forked from scikit-image/scikit-image
-
Notifications
You must be signed in to change notification settings - Fork 0
/
test_exposure.py
760 lines (589 loc) · 24.9 KB
/
test_exposure.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
import warnings
import numpy as np
import pytest
from skimage import util
from skimage import data
from skimage import exposure
from skimage.exposure.exposure import intensity_range
from skimage.color import rgb2gray
from skimage.util.dtype import dtype_range
from skimage._shared._warnings import expected_warnings
from skimage._shared import testing
from skimage._shared.testing import (assert_array_equal,
assert_array_almost_equal,
assert_equal,
assert_almost_equal)
# Test integer histograms
# =======================
def test_wrong_source_range():
im = np.array([-1, 100], dtype=np.int8)
with testing.raises(ValueError):
frequencies, bin_centers = exposure.histogram(im, source_range='foobar')
def test_negative_overflow():
im = np.array([-1, 100], dtype=np.int8)
frequencies, bin_centers = exposure.histogram(im)
assert_array_equal(bin_centers, np.arange(-1, 101))
assert frequencies[0] == 1
assert frequencies[-1] == 1
assert_array_equal(frequencies[1:-1], 0)
def test_all_negative_image():
im = np.array([-100, -1], dtype=np.int8)
frequencies, bin_centers = exposure.histogram(im)
assert_array_equal(bin_centers, np.arange(-100, 0))
assert frequencies[0] == 1
assert frequencies[-1] == 1
assert_array_equal(frequencies[1:-1], 0)
def test_int_range_image():
im = np.array([10, 100], dtype=np.int8)
frequencies, bin_centers = exposure.histogram(im)
assert_equal(len(bin_centers), len(frequencies))
assert_equal(bin_centers[0], 10)
assert_equal(bin_centers[-1], 100)
def test_peak_uint_range_dtype():
im = np.array([10, 100], dtype=np.uint8)
frequencies, bin_centers = exposure.histogram(im, source_range='dtype')
assert_array_equal(bin_centers, np.arange(0, 256))
assert_equal(frequencies[10], 1)
assert_equal(frequencies[100], 1)
assert_equal(frequencies[101], 0)
assert_equal(frequencies.shape, (256,))
def test_peak_int_range_dtype():
im = np.array([10, 100], dtype=np.int8)
frequencies, bin_centers = exposure.histogram(im, source_range='dtype')
assert_array_equal(bin_centers, np.arange(-128, 128))
assert_equal(frequencies[128+10], 1)
assert_equal(frequencies[128+100], 1)
assert_equal(frequencies[128+101], 0)
assert_equal(frequencies.shape, (256,))
def test_flat_uint_range_dtype():
im = np.linspace(0, 255, 256, dtype=np.uint8)
frequencies, bin_centers = exposure.histogram(im, source_range='dtype')
assert_array_equal(bin_centers, np.arange(0, 256))
assert_equal(frequencies.shape, (256,))
def test_flat_int_range_dtype():
im = np.linspace(-128, 128, 256, dtype=np.int8)
frequencies, bin_centers = exposure.histogram(im, source_range='dtype')
assert_array_equal(bin_centers, np.arange(-128, 128))
assert_equal(frequencies.shape, (256,))
def test_peak_float_out_of_range_image():
im = np.array([10, 100], dtype=np.float16)
frequencies, bin_centers = exposure.histogram(im, nbins=90)
# offset values by 0.5 for float...
assert_array_equal(bin_centers, np.arange(10, 100) + 0.5)
def test_peak_float_out_of_range_dtype():
im = np.array([10, 100], dtype=np.float16)
nbins = 10
frequencies, bin_centers = exposure.histogram(im, nbins=nbins, source_range='dtype')
assert_almost_equal(np.min(bin_centers), -0.9, 3)
assert_almost_equal(np.max(bin_centers), 0.9, 3)
assert_equal(len(bin_centers), 10)
def test_normalize():
im = np.array([0, 255, 255], dtype=np.uint8)
frequencies, bin_centers = exposure.histogram(im, source_range='dtype',
normalize=False)
expected = np.zeros(256)
expected[0] = 1
expected[-1] = 2
assert_equal(frequencies, expected)
frequencies, bin_centers = exposure.histogram(im, source_range='dtype',
normalize=True)
expected /= 3.
