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human
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fix react compatibility issues

pull/233/head
Vladimir Mandic 2021-11-04 06:34:13 -04:00
parent 8e15c91e33
commit a5865c8164
18 changed files with 1900 additions and 1711 deletions
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5
.build.json
View File

@ -31,7 +31,7 @@
},
"build": {
"global": {
"target": "es2020",
"target": "es2018",
"sourcemap": false,
"treeShaking": true,
"ignoreAnnotations": true,
@ -114,7 +114,6 @@
{
"name": "tfjs/browser/esm/custom",
"platform": "browser",
"target": "esnext",
"format": "esm",
"input": "tfjs/tf-custom.ts",
"output": "dist/tfjs.esm.js",
@ -134,7 +133,6 @@
{
"name": "human/browser/esm/bundle",
"platform": "browser",
"target": "esnext",
"format": "esm",
"input": "src/human.ts",
"output": "dist/human.esm.js",
@ -147,7 +145,6 @@
{
"name": "demo/browser",
"platform": "browser",
"target": "esnext",
"format": "esm",
"input": "demo/typescript/index.ts",
"output": "demo/typescript/index.js",

3
CHANGELOG.md
View File

@ -9,8 +9,9 @@
## Changelog
### **HEAD -> main** 2021/11/02 mandic00@live.com
### **HEAD -> main** 2021/11/03 mandic00@live.com
- improve precision using wasm backend
- refactor predict with execute
- patch tfjs type defs
- start 2.5 major version

337
dist/human.esm-nobundle.js vendored
View File

@ -232,11 +232,7 @@ __export(tfjs_esm_exports, {
version: () => version9
});
__reExport(tfjs_esm_exports, dist_star);
__reExport(tfjs_esm_exports, dist_star2);
__reExport(tfjs_esm_exports, dist_star3);
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";
import { Tensor } from "@tensorflow/tfjs/dist/index.js";
import { GraphModel } from "@tensorflow/tfjs-converter/dist/index";
var version = "3.11.0";
@ -459,14 +455,14 @@ function GLImageFilter() {
if (drawCount === 0)
source = sourceTexture;
else
source = getTempFramebuffer(currentFramebufferIndex)?.texture || null;
source = getTempFramebuffer(currentFramebufferIndex).texture || null;
drawCount++;
if (lastInChain && !(flags & DRAW.INTERMEDIATE)) {
target = null;
flipY = drawCount % 2 === 0;
} else {
currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;
target = getTempFramebuffer(currentFramebufferIndex)?.fbo || null;
target = getTempFramebuffer(currentFramebufferIndex).fbo || null;
}
gl.bindTexture(gl.TEXTURE_2D, source);
gl.bindFramebuffer(gl.FRAMEBUFFER, target);
@ -476,7 +472,7 @@ function GLImageFilter() {
function compileShader(fragmentSource) {
if (shaderProgramCache[fragmentSource]) {
currentProgram = shaderProgramCache[fragmentSource];
gl.useProgram(currentProgram?.id || null);
gl.useProgram((currentProgram ? currentProgram.id : null) || null);
return currentProgram;
}
currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource);
@ -498,7 +494,7 @@ function GLImageFilter() {
m[19] /= 255;
const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha;
const program = compileShader(shader);
gl.uniform1fv(program?.uniform["m"], m);
gl.uniform1fv(program.uniform["m"], m);
draw2();
},
brightness: (brightness) => {
@ -811,8 +807,8 @@ function GLImageFilter() {
const pixelSizeX = 1 / fxcanvas.width;
const pixelSizeY = 1 / fxcanvas.height;
const program = compileShader(convolution);
gl.uniform1fv(program?.uniform["m"], m);
gl.uniform2f(program?.uniform["px"], pixelSizeX, pixelSizeY);
gl.uniform1fv(program.uniform["m"], m);
gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY);
draw2();
},
detectEdges: () => {
@ -886,16 +882,16 @@ function GLImageFilter() {
const blurSizeX = size2 / 7 / fxcanvas.width;
const blurSizeY = size2 / 7 / fxcanvas.height;
const program = compileShader(blur);
gl.uniform2f(program?.uniform["px"], 0, blurSizeY);
gl.uniform2f(program.uniform["px"], 0, blurSizeY);
draw2(DRAW.INTERMEDIATE);
gl.uniform2f(program?.uniform["px"], blurSizeX, 0);
gl.uniform2f(program.uniform["px"], blurSizeX, 0);
draw2();
},
pixelate: (size2) => {
const blurSizeX = size2 / fxcanvas.width;
const blurSizeY = size2 / fxcanvas.height;
const program = compileShader(pixelate);
gl.uniform2f(program?.uniform["size"], blurSizeX, blurSizeY);
gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY);
draw2();
}
};
@ -1016,7 +1012,7 @@ function process2(input, config3, getTensor = true) {
targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth);
if (!targetWidth || !targetHeight)
throw new Error("input cannot determine dimension");
if (!inCanvas || inCanvas?.width !== targetWidth || inCanvas?.height !== targetHeight)
if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight)
inCanvas = canvas(targetWidth, targetHeight);
const inCtx = inCanvas.getContext("2d");
if (typeof ImageData !== "undefined" && input instanceof ImageData) {
@ -1025,13 +1021,13 @@ function process2(input, config3, getTensor = true) {
if (config3.filter.flip && typeof inCtx.translate !== "undefined") {
inCtx.translate(originalWidth, 0);
inCtx.scale(-1, 1);
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
inCtx.setTransform(1, 0, 0, 1, 0, 0);
} else {
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
}
}
if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas?.height !== outCanvas?.height)
if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height)
outCanvas = canvas(inCanvas.width, inCanvas.height);
if (config3.filter.enabled && env.webgl.supported) {
if (!fx)
@ -1093,7 +1089,7 @@ function process2(input, config3, getTensor = true) {
pixels = tfjs_esm_exports.tensor(arr, [input["height"], input["width"], depth], "int32");
}
} else {
if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas?.height !== tmpCanvas?.height)
if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height)
tmpCanvas = canvas(outCanvas.width, outCanvas.height);
if (tfjs_esm_exports.browser && env.browser) {
if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") {
@ -1249,12 +1245,12 @@ var Env = class {
this.webgpu.supported = this.browser && typeof navigator["gpu"] !== "undefined";
this.webgpu.backend = this.backends.includes("webgpu");
if (this.webgpu.supported)
this.webgpu.adapter = (await navigator["gpu"].requestAdapter())?.name;
this.webgpu.adapter = (await navigator["gpu"].requestAdapter()).name;
this.kernels = tfjs_esm_exports.getKernelsForBackend(tfjs_esm_exports.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());
}
async updateCPU() {
const cpu = { model: "", flags: [] };
if (this.node && this.platform?.startsWith("linux")) {
if (this.node && this.platform.startsWith("linux")) {
const fs = __require("fs");
try {
const data = fs.readFileSync("/proc/cpuinfo").toString();
@ -1266,7 +1262,7 @@ var Env = class {
cpu.flags = line.match(/:(.*)/g)[0].replace(":", "").trim().split(" ").sort();
}
}
} catch {
} catch (e) {
}
}
if (!this["cpu"])
@ -1304,12 +1300,13 @@ var skipped2 = Number.MAX_SAFE_INTEGER;
var lastCount = 0;
var lastTime = 0;
async function load2(config3) {
var _a, _b;
if (env.initial)
model2 = null;
if (!model2) {
model2 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.face.antispoof?.modelPath || ""));
model2 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, ((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath) || ""));
if (!model2 || !model2["modelUrl"])
log("load model failed:", config3.face.antispoof?.modelPath);
log("load model failed:", (_b = config3.face.antispoof) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", model2["modelUrl"]);
} else if (config3.debug)
@ -1317,18 +1314,19 @@ async function load2(config3) {
return model2;
}
async function predict(image25, config3, idx, count2) {
var _a, _b;
if (!model2)
return null;
const skipTime = (config3.face.antispoof?.skipTime || 0) > now() - lastTime;
const skipFrame = skipped2 < (config3.face.antispoof?.skipFrames || 0);
const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime;
const skipFrame = skipped2 < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0);
if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && cached[idx]) {
skipped2++;
return cached[idx];
}
skipped2 = 0;
return new Promise(async (resolve) => {
const resize = tfjs_esm_exports.image.resizeBilinear(image25, [model2?.inputs[0].shape ? model2.inputs[0].shape[2] : 0, model2?.inputs[0].shape ? model2.inputs[0].shape[1] : 0], false);
const res = model2?.execute(resize);
const resize = tfjs_esm_exports.image.resizeBilinear(image25, [(model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[2] : 0, (model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[1] : 0], false);
const res = model2 == null ? void 0 : model2.execute(resize);
const num = (await res.data())[0];
cached[idx] = Math.round(100 * num) / 100;
lastCount = count2;
@ -4774,12 +4772,13 @@ var anchors = null;
var inputSize = 0;
var size = () => inputSize;
async function load3(config3) {
var _a, _b;
if (env.initial)
model3 = null;
if (!model3) {
model3 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.face.detector?.modelPath || ""));
model3 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, ((_a = config3.face.detector) == null ? void 0 : _a.modelPath) || ""));
if (!model3 || !model3["modelUrl"])
log("load model failed:", config3.face.detector?.modelPath);
log("load model failed:", (_b = config3.face.detector) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", model3["modelUrl"]);
} else if (config3.debug)
@ -4806,12 +4805,13 @@ function decodeBounds(boxOutputs) {
return tfjs_esm_exports.concat2d([startNormalized, endNormalized], concatAxis);
}
async function getBoxes(inputImage, config3) {
var _a, _b, _c, _d;
if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
return { boxes: [] };
const [batch, boxes, scores] = tfjs_esm_exports.tidy(() => {
const resizedImage = tfjs_esm_exports.image.resizeBilinear(inputImage, [inputSize, inputSize]);
const normalizedImage = tfjs_esm_exports.sub(tfjs_esm_exports.div(resizedImage, 127.5), 0.5);
const res = model3?.execute(normalizedImage);
const res = model3 == null ? void 0 : model3.execute(normalizedImage);
let batchOut;
if (Array.isArray(res)) {
const sorted = res.sort((a, b) => a.size - b.size);
@ -4827,14 +4827,14 @@ async function getBoxes(inputImage, config3) {
const scoresOut = tfjs_esm_exports.squeeze(tfjs_esm_exports.sigmoid(logits));
return [batchOut, boxesOut, scoresOut];
});
const nmsTensor = await tfjs_esm_exports.image.nonMaxSuppressionAsync(boxes, scores, config3.face.detector?.maxDetected || 0, config3.face.detector?.iouThreshold || 0, config3.face.detector?.minConfidence || 0);
const nmsTensor = await tfjs_esm_exports.image.nonMaxSuppressionAsync(boxes, scores, ((_a = config3.face.detector) == null ? void 0 : _a.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0);
const nms = await 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]];
if (confidence > (config3.face.detector?.minConfidence || 0)) {
if (confidence > (((_d = config3.face.detector) == null ? void 0 : _d.minConfidence) || 0)) {
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]));
annotatedBoxes.push({ box: createBox(boundingBox), landmarks, anchor: anchorsData[nms[i]], confidence });
@ -4918,15 +4918,16 @@ var cache = null;
var padding = [[0, 0], [0, 0], [0, 0], [0, 0]];
var lastTime2 = 0;
async function loadDetect(config3) {
var _a, _b, _c;
if (env3.initial)
models[0] = null;
if (!models[0] && config3.body.detector?.modelPath || "") {
models[0] = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.body.detector?.modelPath || ""));
if (!models[0] && ((_a = config3.body.detector) == null ? void 0 : _a.modelPath) || "") {
models[0] = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, ((_b = config3.body.detector) == null ? void 0 : _b.modelPath) || ""));
const inputs = Object.values(models[0].modelSignature["inputs"]);
inputSize2[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
inputSize2[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
if (!models[0] || !models[0]["modelUrl"])
log("load model failed:", config3.body.detector?.modelPath);
log("load model failed:", (_c = config3.body.detector) == null ? void 0 : _c.modelPath);
else if (config3.debug)
log("load model:", models[0]["modelUrl"]);
} else if (config3.debug && models[0])
@ -4934,6 +4935,7 @@ async function loadDetect(config3) {
return models[0];
}
async function loadPose(config3) {
var _a;
if (env3.initial)
models[1] = null;
if (!models[1]) {
@ -4941,7 +4943,7 @@ async function loadPose(config3) {
const inputs = Object.values(models[1].modelSignature["inputs"]);
inputSize2[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
inputSize2[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
if (config3.body.modelPath?.includes("lite"))
if ((_a = config3.body.modelPath) == null ? void 0 : _a.includes("lite"))
outputNodes = ["ld_3d", "output_segmentation", "output_heatmap", "world_3d", "output_poseflag"];
else
outputNodes = ["Identity", "Identity_2", "Identity_3", "Identity_4", "Identity_1"];
@ -4993,9 +4995,10 @@ function rescaleKeypoints(keypoints, outputSize2) {
}
var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x));
async function detectParts(input, config3, outputSize2) {
var _a;
const t = {};
t.input = await prepareImage(input);
[t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = models[1]?.execute(t.input, outputNodes);
[t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = (_a = models[1]) == null ? void 0 : _a.execute(t.input, outputNodes);
const poseScoreRaw = (await t.poseflag.data())[0];
const poseScore = Math.max(0, (poseScoreRaw - 0.8) / (1 - 0.8));
const points = await t.ld.data();
@ -5209,7 +5212,7 @@ async function predict3(input, config3) {
return new Promise(async (resolve) => {
const outputSize2 = [input.shape[2], input.shape[1]];
const resize = tfjs_esm_exports.image.resizeBilinear(input, [inputSize3, inputSize3]);
const objectT = config3.object.enabled ? model4?.execute(resize, ["tower_0/detections"]) : null;
const objectT = config3.object.enabled ? model4 == null ? void 0 : model4.execute(resize, ["tower_0/detections"]) : null;
lastTime3 = now();
tfjs_esm_exports.dispose(resize);
const obj = await process3(objectT, outputSize2, config3);
@ -5293,8 +5296,9 @@ async function predict4(image25, config3) {
}
skipped5 = 0;
return new Promise(async (resolve) => {
var _a;
const tensor3 = tfjs_esm_exports.tidy(() => {
if (!model5?.inputs[0].shape)
if (!(model5 == null ? void 0 : model5.inputs[0].shape))
return null;
const resize = tfjs_esm_exports.image.resizeBilinear(image25, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
const enhance2 = tfjs_esm_exports.mul(resize, 2);
@ -5303,7 +5307,7 @@ async function predict4(image25, config3) {
});
let resT;
if (config3.body.enabled)
resT = model5?.execute(tensor3);
resT = model5 == null ? void 0 : model5.execute(tensor3);
lastTime4 = now();
tfjs_esm_exports.dispose(tensor3);
if (resT) {
@ -5314,7 +5318,7 @@ async function predict4(image25, config3) {
tfjs_esm_exports.dispose(squeeze8);
for (let id = 0; id < stack3.length; id++) {
const [x2, y2, partScore] = max2d(stack3[id], config3.body.minConfidence);
if (partScore > (config3.body?.minConfidence || 0)) {
if (partScore > (((_a = config3.body) == null ? void 0 : _a.minConfidence) || 0)) {
cache2.keypoints.push({
score: Math.round(100 * partScore) / 100,
part: kpt2[id],
@ -5371,12 +5375,13 @@ var lastTime5 = 0;
var skipped6 = Number.MAX_SAFE_INTEGER;
var rgb = [0.2989, 0.587, 0.114];
async function load6(config3) {
var _a, _b;
if (env.initial)
model6 = null;
if (!model6) {
model6 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.face.emotion?.modelPath || ""));
model6 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, ((_a = config3.face.emotion) == null ? void 0 : _a.modelPath) || ""));
if (!model6 || !model6["modelUrl"])
log("load model failed:", config3.face.emotion?.modelPath);
log("load model failed:", (_b = config3.face.emotion) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", model6["modelUrl"]);
} else if (config3.debug)
@ -5384,19 +5389,21 @@ async function load6(config3) {
return model6;
}
async function predict5(image25, config3, idx, count2) {
var _a, _b;
if (!model6)
return null;
const skipFrame = skipped6 < (config3.face.emotion?.skipFrames || 0);
const skipTime = (config3.face.emotion?.skipTime || 0) > now() - lastTime5;
const skipFrame = skipped6 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0);
const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5;
if (config3.skipAllowed && skipTime && skipFrame && lastCount2 === count2 && last2[idx] && last2[idx].length > 0) {
skipped6++;
return last2[idx];
}
skipped6 = 0;
return new Promise(async (resolve) => {
var _a2, _b2;
const obj = [];
if (config3.face.emotion?.enabled) {
const inputSize8 = model6?.inputs[0].shape ? model6.inputs[0].shape[2] : 0;
if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) {
const inputSize8 = (model6 == null ? void 0 : model6.inputs[0].shape) ? model6.inputs[0].shape[2] : 0;
const resize = tfjs_esm_exports.image.resizeBilinear(image25, [inputSize8, inputSize8], false);
const [red, green, blue] = tfjs_esm_exports.split(resize, 3, 3);
tfjs_esm_exports.dispose(resize);
@ -5412,12 +5419,12 @@ async function predict5(image25, config3, idx, count2) {
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 emotionT = model6?.execute(normalize);
const emotionT = model6 == null ? void 0 : model6.execute(normalize);
lastTime5 = now();
const data = await emotionT.data();
tfjs_esm_exports.dispose(emotionT);
for (let i = 0; i < data.length; i++) {
if (data[i] > (config3.face.emotion?.minConfidence || 0))
if (data[i] > (((_b2 = config3.face.emotion) == null ? void 0 : _b2.minConfidence) || 0))
obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });
}
obj.sort((a, b) => b.score - a.score);
@ -5446,12 +5453,13 @@ var irisLandmarks = {
numCoordinates: 76
};
async function load7(config3) {
var _a, _b;
if (env.initial)
model7 = null;
if (!model7) {
model7 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.face.iris?.modelPath || ""));
model7 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, ((_a = config3.face.iris) == null ? void 0 : _a.modelPath) || ""));
if (!model7 || !model7["modelUrl"])
log("load model failed:", config3.face.iris?.modelPath);
log("load model failed:", (_b = config3.face.iris) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", model7["modelUrl"]);
} else if (config3.debug)
@ -5568,8 +5576,9 @@ var skipped7 = Number.MAX_SAFE_INTEGER;
var lastTime6 = 0;
var enlargeFact = 1.6;
async function predict6(input, config3) {
const skipTime = (config3.face.detector?.skipTime || 0) > now() - lastTime6;
const skipFrame = skipped7 < (config3.face.detector?.skipFrames || 0);
var _a, _b, _c, _d, _e, _f, _g, _h;
const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime6;
const skipFrame = skipped7 < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0);
if (!config3.skipAllowed || !skipTime || !skipFrame || boxCache.length === 0) {
const possibleBoxes = await getBoxes(input, config3);
lastTime6 = now();
@ -5606,14 +5615,14 @@ async function predict6(input, config3) {
faceScore: 0,
annotations: {}
};
if (config3.face.detector?.rotation && config3.face.mesh?.enabled && env.kernels.includes("rotatewithoffset")) {
if (((_c = config3.face.detector) == null ? void 0 : _c.rotation) && ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) && env.kernels.includes("rotatewithoffset")) {
[angle, rotationMatrix, face5.tensor] = correctFaceRotation(box4, input, inputSize5);
} else {
rotationMatrix = fixedRotationMatrix;
face5.tensor = cutBoxFromImageAndResize(box4, input, config3.face.mesh?.enabled ? [inputSize5, inputSize5] : [size(), size()]);
face5.tensor = cutBoxFromImageAndResize(box4, input, ((_e = config3.face.mesh) == null ? void 0 : _e.enabled) ? [inputSize5, inputSize5] : [size(), size()]);
}
face5.boxScore = Math.round(100 * box4.confidence) / 100;
if (!config3.face.mesh?.enabled) {
if (!((_f = config3.face.mesh) == null ? void 0 : _f.enabled)) {
face5.box = getClampedBox(box4, input);
face5.boxRaw = getRawBox(box4, input);
face5.boxScore = Math.round(100 * box4.confidence || 0) / 100;
@ -5635,10 +5644,10 @@ async function predict6(input, config3) {
const coordsReshaped = tfjs_esm_exports.reshape(contourCoords, [-1, 3]);
let rawCoords = await coordsReshaped.array();
tfjs_esm_exports.dispose([contourCoords, coordsReshaped, confidence, contours]);
if (face5.faceScore < (config3.face.detector?.minConfidence || 1)) {
if (face5.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) {
box4.confidence = face5.faceScore;
} else {
if (config3.face.iris?.enabled)
if ((_h = config3.face.iris) == null ? void 0 : _h.enabled)
rawCoords = await augmentIris(rawCoords, face5.tensor, config3, inputSize5);
face5.mesh = transformRawCoords(rawCoords, box4, angle, rotationMatrix, inputSize5);
face5.meshRaw = face5.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize5]);
@ -5657,12 +5666,13 @@ async function predict6(input, config3) {
return faces;
}
async function load8(config3) {
var _a, _b;
if (env.initial)
model8 = null;
if (!model8) {
model8 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.face.mesh?.modelPath || ""));
model8 = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, ((_a = config3.face.mesh) == null ? void 0 : _a.modelPath) || ""));
if (!model8 || !model8["modelUrl"])
log("load model failed:", config3.face.mesh?.modelPath);
log("load model failed:", (_b = config3.face.mesh) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", model8["modelUrl"]);
} else if (config3.debug)
@ -5682,13 +5692,14 @@ var lastTime7 = 0;
var lastCount3 = 0;
var skipped8 = Number.MAX_SAFE_INTEGER;
async function load9(config3) {
const modelUrl = join(config3.modelBasePath, config3.face.description?.modelPath || "");
var _a, _b;
const modelUrl = join(config3.modelBasePath, ((_a = config3.face.description) == null ? void 0 : _a.modelPath) || "");
if (env.initial)
model9 = null;
if (!model9) {
model9 = await tfjs_esm_exports.loadGraphModel(modelUrl);
if (!model9)
log("load model failed:", config3.face.description?.modelPath || "");
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)
@ -5700,7 +5711,7 @@ function enhance(input) {
const tensor3 = input.image || input.tensor || input;
if (!(tensor3 instanceof Tensor))
return null;
if (!model9?.inputs[0].shape)
if (!(model9 == null ? void 0 : model9.inputs[0].shape))
return null;
const crop2 = tfjs_esm_exports.image.resizeBilinear(tensor3, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false);
const norm = tfjs_esm_exports.mul(crop2, 255);
@ -5709,31 +5720,33 @@ function enhance(input) {
return image25;
}
async function predict7(image25, config3, idx, count2) {
var _a, _b, _c, _d;
if (!model9)
return null;
const skipFrame = skipped8 < (config3.face.description?.skipFrames || 0);
const skipTime = (config3.face.description?.skipTime || 0) > now() - lastTime7;
if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && last3[idx]?.age && last3[idx]?.age > 0) {
const skipFrame = skipped8 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0);
const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime7;
if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && ((_c = last3[idx]) == null ? void 0 : _c.age) && ((_d = last3[idx]) == null ? void 0 : _d.age) > 0) {
skipped8++;
return last3[idx];
}
skipped8 = 0;
return new Promise(async (resolve) => {
var _a2, _b2;
const obj = {
age: 0,
gender: "unknown",
genderScore: 0,
descriptor: []
};
if (config3.face.description?.enabled) {
if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) {
const enhanced = enhance(image25);
const resT = model9?.execute(enhanced);
const resT = model9 == null ? void 0 : model9.execute(enhanced);
lastTime7 = now();
tfjs_esm_exports.dispose(enhanced);
const genderT = await resT.find((t) => t.shape[1] === 1);
const gender = await genderT.data();
const confidence = Math.trunc(200 * Math.abs(gender[0] - 0.5)) / 100;
if (confidence > (config3.face.description?.minConfidence || 0)) {
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);
}
@ -9485,22 +9498,23 @@ async function predict8(input, config3) {
return hands;
}
async function load10(config3) {
var _a, _b, _c, _d, _e, _f;
if (env.initial) {
handDetectorModel = null;
handPoseModel = null;
}
if (!handDetectorModel || !handPoseModel) {
[handDetectorModel, handPoseModel] = await Promise.all([
config3.hand.enabled ? tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.hand.detector?.modelPath || ""), { fromTFHub: (config3.hand.detector?.modelPath || "").includes("tfhub.dev") }) : null,
config3.hand.landmarks ? tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.hand.skeleton?.modelPath || ""), { fromTFHub: (config3.hand.skeleton?.modelPath || "").includes("tfhub.dev") }) : null
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
]);
if (config3.hand.enabled) {
if (!handDetectorModel || !handDetectorModel["modelUrl"])
log("load model failed:", config3.hand.detector?.modelPath || "");
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:", config3.hand.skeleton?.modelPath || "");
log("load model failed:", ((_f = config3.hand.skeleton) == null ? void 0 : _f.modelPath) || "");
else if (config3.debug)
log("load model:", handPoseModel["modelUrl"]);
}
@ -9574,16 +9588,17 @@ var fingerMap = {
palm: [0]
};
async function loadDetect2(config3) {
var _a, _b;
if (env.initial)
models2[0] = null;
