forked from jiupinjia/SkyAR
-
Notifications
You must be signed in to change notification settings - Fork 0
/
skyboxengine.py
196 lines (138 loc) · 6.91 KB
/
skyboxengine.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
from skybox_utils import *
from cv2.ximgproc import guidedFilter
import synrain
import os
class SkyBox():
def __init__(self, args):
self.args = args
self.load_skybox()
self.rainmodel = synrain.Rain(
rain_intensity=0.8, haze_intensity=0.0, gamma=1.0, light_correction=1.0)
# motion parameters
self.M = np.array([[1, 0, 0], [0, 1, 0]], dtype=np.float32)
self.frame_id = 0
def tile_skybox_img(self, imgtile):
screen_y1 = int(imgtile.shape[0] / 2 - self.args.out_size_h / 2)
screen_x1 = int(imgtile.shape[1] / 2 - self.args.out_size_w / 2)
imgtile = np.concatenate(
[imgtile[screen_y1:,:,:], imgtile[0:screen_y1,:,:]], axis=0)
imgtile = np.concatenate(
[imgtile[:,screen_x1:,:], imgtile[:,0:screen_x1,:]], axis=1)
return imgtile
def load_skybox(self):
print('initialize skybox...')
if '.jpg' in self.args.skybox:
# static backgroud
skybox_img = cv2.imread(os.path.join(r'./skybox', self.args.skybox), cv2.IMREAD_COLOR)
skybox_img = cv2.cvtColor(skybox_img, cv2.COLOR_BGR2RGB)
self.skybox_img = cv2.resize(
skybox_img, (self.args.out_size_w, self.args.out_size_h))
cc = 1. / self.args.skybox_center_crop
imgtile = cv2.resize(
skybox_img, (int(cc * self.args.out_size_w),
int(cc*self.args.out_size_h)))
self.skybox_imgx2 = self.tile_skybox_img(imgtile)
self.skybox_imgx2 = np.expand_dims(self.skybox_imgx2, axis=0)
else:
# video backgroud
cap = cv2.VideoCapture(os.path.join(r'./skybox', self.args.skybox))
m_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
cc = 1. / self.args.skybox_center_crop
self.skybox_imgx2 = np.zeros(
[m_frames, int(cc*self.args.out_size_h),
int(cc*self.args.out_size_w), 3], np.uint8)
for i in range(m_frames):
_, skybox_img = cap.read()
skybox_img = cv2.cvtColor(skybox_img, cv2.COLOR_BGR2RGB)
imgtile = cv2.resize(
skybox_img, (int(cc * self.args.out_size_w),
int(cc * self.args.out_size_h)))
skybox_imgx2 = self.tile_skybox_img(imgtile)
self.skybox_imgx2[i,:] = skybox_imgx2
def skymask_refinement(self, G_pred, img):
r, eps = 20, 0.01
refined_skymask = guidedFilter(img[:,:,2], G_pred[:,:,0], r, eps)
refined_skymask = np.stack(
[refined_skymask, refined_skymask, refined_skymask], axis=-1)
return np.clip(refined_skymask, a_min=0, a_max=1)
def get_skybg_from_box(self, m):
self.M = update_transformation_matrix(self.M, m)
nbgs, bgh, bgw, c = self.skybox_imgx2.shape
fetch_id = self.frame_id % nbgs
skybg_warp = cv2.warpAffine(
self.skybox_imgx2[fetch_id, :,:,:], self.M,
(bgw, bgh), borderMode=cv2.BORDER_WRAP)
skybg = skybg_warp[0:self.args.out_size_h, 0:self.args.out_size_w, :]
self.frame_id += 1
return np.array(skybg, np.float32)/255.
def skybox_tracking(self, frame, frame_prev, skymask):
if np.mean(skymask) < 0.05:
print('sky area is too small')
return np.array([[1, 0, 0], [0, 1, 0]], dtype=np.float32)
prev_gray = cv2.cvtColor(frame_prev, cv2.COLOR_RGB2GRAY)
prev_gray = np.array(255*prev_gray, dtype=np.uint8)
curr_gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
curr_gray = np.array(255*curr_gray, dtype=np.uint8)
mask = np.array(skymask[:,:,0] > 0.99, dtype=np.uint8)
template_size = int(0.05*mask.shape[0])
mask = cv2.erode(mask, np.ones([template_size, template_size]))
# ShiTomasi corner detection
prev_pts = cv2.goodFeaturesToTrack(
prev_gray, mask=mask, maxCorners=200,
qualityLevel=0.01, minDistance=30, blockSize=3)
if prev_pts is None:
print('no feature point detected')
return np.array([[1, 0, 0], [0, 1, 0]], dtype=np.float32)
# Calculate optical flow (i.e. track feature points)
curr_pts, status, err = cv2.calcOpticalFlowPyrLK(
prev_gray, curr_gray, prev_pts, None)
# Filter only valid points
idx = np.where(status == 1)[0]
if idx.size == 0:
print('no good point matched')
return np.array([[1, 0, 0], [0, 1, 0]], dtype=np.float32)
prev_pts, curr_pts = removeOutliers(prev_pts, curr_pts)
if curr_pts.shape[0] < 10:
print('no good point matched')
return np.array([[1, 0, 0], [0, 1, 0]], dtype=np.float32)
# limit the motion to translation + rotation
dxdyda = estimate_partial_transform((
np.array(prev_pts), np.array(curr_pts)))
m = build_transformation_matrix(dxdyda)
return m
def relighting(self, img, skybg, skymask):
# color matching, reference: skybox_img
step = int(img.shape[0]/20)
skybg_thumb = skybg[::step, ::step, :]
img_thumb = img[::step, ::step, :]
skymask_thumb = skymask[::step, ::step, :]
skybg_mean = np.mean(skybg_thumb, axis=(0, 1), keepdims=True)
img_mean = np.sum(img_thumb * (1-skymask_thumb), axis=(0, 1), keepdims=True) \
/ ((1-skymask_thumb).sum(axis=(0,1), keepdims=True) + 1e-9)
diff = skybg_mean - img_mean
img_colortune = img + self.args.recoloring_factor*diff
if self.args.auto_light_matching:
img = img_colortune
else:
#keep foreground ambient_light and maunally adjust lighting
img = self.args.relighting_factor*(img_colortune + (img.mean() - img_colortune.mean()))
return img
def halo(self, syneth, skybg, skymask):
# reflection
halo = 0.5*cv2.blur(
skybg*skymask, (int(self.args.out_size_w/5),
int(self.args.out_size_w/5)))
# screen blend 1 - (1-a)(1-b)
syneth_with_halo = 1 - (1-syneth) * (1-halo)
return syneth_with_halo
def skyblend(self, img, img_prev, skymask):
m = self.skybox_tracking(img, img_prev, skymask)
skybg = self.get_skybg_from_box(m)
img = self.relighting(img, skybg, skymask)
syneth = img * (1 - skymask) + skybg * skymask
if self.args.halo_effect:
# halo effect brings better visual realism but will slow down the speed
syneth = self.halo(syneth, skybg, skymask)
if 'rainy' in self.args.skybox:
syneth = self.rainmodel.forward(syneth)
return np.clip(syneth, a_min=0, a_max=1)