深度解析：滑块验证码通杀方案--python破解代码
本代码理论上可以通杀市面上所有滑块（如果真出现了识别不到，请自行根据文章末尾的参考内容，自己进行调教）在线测试地址：simple_ocr截至2022.7.13，可以破解的有：1、顶象2、网易易盾以下是识别代码：fromioimportByt
本代码理论上可以通杀市面上所有滑块（如果真出现了识别不到，请自行根据文章末尾的参考内容，自己进行调教）
在线测试地址：
simple_ocr
截至2022.7.13，可以破解的有：
1、
顶象
2、
网易易盾
以下是识别代码：
from
io
import
BytesIO
import
onnxruntime
import
torch
import
torchvision
import
numpy
as
np
from
selenium
.
webdriver
.
common
.
by
import
By
from
selenium
.
webdriver
.
chrome
.
options
import
Options
from
selenium
import
webdriver
import
time
import
cv2
import
base64
from
selenium
.
webdriver
import
ActionChains
import
random
from
PIL
import
Image
from
lxml
import
etree
import
requests
def
padded_resize
(
im
,
new_shape
=
(
640
,
640
)
,
stride
=
32
)
:
try
:
shape
=
im
.
shape
[
:
2
]
r
=
min
(
new_shape
[
0
]
/
shape
[
0
]
,
new_shape
[
1
]
/
shape
[
1
]
)
new_unpad
=
int
(
round
(
shape
[
1
]
*
r
)
)
,
int
(
round
(
shape
[
0
]
*
r
)
)
dw
,
dh
=
new_shape
[
1
]
-
new_unpad
[
0
]
,
new_shape
[
0
]
-
new_unpad
[
1
]
# dw, dh = np.mod(dw, stride), np.mod(dh, stride)
dw
/=
2
dh
/=
2
if
shape
[
:
:
-
1
]
!=
new_unpad
:
# resize
im
=
cv2
.
resize
(
im
,
new_unpad
,
interpolation
=
cv2
.
INTER_LINEAR
)
top
,
bottom
=
int
(
round
(
dh
-
0.1
)
)
,
int
(
round
(
dh
+
0.1
)
)
left
,
right
=
int
(
round
(
dw
-
0.1
)
)
,
int
(
round
(
dw
+
0.1
)
)
im
=
cv2
.
copyMakeBorder
(
im
,
top
,
bottom
,
left
,
right
,
cv2
.
BORDER_CONSTANT
,
value
=
(
114
,
114
,
114
)
)
# add border
# Convert
im
=
im
.
transpose
(
(
2
,
0
,
1
)
)
[
:
:
-
1
]
# HWC to CHW, BGR to RGB
im
=
np
.
ascontiguousarray
(
im
)
im
=
torch
.
from_numpy
(
im
)
im
=
im
.
float
(
)
im
/=
255
im
=
im
[
None
]
im
=
im
.
cpu
(
)
.
numpy
(
)
# torch to numpy
return
im
except
:
(
"123"
)
def
xywh2xyxy
(
x
)
:
# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
y
=
x
.
clone
(
)
if
isinstance
(
x
,
torch
.
Tensor
)
else
np
.
copy
(
x
)
y
[
:
,
0
]
=
x
[
:
,
0
]
-
x
[
:
,
2
]
/
2
# top left x
y
[
:
,
1
]
=
x
[
:
,
1
]
-
x
[
:
,
3
]
/
2
# top left y
y
[
:
,
2
]
=
x
[
:
,
0
]
+
x
[
:
,
2
]
/
2
# bottom right x
y
[
:
,
3
]
=
x
[
:
,
1
]
+
x
[
:
,
3
]
/
2
# bottom right y
return
y
def
box_iou
(
box1
,
box2
)
:
"""
Return intersection-over-union (Jaccard index) of boxes.
Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
Arguments:
box1 (Tensor[N, 4])
box2 (Tensor[M, 4])
Returns:
iou (Tensor[N, M]): the NxM matrix containing the pairwise
IoU values for every element in boxes1 and boxes2
"""
def
box_area
(
box
)
:
# box = 4xn
return
(
box
[
2
]
-
box
[
0
]
)
*
(
box
[
3
]
-
box
[
1
]
)
area1
=
box_area
(
box1
.
T
)
area2
=
box_area
(
box2
.
