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Commit 1a1b018f by vincent
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check in latest build

parent c714a636
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build/commonjs/allFacesFactory.d.ts deleted 100644 → 0
import { TNetInput } from 'tfjs-image-recognition-base';
import { TinyYolov2Types } from 'tfjs-tiny-yolov2';
import { TinyYolov2 } from '.';
import { FullFaceDescription } from './classes/FullFaceDescription';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmark68Net } from './faceLandmarkNet/FaceLandmark68Net';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { Mtcnn } from './mtcnn/Mtcnn';
import { MtcnnForwardParams } from './mtcnn/types';
export declare function allFacesSsdMobilenetv1Factory(ssdMobilenetv1: FaceDetectionNet, landmarkNet: FaceLandmark68Net, recognitionNet: FaceRecognitionNet): (input: TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare function allFacesTinyYolov2Factory(tinyYolov2: TinyYolov2, landmarkNet: FaceLandmark68Net, recognitionNet: FaceRecognitionNet): (input: TNetInput, forwardParams?: TinyYolov2Types.TinyYolov2ForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare function allFacesMtcnnFactory(mtcnn: Mtcnn, recognitionNet: FaceRecognitionNet): (input: TNetInput, mtcnnForwardParams?: MtcnnForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
build/commonjs/allFacesFactory.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 FullFaceDescription_1 = require("./classes/FullFaceDescription");
var dom_1 = require("./dom");
function computeDescriptorsFactory(recognitionNet) {
return function (input, alignedFaceBoxes, useBatchProcessing) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var alignedFaceCanvases, descriptors, _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, dom_1.extractFaces(input, alignedFaceBoxes)];
case 1:
alignedFaceCanvases = _b.sent();
if (!useBatchProcessing) return [3 /*break*/, 3];
return [4 /*yield*/, recognitionNet.computeFaceDescriptor(alignedFaceCanvases)];
case 2:
_a = _b.sent();
return [3 /*break*/, 5];
case 3: return [4 /*yield*/, Promise.all(alignedFaceCanvases.map(function (canvas) { return recognitionNet.computeFaceDescriptor(canvas); }))];
case 4:
_a = _b.sent();
_b.label = 5;
case 5:
descriptors = _a;
return [2 /*return*/, descriptors];
}
});
});
};
}
function allFacesFactory(detectFaces, landmarkNet, recognitionNet) {
var computeDescriptors = computeDescriptorsFactory(recognitionNet);
return function (input, useBatchProcessing) {
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var detections, faceCanvases, faceLandmarksByFace, _a, alignedFaceBoxes, descriptors;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, detectFaces(input)];
case 1:
detections = _b.sent();
return [4 /*yield*/, dom_1.extractFaces(input, detections)];
case 2:
faceCanvases = _b.sent();
if (!useBatchProcessing) return [3 /*break*/, 4];
return [4 /*yield*/, landmarkNet.detectLandmarks(faceCanvases)];
case 3:
_a = _b.sent();
return [3 /*break*/, 6];
case 4: return [4 /*yield*/, Promise.all(faceCanvases.map(function (canvas) { return landmarkNet.detectLandmarks(canvas); }))];
case 5:
_a = _b.sent();
_b.label = 6;
case 6:
faceLandmarksByFace = _a;
alignedFaceBoxes = faceLandmarksByFace.map(function (landmarks, i) { return landmarks.align(detections[i].getBox()); });
return [4 /*yield*/, computeDescriptors(input, alignedFaceBoxes, useBatchProcessing)];
case 7:
descriptors = _b.sent();
return [2 /*return*/, detections.map(function (detection, i) {
return new FullFaceDescription_1.FullFaceDescription(detection, faceLandmarksByFace[i].shiftByPoint(new tfjs_image_recognition_base_1.Point(detection.box.x, detection.box.y)), descriptors[i]);
})];
}
});
});
};
}
function allFacesSsdMobilenetv1Factory(ssdMobilenetv1, landmarkNet, recognitionNet) {
return function (input, minConfidence, useBatchProcessing) {
if (minConfidence === void 0) { minConfidence = 0.8; }
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var detectFaces, allFaces;
return tslib_1.__generator(this, function (_a) {
detectFaces = function (input) { return ssdMobilenetv1.locateFaces(input, minConfidence); };
allFaces = allFacesFactory(detectFaces, landmarkNet, recognitionNet);
return [2 /*return*/, allFaces(input, useBatchProcessing)];
});
});
};
}
exports.allFacesSsdMobilenetv1Factory = allFacesSsdMobilenetv1Factory;
function allFacesTinyYolov2Factory(tinyYolov2, landmarkNet, recognitionNet) {
return function (input, forwardParams, useBatchProcessing) {
if (forwardParams === void 0) { forwardParams = {}; }
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var detectFaces, allFaces;
return tslib_1.__generator(this, function (_a) {
detectFaces = function (input) { return tinyYolov2.locateFaces(input, forwardParams); };
allFaces = allFacesFactory(detectFaces, landmarkNet, recognitionNet);
return [2 /*return*/, allFaces(input, useBatchProcessing)];
});
});
};
}
exports.allFacesTinyYolov2Factory = allFacesTinyYolov2Factory;
function allFacesMtcnnFactory(mtcnn, recognitionNet) {
var computeDescriptors = computeDescriptorsFactory(recognitionNet);
return function (input, mtcnnForwardParams, useBatchProcessing) {
if (mtcnnForwardParams === void 0) { mtcnnForwardParams = {}; }
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var results, alignedFaceBoxes, descriptors;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, mtcnn.forward(input, mtcnnForwardParams)];
case 1:
results = _a.sent();
alignedFaceBoxes = results.map(function (_a) {
var faceLandmarks = _a.faceLandmarks;
return faceLandmarks.align();
});
return [4 /*yield*/, computeDescriptors(input, alignedFaceBoxes, useBatchProcessing)];
case 2:
descriptors = _a.sent();
return [2 /*return*/, results.map(function (_a, i) {
var faceDetection = _a.faceDetection, faceLandmarks = _a.faceLandmarks;
return new FullFaceDescription_1.FullFaceDescription(faceDetection, faceLandmarks, descriptors[i]);
})];
}
});
});
};
}
exports.allFacesMtcnnFactory = allFacesMtcnnFactory;
//# sourceMappingURL=allFacesFactory.js.map
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build/commonjs/allFacesFactory.js.map deleted 100644 → 0
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build/commonjs/faceDetectionNet/FaceDetectionNet.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
import { FaceDetection } from '../classes/FaceDetection';
import { NetParams } from './types';
export declare class FaceDetectionNet extends NeuralNetwork<NetParams> {
constructor();
forwardInput(input: NetInput): {
boxes: tf.Tensor<tf.Rank.R2>[];
scores: tf.Tensor<tf.Rank.R1>[];
};
forward(input: TNetInput): Promise<{
boxes: tf.Tensor<tf.Rank.R2>[];
scores: tf.Tensor<tf.Rank.R1>[];
}>;
locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
protected loadQuantizedParams(uri: string | undefined): Promise<{
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
}>;
protected extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
};
}
build/commonjs/faceDetectionNet/FaceDetectionNet.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
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 mobileNetV1_1 = require("./mobileNetV1");
var nonMaxSuppression_1 = require("./nonMaxSuppression");
var outputLayer_1 = require("./outputLayer");
var predictionLayer_1 = require("./predictionLayer");
var FaceDetectionNet = /** @class */ (function (_super) {
tslib_1.__extends(FaceDetectionNet, _super);
function FaceDetectionNet() {
return _super.call(this, 'FaceDetectionNet') || this;
}
FaceDetectionNet.prototype.forwardInput = function (input) {
var params = this.params;
if (!params) {
throw new Error('FaceDetectionNet - load model before inference');
}
return tf.tidy(function () {
var batchTensor = input.toBatchTensor(512, false).toFloat();
var x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1));
var features = mobileNetV1_1.mobileNetV1(x, params.mobilenetv1);
var _a = predictionLayer_1.predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
return outputLayer_1.outputLayer(boxPredictions, classPredictions, params.output_layer);
});
};
FaceDetectionNet.prototype.forward = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
});
};
FaceDetectionNet.prototype.locateFaces = function (input, minConfidence, maxResults) {
if (minConfidence === void 0) { minConfidence = 0.8; }
if (maxResults === void 0) { maxResults = 100; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var netInput, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, reshapedDims, inputSize, padX, padY, results;
return tslib_1.__generator(this, function (_d) {
switch (_d.label) {
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1:
netInput = _d.sent();
_a = this.forwardInput(netInput), _boxes = _a.boxes, _scores = _a.scores;
boxes = _boxes[0];
scores = _scores[0];
for (i = 1; i < _boxes.length; i++) {
_boxes[i].dispose();
_scores[i].dispose();
}
_c = (_b = Array).from;
return [4 /*yield*/, scores.data()];
case 2:
scoresData = _c.apply(_b, [_d.sent()]);
iouThreshold = 0.5;
indices = nonMaxSuppression_1.nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
reshapedDims = netInput.getReshapedInputDimensions(0);
inputSize = netInput.inputSize;
padX = inputSize / reshapedDims.width;
padY = inputSize / reshapedDims.height;
results = indices
.map(function (idx) {
var _a = [
Math.max(0, boxes.get(idx, 0)),
Math.min(1.0, boxes.get(idx, 2))
].map(function (val) { return val * padY; }), top = _a[0], bottom = _a[1];
var _b = [
Math.max(0, boxes.get(idx, 1)),
Math.min(1.0, boxes.get(idx, 3))
].map(function (val) { return val * padX; }), left = _b[0], right = _b[1];
return new FaceDetection_1.FaceDetection(scoresData[idx], new tfjs_image_recognition_base_1.Rect(left, top, right - left, bottom - top), {
height: netInput.getInputHeight(0),
width: netInput.getInputWidth(0)
});
});
boxes.dispose();
scores.dispose();
return [2 /*return*/, results];
}
});
});
};
FaceDetectionNet.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams_1.loadQuantizedParams(uri);
};
FaceDetectionNet.prototype.extractParams = function (weights) {
return extractParams_1.extractParams(weights);
};
return FaceDetectionNet;
}(tfjs_image_recognition_base_1.NeuralNetwork));
exports.FaceDetectionNet = FaceDetectionNet;
//# sourceMappingURL=FaceDetectionNet.js.map
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build/commonjs/faceDetectionNet/FaceDetectionNet.js.map deleted 100644 → 0
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build/commonjs/faceDetectionNet/boxPredictionLayer.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { BoxPredictionParams } from './types';
export declare function boxPredictionLayer(x: tf.Tensor4D, params: BoxPredictionParams): {
boxPredictionEncoding: tf.Tensor<tf.Rank>;
