face-api.js
Version:
JavaScript API for face detection and face recognition in the browser with tensorflow.js
80 lines • 3.96 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var dom_1 = require("../dom");
var FaceFeatureExtractor_1 = require("../faceFeatureExtractor/FaceFeatureExtractor");
var FaceProcessor_1 = require("../faceProcessor/FaceProcessor");
var FaceExpressions_1 = require("./FaceExpressions");
var FaceExpressionNet = /** @class */ (function (_super) {
tslib_1.__extends(FaceExpressionNet, _super);
function FaceExpressionNet(faceFeatureExtractor) {
if (faceFeatureExtractor === void 0) { faceFeatureExtractor = new FaceFeatureExtractor_1.FaceFeatureExtractor(); }
return _super.call(this, 'FaceExpressionNet', faceFeatureExtractor) || this;
}
FaceExpressionNet.prototype.forwardInput = function (input) {
var _this = this;
return tf.tidy(function () { return tf.softmax(_this.runNet(input)); });
};
FaceExpressionNet.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*/, dom_1.toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
});
};
FaceExpressionNet.prototype.predictExpressions = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var netInput, out, probabilitesByBatch, predictionsByBatch;
var _this = this;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, dom_1.toNetInput(input)];
case 1:
netInput = _a.sent();
return [4 /*yield*/, this.forwardInput(netInput)];
case 2:
out = _a.sent();
return [4 /*yield*/, Promise.all(tf.unstack(out).map(function (t) { return tslib_1.__awaiter(_this, void 0, void 0, function () {
var data;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, t.data()];
case 1:
data = _a.sent();
t.dispose();
return [2 /*return*/, data];
}
});
}); }))];
case 3:
probabilitesByBatch = _a.sent();
out.dispose();
predictionsByBatch = probabilitesByBatch
.map(function (probabilites) { return new FaceExpressions_1.FaceExpressions(probabilites); });
return [2 /*return*/, netInput.isBatchInput
? predictionsByBatch
: predictionsByBatch[0]];
}
});
});
};
FaceExpressionNet.prototype.getDefaultModelName = function () {
return 'face_expression_model';
};
FaceExpressionNet.prototype.getClassifierChannelsIn = function () {
return 256;
};
FaceExpressionNet.prototype.getClassifierChannelsOut = function () {
return 7;
};
return FaceExpressionNet;
}(FaceProcessor_1.FaceProcessor));
exports.FaceExpressionNet = FaceExpressionNet;
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