forked from JonoSax/3DHistologicalReconstruction
-
Notifications
You must be signed in to change notification settings - Fork 4
/
SegSectionsTraining.py
836 lines (643 loc) · 36.5 KB
/
SegSectionsTraining.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
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
'''
This script allows for labelling, training and segmentation of tissue.
'''
import numpy as np
import pandas as pd
from HelperFunctions.Utilities import dictToTxt, dirMaker, nameFromPath, printProgressBar, txtToDict
from glob import glob
from random import random, shuffle
from shutil import copyfile
import tensorflow as tf
import os
import cv2
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Flatten, Dropout, Dense
from multiprocessing import Pool
from itertools import repeat
import matplotlib.pyplot as plt
def labelData(imgDirs, classes):
'''
Label all the features which have been extracted
'''
# create a dictionary which contains all the labels made
labels = {}
featIDs = glob(imgDirs + "*")
featIDOrder = np.argsort([-len(glob(f + "/*.png")) for f in featIDs])
labelStore = []
# label the features based on length (ie the longest features are labelled first)
for n in featIDOrder:
f = featIDs[n]
featID = nameFromPath(f)
imgComb = []
imgStart = cv2.imread(f + "/_referenceImage.jpg")
if imgStart is not None:
imgComb.append(imgStart)
imgFin = cv2.imread(f + "/_finalImg.jpg")
if imgFin is not None:
imgComb.append(imgFin)
sects = glob(f + "/*.png")
# get 5 sections to show (shuffle each time)
imgs = []
shuffle(sects)
sectsToShow = sects[:5]
for s in sectsToShow:
img = cv2.imread(s)
x, y, c = img.shape
imgPlate = np.zeros([x+20, y+20, c]).astype(np.uint8)
imgPlate[10:-10, 10:-10, :] = img
imgs.append(imgPlate)
sectsStack = np.vstack(imgs)
ys, xs, _ = sectsStack.shape
yr, _, _ = imgComb[0].shape
xrs = int(xs * yr/ys)
# resize the section stack to be the same height as the reference image
sectsStackR = cv2.resize(sectsStack, (xrs, yr))
imgComb.append(sectsStackR)
imgComb = np.hstack(imgComb)
imgComb = cv2.cvtColor(imgComb, cv2.COLOR_BGR2RGB)
# show the tissue being labelled
_, ax = plt.subplots()
plt.axis("off")
plt.imshow(imgComb)
ax.set_title("First sample, last sample, random selection of features. Close this window and type in the terminal the key for the tissue types when ready.")
plt.show()
print("\nThere are " + str(len(sects)) + " sections in feature ID " + featID)
labelVals, labelCount = np.unique(sum(list(labels.values()), []), return_counts = True)
print("There are " + str(labelCount) + " for " + str(labelVals))
print("What tissue types are present in feature " + str(featID))
print("type DONE if you want to save and stop labelling")
for c in classes:
print(c + ", ", end = "")
featInputs = input(" : ")
# if there is no matching tissue type then dont add to labels
if featInputs == "":
continue
featSplit = featInputs.split(",")
feats = [f.replace(" ", "") for f in featSplit]
for f in feats:
if f.lower() == "done":
break
labels[featID] = feats
# NOTE if there was adaptive learning, where as features are trained the
# the approximations of what they might be are used to further assist trainig
dictToTxt(labels, imgDirs + "labels.txt")
