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added mask support to cv::norm, cv::meanStdDev
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@ilya-lavrenov
ilya-lavrenov committed Jan 9, 2014
1 parent 3a4d408 commit 6ee0b6e
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Showing 4 changed files with 168 additions and 22 deletions.
2 changes: 1 addition & 1 deletion modules/core/include/opencv2/core/mat.inl.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -60,7 +60,7 @@ inline void _InputArray::init(int _flags, const void* _obj, Size _sz)

inline void* _InputArray::getObj() const { return obj; }

inline _InputArray::_InputArray() { init(0, 0); }
inline _InputArray::_InputArray() { init(NONE, 0); }
inline _InputArray::_InputArray(int _flags, void* _obj) { init(_flags, _obj); }
inline _InputArray::_InputArray(const Mat& m) { init(MAT+ACCESS_READ, &m); }
inline _InputArray::_InputArray(const std::vector<Mat>& vec) { init(STD_VECTOR_MAT+ACCESS_READ, &vec); }
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15 changes: 14 additions & 1 deletion modules/core/src/opencl/reduce.cl
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Original file line number Diff line number Diff line change
Expand Up @@ -51,7 +51,12 @@
#endif

#define noconvert

#ifdef HAVE_MASK
#define EXTRA_PARAMS , __global const uchar * mask, int mask_step, int mask_offset
#else
#define EXTRA_PARAMS
#endif

#if defined OP_SUM || defined OP_SUM_ABS || defined OP_SUM_SQR
#if OP_SUM
Expand All @@ -65,11 +70,19 @@
__local dstT localmem[WGS2_ALIGNED]
#define DEFINE_ACCUMULATOR \
dstT accumulator = (dstT)(0)
#ifdef HAVE_MASK
#define REDUCE_GLOBAL \
dstT temp = convertToDT(src[0]); \
int mask_index = mad24(id / cols, mask_step, mask_offset + (id % cols)); \
if (mask[mask_index]) \
FUNC(accumulator, temp)
#else
#define REDUCE_GLOBAL \
dstT temp = convertToDT(src[0]); \
FUNC(accumulator, temp)
#endif
#define SET_LOCAL_1 \
localmem[lid] = accumulator
localmem[lid] = accumulator
#define REDUCE_LOCAL_1 \
localmem[lid - WGS2_ALIGNED] += accumulator
#define REDUCE_LOCAL_2 \
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61 changes: 41 additions & 20 deletions modules/core/src/stat.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -466,7 +466,7 @@ template <typename T> Scalar ocl_part_sum(Mat m)

enum { OCL_OP_SUM = 0, OCL_OP_SUM_ABS = 1, OCL_OP_SUM_SQR = 2 };

static bool ocl_sum( InputArray _src, Scalar & res, int sum_op )
static bool ocl_sum( InputArray _src, Scalar & res, int sum_op, InputArray _mask = noArray() )
{
CV_Assert(sum_op == OCL_OP_SUM || sum_op == OCL_OP_SUM_ABS || sum_op == OCL_OP_SUM_SQR);

Expand All @@ -481,6 +481,8 @@ static bool ocl_sum( InputArray _src, Scalar & res, int sum_op )

int ddepth = std::max(sum_op == OCL_OP_SUM_SQR ? CV_32F : CV_32S, depth),
dtype = CV_MAKE_TYPE(ddepth, cn);
bool haveMask = _mask.kind() != _InputArray::NONE;
CV_Assert(!haveMask || _mask.type() == CV_8UC1);

