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// Licensed under the MIT License.
import { Tensor } from './tensor-impl.js';
/**
* Create a new tensor object from image object
*
* @param buffer - Extracted image buffer data - assuming RGBA format
* @param imageFormat - input image configuration - required configurations height, width, format
* @param tensorFormat - output tensor configuration - Default is RGB format
*/
export const bufferToTensor = (buffer, options) => {
if (buffer === undefined) {
throw new Error('Image buffer must be defined');
}
if (options.height === undefined || options.width === undefined) {
throw new Error('Image height and width must be defined');
}
if (options.tensorLayout === 'NHWC') {
throw new Error('NHWC Tensor layout is not supported yet');
}
const { height, width } = options;
const norm = options.norm ?? { mean: 255, bias: 0 };
let normMean;
let normBias;
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], norm.mean[3] ?? 255];
}
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], norm.bias[3] ?? 0];
}
const inputformat = options.format !== undefined ? options.format : 'RGBA';
// default value is RGBA since imagedata and HTMLImageElement uses it
const outputformat = options.tensorFormat !== undefined ? (options.tensorFormat !== undefined ? options.tensorFormat : 'RGB') : 'RGB';
const stride = height * width;
const float32Data = outputformat === 'RGBA' ? new Float32Array(stride * 4) : new Float32Array(stride * 3);
// Default pointer assignments
let step = 4, 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 === 'RGB') {
step = 3;
rImagePointer = 0;
gImagePointer = 1;
bImagePointer = 2;
aImagePointer = -1;
}
// Updating the pointer assignments based on the output tensor format
if (outputformat === 'RGBA') {
aTensorPointer = stride * 3;
}
else if (outputformat === 'RBG') {
rTensorPointer = 0;
bTensorPointer = stride;
gTensorPointer = stride * 2;
}
else if (outputformat === 'BGR') {
bTensorPointer = 0;
gTensorPointer = stride;
rTensorPointer = stride * 2;
}
for (let i = 0; i < stride; i++, rImagePointer += step, bImagePointer += step, gImagePointer += step, aImagePointer += step) {
float32Data[rTensorPointer++] = (buffer[rImagePointer] + normBias[0]) / normMean[0];
float32Data[gTensorPointer++] = (buffer[gImagePointer] + normBias[1]) / normMean[1];
float32Data[bTensorPointer++] = (buffer[bImagePointer] + normBias[2]) / normMean[2];
if (aTensorPointer !== -1 && aImagePointer !== -1) {
float32Data[aTensorPointer++] = (buffer[aImagePointer] + normBias[3]) / normMean[3];
}
}
// Float32Array -> ort.Tensor
const outputTensor = outputformat === 'RGBA'
? new Tensor('float32', float32Data, [1, 4, height, width])
: new Tensor('float32', float32Data, [1, 3, height, width]);
return outputTensor;
};
/**
* implementation of Tensor.fromImage().
*/
export const tensorFromImage = async (image, options) => {
// checking the type of image object
const isHTMLImageEle = typeof HTMLImageElement !== 'undefined' && image instanceof HTMLImageElement;
const isImageDataEle = typeof ImageData !== 'undefined' && image instanceof ImageData;
const isImageBitmap = typeof ImageBitmap !== 'undefined' && image instanceof ImageBitmap;
const isString = typeof image === 'string';
let data;
let bufferToTensorOptions = options ?? {};
const createCanvas = () => {
if (typeof document !== 'undefined') {
return document.createElement('canvas');
}
else if (typeof OffscreenCanvas !== 'undefined') {
return new OffscreenCanvas(1, 1);
}
else {
throw new Error('Canvas is not supported');
}
};
const createCanvasContext = (canvas) => {
if (typeof HTMLCanvasElement !== 'undefined' && canvas instanceof HTMLCanvasElement) {
return canvas.getContext('2d');
}
else if (canvas instanceof OffscreenCanvas) {
return canvas.getContext('2d');
}
else {
return null;
}
};
// filling and checking image configuration options
if (isHTMLImageEle) {
// HTMLImageElement - image object - format is RGBA by default
const canvas = createCanvas();
canvas.width = image.width;
canvas.height = image.height;
const pixels2DContext = createCanvasContext(canvas);
if (pixels2DContext != null) {
let height = image.height;
let width = image.width;
if (options !== undefined && options.resizedHeight !== undefined && options.resizedWidth !== undefined) {
height = options.resizedHeight;
