-
Notifications
You must be signed in to change notification settings - Fork 11
/
Copy pathtest.py
314 lines (230 loc) · 9.37 KB
/
test.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
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
#%%
import os
os.environ['KERAS_BACKEND']='tensorflow'
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import glob
from imageio import imread, imsave
import numpy as np
from keras.models import load_model
import matplotlib.pyplot as plt
import keras.backend as K
K.set_image_data_format('channels_last')
channel_axis = -1
import time
import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
tf.Session(config = config)
#%%
SLEEP_TIME = 1000
DISPLAY_ITER = 5000
MAX_ITER = 500000
MODE = 'AIA0304_to_HMI0100'
TRIAL_NAME = 'ORIGINAL_MxLr3'
INPUT1 = 'AIA0304'
INPUT2 = 'EUVI304'
OUTPUT = 'HMI0100'
ISIZE = 1024
NC_IN = 1
NC_OUT = 1
BATCH_SIZE = 1
RSUN = 392
SATURATION = 100
THRESHOLD = 10
#%%
OP1 = INPUT1 + '_to_' + OUTPUT
OP2 = INPUT2 + '_to_' + OUTPUT
IMAGE_PATH1 = './DATA/TEST/' + INPUT1 + '/*.png'
IMAGE_PATH2 = './DATA/TEST/' + INPUT2 + '/*.png'
IMAGE_PATH3 = './DATA/TEST/' + OUTPUT + '/*.png'
IMAGE_LIST1 = sorted(glob.glob(IMAGE_PATH1))
IMAGE_LIST2 = sorted(glob.glob(IMAGE_PATH2))
IMAGE_LIST3 = sorted(glob.glob(IMAGE_PATH3))
RESULT_PATH_MAIN = './RESULTS/' + TRIAL_NAME + '/'
os.mkdir(RESULT_PATH_MAIN) if not os.path.exists(RESULT_PATH_MAIN) else None
RESULT_PATH1 = RESULT_PATH_MAIN + OP1 + '/'
os.mkdir(RESULT_PATH1) if not os.path.exists(RESULT_PATH1) else None
RESULT_PATH2 = RESULT_PATH_MAIN + OP2 + '/'
os.mkdir(RESULT_PATH2) if not os.path.exists(RESULT_PATH2) else None
FIGURE_PATH_MAIN = './FIGURES/' + TRIAL_NAME + '/'
os.mkdir(FIGURE_PATH_MAIN) if not os.path.exists(FIGURE_PATH_MAIN) else None
#%%
def SCALE(DATA, RANGE_IN, RANGE_OUT):
DOMAIN = [RANGE_IN[0], RANGE_OUT[1]]
def INTERP(X):
return RANGE_OUT[0] * (1.0 - X) + RANGE_OUT[1] * X
def UNINTERP(X):
B = 0
if (DOMAIN[1] - DOMAIN[0]) != 0:
B = DOMAIN[1] - DOMAIN[0]
else:
B = 1.0 / DOMAIN[1]
return (X - DOMAIN[0]) / B
return INTERP(UNINTERP(DATA))
def TUMF_VALUE(IMAGE, RSUN, SATURATION, THRESHOLD):
VALUE_POSITIVE = 0
VALUE_NEGATIVE = 0
IMAGE_SCALE = SCALE(IMAGE, RANGE_IN = [0., 255.], RANGE_OUT = [-SATURATION, SATURATION])
SIZE_X, SIZE_Y = IMAGE_SCALE.shape[0], IMAGE_SCALE.shape[1]
for I in range(SIZE_X):
for J in range(SIZE_Y):
if (I-SIZE_X/2) ** 2. + (J-SIZE_Y/2) ** 2. < RSUN ** 2. :
if IMAGE_SCALE[I, J] > THRESHOLD :
VALUE_POSITIVE += IMAGE_SCALE[I, J]
elif IMAGE_SCALE[I, J] < -THRESHOLD :
VALUE_NEGATIVE += IMAGE_SCALE[I, J]
else :
None
FACT = (695500./RSUN) * (695500./RSUN) * 1000 * 1000 * 100 * 100
FLUX_POSITIVE = VALUE_POSITIVE * FACT
FLUX_NEGATIVE = VALUE_NEGATIVE * FACT
FLUX_TOTAL = FLUX_POSITIVE + abs(FLUX_NEGATIVE)
return FLUX_POSITIVE, FLUX_NEGATIVE, FLUX_TOTAL
#%%
ITER = DISPLAY_ITER
while ITER <= MAX_ITER :
SITER = '%07d'%ITER
MODEL_NAME = './MODELS/' + TRIAL_NAME + '/' + MODE + '/' + MODE + '_ITER' + SITER + '.h5'
SAVE_PATH1 = RESULT_PATH1 + 'ITER' + SITER + '/'
os.mkdir(SAVE_PATH1) if not os.path.exists(SAVE_PATH1) else None
SAVE_PATH2 = RESULT_PATH2 + 'ITER' + SITER + '/'
os.mkdir(SAVE_PATH2) if not os.path.exists(SAVE_PATH2) else None
FIGURE_PATH = FIGURE_PATH_MAIN + 'ITER' + SITER
EX = 0
while EX < 1 :
if os.path.exists(MODEL_NAME):
print('Starting Iter ' + str(ITER) + ' ...')
