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imgaug

# 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](https://travis-ci.org/aleju/imgaug.svg?branch=master)](https://travis-ci.org/aleju/imgaug) [![codecov](https://codecov.io/gh/aleju/imgaug/branch/master/graph/badge.svg)](https://codecov.io/gh/aleju/imgaug) [![Codacy Badge](https://api.codacy.com/project/badge/Grade/1370ce38e99e40af842d47a8dd721444)](https://www.codacy.com/app/aleju/imgaug?utm_source=github.com&utm_medium=referral&utm_content=aleju/imgaug&utm_campaign=Badge_Grade) <table> <tr> <th> </th> <th>Image</th> <th>Heatmaps</th> <th>Seg. Maps</th> <th>Keypoints</th> <th>Bounding Boxes, Polygons</th> </tr>  <tr> <td>Original Input</td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/noop_image.jpg?raw=true" height="83" width="124" alt="input images"></td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/noop_heatmap.jpg?raw=true" height="83" width="124" alt="input heatmaps"></td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/noop_segmap.jpg?raw=true" height="83" width="124" alt="input segmentation maps"></td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/noop_kps.jpg?raw=true" height="83" width="124" alt="input keypoints"></td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/noop_bbs.jpg?raw=true" height="83" width="124" alt="input bounding boxes"></td> </tr>  <tr> <td>Gauss. Noise + Contrast + Sharpen</td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/non_geometric_image.jpg?raw=true" height="83" width="124" alt="non geometric augmentations, applied to images"></td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/non_geometric_heatmap.jpg?raw=true" height="83" width="124" alt="non geometric augmentations, applied to heatmaps"></td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/non_geometric_segmap.jpg?raw=true" height="83" width="124" alt="non geometric augmentations, applied to segmentation maps"></td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/non_geometric_kps.jpg?raw=true" height="83" width="124" alt="non geometric augmentations, applied to keypoints"></td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/non_geometric_bbs.jpg?raw=true" height="83" width="124" alt="non geometric augmentations, applied to bounding boxes"></td> </tr>  <tr> <td>Affine</td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/affine_image.jpg?raw=true" height="83" width="124" alt="affine augmentations, applied to images"></td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/affine_heatmap.jpg?raw=true" height="83" width="124" alt="affine augmentations, applied to heatmaps"></td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/affine_segmap.jpg?raw=true" height="83" width="124" alt="affine augmentations, applied to segmentation maps"></td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/affine_kps.jpg?raw=true" height="83" width="124" alt="affine augmentations, applied to keypoints"></td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/affine_bbs.jpg?raw=true" height="83" width="124" alt="affine augmentations, applied to bounding boxes"></td> </tr>  <tr> <td>Crop + Pad</td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/cropandpad_image.jpg?raw=true" height="83" width="124" alt="crop and pad augmentations, applied to images"></td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/cropandpad_heatmap.jpg?raw=true" height="83" width="124" alt="crop and pad augmentations, applied to heatmaps"></td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/cropandpad_segmap.jpg?raw=true" height="83" width="124" alt="crop and pad augmentations, applied to segmentation maps"></td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/cropandpad_kps.jpg?raw=true" height="83" width="124" alt="crop and pad augmentations, applied to keypoints"></td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/cropandpad_bbs.jpg?raw=true" height="83" width="124" alt="crop and pad augmentations, applied to bounding boxes"></td> </tr>  <tr> <td>Fliplr + Perspective</td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/fliplr_perspective_image.jpg" height="83" width="124" alt="Horizontal flip and perspective transform augmentations, applied to images"></td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/fliplr_perspective_heatmap.jpg?raw=true" height="83" width="124" alt="Horizontal flip and perspective transform augmentations, applied to heatmaps"></td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/fliplr_perspective_segmap.jpg?raw=true" height="83" width="124" alt="Horizontal flip and perspective transform augmentations, applied to segmentation maps"></td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/fliplr_perspective_kps.jpg?raw=true" height="83" width="124" alt="Horizontal flip and perspective transform augmentations, applied to keypoints"></td> <td><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/fliplr_perspective_bbs.jpg?raw=true" height="83" width="124" alt="Horizontal flip and perspective transform augmentations, applied to bounding boxes"></td> </tr> </table> **More (strong) example augmentations of one input image:** ![64 quokkas](https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/examples_grid.jpg?raw=true "64 quokkas") ## Table of Contents 1. [Features](#features) 2. [Installation](#installation) 3. [Documentation](#documentation) 4. [Recent Changes](#recent_changes) 5. [Example Images](#example_images) 6. [Code Examples](#code_examples) 7. [Citation](#citation) <a name="features"/> ## Features * Many augmentation techniques * E.g. affine transformations, perspective transformations, contrast changes, gaussian noise, dropout of regions, hue/saturation changes, cropping/padding, blurring, ... * Optimized for high performance * Easy to apply augmentations only to some images * Easy to apply augmentations in random order * Support for * Images (full support for uint8, for other dtypes see [documentation](https://imgaug.readthedocs.io/en/latest/source/dtype_support.html)) * Heatmaps (float32), Segmentation Maps (int), Masks (bool) * May be smaller/larger than their corresponding images. *No* extra lines of code needed for e.g. crop. * Keypoints/Landmarks (int/float coordinates) * Bounding Boxes (int/float coordinates) * Polygons (int/float coordinates) * Line Strings (int/float coordinates) * Automatic alignment of sampled random values * Example: Rotate image and segmentation map on it by the same value sampled from `uniform(-10°, 45°)`. (0 extra lines of code.) * Probability distributions as parameters * Example: Rotate images by values sampled from `uniform(-10°, 45°)`. * Example: Rotate images by values sampled from `ABS(N(0, 20.0))*(1+B(1.0, 1.0))`", where `ABS(.)` is the absolute function, `N(.)` the gaussian distribution and `B(.)` the beta distribution. * Many helper functions * Example: Draw heatmaps, segmentation maps, keypoints, bounding boxes, ... * Example: Scale segmentation maps, average/max pool of images/maps, pad images to aspect ratios (e.g. to square them) * Example: Convert keypoints to distance maps, extract pixels within bounding boxes from images, clip polygon to the image plane, ... * Support for augmentation on multiple CPU cores <a name="installation"/> ## Installation The library supports python 2.7 and 3.4+. ### Installation: Anaconda To install the library in anaconda, perform the following commands: ```bash 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): ```bash pip install imgaug ``` or install the latest version directly from github: ```bash pip install git+https://github.com/aleju/imgaug.git ``` For more details, see the [install guide](https://imgaug.readthedocs.io/en/latest/source/installation.html) To deinstall the library, just execute `pip uninstall imgaug`. <a name="documentation"/> ## Documentation Example jupyter notebooks: * [Load and Augment an Image](https://nbviewer.jupyter.org/github/aleju/imgaug-doc/blob/master/notebooks/A01%20-%20Load%20and%20Augment%20an%20Image.ipynb) * [Multicore Augmentation](https://nbviewer.jupyter.org/github/aleju/imgaug-doc/blob/master/notebooks/A03%20-%20Multicore%20Augmentation.ipynb) * Augment and work with: [Keypoints/Landmarks](https://nbviewer.jupyter.org/github/aleju/imgaug-doc/blob/master/notebooks/B01%20-%20Augment%20Keypoints.ipynb), [Bounding Boxes](https://nbviewer.jupyter.org/github/aleju/imgaug-doc/blob/master/notebooks/B02%20-%20Augment%20Bounding%20Boxes.ipynb), [Polygons](https://nbviewer.jupyter.org/github/aleju/imgaug-doc/blob/master/notebooks/B03%20-%20Augment%20Polygons.ipynb), [Line Strings](https://nbviewer.jupyter.org/github/aleju/imgaug-doc/blob/master/notebooks/B06%20-%20Augment%20Line%20Strings.ipynb), [Heatmaps](https://nbviewer.jupyter.org/github/aleju/imgaug-doc/blob/master/notebooks/B04%20-%20Augment%20Heatmaps.ipynb), [Segmentation Maps](https://nbviewer.jupyter.org/github/aleju/imgaug-doc/blob/master/notebooks/B05%20-%20Augment%20Segmentation%20Maps.ipynb) More notebooks: [imgaug-doc/notebooks](https://github.com/aleju/imgaug-doc/tree/master/notebooks). Example ReadTheDocs pages: * [Quick example code on how to use the library](http://imgaug.readthedocs.io/en/latest/source/examples_basics.html) * [Overview of all Augmenters](https://imgaug.readthedocs.io/en/latest/source/overview_of_augmenters.html) * [API](http://imgaug.readthedocs.io/en/latest/source/api.html) More RTD documentation: [imgaug.readthedocs.io](http://imgaug.readthedocs.io/en/latest/source/examples_basics.html). All documentation related files of this project are hosted in the repository [imgaug-doc](https://github.com/aleju/imgaug-doc). <a name="recent_changes"/> ## 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/](changelogs/) for more details. <a name="example_images"/> ## 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. <table> <tr><td