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human/dist/human.node-wasm.js

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/*
Human
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
"use strict";
var __create = Object.create;
var __defProp = Object.defineProperty;
var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
var __getOwnPropNames = Object.getOwnPropertyNames;
var __getProtoOf = Object.getPrototypeOf;
var __hasOwnProp = Object.prototype.hasOwnProperty;
var __defNormalProp = (obj, key, value) => key in obj ? __defProp(obj, key, { enumerable: true, configurable: true, writable: true, value }) : obj[key] = value;
var __commonJS = (cb, mod3) => function __require() {
return mod3 || (0, cb[__getOwnPropNames(cb)[0]])((mod3 = { exports: {} }).exports, mod3), mod3.exports;
};
var __export = (target, all2) => {
for (var name in all2)
__defProp(target, name, { get: all2[name], enumerable: true });
};
var __copyProps = (to, from, except, desc) => {
if (from && typeof from === "object" || typeof from === "function") {
for (let key of __getOwnPropNames(from))
if (!__hasOwnProp.call(to, key) && key !== except)
__defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable });
}
return to;
};
var __toESM = (mod3, isNodeMode, target) => (target = mod3 != null ? __create(__getProtoOf(mod3)) : {}, __copyProps(
isNodeMode || !mod3 || !mod3.__esModule ? __defProp(target, "default", { value: mod3, enumerable: true }) : target,
mod3
));
var __toCommonJS = (mod3) => __copyProps(__defProp({}, "__esModule", { value: true }), mod3);
var __publicField = (obj, key, value) => {
__defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value);
return value;
};
var __accessCheck = (obj, member, msg) => {
if (!member.has(obj))
throw TypeError("Cannot " + msg);
};
var __privateGet = (obj, member, getter) => {
__accessCheck(obj, member, "read from private field");
return getter ? getter.call(obj) : member.get(obj);
};
var __privateAdd = (obj, member, value) => {
if (member.has(obj))
throw TypeError("Cannot add the same private member more than once");
member instanceof WeakSet ? member.add(obj) : member.set(obj, value);
};
var __privateSet = (obj, member, value, setter) => {
__accessCheck(obj, member, "write to private field");
setter ? setter.call(obj, value) : member.set(obj, value);
return value;
};
// dist/tfjs.esm.js
var require_tfjs_esm = __commonJS({
"dist/tfjs.esm.js"(exports, module2) {
"use strict";
var __defProp2 = Object.defineProperty;
var __getOwnPropDesc2 = Object.getOwnPropertyDescriptor;
var __getOwnPropNames2 = Object.getOwnPropertyNames;
var __hasOwnProp2 = Object.prototype.hasOwnProperty;
var __export2 = (target, all2) => {
for (var name in all2)
__defProp2(target, name, { get: all2[name], enumerable: true });
};
var __copyProps2 = (to, from, except, desc) => {
if (from && typeof from === "object" || typeof from === "function") {
for (let key of __getOwnPropNames2(from))
if (!__hasOwnProp2.call(to, key) && key !== except)
__defProp2(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc2(from, key)) || desc.enumerable });
}
return to;
};
var __reExport = (target, mod3, secondTarget) => (__copyProps2(target, mod3, "default"), secondTarget && __copyProps2(secondTarget, mod3, "default"));
var __toCommonJS2 = (mod3) => __copyProps2(__defProp2({}, "__esModule", { value: true }), mod3);
var tf_node_wasm_exports = {};
__export2(tf_node_wasm_exports, {
version: () => version7
});
module2.exports = __toCommonJS2(tf_node_wasm_exports);
__reExport(tf_node_wasm_exports, require("@tensorflow/tfjs-core"), module2.exports);
__reExport(tf_node_wasm_exports, require("@tensorflow/tfjs-converter"), module2.exports);
__reExport(tf_node_wasm_exports, require("@tensorflow/tfjs-backend-wasm"), module2.exports);
var version4 = "4.1.0";
var version22 = "4.1.0";
var version32 = "4.1.0";
var version42 = "4.1.0";
var version5 = "4.1.0";
var version6 = "0.0.1-alpha.16";
var version7 = {
tfjs: version4,
"tfjs-core": version4,
"tfjs-converter": version22,
"tfjs-backend-cpu": version32,
"tfjs-backend-webgl": version42,
"tfjs-backend-wasm": version5,
"tfjs-backend-webgpu": version6
};
}
});
// src/human.ts
var human_exports = {};
__export(human_exports, {
Env: () => Env,
Human: () => Human,
default: () => Human,
defaults: () => config,
draw: () => draw_exports,
empty: () => empty,
env: () => env,
match: () => match_exports,
models: () => models_exports2
});
module.exports = __toCommonJS(human_exports);
var tf38 = __toESM(require_tfjs_esm());
// src/util/util.ts
function log(...msg) {
const dt = new Date();
const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`;
if (msg)
console.log(ts, "Human:", ...msg);
}
function join(folder, file) {
const separator = folder.endsWith("/") ? "" : "/";
const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:");
const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;
if (!path.toLocaleLowerCase().includes(".json"))
throw new Error(`modelpath error: expecting json file: ${path}`);
return path;
}
var now = () => {
if (typeof performance !== "undefined")
return performance.now();
return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString());
};
function validate(defaults, config3, parent = "config", msgs = []) {
for (const key of Object.keys(config3)) {
if (typeof config3[key] === "object") {
validate(defaults[key], config3[key], key, msgs);
} else {
const defined = defaults && typeof defaults[key] !== "undefined";
if (!defined)
msgs.push({ reason: "unknown property", where: `${parent}.${key} = ${config3[key]}` });
const same = defaults && typeof defaults[key] === typeof config3[key];
if (defined && !same)
msgs.push({ reason: "property type mismatch", where: `${parent}.${key} = ${config3[key]}`, expected: typeof defaults[key] });
}
}
if (config3.debug && parent === "config" && msgs.length > 0)
log("invalid configuration", msgs);
return msgs;
}
function mergeDeep(...objects) {
const isObject = (obj) => obj && typeof obj === "object";
return objects.reduce((prev, obj) => {
Object.keys(obj || {}).forEach((key) => {
const pVal = prev[key];
const oVal = obj[key];
if (Array.isArray(pVal) && Array.isArray(oVal))
prev[key] = pVal.concat(...oVal);
else if (isObject(pVal) && isObject(oVal))
prev[key] = mergeDeep(pVal, oVal);
else
prev[key] = oVal;
});
return prev;
}, {});
}
// src/config.ts
var config = {
backend: "",
modelBasePath: "",
cacheModels: true,
validateModels: true,
wasmPath: "",
wasmPlatformFetch: false,
debug: false,
async: true,
warmup: "full",
cacheSensitivity: 0.7,
skipAllowed: false,
deallocate: false,
flags: {},
softwareKernels: false,
filter: {
enabled: true,
equalization: false,
width: 0,
height: 0,
flip: false,
return: true,
autoBrightness: true,
brightness: 0,
contrast: 0,
sharpness: 0,
blur: 0,
saturation: 0,
hue: 0,
negative: false,
sepia: false,
vintage: false,
kodachrome: false,
technicolor: false,
polaroid: false,
pixelate: 0
},
gesture: {
enabled: true
},
face: {
enabled: true,
detector: {
modelPath: "blazeface.json",
rotation: true,
maxDetected: 1,
skipFrames: 99,
skipTime: 2500,
minConfidence: 0.2,
iouThreshold: 0.1,
mask: false,
return: false
},
mesh: {
enabled: true,
modelPath: "facemesh.json",
keepInvalid: false
},
attention: {
enabled: false,
modelPath: "facemesh-attention.json"
},
iris: {
enabled: true,
modelPath: "iris.json"
},
emotion: {
enabled: true,
minConfidence: 0.1,
skipFrames: 99,
skipTime: 1500,
modelPath: "emotion.json"
},
description: {
enabled: true,
modelPath: "faceres.json",
skipFrames: 99,
skipTime: 3e3,
minConfidence: 0.1
},
antispoof: {
enabled: false,
skipFrames: 99,
skipTime: 4e3,
modelPath: "antispoof.json"
},
liveness: {
enabled: false,
skipFrames: 99,
skipTime: 4e3,
modelPath: "liveness.json"
}
},
body: {
enabled: true,
modelPath: "movenet-lightning.json",
maxDetected: -1,
minConfidence: 0.3,
skipFrames: 1,
skipTime: 200
},
hand: {
enabled: true,
rotation: true,
skipFrames: 99,
skipTime: 1e3,
minConfidence: 0.5,
iouThreshold: 0.2,
maxDetected: -1,
landmarks: true,
detector: {
modelPath: "handtrack.json"
},
skeleton: {
modelPath: "handlandmark-full.json"
}
},
object: {
enabled: false,
modelPath: "centernet.json",
minConfidence: 0.2,
iouThreshold: 0.4,
maxDetected: 10,
skipFrames: 99,
skipTime: 2e3
},
segmentation: {
enabled: false,
modelPath: "rvm.json",
ratio: 0.5,
mode: "default"
}
};
// src/util/env.ts
var tf3 = __toESM(require_tfjs_esm());
// src/image/image.ts
var tf2 = __toESM(require_tfjs_esm());
// src/image/imagefxshaders.ts
var vertexIdentity = `
precision highp float;
attribute vec2 pos;
attribute vec2 uv;
varying vec2 vUv;
uniform float flipY;
void main(void) {
vUv = uv;
gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
}
`;
var colorMatrixWithAlpha = `
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform float m[20];
void main(void) {
vec4 c = texture2D(texture, vUv);
gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];
gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];
gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
}
`;
var colorMatrixWithoutAlpha = `
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform float m[20];
void main(void) {
vec4 c = texture2D(texture, vUv);
gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];
gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];
gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
gl_FragColor.a = c.a;
}
`;
var pixelate = `
precision highp float;
varying vec2 vUv;
uniform vec2 size;
uniform sampler2D texture;
vec2 pixelate(vec2 coord, vec2 size) {
return floor( coord / size ) * size;
}
void main(void) {
gl_FragColor = vec4(0.0);
vec2 coord = pixelate(vUv, size);
gl_FragColor += texture2D(texture, coord);
}
`;
var blur = `
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform vec2 px;
void main(void) {
gl_FragColor = vec4(0.0);
gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;
gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;
gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;
gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;
gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;
gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;
gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;
gl_FragColor += texture2D(texture, vUv )*0.159576912161;
gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;
gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;
gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;
gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;
gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;
gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;
gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;
}
`;
var convolution = `
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform vec2 px;
uniform float m[9];
void main(void) {
vec4 c11 = texture2D(texture, vUv - px); // top left
vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center
vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right
vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left
vec4 c22 = texture2D(texture, vUv); // mid center
vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right
vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left
vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center
vec4 c33 = texture2D(texture, vUv + px ); // bottom right
gl_FragColor =
c11 * m[0] + c12 * m[1] + c22 * m[2] +
c21 * m[3] + c22 * m[4] + c23 * m[5] +
c31 * m[6] + c32 * m[7] + c33 * m[8];
gl_FragColor.a = c22.a;
}
`;
// src/image/imagefx.ts
var collect = (source, prefix, collection) => {
const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig");
source.replace(r, (match2, name) => {
collection[name] = 0;
return match2;
});
};
var GLProgram = class {
constructor(gl, vertexSource, fragmentSource) {
__publicField(this, "uniform", {});
__publicField(this, "attribute", {});
__publicField(this, "gl");
__publicField(this, "id");
__publicField(this, "compile", (source, type) => {
const shader = this.gl.createShader(type);
if (!shader) {
log("filter: could not create shader");
return null;
}
this.gl.shaderSource(shader, source);
this.gl.compileShader(shader);
if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {
log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || "unknown"}`);
return null;
}
return shader;
});
this.gl = gl;
const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);
const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);
this.id = this.gl.createProgram();
if (!vertexShader || !fragmentShader)
return;
if (!this.id) {
log("filter: could not create webgl program");
return;
}
this.gl.attachShader(this.id, vertexShader);
this.gl.attachShader(this.id, fragmentShader);
this.gl.linkProgram(this.id);
if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {
log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || "unknown"}`);
return;
}
this.gl.useProgram(this.id);
collect(vertexSource, "attribute", this.attribute);
for (const a in this.attribute)
this.attribute[a] = this.gl.getAttribLocation(this.id, a);
collect(vertexSource, "uniform", this.uniform);
collect(fragmentSource, "uniform", this.uniform);
for (const u in this.uniform)
this.uniform[u] = this.gl.getUniformLocation(this.id, u);
}
};
function GLImageFilter() {
let drawCount = 0;
let sourceTexture = null;
let lastInChain = false;
let currentFramebufferIndex = -1;
let tempFramebuffers = [null, null];
let filterChain = [];
let vertexBuffer = null;
let currentProgram = null;
const fxcanvas = canvas(100, 100);
const shaderProgramCache = {};
const DRAW = { INTERMEDIATE: 1 };
const gl = fxcanvas.getContext("webgl");
if (!gl) {
log("filter: cannot get webgl context");
return;
}
this.gl = gl;
function resize(width, height) {
if (width === fxcanvas.width && height === fxcanvas.height)
return;
fxcanvas.width = width;
fxcanvas.height = height;
if (!vertexBuffer) {
const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]);
vertexBuffer = gl.createBuffer();
gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);
gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);
gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);
}
gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);
tempFramebuffers = [null, null];
}
function createFramebufferTexture(width, height) {
const fbo = gl.createFramebuffer();
gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);
const renderbuffer = gl.createRenderbuffer();
gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);
const texture = gl.createTexture();
gl.bindTexture(gl.TEXTURE_2D, texture);
gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
gl.bindTexture(gl.TEXTURE_2D, null);
gl.bindFramebuffer(gl.FRAMEBUFFER, null);
return { fbo, texture };
}
function getTempFramebuffer(index2) {
tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);
return tempFramebuffers[index2];
}
function draw(flags = 0) {
if (!currentProgram)
return;
let source = null;
let target = null;
let flipY = false;
if (drawCount === 0)
source = sourceTexture;
else
source = getTempFramebuffer(currentFramebufferIndex).texture || null;
drawCount++;
if (lastInChain && !(flags & DRAW.INTERMEDIATE)) {
target = null;
flipY = drawCount % 2 === 0;
} else {
currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;
target = getTempFramebuffer(currentFramebufferIndex).fbo || null;
}
gl.bindTexture(gl.TEXTURE_2D, source);
gl.bindFramebuffer(gl.FRAMEBUFFER, target);
gl.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1);
gl.drawArrays(gl.TRIANGLES, 0, 6);
}
function compileShader(fragmentSource) {
if (shaderProgramCache[fragmentSource]) {
currentProgram = shaderProgramCache[fragmentSource];
gl.useProgram((currentProgram ? currentProgram.id : null) || null);
return currentProgram;
}
currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource);
if (!currentProgram) {
log("filter: could not get webgl program");
return null;
}
const floatSize = Float32Array.BYTES_PER_ELEMENT;
const vertSize = 4 * floatSize;
gl.enableVertexAttribArray(currentProgram.attribute["pos"]);
gl.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl.FLOAT, false, vertSize, 0 * floatSize);
gl.enableVertexAttribArray(currentProgram.attribute["uv"]);
gl.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl.FLOAT, false, vertSize, 2 * floatSize);
shaderProgramCache[fragmentSource] = currentProgram;
return currentProgram;
}
const filter = {
colorMatrix: (matrix) => {
const m = new Float32Array(matrix);
m[4] /= 255;
m[9] /= 255;
m[14] /= 255;
m[19] /= 255;
const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha;
const program = compileShader(shader);
if (!program)
return;
gl.uniform1fv(program.uniform["m"], m);
draw();
},
brightness: (brightness) => {
const b = (brightness || 0) + 1;
filter.colorMatrix([
b,
0,
0,
0,
0,
0,
b,
0,
0,
0,
0,
0,
b,
0,
0,
0,
0,
0,
1,
0
]);
},
saturation: (amount) => {
const x = (amount || 0) * 2 / 3 + 1;
const y = (x - 1) * -0.5;
filter.colorMatrix([
x,
y,
y,
0,
0,
y,
x,
y,
0,
0,
y,
y,
x,
0,
0,
0,
0,
0,
1,
0
]);
},
desaturate: () => {
filter.saturation(-1);
},
contrast: (amount) => {
const v = (amount || 0) + 1;
const o = -128 * (v - 1);
filter.colorMatrix([
v,
0,
0,
0,
o,
0,
v,
0,
0,
o,
0,
0,
v,
0,
o,
0,
0,
0,
1,
0
]);
},
negative: () => {
filter.contrast(-2);
},
hue: (rotation) => {
rotation = (rotation || 0) / 180 * Math.PI;
const cos = Math.cos(rotation);
const sin = Math.sin(rotation);
const lumR = 0.213;
const lumG = 0.715;
const lumB = 0.072;
filter.colorMatrix([
lumR + cos * (1 - lumR) + sin * -lumR,
lumG + cos * -lumG + sin * -lumG,
lumB + cos * -lumB + sin * (1 - lumB),
0,
0,
lumR + cos * -lumR + sin * 0.143,
lumG + cos * (1 - lumG) + sin * 0.14,
lumB + cos * -lumB + sin * -0.283,
0,
0,
lumR + cos * -lumR + sin * -(1 - lumR),
lumG + cos * -lumG + sin * lumG,
lumB + cos * (1 - lumB) + sin * lumB,
0,
0,
0,
0,
0,
1,
0
]);
},
desaturateLuminance: () => {
filter.colorMatrix([
0.2764723,
0.929708,
0.0938197,
0,
-37.1,
0.2764723,
0.929708,
0.0938197,
0,
-37.1,
0.2764723,
0.929708,
0.0938197,
0,
-37.1,
0,
0,
0,
1,
0
]);
},
sepia: () => {
filter.colorMatrix([
0.393,
0.7689999,
0.18899999,
0,
0,
0.349,
0.6859999,
0.16799999,
0,
0,
0.272,
0.5339999,
0.13099999,
0,
0,
0,
0,
0,
1,
0
]);
},
brownie: () => {
filter.colorMatrix([
0.5997023498159715,
0.34553243048391263,
-0.2708298674538042,
0,
47.43192855600873,
-0.037703249837783157,
0.8609577587992641,
0.15059552388459913,
0,
-36.96841498319127,
0.24113635128153335,
-0.07441037908422492,
0.44972182064877153,
0,
-7.562075277591283,
0,
0,
0,
1,
0
]);
},
vintagePinhole: () => {
filter.colorMatrix([
0.6279345635605994,
0.3202183420819367,
-0.03965408211312453,
0,
9.651285835294123,
0.02578397704808868,
0.6441188644374771,
0.03259127616149294,
0,
7.462829176470591,
0.0466055556782719,
-0.0851232987247891,
0.5241648018700465,
0,
5.159190588235296,
0,
0,
0,
1,
0
]);
},
kodachrome: () => {
filter.colorMatrix([
1.1285582396593525,
-0.3967382283601348,
-0.03992559172921793,
0,
63.72958762196502,
-0.16404339962244616,
1.0835251566291304,
-0.05498805115633132,
0,
24.732407896706203,
-0.16786010706155763,
-0.5603416277695248,
1.6014850761964943,
0,
35.62982807460946,
0,
0,
0,
1,
0
]);
},
technicolor: () => {
filter.colorMatrix([
1.9125277891456083,
-0.8545344976951645,
-0.09155508482755585,
0,
11.793603434377337,
-0.3087833385928097,
1.7658908555458428,
-0.10601743074722245,
0,
-70.35205161461398,
-0.231103377548616,
-0.7501899197440212,
1.847597816108189,
0,
30.950940869491138,
0,
0,
0,
1,
0
]);
},
polaroid: () => {
filter.colorMatrix([
1.438,
-0.062,
-0.062,
0,
0,
-0.122,
1.378,
-0.122,
0,
0,
-0.016,
-0.016,
1.483,
0,
0,
0,
0,
0,
1,
0
]);
},
shiftToBGR: () => {
filter.colorMatrix([
0,
0,
1,
0,
0,
0,
1,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
1,
0
]);
},
convolution: (matrix) => {
const m = new Float32Array(matrix);
const pixelSizeX = 1 / fxcanvas.width;
const pixelSizeY = 1 / fxcanvas.height;
const program = compileShader(convolution);
if (!program)
return;
gl.uniform1fv(program.uniform["m"], m);
gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY);
draw();
},
detectEdges: () => {
filter.convolution.call(this, [
0,
1,
0,
1,
-4,
1,
0,
1,
0
]);
},
sobelX: () => {
filter.convolution.call(this, [
-1,
0,
1,
-2,
0,
2,
-1,
0,
1
]);
},
sobelY: () => {
filter.convolution.call(this, [
-1,
-2,
-1,
0,
0,
0,
1,
2,
1
]);
},
sharpen: (amount) => {
const a = amount || 1;
filter.convolution.call(this, [
0,
-1 * a,
0,
-1 * a,
1 + 4 * a,
-1 * a,
0,
-1 * a,
0
]);
},
emboss: (size2) => {
const s = size2 || 1;
filter.convolution.call(this, [
-2 * s,
-1 * s,
0,
-1 * s,
1,
1 * s,
0,
1 * s,
2 * s
]);
},
blur: (size2) => {
const blurSizeX = size2 / 7 / fxcanvas.width;
const blurSizeY = size2 / 7 / fxcanvas.height;
const program = compileShader(blur);
if (!program)
return;
gl.uniform2f(program.uniform["px"], 0, blurSizeY);
draw(DRAW.INTERMEDIATE);
gl.uniform2f(program.uniform["px"], blurSizeX, 0);
draw();
},
pixelate: (size2) => {
const blurSizeX = size2 / fxcanvas.width;
const blurSizeY = size2 / fxcanvas.height;
const program = compileShader(pixelate);
if (!program)
return;
gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY);
draw();
}
};
this.add = function(name) {
const args = Array.prototype.slice.call(arguments, 1);
const func = filter[name];
filterChain.push({ func, args });
};
this.reset = function() {
filterChain = [];
};
this.get = function() {
return filterChain;
};
this.apply = function(image28) {
resize(image28.width, image28.height);
drawCount = 0;
if (!sourceTexture)
sourceTexture = gl.createTexture();
gl.bindTexture(gl.TEXTURE_2D, sourceTexture);
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
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, image28);
for (let i = 0; i < filterChain.length; i++) {
lastInChain = i === filterChain.length - 1;
const f = filterChain[i];
f.func.apply(this, f.args || []);
}
return fxcanvas;
};
this.draw = function(image28) {
this.add("brightness", 0);
return this.apply(image28);
};
}
// src/image/enhance.ts
var tf = __toESM(require_tfjs_esm());
async function histogramEqualization(inputImage) {
const squeeze14 = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;
const rgb2 = tf.split(squeeze14, 3, 2);
const min2 = [tf.min(rgb2[0]), tf.min(rgb2[1]), tf.min(rgb2[2])];
const max5 = [tf.max(rgb2[0]), tf.max(rgb2[1]), tf.max(rgb2[2])];
const absMax = await Promise.all(max5.map((channel) => channel.data()));
const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);
const maxRange = maxValue > 1 ? 255 : 1;
const factor = maxRange / maxValue;
let final;
if (factor > 1) {
const sub11 = [tf.sub(rgb2[0], min2[0]), tf.sub(rgb2[1], min2[1]), tf.sub(rgb2[2], min2[2])];
const range = [tf.sub(max5[0], min2[0]), tf.sub(max5[1], min2[1]), tf.sub(max5[2], min2[2])];
const enh = [tf.mul(sub11[0], factor), tf.mul(sub11[1], factor), tf.mul(sub11[2], factor)];
const stack5 = tf.stack([enh[0], enh[1], enh[2]], 2);
final = tf.reshape(stack5, [1, squeeze14.shape[0] || 0, squeeze14.shape[1] || 0, 3]);
tf.dispose([...sub11, ...range, ...enh]);
} else {
final = tf.expandDims(squeeze14, 0);
}
tf.dispose([...rgb2, ...min2, ...max5, rgb2, squeeze14, inputImage]);
return final;
}
// src/image/image.ts
var maxSize = 3840;
var inCanvas = null;
var outCanvas = null;
var tmpCanvas = null;
var fx;
var last = {
inputSum: 0,
cacheDiff: 1,
sumMethod: 0,
inputTensor: void 0
};
function reset() {
last.inputSum = 0;
last.cacheDiff = 1;
last.sumMethod = 0;
last.inputTensor = void 0;
}
function canvas(width, height) {
let c;
if (env.browser) {
if (env.worker) {
if (typeof OffscreenCanvas === "undefined")
throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported");
c = new OffscreenCanvas(width, height);
} else {
if (typeof document === "undefined")
throw new Error("canvas error: attempted to run in browser but DOM is not defined");
c = document.createElement("canvas");
c.width = width;
c.height = height;
}
} else {
if (typeof env.Canvas !== "undefined")
c = new env.Canvas(width, height);
else if (typeof globalThis.Canvas !== "undefined")
c = new globalThis.Canvas(width, height);
}
return c;
}
function copy(input, output) {
const outputCanvas = output || canvas(input.width, input.height);
const ctx = outputCanvas.getContext("2d");
ctx.drawImage(input, 0, 0);
return outputCanvas;
}
async function process2(input, config3, getTensor = true) {
var _a, _b, _c;
if (!input) {
if (config3.debug)
log("input error: input is missing");
return { tensor: null, canvas: null };
}
if (!(input instanceof tf2.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof env.Canvas !== "undefined" && input instanceof env.Canvas) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) {
throw new Error("input error: type is not recognized");
}
if (input instanceof tf2.Tensor) {
let tensor7 = null;
if (input["isDisposedInternal"])
throw new Error("input error: attempted to use tensor but it is disposed");
if (!input.shape)
throw new Error("input error: attempted to use tensor without a shape");
if (input.shape.length === 3) {
if (input.shape[2] === 3) {
tensor7 = tf2.expandDims(input, 0);
} else if (input.shape[2] === 4) {
const rgb2 = tf2.slice3d(input, [0, 0, 0], [-1, -1, 3]);
tensor7 = tf2.expandDims(rgb2, 0);
tf2.dispose(rgb2);
}
} else if (input.shape.length === 4) {
if (input.shape[3] === 3) {
tensor7 = tf2.clone(input);
} else if (input.shape[3] === 4) {
tensor7 = tf2.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);
}
}
if (tensor7 == null || tensor7.shape.length !== 4 || tensor7.shape[0] !== 1 || tensor7.shape[3] !== 3)
throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input.shape.toString()}`);
if (tensor7.dtype === "int32") {
const cast8 = tf2.cast(tensor7, "float32");
tf2.dispose(tensor7);
tensor7 = cast8;
}
return { tensor: tensor7, canvas: config3.filter.return ? outCanvas : null };
}
if (typeof input["readyState"] !== "undefined" && input.readyState <= 2) {
if (config3.debug)
log("input stream is not ready");
return { tensor: null, canvas: inCanvas };
}
const originalWidth = input["naturalWidth"] || input["videoWidth"] || input["width"] || input["shape"] && input["shape"][1] > 0;
const originalHeight = input["naturalHeight"] || input["videoHeight"] || input["height"] || input["shape"] && input["shape"][2] > 0;
if (!originalWidth || !originalHeight) {
if (config3.debug)
log("cannot determine input dimensions");
return { tensor: null, canvas: inCanvas };
}
let targetWidth = originalWidth;
let targetHeight = originalHeight;
if (targetWidth > maxSize) {
targetWidth = maxSize;
targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);
}
if (targetHeight > maxSize) {
targetHeight = maxSize;
targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);
}
if ((((_a = config3.filter) == null ? void 0 : _a.width) || 0) > 0)
targetWidth = config3.filter.width;
else if ((((_b = config3.filter) == null ? void 0 : _b.height) || 0) > 0)
targetWidth = originalWidth * ((config3.filter.height || 0) / originalHeight);
if ((config3.filter.height || 0) > 0)
targetHeight = config3.filter.height;
else if ((config3.filter.width || 0) > 0)
targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth);
if (!targetWidth || !targetHeight)
throw new Error("input error: cannot determine dimension");
if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight)
inCanvas = canvas(targetWidth, targetHeight);
const inCtx = inCanvas.getContext("2d");
if (typeof ImageData !== "undefined" && input instanceof ImageData) {
inCtx.putImageData(input, 0, 0);
} else {
if (config3.filter.flip && typeof inCtx.translate !== "undefined") {
inCtx.translate(originalWidth, 0);
inCtx.scale(-1, 1);
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
inCtx.setTransform(1, 0, 0, 1, 0, 0);
} else {
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
}
}
if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height)
outCanvas = canvas(inCanvas.width, inCanvas.height);
if (config3.filter.enabled && env.webgl.supported) {
if (!fx)
fx = env.browser ? new GLImageFilter() : null;
env.filter = !!fx;
if (!(fx == null ? void 0 : fx.add)) {
if (config3.debug)
log("input process error: cannot initialize filters");
env.webgl.supported = false;
config3.filter.enabled = false;
copy(inCanvas, outCanvas);
} else {
fx.reset();
if (config3.filter.brightness !== 0)
fx.add("brightness", config3.filter.brightness);
if (config3.filter.contrast !== 0)
fx.add("contrast", config3.filter.contrast);
if (config3.filter.sharpness !== 0)
fx.add("sharpen", config3.filter.sharpness);
if (config3.filter.blur !== 0)
fx.add("blur", config3.filter.blur);
if (config3.filter.saturation !== 0)
fx.add("saturation", config3.filter.saturation);
if (config3.filter.hue !== 0)
fx.add("hue", config3.filter.hue);
if (config3.filter.negative)
fx.add("negative");
if (config3.filter.sepia)
fx.add("sepia");
if (config3.filter.vintage)
fx.add("brownie");
if (config3.filter.sepia)
fx.add("sepia");
if (config3.filter.kodachrome)
fx.add("kodachrome");
if (config3.filter.technicolor)
fx.add("technicolor");
if (config3.filter.polaroid)
fx.add("polaroid");
if (config3.filter.pixelate !== 0)
fx.add("pixelate", config3.filter.pixelate);
if (((_c = fx.get()) == null ? void 0 : _c.length) > 1)
outCanvas = fx.apply(inCanvas);
else
outCanvas = fx.draw(inCanvas);
}
} else {
copy(inCanvas, outCanvas);
if (fx)
fx = null;
env.filter = !!fx;
}
if (!getTensor)
return { tensor: null, canvas: outCanvas };
if (!outCanvas)
throw new Error("canvas error: cannot create output");
let pixels;
let depth = 3;
if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) {
if (env.browser && tf2.browser) {
pixels = tf2.browser ? tf2.browser.fromPixels(input) : null;
} else {
depth = input.data.length / input.height / input.width;
const arr = new Uint8Array(input.data.buffer);
pixels = tf2.tensor(arr, [input.height, input.width, depth], "int32");
}
} else {
if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height)
tmpCanvas = canvas(outCanvas.width, outCanvas.height);
if (tf2.browser && env.browser) {
if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") {
pixels = tf2.browser.fromPixels(outCanvas);
} else {
tmpCanvas = copy(outCanvas);
pixels = tf2.browser.fromPixels(tmpCanvas);
}
} else {
const tempCanvas = copy(outCanvas);
const tempCtx = tempCanvas.getContext("2d");
const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);
depth = tempData.data.length / targetWidth / targetHeight;
const arr = new Uint8Array(tempData.data.buffer);
pixels = tf2.tensor(arr, [targetWidth, targetHeight, depth]);
}
}
if (depth === 4) {
const rgb2 = tf2.slice3d(pixels, [0, 0, 0], [-1, -1, 3]);
tf2.dispose(pixels);
pixels = rgb2;
}
if (!pixels)
throw new Error("input error: cannot create tensor");
const casted = tf2.cast(pixels, "float32");
const tensor6 = config3.filter.equalization ? await histogramEqualization(casted) : tf2.expandDims(casted, 0);
tf2.dispose([pixels, casted]);
if (config3.filter.autoBrightness) {
const max5 = tf2.max(tensor6);
const maxVal = await max5.data();
config3.filter.brightness = maxVal[0] > 1 ? 1 - maxVal[0] / 255 : 1 - maxVal[0];
tf2.dispose(max5);
}
return { tensor: tensor6, canvas: config3.filter.return ? outCanvas : null };
}
async function skip(config3, input) {
let skipFrame = false;
if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160)
return skipFrame;
if (!last.inputTensor) {
last.inputTensor = tf2.clone(input);
} else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) {
tf2.dispose(last.inputTensor);
last.inputTensor = tf2.clone(input);
} else {
const t2 = {};
t2.diff = tf2.sub(input, last.inputTensor);
t2.squared = tf2.mul(t2.diff, t2.diff);
t2.sum = tf2.sum(t2.squared);
const diffSum = await t2.sum.data();
const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3;
tf2.dispose([last.inputTensor, t2.diff, t2.squared, t2.sum]);
last.inputTensor = tf2.clone(input);
skipFrame = diffRelative <= (config3.cacheSensitivity || 0);
}
return skipFrame;
}
async function compare(config3, input1, input2) {
const t2 = {};
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;
}
t2.input1 = tf2.clone(input1);
t2.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);
t2.diff = tf2.sub(t2.input1, t2.input2);
t2.squared = tf2.mul(t2.diff, t2.diff);
t2.sum = tf2.sum(t2.squared);
const diffSum = await t2.sum.data();
const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;
tf2.dispose([t2.input1, t2.input2, t2.diff, t2.squared, t2.sum]);
return diffRelative;
}
// src/util/env.ts
var Env = class {
constructor() {
__publicField(this, "browser");
__publicField(this, "node");
__publicField(this, "worker");
__publicField(this, "platform", "");
__publicField(this, "agent", "");
__publicField(this, "backends", []);
__publicField(this, "initial");
__publicField(this, "filter");
__publicField(this, "tfjs");
__publicField(this, "offscreen");
__publicField(this, "perfadd", false);
__publicField(this, "tensorflow", {
version: void 0,
gpu: void 0
});
__publicField(this, "wasm", {
supported: void 0,
backend: void 0,
simd: void 0,
multithread: void 0
});
__publicField(this, "webgl", {
supported: void 0,
backend: void 0,
version: void 0,
renderer: void 0,
shader: void 0,
vendor: void 0
});
__publicField(this, "webgpu", {
supported: void 0,
backend: void 0,
adapter: void 0
});
__publicField(this, "cpu", {
model: void 0,
flags: []
});
__publicField(this, "kernels", []);
__publicField(this, "Canvas");
__publicField(this, "Image");
__publicField(this, "ImageData");
this.browser = typeof navigator !== "undefined";
this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined";
this.tfjs = { version: tf3.version["tfjs-core"] };
this.offscreen = typeof OffscreenCanvas !== "undefined";
this.initial = true;
this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0;
if (typeof navigator !== "undefined") {
const raw = navigator.userAgent.match(/\(([^()]+)\)/g);
if (raw == null ? void 0 : raw[0]) {
const platformMatch = raw[0].match(/\(([^()]+)\)/g);
this.platform = (platformMatch == null ? void 0 : platformMatch[0]) ? platformMatch[0].replace(/\(|\)/g, "") : "";
this.agent = navigator.userAgent.replace(raw[0], "");
if (this.platform[1])
this.agent = this.agent.replace(raw[1], "");
this.agent = this.agent.replace(/ /g, " ");
}
} else if (typeof process !== "undefined") {
this.platform = `${process.platform} ${process.arch}`;
this.agent = `NodeJS ${process.version}`;
}
}
async updateBackend() {
this.backends = Object.keys(tf3.engine().registryFactory);
try {
this.tensorflow = {
version: tf3.backend()["binding"] ? tf3.backend()["binding"].TF_Version : void 0,
gpu: tf3.backend()["binding"] ? tf3.backend()["binding"].isUsingGpuDevice() : void 0
};
} catch (e) {
}
this.wasm.supported = typeof WebAssembly !== "undefined";
this.wasm.backend = this.backends.includes("wasm");
if (this.wasm.supported && this.wasm.backend) {
this.wasm.simd = await tf3.env().getAsync("WASM_HAS_SIMD_SUPPORT");
this.wasm.multithread = await tf3.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
}
const c = canvas(100, 100);
const gl = c ? c.getContext("webgl2") : void 0;
this.webgl.supported = typeof gl !== "undefined";
this.webgl.backend = this.backends.includes("webgl");
if (this.webgl.supported && this.webgl.backend && gl) {
this.webgl.version = gl.getParameter(gl.VERSION);
this.webgl.vendor = gl.getParameter(gl.VENDOR);
this.webgl.renderer = gl.getParameter(gl.RENDERER);
this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION);
}
this.webgpu.supported = this.browser && typeof navigator.gpu !== "undefined";
this.webgpu.backend = this.backends.includes("webgpu");
try {
if (this.webgpu.supported) {
const adapter = await navigator.gpu.requestAdapter();
this.webgpu.adapter = await (adapter == null ? void 0 : adapter.requestAdapterInfo());
}
} catch (e) {
this.webgpu.supported = false;
}
try {
this.kernels = tf3.getKernelsForBackend(tf3.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());
