-
Notifications
You must be signed in to change notification settings - Fork 2
/
image_registration.py
478 lines (411 loc) · 26.5 KB
/
image_registration.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
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
import os
import sys
import pathlib
sys.path.append(os.path.join(os.path.dirname(__file__), "."))
import time
import shutil
import numpy as np
import pandas as pd
import torch as tc
import paths as p
import utils as u
### Please Note - registration comes from external package that will be released separately ###
from registration import instance_optimization
from registration import utils_tc
from registration import cost_functions
from registration import regularizers
"""
Script performing the registration of Task 1/3 training sets.
"""
def register_single(source, target):
"""
Registration (purely nonrigid) of the given source and target with fixed registration parameters.
"""
device = "cuda:0"
y_size, x_size, z_size = source.shape
source = tc.from_numpy(source.astype(np.float32))
target = tc.from_numpy(target.astype(np.float32))
source = source.view(1, 1, y_size, x_size, z_size).to(device)
target = target.view(1, 1, y_size, x_size, z_size).to(device)
num_levels = 4
used_levels = 4
num_iters = 50
learning_rate = 0.01
alpha = 100000
cost_function = cost_functions.mse_tc
regularization_function = regularizers.diffusion_tc
cost_function_params = {}
regularization_function_params = {}
transformation = instance_optimization.affine_registration(source, target, num_levels, used_levels-1, num_iters, learning_rate, cost_function, device=device)
displacement_field_tc = utils_tc.tc_transform_to_tc_df(transformation, (1, 1, y_size, x_size, z_size), device=device)
displacement_field_tc = instance_optimization.nonrigid_registration(source, target, num_levels, used_levels, num_iters, learning_rate,
alpha, cost_function, regularization_function, cost_function_params, regularization_function_params, initial_displacement_field=displacement_field_tc, device=device)
displacement_field = utils_tc.tc_df_to_np_df(displacement_field_tc)
return displacement_field
def combine_datasets_by_registration():
"""
Function to combine the Task 1 and Task 3 training sets using affine and nonrigid image registration.
"""
task_1_dataframe = pd.read_csv(p.task_1_training_csv_path)
task_3_dataframe = pd.read_csv(p.task_3_training_csv_path)
print("Task 1 Size: ", len(task_1_dataframe))
print("Task 3 Size: ", len(task_3_dataframe))
task_1_data_path = p.task_1_training_preprocessed_path
task_3_data_path = p.task_3_training_preprocessed_path
combined_data_path = p.combined_training_path
data = []
# Task 1 vs Task 1
all_pairs = list()
all_defects = dict()
for outer_id, outer_case in task_1_dataframe.iterrows():
for inner_id, inner_case in task_1_dataframe.iterrows():
inner_complete_path = inner_case['Complete Skull Path']
outer_complete_path = outer_case['Complete Skull Path']
inner_skull_id = os.path.split(inner_complete_path)[-1].split(".")[0]
outer_skull_id = os.path.split(outer_complete_path)[-1].split(".")[0]
if inner_skull_id != outer_skull_id:
current_pair = (inner_skull_id, outer_skull_id)
all_pairs.append((current_pair))
try:
if inner_complete_path not in all_defects[current_pair]:
all_defects[current_pair].append(inner_complete_path)
except:
all_defects[current_pair] = [inner_complete_path]
all_pairs = list(set(all_pairs))
print("Task 1/1 cases: ", len(all_pairs))
i = 0
for pair in all_pairs:
print("Current pair: " + str(i) + " / " + str(len(all_pairs)))
b_t = time.time()
source_path = task_1_data_path / "complete_skull" / "bilateral" / pathlib.Path(pair[0]).with_suffix('.nrrd')
target_path = task_1_data_path / "complete_skull" / "bilateral" / pathlib.Path(pair[1]).with_suffix('.nrrd')
source, _, _ = u.load_volume(source_path)
target, _, _ = u.load_volume(target_path)
displacement_field = register_single(source, target)
j = 0
for to_warp_case in all_defects[pair]:
complete_skull_path = task_1_data_path / to_warp_case
defective_skull_path = task_1_data_path / to_warp_case.replace("complete_skull", "defective_skull")
implant_path = task_1_data_path / to_warp_case.replace("complete_skull", "implant")
complete_skull, spacing, _ = u.load_volume(complete_skull_path)
defective_skull, _, _ = u.load_volume(defective_skull_path)
implant, _, _ = u.load_volume(implant_path)
warped_complete_skull = u.image_warping(complete_skull, displacement_field, order=0)
warped_defective_skull = u.image_warping(defective_skull, displacement_field, order=0)