classPrediction: tf.Tensor<tf.Rank>;
};
build/commonjs/faceDetectionNet/boxPredictionLayer.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
function boxPredictionLayer(x, params) {
return tf.tidy(function () {
var batchSize = x.shape[0];
var boxPredictionEncoding = tf.reshape(tfjs_tiny_yolov2_1.convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
var classPrediction = tf.reshape(tfjs_tiny_yolov2_1.convLayer(x, params.class_predictor), [batchSize, -1, 3]);
return {
boxPredictionEncoding: boxPredictionEncoding,
classPrediction: classPrediction
};
});
}
exports.boxPredictionLayer = boxPredictionLayer;
//# sourceMappingURL=boxPredictionLayer.js.map
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build/commonjs/faceDetectionNet/boxPredictionLayer.js.map deleted 100644 → 0
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build/commonjs/faceDetectionNet/extractParams.d.ts deleted 100644 → 0
import { ParamMapping } from 'tfjs-image-recognition-base';
import { NetParams } from './types';
export declare function extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: ParamMapping[];
};
build/commonjs/faceDetectionNet/extractParams.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
function extractorsFactory(extractWeights, paramMappings) {
function extractDepthwiseConvParams(numChannels, mappedPrefix) {
var filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
var batch_norm_scale = tf.tensor1d(extractWeights(numChannels));
var batch_norm_offset = tf.tensor1d(extractWeights(numChannels));
var batch_norm_mean = tf.tensor1d(extractWeights(numChannels));
var batch_norm_variance = tf.tensor1d(extractWeights(numChannels));
paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/batch_norm_scale" }, { paramPath: mappedPrefix + "/batch_norm_offset" }, { paramPath: mappedPrefix + "/batch_norm_mean" }, { paramPath: mappedPrefix + "/batch_norm_variance" });
return {
filters: filters,
batch_norm_scale: batch_norm_scale,
batch_norm_offset: batch_norm_offset,
batch_norm_mean: batch_norm_mean,
batch_norm_variance: batch_norm_variance
};
}
function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {
var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
var bias = tf.tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/" + (isPointwiseConv ? 'batch_norm_offset' : 'bias') });
return { filters: filters, bias: bias };
}
function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
var _a = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true), filters = _a.filters, bias = _a.bias;
return {
filters: filters,
batch_norm_offset: bias
};
}
function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) {
var depthwise_conv = extractDepthwiseConvParams(channelsIn, mappedPrefix + "/depthwise_conv");
var pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, mappedPrefix + "/pointwise_conv");
return { depthwise_conv: depthwise_conv, pointwise_conv: pointwise_conv };
}
function extractMobilenetV1Params() {
var conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');
var conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');
var conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');
var conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');
var conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');
var conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');
var conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');
var conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');
var conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');
var conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');
var conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');
var conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');
var conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');
var conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');
return {
conv_0: conv_0,
conv_1: conv_1,
conv_2: conv_2,
conv_3: conv_3,
conv_4: conv_4,
conv_5: conv_5,
conv_6: conv_6,
conv_7: conv_7,
conv_8: conv_8,
conv_9: conv_9,
conv_10: conv_10,
conv_11: conv_11,
conv_12: conv_12,
conv_13: conv_13
};
}
function extractPredictionLayerParams() {
var conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');
var conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');
var conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');
var conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');
var conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');
var conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');
var conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');
var conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');
var box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');
var class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');
var box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');
var class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');
var box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');
var class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');
var box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');
var class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');
var box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');
var class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');
var box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');
var class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');
var box_predictor_0 = {
box_encoding_predictor: box_encoding_0_predictor,
class_predictor: class_predictor_0
};
var box_predictor_1 = {
box_encoding_predictor: box_encoding_1_predictor,
class_predictor: class_predictor_1
};
var box_predictor_2 = {
box_encoding_predictor: box_encoding_2_predictor,
class_predictor: class_predictor_2
};
var box_predictor_3 = {
box_encoding_predictor: box_encoding_3_predictor,
class_predictor: class_predictor_3
};
var box_predictor_4 = {
box_encoding_predictor: box_encoding_4_predictor,
class_predictor: class_predictor_4
};
var box_predictor_5 = {
box_encoding_predictor: box_encoding_5_predictor,
class_predictor: class_predictor_5
};
return {
conv_0: conv_0,
conv_1: conv_1,
conv_2: conv_2,
conv_3: conv_3,
conv_4: conv_4,
conv_5: conv_5,
conv_6: conv_6,
conv_7: conv_7,
box_predictor_0: box_predictor_0,
box_predictor_1: box_predictor_1,
box_predictor_2: box_predictor_2,
box_predictor_3: box_predictor_3,
box_predictor_4: box_predictor_4,
box_predictor_5: box_predictor_5
};
}
return {
extractMobilenetV1Params: extractMobilenetV1Params,
extractPredictionLayerParams: extractPredictionLayerParams
};
}
function extractParams(weights) {
var paramMappings = [];
var _a = tfjs_image_recognition_base_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var _b = extractorsFactory(extractWeights, paramMappings), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams;
var mobilenetv1 = extractMobilenetV1Params();
var prediction_layer = extractPredictionLayerParams();
var extra_dim = tf.tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
var output_layer = {
extra_dim: extra_dim
};
paramMappings.push({ paramPath: 'output_layer/extra_dim' });
if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
}
return {
params: {
mobilenetv1: mobilenetv1,
prediction_layer: prediction_layer,
output_layer: output_layer
},
paramMappings: paramMappings
};
}
exports.extractParams = extractParams;
//# sourceMappingURL=extractParams.js.map
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build/commonjs/faceDetectionNet/extractParams.js.map deleted 100644 → 0
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build/commonjs/faceDetectionNet/index.d.ts deleted 100644 → 0
import { FaceDetectionNet } from './FaceDetectionNet';
export * from './FaceDetectionNet';
export declare function createFaceDetectionNet(weights: Float32Array): FaceDetectionNet;
export declare function faceDetectionNet(weights: Float32Array): FaceDetectionNet;
build/commonjs/faceDetectionNet/index.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var FaceDetectionNet_1 = require("./FaceDetectionNet");
tslib_1.__exportStar(require("./FaceDetectionNet"), exports);
function createFaceDetectionNet(weights) {
var net = new FaceDetectionNet_1.FaceDetectionNet();
net.extractWeights(weights);
return net;
}
exports.createFaceDetectionNet = createFaceDetectionNet;
function faceDetectionNet(weights) {
console.warn('faceDetectionNet(weights: Float32Array) will be deprecated in future, use createFaceDetectionNet instead');
return createFaceDetectionNet(weights);
}
exports.faceDetectionNet = faceDetectionNet;
//# sourceMappingURL=index.js.map
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build/commonjs/faceDetectionNet/index.js.map deleted 100644 → 0
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build/commonjs/faceDetectionNet/loadQuantizedParams.d.ts deleted 100644 → 0
import { ParamMapping } from 'tfjs-image-recognition-base';
import { NetParams } from './types';
export declare function loadQuantizedParams(uri: string | undefined): Promise<{
params: NetParams;
paramMappings: ParamMapping[];
}>;
build/commonjs/faceDetectionNet/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 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) {
var filters = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/weights", 4, mappedPrefix + "/filters");
var batch_norm_offset = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset", 1, mappedPrefix + "/batch_norm_offset");
return { filters: filters, batch_norm_offset: batch_norm_offset };
}
function extractConvPairParams(idx) {
var mappedPrefix = "mobilenetv1/conv_" + idx;
var prefixDepthwiseConv = "MobilenetV1/Conv2d_" + idx + "_depthwise";
var mappedPrefixDepthwiseConv = mappedPrefix + "/depthwise_conv";
var mappedPrefixPointwiseConv = mappedPrefix + "/pointwise_conv";
var filters = extractWeightEntry(prefixDepthwiseConv + "/depthwise_weights", 4, mappedPrefixDepthwiseConv + "/filters");
var batch_norm_scale = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/gamma", 1, mappedPrefixDepthwiseConv + "/batch_norm_scale");
var batch_norm_offset = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/beta", 1, mappedPrefixDepthwiseConv + "/batch_norm_offset");
var batch_norm_mean = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_mean", 1, mappedPrefixDepthwiseConv + "/batch_norm_mean");
var batch_norm_variance = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_variance", 1, mappedPrefixDepthwiseConv + "/batch_norm_variance");
return {
depthwise_conv: {
filters: filters,
batch_norm_scale: batch_norm_scale,
batch_norm_offset: batch_norm_offset,
batch_norm_mean: batch_norm_mean,
batch_norm_variance: batch_norm_variance
},
pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)
};
}
