-
Notifications
You must be signed in to change notification settings - Fork 380
/
preprocess.py
180 lines (166 loc) · 7.8 KB
/
preprocess.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
import numpy as np
from PIL import Image, ImageDraw
import os
import random
from tqdm import tqdm
import cfg
from label import shrink
def batch_reorder_vertexes(xy_list_array):
reorder_xy_list_array = np.zeros_like(xy_list_array)
for xy_list, i in zip(xy_list_array, range(len(xy_list_array))):
reorder_xy_list_array[i] = reorder_vertexes(xy_list)
return reorder_xy_list_array
def reorder_vertexes(xy_list):
reorder_xy_list = np.zeros_like(xy_list)
# determine the first point with the smallest x,
# if two has same x, choose that with smallest y,
ordered = np.argsort(xy_list, axis=0)
xmin1_index = ordered[0, 0]
xmin2_index = ordered[1, 0]
if xy_list[xmin1_index, 0] == xy_list[xmin2_index, 0]:
if xy_list[xmin1_index, 1] <= xy_list[xmin2_index, 1]:
reorder_xy_list[0] = xy_list[xmin1_index]
first_v = xmin1_index
else:
reorder_xy_list[0] = xy_list[xmin2_index]
first_v = xmin2_index
else:
reorder_xy_list[0] = xy_list[xmin1_index]
first_v = xmin1_index
# connect the first point to others, the third point on the other side of
# the line with the middle slope
others = list(range(4))
others.remove(first_v)
k = np.zeros((len(others),))
for index, i in zip(others, range(len(others))):
k[i] = (xy_list[index, 1] - xy_list[first_v, 1]) \
/ (xy_list[index, 0] - xy_list[first_v, 0] + cfg.epsilon)
k_mid = np.argsort(k)[1]
third_v = others[k_mid]
reorder_xy_list[2] = xy_list[third_v]
# determine the second point which on the bigger side of the middle line
others.remove(third_v)
b_mid = xy_list[first_v, 1] - k[k_mid] * xy_list[first_v, 0]
second_v, fourth_v = 0, 0
for index, i in zip(others, range(len(others))):
# delta = y - (k * x + b)
delta_y = xy_list[index, 1] - (k[k_mid] * xy_list[index, 0] + b_mid)
if delta_y > 0:
second_v = index
else:
fourth_v = index
reorder_xy_list[1] = xy_list[second_v]
reorder_xy_list[3] = xy_list[fourth_v]
# compare slope of 13 and 24, determine the final order
k13 = k[k_mid]
k24 = (xy_list[second_v, 1] - xy_list[fourth_v, 1]) / (
xy_list[second_v, 0] - xy_list[fourth_v, 0] + cfg.epsilon)
if k13 < k24:
tmp_x, tmp_y = reorder_xy_list[3, 0], reorder_xy_list[3, 1]
for i in range(2, -1, -1):
reorder_xy_list[i + 1] = reorder_xy_list[i]
reorder_xy_list[0, 0], reorder_xy_list[0, 1] = tmp_x, tmp_y
return reorder_xy_list
def resize_image(im, max_img_size=cfg.max_train_img_size):
im_width = np.minimum(im.width, max_img_size)
if im_width == max_img_size < im.width:
im_height = int((im_width / im.width) * im.height)
else:
im_height = im.height
o_height = np.minimum(im_height, max_img_size)
if o_height == max_img_size < im_height:
o_width = int((o_height / im_height) * im_width)
else:
o_width = im_width
d_wight = o_width - (o_width % 32)
d_height = o_height - (o_height % 32)
return d_wight, d_height
def preprocess():
data_dir = cfg.data_dir
origin_image_dir = os.path.join(data_dir, cfg.origin_image_dir_name)
origin_txt_dir = os.path.join(data_dir, cfg.origin_txt_dir_name)
train_image_dir = os.path.join(data_dir, cfg.train_image_dir_name)
