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Commit 157d15e7 by vincent
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check in latest build

parent 554bbe05
Showing with 173 additions and 54 deletions
build/Rect.d.ts
...@@ -10,5 +10,6 @@ export declare class Rect implements IRect { ...@@ -10,5 +10,6 @@ export declare class Rect implements IRect {
width: number; width: number;
height: number; height: number;
constructor(x: number, y: number, width: number, height: number); constructor(x: number, y: number, width: number, height: number);
toSquare(): Rect;
floor(): Rect; floor(): Rect;
} }
build/Rect.js
...@@ -7,6 +7,19 @@ var Rect = /** @class */ (function () { ...@@ -7,6 +7,19 @@ var Rect = /** @class */ (function () {
this.width = width; this.width = width;
this.height = height; this.height = height;
} }
Rect.prototype.toSquare = function () {
var _a = this, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var diff = Math.abs(width - height);
if (width < height) {
x -= (diff / 2);
width += diff;
}
if (height < width) {
y -= (diff / 2);
height += diff;
}
return new Rect(x, y, width, height);
};
Rect.prototype.floor = function () { Rect.prototype.floor = function () {
return new Rect(Math.floor(this.x), Math.floor(this.y), Math.floor(this.width), Math.floor(this.height)); return new Rect(Math.floor(this.x), Math.floor(this.y), Math.floor(this.width), Math.floor(this.height));
}; };
......
build/Rect.js.map
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\ No newline at end of file \ No newline at end of file
build/faceLandmarkNet/FaceLandmarkNet.d.ts
...@@ -6,5 +6,7 @@ export declare class FaceLandmarkNet { ...@@ -6,5 +6,7 @@ export declare class FaceLandmarkNet {
private _params; private _params;
load(weightsOrUrl: Float32Array | string | undefined): Promise<void>; load(weightsOrUrl: Float32Array | string | undefined): Promise<void>;
extractWeights(weights: Float32Array): void; extractWeights(weights: Float32Array): void;
forwardTensor(imgTensor: tf.Tensor4D): tf.Tensor2D;
forward(input: tf.Tensor | NetInput | TNetInput): Promise<tf.Tensor2D>;
detectLandmarks(input: tf.Tensor | NetInput | TNetInput): Promise<FaceLandmarks>; detectLandmarks(input: tf.Tensor | NetInput | TNetInput): Promise<FaceLandmarks>;
} }
build/faceLandmarkNet/FaceLandmarkNet.js
...@@ -4,8 +4,10 @@ var tslib_1 = require("tslib"); ...@@ -4,8 +4,10 @@ var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core"); var tf = require("@tensorflow/tfjs-core");
var convLayer_1 = require("../commons/convLayer"); var convLayer_1 = require("../commons/convLayer");
var getImageTensor_1 = require("../commons/getImageTensor"); var getImageTensor_1 = require("../commons/getImageTensor");
var padToSquare_1 = require("../padToSquare");
var Point_1 = require("../Point"); var Point_1 = require("../Point");
var toNetInput_1 = require("../toNetInput"); var toNetInput_1 = require("../toNetInput");
var utils_1 = require("../utils");
var extractParams_1 = require("./extractParams"); var extractParams_1 = require("./extractParams");
var FaceLandmarks_1 = require("./FaceLandmarks"); var FaceLandmarks_1 = require("./FaceLandmarks");
var fullyConnectedLayer_1 = require("./fullyConnectedLayer"); var fullyConnectedLayer_1 = require("./fullyConnectedLayer");
...@@ -45,6 +47,72 @@ var FaceLandmarkNet = /** @class */ (function () { ...@@ -45,6 +47,72 @@ var FaceLandmarkNet = /** @class */ (function () {
FaceLandmarkNet.prototype.extractWeights = function (weights) { FaceLandmarkNet.prototype.extractWeights = function (weights) {
this._params = extractParams_1.extractParams(weights); this._params = extractParams_1.extractParams(weights);
}; };
FaceLandmarkNet.prototype.forwardTensor = function (imgTensor) {
var params = this._params;
if (!params) {
throw new Error('FaceLandmarkNet - load model before inference');
}
return tf.tidy(function () {
