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体验新版 GitCode,发现更多精彩内容 >>
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66cc7347
编写于
1月 31, 2023
作者:
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Egrt
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2007_train.txt
2007_train.txt
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2007_val.txt
2007_val.txt
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nets/yolo_training.py
nets/yolo_training.py
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train.py
train.py
+1
-1
utils/dataloader.py
utils/dataloader.py
+4
-98
voc_annotation.py
voc_annotation.py
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2007_train.txt
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2007_val.txt
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,0
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0,0
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,0
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,0
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,0
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E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000177.jpg 58,48,38,8,85,0 257,29,25,9,84,0 400,181,29,9,82
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000180.jpg 52,174,49,12,74,0 158,58,31,9,85,0 281,220,44,10,67,0 400,83,24,9,77,0 127,196,50,7,78
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4,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000228.jpg 170,280,84,19,7,0 420,306,31,16,8
,0
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,0
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,0
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,0
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,0
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0,0
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0,0
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,0
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,0
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75,0
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,0
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,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000482.jpg 103,127,106,33,84
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000513.jpg 432,190,96,12,72
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000515.jpg 71,229,92,13,71
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000532.jpg 64,75,25,68,1
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000540.jpg 334,203,51,137,1
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000553.jpg 429,222,19,58,11
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000561.jpg 290,201,36,112,8
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000571.jpg 261,194,139,49,89
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000572.jpg 109,155,50,140,1
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000594.jpg 283,143,122,25,6
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E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000609.jpg 203,182,25,100,83
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000613.jpg 339,132,25,97,8
4,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000618.jpg 229,165,26,102,83
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000649.jpg 153,304,8,21,50,0 153,49,29,6,69
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E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000656.jpg 95,139,33,7,77
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E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000662.jpg 40,81,59,8,70
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000671.jpg 445,220,15,41,50
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E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000672.jpg 213,288,47,12,69
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E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000675.jpg 411,269,39,11,68
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000700.jpg 287,140,22,57,4
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E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000719.jpg 459,246,20,55,10
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000745.jpg 185,166,155,28,12,0 45,140,69,18,16
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000749.jpg 125,153,104,19,11,0 119,170,106,19,17,0 114,188,106,20,19,0 320,200,229,29,11,0 353,230,153,20,11
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000752.jpg 144,87,140,40,56,0 245,252,210,59,65
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000753.jpg 183,148,28,113,81,0 376,291,13,41,78
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000770.jpg 320,179,30,13,56
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000795.jpg 202,65,12,29,
0,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000838.jpg 352,124,55,31,77
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000855.jpg 250,177,77,12,83
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000917.jpg 351,117,13,65,3
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000918.jpg 350,162,68,25,3
6,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000922.jpg 398,136,40,17,53
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000927.jpg 259,141,25,51,53
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000932.jpg 225,127,85,29,47
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000941.jpg 336,132,22,56,
0,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000946.jpg 245,159,62,23,32
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000951.jpg 432,142,10,31,86
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000954.jpg 184,260,19,53,71,0 226,88,31,20,68
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000966.jpg 125,119,9,34,8
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000967.jpg 391,165,8,34,
7,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000971.jpg 201,67,12,25,52
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E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000993.jpg 112,176,83,38,45
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000996.jpg 147,180,47,24,4
6,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001019.jpg 198,120,12,39,46,0 143,217,38,13,0,0 49,142,29,12,18
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E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001022.jpg 255,226,26,13,45,0 447,139,22,47,77
,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001044.jpg 310,116,16,32,86
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E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001054.jpg 305,115,7,21,90,0 420,233,5,21,85,0 329,269,6,19,78,0 217,210,6,14,82
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E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001058.jpg 196,62,24,7,0,0 311,180,5,21,90,0 221,217,6,20,90,0 108,157,5,15,9
0,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001074.jpg 155,132,9,16,81
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E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001078.jpg 172,92,42,20,63,0 153,161,12,22,
0,0
E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001099.jpg 64,163,12,28,0,0 163,55,9,20,0,0 142,200,12,28,0,0 193,142,10,20,0,0 242,127,17,9,80,0 303,135,10,17,0,0 344,76,20,11,69,0 269,234,15,33,59,0 273,258,14,26,35
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E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001102.jpg 57,24,7,18,5,0 34,171,26,13,90,0 87,114,10,20,0,0 135,98,18,12,90,0 162,205,17,34,53,0 166,228,16,28,50,0 196,107,9,17,0,0 237,47,19,10,63
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E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001128.jpg 69,77,27,11,15,0 86,164,32,9,15,0 214,82,42,11,22,0 202,221,29,10,18,0 326,158,29,10,16,0 392,82,28,11,21,0 426,156,26,11,15
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E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001133.jpg 261,76,41,13,3,0 156,170,22,10,9
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E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001145.jpg 62,76,23,8,0,0 397,121,18,7,12
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E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001158.jpg 104,31,25,7,28,0 36,72,19,7,14,0 34,127,16,7,8,0 87,157,16,6,7,0 226,141,8,19,73
,0
nets/yolo_training.py
浏览文件 @
66cc7347
...
