Merge pull request #7624 from pengli:gaussian_blur

pull/7556/head
Alexander Alekhin 8 years ago
commit f1d93cb23b
  1. 133
      modules/imgproc/src/opencl/gaussianBlur3x3.cl
  2. 69
      modules/imgproc/src/smooth.cpp
  3. 89
      modules/imgproc/test/ocl/test_filters.cpp

@ -0,0 +1,133 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#define DIG(a) a,
__constant float kx[] = { KERNEL_MATRIX_X };
__constant float ky[] = { KERNEL_MATRIX_Y };
#define OP(delta, y, x) (convert_float16(arr[(y + delta) * 3 + x]) * ky[y] * kx[x])
__kernel void gaussianBlur3x3_8UC1_cols16_rows2(__global const uint* src, int src_step,
__global uint* dst, int dst_step, int rows, int cols)
{
int block_x = get_global_id(0);
int y = get_global_id(1) * 2;
int ssx, dsx;
if ((block_x * 16) >= cols || y >= rows) return;
uint4 line[4];
uint4 line_out[2];
uchar a; uchar16 b; uchar c;
uchar d; uchar16 e; uchar f;
uchar g; uchar16 h; uchar i;
uchar j; uchar16 k; uchar l;
ssx = dsx = 1;
int src_index = block_x * 4 * ssx + (y - 1) * (src_step / 4);
line[1] = vload4(0, src + src_index + (src_step / 4));
line[2] = vload4(0, src + src_index + 2 * (src_step / 4));
#ifdef BORDER_CONSTANT
line[0] = (y == 0) ? (uint4)0 : vload4(0, src + src_index);
line[3] = (y == (rows - 2)) ? (uint4)0 : vload4(0, src + src_index + 3 * (src_step / 4));
#elif defined BORDER_REFLECT_101
line[0] = (y == 0) ? line[2] : vload4(0, src + src_index);
line[3] = (y == (rows - 2)) ? line[1] : vload4(0, src + src_index + 3 * (src_step / 4));
#elif defined (BORDER_REPLICATE) || defined(BORDER_REFLECT)
line[0] = (y == 0) ? line[1] : vload4(0, src + src_index);
line[3] = (y == (rows - 2)) ? line[2] : vload4(0, src + src_index + 3 * (src_step / 4));
#endif
__global uchar *src_p = (__global uchar *)src;
src_index = block_x * 16 * ssx + (y - 1) * src_step;
bool line_end = ((block_x + 1) * 16 == cols);
b = as_uchar16(line[0]);
e = as_uchar16(line[1]);
h = as_uchar16(line[2]);
k = as_uchar16(line[3]);
#ifdef BORDER_CONSTANT
a = (block_x == 0 || y == 0) ? 0 : src_p[src_index - 1];
c = (line_end || y == 0) ? 0 : src_p[src_index + 16];
d = (block_x == 0) ? 0 : src_p[src_index + src_step - 1];
f = line_end ? 0 : src_p[src_index + src_step + 16];
g = (block_x == 0) ? 0 : src_p[src_index + 2 * src_step - 1];
i = line_end ? 0 : src_p[src_index + 2 * src_step + 16];
j = (block_x == 0 || y == (rows - 2)) ? 0 : src_p[src_index + 3 * src_step - 1];
l = (line_end || y == (rows - 2))? 0 : src_p[src_index + 3 * src_step + 16];
#elif defined BORDER_REFLECT_101
int offset;
offset = (y == 0) ? (2 * src_step) : 0;
a = (block_x == 0) ? src_p[src_index + offset + 1] : src_p[src_index + offset - 1];
c = line_end ? src_p[src_index + offset + 14] : src_p[src_index + offset + 16];
d = (block_x == 0) ? src_p[src_index + src_step + 1] : src_p[src_index + src_step - 1];
f = line_end ? src_p[src_index + src_step + 14] : src_p[src_index + src_step + 16];
