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human
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add face return tensor

pull/91/head
Vladimir Mandic 2021-03-11 22:04:44 -05:00
parent ca1bc638f8
commit 8b5e7cc2d5
23 changed files with 6386 additions and 443021 deletions
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9
CHANGELOG.md
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@ -9,7 +9,14 @@ Repository: **<git+https://github.com/vladmandic/human.git>**
## Changelog ## Changelog
### **HEAD -> main** 2021/03/10 mandic00@live.com ### **HEAD -> main** 2021/03/11 mandic00@live.com
- wip on embedding
- simplify face box coordinate calculations
- annotated models and removed gender-ssrnet
- autodetect inputsizes
### **origin/main** 2021/03/10 mandic00@live.com
### **1.0.3** 2021/03/10 mandic00@live.com ### **1.0.3** 2021/03/10 mandic00@live.com

1
TODO.md
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@ -4,7 +4,6 @@
- Automated testing - Automated testing
- Guard against corrupt input - Guard against corrupt input
- Improve face embedding - Improve face embedding
- Build Face embedding database
- Dynamic sample processing - Dynamic sample processing
- Explore EfficientPose - Explore EfficientPose
<https://github.com/daniegr/EfficientPose> <https://github.com/daniegr/EfficientPose>

7
config.js
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@ -67,7 +67,7 @@ export default {
// (note: module is not loaded until it is required) // (note: module is not loaded until it is required)
detector: { detector: {
modelPath: '../models/blazeface-back.json', modelPath: '../models/blazeface-back.json',
rotation: true, // use best-guess rotated face image or just box with rotation as-is rotation: false, // use best-guess rotated face image or just box with rotation as-is
// false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees
// this parameter is not valid in nodejs // this parameter is not valid in nodejs
maxFaces: 10, // maximum number of faces detected in the input maxFaces: 10, // maximum number of faces detected in the input
@ -85,7 +85,7 @@ export default {
scoreThreshold: 0.2, // threshold for deciding when to remove boxes based on score scoreThreshold: 0.2, // threshold for deciding when to remove boxes based on score
// in non-maximum suppression, // in non-maximum suppression,
// this is applied on detection objects only and before minConfidence // this is applied on detection objects only and before minConfidence
return: true, // return extracted face as tensor return: false, // return extracted face as tensor
}, },
mesh: { mesh: {
@ -121,7 +121,8 @@ export default {
}, },
embedding: { embedding: {
enabled: false, enabled: false, // to improve accuracy of face embedding extraction it is recommended
// to enable detector.rotation and mesh.enabled
modelPath: '../models/mobilefacenet.json', modelPath: '../models/mobilefacenet.json',
}, },
}, },

6
demo/browser.js
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@ -3,18 +3,20 @@ import Human from '../src/human';
import Menu from './menu.js'; import Menu from './menu.js';
import GLBench from './gl-bench.js'; import GLBench from './gl-bench.js';
// const userConfig = { backend: 'webgl' }; // add any user configuration overrides const userConfig = { backend: 'webgl' }; // add any user configuration overrides
/*
const userConfig = { const userConfig = {
backend: 'wasm', backend: 'wasm',
async: false, async: false,
warmup: 'face', warmup: 'face',
videoOptimized: false, videoOptimized: false,
face: { enabled: true, mesh: { enabled: true }, 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: false } },
hand: { enabled: false }, hand: { enabled: false },
gesture: { enabled: false }, gesture: { enabled: false },
body: { enabled: false, modelPath: '../models/blazepose.json' }, body: { enabled: false, modelPath: '../models/blazepose.json' },
}; };
*/
const human = new Human(userConfig); const human = new Human(userConfig);

