"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
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var tslib_1 = require("tslib");
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var tf = require("@tensorflow/tfjs-core");
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var classes_1 = require("../classes");
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var FaceDetection_1 = require("../classes/FaceDetection");
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var FaceLandmarks5_1 = require("../classes/FaceLandmarks5");
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var dom_1 = require("../dom");
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var factories_1 = require("../factories");
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var NeuralNetwork_1 = require("../NeuralNetwork");
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var bgrToRgbTensor_1 = require("./bgrToRgbTensor");
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var config_1 = require("./config");
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var extractParams_1 = require("./extractParams");
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var extractParamsFromWeigthMap_1 = require("./extractParamsFromWeigthMap");
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var getSizesForScale_1 = require("./getSizesForScale");
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var MtcnnOptions_1 = require("./MtcnnOptions");
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var pyramidDown_1 = require("./pyramidDown");
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var stage1_1 = require("./stage1");
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var stage2_1 = require("./stage2");
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var stage3_1 = require("./stage3");
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var Mtcnn = /** @class */ (function (_super) {
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tslib_1.__extends(Mtcnn, _super);
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function Mtcnn() {
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return _super.call(this, 'Mtcnn') || this;
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}
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Mtcnn.prototype.load = function (weightsOrUrl) {
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return tslib_1.__awaiter(this, void 0, void 0, function () {
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return tslib_1.__generator(this, function (_a) {
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console.warn('mtcnn is deprecated and will be removed soon');
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return [2 /*return*/, _super.prototype.load.call(this, weightsOrUrl)];
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});
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});
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};
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Mtcnn.prototype.loadFromDisk = function (filePath) {
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return tslib_1.__awaiter(this, void 0, void 0, function () {
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return tslib_1.__generator(this, function (_a) {
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console.warn('mtcnn is deprecated and will be removed soon');
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return [2 /*return*/, _super.prototype.loadFromDisk.call(this, filePath)];
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});
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});
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};
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Mtcnn.prototype.forwardInput = function (input, forwardParams) {
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if (forwardParams === void 0) { forwardParams = {}; }
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return tslib_1.__awaiter(this, void 0, void 0, function () {
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var params, inputCanvas, stats, tsTotal, imgTensor, onReturn, _a, height, width, _b, minFaceSize, scaleFactor, maxNumScales, scoreThresholds, scaleSteps, scales, ts, out1, out2, out3, results;
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return tslib_1.__generator(this, function (_c) {
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switch (_c.label) {
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case 0:
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params = this.params;
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if (!params) {
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throw new Error('Mtcnn - load model before inference');
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}
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inputCanvas = input.canvases[0];
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if (!inputCanvas) {
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throw new Error('Mtcnn - inputCanvas is not defined, note that passing tensors into Mtcnn.forwardInput is not supported yet.');
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}
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stats = {};
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tsTotal = Date.now();
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imgTensor = tf.tidy(function () {
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return bgrToRgbTensor_1.bgrToRgbTensor(tf.expandDims(tf.browser.fromPixels(inputCanvas)).toFloat());
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});
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onReturn = function (results) {
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// dispose tensors on return
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imgTensor.dispose();
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stats.total = Date.now() - tsTotal;
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return results;
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};
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_a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
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_b = new MtcnnOptions_1.MtcnnOptions(forwardParams), minFaceSize = _b.minFaceSize, scaleFactor = _b.scaleFactor, maxNumScales = _b.maxNumScales, scoreThresholds = _b.scoreThresholds, scaleSteps = _b.scaleSteps;
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scales = (scaleSteps || pyramidDown_1.pyramidDown(minFaceSize, scaleFactor, [height, width]))
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.filter(function (scale) {
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var sizes = getSizesForScale_1.getSizesForScale(scale, [height, width]);
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return Math.min(sizes.width, sizes.height) > config_1.CELL_SIZE;
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})
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.slice(0, maxNumScales);
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stats.scales = scales;
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stats.pyramid = scales.map(function (scale) { return getSizesForScale_1.getSizesForScale(scale, [height, width]); });
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ts = Date.now();
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return [4 /*yield*/, stage1_1.stage1(imgTensor, scales, scoreThresholds[0], params.pnet, stats)];
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case 1:
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out1 = _c.sent();
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stats.total_stage1 = Date.now() - ts;
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if (!out1.boxes.length) {
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return [2 /*return*/, onReturn({ results: [], stats: stats })];
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}
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stats.stage2_numInputBoxes = out1.boxes.length;
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// using the inputCanvas to extract and resize the image patches, since it is faster
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// than doing this on the gpu
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ts = Date.now();
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return [4 /*yield*/, stage2_1.stage2(inputCanvas, out1.boxes, scoreThresholds[1], params.rnet, stats)];
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case 2:
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out2 = _c.sent();
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stats.total_stage2 = Date.now() - ts;
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if (!out2.boxes.length) {
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return [2 /*return*/, onReturn({ results: [], stats: stats })];
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}
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stats.stage3_numInputBoxes = out2.boxes.length;
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ts = Date.now();
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return [4 /*yield*/, stage3_1.stage3(inputCanvas, out2.boxes, scoreThresholds[2], params.onet, stats)];
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case 3:
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out3 = _c.sent();
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stats.total_stage3 = Date.now() - ts;
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results = out3.boxes.map(function (box, idx) { return factories_1.extendWithFaceLandmarks(factories_1.extendWithFaceDetection({}, new FaceDetection_1.FaceDetection(out3.scores[idx], new classes_1.Rect(box.left / width, box.top / height, box.width / width, box.height / height), {
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height: height,
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width: width
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})), new FaceLandmarks5_1.FaceLandmarks5(out3.points[idx].map(function (pt) { return pt.sub(new classes_1.Point(box.left, box.top)).div(new classes_1.Point(box.width, box.height)); }), { width: box.width, height: box.height })); });
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return [2 /*return*/, onReturn({ results: results, stats: stats })];
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}
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});
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});
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};
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Mtcnn.prototype.forward = function (input, forwardParams) {
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if (forwardParams === void 0) { forwardParams = {}; }
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return tslib_1.__awaiter(this, void 0, void 0, function () {
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var _a;
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return tslib_1.__generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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_a = this.forwardInput;
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return [4 /*yield*/, dom_1.toNetInput(input)];
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case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(),
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forwardParams])];
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case 2: return [2 /*return*/, (_b.sent()).results];
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}
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});
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});
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};
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Mtcnn.prototype.forwardWithStats = function (input, forwardParams) {
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if (forwardParams === void 0) { forwardParams = {}; }
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return tslib_1.__awaiter(this, void 0, void 0, function () {
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var _a;
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return tslib_1.__generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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_a = this.forwardInput;
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return [4 /*yield*/, dom_1.toNetInput(input)];
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case 1: return [2 /*return*/, _a.apply(this, [_b.sent(),
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forwardParams])];
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}
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});
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});
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};
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Mtcnn.prototype.getDefaultModelName = function () {
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return 'mtcnn_model';
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};
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Mtcnn.prototype.extractParamsFromWeigthMap = function (weightMap) {
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return extractParamsFromWeigthMap_1.extractParamsFromWeigthMap(weightMap);
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};
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Mtcnn.prototype.extractParams = function (weights) {
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return extractParams_1.extractParams(weights);
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};
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return Mtcnn;
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}(NeuralNetwork_1.NeuralNetwork));
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exports.Mtcnn = Mtcnn;
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//# sourceMappingURL=Mtcnn.js.map
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