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human
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human/dist/human.esm-nobundle.js

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/*
Human
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
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var __defProp = Object.defineProperty;
var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
var __getOwnPropNames = Object.getOwnPropertyNames;
var __hasOwnProp = Object.prototype.hasOwnProperty;
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var __defNormalProp = (obj, key, value) => key in obj ? __defProp(obj, key, { enumerable: true, configurable: true, writable: true, value }) : obj[key] = value;
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var __markAsModule = (target) => __defProp(target, "__esModule", { value: true });
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var __require = typeof require !== "undefined" ? require : (x) => {
throw new Error('Dynamic require of "' + x + '" is not supported');
};
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var __export = (target, all2) => {
__markAsModule(target);
for (var name in all2)
__defProp(target, name, { get: all2[name], enumerable: true });
};
var __reExport = (target, module, desc) => {
if (module && typeof module === "object" || typeof module === "function") {
for (let key of __getOwnPropNames(module))
if (!__hasOwnProp.call(target, key) && key !== "default")
__defProp(target, key, { get: () => module[key], enumerable: !(desc = __getOwnPropDesc(module, key)) || desc.enumerable });
}
return target;
};
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var __publicField = (obj, key, value) => {
__defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value);
return value;
};
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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;
};
// src/helpers.ts
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(`Human: ModelPath Error: ${path} Expecting JSON file`);
return path;
}
function log(...msg) {
const dt = new Date();
const ts = `${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);
}
var now = () => {
if (typeof performance !== "undefined")
return performance.now();
return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString());
};
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 = {
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backend: "",
modelBasePath: "",
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wasmPath: "",
debug: true,
async: true,
warmup: "full",
cacheSensitivity: 0.75,
skipFrame: false,
filter: {
enabled: true,
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: 15,
skipFrames: 15,
minConfidence: 0.2,
iouThreshold: 0.1,
return: false
},
mesh: {
enabled: true,
modelPath: "facemesh.json"
},
iris: {
enabled: true,
modelPath: "iris.json"
},
description: {
enabled: true,
modelPath: "faceres.json",
skipFrames: 11,
minConfidence: 0.1
},
emotion: {
enabled: true,
minConfidence: 0.1,
skipFrames: 17,
modelPath: "emotion.json"
}
},
body: {
enabled: true,
modelPath: "movenet-lightning.json",
maxDetected: 1,
minConfidence: 0.2,
skipFrames: 1
},
hand: {
enabled: true,
rotation: true,
skipFrames: 18,
minConfidence: 0.8,
iouThreshold: 0.2,
maxDetected: 1,
landmarks: true,
detector: {
modelPath: "handdetect.json"
},
skeleton: {
modelPath: "handskeleton.json"
}
},
object: {
enabled: false,
modelPath: "mb3-centernet.json",
minConfidence: 0.2,
iouThreshold: 0.4,
maxDetected: 10,
skipFrames: 19
},
segmentation: {
enabled: false,
modelPath: "selfie.json"
}
};
// dist/tfjs.esm.js
var tfjs_esm_exports = {};
__export(tfjs_esm_exports, {
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version: () => version9
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});
__reExport(tfjs_esm_exports, dist_star);
__reExport(tfjs_esm_exports, dist_star2);
__reExport(tfjs_esm_exports, dist_star3);
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import * as dist_star from "@tensorflow/tfjs/dist/index.js";
import * as dist_star2 from "@tensorflow/tfjs-backend-webgl/dist/index.js";
import * as dist_star3 from "@tensorflow/tfjs-backend-wasm/dist/index.js";
var version = "3.9.0";
var version2 = "3.9.0";
var version3 = "3.9.0";
var version4 = "3.9.0";
var version5 = "3.9.0";
var version6 = "3.9.0";
var version7 = "3.9.0";
var version8 = "3.9.0";
var version9 = {
tfjs: version,
"tfjs-core": version2,
"tfjs-data": version3,
"tfjs-layers": version4,
"tfjs-converter": version5,
"tfjs-backend-cpu": version6,
"tfjs-backend-webgl": version7,
"tfjs-backend-wasm": version8
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};
// src/blazeface/box.ts
function scaleBoxCoordinates(box6, factor) {
const startPoint = [box6.startPoint[0] * factor[0], box6.startPoint[1] * factor[1]];
const endPoint = [box6.endPoint[0] * factor[0], box6.endPoint[1] * factor[1]];
return { startPoint, endPoint };
}
function getBoxSize(box6) {
return [
Math.abs(box6.endPoint[0] - box6.startPoint[0]),
Math.abs(box6.endPoint[1] - box6.startPoint[1])
];
}
function getBoxCenter(box6) {
return [
box6.startPoint[0] + (box6.endPoint[0] - box6.startPoint[0]) / 2,
box6.startPoint[1] + (box6.endPoint[1] - box6.startPoint[1]) / 2
];
}
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function cutBoxFromImageAndResize(box6, image22, cropSize) {
const h = image22.shape[1];
const w = image22.shape[2];
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const boxes = [[
box6.startPoint[1] / h,
box6.startPoint[0] / w,
box6.endPoint[1] / h,
box6.endPoint[0] / w
]];
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return tfjs_esm_exports.image.cropAndResize(image22, boxes, [0], cropSize);
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}
function enlargeBox(box6, factor = 1.5) {
const center = getBoxCenter(box6);
const size = getBoxSize(box6);
const newHalfSize = [factor * size[0] / 2, factor * size[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, landmarks: box6.landmarks };
}
function squarifyBox(box6) {
const centers = getBoxCenter(box6);
const size = getBoxSize(box6);
const maxEdge = Math.max(...size);
const halfSize = maxEdge / 2;
const startPoint = [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)];
const endPoint = [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)];
return { startPoint, endPoint, landmarks: box6.landmarks };
}
function 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, landmarks };
}
var createBox = (startEndTensor) => ({
startPoint: tfjs_esm_exports.slice(startEndTensor, [0, 0], [-1, 2]),
endPoint: tfjs_esm_exports.slice(startEndTensor, [0, 2], [-1, 2])
});
// src/blazeface/util.ts
var IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];
function normalizeRadians(angle) {
return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));
}
function computeRotation(point1, point2) {
const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);
return normalizeRadians(radians);
}
function buildTranslationMatrix(x, y) {
return [[1, 0, x], [0, 1, y], [0, 0, 1]];
}
function dot(v1, v2) {
let product = 0;
for (let i = 0; i < v1.length; i++) {
product += v1[i] * v2[i];
}
return product;
}
function getColumnFrom2DArr(arr, columnIndex) {
const column = [];
for (let i = 0; i < arr.length; i++) {
column.push(arr[i][columnIndex]);
}
return column;
}
function multiplyTransformMatrices(mat1, mat2) {
const product = [];
const size = mat1.length;
for (let row = 0; row < size; row++) {
product.push([]);
for (let col = 0; col < size; col++) {
product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));
}
}
return product;
}
function 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);
}
function 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]
];
}
function rotatePoint(homogeneousCoordinate, rotationMatrix) {
return [
dot(homogeneousCoordinate, rotationMatrix[0]),
dot(homogeneousCoordinate, rotationMatrix[1])
];
}
function generateAnchors(inputSize) {
const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };
const anchors3 = [];
for (let i = 0; i < spec.strides.length; i++) {
const stride = spec.strides[i];
const gridRows = Math.floor((inputSize + stride - 1) / stride);
const gridCols = Math.floor((inputSize + 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;
}
// src/blazeface/blazeface.ts
var keypointsCount = 6;
function decodeBounds(boxOutputs, anchors3, inputSize) {
const boxStarts = tfjs_esm_exports.slice(boxOutputs, [0, 1], [-1, 2]);
const centers = tfjs_esm_exports.add(boxStarts, anchors3);
const boxSizes = tfjs_esm_exports.slice(boxOutputs, [0, 3], [-1, 2]);
const boxSizesNormalized = tfjs_esm_exports.div(boxSizes, inputSize);
const centersNormalized = tfjs_esm_exports.div(centers, inputSize);
const halfBoxSize = tfjs_esm_exports.div(boxSizesNormalized, 2);
const starts = tfjs_esm_exports.sub(centersNormalized, halfBoxSize);
const ends = tfjs_esm_exports.add(centersNormalized, halfBoxSize);
const startNormalized = tfjs_esm_exports.mul(starts, inputSize);
const endNormalized = tfjs_esm_exports.mul(ends, inputSize);
const concatAxis = 1;
return tfjs_esm_exports.concat2d([startNormalized, endNormalized], concatAxis);
}
var BlazeFaceModel = class {
constructor(model10, config3) {
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__publicField(this, "model");
__publicField(this, "anchorsData");
__publicField(this, "anchors");
__publicField(this, "inputSize");
__publicField(this, "config");
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this.model = model10;
this.anchorsData = generateAnchors(model10.inputs[0].shape[1]);
this.anchors = tfjs_esm_exports.tensor2d(this.anchorsData);
this.inputSize = model10.inputs[0].shape[2];
this.config = config3;
}
async getBoundingBoxes(inputImage, userConfig) {
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var _a, _b, _c, _d;
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if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
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return { boxes: [] };
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const [batch, boxes, scores] = tfjs_esm_exports.tidy(() => {
const resizedImage = tfjs_esm_exports.image.resizeBilinear(inputImage, [this.inputSize, this.inputSize]);
const normalizedImage = tfjs_esm_exports.sub(tfjs_esm_exports.div(resizedImage, 127.5), 0.5);
const res = this.model.execute(normalizedImage);
let batchOut;
if (Array.isArray(res)) {
const sorted = res.sort((a, b) => a.size - b.size);
const concat384 = tfjs_esm_exports.concat([sorted[0], sorted[2]], 2);
const concat512 = tfjs_esm_exports.concat([sorted[1], sorted[3]], 2);
const concat3 = tfjs_esm_exports.concat([concat512, concat384], 1);
batchOut = tfjs_esm_exports.squeeze(concat3, 0);
} else {
batchOut = tfjs_esm_exports.squeeze(res);
}
const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);
const logits = tfjs_esm_exports.slice(batchOut, [0, 0], [-1, 1]);
const scoresOut = tfjs_esm_exports.squeeze(tfjs_esm_exports.sigmoid(logits));
return [batchOut, boxesOut, scoresOut];
});
this.config = mergeDeep(this.config, userConfig);
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const nmsTensor = await tfjs_esm_exports.image.nonMaxSuppressionAsync(boxes, scores, ((_a = this.config.face.detector) == null ? void 0 : _a.maxDetected) || 0, ((_b = this.config.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c = this.config.face.detector) == null ? void 0 : _c.minConfidence) || 0);
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const nms = await nmsTensor.array();
tfjs_esm_exports.dispose(nmsTensor);
const annotatedBoxes = [];
const scoresData = await scores.data();
for (let i = 0; i < nms.length; i++) {
const confidence = scoresData[nms[i]];
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if (confidence > (((_d = this.config.face.detector) == null ? void 0 : _d.minConfidence) || 0)) {
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const boundingBox = tfjs_esm_exports.slice(boxes, [nms[i], 0], [1, -1]);
const landmarks = tfjs_esm_exports.tidy(() => tfjs_esm_exports.reshape(tfjs_esm_exports.squeeze(tfjs_esm_exports.slice(batch, [nms[i], keypointsCount - 1], [1, -1])), [keypointsCount, -1]));
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annotatedBoxes.push({ box: createBox(boundingBox), landmarks, anchor: this.anchorsData[nms[i]], confidence });
tfjs_esm_exports.dispose(boundingBox);
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}
}
tfjs_esm_exports.dispose(batch);
tfjs_esm_exports.dispose(boxes);
tfjs_esm_exports.dispose(scores);
return {
boxes: annotatedBoxes,
scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize]
};
}
};
async function load(config3) {
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var _a, _b, _c;
const model10 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, ((_a = config3.face.detector) == null ? void 0 : _a.modelPath) || ""), { fromTFHub: (((_b = config3.face.detector) == null ? void 0 : _b.modelPath) || "").includes("tfhub.dev") });
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const blazeFace = new BlazeFaceModel(model10, config3);
if (!model10 || !model10.modelUrl)
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log("load model failed:", ((_c = config3.face.detector) == null ? void 0 : _c.modelPath) || "");
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else if (config3.debug)
log("load model:", model10.modelUrl);
return blazeFace;
}
// src/blazeface/coords.ts
var MESH_ANNOTATIONS = {
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: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],
lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],
lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],
lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],
rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],
rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],
rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],
rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],
rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],
rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],
rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],
rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],
rightEyebrowLower: [35, 124, 46, 53, 52, 65],
rightEyeIris: [473, 474, 475, 476, 477],
leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],
leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],
leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],
leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],
leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],
leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],
leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],
leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],
leftEyebrowLower: [265, 353, 276, 283, 282, 295],
leftEyeIris: [468, 469, 470, 471, 472],
midwayBetweenEyes: [168],
noseTip: [1],
noseBottom: [2],
noseRightCorner: [98],
noseLeftCorner: [327],
rightCheek: [205],
leftCheek: [425]
};
var MESH_TO_IRIS_INDICES_MAP = [
{ key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] },
{ key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] },
{ key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] },
{ key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },
{ key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },
{ key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },
{ key: "EyeLower3", indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }
];
var UV468 = [
[0.499976992607117, 0.652534008026123],
[0.500025987625122, 0.547487020492554],
[0.499974012374878, 0.602371990680695],
[0.482113003730774, 0.471979022026062],
[0.500150978565216, 0.527155995368958],
[0.499909996986389, 0.498252987861633],
[0.499523013830185, 0.40106201171875],
[0.289712011814117, 0.380764007568359],
[0.499954998493195, 0.312398016452789],
[0.499987006187439, 0.269918978214264],
[0.500023007392883, 0.107050001621246],
[0.500023007392883, 0.666234016418457],
[0.5000159740448, 0.679224014282227],
[0.500023007392883, 0.692348003387451],
[0.499976992607117, 0.695277988910675],
[0.499976992607117, 0.70593398809433],
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];
var VTX68 = [
127,
234,
132,
58,
172,
150,
149,
148,
152,
377,
378,
379,
397,
288,
361,
454,
356,
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33,
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380,
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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]);
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// src/image/imagefx.ts
function GLProgram(gl, vertexSource, fragmentSource) {
const _collect = function(source, prefix, collection) {
const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig");
source.replace(r, (match3, name) => {
collection[name] = 0;
return match3;
});
};
const _compile = function(source, type) {
const shader = gl.createShader(type);
gl.shaderSource(shader, source);
gl.compileShader(shader);
if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS))
throw new Error("Filter: GL compile failed", gl.getShaderInfoLog(shader));
return shader;
};
this.uniform = {};
this.attribute = {};
const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);
const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);
this.id = gl.createProgram();
gl.attachShader(this.id, _vsh);
gl.attachShader(this.id, _fsh);
gl.linkProgram(this.id);
if (!gl.getProgramParameter(this.id, gl.LINK_STATUS))
throw new Error("Filter: GL link failed", gl.getProgramInfoLog(this.id));
gl.useProgram(this.id);
_collect(vertexSource, "attribute", this.attribute);
for (const a in this.attribute)
this.attribute[a] = gl.getAttribLocation(this.id, a);
_collect(vertexSource, "uniform", this.uniform);
_collect(fragmentSource, "uniform", this.uniform);
for (const u in this.uniform)
this.uniform[u] = gl.getUniformLocation(this.id, u);
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}
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function GLImageFilter(params) {
if (!params)
params = {};
let _drawCount = 0;
let _sourceTexture = null;
let _lastInChain = false;
let _currentFramebufferIndex = -1;
let _tempFramebuffers = [null, null];
let _filterChain = [];
let _width = -1;
let _height = -1;
let _vertexBuffer = null;
let _currentProgram = null;
const _filter = {};
const _canvas = params.canvas || document.createElement("canvas");
const _shaderProgramCache = {};
const DRAW = { INTERMEDIATE: 1 };
const gl = _canvas.getContext("webgl");
if (!gl)
throw new Error("Filter: getContext() failed");
this.addFilter = function(name) {
const args = Array.prototype.slice.call(arguments, 1);
const filter = _filter[name];
_filterChain.push({ func: filter, args });
};
this.reset = function() {
_filterChain = [];
};
const _resize = function(width, height) {
if (width === _width && height === _height) {
return;
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}
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_canvas.width = width;
_width = width;
_canvas.height = height;
_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
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]);
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_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);
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}
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gl.viewport(0, 0, _width, _height);
_tempFramebuffers = [null, null];
};
const _createFramebufferTexture = function(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 };
};
const _getTempFramebuffer = function(index) {
_tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);
return _tempFramebuffers[index];
};
const _draw = function(flags = null) {
var _a, _b;
let source = null;
let target = null;
let flipY = false;
if (_drawCount === 0) {
source = _sourceTexture;
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} else {
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source = (_a = _getTempFramebuffer(_currentFramebufferIndex)) == null ? void 0 : _a.texture;
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}
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_drawCount++;
if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {
target = null;
flipY = _drawCount % 2 === 0;
} else {
_currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;
target = (_b = _getTempFramebuffer(_currentFramebufferIndex)) == null ? void 0 : _b.fbo;
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}
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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);
};
this.apply = function(image22) {
_resize(image22.width, image22.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, image22);