assert_equal(frequencies, expected)
# Test histogram equalization
# ===========================
np.random.seed(0)
test_img_int = data.camera()
# squeeze image intensities to lower image contrast
test_img = util.img_as_float(test_img_int)
test_img = exposure.rescale_intensity(test_img / 5. + 100)
def test_equalize_uint8_approx():
"""Check integer bins used for uint8 images."""
img_eq0 = exposure.equalize_hist(test_img_int)
img_eq1 = exposure.equalize_hist(test_img_int, nbins=3)
np.testing.assert_allclose(img_eq0, img_eq1)
def test_equalize_ubyte():
img = util.img_as_ubyte(test_img)
img_eq = exposure.equalize_hist(img)
cdf, bin_edges = exposure.cumulative_distribution(img_eq)
check_cdf_slope(cdf)
def test_equalize_float():
img = util.img_as_float(test_img)
img_eq = exposure.equalize_hist(img)
cdf, bin_edges = exposure.cumulative_distribution(img_eq)
check_cdf_slope(cdf)
def test_equalize_masked():
img = util.img_as_float(test_img)
mask = np.zeros(test_img.shape)
mask[100:400, 100:400] = 1
img_mask_eq = exposure.equalize_hist(img, mask=mask)
img_eq = exposure.equalize_hist(img)
cdf, bin_edges = exposure.cumulative_distribution(img_mask_eq)
check_cdf_slope(cdf)
assert not (img_eq == img_mask_eq).all()
def check_cdf_slope(cdf):
"""Slope of cdf which should equal 1 for an equalized histogram."""
norm_intensity = np.linspace(0, 1, len(cdf))
slope, intercept = np.polyfit(norm_intensity, cdf, 1)
assert 0.9 < slope < 1.1
# Test intensity range
# ====================
@testing.parametrize("test_input,expected", [
('image', [0, 1]),
('dtype', [0, 255]),
((10, 20), [10, 20])
])
def test_intensity_range_uint8(test_input, expected):
image = np.array([0, 1], dtype=np.uint8)
out = intensity_range(image, range_values=test_input)
assert_array_equal(out, expected)
@testing.parametrize("test_input,expected", [
('image', [0.1, 0.2]),
('dtype', [-1, 1]),
((0.3, 0.4), [0.3, 0.4])
])
def test_intensity_range_float(test_input, expected):
image = np.array([0.1, 0.2], dtype=np.float64)
out = intensity_range(image, range_values=test_input)
assert_array_equal(out, expected)
def test_intensity_range_clipped_float():
image = np.array([0.1, 0.2], dtype=np.float64)
out = intensity_range(image, range_values='dtype', clip_negative=True)
assert_array_equal(out, (0, 1))
# Test rescale intensity
# ======================
uint10_max = 2**10 - 1
uint12_max = 2**12 - 1
uint14_max = 2**14 - 1
uint16_max = 2**16 - 1
def test_rescale_stretch():
image = np.array([51, 102, 153], dtype=np.uint8)
out = exposure.rescale_intensity(image)
assert out.dtype == np.uint8
assert_array_almost_equal(out, [0, 127, 255])
def test_rescale_shrink():
image = np.array([51., 102., 153.])
out = exposure.rescale_intensity(image)
assert_array_almost_equal(out, [0, 0.5, 1])
def test_rescale_in_range():
image = np.array([51., 102., 153.])
out = exposure.rescale_intensity(image, in_range=(0, 255))
assert_array_almost_equal(out, [0.2, 0.4, 0.6])
def test_rescale_in_range_clip():
image = np.array([51., 102., 153.])
out = exposure.rescale_intensity(image, in_range=(0, 102))
assert_array_almost_equal(out, [0.5, 1, 1])
def test_rescale_out_range():
"""Check that output range is correct.
.. versionchanged:: 0.17
This function used to return dtype matching the input dtype. It now
matches the output.