function extractMobilenetV1Params() {
return {
conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),
conv_1: extractConvPairParams(1),
conv_2: extractConvPairParams(2),
conv_3: extractConvPairParams(3),
conv_4: extractConvPairParams(4),
conv_5: extractConvPairParams(5),
conv_6: extractConvPairParams(6),
conv_7: extractConvPairParams(7),
conv_8: extractConvPairParams(8),
conv_9: extractConvPairParams(9),
conv_10: extractConvPairParams(10),
conv_11: extractConvPairParams(11),
conv_12: extractConvPairParams(12),
conv_13: extractConvPairParams(13)
};
}
function extractConvParams(prefix, mappedPrefix) {
var filters = extractWeightEntry(prefix + "/weights", 4, mappedPrefix + "/filters");
var bias = extractWeightEntry(prefix + "/biases", 1, mappedPrefix + "/bias");
return { filters: filters, bias: bias };
}
function extractBoxPredictorParams(idx) {
var box_encoding_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor", "prediction_layer/box_predictor_" + idx + "/box_encoding_predictor");
var class_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/ClassPredictor", "prediction_layer/box_predictor_" + idx + "/class_predictor");
return { box_encoding_predictor: box_encoding_predictor, class_predictor: class_predictor };
}
function extractPredictionLayerParams() {
return {
conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),
conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),
conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),
conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),
conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),
conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),
conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),
conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),
box_predictor_0: extractBoxPredictorParams(0),
box_predictor_1: extractBoxPredictorParams(1),
box_predictor_2: extractBoxPredictorParams(2),
box_predictor_3: extractBoxPredictorParams(3),
box_predictor_4: extractBoxPredictorParams(4),
box_predictor_5: extractBoxPredictorParams(5)
};
}
return {
extractMobilenetV1Params: extractMobilenetV1Params,
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 }];
}
});
});
}
exports.loadQuantizedParams = loadQuantizedParams;
//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file
build/commonjs/faceDetectionNet/loadQuantizedParams.js.map deleted 100644 → 0
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build/commonjs/faceDetectionNet/mobileNetV1.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { MobileNetV1 } from './types';
export declare function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params): {
out: tf.Tensor<tf.Rank.R4>;
conv11: any;
};
build/commonjs/faceDetectionNet/mobileNetV1.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var pointwiseConvLayer_1 = require("./pointwiseConvLayer");
var epsilon = 0.0010000000474974513;
function depthwiseConvLayer(x, params, strides) {
return tf.tidy(function () {
var out = tf.depthwiseConv2d(x, params.filters, strides, 'same');
out = tf.batchNormalization(out, params.batch_norm_mean, params.batch_norm_variance, epsilon, params.batch_norm_scale, params.batch_norm_offset);
return tf.clipByValue(out, 0, 6);
});
}
function getStridesForLayerIdx(layerIdx) {
return [2, 4, 6, 12].some(function (idx) { return idx === layerIdx; }) ? [2, 2] : [1, 1];
}
function mobileNetV1(x, params) {
return tf.tidy(function () {
var conv11 = null;
var out = pointwiseConvLayer_1.pointwiseConvLayer(x, params.conv_0, [2, 2]);
var convPairParams = [
params.conv_1,
params.conv_2,
params.conv_3,
params.conv_4,
params.conv_5,
params.conv_6,
params.conv_7,
params.conv_8,
params.conv_9,
params.conv_10,
params.conv_11,
params.conv_12,
params.conv_13
];
convPairParams.forEach(function (param, i) {
var layerIdx = i + 1;
var depthwiseConvStrides = getStridesForLayerIdx(layerIdx);
out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);
out = pointwiseConvLayer_1.pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);
if (layerIdx === 11) {
conv11 = out;
}
});
if (conv11 === null) {
throw new Error('mobileNetV1 - output of conv layer 11 is null');
}
return {
out: out,
conv11: conv11
};
});
}
exports.mobileNetV1 = mobileNetV1;
//# sourceMappingURL=mobileNetV1.js.map
\ No newline at end of file
build/commonjs/faceDetectionNet/mobileNetV1.js.map deleted 100644 → 0
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\ No newline at end of file
build/commonjs/faceDetectionNet/nonMaxSuppression.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
export declare function nonMaxSuppression(boxes: tf.Tensor2D, scores: number[], maxOutputSize: number, iouThreshold: number, scoreThreshold: number): number[];
build/commonjs/faceDetectionNet/nonMaxSuppression.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
var numBoxes = boxes.shape[0];
var outputSize = Math.min(maxOutputSize, numBoxes);
var candidates = scores
.map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })
.filter(function (c) { return c.score > scoreThreshold; })
.sort(function (c1, c2) { return c2.score - c1.score; });
var suppressFunc = function (x) { return x <= iouThreshold ? 1 : 0; };
var selected = [];
candidates.forEach(function (c) {
if (selected.length >= outputSize) {
return;
}
var originalScore = c.score;
for (var j = selected.length - 1; j >= 0; --j) {
var iou = IOU(boxes, c.boxIndex, selected[j]);
if (iou === 0.0) {
continue;
}
c.score *= suppressFunc(iou);
if (c.score <= scoreThreshold) {
break;
}
}
if (originalScore === c.score) {
selected.push(c.boxIndex);
}
});
return selected;
}
exports.nonMaxSuppression = nonMaxSuppression;
function IOU(boxes, i, j) {
var yminI = Math.min(boxes.get(i, 0), boxes.get(i, 2));
var xminI = Math.min(boxes.get(i, 1), boxes.get(i, 3));
var ymaxI = Math.max(boxes.get(i, 0), boxes.get(i, 2));
var xmaxI = Math.max(boxes.get(i, 1), boxes.get(i, 3));
var yminJ = Math.min(boxes.get(j, 0), boxes.get(j, 2));
var xminJ = Math.min(boxes.get(j, 1), boxes.get(j, 3));
var ymaxJ = Math.max(boxes.get(j, 0), boxes.get(j, 2));
var xmaxJ = Math.max(boxes.get(j, 1), boxes.get(j, 3));
var areaI = (ymaxI - yminI) * (xmaxI - xminI);
var areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
if (areaI <= 0 || areaJ <= 0) {
return 0.0;
}
var intersectionYmin = Math.max(yminI, yminJ);
var intersectionXmin = Math.max(xminI, xminJ);
var intersectionYmax = Math.min(ymaxI, ymaxJ);
var intersectionXmax = Math.min(xmaxI, xmaxJ);
var intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *
Math.max(intersectionXmax - intersectionXmin, 0.0);
return intersectionArea / (areaI + areaJ - intersectionArea);
}
//# sourceMappingURL=nonMaxSuppression.js.map
\ No newline at end of file
build/commonjs/faceDetectionNet/nonMaxSuppression.js.map deleted 100644 → 0
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build/commonjs/faceDetectionNet/outputLayer.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { OutputLayerParams } from './types';
export declare function outputLayer(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams): {
boxes: tf.Tensor<tf.Rank.R2>[];
scores: tf.Tensor<tf.Rank.R1>[];
};
build/commonjs/faceDetectionNet/outputLayer.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function getCenterCoordinatesAndSizesLayer(x) {
var vec = tf.unstack(tf.transpose(x, [1, 0]));
var sizes = [
tf.sub(vec[2], vec[0]),
tf.sub(vec[3], vec[1])
];
var centers = [
tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),
tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))
];
return {
sizes: sizes,
centers: centers
};
}
function decodeBoxesLayer(x0, x1) {
var _a = getCenterCoordinatesAndSizesLayer(x0), sizes = _a.sizes, centers = _a.centers;
var vec = tf.unstack(tf.transpose(x1, [1, 0]));
var div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));
var add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);
var div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));
var add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);
return tf.transpose(tf.stack([
tf.sub(add0_out, div0_out),
tf.sub(add1_out, div1_out),
tf.add(add0_out, div0_out),
tf.add(add1_out, div1_out)
]), [1, 0]);
}
function outputLayer(boxPredictions, classPredictions, params) {
return tf.tidy(function () {
var batchSize = boxPredictions.shape[0];
var boxes = decodeBoxesLayer(tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), tf.reshape(boxPredictions, [-1, 4]));
boxes = tf.reshape(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);
var scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));
var scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]);
scores = tf.reshape(scores, [batchSize, scores.shape[1]]);
var boxesByBatch = tf.unstack(boxes);
var scoresByBatch = tf.unstack(scores);
return {
boxes: boxesByBatch,
scores: scoresByBatch
};
});
}
exports.outputLayer = outputLayer;
//# sourceMappingURL=outputLayer.js.map
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build/commonjs/faceDetectionNet/outputLayer.js.map deleted 100644 → 0
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build/commonjs/faceDetectionNet/pointwiseConvLayer.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { PointwiseConvParams } from './types';
export declare function pointwiseConvLayer(x: tf.Tensor4D, params: PointwiseConvParams, strides: [number, number]): tf.Tensor<tf.Rank.R4>;
build/commonjs/faceDetectionNet/pointwiseConvLayer.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function pointwiseConvLayer(x, params, strides) {
return tf.tidy(function () {
var out = tf.conv2d(x, params.filters, strides, 'same');
out = tf.add(out, params.batch_norm_offset);
return tf.clipByValue(out, 0, 6);
});
}
exports.pointwiseConvLayer = pointwiseConvLayer;
//# sourceMappingURL=pointwiseConvLayer.js.map
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build/commonjs/faceDetectionNet/pointwiseConvLayer.js.map deleted 100644 → 0
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build/commonjs/faceDetectionNet/predictionLayer.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { PredictionLayerParams } from './types';
export declare function predictionLayer(x: tf.Tensor4D, conv11: tf.Tensor4D, params: PredictionLayerParams): {
boxPredictions: tf.Tensor<tf.Rank.R4>;
classPredictions: tf.Tensor<tf.Rank.R4>;
};
build/commonjs/faceDetectionNet/predictionLayer.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var boxPredictionLayer_1 = require("./boxPredictionLayer");
var pointwiseConvLayer_1 = require("./pointwiseConvLayer");
function predictionLayer(x, conv11, params) {
return tf.tidy(function () {
var conv0 = pointwiseConvLayer_1.pointwiseConvLayer(x, params.conv_0, [1, 1]);
var conv1 = pointwiseConvLayer_1.pointwiseConvLayer(conv0, params.conv_1, [2, 2]);
var conv2 = pointwiseConvLayer_1.pointwiseConvLayer(conv1, params.conv_2, [1, 1]);
