|
1
|
|
|
# Copyright 2014 Diamond Light Source Ltd. |
|
2
|
|
|
# |
|
3
|
|
|
# Licensed under the Apache License, Version 2.0 (the "License"); |
|
4
|
|
|
# you may not use this file except in compliance with the License. |
|
5
|
|
|
# You may obtain a copy of the License at |
|
6
|
|
|
# |
|
7
|
|
|
# http://www.apache.org/licenses/LICENSE-2.0 |
|
8
|
|
|
# |
|
9
|
|
|
# Unless required by applicable law or agreed to in writing, software |
|
10
|
|
|
# distributed under the License is distributed on an "AS IS" BASIS, |
|
11
|
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
|
12
|
|
|
# See the License for the specific language governing permissions and |
|
13
|
|
|
# limitations under the License. |
|
14
|
|
|
|
|
15
|
|
|
""" |
|
16
|
|
|
.. module:: image_saver |
|
17
|
|
|
:platform: Unix |
|
18
|
|
|
:synopsis: A class to save output as images |
|
19
|
|
|
|
|
20
|
|
|
.. moduleauthor:: Dan Nixon, Nghia Vo <[email protected]> |
|
21
|
|
|
|
|
22
|
|
|
""" |
|
23
|
|
|
|
|
24
|
|
|
import logging |
|
25
|
|
|
import skimage.exposure |
|
26
|
|
|
import skimage.io |
|
27
|
|
|
import numpy as np |
|
28
|
|
|
from PIL import Image |
|
29
|
|
|
|
|
30
|
|
|
from savu.plugins.savers.base_image_saver import BaseImageSaver |
|
31
|
|
|
from savu.plugins.utils import register_plugin |
|
32
|
|
|
from savu.plugins.driver.cpu_plugin import CpuPlugin |
|
33
|
|
|
import savu.core.utils as cu |
|
34
|
|
|
|
|
35
|
|
|
|
|
36
|
|
|
@register_plugin |
|
37
|
|
|
class ImageSaver(BaseImageSaver, CpuPlugin): |
|
38
|
|
|
""" |
|
39
|
|
|
A class to save tomography data to image files. Run the MaxAndMin plugin\ |
|
40
|
|
|
before this to rescale the data. |
|
41
|
|
|
|
|
42
|
|
|
:param pattern: How to slice the data. Default: 'VOLUME_XZ'. |
|
43
|
|
|
:u*param format: Image format. Default: 'tif'. |
|
44
|
|
|
:u*param num_bit: Bit depth of the tiff format (8, 16 or 32). Default: 16. |
|
45
|
|
|
:param max: Global max for tiff scaling. Default: None. |
|
46
|
|
|
:param min: Global min for tiff scaling. Default: None. |
|
47
|
|
|
:param jpeg_quality: JPEG encoding quality (1 is worst, 100 is best). Default: 75. |
|
48
|
|
|
:param prefix: Override the default output jpg file prefix. Default: None. |
|
49
|
|
|
|
|
50
|
|
|
:config_warn: Do not use this plugin if the raw data is greater than \ |
|
51
|
|
|
100 GB. |
|
52
|
|
|
""" |
|
53
|
|
|
|
|
54
|
|
|
def __init__(self, name='ImageSaver'): |
|
55
|
|
|
super(ImageSaver, self).__init__(name) |
|
56
|
|
|
|
|
57
|
|
|
def setup(self): |
|
58
|
|
|
data_pattern = self.parameters['pattern'] |
|
59
|
|
|
in_pData, _ = self.get_plugin_datasets() |
|
60
|
|
|
try: |
|
61
|
|
|
in_pData[0].plugin_data_setup(data_pattern, 'single') |
|
62
|
|
|
except: |
|
63
|
|
|
msg = "\n***************************************************"\ |
|
64
|
|
|
"**********\n"\ |
|
65
|
|
|
"Can't find the data pattern: {}.\nThe pattern parameter of " \ |
|
66
|
|
|
"this plugin must be relevant to its \nprevious plugin" \ |
|
67
|
|
|
"\n*************************************************************"\ |
|
68
|
|
|
"\n".format(data_pattern) |
|
69
|
|
|
logging.warning(msg) |
|
70
|
|
|
cu.user_message(msg) |
|
71
|
|
|
raise ValueError(msg) |
|
72
|
|
|
|
|
73
|
|
|
def pre_process(self): |
|
74
|
|
|
super(ImageSaver, self).pre_process() |
|
75
|
|
|
self.pData = self.get_plugin_in_datasets()[0] |
|
76
|
|
|
self.file_format = self.parameters['format'] |
|
77
|
|
|
num_bit = self.parameters['num_bit'] |
|
78
|
|
|
if not (num_bit == 8 or num_bit == 16 or num_bit == 32): |
|
79
|
|
|
self.num_bit = 32 |
|
80
|
|
|
msg = "\n***********************************************\n"\ |