g = (block_x == 0) ? src_p[src_index + 2 * src_step + 1] : src_p[src_index + 2 * src_step - 1];
i = line_end ? src_p[src_index + 2 * src_step + 14] : src_p[src_index + 2 * src_step + 16];
offset = (y == (rows - 2)) ? (1 * src_step) : (3 * src_step);
j = (block_x == 0) ? src_p[src_index + offset + 1] : src_p[src_index + offset - 1];
l = line_end ? src_p[src_index + offset + 14] : src_p[src_index + offset + 16];
#elif defined (BORDER_REPLICATE) || defined(BORDER_REFLECT)
int offset;
offset = (y == 0) ? (1 * src_step) : 0;
a = (block_x == 0) ? src_p[src_index + offset] : src_p[src_index + offset - 1];
c = line_end ? src_p[src_index + offset + 15] : src_p[src_index + offset + 16];
d = (block_x == 0) ? src_p[src_index + src_step] : src_p[src_index + src_step - 1];
f = line_end ? src_p[src_index + src_step + 15] : src_p[src_index + src_step + 16];
g = (block_x == 0) ? src_p[src_index + 2 * src_step] : src_p[src_index + 2 * src_step - 1];
i = line_end ? src_p[src_index + 2 * src_step + 15] : src_p[src_index + 2 * src_step + 16];
offset = (y == (rows - 2)) ? (2 * src_step) : (3 * src_step);
j = (block_x == 0) ? src_p[src_index + offset] : src_p[src_index + offset - 1];
l = line_end ? src_p[src_index + offset + 15] : src_p[src_index + offset + 16];
#endif
uchar16 arr[12];
float16 sum[2];
arr[0] = (uchar16)(a, b.s0123, b.s456789ab, b.scde);
arr[1] = b;
arr[2] = (uchar16)(b.s123, b.s4567, b.s89abcdef, c);
arr[3] = (uchar16)(d, e.s0123, e.s456789ab, e.scde);
arr[4] = e;
arr[5] = (uchar16)(e.s123, e.s4567, e.s89abcdef, f);
arr[6] = (uchar16)(g, h.s0123, h.s456789ab, h.scde);
arr[7] = h;
arr[8] = (uchar16)(h.s123, h.s4567, h.s89abcdef, i);
arr[9] = (uchar16)(j, k.s0123, k.s456789ab, k.scde);
arr[10] = k;
arr[11] = (uchar16)(k.s123, k.s4567, k.s89abcdef, l);
sum[0] = OP(0, 0, 0) + OP(0, 0, 1) + OP(0, 0, 2) +
OP(0, 1, 0) + OP(0, 1, 1) + OP(0, 1, 2) +
OP(0, 2, 0) + OP(0, 2, 1) + OP(0, 2, 2);
sum[1] = OP(1, 0, 0) + OP(1, 0, 1) + OP(1, 0, 2) +
OP(1, 1, 0) + OP(1, 1, 1) + OP(1, 1, 2) +
OP(1, 2, 0) + OP(1, 2, 1) + OP(1, 2, 2);
line_out[0] = as_uint4(convert_uchar16_sat_rte(sum[0]));
line_out[1] = as_uint4(convert_uchar16_sat_rte(sum[1]));
int dst_index = block_x * 4 * dsx + y * (dst_step / 4);
vstore4(line_out[0], 0, dst + dst_index);
vstore4(line_out[1], 0, dst + dst_index + (dst_step / 4));
}

@ -2016,9 +2016,68 @@ cv::Ptr<cv::FilterEngine> cv::createGaussianFilter( int type, Size ksize,
return createSeparableLinearFilter( type, type, kx, ky, Point(-1,-1), 0, borderType ); return createSeparableLinearFilter( type, type, kx, ky, Point(-1,-1), 0, borderType );
} }
#ifdef HAVE_IPP
namespace cv namespace cv
{ {
#ifdef HAVE_OPENCL
static bool ocl_GaussianBlur3x3_8UC1(InputArray _src, OutputArray _dst, int ddepth,
InputArray _kernelX, InputArray _kernelY, int borderType)
{
const ocl::Device & dev = ocl::Device::getDefault();
int type = _src.type(), sdepth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
