bpdl.utilities module

The basic module for generating synthetic images and also loading / exporting

Copyright (C) 2015-2020 Jiri Borovec <jiri.borovec@fel.cvut.cz>

bpdl.utilities.convert_numerical(s)

try to convert a string tu numerical

Parameters

s (str) – input string

Returns

>>> convert_numerical('-1')
-1
>>> convert_numerical('-2.0')
-2.0
>>> convert_numerical('.1')
0.1
>>> convert_numerical('-0.')
-0.0
>>> convert_numerical('abc58')
'abc58'

bpdl.utilities.create_clean_folder(path_dir)

create empty folder and while the folder exist clean all files

Parameters

path_dir (str) – path

Return str

>>> path_dir = os.path.abspath('sample_dir')
>>> path_dir = create_clean_folder(path_dir)
>>> os.path.exists(path_dir)
True
>>> shutil.rmtree(path_dir, ignore_errors=True)

bpdl.utilities.estimate_max_circle(point, direction, points, max_diam=1000, step_tol=0.001)

find maximal circe from a given pont in orthogonal direction which just touch the curve with points

Parameters

• point (tuple(float,float)) – particular point on curve

• direction (tuple(float,float)) – orthogonal direction

• float]] points ([[float,) – list of point on curve

• max_diam (float) – maximal diameter

• step_tol (float) – tolerance step in dividing diameter interval

Returns

>>> y = [1] * 10
>>> pts = np.array(list(zip(range(len(y)), y)))
>>> estimate_max_circle([5, 1], [0, 1], pts)   
999.99...
>>> y = [1] * 6 + [2] * 4
>>> pts = np.array(list(zip(range(len(y)), y)))
>>> estimate_max_circle([4, 1], [0, 1], pts)   
4.99...

bpdl.utilities.estimate_point_max_circle(idx, points, tangent_smooth=1, orient=1.0, max_diam=1000000.0, step_tol=0.001)

estimate maximal circle from a particular point on curve

Parameters

• idx (int) – index or point on curve

• float]] points ([[float,) – list of point on curve

• tangent_smooth (int) – distance for tangent

• direct (float) – positive or negative ortogonal

• max_diam (float) – maximal diameter

• step_tol (float) – tolerance step in dividing diameter interval

Returns

>>> y = [1] * 25 + list(range(1, 50)) + [50] * 25
>>> pts = np.array(list(zip(range(len(y)), y)))
>>> estimate_point_max_circle(0, pts)   
60.38...
>>> estimate_point_max_circle(30, pts)   
17.14...
>>> estimate_point_max_circle(90, pts)   
999999.99...

bpdl.utilities.estimate_rolling_ball(points, tangent_smooth=1, max_diam=1000000.0, step_tol=0.001)

roll a ball over curve and get for each particular position a maximal ball which does not intersect the rest of curve

Parameters

• points –

• tangent_smooth –

• max_diam –

• step_tol –

Returns

>>> y = [1] * 6 + [2] * 4
>>> pts = np.array(list(zip(range(len(y)), y)))
>>> diams = estimate_rolling_ball(pts)
>>> list(map(int, diams[0]))
[24, 18, 12, 8, 4, 1, 9, 999999, 999999, 999999]
>>> list(map(int, diams[1]))
[999999, 999999, 999999, 999999, 999999, 10, 1, 4, 8, 12]

bpdl.utilities.generate_gauss_2d(mean, std, im_size=None, norm=None)

Generating a Gaussian distribution in 2D image

Parameters

• norm (float) – normalise the maximal value

• mean (list(int)) – mean position

• std (list(list(int))) – STD

• im_size (tuple(int,int)) – optional image size

Return ndarray

>>> im = generate_gauss_2d([4, 5], [[1, 0], [0, 2]], (8, 10), norm=1.)
>>> np.round(im, 1)  
array([[ 0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ],
       [ 0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ],
       [ 0. ,  0. ,  0. ,  0.1,  0.1,  0.1,  0.1,  0.1,  0. ,  0. ],
       [ 0. ,  0.1,  0.2,  0.4,  0.5,  0.6,  0.5,  0.4,  0.2,  0.1],
       [ 0. ,  0.1,  0.3,  0.6,  0.9,  1. ,  0.9,  0.6,  0.3,  0.1],
       [ 0. ,  0.1,  0.2,  0.4,  0.5,  0.6,  0.5,  0.4,  0.2,  0.1],
       [ 0. ,  0. ,  0. ,  0.1,  0.1,  0.1,  0.1,  0.1,  0. ,  0. ],
       [ 0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ]])
>>> im = generate_gauss_2d([2, 3], [[1., 0], [0, 1.2]])
>>> np.round(im, 2)  
array([[ 0.  ,  0.  ,  0.01,  0.02,  0.01,  0.  ,  0.  ,  0.  ],
       [ 0.  ,  0.02,  0.06,  0.08,  0.06,  0.02,  0.  ,  0.  ],
       [ 0.01,  0.03,  0.09,  0.13,  0.09,  0.03,  0.01,  0.  ],
       [ 0.  ,  0.02,  0.06,  0.08,  0.06,  0.02,  0.  ,  0.  ],
       [ 0.  ,  0.  ,  0.01,  0.02,  0.01,  0.  ,  0.  ,  0.  ]])

bpdl.utilities.is_iterable(var)

check if the variable is iterable

Parameters

var –

Return bool

>>> is_iterable('abc')
False
>>> is_iterable(123.)
False
>>> is_iterable((1, ))
True
>>> is_iterable(range(2))
True

bpdl.utilities.is_list_like(var)

check if the variable is iterable

Parameters

var –

Return bool

>>> is_list_like('abc')
False
>>> is_list_like(123.)
False
>>> is_list_like([0])
True
>>> is_list_like((1, ))
True
>>> is_list_like(range(2))
True