T
)
# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
inter
=
(
torch
.
min
(
box1
[
:
,
None
,
2
:
]
,
box2
[
:
,
2
:
]
)
-
torch
.
max
(
box1
[
:
,
None
,
:
2
]
,
box2
[
:
,
:
2
]
)
)
.
clamp
(
0
)
.
prod
(
2
)
return
inter
/
(
area1
[
:
,
None
]
+
area2
-
inter
)
# iou = inter / (area1 + area2 - inter)
def
non_max_suppression
(
prediction
,
conf_thres
=
0.25
,
iou_thres
=
0.45
,
classes
=
None
,
agnostic
=
False
,
multi_label
=
False
,
labels
=
(
)
,
max_det
=
300
)
:
"""Runs Non-Maximum Suppression (NMS) on inference results
Returns:
list of detections, on (n,6) tensor per image [xyxy, conf, cls]
"""
nc
=
prediction
.
shape
[
2
]
-
5
# number of classes
xc
=
prediction
[
.
.
.
,
4
]
>
conf_thres
# candidates
# Checks
assert
0
<=
conf_thres
<=
1
,
f'Invalid Confidence threshold
{
conf_thres
}
, valid values are between 0.0 and 1.0'
assert
0
<=
iou_thres
<=
1
,
f'Invalid IoU
{
iou_thres
}
, valid values are between 0.0 and 1.0'
# Settings
min_wh
,
max_wh
=
2
,
7680
# (pixels) minimum and maximum box width and height
max_nms
=
30000
# maximum number of boxes into torchvision.ops.nms()
time_limit
=
10.0
# seconds to quit after
redundant
=
True
# require redundant detections
multi_label
&
=
nc
>
1
# multiple labels per box (adds 0.5ms/img)
merge
=
False
# use merge-NMS
t
=
time
.
time
(
)
output
=
[
torch
.
zeros
(
(
0
,
6
)
,
device
=
prediction
.
device
)
]
*
prediction
.
shape
[
0
]
for
xi
,
x
in
enumerate
(
prediction
)
:
# image index, image inference
# Apply constraints
x
[
(
(
x
[
.
.
.
,
2
:
4
]
<
min_wh
)
|
(
x
[
.
.
.
,
2
:
4
]
>
max_wh
)
)
.
any
(
1
)
,
4
]
=
0
# width-height
x
=
x
[
xc
[
xi
]
]
# confidence
# Cat apriori labels if autolabelling
if
labels
and
len
(
labels
[
xi
]
)
:
lb
=
labels
[
xi
]
v
=
torch
.
zeros
(
(
len
(
lb
)
,
nc
+
5
)
,
device
=
x
.
device
)
v
[
:
,
:
4
]
=
lb
[
:
,
1
:
5
]
# box
v
[
:
,
4
]
=
1.0
# conf
v
[
range
(
len
(
lb
)
)
,
lb
[
:
,
0
]
.
long
(
)
+
5
]
=
1.0
# cls
x
=
torch
.
cat
(
(
x
,
v
)
,
0
)
# If none remain process next image
if
not
x
.
shape
[
0
]
:
continue
# Compute conf
x
[
:
,
5
:
]
*=
x
[
:
,
4
:
5
]
# conf = obj_conf * cls_conf
# Box (center x, center y, width, height) to (x1, y1, x2, y2)
box
=
xywh2xyxy
(
x
[
:
,
:
4
]
)
# Detections matrix nx6 (xyxy, conf, cls)
if
multi_label
:
i
,
j
=
(
x
[
:
,
5
:
]
>
conf_thres
)
.
nonzero
(
as_tuple
=
False
)
.
T
x
=
torch
.
cat
(
(
box
[
i
]
,
x
[
i
,
j
+
5
,
None
]
,
j
[
:
,
None
]
.
float
(
)
)
,
1
)
else
:
# best class only
conf
,
j
=
x
[
:
,
5
:
]
.
max
(
1
,
keepdim
=
True
)
x
=
torch
.
cat
(
(
box
,
conf
,
j
.
float
(
)
)
,
1
)
[
conf
.
view
(
-
1
)
>
conf_thres
]
# Filter by class
if
classes
is
not
None
:
x
=
x
[
(
x
[
:
,
5
:
6
]
==
torch
.
tensor
(
classes
,
device
=
x
.
device
)
)
.
