Newer
Older
# ============================================================================================= #
# Author: Pavel Iakubovskii, ZFTurbo, ashawkey, Dominik Müller, #
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
# Copyright: albumentations: : https://github.com/albumentations-team #
# Pavel Iakubovskii : https://github.com/qubvel #
# ZFTurbo : https://github.com/ZFTurbo #
# ashawkey : https://github.com/ashawkey #
# Dominik Müller : https://github.com/muellerdo #
# Lucia Hradecká : lucia.d.hradecka@gmail.com #
# Filip Lux : lux.filip@gmail.com #
# #
# Volumentations History: #
# - Original: https://github.com/albumentations-team/albumentations #
# - 3D Conversion: https://github.com/ashawkey/volumentations #
# - Continued Development: https://github.com/ZFTurbo/volumentations #
# - Enhancements: https://github.com/qubvel/volumentations #
# - Further Enhancements: https://github.com/muellerdo/volumentations #
# - Biomedical Enhancements: https://gitlab.fi.muni.cz/cbia/bio-volumentations #
# #
# MIT License. #
# #
# Permission is hereby granted, free of charge, to any person obtaining a copy #
# of this software and associated documentation files (the "Software"), to deal #
# in the Software without restriction, including without limitation the rights #
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell #
# copies of the Software, and to permit persons to whom the Software is #
# furnished to do so, subject to the following conditions: #
# #
# The above copyright notice and this permission notice shall be included in all #
# copies or substantial portions of the Software. #
# #
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR #
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, #
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE #
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER #
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, #
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE #
# SOFTWARE. #
# ============================================================================================= #
import numpy as np
from functools import wraps
import skimage.transform as skt
from skimage.exposure import equalize_hist
from ..biovol_typing import TypeTripletFloat, TypeSpatioTemporalCoordinate, TypeSextetInt
from .spatial_funcional import get_affine_transform, apply_sitk_transform
MAX_VALUES_BY_DTYPE = {
np.dtype("uint8"): 255,
np.dtype("uint16"): 65535,
np.dtype("uint32"): 4294967295,
np.dtype("float32"): 1.0,
}
# SITK interpolations
SITK_interpolation = {
0: 'sitkNearestNeighbor',
1: 'sitkLinear',
2: 'sitkBSpline',
3: 'sitkGaussian'
}
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
"""
vol: [C, D, H, W (, T)]
you should give (D, H, W) form shape.
skimage interpolation notations:
order = 0: Nearest-Neighbor
order = 1: Bi-Linear (default)
order = 2: Bi-Quadratic
order = 3: Bi-Cubic
order = 4: Bi-Quartic
order = 5: Bi-Quintic
Interpolation behaves strangely when input of type int.
** Be sure to change volume and mask data type to float !!! ** (already done by Float() in compose)
But for parameters use primarily ints.
"""
def preserve_shape(func):
"""
Preserve shape of the image
"""
@wraps(func)
def wrapped_function(img, *args, **kwargs):
shape = img.shape
result = func(img, *args, **kwargs)
result = result.reshape(shape)
return result
return wrapped_function
def get_center_crop_coords(img_shape, crop_shape):
froms = (img_shape - crop_shape) // 2
