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Commit 895c8a21 by vincent
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check in latest build

parent 19e27a14
Showing with 581 additions and 536 deletions
build/commonjs/dom/drawLandmarks.js
......@@ -5,7 +5,7 @@ var FaceLandmarks68_1 = require("../classes/FaceLandmarks68");
var drawContour_1 = require("./drawContour");
function drawLandmarks(canvasArg, faceLandmarks, options) {
var canvas = tfjs_image_recognition_base_1.resolveInput(canvasArg);
if (!(canvas instanceof HTMLCanvasElement)) {
if (!(canvas instanceof tfjs_image_recognition_base_1.env.getEnv().Canvas)) {
throw new Error('drawLandmarks - expected canvas to be of type: HTMLCanvasElement');
}
var drawOptions = Object.assign(tfjs_image_recognition_base_1.getDefaultDrawOptions(options), (options || {}));
......
build/commonjs/dom/drawLandmarks.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commonjs/dom/extractFaceTensors.js
......@@ -17,6 +17,9 @@ var FaceDetection_1 = require("../classes/FaceDetection");
function extractFaceTensors(imageTensor, detections) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
return tslib_1.__generator(this, function (_a) {
if (!tfjs_image_recognition_base_1.isTensor3D(imageTensor) && !tfjs_image_recognition_base_1.isTensor4D(imageTensor)) {
throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');
}
if (tfjs_image_recognition_base_1.isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
throw new Error('extractFaceTensors - batchSize > 1 not supported');
}
......
build/commonjs/dom/extractFaceTensors.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commonjs/dom/extractFaces.js
......@@ -12,12 +12,13 @@ var FaceDetection_1 = require("../classes/FaceDetection");
*/
function extractFaces(input, detections) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var canvas, netInput, tensorOrCanvas, _a, ctx, boxes;
var Canvas, canvas, netInput, tensorOrCanvas, _a, ctx, boxes;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
Canvas = tfjs_image_recognition_base_1.env.getEnv().Canvas;
canvas = input;
if (!!(input instanceof HTMLCanvasElement)) return [3 /*break*/, 5];
if (!!(input instanceof Canvas)) return [3 /*break*/, 5];
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1:
netInput = _b.sent();
......@@ -25,7 +26,7 @@ function extractFaces(input, detections) {
throw new Error('extractFaces - batchSize > 1 not supported');
}
tensorOrCanvas = netInput.getInput(0);
if (!(tensorOrCanvas instanceof HTMLCanvasElement)) return [3 /*break*/, 2];
if (!(tensorOrCanvas instanceof Canvas)) return [3 /*break*/, 2];
_a = tensorOrCanvas;
return [3 /*break*/, 4];
case 2: return [4 /*yield*/, tfjs_image_recognition_base_1.imageTensorToCanvas(tensorOrCanvas)];
......
build/commonjs/dom/extractFaces.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commonjs/faceLandmarkNet/FaceLandmark68Net.d.ts
......@@ -5,13 +5,14 @@ import { NetParams } from './types';
export declare class FaceLandmark68Net extends FaceLandmark68NetBase<NetParams> {
constructor();
runNet(input: NetInput): tf.Tensor2D;
protected loadQuantizedParams(uri: string | undefined): Promise<{
protected getDefaultModelName(): string;
protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
}>;
};
protected extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: {
......
build/commonjs/faceLandmarkNet/FaceLandmark68Net.js
......@@ -5,9 +5,9 @@ var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var depthwiseSeparableConv_1 = require("./depthwiseSeparableConv");
var extractParams_1 = require("./extractParams");
var extractParamsFromWeigthMap_1 = require("./extractParamsFromWeigthMap");
var FaceLandmark68NetBase_1 = require("./FaceLandmark68NetBase");
var fullyConnectedLayer_1 = require("./fullyConnectedLayer");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
function denseBlock(x, denseBlockParams, isFirstLayer) {
if (isFirstLayer === void 0) { isFirstLayer = false; }
return tf.tidy(function () {
......@@ -44,8 +44,11 @@ var FaceLandmark68Net = /** @class */ (function (_super) {
return fullyConnectedLayer_1.fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc);
});
};
FaceLandmark68Net.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams_1.loadQuantizedParams(uri);
FaceLandmark68Net.prototype.getDefaultModelName = function () {
return 'face_landmark_68_model';
};
FaceLandmark68Net.prototype.extractParamsFromWeigthMap = function (weightMap) {
return extractParamsFromWeigthMap_1.extractParamsFromWeigthMap(weightMap);
};
FaceLandmark68Net.prototype.extractParams = function (weights) {
return extractParams_1.extractParams(weights);
......
build/commonjs/faceLandmarkNet/FaceLandmark68Net.js.map
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\ No newline at end of file
build/commonjs/faceLandmarkNet/FaceLandmark68NetBase.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { IDimensions, NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
export declare class FaceLandmark68NetBase<NetParams> extends NeuralNetwork<NetParams> {
export declare abstract class FaceLandmark68NetBase<NetParams> extends NeuralNetwork<NetParams> {
private __name;
constructor(_name: string);
runNet(_: NetInput): tf.Tensor2D;
abstract runNet(netInput: NetInput): tf.Tensor2D;
postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D;
forwardInput(input: NetInput): tf.Tensor2D;
forward(input: TNetInput): Promise<tf.Tensor2D>;
......
build/commonjs/faceLandmarkNet/FaceLandmark68NetBase.js
......@@ -11,9 +11,6 @@ var FaceLandmark68NetBase = /** @class */ (function (_super) {
_this.__name = _name;
return _this;
}
FaceLandmark68NetBase.prototype.runNet = function (_) {
throw new Error(this.__name + " - runNet not implemented");
};
FaceLandmark68NetBase.prototype.postProcess = function (output, inputSize, originalDimensions) {
var inputDimensions = originalDimensions.map(function (_a) {
var width = _a.width, height = _a.height;
......
build/commonjs/faceLandmarkNet/FaceLandmark68NetBase.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commonjs/faceLandmarkNet/FaceLandmark68TinyNet.d.ts
......@@ -5,13 +5,14 @@ import { TinyNetParams } from './types';
export declare class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyNetParams> {
constructor();
runNet(input: NetInput): tf.Tensor2D;
protected loadQuantizedParams(uri: string | undefined): Promise<{
protected getDefaultModelName(): string;
protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: TinyNetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
}>;
};
protected extractParams(weights: Float32Array): {
params: TinyNetParams;
paramMappings: {
......
build/commonjs/faceLandmarkNet/FaceLandmark68TinyNet.js
......@@ -7,7 +7,7 @@ var depthwiseSeparableConv_1 = require("./depthwiseSeparableConv");
var extractParamsTiny_1 = require("./extractParamsTiny");
var FaceLandmark68NetBase_1 = require("./FaceLandmark68NetBase");
var fullyConnectedLayer_1 = require("./fullyConnectedLayer");
var loadQuantizedParamsTiny_1 = require("./loadQuantizedParamsTiny");
var extractParamsFromWeigthMapTiny_1 = require("./extractParamsFromWeigthMapTiny");
function denseBlock(x, denseBlockParams, isFirstLayer) {
if (isFirstLayer === void 0) { isFirstLayer = false; }
return tf.tidy(function () {
......@@ -41,8 +41,11 @@ var FaceLandmark68TinyNet = /** @class */ (function (_super) {
return fullyConnectedLayer_1.fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc);
});
};
FaceLandmark68TinyNet.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParamsTiny_1.loadQuantizedParamsTiny(uri);
FaceLandmark68TinyNet.prototype.getDefaultModelName = function () {
return 'face_landmark_68_tiny_model';
};
FaceLandmark68TinyNet.prototype.extractParamsFromWeigthMap = function (weightMap) {
return extractParamsFromWeigthMapTiny_1.extractParamsFromWeigthMapTiny(weightMap);
};
FaceLandmark68TinyNet.prototype.extractParams = function (weights) {
return extractParamsTiny_1.extractParamsTiny(weights);
......