if (_filterChain.length === 0) {
_draw();
return _canvas;
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}
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for (let i = 0; i < _filterChain.length; i++) {
_lastInChain = i === _filterChain.length - 1;
const f = _filterChain[i];
f.func.apply(this, f.args || []);
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}
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return _canvas;
};
const _compileShader = function(fragmentSource) {
if (_shaderProgramCache[fragmentSource]) {
_currentProgram = _shaderProgramCache[fragmentSource];
gl.useProgram(_currentProgram.id);
return _currentProgram;
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}
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const SHADER = {};
SHADER.VERTEX_IDENTITY = [
"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.);",
"}"
].join("\n");
SHADER.FRAGMENT_IDENTITY = [
"precision highp float;",
"varying vec2 vUv;",
"uniform sampler2D texture;",
"void main(void) {",
"gl_FragColor = texture2D(texture, vUv);",
"}"
].join("\n");
_currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);
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;
};
_filter.colorMatrix = function(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 ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA : _filter.colorMatrix.SHADER.WITH_ALPHA;
const program = _compileShader(shader);
gl.uniform1fv(program.uniform.m, m);
_draw();
};
_filter.colorMatrix.SHADER = {};
_filter.colorMatrix.SHADER.WITH_ALPHA = [
"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];",
"}"
].join("\n");
_filter.colorMatrix.SHADER.WITHOUT_ALPHA = [
"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;",
"}"
].join("\n");
_filter.brightness = function(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
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]);
};
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_filter.saturation = function(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
]);
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};
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_filter.desaturate = function() {
_filter.saturation(-1);
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};
_filter.contrast = function(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
]);
};
_filter.negative = function() {
_filter.contrast(-2);
};
_filter.hue = function(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
]);
};
_filter.desaturateLuminance = function() {
_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
]);
};
_filter.sepia = function() {
_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
]);
};
_filter.brownie = function() {
_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
]);
};
_filter.vintagePinhole = function() {
_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
]);
};
_filter.kodachrome = function() {
_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,
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35.62982807460946,
0,
0,
0,
1,
0
]);
};
_filter.technicolor = function() {
_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
]);
};
_filter.polaroid = function() {
_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
]);
};
_filter.shiftToBGR = function() {
_filter.colorMatrix([
0,
0,
1,
0,
0,
0,
1,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
1,
0
]);
};
_filter.convolution = function(matrix) {
const m = new Float32Array(matrix);
const pixelSizeX = 1 / _width;
const pixelSizeY = 1 / _height;
const program = _compileShader(_filter.convolution.SHADER);
gl.uniform1fv(program.uniform.m, m);
gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);
_draw();
};
_filter.convolution.SHADER = [
"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);",
"vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));",
"vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));",
"vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );",
"vec4 c22 = texture2D(texture, vUv);",
"vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );",
"vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );",
"vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );",
"vec4 c33 = texture2D(texture, vUv + px );",
"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;",
"}"
].join("\n");
_filter.detectEdges = function() {
_filter.convolution.call(this, [
0,
1,
0,
1,
-4,
1,
0,
1,
0
]);
};
_filter.sobelX = function() {
_filter.convolution.call(this, [
-1,
0,
1,
-2,
0,
2,
-1,
0,
1
]);
};
_filter.sobelY = function() {
_filter.convolution.call(this, [
-1,
-2,
-1,
0,
0,
0,
1,
2,
1
]);
};
_filter.sharpen = function(amount) {
const a = amount || 1;
_filter.convolution.call(this, [
0,
-1 * a,
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0,
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-1 * a,
1 + 4 * a,
-1 * a,
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0,
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-1 * a,
0
]);
};
_filter.emboss = function(size) {
const s = size || 1;
_filter.convolution.call(this, [
-2 * s,
-1 * s,
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0,
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-1 * s,
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1,
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1 * s,
0,
1 * s,
2 * s
]);
};
_filter.blur = function(size) {
const blurSizeX = size / 7 / _width;
const blurSizeY = size / 7 / _height;
const program = _compileShader(_filter.blur.SHADER);
gl.uniform2f(program.uniform.px, 0, blurSizeY);
_draw(DRAW.INTERMEDIATE);
gl.uniform2f(program.uniform.px, blurSizeX, 0);
_draw();
};
_filter.blur.SHADER = [
"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;",
"}"
].join("\n");
_filter.pixelate = function(size) {
const blurSizeX = size / _width;
const blurSizeY = size / _height;
const program = _compileShader(_filter.pixelate.SHADER);
gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);
_draw();
};
_filter.pixelate.SHADER = [
"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);",
"}"
].join("\n");
}
// src/image/image.ts
var maxSize = 2048;
var inCanvas;
var outCanvas;
var fx;
function canvas(width, height) {
let c;
if (env.browser) {
if (typeof OffscreenCanvas !== "undefined") {
c = new OffscreenCanvas(width, height);
} else {
c = document.createElement("canvas");
c.width = width;
c.height = height;
}
} else {
c = typeof env.Canvas !== "undefined" ? new env.Canvas(width, height) : null;
}
return c;
}
function process2(input, config3) {
let tensor3;
if (!input)
throw new Error("Human: Input is missing");
if (!(input instanceof tfjs_esm_exports.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof env.Canvas !== "undefined" && input instanceof env.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("Human: Input type is not recognized");
}
if (input instanceof tfjs_esm_exports.Tensor) {
if (input.shape && input.shape.length === 4 && input.shape[0] === 1 && input.shape[3] === 3)
tensor3 = tfjs_esm_exports.clone(input);
else
throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${input.shape}`);
} else {
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if (typeof input["readyState"] !== "undefined" && input["readyState"] <= 2) {
log("input stream is not ready");
return { tensor: null, canvas: inCanvas };
}
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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;
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if (!originalWidth || !originalHeight) {
log("cannot determine input dimensions");
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return { tensor: null, canvas: inCanvas };
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}
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let targetWidth = originalWidth;
let targetHeight = originalHeight;
if (targetWidth > maxSize) {
targetWidth = maxSize;
targetHeight = targetWidth * originalHeight / originalWidth;
}
if (targetHeight > maxSize) {
targetHeight = maxSize;
targetWidth = targetHeight * originalWidth / originalHeight;
}
if ((config3.filter.width || 0) > 0)
targetWidth = config3.filter.width;
else if ((config3.filter.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("Human: Input cannot determine dimension");
if (!inCanvas || (inCanvas == null ? void 0 : inCanvas.width) !== targetWidth || (inCanvas == null ? void 0 : inCanvas.height) !== targetHeight)
inCanvas = canvas(targetWidth, targetHeight);
const ctx = inCanvas.getContext("2d");
if (typeof ImageData !== "undefined" && input instanceof ImageData) {
ctx.putImageData(input, 0, 0);
} else {
if (config3.filter.flip && typeof ctx.translate !== "undefined") {
ctx.translate(originalWidth, 0);
ctx.scale(-1, 1);
ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height);
ctx.setTransform(1, 0, 0, 1, 0, 0);
} else {
ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height);
}
}
if (config3.filter.enabled && env.webgl.supported) {
if (!fx || !outCanvas || inCanvas.width !== outCanvas.width || (inCanvas == null ? void 0 : inCanvas.height) !== (outCanvas == null ? void 0 : outCanvas.height)) {
outCanvas = canvas(inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height);
if ((outCanvas == null ? void 0 : outCanvas.width) !== (inCanvas == null ? void 0 : inCanvas.width))
outCanvas.width = inCanvas == null ? void 0 : inCanvas.width;
if ((outCanvas == null ? void 0 : outCanvas.height) !== (inCanvas == null ? void 0 : inCanvas.height))
outCanvas.height = inCanvas == null ? void 0 : inCanvas.height;
fx = env.browser ? new GLImageFilter({ canvas: outCanvas }) : null;
}
if (!fx)
return { tensor: null, canvas: inCanvas };
fx.reset();
fx.addFilter("brightness", config3.filter.brightness);
if (config3.filter.contrast !== 0)
fx.addFilter("contrast", config3.filter.contrast);
if (config3.filter.sharpness !== 0)
fx.addFilter("sharpen", config3.filter.sharpness);
if (config3.filter.blur !== 0)
fx.addFilter("blur", config3.filter.blur);
if (config3.filter.saturation !== 0)
fx.addFilter("saturation", config3.filter.saturation);
if (config3.filter.hue !== 0)
fx.addFilter("hue", config3.filter.hue);
if (config3.filter.negative)
fx.addFilter("negative");
if (config3.filter.sepia)
fx.addFilter("sepia");
if (config3.filter.vintage)
fx.addFilter("brownie");
if (config3.filter.sepia)
fx.addFilter("sepia");
if (config3.filter.kodachrome)
fx.addFilter("kodachrome");
if (config3.filter.technicolor)
fx.addFilter("technicolor");
if (config3.filter.polaroid)
fx.addFilter("polaroid");
if (config3.filter.pixelate !== 0)
fx.addFilter("pixelate", config3.filter.pixelate);
fx.apply(inCanvas);
} else {
outCanvas = inCanvas;
if (fx)
fx = null;
}
if (!tensor3) {
let pixels;
if (outCanvas.data) {
const shape = [outCanvas.height, outCanvas.width, 3];
pixels = tfjs_esm_exports.tensor3d(outCanvas.data, shape, "int32");
} else if (typeof ImageData !== "undefined" && outCanvas instanceof ImageData) {
pixels = tfjs_esm_exports.browser ? tfjs_esm_exports.browser.fromPixels(outCanvas) : null;
} else if (config3.backend === "webgl" || config3.backend === "humangl") {
const tempCanvas = canvas(targetWidth, targetHeight);
tempCanvas.width = targetWidth;
tempCanvas.height = targetHeight;
const tempCtx = tempCanvas.getContext("2d");
tempCtx == null ? void 0 : tempCtx.drawImage(outCanvas, 0, 0);
pixels = tfjs_esm_exports.browser && env.browser ? tfjs_esm_exports.browser.fromPixels(tempCanvas) : null;
} else {
const tempCanvas = canvas(targetWidth, targetHeight);
tempCanvas.width = targetWidth;
tempCanvas.height = targetHeight;
const tempCtx = tempCanvas.getContext("2d");
tempCtx.drawImage(outCanvas, 0, 0);
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const data = tempCtx.getImageData(0, 0, targetWidth, targetHeight);
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if (tfjs_esm_exports.browser && env.browser) {
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pixels = tfjs_esm_exports.browser.fromPixels(data);
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} else {
pixels = tfjs_esm_exports.tidy(() => {
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const imageData = tfjs_esm_exports.tensor(Array.from(data.data), [targetWidth, targetHeight, 4]);
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const channels = tfjs_esm_exports.split(imageData, 4, 2);
const rgb2 = tfjs_esm_exports.stack([channels[0], channels[1], channels[2]], 2);
const expand = tfjs_esm_exports.reshape(rgb2, [imageData.shape[0], imageData.shape[1], 3]);
return expand;
});
}
}
if (pixels) {
const casted = tfjs_esm_exports.cast(pixels, "float32");
tensor3 = tfjs_esm_exports.expandDims(casted, 0);
tfjs_esm_exports.dispose(pixels);
tfjs_esm_exports.dispose(casted);
} else {
tensor3 = tfjs_esm_exports.zeros([1, targetWidth, targetHeight, 3]);
throw new Error("Human: Cannot create tensor from input");
}
}
}
return { tensor: tensor3, canvas: config3.filter.return ? outCanvas : null };
}
var lastInputSum = 0;
var lastCacheDiff = 1;
async function skip(config3, input) {
if (config3.cacheSensitivity === 0)
return false;
const resizeFact = 32;
if (!input.shape[1] || !input.shape[2])
return false;
const reduced = tfjs_esm_exports.image.resizeBilinear(input, [Math.trunc(input.shape[1] / resizeFact), Math.trunc(input.shape[2] / resizeFact)]);
const reducedData = await reduced.data();
tfjs_esm_exports.dispose(reduced);
let sum = 0;
for (let i = 0; i < reducedData.length / 3; i++)
sum += reducedData[3 * i + 2];
const diff = 100 * (Math.max(sum, lastInputSum) / Math.min(sum, lastInputSum) - 1);
lastInputSum = sum;
const skipFrame = diff < Math.max(config3.cacheSensitivity, lastCacheDiff);
lastCacheDiff = diff > 10 * config3.cacheSensitivity ? 0 : diff;
return skipFrame;
}
// src/env.ts
var env = {
browser: void 0,
node: void 0,
worker: void 0,
platform: void 0,
agent: void 0,
backends: [],
tfjs: {
version: void 0
},
wasm: {
supported: void 0,
backend: void 0,
simd: void 0,
multithread: void 0
},
webgl: {
supported: void 0,
backend: void 0,
version: void 0,
renderer: void 0
},
webgpu: {
supported: void 0,
backend: void 0,
adapter: void 0
},
kernels: [],
Canvas: void 0,
Image: void 0
};
async function backendInfo() {
var _a;
env.backends = Object.keys(tfjs_esm_exports.engine().registryFactory);
env.wasm.supported = typeof WebAssembly !== "undefined";
env.wasm.backend = env.backends.includes("wasm");
if (env.wasm.supported && env.wasm.backend) {
env.wasm.simd = await tfjs_esm_exports.env().getAsync("WASM_HAS_SIMD_SUPPORT");
env.wasm.multithread = await tfjs_esm_exports.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
}
const c = canvas(100, 100);
const ctx = c ? c.getContext("webgl2") : void 0;
env.webgl.supported = typeof ctx !== "undefined";
env.webgl.backend = env.backends.includes("webgl");
if (env.webgl.supported && env.webgl.backend) {
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const gl = tfjs_esm_exports.backend().gpgpu !== "undefined" && tfjs_esm_exports.backend().getGPGPUContext ? await tfjs_esm_exports.backend().getGPGPUContext().gl : null;
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if (gl) {
env.webgl.version = gl.getParameter(gl.VERSION);
env.webgl.renderer = gl.getParameter(gl.RENDERER);
}
}
env.webgpu.supported = env.browser && typeof navigator["gpu"] !== "undefined";
env.webgpu.backend = env.backends.includes("webgpu");
if (env.webgpu.supported)
env.webgpu.adapter = (_a = await navigator["gpu"].requestAdapter()) == null ? void 0 : _a.name;
env.kernels = tfjs_esm_exports.getKernelsForBackend(tfjs_esm_exports.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());
}
async function get() {
env.browser = typeof navigator !== "undefined";
env.node = typeof process !== "undefined";
env.worker = env.browser ? typeof WorkerGlobalScope !== "undefined" : void 0;
env.tfjs.version = tfjs_esm_exports.version_core;
if (typeof navigator !== "undefined") {
const raw = navigator.userAgent.match(/\(([^()]+)\)/g);
if (raw && raw[0]) {
const platformMatch = raw[0].match(/\(([^()]+)\)/g);
env.platform = platformMatch && platformMatch[0] ? platformMatch[0].replace(/\(|\)/g, "") : "";
env.agent = navigator.userAgent.replace(raw[0], "");
if (env.platform[1])
env.agent = env.agent.replace(raw[1], "");
env.agent = env.agent.replace(/ /g, " ");
}
} else if (typeof process !== "undefined") {
env.platform = `${process.platform} ${process.arch}`;
env.agent = `NodeJS ${process.version}`;
}
await backendInfo();
}
// src/blazeface/facepipeline.ts
var leftOutline = MESH_ANNOTATIONS["leftEyeLower0"];
var rightOutline = MESH_ANNOTATIONS["rightEyeLower0"];
var eyeLandmarks = {
leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],
rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]]
};
var meshLandmarks = {
count: 468,
mouth: 13,
symmetryLine: [13, MESH_ANNOTATIONS["midwayBetweenEyes"][0]]
};
var blazeFaceLandmarks = {
leftEye: 0,
rightEye: 1,
nose: 2,
mouth: 3,
leftEar: 4,
rightEar: 5,
symmetryLine: [3, 2]
};
var irisLandmarks = {
upperCenter: 3,
lowerCenter: 4,
index: 71,
numCoordinates: 76
};
function replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {
for (let i = 0; i < MESH_TO_IRIS_INDICES_MAP.length; i++) {
const { key, indices } = MESH_TO_IRIS_INDICES_MAP[i];
const originalIndices = MESH_ANNOTATIONS[`${prefix}${key}`];
if (!keys || keys.includes(key)) {
for (let j = 0; j < indices.length; j++) {
const index = indices[j];
rawCoords[originalIndices[j]] = [
newCoords[index][0],
newCoords[index][1],
(newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2
];
}
}
}
}
var Pipeline = class {
constructor(boundingBoxDetector, meshDetector, irisModel) {
__publicField(this, "storedBoxes");
__publicField(this, "boundingBoxDetector");
__publicField(this, "meshDetector");
__publicField(this, "irisModel");
__publicField(this, "boxSize");
__publicField(this, "meshSize");
__publicField(this, "irisSize");
__publicField(this, "irisEnlarge");
__publicField(this, "skipped");
__publicField(this, "detectedFaces");
var _a, _b;
this.storedBoxes = [];
this.boundingBoxDetector = boundingBoxDetector;
this.meshDetector = meshDetector;
this.irisModel = irisModel;
this.boxSize = ((_a = boundingBoxDetector == null ? void 0 : boundingBoxDetector.model) == null ? void 0 : _a.inputs[0].shape[2]) || 0;
this.meshSize = (meshDetector == null ? void 0 : meshDetector.inputs[0].shape[2]) || ((_b = boundingBoxDetector == null ? void 0 : boundingBoxDetector.model) == null ? void 0 : _b.inputs[0].shape[2]);
this.irisSize = (irisModel == null ? void 0 : irisModel.inputs[0].shape[1]) || 0;
this.irisEnlarge = 2.3;
this.skipped = 0;
this.detectedFaces = 0;
}
transformRawCoords(rawCoords, box6, angle, rotationMatrix) {
const boxSize = getBoxSize({ startPoint: box6.startPoint, endPoint: box6.endPoint });
const coordsScaled = rawCoords.map((coord) => [
boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),
boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),
coord[2]
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]);
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const coordsRotationMatrix = angle !== 0 ? buildRotationMatrix(angle, [0, 0]) : IDENTITY_MATRIX;
const coordsRotated = angle !== 0 ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled;
const inverseRotationMatrix = angle !== 0 ? invertTransformMatrix(rotationMatrix) : IDENTITY_MATRIX;
const boxCenter = [...getBoxCenter({ startPoint: box6.startPoint, endPoint: box6.endPoint }), 1];
return coordsRotated.map((coord) => [
Math.round(coord[0] + dot(boxCenter, inverseRotationMatrix[0])),
Math.round(coord[1] + dot(boxCenter, inverseRotationMatrix[1])),
Math.round(coord[2])
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]);
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}
getLeftToRightEyeDepthDifference(rawCoords) {
const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];
const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];
return leftEyeZ - rightEyeZ;
}
getEyeBox(rawCoords, face5, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {
const box6 = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));
const boxSize = getBoxSize(box6);
let crop = tfjs_esm_exports.image.cropAndResize(face5, [[
box6.startPoint[1] / this.meshSize,
box6.startPoint[0] / this.meshSize,
box6.endPoint[1] / this.meshSize,
box6.endPoint[0] / this.meshSize
]], [0], [this.irisSize, this.irisSize]);
if (flip && env.kernels.includes("flipleftright")) {
const flipped = tfjs_esm_exports.image.flipLeftRight(crop);
tfjs_esm_exports.dispose(crop);
crop = flipped;
}
return { box: box6, boxSize, crop };
}
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 / this.irisSize : x / this.irisSize) * eyeBoxSize[0] + eyeBox.startPoint[0],
y / this.irisSize * eyeBoxSize[1] + eyeBox.startPoint[1],
z
]);
}
return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };
}
