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human
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simplify face box coordinate calculations

pull/91/head
Vladimir Mandic 2021-03-11 11:44:22 -05:00
parent ac5e696cc1
commit 0ee6b8b00c
17 changed files with 163 additions and 164 deletions
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6
demo/browser.js
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@ -6,11 +6,11 @@ import GLBench from './gl-bench.js';
// const userConfig = { backend: 'webgl' }; // add any user configuration overrides
const userConfig = {
backend: 'webgl',
backend: 'wasm',
async: false,
warmup: 'face',
videoOptimized: false,
face: { enabled: true, mesh: { enabled: false }, iris: { enabled: false }, age: { enabled: false }, gender: { enabled: false }, emotion: { enabled: false }, embedding: { enabled: true } },
face: { enabled: true, mesh: { enabled: true }, iris: { enabled: false }, age: { enabled: false }, gender: { enabled: false }, emotion: { enabled: false }, embedding: { enabled: true } },
hand: { enabled: false },
gesture: { enabled: false },
body: { enabled: false, modelPath: '../models/blazepose.json' },
@ -38,7 +38,7 @@ const ui = {
detectFPS: [], // internal, holds fps values for detection performance
drawFPS: [], // internal, holds fps values for draw performance
buffered: false, // experimental, should output be buffered between frames
drawWarmup: true, // debug only, should warmup image processing be displayed on startup
drawWarmup: false, // debug only, should warmup image processing be displayed on startup
drawThread: null, // internl, perform draw operations in a separate thread
detectThread: null, // internl, perform detect operations in a separate thread
framesDraw: 0, // internal, statistics on frames drawn

45
dist/demo-browser-index.js vendored
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@ -75502,22 +75502,19 @@ function decodeBounds(boxOutputs, anchors3, inputSize) {
}
var BlazeFaceModel = class {
constructor(model7, config3) {
this.blazeFaceModel = model7;
this.width = model7.inputs[0].shape[2];
this.height = model7.inputs[0].shape[1];
this.model = model7;
this.anchorsData = generateAnchors(model7.inputs[0].shape[1]);
this.anchors = tensor2d(this.anchorsData);
this.inputSize = tensor1d([this.width, this.height]);
this.inputSize = model7.inputs[0].shape[2];
this.config = config3;
this.scaleFaces = 0.8;
}
async getBoundingBoxes(inputImage) {
if (!inputImage || inputImage.isDisposedInternal || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
return null;
const [batch, boxes, scores] = tidy(() => {
const resizedImage = inputImage.resizeBilinear([this.width, this.height]);
const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);
const normalizedImage = resizedImage.div(127.5).sub(0.5);
const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);
const batchedPrediction = this.model.predict(normalizedImage);
let batchOut;
if (Array.isArray(batchedPrediction)) {
const sorted = batchedPrediction.sort((a, b) => a.size - b.size);
@ -75528,7 +75525,7 @@ var BlazeFaceModel = class {
} else {
batchOut = batchedPrediction.squeeze();
}
const boxesOut = decodeBounds(batchOut, this.anchors, this.inputSize);
const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);
const logits = slice(batchOut, [0, 0], [-1, 1]);
const scoresOut = sigmoid(logits).squeeze();
return [batchOut, boxesOut, scoresOut];
@ -75559,7 +75556,7 @@ var BlazeFaceModel = class {
scores.dispose();
return {
boxes: annotatedBoxes,
scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height]
scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize]
};
}
};
@ -78991,8 +78988,8 @@ var Pipeline = class {
this.boundingBoxDetector = boundingBoxDetector;
this.meshDetector = meshDetector;
this.irisModel = irisModel;
this.boxSize = ((_a = boundingBoxDetector == null ? void 0 : boundingBoxDetector.blazeFaceModel) == null ? void 0 : _a.inputs[0].shape[2]) || 0;
this.meshSize = (meshDetector == null ? void 0 : meshDetector.inputs[0].shape[2]) || ((_b = boundingBoxDetector == null ? void 0 : boundingBoxDetector.blazeFaceModel) == null ? void 0 : _b.inputs[0].shape[2]);
this.boxSize = ((_a = boundingBoxDetector == null ? void 0 : boundingBoxDetector.model) == null ? void 0 : _a.inputs[0].shape[2]) || 0;
this.meshSize = (meshDetector == null ? void 0 : meshDetector.inputs[0].shape[2]) || ((_b = boundingBoxDetector == null ? void 0 : boundingBoxDetector.model) == null ? void 0 : _b.inputs[0].shape[2]);
this.irisSize = (irisModel == null ? void 0 : irisModel.inputs[0].shape[1]) || 0;
this.irisEnlarge = 2.3;
this.skipped = 0;
@ -79000,10 +78997,9 @@ var Pipeline = class {
}
transformRawCoords(rawCoords, box3, angle, rotationMatrix) {
const boxSize = getBoxSize({startPoint: box3.startPoint, endPoint: box3.endPoint});
const scaleFactor = [boxSize[0] / this.meshSize, boxSize[1] / this.boxSize];
const coordsScaled = rawCoords.map((coord) => [
scaleFactor[0] * (coord[0] - this.boxSize / 2),
scaleFactor[1] * (coord[1] - this.boxSize / 2),
boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),
boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),
coord[2]
]);
const coordsRotationMatrix = angle !== 0 ? buildRotationMatrix(angle, [0, 0]) : IDENTITY_MATRIX;
@ -79116,11 +79112,17 @@ var Pipeline = class {
const faceCenterNormalized = [faceCenter[0] / input2.shape[2], faceCenter[1] / input2.shape[1]];
const rotatedImage = image.rotateWithOffset(input2, angle, 0, faceCenterNormalized);
rotationMatrix = buildRotationMatrix(-angle, faceCenter);
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, rotatedImage, [this.meshSize, this.meshSize]).div(255);
if (config3.face.mesh.enabled)
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, rotatedImage, [this.meshSize, this.meshSize]).div(255);
else
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, rotatedImage, [this.boxSize, this.boxSize]).div(255);
} else {
rotationMatrix = IDENTITY_MATRIX;
const cloned = input2.clone();
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, cloned, [this.boxSize, this.boxSize]).div(255);
const clonedImage = input2.clone();
if (config3.face.mesh.enabled)
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, clonedImage, [this.meshSize, this.meshSize]).div(255);
else
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, clonedImage, [this.boxSize, this.boxSize]).div(255);
}
if (!config3.face.mesh.enabled) {
const prediction2 = {
@ -79162,7 +79164,7 @@ var Pipeline = class {
rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);
}
const transformedCoordsData = this.transformRawCoords(rawCoords, box3, angle, rotationMatrix);
const landmarksBox = enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));
const landmarksBox = enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5);
const squarifiedLandmarksBox = squarifyBox(landmarksBox);
const transformedCoords = tensor2d(transformedCoordsData);
const prediction = {
@ -79465,7 +79467,6 @@ async function predict4(image3, config3) {
run("emotion", profileData);
}
}
resize.dispose();
resolve(data2);
});
}
@ -101345,11 +101346,11 @@ var gl_bench_default = GLBench;
// demo/browser.js
var userConfig = {
backend: "webgl",
backend: "wasm",
async: false,
warmup: "face",
videoOptimized: false,
face: {enabled: true, mesh: {enabled: false}, iris: {enabled: false}, age: {enabled: false}, gender: {enabled: false}, emotion: {enabled: false}, embedding: {enabled: true}},
face: {enabled: true, mesh: {enabled: true}, iris: {enabled: false}, age: {enabled: false}, gender: {enabled: false}, emotion: {enabled: false}, embedding: {enabled: true}},
hand: {enabled: false},
gesture: {enabled: false},
body: {enabled: false, modelPath: "../models/blazepose.json"}
@ -101374,7 +101375,7 @@ var ui = {
detectFPS: [],
drawFPS: [],
buffered: false,
drawWarmup: true,
drawWarmup: false,
drawThread: null,
detectThread: null,
framesDraw: 0,

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dist/human.esm-nobundle.js vendored
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@ -406,22 +406,19 @@ function decodeBounds(boxOutputs, anchors3, inputSize) {
}
var BlazeFaceModel = class {
constructor(model6, config3) {
this.blazeFaceModel = model6;
this.width = model6.inputs[0].shape[2];
this.height = model6.inputs[0].shape[1];
this.model = model6;
this.anchorsData = generateAnchors(model6.inputs[0].shape[1]);
this.anchors = tf2.tensor2d(this.anchorsData);
this.inputSize = tf2.tensor1d([this.width, this.height]);
this.inputSize = model6.inputs[0].shape[2];
this.config = config3;
this.scaleFaces = 0.8;
}
async getBoundingBoxes(inputImage) {
if (!inputImage || inputImage.isDisposedInternal || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
return null;
const [batch, boxes, scores] = tf2.tidy(() => {
const resizedImage = inputImage.resizeBilinear([this.width, this.height]);
const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);
const normalizedImage = resizedImage.div(127.5).sub(0.5);
const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);
const batchedPrediction = this.model.predict(normalizedImage);
let batchOut;
if (Array.isArray(batchedPrediction)) {
const sorted = batchedPrediction.sort((a, b) => a.size - b.size);
@ -432,7 +429,7 @@ var BlazeFaceModel = class {
} else {
batchOut = batchedPrediction.squeeze();
}
const boxesOut = decodeBounds(batchOut, this.anchors, this.inputSize);
const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);
const logits = tf2.slice(batchOut, [0, 0], [-1, 1]);
const scoresOut = tf2.sigmoid(logits).squeeze();
return [batchOut, boxesOut, scoresOut];
@ -463,7 +460,7 @@ var BlazeFaceModel = class {
scores.dispose();
return {
boxes: annotatedBoxes,
scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height]
scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize]
};
}
};
@ -3899,8 +3896,8 @@ var Pipeline = class {
this.boundingBoxDetector = boundingBoxDetector;
this.meshDetector = meshDetector;
this.irisModel = irisModel;
this.boxSize = ((_a = boundingBoxDetector == null ? void 0 : boundingBoxDetector.blazeFaceModel) == null ? void 0 : _a.inputs[0].shape[2]) || 0;