build/commonjs/faceLandmarkNet/FaceLandmark68TinyNet.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commonjs/faceLandmarkNet/loadQuantizedParams.d.ts → build/commonjs/faceLandmarkNet/extractParamsFromWeigthMap.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { ParamMapping } from 'tfjs-image-recognition-base';
import { NetParams } from './types';
export declare function loadQuantizedParams(uri: string | undefined): Promise<{
export declare function extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: NetParams;
paramMappings: ParamMapping[];
}>;
};
build/commonjs/faceLandmarkNet/extractParamsFromWeigthMap.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var loadParamsFactory_1 = require("./loadParamsFactory");
function extractParamsFromWeigthMap(weightMap) {
var paramMappings = [];
var _a = loadParamsFactory_1.loadParamsFactory(weightMap, paramMappings), extractDenseBlock4Params = _a.extractDenseBlock4Params, extractFcParams = _a.extractFcParams;
var params = {
dense0: extractDenseBlock4Params('dense0', true),
dense1: extractDenseBlock4Params('dense1'),
dense2: extractDenseBlock4Params('dense2'),
dense3: extractDenseBlock4Params('dense3'),
fc: extractFcParams('fc')
};
tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return { params: params, paramMappings: paramMappings };
}
exports.extractParamsFromWeigthMap = extractParamsFromWeigthMap;
//# sourceMappingURL=extractParamsFromWeigthMap.js.map
\ No newline at end of file
build/commonjs/faceLandmarkNet/extractParamsFromWeigthMap.js.map 0 → 100644
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\ No newline at end of file
build/commonjs/faceLandmarkNet/loadQuantizedParamsTiny.d.ts → build/commonjs/faceLandmarkNet/extractParamsFromWeigthMapTiny.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { ParamMapping } from 'tfjs-image-recognition-base';
import { TinyNetParams } from './types';
export declare function loadQuantizedParamsTiny(uri: string | undefined): Promise<{
export declare function extractParamsFromWeigthMapTiny(weightMap: tf.NamedTensorMap): {
params: TinyNetParams;
paramMappings: ParamMapping[];
}>;
};
build/commonjs/faceLandmarkNet/extractParamsFromWeigthMapTiny.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var loadParamsFactory_1 = require("./loadParamsFactory");
function extractParamsFromWeigthMapTiny(weightMap) {
var paramMappings = [];
var _a = loadParamsFactory_1.loadParamsFactory(weightMap, paramMappings), extractDenseBlock3Params = _a.extractDenseBlock3Params, extractFcParams = _a.extractFcParams;
var params = {
dense0: extractDenseBlock3Params('dense0', true),
dense1: extractDenseBlock3Params('dense1'),
dense2: extractDenseBlock3Params('dense2'),
fc: extractFcParams('fc')
};
tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return { params: params, paramMappings: paramMappings };
}
exports.extractParamsFromWeigthMapTiny = extractParamsFromWeigthMapTiny;
//# sourceMappingURL=extractParamsFromWeigthMapTiny.js.map
\ No newline at end of file
build/commonjs/faceLandmarkNet/extractParamsFromWeigthMapTiny.js.map 0 → 100644
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\ No newline at end of file
build/commonjs/faceLandmarkNet/loadQuantizedParams.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var loadParamsFactory_1 = require("./loadParamsFactory");
var DEFAULT_MODEL_NAME = 'face_landmark_68_model';
function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, paramMappings, _a, extractDenseBlock4Params, extractFcParams, params;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1:
weightMap = _b.sent();
paramMappings = [];
_a = loadParamsFactory_1.loadParamsFactory(weightMap, paramMappings), extractDenseBlock4Params = _a.extractDenseBlock4Params, extractFcParams = _a.extractFcParams;
params = {
dense0: extractDenseBlock4Params('dense0', true),
dense1: extractDenseBlock4Params('dense1'),
dense2: extractDenseBlock4Params('dense2'),
dense3: extractDenseBlock4Params('dense3'),
fc: extractFcParams('fc')
};
tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }];
}
});
});
}
exports.loadQuantizedParams = loadQuantizedParams;
//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file
build/commonjs/faceLandmarkNet/loadQuantizedParams.js.map deleted 100644 → 0
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\ No newline at end of file
build/commonjs/faceLandmarkNet/loadQuantizedParamsTiny.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var loadParamsFactory_1 = require("./loadParamsFactory");
var DEFAULT_MODEL_NAME = 'face_landmark_68_tiny_model';
function loadQuantizedParamsTiny(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, paramMappings, _a, extractDenseBlock3Params, extractFcParams, params;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1:
weightMap = _b.sent();
paramMappings = [];
_a = loadParamsFactory_1.loadParamsFactory(weightMap, paramMappings), extractDenseBlock3Params = _a.extractDenseBlock3Params, extractFcParams = _a.extractFcParams;
params = {
dense0: extractDenseBlock3Params('dense0', true),
dense1: extractDenseBlock3Params('dense1'),
dense2: extractDenseBlock3Params('dense2'),
fc: extractFcParams('fc')
};
tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }];
}
});
});
}
exports.loadQuantizedParamsTiny = loadQuantizedParamsTiny;
//# sourceMappingURL=loadQuantizedParamsTiny.js.map
\ No newline at end of file
build/commonjs/faceLandmarkNet/loadQuantizedParamsTiny.js.map deleted 100644 → 0
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\ No newline at end of file
build/commonjs/faceRecognitionNet/FaceRecognitionNet.d.ts
......@@ -6,13 +6,14 @@ export declare class FaceRecognitionNet extends NeuralNetwork<NetParams> {
forwardInput(input: NetInput): tf.Tensor2D;
forward(input: TNetInput): Promise<tf.Tensor2D>;
computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>;
protected loadQuantizedParams(uri: string | undefined): Promise<{
protected getDefaultModelName(): string;
protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
}>;
};
protected extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: {
......
build/commonjs/faceRecognitionNet/FaceRecognitionNet.js
......@@ -5,7 +5,7 @@ var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var convLayer_1 = require("./convLayer");
var extractParams_1 = require("./extractParams");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
var extractParamsFromWeigthMap_1 = require("./extractParamsFromWeigthMap");
var residualLayer_1 = require("./residualLayer");
var FaceRecognitionNet = /** @class */ (function (_super) {
tslib_1.__extends(FaceRecognitionNet, _super);
......@@ -76,8 +76,11 @@ var FaceRecognitionNet = /** @class */ (function (_super) {
});
});
};
FaceRecognitionNet.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams_1.loadQuantizedParams(uri);
FaceRecognitionNet.prototype.getDefaultModelName = function () {
return 'face_recognition_model';
};
FaceRecognitionNet.prototype.extractParamsFromWeigthMap = function (weightMap) {
return extractParamsFromWeigthMap_1.extractParamsFromWeigthMap(weightMap);
};
FaceRecognitionNet.prototype.extractParams = function (weights) {
return extractParams_1.extractParams(weights);
......
build/commonjs/faceRecognitionNet/FaceRecognitionNet.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commonjs/faceRecognitionNet/loadQuantizedParams.d.ts → build/commonjs/faceRecognitionNet/extractParamsFromWeigthMap.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { ParamMapping } from 'tfjs-image-recognition-base';
import { NetParams } from './types';
export declare function loadQuantizedParams(uri: string | undefined): Promise<{
export declare function extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: NetParams;
paramMappings: ParamMapping[];
}>;
};
build/commonjs/faceRecognitionNet/loadQuantizedParams.js → build/commonjs/faceRecognitionNet/extractParamsFromWeigthMap.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var DEFAULT_MODEL_NAME = 'face_recognition_model';
function extractorsFactory(weightMap, paramMappings) {
var extractWeightEntry = tfjs_image_recognition_base_1.extractWeightEntryFactory(weightMap, paramMappings);
function extractScaleLayerParams(prefix) {
......@@ -27,59 +25,49 @@ function extractorsFactory(weightMap, paramMappings) {
extractResidualLayerParams: extractResidualLayerParams
};
}
function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, paramMappings, _a, extractConvLayerParams, extractResidualLayerParams, conv32_down, conv32_1, conv32_2, conv32_3, conv64_down, conv64_1, conv64_2, conv64_3, conv128_down, conv128_1, conv128_2, conv256_down, conv256_1, conv256_2, conv256_down_out, fc, params;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1:
weightMap = _b.sent();
paramMappings = [];
_a = extractorsFactory(weightMap, paramMappings), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams;
conv32_down = extractConvLayerParams('conv32_down');
conv32_1 = extractResidualLayerParams('conv32_1');
conv32_2 = extractResidualLayerParams('conv32_2');
conv32_3 = extractResidualLayerParams('conv32_3');
conv64_down = extractResidualLayerParams('conv64_down');
conv64_1 = extractResidualLayerParams('conv64_1');
conv64_2 = extractResidualLayerParams('conv64_2');
conv64_3 = extractResidualLayerParams('conv64_3');
conv128_down = extractResidualLayerParams('conv128_down');
conv128_1 = extractResidualLayerParams('conv128_1');
conv128_2 = extractResidualLayerParams('conv128_2');
conv256_down = extractResidualLayerParams('conv256_down');
conv256_1 = extractResidualLayerParams('conv256_1');
conv256_2 = extractResidualLayerParams('conv256_2');
conv256_down_out = extractResidualLayerParams('conv256_down_out');
fc = weightMap['fc'];
paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });
if (!tfjs_image_recognition_base_1.isTensor2D(fc)) {
throw new Error("expected weightMap[fc] to be a Tensor2D, instead have " + fc);
}
params = {
conv32_down: conv32_down,
conv32_1: conv32_1,
conv32_2: conv32_2,
conv32_3: conv32_3,
conv64_down: conv64_down,
conv64_1: conv64_1,
conv64_2: conv64_2,
conv64_3: conv64_3,
