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breaking changes to results.face output properties

pull/134/head
Vladimir Mandic 2021-06-01 07:37:17 -04:00
parent 57ad0c886f
commit 6d31b16bde
14 changed files with 508 additions and 464 deletions
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60
dist/human.esm-nobundle.js vendored
View File

@ -4060,6 +4060,7 @@ var facePipeline;
async function predict(input, config3) {
const predictions = await facePipeline.predict(input, config3);
const results = [];
let id = 0;
for (const prediction of predictions || []) {
if (!prediction || prediction.isDisposedInternal)
continue;
@ -4078,23 +4079,25 @@ async function predict(input, config3) {
Math.max(0, prediction.box.startPoint[1]),
Math.min(input.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),
Math.min(input.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1])
] : 0;
] : [0, 0, 0, 0];
const boxRaw3 = prediction.box ? [
prediction.box.startPoint[0] / input.shape[2],
prediction.box.startPoint[1] / input.shape[1],
(prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],
(prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1]
] : [];
] : [0, 0, 0, 0];
results.push({
confidence: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,
boxConfidence: Math.round(100 * prediction.boxConfidence) / 100,
faceConfidence: Math.round(100 * prediction.faceConfidence) / 100,
id: id++,
score: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,
boxScore: Math.round(100 * prediction.boxConfidence) / 100,
faceScore: Math.round(100 * prediction.faceConfidence) / 100,
box: clampedBox,
boxRaw: boxRaw3,
mesh: prediction.mesh,
meshRaw,
annotations: annotations3,
image: prediction.image
image: prediction.image,
tensor: prediction.image
});
if (prediction.coords)
prediction.coords.dispose();
@ -4403,7 +4406,7 @@ var calculateFaceAngle = (face5, imageSize) => {
return { angle, matrix, gaze };
};
var detectFace = async (parent, input) => {
var _a, _b, _c, _d, _e, _f, _g, _h;
var _a, _b, _c, _d, _e, _f;
let timeStamp;
let ageRes;
let genderRes;
@ -4421,7 +4424,7 @@ var detectFace = async (parent, input) => {
return [];
for (let i = 0; i < faces.length; i++) {
parent.analyze("Get Face");
if (!faces[i].image || faces[i].image.isDisposedInternal) {
if (!faces[i].image || faces[i].image["isDisposedInternal"]) {
log("Face object is disposed:", faces[i].image);
continue;
}
@ -4456,18 +4459,18 @@ var detectFace = async (parent, input) => {
}
const irisSize = ((_e = faces[i].annotations) == null ? void 0 : _e.leftEyeIris) && ((_f = faces[i].annotations) == null ? void 0 : _f.rightEyeIris) ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2] : 0;
faceRes.push({
id: i,
...faces[i],
id: i,
age: descRes.age,
gender: descRes.gender,
genderConfidence: descRes.genderConfidence,
genderScore: descRes.genderConfidence,
embedding: descRes.descriptor,
emotion: emotionRes,
iris: irisSize !== 0 ? Math.trunc(500 / irisSize / 11.7) / 100 : 0,
rotation,
tensor: parent.config.face.detector.return ? (_g = faces[i].image) == null ? void 0 : _g.squeeze() : null
tensor: parent.config.face.detector.return ? tfjs_esm_exports.squeeze(faces[i].image) : null
});
(_h = faces[i].image) == null ? void 0 : _h.dispose();
tfjs_esm_exports.dispose(faces[i].image);
parent.analyze("End Face");
}
parent.analyze("End FaceMesh:");
@ -8163,7 +8166,7 @@ async function predict5(input, config3) {
(predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / input.shape[1]
];
}
hands.push({ id: i, confidence: Math.round(100 * predictions[i].confidence) / 100, box: box6, boxRaw: boxRaw3, keypoints: keypoints3, annotations: annotations3 });
hands.push({ id: i, score: Math.round(100 * predictions[i].confidence) / 100, box: box6, boxRaw: boxRaw3, keypoints: keypoints3, annotations: annotations3 });
}
return hands;
}
@ -8395,10 +8398,10 @@ async function predict7(image15, config3) {
tensor.dispose();
if (resT) {
keypoints.length = 0;
const squeeze2 = resT.squeeze();
const squeeze3 = resT.squeeze();
tfjs_esm_exports.dispose(resT);
const stack2 = squeeze2.unstack(2);
tfjs_esm_exports.dispose(squeeze2);
const stack2 = squeeze3.unstack(2);
tfjs_esm_exports.dispose(squeeze3);
for (let id = 0; id < stack2.length; id++) {
const [x2, y2, partScore] = max2d(stack2[id], config3.body.minConfidence);
if (score > config3.body.minConfidence) {
@ -9892,9 +9895,9 @@ async function face2(inCanvas2, result, drawOptions) {
if (localOptions.drawBoxes)
rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);
const labels2 = [];
labels2.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);
if (f.genderConfidence)
labels2.push(`${f.gender || ""} ${Math.trunc(100 * f.genderConfidence)}% confident`);
labels2.push(`face confidence: ${Math.trunc(100 * f.score)}%`);
if (f.genderScore)
labels2.push(`${f.gender || ""} ${Math.trunc(100 * f.genderScore)}% confident`);
if (f.age)
labels2.push(`age: ${f.age || ""}`);
if (f.iris)
@ -10326,6 +10329,7 @@ function join2(faces, bodies, hands, gestures, shape) {
// src/interpolate.ts
var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0 };
function calc(newResult) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u;
const bufferedFactor = 1e3 / (Date.now() - newResult.timestamp) / 4;
if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) {
bufferedResult.body = JSON.parse(JSON.stringify(newResult.body));
@ -10369,17 +10373,17 @@ function calc(newResult) {
for (let i = 0; i < newResult.face.length; i++) {
const box6 = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor);
const boxRaw3 = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor);
const matrix = newResult.face[i].rotation.matrix;
const angle = {
roll: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.angle.roll + newResult.face[i].rotation.angle.roll) / bufferedFactor,
yaw: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.angle.yaw + newResult.face[i].rotation.angle.yaw) / bufferedFactor,
pitch: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.angle.pitch + newResult.face[i].rotation.angle.pitch) / bufferedFactor
const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };
rotation.matrix = (_a = newResult.face[i].rotation) == null ? void 0 : _a.matrix;
rotation.angle = {
roll: ((bufferedFactor - 1) * (((_c = (_b = bufferedResult.face[i].rotation) == null ? void 0 : _b.angle) == null ? void 0 : _c.roll) || 0) + (((_e = (_d = newResult.face[i].rotation) == null ? void 0 : _d.angle) == null ? void 0 : _e.roll) || 0)) / bufferedFactor,
yaw: ((bufferedFactor - 1) * (((_g = (_f = bufferedResult.face[i].rotation) == null ? void 0 : _f.angle) == null ? void 0 : _g.yaw) || 0) + (((_i = (_h = newResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.yaw) || 0)) / bufferedFactor,
pitch: ((bufferedFactor - 1) * (((_k = (_j = bufferedResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.pitch) || 0) + (((_m = (_l = newResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.pitch) || 0)) / bufferedFactor
};
const gaze = {
bearing: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.gaze.bearing + newResult.face[i].rotation.gaze.bearing) / bufferedFactor,
strength: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.gaze.strength + newResult.face[i].rotation.gaze.strength) / bufferedFactor
rotation.gaze = {
bearing: ((bufferedFactor - 1) * (((_o = (_n = bufferedResult.face[i].rotation) == null ? void 0 : _n.gaze) == null ? void 0 : _o.bearing) || 0) + (((_q = (_p = newResult.face[i].rotation) == null ? void 0 : _p.gaze) == null ? void 0 : _q.bearing) || 0)) / bufferedFactor,
strength: ((bufferedFactor - 1) * (((_s = (_r = bufferedResult.face[i].rotation) == null ? void 0 : _r.gaze) == null ? void 0 : _s.strength) || 0) + (((_u = (_t = newResult.face[i].rotation) == null ? void 0 : _t.gaze) == null ? void 0 : _u.strength) || 0)) / bufferedFactor
};
const rotation = { angle, matrix, gaze };
bufferedResult.face[i] = { ...newResult.face[i], rotation, box: box6, boxRaw: boxRaw3 };
}
}

4
dist/human.esm-nobundle.js.map vendored
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54
dist/human.esm.js vendored
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@ -61996,6 +61996,7 @@ var facePipeline;
async function predict(input2, config3) {
const predictions = await facePipeline.predict(input2, config3);
const results = [];
let id = 0;
for (const prediction of predictions || []) {
if (!prediction || prediction.isDisposedInternal)
continue;
@ -62014,23 +62015,25 @@ async function predict(input2, config3) {
Math.max(0, prediction.box.startPoint[1]),
Math.min(input2.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),
Math.min(input2.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1])
] : 0;
] : [0, 0, 0, 0];
const boxRaw3 = prediction.box ? [
prediction.box.startPoint[0] / input2.shape[2],
prediction.box.startPoint[1] / input2.shape[1],
(prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input2.shape[2],
(prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input2.shape[1]
] : [];
] : [0, 0, 0, 0];
results.push({
confidence: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,
boxConfidence: Math.round(100 * prediction.boxConfidence) / 100,
faceConfidence: Math.round(100 * prediction.faceConfidence) / 100,
id: id++,
score: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,
boxScore: Math.round(100 * prediction.boxConfidence) / 100,
faceScore: Math.round(100 * prediction.faceConfidence) / 100,
box: clampedBox,
boxRaw: boxRaw3,
mesh: prediction.mesh,
meshRaw,
annotations: annotations3,
image: prediction.image
image: prediction.image,
tensor: prediction.image
});
if (prediction.coords)
prediction.coords.dispose();
@ -62339,7 +62342,7 @@ var calculateFaceAngle = (face5, imageSize) => {
return { angle, matrix, gaze };
};
var detectFace = async (parent, input2) => {
var _a, _b, _c, _d, _e, _f, _g, _h;
var _a, _b, _c, _d, _e, _f;
let timeStamp;
let ageRes;
let genderRes;
@ -62357,7 +62360,7 @@ var detectFace = async (parent, input2) => {
return [];
for (let i = 0; i < faces.length; i++) {
parent.analyze("Get Face");
if (!faces[i].image || faces[i].image.isDisposedInternal) {
if (!faces[i].image || faces[i].image["isDisposedInternal"]) {
log("Face object is disposed:", faces[i].image);
continue;
}
@ -62392,18 +62395,18 @@ var detectFace = async (parent, input2) => {
}
const irisSize = ((_e = faces[i].annotations) == null ? void 0 : _e.leftEyeIris) && ((_f = faces[i].annotations) == null ? void 0 : _f.rightEyeIris) ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input2.shape[2] : 0;
faceRes.push({
id: i,
...faces[i],
id: i,
age: descRes.age,
gender: descRes.gender,
genderConfidence: descRes.genderConfidence,
genderScore: descRes.genderConfidence,
embedding: descRes.descriptor,
emotion: emotionRes,
iris: irisSize !== 0 ? Math.trunc(500 / irisSize / 11.7) / 100 : 0,
rotation,
tensor: parent.config.face.detector.return ? (_g = faces[i].image) == null ? void 0 : _g.squeeze() : null
tensor: parent.config.face.detector.return ? squeeze(faces[i].image) : null
});
(_h = faces[i].image) == null ? void 0 : _h.dispose();
dispose(faces[i].image);
parent.analyze("End Face");
}
parent.analyze("End FaceMesh:");
@ -66099,7 +66102,7 @@ async function predict5(input2, config3) {
(predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / input2.shape[1]
];
}
hands.push({ id: i, confidence: Math.round(100 * predictions[i].confidence) / 100, box: box6, boxRaw: boxRaw3, keypoints: keypoints3, annotations: annotations3 });
hands.push({ id: i, score: Math.round(100 * predictions[i].confidence) / 100, box: box6, boxRaw: boxRaw3, keypoints: keypoints3, annotations: annotations3 });
}
return hands;
}
@ -67828,9 +67831,9 @@ async function face2(inCanvas2, result, drawOptions) {
if (localOptions.drawBoxes)
rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);
const labels2 = [];
labels2.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);
if (f.genderConfidence)
labels2.push(`${f.gender || ""} ${Math.trunc(100 * f.genderConfidence)}% confident`);
labels2.push(`face confidence: ${Math.trunc(100 * f.score)}%`);
if (f.genderScore)
labels2.push(`${f.gender || ""} ${Math.trunc(100 * f.genderScore)}% confident`);
if (f.age)
labels2.push(`age: ${f.age || ""}`);
if (f.iris)
@ -68262,6 +68265,7 @@ function join2(faces, bodies, hands, gestures, shape) {
// src/interpolate.ts
var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0 };
function calc(newResult) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u;
const bufferedFactor = 1e3 / (Date.now() - newResult.timestamp) / 4;
if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) {
bufferedResult.body = JSON.parse(JSON.stringify(newResult.body));
@ -68305,17 +68309,17 @@ function calc(newResult) {
for (let i = 0; i < newResult.face.length; i++) {
const box6 = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor);
const boxRaw3 = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor);
const matrix = newResult.face[i].rotation.matrix;
const angle = {
roll: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.angle.roll + newResult.face[i].rotation.angle.roll) / bufferedFactor,
yaw: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.angle.yaw + newResult.face[i].rotation.angle.yaw) / bufferedFactor,
pitch: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.angle.pitch + newResult.face[i].rotation.angle.pitch) / bufferedFactor
const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };
rotation.matrix = (_a = newResult.face[i].rotation) == null ? void 0 : _a.matrix;
rotation.angle = {
roll: ((bufferedFactor - 1) * (((_c = (_b = bufferedResult.face[i].rotation) == null ? void 0 : _b.angle) == null ? void 0 : _c.roll) || 0) + (((_e = (_d = newResult.face[i].rotation) == null ? void 0 : _d.angle) == null ? void 0 : _e.roll) || 0)) / bufferedFactor,
yaw: ((bufferedFactor - 1) * (((_g = (_f = bufferedResult.face[i].rotation) == null ? void 0 : _f.angle) == null ? void 0 : _g.yaw) || 0) + (((_i = (_h = newResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.yaw) || 0)) / bufferedFactor,
pitch: ((bufferedFactor - 1) * (((_k = (_j = bufferedResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.pitch) || 0) + (((_m = (_l = newResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.pitch) || 0)) / bufferedFactor
};
const gaze = {
bearing: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.gaze.bearing + newResult.face[i].rotation.gaze.bearing) / bufferedFactor,
strength: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.gaze.strength + newResult.face[i].rotation.gaze.strength) / bufferedFactor
rotation.gaze = {
bearing: ((bufferedFactor - 1) * (((_o = (_n = bufferedResult.face[i].rotation) == null ? void 0 : _n.gaze) == null ? void 0 : _o.bearing) || 0) + (((_q = (_p = newResult.face[i].rotation) == null ? void 0 : _p.gaze) == null ? void 0 : _q.bearing) || 0)) / bufferedFactor,
strength: ((bufferedFactor - 1) * (((_s = (_r = bufferedResult.face[i].rotation) == null ? void 0 : _r.gaze) == null ? void 0 : _s.strength) || 0) + (((_u = (_t = newResult.face[i].rotation) == null ? void 0 : _t.gaze) == null ? void 0 : _u.strength) || 0)) / bufferedFactor
};
const rotation = { angle, matrix, gaze };
bufferedResult.face[i] = { ...newResult.face[i], rotation, box: box6, boxRaw: boxRaw3 };
}
}

