npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2024 – Pkg Stats / Ryan Hefner

image-augment

v1.1.1

Published

Image augmentation library for machine learning in javascript.

Downloads

86

Readme

image-augment

Augment images (geometric, noise, ...) for visual machine learning data augmentation.

This library has been freely inspired from imgaug

This library is intend to work

Installation

npm install image-augment

Simple example

// First you need a backend for image processing
// this can be one of the following :
// * @tensorflow/tfjs
// * @tensorflow/tfjs-node
// * @tensorflow/tfjs-node-gpu
// * opencv4nodejs

const tf = require('@tensorflow/tfjs-node');

// Then initialize with the backend

const ia = require('image-augment')(tf);

// Create an augmentation pipeline
const basicAugmentation = ia.sequential([
	// Add a noise with a standard deviation of 15
	ia.additiveNoise(15),
	// Rotate 30°
	ia.affine({rotate: 30}),
	// Add a blur kernel of 3 pixel
	ia.blur(3)
]);

// tensorflow backend needs Tensor4d <-> filename function
// see test/examples/simple-example.js for full implementation of those helpers (fileToTensor and tensorToFile)

fileToTensor('test/data/tfjs/lenna.png')
	.then(({images}) => {
		return basicAugmentation.read({images});
	})
	.then(({images}) => {
		return tensorToFile('test/data/tfjs/lenna-example.png', {images});
	})
	.then(() => {
		console.log('done');
	});

Output is :

Grid example with opencv4nodejs

const h = require('hasard');
const cv = require('opencv4nodejs');
const ia = require('image-augment')(cv);

// Random example images
const sometimes = (aug => h.value([aug, ia.identity()]));

const seq = ia.sequential({
	steps: [
		ia.fliplr(0.5),
		ia.flipud(0.5),
		ia.pad({
			percent: h.array({size: 2, value: h.number(0, 0.1)}),
			borderType: ia.RD_BORDER_TYPE,
			borderValue: h.integer(0, 255)
		}),
		sometimes(ia.crop({
			percent: h.array({size: 2, value: h.number(0, 0.1)})
		})),
		sometimes(ia.affine({
			// Scale images to 80-120% of their size, individually per axis
			scale: h.array([h.number(0.6, 1.2), h.number(0.6, 1.2)]),
			// Translate by -20 to +20 percent (per axis)
			translatePercent: h.array([h.number(-0.2, 0.2), h.number(-0.2, 0.2)]),
			// Rotate by -45 to +45 degrees
			rotate: h.number(-45, 45),
			// Shear by -16 to +16 degrees
			shear: h.number(-16, 16),
			// If borderType is constant, use a random rgba value between 0 and 255
			borderValue: h.array({value: h.integer(0, 255), size: 4}),
			borderType: ia.RD_BORDER_TYPE
		}))
	],
	randomOrder: true
});
const image = cv.imread('test/data/opencv4nodejs/lenna.png');

seq.toGrid({images: [image, image, image, image, image, image, image, image]}, {
	filename: 'test/data/opencv4nodejs/lenna-grid.png',
	imageShape: [300, 300],
	gridShape: [4, 2]
});

Output :

Grid Example with tensorflowjs

const h = require('hasard');
const tf = require('@tensorflow/tfjs-node');
const ia = require('image-augment')(tf);


// Random example images
const sometimes = (aug => h.value([aug, ia.identity()]));

const seq = ia.sequential({
	steps: [
		ia.fliplr(0.5),
		ia.flipud(0.5),
		ia.pad({
			percent: h.array({size: 2, value: h.number(0, 0.1)}),
			borderType: ia.RD_BORDER_TYPE,
			borderValue: h.integer(0, 255)
		}),
		sometimes(ia.crop({
			percent: h.array({size: 2, value: h.number(0, 0.1)})
		})),
		sometimes(ia.affine({
			// Scale images to 80-120% of their size, individually per axis
			scale: h.array([h.number(0.6, 1.2), h.number(0.6, 1.2)]),
			// Translate by -20 to +20 percent (per axis)
			translatePercent: h.array([h.number(-0.2, 0.2), h.number(-0.2, 0.2)]),
			// Rotate by -45 to +45 degrees
			rotate: h.number(-45, 45),
			// Shear by -16 to +16 degrees
			shear: h.number(-16, 16),
			// If borderType is constant, use a random rgba value between 0 and 255
			borderValue: h.array({value: h.integer(0, 255), size: 4}),
			borderType: ia.RD_BORDER_TYPE
		}))
	],
	randomOrder: true
});

// tensorflow backend needs Tensor4d <-> filename function
// see test/helpers/files-to-images for full implementation of those helpers (fileToTensor and tensorToFile)
const filenames = new Array(8).fill('test/data/opencv4nodejs/lenna.png');

filesToImages(filenames, seq.backend).then(images => {
	seq.toGrid({images}, {
		filename: 'test/data/tfjs/lenna-grid.png',
		imageShape: [300, 300],
		gridShape: [4, 2]
	});
})

Output :

API documentation

See documentation

Discussion

Opencv4nodejs vs Tensorflowjs

Both librairies have advantages, this is what you need to know

Why opencv4nodejs :

  • easier to manipulate files in node.js (cv.imread ...)
  • Using different image sizes with no impact on performance

Why tensorflowjs :

  • Browser support
  • integrate with DL training
  • Fast Noise image generation (truncatedNormal)

See benchmark for more info about performance

Todo list

Help appreciated, please open an issue if you have any question.

  • [x] Add benchmark test to measure the speed
  • [x] Faster random generator using tensorflow js truncated normal
  • [x] Get affine transform to work with tensorflow backend
  • [x] add examples/explanations/benchmark in the README.md
  • [x] Run all unit tests on Travis
  • [x] Implement perspective Transform using tensorflowjs backend
  • [x] Put documentation on github pages
  • [x] Remove jimp deps
  • [ ] create a demo app running in the browser with tfjs + webgl
  • [ ] Speed up all non-batch implemented tensorflow augmenters
  • [ ] Add more augmenters
  • [ ] Add unit test and examples for cropToBox and DrawBoxes
  • [ ] Stream API
  • [ ] Faster gaussian and poisson noise generators