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3ab2728d
编写于
8月 03, 2011
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
gpu device layer code refactoring
上级
fa0daa48
变更
28
展开全部
隐藏空白更改
内联
并排
Showing
28 changed file
with
4506 addition
and
3766 deletion
+4506
-3766
modules/gpu/CMakeLists.txt
modules/gpu/CMakeLists.txt
+3
-1
modules/gpu/src/color.cpp
modules/gpu/src/color.cpp
+1347
-381
modules/gpu/src/cuda/brute_force_matcher.cu
modules/gpu/src/cuda/brute_force_matcher.cu
+6
-6
modules/gpu/src/cuda/color.cu
modules/gpu/src/cuda/color.cu
+157
-1294
modules/gpu/src/cuda/element_operations.cu
modules/gpu/src/cuda/element_operations.cu
+17
-225
modules/gpu/src/cuda/filters.cu
modules/gpu/src/cuda/filters.cu
+6
-6
modules/gpu/src/cuda/hist.cu
modules/gpu/src/cuda/hist.cu
+5
-7
modules/gpu/src/cuda/match_template.cu
modules/gpu/src/cuda/match_template.cu
+6
-6
modules/gpu/src/cuda/mathfunc.cu
modules/gpu/src/cuda/mathfunc.cu
+2
-2
modules/gpu/src/cuda/matrix_reductions.cu
modules/gpu/src/cuda/matrix_reductions.cu
+58
-58
modules/gpu/src/cuda/stereobp.cu
modules/gpu/src/cuda/stereobp.cu
+3
-3
modules/gpu/src/cuda/stereocsbp.cu
modules/gpu/src/cuda/stereocsbp.cu
+10
-10
modules/gpu/src/cuda/surf.cu
modules/gpu/src/cuda/surf.cu
+10
-40
modules/gpu/src/opencv2/gpu/device/border_interpolate.hpp
modules/gpu/src/opencv2/gpu/device/border_interpolate.hpp
+23
-55
modules/gpu/src/opencv2/gpu/device/color.hpp
modules/gpu/src/opencv2/gpu/device/color.hpp
+221
-0
modules/gpu/src/opencv2/gpu/device/datamov_utils.hpp
modules/gpu/src/opencv2/gpu/device/datamov_utils.hpp
+30
-38
modules/gpu/src/opencv2/gpu/device/detail/color.hpp
modules/gpu/src/opencv2/gpu/device/detail/color.hpp
+1037
-0
modules/gpu/src/opencv2/gpu/device/detail/transform.hpp
modules/gpu/src/opencv2/gpu/device/detail/transform.hpp
+429
-0
modules/gpu/src/opencv2/gpu/device/functional.hpp
modules/gpu/src/opencv2/gpu/device/functional.hpp
+338
-0
modules/gpu/src/opencv2/gpu/device/limits.hpp
modules/gpu/src/opencv2/gpu/device/limits.hpp
+13
-13
modules/gpu/src/opencv2/gpu/device/saturate_cast.hpp
modules/gpu/src/opencv2/gpu/device/saturate_cast.hpp
+107
-113
modules/gpu/src/opencv2/gpu/device/transform.hpp
modules/gpu/src/opencv2/gpu/device/transform.hpp
+17
-404
modules/gpu/src/opencv2/gpu/device/utility.hpp
modules/gpu/src/opencv2/gpu/device/utility.hpp
+206
-0
modules/gpu/src/opencv2/gpu/device/vec_math.hpp
modules/gpu/src/opencv2/gpu/device/vec_math.hpp
+287
-0
modules/gpu/src/opencv2/gpu/device/vec_traits.hpp
modules/gpu/src/opencv2/gpu/device/vec_traits.hpp
+142
-0
modules/gpu/src/opencv2/gpu/device/vecmath.hpp
modules/gpu/src/opencv2/gpu/device/vecmath.hpp
+0
-1097
modules/gpu/src/surf.cpp
modules/gpu/src/surf.cpp
+1
-5
samples/gpu/performance/tests.cpp
samples/gpu/performance/tests.cpp
+25
-2
未找到文件。
modules/gpu/CMakeLists.txt
浏览文件 @
3ab2728d
...
...
@@ -23,7 +23,9 @@ source_group("Include" FILES ${lib_hdrs})
#file(GLOB lib_device_hdrs "include/opencv2/${name}/device/*.h*")
file
(
GLOB lib_device_hdrs
"src/opencv2/gpu/device/*.h*"
)
file
(
GLOB lib_device_hdrs_detail
"src/opencv2/gpu/device/detail/*.h*"
)
source_group
(
"Device"
FILES
${
lib_device_hdrs
}
)
source_group
(
"Device
\\
Detail"
FILES
${
lib_device_hdrs_detail
}
)
if
(
HAVE_CUDA
)
file
(
GLOB_RECURSE ncv_srcs
"src/nvidia/*.cpp"
)
...
...
@@ -83,7 +85,7 @@ foreach(d ${DEPS})
endif
()
endforeach
()
add_library
(
${
the_target
}
${
lib_srcs
}
${
lib_hdrs
}
${
lib_int_hdrs
}
${
lib_cuda
}
${
lib_cuda_hdrs
}
${
lib_device_hdrs
}
${
ncv_srcs
}
${
ncv_hdrs
}
${
ncv_cuda
}
${
cuda_objs
}
)
add_library
(
${
the_target
}
${
lib_srcs
}
${
lib_hdrs
}
${
lib_int_hdrs
}
${
lib_cuda
}
${
lib_cuda_hdrs
}
${
lib_device_hdrs
}
${
lib_device_hdrs_detail
}
${
ncv_srcs
}
${
ncv_hdrs
}
${
ncv_cuda
}
${
cuda_objs
}
)
# For dynamic link numbering convenions
set_target_properties
(
${
the_target
}
PROPERTIES
...
...
modules/gpu/src/color.cpp
浏览文件 @
3ab2728d
此差异已折叠。
点击以展开。
modules/gpu/src/cuda/brute_force_matcher.cu
浏览文件 @
3ab2728d
...
...
@@ -41,7 +41,7 @@
//M*/
#include "internal_shared.hpp"
#include "opencv2/gpu/device/limits
_gpu
.hpp"
#include "opencv2/gpu/device/limits.hpp"
#include "opencv2/gpu/device/datamov_utils.hpp"
using
namespace
cv
::
gpu
;
...
...
@@ -565,7 +565,7 @@ namespace cv { namespace gpu { namespace bfmatcher
int
myBestTrainIdx
=
-
1
;
int
myBestImgIdx
=
-
1
;
typename
Dist
::
ResultType
myMin
=
numeric_limits
_gpu
<
typename
Dist
::
ResultType
>::
max
();
typename
Dist
::
ResultType
myMin
=
numeric_limits
<
typename
Dist
::
ResultType
>::
max
();
{
typename
Dist
::
ResultType
*
sdiff_row
=
smem
+
BLOCK_DIM_X
*
threadIdx
.
y
;
...
...
@@ -821,7 +821,7 @@ namespace cv { namespace gpu { namespace bfmatcher
{
const
T
*
trainDescs
=
trainDescs_
.
ptr
(
trainIdx
);
typename
Dist
::
ResultType
myDist
=
numeric_limits
_gpu
<
typename
Dist
::
ResultType
>::
max
();
typename
Dist
::
ResultType
myDist
=
numeric_limits
<
typename
Dist
::
ResultType
>::
max
();
if
(
mask
(
queryIdx
,
trainIdx
))
{
...
...
@@ -932,7 +932,7 @@ namespace cv { namespace gpu { namespace bfmatcher
{
const
int
tid
=
threadIdx
.
x
;
T
myMin
=
numeric_limits
_gpu
<
T
>::
max
();
T
myMin
=
numeric_limits
<
T
>::
max
();
int
myMinIdx
=
-
1
;
for
(
int
i
=
tid
;
i
<
n
;
i
+=
BLOCK_SIZE
)
...
...
@@ -1007,10 +1007,10 @@ namespace cv { namespace gpu { namespace bfmatcher
if
(
threadIdx
.
x
==
0
)
{
float
dist
=
sdist
[
0
];
if
(
dist
<
numeric_limits
_gpu
<
float
>::
max
())
if
(
dist
<
numeric_limits
<
float
>::
max
())
{
int
bestIdx
=
strainIdx
[
0
];
allDist
[
bestIdx
]
=
numeric_limits
_gpu
<
float
>::
max
();
allDist
[
bestIdx
]
=
numeric_limits
<
float
>::
max
();
trainIdx
[
i
]
=
bestIdx
;
distance
[
i
]
=
dist
;
}
...
...
modules/gpu/src/cuda/color.cu
浏览文件 @
3ab2728d
此差异已折叠。
点击以展开。
modules/gpu/src/cuda/element_operations.cu
浏览文件 @
3ab2728d
...
...
@@ -40,9 +40,10 @@
//
//M*/
#include "opencv2/gpu/device/vecmath.hpp"
#include "opencv2/gpu/device/functional.hpp"
#include "opencv2/gpu/device/vec_math.hpp"
#include "opencv2/gpu/device/transform.hpp"
#include "opencv2/gpu/device/limits
_gpu
.hpp"
#include "opencv2/gpu/device/limits.hpp"
#include "opencv2/gpu/device/saturate_cast.hpp"
#include "internal_shared.hpp"
...
...
@@ -354,114 +355,11 @@ namespace cv { namespace gpu { namespace mathfunc
//////////////////////////////////////////////////////////////////////////
// min/max
struct
MinOp
{
template
<
typename
T
>
__device__
__forceinline__
T
operator
()(
T
a
,
T
b
)
{
return
min
(
a
,
b
);
}
__device__
__forceinline__
float
operator
()(
float
a
,
float
b
)
{
return
fmin
(
a
,
b
);
}
__device__
__forceinline__
double
operator
()(
double
a
,
double
b
)
{
return
fmin
(
a
,
b
);
}
};
struct
MaxOp
{
template
<
typename
T
>
__device__
__forceinline__
T
operator
()(
T
a
,
T
b
)
{
return
max
(
a
,
b
);
}
__device__
__forceinline__
float
operator
()(
float
a
,
float
b
)
{
return
fmax
(
a
,
b
);
}
__device__
__forceinline__
double
operator
()(
double
a
,
double
b
)
{
return
fmax
(
a
,
b
);
}
};
template
<
typename
T
>
struct
ScalarMinOp
{
T
s
;
explicit
ScalarMinOp
(
T
s_
)
:
s
(
s_
)
{}
__device__
__forceinline__
T
operator
()(
T
a
)
{
return
min
(
a
,
s
);
}
};
template
<
>
struct
ScalarMinOp
<
float
>
{
float
s
;
explicit
ScalarMinOp
(
float
s_
)
:
s
(
s_
)
{}
__device__
__forceinline__
float
operator
()(
float
a
)
{
return
fmin
(
a
,
s
);
}
};
template
<
>
struct
ScalarMinOp
<
double
>
{
double
s
;
explicit
ScalarMinOp
(
double
s_
)
:
s
(
s_
)
{}
__device__
__forceinline__
double
operator
()(
double
a
)
{
return
fmin
(
a
,
s
);
}
};
template
<
typename
T
>
struct
ScalarMaxOp
{
T
s
;
explicit
ScalarMaxOp
(
T
s_
)
:
s
(
s_
)
{}
__device__
__forceinline__
T
operator
()(
T
a
)
{
return
max
(
a
,
s
);
}
};
template
<
>
struct
ScalarMaxOp
<
float
>
{
float
s
;
explicit
ScalarMaxOp
(
float
s_
)
:
s
(
s_
)
{}
__device__
__forceinline__
float
operator
()(
float
a
)
{
return
fmax
(
a
,
s
);
}
};
template
<
>
struct
ScalarMaxOp
<
double
>
{
double
s
;
explicit
ScalarMaxOp
(
double
s_
)
:
s
(
s_
)
{}
__device__
__forceinline__
double
operator
()(
double
a
)
{
return
fmax
(
a
,
s
);
}
};
template
<
typename
T
>
void
min_gpu
(
const
DevMem2D_
<
T
>&
src1
,
const
DevMem2D_
<
T
>&
src2
,
const
DevMem2D_
<
T
>&
dst
,
cudaStream_t
stream
)
{
MinOp
op
;
transform
(
src1
,
src2
,
dst
,
op
,
stream
);
transform
(
src1
,
src2
,
dst
,
minimum
<
T
>
(),
stream
);
}
template
void
min_gpu
<
uchar
>(
const
DevMem2D
&
src1
,
const
DevMem2D
&
src2
,
const
DevMem2D
&
dst
,
cudaStream_t
stream
);
...
...
