face-api/build/dom/NetInput.js

import * as tf from '@tensorflow/tfjs-core';
import { env } from '../env';
import { padToSquare } from '../ops/padToSquare';
import { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';
import { createCanvasFromMedia } from './createCanvas';
import { imageToSquare } from './imageToSquare';
export class NetInput {
    constructor(inputs, treatAsBatchInput = false) {
        this._imageTensors = [];
        this._canvases = [];
        this._treatAsBatchInput = false;
        this._inputDimensions = [];
        if (!Array.isArray(inputs)) {
            throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);
        }
        this._treatAsBatchInput = treatAsBatchInput;
        this._batchSize = inputs.length;
        inputs.forEach((input, idx) => {
            if (isTensor3D(input)) {
                this._imageTensors[idx] = input;
                this._inputDimensions[idx] = input.shape;
                return;
            }
            if (isTensor4D(input)) {
                const batchSize = input.shape[0];
                if (batchSize !== 1) {
                    throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);
                }
                this._imageTensors[idx] = input;
                this._inputDimensions[idx] = input.shape.slice(1);
                return;
            }
            const canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);
            this._canvases[idx] = canvas;
            this._inputDimensions[idx] = [canvas.height, canvas.width, 3];
        });
    }
    get imageTensors() {
        return this._imageTensors;
    }
    get canvases() {
        return this._canvases;
    }
    get isBatchInput() {
        return this.batchSize > 1 || this._treatAsBatchInput;
    }
    get batchSize() {
        return this._batchSize;
    }
    get inputDimensions() {
        return this._inputDimensions;
    }
    get inputSize() {
        return this._inputSize;
    }
    get reshapedInputDimensions() {
        return range(this.batchSize, 0, 1).map((_, batchIdx) => this.getReshapedInputDimensions(batchIdx));
    }
    getInput(batchIdx) {
        return this.canvases[batchIdx] || this.imageTensors[batchIdx];
    }
    getInputDimensions(batchIdx) {
        return this._inputDimensions[batchIdx];
    }
    getInputHeight(batchIdx) {
        return this._inputDimensions[batchIdx][0];
    }
    getInputWidth(batchIdx) {
        return this._inputDimensions[batchIdx][1];
    }
    getReshapedInputDimensions(batchIdx) {
        if (typeof this.inputSize !== 'number') {
            throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');
        }
        const width = this.getInputWidth(batchIdx);
        const height = this.getInputHeight(batchIdx);
        return computeReshapedDimensions({ width, height }, this.inputSize);
    }
    /**
     * Create a batch tensor from all input canvases and tensors
     * with size [batchSize, inputSize, inputSize, 3].
     *
     * @param inputSize Height and width of the tensor.
     * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on
     * both sides of the minor dimension oof the image.
     * @returns The batch tensor.
     */
    toBatchTensor(inputSize, isCenterInputs = true) {
        this._inputSize = inputSize;
        return tf.tidy(() => {
            const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {
                const input = this.getInput(batchIdx);
                if (input instanceof tf.Tensor) {
                    // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.
                    let imgTensor = isTensor4D(input) ? input : input.expandDims();
                    // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.
                    imgTensor = padToSquare(imgTensor, isCenterInputs);
                    if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {
                        imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);
                    }
                    return imgTensor.as3D(inputSize, inputSize, 3);
                }
                if (input instanceof env.getEnv().Canvas) {
                    return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));
                }
                throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);
            });
            const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3);
            return batchTensor;
        });
    }
}
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