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https://github.com/saymrwulf/onnxruntime.git
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c10a6a9d17
### Description Enabled the use of per channel Bias and Mean normalization when converting an image <--> tensor. Added a few bug fixes and updates to the relevant E2E tests. --------- Co-authored-by: shalvamist <shalva.mist@microsoft.com>
88 lines
3.4 KiB
JavaScript
88 lines
3.4 KiB
JavaScript
// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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'use strict';
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const IMAGE_HEIGHT = 20
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const IMAGE_WIDTH = 15
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function getRndColor() {
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let r = 255*Math.random()|0,
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g = 255*Math.random()|0,
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b = 255*Math.random()|0,
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a = 255*Math.random()|0;
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return 'rgb(' + r + ',' + g + ',' + b + ',' + a +')';
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}
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function compareTensors(tensorA, tensorB, msg){
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for (let i = 0; i < IMAGE_HEIGHT*IMAGE_WIDTH*3; i++) {
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if(tensorA.data[i]!==tensorB.data[i]){
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console.log("Element - " + i + " - " + tensorA.data[i] + " - " + tensorB.data[i]);
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throw new Error(msg);
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}
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}
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}
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it('Browser E2E testing - Tensor <--> Image E2E test', async function () {
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// Creating Image HTML Image Element
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let img = new Image();
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img.crossOrigin = 'Anonymous';
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// Creating canvas element
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const canvas = document.createElement('canvas');
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canvas.height = IMAGE_HEIGHT;
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canvas.width = IMAGE_WIDTH;
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const context = canvas.getContext('2d');
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let y, x;
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// Filling the canvas with random data
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for(y = 0; y < IMAGE_HEIGHT; y++) {
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for(x = 0; x < IMAGE_WIDTH; x++) {
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context.fillStyle = getRndColor();
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context.fillRect(x, y, 1, 1);
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}
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}
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// Copying the canavas data to the image
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img.src = canvas.toDataURL();
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// Testing HTML Image Element --> Tensor --> ImageData --> Tensor
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img.onload = async () =>{
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// Image HTML element to tensor API - HTML
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const inputTensorHTML = await ort.Tensor.fromImage(img, {norm:{bias:[2,3,9,5],mean:[5,6,17,8]}});
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// Tensor to ImageDAta API
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let newImage = inputTensorHTML.toImageData({norm:{bias:[2/5,3/6,9/17,5/8],mean:[5,6,17,8]}});
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// ImageData to tensor API
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let inputTensorImageData = await ort.Tensor.fromImage(newImage, options={norm:{bias:[2,3,9,5],mean:[5,6,17,8]}});
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compareTensors(inputTensorHTML,inputTensorImageData,'BUG in HTML image element & ImageData use case');
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}
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// Copying the canavas data to the image as Data URL
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let image = canvas.toDataURL();
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// Testing Data URL --> Tensor --> Data URL --> Tensor
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// Data URL to tensor API -
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const inputTensorDataURL = await ort.Tensor.fromImage(image,{format:'RBG', norm:{bias:[1,10,5,0],mean:[5,7,11,0]}});
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// Tensor to ImageDAta API
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let newImage = inputTensorDataURL.toDataURL({norm:{bias:[1/5,10/7,5/11,0],mean:[5,7,11,0]}});
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// ImageData to tensor API
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let inputTensorImageData = await ort.Tensor.fromImage(newImage,{format:'RGB', norm:{bias:[1,10,5,0],mean:[5,7,11,0]}});
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compareTensors(inputTensorDataURL,inputTensorImageData,'BUG in ImageData & Data URL use case');
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// Testing URL --> Tensor --> ImageData --> Tensor
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let online = navigator.onLine;
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if(online){
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// URL element to tensor API
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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]}});
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// Tensor to ImageDAta API
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let newImage = inputTensorURL.toImageData({format:'RGB',norm:{bias:[2/5,3/6,9/17,0],mean:[5,6,17,0]}});
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// ImageData to tensor API
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let inputTensorImageData = await ort.Tensor.fromImage(newImage,{format:'RGB',norm:{bias:[2,3,9,0],mean:[5,6,17,0]}});
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compareTensors(inputTensorURL,inputTensorImageData,'BUG in ImageData & URL');
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}else{
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console.log("No internet connection - didn't test Image URL to tensor API");
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}
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});
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