getAdjustedIrisCoords(rawCoords, irisCoords, direction) {
const upperCenterZ = rawCoords[MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];
const lowerCenterZ = rawCoords[MESH_ANNOTATIONS[`${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];
});
}
correctFaceRotation(config3, box6, input) {
const [indexOfMouth, indexOfForehead] = box6.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;
const angle = computeRotation(box6.landmarks[indexOfMouth], box6.landmarks[indexOfForehead]);
const faceCenter = getBoxCenter({ startPoint: box6.startPoint, endPoint: box6.endPoint });
const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];
const rotated = tfjs_esm_exports.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);
const rotationMatrix = buildRotationMatrix(-angle, faceCenter);
const cut = config3.face.mesh.enabled ? cutBoxFromImageAndResize({ startPoint: box6.startPoint, endPoint: box6.endPoint }, rotated, [this.meshSize, this.meshSize]) : cutBoxFromImageAndResize({ startPoint: box6.startPoint, endPoint: box6.endPoint }, rotated, [this.boxSize, this.boxSize]);
const face5 = tfjs_esm_exports.div(cut, 255);
tfjs_esm_exports.dispose(cut);
tfjs_esm_exports.dispose(rotated);
return [angle, rotationMatrix, face5];
}
async augmentIris(rawCoords, face5) {
const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face5, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);
const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face5, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);
const combined = tfjs_esm_exports.concat([leftEyeCrop, rightEyeCrop]);
tfjs_esm_exports.dispose(leftEyeCrop);
tfjs_esm_exports.dispose(rightEyeCrop);
const eyePredictions = this.irisModel.predict(combined);
tfjs_esm_exports.dispose(combined);
const eyePredictionsData = await eyePredictions.data();
tfjs_esm_exports.dispose(eyePredictions);
const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);
const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);
const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);
const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);
const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);
if (Math.abs(leftToRightEyeDepthDifference) < 30) {
replaceRawCoordinates(rawCoords, leftEyeRawCoords, "left", null);
replaceRawCoordinates(rawCoords, rightEyeRawCoords, "right", null);
} else if (leftToRightEyeDepthDifference < 1) {
replaceRawCoordinates(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]);
} else {
replaceRawCoordinates(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]);
}
const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left");
const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right");
const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);
return newCoords;
}
async predict(input, config3) {
let useFreshBox = false;
let detector;
if (this.skipped === 0 || this.skipped > config3.face.detector.skipFrames || !config3.face.mesh.enabled || !config3.skipFrame) {
detector = await this.boundingBoxDetector.getBoundingBoxes(input, config3);
this.skipped = 0;
}
if (config3.skipFrame)
this.skipped++;
if (!config3.skipFrame || detector && detector.boxes && (!config3.face.mesh.enabled || detector.boxes.length !== this.detectedFaces && this.detectedFaces !== config3.face.detector.maxDetected)) {
this.storedBoxes = [];
this.detectedFaces = 0;
for (const possible of detector.boxes) {
const startPoint = await possible.box.startPoint.data();
const endPoint = await possible.box.endPoint.data();
const landmarks = await possible.landmarks.array();
this.storedBoxes.push({ startPoint, endPoint, landmarks, confidence: possible.confidence });
}
if (this.storedBoxes.length > 0)
useFreshBox = true;
}
if (useFreshBox) {
if (!detector || !detector.boxes || detector.boxes.length === 0) {
this.storedBoxes = [];
this.detectedFaces = 0;
return null;
}
for (let i = 0; i < this.storedBoxes.length; i++) {
const scaledBox = scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);
const enlargedBox = enlargeBox(scaledBox);
const squarifiedBox = squarifyBox(enlargedBox);
const landmarks = this.storedBoxes[i].landmarks;
const confidence = this.storedBoxes[i].confidence;
this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };
}
}
if (detector && detector.boxes) {
detector.boxes.forEach((prediction) => {
tfjs_esm_exports.dispose(prediction.box.startPoint);
tfjs_esm_exports.dispose(prediction.box.endPoint);
tfjs_esm_exports.dispose(prediction.landmarks);
});
}
const results = [];
const newBoxes = [];
for (let box6 of this.storedBoxes) {
let face5;
let angle = 0;
let rotationMatrix;
if (config3.face.detector.rotation && config3.face.mesh.enabled && env.kernels.includes("rotatewithoffset")) {
[angle, rotationMatrix, face5] = this.correctFaceRotation(config3, box6, input);
} else {
rotationMatrix = IDENTITY_MATRIX;
const cloned = input.clone();
const cut = config3.face.mesh.enabled ? cutBoxFromImageAndResize({ startPoint: box6.startPoint, endPoint: box6.endPoint }, cloned, [this.meshSize, this.meshSize]) : cutBoxFromImageAndResize({ startPoint: box6.startPoint, endPoint: box6.endPoint }, cloned, [this.boxSize, this.boxSize]);
face5 = tfjs_esm_exports.div(cut, 255);
tfjs_esm_exports.dispose(cut);
tfjs_esm_exports.dispose(cloned);
}
if (!config3.face.mesh.enabled) {
results.push({
mesh: [],
box: box6,
faceConfidence: null,
boxConfidence: box6.confidence,
confidence: box6.confidence,
image: face5
});
} else {
const [contours, confidence, contourCoords] = this.meshDetector.execute(face5);
tfjs_esm_exports.dispose(contours);
const faceConfidence = (await confidence.data())[0];
tfjs_esm_exports.dispose(confidence);
const coordsReshaped = tfjs_esm_exports.reshape(contourCoords, [-1, 3]);
let rawCoords = await coordsReshaped.array();
tfjs_esm_exports.dispose(contourCoords);
tfjs_esm_exports.dispose(coordsReshaped);
if (faceConfidence < config3.face.detector.minConfidence) {
box6.confidence = faceConfidence;
tfjs_esm_exports.dispose(face5);
} else {
if (config3.face.iris.enabled)
rawCoords = await this.augmentIris(rawCoords, face5);
const mesh = this.transformRawCoords(rawCoords, box6, angle, rotationMatrix);
box6 = { ...enlargeBox(calculateLandmarksBoundingBox(mesh), 1.5), confidence: box6.confidence };
if (config3.face.detector.rotation && config3.face.mesh.enabled && config3.face.description.enabled && env.kernels.includes("rotatewithoffset")) {
tfjs_esm_exports.dispose(face5);
[angle, rotationMatrix, face5] = this.correctFaceRotation(config3, box6, input);
}
results.push({
mesh,
box: box6,
faceConfidence,
boxConfidence: box6.confidence,
confidence: faceConfidence,
image: face5
});
box6 = { ...squarifyBox(box6), confidence: box6.confidence, faceConfidence };
}
}
newBoxes.push(box6);
}
if (config3.face.mesh.enabled)
this.storedBoxes = newBoxes.filter((a) => a.confidence > config3.face.detector.minConfidence);
this.detectedFaces = results.length;
return results;
}
};
// src/blazeface/facemesh.ts
var faceModels = [null, null, null];
var facePipeline;
async function predict(input, config3) {
const predictions = await facePipeline.predict(input, config3);
const results = [];
let id = 0;
for (const prediction of predictions || []) {
if (!prediction || prediction.isDisposedInternal)
continue;
const meshRaw = prediction.mesh.map((pt) => [
pt[0] / (input.shape[2] || 0),
pt[1] / (input.shape[1] || 0),
pt[2] / facePipeline.meshSize
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]);
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const annotations3 = {};
if (prediction.mesh && prediction.mesh.length > 0) {
for (const key of Object.keys(MESH_ANNOTATIONS))
annotations3[key] = MESH_ANNOTATIONS[key].map((index) => prediction.mesh[index]);
}
const clampedBox = prediction.box ? [
Math.trunc(Math.max(0, prediction.box.startPoint[0])),
Math.trunc(Math.max(0, prediction.box.startPoint[1])),
Math.trunc(Math.min(input.shape[2] || 0, prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0])),
Math.trunc(Math.min(input.shape[1] || 0, prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]))
] : [0, 0, 0, 0];
const boxRaw3 = prediction.box ? [
prediction.box.startPoint[0] / (input.shape[2] || 0),
prediction.box.startPoint[1] / (input.shape[1] || 0),
(prediction.box.endPoint[0] - prediction.box.startPoint[0]) / (input.shape[2] || 0),
(prediction.box.endPoint[1] - prediction.box.startPoint[1]) / (input.shape[1] || 0)
] : [0, 0, 0, 0];
results.push({
id: id++,
score: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,
boxScore: Math.round(100 * prediction.boxConfidence) / 100,
faceScore: Math.round(100 * prediction.faceConfidence) / 100,
box: clampedBox,
boxRaw: boxRaw3,
mesh: prediction.mesh,
meshRaw,
annotations: annotations3,
tensor: prediction.image
});
}
return results;
}
async function load2(config3) {
if (!faceModels[0] && config3.face.enabled || !faceModels[1] && config3.face.mesh.enabled || !faceModels[2] && config3.face.iris.enabled) {
faceModels = await Promise.all([
!faceModels[0] && config3.face.enabled ? load(config3) : null,
!faceModels[1] && config3.face.mesh.enabled ? tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.face.mesh.modelPath), { fromTFHub: config3.face.mesh.modelPath.includes("tfhub.dev") }) : null,
!faceModels[2] && config3.face.iris.enabled ? tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.face.iris.modelPath), { fromTFHub: config3.face.iris.modelPath.includes("tfhub.dev") }) : null
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]);
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if (config3.face.mesh.enabled) {
if (!faceModels[1] || !faceModels[1]["modelUrl"])
log("load model failed:", config3.face.mesh.modelPath);
else if (config3.debug)
log("load model:", faceModels[1]["modelUrl"]);
}
if (config3.face.iris.enabled) {
if (!faceModels[2] || !faceModels[2]["modelUrl"])
log("load model failed:", config3.face.iris.modelPath);
else if (config3.debug)
log("load model:", faceModels[2]["modelUrl"]);
}
} else if (config3.debug) {
if (faceModels[0])
log("cached model:", faceModels[0].model["modelUrl"]);
if (faceModels[1])
log("cached model:", faceModels[1]["modelUrl"]);
if (faceModels[2])
log("cached model:", faceModels[2]["modelUrl"]);
}
facePipeline = new Pipeline(faceModels[0], faceModels[1], faceModels[2]);
return faceModels;
}
var triangulation = TRI468;
var uvmap = UV468;
// src/faceres/faceres.ts
var model;
var last = [];
var lastCount = 0;
var skipped = Number.MAX_SAFE_INTEGER;
async function load3(config3) {
var _a, _b;
const modelUrl = join(config3.modelBasePath, ((_a = config3.face.description) == null ? void 0 : _a.modelPath) || "");
if (!model) {
model = await tfjs_esm_exports.loadGraphModel(modelUrl);
if (!model)
log("load model failed:", ((_b = config3.face.description) == null ? void 0 : _b.modelPath) || "");
else if (config3.debug)
log("load model:", modelUrl);
} else if (config3.debug)
log("cached model:", modelUrl);
return model;
}
function similarity(embedding1, embedding2, order = 2) {
if (!embedding1 || !embedding2)
return 0;
if ((embedding1 == null ? void 0 : embedding1.length) === 0 || (embedding2 == null ? void 0 : embedding2.length) === 0)
return 0;
if ((embedding1 == null ? void 0 : embedding1.length) !== (embedding2 == null ? void 0 : embedding2.length))
return 0;
const distance = 5 * embedding1.map((_val, i) => Math.abs(embedding1[i] - embedding2[i]) ** order).reduce((sum, now2) => sum + now2, 0) ** (1 / order);
const res = Math.max(0, 100 - distance) / 100;
return res;
}
function match(embedding, db, threshold = 0) {
let best = { similarity: 0, name: "", source: "", embedding: [] };
if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db))
return best;
for (const f of db) {
if (f.embedding && f.name) {
const perc = similarity(embedding, f.embedding);
if (perc > threshold && perc > best.similarity)
best = { ...f, similarity: perc };
}
}
return best;
}
function enhance(input) {
const image22 = tfjs_esm_exports.tidy(() => {
const tensor3 = input.image || input.tensor || input;
if (!(tensor3 instanceof tfjs_esm_exports.Tensor))
return null;
const box6 = [[0.05, 0.15, 0.85, 0.85]];
if (!model.inputs[0].shape)
return null;
const crop = tensor3.shape.length === 3 ? tfjs_esm_exports.image.cropAndResize(tfjs_esm_exports.expandDims(tensor3, 0), box6, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) : tfjs_esm_exports.image.cropAndResize(tensor3, box6, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);
const norm = tfjs_esm_exports.mul(crop, 255);
return norm;
});
return image22;
}
async function predict2(image22, config3, idx, count2) {
var _a, _b, _c;
if (!model)
return null;
if (skipped < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0) && config3.skipFrame && lastCount === count2 && ((_b = last[idx]) == null ? void 0 : _b.age) && ((_c = last[idx]) == null ? void 0 : _c.age) > 0) {
skipped++;
return last[idx];
}
skipped = 0;
return new Promise(async (resolve) => {
var _a2, _b2;
const enhanced = enhance(image22);
let resT;
const obj = {
age: 0,
gender: "unknown",
genderScore: 0,
descriptor: []
};
if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled)
resT = await model.predict(enhanced);
tfjs_esm_exports.dispose(enhanced);
if (resT) {
const gender = await resT.find((t) => t.shape[1] === 1).data();
const confidence = Math.trunc(200 * Math.abs(gender[0] - 0.5)) / 100;
if (confidence > (((_b2 = config3.face.description) == null ? void 0 : _b2.minConfidence) || 0)) {
obj.gender = gender[0] <= 0.5 ? "female" : "male";
obj.genderScore = Math.min(0.99, confidence);
}
const argmax = tfjs_esm_exports.argMax(resT.find((t) => t.shape[1] === 100), 1);
const age = (await argmax.data())[0];
tfjs_esm_exports.dispose(argmax);
const all2 = await resT.find((t) => t.shape[1] === 100).data();
obj.age = Math.round(all2[age - 1] > all2[age + 1] ? 10 * age - 100 * all2[age - 1] : 10 * age + 100 * all2[age + 1]) / 10;
const desc = resT.find((t) => t.shape[1] === 1024);
const descriptor = await desc.data();
obj.descriptor = [...descriptor];
resT.forEach((t) => tfjs_esm_exports.dispose(t));
}
last[idx] = obj;
lastCount = count2;
resolve(obj);
});
}
// src/emotion/emotion.ts
var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"];
var model2;
var last2 = [];
var lastCount2 = 0;
var skipped2 = Number.MAX_SAFE_INTEGER;
var rgb = [0.2989, 0.587, 0.114];
async function load4(config3) {
var _a, _b;
if (!model2) {
model2 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, ((_a = config3.face.emotion) == null ? void 0 : _a.modelPath) || ""));
if (!model2 || !model2.modelUrl)
log("load model failed:", ((_b = config3.face.emotion) == null ? void 0 : _b.modelPath) || "");
else if (config3.debug)
log("load model:", model2.modelUrl);
} else if (config3.debug)
log("cached model:", model2.modelUrl);
return model2;
}
async function predict3(image22, config3, idx, count2) {
var _a;
if (!model2)
return null;
if (skipped2 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0) && config3.skipFrame && lastCount2 === count2 && last2[idx] && last2[idx].length > 0) {
skipped2++;
return last2[idx];
}
skipped2 = 0;
return new Promise(async (resolve) => {
var _a2, _b;
const resize = tfjs_esm_exports.image.resizeBilinear(image22, [model2.inputs[0].shape[2], model2.inputs[0].shape[1]], false);
const [red, green, blue] = tfjs_esm_exports.split(resize, 3, 3);
tfjs_esm_exports.dispose(resize);
const redNorm = tfjs_esm_exports.mul(red, rgb[0]);
const greenNorm = tfjs_esm_exports.mul(green, rgb[1]);
const blueNorm = tfjs_esm_exports.mul(blue, rgb[2]);
tfjs_esm_exports.dispose(red);
tfjs_esm_exports.dispose(green);
tfjs_esm_exports.dispose(blue);
const grayscale = tfjs_esm_exports.addN([redNorm, greenNorm, blueNorm]);
tfjs_esm_exports.dispose(redNorm);
tfjs_esm_exports.dispose(greenNorm);
tfjs_esm_exports.dispose(blueNorm);
const normalize = tfjs_esm_exports.tidy(() => tfjs_esm_exports.mul(tfjs_esm_exports.sub(grayscale, 0.5), 2));
tfjs_esm_exports.dispose(grayscale);
const obj = [];
if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) {
const emotionT = await model2.predict(normalize);
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const data = await emotionT.data();
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tfjs_esm_exports.dispose(emotionT);
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for (let i = 0; i < data.length; i++) {
if (data[i] > (((_b = config3.face.emotion) == null ? void 0 : _b.minConfidence) || 0))
obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });
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}
obj.sort((a, b) => b.score - a.score);
}
tfjs_esm_exports.dispose(normalize);
last2[idx] = obj;
lastCount2 = count2;
resolve(obj);
});
}
// src/posenet/keypoints.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"]
];
// src/posenet/utils.ts
function getBoundingBox(keypoints3) {
const coord = keypoints3.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(poses2, [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: score3, part, position }) => ({
score: score3,
part,
position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)],
positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight]
}))
});
const scaledPoses = poses2.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 max2 = this.priorityQueue[0];
this.exchange(0, this.numberOfElements--);
this.sink(0);
this.priorityQueue[this.numberOfElements + 1] = null;
return max2;
}
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 t = this.priorityQueue[i];
this.priorityQueue[i] = this.priorityQueue[j];
this.priorityQueue[j] = t;
}
};
function getOffsetPoint(y, x, keypoint, offsets) {
return {
y: offsets.get(y, x, keypoint),
x: offsets.get(y, x, keypoint + count)
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};
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}
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
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};
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}
function clamp(a, min, max2) {
if (a < min)
return min;
if (a > max2)
return max2;
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/posenet/poses.ts
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 score3 = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);
return { position: targetKeypoint, part: partNames[targetId], score: score3 };
}
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 keypoints3 = new Array(numParts);
const rootPoint = getImageCoords(root.part, outputStride, offsets);
keypoints3[root.part.id] = {
score: root.score,
part: partNames[root.part.id],
position: rootPoint
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};
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for (let edge = numEdges - 1; edge >= 0; --edge) {
const sourceId = edgesFwd[edge];
const targetId = edgesBwd[edge];
if (keypoints3[sourceId] && !keypoints3[targetId]) {
keypoints3[targetId] = traverse(edge, keypoints3[sourceId], targetId, scores, offsets, displacementsBwd);
}
}
for (let edge = 0; edge < numEdges; ++edge) {
const sourceId = edgesBwd[edge];
const targetId = edgesFwd[edge];
if (keypoints3[sourceId] && !keypoints3[targetId]) {
keypoints3[targetId] = traverse(edge, keypoints3[sourceId], targetId, scores, offsets, displacementsFwd);
}
}
return keypoints3;
}
function scoreIsMaximumInLocalWindow(keypointId, score3, 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) > score3) {
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: score3 }) => score3);
for (let heatmapY = 0; heatmapY < height; ++heatmapY) {
for (let heatmapX = 0; heatmapX < width; ++heatmapX) {
for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {
const score3 = scores.get(heatmapY, heatmapX, keypointId);
if (score3 < minConfidence2)
continue;
if (scoreIsMaximumInLocalWindow(keypointId, score3, heatmapY, heatmapX, scores))
queue.enqueue({ score: score3, part: { heatmapY, heatmapX, id: keypointId } });
}
}
}
return queue;
}
function withinRadius(poses2, { x, y }, keypointId) {
return poses2.some(({ keypoints: keypoints3 }) => {
var _a;
const correspondingKeypoint = (_a = keypoints3[keypointId]) == null ? void 0 : _a.position;
if (!correspondingKeypoint)
return false;
return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;
});
}
function getInstanceScore(existingPoses, keypoints3) {
const notOverlappedKeypointScores = keypoints3.reduce((result, { position, score: score3 }, keypointId) => {
if (!withinRadius(existingPoses, position, keypointId))
result += score3;
return result;
}, 0);
return notOverlappedKeypointScores / keypoints3.length;
}
function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) {
const poses2 = [];
const queue = buildPartWithScoreQueue(minConfidence2, scores);
while (poses2.length < maxDetected && !queue.empty()) {
const root = queue.dequeue();