any
(
1
)
]
# Apply finite constraint
# if not torch.isfinite(x).all():
# x = x[torch.isfinite(x).all(1)]
# Check shape
n
=
x
.
shape
[
0
]
# number of boxes
if
not
n
:
# no boxes
continue
elif
n
>
max_nms
:
# excess boxes
x
=
x
[
x
[
:
,
4
]
.
argsort
(
descending
=
True
)
[
:
max_nms
]
]
# sort by confidence
# Batched NMS
c
=
x
[
:
,
5
:
6
]
*
(
0
if
agnostic
else
max_wh
)
# classes
boxes
,
scores
=
x
[
:
,
:
4
]
+
c
,
x
[
:
,
4
]
# boxes (offset by class), scores
i
=
torchvision
.
ops
.
nms
(
boxes
,
scores
,
iou_thres
)
# NMS
if
i
.
shape
[
0
]
>
max_det
:
# limit detections
i
=
i
[
:
max_det
]
if
merge
and
(
1
<
n
<
3E3
)
:
# Merge NMS (boxes merged using weighted mean)
# update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
iou
=
box_iou
(
boxes
[
i
]
,
boxes
)
>
iou_thres
# iou matrix
weights
=
iou
*
scores
[
None
]
# box weights
x
[
i
,
:
4
]
=
torch
.
mm
(
weights
,
x
[
:
,
:
4
]
)
.
float
(
)
/
weights
.
sum
(
1
,
keepdim
=
True
)
# merged boxes
if
redundant
:
i
=
i
[
iou
.
sum
(
1
)
>
1
]
# require redundancy
output
[
xi
]
=
x
[
i
]
if
(
time
.
time
(
)
-
t
)
>
time_limit
:
break
# time limit exceeded
return
output
def
xyxy2xywh
(
x
)
:
# Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
y
=
x
.
clone
(
)
if
isinstance
(
x
,
torch
.
Tensor
)
else
np
.
copy
(
x
)
y
[
:
,
0
]
=
(
x
[
:
,
0
]
+
x
[
:
,
2
]
)
/
2
# x center
y
[
:
,
1
]
=
(
x
[
:
,
1
]
+
x
[
:
,
3
]
)
/
2
# y center
y
[
:
,
2
]
=
x
[
:
,
2
]
-
x
[
:
,
0
]
# width
y
[
:
,
3
]
=
x
[
:
,
3
]
-
x
[
:
,
1
]
# height
return
y
def
is_ascii
(
s
=
''
)
:
# Is string composed of all ASCII (no UTF) characters? (note str().isascii() introduced in python 3.7)
s
=
str
(
s
)
# convert list, tuple, None, etc. to str
return
len
(
s
.
encode
(
)
.
decode
(
'ascii'
,
'ignore'
)
)
==
len
(
s
)
def
box_label
(
self
,
box
,
label
=
''
,
color
=
(
128
,
128
,
128
)
,
txt_color
=
(
255
,
255
,
255
)
)
:
# Add one xyxy box to image with label
if
self
.
pil
or
not
is_ascii
(
label
)
:
self
.
draw
.
rectangle
(
box
,
width
=
self
.
lw
,
outline
=
color
)
# box
if
label
:
w
,
h
=
self
.
font
.
getsize
(
label
)
# text width, height
outside
=
box
[
1
]
-
h
>=
0
# label fits outside box
self
.
draw
.
rectangle
(
(
box
[
0
]
,
box
[
1
]
-
h
if
outside
else
box
[
1
]
,
box
[
0
]
+
w
+
1
,
box
[
1
]
+
1
if
outside
else
box
[
1
]
+
h
+
1
)
,
fill
=
color
)
# self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls') # for PIL>8.0
self
.
draw
.
text
(
(
box
[
0
]
,
box
[
1
]
-
h
if
outside
else
box
[
1
]
)
,
label
,
fill
=
txt_color
,
font
=
self
.
font
)
else
:
# cv2
p1
,
p2
=
(
int
(
box
[
0
]
)
,
int
(
box
[
1
]
)
)
,
(
int
(
box
[
2
]
)
,
int
(
box
[
3
]
)
)
cv2
.
rectangle
(
self
.
im
,
p1
,
p2
,
color
,
thickness
=
self
.
lw
,
lineType
=
cv2
.