warped_implant = u.image_warping(implant, displacement_field, order=0)
warped_complete_skull_path = pathlib.Path("complete_skull", "T11", str(i), str(j), "complete_skull.nrrd")
warped_defective_skull_path = pathlib.Path("defective_skull", "T11", str(i), str(j), "defective_skull.nrrd")
warped_implant_path = pathlib.Path("implant", "T11", str(i), str(j), "implant.nrrd")
full_warped_complete_skull_path = combined_data_path / warped_complete_skull_path
full_warped_defective_skull_path = combined_data_path / warped_defective_skull_path
full_warped_implant_path = combined_data_path / warped_implant_path
pathlib.Path(full_warped_complete_skull_path).parents[0].mkdir(parents=True, exist_ok=True)
pathlib.Path(full_warped_defective_skull_path).parents[0].mkdir(parents=True, exist_ok=True)
pathlib.Path(full_warped_implant_path).parents[0].mkdir(parents=True, exist_ok=True)
u.save_volume(warped_complete_skull, spacing, full_warped_complete_skull_path)
u.save_volume(warped_defective_skull, spacing, full_warped_defective_skull_path)
u.save_volume(warped_implant , spacing, full_warped_implant_path)
data.append([warped_complete_skull_path, warped_defective_skull_path, warped_implant_path])
j += 1
i += 1
e_t = time.time()
print("Time for current pair: ", e_t - b_t, " seconds.")
print("Estimated time to end: ", (len(all_pairs) - i) * (e_t - b_t) / 60.0, " minutes.")
# Task 3 vs Task 3
all_pairs = list()
all_defects = dict()
for outer_id, outer_case in task_3_dataframe.iterrows():
for inner_id, inner_case in task_3_dataframe.iterrows():
inner_complete_path = inner_case['Complete Skull Path']
outer_complete_path = outer_case['Complete Skull Path']
inner_skull_id = os.path.split(inner_complete_path)[-1].split(".")[0]
outer_skull_id = os.path.split(outer_complete_path)[-1].split(".")[0]
if inner_skull_id != outer_skull_id:
current_pair = (inner_skull_id, outer_skull_id)
all_pairs.append(current_pair)
try:
if inner_complete_path not in all_defects[current_pair]:
all_defects[current_pair].append(inner_complete_path)
except:
all_defects[current_pair] = [inner_complete_path]
all_pairs = list(set(all_pairs))
print("Task 3/3 cases: ", len(all_pairs))
i = 0
for pair in all_pairs:
print("Current pair: " + str(i) + " / " + str(len(all_pairs)))
b_t = time.time()
source_path = task_3_data_path / "complete_skull" / pathlib.Path(pair[0]).with_suffix('.nrrd')
target_path = task_3_data_path / "complete_skull" / pathlib.Path(pair[1]).with_suffix('.nrrd')
source, _, _ = u.load_volume(source_path)
target, _, _ = u.load_volume(target_path)
displacement_field = register_single(source, target)
j = 0
for to_warp_case in all_defects[pair]:
complete_skull_path = task_3_data_path / to_warp_case
defective_skull_path = task_3_data_path / to_warp_case.replace("complete_skull", "defective_skull")
implant_path = task_3_data_path / to_warp_case.replace("complete_skull", "implant")
complete_skull, spacing, _ = u.load_volume(complete_skull_path)
defective_skull, _, _ = u.load_volume(defective_skull_path)
implant, _, _ = u.load_volume(implant_path)
warped_complete_skull = u.image_warping(complete_skull, displacement_field, order=0)
warped_defective_skull = u.image_warping(defective_skull, displacement_field, order=0)
warped_implant = u.image_warping(implant, displacement_field, order=0)
warped_complete_skull_path = pathlib.Path("complete_skull", "T33", str(i), str(j), "complete_skull.nrrd")
warped_defective_skull_path = pathlib.Path("defective_skull", "T33", str(i), str(j), "defective_skull.nrrd")
warped_implant_path = pathlib.Path("implant", "T33", str(i), str(j), "implant.nrrd")
full_warped_complete_skull_path = combined_data_path / warped_complete_skull_path
full_warped_defective_skull_path = combined_data_path / warped_defective_skull_path
full_warped_implant_path = combined_data_path / warped_implant_path
pathlib.Path(full_warped_complete_skull_path).parents[0].mkdir(parents=True, exist_ok=True)
pathlib.Path(full_warped_defective_skull_path).parents[0].mkdir(parents=True, exist_ok=True)
pathlib.Path(full_warped_implant_path).parents[0].mkdir(parents=True, exist_ok=True)
u.save_volume(warped_complete_skull, spacing, full_warped_complete_skull_path)
u.save_volume(warped_defective_skull, spacing, full_warped_defective_skull_path)
u.save_volume(warped_implant , spacing, full_warped_implant_path)
data.append([warped_complete_skull_path, warped_defective_skull_path, warped_implant_path])
j += 1
i += 1
e_t = time.time()
print("Time for current pair: ", e_t - b_t, " seconds.")
