image-augment
v1.1.1
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Image augmentation library for machine learning in javascript.
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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
- with hasard for randomness
- with 2 different backends :
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