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submodule
opencv
Commits
af33c118
Commit
af33c118
authored
Nov 01, 2013
by
perping
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Plain Diff
fixed a bug of haar.
parent
dfa5a27b
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3 changed files
with
64 additions
and
17 deletions
+64
-17
haar.cpp
modules/ocl/src/haar.cpp
+3
-3
haarobjectdetect.cl
modules/ocl/src/opencl/haarobjectdetect.cl
+37
-7
haarobjectdetect_scaled2.cl
modules/ocl/src/opencl/haarobjectdetect_scaled2.cl
+24
-7
No files found.
modules/ocl/src/haar.cpp
View file @
af33c118
...
...
@@ -1676,9 +1676,9 @@ void cv::ocl::OclCascadeClassifierBuf::CreateFactorRelatedBufs(
{
sz
=
sizev
[
i
];
factor
=
scalev
[
i
];
int
ystep
=
cvRound
(
std
::
max
(
2.
,
factor
)
);
int
width
=
(
cols
-
1
-
sz
.
width
+
ystep
-
1
)
/
ystep
;
int
height
=
(
rows
-
1
-
sz
.
height
+
ystep
-
1
)
/
ystep
;
double
ystep
=
cv
::
max
(
2.
,
factor
);
int
width
=
cvRound
((
cols
-
1
-
sz
.
width
+
ystep
-
1
)
/
ystep
)
;
int
height
=
cvRound
((
rows
-
1
-
sz
.
height
+
ystep
-
1
)
/
ystep
)
;
int
grpnumperline
=
(
width
+
localThreads
[
0
]
-
1
)
/
localThreads
[
0
];
int
totalgrp
=
((
height
+
localThreads
[
1
]
-
1
)
/
localThreads
[
1
])
*
grpnumperline
;
...
...
modules/ocl/src/opencl/haarobjectdetect.cl
View file @
af33c118
...
...
@@ -11,6 +11,7 @@
//
Jia
Haipeng,
jiahaipeng95@gmail.com
//
Nathan,
liujun@multicorewareinc.com
//
Peng
Xiao,
pengxiao@outlook.com
//
Erping
Pang,
erping@multicorewareinc.com
//
Redistribution
and
use
in
source
and
binary
forms,
with
or
without
modification,
//
are
permitted
provided
that
the
following
conditions
are
met:
//
...
...
@@ -321,7 +322,7 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
int
glb_x
=
grpoffx
+
(
lcl_x<<2
)
;
int
glb_y
=
grpoffy
+
lcl_y
;
int
glb_off
=
mad24
(
min
(
glb_y,
height
-
1
)
,
pixelstep,glb_x
)
;
int
glb_off
=
mad24
(
min
(
glb_y,
height
+
WINDOWSIZE
-
1
)
,
pixelstep,glb_x
)
;
int4
data
=
*
(
__global
int4*
)
&sum[glb_off]
;
int
lcl_off
=
mad24
(
lcl_y,
readwidth,
lcl_x<<2
)
;
...
...
@@ -421,12 +422,25 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
result = (stage_sum >= stagethreshold);
}
if(factor < 2)
{
if(result && lclidx %2 ==0 && lclidy %2 ==0 )
{
if(result && (x < width) && (y < height))
int queueindex = atomic_inc(lclcount);
lcloutindex[queueindex<<1] = (lclidy << 16) |
lclidx
;
lcloutindex[
(
queueindex<<1
)
+1]
=
as_int
((
float
)
variance_norm_factor
)
;
}
}
else
{
if
(
result
)
{
int
queueindex
=
atomic_inc
(
lclcount
)
;
lcloutindex[queueindex<<1]
=
(
lclidy
<<
16
)
| lclidx;
lcloutindex[
(
queueindex<<1
)
+1]
=
as_int
(
variance_norm_factor
)
;
lcloutindex[(queueindex<<1)+1] = as_int((float)variance_norm_factor);
}
}
barrier(CLK_LOCAL_MEM_FENCE);
int queuecount = lclcount[0];
...
...
@@ -549,11 +563,27 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
int
y
=
mad24
(
grpidy,grpszy,
((
temp
&
(
int
)
0xffff0000
)
>>
16
))
;
temp
=
glboutindex[0]
;
int4
candidate_result
;
candidate_result.zw
=
(
int2
)
convert_int_rtn
(
factor*20.f
)
;
candidate_result.x
=
convert_int_rtn
(
x*factor
)
;
candidate_result.y
=
convert_int_rtn
(
y*factor
)
;
candidate_result.zw
=
(
int2
)
convert_int_rtn
(
round
(
factor*20.f
)
)
;
candidate_result.x
=
convert_int_rtn
(
round
(
x*factor
)
)
;
candidate_result.y
=
convert_int_rtn
(
round
(
y*factor
)
)
;
atomic_inc
(
glboutindex
)
;
candidate[outputoff+temp+lcl_id]
=
candidate_result
;
int
i
=
outputoff+temp+lcl_id
;
if
(
candidate[i].z
==
0
)
{
candidate[i]
=
candidate_result
;
}
else
{
for
(
i=i+1
;;i++)
{
if
(
candidate[i].z
==
0
)
{
candidate[i]
=
candidate_result
;
break
;
}
}
}
}
barrier
(
CLK_LOCAL_MEM_FENCE
)
;
}//end
for
(
int
grploop=grpidx
;grploop<totalgrp;grploop+=grpnumx)
...