const rootImageCoords = getImageCoords(root.part, outputStride, offsets);
if (withinRadius(poses2, rootImageCoords, root.part.id))
continue;
let keypoints3 = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);
keypoints3 = keypoints3.filter((a) => a.score > minConfidence2);
const score3 = getInstanceScore(poses2, keypoints3);
const box6 = getBoundingBox(keypoints3);
if (score3 > minConfidence2)
poses2.push({ keypoints: keypoints3, box: box6, score: Math.round(100 * score3) / 100 });
}
return poses2;
}
// src/posenet/posenet.ts
var model3;
var poseNetOutputs = ["MobilenetV1/offset_2/BiasAdd", "MobilenetV1/heatmap_2/BiasAdd", "MobilenetV1/displacement_fwd_2/BiasAdd", "MobilenetV1/displacement_bwd_2/BiasAdd"];
async function predict4(input, config3) {
const res = tfjs_esm_exports.tidy(() => {
if (!model3.inputs[0].shape)
return [];
const resized = tfjs_esm_exports.image.resizeBilinear(input, [model3.inputs[0].shape[2], model3.inputs[0].shape[1]]);
const normalized = tfjs_esm_exports.sub(tfjs_esm_exports.div(tfjs_esm_exports.cast(resized, "float32"), 127.5), 1);
const results = model3.execute(normalized, poseNetOutputs);
const results3d = results.map((y) => tfjs_esm_exports.squeeze(y, [0]));
results3d[1] = results3d[1].sigmoid();
return results3d;
});
const buffers = await Promise.all(res.map((tensor3) => tensor3.buffer()));
for (const t of res)
tfjs_esm_exports.dispose(t);
const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence);
if (!model3.inputs[0].shape)
return [];
const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model3.inputs[0].shape[2], model3.inputs[0].shape[1]]);
return scaled;
}
async function load5(config3) {
if (!model3) {
model3 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath || ""));
if (!model3 || !model3["modelUrl"])
log("load model failed:", config3.body.modelPath);
else if (config3.debug)
log("load model:", model3["modelUrl"]);
} else if (config3.debug)
log("cached model:", model3["modelUrl"]);
return model3;
}
// src/handpose/box.ts
function getBoxSize2(box6) {
return [
Math.abs(box6.endPoint[0] - box6.startPoint[0]),
Math.abs(box6.endPoint[1] - box6.startPoint[1])
];
}
function getBoxCenter2(box6) {
return [
box6.startPoint[0] + (box6.endPoint[0] - box6.startPoint[0]) / 2,
box6.startPoint[1] + (box6.endPoint[1] - box6.startPoint[1]) / 2
];
}
function cutBoxFromImageAndResize2(box6, image22, cropSize) {
const h = image22.shape[1];
const w = image22.shape[2];
const boxes = [[
box6.startPoint[1] / h,
box6.startPoint[0] / w,
box6.endPoint[1] / h,
box6.endPoint[0] / w
]];
return tfjs_esm_exports.image.cropAndResize(image22, boxes, [0], cropSize);
}
function scaleBoxCoordinates2(box6, factor) {
const startPoint = [box6.startPoint[0] * factor[0], box6.startPoint[1] * factor[1]];
const endPoint = [box6.endPoint[0] * factor[0], box6.endPoint[1] * factor[1]];
const palmLandmarks = box6.palmLandmarks.map((coord) => {
const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];
return scaledCoord;
});
return { startPoint, endPoint, palmLandmarks, confidence: box6.confidence };
}
function enlargeBox2(box6, factor = 1.5) {
const center = getBoxCenter2(box6);
const size = getBoxSize2(box6);
const newHalfSize = [factor * size[0] / 2, factor * size[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: box6.palmLandmarks };
}
function squarifyBox2(box6) {
const centers = getBoxCenter2(box6);
const size = getBoxSize2(box6);
const maxEdge = Math.max(...size);
const halfSize = maxEdge / 2;
const startPoint = [centers[0] - halfSize, centers[1] - halfSize];
const endPoint = [centers[0] + halfSize, centers[1] + halfSize];
return { startPoint, endPoint, palmLandmarks: box6.palmLandmarks };
}
// src/handpose/anchors.ts
var anchors = [
{ 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 },
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{ 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/handpose/handdetector.ts
var HandDetector = class {
constructor(model10) {
__publicField(this, "model");
__publicField(this, "anchors");
__publicField(this, "anchorsTensor");
__publicField(this, "inputSize");
__publicField(this, "inputSizeTensor");
__publicField(this, "doubleInputSizeTensor");
this.model = model10;
this.anchors = anchors.map((anchor) => [anchor.x, anchor.y]);
this.anchorsTensor = tfjs_esm_exports.tensor2d(this.anchors);
this.inputSize = this.model && this.model.inputs && this.model.inputs[0].shape ? this.model.inputs[0].shape[2] : 0;
this.inputSizeTensor = tfjs_esm_exports.tensor1d([this.inputSize, this.inputSize]);
this.doubleInputSizeTensor = tfjs_esm_exports.tensor1d([this.inputSize * 2, this.inputSize * 2]);
}
normalizeBoxes(boxes) {
return tfjs_esm_exports.tidy(() => {
const boxOffsets = tfjs_esm_exports.slice(boxes, [0, 0], [-1, 2]);
const boxSizes = tfjs_esm_exports.slice(boxes, [0, 2], [-1, 2]);
const boxCenterPoints = tfjs_esm_exports.add(tfjs_esm_exports.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);
const halfBoxSizes = tfjs_esm_exports.div(boxSizes, this.doubleInputSizeTensor);
const startPoints = tfjs_esm_exports.mul(tfjs_esm_exports.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);
const endPoints = tfjs_esm_exports.mul(tfjs_esm_exports.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);
return tfjs_esm_exports.concat2d([startPoints, endPoints], 1);
});
}
normalizeLandmarks(rawPalmLandmarks, index) {
return tfjs_esm_exports.tidy(() => {
const landmarks = tfjs_esm_exports.add(tfjs_esm_exports.div(tfjs_esm_exports.reshape(rawPalmLandmarks, [-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);
return tfjs_esm_exports.mul(landmarks, this.inputSizeTensor);
});
}
async getBoxes(input, config3) {
const t = {};
t.batched = this.model.predict(input);
t.predictions = tfjs_esm_exports.squeeze(t.batched);
t.scores = tfjs_esm_exports.tidy(() => tfjs_esm_exports.squeeze(tfjs_esm_exports.sigmoid(tfjs_esm_exports.slice(t.predictions, [0, 0], [-1, 1]))));
const scores = await t.scores.data();
t.boxes = tfjs_esm_exports.slice(t.predictions, [0, 1], [-1, 4]);
t.norm = this.normalizeBoxes(t.boxes);
t.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t.norm, t.scores, 10 * config3.hand.maxDetected, config3.hand.iouThreshold, config3.hand.minConfidence);
const nms = await t.nms.array();
const hands = [];
for (const index of nms) {
const palmBox = tfjs_esm_exports.slice(t.norm, [index, 0], [1, -1]);
const palmLandmarks = tfjs_esm_exports.tidy(() => tfjs_esm_exports.reshape(this.normalizeLandmarks(tfjs_esm_exports.slice(t.predictions, [index, 5], [1, 14]), index), [-1, 2]));
hands.push({ box: palmBox, palmLandmarks, confidence: scores[index] });
}
for (const tensor3 of Object.keys(t))
tfjs_esm_exports.dispose(t[tensor3]);
return hands;
}
async estimateHandBounds(input, config3) {
const inputHeight = input.shape[1];
const inputWidth = input.shape[2];
const image22 = tfjs_esm_exports.tidy(() => tfjs_esm_exports.sub(tfjs_esm_exports.div(tfjs_esm_exports.image.resizeBilinear(input, [this.inputSize, this.inputSize]), 127.5), 1));
const predictions = await this.getBoxes(image22, config3);
tfjs_esm_exports.dispose(image22);
const hands = [];
if (!predictions || predictions.length === 0)
return hands;
for (const prediction of predictions) {
const boxes = await prediction.box.data();
const startPoint = boxes.slice(0, 2);
const endPoint = boxes.slice(2, 4);
const palmLandmarks = await prediction.palmLandmarks.array();
tfjs_esm_exports.dispose(prediction.box);
tfjs_esm_exports.dispose(prediction.palmLandmarks);
hands.push(scaleBoxCoordinates2({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));
}
return hands;
}
};
// src/handpose/util.ts
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 size = mat1.length;
for (let row = 0; row < size; row++) {
product.push([]);
for (let col = 0; col < size; 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/handpose/handpipeline.ts
var palmBoxEnlargeFactor = 5;
var handBoxEnlargeFactor = 1.65;
var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];
var palmLandmarksPalmBase = 0;
var palmLandmarksMiddleFingerBase = 2;
var HandPipeline = class {
constructor(handDetector, handPoseModel2) {
__publicField(this, "handDetector");
__publicField(this, "handPoseModel");
__publicField(this, "inputSize");
__publicField(this, "storedBoxes");
__publicField(this, "skipped");
__publicField(this, "detectedHands");
this.handDetector = handDetector;
this.handPoseModel = handPoseModel2;
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this.inputSize = this.handPoseModel.inputs[0].shape ? this.handPoseModel.inputs[0].shape[2] : 0;
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this.storedBoxes = [];
this.skipped = 0;
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, box22, angle, rotationMatrix) {
const boxSize = getBoxSize2(box22);
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]
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]);
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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(box22), 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])
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]);
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}
async estimateHands(image22, config3) {
let useFreshBox = false;
let boxes;
if (this.skipped === 0 || this.skipped > config3.hand.skipFrames || !config3.hand.landmarks || !config3.skipFrame) {
boxes = await this.handDetector.estimateHandBounds(image22, config3);
this.skipped = 0;
}
if (config3.skipFrame)
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] / image22.shape[2], palmCenter[1] / image22.shape[1]];
const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tfjs_esm_exports.image.rotateWithOffset(image22, angle, 0, palmCenterNormalized) : image22.clone();
const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
const croppedInput = cutBoxFromImageAndResize2(newBox, rotatedImage, [this.inputSize, this.inputSize]);
const handImage = tfjs_esm_exports.div(croppedInput, 255);
tfjs_esm_exports.dispose(croppedInput);
tfjs_esm_exports.dispose(rotatedImage);
const [confidenceT, keypoints3] = await this.handPoseModel.predict(handImage);
tfjs_esm_exports.dispose(handImage);
const confidence = (await confidenceT.data())[0];
tfjs_esm_exports.dispose(confidenceT);
if (confidence >= config3.hand.minConfidence / 4) {
const keypointsReshaped = tfjs_esm_exports.reshape(keypoints3, [-1, 3]);
const rawCoords = await keypointsReshaped.array();
tfjs_esm_exports.dispose(keypoints3);
tfjs_esm_exports.dispose(keypointsReshaped);
const coords3 = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);
const nextBoundingBox = this.getBoxForHandLandmarks(coords3);
this.storedBoxes[i] = { ...nextBoundingBox, confidence };
const result = {
landmarks: coords3,
confidence,
box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint }
};
hands.push(result);
} else {
this.storedBoxes[i] = null;
}
tfjs_esm_exports.dispose(keypoints3);
} else {
const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor);
const result = {
confidence: currentBox.confidence,
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box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },
landmarks: []
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};
hands.push(result);
}
}
this.storedBoxes = this.storedBoxes.filter((a) => a !== null);
this.detectedHands = hands.length;
return hands;
}
};
// src/fingerpose/description.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]
};
// src/fingerpose/estimator.ts
var options = {
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) {
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 > options.NO_CURL_START_LIMIT)
fingerCurl = FingerCurl.none;
else if (angleOfCurve > options.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 += options.DISTANCE_VOTE_POWER;
else if (start_end_x_y_dist_ratio > 0.66)
voteDiagonal += options.DISTANCE_VOTE_POWER;
else
voteHorizontal += options.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, options.TOTAL_ANGLE_VOTE_POWER);
voteVertical += votes[0];
voteDiagonal += votes[1];
voteHorizontal += votes[2];
for (const fingerSlope of fingerSlopes) {
const fingerVotes = angleOrientationAt(fingerSlope, options.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 };
}
// src/fingerpose/gesture.ts
var Gesture = 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];
}
addCurl(finger, curl, confidence) {
if (typeof this.curls[finger] === "undefined")
this.curls[finger] = [];
this.curls[finger].push([curl, confidence]);
}
addDirection(finger, position, confidence) {
if (!this.directions[finger])
this.directions[finger] = [];
this.directions[finger].push([position, confidence]);
}
setWeight(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, score3] of expectedCurls) {
if (detectedCurl === expectedCurl) {
confidence += score3 * 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, score3] of expectedDirections) {
if (detectedDirection === expectedDirection) {
confidence += score3 * this.weightsRelative[fingerIdx];
break;
}
}
}
return confidence / 10;
}
};
// src/fingerpose/gestures.ts
var ThumbsUp = new Gesture("thumbs up");
ThumbsUp.addCurl(Finger.thumb, FingerCurl.none, 1);
ThumbsUp.addDirection(Finger.thumb, FingerDirection.verticalUp, 1);
ThumbsUp.addDirection(Finger.thumb, FingerDirection.diagonalUpLeft, 0.25);
ThumbsUp.addDirection(Finger.thumb, FingerDirection.diagonalUpRight, 0.25);
for (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {
ThumbsUp.addCurl(finger, FingerCurl.full, 1);
ThumbsUp.addDirection(finger, FingerDirection.horizontalLeft, 1);
ThumbsUp.addDirection(finger, FingerDirection.horizontalRight, 1);
}
var Victory = new Gesture("victory");
Victory.addCurl(Finger.thumb, FingerCurl.half, 0.5);
Victory.addCurl(Finger.thumb, FingerCurl.none, 0.5);
Victory.addDirection(Finger.thumb, FingerDirection.verticalUp, 1);
Victory.addDirection(Finger.thumb, FingerDirection.diagonalUpLeft, 1);
Victory.addCurl(Finger.index, FingerCurl.none, 1);
Victory.addDirection(Finger.index, FingerDirection.verticalUp, 0.75);
Victory.addDirection(Finger.index, FingerDirection.diagonalUpLeft, 1);
Victory.addCurl(Finger.middle, FingerCurl.none, 1);
Victory.addDirection(Finger.middle, FingerDirection.verticalUp, 1);
Victory.addDirection(Finger.middle, FingerDirection.diagonalUpLeft, 0.75);
Victory.addCurl(Finger.ring, FingerCurl.full, 1);
Victory.addDirection(Finger.ring, FingerDirection.verticalUp, 0.2);
Victory.addDirection(Finger.ring, FingerDirection.diagonalUpLeft, 1);
Victory.addDirection(Finger.ring, FingerDirection.horizontalLeft, 0.2);
Victory.addCurl(Finger.pinky, FingerCurl.full, 1);
Victory.addDirection(Finger.pinky, FingerDirection.verticalUp, 0.2);
Victory.addDirection(Finger.pinky, FingerDirection.diagonalUpLeft, 1);
Victory.addDirection(Finger.pinky, FingerDirection.horizontalLeft, 0.2);
Victory.setWeight(Finger.index, 2);
Victory.setWeight(Finger.middle, 2);
var gestures_default = [ThumbsUp, Victory];
// src/fingerpose/fingerpose.ts
var minConfidence = 0.7;
function analyze(keypoints3) {
const estimatorRes = estimate(keypoints3);
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 match2(keypoints3) {
const estimatorRes = estimate(keypoints3);
const poses2 = [];
for (const gesture3 of gestures_default) {
const confidence = gesture3.matchAgainst(estimatorRes.curls, estimatorRes.directions);
if (confidence >= minConfidence)
poses2.push({ name: gesture3.name, confidence });
}
return poses2;
}
// src/handpose/handpose.ts
var meshAnnotations = {
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 predict5(input, config3) {
const predictions = await handPipeline.estimateHands(input, config3);
if (!predictions)
return [];
const hands = [];
for (let i = 0; i < predictions.length; i++) {
const annotations3 = {};
if (predictions[i].landmarks) {
for (const key of Object.keys(meshAnnotations)) {
annotations3[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);
}
}
const keypoints3 = predictions[i].landmarks;
let box6 = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0];
let boxRaw3 = [0, 0, 0, 0];
if (keypoints3 && keypoints3.length > 0) {
for (const pt of keypoints3) {
if (pt[0] < box6[0])
box6[0] = pt[0];
if (pt[1] < box6[1])
box6[1] = pt[1];
if (pt[0] > box6[2])
box6[2] = pt[0];
if (pt[1] > box6[3])
box6[3] = pt[1];
}
box6[2] -= box6[0];
box6[3] -= box6[1];
boxRaw3 = [box6[0] / (input.shape[2] || 0), box6[1] / (input.shape[1] || 0), box6[2] / (input.shape[2] || 0), box6[3] / (input.shape[1] || 0)];
} else {
box6 = 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];
boxRaw3 = [
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(keypoints3);
hands.push({
id: i,
score: Math.round(100 * predictions[i].confidence) / 100,
box: box6,
boxRaw: boxRaw3,
keypoints: keypoints3,
annotations: annotations3,
landmarks
});
}
return hands;
}
async function load6(config3) {
var _a, _b, _c, _d, _e, _f;
if (!handDetectorModel || !handPoseModel) {
[handDetectorModel, handPoseModel] = await Promise.all([
config3.hand.enabled ? tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, ((_a = config3.hand.detector) == null ? void 0 : _a.modelPath) || ""), { fromTFHub: (((_b = config3.hand.detector) == null ? void 0 : _b.modelPath) || "").includes("tfhub.dev") }) : null,
config3.hand.landmarks ? tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, ((_c = config3.hand.skeleton) == null ? void 0 : _c.modelPath) || ""), { fromTFHub: (((_d = config3.hand.skeleton) == null ? void 0 : _d.modelPath) || "").includes("tfhub.dev") }) : null
add benchmark info
2021-09-05 22:42:11 +02:00
]);
add platform and backend capabilities detection
2021-09-14 05:24:04 +02:00
if (config3.hand.enabled) {
if (!handDetectorModel || !handDetectorModel["modelUrl"])
log("load model failed:", ((_e = config3.hand.detector) == null ? void 0 : _e.modelPath) || "");
else if (config3.debug)
log("load model:", handDetectorModel["modelUrl"]);
if (!handPoseModel || !handPoseModel["modelUrl"])
log("load model failed:", ((_f = config3.hand.skeleton) == null ? void 0 : _f.modelPath) || "");
else if (config3.debug)
log("load model:", handPoseModel["modelUrl"]);
}
} else {
if (config3.debug)
log("cached model:", handDetectorModel["modelUrl"]);
if (config3.debug)
log("cached model:", handPoseModel["modelUrl"]);
}
const handDetector = new HandDetector(handDetectorModel);
handPipeline = new HandPipeline(handDetector, handPoseModel);
return [handDetectorModel, handPoseModel];
}
// src/blazepose/annotations.ts
var full = [
"nose",
"leftEyeInside",
"leftEye",
"leftEyeOutside",
"rightEyeInside",
"rightEye",
"rightEyeOutside",
"leftEar",
"rightEar",
"leftMouth",
"rightMouth",
"leftShoulder",
"rightShoulder",
"leftElbow",
"rightElbow",
"leftWrist",
"rightWrist",
"leftPalm",
"rightPalm",
"leftIndex",
"rightIndex",
"leftPinky",
"rightPinky",
"leftHip",
"rightHip",
"leftKnee",
"rightKnee",
"leftAnkle",
"rightAnkle",
"leftHeel",
"rightHeel",
"leftFoot",
"rightFoot",
"midHip",
"forehead",
"leftThumb",
"leftHand",
"rightThumb",
"rightHand"
];
var upper = [
"nose",
"leftEyeInside",
"leftEye",
"leftEyeOutside",
"rightEyeInside",
"rightEye",
"rightEyeOutside",
"leftEar",
"rightEar",
"leftMouth",
"rightMouth",
"leftShoulder",
"rightShoulder",
"leftElbow",
"rightElbow",
"left:15",
"right:16",
"left:17",
"right:18",
"left:19",
"right:20",
"left:21",
"right:22",
"leftChest",
"rightChest",
"neck",
"forehead",
"left:27",
"right:28",
"left:29",
"right:30"
];
// src/blazepose/blazepose.ts
var model4;
async function load7(config3) {
if (!model4) {
model4 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath || ""));
model4["width"] = parseInt(model4["signature"].inputs["input_1:0"].tensorShape.dim[2].size);
model4["height"] = parseInt(model4["signature"].inputs["input_1:0"].tensorShape.dim[1].size);
if (!model4 || !model4["modelUrl"])
log("load model failed:", config3.body.modelPath);
else if (config3.debug)
log("load model:", model4["modelUrl"]);
} else if (config3.debug)
log("cached model:", model4["modelUrl"]);
return model4;
}
async function predict6(image22, config3) {
if (!model4)
return [];
if (!config3.body.enabled)
return [];
const imgSize = { width: image22.shape[2] || 0, height: image22.shape[1] || 0 };
const resize = tfjs_esm_exports.image.resizeBilinear(image22, [model4["width"], model4["height"]], false);
const normalize = tfjs_esm_exports.div(resize, [255]);
tfjs_esm_exports.dispose(resize);
const resT = await model4.predict(normalize);
const findT = resT.find((t) => t.size === 195 || t.size === 155);
const points = await (findT == null ? void 0 : findT.data()) || [];
resT.forEach((t) => tfjs_esm_exports.dispose(t));
tfjs_esm_exports.dispose(normalize);
const keypoints3 = [];
const labels2 = (points == null ? void 0 : points.length) === 195 ? full : upper;
const depth = 5;