this.meshSize = (meshDetector == null ? void 0 : meshDetector.inputs[0].shape[2]) || ((_b = boundingBoxDetector == null ? void 0 : boundingBoxDetector.blazeFaceModel) == null ? void 0 : _b.inputs[0].shape[2]);
this.boxSize = ((_a = boundingBoxDetector == null ? void 0 : boundingBoxDetector.model) == null ? void 0 : _a.inputs[0].shape[2]) || 0;
this.meshSize = (meshDetector == null ? void 0 : meshDetector.inputs[0].shape[2]) || ((_b = boundingBoxDetector == null ? void 0 : boundingBoxDetector.model) == null ? void 0 : _b.inputs[0].shape[2]);
this.irisSize = (irisModel == null ? void 0 : irisModel.inputs[0].shape[1]) || 0;
this.irisEnlarge = 2.3;
this.skipped = 0;
@ -3908,10 +3905,9 @@ var Pipeline = class {
}
transformRawCoords(rawCoords, box3, angle, rotationMatrix) {
const boxSize = getBoxSize({startPoint: box3.startPoint, endPoint: box3.endPoint});
const scaleFactor = [boxSize[0] / this.meshSize, boxSize[1] / this.boxSize];
const coordsScaled = rawCoords.map((coord) => [
scaleFactor[0] * (coord[0] - this.boxSize / 2),
scaleFactor[1] * (coord[1] - this.boxSize / 2),
boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),
boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),
coord[2]
]);
const coordsRotationMatrix = angle !== 0 ? buildRotationMatrix(angle, [0, 0]) : IDENTITY_MATRIX;
@ -4024,11 +4020,17 @@ var Pipeline = class {
const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];
const rotatedImage = tf4.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);
rotationMatrix = buildRotationMatrix(-angle, faceCenter);
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, rotatedImage, [this.meshSize, this.meshSize]).div(255);
if (config3.face.mesh.enabled)
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, rotatedImage, [this.meshSize, this.meshSize]).div(255);
else
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, rotatedImage, [this.boxSize, this.boxSize]).div(255);
} else {
rotationMatrix = IDENTITY_MATRIX;
const cloned = input.clone();
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, cloned, [this.boxSize, this.boxSize]).div(255);
const clonedImage = input.clone();
if (config3.face.mesh.enabled)
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, clonedImage, [this.meshSize, this.meshSize]).div(255);
else
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, clonedImage, [this.boxSize, this.boxSize]).div(255);
}
if (!config3.face.mesh.enabled) {
const prediction2 = {
@ -4070,7 +4072,7 @@ var Pipeline = class {
rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);
}
const transformedCoordsData = this.transformRawCoords(rawCoords, box3, angle, rotationMatrix);
const landmarksBox = enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));
const landmarksBox = enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5);
const squarifiedLandmarksBox = squarifyBox(landmarksBox);
const transformedCoords = tf4.tensor2d(transformedCoordsData);
const prediction = {
@ -4377,7 +4379,6 @@ async function predict4(image13, config3) {
run("emotion", profileData);
}
}
resize.dispose();
resolve(data3);
});
}

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dist/human.esm.js vendored
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@ -75502,22 +75502,19 @@ function decodeBounds(boxOutputs, anchors3, inputSize) {
}
var BlazeFaceModel = class {
constructor(model7, config3) {
this.blazeFaceModel = model7;
this.width = model7.inputs[0].shape[2];
this.height = model7.inputs[0].shape[1];
this.model = model7;
this.anchorsData = generateAnchors(model7.inputs[0].shape[1]);
this.anchors = tensor2d(this.anchorsData);
this.inputSize = tensor1d([this.width, this.height]);
this.inputSize = model7.inputs[0].shape[2];
this.config = config3;
this.scaleFaces = 0.8;
}
async getBoundingBoxes(inputImage) {
if (!inputImage || inputImage.isDisposedInternal || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
return null;
const [batch, boxes, scores] = tidy(() => {
const resizedImage = inputImage.resizeBilinear([this.width, this.height]);
const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);
const normalizedImage = resizedImage.div(127.5).sub(0.5);
const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);
const batchedPrediction = this.model.predict(normalizedImage);
let batchOut;
if (Array.isArray(batchedPrediction)) {
const sorted = batchedPrediction.sort((a, b) => a.size - b.size);
@ -75528,7 +75525,7 @@ var BlazeFaceModel = class {
} else {
batchOut = batchedPrediction.squeeze();
}
const boxesOut = decodeBounds(batchOut, this.anchors, this.inputSize);
const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);
const logits = slice(batchOut, [0, 0], [-1, 1]);
const scoresOut = sigmoid(logits).squeeze();
return [batchOut, boxesOut, scoresOut];
@ -75559,7 +75556,7 @@ var BlazeFaceModel = class {
scores.dispose();
return {
boxes: annotatedBoxes,
scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height]
scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize]
};
}
};
@ -78991,8 +78988,8 @@ var Pipeline = class {
this.boundingBoxDetector = boundingBoxDetector;
this.meshDetector = meshDetector;
this.irisModel = irisModel;
