This project implementing approach which comes from an IEEE paper, Simple Adaptive Mosaic Effects, makes users be able to add mosaic effect to images
Make sure python3 has been installed and that will be used in this project, python2 is not working
And install necessary libraries
$ pip3 install --user -r requirements.txt
If you wanna use GUI then make sure qt5 libraries has been installed
You can use command-line or gui to use thess programs. command-line way is suggested because of more arguments offered to tune
First step, generate seeds from raw image
$ python3 seedgen.py <image-file>
You will get a pickle file (default : seed.pickle) containing seeds data when this process completes
Also, you can tune parameters like minimal area in pixels and error rate, or output the seed distribution to check. Type --help to see more information
Second step, use voronoi diagram generator to create a voronoi diagram and dump as a file
$ python3 voronoigen.py -input <seed-file> -t <iteration time>
The last step, use mosaic-ploting program to put color on
$ python3 mosaicplot.py -input <vor-dig-file> [ -pa | -pc ] [ -pme ] -o <output-file>
$ python3 seedgen.py sample.jpg
$ python3 voronoigen.py
$ python3 mosaicplot.py -pc -pme
This project supports GUI by Qt5. Make sure that you have Qt5 libraries installed
$ python3 gui-qt5.py
to open GUI
If your operation system is Linux, You have a chance to benefit from the speedup of parallelism
To acheive that, you need to compile some files. A script is given for you to compile conveniently
sh compile.sh
$ python3 mosaic.py Lenna.png --error-rate 0.25 --min-area 8 -t 20 -pme -rgb 200 200 200
$ python3 mosaic.py Lenna.png --error-rate 0.25 --min-area 32 -t 20 -pme -rgb 200 200 200
$ python3 mosaic.py Lenna.png --error-rate 0.25 --min-area 64 -t 20 -pme -rgb 200 200 200
$ python3 mosaic.py Lenna.png --error-rate 0.25 --min-area 256 -t 20 -pme -rgb 200 200 200
$ python3 mosaic.py Lenna.png --error-rate 0.25 --min-area 1024 -t 20 -pme -rgb 200 200 200
GNU General Public License v3.0