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demo/embedding.html
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@ -20,12 +20,14 @@
body { margin: 0; background: black; color: white; overflow-x: hidden; scrollbar-width: none; } body { margin: 0; background: black; color: white; overflow-x: hidden; scrollbar-width: none; }
body::-webkit-scrollbar { display: none; } body::-webkit-scrollbar { display: none; }
img { object-fit: contain; } img { object-fit: contain; }
.face { width: 200px; height: 200px; } .face { width: 150px; height: 150px; }
</style> </style>
</head> </head>
<body> <body>
<br>Sample Images: <br>Sample Images:
<div id="images"></div> <div id="images"></div>
<br>Selected Face<br>
<canvas id="orig" style="width: 200px; height: 200px;"></canvas>
<br>Extracted Faces - click on a face to sort by simmilarity:<br> <br>Extracted Faces - click on a face to sort by simmilarity:<br>
<div id="faces"></div> <div id="faces"></div>
</body> </body>

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demo/embedding.js
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@ -9,7 +9,7 @@ const userConfig = {
videoOptimized: false, videoOptimized: false,
face: { face: {
enabled: true, enabled: true,
detector: { rotation: true }, detector: { rotation: true, return: true },
mesh: { enabled: true }, mesh: { enabled: true },
embedding: { enabled: true, modelPath: '../models/mobilefacenet.json' }, embedding: { enabled: true, modelPath: '../models/mobilefacenet.json' },
iris: { enabled: false }, iris: { enabled: false },
@ -22,9 +22,10 @@ const userConfig = {
body: { enabled: false }, body: { enabled: false },
}; };
const human = new Human(userConfig); const human = new Human(userConfig);
const samples = ['../assets/sample-me.jpg', '../assets/sample6.jpg', '../assets/sample1.jpg', '../assets/sample4.jpg', '../assets/sample5.jpg', '../assets/sample3.jpg', '../assets/sample2.jpg', const samples = ['../assets/sample-me.jpg', '../assets/sample6.jpg', '../assets/sample1.jpg', '../assets/sample4.jpg', '../assets/sample5.jpg', '../assets/sample3.jpg', '../assets/sample2.jpg'];
'../private/me (1).jpg', '../private/me (2).jpg', '../private/me (3).jpg', '../private/me (4).jpg', '../private/me (5).jpg', '../private/me (6).jpg', '../private/me (7).jpg', '../private/me (8).jpg', // const samples = ['../assets/sample-me.jpg', '../assets/sample6.jpg', '../assets/sample1.jpg', '../assets/sample4.jpg', '../assets/sample5.jpg', '../assets/sample3.jpg', '../assets/sample2.jpg',
'../private/me (9).jpg', '../private/me (10).jpg', '../private/me (11).jpg', '../private/me (12).jpg', '../private/me (13).jpg']; // '../private/me (1).jpg', '../private/me (2).jpg', '../private/me (3).jpg', '../private/me (4).jpg', '../private/me (5).jpg', '../private/me (6).jpg', '../private/me (7).jpg', '../private/me (8).jpg',
// '../private/me (9).jpg', '../private/me (10).jpg', '../private/me (11).jpg', '../private/me (12).jpg', '../private/me (13).jpg'];
const all = []; const all = [];
function log(...msg) { function log(...msg) {
@ -36,6 +37,12 @@ function log(...msg) {
async function analyze(face) { async function analyze(face) {
log('Face:', face); log('Face:', face);
const box = [[0.05, 0.15, 0.90, 0.85]]; // top, left, bottom, right
const crop = human.tf.image.cropAndResize(face.tensor.expandDims(0), box, [0], [200, 200]); // optionally do a tight box crop
const c = document.getElementById('orig');
human.tf.browser.toPixels(crop.squeeze(), c);
const canvases = document.getElementsByClassName('face'); const canvases = document.getElementsByClassName('face');
for (const canvas of canvases) { for (const canvas of canvases) {
const res = human.simmilarity(face.embedding, all[canvas.tag.sample][canvas.tag.face].embedding); const res = human.simmilarity(face.embedding, all[canvas.tag.sample][canvas.tag.face].embedding);
@ -64,7 +71,7 @@ async function faces(index, res) {
canvas.height = 200; canvas.height = 200;
canvas.className = 'face'; canvas.className = 'face';
canvas.addEventListener('click', (evt) => { canvas.addEventListener('click', (evt) => {
log('Select:', evt.target.tag.sample, evt.target.tag.face); log('Select:', 'Image:', evt.target.tag.sample, 'Face:', evt.target.tag.face);
analyze(all[evt.target.tag.sample][evt.target.tag.face]); analyze(all[evt.target.tag.sample][evt.target.tag.face]);
}); });
human.tf.browser.toPixels(res.face[i].tensor, canvas); human.tf.browser.toPixels(res.face[i].tensor, canvas);
@ -73,7 +80,7 @@ async function faces(index, res) {
} }
async function add(index) { async function add(index) {
log('Add:', samples[index]); log('Add image:', samples[index]);
return new Promise((resolve) => { return new Promise((resolve) => {
const img = new Image(100, 100); const img = new Image(100, 100);
img.onload = () => { img.onload = () => {
@ -89,6 +96,7 @@ async function add(index) {
async function main() { async function main() {
await human.load(); await human.load();
for (const i in samples) await add(i); for (const i in samples) await add(i);
log('Ready');
} }
window.onload = main; window.onload = main;

