/*
  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 __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_exports = {};
    module2.exports = __toCommonJS2(tf_node_exports);
    __reExport(tf_node_exports, require("@tensorflow/tfjs-node"), module2.exports);
  }
});

// src/human.ts
var human_exports = {};
__export(human_exports, {
  Env: () => Env,
  Human: () => Human2,
  default: () => Human2,
  defaults: () => config,
  draw: () => draw_exports,
  env: () => env,
  match: () => match_exports,
  models: () => models_exports2
});
module.exports = __toCommonJS(human_exports);

// 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,
    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: "mb3-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, (match3, name) => {
    collection[name] = 0;
    return match3;
  });
};
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(image27) {
    resize(image27.width, image27.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, image27);
    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(image27) {
    this.add("brightness", 0);
    return this.apply(image27);
  };
}

// 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 channels = tf.split(squeeze14, 3, 2);
  const min2 = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];
  const max4 = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];
  const absMax = await Promise.all(max4.map((channel) => channel.data()));
  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);
  const sub11 = [tf.sub(channels[0], min2[0]), tf.sub(channels[1], min2[1]), tf.sub(channels[2], min2[2])];
  const range = [tf.sub(max4[0], min2[0]), tf.sub(max4[1], min2[1]), tf.sub(max4[2], min2[2])];
  const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];
  const enh = [tf.mul(sub11[0], fact[0]), tf.mul(sub11[1], fact[1]), tf.mul(sub11[2], fact[2])];
  const rgb2 = tf.stack([enh[0], enh[1], enh[2]], 2);
  const reshape8 = tf.reshape(rgb2, [1, squeeze14.shape[0], squeeze14.shape[1], 3]);
  tf.dispose([...channels, ...min2, ...max4, ...sub11, ...range, ...fact, ...enh, rgb2, squeeze14]);
  return reshape8;
}

// 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;
  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 (fx.get() > 0)
        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]);
  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] > 2048 || input.shape[2] > 2048)
    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
    });
    __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);
    this.tensorflow = {
      version: tf3.backend().binding ? tf3.backend().binding.TF_Version : void 0,
      gpu: tf3.backend().binding ? tf3.backend().binding.isUsingGpuDevice() : void 0
    };
    this.wasm.supported = typeof WebAssembly !== "undefined";
    this.wasm.backend = this.backends.includes("wasm");
    if (this.wasm.supported && this.wasm.backend && tf3.getBackend() === "wasm") {
      this.wasm.simd = tf3.env().get("WASM_HAS_SIMD_SUPPORT");
      this.wasm.multithread = tf3.env().get("WASM_HAS_MULTITHREAD_SUPPORT");
    }
    const c = canvas(100, 100);
    const ctx = c ? c.getContext("webgl2") : void 0;
    this.webgl.supported = typeof ctx !== "undefined";
    this.webgl.backend = this.backends.includes("webgl");
    if (this.webgl.supported && this.webgl.backend && (tf3.getBackend() === "webgl" || tf3.getBackend() === "humangl")) {
      const gl = tf3.backend().gpgpu !== "undefined" ? await tf3.backend().getGPGPUContext().gl : null;
      if (gl) {
        this.webgl.version = gl.getParameter(gl.VERSION);
        this.webgl.renderer = gl.getParameter(gl.RENDERER);
      }
    }
    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 = adapter ? adapter.name : void 0;
      }
    } 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, "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.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 }
        }
      };
      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-detect": () => blazepose_detect,
  "blazepose-detector2d": () => blazepose_detector2d,
  "blazepose-detector3d": () => blazepose_detector3d,
  "blazepose-full": () => blazepose_full,
  "blazepose-heavy": () => blazepose_heavy,
  "blazepose-lite": () => blazepose_lite,
  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,
  "mb3-centernet": () => mb3_centernet,
  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,
  selfie: () => selfie
});
var antispoof = 853098;
var blazeface = 538928;
var emotion = 820516;
var facemesh = 1477958;
var faceres = 6978814;
var handlandmark_full = 5431368;
var handtrack = 2964837;
var iris = 2599092;
var liveness = 592976;
var mb3_centernet = 4030290;
var models = 0;
var movenet_lightning = 4650216;
var selfie = 212886;
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 blazepose_detect = 5928804;
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,
  emotion,
  facemesh,
  faceres,
  "handlandmark-full": handlandmark_full,
  handtrack,
  iris,
  liveness,
  "mb3-centernet": mb3_centernet,
  models,
  "movenet-lightning": movenet_lightning,
  selfie,
  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,
  "blazepose-detect": blazepose_detect,
  "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, init3) {
  if (options.debug)
    log("load model fetch:", url, init3);
  return fetch(url, init3);
}
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
  };
  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);
  const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init3) => httpHandler(url, init3) };
  let model21 = new tf4.GraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions);
  let loaded = false;
  try {
    model21.findIOHandler();
    if (options.debug)
      log("model load handler:", model21["handler"]);
  } catch (err) {
    log("error finding model i/o handler:", modelUrl, err);
  }
  try {
    const artifacts = await ((_a = model21.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)
      model21.loadSync(artifacts);
    else
      model21 = await tf4.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions);
    modelStats[shortModelName].sizeLoadedWeights = ((_d = (_c = model21.artifacts) == null ? void 0 : _c.weightData) == null ? void 0 : _d.byteLength) || 0;
    if (options.verbose)
      log("load:", { model: shortModelName, url: model21["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 model21.save(cachedModelName);
      if (options.debug)
        log("model saved:", cachedModelName, saveResult);
    } catch (err) {
      log("error saving model:", modelUrl, err);
    }
  }
  return model21;
}

// src/human.ts
var tf39 = __toESM(require_tfjs_esm());

// package.json
var version2 = "2.11.0";

// src/tfjs/humangl.ts
var tf34 = __toESM(require_tfjs_esm());

// src/models.ts
var models_exports2 = {};
__export(models_exports2, {
  Models: () => Models,
  getModelStats: () => getModelStats,
  load: () => load22,
  reset: () => reset2,
  validate: () => validate2,
  validateModel: () => validateModel
});

// src/face/antispoof.ts
var tf5 = __toESM(require_tfjs_esm());
var model;
var cached = [];
var skipped = Number.MAX_SAFE_INTEGER;
var lastCount = 0;
var lastTime = 0;
async function load(config3) {
  var _a;
  if (env.initial)
    model = null;
  if (!model)
    model = await loadModel((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath);
  else if (config3.debug)
    log("cached model:", model["modelUrl"]);
  return model;
}
async function predict(image27, config3, idx, count2) {
  var _a, _b;
  if (!model || !(model == null ? void 0 : model["executor"]))
    return 0;
  const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime;
  const skipFrame = skipped < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0);
  if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && cached[idx]) {
    skipped++;
    return cached[idx];
  }
  skipped = 0;
  return new Promise(async (resolve) => {
    const resize = tf5.image.resizeBilinear(image27, [(model == null ? void 0 : model.inputs[0].shape) ? model.inputs[0].shape[2] : 0, (model == null ? void 0 : model.inputs[0].shape) ? model.inputs[0].shape[1] : 0], false);
    const res = model == null ? void 0 : model.execute(resize);
    const num = (await res.data())[0];
    cached[idx] = Math.round(100 * num) / 100;
    lastCount = count2;
    lastTime = now();
    tf5.dispose([resize, res]);
    resolve(cached[idx]);
  });
}

// src/face/blazeface.ts
var tf8 = __toESM(require_tfjs_esm());

// src/face/facemeshutil.ts
var tf7 = __toESM(require_tfjs_esm());

// 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],
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var meshLandmarks = {
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  mouth: 13,
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};
var blazeFaceLandmarks = {
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  rightEye: 1,
  nose: 2,
  mouth: 3,
  leftEar: 4,
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  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/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/face/facemeshutil.ts
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, image27, cropSize) => {
  const h = image27.shape[1];
  const w = image27.shape[2];
  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];
  const crop = tf7.image.cropAndResize(image27, [cutBox], [0], cropSize);
  const norm = tf7.div(crop, constants.tf255);
  tf7.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 = tf7.image.rotateWithOffset(input, angle, 0, centerRaw);
      rotationMatrix = buildRotationMatrix(-angle, center);
      face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]);
      tf7.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 model2;
var anchors = null;
var inputSize = 0;
var inputSizeT = null;
var size = () => inputSize;
async function load2(config3) {
  var _a;
  if (env.initial)
    model2 = null;
  if (!model2)
    model2 = await loadModel((_a = config3.face.detector) == null ? void 0 : _a.modelPath);
  else if (config3.debug)
    log("cached model:", model2["modelUrl"]);
  inputSize = model2["executor"] && model2.inputs[0].shape ? model2.inputs[0].shape[2] : 256;
  inputSizeT = tf8.scalar(inputSize, "int32");
  anchors = tf8.tensor2d(generateAnchors(inputSize));
  return model2;
}
function decodeBoxes(boxOutputs) {
  const t2 = {};
  t2.boxStarts = tf8.slice(boxOutputs, [0, 1], [-1, 2]);
  t2.centers = tf8.add(t2.boxStarts, anchors);
  t2.boxSizes = tf8.slice(boxOutputs, [0, 3], [-1, 2]);
  t2.boxSizesNormalized = tf8.div(t2.boxSizes, inputSizeT);
  t2.centersNormalized = tf8.div(t2.centers, inputSizeT);
  t2.halfBoxSize = tf8.div(t2.boxSizesNormalized, constants.tf2);
  t2.starts = tf8.sub(t2.centersNormalized, t2.halfBoxSize);
  t2.ends = tf8.add(t2.centersNormalized, t2.halfBoxSize);
  t2.startNormalized = tf8.mul(t2.starts, inputSizeT);
  t2.endNormalized = tf8.mul(t2.ends, inputSizeT);
  const boxes = tf8.concat2d([t2.startNormalized, t2.endNormalized], 1);
  Object.keys(t2).forEach((tensor6) => tf8.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 = tf8.image.resizeBilinear(inputImage, [inputSize, inputSize]);
  t2.div = tf8.div(t2.resized, constants.tf127);
  t2.normalized = tf8.sub(t2.div, constants.tf05);
  const res = model2 == null ? void 0 : model2.execute(t2.normalized);
  if (Array.isArray(res) && res.length > 2) {
    const sorted = res.sort((a, b) => a.size - b.size);
    t2.concat384 = tf8.concat([sorted[0], sorted[2]], 2);
    t2.concat512 = tf8.concat([sorted[1], sorted[3]], 2);
    t2.concat = tf8.concat([t2.concat512, t2.concat384], 1);
    t2.batch = tf8.squeeze(t2.concat, 0);
  } else if (Array.isArray(res)) {
    t2.batch = tf8.squeeze(res[0]);
  } else {
    t2.batch = tf8.squeeze(res);
  }
  tf8.dispose(res);
  t2.boxes = decodeBoxes(t2.batch);
  t2.logits = tf8.slice(t2.batch, [0, 0], [-1, 1]);
  t2.sigmoid = tf8.sigmoid(t2.logits);
  t2.scores = tf8.squeeze(t2.sigmoid);
  t2.nms = await tf8.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 = tf8.slice(t2.boxes, [nms[i], 0], [1, -1]);
      b.slice = tf8.slice(t2.batch, [nms[i], keypointsCount - 1], [1, -1]);
      b.squeeze = tf8.squeeze(b.slice);
      b.landmarks = tf8.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) / inputSize, (inputImage.shape[1] || 0) / inputSize]);
      const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor);
      const squaredBox = squarifyBox(enlargedBox);
      boxes.push(squaredBox);
      Object.keys(b).forEach((tensor6) => tf8.dispose(b[tensor6]));
    }
  }
  Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6]));
  return boxes;
}

// src/body/blazepose.ts
var tf10 = __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 tf9 = __toESM(require_tfjs_esm());
var inputSize2 = 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(inputSize2 / stride);
    const featureMapWidth = Math.ceil(inputSize2 / 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: tf9.tensor1d(anchors3.map((a) => a.x)), y: tf9.tensor1d(anchors3.map((a) => a.y)) };
}

