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Commit ce3c92eb authored by Alexander Alekhin's avatar Alexander Alekhin
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imgproc: dispatch bilateral_filter

parent b99c9145
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Showing with 50 additions and 1318 deletions
modules/imgproc/CMakeLists.txt
View file @ ce3c92eb
set(the_description "Image Processing")
ocv_add_dispatched_file(accum SSE4_1 AVX AVX2)
ocv_add_dispatched_file(bilateral_filter SSE2 AVX2)
ocv_add_dispatched_file(filter SSE2 SSE4_1 AVX2)
ocv_add_dispatched_file(color_hsv SSE2 SSE4_1 AVX2)
ocv_add_dispatched_file(color_rgb SSE2 SSE4_1 AVX2)
......
modules/imgproc/src/bilateral_filter.dispatch.cpp
View file @ ce3c92eb
......@@ -48,493 +48,14 @@
#include "opencv2/core/hal/intrin.hpp"
#include "opencl_kernels_imgproc.hpp"
#include "bilateral_filter.simd.hpp"
#include "bilateral_filter.simd_declarations.hpp" // defines CV_CPU_DISPATCH_MODES_ALL=AVX2,...,BASELINE based on CMakeLists.txt content
/****************************************************************************************\
Bilateral Filtering
\****************************************************************************************/
namespace cv
{
class BilateralFilter_8u_Invoker :
public ParallelLoopBody
{
public:
BilateralFilter_8u_Invoker(Mat& _dest, const Mat& _temp, int _radius, int _maxk,
int* _space_ofs, float *_space_weight, float *_color_weight) :
temp(&_temp), dest(&_dest), radius(_radius),
maxk(_maxk), space_ofs(_space_ofs), space_weight(_space_weight), color_weight(_color_weight)
{
}
virtual void operator() (const Range& range) const CV_OVERRIDE
{
int i, j, cn = dest->channels(), k;
Size size = dest->size();
for( i = range.start; i < range.end; i++ )
{
const uchar* sptr = temp->ptr(i+radius) + radius*cn;
uchar* dptr = dest->ptr(i);
if( cn == 1 )
{
AutoBuffer<float> buf(alignSize(size.width, CV_SIMD_WIDTH) + size.width + CV_SIMD_WIDTH - 1);
memset(buf.data(), 0, buf.size() * sizeof(float));
float *sum = alignPtr(buf.data(), CV_SIMD_WIDTH);
float *wsum = sum + alignSize(size.width, CV_SIMD_WIDTH);
k = 0;
for(; k <= maxk-4; k+=4)
{
const uchar* ksptr0 = sptr + space_ofs[k];
const uchar* ksptr1 = sptr + space_ofs[k+1];
const uchar* ksptr2 = sptr + space_ofs[k+2];
const uchar* ksptr3 = sptr + space_ofs[k+3];
j = 0;
#if CV_SIMD
v_float32 kweight0 = vx_setall_f32(space_weight[k]);
v_float32 kweight1 = vx_setall_f32(space_weight[k+1]);
v_float32 kweight2 = vx_setall_f32(space_weight[k+2]);
v_float32 kweight3 = vx_setall_f32(space_weight[k+3]);
for (; j <= size.width - v_float32::nlanes; j += v_float32::nlanes)
{
v_uint32 rval = vx_load_expand_q(sptr + j);
v_uint32 val = vx_load_expand_q(ksptr0 + j);
v_float32 w = kweight0 * v_lut(color_weight, v_reinterpret_as_s32(v_absdiff(val, rval)));
v_float32 v_wsum = vx_load_aligned(wsum + j) + w;
v_float32 v_sum = v_muladd(v_cvt_f32(v_reinterpret_as_s32(val)), w, vx_load_aligned(sum + j));
val = vx_load_expand_q(ksptr1 + j);
w = kweight1 * v_lut(color_weight, v_reinterpret_as_s32(v_absdiff(val, rval)));
v_wsum += w;
v_sum = v_muladd(v_cvt_f32(v_reinterpret_as_s32(val)), w, v_sum);
val = vx_load_expand_q(ksptr2 + j);
w = kweight2 * v_lut(color_weight, v_reinterpret_as_s32(v_absdiff(val, rval)));
v_wsum += w;
v_sum = v_muladd(v_cvt_f32(v_reinterpret_as_s32(val)), w, v_sum);
val = vx_load_expand_q(ksptr3 + j);
w = kweight3 * v_lut(color_weight, v_reinterpret_as_s32(v_absdiff(val, rval)));
v_wsum += w;
v_sum = v_muladd(v_cvt_f32(v_reinterpret_as_s32(val)), w, v_sum);
v_store_aligned(wsum + j, v_wsum);
v_store_aligned(sum + j, v_sum);
}
#endif
#if CV_SIMD128
v_float32x4 kweight4 = v_load(space_weight + k);
#endif
for (; j < size.width; j++)
{
#if CV_SIMD128
v_uint32x4 rval = v_setall_u32(sptr[j]);
v_uint32x4 val(ksptr0[j], ksptr1[j], ksptr2[j], ksptr3[j]);
v_float32x4 w = kweight4 * v_lut(color_weight, v_reinterpret_as_s32(v_absdiff(val, rval)));
wsum[j] += v_reduce_sum(w);
sum[j] += v_reduce_sum(v_cvt_f32(v_reinterpret_as_s32(val)) * w);
#else
int rval = sptr[j];
int val = ksptr0[j];
float w = space_weight[k] * color_weight[std::abs(val - rval)];
wsum[j] += w;
sum[j] += val * w;