@@ -475,8 +373,7 @@ namespace cv { namespace gpu { namespace mathfunc
template
<
typename
T
>
void
max_gpu
(
const
DevMem2D_
<
T
>&
src1
,
const
DevMem2D_
<
T
>&
src2
,
const
DevMem2D_
<
T
>&
dst
,
cudaStream_t
stream
)
{
MaxOp
op
;
transform
(
src1
,
src2
,
dst
,
op
,
stream
);
transform
(
src1
,
src2
,
dst
,
maximum
<
T
>
(),
stream
);
}
template
void
max_gpu
<
uchar
>(
const
DevMem2D
&
src1
,
const
DevMem2D
&
src2
,
const
DevMem2D
&
dst
,
cudaStream_t
stream
);
...
...
@@ -490,8 +387,7 @@ namespace cv { namespace gpu { namespace mathfunc
template
<
typename
T
>
void
min_gpu
(
const
DevMem2D_
<
T
>&
src1
,
T
src2
,
const
DevMem2D_
<
T
>&
dst
,
cudaStream_t
stream
)
{
ScalarMinOp
<
T
>
op
(
src2
);
transform
(
src1
,
dst
,
op
,
stream
);
transform
(
src1
,
dst
,
device
::
bind2nd
(
minimum
<
T
>
(),
src2
),
stream
);
}
template
void
min_gpu
<
uchar
>(
const
DevMem2D
&
src1
,
uchar
src2
,
const
DevMem2D
&
dst
,
cudaStream_t
stream
);
...
...
@@ -501,12 +397,11 @@ namespace cv { namespace gpu { namespace mathfunc
template
void
min_gpu
<
int
>(
const
DevMem2D_
<
int
>&
src1
,
int
src2
,
const
DevMem2D_
<
int
>&
dst
,
cudaStream_t
stream
);
template
void
min_gpu
<
float
>(
const
DevMem2D_
<
float
>&
src1
,
float
src2
,
const
DevMem2D_
<
float
>&
dst
,
cudaStream_t
stream
);
template
void
min_gpu
<
double
>(
const
DevMem2D_
<
double
>&
src1
,
double
src2
,
const
DevMem2D_
<
double
>&
dst
,
cudaStream_t
stream
);
template
<
typename
T
>
void
max_gpu
(
const
DevMem2D_
<
T
>&
src1
,
T
src2
,
const
DevMem2D_
<
T
>&
dst
,
cudaStream_t
stream
)
{
ScalarMaxOp
<
T
>
op
(
src2
);
transform
(
src1
,
dst
,
op
,
stream
);
transform
(
src1
,
dst
,
device
::
bind2nd
(
maximum
<
T
>
(),
src2
),
stream
);
}
template
void
max_gpu
<
uchar
>(
const
DevMem2D
&
src1
,
uchar
src2
,
const
DevMem2D
&
dst
,
cudaStream_t
stream
);
...
...
@@ -519,100 +414,7 @@ namespace cv { namespace gpu { namespace mathfunc
//////////////////////////////////////////////////////////////////////////
// threshold
template
<
typename
T
>
struct
ThreshBinary
{
ThreshBinary
(
T
thresh_
,
T
maxVal_
)
:
thresh
(
thresh_
),
maxVal
(
maxVal_
)
{}
__device__
__forceinline__
T
operator
()(
const
T
&
src
)
const
{
return
src
>
thresh
?
maxVal
:
0
;
}
private:
T
thresh
;
T
maxVal
;
};
template
<
typename
T
>
struct
ThreshBinaryInv
{
ThreshBinaryInv
(
T
thresh_
,
T
maxVal_
)
:
thresh
(
thresh_
),
maxVal
(
maxVal_
)
{}
__device__
__forceinline__
T
operator
()(
const
T
&
src
)
const
{
return
src
>
thresh
?
0
:
maxVal
;
}
private:
T
thresh
;
T
maxVal
;
};
template
<
typename
T
>
struct
ThreshTrunc
{
ThreshTrunc
(
T
thresh_
,
T
)
:
thresh
(
thresh_
)
{}
__device__
__forceinline__
T
operator
()(
const
T
&
src
)
const
{
return
min
(
src
,
thresh
);
}
private:
T
thresh
;
};
template
<
>
struct
ThreshTrunc
<
float
>
{
ThreshTrunc
(
float
thresh_
,
float
)
:
thresh
(
thresh_
)
{}
__device__
__forceinline__
float
operator
()(
const
float
&
src
)
const
{
return
fmin
(
src
,
thresh
);
}
private:
float
thresh
;
};
template
<
>
struct
ThreshTrunc
<
double
>
{
ThreshTrunc
(
double
thresh_
,
double
)
:
thresh
(
thresh_
)
{}
__device__
__forceinline__
double
operator
()(
const
double
&
src
)
const
{
return
fmin
(
src
,
thresh
);
}
private:
double
thresh
;
};
template
<
typename
T
>
struct
ThreshToZero
{
public:
ThreshToZero
(
T
thresh_
,
T
)
:
thresh
(
thresh_
)
{}
__device__
__forceinline__
T
operator
()(
const
T
&
src
)
const
{
return
src
>
thresh
?
src
:
0
;
}
private:
T
thresh
;
};
template
<
typename
T
>
struct
ThreshToZeroInv
{
public:
ThreshToZeroInv
(
T
thresh_
,
T
)
:
thresh
(
thresh_
)
{}
__device__
__forceinline__
T
operator
()(
const
T
&
src
)
const
{
return
src
>
thresh
?
0
:
src
;
}
private:
T
thresh
;
};
// threshold
template
<
template
<
typename
>
class
Op
,
typename
T
>
void
threshold_caller
(
const
DevMem2D_
<
T
>&
src
,
const
DevMem2D_
<
T
>&
dst
,
T
thresh
,
T
maxVal
,
...
...
@@ -631,11 +433,11 @@ namespace cv { namespace gpu { namespace mathfunc
static
const
caller_t
callers
[]
=
{
threshold_caller
<
ThreshBinary
,
T
>
,
threshold_caller
<
ThreshBinaryInv
,
T
>
,
threshold_caller
<
ThreshTr
unc
,
T
>
,
threshold_caller
<
ThreshToZero
,
T
>
,
threshold_caller
<
ThreshToZeroInv
,
T
>
threshold_caller
<
thresh_binary_func
,
T
>
,
threshold_caller
<
thresh_binary_inv_func
,
T
>
,
threshold_caller
<
thresh_trunc_f
unc
,
T
>
,
threshold_caller
<
thresh_to_zero_func
,
T
>
,
threshold_caller
<
thresh_to_zero_inv_func
,
T
>
};
callers
[
type
]((
DevMem2D_
<
T
>
)
src
,
(
DevMem2D_
<
T
>
)
dst
,
thresh
,
maxVal
,
stream
);
...
...
@@ -653,20 +455,10 @@ namespace cv { namespace gpu { namespace mathfunc
//////////////////////////////////////////////////////////////////////////
// subtract
template
<
typename
T
>
class
SubtractOp
{
public:
__device__
__forceinline__
T
operator
()(
const
T
&
l
,
const
T
&
r
)
const
{
return
l
-
r
;
}
};
template
<
typename
T
>
void
subtractCaller
(
const
DevMem2D
src1
,
const
DevMem2D
src2
,
DevMem2D
dst
,
cudaStream_t
stream
)
{
transform
((
DevMem2D_
<
T
>
)
src1
,
(
DevMem2D_
<
T
>
)
src2
,
(
DevMem2D_
<
T
>
)
dst
,
SubtractOp
<
T
>
(),
stream
);
transform
((
DevMem2D_
<
T
>
)
src1
,
(
DevMem2D_
<
T
>
)
src2
,
(
DevMem2D_
<
T
>
)
dst
,
minus
<
T
>
(),
stream
);
}
template
void
subtractCaller
<
short
>(
const
DevMem2D
src1
,
const
DevMem2D
src2
,
DevMem2D
dst
,
cudaStream_t
stream
);
...
...
@@ -675,7 +467,7 @@ namespace cv { namespace gpu { namespace mathfunc
//////////////////////////////////////////////////////////////////////////
// pow
template
<
typename
T
,
bool
Signed
=
device
::
numeric_limits
_gpu
<
T
>
::
is_signed
>
template
<
typename
T
,
bool
Signed
=
device
::
numeric_limits
<
T
>
::
is_signed
>
struct
PowOp
{
float
power
;
...
...
@@ -695,7 +487,7 @@ namespace cv { namespace gpu { namespace mathfunc
__device__
__forceinline__
float
operator
()(
const
T
&
e
)
{
T
res
=
saturate_cast
<
T
>
(
__powf
((
float
)
e
,
power
));
T
res
=
saturate_cast
<
T
>
(
__powf
((
float
)
e
,
power
));
if
(
(
e
<
0
)
&&
(
1
&
(
int
)
power
)
)
res
*=
-
1
;
...
...
modules/gpu/src/cuda/filters.cu
浏览文件 @
3ab2728d
...
...
@@ -42,8 +42,8 @@
#include "opencv2/gpu/devmem2d.hpp"
#include "opencv2/gpu/device/saturate_cast.hpp"
#include "opencv2/gpu/device/vecmath.hpp"
#include "opencv2/gpu/device/limits
_gpu
.hpp"
#include "opencv2/gpu/device/vec
_
math.hpp"
#include "opencv2/gpu/device/limits.hpp"
#include "opencv2/gpu/device/border_interpolate.hpp"
#include "safe_call.hpp"
...
...
@@ -76,7 +76,7 @@ namespace filter_krnls
{
template
<
typename
T
,
size_t
size
>
struct
SmemType_
{
typedef
typename
TypeVec
<
float
,
VecTraits
<
T
>::
cn
>::
vec_t
smem_t
;
typedef
typename
TypeVec
<
float
,
VecTraits
<
T
>::
cn
>::
vec_t
ype
smem_t
;
};
template
<
typename
T
>
struct
SmemType_
<
T
,
4
>
{
...
...
@@ -111,7 +111,7 @@ namespace filter_krnls
if
(
x
<
src
.
cols
)
{
typedef
typename
TypeVec
<
float
,
VecTraits
<
T
>::
cn
>::
vec_t
sum_t
;
typedef
typename
TypeVec
<
float
,
VecTraits
<
T
>::
cn
>::
vec_t
ype
sum_t
;
sum_t
sum
=
VecTraits
<
sum_t
>::
all
(
0
);
sDataRow
+=
threadIdx
.
x
+
BLOCK_DIM_X
-
anchor
;
...
...
@@ -253,7 +253,7 @@ namespace filter_krnls
if
(
y
<
src
.
rows
)
{
typedef
typename
TypeVec
<
float
,
VecTraits
<
T
>::
cn
>::
vec_t
sum_t
;
typedef
typename
TypeVec
<
float
,
VecTraits
<
T
>::
cn
>::
vec_t
ype
sum_t
;
sum_t
sum
=
VecTraits
<
sum_t
>::
all
(
0
);
sDataColumn
+=
(
threadIdx
.
y
+
BLOCK_DIM_Y
-
anchor
)
*
BLOCK_DIM_X
;
...
...
@@ -475,7 +475,7 @@ namespace bf_krnls
}
}
float
minimum
=
numeric_limits
_gpu
<
float
>::
max
();
float
minimum
=
numeric_limits
<
float
>::
max
();
int
id
=
0
;
if
(
cost
[
0
]
<
minimum
)
...
...
modules/gpu/src/cuda/hist.cu
浏览文件 @
3ab2728d
...
...
@@ -42,6 +42,7 @@
//M*/
#include "internal_shared.hpp"
#include "opencv2/gpu/device/utility.hpp"
#include "opencv2/gpu/device/saturate_cast.hpp"
using
namespace
cv
::
gpu
;
...
...
@@ -50,14 +51,11 @@ using namespace cv::gpu::device;
#define UINT_BITS 32U
#define LOG2_WARP_SIZE 5U
#define WARP_SIZE (1U << LOG2_WARP_SIZE)
//Warps == subhistograms per threadblock
#define WARP_COUNT 6
//Threadblock size
#define HISTOGRAM256_THREADBLOCK_SIZE (WARP_COUNT * WARP_SIZE)
#define HISTOGRAM256_THREADBLOCK_SIZE (WARP_COUNT *
OPENCV_GPU_
WARP_SIZE)
#define HISTOGRAM256_BIN_COUNT 256
//Shared memory per threadblock
...
...
@@ -73,7 +71,7 @@ namespace cv { namespace gpu { namespace histograms
{
#if (!USE_SMEM_ATOMICS)
#define TAG_MASK ( (1U << (UINT_BITS -
LOG2
_WARP_SIZE)) - 1U )
#define TAG_MASK ( (1U << (UINT_BITS -
OPENCV_GPU_LOG
_WARP_SIZE)) - 1U )
__forceinline__
__device__
void
addByte
(
volatile
uint
*
s_WarpHist
,
uint
data
,
uint
threadTag
)
{
...