4
dist/human.esm.js.map vendored
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54
dist/human.js vendored
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@ -62004,6 +62004,7 @@ return a / b;`;
async function predict(input2, config3) {
const predictions = await facePipeline.predict(input2, config3);
const results = [];
let id = 0;
for (const prediction of predictions || []) {
if (!prediction || prediction.isDisposedInternal)
continue;
@ -62022,23 +62023,25 @@ return a / b;`;
Math.max(0, prediction.box.startPoint[1]),
Math.min(input2.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),
Math.min(input2.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1])
] : 0;
] : [0, 0, 0, 0];
const boxRaw3 = prediction.box ? [
prediction.box.startPoint[0] / input2.shape[2],
prediction.box.startPoint[1] / input2.shape[1],
(prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input2.shape[2],
(prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input2.shape[1]
] : [];
] : [0, 0, 0, 0];
results.push({
confidence: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,
boxConfidence: Math.round(100 * prediction.boxConfidence) / 100,
faceConfidence: Math.round(100 * prediction.faceConfidence) / 100,
id: id++,
score: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,
boxScore: Math.round(100 * prediction.boxConfidence) / 100,
faceScore: Math.round(100 * prediction.faceConfidence) / 100,
box: clampedBox,
boxRaw: boxRaw3,
mesh: prediction.mesh,
meshRaw,
annotations: annotations3,
image: prediction.image
image: prediction.image,
tensor: prediction.image
});
if (prediction.coords)
prediction.coords.dispose();
@ -62347,7 +62350,7 @@ return a / b;`;
return { angle, matrix, gaze };
};
var detectFace = async (parent, input2) => {
var _a, _b, _c, _d, _e, _f, _g, _h;
var _a, _b, _c, _d, _e, _f;
let timeStamp;
let ageRes;
let genderRes;
@ -62365,7 +62368,7 @@ return a / b;`;
return [];
for (let i = 0; i < faces.length; i++) {
parent.analyze("Get Face");
if (!faces[i].image || faces[i].image.isDisposedInternal) {
if (!faces[i].image || faces[i].image["isDisposedInternal"]) {
log("Face object is disposed:", faces[i].image);
continue;
}
@ -62400,18 +62403,18 @@ return a / b;`;
}
const irisSize = ((_e = faces[i].annotations) == null ? void 0 : _e.leftEyeIris) && ((_f = faces[i].annotations) == null ? void 0 : _f.rightEyeIris) ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input2.shape[2] : 0;
faceRes.push({
id: i,
...faces[i],
id: i,
age: descRes.age,
gender: descRes.gender,
genderConfidence: descRes.genderConfidence,
genderScore: descRes.genderConfidence,
embedding: descRes.descriptor,
emotion: emotionRes,
iris: irisSize !== 0 ? Math.trunc(500 / irisSize / 11.7) / 100 : 0,
rotation,
tensor: parent.config.face.detector.return ? (_g = faces[i].image) == null ? void 0 : _g.squeeze() : null
tensor: parent.config.face.detector.return ? squeeze(faces[i].image) : null
});
(_h = faces[i].image) == null ? void 0 : _h.dispose();
dispose(faces[i].image);
parent.analyze("End Face");
}
parent.analyze("End FaceMesh:");
@ -66107,7 +66110,7 @@ return a / b;`;
(predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / input2.shape[1]
];
}
hands.push({ id: i, confidence: Math.round(100 * predictions[i].confidence) / 100, box: box6, boxRaw: boxRaw3, keypoints: keypoints3, annotations: annotations3 });
hands.push({ id: i, score: Math.round(100 * predictions[i].confidence) / 100, box: box6, boxRaw: boxRaw3, keypoints: keypoints3, annotations: annotations3 });
}
return hands;
}
@ -67836,9 +67839,9 @@ return a / b;`;
if (localOptions.drawBoxes)
rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);
const labels2 = [];
labels2.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);
if (f.genderConfidence)
labels2.push(`${f.gender || ""} ${Math.trunc(100 * f.genderConfidence)}% confident`);
labels2.push(`face confidence: ${Math.trunc(100 * f.score)}%`);
if (f.genderScore)
labels2.push(`${f.gender || ""} ${Math.trunc(100 * f.genderScore)}% confident`);
if (f.age)
labels2.push(`age: ${f.age || ""}`);
if (f.iris)
@ -68270,6 +68273,7 @@ return a / b;`;
// src/interpolate.ts
var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0 };
function calc(newResult) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u;
const bufferedFactor = 1e3 / (Date.now() - newResult.timestamp) / 4;
if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) {
bufferedResult.body = JSON.parse(JSON.stringify(newResult.body));
@ -68313,17 +68317,17 @@ return a / b;`;
for (let i = 0; i < newResult.face.length; i++) {
const box6 = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor);
const boxRaw3 = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor);
const matrix = newResult.face[i].rotation.matrix;
const angle = {
roll: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.angle.roll + newResult.face[i].rotation.angle.roll) / bufferedFactor,
yaw: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.angle.yaw + newResult.face[i].rotation.angle.yaw) / bufferedFactor,
pitch: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.angle.pitch + newResult.face[i].rotation.angle.pitch) / bufferedFactor
const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };
rotation.matrix = (_a = newResult.face[i].rotation) == null ? void 0 : _a.matrix;
rotation.angle = {
roll: ((bufferedFactor - 1) * (((_c = (_b = bufferedResult.face[i].rotation) == null ? void 0 : _b.angle) == null ? void 0 : _c.roll) || 0) + (((_e = (_d = newResult.face[i].rotation) == null ? void 0 : _d.angle) == null ? void 0 : _e.roll) || 0)) / bufferedFactor,
yaw: ((bufferedFactor - 1) * (((_g = (_f = bufferedResult.face[i].rotation) == null ? void 0 : _f.angle) == null ? void 0 : _g.yaw) || 0) + (((_i = (_h = newResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.yaw) || 0)) / bufferedFactor,
pitch: ((bufferedFactor - 1) * (((_k = (_j = bufferedResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.pitch) || 0) + (((_m = (_l = newResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.pitch) || 0)) / bufferedFactor
};
const gaze = {
bearing: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.gaze.bearing + newResult.face[i].rotation.gaze.bearing) / bufferedFactor,
strength: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.gaze.strength + newResult.face[i].rotation.gaze.strength) / bufferedFactor
rotation.gaze = {
bearing: ((bufferedFactor - 1) * (((_o = (_n = bufferedResult.face[i].rotation) == null ? void 0 : _n.gaze) == null ? void 0 : _o.bearing) || 0) + (((_q = (_p = newResult.face[i].rotation) == null ? void 0 : _p.gaze) == null ? void 0 : _q.bearing) || 0)) / bufferedFactor,
strength: ((bufferedFactor - 1) * (((_s = (_r = bufferedResult.face[i].rotation) == null ? void 0 : _r.gaze) == null ? void 0 : _s.strength) || 0) + (((_u = (_t = newResult.face[i].rotation) == null ? void 0 : _t.gaze) == null ? void 0 : _u.strength) || 0)) / bufferedFactor
};
const rotation = { angle, matrix, gaze };
bufferedResult.face[i] = { ...newResult.face[i], rotation, box: box6, boxRaw: boxRaw3 };
}
}