int wgs2_aligned = 1;
while (wgs2_aligned < (int)wgs)
Expand All @@ -490,16 +492,24 @@ static bool ocl_sum( InputArray _src, Scalar & res, int sum_op )
static const char * const opMap[3] = { "OP_SUM", "OP_SUM_ABS", "OP_SUM_SQR" };
char cvt[40];
ocl::Kernel k("reduce", ocl::core::reduce_oclsrc,
format("-D srcT=%s -D dstT=%s -D convertToDT=%s -D %s -D WGS=%d -D WGS2_ALIGNED=%d%s",
format("-D srcT=%s -D dstT=%s -D convertToDT=%s -D %s -D WGS=%d -D WGS2_ALIGNED=%d%s%s",
ocl::typeToStr(type), ocl::typeToStr(dtype), ocl::convertTypeStr(depth, ddepth, cn, cvt),
opMap[sum_op], (int)wgs, wgs2_aligned,
doubleSupport ? " -D DOUBLE_SUPPORT" : ""));
doubleSupport ? " -D DOUBLE_SUPPORT" : "",
haveMask ? " -D HAVE_MASK" : ""));
if (k.empty())
return false;

UMat src = _src.getUMat(), db(1, dbsize, dtype);
k.args(ocl::KernelArg::ReadOnlyNoSize(src), src.cols, (int)src.total(),
dbsize, ocl::KernelArg::PtrWriteOnly(db));
UMat src = _src.getUMat(), db(1, dbsize, dtype), mask = _mask.getUMat();

ocl::KernelArg srcarg = ocl::KernelArg::ReadOnlyNoSize(src),
dbarg = ocl::KernelArg::PtrWriteOnly(db),
maskarg = ocl::KernelArg::ReadOnlyNoSize(mask);

if (haveMask)
k.args(srcarg, src.cols, (int)src.total(), dbsize, dbarg, maskarg);
else
k.args(srcarg, src.cols, (int)src.total(), dbsize, dbarg);

size_t globalsize = dbsize * wgs;
if (k.run(1, &globalsize, &wgs, false))
Expand Down Expand Up @@ -807,15 +817,17 @@ cv::Scalar cv::mean( InputArray _src, InputArray _mask )

namespace cv {

static bool ocl_meanStdDev( InputArray _src, OutputArray _mean, OutputArray _sdv )
static bool ocl_meanStdDev( InputArray _src, OutputArray _mean, OutputArray _sdv, InputArray _mask )
{
bool haveMask = _mask.kind() != _InputArray::NONE;

Scalar mean, stddev;
if (!ocl_sum(_src, mean, OCL_OP_SUM))
if (!ocl_sum(_src, mean, OCL_OP_SUM, _mask))
return false;
if (!ocl_sum(_src, stddev, OCL_OP_SUM_SQR))
if (!ocl_sum(_src, stddev, OCL_OP_SUM_SQR, _mask))
return false;

double total = 1.0 / _src.total();
double total = 1.0 / (haveMask ? countNonZero(_mask) : _src.total());
int k, j, cn = _src.channels();
for (int i = 0; i < cn; ++i)
{
Expand Down Expand Up @@ -850,7 +862,7 @@ static bool ocl_meanStdDev( InputArray _src, OutputArray _mean, OutputArray _sdv

void cv::meanStdDev( InputArray _src, OutputArray _mean, OutputArray _sdv, InputArray _mask )
{
if (ocl::useOpenCL() && _src.isUMat() && _mask.empty() && ocl_meanStdDev(_src, _mean, _sdv))
if (ocl::useOpenCL() && _src.isUMat() && ocl_meanStdDev(_src, _mean, _sdv, _mask))
return;

Mat src = _src.getMat(), mask = _mask.getMat();
Expand Down Expand Up @@ -1883,13 +1895,14 @@ static NormDiffFunc getNormDiffFunc(int normType, int depth)

namespace cv {

static bool ocl_norm( InputArray _src, int normType, double & result )
static bool ocl_norm( InputArray _src, int normType, InputArray _mask, double & result )
{
int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
bool doubleSupport = ocl::Device::getDefault().doubleFPConfig() > 0;
bool doubleSupport = ocl::Device::getDefault().doubleFPConfig() > 0,
haveMask = _mask.kind() != _InputArray::NONE;

if ( !(normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2) ||
(!doubleSupport && depth == CV_64F))
(!doubleSupport && depth == CV_64F) || (normType == NORM_INF && haveMask && cn != 1))
return false;