} catch (e) {
}
}
updateCPU() {
const cpu = { model: "", flags: [] };
if (this.node && this.platform.startsWith("linux")) {
}
if (!this.cpu)
Object.defineProperty(this, "cpu", { value: cpu });
else
this.cpu = cpu;
}
};
var env = new Env();
// src/util/webcam.ts
var WebCam = class {
constructor() {
__publicField(this, "config");
__publicField(this, "element");
__publicField(this, "stream");
__publicField(this, "devices", []);
__publicField(this, "enumerate", async () => {
try {
const devices = await navigator.mediaDevices.enumerateDevices();
this.devices = devices.filter((device) => device.kind === "videoinput");
} catch (e) {
this.devices = [];
}
return this.devices;
});
__publicField(this, "start", async (webcamConfig) => {
if (webcamConfig == null ? void 0 : webcamConfig.debug)
this.config.debug = webcamConfig == null ? void 0 : webcamConfig.debug;
if (webcamConfig == null ? void 0 : webcamConfig.crop)
this.config.crop = webcamConfig == null ? void 0 : webcamConfig.crop;
if (webcamConfig == null ? void 0 : webcamConfig.mode)
this.config.mode = webcamConfig == null ? void 0 : webcamConfig.mode;
if (webcamConfig == null ? void 0 : webcamConfig.width)
this.config.width = webcamConfig == null ? void 0 : webcamConfig.width;
if (webcamConfig == null ? void 0 : webcamConfig.height)
this.config.height = webcamConfig == null ? void 0 : webcamConfig.height;
if (webcamConfig == null ? void 0 : webcamConfig.id)
this.config.id = webcamConfig == null ? void 0 : webcamConfig.id;
if (webcamConfig == null ? void 0 : webcamConfig.element) {
if (typeof webcamConfig.element === "string") {
const el = document.getElementById(webcamConfig.element);
if (el && el instanceof HTMLVideoElement) {
this.element = el;
} else {
if (this.config.debug)
log("webcam", "cannot get dom element", webcamConfig.element);
return;
}
} else if (webcamConfig.element instanceof HTMLVideoElement) {
this.element = webcamConfig.element;
} else {
if (this.config.debug)
log("webcam", "unknown dom element", webcamConfig.element);
return;
}
} else {
this.element = document.createElement("video");
}
const requestedConstraints = {
audio: false,
video: {
facingMode: this.config.mode === "front" ? "user" : "environment",
resizeMode: this.config.crop ? "crop-and-scale" : "none",
width: { ideal: this.config.width > 0 ? this.config.width : window.innerWidth },
height: { ideal: this.config.height > 0 ? this.config.height : window.innerHeight }
}
};
if (this.config.id)
requestedConstraints.video.deviceId = this.config.id;
this.element.addEventListener("play", () => {
if (this.config.debug)
log("webcam", "play");
});
this.element.addEventListener("pause", () => {
if (this.config.debug)
log("webcam", "pause");
});
this.element.addEventListener("click", async () => {
if (!this.element || !this.stream)
return;
if (this.element.paused)
await this.element.play();
else
this.element.pause();
});
if (!(navigator == null ? void 0 : navigator.mediaDevices)) {
if (this.config.debug)
log("webcam", "no devices");
return;
}
try {
this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints);
} catch (err) {
log("webcam", err);
return;
}
if (!this.stream) {
if (this.config.debug)
log("webcam", "no stream");
return;
}
this.element.srcObject = this.stream;
const ready3 = new Promise((resolve) => {
if (!this.element)
resolve(false);
else
this.element.onloadeddata = () => resolve(true);
});
await ready3;
await this.element.play();
if (this.config.debug) {
log("webcam", {
width: this.width,
height: this.height,
label: this.label,
stream: this.stream,
track: this.track,
settings: this.settings,
constraints: this.constraints,
capabilities: this.capabilities
});
}
});
__publicField(this, "pause", () => {
if (this.element)
this.element.pause();
});
__publicField(this, "play", async () => {
if (this.element)
await this.element.play();
});
__publicField(this, "stop", () => {
if (this.config.debug)
log("webcam", "stop");
if (this.track)
this.track.stop();
});
this.config = {
element: void 0,
debug: true,
mode: "front",
crop: false,
width: 0,
height: 0
};
}
get track() {
if (!this.stream)
return void 0;
return this.stream.getVideoTracks()[0];
}
get capabilities() {
if (!this.track)
return void 0;
return this.track.getCapabilities ? this.track.getCapabilities() : void 0;
}
get constraints() {
if (!this.track)
return void 0;
return this.track.getConstraints ? this.track.getConstraints() : void 0;
}
get settings() {
if (!this.stream)
return void 0;
const track = this.stream.getVideoTracks()[0];
return track.getSettings ? track.getSettings() : void 0;
}
get label() {
if (!this.track)
return "";
return this.track.label;
}
get paused() {
var _a;
return ((_a = this.element) == null ? void 0 : _a.paused) || false;
}
get width() {
var _a;
return ((_a = this.element) == null ? void 0 : _a.videoWidth) || 0;
}
get height() {
var _a;
return ((_a = this.element) == null ? void 0 : _a.videoHeight) || 0;
}
};
// src/tfjs/load.ts
var tf4 = __toESM(require_tfjs_esm());
// models/models.json
var models_exports = {};
__export(models_exports, {
age: () => age,
"anti-spoofing": () => anti_spoofing,
antispoof: () => antispoof,
blazeface: () => blazeface,
"blazeface-back": () => blazeface_back,
"blazeface-front": () => blazeface_front,
"blazepose-detector": () => blazepose_detector,
"blazepose-detector2d": () => blazepose_detector2d,
"blazepose-detector3d": () => blazepose_detector3d,
"blazepose-full": () => blazepose_full,
"blazepose-heavy": () => blazepose_heavy,
"blazepose-lite": () => blazepose_lite,
centernet: () => centernet,
default: () => models_default,
efficientpose: () => efficientpose,
"efficientpose-i-lite": () => efficientpose_i_lite,
"efficientpose-ii-lite": () => efficientpose_ii_lite,
"efficientpose-iv": () => efficientpose_iv,
emotion: () => emotion,
faceboxes: () => faceboxes,
facemesh: () => facemesh,
"facemesh-attention": () => facemesh_attention,
"facemesh-attention-alt": () => facemesh_attention_alt,
"facemesh-detection-full": () => facemesh_detection_full,
"facemesh-detection-short": () => facemesh_detection_short,
"facemesh-orig": () => facemesh_orig,
faceres: () => faceres,
"faceres-deep": () => faceres_deep,
gear: () => gear,
gender: () => gender,
"gender-ssrnet-imdb": () => gender_ssrnet_imdb,
handdetect: () => handdetect,
"handlandmark-full": () => handlandmark_full,
"handlandmark-lite": () => handlandmark_lite,
"handlandmark-sparse": () => handlandmark_sparse,
handskeleton: () => handskeleton,
handtrack: () => handtrack,
"insightface-efficientnet-b0": () => insightface_efficientnet_b0,
"insightface-ghostnet-strides1": () => insightface_ghostnet_strides1,
"insightface-ghostnet-strides2": () => insightface_ghostnet_strides2,
"insightface-mobilenet-emore": () => insightface_mobilenet_emore,
"insightface-mobilenet-swish": () => insightface_mobilenet_swish,
iris: () => iris,
liveness: () => liveness,
meet: () => meet,
mobileface: () => mobileface,
mobilefacenet: () => mobilefacenet,
models: () => models,
"movenet-lightning": () => movenet_lightning,
"movenet-multipose": () => movenet_multipose,
"movenet-thunder": () => movenet_thunder,
nanodet: () => nanodet,
"nanodet-e": () => nanodet_e,
"nanodet-g": () => nanodet_g,
"nanodet-m": () => nanodet_m,
"nanodet-t": () => nanodet_t,
posenet: () => posenet,
rvm: () => rvm,
selfie: () => selfie
});
var antispoof = 853098;
var blazeface = 538928;
var centernet = 4030290;
var emotion = 820516;
var facemesh = 1477958;
var faceres = 6978814;
var handlandmark_full = 5431368;
var handtrack = 2964837;
var iris = 2599092;
var liveness = 592976;
var models = 0;
var movenet_lightning = 4650216;
var age = 161240;
var blazeface_back = 538928;
var blazeface_front = 402048;
var blazepose_detector2d = 7499400;
var blazepose_detector3d = 5928856;
var blazepose_full = 6338290;
var blazepose_heavy = 27501554;
var blazepose_lite = 2725490;
var efficientpose = 5651240;
var faceboxes = 2013002;
var facemesh_attention_alt = 2387598;
var facemesh_attention = 2382414;
var facemesh_detection_full = 1026192;
var facemesh_detection_short = 201268;
var facemesh_orig = 2955780;
var faceres_deep = 13957620;
var gear = 1498916;
var gender_ssrnet_imdb = 161236;
var gender = 201808;
var handdetect = 3515612;
var handlandmark_lite = 2023432;
var handlandmark_sparse = 5286322;
var handskeleton = 5502280;
var meet = 372228;
var mobileface = 2183192;
var mobilefacenet = 5171976;
var movenet_multipose = 9448838;
var movenet_thunder = 12477112;
var nanodet = 7574558;
var posenet = 5032780;
var rvm = 3739355;
var selfie = 212886;
var blazepose_detector = 5928856;
var anti_spoofing = 853098;
var efficientpose_i_lite = 2269064;
var efficientpose_ii_lite = 5651240;
var efficientpose_iv = 25643252;
var insightface_efficientnet_b0 = 13013224;
var insightface_ghostnet_strides1 = 8093408;
var insightface_ghostnet_strides2 = 8049584;
var insightface_mobilenet_emore = 6938536;
var insightface_mobilenet_swish = 12168584;
var nanodet_e = 12319156;
var nanodet_g = 7574558;
var nanodet_m = 1887474;
var nanodet_t = 5294216;
var models_default = {
antispoof,
blazeface,
centernet,
emotion,
facemesh,
faceres,
"handlandmark-full": handlandmark_full,
handtrack,
iris,
liveness,
models,
"movenet-lightning": movenet_lightning,
age,
"blazeface-back": blazeface_back,
"blazeface-front": blazeface_front,
"blazepose-detector2d": blazepose_detector2d,
"blazepose-detector3d": blazepose_detector3d,
"blazepose-full": blazepose_full,
"blazepose-heavy": blazepose_heavy,
"blazepose-lite": blazepose_lite,
efficientpose,
faceboxes,
"facemesh-attention-alt": facemesh_attention_alt,
"facemesh-attention": facemesh_attention,
"facemesh-detection-full": facemesh_detection_full,
"facemesh-detection-short": facemesh_detection_short,
"facemesh-orig": facemesh_orig,
"faceres-deep": faceres_deep,
gear,
"gender-ssrnet-imdb": gender_ssrnet_imdb,
gender,
handdetect,
"handlandmark-lite": handlandmark_lite,
"handlandmark-sparse": handlandmark_sparse,
handskeleton,
meet,
mobileface,
mobilefacenet,
"movenet-multipose": movenet_multipose,
"movenet-thunder": movenet_thunder,
nanodet,
posenet,
rvm,
selfie,
"blazepose-detector": blazepose_detector,
"anti-spoofing": anti_spoofing,
"efficientpose-i-lite": efficientpose_i_lite,
"efficientpose-ii-lite": efficientpose_ii_lite,
"efficientpose-iv": efficientpose_iv,
"insightface-efficientnet-b0": insightface_efficientnet_b0,
"insightface-ghostnet-strides1": insightface_ghostnet_strides1,
"insightface-ghostnet-strides2": insightface_ghostnet_strides2,
"insightface-mobilenet-emore": insightface_mobilenet_emore,
"insightface-mobilenet-swish": insightface_mobilenet_swish,
"nanodet-e": nanodet_e,
"nanodet-g": nanodet_g,
"nanodet-m": nanodet_m,
"nanodet-t": nanodet_t
};
// src/tfjs/load.ts
var options = {
cacheModels: true,
cacheSupported: true,
verbose: true,
debug: false,
modelBasePath: ""
};
var modelStats = {};
async function httpHandler(url, init4) {
if (options.debug)
log("load model fetch:", url, init4);
return fetch(url, init4);
}
function setModelLoadOptions(config3) {
options.cacheModels = config3.cacheModels;
options.verbose = config3.debug;
options.modelBasePath = config3.modelBasePath;
}
async function loadModel(modelPath) {
var _a, _b, _c, _d;
let modelUrl = join(options.modelBasePath, modelPath || "");
if (!modelUrl.toLowerCase().endsWith(".json"))
modelUrl += ".json";
const modelPathSegments = modelUrl.includes("/") ? modelUrl.split("/") : modelUrl.split("\\");
const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace(".json", "");
const cachedModelName = "indexeddb://" + shortModelName;
modelStats[shortModelName] = {
name: shortModelName,
sizeFromManifest: 0,
sizeLoadedWeights: 0,
sizeDesired: models_exports[shortModelName],
inCache: false,
url: ""
};
options.cacheSupported = typeof indexedDB !== "undefined";
let cachedModels = {};
try {
cachedModels = options.cacheSupported && options.cacheModels ? await tf4.io.listModels() : {};
} catch (e) {
options.cacheSupported = false;
}
modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName);
modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl;
const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init4) => httpHandler(url, init4) };
let model23 = new tf4.GraphModel(modelStats[shortModelName].url, tfLoadOptions);
let loaded = false;
try {
model23.findIOHandler();
if (options.debug)
log("model load handler:", model23["handler"]);
} catch (err) {
log("error finding model i/o handler:", modelUrl, err);
}
try {
const artifacts = await ((_a = model23.handler) == null ? void 0 : _a.load()) || null;
modelStats[shortModelName].sizeFromManifest = ((_b = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _b.byteLength) || 0;
if (artifacts)
model23.loadSync(artifacts);
else
model23 = await tf4.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions);
modelStats[shortModelName].sizeLoadedWeights = ((_d = (_c = model23.artifacts) == null ? void 0 : _c.weightData) == null ? void 0 : _d.byteLength) || 0;
if (options.verbose)
log("load:", { model: shortModelName, url: model23["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights });
loaded = true;
} catch (err) {
log("error loading model:", modelUrl, err);
}
if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) {
try {
const saveResult = await model23.save(cachedModelName);
if (options.debug)
log("model saved:", cachedModelName, saveResult);
} catch (err) {
log("error saving model:", modelUrl, err);
}
}
return model23;
}
// package.json
var version2 = "3.0.0";
// src/tfjs/backend.ts
var tf7 = __toESM(require_tfjs_esm());
// src/tfjs/humangl.ts
var tf5 = __toESM(require_tfjs_esm());
var config2 = {
name: "humangl",
priority: 999,
canvas: null,
gl: null,
extensions: [],
webGLattr: {
alpha: false,
antialias: false,
premultipliedAlpha: false,
preserveDrawingBuffer: false,
depth: false,
stencil: false,
failIfMajorPerformanceCaveat: false,
desynchronized: true
}
};
function extensions() {
const gl = config2.gl;
if (!gl)
return;
config2.extensions = gl.getSupportedExtensions();
}
function register(instance) {
var _a;
if (instance.config.backend !== "humangl")
return;
if (config2.name in tf5.engine().registry && !((_a = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a.getParameter(config2.gl.VERSION))) {
log("humangl error: backend invalid context");
instance.models.reset();
}
if (!tf5.findBackend(config2.name)) {
try {
config2.canvas = canvas(100, 100);
} catch (err) {
log("humangl error: cannot create canvas:", err);
return;
}
try {
config2.gl = config2.canvas.getContext("webgl2", config2.webGLattr);
if (!config2.gl) {
log("humangl error: cannot get webgl context");
return;
}
const glv2 = config2.gl.getParameter(config2.gl.VERSION).includes("2.0");
if (!glv2) {
log("backend override: using fallback webgl backend as webgl 2.0 is not detected");
instance.config.backend = "webgl";
return;
}
if (config2.canvas) {
config2.canvas.addEventListener("webglcontextlost", (e) => {
log("humangl error:", e.type);
log("possible browser memory leak using webgl or conflict with multiple backend registrations");
instance.emit("error");
throw new Error("backend error: webgl context lost");
});
config2.canvas.addEventListener("webglcontextrestored", (e) => {
log("humangl error: context restored:", e);
});
config2.canvas.addEventListener("webglcontextcreationerror", (e) => {
log("humangl error: context create:", e);
});
}
} catch (err) {
log("humangl error: cannot get webgl context:", err);
return;
}
try {
tf5.setWebGLContext(2, config2.gl);
} catch (err) {
log("humangl error: cannot set webgl context:", err);
return;
}
try {
const ctx = new tf5.GPGPUContext(config2.gl);
tf5.registerBackend(config2.name, () => new tf5.MathBackendWebGL(ctx), config2.priority);
} catch (err) {
log("humangl error: cannot register webgl backend:", err);
return;
}
try {
const kernels = tf5.getKernelsForBackend("webgl");
kernels.forEach((kernelConfig) => {
const newKernelConfig = { ...kernelConfig, backendName: config2.name };
tf5.registerKernel(newKernelConfig);
});
} catch (err) {
log("humangl error: cannot update webgl backend registration:", err);
return;
}
try {
if (tf5.env().flagRegistry.WEBGL_VERSION)
tf5.env().set("WEBGL_VERSION", 2);
} catch (err) {
log("humangl error: cannot set WebGL backend flags:", err);
return;
}
extensions();
const backend4 = tf5.backend();
const current = typeof backend4["gpgpu"] !== "undefined" ? backend4["getGPGPUContext"]().gl : null;
if (current) {
if (instance.config.debug)
log("humangl backend registered:", { webgl: current.getParameter(current.VERSION), renderer: current.getParameter(current.RENDERER) });
} else {
log("humangl error: no current gl context:", current, config2.gl);
}
}
}
// src/tfjs/constants.ts
var tf6 = __toESM(require_tfjs_esm());
var constants = {
tf255: 255,
tf1: 1,
tf2: 2,
tf05: 0.5,
tf127: 127.5,
rgb: [0.2989, 0.587, 0.114]
};
function init() {
constants.tf255 = tf6.scalar(255, "float32");
constants.tf1 = tf6.scalar(1, "float32");
constants.tf2 = tf6.scalar(2, "float32");
constants.tf05 = tf6.scalar(0.5, "float32");
constants.tf127 = tf6.scalar(127.5, "float32");
constants.rgb = tf6.tensor1d([0.2989, 0.587, 0.114], "float32");
}
// src/tfjs/backend.ts
async function getBestBackend() {
await env.updateBackend();
if (!env.browser)
return "tensorflow";
if (env.webgpu.supported && env.webgpu.backend)
return "webgpu";
if (env.webgl.supported && env.webgl.backend)
return "webgl";
if (env.wasm.supported && env.wasm.backend)
return "wasm";
return "cpu";
}
function registerCustomOps(config3) {
const newKernels = [];
if (!env.kernels.includes("mod")) {
const kernelMod = {
kernelName: "Mod",
backendName: tf7.getBackend(),
kernelFunc: (op) => tf7.tidy(() => tf7.sub(op.inputs.a, tf7.mul(tf7.div(op.inputs.a, op.inputs.b), op.inputs.b)))
};
tf7.registerKernel(kernelMod);
env.kernels.push("mod");
newKernels.push("mod");
}
if (!env.kernels.includes("floormod")) {
const kernelFloorMod = {
kernelName: "FloorMod",
backendName: tf7.getBackend(),
kernelFunc: (op) => tf7.tidy(() => tf7.add(tf7.mul(tf7.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf7.mod(op.inputs.a, op.inputs.b)))
};
tf7.registerKernel(kernelFloorMod);
env.kernels.push("floormod");
newKernels.push("floormod");
}
if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) {
const kernelRotateWithOffset = {
kernelName: "RotateWithOffset",
backendName: tf7.getBackend(),
kernelFunc: (op) => tf7.tidy(() => {
const backend4 = tf7.getBackend();
tf7.setBackend("cpu");
const t2 = tf7.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);
tf7.setBackend(backend4);
return t2;
})
};
tf7.registerKernel(kernelRotateWithOffset);
env.kernels.push("rotatewithoffset");
newKernels.push("rotatewithoffset");
}
if (newKernels.length > 0 && config3.debug)
log("registered kernels:", newKernels);
}
var defaultFlags = {};
async function check(instance, force = false) {
var _a;
instance.state = "backend";
if (((_a = instance.config.backend) == null ? void 0 : _a.length) === 0)
instance.config.backend = await getBestBackend();
if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && tf7.getBackend() !== instance.config.backend) {
const timeStamp = now();
if (instance.config.backend && instance.config.backend.length > 0) {
if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) {
if (instance.config.debug)
log("running inside web worker");
}
if (env.browser && instance.config.backend === "tensorflow") {
if (instance.config.debug)
log("override: backend set to tensorflow while running in browser");
instance.config.backend = "webgl";
}
if (env.node && (instance.config.backend === "webgl" || instance.config.backend === "humangl")) {
if (instance.config.debug)
log(`override: backend set to ${instance.config.backend} while running in nodejs`);
instance.config.backend = "tensorflow";
}
if (env.browser && instance.config.backend === "webgpu") {
if (typeof navigator === "undefined" || typeof navigator.gpu === "undefined") {
log("override: backend set to webgpu but browser does not support webgpu");
instance.config.backend = "webgl";
} else {
const adapter = await navigator.gpu.requestAdapter();
if (instance.config.debug)
log("enumerated webgpu adapter:", adapter);
if (!adapter) {
log("override: backend set to webgpu but browser reports no available gpu");
instance.config.backend = "webgl";
} else {
const adapterInfo = "requestAdapterInfo" in adapter ? await adapter.requestAdapterInfo() : void 0;
log("webgpu adapter info:", adapterInfo);
}
}
}
let available = Object.keys(tf7.engine().registryFactory);
if (instance.config.backend === "humangl" && !available.includes("humangl")) {
register(instance);
available = Object.keys(tf7.engine().registryFactory);
}
if (instance.config.debug)
log("available backends:", available);
if (!available.includes(instance.config.backend)) {
log(`error: backend ${instance.config.backend} not found in registry`);
instance.config.backend = env.node ? "tensorflow" : "webgl";
if (instance.config.debug)
log(`override: setting backend ${instance.config.backend}`);
}
if (instance.config.debug)
log("setting backend:", [instance.config.backend]);
if (instance.config.backend === "wasm") {
if (tf7.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY)
tf7.env().set("CANVAS2D_WILL_READ_FREQUENTLY", true);
if (instance.config.debug)
log("wasm path:", instance.config.wasmPath);
if (typeof tf7.setWasmPaths !== "undefined")
tf7.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);
else
throw new Error("backend error: attempting to use wasm backend but wasm path is not set");
let mt = false;
let simd = false;
try {
mt = await tf7.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
simd = await tf7.env().getAsync("WASM_HAS_SIMD_SUPPORT");
if (instance.config.debug)
log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`);
if (instance.config.debug && !simd)
log("warning: wasm simd support is not enabled");
} catch (e) {
log("wasm detection failed");
}
}
try {
await tf7.setBackend(instance.config.backend);
await tf7.ready();
} catch (err) {
log("error: cannot set backend:", instance.config.backend, err);
return false;
}
if (instance.config.debug)
defaultFlags = JSON.parse(JSON.stringify(tf7.env().flags));
}
if (tf7.getBackend() === "humangl" || tf7.getBackend() === "webgl") {
if (tf7.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS)
tf7.env().set("WEBGL_USE_SHAPES_UNIFORMS", true);
if (tf7.env().flagRegistry.WEBGL_EXP_CONV)
tf7.env().set("WEBGL_EXP_CONV", true);
if (instance.config.debug && typeof instance.config.deallocate !== "undefined" && instance.config.deallocate) {
log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true);
tf7.env().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0);
}
}
if (tf7.getBackend() === "webgpu") {
}
if (instance.config.debug) {
const newFlags = tf7.env().flags;
const updatedFlags = {};
for (const key of Object.keys(newFlags)) {
if (defaultFlags[key] === newFlags[key])
continue;
updatedFlags[key] = newFlags[key];
}
if (instance.config.debug && Object.keys(updatedFlags).length > 0)
log("backend:", tf7.getBackend(), "flags:", updatedFlags);
}
if (instance.config.flags && Object.keys(instance.config.flags).length > 0) {
if (instance.config.debug)
log("flags:", instance.config["flags"]);
for (const [key, val] of Object.entries(instance.config.flags)) {
tf7.env().set(key, val);
}
}
tf7.enableProdMode();
init();
instance.performance.initBackend = Math.trunc(now() - timeStamp);
instance.config.backend = tf7.getBackend();
await env.updateBackend();
registerCustomOps(instance.config);
env.initial = false;
}
return true;
}
function fakeOps(kernelNames, config3) {
for (const kernelName of kernelNames) {
const kernelConfig = {
kernelName,
backendName: config3.backend,
kernelFunc: (param) => {
var _a;
if (config3.debug)
log("kernelFunc", kernelName, config3.backend, param);
return (_a = param == null ? void 0 : param.inputs) == null ? void 0 : _a.info;
}
};
tf7.registerKernel(kernelConfig);
}
env.kernels = tf7.getKernelsForBackend(tf7.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());
}
// src/draw/draw.ts
var draw_exports = {};
__export(draw_exports, {
all: () => all,
body: () => body,
canvas: () => canvas2,
face: () => face,
gesture: () => gesture,
hand: () => hand,
init: () => init2,
object: () => object,
options: () => options2,
person: () => person
});
// src/draw/primitives.ts
var getCanvasContext = (input) => {
if (!input)
log("draw error: invalid canvas");
else if (!input.getContext)
log("draw error: canvas context not defined");
else {
const ctx = input.getContext("2d");
if (!ctx)
log("draw error: cannot get canvas context");
else
return ctx;
}
return null;
};
var rad2deg = (theta) => Math.round(theta * 180 / Math.PI);
var replace = (str, source, target) => str.replace(source, typeof target === "number" ? target.toFixed(1) : target);
var colorDepth = (z, opt) => {
if (!opt.useDepth || typeof z === "undefined")
return opt.color;
const rgb2 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]);
return `rgba(${rgb2[0]}, ${rgb2[1]}, ${rgb2[2]}, ${opt.alpha})`;
};
function labels(ctx, str, startX, startY, localOptions2) {
const line = str.replace(/\[.*\]/g, "").split("\n").map((l) => l.trim());
const x = Math.max(0, startX);
for (let i = line.length - 1; i >= 0; i--) {
const y = i * localOptions2.lineHeight + startY;
if (localOptions2.shadowColor && localOptions2.shadowColor !== "") {
ctx.fillStyle = localOptions2.shadowColor;
ctx.fillText(line[i], x + 5, y + 16);
}
ctx.fillStyle = localOptions2.labelColor;
ctx.fillText(line[i], x + 4, y + 15);
}
}
function point(ctx, x, y, z, localOptions2) {
ctx.fillStyle = colorDepth(z, localOptions2);
ctx.beginPath();
ctx.arc(x, y, localOptions2.pointSize, 0, 2 * Math.PI);
ctx.fill();
}
function rect(ctx, x, y, width, height, localOptions2) {
ctx.beginPath();
ctx.lineWidth = localOptions2.lineWidth;
if (localOptions2.useCurves) {
const cx = (x + x + width) / 2;
const cy = (y + y + height) / 2;
ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);
} else {
ctx.moveTo(x + localOptions2.roundRect, y);
ctx.lineTo(x + width - localOptions2.roundRect, y);
ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions2.roundRect);
ctx.lineTo(x + width, y + height - localOptions2.roundRect);
ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions2.roundRect, y + height);
ctx.lineTo(x + localOptions2.roundRect, y + height);
ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions2.roundRect);
ctx.lineTo(x, y + localOptions2.roundRect);
ctx.quadraticCurveTo(x, y, x + localOptions2.roundRect, y);
ctx.closePath();
}
ctx.stroke();
}
function lines(ctx, points, localOptions2) {
if (points.length < 2)
return;
ctx.beginPath();
ctx.moveTo(points[0][0], points[0][1]);
for (const pt of points) {
ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions2);
ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));
}
ctx.stroke();
if (localOptions2.fillPolygons) {
ctx.closePath();
ctx.fill();
}
}
function curves(ctx, points, localOptions2) {
if (points.length < 2)
return;
ctx.lineWidth = localOptions2.lineWidth;
if (!localOptions2.useCurves || points.length <= 2) {
lines(ctx, points, localOptions2);
return;
}
ctx.moveTo(points[0][0], points[0][1]);
for (let i = 0; i < points.length - 2; i++) {
const xc = (points[i][0] + points[i + 1][0]) / 2;
const yc = (points[i][1] + points[i + 1][1]) / 2;
ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);
}
ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);
ctx.stroke();
if (localOptions2.fillPolygons) {
ctx.closePath();
ctx.fill();
}
}
function arrow(ctx, from, to, radius = 5) {
let angle;
let x;
let y;
ctx.beginPath();
ctx.moveTo(from[0], from[1]);
ctx.lineTo(to[0], to[1]);
angle = Math.atan2(to[1] - from[1], to[0] - from[0]);
x = radius * Math.cos(angle) + to[0];
y = radius * Math.sin(angle) + to[1];
ctx.moveTo(x, y);
angle += 1 / 3 * (2 * Math.PI);
x = radius * Math.cos(angle) + to[0];
y = radius * Math.sin(angle) + to[1];
ctx.lineTo(x, y);
angle += 1 / 3 * (2 * Math.PI);
x = radius * Math.cos(angle) + to[0];
y = radius * Math.sin(angle) + to[1];
ctx.lineTo(x, y);
ctx.closePath();
ctx.stroke();
ctx.fill();
}
// src/draw/options.ts
var options2 = {
color: "rgba(173, 216, 230, 0.6)",
labelColor: "rgba(173, 216, 230, 1)",
shadowColor: "black",
alpha: 0.5,
font: 'small-caps 16px "Segoe UI"',
lineHeight: 18,
lineWidth: 4,
pointSize: 2,
roundRect: 8,
drawPoints: false,
drawLabels: true,
drawBoxes: true,
drawAttention: true,
drawGestures: true,
drawPolygons: true,
drawGaze: true,
fillPolygons: false,
useDepth: true,
useCurves: false,
faceLabels: "",
bodyLabels: "",
bodyPartLabels: "",
objectLabels: "",
handLabels: "",
fingerLabels: "",
gestureLabels: ""
};
// src/face/facemeshcoords.ts
var meshAnnotations = {
silhouette: [
10,
338,
297,
332,
284,
251,
389,
356,
454,
323,
361,
288,
397,
365,
379,
378,
400,
377,
152,
148,
176,
149,
150,
136,
172,
58,
132,
93,
234,
127,
162,
21,
54,
103,
67,
109
],
lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],
lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],
lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],
lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],
lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],
lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],
lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],
lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],
rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],
rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],
rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],
rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],
rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],
rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],
rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],
rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],
rightEyebrowLower: [35, 124, 46, 53, 52, 65],
rightEyeIris: [473, 474, 475, 476, 477],
leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],
leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],
leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],
leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],
leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],
leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],
leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],
leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],
leftEyebrowLower: [265, 353, 276, 283, 282, 295],
leftEyeIris: [468, 469, 470, 471, 472],
midwayBetweenEyes: [168],
noseTip: [1],
noseBottom: [2],
noseRightCorner: [98],
noseLeftCorner: [327],
rightCheek: [205],
leftCheek: [425]
};
var meshLandmarks = {
count: 468,
mouth: 13,
symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]]
};
var blazeFaceLandmarks = {
leftEye: 0,
rightEye: 1,
nose: 2,
mouth: 3,
leftEar: 4,
rightEar: 5,
symmetryLine: [3, 2]
};
var irisIndices = [
{ key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] },
{ key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] },
{ key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] },
{ key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },
{ key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },
{ key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },
{ key: "EyeLower3", indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },
{ key: "EyebrowUpper", indices: [63, 64, 65, 66, 67, 68, 69, 70] },
{ key: "EyebrowLower", indices: [48, 49, 50, 51, 52, 53] }
];
var UV468 = [
[0.499976992607117, 0.652534008026123],
[0.500025987625122, 0.547487020492554],
[0.499974012374878, 0.602371990680695],
[0.482113003730774, 0.471979022026062],
[0.500150978565216, 0.527155995368958],
[0.499909996986389, 0.498252987861633],
[0.499523013830185, 0.40106201171875],
[0.289712011814117, 0.380764007568359],
[0.499954998493195, 0.312398016452789],
[0.499987006187439, 0.269918978214264],
[0.500023007392883, 0.107050001621246],
[0.500023007392883, 0.666234016418457],
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[0.499976992607117, 0.719385027885437],
[0.499976992607117, 0.737019002437592],
[0.499967992305756, 0.781370997428894],
[0.499816000461578, 0.562981009483337],
[0.473773002624512, 0.573909997940063],
[0.104906998574734, 0.254140973091125],
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[0.393361985683441, 0.403706014156342],
[0.345234006643295, 0.344011008739471],
[0.370094001293182, 0.346076011657715],
[0.319321990013123, 0.347265005111694],
[0.297903001308441, 0.353591024875641],
[0.24779200553894, 0.410809993743896],
[0.396889001131058, 0.842755019664764],
[0.280097991228104, 0.375599980354309],
[0.106310002505779, 0.399955987930298],
[0.2099249958992, 0.391353011131287],
[0.355807989835739, 0.534406006336212],
[0.471751004457474, 0.65040397644043],
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[0.414617002010345, 0.66654098033905],
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[0.332827985286713, 0.460712015628815],
[0.368755996227264, 0.447206974029541],
[0.398963987827301, 0.432654976844788],
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[0.189241006970406, 0.523923993110657],
[0.228962004184723, 0.348950982093811],
[0.490725994110107, 0.562400996685028],
[0.404670000076294, 0.485132992267609],
[0.019469000399113, 0.401564002037048],
[0.426243007183075, 0.420431017875671],
[0.396993011236191, 0.548797011375427],
[0.266469985246658, 0.376977026462555],
[0.439121007919312, 0.51895797252655],
[0.032313998788595, 0.644356966018677],
[0.419054001569748, 0.387154996395111],
[0.462783008813858, 0.505746960639954],
[0.238978996872902, 0.779744982719421],
[0.198220998048782, 0.831938028335571],
[0.107550002634525, 0.540755033493042],
[0.183610007166862, 0.740257024765015],
[0.134409993886948, 0.333683013916016],
[0.385764002799988, 0.883153975009918],
[0.490967005491257, 0.579378008842468],
[0.382384985685349, 0.508572995662689],
[0.174399003386497, 0.397670984268188],
[0.318785011768341, 0.39623498916626],
[0.343364000320435, 0.400596976280212],
[0.396100014448166, 0.710216999053955],
[0.187885001301765, 0.588537991046906],
[0.430987000465393, 0.944064974784851],
[0.318993002176285, 0.898285031318665],
[0.266247987747192, 0.869701027870178],
[0.500023007392883, 0.190576016902924],
[0.499976992607117, 0.954452991485596],
[0.366169989109039, 0.398822009563446],
[0.393207013607025, 0.39553701877594],
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301,
300,
368,
417,
351,
465,
251,
301,
389,
385,
380,
386,
394,
395,
379,
399,
412,
419,
410,
436,
322,
387,
373,
388,
326,
2,
393,
354,
370,
461,
393,
164,
267,
268,
302,
12,
386,
374,
387,
312,
268,
13,
298,
293,
301,
265,
446,
340,
380,
385,
381,
280,
330,
425,
322,
426,
391,
420,
429,
437,
393,
391,
326,
344,
440,
438,
458,
459,
461,
364,
434,
394,
428,
396,
262,
274,
354,
457,
317,
316,
402,
316,
315,
403,
315,
314,
404,
314,
313,
405,
313,
421,
406,
323,
366,
361,
292,
306,
407,
306,
291,
408,
291,
287,
409,
287,
432,
410,
427,
434,
411,
372,
264,
383,
459,
309,
457,
366,
352,
401,
1,
274,
4,
418,
421,
262,
331,
294,
358,
435,
433,
367,
392,
289,
439,
328,
462,
326,
94,
2,
370,
289,
305,
455,
339,
254,
448,
359,
255,
446,
254,
253,
449,
253,
252,
450,
252,
256,
451,
256,
341,
452,
414,
413,
463,
286,
441,
414,
286,
258,
441,
258,
257,
442,
257,
259,
443,
259,
260,
444,
260,
467,
445,
309,
459,
250,
305,
289,
290,
305,
290,
460,
401,
376,
435,
309,
250,
392,
376,
411,
433,
453,
341,
464,
357,
453,
465,
343,
357,
412,
437,
343,
399,
344,
360,
440,
420,
437,
456,
360,
420,
363,
361,
401,
288,
265,
372,
353,
390,
339,
249,
339,
448,
255
];
var VTX68 = [
127,
234,
132,
58,
172,
150,
149,
148,
152,
377,
378,
379,
397,
288,
361,
454,
356,
70,
63,
105,
66,
107,
336,
296,
334,
293,
300,
168,
6,
195,
4,
98,
97,
2,
326,
327,
33,
160,
158,
133,
153,
144,
362,
385,
387,
263,
373,
380,
57,
40,