this.boxSize = ((_a = boundingBoxDetector == null ? void 0 : boundingBoxDetector.blazeFaceModel) == null ? void 0 : _a.inputs[0].shape[2]) || 0;
this.meshSize = (meshDetector == null ? void 0 : meshDetector.inputs[0].shape[2]) || ((_b = boundingBoxDetector == null ? void 0 : boundingBoxDetector.blazeFaceModel) == null ? void 0 : _b.inputs[0].shape[2]);
this.boxSize = ((_a = boundingBoxDetector == null ? void 0 : boundingBoxDetector.model) == null ? void 0 : _a.inputs[0].shape[2]) || 0;
this.meshSize = (meshDetector == null ? void 0 : meshDetector.inputs[0].shape[2]) || ((_b = boundingBoxDetector == null ? void 0 : boundingBoxDetector.model) == null ? void 0 : _b.inputs[0].shape[2]);
this.irisSize = (irisModel == null ? void 0 : irisModel.inputs[0].shape[1]) || 0;
this.irisEnlarge = 2.3;
this.skipped = 0;
@ -79000,10 +78997,9 @@ var Pipeline = class {
}
transformRawCoords(rawCoords, box3, angle, rotationMatrix) {
const boxSize = getBoxSize({startPoint: box3.startPoint, endPoint: box3.endPoint});
const scaleFactor = [boxSize[0] / this.meshSize, boxSize[1] / this.boxSize];
const coordsScaled = rawCoords.map((coord) => [
scaleFactor[0] * (coord[0] - this.boxSize / 2),
scaleFactor[1] * (coord[1] - this.boxSize / 2),
boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),
boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),
coord[2]
]);
const coordsRotationMatrix = angle !== 0 ? buildRotationMatrix(angle, [0, 0]) : IDENTITY_MATRIX;
@ -79116,11 +79112,17 @@ var Pipeline = class {
const faceCenterNormalized = [faceCenter[0] / input2.shape[2], faceCenter[1] / input2.shape[1]];
const rotatedImage = image.rotateWithOffset(input2, angle, 0, faceCenterNormalized);
rotationMatrix = buildRotationMatrix(-angle, faceCenter);
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, rotatedImage, [this.meshSize, this.meshSize]).div(255);
if (config3.face.mesh.enabled)
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, rotatedImage, [this.meshSize, this.meshSize]).div(255);
else
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, rotatedImage, [this.boxSize, this.boxSize]).div(255);
} else {
rotationMatrix = IDENTITY_MATRIX;
const cloned = input2.clone();
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, cloned, [this.boxSize, this.boxSize]).div(255);
const clonedImage = input2.clone();
if (config3.face.mesh.enabled)
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, clonedImage, [this.meshSize, this.meshSize]).div(255);
else
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, clonedImage, [this.boxSize, this.boxSize]).div(255);
}
if (!config3.face.mesh.enabled) {
const prediction2 = {
@ -79162,7 +79164,7 @@ var Pipeline = class {
rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);
}
const transformedCoordsData = this.transformRawCoords(rawCoords, box3, angle, rotationMatrix);
const landmarksBox = enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));
const landmarksBox = enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5);
const squarifiedLandmarksBox = squarifyBox(landmarksBox);
const transformedCoords = tensor2d(transformedCoordsData);
const prediction = {
@ -79465,7 +79467,6 @@ async function predict4(image3, config3) {
run("emotion", profileData);
}
}
resize.dispose();
resolve(data2);
});
}

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dist/human.js vendored
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@ -75509,22 +75509,19 @@ return a / b;`;
}
var BlazeFaceModel = class {
constructor(model7, config3) {
this.blazeFaceModel = model7;
this.width = model7.inputs[0].shape[2];
this.height = model7.inputs[0].shape[1];
this.model = model7;
this.anchorsData = generateAnchors(model7.inputs[0].shape[1]);
this.anchors = tensor2d(this.anchorsData);
this.inputSize = tensor1d([this.width, this.height]);
this.inputSize = model7.inputs[0].shape[2];
this.config = config3;
this.scaleFaces = 0.8;
}
async getBoundingBoxes(inputImage) {
if (!inputImage || inputImage.isDisposedInternal || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
return null;
const [batch, boxes, scores] = tidy(() => {
const resizedImage = inputImage.resizeBilinear([this.width, this.height]);
const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);
const normalizedImage = resizedImage.div(127.5).sub(0.5);
const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);
const batchedPrediction = this.model.predict(normalizedImage);
let batchOut;
if (Array.isArray(batchedPrediction)) {
const sorted = batchedPrediction.sort((a, b) => a.size - b.size);
@ -75535,7 +75532,7 @@ return a / b;`;
} else {
batchOut = batchedPrediction.squeeze();
}
const boxesOut = decodeBounds(batchOut, this.anchors, this.inputSize);
const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);
const logits = slice(batchOut, [0, 0], [-1, 1]);
const scoresOut = sigmoid(logits).squeeze();
return [batchOut, boxesOut, scoresOut];
@ -75566,7 +75563,7 @@ return a / b;`;
scores.dispose();
return {
boxes: annotatedBoxes,
scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height]
scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize]
};
}
};
@ -78998,8 +78995,8 @@ return a / b;`;