...
@@ -137,7 +137,7 @@ class YOLOLoss(nn.Module):
#-------------------------------------------#
xy
=
prediction_pos
[:,
:
2
].
sigmoid
()
*
2.
-
0.5
wh
=
(
prediction_pos
[:,
2
:
4
].
sigmoid
()
*
2
)
**
2
*
anchors
[
i
]
angle
=
(
prediction_pos
[:,
4
:
5
].
sigmoid
()
-
0.5
)
*
torc
h
.
pi
angle
=
(
prediction_pos
[:,
4
:
5
].
sigmoid
()
-
0.5
)
*
mat
h
.
pi
box_theta
=
torch
.
cat
((
xy
,
wh
,
angle
),
1
)
#-------------------------------------------#
# 对真实框进行处理,映射到特征层上
...
...
@@ -150,7 +150,7 @@ class YOLOLoss(nn.Module):
# 计算预测框和真实框的回归损失
#-------------------------------------------#
kldloss
=
self
.
kldbbox
(
box_theta
,
selected_tbox_theta
)
loss
+=
kldloss
.
mean
()
box_loss
+=
kldloss
.
mean
()
#-------------------------------------------#
# 根据预测结果的iou获得置信度损失的gt
#-------------------------------------------#
...
...
@@ -299,7 +299,7 @@ class YOLOLoss(nn.Module):
grid
=
torch
.
stack
([
gi
,
gj
],
dim
=
1
).
type_as
(
fg_pred
)
pxy
=
(
fg_pred
[:,
:
2
].
sigmoid
()
*
2.
-
0.5
+
grid
)
*
self
.
stride
[
i
]
pwh
=
(
fg_pred
[:,
2
:
4
].
sigmoid
()
*
2
)
**
2
*
anch
[
i
][
idx
]
*
self
.
stride
[
i
]
pangle
=
(
fg_pred
[:,
4
:
5
].
sigmoid
()
-
0.5
)
*
torc
h
.
pi
pangle
=
(
fg_pred
[:,
4
:
5
].
sigmoid
()
-
0.5
)
*
mat
h
.
pi
pxywh
=
torch
.
cat
([
pxy
,
pwh
,
pangle
],
dim
=-
1
)
pxyxys
.
append
(
pxywh
)
...
...
train.py
浏览文件 @
66cc7347
...
...
@@ -41,7 +41,7 @@ if __name__ == "__main__":
# Cuda 是否使用Cuda
# 没有GPU可以设置成False
#---------------------------------#
Cuda
=
Fals
e
Cuda
=
Tru
e
#---------------------------------------------------------------------#
# distributed 用于指定是否使用单机多卡分布式运行
# 终端指令仅支持Ubuntu。CUDA_VISIBLE_DEVICES用于在Ubuntu下指定显卡。
...
...
utils/dataloader.py
浏览文件 @
66cc7347
...
...
