forked from huoyijie/AdvancedEAST
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathlabel.py
188 lines (172 loc) · 9.4 KB
/
label.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
import numpy as np
import os
from PIL import Image, ImageDraw
from tqdm import tqdm
import cfg
def point_inside_of_quad(px, py, quad_xy_list, p_min, p_max):
if (p_min[0] <= px <= p_max[0]) and (p_min[1] <= py <= p_max[1]):
xy_list = np.zeros((4, 2))
xy_list[:3, :] = quad_xy_list[1:4, :] - quad_xy_list[:3, :]
xy_list[3] = quad_xy_list[0, :] - quad_xy_list[3, :]
yx_list = np.zeros((4, 2))
yx_list[:, :] = quad_xy_list[:, -1:-3:-1]
a = xy_list * ([py, px] - yx_list)
b = a[:, 0] - a[:, 1]
if np.amin(b) >= 0 or np.amax(b) <= 0:
return True
else:
return False
else:
return False
def point_inside_of_nth_quad(px, py, xy_list, shrink_1, long_edge):
nth = -1
vs = [[[0, 0, 3, 3, 0], [1, 1, 2, 2, 1]],
[[0, 0, 1, 1, 0], [2, 2, 3, 3, 2]]]
for ith in range(2):
quad_xy_list = np.concatenate((
np.reshape(xy_list[vs[long_edge][ith][0]], (1, 2)),
np.reshape(shrink_1[vs[long_edge][ith][1]], (1, 2)),
np.reshape(shrink_1[vs[long_edge][ith][2]], (1, 2)),
np.reshape(xy_list[vs[long_edge][ith][3]], (1, 2))), axis=0)
p_min = np.amin(quad_xy_list, axis=0)
p_max = np.amax(quad_xy_list, axis=0)
if point_inside_of_quad(px, py, quad_xy_list, p_min, p_max):
if nth == -1:
nth = ith
else:
nth = -1
break
return nth
# xy_list = [
# [48.45,231.83],
# [17.87,178.79],
# [179.84,11.1],
# [228.79,47.95]
# ]
# 把四边形框内缩
def shrink(xy_list, ratio=cfg.shrink_ratio):
if ratio == 0.0:
return xy_list, xy_list
diff_1to3 = xy_list[:3, :] - xy_list[1:4, :] # 计算前三个顶点与其后一个点的差值
diff_4 = xy_list[3:4, :] - xy_list[0:1, :] # 计算第四个点与第一个点的差值
diff = np.concatenate((diff_1to3, diff_4), axis=0) # 连接成一个总的diff
dis = np.sqrt(np.sum(np.square(diff), axis=-1)) # 计算欧氏距离
# determine which are long or short edges
long_edge = int(np.argmax(np.sum(np.reshape(dis, (2, 2)), axis=0))) # 求对边的距离之和[dis(1+3), dis(2+4)]
short_edge = 1 - long_edge
# cal r length array
r = [np.minimum(dis[i], dis[(i + 1) % 4]) for i in range(4)] # 求相邻两边更短的那个，得到四个r，[2,2,2,2]
# cal theta array
diff_abs = np.abs(diff)
diff_abs[:, 0] += cfg.epsilon # 防止除零操作
theta = np.arctan(diff_abs[:, 1] / diff_abs[:, 0]) # 计算两点连线的theta角度
# shrink two long edges
temp_new_xy_list = np.copy(xy_list)
shrink_edge(xy_list, temp_new_xy_list, long_edge, r, theta, ratio)
shrink_edge(xy_list, temp_new_xy_list, long_edge + 2, r, theta, ratio)
# shrink two short edges
new_xy_list = np.copy(temp_new_xy_list)
shrink_edge(temp_new_xy_list, new_xy_list, short_edge, r, theta, ratio)
shrink_edge(temp_new_xy_list, new_xy_list, short_edge + 2, r, theta, ratio)
return temp_new_xy_list, new_xy_list, long_edge
def shrink_edge(xy_list, new_xy_list, edge, r, theta, ratio=cfg.shrink_ratio):
if ratio == 0.0:
return
start_point = edge
end_point = (edge + 1) % 4
long_start_sign_x = np.sign(
xy_list[end_point, 0] - xy_list[start_point, 0])
new_xy_list[start_point, 0] = \
xy_list[start_point, 0] + \
long_start_sign_x * ratio * r[start_point] * np.cos(theta[start_point])
long_start_sign_y = np.sign(
xy_list[end_point, 1] - xy_list[start_point, 1])
new_xy_list[start_point, 1] = \
xy_list[start_point, 1] + \
long_start_sign_y * ratio * r[start_point] * np.sin(theta[start_point])
# long edge one, end point
long_end_sign_x = -1 * long_start_sign_x