37,
0,
267,
270,
287,
321,
314,
17,
84,
91,
78,
81,
13,
311,
308,
402,
14,
178
];
var VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];
var VTX7 = [33, 133, 362, 263, 1, 78, 308];
var UV68 = VTX68.map((x) => UV468[x]);
var UV33 = VTX33.map((x) => UV468[x]);
var UV7 = VTX7.map((x) => UV468[x]);
function connectionsToIndices(connections) {
const indices = connections.map((connection) => connection[0]);
indices.push(connections[connections.length - 1][1]);
return indices;
}
var pairsLips = [
[61, 146],
[146, 91],
[91, 181],
[181, 84],
[84, 17],
[17, 314],
[314, 405],
[405, 321],
[321, 375],
[375, 291],
[61, 185],
[185, 40],
[40, 39],
[39, 37],
[37, 0],
[0, 267],
[267, 269],
[269, 270],
[270, 409],
[409, 291],
[78, 95],
[95, 88],
[88, 178],
[178, 87],
[87, 14],
[14, 317],
[317, 402],
[402, 318],
[318, 324],
[324, 308],
[78, 191],
[191, 80],
[80, 81],
[81, 82],
[82, 13],
[13, 312],
[312, 311],
[311, 310],
[310, 415],
[415, 308]
];
var pairsLeftEye = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];
var pairsLeftEyebrow = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];
var pairsLeftIris = [[474, 475], [475, 476], [476, 477], [477, 474]];
var pairsRightEye = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];
var pairsRightEyebrow = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];
var pairsRightIris = [[469, 470], [470, 471], [471, 472], [472, 469]];
var pairsFaceContour = [
[10, 338],
[338, 297],
[297, 332],
[332, 284],
[284, 251],
[251, 389],
[389, 356],
[356, 454],
[454, 323],
[323, 361],
[361, 288],
[288, 397],
[397, 365],
[365, 379],
[379, 378],
[378, 400],
[400, 377],
[377, 152],
[152, 148],
[148, 176],
[176, 149],
[149, 150],
[150, 136],
[136, 172],
[172, 58],
[58, 132],
[132, 93],
[93, 234],
[234, 127],
[127, 162],
[162, 21],
[21, 54],
[54, 103],
[103, 67],
[67, 109],
[109, 10]
];
var contourKeypoints = {
lips: connectionsToIndices(pairsLips),
leftEye: connectionsToIndices(pairsLeftEye),
leftEyebrow: connectionsToIndices(pairsLeftEyebrow),
leftIris: connectionsToIndices(pairsLeftIris),
rightEye: connectionsToIndices(pairsRightEye),
rightEyebrow: connectionsToIndices(pairsRightEyebrow),
rightIris: connectionsToIndices(pairsRightIris),
faceOval: connectionsToIndices(pairsFaceContour)
};
// src/face/constants.ts
var LIPS_CONNECTIONS = [
[61, 146],
[146, 91],
[91, 181],
[181, 84],
[84, 17],
[17, 314],
[314, 405],
[405, 321],
[321, 375],
[375, 291],
[61, 185],
[185, 40],
[40, 39],
[39, 37],
[37, 0],
[0, 267],
[267, 269],
[269, 270],
[270, 409],
[409, 291],
[78, 95],
[95, 88],
[88, 178],
[178, 87],
[87, 14],
[14, 317],
[317, 402],
[402, 318],
[318, 324],
[324, 308],
[78, 191],
[191, 80],
[80, 81],
[81, 82],
[82, 13],
[13, 312],
[312, 311],
[311, 310],
[310, 415],
[415, 308]
];
var LEFT_EYE_CONNECTIONS = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];
var LEFT_EYEBROW_CONNECTIONS = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];
var LEFT_IRIS_CONNECTIONS = [[474, 475], [475, 476], [476, 477], [477, 474]];
var RIGHT_EYE_CONNECTIONS = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];
var RIGHT_EYEBROW_CONNECTIONS = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];
var RIGHT_IRIS_CONNECTIONS = [[469, 470], [470, 471], [471, 472], [472, 469]];
var FACE_OVAL_CONNECTIONS = [
[10, 338],
[338, 297],
[297, 332],
[332, 284],
[284, 251],
[251, 389],
[389, 356],
[356, 454],
[454, 323],
[323, 361],
[361, 288],
[288, 397],
[397, 365],
[365, 379],
[379, 378],
[378, 400],
[400, 377],
[377, 152],
[152, 148],
[148, 176],
[176, 149],
[149, 150],
[150, 136],
[136, 172],
[172, 58],
[58, 132],
[132, 93],
[93, 234],
[234, 127],
[127, 162],
[162, 21],
[21, 54],
[54, 103],
[103, 67],
[67, 109],
[109, 10]
];
function connectionsToIndices2(connections) {
const indices = connections.map((connection) => connection[0]);
indices.push(connections[connections.length - 1][1]);
return indices;
}
var MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {
lips: connectionsToIndices2(LIPS_CONNECTIONS),
leftEye: connectionsToIndices2(LEFT_EYE_CONNECTIONS),
leftEyebrow: connectionsToIndices2(LEFT_EYEBROW_CONNECTIONS),
leftIris: connectionsToIndices2(LEFT_IRIS_CONNECTIONS),
rightEye: connectionsToIndices2(RIGHT_EYE_CONNECTIONS),
rightEyebrow: connectionsToIndices2(RIGHT_EYEBROW_CONNECTIONS),
rightIris: connectionsToIndices2(RIGHT_IRIS_CONNECTIONS),
faceOval: connectionsToIndices2(FACE_OVAL_CONNECTIONS)
};
var indexLabelPairs = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR).map(([label, indices]) => indices.map((index2) => [index2, label])).flat();
var MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);
var LANDMARKS_REFINEMENT_LIPS_CONFIG = [
61,
146,
91,
181,
84,
17,
314,
405,
321,
375,
291,
185,
40,
39,
37,
0,
267,
269,
270,
409,
78,
95,
88,
178,
87,
14,
317,
402,
318,
324,
308,
191,
80,
81,
82,
13,
312,
311,
310,
415,
76,
77,
90,
180,
85,
16,
315,
404,
320,
307,
306,
184,
74,
73,
72,
11,
302,
303,
304,
408,
62,
96,
89,
179,
86,
15,
316,
403,
319,
325,
292,
183,
42,
41,
38,
12,
268,
271,
272,
407
];
var LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [
33,
7,
163,
144,
145,
153,
154,
155,
133,
246,
161,
160,
159,
158,
157,
173,
130,
25,
110,
24,
23,
22,
26,
112,
243,
247,
30,
29,
27,
28,
56,
190,
226,
31,
228,
229,
230,
231,
232,
233,
244,
113,
225,
224,
223,
222,
221,
189,
35,
124,
46,
53,
52,
65,
143,
111,
117,
118,
119,
120,
121,
128,
245,
156,
70,
63,
105,
66,
107,
55,
193
];
var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [
263,
249,
390,
373,
374,
380,
381,
382,
362,
466,
388,
387,
386,
385,
384,
398,
359,
255,
339,
254,
253,
252,
256,
341,
463,
467,
260,
259,
257,
258,
286,
414,
446,
261,
448,
449,
450,
451,
452,
453,
464,
342,
445,
444,
443,
442,
441,
413,
265,
353,
276,
283,
282,
295,
372,
340,
346,
347,
348,
349,
350,
357,
465,
383,
300,
293,
334,
296,
336,
285,
417
];
// src/draw/face.ts
var localOptions;
function drawLabels(f, ctx) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i;
if (!localOptions.drawLabels || ((_a = localOptions.faceLabels) == null ? void 0 : _a.length) === 0)
return;
let l = localOptions.faceLabels.slice();
if (f.score)
l = replace(l, "[score]", 100 * f.score);
if (f.gender)
l = replace(l, "[gender]", f.gender);
if (f.genderScore)
l = replace(l, "[genderScore]", 100 * f.genderScore);
if (f.age)
l = replace(l, "[age]", f.age);
if (f.distance)
l = replace(l, "[distance]", 100 * f.distance);
if (f.real)
l = replace(l, "[real]", 100 * f.real);
if (f.live)
l = replace(l, "[live]", 100 * f.live);
if (f.emotion && f.emotion.length > 0) {
const emotion2 = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);
if (emotion2.length > 3)
emotion2.length = 3;
l = replace(l, "[emotions]", emotion2.join(" "));
}
if ((_c = (_b = f.rotation) == null ? void 0 : _b.angle) == null ? void 0 : _c.roll)
l = replace(l, "[roll]", rad2deg(f.rotation.angle.roll));
if ((_e = (_d = f.rotation) == null ? void 0 : _d.angle) == null ? void 0 : _e.yaw)
l = replace(l, "[yaw]", rad2deg(f.rotation.angle.yaw));
if ((_g = (_f = f.rotation) == null ? void 0 : _f.angle) == null ? void 0 : _g.pitch)
l = replace(l, "[pitch]", rad2deg(f.rotation.angle.pitch));
if ((_i = (_h = f.rotation) == null ? void 0 : _h.gaze) == null ? void 0 : _i.bearing)
l = replace(l, "[gaze]", rad2deg(f.rotation.gaze.bearing));
labels(ctx, l, f.box[0], f.box[1], localOptions);
}
function drawIrisElipse(f, ctx) {
var _a, _b, _c, _d;
if (((_a = f.annotations) == null ? void 0 : _a.leftEyeIris) && ((_b = f.annotations) == null ? void 0 : _b.leftEyeIris[0])) {
ctx.strokeStyle = localOptions.useDepth ? "rgba(255, 200, 255, 0.3)" : localOptions.color;
ctx.beginPath();
const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;
const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;
ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);
ctx.stroke();
if (localOptions.fillPolygons) {
ctx.fillStyle = localOptions.useDepth ? "rgba(255, 255, 200, 0.3)" : localOptions.color;
ctx.fill();
}
}
if (((_c = f.annotations) == null ? void 0 : _c.rightEyeIris) && ((_d = f.annotations) == null ? void 0 : _d.rightEyeIris[0])) {
ctx.strokeStyle = localOptions.useDepth ? "rgba(255, 200, 255, 0.3)" : localOptions.color;
ctx.beginPath();
const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;
const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;
ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);
ctx.stroke();
if (localOptions.fillPolygons) {
ctx.fillStyle = localOptions.useDepth ? "rgba(255, 255, 200, 0.3)" : localOptions.color;
ctx.fill();
}
}
}
function drawGazeSpheres(f, ctx) {
var _a;
if (localOptions.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.angle) && typeof Path2D !== "undefined") {
ctx.strokeStyle = "pink";
const valX = f.box[0] + f.box[2] / 2 - f.box[3] * rad2deg(f.rotation.angle.yaw) / 90;
const valY = f.box[1] + f.box[3] / 2 + f.box[2] * rad2deg(f.rotation.angle.pitch) / 90;
const pathV = new Path2D(`
M ${f.box[0] + f.box[2] / 2} ${f.box[1]}
C
${valX} ${f.box[1]},
${valX} ${f.box[1] + f.box[3]},
${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}
`);
const pathH = new Path2D(`
M ${f.box[0]} ${f.box[1] + f.box[3] / 2}
C
${f.box[0]} ${valY},
${f.box[0] + f.box[2]} ${valY},
${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}
`);
ctx.stroke(pathH);
ctx.stroke(pathV);
}
}
function drawGazeArrows(f, ctx) {
var _a;
if (localOptions.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.gaze.strength) && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) {
ctx.strokeStyle = "pink";
ctx.fillStyle = "pink";
const leftGaze = [
f.annotations.leftEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3],
f.annotations.leftEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]
];
arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4);
const rightGaze = [
f.annotations.rightEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3],
f.annotations.rightEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]
];
arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4);
}
}
function drawFacePolygons(f, ctx) {
if (localOptions.drawPolygons && f.mesh.length >= 468) {
ctx.lineWidth = 1;
for (let i = 0; i < TRI468.length / 3; i++) {
const points = [TRI468[i * 3 + 0], TRI468[i * 3 + 1], TRI468[i * 3 + 2]].map((index2) => f.mesh[index2]);
lines(ctx, points, localOptions);
}
drawIrisElipse(f, ctx);
}
}
function drawFacePoints(f, ctx) {
if (localOptions.drawPoints && f.mesh.length >= 468) {
for (let i = 0; i < f.mesh.length; i++) {
point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions);
if (localOptions.drawAttention) {
if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i))
point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, localOptions);
if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i))
point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions);
if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i))
point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions);
}
}
}
}
function drawFaceBoxes(f, ctx) {
if (localOptions.drawBoxes) {
rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);
}
}
function face(inCanvas2, result, drawOptions) {
localOptions = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
const ctx = getCanvasContext(inCanvas2);
if (!ctx)
return;
ctx.font = localOptions.font;
ctx.strokeStyle = localOptions.color;
ctx.fillStyle = localOptions.color;
for (const f of result) {
drawFaceBoxes(f, ctx);
drawLabels(f, ctx);
if (f.mesh && f.mesh.length > 0) {
drawFacePoints(f, ctx);
drawFacePolygons(f, ctx);
drawGazeSpheres(f, ctx);
drawGazeArrows(f, ctx);
}
}
}
// src/draw/body.ts
function body(inCanvas2, result, drawOptions) {
var _a, _b;
const localOptions2 = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
const ctx = getCanvasContext(inCanvas2);
if (!ctx)
return;
ctx.lineJoin = "round";
for (let i = 0; i < result.length; i++) {
ctx.strokeStyle = localOptions2.color;
ctx.fillStyle = localOptions2.color;
ctx.lineWidth = localOptions2.lineWidth;
ctx.font = localOptions2.font;
if (localOptions2.drawBoxes && result[i].box && result[i].box.length === 4) {
rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2);
if (localOptions2.drawLabels && ((_a = localOptions2.bodyLabels) == null ? void 0 : _a.length) > 0) {
let l = localOptions2.bodyLabels.slice();
l = replace(l, "[score]", 100 * result[i].score);
labels(ctx, l, result[i].box[0], result[i].box[1], localOptions2);
}
}
if (localOptions2.drawPoints && result[i].keypoints) {
for (let pt = 0; pt < result[i].keypoints.length; pt++) {
if (!result[i].keypoints[pt].score || result[i].keypoints[pt].score === 0)
continue;
ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions2);
point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions2);
}
}
if (localOptions2.drawLabels && ((_b = localOptions2.bodyPartLabels) == null ? void 0 : _b.length) > 0 && result[i].keypoints) {
ctx.font = localOptions2.font;
for (const pt of result[i].keypoints) {
if (!pt.score || pt.score === 0)
continue;
let l = localOptions2.bodyPartLabels.slice();
l = replace(l, "[label]", pt.part);
l = replace(l, "[score]", 100 * pt.score);
labels(ctx, l, pt.position[0], pt.position[1], localOptions2);
}
}
if (localOptions2.drawPolygons && result[i].keypoints && result[i].annotations) {
for (const part of Object.values(result[i].annotations)) {
for (const connected4 of part)
curves(ctx, connected4, localOptions2);
}
}
}
}
// src/draw/hand.ts
function hand(inCanvas2, result, drawOptions) {
var _a, _b;
const localOptions2 = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
const ctx = getCanvasContext(inCanvas2);
if (!ctx)
return;
ctx.lineJoin = "round";
ctx.font = localOptions2.font;
for (const h of result) {
if (localOptions2.drawBoxes) {
ctx.strokeStyle = localOptions2.color;
ctx.fillStyle = localOptions2.color;
rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2);
if (localOptions2.drawLabels && ((_a = localOptions2.handLabels) == null ? void 0 : _a.length) > 0) {
let l = localOptions2.handLabels.slice();
l = replace(l, "[label]", h.label);
l = replace(l, "[score]", 100 * h.score);
labels(ctx, l, h.box[0], h.box[1], localOptions2);
}
ctx.stroke();
}
if (localOptions2.drawPoints) {
if (h.keypoints && h.keypoints.length > 0) {
for (const pt of h.keypoints) {
ctx.fillStyle = colorDepth(pt[2], localOptions2);
point(ctx, pt[0], pt[1], 0, localOptions2);
}
}
}
if (localOptions2.drawLabels && h.annotations && ((_b = localOptions2.fingerLabels) == null ? void 0 : _b.length) > 0) {
for (const [part, pt] of Object.entries(h.annotations)) {
let l = localOptions2.fingerLabels.slice();
l = replace(l, "[label]", part);
labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions2);
}
}
if (localOptions2.drawPolygons && h.annotations) {
const addHandLine = (part) => {
if (!part || part.length === 0 || !part[0])
return;
for (let i = 0; i < part.length; i++) {
ctx.beginPath();
const z = part[i][2] || 0;
ctx.strokeStyle = colorDepth(i * z, localOptions2);
ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);
ctx.lineTo(part[i][0], part[i][1]);
ctx.stroke();
}
};
ctx.lineWidth = localOptions2.lineWidth;
addHandLine(h.annotations.index);
addHandLine(h.annotations.middle);
addHandLine(h.annotations.ring);
addHandLine(h.annotations.pinky);
addHandLine(h.annotations.thumb);
}
}
}
// src/draw/object.ts
function object(inCanvas2, result, drawOptions) {
var _a;
const localOptions2 = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
const ctx = getCanvasContext(inCanvas2);
if (!ctx)
return;
ctx.lineJoin = "round";
ctx.font = localOptions2.font;
for (const h of result) {
if (localOptions2.drawBoxes) {
ctx.strokeStyle = localOptions2.color;
ctx.fillStyle = localOptions2.color;
rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2);
if (localOptions2.drawLabels && ((_a = localOptions2.objectLabels) == null ? void 0 : _a.length) > 0) {
let l = localOptions2.objectLabels.slice();
l = replace(l, "[label]", h.label);
l = replace(l, "[score]", 100 * h.score);
labels(ctx, l, h.box[0], h.box[1], localOptions2);
}
ctx.stroke();
}
}
}
// src/draw/gesture.ts
function gesture(inCanvas2, result, drawOptions) {
var _a;
const localOptions2 = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
if (localOptions2.drawGestures && ((_a = localOptions2.gestureLabels) == null ? void 0 : _a.length) > 0) {
const ctx = getCanvasContext(inCanvas2);
if (!ctx)
return;
ctx.font = localOptions2.font;
ctx.fillStyle = localOptions2.color;
let i = 1;
for (let j = 0; j < result.length; j++) {
const [where, what] = Object.entries(result[j]);
if (what.length > 1 && what[1].length > 0) {
const who = where[1] > 0 ? `#${where[1]}` : "";
let l = localOptions2.gestureLabels.slice();
l = replace(l, "[where]", where[0]);
l = replace(l, "[who]", who);
l = replace(l, "[what]", what[1]);
labels(ctx, l, 8, 2 + i * localOptions2.lineHeight, localOptions2);
i += 1;
}
}
}
}
// src/draw/labels.ts
var defaultLabels = {
face: `face
confidence: [score]%
[gender] [genderScore]%
age: [age] years
distance: [distance]cm
real: [real]%
live: [live]%
[emotions]
roll: [roll]\xB0 yaw:[yaw]\xB0 pitch:[pitch]\xB0
gaze: [gaze]\xB0`,
body: "body [score]%",
bodyPart: "[label] [score]%",
object: "[label] [score]%",
hand: "[label] [score]%",
finger: "[label]",
gesture: "[where] [who]: [what]"
};
// src/draw/draw.ts
var drawTime = 0;
function person(inCanvas2, result, drawOptions) {
const localOptions2 = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
const ctx = getCanvasContext(inCanvas2);
if (!ctx)
return;
ctx.lineJoin = "round";
ctx.font = localOptions2.font;
for (let i = 0; i < result.length; i++) {
if (localOptions2.drawBoxes) {
ctx.strokeStyle = localOptions2.color;
ctx.fillStyle = localOptions2.color;
rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2);
if (localOptions2.drawLabels) {
const label = `person #${i}`;
if (localOptions2.shadowColor && localOptions2.shadowColor !== "") {
ctx.fillStyle = localOptions2.shadowColor;
ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]);
}
ctx.fillStyle = localOptions2.labelColor;
ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]);
}
ctx.stroke();
}
}
}
function canvas2(input, output) {
if (!input || !output)
return;
const ctx = getCanvasContext(output);
if (!ctx)
return;
ctx.drawImage(input, 0, 0);
}
async function all(inCanvas2, result, drawOptions) {
if (!(result == null ? void 0 : result.performance) || !inCanvas2)
return null;
const timeStamp = now();
const localOptions2 = mergeDeep(options2, drawOptions);
const promise = Promise.all([
face(inCanvas2, result.face, localOptions2),
body(inCanvas2, result.body, localOptions2),
hand(inCanvas2, result.hand, localOptions2),
object(inCanvas2, result.object, localOptions2),
gesture(inCanvas2, result.gesture, localOptions2)
]);
drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);
result.performance.draw = drawTime;
return promise;
}
function init2() {
options2.faceLabels = defaultLabels.face;
options2.bodyLabels = defaultLabels.body;
options2.bodyPartLabels = defaultLabels.bodyPart;
options2.handLabels = defaultLabels.hand;
options2.fingerLabels = defaultLabels.finger;
options2.objectLabels = defaultLabels.object;
options2.gestureLabels = defaultLabels.gesture;
}
// src/body/blazepose.ts
var tf9 = __toESM(require_tfjs_esm());
// src/body/blazeposecoords.ts
var blazeposecoords_exports = {};
__export(blazeposecoords_exports, {
connected: () => connected,
kpt: () => kpt
});
var kpt = [
"nose",
"leftEyeInside",
"leftEye",
"leftEyeOutside",
"rightEyeInside",
"rightEye",
"rightEyeOutside",
"leftEar",
"rightEar",
"leftMouth",
"rightMouth",
"leftShoulder",
"rightShoulder",
"leftElbow",
"rightElbow",
"leftWrist",
"rightWrist",
"leftPinky",
"rightPinky",
"leftIndex",
"rightIndex",
"leftThumb",
"rightThumb",
"leftHip",
"rightHip",
"leftKnee",
"rightKnee",
"leftAnkle",
"rightAnkle",
"leftHeel",
"rightHeel",
"leftFoot",
"rightFoot",
"bodyCenter",
"bodyTop",
"leftPalm",
"leftHand",
"rightPalm",
"rightHand"
];
var connected = {
shoulders: ["leftShoulder", "rightShoulder"],
hips: ["rightHip", "leftHip"],
mouth: ["leftMouth", "rightMouth"],
leftLegUpper: ["leftHip", "leftKnee"],
leftLegLower: ["leftKnee", "leftAnkle"],
leftFoot: ["leftAnkle", "leftHeel", "leftFoot"],
leftTorso: ["leftShoulder", "leftHip"],
leftArmUpper: ["leftShoulder", "leftElbow"],
leftArmLower: ["leftElbow", "leftWrist"],
leftHand: ["leftWrist", "leftPalm"],
leftHandPinky: ["leftPalm", "leftPinky"],
leftHandIndex: ["leftPalm", "leftIndex"],
leftHandThumb: ["leftPalm", "leftThumb"],
leftEyeOutline: ["leftEyeInside", "leftEyeOutside"],
rightLegUpper: ["rightHip", "rightKnee"],
rightLegLower: ["rightKnee", "rightAnkle"],
rightFoot: ["rightAnkle", "rightHeel", "rightFoot"],
rightTorso: ["rightShoulder", "rightHip"],
rightArmUpper: ["rightShoulder", "rightElbow"],
rightArmLower: ["rightElbow", "rightWrist"],
rightHand: ["rightWrist", "rightPalm"],
rightHandPinky: ["rightPalm", "rightPinky"],
rightHandIndex: ["rightPalm", "rightIndex"],
rightHandThumb: ["rightPalm", "rightThumb"],
rightEyeOutline: ["rightEyeInside", "rightEyeOutside"]
};
// src/body/blazeposedetector.ts
var tf8 = __toESM(require_tfjs_esm());
var model;
var inputSize = 224;
var anchorTensor;
var numLayers = 5;
var strides = [8, 16, 32, 32, 32];
function createAnchors() {
const anchors3 = [];
let layerId = 0;
while (layerId < numLayers) {
let anchorCount = 0;
let lastSameStrideLayer = layerId;
while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {
anchorCount += 2;
lastSameStrideLayer++;
}
const stride = strides[layerId];
const featureMapHeight = Math.ceil(inputSize / stride);
const featureMapWidth = Math.ceil(inputSize / stride);
for (let y = 0; y < featureMapHeight; ++y) {
for (let x = 0; x < featureMapWidth; ++x) {
for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {
anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });
}
}
}
layerId = lastSameStrideLayer;
}
anchorTensor = { x: tf8.tensor1d(anchors3.map((a) => a.x)), y: tf8.tensor1d(anchors3.map((a) => a.y)) };
}
async function loadDetector(config3) {
if (env.initial)
model = null;
if (!model && config3.body["detector"] && config3.body["detector"].modelPath || "") {
model = await loadModel(config3.body["detector"].modelPath);
const inputs = (model == null ? void 0 : model["executor"]) ? Object.values(model.modelSignature["inputs"]) : void 0;
inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
} else if (config3.debug && model)
log("cached model:", model["modelUrl"]);
createAnchors();
return model;
}
var cropFactor = [5, 5];
function decodeBoxes(boxesTensor, anchor) {
return tf8.tidy(() => {
const split6 = tf8.split(boxesTensor, 12, 1);
let xCenter = tf8.squeeze(split6[0]);
let yCenter = tf8.squeeze(split6[1]);
let width = tf8.squeeze(split6[2]);
let height = tf8.squeeze(split6[3]);
xCenter = tf8.add(tf8.div(xCenter, inputSize), anchor.x);
yCenter = tf8.add(tf8.div(yCenter, inputSize), anchor.y);
width = tf8.mul(tf8.div(width, inputSize), cropFactor[0]);
height = tf8.mul(tf8.div(height, inputSize), cropFactor[1]);
const xMin = tf8.sub(xCenter, tf8.div(width, 2));
const yMin = tf8.sub(yCenter, tf8.div(height, 2));
const xMax = tf8.add(xMin, width);
const yMax = tf8.add(yMin, height);
const boxes = tf8.stack([xMin, yMin, xMax, yMax], 1);
return boxes;
});
}
async function decodeResults(boxesTensor, logitsTensor, config3, outputSize2) {
var _a, _b;
const detectedBoxes = [];
const t2 = {};
t2.boxes = decodeBoxes(boxesTensor, anchorTensor);
t2.scores = tf8.sigmoid(logitsTensor);
t2.nms = await tf8.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, 1, ((_a = config3.body["detector"]) == null ? void 0 : _a.minConfidence) || 0.1, ((_b = config3.body["detector"]) == null ? void 0 : _b.iouThreshold) || 0.1);
const nms = await t2.nms.data();
const scores = await t2.scores.data();
const boxes = await t2.boxes.array();
for (const i of Array.from(nms)) {
const score = scores[i];
const boxRaw = boxes[i];
const box = [Math.round(boxRaw[0] * outputSize2[0]), Math.round(boxRaw[1] * outputSize2[1]), Math.round(boxRaw[2] * outputSize2[0]), Math.round(boxRaw[3] * outputSize2[1])];
const detectedBox = { score, boxRaw, box };
detectedBoxes.push(detectedBox);
}
Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6]));
return detectedBoxes;
}
async function detectBoxes(input, config3, outputSize2) {
const t2 = {};
t2.res = model == null ? void 0 : model.execute(input, ["Identity"]);
t2.logitsRaw = tf8.slice(t2.res, [0, 0, 0], [1, -1, 1]);
t2.boxesRaw = tf8.slice(t2.res, [0, 0, 1], [1, -1, -1]);
t2.logits = tf8.squeeze(t2.logitsRaw);
t2.boxes = tf8.squeeze(t2.boxesRaw);
const boxes = await decodeResults(t2.boxes, t2.logits, config3, outputSize2);
Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6]));
return boxes;
}
// src/util/box.ts
function calc(keypoints, outputSize2 = [1, 1]) {
const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])];
const min2 = [Math.min(...coords[0]), Math.min(...coords[1])];
const max5 = [Math.max(...coords[0]), Math.max(...coords[1])];
const box = [min2[0], min2[1], max5[0] - min2[0], max5[1] - min2[1]];
const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]];
return { box, boxRaw };
}
function square(keypoints, outputSize2 = [1, 1]) {
const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])];
const min2 = [Math.min(...coords[0]), Math.min(...coords[1])];
const max5 = [Math.max(...coords[0]), Math.max(...coords[1])];
const center = [(min2[0] + max5[0]) / 2, (min2[1] + max5[1]) / 2];
const dist = Math.max(center[0] - min2[0], center[1] - min2[1], -center[0] + max5[0], -center[1] + max5[1]);
const box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];
const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]];
return { box, boxRaw };
}
function scale(box, scaleFact) {
const dist = [box[2] * scaleFact, box[3] * scaleFact];
const newBox = [
box[0] - (dist[0] - box[2]) / 2,
box[1] - (dist[1] - box[3]) / 2,
dist[0],
dist[1]
];
return newBox;
}
// src/body/blazepose.ts
var model2;
var inputSize2 = 256;
var skipped = Number.MAX_SAFE_INTEGER;
var outputNodes = {
landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"],
detector: []
};
var cache = [];
var padding = [[0, 0], [0, 0], [0, 0], [0, 0]];
var lastTime = 0;
var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x));
var loadDetect = (config3) => loadDetector(config3);
async function loadPose(config3) {
if (env.initial)
model2 = null;
if (!model2) {
model2 = await loadModel(config3.body.modelPath);
const inputs = (model2 == null ? void 0 : model2["executor"]) ? Object.values(model2.modelSignature["inputs"]) : void 0;
inputSize2 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
} else if (config3.debug)
log("cached model:", model2["modelUrl"]);
return model2;
}
function prepareImage(input, size2, cropBox) {
var _a, _b;
const t2 = {};
if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2]))
return input;
let final;
if (cropBox) {
t2.cropped = tf9.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]);
}
if (input.shape[1] !== input.shape[2]) {
const height = [
input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,
input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0
];
const width = [
input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,
input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0
];
padding = [
[0, 0],
height,
width,
[0, 0]
];
t2.pad = tf9.pad(t2.cropped || input, padding);
t2.resize = tf9.image.resizeBilinear(t2.pad, [size2, size2]);
final = tf9.div(t2.resize, constants.tf255);
} else if (input.shape[1] !== size2) {
t2.resize = tf9.image.resizeBilinear(t2.cropped || input, [size2, size2]);
final = tf9.div(t2.resize, constants.tf255);
} else {
final = tf9.div(t2.cropped || input, constants.tf255);
}
Object.keys(t2).forEach((tensor6) => tf9.dispose(t2[tensor6]));
return final;
}
function rescaleKeypoints(keypoints, outputSize2, cropBox) {
for (const kpt4 of keypoints) {
kpt4.position = [
Math.trunc(kpt4.position[0] * (outputSize2[0] + padding[2][0] + padding[2][1]) / outputSize2[0] - padding[2][0]),
Math.trunc(kpt4.position[1] * (outputSize2[1] + padding[1][0] + padding[1][1]) / outputSize2[1] - padding[1][0]),
kpt4.position[2]
];
kpt4.positionRaw = [kpt4.position[0] / outputSize2[0], kpt4.position[1] / outputSize2[1], 2 * kpt4.position[2] / (outputSize2[0] + outputSize2[1])];
}
if (cropBox) {
const width = cropBox[2] - cropBox[0];
const height = cropBox[3] - cropBox[1];
for (const kpt4 of keypoints) {
kpt4.positionRaw = [
kpt4.positionRaw[0] / height + cropBox[1],
kpt4.positionRaw[1] / width + cropBox[0],
kpt4.positionRaw[2]
];
kpt4.position = [
Math.trunc(kpt4.positionRaw[0] * outputSize2[0]),
Math.trunc(kpt4.positionRaw[1] * outputSize2[1]),
kpt4.positionRaw[2]
];
}
}
return keypoints;
}
function fixKeypoints(keypoints) {
const leftPalm = keypoints.find((k) => k.part === "leftPalm");
const leftWrist = keypoints.find((k) => k.part === "leftWrist");
const leftIndex = keypoints.find((k) => k.part === "leftIndex");
leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;
const rightPalm = keypoints.find((k) => k.part === "rightPalm");
const rightWrist = keypoints.find((k) => k.part === "rightWrist");
const rightIndex = keypoints.find((k) => k.part === "rightIndex");
rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;
}
async function detectLandmarks(input, config3, outputSize2) {
if (!(model2 == null ? void 0 : model2["executor"]))
return null;
const t2 = {};
[t2.ld, t2.segmentation, t2.heatmap, t2.world, t2.poseflag] = model2 == null ? void 0 : model2.execute(input, outputNodes.landmarks);
const poseScore = (await t2.poseflag.data())[0];
const points = await t2.ld.data();
const distances = await t2.world.data();
Object.keys(t2).forEach((tensor6) => tf9.dispose(t2[tensor6]));
const keypointsRelative = [];
const depth = 5;
for (let i = 0; i < points.length / depth; i++) {
const score = sigmoid2(points[depth * i + 3]);
const presence = sigmoid2(points[depth * i + 4]);
const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;
const positionRaw = [points[depth * i + 0] / inputSize2, points[depth * i + 1] / inputSize2, points[depth * i + 2] + 0];
const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]];
const distance2 = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];
keypointsRelative.push({ part: kpt[i], positionRaw, position, distance: distance2, score: adjScore });
}
if (poseScore < (config3.body.minConfidence || 0))
return null;
fixKeypoints(keypointsRelative);
const keypoints = rescaleKeypoints(keypointsRelative, outputSize2);
const kpts = keypoints.map((k) => k.position);
const boxes = calc(kpts, [outputSize2[0], outputSize2[1]]);
const annotations2 = {};
for (const [name, indexes] of Object.entries(connected)) {
const pt = [];
for (let i = 0; i < indexes.length - 1; i++) {
const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i]);
const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i + 1]);
if (pt0 && pt1)
pt.push([pt0.position, pt1.position]);
}
annotations2[name] = pt;
}
const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 };
return body4;
}
async function predict(input, config3) {
var _a, _b, _c;
const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
const skipTime = (config3.body.skipTime || 0) > now() - lastTime;
const skipFrame = skipped < (config3.body.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame && cache !== null) {
skipped++;
} else {
let boxes = [];
if ((_b = (_a = config3.body) == null ? void 0 : _a["detector"]) == null ? void 0 : _b["enabled"]) {
const preparedImage = prepareImage(input, 224);
boxes = await detectBoxes(preparedImage, config3, outputSize2);
tf9.dispose(preparedImage);
} else {
boxes = [{ box: [0, 0, 0, 0], boxRaw: [0, 0, 1, 1], score: 0 }];
}
for (let i = 0; i < boxes.length; i++) {
const preparedBox = prepareImage(input, 256, (_c = boxes[i]) == null ? void 0 : _c.boxRaw);
cache.length = 0;
const bodyResult = await detectLandmarks(preparedBox, config3, outputSize2);
tf9.dispose(preparedBox);
if (!bodyResult)
continue;
bodyResult.id = i;
cache.push(bodyResult);
}
lastTime = now();
skipped = 0;
}
return cache;
}
// src/object/centernet.ts
var tf10 = __toESM(require_tfjs_esm());
// src/object/labels.ts
var labels2 = [
{ class: 1, label: "person" },
{ class: 2, label: "bicycle" },
{ class: 3, label: "car" },
{ class: 4, label: "motorcycle" },
{ class: 5, label: "airplane" },
{ class: 6, label: "bus" },
{ class: 7, label: "train" },
{ class: 8, label: "truck" },
{ class: 9, label: "boat" },
{ class: 10, label: "traffic light" },
{ class: 11, label: "fire hydrant" },
{ class: 12, label: "stop sign" },
{ class: 13, label: "parking meter" },
{ class: 14, label: "bench" },
{ class: 15, label: "bird" },
{ class: 16, label: "cat" },
{ class: 17, label: "dog" },
{ class: 18, label: "horse" },
{ class: 19, label: "sheep" },
{ class: 20, label: "cow" },
{ class: 21, label: "elephant" },
{ class: 22, label: "bear" },
{ class: 23, label: "zebra" },
{ class: 24, label: "giraffe" },
{ class: 25, label: "backpack" },
{ class: 26, label: "umbrella" },
{ class: 27, label: "handbag" },
{ class: 28, label: "tie" },
{ class: 29, label: "suitcase" },
{ class: 30, label: "frisbee" },
{ class: 31, label: "skis" },
{ class: 32, label: "snowboard" },
{ class: 33, label: "sports ball" },
{ class: 34, label: "kite" },
{ class: 35, label: "baseball bat" },
{ class: 36, label: "baseball glove" },
{ class: 37, label: "skateboard" },
{ class: 38, label: "surfboard" },
{ class: 39, label: "tennis racket" },
{ class: 40, label: "bottle" },
{ class: 41, label: "wine glass" },
{ class: 42, label: "cup" },
{ class: 43, label: "fork" },
{ class: 44, label: "knife" },
{ class: 45, label: "spoon" },
{ class: 46, label: "bowl" },
{ class: 47, label: "banana" },
{ class: 48, label: "apple" },
{ class: 49, label: "sandwich" },
{ class: 50, label: "orange" },
{ class: 51, label: "broccoli" },
{ class: 52, label: "carrot" },
{ class: 53, label: "hot dog" },
{ class: 54, label: "pizza" },
{ class: 55, label: "donut" },
{ class: 56, label: "cake" },
{ class: 57, label: "chair" },
{ class: 58, label: "couch" },
{ class: 59, label: "potted plant" },
{ class: 60, label: "bed" },
{ class: 61, label: "dining table" },
{ class: 62, label: "toilet" },
{ class: 63, label: "tv" },
{ class: 64, label: "laptop" },
{ class: 65, label: "mouse" },
{ class: 66, label: "remote" },
{ class: 67, label: "keyboard" },
{ class: 68, label: "cell phone" },
{ class: 69, label: "microwave" },
{ class: 70, label: "oven" },
{ class: 71, label: "toaster" },
{ class: 72, label: "sink" },
{ class: 73, label: "refrigerator" },
{ class: 74, label: "book" },
{ class: 75, label: "clock" },
{ class: 76, label: "vase" },
{ class: 77, label: "scissors" },
{ class: 78, label: "teddy bear" },
{ class: 79, label: "hair drier" },
{ class: 80, label: "toothbrush" }
];
// src/object/centernet.ts
var model3;
var inputSize3 = 0;
var last2 = [];
var lastTime2 = 0;
var skipped2 = Number.MAX_SAFE_INTEGER;
async function load(config3) {
if (env.initial)
model3 = null;
if (!model3) {
model3 = await loadModel(config3.object.modelPath);
const inputs = (model3 == null ? void 0 : model3["executor"]) ? Object.values(model3.modelSignature["inputs"]) : void 0;
inputSize3 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
} else if (config3.debug)
log("cached model:", model3["modelUrl"]);
return model3;
}
async function process3(res, outputShape, config3) {
if (!res)
return [];
const t2 = {};
const results = [];
const detections = await res.array();
t2.squeeze = tf10.squeeze(res);
const arr = tf10.split(t2.squeeze, 6, 1);
t2.stack = tf10.stack([arr[1], arr[0], arr[3], arr[2]], 1);
t2.boxes = tf10.squeeze(t2.stack);
t2.scores = tf10.squeeze(arr[4]);
t2.classes = tf10.squeeze(arr[5]);
tf10.dispose([res, ...arr]);
t2.nms = await tf10.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence || 0);