if (!models2[0]) {
fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3);
models2[0] = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.hand.detector?.modelPath || ""));
models2[0] = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, ((_a = config3.hand.detector) == null ? void 0 : _a.modelPath) || ""));
const inputs = Object.values(models2[0].modelSignature["inputs"]);
inputSize6[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
inputSize6[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
if (!models2[0] || !models2[0]["modelUrl"])
log("load model failed:", config3.hand.detector?.modelPath);
log("load model failed:", (_b = config3.hand.detector) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", models2[0]["modelUrl"]);
} else if (config3.debug)
@ -9591,15 +9606,16 @@ async function loadDetect2(config3) {
return models2[0];
}
async function loadSkeleton(config3) {
var _a, _b;
if (env.initial)
models2[1] = null;
if (!models2[1]) {
models2[1] = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, config3.hand.skeleton?.modelPath || ""));
models2[1] = await tfjs_esm_exports.loadGraphModel(join(config3.modelBasePath, ((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath) || ""));
const inputs = Object.values(models2[1].modelSignature["inputs"]);
inputSize6[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
inputSize6[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
if (!models2[1] || !models2[1]["modelUrl"])
log("load model failed:", config3.hand.skeleton?.modelPath);
log("load model failed:", (_b = config3.hand.skeleton) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", models2[1]["modelUrl"]);
} else if (config3.debug)
@ -9692,7 +9708,8 @@ async function detectFingers(input, h, config3) {
return hand3;
}
async function predict9(input, config3) {
if (!models2[0] || !models2[1] || !models2[0]?.inputs[0].shape || !models2[1]?.inputs[0].shape)
var _a, _b;
if (!models2[0] || !models2[1] || !((_a = models2[0]) == null ? void 0 : _a.inputs[0].shape) || !((_b = models2[1]) == null ? void 0 : _b.inputs[0].shape))
return [];
outputSize = [input.shape[2] || 0, input.shape[1] || 0];
skipped9++;
@ -10003,7 +10020,7 @@ async function parseMultiPose(res, config3, image25, inputBox) {
return bodies;
}
async function predict10(input, config3) {
if (!model10 || !model10?.inputs[0].shape)
if (!model10 || !(model10 == null ? void 0 : model10.inputs[0].shape))
return [];
if (!config3.skipAllowed)
cache5.boxes.length = 0;
@ -10017,7 +10034,7 @@ async function predict10(input, config3) {
const t = {};
skipped10 = 0;
t.input = padInput(input, inputSize7);
t.res = model10?.execute(t.input);
t.res = model10 == null ? void 0 : model10.execute(t.input);
cache5.last = now();
const res = await t.res.array();
cache5.bodies = t.res.shape[2] === 17 ? await parseSinglePose(res, config3, input, [0, 0, 1, 1]) : await parseMultiPose(res, config3, input, [0, 0, 1, 1]);
@ -10056,9 +10073,10 @@ async function process4(res, inputSize8, outputShape, config3) {
let results = [];
for (const strideSize of [1, 2, 4]) {
tfjs_esm_exports.tidy(async () => {
var _a, _b;
const baseSize = strideSize * 13;
const scoresT = res.find((a) => a.shape[1] === baseSize ** 2 && a.shape[2] === labels.length)?.squeeze();
const featuresT = res.find((a) => a.shape[1] === baseSize ** 2 && a.shape[2] < labels.length)?.squeeze();
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();
@ -10415,7 +10433,8 @@ function buildPartWithScoreQueue(minConfidence2, scores) {
}
function withinRadius(poses, { x, y }, keypointId) {
return poses.some(({ keypoints }) => {
const correspondingKeypoint = keypoints[keypointId]?.position;
var _a;
const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position;
if (!correspondingKeypoint)
return false;
return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;
@ -10493,14 +10512,15 @@ async function load14(config3) {
return model13;
}
async function process5(input, background, config3) {
var _a, _b;
if (busy)
return { data: [], canvas: null, alpha: null };
busy = true;
if (!model13)
await load14(config3);
const inputImage = process2(input, config3);
const width = inputImage.canvas?.width || 0;
const height = inputImage.canvas?.height || 0;
const width = ((_a = inputImage.canvas) == null ? void 0 : _a.width) || 0;
const height = ((_b = inputImage.canvas) == null ? void 0 : _b.height) || 0;
if (!inputImage.tensor)
return { data: [], canvas: null, alpha: null };
const t = {};
@ -10591,49 +10611,50 @@ function reset(instance) {
instance.models[model14] = null;
}
async function load15(instance) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A, _B, _C, _D, _E;
if (env.initial)
reset(instance);
if (instance.config.hand.enabled) {
if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes("handdetect"))
if (!instance.models.handpose && ((_b = (_a = instance.config.hand.detector) == null ? void 0 : _a.modelPath) == null ? void 0 : _b.includes("handdetect")))
[instance.models.handpose, instance.models.handskeleton] = await load10(instance.config);
if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes("handdetect"))
if (!instance.models.handskeleton && instance.config.hand.landmarks && ((_d = (_c = instance.config.hand.detector) == null ? void 0 : _c.modelPath) == null ? void 0 : _d.includes("handdetect")))
[instance.models.handpose, instance.models.handskeleton] = await load10(instance.config);
}
if (instance.config.face.enabled && !instance.models.facedetect)
instance.models.facedetect = load3(instance.config);
if (instance.config.face.enabled && instance.config.face.mesh?.enabled && !instance.models.facemesh)
if (instance.config.face.enabled && ((_e = instance.config.face.mesh) == null ? void 0 : _e.enabled) && !instance.models.facemesh)
instance.models.facemesh = load8(instance.config);
if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.models.faceiris)
if (instance.config.face.enabled && ((_f = instance.config.face.iris) == null ? void 0 : _f.enabled) && !instance.models.faceiris)
instance.models.faceiris = load7(instance.config);
if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof)
if (instance.config.face.enabled && ((_g = instance.config.face.antispoof) == null ? void 0 : _g.enabled) && !instance.models.antispoof)
instance.models.antispoof = load2(instance.config);
if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes("handtrack"))
if (instance.config.hand.enabled && !instance.models.handtrack && ((_i = (_h = instance.config.hand.detector) == null ? void 0 : _h.modelPath) == null ? void 0 : _i.includes("handtrack")))
instance.models.handtrack = loadDetect2(instance.config);
if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes("handtrack"))
if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && ((_k = (_j = instance.config.hand.detector) == null ? void 0 : _j.modelPath) == null ? void 0 : _k.includes("handtrack")))
instance.models.handskeleton = loadSkeleton(instance.config);
if (instance.config.body.enabled && !instance.models.posenet && instance.config.body?.modelPath?.includes("posenet"))
if (instance.config.body.enabled && !instance.models.posenet && ((_m = (_l = instance.config.body) == null ? void 0 : _l.modelPath) == null ? void 0 : _m.includes("posenet")))
instance.models.posenet = load13(instance.config);
if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes("efficientpose"))
if (instance.config.body.enabled && !instance.models.efficientpose && ((_o = (_n = instance.config.body) == null ? void 0 : _n.modelPath) == null ? void 0 : _o.includes("efficientpose")))
instance.models.efficientpose = load5(instance.config);
if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body?.modelPath?.includes("blazepose"))
if (instance.config.body.enabled && !instance.models.blazepose && ((_q = (_p = instance.config.body) == null ? void 0 : _p.modelPath) == null ? void 0 : _q.includes("blazepose")))
instance.models.blazepose = loadPose(instance.config);
if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body.detector?.modelPath && instance.config.body?.modelPath?.includes("blazepose"))
if (instance.config.body.enabled && !instance.models.blazeposedetect && ((_r = instance.config.body.detector) == null ? void 0 : _r.modelPath) && ((_t = (_s = instance.config.body) == null ? void 0 : _s.modelPath) == null ? void 0 : _t.includes("blazepose")))
instance.models.blazeposedetect = loadDetect(instance.config);
if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes("efficientpose"))
if (instance.config.body.enabled && !instance.models.efficientpose && ((_v = (_u = instance.config.body) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("efficientpose")))
instance.models.efficientpose = load5(instance.config);
if (instance.config.body.enabled && !instance.models.movenet && instance.config.body?.modelPath?.includes("movenet"))
if (instance.config.body.enabled && !instance.models.movenet && ((_x = (_w = instance.config.body) == null ? void 0 : _w.modelPath) == null ? void 0 : _x.includes("movenet")))
instance.models.movenet = load11(instance.config);
if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object?.modelPath?.includes("nanodet"))
if (instance.config.object.enabled && !instance.models.nanodet && ((_z = (_y = instance.config.object) == null ? void 0 : _y.modelPath) == null ? void 0 : _z.includes("nanodet")))
instance.models.nanodet = load12(instance.config);
if (instance.config.object.enabled && !instance.models.centernet && instance.config.object?.modelPath?.includes("centernet"))
if (instance.config.object.enabled && !instance.models.centernet && ((_B = (_A = instance.config.object) == null ? void 0 : _A.modelPath) == null ? void 0 : _B.includes("centernet")))
instance.models.centernet = load4(instance.config);
if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion)
if (instance.config.face.enabled && ((_C = instance.config.face.emotion) == null ? void 0 : _C.enabled) && !instance.models.emotion)
instance.models.emotion = load6(instance.config);
if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres)
if (instance.config.face.enabled && ((_D = instance.config.face.description) == null ? void 0 : _D.enabled) && !instance.models.faceres)
instance.models.faceres = load9(instance.config);
if (instance.config.segmentation.enabled && !instance.models.segmentation)
instance.models.segmentation = load14(instance.config);
if (instance.config.face.enabled && instance.config.face["agegenderrace"]?.enabled && !instance.models.agegenderrace)
if (instance.config.face.enabled && ((_E = instance.config.face["agegenderrace"]) == null ? void 0 : _E.enabled) && !instance.models.agegenderrace)
instance.models.agegenderrace = load(instance.config);
for await (const model14 of Object.keys(instance.models)) {
if (instance.models[model14] && typeof instance.models[model14] !== "undefined")
@ -10657,7 +10678,7 @@ async function validate2(instance) {
continue;
}
const ops = [];
const executor = model14?.executor;
const executor = model14 == null ? void 0 : model14.executor;
if (executor && executor.graph.nodes) {
for (const kernel of Object.values(executor.graph.nodes)) {
const op = kernel.op.toLowerCase();
@ -10706,6 +10727,7 @@ function extensions() {
config2.extensions = gl.getSupportedExtensions();
}
async function register(instance) {
var _a;
if (instance.config.backend !== "humangl")
return;
if (config2.name in tfjs_esm_exports.engine().registry && (!config2.gl || !config2.gl.getParameter(config2.gl.VERSION))) {
@ -10720,7 +10742,7 @@ async function register(instance) {
return;
}
try {
config2.gl = config2.canvas?.getContext("webgl2", config2.webGLattr);
config2.gl = (_a = config2.canvas) == null ? void 0 : _a.getContext("webgl2", config2.webGLattr);
if (config2.canvas) {
config2.canvas.addEventListener("webglcontextlost", async (e) => {
log("error: humangl:", e.type);
@ -10826,7 +10848,7 @@ async function check(instance, force = false) {
if (instance.config.backend === "wasm") {
if (instance.config.debug)
log("wasm path:", instance.config.wasmPath);
if (typeof tfjs_esm_exports?.setWasmPaths !== "undefined")
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("wasm backend is not loaded");
@ -11027,6 +11049,7 @@ async function gesture(inCanvas2, result, drawOptions) {
}
}
async function face(inCanvas2, result, drawOptions) {
var _a, _b, _c, _d, _e;
const localOptions = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
@ -11116,7 +11139,7 @@ async function face(inCanvas2, result, drawOptions) {
ctx.fill();
}
}
if (localOptions.drawGaze && f.rotation?.angle) {
if (localOptions.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.angle)) {
ctx.strokeStyle = "pink";
const valX = f.box[0] + f.box[2] / 2 - f.box[3] * rad2deg(f.rotation.angle.yaw) / 90;
const valY = f.box[1] + f.box[3] / 2 + f.box[2] * rad2deg(f.rotation.angle.pitch) / 90;
@ -11137,7 +11160,7 @@ async function face(inCanvas2, result, drawOptions) {
ctx.stroke(pathH);
ctx.stroke(pathV);
}
if (localOptions.drawGaze && f.rotation?.gaze?.strength && f.rotation?.gaze?.bearing && f.annotations["leftEyeIris"] && f.annotations["rightEyeIris"] && f.annotations["leftEyeIris"][0] && f.annotations["rightEyeIris"][0]) {
if (localOptions.drawGaze && ((_c = (_b = f.rotation) == null ? void 0 : _b.gaze) == null ? void 0 : _c.strength) && ((_e = (_d = f.rotation) == null ? void 0 : _d.gaze) == null ? void 0 : _e.bearing) && f.annotations["leftEyeIris"] && f.annotations["rightEyeIris"] && f.annotations["leftEyeIris"][0] && f.annotations["rightEyeIris"][0]) {
ctx.strokeStyle = "pink";
ctx.fillStyle = "pink";
const leftGaze = [
@ -11156,6 +11179,7 @@ async function face(inCanvas2, result, drawOptions) {
}
}
async function body(inCanvas2, result, drawOptions) {
var _a;
const localOptions = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
@ -11166,7 +11190,7 @@ async function body(inCanvas2, result, drawOptions) {
ctx.fillStyle = localOptions.color;
ctx.lineWidth = localOptions.lineWidth;
ctx.font = localOptions.font;
if (localOptions.drawBoxes && result[i].box && result[i].box?.length === 4) {
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 !== "") {
@ -11445,6 +11469,7 @@ var calculateFaceAngle = (face5, imageSize) => {
// src/face/face.ts
var detectFace = async (parent, input) => {
var _a, _b, _c, _d;
let timeStamp;
let ageRes;
let gearRes;
@ -11503,7 +11528,7 @@ var detectFace = async (parent, input) => {
[ageRes, genderRes, emotionRes, embeddingRes, descRes, gearRes, antispoofRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes, gearRes, antispoofRes]);
}
parent.analyze("Finish Face:");
if (!parent.config.face.iris.enabled && faces[i]?.annotations?.leftEyeIris && faces[i]?.annotations?.rightEyeIris) {
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;
}
@ -11515,10 +11540,10 @@ var detectFace = async (parent, input) => {
faceRes.push({
...faces[i],
id: i,
age: descRes?.age,
gender: descRes?.gender,
genderScore: descRes?.genderScore,
embedding: descRes?.descriptor,
age: descRes == null ? void 0 : descRes.age,
gender: descRes == null ? void 0 : descRes.gender,
genderScore: descRes == null ? void 0 : descRes.genderScore,
embedding: descRes == null ? void 0 : descRes.descriptor,
emotion: emotionRes,
real: antispoofRes,
iris: irisSize !== 0 ? Math.trunc(500 / irisSize / 11.7) / 100 : 0,
@ -11664,6 +11689,7 @@ var hand2 = (res) => {
var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0 };
var interpolateTime = 0;
function calc2(newResult, config3) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A;
const t0 = now();
if (!newResult)
return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0 };
@ -11690,11 +11716,11 @@ function calc2(newResult, config3) {
}));
const annotations2 = {};
let coords8 = { connected: {} };
if (config3.body?.modelPath?.includes("efficientpose"))
if ((_b = (_a = config3.body) == null ? void 0 : _a.modelPath) == null ? void 0 : _b.includes("efficientpose"))
coords8 = efficientposecoords_exports;
else if (config3.body?.modelPath?.includes("blazepose"))
else if ((_d = (_c = config3.body) == null ? void 0 : _c.modelPath) == null ? void 0 : _d.includes("blazepose"))
coords8 = blazeposecoords_exports;
else if (config3.body?.modelPath?.includes("movenet"))
else if ((_f = (_e = config3.body) == null ? void 0 : _e.modelPath) == null ? void 0 : _f.includes("movenet"))
coords8 = movenetcoords_exports;
for (const [name, indexes] of Object.entries(coords8.connected)) {
const pt = [];
@ -11737,15 +11763,15 @@ function calc2(newResult, config3) {
const box4 = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor);
const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor);
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 = newResult.face[i].rotation?.matrix;
rotation.matrix = (_g = newResult.face[i].rotation) == null ? void 0 : _g.matrix;
rotation.angle = {
roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,
yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,
pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor
roll: ((bufferedFactor - 1) * (((_i = (_h = bufferedResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k = (_j = newResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor,
yaw: ((bufferedFactor - 1) * (((_m = (_l = bufferedResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.yaw) || 0) + (((_o = (_n = newResult.face[i].rotation) == null ? void 0 : _n.angle) == null ? void 0 : _o.yaw) || 0)) / bufferedFactor,
pitch: ((bufferedFactor - 1) * (((_q = (_p = bufferedResult.face[i].rotation) == null ? void 0 : _p.angle) == null ? void 0 : _q.pitch) || 0) + (((_s = (_r = newResult.face[i].rotation) == null ? void 0 : _r.angle) == null ? void 0 : _s.pitch) || 0)) / bufferedFactor
};
rotation.gaze = {
bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.bearing || 0) + (newResult.face[i].rotation?.gaze?.bearing || 0)) / bufferedFactor,
strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.strength || 0) + (newResult.face[i].rotation?.gaze?.strength || 0)) / bufferedFactor
bearing: ((bufferedFactor - 1) * (((_u = (_t = bufferedResult.face[i].rotation) == null ? void 0 : _t.gaze) == null ? void 0 : _u.bearing) || 0) + (((_w = (_v = newResult.face[i].rotation) == null ? void 0 : _v.gaze) == null ? void 0 : _w.bearing) || 0)) / bufferedFactor,
strength: ((bufferedFactor - 1) * (((_y = (_x = bufferedResult.face[i].rotation) == null ? void 0 : _x.gaze) == null ? void 0 : _y.strength) || 0) + (((_A = (_z = newResult.face[i].rotation) == null ? void 0 : _z.gaze) == null ? void 0 : _A.strength) || 0)) / bufferedFactor
};
bufferedResult.face[i] = { ...newResult.face[i], rotation, box: box4, boxRaw };
}
@ -11814,6 +11840,7 @@ function match2(descriptor, descriptors, options3 = { order: 2, multiplier: 20,
// src/util/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) {
@ -11837,15 +11864,15 @@ function join2(faces, bodies, hands, gestures, shape) {
}
for (const gesture3 of gestures) {
if (gesture3["face"] !== void 0 && gesture3["face"] === face5.id)
person2.gestures?.push(gesture3);
(_a = person2.gestures) == null ? void 0 : _a.push(gesture3);
else if (gesture3["iris"] !== void 0 && gesture3["iris"] === face5.id)
person2.gestures?.push(gesture3);
else if (gesture3["body"] !== void 0 && gesture3["body"] === person2.body?.id)
person2.gestures?.push(gesture3);
else if (gesture3["hand"] !== void 0 && gesture3["hand"] === person2.hands?.left?.id)
person2.gestures?.push(gesture3);
else if (gesture3["hand"] !== void 0 && gesture3["hand"] === person2.hands?.right?.id)
person2.gestures?.push(gesture3);
(_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 = [];
@ -11855,10 +11882,10 @@ function join2(faces, bodies, hands, gestures, shape) {
y.push(box4[1], box4[1] + box4[3]);
}
};
extractXY(person2.face?.box);
extractXY(person2.body?.box);
extractXY(person2.hands?.left?.box);
extractXY(person2.hands?.right?.box);
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];
@ -12739,7 +12766,7 @@ var Human = class {
return "input must be a tensor";
try {
this.tf.getBackend();
} catch {
} catch (e) {
return "backend not loaded";
}
return null;
@ -12748,8 +12775,9 @@ var Human = class {
__publicField(this, "distance", distance);
__publicField(this, "match", match2);
__publicField(this, "emit", (event) => {
var _a;
if (this.events && this.events.dispatchEvent)
this.events?.dispatchEvent(new Event(event));
(_a = this.events) == null ? void 0 : _a.dispatchEvent(new Event(event));
});
this.env = env;
config.wasmPath = tfjs_esm_exports.version_core.includes("-") ? "https://vladmandic.github.io/tfjs/dist/" : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfjs_esm_exports.version_core}/dist/`;
@ -12860,6 +12888,7 @@ var Human = class {
async detect(input, userConfig) {
this.state = "detect";
return new Promise(async (resolve) => {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v;
this.state = "config";
let timeStamp;
this.config = mergeDeep(this.config, userConfig);
@ -12916,25 +12945,25 @@ var Human = class {
this.state = "detect:body";
const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config;
if (this.config.async) {
if (this.config.body.modelPath?.includes("posenet"))
if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet"))
bodyRes = this.config.body.enabled ? predict12(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("blazepose"))
else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose"))
bodyRes = this.config.body.enabled ? predict2(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("efficientpose"))
else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose"))
bodyRes = this.config.body.enabled ? predict4(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("movenet"))
else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet"))
bodyRes = this.config.body.enabled ? predict10(img.tensor, bodyConfig) : [];
if (this.performance.body)
delete this.performance.body;
} else {
timeStamp = now();
if (this.config.body.modelPath?.includes("posenet"))
if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet"))
bodyRes = this.config.body.enabled ? await predict12(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("blazepose"))
else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose"))
bodyRes = this.config.body.enabled ? await predict2(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("efficientpose"))
else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose"))
bodyRes = this.config.body.enabled ? await predict4(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("movenet"))
else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet"))
bodyRes = this.config.body.enabled ? await predict10(img.tensor, bodyConfig) : [];
this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
@ -12943,17 +12972,17 @@ var Human = class {
this.state = "detect:hand";
const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config;
if (this.config.async) {
if (this.config.hand.detector?.modelPath?.includes("handdetect"))
if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect"))
handRes = this.config.hand.enabled ? predict8(img.tensor, handConfig) : [];
else if (this.config.hand.detector?.modelPath?.includes("handtrack"))
else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack"))
handRes = this.config.hand.enabled ? predict9(img.tensor, handConfig) : [];
if (this.performance.hand)
delete this.performance.hand;
} else {
timeStamp = now();
if (this.config.hand.detector?.modelPath?.includes("handdetect"))
if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect"))
handRes = this.config.hand.enabled ? await predict8(img.tensor, handConfig) : [];
else if (this.config.hand.detector?.modelPath?.includes("handtrack"))
else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack"))
handRes = this.config.hand.enabled ? await predict9(img.tensor, handConfig) : [];
this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
@ -12961,17 +12990,17 @@ var Human = class {
this.analyze("Start Object:");
this.state = "detect:object";
if (this.config.async) {
if (this.config.object.modelPath?.includes("nanodet"))
if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet"))
objectRes = this.config.object.enabled ? predict11(img.tensor, this.config) : [];
else if (this.config.object.modelPath?.includes("centernet"))
else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet"))
objectRes = this.config.object.enabled ? predict3(img.tensor, this.config) : [];
if (this.performance.object)
delete this.performance.object;
} else {
timeStamp = now();
if (this.config.object.modelPath?.includes("nanodet"))
if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet"))
objectRes = this.config.object.enabled ? await predict11(img.tensor, this.config) : [];
else if (this.config.object.modelPath?.includes("centernet"))
else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet"))
objectRes = this.config.object.enabled ? await predict3(img.tensor, this.config) : [];
this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
@ -12990,7 +13019,7 @@ var Human = class {
delete this.performance.gesture;
}
this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);
const shape = this.process?.tensor?.shape || [];
const shape = ((_v = (_u = this.process) == null ? void 0 : _u.tensor) == null ? void 0 : _v.shape) || [];
this.result = {
face: faceRes,
body: bodyRes,