train_label_dir = os.path.join(data_dir, cfg.train_label_dir_name)
if not os.path.exists(train_image_dir):
os.mkdir(train_image_dir)
if not os.path.exists(train_label_dir):
os.mkdir(train_label_dir)
draw_gt_quad = cfg.draw_gt_quad
show_gt_image_dir = os.path.join(data_dir, cfg.show_gt_image_dir_name)
if not os.path.exists(show_gt_image_dir):
os.mkdir(show_gt_image_dir)
show_act_image_dir = os.path.join(cfg.data_dir, cfg.show_act_image_dir_name)
if not os.path.exists(show_act_image_dir):
os.mkdir(show_act_image_dir)
o_img_list = os.listdir(origin_image_dir)
print('found %d origin images.' % len(o_img_list))
train_val_set = []
for o_img_fname, _ in zip(o_img_list, tqdm(range(len(o_img_list)))):
with Image.open(os.path.join(origin_image_dir, o_img_fname)) as im:
# d_wight, d_height = resize_image(im)
d_wight, d_height = cfg.max_train_img_size, cfg.max_train_img_size
scale_ratio_w = d_wight / im.width
scale_ratio_h = d_height / im.height
im = im.resize((d_wight, d_height), Image.NEAREST).convert('RGB')
show_gt_im = im.copy()
# draw on the img
draw = ImageDraw.Draw(show_gt_im)
with open(os.path.join(origin_txt_dir,
o_img_fname[:-4] + '.txt'), 'r') as f:
anno_list = f.readlines()
xy_list_array = np.zeros((len(anno_list), 4, 2))
for anno, i in zip(anno_list, range(len(anno_list))):
anno_colums = anno.strip().split(',')
anno_array = np.array(anno_colums)
xy_list = np.reshape(anno_array[:8].astype(float), (4, 2))
xy_list[:, 0] = xy_list[:, 0] * scale_ratio_w
xy_list[:, 1] = xy_list[:, 1] * scale_ratio_h
xy_list = reorder_vertexes(xy_list)
xy_list_array[i] = xy_list
_, shrink_xy_list, _ = shrink(xy_list, cfg.shrink_ratio)
shrink_1, _, long_edge = shrink(xy_list, cfg.shrink_side_ratio)
if draw_gt_quad:
draw.line([tuple(xy_list[0]), tuple(xy_list[1]),
tuple(xy_list[2]), tuple(xy_list[3]),
tuple(xy_list[0])
],
width=2, fill='green')
draw.line([tuple(shrink_xy_list[0]),
tuple(shrink_xy_list[1]),
tuple(shrink_xy_list[2]),
tuple(shrink_xy_list[3]),
tuple(shrink_xy_list[0])
],
width=2, fill='blue')
vs = [[[0, 0, 3, 3, 0], [1, 1, 2, 2, 1]],
[[0, 0, 1, 1, 0], [2, 2, 3, 3, 2]]]
for q_th in range(2):
draw.line([tuple(xy_list[vs[long_edge][q_th][0]]),
tuple(shrink_1[vs[long_edge][q_th][1]]),
tuple(shrink_1[vs[long_edge][q_th][2]]),
tuple(xy_list[vs[long_edge][q_th][3]]),
tuple(xy_list[vs[long_edge][q_th][4]])],
width=3, fill='yellow')
if cfg.gen_origin_img:
im.save(os.path.join(train_image_dir, o_img_fname))
np.save(os.path.join(
train_label_dir,
o_img_fname[:-4] + '.npy'),
xy_list_array)
if draw_gt_quad:
show_gt_im.save(os.path.join(show_gt_image_dir, o_img_fname))
train_val_set.append('{},{},{}\n'.format(o_img_fname,
d_wight,
d_height))
train_img_list = os.listdir(train_image_dir)
print('found %d train images.' % len(train_img_list))
train_label_list = os.listdir(train_label_dir)
print('found %d train labels.' % len(train_label_list))
random.shuffle(train_val_set)
val_count = int(cfg.validation_split_ratio * len(train_val_set))
with open(os.path.join(data_dir, cfg.val_fname), 'w') as f_val:
f_val.writelines(train_val_set[:val_count])
with open(os.path.join(data_dir, cfg.train_fname), 'w') as f_train:
f_train.writelines(train_val_set[val_count:])
if __name__ == '__main__':
preprocess()