forked from soltanianzadeh/STNeuroNet
-
Notifications
You must be signed in to change notification settings - Fork 0
/
demo_inference.py
175 lines (151 loc) · 6.08 KB
/
demo_inference.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
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
shows how to apply a trained network on a dataset from Allen
@author: Somayyeh Soltanian-Zadeh
%
% Please cite this paper if you use any component of this software:
% S. Soltanian-Zadeh, K. Sahingur, S. Blau, Y. Gong, and S. Farsiu, "Fast and robust
% active neuron segmentation in two-photon calcium imaging using spatio-temporal
% deep learning," Proceedings of the National Academy of Sciences (PNAS), 2019.
%
% Released under a GPL v2 license.
"""
import os
import sys
import niftynet
import math
import numpy as np
from pathlib import Path
import scipy.io as sio
import STNeuroNetPkg
import matlab
matlabLib = STNeuroNetPkg.initialize()
# List of data IDs for Layer275 (DO NOT change the current ordering)
# Order is important for correct mapping between test data and the cross-validation-based trained networks
L = ['524691284', '531006860','502608215', '503109347','501484643', '501574836',
'501729039', '539670003','510214538', '527048992']
# Data to process
name = ['524691284']
# dataType should be either 'Cross Validation' for data from Layer275, or
# 'All' for Layer175 recordings
LayerType = 'Layer275'
if LayerType == 'Layer275':
dataType = 'Cross Validation'
endFile = str(L.index(name[0])+1)
ind = L.index(name[0])
ThreshFile = 'OptParam_Final_JaccardNew_LOO_whitened.mat'
AreaName = 'minArea'
else:
dataType = 'All'
endFile = ''
ind = 0
ThreshFile = 'OptParam_Jaccard_ABO_all275Whitened.mat'
AreaName = 'minA'
## Set directories
dirpath = os.getcwd()
DirData = os.path.join(dirpath,'Dataset','ABO')
DirSaveData = os.path.join(dirpath,'Results','ABO','data')
DirSave = os.path.join(dirpath,'Results','ABO','Probability map')
DirModel = os.path.join(dirpath,'models','ABO','Trained Network Weights',dataType,endFile)
DirMask = os.path.join(dirpath,'Markings','ABO',LayerType,'FinalGT')
DirSaveMask = os.path.join(dirpath,'Results','ABO','Test Masks')
DirThresh = os.path.join(dirpath,'Results','ABO','Thresholds')
## Check if save directories exist
if not os.path.exists(DirSaveMask):
os.makedirs(DirSaveMask)
if not os.path.exists(DirSaveData):
os.makedirs(DirSaveData)
#% Set parameters
pixSize = 0.78 #um
meanR = 5.85 # neuron radius in um
AvgArea = round(math.pi*(meanR/pixSize)**2)
Thresh = 0.5 # IoU threshold for matching
SZ = matlab.double([487,487]) #x and y dimension of data
## read saved threshold values
optThresh = sio.loadmat(os.path.join(DirThresh,ThreshFile))
thresh = matlab.single([optThresh['ProbThresh'][0][ind]])
minArea = matlab.single([optThresh[AreaName][0][ind]])
## Check if HomoFiltered downsampled data is available
data_file = Path(os.path.join(DirSaveData, name[0]+'_dsCropped_HomoNorm.nii.gz'))
if not data_file.exists():
data_file = os.path.join(DirData, name[0]+'_processed.nii.gz')
s = 30
matlabLib.HomoFilt_Normalize(data_file,DirSaveData,name[0],s,nargout=0)
#%%
## Run data through the trained network
# first create a new config file based on the current data
f = open("demo_config_empty.ini")
mylist = f.readlines()
f.close()
indPath = []
indName = []
indNoName = []
indSave = []
indModel = []
for ind in range(len(mylist)):
if mylist[ind].find('path_to_search')>-1:
indPath.append(ind)
if mylist[ind].find('filename_contains')>-1:
indName.append(ind)
if mylist[ind].find('filename_not_contains')>-1:
indNoName.append(ind)
if mylist[ind].find('save_seg_dir')>-1:
indSave.append(ind)
if mylist[ind].find('model_dir')>-1:
indModel.append(ind)
# write path of data
mystr = list(mylist[indPath[0]])
mystr = "".join(mystr[:-1]+ list(DirSaveData) + list('\n'))
mylist[indPath[0]] = mystr
# write name of data
mystr = list(mylist[indName[0]])
#temp = mystr[:-1]
#for ind in range(len(name)):
# temp = temp + list(name[ind]) + list(',')
mystr = "".join(mystr[:-1]+ list('_dsCropped_HomoNorm') + list('\n'))
mylist[indName[0]] = mystr
# exclude any other data not listed in names
AllFiles = os.listdir(DirSaveData)
AllNames = []
for ind in range(len(AllFiles)):
if AllFiles[ind].find('_dsCropped_HomoNorm')>-1:
AllNames.append(AllFiles[ind][:AllFiles[ind].find('_dsCropped_HomoNorm')])
excludeNames = [c for c in AllNames if c not in name]
if len(excludeNames):
mystr = list(mylist[indNoName[0]])
temp = mystr[:-1]
for ind in range(len(excludeNames)):
temp = temp + list(excludeNames[ind]) + list(',')
mystr = "".join(temp[:-1]+ list('\n'))
mylist[indNoName[0]] = mystr
#write where to save result
mystr = list(mylist[indSave[0]])
mystr = "".join(mystr[:-1]+ list(DirSave) + list('\n'))
mylist[indSave[0]] = mystr
#write where model is located
mystr = list(mylist[indModel[0]])
mystr = "".join(mystr[:-1]+ list(DirModel) + list('\n'))
mylist[indModel[0]] = mystr
# Write to a new config file
f = open('config_inf.ini','w')
f.write(''.join(mylist))
f.close()
#%%
sys.argv=['','inference','-a','net_segment','--conf',os.path.join('config_inf.ini'),'--batch_size','1']
niftynet.main()
#%%
## Postprocess to get individual neurons
saveTag = True
for ind in range(len(name)):
print('Postprocessing data {} ...'.format(name[ind]))
Neurons = matlabLib.postProcess(DirSave,name[ind],SZ,AvgArea,minArea,thresh,nargout=2)
if saveTag:
print('Saving results of {} ...'.format(name[ind]))
sio.savemat(os.path.join(DirSaveMask,name[ind]+'_neurons.mat'),{'finalSegments': np.array(Neurons[0],dtype=int),'MaskCenters':np.array(Neurons[1])})
## Compare performance to GT Masks if available
if DirMask is not None:
print('Getting performance metrics for {} ...'.format(name[ind]))
scores = matlabLib.GetPerformance_Jaccard(DirMask,name[ind],Neurons[0],Thresh,nargout=3)
print('data: {} -> recall: {}, precision: {}, and F1 {}:'.format(name[ind],int(10000*scores[0])/100,int(10000*scores[1])/100,int(10000*scores[2])/100))
matlabLib.terminate()