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@ -466,14 +466,14 @@ function GLImageFilter() {
if (drawCount === 0)
source = sourceTexture;
else
source = getTempFramebuffer(currentFramebufferIndex)?.texture || null;
source = getTempFramebuffer(currentFramebufferIndex).texture || null;
drawCount++;
if (lastInChain && !(flags & DRAW.INTERMEDIATE)) {
target = null;
flipY = drawCount % 2 === 0;
} else {
currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;
target = getTempFramebuffer(currentFramebufferIndex)?.fbo || null;
target = getTempFramebuffer(currentFramebufferIndex).fbo || null;
}
gl.bindTexture(gl.TEXTURE_2D, source);
gl.bindFramebuffer(gl.FRAMEBUFFER, target);
@ -483,7 +483,7 @@ function GLImageFilter() {
function compileShader(fragmentSource) {
if (shaderProgramCache[fragmentSource]) {
currentProgram = shaderProgramCache[fragmentSource];
gl.useProgram(currentProgram?.id || null);
gl.useProgram((currentProgram ? currentProgram.id : null) || null);
return currentProgram;
}
currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource);
@ -505,7 +505,7 @@ function GLImageFilter() {
m[19] /= 255;
const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha;
const program = compileShader(shader);
gl.uniform1fv(program?.uniform["m"], m);
gl.uniform1fv(program.uniform["m"], m);
draw2();
},
brightness: (brightness) => {
@ -818,8 +818,8 @@ function GLImageFilter() {
const pixelSizeX = 1 / fxcanvas.width;
const pixelSizeY = 1 / fxcanvas.height;
const program = compileShader(convolution);
gl.uniform1fv(program?.uniform["m"], m);
gl.uniform2f(program?.uniform["px"], pixelSizeX, pixelSizeY);
gl.uniform1fv(program.uniform["m"], m);
gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY);
draw2();
},
detectEdges: () => {
@ -893,16 +893,16 @@ function GLImageFilter() {
const blurSizeX = size2 / 7 / fxcanvas.width;
const blurSizeY = size2 / 7 / fxcanvas.height;
const program = compileShader(blur);
gl.uniform2f(program?.uniform["px"], 0, blurSizeY);
gl.uniform2f(program.uniform["px"], 0, blurSizeY);
draw2(DRAW.INTERMEDIATE);
gl.uniform2f(program?.uniform["px"], blurSizeX, 0);
gl.uniform2f(program.uniform["px"], blurSizeX, 0);
draw2();
},
pixelate: (size2) => {
const blurSizeX = size2 / fxcanvas.width;
const blurSizeY = size2 / fxcanvas.height;
const program = compileShader(pixelate);
gl.uniform2f(program?.uniform["size"], blurSizeX, blurSizeY);
gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY);
draw2();
}
};
@ -1023,7 +1023,7 @@ function process2(input, config3, getTensor = true) {
targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth);
if (!targetWidth || !targetHeight)
throw new Error("input cannot determine dimension");
if (!inCanvas || inCanvas?.width !== targetWidth || inCanvas?.height !== targetHeight)
if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight)
inCanvas = canvas(targetWidth, targetHeight);
const inCtx = inCanvas.getContext("2d");
if (typeof ImageData !== "undefined" && input instanceof ImageData) {
@ -1032,13 +1032,13 @@ function process2(input, config3, getTensor = true) {
if (config3.filter.flip && typeof inCtx.translate !== "undefined") {
inCtx.translate(originalWidth, 0);
inCtx.scale(-1, 1);
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
inCtx.setTransform(1, 0, 0, 1, 0, 0);
} else {
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
}
}
if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas?.height !== outCanvas?.height)
if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height)
outCanvas = canvas(inCanvas.width, inCanvas.height);
if (config3.filter.enabled && env.webgl.supported) {
if (!fx)
@ -1100,7 +1100,7 @@ function process2(input, config3, getTensor = true) {
pixels = tf.tensor(arr, [input["height"], input["width"], depth], "int32");
}
} else {
if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas?.height !== tmpCanvas?.height)
if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height)
tmpCanvas = canvas(outCanvas.width, outCanvas.height);
if (tf.browser && env.browser) {
if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") {
@ -1256,12 +1256,12 @@ var Env = class {
this.webgpu.supported = this.browser && typeof navigator["gpu"] !== "undefined";
this.webgpu.backend = this.backends.includes("webgpu");
if (this.webgpu.supported)
this.webgpu.adapter = (await navigator["gpu"].requestAdapter())?.name;
this.webgpu.adapter = (await navigator["gpu"].requestAdapter()).name;
this.kernels = tf2.getKernelsForBackend(tf2.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());
}
async updateCPU() {
const cpu = { model: "", flags: [] };
if (this.node && this.platform?.startsWith("linux")) {
if (this.node && this.platform.startsWith("linux")) {
const fs = require("fs");
try {
const data = fs.readFileSync("/proc/cpuinfo").toString();
@ -1273,7 +1273,7 @@ var Env = class {
cpu.flags = line.match(/:(.*)/g)[0].replace(":", "").trim().split(" ").sort();
}
}
} catch {
} catch (e) {
}
}
if (!this["cpu"])
@ -1319,12 +1319,13 @@ var skipped2 = Number.MAX_SAFE_INTEGER;
var lastCount = 0;
var lastTime = 0;
async function load2(config3) {
var _a, _b;
if (env.initial)
model2 = null;
if (!model2) {
model2 = await tf4.loadGraphModel(join(config3.modelBasePath, config3.face.antispoof?.modelPath || ""));
model2 = await tf4.loadGraphModel(join(config3.modelBasePath, ((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath) || ""));
if (!model2 || !model2["modelUrl"])
log("load model failed:", config3.face.antispoof?.modelPath);
log("load model failed:", (_b = config3.face.antispoof) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", model2["modelUrl"]);
} else if (config3.debug)
@ -1332,18 +1333,19 @@ async function load2(config3) {
return model2;
}
async function predict(image25, config3, idx, count2) {
var _a, _b;
if (!model2)
return null;
const skipTime = (config3.face.antispoof?.skipTime || 0) > now() - lastTime;
const skipFrame = skipped2 < (config3.face.antispoof?.skipFrames || 0);
const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime;
const skipFrame = skipped2 < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0);
if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && cached[idx]) {
skipped2++;
return cached[idx];
}
skipped2 = 0;
return new Promise(async (resolve) => {
const resize = tf4.image.resizeBilinear(image25, [model2?.inputs[0].shape ? model2.inputs[0].shape[2] : 0, model2?.inputs[0].shape ? model2.inputs[0].shape[1] : 0], false);
const res = model2?.execute(resize);
const resize = tf4.image.resizeBilinear(image25, [(model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[2] : 0, (model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[1] : 0], false);
const res = model2 == null ? void 0 : model2.execute(resize);
const num = (await res.data())[0];
cached[idx] = Math.round(100 * num) / 100;
lastCount = count2;
@ -4795,12 +4797,13 @@ var anchors = null;
var inputSize = 0;
var size = () => inputSize;
async function load3(config3) {
var _a, _b;
if (env.initial)
model3 = null;
if (!model3) {
model3 = await tf6.loadGraphModel(join(config3.modelBasePath, config3.face.detector?.modelPath || ""));
model3 = await tf6.loadGraphModel(join(config3.modelBasePath, ((_a = config3.face.detector) == null ? void 0 : _a.modelPath) || ""));
if (!model3 || !model3["modelUrl"])
log("load model failed:", config3.face.detector?.modelPath);
log("load model failed:", (_b = config3.face.detector) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", model3["modelUrl"]);
} else if (config3.debug)
@ -4827,12 +4830,13 @@ function decodeBounds(boxOutputs) {
return tf6.concat2d([startNormalized, endNormalized], concatAxis);
}
async function getBoxes(inputImage, config3) {
var _a, _b, _c, _d;
if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
return { boxes: [] };
const [batch, boxes, scores] = tf6.tidy(() => {
const resizedImage = tf6.image.resizeBilinear(inputImage, [inputSize, inputSize]);
const normalizedImage = tf6.sub(tf6.div(resizedImage, 127.5), 0.5);
const res = model3?.execute(normalizedImage);
const res = model3 == null ? void 0 : model3.execute(normalizedImage);
let batchOut;
if (Array.isArray(res)) {
const sorted = res.sort((a, b) => a.size - b.size);
@ -4848,14 +4852,14 @@ async function getBoxes(inputImage, config3) {
const scoresOut = tf6.squeeze(tf6.sigmoid(logits));
return [batchOut, boxesOut, scoresOut];
});
const nmsTensor = await tf6.image.nonMaxSuppressionAsync(boxes, scores, config3.face.detector?.maxDetected || 0, config3.face.detector?.iouThreshold || 0, config3.face.detector?.minConfidence || 0);
const nmsTensor = await tf6.image.nonMaxSuppressionAsync(boxes, scores, ((_a = config3.face.detector) == null ? void 0 : _a.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0);
const nms = await nmsTensor.array();
tf6.dispose(nmsTensor);
const annotatedBoxes = [];
const scoresData = await scores.data();
for (let i = 0; i < nms.length; i++) {
const confidence = scoresData[nms[i]];
if (confidence > (config3.face.detector?.minConfidence || 0)) {
if (confidence > (((_d = config3.face.detector) == null ? void 0 : _d.minConfidence) || 0)) {
const boundingBox = tf6.slice(boxes, [nms[i], 0], [1, -1]);
const landmarks = tf6.tidy(() => tf6.reshape(tf6.squeeze(tf6.slice(batch, [nms[i], keypointsCount - 1], [1, -1])), [keypointsCount, -1]));
annotatedBoxes.push({ box: createBox(boundingBox), landmarks, anchor: anchorsData[nms[i]], confidence });
@ -4942,15 +4946,16 @@ var cache = null;
var padding = [[0, 0], [0, 0], [0, 0], [0, 0]];
var lastTime2 = 0;
async function loadDetect(config3) {
var _a, _b, _c;
if (env3.initial)
models[0] = null;
if (!models[0] && config3.body.detector?.modelPath || "") {
models[0] = await tf7.loadGraphModel(join(config3.modelBasePath, config3.body.detector?.modelPath || ""));
if (!models[0] && ((_a = config3.body.detector) == null ? void 0 : _a.modelPath) || "") {
models[0] = await tf7.loadGraphModel(join(config3.modelBasePath, ((_b = config3.body.detector) == null ? void 0 : _b.modelPath) || ""));
const inputs = Object.values(models[0].modelSignature["inputs"]);
inputSize2[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
inputSize2[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
if (!models[0] || !models[0]["modelUrl"])
log("load model failed:", config3.body.detector?.modelPath);
log("load model failed:", (_c = config3.body.detector) == null ? void 0 : _c.modelPath);
else if (config3.debug)
log("load model:", models[0]["modelUrl"]);
} else if (config3.debug && models[0])
@ -4958,6 +4963,7 @@ async function loadDetect(config3) {
return models[0];
}
async function loadPose(config3) {
var _a;
if (env3.initial)
models[1] = null;
if (!models[1]) {
@ -4965,7 +4971,7 @@ async function loadPose(config3) {
const inputs = Object.values(models[1].modelSignature["inputs"]);
inputSize2[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
inputSize2[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
if (config3.body.modelPath?.includes("lite"))
if ((_a = config3.body.modelPath) == null ? void 0 : _a.includes("lite"))
outputNodes = ["ld_3d", "output_segmentation", "output_heatmap", "world_3d", "output_poseflag"];
else
outputNodes = ["Identity", "Identity_2", "Identity_3", "Identity_4", "Identity_1"];
@ -5017,9 +5023,10 @@ function rescaleKeypoints(keypoints, outputSize2) {
}
var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x));
async function detectParts(input, config3, outputSize2) {
var _a;
const t = {};
t.input = await prepareImage(input);
[t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = models[1]?.execute(t.input, outputNodes);
[t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = (_a = models[1]) == null ? void 0 : _a.execute(t.input, outputNodes);
const poseScoreRaw = (await t.poseflag.data())[0];
const poseScore = Math.max(0, (poseScoreRaw - 0.8) / (1 - 0.8));
const points = await t.ld.data();
@ -5236,7 +5243,7 @@ async function predict3(input, config3) {
return new Promise(async (resolve) => {
const outputSize2 = [input.shape[2], input.shape[1]];
const resize = tf8.image.resizeBilinear(input, [inputSize3, inputSize3]);
const objectT = config3.object.enabled ? model4?.execute(resize, ["tower_0/detections"]) : null;
const objectT = config3.object.enabled ? model4 == null ? void 0 : model4.execute(resize, ["tower_0/detections"]) : null;
lastTime3 = now();
tf8.dispose(resize);
const obj = await process3(objectT, outputSize2, config3);
@ -5323,8 +5330,9 @@ async function predict4(image25, config3) {
}
skipped5 = 0;
return new Promise(async (resolve) => {
var _a;
const tensor3 = tf9.tidy(() => {
if (!model5?.inputs[0].shape)
if (!(model5 == null ? void 0 : model5.inputs[0].shape))
return null;
const resize = tf9.image.resizeBilinear(image25, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
const enhance2 = tf9.mul(resize, 2);
@ -5333,7 +5341,7 @@ async function predict4(image25, config3) {
});
let resT;
if (config3.body.enabled)
resT = model5?.execute(tensor3);
resT = model5 == null ? void 0 : model5.execute(tensor3);
lastTime4 = now();
tf9.dispose(tensor3);
if (resT) {
@ -5344,7 +5352,7 @@ async function predict4(image25, config3) {
tf9.dispose(squeeze8);
for (let id = 0; id < stack3.length; id++) {
const [x2, y2, partScore] = max2d(stack3[id], config3.body.minConfidence);
if (partScore > (config3.body?.minConfidence || 0)) {
if (partScore > (((_a = config3.body) == null ? void 0 : _a.minConfidence) || 0)) {
cache2.keypoints.push({
score: Math.round(100 * partScore) / 100,
part: kpt2[id],
@ -5402,12 +5410,13 @@ var lastTime5 = 0;
var skipped6 = Number.MAX_SAFE_INTEGER;
var rgb = [0.2989, 0.587, 0.114];
async function load6(config3) {
var _a, _b;
if (env.initial)
model6 = null;
if (!model6) {
model6 = await tf10.loadGraphModel(join(config3.modelBasePath, config3.face.emotion?.modelPath || ""));
model6 = await tf10.loadGraphModel(join(config3.modelBasePath, ((_a = config3.face.emotion) == null ? void 0 : _a.modelPath) || ""));
if (!model6 || !model6["modelUrl"])
log("load model failed:", config3.face.emotion?.modelPath);
log("load model failed:", (_b = config3.face.emotion) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", model6["modelUrl"]);
} else if (config3.debug)
@ -5415,19 +5424,21 @@ async function load6(config3) {
return model6;
}
async function predict5(image25, config3, idx, count2) {
var _a, _b;
if (!model6)
return null;
const skipFrame = skipped6 < (config3.face.emotion?.skipFrames || 0);
const skipTime = (config3.face.emotion?.skipTime || 0) > now() - lastTime5;
const skipFrame = skipped6 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0);
const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5;
if (config3.skipAllowed && skipTime && skipFrame && lastCount2 === count2 && last2[idx] && last2[idx].length > 0) {
skipped6++;
return last2[idx];
}
skipped6 = 0;
return new Promise(async (resolve) => {
var _a2, _b2;
const obj = [];
if (config3.face.emotion?.enabled) {
const inputSize8 = model6?.inputs[0].shape ? model6.inputs[0].shape[2] : 0;
if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) {
const inputSize8 = (model6 == null ? void 0 : model6.inputs[0].shape) ? model6.inputs[0].shape[2] : 0;
const resize = tf10.image.resizeBilinear(image25, [inputSize8, inputSize8], false);
const [red, green, blue] = tf10.split(resize, 3, 3);
tf10.dispose(resize);
@ -5443,12 +5454,12 @@ async function predict5(image25, config3, idx, count2) {
tf10.dispose(blueNorm);
const normalize = tf10.tidy(() => tf10.mul(tf10.sub(grayscale, 0.5), 2));
tf10.dispose(grayscale);
const emotionT = model6?.execute(normalize);
const emotionT = model6 == null ? void 0 : model6.execute(normalize);
lastTime5 = now();
const data = await emotionT.data();
tf10.dispose(emotionT);
for (let i = 0; i < data.length; i++) {
if (data[i] > (config3.face.emotion?.minConfidence || 0))
if (data[i] > (((_b2 = config3.face.emotion) == null ? void 0 : _b2.minConfidence) || 0))
obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });
}
obj.sort((a, b) => b.score - a.score);
@ -5481,12 +5492,13 @@ var irisLandmarks = {
numCoordinates: 76
};
async function load7(config3) {
var _a, _b;
if (env.initial)
model7 = null;
if (!model7) {
model7 = await tf11.loadGraphModel(join(config3.modelBasePath, config3.face.iris?.modelPath || ""));
model7 = await tf11.loadGraphModel(join(config3.modelBasePath, ((_a = config3.face.iris) == null ? void 0 : _a.modelPath) || ""));
if (!model7 || !model7["modelUrl"])
log("load model failed:", config3.face.iris?.modelPath);
log("load model failed:", (_b = config3.face.iris) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", model7["modelUrl"]);
} else if (config3.debug)
@ -5603,8 +5615,9 @@ var skipped7 = Number.MAX_SAFE_INTEGER;
var lastTime6 = 0;
var enlargeFact = 1.6;
async function predict6(input, config3) {
const skipTime = (config3.face.detector?.skipTime || 0) > now() - lastTime6;
const skipFrame = skipped7 < (config3.face.detector?.skipFrames || 0);
var _a, _b, _c, _d, _e, _f, _g, _h;
const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime6;
const skipFrame = skipped7 < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0);
if (!config3.skipAllowed || !skipTime || !skipFrame || boxCache.length === 0) {
const possibleBoxes = await getBoxes(input, config3);
lastTime6 = now();
@ -5641,14 +5654,14 @@ async function predict6(input, config3) {
faceScore: 0,
annotations: {}
};
if (config3.face.detector?.rotation && config3.face.mesh?.enabled && env.kernels.includes("rotatewithoffset")) {
if (((_c = config3.face.detector) == null ? void 0 : _c.rotation) && ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) && env.kernels.includes("rotatewithoffset")) {
[angle, rotationMatrix, face5.tensor] = correctFaceRotation(box4, input, inputSize5);
} else {
rotationMatrix = fixedRotationMatrix;
face5.tensor = cutBoxFromImageAndResize(box4, input, config3.face.mesh?.enabled ? [inputSize5, inputSize5] : [size(), size()]);
face5.tensor = cutBoxFromImageAndResize(box4, input, ((_e = config3.face.mesh) == null ? void 0 : _e.enabled) ? [inputSize5, inputSize5] : [size(), size()]);
}
face5.boxScore = Math.round(100 * box4.confidence) / 100;
if (!config3.face.mesh?.enabled) {
if (!((_f = config3.face.mesh) == null ? void 0 : _f.enabled)) {
face5.box = getClampedBox(box4, input);
face5.boxRaw = getRawBox(box4, input);
face5.boxScore = Math.round(100 * box4.confidence || 0) / 100;
@ -5670,10 +5683,10 @@ async function predict6(input, config3) {
const coordsReshaped = tf12.reshape(contourCoords, [-1, 3]);
let rawCoords = await coordsReshaped.array();
tf12.dispose([contourCoords, coordsReshaped, confidence, contours]);
if (face5.faceScore < (config3.face.detector?.minConfidence || 1)) {
if (face5.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) {
box4.confidence = face5.faceScore;
} else {
if (config3.face.iris?.enabled)
if ((_h = config3.face.iris) == null ? void 0 : _h.enabled)
rawCoords = await augmentIris(rawCoords, face5.tensor, config3, inputSize5);
face5.mesh = transformRawCoords(rawCoords, box4, angle, rotationMatrix, inputSize5);
face5.meshRaw = face5.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize5]);
@ -5692,12 +5705,13 @@ async function predict6(input, config3) {
return faces;
}
async function load8(config3) {
var _a, _b;
if (env.initial)
model8 = null;
if (!model8) {
model8 = await tf12.loadGraphModel(join(config3.modelBasePath, config3.face.mesh?.modelPath || ""));
model8 = await tf12.loadGraphModel(join(config3.modelBasePath, ((_a = config3.face.mesh) == null ? void 0 : _a.modelPath) || ""));
if (!model8 || !model8["modelUrl"])
log("load model failed:", config3.face.mesh?.modelPath);
log("load model failed:", (_b = config3.face.mesh) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", model8["modelUrl"]);
} else if (config3.debug)
@ -5718,13 +5732,14 @@ var lastTime7 = 0;
var lastCount3 = 0;
var skipped8 = Number.MAX_SAFE_INTEGER;
async function load9(config3) {
const modelUrl = join(config3.modelBasePath, config3.face.description?.modelPath || "");
var _a, _b;
const modelUrl = join(config3.modelBasePath, ((_a = config3.face.description) == null ? void 0 : _a.modelPath) || "");
if (env.initial)
model9 = null;
if (!model9) {
model9 = await tf13.loadGraphModel(modelUrl);
if (!model9)
log("load model failed:", config3.face.description?.modelPath || "");
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)
@ -5736,7 +5751,7 @@ function enhance(input) {
const tensor3 = input.image || input.tensor || input;
if (!(tensor3 instanceof tf13.Tensor))
return null;
if (!model9?.inputs[0].shape)
if (!(model9 == null ? void 0 : model9.inputs[0].shape))
return null;
const crop2 = tf13.image.resizeBilinear(tensor3, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false);
const norm = tf13.mul(crop2, 255);
@ -5745,31 +5760,33 @@ function enhance(input) {
return image25;
}
async function predict7(image25, config3, idx, count2) {
var _a, _b, _c, _d;
if (!model9)
return null;
const skipFrame = skipped8 < (config3.face.description?.skipFrames || 0);
const skipTime = (config3.face.description?.skipTime || 0) > now() - lastTime7;
if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && last3[idx]?.age && last3[idx]?.age > 0) {
const skipFrame = skipped8 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0);
const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime7;
if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && ((_c = last3[idx]) == null ? void 0 : _c.age) && ((_d = last3[idx]) == null ? void 0 : _d.age) > 0) {
skipped8++;
return last3[idx];
}
skipped8 = 0;
return new Promise(async (resolve) => {
var _a2, _b2;
const obj = {
age: 0,
gender: "unknown",
genderScore: 0,
descriptor: []
};
if (config3.face.description?.enabled) {
if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) {
const enhanced = enhance(image25);
const resT = model9?.execute(enhanced);
const resT = model9 == null ? void 0 : model9.execute(enhanced);
lastTime7 = now();
tf13.dispose(enhanced);
const genderT = await resT.find((t) => t.shape[1] === 1);
const gender = await genderT.data();
const confidence = Math.trunc(200 * Math.abs(gender[0] - 0.5)) / 100;
if (confidence > (config3.face.description?.minConfidence || 0)) {
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);
}
@ -9529,22 +9546,23 @@ async function predict8(input, config3) {
return hands;
}
async function load10(config3) {
var _a, _b, _c, _d, _e, _f;
if (env.initial) {
handDetectorModel = null;
handPoseModel = null;
}
if (!handDetectorModel || !handPoseModel) {
[handDetectorModel, handPoseModel] = await Promise.all([
config3.hand.enabled ? tf17.loadGraphModel(join(config3.modelBasePath, config3.hand.detector?.modelPath || ""), { fromTFHub: (config3.hand.detector?.modelPath || "").includes("tfhub.dev") }) : null,
config3.hand.landmarks ? tf17.loadGraphModel(join(config3.modelBasePath, config3.hand.skeleton?.modelPath || ""), { fromTFHub: (config3.hand.skeleton?.modelPath || "").includes("tfhub.dev") }) : null
config3.hand.enabled ? tf17.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 ? tf17.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
]);
if (config3.hand.enabled) {
if (!handDetectorModel || !handDetectorModel["modelUrl"])
log("load model failed:", config3.hand.detector?.modelPath || "");
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:", config3.hand.skeleton?.modelPath || "");
log("load model failed:", ((_f = config3.hand.skeleton) == null ? void 0 : _f.modelPath) || "");
else if (config3.debug)
log("load model:", handPoseModel["modelUrl"]);
}
@ -9619,16 +9637,17 @@ var fingerMap = {
palm: [0]
};
async function loadDetect2(config3) {
var _a, _b;
if (env.initial)
models2[0] = null;
if (!models2[0]) {
fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3);
models2[0] = await tf18.loadGraphModel(join(config3.modelBasePath, config3.hand.detector?.modelPath || ""));
models2[0] = await tf18.loadGraphModel(join(config3.modelBasePath, ((_a = config3.hand.detector) == null ? void 0 : _a.modelPath) || ""));
const inputs = Object.values(models2[0].modelSignature["inputs"]);
inputSize6[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
inputSize6[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
if (!models2[0] || !models2[0]["modelUrl"])
log("load model failed:", config3.hand.detector?.modelPath);
log("load model failed:", (_b = config3.hand.detector) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", models2[0]["modelUrl"]);
} else if (config3.debug)
@ -9636,15 +9655,16 @@ async function loadDetect2(config3) {
return models2[0];
}
async function loadSkeleton(config3) {
var _a, _b;
if (env.initial)
models2[1] = null;
if (!models2[1]) {
models2[1] = await tf18.loadGraphModel(join(config3.modelBasePath, config3.hand.skeleton?.modelPath || ""));
models2[1] = await tf18.loadGraphModel(join(config3.modelBasePath, ((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath) || ""));
const inputs = Object.values(models2[1].modelSignature["inputs"]);
inputSize6[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
inputSize6[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
if (!models2[1] || !models2[1]["modelUrl"])
log("load model failed:", config3.hand.skeleton?.modelPath);
log("load model failed:", (_b = config3.hand.skeleton) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", models2[1]["modelUrl"]);
} else if (config3.debug)
@ -9737,7 +9757,8 @@ async function detectFingers(input, h, config3) {
return hand3;
}
async function predict9(input, config3) {
if (!models2[0] || !models2[1] || !models2[0]?.inputs[0].shape || !models2[1]?.inputs[0].shape)
var _a, _b;
if (!models2[0] || !models2[1] || !((_a = models2[0]) == null ? void 0 : _a.inputs[0].shape) || !((_b = models2[1]) == null ? void 0 : _b.inputs[0].shape))
return [];
outputSize = [input.shape[2] || 0, input.shape[1] || 0];
skipped9++;
@ -10052,7 +10073,7 @@ async function parseMultiPose(res, config3, image25, inputBox) {
return bodies;
}
async function predict10(input, config3) {
if (!model10 || !model10?.inputs[0].shape)
if (!model10 || !(model10 == null ? void 0 : model10.inputs[0].shape))
return [];
if (!config3.skipAllowed)
cache5.boxes.length = 0;
@ -10066,7 +10087,7 @@ async function predict10(input, config3) {
const t = {};
skipped10 = 0;
t.input = padInput(input, inputSize7);
t.res = model10?.execute(t.input);
t.res = model10 == null ? void 0 : model10.execute(t.input);
cache5.last = now();
const res = await t.res.array();
cache5.bodies = t.res.shape[2] === 17 ? await parseSinglePose(res, config3, input, [0, 0, 1, 1]) : await parseMultiPose(res, config3, input, [0, 0, 1, 1]);
@ -10106,9 +10127,10 @@ async function process4(res, inputSize8, outputShape, config3) {
let results = [];
for (const strideSize of [1, 2, 4]) {
tf21.tidy(async () => {
var _a, _b;
const baseSize = strideSize * 13;
const scoresT = res.find((a) => a.shape[1] === baseSize ** 2 && a.shape[2] === labels.length)?.squeeze();
const featuresT = res.find((a) => a.shape[1] === baseSize ** 2 && a.shape[2] < labels.length)?.squeeze();
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();
@ -10468,7 +10490,8 @@ function buildPartWithScoreQueue(minConfidence2, scores) {
}
function withinRadius(poses, { x, y }, keypointId) {
return poses.some(({ keypoints }) => {
const correspondingKeypoint = keypoints[keypointId]?.position;
var _a;
const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position;
if (!correspondingKeypoint)
return false;
return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;
@ -10547,14 +10570,15 @@ async function load14(config3) {
return model13;
}
async function process5(input, background, config3) {
var _a, _b;
if (busy)
return { data: [], canvas: null, alpha: null };
busy = true;
if (!model13)
await load14(config3);
const inputImage = process2(input, config3);
const width = inputImage.canvas?.width || 0;
const height = inputImage.canvas?.height || 0;
const width = ((_a = inputImage.canvas) == null ? void 0 : _a.width) || 0;
const height = ((_b = inputImage.canvas) == null ? void 0 : _b.height) || 0;
if (!inputImage.tensor)
return { data: [], canvas: null, alpha: null };
const t = {};
@ -10645,49 +10669,50 @@ function reset(instance) {
instance.models[model14] = null;
}
async function load15(instance) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A, _B, _C, _D, _E;
if (env.initial)
reset(instance);
if (instance.config.hand.enabled) {
if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes("handdetect"))
if (!instance.models.handpose && ((_b = (_a = instance.config.hand.detector) == null ? void 0 : _a.modelPath) == null ? void 0 : _b.includes("handdetect")))
[instance.models.handpose, instance.models.handskeleton] = await load10(instance.config);
if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes("handdetect"))
if (!instance.models.handskeleton && instance.config.hand.landmarks && ((_d = (_c = instance.config.hand.detector) == null ? void 0 : _c.modelPath) == null ? void 0 : _d.includes("handdetect")))
[instance.models.handpose, instance.models.handskeleton] = await load10(instance.config);
}
if (instance.config.face.enabled && !instance.models.facedetect)
instance.models.facedetect = load3(instance.config);
if (instance.config.face.enabled && instance.config.face.mesh?.enabled && !instance.models.facemesh)
if (instance.config.face.enabled && ((_e = instance.config.face.mesh) == null ? void 0 : _e.enabled) && !instance.models.facemesh)
instance.models.facemesh = load8(instance.config);
if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.models.faceiris)
if (instance.config.face.enabled && ((_f = instance.config.face.iris) == null ? void 0 : _f.enabled) && !instance.models.faceiris)
instance.models.faceiris = load7(instance.config);
if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof)
if (instance.config.face.enabled && ((_g = instance.config.face.antispoof) == null ? void 0 : _g.enabled) && !instance.models.antispoof)
instance.models.antispoof = load2(instance.config);
if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes("handtrack"))
if (instance.config.hand.enabled && !instance.models.handtrack && ((_i = (_h = instance.config.hand.detector) == null ? void 0 : _h.modelPath) == null ? void 0 : _i.includes("handtrack")))
instance.models.handtrack = loadDetect2(instance.config);
if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes("handtrack"))
if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && ((_k = (_j = instance.config.hand.detector) == null ? void 0 : _j.modelPath) == null ? void 0 : _k.includes("handtrack")))
instance.models.handskeleton = loadSkeleton(instance.config);
if (instance.config.body.enabled && !instance.models.posenet && instance.config.body?.modelPath?.includes("posenet"))
if (instance.config.body.enabled && !instance.models.posenet && ((_m = (_l = instance.config.body) == null ? void 0 : _l.modelPath) == null ? void 0 : _m.includes("posenet")))
instance.models.posenet = load13(instance.config);
if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes("efficientpose"))
if (instance.config.body.enabled && !instance.models.efficientpose && ((_o = (_n = instance.config.body) == null ? void 0 : _n.modelPath) == null ? void 0 : _o.includes("efficientpose")))
instance.models.efficientpose = load5(instance.config);
if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body?.modelPath?.includes("blazepose"))
if (instance.config.body.enabled && !instance.models.blazepose && ((_q = (_p = instance.config.body) == null ? void 0 : _p.modelPath) == null ? void 0 : _q.includes("blazepose")))
instance.models.blazepose = loadPose(instance.config);
if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body.detector?.modelPath && instance.config.body?.modelPath?.includes("blazepose"))
if (instance.config.body.enabled && !instance.models.blazeposedetect && ((_r = instance.config.body.detector) == null ? void 0 : _r.modelPath) && ((_t = (_s = instance.config.body) == null ? void 0 : _s.modelPath) == null ? void 0 : _t.includes("blazepose")))
instance.models.blazeposedetect = loadDetect(instance.config);
if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes("efficientpose"))
if (instance.config.body.enabled && !instance.models.efficientpose && ((_v = (_u = instance.config.body) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("efficientpose")))
instance.models.efficientpose = load5(instance.config);
if (instance.config.body.enabled && !instance.models.movenet && instance.config.body?.modelPath?.includes("movenet"))
if (instance.config.body.enabled && !instance.models.movenet && ((_x = (_w = instance.config.body) == null ? void 0 : _w.modelPath) == null ? void 0 : _x.includes("movenet")))
instance.models.movenet = load11(instance.config);
if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object?.modelPath?.includes("nanodet"))
if (instance.config.object.enabled && !instance.models.nanodet && ((_z = (_y = instance.config.object) == null ? void 0 : _y.modelPath) == null ? void 0 : _z.includes("nanodet")))
instance.models.nanodet = load12(instance.config);
if (instance.config.object.enabled && !instance.models.centernet && instance.config.object?.modelPath?.includes("centernet"))
if (instance.config.object.enabled && !instance.models.centernet && ((_B = (_A = instance.config.object) == null ? void 0 : _A.modelPath) == null ? void 0 : _B.includes("centernet")))
instance.models.centernet = load4(instance.config);
if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion)
if (instance.config.face.enabled && ((_C = instance.config.face.emotion) == null ? void 0 : _C.enabled) && !instance.models.emotion)
instance.models.emotion = load6(instance.config);
if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres)
if (instance.config.face.enabled && ((_D = instance.config.face.description) == null ? void 0 : _D.enabled) && !instance.models.faceres)
instance.models.faceres = load9(instance.config);
if (instance.config.segmentation.enabled && !instance.models.segmentation)
instance.models.segmentation = load14(instance.config);
if (instance.config.face.enabled && instance.config.face["agegenderrace"]?.enabled && !instance.models.agegenderrace)
if (instance.config.face.enabled && ((_E = instance.config.face["agegenderrace"]) == null ? void 0 : _E.enabled) && !instance.models.agegenderrace)
instance.models.agegenderrace = load(instance.config);
for await (const model14 of Object.keys(instance.models)) {
if (instance.models[model14] && typeof instance.models[model14] !== "undefined")
@ -10711,7 +10736,7 @@ async function validate2(instance) {
continue;
}
const ops = [];
const executor = model14?.executor;
const executor = model14 == null ? void 0 : model14.executor;
if (executor && executor.graph.nodes) {
for (const kernel of Object.values(executor.graph.nodes)) {
const op = kernel.op.toLowerCase();
@ -10760,6 +10785,7 @@ function extensions() {
config2.extensions = gl.getSupportedExtensions();
}
async function register(instance) {
var _a;
if (instance.config.backend !== "humangl")
return;
if (config2.name in tf24.engine().registry && (!config2.gl || !config2.gl.getParameter(config2.gl.VERSION))) {
@ -10774,7 +10800,7 @@ async function register(instance) {
return;
}
try {
config2.gl = config2.canvas?.getContext("webgl2", config2.webGLattr);
config2.gl = (_a = config2.canvas) == null ? void 0 : _a.getContext("webgl2", config2.webGLattr);
if (config2.canvas) {
config2.canvas.addEventListener("webglcontextlost", async (e) => {
log("error: humangl:", e.type);
@ -10881,7 +10907,7 @@ async function check(instance, force = false) {
if (instance.config.backend === "wasm") {
if (instance.config.debug)
log("wasm path:", instance.config.wasmPath);
if (typeof tf25?.setWasmPaths !== "undefined")
if (typeof (tf25 == null ? void 0 : tf25.setWasmPaths) !== "undefined")
await tf25.setWasmPaths(instance.config.wasmPath);
else
throw new Error("wasm backend is not loaded");
@ -11082,6 +11108,7 @@ async function gesture(inCanvas2, result, drawOptions) {
}
}
async function face(inCanvas2, result, drawOptions) {
var _a, _b, _c, _d, _e;
const localOptions = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
@ -11171,7 +11198,7 @@ async function face(inCanvas2, result, drawOptions) {
ctx.fill();
}
}
if (localOptions.drawGaze && f.rotation?.angle) {
if (localOptions.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.angle)) {
ctx.strokeStyle = "pink";
const valX = f.box[0] + f.box[2] / 2 - f.box[3] * rad2deg(f.rotation.angle.yaw) / 90;
const valY = f.box[1] + f.box[3] / 2 + f.box[2] * rad2deg(f.rotation.angle.pitch) / 90;
@ -11192,7 +11219,7 @@ async function face(inCanvas2, result, drawOptions) {
ctx.stroke(pathH);
ctx.stroke(pathV);
}
if (localOptions.drawGaze && f.rotation?.gaze?.strength && f.rotation?.gaze?.bearing && f.annotations["leftEyeIris"] && f.annotations["rightEyeIris"] && f.annotations["leftEyeIris"][0] && f.annotations["rightEyeIris"][0]) {
if (localOptions.drawGaze && ((_c = (_b = f.rotation) == null ? void 0 : _b.gaze) == null ? void 0 : _c.strength) && ((_e = (_d = f.rotation) == null ? void 0 : _d.gaze) == null ? void 0 : _e.bearing) && f.annotations["leftEyeIris"] && f.annotations["rightEyeIris"] && f.annotations["leftEyeIris"][0] && f.annotations["rightEyeIris"][0]) {
ctx.strokeStyle = "pink";
ctx.fillStyle = "pink";
const leftGaze = [
@ -11211,6 +11238,7 @@ async function face(inCanvas2, result, drawOptions) {
}
}
async function body(inCanvas2, result, drawOptions) {
var _a;
const localOptions = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
@ -11221,7 +11249,7 @@ async function body(inCanvas2, result, drawOptions) {
ctx.fillStyle = localOptions.color;
ctx.lineWidth = localOptions.lineWidth;
ctx.font = localOptions.font;
if (localOptions.drawBoxes && result[i].box && result[i].box?.length === 4) {
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 !== "") {
@ -11503,6 +11531,7 @@ var calculateFaceAngle = (face5, imageSize) => {
// src/face/face.ts
var detectFace = async (parent, input) => {
var _a, _b, _c, _d;
let timeStamp;
let ageRes;
let gearRes;
@ -11561,7 +11590,7 @@ var detectFace = async (parent, input) => {
[ageRes, genderRes, emotionRes, embeddingRes, descRes, gearRes, antispoofRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes, gearRes, antispoofRes]);
}
parent.analyze("Finish Face:");
if (!parent.config.face.iris.enabled && faces[i]?.annotations?.leftEyeIris && faces[i]?.annotations?.rightEyeIris) {
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;
}
@ -11573,10 +11602,10 @@ var detectFace = async (parent, input) => {
faceRes.push({
...faces[i],
id: i,
age: descRes?.age,
gender: descRes?.gender,
genderScore: descRes?.genderScore,
embedding: descRes?.descriptor,
age: descRes == null ? void 0 : descRes.age,
gender: descRes == null ? void 0 : descRes.gender,
genderScore: descRes == null ? void 0 : descRes.genderScore,
embedding: descRes == null ? void 0 : descRes.descriptor,
emotion: emotionRes,
real: antispoofRes,
iris: irisSize !== 0 ? Math.trunc(500 / irisSize / 11.7) / 100 : 0,
@ -11722,6 +11751,7 @@ var hand2 = (res) => {
var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0 };
var interpolateTime = 0;
function calc2(newResult, config3) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A;
const t0 = now();
if (!newResult)
return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0 };
@ -11748,11 +11778,11 @@ function calc2(newResult, config3) {
}));
const annotations2 = {};
let coords8 = { connected: {} };
if (config3.body?.modelPath?.includes("efficientpose"))
if ((_b = (_a = config3.body) == null ? void 0 : _a.modelPath) == null ? void 0 : _b.includes("efficientpose"))
coords8 = efficientposecoords_exports;
else if (config3.body?.modelPath?.includes("blazepose"))
else if ((_d = (_c = config3.body) == null ? void 0 : _c.modelPath) == null ? void 0 : _d.includes("blazepose"))
coords8 = blazeposecoords_exports;
else if (config3.body?.modelPath?.includes("movenet"))
else if ((_f = (_e = config3.body) == null ? void 0 : _e.modelPath) == null ? void 0 : _f.includes("movenet"))
coords8 = movenetcoords_exports;
for (const [name, indexes] of Object.entries(coords8.connected)) {
const pt = [];
@ -11795,15 +11825,15 @@ function calc2(newResult, config3) {
const box4 = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor);
const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor);
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 = newResult.face[i].rotation?.matrix;
rotation.matrix = (_g = newResult.face[i].rotation) == null ? void 0 : _g.matrix;
rotation.angle = {
roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,
yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,
pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor
roll: ((bufferedFactor - 1) * (((_i = (_h = bufferedResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k = (_j = newResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor,
yaw: ((bufferedFactor - 1) * (((_m = (_l = bufferedResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.yaw) || 0) + (((_o = (_n = newResult.face[i].rotation) == null ? void 0 : _n.angle) == null ? void 0 : _o.yaw) || 0)) / bufferedFactor,
pitch: ((bufferedFactor - 1) * (((_q = (_p = bufferedResult.face[i].rotation) == null ? void 0 : _p.angle) == null ? void 0 : _q.pitch) || 0) + (((_s = (_r = newResult.face[i].rotation) == null ? void 0 : _r.angle) == null ? void 0 : _s.pitch) || 0)) / bufferedFactor
};
rotation.gaze = {
bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.bearing || 0) + (newResult.face[i].rotation?.gaze?.bearing || 0)) / bufferedFactor,
strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.strength || 0) + (newResult.face[i].rotation?.gaze?.strength || 0)) / bufferedFactor
bearing: ((bufferedFactor - 1) * (((_u = (_t = bufferedResult.face[i].rotation) == null ? void 0 : _t.gaze) == null ? void 0 : _u.bearing) || 0) + (((_w = (_v = newResult.face[i].rotation) == null ? void 0 : _v.gaze) == null ? void 0 : _w.bearing) || 0)) / bufferedFactor,
strength: ((bufferedFactor - 1) * (((_y = (_x = bufferedResult.face[i].rotation) == null ? void 0 : _x.gaze) == null ? void 0 : _y.strength) || 0) + (((_A = (_z = newResult.face[i].rotation) == null ? void 0 : _z.gaze) == null ? void 0 : _A.strength) || 0)) / bufferedFactor
};
bufferedResult.face[i] = { ...newResult.face[i], rotation, box: box4, boxRaw };
}
@ -11872,6 +11902,7 @@ function match2(descriptor, descriptors, options3 = { order: 2, multiplier: 20,
// src/util/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) {
@ -11895,15 +11926,15 @@ function join2(faces, bodies, hands, gestures, shape) {
}
for (const gesture3 of gestures) {
if (gesture3["face"] !== void 0 && gesture3["face"] === face5.id)
person2.gestures?.push(gesture3);
(_a = person2.gestures) == null ? void 0 : _a.push(gesture3);
else if (gesture3["iris"] !== void 0 && gesture3["iris"] === face5.id)
person2.gestures?.push(gesture3);
else if (gesture3["body"] !== void 0 && gesture3["body"] === person2.body?.id)
person2.gestures?.push(gesture3);
else if (gesture3["hand"] !== void 0 && gesture3["hand"] === person2.hands?.left?.id)
person2.gestures?.push(gesture3);
else if (gesture3["hand"] !== void 0 && gesture3["hand"] === person2.hands?.right?.id)
person2.gestures?.push(gesture3);
(_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 = [];
@ -11913,10 +11944,10 @@ function join2(faces, bodies, hands, gestures, shape) {
y.push(box4[1], box4[1] + box4[3]);
}
};
extractXY(person2.face?.box);
extractXY(person2.body?.box);
extractXY(person2.hands?.left?.box);
extractXY(person2.hands?.right?.box);
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];
@ -12798,7 +12829,7 @@ var Human = class {
return "input must be a tensor";
try {
this.tf.getBackend();
} catch {
} catch (e) {
return "backend not loaded";
}
return null;
@ -12807,8 +12838,9 @@ var Human = class {
__publicField(this, "distance", distance);
__publicField(this, "match", match2);
__publicField(this, "emit", (event) => {
var _a;
if (this.events && this.events.dispatchEvent)
this.events?.dispatchEvent(new Event(event));
(_a = this.events) == null ? void 0 : _a.dispatchEvent(new Event(event));
});
this.env = env;
config.wasmPath = tf28.version_core.includes("-") ? "https://vladmandic.github.io/tfjs/dist/" : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf28.version_core}/dist/`;
@ -12919,6 +12951,7 @@ var Human = class {
async detect(input, userConfig) {
this.state = "detect";
return new Promise(async (resolve) => {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v;
this.state = "config";
let timeStamp;
this.config = mergeDeep(this.config, userConfig);
@ -12975,25 +13008,25 @@ var Human = class {
this.state = "detect:body";
const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config;
if (this.config.async) {
if (this.config.body.modelPath?.includes("posenet"))
if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet"))
bodyRes = this.config.body.enabled ? predict12(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("blazepose"))
else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose"))
bodyRes = this.config.body.enabled ? predict2(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("efficientpose"))
else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose"))
bodyRes = this.config.body.enabled ? predict4(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("movenet"))
else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet"))
bodyRes = this.config.body.enabled ? predict10(img.tensor, bodyConfig) : [];
if (this.performance.body)
delete this.performance.body;
} else {
timeStamp = now();
if (this.config.body.modelPath?.includes("posenet"))
if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet"))
bodyRes = this.config.body.enabled ? await predict12(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("blazepose"))
else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose"))
bodyRes = this.config.body.enabled ? await predict2(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("efficientpose"))
else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose"))
bodyRes = this.config.body.enabled ? await predict4(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("movenet"))
else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet"))
bodyRes = this.config.body.enabled ? await predict10(img.tensor, bodyConfig) : [];
this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
@ -13002,17 +13035,17 @@ var Human = class {
this.state = "detect:hand";
const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config;
if (this.config.async) {
if (this.config.hand.detector?.modelPath?.includes("handdetect"))
if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect"))
handRes = this.config.hand.enabled ? predict8(img.tensor, handConfig) : [];
else if (this.config.hand.detector?.modelPath?.includes("handtrack"))
else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack"))
handRes = this.config.hand.enabled ? predict9(img.tensor, handConfig) : [];
if (this.performance.hand)
delete this.performance.hand;
} else {
timeStamp = now();
if (this.config.hand.detector?.modelPath?.includes("handdetect"))
if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect"))
handRes = this.config.hand.enabled ? await predict8(img.tensor, handConfig) : [];
else if (this.config.hand.detector?.modelPath?.includes("handtrack"))
else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack"))
handRes = this.config.hand.enabled ? await predict9(img.tensor, handConfig) : [];
this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
@ -13020,17 +13053,17 @@ var Human = class {
this.analyze("Start Object:");
this.state = "detect:object";
if (this.config.async) {
if (this.config.object.modelPath?.includes("nanodet"))
if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet"))
objectRes = this.config.object.enabled ? predict11(img.tensor, this.config) : [];
else if (this.config.object.modelPath?.includes("centernet"))
else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet"))
objectRes = this.config.object.enabled ? predict3(img.tensor, this.config) : [];
if (this.performance.object)
delete this.performance.object;
} else {
timeStamp = now();
if (this.config.object.modelPath?.includes("nanodet"))
if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet"))
objectRes = this.config.object.enabled ? await predict11(img.tensor, this.config) : [];
else if (this.config.object.modelPath?.includes("centernet"))
else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet"))
objectRes = this.config.object.enabled ? await predict3(img.tensor, this.config) : [];
this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
@ -13049,7 +13082,7 @@ var Human = class {
delete this.performance.gesture;
}
this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);
const shape = this.process?.tensor?.shape || [];
const shape = ((_v = (_u = this.process) == null ? void 0 : _u.tensor) == null ? void 0 : _v.shape) || [];
this.result = {
face: faceRes,
body: bodyRes,