for (let i = 0; i < points.length / depth; i++) {
keypoints3.push({
id: i,
part: labels2[i],
position: [
Math.trunc(imgSize.width * points[depth * i + 0] / 255),
Math.trunc(imgSize.height * points[depth * i + 1] / 255),
Math.trunc(points[depth * i + 2]) + 0
],
positionRaw: [
points[depth * i + 0] / 255,
points[depth * i + 1] / 255,
points[depth * i + 2] + 0
],
score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100,
presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100
});
}
const x = keypoints3.map((a) => a.position[0]);
const y = keypoints3.map((a) => a.position[1]);
const box6 = [
Math.min(...x),
Math.min(...y),
Math.max(...x) - Math.min(...x),
Math.max(...y) - Math.min(...x)
];
const boxRaw3 = [0, 0, 0, 0];
const score3 = keypoints3.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
return [{ id: 0, score: score3, box: box6, boxRaw: boxRaw3, keypoints: keypoints3 }];
}
// src/efficientpose/efficientpose.ts
var model5;
var keypoints = [];
var box4 = [0, 0, 0, 0];
var boxRaw = [0, 0, 0, 0];
var score = 0;
var skipped3 = Number.MAX_SAFE_INTEGER;
var bodyParts = ["head", "neck", "rightShoulder", "rightElbow", "rightWrist", "chest", "leftShoulder", "leftElbow", "leftWrist", "pelvis", "rightHip", "rightKnee", "rightAnkle", "leftHip", "leftKnee", "leftAnkle"];
async function load8(config3) {
if (!model5) {
model5 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath || ""));
if (!model5 || !model5["modelUrl"])
log("load model failed:", config3.body.modelPath);
else if (config3.debug)
log("load model:", model5["modelUrl"]);
} else if (config3.debug)
log("cached model:", model5["modelUrl"]);
return model5;
}
function max2d(inputs, minScore) {
const [width, height] = inputs.shape;
return tfjs_esm_exports.tidy(() => {
const mod = (a, b) => tfjs_esm_exports.sub(a, tfjs_esm_exports.mul(tfjs_esm_exports.div(a, tfjs_esm_exports.scalar(b, "int32")), tfjs_esm_exports.scalar(b, "int32")));
const reshaped = tfjs_esm_exports.reshape(inputs, [height * width]);
const newScore = tfjs_esm_exports.max(reshaped, 0).dataSync()[0];
if (newScore > minScore) {
const coords3 = tfjs_esm_exports.argMax(reshaped, 0);
const x = mod(coords3, width).dataSync()[0];
const y = tfjs_esm_exports.div(coords3, tfjs_esm_exports.scalar(width, "int32")).dataSync()[0];
return [x, y, newScore];
}
return [0, 0, newScore];
});
}
async function predict7(image22, config3) {
var _a;
if (skipped3 < (((_a = config3.body) == null ? void 0 : _a.skipFrames) || 0) && config3.skipFrame && Object.keys(keypoints).length > 0) {
skipped3++;
return [{ id: 0, score, box: box4, boxRaw, keypoints }];
}
skipped3 = 0;
return new Promise(async (resolve) => {
var _a2;
const tensor3 = tfjs_esm_exports.tidy(() => {
if (!model5.inputs[0].shape)
return null;
const resize = tfjs_esm_exports.image.resizeBilinear(image22, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
const enhance2 = tfjs_esm_exports.mul(resize, 2);
const norm = enhance2.sub(1);
return norm;
});
let resT;
if (config3.body.enabled)
resT = await model5.predict(tensor3);
tfjs_esm_exports.dispose(tensor3);
if (resT) {
keypoints.length = 0;
const squeeze7 = resT.squeeze();
tfjs_esm_exports.dispose(resT);
const stack3 = squeeze7.unstack(2);
tfjs_esm_exports.dispose(squeeze7);
for (let id = 0; id < stack3.length; id++) {
const [x2, y2, partScore] = max2d(stack3[id], config3.body.minConfidence);
if (score > (((_a2 = config3.body) == null ? void 0 : _a2.minConfidence) || 0)) {
keypoints.push({
score: Math.round(100 * partScore) / 100,
part: bodyParts[id],
positionRaw: [
x2 / model5.inputs[0].shape[2],
y2 / model5.inputs[0].shape[1]
],
position: [
Math.round(image22.shape[2] * x2 / model5.inputs[0].shape[2]),
Math.round(image22.shape[1] * y2 / model5.inputs[0].shape[1])
]
});
}
}
stack3.forEach((s) => tfjs_esm_exports.dispose(s));
}
score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
const x = keypoints.map((a) => a.position[0]);
const y = keypoints.map((a) => a.position[1]);
box4 = [
Math.min(...x),
Math.min(...y),
Math.max(...x) - Math.min(...x),
Math.max(...y) - Math.min(...y)
];
const xRaw = keypoints.map((a) => a.positionRaw[0]);
const yRaw = keypoints.map((a) => a.positionRaw[1]);
boxRaw = [
Math.min(...xRaw),
Math.min(...yRaw),
Math.max(...xRaw) - Math.min(...xRaw),
Math.max(...yRaw) - Math.min(...yRaw)
];
resolve([{ id: 0, score, box: box4, boxRaw, keypoints }]);
});
}
// src/movenet/movenet.ts
var model6;
var keypoints2 = [];
var box5 = [0, 0, 0, 0];
var boxRaw2 = [0, 0, 0, 0];
var score2 = 0;
var skipped4 = Number.MAX_SAFE_INTEGER;
var bodyParts2 = ["nose", "leftEye", "rightEye", "leftEar", "rightEar", "leftShoulder", "rightShoulder", "leftElbow", "rightElbow", "leftWrist", "rightWrist", "leftHip", "rightHip", "leftKnee", "rightKnee", "leftAnkle", "rightAnkle"];
async function load9(config3) {
if (!model6) {
model6 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath || ""));
if (!model6 || !model6["modelUrl"])
log("load model failed:", config3.body.modelPath);
else if (config3.debug)
log("load model:", model6["modelUrl"]);
} else if (config3.debug)
log("cached model:", model6["modelUrl"]);
return model6;
}
async function parseSinglePose(res, config3, image22) {
keypoints2.length = 0;
const kpt3 = res[0][0];
for (let id = 0; id < kpt3.length; id++) {
score2 = kpt3[id][2];
if (score2 > config3.body.minConfidence) {
keypoints2.push({
score: Math.round(100 * score2) / 100,
part: bodyParts2[id],
positionRaw: [
kpt3[id][1],
kpt3[id][0]
],
position: [
Math.round((image22.shape[2] || 0) * kpt3[id][1]),
Math.round((image22.shape[1] || 0) * kpt3[id][0])
]
});
}
}
score2 = keypoints2.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
const x = keypoints2.map((a) => a.position[0]);
const y = keypoints2.map((a) => a.position[1]);
box5 = [
Math.min(...x),
Math.min(...y),
Math.max(...x) - Math.min(...x),
Math.max(...y) - Math.min(...y)
];
const xRaw = keypoints2.map((a) => a.positionRaw[0]);
const yRaw = keypoints2.map((a) => a.positionRaw[1]);
boxRaw2 = [
Math.min(...xRaw),
Math.min(...yRaw),
Math.max(...xRaw) - Math.min(...xRaw),
Math.max(...yRaw) - Math.min(...yRaw)
];
const persons2 = [];
persons2.push({ id: 0, score: score2, box: box5, boxRaw: boxRaw2, keypoints: keypoints2 });
return persons2;
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}
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async function parseMultiPose(res, config3, image22) {
const persons2 = [];
for (let p = 0; p < res[0].length; p++) {
const kpt3 = res[0][p];
score2 = Math.round(100 * kpt3[51 + 4]) / 100;
if (score2 < config3.body.minConfidence)
continue;
keypoints2.length = 0;
for (let i = 0; i < 17; i++) {
const partScore = Math.round(100 * kpt3[3 * i + 2]) / 100;
if (partScore > config3.body.minConfidence) {
keypoints2.push({
part: bodyParts2[i],
score: partScore,
positionRaw: [
kpt3[3 * i + 1],
kpt3[3 * i + 0]
],
position: [
Math.trunc(kpt3[3 * i + 1] * (image22.shape[2] || 0)),
Math.trunc(kpt3[3 * i + 0] * (image22.shape[1] || 0))
]
});
}
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}
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boxRaw2 = [kpt3[51 + 1], kpt3[51 + 0], kpt3[51 + 3] - kpt3[51 + 1], kpt3[51 + 2] - kpt3[51 + 0]];
persons2.push({
id: p,
score: score2,
boxRaw: boxRaw2,
box: [
Math.trunc(boxRaw2[0] * (image22.shape[2] || 0)),
Math.trunc(boxRaw2[1] * (image22.shape[1] || 0)),
Math.trunc(boxRaw2[2] * (image22.shape[2] || 0)),
Math.trunc(boxRaw2[3] * (image22.shape[1] || 0))
],
keypoints: keypoints2
});
}
return persons2;
}
async function predict8(image22, config3) {
if (skipped4 < (config3.body.skipFrames || 0) && config3.skipFrame && Object.keys(keypoints2).length > 0) {
skipped4++;
return [{ id: 0, score: score2, box: box5, boxRaw: boxRaw2, keypoints: keypoints2 }];
}
skipped4 = 0;
return new Promise(async (resolve) => {
const tensor3 = tfjs_esm_exports.tidy(() => {
if (!model6.inputs[0].shape)
return null;
let inputSize = model6.inputs[0].shape[2];
if (inputSize === -1)
inputSize = 256;
const resize = tfjs_esm_exports.image.resizeBilinear(image22, [inputSize, inputSize], false);
const cast4 = tfjs_esm_exports.cast(resize, "int32");
return cast4;
});
let resT;
if (config3.body.enabled)
resT = await model6.predict(tensor3);
tfjs_esm_exports.dispose(tensor3);
if (!resT)
resolve([]);
const res = await resT.array();
let persons2;
if (resT.shape[2] === 17)
persons2 = await parseSinglePose(res, config3, image22);
else if (resT.shape[2] === 56)
persons2 = await parseMultiPose(res, config3, image22);
tfjs_esm_exports.dispose(resT);
resolve(persons2);
});
}
// 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/nanodet.ts
var model7;
var last3 = [];
var skipped5 = Number.MAX_SAFE_INTEGER;
var scaleBox = 2.5;
async function load10(config3) {
if (!model7) {
model7 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.object.modelPath || ""));
const inputs = Object.values(model7.modelSignature["inputs"]);
model7.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;
if (!model7.inputSize)
throw new Error(`Human: Cannot determine model inputSize: ${config3.object.modelPath}`);
if (!model7 || !model7.modelUrl)
log("load model failed:", config3.object.modelPath);
else if (config3.debug)
log("load model:", model7.modelUrl);
} else if (config3.debug)
log("cached model:", model7.modelUrl);
return model7;
}
async function process3(res, inputSize, outputShape, config3) {
let id = 0;
let results = [];
for (const strideSize of [1, 2, 4]) {
tfjs_esm_exports.tidy(async () => {
var _a, _b;
const baseSize = strideSize * 13;
const scoresT = (_a = res.find((a) => a.shape[1] === baseSize ** 2 && a.shape[2] === labels.length)) == null ? void 0 : _a.squeeze();
const featuresT = (_b = res.find((a) => a.shape[1] === baseSize ** 2 && a.shape[2] < labels.length)) == null ? void 0 : _b.squeeze();
const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]);
const boxIdx = await boxesMax.argMax(2).array();
const scores = await scoresT.array();
for (let i = 0; i < scoresT.shape[0]; i++) {
for (let j = 0; j < scoresT.shape[1]; j++) {
const score3 = scores[i][j];
if (score3 > config3.object.minConfidence && 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 / inputSize));
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 boxRaw3 = [x, y, w, h];
boxRaw3 = boxRaw3.map((a) => Math.max(0, Math.min(a, 1)));
const box6 = [
boxRaw3[0] * outputShape[0],
boxRaw3[1] * outputShape[1],
boxRaw3[2] * outputShape[0],
boxRaw3[3] * outputShape[1]
];
const result = {
id: id++,
score: Math.round(100 * score3) / 100,
class: j + 1,
label: labels[j].label,
box: box6.map((a) => Math.trunc(a)),
boxRaw: boxRaw3
};
results.push(result);
}
}
}
});
}
res.forEach((t) => tfjs_esm_exports.dispose(t));
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 tfjs_esm_exports.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
nmsIdx = await nms.data();
tfjs_esm_exports.dispose(nms);
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}
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results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
return results;
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}
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async function predict9(image22, config3) {
if (skipped5 < (config3.object.skipFrames || 0) && config3.skipFrame && last3.length > 0) {
skipped5++;
return last3;
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}
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skipped5 = 0;
if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense"))
return last3;
return new Promise(async (resolve) => {
const outputSize = [image22.shape[2], image22.shape[1]];
const resize = tfjs_esm_exports.image.resizeBilinear(image22, [model7.inputSize, model7.inputSize], false);
const norm = tfjs_esm_exports.div(resize, 255);
const transpose = norm.transpose([0, 3, 1, 2]);
tfjs_esm_exports.dispose(norm);
tfjs_esm_exports.dispose(resize);
let objectT;
if (config3.object.enabled)
objectT = await model7.predict(transpose);
tfjs_esm_exports.dispose(transpose);
const obj = await process3(objectT, model7.inputSize, outputSize, config3);
last3 = obj;
resolve(obj);
});
}
// src/object/centernet.ts
var model8;
var last4 = [];
var skipped6 = Number.MAX_SAFE_INTEGER;
async function load11(config3) {
if (!model8) {
model8 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.object.modelPath || ""));
const inputs = Object.values(model8.modelSignature["inputs"]);
model8.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;
if (!model8.inputSize)
throw new Error(`Human: Cannot determine model inputSize: ${config3.object.modelPath}`);
if (!model8 || !model8.modelUrl)
log("load model failed:", config3.object.modelPath);
else if (config3.debug)
log("load model:", model8.modelUrl);
} else if (config3.debug)
log("cached model:", model8.modelUrl);
return model8;
}
async function process4(res, inputSize, outputShape, config3) {
if (!res)
return [];
const results = [];
const detections = await res.array();
const squeezeT = tfjs_esm_exports.squeeze(res);
tfjs_esm_exports.dispose(res);
const arr = tfjs_esm_exports.split(squeezeT, 6, 1);
tfjs_esm_exports.dispose(squeezeT);
const stackT = tfjs_esm_exports.stack([arr[1], arr[0], arr[3], arr[2]], 1);
const boxesT = tfjs_esm_exports.squeeze(stackT);
tfjs_esm_exports.dispose(stackT);
const scoresT = tfjs_esm_exports.squeeze(arr[4]);
const classesT = tfjs_esm_exports.squeeze(arr[5]);
arr.forEach((t) => tfjs_esm_exports.dispose(t));
const nmsT = await tfjs_esm_exports.image.nonMaxSuppressionAsync(boxesT, scoresT, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
tfjs_esm_exports.dispose(boxesT);
tfjs_esm_exports.dispose(scoresT);
tfjs_esm_exports.dispose(classesT);
const nms = await nmsT.data();
tfjs_esm_exports.dispose(nmsT);
let i = 0;
for (const id of nms) {
const score3 = Math.trunc(100 * detections[0][id][4]) / 100;
const classVal = detections[0][id][5];
const label = labels[classVal].label;
const [x, y] = [
detections[0][id][0] / inputSize,
detections[0][id][1] / inputSize
];
const boxRaw3 = [
x,
y,
detections[0][id][2] / inputSize - x,
detections[0][id][3] / inputSize - y
];
const box6 = [
Math.trunc(boxRaw3[0] * outputShape[0]),
Math.trunc(boxRaw3[1] * outputShape[1]),
Math.trunc(boxRaw3[2] * outputShape[0]),
Math.trunc(boxRaw3[3] * outputShape[1])
];
results.push({ id: i++, score: score3, class: classVal, label, box: box6, boxRaw: boxRaw3 });
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}
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return results;
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}
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async function predict10(input, config3) {
if (skipped6 < (config3.object.skipFrames || 0) && config3.skipFrame && last4.length > 0) {
skipped6++;
return last4;
}
skipped6 = 0;
if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense"))
return last4;
return new Promise(async (resolve) => {
const outputSize = [input.shape[2], input.shape[1]];
const resize = tfjs_esm_exports.image.resizeBilinear(input, [model8.inputSize, model8.inputSize]);
const objectT = config3.object.enabled ? model8.execute(resize, ["tower_0/detections"]) : null;
tfjs_esm_exports.dispose(resize);
const obj = await process4(objectT, model8.inputSize, outputSize, config3);
last4 = obj;
resolve(obj);
});
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}
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// src/segmentation/segmentation.ts
var model9;
var busy = false;
async function load12(config3) {
if (!model9) {
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model9 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.segmentation.modelPath || ""));
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if (!model9 || !model9["modelUrl"])
log("load model failed:", config3.segmentation.modelPath);
else if (config3.debug)
log("load model:", model9["modelUrl"]);
} else if (config3.debug)
log("cached model:", model9["modelUrl"]);
return model9;
}
async function predict11(input) {
var _a, _b;
const width = ((_a = input.tensor) == null ? void 0 : _a.shape[1]) || 0;
const height = ((_b = input.tensor) == null ? void 0 : _b.shape[2]) || 0;
if (!input.tensor)
return null;
if (!model9 || !model9.inputs[0].shape)
return null;
const resizeInput = tfjs_esm_exports.image.resizeBilinear(input.tensor, [model9.inputs[0].shape[1], model9.inputs[0].shape[2]], false);
const norm = tfjs_esm_exports.div(resizeInput, 255);
const res = model9.predict(norm);
tfjs_esm_exports.dispose(resizeInput);
tfjs_esm_exports.dispose(norm);
const squeeze7 = tfjs_esm_exports.squeeze(res, 0);
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tfjs_esm_exports.dispose(res);
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let resizeOutput;
if (squeeze7.shape[2] === 2) {
const softmax = squeeze7.softmax();
const [bg, fg] = tfjs_esm_exports.unstack(softmax, 2);
const expand = tfjs_esm_exports.expandDims(fg, 2);
const pad = tfjs_esm_exports.expandDims(expand, 0);
tfjs_esm_exports.dispose(softmax);
tfjs_esm_exports.dispose(bg);
tfjs_esm_exports.dispose(fg);
const crop = tfjs_esm_exports.image.cropAndResize(pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);
resizeOutput = tfjs_esm_exports.squeeze(crop, 0);
tfjs_esm_exports.dispose(crop);
tfjs_esm_exports.dispose(expand);
tfjs_esm_exports.dispose(pad);
} else {
resizeOutput = tfjs_esm_exports.image.resizeBilinear(squeeze7, [width, height]);
}
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tfjs_esm_exports.dispose(squeeze7);
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if (env.node) {
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const data = await resizeOutput.data();
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tfjs_esm_exports.dispose(resizeOutput);
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return data;
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}
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const overlay = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(width, height) : document.createElement("canvas");
overlay.width = width;
overlay.height = height;
if (tfjs_esm_exports.browser)
await tfjs_esm_exports.browser.toPixels(resizeOutput, overlay);
tfjs_esm_exports.dispose(resizeOutput);
const alphaCanvas = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(width, height) : document.createElement("canvas");
alphaCanvas.width = width;
alphaCanvas.height = height;
const ctxAlpha = alphaCanvas.getContext("2d");
ctxAlpha.filter = "blur(8px";
await ctxAlpha.drawImage(overlay, 0, 0);
const alpha = ctxAlpha.getImageData(0, 0, width, height).data;
const original = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(width, height) : document.createElement("canvas");
original.width = width;
original.height = height;
const ctx = original.getContext("2d");
if (input.canvas)
await ctx.drawImage(input.canvas, 0, 0);
ctx.globalCompositeOperation = "darken";
ctx.filter = "blur(8px)";
await ctx.drawImage(overlay, 0, 0);
ctx.globalCompositeOperation = "source-over";
ctx.filter = "none";
input.canvas = original;
return alpha;
}
async function process5(input, background, config3) {
var _a;
if (busy)
return null;
busy = true;
if (!model9)
await load12(config3);
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const img = process2(input, config3);
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const alpha = await predict11(img);
tfjs_esm_exports.dispose(img.tensor);
if (background && alpha) {
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const tmp = process2(background, config3);
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const bg = tmp.canvas;
tfjs_esm_exports.dispose(tmp.tensor);
const fg = img.canvas;
const fgData = (_a = fg.getContext("2d")) == null ? void 0 : _a.getImageData(0, 0, fg.width, fg.height).data;
const c = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(fg.width, fg.height) : document.createElement("canvas");
c.width = fg.width;
c.height = fg.height;
const ctx = c.getContext("2d");
ctx.globalCompositeOperation = "copy";
ctx.drawImage(bg, 0, 0, c.width, c.height);
const cData = ctx.getImageData(0, 0, c.width, c.height);
for (let i = 0; i < c.width * c.height; i++) {
cData.data[4 * i + 0] = (255 - alpha[4 * i + 0]) / 255 * cData.data[4 * i + 0] + alpha[4 * i + 0] / 255 * fgData[4 * i + 0];
cData.data[4 * i + 1] = (255 - alpha[4 * i + 1]) / 255 * cData.data[4 * i + 1] + alpha[4 * i + 1] / 255 * fgData[4 * i + 1];
cData.data[4 * i + 2] = (255 - alpha[4 * i + 2]) / 255 * cData.data[4 * i + 2] + alpha[4 * i + 2] / 255 * fgData[4 * i + 2];
cData.data[4 * i + 3] = (255 - alpha[4 * i + 3]) / 255 * cData.data[4 * i + 3] + alpha[4 * i + 3] / 255 * fgData[4 * i + 3];
}
ctx.putImageData(cData, 0, 0);
img.canvas = c;
}
busy = false;
return img.canvas;
}
// src/models.ts