...
@@ -111,7 +109,7 @@ namespace cv { namespace gpu { namespace histograms
{
//Per-warp subhistogram storage
__shared__
uint
s_Hist
[
HISTOGRAM256_THREADBLOCK_MEMORY
];
uint
*
s_WarpHist
=
s_Hist
+
(
threadIdx
.
x
>>
LOG2
_WARP_SIZE
)
*
HISTOGRAM256_BIN_COUNT
;
uint
*
s_WarpHist
=
s_Hist
+
(
threadIdx
.
x
>>
OPENCV_GPU_LOG
_WARP_SIZE
)
*
HISTOGRAM256_BIN_COUNT
;
//Clear shared memory storage for current threadblock before processing
#pragma unroll
...
...
@@ -119,7 +117,7 @@ namespace cv { namespace gpu { namespace histograms
s_Hist
[
threadIdx
.
x
+
i
*
HISTOGRAM256_THREADBLOCK_SIZE
]
=
0
;
//Cycle through the entire data set, update subhistograms for each warp
const
uint
tag
=
threadIdx
.
x
<<
(
UINT_BITS
-
LOG2
_WARP_SIZE
);
const
uint
tag
=
threadIdx
.
x
<<
(
UINT_BITS
-
OPENCV_GPU_LOG
_WARP_SIZE
);
__syncthreads
();
const
uint
colsui
=
d_Data
.
step
/
sizeof
(
uint
);
...
...
modules/gpu/src/cuda/match_template.cu
浏览文件 @
3ab2728d
...
...
@@ -41,7 +41,7 @@
//M*/
#include "internal_shared.hpp"
#include "opencv2/gpu/device/vecmath.hpp"
#include "opencv2/gpu/device/vec
_
math.hpp"
using
namespace
cv
::
gpu
;
using
namespace
cv
::
gpu
::
device
;
...
...
@@ -84,8 +84,8 @@ __global__ void matchTemplateNaiveKernel_CCORR(
int
w
,
int
h
,
const
PtrStep
image
,
const
PtrStep
templ
,
DevMem2Df
result
)
{
typedef
typename
TypeVec
<
T
,
cn
>::
vec_t
Type
;
typedef
typename
TypeVec
<
float
,
cn
>::
vec_t
Typef
;
typedef
typename
TypeVec
<
T
,
cn
>::
vec_t
ype
Type
;
typedef
typename
TypeVec
<
float
,
cn
>::
vec_t
ype
Typef
;
int
x
=
blockDim
.
x
*
blockIdx
.
x
+
threadIdx
.
x
;
int
y
=
blockDim
.
y
*
blockIdx
.
y
+
threadIdx
.
y
;
...
...
@@ -174,8 +174,8 @@ __global__ void matchTemplateNaiveKernel_SQDIFF(
int
w
,
int
h
,
const
PtrStep
image
,
const
PtrStep
templ
,
DevMem2Df
result
)
{
typedef
typename
TypeVec
<
T
,
cn
>::
vec_t
Type
;
typedef
typename
TypeVec
<
float
,
cn
>::
vec_t
Typef
;
typedef
typename
TypeVec
<
T
,
cn
>::
vec_t
ype
Type
;
typedef
typename
TypeVec
<
float
,
cn
>::
vec_t
ype
Typef
;
int
x
=
blockDim
.
x
*
blockIdx
.
x
+
threadIdx
.
x
;
int
y
=
blockDim
.
y
*
blockIdx
.
y
+
threadIdx
.
y
;
...
...
@@ -884,7 +884,7 @@ void normalize_8U(int w, int h, const DevMem2D_<unsigned long long> image_sqsum,
template
<
int
cn
>
__global__
void
extractFirstChannel_32F
(
const
PtrStep
image
,
DevMem2Df
result
)
{
typedef
typename
TypeVec
<
float
,
cn
>::
vec_t
Typef
;
typedef
typename
TypeVec
<
float
,
cn
>::
vec_t
ype
Typef
;
int
x
=
blockDim
.
x
*
blockIdx
.
x
+
threadIdx
.
x
;
int
y
=
blockDim
.
y
*
blockIdx
.
y
+
threadIdx
.
y
;
...
...
modules/gpu/src/cuda/mathfunc.cu
浏览文件 @
3ab2728d
...
...
@@ -40,9 +40,9 @@
//
//M*/
#include "opencv2/gpu/device/limits
_gpu
.hpp"
#include "opencv2/gpu/device/limits.hpp"
#include "opencv2/gpu/device/saturate_cast.hpp"
#include "opencv2/gpu/device/vecmath.hpp"
#include "opencv2/gpu/device/vec
_
math.hpp"
#include "opencv2/gpu/device/transform.hpp"
#include "internal_shared.hpp"
...
...
modules/gpu/src/cuda/matrix_reductions.cu
浏览文件 @
3ab2728d
...
...
@@ -40,9 +40,9 @@
//
//M*/
#include "opencv2/gpu/device/limits
_gpu
.hpp"
#include "opencv2/gpu/device/limits.hpp"
#include "opencv2/gpu/device/saturate_cast.hpp"
#include "opencv2/gpu/device/vecmath.hpp"
#include "opencv2/gpu/device/vec
_
math.hpp"
#include "opencv2/gpu/device/transform.hpp"
#include "internal_shared.hpp"
...
...
@@ -190,8 +190,8 @@ namespace cv { namespace gpu { namespace mathfunc
uint
y0
=
blockIdx
.
y
*
blockDim
.
y
*
ctheight
+
threadIdx
.
y
;
uint
tid
=
threadIdx
.
y
*
blockDim
.
x
+
threadIdx
.
x
;
T
mymin
=
numeric_limits
_gpu
<
T
>::
max
();
T
mymax
=
numeric_limits
_gpu
<
T
>::
is_signed
?
-
numeric_limits_gpu
<
T
>::
max
()
:
numeric_limits_gpu
<
T
>::
min
();
T
mymin
=
numeric_limits
<
T
>::
max
();
T
mymax
=
numeric_limits
<
T
>::
is_signed
?
-
numeric_limits
<
T
>::
max
()
:
numeric_limits
<
T
>::
min
();
uint
y_end
=
min
(
y0
+
(
ctheight
-
1
)
*
blockDim
.
y
+
1
,
src
.
rows
);
uint
x_end
=
min
(
x0
+
(
ctwidth
-
1
)
*
blockDim
.
x
+
1
,
src
.
cols
);
for
(
uint
y
=
y0
;
y
<
y_end
;
y
+=
blockDim
.
y
)
...
...
@@ -512,9 +512,9 @@ namespace cv { namespace gpu { namespace mathfunc
uint
y0
=
blockIdx
.
y
*
blockDim
.
y
*
ctheight
+
threadIdx
.
y
;
uint
tid
=
threadIdx
.
y
*
blockDim
.
x
+
threadIdx
.
x
;
T
mymin
=
numeric_limits
_gpu
<
T
>::
max
();
T
mymax
=
numeric_limits
_gpu
<
T
>::
is_signed
?
-
numeric_limits_gpu
<
T
>::
max
()
:
numeric_limits
_gpu
<
T
>::
min
();
T
mymin
=
numeric_limits
<
T
>::
max
();
T
mymax
=
numeric_limits
<
T
>::
is_signed
?
-
numeric_limits
<
T
>::
max
()
:
numeric_limits
<
T
>::
min
();
uint
myminloc
=
0
;
uint
mymaxloc
=
0
;
uint
y_end
=
min
(
y0
+
(
ctheight
-
1
)
*
blockDim
.
y
+
1
,
src
.
rows
);
...
...
@@ -1094,10 +1094,10 @@ namespace cv { namespace gpu { namespace mathfunc
template
<
typename
T
,
typename
R
,
typename
Op
,
int
nthreads
>
__global__
void
sumKernel_C2
(
const
DevMem2D
src
,
typename
TypeVec
<
R
,
2
>::
vec_t
*
result
)
__global__
void
sumKernel_C2
(
const
DevMem2D
src
,
typename
TypeVec
<
R
,
2
>::
vec_t
ype
*
result
)
{
typedef
typename
TypeVec
<
T
,
2
>::
vec_t
SrcType
;
typedef
typename
TypeVec
<
R
,
2
>::
vec_t
DstType
;
typedef
typename
TypeVec
<
T
,
2
>::
vec_t
ype
SrcType
;
typedef
typename
TypeVec
<
R
,
2
>::
vec_t
ype
DstType
;
__shared__
R
smem
[
nthreads
*
2
];
...
...
@@ -1173,9 +1173,9 @@ namespace cv { namespace gpu { namespace mathfunc
template
<
typename
T
,
typename
R
,
int
nthreads
>
__global__
void
sumPass2Kernel_C2
(
typename
TypeVec
<
R
,
2
>::
vec_t
*
result
,
int
size
)
__global__
void
sumPass2Kernel_C2
(
typename
TypeVec
<
R
,
2
>::
vec_t
ype
*
result
,
int
size
)
{
typedef
typename
TypeVec
<
R
,
2
>::
vec_t
DstType
;
typedef
typename
TypeVec
<
R
,
2
>::
vec_t
ype
DstType
;
__shared__
R
smem
[
nthreads
*
2
];
...
...
@@ -1199,10 +1199,10 @@ namespace cv { namespace gpu { namespace mathfunc
template
<
typename
T
,
typename
R
,
typename
Op
,
int
nthreads
>
__global__
void
sumKernel_C3
(
const
DevMem2D
src
,
typename
TypeVec
<
R
,
3
>::
vec_t
*
result
)
__global__
void
sumKernel_C3
(
const
DevMem2D
src
,
typename
TypeVec
<
R
,
3
>::
vec_t
ype
*
result
)
{
typedef
typename
TypeVec
<
T
,
3
>::
vec_t
SrcType
;
typedef
typename
TypeVec
<
R
,
3
>::
vec_t
DstType
;
typedef
typename
TypeVec
<
T
,
3
>::
vec_t
ype
SrcType
;
typedef
typename
TypeVec
<
R
,
3
>::
vec_t
ype
DstType
;
__shared__
R
smem
[
nthreads
*
3
];
...
...
@@ -1285,9 +1285,9 @@ namespace cv { namespace gpu { namespace mathfunc
template
<
typename
T
,
typename
R
,
int
nthreads
>
__global__
void
sumPass2Kernel_C3
(
typename
TypeVec
<
R
,
3
>::
vec_t
*
result
,
int
size
)
__global__
void
sumPass2Kernel_C3
(
typename
TypeVec
<
R
,
3
>::
vec_t
ype
*
result
,
int
size
)
{
typedef
typename
TypeVec
<
R
,
3
>::
vec_t
DstType
;
typedef
typename
TypeVec
<
R
,
3
>::
vec_t
ype
DstType
;
__shared__
R
smem
[
nthreads
*
3
];
...
...
@@ -1313,10 +1313,10 @@ namespace cv { namespace gpu { namespace mathfunc
}
template
<
typename
T
,
typename
R
,
typename
Op
,
int
nthreads
>
__global__
void
sumKernel_C4
(
const
DevMem2D
src
,
typename
TypeVec
<
R
,
4
>::
vec_t
*
result
)
__global__
void
sumKernel_C4
(
const
DevMem2D
src
,
typename
TypeVec
<
R
,
4
>::
vec_t
ype
*
result
)
{
typedef
typename
TypeVec
<
T
,
4
>::
vec_t
SrcType
;
typedef
typename
TypeVec
<
R
,
4
>::
vec_t
DstType
;
typedef
typename
TypeVec
<
T
,
4
>::
vec_t
ype
SrcType
;
typedef
typename
TypeVec
<
R
,
4
>::
vec_t
ype
DstType
;
__shared__
R
smem
[
nthreads
*
4
];
...
...
@@ -1407,9 +1407,9 @@ namespace cv { namespace gpu { namespace mathfunc
template
<
typename
T
,
typename
R
,
int
nthreads
>
__global__
void
sumPass2Kernel_C4
(
typename
TypeVec
<
R
,
4
>::
vec_t
*
result
,
int
size
)
__global__
void
sumPass2Kernel_C4
(
typename
TypeVec
<
R
,
4
>::
vec_t
ype
*
result
,
int
size
)
{
typedef
typename
TypeVec
<
R
,
4
>::
vec_t
DstType
;
typedef
typename
TypeVec
<
R
,
4
>::
vec_t
ype
DstType
;
__shared__
R
smem
[
nthreads
*
4
];
...
...