width = options.resizedWidth;
}
if (options !== undefined) {
bufferToTensorOptions = options;
if (options.tensorFormat !== undefined) {
throw new Error('Image input config format must be RGBA for HTMLImageElement');
}
else {
bufferToTensorOptions.tensorFormat = 'RGBA';
}
bufferToTensorOptions.height = height;
bufferToTensorOptions.width = width;
}
else {
bufferToTensorOptions.tensorFormat = 'RGBA';
bufferToTensorOptions.height = height;
bufferToTensorOptions.width = width;
}
pixels2DContext.drawImage(image, 0, 0);
data = pixels2DContext.getImageData(0, 0, width, height).data;
}
else {
throw new Error('Can not access image data');
}
}
else if (isImageDataEle) {
let height;
let width;
if (options !== undefined && options.resizedWidth !== undefined && options.resizedHeight !== undefined) {
height = options.resizedHeight;
width = options.resizedWidth;
}
else {
height = image.height;
width = image.width;
}
if (options !== undefined) {
bufferToTensorOptions = options;
}
bufferToTensorOptions.format = 'RGBA';
bufferToTensorOptions.height = height;
bufferToTensorOptions.width = width;
if (options !== undefined) {
const tempCanvas = createCanvas();
tempCanvas.width = width;
tempCanvas.height = height;
const pixels2DContext = createCanvasContext(tempCanvas);
if (pixels2DContext != null) {
pixels2DContext.putImageData(image, 0, 0);
data = pixels2DContext.getImageData(0, 0, width, height).data;
}
else {
throw new Error('Can not access image data');
}
}
else {
data = image.data;
}
}
else if (isImageBitmap) {
// ImageBitmap - image object - format must be provided by user
if (options === undefined) {
throw new Error('Please provide image config with format for Imagebitmap');
}
const canvas = createCanvas();
canvas.width = image.width;
canvas.height = image.height;
const pixels2DContext = createCanvasContext(canvas);
if (pixels2DContext != null) {
const height = image.height;
const width = image.width;
pixels2DContext.drawImage(image, 0, 0, width, height);
data = pixels2DContext.getImageData(0, 0, width, height).data;
bufferToTensorOptions.height = height;
bufferToTensorOptions.width = width;
return bufferToTensor(data, bufferToTensorOptions);
}
else {
throw new Error('Can not access image data');
}
}
else if (isString) {
return new Promise((resolve, reject) => {
const canvas = createCanvas();
const context = createCanvasContext(canvas);
if (!image || !context) {
return reject();
}
const newImage = new Image();
newImage.crossOrigin = 'Anonymous';
newImage.src = image;
newImage.onload = () => {
canvas.width = newImage.width;
canvas.height = newImage.height;
context.drawImage(newImage, 0, 0, canvas.width, canvas.height);
const img = context.getImageData(0, 0, canvas.width, canvas.height);
bufferToTensorOptions.height = canvas.height;
bufferToTensorOptions.width = canvas.width;
resolve(bufferToTensor(img.data, bufferToTensorOptions));
};
});
}
else {
throw new Error('Input data provided is not supported - aborted tensor creation');
}
if (data !== undefined) {
return bufferToTensor(data, bufferToTensorOptions);
}
else {
throw new Error('Input data provided is not supported - aborted tensor creation');
}
};
/**
* implementation of Tensor.fromTexture().
*/
export const tensorFromTexture = (texture, options) => {
const { width, height, download, dispose } = options;
// Always assume RGBAF32. TODO: support different texture format
const dims = [1, height, width, 4];
return new Tensor({ location: 'texture', type: 'float32', texture, dims, download, dispose });
};
/**
* implementation of Tensor.fromGpuBuffer().
*/
export const tensorFromGpuBuffer = (gpuBuffer, options) => {
const { dataType, dims, download, dispose } = options;
return new Tensor({ location: 'gpu-buffer', type: dataType ?? 'float32', gpuBuffer, dims, download, dispose });
};
/**
* implementation of Tensor.fromMLTensor().
*/
export const tensorFromMLTensor = (mlTensor, options) => {
const { dataType, dims, download, dispose } = options;
return new Tensor({ location: 'ml-tensor', type: dataType ?? 'float32', mlTensor, dims, download, dispose });
};
/**
* implementation of Tensor.fromPinnedBuffer().
*/
export const tensorFromPinnedBuffer = (type, buffer, dims) => new Tensor({ location: 'cpu-pinned', type, data: buffer, dims: dims ?? [buffer.length] });
//# sourceMappingURL=tensor-factory-impl.js.map |