-
Notifications
You must be signed in to change notification settings - Fork 0
/
new-top-hat-gpu.py
67 lines (47 loc) · 1.57 KB
/
new-top-hat-gpu.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
from skimage import io
import matplotlib.pyplot as plt
from timeit import default_timer as timer
from morph_cupy.morphology_cupy import *
def square_closing(img, db, bb):
return grey_closing_cuda(grey_opening_cuda(img, db), bb)
def square_opening(img, db, bb):
return grey_opening_cuda(grey_closing_cuda(img, db), bb)
def top_hat(img, db, bb):
NWTH = img - cp.minimum(img, square_closing(img, db, bb))
NBTH = cp.maximum(img, square_opening(img, db, bb)) - img
return [NWTH, NBTH]
def multiscale_top_hat(img, nw, nl, nm, ns, n):
NWTH_out = cp.zeros_like(img)
NBTH_out = cp.zeros_like(img)
for i in range(n):
bb = nl + ns * i
db = nw + ns * i + 2 * nm
single_scale_top_hat = top_hat(img, db, bb)
NWTH_out = cp.maximum(NWTH_out, single_scale_top_hat[0])
NBTH_out = cp.maximum(NBTH_out, single_scale_top_hat[1])
return [NWTH_out, NBTH_out]
if __name__ == '__main__':
image = io.imread('01.jpg')
image = np.array(image[:, :, 0]).astype(int)
ax = plt.hist(image.ravel(), bins=256)
plt.show()
plt.imshow(image, cmap='gray', vmin=0, vmax=255)
plt.show()
image = cp.array(image)
nW = 5
nL = 5
nM = 2
nS = 11
n = 9
start = timer()
[NWTH, NBTH] = multiscale_top_hat(image, nW, nL, nM, nS, n)
end = timer()
print(end - start)
out = image * 0.2 + 5 * NWTH - 3 * NBTH
out[out > 255] = 255
out[out < 0] = 0
out = cp.asnumpy(out)
ax = plt.hist(out.ravel(), bins=256)
plt.show()
plt.imshow(out, cmap='gray', vmin=0, vmax=255)
plt.show()