Base64-to-Tensor

This package enables the conversion of Base64-encoded images to tensor objects using pure JavaScript, compatible with TensorFlow.js.

Installation

Install the package using npm:

``bash
npm install base64-to-tensor --save
`

Prerequisites

Ensure @tensorflow/tfjs-core is installed alongside a valid TensorFlow backend. Choose between the synchronous package jpeg-js for full blocking sync or the asynchronous package sharp for non-blocking async operations:

`sh


For synchronous operations:

npm install @tensorflow/tfjs-core jpeg-js

For asynchronous operations:

npm install @tensorflow/tfjs-core sharp
`

Usage

Refer to the convert.test.ts file for example usage. Below are snippets demonstrating both synchronous and asynchronous conversions:

`typescript
import { convert, convertAsync } from "base64-to-tensor";
import { setBackend } from "@tensorflow/tfjs-core";
import "@tensorflow/tfjs-backend-wasm";

await setBackend("wasm");

// Synchronous conversion (jpeg-js)
const tensorSync = convert(mybase64); // Ensure mybase64 is a valid JPEG

// Asynchronous conversion (sharp)
const tensorAsync = await convertAsync(mybase64); // Enhanced performance

// Example tensor output
{
kept: false,
isDisposedInternal: false,
shape: [189, 300, 3],
dtype: "int32",
size: 170100,
strides: [900, 3],
dataId: { id: 1 },
id: 1,
rankType: "3",
}
`

Benefits

Using pure JavaScript for image conversion to tensors offers several advantages:

1. Reduced Size and Portability: Eliminates the need for cairo` or other native image development converters.
2. Increased Speed: Performs calculations on-the-fly without external call overheads.
3. Worker Thread Compatibility: Facilitates the use of TensorFlow WASM backends within API services, enhancing performance and scalability.

Benchmarks

Performance Benchmarks

Performance benchmarks conducted on a Mac M1 (64GB RAM):

Test Case	Characters	Size	Synchronous	Asynchronous
JPEG	26,791	26.16 KB	100 ms	50 ms