var conv3 = pointwiseConvLayer_1.pointwiseConvLayer(conv2, params.conv_3, [2, 2]);
var conv4 = pointwiseConvLayer_1.pointwiseConvLayer(conv3, params.conv_4, [1, 1]);
var conv5 = pointwiseConvLayer_1.pointwiseConvLayer(conv4, params.conv_5, [2, 2]);
var conv6 = pointwiseConvLayer_1.pointwiseConvLayer(conv5, params.conv_6, [1, 1]);
var conv7 = pointwiseConvLayer_1.pointwiseConvLayer(conv6, params.conv_7, [2, 2]);
var boxPrediction0 = boxPredictionLayer_1.boxPredictionLayer(conv11, params.box_predictor_0);
var boxPrediction1 = boxPredictionLayer_1.boxPredictionLayer(x, params.box_predictor_1);
var boxPrediction2 = boxPredictionLayer_1.boxPredictionLayer(conv1, params.box_predictor_2);
var boxPrediction3 = boxPredictionLayer_1.boxPredictionLayer(conv3, params.box_predictor_3);
var boxPrediction4 = boxPredictionLayer_1.boxPredictionLayer(conv5, params.box_predictor_4);
var boxPrediction5 = boxPredictionLayer_1.boxPredictionLayer(conv7, params.box_predictor_5);
var boxPredictions = tf.concat([
boxPrediction0.boxPredictionEncoding,
boxPrediction1.boxPredictionEncoding,
boxPrediction2.boxPredictionEncoding,
boxPrediction3.boxPredictionEncoding,
boxPrediction4.boxPredictionEncoding,
boxPrediction5.boxPredictionEncoding
], 1);
var classPredictions = tf.concat([
boxPrediction0.classPrediction,
boxPrediction1.classPrediction,
boxPrediction2.classPrediction,
boxPrediction3.classPrediction,
boxPrediction4.classPrediction,
boxPrediction5.classPrediction
], 1);
return {
boxPredictions: boxPredictions,
classPredictions: classPredictions
};
});
}
exports.predictionLayer = predictionLayer;
//# sourceMappingURL=predictionLayer.js.map
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build/commonjs/faceDetectionNet/predictionLayer.js.map deleted 100644 → 0
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build/commonjs/faceDetectionNet/types.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from 'tfjs-tiny-yolov2';
export declare type PointwiseConvParams = {
filters: tf.Tensor4D;
batch_norm_offset: tf.Tensor1D;
};
export declare namespace MobileNetV1 {
type DepthwiseConvParams = {
filters: tf.Tensor4D;
batch_norm_scale: tf.Tensor1D;
batch_norm_offset: tf.Tensor1D;
batch_norm_mean: tf.Tensor1D;
batch_norm_variance: tf.Tensor1D;
};
type ConvPairParams = {
depthwise_conv: DepthwiseConvParams;
pointwise_conv: PointwiseConvParams;
};
type Params = {
conv_0: PointwiseConvParams;
conv_1: ConvPairParams;
conv_2: ConvPairParams;
conv_3: ConvPairParams;
conv_4: ConvPairParams;
conv_5: ConvPairParams;
conv_6: ConvPairParams;
conv_7: ConvPairParams;
conv_8: ConvPairParams;
conv_9: ConvPairParams;
conv_10: ConvPairParams;
conv_11: ConvPairParams;
conv_12: ConvPairParams;
conv_13: ConvPairParams;
};
}
export declare type BoxPredictionParams = {
box_encoding_predictor: ConvParams;
class_predictor: ConvParams;
};
export declare type PredictionLayerParams = {
conv_0: PointwiseConvParams;
conv_1: PointwiseConvParams;
conv_2: PointwiseConvParams;
conv_3: PointwiseConvParams;
conv_4: PointwiseConvParams;
conv_5: PointwiseConvParams;
conv_6: PointwiseConvParams;
conv_7: PointwiseConvParams;
box_predictor_0: BoxPredictionParams;
box_predictor_1: BoxPredictionParams;
box_predictor_2: BoxPredictionParams;
box_predictor_3: BoxPredictionParams;
box_predictor_4: BoxPredictionParams;
box_predictor_5: BoxPredictionParams;
};
export declare type OutputLayerParams = {
extra_dim: tf.Tensor3D;
};
export declare type NetParams = {
mobilenetv1: MobileNetV1.Params;
prediction_layer: PredictionLayerParams;
output_layer: OutputLayerParams;
};
build/commonjs/faceDetectionNet/types.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
//# sourceMappingURL=types.js.map
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build/commonjs/faceDetectionNet/types.js.map deleted 100644 → 0
{"version":3,"file":"types.js","sourceRoot":"","sources":["../../../src/faceDetectionNet/types.ts"],"names":[],"mappings":""}
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build/commonjs/globalApi.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { NetInput, TNetInput } from 'tfjs-image-recognition-base';
import { TinyYolov2Types } from 'tfjs-tiny-yolov2';
import { FaceDetection } from './classes/FaceDetection';
import { FaceLandmarks68 } from './classes/FaceLandmarks68';
import { FullFaceDescription } from './classes/FullFaceDescription';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmark68Net } from './faceLandmarkNet/FaceLandmark68Net';
import { FaceLandmark68TinyNet } from './faceLandmarkNet/FaceLandmark68TinyNet';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { Mtcnn } from './mtcnn/Mtcnn';
import { MtcnnForwardParams, MtcnnResult } from './mtcnn/types';
import { TinyYolov2 } from './tinyYolov2/TinyYolov2';
export declare const detectionNet: FaceDetectionNet;
export declare const landmarkNet: FaceLandmark68Net;
export declare const recognitionNet: FaceRecognitionNet;
export declare const nets: {
ssdMobilenetv1: FaceDetectionNet;
faceLandmark68Net: FaceLandmark68Net;
faceLandmark68TinyNet: FaceLandmark68TinyNet;
faceRecognitionNet: FaceRecognitionNet;
mtcnn: Mtcnn;
tinyYolov2: TinyYolov2;
};
export declare function loadSsdMobilenetv1Model(url: string): Promise<void>;
export declare function loadFaceLandmarkModel(url: string): Promise<void>;
export declare function loadFaceLandmarkTinyModel(url: string): Promise<void>;
export declare function loadFaceRecognitionModel(url: string): Promise<void>;
export declare function loadMtcnnModel(url: string): Promise<void>;
export declare function loadTinyYolov2Model(url: string): Promise<void>;
export declare function loadFaceDetectionModel(url: string): Promise<void>;
export declare function loadModels(url: string): Promise<[void, void, void, void, void]>;
export declare function locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
export declare const ssdMobilenetv1: typeof locateFaces;
export declare function detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>;
export declare function detectLandmarksTiny(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>;
export declare function computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>;
export declare function mtcnn(input: TNetInput, forwardParams: MtcnnForwardParams): Promise<MtcnnResult[]>;
export declare function tinyYolov2(input: TNetInput, forwardParams: TinyYolov2Types.TinyYolov2ForwardParams): Promise<FaceDetection[]>;
export declare type allFacesSsdMobilenetv1Function = (input: tf.Tensor | NetInput | TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare const allFacesSsdMobilenetv1: allFacesSsdMobilenetv1Function;
export declare type allFacesTinyYolov2Function = (input: tf.Tensor | NetInput | TNetInput, forwardParams?: TinyYolov2Types.TinyYolov2ForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare const allFacesTinyYolov2: allFacesTinyYolov2Function;
export declare type allFacesMtcnnFunction = (input: tf.Tensor | NetInput | TNetInput, mtcnnForwardParams?: MtcnnForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare const allFacesMtcnn: allFacesMtcnnFunction;
export declare const allFaces: allFacesSsdMobilenetv1Function;
build/commonjs/globalApi.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var allFacesFactory_1 = require("./allFacesFactory");
var FaceDetectionNet_1 = require("./faceDetectionNet/FaceDetectionNet");
var FaceLandmark68Net_1 = require("./faceLandmarkNet/FaceLandmark68Net");
var FaceLandmark68TinyNet_1 = require("./faceLandmarkNet/FaceLandmark68TinyNet");
var FaceRecognitionNet_1 = require("./faceRecognitionNet/FaceRecognitionNet");
var Mtcnn_1 = require("./mtcnn/Mtcnn");
var TinyYolov2_1 = require("./tinyYolov2/TinyYolov2");
exports.detectionNet = new FaceDetectionNet_1.FaceDetectionNet();
exports.landmarkNet = new FaceLandmark68Net_1.FaceLandmark68Net();
exports.recognitionNet = new FaceRecognitionNet_1.FaceRecognitionNet();
// nets need more specific names, to avoid ambiguity in future
// when alternative net implementations are provided
exports.nets = {
ssdMobilenetv1: exports.detectionNet,
faceLandmark68Net: exports.landmarkNet,
faceLandmark68TinyNet: new FaceLandmark68TinyNet_1.FaceLandmark68TinyNet(),
faceRecognitionNet: exports.recognitionNet,
mtcnn: new Mtcnn_1.Mtcnn(),
tinyYolov2: new TinyYolov2_1.TinyYolov2()
};
function loadSsdMobilenetv1Model(url) {
return exports.nets.ssdMobilenetv1.load(url);
}
exports.loadSsdMobilenetv1Model = loadSsdMobilenetv1Model;
function loadFaceLandmarkModel(url) {
return exports.nets.faceLandmark68Net.load(url);
}
exports.loadFaceLandmarkModel = loadFaceLandmarkModel;
function loadFaceLandmarkTinyModel(url) {
return exports.nets.faceLandmark68TinyNet.load(url);
}
exports.loadFaceLandmarkTinyModel = loadFaceLandmarkTinyModel;
function loadFaceRecognitionModel(url) {
return exports.nets.faceRecognitionNet.load(url);
}
exports.loadFaceRecognitionModel = loadFaceRecognitionModel;
function loadMtcnnModel(url) {
return exports.nets.mtcnn.load(url);
}
exports.loadMtcnnModel = loadMtcnnModel;
function loadTinyYolov2Model(url) {
return exports.nets.tinyYolov2.load(url);
}
exports.loadTinyYolov2Model = loadTinyYolov2Model;
function loadFaceDetectionModel(url) {
return loadSsdMobilenetv1Model(url);
}
exports.loadFaceDetectionModel = loadFaceDetectionModel;
function loadModels(url) {
console.warn('loadModels will be deprecated in future');
return Promise.all([
loadSsdMobilenetv1Model(url),
loadFaceLandmarkModel(url),
loadFaceRecognitionModel(url),
loadMtcnnModel(url),
loadTinyYolov2Model(url)
]);
}
exports.loadModels = loadModels;
function locateFaces(input, minConfidence, maxResults) {
return exports.nets.ssdMobilenetv1.locateFaces(input, minConfidence, maxResults);
}
exports.locateFaces = locateFaces;
exports.ssdMobilenetv1 = locateFaces;
function detectLandmarks(input) {
return exports.nets.faceLandmark68Net.detectLandmarks(input);
}
exports.detectLandmarks = detectLandmarks;
function detectLandmarksTiny(input) {
return exports.nets.faceLandmark68TinyNet.detectLandmarks(input);
}
exports.detectLandmarksTiny = detectLandmarksTiny;
function computeFaceDescriptor(input) {
return exports.nets.faceRecognitionNet.computeFaceDescriptor(input);
}
exports.computeFaceDescriptor = computeFaceDescriptor;
function mtcnn(input, forwardParams) {
return exports.nets.mtcnn.forward(input, forwardParams);
}
exports.mtcnn = mtcnn;
function tinyYolov2(input, forwardParams) {
return exports.nets.tinyYolov2.locateFaces(input, forwardParams);
}
exports.tinyYolov2 = tinyYolov2;
exports.allFacesSsdMobilenetv1 = allFacesFactory_1.allFacesSsdMobilenetv1Factory(exports.nets.ssdMobilenetv1, exports.nets.faceLandmark68Net, exports.nets.faceRecognitionNet);