333
dist/human.node-wasm.js vendored
View File

@ -467,14 +467,14 @@ function GLImageFilter() {
if (drawCount === 0)
source = sourceTexture;
else
source = getTempFramebuffer(currentFramebufferIndex)?.texture || null;
source = getTempFramebuffer(currentFramebufferIndex).texture || null;
drawCount++;
if (lastInChain && !(flags & DRAW.INTERMEDIATE)) {
target = null;
flipY = drawCount % 2 === 0;
} else {
currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;
target = getTempFramebuffer(currentFramebufferIndex)?.fbo || null;
target = getTempFramebuffer(currentFramebufferIndex).fbo || null;
}
gl.bindTexture(gl.TEXTURE_2D, source);
gl.bindFramebuffer(gl.FRAMEBUFFER, target);
@ -484,7 +484,7 @@ function GLImageFilter() {
function compileShader(fragmentSource) {
if (shaderProgramCache[fragmentSource]) {
currentProgram = shaderProgramCache[fragmentSource];
gl.useProgram(currentProgram?.id || null);
gl.useProgram((currentProgram ? currentProgram.id : null) || null);
return currentProgram;
}
currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource);
@ -506,7 +506,7 @@ function GLImageFilter() {
m[19] /= 255;
const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha;
const program = compileShader(shader);
gl.uniform1fv(program?.uniform["m"], m);
gl.uniform1fv(program.uniform["m"], m);
draw2();
},
brightness: (brightness) => {
@ -819,8 +819,8 @@ function GLImageFilter() {
const pixelSizeX = 1 / fxcanvas.width;
const pixelSizeY = 1 / fxcanvas.height;
const program = compileShader(convolution);
gl.uniform1fv(program?.uniform["m"], m);
gl.uniform2f(program?.uniform["px"], pixelSizeX, pixelSizeY);
gl.uniform1fv(program.uniform["m"], m);
gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY);
draw2();
},
detectEdges: () => {
@ -894,16 +894,16 @@ function GLImageFilter() {
const blurSizeX = size2 / 7 / fxcanvas.width;
const blurSizeY = size2 / 7 / fxcanvas.height;
const program = compileShader(blur);
gl.uniform2f(program?.uniform["px"], 0, blurSizeY);
gl.uniform2f(program.uniform["px"], 0, blurSizeY);
draw2(DRAW.INTERMEDIATE);
gl.uniform2f(program?.uniform["px"], blurSizeX, 0);
gl.uniform2f(program.uniform["px"], blurSizeX, 0);
draw2();
},
pixelate: (size2) => {
const blurSizeX = size2 / fxcanvas.width;
const blurSizeY = size2 / fxcanvas.height;
const program = compileShader(pixelate);
gl.uniform2f(program?.uniform["size"], blurSizeX, blurSizeY);
gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY);
draw2();
}
};
@ -1024,7 +1024,7 @@ function process2(input, config3, getTensor = true) {
targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth);
if (!targetWidth || !targetHeight)
throw new Error("input cannot determine dimension");
if (!inCanvas || inCanvas?.width !== targetWidth || inCanvas?.height !== targetHeight)
if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight)
inCanvas = canvas(targetWidth, targetHeight);
const inCtx = inCanvas.getContext("2d");
if (typeof ImageData !== "undefined" && input instanceof ImageData) {
@ -1033,13 +1033,13 @@ function process2(input, config3, getTensor = true) {
if (config3.filter.flip && typeof inCtx.translate !== "undefined") {
inCtx.translate(originalWidth, 0);
inCtx.scale(-1, 1);
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
inCtx.setTransform(1, 0, 0, 1, 0, 0);
} else {
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
}
}
if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas?.height !== outCanvas?.height)
if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height)
outCanvas = canvas(inCanvas.width, inCanvas.height);
if (config3.filter.enabled && env.webgl.supported) {
if (!fx)
@ -1101,7 +1101,7 @@ function process2(input, config3, getTensor = true) {
pixels = tf.tensor(arr, [input["height"], input["width"], depth], "int32");
}
} else {
if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas?.height !== tmpCanvas?.height)
if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height)
tmpCanvas = canvas(outCanvas.width, outCanvas.height);
if (tf.browser && env.browser) {
if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") {
@ -1257,12 +1257,12 @@ var Env = class {
this.webgpu.supported = this.browser && typeof navigator["gpu"] !== "undefined";
this.webgpu.backend = this.backends.includes("webgpu");
if (this.webgpu.supported)
this.webgpu.adapter = (await navigator["gpu"].requestAdapter())?.name;
this.webgpu.adapter = (await navigator["gpu"].requestAdapter()).name;
this.kernels = tf2.getKernelsForBackend(tf2.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());
}
async updateCPU() {
const cpu = { model: "", flags: [] };
if (this.node && this.platform?.startsWith("linux")) {
if (this.node && this.platform.startsWith("linux")) {
const fs = require("fs");
try {
const data = fs.readFileSync("/proc/cpuinfo").toString();
@ -1274,7 +1274,7 @@ var Env = class {
cpu.flags = line.match(/:(.*)/g)[0].replace(":", "").trim().split(" ").sort();
}
}
} catch {
} catch (e) {
}
}
if (!this["cpu"])
@ -1320,12 +1320,13 @@ var skipped2 = Number.MAX_SAFE_INTEGER;
var lastCount = 0;
var lastTime = 0;
async function load2(config3) {
var _a, _b;
if (env.initial)
model2 = null;
if (!model2) {
model2 = await tf4.loadGraphModel(join(config3.modelBasePath, config3.face.antispoof?.modelPath || ""));
model2 = await tf4.loadGraphModel(join(config3.modelBasePath, ((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath) || ""));
if (!model2 || !model2["modelUrl"])
log("load model failed:", config3.face.antispoof?.modelPath);
log("load model failed:", (_b = config3.face.antispoof) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", model2["modelUrl"]);
} else if (config3.debug)
@ -1333,18 +1334,19 @@ async function load2(config3) {
return model2;
}
async function predict(image25, config3, idx, count2) {
var _a, _b;
if (!model2)
return null;
const skipTime = (config3.face.antispoof?.skipTime || 0) > now() - lastTime;
const skipFrame = skipped2 < (config3.face.antispoof?.skipFrames || 0);
const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime;
const skipFrame = skipped2 < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0);
if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && cached[idx]) {
skipped2++;
return cached[idx];
}
skipped2 = 0;
return new Promise(async (resolve) => {
const resize = tf4.image.resizeBilinear(image25, [model2?.inputs[0].shape ? model2.inputs[0].shape[2] : 0, model2?.inputs[0].shape ? model2.inputs[0].shape[1] : 0], false);
const res = model2?.execute(resize);
const resize = tf4.image.resizeBilinear(image25, [(model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[2] : 0, (model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[1] : 0], false);
const res = model2 == null ? void 0 : model2.execute(resize);
const num = (await res.data())[0];
cached[idx] = Math.round(100 * num) / 100;
lastCount = count2;
@ -4796,12 +4798,13 @@ var anchors = null;
var inputSize = 0;
var size = () => inputSize;
async function load3(config3) {
var _a, _b;
if (env.initial)
model3 = null;
if (!model3) {
model3 = await tf6.loadGraphModel(join(config3.modelBasePath, config3.face.detector?.modelPath || ""));
model3 = await tf6.loadGraphModel(join(config3.modelBasePath, ((_a = config3.face.detector) == null ? void 0 : _a.modelPath) || ""));
if (!model3 || !model3["modelUrl"])
log("load model failed:", config3.face.detector?.modelPath);
log("load model failed:", (_b = config3.face.detector) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", model3["modelUrl"]);
} else if (config3.debug)
@ -4828,12 +4831,13 @@ function decodeBounds(boxOutputs) {
return tf6.concat2d([startNormalized, endNormalized], concatAxis);
}
async function getBoxes(inputImage, config3) {
var _a, _b, _c, _d;
if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
return { boxes: [] };
const [batch, boxes, scores] = tf6.tidy(() => {
const resizedImage = tf6.image.resizeBilinear(inputImage, [inputSize, inputSize]);
const normalizedImage = tf6.sub(tf6.div(resizedImage, 127.5), 0.5);
const res = model3?.execute(normalizedImage);
const res = model3 == null ? void 0 : model3.execute(normalizedImage);
let batchOut;
if (Array.isArray(res)) {
const sorted = res.sort((a, b) => a.size - b.size);
@ -4849,14 +4853,14 @@ async function getBoxes(inputImage, config3) {
const scoresOut = tf6.squeeze(tf6.sigmoid(logits));
return [batchOut, boxesOut, scoresOut];
});
const nmsTensor = await tf6.image.nonMaxSuppressionAsync(boxes, scores, config3.face.detector?.maxDetected || 0, config3.face.detector?.iouThreshold || 0, config3.face.detector?.minConfidence || 0);
const nmsTensor = await tf6.image.nonMaxSuppressionAsync(boxes, scores, ((_a = config3.face.detector) == null ? void 0 : _a.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0);
const nms = await nmsTensor.array();
tf6.dispose(nmsTensor);
const annotatedBoxes = [];
const scoresData = await scores.data();
for (let i = 0; i < nms.length; i++) {
const confidence = scoresData[nms[i]];
if (confidence > (config3.face.detector?.minConfidence || 0)) {
if (confidence > (((_d = config3.face.detector) == null ? void 0 : _d.minConfidence) || 0)) {
const boundingBox = tf6.slice(boxes, [nms[i], 0], [1, -1]);
const landmarks = tf6.tidy(() => tf6.reshape(tf6.squeeze(tf6.slice(batch, [nms[i], keypointsCount - 1], [1, -1])), [keypointsCount, -1]));
annotatedBoxes.push({ box: createBox(boundingBox), landmarks, anchor: anchorsData[nms[i]], confidence });
@ -4943,15 +4947,16 @@ var cache = null;
var padding = [[0, 0], [0, 0], [0, 0], [0, 0]];
var lastTime2 = 0;
async function loadDetect(config3) {
var _a, _b, _c;
if (env3.initial)
models[0] = null;
if (!models[0] && config3.body.detector?.modelPath || "") {
models[0] = await tf7.loadGraphModel(join(config3.modelBasePath, config3.body.detector?.modelPath || ""));
if (!models[0] && ((_a = config3.body.detector) == null ? void 0 : _a.modelPath) || "") {
models[0] = await tf7.loadGraphModel(join(config3.modelBasePath, ((_b = config3.body.detector) == null ? void 0 : _b.modelPath) || ""));
const inputs = Object.values(models[0].modelSignature["inputs"]);
inputSize2[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
inputSize2[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
if (!models[0] || !models[0]["modelUrl"])
log("load model failed:", config3.body.detector?.modelPath);
log("load model failed:", (_c = config3.body.detector) == null ? void 0 : _c.modelPath);
else if (config3.debug)
log("load model:", models[0]["modelUrl"]);
} else if (config3.debug && models[0])
@ -4959,6 +4964,7 @@ async function loadDetect(config3) {
return models[0];
}
async function loadPose(config3) {
var _a;
if (env3.initial)
models[1] = null;
if (!models[1]) {
@ -4966,7 +4972,7 @@ async function loadPose(config3) {
const inputs = Object.values(models[1].modelSignature["inputs"]);
inputSize2[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
inputSize2[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
if (config3.body.modelPath?.includes("lite"))
if ((_a = config3.body.modelPath) == null ? void 0 : _a.includes("lite"))
outputNodes = ["ld_3d", "output_segmentation", "output_heatmap", "world_3d", "output_poseflag"];
else
outputNodes = ["Identity", "Identity_2", "Identity_3", "Identity_4", "Identity_1"];
@ -5018,9 +5024,10 @@ function rescaleKeypoints(keypoints, outputSize2) {
}
var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x));
async function detectParts(input, config3, outputSize2) {
var _a;
const t = {};
t.input = await prepareImage(input);
[t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = models[1]?.execute(t.input, outputNodes);
[t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = (_a = models[1]) == null ? void 0 : _a.execute(t.input, outputNodes);
const poseScoreRaw = (await t.poseflag.data())[0];
const poseScore = Math.max(0, (poseScoreRaw - 0.8) / (1 - 0.8));
const points = await t.ld.data();
@ -5237,7 +5244,7 @@ async function predict3(input, config3) {
return new Promise(async (resolve) => {
const outputSize2 = [input.shape[2], input.shape[1]];
const resize = tf8.image.resizeBilinear(input, [inputSize3, inputSize3]);
const objectT = config3.object.enabled ? model4?.execute(resize, ["tower_0/detections"]) : null;
const objectT = config3.object.enabled ? model4 == null ? void 0 : model4.execute(resize, ["tower_0/detections"]) : null;
lastTime3 = now();
tf8.dispose(resize);
const obj = await process3(objectT, outputSize2, config3);
@ -5324,8 +5331,9 @@ async function predict4(image25, config3) {
}
skipped5 = 0;
return new Promise(async (resolve) => {
var _a;
const tensor3 = tf9.tidy(() => {
if (!model5?.inputs[0].shape)
if (!(model5 == null ? void 0 : model5.inputs[0].shape))
return null;
const resize = tf9.image.resizeBilinear(image25, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
const enhance2 = tf9.mul(resize, 2);
@ -5334,7 +5342,7 @@ async function predict4(image25, config3) {
});
let resT;
if (config3.body.enabled)
resT = model5?.execute(tensor3);
resT = model5 == null ? void 0 : model5.execute(tensor3);
lastTime4 = now();
tf9.dispose(tensor3);
if (resT) {
@ -5345,7 +5353,7 @@ async function predict4(image25, config3) {
tf9.dispose(squeeze8);
for (let id = 0; id < stack3.length; id++) {
const [x2, y2, partScore] = max2d(stack3[id], config3.body.minConfidence);
if (partScore > (config3.body?.minConfidence || 0)) {
if (partScore > (((_a = config3.body) == null ? void 0 : _a.minConfidence) || 0)) {
cache2.keypoints.push({
score: Math.round(100 * partScore) / 100,
part: kpt2[id],
@ -5403,12 +5411,13 @@ var lastTime5 = 0;
var skipped6 = Number.MAX_SAFE_INTEGER;
var rgb = [0.2989, 0.587, 0.114];
async function load6(config3) {
var _a, _b;
if (env.initial)
model6 = null;
if (!model6) {
model6 = await tf10.loadGraphModel(join(config3.modelBasePath, config3.face.emotion?.modelPath || ""));
model6 = await tf10.loadGraphModel(join(config3.modelBasePath, ((_a = config3.face.emotion) == null ? void 0 : _a.modelPath) || ""));
if (!model6 || !model6["modelUrl"])
log("load model failed:", config3.face.emotion?.modelPath);
log("load model failed:", (_b = config3.face.emotion) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", model6["modelUrl"]);
} else if (config3.debug)
@ -5416,19 +5425,21 @@ async function load6(config3) {
return model6;
}
async function predict5(image25, config3, idx, count2) {
var _a, _b;
if (!model6)
return null;
const skipFrame = skipped6 < (config3.face.emotion?.skipFrames || 0);
const skipTime = (config3.face.emotion?.skipTime || 0) > now() - lastTime5;
const skipFrame = skipped6 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0);
const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5;
if (config3.skipAllowed && skipTime && skipFrame && lastCount2 === count2 && last2[idx] && last2[idx].length > 0) {
skipped6++;
return last2[idx];
}
skipped6 = 0;
return new Promise(async (resolve) => {
var _a2, _b2;
const obj = [];
if (config3.face.emotion?.enabled) {
const inputSize8 = model6?.inputs[0].shape ? model6.inputs[0].shape[2] : 0;
if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) {
const inputSize8 = (model6 == null ? void 0 : model6.inputs[0].shape) ? model6.inputs[0].shape[2] : 0;
const resize = tf10.image.resizeBilinear(image25, [inputSize8, inputSize8], false);
const [red, green, blue] = tf10.split(resize, 3, 3);
tf10.dispose(resize);
@ -5444,12 +5455,12 @@ async function predict5(image25, config3, idx, count2) {
tf10.dispose(blueNorm);
const normalize = tf10.tidy(() => tf10.mul(tf10.sub(grayscale, 0.5), 2));
tf10.dispose(grayscale);
const emotionT = model6?.execute(normalize);
const emotionT = model6 == null ? void 0 : model6.execute(normalize);
lastTime5 = now();
const data = await emotionT.data();
tf10.dispose(emotionT);
for (let i = 0; i < data.length; i++) {
if (data[i] > (config3.face.emotion?.minConfidence || 0))
if (data[i] > (((_b2 = config3.face.emotion) == null ? void 0 : _b2.minConfidence) || 0))
obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });
}
obj.sort((a, b) => b.score - a.score);
@ -5482,12 +5493,13 @@ var irisLandmarks = {
numCoordinates: 76
};
async function load7(config3) {
var _a, _b;
if (env.initial)
model7 = null;
if (!model7) {
model7 = await tf11.loadGraphModel(join(config3.modelBasePath, config3.face.iris?.modelPath || ""));
model7 = await tf11.loadGraphModel(join(config3.modelBasePath, ((_a = config3.face.iris) == null ? void 0 : _a.modelPath) || ""));
if (!model7 || !model7["modelUrl"])
log("load model failed:", config3.face.iris?.modelPath);
log("load model failed:", (_b = config3.face.iris) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", model7["modelUrl"]);
} else if (config3.debug)
@ -5604,8 +5616,9 @@ var skipped7 = Number.MAX_SAFE_INTEGER;
var lastTime6 = 0;
var enlargeFact = 1.6;
async function predict6(input, config3) {
const skipTime = (config3.face.detector?.skipTime || 0) > now() - lastTime6;
const skipFrame = skipped7 < (config3.face.detector?.skipFrames || 0);
var _a, _b, _c, _d, _e, _f, _g, _h;
const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime6;
const skipFrame = skipped7 < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0);
if (!config3.skipAllowed || !skipTime || !skipFrame || boxCache.length === 0) {
const possibleBoxes = await getBoxes(input, config3);
lastTime6 = now();
@ -5642,14 +5655,14 @@ async function predict6(input, config3) {
faceScore: 0,
annotations: {}
};
if (config3.face.detector?.rotation && config3.face.mesh?.enabled && env.kernels.includes("rotatewithoffset")) {
if (((_c = config3.face.detector) == null ? void 0 : _c.rotation) && ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) && env.kernels.includes("rotatewithoffset")) {
[angle, rotationMatrix, face5.tensor] = correctFaceRotation(box4, input, inputSize5);
} else {
rotationMatrix = fixedRotationMatrix;
face5.tensor = cutBoxFromImageAndResize(box4, input, config3.face.mesh?.enabled ? [inputSize5, inputSize5] : [size(), size()]);
face5.tensor = cutBoxFromImageAndResize(box4, input, ((_e = config3.face.mesh) == null ? void 0 : _e.enabled) ? [inputSize5, inputSize5] : [size(), size()]);
}
face5.boxScore = Math.round(100 * box4.confidence) / 100;
if (!config3.face.mesh?.enabled) {
if (!((_f = config3.face.mesh) == null ? void 0 : _f.enabled)) {
face5.box = getClampedBox(box4, input);
face5.boxRaw = getRawBox(box4, input);
face5.boxScore = Math.round(100 * box4.confidence || 0) / 100;
@ -5671,10 +5684,10 @@ async function predict6(input, config3) {
const coordsReshaped = tf12.reshape(contourCoords, [-1, 3]);
let rawCoords = await coordsReshaped.array();
tf12.dispose([contourCoords, coordsReshaped, confidence, contours]);
if (face5.faceScore < (config3.face.detector?.minConfidence || 1)) {
if (face5.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) {
box4.confidence = face5.faceScore;
} else {
if (config3.face.iris?.enabled)
if ((_h = config3.face.iris) == null ? void 0 : _h.enabled)
rawCoords = await augmentIris(rawCoords, face5.tensor, config3, inputSize5);
face5.mesh = transformRawCoords(rawCoords, box4, angle, rotationMatrix, inputSize5);
face5.meshRaw = face5.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize5]);
@ -5693,12 +5706,13 @@ async function predict6(input, config3) {
return faces;
}
async function load8(config3) {
var _a, _b;
if (env.initial)
model8 = null;
if (!model8) {
model8 = await tf12.loadGraphModel(join(config3.modelBasePath, config3.face.mesh?.modelPath || ""));
model8 = await tf12.loadGraphModel(join(config3.modelBasePath, ((_a = config3.face.mesh) == null ? void 0 : _a.modelPath) || ""));
if (!model8 || !model8["modelUrl"])
log("load model failed:", config3.face.mesh?.modelPath);
log("load model failed:", (_b = config3.face.mesh) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", model8["modelUrl"]);
} else if (config3.debug)
@ -5719,13 +5733,14 @@ var lastTime7 = 0;
var lastCount3 = 0;
var skipped8 = Number.MAX_SAFE_INTEGER;
async function load9(config3) {
const modelUrl = join(config3.modelBasePath, config3.face.description?.modelPath || "");
var _a, _b;
const modelUrl = join(config3.modelBasePath, ((_a = config3.face.description) == null ? void 0 : _a.modelPath) || "");
if (env.initial)
model9 = null;
if (!model9) {
model9 = await tf13.loadGraphModel(modelUrl);
if (!model9)
log("load model failed:", config3.face.description?.modelPath || "");
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)
@ -5737,7 +5752,7 @@ function enhance(input) {
const tensor3 = input.image || input.tensor || input;
if (!(tensor3 instanceof tf13.Tensor))
return null;
if (!model9?.inputs[0].shape)
if (!(model9 == null ? void 0 : model9.inputs[0].shape))
return null;
const crop2 = tf13.image.resizeBilinear(tensor3, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false);
const norm = tf13.mul(crop2, 255);
@ -5746,31 +5761,33 @@ function enhance(input) {
return image25;
}
async function predict7(image25, config3, idx, count2) {
var _a, _b, _c, _d;
if (!model9)
return null;
const skipFrame = skipped8 < (config3.face.description?.skipFrames || 0);
const skipTime = (config3.face.description?.skipTime || 0) > now() - lastTime7;
if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && last3[idx]?.age && last3[idx]?.age > 0) {
const skipFrame = skipped8 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0);
const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime7;
if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && ((_c = last3[idx]) == null ? void 0 : _c.age) && ((_d = last3[idx]) == null ? void 0 : _d.age) > 0) {
skipped8++;
return last3[idx];
}
skipped8 = 0;
return new Promise(async (resolve) => {
var _a2, _b2;
const obj = {
age: 0,
gender: "unknown",
genderScore: 0,
descriptor: []
};
if (config3.face.description?.enabled) {
if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) {
const enhanced = enhance(image25);
const resT = model9?.execute(enhanced);
const resT = model9 == null ? void 0 : model9.execute(enhanced);
lastTime7 = now();
tf13.dispose(enhanced);
const genderT = await resT.find((t) => t.shape[1] === 1);
const gender = await genderT.data();
const confidence = Math.trunc(200 * Math.abs(gender[0] - 0.5)) / 100;
if (confidence > (config3.face.description?.minConfidence || 0)) {
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);
}
@ -9530,22 +9547,23 @@ async function predict8(input, config3) {
return hands;
}
async function load10(config3) {
var _a, _b, _c, _d, _e, _f;
if (env.initial) {
handDetectorModel = null;
handPoseModel = null;
}
if (!handDetectorModel || !handPoseModel) {
[handDetectorModel, handPoseModel] = await Promise.all([
config3.hand.enabled ? tf17.loadGraphModel(join(config3.modelBasePath, config3.hand.detector?.modelPath || ""), { fromTFHub: (config3.hand.detector?.modelPath || "").includes("tfhub.dev") }) : null,
config3.hand.landmarks ? tf17.loadGraphModel(join(config3.modelBasePath, config3.hand.skeleton?.modelPath || ""), { fromTFHub: (config3.hand.skeleton?.modelPath || "").includes("tfhub.dev") }) : null
config3.hand.enabled ? tf17.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 ? tf17.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
]);
if (config3.hand.enabled) {
if (!handDetectorModel || !handDetectorModel["modelUrl"])
log("load model failed:", config3.hand.detector?.modelPath || "");
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:", config3.hand.skeleton?.modelPath || "");
log("load model failed:", ((_f = config3.hand.skeleton) == null ? void 0 : _f.modelPath) || "");
else if (config3.debug)
log("load model:", handPoseModel["modelUrl"]);
}
@ -9620,16 +9638,17 @@ var fingerMap = {
palm: [0]
};
async function loadDetect2(config3) {
var _a, _b;
if (env.initial)
models2[0] = null;
if (!models2[0]) {
fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3);
models2[0] = await tf18.loadGraphModel(join(config3.modelBasePath, config3.hand.detector?.modelPath || ""));
models2[0] = await tf18.loadGraphModel(join(config3.modelBasePath, ((_a = config3.hand.detector) == null ? void 0 : _a.modelPath) || ""));
const inputs = Object.values(models2[0].modelSignature["inputs"]);
inputSize6[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
inputSize6[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
if (!models2[0] || !models2[0]["modelUrl"])
log("load model failed:", config3.hand.detector?.modelPath);
log("load model failed:", (_b = config3.hand.detector) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", models2[0]["modelUrl"]);
} else if (config3.debug)
@ -9637,15 +9656,16 @@ async function loadDetect2(config3) {