tos = froms + crop_shape
return froms, tos
# Too similar to the random_crop. Could be made into one function
def center_crop(img, input_crop_shape, border_mode, cval, mask):
for i in input_crop_shape:
if i < 0:
warn(f'CenterCrop(): shape {input_crop_shape} contains zero or negative number, continuing'
f'without CenterCrop.', UserWarning)
return img
if not mask:
crop_shape = np.insert(input_crop_shape, 0, img.shape[0])
else:
crop_shape = input_crop_shape
img_shape = np.array(img.shape)
# Adding last dimension, if there is one less in the crop_shape
if len(img_shape) == len(crop_shape) + 1:
crop_shape = np.append(crop_shape, img_shape[-1])
if np.any(img_shape < crop_shape):
warn(f'CenterCrop(): image size {img_shape} smaller than crop size {crop_shape}, pad by {border_mode}.', UserWarning)
img = pad(img, img_shape, crop_shape, border_mode , cval)
img_shape = np.array(img.shape)
from_indices, to_indices = get_center_crop_coords(img_shape, crop_shape)
return crop_from_to(img, from_indices, to_indices)
def pad(img, img_shape, crop_shape, border_mode, cval):
axes_to_pad = np.where(img_shape < crop_shape)[0]
pad_width = []
for i in range(len(img_shape)):
if i in axes_to_pad:
how_many_to_pad = crop_shape[i] - img_shape[i]
if how_many_to_pad % 2:
pad_width.append((int(how_many_to_pad // 2 + 1), int(how_many_to_pad // 2)))
else:
pad_width.append((int(how_many_to_pad // 2), int(how_many_to_pad // 2)))
else:
pad_width.append((0, 0))
if border_mode == "constant":
return np.pad(img, pad_width, border_mode, constant_values=cval)
if border_mode == "linear_ramp":
return np.pad(img, pad_width, border_mode, end_values=cval)
return np.pad(img, pad_width, border_mode)
def pad_keypoints(keypoints, pad_size):
a, b, c, d, e, f = pad_size
res = []
for coo in keypoints:
padding = np.array((a, c, e)) if len(coo) == 3 else np.array((a, c, e, 0))
res.append(coo + padding)
return res
def pad_pixels(img, input_pad_width: TypeSextetInt, border_mode, cval, mask=False):
"""
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
if not mask:
img_shape = img_shape[1:]
if isinstance(input_pad_width, (int, tuple)):
pad_width = input_pad_width
else:
pad_width = input_pad_width.copy()
if isinstance(pad_width, int):
# padding only spatial dimensions
pad_width = [(pad_width, pad_width) if i < 3 else (0, 0) for i in range(len(img_shape))]
elif isinstance(pad_width, tuple):
if len(pad_width) > 2:
warn(f'Pad(): tuple for pad_size {pad_width} have more than 2 elements, ignoring elements after the ' +
f'second one.', UserWarning)
pad_width = [(pad_width[0], pad_width[1]) if i < 3 else (0, 0) for i in range(len(img_shape))]
else:
# Making tuples out of single numbers
for i in range(len(pad_width)):
if isinstance(pad_width[i], int):
pad_width[i] = (pad_width[i], pad_width[i])
# Padding with zeroes
if len(pad_width) < len(img_shape):
pad_width = pad_width + [(0, 0)] * (len(img_shape) - len(pad_width))
"""
a, b, c, d, e, f = input_pad_width
pad_width = [(a, b), (c, d), (e, f)]
# zeroes for channel dimension
if not mask:
pad_width = [(0, 0)] + pad_width
# zeroes for temporal dimension
if len(img.shape) == 5:
pad_width = pad_width + [(0, 0)]
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
if border_mode == "constant":
return np.pad(img, pad_width, border_mode, constant_values=cval)
if border_mode == "linear_ramp":
return np.pad(img, pad_width, border_mode, end_values=cval)
return np.pad(img, pad_width, border_mode)
