Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
Greenplum
Opencv
提交
f610c881
O
Opencv
项目概览
Greenplum
/
Opencv
10 个月 前同步成功
通知
7
Star
0
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
0
列表
看板
标记
里程碑
合并请求
0
DevOps
流水线
流水线任务
计划
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
O
Opencv
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
0
Issue
0
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
DevOps
DevOps
流水线
流水线任务
计划
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
流水线任务
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
提交
f610c881
编写于
9月 17, 2014
作者:
J
Juan Carlos Niebles
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
extended python interface for KalmanFilter
上级
458bde5e
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
120 addition
and
0 deletion
+120
-0
modules/video/include/opencv2/video/tracking.hpp
modules/video/include/opencv2/video/tracking.hpp
+17
-0
samples/python2/kalman.py
samples/python2/kalman.py
+103
-0
未找到文件。
modules/video/include/opencv2/video/tracking.hpp
浏览文件 @
f610c881
...
...
@@ -129,6 +129,23 @@ public:
//! updates the predicted state from the measurement
CV_WRAP
const
Mat
&
correct
(
const
Mat
&
measurement
);
//! sets predicted state
CV_WRAP
void
setStatePre
(
const
Mat
&
state
)
{
statePre
=
state
;
}
//! sets corrected state
CV_WRAP
void
setStatePost
(
const
Mat
&
state
)
{
statePost
=
state
;
}
//! sets transition matrix
CV_WRAP
void
setTransitionMatrix
(
const
Mat
&
transition
)
{
transitionMatrix
=
transition
;
}
//! sets control matrix
CV_WRAP
void
setControlMatrix
(
const
Mat
&
control
)
{
controlMatrix
=
control
;
}
//! sets measurement matrix
CV_WRAP
void
setMeasurementMatrix
(
const
Mat
&
measurement
)
{
measurementMatrix
=
measurement
;
}
//! sets process noise covariance matrix
CV_WRAP
void
setProcessNoiseCov
(
const
Mat
&
noise
)
{
processNoiseCov
=
noise
;
}
//! sets measurement noise covariance matrix
CV_WRAP
void
setMeasurementNoiseCov
(
const
Mat
&
noise
)
{
measurementNoiseCov
=
noise
;
}
//! sets posteriori error covariance
CV_WRAP
void
setErrorCovPost
(
const
Mat
&
error
)
{
errorCovPost
=
error
;
}
Mat
statePre
;
//!< predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
Mat
statePost
;
//!< corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
Mat
transitionMatrix
;
//!< state transition matrix (A)
...
...
samples/python2/kalman.py
0 → 100755
浏览文件 @
f610c881
#!/usr/bin/python
"""
Tracking of rotating point.
Rotation speed is constant.
Both state and measurements vectors are 1D (a point angle),
Measurement is the real point angle + gaussian noise.
The real and the estimated points are connected with yellow line segment,
the real and the measured points are connected with red line segment.
(if Kalman filter works correctly,
the yellow segment should be shorter than the red one).
Pressing any key (except ESC) will reset the tracking with a different speed.
Pressing ESC will stop the program.
"""
import
urllib2
import
cv2
from
math
import
cos
,
sin
,
sqrt
import
sys
import
numpy
as
np
if
__name__
==
"__main__"
:
img_height
=
500
img_width
=
500
img
=
np
.
array
((
img_height
,
img_width
,
3
),
np
.
uint8
)
kalman
=
cv2
.
KalmanFilter
(
2
,
1
,
0
)
state
=
np
.
zeros
((
2
,
1
))
# (phi, delta_phi)
process_noise
=
np
.
zeros
((
2
,
1
))
measurement
=
np
.
zeros
((
1
,
1
))
code
=
-
1L
cv2
.
namedWindow
(
"Kalman"
)
while
True
:
state
=
0.1
*
np
.
random
.
randn
(
2
,
1
)
transition_matrix
=
np
.
array
([[
1.
,
1.
],
[
0.
,
1.
]])
kalman
.
setTransitionMatrix
(
transition_matrix
)
measurement_matrix
=
1.
*
np
.
ones
((
1
,
2
))
kalman
.
setMeasurementMatrix
(
measurement_matrix
)
process_noise_cov
=
1e-5
kalman
.
setProcessNoiseCov
(
process_noise_cov
*
np
.
eye
(
2
))
measurement_noise_cov
=
1e-1
kalman
.
setMeasurementNoiseCov
(
measurement_noise_cov
*
np
.
ones
((
1
,
1
)))
kalman
.
setErrorCovPost
(
1.
*
np
.
ones
((
2
,
2
)))
kalman
.
setStatePost
(
0.1
*
np
.
random
.
randn
(
2
,
1
))
while
True
:
def
calc_point
(
angle
):
return
(
np
.
around
(
img_width
/
2
+
img_width
/
3
*
cos
(
angle
),
0
).
astype
(
int
),
np
.
around
(
img_height
/
2
-
img_width
/
3
*
sin
(
angle
),
1
).
astype
(
int
))
state_angle
=
state
[
0
,
0
]
state_pt
=
calc_point
(
state_angle
)
prediction
=
kalman
.
predict
()
predict_angle
=
prediction
[
0
,
0
]
predict_pt
=
calc_point
(
predict_angle
)
measurement
=
measurement_noise_cov
*
np
.
random
.
randn
(
1
,
1
)
# generate measurement
measurement
=
np
.
dot
(
measurement_matrix
,
state
)
+
measurement
measurement_angle
=
measurement
[
0
,
0
]
measurement_pt
=
calc_point
(
measurement_angle
)
# plot points
def
draw_cross
(
center
,
color
,
d
):
cv2
.
line
(
img
,
(
center
[
0
]
-
d
,
center
[
1
]
-
d
),
(
center
[
0
]
+
d
,
center
[
1
]
+
d
),
color
,
1
,
cv2
.
LINE_AA
,
0
)
cv2
.
line
(
img
,
(
center
[
0
]
+
d
,
center
[
1
]
-
d
),
(
center
[
0
]
-
d
,
center
[
1
]
+
d
),
color
,
1
,
cv2
.
LINE_AA
,
0
)
img
=
np
.
zeros
((
img_height
,
img_width
,
3
),
np
.
uint8
)
draw_cross
(
np
.
int32
(
state_pt
),
(
255
,
255
,
255
),
3
)
draw_cross
(
np
.
int32
(
measurement_pt
),
(
0
,
0
,
255
),
3
)
draw_cross
(
np
.
int32
(
predict_pt
),
(
0
,
255
,
0
),
3
)
cv2
.
line
(
img
,
state_pt
,
measurement_pt
,
(
0
,
0
,
255
),
3
,
cv2
.
LINE_AA
,
0
)
cv2
.
line
(
img
,
state_pt
,
predict_pt
,
(
0
,
255
,
255
),
3
,
cv2
.
LINE_AA
,
0
)
kalman
.
correct
(
measurement
)
process_noise
=
process_noise_cov
*
np
.
random
.
randn
(
2
,
1
)
state
=
np
.
dot
(
transition_matrix
,
state
)
+
process_noise
cv2
.
imshow
(
"Kalman"
,
img
)
code
=
cv2
.
waitKey
(
100
)
%
0x100
if
code
!=
-
1
:
break
if
code
in
[
27
,
ord
(
'q'
),
ord
(
'Q'
)]:
break
cv2
.
destroyWindow
(
"Kalman"
)
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录