print("Labels saved as: " + imgDirs + "labels.txt")
def getLabels(imgDir, infoDir, labels):
'''
Get the labels from the annotationed folders and convert into a sparse matrix
Inputs:\n
imgDir, directory of the images to perform the search for labels on \n
infoDir, directory to save the csv of the labels
Outputs:\n
valK, pandas dataframe of the labels, also saved as a csv
'''
# NOTE ssh into the drive and do os.listdir for each specimen
# All the folder names
# vals = ['3505', '2238_d,v,b', '2327_d,g,b', '315_v,b', '1960_m,d,g,b', '554_d,g,b', '118_d,g', '3_m,b', '3362_m,d,g,b', '3295_v,b', '2897', '930_d,g,b', '1076_m,d,g,b', '597_d,v,b', '550_v,b', '884_d,g,b', '1699_v,b', '2850_v,b', '667_v,b', '3063_d,v,g,b', '3305', '44_v,b', '2962_m,d,g,b', '1707_m,d,g,b', '588_d,v,g,b', '979_m,d,g,b', '841_v,b', '2874_d,g', '3604_v,b', '2836_d,v,b', '1628_m,d,g,b', '1954', '3426_v,b', '3195_d,g,b', '2236_v,b', '1588_v,b', '225_d,g', '1602_d,v,g,b', '1075_d,g,b', '695_d,v,g,b', '333_v,b', '915_d,g,b', '3045_v,b', '3539_m,d,g,b', '3029_d,v,g,b', '3283_d,v,b', '3370_v,b', '1014_m,d,g,b', '1052_m,d,g,b', '33_d,g', '699_d,v,g,b', '3093_v,b', '1088_v,b', '2993_d,v,g,b', '860_m,b', '2786_v,b', '726_d,g,b', '3414_v,b', '669_m,d,g,b', '592_d,g,b', '412_d,v,b', '1675_v,b', '2979', '892', '3216_v,b', '178_d,v,b', '2921_d,v,b', '3451_d,g,b', '2910_d,g,b', '254_d,g', '634_v,b', '214_d,v,g', '641_m,d,v,g,b', '901', '3292_d,v,b', '3402_v,b', '2853_v,b', '276_m,d,v,g,b', '738_m,d,g,b', '351_v,b', '414_m,d,g,b', '31_d,v', '3100_d,v,b', '961_m,d,g,b', '956_d,g,b', '104_d,g', '3449_v,b', '212_d,g,b', '2828_d,g,b', '264_v', '377', '129_m,b', '934_m,b', '259_m,d,g,b', '889_d,g,b', '466_d,v,b,g', '36_d,g,b', '863_m,b', '110_d,v,g', '3393_v,b', '3472_v,b', '2801_v,b', '1938', '580_d,v,b', '2957_d,v,g,b', '274_m,d,v,g,b', '34_d,v,g', '2888_v,b', '561_d,v,b', '3282_d,v,b', '3516_v,b', '475_d,g', '311_m,g,b', '373_d,g,b', '874_v', '1524_d,g,b', '2212_d,v,g,b', '3056_v,b', '2934_d,g,b', '2749_v,b', '381_m,b', '3435_d,v,g,b', '3016', '3215_d,g,b', '879_m,d,g,b', '347_d,g,b', '3015_d,g,b', '2669_m,d,g,b', '928_d,g,b', '3524_v,b', '2265_d,v,g,b', '2929_m,d,g,b', '2845_d,g,b', '3042_d,v,b', '1074_d,g,b', '2777_v,b', '872_m,b', '2840_m,d,v,g,b', '3299_d,g,b', '740_d,v,b', '329_m,d,v,g,b', '1547_v,b', '944_d,v,g,b', '76_d,v,g,b', '3289_v,b', '63_d,v,g,b', '41_d,g,b', '324_d,v,g,b', '3623', '181_v', '693_d,v,b', '819_v,b', '3168_m,d,g,b', '2745_v,b', '431_v,b', '900_v,b', '2961_d,g,b', '2821_m,d,g,b', '2303_m,d,g,b', '536_v,b', '3485_d,g,b', '2946_d,v,g,b', '2804_m,d,g,b', '2759_d,v,g,b', '2875_d,g', '317_m,d,g,b', '2855', '2860_d,g,b', '859_m,b', '284_m,d,g,b', '2832_v,b', '267_m,d,g,b', '1926', '169_v', '314_d,g,b', '3388_d,v,g,b', '3384_v,b', '578_d,v,g,b', '2948_d,v,b', '3000_v,b', '567_m,b', '912_d,v,b', '2824_d,g,b', '173_m,d,g,b', '2958_v,b', '650_d,v,b', '2936', '2983', '1_d,v,g', '3146_v,b', '2820_v,b', '2587_d,g,b', '1641_m,b', '206_m,d,v,g,b', '2800_v,b', '3550_v,b', '393_m,b', '328_m,d,g,b', '3372_v,b', '345_d,v,g,b', '560_d,v,g,b', '2943_d,g,b', '16_d,v,g', '1682_v,d,b', '718_d,v,g,b', '2788_v,b', '899_m,b', '1999_d,v,g,b', '836_m,b', '2870_m,d,g,b', '2908_d,v,b', '972_d,g,b', '685_m,d,g,b', '3405_d,v,g,b', '3180