@@ -1454,41 +1454,41 @@ namespace cv { namespace gpu { namespace mathfunc
{
case
1
:
sumKernel
<
T
,
R
,
IdentityOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
1
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
1
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
cudaSafeCall
(
cudaGetLastError
()
);
sumPass2Kernel
<
T
,
R
,
threads_x
*
threads_y
><<<
1
,
threads_x
*
threads_y
>>>
(
(
typename
TypeVec
<
R
,
1
>::
vec_t
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
(
typename
TypeVec
<
R
,
1
>::
vec_t
ype
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
cudaSafeCall
(
cudaGetLastError
()
);
break
;
case
2
:
sumKernel_C2
<
T
,
R
,
IdentityOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
2
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
2
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
cudaSafeCall
(
cudaGetLastError
()
);
sumPass2Kernel_C2
<
T
,
R
,
threads_x
*
threads_y
><<<
1
,
threads_x
*
threads_y
>>>
(
(
typename
TypeVec
<
R
,
2
>::
vec_t
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
(
typename
TypeVec
<
R
,
2
>::
vec_t
ype
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
cudaSafeCall
(
cudaGetLastError
()
);
break
;
case
3
:
sumKernel_C3
<
T
,
R
,
IdentityOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
3
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
3
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
cudaSafeCall
(
cudaGetLastError
()
);
sumPass2Kernel_C3
<
T
,
R
,
threads_x
*
threads_y
><<<
1
,
threads_x
*
threads_y
>>>
(
(
typename
TypeVec
<
R
,
3
>::
vec_t
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
(
typename
TypeVec
<
R
,
3
>::
vec_t
ype
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
cudaSafeCall
(
cudaGetLastError
()
);
break
;
case
4
:
sumKernel_C4
<
T
,
R
,
IdentityOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
4
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
4
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
cudaSafeCall
(
cudaGetLastError
()
);
sumPass2Kernel_C4
<
T
,
R
,
threads_x
*
threads_y
><<<
1
,
threads_x
*
threads_y
>>>
(
(
typename
TypeVec
<
R
,
4
>::
vec_t
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
(
typename
TypeVec
<
R
,
4
>::
vec_t
ype
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
cudaSafeCall
(
cudaGetLastError
()
);
break
;
...
...
@@ -1526,19 +1526,19 @@ namespace cv { namespace gpu { namespace mathfunc
{
case
1
:
sumKernel
<
T
,
R
,
IdentityOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
1
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
1
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
break
;
case
2
:
sumKernel_C2
<
T
,
R
,
IdentityOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
2
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
2
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
break
;
case
3
:
sumKernel_C3
<
T
,
R
,
IdentityOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
3
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
3
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
break
;
case
4
:
sumKernel_C4
<
T
,
R
,
IdentityOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
4
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
4
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
break
;
}
cudaSafeCall
(
cudaGetLastError
()
);
...
...
@@ -1576,41 +1576,41 @@ namespace cv { namespace gpu { namespace mathfunc
{
case
1
:
sumKernel
<
T
,
R
,
AbsOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
1
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
1
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
cudaSafeCall
(
cudaGetLastError
()
);
sumPass2Kernel
<
T
,
R
,
threads_x
*
threads_y
><<<
1
,
threads_x
*
threads_y
>>>
(
(
typename
TypeVec
<
R
,
1
>::
vec_t
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
(
typename
TypeVec
<
R
,
1
>::
vec_t
ype
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
cudaSafeCall
(
cudaGetLastError
()
);
break
;
case
2
:
sumKernel_C2
<
T
,
R
,
AbsOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
2
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
2
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
cudaSafeCall
(
cudaGetLastError
()
);
sumPass2Kernel_C2
<
T
,
R
,
threads_x
*
threads_y
><<<
1
,
threads_x
*
threads_y
>>>
(
(
typename
TypeVec
<
R
,
2
>::
vec_t
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
(
typename
TypeVec
<
R
,
2
>::
vec_t
ype
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
cudaSafeCall
(
cudaGetLastError
()
);
break
;
case
3
:
sumKernel_C3
<
T
,
R
,
AbsOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
3
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
3
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
cudaSafeCall
(
cudaGetLastError
()
);
sumPass2Kernel_C3
<
T
,
R
,
threads_x
*
threads_y
><<<
1
,
threads_x
*
threads_y
>>>
(
(
typename
TypeVec
<
R
,
3
>::
vec_t
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
(
typename
TypeVec
<
R
,
3
>::
vec_t
ype
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
cudaSafeCall
(
cudaGetLastError
()
);
break
;
case
4
:
sumKernel_C4
<
T
,
R
,
AbsOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
4
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
4
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
cudaSafeCall
(
cudaGetLastError
()
);
sumPass2Kernel_C4
<
T
,
R
,
threads_x
*
threads_y
><<<
1
,
threads_x
*
threads_y
>>>
(
(
typename
TypeVec
<
R
,
4
>::
vec_t
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
(
typename
TypeVec
<
R
,
4
>::
vec_t
ype
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
cudaSafeCall
(
cudaGetLastError
()
);
break
;
...
...
@@ -1648,19 +1648,19 @@ namespace cv { namespace gpu { namespace mathfunc
{
case
1
:
sumKernel
<
T
,
R
,
AbsOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
1
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
1
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
break
;
case
2
:
sumKernel_C2
<
T
,
R
,
AbsOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
2
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
2
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
break
;
case
3
:
sumKernel_C3
<
T
,
R
,
AbsOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
3
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
3
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
break
;
case
4
:
sumKernel_C4
<
T
,
R
,
AbsOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
4
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
4
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
break
;
}
cudaSafeCall
(
cudaGetLastError
()
);
...
...
@@ -1698,41 +1698,41 @@ namespace cv { namespace gpu { namespace mathfunc
{
case
1
:
sumKernel
<
T
,
R
,
SqrOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
1
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
1
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
cudaSafeCall
(
cudaGetLastError
()
);
sumPass2Kernel
<
T
,
R
,
threads_x
*
threads_y
><<<
1
,
threads_x
*
threads_y
>>>
(
(
typename
TypeVec
<
R
,
1
>::
vec_t
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
(
typename
TypeVec
<
R
,
1
>::
vec_t
ype
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
cudaSafeCall
(
cudaGetLastError
()
);
break
;
case
2
:
sumKernel_C2
<
T
,
R
,
SqrOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
2
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
2
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
cudaSafeCall
(
cudaGetLastError
()
);
sumPass2Kernel_C2
<
T
,
R
,
threads_x
*
threads_y
><<<
1
,
threads_x
*
threads_y
>>>
(
(
typename
TypeVec
<
R
,
2
>::
vec_t
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
(
typename
TypeVec
<
R
,
2
>::
vec_t
ype
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
cudaSafeCall
(
cudaGetLastError
()
);
break
;
case
3
:
sumKernel_C3
<
T
,
R
,
SqrOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
3
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
3
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
cudaSafeCall
(
cudaGetLastError
()
);
sumPass2Kernel_C3
<
T
,
R
,
threads_x
*
threads_y
><<<
1
,
threads_x
*
threads_y
>>>
(
(
typename
TypeVec
<
R
,
3
>::
vec_t
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
(
typename
TypeVec
<
R
,
3
>::
vec_t
ype
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
cudaSafeCall
(
cudaGetLastError
()
);
break
;
case
4
:
sumKernel_C4
<
T
,
R
,
SqrOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
4
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
4
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
cudaSafeCall
(
cudaGetLastError
()
);
sumPass2Kernel_C4
<
T
,
R
,
threads_x
*
threads_y
><<<
1
,
threads_x
*
threads_y
>>>
(
(
typename
TypeVec
<
R
,
4
>::
vec_t
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
(
typename
TypeVec
<
R
,
4
>::
vec_t
ype
*
)
buf
.
ptr
(
0
),
grid
.
x
*
grid
.
y
);
cudaSafeCall
(
cudaGetLastError
()
);
break
;
...
...
@@ -1770,19 +1770,19 @@ namespace cv { namespace gpu { namespace mathfunc
{
case
1
:
sumKernel
<
T
,
R
,
SqrOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
1
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
1
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
break
;
case
2
:
sumKernel_C2
<
T
,
R
,
SqrOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
2
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
2
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
break
;
case
3
:
sumKernel_C3
<
T
,
R
,
SqrOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
3
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
3
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
break
;
case
4
:
sumKernel_C4
<
T
,
R
,
SqrOp
<
R
>
,
threads_x
*
threads_y
><<<
grid
,
threads
>>>
(
src
,
(
typename
TypeVec
<
R
,
4
>::
vec_t
*
)
buf
.
ptr
(
0
));
src
,
(
typename
TypeVec
<
R
,
4
>::
vec_t
ype
*
)
buf
.
ptr
(
0
));
break
;
}
cudaSafeCall
(
cudaGetLastError
()
);
...
...
modules/gpu/src/cuda/stereobp.cu
浏览文件 @
3ab2728d
...
...
@@ -42,7 +42,7 @@
#include "opencv2/gpu/devmem2d.hpp"
#include "opencv2/gpu/device/saturate_cast.hpp"
#include "opencv2/gpu/device/limits
_gpu
.hpp"
#include "opencv2/gpu/device/limits.hpp"
#include "safe_call.hpp"
using
namespace
cv
::
gpu
;
...
...
@@ -381,7 +381,7 @@ namespace cv { namespace gpu { namespace bp
template
<
typename
T
>
__device__
void
message
(
const
T
*
msg1
,
const
T
*
msg2
,
const
T
*
msg3
,
const
T
*
data
,
T
*
dst
,
size_t
msg_disp_step
,
size_t
data_disp_step
)
{
float
minimum
=
numeric_limits
_gpu
<
float
>::
max
();
float
minimum
=
numeric_limits
<
float
>::
max
();
for
(
int
i
=
0
;
i
<
cndisp
;
++
i
)
{
...
...
@@ -486,7 +486,7 @@ namespace cv { namespace gpu { namespace bp
size_t
disp_step
=
disp
.
rows
*
u
.
step
;
int
best
=
0
;
float
best_val
=
numeric_limits
_gpu
<
float
>::
max
();
float
best_val
=
numeric_limits
<
float
>::
max
();
for
(
int
d
=
0
;
d
<
cndisp
;
++
d
)
{
float
val
=
us
[
d
*
disp_step
];
...
...
modules/gpu/src/cuda/stereocsbp.cu
浏览文件 @
3ab2728d
...
...
@@ -42,7 +42,7 @@
#include "opencv2/gpu/devmem2d.hpp"
#include "opencv2/gpu/device/saturate_cast.hpp"
#include "opencv2/gpu/device/limits
_gpu
.hpp"
#include "opencv2/gpu/device/limits.hpp"
#include "safe_call.hpp"
using
namespace
cv
::
gpu
;
...
...
@@ -147,7 +147,7 @@ namespace cv { namespace gpu { namespace csbp
for
(
int
i
=
0
;
i
<
nr_plane
;
i
++
)
{
T
minimum
=
numeric_limits
_gpu
<
T
>::
max
();
T
minimum
=
numeric_limits
<
T
>::
max
();
int
id
=
0
;
for
(
int
d
=
0
;
d
<
cndisp
;
d
++
)
{
...
...
@@ -161,7 +161,7 @@ namespace cv { namespace gpu { namespace csbp
data_cost_selected
[
i
*
cdisp_step1
]
=
minimum
;
selected_disparity
[
i
*
cdisp_step1
]
=
id
;
data_cost
[
id
*
cdisp_step1
]
=
numeric_limits
_gpu
<
T
>::
max
();
data_cost
[
id
*
cdisp_step1
]
=
numeric_limits
<
T
>::
max
();
}
}
}
...
...
@@ -192,7 +192,7 @@ namespace cv { namespace gpu { namespace csbp
data_cost_selected
[
nr_local_minimum
*
cdisp_step1
]
=
cur
;
selected_disparity
[
nr_local_minimum
*
cdisp_step1
]
=
d
;
data_cost
[
d
*
cdisp_step1
]
=
numeric_limits
_gpu
<
T
>::
max
();
data_cost
[
d
*
cdisp_step1
]
=
numeric_limits
<
T
>::
max
();
nr_local_minimum
++
;
}
...
...
@@ -203,7 +203,7 @@ namespace cv { namespace gpu { namespace csbp
for
(
int
i
=
nr_local_minimum
;
i
<
nr_plane
;
i
++
)
{
T
minimum
=
numeric_limits
_gpu
<
T
>::
max
();
T
minimum
=
numeric_limits
<
T
>::
max
();
int
id
=
0
;
for
(
int
d
=
0
;
d
<
cndisp
;
d
++
)
...
...