// 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 max4 = [Math.max(...coords[0]), Math.max(...coords[1])];
  const box = [min2[0], min2[1], max4[0] - min2[0], max4[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 max4 = [Math.max(...coords[0]), Math.max(...coords[1])];
  const center = [(min2[0] + max4[0]) / 2, (min2[1] + max4[1]) / 2];
  const dist = Math.max(center[0] - min2[0], center[1] - min2[1], -center[0] + max4[0], -center[1] + max4[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 env3 = { initial: true };
var models2 = { detector: null, landmarks: null };
var inputSize3 = { detector: [224, 224], landmarks: [256, 256] };
var skipped2 = Number.MAX_SAFE_INTEGER;
var outputNodes = {
  landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"],
  detector: []
};
var cache = null;
var cropBox;
var padding = [[0, 0], [0, 0], [0, 0], [0, 0]];
var lastTime2 = 0;
var sigmoid3 = (x) => 1 - 1 / (1 + Math.exp(x));
async function loadDetect(config3) {
  var _a;
  if (env3.initial)
    models2.detector = null;
  if (!models2.detector && config3.body["detector"] && config3.body["detector"].modelPath || "") {
    models2.detector = await loadModel(config3.body["detector"].modelPath);
    const inputs = ((_a = models2.detector) == null ? void 0 : _a["executor"]) ? Object.values(models2.detector.modelSignature["inputs"]) : void 0;
    inputSize3.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
    inputSize3.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
  } else if (config3.debug && models2.detector)
    log("cached model:", models2.detector["modelUrl"]);
  createAnchors();
  return models2.detector;
}
async function loadPose(config3) {
  var _a;
  if (env3.initial)
    models2.landmarks = null;
  if (!models2.landmarks) {
    models2.landmarks = await loadModel(config3.body.modelPath);
    const inputs = ((_a = models2.landmarks) == null ? void 0 : _a["executor"]) ? Object.values(models2.landmarks.modelSignature["inputs"]) : void 0;
    inputSize3.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
    inputSize3.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
  } else if (config3.debug)
    log("cached model:", models2.landmarks["modelUrl"]);
  return models2.landmarks;
}
function prepareImage(input, size2) {
  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 = tf10.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 = tf10.pad(t2.cropped || input, padding);
    t2.resize = tf10.image.resizeBilinear(t2.pad, [size2, size2]);
    final = tf10.div(t2.resize, constants.tf255);
  } else if (input.shape[1] !== size2) {
    t2.resize = tf10.image.resizeBilinear(t2.cropped || input, [size2, size2]);
    final = tf10.div(t2.resize, constants.tf255);
  } else {
    final = tf10.div(t2.cropped || input, constants.tf255);
  }
  Object.keys(t2).forEach((tensor6) => tf10.dispose(t2[tensor6]));
  return final;
}
function rescaleKeypoints(keypoints, outputSize2) {
  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) {
    for (const kpt4 of keypoints) {
      kpt4.positionRaw = [
        kpt4.positionRaw[0] + cropBox[1],
        kpt4.positionRaw[1] + 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) {
  var _a, _b;
  if (!((_a = models2.landmarks) == null ? void 0 : _a["executor"]))
    return null;
  const t2 = {};
  [t2.ld, t2.segmentation, t2.heatmap, t2.world, t2.poseflag] = (_b = models2.landmarks) == null ? void 0 : _b.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) => tf10.dispose(t2[tensor6]));
  const keypointsRelative = [];
  const depth = 5;
  for (let i = 0; i < points.length / depth; i++) {
    const score = sigmoid3(points[depth * i + 3]);
    const presence = sigmoid3(points[depth * i + 4]);
    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;
    const positionRaw = [points[depth * i + 0] / inputSize3.landmarks[0], points[depth * i + 1] / inputSize3.landmarks[1], 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 predict2(input, config3) {
  const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
  const skipTime = (config3.body.skipTime || 0) > now() - lastTime2;
  const skipFrame = skipped2 < (config3.body.skipFrames || 0);
  if (config3.skipAllowed && skipTime && skipFrame && cache !== null) {
    skipped2++;
  } else {
    const t2 = {};
    t2.landmarks = prepareImage(input, 256);
    cache = await detectLandmarks(t2.landmarks, config3, outputSize2);
    Object.keys(t2).forEach((tensor6) => tf10.dispose(t2[tensor6]));
    lastTime2 = now();
    skipped2 = 0;
  }
  return cache ? [cache] : [];
}

// src/object/centernet.ts
var tf11 = __toESM(require_tfjs_esm());

// src/object/labels.ts
var labels = [
  { 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 inputSize4 = 0;
var last2 = [];
var lastTime3 = 0;
var skipped3 = Number.MAX_SAFE_INTEGER;
async function load3(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;
    inputSize4 = 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 = tf11.squeeze(res);
  const arr = tf11.split(t2.squeeze, 6, 1);
  t2.stack = tf11.stack([arr[1], arr[0], arr[3], arr[2]], 1);
  t2.boxes = tf11.squeeze(t2.stack);
  t2.scores = tf11.squeeze(arr[4]);
  t2.classes = tf11.squeeze(arr[5]);
  tf11.dispose([res, ...arr]);
  t2.nms = await tf11.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, config3.object.maxDetected, 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 = labels[classVal].label;
    const [x, y] = [
      detections[0][id][0] / inputSize4,
      detections[0][id][1] / inputSize4
    ];
    const boxRaw = [
      x,
      y,
      detections[0][id][2] / inputSize4 - x,
      detections[0][id][3] / inputSize4 - 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) => tf11.dispose(t2[tensor6]));
  return results;
}
async function predict3(input, config3) {
  if (!(model3 == null ? void 0 : model3["executor"]))
    return [];
  const skipTime = (config3.object.skipTime || 0) > now() - lastTime3;
  const skipFrame = skipped3 < (config3.object.skipFrames || 0);
  if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) {
    skipped3++;
    return last2;
  }
  skipped3 = 0;
  return new Promise(async (resolve) => {
    const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
    const resize = tf11.image.resizeBilinear(input, [inputSize4, inputSize4]);
    const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null;
    lastTime3 = now();
    tf11.dispose(resize);
    const obj = await process3(objectT, outputSize2, config3);
    last2 = obj;
    resolve(obj);
  });
}

// src/body/efficientpose.ts
var tf12 = __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 lastTime4 = 0;
var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} };
var skipped4 = Number.MAX_SAFE_INTEGER;
async function load4(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 = tf12.reshape(inputs, [height * width]);
  const max4 = tf12.max(reshaped, 0);
  const newScore = (await max4.data())[0];
  if (newScore > minScore) {
    const coordinates = tf12.argMax(reshaped, 0);
    const mod3 = tf12.mod(coordinates, width);
    const x = (await mod3.data())[0];
    const div16 = tf12.div(coordinates, width);
    const y = (await div16.data())[0];
    tf12.dispose([reshaped, max4, coordinates, mod3, div16]);
    return [x, y, newScore];
  }
  tf12.dispose([reshaped, max4]);
  return [0, 0, newScore];
}
async function predict4(image27, config3) {
  if (!(model4 == null ? void 0 : model4["executor"]))
    return [];
  const skipTime = (config3.body.skipTime || 0) > now() - lastTime4;
  const skipFrame = skipped4 < (config3.body.skipFrames || 0);
  if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) {
    skipped4++;
    return [cache2];
  }
  skipped4 = 0;
  return new Promise(async (resolve) => {
    const tensor6 = tf12.tidy(() => {
      if (!(model4 == null ? void 0 : model4.inputs[0].shape))
        return null;
      const resize = tf12.image.resizeBilinear(image27, [model4.inputs[0].shape[2], model4.inputs[0].shape[1]], false);
      const enhance2 = tf12.mul(resize, constants.tf2);
      const norm = tf12.sub(enhance2, constants.tf1);
      return norm;
    });
    let resT;
    if (config3.body.enabled)
      resT = model4 == null ? void 0 : model4.execute(tensor6);
    lastTime4 = now();
    tf12.dispose(tensor6);
    if (resT) {
      cache2.keypoints.length = 0;
      const squeeze14 = tf12.squeeze(resT);
      tf12.dispose(resT);
      const stack5 = tf12.unstack(squeeze14, 2);
      tf12.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(image27.shape[2] * x2 / model4.inputs[0].shape[2]),
              Math.round(image27.shape[1] * y2 / model4.inputs[0].shape[1])
            ]
          });
        }
      }
      stack5.forEach((s) => tf12.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/gear/emotion.ts
var tf13 = __toESM(require_tfjs_esm());
var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"];
var model5;
var last3 = [];
var lastCount2 = 0;
var lastTime5 = 0;
var skipped5 = Number.MAX_SAFE_INTEGER;
async function load5(config3) {
  var _a;
  if (env.initial)
    model5 = null;
  if (!model5)
    model5 = await loadModel((_a = config3.face.emotion) == null ? void 0 : _a.modelPath);
  else if (config3.debug)
    log("cached model:", model5["modelUrl"]);
  return model5;
}
async function predict5(image27, config3, idx, count2) {
  var _a, _b;
  if (!model5)
    return [];
  const skipFrame = skipped5 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0);
  const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5;
  if (config3.skipAllowed && skipTime && skipFrame && lastCount2 === count2 && last3[idx] && last3[idx].length > 0) {
    skipped5++;
    return last3[idx];
  }
  skipped5 = 0;
  return new Promise(async (resolve) => {
    var _a2;
    const obj = [];
    if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) {
      const t2 = {};
      const inputSize10 = (model5 == null ? void 0 : model5.inputs[0].shape) ? model5.inputs[0].shape[2] : 0;
      t2.resize = tf13.image.resizeBilinear(image27, [inputSize10, inputSize10], false);
      t2.channels = tf13.mul(t2.resize, constants.rgb);
      t2.grayscale = tf13.sum(t2.channels, 3, true);
      t2.grayscaleSub = tf13.sub(t2.grayscale, constants.tf05);
      t2.grayscaleMul = tf13.mul(t2.grayscaleSub, constants.tf2);
      t2.emotion = model5 == null ? void 0 : model5.execute(t2.grayscaleMul);
      lastTime5 = 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) => tf13.dispose(t2[tensor6]));
    }
    last3[idx] = obj;
    lastCount2 = count2;
    resolve(obj);
  });
}

// src/face/facemesh.ts
var tf15 = __toESM(require_tfjs_esm());

// 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 load6(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/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/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 predict6(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 load7(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/face/faceres.ts
var tf16 = __toESM(require_tfjs_esm());
var model8;
var last4 = [];
var lastTime6 = 0;
var lastCount3 = 0;
var skipped6 = Number.MAX_SAFE_INTEGER;
async function load8(config3) {
  var _a;
  if (env.initial)
    model8 = null;
  if (!model8)
    model8 = await loadModel((_a = config3.face.description) == null ? void 0 : _a.modelPath);
  else if (config3.debug)
    log("cached model:", model8["modelUrl"]);
  return model8;
}
function enhance(input) {
  const tensor6 = input.image || input.tensor || input;
  if (!(model8 == null ? void 0 : model8.inputs[0].shape))
    return tensor6;
  const crop = tf16.image.resizeBilinear(tensor6, [model8.inputs[0].shape[2], model8.inputs[0].shape[1]], false);
  const norm = tf16.mul(crop, constants.tf255);
  tf16.dispose(crop);
  return norm;
}
async function predict7(image27, config3, idx, count2) {
  var _a, _b, _c, _d;
  const obj = {
    age: 0,
    gender: "unknown",
    genderScore: 0,
    descriptor: []
  };
  if (!(model8 == null ? void 0 : model8["executor"]))
    return obj;
  const skipFrame = skipped6 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0);
  const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime6;
  if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === 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) {
    skipped6++;
    return last4[idx];
  }
  skipped6 = 0;
  return new Promise(async (resolve) => {
    var _a2;
    if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) {
      const enhanced = enhance(image27);
      const resT = model8 == null ? void 0 : model8.execute(enhanced);
      lastTime6 = now();
      tf16.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 = tf16.argMax(resT.find((t2) => t2.shape[1] === 100), 1);
      const ageIdx = (await argmax.data())[0];
      tf16.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: model8, 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) => tf16.dispose(t2));
    }
    last4[idx] = obj;
    lastCount3 = count2;
    resolve(obj);
  });
}

