-
Notifications
You must be signed in to change notification settings - Fork 7
/
Copy pathBatchMaker.py
209 lines (193 loc) · 9.02 KB
/
BatchMaker.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
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
from tifffile import imread, imsave
import numpy as np
import random
import os
import torch
import torch.utils.data
import ImageTools
import LearnTools
import math
from PIL import Image
import matplotlib.pyplot as plt
perms = [[1, 2, 3], [2, 1, 3], [3, 1, 2]] # permutations for a 4d array.
perms_3d = np.array(perms) + 1 # permutations for a 5d array.
crop_size = 4 # crop pixels in each dimension when choosing train slices
# LOW_L_3D = 45 # length of low resolution 3d
HIGH_L_3D = 64 # length of high resolution 3d
if os.getcwd().endswith('code'):
os.chdir('..') # current directory from /SuperRes/code to SuperRes/
NMC_PATH = 'data/NMC.tif'
SOFC_CATHODE_PATH = 'netl-sofc-cathode-segmented.tif'
class BatchMaker:
"""
Makes and saves training and test batch images.
"""
def __init__(self, device, to_low_idx=None, path=NMC_PATH, sf=4, dims=3,
stack=True, down_sample=False, separator=False,
low_res=False, rot_and_mir=True, squash=False,
super_sample=False):
"""
:param device: the device that the image is on.
:param to_low_idx: the indices of the phases to be down-sampled.
:param path: the path of the tif file
TODO make it more general than just tif..
:param sf: the scale factor between low and high res.
:param dims: number of dimensions for the batches (2 or 3)
:param stack: if the data is a stack of 2D images
:param down_sample: whether to down-sample the data or not.
:param separator: whether the material is a separator.
:param rot_and_mir: if True, the stack of 2D images will rotate and
mirror for another 8 configurations
:param squash: whether to squash all phases (other than pore) to one phase.
:param super_sample: whether when down sampling to make it with
super-sampling and not with blurring.
"""
# TODO currently training images are on cpu, maybe better to move
# them to gpu.
# down-sample parameters:
self.down_sample, self.to_low_idx, self.squash = down_sample, \
to_low_idx, squash
self.scale_factor = sf
self.path = path # the path of the tif file
self.dims = dims # if G is 3D to 3D or 2D to 2D
self.device = device
self.stack = stack # if the data is a stack of 2D images
self.im = imread(path)
self.high_l = HIGH_L_3D
if stack and not low_res: # it is the high-res training data
if len(self.im.shape) == 2:
# unsqueeze the np array to make it 1 X height X width
self.im = np.expand_dims(self.im, axis=0)
self.hr_metrics = ImageTools.vf_sa_metrics(self.im)
self.high_l -= crop_size*2
if rot_and_mir:
# If the image is not square, the rotations will not work, so there is a
# need to crop and batch the image(s) to squares:
if stack:
self.make_images_square()
self.rotate_and_mirror()
self.dim_im = len(self.im.shape) # the dimension of the image
self.phases = np.unique(self.im) # the unique values in image
self.im = ImageTools.one_hot_encoding(self.im, self.phases)
if low_res:
self.high_l = int(HIGH_L_3D/self.scale_factor)
if self.dims == 2:
self.high_l = self.high_l*2
if self.down_sample:
self.down_sample_object = LearnTools.\
DownSample(self.squash, self.dims, self.to_low_idx,
self.scale_factor, device, super_sample,
separator).to(self.device)
self.im = np.array(self.down_sample_im(self.im).detach().cpu())
self.phases = [self.phases[0]] + list(np.array(self.phases)[
np.array(self.to_low_idx.detach().cpu())])
self.high_l = int(HIGH_L_3D / self.scale_factor)
def make_images_square(self):
if self.im.shape[1] != self.im.shape[2]:
min_len = min(self.im.shape[1], self.im.shape[2])
max_len = max(self.im.shape[1], self.im.shape[2])
max_idx = np.argmax(self.im.shape)
# Generate a set of square images that cover the whole image:
# the number of squares in the image:
num_squares = int(np.ceil(max_len/min_len))
new_image_stack = []
for i in range(num_squares):
slices = [slice(None)]*len(self.im.shape)
if i == num_squares - 1:
slices[max_idx] = slice(max_len - min_len, max_len)
else:
slices[max_idx] = slice(i*min_len, (i+1)*min_len)
new_image_stack.append(self.im[tuple(slices)])
# stack the new images:
self.im = np.concatenate(new_image_stack, axis=0)
def down_sample_im(self, image):
"""
:return: a down-sample image of the high resolution image for the input
of G.
