Segmentation/segmentation_skimage.py at master · Marcos001/Segmentation · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97

import numpy as np
import cv2
import example_plot as plot
import os

path_relative = os.getcwd()

from skimage.feature import canny
from scipy import ndimage as ndi


def segmentacao_spectral():
    import numpy as np
    import matplotlib.pyplot as plt

    from sklearn.feature_extraction import image
    from sklearn.cluster import spectral_clustering

    l = 100
    x, y = np.indices((l, l))

    center1 = (28, 24)
    center2 = (40, 50)
    center3 = (67, 58)
    center4 = (24, 70)

    radius1, radius2, radius3, radius4 = 16, 14, 15, 14

    circle1 = (x - center1[0]) ** 2 + (y - center1[1]) ** 2 < radius1 ** 2
    circle2 = (x - center2[0]) ** 2 + (y - center2[1]) ** 2 < radius2 ** 2
    circle3 = (x - center3[0]) ** 2 + (y - center3[1]) ** 2 < radius3 ** 2
    circle4 = (x - center4[0]) ** 2 + (y - center4[1]) ** 2 < radius4 ** 2

    # #############################################################################
    # 4 circles
    img = circle1 + circle2 + circle3 + circle4

    # We use a mask that limits to the foreground: the problem that we are
    # interested in here is not separating the objects from the background,
    # but separating them one from the other.
    mask = img.astype(bool)

    img = img.astype(float)
    img += 1 + 0.2 * np.random.randn(*img.shape)

    # Convert the image into a graph with the value of the gradient on the
    # edges.
    graph = image.img_to_graph(img, mask=mask)

    # Take a decreasing function of the gradient: we take it weakly
    # dependent from the gradient the segmentation is close to a voronoi
    graph.data = np.exp(-graph.data / graph.data.std())

    # Force the solver to be arpack, since amg is numerically
    # unstable on this example
    labels = spectral_clustering(graph, n_clusters=4, eigen_solver='arpack')
    label_im = -np.ones(mask.shape)
    label_im[mask] = labels

    plt.matshow(img)
    plt.matshow(label_im)

    # #############################################################################
    # 2 circles
    img = circle1 + circle2
    mask = img.astype(bool)
    img = img.astype(float)

    img += 1 + 0.2 * np.random.randn(*img.shape)

    graph = image.img_to_graph(img, mask=mask)
    graph.data = np.exp(-graph.data / graph.data.std())

    labels = spectral_clustering(graph, n_clusters=2, eigen_solver='arpack')
    label_im = -np.ones(mask.shape)
    label_im[mask] = labels

    plt.matshow(img)
    plt.matshow(label_im)

    plt.show()


#le a imagem
#retina = cv2.imread(path_relative+'/data/imagens/retina/400/rgb/1.png',0)
retina = cv2.imread('/media/nig/Arquivos/Imagens/G Drive/P_20160802_145622_BF.jpg',0)

histo = np.histogram(retina, bins=np.arange(0, 256))

# ver histograma da imagem
# plot.histograma(retina, histo)

edges = canny(retina/255.)
fill_coins = ndi.binary_fill_holes(edges)

plot.ver_uma_imagem('edges', edges)