@@ -218,7 +218,7 @@ namespace cv { namespace gpu { namespace csbp
data_cost_selected
[
i
*
cdisp_step1
]
=
minimum
;
selected_disparity
[
i
*
cdisp_step1
]
=
id
;
data_cost
[
id
*
cdisp_step1
]
=
numeric_limits
_gpu
<
T
>::
max
();
data_cost
[
id
*
cdisp_step1
]
=
numeric_limits
<
T
>::
max
();
}
}
}
...
...
@@ -610,7 +610,7 @@ namespace cv { namespace gpu { namespace csbp
{
for
(
int
i
=
0
;
i
<
nr_plane
;
i
++
)
{
T
minimum
=
numeric_limits
_gpu
<
T
>::
max
();
T
minimum
=
numeric_limits
<
T
>::
max
();
int
id
=
0
;
for
(
int
j
=
0
;
j
<
nr_plane2
;
j
++
)
{
...
...
@@ -630,7 +630,7 @@ namespace cv { namespace gpu { namespace csbp
l_new
[
i
*
cdisp_step1
]
=
l_cur
[
id
*
cdisp_step2
];
r_new
[
i
*
cdisp_step1
]
=
r_cur
[
id
*
cdisp_step2
];
data_cost_new
[
id
*
cdisp_step1
]
=
numeric_limits
_gpu
<
T
>::
max
();
data_cost_new
[
id
*
cdisp_step1
]
=
numeric_limits
<
T
>::
max
();
}
}
...
...
@@ -737,7 +737,7 @@ namespace cv { namespace gpu { namespace csbp
__device__
void
message_per_pixel
(
const
T
*
data
,
T
*
msg_dst
,
const
T
*
msg1
,
const
T
*
msg2
,
const
T
*
msg3
,
const
T
*
dst_disp
,
const
T
*
src_disp
,
int
nr_plane
,
T
*
temp
)
{
T
minimum
=
numeric_limits
_gpu
<
T
>::
max
();
T
minimum
=
numeric_limits
<
T
>::
max
();
for
(
int
d
=
0
;
d
<
nr_plane
;
d
++
)
{
...
...
@@ -850,7 +850,7 @@ namespace cv { namespace gpu { namespace csbp
const
T
*
r
=
r_
+
(
y
+
0
)
*
cmsg_step1
+
(
x
-
1
);
int
best
=
0
;
T
best_val
=
numeric_limits
_gpu
<
T
>::
max
();
T
best_val
=
numeric_limits
<
T
>::
max
();
for
(
int
i
=
0
;
i
<
nr_plane
;
++
i
)
{
int
idx
=
i
*
cdisp_step1
;
...
...
modules/gpu/src/cuda/surf.cu
浏览文件 @
3ab2728d
...
...
@@ -46,8 +46,10 @@
//M*/
#include "internal_shared.hpp"
#include "opencv2/gpu/device/limits
_gpu
.hpp"
#include "opencv2/gpu/device/limits.hpp"
#include "opencv2/gpu/device/saturate_cast.hpp"
#include "opencv2/gpu/device/utility.hpp"
#include "opencv2/gpu/device/functional.hpp"
using
namespace
cv
::
gpu
;
using
namespace
cv
::
gpu
::
device
;
...
...
@@ -393,31 +395,10 @@ namespace cv { namespace gpu { namespace surf
//dss
H
[
2
][
2
]
=
N9
[
0
][
1
][
1
]
-
2.0
f
*
N9
[
1
][
1
][
1
]
+
N9
[
2
][
1
][
1
];
float
det
=
H
[
0
][
0
]
*
(
H
[
1
][
1
]
*
H
[
2
][
2
]
-
H
[
1
][
2
]
*
H
[
2
][
1
])
-
H
[
0
][
1
]
*
(
H
[
1
][
0
]
*
H
[
2
][
2
]
-
H
[
1
][
2
]
*
H
[
2
][
0
])
+
H
[
0
][
2
]
*
(
H
[
1
][
0
]
*
H
[
2
][
1
]
-
H
[
1
][
1
]
*
H
[
2
][
0
]);
__shared__
float
x
[
3
];
if
(
det
!=
0.0
f
)
if
(
solve3x3
(
H
,
dD
,
x
)
)
{
float
invdet
=
1.0
f
/
det
;
__shared__
float
x
[
3
];
x
[
0
]
=
invdet
*
(
dD
[
0
]
*
(
H
[
1
][
1
]
*
H
[
2
][
2
]
-
H
[
1
][
2
]
*
H
[
2
][
1
])
-
H
[
0
][
1
]
*
(
dD
[
1
]
*
H
[
2
][
2
]
-
H
[
1
][
2
]
*
dD
[
2
])
+
H
[
0
][
2
]
*
(
dD
[
1
]
*
H
[
2
][
1
]
-
H
[
1
][
1
]
*
dD
[
2
]));
x
[
1
]
=
invdet
*
(
H
[
0
][
0
]
*
(
dD
[
1
]
*
H
[
2
][
2
]
-
H
[
1
][
2
]
*
dD
[
2
])
-
dD
[
0
]
*
(
H
[
1
][
0
]
*
H
[
2
][
2
]
-
H
[
1
][
2
]
*
H
[
2
][
0
])
+
H
[
0
][
2
]
*
(
H
[
1
][
0
]
*
dD
[
2
]
-
dD
[
1
]
*
H
[
2
][
0
]));
x
[
2
]
=
invdet
*
(
H
[
0
][
0
]
*
(
H
[
1
][
1
]
*
dD
[
2
]
-
dD
[
1
]
*
H
[
2
][
1
])
-
H
[
0
][
1
]
*
(
H
[
1
][
0
]
*
dD
[
2
]
-
dD
[
1
]
*
H
[
2
][
0
])
+
dD
[
0
]
*
(
H
[
1
][
0
]
*
H
[
2
][
1
]
-
H
[
1
][
1
]
*
H
[
2
][
0
]));
if
(
fabs
(
x
[
0
])
<=
1.
f
&&
fabs
(
x
[
1
])
<=
1.
f
&&
fabs
(
x
[
2
])
<=
1.
f
)
{
// if the step is within the interpolation region, perform it
...
...
@@ -500,20 +481,6 @@ namespace cv { namespace gpu { namespace surf
__constant__
float
c_NX
[
2
][
5
]
=
{{
0
,
0
,
2
,
4
,
-
1
},
{
2
,
0
,
4
,
4
,
1
}};
__constant__
float
c_NY
[
2
][
5
]
=
{{
0
,
0
,
4
,
2
,
1
},
{
0
,
2
,
4
,
4
,
-
1
}};
__device__
void
reduceSum32
(
volatile
float
*
v_sum
,
float
&
sum
)
{
v_sum
[
threadIdx
.
x
]
=
sum
;
if
(
threadIdx
.
x
<
16
)
{
v_sum
[
threadIdx
.
x
]
=
sum
+=
v_sum
[
threadIdx
.
x
+
16
];
v_sum
[
threadIdx
.
x
]
=
sum
+=
v_sum
[
threadIdx
.
x
+
8
];
v_sum
[
threadIdx
.
x
]
=
sum
+=
v_sum
[
threadIdx
.
x
+
4
];
v_sum
[
threadIdx
.
x
]
=
sum
+=
v_sum
[
threadIdx
.
x
+
2
];
v_sum
[
threadIdx
.
x
]
=
sum
+=
v_sum
[
threadIdx
.
x
+
1
];
}
}
__global__
void
icvCalcOrientation
(
const
float
*
featureX
,
const
float
*
featureY
,
const
float
*
featureSize
,
float
*
featureDir
)
{
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 110
...
...
@@ -599,8 +566,11 @@ namespace cv { namespace gpu { namespace surf
float
*
s_sum_row
=
s_sum
+
threadIdx
.
y
*
32
;
reduceSum32
(
s_sum_row
,
sumx
);
reduceSum32
(
s_sum_row
,
sumy
);
//reduceSum32(s_sum_row, sumx);
//reduceSum32(s_sum_row, sumy);
warpReduce32
(
s_sum_row
,
sumx
,
threadIdx
.
x
,
plus
<
volatile
float
>
());
warpReduce32
(
s_sum_row
,
sumy
,
threadIdx
.
x
,
plus
<
volatile
float
>
());
const
float
temp_mod
=
sumx
*
sumx
+
sumy
*
sumy
;
if
(
temp_mod
>
best_mod
)
...
...
modules/gpu/src/opencv2/gpu/device/border_interpolate.hpp
浏览文件 @
3ab2728d
...
...
@@ -43,8 +43,8 @@
#ifndef __OPENCV_GPU_BORDER_INTERPOLATE_HPP__
#define __OPENCV_GPU_BORDER_INTERPOLATE_HPP__
#include "
opencv2/gpu/device/
saturate_cast.hpp"
#include "
opencv2/gpu/device/vecmath
.hpp"
#include "saturate_cast.hpp"
#include "
vec_traits
.hpp"
namespace
cv
{
namespace
gpu
{
namespace
device
{
...
...
@@ -72,64 +72,53 @@ namespace cv { namespace gpu { namespace device
return
-
last
<=
mini
&&
maxi
<=
2
*
last
;
}
private:
int
last
;
};
template
<
typename
D
>
struct
BrdRowReflect101
:
BrdReflect101
template
<
typename
D
>
struct
BrdRowReflect101
:
BrdReflect101
{
explicit
BrdRowReflect101
(
int
len
)
:
BrdReflect101
(
len
)
{}
template
<
typename
T
>
__device__
__forceinline__
D
at_low
(
int
i
,
const
T
*
data
)
const
template
<
typename
T
>
__device__
__forceinline__
D
at_low
(
int
i
,
const
T
*
data
)
const
{
return
saturate_cast
<
D
>
(
data
[
idx_low
(
i
)]);
}
template
<
typename
T
>
__device__
__forceinline__
D
at_high
(
int
i
,
const
T
*
data
)
const
template
<
typename
T
>
__device__
__forceinline__
D
at_high
(
int
i
,
const
T
*
data
)
const
{
return
saturate_cast
<
D
>
(
data
[
idx_high
(
i
)]);
}
};
template
<
typename
D
>
struct
BrdColReflect101
:
BrdReflect101
template
<
typename
D
>
struct
BrdColReflect101
:
BrdReflect101
{
BrdColReflect101
(
int
len
,
int
step
)
:
BrdReflect101
(
len
),
step
(
step
)
{}
template
<
typename
T
>
__device__
__forceinline__
D
at_low
(
int
i
,
const
T
*
data
)
const
template
<
typename
T
>
__device__
__forceinline__
D
at_low
(
int
i
,
const
T
*
data
)
const
{
return
saturate_cast
<
D
>
(
*
(
const
D
*
)((
const
char
*
)
data
+
idx_low
(
i
)
*
step
));
}
template
<
typename
T
>
__device__
__forceinline__
D
at_high
(
int
i
,
const
T
*
data
)
const
template
<
typename
T
>
__device__
__forceinline__
D
at_high
(
int
i
,
const
T
*
data
)
const
{
return
saturate_cast
<
D
>
(
*
(
const
D
*
)((
const
char
*
)
data
+
idx_high
(
i
)
*
step
));
}
private:
int
step
;
};
struct
BrdReplicate
{
explicit
BrdReplicate
(
int
len
)
:
last
(
len
-
1
)
{}
__device__
__forceinline__
int
idx_low
(
int
i
)
const
{
return
max
(
i
,
0
);
return
::
max
(
i
,
0
);
}
__device__
__forceinline__
int
idx_high
(
int
i
)
const
{
return
min
(
i
,
last
);
return
::
min
(
i
,
last
);
}
__device__
__forceinline__
int
idx
(
int
i
)
const
...
...
