-
Notifications
You must be signed in to change notification settings - Fork 6
/
Copy pathimage.py
170 lines (142 loc) · 5.97 KB
/
image.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
#!/usr/bin/python
# encoding: utf-8
import random
from PIL import Image
import numpy as np
####################################################
import shapely.geometry as geometry
import dota_process
####################################################
def distort_image(im, hue, sat, val):
im = im.convert('HSV')
cs = list(im.split())
cs[1] = cs[1].point(lambda i: i * sat)
cs[2] = cs[2].point(lambda i: i * val)
def change_hue(x):
x += hue*255
if x > 255:
x -= 255
if x < 0:
x += 255
return x
cs[0] = cs[0].point(change_hue)
im = Image.merge(im.mode, tuple(cs))
im = im.convert('RGB')
return im
def rand_scale(s):
scale = random.uniform(1, s)
if random.randint(1, 10000) % 2:
return scale
return 1./scale
def random_distort_image(im, hue, saturation, exposure):
dhue = random.uniform(-hue, hue)
dsat = rand_scale(saturation)
dexp = rand_scale(exposure)
res = distort_image(im, dhue, dsat, dexp)
return res
####################################################
def data_augmentation(img, shape, jitter, hue, saturation, exposure):
oh = img.height
ow = img.width
dw = int(ow * jitter)
dh = int(oh * jitter)
pleft = random.randint(-dw, dw)
pright = random.randint(-dw, dw)
ptop = random.randint(-dh, dh)
pbot = random.randint(-dh, dh)
swidth = ow - pleft - pright
sheight = oh - ptop - pbot
flip = random.randint(1, 10000) % 2
cropped = img.crop((pleft, ptop, pleft + swidth - 1, ptop + sheight - 1))
sized = cropped.resize(shape)
if flip:
sized = sized.transpose(Image.FLIP_LEFT_RIGHT)
img = random_distort_image(sized, hue, saturation, exposure)
return img, flip, swidth, sheight, pleft, ptop
####################################################
def fill_truth_detection(labpath, w, h, flip, swidth, sheight, pleft, ptop):
max_boxes = 800
cc = 0
label = np.zeros((max_boxes, 5))
left = max(pleft, 0)
right = min((pleft + swidth), w)
up = max(ptop, 0)
down = min((ptop + sheight), h)
objects = dota_process.read_dota_gt(labpath)
if objects is None:
return label
imgbox = geometry.Polygon([(left, up), (right, up), (right, down),
(left, down)])
for obj in objects:
gtbox = geometry.Polygon([(obj['box'][0], obj['box'][1]),
(obj['box'][2], obj['box'][3]),
(obj['box'][4], obj['box'][5]),
(obj['box'][6], obj['box'][7])])
if gtbox.area <= 0:
continue
inter_box, half_iou = dota_process.calc_half_iou(gtbox, imgbox)
if half_iou == 1:
boxInsub = dota_process.boxorig2sub(pleft, ptop, obj['box'])
elif half_iou > 0.3:
inter_box = geometry.polygon.orient(inter_box, sign=1)
out_box = list(inter_box.exterior.coords)[0: -1]
if len(out_box) < 4:
continue
out_box2 = []
for i in range(len(out_box)):
out_box2.append(out_box[i][0])
out_box2.append(out_box[i][1])
if len(out_box) == 5:
out_box2 = dota_process.box5_box4(out_box2)
elif len(out_box) > 5:
continue
out_box2 = dota_process.choose_best_point_order_fit_another(out_box2, obj['box'])
boxInsub = dota_process.boxorig2sub(left, up, out_box2)
for index, item in enumerate(boxInsub):
if index % 2 == 0:
if item <= 1:
boxInsub[index] = 1
elif item >= swidth:
boxInsub[index] = swidth
elif index % 2 == 1:
if item <= 1:
boxInsub[index] = 1
elif item >= sheight:
boxInsub[index] = sheight
else:
continue
length = max(np.abs(np.subtract(boxInsub[0], boxInsub[4])), np.abs(np.subtract(boxInsub[1], boxInsub[5])))
boxInsub = [(boxInsub[0], boxInsub[1]), (boxInsub[2], boxInsub[3]), (boxInsub[4], boxInsub[5]),
(boxInsub[6], boxInsub[7])]
if (length / min(swidth, sheight)) < (5.0/704.0) and geometry.Polygon(boxInsub).area < 15:
continue
rect_minx = geometry.Polygon(boxInsub).bounds[0]
rect_miny = geometry.Polygon(boxInsub).bounds[1]
rect_maxx = geometry.Polygon(boxInsub).bounds[2]
rect_maxy = geometry.Polygon(boxInsub).bounds[3]
if max(abs(rect_maxx - rect_minx)/swidth, abs(rect_maxy - rect_miny)/sheight) < (5.0/704.0) \
or min(abs(rect_maxx - rect_minx)/swidth, abs(rect_maxy - rect_miny)/sheight) <= 0:
continue
label[cc][0] = dota_process.classnames.index(obj['name'])
if flip:
label[cc][1] = 0.999 - abs(rect_maxx + rect_minx) / (2.0 * swidth)
else:
label[cc][1] = abs(rect_maxx + rect_minx) / (2.0 * swidth)
label[cc][2] = abs(rect_maxy + rect_miny) / (2.0 * sheight)
label[cc][3] = abs(rect_maxx - rect_minx) / swidth
label[cc][4] = abs(rect_maxy - rect_miny) / sheight
cc += 1
if cc >= 800:
break
label = np.reshape(label, (-1))
return label
####################################################
def load_data_detection(imgpath, shape, jitter, hue, saturation, exposure):
labpath = imgpath.replace('images', 'labels').replace('JPEGImages', 'labels').replace('.jpg', '.txt').replace('.png', '.txt')
## data augmentation
img = Image.open(imgpath).convert('RGB')
####################################################
img, flip, swidth, sheight, pleft, ptop = data_augmentation(img, shape, jitter, hue, saturation, exposure)
label = fill_truth_detection(labpath, img.width, img.height, flip, swidth, sheight, pleft, ptop)
####################################################
return img, label