LINE_AA
)
if
label
:
tf
=
max
(
self
.
lw
-
1
,
1
)
# font thickness
w
,
h
=
cv2
.
getTextSize
(
label
,
0
,
fontScale
=
self
.
lw
/
3
,
thickness
=
tf
)
[
0
]
# text width, height
outside
=
p1
[
1
]
-
h
-
3
>=
0
# label fits outside box
p2
=
p1
[
0
]
+
w
,
p1
[
1
]
-
h
-
3
if
outside
else
p1
[
1
]
+
h
+
3
cv2
.
rectangle
(
self
.
im
,
p1
,
p2
,
color
,
-
1
,
cv2
.
LINE_AA
)
# filled
cv2
.
putText
(
self
.
im
,
label
,
(
p1
[
0
]
,
p1
[
1
]
-
2
if
outside
else
p1
[
1
]
+
h
+
2
)
,
0
,
self
.
lw
/
3
,
txt_color
,
thickness
=
tf
,
lineType
=
cv2
.
LINE_AA
)
def
return_coordinates
(
xyxy
,
conf
)
:
conf
=
float
(
conf
.
numpy
(
)
)
gain
=
1.02
pad
=
10
xyxy
=
torch
.
tensor
(
xyxy
)
.
view
(
-
1
,
4
)
b
=
xyxy2xywh
(
xyxy
)
# boxes
b
[
:
,
2
:
]
=
b
[
:
,
2
:
]
*
gain
+
pad
# box wh * gain + pad
xyxy
=
xywh2xyxy
(
b
)
.
long
(
)
c1
,
c2
=
(
int
(
xyxy
[
0
,
0
]
)
+
6
,
int
(
xyxy
[
0
,
1
]
)
+
6
)
,
(
int
(
xyxy
[
0
,
2
]
)
-
6
,
int
(
xyxy
[
0
,
3
]
)
-
6
)
# print(f"leftTop:{c1},rightBottom:{c2},Confidence:{conf*100}%")
result_dict
=
{
"leftTop"
:
c1
,
"rightBottom"
:
c2
,
"Confidence"
:
conf
}
return
result_dict
def
clip_coords
(
boxes
,
shape
)
:
# Clip bounding xyxy bounding boxes to image shape (height, width)
if
isinstance
(
boxes
,
torch
.
Tensor
)
:
# faster individually
boxes
[
:
,
0
]
.
clamp_
(
0
,
shape
[
1
]
)
# x1
boxes
[
:
,
1
]
.
clamp_
(
0
,
shape
[
0
]
)
# y1
boxes
[
:
,
2
]
.
clamp_
(
0
,
shape
[
1
]
)
# x2
boxes
[
:
,
3
]
.
clamp_
(
0
,
shape
[
0
]
)
# y2
else
:
# np.array (faster grouped)
boxes
[
:
,
[
0
,
2
]
]
=
boxes
[
:
,
[
0
,
2
]
]
.
clip
(
0
,
shape
[
1
]
)
# x1, x2
boxes
[
:
,
[
1
,
3
]
]
=
boxes
[
:
,
[
1
,
3
]
]
.
clip
(
0
,
shape
[
0
]
)
# y1, y2
def
scale_coords
(
img1_shape
,
coords
,
img0_shape
,
ratio_pad
=
None
)
:
# Rescale coords (xyxy) from img1_shape to img0_shape
if
ratio_pad
is
None
:
# calculate from img0_shape
gain
=
min
(
img1_shape
[
0
]
/
img0_shape
[
0
]
,
img1_shape
[
1
]
/
img0_shape
[
1
]
)
# gain = old / new
pad
=
(
img1_shape
[
1
]
-
img0_shape
[
1
]
*
gain
)
/
2
,
(
img1_shape
[
0
]
-
img0_shape
[
0
]
*
gain
)
/
2
# wh padding
else
:
gain
=
ratio_pad
[
0
]
[
0
]
pad
=
ratio_pad
[
1
]
coords
[
:
,
[
0
,
2
]
]
-=
pad
[
0
]
# x padding
coords
[
:
,
[
1
,
3
]
]
-=
pad
[
1
]
# y padding
coords
[
:
,
:
4
]
/=
gain
clip_coords
(
coords
,
img0_shape
)
return
coords
def
onnx_model_main
(
path
)
:
# onnx
session
=
onnxruntime
.
