Skip to content
GitLab
Explore
Sign in
Primary navigation
Search or go to…
Project
hydra-image-processor
Manage
Activity
Members
Labels
Plan
Issues
Issue boards
Milestones
Wiki
Code
Merge requests
Repository
Branches
Commits
Tags
Repository graph
Compare revisions
Snippets
Deploy
Releases
Model registry
Monitor
Incidents
Analyze
Value stream analytics
Contributor analytics
Repository analytics
Model experiments
Help
Help
Support
GitLab documentation
Compare GitLab plans
Community forum
Contribute to GitLab
Provide feedback
Keyboard shortcuts
?
Snippets
Groups
Projects
Show more breadcrumbs
OpenSource
hydra-image-processor
Commits
29f554c6
Commit
29f554c6
authored
6 years ago
by
Eric Wait
Browse files
Options
Downloads
Patches
Plain Diff
Fixed LoG (still bleeds on edges the same way MATLAB does)
parent
9e5d22a3
No related branches found
No related tags found
No related merge requests found
Changes
3
Hide whitespace changes
Inline
Side-by-side
Showing
3 changed files
src/c/Cuda/CudaLoG.cuh
+85
-18
85 additions, 18 deletions
src/c/Cuda/CudaLoG.cuh
src/c/Cuda/KernelGenerators.h
+1
-1
1 addition, 1 deletion
src/c/Cuda/KernelGenerators.h
src/c/Cuda/LoGKernel.cpp
+106
-61
106 additions, 61 deletions
src/c/Cuda/LoGKernel.cpp
with
192 additions
and
80 deletions
src/c/Cuda/CudaLoG.cuh
+
85
−
18
View file @
29f554c6
...
...
@@ -10,7 +10,7 @@
#include
"Defines.h"
#include
"Vec.h"
#include
"KernelGenerators.h"
#include
"
SeparableMultiplySum
.cuh"
#include
"
CudaAddTwoImages
.cuh"
#include
<cuda_runtime.h>
#include
<limits>
...
...
@@ -21,16 +21,16 @@ void cLoG(ImageContainer<PixelTypeIn> imageIn, ImageContainer<float>& imageOut,
{
const
float
MIN_VAL
=
std
::
numeric_limits
<
float
>::
lowest
();
const
float
MAX_VAL
=
std
::
numeric_limits
<
float
>::
max
();
const
int
NUM_BUFF_NEEDED
=
2
;
const
int
NUM_BUFF_NEEDED
=
3
;
setUpOutIm
<
float
>
(
imageIn
.
getDims
(),
imageOut
);
CudaDevices
cudaDevs
(
cuda
MultiplySum
<
float
,
float
>
,
device
);
CudaDevices
cudaDevs
(
cuda
AddTwoImages
<
float
,
float
,
float
>
,
device
);
Vec
<
size_t
>
kernDims
(
0
);
float
*
hostKernels
=
createGaus
sian
Kernel
(
sigmas
,
kernDims
);
Vec
<
size_t
>
kern
el
Dims
(
0
);
float
*
host
LoG_Gaus
Kernels
=
create
LoG_
GausKernel
s
(
sigmas
,
kern
el
Dims
);
std
::
vector
<
ImageChunk
>
chunks
=
calculateBuffers
(
imageIn
.
getDims
(),
NUM_BUFF_NEEDED
,
cudaDevs
,
sizeof
(
float
),
kernDims
);
std
::
vector
<
ImageChunk
>
chunks
=
calculateBuffers
(
imageIn
.
getDims
(),
NUM_BUFF_NEEDED
,
cudaDevs
,
sizeof
(
float
),
kern
el
Dims
);
Vec
<
size_t
>
maxDeviceDims
;
setMaxDeviceDims
(
chunks
,
maxDeviceDims
);
...
...
