onnxruntime/js/web/test/e2e/browser-test-wasm-image-tensor-image.js

// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.

'use strict';

const IMAGE_HEIGHT = 20
const IMAGE_WIDTH = 15

function getRndColor() {
  let r = 255 * Math.random() | 0, g = 255 * Math.random() | 0, b = 255 * Math.random() | 0,
      a = 255 * Math.random() | 0;
  return 'rgb(' + r + ',' + g + ',' + b + ',' + a + ')';
}

function compareTensors(tensorA, tensorB, msg) {
  for (let i = 0; i < IMAGE_HEIGHT * IMAGE_WIDTH * 3; i++) {
    if (tensorA.data[i] !== tensorB.data[i]) {
      console.log('Element - ' + i + ' - ' + tensorA.data[i] + ' - ' + tensorB.data[i]);
      throw new Error(msg);
    }
  }
}

// TODO: this testing need to be revised
//
// work item list:
// - format the code
// - remove 'wasm' from file name
// - test depending on public website (https://media.istockphoto.com/) should be changed to depends on a localhost
// - the test is composed by 3 different test cases. split them to 3 different cases.
// - some test cases are wriiten incorrectly.
//
it('Browser E2E testing - Tensor <--> Image E2E test', async function() {
  // Creating Image HTML Image Element
  let img = new Image();
  img.crossOrigin = 'Anonymous';

  // Creating canvas element
  const canvas = document.createElement('canvas');
  canvas.height = IMAGE_HEIGHT;
  canvas.width = IMAGE_WIDTH;
  const context = canvas.getContext('2d');
  let y, x;

  // Filling the canvas with random data
  for (y = 0; y < IMAGE_HEIGHT; y++) {
    for (x = 0; x < IMAGE_WIDTH; x++) {
      context.fillStyle = getRndColor();
      context.fillRect(x, y, 1, 1);
    }
  }

  // Copying the canavas data to the image
  img.src = canvas.toDataURL();

  // Testing HTML Image Element --> Tensor --> ImageData --> Tensor
  img.onload =
      async () => {
    // Image HTML element to tensor API - HTML
    const inputTensorHTML = await ort.Tensor.fromImage(img, {norm: {bias: [2, 3, 9, 5], mean: [5, 6, 17, 8]}});
    // Tensor to ImageDAta API
    let newImage = inputTensorHTML.toImageData({norm: {bias: [2 / 5, 3 / 6, 9 / 17, 5 / 8], mean: [5, 6, 17, 8]}});
    // ImageData to tensor API
    let inputTensorImageData =
        await ort.Tensor.fromImage(newImage, options = {norm: {bias: [2, 3, 9, 5], mean: [5, 6, 17, 8]}});

    // TODO: fix this test case
    //
    // the line above does not return as expected because syntax error.
    // the reason why it does not fail is because it throws exception, and the exception is not caught. the line below
    // is not executed. to fix this, wrap a try-catch to deal with exceptions.

    compareTensors(inputTensorHTML, inputTensorImageData, 'BUG in HTML image element & ImageData use case');
  }

  // Copying the canavas data to the image as Data URL
  let image = canvas.toDataURL();

  // Testing Data URL --> Tensor --> Data URL --> Tensor
  // Data URL to tensor API -
  const inputTensorDataURL =
      await ort.Tensor.fromImage(image, {format: 'RBG', norm: {bias: [1, 10, 5, 0], mean: [5, 7, 11, 0]}});
  // Tensor to ImageDAta API
  let newImage = inputTensorDataURL.toDataURL({norm: {bias: [1 / 5, 10 / 7, 5 / 11, 0], mean: [5, 7, 11, 0]}});
  // ImageData to tensor API
  let inputTensorImageData =
      await ort.Tensor.fromImage(newImage, {format: 'RGBA', norm: {bias: [1, 10, 5, 0], mean: [5, 7, 11, 0]}});

  // TODO: fix this
  // creating tensor from image data should not depend on `options.format`.
  // data url with type 'image/png' has a determined 'RGBA' format

  compareTensors(inputTensorDataURL, inputTensorImageData, 'BUG in ImageData & Data URL use case');

  // Testing URL --> Tensor --> ImageData --> Tensor
  let online = navigator.onLine;
  if (online) {
    // URL element to tensor API
    const inputTensorURL = await ort.Tensor.fromImage(
        'https://media.istockphoto.com/id/172859087/photo/square-eggs.jpg?s=2048x2048&w=is&k=20&c=KiBRyyYaoUUSjcJLBh1-qqVu7LW6UQZBopZdva0f5e4=',
        {norm: {bias: [2, 3, 9, 0], mean: [5, 6, 17, 0]}});
    // Tensor to ImageDAta API
    let newImage =
        inputTensorURL.toImageData({format: 'RGB', norm: {bias: [2 / 5, 3 / 6, 9 / 17, 0], mean: [5, 6, 17, 0]}});
    // ImageData to tensor API
    let inputTensorImageData =
        await ort.Tensor.fromImage(newImage, {format: 'RGB', norm: {bias: [2, 3, 9, 0], mean: [5, 6, 17, 0]}});

    compareTensors(inputTensorURL, inputTensorImageData, 'BUG in ImageData & URL');
  } else {
    console.log('No internet connection - didn\'t test Image URL to tensor API');
  }
});