Newer
Older
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
import time
import numpy as np
from src.augmentations import functional, atleast_kd, get_nonchannel_axes
class TestNormalization(unittest.TestCase):
def normalize_cycle(self, img, mean, std):
# def normalize_channel(img, mean, std):
# return (img - img.mean()) * (std / img.std()) + mean
# for-cycle implementation
for i in range(img.shape[0]):
img[i] = (img[i] - img[i].mean()) * (std[i] / img[i].std()) + mean[i]
# img[i] = normalize_channel(img[i], mean[i], std[i])
return img
def normalize_vect(self, img, mean, std):
mean = atleast_kd(mean, img.ndim)
std = atleast_kd(std, img.ndim)
img_mean = atleast_kd(img.mean(axis=get_nonchannel_axes(img)), img.ndim)
img_std = atleast_kd(img.std(axis=get_nonchannel_axes(img)), img.ndim)
return (img - img_mean) * (std / img_std) + mean
def test_normalize_cycle_imlp(self):
img = np.random.random(size=(3, 5, 6, 7))
mean = [0, 0, 1]
std = [1, 2, 1]
img = self.normalize_cycle(img, mean, std)
# verify it
for i in range(img.shape[0]):
self.assertAlmostEqual(img[i].mean(), mean[i])
self.assertAlmostEqual(img[i].std(), std[i])
def test_normalize_vect_imlp(self):
img = np.random.random(size=(3, 5, 6, 7))
mean = [0, 0, 1]
std = [1, 2, 1]
# numpy-vectorised implementation
img = self.normalize_vect(img, mean, std)
# verify it
for i in range(img.shape[0]):
self.assertAlmostEqual(img[i].mean(), mean[i])
self.assertAlmostEqual(img[i].std(), std[i])
def test_runtime(self):
n = 30
img_size = (3, 256, 256, 256)
img_size = (3, 130, 130, 100, 4)
# img_size = (8, 200, 200, 200)
time_cycle = self.measure_exec_time(self.normalize_cycle, n=n, img_size=img_size)
print(f'Runtime (for cycle implementation): {time_cycle}')
time_vect = self.measure_exec_time(self.normalize_vect, n=n, img_size=img_size)
print(f'Runtime (vectorised implementation): {time_vect}')
def measure_exec_time(self, fn, n=100, img_size=(3, 256, 256, 256)):
total_time = 0
for i in range(n):
img = np.random.random(size=img_size)
mean = [0, 0, 1] + [-1] * (img_size[0] - 3)
std = [1, 2, 1] + [3] * (img_size[0] - 3)
start = time.time()
res = fn(img, mean, std)
res2 = res.shape
end = time.time()
total_time += (end - start)
return total_time / n
def test_normalize_fn_1(self):
img = np.random.random(size=(3, 5, 6, 7))
mean = [0, 0, 1]
std = [1, 2, 1]
res = functional.normalize(img, mean, std)
for i in range(res.shape[0]):
self.assertAlmostEqual(res[i].mean(), mean[i])
self.assertAlmostEqual(res[i].std(), std[i])
def test_normalize_fn_2(self):
img = np.random.random(size=(3, 5, 6, 7, 4))
mean = [0, 0, 1]
std = [1, 2, 2]
res = functional.normalize(img, mean, std)
for i in range(res.shape[0]):
self.assertAlmostEqual(res[i].mean(), mean[i])
self.assertAlmostEqual(res[i].std(), std[i])
def test_normalize_fn_3(self):
img = np.random.random(size=(1, 5, 6, 7))
mean = [1]
std = [2]
res = functional.normalize(img, mean, std)
for i in range(res.shape[0]):
self.assertAlmostEqual(res[i].mean(), mean[i])
self.assertAlmostEqual(res[i].std(), std[i])
class TestGaussianBlur(unittest.TestCase):
def gaussian_blur_vect(self, img, sigma, border_mode, cval):