var _a = imgTensor.shape.slice(), batchSize = _a[0], height = _a[1], width = _a[2];
var x = padToSquare_1.padToSquare(imgTensor, true);
var _b = x.shape.slice(1), heightAfterPadding = _b[0], widthAfterPadding = _b[1];
// work with 128 x 128 sized face images
if (heightAfterPadding !== 128 || widthAfterPadding !== 128) {
x = tf.image.resizeBilinear(x, [128, 128]);
}
var out = conv(x, params.conv0_params);
out = maxPool(out);
out = conv(out, params.conv1_params);
out = conv(out, params.conv2_params);
out = maxPool(out);
out = conv(out, params.conv3_params);
out = conv(out, params.conv4_params);
out = maxPool(out);
out = conv(out, params.conv5_params);
out = conv(out, params.conv6_params);
out = maxPool(out, [1, 1]);
out = conv(out, params.conv7_params);
var fc0 = tf.relu(fullyConnectedLayer_1.fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0_params));
var fc1 = fullyConnectedLayer_1.fullyConnectedLayer(fc0, params.fc1_params);
var createInterleavedTensor = function (fillX, fillY) {
return tf.stack([
tf.fill([68], fillX),
tf.fill([68], fillY)
], 1).as2D(batchSize, 136);
};
/* shift coordinates back, to undo centered padding
((x * widthAfterPadding) - shiftX) / width
((y * heightAfterPadding) - shiftY) / height
*/
var shiftX = Math.floor(Math.abs(widthAfterPadding - width) / 2);
var shiftY = Math.floor(Math.abs(heightAfterPadding - height) / 2);
var landmarkTensor = fc1
.mul(createInterleavedTensor(widthAfterPadding, heightAfterPadding))
.sub(createInterleavedTensor(shiftX, shiftY))
.div(createInterleavedTensor(width, height));
return landmarkTensor;
});
};
FaceLandmarkNet.prototype.forward = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var netInput, _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
if (!(input instanceof tf.Tensor)) return [3 /*break*/, 1];
_a = input;
return [3 /*break*/, 3];
case 1: return [4 /*yield*/, toNetInput_1.toNetInput(input)];
case 2:
_a = _b.sent();
_b.label = 3;
case 3:
netInput = _a;
return [2 /*return*/, this.forwardTensor(getImageTensor_1.getImageTensor(netInput))];
}
});
});
};
FaceLandmarkNet.prototype.detectLandmarks = function (input) { FaceLandmarkNet.prototype.detectLandmarks = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () { return tslib_1.__awaiter(this, void 0, void 0, function () {
var _this = this; var _this = this;
...@@ -52,9 +120,6 @@ var FaceLandmarkNet = /** @class */ (function () { ...@@ -52,9 +120,6 @@ var FaceLandmarkNet = /** @class */ (function () {
return tslib_1.__generator(this, function (_d) { return tslib_1.__generator(this, function (_d) {
switch (_d.label) { switch (_d.label) {
case 0: case 0:
if (!this._params) {
throw new Error('FaceLandmarkNet - load model before inference');
}
if (!(input instanceof tf.Tensor)) return [3 /*break*/, 1]; if (!(input instanceof tf.Tensor)) return [3 /*break*/, 1];
_a = input; _a = input;
return [3 /*break*/, 3]; return [3 /*break*/, 3];
...@@ -65,37 +130,18 @@ var FaceLandmarkNet = /** @class */ (function () { ...@@ -65,37 +130,18 @@ var FaceLandmarkNet = /** @class */ (function () {
case 3: case 3:
netInput = _a; netInput = _a;
outTensor = tf.tidy(function () { outTensor = tf.tidy(function () {
var params = _this._params;
var imgTensor = getImageTensor_1.getImageTensor(netInput); var imgTensor = getImageTensor_1.getImageTensor(netInput);
var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1]; var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
imageDimensions = { width: width, height: height }; imageDimensions = { width: width, height: height };
// work with 128 x 128 sized face images return _this.forwardTensor(imgTensor);
if (imgTensor.shape[1] !== 128 || imgTensor.shape[2] !== 128) {
imgTensor = tf.image.resizeBilinear(imgTensor, [128, 128]);
}
var out = conv(imgTensor, params.conv0_params);
out = maxPool(out);
out = conv(out, params.conv1_params);
out = conv(out, params.conv2_params);
out = maxPool(out);