async function load13(instance) {
if (instance.config.async) {
[
instance.models.face,
instance.models.emotion,
instance.models.handpose,
instance.models.posenet,
instance.models.blazepose,
instance.models.efficientpose,
instance.models.movenet,
instance.models.nanodet,
instance.models.centernet,
instance.models.faceres,
instance.models.segmentation
] = await Promise.all([
instance.models.face || (instance.config.face.enabled ? load2(instance.config) : null),
instance.models.emotion || (instance.config.face.enabled && instance.config.face.emotion.enabled ? load4(instance.config) : null),
instance.models.handpose || (instance.config.hand.enabled ? load6(instance.config) : null),
instance.models.posenet || (instance.config.body.enabled && instance.config.body.modelPath.includes("posenet") ? load5(instance.config) : null),
instance.models.blazepose || (instance.config.body.enabled && instance.config.body.modelPath.includes("blazepose") ? load7(instance.config) : null),
instance.models.efficientpose || (instance.config.body.enabled && instance.config.body.modelPath.includes("efficientpose") ? load8(instance.config) : null),
instance.models.movenet || (instance.config.body.enabled && instance.config.body.modelPath.includes("movenet") ? load9(instance.config) : null),
instance.models.nanodet || (instance.config.object.enabled && instance.config.object.modelPath.includes("nanodet") ? load10(instance.config) : null),
instance.models.centernet || (instance.config.object.enabled && instance.config.object.modelPath.includes("centernet") ? load11(instance.config) : null),
instance.models.faceres || (instance.config.face.enabled && instance.config.face.description.enabled ? load3(instance.config) : null),
instance.models.segmentation || (instance.config.segmentation.enabled ? load12(instance.config) : null)
]);
} else {
if (instance.config.face.enabled && !instance.models.face)
instance.models.face = await load2(instance.config);
if (instance.config.face.enabled && instance.config.face.emotion.enabled && !instance.models.emotion)
instance.models.emotion = await load4(instance.config);
if (instance.config.hand.enabled && !instance.models.handpose)
instance.models.handpose = await load6(instance.config);
if (instance.config.body.enabled && !instance.models.posenet && instance.config.body.modelPath.includes("posenet"))
instance.models.posenet = await load5(instance.config);
if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body.modelPath.includes("blazepose"))
instance.models.blazepose = await load7(instance.config);
if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body.modelPath.includes("efficientpose"))
instance.models.efficientpose = await load7(instance.config);
if (instance.config.body.enabled && !instance.models.movenet && instance.config.body.modelPath.includes("movenet"))
instance.models.movenet = await load9(instance.config);
if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object.modelPath.includes("nanodet"))
instance.models.nanodet = await load10(instance.config);
if (instance.config.object.enabled && !instance.models.centernet && instance.config.object.modelPath.includes("centernet"))
instance.models.centernet = await load11(instance.config);
if (instance.config.face.enabled && instance.config.face.description.enabled && !instance.models.faceres)
instance.models.faceres = await load3(instance.config);
if (instance.config.segmentation.enabled && !instance.models.segmentation)
instance.models.segmentation = await load12(instance.config);
}
}
modularize human class and add model validation
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async function validate(instance) {
const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"];
for (const defined of Object.keys(instance.models)) {
if (instance.models[defined]) {
let models2 = [];
if (Array.isArray(instance.models[defined]))
models2 = instance.models[defined].map((model10) => model10.executor ? model10 : model10.model);
else
models2 = [instance.models[defined]];
for (const model10 of models2) {
const ops = [];
const executor = model10 == null ? void 0 : model10.executor;
if (executor) {
for (const kernel of Object.values(executor.graph.nodes)) {
const op = kernel.op.toLowerCase();
if (!ops.includes(op))
ops.push(op);
}
}
const missing = [];
for (const op of ops) {
if (!simpleOps.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 (!executor && instance.config.debug)
log("model executor not found:", defined);
if (missing.length > 0 && instance.config.debug)
log("model validation:", defined, missing);
}
}
}
}
add benchmark info
2021-09-05 22:42:11 +02:00
// src/face.ts
var calculateGaze = (face5) => {
const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]);
if (!face5.annotations["rightEyeIris"] || !face5.annotations["leftEyeIris"])
return { bearing: 0, strength: 0 };
const offsetIris = [0, -0.1];
const eyeRatio = 1;
const left = face5.mesh[33][2] > face5.mesh[263][2];
const irisCenter = left ? face5.mesh[473] : face5.mesh[468];
const eyeCenter = left ? [(face5.mesh[133][0] + face5.mesh[33][0]) / 2, (face5.mesh[133][1] + face5.mesh[33][1]) / 2] : [(face5.mesh[263][0] + face5.mesh[362][0]) / 2, (face5.mesh[263][1] + face5.mesh[362][1]) / 2];
const eyeSize = left ? [face5.mesh[133][0] - face5.mesh[33][0], face5.mesh[23][1] - face5.mesh[27][1]] : [face5.mesh[263][0] - face5.mesh[362][0], face5.mesh[253][1] - face5.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] ** 2 + eyeDiff[1] ** 2);
strength = Math.min(strength, face5.boxRaw[2] / 2, face5.boxRaw[3] / 2);
const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI;
return { bearing, strength };
};
var calculateFaceAngle = (face5, imageSize) => {
const normalize = (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 (isNaN(thetaX))
thetaX = 0;
if (isNaN(thetaY))
thetaY = 0;
if (isNaN(thetaZ))
thetaZ = 0;
return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };
};
const meshToEulerAngle = (mesh2) => {
const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);
const angle2 = {
pitch: radians(mesh2[10][1], mesh2[10][2], mesh2[152][1], mesh2[152][2]),
yaw: radians(mesh2[33][0], mesh2[33][2], mesh2[263][0], mesh2[263][2]),
roll: radians(mesh2[33][0], mesh2[33][1], mesh2[263][0], mesh2[263][1])
};
return angle2;
};
const mesh = face5.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 size = Math.max(face5.boxRaw[2] * imageSize[0], face5.boxRaw[3] * imageSize[1]) / 1.5;
const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [
pt[0] * imageSize[0] / size,
pt[1] * imageSize[1] / size,
pt[2]
]);
const y_axis = normalize(subVectors(pts[1], pts[0]));
let x_axis = normalize(subVectors(pts[3], pts[2]));
const z_axis = normalize(crossVectors(x_axis, y_axis));
x_axis = crossVectors(y_axis, z_axis);
const matrix = [
x_axis[0],
x_axis[1],
x_axis[2],
y_axis[0],
y_axis[1],
y_axis[2],
z_axis[0],
z_axis[1],
z_axis[2]
];
const angle = rotationMatrixToEulerAngle(matrix);
const gaze = mesh.length === 478 ? calculateGaze(face5) : { bearing: 0, strength: 0 };
return { angle, matrix, gaze };
};
var detectFace = async (parent, input) => {
var _a, _b, _c, _d, _e, _f;
let timeStamp;
let ageRes;
let gearRes;
let genderRes;
let emotionRes;
let embeddingRes;
let descRes;
const faceRes = [];
parent.state = "run:face";
timeStamp = now();
const faces = await predict(input, parent.config);
parent.performance.face = Math.trunc(now() - timeStamp);
if (!input.shape || input.shape.length !== 4)
return [];
if (!faces)
return [];
for (let i = 0; i < faces.length; i++) {
parent.analyze("Get Face");
if (!faces[i].tensor || faces[i].tensor["isDisposedInternal"]) {
log("Face object is disposed:", faces[i].tensor);
continue;
}
const rotation = calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]);
parent.analyze("Start Emotion:");
if (parent.config.async) {
emotionRes = parent.config.face.emotion.enabled ? predict3(faces[i].tensor || tfjs_esm_exports.tensor([]), parent.config, i, faces.length) : {};
} else {
parent.state = "run:emotion";
timeStamp = now();
emotionRes = parent.config.face.emotion.enabled ? await predict3(faces[i].tensor || tfjs_esm_exports.tensor([]), parent.config, i, faces.length) : {};
parent.performance.emotion = Math.trunc(now() - timeStamp);
}
parent.analyze("End Emotion:");
parent.analyze("Start Description:");
if (parent.config.async) {
descRes = parent.config.face.description.enabled ? predict2(faces[i].tensor || tfjs_esm_exports.tensor([]), parent.config, i, faces.length) : [];
} else {
parent.state = "run:description";
timeStamp = now();
descRes = parent.config.face.description.enabled ? await predict2(faces[i].tensor || tfjs_esm_exports.tensor([]), parent.config, i, faces.length) : [];
parent.performance.embedding = Math.trunc(now() - timeStamp);
}
parent.analyze("End Description:");
if (parent.config.async) {
[ageRes, genderRes, emotionRes, embeddingRes, descRes, gearRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes, gearRes]);
}
parent.analyze("Finish Face:");
if (!parent.config.face.iris.enabled && ((_b = (_a = faces[i]) == null ? void 0 : _a.annotations) == null ? void 0 : _b.leftEyeIris) && ((_d = (_c = faces[i]) == null ? void 0 : _c.annotations) == null ? void 0 : _d.rightEyeIris)) {
delete faces[i].annotations.leftEyeIris;
delete faces[i].annotations.rightEyeIris;
}
const irisSize = ((_e = faces[i].annotations) == null ? void 0 : _e.leftEyeIris) && ((_f = faces[i].annotations) == null ? void 0 : _f.rightEyeIris) ? 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;
fix multiple memory leaks
2021-09-13 19:28:35 +02:00
const tensor3 = parent.config.face.detector.return ? tfjs_esm_exports.squeeze(faces[i].tensor) : null;
add benchmark info
2021-09-05 22:42:11 +02:00
tfjs_esm_exports.dispose(faces[i].tensor);
if (faces[i].tensor)
delete faces[i].tensor;
faceRes.push({
...faces[i],
id: i,
age: descRes.age,
gender: descRes.gender,
genderScore: descRes.genderScore,
embedding: descRes.descriptor,
emotion: emotionRes,
iris: irisSize !== 0 ? Math.trunc(500 / irisSize / 11.7) / 100 : 0,
rotation,
fix multiple memory leaks
2021-09-13 19:28:35 +02:00
tensor: tensor3
add benchmark info
2021-09-05 22:42:11 +02:00
});
parent.analyze("End Face");
}
parent.analyze("End FaceMesh:");
if (parent.config.async) {
if (parent.performance.face)
delete parent.performance.face;
if (parent.performance.age)
delete parent.performance.age;
if (parent.performance.gender)
delete parent.performance.gender;
if (parent.performance.emotion)
delete parent.performance.emotion;
}
return faceRes;
};
// src/gesture/gesture.ts
var body = (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.y < nose.position.y && rightWrist.position.y < nose.position.y)
gestures.push({ body: i, gesture: "i give up" });
else if (nose && leftWrist && leftWrist.position.y < nose.position.y)
gestures.push({ body: i, gesture: "raise left hand" });
else if (nose && rightWrist && rightWrist.position.y < nose.position.y)
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)
gestures.push({ body: i, gesture: `leaning ${leftShoulder.position.y > rightShoulder.position.y ? "left" : "right"}` });
}
return gestures;
};
var face = (res) => {
if (!res)
return [];
const gestures = [];
for (let i = 0; i < res.length; i++) {
if (res[i].mesh && res[i].mesh.length > 0) {
const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];
if (Math.abs(eyeFacing) < 10)
gestures.push({ face: i, gesture: "facing center" });
else
gestures.push({ face: i, gesture: `facing ${eyeFacing < 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];
if (Math.abs(chinDepth) > 10)
gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` });
}
}
return gestures;
};
var iris = (res) => {
if (!res)
return [];
const gestures = [];
for (let i = 0; i < res.length; i++) {
if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris)
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 rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];
const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];
if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06)
center = false;
if (leftIrisCenterX > 0.06)
gestures.push({ iris: i, gesture: "looking right" });
if (rightIrisCenterX > 0.06)
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 hand = (res) => {
if (!res)
return [];
const gestures = [];
for (let i = 0; i < res.length; i++) {
const fingers = [];
for (const [finger, pos] of Object.entries(res[i]["annotations"])) {
if (finger !== "palmBase" && Array.isArray(pos))
fingers.push({ name: finger.toLowerCase(), position: pos[0] });
}
if (fingers && fingers.length > 0) {
const closest = fingers.reduce((best, a) => best.position[2] < a.position[2] ? 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: `${highest.name} up` });
}
const poses2 = match2(res[i]["keypoints"]);
for (const pose of poses2)
gestures.push({ hand: i, gesture: pose.name });
}
return gestures;
};
redefine draw helpers interface
2021-09-16 00:58:54 +02:00
// src/draw.ts
add benchmark info
2021-09-05 22:42:11 +02:00
var options2 = {
color: "rgba(173, 216, 230, 0.6)",
labelColor: "rgba(173, 216, 230, 1)",
shadowColor: "black",
font: 'small-caps 14px "Segoe UI"',
lineHeight: 18,
lineWidth: 4,
pointSize: 2,
roundRect: 8,
drawPoints: false,
drawLabels: true,
drawBoxes: true,
drawPolygons: true,
drawGaze: true,
fillPolygons: false,
useDepth: true,
useCurves: false,
bufferedOutput: true
};
var getCanvasContext = (input) => {
if (input && input.getContext)
return input.getContext("2d");
throw new Error("Human: Invalid Canvas");
};
var rad2deg = (theta) => Math.round(theta * 180 / Math.PI);
function point(ctx, x, y, z = 0, localOptions) {
ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + 2 * z}, ${127.5 - 2 * z}, 255, 0.3)` : localOptions.color;
ctx.beginPath();
ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);
ctx.fill();
}
function rect(ctx, x, y, width, height, localOptions) {
ctx.beginPath();
if (localOptions.useCurves) {
const cx = (x + x + width) / 2;
const cy = (y + y + height) / 2;
ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);
} else {
ctx.lineWidth = localOptions.lineWidth;
ctx.moveTo(x + localOptions.roundRect, y);
ctx.lineTo(x + width - localOptions.roundRect, y);
ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);
ctx.lineTo(x + width, y + height - localOptions.roundRect);
ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);
ctx.lineTo(x + localOptions.roundRect, y + height);
ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);
ctx.lineTo(x, y + localOptions.roundRect);
ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);
ctx.closePath();
}
ctx.stroke();
}
function lines(ctx, points = [], localOptions) {
if (points === void 0 || points.length === 0)
return;
ctx.beginPath();
ctx.moveTo(points[0][0], points[0][1]);
for (const pt of points) {
const z = pt[2] || 0;
ctx.strokeStyle = localOptions.useDepth && z ? `rgba(${127.5 + 2 * z}, ${127.5 - 2 * z}, 255, 0.3)` : localOptions.color;
ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + 2 * z}, ${127.5 - 2 * z}, 255, 0.3)` : localOptions.color;
ctx.lineTo(pt[0], Math.round(pt[1]));
}
ctx.stroke();
if (localOptions.fillPolygons) {
ctx.closePath();
ctx.fill();
}
}
function curves(ctx, points = [], localOptions) {
if (points === void 0 || points.length === 0)
return;
if (!localOptions.useCurves || points.length <= 2) {
lines(ctx, points, localOptions);
return;
}
ctx.moveTo(points[0][0], points[0][1]);
for (let i = 0; i < points.length - 2; i++) {
const xc = (points[i][0] + points[i + 1][0]) / 2;
const yc = (points[i][1] + points[i + 1][1]) / 2;
ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);
}
ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);
ctx.stroke();
if (localOptions.fillPolygons) {
ctx.closePath();
ctx.fill();
}
}
async function gesture(inCanvas2, result, drawOptions) {
const localOptions = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
const ctx = getCanvasContext(inCanvas2);
ctx.font = localOptions.font;
ctx.fillStyle = localOptions.color;
let i = 1;
for (let j = 0; j < result.length; j++) {
let where = [];
let what = [];
[where, what] = Object.entries(result[j]);
if (what.length > 1 && what[1].length > 0) {
const who = where[1] > 0 ? `#${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;
}
}
}
async function face2(inCanvas2, result, drawOptions) {
var _a, _b, _c, _d;
const localOptions = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
const ctx = getCanvasContext(inCanvas2);
for (const f of result) {
ctx.font = localOptions.font;
ctx.strokeStyle = localOptions.color;
ctx.fillStyle = localOptions.color;
if (localOptions.drawBoxes)
rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);
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: ${f.iris}`);
if (f.emotion && f.emotion.length > 0) {
const emotion3 = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);
if (emotion3.length > 3)
emotion3.length = 3;
labels2.push(emotion3.join(" "));
}
if (f.rotation && f.rotation.angle && f.rotation.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 = localOptions.color;
for (let i = labels2.length - 1; i >= 0; i--) {
const x = Math.max(f.box[0], 0);
const y = i * localOptions.lineHeight + f.box[1];
if (localOptions.shadowColor && localOptions.shadowColor !== "") {
ctx.fillStyle = localOptions.shadowColor;
ctx.fillText(labels2[i], x + 5, y + 16);
}
ctx.fillStyle = localOptions.labelColor;
ctx.fillText(labels2[i], x + 4, y + 15);
}
ctx.lineWidth = 1;
if (f.mesh && f.mesh.length > 0) {
if (localOptions.drawPoints) {
for (const pt of f.mesh)
point(ctx, pt[0], pt[1], pt[2], localOptions);
}
if (localOptions.drawPolygons) {
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((index) => f.mesh[index]);
lines(ctx, points, localOptions);
}
if (f.annotations && f.annotations["leftEyeIris"]) {
ctx.strokeStyle = localOptions.useDepth ? "rgba(255, 200, 255, 0.3)" : localOptions.color;
ctx.beginPath();
const sizeX = Math.abs(f.annotations["leftEyeIris"][3][0] - f.annotations["leftEyeIris"][1][0]) / 2;
const sizeY = Math.abs(f.annotations["leftEyeIris"][4][1] - f.annotations["leftEyeIris"][2][1]) / 2;
ctx.ellipse(f.annotations["leftEyeIris"][0][0], f.annotations["leftEyeIris"][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);
ctx.stroke();
if (localOptions.fillPolygons) {
ctx.fillStyle = localOptions.useDepth ? "rgba(255, 255, 200, 0.3)" : localOptions.color;
ctx.fill();
}
}
if (f.annotations && f.annotations["rightEyeIris"]) {
ctx.strokeStyle = localOptions.useDepth ? "rgba(255, 200, 255, 0.3)" : localOptions.color;
ctx.beginPath();
const sizeX = Math.abs(f.annotations["rightEyeIris"][3][0] - f.annotations["rightEyeIris"][1][0]) / 2;
const sizeY = Math.abs(f.annotations["rightEyeIris"][4][1] - f.annotations["rightEyeIris"][2][1]) / 2;
ctx.ellipse(f.annotations["rightEyeIris"][0][0], f.annotations["rightEyeIris"][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);
ctx.stroke();
if (localOptions.fillPolygons) {
ctx.fillStyle = localOptions.useDepth ? "rgba(255, 255, 200, 0.3)" : localOptions.color;
ctx.fill();
}
}
if (localOptions.drawGaze && ((_b = (_a = f.rotation) == null ? void 0 : _a.gaze) == null ? void 0 : _b.strength) && ((_d = (_c = f.rotation) == null ? void 0 : _c.gaze) == null ? void 0 : _d.bearing) && f.annotations["leftEyeIris"] && f.annotations["rightEyeIris"] && f.annotations["leftEyeIris"][0] && f.annotations["rightEyeIris"][0]) {
ctx.strokeStyle = "pink";
ctx.beginPath();
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]
];
ctx.moveTo(f.annotations["leftEyeIris"][0][0], f.annotations["leftEyeIris"][0][1]);
ctx.lineTo(leftGaze[0], leftGaze[1]);
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]
];
ctx.moveTo(f.annotations["rightEyeIris"][0][0], f.annotations["rightEyeIris"][0][1]);
ctx.lineTo(rightGaze[0], rightGaze[1]);
ctx.stroke();
}
}
}
}
}
async function body2(inCanvas2, result, drawOptions) {
var _a;
const localOptions = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
const ctx = getCanvasContext(inCanvas2);
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 && ((_a = result[i].box) == null ? void 0 : _a.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 ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
}
}
if (localOptions.drawPoints) {
for (let pt = 0; pt < result[i].keypoints.length; pt++) {
ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position[2] ? `rgba(${127.5 + 2 * (result[i].keypoints[pt].position[2] || 0)}, ${127.5 - 2 * (result[i].keypoints[pt].position[2] || 0)}, 255, 0.5)` : localOptions.color;
point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);
}
}
if (localOptions.drawLabels) {
ctx.font = localOptions.font;
if (result[i].keypoints) {
for (const pt of result[i].keypoints) {
ctx.fillStyle = localOptions.useDepth && pt.position[2] ? `rgba(${127.5 + 2 * pt.position[2]}, ${127.5 - 2 * pt.position[2]}, 255, 0.5)` : localOptions.color;
ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);
}
}
}
if (localOptions.drawPolygons && result[i].keypoints) {
let part;
const points = [];
points.length = 0;
part = result[i].keypoints.find((a) => a.part === "leftShoulder");
if (part)
points.push([part.position[0], part.position[1]]);
part = result[i].keypoints.find((a) => a.part === "rightShoulder");
if (part)
points.push([part.position[0], part.position[1]]);
curves(ctx, points, localOptions);
points.length = 0;
part = result[i].keypoints.find((a) => a.part === "rightShoulder");
if (part)
points.push([part.position[0], part.position[1]]);
part = result[i].keypoints.find((a) => a.part === "rightHip");
if (part)
points.push([part.position[0], part.position[1]]);
part = result[i].keypoints.find((a) => a.part === "leftHip");
if (part)
points.push([part.position[0], part.position[1]]);
part = result[i].keypoints.find((a) => a.part === "leftShoulder");
if (part)
points.push([part.position[0], part.position[1]]);
if (points.length === 4)
lines(ctx, points, localOptions);
points.length = 0;
part = result[i].keypoints.find((a) => a.part === "leftHip");
if (part)
points.push([part.position[0], part.position[1]]);
part = result[i].keypoints.find((a) => a.part === "leftKnee");
if (part)
points.push([part.position[0], part.position[1]]);
part = result[i].keypoints.find((a) => a.part === "leftAnkle");
if (part)
points.push([part.position[0], part.position[1]]);
part = result[i].keypoints.find((a) => a.part === "leftHeel");
if (part)
points.push([part.position[0], part.position[1]]);
part = result[i].keypoints.find((a) => a.part === "leftFoot");
if (part)
points.push([part.position[0], part.position[1]]);
curves(ctx, points, localOptions);
points.length = 0;
part = result[i].keypoints.find((a) => a.part === "rightHip");
if (part)
points.push([part.position[0], part.position[1]]);
part = result[i].keypoints.find((a) => a.part === "rightKnee");
if (part)
points.push([part.position[0], part.position[1]]);
part = result[i].keypoints.find((a) => a.part === "rightAnkle");
if (part)
points.push([part.position[0], part.position[1]]);
part = result[i].keypoints.find((a) => a.part === "rightHeel");
if (part)
points.push([part.position[0], part.position[1]]);
part = result[i].keypoints.find((a) => a.part === "rightFoot");
if (part)
points.push([part.position[0], part.position[1]]);
curves(ctx, points, localOptions);
points.length = 0;
part = result[i].keypoints.find((a) => a.part === "leftShoulder");
if (part)
points.push([part.position[0], part.position[1]]);
part = result[i].keypoints.find((a) => a.part === "leftElbow");
if (part)
points.push([part.position[0], part.position[1]]);
part = result[i].keypoints.find((a) => a.part === "leftWrist");
if (part)
points.push([part.position[0], part.position[1]]);
part = result[i].keypoints.find((a) => a.part === "leftPalm");
if (part)
points.push([part.position[0], part.position[1]]);
curves(ctx, points, localOptions);
points.length = 0;
part = result[i].keypoints.find((a) => a.part === "rightShoulder");
if (part)
points.push([part.position[0], part.position[1]]);
part = result[i].keypoints.find((a) => a.part === "rightElbow");
if (part)
points.push([part.position[0], part.position[1]]);
part = result[i].keypoints.find((a) => a.part === "rightWrist");
if (part)
points.push([part.position[0], part.position[1]]);
part = result[i].keypoints.find((a) => a.part === "rightPalm");
if (part)
points.push([part.position[0], part.position[1]]);
curves(ctx, points, localOptions);
}
}
}
async function hand2(inCanvas2, result, drawOptions) {
const localOptions = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
const ctx = getCanvasContext(inCanvas2);
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", h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);
}
ctx.fillStyle = localOptions.labelColor;
ctx.fillText("hand", 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 = localOptions.useDepth ? `rgba(${127.5 + 2 * pt[2]}, ${127.5 - 2 * pt[2]}, 255, 0.5)` : localOptions.color;
point(ctx, pt[0], pt[1], 0, localOptions);
}
}
}
if (localOptions.drawLabels) {
const addHandLabel = (part, title) => {
if (!part)
return;
ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + 2 * part[part.length - 1][2]}, ${127.5 - 2 * part[part.length - 1][2]}, 255, 0.5)` : localOptions.color;
ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);
};
ctx.font = localOptions.font;
addHandLabel(h.annotations["index"], "index");
addHandLabel(h.annotations["middle"], "middle");
addHandLabel(h.annotations["ring"], "ring");
addHandLabel(h.annotations["pinky"], "pinky");
addHandLabel(h.annotations["thumb"], "thumb");
addHandLabel(h.annotations["palm"], "palm");
}
if (localOptions.drawPolygons) {
const addHandLine = (part) => {
if (!part)
return;
for (let i = 0; i < part.length; i++) {
ctx.beginPath();
ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + 2 * part[i][2]}, ${127.5 - 2 * part[i][2]}, 255, 0.5)` : localOptions.color;
ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);
ctx.lineTo(part[i][0], part[i][1]);
ctx.stroke();
}
};
ctx.lineWidth = localOptions.lineWidth;
addHandLine(h.annotations["index"]);
addHandLine(h.annotations["middle"]);
addHandLine(h.annotations["ring"]);
addHandLine(h.annotations["pinky"]);
addHandLine(h.annotations["thumb"]);
}
}
}
async function object(inCanvas2, result, drawOptions) {
const localOptions = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
const ctx = getCanvasContext(inCanvas2);
ctx.lineJoin = "round";
ctx.font = localOptions.font;
for (const h of result) {
if (localOptions.drawBoxes) {
ctx.strokeStyle = localOptions.color;
ctx.fillStyle = localOptions.color;
rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);
if (localOptions.drawLabels) {
const label = `${h.label} ${Math.round(100 * h.score)}%`;
if (localOptions.shadowColor && localOptions.shadowColor !== "") {
ctx.fillStyle = localOptions.shadowColor;
ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);
}
ctx.fillStyle = localOptions.labelColor;
ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);
}
ctx.stroke();
}
}
}
async function person(inCanvas2, result, drawOptions) {
const localOptions = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
const ctx = getCanvasContext(inCanvas2);
ctx.lineJoin = "round";
ctx.font = localOptions.font;
for (let i = 0; i < result.length; i++) {
if (localOptions.drawBoxes) {
ctx.strokeStyle = localOptions.color;
ctx.fillStyle = localOptions.color;
rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);
if (localOptions.drawLabels) {
const label = `person #${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();
}
}
}
add simple webcam and webrtc demo
2021-09-15 19:59:18 +02:00
async function canvas2(input, output) {
if (!input || !output)
add benchmark info
2021-09-05 22:42:11 +02:00
return;
add simple webcam and webrtc demo
2021-09-15 19:59:18 +02:00
const ctx = getCanvasContext(output);
ctx.drawImage(input, 0, 0);
add benchmark info
2021-09-05 22:42:11 +02:00
}
async function all(inCanvas2, result, drawOptions) {
add simple webcam and webrtc demo
2021-09-15 19:59:18 +02:00
if (!result || !result.performance || !result || !inCanvas2)
return null;
add benchmark info
2021-09-05 22:42:11 +02:00
const timestamp = now();
const localOptions = mergeDeep(options2, drawOptions);
const promise = Promise.all([
face2(inCanvas2, result.face, localOptions),
body2(inCanvas2, result.body, localOptions),
hand2(inCanvas2, result.hand, localOptions),
object(inCanvas2, result.object, localOptions),
gesture(inCanvas2, result.gesture, localOptions)
]);
result.performance.draw = Math.trunc(now() - timestamp);
return promise;
}
// src/persons.ts
function join2(faces, bodies, hands, gestures, shape) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p;
let id = 0;
const persons2 = [];
for (const face5 of faces) {
const person2 = { id: id++, face: face5, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };
for (const body4 of bodies) {
if (face5.box[0] > body4.box[0] && face5.box[0] < body4.box[0] + body4.box[2] && face5.box[1] + face5.box[3] > body4.box[1] && face5.box[1] + face5.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 gesture3 of gestures) {
if (gesture3["face"] !== void 0 && gesture3["face"] === face5.id)
(_a = person2.gestures) == null ? void 0 : _a.push(gesture3);
else if (gesture3["iris"] !== void 0 && gesture3["iris"] === face5.id)
(_b = person2.gestures) == null ? void 0 : _b.push(gesture3);
else if (gesture3["body"] !== void 0 && gesture3["body"] === ((_c = person2.body) == null ? void 0 : _c.id))
(_d = person2.gestures) == null ? void 0 : _d.push(gesture3);
else if (gesture3["hand"] !== void 0 && gesture3["hand"] === ((_f = (_e = person2.hands) == null ? void 0 : _e.left) == null ? void 0 : _f.id))
(_g = person2.gestures) == null ? void 0 : _g.push(gesture3);
else if (gesture3["hand"] !== void 0 && gesture3["hand"] === ((_i = (_h = person2.hands) == null ? void 0 : _h.right) == null ? void 0 : _i.id))
(_j = person2.gestures) == null ? void 0 : _j.push(gesture3);
}
const x = [];
const y = [];
const extractXY = (box6) => {
if (box6 && box6.length === 4) {
x.push(box6[0], box6[0] + box6[2]);
y.push(box6[1], box6[1] + box6[3]);
}
};
extractXY((_k = person2.face) == null ? void 0 : _k.box);
extractXY((_l = person2.body) == null ? void 0 : _l.box);
extractXY((_n = (_m = person2.hands) == null ? void 0 : _m.left) == null ? void 0 : _n.box);
extractXY((_p = (_o = person2.hands) == null ? void 0 : _o.right) == null ? void 0 : _p.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 && shape[1] && shape[2])
person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]];
persons2.push(person2);
}
return persons2;
}
// src/interpolate.ts
var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0 };
function calc(newResult) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u;
if (!newResult)
return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0 };
const elapsed = Date.now() - newResult.timestamp;
const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1;
bufferedResult.canvas = newResult.canvas;
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 box6 = newResult.body[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + b) / bufferedFactor);
const boxRaw3 = newResult.body[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + b) / bufferedFactor);
const keypoints3 = newResult.body[i].keypoints.map((keypoint, j) => ({
score: keypoint.score,
part: keypoint.part,
position: [
bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * bufferedResult.body[i].keypoints[j].position[0] + keypoint.position[0]) / bufferedFactor : keypoint.position[0],
bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * bufferedResult.body[i].keypoints[j].position[1] + keypoint.position[1]) / bufferedFactor : keypoint.position[1]
],
positionRaw: [
bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * bufferedResult.body[i].keypoints[j].positionRaw[0] + keypoint.positionRaw[0]) / bufferedFactor : keypoint.position[0],
bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * bufferedResult.body[i].keypoints[j].positionRaw[1] + keypoint.positionRaw[1]) / bufferedFactor : keypoint.position[1]
]
}));
bufferedResult.body[i] = { ...newResult.body[i], box: box6, boxRaw: boxRaw3, keypoints: keypoints3 };
}
}
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 box6 = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor);
const boxRaw3 = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor);
const keypoints3 = newResult.hand[i].keypoints ? newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].keypoints[j][k] + coord) / bufferedFactor)) : [];
const keys = Object.keys(newResult.hand[i].annotations);
const annotations3 = {};
for (const key of keys) {
annotations3[key] = newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor));
}
bufferedResult.hand[i] = { ...newResult.hand[i], box: box6, boxRaw: boxRaw3, keypoints: keypoints3, annotations: annotations3 };
}
}
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 box6 = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor);
const boxRaw3 = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor);
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 = (_a = newResult.face[i].rotation) == null ? void 0 : _a.matrix;
rotation.angle = {
roll: ((bufferedFactor - 1) * (((_c = (_b = bufferedResult.face[i].rotation) == null ? void 0 : _b.angle) == null ? void 0 : _c.roll) || 0) + (((_e = (_d = newResult.face[i].rotation) == null ? void 0 : _d.angle) == null ? void 0 : _e.roll) || 0)) / bufferedFactor,
yaw: ((bufferedFactor - 1) * (((_g = (_f = bufferedResult.face[i].rotation) == null ? void 0 : _f.angle) == null ? void 0 : _g.yaw) || 0) + (((_i = (_h = newResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.yaw) || 0)) / bufferedFactor,
pitch: ((bufferedFactor - 1) * (((_k = (_j = bufferedResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.pitch) || 0) + (((_m = (_l = newResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.pitch) || 0)) / bufferedFactor
};
rotation.gaze = {
bearing: ((bufferedFactor - 1) * (((_o = (_n = bufferedResult.face[i].rotation) == null ? void 0 : _n.gaze) == null ? void 0 : _o.bearing) || 0) + (((_q = (_p = newResult.face[i].rotation) == null ? void 0 : _p.gaze) == null ? void 0 : _q.bearing) || 0)) / bufferedFactor,
strength: ((bufferedFactor - 1) * (((_s = (_r = bufferedResult.face[i].rotation) == null ? void 0 : _r.gaze) == null ? void 0 : _s.strength) || 0) + (((_u = (_t = newResult.face[i].rotation) == null ? void 0 : _t.gaze) == null ? void 0 : _u.strength) || 0)) / bufferedFactor
};
bufferedResult.face[i] = { ...newResult.face[i], rotation, box: box6, boxRaw: boxRaw3 };
}
}
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 box6 = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor);
const boxRaw3 = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor);
bufferedResult.object[i] = { ...newResult.object[i], box: box6, boxRaw: boxRaw3 };
}
}
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((box6, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box6) / bufferedFactor);
}
}
}
if (newResult.gesture)
bufferedResult.gesture = newResult.gesture;
if (newResult.performance)
bufferedResult.performance = newResult.performance;
return bufferedResult;
}
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// src/tfjs/humangl.ts
var config2 = {
name: "humangl",
priority: 99,
canvas: null,
gl: null,
width: 1024,
height: 1024,
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() {
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var _a;
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if (!tfjs_esm_exports.findBackend(config2.name)) {
try {
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config2.canvas = canvas(100, 100);
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} catch (err) {
log("error: cannot create canvas:", err);
return;
}
try {
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config2.gl = (_a = config2.canvas) == null ? void 0 : _a.getContext("webgl2", config2.webGLattr);
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} catch (err) {
log("error: cannot get WebGL2 context:", err);
return;
}
try {
tfjs_esm_exports.setWebGLContext(2, config2.gl);
} catch (err) {
log("error: cannot set WebGL2 context:", err);
return;
}
try {
const ctx = new tfjs_esm_exports.GPGPUContext(config2.gl);
tfjs_esm_exports.registerBackend(config2.name, () => new tfjs_esm_exports.MathBackendWebGL(ctx), config2.priority);
} catch (err) {
log("error: cannot register WebGL backend:", err);
return;
}
try {
const kernels = tfjs_esm_exports.getKernelsForBackend("webgl");
kernels.forEach((kernelConfig) => {
const newKernelConfig = { ...kernelConfig, backendName: config2.name };
tfjs_esm_exports.registerKernel(newKernelConfig);
});
} catch (err) {
log("error: cannot update WebGL backend registration:", err);
return;
}
try {
tfjs_esm_exports.ENV.set("WEBGL_VERSION", 2);
} catch (err) {
log("error: cannot set WebGL backend flags:", err);
return;
}
extensions();
log("backend registered:", config2.name);
}
}
// src/tfjs/backend.ts
async function check(instance) {
if (instance.initial || instance.config.backend && instance.config.backend.length > 0 && tfjs_esm_exports.getBackend() !== instance.config.backend) {
const timeStamp = now();
instance.state = "backend";
if (instance.config.backend && instance.config.backend.length > 0) {
if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) {
log("running inside web worker");
}
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if (env.browser && instance.config.backend === "tensorflow") {
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log("override: backend set to tensorflow while running in browser");
instance.config.backend = "humangl";
}
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if (env.node && (instance.config.backend === "webgl" || instance.config.backend === "humangl")) {
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log(`override: backend set to ${instance.config.backend} while running in nodejs`);
instance.config.backend = "tensorflow";
}
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if (env.browser && instance.config.backend === "webgpu") {
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if (typeof navigator === "undefined" || typeof navigator["gpu"] === "undefined") {
log("override: backend set to webgpu but browser does not support webgpu");
instance.config.backend = "humangl";
} else {
const adapter = await navigator["gpu"].requestAdapter();
if (instance.config.debug)
log("enumerated webgpu adapter:", adapter);
}
}
if (instance.config.backend === "humangl")
register();
const available = Object.keys(tfjs_esm_exports.engine().registryFactory);
if (instance.config.debug)
log("available backends:", available);
if (!available.includes(instance.config.backend)) {
log(`error: backend ${instance.config.backend} not found in registry`);
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instance.config.backend = env.node ? "tensorflow" : "humangl";
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log(`override: setting backend ${instance.config.backend}`);
}
if (instance.config.debug)
log("setting backend:", instance.config.backend);
if (instance.config.backend === "wasm") {
if (instance.config.debug)
log("wasm path:", instance.config.wasmPath);
if (typeof (tfjs_esm_exports == null ? void 0 : tfjs_esm_exports.setWasmPaths) !== "undefined")
await tfjs_esm_exports.setWasmPaths(instance.config.wasmPath);
else
throw new Error("Human: WASM backend is not loaded");
const simd = await tfjs_esm_exports.env().getAsync("WASM_HAS_SIMD_SUPPORT");
const mt = await tfjs_esm_exports.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
if (instance.config.debug)
log(`wasm execution: ${simd ? "SIMD" : "no SIMD"} ${mt ? "multithreaded" : "singlethreaded"}`);
if (instance.config.debug && !simd)
log("warning: wasm simd support is not enabled");
}
await tfjs_esm_exports.setBackend(instance.config.backend);
try {
await tfjs_esm_exports.setBackend(instance.config.backend);
await tfjs_esm_exports.ready();
} catch (err) {
log("error: cannot set backend:", instance.config.backend, err);
}
}
if (tfjs_esm_exports.getBackend() === "humangl") {
tfjs_esm_exports.ENV.set("CHECK_COMPUTATION_FOR_ERRORS", false);
tfjs_esm_exports.ENV.set("WEBGL_CPU_FORWARD", true);
tfjs_esm_exports.ENV.set("WEBGL_PACK_DEPTHWISECONV", false);
tfjs_esm_exports.ENV.set("WEBGL_USE_SHAPES_UNIFORMS", true);
if (typeof instance.config["deallocate"] !== "undefined" && instance.config["deallocate"]) {
log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true);
tfjs_esm_exports.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0);
}
const gl = await tfjs_esm_exports.backend().getGPGPUContext().gl;
if (instance.config.debug)
log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);
}
tfjs_esm_exports.enableProdMode();
await tfjs_esm_exports.ready();
instance.performance.backend = Math.trunc(now() - timeStamp);
instance.config.backend = tfjs_esm_exports.getBackend();
get();
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instance.env = env;
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}
}
// package.json
major release
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var version10 = "2.2.1";
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add benchmark info
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// src/sample.ts
var face3 = `
rebuild
2020-12-10 21:47:43 +01:00
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add benchmark info
2021-09-05 22:42:11 +02:00
euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`;
var body3 = `