_m,d,v,g,b', '3052_d,v,g,b', '3381_m,d,g,b', '894_m,d,g,b', '694_m,b', '940_d,g,b', '2791_v,b', '1609_d,v,b', '867_v,b', '98_d,v,g,b', '2954_v,b', '2937_d,v,g,b', '221_d,v,g,b', '364_m,d,v,g,b', '3221_v,b', '3591_d,v,g,b', '3488_d,g,b', '1645_v,b', '140_m,d,g,b', '885_d,g,b', '2965_d,v,g,b', '705_d,v,b', '2904_d,g,b', '2877_m,d,v,g,b', '3260_m,d,g,b', '3484_d,g,b', '862_m,d,g,b', '1066_v,b', '57_d,v,g', '647_d,g,b', '27_d,v,g,b', '687', '2038_v,b', '2923_d,v,g,b', '3395_v,b', '950_m,d,g,b', '395_m,b', '637', '471_m,d,b,g', '3454_v,b', '1046_m,d,v,g,b', '552_m,d,g,b', '1062_d,v,b', '3554_m,d,g,b', '3419_v,b', '3490_d,v,b', '2914_m,d,g,b', '917_d,v,g,b', '1079_d,g,b', '541_v,b', '662_d,g,b', '2809_d,g', '702_d,g,b', '53_d,g', '3294_d,v,b', '496_d,b,g', '2876_m,b', '35_m,b', '428_m,d,v,g,b', '116_v,b', '724_d,g,b', '535_d,b,g', '483_m,d,b,g', '409_v,b', '2927_d,v,g,b', '2586_d,g,b', '3266_d,v,b', '3145_v,b', '3217_m,d,g,b', '668_v,b', '1668_v,b', '60_d,g,b', '3026_v,b', '2818_v,b', '2834_d,g,b', '587_d,g,b', '1827_d,g,b', '3486_d,g,b', '1595_m,b', '1598_m,d,g,b', '2916_d,v,g,b', '1988_m,b', '3153_v,b', '122_v', '334_d,g,b', '519', '3133_v,b', '3101_d,v,g,b', '3022', '690_d,v,g,b', '2872_d,v,b', '1057_d,g,b', '2032_d,v,b', '611_m,b', '323_d,v,g,b', '310_v,b', '152_m,d,v,g,b', '1275_v,b', '297_d,v,g,b', '2002_m,b', '2815_d,g,b', '3377_d,v,g,b', '136_d,v', '38_d,v', '112_m,d,v,g,b', '.DS_Store', '183_d,v,g,b', '3404_d,v,g,b', '427_d,v,b', '2715_v,b', '2884_d,v,g,b', '352_d,g,b', '1325', '2795_d,g,b', '2807_d,v,b', '257_m,b', '3034_d,v,g,b', '652_m,b', '937_d,v,b', '1015_d,g,b', '366_m,d,v,g,b', '3141_m,d,g,b', '2864_d,g,b', '543_d,v,b,g', '3038', '976', '369_m,d,g,b', '3247_d,v,g,b', '271_v', '3473_v,b', '1049_m,b', '1632', '633_v,b', '391_m,d,g,b', '3503_m,d,g,b', '3300_m,d,g,b', '2922_d,v,g,b', '21_d,v,g,b', '2319_d,g,b', '2799_d,v,g,b', '2781_d,g,b', '197_m,d,g,b', '638_m,d,b,g', '2764_d,g,b', '62_v,b', '3580_v,b', '1069_m,d,g,b', '1033_v,b', '3599_v,b', '2842_d,g,b', '346_d,v,b', '208_d,g,b', '924_v,b', '996_m,d,g,b', '882_m,b', '3027_d,g,b', '890_v,b', '69_m,d,g,b', '2372_m,d,g,b', '3001_d,v,b', '3520_v,b', '1964_v,b', '1640_m,d,g,b', '2956_v,b', '973_d,v,b', '244_d,v,g,b', '3053_m,d,g,b', '3363_m,d,g,b', '2973_m,d,g,b', '2782_d,v,g,b', '1822_d,g,b', '3408_d,g,b', '3018_m,d,g,b', '3587_v,b', '651_v,b', '703', '295_v', '3369_m,d,g,b', '2817_v,b', '2879_d,v,g,b', '2917', '3389_v,b', '1589_m,b', '299_v,b', '542_d,v,b,g', '854_m,d,g,b', '710_m,b', '3448_v,b', '3445_d,v,g,b', '572_m,d,v,b,g', '70_d,v,g,b', '600_v,b', '55_v', '488_d,v,b', '2444_m,d,g,b', '1051_d,v,g,b', '790_m,d,g,b', '90_d,v,g', '2903', '2726_v,b', '3013_v,b', '2805_v,b', '908_d,g,b', '517_v,b', '909', '3054_d,v,g,b', '515_m,d,v,b,g', '2806_d,g,b', '2334_d,g,b', '3347', '876', '1241_m,d,g,b', '156_d,v,g,b', '2147_v,b', '198_m,b', '472_m,d,b,g', '919_d,v,g,b', '620_v,b', '1081_d,v,g,b', '962_v,b', '929_d,v,g,b', '689_m,d,v,g,b', '59_m,d,g', '1068_m,d,g,b', '660_m,d,v,g,b', '2001_m,v,g,b', '1073_d,v,b', '514', '3014_d,g,b', '3394_d,v,g,b', '3340', '960_v,b', '245_v', '967_d,g,b', '478_d,v,b', '3249', '355_d,g,b', '507_m,b', '659_m,d,g,b', '594', '263_v', '2578_v,b', '176_d,v,g', '385_d,g,b', '1616_m,d,g,