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aleju/imgaug: Image augmentation for machine learning experiments.

原作者: [db:作者] 来自: 网络 收藏 邀请

开源软件名称(OpenSource Name):

aleju/imgaug

开源软件地址(OpenSource Url):

https://github.com/aleju/imgaug

开源编程语言(OpenSource Language):

Python 100.0%

开源软件介绍(OpenSource Introduction):

imgaug

This python library helps you with augmenting images for your machine learning projects. It converts a set of input images into a new, much larger set of slightly altered images.

Build Status codecov Codacy Badge

  Image Heatmaps Seg. Maps Keypoints Bounding Boxes,
Polygons
Original Input input images input heatmaps input segmentation maps input keypoints input bounding boxes
Gauss. Noise
+ Contrast
+ Sharpen
non geometric augmentations, applied to images non geometric augmentations, applied to heatmaps non geometric augmentations, applied to segmentation maps non geometric augmentations, applied to keypoints non geometric augmentations, applied to bounding boxes
Affine affine augmentations, applied to images affine augmentations, applied to heatmaps affine augmentations, applied to segmentation maps affine augmentations, applied to keypoints affine augmentations, applied to bounding boxes
Crop
+ Pad
crop and pad augmentations, applied to images crop and pad augmentations, applied to heatmaps crop and pad augmentations, applied to segmentation maps crop and pad augmentations, applied to keypoints crop and pad augmentations, applied to bounding boxes
Fliplr
+ Perspective
Horizontal flip and perspective transform augmentations, applied to images Horizontal flip and perspective transform augmentations, applied to heatmaps Horizontal flip and perspective transform augmentations, applied to segmentation maps Horizontal flip and perspective transform augmentations, applied to keypoints Horizontal flip and perspective transform augmentations, applied to bounding boxes

More (strong) example augmentations of one input image:

64 quokkas

Table of Contents

  1. Features
  2. Installation
  3. Documentation
  4. Recent Changes
  5. Example Images
  6. Code Examples
  7. Citation

Features

Installation

The library supports python 2.7 and 3.4+.

Installation: Anaconda

To install the library in anaconda, perform the following commands:

conda config --add channels conda-forge
conda install imgaug

You can deinstall the library again via conda remove imgaug.

Installation: pip

Then install imgaug either via pypi (can lag behind the github version):

pip install imgaug

or install the latest version directly from github:

pip install git+https://github.com/aleju/imgaug.git

For more details, see the install guide

To deinstall the library, just execute pip uninstall imgaug.

Documentation

Example jupyter notebooks:

More notebooks: imgaug-doc/notebooks.

Example ReadTheDocs pages:

More RTD documentation: imgaug.readthedocs.io.

All documentation related files of this project are hosted in the repository imgaug-doc.

Recent Changes

  • 0.4.0: Added new augmenters, changed backend to batchwise augmentation, support for numpy 1.18 and python 3.8.
  • 0.3.0: Reworked segmentation map augmentation, adapted to numpy 1.17+ random number sampling API, several new augmenters.
  • 0.2.9: Added polygon augmentation, added line string augmentation, simplified augmentation interface.
  • 0.2.8: Improved performance, dtype support and multicore augmentation.

See changelogs/ for more details.

Example Images

The images below show examples for most augmentation techniques.

Values written in the form (a, b) denote a uniform distribution, i.e. the value is randomly picked from the interval [a, b]. Line strings are supported by (almost) all augmenters, but are not explicitly visualized here.

