docs: add quixzoom-auth-core product to AAMOS
- Product documentation in docs/products/ - Updated MEMORY.md with product info - quiXzoom Auth Core as AAMOS Identity product
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// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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import { TensorToDataUrlOptions, TensorToImageDataOptions } from './tensor-conversion.js';
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import { Tensor } from './tensor.js';
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/**
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* implementation of Tensor.toDataURL()
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*/
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export const tensorToDataURL = (tensor: Tensor, options?: TensorToDataUrlOptions): string => {
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const canvas = typeof document !== 'undefined' ? document.createElement('canvas') : new OffscreenCanvas(1, 1);
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canvas.width = tensor.dims[3];
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canvas.height = tensor.dims[2];
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const pixels2DContext = canvas.getContext('2d') as
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| CanvasRenderingContext2D
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| OffscreenCanvasRenderingContext2D
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| null;
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if (pixels2DContext != null) {
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// Default values for height and width & format
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let width: number;
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let height: number;
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if (options?.tensorLayout !== undefined && options.tensorLayout === 'NHWC') {
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width = tensor.dims[2];
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height = tensor.dims[3];
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} else {
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// Default layout is NCWH
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width = tensor.dims[3];
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height = tensor.dims[2];
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}
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const inputformat = options?.format !== undefined ? options.format : 'RGB';
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const norm = options?.norm;
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let normMean: [number, number, number, number];
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let normBias: [number, number, number, number];
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if (norm === undefined || norm.mean === undefined) {
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normMean = [255, 255, 255, 255];
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} else {
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if (typeof norm.mean === 'number') {
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normMean = [norm.mean, norm.mean, norm.mean, norm.mean];
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} else {
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normMean = [norm.mean[0], norm.mean[1], norm.mean[2], 0];
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if (norm.mean[3] !== undefined) {
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normMean[3] = norm.mean[3];
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}
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}
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}
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if (norm === undefined || norm.bias === undefined) {
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normBias = [0, 0, 0, 0];
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} else {
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if (typeof norm.bias === 'number') {
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normBias = [norm.bias, norm.bias, norm.bias, norm.bias];
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} else {
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normBias = [norm.bias[0], norm.bias[1], norm.bias[2], 0];
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if (norm.bias[3] !== undefined) {
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normBias[3] = norm.bias[3];
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}
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}
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}
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const stride = height * width;
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// Default pointer assignments
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let rTensorPointer = 0,
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gTensorPointer = stride,
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bTensorPointer = stride * 2,
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aTensorPointer = -1;
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// Updating the pointer assignments based on the input image format
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if (inputformat === 'RGBA') {
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rTensorPointer = 0;
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gTensorPointer = stride;
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bTensorPointer = stride * 2;
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aTensorPointer = stride * 3;
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} else if (inputformat === 'RGB') {
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rTensorPointer = 0;
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gTensorPointer = stride;
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bTensorPointer = stride * 2;
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} else if (inputformat === 'RBG') {
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rTensorPointer = 0;
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bTensorPointer = stride;
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gTensorPointer = stride * 2;
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}
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for (let i = 0; i < height; i++) {
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for (let j = 0; j < width; j++) {
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const R = ((tensor.data[rTensorPointer++] as number) - normBias[0]) * normMean[0]; // R value
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const G = ((tensor.data[gTensorPointer++] as number) - normBias[1]) * normMean[1]; // G value
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const B = ((tensor.data[bTensorPointer++] as number) - normBias[2]) * normMean[2]; // B value
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const A = aTensorPointer === -1 ? 255 : ((tensor.data[aTensorPointer++] as number) - normBias[3]) * normMean[3]; // A value
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pixels2DContext.fillStyle = 'rgba(' + R + ',' + G + ',' + B + ',' + A + ')';
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pixels2DContext.fillRect(j, i, 1, 1);
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}
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}
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if ('toDataURL' in canvas) {
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return canvas.toDataURL();
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} else {
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throw new Error('toDataURL is not supported');
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}
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} else {
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throw new Error('Can not access image data');
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}
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};
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/**
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* implementation of Tensor.toImageData()
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*/