InferenceSession
(
"last.onnx"
,
providers
=
[
"CPUExecutionProvider"
]
)
start
=
time
.
time
(
)
image
=
open
(
path
,
"rb"
)
.
read
(
)
img
=
np
.
array
(
Image
.
open
(
BytesIO
(
image
)
)
)
# img = cv2.imread(path)
# 图像处理
img
=
img
[
:
,
:
,
:
3
]
im
=
padded_resize
(
img
)
# 模型调度
pred
=
session
.
run
(
[
session
.
get_outputs
(
)
[
0
]
.
name
]
,
{
session
.
get_inputs
(
)
[
0
]
.
name
:
im
}
)
[
0
]
pred
=
torch
.
tensor
(
pred
)
pred
=
non_max_suppression
(
pred
,
conf_thres
=
0.25
,
iou_thres
=
0.60
,
max_det
=
1000
)
# 大于百分之六十的置信度
coordinate_list
=
[
]
for
i
,
det
in
enumerate
(
pred
)
:
det
[
:
,
:
4
]
=
scale_coords
(
im
.
shape
[
2
:
]
,
det
[
:
,
:
4
]
,
img
.
shape
)
.
round
(
)
for
*
xyxy
,
conf
,
cls
in
reversed
(
det
)
:
# 返回坐标和置信度
coordinates
=
return_coordinates
(
xyxy
,
conf
)
coordinate_list
.
append
(
coordinates
)
# 坐标列表
coordinate
=
sorted
(
coordinate_list
,
key
=
lambda
a
:
a
[
"Confidence"
]
)
# 用时
duration
=
str
(
(
time
.
time
(
)
-
start
)
)
if
len
(
coordinate
)
==
0
:
data
=
{
'message'
:
'error'
,
'time'
:
duration
}
else
:
coordinate
=
coordinate
[
-
1
]
x
=
coordinate
.
get
(
'leftTop'
)
[
0
]
y
=
coordinate
.
get
(
'leftTop'
)
[
1
]
w
=
coordinate
.
get
(
'rightBottom'
)
[
0
]
-
coordinate
.
get
(
'leftTop'
)
[
0
]
h
=
coordinate
.
get
(
'rightBottom'
)
[
1
]
-
coordinate
.
get
(
'leftTop'
)
[
1
]
point
=
f"
{
x
}
|
{
y
}
|
{
w
}
|
{
h
}
"
data
=
{
'message'
:
'success'
,
'time'
:
duration
,
'point'
:
point
}
data
.
update
(
coordinate
)
(
data
)
return
data
def
drow_rectangle
(
coordinate
,
path
)
:
img
=
cv2
.
imread
(
path
)
# 画框
result
=
cv2
.
rectangle
(
img
,
coordinate
.
get
(
"leftTop"
)
,
coordinate
.
get
(
"rightBottom"
)
,
(
0
,
0
,
255
)
,
2
)
cv2
.
imwrite
(
"drow_rectangle.jpg"
,
result
)
# 返回圈中矩形的图片
(
"返回坐标矩形成功"
)
调用方法：
coordinate_onnx
=
onnx_model_main
(
"1.png"
)
#需要识别的图片路径
#返回内容
#{'message': 'success', 'time': '0.5251204967498779', 'point': '260|90|59|56', 'leftTop': (260, 90), 'rightBottom': (319, 146), 'Confidence': 0.31054314970970154}
#
#这个是根据返回信息在同级目录下生成一个加了框的图片
drow_rectangle
(
coordinate_onnx
,
"1.png"
)
#需要识别的图片路径
效果类似于这样子
附带个顶象的测试代码，基于selenium的：（这俩放一起就行,别问我为什么不是协议，问就是不会行了吧）
class
Test
(
)
:
def
__init__
(
self
)
:
option
=
Options
(
)
option
.
add_experimental_option
(
'excludeSwitches'
,
[
'enable-automation'
]
)
option
.
add_argument
(
'--disable-blink-features=AutomationControlled'
)
self
.
driver
=
webdriver
.
