-
Notifications
You must be signed in to change notification settings - Fork 7
/
review_images_Create_Labels_out_gray.py
152 lines (107 loc) · 4.47 KB
/
review_images_Create_Labels_out_gray.py
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
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
# -*- coding: utf-8 -*-
"""
Created on Sat Feb 8 09:41:41 2020
@author: craig
"""
from PIL import Image
import os.path
#import skimage
from skimage.color import rgb2gray
from skimage.filters import sobel,gaussian,hessian,frangi,laplace,median
# meijering, prewitt, prewitt_h, prewitt_v, sato
#from skimage.segmentation import random_walker
#from skimage.data import binary_blobs, moon, astronaut, hubble_deep_field, ts
from skimage.io import imsave
import numpy as np
import matplotlib.pyplot as plt
for i in range(1,13,1):
TS=str(i)
#offset_plus = 50
img=os.path.join('dataset1/images_prepped_test', TS + '.png')
# TS_Im = Image.open(img).convert('L')
TS_Im = Image.open(img)
# a = np.array(TS_Im)
data = np.array(TS_Im)
#gradient = np.array(TS_Im)
#gradient = sobel(rgb2gray(data)) # true gradients
gradient = gaussian(rgb2gray(data)) #averagint
#gradient = median(rgb2gray(data)) #average this is best for thin sections scaled 0 to 256
#gradient = hessian(rgb2gray(data)) # picks out grains
#gradient = frangi(rgb2gray(data)) # really weird
#gradient = laplace(rgb2gray(data)) # creates higher grains and lower matrix extremely well
#gradient = meijering(rgb2gray(data)) #shows paths ofpossible flow????
#gradient = prewitt(rgb2gray(data)) #gradient like sobel
#gradient = sobel(rgb2gray(data)) # true gradients
#gradient = prewitt_h(rgb2gray(data)) #creates higher grains and lower matrix extremely well
#gradient = prewitt_v(rgb2gray(data)) #creates higher grains and lower matrix extremely well with vertical bias
#gradient = sato(rgb2gray(data)) #weidrd like frangi
'''
#------------------------------------------------------------------------------
#
# Partition Image from 0 to n_classes
#
#------------------------------------------------------------------------------
'''
label = np.zeros(gradient.shape )
label[gradient < 0.25] = 1 #black grains
label[gradient > 0.25] = 2 #darker grains
label[gradient > 0.4] = 3 #blue-dye epoxy or visual porosity
label[gradient > 0.6] = 4 #darker grains
label[gradient > 0.75] = 5 #bright quartz grains
'''
#------------------------------------------------------------------------------
#
# Verification of Input and Labeled Results
#
#------------------------------------------------------------------------------
'''
plt.figure(0)
plt.imshow(data) #Original Image
plt.figure(1)
histogram, bin_edges = np.histogram(data, bins=256, range=(0.001, 250))
plt.title(" Histogram Original Image")
plt.xlabel(" value")
plt.ylabel("pixels")
plt.plot(bin_edges[0:-1], histogram) # <- or here
plt.figure(2)
plt.imshow(gradient) #Original Image
plt.figure(3)
histogram, bin_edges = np.histogram(gradient, bins=256, range=(0.0, 1))
plt.title(" Histogram Gradient Filter")
plt.xlabel(" value")
plt.ylabel("pixels")
plt.plot(bin_edges[0:-1], histogram) # <- or here
plt.figure(4)
plt.imshow(data) #Original Image
plt.figure(5)
plt.imshow(label) #Original Image
plt.figure(6)
histogram, bin_edges = np.histogram(label, bins=256, range=(0.0, 10))
plt.title(" Histogram Labels")
plt.xlabel(" value")
plt.ylabel("pixels")
plt.plot(bin_edges[0:-1], histogram) # <- or here
### Original way of doing this.
# img_out=os.path.join('label/', TS + '.png')
#
# im = Image.fromarray(label)
# im = im.convert("L")
#
# im.save(img_out)
# ##### for images scaled 0 to 1
# img_out=os.path.join('ClasticThinSectionsLabels', TS + '.png')
# #############im = Image.fromarray(label)
# ############im = label.convert("L")
# imsave(img_out,label)
##### for images 0 to whatever
'''
#------------------------------------------------------------------------------
#
# Save Label Image Results to dataset1/annotations_prepped_test
#
#------------------------------------------------------------------------------
'''
img_out=os.path.join('dataset1/annotations_prepped_test', TS + '.png')
im = Image.fromarray(label)
im = im.convert("L")
im.save(img_out)