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docs: add quixzoom-auth-core product to AAMOS

Browse Source 
- Product documentation in docs/products/
- Updated MEMORY.md with product info
- quiXzoom Auth Core as AAMOS Identity product
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Bernt
2026-07-14 09:58:53 +00:00
parent 58ca4e68db
commit 48ea61cdcc
2730 changed files with 293827 additions and 7213 deletions
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aamos-api-v1/node_modules/onnxruntime-common/dist/esm/tensor-conversion-impl.js
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// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
/**
* implementation of Tensor.toDataURL()
*/
export const tensorToDataURL = (tensor, options) => {
const canvas = typeof document !== 'undefined' ? document.createElement('canvas') : new OffscreenCanvas(1, 1);
canvas.width = tensor.dims[3];
canvas.height = tensor.dims[2];
const pixels2DContext = canvas.getContext('2d');
if (pixels2DContext != null) {
// Default values for height and width & format
let width;
let height;
if (options?.tensorLayout !== undefined && options.tensorLayout === 'NHWC') {
width = tensor.dims[2];
height = tensor.dims[3];
}
else {
// Default layout is NCWH
width = tensor.dims[3];
height = tensor.dims[2];
}
const inputformat = options?.format !== undefined ? options.format : 'RGB';
const norm = options?.norm;
let normMean;
let normBias;
if (norm === undefined || norm.mean === undefined) {
normMean = [255, 255, 255, 255];
}
else {
if (typeof norm.mean === 'number') {
normMean = [norm.mean, norm.mean, norm.mean, norm.mean];
}
else {
normMean = [norm.mean[0], norm.mean[1], norm.mean[2], 0];
if (norm.mean[3] !== undefined) {
normMean[3] = norm.mean[3];
}
}
}
if (norm === undefined || norm.bias === undefined) {
normBias = [0, 0, 0, 0];
}
else {
if (typeof norm.bias === 'number') {
normBias = [norm.bias, norm.bias, norm.bias, norm.bias];
}
else {
normBias = [norm.bias[0], norm.bias[1], norm.bias[2], 0];
if (norm.bias[3] !== undefined) {
normBias[3] = norm.bias[3];
}
}
}
const stride = height * width;
// Default pointer assignments
let rTensorPointer = 0, gTensorPointer = stride, bTensorPointer = stride * 2, aTensorPointer = -1;
// Updating the pointer assignments based on the input image format
if (inputformat === 'RGBA') {
rTensorPointer = 0;
gTensorPointer = stride;
bTensorPointer = stride * 2;
aTensorPointer = stride * 3;
}
else if (inputformat === 'RGB') {
rTensorPointer = 0;
gTensorPointer = stride;
bTensorPointer = stride * 2;
}
else if (inputformat === 'RBG') {
rTensorPointer = 0;
bTensorPointer = stride;
gTensorPointer = stride * 2;
}
for (let i = 0; i < height; i++) {
for (let j = 0; j < width; j++) {
const R = (tensor.data[rTensorPointer++] - normBias[0]) * normMean[0]; // R value
const G = (tensor.data[gTensorPointer++] - normBias[1]) * normMean[1]; // G value
const B = (tensor.data[bTensorPointer++] - normBias[2]) * normMean[2]; // B value
const A = aTensorPointer === -1 ? 255 : (tensor.data[aTensorPointer++] - normBias[3]) * normMean[3]; // A value
pixels2DContext.fillStyle = 'rgba(' + R + ',' + G + ',' + B + ',' + A + ')';
pixels2DContext.fillRect(j, i, 1, 1);
}
}
if ('toDataURL' in canvas) {
return canvas.toDataURL();
}
else {
throw new Error('toDataURL is not supported');
}
}
else {
throw new Error('Can not access image data');
}
};
/**
* implementation of Tensor.toImageData()
*/
export const tensorToImageData = (tensor, options) => {
const pixels2DContext = typeof document !== 'undefined'
? document.createElement('canvas').getContext('2d')
: new OffscreenCanvas(1, 1).getContext('2d');
let image;
if (pixels2DContext != null) {
// Default values for height and width & format
let width;
let height;
let channels;
if (options?.tensorLayout !== undefined && options.tensorLayout === 'NHWC') {
width = tensor.dims[2];
height = tensor.dims[1];
channels = tensor.dims[3];
}
else {
// Default layout is NCWH
width = tensor.dims[3];
height = tensor.dims[2];
channels = tensor.dims[1];
}
const inputformat = options !== undefined ? (options.format !== undefined ? options.format : 'RGB') : 'RGB';
const norm = options?.norm;
let normMean;
let normBias;
if (norm === undefined || norm.mean === undefined) {
normMean = [255, 255, 255, 255];
}
else {
if (typeof norm.mean === 'number') {
normMean = [norm.mean, norm.mean, norm.mean, norm.mean];
}
else {
normMean = [norm.mean[0], norm.mean[1], norm.mean[2], 255];
if (norm.mean[3] !== undefined) {
normMean[3] = norm.mean[3];
}
}
}
if (norm === undefined || norm.bias === undefined) {
normBias = [0, 0, 0, 0];
}
else {
if (typeof norm.bias === 'number') {
normBias = [norm.bias, norm.bias, norm.bias, norm.bias];
}
else {
normBias = [norm.bias[0], norm.bias[1], norm.bias[2], 0];
if (norm.bias[3] !== undefined) {
normBias[3] = norm.bias[3];
}
}
}
const stride = height * width;
if (options !== undefined) {
if ((options.format !== undefined && channels === 4 && options.format !== 'RGBA') ||
(channels === 3 && options.format !== 'RGB' && options.format !== 'BGR')) {
throw new Error("Tensor format doesn't match input tensor dims");
}
}
// Default pointer assignments
const step = 4;
let rImagePointer = 0, gImagePointer = 1, bImagePointer = 2, aImagePointer = 3;
let rTensorPointer = 0, gTensorPointer = stride, bTensorPointer = stride * 2, aTensorPointer = -1;
// Updating the pointer assignments based on the input image format
if (inputformat === 'RGBA') {
rTensorPointer = 0;
gTensorPointer = stride;
bTensorPointer = stride * 2;
aTensorPointer = stride * 3;
}
else if (inputformat === 'RGB') {
rTensorPointer = 0;
gTensorPointer = stride;
bTensorPointer = stride * 2;
}
else if (inputformat === 'RBG') {
rTensorPointer = 0;
bTensorPointer = stride;
gTensorPointer = stride * 2;
}
image = pixels2DContext.createImageData(width, height);
for (let i = 0; i < height * width; rImagePointer += step, gImagePointer += step, bImagePointer += step, aImagePointer += step, i++) {
image.data[rImagePointer] = (tensor.data[rTensorPointer++] - normBias[0]) * normMean[0]; // R value
image.data[gImagePointer] = (tensor.data[gTensorPointer++] - normBias[1]) * normMean[1]; // G value
image.data[bImagePointer] = (tensor.data[bTensorPointer++] - normBias[2]) * normMean[2]; // B value
image.data[aImagePointer] =
aTensorPointer === -1 ? 255 : (tensor.data[aTensorPointer++] - normBias[3]) * normMean[3]; // A value
}
}
else {
throw new Error('Can not access image data');
}
return image;
};
//# sourceMappingURL=tensor-conversion-impl.js.map