Chrome
(
options
=
option
)
def
__ease_out_expo
(
self
,
sep
)
:
if
sep
==
1
:
return
1
else
:
return
1
-
pow
(
2
,
-
10
*
sep
)
def
generate_tracks
(
self
,
distance
)
:
"""
根据滑动距离生成滑动轨迹
:param distance: 需要滑动的距离
:return: 滑动轨迹<type 'list'>: [[x,y,t], ...]
x: 已滑动的横向距离
y: 已滑动的纵向距离, 除起点外, 均为0
t: 滑动过程消耗的时间, 单位: 毫秒
"""
if
not
isinstance
(
distance
,
int
)
or
distance
<
0
:
raise
ValueError
(
f"distance类型必须是大于等于0的整数: distance:
{
distance
}
, type:
{
type
(
distance
)
}
"
)
# 初始化轨迹列表
slide_track
=
[
[
random
.
randint
(
-
50
,
-
10
)
,
random
.
randint
(
-
50
,
-
10
)
,
0
]
,
[
0
,
0
,
0
]
,
]
# 共记录count次滑块位置信息
count
=
30
+
int
(
distance
/
2
)
# 初始化滑动时间
t
=
random
.
randint
(
50
,
100
)
# 记录上一次滑动的距离
_x
=
0
_y
=
0
for
i
in
range
(
count
)
:
# 已滑动的横向距离
x
=
round
(
self
.
__ease_out_expo
(
i
/
count
)
*
distance
)
# 滑动过程消耗的时间
t
+=
random
.
randint
(
10
,
20
)
if
x
==
_x
:
continue
slide_track
.
append
(
[
x
,
_y
,
t
]
)
_x
=
x
slide_track
.
append
(
slide_track
[
-
1
]
)
return
slide_track
# 顶象测试
def
dx_test
(
self
)
:
url
=
'https://www.dingxiang-inc.com/business/captcha?utm_source=baidu1sem&utm_medium=%E4%BA%A7%E5%93%81%E8%AF%8D&utm_campaign=%E4%BA%A7%E5%93%81%E8%AF%8D-%E9%AA%8C%E8%AF%81%E7%A0%81&utm_term=%E9%AA%8C%E8%AF%81%E7%A0%81&e_matchtype=1&e_keywordid=317912325143&bd_vid=9281303599717237405'
# Chrome浏览器
self
.
driver
.
get
(
url
=
url
)
# //li[@class="item-2"]/h3
self
.
driver
.
maximize_window
(
)
# 滑动到最底部
js_button
=
'q=document.body.scrollTop=500'
# 执行js，滑动到最底部
self
.
driver
.
execute_script
(
js_button
)
self
.
driver
.
find_element
(
By
.
XPATH
,
'//li[@class="item-2"]/h3'
)
.
click
(
)
time
.
sleep
(
2
)
self
.
driver
.
find_element
(
By
.
XPATH
,
'//div[@id="dx_captcha_oneclick_bar-inform_3"]'
)
.
click
(
)
time
.
sleep
(
2
)
self
.
download_yzm
(
)
distence
=
onnx_model_main
(
'1.png'
)
drow_rectangle
(
distence
,
'1.png'
)
time
.
sleep
(
1
)
distence
=
int
(
distence
[
'leftTop'
]
[
0
]
)
-
30
source
=
self
.
driver
.
find_element
(
By
.
XPATH
,
'//div[@id="dx_captcha_basic_slider-img-animated-wrap_4"]/span[1]'
)
action
=
ActionChains
(
self
.
driver
,
duration
=
20
)
action
.
click_and_hold
(
source
)
a
=
0
time
.
sleep
(
1
)
for
x
in
self
.
generate_tracks
(
distence
)
:
(
x
)
action
.
move_by_offset
(
xoffset
=
x
[
0
]
-
a
,
yoffset
=
x
[
1
]
)
a
=
x
[
0
]
time
.
sleep
(
0.5
)
action
.
release
(
)
.
perform
(
)
input
(
)
# 网易测试
def
wy_test
(
self
)
:
url
=
'https://dun.163.com/trial/jigsaw'
# Chrome浏览器
self
.
driver
.
get
(
url
=
url
)
self
.
driver
.
maximize_window
(
)
js_button
=
'q=document.body.scrollTop=500'
# 执行js，滑动到最底部
self
.
driver
.
execute_script
(
js_button
)
self
.
driver
.
find_element
(
By
.
XPATH
,
'//li[@class="tcapt-tabs__tab"][1]'
)
.
click
(
)
time
.
sleep
(
2
)
self
.
driver
.
find_element
(
By
.
