|
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
|
|
|
.. module:: min_and_max |
|
16
|
|
|
:platform: Unix |
|
17
|
|
|
:synopsis: A plugin to calculate the min and max of each frame |
|
18
|
|
|
.. moduleauthor:: Nicola Wadeson <[email protected]> |
|
19
|
|
|
""" |
|
20
|
|
|
import logging |
|
21
|
|
|
import numpy as np |
|
22
|
|
|
|
|
23
|
|
|
from scipy.ndimage import gaussian_filter |
|
24
|
|
|
from savu.plugins.plugin import Plugin |
|
25
|
|
|
from savu.plugins.utils import register_plugin |
|
26
|
|
|
from savu.plugins.driver.cpu_plugin import CpuPlugin |
|
27
|
|
|
import savu.core.utils as cu |
|
28
|
|
|
|
|
29
|
|
|
|
|
30
|
|
|
@register_plugin |
|
31
|
|
|
class MinAndMaxDeprecated(Plugin, CpuPlugin): |
|
32
|
|
|
|
|
33
|
|
|
def __init__(self): |
|
34
|
|
|
super(MinAndMaxDeprecated, self).__init__("MinAndMax") |
|
35
|
|
|
|
|
36
|
|
|
def circle_mask(self, width, ratio): |
|
37
|
|
|
# Create a circle mask. |
|
38
|
|
|
mask = np.zeros((width, width), dtype=np.float32) |
|
39
|
|
|
center = width // 2 |
|
40
|
|
|
radius = ratio * center |
|
41
|
|
|
y, x = np.ogrid[-center:width - center, -center:width - center] |
|
42
|
|
|
mask_check = x * x + y * y <= radius * radius |
|
43
|
|
|
mask[mask_check] = 1.0 |
|
44
|
|
|
return mask |
|
45
|
|
|
|
|
46
|
|
|
def pre_process(self): |
|
47
|
|
|
in_pData = self.get_plugin_in_datasets()[0] |
|
48
|
|
|
in_meta_data = self.get_in_meta_data()[0] |
|
49
|
|
|
data = self.get_in_datasets()[0] |
|
50
|
|
|
data_shape = data.get_shape() |
|
51
|
|
|
width = data_shape[0] |
|
52
|
|
|
self.use_mask = self.parameters['masking'] |
|
53
|
|
|
self.data_pattern = self.parameters['pattern'] |
|
54
|
|
|
self.mask = np.ones((width, width), dtype=np.float32) |
|
55
|
|
|
if self.use_mask is True: |
|
56
|
|
|
ratio = self.parameters['ratio'] |
|
57
|
|
|
if ratio is None: |
|
58
|
|
|
try: |
|
59
|
|
|
cor = np.min(in_meta_data.get('centre_of_rotation')) |
|
60
|
|
|
ratio = (min(cor, abs(width - cor))) / (width * 0.5) |
|
61
|
|
|
except KeyError: |
|
62
|
|
|
ratio = 1.0 |
|
63
|
|
|
self.mask = self.circle_mask(width, ratio) |
|
64
|
|
|
self.method = self.parameters['method'] |
|
65
|
|
|
if not (self.method == 'percentile' or self.method == 'extrema'): |
|
66
|
|
|
msg = "\n***********************************************\n" \ |
|
67
|
|
|
"!!! ERROR !!! -> Wrong method. Please use only one of " \ |
|
68
|
|
|
"the provided options \n" \ |
|
69
|
|
|
"***********************************************\n" |
|
70
|
|
|
logging.warning(msg) |
|
71
|
|
|
cu.user_message(msg) |
|
72
|
|
|
raise ValueError(msg) |
|
73
|
|
|
self.p_min, self.p_max = np.sort(np.clip(np.asarray( |
|
74
|
|
|
self.parameters['p_range'], dtype=np.float32), 0.0, 100.0)) |
|
75
|
|
|
|
|
76
|
|
|
def process_frames(self, data): |