b', '547_v,b', '11_m,d,g,b', '1522', '18_m,b', '92_m,b', '2867_d,v,b', '970_v,b', '3008_v,b', '728', '356_m,d,g,b', '518_d,b,g', '2883_v,b', '1576_d,v,b', '2982_d,g,b', '363_d,g,b', '3071_v,b', '3450_d,v,g,b', '1022_d,g,b', '3560_v,d,b', '2964_d,g,b', '3193_d,v,b', '231_m,b', '1012_v,b', '2758_d,g,b', '570_m,b', '808_m,b', '3374_m,d,g,b', '89_d,v,b', '2970_v,b', '51_d,v,g', '2873_m,d,g,b', '931_m,d,g,b', '171_m,d,g,b', '743_m,d,g,b', '143_m,d,g,b', '658_d,v,g,b', '1264_m,b', '272_v,b', '1661_v,b', '358_m,d,v,g,b', '2229_v,b', '365_d,g,b', '2915_v,b', '308_d,g,b', '1043_m,d,g,b', '278_m,d,g,b', '2894', '727_d,g,b', '563_m,b', '549_d,g,b', '2953_d,g,b', '903_m,d,g,b', '849_m,b', '2257_v,b', '376_d,v,g,b', '516_d,v,b,g', '300_m,d,g,b', '565_d,v,b', '3430_d,g,b', '943_m,d,g,b', '655_d,v,g,b', '2920_d,g,b', '326_v,b', '407_m,b', '222_d,v,g,b', '2641_v,b', '1569_v,b', '2774_d,g,b', '3135_d,v,b', '700_v,b', '524_v,b', '2928_d,v,g,b', '10_m,b', '2006_m,d,g,b', '3415_m,d,g,b', '3130_d,v,g,b', '1050_v,b', '3348_d,g,b', '951_m,b', '2998_d,g,b', '1082_d,v,b', '888_d,v,g,b', '551_m,d,g,b', '96_m,b', '67_v,b', '708_m,b', '2866_m,d,g,b', '3386_m,d,g,b', '795_d,v,g,b', '1011_m,d,g,b', '858_m,b', '966', '37_d,g', '2969', '3367_m,d,v,g,b', '3431_d,v,b', '1097_v,b', '945_m,d,g,b', '2885_d,g,b', '450_d,v,b', '3423_v,b', '188_d,v,g,b', '1804_d,g,b', '7_m,b', '3062_v,b', '2787_d,v,g,b', '1032', '531_d,v,b,g', '1934_d,v,g,b', '2755_v,b', '2839_v,b', '415_m,d,g,b', '878_d,v,b', '2918_v,b', '3205_v,b', '534_m,b', '2900_d,g,b', '2789_d,g,b', '2955', '2_m,d,v,g,b', '2161_d,g,b', '3220_d,v,g,b', '361_v', '1987_m,d,g,b', '2776', '756_m,d,g,b', '3041_m,d,g,b', '3411_v,b', '2919_d,b,g', '445_m,d,g,b', '3434_d,g,b', '239_m,d,g,b']
valDict = txtToDict(imgDir + "labels.txt", typeID = int, typeV = str)[0]
vals = list(valDict.keys())
# select only the values which have been labelled
# valsC = np.array(vals)[np.where([v.find("_")>0 for v in vals])[0]]
# split the values into their id and features present
# valsS = [v.split("_") for v in valsC]
labels = sorted(labels)
# create a sparse matrix of the features present
valKs = []
for vi in valDict:
vals = valDict[vi]
# identify which features are observed for each sample
valArr = np.zeros(len(labels)+1).astype(int)
valArr[0] = int(vi)
for v in vals:
for n, l in enumerate(labels):
if v == l:
# create np array for features
valArr[n+1] = 1
valKs.append(valArr)
# create the data frame
valK = pd.DataFrame(np.array(valKs), columns=["featID"] + labels).sort_values(by = ["featID"]).set_index("featID")
valK.to_csv(infoDir + "Classes.txt")
def dataOrganiser(imgDirs, destDir, ratios = [0.8, 0.1, 0.1]):
'''
This organises the raw data into the apporpriate directories for training
Inputs:\n
imgDirs, directory containing the sectioned images\n
destDir, directory to save the newly arranged data
ratios, training validation testing ratios
Outputs:\n
, information seperated into training, validation and testing for each class
'''
l = pd.read_csv(destDir + "Classes.txt")
# create destination dirs
testDir = destDir + "test/"
trainDir = destDir + "train/"
valDir = destDir + "val/"
trainR, valR, testR = ratios
if np.sum(ratios) != 1:
print("!!! Ensure ratios sum to 1 !!!")