conv128_down: conv128_down,
conv128_1: conv128_1,
conv128_2: conv128_2,
conv256_down: conv256_down,
conv256_1: conv256_1,
conv256_2: conv256_2,
conv256_down_out: conv256_down_out,
fc: fc
};
tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }];
}
});
});
function extractParamsFromWeigthMap(weightMap) {
var paramMappings = [];
var _a = extractorsFactory(weightMap, paramMappings), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams;
var conv32_down = extractConvLayerParams('conv32_down');
var conv32_1 = extractResidualLayerParams('conv32_1');
var conv32_2 = extractResidualLayerParams('conv32_2');
var conv32_3 = extractResidualLayerParams('conv32_3');
var conv64_down = extractResidualLayerParams('conv64_down');
var conv64_1 = extractResidualLayerParams('conv64_1');
var conv64_2 = extractResidualLayerParams('conv64_2');
var conv64_3 = extractResidualLayerParams('conv64_3');
var conv128_down = extractResidualLayerParams('conv128_down');
var conv128_1 = extractResidualLayerParams('conv128_1');
var conv128_2 = extractResidualLayerParams('conv128_2');
var conv256_down = extractResidualLayerParams('conv256_down');
var conv256_1 = extractResidualLayerParams('conv256_1');
var conv256_2 = extractResidualLayerParams('conv256_2');
var conv256_down_out = extractResidualLayerParams('conv256_down_out');
var fc = weightMap['fc'];
paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });
if (!tfjs_image_recognition_base_1.isTensor2D(fc)) {
throw new Error("expected weightMap[fc] to be a Tensor2D, instead have " + fc);
}
var params = {
conv32_down: conv32_down,
conv32_1: conv32_1,
conv32_2: conv32_2,
conv32_3: conv32_3,
conv64_down: conv64_down,
conv64_1: conv64_1,
conv64_2: conv64_2,
conv64_3: conv64_3,
conv128_down: conv128_down,
conv128_1: conv128_1,
conv128_2: conv128_2,
conv256_down: conv256_down,
conv256_1: conv256_1,
conv256_2: conv256_2,
conv256_down_out: conv256_down_out,
fc: fc
};
tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return { params: params, paramMappings: paramMappings };
}
exports.loadQuantizedParams = loadQuantizedParams;
//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file
exports.extractParamsFromWeigthMap = extractParamsFromWeigthMap;
//# sourceMappingURL=extractParamsFromWeigthMap.js.map
\ No newline at end of file
build/commonjs/faceRecognitionNet/extractParamsFromWeigthMap.js.map 0 → 100644
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\ No newline at end of file
build/commonjs/faceRecognitionNet/loadQuantizedParams.js.map deleted 100644 → 0
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\ No newline at end of file
build/commonjs/globalApi/ComputeFaceDescriptorsTasks.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var FullFaceDescription_1 = require("../classes/FullFaceDescription");
var dom_1 = require("../dom");
var ComposableTask_1 = require("./ComposableTask");
......@@ -24,25 +25,34 @@ var ComputeAllFaceDescriptorsTask = /** @class */ (function (_super) {
ComputeAllFaceDescriptorsTask.prototype.run = function () {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _this = this;
var facesWithLandmarks, alignedFaceCanvases;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
var facesWithLandmarks, alignedRects, alignedFaces, _a, fullFaceDescriptions;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, this.detectFaceLandmarksTask];
case 1:
facesWithLandmarks = _a.sent();
return [4 /*yield*/, dom_1.extractFaces(this.input, facesWithLandmarks.map(function (_a) {
var landmarks = _a.landmarks;
return landmarks.align();
}))];
facesWithLandmarks = _b.sent();
alignedRects = facesWithLandmarks.map(function (_a) {
var alignedRect = _a.alignedRect;
return alignedRect;
});
if (!(this.input instanceof tf.Tensor)) return [3 /*break*/, 3];
return [4 /*yield*/, dom_1.extractFaceTensors(this.input, alignedRects)];
case 2:
alignedFaceCanvases = _a.sent();
_a = _b.sent();
return [3 /*break*/, 5];
case 3: return [4 /*yield*/, dom_1.extractFaces(this.input, alignedRects)];
case 4:
_a = _b.sent();
_b.label = 5;
case 5:
alignedFaces = _a;
return [4 /*yield*/, Promise.all(facesWithLandmarks.map(function (_a, i) {
var detection = _a.detection, landmarks = _a.landmarks;
return tslib_1.__awaiter(_this, void 0, void 0, function () {
var descriptor;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, nets_1.nets.faceRecognitionNet.computeFaceDescriptor(alignedFaceCanvases[i])];
case 0: return [4 /*yield*/, nets_1.nets.faceRecognitionNet.computeFaceDescriptor(alignedFaces[i])];
case 1:
descriptor = _b.sent();
return [2 /*return*/, new FullFaceDescription_1.FullFaceDescription(detection, landmarks, descriptor)];
......@@ -50,7 +60,10 @@ var ComputeAllFaceDescriptorsTask = /** @class */ (function (_super) {
});
});
}))];
case 3: return [2 /*return*/, _a.sent()];
case 6:
fullFaceDescriptions = _b.sent();
alignedFaces.forEach(function (f) { return f instanceof tf.Tensor && f.dispose(); });
return [2 /*return*/, fullFaceDescriptions];
}
});
});
......@@ -65,22 +78,31 @@ var ComputeSingleFaceDescriptorTask = /** @class */ (function (_super) {
}
ComputeSingleFaceDescriptorTask.prototype.run = function () {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var detectionWithLandmarks, detection, landmarks, alignedRect, alignedFaceCanvas, descriptor;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
var detectionWithLandmarks, detection, landmarks, alignedRect, alignedFaces, _a, descriptor;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, this.detectFaceLandmarksTask];
case 1:
detectionWithLandmarks = _a.sent();
detectionWithLandmarks = _b.sent();
if (!detectionWithLandmarks) {
return [2 /*return*/];
}
detection = detectionWithLandmarks.detection, landmarks = detectionWithLandmarks.landmarks, alignedRect = detectionWithLandmarks.alignedRect;
return [4 /*yield*/, dom_1.extractFaces(this.input, [alignedRect])];
if (!(this.input instanceof tf.Tensor)) return [3 /*break*/, 3];
return [4 /*yield*/, dom_1.extractFaceTensors(this.input, [alignedRect])];
case 2:
alignedFaceCanvas = (_a.sent())[0];
return [4 /*yield*/, nets_1.nets.faceRecognitionNet.computeFaceDescriptor(alignedFaceCanvas)];
case 3:
descriptor = _a.sent();
_a = _b.sent();
return [3 /*break*/, 5];
case 3: return [4 /*yield*/, dom_1.extractFaces(this.input, [alignedRect])];
case 4:
_a = _b.sent();
_b.label = 5;
case 5:
alignedFaces = _a;
return [4 /*yield*/, nets_1.nets.faceRecognitionNet.computeFaceDescriptor(alignedFaces[0])];
case 6:
descriptor = _b.sent();
alignedFaces.forEach(function (f) { return f instanceof tf.Tensor && f.dispose(); });
return [2 /*return*/, new FullFaceDescription_1.FullFaceDescription(detection, landmarks, descriptor)];
}
});
......
build/commonjs/globalApi/ComputeFaceDescriptorsTasks.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commonjs/globalApi/DetectFaceLandmarksTasks.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var FaceDetectionWithLandmarks_1 = require("../classes/FaceDetectionWithLandmarks");
var dom_1 = require("../dom");
var ComposableTask_1 = require("./ComposableTask");
......@@ -35,18 +36,27 @@ var DetectAllFaceLandmarksTask = /** @class */ (function (_super) {
DetectAllFaceLandmarksTask.prototype.run = function () {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _this = this;
var detections, faceCanvases, faceLandmarksByFace;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
var detections, faces, _a, faceLandmarksByFace;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, this.detectFacesTask];
case 1:
detections = _a.sent();
return [4 /*yield*/, dom_1.extractFaces(this.input, detections)];
detections = _b.sent();
if (!(this.input instanceof tf.Tensor)) return [3 /*break*/, 3];
return [4 /*yield*/, dom_1.extractFaceTensors(this.input, detections)];
case 2:
faceCanvases = _a.sent();
return [4 /*yield*/, Promise.all(faceCanvases.map(function (canvas) { return _this.landmarkNet.detectLandmarks(canvas); }))];
case 3:
faceLandmarksByFace = _a.sent();
_a = _b.sent();
return [3 /*break*/, 5];
case 3: return [4 /*yield*/, dom_1.extractFaces(this.input, detections)];
case 4:
_a = _b.sent();
_b.label = 5;
case 5:
faces = _a;
return [4 /*yield*/, Promise.all(faces.map(function (face) { return _this.landmarkNet.detectLandmarks(face); }))];
case 6:
faceLandmarksByFace = _b.sent();
faces.forEach(function (f) { return f instanceof tf.Tensor && f.dispose(); });
return [2 /*return*/, detections.map(function (detection, i) {
return new FaceDetectionWithLandmarks_1.FaceDetectionWithLandmarks(detection, faceLandmarksByFace[i]);
})];
......@@ -67,22 +77,31 @@ var DetectSingleFaceLandmarksTask = /** @class */ (function (_super) {
}
DetectSingleFaceLandmarksTask.prototype.run = function () {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var detection, faceCanvas, _a, _b;
return tslib_1.__generator(this, function (_c) {
switch (_c.label) {
var detection, faces, _a, landmarks;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, this.detectFacesTask];
case 1:
detection = _c.sent();
detection = _b.sent();
if (!detection) {
return [2 /*return*/];
}
return [4 /*yield*/, dom_1.extractFaces(this.input, [detection])];
if (!(this.input instanceof tf.Tensor)) return [3 /*break*/, 3];
return [4 /*yield*/, dom_1.extractFaceTensors(this.input, [detection])];
case 2:
faceCanvas = (_c.sent())[0];
_a = FaceDetectionWithLandmarks_1.FaceDetectionWithLandmarks.bind;
_b = [void 0, detection];
return [4 /*yield*/, this.landmarkNet.detectLandmarks(faceCanvas)];
case 3: return [2 /*return*/, new (_a.apply(FaceDetectionWithLandmarks_1.FaceDetectionWithLandmarks, _b.concat([_c.sent()])))()];
_a = _b.sent();
return [3 /*break*/, 5];
case 3: return [4 /*yield*/, dom_1.extractFaces(this.input, [detection])];
case 4:
_a = _b.sent();
_b.label = 5;
case 5:
faces = _a;
return [4 /*yield*/, this.landmarkNet.detectLandmarks(faces[0])];
case 6:
landmarks = _b.sent();
faces.forEach(function (f) { return f instanceof tf.Tensor && f.dispose(); });
return [2 /*return*/, new FaceDetectionWithLandmarks_1.FaceDetectionWithLandmarks(detection, landmarks)];
}
});
});
......
