Adaptive-Boxes

Python Library for rectangular decomposition of 2D binary images.

See also the ultra-fast CUDA GPU version: adaptive-boxes-gpu

Quick Start

Install adabox from PiP:

pip install adaptive-boxes

Call adaptive-boxes library

from adabox import proc
from adabox.plot_tools import plot_rectangles, plot_rectangles_only_lines

Call others too:

import numpy as np
import matplotlib.pyplot as plt

Load data in .csv format. File should contain data with columns: [x1_position x2_position flag]. Initially, flag = 0 (See sample_data folder).

# Input Path
in_path = './sample_data/sample_2.csv'

# Load Demo data with columns [x_position y_position flag]
data_2d = np.loadtxt(in_path, delimiter=",")

If you want to see data, plot using:

# Plot demo data
plt.scatter(data_2d[:, 0], data_2d[:, 1])
plt.axis('scaled')    

Decompose data in rectangles, it returns a list of rectangles and a separation value needed to plot them.

rectangles = []
# Number of random searches, more is better!
searches = 2        
(rectangles, sep_value) = proc.decompose(data_2d, searches)
print('Number of rectangles found: ' + str(len(rectangles)))   

Plot resulting rectangles

plot_rectangles(rectangles, sep_value)

or

plot_rectangles_only_lines(rectangles, sep_value) 

Output

Adabox applied over: ./sample_data/ files. Click in the images to expand.

Hi-res images

File: sample_1.csv

File: sample_2.csv

Repo Content

Each folder contains the next information:

• data: Files with voxel information in Blender (.ply extension)
• proto: Prototype scripts
• results: Results of the heuristic process (.json extension)
• lib: library scripts
• postproc_gpu: post-processing scripts for handling csv files generated by adaptive-boxes-gpu.

More info