// src/gear/gear.ts
var tf17 = __toESM(require_tfjs_esm());
var model9;
var last5 = [];
var raceNames = ["white", "black", "asian", "indian", "other"];
var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];
var lastCount4 = 0;
var lastTime7 = 0;
var skipped7 = Number.MAX_SAFE_INTEGER;
async function load9(config3) {
  var _a;
  if (env.initial)
    model9 = null;
  if (!model9)
    model9 = await loadModel((_a = config3.face.gear) == null ? void 0 : _a.modelPath);
  else if (config3.debug)
    log("cached model:", model9["modelUrl"]);
  return model9;
}
async function predict8(image27, config3, idx, count2) {
  var _a, _b;
  if (!model9)
    return { age: 0, gender: "unknown", genderScore: 0, race: [] };
  const skipFrame = skipped7 < (((_a = config3.face.gear) == null ? void 0 : _a.skipFrames) || 0);
  const skipTime = (((_b = config3.face.gear) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime7;
  if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && last5[idx]) {
    skipped7++;
    return last5[idx];
  }
  skipped7 = 0;
  return new Promise(async (resolve) => {
    var _a2, _b2;
    if (!(model9 == null ? void 0 : model9.inputs[0].shape))
      return;
    const t2 = {};
    const box = [[0, 0.1, 0.9, 0.9]];
    t2.resize = tf17.image.cropAndResize(image27, box, [0], [model9.inputs[0].shape[2], model9.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] = model9.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) => tf17.dispose(t2[tensor6]));
    last5[idx] = obj;
    lastCount4 = count2;
    lastTime7 = now();
    resolve(obj);
  });
}

// src/hand/handposedetector.ts
var tf19 = __toESM(require_tfjs_esm());

// src/hand/handposeutil.ts
var tf18 = __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, image27, cropSize) {
  const h = image27.shape[1];
  const w = image27.shape[2];
  const boxes = [[
    box.startPoint[1] / h,
    box.startPoint[0] / w,
    box.endPoint[1] / h,
    box.endPoint[0] / w
  ]];
  return tf18.image.cropAndResize(image27, 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 },
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  { 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(model21) {
    __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 = model21;
    this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]);
    this.anchorsTensor = tf19.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 = tf19.tensor1d([this.inputSize, this.inputSize]);
    this.doubleInputSizeTensor = tf19.tensor1d([this.inputSize * 2, this.inputSize * 2]);
  }
  normalizeBoxes(boxes) {
    const t2 = {};
    t2.boxOffsets = tf19.slice(boxes, [0, 0], [-1, 2]);
    t2.boxSizes = tf19.slice(boxes, [0, 2], [-1, 2]);
    t2.div = tf19.div(t2.boxOffsets, this.inputSizeTensor);
    t2.boxCenterPoints = tf19.add(t2.div, this.anchorsTensor);
    t2.halfBoxSizes = tf19.div(t2.boxSizes, this.doubleInputSizeTensor);
    t2.sub = tf19.sub(t2.boxCenterPoints, t2.halfBoxSizes);
    t2.startPoints = tf19.mul(t2.sub, this.inputSizeTensor);
    t2.add = tf19.add(t2.boxCenterPoints, t2.halfBoxSizes);
    t2.endPoints = tf19.mul(t2.add, this.inputSizeTensor);
    const res = tf19.concat2d([t2.startPoints, t2.endPoints], 1);
    Object.keys(t2).forEach((tensor6) => tf19.dispose(t2[tensor6]));
    return res;
  }
  normalizeLandmarks(rawPalmLandmarks, index2) {
    const t2 = {};
    t2.reshape = tf19.reshape(rawPalmLandmarks, [-1, 7, 2]);
    t2.div = tf19.div(t2.reshape, this.inputSizeTensor);
    t2.landmarks = tf19.add(t2.div, this.anchors[index2] ? this.anchors[index2] : 0);
    const res = tf19.mul(t2.landmarks, this.inputSizeTensor);
    Object.keys(t2).forEach((tensor6) => tf19.dispose(t2[tensor6]));
    return res;
  }
  async predict(input, config3) {
    var _a;
    const t2 = {};
    t2.resize = tf19.image.resizeBilinear(input, [this.inputSize, this.inputSize]);
    t2.div = tf19.div(t2.resize, constants.tf127);
    t2.image = tf19.sub(t2.div, constants.tf1);
    t2.batched = this.model.execute(t2.image);
    t2.predictions = tf19.squeeze(t2.batched);
    t2.slice = tf19.slice(t2.predictions, [0, 0], [-1, 1]);
    t2.sigmoid = tf19.sigmoid(t2.slice);
    t2.scores = tf19.squeeze(t2.sigmoid);
    const scores = await t2.scores.data();
    t2.boxes = tf19.slice(t2.predictions, [0, 1], [-1, 4]);
    t2.norm = this.normalizeBoxes(t2.boxes);
    t2.nms = await tf19.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 = tf19.slice(t2.norm, [index2, 0], [1, -1]);
      p.slice = tf19.slice(t2.predictions, [index2, 5], [1, 14]);
      p.norm = this.normalizeLandmarks(p.slice, index2);
      p.palmLandmarks = tf19.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) => tf19.dispose(p[tensor6]));
    }
    Object.keys(t2).forEach((tensor6) => tf19.dispose(t2[tensor6]));
    return hands;
  }
};

// src/hand/handposepipeline.ts
var tf20 = __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 lastTime8 = 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(image27, config3) {
    let useFreshBox = false;
    let boxes;
    const skipTime = (config3.hand.skipTime || 0) > now() - lastTime8;
    const skipFrame = this.skipped < (config3.hand.skipFrames || 0);
    if (config3.skipAllowed && skipTime && skipFrame) {
      boxes = await this.handDetector.predict(image27, 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] / image27.shape[2], palmCenter[1] / image27.shape[1]];
        const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf20.image.rotateWithOffset(image27, angle, 0, palmCenterNormalized) : image27.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 = tf20.div(croppedInput, constants.tf255);
        tf20.dispose(croppedInput);
        tf20.dispose(rotatedImage);
        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage);
        lastTime8 = now();
        tf20.dispose(handImage);
        const confidence = (await confidenceT.data())[0];
        tf20.dispose(confidenceT);
        if (confidence >= config3.hand.minConfidence / 4) {
          const keypointsReshaped = tf20.reshape(keypoints, [-1, 3]);
          const rawCoords = await keypointsReshaped.array();
          tf20.dispose(keypoints);
          tf20.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;
        }
        tf20.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/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 options2 = {
  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 > options2.NO_CURL_START_LIMIT)
    fingerCurl = FingerCurl.none;
  else if (angleOfCurve > options2.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 += options2.DISTANCE_VOTE_POWER;
  else if (start_end_x_y_dist_ratio > 0.66)
    voteDiagonal += options2.DISTANCE_VOTE_POWER;
  else
    voteHorizontal += options2.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, options2.TOTAL_ANGLE_VOTE_POWER);
  voteVertical += votes[0];
  voteDiagonal += votes[1];
  voteHorizontal += votes[2];
  for (const fingerSlope of fingerSlopes) {
    const fingerVotes = angleOrientationAt(fingerSlope, options2.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/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 predict9(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 load10(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 tf21 = __toESM(require_tfjs_esm());
var models3 = [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 skipped8 = Number.MAX_SAFE_INTEGER;
var lastTime9 = 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)
    models3[0] = null;
  if (!models3[0]) {
    fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3);
    models3[0] = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath);
    const inputs = models3[0]["executor"] ? Object.values(models3[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:", models3[0]["modelUrl"]);
  return models3[0];
}
async function loadSkeleton(config3) {
  var _a;
  if (env.initial)
    models3[1] = null;
  if (!models3[1]) {
    models3[1] = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath);
    const inputs = models3[1]["executor"] ? Object.values(models3[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:", models3[1]["modelUrl"]);
  return models3[1];
}
async function detectHands(input, config3) {
  const hands = [];
  if (!input || !models3[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 = tf21.image.resizeBilinear(input, [height, width]);
  t2.cast = tf21.cast(t2.resize, "int32");
  [t2.rawScores, t2.rawBoxes] = await models3[0].executeAsync(t2.cast, modelOutputNodes);
  t2.boxes = tf21.squeeze(t2.rawBoxes, [0, 2]);
  t2.scores = tf21.squeeze(t2.rawScores, [0]);
  const classScores = tf21.unstack(t2.scores, 1);
  tf21.dispose(classScores[faceIndex]);
  classScores.splice(faceIndex, 1);
  t2.filtered = tf21.stack(classScores, 1);
  tf21.dispose(classScores);
  t2.max = tf21.max(t2.filtered, 1);
  t2.argmax = tf21.argMax(t2.filtered, 1);
  let id = 0;
  t2.nms = await tf21.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 = tf21.slice(t2.boxes, nmsIndex, 1);
    const boxYX = await boxSlice.data();
    tf21.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) => tf21.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 && models3[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 = tf21.image.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear");
    t2.div = tf21.div(t2.crop, constants.tf255);
    [t2.score, t2.keypoints] = models3[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 = tf21.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) => tf21.dispose(t2[tensor6]));
  }
  return hand3;
}
async function predict10(input, config3) {
  var _a, _b;
  if (!((_a = models3[0]) == null ? void 0 : _a["executor"]) || !((_b = models3[1]) == null ? void 0 : _b["executor"]) || !models3[0].inputs[0].shape || !models3[1].inputs[0].shape)
    return [];
  outputSize = [input.shape[2] || 0, input.shape[1] || 0];
  skipped8++;
  const skipTime = (config3.hand.skipTime || 0) > now() - lastTime9;
  const skipFrame = skipped8 < (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() - lastTime9;
    const skipFrameExtended = skipped8 < 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);
      lastTime9 = now();
      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
      skipped8 = 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/face/insightface.ts
var tf22 = __toESM(require_tfjs_esm());
var model10;
var last6 = [];
var lastCount5 = 0;
var lastTime10 = 0;
var skipped9 = Number.MAX_SAFE_INTEGER;
async function load11(config3) {
  if (env.initial)
    model10 = null;
  if (!model10)
    model10 = await loadModel(config3.face["insightface"].modelPath);
  else if (config3.debug)
    log("cached model:", model10["modelUrl"]);
  return model10;
}
async function predict11(input, config3, idx, count2) {
  var _a, _b;
  if (!(model10 == null ? void 0 : model10["executor"]))
    return [];
  const skipFrame = skipped9 < (((_a = config3.face["insightface"]) == null ? void 0 : _a.skipFrames) || 0);
  const skipTime = (((_b = config3.face["insightface"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10;
  if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last6[idx]) {
    skipped9++;
    return last6[idx];
  }
  return new Promise(async (resolve) => {
    var _a2;
    let data = [];
    if (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.enabled) && (model10 == null ? void 0 : model10.inputs[0].shape)) {
      const t2 = {};
      t2.crop = tf22.image.resizeBilinear(input, [model10.inputs[0].shape[2], model10.inputs[0].shape[1]], false);
      t2.data = model10.execute(t2.crop);
      const output = await t2.data.data();
      data = Array.from(output);
      Object.keys(t2).forEach((tensor6) => tf22.dispose(t2[tensor6]));
    }
    last6[idx] = data;
    lastCount5 = count2;
    lastTime10 = now();
    resolve(data);
  });
}

// src/face/liveness.ts
var tf23 = __toESM(require_tfjs_esm());
var model11;
var cached2 = [];
var skipped10 = Number.MAX_SAFE_INTEGER;
var lastCount6 = 0;
var lastTime11 = 0;
async function load12(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 predict12(image27, 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() - lastTime11;
  const skipFrame = skipped10 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0);
  if (config3.skipAllowed && skipTime && skipFrame && lastCount6 === count2 && cached2[idx]) {
    skipped10++;
    return cached2[idx];
  }
  skipped10 = 0;
  return new Promise(async (resolve) => {
    const resize = tf23.image.resizeBilinear(image27, [(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;
    lastCount6 = count2;
    lastTime11 = now();
    tf23.dispose([resize, res]);
    resolve(cached2[idx]);
  });
}