this.boundingBoxDetector = boundingBoxDetector;
this.meshDetector = meshDetector;
this.irisModel = irisModel;
this.boxSize = ((_a = boundingBoxDetector == null ? void 0 : boundingBoxDetector.blazeFaceModel) == null ? void 0 : _a.inputs[0].shape[2]) || 0;
this.meshSize = (meshDetector == null ? void 0 : meshDetector.inputs[0].shape[2]) || ((_b = boundingBoxDetector == null ? void 0 : boundingBoxDetector.blazeFaceModel) == null ? void 0 : _b.inputs[0].shape[2]);
this.boxSize = ((_a = boundingBoxDetector == null ? void 0 : boundingBoxDetector.model) == null ? void 0 : _a.inputs[0].shape[2]) || 0;
this.meshSize = (meshDetector == null ? void 0 : meshDetector.inputs[0].shape[2]) || ((_b = boundingBoxDetector == null ? void 0 : boundingBoxDetector.model) == null ? void 0 : _b.inputs[0].shape[2]);
this.irisSize = (irisModel == null ? void 0 : irisModel.inputs[0].shape[1]) || 0;
this.irisEnlarge = 2.3;
this.skipped = 0;
@ -79007,10 +79004,9 @@ return a / b;`;
}
transformRawCoords(rawCoords, box3, angle, rotationMatrix) {
const boxSize = getBoxSize({startPoint: box3.startPoint, endPoint: box3.endPoint});
const scaleFactor = [boxSize[0] / this.meshSize, boxSize[1] / this.boxSize];
const coordsScaled = rawCoords.map((coord) => [
scaleFactor[0] * (coord[0] - this.boxSize / 2),
scaleFactor[1] * (coord[1] - this.boxSize / 2),
boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),
boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),
coord[2]
]);
const coordsRotationMatrix = angle !== 0 ? buildRotationMatrix(angle, [0, 0]) : IDENTITY_MATRIX;
@ -79123,11 +79119,17 @@ return a / b;`;
const faceCenterNormalized = [faceCenter[0] / input2.shape[2], faceCenter[1] / input2.shape[1]];
const rotatedImage = image.rotateWithOffset(input2, angle, 0, faceCenterNormalized);
rotationMatrix = buildRotationMatrix(-angle, faceCenter);
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, rotatedImage, [this.meshSize, this.meshSize]).div(255);
if (config3.face.mesh.enabled)
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, rotatedImage, [this.meshSize, this.meshSize]).div(255);
else
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, rotatedImage, [this.boxSize, this.boxSize]).div(255);
} else {
rotationMatrix = IDENTITY_MATRIX;
const cloned = input2.clone();
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, cloned, [this.boxSize, this.boxSize]).div(255);
const clonedImage = input2.clone();
if (config3.face.mesh.enabled)
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, clonedImage, [this.meshSize, this.meshSize]).div(255);
else
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, clonedImage, [this.boxSize, this.boxSize]).div(255);
}
if (!config3.face.mesh.enabled) {
const prediction2 = {
@ -79169,7 +79171,7 @@ return a / b;`;
rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);
}
const transformedCoordsData = this.transformRawCoords(rawCoords, box3, angle, rotationMatrix);
const landmarksBox = enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));
const landmarksBox = enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5);
const squarifiedLandmarksBox = squarifyBox(landmarksBox);
const transformedCoords = tensor2d(transformedCoordsData);
const prediction = {
@ -79472,7 +79474,6 @@ return a / b;`;
run("emotion", profileData);
}
}
resize.dispose();
resolve(data2);
});
}

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dist/human.node-gpu.js vendored
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@ -386,22 +386,19 @@ function decodeBounds(boxOutputs, anchors3, inputSize) {
}
var BlazeFaceModel = class {
constructor(model6, config3) {
this.blazeFaceModel = model6;
this.width = model6.inputs[0].shape[2];
this.height = model6.inputs[0].shape[1];
this.model = model6;
this.anchorsData = generateAnchors(model6.inputs[0].shape[1]);
this.anchors = tf2.tensor2d(this.anchorsData);
this.inputSize = tf2.tensor1d([this.width, this.height]);
this.inputSize = model6.inputs[0].shape[2];
this.config = config3;
this.scaleFaces = 0.8;
}
async getBoundingBoxes(inputImage) {
if (!inputImage || inputImage.isDisposedInternal || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
return null;
const [batch, boxes, scores] = tf2.tidy(() => {
const resizedImage = inputImage.resizeBilinear([this.width, this.height]);
const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);
const normalizedImage = resizedImage.div(127.5).sub(0.5);
const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);
const batchedPrediction = this.model.predict(normalizedImage);
let batchOut;
if (Array.isArray(batchedPrediction)) {
const sorted = batchedPrediction.sort((a, b) => a.size - b.size);
@ -412,7 +409,7 @@ var BlazeFaceModel = class {
} else {
batchOut = batchedPrediction.squeeze();
}
const boxesOut = decodeBounds(batchOut, this.anchors, this.inputSize);
const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);
const logits = tf2.slice(batchOut, [0, 0], [-1, 1]);
const scoresOut = tf2.sigmoid(logits).squeeze();
return [batchOut, boxesOut, scoresOut];
@ -443,7 +440,7 @@ var BlazeFaceModel = class {
scores.dispose();
return {