def crop_from_to(img, froms, tos):
if len(froms) == 3:
c1, x1, y1 = froms
c2, x2, y2 = tos
return img[c1:c2, x1:x2, y1:y2]
if len(froms) == 4:
c1, x1, y1, z1 = froms
c2, x2, y2, z2 = tos
return img[c1:c2, x1:x2, y1:y2, z1:z2]
if len(froms) == 5:
c1, x1, y1, z1, t1 = froms
c2, x2, y2, z2, t2 = tos
return img[c1:c2, x1:x2, y1:y2, z1:z2, t1:t2]
def get_random_crop_coords(img_shape, crop_shape, crop_start):
froms = ((img_shape - crop_shape) * crop_start).astype(int)
tos = froms + crop_shape
return froms, tos
# Too similar to the center_crop. Could be made into one function
def random_crop(img, input_crop_shape, input_crop_start, border_mode, cval, mask):
if not mask:
crop_shape = np.insert(input_crop_shape, 0, img.shape[0])
crop_start = np.insert(input_crop_start, 0, 0)
else:
crop_shape = input_crop_shape
crop_start = input_crop_start
img_shape = np.array(img.shape)
# Adding last dimension, if there is one less in the crop_shape
if len(img_shape) == len(crop_shape) + 1:
crop_shape = np.append(crop_shape, img_shape[-1])
crop_start = np.append(crop_start, 0)
if np.any(img_shape < crop_shape):
warn(
f'BioVolumentations transformation RandomCrop():' +
f' image size {img_shape} smaller than crop size {crop_shape}, pad by {border_mode}.',
UserWarning)
img = pad(img, img_shape, crop_shape, border_mode,cval)
img_shape = np.array(img.shape)
froms, tos = get_random_crop_coords(img_shape, crop_shape, crop_start)
print('RANDOM_CROP', froms, tos, input_crop_start)
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
return crop_from_to(img, froms, tos)
def normalize_mean_std(img, mean, denominator):
if len(mean.shape) == 0:
mean = mean[..., None]
if len(denominator.shape) == 0:
denominator = denominator[..., None]
new_axis = [i + 1 for i in range(len(img.shape) - 1)]
img -= np.expand_dims(mean, axis=new_axis)
img *= np.expand_dims(denominator, axis=new_axis)
return img
# formula taken from
# https://stats.stackexchange.com/questions/46429/transform-data-to-desired-mean-and-standard-deviation
def normalize_channel(img, mean, std):
return (img - img.mean()) * (std / img.std()) + mean
def value_to_list(value, length):
if isinstance(value, (float, int)):
return [value for _ in range(length)]
else:
return value
def correct_length_list(list_to_check, length, value_to_fill=1, list_name="###Default###"):
if len(list_to_check) < length:
warn(f"{list_name} have elements {len(list_to_check)}, should be {length} appending {value_to_fill} " +
"till length matches", UserWarning)
for i in range(length - len(list_to_check)):
list_to_check = list_to_check + [value_to_fill]
if len(list_to_check) > length:
warn(f"{list_name} have elements {len(list_to_check)}, should be {length} removing elements from behind " +
" till length matches", UserWarning)
list_to_check = [list_to_check[i] for i in range(length)]
return list_to_check
def normalize(img, input_mean, input_std):
mean = value_to_list(input_mean, img.shape[0])
std = value_to_list(input_std, img.shape[0])
mean = correct_length_list(mean, img.shape[0], value_to_fill=0, list_name="mean")
std = correct_length_list(std, img.shape[0], value_to_fill=1, list_name="std")
for i in range(img.shape[0]):
img[i] = normalize_channel(img[i], mean[i], std[i])
return img
def gaussian_noise(img, mean, sigma):
img = img.astype("float32")
noise = np.random.normal(mean, sigma, img.shape).astype(np.float32)
return (np.random.poisson(img * peak) / peak).astype(np.float32)