@@ -142,64 +131,52 @@ namespace cv { namespace gpu { namespace device
return
true
;
}
private:
int
last
;
};
template
<
typename
D
>
struct
BrdRowReplicate
:
BrdReplicate
template
<
typename
D
>
struct
BrdRowReplicate
:
BrdReplicate
{
explicit
BrdRowReplicate
(
int
len
)
:
BrdReplicate
(
len
)
{}
template
<
typename
T
>
__device__
__forceinline__
D
at_low
(
int
i
,
const
T
*
data
)
const
template
<
typename
T
>
__device__
__forceinline__
D
at_low
(
int
i
,
const
T
*
data
)
const
{
return
saturate_cast
<
D
>
(
data
[
idx_low
(
i
)]);
}
template
<
typename
T
>
__device__
__forceinline__
D
at_high
(
int
i
,
const
T
*
data
)
const
template
<
typename
T
>
__device__
__forceinline__
D
at_high
(
int
i
,
const
T
*
data
)
const
{
return
saturate_cast
<
D
>
(
data
[
idx_high
(
i
)]);
}
};
template
<
typename
D
>
struct
BrdColReplicate
:
BrdReplicate
template
<
typename
D
>
struct
BrdColReplicate
:
BrdReplicate
{
BrdColReplicate
(
int
len
,
int
step
)
:
BrdReplicate
(
len
),
step
(
step
)
{}
template
<
typename
T
>
__device__
__forceinline__
D
at_low
(
int
i
,
const
T
*
data
)
const
template
<
typename
T
>
__device__
__forceinline__
D
at_low
(
int
i
,
const
T
*
data
)
const
{
return
saturate_cast
<
D
>
(
*
(
const
D
*
)((
const
char
*
)
data
+
idx_low
(
i
)
*
step
));
}
template
<
typename
T
>
__device__
__forceinline__
D
at_high
(
int
i
,
const
T
*
data
)
const
template
<
typename
T
>
__device__
__forceinline__
D
at_high
(
int
i
,
const
T
*
data
)
const
{
return
saturate_cast
<
D
>
(
*
(
const
D
*
)((
const
char
*
)
data
+
idx_high
(
i
)
*
step
));
}
private:
int
step
;
};
template
<
typename
D
>
struct
BrdRowConstant
template
<
typename
D
>
struct
BrdRowConstant
{
explicit
BrdRowConstant
(
int
len_
,
const
D
&
val_
=
VecTraits
<
D
>::
all
(
0
))
:
len
(
len_
),
val
(
val_
)
{}
template
<
typename
T
>
__device__
__forceinline__
D
at_low
(
int
i
,
const
T
*
data
)
const
template
<
typename
T
>
__device__
__forceinline__
D
at_low
(
int
i
,
const
T
*
data
)
const
{
return
i
>=
0
?
saturate_cast
<
D
>
(
data
[
i
])
:
val
;
}
template
<
typename
T
>
__device__
__forceinline__
D
at_high
(
int
i
,
const
T
*
data
)
const
template
<
typename
T
>
__device__
__forceinline__
D
at_high
(
int
i
,
const
T
*
data
)
const
{
return
i
<
len
?
saturate_cast
<
D
>
(
data
[
i
])
:
val
;
}
...
...
@@ -209,24 +186,20 @@ namespace cv { namespace gpu { namespace device
return
true
;
}
private:
int
len
;
D
val
;
};
template
<
typename
D
>
struct
BrdColConstant
template
<
typename
D
>
struct
BrdColConstant
{
BrdColConstant
(
int
len_
,
int
step_
,
const
D
&
val_
=
VecTraits
<
D
>::
all
(
0
))
:
len
(
len_
),
step
(
step_
),
val
(
val_
)
{}
template
<
typename
T
>
__device__
__forceinline__
D
at_low
(
int
i
,
const
T
*
data
)
const
template
<
typename
T
>
__device__
__forceinline__
D
at_low
(
int
i
,
const
T
*
data
)
const
{
return
i
>=
0
?
saturate_cast
<
D
>
(
*
(
const
D
*
)((
const
char
*
)
data
+
i
*
step
))
:
val
;
}
template
<
typename
T
>
__device__
__forceinline__
D
at_high
(
int
i
,
const
T
*
data
)
const
template
<
typename
T
>
__device__
__forceinline__
D
at_high
(
int
i
,
const
T
*
data
)
const
{
return
i
<
len
?
saturate_cast
<
D
>
(
*
(
const
D
*
)((
const
char
*
)
data
+
i
*
step
))
:
val
;
}
...
...
@@ -236,15 +209,12 @@ namespace cv { namespace gpu { namespace device
return
true
;
}
private:
int
len
;
int
step
;
D
val
;
};
template
<
typename
OutT
>
struct
BrdConstant
template
<
typename
OutT
>
struct
BrdConstant
{
BrdConstant
(
int
w
,
int
h
,
const
OutT
&
val
=
VecTraits
<
OutT
>::
all
(
0
))
:
w
(
w
),
h
(
h
),
val
(
val
)
{}
...
...
@@ -255,11 +225,9 @@ namespace cv { namespace gpu { namespace device
return
val
;
}
private:
int
w
,
h
;
OutT
val
;
};
}}}
#endif // __OPENCV_GPU_BORDER_INTERPOLATE_HPP__
modules/gpu/src/opencv2/gpu/device/color.hpp
0 → 100644
浏览文件 @
3ab2728d
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or bpied warranties, including, but not limited to, the bpied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_GPU_COLOR_HPP__
#define __OPENCV_GPU_COLOR_HPP__
#include "detail/color.hpp"
namespace
cv
{
namespace
gpu
{
namespace
device
{
// All OPENCV_GPU_IMPLEMENT_*_TRAITS(ColorSpace1_to_ColorSpace2, ...) macros implements
// template <typename T> class ColorSpace1_to_ColorSpace2_traits
// {
// typedef ... functor_type;
// static __host__ __device__ functor_type create_functor();
// };
OPENCV_GPU_IMPLEMENT_RGB2RGB_TRAITS
(
bgr_to_rgb
,
3
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2RGB_TRAITS
(
bgr_to_bgra
,
3
,
4
,
0
)
OPENCV_GPU_IMPLEMENT_RGB2RGB_TRAITS
(
bgr_to_rgba
,
3
,
4
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2RGB_TRAITS
(
bgra_to_bgr
,
4
,
3
,
0
)
OPENCV_GPU_IMPLEMENT_RGB2RGB_TRAITS
(
bgra_to_rgb
,
4
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2RGB_TRAITS
(
bgra_to_rgba
,
4
,
4
,
2
)
#undef OPENCV_GPU_IMPLEMENT_RGB2RGB_TRAITS
OPENCV_GPU_IMPLEMENT_RGB2RGB5x5_TRAITS
(
bgr_to_bgr555
,
3
,
0
,
5
)
OPENCV_GPU_IMPLEMENT_RGB2RGB5x5_TRAITS
(
bgr_to_bgr565
,
3
,
0
,
6
)
OPENCV_GPU_IMPLEMENT_RGB2RGB5x5_TRAITS
(
rgb_to_bgr555
,
3
,
2
,
5
)
OPENCV_GPU_IMPLEMENT_RGB2RGB5x5_TRAITS
(
rgb_to_bgr565
,
3
,
2
,
6
)
OPENCV_GPU_IMPLEMENT_RGB2RGB5x5_TRAITS
(
bgra_to_bgr555
,
4
,
0
,
5
)
OPENCV_GPU_IMPLEMENT_RGB2RGB5x5_TRAITS
(
bgra_to_bgr565
,
4
,
0
,
6
)
OPENCV_GPU_IMPLEMENT_RGB2RGB5x5_TRAITS
(
rgba_to_bgr555
,
4
,
2
,
5
)
OPENCV_GPU_IMPLEMENT_RGB2RGB5x5_TRAITS
(
rgba_to_bgr565
,
4
,
2
,
6
)
#undef OPENCV_GPU_IMPLEMENT_RGB2RGB5x5_TRAITS
OPENCV_GPU_IMPLEMENT_RGB5x52RGB_TRAITS
(
bgr555_to_rgb
,
3
,
2
,
5
)
OPENCV_GPU_IMPLEMENT_RGB5x52RGB_TRAITS
(
bgr565_to_rgb
,
3
,
2
,
6
)
OPENCV_GPU_IMPLEMENT_RGB5x52RGB_TRAITS
(
bgr555_to_bgr
,
3
,
0
,
5
)
OPENCV_GPU_IMPLEMENT_RGB5x52RGB_TRAITS
(
bgr565_to_bgr
,
3
,
0
,
6
)
OPENCV_GPU_IMPLEMENT_RGB5x52RGB_TRAITS
(
bgr555_to_rgba
,
4
,
2
,
5
)
OPENCV_GPU_IMPLEMENT_RGB5x52RGB_TRAITS
(
bgr565_to_rgba
,
4
,
2
,
6
)
OPENCV_GPU_IMPLEMENT_RGB5x52RGB_TRAITS
(
bgr555_to_bgra
,
4
,
0
,
5
)
OPENCV_GPU_IMPLEMENT_RGB5x52RGB_TRAITS
(
bgr565_to_bgra
,
4
,
0
,
6
)
#undef OPENCV_GPU_IMPLEMENT_RGB5x52RGB_TRAITS
OPENCV_GPU_IMPLEMENT_GRAY2RGB_TRAITS
(
gray_to_bgr
,
3
)
OPENCV_GPU_IMPLEMENT_GRAY2RGB_TRAITS
(
gray_to_bgra
,
4
)
#undef OPENCV_GPU_IMPLEMENT_GRAY2RGB_TRAITS
OPENCV_GPU_IMPLEMENT_GRAY2RGB5x5_TRAITS
(
gray_to_bgr555
,
5
)
OPENCV_GPU_IMPLEMENT_GRAY2RGB5x5_TRAITS
(
gray_to_bgr565
,
6
)
#undef OPENCV_GPU_IMPLEMENT_GRAY2RGB5x5_TRAITS
OPENCV_GPU_IMPLEMENT_RGB5x52GRAY_TRAITS
(
bgr555_to_gray
,
5
)
OPENCV_GPU_IMPLEMENT_RGB5x52GRAY_TRAITS
(
bgr565_to_gray
,
6
)
#undef OPENCV_GPU_IMPLEMENT_RGB5x52GRAY_TRAITS
OPENCV_GPU_IMPLEMENT_RGB2GRAY_TRAITS
(
rgb_to_gray
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2GRAY_TRAITS
(
bgr_to_gray
,
3
,
0
)
OPENCV_GPU_IMPLEMENT_RGB2GRAY_TRAITS
(
rgba_to_gray
,
4
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2GRAY_TRAITS
(
bgra_to_gray
,
4
,
0
)
#undef OPENCV_GPU_IMPLEMENT_RGB2GRAY_TRAITS
OPENCV_GPU_IMPLEMENT_RGB2YUV_TRAITS
(
rgb_to_yuv
,
3
,
3
,
0
)
OPENCV_GPU_IMPLEMENT_RGB2YUV_TRAITS
(
rgba_to_yuv
,
4
,
3
,
0
)
OPENCV_GPU_IMPLEMENT_RGB2YUV_TRAITS
(
rgb_to_yuv4
,
3
,
4
,
0
)
OPENCV_GPU_IMPLEMENT_RGB2YUV_TRAITS
(
rgba_to_yuv4
,
4
,
4
,
0
)
OPENCV_GPU_IMPLEMENT_RGB2YUV_TRAITS
(
bgr_to_yuv
,
3
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2YUV_TRAITS
(
bgra_to_yuv
,
4
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2YUV_TRAITS
(
bgr_to_yuv4
,
3
,
4
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2YUV_TRAITS
(
bgra_to_yuv4
,
4
,
4
,
2
)
#undef OPENCV_GPU_IMPLEMENT_RGB2YUV_TRAITS
OPENCV_GPU_IMPLEMENT_YUV2RGB_TRAITS
(
yuv_to_rgb
,
3
,
3
,
0
)
OPENCV_GPU_IMPLEMENT_YUV2RGB_TRAITS
(
yuv_to_rgba
,
3
,
4
,
0
)
OPENCV_GPU_IMPLEMENT_YUV2RGB_TRAITS
(
yuv4_to_rgb
,
4
,
3
,
0
)
OPENCV_GPU_IMPLEMENT_YUV2RGB_TRAITS
(
yuv4_to_rgba
,
4
,
4
,
0
)
OPENCV_GPU_IMPLEMENT_YUV2RGB_TRAITS
(
yuv_to_bgr
,
3
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_YUV2RGB_TRAITS
(
yuv_to_bgra
,
3
,
4
,
2
)
OPENCV_GPU_IMPLEMENT_YUV2RGB_TRAITS
(
yuv4_to_bgr
,
4
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_YUV2RGB_TRAITS
(
yuv4_to_bgra
,
4
,
4
,
2
)
#undef OPENCV_GPU_IMPLEMENT_YUV2RGB_TRAITS