print("Estimated time to end: ", (len(all_pairs) - i) * (e_t - b_t) / 60.0, " minutes.")
# Task 3 vs Task 1
all_pairs = list()
all_defects = dict()
for outer_id, outer_case in task_1_dataframe.iterrows():
for inner_id, inner_case in task_3_dataframe.iterrows():
inner_complete_path = inner_case['Complete Skull Path']
outer_complete_path = outer_case['Complete Skull Path']
inner_skull_id = os.path.split(inner_complete_path)[-1].split(".")[0]
outer_skull_id = os.path.split(outer_complete_path)[-1].split(".")[0]
current_pair = (inner_skull_id, outer_skull_id)
all_pairs.append(current_pair)
try:
if inner_complete_path not in all_defects[current_pair]:
all_defects[current_pair].append(inner_complete_path)
except:
all_defects[current_pair] = [inner_complete_path]
all_pairs = list(set(all_pairs))
print("Task 1/3 cases: ", len(all_pairs))
i = 0
for pair in all_pairs:
print("Current pair: " + str(i) + " / " + str(len(all_pairs)))
b_t = time.time()
source_path = task_3_data_path / "complete_skull" / pathlib.Path(pair[0]).with_suffix('.nrrd')
target_path = task_1_data_path / "complete_skull" / "bilateral" / pathlib.Path(pair[1]).with_suffix('.nrrd')
source, _, _ = u.load_volume(source_path)
target, _, _ = u.load_volume(target_path)
displacement_field = register_single(source, target)
j = 0
for to_warp_case in all_defects[pair]:
complete_skull_path = task_3_data_path / to_warp_case
defective_skull_path = task_3_data_path / to_warp_case.replace("complete_skull", "defective_skull")
implant_path = task_3_data_path / to_warp_case.replace("complete_skull", "implant")
complete_skull, spacing, _ = u.load_volume(complete_skull_path)
defective_skull, _, _ = u.load_volume(defective_skull_path)
implant, _, _ = u.load_volume(implant_path)
warped_complete_skull = u.image_warping(complete_skull, displacement_field, order=0)
warped_defective_skull = u.image_warping(defective_skull, displacement_field, order=0)
warped_implant = u.image_warping(implant, displacement_field, order=0)
warped_complete_skull_path = pathlib.Path("complete_skull", "T31", str(i), str(j), "complete_skull.nrrd")
warped_defective_skull_path = pathlib.Path("defective_skull", "T31", str(i), str(j), "defective_skull.nrrd")
warped_implant_path = pathlib.Path("implant", "T31", str(i), str(j), "implant.nrrd")
full_warped_complete_skull_path = combined_data_path / warped_complete_skull_path
full_warped_defective_skull_path = combined_data_path / warped_defective_skull_path
full_warped_implant_path = combined_data_path / warped_implant_path
pathlib.Path(full_warped_complete_skull_path).parents[0].mkdir(parents=True, exist_ok=True)
pathlib.Path(full_warped_defective_skull_path).parents[0].mkdir(parents=True, exist_ok=True)
pathlib.Path(full_warped_implant_path).parents[0].mkdir(parents=True, exist_ok=True)
u.save_volume(warped_complete_skull, spacing, full_warped_complete_skull_path)
u.save_volume(warped_defective_skull, spacing, full_warped_defective_skull_path)
u.save_volume(warped_implant , spacing, full_warped_implant_path)
data.append([warped_complete_skull_path, warped_defective_skull_path, warped_implant_path])
j += 1
i += 1
e_t = time.time()
print("Time for current pair: ", e_t - b_t, " seconds.")
print("Estimated time to end: ", (len(all_pairs) - i) * (e_t - b_t) / 60.0, " minutes.")