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src/blazeface/facepipeline.ts
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@ -4,20 +4,36 @@ import * as bounding from './box';
import * as util from './util'; import * as util from './util';
import * as coords from './coords'; import * as coords from './coords';
const LANDMARKS_COUNT = 468; const leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];
const MESH_MOUTH_INDEX = 13; const rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];
const MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];
const BLAZEFACE_MOUTH_INDEX = 3; const eyeLandmarks = {
const BLAZEFACE_NOSE_INDEX = 2; leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],
const BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX]; rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],
const LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0']; };
const LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];
const RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0']; const meshLandmarks = {
const RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]]; count: 468,
const IRIS_UPPER_CENTER_INDEX = 3; mouth: 13,
const IRIS_LOWER_CENTER_INDEX = 4; symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],
const IRIS_IRIS_INDEX = 71; };
const IRIS_NUM_COORDINATES = 76;
const blazeFaceLandmarks = {
leftEye: 0,
rightEye: 1,
nose: 2,
mouth: 3,
leftEar: 4,
rightEar: 5,
symmetryLine: [3, 2],
};
const irisLandmarks = {
upperCenter: 3,
lowerCenter: 4,
index: 71,
numCoordinates: 76,
};
// Replace the raw coordinates returned by facemesh with refined iris model coordinates // Replace the raw coordinates returned by facemesh with refined iris model coordinates
// Update the z coordinate to be an average of the original and the new. // Update the z coordinate to be an average of the original and the new.
@ -83,8 +99,8 @@ export class Pipeline {
} }
getLeftToRightEyeDepthDifference(rawCoords) { getLeftToRightEyeDepthDifference(rawCoords) {
const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2]; const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];
const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2]; const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];
return leftEyeZ - rightEyeZ; return leftEyeZ - rightEyeZ;
} }
@ -106,7 +122,7 @@ export class Pipeline {
// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris. // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.
getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) { getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {
const eyeRawCoords: Array<any[]> = []; const eyeRawCoords: Array<any[]> = [];
for (let i = 0; i < IRIS_NUM_COORDINATES; i++) { for (let i = 0; i < irisLandmarks.numCoordinates; i++) {
const x = eyeData[i * 3]; const x = eyeData[i * 3];
const y = eyeData[i * 3 + 1]; const y = eyeData[i * 3 + 1];
const z = eyeData[i * 3 + 2]; const z = eyeData[i * 3 + 2];
@ -115,13 +131,13 @@ export class Pipeline {
(y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z, (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,
]); ]);
} }
return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) }; return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };
} }
// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints. // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.
getAdjustedIrisCoords(rawCoords, irisCoords, direction) { getAdjustedIrisCoords(rawCoords, irisCoords, direction) {
const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2]; const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];
const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2]; const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];
const averageZ = (upperCenterZ + lowerCenterZ) / 2; const averageZ = (upperCenterZ + lowerCenterZ) / 2;
// Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below
return irisCoords.map((coord, i) => { return irisCoords.map((coord, i) => {
@ -187,7 +203,7 @@ export class Pipeline {
let rotationMatrix; let rotationMatrix;
if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) { 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; const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;
angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]); angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);
const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }); const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });
const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]]; const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];
@ -222,13 +238,13 @@ export class Pipeline {
let rawCoords = coordsReshaped.arraySync(); let rawCoords = coordsReshaped.arraySync();
if (config.face.iris.enabled) { if (config.face.iris.enabled) {
const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true); const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);
const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]); const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);
const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop])); const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));
const eyePredictionsData = eyePredictions.dataSync(); const eyePredictionsData = eyePredictions.dataSync();
const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3); const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);
const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true); const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);
const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3); const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);
const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize); const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);
const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords); const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);
if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead. if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.
@ -246,18 +262,33 @@ export class Pipeline {
rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords); rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);
} }
// override box from detection with one calculated from mesh
const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix); const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);
const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one
const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);
const transformedCoords = tf.tensor2d(transformedCoordsData); const transformedCoords = tf.tensor2d(transformedCoordsData);
// do rotation one more time with mesh keypoints if we want to return perfect image
if (config.face.detector.rotation && config.face.mesh.enabled && config.face.detector.return && tf.ENV.flags.IS_BROWSER) {
const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;
angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);
const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });
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);
}
const prediction = { const prediction = {
coords: transformedCoords, coords: transformedCoords,
box: landmarksBox, box,
faceConfidence, faceConfidence,
boxConfidence: box.confidence, boxConfidence: box.confidence,
image: face, image: face,
rawCoords, rawCoords,
}; };
// updated stored cache values
const squarifiedLandmarksBox = bounding.squarifyBox(box);
this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence }; this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };
return prediction; return prediction;