...
modules/ocl/src/opencl/haarobjectdetect_scaled2.cl
View file @
af33c118
...
...
@@ -18,6 +18,7 @@
//
Wu
Xinglong,
wxl370@126.com
//
Sen
Liu,
swjtuls1987@126.com
//
Peng
Xiao,
pengxiao@outlook.com
//
Erping
Pang,
erping@multicorewareinc.com
//
Redistribution
and
use
in
source
and
binary
forms,
with
or
without
modification,
//
are
permitted
provided
that
the
following
conditions
are
met:
//
...
...
@@ -142,7 +143,7 @@ __kernel void gpuRunHaarClassifierCascade_scaled2(
int
totalgrp
=
scaleinfo1.y
&
0xffff
;
float
factor
=
as_float
(
scaleinfo1.w
)
;
float
correction_t
=
correction[scalei]
;
int
ystep
=
(
int
)(
max
(
2.0f,
factor
)
+
0.5f
)
;
float
ystep
=
max
(
2.0f,
factor
)
;
for
(
int
grploop
=
get_group_id
(
0
)
; grploop < totalgrp; grploop += grpnumx)
{
...
...
@@ -151,8 +152,8 @@ __kernel void gpuRunHaarClassifierCascade_scaled2(
int
grpidx
=
grploop
-
mul24
(
grpidy,
grpnumperline
)
;
int
ix
=
mad24
(
grpidx,
grpszx,
lclidx
)
;
int
iy
=
mad24
(
grpidy,
grpszy,
lclidy
)
;
int
x
=
ix
*
ystep
;
int
y
=
iy
*
ystep
;
int
x
=
round
(
ix
*
ystep
)
;
int
y
=
round
(
iy
*
ystep
)
;
lcloutindex[lcl_id]
=
0
;
lclcount[0]
=
0
;
int
nodecounter
;
...
...
@@ -243,7 +244,7 @@ __kernel void gpuRunHaarClassifierCascade_scaled2(
barrier(CLK_LOCAL_MEM_FENCE);
if (result
&& (ix < width) && (iy < height)
)
if (result)
{
int queueindex = atomic_inc(lclcount);
lcloutindex[queueindex] = (y << 16) |
x
;
...
...
@@ -258,10 +259,26 @@ __kernel void gpuRunHaarClassifierCascade_scaled2(
int
y
=
(
temp
&
(
int
)
0xffff0000
)
>>
16
;
temp
=
atomic_inc
(
glboutindex
)
;
int4
candidate_result
;
candidate_result.zw
=
(
int2
)
convert_int_rtn
(
factor
*
20.f
)
;
candidate_result.zw
=
(
int2
)
convert_int_rtn
(
round
(
factor
*
20.f
)
)
;
candidate_result.x
=
x
;
candidate_result.y
=
y
;
candidate[outputoff
+
temp
+
lcl_id]
=
candidate_result
;
int
i
=
outputoff+temp+lcl_id
;
if
(
candidate[i].z
==
0
)
{
candidate[i]
=
candidate_result
;
}
else
{
for
(
i=i+1
;;i++)
{
if
(
candidate[i].z
==
0
)
{
candidate[i]
=
candidate_result
;
break
;
}
}
}
}
barrier
(
CLK_LOCAL_MEM_FENCE
)
;
...
...
@@ -284,7 +301,7 @@ __kernel void gpuscaleclassifier(global GpuHidHaarTreeNode *orinode, global GpuH
tr_h[i]
=
(
int
)(
t1.p[i][3]
*
scale
+
0.5f
)
;
}
t1.weight[0]
=
t1.p[2][0]
?
-
(
t1.weight[1]
*
tr_h[1]
*
tr_w[1]
+
t1.weight[2]
*
tr_h[2]
*
tr_w[2]
)
/
(
tr_h[0]
*
tr_w[0]
)
:
-t1.weight[1]
*
tr_h[1]
*
tr_w[1]
/
(
tr_h[0]
*
tr_w[0]
)
;
t1.weight[0]
=
-
(
t1.weight[1]
*
tr_h[1]
*
tr_w[1]
+
t1.weight[2]
*
tr_h[2]
*
tr_w[2]
)
/
(
tr_h[0]
*
tr_w[0]
)
;
counter
+=
nodenum
;
#
pragma
unroll
...
...
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