from skimage.filters import gaussian
# If None, input is filtered along all axes. Otherwise, input is filtered along the specified axes.
# When axes is specified, any tuples used for sigma, order, mode and/or radius must match the length of axes.
# The ith entry in any of these tuples corresponds to the ith entry in axes.
# return functional.gaussian_filter(img, sigma=sigma, mode=border_mode, cval=cval, axes=[0]) # compute
return gaussian(img, sigma=sigma, channel_axis=0, preserve_range=True) # compute
def test_gaussian_blur_fn_1(self):
img = np.random.random(size=(3, 100, 101, 120))
sigma = [2, 1, 1.5]
res = self.gaussian_blur_vect(img, sigma, 'reflect', 0)
res1 = functional.gaussian_blur_stack(img, sigma, 'reflect', 0)
self.assertTrue(np.allclose(res, res1))
class TestCropPadEtc(unittest.TestCase):
def test_keypoints_crop_1(self):
keypoints = [(1, 2, 3), (2, 2, 2), (10, 2, 1), (20, 23, 20), (4, 5, 0)]
out_shape = np.asarray((5, 5, 5))
corner_position = np.asarray((1, 0, 1))
pad = np.asarray((0, 0, 0))
# res = self.crop_keypoints_cycle(keypoints, corner_position, pad, False, out_shape)
res = self.crop_keypoints_vect(keypoints, corner_position, pad, False, out_shape)
self.assertEqual(len(res), 2)
self.assertTupleEqual(tuple(res[0]), (0, 2, 2))
self.assertTupleEqual(tuple(res[1]), (1, 2, 1))
# res = self.crop_keypoints_cycle(keypoints, corner_position, pad, True, out_shape)
res = self.crop_keypoints_vect(keypoints, corner_position, pad, True, out_shape)
self.assertEqual(len(res), 5)
self.assertTupleEqual(tuple(res[0]), (0, 2, 2))
self.assertTupleEqual(tuple(res[1]), (1, 2, 1))
self.assertTupleEqual(tuple(res[2]), (9, 2, 0))
self.assertTupleEqual(tuple(res[3]), (19, 23, 19))
self.assertTupleEqual(tuple(res[4]), (3, 5, -1))
def test_keypoints_crop_2(self):
keypoints = [(1, 2, 3, 0), (2, 2, 2, 1), (10, 2, 1, 3), (20, 23, 20, 1), (4, 5, 0, 0)]
out_shape = np.asarray((5, 5, 5))
corner_position = np.asarray((1, 0, 1))
pad = np.asarray((0, 0, 0))
# res = self.crop_keypoints_cycle(keypoints, corner_position, pad, False, out_shape)
res = self.crop_keypoints_vect(keypoints, corner_position, pad, False, out_shape)
self.assertEqual(len(res), 2)
self.assertTupleEqual(tuple(res[0]), (0, 2, 2))
self.assertTupleEqual(tuple(res[1]), (1, 2, 1))
# res = self.crop_keypoints_cycle(keypoints, corner_position, pad, True, out_shape)
res = self.crop_keypoints_vect(keypoints, corner_position, pad, True, out_shape)
self.assertEqual(len(res), 5)
self.assertTupleEqual(tuple(res[0]), (0, 2, 2))
self.assertTupleEqual(tuple(res[1]), (1, 2, 1))
self.assertTupleEqual(tuple(res[2]), (9, 2, 0))
self.assertTupleEqual(tuple(res[3]), (19, 23, 19))
self.assertTupleEqual(tuple(res[4]), (3, 5, -1))
def crop_keypoints_cycle(self, keypoints, crop_position, pad, keep_all, crop_shape):
res = []
for keypoint in keypoints: # keypoints = list of tuples of floats
k = keypoint[:3] - crop_position + pad # ignore time-dim keypoint position
if keep_all or (np.all(k >= 0) and np.all((k + .5) < crop_shape)):
res.append(k)
return res