// src/segmentation/meet.ts
var tf24 = __toESM(require_tfjs_esm());
var model12;
async function load13(config3) {
  if (!model12 || env.initial)
    model12 = await loadModel(config3.segmentation.modelPath);
  else if (config3.debug)
    log("cached model:", model12["modelUrl"]);
  return model12;
}
async function predict13(input, config3) {
  var _a;
  if (!model12)
    model12 = await load13(config3);
  if (!(model12 == null ? void 0 : model12["executor"]) || !((_a = model12 == null ? void 0 : model12.inputs) == null ? void 0 : _a[0].shape))
    return null;
  const t2 = {};
  t2.resize = tf24.image.resizeBilinear(input, [model12.inputs[0].shape ? model12.inputs[0].shape[1] : 0, model12.inputs[0].shape ? model12.inputs[0].shape[2] : 0], false);
  t2.norm = tf24.div(t2.resize, constants.tf255);
  t2.res = model12.execute(t2.norm);
  t2.squeeze = tf24.squeeze(t2.res, 0);
  [t2.bgRaw, t2.fgRaw] = tf24.unstack(t2.squeeze, 2);
  t2.fg = tf24.softmax(t2.fgRaw);
  t2.mul = tf24.mul(t2.fg, constants.tf255);
  t2.expand = tf24.expandDims(t2.mul, 2);
  t2.output = tf24.image.resizeBilinear(t2.expand, [input.shape[1], input.shape[2]]);
  let rgba;
  switch (config3.segmentation.mode || "default") {
    case "default":
      t2.input = tf24.squeeze(input);
      t2.concat = tf24.concat([t2.input, t2.output], -1);
      rgba = tf24.cast(t2.concat, "int32");
      break;
    case "alpha":
      rgba = tf24.cast(t2.output, "int32");
      break;
    default:
      rgba = tf24.tensor(0);
  }
  Object.keys(t2).forEach((tensor6) => tf24.dispose(t2[tensor6]));
  return rgba;
}

// src/face/mobilefacenet.ts
var tf25 = __toESM(require_tfjs_esm());
var model13;
var last7 = [];
var lastCount7 = 0;
var lastTime12 = 0;
var skipped11 = Number.MAX_SAFE_INTEGER;
async function load14(config3) {
  var _a;
  if (env.initial)
    model13 = null;
  if (!model13)
    model13 = await loadModel((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.modelPath);
  else if (config3.debug)
    log("cached model:", model13["modelUrl"]);
  return model13;
}
async function predict14(input, config3, idx, count2) {
  var _a, _b;
  if (!(model13 == null ? void 0 : model13["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() - lastTime12;
  if (config3.skipAllowed && skipTime && skipFrame && lastCount7 === count2 && last7[idx]) {
    skipped11++;
    return last7[idx];
  }
  return new Promise(async (resolve) => {
    var _a2;
    let data = [];
    if (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.enabled) && (model13 == null ? void 0 : model13.inputs[0].shape)) {
      const t2 = {};
      t2.crop = tf25.image.resizeBilinear(input, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false);
      t2.data = model13.execute(t2.crop);
      const output = await t2.data.data();
      data = Array.from(output);
      Object.keys(t2).forEach((tensor6) => tf25.dispose(t2[tensor6]));
    }
    last7[idx] = data;
    lastCount7 = count2;
    lastTime12 = now();
    resolve(data);
  });
}

// src/body/movenet.ts
var tf27 = __toESM(require_tfjs_esm());

// 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/body/movenetfix.ts
var tf26 = __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 = tf26.pad(input, cache5.padding);
  t2.resize = tf26.image.resizeBilinear(t2.pad, [inputSize10, inputSize10]);
  const final = tf26.cast(t2.resize, "int32");
  Object.keys(t2).forEach((tensor6) => tf26.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 model14;
var inputSize8 = 0;
var skipped12 = Number.MAX_SAFE_INTEGER;
var cache6 = {
  boxes: [],
  bodies: [],
  last: 0
};
async function load15(config3) {
  var _a;
  if (env.initial)
    model14 = null;
  if (!model14) {
    fakeOps(["size"], config3);
    model14 = await loadModel(config3.body.modelPath);
  } else if (config3.debug)
    log("cached model:", model14["modelUrl"]);
  inputSize8 = (model14 == null ? void 0 : model14["executor"]) && ((_a = model14 == null ? void 0 : model14.inputs) == null ? void 0 : _a[0].shape) ? model14.inputs[0].shape[2] : 0;
  if (inputSize8 < 64)
    inputSize8 = 256;
  return model14;
}
function parseSinglePose(res, config3, image27) {
  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((image27.shape[2] || 0) * positionRaw[0]),
          Math.round((image27.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), [image27.shape[2], image27.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, image27) {
  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((image27.shape[2] || 0) * positionRaw[0]), Math.round((image27.shape[1] || 0) * positionRaw[1])]
          });
        }
      }
      const newBox = calc(keypoints.map((pt) => pt.position), [image27.shape[2], image27.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 predict15(input, config3) {
  var _a;
  if (!(model14 == null ? void 0 : model14["executor"]) || !((_a = model14 == null ? void 0 : model14.inputs) == null ? void 0 : _a[0].shape))
    return [];
  if (!config3.skipAllowed)
    cache6.boxes.length = 0;
  skipped12++;
  const skipTime = (config3.body.skipTime || 0) > now() - cache6.last;
  const skipFrame = skipped12 < (config3.body.skipFrames || 0);
  if (config3.skipAllowed && skipTime && skipFrame) {
    return cache6.bodies;
  }
  return new Promise(async (resolve) => {
    const t2 = {};
    skipped12 = 0;
    t2.input = padInput(input, inputSize8);
    t2.res = model14 == null ? void 0 : model14.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) => tf27.dispose(t2[tensor6]));
    resolve(cache6.bodies);
  });
}

// src/object/nanodet.ts
var tf28 = __toESM(require_tfjs_esm());
var model15;
var last8 = [];
var lastTime13 = 0;
var skipped13 = Number.MAX_SAFE_INTEGER;
var inputSize9 = 0;
var scaleBox = 2.5;
async function load16(config3) {
  if (!model15 || env.initial) {
    model15 = await loadModel(config3.object.modelPath);
    const inputs = (model15 == null ? void 0 : model15["executor"]) ? Object.values(model15.modelSignature["inputs"]) : void 0;
    inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;
  } else if (config3.debug)
    log("cached model:", model15["modelUrl"]);
  return model15;
}
async function process4(res, outputShape, config3) {
  let id = 0;
  let results = [];
  const size2 = inputSize9;
  for (const strideSize of [1, 2, 4]) {
    const baseSize = strideSize * 13;
    const scoresT = tf28.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels.length));
    const scores = await scoresT.array();
    const featuresT = tf28.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels.length));
    const boxesMaxT = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]);
    const boxIdxT = boxesMaxT.argMax(2);
    const boxIdx = await boxIdxT.array();
    for (let i = 0; i < scoresT.shape[0]; i++) {
      for (let j = 0; j < scoresT.shape[1]; 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: labels[j].label,
            box: box.map((a) => Math.trunc(a)),
            boxRaw
          };
          results.push(result);
        }
      }
    }
    tf28.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 tf28.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
    nmsIdx = await nms.data();
    tf28.dispose(nms);
  }
  results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
  return results;
}
async function predict16(image27, config3) {
  if (!(model15 == null ? void 0 : model15["executor"]))
    return [];
  const skipTime = (config3.object.skipTime || 0) > now() - lastTime13;
  const skipFrame = skipped13 < (config3.object.skipFrames || 0);
  if (config3.skipAllowed && skipTime && skipFrame && last8.length > 0) {
    skipped13++;
    return last8;
  }
  skipped13 = 0;
  if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense"))
    return last8;
  return new Promise(async (resolve) => {
    const outputSize2 = [image27.shape[2] || 0, image27.shape[1] || 0];
    const resizeT = tf28.image.resizeBilinear(image27, [inputSize9, inputSize9], false);
    const normT = tf28.div(resizeT, constants.tf255);
    const transposeT = tf28.transpose(normT, [0, 3, 1, 2]);
    let objectT;
    if (config3.object.enabled)
      objectT = model15.execute(transposeT);
    lastTime13 = now();
    const obj = await process4(objectT, outputSize2, config3);
    last8 = obj;
    tf28.dispose([resizeT, normT, transposeT, ...objectT]);
    resolve(obj);
  });
}

// src/body/posenet.ts
var tf29 = __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 max4 = this.priorityQueue[0];
    this.exchange(0, this.numberOfElements--);
    this.sink(0);
    this.priorityQueue[this.numberOfElements + 1] = null;
    return max4;
  }
  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, max4) {
  if (a < min2)
    return min2;
  if (a > max4)
    return max4;
  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 model16;
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 predict17(input, config3) {
  if (!(model16 == null ? void 0 : model16["executor"]))
    return [];
  const res = tf29.tidy(() => {
    if (!model16.inputs[0].shape)
      return [];
    const resized = tf29.image.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]]);
    const normalized = tf29.sub(tf29.div(tf29.cast(resized, "float32"), 127.5), 1);
    const results = model16.execute(normalized, poseNetOutputs);
    const results3d = results.map((y) => tf29.squeeze(y, [0]));
    results3d[1] = tf29.sigmoid(results3d[1]);
    return results3d;
  });
  const buffers = await Promise.all(res.map((tensor6) => tensor6.buffer()));
  for (const t2 of res)
    tf29.dispose(t2);
  const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence);
  if (!model16.inputs[0].shape)
    return [];
  const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model16.inputs[0].shape[2], model16.inputs[0].shape[1]]);
  return scaled;
}
async function load17(config3) {
  if (!model16 || env.initial)
    model16 = await loadModel(config3.body.modelPath);
  else if (config3.debug)
    log("cached model:", model16["modelUrl"]);
  return model16;
}

// src/segmentation/rvm.ts
var tf30 = __toESM(require_tfjs_esm());
var model17;
var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"];
var t = {};
var ratio = 0;
function init2(config3) {
  tf30.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);
  t.r1i = tf30.tensor(0);
  t.r2i = tf30.tensor(0);
  t.r3i = tf30.tensor(0);
  t.r4i = tf30.tensor(0);
  ratio = config3.segmentation.ratio || 0.5;
  t.downsample_ratio = tf30.tensor(ratio);
}
async function load18(config3) {
  if (!model17 || env.initial)
    model17 = await loadModel(config3.segmentation.modelPath);
  else if (config3.debug)
    log("cached model:", model17["modelUrl"]);
  init2(config3);
  return model17;
}
var normalize = (r) => tf30.tidy(() => {
  const squeeze14 = tf30.squeeze(r, [0]);
  const mul15 = tf30.mul(squeeze14, constants.tf255);
  const cast8 = tf30.cast(mul15, "int32");
  return cast8;
});
function getRGBA(fgr, pha) {
  const rgb2 = fgr ? normalize(fgr) : tf30.fill([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32");
  const a = pha ? normalize(pha) : tf30.fill([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32");
  const rgba = tf30.concat([rgb2, a], -1);
  tf30.dispose([rgb2, a]);
  return rgba;
}
function getState(state) {
  return tf30.tidy(() => {
    const r = {};
    r.unstack = tf30.unstack(state, -1);
    r.concat = tf30.concat(r.unstack, 1);
    r.split = tf30.split(r.concat, 4, 1);
    r.stack = tf30.concat(r.split, 2);
    r.squeeze = tf30.squeeze(r.stack, [0]);
    r.expand = tf30.expandDims(r.squeeze, -1);
    r.add = tf30.add(r.expand, 1);
    r.mul = tf30.mul(r.add, 127.5);
    r.cast = tf30.cast(r.mul, "int32");
    r.tile = tf30.tile(r.cast, [1, 1, 3]);
    r.alpha = tf30.fill([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32");
    return tf30.concat([r.tile, r.alpha], -1);
  });
}
async function predict18(input, config3) {
  if (!model17)
    model17 = await load18(config3);
  if (!(model17 == null ? void 0 : model17["executor"]))
    return null;
  t.src = tf30.div(input, 255);
  if (ratio !== config3.segmentation.ratio)
    init2(config3);
  const [fgr, pha, r1o, r2o, r3o, r4o] = await model17.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 = tf30.tensor(0);
  }
  tf30.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 tf31 = __toESM(require_tfjs_esm());
var model18;
async function load19(config3) {
  if (!model18 || env.initial)
    model18 = await loadModel(config3.segmentation.modelPath);
  else if (config3.debug)
    log("cached model:", model18["modelUrl"]);
  return model18;
}
async function predict19(input, config3) {
  var _a;
  if (!model18)
    model18 = await load19(config3);
  if (!(model18 == null ? void 0 : model18["executor"]) || !((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape))
    return null;
  const t2 = {};
  t2.resize = tf31.image.resizeBilinear(input, [model18.inputs[0].shape ? model18.inputs[0].shape[1] : 0, model18.inputs[0].shape ? model18.inputs[0].shape[2] : 0], false);
  t2.norm = tf31.div(t2.resize, constants.tf255);
  t2.res = model18.execute(t2.norm);
  t2.squeeze = tf31.squeeze(t2.res, 0);
  t2.alpha = tf31.image.resizeBilinear(t2.squeeze, [input.shape[1], input.shape[2]]);
  t2.mul = tf31.mul(t2.alpha, constants.tf255);
  let rgba;
  switch (config3.segmentation.mode || "default") {
    case "default":
      t2.input = tf31.squeeze(input);
      t2.concat = tf31.concat([t2.input, t2.mul], -1);
      rgba = tf31.cast(t2.concat, "int32");
      break;
    case "alpha":
      rgba = tf31.cast(t2.mul, "int32");
      break;
    default:
      rgba = tf31.tensor(0);
  }
  Object.keys(t2).forEach((tensor6) => tf31.dispose(t2[tensor6]));
  return rgba;
}