233
dist/human.node-gpu.js vendored
View File

@ -236,7 +236,7 @@ function info() {
}
// src/human.ts
var tf19 = __toModule(require_tfjs_esm());
var tf20 = __toModule(require_tfjs_esm());
// src/tfjs/backend.ts
var tf = __toModule(require_tfjs_esm());
@ -306,6 +306,9 @@ function register() {
}
}
// src/face.ts
var tf8 = __toModule(require_tfjs_esm());
// src/blazeface/facemesh.ts
var facemesh_exports = {};
__export(facemesh_exports, {
@ -4076,6 +4079,7 @@ var facePipeline;
async function predict(input, config3) {
const predictions = await facePipeline.predict(input, config3);
const results = [];
let id = 0;
for (const prediction of predictions || []) {
if (!prediction || prediction.isDisposedInternal)
continue;
@ -4094,23 +4098,25 @@ async function predict(input, config3) {
Math.max(0, prediction.box.startPoint[1]),
Math.min(input.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),
Math.min(input.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1])
] : 0;
] : [0, 0, 0, 0];
const boxRaw3 = prediction.box ? [
prediction.box.startPoint[0] / input.shape[2],
prediction.box.startPoint[1] / input.shape[1],
(prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],
(prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1]
] : [];
] : [0, 0, 0, 0];
results.push({
confidence: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,
boxConfidence: Math.round(100 * prediction.boxConfidence) / 100,
faceConfidence: Math.round(100 * prediction.faceConfidence) / 100,
id: id++,
score: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,
boxScore: Math.round(100 * prediction.boxConfidence) / 100,
faceScore: Math.round(100 * prediction.faceConfidence) / 100,
box: clampedBox,
boxRaw: boxRaw3,
mesh: prediction.mesh,
meshRaw,
annotations: annotations3,
image: prediction.image
image: prediction.image,
tensor: prediction.image
});
if (prediction.coords)
prediction.coords.dispose();
@ -4421,7 +4427,7 @@ var calculateFaceAngle = (face5, imageSize) => {
return { angle, matrix, gaze };
};
var detectFace = async (parent, input) => {
var _a, _b, _c, _d, _e, _f, _g, _h;
var _a, _b, _c, _d, _e, _f;
let timeStamp;
let ageRes;
let genderRes;
@ -4439,7 +4445,7 @@ var detectFace = async (parent, input) => {
return [];
for (let i = 0; i < faces.length; i++) {
parent.analyze("Get Face");
if (!faces[i].image || faces[i].image.isDisposedInternal) {
if (!faces[i].image || faces[i].image["isDisposedInternal"]) {
log("Face object is disposed:", faces[i].image);
continue;
}
@ -4474,18 +4480,18 @@ var detectFace = async (parent, input) => {
}
const irisSize = ((_e = faces[i].annotations) == null ? void 0 : _e.leftEyeIris) && ((_f = faces[i].annotations) == null ? void 0 : _f.rightEyeIris) ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2] : 0;
faceRes.push({
id: i,
...faces[i],
id: i,
age: descRes.age,
gender: descRes.gender,
genderConfidence: descRes.genderConfidence,
genderScore: descRes.genderConfidence,
embedding: descRes.descriptor,
emotion: emotionRes,
iris: irisSize !== 0 ? Math.trunc(500 / irisSize / 11.7) / 100 : 0,
rotation,
tensor: parent.config.face.detector.return ? (_g = faces[i].image) == null ? void 0 : _g.squeeze() : null
tensor: parent.config.face.detector.return ? tf8.squeeze(faces[i].image) : null
});
(_h = faces[i].image) == null ? void 0 : _h.dispose();
tf8.dispose(faces[i].image);
parent.analyze("End Face");
}
parent.analyze("End FaceMesh:");
@ -4508,7 +4514,7 @@ __export(posenet_exports, {
load: () => load5,
predict: () => predict4
});
var tf8 = __toModule(require_tfjs_esm());
var tf9 = __toModule(require_tfjs_esm());
// src/posenet/keypoints.ts
var partNames = [
@ -4820,7 +4826,7 @@ function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected
var model3;
var poseNetOutputs = ["MobilenetV1/offset_2/BiasAdd", "MobilenetV1/heatmap_2/BiasAdd", "MobilenetV1/displacement_fwd_2/BiasAdd", "MobilenetV1/displacement_bwd_2/BiasAdd"];
async function predict4(input, config3) {
const res = tf8.tidy(() => {
const res = tf9.tidy(() => {
if (!model3.inputs[0].shape)
return [];
const resized = input.resizeBilinear([model3.inputs[0].shape[2], model3.inputs[0].shape[1]]);
@ -4841,7 +4847,7 @@ async function predict4(input, config3) {
}
async function load5(config3) {
if (!model3) {
model3 = await tf8.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
model3 = await tf9.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
if (!model3 || !model3["modelUrl"])
log("load model failed:", config3.body.modelPath);
else if (config3.debug)
@ -4857,13 +4863,13 @@ __export(handpose_exports, {
load: () => load6,
predict: () => predict5
});
var tf12 = __toModule(require_tfjs_esm());
var tf13 = __toModule(require_tfjs_esm());
// src/handpose/handdetector.ts
var tf10 = __toModule(require_tfjs_esm());
var tf11 = __toModule(require_tfjs_esm());
// src/handpose/box.ts
var tf9 = __toModule(require_tfjs_esm());
var tf10 = __toModule(require_tfjs_esm());
function getBoxSize2(box6) {
return [
Math.abs(box6.endPoint[0] - box6.startPoint[0]),
@ -4885,7 +4891,7 @@ function cutBoxFromImageAndResize2(box6, image15, cropSize) {
box6.endPoint[1] / h,
box6.endPoint[0] / w
]];
return tf9.image.cropAndResize(image15, boxes, [0], cropSize);
return tf10.image.cropAndResize(image15, boxes, [0], cropSize);
}
function scaleBoxCoordinates2(box6, factor) {
const startPoint = [box6.startPoint[0] * factor[0], box6.startPoint[1] * factor[1]];
@ -7868,47 +7874,47 @@ var HandDetector = class {
var _a;
this.model = model9;
this.anchors = anchors.map((anchor) => [anchor.x, anchor.y]);
this.anchorsTensor = tf10.tensor2d(this.anchors);
this.anchorsTensor = tf11.tensor2d(this.anchors);
this.inputSize = (_a = this.model) == null ? void 0 : _a.inputs[0].shape[2];
this.inputSizeTensor = tf10.tensor1d([this.inputSize, this.inputSize]);
this.doubleInputSizeTensor = tf10.tensor1d([this.inputSize * 2, this.inputSize * 2]);
this.inputSizeTensor = tf11.tensor1d([this.inputSize, this.inputSize]);
this.doubleInputSizeTensor = tf11.tensor1d([this.inputSize * 2, this.inputSize * 2]);
}
normalizeBoxes(boxes) {
return tf10.tidy(() => {
const boxOffsets = tf10.slice(boxes, [0, 0], [-1, 2]);
const boxSizes = tf10.slice(boxes, [0, 2], [-1, 2]);
const boxCenterPoints = tf10.add(tf10.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);
const halfBoxSizes = tf10.div(boxSizes, this.doubleInputSizeTensor);
const startPoints = tf10.mul(tf10.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);
const endPoints = tf10.mul(tf10.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);
return tf10.concat2d([startPoints, endPoints], 1);
return tf11.tidy(() => {
const boxOffsets = tf11.slice(boxes, [0, 0], [-1, 2]);
const boxSizes = tf11.slice(boxes, [0, 2], [-1, 2]);
const boxCenterPoints = tf11.add(tf11.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);
const halfBoxSizes = tf11.div(boxSizes, this.doubleInputSizeTensor);
const startPoints = tf11.mul(tf11.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);
const endPoints = tf11.mul(tf11.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);
return tf11.concat2d([startPoints, endPoints], 1);
});
}
normalizeLandmarks(rawPalmLandmarks, index) {
return tf10.tidy(() => {
const landmarks = tf10.add(tf10.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);
return tf10.mul(landmarks, this.inputSizeTensor);
return tf11.tidy(() => {
const landmarks = tf11.add(tf11.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);
return tf11.mul(landmarks, this.inputSizeTensor);
});
}
async getBoxes(input, config3) {
const batched = this.model.predict(input);
const predictions = batched.squeeze();
batched.dispose();
const scoresT = tf10.tidy(() => tf10.sigmoid(tf10.slice(predictions, [0, 0], [-1, 1])).squeeze());
const scoresT = tf11.tidy(() => tf11.sigmoid(tf11.slice(predictions, [0, 0], [-1, 1])).squeeze());
const scores = scoresT.dataSync();
const rawBoxes = tf10.slice(predictions, [0, 1], [-1, 4]);
const rawBoxes = tf11.slice(predictions, [0, 1], [-1, 4]);
const boxes = this.normalizeBoxes(rawBoxes);
rawBoxes.dispose();
const filteredT = await tf10.image.nonMaxSuppressionAsync(boxes, scores, config3.hand.maxDetected, config3.hand.iouThreshold, config3.hand.minConfidence);
const filteredT = await tf11.image.nonMaxSuppressionAsync(boxes, scores, config3.hand.maxDetected, config3.hand.iouThreshold, config3.hand.minConfidence);
const filtered = filteredT.arraySync();
scoresT.dispose();
filteredT.dispose();
const hands = [];
for (const index of filtered) {
if (scores[index] >= config3.hand.minConfidence) {
const matchingBox = tf10.slice(boxes, [index, 0], [1, -1]);
const rawPalmLandmarks = tf10.slice(predictions, [index, 5], [1, 14]);
const palmLandmarks = tf10.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));
const matchingBox = tf11.slice(boxes, [index, 0], [1, -1]);
const rawPalmLandmarks = tf11.slice(predictions, [index, 5], [1, 14]);
const palmLandmarks = tf11.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));
rawPalmLandmarks.dispose();
hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });
}
@ -7920,7 +7926,7 @@ var HandDetector = class {
async estimateHandBounds(input, config3) {
const inputHeight = input.shape[1];
const inputWidth = input.shape[2];
const image15 = tf10.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));
const image15 = tf11.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));
const predictions = await this.getBoxes(image15, config3);
image15.dispose();
const hands = [];
@ -7940,7 +7946,7 @@ var HandDetector = class {
};
// src/handpose/handpipeline.ts
var tf11 = __toModule(require_tfjs_esm());
var tf12 = __toModule(require_tfjs_esm());
// src/handpose/util.ts
function normalizeRadians2(angle) {
@ -8091,7 +8097,7 @@ var HandPipeline = class {
const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;
const palmCenter = getBoxCenter2(currentBox);
const palmCenterNormalized = [palmCenter[0] / image15.shape[2], palmCenter[1] / image15.shape[1]];
const rotatedImage = config3.hand.rotation && tf11.ENV.flags.IS_BROWSER ? tf11.image.rotateWithOffset(image15, angle, 0, palmCenterNormalized) : image15.clone();
const rotatedImage = config3.hand.rotation && tf12.ENV.flags.IS_BROWSER ? tf12.image.rotateWithOffset(image15, angle, 0, palmCenterNormalized) : image15.clone();
const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
const croppedInput = cutBoxFromImageAndResize2(newBox, rotatedImage, [this.inputSize, this.inputSize]);
@ -8103,7 +8109,7 @@ var HandPipeline = class {
const confidence = confidenceT.dataSync()[0];
confidenceT.dispose();
if (confidence >= config3.hand.minConfidence) {
const keypointsReshaped = tf11.reshape(keypoints3, [-1, 3]);
const keypointsReshaped = tf12.reshape(keypoints3, [-1, 3]);
const rawCoords = keypointsReshaped.arraySync();
keypoints3.dispose();
keypointsReshaped.dispose();
@ -8190,15 +8196,15 @@ async function predict5(input, config3) {
(predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / input.shape[1]
];
}
hands.push({ id: i, confidence: Math.round(100 * predictions[i].confidence) / 100, box: box6, boxRaw: boxRaw3, keypoints: keypoints3, annotations: annotations3 });
hands.push({ id: i, score: Math.round(100 * predictions[i].confidence) / 100, box: box6, boxRaw: boxRaw3, keypoints: keypoints3, annotations: annotations3 });
}
return hands;
}
async function load6(config3) {
if (!handDetectorModel || !handPoseModel) {
[handDetectorModel, handPoseModel] = await Promise.all([
config3.hand.enabled ? tf12.loadGraphModel(join(config3.modelBasePath, config3.hand.detector.modelPath), { fromTFHub: config3.hand.detector.modelPath.includes("tfhub.dev") }) : null,
config3.hand.landmarks ? tf12.loadGraphModel(join(config3.modelBasePath, config3.hand.skeleton.modelPath), { fromTFHub: config3.hand.skeleton.modelPath.includes("tfhub.dev") }) : null
config3.hand.enabled ? tf13.loadGraphModel(join(config3.modelBasePath, config3.hand.detector.modelPath), { fromTFHub: config3.hand.detector.modelPath.includes("tfhub.dev") }) : null,
config3.hand.landmarks ? tf13.loadGraphModel(join(config3.modelBasePath, config3.hand.skeleton.modelPath), { fromTFHub: config3.hand.skeleton.modelPath.includes("tfhub.dev") }) : null
]);
if (config3.hand.enabled) {
if (!handDetectorModel || !handDetectorModel["modelUrl"])
@ -8227,7 +8233,7 @@ __export(blazepose_exports, {
load: () => load7,
predict: () => predict6
});
var tf13 = __toModule(require_tfjs_esm());
var tf14 = __toModule(require_tfjs_esm());
// src/blazepose/annotations.ts
var full = [
@ -8309,7 +8315,7 @@ var upper = [
var model4;
async function load7(config3) {
if (!model4) {
model4 = await tf13.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
model4 = await tf14.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
model4["width"] = parseInt(model4["signature"].inputs["input_1:0"].tensorShape.dim[2].size);
model4["height"] = parseInt(model4["signature"].inputs["input_1:0"].tensorShape.dim[1].size);
if (!model4 || !model4["modelUrl"])
@ -8327,8 +8333,8 @@ async function predict6(image15, config3) {
if (!config3.body.enabled)
return [];
const imgSize = { width: image15.shape[2], height: image15.shape[1] };
const resize = tf13.image.resizeBilinear(image15, [model4["width"], model4["height"]], false);
const normalize = tf13.div(resize, [255]);
const resize = tf14.image.resizeBilinear(image15, [model4["width"], model4["height"]], false);
const normalize = tf14.div(resize, [255]);
resize.dispose();
const resT = await model4.predict(normalize);
const points = ((_a = resT.find((t) => t.size === 195 || t.size === 155)) == null ? void 0 : _a.dataSync()) || [];
@ -8369,7 +8375,7 @@ async function predict6(image15, config3) {
}
// src/efficientpose/efficientpose.ts
var tf14 = __toModule(require_tfjs_esm());
var tf15 = __toModule(require_tfjs_esm());
var model5;
var keypoints = [];
var box4 = [0, 0, 0, 0];
@ -8379,7 +8385,7 @@ var skipped3 = Number.MAX_SAFE_INTEGER;
var bodyParts = ["head", "neck", "rightShoulder", "rightElbow", "rightWrist", "chest", "leftShoulder", "leftElbow", "leftWrist", "pelvis", "rightHip", "rightKnee", "rightAnkle", "leftHip", "leftKnee", "leftAnkle"];
async function load8(config3) {
if (!model5) {
model5 = await tf14.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
model5 = await tf15.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
if (!model5 || !model5["modelUrl"])
log("load model failed:", config3.body.modelPath);
else if (config3.debug)
@ -8390,14 +8396,14 @@ async function load8(config3) {
}
function max2d(inputs, minScore) {
const [width, height] = inputs.shape;
return tf14.tidy(() => {
const mod = (a, b) => tf14.sub(a, tf14.mul(tf14.div(a, tf14.scalar(b, "int32")), tf14.scalar(b, "int32")));
const reshaped = tf14.reshape(inputs, [height * width]);
const newScore = tf14.max(reshaped, 0).dataSync()[0];
return tf15.tidy(() => {
const mod = (a, b) => tf15.sub(a, tf15.mul(tf15.div(a, tf15.scalar(b, "int32")), tf15.scalar(b, "int32")));
const reshaped = tf15.reshape(inputs, [height * width]);
const newScore = tf15.max(reshaped, 0).dataSync()[0];
if (newScore > minScore) {
const coords3 = tf14.argMax(reshaped, 0);
const coords3 = tf15.argMax(reshaped, 0);
const x = mod(coords3, width).dataSync()[0];
const y = tf14.div(coords3, tf14.scalar(width, "int32")).dataSync()[0];
const y = tf15.div(coords3, tf15.scalar(width, "int32")).dataSync()[0];
return [x, y, newScore];
}
return [0, 0, newScore];
@ -8410,11 +8416,11 @@ async function predict7(image15, config3) {
}
skipped3 = 0;
return new Promise(async (resolve) => {
const tensor = tf14.tidy(() => {
const tensor = tf15.tidy(() => {
if (!model5.inputs[0].shape)
return null;
const resize = tf14.image.resizeBilinear(image15, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
const enhance2 = tf14.mul(resize, 2);
const resize = tf15.image.resizeBilinear(image15, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
const enhance2 = tf15.mul(resize, 2);
const norm = enhance2.sub(1);
return norm;
});
@ -8424,10 +8430,10 @@ async function predict7(image15, config3) {
tensor.dispose();
if (resT) {
keypoints.length = 0;
const squeeze2 = resT.squeeze();
tf14.dispose(resT);
const stack2 = squeeze2.unstack(2);
tf14.dispose(squeeze2);
const squeeze3 = resT.squeeze();
tf15.dispose(resT);
const stack2 = squeeze3.unstack(2);
tf15.dispose(squeeze3);
for (let id = 0; id < stack2.length; id++) {
const [x2, y2, partScore] = max2d(stack2[id], config3.body.minConfidence);
if (score > config3.body.minConfidence) {
@ -8445,7 +8451,7 @@ async function predict7(image15, config3) {
});
}
}
stack2.forEach((s) => tf14.dispose(s));
stack2.forEach((s) => tf15.dispose(s));
}
score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
const x = keypoints.map((a) => a.position.x);
@ -8469,7 +8475,7 @@ async function predict7(image15, config3) {
}
// src/movenet/movenet.ts
var tf15 = __toModule(require_tfjs_esm());
var tf16 = __toModule(require_tfjs_esm());
var model6;
var keypoints2 = [];
var box5 = [0, 0, 0, 0];
@ -8479,7 +8485,7 @@ var skipped4 = Number.MAX_SAFE_INTEGER;
var bodyParts2 = ["nose", "leftEye", "rightEye", "leftEar", "rightEar", "leftShoulder", "rightShoulder", "leftElbow", "rightElbow", "leftWrist", "rightWrist", "leftHip", "rightHip", "leftKnee", "rightKnee", "leftAnkle", "rightAnkle"];
async function load9(config3) {
if (!model6) {
model6 = await tf15.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
model6 = await tf16.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
if (!model6 || !model6["modelUrl"])
log("load model failed:", config3.body.modelPath);
else if (config3.debug)
@ -8495,11 +8501,11 @@ async function predict8(image15, config3) {
}
skipped4 = 0;
return new Promise(async (resolve) => {
const tensor = tf15.tidy(() => {
const tensor = tf16.tidy(() => {
if (!model6.inputs[0].shape)
return null;
const resize = tf15.image.resizeBilinear(image15, [model6.inputs[0].shape[2], model6.inputs[0].shape[1]], false);
const cast2 = tf15.cast(resize, "int32");
const resize = tf16.image.resizeBilinear(image15, [model6.inputs[0].shape[2], model6.inputs[0].shape[1]], false);
const cast2 = tf16.cast(resize, "int32");
return cast2;
});
let resT;
@ -8509,7 +8515,7 @@ async function predict8(image15, config3) {
if (resT) {
keypoints2.length = 0;
const res = resT.arraySync();
tf15.dispose(resT);
tf16.dispose(resT);
const kpt3 = res[0][0];
for (let id = 0; id < kpt3.length; id++) {
score2 = kpt3[id][2];
@ -8556,7 +8562,7 @@ __export(nanodet_exports, {
load: () => load10,
predict: () => predict9
});
var tf16 = __toModule(require_tfjs_esm());
var tf17 = __toModule(require_tfjs_esm());
// src/object/labels.ts
var labels = [
@ -8649,7 +8655,7 @@ var skipped5 = Number.MAX_SAFE_INTEGER;
var scaleBox = 2.5;
async function load10(config3) {
if (!model7) {
model7 = await tf16.loadGraphModel(join(config3.modelBasePath, config3.object.modelPath));
model7 = await tf17.loadGraphModel(join(config3.modelBasePath, config3.object.modelPath));
const inputs = Object.values(model7.modelSignature["inputs"]);
model7.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;
if (!model7.inputSize)
@ -8666,7 +8672,7 @@ async function process2(res, inputSize, outputShape, config3) {
let id = 0;
let results = [];
for (const strideSize of [1, 2, 4]) {
tf16.tidy(() => {
tf17.tidy(() => {
var _a, _b;
const baseSize = strideSize * 13;
const scoresT = (_a = res.find((a) => a.shape[1] === baseSize ** 2 && a.shape[2] === labels.length)) == null ? void 0 : _a.squeeze();
@ -8711,14 +8717,14 @@ async function process2(res, inputSize, outputShape, config3) {
}
});
}
res.forEach((t) => tf16.dispose(t));
res.forEach((t) => tf17.dispose(t));
const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]);
const nmsScores = results.map((a) => a.score);
let nmsIdx = [];
if (nmsBoxes && nmsBoxes.length > 0) {
const nms = await tf16.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
const nms = await tf17.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
nmsIdx = nms.dataSync();
tf16.dispose(nms);
tf17.dispose(nms);
}
results = results.filter((a, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
return results;
@ -8731,7 +8737,7 @@ async function predict9(image15, config3) {
skipped5 = 0;
return new Promise(async (resolve) => {
const outputSize = [image15.shape[2], image15.shape[1]];
const resize = tf16.image.resizeBilinear(image15, [model7.inputSize, model7.inputSize], false);
const resize = tf17.image.resizeBilinear(image15, [model7.inputSize, model7.inputSize], false);
const norm = resize.div(255);
const transpose = norm.transpose([0, 3, 1, 2]);
norm.dispose();
@ -8752,13 +8758,13 @@ __export(centernet_exports, {
load: () => load11,
predict: () => predict10
});
var tf17 = __toModule(require_tfjs_esm());
var tf18 = __toModule(require_tfjs_esm());
var model8;
var last4 = [];
var skipped6 = Number.MAX_SAFE_INTEGER;
async function load11(config3) {
if (!model8) {
model8 = await tf17.loadGraphModel(join(config3.modelBasePath, config3.object.modelPath));
model8 = await tf18.loadGraphModel(join(config3.modelBasePath, config3.object.modelPath));
const inputs = Object.values(model8.modelSignature["inputs"]);
model8.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;
if (!model8.inputSize)
@ -8776,16 +8782,16 @@ async function process3(res, inputSize, outputShape, config3) {
return [];
const results = [];
const detections = res.arraySync();
const squeezeT = tf17.squeeze(res);
const squeezeT = tf18.squeeze(res);
res.dispose();
const arr = tf17.split(squeezeT, 6, 1);
const arr = tf18.split(squeezeT, 6, 1);
squeezeT.dispose();
const stackT = tf17.stack([arr[1], arr[0], arr[3], arr[2]], 1);
const stackT = tf18.stack([arr[1], arr[0], arr[3], arr[2]], 1);
const boxesT = stackT.squeeze();
const scoresT = arr[4].squeeze();
const classesT = arr[5].squeeze();
arr.forEach((t) => t.dispose());
const nmsT = await tf17.image.nonMaxSuppressionAsync(boxesT, scoresT, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
const nmsT = await tf18.image.nonMaxSuppressionAsync(boxesT, scoresT, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
boxesT.dispose();
scoresT.dispose();
classesT.dispose();
@ -8820,7 +8826,7 @@ async function predict10(input, config3) {
skipped6 = 0;
return new Promise(async (resolve) => {
const outputSize = [input.shape[2], input.shape[1]];
const resize = tf17.image.resizeBilinear(input, [model8.inputSize, model8.inputSize]);
const resize = tf18.image.resizeBilinear(input, [model8.inputSize, model8.inputSize]);
const objectT = config3.object.enabled ? model8.execute(resize, ["tower_0/detections"]) : null;
resize.dispose();
const obj = await process3(objectT, model8.inputSize, outputSize, config3);
@ -8938,7 +8944,7 @@ var hand = (res) => {
};
// src/image/image.ts
var tf18 = __toModule(require_tfjs_esm());
var tf19 = __toModule(require_tfjs_esm());
// src/image/imagefx.js
function GLProgram(gl, vertexSource, fragmentSource) {
@ -9655,12 +9661,12 @@ function process4(input, config3) {
let tensor;
if (!input)
throw new Error("Human: Input is missing");
if (!(input instanceof tf18.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) {
if (!(input instanceof tf19.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) {
throw new Error("Human: Input type is not recognized");
}
if (input instanceof tf18.Tensor) {
if (input instanceof tf19.Tensor) {
if (input.shape && input.shape.length === 4 && input.shape[0] === 1 && input.shape[3] === 3)
tensor = tf18.clone(input);
tensor = tf19.clone(input);
else
throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${input.shape}`);
} else {
@ -9715,7 +9721,7 @@ function process4(input, config3) {
outCanvas.width = inCanvas == null ? void 0 : inCanvas.width;
if ((outCanvas == null ? void 0 : outCanvas.height) !== (inCanvas == null ? void 0 : inCanvas.height))
outCanvas.height = inCanvas == null ? void 0 : inCanvas.height;
fx = tf18.ENV.flags.IS_BROWSER ? new GLImageFilter({ canvas: outCanvas }) : null;
fx = tf19.ENV.flags.IS_BROWSER ? new GLImageFilter({ canvas: outCanvas }) : null;
}
if (!fx)
return { tensor: null, canvas: inCanvas };
@ -9756,16 +9762,16 @@ function process4(input, config3) {
let pixels;
if (outCanvas.data) {
const shape = [outCanvas.height, outCanvas.width, 3];
pixels = tf18.tensor3d(outCanvas.data, shape, "int32");
pixels = tf19.tensor3d(outCanvas.data, shape, "int32");
} else if (outCanvas instanceof ImageData) {
pixels = tf18.browser.fromPixels(outCanvas);
pixels = tf19.browser.fromPixels(outCanvas);
} else if (config3.backend === "webgl" || config3.backend === "humangl") {
const tempCanvas = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement("canvas");
tempCanvas.width = targetWidth;
tempCanvas.height = targetHeight;
const tempCtx = tempCanvas.getContext("2d");
tempCtx == null ? void 0 : tempCtx.drawImage(outCanvas, 0, 0);
pixels = tf18.browser.fromPixels(tempCanvas);
pixels = tf19.browser.fromPixels(tempCanvas);
} else {
const tempCanvas = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement("canvas");
tempCanvas.width = targetWidth;
@ -9773,7 +9779,7 @@ function process4(input, config3) {
const tempCtx = tempCanvas.getContext("2d");
tempCtx == null ? void 0 : tempCtx.drawImage(outCanvas, 0, 0);
const data = tempCtx == null ? void 0 : tempCtx.getImageData(0, 0, targetWidth, targetHeight);
pixels = tf18.browser.fromPixels(data);
pixels = tf19.browser.fromPixels(data);
}
const casted = pixels.toFloat();
tensor = casted.expandDims(0);
@ -9927,9 +9933,9 @@ async function face2(inCanvas2, result, drawOptions) {
if (localOptions.drawBoxes)
rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);
const labels2 = [];
labels2.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);
if (f.genderConfidence)
labels2.push(`${f.gender || ""} ${Math.trunc(100 * f.genderConfidence)}% confident`);
labels2.push(`face confidence: ${Math.trunc(100 * f.score)}%`);
if (f.genderScore)
labels2.push(`${f.gender || ""} ${Math.trunc(100 * f.genderScore)}% confident`);
if (f.age)
labels2.push(`age: ${f.age || ""}`);
if (f.iris)
@ -10361,6 +10367,7 @@ function join2(faces, bodies, hands, gestures, shape) {
// src/interpolate.ts
var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0 };
function calc(newResult) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u;
const bufferedFactor = 1e3 / (Date.now() - newResult.timestamp) / 4;
if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) {
bufferedResult.body = JSON.parse(JSON.stringify(newResult.body));
@ -10404,17 +10411,17 @@ function calc(newResult) {
for (let i = 0; i < newResult.face.length; i++) {
const box6 = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor);
const boxRaw3 = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor);
const matrix = newResult.face[i].rotation.matrix;
const angle = {
roll: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.angle.roll + newResult.face[i].rotation.angle.roll) / bufferedFactor,
yaw: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.angle.yaw + newResult.face[i].rotation.angle.yaw) / bufferedFactor,
pitch: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.angle.pitch + newResult.face[i].rotation.angle.pitch) / bufferedFactor
const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };
rotation.matrix = (_a = newResult.face[i].rotation) == null ? void 0 : _a.matrix;
rotation.angle = {
roll: ((bufferedFactor - 1) * (((_c = (_b = bufferedResult.face[i].rotation) == null ? void 0 : _b.angle) == null ? void 0 : _c.roll) || 0) + (((_e = (_d = newResult.face[i].rotation) == null ? void 0 : _d.angle) == null ? void 0 : _e.roll) || 0)) / bufferedFactor,
yaw: ((bufferedFactor - 1) * (((_g = (_f = bufferedResult.face[i].rotation) == null ? void 0 : _f.angle) == null ? void 0 : _g.yaw) || 0) + (((_i = (_h = newResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.yaw) || 0)) / bufferedFactor,
pitch: ((bufferedFactor - 1) * (((_k = (_j = bufferedResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.pitch) || 0) + (((_m = (_l = newResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.pitch) || 0)) / bufferedFactor
};
const gaze = {
bearing: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.gaze.bearing + newResult.face[i].rotation.gaze.bearing) / bufferedFactor,
strength: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.gaze.strength + newResult.face[i].rotation.gaze.strength) / bufferedFactor
rotation.gaze = {
bearing: ((bufferedFactor - 1) * (((_o = (_n = bufferedResult.face[i].rotation) == null ? void 0 : _n.gaze) == null ? void 0 : _o.bearing) || 0) + (((_q = (_p = newResult.face[i].rotation) == null ? void 0 : _p.gaze) == null ? void 0 : _q.bearing) || 0)) / bufferedFactor,
strength: ((bufferedFactor - 1) * (((_s = (_r = bufferedResult.face[i].rotation) == null ? void 0 : _r.gaze) == null ? void 0 : _s.strength) || 0) + (((_u = (_t = newResult.face[i].rotation) == null ? void 0 : _t.gaze) == null ? void 0 : _u.strength) || 0)) / bufferedFactor
};
const rotation = { angle, matrix, gaze };
bufferedResult.face[i] = { ...newResult.face[i], rotation, box: box6, boxRaw: boxRaw3 };
}
}
@ -11191,7 +11198,7 @@ var Human = class {
return null;
if (!input)
return "input is not defined";
if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf19.Tensor))
if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf20.Tensor))
return "input must be a tensor";
try {
this.tf.getBackend();
@ -11334,8 +11341,8 @@ var Human = class {
if (!img)
return null;
let res;
if (typeof tf19["node"] !== "undefined") {
const data = tf19["node"].decodeJpeg(img);
if (typeof tf20["node"] !== "undefined") {
const data = tf20["node"].decodeJpeg(img);
const expanded = data.expandDims(0);
this.tf.dispose(data);
res = await this.detect(expanded, this.config);
@ -11347,7 +11354,7 @@ var Human = class {
return res;
});
this.config = mergeDeep(config, userConfig || {});
this.tf = tf19;
this.tf = tf20;
this.draw = draw_exports;
this.version = version;
this.state = "idle";
@ -11613,7 +11620,7 @@ var Human = class {
return join2(faceRes, bodyRes, handRes, gestureRes, (_a = process5 == null ? void 0 : process5.tensor) == null ? void 0 : _a.shape);
}
};
tf19.dispose(process5.tensor);
tf20.dispose(process5.tensor);
resolve(this.result);
});
}