# TODO parameter
# Anti-aliasing - gaussian filter to smooth. using automatically when downsampling, except when integer
# and interpolation is 0. (so mask)
# float mask - how, for now no gaussian filter.
def resize(img, input_new_shape, interpolation=1, border_mode='reflect', cval=0, mask=False,
anti_aliasing_downsample=True):
# TODO: random fix, check if it is correct
new_shape = list(input_new_shape)[:-1]
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
# Zero or negative check
for dimension in new_shape:
if dimension <= 0:
warn(f"Resize(): shape: {new_shape} contains zero or negative number, continuing without Resize.",
UserWarning)
return img
# shape check
if mask:
# too many or few dimensions of new_shape
if len(new_shape) < len(img.shape) - 1 or len(new_shape) > len(img.shape):
warn(f"Resize(): wrong parameter shape: {new_shape}," +
f"expecting something with dimensions of {img.shape } or {img.shape[0:-1] }, " +
"continuing without resizing ", UserWarning)
return img
# Adding time dimension
elif len(new_shape) == len(img.shape) - 1:
new_shape = np.append(new_shape, img.shape[-1])
else:
if len(new_shape) < len(img.shape[1:]) - 1 or len(new_shape) > len(img.shape[1:]):
warn(f"Resize(): wrong dimensions of shape: {new_shape}," +
f"expecting something with dimensions of {img.shape[1:] } or {img.shape[1:-1] }, continuing " +
"without resizing ", UserWarning)
return img
# adding time dimension
elif len(new_shape) == len(img.shape[1:]) - 1:
new_shape = np.append(new_shape, img.shape[-1])
anti_aliasing = False
if mask:
new_img = skt.resize(
img,
new_shape,
order=interpolation,
mode=border_mode,
cval=cval,
clip=True,
anti_aliasing=anti_aliasing
)
return new_img
if anti_aliasing_downsample and np.any(np.array(img.shape[1:]) < np.array(new_shape)):
anti_aliasing = True
data = []
for i in range(img.shape[0]):
subimg = img[i].copy()
d0 = skt.resize(
subimg,
new_shape,
order=interpolation,
mode=border_mode,
cval=cval,
clip=True,
anti_aliasing=anti_aliasing
)
data.append(d0.copy())
new_img = np.stack(data, axis=0)
return new_img
def resize_keypoints(keypoints,
domain_limit: TypeSpatioTemporalCoordinate,
new_shape: TypeSpatioTemporalCoordinate):
assert len(domain_limit) == len(new_shape) == 4
# for each dim compute ratio
ratio = np.array(new_shape[:3]) / np.array(domain_limit[:3])
# it supposes that length of keypoint is 3
return [keypoint * ratio for keypoint in keypoints]
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
# TODO compare with skt.rescale, new version got channel_axis
def scale(img, input_scale_factor, interpolation=0, border_mode='reflect', cval=0, mask=True):
scale_factor = input_scale_factor
# check for zero or negative numbers
if isinstance(scale_factor, (int, float)):
if scale_factor <= 0:
warn(f"RandomScale()/Scale(): scale_factor: {len(scale_factor)} is zero or negative number" +
f" continuing without scaling ", UserWarning)
return img
else:
for dimension in scale_factor:
if dimension <= 0:
warn(f"RandomScale()/Scale(): scale_factor: {len(scale_factor)} contains zero or negative number " +
"continuing without scaling ", UserWarning)
return img
img_shape = img.shape
if scale_factor is None:
return img
if isinstance(scale_factor, (list, tuple)):
scale_factor = np.array(scale_factor)
if not mask:
img_shape = img_shape[1:]
# TODO, maybe user wants to add shape for only spatial dimensions
if len(img_shape) != len(scale_factor) and len(img_shape) - 1 != len(scale_factor):
warn(f"RandomScale()/Scale(): Wrong dimension of scaling factor list: {len(scale_factor)}," +
f"expecting {len(img_shape)} or {len(img_shape[:-1]) }, continuing without scaling ", UserWarning)
return img
elif len(img_shape) - 1 == len(scale_factor):
scale_factor = np.append(scale_factor, 1)
else:
scale_factor = [scale_factor for _ in range(len(img_shape) - 1)]