return models2[0];
}
async function loadSkeleton(config3) {
var _a, _b;
if (env.initial)
models2[1] = null;
if (!models2[1]) {
models2[1] = await tf18.loadGraphModel(join(config3.modelBasePath, config3.hand.skeleton?.modelPath || ""));
models2[1] = await tf18.loadGraphModel(join(config3.modelBasePath, ((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath) || ""));
const inputs = Object.values(models2[1].modelSignature["inputs"]);
inputSize6[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
inputSize6[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
if (!models2[1] || !models2[1]["modelUrl"])
log("load model failed:", config3.hand.skeleton?.modelPath);
log("load model failed:", (_b = config3.hand.skeleton) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", models2[1]["modelUrl"]);
} else if (config3.debug)
@ -9738,7 +9758,8 @@ async function detectFingers(input, h, config3) {
return hand3;
}
async function predict9(input, config3) {
if (!models2[0] || !models2[1] || !models2[0]?.inputs[0].shape || !models2[1]?.inputs[0].shape)
var _a, _b;
if (!models2[0] || !models2[1] || !((_a = models2[0]) == null ? void 0 : _a.inputs[0].shape) || !((_b = models2[1]) == null ? void 0 : _b.inputs[0].shape))
return [];
outputSize = [input.shape[2] || 0, input.shape[1] || 0];
skipped9++;
@ -10053,7 +10074,7 @@ async function parseMultiPose(res, config3, image25, inputBox) {
return bodies;
}
async function predict10(input, config3) {
if (!model10 || !model10?.inputs[0].shape)
if (!model10 || !(model10 == null ? void 0 : model10.inputs[0].shape))
return [];
if (!config3.skipAllowed)
cache5.boxes.length = 0;
@ -10067,7 +10088,7 @@ async function predict10(input, config3) {
const t = {};
skipped10 = 0;
t.input = padInput(input, inputSize7);
t.res = model10?.execute(t.input);
t.res = model10 == null ? void 0 : model10.execute(t.input);
cache5.last = now();
const res = await t.res.array();
cache5.bodies = t.res.shape[2] === 17 ? await parseSinglePose(res, config3, input, [0, 0, 1, 1]) : await parseMultiPose(res, config3, input, [0, 0, 1, 1]);
@ -10107,9 +10128,10 @@ async function process4(res, inputSize8, outputShape, config3) {
let results = [];
for (const strideSize of [1, 2, 4]) {
tf21.tidy(async () => {
var _a, _b;
const baseSize = strideSize * 13;
const scoresT = res.find((a) => a.shape[1] === baseSize ** 2 && a.shape[2] === labels.length)?.squeeze();
const featuresT = res.find((a) => a.shape[1] === baseSize ** 2 && a.shape[2] < labels.length)?.squeeze();
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();
@ -10469,7 +10491,8 @@ function buildPartWithScoreQueue(minConfidence2, scores) {
}
function withinRadius(poses, { x, y }, keypointId) {
return poses.some(({ keypoints }) => {
const correspondingKeypoint = keypoints[keypointId]?.position;
var _a;
const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position;
if (!correspondingKeypoint)
return false;
return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;
@ -10548,14 +10571,15 @@ async function load14(config3) {
return model13;
}
async function process5(input, background, config3) {
var _a, _b;
if (busy)
return { data: [], canvas: null, alpha: null };
busy = true;
if (!model13)
await load14(config3);
const inputImage = process2(input, config3);
const width = inputImage.canvas?.width || 0;
const height = inputImage.canvas?.height || 0;
const width = ((_a = inputImage.canvas) == null ? void 0 : _a.width) || 0;
const height = ((_b = inputImage.canvas) == null ? void 0 : _b.height) || 0;
if (!inputImage.tensor)
return { data: [], canvas: null, alpha: null };
const t = {};
@ -10646,49 +10670,50 @@ function reset(instance) {
instance.models[model14] = null;
}
async function load15(instance) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A, _B, _C, _D, _E;
if (env.initial)
reset(instance);
if (instance.config.hand.enabled) {
if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes("handdetect"))
if (!instance.models.handpose && ((_b = (_a = instance.config.hand.detector) == null ? void 0 : _a.modelPath) == null ? void 0 : _b.includes("handdetect")))
[instance.models.handpose, instance.models.handskeleton] = await load10(instance.config);
if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes("handdetect"))
if (!instance.models.handskeleton && instance.config.hand.landmarks && ((_d = (_c = instance.config.hand.detector) == null ? void 0 : _c.modelPath) == null ? void 0 : _d.includes("handdetect")))
[instance.models.handpose, instance.models.handskeleton] = await load10(instance.config);
}
if (instance.config.face.enabled && !instance.models.facedetect)
instance.models.facedetect = load3(instance.config);
if (instance.config.face.enabled && instance.config.face.mesh?.enabled && !instance.models.facemesh)
if (instance.config.face.enabled && ((_e = instance.config.face.mesh) == null ? void 0 : _e.enabled) && !instance.models.facemesh)
instance.models.facemesh = load8(instance.config);
if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.models.faceiris)
if (instance.config.face.enabled && ((_f = instance.config.face.iris) == null ? void 0 : _f.enabled) && !instance.models.faceiris)
instance.models.faceiris = load7(instance.config);
if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof)
if (instance.config.face.enabled && ((_g = instance.config.face.antispoof) == null ? void 0 : _g.enabled) && !instance.models.antispoof)
instance.models.antispoof = load2(instance.config);
if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes("handtrack"))
if (instance.config.hand.enabled && !instance.models.handtrack && ((_i = (_h = instance.config.hand.detector) == null ? void 0 : _h.modelPath) == null ? void 0 : _i.includes("handtrack")))
instance.models.handtrack = loadDetect2(instance.config);
if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes("handtrack"))
if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && ((_k = (_j = instance.config.hand.detector) == null ? void 0 : _j.modelPath) == null ? void 0 : _k.includes("handtrack")))
instance.models.handskeleton = loadSkeleton(instance.config);
if (instance.config.body.enabled && !instance.models.posenet && instance.config.body?.modelPath?.includes("posenet"))
if (instance.config.body.enabled && !instance.models.posenet && ((_m = (_l = instance.config.body) == null ? void 0 : _l.modelPath) == null ? void 0 : _m.includes("posenet")))
instance.models.posenet = load13(instance.config);
if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes("efficientpose"))
if (instance.config.body.enabled && !instance.models.efficientpose && ((_o = (_n = instance.config.body) == null ? void 0 : _n.modelPath) == null ? void 0 : _o.includes("efficientpose")))
instance.models.efficientpose = load5(instance.config);
if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body?.modelPath?.includes("blazepose"))
if (instance.config.body.enabled && !instance.models.blazepose && ((_q = (_p = instance.config.body) == null ? void 0 : _p.modelPath) == null ? void 0 : _q.includes("blazepose")))
instance.models.blazepose = loadPose(instance.config);
if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body.detector?.modelPath && instance.config.body?.modelPath?.includes("blazepose"))
if (instance.config.body.enabled && !instance.models.blazeposedetect && ((_r = instance.config.body.detector) == null ? void 0 : _r.modelPath) && ((_t = (_s = instance.config.body) == null ? void 0 : _s.modelPath) == null ? void 0 : _t.includes("blazepose")))
instance.models.blazeposedetect = loadDetect(instance.config);
if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes("efficientpose"))
if (instance.config.body.enabled && !instance.models.efficientpose && ((_v = (_u = instance.config.body) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("efficientpose")))
instance.models.efficientpose = load5(instance.config);
if (instance.config.body.enabled && !instance.models.movenet && instance.config.body?.modelPath?.includes("movenet"))
if (instance.config.body.enabled && !instance.models.movenet && ((_x = (_w = instance.config.body) == null ? void 0 : _w.modelPath) == null ? void 0 : _x.includes("movenet")))
instance.models.movenet = load11(instance.config);
if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object?.modelPath?.includes("nanodet"))
if (instance.config.object.enabled && !instance.models.nanodet && ((_z = (_y = instance.config.object) == null ? void 0 : _y.modelPath) == null ? void 0 : _z.includes("nanodet")))
instance.models.nanodet = load12(instance.config);
if (instance.config.object.enabled && !instance.models.centernet && instance.config.object?.modelPath?.includes("centernet"))
if (instance.config.object.enabled && !instance.models.centernet && ((_B = (_A = instance.config.object) == null ? void 0 : _A.modelPath) == null ? void 0 : _B.includes("centernet")))
instance.models.centernet = load4(instance.config);
if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion)
if (instance.config.face.enabled && ((_C = instance.config.face.emotion) == null ? void 0 : _C.enabled) && !instance.models.emotion)
instance.models.emotion = load6(instance.config);
if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres)
if (instance.config.face.enabled && ((_D = instance.config.face.description) == null ? void 0 : _D.enabled) && !instance.models.faceres)
instance.models.faceres = load9(instance.config);
if (instance.config.segmentation.enabled && !instance.models.segmentation)
instance.models.segmentation = load14(instance.config);
if (instance.config.face.enabled && instance.config.face["agegenderrace"]?.enabled && !instance.models.agegenderrace)
if (instance.config.face.enabled && ((_E = instance.config.face["agegenderrace"]) == null ? void 0 : _E.enabled) && !instance.models.agegenderrace)
instance.models.agegenderrace = load(instance.config);
for await (const model14 of Object.keys(instance.models)) {
if (instance.models[model14] && typeof instance.models[model14] !== "undefined")
@ -10712,7 +10737,7 @@ async function validate2(instance) {
continue;
}
const ops = [];
const executor = model14?.executor;
const executor = model14 == null ? void 0 : model14.executor;
if (executor && executor.graph.nodes) {
for (const kernel of Object.values(executor.graph.nodes)) {
const op = kernel.op.toLowerCase();
@ -10761,6 +10786,7 @@ function extensions() {
config2.extensions = gl.getSupportedExtensions();
}
async function register(instance) {
var _a;
if (instance.config.backend !== "humangl")
return;
if (config2.name in tf24.engine().registry && (!config2.gl || !config2.gl.getParameter(config2.gl.VERSION))) {
@ -10775,7 +10801,7 @@ async function register(instance) {
return;
}
try {
config2.gl = config2.canvas?.getContext("webgl2", config2.webGLattr);
config2.gl = (_a = config2.canvas) == null ? void 0 : _a.getContext("webgl2", config2.webGLattr);
if (config2.canvas) {
config2.canvas.addEventListener("webglcontextlost", async (e) => {
log("error: humangl:", e.type);
@ -10882,7 +10908,7 @@ async function check(instance, force = false) {
if (instance.config.backend === "wasm") {
if (instance.config.debug)
log("wasm path:", instance.config.wasmPath);
if (typeof tf25?.setWasmPaths !== "undefined")
if (typeof (tf25 == null ? void 0 : tf25.setWasmPaths) !== "undefined")
await tf25.setWasmPaths(instance.config.wasmPath);
else
throw new Error("wasm backend is not loaded");
@ -11083,6 +11109,7 @@ async function gesture(inCanvas2, result, drawOptions) {
}
}
async function face(inCanvas2, result, drawOptions) {
var _a, _b, _c, _d, _e;
const localOptions = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
@ -11172,7 +11199,7 @@ async function face(inCanvas2, result, drawOptions) {
ctx.fill();
}
}
if (localOptions.drawGaze && f.rotation?.angle) {
if (localOptions.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.angle)) {
ctx.strokeStyle = "pink";
const valX = f.box[0] + f.box[2] / 2 - f.box[3] * rad2deg(f.rotation.angle.yaw) / 90;
const valY = f.box[1] + f.box[3] / 2 + f.box[2] * rad2deg(f.rotation.angle.pitch) / 90;
@ -11193,7 +11220,7 @@ async function face(inCanvas2, result, drawOptions) {
ctx.stroke(pathH);
ctx.stroke(pathV);
}
if (localOptions.drawGaze && f.rotation?.gaze?.strength && f.rotation?.gaze?.bearing && f.annotations["leftEyeIris"] && f.annotations["rightEyeIris"] && f.annotations["leftEyeIris"][0] && f.annotations["rightEyeIris"][0]) {
if (localOptions.drawGaze && ((_c = (_b = f.rotation) == null ? void 0 : _b.gaze) == null ? void 0 : _c.strength) && ((_e = (_d = f.rotation) == null ? void 0 : _d.gaze) == null ? void 0 : _e.bearing) && f.annotations["leftEyeIris"] && f.annotations["rightEyeIris"] && f.annotations["leftEyeIris"][0] && f.annotations["rightEyeIris"][0]) {
ctx.strokeStyle = "pink";
ctx.fillStyle = "pink";
const leftGaze = [
@ -11212,6 +11239,7 @@ async function face(inCanvas2, result, drawOptions) {
}
}
async function body(inCanvas2, result, drawOptions) {
var _a;
const localOptions = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
@ -11222,7 +11250,7 @@ async function body(inCanvas2, result, drawOptions) {
ctx.fillStyle = localOptions.color;
ctx.lineWidth = localOptions.lineWidth;
ctx.font = localOptions.font;
if (localOptions.drawBoxes && result[i].box && result[i].box?.length === 4) {
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 !== "") {
@ -11504,6 +11532,7 @@ var calculateFaceAngle = (face5, imageSize) => {
// src/face/face.ts
var detectFace = async (parent, input) => {
var _a, _b, _c, _d;
let timeStamp;
let ageRes;
let gearRes;
@ -11562,7 +11591,7 @@ var detectFace = async (parent, input) => {
[ageRes, genderRes, emotionRes, embeddingRes, descRes, gearRes, antispoofRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes, gearRes, antispoofRes]);
}
parent.analyze("Finish Face:");
if (!parent.config.face.iris.enabled && faces[i]?.annotations?.leftEyeIris && faces[i]?.annotations?.rightEyeIris) {
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;
}
@ -11574,10 +11603,10 @@ var detectFace = async (parent, input) => {
faceRes.push({
...faces[i],
id: i,
age: descRes?.age,
gender: descRes?.gender,
genderScore: descRes?.genderScore,
embedding: descRes?.descriptor,
age: descRes == null ? void 0 : descRes.age,
gender: descRes == null ? void 0 : descRes.gender,
genderScore: descRes == null ? void 0 : descRes.genderScore,
embedding: descRes == null ? void 0 : descRes.descriptor,
emotion: emotionRes,
real: antispoofRes,
iris: irisSize !== 0 ? Math.trunc(500 / irisSize / 11.7) / 100 : 0,
@ -11723,6 +11752,7 @@ var hand2 = (res) => {
var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0 };
var interpolateTime = 0;
function calc2(newResult, config3) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A;
const t0 = now();
if (!newResult)
return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0 };
@ -11749,11 +11779,11 @@ function calc2(newResult, config3) {
}));
const annotations2 = {};
let coords8 = { connected: {} };
if (config3.body?.modelPath?.includes("efficientpose"))
if ((_b = (_a = config3.body) == null ? void 0 : _a.modelPath) == null ? void 0 : _b.includes("efficientpose"))
coords8 = efficientposecoords_exports;
else if (config3.body?.modelPath?.includes("blazepose"))
else if ((_d = (_c = config3.body) == null ? void 0 : _c.modelPath) == null ? void 0 : _d.includes("blazepose"))
coords8 = blazeposecoords_exports;
else if (config3.body?.modelPath?.includes("movenet"))
else if ((_f = (_e = config3.body) == null ? void 0 : _e.modelPath) == null ? void 0 : _f.includes("movenet"))
coords8 = movenetcoords_exports;
for (const [name, indexes] of Object.entries(coords8.connected)) {
const pt = [];
@ -11796,15 +11826,15 @@ function calc2(newResult, config3) {
const box4 = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor);
const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor);
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 = newResult.face[i].rotation?.matrix;
rotation.matrix = (_g = newResult.face[i].rotation) == null ? void 0 : _g.matrix;
rotation.angle = {
roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,
yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,
pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor
roll: ((bufferedFactor - 1) * (((_i = (_h = bufferedResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k = (_j = newResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor,
yaw: ((bufferedFactor - 1) * (((_m = (_l = bufferedResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.yaw) || 0) + (((_o = (_n = newResult.face[i].rotation) == null ? void 0 : _n.angle) == null ? void 0 : _o.yaw) || 0)) / bufferedFactor,
pitch: ((bufferedFactor - 1) * (((_q = (_p = bufferedResult.face[i].rotation) == null ? void 0 : _p.angle) == null ? void 0 : _q.pitch) || 0) + (((_s = (_r = newResult.face[i].rotation) == null ? void 0 : _r.angle) == null ? void 0 : _s.pitch) || 0)) / bufferedFactor
};
rotation.gaze = {
bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.bearing || 0) + (newResult.face[i].rotation?.gaze?.bearing || 0)) / bufferedFactor,
strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.strength || 0) + (newResult.face[i].rotation?.gaze?.strength || 0)) / bufferedFactor
bearing: ((bufferedFactor - 1) * (((_u = (_t = bufferedResult.face[i].rotation) == null ? void 0 : _t.gaze) == null ? void 0 : _u.bearing) || 0) + (((_w = (_v = newResult.face[i].rotation) == null ? void 0 : _v.gaze) == null ? void 0 : _w.bearing) || 0)) / bufferedFactor,
strength: ((bufferedFactor - 1) * (((_y = (_x = bufferedResult.face[i].rotation) == null ? void 0 : _x.gaze) == null ? void 0 : _y.strength) || 0) + (((_A = (_z = newResult.face[i].rotation) == null ? void 0 : _z.gaze) == null ? void 0 : _A.strength) || 0)) / bufferedFactor
};
bufferedResult.face[i] = { ...newResult.face[i], rotation, box: box4, boxRaw };
}
@ -11873,6 +11903,7 @@ function match2(descriptor, descriptors, options3 = { order: 2, multiplier: 20,
// src/util/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) {
@ -11896,15 +11927,15 @@ function join2(faces, bodies, hands, gestures, shape) {
}
for (const gesture3 of gestures) {
if (gesture3["face"] !== void 0 && gesture3["face"] === face5.id)
person2.gestures?.push(gesture3);
(_a = person2.gestures) == null ? void 0 : _a.push(gesture3);
else if (gesture3["iris"] !== void 0 && gesture3["iris"] === face5.id)
person2.gestures?.push(gesture3);
else if (gesture3["body"] !== void 0 && gesture3["body"] === person2.body?.id)
person2.gestures?.push(gesture3);
else if (gesture3["hand"] !== void 0 && gesture3["hand"] === person2.hands?.left?.id)
person2.gestures?.push(gesture3);
else if (gesture3["hand"] !== void 0 && gesture3["hand"] === person2.hands?.right?.id)
person2.gestures?.push(gesture3);
(_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 = [];
@ -11914,10 +11945,10 @@ function join2(faces, bodies, hands, gestures, shape) {
y.push(box4[1], box4[1] + box4[3]);
}
};
extractXY(person2.face?.box);
extractXY(person2.body?.box);
extractXY(person2.hands?.left?.box);
extractXY(person2.hands?.right?.box);
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];
@ -12799,7 +12830,7 @@ var Human = class {
return "input must be a tensor";
try {
this.tf.getBackend();
} catch {
} catch (e) {
return "backend not loaded";
}
return null;
@ -12808,8 +12839,9 @@ var Human = class {
__publicField(this, "distance", distance);
__publicField(this, "match", match2);
__publicField(this, "emit", (event) => {
var _a;
if (this.events && this.events.dispatchEvent)
this.events?.dispatchEvent(new Event(event));
(_a = this.events) == null ? void 0 : _a.dispatchEvent(new Event(event));
});
this.env = env;
config.wasmPath = tf28.version_core.includes("-") ? "https://vladmandic.github.io/tfjs/dist/" : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf28.version_core}/dist/`;
@ -12920,6 +12952,7 @@ var Human = class {
async detect(input, userConfig) {
this.state = "detect";
return new Promise(async (resolve) => {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v;
this.state = "config";
let timeStamp;
this.config = mergeDeep(this.config, userConfig);
@ -12976,25 +13009,25 @@ var Human = class {
this.state = "detect:body";
const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config;
if (this.config.async) {
if (this.config.body.modelPath?.includes("posenet"))
if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet"))
bodyRes = this.config.body.enabled ? predict12(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("blazepose"))
else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose"))
bodyRes = this.config.body.enabled ? predict2(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("efficientpose"))
else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose"))
bodyRes = this.config.body.enabled ? predict4(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("movenet"))
else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet"))
bodyRes = this.config.body.enabled ? predict10(img.tensor, bodyConfig) : [];
if (this.performance.body)
delete this.performance.body;
} else {
timeStamp = now();
if (this.config.body.modelPath?.includes("posenet"))
if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet"))
bodyRes = this.config.body.enabled ? await predict12(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("blazepose"))
else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose"))
bodyRes = this.config.body.enabled ? await predict2(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("efficientpose"))
else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose"))
bodyRes = this.config.body.enabled ? await predict4(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("movenet"))
else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet"))
bodyRes = this.config.body.enabled ? await predict10(img.tensor, bodyConfig) : [];
this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
@ -13003,17 +13036,17 @@ var Human = class {
this.state = "detect:hand";
const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config;
if (this.config.async) {
if (this.config.hand.detector?.modelPath?.includes("handdetect"))
if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect"))
handRes = this.config.hand.enabled ? predict8(img.tensor, handConfig) : [];
else if (this.config.hand.detector?.modelPath?.includes("handtrack"))
else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack"))
handRes = this.config.hand.enabled ? predict9(img.tensor, handConfig) : [];
if (this.performance.hand)
delete this.performance.hand;
} else {
timeStamp = now();
if (this.config.hand.detector?.modelPath?.includes("handdetect"))
if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect"))
handRes = this.config.hand.enabled ? await predict8(img.tensor, handConfig) : [];
else if (this.config.hand.detector?.modelPath?.includes("handtrack"))
else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack"))
handRes = this.config.hand.enabled ? await predict9(img.tensor, handConfig) : [];
this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
@ -13021,17 +13054,17 @@ var Human = class {
this.analyze("Start Object:");
this.state = "detect:object";
if (this.config.async) {
if (this.config.object.modelPath?.includes("nanodet"))
if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet"))
objectRes = this.config.object.enabled ? predict11(img.tensor, this.config) : [];
else if (this.config.object.modelPath?.includes("centernet"))
else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet"))
objectRes = this.config.object.enabled ? predict3(img.tensor, this.config) : [];
if (this.performance.object)
delete this.performance.object;
} else {
timeStamp = now();
if (this.config.object.modelPath?.includes("nanodet"))
if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet"))
objectRes = this.config.object.enabled ? await predict11(img.tensor, this.config) : [];
else if (this.config.object.modelPath?.includes("centernet"))
else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet"))
objectRes = this.config.object.enabled ? await predict3(img.tensor, this.config) : [];
this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
@ -13050,7 +13083,7 @@ var Human = class {
delete this.performance.gesture;
}
this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);
const shape = this.process?.tensor?.shape || [];
const shape = ((_v = (_u = this.process) == null ? void 0 : _u.tensor) == null ? void 0 : _v.shape) || [];
this.result = {
face: faceRes,
body: bodyRes,