UMat src = _src.getUMat();
Expand Down Expand Up @@ -1921,17 +1934,25 @@ static bool ocl_norm( InputArray _src, int normType, double & result )
else
abssrc = src;

cv::minMaxIdx(abssrc.reshape(1), NULL, &result);
cv::minMaxIdx(haveMask ? abssrc : abssrc.reshape(1), NULL, &result, NULL, NULL, _mask);
}
else if (normType == NORM_L1 || normType == NORM_L2)
{
Scalar s;
Scalar sc;
bool unstype = depth == CV_8U || depth == CV_16U;

if ( !ocl_sum(src.reshape(1), s, normType == NORM_L2 ?
OCL_OP_SUM_SQR : (unstype ? OCL_OP_SUM : OCL_OP_SUM_ABS)) )
if ( !ocl_sum(haveMask ? src : src.reshape(1), sc, normType == NORM_L2 ?
OCL_OP_SUM_SQR : (unstype ? OCL_OP_SUM : OCL_OP_SUM_ABS), _mask) )
return false;
result = normType == NORM_L1 ? s[0] : std::sqrt(s[0]);

if (!haveMask)
cn = 1;

double s = 0.0;
for (int i = 0; i < cn; ++i)
s += sc[i];

result = normType == NORM_L1 ? s : std::sqrt(s);
}

return true;
Expand All @@ -1947,7 +1968,7 @@ double cv::norm( InputArray _src, int normType, InputArray _mask )
((normType == NORM_HAMMING || normType == NORM_HAMMING2) && _src.type() == CV_8U) );

double _result = 0;
if (ocl::useOpenCL() && _mask.empty() && _src.isUMat() && _src.dims() <= 2 && ocl_norm(_src, normType, _result))
if (ocl::useOpenCL() && _src.isUMat() && _src.dims() <= 2 && ocl_norm(_src, normType, _mask, _result))
return _result;

Mat src = _src.getMat(), mask = _mask.getMat();
Expand Down
112 changes: 112 additions & 0 deletions modules/core/test/ocl/test_arithm.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -924,6 +924,25 @@ OCL_TEST_P(MeanStdDev, Mat)
}
}

OCL_TEST_P(MeanStdDev, Mat_Mask)
{
for (int j = 0; j < test_loop_times; j++)
{
generateTestData();

Scalar cpu_mean, cpu_stddev;
Scalar gpu_mean, gpu_stddev;

OCL_OFF(cv::meanStdDev(src1_roi, cpu_mean, cpu_stddev, mask_roi));
OCL_ON(cv::meanStdDev(usrc1_roi, gpu_mean, gpu_stddev, umask_roi));

for (int i = 0; i < cn; ++i)
{
EXPECT_NEAR(cpu_mean[i], gpu_mean[i], 0.1);
EXPECT_NEAR(cpu_stddev[i], gpu_stddev[i], 0.1);
}
}
}

//////////////////////////////////////// Log /////////////////////////////////////////

Expand Down Expand Up @@ -1124,6 +1143,19 @@ OCL_TEST_P(Norm, NORM_INF_1arg)
}
}

OCL_TEST_P(Norm, NORM_INF_1arg_mask)
{
for (int j = 0; j < test_loop_times; j++)
{
generateTestData();

OCL_OFF(const double cpuRes = cv::norm(src1_roi, NORM_INF, mask_roi));
OCL_ON(const double gpuRes = cv::norm(usrc1_roi, NORM_INF, umask_roi));

EXPECT_NEAR(cpuRes, gpuRes, 0.1);
}
}

OCL_TEST_P(Norm, NORM_L1_1arg)
{
for (int j = 0; j < test_loop_times; j++)
Expand All @@ -1137,6 +1169,19 @@ OCL_TEST_P(Norm, NORM_L1_1arg)
}
}