def crop_keypoints_vect(self, keypoints, crop_position, pad, keep_all, crop_shape):
keys = np.asarray(keypoints)[:, :3] - crop_position + pad
if keep_all:
return keys
mask = (keys >= 0) & (keys+.5 < crop_shape)
res = keys[np.sum(mask, axis=1) == 3, :]
return res
def pad_keypoints_cycle(self, keypoints, pad):
res = []
for coo in keypoints:
padding = np.array(pad) if len(coo) == 3 else np.array(pad + (0,))
res.append(coo + padding)
return res
def pad_keypoints_vect(self, keypoints, pad):
keys = np.asarray(keypoints)
padding = np.asarray(pad if keys.shape[1] == 3 else pad+(0,)) # we only need the 'before' pad size
return keys + padding
def test_keypoints_pad_1(self):
keypoints = [(1, 2, 3), (2, 2, 2), (10, 2, 1), (20, 23, 20), (4, 5, 0)]
pad = (0, 1, 3)
# res = self.pad_keypoints_cycle(keypoints, pad)
res = self.pad_keypoints_vect(keypoints, pad)
self.assertEqual(len(res), 5)
self.assertTupleEqual(tuple(res[0]), (1, 3, 6))
self.assertTupleEqual(tuple(res[1]), (2, 3, 5))
self.assertTupleEqual(tuple(res[2]), (10, 3, 4))
self.assertTupleEqual(tuple(res[3]), (20, 24, 23))
self.assertTupleEqual(tuple(res[4]), (4, 6, 3))
def test_keypoints_pad_2(self):
keypoints = [(1, 2, 3, 0), (2, 2, 2, 1), (10, 2, 1, 4), (20, 23, 20, 2), (4, 5, 0, 1)]
pad = (0, 1, 3)
# res = self.pad_keypoints_cycle(keypoints, pad)
res = self.pad_keypoints_vect(keypoints, pad)
self.assertEqual(len(res), 5)
self.assertTupleEqual(tuple(res[0]), (1, 3, 6, 0))
self.assertTupleEqual(tuple(res[1]), (2, 3, 5, 1))
self.assertTupleEqual(tuple(res[2]), (10, 3, 4, 4))
self.assertTupleEqual(tuple(res[3]), (20, 24, 23, 2))
self.assertTupleEqual(tuple(res[4]), (4, 6, 3, 1))
def transpose_keypoints_cycle(self, keypoints, ax1, ax2):
res = []
for k in keypoints:
k = list(k)
k[ax1 - 1], k[ax2 - 1] = k[ax2 - 1], k[ax1 - 1]
res.append(tuple(k))
return res
def transpose_keypoints_vect(self, keypoints, ax1, ax2):
keys = np.asarray(keypoints)
ax1, ax2 = ax1-1, ax2-1
keys[:, ax1], keys[:, ax2] = keys[:, ax2], keys[:, ax1].copy()
return keys
def test_keypoints_transpose_1(self):
keypoints = [(1, 2, 3), (2, 2, 2), (10, 2, 1), (20, 23, 20), (4, 5, 0)]
# res = self.transpose_keypoints_cycle(keypoints, 1, 2)
res = self.transpose_keypoints_vect(keypoints, 1, 2)
self.assertEqual(len(res), 5)
self.assertTupleEqual(tuple(res[0]), (2, 1, 3))
self.assertTupleEqual(tuple(res[4]), (5, 4, 0))
# res = self.transpose_keypoints_cycle(keypoints, 1, 3)
res = self.transpose_keypoints_vect(keypoints, 1, 3)
self.assertEqual(len(res), 5)
self.assertTupleEqual(tuple(res[0]), (3, 2, 1))
self.assertTupleEqual(tuple(res[4]), (0, 5, 4))
# res = self.transpose_keypoints_cycle(keypoints, 3, 2)
res = self.transpose_keypoints_vect(keypoints, 3, 2)
self.assertEqual(len(res), 5)
self.assertTupleEqual(tuple(res[0]), (1, 3, 2))
self.assertTupleEqual(tuple(res[4]), (4, 0, 5))
def test_keypoints_transpose_2(self):
keypoints = [(1, 2, 3, 1), (2, 2, 2, 1), (10, 2, 1, 1), (20, 23, 20, 1), (4, 5, 0, 1)]
# res = self.transpose_keypoints_cycle(keypoints, 1, 2)
res = self.transpose_keypoints_vect(keypoints, 1, 2)
self.assertEqual(len(res), 5)