colspan="5">meta</td></tr> <tr> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/meta.html#identity">Identity</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/meta.html#channelshuffle">ChannelShuffle</a></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/meta/identity.gif" height="148" width="100" alt="Identity"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/meta/channelshuffle.gif" height="148" width="100" alt="ChannelShuffle"></td> <td> </td> <td> </td> <td> </td> </tr> <tr> </tr> <tr> <td colspan="5">See also: <a href="https://imgaug.readthedocs.io/en/latest/source/overview/meta.html#sequential">Sequential</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/meta.html#someof">SomeOf</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/meta.html#oneof">OneOf</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/meta.html#sometimes">Sometimes</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/meta.html#withchannels">WithChannels</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/meta.html#lambda">Lambda</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/meta.html#assertlambda">AssertLambda</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/meta.html#assertshape">AssertShape</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/meta.html#removecbasbyoutofimagefraction">RemoveCBAsByOutOfImageFraction</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/meta.html#clipcbastoimageplanes">ClipCBAsToImagePlanes</a></td> </tr> <tr><td colspan="5">arithmetic</td></tr> <tr> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#add">Add</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#add">Add</a> (per_channel=True)</td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#additivegaussiannoise">AdditiveGaussianNoise</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#additivegaussiannoise">AdditiveGaussianNoise</a> (per_channel=True)</td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#multiply">Multiply</a></td> </tr> <tr> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/arithmetic/add.gif" height="148" width="100" alt="Add"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/arithmetic/add_per_channel_true.gif" height="148" width="100" alt="Add per_channel=True"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/arithmetic/additivegaussiannoise.gif" height="148" width="100" alt="AdditiveGaussianNoise"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/arithmetic/additivegaussiannoise_per_channel_true.gif" height="148" width="100" alt="AdditiveGaussianNoise per_channel=True"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/arithmetic/multiply.gif" height="148" width="100" alt="Multiply"></td> </tr> <tr> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#cutout">Cutout</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#dropout">Dropout</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#coarsedropout">CoarseDropout</a> (p=0.2)</td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#coarsedropout">CoarseDropout</a> (p=0.2, per_channel=True)</td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#dropout2d">Dropout2d</a></td> </tr> <tr> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/arithmetic/cutout.gif" height="148" width="100" alt="Cutout"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/arithmetic/dropout.gif" height="148" width="100" alt="Dropout"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/arithmetic/coarsedropout_p_0_2.gif" height="148" width="100" alt="CoarseDropout p=0.2"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/arithmetic/coarsedropout_p_0_2_per_channel_true.gif" height="148" width="100" alt="CoarseDropout p=0.2, per_channel=True"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/arithmetic/dropout2d.gif" height="148" width="100" alt="Dropout2d"></td> </tr> <tr> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#saltandpepper">SaltAndPepper</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#coarsesaltandpepper">CoarseSaltAndPepper</a> (p=0.2)</td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#invert">Invert</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#solarize">Solarize</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#jpegcompression">JpegCompression</a></td> </tr> <tr> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/arithmetic/saltandpepper.gif" height="148" width="100" alt="SaltAndPepper"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/arithmetic/coarsesaltandpepper_p_0_2.gif" height="148" width="100" alt="CoarseSaltAndPepper p=0.2"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/arithmetic/invert.gif" height="148" width="100" alt="Invert"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/arithmetic/solarize.gif" height="148" width="100" alt="Solarize"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/arithmetic/jpegcompression.gif" height="148" width="100" alt="JpegCompression"></td> </tr> <tr> </tr> <tr> <td colspan="5">See also: <a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#addelementwise">AddElementwise</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#additivelaplacenoise">AdditiveLaplaceNoise</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#additivepoissonnoise">AdditivePoissonNoise</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#multiplyelementwise">MultiplyElementwise</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#totaldropout">TotalDropout</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#replaceelementwise">ReplaceElementwise</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#impulsenoise">ImpulseNoise</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#salt">Salt</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#pepper">Pepper</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#coarsesalt">CoarseSalt</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#coarsepepper">CoarsePepper</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/arithmetic.html#solarize">Solarize</a></td> </tr> <tr><td colspan="5">artistic</td></tr> <tr> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/artistic.html#cartoon">Cartoon</a></td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/artistic/cartoon.gif" height="144" width="128" alt="Cartoon"></td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr><td colspan="5">blend</td></tr> <tr> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/blend.html#blendalpha">BlendAlpha</a> with EdgeDetect(1.0)</td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/blend.html#blendalphasimplexnoise">BlendAlphaSimplexNoise</a> with EdgeDetect(1.0)</td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/blend.html#blendalphafrequencynoise">BlendAlphaFrequencyNoise</a> with EdgeDetect(1.0)</td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/blend.html#blendalphasomecolors">BlendAlphaSomeColors</a> with RemoveSaturation(1.0)</td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/blend.html#blendalpharegulargrid">BlendAlphaRegularGrid</a> with Multiply((0.0, 0.5))</td> </tr> <tr> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/blend/blendalpha_with_edgedetect_1_0.gif" height="148" width="100" alt="BlendAlpha with EdgeDetect1.0"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/blend/blendalphasimplexnoise_with_edgedetect_1_0.gif" height="148" width="100" alt="BlendAlphaSimplexNoise with EdgeDetect1.0"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/blend/blendalphafrequencynoise_with_edgedetect_1_0.gif" height="148" width="100" alt="BlendAlphaFrequencyNoise with EdgeDetect1.0"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/blend/blendalphasomecolors_with_removesaturation_1_0.gif" height="144" width="128" alt="BlendAlphaSomeColors with RemoveSaturation1.0"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/blend/blendalpharegulargrid_with_multiply_0_0_0_5.gif" height="148" width="100" alt="BlendAlphaRegularGrid with Multiply0.0, 0.5"></td> </tr> <tr> </tr> <tr> <td colspan="5">See also: <a href="https://imgaug.readthedocs.io/en/latest/source/overview/blend.html#blendalphamask">BlendAlphaMask</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/blend.html#blendalphaelementwise">BlendAlphaElementwise</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/blend.html#blendalphaverticallineargradient">BlendAlphaVerticalLinearGradient</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/blend.html#blendalphahorizontallineargradient">BlendAlphaHorizontalLinearGradient</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/blend.html#blendalphasegmapclassids">BlendAlphaSegMapClassIds</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/blend.html#blendalphaboundingboxes">BlendAlphaBoundingBoxes</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/blend.html#blendalphacheckerboard">BlendAlphaCheckerboard</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/api_augmenters_blend.html#imgaug.augmenters.blend.SomeColorsMaskGen">SomeColorsMaskGen</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/api_augmenters_blend.html#imgaug.augmenters.blend.HorizontalLinearGradientMaskGen">HorizontalLinearGradientMaskGen</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/api_augmenters_blend.html#imgaug.augmenters.blend.VerticalLinearGradientMaskGen">VerticalLinearGradientMaskGen</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/api_augmenters_blend.html#imgaug.augmenters.blend.RegularGridMaskGen">RegularGridMaskGen</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/api_augmenters_blend.html#imgaug.augmenters.blend.CheckerboardMaskGen">CheckerboardMaskGen</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/api_augmenters_blend.html#imgaug.augmenters.blend.SegMapClassIdsMaskGen">SegMapClassIdsMaskGen</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/api_augmenters_blend.html#imgaug.augmenters.blend.BoundingBoxesMaskGen">BoundingBoxesMaskGen</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/api_augmenters_blend.html#imgaug.augmenters.blend.InvertMaskGen">InvertMaskGen</a></td> </tr> <tr><td