osaic is a simple python module which let you create mosaics from images by issuing a simple python -mosaic foo.jpg command.
The basic idea is to take as input a set of images: the first is used as background for the final mosaic; the others are indexed by average colors and pated, as tiles, over the final mosaic.
In addition, users are enabled to specify the final size of the mosaic (relative to the size of the initial target image) and the number of tiles to use per side.
The module depends on python PIL library (automatically fetched by the installation script) for all the operations concerning image processing. In addition, to add support for jpegs and pngs images, and to enable the module to display mosaics on screen, please install libjpeg, libpng and libtk as well.
To install osaic, you are enabled to grab it from both the mercurial repository and from the Python Package Index (PyPI). The former is preferred for bleeding-edge users, even tough the latter is not guaranteed to be very stable as well.
cd /wherever/you/want hg clone https://bitbucket.org/iamFIREcracker/osaic python setup.py install
cd /path/to/workspace wget http://pypi.python.org/packages/source/o/osaic/osaic-2.0.0.tar.gz tar zxvf osaic-2.0.0.tar.gz cd osaic-2.0.0 python setup.py install
From the PyPI:
pip install osaic
osaic is a module that can be used both as a standalone application and as a standard python module.
A typical usage of the application is to display a mosaic composition created from a source image:
python -mosaic image.jpg
If you want to save the output to a file instead of showing it:
python -mosaic image.jpg -o mosaic-image.jpg
Finally, if you want to create a mosaic which is 4 times bigger than the original image, and with 100 tiles per side, just issue:
python -mosaic -z4 -t100 image.jpg
For everything else use the help message:
python -mosaic -h
The module is a collection of objects and functions with different capabilities: functions for vectors, color transformations, image objects, image indexes and procedure wrappers.
Regarding operation with vectors, the module implement some basic functions not included in standard Python but useful while working with colors:
>>> dotproduct([1, 2, 3], [4, 5, 6]) 70 >>> difference([1, 2, 3], [1, 2, 3]) [0, 0, 0] >>> squaredistance([1, 2, 3], [0, 0, 0]) 30
It is possible to find also a couple of functions wrapping up common colors operations, like computation of the average color of an image and color quantization. The latter is particularly useful while trying to keep the CPU work load at low levels:
>>> average_color('almost-red.png') (240, 10, 20) >>> quantize_color((240, 10, 20), levels=2) (192, 64, 64)
As noted earlier, the module is built on top of the Python PIL library. However, we chose not export such external objects, but rather present to users some wrappers, namely ImageWrapper:
>>> img = Image.open('foo.png') >>> img.size (640, 480) >>> img.reratio(5 / 1) >>> img.crop((0, 0, 10, 10)) >>> img.size (10, 10) >>> img.show()
While creating mosaics, it comes in handy to have to possibility to index a set of images and make it possible to search which of them is the most similar, in terms of average color, to another one. The ImageList object is shipped with the module for this reason:
>>> img_list = ImageList(['1.png', '2.png', '3.png']) >>> img_list.search((255, 0, 0)) ImageTuple(filename='1.png', color=(255, 0, 0), image=None)
Finally, the module is shipped with a mosaicify function which wraps up all the operations needed to create mosaic, including source images indexing, and search of neighbour images depending on the average color:
>>> import osaic >>> osaic.mosaificy( ... target='foo.png', ... sources=['bar.png', 'asd.png', 'bazinga.png'], ... tiles=128, ... zoom=4, ... output='mosaic.png', ... )