OPENCV_GPU_IMPLEMENT_RGB2YCrCb_TRAITS
(
rgb_to_YCrCb
,
3
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2YCrCb_TRAITS
(
rgba_to_YCrCb
,
4
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2YCrCb_TRAITS
(
rgb_to_YCrCb4
,
3
,
4
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2YCrCb_TRAITS
(
rgba_to_YCrCb4
,
4
,
4
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2YCrCb_TRAITS
(
bgr_to_YCrCb
,
3
,
3
,
0
)
OPENCV_GPU_IMPLEMENT_RGB2YCrCb_TRAITS
(
bgra_to_YCrCb
,
4
,
3
,
0
)
OPENCV_GPU_IMPLEMENT_RGB2YCrCb_TRAITS
(
bgr_to_YCrCb4
,
3
,
4
,
0
)
OPENCV_GPU_IMPLEMENT_RGB2YCrCb_TRAITS
(
bgra_to_YCrCb4
,
4
,
4
,
0
)
#undef OPENCV_GPU_IMPLEMENT_RGB2YCrCb_TRAITS
OPENCV_GPU_IMPLEMENT_YCrCb2RGB_TRAITS
(
YCrCb_to_rgb
,
3
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_YCrCb2RGB_TRAITS
(
YCrCb_to_rgba
,
3
,
4
,
2
)
OPENCV_GPU_IMPLEMENT_YCrCb2RGB_TRAITS
(
YCrCb4_to_rgb
,
4
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_YCrCb2RGB_TRAITS
(
YCrCb4_to_rgba
,
4
,
4
,
2
)
OPENCV_GPU_IMPLEMENT_YCrCb2RGB_TRAITS
(
YCrCb_to_bgr
,
3
,
3
,
0
)
OPENCV_GPU_IMPLEMENT_YCrCb2RGB_TRAITS
(
YCrCb_to_bgra
,
3
,
4
,
0
)
OPENCV_GPU_IMPLEMENT_YCrCb2RGB_TRAITS
(
YCrCb4_to_bgr
,
4
,
3
,
0
)
OPENCV_GPU_IMPLEMENT_YCrCb2RGB_TRAITS
(
YCrCb4_to_bgra
,
4
,
4
,
0
)
#undef OPENCV_GPU_IMPLEMENT_YCrCb2RGB_TRAITS
OPENCV_GPU_IMPLEMENT_RGB2XYZ_TRAITS
(
rgb_to_xyz
,
3
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2XYZ_TRAITS
(
rgba_to_xyz
,
4
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2XYZ_TRAITS
(
rgb_to_xyz4
,
3
,
4
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2XYZ_TRAITS
(
rgba_to_xyz4
,
4
,
4
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2XYZ_TRAITS
(
bgr_to_xyz
,
3
,
3
,
0
)
OPENCV_GPU_IMPLEMENT_RGB2XYZ_TRAITS
(
bgra_to_xyz
,
4
,
3
,
0
)
OPENCV_GPU_IMPLEMENT_RGB2XYZ_TRAITS
(
bgr_to_xyz4
,
3
,
4
,
0
)
OPENCV_GPU_IMPLEMENT_RGB2XYZ_TRAITS
(
bgra_to_xyz4
,
4
,
4
,
0
)
#undef OPENCV_GPU_IMPLEMENT_RGB2XYZ_TRAITS
OPENCV_GPU_IMPLEMENT_XYZ2RGB_TRAITS
(
xyz_to_rgb
,
3
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_XYZ2RGB_TRAITS
(
xyz4_to_rgb
,
4
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_XYZ2RGB_TRAITS
(
xyz_to_rgba
,
3
,
4
,
2
)
OPENCV_GPU_IMPLEMENT_XYZ2RGB_TRAITS
(
xyz4_to_rgba
,
4
,
4
,
2
)
OPENCV_GPU_IMPLEMENT_XYZ2RGB_TRAITS
(
xyz_to_bgr
,
3
,
3
,
0
)
OPENCV_GPU_IMPLEMENT_XYZ2RGB_TRAITS
(
xyz4_to_bgr
,
4
,
3
,
0
)
OPENCV_GPU_IMPLEMENT_XYZ2RGB_TRAITS
(
xyz_to_bgra
,
3
,
4
,
0
)
OPENCV_GPU_IMPLEMENT_XYZ2RGB_TRAITS
(
xyz4_to_bgra
,
4
,
4
,
0
)
#undef OPENCV_GPU_IMPLEMENT_XYZ2RGB_TRAITS
OPENCV_GPU_IMPLEMENT_RGB2HSV_TRAITS
(
rgb_to_hsv
,
3
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2HSV_TRAITS
(
rgba_to_hsv
,
4
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2HSV_TRAITS
(
rgb_to_hsv4
,
3
,
4
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2HSV_TRAITS
(
rgba_to_hsv4
,
4
,
4
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2HSV_TRAITS
(
bgr_to_hsv
,
3
,
3
,
0
)
OPENCV_GPU_IMPLEMENT_RGB2HSV_TRAITS
(
bgra_to_hsv
,
4
,
3
,
0
)
OPENCV_GPU_IMPLEMENT_RGB2HSV_TRAITS
(
bgr_to_hsv4
,
3
,
4
,
0
)
OPENCV_GPU_IMPLEMENT_RGB2HSV_TRAITS
(
bgra_to_hsv4
,
4
,
4
,
0
)
#undef OPENCV_GPU_IMPLEMENT_RGB2HSV_TRAITS
OPENCV_GPU_IMPLEMENT_HSV2RGB_TRAITS
(
hsv_to_rgb
,
3
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_HSV2RGB_TRAITS
(
hsv_to_rgba
,
3
,
4
,
2
)
OPENCV_GPU_IMPLEMENT_HSV2RGB_TRAITS
(
hsv4_to_rgb
,
4
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_HSV2RGB_TRAITS
(
hsv4_to_rgba
,
4
,
4
,
2
)
OPENCV_GPU_IMPLEMENT_HSV2RGB_TRAITS
(
hsv_to_bgr
,
3
,
3
,
0
)
OPENCV_GPU_IMPLEMENT_HSV2RGB_TRAITS
(
hsv_to_bgra
,
3
,
4
,
0
)
OPENCV_GPU_IMPLEMENT_HSV2RGB_TRAITS
(
hsv4_to_bgr
,
4
,
3
,
0
)
OPENCV_GPU_IMPLEMENT_HSV2RGB_TRAITS
(
hsv4_to_bgra
,
4
,
4
,
0
)
#undef OPENCV_GPU_IMPLEMENT_HSV2RGB_TRAITS
OPENCV_GPU_IMPLEMENT_RGB2HLS_TRAITS
(
rgb_to_hls
,
3
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2HLS_TRAITS
(
rgba_to_hls
,
4
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2HLS_TRAITS
(
rgb_to_hls4
,
3
,
4
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2HLS_TRAITS
(
rgba_to_hls4
,
4
,
4
,
2
)
OPENCV_GPU_IMPLEMENT_RGB2HLS_TRAITS
(
bgr_to_hls
,
3
,
3
,
0
)
OPENCV_GPU_IMPLEMENT_RGB2HLS_TRAITS
(
bgra_to_hls
,
4
,
3
,
0
)
OPENCV_GPU_IMPLEMENT_RGB2HLS_TRAITS
(
bgr_to_hls4
,
3
,
4
,
0
)
OPENCV_GPU_IMPLEMENT_RGB2HLS_TRAITS
(
bgra_to_hls4
,
4
,
4
,
0
)
#undef OPENCV_GPU_IMPLEMENT_RGB2HLS_TRAITS
OPENCV_GPU_IMPLEMENT_HLS2RGB_TRAITS
(
hls_to_rgb
,
3
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_HLS2RGB_TRAITS
(
hls_to_rgba
,
3
,
4
,
2
)
OPENCV_GPU_IMPLEMENT_HLS2RGB_TRAITS
(
hls4_to_rgb
,
4
,
3
,
2
)
OPENCV_GPU_IMPLEMENT_HLS2RGB_TRAITS
(
hls4_to_rgba
,
4
,
4
,
2
)
OPENCV_GPU_IMPLEMENT_HLS2RGB_TRAITS
(
hls_to_bgr
,
3
,
3
,
0
)
OPENCV_GPU_IMPLEMENT_HLS2RGB_TRAITS
(
hls_to_bgra
,
3
,
4
,
0
)
OPENCV_GPU_IMPLEMENT_HLS2RGB_TRAITS
(
hls4_to_bgr
,
4
,
3
,
0
)
OPENCV_GPU_IMPLEMENT_HLS2RGB_TRAITS
(
hls4_to_bgra
,
4
,
4
,
0
)
#undef OPENCV_GPU_IMPLEMENT_HLS2RGB_TRAITS
}}}
#endif // __OPENCV_GPU_BORDER_INTERPOLATE_HPP__
modules/gpu/src/opencv2/gpu/device/datamov_utils.hpp
浏览文件 @
3ab2728d
...
...
@@ -44,6 +44,7 @@
#define __OPENCV_GPU_DATAMOV_UTILS_HPP__
#include "internal_shared.hpp"
#include "utility.hpp"
namespace
cv
{
namespace
gpu
{
namespace
device
{
...
...
@@ -55,49 +56,40 @@ namespace cv { namespace gpu { namespace device
__device__
__forceinline__
static
void
Load
(
const
T
*
ptr
,
int
offset
,
T
&
val
)
{
val
=
ptr
[
offset
];
}
};
#else // __CUDA_ARCH__ >= 200
#if defined(_WIN64) || defined(__LP64__)
// 64-bit register modifier for inlined asm
#define _OPENCV_ASM_PTR_ "l"
#else
// 32-bit register modifier for inlined asm
#define _OPENCV_ASM_PTR_ "r"
#endif
#else // __CUDA_ARCH__ >= 200
template
<
class
T
>
struct
ForceGlob
;
#define DEFINE_FORCE_GLOB(base_type, ptx_type, reg_mod) \
template
<
>
struct
ForceGlob
<
base_type
>
\
{
\
__device__
__forceinline__
static
void
Load
(
const
base_type
*
ptr
,
int
offset
,
base_type
&
val
)
\
{
\
asm
(
"ld.global."
#
ptx_type
" %0, [%1];"
:
"="
#
reg_mod
(
val
)
:
_OPENCV_ASM_PTR_
(
ptr
+
offset
));
\
}
\
};
#define DEFINE_FORCE_GLOB_B(base_type, ptx_type) \
template
<
>
struct
ForceGlob
<
base_type
>
\
{
\
__device__
__forceinline__
static
void
Load
(
const
base_type
*
ptr
,
int
offset
,
base_type
&
val
)
\
{
\
asm
(
"ld.global."
#
ptx_type
" %0, [%1];"
:
"=r"
(
*
reinterpret_cast
<
uint
*>
(
&
val
))
:
_OPENCV_ASM_PTR_
(
ptr
+
offset
));
\
}
\
};
#define OPENCV_GPU_DEFINE_FORCE_GLOB(base_type, ptx_type, reg_mod) \
template
<
>
struct
ForceGlob
<
base_type
>
\
{
\
__device__
__forceinline__
static
void
Load
(
const
base_type
*
ptr
,
int
offset
,
base_type
&
val
)
\
{
\
asm
(
"ld.global."
#
ptx_type
" %0, [%1];"
:
"="
#
reg_mod
(
val
)
:
OPENCV_GPU_ASM_PTR
(
ptr
+
offset
));
\
}
\
};
#define OPENCV_GPU_DEFINE_FORCE_GLOB_B(base_type, ptx_type) \
template
<
>
struct
ForceGlob
<
base_type
>
\
{
\
__device__
__forceinline__
static
void
Load
(
const
base_type
*
ptr
,
int
offset
,
base_type
&
val
)
\
{
\
asm
(
"ld.global."
#
ptx_type
" %0, [%1];"
:
"=r"
(
*
reinterpret_cast
<
uint
*>
(
&
val
))
:
OPENCV_GPU_ASM_PTR
(
ptr
+
offset
));
\
}
\
};
DEFINE_FORCE_GLOB_B
(
uchar
,
u8
)
DEFINE_FORCE_GLOB_B
(
schar
,
s8
)
DEFINE_FORCE_GLOB_B
(
char
,
b8
)
DEFINE_FORCE_GLOB
(
ushort
,
u16
,
h
)
DEFINE_FORCE_GLOB
(
short
,
s16
,
h
)
DEFINE_FORCE_GLOB
(
uint
,
u32
,
r
)
DEFINE_FORCE_GLOB
(
int
,
s32
,
r
)
DEFINE_FORCE_GLOB
(
float
,
f32
,
f
)
DEFINE_FORCE_GLOB
(
double
,
f64
,
d
)
OPENCV_GPU_DEFINE_FORCE_GLOB_B
(
uchar
,
u8
)
OPENCV_GPU_DEFINE_FORCE_GLOB_B
(
schar
,
s8
)
OPENCV_GPU_DEFINE_FORCE_GLOB_B
(
char
,
b8
)
OPENCV_GPU_DEFINE_FORCE_GLOB
(
ushort
,
u16
,
h
)
OPENCV_GPU_DEFINE_FORCE_GLOB
(
short
,
s16
,
h
)
OPENCV_GPU_DEFINE_FORCE_GLOB
(
uint
,
u32
,
r
)
OPENCV_GPU_DEFINE_FORCE_GLOB
(
int
,
s32
,
r
)
OPENCV_GPU_DEFINE_FORCE_GLOB
(
float
,
f32
,
f
)
OPENCV_GPU_DEFINE_FORCE_GLOB
(
double
,
f64
,
d
)
#undef DEFINE_FORCE_GLOB
#undef DEFINE_FORCE_GLOB_B
#undef _OPENCV_ASM_PTR_
#undef OPENCV_GPU_DEFINE_FORCE_GLOB
#undef OPENCV_GPU_DEFINE_FORCE_GLOB_B
#endif // __CUDA_ARCH__ >= 200
}}}
...