meta
Identity ChannelShuffle      
Identity ChannelShuffle      
See also: Sequential, SomeOf, OneOf, Sometimes, WithChannels, Lambda, AssertLambda, AssertShape, RemoveCBAsByOutOfImageFraction, ClipCBAsToImagePlanes
arithmetic
Add Add
(per_channel=True)
AdditiveGaussianNoise AdditiveGaussianNoise
(per_channel=True)
Multiply
Add Add per_channel=True AdditiveGaussianNoise AdditiveGaussianNoise per_channel=True Multiply
Cutout Dropout CoarseDropout
(p=0.2)
CoarseDropout
(p=0.2, per_channel=True)
Dropout2d
Cutout Dropout CoarseDropout p=0.2 CoarseDropout p=0.2, per_channel=True Dropout2d
SaltAndPepper CoarseSaltAndPepper
(p=0.2)
Invert Solarize JpegCompression
SaltAndPepper CoarseSaltAndPepper p=0.2 Invert Solarize JpegCompression
See also: AddElementwise, AdditiveLaplaceNoise, AdditivePoissonNoise, MultiplyElementwise, TotalDropout, ReplaceElementwise, ImpulseNoise, Salt, Pepper, CoarseSalt, CoarsePepper, Solarize
artistic
Cartoon        
Cartoon        
blend
BlendAlpha
with EdgeDetect(1.0)
BlendAlphaSimplexNoise
with EdgeDetect(1.0)
BlendAlphaFrequencyNoise
with EdgeDetect(1.0)
BlendAlphaSomeColors
with RemoveSaturation(1.0)
BlendAlphaRegularGrid
with Multiply((0.0, 0.5))
BlendAlpha with EdgeDetect1.0 BlendAlphaSimplexNoise with EdgeDetect1.0 BlendAlphaFrequencyNoise with EdgeDetect1.0 BlendAlphaSomeColors with RemoveSaturation1.0 BlendAlphaRegularGrid with Multiply0.0, 0.5
See also: BlendAlphaMask, BlendAlphaElementwise, BlendAlphaVerticalLinearGradient, BlendAlphaHorizontalLinearGradient, BlendAlphaSegMapClassIds, BlendAlphaBoundingBoxes, BlendAlphaCheckerboard, SomeColorsMaskGen, HorizontalLinearGradientMaskGen, VerticalLinearGradientMaskGen, RegularGridMaskGen, CheckerboardMaskGen, SegMapClassIdsMaskGen, BoundingBoxesMaskGen, InvertMaskGen
blur
GaussianBlur AverageBlur MedianBlur BilateralBlur
(sigma_color=250,
sigma_space=250)
MotionBlur
(angle=0)
GaussianBlur AverageBlur MedianBlur BilateralBlur sigma_color=250, sigma_space=250 MotionBlur angle=0
MotionBlur
(k=5)
MeanShiftBlur      
MotionBlur k=5 MeanShiftBlur      
collections
RandAugment        
RandAugment        
color
MultiplyAndAddToBrightness MultiplyHueAndSaturation MultiplyHue MultiplySaturation AddToHueAndSaturation
MultiplyAndAddToBrightness MultiplyHueAndSaturation MultiplyHue MultiplySaturation AddToHueAndSaturation
Grayscale RemoveSaturation ChangeColorTemperature KMeansColorQuantization
(to_colorspace=RGB)
UniformColorQuantization
(to_colorspace=RGB)
Grayscale RemoveSaturation ChangeColorTemperature KMeansColorQuantization to_colorspace=RGB UniformColorQuantization to_colorspace=RGB
See also: WithColorspace, WithBrightnessChannels, MultiplyBrightness, AddToBrightness, WithHueAndSaturation, AddToHue, AddToSaturation, ChangeColorspace, Posterize
contrast
GammaContrast GammaContrast
(per_channel=True)
SigmoidContrast
(cutoff=0.5)
SigmoidContrast
(gain=10)
LogContrast
GammaContrast GammaContrast per_channel=True SigmoidContrast cutoff=0.5 SigmoidContrast gain=10 LogContrast
LinearContrast AllChannels-
HistogramEqualization
HistogramEqualization AllChannelsCLAHE CLAHE
LinearContrast AllChannels- HistogramEqualization HistogramEqualization AllChannelsCLAHE CLAHE
See also: Equalize
convolutional
Sharpen
(alpha=1)
Emboss
(alpha=1)
EdgeDetect DirectedEdgeDetect
(alpha=1)
 
Sharpen alpha=1 Emboss alpha=1 EdgeDetect DirectedEdgeDetect alpha=1  
See also: Convolve
debug
See also: SaveDebugImageEveryNBatches
edges
Canny        
Canny        
flip
Fliplr Flipud  
Fliplr Flipud  
See also: HorizontalFlip, VerticalFlip
geometric
Affine Affine: Modes  
Affine Affine: Modes  
Affine: cval PiecewiseAffine  
Affine: cval PiecewiseAffine  
PerspectiveTransform ElasticTransformation
(sigma=1.0)
 
PerspectiveTransform ElasticTransformation sigma=1.0  
ElasticTransformation
(sigma=4.0)
Rot90  
ElasticTransformation sigma=4.0 Rot90  
WithPolarWarping
+Affine
Jigsaw
(5x5 grid)
 
WithPolarWarping +Affine Jigsaw 5x5 grid  
See also: ScaleX, ScaleY, TranslateX, TranslateY,

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