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export const tensorToImageData = (tensor: Tensor, options?: TensorToImageDataOptions): ImageData => {
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const pixels2DContext =
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typeof document !== 'undefined'
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? document.createElement('canvas').getContext('2d')
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: (new OffscreenCanvas(1, 1).getContext('2d') as OffscreenCanvasRenderingContext2D);
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let image: ImageData;
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if (pixels2DContext != null) {
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// Default values for height and width & format
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let width: number;
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let height: number;
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let channels: number;
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if (options?.tensorLayout !== undefined && options.tensorLayout === 'NHWC') {
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width = tensor.dims[2];
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height = tensor.dims[1];
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channels = tensor.dims[3];
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} else {
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// Default layout is NCWH
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width = tensor.dims[3];
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height = tensor.dims[2];
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channels = tensor.dims[1];
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}
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const inputformat = options !== undefined ? (options.format !== undefined ? options.format : 'RGB') : 'RGB';
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const norm = options?.norm;
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let normMean: [number, number, number, number];
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let normBias: [number, number, number, number];
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if (norm === undefined || norm.mean === undefined) {
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normMean = [255, 255, 255, 255];
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} else {
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if (typeof norm.mean === 'number') {
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normMean = [norm.mean, norm.mean, norm.mean, norm.mean];
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} else {
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normMean = [norm.mean[0], norm.mean[1], norm.mean[2], 255];
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if (norm.mean[3] !== undefined) {
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normMean[3] = norm.mean[3];
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}
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}
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}
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if (norm === undefined || norm.bias === undefined) {
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normBias = [0, 0, 0, 0];
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} else {
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if (typeof norm.bias === 'number') {
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normBias = [norm.bias, norm.bias, norm.bias, norm.bias];
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} else {
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normBias = [norm.bias[0], norm.bias[1], norm.bias[2], 0];
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if (norm.bias[3] !== undefined) {
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normBias[3] = norm.bias[3];
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}
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}
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}
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const stride = height * width;
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if (options !== undefined) {
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if (
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(options.format !== undefined && channels === 4 && options.format !== 'RGBA') ||
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(channels === 3 && options.format !== 'RGB' && options.format !== 'BGR')
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) {
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throw new Error("Tensor format doesn't match input tensor dims");
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}
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}
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// Default pointer assignments
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const step = 4;
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let rImagePointer = 0,
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gImagePointer = 1,
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bImagePointer = 2,
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aImagePointer = 3;
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let rTensorPointer = 0,
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gTensorPointer = stride,
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bTensorPointer = stride * 2,
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aTensorPointer = -1;
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// Updating the pointer assignments based on the input image format
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if (inputformat === 'RGBA') {
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rTensorPointer = 0;
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gTensorPointer = stride;
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bTensorPointer = stride * 2;
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aTensorPointer = stride * 3;
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} else if (inputformat === 'RGB') {
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rTensorPointer = 0;
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gTensorPointer = stride;
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bTensorPointer = stride * 2;
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} else if (inputformat === 'RBG') {
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rTensorPointer = 0;
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bTensorPointer = stride;
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gTensorPointer = stride * 2;
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}
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image = pixels2DContext.createImageData(width, height);
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for (
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let i = 0;
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i < height * width;
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rImagePointer += step, gImagePointer += step, bImagePointer += step, aImagePointer += step, i++
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) {
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image.data[rImagePointer] = ((tensor.data[rTensorPointer++] as number) - normBias[0]) * normMean[0]; // R value
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image.data[gImagePointer] = ((tensor.data[gTensorPointer++] as number) - normBias[1]) * normMean[1]; // G value
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image.data[bImagePointer] = ((tensor.data[bTensorPointer++] as number) - normBias[2]) * normMean[2]; // B value
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image.data[aImagePointer] =
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aTensorPointer === -1 ? 255 : ((tensor.data[aTensorPointer++] as number) - normBias[3]) * normMean[3]; // A value
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}
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} else {
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throw new Error('Can not access image data');
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}
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return image;
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};
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