...
modules/gpu/src/opencv2/gpu/device/detail/color.hpp
0 → 100644
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modules/gpu/src/opencv2/gpu/device/detail/transform.hpp
0 → 100644
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此差异已折叠。
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modules/gpu/src/opencv2/gpu/device/functional.hpp
0 → 100644
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此差异已折叠。
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modules/gpu/src/opencv2/gpu/device/limits
_gpu
.hpp
→
modules/gpu/src/opencv2/gpu/device/limits.hpp
浏览文件 @
3ab2728d
...
...
@@ -45,7 +45,7 @@
namespace
cv
{
namespace
gpu
{
namespace
device
{
template
<
class
T
>
struct
numeric_limits
_gpu
template
<
class
T
>
struct
numeric_limits
{
typedef
T
type
;
__device__
__forceinline__
static
type
min
()
{
return
type
();
};
...
...
@@ -59,7 +59,7 @@ namespace cv { namespace gpu { namespace device
static
const
bool
is_signed
;
};
template
<
>
struct
numeric_limits
_gpu
<
bool
>
template
<
>
struct
numeric_limits
<
bool
>
{
typedef
bool
type
;
__device__
__forceinline__
static
type
min
()
{
return
false
;
};
...
...
@@ -73,7 +73,7 @@ namespace cv { namespace gpu { namespace device
static
const
bool
is_signed
=
false
;
};
template
<
>
struct
numeric_limits
_gpu
<
char
>
template
<
>
struct
numeric_limits
<
char
>
{
typedef
char
type
;
__device__
__forceinline__
static
type
min
()
{
return
CHAR_MIN
;
};
...
...
@@ -87,7 +87,7 @@ namespace cv { namespace gpu { namespace device
static
const
bool
is_signed
=
(
char
)
-
1
==
-
1
;
};
template
<
>
struct
numeric_limits
_gpu
<
signed
char
>
template
<
>
struct
numeric_limits
<
signed
char
>
{
typedef
char
type
;
__device__
__forceinline__
static
type
min
()
{
return
CHAR_MIN
;
};
...
...
@@ -101,7 +101,7 @@ namespace cv { namespace gpu { namespace device
static
const
bool
is_signed
=
(
signed
char
)
-
1
==
-
1
;
};
template
<
>
struct
numeric_limits
_gpu
<
unsigned
char
>
template
<
>
struct
numeric_limits
<
unsigned
char
>
{
typedef
unsigned
char
type
;
__device__
__forceinline__
static
type
min
()
{
return
0
;
};
...
...
@@ -115,7 +115,7 @@ namespace cv { namespace gpu { namespace device
static
const
bool
is_signed
=
false
;
};
template
<
>
struct
numeric_limits
_gpu
<
short
>
template
<
>
struct
numeric_limits
<
short
>
{
typedef
short
type
;
__device__
__forceinline__
static
type
min
()
{
return
SHRT_MIN
;
};
...
...
@@ -129,7 +129,7 @@ namespace cv { namespace gpu { namespace device
static
const
bool
is_signed
=
true
;
};
template
<
>
struct
numeric_limits
_gpu
<
unsigned
short
>
template
<
>
struct
numeric_limits
<
unsigned
short
>
{
typedef
unsigned
short
type
;
__device__
__forceinline__
static
type
min
()
{
return
0
;
};
...
...
@@ -143,7 +143,7 @@ namespace cv { namespace gpu { namespace device
static
const
bool
is_signed
=
false
;
};
template
<
>
struct
numeric_limits
_gpu
<
int
>
template
<
>
struct
numeric_limits
<
int
>
{
typedef
int
type
;
__device__
__forceinline__
static
type
min
()
{
return
INT_MIN
;
};
...
...
@@ -158,7 +158,7 @@ namespace cv { namespace gpu { namespace device
};
template
<
>
struct
numeric_limits
_gpu
<
unsigned
int
>
template
<
>
struct
numeric_limits
<
unsigned
int
>
{
typedef
unsigned
int
type
;
__device__
__forceinline__
static
type
min
()
{
return
0
;
};
...
...
@@ -172,7 +172,7 @@ namespace cv { namespace gpu { namespace device
static
const
bool
is_signed
=
false
;
};
template
<
>
struct
numeric_limits
_gpu
<
long
>
template
<
>
struct
numeric_limits
<
long
>
{
typedef
long
type
;
__device__
__forceinline__
static
type
min
()
{
return
LONG_MIN
;
};
...
...
@@ -186,7 +186,7 @@ namespace cv { namespace gpu { namespace device
static
const
bool
is_signed
=
true
;
};
template
<
>
struct
numeric_limits
_gpu
<
unsigned
long
>
template
<
>
struct
numeric_limits
<
unsigned
long
>
{
typedef
unsigned
long
type
;
__device__
__forceinline__
static
type
min
()
{
return
0
;
};
...
...
@@ -200,7 +200,7 @@ namespace cv { namespace gpu { namespace device
static
const
bool
is_signed
=
false
;
};
template
<
>
struct
numeric_limits
_gpu
<
float
>
template
<
>
struct
numeric_limits
<
float
>
{
typedef
float
type
;
__device__
__forceinline__
static
type
min
()
{
return
1.175494351e-38
f
/*FLT_MIN*/
;
};
...
...
@@ -214,7 +214,7 @@ namespace cv { namespace gpu { namespace device
static
const
bool
is_signed
=
true
;
};
template
<
>
struct
numeric_limits
_gpu
<
double
>
template
<
>
struct
numeric_limits
<
double
>
{
typedef
double
type
;
__device__
__forceinline__
static
type
min
()
{
return
2.2250738585072014e-308
/*DBL_MIN*/
;
};
...
...
modules/gpu/src/opencv2/gpu/device/saturate_cast.hpp
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modules/gpu/src/opencv2/gpu/device/transform.hpp
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modules/gpu/src/opencv2/gpu/device/vec_math.hpp
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modules/gpu/src/opencv2/gpu/device/vec_traits.hpp
0 → 100644
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_GPU_VEC_TRAITS_HPP__
#define __OPENCV_GPU_VEC_TRAITS_HPP__
#include "internal_shared.hpp"
namespace
cv
{
namespace
gpu
{
namespace
device
{
template
<
typename
T
,
int
N
>
struct
TypeVec
;
#define OPENCV_GPU_IMPLEMENT_TYPE_VEC(type) \
template
<
>
struct
TypeVec
<
type
,
1
>
{
typedef
type
vec_type
;
};
\
template
<
>
struct
TypeVec
<
type
##
1
,
1
>
{
typedef
type
##
1
vec_type
;
};
\
template
<
>
struct
TypeVec
<
type
,
2
>
{
typedef
type
##
2
vec_type
;
};
\
template
<
>
struct
TypeVec
<
type
##
2
,
2
>
{
typedef
type
##
2
vec_type
;
};
\
template
<
>
struct
TypeVec
<
type
,
3
>
{
typedef
type
##
3
vec_type
;
};
\
template
<
>
struct
TypeVec
<
type
##
3
,
3
>
{
typedef
type
##
3
vec_type
;
};
\
template
<
>
struct
TypeVec
<
type
,
4
>
{
typedef
type
##
4
vec_type
;
};
\
template
<
>
struct
TypeVec
<
type
##
4
,
4
>
{
typedef
type
##
4
vec_type
;
};
OPENCV_GPU_IMPLEMENT_TYPE_VEC
(
uchar
)
OPENCV_GPU_IMPLEMENT_TYPE_VEC
(
char
)
OPENCV_GPU_IMPLEMENT_TYPE_VEC
(
ushort
)
OPENCV_GPU_IMPLEMENT_TYPE_VEC
(
short
)
OPENCV_GPU_IMPLEMENT_TYPE_VEC
(
int
)
OPENCV_GPU_IMPLEMENT_TYPE_VEC
(
uint
)
OPENCV_GPU_IMPLEMENT_TYPE_VEC
(
float
)
OPENCV_GPU_IMPLEMENT_TYPE_VEC
(
double
)
#undef OPENCV_GPU_IMPLEMENT_TYPE_VEC
template
<
>
struct
TypeVec
<
schar
,
1
>
{
typedef
schar
vec_type
;
};
template
<
>
struct
TypeVec
<
schar
,
2
>
{
typedef
char2
vec_type
;
};
template
<
>
struct
TypeVec
<
schar
,
3
>
{
typedef
char3
vec_type
;
};
template
<
>
struct
TypeVec
<
schar
,
4
>
{
typedef
char4
vec_type
;
};
template
<
>
struct
TypeVec
<
bool
,
1
>
{
typedef
uchar
vec_type
;
};
template
<
>
struct
TypeVec
<
bool
,
2
>
{
typedef
uchar2
vec_type
;
};
template
<
>
struct
TypeVec
<
bool
,
3
>
{
typedef
uchar3
vec_type
;
};
template
<
>
struct
TypeVec
<
bool
,
4
>
{
typedef
uchar4
vec_type
;
};
template
<
typename
T
>
struct
VecTraits
;
#define OPENCV_GPU_IMPLEMENT_VEC_TRAITS(type) \
template
<
>
struct
VecTraits
<
type
>
\
{
\
typedef
type
elem_type
;
\
enum
{
cn
=
1
};
\
static
__device__
__host__
type
all
(
type
v
)
{
return
v
;}
\
static
__device__
__host__
type
make
(
type
x
)
{
return
x
;}
\
};
\
template
<
>
struct
VecTraits
<
type
##
1
>
\
{
\
typedef
type
elem_type
;
\
enum
{
cn
=
1
};
\
static
__device__
__host__
type
##
1
all
(
type
v
)
{
return
make_
##
type
##
1
(
v
);}
\
static
__device__
__host__
type
##
1
make
(
type
x
)
{
return
make_
##
type
##
1
(
x
);}
\
};
\
template
<
>
struct
VecTraits
<
type
##
2
>
\
{
\
typedef
type
elem_type
;
\
enum
{
cn
=
2
};
\
static
__device__
__host__
type
##
2
all
(
type
v
)
{
return
make_
##
type
##
2
(
v
,
v
);}
\
static
__device__
__host__
type
##
2
make
(
type
x
,
type
y
)
{
return
make_
##
type
##
2
(
x
,
y
);}
\
};
\
template
<
>
struct
VecTraits
<
type
##
3
>
\
{
\
typedef
type
elem_type
;
\
enum
{
cn
=
3
};
\
static
__device__
__host__
type
##
3
all
(
type
v
)
{
return
make_
##
type
##
3
(
v
,
v
,
v
);}
\
static
__device__
__host__
type
##
3
make
(
type
x
,
type
y
,
type
z
)
{
return
make_
##
type
##
3
(
x
,
y
,
z
);}
\
};
\
template
<
>
struct
VecTraits
<
type
##
4
>
\
{
\
typedef
type
elem_type
;
\
enum
{
cn
=
4
};
\
static
__device__
__host__
type
##
4
all
(
type
v
)
{
return
make_
##
type
##
4
(
v
,
v
,
v
,
v
);}
\
static
__device__
__host__
type
##
4
make
(
type
x
,
type
y
,
type
z
,
type
w
)
{
return
make_
##
type
##
4
(
x
,
y
,
z
,
w
);}
\
};
OPENCV_GPU_IMPLEMENT_VEC_TRAITS
(
uchar
)
OPENCV_GPU_IMPLEMENT_VEC_TRAITS
(
char
)
OPENCV_GPU_IMPLEMENT_VEC_TRAITS
(
ushort
)
OPENCV_GPU_IMPLEMENT_VEC_TRAITS
(
short
)
OPENCV_GPU_IMPLEMENT_VEC_TRAITS
(
int
)
OPENCV_GPU_IMPLEMENT_VEC_TRAITS
(
uint
)
OPENCV_GPU_IMPLEMENT_VEC_TRAITS
(
float
)
OPENCV_GPU_IMPLEMENT_VEC_TRAITS
(
double
)
#undef OPENCV_GPU_IMPLEMENT_VEC_TRAITS
template
<
>
struct
VecTraits
<
schar
>
{
typedef
schar
elem_type
;
enum
{
cn
=
1
};
static
__device__
__host__
schar
all
(
schar
v
)
{
return
v
;}
static
__device__
__host__
schar
make
(
schar
x
)
{
return
x
;}
};
}}}
#endif // __OPENCV_GPU_VEC_TRAITS_HPP__
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