texturize

.. image:: docs/gravel-x4.webp

A command-line tool and Python library to automatically generate new textures similar to a source image or photograph. It's useful in the context of computer graphics if you want to make variations on a theme or expand the size of an existing texture.

This software is powered by deep learning technology — using a combination of convolution networks and example-based optimization to synthesize images. We're building texturize as the highest-quality open source library available!

1. Examples & Demos <#1-examples--demos>_
2. Commands <#2-commands>_
3. Options & Usage <#3-options--usage>_
4. Installation <#4-installation>_

|Python Version| |License Type| |Project Stars| |Package Version| |Project Status| |Build Status|

1. Examples & Demos ===================

The examples are available as notebooks, and you can run them directly in-browser thanks to Jupyter and Google Colab:

• Gravel — online demo <https://colab.research.google.com/github/photogeniq/texturize/blob/master/examples/Demo_Gravel.ipynb>__ and source notebook <https://github.com/photogeniq/texturize/blob/master/examples/Demo_Gravel.ipynb>__.
• Grass — online demo <https://colab.research.google.com/github/photogeniq/texturize/blob/master/examples/Demo_Grass.ipynb>__ and source notebook <https://github.com/photogeniq/texturize/blob/master/examples/Demo_Grass.ipynb>__.

These demo materials are released under the Creative Commons BY-NC-SA license <https://creativecommons.org/licenses/by-nc-sa/3.0/>_, including the text, images and code.

.. image:: docs/grass-x4.webp

2. Commands ===========

a) REMIX

Generate variations of any shape from a single texture.

Remix Command-Line

.. code-block:: bash

    Usage:
        texturize remix SOURCE...

    Examples:
        texturize remix samples/grass.webp --size=720x360
        texturize remix samples/gravel.png --size=512x512

Remix Library API
~~~~~~~~~~~~~~~~~

.. code-block:: python

    from texturize import api, commands, io

    # The input could be any PIL Image in RGB mode.
    image = io.load_image_from_file("input.png")

    # Coarse-to-fine synthesis runs one octave at a time.
    remix = commands.Remix(image)
    for result in api.process_octaves(remix, octaves=5):
        pass

    # The output can be saved in any PIL-supported format.
    result.image.save("output.png")


Remix Examples
~~~~~~~~~~~~~~

.. image:: docs/remix-gravel.webp

.. Remix Online Tool
.. ~~~~~~~~~~~~~~~~~
.. * `colab notebook <https://colab.research.google.com/github/photogeniq/texturize/blob/master/examples/Tool_Remix.ipynb>`__

----

b) REMAKE
---------

    Reproduce an original texture in the style of another.


Remake Command-Line

.. code-block:: bash

Usage:
    texturize remake TARGET [like] SOURCE

Examples:
    texturize remake samples/grass1.webp like samples/grass2.webp
    texturize remake samples/gravel1.png like samples/gravel2.png --weight 0.5

Remake Library API

.. code-block:: python

    from texturize import api, commands

    # The input could be any PIL Image in RGB mode.
    target = io.load_image_from_file("input1.png")
    source = io.load_image_from_file("input2.png")

    # Only process one octave to retain photo-realistic output.
    remake = commands.Remake(target, source)
    for result in api.process_octaves(remake, octaves=1):
        pass

    # The output can be saved in any PIL-supported format.
    result.image.save("output.png")


Remake Examples
~~~~~~~~~~~~~~~

.. image:: docs/remake-grass.webp

.. Remake Online Tool
.. ~~~~~~~~~~~~~~~~~~
.. * `colab notebook <https://colab.research.google.com/github/photogeniq/texturize/blob/master/examples/Tool_Remake.ipynb>`__

----

c) MASHUP
---------

    Combine multiple textures together into one output.


Mashup Command-Line

.. code-block:: bash

Usage:
    texturize mashup SOURCE...

Examples:
    texturize mashup samples/grass1.webp samples/grass2.webp
    texturize mashup samples/gravel1.png samples/gravel2.png

Mashup Library API

.. code-block:: python

    from texturize import api, commands

    # The input could be any PIL Image in RGB mode.
    sources = [
        io.load_image_from_file("input1.png"),
        io.load_image_from_file("input2.png"),
    ]

    # Only process one octave to retain photo-realistic output.
    mashup = commands.Mashup(sources)
    for result in api.process_octaves(mashup, octaves=5):
        pass

    # The output can be saved in any PIL-supported format.
    result.image.save("output.png")


Mashup Examples
~~~~~~~~~~~~~~~

.. image:: docs/mashup-gravel.webp

.. Mashup Online Tool
.. ~~~~~~~~~~~~~~~~~~
.. * `colab notebook <https://colab.research.google.com/github/photogeniq/texturize/blob/master/examples/Tool_Mashup.ipynb>`__

----

d) ENHANCE
----------

    Increase the resolution or quality of a texture using another as an example.


