-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathblender.py
156 lines (132 loc) · 5.14 KB
/
blender.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
import cv2
import numpy as np
import scipy.sparse
# import pyamg
def mask_from_points(size, points):
""" Create a mask of supplied size from supplied points
:param size: tuple of output mask size
:param points: array of [x, y] points
:returns: mask of values 0 and 255 where
255 indicates the convex hull containing the points
"""
radius = 10 # kernel size
kernel = np.ones((radius, radius), np.uint8)
mask = np.zeros(size, np.uint8)
cv2.fillConvexPoly(mask, cv2.convexHull(points), 255)
mask = cv2.erode(mask, kernel)
return mask
def overlay_image(foreground_image, mask, background_image):
""" Overlay foreground image onto the background given a mask
:param foreground_image: foreground image points
:param mask: [0-255] values in mask
:param background_image: background image points
:returns: image with foreground where mask > 0 overlaid on background image
"""
foreground_pixels = mask > 0
background_image[..., :3][foreground_pixels] = foreground_image[..., :3][foreground_pixels]
return background_image
def apply_mask(img, mask):
""" Apply mask to supplied image
:param img: max 3 channel image
:param mask: [0-255] values in mask
:returns: new image with mask applied
"""
masked_img = np.copy(img)
num_channels = 3
for c in range(num_channels):
masked_img[..., c] = img[..., c] * (mask / 255)
return masked_img
def correct_colours(im1, im2, landmarks1):
COLOUR_CORRECT_BLUR_FRAC = 0.9
LEFT_EYE_POINTS = list(range(37, 43))
RIGHT_EYE_POINTS = list(range(43, 49))
blur_amount = COLOUR_CORRECT_BLUR_FRAC * np.linalg.norm(
np.mean(landmarks1[LEFT_EYE_POINTS], axis=0) -
np.mean(landmarks1[RIGHT_EYE_POINTS], axis=0))
blur_amount = int(blur_amount)
if blur_amount % 2 == 0:
blur_amount += 1
im1_blur = cv2.GaussianBlur(im1, (blur_amount, blur_amount), 0)
im2_blur = cv2.GaussianBlur(im2, (blur_amount, blur_amount), 0)
# Avoid divide-by-zero errors.
im2_blur = im2_blur.astype(int)
im2_blur += 128*(im2_blur <= 1)
result = im2.astype(np.float64) * im1_blur.astype(np.float64) / im2_blur.astype(np.float64)
result = np.clip(result, 0, 255).astype(np.uint8)
return result
def weighted_average(img1, img2, percent=0.5):
if percent <= 0:
return img2
elif percent >= 1:
return img1
else:
return cv2.addWeighted(img1, percent, img2, 1-percent, 0)
def alpha_feathering(src_img, dest_img, img_mask, blur_radius=15):
mask = cv2.blur(img_mask, (blur_radius, blur_radius))
mask = mask / 255.0
result_img = np.empty(src_img.shape, np.uint8)
for i in range(3):
result_img[..., i] = src_img[..., i] * mask + dest_img[..., i] * (1-mask)
return result_img
def poisson_blend(img_source, dest_img, img_mask, offset=(0, 0)):
img_target = np.copy(dest_img)
# compute regions to be blended
region_source = (
max(-offset[0], 0),
max(-offset[1], 0),
min(img_target.shape[0] - offset[0], img_source.shape[0]),
min(img_target.shape[1] - offset[1], img_source.shape[1]))
region_target = (
max(offset[0], 0),
max(offset[1], 0),
min(img_target.shape[0], img_source.shape[0] + offset[0]),
min(img_target.shape[1], img_source.shape[1] + offset[1]))
region_size = (region_source[2] - region_source[0],
region_source[3] - region_source[1])
# clip and normalize mask image
img_mask = img_mask[region_source[0]:region_source[2],
region_source[1]:region_source[3]]
# create coefficient matrix
coff_mat = scipy.sparse.identity(np.prod(region_size), format='lil')
for y in range(region_size[0]):
for x in range(region_size[1]):
if img_mask[y, x]:
index = x + y * region_size[1]
coff_mat[index, index] = 4
if index + 1 < np.prod(region_size):
coff_mat[index, index + 1] = -1
if index - 1 >= 0:
coff_mat[index, index - 1] = -1
if index + region_size[1] < np.prod(region_size):
coff_mat[index, index + region_size[1]] = -1
if index - region_size[1] >= 0:
coff_mat[index, index - region_size[1]] = -1
coff_mat = coff_mat.tocsr()
# create poisson matrix for b
poisson_mat = pyamg.gallery.poisson(img_mask.shape)
# for each layer (ex. RGB)
for num_layer in range(img_target.shape[2]):
# get subimages
t = img_target[region_target[0]:region_target[2],
region_target[1]:region_target[3], num_layer]
s = img_source[region_source[0]:region_source[2],
region_source[1]:region_source[3], num_layer]
t = t.flatten()
s = s.flatten()
# create b
b = poisson_mat * s
for y in range(region_size[0]):
for x in range(region_size[1]):
if not img_mask[y, x]:
index = x + y * region_size[1]
b[index] = t[index]
# solve Ax = b
x = pyamg.solve(coff_mat, b, verb=False, tol=1e-10)
# assign x to target image
x = np.reshape(x, region_size)
x[x > 255] = 255
x[x < 0] = 0
x = np.array(x, img_target.dtype)
img_target[region_target[0]:region_target[2],
region_target[1]:region_target[3], num_layer] = x
return img_target