if mask:
scale_factor.append(scale_factor[0])
# Not scaling time dimensions
if len(scale_factor) == 4:
scale_factor[-1] = 1
if mask:
return zoom(img, scale_factor, order=interpolation, mode=border_mode, cval=cval)
data = []
for i in range(img.shape[0]):
subimg = img[i].copy()
d0 = zoom(subimg, scale_factor, order=interpolation, mode=border_mode, cval=cval)
data.append(d0.copy())
new_img = np.stack(data, axis=0)
return new_img
'''
#TODO maybe add parameter for order of rotations
#LIMIT dimensions
def affine_transform(img, input_x_angle, input_y_angle, input_z_angle, translantion, interpolation = 1, border_mode = 'constant',
value = 0, input_scaling_coef = None, scale_back = True, mask = False ):
if mask:
img = img[np.newaxis, :]
x_angle, y_angle, z_angle = [np.pi * i / 180 for i in [input_x_angle, input_y_angle, input_z_angle]]
if not(input_scaling_coef is None):
scaling_coef = np.array(input_scaling_coef)
#no scaling on the channels if the scaling_coef is in wrong format
if(len(scaling_coef) != 3):
warn(f"Rotate transform: Wrong dimension of scaling coeficient list: {len(scaling_coef)}, expecting {3}, continuing without scaling ", UserWarning)
inverse_affine_matrix = np.linalg.inv(rotation_matrix_calculation(len(img.shape),x_angle,y_angle,z_angle ))
else:
scaling_coef = np.insert(scaling_coef, 0, 1 )
if len(scaling_coef) < len(img.shape):
scaling_coef = np.append(scaling_coef, 1 )
inverse_scaling_matrix = np.diag([ 1/i for i in scaling_coef])
inverse_rotation_matrix = np.linalg.inv(rotation_matrix_calculation(len(img.shape),x_angle,y_angle,z_angle ))
inverse_affine_matrix = inverse_scaling_matrix @ inverse_rotation_matrix
if scale_back:
inverse_scale_back_matrix = np.diag([ i for i in scaling_coef])
inverse_affine_matrix = inverse_affine_matrix @ inverse_scale_back_matrix
else:
inverse_affine_matrix = np.linalg.inv(rotation_matrix_calculation(len(img.shape),x_angle,y_angle,z_angle ))
c_in=0.5*np.array(img.shape)
offset=c_in-inverse_affine_matrix.dot(c_in)
if not(translantion is None):
if len(translantion) > len(img.shape) - 1:
warn(f"Rotate transform(): translation list has wrong length {len(translantion)}, expected {len(img.shape) - 1}", UserWarning)
else:
for i in range(len(translantion)):
offset[i + 1] -= translantion[i]
img = sci.affine_transform(img, inverse_affine_matrix, offset, order=interpolation, mode=border_mode, cval= value)
if mask:
img = img[0]
return img
'''
def affine(img: np.array,
degrees: TypeTripletFloat = (0, 0, 0),
scales: TypeTripletFloat = (1, 1, 1),
translation: TypeTripletFloat = (0, 0, 0),
border_mode: str = 'constant',
value: float = 0,
spacing: TypeTripletFloat = (1, 1, 1)):
"""
img (np.array) : format (channel, ax1, ax2, ax3, [time])
"""
shape = img.shape[1:]
transform = get_affine_transform(shape,
scales=scales,
degrees=degrees,
translation=translation,
spacing=spacing)
return apply_sitk_transform(img,
sitk_transform=transform,
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
def affine_keypoints(keypoints: list,
domain_limit: TypeSpatioTemporalCoordinate,
degrees: TypeTripletFloat = (0, 0, 0),
scales: TypeTripletFloat = (1, 1, 1),
translation: TypeTripletFloat = (0, 0, 0),
border_mode: str = 'constant',
keep_all: bool = False,
spacing: TypeTripletFloat = (1, 1, 1)):
"""
Args:
keypoints: list of input keypoints
domain_limit: limit of the domain, there keyp-points can appear, it is used to define center of transforms
and to filter out output key-point from the outside of the domain
degrees:
scales:
translation:
border_mode: not used
keep_all: True to keep also key_point frou poutside the domain
spacing: relative voxel size
Returns: list of transformed key-points
"""
transform = get_affine_transform(domain_limit,
scales=scales,
degrees=degrees,
translation=translation,
spacing=spacing)