# Task 1 vs Task 3
all_pairs = list()
all_defects = dict()
for outer_id, outer_case in task_3_dataframe.iterrows():
for inner_id, inner_case in task_1_dataframe.iterrows():
inner_complete_path = inner_case['Complete Skull Path']
outer_complete_path = outer_case['Complete Skull Path']
inner_skull_id = os.path.split(inner_complete_path)[-1].split(".")[0]
outer_skull_id = os.path.split(outer_complete_path)[-1].split(".")[0]
current_pair = (inner_skull_id, outer_skull_id)
all_pairs.append(current_pair)
try:
if inner_complete_path not in all_defects[current_pair]:
all_defects[current_pair].append(inner_complete_path)
except:
all_defects[current_pair] = [inner_complete_path]
all_pairs = list(set(all_pairs))
print("Task 3/1 cases: ",len(all_pairs))
i = 0
for pair in all_pairs:
print("Current pair: " + str(i) + " / " + str(len(all_pairs)))
b_t = time.time()
source_path = task_1_data_path / "complete_skull" / "bilateral" / pathlib.Path(pair[0]).with_suffix('.nrrd')
target_path = task_3_data_path / "complete_skull" / pathlib.Path(pair[1]).with_suffix('.nrrd')
source, _, _ = u.load_volume(source_path)
target, _, _ = u.load_volume(target_path)
displacement_field = register_single(source, target)
j = 0
for to_warp_case in all_defects[pair]:
complete_skull_path = task_1_data_path / to_warp_case
defective_skull_path = task_1_data_path / to_warp_case.replace("complete_skull", "defective_skull")
implant_path = task_1_data_path / to_warp_case.replace("complete_skull", "implant")
complete_skull, spacing, _ = u.load_volume(complete_skull_path)
defective_skull, _, _ = u.load_volume(defective_skull_path)
implant, _, _ = u.load_volume(implant_path)
warped_complete_skull = u.image_warping(complete_skull, displacement_field, order=0)
warped_defective_skull = u.image_warping(defective_skull, displacement_field, order=0)
warped_implant = u.image_warping(implant, displacement_field, order=0)
warped_complete_skull_path = pathlib.Path("complete_skull", "T13", str(i), str(j), "complete_skull.nrrd")
warped_defective_skull_path = pathlib.Path("defective_skull", "T13", str(i), str(j), "defective_skull.nrrd")
warped_implant_path = pathlib.Path("implant", "T13", str(i), str(j), "implant.nrrd")
full_warped_complete_skull_path = combined_data_path / warped_complete_skull_path
full_warped_defective_skull_path = combined_data_path / warped_defective_skull_path
full_warped_implant_path = combined_data_path / warped_implant_path
pathlib.Path(full_warped_complete_skull_path).parents[0].mkdir(parents=True, exist_ok=True)
pathlib.Path(full_warped_defective_skull_path).parents[0].mkdir(parents=True, exist_ok=True)
pathlib.Path(full_warped_implant_path).parents[0].mkdir(parents=True, exist_ok=True)
u.save_volume(warped_complete_skull, spacing, full_warped_complete_skull_path)
u.save_volume(warped_defective_skull, spacing, full_warped_defective_skull_path)
u.save_volume(warped_implant , spacing, full_warped_implant_path)
data.append([warped_complete_skull_path, warped_defective_skull_path, warped_implant_path])
j += 1
i += 1
e_t = time.time()
print("Time for current pair: ", e_t - b_t, " seconds.")
print("Estimated time to end: ", (len(all_pairs) - i) * (e_t - b_t) / 60.0, " minutes.")
output_dataframe = pd.DataFrame(data, columns=['Complete Skull Path', "Defective Skull Path", "Implant Path"])
output_dataframe.to_csv(p.combined_training_csv_path, index=False)
def combine_validation_sets():
"""
Function to combine Task 1/3 validation sets (w/o registration).