exports.allFacesTinyYolov2 = allFacesFactory_1.allFacesTinyYolov2Factory(exports.nets.tinyYolov2, exports.nets.faceLandmark68Net, exports.nets.faceRecognitionNet);
exports.allFacesMtcnn = allFacesFactory_1.allFacesMtcnnFactory(exports.nets.mtcnn, exports.nets.faceRecognitionNet);
exports.allFaces = exports.allFacesSsdMobilenetv1;
//# sourceMappingURL=globalApi.js.map
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build/commonjs/globalApi.js.map deleted 100644 → 0
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build/commonjs/mtcnn/getDefaultMtcnnForwardParams.d.ts deleted 100644 → 0
export declare function getDefaultMtcnnForwardParams(): {
minFaceSize: number;
scaleFactor: number;
maxNumScales: number;
scoreThresholds: number[];
};
build/commonjs/mtcnn/getDefaultMtcnnForwardParams.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function getDefaultMtcnnForwardParams() {
return {
minFaceSize: 20,
scaleFactor: 0.709,
maxNumScales: 10,
scoreThresholds: [0.6, 0.7, 0.7]
};
}
exports.getDefaultMtcnnForwardParams = getDefaultMtcnnForwardParams;
//# sourceMappingURL=getDefaultMtcnnForwardParams.js.map
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build/commonjs/mtcnn/getDefaultMtcnnForwardParams.js.map deleted 100644 → 0
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build/es6/allFacesFactory.d.ts deleted 100644 → 0
import { TNetInput } from 'tfjs-image-recognition-base';
import { TinyYolov2Types } from 'tfjs-tiny-yolov2';
import { TinyYolov2 } from '.';
import { FullFaceDescription } from './classes/FullFaceDescription';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmark68Net } from './faceLandmarkNet/FaceLandmark68Net';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { Mtcnn } from './mtcnn/Mtcnn';
import { MtcnnForwardParams } from './mtcnn/types';
export declare function allFacesSsdMobilenetv1Factory(ssdMobilenetv1: FaceDetectionNet, landmarkNet: FaceLandmark68Net, recognitionNet: FaceRecognitionNet): (input: TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare function allFacesTinyYolov2Factory(tinyYolov2: TinyYolov2, landmarkNet: FaceLandmark68Net, recognitionNet: FaceRecognitionNet): (input: TNetInput, forwardParams?: TinyYolov2Types.TinyYolov2ForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare function allFacesMtcnnFactory(mtcnn: Mtcnn, recognitionNet: FaceRecognitionNet): (input: TNetInput, mtcnnForwardParams?: MtcnnForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
build/es6/allFacesFactory.js deleted 100644 → 0
import * as tslib_1 from "tslib";
import { Point } from 'tfjs-image-recognition-base';
import { FullFaceDescription } from './classes/FullFaceDescription';
import { extractFaces } from './dom';
function computeDescriptorsFactory(recognitionNet) {
return function (input, alignedFaceBoxes, useBatchProcessing) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var alignedFaceCanvases, descriptors, _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, extractFaces(input, alignedFaceBoxes)];
case 1:
alignedFaceCanvases = _b.sent();
if (!useBatchProcessing) return [3 /*break*/, 3];
return [4 /*yield*/, recognitionNet.computeFaceDescriptor(alignedFaceCanvases)];
case 2:
_a = _b.sent();
return [3 /*break*/, 5];
case 3: return [4 /*yield*/, Promise.all(alignedFaceCanvases.map(function (canvas) { return recognitionNet.computeFaceDescriptor(canvas); }))];
case 4:
_a = _b.sent();
_b.label = 5;
case 5:
descriptors = _a;
return [2 /*return*/, descriptors];
}
});
});
};
}
function allFacesFactory(detectFaces, landmarkNet, recognitionNet) {
var computeDescriptors = computeDescriptorsFactory(recognitionNet);
return function (input, useBatchProcessing) {
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var detections, faceCanvases, faceLandmarksByFace, _a, alignedFaceBoxes, descriptors;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, detectFaces(input)];
case 1:
detections = _b.sent();
return [4 /*yield*/, extractFaces(input, detections)];
case 2:
faceCanvases = _b.sent();
if (!useBatchProcessing) return [3 /*break*/, 4];
return [4 /*yield*/, landmarkNet.detectLandmarks(faceCanvases)];
case 3:
_a = _b.sent();
return [3 /*break*/, 6];
case 4: return [4 /*yield*/, Promise.all(faceCanvases.map(function (canvas) { return landmarkNet.detectLandmarks(canvas); }))];
case 5:
_a = _b.sent();
_b.label = 6;
case 6:
faceLandmarksByFace = _a;
alignedFaceBoxes = faceLandmarksByFace.map(function (landmarks, i) { return landmarks.align(detections[i].getBox()); });
return [4 /*yield*/, computeDescriptors(input, alignedFaceBoxes, useBatchProcessing)];
case 7:
descriptors = _b.sent();
return [2 /*return*/, detections.map(function (detection, i) {
return new FullFaceDescription(detection, faceLandmarksByFace[i].shiftByPoint(new Point(detection.box.x, detection.box.y)), descriptors[i]);
})];
}
});
});
};
}
export function allFacesSsdMobilenetv1Factory(ssdMobilenetv1, landmarkNet, recognitionNet) {
return function (input, minConfidence, useBatchProcessing) {
if (minConfidence === void 0) { minConfidence = 0.8; }
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var detectFaces, allFaces;
return tslib_1.__generator(this, function (_a) {
detectFaces = function (input) { return ssdMobilenetv1.locateFaces(input, minConfidence); };
allFaces = allFacesFactory(detectFaces, landmarkNet, recognitionNet);
return [2 /*return*/, allFaces(input, useBatchProcessing)];
});
});
};
}
export function allFacesTinyYolov2Factory(tinyYolov2, landmarkNet, recognitionNet) {
return function (input, forwardParams, useBatchProcessing) {
if (forwardParams === void 0) { forwardParams = {}; }
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var detectFaces, allFaces;
return tslib_1.__generator(this, function (_a) {
detectFaces = function (input) { return tinyYolov2.locateFaces(input, forwardParams); };
allFaces = allFacesFactory(detectFaces, landmarkNet, recognitionNet);
return [2 /*return*/, allFaces(input, useBatchProcessing)];
});
});
};
}
export function allFacesMtcnnFactory(mtcnn, recognitionNet) {
var computeDescriptors = computeDescriptorsFactory(recognitionNet);
return function (input, mtcnnForwardParams, useBatchProcessing) {
if (mtcnnForwardParams === void 0) { mtcnnForwardParams = {}; }
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var results, alignedFaceBoxes, descriptors;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, mtcnn.forward(input, mtcnnForwardParams)];
case 1:
results = _a.sent();
alignedFaceBoxes = results.map(function (_a) {
var faceLandmarks = _a.faceLandmarks;
return faceLandmarks.align();
});
return [4 /*yield*/, computeDescriptors(input, alignedFaceBoxes, useBatchProcessing)];
case 2:
descriptors = _a.sent();
return [2 /*return*/, results.map(function (_a, i) {
var faceDetection = _a.faceDetection, faceLandmarks = _a.faceLandmarks;
return new FullFaceDescription(faceDetection, faceLandmarks, descriptors[i]);
})];
}
});
});
};
}
//# sourceMappingURL=allFacesFactory.js.map
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build/es6/faceDetectionNet/FaceDetectionNet.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
import { FaceDetection } from '../classes/FaceDetection';
import { NetParams } from './types';
export declare class FaceDetectionNet extends NeuralNetwork<NetParams> {
constructor();
forwardInput(input: NetInput): {
boxes: tf.Tensor<tf.Rank.R2>[];
scores: tf.Tensor<tf.Rank.R1>[];
};
forward(input: TNetInput): Promise<{
boxes: tf.Tensor<tf.Rank.R2>[];
scores: tf.Tensor<tf.Rank.R1>[];
}>;
locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
protected loadQuantizedParams(uri: string | undefined): Promise<{
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
}>;
protected extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
};
}
build/es6/faceDetectionNet/FaceDetectionNet.js deleted 100644 → 0
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
import { NeuralNetwork, Rect, toNetInput } from 'tfjs-image-recognition-base';
import { FaceDetection } from '../classes/FaceDetection';
import { extractParams } from './extractParams';
import { loadQuantizedParams } from './loadQuantizedParams';
import { mobileNetV1 } from './mobileNetV1';
import { nonMaxSuppression } from './nonMaxSuppression';
import { outputLayer } from './outputLayer';
import { predictionLayer } from './predictionLayer';
var FaceDetectionNet = /** @class */ (function (_super) {
tslib_1.__extends(FaceDetectionNet, _super);
function FaceDetectionNet() {
return _super.call(this, 'FaceDetectionNet') || this;
}
FaceDetectionNet.prototype.forwardInput = function (input) {
var params = this.params;
if (!params) {
throw new Error('FaceDetectionNet - load model before inference');
}
return tf.tidy(function () {
var batchTensor = input.toBatchTensor(512, false).toFloat();
var x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1));
var features = mobileNetV1(x, params.mobilenetv1);
var _a = predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
return outputLayer(boxPredictions, classPredictions, params.output_layer);
});
};
FaceDetectionNet.prototype.forward = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
});
};
FaceDetectionNet.prototype.locateFaces = function (input, minConfidence, maxResults) {
if (minConfidence === void 0) { minConfidence = 0.8; }
if (maxResults === void 0) { maxResults = 100; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var netInput, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, reshapedDims, inputSize, padX, padY, results;
return tslib_1.__generator(this, function (_d) {
switch (_d.label) {
case 0: return [4 /*yield*/, toNetInput(input)];
case 1:
netInput = _d.sent();
_a = this.forwardInput(netInput), _boxes = _a.boxes, _scores = _a.scores;
boxes = _boxes[0];
scores = _scores[0];
for (i = 1; i < _boxes.length; i++) {
_boxes[i].dispose();
_scores[i].dispose();
}
_c = (_b = Array).from;
return [4 /*yield*/, scores.data()];
case 2:
scoresData = _c.apply(_b, [_d.sent()]);
iouThreshold = 0.5;
indices = nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
reshapedDims = netInput.getReshapedInputDimensions(0);
inputSize = netInput.inputSize;
padX = inputSize / reshapedDims.width;
padY = inputSize / reshapedDims.height;
results = indices
.map(function (idx) {
var _a = [
Math.max(0, boxes.get(idx, 0)),
Math.min(1.0, boxes.get(idx, 2))
].map(function (val) { return val * padY; }), top = _a[0], bottom = _a[1];
var _b = [
Math.max(0, boxes.get(idx, 1)),
Math.min(1.0, boxes.get(idx, 3))
].map(function (val) { return val * padX; }), left = _b[0], right = _b[1];
return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), {
height: netInput.getInputHeight(0),
width: netInput.getInputWidth(0)
});
});
boxes.dispose();
scores.dispose();
return [2 /*return*/, results];
}
});
});
};
FaceDetectionNet.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams(uri);