@@ -42,29 +42,96 @@ void cLoG(ImageContainer<PixelTypeIn> imageIn, ImageContainer<float>& imageOut,
const
int
N_THREADS
=
omp_get_num_threads
();
const
int
CUR_DEVICE
=
cudaDevs
.
getDeviceIdx
(
CUDA_IDX
);
CudaDeviceImages
<
float
>
deviceImages
(
NUM_BUFF_NEEDED
,
maxDeviceDims
,
CUR_DEVICE
);
CudaDeviceImages
<
float
>
deviceImages
(
NUM_BUFF_NEEDED
-
1
,
maxDeviceDims
,
CUR_DEVICE
);
CudaDeviceImages
<
float
>
deviceImagesScratch
(
1
,
maxDeviceDims
,
CUR_DEVICE
);
Kernel
constFullKern
(
kernDims
.
sum
(),
hostKernels
,
CUR_DEVICE
);
Kernel
constKernelMem_x
=
constFullKern
.
getOffsetCopy
(
Vec
<
size_t
>
(
kernDims
.
x
,
1
,
1
),
0
);
Kernel
constKernelMem_y
=
constFullKern
.
getOffsetCopy
(
Vec
<
size_t
>
(
1
,
kernDims
.
y
,
1
),
kernDims
.
x
);
Kernel
constKernelMem_z
=
constFullKern
.
getOffsetCopy
(
Vec
<
size_t
>
(
1
,
1
,
kernDims
.
z
),
kernDims
.
x
+
kernDims
.
y
);
size_t
logStride
=
kernelDims
.
sum
();
Kernel
constFullKern
(
logStride
*
2
,
hostLoG_GausKernels
,
CUR_DEVICE
);
Kernel
constLoGKernelMem_x
=
constFullKern
.
getOffsetCopy
(
Vec
<
size_t
>
(
kernelDims
.
x
,
1
,
1
),
0
);
Kernel
constLoGKernelMem_y
=
constFullKern
.
getOffsetCopy
(
Vec
<
size_t
>
(
1
,
kernelDims
.
y
,
1
),
kernelDims
.
x
);
Kernel
constLoGKernelMem_z
=
constFullKern
.
getOffsetCopy
(
Vec
<
size_t
>
(
1
,
1
,
kernelDims
.
z
),
kernelDims
.
x
+
kernelDims
.
y
);
Kernel
constGausKernelMem_x
=
constFullKern
.
getOffsetCopy
(
Vec
<
size_t
>
(
kernelDims
.
x
,
1
,
1
),
logStride
);
Kernel
constGausKernelMem_y
=
constFullKern
.
getOffsetCopy
(
Vec
<
size_t
>
(
1
,
kernelDims
.
y
,
1
),
kernelDims
.
x
+
logStride
);
Kernel
constGausKernelMem_z
=
constFullKern
.
getOffsetCopy
(
Vec
<
size_t
>
(
1
,
1
,
kernelDims
.
z
),
kernelDims
.
x
+
kernelDims
.
y
+
logStride
);
for
(
int
i
=
CUDA_IDX
;
i
<
chunks
.
size
();
i
+=
N_THREADS
)
{
if
(
!
chunks
[
i
].
sendROI
(
imageIn
,
deviceImages
.
getCurBuffer
()))
std
::
runtime_error
(
"Error sending ROI to device!"
);
size_t
memsize
=
sizeof
(
float
)
*
chunks
[
i
].
getFullChunkSize
().
product
();
deviceImages
.
setAllDims
(
chunks
[
i
].
getFullChunkSize
());
deviceImagesScratch
.
setAllDims
(
chunks
[
i
].
getFullChunkSize
());
for
(
int
j
=
0
;
j
<
numIterations
;
++
j
)
HANDLE_ERROR
(
cudaMemset
(
deviceImagesScratch
.
getCurBuffer
()
->
getDeviceImagePointer
(),
0
,
memsize
));
// apply LoG in X
if
(
sigmas
.
x
!=
0
)
{
SeparableMultiplySum
(
chunks
[
i
],
deviceImages
,
constKernelMem_x
,
constKernelMem_y
,
constKernelMem_z
,
MIN_VAL
,
MAX_VAL
);
if
(
!
chunks
[
i
].
sendROI
(
imageIn
,
deviceImages
.
getCurBuffer
()))
std
::
runtime_error
(
"Error sending ROI to device!"
