"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 FaceLandmarks68_1 = require("../classes/FaceLandmarks68");
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var dom_1 = require("../dom");
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var FaceProcessor_1 = require("../faceProcessor/FaceProcessor");
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var utils_1 = require("../utils");
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var FaceLandmark68NetBase = /** @class */ (function (_super) {
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tslib_1.__extends(FaceLandmark68NetBase, _super);
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function FaceLandmark68NetBase() {
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return _super !== null && _super.apply(this, arguments) || this;
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}
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FaceLandmark68NetBase.prototype.postProcess = function (output, inputSize, originalDimensions) {
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var inputDimensions = originalDimensions.map(function (_a) {
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var width = _a.width, height = _a.height;
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var scale = inputSize / Math.max(height, width);
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return {
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width: width * scale,
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height: height * scale
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};
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});
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var batchSize = inputDimensions.length;
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return tf.tidy(function () {
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var createInterleavedTensor = function (fillX, fillY) {
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return tf.stack([
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tf.fill([68], fillX),
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tf.fill([68], fillY)
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], 1).as2D(1, 136).as1D();
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};
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var getPadding = function (batchIdx, cond) {
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var _a = inputDimensions[batchIdx], width = _a.width, height = _a.height;
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return cond(width, height) ? Math.abs(width - height) / 2 : 0;
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};
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var getPaddingX = function (batchIdx) { return getPadding(batchIdx, function (w, h) { return w < h; }); };
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var getPaddingY = function (batchIdx) { return getPadding(batchIdx, function (w, h) { return h < w; }); };
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var landmarkTensors = output
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.mul(tf.fill([batchSize, 136], inputSize))
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.sub(tf.stack(Array.from(Array(batchSize), function (_, batchIdx) {
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return createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx));
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})))
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.div(tf.stack(Array.from(Array(batchSize), function (_, batchIdx) {
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return createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height);
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})));
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return landmarkTensors;
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});
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};
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FaceLandmark68NetBase.prototype.forwardInput = function (input) {
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var _this = this;
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return tf.tidy(function () {
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var out = _this.runNet(input);
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return _this.postProcess(out, input.inputSize, input.inputDimensions.map(function (_a) {
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var height = _a[0], width = _a[1];
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return ({ height: height, width: width });
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}));
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});
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};
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FaceLandmark68NetBase.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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FaceLandmark68NetBase.prototype.detectLandmarks = function (input) {
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return tslib_1.__awaiter(this, void 0, void 0, function () {
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var netInput, landmarkTensors, landmarksForBatch;
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var _this = this;
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return tslib_1.__generator(this, function (_a) {
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switch (_a.label) {
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case 0: return [4 /*yield*/, dom_1.toNetInput(input)];
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case 1:
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netInput = _a.sent();
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landmarkTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); });
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return [4 /*yield*/, Promise.all(landmarkTensors.map(function (landmarkTensor, batchIdx) { return tslib_1.__awaiter(_this, void 0, void 0, function () {
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var landmarksArray, _a, _b, xCoords, yCoords;
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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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_b = (_a = Array).from;
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return [4 /*yield*/, landmarkTensor.data()];
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case 1:
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landmarksArray = _b.apply(_a, [_c.sent()]);
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xCoords = landmarksArray.filter(function (_, i) { return utils_1.isEven(i); });
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yCoords = landmarksArray.filter(function (_, i) { return !utils_1.isEven(i); });
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return [2 /*return*/, new FaceLandmarks68_1.FaceLandmarks68(Array(68).fill(0).map(function (_, i) { return new classes_1.Point(xCoords[i], yCoords[i]); }), {
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height: netInput.getInputHeight(batchIdx),
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width: netInput.getInputWidth(batchIdx),
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})];
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}
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});
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}); }))];
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case 2:
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landmarksForBatch = _a.sent();
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landmarkTensors.forEach(function (t) { return t.dispose(); });
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return [2 /*return*/, netInput.isBatchInput
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? landmarksForBatch
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: landmarksForBatch[0]];
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}
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});
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});
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};
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FaceLandmark68NetBase.prototype.getClassifierChannelsOut = function () {
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return 136;
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};
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return FaceLandmark68NetBase;
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}(FaceProcessor_1.FaceProcessor));
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exports.FaceLandmark68NetBase = FaceLandmark68NetBase;
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//# sourceMappingURL=FaceLandmark68NetBase.js.map
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