build/commonjs/globalApi/DetectFaceLandmarksTasks.js.map
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\ No newline at end of file
build/commonjs/mtcnn/Mtcnn.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
import { FaceDetectionWithLandmarks } from '../classes/FaceDetectionWithLandmarks';
import { FaceLandmarks5 } from '../classes/FaceLandmarks5';
......@@ -14,13 +15,14 @@ export declare class Mtcnn extends NeuralNetwork<NetParams> {
results: FaceDetectionWithLandmarks<FaceLandmarks5>[];
stats: any;
}>;
protected loadQuantizedParams(uri: string | undefined): Promise<{
protected getDefaultModelName(): string;
protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
}>;
};
protected extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: {
......
build/commonjs/mtcnn/Mtcnn.js
......@@ -9,8 +9,8 @@ var FaceLandmarks5_1 = require("../classes/FaceLandmarks5");
var bgrToRgbTensor_1 = require("./bgrToRgbTensor");
var config_1 = require("./config");
var extractParams_1 = require("./extractParams");
var extractParamsFromWeigthMap_1 = require("./extractParamsFromWeigthMap");
var getSizesForScale_1 = require("./getSizesForScale");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
var MtcnnOptions_1 = require("./MtcnnOptions");
var pyramidDown_1 = require("./pyramidDown");
var stage1_1 = require("./stage1");
......@@ -122,9 +122,11 @@ var Mtcnn = /** @class */ (function (_super) {
});
});
};
// none of the param tensors are quantized yet
Mtcnn.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams_1.loadQuantizedParams(uri);
Mtcnn.prototype.getDefaultModelName = function () {
return 'mtcnn_model';
};
Mtcnn.prototype.extractParamsFromWeigthMap = function (weightMap) {
return extractParamsFromWeigthMap_1.extractParamsFromWeigthMap(weightMap);
};
Mtcnn.prototype.extractParams = function (weights) {
return extractParams_1.extractParams(weights);
......
build/commonjs/mtcnn/Mtcnn.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commonjs/mtcnn/extractImagePatches.js
......@@ -20,7 +20,7 @@ function extractImagePatches(img, boxes, _a) {
fromX = x - 1;
fromY = y - 1;
imgData = imgCtx.getImageData(fromX, fromY, (ex - fromX), (ey - fromY));
return [2 /*return*/, createImageBitmap(imgData)];
return [2 /*return*/, tfjs_image_recognition_base_1.env.isNodejs() ? tfjs_image_recognition_base_1.createCanvasFromMedia(imgData) : createImageBitmap(imgData)];
});
}); }))];
case 1:
......
build/commonjs/mtcnn/extractImagePatches.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commonjs/mtcnn/loadQuantizedParams.d.ts → build/commonjs/mtcnn/extractParamsFromWeigthMap.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { ParamMapping } from 'tfjs-image-recognition-base';
import { NetParams } from './types';
export declare function loadQuantizedParams(uri: string | undefined): Promise<{
export declare function extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: NetParams;
paramMappings: ParamMapping[];
}>;
};
build/commonjs/mtcnn/loadQuantizedParams.js → build/commonjs/mtcnn/extractParamsFromWeigthMap.js
......@@ -2,7 +2,6 @@
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var DEFAULT_MODEL_NAME = 'mtcnn_model';
function extractorsFactory(weightMap, paramMappings) {
var extractWeightEntry = tfjs_image_recognition_base_1.extractWeightEntryFactory(weightMap, paramMappings);
function extractConvParams(prefix) {
......@@ -58,24 +57,14 @@ function extractorsFactory(weightMap, paramMappings) {
extractONetParams: extractONetParams
};
}
function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, paramMappings, _a, extractPNetParams, extractRNetParams, extractONetParams, pnet, rnet, onet;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1:
weightMap = _b.sent();
paramMappings = [];
_a = extractorsFactory(weightMap, paramMappings), extractPNetParams = _a.extractPNetParams, extractRNetParams = _a.extractRNetParams, extractONetParams = _a.extractONetParams;
pnet = extractPNetParams();
rnet = extractRNetParams();
onet = extractONetParams();
tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: { pnet: pnet, rnet: rnet, onet: onet }, paramMappings: paramMappings }];
}
});
});
function extractParamsFromWeigthMap(weightMap) {
var paramMappings = [];
var _a = extractorsFactory(weightMap, paramMappings), extractPNetParams = _a.extractPNetParams, extractRNetParams = _a.extractRNetParams, extractONetParams = _a.extractONetParams;
var pnet = extractPNetParams();
var rnet = extractRNetParams();
var onet = extractONetParams();
tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return { params: { pnet: pnet, rnet: rnet, onet: onet }, paramMappings: paramMappings };
}
exports.loadQuantizedParams = loadQuantizedParams;
//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file
exports.extractParamsFromWeigthMap = extractParamsFromWeigthMap;
//# sourceMappingURL=extractParamsFromWeigthMap.js.map
\ No newline at end of file
build/commonjs/mtcnn/extractParamsFromWeigthMap.js.map 0 → 100644
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\ No newline at end of file
build/commonjs/mtcnn/loadQuantizedParams.js.map deleted 100644 → 0
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\ No newline at end of file
build/commonjs/ssdMobilenetv1/SsdMobilenetv1.d.ts
......@@ -14,13 +14,14 @@ export declare class SsdMobilenetv1 extends NeuralNetwork<NetParams> {
scores: tf.Tensor<tf.Rank.R1>[];
}>;
locateFaces(input: TNetInput, options?: ISsdMobilenetv1Options): Promise<FaceDetection[]>;
protected loadQuantizedParams(uri: string | undefined): Promise<{
protected getDefaultModelName(): string;
protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
}>;
};
protected extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: {
......
build/commonjs/ssdMobilenetv1/SsdMobilenetv1.js
......@@ -5,7 +5,7 @@ var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceDetection_1 = require("../classes/FaceDetection");
var extractParams_1 = require("./extractParams");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
var extractParamsFromWeigthMap_1 = require("./extractParamsFromWeigthMap");
var mobileNetV1_1 = require("./mobileNetV1");
var nonMaxSuppression_1 = require("./nonMaxSuppression");
var outputLayer_1 = require("./outputLayer");
......@@ -92,8 +92,11 @@ var SsdMobilenetv1 = /** @class */ (function (_super) {
});
});
};
SsdMobilenetv1.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams_1.loadQuantizedParams(uri);
SsdMobilenetv1.prototype.getDefaultModelName = function () {
return 'ssd_mobilenetv1_model';
};
SsdMobilenetv1.prototype.extractParamsFromWeigthMap = function (weightMap) {
return extractParamsFromWeigthMap_1.extractParamsFromWeigthMap(weightMap);
};
SsdMobilenetv1.prototype.extractParams = function (weights) {
return extractParams_1.extractParams(weights);
......