};
FaceDetectionNet.prototype.extractParams = function (weights) {
return extractParams(weights);
};
return FaceDetectionNet;
}(NeuralNetwork));
export { FaceDetectionNet };
//# sourceMappingURL=FaceDetectionNet.js.map
\ No newline at end of file
build/es6/faceDetectionNet/FaceDetectionNet.js.map deleted 100644 → 0
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\ No newline at end of file
build/es6/faceDetectionNet/boxPredictionLayer.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { BoxPredictionParams } from './types';
export declare function boxPredictionLayer(x: tf.Tensor4D, params: BoxPredictionParams): {
boxPredictionEncoding: tf.Tensor<tf.Rank>;
classPrediction: tf.Tensor<tf.Rank>;
};
build/es6/faceDetectionNet/boxPredictionLayer.js deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { convLayer } from 'tfjs-tiny-yolov2';
export function boxPredictionLayer(x, params) {
return tf.tidy(function () {
var batchSize = x.shape[0];
var boxPredictionEncoding = tf.reshape(convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
var classPrediction = tf.reshape(convLayer(x, params.class_predictor), [batchSize, -1, 3]);
return {
boxPredictionEncoding: boxPredictionEncoding,
classPrediction: classPrediction
};
});
}
//# sourceMappingURL=boxPredictionLayer.js.map
\ No newline at end of file
build/es6/faceDetectionNet/boxPredictionLayer.js.map deleted 100644 → 0
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\ No newline at end of file
build/es6/faceDetectionNet/extractParams.d.ts deleted 100644 → 0
import { ParamMapping } from 'tfjs-image-recognition-base';
import { NetParams } from './types';
export declare function extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: ParamMapping[];
};
build/es6/faceDetectionNet/extractParams.js deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { extractWeightsFactory } from 'tfjs-image-recognition-base';
function extractorsFactory(extractWeights, paramMappings) {
function extractDepthwiseConvParams(numChannels, mappedPrefix) {
var filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
var batch_norm_scale = tf.tensor1d(extractWeights(numChannels));
var batch_norm_offset = tf.tensor1d(extractWeights(numChannels));
var batch_norm_mean = tf.tensor1d(extractWeights(numChannels));
var batch_norm_variance = tf.tensor1d(extractWeights(numChannels));
paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/batch_norm_scale" }, { paramPath: mappedPrefix + "/batch_norm_offset" }, { paramPath: mappedPrefix + "/batch_norm_mean" }, { paramPath: mappedPrefix + "/batch_norm_variance" });
return {
filters: filters,
batch_norm_scale: batch_norm_scale,
batch_norm_offset: batch_norm_offset,
batch_norm_mean: batch_norm_mean,
batch_norm_variance: batch_norm_variance
};
}
function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {
var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
var bias = tf.tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/" + (isPointwiseConv ? 'batch_norm_offset' : 'bias') });
return { filters: filters, bias: bias };
}
function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
var _a = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true), filters = _a.filters, bias = _a.bias;
return {
filters: filters,
batch_norm_offset: bias
};
}
function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) {
var depthwise_conv = extractDepthwiseConvParams(channelsIn, mappedPrefix + "/depthwise_conv");
var pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, mappedPrefix + "/pointwise_conv");
return { depthwise_conv: depthwise_conv, pointwise_conv: pointwise_conv };
}
function extractMobilenetV1Params() {
var conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');
var conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');
var conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');
var conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');
var conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');
var conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');
var conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');
var conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');
var conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');
var conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');
var conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');
var conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');
var conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');
var conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');
return {
conv_0: conv_0,
conv_1: conv_1,
conv_2: conv_2,
conv_3: conv_3,
conv_4: conv_4,
conv_5: conv_5,
conv_6: conv_6,
conv_7: conv_7,
conv_8: conv_8,
conv_9: conv_9,
conv_10: conv_10,
conv_11: conv_11,
conv_12: conv_12,
conv_13: conv_13
};
}
function extractPredictionLayerParams() {
var conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');
var conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');
var conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');
var conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');
var conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');
var conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');
var conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');
var conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');
var box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');
var class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');
var box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');
var class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');
var box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');
var class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');
var box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');
var class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');
var box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');
var class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');
var box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');
var class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');
var box_predictor_0 = {
box_encoding_predictor: box_encoding_0_predictor,
class_predictor: class_predictor_0
};
var box_predictor_1 = {
box_encoding_predictor: box_encoding_1_predictor,
class_predictor: class_predictor_1
};
var box_predictor_2 = {
box_encoding_predictor: box_encoding_2_predictor,
class_predictor: class_predictor_2
};
var box_predictor_3 = {
box_encoding_predictor: box_encoding_3_predictor,
class_predictor: class_predictor_3
};
var box_predictor_4 = {
box_encoding_predictor: box_encoding_4_predictor,
class_predictor: class_predictor_4
};
var box_predictor_5 = {
box_encoding_predictor: box_encoding_5_predictor,
class_predictor: class_predictor_5
};
return {
conv_0: conv_0,
conv_1: conv_1,
conv_2: conv_2,
conv_3: conv_3,
conv_4: conv_4,
conv_5: conv_5,
conv_6: conv_6,
conv_7: conv_7,
box_predictor_0: box_predictor_0,
box_predictor_1: box_predictor_1,
box_predictor_2: box_predictor_2,
box_predictor_3: box_predictor_3,
box_predictor_4: box_predictor_4,
box_predictor_5: box_predictor_5
};
}
return {
extractMobilenetV1Params: extractMobilenetV1Params,
extractPredictionLayerParams: extractPredictionLayerParams
};
}
export function extractParams(weights) {
var paramMappings = [];
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var _b = extractorsFactory(extractWeights, paramMappings), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams;
var mobilenetv1 = extractMobilenetV1Params();
var prediction_layer = extractPredictionLayerParams();
var extra_dim = tf.tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
var output_layer = {
extra_dim: extra_dim
};
paramMappings.push({ paramPath: 'output_layer/extra_dim' });
if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
}
return {
params: {
mobilenetv1: mobilenetv1,
prediction_layer: prediction_layer,
output_layer: output_layer
},
paramMappings: paramMappings
};
}
//# sourceMappingURL=extractParams.js.map
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build/es6/faceDetectionNet/extractParams.js.map deleted 100644 → 0
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build/es6/faceDetectionNet/index.d.ts deleted 100644 → 0
import { FaceDetectionNet } from './FaceDetectionNet';
export * from './FaceDetectionNet';
export declare function createFaceDetectionNet(weights: Float32Array): FaceDetectionNet;
export declare function faceDetectionNet(weights: Float32Array): FaceDetectionNet;
build/es6/faceDetectionNet/index.js deleted 100644 → 0
import { FaceDetectionNet } from './FaceDetectionNet';
export * from './FaceDetectionNet';
export function createFaceDetectionNet(weights) {
var net = new FaceDetectionNet();
net.extractWeights(weights);
return net;
}
export function faceDetectionNet(weights) {
console.warn('faceDetectionNet(weights: Float32Array) will be deprecated in future, use createFaceDetectionNet instead');
return createFaceDetectionNet(weights);
}
//# sourceMappingURL=index.js.map
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build/es6/faceDetectionNet/loadQuantizedParams.d.ts deleted 100644 → 0
import { ParamMapping } from 'tfjs-image-recognition-base';
import { NetParams } from './types';
export declare function loadQuantizedParams(uri: string | undefined): Promise<{
params: NetParams;
paramMappings: ParamMapping[];
}>;
build/es6/faceDetectionNet/loadQuantizedParams.js deleted 100644 → 0
import * as tslib_1 from "tslib";
import { disposeUnusedWeightTensors, extractWeightEntryFactory, isTensor3D, loadWeightMap, } from 'tfjs-image-recognition-base';
var DEFAULT_MODEL_NAME = 'ssd_mobilenetv1_model';
function extractorsFactory(weightMap, paramMappings) {
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
var filters = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/weights", 4, mappedPrefix + "/filters");
var batch_norm_offset = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset", 1, mappedPrefix + "/batch_norm_offset");
return { filters: filters, batch_norm_offset: batch_norm_offset };
}
function extractConvPairParams(idx) {
var mappedPrefix = "mobilenetv1/conv_" + idx;
var prefixDepthwiseConv = "MobilenetV1/Conv2d_" + idx + "_depthwise";
var mappedPrefixDepthwiseConv = mappedPrefix + "/depthwise_conv";
var mappedPrefixPointwiseConv = mappedPrefix + "/pointwise_conv";
var filters = extractWeightEntry(prefixDepthwiseConv + "/depthwise_weights", 4, mappedPrefixDepthwiseConv + "/filters");
var batch_norm_scale = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/gamma", 1, mappedPrefixDepthwiseConv + "/batch_norm_scale");
var batch_norm_offset = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/beta", 1, mappedPrefixDepthwiseConv + "/batch_norm_offset");