const nms = await t2.nms.data();
let i = 0;
for (const id of Array.from(nms)) {
const score = Math.trunc(100 * detections[0][id][4]) / 100;
const classVal = detections[0][id][5];
if (Number.isNaN(classVal))
continue;
const label = labels2[classVal].label;
const [x, y] = [
detections[0][id][0] / inputSize3,
detections[0][id][1] / inputSize3
];
const boxRaw = [
x,
y,
detections[0][id][2] / inputSize3 - x,
detections[0][id][3] / inputSize3 - y
];
const box = [
Math.trunc(boxRaw[0] * outputShape[0]),
Math.trunc(boxRaw[1] * outputShape[1]),
Math.trunc(boxRaw[2] * outputShape[0]),
Math.trunc(boxRaw[3] * outputShape[1])
];
results.push({ id: i++, score, class: classVal, label, box, boxRaw });
}
Object.keys(t2).forEach((tensor6) => tf10.dispose(t2[tensor6]));
return results;
}
async function predict2(input, config3) {
if (!(model3 == null ? void 0 : model3["executor"]))
return [];
const skipTime = (config3.object.skipTime || 0) > now() - lastTime2;
const skipFrame = skipped2 < (config3.object.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) {
skipped2++;
return last2;
}
skipped2 = 0;
return new Promise(async (resolve) => {
const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
const resize = tf10.image.resizeBilinear(input, [inputSize3, inputSize3]);
const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null;
lastTime2 = now();
tf10.dispose(resize);
const obj = await process3(objectT, outputSize2, config3);
last2 = obj;
resolve(obj);
});
}
// src/body/efficientpose.ts
var tf11 = __toESM(require_tfjs_esm());
// src/body/efficientposecoords.ts
var efficientposecoords_exports = {};
__export(efficientposecoords_exports, {
connected: () => connected2,
kpt: () => kpt2
});
var kpt2 = [
"head",
"neck",
"rightShoulder",
"rightElbow",
"rightWrist",
"chest",
"leftShoulder",
"leftElbow",
"leftWrist",
"bodyCenter",
"rightHip",
"rightKnee",
"rightAnkle",
"leftHip",
"leftKnee",
"leftAnkle"
];
var connected2 = {
leftLeg: ["leftHip", "leftKnee", "leftAnkle"],
rightLeg: ["rightHip", "rightKnee", "rightAnkle"],
torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"],
leftArm: ["leftShoulder", "leftElbow", "leftWrist"],
rightArm: ["rightShoulder", "rightElbow", "rightWrist"],
head: []
};
// src/body/efficientpose.ts
var model4;
var lastTime3 = 0;
var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} };
var skipped3 = Number.MAX_SAFE_INTEGER;
async function load2(config3) {
if (env.initial)
model4 = null;
if (!model4)
model4 = await loadModel(config3.body.modelPath);
else if (config3.debug)
log("cached model:", model4["modelUrl"]);
return model4;
}
async function max2d(inputs, minScore) {
const [width, height] = inputs.shape;
const reshaped = tf11.reshape(inputs, [height * width]);
const max5 = tf11.max(reshaped, 0);
const newScore = (await max5.data())[0];
if (newScore > minScore) {
const coordinates = tf11.argMax(reshaped, 0);
const mod3 = tf11.mod(coordinates, width);
const x = (await mod3.data())[0];
const div15 = tf11.div(coordinates, width);
const y = (await div15.data())[0];
tf11.dispose([reshaped, max5, coordinates, mod3, div15]);
return [x, y, newScore];
}
tf11.dispose([reshaped, max5]);
return [0, 0, newScore];
}
async function predict3(image28, config3) {
if (!(model4 == null ? void 0 : model4["executor"]) || !(model4 == null ? void 0 : model4.inputs[0].shape))
return [];
const skipTime = (config3.body.skipTime || 0) > now() - lastTime3;
const skipFrame = skipped3 < (config3.body.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) {
skipped3++;
return [cache2];
}
skipped3 = 0;
return new Promise(async (resolve) => {
const tensor6 = tf11.tidy(() => {
var _a, _b;
const resize = tf11.image.resizeBilinear(image28, [((_a = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _a[2]) || 0, ((_b = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _b[1]) || 0], false);
const enhance2 = tf11.mul(resize, constants.tf2);
const norm = tf11.sub(enhance2, constants.tf1);
return norm;
});
let resT;
if (config3.body.enabled)
resT = model4 == null ? void 0 : model4.execute(tensor6);
lastTime3 = now();
tf11.dispose(tensor6);
if (resT) {
cache2.keypoints.length = 0;
const squeeze14 = tf11.squeeze(resT);
tf11.dispose(resT);
const stack5 = tf11.unstack(squeeze14, 2);
tf11.dispose(squeeze14);
for (let id = 0; id < stack5.length; id++) {
const [x2, y2, partScore] = await max2d(stack5[id], config3.body.minConfidence);
if (partScore > (config3.body.minConfidence || 0)) {
cache2.keypoints.push({
score: Math.round(100 * partScore) / 100,
part: kpt2[id],
positionRaw: [
x2 / model4.inputs[0].shape[2],
y2 / model4.inputs[0].shape[1]
],
position: [
Math.round(image28.shape[2] * x2 / model4.inputs[0].shape[2]),
Math.round(image28.shape[1] * y2 / model4.inputs[0].shape[1])
]
});
}
}
stack5.forEach((s) => tf11.dispose(s));
}
cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
const x = cache2.keypoints.map((a) => a.position[0]);
const y = cache2.keypoints.map((a) => a.position[1]);
cache2.box = [
Math.min(...x),
Math.min(...y),
Math.max(...x) - Math.min(...x),
Math.max(...y) - Math.min(...y)
];
const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]);
const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]);
cache2.boxRaw = [
Math.min(...xRaw),
Math.min(...yRaw),
Math.max(...xRaw) - Math.min(...xRaw),
Math.max(...yRaw) - Math.min(...yRaw)
];
for (const [name, indexes] of Object.entries(connected2)) {
const pt = [];
for (let i = 0; i < indexes.length - 1; i++) {
const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i]);
const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i + 1]);
if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
pt.push([pt0.position, pt1.position]);
}
cache2.annotations[name] = pt;
}
resolve([cache2]);
});
}
// src/face/face.ts
var tf25 = __toESM(require_tfjs_esm());
// src/face/facemesh.ts
var tf15 = __toESM(require_tfjs_esm());
// src/face/blazeface.ts
var tf13 = __toESM(require_tfjs_esm());
// src/face/facemeshutil.ts
var tf12 = __toESM(require_tfjs_esm());
var getBoxSize = (box) => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];
var getBoxCenter = (box) => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];
var clampBox = (box, input) => box ? [
Math.trunc(Math.max(0, box.startPoint[0])),
Math.trunc(Math.max(0, box.startPoint[1])),
Math.trunc(Math.min(input.shape[2] || 0, box.endPoint[0]) - Math.max(0, box.startPoint[0])),
Math.trunc(Math.min(input.shape[1] || 0, box.endPoint[1]) - Math.max(0, box.startPoint[1]))
] : [0, 0, 0, 0];
var getRawBox = (box, input) => box ? [
box.startPoint[0] / (input.shape[2] || 0),
box.startPoint[1] / (input.shape[1] || 0),
(box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),
(box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0)
] : [0, 0, 0, 0];
var scaleBoxCoordinates = (box, factor) => {
const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };
};
var cutAndResize = (box, image28, cropSize) => {
const h = image28.shape[1];
const w = image28.shape[2];
const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];
const crop = tf12.image.cropAndResize(image28, [cutBox], [0], cropSize);
const norm = tf12.div(crop, constants.tf255);
tf12.dispose(crop);
return norm;
};
var enlargeBox = (box, factor) => {
const center = getBoxCenter(box);
const size2 = getBoxSize(box);
const halfSize = [factor * size2[0] / 2, factor * size2[1] / 2];
return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]], endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]], landmarks: box.landmarks, confidence: box.confidence };
};
var squarifyBox = (box) => {
const centers = getBoxCenter(box);
const size2 = getBoxSize(box);
const halfSize = Math.max(...size2) / 2;
return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)], endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)], landmarks: box.landmarks, confidence: box.confidence };
};
var calculateLandmarksBoundingBox = (landmarks) => {
const x = landmarks.map((d) => d[0]);
const y = landmarks.map((d) => d[1]);
return { startPoint: [Math.min(...x), Math.min(...y)], endPoint: [Math.max(...x), Math.max(...y)], landmarks };
};
var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];
var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));
var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));
var buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];
var dot = (v1, v2) => {
let product = 0;
for (let i = 0; i < v1.length; i++)
product += v1[i] * v2[i];
return product;
};
var getColumnFrom2DArr = (arr, columnIndex) => {
const column = [];
for (let i = 0; i < arr.length; i++)
column.push(arr[i][columnIndex]);
return column;
};
var multiplyTransformMatrices = (mat1, mat2) => {
const product = [];
const size2 = mat1.length;
for (let row = 0; row < size2; row++) {
product.push([]);
for (let col = 0; col < size2; col++)
product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));
}
return product;
};
var buildRotationMatrix = (rotation, center) => {
const cosA = Math.cos(rotation);
const sinA = Math.sin(rotation);
const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];
const translationMatrix = buildTranslationMatrix(center[0], center[1]);
const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);
const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);
return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);
};
var invertTransformMatrix = (matrix) => {
const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];
const translationComponent = [matrix[0][2], matrix[1][2]];
const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];
return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];
};
var rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];
function generateAnchors(inputSize10) {
const spec = inputSize10 === 192 ? { strides: [4], anchors: [1] } : { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] };
const anchors3 = [];
for (let i = 0; i < spec.strides.length; i++) {
const stride = spec.strides[i];
const gridRows = Math.floor((inputSize10 + stride - 1) / stride);
const gridCols = Math.floor((inputSize10 + stride - 1) / stride);
const anchorsNum = spec.anchors[i];
for (let gridY = 0; gridY < gridRows; gridY++) {
const anchorY = stride * (gridY + 0.5);
for (let gridX = 0; gridX < gridCols; gridX++) {
const anchorX = stride * (gridX + 0.5);
for (let n = 0; n < anchorsNum; n++)
anchors3.push([anchorX, anchorY]);
}
}
}
return anchors3;
}
function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize10) {
const boxSize = getBoxSize(box);
const coordsScaled = coordsRaw.map((coord) => [
boxSize[0] / inputSize10 * (coord[0] - inputSize10 / 2),
boxSize[1] / inputSize10 * (coord[1] - inputSize10 / 2),
coord[2] || 0
]);
const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2;
const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;
const coordsRotated = largeAngle ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled;
const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;
const boxCenter = getBoxCenter(box);
const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];
return coordsRotated.map((coord) => [
Math.trunc(coord[0] + offsets[0]),
Math.trunc(coord[1] + offsets[1]),
Math.trunc(coord[2] || 0)
]);
}
function correctFaceRotation(rotate, box, input, inputSize10) {
const symmetryLine = box.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;
let angle = 0;
let rotationMatrix = fixedRotationMatrix;
let face4;
if (rotate && env.kernels.includes("rotatewithoffset")) {
angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);
const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2;
if (largeAngle) {
const center = getBoxCenter(box);
const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]];
const rotated = tf12.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]);
rotationMatrix = buildRotationMatrix(-angle, center);
face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]);
tf12.dispose(rotated);
} else {
face4 = cutAndResize(box, input, [inputSize10, inputSize10]);
}
} else {
face4 = cutAndResize(box, input, [inputSize10, inputSize10]);
}
return [angle, rotationMatrix, face4];
}
var findFaceCenter = (mesh) => {
const x = mesh.map((m) => m[0]);
const y = mesh.map((m) => m[1]);
return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];
};
var calculateFaceBox = (mesh, previousBox) => {
const center = findFaceCenter(mesh);
const boxSize = getBoxSize(previousBox);
const calculatedBox = {
startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2],
endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2]
};
return calculatedBox;
};
// src/face/blazeface.ts
var keypointsCount = 6;
var faceBoxScaleFactor = 1.4;
var model5;
var anchors = null;
var inputSize4 = 0;
var inputSizeT = null;
var size = () => inputSize4;
async function load3(config3) {
var _a;
if (env.initial)
model5 = null;
if (!model5)
model5 = await loadModel((_a = config3.face.detector) == null ? void 0 : _a.modelPath);
else if (config3.debug)
log("cached model:", model5["modelUrl"]);
inputSize4 = model5["executor"] && model5.inputs[0].shape ? model5.inputs[0].shape[2] : 256;
inputSizeT = tf13.scalar(inputSize4, "int32");
anchors = tf13.tensor2d(generateAnchors(inputSize4));
return model5;
}
function decodeBoxes2(boxOutputs) {
if (!anchors || !inputSizeT)
return tf13.zeros([0, 0]);
const t2 = {};
t2.boxStarts = tf13.slice(boxOutputs, [0, 1], [-1, 2]);
t2.centers = tf13.add(t2.boxStarts, anchors);
t2.boxSizes = tf13.slice(boxOutputs, [0, 3], [-1, 2]);
t2.boxSizesNormalized = tf13.div(t2.boxSizes, inputSizeT);
t2.centersNormalized = tf13.div(t2.centers, inputSizeT);
t2.halfBoxSize = tf13.div(t2.boxSizesNormalized, constants.tf2);
t2.starts = tf13.sub(t2.centersNormalized, t2.halfBoxSize);
t2.ends = tf13.add(t2.centersNormalized, t2.halfBoxSize);
t2.startNormalized = tf13.mul(t2.starts, inputSizeT);
t2.endNormalized = tf13.mul(t2.ends, inputSizeT);
const boxes = tf13.concat2d([t2.startNormalized, t2.endNormalized], 1);
Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6]));
return boxes;
}
async function getBoxes(inputImage, config3) {
var _a, _b, _c, _d;
if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
return [];
const t2 = {};
t2.resized = tf13.image.resizeBilinear(inputImage, [inputSize4, inputSize4]);
t2.div = tf13.div(t2.resized, constants.tf127);
t2.normalized = tf13.sub(t2.div, constants.tf05);
const res = model5 == null ? void 0 : model5.execute(t2.normalized);
if (Array.isArray(res) && res.length > 2) {
const sorted = res.sort((a, b) => a.size - b.size);
t2.concat384 = tf13.concat([sorted[0], sorted[2]], 2);
t2.concat512 = tf13.concat([sorted[1], sorted[3]], 2);
t2.concat = tf13.concat([t2.concat512, t2.concat384], 1);
t2.batch = tf13.squeeze(t2.concat, [0]);
} else if (Array.isArray(res)) {
t2.batch = tf13.squeeze(res[0]);
} else {
t2.batch = tf13.squeeze(res);
}
tf13.dispose(res);
t2.boxes = decodeBoxes2(t2.batch);
t2.logits = tf13.slice(t2.batch, [0, 0], [-1, 1]);
t2.sigmoid = tf13.sigmoid(t2.logits);
t2.scores = tf13.squeeze(t2.sigmoid);
t2.nms = await tf13.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, ((_a = config3.face.detector) == null ? void 0 : _a.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0);
const nms = await t2.nms.array();
const boxes = [];
const scores = await t2.scores.data();
for (let i = 0; i < nms.length; i++) {
const confidence = scores[nms[i]];
if (confidence > (((_d = config3.face.detector) == null ? void 0 : _d.minConfidence) || 0)) {
const b = {};
b.bbox = tf13.slice(t2.boxes, [nms[i], 0], [1, -1]);
b.slice = tf13.slice(t2.batch, [nms[i], keypointsCount - 1], [1, -1]);
b.squeeze = tf13.squeeze(b.slice);
b.landmarks = tf13.reshape(b.squeeze, [keypointsCount, -1]);
const points = await b.bbox.data();
const rawBox = {
startPoint: [points[0], points[1]],
endPoint: [points[2], points[3]],
landmarks: await b.landmarks.array(),
confidence
};
const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize4, (inputImage.shape[1] || 0) / inputSize4]);
const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor);
const squaredBox = squarifyBox(enlargedBox);
boxes.push(squaredBox);
Object.keys(b).forEach((tensor6) => tf13.dispose(b[tensor6]));
}
}
Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6]));
return boxes;
}
// src/face/iris.ts
var tf14 = __toESM(require_tfjs_esm());
var model6;
var inputSize5 = 0;
var irisEnlarge = 2.3;
var leftOutline = meshAnnotations.leftEyeLower0;
var rightOutline = meshAnnotations.rightEyeLower0;
var eyeLandmarks = {
leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],
rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]]
};
var irisLandmarks = {
upperCenter: 3,
lowerCenter: 4,
index: 71,
numCoordinates: 76
};
async function load4(config3) {
var _a, _b;
if (env.initial)
model6 = null;
if (!model6)
model6 = await loadModel((_a = config3.face.iris) == null ? void 0 : _a.modelPath);
else if (config3.debug)
log("cached model:", model6["modelUrl"]);
inputSize5 = (model6 == null ? void 0 : model6["executor"]) && ((_b = model6.inputs) == null ? void 0 : _b[0].shape) ? model6.inputs[0].shape[2] : 0;
if (inputSize5 === -1)
inputSize5 = 64;
return model6;
}
function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {
for (let i = 0; i < irisIndices.length; i++) {
const { key, indices } = irisIndices[i];
const originalIndices = meshAnnotations[`${prefix}${key}`];
if (!keys || keys.includes(key)) {
for (let j = 0; j < indices.length; j++) {
const index2 = indices[j];
rawCoords[originalIndices[j]] = [
newCoords[index2][0],
newCoords[index2][1],
(newCoords[index2][2] + rawCoords[originalIndices[j]][2]) / 2
];
}
}
}
}
var getLeftToRightEyeDepthDifference = (rawCoords) => {
const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];
const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];
return leftEyeZ - rightEyeZ;
};
var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {
const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));
const boxSize = getBoxSize(box);
let crop = tf14.image.cropAndResize(face4, [[
box.startPoint[1] / meshSize,
box.startPoint[0] / meshSize,
box.endPoint[1] / meshSize,
box.endPoint[0] / meshSize
]], [0], [inputSize5, inputSize5]);
if (flip && env.kernels.includes("flipleftright")) {
const flipped = tf14.image.flipLeftRight(crop);
tf14.dispose(crop);
crop = flipped;
}
return { box, boxSize, crop };
};
var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {
const eyeRawCoords = [];
for (let i = 0; i < irisLandmarks.numCoordinates; i++) {
const x = eyeData[i * 3];
const y = eyeData[i * 3 + 1];
const z = eyeData[i * 3 + 2];
eyeRawCoords.push([
(flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0],
y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1],
z
]);
}
return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };
};
var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {
const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];
const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];
const averageZ = (upperCenterZ + lowerCenterZ) / 2;
return irisCoords.map((coord, i) => {
let z = averageZ;
if (i === 2) {
z = upperCenterZ;
} else if (i === 4) {
z = lowerCenterZ;
}
return [coord[0], coord[1], z];
});
};
async function augmentIris(rawCoords, face4, meshSize) {
if (!(model6 == null ? void 0 : model6["executor"]))
return rawCoords;
const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);
const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);
const combined = tf14.concat([leftEyeCrop, rightEyeCrop]);
tf14.dispose(leftEyeCrop);
tf14.dispose(rightEyeCrop);
const eyePredictions = model6.execute(combined);
tf14.dispose(combined);
const eyePredictionsData = await eyePredictions.data();
tf14.dispose(eyePredictions);
const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);
const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);
const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);
const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);
const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);
if (Math.abs(leftToRightEyeDepthDifference) < 30) {
replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", null);
replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", null);
} else if (leftToRightEyeDepthDifference < 1) {
replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]);
} else {
replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]);
}
const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left");
const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right");
const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);
return newCoords;
}
// src/face/attention.ts
async function augment(rawCoords, results) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j;
const t2 = {
lips: await ((_b = (_a = results.filter((r) => r.size === 160)) == null ? void 0 : _a[0]) == null ? void 0 : _b.data()),
irisL: await ((_d = (_c = results.filter((r) => r.size === 10)) == null ? void 0 : _c[0]) == null ? void 0 : _d.data()),
eyeL: await ((_f = (_e = results.filter((r) => r.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f.data()),
irisR: await ((_h = (_g = results.filter((r) => r.size === 10)) == null ? void 0 : _g[1]) == null ? void 0 : _h.data()),
eyeR: await ((_j = (_i = results.filter((r) => r.size === 142)) == null ? void 0 : _i[1]) == null ? void 0 : _j.data())
};
for (const val of Object.values(t2)) {
if (!val)
return rawCoords;
}
const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length;
for (let i = 0; i < t2.irisL.length / 2; i++)
rawCoords.push([t2.irisL[2 * i + 0], t2.irisL[2 * i + 1], irisLDepth]);
const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length;
for (let i = 0; i < t2.irisR.length / 2; i++)
rawCoords.push([t2.irisR[2 * i + 0], t2.irisR[2 * i + 1], irisRDepth]);
for (let i = 0; i < t2.eyeL.length / 2; i++)
rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t2.eyeL[2 * i + 0], t2.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];
for (let i = 0; i < t2.eyeR.length / 2; i++)
rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t2.eyeR[2 * i + 0], t2.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];
for (let i = 0; i < t2.lips.length / 2; i++)
rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t2.lips[2 * i + 0], t2.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];
return rawCoords;
}
// src/face/facemesh.ts
var cache3 = {
boxes: [],
skipped: Number.MAX_SAFE_INTEGER,
timestamp: 0
};
var model7 = null;
var inputSize6 = 0;
async function predict4(input, config3) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j;
if (!(model7 == null ? void 0 : model7["executor"]))
return [];
const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - cache3.timestamp;
const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0);
if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) {
cache3.boxes = await getBoxes(input, config3);
cache3.timestamp = now();
cache3.skipped = 0;
} else {
cache3.skipped++;
}
const faces = [];
const newCache = [];
let id = 0;
const size2 = inputSize6;
for (let i = 0; i < cache3.boxes.length; i++) {
const box = cache3.boxes[i];
let angle = 0;
let rotationMatrix;
const face4 = {
id: id++,
mesh: [],
meshRaw: [],
box: [0, 0, 0, 0],
boxRaw: [0, 0, 0, 0],
score: 0,
boxScore: 0,
faceScore: 0,
annotations: {}
};
[angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c = config3.face.detector) == null ? void 0 : _c.rotation, box, input, ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) ? inputSize6 : size());
if (config3.filter.equalization) {
const equilized = face4.tensor ? await histogramEqualization(face4.tensor) : void 0;
tf15.dispose(face4.tensor);
if (equilized)
face4.tensor = equilized;
}
face4.boxScore = Math.round(100 * box.confidence) / 100;
if (!((_e = config3.face.mesh) == null ? void 0 : _e.enabled)) {
face4.box = clampBox(box, input);
face4.boxRaw = getRawBox(box, input);
face4.score = face4.boxScore;
face4.mesh = box.landmarks.map((pt) => [
(box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(),
(box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size()
]);
face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]);
for (const key of Object.keys(blazeFaceLandmarks)) {
face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]];
}
} else if (!model7) {
if (config3.debug)
log("face mesh detection requested, but model is not loaded");
} else {
if (((_f = config3.face.attention) == null ? void 0 : _f.enabled) && !env.kernels.includes("atan2")) {
config3.face.attention.enabled = false;
tf15.dispose(face4.tensor);
return faces;
}
const results = model7.execute(face4.tensor);
const confidenceT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1);
const faceConfidence = await confidenceT.data();
face4.faceScore = Math.round(100 * faceConfidence[0]) / 100;
if (face4.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) {
box.confidence = face4.faceScore;
if (config3.face.mesh.keepInvalid) {
face4.box = clampBox(box, input);
face4.boxRaw = getRawBox(box, input);
face4.score = face4.boxScore;
face4.mesh = box.landmarks.map((pt) => [
(box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(),
(box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size()
]);
face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size2]);
for (const key of Object.keys(blazeFaceLandmarks)) {
face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]];
}
}
} else {
const meshT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1404);
const coordsReshaped = tf15.reshape(meshT, [-1, 3]);
let rawCoords = await coordsReshaped.array();
tf15.dispose(coordsReshaped);
if ((_h = config3.face.attention) == null ? void 0 : _h.enabled) {
rawCoords = await augment(rawCoords, results);
} else if ((_i = config3.face.iris) == null ? void 0 : _i.enabled) {
rawCoords = await augmentIris(rawCoords, face4.tensor, inputSize6);
}
face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6);
face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]);
for (const key of Object.keys(meshAnnotations))
face4.annotations[key] = meshAnnotations[key].map((index2) => face4.mesh[index2]);
face4.score = face4.faceScore;
const calculatedBox = { ...calculateFaceBox(face4.mesh, box), confidence: box.confidence, landmarks: box.landmarks };
face4.box = clampBox(calculatedBox, input);
face4.boxRaw = getRawBox(calculatedBox, input);
newCache.push(calculatedBox);
}
tf15.dispose(results);
}
if (face4.score > (((_j = config3.face.detector) == null ? void 0 : _j.minConfidence) || 1))
faces.push(face4);
else
tf15.dispose(face4.tensor);
}
cache3.boxes = newCache;
return faces;
}
async function load5(config3) {
var _a, _b, _c, _d, _e, _f;
if (env.initial)
model7 = null;
if (((_a = config3.face.attention) == null ? void 0 : _a.enabled) && (model7 == null ? void 0 : model7["signature"])) {
if (Object.keys(((_b = model7 == null ? void 0 : model7["signature"]) == null ? void 0 : _b.outputs) || {}).length < 6)
model7 = null;
}
if (!model7) {
if ((_c = config3.face.attention) == null ? void 0 : _c.enabled)
model7 = await loadModel(config3.face.attention.modelPath);
else
model7 = await loadModel((_d = config3.face.mesh) == null ? void 0 : _d.modelPath);
} else if (config3.debug) {
log("cached model:", model7["modelUrl"]);
}
inputSize6 = model7["executor"] && ((_e = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _e[0].shape) ? (_f = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _f[0].shape[2] : 256;
return model7;
}
var triangulation = TRI468;
var uvmap = UV468;
// src/gear/emotion.ts
var tf16 = __toESM(require_tfjs_esm());
var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"];
var model8;
var last3 = [];
var lastCount = 0;
var lastTime4 = 0;
var skipped4 = Number.MAX_SAFE_INTEGER;
async function load6(config3) {
var _a;
if (env.initial)
model8 = null;
if (!model8)
model8 = await loadModel((_a = config3.face.emotion) == null ? void 0 : _a.modelPath);
else if (config3.debug)
log("cached model:", model8["modelUrl"]);
return model8;
}
async function predict5(image28, config3, idx, count2) {
var _a, _b;
if (!model8)
return [];
const skipFrame = skipped4 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0);
const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime4;
if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && last3[idx] && last3[idx].length > 0) {
skipped4++;
return last3[idx];
}
skipped4 = 0;
return new Promise(async (resolve) => {
var _a2;
const obj = [];
if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) {
const t2 = {};
const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0;
t2.resize = tf16.image.resizeBilinear(image28, [inputSize10, inputSize10], false);
t2.channels = tf16.mul(t2.resize, constants.rgb);
t2.grayscale = tf16.sum(t2.channels, 3, true);
t2.grayscaleSub = tf16.sub(t2.grayscale, constants.tf05);
t2.grayscaleMul = tf16.mul(t2.grayscaleSub, constants.tf2);
t2.emotion = model8 == null ? void 0 : model8.execute(t2.grayscaleMul);
lastTime4 = now();
const data = await t2.emotion.data();
for (let i = 0; i < data.length; i++) {
if (data[i] > (config3.face.emotion.minConfidence || 0))
obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });
}
obj.sort((a, b) => b.score - a.score);
Object.keys(t2).forEach((tensor6) => tf16.dispose(t2[tensor6]));
}
last3[idx] = obj;
lastCount = count2;
resolve(obj);
});
}
// src/face/faceres.ts
var tf17 = __toESM(require_tfjs_esm());
var model9;
var last4 = [];
var lastTime5 = 0;
var lastCount2 = 0;
var skipped5 = Number.MAX_SAFE_INTEGER;
async function load7(config3) {
var _a;
if (env.initial)
model9 = null;
if (!model9)
model9 = await loadModel((_a = config3.face.description) == null ? void 0 : _a.modelPath);
else if (config3.debug)
log("cached model:", model9["modelUrl"]);
return model9;
}
function enhance(input) {
const tensor6 = input.image || input.tensor || input;
if (!(model9 == null ? void 0 : model9.inputs[0].shape))
return tensor6;
const crop = tf17.image.resizeBilinear(tensor6, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false);
const norm = tf17.mul(crop, constants.tf255);
tf17.dispose(crop);
return norm;
}
async function predict6(image28, config3, idx, count2) {
var _a, _b, _c, _d;
const obj = {
age: 0,
gender: "unknown",
genderScore: 0,
descriptor: []
};
if (!(model9 == null ? void 0 : model9["executor"]))
return obj;
const skipFrame = skipped5 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0);
const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5;
if (config3.skipAllowed && skipFrame && skipTime && lastCount2 === count2 && ((_c = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _c.age) > 0 && ((_d = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _d.genderScore) > 0) {
skipped5++;
return last4[idx];
}
skipped5 = 0;
return new Promise(async (resolve) => {
var _a2;
if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) {
const enhanced = enhance(image28);
const resT = model9 == null ? void 0 : model9.execute(enhanced);
lastTime5 = now();
tf17.dispose(enhanced);
const genderT = resT.find((t2) => t2.shape[1] === 1);
const gender2 = await genderT.data();
const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100;
if (confidence > (config3.face.description.minConfidence || 0)) {
obj.gender = gender2[0] <= 0.5 ? "female" : "male";
obj.genderScore = Math.min(0.99, confidence);
}
const argmax = tf17.argMax(resT.find((t2) => t2.shape[1] === 100), 1);
const ageIdx = (await argmax.data())[0];
tf17.dispose(argmax);
const ageT = resT.find((t2) => t2.shape[1] === 100);
const all2 = await ageT.data();
obj.age = Math.round(all2[ageIdx - 1] > all2[ageIdx + 1] ? 10 * ageIdx - 100 * all2[ageIdx - 1] : 10 * ageIdx + 100 * all2[ageIdx + 1]) / 10;
if (Number.isNaN(gender2[0]) || Number.isNaN(all2[0]))
log("faceres error:", { model: model9, result: resT });
const desc = resT.find((t2) => t2.shape[1] === 1024);
const descriptor = desc ? await desc.data() : [];
obj.descriptor = Array.from(descriptor);
resT.forEach((t2) => tf17.dispose(t2));
}
last4[idx] = obj;
lastCount2 = count2;
resolve(obj);
});
}
// src/face/mask.ts
var expandFact = 0.1;
var alpha = 0.5;
function insidePoly(x, y, polygon) {
let inside = false;
let j = polygon.length - 1;
for (let i = 0; i < polygon.length; j = i++) {
if (polygon[i].y > y !== polygon[j].y > y && x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)
inside = !inside;
}
return inside;
}
async function mask(face4) {
if (!face4.tensor)
return face4.tensor;
if (!face4.mesh || face4.mesh.length < 100)
return face4.tensor;
const width = face4.tensor.shape[2] || 0;
const height = face4.tensor.shape[1] || 0;
const buffer = await face4.tensor.buffer();
let silhouette = [];
for (const pt of meshAnnotations.silhouette)
silhouette.push({ x: (face4.mesh[pt][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt][1] - face4.box[1]) / face4.box[3] });
if (expandFact && expandFact > 0)
silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact }));
for (let x = 0; x < width; x++) {
for (let y = 0; y < height; y++) {
const inside = insidePoly(x / width, y / width, silhouette);
if (!inside) {
buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);
buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);
buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);
}
}
}
const output = buffer.toTensor();
return output;
}
// src/face/antispoof.ts
var tf18 = __toESM(require_tfjs_esm());
var model10;
var cached = [];
var skipped6 = Number.MAX_SAFE_INTEGER;
var lastCount3 = 0;
var lastTime6 = 0;
async function load8(config3) {
var _a;
if (env.initial)
model10 = null;
if (!model10)
model10 = await loadModel((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath);
else if (config3.debug)
log("cached model:", model10["modelUrl"]);
return model10;
}
async function predict7(image28, config3, idx, count2) {
var _a, _b;
if (!(model10 == null ? void 0 : model10["executor"]))
return 0;
const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime6;
const skipFrame = skipped6 < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0);
if (config3.skipAllowed && skipTime && skipFrame && lastCount3 === count2 && cached[idx]) {
skipped6++;
return cached[idx];
}
skipped6 = 0;
return new Promise(async (resolve) => {
const resize = tf18.image.resizeBilinear(image28, [(model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[2] : 0, (model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[1] : 0], false);
const res = model10 == null ? void 0 : model10.execute(resize);
const num = (await res.data())[0];
cached[idx] = Math.round(100 * num) / 100;
lastCount3 = count2;
lastTime6 = now();
tf18.dispose([resize, res]);
resolve(cached[idx]);
});
}
// src/face/liveness.ts
var tf19 = __toESM(require_tfjs_esm());
var model11;
var cached2 = [];
var skipped7 = Number.MAX_SAFE_INTEGER;
var lastCount4 = 0;
var lastTime7 = 0;
async function load9(config3) {
var _a;
if (env.initial)
model11 = null;
if (!model11)
model11 = await loadModel((_a = config3.face.liveness) == null ? void 0 : _a.modelPath);
else if (config3.debug)
log("cached model:", model11["modelUrl"]);
return model11;
}
async function predict8(image28, config3, idx, count2) {
var _a, _b;
if (!(model11 == null ? void 0 : model11["executor"]))
return 0;
const skipTime = (((_a = config3.face.liveness) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime7;
const skipFrame = skipped7 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0);
if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && cached2[idx]) {
skipped7++;
return cached2[idx];
}
skipped7 = 0;
return new Promise(async (resolve) => {
const resize = tf19.image.resizeBilinear(image28, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false);
const res = model11 == null ? void 0 : model11.execute(resize);
const num = (await res.data())[0];
cached2[idx] = Math.round(100 * num) / 100;