build/commonjs/ssdMobilenetv1/SsdMobilenetv1.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commonjs/ssdMobilenetv1/loadQuantizedParams.d.ts → build/commonjs/ssdMobilenetv1/extractParamsFromWeigthMap.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { ParamMapping } from 'tfjs-image-recognition-base';
import { NetParams } from './types';
export declare function loadQuantizedParams(uri: string | undefined): Promise<{
export declare function extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: NetParams;
paramMappings: ParamMapping[];
}>;
};
build/commonjs/ssdMobilenetv1/loadQuantizedParams.js → build/commonjs/ssdMobilenetv1/extractParamsFromWeigthMap.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var DEFAULT_MODEL_NAME = 'ssd_mobilenetv1_model';
function extractorsFactory(weightMap, paramMappings) {
var extractWeightEntry = tfjs_image_recognition_base_1.extractWeightEntryFactory(weightMap, paramMappings);
function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
......@@ -82,33 +80,23 @@ function extractorsFactory(weightMap, paramMappings) {
extractPredictionLayerParams: extractPredictionLayerParams
};
}
function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, paramMappings, _a, extractMobilenetV1Params, extractPredictionLayerParams, extra_dim, params;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1:
weightMap = _b.sent();
paramMappings = [];
_a = extractorsFactory(weightMap, paramMappings), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;
extra_dim = weightMap['Output/extra_dim'];
paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });
if (!tfjs_image_recognition_base_1.isTensor3D(extra_dim)) {
throw new Error("expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have " + extra_dim);
}
params = {
mobilenetv1: extractMobilenetV1Params(),
prediction_layer: extractPredictionLayerParams(),
output_layer: {
extra_dim: extra_dim
}
};
tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }];
}
});
});
function extractParamsFromWeigthMap(weightMap) {
var paramMappings = [];
var _a = extractorsFactory(weightMap, paramMappings), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;
var extra_dim = weightMap['Output/extra_dim'];
paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });
if (!tfjs_image_recognition_base_1.isTensor3D(extra_dim)) {
throw new Error("expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have " + extra_dim);
}
var params = {
mobilenetv1: extractMobilenetV1Params(),
prediction_layer: extractPredictionLayerParams(),
output_layer: {
extra_dim: extra_dim
}
};
tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return { params: params, paramMappings: paramMappings };
}
exports.loadQuantizedParams = loadQuantizedParams;
//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file
exports.extractParamsFromWeigthMap = extractParamsFromWeigthMap;
//# sourceMappingURL=extractParamsFromWeigthMap.js.map
\ No newline at end of file
build/commonjs/ssdMobilenetv1/extractParamsFromWeigthMap.js.map 0 → 100644
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\ No newline at end of file
build/commonjs/ssdMobilenetv1/loadQuantizedParams.js.map deleted 100644 → 0
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\ No newline at end of file
build/commonjs/tinyFaceDetector/TinyFaceDetector.d.ts
import { Point, TNetInput } from 'tfjs-image-recognition-base';
import { TinyYolov2 as TinyYolov2Base, ITinyYolov2Options } from 'tfjs-tiny-yolov2';
import * as tf from '@tensorflow/tfjs-core';
import { ParamMapping, Point, TNetInput } from 'tfjs-image-recognition-base';
import { ITinyYolov2Options, TinyYolov2 as TinyYolov2Base } from 'tfjs-tiny-yolov2';
import { TinyYolov2NetParams } from 'tfjs-tiny-yolov2/build/commonjs/tinyYolov2/types';
import { FaceDetection } from '../classes';
export declare class TinyFaceDetector extends TinyYolov2Base {
constructor();
readonly anchors: Point[];
locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]>;
protected loadQuantizedParams(modelUri: string | undefined): any;
protected getDefaultModelName(): string;
protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: TinyYolov2NetParams;
paramMappings: ParamMapping[];
};
}
build/commonjs/tinyFaceDetector/TinyFaceDetector.js
......@@ -40,9 +40,11 @@ var TinyFaceDetector = /** @class */ (function (_super) {
});
});
};
TinyFaceDetector.prototype.loadQuantizedParams = function (modelUri) {
var defaultModelName = const_1.DEFAULT_MODEL_NAME;
return _super.prototype.loadQuantizedParams.call(this, modelUri, defaultModelName);
TinyFaceDetector.prototype.getDefaultModelName = function () {
return 'tiny_face_detector_model';
};
TinyFaceDetector.prototype.extractParamsFromWeigthMap = function (weightMap) {
return _super.prototype.extractParamsFromWeigthMap.call(this, weightMap);
};
return TinyFaceDetector;
}(tfjs_tiny_yolov2_1.TinyYolov2));
......
build/commonjs/tinyFaceDetector/TinyFaceDetector.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commonjs/tinyFaceDetector/const.d.ts
......@@ -2,4 +2,3 @@ import { Point } from 'tfjs-image-recognition-base';
export declare const IOU_THRESHOLD = 0.4;
export declare const BOX_ANCHORS: Point[];
export declare const MEAN_RGB: [number, number, number];
export declare const DEFAULT_MODEL_NAME = "tiny_face_detector_model";
build/commonjs/tinyFaceDetector/const.js
......@@ -10,5 +10,4 @@ exports.BOX_ANCHORS = [
new tfjs_image_recognition_base_1.Point(9.041765, 10.66308)
];
exports.MEAN_RGB = [117.001, 114.697, 97.404];
exports.DEFAULT_MODEL_NAME = 'tiny_face_detector_model';
//# sourceMappingURL=const.js.map
\ No newline at end of file
build/commonjs/tinyFaceDetector/const.js.map
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\ No newline at end of file
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\ No newline at end of file
build/commonjs/tinyYolov2/TinyYolov2.d.ts
import { Point, TNetInput } from 'tfjs-image-recognition-base';
import { ITinyYolov2Options, TinyYolov2 as TinyYolov2Base } from 'tfjs-tiny-yolov2';
import * as tf from '@tensorflow/tfjs-core';
import { ParamMapping, Point, TNetInput } from 'tfjs-image-recognition-base';
import { ITinyYolov2Options, TinyYolov2 as TinyYolov2Base, TinyYolov2NetParams } from 'tfjs-tiny-yolov2';
import { FaceDetection } from '../classes';
export declare class TinyYolov2 extends TinyYolov2Base {
constructor(withSeparableConvs?: boolean);
readonly withSeparableConvs: boolean;
readonly anchors: Point[];
locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]>;
protected loadQuantizedParams(modelUri: string | undefined): any;
protected getDefaultModelName(): string;
protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: TinyYolov2NetParams;
paramMappings: ParamMapping[];
};
}
build/commonjs/tinyYolov2/TinyYolov2.js
......@@ -52,9 +52,11 @@ var TinyYolov2 = /** @class */ (function (_super) {
});
});
};
TinyYolov2.prototype.loadQuantizedParams = function (modelUri) {
var defaultModelName = this.withSeparableConvs ? const_1.DEFAULT_MODEL_NAME_SEPARABLE_CONV : const_1.DEFAULT_MODEL_NAME;
return _super.prototype.loadQuantizedParams.call(this, modelUri, defaultModelName);
TinyYolov2.prototype.getDefaultModelName = function () {
return this.withSeparableConvs ? const_1.DEFAULT_MODEL_NAME_SEPARABLE_CONV : const_1.DEFAULT_MODEL_NAME;
};
TinyYolov2.prototype.extractParamsFromWeigthMap = function (weightMap) {
return _super.prototype.extractParamsFromWeigthMap.call(this, weightMap);
};
return TinyYolov2;
}(tfjs_tiny_yolov2_1.TinyYolov2));
......
build/commonjs/tinyYolov2/TinyYolov2.js.map
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\ No newline at end of file
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\ No newline at end of file
build/es6/dom/drawLandmarks.js
import { getContext2dOrThrow, getDefaultDrawOptions, resolveInput } from 'tfjs-image-recognition-base';
import { env, getContext2dOrThrow, getDefaultDrawOptions, resolveInput } from 'tfjs-image-recognition-base';
import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
import { drawContour } from './drawContour';
export function drawLandmarks(canvasArg, faceLandmarks, options) {
var canvas = resolveInput(canvasArg);
if (!(canvas instanceof HTMLCanvasElement)) {
if (!(canvas instanceof env.getEnv().Canvas)) {
throw new Error('drawLandmarks - expected canvas to be of type: HTMLCanvasElement');
}
var drawOptions = Object.assign(getDefaultDrawOptions(options), (options || {}));
......
build/es6/dom/drawLandmarks.js.map
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\ No newline at end of file
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\ No newline at end of file
build/es6/dom/extractFaceTensors.js
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
import { isTensor4D } from 'tfjs-image-recognition-base';
import { isTensor4D, isTensor3D } from 'tfjs-image-recognition-base';
import { FaceDetection } from '../classes/FaceDetection';
/**
* Extracts the tensors of the image regions containing the detected faces.
......@@ -15,6 +15,9 @@ import { FaceDetection } from '../classes/FaceDetection';
export function extractFaceTensors(imageTensor, detections) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
return tslib_1.__generator(this, function (_a) {
if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {
throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');
}
if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
throw new Error('extractFaceTensors - batchSize > 1 not supported');
}
......
build/es6/dom/extractFaceTensors.js.map
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\ No newline at end of file
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\ No newline at end of file
build/es6/dom/extractFaces.js
import * as tslib_1 from "tslib";
import { createCanvas, getContext2dOrThrow, imageTensorToCanvas, toNetInput, } from 'tfjs-image-recognition-base';
import { createCanvas, env, getContext2dOrThrow, imageTensorToCanvas, toNetInput, } from 'tfjs-image-recognition-base';
import { FaceDetection } from '../classes/FaceDetection';
/**
* Extracts the image regions containing the detected faces.