var batch_norm_mean = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_mean", 1, mappedPrefixDepthwiseConv + "/batch_norm_mean");
var batch_norm_variance = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_variance", 1, mappedPrefixDepthwiseConv + "/batch_norm_variance");
return {
depthwise_conv: {
filters: filters,
batch_norm_scale: batch_norm_scale,
batch_norm_offset: batch_norm_offset,
batch_norm_mean: batch_norm_mean,
batch_norm_variance: batch_norm_variance
},
pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)
};
}
function extractMobilenetV1Params() {
return {
conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),
conv_1: extractConvPairParams(1),
conv_2: extractConvPairParams(2),
conv_3: extractConvPairParams(3),
conv_4: extractConvPairParams(4),
conv_5: extractConvPairParams(5),
conv_6: extractConvPairParams(6),
conv_7: extractConvPairParams(7),
conv_8: extractConvPairParams(8),
conv_9: extractConvPairParams(9),
conv_10: extractConvPairParams(10),
conv_11: extractConvPairParams(11),
conv_12: extractConvPairParams(12),
conv_13: extractConvPairParams(13)
};
}
function extractConvParams(prefix, mappedPrefix) {
var filters = extractWeightEntry(prefix + "/weights", 4, mappedPrefix + "/filters");
var bias = extractWeightEntry(prefix + "/biases", 1, mappedPrefix + "/bias");
return { filters: filters, bias: bias };
}
function extractBoxPredictorParams(idx) {
var box_encoding_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor", "prediction_layer/box_predictor_" + idx + "/box_encoding_predictor");
var class_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/ClassPredictor", "prediction_layer/box_predictor_" + idx + "/class_predictor");
return { box_encoding_predictor: box_encoding_predictor, class_predictor: class_predictor };
}
function extractPredictionLayerParams() {
return {
conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),
conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),
conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),
conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),
conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),
conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),
conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),
conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),
box_predictor_0: extractBoxPredictorParams(0),
box_predictor_1: extractBoxPredictorParams(1),
box_predictor_2: extractBoxPredictorParams(2),
box_predictor_3: extractBoxPredictorParams(3),
box_predictor_4: extractBoxPredictorParams(4),
box_predictor_5: extractBoxPredictorParams(5)
};
}
return {
extractMobilenetV1Params: extractMobilenetV1Params,
extractPredictionLayerParams: extractPredictionLayerParams
};
}
export 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*/, 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 (!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
}
};
disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }];
}
});
});
}
//# sourceMappingURL=loadQuantizedParams.js.map
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build/es6/faceDetectionNet/loadQuantizedParams.js.map deleted 100644 → 0
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build/es6/faceDetectionNet/mobileNetV1.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { MobileNetV1 } from './types';
export declare function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params): {
out: tf.Tensor<tf.Rank.R4>;
conv11: any;
};
build/es6/faceDetectionNet/mobileNetV1.js deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { pointwiseConvLayer } from './pointwiseConvLayer';
var epsilon = 0.0010000000474974513;
function depthwiseConvLayer(x, params, strides) {
return tf.tidy(function () {
var out = tf.depthwiseConv2d(x, params.filters, strides, 'same');
out = tf.batchNormalization(out, params.batch_norm_mean, params.batch_norm_variance, epsilon, params.batch_norm_scale, params.batch_norm_offset);
return tf.clipByValue(out, 0, 6);
});
}
function getStridesForLayerIdx(layerIdx) {
return [2, 4, 6, 12].some(function (idx) { return idx === layerIdx; }) ? [2, 2] : [1, 1];
}
export function mobileNetV1(x, params) {
return tf.tidy(function () {
var conv11 = null;
var out = pointwiseConvLayer(x, params.conv_0, [2, 2]);
var convPairParams = [
params.conv_1,
params.conv_2,
params.conv_3,
params.conv_4,
params.conv_5,
params.conv_6,
params.conv_7,
params.conv_8,
params.conv_9,
params.conv_10,
params.conv_11,
params.conv_12,
params.conv_13
];
convPairParams.forEach(function (param, i) {
var layerIdx = i + 1;
var depthwiseConvStrides = getStridesForLayerIdx(layerIdx);
out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);
out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);
if (layerIdx === 11) {
conv11 = out;
}
});
if (conv11 === null) {
throw new Error('mobileNetV1 - output of conv layer 11 is null');
}
return {
out: out,
conv11: conv11
};
});
}
//# sourceMappingURL=mobileNetV1.js.map
\ No newline at end of file
build/es6/faceDetectionNet/mobileNetV1.js.map deleted 100644 → 0
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build/es6/faceDetectionNet/nonMaxSuppression.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
export declare function nonMaxSuppression(boxes: tf.Tensor2D, scores: number[], maxOutputSize: number, iouThreshold: number, scoreThreshold: number): number[];
build/es6/faceDetectionNet/nonMaxSuppression.js deleted 100644 → 0
export function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
var numBoxes = boxes.shape[0];
var outputSize = Math.min(maxOutputSize, numBoxes);
var candidates = scores
.map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })
.filter(function (c) { return c.score > scoreThreshold; })
.sort(function (c1, c2) { return c2.score - c1.score; });
var suppressFunc = function (x) { return x <= iouThreshold ? 1 : 0; };
var selected = [];
candidates.forEach(function (c) {
if (selected.length >= outputSize) {
return;
}
var originalScore = c.score;
for (var j = selected.length - 1; j >= 0; --j) {
var iou = IOU(boxes, c.boxIndex, selected[j]);
if (iou === 0.0) {
continue;
}
c.score *= suppressFunc(iou);
if (c.score <= scoreThreshold) {
break;
}
}
if (originalScore === c.score) {
selected.push(c.boxIndex);
}
});
return selected;
}
function IOU(boxes, i, j) {
var yminI = Math.min(boxes.get(i, 0), boxes.get(i, 2));
var xminI = Math.min(boxes.get(i, 1), boxes.get(i, 3));
var ymaxI = Math.max(boxes.get(i, 0), boxes.get(i, 2));
var xmaxI = Math.max(boxes.get(i, 1), boxes.get(i, 3));
var yminJ = Math.min(boxes.get(j, 0), boxes.get(j, 2));
var xminJ = Math.min(boxes.get(j, 1), boxes.get(j, 3));
var ymaxJ = Math.max(boxes.get(j, 0), boxes.get(j, 2));
var xmaxJ = Math.max(boxes.get(j, 1), boxes.get(j, 3));
var areaI = (ymaxI - yminI) * (xmaxI - xminI);
var areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
if (areaI <= 0 || areaJ <= 0) {
return 0.0;
}
var intersectionYmin = Math.max(yminI, yminJ);
var intersectionXmin = Math.max(xminI, xminJ);
var intersectionYmax = Math.min(ymaxI, ymaxJ);
var intersectionXmax = Math.min(xmaxI, xmaxJ);
var intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *
Math.max(intersectionXmax - intersectionXmin, 0.0);
return intersectionArea / (areaI + areaJ - intersectionArea);
}
//# sourceMappingURL=nonMaxSuppression.js.map
\ No newline at end of file
build/es6/faceDetectionNet/nonMaxSuppression.js.map deleted 100644 → 0
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\ No newline at end of file
build/es6/faceDetectionNet/outputLayer.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { OutputLayerParams } from './types';
export declare function outputLayer(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams): {
boxes: tf.Tensor<tf.Rank.R2>[];
scores: tf.Tensor<tf.Rank.R1>[];
};
build/es6/faceDetectionNet/outputLayer.js deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
function getCenterCoordinatesAndSizesLayer(x) {
var vec = tf.unstack(tf.transpose(x, [1, 0]));
var sizes = [
tf.sub(vec[2], vec[0]),
tf.sub(vec[3], vec[1])
];
var centers = [
tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),
tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))
];
return {
sizes: sizes,
centers: centers
};
}
function decodeBoxesLayer(x0, x1) {
var _a = getCenterCoordinatesAndSizesLayer(x0), sizes = _a.sizes, centers = _a.centers;
var vec = tf.unstack(tf.transpose(x1, [1, 0]));
var div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));
var add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);
var div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));
var add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);
return tf.transpose(tf.stack([
tf.sub(add0_out, div0_out),
tf.sub(add1_out, div1_out),
tf.add(add0_out, div0_out),
tf.add(add1_out, div1_out)
]), [1, 0]);
}
export function outputLayer(boxPredictions, classPredictions, params) {
return tf.tidy(function () {
var batchSize = boxPredictions.shape[0];
var boxes = decodeBoxesLayer(tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), tf.reshape(boxPredictions, [-1, 4]));
boxes = tf.reshape(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);
var scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));
var scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]);
scores = tf.reshape(scores, [batchSize, scores.shape[1]]);
var boxesByBatch = tf.unstack(boxes);
var scoresByBatch = tf.unstack(scores);
return {
boxes: boxesByBatch,
scores: scoresByBatch
};
});
}
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build/es6/faceDetectionNet/pointwiseConvLayer.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { PointwiseConvParams } from './types';
export declare function pointwiseConvLayer(x: tf.Tensor4D, params: PointwiseConvParams, strides: [number, number]): tf.Tensor<tf.Rank.R4>;
build/es6/faceDetectionNet/pointwiseConvLayer.js deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
export function pointwiseConvLayer(x, params, strides) {
return tf.tidy(function () {
var out = tf.conv2d(x, params.filters, strides, 'same');
out = tf.add(out, params.batch_norm_offset);
return tf.clipByValue(out, 0, 6);
});
}
//# sourceMappingURL=pointwiseConvLayer.js.map
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build/es6/faceDetectionNet/predictionLayer.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { PredictionLayerParams } from './types';
export declare function predictionLayer(x: tf.Tensor4D, conv11: tf.Tensor4D, params: PredictionLayerParams): {
boxPredictions: tf.Tensor<tf.Rank.R4>;
classPredictions: tf.Tensor<tf.Rank.R4>;
};
build/es6/faceDetectionNet/predictionLayer.js deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { boxPredictionLayer } from './boxPredictionLayer';
import { pointwiseConvLayer } from './pointwiseConvLayer';