Enhance Command-Line

.. code-block:: bash

Usage:
    texturize enhance TARGET [with] SOURCE --zoom=ZOOM

Examples:
    texturize enhance samples/grass1.webp with samples/grass2.webp --zoom=2
    texturize enhance samples/gravel1.png with samples/gravel2.png --zoom=4

Enhance Library API

.. code-block:: python

    from texturize import api, commands

    # The input could be any PIL Image in RGB mode.
    target = io.load_image_from_file("input1.png")
    source = io.load_image_from_file("input2.png")

    # Only process one octave to retain photo-realistic output.
    enhance = commands.Enhance(target, source, zoom=2)
    for result in api.process_octaves(enhance, octaves=2):
        pass

    # The output can be saved in any PIL-supported format.
    result.image.save("output.png")


Enhance Examples
~~~~~~~~~~~~~~~~

.. image:: docs/enhance-grass.webp

.. Enhance Online Tool
.. ~~~~~~~~~~~~~~~~~~~
.. * `colab notebook <https://colab.research.google.com/github/photogeniq/texturize/blob/master/examples/Tool_Enhance.ipynb>`__

----


3. Options & Usage
==================

For details about the command-line usage of the tool, see the tool itself:

.. code-block:: bash

    texturize --help

Here are the command-line options currently available, which apply to most of the
commands above::

    Options:
        SOURCE                  Path to source image to use as texture.
        -s WxH, --size=WxH      Output resolution as WIDTHxHEIGHT. [default: 640x480]
        -o FILE, --output=FILE  Filename for saving the result, includes format variables.
                                [default: {command}_{source}{variation}.png]

        --weights=WEIGHTS       Comma-separated list of blend weights. [default: 1.0]
        --zoom=ZOOM             Integer zoom factor for enhancing. [default: 2]

        --variations=V          Number of images to generate at same time. [default: 1]
        --seed=SEED             Configure the random number generation.
        --mode=MODE             Either "patch" or "gram" to manually specify critics.
        --octaves=O             Number of octaves to process. Defaults to 5 for 512x512, or
                                4 for 256x256 equivalent pixel count.
        --quality=Q             Quality for optimization, higher is better. [default: 5]
        --device=DEVICE         Hardware to use, either "cpu" or "cuda".
        --precision=PRECISION   Floating-point format to use, "float16" or "float32".
        --quiet                 Suppress any messages going to stdout.
        --verbose               Display more information on stdout.
        -h, --help              Show this message.


4. Installation
===============

Latest Release [recommended]
----------------------------

We suggest using `Miniconda 3.x <https://docs.conda.io/en/latest/miniconda.html>`__ to
manage your Python environments.  Once the ``conda`` command-line tool is installed on
your machine, there are setup scripts you can download directly from the repository:

.. code-block:: bash

    # a) Use this if you have an *Nvidia GPU only*.
    curl -s https://raw.githubusercontent.com/photogeniq/texturize/master/tasks/setup-cuda.yml -o setup.yml

    # b) Fallback if you just want to run on CPU.
    curl -s https://raw.githubusercontent.com/photogeniq/texturize/master/tasks/setup-cpu.yml -o setup.yml

Now you can create a fresh Conda environment for texture synthesis:

.. code-block:: bash

    conda env create -n myenv -f setup.yml
    conda activate myenv

**NOTE**: Any version of CUDA is suitable to run ``texturize`` as long as PyTorch is
working.  See the official `PyTorch installation guide <https://pytorch.org/get-started/locally/>`__
for alternatives ways to install the ``pytorch`` library.

Then, you can fetch the latest version of the library from the Python Package Index
(PyPI) using the following command:

.. code-block:: bash

    pip install texturize

Finally, you can check if everything worked by calling the command-line script:

.. code-block:: bash

    texturize --help

You can use ``conda env remove -n myenv`` to delete the virtual environment once you
are done.


Repository Install [developers]
-------------------------------

If you're a developer and want to install the library locally, start by cloning the
repository to your local disk:

.. code-block:: bash

    git clone https://github.com/photogeniq/texturize.git

We also recommend using `Miniconda 3.x <https://docs.conda.io/en/latest/miniconda.html>`__
for development.  You can set up a new virtual environment called ``myenv`` by running
the following commands, depending whether you want to run on CPU or GPU (via CUDA).
For advanced setups like specifying which CUDA version to use, see the official
`PyTorch installation guide <https://pytorch.org/get-started/locally/>`__.

.. code-block:: bash

    cd texturize

    # a) Use this if you have an *Nvidia GPU only*.
    conda env create -n myenv -f tasks/setup-cuda.yml

    # b) Fallback if you just want to run on CPU.
    conda env create -n myenv -f tasks/setup-cpu.yml

Once the virtual environment is created, you can activate it and finish the setup of
``texturize`` with these commands:

.. code-block:: bash

    conda activate myenv
    poetry install

Finally, you can check if everything worked by calling the script:

.. code-block:: bash

    texturize --help

Use ``conda env remove -n myenv`` to remove the virtual environment once you are done.

----

|Python Version| |License Type| |Project Stars| |Package Version| |Project Status| |Build Status|

.. |Python Version| image:: https://img.shields.io/pypi/pyversions/texturize
    :target: https://docs.conda.io/en/latest/miniconda.html

.. |License Type| image:: https://img.shields.io/badge/license-AGPL-blue.svg
    :target: https://github.com/photogeniq/texturize/blob/master/LICENSE

.. |Project Stars| image:: https://img.shields.io/github/stars/photogeniq/texturize.svg?color=turquoise
    :target: https://github.com/photogeniq/texturize/stargazers

.. |Package Version| image:: https://img.shields.io/pypi/v/texturize?color=turquoise
    :alt: PyPI - Version
    :target: https://pypi.org/project/texturize/

.. |Project Status| image:: https://img.shields.io/pypi/status/texturize?color=#00ff00
    :alt: PyPI - Status
    :target: https://github.com/photogeniq/texturize

.. |Build Status| image:: https://img.shields.io/github/workflow/status/photogeniq/texturize/build
    :alt: GitHub Workflow Status
    :target: https://github.com/photogeniq/texturize/actions?query=workflow%3Abuild