Coverage for mlstatpy/image/detection_segment/random_image.py: 100%

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47 statements

1# -*- coding: utf-8 -*-

2"""

3@file

4@brief Génère des images aléatoires.

5"""

6import math

7import numpy

8import numpy.random as nprnd # pylint: disable=E1101

11def random_noise_image(size, ratio=0.1):

12 """

13 Construit une image blanche de taille *size*,

14 noircit aléatoirement *ratio x nb pixels*

15 pixels.

17 @param size taille de l'image

18 @param ratio proportion de pixels à noircir

19 @return :epkg:`numpy:array`

20 """

21 img = numpy.zeros((size[1], size[0]), dtype=numpy.float32)

22 nb = int(ratio * size[0] * size[1])

23 xr = nprnd.randint(0, size[0] - 1, nb)

24 yr = nprnd.randint(0, size[1] - 1, nb)

25 img[yr, xr] = 1

26 return img

29def random_segment_image(image, lmin=0.1, lmax=1., noise=0.01, density=1.):

30 """

31 Ajoute un segment aléatoire à une image.

32 Génère des points le long d'un segment aléatoire.

34 @param image :epkg:`numpy:array` (modifié par la fonction)

35 @param lmin taille minimal du segment

36 @param lmax taille maximam du segment

37 @param density nombre de pixel à tirer le long de l'axe

38 @param noise bruit

39 @return dictionary with *size, angle, x1, y1, x2, y2, nbpoints*

40 """

41 def move_coordinate(x1, y1, x2, y2, X, Y):

42 if x2 < 0:

43 x1 -= x2

44 x2 = 0

45 x1 = min(max(x1, 0), X - 1)

46 x2 = min(max(x2, 0), X - 1)

47 y1 = min(max(y1, 0), Y - 1)

48 y2 = min(max(y2, 0), Y - 1)

49 size = int(((x1 - x2)**2 + (y1 - y2) ** 2) ** 0.5)

50 return x1, y1, x2, y2, size

52 mind = min(image.shape)

53 lmin = int(mind * lmin)

54 lmax = int(mind * lmax)

55 size = nprnd.randint(lmin, lmax)

56 angle = nprnd.random() * math.pi # pylint: disable=E1101

57 x1 = nprnd.randint(

58 image.shape[1] - int(size * abs(math.cos(angle)) - 1))

59 y1 = nprnd.randint(image.shape[0] - int(size * math.sin(angle) - 1))

60 x2 = x1 + size * math.cos(angle)

61 y2 = y1 + size * math.sin(angle)

62 x1, y1, x2, y2, size = move_coordinate(

63 x1, y1, x2, y2, image.shape[1], image.shape[0])

64 t = nprnd.randint(0, size, int(size * density))

65 xs = t * math.cos(angle) + x1

66 ys = t * math.sin(angle) + x2

67 noise = nprnd.randn( # pylint: disable=E1101

68 xs.shape[0] * 2).reshape(xs.shape[0], 2) * noise * mind

69 xs += noise[:, 0]

70 ys += noise[:, 1]

71 xs = numpy.maximum(xs, numpy.zeros(xs.shape[0])) # pylint: disable=E1111

72 ys = numpy.maximum(ys, numpy.zeros(

73 xs.shape[0])) # pylint: disable=E1111,E1101,E1136

74 xs = numpy.minimum(xs, numpy.zeros( # pylint: disable=E1101,E1136

75 xs.shape[0]) + image.shape[1] - 1) # pylint: disable=E1111,E1101,E1136

76 ys = numpy.minimum(ys, numpy.zeros( # pylint: disable=E1101,E1136

77 xs.shape[0]) + image.shape[0] - 1) # pylint: disable=E1111,E1101,E1136

78 xs = xs.astype(numpy.int32) # pylint: disable=E1101

79 ys = ys.astype(numpy.int32) # pylint: disable=E1101

80 image[ys, xs] = 1

81 res = dict(size=size, angle=angle, x1=x1, y1=y1, x2=x2, y2=y2,

82 nbpoints=xs.shape[0]) # pylint: disable=E1136

83 return res