export function predictionLayer(x, conv11, params) {
return tf.tidy(function () {
var conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);
var conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);
var conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);
var conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);
var conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);
var conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);
var conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);
var conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);
var boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);
var boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);
var boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);
var boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);
var boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);
var boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);
var boxPredictions = tf.concat([
boxPrediction0.boxPredictionEncoding,
boxPrediction1.boxPredictionEncoding,
boxPrediction2.boxPredictionEncoding,
boxPrediction3.boxPredictionEncoding,
boxPrediction4.boxPredictionEncoding,
boxPrediction5.boxPredictionEncoding
], 1);
var classPredictions = tf.concat([
boxPrediction0.classPrediction,
boxPrediction1.classPrediction,
boxPrediction2.classPrediction,
boxPrediction3.classPrediction,
boxPrediction4.classPrediction,
boxPrediction5.classPrediction
], 1);
return {
boxPredictions: boxPredictions,
classPredictions: classPredictions
};
});
}
//# sourceMappingURL=predictionLayer.js.map
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build/es6/faceDetectionNet/predictionLayer.js.map deleted 100644 → 0
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build/es6/faceDetectionNet/types.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from 'tfjs-tiny-yolov2';
export declare type PointwiseConvParams = {
filters: tf.Tensor4D;
batch_norm_offset: tf.Tensor1D;
};
export declare namespace MobileNetV1 {
type DepthwiseConvParams = {
filters: tf.Tensor4D;
batch_norm_scale: tf.Tensor1D;
batch_norm_offset: tf.Tensor1D;
batch_norm_mean: tf.Tensor1D;
batch_norm_variance: tf.Tensor1D;
};
type ConvPairParams = {
depthwise_conv: DepthwiseConvParams;
pointwise_conv: PointwiseConvParams;
};
type Params = {
conv_0: PointwiseConvParams;
conv_1: ConvPairParams;
conv_2: ConvPairParams;
conv_3: ConvPairParams;
conv_4: ConvPairParams;
conv_5: ConvPairParams;
conv_6: ConvPairParams;
conv_7: ConvPairParams;
conv_8: ConvPairParams;
conv_9: ConvPairParams;
conv_10: ConvPairParams;
conv_11: ConvPairParams;
conv_12: ConvPairParams;
conv_13: ConvPairParams;
};
}
export declare type BoxPredictionParams = {
box_encoding_predictor: ConvParams;
class_predictor: ConvParams;
};
export declare type PredictionLayerParams = {
conv_0: PointwiseConvParams;
conv_1: PointwiseConvParams;
conv_2: PointwiseConvParams;
conv_3: PointwiseConvParams;
conv_4: PointwiseConvParams;
conv_5: PointwiseConvParams;
conv_6: PointwiseConvParams;
conv_7: PointwiseConvParams;
box_predictor_0: BoxPredictionParams;
box_predictor_1: BoxPredictionParams;
box_predictor_2: BoxPredictionParams;
box_predictor_3: BoxPredictionParams;
box_predictor_4: BoxPredictionParams;
box_predictor_5: BoxPredictionParams;
};
export declare type OutputLayerParams = {
extra_dim: tf.Tensor3D;
};
export declare type NetParams = {
mobilenetv1: MobileNetV1.Params;
prediction_layer: PredictionLayerParams;
output_layer: OutputLayerParams;
};
build/es6/faceDetectionNet/types.js deleted 100644 → 0
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build/es6/faceDetectionNet/types.js.map deleted 100644 → 0
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build/es6/globalApi.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { NetInput, TNetInput } from 'tfjs-image-recognition-base';
import { TinyYolov2Types } from 'tfjs-tiny-yolov2';
import { FaceDetection } from './classes/FaceDetection';
import { FaceLandmarks68 } from './classes/FaceLandmarks68';
import { FullFaceDescription } from './classes/FullFaceDescription';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmark68Net } from './faceLandmarkNet/FaceLandmark68Net';
import { FaceLandmark68TinyNet } from './faceLandmarkNet/FaceLandmark68TinyNet';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { Mtcnn } from './mtcnn/Mtcnn';
import { MtcnnForwardParams, MtcnnResult } from './mtcnn/types';
import { TinyYolov2 } from './tinyYolov2/TinyYolov2';
export declare const detectionNet: FaceDetectionNet;
export declare const landmarkNet: FaceLandmark68Net;
export declare const recognitionNet: FaceRecognitionNet;
export declare const nets: {
ssdMobilenetv1: FaceDetectionNet;
faceLandmark68Net: FaceLandmark68Net;
faceLandmark68TinyNet: FaceLandmark68TinyNet;
faceRecognitionNet: FaceRecognitionNet;
mtcnn: Mtcnn;
tinyYolov2: TinyYolov2;
};
export declare function loadSsdMobilenetv1Model(url: string): Promise<void>;
export declare function loadFaceLandmarkModel(url: string): Promise<void>;
export declare function loadFaceLandmarkTinyModel(url: string): Promise<void>;
export declare function loadFaceRecognitionModel(url: string): Promise<void>;
export declare function loadMtcnnModel(url: string): Promise<void>;
export declare function loadTinyYolov2Model(url: string): Promise<void>;
export declare function loadFaceDetectionModel(url: string): Promise<void>;
export declare function loadModels(url: string): Promise<[void, void, void, void, void]>;
export declare function locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
export declare const ssdMobilenetv1: typeof locateFaces;
export declare function detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>;
export declare function detectLandmarksTiny(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>;
export declare function computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>;
export declare function mtcnn(input: TNetInput, forwardParams: MtcnnForwardParams): Promise<MtcnnResult[]>;
export declare function tinyYolov2(input: TNetInput, forwardParams: TinyYolov2Types.TinyYolov2ForwardParams): Promise<FaceDetection[]>;
export declare type allFacesSsdMobilenetv1Function = (input: tf.Tensor | NetInput | TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare const allFacesSsdMobilenetv1: allFacesSsdMobilenetv1Function;
export declare type allFacesTinyYolov2Function = (input: tf.Tensor | NetInput | TNetInput, forwardParams?: TinyYolov2Types.TinyYolov2ForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare const allFacesTinyYolov2: allFacesTinyYolov2Function;
export declare type allFacesMtcnnFunction = (input: tf.Tensor | NetInput | TNetInput, mtcnnForwardParams?: MtcnnForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare const allFacesMtcnn: allFacesMtcnnFunction;
export declare const allFaces: allFacesSsdMobilenetv1Function;
build/es6/globalApi.js deleted 100644 → 0
import { allFacesMtcnnFactory, allFacesSsdMobilenetv1Factory, allFacesTinyYolov2Factory } from './allFacesFactory';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmark68Net } from './faceLandmarkNet/FaceLandmark68Net';
import { FaceLandmark68TinyNet } from './faceLandmarkNet/FaceLandmark68TinyNet';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { Mtcnn } from './mtcnn/Mtcnn';
import { TinyYolov2 } from './tinyYolov2/TinyYolov2';
export var detectionNet = new FaceDetectionNet();
export var landmarkNet = new FaceLandmark68Net();
export var recognitionNet = new FaceRecognitionNet();
// nets need more specific names, to avoid ambiguity in future
// when alternative net implementations are provided
export var nets = {
ssdMobilenetv1: detectionNet,
faceLandmark68Net: landmarkNet,
faceLandmark68TinyNet: new FaceLandmark68TinyNet(),
faceRecognitionNet: recognitionNet,
mtcnn: new Mtcnn(),
tinyYolov2: new TinyYolov2()
};
export function loadSsdMobilenetv1Model(url) {
return nets.ssdMobilenetv1.load(url);
}
export function loadFaceLandmarkModel(url) {
return nets.faceLandmark68Net.load(url);
}
export function loadFaceLandmarkTinyModel(url) {
return nets.faceLandmark68TinyNet.load(url);
}
export function loadFaceRecognitionModel(url) {
return nets.faceRecognitionNet.load(url);
}
export function loadMtcnnModel(url) {
return nets.mtcnn.load(url);
}
export function loadTinyYolov2Model(url) {
return nets.tinyYolov2.load(url);
}
export function loadFaceDetectionModel(url) {
return loadSsdMobilenetv1Model(url);
}
export function loadModels(url) {
console.warn('loadModels will be deprecated in future');
return Promise.all([
loadSsdMobilenetv1Model(url),
loadFaceLandmarkModel(url),
loadFaceRecognitionModel(url),
loadMtcnnModel(url),
loadTinyYolov2Model(url)
]);
}
export function locateFaces(input, minConfidence, maxResults) {
return nets.ssdMobilenetv1.locateFaces(input, minConfidence, maxResults);
}
export var ssdMobilenetv1 = locateFaces;
export function detectLandmarks(input) {
return nets.faceLandmark68Net.detectLandmarks(input);
}
export function detectLandmarksTiny(input) {
return nets.faceLandmark68TinyNet.detectLandmarks(input);
}
export function computeFaceDescriptor(input) {
return nets.faceRecognitionNet.computeFaceDescriptor(input);
}
export function mtcnn(input, forwardParams) {
return nets.mtcnn.forward(input, forwardParams);
}
export function tinyYolov2(input, forwardParams) {
return nets.tinyYolov2.locateFaces(input, forwardParams);
}
export var allFacesSsdMobilenetv1 = allFacesSsdMobilenetv1Factory(nets.ssdMobilenetv1, nets.faceLandmark68Net, nets.faceRecognitionNet);
export var allFacesTinyYolov2 = allFacesTinyYolov2Factory(nets.tinyYolov2, nets.faceLandmark68Net, nets.faceRecognitionNet);
export var allFacesMtcnn = allFacesMtcnnFactory(nets.mtcnn, nets.faceRecognitionNet);
export var allFaces = allFacesSsdMobilenetv1;
//# sourceMappingURL=globalApi.js.map
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build/es6/globalApi.js.map deleted 100644 → 0
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\ No newline at end of file
build/es6/mtcnn/getDefaultMtcnnForwardParams.d.ts deleted 100644 → 0
export declare function getDefaultMtcnnForwardParams(): {
minFaceSize: number;
scaleFactor: number;
maxNumScales: number;
scoreThresholds: number[];
};
build/es6/mtcnn/getDefaultMtcnnForwardParams.js deleted 100644 → 0
export function getDefaultMtcnnForwardParams() {
return {
minFaceSize: 20,
scaleFactor: 0.709,
maxNumScales: 10,
scoreThresholds: [0.6, 0.7, 0.7]
};
}
//# sourceMappingURL=getDefaultMtcnnForwardParams.js.map
\ No newline at end of file
build/es6/mtcnn/getDefaultMtcnnForwardParams.js.map deleted 100644 → 0
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\ No newline at end of file
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