lastCount4 = count2;
lastTime7 = now();
tf19.dispose([resize, res]);
resolve(cached2[idx]);
});
}
// src/gear/gear.ts
var tf20 = __toESM(require_tfjs_esm());
var model12;
var last5 = [];
var raceNames = ["white", "black", "asian", "indian", "other"];
var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];
var lastCount5 = 0;
var lastTime8 = 0;
var skipped8 = Number.MAX_SAFE_INTEGER;
async function load10(config3) {
var _a;
if (env.initial)
model12 = null;
if (!model12)
model12 = await loadModel((_a = config3.face.gear) == null ? void 0 : _a.modelPath);
else if (config3.debug)
log("cached model:", model12["modelUrl"]);
return model12;
}
async function predict9(image28, config3, idx, count2) {
var _a, _b;
if (!model12)
return { age: 0, gender: "unknown", genderScore: 0, race: [] };
const skipFrame = skipped8 < (((_a = config3.face.gear) == null ? void 0 : _a.skipFrames) || 0);
const skipTime = (((_b = config3.face.gear) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime8;
if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last5[idx]) {
skipped8++;
return last5[idx];
}
skipped8 = 0;
return new Promise(async (resolve) => {
var _a2, _b2;
if (!(model12 == null ? void 0 : model12.inputs[0].shape))
return;
const t2 = {};
const box = [[0, 0.1, 0.9, 0.9]];
t2.resize = tf20.image.cropAndResize(image28, box, [0], [model12.inputs[0].shape[2], model12.inputs[0].shape[1]]);
const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] };
if ((_a2 = config3.face.gear) == null ? void 0 : _a2.enabled)
[t2.age, t2.gender, t2.race] = model12.execute(t2.resize, ["age_output", "gender_output", "race_output"]);
const gender2 = await t2.gender.data();
obj.gender = gender2[0] > gender2[1] ? "male" : "female";
obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100;
const race = await t2.race.data();
for (let i = 0; i < race.length; i++) {
if (race[i] > (((_b2 = config3.face.gear) == null ? void 0 : _b2.minConfidence) || 0.2))
obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] });
}
obj.race.sort((a, b) => b.score - a.score);
const ageDistribution = Array.from(await t2.age.data());
const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);
let age2 = ageSorted[0][0];
for (let i = 1; i < ageSorted.length; i++)
age2 += ageSorted[i][1] * (ageSorted[i][0] - age2);
obj.age = Math.round(10 * age2) / 10;
Object.keys(t2).forEach((tensor6) => tf20.dispose(t2[tensor6]));
last5[idx] = obj;
lastCount5 = count2;
lastTime8 = now();
resolve(obj);
});
}
// src/gear/ssrnet-age.ts
var tf21 = __toESM(require_tfjs_esm());
var model13;
var last6 = [];
var lastCount6 = 0;
var lastTime9 = 0;
var skipped9 = Number.MAX_SAFE_INTEGER;
async function load11(config3) {
if (env.initial)
model13 = null;
if (!model13)
model13 = await loadModel(config3.face["ssrnet"].modelPathAge);
else if (config3.debug)
log("cached model:", model13["modelUrl"]);
return model13;
}
async function predict10(image28, config3, idx, count2) {
var _a, _b, _c, _d;
if (!model13)
return { age: 0 };
const skipFrame = skipped9 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0);
const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime9;
if (config3.skipAllowed && skipFrame && skipTime && lastCount6 === count2 && ((_c = last6[idx]) == null ? void 0 : _c.age) && ((_d = last6[idx]) == null ? void 0 : _d.age) > 0) {
skipped9++;
return last6[idx];
}
skipped9 = 0;
return new Promise(async (resolve) => {
var _a2;
if (!(model13 == null ? void 0 : model13.inputs) || !model13.inputs[0] || !model13.inputs[0].shape)
return;
const t2 = {};
t2.resize = tf21.image.resizeBilinear(image28, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false);
t2.enhance = tf21.mul(t2.resize, constants.tf255);
const obj = { age: 0 };
if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled)
t2.age = model13.execute(t2.enhance);
if (t2.age) {
const data = await t2.age.data();
obj.age = Math.trunc(10 * data[0]) / 10;
}
Object.keys(t2).forEach((tensor6) => tf21.dispose(t2[tensor6]));
last6[idx] = obj;
lastCount6 = count2;
lastTime9 = now();
resolve(obj);
});
}
// src/gear/ssrnet-gender.ts
var tf22 = __toESM(require_tfjs_esm());
var model14;
var last7 = [];
var lastCount7 = 0;
var lastTime10 = 0;
var skipped10 = Number.MAX_SAFE_INTEGER;
var rgb = [0.2989, 0.587, 0.114];
async function load12(config3) {
var _a;
if (env.initial)
model14 = null;
if (!model14)
model14 = await loadModel((_a = config3.face["ssrnet"]) == null ? void 0 : _a.modelPathGender);
else if (config3.debug)
log("cached model:", model14["modelUrl"]);
return model14;
}
async function predict11(image28, config3, idx, count2) {
var _a, _b, _c, _d;
if (!model14)
return { gender: "unknown", genderScore: 0 };
const skipFrame = skipped10 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0);
const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10;
if (config3.skipAllowed && skipFrame && skipTime && lastCount7 === count2 && ((_c = last7[idx]) == null ? void 0 : _c.gender) && ((_d = last7[idx]) == null ? void 0 : _d.genderScore) > 0) {
skipped10++;
return last7[idx];
}
skipped10 = 0;
return new Promise(async (resolve) => {
var _a2;
if (!(model14 == null ? void 0 : model14.inputs[0].shape))
return;
const t2 = {};
t2.resize = tf22.image.resizeBilinear(image28, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false);
t2.enhance = tf22.tidy(() => {
const [red, green, blue] = tf22.split(t2.resize, 3, 3);
const redNorm = tf22.mul(red, rgb[0]);
const greenNorm = tf22.mul(green, rgb[1]);
const blueNorm = tf22.mul(blue, rgb[2]);
const grayscale = tf22.addN([redNorm, greenNorm, blueNorm]);
const normalize2 = tf22.mul(tf22.sub(grayscale, constants.tf05), 2);
return normalize2;
});
const obj = { gender: "unknown", genderScore: 0 };
if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled)
t2.gender = model14.execute(t2.enhance);
const data = await t2.gender.data();
obj.gender = data[0] > data[1] ? "female" : "male";
obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100;
Object.keys(t2).forEach((tensor6) => tf22.dispose(t2[tensor6]));
last7[idx] = obj;
lastCount7 = count2;
lastTime10 = now();
resolve(obj);
});
}
// src/face/mobilefacenet.ts
var tf23 = __toESM(require_tfjs_esm());
var model15;
var last8 = [];
var lastCount8 = 0;
var lastTime11 = 0;
var skipped11 = Number.MAX_SAFE_INTEGER;
async function load13(config3) {
var _a;
if (env.initial)
model15 = null;
if (!model15)
model15 = await loadModel((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.modelPath);
else if (config3.debug)
log("cached model:", model15["modelUrl"]);
return model15;
}
async function predict12(input, config3, idx, count2) {
var _a, _b;
if (!(model15 == null ? void 0 : model15["executor"]))
return [];
const skipFrame = skipped11 < (((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.skipFrames) || 0);
const skipTime = (((_b = config3.face["mobilefacenet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime11;
if (config3.skipAllowed && skipTime && skipFrame && lastCount8 === count2 && last8[idx]) {
skipped11++;
return last8[idx];
}
return new Promise(async (resolve) => {
var _a2;
let data = [];
if (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.enabled) && (model15 == null ? void 0 : model15.inputs[0].shape)) {
const t2 = {};
t2.crop = tf23.image.resizeBilinear(input, [model15.inputs[0].shape[2], model15.inputs[0].shape[1]], false);
t2.data = model15.execute(t2.crop);
const output = await t2.data.data();
data = Array.from(output);
Object.keys(t2).forEach((tensor6) => tf23.dispose(t2[tensor6]));
}
last8[idx] = data;
lastCount8 = count2;
lastTime11 = now();
resolve(data);
});
}
// src/face/insightface.ts
var tf24 = __toESM(require_tfjs_esm());
var model16;
var last9 = [];
var lastCount9 = 0;
var lastTime12 = 0;
var skipped12 = Number.MAX_SAFE_INTEGER;
async function load14(config3) {
if (env.initial)
model16 = null;
if (!model16)
model16 = await loadModel(config3.face["insightface"].modelPath);
else if (config3.debug)
log("cached model:", model16["modelUrl"]);
return model16;
}
async function predict13(input, config3, idx, count2) {
var _a, _b;
if (!(model16 == null ? void 0 : model16["executor"]))
return [];
const skipFrame = skipped12 < (((_a = config3.face["insightface"]) == null ? void 0 : _a.skipFrames) || 0);
const skipTime = (((_b = config3.face["insightface"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime12;
if (config3.skipAllowed && skipTime && skipFrame && lastCount9 === count2 && last9[idx]) {
skipped12++;
return last9[idx];
}
return new Promise(async (resolve) => {
var _a2;
let data = [];
if (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.enabled) && (model16 == null ? void 0 : model16.inputs[0].shape)) {
const t2 = {};
t2.crop = tf24.image.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]], false);
t2.data = model16.execute(t2.crop);
const output = await t2.data.data();
data = Array.from(output);
Object.keys(t2).forEach((tensor6) => tf24.dispose(t2[tensor6]));
}
last9[idx] = data;
lastCount9 = count2;
lastTime12 = now();
resolve(data);
});
}
// src/face/angles.ts
var calculateGaze = (face4) => {
const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]);
if (!face4.annotations.rightEyeIris || !face4.annotations.leftEyeIris)
return { bearing: 0, strength: 0 };
const offsetIris = [0, -0.1];
const eyeRatio = 1;
const left = (face4.mesh[33][2] || 0) > (face4.mesh[263][2] || 0);
const irisCenter = left ? face4.mesh[473] : face4.mesh[468];
const eyeCenter = left ? [(face4.mesh[133][0] + face4.mesh[33][0]) / 2, (face4.mesh[133][1] + face4.mesh[33][1]) / 2] : [(face4.mesh[263][0] + face4.mesh[362][0]) / 2, (face4.mesh[263][1] + face4.mesh[362][1]) / 2];
const eyeSize = left ? [face4.mesh[133][0] - face4.mesh[33][0], face4.mesh[23][1] - face4.mesh[27][1]] : [face4.mesh[263][0] - face4.mesh[362][0], face4.mesh[253][1] - face4.mesh[257][1]];
const eyeDiff = [
(eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],
eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1]
];
let strength = Math.sqrt(eyeDiff[0] * eyeDiff[0] + eyeDiff[1] * eyeDiff[1]);
strength = Math.min(strength, face4.boxRaw[2] / 2, face4.boxRaw[3] / 2);
const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI;
return { bearing, strength };
};
var calculateFaceAngle = (face4, imageSize) => {
const normalize2 = (v) => {
const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);
v[0] /= length;
v[1] /= length;
v[2] /= length;
return v;
};
const subVectors = (a, b) => {
const x = a[0] - b[0];
const y = a[1] - b[1];
const z = a[2] - b[2];
return [x, y, z];
};
const crossVectors = (a, b) => {
const x = a[1] * b[2] - a[2] * b[1];
const y = a[2] * b[0] - a[0] * b[2];
const z = a[0] * b[1] - a[1] * b[0];
return [x, y, z];
};
const rotationMatrixToEulerAngle = (r) => {
const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;
let thetaX;
let thetaY;
let thetaZ;
if (r10 < 1) {
if (r10 > -1) {
thetaZ = Math.asin(r10);
thetaY = Math.atan2(-r20, r00);
thetaX = Math.atan2(-r12, r11);
} else {
thetaZ = -Math.PI / 2;
thetaY = -Math.atan2(r21, r22);
thetaX = 0;
}
} else {
thetaZ = Math.PI / 2;
thetaY = Math.atan2(r21, r22);
thetaX = 0;
}
if (Number.isNaN(thetaX))
thetaX = 0;
if (Number.isNaN(thetaY))
thetaY = 0;
if (Number.isNaN(thetaZ))
thetaZ = 0;
return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };
};
const mesh = face4.meshRaw;
if (!mesh || mesh.length < 300)
return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };
const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5;
const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size2, pt[1] * imageSize[1] / size2, pt[2]]);
const yAxis = normalize2(subVectors(pts[1], pts[0]));
let xAxis = normalize2(subVectors(pts[3], pts[2]));
const zAxis = normalize2(crossVectors(xAxis, yAxis));
xAxis = crossVectors(yAxis, zAxis);
const matrix = [
xAxis[0],
xAxis[1],
xAxis[2],
yAxis[0],
yAxis[1],
yAxis[2],
zAxis[0],
zAxis[1],
zAxis[2]
];
const angle = rotationMatrixToEulerAngle(matrix);
const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 };
return { angle, matrix, gaze };
};
// src/face/anthropometry.ts
function calculateCameraDistance(face4, width) {
const f = face4 == null ? void 0 : face4.annotations;
if (!f)
return 0;
const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;
const cameraDistance = Math.round(1.17 / irisSize) / 100;
return cameraDistance;
}
// src/face/face.ts
var detectFace = async (instance, input) => {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w;
let timeStamp = now();
let ageRes;
let gearRes;
let genderRes;
let emotionRes;
let mobilefacenetRes;
let insightfaceRes;
let antispoofRes;
let livenessRes;
let descRes;
const faceRes = [];
instance.state = "run:face";
const faces = await predict4(input, instance.config);
instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
if (!input.shape || input.shape.length !== 4)
return [];
if (!faces)
return [];
for (let i = 0; i < faces.length; i++) {
instance.analyze("Get Face");
if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {
log("Face object is disposed:", faces[i].tensor);
continue;
}
if ((_a = instance.config.face.detector) == null ? void 0 : _a.mask) {
const masked = await mask(faces[i]);
tf25.dispose(faces[i].tensor);
if (masked)
faces[i].tensor = masked;
}
const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;
instance.analyze("Start Emotion:");
if (instance.config.async) {
emotionRes = ((_b = instance.config.face.emotion) == null ? void 0 : _b.enabled) ? predict5(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : [];
} else {
instance.state = "run:emotion";
timeStamp = now();
emotionRes = ((_c = instance.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict5(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : [];
instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
instance.analyze("End Emotion:");
instance.analyze("Start AntiSpoof:");
if (instance.config.async) {
antispoofRes = ((_d = instance.config.face.antispoof) == null ? void 0 : _d.enabled) ? predict7(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0;
} else {
instance.state = "run:antispoof";
timeStamp = now();
antispoofRes = ((_e = instance.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict7(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0;
instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
instance.analyze("End AntiSpoof:");
instance.analyze("Start Liveness:");
if (instance.config.async) {
livenessRes = ((_f = instance.config.face.liveness) == null ? void 0 : _f.enabled) ? predict8(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0;
} else {
instance.state = "run:liveness";
timeStamp = now();
livenessRes = ((_g = instance.config.face.liveness) == null ? void 0 : _g.enabled) ? await predict8(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0;
instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
instance.analyze("End Liveness:");
instance.analyze("Start GEAR:");
if (instance.config.async) {
gearRes = ((_h = instance.config.face.gear) == null ? void 0 : _h.enabled) ? predict9(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null;
} else {
instance.state = "run:gear";
timeStamp = now();
gearRes = ((_i = instance.config.face.gear) == null ? void 0 : _i.enabled) ? await predict9(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null;
instance.performance.gear = Math.trunc(now() - timeStamp);
}
instance.analyze("End GEAR:");
instance.analyze("Start SSRNet:");
if (instance.config.async) {
ageRes = ((_j = instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) ? predict10(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null;
genderRes = ((_k = instance.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict11(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null;
} else {
instance.state = "run:ssrnet";
timeStamp = now();
ageRes = ((_l = instance.config.face["ssrnet"]) == null ? void 0 : _l.enabled) ? await predict10(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null;
genderRes = ((_m = instance.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict11(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null;
instance.performance.ssrnet = Math.trunc(now() - timeStamp);
}
instance.analyze("End SSRNet:");
instance.analyze("Start MobileFaceNet:");
if (instance.config.async) {
mobilefacenetRes = ((_n = instance.config.face["mobilefacenet"]) == null ? void 0 : _n.enabled) ? predict12(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null;
} else {
instance.state = "run:mobilefacenet";
timeStamp = now();
mobilefacenetRes = ((_o = instance.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict12(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null;
instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);
}
instance.analyze("End MobileFaceNet:");
instance.analyze("Start InsightFace:");
if (instance.config.async) {
insightfaceRes = ((_p = instance.config.face["insightface"]) == null ? void 0 : _p.enabled) ? predict13(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null;
} else {
instance.state = "run:mobilefacenet";
timeStamp = now();
insightfaceRes = ((_q = instance.config.face["insightface"]) == null ? void 0 : _q.enabled) ? await predict13(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null;
instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);
}
instance.analyze("End InsightFace:");
instance.analyze("Start Description:");
if (instance.config.async) {
descRes = predict6(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length);
} else {
instance.state = "run:description";
timeStamp = now();
descRes = await predict6(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length);
instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
instance.analyze("End Description:");
if (instance.config.async) {
[ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]);
}
instance.analyze("Finish Face:");
if (((_r = instance.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && ageRes && genderRes) {
descRes = {
...descRes,
age: ageRes.age,
gender: genderRes.gender,
genderScore: genderRes.genderScore
};
}
if (((_s = instance.config.face.gear) == null ? void 0 : _s.enabled) && gearRes) {
descRes = {
...descRes,
age: gearRes.age,
gender: gearRes.gender,
genderScore: gearRes.genderScore,
race: gearRes.race
};
}
if (((_t = instance.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) {
descRes.descriptor = mobilefacenetRes;
}
if (((_u = instance.config.face["insightface"]) == null ? void 0 : _u.enabled) && insightfaceRes) {
descRes.descriptor = insightfaceRes;
}
const irisSize = ((_v = instance.config.face.iris) == null ? void 0 : _v.enabled) ? calculateCameraDistance(faces[i], input.shape[2]) : 0;
const tensor6 = ((_w = instance.config.face.detector) == null ? void 0 : _w.return) ? tf25.squeeze(faces[i].tensor) : null;
tf25.dispose(faces[i].tensor);
if (faces[i].tensor)
delete faces[i].tensor;
const res = {
...faces[i],
id: i
};
if (descRes.age)
res.age = descRes.age;
if (descRes.gender)
res.gender = descRes.gender;
if (descRes.genderScore)
res.genderScore = descRes.genderScore;
if (descRes.descriptor)
res.embedding = descRes.descriptor;
if (descRes.race)
res.race = descRes.race;
if (emotionRes)
res.emotion = emotionRes;
if (antispoofRes)
res.real = antispoofRes;
if (livenessRes)
res.live = livenessRes;
if (irisSize > 0)
res.distance = irisSize;
if (rotation)
res.rotation = rotation;
if (tensor6)
res.tensor = tensor6;
faceRes.push(res);
instance.analyze("End Face");
}
instance.analyze("End FaceMesh:");
if (instance.config.async) {
if (instance.performance.face)
delete instance.performance.face;
if (instance.performance.age)
delete instance.performance.age;
if (instance.performance.gender)
delete instance.performance.gender;
if (instance.performance.emotion)
delete instance.performance.emotion;
}
return faceRes;
};
// src/hand/fingerdef.ts
var Finger = {
thumb: 0,
index: 1,
middle: 2,
ring: 3,
pinky: 4,
all: [0, 1, 2, 3, 4],
nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" },
pointsMapping: {
0: [[0, 1], [1, 2], [2, 3], [3, 4]],
1: [[0, 5], [5, 6], [6, 7], [7, 8]],
2: [[0, 9], [9, 10], [10, 11], [11, 12]],
3: [[0, 13], [13, 14], [14, 15], [15, 16]],
4: [[0, 17], [17, 18], [18, 19], [19, 20]]
},
getName: (value) => Finger.nameMapping[value],
getPoints: (value) => Finger.pointsMapping[value]
};
var FingerCurl = {
none: 0,
half: 1,
full: 2,
nameMapping: { 0: "none", 1: "half", 2: "full" },
getName: (value) => FingerCurl.nameMapping[value]
};
var FingerDirection = {
verticalUp: 0,
verticalDown: 1,
horizontalLeft: 2,
horizontalRight: 3,
diagonalUpRight: 4,
diagonalUpLeft: 5,
diagonalDownRight: 6,
diagonalDownLeft: 7,
nameMapping: { 0: "verticalUp", 1: "verticalDown", 2: "horizontalLeft", 3: "horizontalRight", 4: "diagonalUpRight", 5: "diagonalUpLeft", 6: "diagonalDownRight", 7: "diagonalDownLeft" },
getName: (value) => FingerDirection.nameMapping[value]
};
var FingerGesture = class {
constructor(name) {
__publicField(this, "name");
__publicField(this, "curls");
__publicField(this, "directions");
__publicField(this, "weights");
__publicField(this, "weightsRelative");
this.name = name;
this.curls = {};
this.directions = {};
this.weights = [1, 1, 1, 1, 1];
this.weightsRelative = [1, 1, 1, 1, 1];
}
curl(finger, curl, confidence) {
if (typeof this.curls[finger] === "undefined")
this.curls[finger] = [];
this.curls[finger].push([curl, confidence]);
}
direction(finger, position, confidence) {
if (!this.directions[finger])
this.directions[finger] = [];
this.directions[finger].push([position, confidence]);
}
weight(finger, weight) {
this.weights[finger] = weight;
const total = this.weights.reduce((a, b) => a + b, 0);
this.weightsRelative = this.weights.map((el) => el * 5 / total);
}
matchAgainst(detectedCurls, detectedDirections) {
let confidence = 0;
for (const fingerIdx in detectedCurls) {
const detectedCurl = detectedCurls[fingerIdx];
const expectedCurls = this.curls[fingerIdx];
if (typeof expectedCurls === "undefined") {
confidence += this.weightsRelative[fingerIdx];
continue;
}
for (const [expectedCurl, score] of expectedCurls) {
if (detectedCurl === expectedCurl) {
confidence += score * this.weightsRelative[fingerIdx];
break;
}
}
}
for (const fingerIdx in detectedDirections) {
const detectedDirection = detectedDirections[fingerIdx];
const expectedDirections = this.directions[fingerIdx];
if (typeof expectedDirections === "undefined") {
confidence += this.weightsRelative[fingerIdx];
continue;
}
for (const [expectedDirection, score] of expectedDirections) {
if (detectedDirection === expectedDirection) {
confidence += score * this.weightsRelative[fingerIdx];
break;
}
}
}
return confidence / 10;
}
};
// src/hand/fingergesture.ts
var { thumb, index, middle, ring, pinky } = Finger;
var { none, half, full } = FingerCurl;
var { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;
var ThumbsUp = new FingerGesture("thumbs up");
ThumbsUp.curl(thumb, none, 1);
ThumbsUp.direction(thumb, verticalUp, 1);
ThumbsUp.direction(thumb, diagonalUpLeft, 0.25);
ThumbsUp.direction(thumb, diagonalUpRight, 0.25);
for (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {
ThumbsUp.curl(finger, full, 1);
ThumbsUp.direction(finger, horizontalLeft, 1);
ThumbsUp.direction(finger, horizontalRight, 1);
}
var Victory = new FingerGesture("victory");
Victory.curl(thumb, half, 0.5);
Victory.curl(thumb, none, 0.5);
Victory.direction(thumb, verticalUp, 1);
Victory.direction(thumb, diagonalUpLeft, 1);
Victory.curl(index, none, 1);
Victory.direction(index, verticalUp, 0.75);
Victory.direction(index, diagonalUpLeft, 1);
Victory.curl(middle, none, 1);
Victory.direction(middle, verticalUp, 1);
Victory.direction(middle, diagonalUpLeft, 0.75);
Victory.curl(ring, full, 1);
Victory.direction(ring, verticalUp, 0.2);
Victory.direction(ring, diagonalUpLeft, 1);
Victory.direction(ring, horizontalLeft, 0.2);
Victory.curl(pinky, full, 1);
Victory.direction(pinky, verticalUp, 0.2);
Victory.direction(pinky, diagonalUpLeft, 1);
Victory.direction(pinky, horizontalLeft, 0.2);
Victory.weight(index, 2);
Victory.weight(middle, 2);
var Point = new FingerGesture("point");
Point.curl(thumb, full, 1);
Point.curl(index, none, 0.5);
Point.curl(middle, full, 0.5);
Point.curl(ring, full, 0.5);
Point.curl(pinky, full, 0.5);
Point.weight(index, 2);
Point.weight(middle, 2);
var MiddleFinger = new FingerGesture("middle finger");
MiddleFinger.curl(thumb, none, 1);
MiddleFinger.curl(index, full, 0.5);
MiddleFinger.curl(middle, full, 0.5);
MiddleFinger.curl(ring, full, 0.5);
MiddleFinger.curl(pinky, full, 0.5);
MiddleFinger.weight(index, 2);
MiddleFinger.weight(middle, 2);
var OpenPalm = new FingerGesture("open palm");
OpenPalm.curl(thumb, none, 0.75);
OpenPalm.curl(index, none, 0.75);
OpenPalm.curl(middle, none, 0.75);
OpenPalm.curl(ring, none, 0.75);
OpenPalm.curl(pinky, none, 0.75);
var fingergesture_default = [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];
// src/hand/fingerpose.ts
var minConfidence = 0.7;
var options3 = {
HALF_CURL_START_LIMIT: 60,
NO_CURL_START_LIMIT: 130,
DISTANCE_VOTE_POWER: 1.1,
SINGLE_ANGLE_VOTE_POWER: 0.9,
TOTAL_ANGLE_VOTE_POWER: 1.6
};
function calculateSlope(point1x, point1y, point2x, point2y) {
const value = (point1y - point2y) / (point1x - point2x);
let slope = Math.atan(value) * 180 / Math.PI;
if (slope <= 0)
slope = -slope;
else if (slope > 0)
slope = 180 - slope;
return slope;
}
function getSlopes(point1, point2) {
if (!point1 || !point2)
return [0, 0];
const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);
if (point1.length === 2)
return slopeXY;
const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);
return [slopeXY, slopeYZ];
}
function angleOrientationAt(angle, weightageAt = 1) {
let isVertical = 0;
let isDiagonal = 0;
let isHorizontal = 0;
if (angle >= 75 && angle <= 105)
isVertical = 1 * weightageAt;
else if (angle >= 25 && angle <= 155)
isDiagonal = 1 * weightageAt;
else
isHorizontal = 1 * weightageAt;
return [isVertical, isDiagonal, isHorizontal];
}
function estimateFingerCurl(startPoint, midPoint, endPoint) {
const start_mid_x_dist = startPoint[0] - midPoint[0];
const start_end_x_dist = startPoint[0] - endPoint[0];
const mid_end_x_dist = midPoint[0] - endPoint[0];
const start_mid_y_dist = startPoint[1] - midPoint[1];
const start_end_y_dist = startPoint[1] - endPoint[1];
const mid_end_y_dist = midPoint[1] - endPoint[1];
const start_mid_z_dist = startPoint[2] - midPoint[2];
const start_end_z_dist = startPoint[2] - endPoint[2];
const mid_end_z_dist = midPoint[2] - endPoint[2];
const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);
const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);
const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);
let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);
if (cos_in > 1)
cos_in = 1;
else if (cos_in < -1)
cos_in = -1;
let angleOfCurve = Math.acos(cos_in);
angleOfCurve = 57.2958 * angleOfCurve % 180;
let fingerCurl;
if (angleOfCurve > options3.NO_CURL_START_LIMIT)
fingerCurl = FingerCurl.none;
else if (angleOfCurve > options3.HALF_CURL_START_LIMIT)
fingerCurl = FingerCurl.half;
else
fingerCurl = FingerCurl.full;
return fingerCurl;
}
function estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {
let estimatedDirection;
if (max_dist_x === Math.abs(start_end_x_dist)) {
if (start_end_x_dist > 0)
estimatedDirection = FingerDirection.horizontalLeft;
else
estimatedDirection = FingerDirection.horizontalRight;
} else if (max_dist_x === Math.abs(start_mid_x_dist)) {
if (start_mid_x_dist > 0)
estimatedDirection = FingerDirection.horizontalLeft;
else
estimatedDirection = FingerDirection.horizontalRight;
} else {
if (mid_end_x_dist > 0)
estimatedDirection = FingerDirection.horizontalLeft;
else
estimatedDirection = FingerDirection.horizontalRight;
}
return estimatedDirection;
}
function estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {
let estimatedDirection;
if (max_dist_y === Math.abs(start_end_y_dist)) {
if (start_end_y_dist < 0)
estimatedDirection = FingerDirection.verticalDown;
else
estimatedDirection = FingerDirection.verticalUp;
} else if (max_dist_y === Math.abs(start_mid_y_dist)) {
if (start_mid_y_dist < 0)
estimatedDirection = FingerDirection.verticalDown;
else
estimatedDirection = FingerDirection.verticalUp;
} else {
if (mid_end_y_dist < 0)
estimatedDirection = FingerDirection.verticalDown;
else
estimatedDirection = FingerDirection.verticalUp;
}
return estimatedDirection;
}
function estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {
let estimatedDirection;
const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);
const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
if (reqd_vertical_direction === FingerDirection.verticalUp) {
if (reqd_horizontal_direction === FingerDirection.horizontalLeft)
estimatedDirection = FingerDirection.diagonalUpLeft;
else
estimatedDirection = FingerDirection.diagonalUpRight;
} else {
if (reqd_horizontal_direction === FingerDirection.horizontalLeft)
estimatedDirection = FingerDirection.diagonalDownLeft;
else
estimatedDirection = FingerDirection.diagonalDownRight;
}
return estimatedDirection;
}
function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {
const start_mid_x_dist = startPoint[0] - midPoint[0];
const start_end_x_dist = startPoint[0] - endPoint[0];
const mid_end_x_dist = midPoint[0] - endPoint[0];
const start_mid_y_dist = startPoint[1] - midPoint[1];
const start_end_y_dist = startPoint[1] - endPoint[1];
const mid_end_y_dist = midPoint[1] - endPoint[1];
const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));
const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));
let voteVertical = 0;
let voteDiagonal = 0;
let voteHorizontal = 0;
const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5);
if (start_end_x_y_dist_ratio > 1.5)
voteVertical += options3.DISTANCE_VOTE_POWER;
else if (start_end_x_y_dist_ratio > 0.66)
voteDiagonal += options3.DISTANCE_VOTE_POWER;
else
voteHorizontal += options3.DISTANCE_VOTE_POWER;
const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);
const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);
const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);
const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);
let calc_start_point_x = startPoint[0];
let calc_start_point_y = startPoint[1];
let calc_end_point_x = endPoint[0];
let calc_end_point_y = endPoint[1];
if (max_dist === start_mid_dist) {
calc_end_point_x = endPoint[0];
calc_end_point_y = endPoint[1];
} else if (max_dist === mid_end_dist) {
calc_start_point_x = midPoint[0];
calc_start_point_y = midPoint[1];
}
const calcStartPoint = [calc_start_point_x, calc_start_point_y];
const calcEndPoint = [calc_end_point_x, calc_end_point_y];
const totalAngle = getSlopes(calcStartPoint, calcEndPoint);
const votes = angleOrientationAt(totalAngle, options3.TOTAL_ANGLE_VOTE_POWER);
voteVertical += votes[0];
voteDiagonal += votes[1];
voteHorizontal += votes[2];
for (const fingerSlope of fingerSlopes) {
const fingerVotes = angleOrientationAt(fingerSlope, options3.SINGLE_ANGLE_VOTE_POWER);
voteVertical += fingerVotes[0];
voteDiagonal += fingerVotes[1];
voteHorizontal += fingerVotes[2];
}
let estimatedDirection;
if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {
estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);
} else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {
estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
} else {
estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
}
return estimatedDirection;
}
function estimate(landmarks) {
const slopesXY = [];
const slopesYZ = [];
const fingerCurls = [];
const fingerDirections = [];
if (!landmarks)
return { curls: fingerCurls, directions: fingerDirections };
for (const finger of Finger.all) {
const points = Finger.getPoints(finger);
const slopeAtXY = [];
const slopeAtYZ = [];
for (const point2 of points) {
const point1 = landmarks[point2[0]];
const point22 = landmarks[point2[1]];
const slopes = getSlopes(point1, point22);
const slopeXY = slopes[0];
const slopeYZ = slopes[1];
slopeAtXY.push(slopeXY);
slopeAtYZ.push(slopeYZ);
}
slopesXY.push(slopeAtXY);
slopesYZ.push(slopeAtYZ);
}
for (const finger of Finger.all) {
const pointIndexAt = finger === Finger.thumb ? 1 : 0;
const fingerPointsAt = Finger.getPoints(finger);
const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];
const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];
const endPoint = landmarks[fingerPointsAt[3][1]];
const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);
const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));
fingerCurls[finger] = fingerCurled;
fingerDirections[finger] = fingerPosition;
}
return { curls: fingerCurls, directions: fingerDirections };
}
function analyze(keypoints) {
if (!keypoints || keypoints.length === 0)
return null;
const estimatorRes = estimate(keypoints);
const landmarks = {};
for (const fingerIdx of Finger.all) {
landmarks[Finger.getName(fingerIdx)] = {
curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),
direction: FingerDirection.getName(estimatorRes.directions[fingerIdx])
};
}
return landmarks;
}
function match(keypoints) {
const poses = [];
if (!keypoints || keypoints.length === 0)
return poses;
const estimatorRes = estimate(keypoints);
for (const gesture2 of fingergesture_default) {
const confidence = gesture2.matchAgainst(estimatorRes.curls, estimatorRes.directions);
if (confidence >= minConfidence)
poses.push({ name: gesture2.name, confidence });
}
return poses;
}
// src/gesture/gesture.ts
var body2 = (res) => {
if (!res)
return [];
const gestures = [];
for (let i = 0; i < res.length; i++) {
const leftWrist = res[i].keypoints.find((a) => a.part === "leftWrist");
const rightWrist = res[i].keypoints.find((a) => a.part === "rightWrist");
const nose = res[i].keypoints.find((a) => a.part === "nose");
if (nose && leftWrist && rightWrist && leftWrist.position[1] < nose.position[1] && rightWrist.position[1] < nose.position[1])
gestures.push({ body: i, gesture: "i give up" });
else if (nose && leftWrist && leftWrist.position[1] < nose.position[1])
gestures.push({ body: i, gesture: "raise left hand" });
else if (nose && rightWrist && rightWrist.position[1] < nose.position[1])
gestures.push({ body: i, gesture: "raise right hand" });
const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder");
const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder");
if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {
gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` });
}
}
return gestures;
};
var face2 = (res) => {
if (!res)
return [];
const gestures = [];
for (let i = 0; i < res.length; i++) {
if (res[i].mesh && res[i].mesh.length > 450) {
const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);
const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];
if (Math.abs(zDiff / xDiff) <= 0.15)
gestures.push({ face: i, gesture: "facing center" });
else
gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? "left" : "right"}` });
const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]);
if (openLeft < 0.2)
gestures.push({ face: i, gesture: "blink left eye" });
const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]);
if (openRight < 0.2)
gestures.push({ face: i, gesture: "blink right eye" });
const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));
if (mouthOpen > 10)
gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });
const chinDepth = res[i].mesh[152][2] || 0;
if (Math.abs(chinDepth) > 10)
gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` });
}
}
return gestures;
};
var iris2 = (res) => {
var _a, _b, _c, _d;
if (!res)
return [];
const gestures = [];
for (let i = 0; i < res.length; i++) {
if (!((_b = (_a = res[i].annotations) == null ? void 0 : _a.leftEyeIris) == null ? void 0 : _b[0]) || !((_d = (_c = res[i].annotations) == null ? void 0 : _c.rightEyeIris) == null ? void 0 : _d[0]))