new_xy_list[end_point, 0] = \
xy_list[end_point, 0] + \
long_end_sign_x * ratio * r[end_point] * np.cos(theta[start_point])
long_end_sign_y = -1 * long_start_sign_y
new_xy_list[end_point, 1] = \
xy_list[end_point, 1] + \
long_end_sign_y * ratio * r[end_point] * np.sin(theta[start_point])
def process_label(data_dir=cfg.data_dir):
# 现在已经有验证和训练样本文本，把他们统一成一个变量f_list = > file_list
with open(os.path.join(data_dir, cfg.val_fname), 'r') as f_val:
f_list = f_val.readlines()
with open(os.path.join(data_dir, cfg.train_fname), 'r') as f_train:
f_list.extend(f_train.readlines())
# 遍历每一个图片文件名
for line, _ in zip(f_list, tqdm(range(len(f_list)))):
line_cols = str(line).strip().split(',')
img_name, width, height = \
line_cols[0].strip(), int(line_cols[1].strip()), \
int(line_cols[2].strip())
# gt用来储存图片在7种尺寸下ground truth，注意在gt中，原图的4个像素被聚合成了一个
gt = np.zeros((height // cfg.pixel_size, width // cfg.pixel_size, 7))
train_label_dir = os.path.join(data_dir, cfg.train_label_dir_name) # label保存位置
xy_list_array = np.load(os.path.join(train_label_dir,
img_name[:-4] + '.npy')) # 图片的box们
train_image_dir = os.path.join(data_dir, cfg.train_image_dir_name) # image保存位置
with Image.open(os.path.join(train_image_dir, img_name)) as im: # 打开这张图片
draw = ImageDraw.Draw(im)
for xy_list in xy_list_array:
_, shrink_xy_list, _ = shrink(xy_list, cfg.shrink_ratio)
shrink_1, _, long_edge = shrink(xy_list, cfg.shrink_side_ratio)
p_min = np.amin(shrink_xy_list, axis=0) # 求每一列的最小值
p_max = np.amax(shrink_xy_list, axis=0) # 每一列的最大值
# floor of the float
ji_min = (p_min / cfg.pixel_size - 0.5).astype(int) - 1 # 从原图的像素坐标转为像素合并图中的像素坐标
# +1 for ceil of the float and +1 for include the end
ji_max = (p_max / cfg.pixel_size - 0.5).astype(int) + 3 # 大像素图中的：
imin = np.maximum(0, ji_min[1]) # 最小行
imax = np.minimum(height // cfg.pixel_size, ji_max[1]) # 最大行
jmin = np.maximum(0, ji_min[0]) # 最小列
jmax = np.minimum(width // cfg.pixel_size, ji_max[0]) # 最大列
for i in range(imin, imax):
for j in range(jmin, jmax):
px = (j + 0.5) * cfg.pixel_size # 四个像素合并的大像素中心在原图中的实际像素位置
py = (i + 0.5) * cfg.pixel_size # 四个像素合并的大像素中心在原图中的实际像素位置
if point_inside_of_quad(px, py, # 四边形的外包矩形中的像素如果位于四边形内的话，
shrink_xy_list, p_min, p_max):
gt[i, j, 0] = 1
line_width, line_color = 1, 'red'
ith = point_inside_of_nth_quad(px, py,
xy_list,
shrink_1,
long_edge)
vs = [[[3, 0], [1, 2]], [[0, 1], [2, 3]]]
if ith in range(2):
gt[i, j, 1] = 1
if ith == 0:
line_width, line_color = 2, 'yellow'
else:
line_width, line_color = 2, 'green'
gt[i, j, 2:3] = ith
gt[i, j, 3:5] = \
xy_list[vs[long_edge][ith][0]] - [px, py] # 相对位置？
gt[i, j, 5:] = \
xy_list[vs[long_edge][ith][1]] - [px, py]
draw.line([(px - 0.5 * cfg.pixel_size,
py - 0.5 * cfg.pixel_size),
(px + 0.5 * cfg.pixel_size,
py - 0.5 * cfg.pixel_size),
(px + 0.5 * cfg.pixel_size,
py + 0.5 * cfg.pixel_size),
(px - 0.5 * cfg.pixel_size,
py + 0.5 * cfg.pixel_size),
(px - 0.5 * cfg.pixel_size,
py - 0.5 * cfg.pixel_size)],
width=line_width, fill=line_color)
act_image_dir = os.path.join(cfg.data_dir,
cfg.show_act_image_dir_name)
if cfg.draw_act_quad:
im.save(os.path.join(act_image_dir, img_name))
train_label_dir = os.path.join(data_dir, cfg.train_label_dir_name)
np.save(os.path.join(train_label_dir,
img_name[:-4] + '_gt.npy'), gt) # 从box转为gt，gt = [内部像素？，边界像素？，头尾？，左上（右上）x，左上（右上）y，左下（右下）x，左下（右下）y]
if __name__ == '__main__':
process_label()