colspan="5">blur</td></tr> <tr> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/blur.html#gaussianblur">GaussianBlur</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/blur.html#averageblur">AverageBlur</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/blur.html#medianblur">MedianBlur</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/blur.html#bilateralblur">BilateralBlur</a> (sigma_color=250, sigma_space=250)</td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/blur.html#motionblur">MotionBlur</a> (angle=0)</td> </tr> <tr> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/blur/gaussianblur.gif" height="148" width="100" alt="GaussianBlur"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/blur/averageblur.gif" height="148" width="100" alt="AverageBlur"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/blur/medianblur.gif" height="148" width="100" alt="MedianBlur"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/blur/bilateralblur_sigma_color_250_sigma_space_250.gif" height="148" width="100" alt="BilateralBlur sigma_color=250, sigma_space=250"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/blur/motionblur_angle_0.gif" height="148" width="100" alt="MotionBlur angle=0"></td> </tr> <tr> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/blur.html#motionblur">MotionBlur</a> (k=5)</td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/blur.html#meanshiftblur">MeanShiftBlur</a></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/blur/motionblur_k_5.gif" height="148" width="100" alt="MotionBlur k=5"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/blur/meanshiftblur.gif" height="148" width="100" alt="MeanShiftBlur"></td> <td> </td> <td> </td> <td> </td> </tr> <tr><td colspan="5">collections</td></tr> <tr> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/collections.html#randaugment">RandAugment</a></td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/collections/randaugment.gif" height="148" width="100" alt="RandAugment"></td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr><td colspan="5">color</td></tr> <tr> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/color.html#multiplyandaddtobrightness">MultiplyAndAddToBrightness</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/color.html#multiplyhueandsaturation">MultiplyHueAndSaturation</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/color.html#multiplyhue">MultiplyHue</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/color.html#multiplysaturation">MultiplySaturation</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/color.html#addtohueandsaturation">AddToHueAndSaturation</a></td> </tr> <tr> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/color/multiplyandaddtobrightness.gif" height="148" width="100" alt="MultiplyAndAddToBrightness"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/color/multiplyhueandsaturation.gif" height="148" width="100" alt="MultiplyHueAndSaturation"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/color/multiplyhue.gif" height="148" width="100" alt="MultiplyHue"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/color/multiplysaturation.gif" height="148" width="100" alt="MultiplySaturation"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/color/addtohueandsaturation.gif" height="148" width="100" alt="AddToHueAndSaturation"></td> </tr> <tr> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/color.html#grayscale">Grayscale</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/color.html#removesaturation">RemoveSaturation</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/color.html#changecolortemperature">ChangeColorTemperature</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/color.html#kmeanscolorquantization">KMeansColorQuantization</a> (to_colorspace=RGB)</td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/color.html#uniformcolorquantization">UniformColorQuantization</a> (to_colorspace=RGB)</td> </tr> <tr> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/color/grayscale.gif" height="148" width="100" alt="Grayscale"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/color/removesaturation.gif" height="148" width="100" alt="RemoveSaturation"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/color/changecolortemperature.gif" height="148" width="100" alt="ChangeColorTemperature"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/color/kmeanscolorquantization_to_colorspace_rgb.gif" height="148" width="100" alt="KMeansColorQuantization to_colorspace=RGB"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/color/uniformcolorquantization_to_colorspace_rgb.gif" height="148" width="100" alt="UniformColorQuantization to_colorspace=RGB"></td> </tr> <tr> </tr> <tr> <td colspan="5">See also: <a href="https://imgaug.readthedocs.io/en/latest/source/overview/color.html#withcolorspace">WithColorspace</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/color.html#withbrightnesschannels">WithBrightnessChannels</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/color.html#multiplybrightness">MultiplyBrightness</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/color.html#addtobrightness">AddToBrightness</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/color.html#withhueandsaturation">WithHueAndSaturation</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/color.html#addtohue">AddToHue</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/color.html#addtosaturation">AddToSaturation</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/color.html#changecolorspace">ChangeColorspace</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/color.html#posterize">Posterize</a></td> </tr> <tr><td colspan="5">contrast</td></tr> <tr> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/contrast.html#gammacontrast">GammaContrast</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/contrast.html#gammacontrast">GammaContrast</a> (per_channel=True)</td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/contrast.html#sigmoidcontrast">SigmoidContrast</a> (cutoff=0.5)</td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/contrast.html#sigmoidcontrast">SigmoidContrast</a> (gain=10)</td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/contrast.html#logcontrast">LogContrast</a></td> </tr> <tr> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/contrast/gammacontrast.gif" height="148" width="100" alt="GammaContrast"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/contrast/gammacontrast_per_channel_true.gif" height="148" width="100" alt="GammaContrast per_channel=True"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/contrast/sigmoidcontrast_cutoff_0_5.gif" height="148" width="100" alt="SigmoidContrast cutoff=0.5"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/contrast/sigmoidcontrast_gain_10.gif" height="148" width="100" alt="SigmoidContrast gain=10"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/contrast/logcontrast.gif" height="148" width="100" alt="LogContrast"></td> </tr> <tr> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/contrast.html#linearcontrast">LinearContrast</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/contrast.html#allchannelshistogramequalization">AllChannels-</a> HistogramEqualization</td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/contrast.html#histogramequalization">HistogramEqualization</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/contrast.html#allchannelsclahe">AllChannelsCLAHE</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/contrast.html#clahe">CLAHE</a></td> </tr> <tr> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/contrast/linearcontrast.gif" height="148" width="100" alt="LinearContrast"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/contrast/allchannels_histogramequalization.gif" height="148" width="100" alt="AllChannels- HistogramEqualization"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/contrast/histogramequalization.gif" height="148" width="100" alt="HistogramEqualization"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/contrast/allchannelsclahe.gif" height="148" width="100" alt="AllChannelsCLAHE"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/contrast/clahe.gif" height="148" width="100" alt="CLAHE"></td> </tr> <tr> </tr> <tr> <td colspan="5">See also: <a href="https://imgaug.readthedocs.io/en/latest/source/overview/contrast.html#equalize">Equalize</a></td> </tr> <tr><td colspan="5">convolutional</td></tr> <tr> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/convolutional.html#sharpen">Sharpen</a> (alpha=1)</td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/convolutional.html#emboss">Emboss</a> (alpha=1)</td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/convolutional.html#edgedetect">EdgeDetect</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/convolutional.html#directededgedetect">DirectedEdgeDetect</a> (alpha=1)</td> <td> </td> </tr> <tr> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/convolutional/sharpen_alpha_1.gif" height="148" width="100" alt="Sharpen alpha=1"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/convolutional/emboss_alpha_1.gif" height="148" width="100" alt="Emboss alpha=1"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/convolutional/edgedetect.gif" height="148" width="100" alt="EdgeDetect"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/convolutional/directededgedetect_alpha_1.gif" height="148" width="100" alt="DirectedEdgeDetect alpha=1"></td> <td> </td> </tr> <tr> </tr> <tr> <td colspan="5">See also: <a href="https://imgaug.readthedocs.io/en/latest/source/overview/convolutional.html#convolve">Convolve</a></td> </tr> <tr> <td colspan="5">debug</td></tr> <tr> <td colspan="5">See also: <a href="https://imgaug.readthedocs.io/en/latest/source/overview/debug.html#savedebugimageeverynbatches">SaveDebugImageEveryNBatches</a></td> </tr> <tr><td