OCL_TEST_P(Norm, NORM_L1_1arg_mask)
{
for (int j = 0; j < test_loop_times; j++)
{
generateTestData();

OCL_OFF(const double cpuRes = cv::norm(src1_roi, NORM_L1, mask_roi));
OCL_ON(const double gpuRes = cv::norm(usrc1_roi, NORM_L1, umask_roi));

EXPECT_PRED3(relativeError, cpuRes, gpuRes, 1e-6);
}
}

OCL_TEST_P(Norm, NORM_L2_1arg)
{
for (int j = 0; j < test_loop_times; j++)
Expand All @@ -1150,6 +1195,19 @@ OCL_TEST_P(Norm, NORM_L2_1arg)
}
}

OCL_TEST_P(Norm, NORM_L2_1arg_mask)
{
for (int j = 0; j < test_loop_times; j++)
{
generateTestData();

OCL_OFF(const double cpuRes = cv::norm(src1_roi, NORM_L2, mask_roi));
OCL_ON(const double gpuRes = cv::norm(usrc1_roi, NORM_L2, umask_roi));

EXPECT_PRED3(relativeError, cpuRes, gpuRes, 1e-6);
}
}

OCL_TEST_P(Norm, NORM_INF_2args)
{
for (int relative = 0; relative < 2; ++relative)
Expand All @@ -1168,6 +1226,24 @@ OCL_TEST_P(Norm, NORM_INF_2args)
}
}

OCL_TEST_P(Norm, NORM_INF_2args_mask)
{
for (int relative = 0; relative < 2; ++relative)
for (int j = 0; j < test_loop_times; j++)
{
generateTestData();

int type = NORM_INF;
if (relative == 1)
type |= NORM_RELATIVE;

OCL_OFF(const double cpuRes = cv::norm(src1_roi, src2_roi, type, mask_roi));
OCL_ON(const double gpuRes = cv::norm(usrc1_roi, usrc2_roi, type, umask_roi));

EXPECT_NEAR(cpuRes, gpuRes, 0.1);
}
}

OCL_TEST_P(Norm, NORM_L1_2args)
{
for (int relative = 0; relative < 2; ++relative)
Expand All @@ -1186,6 +1262,24 @@ OCL_TEST_P(Norm, NORM_L1_2args)
}
}

OCL_TEST_P(Norm, NORM_L1_2args_mask)
{
for (int relative = 0; relative < 2; ++relative)
for (int j = 0; j < test_loop_times; j++)
{
generateTestData();

int type = NORM_L1;
if (relative == 1)
type |= NORM_RELATIVE;

OCL_OFF(const double cpuRes = cv::norm(src1_roi, src2_roi, type, mask_roi));
OCL_ON(const double gpuRes = cv::norm(usrc1_roi, usrc2_roi, type, umask_roi));

EXPECT_PRED3(relativeError, cpuRes, gpuRes, 1e-6);
}
}

OCL_TEST_P(Norm, NORM_L2_2args)
{
for (int relative = 0; relative < 2; ++relative)
Expand All @@ -1204,6 +1298,24 @@ OCL_TEST_P(Norm, NORM_L2_2args)
}
}

OCL_TEST_P(Norm, NORM_L2_2args_mask)
{
for (int relative = 0; relative < 2; ++relative)
for (int j = 0; j < test_loop_times; j++)
{
generateTestData();

int type = NORM_L2;
if (relative == 1)
type |= NORM_RELATIVE;

OCL_OFF(const double cpuRes = cv::norm(src1_roi, src2_roi, type, mask_roi));
OCL_ON(const double gpuRes = cv::norm(usrc1_roi, usrc2_roi, type, umask_roi));

EXPECT_PRED3(relativeError, cpuRes, gpuRes, 1e-6);
}
}

//////////////////////////////// Sqrt ////////////////////////////////////////////////

typedef ArithmTestBase Sqrt;
Expand Down

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