"""
image = np.array([-10, 0, 10], dtype=np.int8)
out = exposure.rescale_intensity(image, out_range=(0, 127))
assert out.dtype == float
assert_array_almost_equal(out, [0, 63.5, 127])
def test_rescale_named_in_range():
image = np.array([0, uint10_max, uint10_max + 100], dtype=np.uint16)
out = exposure.rescale_intensity(image, in_range='uint10')
assert_array_almost_equal(out, [0, uint16_max, uint16_max])
def test_rescale_named_out_range():
image = np.array([0, uint16_max], dtype=np.uint16)
out = exposure.rescale_intensity(image, out_range='uint10')
assert_array_almost_equal(out, [0, uint10_max])
def test_rescale_uint12_limits():
image = np.array([0, uint16_max], dtype=np.uint16)
out = exposure.rescale_intensity(image, out_range='uint12')
assert_array_almost_equal(out, [0, uint12_max])
def test_rescale_uint14_limits():
image = np.array([0, uint16_max], dtype=np.uint16)
out = exposure.rescale_intensity(image, out_range='uint14')
assert_array_almost_equal(out, [0, uint14_max])
def test_rescale_all_zeros():
image = np.zeros((2, 2), dtype=np.uint8)
out = exposure.rescale_intensity(image)
assert ~np.isnan(out).all()
assert_array_almost_equal(out, image)
def test_rescale_constant():
image = np.array([130, 130], dtype=np.uint16)
out = exposure.rescale_intensity(image, out_range=(0, 127))
assert_array_almost_equal(out, [127, 127])
def test_rescale_same_values():
image = np.ones((2, 2))
out = exposure.rescale_intensity(image)
assert ~np.isnan(out).all()
assert_array_almost_equal(out, image)
@pytest.mark.parametrize(
"in_range,out_range", [("image", "dtype"),
("dtype", "image")]
)
def test_rescale_nan_warning(in_range, out_range):
image = np.arange(12, dtype=float).reshape(3, 4)
image[1, 1] = np.nan
msg = (
r"One or more intensity levels are NaN\."
r" Rescaling will broadcast NaN to the full image\."
)
# 2019/11/10 Passing NaN to np.clip raises a DeprecationWarning for
# versions above 1.17
# TODO: Remove once NumPy removes this DeprecationWarning
numpy_warning_1_17_plus = (
r"Passing `np.nan` to mean no clipping in np.clip "
r"has always been unreliable|\A\Z"
)
with expected_warnings(
[msg, numpy_warning_1_17_plus]
):
exposure.rescale_intensity(image, in_range, out_range)
@pytest.mark.parametrize(
"out_range, out_dtype", [
('uint8', np.uint8),
('uint10', np.uint16),
('uint12', np.uint16),
('uint16', np.uint16),
('float', float),
]
)
def test_rescale_output_dtype(out_range, out_dtype):
image = np.array([-128, 0, 127], dtype=np.int8)
output_image = exposure.rescale_intensity(image, out_range=out_range)
assert output_image.dtype == out_dtype
def test_rescale_no_overflow():
image = np.array([-128, 0, 127], dtype=np.int8)
output_image = exposure.rescale_intensity(image, out_range=np.uint8)
testing.assert_array_equal(output_image, [0, 128, 255])
assert output_image.dtype == np.uint8
def test_rescale_float_output():
image = np.array([-128, 0, 127], dtype=np.int8)
output_image = exposure.rescale_intensity(image, out_range=(0, 255))
testing.assert_array_equal(output_image, [0, 128, 255])
assert output_image.dtype == float
def test_rescale_raises_on_incorrect_out_range():
image = np.array([-128, 0, 127], dtype=np.int8)
with testing.raises(ValueError):
_ = exposure.rescale_intensity(image, out_range='flat')
# Test adaptive histogram equalization
# ====================================
def test_adapthist_grayscale():
"""Test a grayscale float image
"""
img = util.img_as_float(data.astronaut())
img = rgb2gray(img)
img = np.dstack((img, img, img))
adapted = exposure.equalize_adapthist(img, kernel_size=(57, 51),
clip_limit=0.01, nbins=128)
assert img.shape == adapted.shape
assert_almost_equal(peak_snr(img, adapted), 100.140, 3)
assert_almost_equal(norm_brightness_err(img, adapted), 0.0529, 3)
def test_adapthist_color():
"""Test an RGB color uint16 image
"""
img = util.img_as_uint(data.astronaut())
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