colspan="5">edges</td></tr> <tr> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/edges.html#canny">Canny</a></td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/edges/canny.gif" height="148" width="100" alt="Canny"></td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr><td colspan="5">flip</td></tr> <tr> <td colspan="2"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/flip.html#fliplr">Fliplr</a></td> <td colspan="2"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/flip.html#flipud">Flipud</a></td> <td> </td> </tr> <tr> <td colspan="2"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/flip/fliplr.gif" height="148" width="300" alt="Fliplr"></td> <td colspan="2"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/flip/flipud.gif" height="148" width="300" alt="Flipud"></td> <td> </td> </tr> <tr> </tr> <tr> <td colspan="5">See also: <a href="https://imgaug.readthedocs.io/en/latest/source/overview/color.html#horizontalflip">HorizontalFlip</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/color.html#verticalflip">VerticalFlip</a></td> </tr> <tr><td colspan="5">geometric</td></tr> <tr> <td colspan="2"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/geometric.html#affine">Affine</a></td> <td colspan="2"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/geometric.html#affine">Affine: Modes</a></td> <td> </td> </tr> <tr> <td colspan="2"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/geometric/affine.gif" height="148" width="300" alt="Affine"></td> <td colspan="2"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/geometric/affine_modes.gif" height="148" width="300" alt="Affine: Modes"></td> <td> </td> </tr> <tr> <td colspan="2"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/geometric.html#affine">Affine: cval</a></td> <td colspan="2"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/geometric.html#piecewiseaffine">PiecewiseAffine</a></td> <td> </td> </tr> <tr> <td colspan="2"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/geometric/affine_cval.gif" height="148" width="300" alt="Affine: cval"></td> <td colspan="2"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/geometric/piecewiseaffine.gif" height="148" width="300" alt="PiecewiseAffine"></td> <td> </td> </tr> <tr> <td colspan="2"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/geometric.html#perspectivetransform">PerspectiveTransform</a></td> <td colspan="2"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/geometric.html#elastictransformation">ElasticTransformation</a> (sigma=1.0)</td> <td> </td> </tr> <tr> <td colspan="2"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/geometric/perspectivetransform.gif" height="148" width="300" alt="PerspectiveTransform"></td> <td colspan="2"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/geometric/elastictransformation_sigma_1_0.gif" height="148" width="300" alt="ElasticTransformation sigma=1.0"></td> <td> </td> </tr> <tr> <td colspan="2"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/geometric.html#elastictransformation">ElasticTransformation</a> (sigma=4.0)</td> <td colspan="2"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/geometric.html#rot90">Rot90</a></td> <td> </td> </tr> <tr> <td colspan="2"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/geometric/elastictransformation_sigma_4_0.gif" height="148" width="300" alt="ElasticTransformation sigma=4.0"></td> <td colspan="2"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/geometric/rot90.gif" height="148" width="300" alt="Rot90"></td> <td> </td> </tr> <tr> <td colspan="2"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/geometric.html#withpolarwarping">WithPolarWarping</a> +Affine</td> <td colspan="2"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/geometric.html#jigsaw">Jigsaw</a> (5x5 grid)</td> <td> </td> </tr> <tr> <td colspan="2"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/geometric/withpolarwarping_affine.gif" height="148" width="300" alt="WithPolarWarping +Affine"></td> <td colspan="2"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/geometric/jigsaw_5x5_grid.gif" height="148" width="300" alt="Jigsaw 5x5 grid"></td> <td> </td> </tr> <tr> </tr> <tr> <td colspan="5">See also: <a href="https://imgaug.readthedocs.io/en/latest/source/overview/geometric.html#scalex">ScaleX</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/geometric.html#scaley">ScaleY</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/geometric.html#translatex">TranslateX</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/geometric.html#translatey">TranslateY</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/geometric.html#rotate">Rotate</a></td> </tr> <tr><td colspan="5">imgcorruptlike</td></tr> <tr> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/imgcorruptlike.html#glassblur">GlassBlur</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/imgcorruptlike.html#defocusblur">DefocusBlur</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/imgcorruptlike.html#zoomblur">ZoomBlur</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/imgcorruptlike.html#snow">Snow</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/imgcorruptlike.html#spatter">Spatter</a></td> </tr> <tr> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/imgcorruptlike/glassblur.gif" height="148" width="100" alt="GlassBlur"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/imgcorruptlike/defocusblur.gif" height="148" width="100" alt="DefocusBlur"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/imgcorruptlike/zoomblur.gif" height="148" width="100" alt="ZoomBlur"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/imgcorruptlike/snow.gif" height="148" width="100" alt="Snow"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/imgcorruptlike/spatter.gif" height="148" width="100" alt="Spatter"></td> </tr> <tr> </tr> <tr> <td colspan="5">See also: <a href="https://imgaug.readthedocs.io/en/latest/source/overview/imgcorruptlike.html#gaussiannoise">GaussianNoise</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/imgcorruptlike.html#shotnoise">ShotNoise</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/imgcorruptlike.html#impulsenoise">ImpulseNoise</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/imgcorruptlike.html#specklenoise">SpeckleNoise</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/imgcorruptlike.html#gaussianblur">GaussianBlur</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/imgcorruptlike.html#motionblur">MotionBlur</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/imgcorruptlike.html#fog">Fog</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/imgcorruptlike.html#frost">Frost</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/imgcorruptlike.html#contrast">Contrast</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/imgcorruptlike.html#brightness">Brightness</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/imgcorruptlike.html#saturate">Saturate</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/imgcorruptlike.html#jpegcompression">JpegCompression</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/imgcorruptlike.html#pixelate">Pixelate</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/imgcorruptlike.html#elastictransform">ElasticTransform</a></td> </tr> <tr><td colspan="5">pillike</td></tr> <tr> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/pillike.html#autocontrast">Autocontrast</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/pillike.html#enhancecolor">EnhanceColor</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/pillike.html#enhancesharpness">EnhanceSharpness</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/pillike.html#filteredgeenhancemore">FilterEdgeEnhanceMore</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/pillike.html#filtercontour">FilterContour</a></td> </tr> <tr> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/pillike/autocontrast.gif" height="148" width="100" alt="Autocontrast"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/pillike/enhancecolor.gif" height="148" width="100" alt="EnhanceColor"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/pillike/enhancesharpness.gif" height="148" width="100" alt="EnhanceSharpness"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/pillike/filteredgeenhancemore.gif" height="148" width="100" alt="FilterEdgeEnhanceMore"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/pillike/filtercontour.gif" height="148" width="100" alt="FilterContour"></td> </tr> <tr> </tr> <tr> <td colspan="5">See also: <a href="https://imgaug.readthedocs.io/en/latest/source/overview/pillike.html#solarize">Solarize</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/pillike.html#posterize">Posterize</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/pillike.html#equalize">Equalize</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/pillike.html#enhancecontrast">EnhanceContrast</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/pillike.html#enhancebrightness">EnhanceBrightness</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/pillike.html#filterblur">FilterBlur</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/pillike.html#filtersmooth">FilterSmooth</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/pillike.html#filtersmoothmore">FilterSmoothMore</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/pillike.html#filteredgeenhance">FilterEdgeEnhance</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/pillike.html#filterfindedges">FilterFindEdges</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/pillike.html#filteremboss">FilterEmboss</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/pillike.html#filtersharpen">FilterSharpen</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/pillike.html#filterdetail">FilterDetail</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/pillike.html#affine">Affine</a></td> </tr> <tr><td