self.assertTupleEqual(tuple(res[0]), (2, 1, 3, 1))
self.assertTupleEqual(tuple(res[4]), (5, 4, 0, 1))
# res = self.transpose_keypoints_cycle(keypoints, 1, 3)
res = self.transpose_keypoints_vect(keypoints, 1, 3)
self.assertEqual(len(res), 5)
self.assertTupleEqual(tuple(res[0]), (3, 2, 1, 1))
self.assertTupleEqual(tuple(res[4]), (0, 5, 4, 1))
# res = self.transpose_keypoints_cycle(keypoints, 3, 2)
res = self.transpose_keypoints_vect(keypoints, 3, 2)
self.assertEqual(len(res), 5)
self.assertTupleEqual(tuple(res[0]), (1, 3, 2, 1))
self.assertTupleEqual(tuple(res[4]), (4, 0, 5, 1))
def flip_keypoints_cycle(self, keypoints, axes, img_shape):
mult, add = np.ones(3, int), np.zeros(3, int)
for ax in axes:
mult[ax - 1] = -1
add[ax - 1] = img_shape[ax - 1] - 1
res = []
for k in keypoints:
flipped = list(np.array(k[:3]) * mult + add)
if len(k) == 4:
flipped.append(k[-1])
res.append(tuple(flipped))
return res
def flip_keypoints_vect(self, keypoints, axes, img_shape):
keys = np.asarray(keypoints)
ndim = keys.shape[1]
mult, add = np.ones(ndim, int), np.zeros(ndim, int)
for ax in axes:
mult[ax - 1] = -1
add[ax - 1] = img_shape[ax - 1] - 1
flipped = keys * mult + add
return flipped
def test_keypoints_flip_1(self):
keypoints = [(1, 2, 3), (2, 2, 2), (10, 2, 1), (20, 23, 20), (4, 5, 0)]
img_shape = (25, 25, 25)
axes = []
# res = self.flip_keypoints_cycle(keypoints, axes, img_shape)
res = self.flip_keypoints_vect(keypoints, axes, img_shape)
self.assertEqual(len(res), 5)
self.assertTupleEqual(tuple(res[0]), (1, 2, 3))
self.assertTupleEqual(tuple(res[4]), (4, 5, 0))
axes = [1]
# res = self.flip_keypoints_cycle(keypoints, axes, img_shape)
res = self.flip_keypoints_vect(keypoints, axes, img_shape)
self.assertEqual(len(res), 5)
self.assertTupleEqual(tuple(res[0]), (23, 2, 3))
self.assertTupleEqual(tuple(res[4]), (20, 5, 0))
axes = [1, 3]
# res = self.flip_keypoints_cycle(keypoints, axes, img_shape)
res = self.flip_keypoints_vect(keypoints, axes, img_shape)
self.assertEqual(len(res), 5)
self.assertTupleEqual(tuple(res[0]), (23, 2, 21))
self.assertTupleEqual(tuple(res[4]), (20, 5, 24))
def test_keypoints_flip_2(self):
keypoints = [(1, 2, 3, 0), (2, 2, 2, 1), (10, 2, 1, 3), (20, 23, 20, 1), (4, 5, 0, 1)]
img_shape = (25, 25, 25)
axes = []
# res = self.flip_keypoints_cycle(keypoints, axes, img_shape)
res = self.flip_keypoints_vect(keypoints, axes, img_shape)
self.assertEqual(len(res), 5)
self.assertTupleEqual(tuple(res[0]), (1, 2, 3, 0))
self.assertTupleEqual(tuple(res[4]), (4, 5, 0, 1))
axes = [1]
# res = self.flip_keypoints_cycle(keypoints, axes, img_shape)
res = self.flip_keypoints_vect(keypoints, axes, img_shape)
self.assertEqual(len(res), 5)
self.assertTupleEqual(tuple(res[0]), (23, 2, 3, 0))
self.assertTupleEqual(tuple(res[4]), (20, 5, 0, 1))
axes = [1, 3]
# res = self.flip_keypoints_cycle(keypoints, axes, img_shape)
res = self.flip_keypoints_vect(keypoints, axes, img_shape)
self.assertEqual(len(res), 5)
self.assertTupleEqual(tuple(res[0]), (23, 2, 21, 0))
self.assertTupleEqual(tuple(res[4]), (20, 5, 24, 1))