hist, bin_centers = exposure.histogram(img)
assert len(w) > 0
adapted = exposure.equalize_adapthist(img, clip_limit=0.01)
assert adapted.min() == 0
assert adapted.max() == 1.0
assert img.shape == adapted.shape
full_scale = exposure.rescale_intensity(img)
assert_almost_equal(peak_snr(full_scale, adapted), 109.393, 1)
assert_almost_equal(norm_brightness_err(full_scale, adapted), 0.02, 2)
return data, adapted
def test_adapthist_alpha():
"""Test an RGBA color image
"""
img = util.img_as_float(data.astronaut())
alpha = np.ones((img.shape[0], img.shape[1]), dtype=float)
img = np.dstack((img, alpha))
adapted = exposure.equalize_adapthist(img)
assert adapted.shape != img.shape
img = img[:, :, :3]
full_scale = exposure.rescale_intensity(img)
assert img.shape == adapted.shape
assert_almost_equal(peak_snr(full_scale, adapted), 109.393, 2)
assert_almost_equal(norm_brightness_err(full_scale, adapted), 0.0248, 3)
def test_adapthist_grayscale_Nd():
"""
Test for n-dimensional consistency with float images
Note: Currently if img.ndim == 3, img.shape[2] > 4 must hold for the image
not to be interpreted as a color image by @adapt_rgb
"""
# take 2d image, subsample and stack it
img = util.img_as_float(data.astronaut())
img = rgb2gray(img)
a = 15
img2d = util.img_as_float(img[0:-1:a, 0:-1:a])
img3d = np.array([img2d] * (img.shape[0] // a))
# apply CLAHE
adapted2d = exposure.equalize_adapthist(img2d,
kernel_size=5,
clip_limit=0.05)
adapted3d = exposure.equalize_adapthist(img3d,
kernel_size=5,
clip_limit=0.05)
# check that dimensions of input and output match
assert img2d.shape == adapted2d.shape
assert img3d.shape == adapted3d.shape
# check that the result from the stack of 2d images is similar
# to the underlying 2d image
assert np.mean(np.abs(adapted2d
- adapted3d[adapted3d.shape[0] // 2])) < 0.02
def test_adapthist_constant():
"""Test constant image, float and uint
"""
img = np.zeros((8, 8))
img += 2
img = img.astype(np.uint16)
adapted = exposure.equalize_adapthist(img, 3)
assert np.min(adapted) == np.max(adapted)
img = np.zeros((8, 8))
img += 0.1
img = img.astype(np.float64)
adapted = exposure.equalize_adapthist(img, 3)
assert np.min(adapted) == np.max(adapted)
def test_adapthist_borders():
"""Test border processing
"""
img = rgb2gray(util.img_as_float(data.astronaut()))
# maximize difference between orig and processed img
img /= 100.
img[img.shape[0] // 2, img.shape[1] // 2] = 1.
# check borders are processed for different kernel sizes
border_index = -1
for kernel_size in range(51, 71, 2):
adapted = exposure.equalize_adapthist(img, kernel_size, clip_limit=0.5)
# Check last columns are processed
assert norm_brightness_err(adapted[:, border_index],
img[:, border_index]) > 0.1
# Check last rows are processed
assert norm_brightness_err(adapted[border_index, :],
img[border_index, :]) > 0.1
def test_adapthist_clip_limit():
img_u = data.moon()
img_f = util.img_as_float(img_u)
# uint8 input
img_clahe0 = exposure.equalize_adapthist(img_u, clip_limit=0)
img_clahe1 = exposure.equalize_adapthist(img_u, clip_limit=1)
assert_array_equal(img_clahe0, img_clahe1)
# float64 input
img_clahe0 = exposure.equalize_adapthist(img_f, clip_limit=0)
img_clahe1 = exposure.equalize_adapthist(img_f, clip_limit=1)
assert_array_equal(img_clahe0, img_clahe1)
def peak_snr(img1, img2):
"""Peak signal to noise ratio of two images
Parameters
----------
img1 : array-like
img2 : array-like
Returns
-------
peak_snr : float
Peak signal to noise ratio
"""
if img1.ndim == 3:
img1, img2 = rgb2gray(img1.copy()), rgb2gray(img2.copy())
img1 = util.img_as_float(img1)
img2 = util.img_as_float(img2)
mse = 1. / img1.size * np.square(img1 - img2).sum()
_, max_ = dtype_range[img1.dtype.type]
return 20 * np.log(max_ / mse)
def norm_brightness_err(img1, img2):
"""Normalized Absolute Mean Brightness Error between two images
Parameters
----------
img1 : array-like