233
dist/human.node-wasm.js vendored
View File

@ -237,7 +237,7 @@ function info() {
}
// src/human.ts
var tf19 = __toModule(require_tfjs_esm());
var tf20 = __toModule(require_tfjs_esm());
// src/tfjs/backend.ts
var tf = __toModule(require_tfjs_esm());
@ -307,6 +307,9 @@ function register() {
}
}
// src/face.ts
var tf8 = __toModule(require_tfjs_esm());
// src/blazeface/facemesh.ts
var facemesh_exports = {};
__export(facemesh_exports, {
@ -4077,6 +4080,7 @@ var facePipeline;
async function predict(input, config3) {
const predictions = await facePipeline.predict(input, config3);
const results = [];
let id = 0;
for (const prediction of predictions || []) {
if (!prediction || prediction.isDisposedInternal)
continue;
@ -4095,23 +4099,25 @@ async function predict(input, config3) {
Math.max(0, prediction.box.startPoint[1]),
Math.min(input.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),
Math.min(input.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1])
] : 0;
] : [0, 0, 0, 0];
const boxRaw3 = prediction.box ? [
prediction.box.startPoint[0] / input.shape[2],
prediction.box.startPoint[1] / input.shape[1],
(prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],
(prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1]
] : [];
] : [0, 0, 0, 0];
results.push({
confidence: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,
boxConfidence: Math.round(100 * prediction.boxConfidence) / 100,
faceConfidence: Math.round(100 * prediction.faceConfidence) / 100,
id: id++,
score: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,
boxScore: Math.round(100 * prediction.boxConfidence) / 100,
faceScore: Math.round(100 * prediction.faceConfidence) / 100,
box: clampedBox,
boxRaw: boxRaw3,
mesh: prediction.mesh,
meshRaw,
annotations: annotations3,
image: prediction.image
image: prediction.image,
tensor: prediction.image
});
if (prediction.coords)
prediction.coords.dispose();
@ -4422,7 +4428,7 @@ var calculateFaceAngle = (face5, imageSize) => {
return { angle, matrix, gaze };
};
var detectFace = async (parent, input) => {
var _a, _b, _c, _d, _e, _f, _g, _h;
var _a, _b, _c, _d, _e, _f;
let timeStamp;
let ageRes;
let genderRes;
@ -4440,7 +4446,7 @@ var detectFace = async (parent, input) => {
return [];
for (let i = 0; i < faces.length; i++) {
parent.analyze("Get Face");
if (!faces[i].image || faces[i].image.isDisposedInternal) {
if (!faces[i].image || faces[i].image["isDisposedInternal"]) {
log("Face object is disposed:", faces[i].image);
continue;
}
@ -4475,18 +4481,18 @@ var detectFace = async (parent, input) => {
}
const irisSize = ((_e = faces[i].annotations) == null ? void 0 : _e.leftEyeIris) && ((_f = faces[i].annotations) == null ? void 0 : _f.rightEyeIris) ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2] : 0;
faceRes.push({
id: i,
...faces[i],
id: i,
age: descRes.age,
gender: descRes.gender,
genderConfidence: descRes.genderConfidence,
genderScore: descRes.genderConfidence,
embedding: descRes.descriptor,
emotion: emotionRes,
iris: irisSize !== 0 ? Math.trunc(500 / irisSize / 11.7) / 100 : 0,
rotation,
tensor: parent.config.face.detector.return ? (_g = faces[i].image) == null ? void 0 : _g.squeeze() : null
tensor: parent.config.face.detector.return ? tf8.squeeze(faces[i].image) : null
});
(_h = faces[i].image) == null ? void 0 : _h.dispose();
tf8.dispose(faces[i].image);
parent.analyze("End Face");
}
parent.analyze("End FaceMesh:");
@ -4509,7 +4515,7 @@ __export(posenet_exports, {
load: () => load5,
predict: () => predict4
});
var tf8 = __toModule(require_tfjs_esm());
var tf9 = __toModule(require_tfjs_esm());
// src/posenet/keypoints.ts
var partNames = [
@ -4821,7 +4827,7 @@ function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected
var model3;
var poseNetOutputs = ["MobilenetV1/offset_2/BiasAdd", "MobilenetV1/heatmap_2/BiasAdd", "MobilenetV1/displacement_fwd_2/BiasAdd", "MobilenetV1/displacement_bwd_2/BiasAdd"];
async function predict4(input, config3) {
const res = tf8.tidy(() => {
const res = tf9.tidy(() => {
if (!model3.inputs[0].shape)
return [];
const resized = input.resizeBilinear([model3.inputs[0].shape[2], model3.inputs[0].shape[1]]);
@ -4842,7 +4848,7 @@ async function predict4(input, config3) {
}
async function load5(config3) {
if (!model3) {
model3 = await tf8.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
model3 = await tf9.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
if (!model3 || !model3["modelUrl"])
log("load model failed:", config3.body.modelPath);
else if (config3.debug)
@ -4858,13 +4864,13 @@ __export(handpose_exports, {
load: () => load6,
predict: () => predict5
});
var tf12 = __toModule(require_tfjs_esm());
var tf13 = __toModule(require_tfjs_esm());
// src/handpose/handdetector.ts
var tf10 = __toModule(require_tfjs_esm());
var tf11 = __toModule(require_tfjs_esm());
// src/handpose/box.ts
var tf9 = __toModule(require_tfjs_esm());
var tf10 = __toModule(require_tfjs_esm());
function getBoxSize2(box6) {
return [
Math.abs(box6.endPoint[0] - box6.startPoint[0]),
@ -4886,7 +4892,7 @@ function cutBoxFromImageAndResize2(box6, image15, cropSize) {
box6.endPoint[1] / h,
box6.endPoint[0] / w
]];
return tf9.image.cropAndResize(image15, boxes, [0], cropSize);
return tf10.image.cropAndResize(image15, boxes, [0], cropSize);
}
function scaleBoxCoordinates2(box6, factor) {
const startPoint = [box6.startPoint[0] * factor[0], box6.startPoint[1] * factor[1]];
@ -7869,47 +7875,47 @@ var HandDetector = class {
var _a;
this.model = model9;
this.anchors = anchors.map((anchor) => [anchor.x, anchor.y]);
this.anchorsTensor = tf10.tensor2d(this.anchors);
this.anchorsTensor = tf11.tensor2d(this.anchors);
this.inputSize = (_a = this.model) == null ? void 0 : _a.inputs[0].shape[2];
this.inputSizeTensor = tf10.tensor1d([this.inputSize, this.inputSize]);
this.doubleInputSizeTensor = tf10.tensor1d([this.inputSize * 2, this.inputSize * 2]);
this.inputSizeTensor = tf11.tensor1d([this.inputSize, this.inputSize]);
this.doubleInputSizeTensor = tf11.tensor1d([this.inputSize * 2, this.inputSize * 2]);
}
normalizeBoxes(boxes) {
return tf10.tidy(() => {
const boxOffsets = tf10.slice(boxes, [0, 0], [-1, 2]);
const boxSizes = tf10.slice(boxes, [0, 2], [-1, 2]);
const boxCenterPoints = tf10.add(tf10.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);
const halfBoxSizes = tf10.div(boxSizes, this.doubleInputSizeTensor);
const startPoints = tf10.mul(tf10.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);
const endPoints = tf10.mul(tf10.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);
return tf10.concat2d([startPoints, endPoints], 1);
return tf11.tidy(() => {
const boxOffsets = tf11.slice(boxes, [0, 0], [-1, 2]);
const boxSizes = tf11.slice(boxes, [0, 2], [-1, 2]);
const boxCenterPoints = tf11.add(tf11.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);
const halfBoxSizes = tf11.div(boxSizes, this.doubleInputSizeTensor);
const startPoints = tf11.mul(tf11.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);
const endPoints = tf11.mul(tf11.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);
return tf11.concat2d([startPoints, endPoints], 1);
});
}
normalizeLandmarks(rawPalmLandmarks, index) {
return tf10.tidy(() => {
const landmarks = tf10.add(tf10.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);
return tf10.mul(landmarks, this.inputSizeTensor);
return tf11.tidy(() => {
const landmarks = tf11.add(tf11.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);
return tf11.mul(landmarks, this.inputSizeTensor);
});
}
async getBoxes(input, config3) {
const batched = this.model.predict(input);
const predictions = batched.squeeze();
batched.dispose();
const scoresT = tf10.tidy(() => tf10.sigmoid(tf10.slice(predictions, [0, 0], [-1, 1])).squeeze());
const scoresT = tf11.tidy(() => tf11.sigmoid(tf11.slice(predictions, [0, 0], [-1, 1])).squeeze());
const scores = scoresT.dataSync();
const rawBoxes = tf10.slice(predictions, [0, 1], [-1, 4]);
const rawBoxes = tf11.slice(predictions, [0, 1], [-1, 4]);
const boxes = this.normalizeBoxes(rawBoxes);
rawBoxes.dispose();
const filteredT = await tf10.image.nonMaxSuppressionAsync(boxes, scores, config3.hand.maxDetected, config3.hand.iouThreshold, config3.hand.minConfidence);
const filteredT = await tf11.image.nonMaxSuppressionAsync(boxes, scores, config3.hand.maxDetected, config3.hand.iouThreshold, config3.hand.minConfidence);
const filtered = filteredT.arraySync();
scoresT.dispose();
filteredT.dispose();
const hands = [];
for (const index of filtered) {
if (scores[index] >= config3.hand.minConfidence) {
const matchingBox = tf10.slice(boxes, [index, 0], [1, -1]);
const rawPalmLandmarks = tf10.slice(predictions, [index, 5], [1, 14]);
const palmLandmarks = tf10.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));
const matchingBox = tf11.slice(boxes, [index, 0], [1, -1]);
const rawPalmLandmarks = tf11.slice(predictions, [index, 5], [1, 14]);
const palmLandmarks = tf11.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));
rawPalmLandmarks.dispose();
hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });
}
@ -7921,7 +7927,7 @@ var HandDetector = class {
async estimateHandBounds(input, config3) {
const inputHeight = input.shape[1];
const inputWidth = input.shape[2];
const image15 = tf10.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));
const image15 = tf11.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));
const predictions = await this.getBoxes(image15, config3);
image15.dispose();
const hands = [];
@ -7941,7 +7947,7 @@ var HandDetector = class {
};
// src/handpose/handpipeline.ts
var tf11 = __toModule(require_tfjs_esm());
var tf12 = __toModule(require_tfjs_esm());
// src/handpose/util.ts
function normalizeRadians2(angle) {
@ -8092,7 +8098,7 @@ var HandPipeline = class {
const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;
const palmCenter = getBoxCenter2(currentBox);
const palmCenterNormalized = [palmCenter[0] / image15.shape[2], palmCenter[1] / image15.shape[1]];
const rotatedImage = config3.hand.rotation && tf11.ENV.flags.IS_BROWSER ? tf11.image.rotateWithOffset(image15, angle, 0, palmCenterNormalized) : image15.clone();
const rotatedImage = config3.hand.rotation && tf12.ENV.flags.IS_BROWSER ? tf12.image.rotateWithOffset(image15, angle, 0, palmCenterNormalized) : image15.clone();
const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
const croppedInput = cutBoxFromImageAndResize2(newBox, rotatedImage, [this.inputSize, this.inputSize]);
@ -8104,7 +8110,7 @@ var HandPipeline = class {
const confidence = confidenceT.dataSync()[0];
confidenceT.dispose();
if (confidence >= config3.hand.minConfidence) {
const keypointsReshaped = tf11.reshape(keypoints3, [-1, 3]);
const keypointsReshaped = tf12.reshape(keypoints3, [-1, 3]);
const rawCoords = keypointsReshaped.arraySync();
keypoints3.dispose();
keypointsReshaped.dispose();
@ -8191,15 +8197,15 @@ async function predict5(input, config3) {
(predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / input.shape[1]
];
}
hands.push({ id: i, confidence: Math.round(100 * predictions[i].confidence) / 100, box: box6, boxRaw: boxRaw3, keypoints: keypoints3, annotations: annotations3 });
hands.push({ id: i, score: Math.round(100 * predictions[i].confidence) / 100, box: box6, boxRaw: boxRaw3, keypoints: keypoints3, annotations: annotations3 });
}
return hands;
}
async function load6(config3) {
if (!handDetectorModel || !handPoseModel) {
[handDetectorModel, handPoseModel] = await Promise.all([
config3.hand.enabled ? tf12.loadGraphModel(join(config3.modelBasePath, config3.hand.detector.modelPath), { fromTFHub: config3.hand.detector.modelPath.includes("tfhub.dev") }) : null,
config3.hand.landmarks ? tf12.loadGraphModel(join(config3.modelBasePath, config3.hand.skeleton.modelPath), { fromTFHub: config3.hand.skeleton.modelPath.includes("tfhub.dev") }) : null
config3.hand.enabled ? tf13.loadGraphModel(join(config3.modelBasePath, config3.hand.detector.modelPath), { fromTFHub: config3.hand.detector.modelPath.includes("tfhub.dev") }) : null,
config3.hand.landmarks ? tf13.loadGraphModel(join(config3.modelBasePath, config3.hand.skeleton.modelPath), { fromTFHub: config3.hand.skeleton.modelPath.includes("tfhub.dev") }) : null
]);
if (config3.hand.enabled) {
if (!handDetectorModel || !handDetectorModel["modelUrl"])
@ -8228,7 +8234,7 @@ __export(blazepose_exports, {
load: () => load7,
predict: () => predict6
});
var tf13 = __toModule(require_tfjs_esm());
var tf14 = __toModule(require_tfjs_esm());
// src/blazepose/annotations.ts
var full = [
@ -8310,7 +8316,7 @@ var upper = [
var model4;
async function load7(config3) {
if (!model4) {
model4 = await tf13.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
model4 = await tf14.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
model4["width"] = parseInt(model4["signature"].inputs["input_1:0"].tensorShape.dim[2].size);
model4["height"] = parseInt(model4["signature"].inputs["input_1:0"].tensorShape.dim[1].size);
if (!model4 || !model4["modelUrl"])
@ -8328,8 +8334,8 @@ async function predict6(image15, config3) {
if (!config3.body.enabled)
return [];
const imgSize = { width: image15.shape[2], height: image15.shape[1] };
const resize = tf13.image.resizeBilinear(image15, [model4["width"], model4["height"]], false);
const normalize = tf13.div(resize, [255]);
const resize = tf14.image.resizeBilinear(image15, [model4["width"], model4["height"]], false);
const normalize = tf14.div(resize, [255]);
resize.dispose();
const resT = await model4.predict(normalize);
const points = ((_a = resT.find((t) => t.size === 195 || t.size === 155)) == null ? void 0 : _a.dataSync()) || [];
@ -8370,7 +8376,7 @@ async function predict6(image15, config3) {
}
// src/efficientpose/efficientpose.ts
var tf14 = __toModule(require_tfjs_esm());
var tf15 = __toModule(require_tfjs_esm());
var model5;
var keypoints = [];
var box4 = [0, 0, 0, 0];
@ -8380,7 +8386,7 @@ var skipped3 = Number.MAX_SAFE_INTEGER;
var bodyParts = ["head", "neck", "rightShoulder", "rightElbow", "rightWrist", "chest", "leftShoulder", "leftElbow", "leftWrist", "pelvis", "rightHip", "rightKnee", "rightAnkle", "leftHip", "leftKnee", "leftAnkle"];
async function load8(config3) {
if (!model5) {
model5 = await tf14.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
model5 = await tf15.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
if (!model5 || !model5["modelUrl"])
log("load model failed:", config3.body.modelPath);
else if (config3.debug)
@ -8391,14 +8397,14 @@ async function load8(config3) {
}
function max2d(inputs, minScore) {
const [width, height] = inputs.shape;
return tf14.tidy(() => {
const mod = (a, b) => tf14.sub(a, tf14.mul(tf14.div(a, tf14.scalar(b, "int32")), tf14.scalar(b, "int32")));
const reshaped = tf14.reshape(inputs, [height * width]);
const newScore = tf14.max(reshaped, 0).dataSync()[0];
return tf15.tidy(() => {
const mod = (a, b) => tf15.sub(a, tf15.mul(tf15.div(a, tf15.scalar(b, "int32")), tf15.scalar(b, "int32")));
const reshaped = tf15.reshape(inputs, [height * width]);
const newScore = tf15.max(reshaped, 0).dataSync()[0];
if (newScore > minScore) {