......@@ -10,12 +10,13 @@ import { FaceDetection } from '../classes/FaceDetection';
*/
export function extractFaces(input, detections) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var canvas, netInput, tensorOrCanvas, _a, ctx, boxes;
var Canvas, canvas, netInput, tensorOrCanvas, _a, ctx, boxes;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
Canvas = env.getEnv().Canvas;
canvas = input;
if (!!(input instanceof HTMLCanvasElement)) return [3 /*break*/, 5];
if (!!(input instanceof Canvas)) return [3 /*break*/, 5];
return [4 /*yield*/, toNetInput(input)];
case 1:
netInput = _b.sent();
......@@ -23,7 +24,7 @@ export function extractFaces(input, detections) {
throw new Error('extractFaces - batchSize > 1 not supported');
}
tensorOrCanvas = netInput.getInput(0);
if (!(tensorOrCanvas instanceof HTMLCanvasElement)) return [3 /*break*/, 2];
if (!(tensorOrCanvas instanceof Canvas)) return [3 /*break*/, 2];
_a = tensorOrCanvas;
return [3 /*break*/, 4];
case 2: return [4 /*yield*/, imageTensorToCanvas(tensorOrCanvas)];
......
build/es6/dom/extractFaces.js.map
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\ No newline at end of file
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\ No newline at end of file
build/es6/faceLandmarkNet/FaceLandmark68Net.d.ts
......@@ -5,13 +5,14 @@ import { NetParams } from './types';
export declare class FaceLandmark68Net extends FaceLandmark68NetBase<NetParams> {
constructor();
runNet(input: NetInput): tf.Tensor2D;
protected loadQuantizedParams(uri: string | undefined): Promise<{
protected getDefaultModelName(): string;
protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
}>;
};
protected extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: {
......
build/es6/faceLandmarkNet/FaceLandmark68Net.js
......@@ -3,9 +3,9 @@ import * as tf from '@tensorflow/tfjs-core';
import { normalize } from 'tfjs-image-recognition-base';
import { depthwiseSeparableConv } from './depthwiseSeparableConv';
import { extractParams } from './extractParams';
import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
import { FaceLandmark68NetBase } from './FaceLandmark68NetBase';
import { fullyConnectedLayer } from './fullyConnectedLayer';
import { loadQuantizedParams } from './loadQuantizedParams';
function denseBlock(x, denseBlockParams, isFirstLayer) {
if (isFirstLayer === void 0) { isFirstLayer = false; }
return tf.tidy(function () {
......@@ -42,8 +42,11 @@ var FaceLandmark68Net = /** @class */ (function (_super) {
return fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc);
});
};
FaceLandmark68Net.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams(uri);
FaceLandmark68Net.prototype.getDefaultModelName = function () {
return 'face_landmark_68_model';
};
FaceLandmark68Net.prototype.extractParamsFromWeigthMap = function (weightMap) {
return extractParamsFromWeigthMap(weightMap);
};
FaceLandmark68Net.prototype.extractParams = function (weights) {
return extractParams(weights);
......
build/es6/faceLandmarkNet/FaceLandmark68Net.js.map
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\ No newline at end of file
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\ No newline at end of file
build/es6/faceLandmarkNet/FaceLandmark68NetBase.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { IDimensions, NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
export declare class FaceLandmark68NetBase<NetParams> extends NeuralNetwork<NetParams> {
export declare abstract class FaceLandmark68NetBase<NetParams> extends NeuralNetwork<NetParams> {
private __name;
constructor(_name: string);
runNet(_: NetInput): tf.Tensor2D;
abstract runNet(netInput: NetInput): tf.Tensor2D;
postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D;
forwardInput(input: NetInput): tf.Tensor2D;
forward(input: TNetInput): Promise<tf.Tensor2D>;
......
build/es6/faceLandmarkNet/FaceLandmark68NetBase.js
......@@ -9,9 +9,6 @@ var FaceLandmark68NetBase = /** @class */ (function (_super) {
_this.__name = _name;
return _this;
}
FaceLandmark68NetBase.prototype.runNet = function (_) {
throw new Error(this.__name + " - runNet not implemented");
};
FaceLandmark68NetBase.prototype.postProcess = function (output, inputSize, originalDimensions) {
var inputDimensions = originalDimensions.map(function (_a) {
var width = _a.width, height = _a.height;
......
build/es6/faceLandmarkNet/FaceLandmark68NetBase.js.map
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\ No newline at end of file
build/es6/faceLandmarkNet/FaceLandmark68TinyNet.d.ts
......@@ -5,13 +5,14 @@ import { TinyNetParams } from './types';
export declare class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyNetParams> {
constructor();
runNet(input: NetInput): tf.Tensor2D;
protected loadQuantizedParams(uri: string | undefined): Promise<{
protected getDefaultModelName(): string;
protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: TinyNetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
}>;
};
protected extractParams(weights: Float32Array): {
params: TinyNetParams;
paramMappings: {
......
build/es6/faceLandmarkNet/FaceLandmark68TinyNet.js
......@@ -5,7 +5,7 @@ import { depthwiseSeparableConv } from './depthwiseSeparableConv';
import { extractParamsTiny } from './extractParamsTiny';
import { FaceLandmark68NetBase } from './FaceLandmark68NetBase';
import { fullyConnectedLayer } from './fullyConnectedLayer';
import { loadQuantizedParamsTiny } from './loadQuantizedParamsTiny';
import { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';
function denseBlock(x, denseBlockParams, isFirstLayer) {
if (isFirstLayer === void 0) { isFirstLayer = false; }
return tf.tidy(function () {
......@@ -39,8 +39,11 @@ var FaceLandmark68TinyNet = /** @class */ (function (_super) {
return fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc);
});
};
FaceLandmark68TinyNet.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParamsTiny(uri);
FaceLandmark68TinyNet.prototype.getDefaultModelName = function () {
return 'face_landmark_68_tiny_model';
};
FaceLandmark68TinyNet.prototype.extractParamsFromWeigthMap = function (weightMap) {
return extractParamsFromWeigthMapTiny(weightMap);
};
FaceLandmark68TinyNet.prototype.extractParams = function (weights) {
return extractParamsTiny(weights);
......
build/es6/faceLandmarkNet/FaceLandmark68TinyNet.js.map
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\ No newline at end of file
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\ No newline at end of file
build/es6/faceLandmarkNet/loadQuantizedParams.d.ts → build/es6/faceLandmarkNet/extractParamsFromWeigthMap.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { ParamMapping } from 'tfjs-image-recognition-base';
import { NetParams } from './types';
export declare function loadQuantizedParams(uri: string | undefined): Promise<{
export declare function extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: NetParams;
paramMappings: ParamMapping[];
}>;
};
build/es6/faceLandmarkNet/extractParamsFromWeigthMap.js 0 → 100644
import { disposeUnusedWeightTensors } from 'tfjs-image-recognition-base';
import { loadParamsFactory } from './loadParamsFactory';
export function extractParamsFromWeigthMap(weightMap) {
var paramMappings = [];
var _a = loadParamsFactory(weightMap, paramMappings), extractDenseBlock4Params = _a.extractDenseBlock4Params, extractFcParams = _a.extractFcParams;
var params = {
dense0: extractDenseBlock4Params('dense0', true),
dense1: extractDenseBlock4Params('dense1'),
dense2: extractDenseBlock4Params('dense2'),
dense3: extractDenseBlock4Params('dense3'),
fc: extractFcParams('fc')
};
disposeUnusedWeightTensors(weightMap, paramMappings);
return { params: params, paramMappings: paramMappings };
}
//# sourceMappingURL=extractParamsFromWeigthMap.js.map
\ No newline at end of file
build/es6/faceLandmarkNet/extractParamsFromWeigthMap.js.map 0 → 100644
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\ No newline at end of file
build/es6/faceLandmarkNet/loadQuantizedParamsTiny.d.ts → build/es6/faceLandmarkNet/extractParamsFromWeigthMapTiny.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { ParamMapping } from 'tfjs-image-recognition-base';
import { TinyNetParams } from './types';
export declare function loadQuantizedParamsTiny(uri: string | undefined): Promise<{
export declare function extractParamsFromWeigthMapTiny(weightMap: tf.NamedTensorMap): {
params: TinyNetParams;
paramMappings: ParamMapping[];
}>;
};
build/es6/faceLandmarkNet/extractParamsFromWeigthMapTiny.js 0 → 100644
import { disposeUnusedWeightTensors } from 'tfjs-image-recognition-base';
import { loadParamsFactory } from './loadParamsFactory';
export function extractParamsFromWeigthMapTiny(weightMap) {
var paramMappings = [];
var _a = loadParamsFactory(weightMap, paramMappings), extractDenseBlock3Params = _a.extractDenseBlock3Params, extractFcParams = _a.extractFcParams;
var params = {
dense0: extractDenseBlock3Params('dense0', true),
dense1: extractDenseBlock3Params('dense1'),
dense2: extractDenseBlock3Params('dense2'),
fc: extractFcParams('fc')
};
disposeUnusedWeightTensors(weightMap, paramMappings);
return { params: params, paramMappings: paramMappings };
}
//# sourceMappingURL=extractParamsFromWeigthMapTiny.js.map
\ No newline at end of file
build/es6/faceLandmarkNet/extractParamsFromWeigthMapTiny.js.map 0 → 100644
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\ No newline at end of file
build/es6/faceLandmarkNet/loadQuantizedParams.js deleted 100644 → 0
import * as tslib_1 from "tslib";
import { disposeUnusedWeightTensors, loadWeightMap } from 'tfjs-image-recognition-base';
import { loadParamsFactory } from './loadParamsFactory';
var DEFAULT_MODEL_NAME = 'face_landmark_68_model';
export function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, paramMappings, _a, extractDenseBlock4Params, extractFcParams, params;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1:
weightMap = _b.sent();
paramMappings = [];
_a = loadParamsFactory(weightMap, paramMappings), extractDenseBlock4Params = _a.extractDenseBlock4Params, extractFcParams = _a.extractFcParams;
params = {
dense0: extractDenseBlock4Params('dense0', true),
dense1: extractDenseBlock4Params('dense1'),
dense2: extractDenseBlock4Params('dense2'),
dense3: extractDenseBlock4Params('dense3'),
fc: extractFcParams('fc')
};
disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }];
}
});
});
}
//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file
build/es6/faceLandmarkNet/loadQuantizedParams.js.map deleted 100644 → 0
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\ No newline at end of file
build/es6/faceLandmarkNet/loadQuantizedParamsTiny.js deleted 100644 → 0
import * as tslib_1 from "tslib";
import { disposeUnusedWeightTensors, loadWeightMap } from 'tfjs-image-recognition-base';
import { loadParamsFactory } from './loadParamsFactory';
var DEFAULT_MODEL_NAME = 'face_landmark_68_tiny_model';
export function loadQuantizedParamsTiny(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, paramMappings, _a, extractDenseBlock3Params, extractFcParams, params;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1:
weightMap = _b.sent();
paramMappings = [];
_a = loadParamsFactory(weightMap, paramMappings), extractDenseBlock3Params = _a.extractDenseBlock3Params, extractFcParams = _a.extractFcParams;
params = {
dense0: extractDenseBlock3Params('dense0', true),
dense1: extractDenseBlock3Params('dense1'),
dense2: extractDenseBlock3Params('dense2'),
fc: extractFcParams('fc')
};
disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }];
}
});
});
}
//# sourceMappingURL=loadQuantizedParamsTiny.js.map
\ No newline at end of file
build/es6/faceLandmarkNet/loadQuantizedParamsTiny.js.map deleted 100644 → 0
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\ No newline at end of file
build/es6/faceRecognitionNet/FaceRecognitionNet.d.ts
......@@ -6,13 +6,14 @@ export declare class FaceRecognitionNet extends NeuralNetwork<NetParams> {
forwardInput(input: NetInput): tf.Tensor2D;
forward(input: TNetInput): Promise<tf.Tensor2D>;
computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>;
protected loadQuantizedParams(uri: string | undefined): Promise<{
protected getDefaultModelName(): string;
protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
}>;
};
protected extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: {
......