img2 : array-like
Returns
-------
norm_brightness_error : float
Normalized absolute mean brightness error
"""
if img1.ndim == 3:
img1, img2 = rgb2gray(img1), rgb2gray(img2)
ambe = np.abs(img1.mean() - img2.mean())
nbe = ambe / dtype_range[img1.dtype.type][1]
return nbe
# Test Gamma Correction
# =====================
def test_adjust_gamma_1x1_shape():
"""Check that the shape is maintained"""
img = np.ones([1,1])
result = exposure.adjust_gamma(img, 1.5)
assert img.shape == result.shape
def test_adjust_gamma_one():
"""Same image should be returned for gamma equal to one"""
image = np.random.uniform(0, 255, (8, 8))
result = exposure.adjust_gamma(image, 1)
assert_array_equal(result, image)
def test_adjust_gamma_zero():
"""White image should be returned for gamma equal to zero"""
image = np.random.uniform(0, 255, (8, 8))
result = exposure.adjust_gamma(image, 0)
dtype = image.dtype.type
assert_array_equal(result, dtype_range[dtype][1])
def test_adjust_gamma_less_one():
"""Verifying the output with expected results for gamma
correction with gamma equal to half"""
image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))
expected = np.array([
[ 0, 31, 45, 55, 63, 71, 78, 84],
[ 90, 95, 100, 105, 110, 115, 119, 123],
[127, 131, 135, 139, 142, 146, 149, 153],
[156, 159, 162, 165, 168, 171, 174, 177],
[180, 183, 186, 188, 191, 194, 196, 199],
[201, 204, 206, 209, 211, 214, 216, 218],
[221, 223, 225, 228, 230, 232, 234, 236],
[238, 241, 243, 245, 247, 249, 251, 253]], dtype=np.uint8)
result = exposure.adjust_gamma(image, 0.5)
assert_array_equal(result, expected)
def test_adjust_gamma_greater_one():
"""Verifying the output with expected results for gamma
correction with gamma equal to two"""
image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))
expected = np.array([
[ 0, 0, 0, 0, 1, 1, 2, 3],
[ 4, 5, 6, 7, 9, 10, 12, 14],
[ 16, 18, 20, 22, 25, 27, 30, 33],
[ 36, 39, 42, 45, 49, 52, 56, 60],
[ 64, 68, 72, 76, 81, 85, 90, 95],
[100, 105, 110, 116, 121, 127, 132, 138],
[144, 150, 156, 163, 169, 176, 182, 189],
[196, 203, 211, 218, 225, 233, 241, 249]], dtype=np.uint8)
result = exposure.adjust_gamma(image, 2)
assert_array_equal(result, expected)
def test_adjust_gamma_neggative():
image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))
with testing.raises(ValueError):
exposure.adjust_gamma(image, -1)
# Test Logarithmic Correction
# ===========================
def test_adjust_log_1x1_shape():
"""Check that the shape is maintained"""
img = np.ones([1, 1])
result = exposure.adjust_log(img, 1)
assert img.shape == result.shape
def test_adjust_log():
"""Verifying the output with expected results for logarithmic
correction with multiplier constant multiplier equal to unity"""
image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))
expected = np.array([
[ 0, 5, 11, 16, 22, 27, 33, 38],
[ 43, 48, 53, 58, 63, 68, 73, 77],
[ 82, 86, 91, 95, 100, 104, 109, 113],
[117, 121, 125, 129, 133, 137, 141, 145],
[149, 153, 157, 160, 164, 168, 172, 175],
[179, 182, 186, 189, 193, 196, 199, 203],
[206, 209, 213, 216, 219, 222, 225, 228],
[231, 234, 238, 241, 244, 246, 249, 252]], dtype=np.uint8)
result = exposure.adjust_log(image, 1)
assert_array_equal(result, expected)
def test_adjust_inv_log():
"""Verifying the output with expected results for inverse logarithmic
correction with multiplier constant multiplier equal to unity"""
image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))
expected = np.array([
[ 0, 2, 5, 8, 11, 14, 17, 20],
[ 23, 26, 29, 32, 35, 38, 41, 45],
[ 48, 51, 55, 58, 61, 65, 68, 72],
[ 76, 79, 83, 87, 90, 94, 98, 102],
[106, 110, 114, 118, 122, 126, 130, 134],
[138, 143, 147, 151, 156, 160, 165, 170],
[174, 179, 184, 188, 193, 198, 203, 208],
[213, 218, 224, 229, 234, 239, 245, 250]], dtype=np.uint8)
result = exposure.adjust_log(image, 1, True)
assert_array_equal(result, expected)