out = conv(out, params.conv3_params);
out = conv(out, params.conv4_params);
out = maxPool(out);
out = conv(out, params.conv5_params);
out = conv(out, params.conv6_params);
out = maxPool(out, [1, 1]);
out = conv(out, params.conv7_params);
var fc0 = tf.relu(fullyConnectedLayer_1.fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0_params));
var fc1 = fullyConnectedLayer_1.fullyConnectedLayer(fc0, params.fc1_params);
return fc1;
}); });
_c = (_b = Array).from; _c = (_b = Array).from;
return [4 /*yield*/, outTensor.data()]; return [4 /*yield*/, outTensor.data()];
case 4: case 4:
faceLandmarksArray = _c.apply(_b, [_d.sent()]); faceLandmarksArray = _c.apply(_b, [_d.sent()]);
outTensor.dispose(); outTensor.dispose();
xCoords = faceLandmarksArray.filter(function (c, i) { return (i - 1) % 2; }); xCoords = faceLandmarksArray.filter(function (_, i) { return utils_1.isEven(i); });
yCoords = faceLandmarksArray.filter(function (c, i) { return i % 2; }); yCoords = faceLandmarksArray.filter(function (_, i) { return !utils_1.isEven(i); });
return [2 /*return*/, new FaceLandmarks_1.FaceLandmarks(Array(68).fill(0).map(function (_, i) { return new Point_1.Point(xCoords[i], yCoords[i]); }), imageDimensions)]; return [2 /*return*/, new FaceLandmarks_1.FaceLandmarks(Array(68).fill(0).map(function (_, i) { return new Point_1.Point(xCoords[i], yCoords[i]); }), imageDimensions)];
} }
}); });
......
build/faceLandmarkNet/FaceLandmarkNet.js.map
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\ No newline at end of file \ No newline at end of file
dist/face-api.js
...@@ -510,6 +510,19 @@ ...@@ -510,6 +510,19 @@
this.width = width; this.width = width;
this.height = height; this.height = height;
} }
Rect.prototype.toSquare = function () {
var _a = this, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var diff = Math.abs(width - height);
if (width < height) {
x -= (diff / 2);
width += diff;
}
if (height < width) {
y -= (diff / 2);
height += diff;
}
return new Rect(x, y, width, height);
};
Rect.prototype.floor = function () { Rect.prototype.floor = function () {
return new Rect(Math.floor(this.x), Math.floor(this.y), Math.floor(this.width), Math.floor(this.height)); return new Rect(Math.floor(this.x), Math.floor(this.y), Math.floor(this.width), Math.floor(this.height));
}; };
...@@ -1732,6 +1745,72 @@ ...@@ -1732,6 +1745,72 @@
FaceLandmarkNet.prototype.extractWeights = function (weights) { FaceLandmarkNet.prototype.extractWeights = function (weights) {
this._params = extractParams$1(weights); this._params = extractParams$1(weights);
}; };
FaceLandmarkNet.prototype.forwardTensor = function (imgTensor) {
var params = this._params;
if (!params) {
throw new Error('FaceLandmarkNet - load model before inference');
}
return tidy(function () {
var _a = imgTensor.shape.slice(), batchSize = _a[0], height = _a[1], width = _a[2];
var x = padToSquare(imgTensor, true);
var _b = x.shape.slice(1), heightAfterPadding = _b[0], widthAfterPadding = _b[1];
// work with 128 x 128 sized face images
if (heightAfterPadding !== 128 || widthAfterPadding !== 128) {
x = image.resizeBilinear(x, [128, 128]);
}
var out = conv(x, params.conv0_params);
out = maxPool$1(out);
out = conv(out, params.conv1_params);
out = conv(out, params.conv2_params);
out = maxPool$1(out);
out = conv(out, params.conv3_params);
out = conv(out, params.conv4_params);
out = maxPool$1(out);
out = conv(out, params.conv5_params);
out = conv(out, params.conv6_params);
out = maxPool$1(out, [1, 1]);
out = conv(out, params.conv7_params);
var fc0 = relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0_params));
var fc1 = fullyConnectedLayer(fc0, params.fc1_params);
var createInterleavedTensor = function (fillX, fillY) {
return stack([
fill([68], fillX),
fill([68], fillY)
], 1).as2D(batchSize, 136);
};
/* shift coordinates back, to undo centered padding
((x * widthAfterPadding) - shiftX) / width
((y * heightAfterPadding) - shiftY) / height