);
cudaMultiplySum
<<
<
chunks
[
i
].
blocks
,
chunks
[
i
].
threads
>>
>
(
*
(
deviceImages
.
getCurBuffer
()),
*
(
deviceImages
.
getNextBuffer
()),
constLoGKernelMem_x
,
MIN_VAL
,
MAX_VAL
,
false
);
deviceImages
.
incrementBuffer
();
if
(
sigmas
.
y
!=
0
)
{
cudaMultiplySum
<<
<
chunks
[
i
].
blocks
,
chunks
[
i
].
threads
>>
>
(
*
(
deviceImages
.
getCurBuffer
()),
*
(
deviceImages
.
getNextBuffer
()),
constGausKernelMem_y
,
MIN_VAL
,
MAX_VAL
);
deviceImages
.
incrementBuffer
();
}
if
(
sigmas
.
z
!=
0
)
{
cudaMultiplySum
<<
<
chunks
[
i
].
blocks
,
chunks
[
i
].
threads
>>
>
(
*
(
deviceImages
.
getCurBuffer
()),
*
(
deviceImages
.
getNextBuffer
()),
constGausKernelMem_z
,
MIN_VAL
,
MAX_VAL
);
deviceImages
.
incrementBuffer
();
}
cudaAddTwoImages
<<
<
chunks
[
i
].
blocks
,
chunks
[
i
].
threads
>>
>
(
*
(
deviceImages
.
getCurBuffer
()),
*
(
deviceImagesScratch
.
getCurBuffer
()),
*
(
deviceImagesScratch
.
getCurBuffer
()),
MIN_VAL
,
MAX_VAL
);
DEBUG_KERNEL_CHECK
();
}
chunks
[
i
].
retriveROI
(
imageOut
,
deviceImages
.
getCurBuffer
());
// apply LoG in Y
if
(
sigmas
.
y
!=
0
)
{
if
(
!
chunks
[
i
].
sendROI
(
imageIn
,
deviceImages
.
getCurBuffer
()))
std
::
runtime_error
(
"Error sending ROI to device!"
);
if
(
sigmas
.
x
!=
0
)
{
cudaMultiplySum
<<
<
chunks
[
i
].
blocks
,
chunks
[
i
].
threads
>>
>
(
*
(
deviceImages
.
getCurBuffer
()),
*
(
deviceImages
.
getNextBuffer
()),
constGausKernelMem_x
,
MIN_VAL
,
MAX_VAL
);
deviceImages
.
incrementBuffer
();
}
cudaMultiplySum
<<
<
chunks
[
i
].
blocks
,
chunks
[
i
].
threads
>>
>
(
*
(
deviceImages
.
getCurBuffer
()),
*
(
deviceImages
.
getNextBuffer
()),
constLoGKernelMem_y
,
MIN_VAL
,
MAX_VAL
,
false
);
deviceImages
.
incrementBuffer
();
if
(
sigmas
.
z
!=
0
)
{
cudaMultiplySum
<<
<
chunks
[
i
].
blocks
,
chunks
[
i
].
threads
>>
>
(
*
(
deviceImages
.
getCurBuffer
()),
*
(
deviceImages
.
getNextBuffer
()),
constGausKernelMem_z
,
MIN_VAL
,
MAX_VAL
);
deviceImages
.
incrementBuffer
();
}
cudaAddTwoImages
<<
<
chunks
[
i
].
blocks
,
chunks
[
i
].
threads
>>
>
(
*
(
deviceImages
.
getCurBuffer
()),
*
(
deviceImagesScratch
.
getCurBuffer
()),
*
(
deviceImagesScratch
.
getCurBuffer
()),
MIN_VAL
,
MAX_VAL
);
DEBUG_KERNEL_CHECK
();
}
// apply LoG in Z
if
(
sigmas
.
z
!=
0
)
{
if
(
!
chunks
[
i
].
sendROI
(
imageIn
,
deviceImages
.
getCurBuffer
()))
std
::
runtime_error
(
"Error sending ROI to device!"
);
if
(
sigmas
.
x
!=
0
)
{
cudaMultiplySum
<<
<
chunks
[
i
].
blocks
,
chunks
[
i
].
threads
>>
>
(
*
(
deviceImages
.
getCurBuffer
()),
*
(
deviceImages
.
getNextBuffer
()),
constGausKernelMem_x
,
MIN_VAL
,
MAX_VAL
);
deviceImages
.
incrementBuffer
();
}
if
(
sigmas
.
y
!=
0
)
{
cudaMultiplySum
<<
<
chunks
[
i
].
blocks
,
chunks
[
i
].
threads
>>
>
(
*
(
deviceImages
.
