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体验新版 GitCode,发现更多精彩内容 >>
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da38a95d
编写于
8月 09, 2012
作者:
V
Vladislav Vinogradov
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fixed number of update operation
上级
9ec96597
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
26 addition
and
23 deletion
+26
-23
modules/gpu/src/bgfg_gmg.cpp
modules/gpu/src/bgfg_gmg.cpp
+13
-5
modules/gpu/src/cuda/bgfg_gmg.cu
modules/gpu/src/cuda/bgfg_gmg.cu
+13
-18
未找到文件。
modules/gpu/src/bgfg_gmg.cpp
浏览文件 @
da38a95d
...
...
@@ -96,7 +96,8 @@ void cv::gpu::GMG_GPU::initialize(cv::Size frameSize, float min, float max)
nfeatures_
.
setTo
(
cv
::
Scalar
::
all
(
0
));
boxFilter_
=
cv
::
gpu
::
createBoxFilter_GPU
(
CV_8UC1
,
CV_8UC1
,
cv
::
Size
(
smoothingRadius
,
smoothingRadius
));
if
(
smoothingRadius
>
0
)
boxFilter_
=
cv
::
gpu
::
createBoxFilter_GPU
(
CV_8UC1
,
CV_8UC1
,
cv
::
Size
(
smoothingRadius
,
smoothingRadius
));
loadConstants
(
frameSize_
.
width
,
frameSize_
.
height
,
minVal_
,
maxVal_
,
quantizationLevels
,
backgroundPrior
,
decisionThreshold
,
maxFeatures
,
numInitializationFrames
);
}
...
...
@@ -130,14 +131,21 @@ void cv::gpu::GMG_GPU::operator ()(const cv::gpu::GpuMat& frame, cv::gpu::GpuMat
initialize
(
frame
.
size
(),
0.0
f
,
frame
.
depth
()
==
CV_8U
?
255.0
f
:
frame
.
depth
()
==
CV_16U
?
std
::
numeric_limits
<
ushort
>::
max
()
:
1.0
f
);
fgmask
.
create
(
frameSize_
,
CV_8UC1
);
if
(
stream
)
stream
.
enqueueMemSet
(
fgmask
,
cv
::
Scalar
::
all
(
0
));
else
fgmask
.
setTo
(
cv
::
Scalar
::
all
(
0
));
funcs
[
frame
.
depth
()][
frame
.
channels
()
-
1
](
frame
,
fgmask
,
colors_
,
weights_
,
nfeatures_
,
frameNum_
,
learningRate
,
cv
::
gpu
::
StreamAccessor
::
getStream
(
stream
));
// medianBlur
boxFilter_
->
apply
(
fgmask
,
buf_
,
cv
::
Rect
(
0
,
0
,
-
1
,
-
1
),
stream
);
int
minCount
=
(
smoothingRadius
*
smoothingRadius
+
1
)
/
2
;
double
thresh
=
255.0
*
minCount
/
(
smoothingRadius
*
smoothingRadius
);
cv
::
gpu
::
threshold
(
buf_
,
fgmask
,
thresh
,
255.0
,
cv
::
THRESH_BINARY
,
stream
);
if
(
smoothingRadius
>
0
)
{
boxFilter_
->
apply
(
fgmask
,
buf_
,
cv
::
Rect
(
0
,
0
,
-
1
,
-
1
),
stream
);
int
minCount
=
(
smoothingRadius
*
smoothingRadius
+
1
)
/
2
;
double
thresh
=
255.0
*
minCount
/
(
smoothingRadius
*
smoothingRadius
);
cv
::
gpu
::
threshold
(
buf_
,
fgmask
,
thresh
,
255.0
,
cv
::
THRESH_BINARY
,
stream
);
}
// keep track of how many frames we have processed
++
frameNum_
;
...
...
modules/gpu/src/cuda/bgfg_gmg.cu
浏览文件 @
da38a95d
...
...
@@ -181,32 +181,18 @@ namespace cv { namespace gpu { namespace device {
int
nfeatures
=
nfeatures_
(
y
,
x
);
bool
isForeground
=
false
;
if
(
frameNum
>
c_numInitializationFrames
)
if
(
frameNum
>=
c_numInitializationFrames
)
{
// typical operation
const
float
weight
=
findFeature
(
newFeatureColor
,
colors_
,
weights_
,
x
,
y
,
nfeatures
);
// see Godbehere, Matsukawa, Goldberg (2012) for reasoning behind this implementation of Bayes rule
const
float
posterior
=
(
weight
*
c_backgroundPrior
)
/
(
weight
*
c_backgroundPrior
+
(
1.0
f
-
weight
)
*
(
1.0
f
-
c_backgroundPrior
));
isForeground
=
((
1.0
f
-
posterior
)
>
c_decisionThreshold
);
}
fgmask
(
y
,
x
)
=
(
uchar
)(
-
isForeground
);
const
bool
isForeground
=
((
1.0
f
-
posterior
)
>
c_decisionThreshold
);
fgmask
(
y
,
x
)
=
(
uchar
)(
-
isForeground
);
if
(
frameNum
<=
c_numInitializationFrames
+
1
)
{
// training-mode update
insertFeature
(
newFeatureColor
,
1.0
f
,
colors_
,
weights_
,
x
,
y
,
nfeatures
);
if
(
frameNum
==
c_numInitializationFrames
+
1
)
normalizeHistogram
(
weights_
,
x
,
y
,
nfeatures
);
}
else
{
// update histogram.
for
(
int
i
=
0
,
fy
=
y
;
i
<
nfeatures
;
++
i
,
fy
+=
c_height
)
...
...
@@ -220,6 +206,15 @@ namespace cv { namespace gpu { namespace device {
nfeatures_
(
y
,
x
)
=
nfeatures
;
}
}
else
{
// training-mode update
insertFeature
(
newFeatureColor
,
1.0
f
,
colors_
,
weights_
,
x
,
y
,
nfeatures
);
if
(
frameNum
==
c_numInitializationFrames
-
1
)
normalizeHistogram
(
weights_
,
x
,
y
,
nfeatures
);
}
}
template
<
typename
SrcT
>
...
...
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