return
# label txt file (except featID) are the labels
label = list(l.keys())[1:]
# ensure each directory is empty
for lb in label:
dirMaker(testDir + lb + "/", True)
dirMaker(trainDir + lb + "/", True)
dirMaker(valDir + lb + "/", True)
for n, lb in enumerate(label):
imgStore = []
# get the spec ID for images which are only a single class
classID = l[~(l.loc[:, l.columns!=lb]==1).any(axis = 1)]
classSpec = np.array(classID.featID)
# get the image path for all these images
for c in classSpec:
c = str(c)
imgAll = glob(imgDirs + str(c) + "/H*")
for i in imgAll:
imgStore.append(i)
# randomally move images to either the train, val or test directories
for n, i in enumerate(imgStore):
r = random()
if r <= trainR:
# move images to traindir
copyfile(i, trainDir + lb + "/" + str(n) + ".png")
elif r <= trainR + valR:
# move images to valdir
copyfile(i, valDir + lb + "/" + str(n) + ".png")
else:
# move images ot testdir
copyfile(i, testDir + lb + "/" + str(n) + ".png")
def modelTrainer(src, modelName, gpu = 0, layerNo = 0, epochs = 100, batch_size = 64, name = ""):
# this function takes the compiled model and trains it on the imagenet data
# Inputs: (src), the data organised as test, train, val directories with each
# class organised as a directory
# (modelName), the specific model being used
# (gpu), the gpu being used. If no GPU is being used (ie on a CPU) then
# this variable is not important
# (epochs), number of training rounds for the model
# (batch_size), number of images processed for each round of parameter updates
# Outputs: (), atm no output but will eventually save the model to be used for
# evaluation
# some basic info
print("start, MODEL = " + modelName + " training on GPU " + str(gpu) + ", PID = " + str(os.getpid()))
# set the GPU to be used
CUDA_VISIBLE_DEVICES = gpu
# info paths
testDir = src + "test/"
trainDir = src + "train/"
valDir = src + "val/"
# create a dictionary which will contain the classes and their codes being used
classes = os.listdir(trainDir)
# get the image size (assumes all images are the same size)
trainImages = glob(valDir + "*/*")
IMAGE_SIZE = list(cv2.imread(trainImages[0]).shape)
# create the model topology, make the final convolutional layers retrainable
model, preproFunc = makeModel(modelName, IMAGE_SIZE, len(classes), layerNo)
# create the data augmentation
# NOTE I don't think this is augmenting the data beyond the number of samples
# that are present....
gen_Aug = ImageDataGenerator(
preprocessing_function=preproFunc,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.2,
rotation_range = 360,
zoom_range = [0.8, 1.2],
horizontal_flip=True,
vertical_flip = True
)
# create the data generator WITH augmentation
train_generator = gen_Aug.flow_from_directory(
trainDir,
target_size=IMAGE_SIZE[:2], # only use the first two dimensions of the images
batch_size=batch_size,
class_mode='binary',
save_to_dir = None
)
# create the validating data generator (NO augmentation)
gen_noAug = ImageDataGenerator(preprocessing_function=preproFunc)
valid_generator = gen_noAug.flow_from_directory(
valDir,
target_size=IMAGE_SIZE[:2],
batch_size=batch_size,
class_mode='binary',
)
# create checkpoints, save every epoch
checkpoint_path = "training/cp-{epoch:04d}.ckpt"
checkpoint_dir = os.path.dirname(checkpoint_path)
cp_callback = tf.keras.callbacks.ModelCheckpoint(
filepath=checkpoint_path,
verbose=1,
save_weights_only=True,
save_freq=int(len(trainImages) / batch_size))
# train the model
r = model.fit(x = train_generator,
validation_data=valid_generator,
epochs=epochs
)
# save the entire model
model.save(name + 'saved_model')
print("done\n\n")
def makeModel(modelType, IMAGE_SIZE, noClasses, layerNo):
# create the model topology
# Inputs: (modelType), text variable which leads to a different model which has been made below
# (IMAGE_SIZE), size of the images which are inputted
# (noClasses), number of classes (ie number of neurons to use)
# Outputs: (model), compiled model as constructd per modelType choice
if modelType == "VGG19":
model, preproFunc = VGG19Maker(IMAGE_SIZE, noClasses, layerNo)
elif modelType == "VGG16":
model, preproFunc = VGG16Maker(IMAGE_SIZE, noClasses, layerNo)
elif modelType == "ResNet50":
model, preproFunc = ResNet50Maker(IMAGE_SIZE, noClasses, layerNo)
elif modelType == "ResNet101":
model, preproFunc = ResNet101Maker(IMAGE_SIZE, noClasses, layerNo)
elif modelType == "EfficientNetB7":
model, preproFunc = EfficientNetB7Maker(IMAGE_SIZE, noClasses, layerNo)
elif modelType == "unet":
model, preproFunc = UNETMaker(IMAGE_SIZE, noClasses)
else:
raise("No valid model selected")
# print the model toplogy
# model.summary()
return(model, preproFunc)
def fineTuning(ptm, layerNo, structure = 'block'):
'''
Specify the blocks to be trainable
Inputs:\n
(ptm), model
(layerNo), the number of layers to modify. Starts with the blocks the closest to the output.