EX = 1
else :
print('Waiting Iter ' + str(ITER) + ' ...')
time.sleep(SLEEP_TIME)
MODEL = load_model(MODEL_NAME)
REAL_A = MODEL.input
FAKE_B = MODEL.output
NET_G_GENERATE = K.function([REAL_A], [FAKE_B])
def NET_G_GEN(A):
return np.concatenate([NET_G_GENERATE([A[I:I+1]])[0] for I in range(A.shape[0])], axis=0)
UTMF_REAL = []
UTMF_FAKE = []
for I in range(len(IMAGE_LIST1)) :
IMG = np.float32(imread(IMAGE_LIST1[I]) / 255.0 * 2 - 1)
REAL = np.float32(imread(IMAGE_LIST3[I]))
DATE = IMAGE_LIST1[I][-19:-4]
IMG.shape = (BATCH_SIZE, ISIZE, ISIZE, NC_IN)
FAKE = NET_G_GEN(IMG)
FAKE = ((FAKE[0] + 1) / 2.0 * 255.).clip(0, 255).astype('uint8')
FAKE.shape = (ISIZE, ISIZE) if NC_IN == 1 else (ISIZE, ISIZE, NC_OUT)
SAVE_NAME = SAVE_PATH1 + OP1 + '_' + DATE + '.png'
imsave(SAVE_NAME, FAKE)
RP, RN, RT = TUMF_VALUE(REAL, RSUN, SATURATION, THRESHOLD)
FP, FN, FT = TUMF_VALUE(FAKE, RSUN, SATURATION, THRESHOLD)
UTMF_REAL.append(RT)
UTMF_FAKE.append(FT)
for J in range(len(IMAGE_LIST2)) :
IMG = np.float32(imread(IMAGE_LIST2[J]) / 255.0 * 2 - 1)
DATE = IMAGE_LIST2[J][-19:-4]
IMG.shape = (BATCH_SIZE, ISIZE, ISIZE, NC_IN)
FAKE = NET_G_GEN(IMG)
FAKE = ((FAKE[0] + 1) / 2.0 * 255.).clip(0, 255).astype('uint8')
FAKE.shape = (ISIZE, ISIZE) if NC_IN == 1 else (ISIZE, ISIZE, NC_OUT)
SAVE_NAME = SAVE_PATH2 + OP2 + '_' + DATE + '.png'
imsave(SAVE_NAME, FAKE)
def MAKE_FIGURE():
I1 = np.array(imread('./DATA/TEST/AIA0304/AIA0304_20170901_120005.png'))
I2 = np.array(imread('./DATA/TEST/AIA0304/AIA0304_20170903_120005.png'))
I3 = np.array(imread('./DATA/TEST/AIA0304/AIA0304_20170905_120005.png'))
I4 = np.array(imread('./DATA/TEST/AIA0304/AIA0304_20170907_120005.png'))
T1 = np.array(imread('./DATA/TEST/HMI0100/HMI_20170901_120130.png'))
T2 = np.array(imread('./DATA/TEST/HMI0100/HMI_20170903_120130.png'))
T3 = np.array(imread('./DATA/TEST/HMI0100/HMI_20170905_120130.png'))
T4 = np.array(imread('./DATA/TEST/HMI0100/HMI_20170907_120130.png'))
O1 = np.array(imread(SAVE_PATH1 + '/' + OP1 + '_20170901_120005.png'))
O2 = np.array(imread(SAVE_PATH1 + '/' + OP1 + '_20170903_120005.png'))
O3 = np.array(imread(SAVE_PATH1 + '/' + OP1 + '_20170905_120005.png'))
O4 = np.array(imread(SAVE_PATH1 + '/' + OP1 + '_20170907_120005.png'))
fig2 = plt.figure()
ax211 = fig2.add_subplot(3, 4, 1)
ax211.imshow(I1, cmap = 'gray')
ax211.axis('off')
ax212 = fig2.add_subplot(3, 4, 2)
ax212.imshow(I2, cmap = 'gray')
ax212.axis('off')
ax213 = fig2.add_subplot(3, 4, 3)
ax213.imshow(I3, cmap = 'gray')