const coords3 = tf14.argMax(reshaped, 0);
const coords3 = tf15.argMax(reshaped, 0);
const x = mod(coords3, width).dataSync()[0];
const y = tf14.div(coords3, tf14.scalar(width, "int32")).dataSync()[0];
const y = tf15.div(coords3, tf15.scalar(width, "int32")).dataSync()[0];
return [x, y, newScore];
}
return [0, 0, newScore];
@ -8411,11 +8417,11 @@ async function predict7(image15, config3) {
}
skipped3 = 0;
return new Promise(async (resolve) => {
const tensor = tf14.tidy(() => {
const tensor = tf15.tidy(() => {
if (!model5.inputs[0].shape)
return null;
const resize = tf14.image.resizeBilinear(image15, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
const enhance2 = tf14.mul(resize, 2);
const resize = tf15.image.resizeBilinear(image15, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
const enhance2 = tf15.mul(resize, 2);
const norm = enhance2.sub(1);
return norm;
});
@ -8425,10 +8431,10 @@ async function predict7(image15, config3) {
tensor.dispose();
if (resT) {
keypoints.length = 0;
const squeeze2 = resT.squeeze();
tf14.dispose(resT);
const stack2 = squeeze2.unstack(2);
tf14.dispose(squeeze2);
const squeeze3 = resT.squeeze();
tf15.dispose(resT);
const stack2 = squeeze3.unstack(2);
tf15.dispose(squeeze3);
for (let id = 0; id < stack2.length; id++) {
const [x2, y2, partScore] = max2d(stack2[id], config3.body.minConfidence);
if (score > config3.body.minConfidence) {
@ -8446,7 +8452,7 @@ async function predict7(image15, config3) {
});
}
}
stack2.forEach((s) => tf14.dispose(s));
stack2.forEach((s) => tf15.dispose(s));
}
score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
const x = keypoints.map((a) => a.position.x);
@ -8470,7 +8476,7 @@ async function predict7(image15, config3) {
}
// src/movenet/movenet.ts
var tf15 = __toModule(require_tfjs_esm());
var tf16 = __toModule(require_tfjs_esm());
var model6;
var keypoints2 = [];
var box5 = [0, 0, 0, 0];
@ -8480,7 +8486,7 @@ var skipped4 = Number.MAX_SAFE_INTEGER;
var bodyParts2 = ["nose", "leftEye", "rightEye", "leftEar", "rightEar", "leftShoulder", "rightShoulder", "leftElbow", "rightElbow", "leftWrist", "rightWrist", "leftHip", "rightHip", "leftKnee", "rightKnee", "leftAnkle", "rightAnkle"];
async function load9(config3) {
if (!model6) {
model6 = await tf15.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
model6 = await tf16.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
if (!model6 || !model6["modelUrl"])
log("load model failed:", config3.body.modelPath);
else if (config3.debug)
@ -8496,11 +8502,11 @@ async function predict8(image15, config3) {
}
skipped4 = 0;
return new Promise(async (resolve) => {
const tensor = tf15.tidy(() => {
const tensor = tf16.tidy(() => {
if (!model6.inputs[0].shape)
return null;
const resize = tf15.image.resizeBilinear(image15, [model6.inputs[0].shape[2], model6.inputs[0].shape[1]], false);
const cast2 = tf15.cast(resize, "int32");
const resize = tf16.image.resizeBilinear(image15, [model6.inputs[0].shape[2], model6.inputs[0].shape[1]], false);
const cast2 = tf16.cast(resize, "int32");
return cast2;
});
let resT;
@ -8510,7 +8516,7 @@ async function predict8(image15, config3) {
if (resT) {
keypoints2.length = 0;
const res = resT.arraySync();
tf15.dispose(resT);
tf16.dispose(resT);
const kpt3 = res[0][0];
for (let id = 0; id < kpt3.length; id++) {
score2 = kpt3[id][2];
@ -8557,7 +8563,7 @@ __export(nanodet_exports, {
load: () => load10,
predict: () => predict9
});
var tf16 = __toModule(require_tfjs_esm());
var tf17 = __toModule(require_tfjs_esm());
// src/object/labels.ts
var labels = [
@ -8650,7 +8656,7 @@ var skipped5 = Number.MAX_SAFE_INTEGER;
var scaleBox = 2.5;
async function load10(config3) {
if (!model7) {
model7 = await tf16.loadGraphModel(join(config3.modelBasePath, config3.object.modelPath));
model7 = await tf17.loadGraphModel(join(config3.modelBasePath, config3.object.modelPath));
const inputs = Object.values(model7.modelSignature["inputs"]);
model7.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;
if (!model7.inputSize)
@ -8667,7 +8673,7 @@ async function process2(res, inputSize, outputShape, config3) {
let id = 0;
let results = [];
for (const strideSize of [1, 2, 4]) {
tf16.tidy(() => {
tf17.tidy(() => {
var _a, _b;
const baseSize = strideSize * 13;
const scoresT = (_a = res.find((a) => a.shape[1] === baseSize ** 2 && a.shape[2] === labels.length)) == null ? void 0 : _a.squeeze();
@ -8712,14 +8718,14 @@ async function process2(res, inputSize, outputShape, config3) {
}
});
}
res.forEach((t) => tf16.dispose(t));
res.forEach((t) => tf17.dispose(t));
const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]);
const nmsScores = results.map((a) => a.score);
let nmsIdx = [];
if (nmsBoxes && nmsBoxes.length > 0) {
const nms = await tf16.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
const nms = await tf17.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
nmsIdx = nms.dataSync();
tf16.dispose(nms);
tf17.dispose(nms);
}
results = results.filter((a, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
return results;
@ -8732,7 +8738,7 @@ async function predict9(image15, config3) {
skipped5 = 0;
return new Promise(async (resolve) => {
const outputSize = [image15.shape[2], image15.shape[1]];
const resize = tf16.image.resizeBilinear(image15, [model7.inputSize, model7.inputSize], false);
const resize = tf17.image.resizeBilinear(image15, [model7.inputSize, model7.inputSize], false);
const norm = resize.div(255);
const transpose = norm.transpose([0, 3, 1, 2]);
norm.dispose();
@ -8753,13 +8759,13 @@ __export(centernet_exports, {
load: () => load11,
predict: () => predict10
});
var tf17 = __toModule(require_tfjs_esm());
var tf18 = __toModule(require_tfjs_esm());
var model8;
var last4 = [];
var skipped6 = Number.MAX_SAFE_INTEGER;
async function load11(config3) {
if (!model8) {
model8 = await tf17.loadGraphModel(join(config3.modelBasePath, config3.object.modelPath));
model8 = await tf18.loadGraphModel(join(config3.modelBasePath, config3.object.modelPath));
const inputs = Object.values(model8.modelSignature["inputs"]);
model8.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;
if (!model8.inputSize)
@ -8777,16 +8783,16 @@ async function process3(res, inputSize, outputShape, config3) {
return [];
const results = [];
const detections = res.arraySync();
const squeezeT = tf17.squeeze(res);
const squeezeT = tf18.squeeze(res);
res.dispose();
const arr = tf17.split(squeezeT, 6, 1);
const arr = tf18.split(squeezeT, 6, 1);
squeezeT.dispose();
const stackT = tf17.stack([arr[1], arr[0], arr[3], arr[2]], 1);
const stackT = tf18.stack([arr[1], arr[0], arr[3], arr[2]], 1);
const boxesT = stackT.squeeze();
const scoresT = arr[4].squeeze();
const classesT = arr[5].squeeze();
arr.forEach((t) => t.dispose());
const nmsT = await tf17.image.nonMaxSuppressionAsync(boxesT, scoresT, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
const nmsT = await tf18.image.nonMaxSuppressionAsync(boxesT, scoresT, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
boxesT.dispose();
scoresT.dispose();
classesT.dispose();
@ -8821,7 +8827,7 @@ async function predict10(input, config3) {
skipped6 = 0;
return new Promise(async (resolve) => {
const outputSize = [input.shape[2], input.shape[1]];
const resize = tf17.image.resizeBilinear(input, [model8.inputSize, model8.inputSize]);
const resize = tf18.image.resizeBilinear(input, [model8.inputSize, model8.inputSize]);
const objectT = config3.object.enabled ? model8.execute(resize, ["tower_0/detections"]) : null;
resize.dispose();
const obj = await process3(objectT, model8.inputSize, outputSize, config3);
@ -8939,7 +8945,7 @@ var hand = (res) => {
};
// src/image/image.ts
var tf18 = __toModule(require_tfjs_esm());
var tf19 = __toModule(require_tfjs_esm());
// src/image/imagefx.js
function GLProgram(gl, vertexSource, fragmentSource) {
@ -9656,12 +9662,12 @@ function process4(input, config3) {
let tensor;
if (!input)
throw new Error("Human: Input is missing");
if (!(input instanceof tf18.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) {
if (!(input instanceof tf19.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) {
throw new Error("Human: Input type is not recognized");
}
if (input instanceof tf18.Tensor) {
if (input instanceof tf19.Tensor) {
if (input.shape && input.shape.length === 4 && input.shape[0] === 1 && input.shape[3] === 3)
tensor = tf18.clone(input);
tensor = tf19.clone(input);
else
throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${input.shape}`);
} else {
@ -9716,7 +9722,7 @@ function process4(input, config3) {
outCanvas.width = inCanvas == null ? void 0 : inCanvas.width;
if ((outCanvas == null ? void 0 : outCanvas.height) !== (inCanvas == null ? void 0 : inCanvas.height))
outCanvas.height = inCanvas == null ? void 0 : inCanvas.height;
fx = tf18.ENV.flags.IS_BROWSER ? new GLImageFilter({ canvas: outCanvas }) : null;
fx = tf19.ENV.flags.IS_BROWSER ? new GLImageFilter({ canvas: outCanvas }) : null;
}
if (!fx)
return { tensor: null, canvas: inCanvas };
@ -9757,16 +9763,16 @@ function process4(input, config3) {
let pixels;
if (outCanvas.data) {
const shape = [outCanvas.height, outCanvas.width, 3];
pixels = tf18.tensor3d(outCanvas.data, shape, "int32");
pixels = tf19.tensor3d(outCanvas.data, shape, "int32");
} else if (outCanvas instanceof ImageData) {
pixels = tf18.browser.fromPixels(outCanvas);
pixels = tf19.browser.fromPixels(outCanvas);
} else if (config3.backend === "webgl" || config3.backend === "humangl") {
const tempCanvas = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement("canvas");
tempCanvas.width = targetWidth;
tempCanvas.height = targetHeight;
const tempCtx = tempCanvas.getContext("2d");
tempCtx == null ? void 0 : tempCtx.drawImage(outCanvas, 0, 0);
pixels = tf18.browser.fromPixels(tempCanvas);
pixels = tf19.browser.fromPixels(tempCanvas);
} else {
const tempCanvas = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement("canvas");
tempCanvas.width = targetWidth;
@ -9774,7 +9780,7 @@ function process4(input, config3) {
const tempCtx = tempCanvas.getContext("2d");
tempCtx == null ? void 0 : tempCtx.drawImage(outCanvas, 0, 0);
const data = tempCtx == null ? void 0 : tempCtx.getImageData(0, 0, targetWidth, targetHeight);
pixels = tf18.browser.fromPixels(data);
pixels = tf19.browser.fromPixels(data);
}
const casted = pixels.toFloat();
tensor = casted.expandDims(0);
@ -9928,9 +9934,9 @@ async function face2(inCanvas2, result, drawOptions) {
if (localOptions.drawBoxes)
rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);
const labels2 = [];
labels2.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);
if (f.genderConfidence)
labels2.push(`${f.gender || ""} ${Math.trunc(100 * f.genderConfidence)}% confident`);
labels2.push(`face confidence: ${Math.trunc(100 * f.score)}%`);
if (f.genderScore)
labels2.push(`${f.gender || ""} ${Math.trunc(100 * f.genderScore)}% confident`);
if (f.age)
labels2.push(`age: ${f.age || ""}`);
if (f.iris)
@ -10362,6 +10368,7 @@ function join2(faces, bodies, hands, gestures, shape) {
// src/interpolate.ts
var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0 };
function calc(newResult) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u;
const bufferedFactor = 1e3 / (Date.now() - newResult.timestamp) / 4;
if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) {
bufferedResult.body = JSON.parse(JSON.stringify(newResult.body));
@ -10405,17 +10412,17 @@ function calc(newResult) {
for (let i = 0; i < newResult.face.length; i++) {
const box6 = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor);
const boxRaw3 = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor);
const matrix = newResult.face[i].rotation.matrix;
const angle = {
roll: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.angle.roll + newResult.face[i].rotation.angle.roll) / bufferedFactor,
yaw: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.angle.yaw + newResult.face[i].rotation.angle.yaw) / bufferedFactor,
pitch: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.angle.pitch + newResult.face[i].rotation.angle.pitch) / bufferedFactor
const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };
rotation.matrix = (_a = newResult.face[i].rotation) == null ? void 0 : _a.matrix;
rotation.angle = {
roll: ((bufferedFactor - 1) * (((_c = (_b = bufferedResult.face[i].rotation) == null ? void 0 : _b.angle) == null ? void 0 : _c.roll) || 0) + (((_e = (_d = newResult.face[i].rotation) == null ? void 0 : _d.angle) == null ? void 0 : _e.roll) || 0)) / bufferedFactor,
yaw: ((bufferedFactor - 1) * (((_g = (_f = bufferedResult.face[i].rotation) == null ? void 0 : _f.angle) == null ? void 0 : _g.yaw) || 0) + (((_i = (_h = newResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.yaw) || 0)) / bufferedFactor,
pitch: ((bufferedFactor - 1) * (((_k = (_j = bufferedResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.pitch) || 0) + (((_m = (_l = newResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.pitch) || 0)) / bufferedFactor
};
const gaze = {
bearing: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.gaze.bearing + newResult.face[i].rotation.gaze.bearing) / bufferedFactor,
strength: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.gaze.strength + newResult.face[i].rotation.gaze.strength) / bufferedFactor
rotation.gaze = {
bearing: ((bufferedFactor - 1) * (((_o = (_n = bufferedResult.face[i].rotation) == null ? void 0 : _n.gaze) == null ? void 0 : _o.bearing) || 0) + (((_q = (_p = newResult.face[i].rotation) == null ? void 0 : _p.gaze) == null ? void 0 : _q.bearing) || 0)) / bufferedFactor,
strength: ((bufferedFactor - 1) * (((_s = (_r = bufferedResult.face[i].rotation) == null ? void 0 : _r.gaze) == null ? void 0 : _s.strength) || 0) + (((_u = (_t = newResult.face[i].rotation) == null ? void 0 : _t.gaze) == null ? void 0 : _u.strength) || 0)) / bufferedFactor
};
const rotation = { angle, matrix, gaze };
bufferedResult.face[i] = { ...newResult.face[i], rotation, box: box6, boxRaw: boxRaw3 };
}
}
@ -11192,7 +11199,7 @@ var Human = class {
return null;
if (!input)
return "input is not defined";
if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf19.Tensor))
if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf20.Tensor))
return "input must be a tensor";
try {
this.tf.getBackend();
@ -11335,8 +11342,8 @@ var Human = class {
if (!img)
return null;
let res;
if (typeof tf19["node"] !== "undefined") {
const data = tf19["node"].decodeJpeg(img);
if (typeof tf20["node"] !== "undefined") {
const data = tf20["node"].decodeJpeg(img);
const expanded = data.expandDims(0);
this.tf.dispose(data);
res = await this.detect(expanded, this.config);
@ -11348,7 +11355,7 @@ var Human = class {
return res;
});
this.config = mergeDeep(config, userConfig || {});
this.tf = tf19;
this.tf = tf20;
this.draw = draw_exports;
this.version = version;
this.state = "idle";
@ -11614,7 +11621,7 @@ var Human = class {
return join2(faceRes, bodyRes, handRes, gestureRes, (_a = process5 == null ? void 0 : process5.tensor) == null ? void 0 : _a.shape);
}
};
tf19.dispose(process5.tensor);
tf20.dispose(process5.tensor);
resolve(this.result);
});
}