// src/gear/ssrnet-age.ts
var tf32 = __toESM(require_tfjs_esm());
var model19;
var last9 = [];
var lastCount8 = 0;
var lastTime14 = 0;
var skipped14 = Number.MAX_SAFE_INTEGER;
async function load20(config3) {
  if (env.initial)
    model19 = null;
  if (!model19)
    model19 = await loadModel(config3.face["ssrnet"].modelPathAge);
  else if (config3.debug)
    log("cached model:", model19["modelUrl"]);
  return model19;
}
async function predict20(image27, config3, idx, count2) {
  var _a, _b, _c, _d;
  if (!model19)
    return { age: 0 };
  const skipFrame = skipped14 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0);
  const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime14;
  if (config3.skipAllowed && skipFrame && skipTime && lastCount8 === count2 && ((_c = last9[idx]) == null ? void 0 : _c.age) && ((_d = last9[idx]) == null ? void 0 : _d.age) > 0) {
    skipped14++;
    return last9[idx];
  }
  skipped14 = 0;
  return new Promise(async (resolve) => {
    var _a2;
    if (!(model19 == null ? void 0 : model19.inputs) || !model19.inputs[0] || !model19.inputs[0].shape)
      return;
    const t2 = {};
    t2.resize = tf32.image.resizeBilinear(image27, [model19.inputs[0].shape[2], model19.inputs[0].shape[1]], false);
    t2.enhance = tf32.mul(t2.resize, constants.tf255);
    const obj = { age: 0 };
    if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled)
      t2.age = model19.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) => tf32.dispose(t2[tensor6]));
    last9[idx] = obj;
    lastCount8 = count2;
    lastTime14 = now();
    resolve(obj);
  });
}

// src/gear/ssrnet-gender.ts
var tf33 = __toESM(require_tfjs_esm());
var model20;
var last10 = [];
var lastCount9 = 0;
var lastTime15 = 0;
var skipped15 = Number.MAX_SAFE_INTEGER;
var rgb = [0.2989, 0.587, 0.114];
async function load21(config3) {
  var _a;
  if (env.initial)
    model20 = null;
  if (!model20)
    model20 = await loadModel((_a = config3.face["ssrnet"]) == null ? void 0 : _a.modelPathGender);
  else if (config3.debug)
    log("cached model:", model20["modelUrl"]);
  return model20;
}
async function predict21(image27, config3, idx, count2) {
  var _a, _b, _c, _d;
  if (!model20)
    return { gender: "unknown", genderScore: 0 };
  const skipFrame = skipped15 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0);
  const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime15;
  if (config3.skipAllowed && skipFrame && skipTime && lastCount9 === count2 && ((_c = last10[idx]) == null ? void 0 : _c.gender) && ((_d = last10[idx]) == null ? void 0 : _d.genderScore) > 0) {
    skipped15++;
    return last10[idx];
  }
  skipped15 = 0;
  return new Promise(async (resolve) => {
    var _a2;
    if (!(model20 == null ? void 0 : model20.inputs[0].shape))
      return;
    const t2 = {};
    t2.resize = tf33.image.resizeBilinear(image27, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]], false);
    t2.enhance = tf33.tidy(() => {
      const [red, green, blue] = tf33.split(t2.resize, 3, 3);
      const redNorm = tf33.mul(red, rgb[0]);
      const greenNorm = tf33.mul(green, rgb[1]);
      const blueNorm = tf33.mul(blue, rgb[2]);
      const grayscale = tf33.addN([redNorm, greenNorm, blueNorm]);
      const normalize2 = tf33.mul(tf33.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 = model20.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) => tf33.dispose(t2[tensor6]));
    last10[idx] = obj;
    lastCount9 = count2;
    lastTime15 = now();
    resolve(obj);
  });
}

// src/models.ts
var Models = class {
  constructor() {
    __publicField(this, "ssrnetage", null);
    __publicField(this, "gear", null);
    __publicField(this, "blazeposedetect", null);
    __publicField(this, "blazepose", null);
    __publicField(this, "centernet", null);
    __publicField(this, "efficientpose", null);
    __publicField(this, "mobilefacenet", null);
    __publicField(this, "insightface", null);
    __publicField(this, "emotion", null);
    __publicField(this, "facedetect", null);
    __publicField(this, "faceiris", null);
    __publicField(this, "facemesh", null);
    __publicField(this, "faceres", null);
    __publicField(this, "ssrnetgender", null);
    __publicField(this, "handpose", null);
    __publicField(this, "handskeleton", null);
    __publicField(this, "handtrack", null);
    __publicField(this, "liveness", null);
    __publicField(this, "meet", null);
    __publicField(this, "movenet", null);
    __publicField(this, "nanodet", null);
    __publicField(this, "posenet", null);
    __publicField(this, "selfie", null);
    __publicField(this, "rvm", null);
    __publicField(this, "antispoof", null);
  }
};
var instance;
var getModelStats = (currentInstance) => {
  if (currentInstance)
    instance = currentInstance;
  if (!instance)
    log("instance not registred");
  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(instance.models).length,
    percentageLoaded,
    totalSizeFromManifest,
    totalSizeWeights,
    totalSizeLoading,
    totalSizeEnabled: void 0,
    modelStats: Object.values(modelStats)
  };
};
function reset2(currentInstance) {
  if (currentInstance)
    instance = currentInstance;
  for (const model21 of Object.keys(instance.models))
    instance.models[model21] = null;
}
async function load22(currentInstance) {
  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, _B, _C;
  if (currentInstance)
    instance = currentInstance;
  if (!instance)
    log("instance not registred");
  if (env.initial)
    reset2(instance);
  if (instance.config.hand.enabled) {
    if (!instance.models.handpose && ((_b = (_a = instance.config.hand.detector) == null ? void 0 : _a.modelPath) == null ? void 0 : _b.includes("handdetect"))) {
      [instance.models.handpose, instance.models.handskeleton] = await load10(instance.config);
    }
    if (!instance.models.handskeleton && instance.config.hand.landmarks && ((_d = (_c = instance.config.hand.detector) == null ? void 0 : _c.modelPath) == null ? void 0 : _d.includes("handdetect"))) {
      [instance.models.handpose, instance.models.handskeleton] = await load10(instance.config);
    }
  }
  if (instance.config.body.enabled && !instance.models.blazepose && ((_e = instance.config.body.modelPath) == null ? void 0 : _e.includes("blazepose")))
    instance.models.blazepose = loadPose(instance.config);
  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body["detector"] && instance.config.body["detector"].modelPath)
    instance.models.blazeposedetect = loadDetect(instance.config);
  if (instance.config.body.enabled && !instance.models.efficientpose && ((_f = instance.config.body.modelPath) == null ? void 0 : _f.includes("efficientpose")))
    instance.models.efficientpose = load4(instance.config);
  if (instance.config.body.enabled && !instance.models.movenet && ((_g = instance.config.body.modelPath) == null ? void 0 : _g.includes("movenet")))
    instance.models.movenet = load15(instance.config);
  if (instance.config.body.enabled && !instance.models.posenet && ((_h = instance.config.body.modelPath) == null ? void 0 : _h.includes("posenet")))
    instance.models.posenet = load17(instance.config);
  if (instance.config.face.enabled && !instance.models.facedetect)
    instance.models.facedetect = load2(instance.config);
  if (instance.config.face.enabled && ((_i = instance.config.face.antispoof) == null ? void 0 : _i.enabled) && !instance.models.antispoof)
    instance.models.antispoof = load(instance.config);
  if (instance.config.face.enabled && ((_j = instance.config.face.liveness) == null ? void 0 : _j.enabled) && !instance.models.liveness)
    instance.models.liveness = load12(instance.config);
  if (instance.config.face.enabled && ((_k = instance.config.face.description) == null ? void 0 : _k.enabled) && !instance.models.faceres)
    instance.models.faceres = load8(instance.config);
  if (instance.config.face.enabled && ((_l = instance.config.face.emotion) == null ? void 0 : _l.enabled) && !instance.models.emotion)
    instance.models.emotion = load5(instance.config);
  if (instance.config.face.enabled && ((_m = instance.config.face.iris) == null ? void 0 : _m.enabled) && !((_n = instance.config.face.attention) == null ? void 0 : _n.enabled) && !instance.models.faceiris)
    instance.models.faceiris = load6(instance.config);
  if (instance.config.face.enabled && ((_o = instance.config.face.mesh) == null ? void 0 : _o.enabled) && !instance.models.facemesh)
    instance.models.facemesh = load7(instance.config);
  if (instance.config.face.enabled && ((_p = instance.config.face["gear"]) == null ? void 0 : _p.enabled) && !instance.models.gear)
    instance.models.gear = load9(instance.config);
  if (instance.config.face.enabled && ((_q = instance.config.face["ssrnet"]) == null ? void 0 : _q.enabled) && !instance.models.ssrnetage)
    instance.models.ssrnetage = load20(instance.config);
  if (instance.config.face.enabled && ((_r = instance.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && !instance.models.ssrnetgender)
    instance.models.ssrnetgender = load21(instance.config);
  if (instance.config.face.enabled && ((_s = instance.config.face["mobilefacenet"]) == null ? void 0 : _s.enabled) && !instance.models.mobilefacenet)
    instance.models.mobilefacenet = load14(instance.config);
  if (instance.config.face.enabled && ((_t = instance.config.face["insightface"]) == null ? void 0 : _t.enabled) && !instance.models.insightface)
    instance.models.insightface = load11(instance.config);
  if (instance.config.hand.enabled && !instance.models.handtrack && ((_v = (_u = instance.config.hand.detector) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("handtrack")))
    instance.models.handtrack = loadDetect2(instance.config);
  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && ((_x = (_w = instance.config.hand.detector) == null ? void 0 : _w.modelPath) == null ? void 0 : _x.includes("handtrack")))
    instance.models.handskeleton = loadSkeleton(instance.config);
  if (instance.config.object.enabled && !instance.models.centernet && ((_y = instance.config.object.modelPath) == null ? void 0 : _y.includes("centernet")))
    instance.models.centernet = load3(instance.config);
  if (instance.config.object.enabled && !instance.models.nanodet && ((_z = instance.config.object.modelPath) == null ? void 0 : _z.includes("nanodet")))
    instance.models.nanodet = load16(instance.config);
  if (instance.config.segmentation.enabled && !instance.models.selfie && ((_A = instance.config.segmentation.modelPath) == null ? void 0 : _A.includes("selfie")))
    instance.models.selfie = load19(instance.config);
  if (instance.config.segmentation.enabled && !instance.models.meet && ((_B = instance.config.segmentation.modelPath) == null ? void 0 : _B.includes("meet")))
    instance.models.meet = load13(instance.config);
  if (instance.config.segmentation.enabled && !instance.models.rvm && ((_C = instance.config.segmentation.modelPath) == null ? void 0 : _C.includes("rvm")))
    instance.models.rvm = load18(instance.config);
  for await (const model21 of Object.keys(instance.models)) {
    if (instance.models[model21] && typeof instance.models[model21] !== "undefined") {
      instance.models[model21] = await instance.models[model21];
    }
  }
}
function validateModel(currentInstance, model21, name) {
  var _a, _b;
  if (!model21)
    return null;
  if (currentInstance)
    instance = currentInstance;
  if (!instance)
    log("instance not registred");
  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 = model21["modelUrl"];
  const executor = model21["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;
}
function validate2(currentInstance) {
  if (currentInstance)
    instance = currentInstance;
  if (!instance)
    log("instance not registred");
  const missing = [];
  for (const defined of Object.keys(currentInstance.models)) {
    const model21 = currentInstance.models[defined];
    if (!model21)
      continue;
    const res = validateModel(currentInstance, model21, defined);
    if (res)
      missing.push(res);
  }
  return missing;
}