333
dist/human.node.js vendored
View File

@ -466,14 +466,14 @@ function GLImageFilter() {
if (drawCount === 0)
source = sourceTexture;
else
source = getTempFramebuffer(currentFramebufferIndex)?.texture || null;
source = getTempFramebuffer(currentFramebufferIndex).texture || null;
drawCount++;
if (lastInChain && !(flags & DRAW.INTERMEDIATE)) {
target = null;
flipY = drawCount % 2 === 0;
} else {
currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;
target = getTempFramebuffer(currentFramebufferIndex)?.fbo || null;
target = getTempFramebuffer(currentFramebufferIndex).fbo || null;
}
gl.bindTexture(gl.TEXTURE_2D, source);
gl.bindFramebuffer(gl.FRAMEBUFFER, target);
@ -483,7 +483,7 @@ function GLImageFilter() {
function compileShader(fragmentSource) {
if (shaderProgramCache[fragmentSource]) {
currentProgram = shaderProgramCache[fragmentSource];
gl.useProgram(currentProgram?.id || null);
gl.useProgram((currentProgram ? currentProgram.id : null) || null);
return currentProgram;
}
currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource);
@ -505,7 +505,7 @@ function GLImageFilter() {
m[19] /= 255;
const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha;
const program = compileShader(shader);
gl.uniform1fv(program?.uniform["m"], m);
gl.uniform1fv(program.uniform["m"], m);
draw2();
},
brightness: (brightness) => {
@ -818,8 +818,8 @@ function GLImageFilter() {
const pixelSizeX = 1 / fxcanvas.width;
const pixelSizeY = 1 / fxcanvas.height;
const program = compileShader(convolution);
gl.uniform1fv(program?.uniform["m"], m);
gl.uniform2f(program?.uniform["px"], pixelSizeX, pixelSizeY);
gl.uniform1fv(program.uniform["m"], m);
gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY);
draw2();
},
detectEdges: () => {
@ -893,16 +893,16 @@ function GLImageFilter() {
const blurSizeX = size2 / 7 / fxcanvas.width;
const blurSizeY = size2 / 7 / fxcanvas.height;
const program = compileShader(blur);
gl.uniform2f(program?.uniform["px"], 0, blurSizeY);
gl.uniform2f(program.uniform["px"], 0, blurSizeY);
draw2(DRAW.INTERMEDIATE);
gl.uniform2f(program?.uniform["px"], blurSizeX, 0);
gl.uniform2f(program.uniform["px"], blurSizeX, 0);
draw2();
},
pixelate: (size2) => {
const blurSizeX = size2 / fxcanvas.width;
const blurSizeY = size2 / fxcanvas.height;
const program = compileShader(pixelate);
gl.uniform2f(program?.uniform["size"], blurSizeX, blurSizeY);
gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY);
draw2();
}
};
@ -1023,7 +1023,7 @@ function process2(input, config3, getTensor = true) {
targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth);
if (!targetWidth || !targetHeight)
throw new Error("input cannot determine dimension");
if (!inCanvas || inCanvas?.width !== targetWidth || inCanvas?.height !== targetHeight)
if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight)
inCanvas = canvas(targetWidth, targetHeight);
const inCtx = inCanvas.getContext("2d");
if (typeof ImageData !== "undefined" && input instanceof ImageData) {
@ -1032,13 +1032,13 @@ function process2(input, config3, getTensor = true) {
if (config3.filter.flip && typeof inCtx.translate !== "undefined") {
inCtx.translate(originalWidth, 0);
inCtx.scale(-1, 1);
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
inCtx.setTransform(1, 0, 0, 1, 0, 0);
} else {
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
}
}
if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas?.height !== outCanvas?.height)
if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height)
outCanvas = canvas(inCanvas.width, inCanvas.height);
if (config3.filter.enabled && env.webgl.supported) {
if (!fx)
@ -1100,7 +1100,7 @@ function process2(input, config3, getTensor = true) {
pixels = tf.tensor(arr, [input["height"], input["width"], depth], "int32");
}
} else {
if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas?.height !== tmpCanvas?.height)
if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height)
tmpCanvas = canvas(outCanvas.width, outCanvas.height);
if (tf.browser && env.browser) {
if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") {
@ -1256,12 +1256,12 @@ var Env = class {
this.webgpu.supported = this.browser && typeof navigator["gpu"] !== "undefined";
this.webgpu.backend = this.backends.includes("webgpu");
if (this.webgpu.supported)
this.webgpu.adapter = (await navigator["gpu"].requestAdapter())?.name;
this.webgpu.adapter = (await navigator["gpu"].requestAdapter()).name;
this.kernels = tf2.getKernelsForBackend(tf2.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());
}
async updateCPU() {
const cpu = { model: "", flags: [] };
if (this.node && this.platform?.startsWith("linux")) {
if (this.node && this.platform.startsWith("linux")) {
const fs = require("fs");
try {
const data = fs.readFileSync("/proc/cpuinfo").toString();
@ -1273,7 +1273,7 @@ var Env = class {
cpu.flags = line.match(/:(.*)/g)[0].replace(":", "").trim().split(" ").sort();
}
}
} catch {
} catch (e) {
}
}
if (!this["cpu"])
@ -1319,12 +1319,13 @@ var skipped2 = Number.MAX_SAFE_INTEGER;
var lastCount = 0;
var lastTime = 0;
async function load2(config3) {
var _a, _b;
if (env.initial)
model2 = null;
if (!model2) {
model2 = await tf4.loadGraphModel(join(config3.modelBasePath, config3.face.antispoof?.modelPath || ""));
model2 = await tf4.loadGraphModel(join(config3.modelBasePath, ((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath) || ""));
if (!model2 || !model2["modelUrl"])
log("load model failed:", config3.face.antispoof?.modelPath);
log("load model failed:", (_b = config3.face.antispoof) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", model2["modelUrl"]);
} else if (config3.debug)
@ -1332,18 +1333,19 @@ async function load2(config3) {
return model2;
}
async function predict(image25, config3, idx, count2) {
var _a, _b;
if (!model2)
return null;
const skipTime = (config3.face.antispoof?.skipTime || 0) > now() - lastTime;
const skipFrame = skipped2 < (config3.face.antispoof?.skipFrames || 0);
const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime;
const skipFrame = skipped2 < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0);
if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && cached[idx]) {
skipped2++;
return cached[idx];
}
skipped2 = 0;
return new Promise(async (resolve) => {
const resize = tf4.image.resizeBilinear(image25, [model2?.inputs[0].shape ? model2.inputs[0].shape[2] : 0, model2?.inputs[0].shape ? model2.inputs[0].shape[1] : 0], false);
const res = model2?.execute(resize);
const resize = tf4.image.resizeBilinear(image25, [(model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[2] : 0, (model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[1] : 0], false);
const res = model2 == null ? void 0 : model2.execute(resize);
const num = (await res.data())[0];
cached[idx] = Math.round(100 * num) / 100;
lastCount = count2;
@ -4795,12 +4797,13 @@ var anchors = null;
var inputSize = 0;
var size = () => inputSize;
async function load3(config3) {
var _a, _b;
if (env.initial)
model3 = null;
if (!model3) {
model3 = await tf6.loadGraphModel(join(config3.modelBasePath, config3.face.detector?.modelPath || ""));
model3 = await tf6.loadGraphModel(join(config3.modelBasePath, ((_a = config3.face.detector) == null ? void 0 : _a.modelPath) || ""));
if (!model3 || !model3["modelUrl"])
log("load model failed:", config3.face.detector?.modelPath);
log("load model failed:", (_b = config3.face.detector) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", model3["modelUrl"]);
} else if (config3.debug)
@ -4827,12 +4830,13 @@ function decodeBounds(boxOutputs) {
return tf6.concat2d([startNormalized, endNormalized], concatAxis);
}
async function getBoxes(inputImage, config3) {
var _a, _b, _c, _d;
if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
return { boxes: [] };
const [batch, boxes, scores] = tf6.tidy(() => {
const resizedImage = tf6.image.resizeBilinear(inputImage, [inputSize, inputSize]);
const normalizedImage = tf6.sub(tf6.div(resizedImage, 127.5), 0.5);
const res = model3?.execute(normalizedImage);
const res = model3 == null ? void 0 : model3.execute(normalizedImage);
let batchOut;
if (Array.isArray(res)) {
const sorted = res.sort((a, b) => a.size - b.size);
@ -4848,14 +4852,14 @@ async function getBoxes(inputImage, config3) {
const scoresOut = tf6.squeeze(tf6.sigmoid(logits));
return [batchOut, boxesOut, scoresOut];
});
const nmsTensor = await tf6.image.nonMaxSuppressionAsync(boxes, scores, config3.face.detector?.maxDetected || 0, config3.face.detector?.iouThreshold || 0, config3.face.detector?.minConfidence || 0);
const nmsTensor = await tf6.image.nonMaxSuppressionAsync(boxes, scores, ((_a = config3.face.detector) == null ? void 0 : _a.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0);
const nms = await nmsTensor.array();
tf6.dispose(nmsTensor);
const annotatedBoxes = [];
const scoresData = await scores.data();
for (let i = 0; i < nms.length; i++) {
const confidence = scoresData[nms[i]];
if (confidence > (config3.face.detector?.minConfidence || 0)) {
if (confidence > (((_d = config3.face.detector) == null ? void 0 : _d.minConfidence) || 0)) {
const boundingBox = tf6.slice(boxes, [nms[i], 0], [1, -1]);
const landmarks = tf6.tidy(() => tf6.reshape(tf6.squeeze(tf6.slice(batch, [nms[i], keypointsCount - 1], [1, -1])), [keypointsCount, -1]));
annotatedBoxes.push({ box: createBox(boundingBox), landmarks, anchor: anchorsData[nms[i]], confidence });
@ -4942,15 +4946,16 @@ var cache = null;
var padding = [[0, 0], [0, 0], [0, 0], [0, 0]];
var lastTime2 = 0;
async function loadDetect(config3) {
var _a, _b, _c;
if (env3.initial)
models[0] = null;
if (!models[0] && config3.body.detector?.modelPath || "") {
models[0] = await tf7.loadGraphModel(join(config3.modelBasePath, config3.body.detector?.modelPath || ""));
if (!models[0] && ((_a = config3.body.detector) == null ? void 0 : _a.modelPath) || "") {
models[0] = await tf7.loadGraphModel(join(config3.modelBasePath, ((_b = config3.body.detector) == null ? void 0 : _b.modelPath) || ""));
const inputs = Object.values(models[0].modelSignature["inputs"]);
inputSize2[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
inputSize2[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
if (!models[0] || !models[0]["modelUrl"])
log("load model failed:", config3.body.detector?.modelPath);
log("load model failed:", (_c = config3.body.detector) == null ? void 0 : _c.modelPath);
else if (config3.debug)
log("load model:", models[0]["modelUrl"]);
} else if (config3.debug && models[0])
@ -4958,6 +4963,7 @@ async function loadDetect(config3) {
return models[0];
}
async function loadPose(config3) {
var _a;
if (env3.initial)
models[1] = null;
if (!models[1]) {
@ -4965,7 +4971,7 @@ async function loadPose(config3) {
const inputs = Object.values(models[1].modelSignature["inputs"]);
inputSize2[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
inputSize2[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
if (config3.body.modelPath?.includes("lite"))
if ((_a = config3.body.modelPath) == null ? void 0 : _a.includes("lite"))
outputNodes = ["ld_3d", "output_segmentation", "output_heatmap", "world_3d", "output_poseflag"];
else
outputNodes = ["Identity", "Identity_2", "Identity_3", "Identity_4", "Identity_1"];
@ -5017,9 +5023,10 @@ function rescaleKeypoints(keypoints, outputSize2) {
}
var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x));
async function detectParts(input, config3, outputSize2) {
var _a;
const t = {};
t.input = await prepareImage(input);
[t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = models[1]?.execute(t.input, outputNodes);
[t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = (_a = models[1]) == null ? void 0 : _a.execute(t.input, outputNodes);
const poseScoreRaw = (await t.poseflag.data())[0];
const poseScore = Math.max(0, (poseScoreRaw - 0.8) / (1 - 0.8));
const points = await t.ld.data();
@ -5236,7 +5243,7 @@ async function predict3(input, config3) {
return new Promise(async (resolve) => {
const outputSize2 = [input.shape[2], input.shape[1]];
const resize = tf8.image.resizeBilinear(input, [inputSize3, inputSize3]);
const objectT = config3.object.enabled ? model4?.execute(resize, ["tower_0/detections"]) : null;
const objectT = config3.object.enabled ? model4 == null ? void 0 : model4.execute(resize, ["tower_0/detections"]) : null;
lastTime3 = now();
tf8.dispose(resize);
const obj = await process3(objectT, outputSize2, config3);
@ -5323,8 +5330,9 @@ async function predict4(image25, config3) {
}
skipped5 = 0;
return new Promise(async (resolve) => {
var _a;
const tensor3 = tf9.tidy(() => {
if (!model5?.inputs[0].shape)
if (!(model5 == null ? void 0 : model5.inputs[0].shape))
return null;
const resize = tf9.image.resizeBilinear(image25, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
const enhance2 = tf9.mul(resize, 2);
@ -5333,7 +5341,7 @@ async function predict4(image25, config3) {
});
let resT;
if (config3.body.enabled)
resT = model5?.execute(tensor3);
resT = model5 == null ? void 0 : model5.execute(tensor3);
lastTime4 = now();
tf9.dispose(tensor3);
if (resT) {
@ -5344,7 +5352,7 @@ async function predict4(image25, config3) {
tf9.dispose(squeeze8);
for (let id = 0; id < stack3.length; id++) {
const [x2, y2, partScore] = max2d(stack3[id], config3.body.minConfidence);
if (partScore > (config3.body?.minConfidence || 0)) {
if (partScore > (((_a = config3.body) == null ? void 0 : _a.minConfidence) || 0)) {
cache2.keypoints.push({
score: Math.round(100 * partScore) / 100,
part: kpt2[id],
@ -5402,12 +5410,13 @@ var lastTime5 = 0;
var skipped6 = Number.MAX_SAFE_INTEGER;
var rgb = [0.2989, 0.587, 0.114];
async function load6(config3) {
var _a, _b;
if (env.initial)
model6 = null;
if (!model6) {
model6 = await tf10.loadGraphModel(join(config3.modelBasePath, config3.face.emotion?.modelPath || ""));
model6 = await tf10.loadGraphModel(join(config3.modelBasePath, ((_a = config3.face.emotion) == null ? void 0 : _a.modelPath) || ""));
if (!model6 || !model6["modelUrl"])
log("load model failed:", config3.face.emotion?.modelPath);
log("load model failed:", (_b = config3.face.emotion) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", model6["modelUrl"]);
} else if (config3.debug)
@ -5415,19 +5424,21 @@ async function load6(config3) {
return model6;
}
async function predict5(image25, config3, idx, count2) {
var _a, _b;
if (!model6)
return null;
const skipFrame = skipped6 < (config3.face.emotion?.skipFrames || 0);
const skipTime = (config3.face.emotion?.skipTime || 0) > now() - lastTime5;
const skipFrame = skipped6 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0);
const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5;
if (config3.skipAllowed && skipTime && skipFrame && lastCount2 === count2 && last2[idx] && last2[idx].length > 0) {
skipped6++;
return last2[idx];
}
skipped6 = 0;
return new Promise(async (resolve) => {
var _a2, _b2;
const obj = [];
if (config3.face.emotion?.enabled) {
const inputSize8 = model6?.inputs[0].shape ? model6.inputs[0].shape[2] : 0;
if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) {
const inputSize8 = (model6 == null ? void 0 : model6.inputs[0].shape) ? model6.inputs[0].shape[2] : 0;
const resize = tf10.image.resizeBilinear(image25, [inputSize8, inputSize8], false);
const [red, green, blue] = tf10.split(resize, 3, 3);
tf10.dispose(resize);
@ -5443,12 +5454,12 @@ async function predict5(image25, config3, idx, count2) {
tf10.dispose(blueNorm);
const normalize = tf10.tidy(() => tf10.mul(tf10.sub(grayscale, 0.5), 2));
tf10.dispose(grayscale);
const emotionT = model6?.execute(normalize);
const emotionT = model6 == null ? void 0 : model6.execute(normalize);
lastTime5 = now();
const data = await emotionT.data();
tf10.dispose(emotionT);
for (let i = 0; i < data.length; i++) {
if (data[i] > (config3.face.emotion?.minConfidence || 0))
if (data[i] > (((_b2 = config3.face.emotion) == null ? void 0 : _b2.minConfidence) || 0))
obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });
}
obj.sort((a, b) => b.score - a.score);
@ -5481,12 +5492,13 @@ var irisLandmarks = {
numCoordinates: 76
};
async function load7(config3) {
var _a, _b;
if (env.initial)
model7 = null;
if (!model7) {
model7 = await tf11.loadGraphModel(join(config3.modelBasePath, config3.face.iris?.modelPath || ""));
model7 = await tf11.loadGraphModel(join(config3.modelBasePath, ((_a = config3.face.iris) == null ? void 0 : _a.modelPath) || ""));
if (!model7 || !model7["modelUrl"])
log("load model failed:", config3.face.iris?.modelPath);
log("load model failed:", (_b = config3.face.iris) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", model7["modelUrl"]);
} else if (config3.debug)
@ -5603,8 +5615,9 @@ var skipped7 = Number.MAX_SAFE_INTEGER;
var lastTime6 = 0;
var enlargeFact = 1.6;
async function predict6(input, config3) {
const skipTime = (config3.face.detector?.skipTime || 0) > now() - lastTime6;
const skipFrame = skipped7 < (config3.face.detector?.skipFrames || 0);
var _a, _b, _c, _d, _e, _f, _g, _h;
const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime6;
const skipFrame = skipped7 < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0);
if (!config3.skipAllowed || !skipTime || !skipFrame || boxCache.length === 0) {
const possibleBoxes = await getBoxes(input, config3);
lastTime6 = now();
@ -5641,14 +5654,14 @@ async function predict6(input, config3) {
faceScore: 0,
annotations: {}
};
if (config3.face.detector?.rotation && config3.face.mesh?.enabled && env.kernels.includes("rotatewithoffset")) {
if (((_c = config3.face.detector) == null ? void 0 : _c.rotation) && ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) && env.kernels.includes("rotatewithoffset")) {
[angle, rotationMatrix, face5.tensor] = correctFaceRotation(box4, input, inputSize5);
} else {
rotationMatrix = fixedRotationMatrix;
face5.tensor = cutBoxFromImageAndResize(box4, input, config3.face.mesh?.enabled ? [inputSize5, inputSize5] : [size(), size()]);
face5.tensor = cutBoxFromImageAndResize(box4, input, ((_e = config3.face.mesh) == null ? void 0 : _e.enabled) ? [inputSize5, inputSize5] : [size(), size()]);
}
face5.boxScore = Math.round(100 * box4.confidence) / 100;
if (!config3.face.mesh?.enabled) {
if (!((_f = config3.face.mesh) == null ? void 0 : _f.enabled)) {
face5.box = getClampedBox(box4, input);
face5.boxRaw = getRawBox(box4, input);
face5.boxScore = Math.round(100 * box4.confidence || 0) / 100;
@ -5670,10 +5683,10 @@ async function predict6(input, config3) {
const coordsReshaped = tf12.reshape(contourCoords, [-1, 3]);
let rawCoords = await coordsReshaped.array();
tf12.dispose([contourCoords, coordsReshaped, confidence, contours]);
if (face5.faceScore < (config3.face.detector?.minConfidence || 1)) {
if (face5.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) {
box4.confidence = face5.faceScore;
} else {
if (config3.face.iris?.enabled)
if ((_h = config3.face.iris) == null ? void 0 : _h.enabled)
rawCoords = await augmentIris(rawCoords, face5.tensor, config3, inputSize5);
face5.mesh = transformRawCoords(rawCoords, box4, angle, rotationMatrix, inputSize5);
face5.meshRaw = face5.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize5]);
@ -5692,12 +5705,13 @@ async function predict6(input, config3) {
return faces;
}
async function load8(config3) {
var _a, _b;
if (env.initial)
model8 = null;
if (!model8) {
model8 = await tf12.loadGraphModel(join(config3.modelBasePath, config3.face.mesh?.modelPath || ""));
model8 = await tf12.loadGraphModel(join(config3.modelBasePath, ((_a = config3.face.mesh) == null ? void 0 : _a.modelPath) || ""));
if (!model8 || !model8["modelUrl"])
log("load model failed:", config3.face.mesh?.modelPath);
log("load model failed:", (_b = config3.face.mesh) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", model8["modelUrl"]);
} else if (config3.debug)
@ -5718,13 +5732,14 @@ var lastTime7 = 0;
var lastCount3 = 0;
var skipped8 = Number.MAX_SAFE_INTEGER;
async function load9(config3) {
const modelUrl = join(config3.modelBasePath, config3.face.description?.modelPath || "");
var _a, _b;
const modelUrl = join(config3.modelBasePath, ((_a = config3.face.description) == null ? void 0 : _a.modelPath) || "");
if (env.initial)
model9 = null;
if (!model9) {
model9 = await tf13.loadGraphModel(modelUrl);
if (!model9)
log("load model failed:", config3.face.description?.modelPath || "");
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)
@ -5736,7 +5751,7 @@ function enhance(input) {
const tensor3 = input.image || input.tensor || input;
if (!(tensor3 instanceof tf13.Tensor))
return null;
if (!model9?.inputs[0].shape)
if (!(model9 == null ? void 0 : model9.inputs[0].shape))
return null;
const crop2 = tf13.image.resizeBilinear(tensor3, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false);
const norm = tf13.mul(crop2, 255);
@ -5745,31 +5760,33 @@ function enhance(input) {
return image25;
}
async function predict7(image25, config3, idx, count2) {
var _a, _b, _c, _d;
if (!model9)
return null;
const skipFrame = skipped8 < (config3.face.description?.skipFrames || 0);
const skipTime = (config3.face.description?.skipTime || 0) > now() - lastTime7;
if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && last3[idx]?.age && last3[idx]?.age > 0) {
const skipFrame = skipped8 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0);
const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime7;
if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && ((_c = last3[idx]) == null ? void 0 : _c.age) && ((_d = last3[idx]) == null ? void 0 : _d.age) > 0) {
skipped8++;
return last3[idx];
}
skipped8 = 0;
return new Promise(async (resolve) => {
var _a2, _b2;
const obj = {
age: 0,
gender: "unknown",
genderScore: 0,
descriptor: []
};
if (config3.face.description?.enabled) {
if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) {
const enhanced = enhance(image25);
const resT = model9?.execute(enhanced);
const resT = model9 == null ? void 0 : model9.execute(enhanced);
lastTime7 = now();
tf13.dispose(enhanced);
const genderT = await resT.find((t) => t.shape[1] === 1);
const gender = await genderT.data();
const confidence = Math.trunc(200 * Math.abs(gender[0] - 0.5)) / 100;
if (confidence > (config3.face.description?.minConfidence || 0)) {
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);
}
@ -9529,22 +9546,23 @@ async function predict8(input, config3) {
return hands;
}
async function load10(config3) {
var _a, _b, _c, _d, _e, _f;
if (env.initial) {
handDetectorModel = null;
handPoseModel = null;
}
if (!handDetectorModel || !handPoseModel) {
[handDetectorModel, handPoseModel] = await Promise.all([
config3.hand.enabled ? tf17.loadGraphModel(join(config3.modelBasePath, config3.hand.detector?.modelPath || ""), { fromTFHub: (config3.hand.detector?.modelPath || "").includes("tfhub.dev") }) : null,
config3.hand.landmarks ? tf17.loadGraphModel(join(config3.modelBasePath, config3.hand.skeleton?.modelPath || ""), { fromTFHub: (config3.hand.skeleton?.modelPath || "").includes("tfhub.dev") }) : null
config3.hand.enabled ? tf17.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 ? tf17.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
]);
if (config3.hand.enabled) {
if (!handDetectorModel || !handDetectorModel["modelUrl"])
log("load model failed:", config3.hand.detector?.modelPath || "");
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:", config3.hand.skeleton?.modelPath || "");
log("load model failed:", ((_f = config3.hand.skeleton) == null ? void 0 : _f.modelPath) || "");
else if (config3.debug)
log("load model:", handPoseModel["modelUrl"]);
}
@ -9619,16 +9637,17 @@ var fingerMap = {
palm: [0]
};
async function loadDetect2(config3) {
var _a, _b;
if (env.initial)
models2[0] = null;
if (!models2[0]) {
fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3);
models2[0] = await tf18.loadGraphModel(join(config3.modelBasePath, config3.hand.detector?.modelPath || ""));
models2[0] = await tf18.loadGraphModel(join(config3.modelBasePath, ((_a = config3.hand.detector) == null ? void 0 : _a.modelPath) || ""));
const inputs = Object.values(models2[0].modelSignature["inputs"]);
inputSize6[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
inputSize6[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
if (!models2[0] || !models2[0]["modelUrl"])
log("load model failed:", config3.hand.detector?.modelPath);
log("load model failed:", (_b = config3.hand.detector) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", models2[0]["modelUrl"]);
} else if (config3.debug)
@ -9636,15 +9655,16 @@ async function loadDetect2(config3) {