getCurBuffer
()),
*
(
deviceImages
.
getNextBuffer
()),
constGausKernelMem_y
,
MIN_VAL
,
MAX_VAL
);
deviceImages
.
incrementBuffer
();
}
cudaMultiplySum
<<
<
chunks
[
i
].
blocks
,
chunks
[
i
].
threads
>>
>
(
*
(
deviceImages
.
getCurBuffer
()),
*
(
deviceImages
.
getNextBuffer
()),
constLoGKernelMem_z
,
MIN_VAL
,
MAX_VAL
,
false
);
deviceImages
.
incrementBuffer
();
cudaAddTwoImages
<<
<
chunks
[
i
].
blocks
,
chunks
[
i
].
threads
>>
>
(
*
(
deviceImages
.
getCurBuffer
()),
*
(
deviceImagesScratch
.
getCurBuffer
()),
*
(
deviceImagesScratch
.
getCurBuffer
()),
MIN_VAL
,
MAX_VAL
);
DEBUG_KERNEL_CHECK
();
}
chunks
[
i
].
retriveROI
(
imageOut
,
deviceImagesScratch
.
getCurBuffer
());
}
constFullKern
.
clean
();
}
delete
[]
hostKernels
;
delete
[]
host
LoG_Gaus
Kernels
;
}
This diff is collapsed.
Click to expand it.
src/c/Cuda/KernelGenerators.h
+
1
−
1
View file @
29f554c6
...
...
@@ -5,4 +5,4 @@
#include
<vector>
float
*
createGaussianKernel
(
Vec
<
double
>
sigmas
,
Vec
<
size_t
>&
dimsOut
);
float
*
createLoGKernel
(
Vec
<
double
>
sigmas
,
Vec
<
size_t
>&
dimsOut
);
float
*
createLoG
_Gaus
Kernel
s
(
Vec
<
double
>
sigmas
,
Vec
<
size_t
>&
dimsOut
);
This diff is collapsed.
Click to expand it.
src/c/Cuda/LoGKernel.cpp
+
106
−
61
View file @
29f554c6
...
...
@@ -2,7 +2,7 @@
#include
<functional>
float
*
createLoGKernel
(
Vec
<
double
>
sigmas
,
Vec
<
size_t
>&
dimsOut
)
float
*
createLoG
_Gaus
Kernel
s
(
Vec
<
double
>
sigmas
,
Vec
<
size_t
>&
dimsOut
)
{
const
double
PI
=
std
::
atan
(
1.0
)
*
4
;
...
...
@@ -16,79 +16,124 @@ float* createLoGKernel(Vec<double> sigmas, Vec<size_t>& dimsOut)
Vec
<
double
>
mid
=
Vec
<
double
>
(
dimsOut
)
/
2.0
-
0.5
;
float
*
kernelOut
=
new
float
[
dimsOut
.
sum
()];
bool
is3d
=
sigmas
!=
Vec
<
double
>
(
0.0
);
float
*
kernelOut
=
new
float
[
dimsOut
.
sum
()
*
2
];
Vec
<
double
>
sigmaSqr
=
sigmas
.
pwr
(
2
);
Vec
<
double
>
oneOverSigSqr
=
Vec
<
double
>
(
1.0
)
/
sigmaSqr
;
Vec
<
double
>
twoSigmaSqr
=
sigmaSqr
*
2
;
Vec
<
double
>
sigmaForth
=
sigmas
.
pwr
(
4
);
int
loGstride
=
dimsOut
.
sum
();
if
(
is3d
)
for
(
int
i
=
0
;
i
<
3
;
++
i
)
{
// LaTeX form of LoG
// $\frac{\Big(\frac{(x-\mu_x)^2}{\sigma_x^4}-\frac{1}{\sigma_x^2}+\frac{(y-\mu_y)^2}{\sigma_y^4}-\frac{1}{\sigma_y^2}+\frac{(z-\mu_z)^2}{\sigma_z^4}-\frac{1}{\sigma_z^2}\Big)\exp\Big(-\frac{(x-\mu_x)^2}{2\sigma_x^2}-\frac{(y-\mu_y)^2}{2\sigma_y^2}-\frac{(z-\mu)^2}{2\sigma_z^2}\Big)}{(2\pi)^{\frac{3}{2}}\sigma_x\sigma_y\sigma_z}$
Vec
<
double
>
sigma4th
=
sigmas
.