XPATH
,
'//button[@class="yidun_refresh"]'
)
.
click
(
)
html
=
self
.
driver
.
page_source
html
=
etree
.
HTML
(
html
)
url
=
html
.
xpath
(
'//img[@class="yidun_bg-img"]/@src'
)
[
0
]
response
=
requests
.
get
(
url
)
(
url
)
with
open
(
'1.png'
,
'wb'
)
as
f
:
f
.
write
(
response
.
content
)
time
.
sleep
(
2
)
input
(
11111111111111
)
self
.
download_yzm
(
)
distence
=
onnx_model_main
(
'1.png'
)
drow_rectangle
(
distence
,
'1.png'
)
time
.
sleep
(
1
)
distence
=
int
(
distence
[
'leftTop'
]
[
0
]
)
-
30
source
=
self
.
driver
.
find_element
(
By
.
XPATH
,
'//div[@id="dx_captcha_basic_slider-img-animated-wrap_4"]/span[1]'
)
action
=
ActionChains
(
self
.
driver
,
duration
=
20
)
action
.
click_and_hold
(
source
)
a
=
0
time
.
sleep
(
1
)
for
x
in
self
.
generate_tracks
(
distence
)
:
(
x
)
action
.
move_by_offset
(
xoffset
=
x
[
0
]
-
a
,
yoffset
=
x
[
1
]
)
a
=
x
[
0
]
time
.
sleep
(
0.5
)
action
.
release
(
)
.
perform
(
)
input
(
)
# 测试代码用到的下载图片的东西
def
download_yzm
(
self
)
:
js
=
"return document.querySelector('.dx_captcha_basic_bg > canvas').toDataURL('image/png')"
image_data
=
self
.
driver
.
execute_script
(
js
)
# 执行js代码得到图片数据
data
=
image_data
.
split
(
','
)
[
1
]
image_data
=
base64
.
b64decode
(
data
)
with
open
(
'1.png'
,
'wb'
)
as
f
:
f
.
write
(
image_data
)
if
__name__
==
'__main__'
:
a
=
Test
(
)
a
.
dx_test
(
)
# coordinate_onnx = onnx_model_main("1.png")
# drow_rectangle(coordinate_onnx, "1.png")
可能出现的错误：
1、xpath定位错误
2 、action = ActionChains(self.driver,duration=20) 这里报错
3、self.driver.find_element(By.XPATH,‘xxx"]’).click()这里报错
4、 self.driver = webdriver.Chrome(options=option)这里报错
5、环境问题
解决方法：
1、自己改改嘛，挺简单的。
2、删掉duration=20就好了 或 升级下selenium版本。
3、改成find_element_by_xpath(‘xxx’) 或 升级下selenium版本。
4、指定下chromedriver.exe位置 或 放在python目录下。
5、仅仅是用的话笔者提供的示例的话，应该没啥问题吧…应该，实在不行加我qq问吧（请我吃根棒棒糖就可以了！我这里棒棒糖仅需5毛！！！）
模型下载：
1、csdn下载：
通用滑块验证码识别模型
（我设置的0积分，如果收费了记得给我说下，或者直接加我，问我要也可以）
2、百度云下载： https://pan.baidu.com/s/1Nt2Z2Pu45RaQBs4dsS2kKg?pwd=82xg
提取码：82xg （老慢了）
3、github：暂无
百度云下载方式更新在了github
github:
https://github.com/Bump-mann/simple_ocr
测试：http://121.4.108.95:8000/index/
参考：
1、
深度学习训练滑动验证码(Yolov5)
2、
【Yolov5】1.认真总结6000字Yolov5保姆级教程（全新版本2022.06.28）
---------------------------------------笔者碎碎念---------------------------------------
本来吧，是想先出旋转验证码的，代码都写好了，但是大家都想看滑块的，就先出滑块的啦…
开始做的时候想着就图片处理嘛，蛮简单的，结果…一家一个类型，参数没法写死，不同的图片不同的算法效果不稳定，全是坑，被逼无奈改成写模型了
什么？你问我代码能通杀吗？能，当然能，前提是你数据量够
观众老爷：你这不是欺负老人吗？标题党系内！
笔者：阿巴阿巴，如果催的人多的话，我会持续更新的，做到真正的通杀
本代码的数据标注都是笔者个人一个框一个框标注的，呜呜呜，我也想对接打码平台啊，穷啊