build/es6/faceRecognitionNet/FaceRecognitionNet.js
......@@ -3,7 +3,7 @@ import * as tf from '@tensorflow/tfjs-core';
import { NeuralNetwork, normalize, toNetInput } from 'tfjs-image-recognition-base';
import { convDown } from './convLayer';
import { extractParams } from './extractParams';
import { loadQuantizedParams } from './loadQuantizedParams';
import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
import { residual, residualDown } from './residualLayer';
var FaceRecognitionNet = /** @class */ (function (_super) {
tslib_1.__extends(FaceRecognitionNet, _super);
......@@ -74,8 +74,11 @@ var FaceRecognitionNet = /** @class */ (function (_super) {
});
});
};
FaceRecognitionNet.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams(uri);
FaceRecognitionNet.prototype.getDefaultModelName = function () {
return 'face_recognition_model';
};
FaceRecognitionNet.prototype.extractParamsFromWeigthMap = function (weightMap) {
return extractParamsFromWeigthMap(weightMap);
};
FaceRecognitionNet.prototype.extractParams = function (weights) {
return extractParams(weights);
......
build/es6/faceRecognitionNet/FaceRecognitionNet.js.map
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build/es6/faceRecognitionNet/loadQuantizedParams.d.ts → build/es6/faceRecognitionNet/extractParamsFromWeigthMap.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { ParamMapping } from 'tfjs-image-recognition-base';
import { NetParams } from './types';
export declare function loadQuantizedParams(uri: string | undefined): Promise<{
export declare function extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: NetParams;
paramMappings: ParamMapping[];
}>;
};
build/es6/faceRecognitionNet/loadQuantizedParams.js → build/es6/faceRecognitionNet/extractParamsFromWeigthMap.js
import * as tslib_1 from "tslib";
import { disposeUnusedWeightTensors, extractWeightEntryFactory, isTensor2D, loadWeightMap, } from 'tfjs-image-recognition-base';
var DEFAULT_MODEL_NAME = 'face_recognition_model';
import { disposeUnusedWeightTensors, extractWeightEntryFactory, isTensor2D, } from 'tfjs-image-recognition-base';
function extractorsFactory(weightMap, paramMappings) {
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
function extractScaleLayerParams(prefix) {
......@@ -25,58 +23,48 @@ function extractorsFactory(weightMap, paramMappings) {
extractResidualLayerParams: extractResidualLayerParams
};
}
export function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, paramMappings, _a, extractConvLayerParams, extractResidualLayerParams, conv32_down, conv32_1, conv32_2, conv32_3, conv64_down, conv64_1, conv64_2, conv64_3, conv128_down, conv128_1, conv128_2, conv256_down, conv256_1, conv256_2, conv256_down_out, fc, params;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1:
weightMap = _b.sent();
paramMappings = [];
_a = extractorsFactory(weightMap, paramMappings), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams;
conv32_down = extractConvLayerParams('conv32_down');
conv32_1 = extractResidualLayerParams('conv32_1');
conv32_2 = extractResidualLayerParams('conv32_2');
conv32_3 = extractResidualLayerParams('conv32_3');
conv64_down = extractResidualLayerParams('conv64_down');
conv64_1 = extractResidualLayerParams('conv64_1');
conv64_2 = extractResidualLayerParams('conv64_2');
conv64_3 = extractResidualLayerParams('conv64_3');
conv128_down = extractResidualLayerParams('conv128_down');
conv128_1 = extractResidualLayerParams('conv128_1');
conv128_2 = extractResidualLayerParams('conv128_2');
conv256_down = extractResidualLayerParams('conv256_down');
conv256_1 = extractResidualLayerParams('conv256_1');
conv256_2 = extractResidualLayerParams('conv256_2');
conv256_down_out = extractResidualLayerParams('conv256_down_out');
fc = weightMap['fc'];
paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });
if (!isTensor2D(fc)) {
throw new Error("expected weightMap[fc] to be a Tensor2D, instead have " + fc);
}
params = {
conv32_down: conv32_down,
conv32_1: conv32_1,
conv32_2: conv32_2,
conv32_3: conv32_3,
conv64_down: conv64_down,
conv64_1: conv64_1,
conv64_2: conv64_2,
conv64_3: conv64_3,
conv128_down: conv128_down,
conv128_1: conv128_1,
conv128_2: conv128_2,
conv256_down: conv256_down,
conv256_1: conv256_1,
conv256_2: conv256_2,
conv256_down_out: conv256_down_out,
fc: fc
};
disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }];
}
});
});
export function extractParamsFromWeigthMap(weightMap) {
var paramMappings = [];
var _a = extractorsFactory(weightMap, paramMappings), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams;
var conv32_down = extractConvLayerParams('conv32_down');
var conv32_1 = extractResidualLayerParams('conv32_1');
var conv32_2 = extractResidualLayerParams('conv32_2');
var conv32_3 = extractResidualLayerParams('conv32_3');
var conv64_down = extractResidualLayerParams('conv64_down');
var conv64_1 = extractResidualLayerParams('conv64_1');
var conv64_2 = extractResidualLayerParams('conv64_2');
var conv64_3 = extractResidualLayerParams('conv64_3');
var conv128_down = extractResidualLayerParams('conv128_down');
var conv128_1 = extractResidualLayerParams('conv128_1');
var conv128_2 = extractResidualLayerParams('conv128_2');
var conv256_down = extractResidualLayerParams('conv256_down');
var conv256_1 = extractResidualLayerParams('conv256_1');
var conv256_2 = extractResidualLayerParams('conv256_2');
var conv256_down_out = extractResidualLayerParams('conv256_down_out');
var fc = weightMap['fc'];
paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });
if (!isTensor2D(fc)) {
throw new Error("expected weightMap[fc] to be a Tensor2D, instead have " + fc);
}
var params = {
conv32_down: conv32_down,
conv32_1: conv32_1,
conv32_2: conv32_2,
conv32_3: conv32_3,
conv64_down: conv64_down,
conv64_1: conv64_1,
conv64_2: conv64_2,
conv64_3: conv64_3,
conv128_down: conv128_down,
conv128_1: conv128_1,
conv128_2: conv128_2,
conv256_down: conv256_down,
conv256_1: conv256_1,
conv256_2: conv256_2,
conv256_down_out: conv256_down_out,
fc: fc
};
disposeUnusedWeightTensors(weightMap, paramMappings);
return { params: params, paramMappings: paramMappings };
}
//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file
//# sourceMappingURL=extractParamsFromWeigthMap.js.map
\ No newline at end of file
build/es6/faceRecognitionNet/extractParamsFromWeigthMap.js.map 0 → 100644
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\ No newline at end of file
build/es6/faceRecognitionNet/loadQuantizedParams.js.map deleted 100644 → 0
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\ No newline at end of file
build/es6/globalApi/ComputeFaceDescriptorsTasks.js
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
import { FullFaceDescription } from '../classes/FullFaceDescription';
import { extractFaces } from '../dom';
import { extractFaces, extractFaceTensors } from '../dom';
import { ComposableTask } from './ComposableTask';
import { nets } from './nets';
var ComputeFaceDescriptorsTaskBase = /** @class */ (function (_super) {
......@@ -22,25 +23,34 @@ var ComputeAllFaceDescriptorsTask = /** @class */ (function (_super) {
ComputeAllFaceDescriptorsTask.prototype.run = function () {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _this = this;
var facesWithLandmarks, alignedFaceCanvases;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
var facesWithLandmarks, alignedRects, alignedFaces, _a, fullFaceDescriptions;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, this.detectFaceLandmarksTask];
case 1:
facesWithLandmarks = _a.sent();
return [4 /*yield*/, extractFaces(this.input, facesWithLandmarks.map(function (_a) {
var landmarks = _a.landmarks;
return landmarks.align();
}))];
facesWithLandmarks = _b.sent();
alignedRects = facesWithLandmarks.map(function (_a) {
var alignedRect = _a.alignedRect;
return alignedRect;
});
if (!(this.input instanceof tf.Tensor)) return [3 /*break*/, 3];
return [4 /*yield*/, extractFaceTensors(this.input, alignedRects)];
case 2:
alignedFaceCanvases = _a.sent();
_a = _b.sent();
return [3 /*break*/, 5];
case 3: return [4 /*yield*/, extractFaces(this.input, alignedRects)];
case 4:
_a = _b.sent();
_b.label = 5;
case 5:
alignedFaces = _a;
return [4 /*yield*/, Promise.all(facesWithLandmarks.map(function (_a, i) {
