1.帧差法原理
移动侦测即是根据视频每帧或者几帧之间像素的差异，对差异值设置阈值，筛选大于阈值的像素点，做掩模图即可选出视频中存在变化的桢。 帧差法 较为简单的视频中 物体移动侦测 ，帧差法分为： 单帧差 、 两桢差 、和 三桢差 。随着帧数的增加是防止检测结果的重影。
2.算法思路
文章以截取视频为例进行单帧差法移动侦测
3.python 实现代码
def
threh
(
video
,
save_video
,
thres1
,
area_threh
)
:
cam
=
cv2
.
VideoCapture
(
video
)
#打开一个视频
input_fps
=
cam
.
get
(
cv2
.
CAP_PROP_FPS
)
ret_val
,
input_image
=
cam
.
read
(
)
index
=
[
]
images
=
[
]
images
.
append
(
input_image
)
video_length
=
int
(
cam
.
get
(
cv2
.
CAP_PROP_FRAME_COUNT
)
)
input_image
=
cv2
.
resize
(
input_image
,
(
512
,
512
)
)
ending_frame
=
video_length
fourcc
=
cv2
.
VideoWriter_fourcc
(
*
'XVID'
)
out
=
cv2
.
VideoWriter
(
save_video
,
fourcc
,
input_fps
,
(
512
,
512
)
)
gray_lwpCV
=
cv2
.
cvtColor
(
input_image
,
cv2
.
COLOR_BGR2GRAY
)
gray_lwpCV
=
cv2
.
GaussianBlur
(
gray_lwpCV
,
(
21
,
21
)
,
0
)
background
=
gray_lwpCV
# es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9, 4))
i
=
0
# default is 0
outt
=
[
]
while
(
cam
.
isOpened
(
)
)
and
ret_val
==
True
and
i
<
2999
:
## if i % 2==1:
ret_val
,
input_image
=
cam
.
read
(
)
input_image
=
cv2
.
resize
(
input_image
,
(
512
,
512
)
)
gray_lwpCV
=
cv2
.
cvtColor
(
input_image
,
cv2
.
COLOR_BGR2GRAY
)
gray_lwpCV
=
cv2
.
GaussianBlur
(
gray_lwpCV
,
(
21
,
21
)
,
0
)
diff
=
cv2
.
absdiff
(
background
,
gray_lwpCV
)
outt
.
append
(
diff
)
#跟着图像变换背景
tem_diff
=
diff
.
flatten
(
)
tem_ds
=
pd
.
Series
(
tem_diff
)
tem_per
=
1
-
len
(
tem_ds
[
tem_ds
==
0
]
)
/
len
(
tem_ds
)
if
(
tem_per
<
0.2
)
|
(
tem_per
>
0.75
)
:
background
=
gray_lwpCV
else
:
diff
=
cv2
.
threshold
(
diff
,
thres1
,
255
,
cv2
.
THRESH_BINARY
)
[
1
]
ret
,
thresh
=
cv2
.
threshold
(
diff
.
copy
(
)
,
150
,
255
,
0
)
contours
,
hierarchy
=
cv2
.
findContours
(
thresh
,
cv2
.
RETR_EXTERNAL
,
cv2
.
CHAIN_APPROX_SIMPLE
)
# contours, hierarchy = cv2.findContours(diff.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for
c
in
contours
:
if
(
cv2
.
contourArea
(
c
)
<
area_threh
)
|
(
cv2
.
contourArea
(
c
)
>
int
(
512
*
512
*
0.3
)
)
:
# 对于矩形区域，只显示大于给定阈值的轮廓（去除微小的变化等噪点）
continue
(
x
,
y
,
w
,
h
)
=
cv2
.
boundingRect
(
c
)
# 该函数计算矩形的边界框
cv2
.
rectangle
(
input_image
,
(
x
,
y
)
,
(
x
+
w
,
y
+
h
)
,
(
0
,
255
,
0
)
,
2
)
index
.
append
(
i
)
# cv2.imshow('contours', input_image)
# cv2.imshow('dis', diff)
out
.
write
(
input_image
)
images
.
append
(
input_image
)
i
=
i
+
1
out
.
release
(
)
cam
.
release
(
)
return
outt
,
index
,
images```
##调取函数
outt
=
threh
(
'new_video.mp4'
,
'test6.mp4'
,
25
,
3000
)