ax213.axis('off')
ax214 = fig2.add_subplot(3, 4, 4)
ax214.imshow(I4, cmap = 'gray')
ax214.axis('off')
ax221 = fig2.add_subplot(3, 4, 5)
ax221.imshow(O1, cmap = 'gray')
ax221.axis('off')
ax222 = fig2.add_subplot(3, 4, 6)
ax222.imshow(O2, cmap = 'gray')
ax222.axis('off')
ax223 = fig2.add_subplot(3, 4, 7)
ax223.imshow(O3, cmap = 'gray')
ax223.axis('off')
ax224 = fig2.add_subplot(3, 4, 8)
ax224.imshow(O4, cmap = 'gray')
ax224.axis('off')
ax231 = fig2.add_subplot(3, 4, 9)
ax231.imshow(T1, cmap = 'gray')
ax231.axis('off')
ax232 = fig2.add_subplot(3, 4, 10)
ax232.imshow(T2, cmap = 'gray')
ax232.axis('off')
ax233 = fig2.add_subplot(3, 4, 11)
ax233.imshow(T3, cmap = 'gray')
ax233.axis('off')
ax234 = fig2.add_subplot(3, 4, 12)
ax234.imshow(T4, cmap = 'gray')
ax234.axis('off')
fig2.savefig(FIGURE_PATH + '_FIGURE2.png')
plt.close(fig2)
CC = np.corrcoef(UTMF_REAL, UTMF_FAKE)[0, 1]
fig3 = plt.figure()
fig3.suptitle('CC : %6.3f' % (CC))
ax3 = fig3.add_subplot(1, 1, 1)
ax3.plot(UTMF_REAL, UTMF_FAKE, 'ro')
fig3.savefig(FIGURE_PATH + '_FIGURE3.png')
plt.close(fig3)
U1 = np.array(imread('./DATA/TEST/EUVI304/EUVI304_20140604_121615.png'))
U2 = np.array(imread('./DATA/TEST/EUVI304/EUVI304_20140607_120615.png'))
U3 = np.array(imread('./DATA/TRAIN/AIA0304/AIA0304_20140610_120007.png'))
U4 = np.array(imread('./DATA/TRAIN/AIA0304/AIA0304_20140613_120007.png'))
D1 = np.array(imread(SAVE_PATH2 + '/' + OP2 + '_20140604_121615.png'))
D2 = np.array(imread(SAVE_PATH2 + '/' + OP2 + '_20140607_120615.png'))
D3 = np.array(imread('./DATA/TRAIN/HMI0100/HMI_20140610_120130.png'))
D4 = np.array(imread('./DATA/TRAIN/HMI0100/HMI_20140613_120130.png'))
fig4 = plt.figure()
ax411 = fig4.add_subplot(2, 4, 1)
ax411.imshow(U1, cmap = 'gray')
ax411.axis('off')
ax412 = fig4.add_subplot(2, 4, 2)
ax412.imshow(U2, cmap = 'gray')
ax412.axis('off')
ax413 = fig4.add_subplot(2, 4, 3)
ax413.imshow(U3, cmap = 'gray')
ax413.axis('off')
ax414 = fig4.add_subplot(2, 4, 4)
ax414.imshow(U4, cmap = 'gray')
ax414.axis('off')
ax421 = fig4.add_subplot(2, 4, 5)
ax421.imshow(D1, cmap = 'gray')
ax421.axis('off')
ax422 = fig4.add_subplot(2, 4, 6)
ax422.imshow(D2, cmap = 'gray')
ax422.axis('off')
ax423 = fig4.add_subplot(2, 4, 7)
ax423.imshow(D3, cmap = 'gray')
ax423.axis('off')
ax424 = fig4.add_subplot(2, 4, 8)
ax424.imshow(D4, cmap = 'gray')
ax424.axis('off')
fig4.savefig(FIGURE_PATH + '_FIGURE4.png')
plt.close(fig4)
MAKE_FIGURE()
del MODEL
K.clear_session()
ITER += DISPLAY_ITER