug</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#face" class="tsd-kind-icon">face</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#filter" class="tsd-kind-icon">filter</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#gesture" class="tsd-kind-icon">gesture</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#hand" class="tsd-kind-icon">hand</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#modelBasePath" class="tsd-kind-icon">model<wbr/>Base<wbr/>Path</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#object" class="tsd-kind-icon">object</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#segmentation" class="tsd-kind-icon">segmentation</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#skipAllowed" class="tsd-kind-icon">skip<wbr/>Allowed</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#warmup" class="tsd-kind-icon">warmup</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#wasmPath" class="tsd-kind-icon">wasm<wbr/>Path</a></li></ul></li></ul></nav></div></div></div><footer class=""><div class="container"><h2>Legend</h2><div class="tsd-legend-group"><ul class="tsd-legend"><li class="tsd-kind-constructor tsd-parent-kind-class"><span class="tsd-kind-icon">Constructor</span></li><li class="tsd-kind-property tsd-parent-kind-class"><span class="tsd-kind-icon">Property</span></li><li class="tsd-kind-method tsd-parent-kind-class"><span class="tsd-kind-icon">Method</span></li></ul><ul class="tsd-legend"><li class="tsd-kind-property tsd-parent-kind-interface"><span class="tsd-kind-icon">Property</span></li></ul></div><h2>Settings</h2><p>Theme <select id="theme"><option value="os">OS</option><option value="light">Light</option><option value="dark">Dark</option></select></p></div></footer><div class="overlay"></div><script src="../assets/main.js"></script></body></html>

6
types/src/config.d.ts vendored
View File

@ -181,31 +181,37 @@ export interface Config {
*/
backend: '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';
/** Path to *.wasm files if backend is set to `wasm`
*
* default: auto-detects to link to CDN `jsdelivr` when running in browser
*/
wasmPath: string;
/** Print debug statements to console
*
* default: `true`
*/
debug: boolean;
/** Perform model loading and inference concurrently or sequentially
*
* default: `true`
*/
async: boolean;
/** What to use for `human.warmup()`
* - warmup pre-initializes all models for faster inference but can take significant time on startup
* - used by `webgl`, `humangl` and `webgpu` backends
*
* default: `full`
*/
warmup: 'none' | 'face' | 'full' | 'body';
/** Base model path (typically starting with file://, http:// or https://) for all models
* - individual modelPath values are relative to this path
*
* default: `../models/` for browsers and `file://models/` for nodejs
*/
modelBasePath: string;
/** Cache sensitivity
* - values 0..1 where 0.01 means reset cache if input changed more than 1%
* - set to 0 to disable caching
*
* default: 0.7
*/
cacheSensitivity: number;

9
types/src/human.d.ts vendored
View File

@ -160,6 +160,15 @@ export declare class Human {
* @returns Tensor
*/
enhance(input: Tensor): Tensor | null;
/** Compare two input tensors for pixel simmilarity
* - use `human.image` to process any valid input and get a tensor that can be used for compare
* - when passing manually generated tensors:
* - both input tensors must be in format [1, height, width, 3]
* - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor
* @returns {number}
* - return value is pixel similarity score normalized by input resolution and rgb channels
*/
compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number>;
/** Explicit backend initialization
* - Normally done implicitly during initial load phase
* - Call to explictly register and initialize TFJS backend without any other operations

1
types/src/image/image.d.ts vendored
View File

@ -9,3 +9,4 @@ export declare function process(input: Input, config: Config, getTensor?: boolea
canvas: AnyCanvas | null;
}>;
export declare function skip(config: any, input: Tensor): Promise<boolean>;
export declare function compare(config: any, input1: Tensor, input2: Tensor): Promise<number>;