// src/tfjs/humangl.ts
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(instance2) {
  var _a;
  if (instance2.config.backend !== "humangl")
    return;
  if (config2.name in tf34.engine().registry && !((_a = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a.getParameter(config2.gl.VERSION))) {
    log("humangl error: backend invalid context");
    reset2(instance2);
  }
  if (!tf34.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");
        instance2.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");
          instance2.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 {
      tf34.setWebGLContext(2, config2.gl);
    } catch (err) {
      log("humangl error: cannot set webgl context:", err);
      return;
    }
    try {
      const ctx = new tf34.GPGPUContext(config2.gl);
      tf34.registerBackend(config2.name, () => new tf34.MathBackendWebGL(ctx), config2.priority);
    } catch (err) {
      log("humangl error: cannot register webgl backend:", err);
      return;
    }
    try {
      const kernels = tf34.getKernelsForBackend("webgl");
      kernels.forEach((kernelConfig) => {
        const newKernelConfig = { ...kernelConfig, backendName: config2.name };
        tf34.registerKernel(newKernelConfig);
      });
    } catch (err) {
      log("humangl error: cannot update webgl backend registration:", err);
      return;
    }
    try {
      if (tf34.env().flagRegistry.WEBGL_VERSION)
        tf34.env().set("WEBGL_VERSION", 2);
    } catch (err) {
      log("humangl error: cannot set WebGL backend flags:", err);
      return;
    }
    extensions();
    const current = tf34.backend().getGPGPUContext ? tf34.backend().getGPGPUContext().gl : null;
    if (current) {
      if (instance2.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/backend.ts
var tf35 = __toESM(require_tfjs_esm());
function registerCustomOps(config3) {
  const newKernels = [];
  if (!env.kernels.includes("mod")) {
    const kernelMod = {
      kernelName: "Mod",
      backendName: tf35.getBackend(),
      kernelFunc: (op) => tf35.tidy(() => tf35.sub(op.inputs.a, tf35.mul(tf35.div(op.inputs.a, op.inputs.b), op.inputs.b)))
    };
    tf35.registerKernel(kernelMod);
    env.kernels.push("mod");
    newKernels.push("mod");
  }
  if (!env.kernels.includes("floormod")) {
    const kernelFloorMod = {
      kernelName: "FloorMod",
      backendName: tf35.getBackend(),
      kernelFunc: (op) => tf35.tidy(() => tf35.add(tf35.mul(tf35.floorDiv(op.inputs.a / op.inputs.b), op.inputs.b), tf35.mod(op.inputs.a, op.inputs.b)))
    };
    tf35.registerKernel(kernelFloorMod);
    env.kernels.push("floormod");
    newKernels.push("floormod");
  }
  if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) {
    const kernelRotateWithOffset = {
      kernelName: "RotateWithOffset",
      backendName: tf35.getBackend(),
      kernelFunc: (op) => tf35.tidy(() => {
        const backend4 = tf35.getBackend();
        tf35.setBackend("cpu");
        const t2 = tf35.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);
        tf35.setBackend(backend4);
        return t2;
      })
    };
    tf35.registerKernel(kernelRotateWithOffset);
    env.kernels.push("rotatewithoffset");
    newKernels.push("rotatewithoffset");
  }
  if (newKernels.length > 0 && config3.debug)
    log("registered kernels:", newKernels);
}
var defaultFlags = {};
async function check(instance2, force = false) {
  instance2.state = "backend";
  if (force || env.initial || instance2.config.backend && instance2.config.backend.length > 0 && tf35.getBackend() !== instance2.config.backend) {
    const timeStamp = now();
    if (instance2.config.backend && instance2.config.backend.length > 0) {
      if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance2.config.debug) {
        if (instance2.config.debug)
          log("running inside web worker");
      }
      if (env.browser && instance2.config.backend === "tensorflow") {
        if (instance2.config.debug)
          log("override: backend set to tensorflow while running in browser");
        instance2.config.backend = "webgl";
      }
      if (env.node && (instance2.config.backend === "webgl" || instance2.config.backend === "humangl")) {
        if (instance2.config.debug)
          log(`override: backend set to ${instance2.config.backend} while running in nodejs`);
        instance2.config.backend = "tensorflow";
      }
      if (env.browser && instance2.config.backend === "webgpu") {
        if (typeof navigator === "undefined" || typeof navigator.gpu === "undefined") {
          log("override: backend set to webgpu but browser does not support webgpu");
          instance2.config.backend = "webgl";
        } else {
          const adapter = await navigator.gpu.requestAdapter();
          if (instance2.config.debug)
            log("enumerated webgpu adapter:", adapter);
          if (!adapter) {
            log("override: backend set to webgpu but browser reports no available gpu");
            instance2.config.backend = "webgl";
          } else {
            const adapterInfo = "requestAdapterInfo" in adapter ? await adapter.requestAdapterInfo() : void 0;
            log("webgpu adapter info:", adapterInfo);
          }
        }
      }
      let available = Object.keys(tf35.engine().registryFactory);
      if (instance2.config.backend === "humangl" && !available.includes("humangl")) {
        register(instance2);
        available = Object.keys(tf35.engine().registryFactory);
      }
      if (instance2.config.debug)
        log("available backends:", available);
      if (!available.includes(instance2.config.backend)) {
        log(`error: backend ${instance2.config.backend} not found in registry`);
        instance2.config.backend = env.node ? "tensorflow" : "webgl";
        if (instance2.config.debug)
          log(`override: setting backend ${instance2.config.backend}`);
      }
      if (instance2.config.debug)
        log("setting backend:", [instance2.config.backend]);
      if (instance2.config.backend === "wasm") {
        if (tf35.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY)
          tf35.env().set("CANVAS2D_WILL_READ_FREQUENTLY", true);
        if (instance2.config.debug)
          log("wasm path:", instance2.config.wasmPath);
        if (typeof tf35.setWasmPaths !== "undefined")
          tf35.setWasmPaths(instance2.config.wasmPath, instance2.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 tf35.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
          simd = await tf35.env().getAsync("WASM_HAS_SIMD_SUPPORT");
          if (instance2.config.debug)
            log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`);
          if (instance2.config.debug && !simd)
            log("warning: wasm simd support is not enabled");
        } catch (e) {
          log("wasm detection failed");
        }
      }
      try {
        await tf35.setBackend(instance2.config.backend);
        await tf35.ready();
      } catch (err) {
        log("error: cannot set backend:", instance2.config.backend, err);
        return false;
      }
      if (instance2.config.debug)
        defaultFlags = JSON.parse(JSON.stringify(tf35.env().flags));
    }
    if (tf35.getBackend() === "humangl" || tf35.getBackend() === "webgl") {
      if (tf35.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS)
        tf35.env().set("WEBGL_USE_SHAPES_UNIFORMS", true);
      if (tf35.env().flagRegistry.WEBGL_EXP_CONV)
        tf35.env().set("WEBGL_EXP_CONV", true);
      if (instance2.config.debug && typeof instance2.config.deallocate !== "undefined" && instance2.config.deallocate) {
        log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true);
        tf35.env().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0);
      }
    }
    if (tf35.getBackend() === "webgpu") {
    }
    if (instance2.config.debug) {
      const newFlags = tf35.env().flags;
      const updatedFlags = {};
      for (const key of Object.keys(newFlags)) {
        if (defaultFlags[key] === newFlags[key])
          continue;
        updatedFlags[key] = newFlags[key];
      }
      if (instance2.config.debug && Object.keys(updatedFlags).length > 0)
        log("backend:", tf35.getBackend(), "flags:", updatedFlags);
    }
    if (instance2.config.flags && Object.keys(instance2.config.flags).length > 0) {
      if (instance2.config.debug)
        log("flags:", instance2.config["flags"]);
      for (const [key, val] of Object.entries(instance2.config.flags)) {
        tf35.env().set(key, val);
      }
    }
    tf35.enableProdMode();
    init();
    instance2.performance.initBackend = Math.trunc(now() - timeStamp);
    instance2.config.backend = tf35.getBackend();
    await env.updateBackend();
    registerCustomOps(instance2.config);
    env.initial = false;
  }
  return true;
}
function fakeOps(kernelNames, config3) {
  for (const kernelName of kernelNames) {
    const kernelConfig = {
      kernelName,
      backendName: config3.backend,
      kernelFunc: () => {
        if (config3.debug)
          log("kernelFunc", kernelName, config3.backend);
      }
    };
    tf35.registerKernel(kernelConfig);
  }
  env.kernels = tf35.getKernelsForBackend(tf35.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,
  object: () => object,
  options: () => options3,
  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 colorDepth = (z, opt2) => {
  if (!opt2.useDepth || typeof z === "undefined")
    return opt2.color;
  const rgb2 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]);
  return `rgba(${rgb2[0]}, ${rgb2[1]}, ${rgb2[2]}, ${opt2.alpha})`;
};
function point(ctx, x, y, z, localOptions) {
  ctx.fillStyle = colorDepth(z, localOptions);
  ctx.beginPath();
  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);
  ctx.fill();
}
function rect(ctx, x, y, width, height, localOptions) {
  ctx.beginPath();
  ctx.lineWidth = localOptions.lineWidth;
  if (localOptions.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 + localOptions.roundRect, y);
    ctx.lineTo(x + width - localOptions.roundRect, y);
    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);
    ctx.lineTo(x + width, y + height - localOptions.roundRect);
    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);
    ctx.lineTo(x + localOptions.roundRect, y + height);
    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);
    ctx.lineTo(x, y + localOptions.roundRect);
    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);
    ctx.closePath();
  }
  ctx.stroke();
}
function lines(ctx, points, localOptions) {
  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, localOptions);
    ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));
  }
  ctx.stroke();
  if (localOptions.fillPolygons) {
    ctx.closePath();
    ctx.fill();
  }
}
function curves(ctx, points, localOptions) {
  if (points.length < 2)
    return;
  ctx.lineWidth = localOptions.lineWidth;
  if (!localOptions.useCurves || points.length <= 2) {
    lines(ctx, points, localOptions);
    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 (localOptions.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 options3 = {
  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
};