233
dist/human.node.js vendored
View File

@ -236,7 +236,7 @@ function info() {
}
// src/human.ts
var tf19 = __toModule(require_tfjs_esm());
var tf20 = __toModule(require_tfjs_esm());
// src/tfjs/backend.ts
var tf = __toModule(require_tfjs_esm());
@ -306,6 +306,9 @@ function register() {
}
}
// src/face.ts
var tf8 = __toModule(require_tfjs_esm());
// src/blazeface/facemesh.ts
var facemesh_exports = {};
__export(facemesh_exports, {
@ -4076,6 +4079,7 @@ var facePipeline;
async function predict(input, config3) {
const predictions = await facePipeline.predict(input, config3);
const results = [];
let id = 0;
for (const prediction of predictions || []) {
if (!prediction || prediction.isDisposedInternal)
continue;
@ -4094,23 +4098,25 @@ async function predict(input, config3) {
Math.max(0, prediction.box.startPoint[1]),
Math.min(input.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),
Math.min(input.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1])
] : 0;
] : [0, 0, 0, 0];
const boxRaw3 = prediction.box ? [
prediction.box.startPoint[0] / input.shape[2],
prediction.box.startPoint[1] / input.shape[1],
(prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],
(prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1]
] : [];
] : [0, 0, 0, 0];
results.push({
confidence: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,
boxConfidence: Math.round(100 * prediction.boxConfidence) / 100,
faceConfidence: Math.round(100 * prediction.faceConfidence) / 100,
id: id++,
score: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,
boxScore: Math.round(100 * prediction.boxConfidence) / 100,
faceScore: Math.round(100 * prediction.faceConfidence) / 100,
box: clampedBox,
boxRaw: boxRaw3,
mesh: prediction.mesh,
meshRaw,
annotations: annotations3,
image: prediction.image
image: prediction.image,
tensor: prediction.image
});
if (prediction.coords)
prediction.coords.dispose();
@ -4421,7 +4427,7 @@ var calculateFaceAngle = (face5, imageSize) => {
return { angle, matrix, gaze };
};
var detectFace = async (parent, input) => {
var _a, _b, _c, _d, _e, _f, _g, _h;
var _a, _b, _c, _d, _e, _f;
let timeStamp;
let ageRes;
let genderRes;
@ -4439,7 +4445,7 @@ var detectFace = async (parent, input) => {
return [];
for (let i = 0; i < faces.length; i++) {
parent.analyze("Get Face");
if (!faces[i].image || faces[i].image.isDisposedInternal) {
if (!faces[i].image || faces[i].image["isDisposedInternal"]) {
log("Face object is disposed:", faces[i].image);
continue;
}
@ -4474,18 +4480,18 @@ var detectFace = async (parent, input) => {
}
const irisSize = ((_e = faces[i].annotations) == null ? void 0 : _e.leftEyeIris) && ((_f = faces[i].annotations) == null ? void 0 : _f.rightEyeIris) ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2] : 0;
faceRes.push({
id: i,
...faces[i],
id: i,
age: descRes.age,
gender: descRes.gender,
genderConfidence: descRes.genderConfidence,
genderScore: descRes.genderConfidence,
embedding: descRes.descriptor,
emotion: emotionRes,
iris: irisSize !== 0 ? Math.trunc(500 / irisSize / 11.7) / 100 : 0,
rotation,
tensor: parent.config.face.detector.return ? (_g = faces[i].image) == null ? void 0 : _g.squeeze() : null
tensor: parent.config.face.detector.return ? tf8.squeeze(faces[i].image) : null
});
(_h = faces[i].image) == null ? void 0 : _h.dispose();
tf8.dispose(faces[i].image);
parent.analyze("End Face");
}
parent.analyze("End FaceMesh:");
@ -4508,7 +4514,7 @@ __export(posenet_exports, {
load: () => load5,
predict: () => predict4
});
var tf8 = __toModule(require_tfjs_esm());
var tf9 = __toModule(require_tfjs_esm());
// src/posenet/keypoints.ts
var partNames = [
@ -4820,7 +4826,7 @@ function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected
var model3;
var poseNetOutputs = ["MobilenetV1/offset_2/BiasAdd", "MobilenetV1/heatmap_2/BiasAdd", "MobilenetV1/displacement_fwd_2/BiasAdd", "MobilenetV1/displacement_bwd_2/BiasAdd"];
async function predict4(input, config3) {
const res = tf8.tidy(() => {
const res = tf9.tidy(() => {
if (!model3.inputs[0].shape)
return [];
const resized = input.resizeBilinear([model3.inputs[0].shape[2], model3.inputs[0].shape[1]]);
@ -4841,7 +4847,7 @@ async function predict4(input, config3) {
}
async function load5(config3) {
if (!model3) {
model3 = await tf8.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
model3 = await tf9.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
if (!model3 || !model3["modelUrl"])
log("load model failed:", config3.body.modelPath);
else if (config3.debug)
@ -4857,13 +4863,13 @@ __export(handpose_exports, {
load: () => load6,
predict: () => predict5
});
var tf12 = __toModule(require_tfjs_esm());
var tf13 = __toModule(require_tfjs_esm());
// src/handpose/handdetector.ts
var tf10 = __toModule(require_tfjs_esm());
var tf11 = __toModule(require_tfjs_esm());
// src/handpose/box.ts
var tf9 = __toModule(require_tfjs_esm());
var tf10 = __toModule(require_tfjs_esm());
function getBoxSize2(box6) {
return [
Math.abs(box6.endPoint[0] - box6.startPoint[0]),
@ -4885,7 +4891,7 @@ function cutBoxFromImageAndResize2(box6, image15, cropSize) {
box6.endPoint[1] / h,
box6.endPoint[0] / w
]];
return tf9.image.cropAndResize(image15, boxes, [0], cropSize);
return tf10.image.cropAndResize(image15, boxes, [0], cropSize);
}
function scaleBoxCoordinates2(box6, factor) {
const startPoint = [box6.startPoint[0] * factor[0], box6.startPoint[1] * factor[1]];
@ -7868,47 +7874,47 @@ var HandDetector = class {
var _a;
this.model = model9;
this.anchors = anchors.map((anchor) => [anchor.x, anchor.y]);
this.anchorsTensor = tf10.tensor2d(this.anchors);
this.anchorsTensor = tf11.tensor2d(this.anchors);
this.inputSize = (_a = this.model) == null ? void 0 : _a.inputs[0].shape[2];
this.inputSizeTensor = tf10.tensor1d([this.inputSize, this.inputSize]);
this.doubleInputSizeTensor = tf10.tensor1d([this.inputSize * 2, this.inputSize * 2]);
this.inputSizeTensor = tf11.tensor1d([this.inputSize, this.inputSize]);
this.doubleInputSizeTensor = tf11.tensor1d([this.inputSize * 2, this.inputSize * 2]);
}
normalizeBoxes(boxes) {
return tf10.tidy(() => {
const boxOffsets = tf10.slice(boxes, [0, 0], [-1, 2]);
const boxSizes = tf10.slice(boxes, [0, 2], [-1, 2]);
const boxCenterPoints = tf10.add(tf10.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);
const halfBoxSizes = tf10.div(boxSizes, this.doubleInputSizeTensor);
const startPoints = tf10.mul(tf10.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);
const endPoints = tf10.mul(tf10.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);
return tf10.concat2d([startPoints, endPoints], 1);
return tf11.tidy(() => {
const boxOffsets = tf11.slice(boxes, [0, 0], [-1, 2]);
const boxSizes = tf11.slice(boxes, [0, 2], [-1, 2]);
const boxCenterPoints = tf11.add(tf11.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);
const halfBoxSizes = tf11.div(boxSizes, this.doubleInputSizeTensor);
const startPoints = tf11.mul(tf11.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);
const endPoints = tf11.mul(tf11.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);
return tf11.concat2d([startPoints, endPoints], 1);
});
}
normalizeLandmarks(rawPalmLandmarks, index) {
return tf10.tidy(() => {
const landmarks = tf10.add(tf10.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);
return tf10.mul(landmarks, this.inputSizeTensor);
return tf11.tidy(() => {
const landmarks = tf11.add(tf11.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);
return tf11.mul(landmarks, this.inputSizeTensor);
});
}
async getBoxes(input, config3) {
const batched = this.model.predict(input);
const predictions = batched.squeeze();
batched.dispose();
const scoresT = tf10.tidy(() => tf10.sigmoid(tf10.slice(predictions, [0, 0], [-1, 1])).squeeze());
const scoresT = tf11.tidy(() => tf11.sigmoid(tf11.slice(predictions, [0, 0], [-1, 1])).squeeze());
const scores = scoresT.dataSync();
const rawBoxes = tf10.slice(predictions, [0, 1], [-1, 4]);
const rawBoxes = tf11.slice(predictions, [0, 1], [-1, 4]);
const boxes = this.normalizeBoxes(rawBoxes);
rawBoxes.dispose();
const filteredT = await tf10.image.nonMaxSuppressionAsync(boxes, scores, config3.hand.maxDetected, config3.hand.iouThreshold, config3.hand.minConfidence);
const filteredT = await tf11.image.nonMaxSuppressionAsync(boxes, scores, config3.hand.maxDetected, config3.hand.iouThreshold, config3.hand.minConfidence);
const filtered = filteredT.arraySync();
scoresT.dispose();
filteredT.dispose();
const hands = [];
for (const index of filtered) {
if (scores[index] >= config3.hand.minConfidence) {
const matchingBox = tf10.slice(boxes, [index, 0], [1, -1]);
const rawPalmLandmarks = tf10.slice(predictions, [index, 5], [1, 14]);
const palmLandmarks = tf10.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));
const matchingBox = tf11.slice(boxes, [index, 0], [1, -1]);
const rawPalmLandmarks = tf11.slice(predictions, [index, 5], [1, 14]);
const palmLandmarks = tf11.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));
rawPalmLandmarks.dispose();
hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });
}
@ -7920,7 +7926,7 @@ var HandDetector = class {
async estimateHandBounds(input, config3) {
const inputHeight = input.shape[1];
const inputWidth = input.shape[2];
const image15 = tf10.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));
const image15 = tf11.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));
const predictions = await this.getBoxes(image15, config3);
image15.dispose();
const hands = [];
@ -7940,7 +7946,7 @@ var HandDetector = class {
};
// src/handpose/handpipeline.ts
var tf11 = __toModule(require_tfjs_esm());
var tf12 = __toModule(require_tfjs_esm());
// src/handpose/util.ts
function normalizeRadians2(angle) {
@ -8091,7 +8097,7 @@ var HandPipeline = class {
const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;
const palmCenter = getBoxCenter2(currentBox);
const palmCenterNormalized = [palmCenter[0] / image15.shape[2], palmCenter[1] / image15.shape[1]];
const rotatedImage = config3.hand.rotation && tf11.ENV.flags.IS_BROWSER ? tf11.image.rotateWithOffset(image15, angle, 0, palmCenterNormalized) : image15.clone();
const rotatedImage = config3.hand.rotation && tf12.ENV.flags.IS_BROWSER ? tf12.image.rotateWithOffset(image15, angle, 0, palmCenterNormalized) : image15.clone();
const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
const croppedInput = cutBoxFromImageAndResize2(newBox, rotatedImage, [this.inputSize, this.inputSize]);
@ -8103,7 +8109,7 @@ var HandPipeline = class {
const confidence = confidenceT.dataSync()[0];
confidenceT.dispose();
if (confidence >= config3.hand.minConfidence) {
const keypointsReshaped = tf11.reshape(keypoints3, [-1, 3]);
const keypointsReshaped = tf12.reshape(keypoints3, [-1, 3]);
const rawCoords = keypointsReshaped.arraySync();
keypoints3.dispose();
keypointsReshaped.dispose();
@ -8190,15 +8196,15 @@ async function predict5(input, config3) {
(predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / input.shape[1]
];
}
hands.push({ id: i, confidence: Math.round(100 * predictions[i].confidence) / 100, box: box6, boxRaw: boxRaw3, keypoints: keypoints3, annotations: annotations3 });
hands.push({ id: i, score: Math.round(100 * predictions[i].confidence) / 100, box: box6, boxRaw: boxRaw3, keypoints: keypoints3, annotations: annotations3 });
}
return hands;
}
async function load6(config3) {
if (!handDetectorModel || !handPoseModel) {
[handDetectorModel, handPoseModel] = await Promise.all([
config3.hand.enabled ? tf12.loadGraphModel(join(config3.modelBasePath, config3.hand.detector.modelPath), { fromTFHub: config3.hand.detector.modelPath.includes("tfhub.dev") }) : null,
config3.hand.landmarks ? tf12.loadGraphModel(join(config3.modelBasePath, config3.hand.skeleton.modelPath), { fromTFHub: config3.hand.skeleton.modelPath.includes("tfhub.dev") }) : null
config3.hand.enabled ? tf13.loadGraphModel(join(config3.modelBasePath, config3.hand.detector.modelPath), { fromTFHub: config3.hand.detector.modelPath.includes("tfhub.dev") }) : null,
config3.hand.landmarks ? tf13.loadGraphModel(join(config3.modelBasePath, config3.hand.skeleton.modelPath), { fromTFHub: config3.hand.skeleton.modelPath.includes("tfhub.dev") }) : null
]);
if (config3.hand.enabled) {
if (!handDetectorModel || !handDetectorModel["modelUrl"])
@ -8227,7 +8233,7 @@ __export(blazepose_exports, {
load: () => load7,
predict: () => predict6
});
var tf13 = __toModule(require_tfjs_esm());
var tf14 = __toModule(require_tfjs_esm());
// src/blazepose/annotations.ts
var full = [
@ -8309,7 +8315,7 @@ var upper = [
var model4;
async function load7(config3) {
if (!model4) {
model4 = await tf13.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
model4 = await tf14.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
model4["width"] = parseInt(model4["signature"].inputs["input_1:0"].tensorShape.dim[2].size);
model4["height"] = parseInt(model4["signature"].inputs["input_1:0"].tensorShape.dim[1].size);
if (!model4 || !model4["modelUrl"])
@ -8327,8 +8333,8 @@ async function predict6(image15, config3) {
if (!config3.body.enabled)
return [];
const imgSize = { width: image15.shape[2], height: image15.shape[1] };
const resize = tf13.image.resizeBilinear(image15, [model4["width"], model4["height"]], false);
const normalize = tf13.div(resize, [255]);
const resize = tf14.image.resizeBilinear(image15, [model4["width"], model4["height"]], false);
const normalize = tf14.div(resize, [255]);
resize.dispose();
const resT = await model4.predict(normalize);
const points = ((_a = resT.find((t) => t.size === 195 || t.size === 155)) == null ? void 0 : _a.dataSync()) || [];
@ -8369,7 +8375,7 @@ async function predict6(image15, config3) {
}
// src/efficientpose/efficientpose.ts
var tf14 = __toModule(require_tfjs_esm());
var tf15 = __toModule(require_tfjs_esm());
var model5;
var keypoints = [];
var box4 = [0, 0, 0, 0];
@ -8379,7 +8385,7 @@ var skipped3 = Number.MAX_SAFE_INTEGER;
var bodyParts = ["head", "neck", "rightShoulder", "rightElbow", "rightWrist", "chest", "leftShoulder", "leftElbow", "leftWrist", "pelvis", "rightHip", "rightKnee", "rightAnkle", "leftHip", "leftKnee", "leftAnkle"];
async function load8(config3) {
if (!model5) {
model5 = await tf14.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
model5 = await tf15.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
if (!model5 || !model5["modelUrl"])
log("load model failed:", config3.body.modelPath);
else if (config3.debug)
@ -8390,14 +8396,14 @@ async function load8(config3) {
}
function max2d(inputs, minScore) {
const [width, height] = inputs.shape;
return tf14.tidy(() => {
const mod = (a, b) => tf14.sub(a, tf14.mul(tf14.div(a, tf14.scalar(b, "int32")), tf14.scalar(b, "int32")));
const reshaped = tf14.reshape(inputs, [height * width]);
const newScore = tf14.max(reshaped, 0).dataSync()[0];
return tf15.tidy(() => {
const mod = (a, b) => tf15.sub(a, tf15.mul(tf15.div(a, tf15.scalar(b, "int32")), tf15.scalar(b, "int32")));
const reshaped = tf15.reshape(inputs, [height * width]);
const newScore = tf15.max(reshaped, 0).dataSync()[0];
if (newScore > minScore) {