11
src/embedding/embedding.ts
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@ -26,7 +26,7 @@ export function simmilarity(embedding1, embedding2, order = 2) {
.map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared
.reduce((sum, now) => (sum + now), 0) // sum all distances .reduce((sum, now) => (sum + now), 0) // sum all distances
** (1 / order); // get root of ** (1 / order); // get root of
const res = Math.trunc(1000 * (1 - (20 * distance))) / 1000; const res = Math.max(Math.trunc(1000 * (1 - (50 * distance))) / 1000, 0);
return res; return res;
} }
@ -35,9 +35,10 @@ export async function predict(input, config) {
return new Promise(async (resolve) => { return new Promise(async (resolve) => {
const image = tf.tidy(() => { const image = tf.tidy(() => {
const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // input is already normalized to 0..1 const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // input is already normalized to 0..1
// const box = [[0.05, 0.10, 0.85, 0.90]]; // top, left, bottom, right const box = [[0.05, 0.15, 0.90, 0.85]]; // top, left, bottom, right
// const crop = tf.image.cropAndResize(data, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]); // optionally do a tight box crop const crop = tf.image.cropAndResize(data, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]); // optionally do a tight box crop
const norm = data.sub(data.mean()); // trick to normalize around image mean value // const norm = crop.sub(crop.min()).sub(0.5); // trick to normalize around image mean value
const norm = crop.sub(0.5);
return norm; return norm;
}); });
let data: Array<[]> = []; let data: Array<[]> = [];
@ -49,7 +50,7 @@ export async function predict(input, config) {
const scale = res.div(l2); const scale = res.div(l2);
return scale; return scale;
}); });
data = [...scaled.dataSync()]; // convert object array to standard array data = scaled.dataSync(); // convert object array to standard array
tf.dispose(scaled); tf.dispose(scaled);
tf.dispose(res); tf.dispose(res);
} else { } else {

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@ -1 +1 @@
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