return models2[0];
}
async function loadSkeleton(config3) {
var _a, _b;
if (env.initial)
models2[1] = null;
if (!models2[1]) {
models2[1] = await tf18.loadGraphModel(join(config3.modelBasePath, config3.hand.skeleton?.modelPath || ""));
models2[1] = await tf18.loadGraphModel(join(config3.modelBasePath, ((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath) || ""));
const inputs = Object.values(models2[1].modelSignature["inputs"]);
inputSize6[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
inputSize6[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
if (!models2[1] || !models2[1]["modelUrl"])
log("load model failed:", config3.hand.skeleton?.modelPath);
log("load model failed:", (_b = config3.hand.skeleton) == null ? void 0 : _b.modelPath);
else if (config3.debug)
log("load model:", models2[1]["modelUrl"]);
} else if (config3.debug)
@ -9737,7 +9757,8 @@ async function detectFingers(input, h, config3) {
return hand3;
}
async function predict9(input, config3) {
if (!models2[0] || !models2[1] || !models2[0]?.inputs[0].shape || !models2[1]?.inputs[0].shape)
var _a, _b;
if (!models2[0] || !models2[1] || !((_a = models2[0]) == null ? void 0 : _a.inputs[0].shape) || !((_b = models2[1]) == null ? void 0 : _b.inputs[0].shape))
return [];
outputSize = [input.shape[2] || 0, input.shape[1] || 0];
skipped9++;
@ -10052,7 +10073,7 @@ async function parseMultiPose(res, config3, image25, inputBox) {
return bodies;
}
async function predict10(input, config3) {
if (!model10 || !model10?.inputs[0].shape)
if (!model10 || !(model10 == null ? void 0 : model10.inputs[0].shape))
return [];
if (!config3.skipAllowed)
cache5.boxes.length = 0;
@ -10066,7 +10087,7 @@ async function predict10(input, config3) {
const t = {};
skipped10 = 0;
t.input = padInput(input, inputSize7);
t.res = model10?.execute(t.input);
t.res = model10 == null ? void 0 : model10.execute(t.input);
cache5.last = now();
const res = await t.res.array();
cache5.bodies = t.res.shape[2] === 17 ? await parseSinglePose(res, config3, input, [0, 0, 1, 1]) : await parseMultiPose(res, config3, input, [0, 0, 1, 1]);
@ -10106,9 +10127,10 @@ async function process4(res, inputSize8, outputShape, config3) {
let results = [];
for (const strideSize of [1, 2, 4]) {
tf21.tidy(async () => {
var _a, _b;
const baseSize = strideSize * 13;
const scoresT = res.find((a) => a.shape[1] === baseSize ** 2 && a.shape[2] === labels.length)?.squeeze();
const featuresT = res.find((a) => a.shape[1] === baseSize ** 2 && a.shape[2] < labels.length)?.squeeze();
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();
@ -10468,7 +10490,8 @@ function buildPartWithScoreQueue(minConfidence2, scores) {
}
function withinRadius(poses, { x, y }, keypointId) {
return poses.some(({ keypoints }) => {
const correspondingKeypoint = keypoints[keypointId]?.position;
var _a;
const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position;
if (!correspondingKeypoint)
return false;
return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;
@ -10547,14 +10570,15 @@ async function load14(config3) {
return model13;
}
async function process5(input, background, config3) {
var _a, _b;
if (busy)
return { data: [], canvas: null, alpha: null };
busy = true;
if (!model13)
await load14(config3);
const inputImage = process2(input, config3);
const width = inputImage.canvas?.width || 0;
const height = inputImage.canvas?.height || 0;
const width = ((_a = inputImage.canvas) == null ? void 0 : _a.width) || 0;
const height = ((_b = inputImage.canvas) == null ? void 0 : _b.height) || 0;
if (!inputImage.tensor)
return { data: [], canvas: null, alpha: null };
const t = {};
@ -10645,49 +10669,50 @@ function reset(instance) {
instance.models[model14] = null;
}
async function load15(instance) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A, _B, _C, _D, _E;
if (env.initial)
reset(instance);
if (instance.config.hand.enabled) {
if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes("handdetect"))
if (!instance.models.handpose && ((_b = (_a = instance.config.hand.detector) == null ? void 0 : _a.modelPath) == null ? void 0 : _b.includes("handdetect")))
[instance.models.handpose, instance.models.handskeleton] = await load10(instance.config);
if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes("handdetect"))
if (!instance.models.handskeleton && instance.config.hand.landmarks && ((_d = (_c = instance.config.hand.detector) == null ? void 0 : _c.modelPath) == null ? void 0 : _d.includes("handdetect")))
[instance.models.handpose, instance.models.handskeleton] = await load10(instance.config);
}
if (instance.config.face.enabled && !instance.models.facedetect)
instance.models.facedetect = load3(instance.config);
if (instance.config.face.enabled && instance.config.face.mesh?.enabled && !instance.models.facemesh)
if (instance.config.face.enabled && ((_e = instance.config.face.mesh) == null ? void 0 : _e.enabled) && !instance.models.facemesh)
instance.models.facemesh = load8(instance.config);
if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.models.faceiris)
if (instance.config.face.enabled && ((_f = instance.config.face.iris) == null ? void 0 : _f.enabled) && !instance.models.faceiris)
instance.models.faceiris = load7(instance.config);
if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof)
if (instance.config.face.enabled && ((_g = instance.config.face.antispoof) == null ? void 0 : _g.enabled) && !instance.models.antispoof)
instance.models.antispoof = load2(instance.config);
if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes("handtrack"))
if (instance.config.hand.enabled && !instance.models.handtrack && ((_i = (_h = instance.config.hand.detector) == null ? void 0 : _h.modelPath) == null ? void 0 : _i.includes("handtrack")))
instance.models.handtrack = loadDetect2(instance.config);
if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes("handtrack"))
if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && ((_k = (_j = instance.config.hand.detector) == null ? void 0 : _j.modelPath) == null ? void 0 : _k.includes("handtrack")))
instance.models.handskeleton = loadSkeleton(instance.config);
if (instance.config.body.enabled && !instance.models.posenet && instance.config.body?.modelPath?.includes("posenet"))
if (instance.config.body.enabled && !instance.models.posenet && ((_m = (_l = instance.config.body) == null ? void 0 : _l.modelPath) == null ? void 0 : _m.includes("posenet")))
instance.models.posenet = load13(instance.config);
if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes("efficientpose"))
if (instance.config.body.enabled && !instance.models.efficientpose && ((_o = (_n = instance.config.body) == null ? void 0 : _n.modelPath) == null ? void 0 : _o.includes("efficientpose")))
instance.models.efficientpose = load5(instance.config);
if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body?.modelPath?.includes("blazepose"))
if (instance.config.body.enabled && !instance.models.blazepose && ((_q = (_p = instance.config.body) == null ? void 0 : _p.modelPath) == null ? void 0 : _q.includes("blazepose")))
instance.models.blazepose = loadPose(instance.config);
if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body.detector?.modelPath && instance.config.body?.modelPath?.includes("blazepose"))
if (instance.config.body.enabled && !instance.models.blazeposedetect && ((_r = instance.config.body.detector) == null ? void 0 : _r.modelPath) && ((_t = (_s = instance.config.body) == null ? void 0 : _s.modelPath) == null ? void 0 : _t.includes("blazepose")))
instance.models.blazeposedetect = loadDetect(instance.config);
if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes("efficientpose"))
if (instance.config.body.enabled && !instance.models.efficientpose && ((_v = (_u = instance.config.body) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("efficientpose")))
instance.models.efficientpose = load5(instance.config);
if (instance.config.body.enabled && !instance.models.movenet && instance.config.body?.modelPath?.includes("movenet"))
if (instance.config.body.enabled && !instance.models.movenet && ((_x = (_w = instance.config.body) == null ? void 0 : _w.modelPath) == null ? void 0 : _x.includes("movenet")))
instance.models.movenet = load11(instance.config);
if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object?.modelPath?.includes("nanodet"))
if (instance.config.object.enabled && !instance.models.nanodet && ((_z = (_y = instance.config.object) == null ? void 0 : _y.modelPath) == null ? void 0 : _z.includes("nanodet")))
instance.models.nanodet = load12(instance.config);
if (instance.config.object.enabled && !instance.models.centernet && instance.config.object?.modelPath?.includes("centernet"))
if (instance.config.object.enabled && !instance.models.centernet && ((_B = (_A = instance.config.object) == null ? void 0 : _A.modelPath) == null ? void 0 : _B.includes("centernet")))
instance.models.centernet = load4(instance.config);
if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion)
if (instance.config.face.enabled && ((_C = instance.config.face.emotion) == null ? void 0 : _C.enabled) && !instance.models.emotion)
instance.models.emotion = load6(instance.config);
if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres)
if (instance.config.face.enabled && ((_D = instance.config.face.description) == null ? void 0 : _D.enabled) && !instance.models.faceres)
instance.models.faceres = load9(instance.config);
if (instance.config.segmentation.enabled && !instance.models.segmentation)
instance.models.segmentation = load14(instance.config);
if (instance.config.face.enabled && instance.config.face["agegenderrace"]?.enabled && !instance.models.agegenderrace)
if (instance.config.face.enabled && ((_E = instance.config.face["agegenderrace"]) == null ? void 0 : _E.enabled) && !instance.models.agegenderrace)
instance.models.agegenderrace = load(instance.config);
for await (const model14 of Object.keys(instance.models)) {
if (instance.models[model14] && typeof instance.models[model14] !== "undefined")
@ -10711,7 +10736,7 @@ async function validate2(instance) {
continue;
}
const ops = [];
const executor = model14?.executor;
const executor = model14 == null ? void 0 : model14.executor;
if (executor && executor.graph.nodes) {
for (const kernel of Object.values(executor.graph.nodes)) {
const op = kernel.op.toLowerCase();
@ -10760,6 +10785,7 @@ function extensions() {
config2.extensions = gl.getSupportedExtensions();
}
async function register(instance) {
var _a;
if (instance.config.backend !== "humangl")
return;
if (config2.name in tf24.engine().registry && (!config2.gl || !config2.gl.getParameter(config2.gl.VERSION))) {
@ -10774,7 +10800,7 @@ async function register(instance) {
return;
}
try {
config2.gl = config2.canvas?.getContext("webgl2", config2.webGLattr);
config2.gl = (_a = config2.canvas) == null ? void 0 : _a.getContext("webgl2", config2.webGLattr);
if (config2.canvas) {
config2.canvas.addEventListener("webglcontextlost", async (e) => {
log("error: humangl:", e.type);
@ -10881,7 +10907,7 @@ async function check(instance, force = false) {
if (instance.config.backend === "wasm") {
if (instance.config.debug)
log("wasm path:", instance.config.wasmPath);
if (typeof tf25?.setWasmPaths !== "undefined")
if (typeof (tf25 == null ? void 0 : tf25.setWasmPaths) !== "undefined")
await tf25.setWasmPaths(instance.config.wasmPath);
else
throw new Error("wasm backend is not loaded");
@ -11082,6 +11108,7 @@ async function gesture(inCanvas2, result, drawOptions) {
}
}
async function face(inCanvas2, result, drawOptions) {
var _a, _b, _c, _d, _e;
const localOptions = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
@ -11171,7 +11198,7 @@ async function face(inCanvas2, result, drawOptions) {
ctx.fill();
}
}
if (localOptions.drawGaze && f.rotation?.angle) {
if (localOptions.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.angle)) {
ctx.strokeStyle = "pink";
const valX = f.box[0] + f.box[2] / 2 - f.box[3] * rad2deg(f.rotation.angle.yaw) / 90;
const valY = f.box[1] + f.box[3] / 2 + f.box[2] * rad2deg(f.rotation.angle.pitch) / 90;
@ -11192,7 +11219,7 @@ async function face(inCanvas2, result, drawOptions) {
ctx.stroke(pathH);
ctx.stroke(pathV);
}
if (localOptions.drawGaze && f.rotation?.gaze?.strength && f.rotation?.gaze?.bearing && f.annotations["leftEyeIris"] && f.annotations["rightEyeIris"] && f.annotations["leftEyeIris"][0] && f.annotations["rightEyeIris"][0]) {
if (localOptions.drawGaze && ((_c = (_b = f.rotation) == null ? void 0 : _b.gaze) == null ? void 0 : _c.strength) && ((_e = (_d = f.rotation) == null ? void 0 : _d.gaze) == null ? void 0 : _e.bearing) && f.annotations["leftEyeIris"] && f.annotations["rightEyeIris"] && f.annotations["leftEyeIris"][0] && f.annotations["rightEyeIris"][0]) {
ctx.strokeStyle = "pink";
ctx.fillStyle = "pink";
const leftGaze = [
@ -11211,6 +11238,7 @@ async function face(inCanvas2, result, drawOptions) {
}
}
async function body(inCanvas2, result, drawOptions) {
var _a;
const localOptions = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
@ -11221,7 +11249,7 @@ async function body(inCanvas2, result, drawOptions) {
ctx.fillStyle = localOptions.color;
ctx.lineWidth = localOptions.lineWidth;
ctx.font = localOptions.font;
if (localOptions.drawBoxes && result[i].box && result[i].box?.length === 4) {
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 !== "") {
@ -11503,6 +11531,7 @@ var calculateFaceAngle = (face5, imageSize) => {
// src/face/face.ts
var detectFace = async (parent, input) => {
var _a, _b, _c, _d;
let timeStamp;
let ageRes;
let gearRes;
@ -11561,7 +11590,7 @@ var detectFace = async (parent, input) => {
[ageRes, genderRes, emotionRes, embeddingRes, descRes, gearRes, antispoofRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes, gearRes, antispoofRes]);
}
parent.analyze("Finish Face:");
if (!parent.config.face.iris.enabled && faces[i]?.annotations?.leftEyeIris && faces[i]?.annotations?.rightEyeIris) {
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;
}
@ -11573,10 +11602,10 @@ var detectFace = async (parent, input) => {
faceRes.push({
...faces[i],
id: i,
age: descRes?.age,
gender: descRes?.gender,
genderScore: descRes?.genderScore,
embedding: descRes?.descriptor,
age: descRes == null ? void 0 : descRes.age,
gender: descRes == null ? void 0 : descRes.gender,
genderScore: descRes == null ? void 0 : descRes.genderScore,
embedding: descRes == null ? void 0 : descRes.descriptor,
emotion: emotionRes,
real: antispoofRes,
iris: irisSize !== 0 ? Math.trunc(500 / irisSize / 11.7) / 100 : 0,
@ -11722,6 +11751,7 @@ var hand2 = (res) => {
var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0 };
var interpolateTime = 0;
function calc2(newResult, config3) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A;
const t0 = now();
if (!newResult)
return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0 };
@ -11748,11 +11778,11 @@ function calc2(newResult, config3) {
}));
const annotations2 = {};
let coords8 = { connected: {} };
if (config3.body?.modelPath?.includes("efficientpose"))
if ((_b = (_a = config3.body) == null ? void 0 : _a.modelPath) == null ? void 0 : _b.includes("efficientpose"))
coords8 = efficientposecoords_exports;
else if (config3.body?.modelPath?.includes("blazepose"))
else if ((_d = (_c = config3.body) == null ? void 0 : _c.modelPath) == null ? void 0 : _d.includes("blazepose"))
coords8 = blazeposecoords_exports;
else if (config3.body?.modelPath?.includes("movenet"))
else if ((_f = (_e = config3.body) == null ? void 0 : _e.modelPath) == null ? void 0 : _f.includes("movenet"))
coords8 = movenetcoords_exports;
for (const [name, indexes] of Object.entries(coords8.connected)) {
const pt = [];
@ -11795,15 +11825,15 @@ function calc2(newResult, config3) {
const box4 = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor);
const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor);
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 = newResult.face[i].rotation?.matrix;
rotation.matrix = (_g = newResult.face[i].rotation) == null ? void 0 : _g.matrix;
rotation.angle = {
roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,
yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,
pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor
roll: ((bufferedFactor - 1) * (((_i = (_h = bufferedResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k = (_j = newResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor,
yaw: ((bufferedFactor - 1) * (((_m = (_l = bufferedResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.yaw) || 0) + (((_o = (_n = newResult.face[i].rotation) == null ? void 0 : _n.angle) == null ? void 0 : _o.yaw) || 0)) / bufferedFactor,
pitch: ((bufferedFactor - 1) * (((_q = (_p = bufferedResult.face[i].rotation) == null ? void 0 : _p.angle) == null ? void 0 : _q.pitch) || 0) + (((_s = (_r = newResult.face[i].rotation) == null ? void 0 : _r.angle) == null ? void 0 : _s.pitch) || 0)) / bufferedFactor
};
rotation.gaze = {
bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.bearing || 0) + (newResult.face[i].rotation?.gaze?.bearing || 0)) / bufferedFactor,
strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.strength || 0) + (newResult.face[i].rotation?.gaze?.strength || 0)) / bufferedFactor
bearing: ((bufferedFactor - 1) * (((_u = (_t = bufferedResult.face[i].rotation) == null ? void 0 : _t.gaze) == null ? void 0 : _u.bearing) || 0) + (((_w = (_v = newResult.face[i].rotation) == null ? void 0 : _v.gaze) == null ? void 0 : _w.bearing) || 0)) / bufferedFactor,
strength: ((bufferedFactor - 1) * (((_y = (_x = bufferedResult.face[i].rotation) == null ? void 0 : _x.gaze) == null ? void 0 : _y.strength) || 0) + (((_A = (_z = newResult.face[i].rotation) == null ? void 0 : _z.gaze) == null ? void 0 : _A.strength) || 0)) / bufferedFactor
};
bufferedResult.face[i] = { ...newResult.face[i], rotation, box: box4, boxRaw };
}
@ -11872,6 +11902,7 @@ function match2(descriptor, descriptors, options3 = { order: 2, multiplier: 20,
// src/util/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) {
@ -11895,15 +11926,15 @@ function join2(faces, bodies, hands, gestures, shape) {
}
for (const gesture3 of gestures) {
if (gesture3["face"] !== void 0 && gesture3["face"] === face5.id)
person2.gestures?.push(gesture3);
(_a = person2.gestures) == null ? void 0 : _a.push(gesture3);
else if (gesture3["iris"] !== void 0 && gesture3["iris"] === face5.id)
person2.gestures?.push(gesture3);
else if (gesture3["body"] !== void 0 && gesture3["body"] === person2.body?.id)
person2.gestures?.push(gesture3);
else if (gesture3["hand"] !== void 0 && gesture3["hand"] === person2.hands?.left?.id)
person2.gestures?.push(gesture3);
else if (gesture3["hand"] !== void 0 && gesture3["hand"] === person2.hands?.right?.id)
person2.gestures?.push(gesture3);
(_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 = [];
@ -11913,10 +11944,10 @@ function join2(faces, bodies, hands, gestures, shape) {
y.push(box4[1], box4[1] + box4[3]);
}
};
extractXY(person2.face?.box);
extractXY(person2.body?.box);
extractXY(person2.hands?.left?.box);
extractXY(person2.hands?.right?.box);
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];
@ -12798,7 +12829,7 @@ var Human = class {
return "input must be a tensor";
try {
this.tf.getBackend();
} catch {
} catch (e) {
return "backend not loaded";
}
return null;
@ -12807,8 +12838,9 @@ var Human = class {
__publicField(this, "distance", distance);
__publicField(this, "match", match2);
__publicField(this, "emit", (event) => {
var _a;
if (this.events && this.events.dispatchEvent)
this.events?.dispatchEvent(new Event(event));
(_a = this.events) == null ? void 0 : _a.dispatchEvent(new Event(event));
});
this.env = env;
config.wasmPath = tf28.version_core.includes("-") ? "https://vladmandic.github.io/tfjs/dist/" : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf28.version_core}/dist/`;
@ -12919,6 +12951,7 @@ var Human = class {
async detect(input, userConfig) {
this.state = "detect";
return new Promise(async (resolve) => {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v;
this.state = "config";
let timeStamp;
this.config = mergeDeep(this.config, userConfig);
@ -12975,25 +13008,25 @@ var Human = class {
this.state = "detect:body";
const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config;
if (this.config.async) {
if (this.config.body.modelPath?.includes("posenet"))
if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet"))
bodyRes = this.config.body.enabled ? predict12(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("blazepose"))
else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose"))
bodyRes = this.config.body.enabled ? predict2(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("efficientpose"))
else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose"))
bodyRes = this.config.body.enabled ? predict4(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("movenet"))
else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet"))
bodyRes = this.config.body.enabled ? predict10(img.tensor, bodyConfig) : [];
if (this.performance.body)
delete this.performance.body;
} else {
timeStamp = now();
if (this.config.body.modelPath?.includes("posenet"))
if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet"))
bodyRes = this.config.body.enabled ? await predict12(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("blazepose"))
else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose"))
bodyRes = this.config.body.enabled ? await predict2(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("efficientpose"))
else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose"))
bodyRes = this.config.body.enabled ? await predict4(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes("movenet"))
else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet"))
bodyRes = this.config.body.enabled ? await predict10(img.tensor, bodyConfig) : [];
this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
@ -13002,17 +13035,17 @@ var Human = class {
this.state = "detect:hand";
const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config;
if (this.config.async) {
if (this.config.hand.detector?.modelPath?.includes("handdetect"))
if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect"))
handRes = this.config.hand.enabled ? predict8(img.tensor, handConfig) : [];
else if (this.config.hand.detector?.modelPath?.includes("handtrack"))
else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack"))
handRes = this.config.hand.enabled ? predict9(img.tensor, handConfig) : [];
if (this.performance.hand)
delete this.performance.hand;
} else {
timeStamp = now();
if (this.config.hand.detector?.modelPath?.includes("handdetect"))
if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect"))
handRes = this.config.hand.enabled ? await predict8(img.tensor, handConfig) : [];
else if (this.config.hand.detector?.modelPath?.includes("handtrack"))
else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack"))
handRes = this.config.hand.enabled ? await predict9(img.tensor, handConfig) : [];
this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
@ -13020,17 +13053,17 @@ var Human = class {
this.analyze("Start Object:");
this.state = "detect:object";
if (this.config.async) {
if (this.config.object.modelPath?.includes("nanodet"))
if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet"))
objectRes = this.config.object.enabled ? predict11(img.tensor, this.config) : [];
else if (this.config.object.modelPath?.includes("centernet"))
else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet"))
objectRes = this.config.object.enabled ? predict3(img.tensor, this.config) : [];
if (this.performance.object)
delete this.performance.object;
} else {
timeStamp = now();
if (this.config.object.modelPath?.includes("nanodet"))
if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet"))
objectRes = this.config.object.enabled ? await predict11(img.tensor, this.config) : [];
else if (this.config.object.modelPath?.includes("centernet"))
else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet"))
objectRes = this.config.object.enabled ? await predict3(img.tensor, this.config) : [];
this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
@ -13049,7 +13082,7 @@ var Human = class {
delete this.performance.gesture;
}
this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);
const shape = this.process?.tensor?.shape || [];
const shape = ((_v = (_u = this.process) == null ? void 0 : _u.tensor) == null ? void 0 : _v.shape) || [];
this.result = {
face: faceRes,
body: bodyRes,