if ( !(dev.isIntel() && (type == CV_8UC1) &&
(_src.offset() == 0) && (_src.step() % 4 == 0) &&
(_src.cols() % 16 == 0) && (_src.rows() % 2 == 0)) )
return false;
Mat kernelX = _kernelX.getMat().reshape(1, 1);
if (kernelX.cols % 2 != 1)
return false;
Mat kernelY = _kernelY.getMat().reshape(1, 1);
if (kernelY.cols % 2 != 1)
return false;
if (ddepth < 0)
ddepth = sdepth;
Size size = _src.size();
size_t globalsize[2] = { 0, 0 };
size_t localsize[2] = { 0, 0 };
globalsize[0] = size.width / 16;
globalsize[1] = size.height / 2;
const char * const borderMap[] = { "BORDER_CONSTANT", "BORDER_REPLICATE", "BORDER_REFLECT", 0, "BORDER_REFLECT_101" };
char build_opts[1024];
sprintf(build_opts, "-D %s %s%s", borderMap[borderType],
ocl::kernelToStr(kernelX, CV_32F, "KERNEL_MATRIX_X").c_str(),
ocl::kernelToStr(kernelY, CV_32F, "KERNEL_MATRIX_Y").c_str());
ocl::Kernel kernel("gaussianBlur3x3_8UC1_cols16_rows2", cv::ocl::imgproc::gaussianBlur3x3_oclsrc, build_opts);
if (kernel.empty())
return false;
UMat src = _src.getUMat();
_dst.create(size, CV_MAKETYPE(ddepth, cn));
if (!(_dst.offset() == 0 && _dst.step() % 4 == 0))
return false;
UMat dst = _dst.getUMat();
int idxArg = kernel.set(0, ocl::KernelArg::PtrReadOnly(src));
idxArg = kernel.set(idxArg, (int)src.step);
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrWriteOnly(dst));
idxArg = kernel.set(idxArg, (int)dst.step);
idxArg = kernel.set(idxArg, (int)dst.rows);
idxArg = kernel.set(idxArg, (int)dst.cols);
return kernel.run(2, globalsize, (localsize[0] == 0) ? NULL : localsize, false);
}
#endif
#ifdef HAVE_IPP
static bool ipp_GaussianBlur( InputArray _src, OutputArray _dst, Size ksize, static bool ipp_GaussianBlur( InputArray _src, OutputArray _dst, Size ksize,
double sigma1, double sigma2, double sigma1, double sigma2,
int borderType ) int borderType )
@ -2109,8 +2168,8 @@ static bool ipp_GaussianBlur( InputArray _src, OutputArray _dst, Size ksize,
#endif #endif
return false; return false;
} }
}
#endif #endif
}
void cv::GaussianBlur( InputArray _src, OutputArray _dst, Size ksize, void cv::GaussianBlur( InputArray _src, OutputArray _dst, Size ksize,
@ -2148,6 +2207,12 @@ void cv::GaussianBlur( InputArray _src, OutputArray _dst, Size ksize,
Mat kx, ky; Mat kx, ky;
createGaussianKernels(kx, ky, type, ksize, sigma1, sigma2); createGaussianKernels(kx, ky, type, ksize, sigma1, sigma2);
CV_OCL_RUN(_dst.isUMat() && _src.dims() <= 2 &&
ksize.width == 3 && ksize.height == 3 &&
(size_t)_src.rows() > ky.total() && (size_t)_src.cols() > kx.total(),
ocl_GaussianBlur3x3_8UC1(_src, _dst, CV_MAT_DEPTH(type), kx, ky, borderType));
sepFilter2D(_src, _dst, CV_MAT_DEPTH(type), kx, ky, Point(-1,-1), 0, borderType ); sepFilter2D(_src, _dst, CV_MAT_DEPTH(type), kx, ky, Point(-1,-1), 0, borderType );
} }

@ -229,6 +229,86 @@ OCL_TEST_P(GaussianBlurTest, Mat)
} }
} }
PARAM_TEST_CASE(GaussianBlur3x3_cols16_rows2_Base, MatType,