continue;
const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];
const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];
const areaLeft = Math.abs(sizeXLeft * sizeYLeft);
const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];
const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];
const areaRight = Math.abs(sizeXRight * sizeYRight);
let center = false;
const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);
if (difference < 0.25) {
center = true;
gestures.push({ iris: i, gesture: "facing center" });
}
const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];
const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];
if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06)
center = false;
if (leftIrisCenterX > rightIrisCenterX) {
if (leftIrisCenterX > 0.05)
gestures.push({ iris: i, gesture: "looking right" });
} else {
if (rightIrisCenterX > 0.05)
gestures.push({ iris: i, gesture: "looking left" });
}
const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];
const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];
if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022)
center = false;
if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01)
gestures.push({ iris: i, gesture: "looking down" });
if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022)
gestures.push({ iris: i, gesture: "looking up" });
if (center)
gestures.push({ iris: i, gesture: "looking center" });
}
return gestures;
};
var hand2 = (res) => {
if (!res)
return [];
const gestures = [];
for (let i = 0; i < res.length; i++) {
const fingers = [];
if (res[i].annotations) {
for (const [finger, pos] of Object.entries(res[i].annotations)) {
if (finger !== "palmBase" && Array.isArray(pos) && pos[0])
fingers.push({ name: finger.toLowerCase(), position: pos[0] });
}
}
if (fingers && fingers.length > 0) {
const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a);
gestures.push({ hand: i, gesture: `${closest.name} forward` });
const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a);
gestures.push({ hand: i, gesture: `${highest.name} up` });
}
if (res[i].keypoints) {
const poses = match(res[i].keypoints);
for (const pose of poses)
gestures.push({ hand: i, gesture: pose.name });
}
}
return gestures;
};
// src/hand/handposedetector.ts
var tf27 = __toESM(require_tfjs_esm());
// src/hand/handposeutil.ts
var tf26 = __toESM(require_tfjs_esm());
function getBoxSize2(box) {
return [
Math.abs(box.endPoint[0] - box.startPoint[0]),
Math.abs(box.endPoint[1] - box.startPoint[1])
];
}
function getBoxCenter2(box) {
return [
box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,
box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2
];
}
function cutBoxFromImageAndResize(box, image28, cropSize) {
const h = image28.shape[1];
const w = image28.shape[2];
const boxes = [[
box.startPoint[1] / h,
box.startPoint[0] / w,
box.endPoint[1] / h,
box.endPoint[0] / w
]];
return tf26.image.cropAndResize(image28, boxes, [0], cropSize);
}
function scaleBoxCoordinates2(box, factor) {
const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
const palmLandmarks = box.palmLandmarks.map((coord) => {
const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];
return scaledCoord;
});
return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };
}
function enlargeBox2(box, factor = 1.5) {
const center = getBoxCenter2(box);
const size2 = getBoxSize2(box);
const newHalfSize = [factor * size2[0] / 2, factor * size2[1] / 2];
const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];
const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];
return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };
}
function squarifyBox2(box) {
const centers = getBoxCenter2(box);
const size2 = getBoxSize2(box);
const maxEdge = Math.max(...size2);
const halfSize = maxEdge / 2;
const startPoint = [centers[0] - halfSize, centers[1] - halfSize];
const endPoint = [centers[0] + halfSize, centers[1] + halfSize];
return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };
}
function normalizeRadians2(angle) {
return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));
}
function computeRotation2(point1, point2) {
const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);
return normalizeRadians2(radians);
}
var buildTranslationMatrix2 = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];
function dot2(v1, v2) {
let product = 0;
for (let i = 0; i < v1.length; i++) {
product += v1[i] * v2[i];
}
return product;
}
function getColumnFrom2DArr2(arr, columnIndex) {
const column = [];
for (let i = 0; i < arr.length; i++) {
column.push(arr[i][columnIndex]);
}
return column;
}
function multiplyTransformMatrices2(mat1, mat2) {
const product = [];
const size2 = mat1.length;
for (let row = 0; row < size2; row++) {
product.push([]);
for (let col = 0; col < size2; col++) {
product[row].push(dot2(mat1[row], getColumnFrom2DArr2(mat2, col)));
}
}
return product;
}
function buildRotationMatrix2(rotation, center) {
const cosA = Math.cos(rotation);
const sinA = Math.sin(rotation);
const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];
const translationMatrix = buildTranslationMatrix2(center[0], center[1]);
const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix);
const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]);
return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix);
}
function invertTransformMatrix2(matrix) {
const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];
const translationComponent = [matrix[0][2], matrix[1][2]];
const invertedTranslation = [
-dot2(rotationComponent[0], translationComponent),
-dot2(rotationComponent[1], translationComponent)
];
return [
rotationComponent[0].concat(invertedTranslation[0]),
rotationComponent[1].concat(invertedTranslation[1]),
[0, 0, 1]
];
}
function rotatePoint2(homogeneousCoordinate, rotationMatrix) {
return [
dot2(homogeneousCoordinate, rotationMatrix[0]),
dot2(homogeneousCoordinate, rotationMatrix[1])
];
}
// src/hand/handposeanchors.ts
var anchors2 = [
{ x: 0.015625, y: 0.015625 },
{ x: 0.015625, y: 0.015625 },
{ x: 0.046875, y: 0.015625 },
{ x: 0.046875, y: 0.015625 },
{ x: 0.078125, y: 0.015625 },
{ x: 0.078125, y: 0.015625 },
{ x: 0.109375, y: 0.015625 },
{ x: 0.109375, y: 0.015625 },
{ x: 0.140625, y: 0.015625 },
{ x: 0.140625, y: 0.015625 },
{ x: 0.171875, y: 0.015625 },
{ x: 0.171875, y: 0.015625 },
{ x: 0.203125, y: 0.015625 },
{ x: 0.203125, y: 0.015625 },
{ x: 0.234375, y: 0.015625 },
{ x: 0.234375, y: 0.015625 },
{ x: 0.265625, y: 0.015625 },
{ x: 0.265625, y: 0.015625 },
{ x: 0.296875, y: 0.015625 },
{ x: 0.296875, y: 0.015625 },
{ x: 0.328125, y: 0.015625 },
{ x: 0.328125, y: 0.015625 },
{ x: 0.359375, y: 0.015625 },
{ x: 0.359375, y: 0.015625 },
{ x: 0.390625, y: 0.015625 },
{ x: 0.390625, y: 0.015625 },
{ x: 0.421875, y: 0.015625 },
{ x: 0.421875, y: 0.015625 },
{ x: 0.453125, y: 0.015625 },
{ x: 0.453125, y: 0.015625 },
{ x: 0.484375, y: 0.015625 },
{ x: 0.484375, y: 0.015625 },
{ x: 0.515625, y: 0.015625 },
{ x: 0.515625, y: 0.015625 },
{ x: 0.546875, y: 0.015625 },
{ x: 0.546875, y: 0.015625 },
{ x: 0.578125, y: 0.015625 },
{ x: 0.578125, y: 0.015625 },
{ x: 0.609375, y: 0.015625 },
{ x: 0.609375, y: 0.015625 },
{ x: 0.640625, y: 0.015625 },
{ x: 0.640625, y: 0.015625 },
{ x: 0.671875, y: 0.015625 },
{ x: 0.671875, y: 0.015625 },
{ x: 0.703125, y: 0.015625 },
{ x: 0.703125, y: 0.015625 },
{ x: 0.734375, y: 0.015625 },
{ x: 0.734375, y: 0.015625 },
{ x: 0.765625, y: 0.015625 },
{ x: 0.765625, y: 0.015625 },
{ x: 0.796875, y: 0.015625 },
{ x: 0.796875, y: 0.015625 },
{ x: 0.828125, y: 0.015625 },
{ x: 0.828125, y: 0.015625 },
{ x: 0.859375, y: 0.015625 },
{ x: 0.859375, y: 0.015625 },
{ x: 0.890625, y: 0.015625 },
{ x: 0.890625, y: 0.015625 },
{ x: 0.921875, y: 0.015625 },
{ x: 0.921875, y: 0.015625 },
{ x: 0.953125, y: 0.015625 },
{ x: 0.953125, y: 0.015625 },
{ x: 0.984375, y: 0.015625 },
{ x: 0.984375, y: 0.015625 },
{ x: 0.015625, y: 0.046875 },
{ x: 0.015625, y: 0.046875 },
{ x: 0.046875, y: 0.046875 },
{ x: 0.046875, y: 0.046875 },
{ x: 0.078125, y: 0.046875 },
{ x: 0.078125, y: 0.046875 },
{ x: 0.109375, y: 0.046875 },
{ x: 0.109375, y: 0.046875 },
{ x: 0.140625, y: 0.046875 },
{ x: 0.140625, y: 0.046875 },
{ x: 0.171875, y: 0.046875 },
{ x: 0.171875, y: 0.046875 },
{ x: 0.203125, y: 0.046875 },
{ x: 0.203125, y: 0.046875 },
{ x: 0.234375, y: 0.046875 },
{ x: 0.234375, y: 0.046875 },
{ x: 0.265625, y: 0.046875 },
{ x: 0.265625, y: 0.046875 },
{ x: 0.296875, y: 0.046875 },
{ x: 0.296875, y: 0.046875 },
{ x: 0.328125, y: 0.046875 },
{ x: 0.328125, y: 0.046875 },
{ x: 0.359375, y: 0.046875 },
{ x: 0.359375, y: 0.046875 },
{ x: 0.390625, y: 0.046875 },
{ x: 0.390625, y: 0.046875 },
{ x: 0.421875, y: 0.046875 },
{ x: 0.421875, y: 0.046875 },
{ x: 0.453125, y: 0.046875 },
{ x: 0.453125, y: 0.046875 },
{ x: 0.484375, y: 0.046875 },
{ x: 0.484375, y: 0.046875 },
{ x: 0.515625, y: 0.046875 },
{ x: 0.515625, y: 0.046875 },
{ x: 0.546875, y: 0.046875 },
{ x: 0.546875, y: 0.046875 },
{ x: 0.578125, y: 0.046875 },
{ x: 0.578125, y: 0.046875 },
{ x: 0.609375, y: 0.046875 },
{ x: 0.609375, y: 0.046875 },
{ x: 0.640625, y: 0.046875 },
{ x: 0.640625, y: 0.046875 },
{ x: 0.671875, y: 0.046875 },
{ x: 0.671875, y: 0.046875 },
{ x: 0.703125, y: 0.046875 },
{ x: 0.703125, y: 0.046875 },
{ x: 0.734375, y: 0.046875 },
{ x: 0.734375, y: 0.046875 },
{ x: 0.765625, y: 0.046875 },
{ x: 0.765625, y: 0.046875 },
{ x: 0.796875, y: 0.046875 },
{ x: 0.796875, y: 0.046875 },
{ x: 0.828125, y: 0.046875 },
{ x: 0.828125, y: 0.046875 },
{ x: 0.859375, y: 0.046875 },
{ x: 0.859375, y: 0.046875 },
{ x: 0.890625, y: 0.046875 },
{ x: 0.890625, y: 0.046875 },
{ x: 0.921875, y: 0.046875 },
{ x: 0.921875, y: 0.046875 },
{ x: 0.953125, y: 0.046875 },
{ x: 0.953125, y: 0.046875 },
{ x: 0.984375, y: 0.046875 },
{ x: 0.984375, y: 0.046875 },
{ x: 0.015625, y: 0.078125 },
{ x: 0.015625, y: 0.078125 },
{ x: 0.046875, y: 0.078125 },
{ x: 0.046875, y: 0.078125 },
{ x: 0.078125, y: 0.078125 },
{ x: 0.078125, y: 0.078125 },
{ x: 0.109375, y: 0.078125 },
{ x: 0.109375, y: 0.078125 },
{ x: 0.140625, y: 0.078125 },
{ x: 0.140625, y: 0.078125 },
{ x: 0.171875, y: 0.078125 },
{ x: 0.171875, y: 0.078125 },
{ x: 0.203125, y: 0.078125 },
{ x: 0.203125, y: 0.078125 },
{ x: 0.234375, y: 0.078125 },
{ x: 0.234375, y: 0.078125 },
{ x: 0.265625, y: 0.078125 },
{ x: 0.265625, y: 0.078125 },
{ x: 0.296875, y: 0.078125 },
{ x: 0.296875, y: 0.078125 },
{ x: 0.328125, y: 0.078125 },
{ x: 0.328125, y: 0.078125 },
{ x: 0.359375, y: 0.078125 },
{ x: 0.359375, y: 0.078125 },
{ x: 0.390625, y: 0.078125 },
{ x: 0.390625, y: 0.078125 },
{ x: 0.421875, y: 0.078125 },
{ x: 0.421875, y: 0.078125 },
{ x: 0.453125, y: 0.078125 },
{ x: 0.453125, y: 0.078125 },
{ x: 0.484375, y: 0.078125 },
{ x: 0.484375, y: 0.078125 },
{ x: 0.515625, y: 0.078125 },
{ x: 0.515625, y: 0.078125 },
{ x: 0.546875, y: 0.078125 },
{ x: 0.546875, y: 0.078125 },
{ x: 0.578125, y: 0.078125 },
{ x: 0.578125, y: 0.078125 },
{ x: 0.609375, y: 0.078125 },
{ x: 0.609375, y: 0.078125 },
{ x: 0.640625, y: 0.078125 },
{ x: 0.640625, y: 0.078125 },
{ x: 0.671875, y: 0.078125 },
{ x: 0.671875, y: 0.078125 },
{ x: 0.703125, y: 0.078125 },
{ x: 0.703125, y: 0.078125 },
{ x: 0.734375, y: 0.078125 },
{ x: 0.734375, y: 0.078125 },
{ x: 0.765625, y: 0.078125 },
{ x: 0.765625, y: 0.078125 },
{ x: 0.796875, y: 0.078125 },
{ x: 0.796875, y: 0.078125 },
{ x: 0.828125, y: 0.078125 },
{ x: 0.828125, y: 0.078125 },
{ x: 0.859375, y: 0.078125 },
{ x: 0.859375, y: 0.078125 },
{ x: 0.890625, y: 0.078125 },
{ x: 0.890625, y: 0.078125 },
{ x: 0.921875, y: 0.078125 },
{ x: 0.921875, y: 0.078125 },
{ x: 0.953125, y: 0.078125 },
{ x: 0.953125, y: 0.078125 },
{ x: 0.984375, y: 0.078125 },
{ x: 0.984375, y: 0.078125 },
{ x: 0.015625, y: 0.109375 },
{ x: 0.015625, y: 0.109375 },
{ x: 0.046875, y: 0.109375 },
{ x: 0.046875, y: 0.109375 },
{ x: 0.078125, y: 0.109375 },
{ x: 0.078125, y: 0.109375 },
{ x: 0.109375, y: 0.109375 },
{ x: 0.109375, y: 0.109375 },
{ x: 0.140625, y: 0.109375 },
{ x: 0.140625, y: 0.109375 },
{ x: 0.171875, y: 0.109375 },
{ x: 0.171875, y: 0.109375 },
{ x: 0.203125, y: 0.109375 },
{ x: 0.203125, y: 0.109375 },
{ x: 0.234375, y: 0.109375 },
{ x: 0.234375, y: 0.109375 },
{ x: 0.265625, y: 0.109375 },
{ x: 0.265625, y: 0.109375 },
{ x: 0.296875, y: 0.109375 },
{ x: 0.296875, y: 0.109375 },
{ x: 0.328125, y: 0.109375 },
{ x: 0.328125, y: 0.109375 },
{ x: 0.359375, y: 0.109375 },
{ x: 0.359375, y: 0.109375 },
{ x: 0.390625, y: 0.109375 },
{ x: 0.390625, y: 0.109375 },
{ x: 0.421875, y: 0.109375 },
{ x: 0.421875, y: 0.109375 },
{ x: 0.453125, y: 0.109375 },
{ x: 0.453125, y: 0.109375 },
{ x: 0.484375, y: 0.109375 },
{ x: 0.484375, y: 0.109375 },
{ x: 0.515625, y: 0.109375 },
{ x: 0.515625, y: 0.109375 },
{ x: 0.546875, y: 0.109375 },
{ x: 0.546875, y: 0.109375 },
{ x: 0.578125, y: 0.109375 },
{ x: 0.578125, y: 0.109375 },
{ x: 0.609375, y: 0.109375 },
{ x: 0.609375, y: 0.109375 },
{ x: 0.640625, y: 0.109375 },
{ x: 0.640625, y: 0.109375 },
{ x: 0.671875, y: 0.109375 },
{ x: 0.671875, y: 0.109375 },
{ x: 0.703125, y: 0.109375 },
{ x: 0.703125, y: 0.109375 },
{ x: 0.734375, y: 0.109375 },
{ x: 0.734375, y: 0.109375 },
{ x: 0.765625, y: 0.109375 },
{ x: 0.765625, y: 0.109375 },
{ x: 0.796875, y: 0.109375 },
{ x: 0.796875, y: 0.109375 },
{ x: 0.828125, y: 0.109375 },
{ x: 0.828125, y: 0.109375 },
{ x: 0.859375, y: 0.109375 },
{ x: 0.859375, y: 0.109375 },
{ x: 0.890625, y: 0.109375 },
{ x: 0.890625, y: 0.109375 },
{ x: 0.921875, y: 0.109375 },
{ x: 0.921875, y: 0.109375 },
{ x: 0.953125, y: 0.109375 },
{ x: 0.953125, y: 0.109375 },
{ x: 0.984375, y: 0.109375 },
{ x: 0.984375, y: 0.109375 },
{ x: 0.015625, y: 0.140625 },
{ x: 0.015625, y: 0.140625 },
{ x: 0.046875, y: 0.140625 },
{ x: 0.046875, y: 0.140625 },
{ x: 0.078125, y: 0.140625 },
{ x: 0.078125, y: 0.140625 },
{ x: 0.109375, y: 0.140625 },
{ x: 0.109375, y: 0.140625 },
{ x: 0.140625, y: 0.140625 },
{ x: 0.140625, y: 0.140625 },
{ x: 0.171875, y: 0.140625 },
{ x: 0.171875, y: 0.140625 },
{ x: 0.203125, y: 0.140625 },
{ x: 0.203125, y: 0.140625 },
{ x: 0.234375, y: 0.140625 },
{ x: 0.234375, y: 0.140625 },
{ x: 0.265625, y: 0.140625 },
{ x: 0.265625, y: 0.140625 },
{ x: 0.296875, y: 0.140625 },
{ x: 0.296875, y: 0.140625 },
{ x: 0.328125, y: 0.140625 },
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{ x: 0.59375, y: 0.96875 },
{ x: 0.59375, y: 0.96875 },
{ x: 0.65625, y: 0.96875 },
{ x: 0.65625, y: 0.96875 },
{ x: 0.71875, y: 0.96875 },
{ x: 0.71875, y: 0.96875 },
{ x: 0.78125, y: 0.96875 },
{ x: 0.78125, y: 0.96875 },
{ x: 0.84375, y: 0.96875 },
{ x: 0.84375, y: 0.96875 },
{ x: 0.90625, y: 0.96875 },
{ x: 0.90625, y: 0.96875 },
{ x: 0.96875, y: 0.96875 },
{ x: 0.96875, y: 0.96875 },
{ x: 0.0625, y: 0.0625 },
{ x: 0.0625, y: 0.0625 },
{ x: 0.0625, y: 0.0625 },
{ x: 0.0625, y: 0.0625 },
{ x: 0.0625, y: 0.0625 },
{ x: 0.0625, y: 0.0625 },
{ x: 0.1875, y: 0.0625 },
{ x: 0.1875, y: 0.0625 },
{ x: 0.1875, y: 0.0625 },
{ x: 0.1875, y: 0.0625 },
{ x: 0.1875, y: 0.0625 },
{ x: 0.1875, y: 0.0625 },
{ x: 0.3125, y: 0.0625 },
{ x: 0.3125, y: 0.0625 },
{ x: 0.3125, y: 0.0625 },
{ x: 0.3125, y: 0.0625 },
{ x: 0.3125, y: 0.0625 },
{ x: 0.3125, y: 0.0625 },
{ x: 0.4375, y: 0.0625 },
{ x: 0.4375, y: 0.0625 },
{ x: 0.4375, y: 0.0625 },
{ x: 0.4375, y: 0.0625 },
{ x: 0.4375, y: 0.0625 },
{ x: 0.4375, y: 0.0625 },
{ x: 0.5625, y: 0.0625 },
{ x: 0.5625, y: 0.0625 },
{ x: 0.5625, y: 0.0625 },
{ x: 0.5625, y: 0.0625 },
{ x: 0.5625, y: 0.0625 },
{ x: 0.5625, y: 0.0625 },
{ x: 0.6875, y: 0.0625 },
{ x: 0.6875, y: 0.0625 },
{ x: 0.6875, y: 0.0625 },
{ x: 0.6875, y: 0.0625 },
{ x: 0.6875, y: 0.0625 },
{ x: 0.6875, y: 0.0625 },
{ x: 0.8125, y: 0.0625 },
{ x: 0.8125, y: 0.0625 },
{ x: 0.8125, y: 0.0625 },
{ x: 0.8125, y: 0.0625 },
{ x: 0.8125, y: 0.0625 },
{ x: 0.8125, y: 0.0625 },
{ x: 0.9375, y: 0.0625 },
{ x: 0.9375, y: 0.0625 },
{ x: 0.9375, y: 0.0625 },
{ x: 0.9375, y: 0.0625 },
{ x: 0.9375, y: 0.0625 },
{ x: 0.9375, y: 0.0625 },
{ x: 0.0625, y: 0.1875 },
{ x: 0.0625, y: 0.1875 },
{ x: 0.0625, y: 0.1875 },
{ x: 0.0625, y: 0.1875 },
{ x: 0.0625, y: 0.1875 },
{ x: 0.0625, y: 0.1875 },
{ x: 0.1875, y: 0.1875 },
{ x: 0.1875, y: 0.1875 },
{ x: 0.1875, y: 0.1875 },
{ x: 0.1875, y: 0.1875 },
{ x: 0.1875, y: 0.1875 },
{ x: 0.1875, y: 0.1875 },
{ x: 0.3125, y: 0.1875 },
{ x: 0.3125, y: 0.1875 },
{ x: 0.3125, y: 0.1875 },
{ x: 0.3125, y: 0.1875 },
{ x: 0.3125, y: 0.1875 },
{ x: 0.3125, y: 0.1875 },
{ x: 0.4375, y: 0.1875 },
{ x: 0.4375, y: 0.1875 },
{ x: 0.4375, y: 0.1875 },
{ x: 0.4375, y: 0.1875 },
{ x: 0.4375, y: 0.1875 },
{ x: 0.4375, y: 0.1875 },
{ x: 0.5625, y: 0.1875 },
{ x: 0.5625, y: 0.1875 },
{ x: 0.5625, y: 0.1875 },
{ x: 0.5625, y: 0.1875 },
{ x: 0.5625, y: 0.1875 },
{ x: 0.5625, y: 0.1875 },
{ x: 0.6875, y: 0.1875 },
{ x: 0.6875, y: 0.1875 },
{ x: 0.6875, y: 0.1875 },
{ x: 0.6875, y: 0.1875 },
{ x: 0.6875, y: 0.1875 },
{ x: 0.6875, y: 0.1875 },
{ x: 0.8125, y: 0.1875 },
{ x: 0.8125, y: 0.1875 },
{ x: 0.8125, y: 0.1875 },
{ x: 0.8125, y: 0.1875 },
{ x: 0.8125, y: 0.1875 },
{ x: 0.8125, y: 0.1875 },
{ x: 0.9375, y: 0.1875 },
{ x: 0.9375, y: 0.1875 },
{ x: 0.9375, y: 0.1875 },
{ x: 0.9375, y: 0.1875 },
{ x: 0.9375, y: 0.1875 },
{ x: 0.9375, y: 0.1875 },
{ x: 0.0625, y: 0.3125 },
{ x: 0.0625, y: 0.3125 },
{ x: 0.0625, y: 0.3125 },
{ x: 0.0625, y: 0.3125 },
{ x: 0.0625, y: 0.3125 },
{ x: 0.0625, y: 0.3125 },
{ x: 0.1875, y: 0.3125 },
{ x: 0.1875, y: 0.3125 },
{ x: 0.1875, y: 0.3125 },
{ x: 0.1875, y: 0.3125 },
{ x: 0.1875, y: 0.3125 },
{ x: 0.1875, y: 0.3125 },
{ x: 0.3125, y: 0.3125 },
{ x: 0.3125, y: 0.3125 },
{ x: 0.3125, y: 0.3125 },
{ x: 0.3125, y: 0.3125 },
{ x: 0.3125, y: 0.3125 },
{ x: 0.3125, y: 0.3125 },
{ x: 0.4375, y: 0.3125 },
{ x: 0.4375, y: 0.3125 },
{ x: 0.4375, y: 0.3125 },
{ x: 0.4375, y: 0.3125 },
{ x: 0.4375, y: 0.3125 },
{ x: 0.4375, y: 0.3125 },
{ x: 0.5625, y: 0.3125 },
{ x: 0.5625, y: 0.3125 },
{ x: 0.5625, y: 0.3125 },
{ x: 0.5625, y: 0.3125 },
{ x: 0.5625, y: 0.3125 },
{ x: 0.5625, y: 0.3125 },
{ x: 0.6875, y: 0.3125 },
{ x: 0.6875, y: 0.3125 },
{ x: 0.6875, y: 0.3125 },
{ x: 0.6875, y: 0.3125 },
{ x: 0.6875, y: 0.3125 },
{ x: 0.6875, y: 0.3125 },
{ x: 0.8125, y: 0.3125 },
{ x: 0.8125, y: 0.3125 },
{ x: 0.8125, y: 0.3125 },
{ x: 0.8125, y: 0.3125 },
{ x: 0.8125, y: 0.3125 },
{ x: 0.8125, y: 0.3125 },
{ x: 0.9375, y: 0.3125 },
{ x: 0.9375, y: 0.3125 },
{ x: 0.9375, y: 0.3125 },
{ x: 0.9375, y: 0.3125 },
{ x: 0.9375, y: 0.3125 },
{ x: 0.9375, y: 0.3125 },
{ x: 0.0625, y: 0.4375 },
{ x: 0.0625, y: 0.4375 },
{ x: 0.0625, y: 0.4375 },
{ x: 0.0625, y: 0.4375 },
{ x: 0.0625, y: 0.4375 },
{ x: 0.0625, y: 0.4375 },
{ x: 0.1875, y: 0.4375 },
{ x: 0.1875, y: 0.4375 },
{ x: 0.1875, y: 0.4375 },
{ x: 0.1875, y: 0.4375 },
{ x: 0.1875, y: 0.4375 },
{ x: 0.1875, y: 0.4375 },
{ x: 0.3125, y: 0.4375 },
{ x: 0.3125, y: 0.4375 },
{ x: 0.3125, y: 0.4375 },
{ x: 0.3125, y: 0.4375 },
{ x: 0.3125, y: 0.4375 },
{ x: 0.3125, y: 0.4375 },
{ x: 0.4375, y: 0.4375 },
{ x: 0.4375, y: 0.4375 },
{ x: 0.4375, y: 0.4375 },
{ x: 0.4375, y: 0.4375 },
{ x: 0.4375, y: 0.4375 },
{ x: 0.4375, y: 0.4375 },
{ x: 0.5625, y: 0.4375 },
{ x: 0.5625, y: 0.4375 },
{ x: 0.5625, y: 0.4375 },
{ x: 0.5625, y: 0.4375 },
{ x: 0.5625, y: 0.4375 },
{ x: 0.5625, y: 0.4375 },
{ x: 0.6875, y: 0.4375 },
{ x: 0.6875, y: 0.4375 },
{ x: 0.6875, y: 0.4375 },
{ x: 0.6875, y: 0.4375 },
{ x: 0.6875, y: 0.4375 },
{ x: 0.6875, y: 0.4375 },
{ x: 0.8125, y: 0.4375 },
{ x: 0.8125, y: 0.4375 },
{ x: 0.8125, y: 0.4375 },
{ x: 0.8125, y: 0.4375 },
{ x: 0.8125, y: 0.4375 },
{ x: 0.8125, y: 0.4375 },
{ x: 0.9375, y: 0.4375 },
{ x: 0.9375, y: 0.4375 },
{ x: 0.9375, y: 0.4375 },
{ x: 0.9375, y: 0.4375 },
{ x: 0.9375, y: 0.4375 },
{ x: 0.9375, y: 0.4375 },
{ x: 0.0625, y: 0.5625 },
{ x: 0.0625, y: 0.5625 },
{ x: 0.0625, y: 0.5625 },
{ x: 0.0625, y: 0.5625 },
{ x: 0.0625, y: 0.5625 },
{ x: 0.0625, y: 0.5625 },
{ x: 0.1875, y: 0.5625 },
{ x: 0.1875, y: 0.5625 },
{ x: 0.1875, y: 0.5625 },
{ x: 0.1875, y: 0.5625 },
{ x: 0.1875, y: 0.5625 },
{ x: 0.1875, y: 0.5625 },
{ x: 0.3125, y: 0.5625 },
{ x: 0.3125, y: 0.5625 },
{ x: 0.3125, y: 0.5625 },
{ x: 0.3125, y: 0.5625 },
{ x: 0.3125, y: 0.5625 },
{ x: 0.3125, y: 0.5625 },
{ x: 0.4375, y: 0.5625 },
{ x: 0.4375, y: 0.5625 },
{ x: 0.4375, y: 0.5625 },
{ x: 0.4375, y: 0.5625 },
{ x: 0.4375, y: 0.5625 },
{ x: 0.4375, y: 0.5625 },
{ x: 0.5625, y: 0.5625 },
{ x: 0.5625, y: 0.5625 },
{ x: 0.5625, y: 0.5625 },
{ x: 0.5625, y: 0.5625 },
{ x: 0.5625, y: 0.5625 },
{ x: 0.5625, y: 0.5625 },
{ x: 0.6875, y: 0.5625 },
{ x: 0.6875, y: 0.5625 },
{ x: 0.6875, y: 0.5625 },
{ x: 0.6875, y: 0.5625 },
{ x: 0.6875, y: 0.5625 },
{ x: 0.6875, y: 0.5625 },
{ x: 0.8125, y: 0.5625 },
{ x: 0.8125, y: 0.5625 },
{ x: 0.8125, y: 0.5625 },
{ x: 0.8125, y: 0.5625 },
{ x: 0.8125, y: 0.5625 },
{ x: 0.8125, y: 0.5625 },
{ x: 0.9375, y: 0.5625 },
{ x: 0.9375, y: 0.5625 },
{ x: 0.9375, y: 0.5625 },
{ x: 0.9375, y: 0.5625 },
{ x: 0.9375, y: 0.5625 },
{ x: 0.9375, y: 0.5625 },
{ x: 0.0625, y: 0.6875 },
{ x: 0.0625, y: 0.6875 },
{ x: 0.0625, y: 0.6875 },
{ x: 0.0625, y: 0.6875 },
{ x: 0.0625, y: 0.6875 },
{ x: 0.0625, y: 0.6875 },
{ x: 0.1875, y: 0.6875 },
{ x: 0.1875, y: 0.6875 },
{ x: 0.1875, y: 0.6875 },
{ x: 0.1875, y: 0.6875 },
{ x: 0.1875, y: 0.6875 },
{ x: 0.1875, y: 0.6875 },
{ x: 0.3125, y: 0.6875 },
{ x: 0.3125, y: 0.6875 },
{ x: 0.3125, y: 0.6875 },
{ x: 0.3125, y: 0.6875 },
{ x: 0.3125, y: 0.6875 },
{ x: 0.3125, y: 0.6875 },
{ x: 0.4375, y: 0.6875 },
{ x: 0.4375, y: 0.6875 },
{ x: 0.4375, y: 0.6875 },
{ x: 0.4375, y: 0.6875 },
{ x: 0.4375, y: 0.6875 },
{ x: 0.4375, y: 0.6875 },
{ x: 0.5625, y: 0.6875 },
{ x: 0.5625, y: 0.6875 },
{ x: 0.5625, y: 0.6875 },
{ x: 0.5625, y: 0.6875 },
{ x: 0.5625, y: 0.6875 },
{ x: 0.5625, y: 0.6875 },
{ x: 0.6875, y: 0.6875 },
{ x: 0.6875, y: 0.6875 },
{ x: 0.6875, y: 0.6875 },
{ x: 0.6875, y: 0.6875 },
{ x: 0.6875, y: 0.6875 },
{ x: 0.6875, y: 0.6875 },
{ x: 0.8125, y: 0.6875 },
{ x: 0.8125, y: 0.6875 },
{ x: 0.8125, y: 0.6875 },
{ x: 0.8125, y: 0.6875 },
{ x: 0.8125, y: 0.6875 },
{ x: 0.8125, y: 0.6875 },
{ x: 0.9375, y: 0.6875 },
{ x: 0.9375, y: 0.6875 },
{ x: 0.9375, y: 0.6875 },
{ x: 0.9375, y: 0.6875 },
{ x: 0.9375, y: 0.6875 },
{ x: 0.9375, y: 0.6875 },
{ x: 0.0625, y: 0.8125 },
{ x: 0.0625, y: 0.8125 },
{ x: 0.0625, y: 0.8125 },
{ x: 0.0625, y: 0.8125 },
{ x: 0.0625, y: 0.8125 },
{ x: 0.0625, y: 0.8125 },
{ x: 0.1875, y: 0.8125 },
{ x: 0.1875, y: 0.8125 },
{ x: 0.1875, y: 0.8125 },
{ x: 0.1875, y: 0.8125 },
{ x: 0.1875, y: 0.8125 },
{ x: 0.1875, y: 0.8125 },
{ x: 0.3125, y: 0.8125 },
{ x: 0.3125, y: 0.8125 },
{ x: 0.3125, y: 0.8125 },
{ x: 0.3125, y: 0.8125 },
{ x: 0.3125, y: 0.8125 },
{ x: 0.3125, y: 0.8125 },
{ x: 0.4375, y: 0.8125 },
{ x: 0.4375, y: 0.8125 },
{ x: 0.4375, y: 0.8125 },
{ x: 0.4375, y: 0.8125 },
{ x: 0.4375, y: 0.8125 },
{ x: 0.4375, y: 0.8125 },
{ x: 0.5625, y: 0.8125 },
{ x: 0.5625, y: 0.8125 },
{ x: 0.5625, y: 0.8125 },
{ x: 0.5625, y: 0.8125 },
{ x: 0.5625, y: 0.8125 },
{ x: 0.5625, y: 0.8125 },
{ x: 0.6875, y: 0.8125 },
{ x: 0.6875, y: 0.8125 },
{ x: 0.6875, y: 0.8125 },
{ x: 0.6875, y: 0.8125 },
{ x: 0.6875, y: 0.8125 },
{ x: 0.6875, y: 0.8125 },
{ x: 0.8125, y: 0.8125 },
{ x: 0.8125, y: 0.8125 },
{ x: 0.8125, y: 0.8125 },
{ x: 0.8125, y: 0.8125 },
{ x: 0.8125, y: 0.8125 },
{ x: 0.8125, y: 0.8125 },
{ x: 0.9375, y: 0.8125 },
{ x: 0.9375, y: 0.8125 },
{ x: 0.9375, y: 0.8125 },
{ x: 0.9375, y: 0.8125 },
{ x: 0.9375, y: 0.8125 },
{ x: 0.9375, y: 0.8125 },
{ x: 0.0625, y: 0.9375 },
{ x: 0.0625, y: 0.9375 },
{ x: 0.0625, y: 0.9375 },
{ x: 0.0625, y: 0.9375 },
{ x: 0.0625, y: 0.9375 },
{ x: 0.0625, y: 0.9375 },
{ x: 0.1875, y: 0.9375 },
{ x: 0.1875, y: 0.9375 },
{ x: 0.1875, y: 0.9375 },
{ x: 0.1875, y: 0.9375 },
{ x: 0.1875, y: 0.9375 },
{ x: 0.1875, y: 0.9375 },
{ x: 0.3125, y: 0.9375 },
{ x: 0.3125, y: 0.9375 },
{ x: 0.3125, y: 0.9375 },
{ x: 0.3125, y: 0.9375 },
{ x: 0.3125, y: 0.9375 },
{ x: 0.3125, y: 0.9375 },
{ x: 0.4375, y: 0.9375 },
{ x: 0.4375, y: 0.9375 },
{ x: 0.4375, y: 0.9375 },
{ x: 0.4375, y: 0.9375 },
{ x: 0.4375, y: 0.9375 },
{ x: 0.4375, y: 0.9375 },
{ x: 0.5625, y: 0.9375 },
{ x: 0.5625, y: 0.9375 },
{ x: 0.5625, y: 0.9375 },
{ x: 0.5625, y: 0.9375 },
{ x: 0.5625, y: 0.9375 },
{ x: 0.5625, y: 0.9375 },
{ x: 0.6875, y: 0.9375 },
{ x: 0.6875, y: 0.9375 },
{ x: 0.6875, y: 0.9375 },
{ x: 0.6875, y: 0.9375 },
{ x: 0.6875, y: 0.9375 },
{ x: 0.6875, y: 0.9375 },
{ x: 0.8125, y: 0.9375 },
{ x: 0.8125, y: 0.9375 },
{ x: 0.8125, y: 0.9375 },
{ x: 0.8125, y: 0.9375 },
{ x: 0.8125, y: 0.9375 },
{ x: 0.8125, y: 0.9375 },
{ x: 0.9375, y: 0.9375 },
{ x: 0.9375, y: 0.9375 },
{ x: 0.9375, y: 0.9375 },
{ x: 0.9375, y: 0.9375 },
{ x: 0.9375, y: 0.9375 },
{ x: 0.9375, y: 0.9375 }
];
// src/hand/handposedetector.ts
var HandDetector = class {
constructor(model23) {
__publicField(this, "model");
__publicField(this, "anchors");
__publicField(this, "anchorsTensor");
__publicField(this, "inputSize");
__publicField(this, "inputSizeTensor");
__publicField(this, "doubleInputSizeTensor");
var _a, _b, _c, _d;
this.model = model23;
this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]);
this.anchorsTensor = tf27.tensor2d(this.anchors);
this.inputSize = ((_d = (_c = (_b = (_a = this == null ? void 0 : this.model) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0]) == null ? void 0 : _c.shape) == null ? void 0 : _d[2]) || 0;
this.inputSizeTensor = tf27.tensor1d([this.inputSize, this.inputSize]);
this.doubleInputSizeTensor = tf27.tensor1d([this.inputSize * 2, this.inputSize * 2]);
}
normalizeBoxes(boxes) {
const t2 = {};
t2.boxOffsets = tf27.slice(boxes, [0, 0], [-1, 2]);
t2.boxSizes = tf27.slice(boxes, [0, 2], [-1, 2]);
t2.div = tf27.div(t2.boxOffsets, this.inputSizeTensor);
t2.boxCenterPoints = tf27.add(t2.div, this.anchorsTensor);
t2.halfBoxSizes = tf27.div(t2.boxSizes, this.doubleInputSizeTensor);
t2.sub = tf27.sub(t2.boxCenterPoints, t2.halfBoxSizes);
t2.startPoints = tf27.mul(t2.sub, this.inputSizeTensor);
t2.add = tf27.add(t2.boxCenterPoints, t2.halfBoxSizes);
t2.endPoints = tf27.mul(t2.add, this.inputSizeTensor);
const res = tf27.concat2d([t2.startPoints, t2.endPoints], 1);
Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6]));
return res;
}
normalizeLandmarks(rawPalmLandmarks, index2) {
const t2 = {};
t2.reshape = tf27.reshape(rawPalmLandmarks, [-1, 7, 2]);
t2.div = tf27.div(t2.reshape, this.inputSizeTensor);
t2.landmarks = tf27.add(t2.div, this.anchors[index2] ? this.anchors[index2] : 0);
const res = tf27.mul(t2.landmarks, this.inputSizeTensor);
Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6]));
return res;
}
async predict(input, config3) {
var _a;
const t2 = {};
t2.resize = tf27.image.resizeBilinear(input, [this.inputSize, this.inputSize]);
t2.div = tf27.div(t2.resize, constants.tf127);
t2.image = tf27.sub(t2.div, constants.tf1);
t2.batched = this.model.execute(t2.image);
t2.predictions = tf27.squeeze(t2.batched);
t2.slice = tf27.slice(t2.predictions, [0, 0], [-1, 1]);
t2.sigmoid = tf27.sigmoid(t2.slice);
t2.scores = tf27.squeeze(t2.sigmoid);
const scores = await t2.scores.data();
t2.boxes = tf27.slice(t2.predictions, [0, 1], [-1, 4]);
t2.norm = this.normalizeBoxes(t2.boxes);
t2.nms = await tf27.image.nonMaxSuppressionAsync(t2.norm, t2.scores, 3 * (((_a = config3.hand) == null ? void 0 : _a.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence);
const nms = await t2.nms.array();
const hands = [];
for (const index2 of nms) {
const p = {};
p.box = tf27.slice(t2.norm, [index2, 0], [1, -1]);
p.slice = tf27.slice(t2.predictions, [index2, 5], [1, 14]);
p.norm = this.normalizeLandmarks(p.slice, index2);
p.palmLandmarks = tf27.reshape(p.norm, [-1, 2]);
const box = await p.box.data();
const startPoint = box.slice(0, 2);
const endPoint = box.slice(2, 4);
const palmLandmarks = await p.palmLandmarks.array();
const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] };
const scaled = scaleBoxCoordinates2(hand3, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]);
hands.push(scaled);
Object.keys(p).forEach((tensor6) => tf27.dispose(p[tensor6]));
}
Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6]));
return hands;
}
};
// src/hand/handposepipeline.ts
var tf28 = __toESM(require_tfjs_esm());
var palmBoxEnlargeFactor = 5;
var handBoxEnlargeFactor = 1.65;
var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];
var palmLandmarksPalmBase = 0;
var palmLandmarksMiddleFingerBase = 2;
var lastTime13 = 0;
var HandPipeline = class {
constructor(handDetector, handPoseModel2) {
__publicField(this, "handDetector");
__publicField(this, "handPoseModel");
__publicField(this, "inputSize");
__publicField(this, "storedBoxes");
__publicField(this, "skipped");
__publicField(this, "detectedHands");
var _a, _b, _c;
this.handDetector = handDetector;
this.handPoseModel = handPoseModel2;
this.inputSize = ((_c = (_b = (_a = this.handPoseModel) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c[2]) || 0;
this.storedBoxes = [];
this.skipped = Number.MAX_SAFE_INTEGER;
this.detectedHands = 0;
}
calculateLandmarksBoundingBox(landmarks) {
const xs = landmarks.map((d) => d[0]);
const ys = landmarks.map((d) => d[1]);
const startPoint = [Math.min(...xs), Math.min(...ys)];
const endPoint = [Math.max(...xs), Math.max(...ys)];
return { startPoint, endPoint };
}
getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {
const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix));
const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);
return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor);
}
getBoxForHandLandmarks(landmarks) {
const boundingBox = this.calculateLandmarksBoundingBox(landmarks);
const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor);
boxAroundHand.palmLandmarks = [];
for (let i = 0; i < palmLandmarkIds.length; i++) {
boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));
}
return boxAroundHand;
}
transformRawCoords(rawCoords, box2, angle, rotationMatrix) {
const boxSize = getBoxSize2(box2);
const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];
const coordsScaled = rawCoords.map((coord) => [
scaleFactor[0] * (coord[0] - this.inputSize / 2),
scaleFactor[1] * (coord[1] - this.inputSize / 2),
scaleFactor[2] * coord[2]
]);
const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]);
const coordsRotated = coordsScaled.map((coord) => {
const rotated = rotatePoint2(coord, coordsRotationMatrix);
return [...rotated, coord[2]];
});
const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix);
const boxCenter = [...getBoxCenter2(box2), 1];
const originalBoxCenter = [
dot2(boxCenter, inverseRotationMatrix[0]),
dot2(boxCenter, inverseRotationMatrix[1])
];
return coordsRotated.map((coord) => [
Math.trunc(coord[0] + originalBoxCenter[0]),
Math.trunc(coord[1] + originalBoxCenter[1]),
Math.trunc(coord[2])
]);
}
async estimateHands(image28, config3) {
let useFreshBox = false;
let boxes;
const skipTime = (config3.hand.skipTime || 0) > now() - lastTime13;
const skipFrame = this.skipped < (config3.hand.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame) {
boxes = await this.handDetector.predict(image28, config3);
this.skipped = 0;
}
if (config3.skipAllowed)
this.skipped++;
if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) {
this.detectedHands = 0;
this.storedBoxes = [...boxes];
if (this.storedBoxes.length > 0)
useFreshBox = true;
}
const hands = [];
for (let i = 0; i < this.storedBoxes.length; i++) {
const currentBox = this.storedBoxes[i];
if (!currentBox)
continue;
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] / image28.shape[2], palmCenter[1] / image28.shape[1]];
const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf28.image.rotateWithOffset(image28, angle, 0, palmCenterNormalized) : image28.clone();
const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);
const handImage = tf28.div(croppedInput, constants.tf255);
tf28.dispose(croppedInput);
tf28.dispose(rotatedImage);
const [confidenceT, keypoints] = this.handPoseModel.execute(handImage);
lastTime13 = now();
tf28.dispose(handImage);
const confidence = (await confidenceT.data())[0];
tf28.dispose(confidenceT);
if (confidence >= config3.hand.minConfidence / 4) {
const keypointsReshaped = tf28.reshape(keypoints, [-1, 3]);
const rawCoords = await keypointsReshaped.array();
tf28.dispose(keypoints);
tf28.dispose(keypointsReshaped);
const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);
const nextBoundingBox = this.getBoxForHandLandmarks(coords);
this.storedBoxes[i] = { ...nextBoundingBox, confidence };
const result = {
landmarks: coords,
confidence,
boxConfidence: currentBox.confidence,
fingerConfidence: confidence,
box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint }
};
hands.push(result);
} else {
this.storedBoxes[i] = null;
}
tf28.dispose(keypoints);
} else {
const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor);
const result = {
confidence: currentBox.confidence,
boxConfidence: currentBox.confidence,
fingerConfidence: 0,
box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },
landmarks: []
};
hands.push(result);
}
}
this.storedBoxes = this.storedBoxes.filter((a) => a !== null);
this.detectedHands = hands.length;
if (hands.length > config3.hand.maxDetected)
hands.length = config3.hand.maxDetected;
return hands;
}
};
// src/hand/handpose.ts
var meshAnnotations2 = {
thumb: [1, 2, 3, 4],
index: [5, 6, 7, 8],
middle: [9, 10, 11, 12],
ring: [13, 14, 15, 16],
pinky: [17, 18, 19, 20],
palm: [0]
};
var handDetectorModel;
var handPoseModel;
var handPipeline;
async function predict14(input, config3) {
const predictions = await handPipeline.estimateHands(input, config3);
if (!predictions)
return [];
const hands = [];
for (let i = 0; i < predictions.length; i++) {
const annotations2 = {};
if (predictions[i].landmarks) {
for (const key of Object.keys(meshAnnotations2)) {
annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i].landmarks[index2]);
}
}
const keypoints = predictions[i].landmarks;
let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0];
let boxRaw = [0, 0, 0, 0];
if (keypoints && keypoints.length > 0) {
for (const pt of keypoints) {
if (pt[0] < box[0])
box[0] = pt[0];
if (pt[1] < box[1])
box[1] = pt[1];
if (pt[0] > box[2])
box[2] = pt[0];
if (pt[1] > box[3])
box[3] = pt[1];
}
box[2] -= box[0];
box[3] -= box[1];
boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];
} else {
box = predictions[i].box ? [
Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),
Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),
Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),
Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1]))
] : [0, 0, 0, 0];
boxRaw = [
predictions[i].box.topLeft[0] / (input.shape[2] || 0),
predictions[i].box.topLeft[1] / (input.shape[1] || 0),
(predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),
(predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0)
];
}
const landmarks = analyze(keypoints);
hands.push({
id: i,
score: Math.round(100 * predictions[i].confidence) / 100,
boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,
fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,
label: "hand",
box,
boxRaw,
keypoints,
annotations: annotations2,
landmarks
});
}
return hands;
}
async function load15(config3) {
var _a, _b;
if (env.initial) {
handDetectorModel = null;
handPoseModel = null;
}
if (!handDetectorModel || !handPoseModel) {
[handDetectorModel, handPoseModel] = await Promise.all([
config3.hand.enabled ? loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath) : null,
config3.hand.landmarks ? loadModel((_b = config3.hand.skeleton) == null ? void 0 : _b.modelPath) : null
]);
} else {
if (config3.debug)
log("cached model:", handDetectorModel["modelUrl"]);
if (config3.debug)
log("cached model:", handPoseModel["modelUrl"]);