if the input is False then nothing will be trained. If True then the WHOLE model will
be trainiable. If an integer number is specified then than number structures will be trained
(structure), what structure is being specified, defaults block
Outputs:\n
(ptm), same network but with the trainiable parameter modified as necessary
'''
# if the number of layers is 0 then set the whole thing to being untrainable
if layerNo == 0:
ptm.trainable = False
return ptm
ptm.trainable = layerNo
# if a specificed number of layers selected then state their trainability
if type(layerNo)==int:
blocks = sorted(list(np.unique(np.array([p.name.split("_")[0] for p in ptm.layers]))))
blocksCopy = blocks.copy()
for b in blocksCopy:
if b.find(structure)==-1:
blocks.remove(b)
# select the highest block numbers first
tarB = blocks[-layerNo:]
for p in ptm.layers:
# if the selected layers aren't matched then it is not being trained
for t in tarB:
if p.name.find(t)!=-1:
print(p.name + " trainable")
p.trainable = True
return(ptm)
def VGG16Maker(IMAGE_SIZE, noClasses, layerNo = 0, Weights = 'imagenet', Top = False):
# create a model with the VGG19 topology
# Inputs: (IMAGE_SIZE), size of the inputs
# (noClasses), number of classes the network will identify
# (Trainable), boolean whether the CNN component will be trainiable, defaults False
# (Weights), the pre-loaded weights that can be used, defaults to imagenet
# (Top), the dense layer which comes with the model, defaults to not being included
# Outputs: (model), compiled model
# load the VGG16 and necessary layers from keras
from tensorflow.keras.applications import VGG16 as PretrainedModel
from tensorflow.keras.applications.vgg16 import preprocess_input
# load the pretrained model and specify the weights being used
ptm = PretrainedModel(
input_shape=IMAGE_SIZE,
weights=Weights,
include_top=Top)
# ---- Fine tuning ---
# get the name of all the blocks
ptm = fineTuning(ptm, layerNo)
# create the dense layer. This is always trainable
x = denseLayer(ptm.output, noClasses)
# combine the convolutional imagenet pretrained model with the denselayer
model = Model(inputs=ptm.input, outputs=x)
# bolt the whole thing together, aka compile it
model.compile(
loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# set the pre-processing function as the inbuilt vgg19 function
preprocessingFunc = preprocess_input
return(model, preprocessingFunc)
def VGG19Maker(IMAGE_SIZE, noClasses, layerNo = 0, Weights = 'imagenet', Top = False):
# create a model with the VGG19 topology
# Inputs: (IMAGE_SIZE), size of the inputs
# (noClasses), number of classes the network will identify
# (Trainable), boolean whether the CNN component will be trainiable, defaults False
# (Weights), the pre-loaded weights that can be used, defaults to imagenet
# (Top), the dense layer which comes with the model, defaults to not being included
# Outputs: (model), compiled model
# load the VGG19 and necessary layers from keras
from tensorflow.keras.applications import VGG19 as PretrainedModel
from tensorflow.keras.applications.vgg19 import preprocess_input
from tensorflow.keras.layers import Flatten, Dropout, Dense
from tensorflow.keras.models import Model
# load the pretrained model and specify the weights being used
ptm = PretrainedModel(
input_shape=IMAGE_SIZE,
weights=Weights,
include_top=Top)
# boolean to fine-tune the CNN layers
ptm = fineTuning(ptm, layerNo)
# create the dense layer
x = denseLayer(ptm.output, noClasses)
model = Model(inputs=ptm.input, outputs=x) # substitute the 4D CNN output for a 1D shape for the dense network input
# bolt the whole thing together, aka compile it
model.compile(
loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# set the pre-processing function as the inbuilt vgg19 function
preprocessingFunc = preprocess_input
return(model, preprocessingFunc)
def ResNet50Maker(IMAGE_SIZE, noClasses, layerNo = 0, Weights = 'imagenet', Top = False):
# create a model with the VGG19 topology
# Inputs: (IMAGE_SIZE), size of the inputs
# (noClasses), number of classes the network will identify
# (Trainable), boolean whether the CNN component will be trainiable, defaults False
# (Weights), the pre-loaded weights that can be used, defaults to imagenet
# (Top), the dense layer which comes with the model, defaults to not being included
# Outputs: (model), compiled model
# load the ResNet and necessary layers from keras
from tensorflow.keras.applications import ResNet50 as PretrainedModel
from tensorflow.keras.applications.resnet50 import preprocess_input
from tensorflow.keras.layers import Flatten, Dropout, Dense
from tensorflow.keras.models import Model
# load the pretrained model and specify the weights being used
ptm = PretrainedModel(
input_shape=IMAGE_SIZE,
weights=Weights,
include_top=Top)
