81 lines
3.5 KiB
TypeScript
81 lines
3.5 KiB
TypeScript
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/**
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* @license
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* Copyright 2019 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* https://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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import * as tfconv from '@tensorflow/tfjs-converter';
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import * as tf from '@tensorflow/tfjs-core';
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import { Box } from './box';
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export interface NormalizedFace {
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/** The upper left-hand corner of the face. */
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topLeft: [number, number] | tf.Tensor1D;
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/** The lower right-hand corner of the face. */
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bottomRight: [number, number] | tf.Tensor1D;
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/** Facial landmark coordinates. */
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landmarks?: number[][] | tf.Tensor2D;
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/** Probability of the face detection. */
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probability?: number | tf.Tensor1D;
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}
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export declare type BlazeFacePrediction = {
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box: Box;
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landmarks: tf.Tensor2D;
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probability: tf.Tensor1D;
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anchor: tf.Tensor2D | [number, number];
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};
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export declare class BlazeFaceModel {
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private blazeFaceModel;
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private width;
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private height;
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private maxFaces;
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private anchors;
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private anchorsData;
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private inputSize;
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private iouThreshold;
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private scoreThreshold;
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constructor(model: tfconv.GraphModel, width: number, height: number, maxFaces: number, iouThreshold: number, scoreThreshold: number);
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resizeAspectRatio(inputImage: tf.Tensor4D, width: number, height: number): {
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ratio: number;
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image: tf.Tensor<tf.Rank.R4>;
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};
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getBoundingBoxes(inputImage: tf.Tensor4D, returnTensors: boolean, annotateBoxes?: boolean): Promise<{
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boxes: Array<BlazeFacePrediction | Box>;
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scaleFactor: tf.Tensor | [number, number];
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}>;
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/**
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* Returns an array of faces in an image.
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*
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* @param input The image to classify. Can be a tensor, DOM element image,
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* video, or canvas.
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* @param returnTensors (defaults to `false`) Whether to return tensors as
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* opposed to values.
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* @param flipHorizontal Whether to flip/mirror the facial keypoints
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* horizontally. Should be true for videos that are flipped by default (e.g.
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* webcams).
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* @param annotateBoxes (defaults to `true`) Whether to annotate bounding
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* boxes with additional properties such as landmarks and probability. Pass in
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* `false` for faster inference if annotations are not needed.
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*
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* @return An array of detected faces, each with the following properties:
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* `topLeft`: the upper left coordinate of the face in the form `[x, y]`
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* `bottomRight`: the lower right coordinate of the face in the form `[x, y]`
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* `landmarks`: facial landmark coordinates
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* `probability`: the probability of the face being present
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*/
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estimateFaces(input: tf.Tensor3D | ImageData | HTMLVideoElement | HTMLImageElement | HTMLCanvasElement, returnTensors?: boolean, flipHorizontal?: boolean, annotateBoxes?: boolean): Promise<NormalizedFace[]>;
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/**
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* Dispose the WebGL memory held by the underlying model.
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*/
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dispose(): void;
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}
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