84 lines
5.1 KiB
JavaScript
84 lines
5.1 KiB
JavaScript
"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.detectLandmarks = exports.locateFaces = exports.loadFaceDetectionModel = exports.loadAgeGenderModel = exports.loadFaceExpressionModel = exports.loadFaceRecognitionModel = exports.loadFaceLandmarkTinyModel = exports.loadFaceLandmarkModel = exports.loadTinyYolov2Model = exports.loadTinyFaceDetectorModel = exports.predictAgeAndGender = exports.recognizeFaceExpressions = exports.computeFaceDescriptor = exports.detectFaceLandmarksTiny = exports.detectFaceLandmarks = exports.tinyFaceDetector = exports.nets = void 0;
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const AgeGenderNet_1 = require("../ageGenderNet/AgeGenderNet");
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const FaceExpressionNet_1 = require("../faceExpressionNet/FaceExpressionNet");
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const FaceLandmark68Net_1 = require("../faceLandmarkNet/FaceLandmark68Net");
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const FaceLandmark68TinyNet_1 = require("../faceLandmarkNet/FaceLandmark68TinyNet");
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const FaceRecognitionNet_1 = require("../faceRecognitionNet/FaceRecognitionNet");
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const TinyFaceDetector_1 = require("../tinyFaceDetector/TinyFaceDetector");
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const tinyYolov2_1 = require("../tinyYolov2");
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exports.nets = {
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tinyFaceDetector: new TinyFaceDetector_1.TinyFaceDetector(),
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tinyYolov2: new tinyYolov2_1.TinyYolov2(),
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faceLandmark68Net: new FaceLandmark68Net_1.FaceLandmark68Net(),
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faceLandmark68TinyNet: new FaceLandmark68TinyNet_1.FaceLandmark68TinyNet(),
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faceRecognitionNet: new FaceRecognitionNet_1.FaceRecognitionNet(),
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faceExpressionNet: new FaceExpressionNet_1.FaceExpressionNet(),
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ageGenderNet: new AgeGenderNet_1.AgeGenderNet()
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};
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/**
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* Attempts to detect all faces in an image using the Tiny Face Detector.
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*
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* @param input The input image.
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* @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).
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* @returns Bounding box of each face with score.
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*/
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exports.tinyFaceDetector = (input, options) => exports.nets.tinyFaceDetector.locateFaces(input, options);
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/**
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* Detects the 68 point face landmark positions of the face shown in an image.
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*
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* @param inputs The face image extracted from the bounding box of a face. Can
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* also be an array of input images, which will be batch processed.
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* @returns 68 point face landmarks or array thereof in case of batch input.
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*/
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exports.detectFaceLandmarks = (input) => exports.nets.faceLandmark68Net.detectLandmarks(input);
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/**
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* Detects the 68 point face landmark positions of the face shown in an image
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* using a tinier version of the 68 point face landmark model, which is slightly
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* faster at inference, but also slightly less accurate.
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*
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* @param inputs The face image extracted from the bounding box of a face. Can
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* also be an array of input images, which will be batch processed.
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* @returns 68 point face landmarks or array thereof in case of batch input.
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*/
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exports.detectFaceLandmarksTiny = (input) => exports.nets.faceLandmark68TinyNet.detectLandmarks(input);
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/**
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* Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,
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* which uniquely represents the features of that persons face. The computed face descriptor can
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* be used to measure the similarity between faces, by computing the euclidean distance of two
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* face descriptors.
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*
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* @param inputs The face image extracted from the aligned bounding box of a face. Can
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* also be an array of input images, which will be batch processed.
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* @returns Face descriptor with 128 entries or array thereof in case of batch input.
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*/
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exports.computeFaceDescriptor = (input) => exports.nets.faceRecognitionNet.computeFaceDescriptor(input);
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/**
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* Recognizes the facial expressions from a face image.
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*
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* @param inputs The face image extracted from the bounding box of a face. Can
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* also be an array of input images, which will be batch processed.
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* @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.
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*/
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exports.recognizeFaceExpressions = (input) => exports.nets.faceExpressionNet.predictExpressions(input);
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/**
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* Predicts age and gender from a face image.
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*
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* @param inputs The face image extracted from the bounding box of a face. Can
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* also be an array of input images, which will be batch processed.
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* @returns Predictions with age, gender and gender probability or array thereof in case of batch input.
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*/
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exports.predictAgeAndGender = (input) => exports.nets.ageGenderNet.predictAgeAndGender(input);
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exports.loadTinyFaceDetectorModel = (url) => exports.nets.tinyFaceDetector.load(url);
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exports.loadTinyYolov2Model = (url) => exports.nets.tinyYolov2.load(url);
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exports.loadFaceLandmarkModel = (url) => exports.nets.faceLandmark68Net.load(url);
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exports.loadFaceLandmarkTinyModel = (url) => exports.nets.faceLandmark68TinyNet.load(url);
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exports.loadFaceRecognitionModel = (url) => exports.nets.faceRecognitionNet.load(url);
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exports.loadFaceExpressionModel = (url) => exports.nets.faceExpressionNet.load(url);
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exports.loadAgeGenderModel = (url) => exports.nets.ageGenderNet.load(url);
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// backward compatibility
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exports.loadFaceDetectionModel = exports.loadTinyFaceDetectorModel;
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exports.locateFaces = TinyFaceDetector_1.TinyFaceDetector;
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exports.detectLandmarks = exports.detectFaceLandmarks;
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//# sourceMappingURL=nets.js.map
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