}
const handDetector = handDetectorModel ? new HandDetector(handDetectorModel) : void 0;
if (handDetector && handPoseModel)
handPipeline = new HandPipeline(handDetector, handPoseModel);
return [handDetectorModel, handPoseModel];
}
// src/hand/handtrack.ts
var tf29 = __toESM(require_tfjs_esm());
var models2 = [null, null];
var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"];
var inputSize7 = [[0, 0], [0, 0]];
var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"];
var faceIndex = 4;
var boxExpandFact = 1.6;
var maxDetectorResolution = 512;
var detectorExpandFact = 1.4;
var skipped13 = Number.MAX_SAFE_INTEGER;
var lastTime14 = 0;
var outputSize = [0, 0];
var cache4 = {
boxes: [],
hands: []
};
var fingerMap = {
thumb: [1, 2, 3, 4],
index: [5, 6, 7, 8],
middle: [9, 10, 11, 12],
ring: [13, 14, 15, 16],
pinky: [17, 18, 19, 20],
base: [0],
palm: [0, 17, 13, 9, 5, 1, 0]
};
async function loadDetect2(config3) {
var _a;
if (env.initial)
models2[0] = null;
if (!models2[0]) {
fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3);
models2[0] = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath);
const inputs = models2[0]["executor"] ? Object.values(models2[0].modelSignature["inputs"]) : void 0;
inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
} else if (config3.debug)
log("cached model:", models2[0]["modelUrl"]);
return models2[0];
}
async function loadSkeleton(config3) {
var _a;
if (env.initial)
models2[1] = null;
if (!models2[1]) {
models2[1] = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath);
const inputs = models2[1]["executor"] ? Object.values(models2[1].modelSignature["inputs"]) : void 0;
inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
} else if (config3.debug)
log("cached model:", models2[1]["modelUrl"]);
return models2[1];
}
async function detectHands(input, config3) {
const hands = [];
if (!input || !models2[0])
return hands;
const t2 = {};
const ratio2 = (input.shape[2] || 1) / (input.shape[1] || 1);
const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution);
const width = Math.round(height * ratio2 / 8) * 8;
t2.resize = tf29.image.resizeBilinear(input, [height, width]);
t2.cast = tf29.cast(t2.resize, "int32");
[t2.rawScores, t2.rawBoxes] = await models2[0].executeAsync(t2.cast, modelOutputNodes);
t2.boxes = tf29.squeeze(t2.rawBoxes, [0, 2]);
t2.scores = tf29.squeeze(t2.rawScores, [0]);
const classScores = tf29.unstack(t2.scores, 1);
tf29.dispose(classScores[faceIndex]);
classScores.splice(faceIndex, 1);
t2.filtered = tf29.stack(classScores, 1);
tf29.dispose(classScores);
t2.max = tf29.max(t2.filtered, 1);
t2.argmax = tf29.argMax(t2.filtered, 1);
let id = 0;
t2.nms = await tf29.image.nonMaxSuppressionAsync(t2.boxes, t2.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1);
const nms = await t2.nms.data();
const scores = await t2.max.data();
const classNum = await t2.argmax.data();
for (const nmsIndex of Array.from(nms)) {
const boxSlice = tf29.slice(t2.boxes, nmsIndex, 1);
const boxYX = await boxSlice.data();
tf29.dispose(boxSlice);
const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]];
const boxRaw = scale(boxData, detectorExpandFact);
const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];
const score = scores[nmsIndex];
const label = classes[classNum[nmsIndex]];
const hand3 = { id: id++, score, box: boxFull, boxRaw, label };
hands.push(hand3);
}
Object.keys(t2).forEach((tensor6) => tf29.dispose(t2[tensor6]));
hands.sort((a, b) => b.score - a.score);
if (hands.length > (config3.hand.maxDetected || 1))
hands.length = config3.hand.maxDetected || 1;
return hands;
}
async function detectFingers(input, h, config3) {
const hand3 = {
id: h.id,
score: Math.round(100 * h.score) / 100,
boxScore: Math.round(100 * h.score) / 100,
fingerScore: 0,
box: h.box,
boxRaw: h.boxRaw,
label: h.label,
keypoints: [],
landmarks: {},
annotations: {}
};
if (input && models2[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) {
const t2 = {};
const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]];
t2.crop = tf29.image.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear");
t2.div = tf29.div(t2.crop, constants.tf255);
[t2.score, t2.keypoints] = models2[1].execute(t2.div, ["Identity_1", "Identity"]);
const rawScore = (await t2.score.data())[0];
const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100;
if (score >= (config3.hand.minConfidence || 0)) {
hand3.fingerScore = score;
t2.reshaped = tf29.reshape(t2.keypoints, [-1, 3]);
const coordsData = await t2.reshaped.array();
const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]);
const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]);
hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]);
hand3.landmarks = analyze(hand3.keypoints);
for (const key of Object.keys(fingerMap)) {
hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null);
}
}
Object.keys(t2).forEach((tensor6) => tf29.dispose(t2[tensor6]));
}
return hand3;
}
async function predict15(input, config3) {
var _a, _b;
if (!((_a = models2[0]) == null ? void 0 : _a["executor"]) || !((_b = models2[1]) == null ? void 0 : _b["executor"]) || !models2[0].inputs[0].shape || !models2[1].inputs[0].shape)
return [];
outputSize = [input.shape[2] || 0, input.shape[1] || 0];
skipped13++;
const skipTime = (config3.hand.skipTime || 0) > now() - lastTime14;
const skipFrame = skipped13 < (config3.hand.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame) {
return cache4.hands;
}
return new Promise(async (resolve) => {
const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime14;
const skipFrameExtended = skipped13 < 3 * (config3.hand.skipFrames || 0);
if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) {
cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
} else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) {
cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
} else {
cache4.boxes = await detectHands(input, config3);
lastTime14 = now();
cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
skipped13 = 0;
}
const oldCache = [...cache4.boxes];
cache4.boxes.length = 0;
if (config3.cacheSensitivity > 0) {
for (let i = 0; i < cache4.hands.length; i++) {
const boxKpt = square(cache4.hands[i].keypoints, outputSize);
if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache4.hands[i].fingerScore && cache4.hands[i].fingerScore > (config3.hand.minConfidence || 0)) {
const boxScale = scale(boxKpt.box, boxExpandFact);
const boxScaleRaw = scale(boxKpt.boxRaw, boxExpandFact);
cache4.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });
}
}
}
for (let i = 0; i < cache4.hands.length; i++) {
const bbox = calc(cache4.hands[i].keypoints, outputSize);
cache4.hands[i].box = bbox.box;
cache4.hands[i].boxRaw = bbox.boxRaw;
}
resolve(cache4.hands);
});
}
// src/result.ts
var empty = (error = null) => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error });
// src/body/movenetcoords.ts
var movenetcoords_exports = {};
__export(movenetcoords_exports, {
connected: () => connected3,
horizontal: () => horizontal,
kpt: () => kpt3,
relative: () => relative,
vertical: () => vertical
});
var kpt3 = [
"nose",
"leftEye",
"rightEye",
"leftEar",
"rightEar",
"leftShoulder",
"rightShoulder",
"leftElbow",
"rightElbow",
"leftWrist",
"rightWrist",
"leftHip",
"rightHip",
"leftKnee",
"rightKnee",
"leftAnkle",
"rightAnkle"
];
var horizontal = [
["leftEye", "rightEye"],
["leftEar", "rightEar"],
["leftShoulder", "rightShoulder"],
["leftElbow", "rightElbow"],
["leftWrist", "rightWrist"],
["leftHip", "rightHip"],
["leftKnee", "rightKnee"],
["leftAnkle", "rightAnkle"]
];
var vertical = [
["leftKnee", "leftShoulder"],
["rightKnee", "rightShoulder"],
["leftAnkle", "leftKnee"],
["rightAnkle", "rightKnee"]
];
var relative = [
[["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]],
[["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]]
];
var connected3 = {
leftLeg: ["leftHip", "leftKnee", "leftAnkle"],
rightLeg: ["rightHip", "rightKnee", "rightAnkle"],
torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"],
leftArm: ["leftShoulder", "leftElbow", "leftWrist"],
rightArm: ["rightShoulder", "rightElbow", "rightWrist"],
head: []
};
// src/util/interpolate.ts
var bufferedResult = empty();
var interpolateTime = 0;
function calc2(newResult, config3) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w;
const t0 = now();
if (!newResult)
return empty();
const elapsed = Date.now() - newResult.timestamp;
const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1;
if (newResult.canvas)
bufferedResult.canvas = newResult.canvas;
if (newResult.error)
bufferedResult.error = newResult.error;
if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) {
bufferedResult.body = JSON.parse(JSON.stringify(newResult.body));
} else {
for (let i = 0; i < newResult.body.length; i++) {
const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor);
const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor);
const keypoints = newResult.body[i].keypoints.map((newKpt, j) => {
var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2;
return {
score: newKpt.score,
part: newKpt.part,
position: [
bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],
bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],
bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2]
],
positionRaw: [
bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],
bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],
bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2]
],
distance: [
bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a2[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0],
bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d2[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1],
bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g2[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2]
]
};
});
const annotations2 = {};
let coords = { connected: {} };
if ((_a = config3.body.modelPath) == null ? void 0 : _a.includes("efficientpose"))
coords = efficientposecoords_exports;
else if ((_b = config3.body.modelPath) == null ? void 0 : _b.includes("blazepose"))
coords = blazeposecoords_exports;
else if ((_c = config3.body.modelPath) == null ? void 0 : _c.includes("movenet"))
coords = movenetcoords_exports;
for (const [name, indexes] of Object.entries(coords.connected)) {
const pt = [];
for (let j = 0; j < indexes.length - 1; j++) {
const pt0 = keypoints.find((kp) => kp.part === indexes[j]);
const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);
if (pt0 && pt1)
pt.push([pt0.position, pt1.position]);
}
annotations2[name] = pt;
}
bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations2 };
}
}
if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) {
bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand));
} else {
for (let i = 0; i < newResult.hand.length; i++) {
const box = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor);
const boxRaw = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor);
if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length)
bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints;
const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : [];
let annotations2 = {};
if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {
bufferedResult.hand[i].annotations = newResult.hand[i].annotations;
annotations2 = bufferedResult.hand[i].annotations;
} else if (newResult.hand[i].annotations) {
for (const key of Object.keys(newResult.hand[i].annotations)) {
annotations2[key] = ((_f = (_e = (_d = newResult.hand[i]) == null ? void 0 : _d.annotations) == null ? void 0 : _e[key]) == null ? void 0 : _f[0]) ? newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor)) : null;
}
}
bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 };
}
}
if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) {
bufferedResult.face = JSON.parse(JSON.stringify(newResult.face));
} else {
for (let i = 0; i < newResult.face.length; i++) {
const box = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor);
const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor);
if (newResult.face[i].rotation) {
const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };
rotation.matrix = (_g = newResult.face[i].rotation) == null ? void 0 : _g.matrix;
rotation.angle = {
roll: ((bufferedFactor - 1) * (((_i = (_h = bufferedResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k = (_j = newResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor,
yaw: ((bufferedFactor - 1) * (((_m = (_l = bufferedResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.yaw) || 0) + (((_o = (_n = newResult.face[i].rotation) == null ? void 0 : _n.angle) == null ? void 0 : _o.yaw) || 0)) / bufferedFactor,
pitch: ((bufferedFactor - 1) * (((_q = (_p = bufferedResult.face[i].rotation) == null ? void 0 : _p.angle) == null ? void 0 : _q.pitch) || 0) + (((_s = (_r = newResult.face[i].rotation) == null ? void 0 : _r.angle) == null ? void 0 : _s.pitch) || 0)) / bufferedFactor
};
rotation.gaze = {
bearing: ((bufferedFactor - 1) * (((_t = bufferedResult.face[i].rotation) == null ? void 0 : _t.gaze.bearing) || 0) + (((_u = newResult.face[i].rotation) == null ? void 0 : _u.gaze.bearing) || 0)) / bufferedFactor,
strength: ((bufferedFactor - 1) * (((_v = bufferedResult.face[i].rotation) == null ? void 0 : _v.gaze.strength) || 0) + (((_w = newResult.face[i].rotation) == null ? void 0 : _w.gaze.strength) || 0)) / bufferedFactor
};
bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw };
} else {
bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw };
}
}
}
if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) {
bufferedResult.object = JSON.parse(JSON.stringify(newResult.object));
} else {
for (let i = 0; i < newResult.object.length; i++) {
const box = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor);
const boxRaw = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor);
bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw };
}
}
if (newResult.persons) {
const newPersons = newResult.persons;
if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) {
bufferedResult.persons = JSON.parse(JSON.stringify(newPersons));
} else {
for (let i = 0; i < newPersons.length; i++) {
bufferedResult.persons[i].box = newPersons[i].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor);
}
}
}
if (newResult.gesture)
bufferedResult.gesture = newResult.gesture;
bufferedResult.width = newResult.width;
bufferedResult.height = newResult.height;
const t1 = now();
interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);
if (newResult.performance)
bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };
return bufferedResult;
}
// src/segmentation/meet.ts
var tf30 = __toESM(require_tfjs_esm());
var model17;
async function load16(config3) {
if (!model17 || env.initial)
model17 = await loadModel(config3.segmentation.modelPath);
else if (config3.debug)
log("cached model:", model17["modelUrl"]);
return model17;
}
async function predict16(input, config3) {
var _a;
if (!model17)
model17 = await load16(config3);
if (!(model17 == null ? void 0 : model17["executor"]) || !((_a = model17 == null ? void 0 : model17.inputs) == null ? void 0 : _a[0].shape))
return null;
const t2 = {};
t2.resize = tf30.image.resizeBilinear(input, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false);
t2.norm = tf30.div(t2.resize, constants.tf255);
t2.res = model17.execute(t2.norm);
t2.squeeze = tf30.squeeze(t2.res, [0]);
[t2.bgRaw, t2.fgRaw] = tf30.unstack(t2.squeeze, 2);
t2.fg = tf30.softmax(t2.fgRaw);
t2.mul = tf30.mul(t2.fg, constants.tf255);
t2.expand = tf30.expandDims(t2.mul, 2);
t2.output = tf30.image.resizeBilinear(t2.expand, [input.shape[1] || 0, input.shape[2] || 0]);
let rgba;
switch (config3.segmentation.mode || "default") {
case "default":
t2.input = tf30.squeeze(input);
t2.concat = tf30.concat([t2.input, t2.output], -1);
rgba = tf30.cast(t2.concat, "int32");
break;
case "alpha":
rgba = tf30.cast(t2.output, "int32");
break;
default:
rgba = tf30.tensor(0);
}
Object.keys(t2).forEach((tensor6) => tf30.dispose(t2[tensor6]));
return rgba;
}
// src/face/match.ts
var match_exports = {};
__export(match_exports, {
distance: () => distance,
find: () => find,
similarity: () => similarity
});
function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25 }) {
if (!descriptor1 || !descriptor1)
return Number.MAX_SAFE_INTEGER;
let sum3 = 0;
for (let i = 0; i < descriptor1.length; i++) {
const diff = !options4.order || options4.order === 2 ? descriptor1[i] - descriptor2[i] : Math.abs(descriptor1[i] - descriptor2[i]);
sum3 += !options4.order || options4.order === 2 ? diff * diff : diff ** options4.order;
}
return (options4.multiplier || 20) * sum3;
}
var normalizeDistance = (dist, order, min2, max5) => {
if (dist === 0)
return 1;
const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order);
const norm = (1 - root / 100 - min2) / (max5 - min2);
const clamp2 = Math.max(Math.min(norm, 1), 0);
return clamp2;
};
function similarity(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {
const dist = distance(descriptor1, descriptor2, options4);
return normalizeDistance(dist, options4.order || 2, options4.min || 0, options4.max || 1);
}
function find(descriptor, descriptors, options4 = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {
if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) {
return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };
}
let lowestDistance = Number.MAX_SAFE_INTEGER;
let index2 = -1;
for (let i = 0; i < descriptors.length; i++) {
const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options4) : Number.MAX_SAFE_INTEGER;
if (res < lowestDistance) {
lowestDistance = res;
index2 = i;
}
if (lowestDistance < (options4.threshold || 0))
break;
}
const normalizedSimilarity = normalizeDistance(lowestDistance, options4.order || 2, options4.min || 0, options4.max || 1);
return { index: index2, distance: lowestDistance, similarity: normalizedSimilarity };
}
// src/models.ts
var models_exports2 = {};
__export(models_exports2, {
Models: () => Models,
validateModel: () => validateModel
});
// src/body/movenet.ts
var tf32 = __toESM(require_tfjs_esm());
// src/body/movenetfix.ts
var tf31 = __toESM(require_tfjs_esm());
var maxJitter = 5e-3;
var cache5 = {
keypoints: [],
padding: [[0, 0], [0, 0], [0, 0], [0, 0]]
};
function bodyParts(body4) {
for (const pair of horizontal) {
const left = body4.keypoints.findIndex((kp) => kp.part === pair[0]);
const right = body4.keypoints.findIndex((kp) => kp.part === pair[1]);
if (body4.keypoints[left] && body4.keypoints[right]) {
if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) {
const tmp = body4.keypoints[left];
body4.keypoints[left] = body4.keypoints[right];
body4.keypoints[right] = tmp;
}
}
}
for (const pair of vertical) {
const lower = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]);
const higher = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]);
if (body4.keypoints[lower] && body4.keypoints[higher]) {
if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) {
body4.keypoints.splice(lower, 1);
}
}
}
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 === 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] ? [
Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]),
Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0])
] : [0, 0];
const distanceRight = body4.keypoints[right] ? [
Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]),
Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0])
] : [0, 0];
if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) {
const tmp = body4.keypoints[left];
body4.keypoints[left] = body4.keypoints[right];
body4.keypoints[right] = tmp;
}
}
}
function jitter(keypoints) {
for (let i = 0; i < keypoints.length; i++) {
if (keypoints[i] && cache5.keypoints[i]) {
const diff = [Math.abs(keypoints[i].positionRaw[0] - cache5.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache5.keypoints[i].positionRaw[1])];
if (diff[0] < maxJitter && diff[1] < maxJitter) {
keypoints[i] = cache5.keypoints[i];
} else {
cache5.keypoints[i] = keypoints[i];
}
} else {
cache5.keypoints[i] = keypoints[i];
}
}
return keypoints;
}
function padInput(input, inputSize10) {
var _a, _b;
const t2 = {};
if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2]))
return input;
cache5.padding = [
[0, 0],
[input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0],
[input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0],
[0, 0]
];
t2.pad = tf31.pad(input, cache5.padding);
t2.resize = tf31.image.resizeBilinear(t2.pad, [inputSize10, inputSize10]);
const final = tf31.cast(t2.resize, "int32");
Object.keys(t2).forEach((tensor6) => tf31.dispose(t2[tensor6]));
return final;
}
function rescaleBody(body4, outputSize2) {
body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 == null ? void 0 : kpt4.position);
for (const kpt4 of body4.keypoints) {
kpt4.position = [
kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0],
kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0]
];
kpt4.positionRaw = [
kpt4.position[0] / outputSize2[0],
kpt4.position[1] / outputSize2[1]
];
}
const rescaledBoxes = calc(body4.keypoints.map((pt) => pt.position), outputSize2);
body4.box = rescaledBoxes.box;
body4.boxRaw = rescaledBoxes.boxRaw;
return body4;
}
// src/body/movenet.ts
var model18;
var inputSize8 = 0;
var skipped14 = Number.MAX_SAFE_INTEGER;
var cache6 = {
boxes: [],
bodies: [],
last: 0
};
async function load17(config3) {
var _a;
if (env.initial)
model18 = null;
if (!model18) {
fakeOps(["size"], config3);
model18 = await loadModel(config3.body.modelPath);
} else if (config3.debug)
log("cached model:", model18["modelUrl"]);
inputSize8 = (model18 == null ? void 0 : model18["executor"]) && ((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape) ? model18.inputs[0].shape[2] : 0;
if (inputSize8 < 64)
inputSize8 = 256;
return model18;
}
function parseSinglePose(res, config3, image28) {
const kpt4 = res[0][0];
const keypoints = [];
let score = 0;
for (let id = 0; id < kpt4.length; id++) {
score = kpt4[id][2];
if (score > config3.body.minConfidence) {
const positionRaw = [kpt4[id][1], kpt4[id][0]];
keypoints.push({
score: Math.round(100 * score) / 100,
part: kpt3[id],
positionRaw,
position: [
Math.round((image28.shape[2] || 0) * positionRaw[0]),
Math.round((image28.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), [image28.shape[2], image28.shape[1]]);
const annotations2 = {};
for (const [name, indexes] of Object.entries(connected3)) {
const pt = [];
for (let i = 0; i < indexes.length - 1; i++) {
const pt0 = keypoints.find((kp) => kp.part === indexes[i]);
const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);
if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
pt.push([pt0.position, pt1.position]);
}
annotations2[name] = pt;
}
const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 };
bodyParts(body4);
bodies.push(body4);
return bodies;
}
function parseMultiPose(res, config3, image28) {
const bodies = [];
for (let id = 0; id < res[0].length; id++) {
const kpt4 = res[0][id];
const totalScore = Math.round(100 * kpt4[51 + 4]) / 100;
if (totalScore > config3.body.minConfidence) {
const keypoints = [];
for (let i = 0; i < 17; i++) {
const score = kpt4[3 * i + 2];
if (score > config3.body.minConfidence) {
const positionRaw = [kpt4[3 * i + 1], kpt4[3 * i + 0]];
keypoints.push({
part: kpt3[i],
score: Math.round(100 * score) / 100,
positionRaw,
position: [Math.round((image28.shape[2] || 0) * positionRaw[0]), Math.round((image28.shape[1] || 0) * positionRaw[1])]
});
}
}
const newBox = calc(keypoints.map((pt) => pt.position), [image28.shape[2], image28.shape[1]]);
const annotations2 = {};
for (const [name, indexes] of Object.entries(connected3)) {
const pt = [];
for (let i = 0; i < indexes.length - 1; i++) {
const pt0 = keypoints.find((kp) => kp.part === indexes[i]);
const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);
if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
pt.push([pt0.position, pt1.position]);
}
annotations2[name] = pt;
}
const body4 = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations: annotations2 };
bodyParts(body4);
bodies.push(body4);
}
}
bodies.sort((a, b) => b.score - a.score);
if (bodies.length > config3.body.maxDetected)
bodies.length = config3.body.maxDetected;
return bodies;
}
async function predict17(input, config3) {
var _a;
if (!(model18 == null ? void 0 : model18["executor"]) || !((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape))
return [];
if (!config3.skipAllowed)
cache6.boxes.length = 0;
skipped14++;
const skipTime = (config3.body.skipTime || 0) > now() - cache6.last;
const skipFrame = skipped14 < (config3.body.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame) {
return cache6.bodies;
}
return new Promise(async (resolve) => {
const t2 = {};
skipped14 = 0;
t2.input = padInput(input, inputSize8);
t2.res = model18 == null ? void 0 : model18.execute(t2.input);
cache6.last = now();
const res = await t2.res.array();
cache6.bodies = t2.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input);
for (const body4 of cache6.bodies) {
rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]);
jitter(body4.keypoints);
}
Object.keys(t2).forEach((tensor6) => tf32.dispose(t2[tensor6]));
resolve(cache6.bodies);
});
}
// src/object/nanodet.ts
var tf33 = __toESM(require_tfjs_esm());
var model19;
var last10 = [];
var lastTime15 = 0;
var skipped15 = Number.MAX_SAFE_INTEGER;
var inputSize9 = 0;
var scaleBox = 2.5;
async function load18(config3) {
if (!model19 || env.initial) {
model19 = await loadModel(config3.object.modelPath);
const inputs = (model19 == null ? void 0 : model19["executor"]) ? Object.values(model19.modelSignature["inputs"]) : void 0;
inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;
} else if (config3.debug)
log("cached model:", model19["modelUrl"]);
return model19;
}
async function process4(res, outputShape, config3) {
var _a, _b;
let id = 0;
let results = [];
const size2 = inputSize9;
for (const strideSize of [1, 2, 4]) {
const baseSize = strideSize * 13;
const scoresT = tf33.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels2.length));
const scores = await scoresT.array();
const featuresT = tf33.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels2.length));
const boxesMaxT = tf33.reshape(featuresT, [-1, 4, (((_a = featuresT.shape) == null ? void 0 : _a[1]) || 0) / 4]);
const boxIdxT = tf33.argMax(boxesMaxT, 2);
const boxIdx = await boxIdxT.array();
for (let i = 0; i < scoresT.shape[0]; i++) {
for (let j = 0; j < (((_b = scoresT.shape) == null ? void 0 : _b[1]) || 0); j++) {
const score = scores[i][j];
if (score > (config3.object.minConfidence || 0) && j !== 61) {
const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize;
const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize;
const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / size2));
const [x, y] = [
cx - scaleBox / strideSize * boxOffset[0],
cy - scaleBox / strideSize * boxOffset[1]
];
const [w, h] = [
cx + scaleBox / strideSize * boxOffset[2] - x,
cy + scaleBox / strideSize * boxOffset[3] - y
];
let boxRaw = [x, y, w, h];
boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1)));
const box = [
boxRaw[0] * outputShape[0],
boxRaw[1] * outputShape[1],
boxRaw[2] * outputShape[0],
boxRaw[3] * outputShape[1]
];
const result = {
id: id++,
score: Math.round(100 * score) / 100,
class: j + 1,
label: labels2[j].label,
box: box.map((a) => Math.trunc(a)),
boxRaw
};
results.push(result);
}
}
}
tf33.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);
}
const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]);
const nmsScores = results.map((a) => a.score);
let nmsIdx = [];
if (nmsBoxes && nmsBoxes.length > 0) {
const nms = await tf33.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence);
nmsIdx = Array.from(await nms.data());
tf33.dispose(nms);
}
results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
return results;
}
async function predict18(image28, config3) {
if (!(model19 == null ? void 0 : model19["executor"]))
return [];
const skipTime = (config3.object.skipTime || 0) > now() - lastTime15;
const skipFrame = skipped15 < (config3.object.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame && last10.length > 0) {
skipped15++;
return last10;
}
skipped15 = 0;
if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense"))
return last10;
return new Promise(async (resolve) => {
const outputSize2 = [image28.shape[2] || 0, image28.shape[1] || 0];
const resizeT = tf33.image.resizeBilinear(image28, [inputSize9, inputSize9], false);
const normT = tf33.div(resizeT, constants.tf255);
const transposeT = tf33.transpose(normT, [0, 3, 1, 2]);
let objectT;
if (config3.object.enabled)
objectT = model19.execute(transposeT);
lastTime15 = now();
const obj = await process4(objectT, outputSize2, config3);
last10 = obj;
tf33.dispose([resizeT, normT, transposeT, ...objectT]);
resolve(obj);
});
}
// src/body/posenet.ts
var tf34 = __toESM(require_tfjs_esm());
// src/body/posenetutils.ts
var partNames = [
"nose",
"leftEye",
"rightEye",
"leftEar",
"rightEar",
"leftShoulder",
"rightShoulder",
"leftElbow",
"rightElbow",
"leftWrist",
"rightWrist",
"leftHip",
"rightHip",
"leftKnee",
"rightKnee",
"leftAnkle",
"rightAnkle"
];
var count = partNames.length;
var partIds = partNames.reduce((result, jointName, i) => {
result[jointName] = i;
return result;
}, {});
var connectedPartNames = [
["leftHip", "leftShoulder"],
["leftElbow", "leftShoulder"],
["leftElbow", "leftWrist"],
["leftHip", "leftKnee"],
["leftKnee", "leftAnkle"],
["rightHip", "rightShoulder"],
["rightElbow", "rightShoulder"],
["rightElbow", "rightWrist"],
["rightHip", "rightKnee"],
["rightKnee", "rightAnkle"],
["leftShoulder", "rightShoulder"],
["leftHip", "rightHip"]
];
var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]);
var poseChain = [
["nose", "leftEye"],
["leftEye", "leftEar"],
["nose", "rightEye"],
["rightEye", "rightEar"],
["nose", "leftShoulder"],
["leftShoulder", "leftElbow"],
["leftElbow", "leftWrist"],
["leftShoulder", "leftHip"],
["leftHip", "leftKnee"],
["leftKnee", "leftAnkle"],
["nose", "rightShoulder"],
["rightShoulder", "rightElbow"],
["rightElbow", "rightWrist"],
["rightShoulder", "rightHip"],
["rightHip", "rightKnee"],
["rightKnee", "rightAnkle"]
];
function getBoundingBox(keypoints) {
const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({
maxX: Math.max(maxX, x),
maxY: Math.max(maxY, y),
minX: Math.min(minX, x),
minY: Math.min(minY, y)
}), {
maxX: Number.NEGATIVE_INFINITY,
maxY: Number.NEGATIVE_INFINITY,
minX: Number.POSITIVE_INFINITY,
minY: Number.POSITIVE_INFINITY
});
return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];
}
function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {
const scaleY = height / inputResolutionHeight;
const scaleX = width / inputResolutionWidth;
const scalePose = (pose, i) => ({
id: i,
score: pose.score,
boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],
box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],
keypoints: pose.keypoints.map(({ score, part, position }) => ({
score,
part,
position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)],
positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight]
})),
annotations: {}
});
const scaledPoses = poses.map((pose, i) => scalePose(pose, i));
return scaledPoses;
}
var MaxHeap = class {
constructor(maxSize2, getElementValue) {
__publicField(this, "priorityQueue");
__publicField(this, "numberOfElements");
__publicField(this, "getElementValue");
this.priorityQueue = new Array(maxSize2);
this.numberOfElements = -1;
this.getElementValue = getElementValue;
}
enqueue(x) {
this.priorityQueue[++this.numberOfElements] = x;
this.swim(this.numberOfElements);
}
dequeue() {
const max5 = this.priorityQueue[0];
this.exchange(0, this.numberOfElements--);
this.sink(0);
this.priorityQueue[this.numberOfElements + 1] = null;
return max5;
}
empty() {
return this.numberOfElements === -1;
}
size() {
return this.numberOfElements + 1;
}
all() {
return this.priorityQueue.slice(0, this.numberOfElements + 1);
}
max() {
return this.priorityQueue[0];
}
swim(k) {
while (k > 0 && this.less(Math.floor(k / 2), k)) {
this.exchange(k, Math.floor(k / 2));
k = Math.floor(k / 2);
}
}
sink(k) {
while (2 * k <= this.numberOfElements) {
let j = 2 * k;
if (j < this.numberOfElements && this.less(j, j + 1))
j++;
if (!this.less(k, j))
break;
this.exchange(k, j);
k = j;
}
}
getValueAt(i) {
return this.getElementValue(this.priorityQueue[i]);
}
less(i, j) {
return this.getValueAt(i) < this.getValueAt(j);
}
exchange(i, j) {
const t2 = this.priorityQueue[i];
this.priorityQueue[i] = this.priorityQueue[j];
this.priorityQueue[j] = t2;
}
};
function getOffsetPoint(y, x, keypoint, offsets) {
return {
y: offsets.get(y, x, keypoint),
x: offsets.get(y, x, keypoint + count)
};
}
function getImageCoords(part, outputStride2, offsets) {
const { heatmapY, heatmapX, id: keypoint } = part;
const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);
return {
x: part.heatmapX * outputStride2 + x,
y: part.heatmapY * outputStride2 + y
};
}
function clamp(a, min2, max5) {
if (a < min2)
return min2;
if (a > max5)
return max5;
return a;
}
function squaredDistance(y1, x1, y2, x2) {
const dy = y2 - y1;
const dx = x2 - x1;
return dy * dy + dx * dx;
}
function addVectors(a, b) {
return { x: a.x + b.x, y: a.y + b.y };
}
// src/body/posenet.ts
var model20;
var poseNetOutputs = ["MobilenetV1/offset_2/BiasAdd", "MobilenetV1/heatmap_2/BiasAdd", "MobilenetV1/displacement_fwd_2/BiasAdd", "MobilenetV1/displacement_bwd_2/BiasAdd"];
var localMaximumRadius = 1;
var outputStride = 16;
var squaredNmsRadius = 50 ** 2;
function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {
const getDisplacement = (point2) => ({
y: displacements.get(point2.y, point2.x, edgeId),
x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId)
});
const getStridedIndexNearPoint = (point2, height2, width2) => ({
y: clamp(Math.round(point2.y / outputStride), 0, height2 - 1),
x: clamp(Math.round(point2.x / outputStride), 0, width2 - 1)
});
const [height, width] = scores.shape;
const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);
const displacement = getDisplacement(sourceKeypointIndices);
const displacedPoint = addVectors(sourceKeypoint.position, displacement);
let targetKeypoint = displacedPoint;
for (let i = 0; i < offsetRefineStep; i++) {
const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);
const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);
targetKeypoint = addVectors(
{ x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },
{ x: offsetPoint.x, y: offsetPoint.y }
);
}
const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);
const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);
return { position: targetKeypoint, part: partNames[targetId], score };
}
function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {
const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]);
const edgesFwd = tuples.map(([, childJointId]) => childJointId);
const edgesBwd = tuples.map(([parentJointId]) => parentJointId);
const numParts = scores.shape[2];
const numEdges = edgesFwd.length;
const keypoints = new Array(numParts);
const rootPoint = getImageCoords(root.part, outputStride, offsets);
keypoints[root.part.id] = {
score: root.score,
part: partNames[root.part.id],
position: rootPoint
};
for (let edge = numEdges - 1; edge >= 0; --edge) {
const sourceId = edgesFwd[edge];
const targetId = edgesBwd[edge];
if (keypoints[sourceId] && !keypoints[targetId]) {
keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);
}
}
for (let edge = 0; edge < numEdges; ++edge) {
const sourceId = edgesBwd[edge];
const targetId = edgesFwd[edge];
if (keypoints[sourceId] && !keypoints[targetId]) {
keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);
}
}
return keypoints;
}