const coords3 = tf14.argMax(reshaped, 0);
const coords3 = tf15.argMax(reshaped, 0);
const x = mod(coords3, width).dataSync()[0];
const y = tf14.div(coords3, tf14.scalar(width, "int32")).dataSync()[0];
const y = tf15.div(coords3, tf15.scalar(width, "int32")).dataSync()[0];
return [x, y, newScore];
}
return [0, 0, newScore];
@ -8410,11 +8416,11 @@ async function predict7(image15, config3) {
}
skipped3 = 0;
return new Promise(async (resolve) => {
const tensor = tf14.tidy(() => {
const tensor = tf15.tidy(() => {
if (!model5.inputs[0].shape)
return null;
const resize = tf14.image.resizeBilinear(image15, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
const enhance2 = tf14.mul(resize, 2);
const resize = tf15.image.resizeBilinear(image15, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
const enhance2 = tf15.mul(resize, 2);
const norm = enhance2.sub(1);
return norm;
});
@ -8424,10 +8430,10 @@ async function predict7(image15, config3) {
tensor.dispose();
if (resT) {
keypoints.length = 0;
const squeeze2 = resT.squeeze();
tf14.dispose(resT);
const stack2 = squeeze2.unstack(2);
tf14.dispose(squeeze2);
const squeeze3 = resT.squeeze();
tf15.dispose(resT);
const stack2 = squeeze3.unstack(2);
tf15.dispose(squeeze3);
for (let id = 0; id < stack2.length; id++) {
const [x2, y2, partScore] = max2d(stack2[id], config3.body.minConfidence);
if (score > config3.body.minConfidence) {
@ -8445,7 +8451,7 @@ async function predict7(image15, config3) {
});
}
}
stack2.forEach((s) => tf14.dispose(s));
stack2.forEach((s) => tf15.dispose(s));
}
score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
const x = keypoints.map((a) => a.position.x);
@ -8469,7 +8475,7 @@ async function predict7(image15, config3) {
}
// src/movenet/movenet.ts
var tf15 = __toModule(require_tfjs_esm());
var tf16 = __toModule(require_tfjs_esm());
var model6;
var keypoints2 = [];
var box5 = [0, 0, 0, 0];
@ -8479,7 +8485,7 @@ var skipped4 = Number.MAX_SAFE_INTEGER;
var bodyParts2 = ["nose", "leftEye", "rightEye", "leftEar", "rightEar", "leftShoulder", "rightShoulder", "leftElbow", "rightElbow", "leftWrist", "rightWrist", "leftHip", "rightHip", "leftKnee", "rightKnee", "leftAnkle", "rightAnkle"];
async function load9(config3) {
if (!model6) {
model6 = await tf15.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
model6 = await tf16.loadGraphModel(join(config3.modelBasePath, config3.body.modelPath));
if (!model6 || !model6["modelUrl"])
log("load model failed:", config3.body.modelPath);
else if (config3.debug)
@ -8495,11 +8501,11 @@ async function predict8(image15, config3) {
}
skipped4 = 0;
return new Promise(async (resolve) => {
const tensor = tf15.tidy(() => {
const tensor = tf16.tidy(() => {
if (!model6.inputs[0].shape)
return null;
const resize = tf15.image.resizeBilinear(image15, [model6.inputs[0].shape[2], model6.inputs[0].shape[1]], false);
const cast2 = tf15.cast(resize, "int32");
const resize = tf16.image.resizeBilinear(image15, [model6.inputs[0].shape[2], model6.inputs[0].shape[1]], false);
const cast2 = tf16.cast(resize, "int32");
return cast2;
});
let resT;
@ -8509,7 +8515,7 @@ async function predict8(image15, config3) {
if (resT) {
keypoints2.length = 0;
const res = resT.arraySync();
tf15.dispose(resT);
tf16.dispose(resT);
const kpt3 = res[0][0];
for (let id = 0; id < kpt3.length; id++) {
score2 = kpt3[id][2];
@ -8556,7 +8562,7 @@ __export(nanodet_exports, {
load: () => load10,
predict: () => predict9
});
var tf16 = __toModule(require_tfjs_esm());
var tf17 = __toModule(require_tfjs_esm());
// src/object/labels.ts
var labels = [
@ -8649,7 +8655,7 @@ var skipped5 = Number.MAX_SAFE_INTEGER;
var scaleBox = 2.5;
async function load10(config3) {
if (!model7) {
model7 = await tf16.loadGraphModel(join(config3.modelBasePath, config3.object.modelPath));
model7 = await tf17.loadGraphModel(join(config3.modelBasePath, config3.object.modelPath));
const inputs = Object.values(model7.modelSignature["inputs"]);
model7.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;
if (!model7.inputSize)
@ -8666,7 +8672,7 @@ async function process2(res, inputSize, outputShape, config3) {
let id = 0;
let results = [];
for (const strideSize of [1, 2, 4]) {
tf16.tidy(() => {
tf17.tidy(() => {
var _a, _b;
const baseSize = strideSize * 13;
const scoresT = (_a = res.find((a) => a.shape[1] === baseSize ** 2 && a.shape[2] === labels.length)) == null ? void 0 : _a.squeeze();
@ -8711,14 +8717,14 @@ async function process2(res, inputSize, outputShape, config3) {
}
});
}
res.forEach((t) => tf16.dispose(t));
res.forEach((t) => tf17.dispose(t));
const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]);
const nmsScores = results.map((a) => a.score);
let nmsIdx = [];
if (nmsBoxes && nmsBoxes.length > 0) {
const nms = await tf16.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
const nms = await tf17.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
nmsIdx = nms.dataSync();
tf16.dispose(nms);
tf17.dispose(nms);
}
results = results.filter((a, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
return results;
@ -8731,7 +8737,7 @@ async function predict9(image15, config3) {
skipped5 = 0;
return new Promise(async (resolve) => {
const outputSize = [image15.shape[2], image15.shape[1]];
const resize = tf16.image.resizeBilinear(image15, [model7.inputSize, model7.inputSize], false);
const resize = tf17.image.resizeBilinear(image15, [model7.inputSize, model7.inputSize], false);
const norm = resize.div(255);
const transpose = norm.transpose([0, 3, 1, 2]);
norm.dispose();
@ -8752,13 +8758,13 @@ __export(centernet_exports, {
load: () => load11,
predict: () => predict10
});
var tf17 = __toModule(require_tfjs_esm());
var tf18 = __toModule(require_tfjs_esm());
var model8;
var last4 = [];
var skipped6 = Number.MAX_SAFE_INTEGER;
async function load11(config3) {
if (!model8) {
model8 = await tf17.loadGraphModel(join(config3.modelBasePath, config3.object.modelPath));
model8 = await tf18.loadGraphModel(join(config3.modelBasePath, config3.object.modelPath));
const inputs = Object.values(model8.modelSignature["inputs"]);
model8.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;
if (!model8.inputSize)
@ -8776,16 +8782,16 @@ async function process3(res, inputSize, outputShape, config3) {
return [];
const results = [];
const detections = res.arraySync();
const squeezeT = tf17.squeeze(res);
const squeezeT = tf18.squeeze(res);
res.dispose();
const arr = tf17.split(squeezeT, 6, 1);
const arr = tf18.split(squeezeT, 6, 1);
squeezeT.dispose();
const stackT = tf17.stack([arr[1], arr[0], arr[3], arr[2]], 1);
const stackT = tf18.stack([arr[1], arr[0], arr[3], arr[2]], 1);
const boxesT = stackT.squeeze();
const scoresT = arr[4].squeeze();
const classesT = arr[5].squeeze();
arr.forEach((t) => t.dispose());
const nmsT = await tf17.image.nonMaxSuppressionAsync(boxesT, scoresT, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
const nmsT = await tf18.image.nonMaxSuppressionAsync(boxesT, scoresT, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
boxesT.dispose();
scoresT.dispose();
classesT.dispose();
@ -8820,7 +8826,7 @@ async function predict10(input, config3) {
skipped6 = 0;
return new Promise(async (resolve) => {
const outputSize = [input.shape[2], input.shape[1]];
const resize = tf17.image.resizeBilinear(input, [model8.inputSize, model8.inputSize]);
const resize = tf18.image.resizeBilinear(input, [model8.inputSize, model8.inputSize]);
const objectT = config3.object.enabled ? model8.execute(resize, ["tower_0/detections"]) : null;
resize.dispose();
const obj = await process3(objectT, model8.inputSize, outputSize, config3);
@ -8938,7 +8944,7 @@ var hand = (res) => {
};
// src/image/image.ts
var tf18 = __toModule(require_tfjs_esm());
var tf19 = __toModule(require_tfjs_esm());
// src/image/imagefx.js
function GLProgram(gl, vertexSource, fragmentSource) {
@ -9655,12 +9661,12 @@ function process4(input, config3) {
let tensor;
if (!input)
throw new Error("Human: Input is missing");
if (!(input instanceof tf18.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) {
if (!(input instanceof tf19.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) {
throw new Error("Human: Input type is not recognized");
}
if (input instanceof tf18.Tensor) {
if (input instanceof tf19.Tensor) {
if (input.shape && input.shape.length === 4 && input.shape[0] === 1 && input.shape[3] === 3)
tensor = tf18.clone(input);
tensor = tf19.clone(input);
else
throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${input.shape}`);
} else {
@ -9715,7 +9721,7 @@ function process4(input, config3) {
outCanvas.width = inCanvas == null ? void 0 : inCanvas.width;
if ((outCanvas == null ? void 0 : outCanvas.height) !== (inCanvas == null ? void 0 : inCanvas.height))
outCanvas.height = inCanvas == null ? void 0 : inCanvas.height;
fx = tf18.ENV.flags.IS_BROWSER ? new GLImageFilter({ canvas: outCanvas }) : null;
fx = tf19.ENV.flags.IS_BROWSER ? new GLImageFilter({ canvas: outCanvas }) : null;
}
if (!fx)
return { tensor: null, canvas: inCanvas };
@ -9756,16 +9762,16 @@ function process4(input, config3) {
let pixels;
if (outCanvas.data) {
const shape = [outCanvas.height, outCanvas.width, 3];
pixels = tf18.tensor3d(outCanvas.data, shape, "int32");
pixels = tf19.tensor3d(outCanvas.data, shape, "int32");
} else if (outCanvas instanceof ImageData) {
pixels = tf18.browser.fromPixels(outCanvas);
pixels = tf19.browser.fromPixels(outCanvas);
} else if (config3.backend === "webgl" || config3.backend === "humangl") {
const tempCanvas = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement("canvas");
tempCanvas.width = targetWidth;
tempCanvas.height = targetHeight;
const tempCtx = tempCanvas.getContext("2d");
tempCtx == null ? void 0 : tempCtx.drawImage(outCanvas, 0, 0);
pixels = tf18.browser.fromPixels(tempCanvas);
pixels = tf19.browser.fromPixels(tempCanvas);
} else {
const tempCanvas = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement("canvas");
tempCanvas.width = targetWidth;
@ -9773,7 +9779,7 @@ function process4(input, config3) {
const tempCtx = tempCanvas.getContext("2d");
tempCtx == null ? void 0 : tempCtx.drawImage(outCanvas, 0, 0);
const data = tempCtx == null ? void 0 : tempCtx.getImageData(0, 0, targetWidth, targetHeight);
pixels = tf18.browser.fromPixels(data);
pixels = tf19.browser.fromPixels(data);
}
const casted = pixels.toFloat();
tensor = casted.expandDims(0);
@ -9927,9 +9933,9 @@ async function face2(inCanvas2, result, drawOptions) {
if (localOptions.drawBoxes)
rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);
const labels2 = [];
labels2.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);
if (f.genderConfidence)
labels2.push(`${f.gender || ""} ${Math.trunc(100 * f.genderConfidence)}% confident`);
labels2.push(`face confidence: ${Math.trunc(100 * f.score)}%`);
if (f.genderScore)
labels2.push(`${f.gender || ""} ${Math.trunc(100 * f.genderScore)}% confident`);
if (f.age)
labels2.push(`age: ${f.age || ""}`);
if (f.iris)
@ -10361,6 +10367,7 @@ function join2(faces, bodies, hands, gestures, shape) {
// src/interpolate.ts
var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0 };
function calc(newResult) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u;
const bufferedFactor = 1e3 / (Date.now() - newResult.timestamp) / 4;
if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) {
bufferedResult.body = JSON.parse(JSON.stringify(newResult.body));
@ -10404,17 +10411,17 @@ function calc(newResult) {
for (let i = 0; i < newResult.face.length; i++) {
const box6 = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor);
const boxRaw3 = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor);
const matrix = newResult.face[i].rotation.matrix;
const angle = {
roll: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.angle.roll + newResult.face[i].rotation.angle.roll) / bufferedFactor,
yaw: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.angle.yaw + newResult.face[i].rotation.angle.yaw) / bufferedFactor,
pitch: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.angle.pitch + newResult.face[i].rotation.angle.pitch) / bufferedFactor
const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };
rotation.matrix = (_a = newResult.face[i].rotation) == null ? void 0 : _a.matrix;
rotation.angle = {
roll: ((bufferedFactor - 1) * (((_c = (_b = bufferedResult.face[i].rotation) == null ? void 0 : _b.angle) == null ? void 0 : _c.roll) || 0) + (((_e = (_d = newResult.face[i].rotation) == null ? void 0 : _d.angle) == null ? void 0 : _e.roll) || 0)) / bufferedFactor,
yaw: ((bufferedFactor - 1) * (((_g = (_f = bufferedResult.face[i].rotation) == null ? void 0 : _f.angle) == null ? void 0 : _g.yaw) || 0) + (((_i = (_h = newResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.yaw) || 0)) / bufferedFactor,
pitch: ((bufferedFactor - 1) * (((_k = (_j = bufferedResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.pitch) || 0) + (((_m = (_l = newResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.pitch) || 0)) / bufferedFactor
};
const gaze = {
bearing: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.gaze.bearing + newResult.face[i].rotation.gaze.bearing) / bufferedFactor,
strength: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.gaze.strength + newResult.face[i].rotation.gaze.strength) / bufferedFactor
rotation.gaze = {
bearing: ((bufferedFactor - 1) * (((_o = (_n = bufferedResult.face[i].rotation) == null ? void 0 : _n.gaze) == null ? void 0 : _o.bearing) || 0) + (((_q = (_p = newResult.face[i].rotation) == null ? void 0 : _p.gaze) == null ? void 0 : _q.bearing) || 0)) / bufferedFactor,
strength: ((bufferedFactor - 1) * (((_s = (_r = bufferedResult.face[i].rotation) == null ? void 0 : _r.gaze) == null ? void 0 : _s.strength) || 0) + (((_u = (_t = newResult.face[i].rotation) == null ? void 0 : _t.gaze) == null ? void 0 : _u.strength) || 0)) / bufferedFactor
};
const rotation = { angle, matrix, gaze };
bufferedResult.face[i] = { ...newResult.face[i], rotation, box: box6, boxRaw: boxRaw3 };
}
}
@ -11191,7 +11198,7 @@ var Human = class {
return null;
if (!input)
return "input is not defined";
if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf19.Tensor))
if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf20.Tensor))
return "input must be a tensor";
try {
this.tf.getBackend();
@ -11334,8 +11341,8 @@ var Human = class {
if (!img)
return null;
let res;
if (typeof tf19["node"] !== "undefined") {
const data = tf19["node"].decodeJpeg(img);
if (typeof tf20["node"] !== "undefined") {
const data = tf20["node"].decodeJpeg(img);
const expanded = data.expandDims(0);
this.tf.dispose(data);
res = await this.detect(expanded, this.config);
@ -11347,7 +11354,7 @@ var Human = class {
return res;
});
this.config = mergeDeep(config, userConfig || {});
this.tf = tf19;
this.tf = tf20;
this.draw = draw_exports;
this.version = version;
this.state = "idle";
@ -11613,7 +11620,7 @@ var Human = class {
return join2(faceRes, bodyRes, handRes, gestureRes, (_a = process5 == null ? void 0 : process5.tensor) == null ? void 0 : _a.shape);
}
};
tf19.dispose(process5.tensor);
tf20.dispose(process5.tensor);
resolve(this.result);
});
}