@@ -74,8 +74,8 @@ class YoloDataset(Dataset):
# 序号为4的部分,为真实框的旋转角度
# 序号为5的部分,为真实框的种类
#---------------------------------------------------#
box
[:,
2
:
4
]
=
box
[:,
2
:
4
]
-
box
[:,
0
:
2
]
box
[:,
0
:
2
]
=
box
[:,
0
:
2
]
+
box
[:,
2
:
4
]
/
2
#
box[:, 2:4] = box[:, 2:4] - box[:, 0:2]
#
box[:, 0:2] = box[:, 0:2] + box[:, 2:4] / 2
#---------------------------------------------------#
# 调整顺序,符合训练的格式
...
...
@@ -105,102 +105,8 @@ class YoloDataset(Dataset):
# 获得预测框
#------------------------------#
box
=
np
.
array
([
np
.
array
(
list
(
map
(
int
,
box
.
split
(
','
))))
for
box
in
line
[
1
:]])
if
not
random
:
scale
=
min
(
w
/
iw
,
h
/
ih
)
nw
=
int
(
iw
*
scale
)
nh
=
int
(
ih
*
scale
)
dx
=
(
w
-
nw
)
//
2
dy
=
(
h
-
nh
)
//
2
#---------------------------------#
# 将图像多余的部分加上灰条
#---------------------------------#
image
=
image
.
resize
((
nw
,
nh
),
Image
.
BICUBIC
)
new_image
=
Image
.
new
(
'RGB'
,
(
w
,
h
),
(
128
,
128
,
128
))
new_image
.
paste
(
image
,
(
dx
,
dy
))
image_data
=
np
.
array
(
new_image
,
np
.
float32
)
#---------------------------------#
# 对真实框进行调整
#---------------------------------#
if
len
(
box
)
>
0
:
np
.
random
.
shuffle
(
box
)
box
[:,
[
0
,
2
]]
=
box
[:,
[
0
,
2
]]
*
nw
/
iw
+
dx
box
[:,
[
1
,
3
]]
=
box
[:,
[
1
,
3
]]
*
nh
/
ih
+
dy
box
[:,
0
:
2
][
box
[:,
0
:
2
]
<
0
]
=
0
box
[:,
2
][
box
[:,
2
]
>
w
]
=
w
box
[:,
3
][
box
[:,
3
]
>
h
]
=
h
box_w
=
box
[:,
2
]
-
box
[:,
0
]
box_h
=
box
[:,
3
]
-
box
[:,
1
]
box
=
box
[
np
.
logical_and
(
box_w
>
1
,
box_h
>
1
)]
# discard invalid box
return
image_data
,
box
#------------------------------------------#
# 对图像进行缩放并且进行长和宽的扭曲
#------------------------------------------#
new_ar
=
iw
/
ih
*
self
.
rand
(
1
-
jitter
,
1
+
jitter
)
/
self
.
rand
(
1
-
jitter
,
1
+
jitter
)
scale
=
self
.
rand
(.
25
,
2
)
if
new_ar
<
1
:
nh
=
int
(
scale
*
h
)
nw
=
int
(
nh
*
new_ar
)
else
:
nw
=
int
(
scale
*
w
)
nh
=
int
(
nw
/
new_ar
)
image
=
image
.
resize
((
nw
,
nh
),
Image
.
BICUBIC
)
#------------------------------------------#
# 将图像多余的部分加上灰条
#------------------------------------------#
dx
=
int
(
self
.
rand
(
0
,
w
-
nw
))
dy
=
int
(
self
.
rand
(
0
,
h
-
nh
))
new_image
=
Image
.
new
(
'RGB'
,
(
w
,
h
),
(
128
,
128
,
128
))
new_image
.
paste
(
image
,
(
dx
,
dy
))
image
=
new_image
#------------------------------------------#
# 翻转图像
#------------------------------------------#
flip
=
self
.
rand
()
<
.
5
if
flip
:
image
=
image
.
transpose
(
Image
.