|
81
|
|
|
"This option %s is not available. Reset to 32 \n"\ |
|
82
|
|
|
% str(num_bit) |
|
83
|
|
|
cu.user_message(msg) |
|
84
|
|
|
else: |
|
85
|
|
|
self.num_bit = num_bit |
|
86
|
|
|
self._data_range = self._get_min_and_max() |
|
87
|
|
|
|
|
88
|
|
|
def process_frames(self, data): |
|
89
|
|
|
frame = self.pData.get_current_frame_idx()[0] |
|
90
|
|
|
filename = '%s%05i.%s' % (self.filename, frame, self.file_format) |
|
91
|
|
|
frame = np.nan_to_num(data[0]) |
|
92
|
|
|
if (self.file_format == "tiff") or (self.file_format == "tif"): |
|
93
|
|
|
global_min = self.parameters['min'] |
|
94
|
|
|
global_max = self.parameters['max'] |
|
95
|
|
|
if self.num_bit == 32: |
|
96
|
|
|
rescaled_image = frame |
|
97
|
|
|
else: |
|
98
|
|
|
if global_min is None: |
|
99
|
|
|
if self.the_min is not None: |
|
100
|
|
|
global_min = self.the_min |
|
101
|
|
|
else: |
|
102
|
|
|
global_min = np.min(frame) |
|
103
|
|
|
if global_max is None: |
|
104
|
|
|
if self.the_max is not None: |
|
105
|
|
|
global_max = self.the_max |
|
106
|
|
|
else: |
|
107
|
|
|
global_max = np.max(frame) |
|
108
|
|
|
rescaled_image = np.clip(frame, global_min, global_max) |
|
109
|
|
|
rescaled_image = (rescaled_image - global_min) \ |
|
110
|
|
|
/ (global_max - global_min) |
|
111
|
|
|
if self.num_bit == 16: |
|
112
|
|
|
rescaled_image = np.clip( |
|
113
|
|
|
np.uint16(rescaled_image * 65535), 0, 65535) |
|
114
|
|
|
else: |
|
115
|
|
|
rescaled_image = np.clip( |
|
116
|
|
|
np.uint8(rescaled_image * 255), 0, 255) |
|
117
|
|
|
img = Image.fromarray(rescaled_image) |
|
118
|
|
|
img.save(filename) |
|
119
|
|
|
else: |
|
120
|
|
|
# Rescale image to (0.0, 1.0) range |
|
121
|
|
|
rescaled_image = skimage.exposure.rescale_intensity( |
|
122
|
|
|
frame, in_range=self._data_range, out_range=(0.0, 1.0)) |
|
123
|
|
|
# Save image |
|
124
|
|
|
skimage.io.imsave( |
|
125
|
|
|
filename, rescaled_image, quality=self.parameters['jpeg_quality']) |
|
126
|
|
|
|
|
127
|
|
|
def _get_min_and_max(self): |
|
128
|
|
|
data = self.get_in_datasets()[0] |
|
129
|
|
|
pattern = self.parameters['pattern'] |
|
130
|
|
|
try: |
|
131
|
|
|
self.the_min = np.min( |
|
132
|
|
|
data.meta_data.get(['stats', 'min', pattern])) |
|
133
|
|
|
self.the_max = np.max( |
|
134
|
|
|
data.meta_data.get(['stats', 'max', pattern])) |
|
135
|
|
|
self._data_range = (self.the_min, self.the_max) |
|
136
|
|
|
except KeyError: |
|
137
|
|
|
self._data_range = 'image' |
|
138
|
|
|
if (self.file_format == "tiff") or (self.file_format == "tif"): |
|
139
|
|
|
self.the_min = None |
|
140
|
|
|
self.the_max = None |
|
141
|
|
|
msg = "\n***********************************************\n"\ |
|
142
|
|
|
"!!!Warning!!!-> No global maximum and global minimum found\n"\ |
|
143
|
|
|
"in the metadata. Please run the MaxAndMin plugin before\n" \ |
|
144
|
|
|
"ImageSaver or input manually. Otherwise, local minimum\n" \ |
|
145
|
|
|
"and local maximum will be used for rescaling. This may\n"\ |
|
146
|
|
|
"result the fluctuation of brightness between slices.\n"\ |
|
147
|
|
|
"***********************************************\n" |
|
148
|
|
|
if (self.num_bit == 8) or (self.num_bit == 16): |
|
149
|
|
|
cu.user_message(msg) |
|
150
|
|
|
return self._data_range |
|
151
|
|
|
|
|
152
|
|
|
def executive_summary(self): |
|
153
|
|
|
if self._data_range == 'image': |
|
154
|
|
|
return ["To rescale and normalise the data between global max and " |
|
155
|
|
|
"min values, please run MaxAndMin plugin before " |
|
156
|
|
|
"ImageSaver."] |
|
157
|
|
|
return ["Nothing to Report"] |
|
158
|
|
|
|
DiamondLightSource /
Savu