# Test Sigmoid Correction
# =======================
def test_adjust_sigmoid_1x1_shape():
"""Check that the shape is maintained"""
img = np.ones([1, 1])
result = exposure.adjust_sigmoid(img, 1, 5)
assert img.shape == result.shape
def test_adjust_sigmoid_cutoff_one():
"""Verifying the output with expected results for sigmoid correction
with cutoff equal to one and gain of 5"""
image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))
expected = np.array([
[ 1, 1, 1, 2, 2, 2, 2, 2],
[ 3, 3, 3, 4, 4, 4, 5, 5],
[ 5, 6, 6, 7, 7, 8, 9, 10],
[ 10, 11, 12, 13, 14, 15, 16, 18],
[ 19, 20, 22, 24, 25, 27, 29, 32],
[ 34, 36, 39, 41, 44, 47, 50, 54],
[ 57, 61, 64, 68, 72, 76, 80, 85],
[ 89, 94, 99, 104, 108, 113, 118, 123]], dtype=np.uint8)
result = exposure.adjust_sigmoid(image, 1, 5)
assert_array_equal(result, expected)
def test_adjust_sigmoid_cutoff_zero():
"""Verifying the output with expected results for sigmoid correction
with cutoff equal to zero and gain of 10"""
image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))
expected = np.array([
[127, 137, 147, 156, 166, 175, 183, 191],
[198, 205, 211, 216, 221, 225, 229, 232],
[235, 238, 240, 242, 244, 245, 247, 248],
[249, 250, 250, 251, 251, 252, 252, 253],
[253, 253, 253, 253, 254, 254, 254, 254],
[254, 254, 254, 254, 254, 254, 254, 254],
[254, 254, 254, 254, 254, 254, 254, 254],
[254, 254, 254, 254, 254, 254, 254, 254]], dtype=np.uint8)
result = exposure.adjust_sigmoid(image, 0, 10)
assert_array_equal(result, expected)
def test_adjust_sigmoid_cutoff_half():
"""Verifying the output with expected results for sigmoid correction
with cutoff equal to half and gain of 10"""
image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))
expected = np.array([
[ 1, 1, 2, 2, 3, 3, 4, 5],
[ 5, 6, 7, 9, 10, 12, 14, 16],
[ 19, 22, 25, 29, 34, 39, 44, 50],
[ 57, 64, 72, 80, 89, 99, 108, 118],
[128, 138, 148, 158, 167, 176, 184, 192],
[199, 205, 211, 217, 221, 226, 229, 233],
[236, 238, 240, 242, 244, 246, 247, 248],
[249, 250, 250, 251, 251, 252, 252, 253]], dtype=np.uint8)
result = exposure.adjust_sigmoid(image, 0.5, 10)
assert_array_equal(result, expected)
def test_adjust_inv_sigmoid_cutoff_half():
"""Verifying the output with expected results for inverse sigmoid
correction with cutoff equal to half and gain of 10"""
image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))
expected = np.array([
[253, 253, 252, 252, 251, 251, 250, 249],
[249, 248, 247, 245, 244, 242, 240, 238],
[235, 232, 229, 225, 220, 215, 210, 204],
[197, 190, 182, 174, 165, 155, 146, 136],
[126, 116, 106, 96, 87, 78, 70, 62],
[ 55, 49, 43, 37, 33, 28, 25, 21],
[ 18, 16, 14, 12, 10, 8, 7, 6],
[ 5, 4, 4, 3, 3, 2, 2, 1]], dtype=np.uint8)
result = exposure.adjust_sigmoid(image, 0.5, 10, True)
assert_array_equal(result, expected)
def test_negative():
image = np.arange(-10, 245, 4).reshape((8, 8)).astype(np.double)
with testing.raises(ValueError):
exposure.adjust_gamma(image)
def test_is_low_contrast():
image = np.linspace(0, 0.04, 100)
assert exposure.is_low_contrast(image)
image[-1] = 1
assert exposure.is_low_contrast(image)
assert not exposure.is_low_contrast(image, upper_percentile=100)
image = (image * 255).astype(np.uint8)
assert exposure.is_low_contrast(image)
assert not exposure.is_low_contrast(image, upper_percentile=100)
image = (image.astype(np.uint16)) * 2**8
assert exposure.is_low_contrast(image)
assert not exposure.is_low_contrast(image, upper_percentile=100)
# Test Dask Compatibility
# =======================
def test_dask_histogram():
pytest.importorskip('dask', reason="dask python library is not installed")
import dask.array as da
dask_array = da.from_array(np.array([[0, 1], [1, 2]]), chunks=(1, 2))
output_hist, output_bins = exposure.histogram(dask_array)
expected_bins = [0, 1, 2]
expected_hist = [1, 2, 1]
assert np.allclose(expected_bins, output_bins)
assert np.allclose(expected_hist, output_hist)