int, // kernel size
Size, // dx, dy
BorderType, // border type
double, // optional parameter
bool, // roi or not
int) // width multiplier
{
int type, borderType, ksize;
Size size;
double param;
bool useRoi;
int widthMultiple;
TEST_DECLARE_INPUT_PARAMETER(src);
TEST_DECLARE_OUTPUT_PARAMETER(dst);
virtual void SetUp()
{
type = GET_PARAM(0);
ksize = GET_PARAM(1);
size = GET_PARAM(2);
borderType = GET_PARAM(3);
param = GET_PARAM(4);
useRoi = GET_PARAM(5);
widthMultiple = GET_PARAM(6);
}
void random_roi()
{
size = Size(3, 3);
Size roiSize = randomSize(size.width, MAX_VALUE, size.height, MAX_VALUE);
roiSize.width = std::max(size.width + 13, roiSize.width & (~0xf));
roiSize.height = std::max(size.height + 1, roiSize.height & (~0x1));
Border srcBorder = randomBorder(0, useRoi ? MAX_VALUE : 0);
randomSubMat(src, src_roi, roiSize, srcBorder, type, 5, 256);
Border dstBorder = randomBorder(0, useRoi ? MAX_VALUE : 0);
randomSubMat(dst, dst_roi, roiSize, dstBorder, type, -60, 70);
UMAT_UPLOAD_INPUT_PARAMETER(src);
UMAT_UPLOAD_OUTPUT_PARAMETER(dst);
}
void Near()
{
Near(1, false);
}
void Near(double threshold, bool relative)
{
if (relative)
OCL_EXPECT_MATS_NEAR_RELATIVE(dst, threshold);
else
OCL_EXPECT_MATS_NEAR(dst, threshold);
}
};
typedef GaussianBlur3x3_cols16_rows2_Base GaussianBlur3x3_cols16_rows2;
OCL_TEST_P(GaussianBlur3x3_cols16_rows2, Mat)
{
Size kernelSize(ksize, ksize);
for (int j = 0; j < test_loop_times; j++)
{
random_roi();
double sigma1 = rng.uniform(0.1, 1.0);
double sigma2 = j % 2 == 0 ? sigma1 : rng.uniform(0.1, 1.0);
OCL_OFF(cv::GaussianBlur(src_roi, dst_roi, Size(ksize, ksize), sigma1, sigma2, borderType));
OCL_ON(cv::GaussianBlur(usrc_roi, udst_roi, Size(ksize, ksize), sigma1, sigma2, borderType));
Near(CV_MAT_DEPTH(type) >= CV_32F ? 1e-3 : 4, CV_MAT_DEPTH(type) >= CV_32F);
}
}
///////////////////////////////////////////////////////////////////////////////////////////////// /////////////////////////////////////////////////////////////////////////////////////////////////
// Erode // Erode
@ -490,6 +570,15 @@ OCL_INSTANTIATE_TEST_CASE_P(Filter, GaussianBlurTest, Combine(
Bool(), Bool(),
Values(1))); // not used Values(1))); // not used
OCL_INSTANTIATE_TEST_CASE_P(Filter, GaussianBlur3x3_cols16_rows2, Combine(
Values((MatType)CV_8UC1),
Values(3), // kernel size
Values(Size(0, 0)), // not used
FILTER_BORDER_SET_NO_WRAP_NO_ISOLATED,
Values(0.0), // not used
Bool(),
Values(1))); // not used
OCL_INSTANTIATE_TEST_CASE_P(Filter, Erode, Combine( OCL_INSTANTIATE_TEST_CASE_P(Filter, Erode, Combine(
Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC3, CV_32FC4, CV_64FC1, CV_64FC4), Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC3, CV_32FC4, CV_64FC1, CV_64FC4),
Values(0, 3, 5, 7), // kernel size, 0 means kernel = Mat() Values(0, 3, 5, 7), // kernel size, 0 means kernel = Mat()

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