colspan="5">pooling</td></tr> <tr> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/pooling.html#averagepooling">AveragePooling</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/pooling.html#maxpooling">MaxPooling</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/pooling.html#minpooling">MinPooling</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/pooling.html#medianpooling">MedianPooling</a></td> <td> </td> </tr> <tr> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/pooling/averagepooling.gif" height="148" width="100" alt="AveragePooling"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/pooling/maxpooling.gif" height="148" width="100" alt="MaxPooling"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/pooling/minpooling.gif" height="148" width="100" alt="MinPooling"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/pooling/medianpooling.gif" height="148" width="100" alt="MedianPooling"></td> <td> </td> </tr> <tr><td colspan="5">segmentation</td></tr> <tr> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/segmentation.html#superpixels">Superpixels</a> (p_replace=1)</td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/segmentation.html#superpixels">Superpixels</a> (n_segments=100)</td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/segmentation.html#uniformvoronoi">UniformVoronoi</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/segmentation.html#regulargridvoronoi">RegularGridVoronoi: rows/cols</a> (p_drop_points=0)</td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/segmentation.html#regulargridvoronoi">RegularGridVoronoi: p_drop_points</a> (n_rows=n_cols=30)</td> </tr> <tr> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/segmentation/superpixels_p_replace_1.gif" height="148" width="100" alt="Superpixels p_replace=1"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/segmentation/superpixels_n_segments_100.gif" height="148" width="100" alt="Superpixels n_segments=100"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/segmentation/uniformvoronoi.gif" height="148" width="100" alt="UniformVoronoi"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/segmentation/regulargridvoronoi_rows_cols_p_drop_points_0.gif" height="148" width="100" alt="RegularGridVoronoi: rows/cols p_drop_points=0"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/segmentation/regulargridvoronoi_p_drop_points_n_rows_n_cols_30.gif" height="148" width="100" alt="RegularGridVoronoi: p_drop_points n_rows=n_cols=30"></td> </tr> <tr> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/segmentation.html#regulargridvoronoi">RegularGridVoronoi: p_replace</a> (n_rows=n_cols=16)</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/segmentation/regulargridvoronoi_p_replace_n_rows_n_cols_16.gif" height="148" width="100" alt="RegularGridVoronoi: p_replace n_rows=n_cols=16"></td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> </tr> <tr> <td colspan="5">See also: <a href="https://imgaug.readthedocs.io/en/latest/source/overview/segmentation.html#voronoi">Voronoi</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/segmentation.html#relativeregulargridvoronoi">RelativeRegularGridVoronoi</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/api_augmenters_segmentation.html#imgaug.augmenters.segmentation.RegularGridPointsSampler">RegularGridPointsSampler</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/api_augmenters_segmentation.html#imgaug.augmenters.segmentation.RelativeRegularGridPointsSampler">RelativeRegularGridPointsSampler</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/api_augmenters_segmentation.html#imgaug.augmenters.segmentation.DropoutPointsSampler">DropoutPointsSampler</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/api_augmenters_segmentation.html#imgaug.augmenters.segmentation.UniformPointsSampler">UniformPointsSampler</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/api_augmenters_segmentation.html#imgaug.augmenters.segmentation.SubsamplingPointsSampler">SubsamplingPointsSampler</a></td> </tr> <tr><td colspan="5">size</td></tr> <tr> <td colspan="2"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#cropandpad">CropAndPad</a></td> <td colspan="2"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#crop">Crop</a></td> <td> </td> </tr> <tr> <td colspan="2"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/size/cropandpad.gif" height="148" width="300" alt="CropAndPad"></td> <td colspan="2"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/size/crop.gif" height="148" width="300" alt="Crop"></td> <td> </td> </tr> <tr> <td colspan="2"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#pad">Pad</a></td> <td colspan="2"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#padtofixedsize">PadToFixedSize</a> (height'=height+32, width'=width+32)</td> <td> </td> </tr> <tr> <td colspan="2"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/size/pad.gif" height="148" width="300" alt="Pad"></td> <td colspan="2"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/size/padtofixedsize_height_height_32_width_width_32.gif" height="148" width="300" alt="PadToFixedSize height'=height+32, width'=width+32"></td> <td> </td> </tr> <tr> <td colspan="2"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#croptofixedsize">CropToFixedSize</a> (height'=height-32, width'=width-32)</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td colspan="2"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/size/croptofixedsize_height_height_32_width_width_32.gif" height="148" width="300" alt="CropToFixedSize height'=height-32, width'=width-32"></td> <td> </td> <td> </td> <td> </td> </tr> <tr> </tr> <tr> <td colspan="5">See also: <a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#resize">Resize</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#croptomultiplesof">CropToMultiplesOf</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#padtomultiplesof">PadToMultiplesOf</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#croptopowersof">CropToPowersOf</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#padtopowersof">PadToPowersOf</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#croptoaspectratio">CropToAspectRatio</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#padtoaspectratio">PadToAspectRatio</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#croptosquare">CropToSquare</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#padtosquare">PadToSquare</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#centercroptofixedsize">CenterCropToFixedSize</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#centerpadtofixedsize">CenterPadToFixedSize</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#centercroptomultiplesof">CenterCropToMultiplesOf</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#centerpadtomultiplesof">CenterPadToMultiplesOf</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#centercroptopowersof">CenterCropToPowersOf</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#centerpadtopowersof">CenterPadToPowersOf</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#centercroptoaspectratio">CenterCropToAspectRatio</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#centerpadtoaspectratio">CenterPadToAspectRatio</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#centercroptosquare">CenterCropToSquare</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#centerpadtosquare">CenterPadToSquare</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/size.html#keepsizebyresize">KeepSizeByResize</a></td> </tr> <tr><td colspan="5">weather</td></tr> <tr> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/weather.html#fastsnowylandscape">FastSnowyLandscape</a> (lightness_multiplier=2.0)</td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/weather.html#clouds">Clouds</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/weather.html#fog">Fog</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/weather.html#snowflakes">Snowflakes</a></td> <td colspan="1"><a href="https://imgaug.readthedocs.io/en/latest/source/overview/weather.html#rain">Rain</a></td> </tr> <tr> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/weather/fastsnowylandscape_lightness_multiplier_2_0.gif" height="144" width="128" alt="FastSnowyLandscape lightness_multiplier=2.0"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/weather/clouds.gif" height="144" width="128" alt="Clouds"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/weather/fog.gif" height="144" width="128" alt="Fog"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/weather/snowflakes.gif" height="144" width="128" alt="Snowflakes"></td> <td colspan="1"><img src="https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/augmenter_videos/weather/rain.gif" height="144" width="128" alt="Rain"></td> </tr> <tr> </tr> <tr> <td colspan="5">See also: <a href="https://imgaug.readthedocs.io/en/latest/source/overview/weather.html#cloudlayer">CloudLayer</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/weather.html#snowflakeslayer">SnowflakesLayer</a>, <a href="https://imgaug.readthedocs.io/en/latest/source/overview/weather.html#rainlayer">RainLayer</a></td> </tr> </table> <a name="code_examples"/> ## Code Examples ### Example: Simple Training Setting A standard machine learning situation. Train on batches of images and augment each batch via crop, horizontal flip ("Fliplr") and gaussian blur: ```python import numpy as np import imgaug.augmenters as iaa def load_batch(batch_idx): # dummy function, implement this # Return a numpy array of shape (N, height, width, #channels) # or a list of (height, width, #channels) arrays (may have different image # sizes). # Images should be in RGB for colorspace augmentations. # (cv2.imread() returns BGR!) # Images should usually be in uint8 with values from 0-255. return np.zeros((128, 32, 32, 3), dtype=np.uint8) + (batch_idx % 255) def train_on_images(images): # dummy function, implement this pass # Pipeline: # (1) Crop images from each side by 1-16px, do not resize the results # images back to the input size. Keep them at the cropped size. # (2) Horizontally flip 50% of the images. # (3) Blur images using a gaussian kernel with sigma between 0.0 and 3.0. seq = iaa.Sequential([ iaa.Crop(px=(1, 16), keep_size=False), iaa.Fliplr(0.5), iaa.GaussianBlur(sigma=(0, 3.0)) ]) for batch_idx in range(100): images = load_batch(batch_idx) images_aug = seq(images=images) # done by the library train_on_images(images_aug) ``` ### Example: Very Complex Augmentation Pipeline Apply a very heavy augmentation pipeline to images (used to create the image at the very top of this readme): ```python import numpy as np import imgaug as ia import imgaug.augmenters as iaa # random example images images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8) # Sometimes(0.5, ...) applies the given augmenter in 50% of all cases, # e.g. Sometimes(0.5, GaussianBlur(0.3)) would blur roughly every second image. sometimes = lambda aug: iaa.Sometimes(0.5, aug) # Define our sequence of augmentation steps that will be applied to every image # All augmenters with per_channel=0.5 will sample one value _per image_ # in 50% of all cases. In all other cases they will sample new values # _per channel_. seq = iaa.Sequential( [ # apply the following augmenters to most images iaa.Fliplr(0.5), # horizontally flip 50% of all images iaa.Flipud(0.2), # vertically flip 20% of all images # crop images by -5% to 10% of their height/width sometimes(iaa.CropAndPad( percent=(-0.05, 0.1), pad_mode=ia.ALL, pad_cval=(0, 255) )), sometimes(iaa.Affine( scale={"x": (0.8, 1.2), "y": (0.8, 1.2)}, # scale images to 80-120% of their size, individually per axis translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)}, # translate by -20 to +20 percent (per axis) rotate=(-45, 45), # rotate by -45 to +45 degrees shear=(-16, 16), # shear by -16 to +16 degrees order=[0, 1], # use nearest neighbour or bilinear interpolation (fast) cval=(0, 255), # if mode is constant, use a cval between 0 and 255 mode=ia.ALL # use any of scikit-image's warping modes (see 2nd image from the top for examples) )), # execute 0 to 5 of the following (less important) augmenters per image # don't execute all of them, as that would often be way too strong iaa.SomeOf((0, 5), [ sometimes(iaa.Superpixels(p_replace=(0, 1.0), n_segments=(20, 200))), # convert images into their superpixel representation iaa.OneOf([ iaa.GaussianBlur((0, 3.0)), # blur images with a sigma between 0 and 3.0 iaa.AverageBlur(k=(2, 7)), # blur image using local means with kernel sizes between 2 and 7 iaa.MedianBlur(k=(3, 11)), # blur image using local medians with kernel sizes between 2 and 7 ]), iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)), # sharpen images iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)), # emboss images # search either for all edges or for directed edges, # blend the result with the original image using a blobby mask iaa.SimplexNoiseAlpha(iaa.OneOf([ iaa.EdgeDetect(alpha=(0.5, 1.0)), iaa.DirectedEdgeDetect(alpha=(0.5, 1.0), direction=(0.0, 1.0)), ])), iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5), # add gaussian noise to images iaa.OneOf([ iaa.Dropout((0.01, 0.1), per_channel=0.5), # randomly remove up to 10% of the pixels iaa.CoarseDropout((0.03, 0.15), size_percent=(0.02, 0.05), per_channel=0.2), ]), iaa.Invert(0.05, per_channel=True), # invert color channels iaa.Add((-10, 10), per_channel=0.5), # change brightness of images (by -10 to 10 of original value) iaa.AddToHueAndSaturation((-20, 20)), # change hue and saturation # either change the brightness of the whole image (sometimes # per channel) or change the brightness of subareas iaa.OneOf([ iaa.Multiply((0.5, 1.5), per_channel=0.5), iaa.FrequencyNoiseAlpha( exponent=(-4, 0), first=iaa.Multiply((0.5, 1.5), per_channel=True), second=iaa.LinearContrast((0.5, 2.0)) ) ]), iaa.LinearContrast((0.5, 2.0), per_channel=0.5), # improve or worsen the contrast iaa.Grayscale(alpha=(0.0, 1.0)), sometimes(iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)), # move pixels locally around (with random strengths) sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.05))), # sometimes move parts of the image around sometimes(iaa.PerspectiveTransform(scale=(0.01, 0.1))) ], random_order=True ) ], random_order=True ) images_aug = seq(images=images) ``` ### Example: Augment Images and Keypoints Augment images and keypoints/landmarks on the same images: ```python import numpy as np import imgaug.augmenters as iaa images = np.zeros((2, 128, 128, 3), dtype=np.uint8) # two example images images[:, 64, 64, :] = 255 points = [ [(10.5, 20.5)], # points on first image [(50.5, 50.5), (60.5, 60.5), (70.5, 70.5)] # points on second image ] seq = iaa.Sequential([ iaa.AdditiveGaussianNoise(scale=0.05*255), iaa.Affine(translate_px={"x": (1, 5)}) ]) # augment keypoints and images images_aug, points_aug = seq(images=images, keypoints=points) print("Image 1 center", np.argmax(images_aug[0, 64, 64:64+6, 0])) print("Image 2 center", np.argmax(images_aug[1, 64, 64:64+6, 0])) print("Points 1", points_aug[0]) print("Points 2", points_aug[1]) ``` Note that all coordinates in `imgaug` are subpixel-accurate, which is why `x=0.5, y=0.5` denotes the center of the top left pixel. ### Example: Augment Images and Bounding Boxes ```python import numpy as np import imgaug as ia import imgaug.augmenters as iaa images = np.zeros((2, 128, 128, 3), dtype=np.uint8) # two example images images[:, 64, 64, :] = 255 bbs = [ [ia.BoundingBox(x1=10.5, y1=15.5, x2=30.5, y2=50.5)], [ia.BoundingBox(x1=10.5, y1=20.5, x2=50.5, y2=50.5), ia.BoundingBox(x1=40.5, y1=75.5, x2=70.5, y2=100.5)] ] seq = iaa.Sequential([ iaa.AdditiveGaussianNoise(scale=0.05*255), iaa.Affine(translate_px={"x": (1, 5)}) ]) images_aug, bbs_aug = seq(images=images, bounding_boxes=bbs) ``` ### Example: Augment Images and Polygons ```python import numpy as np import imgaug as ia import imgaug.augmenters as iaa images = np.zeros((2, 128, 128, 3), dtype=np.uint8) # two example images images[:, 64, 64, :] = 255 polygons = [ [ia.Polygon([(10.5, 10.5), (50.5, 10.5), (50.5, 50.5)])], [ia.Polygon([(0.0, 64.5), (64.5, 0.0), (128.0, 128.0), (64.5, 128.0)])] ] seq = iaa.Sequential([ iaa.AdditiveGaussianNoise(scale=0.05*255), iaa.Affine(translate_px={"x": (1, 5)}) ]) images_aug, polygons_aug = seq(images=images, polygons=polygons) ``` ### Example: Augment Images and LineStrings LineStrings are similar to polygons, but are not closed, may intersect with themselves and don't have an inner area. ```python import numpy as np import imgaug as ia import imgaug.augmenters as iaa images = np.zeros((2, 128, 128, 3), dtype=np.uint8) # two example images images[:, 64, 64, :] = 255 ls = [ [ia.LineString([(10.5, 10.5), (50.5, 10.5), (50.5, 50.5)])], [ia.LineString([(0.0, 64.5), (64.5, 0.0), (128.0, 128.0), (64.5, 128.0), (128.0, 0.0)])] ] seq = iaa.Sequential([ iaa.AdditiveGaussianNoise(scale=0.05*255), iaa.Affine(translate_px={"x": (1, 5)}) ]) images_aug, ls_aug = seq(images=images, line_strings=ls) ``` ### Example: Augment Images and Heatmaps Heatmaps are dense float arrays with values between `0.0` and `1.0`. They can be used e.g. when training models to predict facial landmark locations. Note that the heatmaps here have lower height and width than the images. `imgaug` handles that case automatically. The crop pixel amounts will be halved for the heatmaps. ```python import numpy as np import imgaug.augmenters as iaa # Standard scenario: You have N RGB-images and additionally 21 heatmaps per # image. You want to augment each image and its heatmaps identically. images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8) heatmaps = np.random.random(size=(16, 64, 64, 1)).astype(np.float32) seq = iaa.Sequential([ iaa.GaussianBlur((0, 3.0)), iaa.Affine(translate_px={"x": (-40, 40)}), iaa.Crop(px=(0, 10)) ]) images_aug, heatmaps_aug = seq(images=images, heatmaps=heatmaps) ``` ### Example: Augment Images and Segmentation Maps This is similar to heatmaps, but the dense arrays have dtype `int32`. Operations such as resizing will automatically use nearest neighbour interpolation. ```python import numpy as np import imgaug.augmenters as iaa # Standard scenario: You have N=16 RGB-images and additionally one segmentation # map per image. You want to augment each image and its heatmaps identically. images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8) segmaps = np.random.randint(0, 10, size=(16, 64, 64, 1), dtype=np.int32) seq = iaa.Sequential([ iaa.GaussianBlur((0, 3.0)), iaa.Affine(translate_px={"x": (-40, 40)}), iaa.Crop(px=(0, 10)) ]) images_aug, segmaps_aug = seq(images=images, segmentation_maps=segmaps) ``` ### Example: Visualize Augmented Images Quickly show example results of your augmentation sequence: ```python import numpy as np import imgaug.augmenters as iaa images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8) seq = iaa.Sequential([iaa.Fliplr(0.5), iaa.GaussianBlur((0, 3.0))]) # Show an image with 8*8 augmented versions of image 0 and 8*8 augmented # versions of image 1. Identical augmentations will be applied to # image 0 and 1. seq.show_grid([images[0], images[1]], cols=8, rows=8) ``` ### Example: Visualize Augmented Non-Image Data `imgaug` contains many helper function, among these functions to quickly visualize augmented non-image results, such as bounding boxes or heatmaps. ```python import numpy as np import imgaug as ia image = np.zeros((64, 64, 3), dtype=np.uint8) # points kps = [ia.Keypoint(x=10.5, y=20.5), ia.Keypoint(x=60.5, y=60.5)] kpsoi = ia.KeypointsOnImage(kps, shape=image.shape) image_with_kps = kpsoi.draw_on_image(image, size=7, color=(0, 0, 255)) ia.imshow(image_with_kps) # bbs bbsoi = ia.BoundingBoxesOnImage([ ia.BoundingBox(x1=10.5, y1=20.5, x2=50.5, y2=30.5) ], shape=image.shape) image_with_bbs = bbsoi.draw_on_image(image) image_with_bbs = ia.BoundingBox( x1=50.5, y1=10.5, x2=100.5, y2=16.5 ).draw_on_image(image_with_bbs, color=(255, 0, 0), size=3) ia.imshow(image_with_bbs) # polygons psoi = ia.PolygonsOnImage([ ia.Polygon([(10.5, 20.5), (50.5, 30.5), (10.5, 50.5)]) ], shape=image.shape) image_with_polys = psoi.draw_on_image( image, alpha_points=0, alpha_face=0.5, color_lines=(255, 0, 0)) ia.imshow(image_with_polys) # heatmaps hms = ia.HeatmapsOnImage(np.random.random(size=(32, 32, 1)).astype(np.float32), shape=image.shape) image_with_hms = hms.draw_on_image(image) ia.imshow(image_with_hms) ``` LineStrings and segmentation maps support similar methods as shown above. ### Example: Using Augmenters Only Once While the interface is adapted towards re-using instances of augmenters many times, you are also free to use them only once. The overhead to instantiate the augmenters each time is usually negligible. ```python from imgaug import augmenters as iaa import numpy as np images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8) # always horizontally flip each input image images_aug = iaa.Fliplr(1.0)(images=images) # vertically flip each input image with 90% probability images_aug = iaa.Flipud(0.9)(images=images) # blur 50% of all images using a gaussian kernel with a sigma of 3.0 images_aug = iaa.Sometimes(0.5, iaa.GaussianBlur(3.0))(images=images) ``` ### Example: Multicore Augmentation Images can be augmented in **background processes** using the method `augment_batches(batches, background=True)`, where `batches` is a list/generator of [imgaug.augmentables.batches.UnnormalizedBatch](https://imgaug.readthedocs.io/en/latest/_modules/imgaug/augmentables/batches.html#UnnormalizedBatch) or [imgaug.augmentables.batches.Batch](https://imgaug.readthedocs.io/en/latest/source/api_augmentables_batches.html#imgaug.augmentables.batches.Batch). The following example augments a list of image batches in the background: ```python import skimage.data import imgaug as ia import imgaug.augmenters as iaa from imgaug.augmentables.batches import UnnormalizedBatch # Number of batches and batch size for this example nb_batches = 10 batch_size = 32 # Example augmentation sequence to run in the background augseq = iaa.Sequential([ iaa.Fliplr(0.5), iaa.CoarseDropout(p=0.1, size_percent=0.1) ]) # For simplicity, we use the same image here many times astronaut = skimage.data.astronaut() astronaut = ia.imresize_single_image(astronaut, (64, 64)) # Make batches out of the example image (here: 10 batches, each 32 times # the example image) batches = [] for _ in range(nb_batches): batches.append(UnnormalizedBatch(images=[astronaut] * batch_size)) # Show the augmented images. # Note that augment_batches() returns a generator. for images_aug in augseq.augment_batches(batches, background=True): ia.imshow(ia.draw_grid(images_aug.images_aug, cols=8)) ``` If you need more control over the background augmentation, e.g. to set seeds, control the number of used CPU cores or constraint the memory usage, see the corresponding [multicore augmentation notebook](https://nbviewer.jupyter.org/github/aleju/imgaug-doc/blob/master/notebooks/A03%20-%20Multicore%20Augmentation.ipynb) or the API about [Augmenter.pool()](https://imgaug.readthedocs.io/en/latest/source/api_augmenters_meta.html#imgaug.augmenters.meta.Augmenter.pool) and [imgaug.multicore.Pool](https://imgaug.readthedocs.io/en/latest/source/api_multicore.html#imgaug.multicore.Pool). ### Example: Probability Distributions as Parameters Most augmenters support using tuples `(a, b)` as a shortcut to denote `uniform(a, b)` or lists `[a, b, c]` to denote a set of allowed values from which one will be picked randomly. If you require more complex probability distributions (e.g. gaussians, truncated gaussians or poisson distributions) you can use stochastic parameters from `imgaug.parameters`: ```python import numpy as np from imgaug import augmenters as iaa from imgaug import parameters as iap images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8) # Blur by a value sigma which is sampled from a uniform distribution # of range 10.1 <= x < 13.0. # The convenience shortcut for this is: GaussianBlur((10.1, 13.0)) blurer = iaa.GaussianBlur(10 + iap.Uniform(0.1, 3.0)) images_aug = blurer(images=images) # Blur by a value sigma which is sampled from a gaussian distribution # N(1.0, 0.1), i.e. sample a value that is usually around 1.0. # Clip the resulting value so that it never gets below 0.1 or above 3.0. blurer = iaa.GaussianBlur(iap.Clip(iap.Normal(1.0, 0.1), 0.1, 3.0)) images_aug = blurer(images=images) ``` There are many more probability distributions in the library, e.g. truncated gaussian distribution, poisson distribution or beta distribution. ### Example: WithChannels Apply an augmenter only to specific image channels: ```python import numpy as np import imgaug.augmenters as iaa # fake RGB images images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8) # add a random value from the range (-30, 30) to the first two channels of # input images (e.g. to the R and G channels) aug = iaa.WithChannels( channels=[0, 1], children=iaa.Add((-30, 30)) ) images_aug = aug(images=images) ``` <a name="citation"/> ## Citation  If this library has helped you during your research, feel free to cite it: ```latex @misc{imgaug, author = {Jung, Alexander B. and Wada, Kentaro and Crall, Jon and Tanaka, Satoshi and Graving, Jake and Reinders, Christoph and Yadav, Sarthak and Banerjee, Joy and Vecsei, Gábor and Kraft, Adam and Rui, Zheng and Borovec, Jirka and Vallentin, Christian and Zhydenko, Semen and Pfeiffer, Kilian and Cook, Ben and Fernández, Ismael and De Rainville, François-Michel and Weng, Chi-Hung and Ayala-Acevedo, Abner and Meudec, Raphael and Laporte, Matias and others}, title = {{imgaug}}, howpublished = {\url{https://github.com/aleju/imgaug}}, year = {2020}, note = {Online; accessed 01-Feb-2020} } ```

Data Labeling

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papers

# About This repository contains short summaries of some machine learning papers. ## Added 2018/10/01: * ****`UNSUPERVISED LEARNING`**** ****`ECCV 2018`**** [Deep Clustering for Unsupervised Learning of Visual Features](neural-nets/Deep_Clustering_for_Unsupervised_Learning_of_Visual_Features.md) * ****`OBJECT DETECTION`**** ****`POINT CLOUD`**** ****`SELF-DRIVING CARS`**** ****`ECCV 2018`**** [Deep Continuous Fusion for Multi-Sensor 3D Object Detection](neural-nets/Deep_Continuous_Fusion_for_Multi-Sensor_3D_Object_Detection.md) * ****`AUDIO`**** ****`SOUND SOURCE LOCALIZATION`**** ****`ACTION RECOGNITION`**** ****`SOUND SOURCE SEPARATION`**** ****`SELF-SUPERVISED`**** ****`ECCV 2018`**** [Audio-Visual Scene Analysis with Self-Supervised Multisensory Features](neural-nets/Audio-Visual_Scene_Analysis_with_Self-Supervised_Multisensory_Features.md) * ****`UNCERTAINTY`**** ****`ECCV 2018`**** [Towards Realistic Predictors](neural-nets/Towards_Realistic_Predictors.md) * ****`OBJECT DETECTION`**** ****`ECCV 2018`**** [Acquisition of Localization Confidence for Accurate Object Detection](neural-nets/Acquisition_of_Localization_Confidence_for_Accurate_OD.md) * ****`OBJECT DETECTION`**** ****`ECCV 2018`**** [CornerNet: Detecting Objects as Paired Keypoints](neural-nets/CornerNet.md) * ****`NORMALIZATION`**** ****`ECCV 2018`**** [Group Normalization](neural-nets/Group_Normalization.md) * ****`ARCHITECTURES`**** ****`ATTENTION`**** ****`ECCV 2018`**** [Convolutional Networks with Adaptive Inference Graphs](neural-nets/Convolutional_Networks_with_Adaptive_Inference_Graphs.md) ## Added 2018/03/08: * ****`ARCHITECTURES`**** ****`ATTENTION`**** [Spatial Transformer Networks](neural-nets/STN.md) (thanks, [alexobednikov](https://github.com/alexobednikov)) ## Added 2018/03/06: * ****`LOSS FUNCTIONS`**** ****`RECOGNITION`**** [Working hard to know your neighbor’s margins: Local descriptor learning loss](neural-nets/HardNet.md) (thanks, [alexobednikov](https://github.com/alexobednikov)) ## Added 2017/12/13: * ****`FACE RECOGNITION`**** ****`FACES`**** [Neural Aggregation Network for Video Face Recognition](neural-nets/NAN_for_Video_Face_Recognition.md) (thanks, [alexobednikov](https://github.com/alexobednikov)) ## Added 2017/12/03: * [Critical Learning Periods in Deep Neural Networks](neural-nets/Critical_Learning_Periods_in_Deep_Neural_Networks.md) * ****`GAN`**** ****`SELF-DRIVING CARS`**** [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](neural-nets/High_Resolution_Image_Synthesis_with_Conditional_GANs.md) * ****`SELF-DRIVING CARS`**** [Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art](mixed/Computer_Vision_for_Autonomous_Vehicles_Overview.md) ## Added 2017/10/28: * ****`GAN`**** [Progressive Growing of GANs for Improved Quality, Stability, and Variation](neural-nets/Progressive_Growing_of_GANs.md) ## Added 2017/10/24: * ****`SELF-DRIVING CARS`**** [Systematic Testing of Convolutional Neural Networks for Autonomous Driving](neural-nets/Systematic_Testing_of_CNNs_for_Autonomous_Driving.md) * ****`SELF-DRIVING CARS`**** ****`SEGMENTATION`**** [Fast Scene Understanding for Autonomous Driving](neural-nets/Fast_Scene_Understanding_for_Autonomous_Driving.md) * ****`SELF-DRIVING CARS`**** [Arguing Machines: Perception-Control System Redundancy and Edge Case Discovery in Real-World Autonomous Driving](neural-nets/Arguing_Machines.md) * ****`SELF-DRIVING CARS`**** ****`GAN`**** ****`REINFORCEMENT`**** [Virtual to Real Reinforcement Learning for Autonomous Driving](neural-nets/Virtual_to_Real_RL_for_AD.md) * ****`SELF-DRIVING CARS`**** [End to End Learning for Self-Driving Cars](neural-nets/End_to_End_Learning_for_Self-Driving_Cars.md) ## Added 2017/10/21: * [Snapshot Ensembles: Train 1, get M for free](neural-nets/Snapshot_Ensembles.md) * [Image Crowd Counting Using Convolutional Neural Network and