change default ports
2020-12-12 16:15:51 +01:00
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AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
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AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
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AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
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AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA0OIYfpOHPijvNNo+fdvtXJO18k/
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AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
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K2mbT0itesy2cHsvbvqtbmyz5xERWP2jd1tJwrTaONsOKtZ8585+cnDrzmn4Rq+IZObUROHD32n7
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eF4dqRzZbdb5J72l160WVK02ZxCYhOwI23TsnY2BGxsnYBjsiYZsZBjMMZZSgGEolMsQDdG6NwZ7
piVe6YkFsSziVMWZRILolMSriWUSCyJTuwhMSDMRCQSI3SAlACRCQAAEoAEoASAAAAAAAAACUACR
ACQAAAAAAAAAAAAASAAAAAAAAAAAAAAAAAAACAAAAAAAAAAAAAABAAAAAAAAAAAAACBKAAAAAAAQ
JQAAAhICEbJAYTWJ7wx8KvpC0BV4ceieWGewDHlNmWwCNjZICNhIDmcZredBecdpiY69FXCOLW+i
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lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/
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2Q==`;
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// src/warmup.ts
async function warmupBitmap(instance) {
const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob());
let blob;
let res;
switch (instance.config.warmup) {
case "face":
blob = await b64toBlob(face3);
break;
case "full":
blob = await b64toBlob(body3);
break;
default:
blob = null;
}
if (blob) {
const bitmap = await createImageBitmap(blob);
res = await instance.detect(bitmap, instance.config);
bitmap.close();
}
return res;
}
async function warmupCanvas(instance) {
return new Promise((resolve) => {
let src;
switch (instance.config.warmup) {
case "face":
src = "data:image/jpeg;base64," + face3;
break;
case "full":
case "body":
src = "data:image/jpeg;base64," + body3;
break;
default:
src = null;
}
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let img;
if (typeof Image !== "undefined")
img = new Image();
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else if (env.Image)
img = new env.Image();
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img.onload = async () => {
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const canvas3 = canvas(img.naturalWidth, img.naturalHeight);
if (!canvas3) {
log("Warmup: Canvas not found");
resolve({});
} else {
const ctx = canvas3.getContext("2d");
ctx.drawImage(img, 0, 0);
const tensor3 = await instance.image(canvas3);
const res = await instance.detect(tensor3.tensor, instance.config);
resolve(res);
}
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};
if (src)
img.src = src;
else
resolve(null);
});
}
async function warmupNode(instance) {
const atob = (str) => Buffer.from(str, "base64");
let img;
if (instance.config.warmup === "face")
img = atob(face3);
if (instance.config.warmup === "body" || instance.config.warmup === "full")
img = atob(body3);
if (!img)
return null;
let res;
if (typeof tfjs_esm_exports["node"] !== "undefined") {
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const data = tfjs_esm_exports["node"].decodeJpeg(img);
const expanded = data.expandDims(0);
instance.tf.dispose(data);
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res = await instance.detect(expanded, instance.config);
instance.tf.dispose(expanded);
} else {
if (instance.config.debug)
log("Warmup tfjs-node not loaded");
}
return res;
}
async function warmup(instance, userConfig) {
const t0 = now();
if (userConfig)
instance.config = mergeDeep(instance.config, userConfig);
if (!instance.config.warmup || instance.config.warmup === "none")
return { error: "null" };
let res;
if (typeof createImageBitmap === "function")
res = await warmupBitmap(instance);
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else if (typeof Image !== "undefined" || env.Canvas !== void 0)
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res = await warmupCanvas(instance);
else
res = await warmupNode(instance);
const t1 = now();
if (instance.config.debug)
log("Warmup", instance.config.warmup, Math.round(t1 - t0), "ms");
instance.emit("warmup");
return res;
}
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// src/human.ts
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var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity;
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var Human = class {
constructor(userConfig) {
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__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");
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__privateAdd(this, _numTensors, void 0);
__privateAdd(this, _analyzeMemoryLeaks, void 0);
__privateAdd(this, _checkSanity, void 0);
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__publicField(this, "initial");
__publicField(this, "analyze", (...msg) => {
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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);
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});
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__privateAdd(this, _sanity, (input) => {
if (!__privateGet(this, _checkSanity))
return null;
if (!input)
return "input is not defined";
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if (this.env.node && !(input instanceof tfjs_esm_exports.Tensor))
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return "input must be a tensor";
try {
this.tf.getBackend();
} catch (e) {
return "backend not loaded";
}
return null;
});
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__publicField(this, "image", (input) => process2(input, this.config));
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__publicField(this, "emit", (event) => {
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var _a;
return (_a = this.events) == null ? void 0 : _a.dispatchEvent(new Event(event));
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});
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get();
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this.env = env;
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config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfjs_esm_exports.version_core}/dist/`;
config.modelBasePath = this.env.browser ? "../models/" : "file://models/";
config.backend = this.env.browser ? "humangl" : "tensorflow";
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this.version = version10;
Object.defineProperty(this, "version", { value: version10 });
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this.config = mergeDeep(config, userConfig || {});
this.tf = tfjs_esm_exports;
this.state = "idle";
__privateSet(this, _numTensors, 0);
__privateSet(this, _analyzeMemoryLeaks, false);
__privateSet(this, _checkSanity, false);
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this.initial = true;
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this.performance = { backend: 0, load: 0, image: 0, frames: 0, cached: 0, changed: 0, total: 0, draw: 0 };
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this.events = new EventTarget();
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this.models = {
face: null,
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handpose: null,
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posenet: null,
blazepose: null,
efficientpose: null,
movenet: null,
age: null,
gender: null,
emotion: null,
embedding: null,
nanodet: null,
centernet: null,
faceres: null,
segmentation: null
};
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this.draw = {
options: options2,
canvas: (input, output) => canvas2(input, output),
face: (output, result, options3) => face2(output, result, options3),
body: (output, result, options3) => body2(output, result, options3),
hand: (output, result, options3) => hand2(output, result, options3),
gesture: (output, result, options3) => gesture(output, result, options3),
object: (output, result, options3) => object(output, result, options3),
person: (output, result, options3) => person(output, result, options3),
all: (output, result, options3) => all(output, result, options3)
};
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this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [] };
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this.process = { tensor: null, canvas: null };
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this.faceTriangulation = triangulation;
this.faceUVMap = uvmap;
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this.emit("create");
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}
similarity(embedding1, embedding2) {
return similarity(embedding1, embedding2);
}
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async segmentation(input, background) {
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return input ? process5(input, background, this.config) : null;
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}
enhance(input) {
return enhance(input);
}
match(faceEmbedding, db, threshold = 0) {
return match(faceEmbedding, db, threshold);
}
async load(userConfig) {
this.state = "load";
const timeStamp = now();
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const count2 = Object.values(this.models).filter((model10) => model10).length;
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if (userConfig)
this.config = mergeDeep(this.config, userConfig);
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if (this.initial) {
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if (this.config.debug)
log(`version: ${this.version}`);
if (this.config.debug)
log(`tfjs version: ${this.tf.version_core}`);
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await check(this);
await tfjs_esm_exports.ready();
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if (this.env.browser) {
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if (this.config.debug)
log("configuration:", this.config);
if (this.config.debug)
log("tf flags:", this.tf.ENV.flags);
}
}
await load13(this);
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if (this.initial && this.config.debug)
log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors");
this.initial = false;
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const loaded = Object.values(this.models).filter((model10) => model10).length;
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if (loaded !== count2) {
await validate(this);
this.emit("load");
}
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const current = Math.trunc(now() - timeStamp);
if (current > (this.performance.load || 0))
this.performance.load = current;
}
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next(result = this.result) {
return calc(result);
}
async warmup(userConfig) {
return warmup(this, userConfig);
}
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async detect(input, userConfig) {
return new Promise(async (resolve) => {
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var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n;
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this.state = "config";
let timeStamp;
let elapsedTime;
this.config = mergeDeep(this.config, userConfig);
this.state = "check";
const error = __privateGet(this, _sanity).call(this, input);
if (error) {
log(error, input);
resolve({ error });
}
const timeStart = now();
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await check(this);
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await this.load();
timeStamp = now();
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let img = process2(input, this.config);
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this.process = img;
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this.performance.image = Math.trunc(now() - timeStamp);
this.analyze("Get Image:");
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if (this.config.segmentation.enabled && this.process && img.tensor && img.canvas) {
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this.analyze("Start Segmentation:");
this.state = "run:segmentation";
timeStamp = now();
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await predict11(img);
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elapsedTime = Math.trunc(now() - timeStamp);
if (elapsedTime > 0)
this.performance.segmentation = elapsedTime;
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if (img.canvas) {
tfjs_esm_exports.dispose(img.tensor);
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img = process2(img.canvas, this.config);
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}
this.analyze("End Segmentation:");
}
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if (!img.tensor) {
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log("could not convert input to tensor");
resolve({ error: "could not convert input to tensor" });
return;
}
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this.emit("image");
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timeStamp = now();
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this.config.skipFrame = await skip(this.config, img.tensor);
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if (!this.performance.frames)
this.performance.frames = 0;
if (!this.performance.cached)
this.performance.cached = 0;
this.performance.frames++;
if (this.config.skipFrame)
this.performance.cached++;
this.performance.changed = Math.trunc(now() - timeStamp);
this.analyze("Check Changed:");
let faceRes = [];
let bodyRes = [];
let handRes = [];
let objectRes = [];
if (this.config.async) {
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faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : [];
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if (this.performance.face)
delete this.performance.face;
} else {
this.state = "run:face";
timeStamp = now();
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faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : [];
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elapsedTime = Math.trunc(now() - timeStamp);
if (elapsedTime > 0)
this.performance.face = elapsedTime;
}
this.analyze("Start Body:");
if (this.config.async) {
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if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet"))
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bodyRes = this.config.body.enabled ? predict4(img.tensor, this.config) : [];
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else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose"))
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bodyRes = this.config.body.enabled ? predict6(img.tensor, this.config) : [];
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else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose"))
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bodyRes = this.config.body.enabled ? predict7(img.tensor, this.config) : [];
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else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet"))
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bodyRes = this.config.body.enabled ? predict8(img.tensor, this.config) : [];
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if (this.performance.body)
delete this.performance.body;
} else {
this.state = "run:body";
timeStamp = now();
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if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet"))
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bodyRes = this.config.body.enabled ? await predict4(img.tensor, this.config) : [];
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else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose"))
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bodyRes = this.config.body.enabled ? await predict6(img.tensor, this.config) : [];
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else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose"))
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bodyRes = this.config.body.enabled ? await predict7(img.tensor, this.config) : [];
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else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet"))
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bodyRes = this.config.body.enabled ? await predict8(img.tensor, this.config) : [];
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elapsedTime = Math.trunc(now() - timeStamp);
if (elapsedTime > 0)
this.performance.body = elapsedTime;
}
this.analyze("End Body:");
this.analyze("Start Hand:");
if (this.config.async) {
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handRes = this.config.hand.enabled ? predict5(img.tensor, this.config) : [];
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if (this.performance.hand)
delete this.performance.hand;
} else {
this.state = "run:hand";
timeStamp = now();
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handRes = this.config.hand.enabled ? await predict5(img.tensor, this.config) : [];
add benchmark info
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elapsedTime = Math.trunc(now() - timeStamp);
if (elapsedTime > 0)
this.performance.hand = elapsedTime;
}
this.analyze("End Hand:");
this.analyze("Start Object:");
if (this.config.async) {
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if ((_i = this.config.object.modelPath) == null ? void 0 : _i.includes("nanodet"))
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objectRes = this.config.object.enabled ? predict9(img.tensor, this.config) : [];
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else if ((_j = this.config.object.modelPath) == null ? void 0 : _j.includes("centernet"))
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objectRes = this.config.object.enabled ? predict10(img.tensor, this.config) : [];
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if (this.performance.object)
delete this.performance.object;
} else {
this.state = "run:object";
timeStamp = now();
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if ((_k = this.config.object.modelPath) == null ? void 0 : _k.includes("nanodet"))
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objectRes = this.config.object.enabled ? await predict9(img.tensor, this.config) : [];
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else if ((_l = this.config.object.modelPath) == null ? void 0 : _l.includes("centernet"))
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objectRes = this.config.object.enabled ? await predict10(img.tensor, this.config) : [];
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elapsedTime = Math.trunc(now() - timeStamp);
if (elapsedTime > 0)
this.performance.object = elapsedTime;
}
this.analyze("End Object:");
if (this.config.async)
[faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);
let gestureRes = [];
if (this.config.gesture.enabled) {
timeStamp = now();
gestureRes = [...face(faceRes), ...body(bodyRes), ...hand(handRes), ...iris(faceRes)];
if (!this.config.async)
this.performance.gesture = Math.trunc(now() - timeStamp);
else if (this.performance.gesture)
delete this.performance.gesture;
}
this.performance.total = Math.trunc(now() - timeStart);
this.state = "idle";
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const shape = ((_n = (_m = this.process) == null ? void 0 : _m.tensor) == null ? void 0 : _n.shape) || [];
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this.result = {
face: faceRes,
body: bodyRes,
hand: handRes,
gesture: gestureRes,
object: objectRes,
performance: this.performance,
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canvas: this.process.canvas,
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timestamp: Date.now(),
get persons() {
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return join2(faceRes, bodyRes, handRes, gestureRes, shape);
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}
};
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tfjs_esm_exports.dispose(img.tensor);
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this.emit("detect");
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resolve(this.result);
});
}
};
_numTensors = new WeakMap();
_analyzeMemoryLeaks = new WeakMap();
_checkSanity = new WeakMap();
_sanity = new WeakMap();
export {
Human,
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Human as default,
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config as defaults,
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env
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};
added multi backend support
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//# sourceMappingURL=human.esm-nobundle.js.map