function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {
const [height, width] = scores.shape;
let localMaximum = true;
const yStart = Math.max(heatmapY - localMaximumRadius, 0);
const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);
for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {
const xStart = Math.max(heatmapX - localMaximumRadius, 0);
const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);
for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {
if (scores.get(yCurrent, xCurrent, keypointId) > score) {
localMaximum = false;
break;
}
}
if (!localMaximum)
break;
}
return localMaximum;
}
function buildPartWithScoreQueue(minConfidence2, scores) {
const [height, width, numKeypoints] = scores.shape;
const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score);
for (let heatmapY = 0; heatmapY < height; ++heatmapY) {
for (let heatmapX = 0; heatmapX < width; ++heatmapX) {
for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {
const score = scores.get(heatmapY, heatmapX, keypointId);
if (score < minConfidence2)
continue;
if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores))
queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });
}
}
}
return queue;
}
function withinRadius(poses, { x, y }, keypointId) {
return poses.some(({ keypoints }) => {
var _a;
const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position;
if (!correspondingKeypoint)
return false;
return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;
});
}
function getInstanceScore(existingPoses, keypoints) {
const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {
if (!withinRadius(existingPoses, position, keypointId))
result += score;
return result;
}, 0);
return notOverlappedKeypointScores / keypoints.length;
}
function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) {
const poses = [];
const queue = buildPartWithScoreQueue(minConfidence2, scores);
while (poses.length < maxDetected && !queue.empty()) {
const root = queue.dequeue();
const rootImageCoords = getImageCoords(root.part, outputStride, offsets);
if (withinRadius(poses, rootImageCoords, root.part.id))
continue;
let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);
keypoints = keypoints.filter((a) => a.score > minConfidence2);
const score = getInstanceScore(poses, keypoints);
const box = getBoundingBox(keypoints);
if (score > minConfidence2)
poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });
}
return poses;
}
async function predict19(input, config3) {
if (!(model20 == null ? void 0 : model20["executor"]))
return [];
const res = tf34.tidy(() => {
if (!model20.inputs[0].shape)
return [];
const resized = tf34.image.resizeBilinear(input, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]);
const normalized = tf34.sub(tf34.div(tf34.cast(resized, "float32"), 127.5), 1);
const results = model20.execute(normalized, poseNetOutputs);
const results3d = results.map((y) => tf34.squeeze(y, [0]));
results3d[1] = tf34.sigmoid(results3d[1]);
return results3d;
});
const buffers = await Promise.all(res.map((tensor6) => tensor6.buffer()));
for (const t2 of res)
tf34.dispose(t2);
const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence);
if (!model20.inputs[0].shape)
return [];
const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]);
return scaled;
}
async function load19(config3) {
if (!model20 || env.initial)
model20 = await loadModel(config3.body.modelPath);
else if (config3.debug)
log("cached model:", model20["modelUrl"]);
return model20;
}
// src/segmentation/rvm.ts
var tf35 = __toESM(require_tfjs_esm());
var model21;
var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"];
var t = {};
var ratio = 0;
function init3(config3) {
tf35.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);
t.r1i = tf35.tensor(0);
t.r2i = tf35.tensor(0);
t.r3i = tf35.tensor(0);
t.r4i = tf35.tensor(0);
ratio = config3.segmentation.ratio || 0.5;
t.downsample_ratio = tf35.tensor(ratio);
}
async function load20(config3) {
if (!model21 || env.initial)
model21 = await loadModel(config3.segmentation.modelPath);
else if (config3.debug)
log("cached model:", model21["modelUrl"]);
init3(config3);
return model21;
}
var normalize = (r) => tf35.tidy(() => {
const squeeze14 = tf35.squeeze(r, [0]);
const mul15 = tf35.mul(squeeze14, constants.tf255);
const cast8 = tf35.cast(mul15, "int32");
return cast8;
});
function getRGBA(fgr, pha) {
const rgb2 = fgr ? normalize(fgr) : tf35.fill([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32");
const a = pha ? normalize(pha) : tf35.fill([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32");
const rgba = tf35.concat([rgb2, a], -1);
tf35.dispose([rgb2, a]);
return rgba;
}
function getState(state) {
return tf35.tidy(() => {
const r = {};
r.unstack = tf35.unstack(state, -1);
r.concat = tf35.concat(r.unstack, 1);
r.split = tf35.split(r.concat, 4, 1);
r.stack = tf35.concat(r.split, 2);
r.squeeze = tf35.squeeze(r.stack, [0]);
r.expand = tf35.expandDims(r.squeeze, -1);
r.add = tf35.add(r.expand, 1);
r.mul = tf35.mul(r.add, 127.5);
r.cast = tf35.cast(r.mul, "int32");
r.tile = tf35.tile(r.cast, [1, 1, 3]);
r.alpha = tf35.fill([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32");
return tf35.concat([r.tile, r.alpha], -1);
});
}
async function predict20(input, config3) {
if (!model21)
model21 = await load20(config3);
if (!(model21 == null ? void 0 : model21["executor"]))
return null;
t.src = tf35.div(input, 255);
if (ratio !== config3.segmentation.ratio)
init3(config3);
const [fgr, pha, r1o, r2o, r3o, r4o] = await model21.executeAsync(t, outputNodes2);
let rgba;
switch (config3.segmentation.mode || "default") {
case "default":
rgba = getRGBA(fgr, pha);
break;
case "alpha":
rgba = getRGBA(null, pha);
break;
case "foreground":
rgba = getRGBA(fgr, null);
break;
case "state":
rgba = getState(r1o);
break;
default:
rgba = tf35.tensor(0);
}
tf35.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);
[t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o];
return rgba;
}
// src/segmentation/selfie.ts
var tf36 = __toESM(require_tfjs_esm());
var model22;
async function load21(config3) {
if (!model22 || env.initial)
model22 = await loadModel(config3.segmentation.modelPath);
else if (config3.debug)
log("cached model:", model22["modelUrl"]);
return model22;
}
async function predict21(input, config3) {
var _a;
if (!model22)
model22 = await load21(config3);
if (!(model22 == null ? void 0 : model22["executor"]) || !((_a = model22 == null ? void 0 : model22.inputs) == null ? void 0 : _a[0].shape))
return null;
const t2 = {};
t2.resize = tf36.image.resizeBilinear(input, [model22.inputs[0].shape ? model22.inputs[0].shape[1] : 0, model22.inputs[0].shape ? model22.inputs[0].shape[2] : 0], false);
t2.norm = tf36.div(t2.resize, constants.tf255);
t2.res = model22.execute(t2.norm);
t2.squeeze = tf36.squeeze(t2.res, [0]);
t2.alpha = tf36.image.resizeBilinear(t2.squeeze, [input.shape[1] || 0, input.shape[2] || 0]);
t2.mul = tf36.mul(t2.alpha, constants.tf255);
let rgba;
switch (config3.segmentation.mode || "default") {
case "default":
t2.input = tf36.squeeze(input);
t2.concat = tf36.concat([t2.input, t2.mul], -1);
rgba = tf36.cast(t2.concat, "int32");
break;
case "alpha":
rgba = tf36.cast(t2.mul, "int32");
break;
default:
rgba = tf36.tensor(0);
}
Object.keys(t2).forEach((tensor6) => tf36.dispose(t2[tensor6]));
return rgba;
}
// src/models.ts
function validateModel(instance, model23, name) {
var _a, _b;
if (!model23)
return null;
if (!((_a = instance == null ? void 0 : instance.config) == null ? void 0 : _a.validateModels))
return null;
const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"];
const ignoreOps = ["biasadd", "fusedbatchnormv3", "matmul", "switch", "shape", "merge", "split", "broadcastto"];
const ops = [];
const missing = [];
const url = model23["modelUrl"];
const executor = model23["executor"];
if ((_b = executor == null ? void 0 : executor.graph) == null ? void 0 : _b.nodes) {
for (const kernel of Object.values(executor.graph.nodes)) {
const op = kernel.op.toLowerCase();
if (!ops.includes(op))
ops.push(op);
}
} else {
if (!executor && instance.config.debug) {
log("model not loaded", name);
}
}
for (const op of ops) {
if (!simpleOps.includes(op) && !ignoreOps.includes(op) && !instance.env.kernels.includes(op) && !instance.env.kernels.includes(op.replace("_", "")) && !instance.env.kernels.includes(op.replace("native", "")) && !instance.env.kernels.includes(op.replace("v2", ""))) {
missing.push(op);
}
}
if (instance.config.debug && missing.length > 0)
log("model validation failed:", name, missing);
return missing.length > 0 ? { name, missing, ops, url } : null;
}
var Models = class {
constructor(currentInstance) {
__publicField(this, "instance");
__publicField(this, "models", {});
this.models = {};
this.instance = currentInstance;
}
stats() {
let totalSizeFromManifest = 0;
let totalSizeWeights = 0;
let totalSizeLoading = 0;
for (const m of Object.values(modelStats)) {
totalSizeFromManifest += m.sizeFromManifest;
totalSizeWeights += m.sizeLoadedWeights;
totalSizeLoading += m.sizeDesired;
}
const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;
return {
numLoadedModels: Object.values(modelStats).length,
numDefinedModels: Object.keys(this.models).length,
percentageLoaded,
totalSizeFromManifest,
totalSizeWeights,
totalSizeLoading,
modelStats: Object.values(modelStats)
};
}
reset() {
for (const model23 of Object.keys(this.models))
this.models[model23] = null;
}
async load(instance) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A;
if (env.initial)
this.reset();
if (instance)
this.instance = instance;
const m = {};
m.blazeface = this.instance.config.face.enabled && !this.models.blazeface ? load3(this.instance.config) : null;
m.antispoof = this.instance.config.face.enabled && ((_a = this.instance.config.face.antispoof) == null ? void 0 : _a.enabled) && !this.models.antispoof ? load8(this.instance.config) : null;
m.liveness = this.instance.config.face.enabled && ((_b = this.instance.config.face.liveness) == null ? void 0 : _b.enabled) && !this.models.liveness ? load9(this.instance.config) : null;
m.faceres = this.instance.config.face.enabled && ((_c = this.instance.config.face.description) == null ? void 0 : _c.enabled) && !this.models.faceres ? load7(this.instance.config) : null;
m.emotion = this.instance.config.face.enabled && ((_d = this.instance.config.face.emotion) == null ? void 0 : _d.enabled) && !this.models.emotion ? load6(this.instance.config) : null;
m.iris = this.instance.config.face.enabled && ((_e = this.instance.config.face.iris) == null ? void 0 : _e.enabled) && !((_f = this.instance.config.face.attention) == null ? void 0 : _f.enabled) && !this.models.iris ? load4(this.instance.config) : null;
m.facemesh = this.instance.config.face.enabled && ((_g = this.instance.config.face.mesh) == null ? void 0 : _g.enabled) && !this.models.facemesh ? load5(this.instance.config) : null;
m.gear = this.instance.config.face.enabled && ((_h = this.instance.config.face["gear"]) == null ? void 0 : _h.enabled) && !this.models.gear ? load10(this.instance.config) : null;
m.ssrnetage = this.instance.config.face.enabled && ((_i = this.instance.config.face["ssrnet"]) == null ? void 0 : _i.enabled) && !this.models.ssrnetage ? load11(this.instance.config) : null;
m.ssrnetgender = this.instance.config.face.enabled && ((_j = this.instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) && !this.models.ssrnetgender ? load12(this.instance.config) : null;
m.mobilefacenet = this.instance.config.face.enabled && ((_k = this.instance.config.face["mobilefacenet"]) == null ? void 0 : _k.enabled) && !this.models.mobilefacenet ? load13(this.instance.config) : null;
m.insightface = this.instance.config.face.enabled && ((_l = this.instance.config.face["insightface"]) == null ? void 0 : _l.enabled) && !this.models.insightface ? load14(this.instance.config) : null;
m.blazepose = this.instance.config.body.enabled && !this.models.blazepose && ((_m = this.instance.config.body.modelPath) == null ? void 0 : _m.includes("blazepose")) ? loadPose(this.instance.config) : null;
m.blazeposedetect = this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body["detector"] && this.instance.config.body["detector"].modelPath ? loadDetect(this.instance.config) : null;
m.efficientpose = this.instance.config.body.enabled && !this.models.efficientpose && ((_n = this.instance.config.body.modelPath) == null ? void 0 : _n.includes("efficientpose")) ? load2(this.instance.config) : null;
m.movenet = this.instance.config.body.enabled && !this.models.movenet && ((_o = this.instance.config.body.modelPath) == null ? void 0 : _o.includes("movenet")) ? load17(this.instance.config) : null;
m.posenet = this.instance.config.body.enabled && !this.models.posenet && ((_p = this.instance.config.body.modelPath) == null ? void 0 : _p.includes("posenet")) ? load19(this.instance.config) : null;
m.handtrack = this.instance.config.hand.enabled && !this.models.handtrack && ((_r = (_q = this.instance.config.hand.detector) == null ? void 0 : _q.modelPath) == null ? void 0 : _r.includes("handtrack")) ? loadDetect2(this.instance.config) : null;
m.handskeleton = this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && ((_t = (_s = this.instance.config.hand.detector) == null ? void 0 : _s.modelPath) == null ? void 0 : _t.includes("handtrack")) ? loadSkeleton(this.instance.config) : null;
if ((_v = (_u = this.instance.config.hand.detector) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("handdetect"))
[m.handpose, m.handskeleton] = !this.models.handpose ? await load15(this.instance.config) : [null, null];
m.centernet = this.instance.config.object.enabled && !this.models.centernet && ((_w = this.instance.config.object.modelPath) == null ? void 0 : _w.includes("centernet")) ? load(this.instance.config) : null;
m.nanodet = this.instance.config.object.enabled && !this.models.nanodet && ((_x = this.instance.config.object.modelPath) == null ? void 0 : _x.includes("nanodet")) ? load18(this.instance.config) : null;
m.selfie = this.instance.config.segmentation.enabled && !this.models.selfie && ((_y = this.instance.config.segmentation.modelPath) == null ? void 0 : _y.includes("selfie")) ? load21(this.instance.config) : null;
m.meet = this.instance.config.segmentation.enabled && !this.models.meet && ((_z = this.instance.config.segmentation.modelPath) == null ? void 0 : _z.includes("meet")) ? load16(this.instance.config) : null;
m.rvm = this.instance.config.segmentation.enabled && !this.models.rvm && ((_A = this.instance.config.segmentation.modelPath) == null ? void 0 : _A.includes("rvm")) ? load20(this.instance.config) : null;
await Promise.all([...Object.values(m)]);
for (const model23 of Object.keys(m))
this.models[model23] = m[model23] || this.models[model23] || null;
}
list() {
const models3 = Object.keys(this.models).map((model23) => {
var _a;
return { name: model23, loaded: this.models[model23] !== null, size: 0, url: this.models[model23] ? (_a = this.models[model23]) == null ? void 0 : _a["modelUrl"] : null };
});
for (const m of models3) {
const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name));
if (!stats)
continue;
m.size = modelStats[stats].sizeLoadedWeights;
m.url = modelStats[stats].url;
}
return models3;
}
loaded() {
const list = this.list();
const loaded = list.filter((model23) => model23.loaded).map((model23) => model23.name);
return loaded;
}
validate() {
const missing = [];
for (const defined of Object.keys(this.models)) {
const model23 = this.models[defined];
if (!model23)
continue;
const res = validateModel(this.instance, model23, defined);
if (res)
missing.push(res);
}
return missing;
}
};
// src/util/persons.ts
function join2(faces, bodies, hands, gestures, shape) {
var _a, _b, _c, _d, _e, _f;
let id = 0;
const persons = [];
for (const face4 of faces) {
const person2 = { id: id++, face: face4, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };
for (const body4 of bodies) {
if (face4.box[0] > body4.box[0] && face4.box[0] < body4.box[0] + body4.box[2] && face4.box[1] + face4.box[3] > body4.box[1] && face4.box[1] + face4.box[3] < body4.box[1] + body4.box[3]) {
person2.body = body4;
}
}
if (person2.body) {
for (const hand3 of hands) {
if (hand3.box[0] + hand3.box[2] > person2.body.box[0] && hand3.box[0] + hand3.box[2] < person2.body.box[0] + person2.body.box[2] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) {
if (person2.hands)
person2.hands.left = hand3;
}
if (hand3.box[0] < person2.body.box[0] + person2.body.box[2] && hand3.box[0] > person2.body.box[0] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) {
if (person2.hands)
person2.hands.right = hand3;
}
}
}
for (const gesture2 of gestures) {
if (gesture2["face"] !== void 0 && gesture2["face"] === face4.id)
person2.gestures.push(gesture2);
else if (gesture2["iris"] !== void 0 && gesture2["iris"] === face4.id)
person2.gestures.push(gesture2);
else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_a = person2.body) == null ? void 0 : _a.id))
person2.gestures.push(gesture2);
else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_b = person2.hands.left) == null ? void 0 : _b.id))
person2.gestures.push(gesture2);
else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_c = person2.hands.right) == null ? void 0 : _c.id))
person2.gestures.push(gesture2);
}
const x = [];
const y = [];
const extractXY = (box) => {
if (box && box.length === 4) {
x.push(box[0], box[0] + box[2]);
y.push(box[1], box[1] + box[3]);
}
};
extractXY(person2.face.box);
extractXY((_d = person2.body) == null ? void 0 : _d.box);
extractXY((_e = person2.hands.left) == null ? void 0 : _e.box);
extractXY((_f = person2.hands.right) == null ? void 0 : _f.box);
const minX = Math.min(...x);
const minY = Math.min(...y);
person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY];
if ((shape == null ? void 0 : shape[1]) && (shape == null ? void 0 : shape[2]))
person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]];
persons.push(person2);
}
return persons;
}
// src/warmup.ts
var tf37 = __toESM(require_tfjs_esm());
// src/sample.ts
var face3 = `
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// src/warmup.ts
async function warmupBitmap(instance) {
const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob());
let blob;
let res;
switch (instance.config.warmup) {
case "face":
blob = await b64toBlob(face3);
break;
case "body":
case "full":
blob = await b64toBlob(body3);
break;
default:
blob = null;
}
if (blob) {
const bitmap = await createImageBitmap(blob);
res = await instance.detect(bitmap, instance.config);
bitmap.close();
}
return res;
}
async function warmupCanvas(instance) {
return new Promise((resolve) => {
let src;
switch (instance.config.warmup) {
case "face":
src = "data:image/jpeg;base64," + face3;
break;
case "full":
case "body":
src = "data:image/jpeg;base64," + body3;
break;
default:
src = "";
}
let img;
if (typeof Image !== "undefined")
img = new Image();
else if (env.Image)
img = new env.Image();
else
return;
img.onload = async () => {
const canvas3 = canvas(img.naturalWidth, img.naturalHeight);
if (!canvas3) {
log("Warmup: Canvas not found");
resolve(void 0);
} else {
const ctx = canvas3.getContext("2d");
if (ctx)
ctx.drawImage(img, 0, 0);
const tensor6 = await instance.image(canvas3, true);
const res = tensor6.tensor ? await instance.detect(tensor6.tensor, instance.config) : void 0;
resolve(res);
}
};
if (src)
img.src = src;
else
resolve(void 0);
});
}
async function warmupNode(instance) {
const atob = (str) => Buffer.from(str, "base64");
let img;
if (instance.config.warmup === "face")
img = atob(face3);
else
img = atob(body3);
let res;
if ("node" in tf37 && tf37.getBackend() === "tensorflow") {
const data = tf37["node"].decodeJpeg(img);
const expanded = tf37.expandDims(data, 0);
instance.tf.dispose(data);
res = await instance.detect(expanded, instance.config);
instance.tf.dispose(expanded);
} else {
if (instance.config.debug)
log("Warmup tfjs-node not loaded");
}
return res;
}
async function runInference(instance) {
let res;
if (typeof createImageBitmap === "function")
res = await warmupBitmap(instance);
else if (typeof Image !== "undefined" || env.Canvas !== void 0)
res = await warmupCanvas(instance);
else
res = await warmupNode(instance);
return res;
}
async function runCompile(instance) {
var _a, _b, _c, _d;
if (!tf37.env().flagRegistry.ENGINE_COMPILE_ONLY)
return;
const backendType = tf37.getBackend();
const webGLBackend = tf37.backend();
if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend["checkCompileCompletion"])) {
return;
}
tf37.env().set("ENGINE_COMPILE_ONLY", true);
const numTensorsStart = tf37.engine().state.numTensors;
const compiledModels = [];
for (const [modelName, model23] of Object.entries(instance.models).filter(([key, val]) => key !== null && val !== null)) {
const shape = (model23 == null ? void 0 : model23.modelSignature) && ((_b = (_a = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _a[0]) == null ? void 0 : _b.shape) ? [...model23.inputs[0].shape] : [1, 64, 64, 3];
const dtype = (model23 == null ? void 0 : model23.modelSignature) && ((_d = (_c = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _c[0]) == null ? void 0 : _d.dtype) ? model23.inputs[0].dtype : "float32";
for (let dim = 0; dim < shape.length; dim++) {
if (shape[dim] === -1)
shape[dim] = dim === 0 ? 1 : 64;
}
const tensor6 = tf37.zeros(shape, dtype);
try {
const res = model23.execute(tensor6);
compiledModels.push(modelName);
if (Array.isArray(res))
res.forEach((t2) => tf37.dispose(t2));
else
tf37.dispose(res);
} catch (e) {
if (instance.config.debug)
log("compile fail model:", modelName);
}
tf37.dispose(tensor6);
}
const kernels = await webGLBackend["checkCompileCompletionAsync"]();
webGLBackend["getUniformLocations"]();
if (instance.config.debug)
log("compile pass:", { models: compiledModels, kernels: kernels.length });
tf37.env().set("ENGINE_COMPILE_ONLY", false);
const numTensorsEnd = tf37.engine().state.numTensors;
if (numTensorsEnd - numTensorsStart > 0)
log("tensor leak:", numTensorsEnd - numTensorsStart);
}
async function warmup(instance, userConfig) {
await check(instance, false);
const t0 = now();
instance.state = "warmup";
if (userConfig)
instance.config = mergeDeep(instance.config, userConfig);
if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none") {
return empty();
}
return new Promise(async (resolve) => {
await instance.models.load();
await runCompile(instance);
const res = await runInference(instance);
const t1 = now();
if (instance.config.debug)
log("warmup", instance.config.warmup, Math.round(t1 - t0), "ms");
instance.emit("warmup");
resolve(res);
});
}
// src/human.ts
var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity, _loops;
var Human = class {
constructor(userConfig) {
__publicField(this, "version");
__publicField(this, "config");
__publicField(this, "result");
__publicField(this, "state");
__publicField(this, "process");
__publicField(this, "tf");
__publicField(this, "env", env);
__publicField(this, "draw", draw_exports);
__publicField(this, "match", match_exports);
__publicField(this, "models");
__publicField(this, "events");
__publicField(this, "faceTriangulation");
__publicField(this, "faceUVMap");
__publicField(this, "performance");
__privateAdd(this, _numTensors, void 0);
__privateAdd(this, _analyzeMemoryLeaks, void 0);
__privateAdd(this, _checkSanity, void 0);
__publicField(this, "analyze", (...msg) => {
if (!__privateGet(this, _analyzeMemoryLeaks))
return;
const currentTensors = this.tf.engine().state.numTensors;
const previousTensors = __privateGet(this, _numTensors);
__privateSet(this, _numTensors, currentTensors);
const leaked = currentTensors - previousTensors;
if (leaked !== 0)
log(...msg, leaked);
});
__privateAdd(this, _sanity, (input) => {
if (!__privateGet(this, _checkSanity))
return null;
if (!input)
return "input is not defined";
if (this.env.node && !(input instanceof tf38.Tensor))
return "input must be a tensor";
try {
this.tf.getBackend();
} catch (e) {
return "backend not loaded";
}
return null;
});
__publicField(this, "webcam", new WebCam());
__publicField(this, "emit", (event) => {
var _a;
if ((_a = this.events) == null ? void 0 : _a.dispatchEvent)
this.events.dispatchEvent(new Event(event));
});
__privateAdd(this, _loops, {});
const tfVersion = (tf38.version.tfjs || tf38.version_core).replace(/-(.*)/, "");
config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;
config.modelBasePath = env.browser ? "../models/" : "file://models/";
this.version = version2;
Object.defineProperty(this, "version", { value: version2 });
this.config = JSON.parse(JSON.stringify(config));
Object.seal(this.config);
this.config.cacheModels = typeof indexedDB !== "undefined";
if (userConfig)
this.config = mergeDeep(this.config, userConfig);
setModelLoadOptions(this.config);
this.tf = tf38;
this.state = "idle";
__privateSet(this, _numTensors, 0);
__privateSet(this, _analyzeMemoryLeaks, false);
__privateSet(this, _checkSanity, false);
this.performance = {};
this.events = typeof EventTarget !== "undefined" ? new EventTarget() : void 0;
this.models = new Models(this);
init2();
this.result = empty();
this.process = { tensor: null, canvas: null };
this.faceTriangulation = triangulation;
this.faceUVMap = uvmap;
validateModel(this, null, "");
this.emit("create");
if (this.config.debug || this.env.browser)
log(`version: ${this.version}`);
if (this.config.debug)
log(`tfjs version: ${this.tf.version["tfjs-core"]}`);
const envTemp = JSON.parse(JSON.stringify(this.env));
delete envTemp.kernels;
delete envTemp.initial;
delete envTemp.perfadd;
if (this.config.debug)
log("environment:", envTemp);
}
reset() {
const currentBackend = this.config.backend;
this.config = JSON.parse(JSON.stringify(config));
this.config.backend = currentBackend;
reset();
env.initial = true;
}
validate(userConfig) {
const msgs = validate(config, userConfig || this.config);
if (msgs.length === 0)
this.config = mergeDeep(this.config, userConfig);
return msgs;
}
now() {
return now();
}
image(input, getTensor = false) {
return process2(input, this.config, getTensor);
}
async segmentation(input, userConfig) {
var _a, _b, _c;
if (userConfig)
this.config = mergeDeep(this.config, userConfig);
if (!this.config.segmentation.enabled)
return null;
const processed = await process2(input, this.config);
if (!processed.tensor)
return null;
let tensor6 = null;
if ((_a = this.config.segmentation.modelPath) == null ? void 0 : _a.includes("rvm"))
tensor6 = await predict20(processed.tensor, this.config);
if ((_b = this.config.segmentation.modelPath) == null ? void 0 : _b.includes("meet"))
tensor6 = await predict16(processed.tensor, this.config);
if ((_c = this.config.segmentation.modelPath) == null ? void 0 : _c.includes("selfie"))
tensor6 = await predict21(processed.tensor, this.config);
tf38.dispose(processed.tensor);
return tensor6;
}
compare(firstImageTensor, secondImageTensor) {
return compare(this.config, firstImageTensor, secondImageTensor);
}
async init() {
await check(this, true);
await this.tf.ready();
reset();
}
async load(userConfig) {
this.state = "load";
const timeStamp = now();
const count2 = Object.values(this.models.models).filter((model23) => model23).length;
if (userConfig)
this.config = mergeDeep(this.config, userConfig);
if (this.env.initial) {
if (!await check(this, false))
log("error: backend check failed");
await tf38.ready();
if (this.env.browser) {
if (this.config.debug)
log("configuration:", this.config);
if (this.config.debug)
log("tf flags:", this.tf.ENV.flags);
}
}
await this.models.load(this);
if (this.env.initial && this.config.debug)
log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors");
this.env.initial = false;
const loaded = Object.values(this.models.models).filter((model23) => model23).length;
if (loaded !== count2) {
this.models.validate();
this.emit("load");
}
const current = Math.trunc(now() - timeStamp);
if (current > (this.performance.loadModels || 0))
this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;
}
next(result = this.result) {
return calc2(result, this.config);
}
async warmup(userConfig) {
const t0 = now();
const res = await warmup(this, userConfig);
const t1 = now();
this.performance.warmup = Math.trunc(t1 - t0);
return res;
}
async profile(input, userConfig) {
const profile = await this.tf.profile(() => this.detect(input, userConfig));
const kernels = {};
let total = 0;
for (const kernel of profile.kernels) {
const ms = Number(kernel.kernelTimeMs) || 0;
if (kernels[kernel.name])
kernels[kernel.name] += ms;
else
kernels[kernel.name] = ms;
total += ms;
}
const kernelArr = [];
Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1], perc: 0 }));
for (const kernel of kernelArr) {
kernel.perc = Math.round(1e3 * kernel.time / total) / 1e3;
kernel.time = Math.round(1e3 * kernel.time) / 1e3;
}
kernelArr.sort((a, b) => b.time - a.time);
kernelArr.length = 20;
return kernelArr;
}
async detect(input, userConfig) {
this.state = "detect";
return new Promise(async (resolve) => {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u;
this.state = "config";
let timeStamp;
this.config = mergeDeep(this.config, userConfig);
this.state = "check";
const error = __privateGet(this, _sanity).call(this, input);
if (error) {
log(error, input);
this.emit("error");
resolve(empty(error));
}
const timeStart = now();
await this.load();
timeStamp = now();
this.state = "image";
const img = await process2(input, this.config);
this.process = img;
this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
this.analyze("Get Image:");
if (!img.tensor) {
if (this.config.debug)
log("could not convert input to tensor");
this.emit("error");
resolve(empty("could not convert input to tensor"));
return;
}
this.emit("image");
timeStamp = now();
this.config.skipAllowed = await skip(this.config, img.tensor);
this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed;
if (!this.performance.totalFrames)
this.performance.totalFrames = 0;
if (!this.performance.cachedFrames)
this.performance.cachedFrames = 0;
this.performance.totalFrames++;
if (this.config.skipAllowed)
this.performance.cachedFrames++;
this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
this.analyze("Check Changed:");
let faceRes = [];
let bodyRes = [];
let handRes = [];
let objectRes = [];
this.state = "detect:face";
if (this.config.async) {
faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : [];
if (this.performance.face)
delete this.performance.face;
} else {
timeStamp = now();
faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : [];
this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1))
faceRes = await faceRes;
this.analyze("Start Body:");
this.state = "detect:body";
const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config;
if (this.config.async) {
if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet"))
bodyRes = this.config.body.enabled ? predict19(img.tensor, bodyConfig) : [];
else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose"))
bodyRes = this.config.body.enabled ? predict(img.tensor, bodyConfig) : [];
else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose"))
bodyRes = this.config.body.enabled ? predict3(img.tensor, bodyConfig) : [];
else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet"))
bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : [];
if (this.performance.body)
delete this.performance.body;
} else {
timeStamp = now();
if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet"))
bodyRes = this.config.body.enabled ? await predict19(img.tensor, bodyConfig) : [];
else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose"))
bodyRes = this.config.body.enabled ? await predict(img.tensor, bodyConfig) : [];
else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose"))
bodyRes = this.config.body.enabled ? await predict3(img.tensor, bodyConfig) : [];
else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet"))
bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : [];
this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
this.analyze("End Body:");
this.analyze("Start Hand:");
this.state = "detect:hand";
const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config;
if (this.config.async) {
if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect"))
handRes = this.config.hand.enabled ? predict14(img.tensor, handConfig) : [];
else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack"))
handRes = this.config.hand.enabled ? predict15(img.tensor, handConfig) : [];
if (this.performance.hand)
delete this.performance.hand;
} else {
timeStamp = now();
if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect"))
handRes = this.config.hand.enabled ? await predict14(img.tensor, handConfig) : [];
else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack"))
handRes = this.config.hand.enabled ? await predict15(img.tensor, handConfig) : [];
this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
this.analyze("End Hand:");
this.analyze("Start Object:");
this.state = "detect:object";
if (this.config.async) {
if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet"))
objectRes = this.config.object.enabled ? predict18(img.tensor, this.config) : [];
else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet"))
objectRes = this.config.object.enabled ? predict2(img.tensor, this.config) : [];
if (this.performance.object)
delete this.performance.object;
} else {
timeStamp = now();
if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet"))
objectRes = this.config.object.enabled ? await predict18(img.tensor, this.config) : [];
else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet"))
objectRes = this.config.object.enabled ? await predict2(img.tensor, this.config) : [];
this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
this.analyze("End Object:");
this.state = "detect:await";
if (this.config.async)
[faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);
this.state = "detect:gesture";
let gestureRes = [];
if (this.config.gesture.enabled) {
timeStamp = now();
gestureRes = [...face2(faceRes), ...body2(bodyRes), ...hand2(handRes), ...iris2(faceRes)];
if (!this.config.async)
this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
else if (this.performance.gesture)
delete this.performance.gesture;
}
this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);
const shape = ((_u = this.process.tensor) == null ? void 0 : _u.shape) || [0, 0, 0, 0];
this.result = {
face: faceRes,
body: bodyRes,
hand: handRes,
gesture: gestureRes,
object: objectRes,
performance: this.performance,
canvas: this.process.canvas,
timestamp: Date.now(),
error: null,
width: shape[2],
height: shape[1],
get persons() {
return join2(faceRes, bodyRes, handRes, gestureRes, shape);
}
};
tf38.dispose(img.tensor);
this.emit("detect");
this.state = "idle";
resolve(this.result);
});
}
async sleep(ms) {
return new Promise((resolve) => {
setTimeout(resolve, ms);
});
}
async video(element, run = true, delay = 0) {
if (run) {
if (!__privateGet(this, _loops)[element.id]) {
if (this.config.debug)
log("video start", element.id);
__privateGet(this, _loops)[element.id] = true;
}
if (!element.paused && __privateGet(this, _loops)[element.id] && element.readyState >= 2)
await this.detect(element);
if (delay > 0)
await this.sleep(delay);
if (__privateGet(this, _loops)[element.id])
requestAnimationFrame(() => this.video(element, run, delay));
} else {
if (this.config.debug)
log("video stop", element.id);
__privateGet(this, _loops)[element.id] = false;
}
}
};
_numTensors = new WeakMap();
_analyzeMemoryLeaks = new WeakMap();
_checkSanity = new WeakMap();
_sanity = new WeakMap();
_loops = new WeakMap();
// Annotate the CommonJS export names for ESM import in node:
0 && (module.exports = {
Env,
Human,
defaults,
draw,
empty,
env,
match,
models
});
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