"""
task_1_val_dataframe = pd.read_csv(p.task_1_validation_csv_path)
task_3_val_dataframe = pd.read_csv(p.task_3_validation_csv_path)
task_1_data_path = p.task_1_training_preprocessed_path
task_3_data_path = p.task_3_training_preprocessed_path
combined_data_path = p.combined_training_path
data = []
for current_id, case in task_1_val_dataframe.iterrows():
input_complete_skull_path = task_1_data_path / case['Complete Skull Path']
input_defective_skull_path = task_1_data_path / case['Defective Skull Path']
input_implant_path = task_1_data_path / case['Implant Path']
output_complete_skull_path = combined_data_path / "Validation" / case['Complete Skull Path']
output_defective_skull_path = combined_data_path / "Validation" / case['Defective Skull Path']
output_implant_path = combined_data_path / "Validation" / case['Implant Path']
pathlib.Path(output_complete_skull_path).parents[0].mkdir(parents=True, exist_ok=True)
pathlib.Path(output_defective_skull_path).parents[0].mkdir(parents=True, exist_ok=True)
pathlib.Path(output_implant_path).parents[0].mkdir(parents=True, exist_ok=True)
shutil.copy(input_complete_skull_path, output_complete_skull_path)
shutil.copy(input_defective_skull_path, output_defective_skull_path)
shutil.copy(input_implant_path, output_implant_path)
data.append([output_complete_skull_path, output_defective_skull_path, output_implant_path])
for current_id, case in task_3_val_dataframe.iterrows():
input_complete_skull_path = task_3_data_path / case['Complete Skull Path']
input_defective_skull_path = task_3_data_path / case['Defective Skull Path']
input_implant_path = task_3_data_path / case['Implant Path']
output_complete_skull_path = combined_data_path / "Validation" / case['Complete Skull Path']
output_defective_skull_path = combined_data_path / "Validation" / case['Defective Skull Path']
output_implant_path = combined_data_path / "Validation" / case['Implant Path']
pathlib.Path(output_complete_skull_path).parents[0].mkdir(parents=True, exist_ok=True)
pathlib.Path(output_defective_skull_path).parents[0].mkdir(parents=True, exist_ok=True)
pathlib.Path(output_implant_path).parents[0].mkdir(parents=True, exist_ok=True)
shutil.copy(input_complete_skull_path, output_complete_skull_path)
shutil.copy(input_defective_skull_path, output_defective_skull_path)
shutil.copy(input_implant_path, output_implant_path)
data.append([output_complete_skull_path, output_defective_skull_path, output_implant_path])
validation_dataframe = pd.DataFrame(data, columns=['Complete Skull Path', "Defective Skull Path", "Implant Path"])
validation_dataframe.to_csv(p.combined_validation_csv_path, index=False)
def combine_training_sets_wor():
"""
Function to combine Task 1/3 training sets (w/o registration).
"""
task_1_tr_dataframe = pd.read_csv(p.task_1_training_csv_path)
task_3_tr_dataframe = pd.read_csv(p.task_3_training_csv_path)
task_1_data_path = p.task_1_training_preprocessed_path
task_3_data_path = p.task_3_training_preprocessed_path
combined_data_path = p.combined_training_path
data = []
for current_id, case in task_1_tr_dataframe.iterrows():
input_complete_skull_path = task_1_data_path / case['Complete Skull Path']
input_defective_skull_path = task_1_data_path / case['Defective Skull Path']
input_implant_path = task_1_data_path / case['Implant Path']
output_complete_skull_path = combined_data_path / "TRWR" / case['Complete Skull Path']
output_defective_skull_path = combined_data_path / "TRWR" / case['Defective Skull Path']
output_implant_path = combined_data_path / "TRWR" / case['Implant Path']
pathlib.Path(output_complete_skull_path).parents[0].mkdir(parents=True, exist_ok=True)
pathlib.Path(output_defective_skull_path).parents[0].mkdir(parents=True, exist_ok=True)
pathlib.Path(output_implant_path).parents[0].mkdir(parents=True, exist_ok=True)
shutil.copy(input_complete_skull_path, output_complete_skull_path)
shutil.copy(input_defective_skull_path, output_defective_skull_path)
shutil.copy(input_implant_path, output_implant_path)
data.append([output_complete_skull_path, output_defective_skull_path, output_implant_path])
