"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 dom_1 = require("../dom");
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var NeuralNetwork_1 = require("../NeuralNetwork");
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var extractParams_1 = require("./extractParams");
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var extractParamsFromWeigthMap_1 = require("./extractParamsFromWeigthMap");
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var mobileNetV1_1 = require("./mobileNetV1");
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var nonMaxSuppression_1 = require("./nonMaxSuppression");
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var outputLayer_1 = require("./outputLayer");
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var predictionLayer_1 = require("./predictionLayer");
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var SsdMobilenetv1Options_1 = require("./SsdMobilenetv1Options");
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var SsdMobilenetv1 = /** @class */ (function (_super) {
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tslib_1.__extends(SsdMobilenetv1, _super);
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function SsdMobilenetv1() {
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return _super.call(this, 'SsdMobilenetv1') || this;
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}
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SsdMobilenetv1.prototype.forwardInput = function (input) {
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var params = this.params;
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if (!params) {
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throw new Error('SsdMobilenetv1 - load model before inference');
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}
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return tf.tidy(function () {
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var batchTensor = input.toBatchTensor(512, false).toFloat();
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var x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1));
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var features = mobileNetV1_1.mobileNetV1(x, params.mobilenetv1);
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var _a = predictionLayer_1.predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
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return outputLayer_1.outputLayer(boxPredictions, classPredictions, params.output_layer);
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});
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};
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SsdMobilenetv1.prototype.forward = function (input) {
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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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}
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});
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});
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};
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SsdMobilenetv1.prototype.locateFaces = function (input, options) {
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if (options === void 0) { options = {}; }
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return tslib_1.__awaiter(this, void 0, void 0, function () {
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var _a, maxResults, minConfidence, netInput, _b, _boxes, _scores, boxes, scores, i, scoresData, _c, _d, iouThreshold, indices, reshapedDims, inputSize, padX, padY, boxesData, results;
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return tslib_1.__generator(this, function (_e) {
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switch (_e.label) {
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case 0:
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_a = new SsdMobilenetv1Options_1.SsdMobilenetv1Options(options), maxResults = _a.maxResults, minConfidence = _a.minConfidence;
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return [4 /*yield*/, dom_1.toNetInput(input)];
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case 1:
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netInput = _e.sent();
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_b = this.forwardInput(netInput), _boxes = _b.boxes, _scores = _b.scores;
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boxes = _boxes[0];
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scores = _scores[0];
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for (i = 1; i < _boxes.length; i++) {
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_boxes[i].dispose();
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_scores[i].dispose();
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}
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_d = (_c = Array).from;
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return [4 /*yield*/, scores.data()];
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case 2:
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scoresData = _d.apply(_c, [_e.sent()]);
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iouThreshold = 0.5;
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indices = nonMaxSuppression_1.nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
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reshapedDims = netInput.getReshapedInputDimensions(0);
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inputSize = netInput.inputSize;
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padX = inputSize / reshapedDims.width;
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padY = inputSize / reshapedDims.height;
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boxesData = boxes.arraySync();
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results = indices
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.map(function (idx) {
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var _a = [
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Math.max(0, boxesData[idx][0]),
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Math.min(1.0, boxesData[idx][2])
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].map(function (val) { return val * padY; }), top = _a[0], bottom = _a[1];
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var _b = [
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Math.max(0, boxesData[idx][1]),
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Math.min(1.0, boxesData[idx][3])
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].map(function (val) { return val * padX; }), left = _b[0], right = _b[1];
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return new FaceDetection_1.FaceDetection(scoresData[idx], new classes_1.Rect(left, top, right - left, bottom - top), {
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height: netInput.getInputHeight(0),
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width: netInput.getInputWidth(0)
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});
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});
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boxes.dispose();
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scores.dispose();
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return [2 /*return*/, results];
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}
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});
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});
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};
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SsdMobilenetv1.prototype.getDefaultModelName = function () {
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return 'ssd_mobilenetv1_model';
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};
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SsdMobilenetv1.prototype.extractParamsFromWeigthMap = function (weightMap) {
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return extractParamsFromWeigthMap_1.extractParamsFromWeigthMap(weightMap);
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};
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SsdMobilenetv1.prototype.extractParams = function (weights) {
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return extractParams_1.extractParams(weights);
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};
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return SsdMobilenetv1;
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}(NeuralNetwork_1.NeuralNetwork));
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exports.SsdMobilenetv1 = SsdMobilenetv1;
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//# sourceMappingURL=SsdMobilenetv1.js.map
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