"""
torch_im = torch.FloatTensor(image).unsqueeze(0).to(self.device)
return self.down_sample_object(torch_im, low_res_input=True).squeeze(0)
def rotate_and_mirror(self):
"""
Given a stack of 2D images, in the form of num_images X width X heigth
return a num_images*8 X width X height stack, with all 8 different
90deg rotations and mirrors of the images.
"""
num_ims = self.im.shape[0]
flip_im = np.flip(self.im, -1)
res = np.zeros((num_ims*8, *self.im.shape[1:]), dtype=self.im.dtype)
for k in np.arange(4): # for each 90 deg rotation
first_i, second_i = 2*k*num_ims, (2*k+1)*num_ims
# rotation images of original image:
res[first_i:second_i, ...] = np.rot90(self.im, k, [-2, -1])
# rotation images of flipped image:
res[second_i:second_i + num_ims, ...] = np.rot90(flip_im, k,
[-2, -1])
self.im = res
def random_batch_for_real(self, batch_size):
return self.random_batch2d(batch_size)
def random_batch_for_fake(self, batch_size, dim_chosen):
if self.dims == 3:
return self.random_batch3d(batch_size, dim_chosen)
else: # dims = 2
return self.random_batch2d(batch_size)
def random_batch3d(self, batch_size, dim_chosen):
"""
:return: A batch of high resolution images,
along the dimension chosen (0->x,1->y,2->z) in the 3d tif image.
"""
res = np.zeros((batch_size, len(self.phases),
*self.high_l * np.ones(self.dims, dtype=int)),
dtype=self.im.dtype)
for i in range(batch_size):
res[i, ...] = self.generate_a_random_image3d(dim_chosen)
# return a torch tensor:
return torch.FloatTensor(res).to(device=self.device)
def generate_a_random_image3d(self, dim_chosen):
"""
:param dim_chosen: the dimension chosen for the slice
:return: A random image of size res from the dimension chosen of the
image.
"""
h_r = self.high_l
# starting voxels
s_ind = np.random.randint(np.array(self.im.shape[1:]) - h_r)
e_ind = s_ind + h_r # the end indices
res_image = self.im[:, s_ind[0]:e_ind[0], s_ind[1]:e_ind[1],
s_ind[2]:e_ind[2]]
# for different view, change the cube around..
return res_image.transpose(0, *perms[dim_chosen])
def random_batch2d(self, batch_size):
"""
:return: A batch of high resolution images,
along the dimension chosen (0->x,1->y,2->z) in the 3d tif image.
"""
res = np.zeros((batch_size, len(self.phases), self.high_l,
self.high_l), dtype=self.im.dtype)
for i in range(batch_size):
res[i, :, :, :] = self.generate_a_random_image2d()
# return a torch tensor:
return torch.FloatTensor(res).to(device=self.device)
def generate_a_random_image2d(self):
"""
:return: A random image of size res from the dimension chosen of the
image.
"""
# the starting pixels of the other dimensions:
if self.stack: # has to be, since this function is only called for
# by the discriminator batch maker.
s_ind = np.random.randint(np.array(self.im.shape[2:]) -
self.high_l)
e_ind = s_ind + self.high_l
slice_chosen = np.random.randint(self.im.shape[1])
return self.im[:, slice_chosen, s_ind[0]:e_ind[0],
s_ind[1]:e_ind[1]]
else:
raise ValueError("2D images should be stacked as "
"n_stack X height X width")
def all_image_batch(self):
"""
:return: the 3d image ready to be fed into the G with dimensions
1xCxDxHxW or 1xCxHxW
"""
return torch.FloatTensor(self.im).to(self.device).unsqueeze(0)