pyrebel

A pure python library that implements abstraction of data.

Installation

From PyPI

python3 -m pip install --upgrade pyrebel

From source

git clone https://github.com/ps-nithin/pyrebel

cd pyrebel

python3 -m pip install .

Running demo programs

Demo programs are found in 'demo/' directory.

cd demo/

1. Image abstraction demo

Usage:

python3 pyrebel_main.py --input <filename.png>


Optional arguments

--abs_threshold <value> Selects the threshold of abstraction. (Defaults to 5)


For example,

python3 pyrebel_main.py --input images/abc.png --abs_threshold 10


The output is written to 'output.png'

2. Edge detection demo

This is a demo of edge detection achieved using data abstraction.

Usage:

python3 pyrebel_main_edge.py --input <filename>


For example,

python3 pyrebel_main_edge.py --input images/wildlife.jpg


The output is written to 'output.png'.
Below is a sample input image,



Below is the output image,

3. 2D sketch demo

This is a demo of 2D sketch formation using data abstraction.

Usage:

python3 pyrebel_main_vision.py --input <filename>


Optional arguments for tweaking the result,

1. --edge_threshold <value> Selects the threshold of edge detection.(Defaults to 5)
2. --abs_threshold <value> Selects the threshold of output abstraction. (Defaults to 10)
3. --bound_threshold <value> Selects the threshold of boundary size. (Defaults to 100)

For example,

python3 pyrebel_main_vision.py --input images/lotus.jpg


Below is a sample input image,



Below is the output image,

4. Abstract painting

This is a demo of abstract painting using data abstraction. The output of edge detection is painted to obtain the desired output.

Usage:

python3 pyrebel_main_paint.py --input <filename>


Optional arguments for tweaking the result,

1. --edge_threshold <value> Selects the threshold of edge detection. (Defaults to 10).
2. --paint_threshold <value> Selects the threshold of painting. (Defaults to 5).
3. --block_threshold <value> Selects the threshold of block size. (Defaults to 20).


For example,

Running python3 pyrebel_main_paint.py --input images/elephant.jpg --edge_threshold 10 --block_threshold 50 --paint_threshold 1


Below is the sample input image,



Below is the output image,

5. Pattern recognition demo

This is a demo of pattern recognition achieved using data abstraction.

1. Learning

Usage: python3 pyrebel_main_learn.py --learn /path/to/image/directory/

For example running
python3 pyrebel_main_learn.py --learn images/train-hand/ learns all the images in the directory and links the filename with the signatures.


3. Recognition

Usage: python3 pyrebel_main_learn.py --recognize <filename>

For example running
python3 pyrebel_main_learn.py --recognize images/recognize.png displays the symbols recognized in the file 'images/recognize.png'.

To reset the knowledge base just delete file 'know_base.pkl' in the current working directory. The program expects a single pattern in the input image. Otherwise, a pattern has to be selected by changing variable 'blob_index' accordingly.

For learning / recognizing multiple patterns, use demo script pyrebel_main_learn_multiple.py instead of pyrebel_main_learn.py.

Docs here

Read more about abstraction here

Let the data shine!