boxes: annotatedBoxes,
scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height]
scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize]
};
}
};
@ -3879,8 +3876,8 @@ var Pipeline = class {
this.boundingBoxDetector = boundingBoxDetector;
this.meshDetector = meshDetector;
this.irisModel = irisModel;
this.boxSize = ((_a = boundingBoxDetector == null ? void 0 : boundingBoxDetector.blazeFaceModel) == null ? void 0 : _a.inputs[0].shape[2]) || 0;
this.meshSize = (meshDetector == null ? void 0 : meshDetector.inputs[0].shape[2]) || ((_b = boundingBoxDetector == null ? void 0 : boundingBoxDetector.blazeFaceModel) == null ? void 0 : _b.inputs[0].shape[2]);
this.boxSize = ((_a = boundingBoxDetector == null ? void 0 : boundingBoxDetector.model) == null ? void 0 : _a.inputs[0].shape[2]) || 0;
this.meshSize = (meshDetector == null ? void 0 : meshDetector.inputs[0].shape[2]) || ((_b = boundingBoxDetector == null ? void 0 : boundingBoxDetector.model) == null ? void 0 : _b.inputs[0].shape[2]);
this.irisSize = (irisModel == null ? void 0 : irisModel.inputs[0].shape[1]) || 0;
this.irisEnlarge = 2.3;
this.skipped = 0;
@ -3888,10 +3885,9 @@ var Pipeline = class {
}
transformRawCoords(rawCoords, box3, angle, rotationMatrix) {
const boxSize = getBoxSize({startPoint: box3.startPoint, endPoint: box3.endPoint});
const scaleFactor = [boxSize[0] / this.meshSize, boxSize[1] / this.boxSize];
const coordsScaled = rawCoords.map((coord) => [
scaleFactor[0] * (coord[0] - this.boxSize / 2),
scaleFactor[1] * (coord[1] - this.boxSize / 2),
boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),
boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),
coord[2]
]);
const coordsRotationMatrix = angle !== 0 ? buildRotationMatrix(angle, [0, 0]) : IDENTITY_MATRIX;
@ -4004,11 +4000,17 @@ var Pipeline = class {
const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];
const rotatedImage = tf4.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);
rotationMatrix = buildRotationMatrix(-angle, faceCenter);
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, rotatedImage, [this.meshSize, this.meshSize]).div(255);
if (config3.face.mesh.enabled)
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, rotatedImage, [this.meshSize, this.meshSize]).div(255);
else
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, rotatedImage, [this.boxSize, this.boxSize]).div(255);
} else {
rotationMatrix = IDENTITY_MATRIX;
const cloned = input.clone();
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, cloned, [this.boxSize, this.boxSize]).div(255);
const clonedImage = input.clone();
if (config3.face.mesh.enabled)
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, clonedImage, [this.meshSize, this.meshSize]).div(255);
else
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, clonedImage, [this.boxSize, this.boxSize]).div(255);
}
if (!config3.face.mesh.enabled) {
const prediction2 = {
@ -4050,7 +4052,7 @@ var Pipeline = class {
rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);
}
const transformedCoordsData = this.transformRawCoords(rawCoords, box3, angle, rotationMatrix);
const landmarksBox = enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));
const landmarksBox = enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5);
const squarifiedLandmarksBox = squarifyBox(landmarksBox);
const transformedCoords = tf4.tensor2d(transformedCoordsData);
const prediction = {
@ -4357,7 +4359,6 @@ async function predict4(image13, config3) {
run("emotion", profileData);
}
}
resize.dispose();
resolve(data2);
});
}

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@ -386,22 +386,19 @@ function decodeBounds(boxOutputs, anchors3, inputSize) {
}
var BlazeFaceModel = class {
constructor(model6, config3) {
this.blazeFaceModel = model6;
this.width = model6.inputs[0].shape[2];
this.height = model6.inputs[0].shape[1];
this.model = model6;
this.anchorsData = generateAnchors(model6.inputs[0].shape[1]);
this.anchors = tf2.tensor2d(this.anchorsData);
this.inputSize = tf2.tensor1d([this.width, this.height]);
this.inputSize = model6.inputs[0].shape[2];
this.config = config3;
this.scaleFaces = 0.8;
}
async getBoundingBoxes(inputImage) {
if (!inputImage || inputImage.isDisposedInternal || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
return null;
const [batch, boxes, scores] = tf2.tidy(() => {
const resizedImage = inputImage.resizeBilinear([this.width, this.height]);
const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);
const normalizedImage = resizedImage.div(127.5).sub(0.5);
const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);
const batchedPrediction = this.model.predict(normalizedImage);
let batchOut;
if (Array.isArray(batchedPrediction)) {
const sorted = batchedPrediction.sort((a, b) => a.size - b.size);
@ -412,7 +409,7 @@ var BlazeFaceModel = class {
} else {
batchOut = batchedPrediction.squeeze();
}
const boxesOut = decodeBounds(batchOut, this.anchors, this.inputSize);
const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);
const logits = tf2.slice(batchOut, [0, 0], [-1, 1]);
const scoresOut = tf2.sigmoid(logits).squeeze();
