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autocolorize

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Commits

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b2c8fff6b5724ae58893efa0d92946f6d869d0bb

Removed fractalnet lines added by mistake

ggustavla committed 8 years ago
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c359fd6ec1976cf0fb58ade91371aa92c301e880

Refactor calc_rgb

ggustavla committed 8 years ago
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8faa1c526327f773a4403545832b4e75c02e347b

Add missing header file

ggustavla committed 8 years ago
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978e0e21771aea0136f625bc26e1c066eb23a187

Forgot add_to_caffe.proto in last commit

ggustavla committed 8 years ago
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d9b46deacce904b51cda16587edd9f24a552f0c2

Added note about training

ggustavla committed 8 years ago
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3201071b8f74e30541ee0ff101767f006dd80c0b

Add training files and instructions

ggustavla committed 8 years ago

README

The README file for this repository.

.. image:: https://img.shields.io/pypi/v/autocolorize.svg :target: https://pypi.python.org/pypi/autocolorize

autocolorize

Automatically colorize images using Machine Learning.

  • Project page <http://people.cs.uchicago.edu/~larsson/colorization/>__
  • ECCV 2016 paper (arXiv <http://arxiv.org/abs/1603.06668>__)

Installation

Make sure that you have Caffe (with Python bindings). Then run::

pip install autocolorize

Run::

autocolorize grayscale.png -o colorized.png

API

You can also colorize from Python (assuming grayscale is the image that you want to colorize)::

import autocolorize
classifier = autocolorize.load_default_classifier()
rgb = autocolorize.colorize(grayscale, classifier=classifier)

Sparse training

We provide custom layers for doing sparse hypercolumn training in both Caffe (see caffe/) and Tensorflow (see tensorflow/). This can be used for other image-to-image tasks, such as semantic segmentation or edge prediction.

Look inside the train folder if you want to train from scratch.