pwr
(
4
)
;
double
subtractor
=
(
Vec
<
double
>
(
1.0
f
,
1.0
f
,
1.0
f
)
/
sigmaSqr
).
sum
();
double
denominator
=
pow
(
2.0
*
PI
,
3.0
/
2.0
)
*
sigmas
.
product
()
;
int
stride
=
0
;
if
(
i
>
0
)
stride
=
dimsOut
.
x
;
if
(
i
>
1
)
stride
+=
dimsOut
.
y
;
for
(
int
i
=
0
;
i
<
3
;
++
i
)
if
(
sigmas
.
e
[
i
]
==
0
)
{
size_t
startOffset
=
0
;
if
(
i
>
0
)
startOffset
+=
dimsOut
.
x
;
if
(
i
>
1
)
startOffset
+=
dimsOut
.
y
;
for
(
int
j
=
0
;
j
<
dimsOut
.
e
[
i
];
++
j
)
{
int
firstOther
=
0
;
if
(
j
==
0
)
firstOther
=
1
;
int
secondOther
=
2
;
if
(
j
==
2
)
secondOther
=
1
;
double
firstAdditive
=
-
1.0
/
sigmaSqr
.
e
[
firstOther
];
double
secondAdditive
=
-
1.0
/
sigmaSqr
.
e
[
secondOther
];
double
muSub
=
SQR
(
j
-
mid
.
e
[
i
]);
double
muSubSigSqr
=
muSub
/
(
2
*
sigmaSqr
.
e
[
i
]);
double
additive
=
muSub
/
sigma4th
.
e
[
i
]
-
1.0
/
sigmaSqr
.
e
[
i
];
kernelOut
[
stride
]
=
0.0
f
;
kernelOut
[
stride
+
loGstride
]
=
1.0
f
;
continue
;
}
double
posVal
=
((
additive
+
firstAdditive
+
secondAdditive
)
*
exp
(
-
muSubSigSqr
))
/
denominator
;
kernelOut
[
startOffset
+
j
]
=
(
float
)
posVal
;
}
double
gaussSum
=
0.0
;
for
(
int
j
=
0
;
j
<
dimsOut
.
e
[
i
];
++
j
)
{
double
pos
=
j
-
mid
.
e
[
i
];
double
posSqr
=
SQR
(
pos
);
double
gauss
=
exp
(
-
(
posSqr
/
twoSigmaSqr
.
e
[
i
]));
double
logVal
=
(
posSqr
/
sigmaForth
.
e
[
i
]
-
oneOverSigSqr
.
e
[
i
])
*
gauss
;
kernelOut
[
j
+
stride
]
=
(
float
)
logVal
;
kernelOut
[
j
+
stride
+
loGstride
]
=
gauss
;
gaussSum
+=
gauss
;
}
double
sumVal
=
0.0
;
for
(
int
j
=
0
;
j
<
dimsOut
.
e
[
i
];
++
j
)
{
kernelOut
[
j
+
stride
]
/=
(
float
)
gaussSum
;
sumVal
+=
kernelOut
[
j
+
stride
];
kernelOut
[
j
+
stride
+
loGstride
]
/=
(
float
)
gaussSum
;
}
}
else
{
// LaTeX form of LoG
// $\frac{-1}{\pi\sigma_x^2\sigma_y^2}\Bigg(1-\frac{(x-\mu_x)^2}{2\sigma_x^2}-\frac{(y-\mu_y)^2}{2\sigma_y^2}\Bigg)\exp\Bigg(-\frac{(x-\mu_x)^2}{2\sigma_x^2}-\frac{(y-\mu_y)^2}{2\sigma_y^2}\Bigg)$
// figure out which dim is zero
double
sigProd
=
1.0
;
if
(
sigmas
.
x
!=
0
)
sigProd
*=
sigmas
.
x
;
if
(
sigmas
.
y
!=
0
)
sigProd
*=
sigmas
.
y
;
if
(
sigmas
.
z
!=
0
)
sigProd
*=
sigmas
.
z
;
double
denominator
=
-
PI
*
sigProd
;
for
(
int
i
=
0
;
i
<
2
;
++
i
)
for
(
int
j
=
0
;
j
<
dimsOut
.