*/
var shiftX = Math.floor(Math.abs(widthAfterPadding - width) / 2);
var shiftY = Math.floor(Math.abs(heightAfterPadding - height) / 2);
var landmarkTensor = fc1
.mul(createInterleavedTensor(widthAfterPadding, heightAfterPadding))
.sub(createInterleavedTensor(shiftX, shiftY))
.div(createInterleavedTensor(width, height));
return landmarkTensor;
});
};
FaceLandmarkNet.prototype.forward = function (input) {
return __awaiter$1(this, void 0, void 0, function () {
var netInput, _a;
return __generator$1(this, function (_b) {
switch (_b.label) {
case 0:
if (!(input instanceof Tensor)) return [3 /*break*/, 1];
_a = input;
return [3 /*break*/, 3];
case 1: return [4 /*yield*/, toNetInput(input)];
case 2:
_a = _b.sent();
_b.label = 3;
case 3:
netInput = _a;
return [2 /*return*/, this.forwardTensor(getImageTensor(netInput))];
}
});
});
};
FaceLandmarkNet.prototype.detectLandmarks = function (input) { FaceLandmarkNet.prototype.detectLandmarks = function (input) {
return __awaiter$1(this, void 0, void 0, function () { return __awaiter$1(this, void 0, void 0, function () {
var _this = this; var _this = this;
...@@ -1739,9 +1818,6 @@ ...@@ -1739,9 +1818,6 @@
return __generator$1(this, function (_d) { return __generator$1(this, function (_d) {
switch (_d.label) { switch (_d.label) {
case 0: case 0:
if (!this._params) {
throw new Error('FaceLandmarkNet - load model before inference');
}
if (!(input instanceof Tensor)) return [3 /*break*/, 1]; if (!(input instanceof Tensor)) return [3 /*break*/, 1];
_a = input; _a = input;
return [3 /*break*/, 3]; return [3 /*break*/, 3];
...@@ -1752,37 +1828,18 @@ ...@@ -1752,37 +1828,18 @@
case 3: case 3:
netInput = _a; netInput = _a;
outTensor = tidy(function () { outTensor = tidy(function () {
var params = _this._params;
var imgTensor = getImageTensor(netInput); var imgTensor = getImageTensor(netInput);
var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1]; var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
imageDimensions = { width: width, height: height }; imageDimensions = { width: width, height: height };
// work with 128 x 128 sized face images return _this.forwardTensor(imgTensor);
if (imgTensor.shape[1] !== 128 || imgTensor.shape[2] !== 128) {
imgTensor = image.resizeBilinear(imgTensor, [128, 128]);
}
var out = conv(imgTensor, params.conv0_params);
out = maxPool$1(out);
out = conv(out, params.conv1_params);
out = conv(out, params.conv2_params);
out = maxPool$1(out);
out = conv(out, params.conv3_params);
out = conv(out, params.conv4_params);
out = maxPool$1(out);
out = conv(out, params.conv5_params);
out = conv(out, params.conv6_params);
out = maxPool$1(out, [1, 1]);
out = conv(out, params.conv7_params);
var fc0 = relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0_params));
var fc1 = fullyConnectedLayer(fc0, params.fc1_params);
return fc1;
}); });
_c = (_b = Array).from; _c = (_b = Array).from;
return [4 /*yield*/, outTensor.data()]; return [4 /*yield*/, outTensor.data()];
case 4: case 4:
faceLandmarksArray = _c.apply(_b, [_d.sent()]); faceLandmarksArray = _c.apply(_b, [_d.sent()]);
outTensor.dispose(); outTensor.dispose();
xCoords = faceLandmarksArray.filter(function (c, i) { return (i - 1) % 2; }); xCoords = faceLandmarksArray.filter(function (_, i) { return isEven(i); });
yCoords = faceLandmarksArray.filter(function (c, i) { return i % 2; }); yCoords = faceLandmarksArray.filter(function (_, i) { return !isEven(i); });
return [2 /*return*/, new FaceLandmarks(Array(68).fill(0).map(function (_, i) { return new Point(xCoords[i], yCoords[i]); }), imageDimensions)]; return [2 /*return*/, new FaceLandmarks(Array(68).fill(0).map(function (_, i) { return new Point(xCoords[i], yCoords[i]); }), imageDimensions)];
} }
}); });
......
dist/face-api.js.map
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dist/face-api.min.js
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