23
src/blazeface/facemesh.ts
View File

@ -8,13 +8,16 @@ import * as blazeface from './blazeface';
import * as facepipeline from './facepipeline';
import * as coords from './coords';
import { GraphModel } from '../tfjs/types';
import { Face } from '../result';
let faceModels: [blazeface.BlazeFaceModel | null, GraphModel | null, GraphModel | null] = [null, null, null];
let facePipeline;
export async function predict(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {
// export async function predict(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {
export async function predict(input, config): Promise<Face[]> {
const predictions = await facePipeline.predict(input, config);
const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];
const results: Array<Face> = [];
let id = 0;
for (const prediction of (predictions || [])) {
if (!prediction || prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing
const meshRaw = prediction.mesh.map((pt) => [
@ -26,28 +29,30 @@ export async function predict(input, config): Promise<{ confidence, boxConfidenc
if (prediction.mesh && prediction.mesh.length > 0) {
for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => prediction.mesh[index]);
}
const clampedBox = prediction.box ? [
const clampedBox: [number, number, number, number] = prediction.box ? [
Math.max(0, prediction.box.startPoint[0]),
Math.max(0, prediction.box.startPoint[1]),
Math.min(input.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),
Math.min(input.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),
] : 0;
const boxRaw = prediction.box ? [
] : [0, 0, 0, 0];
const boxRaw: [number, number, number, number] = prediction.box ? [
prediction.box.startPoint[0] / input.shape[2],
prediction.box.startPoint[1] / input.shape[1],
(prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],
(prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],
] : [];
] : [0, 0, 0, 0];
results.push({
confidence: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,
boxConfidence: Math.round(100 * prediction.boxConfidence) / 100,
faceConfidence: Math.round(100 * prediction.faceConfidence) / 100,
id: id++,
score: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,
boxScore: Math.round(100 * prediction.boxConfidence) / 100,
faceScore: Math.round(100 * prediction.faceConfidence) / 100,
box: clampedBox,
boxRaw,
mesh: prediction.mesh,
meshRaw,
annotations,
image: prediction.image,
tensor: prediction.image,
});
if (prediction.coords) prediction.coords.dispose();
}

4
src/draw/draw.ts
View File

@ -174,8 +174,8 @@ export async function face(inCanvas: HTMLCanvasElement, result: Array<Face>, dra
if (localOptions.drawBoxes) rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);
// silly hack since fillText does not suport new line
const labels:string[] = [];
labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);
if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);
labels.push(`face confidence: ${Math.trunc(100 * f.score)}%`);
if (f.genderScore) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderScore)}% confident`);
// if (f.genderConfidence) labels.push(f.gender);
if (f.age) labels.push(`age: ${f.age || ''}`);
if (f.iris) labels.push(`distance: ${f.iris}`);

11
src/face.ts
View File

@ -4,6 +4,7 @@
*/
import { log, now } from './helpers';
import * as tf from '../dist/tfjs.esm.js';
import * as facemesh from './blazeface/facemesh';
import * as emotion from './emotion/emotion';
import * as faceres from './faceres/faceres';
@ -159,7 +160,7 @@ export const detectFace = async (parent /* instance of human */, input: Tensor):
parent.analyze('Get Face');
// is something went wrong, skip the face
if (!faces[i].image || faces[i].image.isDisposedInternal) {
if (!faces[i].image || faces[i].image['isDisposedInternal']) {
log('Face object is disposed:', faces[i].image);
continue;
}
@ -210,19 +211,19 @@ export const detectFace = async (parent /* instance of human */, input: Tensor):
// combine results
faceRes.push({
id: i,
...faces[i],
id: i,
age: descRes.age,
gender: descRes.gender,
genderConfidence: descRes.genderConfidence,
genderScore: descRes.genderConfidence,
embedding: descRes.descriptor,
emotion: emotionRes,
iris: irisSize !== 0 ? Math.trunc(500 / irisSize / 11.7) / 100 : 0,
rotation,
tensor: parent.config.face.detector.return ? faces[i].image?.squeeze() : null,
tensor: parent.config.face.detector.return ? tf.squeeze(faces[i].image) : null,
});
// dispose original face tensor
faces[i].image?.dispose();
tf.dispose(faces[i].image);
parent.analyze('End Face');
}

2
src/handpose/handpose.ts
View File

@ -63,7 +63,7 @@ export async function predict(input, config): Promise<Hand[]> {
(predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / input.shape[1],
];
}
hands.push({ id: i, confidence: Math.round(100 * predictions[i].confidence) / 100, box, boxRaw, keypoints, annotations });
hands.push({ id: i, score: Math.round(100 * predictions[i].confidence) / 100, box, boxRaw, keypoints, annotations });
}
return hands;
}

22
src/interpolate.ts
View File

@ -70,18 +70,22 @@ export function calc(newResult: Result): Result {
.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as [number, number, number, number];
const boxRaw = (newResult.face[i].boxRaw // update boxRaw
.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as [number, number, number, number];
const matrix = newResult.face[i].rotation.matrix;
const angle = {
roll: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.angle.roll + newResult.face[i].rotation.angle.roll) / bufferedFactor,
yaw: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.angle.yaw + newResult.face[i].rotation.angle.yaw) / bufferedFactor,
pitch: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.angle.pitch + newResult.face[i].rotation.angle.pitch) / bufferedFactor,
const rotation: {
matrix: [number, number, number, number, number, number, number, number, number],
angle: { roll: number, yaw: number, pitch: number },
gaze: { bearing: number, strength: number }
} = { 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 as [number, number, number, number, number, number, number, number, number];
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,
};
const gaze = {
rotation.gaze = {
// not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive
bearing: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.gaze.bearing + newResult.face[i].rotation.gaze.bearing) / bufferedFactor,
strength: ((bufferedFactor - 1) * bufferedResult.face[i].rotation.gaze.strength + newResult.face[i].rotation.gaze.strength) / bufferedFactor,
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,
};
const rotation = { angle, matrix, gaze };
bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values
}
}

35
src/result.ts
View File

@ -10,9 +10,9 @@ import { Tensor } from '../dist/tfjs.esm.js';
*
* Each result has:
* - id: face id number
* - confidence: overal detection confidence value
* - boxConfidence: face box detection confidence value
* - faceConfidence: face keypoints detection confidence value
* - score: overal detection confidence score value
* - boxScore: face box detection confidence score value
* - faceScore: face keypoints detection confidence score value
* - box: face bounding box as array of [x, y, width, height], normalized to image resolution
* - boxRaw: face bounding box as array of [x, y, width, height], normalized to range 0..1
* - mesh: face keypoints as array of [x, y, z] points of face mesh, normalized to image resolution
@ -20,7 +20,7 @@ import { Tensor } from '../dist/tfjs.esm.js';
* - annotations: annotated face keypoints as array of annotated face mesh points
* - age: age as value
* - gender: gender as value
* - genderConfidence: gender detection confidence as value
* - genderScore: gender detection confidence score as value
* - emotion: emotions as array of possible emotions with their individual scores
* - embedding: facial descriptor as array of numerical elements
* - iris: iris distance from current viewpoint as distance value in centimeters for a typical camera
@ -33,25 +33,26 @@ import { Tensor } from '../dist/tfjs.esm.js';
*/
export interface Face {
id: number
confidence: number,
boxConfidence: number,
faceConfidence: number,
score: number,
boxScore: number,
faceScore: number,
box: [number, number, number, number],
boxRaw: [number, number, number, number],
mesh: Array<[number, number, number]>
meshRaw: Array<[number, number, number]>
annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,
age: number,
gender: string,
genderConfidence: number,
emotion: Array<{ score: number, emotion: string }>,
embedding: Array<number>,
iris: number,
rotation: {
annotations: Record<string, Array<[number, number, number]>>,
age?: number,
gender?: string,
genderScore?: number,
emotion?: Array<{ score: number, emotion: string }>,
embedding?: Array<number>,
iris?: number,
rotation?: {
angle: { roll: number, yaw: number, pitch: number },
matrix: [number, number, number, number, number, number, number, number, number],
gaze: { bearing: number, strength: number },
}
image: typeof Tensor;
tensor: typeof Tensor,
}
@ -86,7 +87,7 @@ export interface Body {
*
* Each result has:
* - id: hand id number
* - confidence: detection confidence score as value
* - score: detection confidence score as value
* - box: bounding box: x, y, width, height normalized to input image resolution
* - boxRaw: bounding box: x, y, width, height normalized to 0..1
* - landmarks: landmarks as array of [x, y, z] points of hand, normalized to image resolution
@ -94,7 +95,7 @@ export interface Body {
*/
export interface Hand {
id: number,
confidence: number,
score: number,
box: [number, number, number, number],
boxRaw: [number, number, number, number],
keypoints: Array<[number, number, number]>,