// src/draw/face.ts
var opt;
function drawLabels(f, ctx) {
  var _a, _b;
  if (opt.drawLabels) {
    const labels2 = [];
    labels2.push(`face: ${Math.trunc(100 * f.score)}%`);
    if (f.genderScore)
      labels2.push(`${f.gender || ""} ${Math.trunc(100 * f.genderScore)}%`);
    if (f.age)
      labels2.push(`age: ${f.age || ""}`);
    if (f.iris)
      labels2.push(`distance: ${f.iris}`);
    if (f.real)
      labels2.push(`real: ${Math.trunc(100 * f.real)}%`);
    if (f.live)
      labels2.push(`live: ${Math.trunc(100 * f.live)}%`);
    if (f.emotion && f.emotion.length > 0) {
      const emotion2 = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);
      if (emotion2.length > 3)
        emotion2.length = 3;
      labels2.push(emotion2.join(" "));
    }
    if (((_a = f.rotation) == null ? void 0 : _a.angle) && ((_b = f.rotation) == null ? void 0 : _b.gaze)) {
      if (f.rotation.angle.roll)
        labels2.push(`roll: ${rad2deg(f.rotation.angle.roll)}\xB0 yaw:${rad2deg(f.rotation.angle.yaw)}\xB0 pitch:${rad2deg(f.rotation.angle.pitch)}\xB0`);
      if (f.rotation.gaze.bearing)
        labels2.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\xB0`);
    }
    if (labels2.length === 0)
      labels2.push("face");
    ctx.fillStyle = opt.color;
    for (let i = labels2.length - 1; i >= 0; i--) {
      const x = Math.max(f.box[0], 0);
      const y = i * opt.lineHeight + f.box[1];
      if (opt.shadowColor && opt.shadowColor !== "") {
        ctx.fillStyle = opt.shadowColor;
        ctx.fillText(labels2[i], x + 5, y + 16);
      }
      ctx.fillStyle = opt.labelColor;
      ctx.fillText(labels2[i], x + 4, y + 15);
    }
  }
}
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 = opt.useDepth ? "rgba(255, 200, 255, 0.3)" : opt.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 (opt.fillPolygons) {
      ctx.fillStyle = opt.useDepth ? "rgba(255, 255, 200, 0.3)" : opt.color;
      ctx.fill();
    }
  }
  if (((_c = f.annotations) == null ? void 0 : _c.rightEyeIris) && ((_d = f.annotations) == null ? void 0 : _d.rightEyeIris[0])) {
    ctx.strokeStyle = opt.useDepth ? "rgba(255, 200, 255, 0.3)" : opt.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 (opt.fillPolygons) {
      ctx.fillStyle = opt.useDepth ? "rgba(255, 255, 200, 0.3)" : opt.color;
      ctx.fill();
    }
  }
}
function drawGazeSpheres(f, ctx) {
  var _a;
  if (opt.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 (opt.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 (opt.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, opt);
    }
    drawIrisElipse(f, ctx);
  }
}
function drawFacePoints(f, ctx) {
  if (opt.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], opt);
      if (opt.drawAttention) {
        if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i))
          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, opt);
        if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i))
          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, opt);
        if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i))
          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, opt);
      }
    }
  }
}
function drawFaceBoxes(f, ctx) {
  if (opt.drawBoxes) {
    rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], opt);
  }
}
function face(inCanvas2, result, drawOptions) {
  opt = mergeDeep(options3, drawOptions);
  if (!result || !inCanvas2)
    return;
  const ctx = getCanvasContext(inCanvas2);
  if (!ctx)
    return;
  ctx.font = opt.font;
  ctx.strokeStyle = opt.color;
  ctx.fillStyle = opt.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) {
  const localOptions = mergeDeep(options3, 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 = localOptions.color;
    ctx.fillStyle = localOptions.color;
    ctx.lineWidth = localOptions.lineWidth;
    ctx.font = localOptions.font;
    if (localOptions.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], localOptions);
      if (localOptions.drawLabels) {
        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
          ctx.fillStyle = localOptions.shadowColor;
          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
        }
        ctx.fillStyle = localOptions.labelColor;
        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
      }
    }
    if (localOptions.drawPoints && result[i].keypoints) {
      for (let pt = 0; pt < result[i].keypoints.length; pt++) {
        if (!result[i].keypoints[pt].score || result[i].keypoints[pt].score === 0)
          continue;
        ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);
        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);
      }
    }
    if (localOptions.drawLabels && result[i].keypoints) {
      ctx.font = localOptions.font;
      for (const pt of result[i].keypoints) {
        if (!pt.score || pt.score === 0)
          continue;
        ctx.fillStyle = colorDepth(pt.position[2], localOptions);
        ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);
      }
    }
    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {
      for (const part of Object.values(result[i].annotations)) {
        for (const connected4 of part)
          curves(ctx, connected4, localOptions);
      }
    }
  }
}

// src/draw/hand.ts
function hand(inCanvas2, result, drawOptions) {
  const localOptions = mergeDeep(options3, drawOptions);
  if (!result || !inCanvas2)
    return;
  const ctx = getCanvasContext(inCanvas2);
  if (!ctx)
    return;
  ctx.lineJoin = "round";
  ctx.font = localOptions.font;
  for (const h of result) {
    if (localOptions.drawBoxes) {
      ctx.strokeStyle = localOptions.color;
      ctx.fillStyle = localOptions.color;
      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);
      if (localOptions.drawLabels) {
        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
          ctx.fillStyle = localOptions.shadowColor;
          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);
        }
        ctx.fillStyle = localOptions.labelColor;
        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);
      }
      ctx.stroke();
    }
    if (localOptions.drawPoints) {
      if (h.keypoints && h.keypoints.length > 0) {
        for (const pt of h.keypoints) {
          ctx.fillStyle = colorDepth(pt[2], localOptions);
          point(ctx, pt[0], pt[1], 0, localOptions);
        }
      }
    }
    if (localOptions.drawLabels && h.annotations) {
      const addHandLabel = (part, title) => {
        if (!part || part.length === 0 || !part[0])
          return;
        const z = part[part.length - 1][2] || -256;
        ctx.fillStyle = colorDepth(z, localOptions);
        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);
      };
      ctx.font = localOptions.font;
      addHandLabel(h.annotations.index, "index");
      addHandLabel(h.annotations.middle, "middle");
      addHandLabel(h.annotations.ring, "ring");
      addHandLabel(h.annotations.pinky, "pinky");
      addHandLabel(h.annotations.thumb, "thumb");
      addHandLabel(h.annotations.palm, "palm");
    }
    if (localOptions.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, localOptions);
          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 = localOptions.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) {
  const localOptions = mergeDeep(options3, drawOptions);
  if (!result || !inCanvas2)
    return;
  const ctx = getCanvasContext(inCanvas2);
  if (!ctx)
    return;
  ctx.lineJoin = "round";
  ctx.font = localOptions.font;
  for (const h of result) {
    if (localOptions.drawBoxes) {
      ctx.strokeStyle = localOptions.color;
      ctx.fillStyle = localOptions.color;
      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);
      if (localOptions.drawLabels) {
        const label = `${h.label} ${Math.round(100 * h.score)}%`;
        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
          ctx.fillStyle = localOptions.shadowColor;
          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);
        }
        ctx.fillStyle = localOptions.labelColor;
        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);
      }
      ctx.stroke();
    }
  }
}

// src/draw/gesture.ts
function gesture(inCanvas2, result, drawOptions) {
  const localOptions = mergeDeep(options3, drawOptions);
  if (!result || !inCanvas2)
    return;
  if (localOptions.drawGestures) {
    const ctx = getCanvasContext(inCanvas2);
    if (!ctx)
      return;
    ctx.font = localOptions.font;
    ctx.fillStyle = localOptions.color;
    let i = 1;
    for (let j = 0; j < result.length; j++) {
      let where = [];
      let what = [];
      [where, what] = Object.entries(result[j]);
      if (what.length > 1 && what[1].length > 0) {
        const who = where[1] > 0 ? `#${where[1]}` : "";
        const label = `${where[0]} ${who}: ${what[1]}`;
        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
          ctx.fillStyle = localOptions.shadowColor;
          ctx.fillText(label, 8, 2 + i * localOptions.lineHeight);
        }
        ctx.fillStyle = localOptions.labelColor;
        ctx.fillText(label, 6, 0 + i * localOptions.lineHeight);
        i += 1;
      }
    }
  }
}

// src/draw/draw.ts
var drawTime = 0;
function person(inCanvas2, result, drawOptions) {
  const localOptions = mergeDeep(options3, drawOptions);
  if (!result || !inCanvas2)
    return;
  const ctx = getCanvasContext(inCanvas2);
  if (!ctx)
    return;
  ctx.lineJoin = "round";
  ctx.font = localOptions.font;
  for (let i = 0; i < result.length; i++) {
    if (localOptions.drawBoxes) {
      ctx.strokeStyle = localOptions.color;
      ctx.fillStyle = localOptions.color;
      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);
      if (localOptions.drawLabels) {
        const label = `person #${i}`;
        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
          ctx.fillStyle = localOptions.shadowColor;
          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
        }
        ctx.fillStyle = localOptions.labelColor;
        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.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 localOptions = mergeDeep(options3, drawOptions);
  const promise = Promise.all([
    face(inCanvas2, result.face, localOptions),
    body(inCanvas2, result.body, localOptions),
    hand(inCanvas2, result.hand, localOptions),
    object(inCanvas2, result.object, localOptions),
    gesture(inCanvas2, result.gesture, localOptions)
  ]);
  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);
  result.performance.draw = drawTime;
  return promise;
}

// src/face/face.ts
var tf37 = __toESM(require_tfjs_esm());

// src/face/mask.ts
var tf36 = __toESM(require_tfjs_esm());
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();
  tf36.dispose(buffer);
  return output;
}

// 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/face.ts
var detectFace = async (instance2, 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, _x, _y, _z, _A, _B, _C;
  let timeStamp = now();
  let ageRes;
  let gearRes;
  let genderRes;
  let emotionRes;
  let mobilefacenetRes;
  let insightfaceRes;
  let antispoofRes;
  let livenessRes;
  let descRes;
  const faceRes = [];
  instance2.state = "run:face";
  const faces = await predict6(input, instance2.config);
  instance2.performance.face = env.perfadd ? (instance2.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++) {
    instance2.analyze("Get Face");
    if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {
      log("Face object is disposed:", faces[i].tensor);
      continue;
    }
    if ((_a = instance2.config.face.detector) == null ? void 0 : _a.mask) {
      const masked = await mask(faces[i]);
      tf37.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;
    instance2.analyze("Start Emotion:");
    if (instance2.config.async) {
      emotionRes = ((_b = instance2.config.face.emotion) == null ? void 0 : _b.enabled) ? predict5(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : [];
    } else {
      instance2.state = "run:emotion";
      timeStamp = now();
      emotionRes = ((_c = instance2.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict5(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : [];
      instance2.performance.emotion = env.perfadd ? (instance2.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
    }
    instance2.analyze("End Emotion:");
    instance2.analyze("Start AntiSpoof:");
    if (instance2.config.async) {
      antispoofRes = ((_d = instance2.config.face.antispoof) == null ? void 0 : _d.enabled) ? predict(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : 0;
    } else {
      instance2.state = "run:antispoof";
      timeStamp = now();
      antispoofRes = ((_e = instance2.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : 0;
      instance2.performance.antispoof = env.perfadd ? (instance2.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
    }
    instance2.analyze("End AntiSpoof:");
    instance2.analyze("Start Liveness:");
    if (instance2.config.async) {
      livenessRes = ((_f = instance2.config.face.liveness) == null ? void 0 : _f.enabled) ? predict12(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : 0;
    } else {
      instance2.state = "run:liveness";
      timeStamp = now();
      livenessRes = ((_g = instance2.config.face.liveness) == null ? void 0 : _g.enabled) ? await predict12(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : 0;
      instance2.performance.liveness = env.perfadd ? (instance2.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
    }
    instance2.analyze("End Liveness:");
    instance2.analyze("Start GEAR:");
    if (instance2.config.async) {
      gearRes = ((_h = instance2.config.face.gear) == null ? void 0 : _h.enabled) ? predict8(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
    } else {
      instance2.state = "run:gear";
      timeStamp = now();
      gearRes = ((_i = instance2.config.face.gear) == null ? void 0 : _i.enabled) ? await predict8(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
      instance2.performance.gear = Math.trunc(now() - timeStamp);
    }
    instance2.analyze("End GEAR:");
    instance2.analyze("Start SSRNet:");
    if (instance2.config.async) {
      ageRes = ((_j = instance2.config.face["ssrnet"]) == null ? void 0 : _j.enabled) ? predict20(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
      genderRes = ((_k = instance2.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict21(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
    } else {
      instance2.state = "run:ssrnet";
      timeStamp = now();
      ageRes = ((_l = instance2.config.face["ssrnet"]) == null ? void 0 : _l.enabled) ? await predict20(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
      genderRes = ((_m = instance2.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict21(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
      instance2.performance.ssrnet = Math.trunc(now() - timeStamp);
    }
    instance2.analyze("End SSRNet:");
    instance2.analyze("Start MobileFaceNet:");
    if (instance2.config.async) {
      mobilefacenetRes = ((_n = instance2.config.face["mobilefacenet"]) == null ? void 0 : _n.enabled) ? predict14(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
    } else {
      instance2.state = "run:mobilefacenet";
      timeStamp = now();
      mobilefacenetRes = ((_o = instance2.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict14(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
      instance2.performance.mobilefacenet = Math.trunc(now() - timeStamp);
    }
    instance2.analyze("End MobileFaceNet:");
    instance2.analyze("Start InsightFace:");
    if (instance2.config.async) {
      insightfaceRes = ((_p = instance2.config.face["insightface"]) == null ? void 0 : _p.enabled) ? predict11(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
    } else {
      instance2.state = "run:mobilefacenet";
      timeStamp = now();
      insightfaceRes = ((_q = instance2.config.face["insightface"]) == null ? void 0 : _q.enabled) ? await predict11(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
      instance2.performance.mobilefacenet = Math.trunc(now() - timeStamp);
    }
    instance2.analyze("End InsightFace:");
    instance2.analyze("Start Description:");
    if (instance2.config.async) {
      descRes = predict7(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length);
    } else {
      instance2.state = "run:description";
      timeStamp = now();
      descRes = await predict7(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length);
      instance2.performance.description = env.perfadd ? (instance2.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
    }
    instance2.analyze("End Description:");
    if (instance2.config.async) {
      [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]);
    }
    instance2.analyze("Finish Face:");
    if (((_r = instance2.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && ageRes && genderRes) {
      descRes = {
        ...descRes,
        age: ageRes.age,
        gender: genderRes.gender,
        genderScore: genderRes.genderScore
      };
    }
    if (((_s = instance2.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 = instance2.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) {
      descRes.descriptor = mobilefacenetRes;
    }
    if (((_u = instance2.config.face["insightface"]) == null ? void 0 : _u.enabled) && insightfaceRes) {
      descRes.descriptor = insightfaceRes;
    }
    if (!((_v = instance2.config.face.iris) == null ? void 0 : _v.enabled)) {
    }
    const irisSize = ((_y = (_x = (_w = faces[i]) == null ? void 0 : _w.annotations) == null ? void 0 : _x.leftEyeIris) == null ? void 0 : _y[0]) && ((_B = (_A = (_z = faces[i]) == null ? void 0 : _z.annotations) == null ? void 0 : _A.rightEyeIris) == null ? void 0 : _B[0]) && faces[i].annotations.leftEyeIris.length > 0 && faces[i].annotations.rightEyeIris.length > 0 && faces[i].annotations.leftEyeIris[0] !== null && faces[i].annotations.rightEyeIris[0] !== null ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2] : 0;
    const tensor6 = ((_C = instance2.config.face.detector) == null ? void 0 : _C.return) ? tf37.squeeze(faces[i].tensor) : null;
    tf37.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 && irisSize !== 0)
      res.iris = Math.trunc(500 / irisSize / 11.7) / 100;
    if (rotation)
      res.rotation = rotation;
    if (tensor6)
      res.tensor = tensor6;
    faceRes.push(res);
    instance2.analyze("End Face");
  }
  instance2.analyze("End FaceMesh:");
  if (instance2.config.async) {
    if (instance2.performance.face)
      delete instance2.performance.face;
    if (instance2.performance.age)
      delete instance2.performance.age;
    if (instance2.performance.gender)
      delete instance2.performance.gender;
    if (instance2.performance.emotion)
      delete instance2.performance.emotion;
  }
  return faceRes;
};