return [batchOut, boxesOut, scoresOut];
@ -443,7 +440,7 @@ var BlazeFaceModel = class {
scores.dispose();
return {
boxes: annotatedBoxes,
scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height]
scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize]
};
}
};
@ -3879,8 +3876,8 @@ var Pipeline = class {
this.boundingBoxDetector = boundingBoxDetector;
this.meshDetector = meshDetector;
this.irisModel = irisModel;
this.boxSize = ((_a = boundingBoxDetector == null ? void 0 : boundingBoxDetector.blazeFaceModel) == null ? void 0 : _a.inputs[0].shape[2]) || 0;
this.meshSize = (meshDetector == null ? void 0 : meshDetector.inputs[0].shape[2]) || ((_b = boundingBoxDetector == null ? void 0 : boundingBoxDetector.blazeFaceModel) == null ? void 0 : _b.inputs[0].shape[2]);
this.boxSize = ((_a = boundingBoxDetector == null ? void 0 : boundingBoxDetector.model) == null ? void 0 : _a.inputs[0].shape[2]) || 0;
this.meshSize = (meshDetector == null ? void 0 : meshDetector.inputs[0].shape[2]) || ((_b = boundingBoxDetector == null ? void 0 : boundingBoxDetector.model) == null ? void 0 : _b.inputs[0].shape[2]);
this.irisSize = (irisModel == null ? void 0 : irisModel.inputs[0].shape[1]) || 0;
this.irisEnlarge = 2.3;
this.skipped = 0;
@ -3888,10 +3885,9 @@ var Pipeline = class {
}
transformRawCoords(rawCoords, box3, angle, rotationMatrix) {
const boxSize = getBoxSize({startPoint: box3.startPoint, endPoint: box3.endPoint});
const scaleFactor = [boxSize[0] / this.meshSize, boxSize[1] / this.boxSize];
const coordsScaled = rawCoords.map((coord) => [
scaleFactor[0] * (coord[0] - this.boxSize / 2),
scaleFactor[1] * (coord[1] - this.boxSize / 2),
boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),
boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),
coord[2]
]);
const coordsRotationMatrix = angle !== 0 ? buildRotationMatrix(angle, [0, 0]) : IDENTITY_MATRIX;
@ -4004,11 +4000,17 @@ var Pipeline = class {
const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];
const rotatedImage = tf4.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);
rotationMatrix = buildRotationMatrix(-angle, faceCenter);
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, rotatedImage, [this.meshSize, this.meshSize]).div(255);
if (config3.face.mesh.enabled)
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, rotatedImage, [this.meshSize, this.meshSize]).div(255);
else
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, rotatedImage, [this.boxSize, this.boxSize]).div(255);
} else {
rotationMatrix = IDENTITY_MATRIX;
const cloned = input.clone();
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, cloned, [this.boxSize, this.boxSize]).div(255);
const clonedImage = input.clone();
if (config3.face.mesh.enabled)
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, clonedImage, [this.meshSize, this.meshSize]).div(255);
else
face4 = cutBoxFromImageAndResize({startPoint: box3.startPoint, endPoint: box3.endPoint}, clonedImage, [this.boxSize, this.boxSize]).div(255);
}
if (!config3.face.mesh.enabled) {
const prediction2 = {
@ -4050,7 +4052,7 @@ var Pipeline = class {
rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);
}
const transformedCoordsData = this.transformRawCoords(rawCoords, box3, angle, rotationMatrix);
const landmarksBox = enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));
const landmarksBox = enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5);
const squarifiedLandmarksBox = squarifyBox(landmarksBox);
const transformedCoords = tf4.tensor2d(transformedCoordsData);
const prediction = {
@ -4357,7 +4359,6 @@ async function predict4(image13, config3) {
run("emotion", profileData);
}
}
resize.dispose();
resolve(data2);
});
}

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22
src/blazeface/blazeface.ts
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@ -52,34 +52,28 @@ function decodeBounds(boxOutputs, anchors, inputSize) {
}
export class BlazeFaceModel {
blazeFaceModel: any;
width: number;
height: number;
model: any;
anchorsData: any;
anchors: any;
inputSize: any;
inputSize: number;
config: any;
scaleFaces: number;
constructor(model, config) {
this.blazeFaceModel = model;
this.width = model.inputs[0].shape[2];
this.height = model.inputs[0].shape[1];
this.model = model;
this.anchorsData = generateAnchors(model.inputs[0].shape[1]);
this.anchors = tf.tensor2d(this.anchorsData);
this.inputSize = tf.tensor1d([this.width, this.height]);
this.inputSize = model.inputs[0].shape[2];
this.config = config;
this.scaleFaces = 0.8;
}
async getBoundingBoxes(inputImage) {
// sanity check on input
if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;
const [batch, boxes, scores] = tf.tidy(() => {
const resizedImage = inputImage.resizeBilinear([this.width, this.height]);
const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);
// const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);
const normalizedImage = resizedImage.div(127.5).sub(0.5);
const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);
const batchedPrediction = this.model.predict(normalizedImage);
let batchOut;
// are we using tfhub or pinto converted model?
