1.幀差法原理
移動偵測即是根據視頻每幀或者幾幀之間像素的差異，對差異值設置閾值，篩選大于閾值的像素點，做掩模圖即可選出視頻中存在變化的楨。 幀差法 較為簡單的視頻中 物體移動偵測 ，幀差法分為： 單幀差 、 兩楨差 、和 三楨差 。隨著幀數的增加是防止檢測結果的重影。
2.算法思路
文章以截取視頻為例進行單幀差法移動偵測
3.python 實現代碼
def
threh
(
video
,
save_video
,
thres1
,
area_threh
)
:
cam
=
cv2
.
VideoCapture
(
video
)
#打開一個視頻
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
)
#跟著圖像變換背景
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
)
)
:
# 對于矩形區域，只顯示大于給定閾值的輪廓（去除微小的變化等噪點）
continue
(
x
,
y
,
w
,
h
)
=
cv2
.
boundingRect
(
c
)
# 該函數計算矩形的邊界框
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```
##調取函數
outt
=
threh
(
'new_video.mp4'
,
'test6.mp4'
,
25
,
3000
)
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