// 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/util/interpolate.ts
var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };
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 { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };
  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;
  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/face/match.ts
var match_exports = {};
__export(match_exports, {
  distance: () => distance,
  match: () => match2,
  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, max4) => {
  if (dist === 0)
    return 1;
  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order);
  const norm = (1 - root / 100 - min2) / (max4 - 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 match2(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/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/sample.ts
var face3 = `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// src/warmup.ts
var tf38 = __toESM(require_tfjs_esm());
async function warmupBitmap(instance2) {
  const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob());
  let blob;
  let res;
  switch (instance2.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 instance2.detect(bitmap, instance2.config);
    bitmap.close();
  }
  return res;
}
async function warmupCanvas(instance2) {
  return new Promise((resolve) => {
    let src;
    switch (instance2.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 instance2.image(canvas3);
        const res = tensor6.tensor ? await instance2.detect(tensor6.tensor, instance2.config) : void 0;
        resolve(res);
      }
    };
    if (src)
      img.src = src;
    else
      resolve(void 0);
  });
}
async function warmupNode(instance2) {
  const atob = (str) => Buffer.from(str, "base64");
  let img;
  if (instance2.config.warmup === "face")
    img = atob(face3);
  else
    img = atob(body3);
  let res;
  if ("node" in tf38 && tf38.getBackend() === "tensorflow") {
    const data = tf38["node"].decodeJpeg(img);
    const expanded = tf38.expandDims(data, 0);
    instance2.tf.dispose(data);
    res = await instance2.detect(expanded, instance2.config);
    instance2.tf.dispose(expanded);
  } else {
    if (instance2.config.debug)
      log("Warmup tfjs-node not loaded");
  }
  return res;
}
async function runInference(instance2) {
  let res;
  if (typeof createImageBitmap === "function")
    res = await warmupBitmap(instance2);
  else if (typeof Image !== "undefined" || env.Canvas !== void 0)
    res = await warmupCanvas(instance2);
  else
    res = await warmupNode(instance2);
  return res;
}
async function runCompile(instance2) {
  var _a, _b, _c, _d;
  if (!tf38.env().flagRegistry.ENGINE_COMPILE_ONLY)
    return;
  const backendType = tf38.getBackend();
  const webGLBackend = tf38.backend();
  if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend.checkCompileCompletion)) {
    return;
  }
  tf38.env().set("ENGINE_COMPILE_ONLY", true);
  const numTensorsStart = tf38.engine().state.numTensors;
  const compiledModels = [];
  for (const [modelName, model21] of Object.entries(instance2.models).filter(([key, val]) => key !== null && val !== null)) {
    const shape = ((_b = (_a = model21.inputs) == null ? void 0 : _a[0]) == null ? void 0 : _b.shape) ? [...model21.inputs[0].shape] : [1, 64, 64, 3];
    const dtype = ((_d = (_c = model21.inputs) == null ? void 0 : _c[0]) == null ? void 0 : _d.dtype) ? model21.inputs[0].dtype : "float32";
    for (let dim = 0; dim < shape.length; dim++) {
      if (shape[dim] === -1)
        shape[dim] = dim === 0 ? 1 : 64;
    }
    const tensor6 = tf38.zeros(shape, dtype);
    try {
      const res = model21.execute(tensor6);
      compiledModels.push(modelName);
      if (Array.isArray(res))
        res.forEach((t2) => tf38.dispose(t2));
      else
        tf38.dispose(res);
    } catch (e) {
      if (instance2.config.debug)
        log("compile fail model:", modelName);
    }
    tf38.dispose(tensor6);
  }
  const kernels = await webGLBackend.checkCompileCompletionAsync();
  webGLBackend.getUniformLocations();
  if (instance2.config.debug)
    log("compile pass:", { models: compiledModels, kernels: kernels.length });
  tf38.env().set("ENGINE_COMPILE_ONLY", false);
  const numTensorsEnd = tf38.engine().state.numTensors;
  if (numTensorsEnd - numTensorsStart > 0)
    log("tensor leak:", numTensorsEnd - numTensorsStart);
}
async function warmup(instance2, userConfig) {
  await check(instance2, false);
  const t0 = now();
  instance2.state = "warmup";
  if (userConfig)
    instance2.config = mergeDeep(instance2.config, userConfig);
  if (!instance2.config.warmup || instance2.config.warmup.length === 0 || instance2.config.warmup === "none") {
    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance2.performance, timestamp: now(), persons: [], error: null };
  }
  return new Promise(async (resolve) => {
    await models_exports2.load(instance2);
    await runCompile(instance2);
    const res = await runInference(instance2);
    const t1 = now();
    if (instance2.config.debug)
      log("warmup", instance2.config.warmup, Math.round(t1 - t0), "ms");
    instance2.emit("warmup");
    resolve(res);
  });
}

// src/human.ts
var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity, _loops;
var Human2 = class {
  constructor(userConfig) {
    __publicField(this, "version");
    __publicField(this, "config");
    __publicField(this, "result");
    __publicField(this, "state");
    __publicField(this, "process");
    __publicField(this, "tf");
    __publicField(this, "env");
    __publicField(this, "draw");
    __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, "gl");
    __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 tf39.Tensor))
        return "input must be a tensor";
      try {
        this.tf.getBackend();
      } catch (e) {
        return "backend not loaded";
      }
      return null;
    });
    __publicField(this, "similarity", similarity);
    __publicField(this, "distance", distance);
    __publicField(this, "match", match2);
    __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, {});
    this.env = env;
    const tfVersion = (tf39.version.tfjs || tf39.version_core).replace(/-(.*)/, "");
    config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;
    config.modelBasePath = env.browser ? "../models/" : "file://models/";
    config.backend = env.browser ? "webgl" : "tensorflow";
    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 = tf39;
    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.draw = {
      options: options3,
      canvas: (input, output) => canvas2(input, output),
      face: (output, result, options4) => face(output, result, options4),
      body: (output, result, options4) => body(output, result, options4),
      hand: (output, result, options4) => hand(output, result, options4),
      gesture: (output, result, options4) => gesture(output, result, options4),
      object: (output, result, options4) => object(output, result, options4),
      person: (output, result, options4) => person(output, result, options4),
      all: (output, result, options4) => all(output, result, options4)
    };
    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };
    this.process = { tensor: null, canvas: null };
    this.faceTriangulation = triangulation;
    this.faceUVMap = uvmap;
    this.gl = config2;
    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;
  }
  check() {
    return validate2(this);
  }
  now() {
    return now();
  }
  image(input, getTensor = true) {
    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 predict18(processed.tensor, this.config);
    if ((_b = this.config.segmentation.modelPath) == null ? void 0 : _b.includes("meet"))
      tensor6 = await predict13(processed.tensor, this.config);
    if ((_c = this.config.segmentation.modelPath) == null ? void 0 : _c.includes("selfie"))
      tensor6 = await predict19(processed.tensor, this.config);
    tf39.dispose(processed.tensor);
    return tensor6;
  }
  enhance(input) {
    return enhance(input);
  }
  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).filter((model21) => model21).length;
    if (userConfig)
      this.config = mergeDeep(this.config, userConfig);
    if (this.env.initial) {
      if (!await check(this, false))
        log("error: backend check failed");
      await tf39.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 load22(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).filter((model21) => model21).length;
    if (loaded !== count2) {
      validate2(this);
      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);
  }
  getModelStats() {
    return getModelStats(this);
  }
  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) {
      if (kernels[kernel.name])
        kernels[kernel.name] += kernel.kernelTimeMs;
      else
        kernels[kernel.name] = kernel.kernelTimeMs;
      total += kernel.kernelTimeMs;
    }
    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({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], 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({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: "could not convert input to tensor" });
        return;
      }
      this.emit("image");
      timeStamp = now();
      this.config.skipAllowed = await skip(this.config, img.tensor);
      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 ? predict17(img.tensor, bodyConfig) : [];
        else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose"))
          bodyRes = this.config.body.enabled ? predict2(img.tensor, bodyConfig) : [];
        else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose"))
          bodyRes = this.config.body.enabled ? predict4(img.tensor, bodyConfig) : [];
        else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet"))
          bodyRes = this.config.body.enabled ? predict15(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 predict17(img.tensor, bodyConfig) : [];
        else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose"))
          bodyRes = this.config.body.enabled ? await predict2(img.tensor, bodyConfig) : [];
        else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose"))
          bodyRes = this.config.body.enabled ? await predict4(img.tensor, bodyConfig) : [];
        else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet"))
          bodyRes = this.config.body.enabled ? await predict15(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 ? predict9(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 ? predict10(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 predict9(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 predict10(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 ? predict16(img.tensor, this.config) : [];
        else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet"))
          objectRes = this.config.object.enabled ? predict3(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 predict16(img.tensor, this.config) : [];
        else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet"))
          objectRes = this.config.object.enabled ? await predict3(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) || [];
      this.result = {
        face: faceRes,
        body: bodyRes,
        hand: handRes,
        gesture: gestureRes,
        object: objectRes,
        performance: this.performance,
        canvas: this.process.canvas,
        timestamp: Date.now(),
        error: null,
        get persons() {
          return join2(faceRes, bodyRes, handRes, gestureRes, shape);
        }
      };
      tf39.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,
  env,
  match,
  models
});