FLIP_LEFT_RIGHT
)
image_data
=
np
.
array
(
image
,
np
.
uint8
)
#---------------------------------#
# 对图像进行色域变换
# 计算色域变换的参数
#---------------------------------#
r
=
np
.
random
.
uniform
(
-
1
,
1
,
3
)
*
[
hue
,
sat
,
val
]
+
1
#---------------------------------#
# 将图像转到HSV上
#---------------------------------#
hue
,
sat
,
val
=
cv2
.
split
(
cv2
.
cvtColor
(
image_data
,
cv2
.
COLOR_RGB2HSV
))
dtype
=
image_data
.
dtype
#---------------------------------#
# 应用变换
#---------------------------------#
x
=
np
.
arange
(
0
,
256
,
dtype
=
r
.
dtype
)
lut_hue
=
((
x
*
r
[
0
])
%
180
).
astype
(
dtype
)
lut_sat
=
np
.
clip
(
x
*
r
[
1
],
0
,
255
).
astype
(
dtype
)
lut_val
=
np
.
clip
(
x
*
r
[
2
],
0
,
255
).
astype
(
dtype
)
image_data
=
cv2
.
merge
((
cv2
.
LUT
(
hue
,
lut_hue
),
cv2
.
LUT
(
sat
,
lut_sat
),
cv2
.
LUT
(
val
,
lut_val
)))
image_data
=
cv2
.
cvtColor
(
image_data
,
cv2
.
COLOR_HSV2RGB
)
#---------------------------------#
# 对真实框进行调整
#---------------------------------#
if
len
(
box
)
>
0
:
np
.
random
.
shuffle
(
box
)
box
[:,
[
0
,
2
]]
=
box
[:,
[
0
,
2
]]
*
nw
/
iw
+
dx
box
[:,
[
1
,
3
]]
=
box
[:,
[
1
,
3
]]
*
nh
/
ih
+
dy
if
flip
:
box
[:,
[
0
,
2
]]
=
w
-
box
[:,
[
2
,
0
]]
box
[:,
0
:
2
][
box
[:,
0
:
2
]
<
0
]
=
0
box
[:,
2
][
box
[:,
2
]
>
w
]
=
w
box
[:,
3
][
box
[:,
3
]
>
h
]
=
h
box_w
=
box
[:,
2
]
-
box
[:,
0
]
box_h
=
box
[:,
3
]
-
box
[:,
1
]
box
=
box
[
np
.
logical_and
(
box_w
>
1
,
box_h
>
1
)]
image
=
image
.
resize
((
w
,
h
),
Image
.
BICUBIC
)
image_data
=
np
.
array
(
image
,
np
.
float32
)
return
image_data
,
box
...
...
voc_annotation.py
浏览文件 @
66cc7347
...
...
@@ -56,7 +56,7 @@ def convert_annotation(year, image_id, list_file):
continue
cls_id
=
classes
.
index
(
cls
)
xmlbox
=
obj
.
find
(
'rotated_bndbox'
)
b
=
(
int
(
float
(
xmlbox
.
find
(
'rotated_bbox_cx'
).
text
)),
int
(
float
(
xmlbox
.
find
(
'rotated_bbox_cy'
).
text
)),
int
(
float
(
xmlbox
.
find
(
'rotated_bbox_w'
).
text
)),
int
(
float
(
xmlbox
.
find
(
'rotated_bbox_h'
).
text
)),
float
(
xmlbox
.
find
(
'rotated_bbox_theta'
).
text
))
b
=
(
int
(
float
(
xmlbox
.
find
(
'rotated_bbox_cx'
).
text
)),
int
(
float
(
xmlbox
.
find
(
'rotated_bbox_cy'
).
text
)),
int
(
float
(
xmlbox
.
find
(
'rotated_bbox_w'
).
text
)),
int
(
float
(
xmlbox
.
find
(
'rotated_bbox_h'
).
text
)),
int
(
float
(
xmlbox
.
find
(
'rotated_bbox_theta'
).
text
)
))
list_file
.
write
(
" "
+
","
.
join
([
str
(
a
)
for
a
in
b
])
+
','
+
str
(
cls_id
))
nums
[
classes
.
index
(
cls
)]
=
nums
[
classes
.
index
(
cls
)]
+
1
...
...
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