# boolean to fine-tune the CNN layers
ptm = fineTuning(ptm, layerNo)
# create the dense layer
x = denseLayer(ptm.output, noClasses)
model = Model(inputs=ptm.input, outputs=x) # substitute the 4D CNN output for a 1D shape for the dense network input
# bolt the whole thing together, aka compile it
model.compile(
loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# set the pre-processing function as the inbuilt vgg19 function
preprocessingFunc = preprocess_input
return(model, preprocessingFunc)
def ResNet101Maker(IMAGE_SIZE, noClasses, layerNo = 0, Weights = 'imagenet', Top = False):
# create a model with the ResNet50 topology
# Inputs: (IMAGE_SIZE), size of the inputs
# (noClasses), number of classes the network will identify
# (Trainable), boolean whether the CNN component will be trainiable, defaults False
# (Weights), the pre-loaded weights that can be used, defaults to imagenet
# (Top), the dense layer which comes with the model, defaults to not being included
# Outputs: (model), compiled model
# load the ResNet and necessary layers from keras
from tensorflow.keras.applications import ResNet101 as PretrainedModel
from tensorflow.keras.applications.resnet import preprocess_input
# load the pretrained model and specify the weights being used
ptm = PretrainedModel(
input_shape=IMAGE_SIZE,
weights=Weights,
include_top=Top)
# boolean whether to fine-tune the CNN layers
ptm = fineTuning(ptm, layerNo)
# create the dense layer
x = denseLayer(ptm.output, noClasses)
model = Model(inputs=ptm.input, outputs=x) # substitute the 4D CNN output for a 1D shape for the dense network input
# bolt the whole thing together, aka compile it
model.compile(
loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# set the pre-processing function as the inbuilt vgg19 function
preprocessingFunc = preprocess_input
return(model, preprocessingFunc)
def EfficientNetB7Maker(IMAGE_SIZE, noClasses, layerNo = 0, Weights = 'imagenet', Top = False):
# create a model with the ResNet50 topology
# Inputs: (IMAGE_SIZE), size of the inputs
# (noClasses), number of classes the network will identify
# (Trainable), boolean whether the CNN component will be trainiable, defaults False
# (Weights), the pre-loaded weights that can be used, defaults to imagenet
# (Top), the dense layer which comes with the model, defaults to not being included
# Outputs: (model), compiled model
# load the ResNet and necessary layers from keras
from tensorflow.keras.applications import EfficientNetB7 as PretrainedModel
from tensorflow.keras.applications.efficientnet import preprocess_input
# load the pretrained model and specify the weights being used
ptm = PretrainedModel(
input_shape=IMAGE_SIZE,
weights=Weights,
include_top=Top)
# boolean whether to fine-tune the CNN layers
ptm = fineTuning(ptm, layerNo)
# create the dense layer
x = denseLayer(ptm.output, noClasses)
model = Model(inputs=ptm.input, outputs=x) # substitute the 4D CNN output for a 1D shape for the dense network input
# bolt the whole thing together, aka compile it
model.compile(
loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# set the pre-processing function as the inbuilt vgg19 function
preprocessingFunc = preprocess_input
return(model, preprocessingFunc)
def UNETMaker(IMAGE_SIZE, noClasses, Trainable = False, Weights = 'imagenet', Top = False):
from tensorflow.keras.applications import MobileNetV2 as PretrainedModel
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
def upsample(filters, size, norm_type='batchnorm', apply_dropout=False):
"""Upsamples an input.
Conv2DTranspose => Batchnorm => Dropout => Relu
Args:
filters: number of filters
size: filter size
norm_type: Normalization type; either 'batchnorm' or 'instancenorm'.
apply_dropout: If True, adds the dropout layer
Returns:
Upsample Sequential Model
"""
class InstanceNormalization(tf.keras.layers.Layer):
"""Instance Normalization Layer (https://arxiv.org/abs/1607.08022)."""
def __init__(self, epsilon=1e-5):
super(InstanceNormalization, self).__init__()
self.epsilon = epsilon
def build(self, input_shape):
self.scale = self.add_weight(
name='scale',
shape=input_shape[-1:],
initializer=tf.random_normal_initializer(1., 0.02),
trainable=True)
self.offset = self.add_weight(
name='offset',
shape=input_shape[-1:],
initializer='zeros',
trainable=True)
def call(self, x):
mean, variance = tf.nn.moments(x, axes=[1, 2], keepdims=True)
inv = tf.math.rsqrt(variance + self.epsilon)
normalized = (x - mean) * inv
return self.scale * normalized + self.offset
initializer = tf.random_normal_initializer(0., 0.02)
result = tf.keras.Sequential()
result.add(
tf.keras.layers.Conv2DTranspose(filters, size, strides=2,
padding='same',
kernel_initializer=initializer,
use_bias=False))
if norm_type.lower() == 'batchnorm':
result.add(tf.keras.layers.BatchNormalization())
elif norm_type.lower() == 'instancenorm':
