Python package for rectangular decomposition of 2D scenes/binary images
Project description
Adaptive-Boxes
Python Library for rectangular decomposition of 2D binary images.
See the 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
More info
Project details
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