var detection = _a.detection, landmarks = _a.landmarks;
return tslib_1.__awaiter(_this, void 0, void 0, function () {
var descriptor;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, nets.faceRecognitionNet.computeFaceDescriptor(alignedFaceCanvases[i])];
case 0: return [4 /*yield*/, nets.faceRecognitionNet.computeFaceDescriptor(alignedFaces[i])];
case 1:
descriptor = _b.sent();
return [2 /*return*/, new FullFaceDescription(detection, landmarks, descriptor)];
......@@ -48,7 +58,10 @@ var ComputeAllFaceDescriptorsTask = /** @class */ (function (_super) {
});
});
}))];
case 3: return [2 /*return*/, _a.sent()];
case 6:
fullFaceDescriptions = _b.sent();
alignedFaces.forEach(function (f) { return f instanceof tf.Tensor && f.dispose(); });
return [2 /*return*/, fullFaceDescriptions];
}
});
});
......@@ -63,22 +76,31 @@ var ComputeSingleFaceDescriptorTask = /** @class */ (function (_super) {
}
ComputeSingleFaceDescriptorTask.prototype.run = function () {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var detectionWithLandmarks, detection, landmarks, alignedRect, alignedFaceCanvas, descriptor;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
var detectionWithLandmarks, detection, landmarks, alignedRect, alignedFaces, _a, descriptor;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, this.detectFaceLandmarksTask];
case 1:
detectionWithLandmarks = _a.sent();
detectionWithLandmarks = _b.sent();
if (!detectionWithLandmarks) {
return [2 /*return*/];
}
detection = detectionWithLandmarks.detection, landmarks = detectionWithLandmarks.landmarks, alignedRect = detectionWithLandmarks.alignedRect;
return [4 /*yield*/, extractFaces(this.input, [alignedRect])];
if (!(this.input instanceof tf.Tensor)) return [3 /*break*/, 3];
return [4 /*yield*/, extractFaceTensors(this.input, [alignedRect])];
case 2:
alignedFaceCanvas = (_a.sent())[0];
return [4 /*yield*/, nets.faceRecognitionNet.computeFaceDescriptor(alignedFaceCanvas)];
case 3:
descriptor = _a.sent();
_a = _b.sent();
return [3 /*break*/, 5];
case 3: return [4 /*yield*/, extractFaces(this.input, [alignedRect])];
case 4:
_a = _b.sent();
_b.label = 5;
case 5:
alignedFaces = _a;
return [4 /*yield*/, nets.faceRecognitionNet.computeFaceDescriptor(alignedFaces[0])];
case 6:
descriptor = _b.sent();
alignedFaces.forEach(function (f) { return f instanceof tf.Tensor && f.dispose(); });
return [2 /*return*/, new FullFaceDescription(detection, landmarks, descriptor)];
}
});
......
build/es6/globalApi/ComputeFaceDescriptorsTasks.js.map
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\ No newline at end of file
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\ No newline at end of file
build/es6/globalApi/DetectFaceLandmarksTasks.js
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
import { FaceDetectionWithLandmarks } from '../classes/FaceDetectionWithLandmarks';
import { extractFaces } from '../dom';
import { extractFaces, extractFaceTensors } from '../dom';
import { ComposableTask } from './ComposableTask';
import { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';
import { nets } from './nets';
......@@ -33,18 +34,27 @@ var DetectAllFaceLandmarksTask = /** @class */ (function (_super) {
DetectAllFaceLandmarksTask.prototype.run = function () {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _this = this;
var detections, faceCanvases, faceLandmarksByFace;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
var detections, faces, _a, faceLandmarksByFace;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, this.detectFacesTask];
case 1:
detections = _a.sent();
return [4 /*yield*/, extractFaces(this.input, detections)];
detections = _b.sent();
if (!(this.input instanceof tf.Tensor)) return [3 /*break*/, 3];
return [4 /*yield*/, extractFaceTensors(this.input, detections)];
case 2:
faceCanvases = _a.sent();
return [4 /*yield*/, Promise.all(faceCanvases.map(function (canvas) { return _this.landmarkNet.detectLandmarks(canvas); }))];
case 3:
faceLandmarksByFace = _a.sent();
_a = _b.sent();
return [3 /*break*/, 5];
case 3: return [4 /*yield*/, extractFaces(this.input, detections)];
case 4:
_a = _b.sent();
_b.label = 5;
case 5:
faces = _a;
return [4 /*yield*/, Promise.all(faces.map(function (face) { return _this.landmarkNet.detectLandmarks(face); }))];
case 6:
faceLandmarksByFace = _b.sent();
faces.forEach(function (f) { return f instanceof tf.Tensor && f.dispose(); });
return [2 /*return*/, detections.map(function (detection, i) {
return new FaceDetectionWithLandmarks(detection, faceLandmarksByFace[i]);
})];
......@@ -65,22 +75,31 @@ var DetectSingleFaceLandmarksTask = /** @class */ (function (_super) {
}
DetectSingleFaceLandmarksTask.prototype.run = function () {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var detection, faceCanvas, _a, _b;
return tslib_1.__generator(this, function (_c) {
switch (_c.label) {
var detection, faces, _a, landmarks;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, this.detectFacesTask];
case 1:
detection = _c.sent();
detection = _b.sent();
if (!detection) {
return [2 /*return*/];
}
return [4 /*yield*/, extractFaces(this.input, [detection])];
if (!(this.input instanceof tf.Tensor)) return [3 /*break*/, 3];
return [4 /*yield*/, extractFaceTensors(this.input, [detection])];
case 2:
faceCanvas = (_c.sent())[0];
_a = FaceDetectionWithLandmarks.bind;
_b = [void 0, detection];
return [4 /*yield*/, this.landmarkNet.detectLandmarks(faceCanvas)];
case 3: return [2 /*return*/, new (_a.apply(FaceDetectionWithLandmarks, _b.concat([_c.sent()])))()];
_a = _b.sent();
return [3 /*break*/, 5];
case 3: return [4 /*yield*/, extractFaces(this.input, [detection])];
case 4:
_a = _b.sent();
_b.label = 5;
case 5:
faces = _a;
return [4 /*yield*/, this.landmarkNet.detectLandmarks(faces[0])];
case 6:
landmarks = _b.sent();
faces.forEach(function (f) { return f instanceof tf.Tensor && f.dispose(); });
return [2 /*return*/, new FaceDetectionWithLandmarks(detection, landmarks)];
}
});
});
......
build/es6/globalApi/DetectFaceLandmarksTasks.js.map
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\ No newline at end of file
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\ No newline at end of file
build/es6/mtcnn/Mtcnn.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
import { FaceDetectionWithLandmarks } from '../classes/FaceDetectionWithLandmarks';
import { FaceLandmarks5 } from '../classes/FaceLandmarks5';
......@@ -14,13 +15,14 @@ export declare class Mtcnn extends NeuralNetwork<NetParams> {
results: FaceDetectionWithLandmarks<FaceLandmarks5>[];
stats: any;
}>;
protected loadQuantizedParams(uri: string | undefined): Promise<{
protected getDefaultModelName(): string;
protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
}>;
};
protected extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: {
......
build/es6/mtcnn/Mtcnn.js
......@@ -7,8 +7,8 @@ import { FaceLandmarks5 } from '../classes/FaceLandmarks5';
import { bgrToRgbTensor } from './bgrToRgbTensor';
import { CELL_SIZE } from './config';
import { extractParams } from './extractParams';
import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
import { getSizesForScale } from './getSizesForScale';
import { loadQuantizedParams } from './loadQuantizedParams';
import { MtcnnOptions } from './MtcnnOptions';
import { pyramidDown } from './pyramidDown';
import { stage1 } from './stage1';
......@@ -120,9 +120,11 @@ var Mtcnn = /** @class */ (function (_super) {
});
});
};
// none of the param tensors are quantized yet
Mtcnn.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams(uri);
Mtcnn.prototype.getDefaultModelName = function () {
return 'mtcnn_model';
};
Mtcnn.prototype.extractParamsFromWeigthMap = function (weightMap) {
return extractParamsFromWeigthMap(weightMap);
};
Mtcnn.prototype.extractParams = function (weights) {
return extractParams(weights);
......
build/es6/mtcnn/Mtcnn.js.map
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\ No newline at end of file
dist/face-api.js
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dist/face-api.js.map
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dist/face-api.min.js
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