transform = transform.GetInverse()
res = []
for point in keypoints:
transformed_point = transform.TransformPoint(point)
if keep_all or is_included(domain_limit, transformed_point):
res.append(transformed_point)
return res
# TO REMOVE
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
def rotation_matrix_calculation(dim, x_angle, y_angle, z_angle):
rot_matrix = np.identity(dim).astype(np.float32)
rot_matrix = rot_matrix @ rot_x(x_angle, dim)
rot_matrix = rot_matrix @ rot_y(y_angle, dim)
rot_matrix = rot_matrix @ rot_z(z_angle, dim)
return rot_matrix
def rot_x(angle, dim):
if dim == 4:
rotation_x = np.array([[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, np.cos(angle), -np.sin(angle)],
[0, 0, np.sin(angle), np.cos(angle)]])
if dim == 5:
rotation_x = np.array([[1, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 0, np.cos(angle), -np.sin(angle), 0],
[0, 0, np.sin(angle), np.cos(angle), 0],
[0, 0, 0, 0, 1]])
return rotation_x
def rot_y(angle, dim):
if dim == 4:
rotation_y = np.array([[1, 0, 0, 0],
[0, np.cos(angle), 0, np.sin(angle)],
[0, 0, 1, 0],
[0, -np.sin(angle), 0, np.cos(angle)]])
if dim == 5:
rotation_y = np.array([[1, 0, 0, 0, 0],
[0, np.cos(angle), 0, np.sin(angle), 0],
[0, 0, 1, 0, 0],
[0, -np.sin(angle), 0, np.cos(angle), 0],
[0, 0, 0, 0, 1]])
return rotation_y
def rot_z(angle, dim):
if dim == 4:
rotation_z = np.array([[1, 0, 0, 0],
[0, np.cos(angle), -np.sin(angle), 0],
[0, np.sin(angle), np.cos(angle), 0],
[0, 0, 0, 1]])
if dim == 5:
rotation_z = np.array([[1, 0, 0, 0, 0],
[0, np.cos(angle), -np.sin(angle), 0, 0],
[0, np.sin(angle), np.cos(angle), 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1]])
return rotation_z
# TODO clipped tag may be important for types other that float32, but tags are from fork and not tested
# @clipped
def brightness_contrast_adjust(img, alpha=1, beta=0):
if alpha != 1:
img *= alpha
if beta != 0:
img += beta
return img
def histogram_equalization(img, bins):
for i in range(img.shape[0]):
img[i] = equalize_hist(img[i], bins)
return img
def gaussian_blur(img, input_sigma, border_mode, cval):
sigma = input_sigma
if isinstance(sigma, list):
if img.shape[0] != len(sigma):
warn(f'GaussianBlur(): wrong list size {len(sigma)}, expecting same as number of dimensions {img.shape[0]}. Ignoring', UserWarning)
return img
return gaussian_blur_stack(img, sigma, border_mode, cval)
if isinstance(sigma, (int, float)):
sigma = np.repeat(sigma, len(img.shape))
sigma[0] = 0
# Checking for time dimension
if len(img.shape) > 4:
sigma[-1] = 0
else:
# TODO what to expect in the input.
if len(sigma) == len(img.shape) - 2:
sigma = np.append(sigma, 0)
if len(sigma) == len(img.shape) - 1:
sigma = np.insert(sigma, 0, 0)
# TODO better warning
if len(sigma) != len(img.shape):
warn(f'GaussianBlur(): wrong sigma tuple, ignoring', UserWarning)
return img
return gaussian_filter(img, sigma=sigma, mode=border_mode, cval=cval)
def gaussian_blur_stack(img, input_sigma, border_mode, cval):
sigma = list(np.asarray(input_sigma).copy())
# simple sigma check
for channel in sigma:
if not isinstance(channel, (float, int, tuple)):
warn(f'GaussianBlur(): wrong sigma format, Inside list can be only tuple,float or int. Ignoring',
UserWarning)
return img
# TODO try different techniques for better optimalization.
for i in range(len(sigma)):
if isinstance(sigma[i], (float, int)):
sigma[i] = np.repeat(sigma[i], len(img.shape) - 1)
if len(sigma[i]) >= 4:
sigma[i][-1] = 0
else:
if len(sigma[i]) == len(img.shape) - 2:
sigma[i] = np.append(sigma[i], 0)
img[i] = gaussian_filter(img[i], sigma=sigma[i], mode=border_mode, cval=cval)
return img
def gamma_transform(img, gamma):
if np.all(img < 0) or np.all(img > 1) :
warn(f"Gamma transform: image is not in range [0,1]. continuing without transform", UserWarning)
return img
else:
return np.power(img, gamma)