for current_id, case in task_3_tr_dataframe.iterrows():
input_complete_skull_path = task_3_data_path / case['Complete Skull Path']
input_defective_skull_path = task_3_data_path / case['Defective Skull Path']
input_implant_path = task_3_data_path / case['Implant Path']
output_complete_skull_path = combined_data_path / "TRWR" / case['Complete Skull Path']
output_defective_skull_path = combined_data_path / "TRWR" / case['Defective Skull Path']
output_implant_path = combined_data_path / "TRWR" / case['Implant Path']
pathlib.Path(output_complete_skull_path).parents[0].mkdir(parents=True, exist_ok=True)
pathlib.Path(output_defective_skull_path).parents[0].mkdir(parents=True, exist_ok=True)
pathlib.Path(output_implant_path).parents[0].mkdir(parents=True, exist_ok=True)
shutil.copy(input_complete_skull_path, output_complete_skull_path)
shutil.copy(input_defective_skull_path, output_defective_skull_path)
shutil.copy(input_implant_path, output_implant_path)
data.append([output_complete_skull_path, output_defective_skull_path, output_implant_path])
training_dataframe = pd.DataFrame(data, columns=['Complete Skull Path', "Defective Skull Path", "Implant Path"])
training_dataframe.to_csv(p.combined_training_wr_csv_path, index=False)
def combine_validation_sets():
"""
Function to combine Task 1/3 validation sets (w/o registration).
"""
task_1_tr_dataframe = pd.read_csv(p.task_1_validation_csv_path)
task_3_tr_dataframe = pd.read_csv(p.task_3_validation_csv_path)
task_1_data_path = p.task_1_training_preprocessed_path
task_3_data_path = p.task_3_training_preprocessed_path
combined_data_path = p.combined_training_path
data = []
for current_id, case in task_1_tr_dataframe.iterrows():
input_complete_skull_path = task_1_data_path / case['Complete Skull Path']
input_defective_skull_path = task_1_data_path / case['Defective Skull Path']
input_implant_path = task_1_data_path / case['Implant Path']
output_complete_skull_path = combined_data_path / "Validation" / case['Complete Skull Path']
output_defective_skull_path = combined_data_path / "Validation" / case['Defective Skull Path']
output_implant_path = combined_data_path / "Validation" / case['Implant Path']
pathlib.Path(output_complete_skull_path).parents[0].mkdir(parents=True, exist_ok=True)
pathlib.Path(output_defective_skull_path).parents[0].mkdir(parents=True, exist_ok=True)
pathlib.Path(output_implant_path).parents[0].mkdir(parents=True, exist_ok=True)
shutil.copy(input_complete_skull_path, output_complete_skull_path)
shutil.copy(input_defective_skull_path, output_defective_skull_path)
shutil.copy(input_implant_path, output_implant_path)
data.append([output_complete_skull_path, output_defective_skull_path, output_implant_path])
for current_id, case in task_3_tr_dataframe.iterrows():
input_complete_skull_path = task_3_data_path / case['Complete Skull Path']
input_defective_skull_path = task_3_data_path / case['Defective Skull Path']
input_implant_path = task_3_data_path / case['Implant Path']
output_complete_skull_path = combined_data_path / "Validation" / case['Complete Skull Path']
output_defective_skull_path = combined_data_path / "Validation" / case['Defective Skull Path']
output_implant_path = combined_data_path / "Validation" / case['Implant Path']
pathlib.Path(output_complete_skull_path).parents[0].mkdir(parents=True, exist_ok=True)
pathlib.Path(output_defective_skull_path).parents[0].mkdir(parents=True, exist_ok=True)
pathlib.Path(output_implant_path).parents[0].mkdir(parents=True, exist_ok=True)
shutil.copy(input_complete_skull_path, output_complete_skull_path)
shutil.copy(input_defective_skull_path, output_defective_skull_path)
shutil.copy(input_implant_path, output_implant_path)
data.append([output_complete_skull_path, output_defective_skull_path, output_implant_path])
validation_dataframe = pd.DataFrame(data, columns=['Complete Skull Path', "Defective Skull Path", "Implant Path"])
validation_dataframe.to_csv(p.combined_validation_csv_path, index=False)
def run():
# combine_datasets_by_registration()
# combine_training_sets_wor()
# combine_validation_sets()
pass
if __name__ == "__main__":
run()