result.add(InstanceNormalization())
if apply_dropout:
result.add(tf.keras.layers.Dropout(0.5))
result.add(tf.keras.layers.ReLU())
return result
# creat model
def unet_model(output_channels):
# load the pretrained model and specify the weights being used
ptm = PretrainedModel(
input_shape=IMAGE_SIZE,
weights=Weights,
include_top=Top)
# --- create encoder ---
# Use the activations of these layers
layer_names = [
'block_1_expand_relu', # 64x64
'block_3_expand_relu', # 32x32
'block_6_expand_relu', # 16x16
'block_13_expand_relu', # 8x8
'block_16_project', # 4x4
]
layers = [ptm.get_layer(name).output for name in layer_names]
# Create the feature extraction model
down_stack = Model(inputs=ptm.input, outputs=layers)
down_stack.trainable = False
# --- create decoder ---
# upscaling function
up_stack = [
upsample(576, 3), # 4x4 -> 8x8
upsample(272, 3), # 8x8 -> 16x16
upsample(136, 3), # 16x16 -> 32x32
upsample(68, 3), # 32x32 -> 64x64
]
# Downsampling through the model
x = ptm.input
skips = down_stack(x)
x = skips[-1]
skips = reversed(skips[:-1])
# Upsampling and establishing the skip connections
for up, skip in zip(up_stack, skips):
x = up(x)
concat = tf.keras.layers.Concatenate()
x = concat([x, skip])
# This is the last layer of the model
last = tf.keras.layers.Conv2DTranspose(
output_channels, 3, strides=2,
padding='same') #64x64 -> 128x128
x = last(x)
return Model(inputs=inputs, outputs=x)
model = unet_model(noClasses)
# bolt the whole thing together, aka compile it
model.compile(
loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# set the pre-processing function as the inbuilt vgg19 function
preprocessingFunc = preprocess_input
return(model, preprocessingFunc)
def denseLayer(ptmoutput, noClasses):
# creates the dense layer
# Inputs: (ptmoutput), takes the CNN output layer
# (noClasses), number of classifiers to train
# Outputs: (x), constructed dense layer
# create the dense layer
x = Flatten()(ptmoutput)
x = Dropout(0.2)(x)
x = Dense(noClasses, activation='softmax')(x)
return(x)
def segment(imgPath, model, sectL, sc):
'''
Perform segmentation with the model generated
Inputs:\n
imgPath, image path to segment
modelPath, model directory to use for segmentation
sectL, length of the section tile
sc, window size
Outputs:\n
(), creates a segmented mask
'''
# load the image and get info
name = nameFromPath(imgPath)
img = cv2.imread(imgPath)
h, w, c = img.shape
hrange = int(np.floor(h/sc))
wrange = int(np.floor(w/sc))
# create a segmentation mask
predict = np.zeros([hrange, wrange])
for x in range(wrange):
for y in range(hrange):
printProgressBar(y + x * hrange, hrange * wrange, name + " processed", length=20)
sect = img[y*sc:y*sc+sectL, x*sc:x*sc+sectL]
if np.count_nonzero(sect) < sect.size * 0.5:
# if less than 50% of the sect is information then
# assign to 0
predict[y, x] = 0
else:
# if there is sufficient information to process
# then categorise. NOTE that background is assigned 0
# and the labels are l + 1
predict[y, x] = np.argmax(model.predict(np.expand_dims(sect, 0), use_multiprocessing = False)) + 1
cv2.imwrite(name + ".png", predict * 255/np.max(predict))
print(" " + name + " DONE")
if __name__ == "__main__":
src = '/Volumes/USB/H653A_11.3/'
size = 3
dataHome = src + str(size) + "/"
# directory which contains the sections
featDir = dataHome + 'FeatureSections/'
imgDirs = featDir + 'NLAlignedSamplesSmallpng_False/'
# three classes (myometrium, decidua, villous)
# Just change what classes you want here
classes = ["m", "d", "v"]
labelData(imgDirs, classes)
# from all of the images get their corresponding labels
getLabels(imgDirs, featDir, classes)
# oragnise the data for TF usage
dataOrganiser(imgDirs, featDir)
# train the models
# NOTE if you don't want to train multiple models just select only one
for m in ["ResNet101", "VGG16", "VGG19"]:
print("---- " + m + " ----")
modelTrainer(featDir, m, epochs=50, name = m, layerNo = 1)
'''
# get the labels
vals = os.listdir(featDir + 'test/')
# from the testing data enable evaluation
# NOTE labels dont' seem to match???
evalStore = []
evalID = []
for v, va in enumerate(vals):
testImgs = glob(featDir + 'test/' + va + '/*')[:20]
for t in testImgs:
evalStore.append(cv2.imread(t))
evalID.append(v)
evalStore = np.array(evalStore)
evalID = np.array(evalID)
'''
# segment the final reconstructions
imgssrc = sorted(glob(dataHome + '/NLAlignedSamplesSmall/*'))
# segment the image
sectL = 136 # this is the x-y dimension of the patches
sc = 100 # the size (in pixels) for the window to slide along
# get the paths of all the models trained
models = sorted(glob("*saved_model"))
# segment each image with the selected model
for m in models:
print(m + " is being processed")
model = tf.keras.models.load_model(m)
# for every image
for i in imgssrc:
segment(i, model, sectL, sc)