e
[
i
];
++
j
)
{
size_t
startOffset
=
i
*
dimsOut
.
e
[
0
];
for
(
int
j
=
0
;
j
<
dimsOut
.
e
[
i
];
++
j
)
{
double
gaussComp
=
SQR
(
j
-
mid
.
e
[
i
])
/
twoSigmaSqr
.
e
[
i
];
double
posVal
=
((
1.0
-
gaussComp
)
*
exp
(
-
gaussComp
))
/
denominator
;
kernelOut
[
startOffset
+
j
]
=
(
float
)
posVal
;
}
kernelOut
[
j
+
stride
]
-=
(
float
)
sumVal
;
}
}
//bool is3d = sigmas != Vec<double>(0.0);
//
//if (is3d)
//{
// // LaTeX form of LoG
// // $\frac{\Big(\frac{(x-\mu_x)^2}{\sigma_x^4}-\frac{1}{\sigma_x^2}+\frac{(y-\mu_y)^2}{\sigma_y^4}-\frac{1}{\sigma_y^2}+\frac{(z-\mu_z)^2}{\sigma_z^4}-\frac{1}{\sigma_z^2}\Big)\exp\Big(-\frac{(x-\mu_x)^2}{2\sigma_x^2}-\frac{(y-\mu_y)^2}{2\sigma_y^2}-\frac{(z-\mu)^2}{2\sigma_z^2}\Big)}{(2\pi)^{\frac{3}{2}}\sigma_x\sigma_y\sigma_z}$
// Vec<double> sigma4th = sigmas.pwr(4);
// double subtractor = (Vec<double>(1.0f, 1.0f, 1.0f) / sigmaSqr).sum();
// double denominator = pow(2.0*PI, 3.0 / 2.0)*sigmas.product();
// for (int i =0; i<3; ++i)
// {
// size_t startOffset = 0;
// if (i > 0)
// startOffset += dimsOut.x;
// if (i > 1)
// startOffset += dimsOut.y;
// for (int j = 0; j<dimsOut.e[i]; ++j)
// {
// int firstOther = 0;
// if (j == 0)
// firstOther = 1;
// int secondOther = 2;
// if (j == 2)
// secondOther = 1;
// double firstAdditive = -1.0 / sigmaSqr.e[firstOther];
// double secondAdditive = -1.0 / sigmaSqr.e[secondOther];
// double muSub = SQR(j - mid.e[i]);
// double muSubSigSqr = muSub / (2 * sigmaSqr.e[i]);
// double additive = muSub / sigma4th.e[i] - 1.0 / sigmaSqr.e[i];
// double posVal = ((additive + firstAdditive + secondAdditive) * exp(-muSubSigSqr)) / denominator;
// kernelOut[startOffset + j] = (float)posVal;
// }
// }
//}
//else
//{
// // LaTeX form of LoG
// // $\frac{-1}{\pi\sigma_x^2\sigma_y^2}\Bigg(1-\frac{(x-\mu_x)^2}{2\sigma_x^2}-\frac{(y-\mu_y)^2}{2\sigma_y^2}\Bigg)\exp\Bigg(-\frac{(x-\mu_x)^2}{2\sigma_x^2}-\frac{(y-\mu_y)^2}{2\sigma_y^2}\Bigg)$
// // figure out which dim is zero
// double sigProd = 1.0;
// if (sigmas.x != 0)
// sigProd *= sigmas.x;
// if (sigmas.y != 0)
// sigProd *= sigmas.y;
// if (sigmas.z != 0)
// sigProd *= sigmas.z;
// double denominator = -PI*sigProd;
// for (int i=0; i<2; ++i)
// {
// size_t startOffset = i*dimsOut.e[0];
// for (int j = 0; j < dimsOut.e[i]; ++j)
// {
// double gaussComp = SQR(j - mid.e[i]) / twoSigmaSqr.e[i];
// double posVal = ((1.0 - gaussComp)*exp(-gaussComp)) / denominator;
// kernelOut[startOffset + j] = (float)posVal;
// }
// }
//}
return
kernelOut
;
}
This diff is collapsed.
Click to expand it.
Preview
0%
Loading
Try again
or
attach a new file
.
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Save comment
Cancel
Please
register
or
sign in
to comment