|
77
|
|
|
use_filter = self.parameters['smoothing'] |
|
78
|
|
|
frame = np.nan_to_num(data[0]) |
|
79
|
|
|
if use_filter is True: |
|
80
|
|
|
frame = gaussian_filter(frame, (3, 3)) |
|
81
|
|
|
if (self.use_mask is True) and (self.data_pattern == 'VOLUME_XZ') \ |
|
82
|
|
|
and (self.mask.shape == frame.shape): |
|
83
|
|
|
frame = frame * self.mask |
|
84
|
|
|
if self.method == 'percentile': |
|
85
|
|
|
list_out = [np.array( |
|
86
|
|
|
[np.percentile(frame, self.p_min)], dtype=np.float32), |
|
87
|
|
|
np.array([np.percentile(frame, self.p_max)], dtype=np.float32)] |
|
88
|
|
|
else: |
|
89
|
|
|
list_out = [np.array([np.min(frame)], dtype=np.float32), |
|
90
|
|
|
np.array([np.max(frame)], dtype=np.float32)] |
|
91
|
|
|
return list_out |
|
92
|
|
|
|
|
93
|
|
|
def post_process(self): |
|
94
|
|
|
in_datasets, out_datasets = self.get_datasets() |
|
95
|
|
|
the_min = np.squeeze(out_datasets[0].data[...]) |
|
96
|
|
|
the_max = np.squeeze(out_datasets[1].data[...]) |
|
97
|
|
|
pattern = self._get_pattern() |
|
98
|
|
|
in_datasets[0].meta_data.set(['stats', 'min', pattern], the_min) |
|
99
|
|
|
in_datasets[0].meta_data.set(['stats', 'max', pattern], the_max) |
|
100
|
|
|
|
|
101
|
|
|
def setup(self): |
|
102
|
|
|
in_dataset, out_datasets = self.get_datasets() |
|
103
|
|
|
in_pData, out_pData = self.get_plugin_datasets() |
|
104
|
|
|
try: |
|
105
|
|
|
in_pData[0].plugin_data_setup(self._get_pattern(), 'single') |
|
106
|
|
|
except: |
|
107
|
|
|
msg = "\n***************************************************" \ |
|
108
|
|
|
"**********\nCan't find the data pattern: {}.\nThe pattern " \ |
|
109
|
|
|
"parameter of this plugin must be relevant to its \n" \ |
|
110
|
|
|
"previous plugin\n****************************************" \ |
|
111
|
|
|
"*********************\n".format(self._get_pattern()) |
|
112
|
|
|
logging.warning(msg) |
|
113
|
|
|
cu.user_message(msg) |
|
114
|
|
|
raise ValueError(msg) |
|
115
|
|
|
|
|
116
|
|
|
slice_dirs = list(in_dataset[0].get_slice_dimensions()) |
|
117
|
|
|
orig_shape = in_dataset[0].get_shape() |
|
118
|
|
|
new_shape = (np.prod(np.array(orig_shape)[slice_dirs]), 1) |
|
119
|
|
|
|
|
120
|
|
|
labels = ['x.pixels', 'y.pixels'] |
|
121
|
|
|
for i in range(len(out_datasets)): |
|
122
|
|
|
out_datasets[i].create_dataset(shape=new_shape, axis_labels=labels, |
|
123
|
|
|
remove=True, transport='hdf5') |
|
124
|
|
|
out_datasets[i].add_pattern( |
|
125
|
|
|
"METADATA", core_dims=(1,), slice_dims=(0,)) |
|
126
|
|
|
out_pData[i].plugin_data_setup('METADATA', 'single') |
|
127
|
|
|
|
|
128
|
|
|
def _get_pattern(self): |
|
129
|
|
|
return self.parameters['pattern'] |
|
130
|
|
|
|
|
131
|
|
|
def nOutput_datasets(self): |
|
132
|
|
|
return 2 |
|
133
|
|
|
|
DiamondLightSource /
Savu