10
dist/tfjs.esm.js vendored
View File

@ -6395,7 +6395,7 @@ var Tensor = class {
const bytes = await data;
try {
return bytes.map((b) => decodeString(b));
} catch {
} catch (e) {
throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
}
}
@ -6407,7 +6407,7 @@ var Tensor = class {
if (this.dtype === "string") {
try {
return data.map((b) => decodeString(b));
} catch {
} catch (e) {
throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
}
}
@ -38826,7 +38826,7 @@ var _MathBackendCPU = class extends KernelBackend {
if (t.dtype === "string") {
try {
decodedData = data.map((d) => util_exports.decodeString(d));
} catch {
} catch (e) {
throw new Error("Failed to decode encoded string bytes into utf-8");
}
}
@ -49188,7 +49188,7 @@ var _MathBackendWebGL = class extends KernelBackend {
if (t.dtype === "string") {
try {
decodedData = data.map((d) => util_exports.decodeString(d));
} catch {
} catch (e) {
throw new Error("Failed to decode encoded string bytes into utf-8");
}
}
@ -66376,7 +66376,7 @@ var _WebGPUBackend = class extends KernelBackend {
if (t.dtype === "string") {
try {
decodedData = data.map((d) => util_exports.decodeString(d));
} catch {
} catch (e) {
throw new Error("Failed to decode encoded string bytes into utf-8");
}
}

2
src/face/facemesh.ts
View File

@ -108,7 +108,7 @@ export async function predict(input: Tensor, config: Config): Promise<FaceResult
face.score = face.faceScore;
newCache.push(box);
// other modules prefer wider crop for a face so we dispose it and do it again
// other modules prefer different crop for a face so we dispose it and do it again
/*
tf.dispose(face.tensor);
face.tensor = config.face.detector?.rotation && config.face.mesh?.enabled && env.kernels.includes('rotatewithoffset')

10
src/image/image.ts
View File

@ -107,7 +107,7 @@ export function process(input: Input, config: Config, getTensor: boolean = true)
if ((config.filter.height || 0) > 0) targetHeight = config.filter.height;
else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);
if (!targetWidth || !targetHeight) throw new Error('input cannot determine dimension');
if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);
if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);
// draw input to our canvas
const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;
@ -117,14 +117,14 @@ export function process(input: Input, config: Config, getTensor: boolean = true)
if (config.filter.flip && typeof inCtx.translate !== 'undefined') {
inCtx.translate(originalWidth, 0);
inCtx.scale(-1, 1);
inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);
inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults
} else {
inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);
inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
}
}
if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas
if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas
// imagefx transforms using gl from input canvas to output canvas
if (config.filter.enabled && env.webgl.supported) {
@ -170,7 +170,7 @@ export function process(input: Input, config: Config, getTensor: boolean = true)
pixels = tf.tensor(arr, [input['height'], input['width'], depth], 'int32');
}
} else {
if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas?.height !== tmpCanvas?.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas
if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas
if (tf.browser && env.browser) {
if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {
pixels = tf.browser.fromPixels(outCanvas); // safe to reuse since both backend and context are gl based

28
src/image/imagefx.ts
View File

@ -85,11 +85,11 @@ export function GLImageFilter() {
}
function createFramebufferTexture(width, height) {
const fbo = gl.createFramebuffer();
const fbo = gl.createFramebuffer() as WebGLFramebuffer;
gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);
const renderbuffer = gl.createRenderbuffer();
gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);
const texture = gl.createTexture();
const texture = gl.createTexture() as WebGLTexture;
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);
@ -104,7 +104,7 @@ export function GLImageFilter() {
function getTempFramebuffer(index) {
tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);
return tempFramebuffers[index];
return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };
}
function draw(flags = 0) {
@ -113,14 +113,14 @@ export function GLImageFilter() {
let target: WebGLFramebuffer | null = null;
let flipY = false;
if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture
else source = getTempFramebuffer(currentFramebufferIndex)?.texture || null; // All following draw calls use the temp buffer last drawn to
else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to
drawCount++;
if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now
target = null;
flipY = drawCount % 2 === 0;
} else {
currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;
target = getTempFramebuffer(currentFramebufferIndex)?.fbo || null; // Intermediate draw call - get a temp buffer to draw to
target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to
}
gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles
gl.bindFramebuffer(gl.FRAMEBUFFER, target);
@ -131,8 +131,8 @@ export function GLImageFilter() {
function compileShader(fragmentSource) {
if (shaderProgramCache[fragmentSource]) {
currentProgram = shaderProgramCache[fragmentSource];
gl.useProgram(currentProgram?.id || null);
return currentProgram;
gl.useProgram((currentProgram ? currentProgram.id : null) || null);
return currentProgram as GLProgram;
}
currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);
const floatSize = Float32Array.BYTES_PER_ELEMENT;
@ -142,7 +142,7 @@ export function GLImageFilter() {
gl.enableVertexAttribArray(currentProgram.attribute['uv']);
gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);
shaderProgramCache[fragmentSource] = currentProgram;
return currentProgram;
return currentProgram as GLProgram;
}
const filter = {
@ -156,7 +156,7 @@ export function GLImageFilter() {
? shaders.colorMatrixWithoutAlpha
: shaders.colorMatrixWithAlpha;
const program = compileShader(shader);
gl.uniform1fv(program?.uniform['m'], m);
gl.uniform1fv(program.uniform['m'], m);
draw();
},
@ -292,8 +292,8 @@ export function GLImageFilter() {
const pixelSizeX = 1 / fxcanvas.width;
const pixelSizeY = 1 / fxcanvas.height;
const program = compileShader(shaders.convolution);
gl.uniform1fv(program?.uniform['m'], m);
gl.uniform2f(program?.uniform['px'], pixelSizeX, pixelSizeY);
gl.uniform1fv(program.uniform['m'], m);
gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);
draw();
},
@ -349,10 +349,10 @@ export function GLImageFilter() {
const blurSizeY = (size / 7) / fxcanvas.height;
const program = compileShader(shaders.blur);
// Vertical
gl.uniform2f(program?.uniform['px'], 0, blurSizeY);
gl.uniform2f(program.uniform['px'], 0, blurSizeY);
draw(DRAW.INTERMEDIATE);
// Horizontal
gl.uniform2f(program?.uniform['px'], blurSizeX, 0);
gl.uniform2f(program.uniform['px'], blurSizeX, 0);
draw();
},
@ -360,7 +360,7 @@ export function GLImageFilter() {
const blurSizeX = (size) / fxcanvas.width;
const blurSizeY = (size) / fxcanvas.height;
const program = compileShader(shaders.pixelate);
gl.uniform2f(program?.uniform['size'], blurSizeX, blurSizeY);
gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);
draw();
},
};

4
src/util/env.ts
View File

@ -134,14 +134,14 @@ export class Env {
}
this.webgpu.supported = this.browser && typeof navigator['gpu'] !== 'undefined';
this.webgpu.backend = this.backends.includes('webgpu');
if (this.webgpu.supported) this.webgpu.adapter = (await navigator['gpu'].requestAdapter())?.name;
if (this.webgpu.supported) this.webgpu.adapter = (await navigator['gpu'].requestAdapter()).name;
// enumerate kernels
this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());
}
async updateCPU() {
const cpu = { model: '', flags: [] };
if (this.node && this.platform?.startsWith('linux')) {
if (this.node && this.platform.startsWith('linux')) {
// eslint-disable-next-line global-require
const fs = require('fs');
try {

50
test/build.log
View File

@ -1,25 +1,25 @@
2021-11-03 16:30:50 INFO:  @vladmandic/human version 2.5.0
2021-11-03 16:30:50 INFO:  User: vlado Platform: linux Arch: x64 Node: v17.0.1
2021-11-03 16:30:50 INFO:  Application: {"name":"@vladmandic/human","version":"2.5.0"}
2021-11-03 16:30:50 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true}
2021-11-03 16:30:50 INFO:  Toolchain: {"build":"0.6.3","esbuild":"0.13.12","typescript":"4.4.4","typedoc":"0.22.7","eslint":"8.1.0"}
2021-11-03 16:30:50 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]}
2021-11-03 16:30:50 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]}
2021-11-03 16:30:50 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":1275}
2021-11-03 16:30:51 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":55,"inputBytes":516475,"outputBytes":431829}
2021-11-03 16:30:51 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":1283}
2021-11-03 16:30:51 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":55,"inputBytes":516483,"outputBytes":431833}
2021-11-03 16:30:51 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":149,"outputBytes":1350}
2021-11-03 16:30:51 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":55,"inputBytes":516550,"outputBytes":431905}
2021-11-03 16:30:51 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1063,"outputBytes":1652}
2021-11-03 16:30:51 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2323,"outputBytes":973}
2021-11-03 16:30:51 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":55,"inputBytes":516173,"outputBytes":433664}
2021-11-03 16:30:51 STATE: Compile: {"name":"tfjs/browser/esm/custom","format":"esm","platform":"browser","input":"tfjs/tf-custom.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2564340,"outputBytes":2499559}
2021-11-03 16:30:51 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":55,"inputBytes":3014759,"outputBytes":1607128}
2021-11-03 16:30:52 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":55,"inputBytes":3014759,"outputBytes":2935423}
2021-11-03 16:31:13 STATE: Typings: {"input":"src/human.ts","output":"types","files":48}
2021-11-03 16:31:19 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":48,"generated":true}
2021-11-03 16:31:19 STATE: Compile: {"name":"demo/browser","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":3807,"outputBytes":3340}
2021-11-03 16:31:50 STATE: Lint: {"locations":["*.json","src/**/*.ts","test/**/*.js","demo/**/*.js"],"files":92,"errors":0,"warnings":0}
2021-11-03 16:31:51 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"}
2021-11-03 16:31:51 INFO:  Done...
2021-11-04 06:30:52 INFO:  @vladmandic/human version 2.5.0
2021-11-04 06:30:52 INFO:  User: vlado Platform: linux Arch: x64 Node: v17.0.1
2021-11-04 06:30:52 INFO:  Application: {"name":"@vladmandic/human","version":"2.5.0"}
2021-11-04 06:30:52 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true}
2021-11-04 06:30:52 INFO:  Toolchain: {"build":"0.6.3","esbuild":"0.13.12","typescript":"4.4.4","typedoc":"0.22.7","eslint":"8.1.0"}
2021-11-04 06:30:52 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]}
2021-11-04 06:30:52 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]}
2021-11-04 06:30:52 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":1275}
2021-11-04 06:30:52 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":55,"inputBytes":516598,"outputBytes":438074}
2021-11-04 06:30:52 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":1283}
2021-11-04 06:30:52 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":55,"inputBytes":516606,"outputBytes":438078}
2021-11-04 06:30:52 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":149,"outputBytes":1350}
2021-11-04 06:30:52 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":55,"inputBytes":516673,"outputBytes":438150}
2021-11-04 06:30:52 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1063,"outputBytes":1652}
2021-11-04 06:30:52 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2329,"outputBytes":850}
2021-11-04 06:30:53 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":55,"inputBytes":516173,"outputBytes":439686}
2021-11-04 06:30:53 STATE: Compile: {"name":"tfjs/browser/esm/custom","format":"esm","platform":"browser","input":"tfjs/tf-custom.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2564340,"outputBytes":2499579}
2021-11-04 06:30:53 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":55,"inputBytes":3014902,"outputBytes":1611458}
2021-11-04 06:30:54 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":55,"inputBytes":3014902,"outputBytes":2945275}
2021-11-04 06:31:10 STATE: Typings: {"input":"src/human.ts","output":"types","files":48}
2021-11-04 06:31:16 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":48,"generated":true}
2021-11-04 06:31:16 STATE: Compile: {"name":"demo/browser","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":3807,"outputBytes":3340}
2021-11-04 06:31:46 STATE: Lint: {"locations":["*.json","src/**/*.ts","test/**/*.js","demo/**/*.js"],"files":92,"errors":0,"warnings":0}
2021-11-04 06:31:47 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"}
2021-11-04 06:31:47 INFO:  Done...

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test/test.log
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4
tfjs/tf-browser.ts
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@ -5,8 +5,8 @@
// export all from build bundle
export * from '@tensorflow/tfjs/dist/index.js';
export * from '@tensorflow/tfjs-backend-webgl/dist/index.js';
export * from '@tensorflow/tfjs-backend-wasm/dist/index.js';
// export * from '@tensorflow/tfjs-backend-webgl/dist/index.js';
// export * from '@tensorflow/tfjs-backend-wasm/dist/index.js';
// add webgpu to bundle, experimental
// export * from '@tensorflow/tfjs-backend-webgpu/dist/index.js';