face-api.js
Version:
JavaScript API for face detection and face recognition in the browser with tensorflow.js
93 lines • 4.79 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 NeuralNetwork_1 = require("../NeuralNetwork");
var ops_1 = require("../ops");
var convLayer_1 = require("./convLayer");
var extractParams_1 = require("./extractParams");
var extractParamsFromWeigthMap_1 = require("./extractParamsFromWeigthMap");
var residualLayer_1 = require("./residualLayer");
var FaceRecognitionNet = /** @class */ (function (_super) {
tslib_1.__extends(FaceRecognitionNet, _super);
function FaceRecognitionNet() {
return _super.call(this, 'FaceRecognitionNet') || this;
}
FaceRecognitionNet.prototype.forwardInput = function (input) {
var params = this.params;
if (!params) {
throw new Error('FaceRecognitionNet - load model before inference');
}
return tf.tidy(function () {
var batchTensor = input.toBatchTensor(150, true).toFloat();
var meanRgb = [122.782, 117.001, 104.298];
var normalized = ops_1.normalize(batchTensor, meanRgb).div(tf.scalar(256));
var out = convLayer_1.convDown(normalized, params.conv32_down);
out = tf.maxPool(out, 3, 2, 'valid');
out = residualLayer_1.residual(out, params.conv32_1);
out = residualLayer_1.residual(out, params.conv32_2);
out = residualLayer_1.residual(out, params.conv32_3);
out = residualLayer_1.residualDown(out, params.conv64_down);
out = residualLayer_1.residual(out, params.conv64_1);
out = residualLayer_1.residual(out, params.conv64_2);
out = residualLayer_1.residual(out, params.conv64_3);
out = residualLayer_1.residualDown(out, params.conv128_down);
out = residualLayer_1.residual(out, params.conv128_1);
out = residualLayer_1.residual(out, params.conv128_2);
out = residualLayer_1.residualDown(out, params.conv256_down);
out = residualLayer_1.residual(out, params.conv256_1);
out = residualLayer_1.residual(out, params.conv256_2);
out = residualLayer_1.residualDown(out, params.conv256_down_out);
var globalAvg = out.mean([1, 2]);
var fullyConnected = tf.matMul(globalAvg, params.fc);
return fullyConnected;
});
};
FaceRecognitionNet.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()])];
}
});
});
};
FaceRecognitionNet.prototype.computeFaceDescriptor = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var netInput, faceDescriptorTensors, faceDescriptorsForBatch;
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();
faceDescriptorTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); });
return [4 /*yield*/, Promise.all(faceDescriptorTensors.map(function (t) { return t.data(); }))];
case 2:
faceDescriptorsForBatch = _a.sent();
faceDescriptorTensors.forEach(function (t) { return t.dispose(); });
return [2 /*return*/, netInput.isBatchInput
? faceDescriptorsForBatch
: faceDescriptorsForBatch[0]];
}
});
});
};
FaceRecognitionNet.prototype.getDefaultModelName = function () {
return 'face_recognition_model';
};
FaceRecognitionNet.prototype.extractParamsFromWeigthMap = function (weightMap) {
return extractParamsFromWeigthMap_1.extractParamsFromWeigthMap(weightMap);
};
FaceRecognitionNet.prototype.extractParams = function (weights) {
return extractParams_1.extractParams(weights);
};
return FaceRecognitionNet;
}(NeuralNetwork_1.NeuralNetwork));
exports.FaceRecognitionNet = FaceRecognitionNet;
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