Markov Random Field](neural-nets/Image_Crowd_Counting_using_CNN_and_MRF.md) * ****`REINFORCEMENT`**** [Rainbow: Combining Improvements in Deep Reinforcement Learning](neural-nets/Rainbow.md) * ****`REINFORCEMENT`**** [Learning to Navigate in Complex Environments](neural-nets/Learning_to_Navigate_in_Complex_Environments.md) * ****`GAN`**** [Unsupervised Image-to-Image Translation Networks](neural-nets/Unsupervised_Image-to-Image_Translation_Networks.md) * ****`RNN`**** [Dilated Recurrent Neural Networks](neural-nets/Dilated_Recurrent_Neural_Networks.md) * ****`OBJECT DETECTION`**** ****`TRACKING`**** [Detect to Track and Track to Detect](neural-nets/Detect_to_Track_and_Track_to_Detect.md) * ****`ARCHITECTURES`**** [Dilated Residual Networks](neural-nets/Dilated_Residual_Networks.md) ## Added 2017/09/24: * ****`OBJECT DETECTION`**** [Feature Pyramid Networks for Object Detection](neural-nets/Feature_Pyramid_Networks_for_Object_Detection.md) * ****`OBJECT DETECTION`**** [SSD: Single Shot MultiBox Detector](neural-nets/SSD.md) * ****`OBJECT DETECTION`**** ****`EFFICIENT NETWORKS`**** [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](neural-nets/MobileNets.md) * ****`OBJECT DETECTION`**** [Mask R-CNN](neural-nets/Mask_R-CNN.md) ## Added 2017/08/08: * ****`FACES`**** [Multi-view Face Detection Using Deep Convolutional Neural Networks](neural-nets/Multi-view_Face_Detection_Using_Deep_Convolutional_Neural_Networks.md) (aka DDFD) (thanks, [arnaldog12](https://github.com/arnaldog12)) ## Added 2017/06/11: * ****`GAN`**** [On the Effects of Batch and Weight Normalization in Generative Adversarial Networks](neural-nets/On_The_Effects_of_BN_and_WN_in_GANs.md) * ****`GAN`**** [BEGAN](neural-nets/BEGAN.md) * ****`GAN`**** [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks](neural-nets/StackGAN.md) * ****`ACTIVATION FUNCTIONS`**** [Self-Normalizing Neural Networks](neural-nets/Self-Normalizing_Neural_Networks.md) * ****`GAN`**** [Wasserstein GAN](neural-nets/WGAN.md) (aka WGAN) ## Added 2017/03/15: * ****`OBJECT DETECTION`**** [YOLO9000: Better, Faster, Stronger](neural-nets/YOLO9000.md) (aka YOLOv2) * ****`OBJECT DETECTION`**** [You Only Look Once: Unified, Real-Time Object Detection](neural-nets/YOLO.md) (aka YOLO) * ****`OBJECT DETECTION`**** [PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection](neural-nets/PVANET.md) ## Added 2017/03/14: * ****`OBJECT DETECTION`**** [R-FCN: Object Detection via Region-based Fully Convolutional Networks](neural-nets/R-FCN.md) * ****`OBJECT DETECTION`**** [Faster R-CNN](neural-nets/Faster_R-CNN.md) * ****`OBJECT DETECTION`**** [Fast R-CNN](neural-nets/Fast_R-CNN.md) * ****`OBJECT DETECTION`**** [Rich feature hierarchies for accurate object detection and semantic segmentation](neural-nets/Rich_feature_hierarchies_for_accurate_object_detection_and_semantic_segmentation.md) (aka R-CNN) * ****`PEDESTRIANS`**** [Ten Years of Pedestrian Detection, What Have We Learned?](mixed/Ten_Years_of_Pedestrian_Detection_What_Have_We_Learned.md) * ****`NEURAL STYLE`**** [Instance Normalization: The Missing Ingredient for Fast Stylization](neural-nets/Instance_Normalization_The_Missing_Ingredient_for_Fast_Stylization.md) ## Added 2016/07/29: * ****`HUMAN POSE ESTIMATION`**** [Stacked Hourglass Networks for Human Pose Estimation](neural-nets/Stacked_Hourglass_Networks_for_Human_Pose_Estimation.md) * ****`FACES`**** [DeepFace: Closing the Gap to Human-Level Performance in Face Verification](neural-nets/DeepFace.md) * ****`TRANSLATION`**** [Character-based Neural Machine Translation](neural-nets/Character-based_Neural_Machine_Translation.md) ## Added 2016/07/01: * ****`HUMAN POSE ESTIMATION`**** [Convolutional Pose Machines](neural-nets/Convolutional_Pose_Machines.md) * ****`FACES`**** [HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition](neural-nets/HyperFace.md) * ****`FACES`**** [Face Attribute Prediction Using Off-the-Shelf CNN Features](neural-nets/Face_Attribute_Prediction_Using_Off-the-Shelf_CNN_Features.md) * ****`FACES`**** [CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection](neural-nets/CMS-RCNN.md) * [Conditional Image Generation with PixelCNN Decoders](neural-nets/Conditional_Image_Generation_with_PixelCNN_Decoders.md) * ****`GAN`**** [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](neural-nets/InfoGAN.md) * ****`GAN`**** [Improved Techniques for Training GANs](neural-nets/Improved_Techniques_for_Training_GANs.md) * [Synthesizing the preferred inputs for neurons in neural networks via deep generator networks](neural-nets/Synthesizing_the_preferred_inputs_for_neurons_in_neural_networks_via_deep_generator_networks.md) ## Added 2016/06/06: * ****`ARCHITECTURES`**** [FractalNet: Ultra-Deep Neural Networks without Residuals](neural-nets/FractalNet_Ultra-Deep_Networks_without_Residuals.md) * [PlaNet - Photo Geolocation with Convolutional Neural Networks](neural-nets/PlaNet.md) * ****`OPTIMIZERS`**** [Adam: A Method for Stochastic Optimization](neural-nets/Adam.md) * ****`GAN`**** ****`RNN`**** [Generating images with recurrent adversarial networks](neural-nets/Generating_Images_with_Recurrent_Adversarial_Networks.md) * ****`GAN`**** [Adversarially Learned Inference](neural-nets/Adversarially_Learned_Inference.md) ## Added 2016/06/02: * ****`ARCHITECTURES`**** [Resnet in Resnet: Generalizing Residual Architectures](neural-nets/Resnet_in_Resnet.md) * ****`AUTOENCODERS`**** [Rank Ordered Autoencoders](neural-nets/Rank_Ordered_Autoencoders.md) * ****`ARCHITECTURES`**** [Wide Residual Networks](neural-nets/Wide_Residual_Networks.md) * ****`ARCHITECTURES`**** [Identity Mappings in Deep Residual Networks](neural-nets/Identity_Mappings_in_Deep_Residual_Networks.md) * ****`REGULARIZATION`**** [Swapout: Learning an ensemble of deep architectures](neural-nets/Swapout.md) * [Multi-Scale Context Aggregation by Dilated Convolutions](neural-nets/Multi-Scale_Context_Aggregation_by_Dilated_Convolutions.md) * [Texture Synthesis Through Convolutional Neural Networks and Spectrum Constraints](neural-nets/Texture_Synthesis_Through_CNNs_and_Spectrum_Constraints.md) * [Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks](neural-nets/Markovian_GANs.md) ## Added 2016/05/15: * ****`NEURAL STYLE`**** [Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artwork](neural-nets/Neural_Doodle.md) * [Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis](neural-nets/Combining_MRFs_and_CNNs_for_Image_Synthesis.md) * ****`SUPERRESOLUTION`**** [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](neural-nets/Accurate_Image_Super-Resolution.md) * ****`HUMAN POSE ESTIMATION`**** [Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation](neural-nets/Joint_Training_of_a_ConvNet_and_a_PGM_for_HPE.md) * ****`REINFORCEMENT`**** [Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation](neural-nets/Hierarchical_Deep_Reinforcement_Learning.md) * ****`COLORIZATION`**** [Let there be Color](neural-nets/Let_there_be_Color.md) ## Added 2016/05/08: * ****`NEURAL STYLE`**** [Artistic Style Transfer for Videos](neural-nets/Artistic_Style_Transfer_for_Videos.md) ## Added 2016/05/03: * ****`REINFORCEMENT`**** [Playing Atari with Deep Reinforcement Learning](neural-nets/Playing_Atari_with_Deep_Reinforcement_Learning.md) * ****`GENERATIVE`**** [Attend, Infer, Repeat: Fast Scene Understanding with Generative Models](neural-nets/Attend_Infer_Repeat.md) * ****`ARCHITECTURES`**** ****`EFFICIENT NETWORKS`**** [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](neural-nets/SqueezeNet.md) * ****`ACTIVATION FUNCTIONS`**** [Noisy Activation Functions](neural-nets/Noisy_Activation_Functions.md) * ****`OBJECT DETECTION`**** ****`IMAGE TO TEXT`**** [DenseCap: Fully Convolutional Localization Networks for Dense Captioning](neural-nets/DenseCap.md) ## Added 2016/04/01: * ****`REGULARIZATION`**** [Deep Networks with Stochastic Depth](neural-nets/Deep_Networks_with_Stochastic_Depth.md) ## Added 2016/03/31: * ****`GAN`**** [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks](neural-nets/Deep_Generative_Image_Models_using_a_Laplacian_Pyramid_of_Adversarial_Networks.md) * ****`GENERATIVE`**** ****`RNN`**** ****`ATTENTION`**** [DRAW A Recurrent Neural Network for Image Generation](neural-nets/DRAW_A_Recurrent_Neural_Network_for_Image_Generation.md) * [Generating Images with Perceptual Similarity Metrics based on Deep Networks](neural-nets/Generating_Images_with_Perceptual_Similarity_Metrics_based_on_Deep_Networks.md) * ****`GENERATIVE`**** [Generative Moment Matching Networks](neural-nets/Generative_Moment_Matching_Networks.md) * ****`GENERATIVE`**** ****`RNN`**** [Pixel Recurrent Neural Networks](neural-nets/Pixel_Recurrent_Neural_Networks.md) * ****`GAN`**** [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](neural-nets/Unsupervised_Representation_Learning_with_Deep_Convolutional_Generative_Adversarial_Networks.md) ## Added 2016/03/??: * ****`NEURAL STYLE`**** [A Neural Algorithm for Artistic Style](neural-nets/A_Neural_Algorithm_for_Artistic_Style.md) * ****`NORMALIZATION`**** ****`REGULARIZATION`**** [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](neural-nets/Batch_Normalization.md) * ****`ARCHITECTURES`**** [Deep Residual Learning for Image Recognition](neural-nets/Deep_Residual_Learning_for_Image_Recognition.md) * ****`ACTIVATION FUNCTIONS`**** [Fast and Accurate Deep Networks Learning By Exponential Linear Units (ELUs)](neural-nets/ELUs.md) * [Fractional Max-Pooling](neural-nets/Fractional_Max_Pooling.md) * ****`GAN`**** [Generative Adversarial Networks](neural-nets/Generative_Adversarial_Networks.md) * ****`ARCHITECTURES`**** [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](neural-nets/Inception_v4.md) * ****`NORMALIZATION`**** [Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks](neural-nets/Weight_Normalization.md)

Education & Learning ML Frameworks

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