if (Array.isArray(batchedPrediction)) {
@ -91,7 +85,7 @@ export class BlazeFaceModel {
} else {
batchOut = batchedPrediction.squeeze(); // when using tfhub model
}
const boxesOut = decodeBounds(batchOut, this.anchors, this.inputSize);
const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);
const logits = tf.slice(batchOut, [0, 0], [-1, 1]);
const scoresOut = tf.sigmoid(logits).squeeze();
return [batchOut, boxesOut, scoresOut];
@ -123,7 +117,7 @@ export class BlazeFaceModel {
scores.dispose();
return {
boxes: annotatedBoxes,
scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],
scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],
};
}
}

21
src/blazeface/facepipeline.ts
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@ -56,8 +56,8 @@ export class Pipeline {
this.boundingBoxDetector = boundingBoxDetector;
this.meshDetector = meshDetector;
this.irisModel = irisModel;
this.boxSize = boundingBoxDetector?.blazeFaceModel?.inputs[0].shape[2] || 0;
this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.blazeFaceModel?.inputs[0].shape[2];
this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;
this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];
this.irisSize = irisModel?.inputs[0].shape[1] || 0;
this.irisEnlarge = 2.3;
this.skipped = 0;
@ -66,10 +66,10 @@ export class Pipeline {
transformRawCoords(rawCoords, box, angle, rotationMatrix) {
const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });
const scaleFactor = [boxSize[0] / this.meshSize, boxSize[1] / this.boxSize];
const coordsScaled = rawCoords.map((coord) => ([
scaleFactor[0] * (coord[0] - this.boxSize / 2),
scaleFactor[1] * (coord[1] - this.boxSize / 2), coord[2],
boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),
boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),
coord[2],
]));
const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;
const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;
@ -185,6 +185,7 @@ export class Pipeline {
let face;
let angle = 0;
let rotationMatrix;
if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {
const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;
angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);
@ -192,11 +193,13 @@ export class Pipeline {
const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];
const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node
rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);
face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);
if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);
else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);
} else {
rotationMatrix = util.IDENTITY_MATRIX;
const cloned = input.clone();
face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.boxSize, this.boxSize]).div(255);
const clonedImage = input.clone();
if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);
else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);
}
// if we're not going to produce mesh, don't spend time with further processing
@ -244,7 +247,7 @@ export class Pipeline {
}
const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);
const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));
const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5);
const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);
const transformedCoords = tf.tensor2d(transformedCoordsData);
const prediction = {

7
src/embedding/embedding.ts
View File

@ -30,8 +30,9 @@ export function simmilarity(embedding1, embedding2) {
export async function predict(image, config) {
if (!model) return null;
return new Promise(async (resolve) => {
const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
// const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???
const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // input is already normalized to 0..1
// const mean = resize.mean();
// const whiten = resize.sub(mean); // normalizes with mean value being at point 0
let data: Array<[]> = [];
if (config.face.embedding.enabled) {
if (!config.profile) {
@ -45,8 +46,6 @@ export async function predict(image, config) {
profile.run('emotion', profileData);
}
}
resize.dispose();
// normalize.dispose();
resolve(data);
});
}

7
types/blazeface/blazeface.d.ts vendored
View File

@ -1,13 +1,10 @@
export declare const disposeBox: (box: any) => void;
export declare class BlazeFaceModel {
blazeFaceModel: any;
width: number;
height: number;
model: any;
anchorsData: any;
anchors: any;
inputSize: any;
inputSize: number;
config: any;
scaleFaces: number;
constructor(model: any, config: any);
getBoundingBoxes(inputImage: any): Promise<{
boxes: {