val = ksptr1[j];
w = space_weight[k+1] * color_weight[std::abs(val - rval)];
wsum[j] += w;
sum[j] += val * w;
val = ksptr2[j];
w = space_weight[k+2] * color_weight[std::abs(val - rval)];
wsum[j] += w;
sum[j] += val * w;
val = ksptr3[j];
w = space_weight[k+3] * color_weight[std::abs(val - rval)];
wsum[j] += w;
sum[j] += val * w;
#endif
}
}
for(; k < maxk; k++)
{
const uchar* ksptr = sptr + space_ofs[k];
j = 0;
#if CV_SIMD
v_float32 kweight = vx_setall_f32(space_weight[k]);
for (; j <= size.width - v_float32::nlanes; j += v_float32::nlanes)
{
v_uint32 val = vx_load_expand_q(ksptr + j);
v_float32 w = kweight * v_lut(color_weight, v_reinterpret_as_s32(v_absdiff(val, vx_load_expand_q(sptr + j))));
v_store_aligned(wsum + j, vx_load_aligned(wsum + j) + w);
v_store_aligned(sum + j, v_muladd(v_cvt_f32(v_reinterpret_as_s32(val)), w, vx_load_aligned(sum + j)));
}
#endif
for (; j < size.width; j++)
{
int val = ksptr[j];
float w = space_weight[k] * color_weight[std::abs(val - sptr[j])];
wsum[j] += w;
sum[j] += val * w;
}
}
j = 0;
#if CV_SIMD
for (; j <= size.width - 2*v_float32::nlanes; j += 2*v_float32::nlanes)
v_pack_u_store(dptr + j, v_pack(v_round(vx_load_aligned(sum + j ) / vx_load_aligned(wsum + j )),
v_round(vx_load_aligned(sum + j + v_float32::nlanes) / vx_load_aligned(wsum + j + v_float32::nlanes))));
#endif
for (; j < size.width; j++)
{
// overflow is not possible here => there is no need to use cv::saturate_cast
CV_DbgAssert(fabs(wsum[j]) > 0);
dptr[j] = (uchar)cvRound(sum[j]/wsum[j]);
}
}
else
{
assert( cn == 3 );
AutoBuffer<float> buf(alignSize(size.width, CV_SIMD_WIDTH)*3 + size.width + CV_SIMD_WIDTH - 1);
memset(buf.data(), 0, buf.size() * sizeof(float));
float *sum_b = alignPtr(buf.data(), CV_SIMD_WIDTH);
float *sum_g = sum_b + alignSize(size.width, CV_SIMD_WIDTH);
float *sum_r = sum_g + alignSize(size.width, CV_SIMD_WIDTH);
float *wsum = sum_r + alignSize(size.width, CV_SIMD_WIDTH);
k = 0;
for(; k <= maxk-4; k+=4)
{
const uchar* ksptr0 = sptr + space_ofs[k];
const uchar* ksptr1 = sptr + space_ofs[k+1];
const uchar* ksptr2 = sptr + space_ofs[k+2];
const uchar* ksptr3 = sptr + space_ofs[k+3];
const uchar* rsptr = sptr;
j = 0;
#if CV_SIMD
v_float32 kweight0 = vx_setall_f32(space_weight[k]);
v_float32 kweight1 = vx_setall_f32(space_weight[k+1]);
v_float32 kweight2 = vx_setall_f32(space_weight[k+2]);
v_float32 kweight3 = vx_setall_f32(space_weight[k+3]);
for (; j <= size.width - v_uint8::nlanes; j += v_uint8::nlanes, rsptr += 3*v_uint8::nlanes,
ksptr0 += 3*v_uint8::nlanes, ksptr1 += 3*v_uint8::nlanes, ksptr2 += 3*v_uint8::nlanes, ksptr3 += 3*v_uint8::nlanes)
{
v_uint8 kb, kg, kr, rb, rg, rr;
v_load_deinterleave(rsptr, rb, rg, rr);
v_load_deinterleave(ksptr0, kb, kg, kr);
v_uint16 val0, val1, val2, val3, val4;
v_expand(v_absdiff(kb, rb), val0, val1);
v_expand(v_absdiff(kg, rg), val2, val3);
val0 += val2; val1 += val3;
v_expand(v_absdiff(kr, rr), val2, val3);
val0 += val2; val1 += val3;
v_uint32 vall, valh;
v_expand(val0, vall, valh);
v_float32 w0 = kweight0 * v_lut(color_weight, v_reinterpret_as_s32(vall));
v_float32 w1 = kweight0 * v_lut(color_weight, v_reinterpret_as_s32(valh));
v_store_aligned(wsum + j, w0 + vx_load_aligned(wsum + j));
v_store_aligned(wsum + j + v_float32::nlanes, w1 + vx_load_aligned(wsum + j + v_float32::nlanes));
v_expand(kb, val0, val2);
v_expand(val0, vall, valh);
v_store_aligned(sum_b + j , v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_b + j)));
v_store_aligned(sum_b + j + v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_b + j + v_float32::nlanes)));
v_expand(kg, val0, val3);
v_expand(val0, vall, valh);
v_store_aligned(sum_g + j , v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_g + j)));
v_store_aligned(sum_g + j + v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_g + j + v_float32::nlanes)));
v_expand(kr, val0, val4);
v_expand(val0, vall, valh);
v_store_aligned(sum_r + j , v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_r + j)));
v_store_aligned(sum_r + j + v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_r + j + v_float32::nlanes)));
v_expand(val1, vall, valh);
w0 = kweight0 * v_lut(color_weight, v_reinterpret_as_s32(vall));
w1 = kweight0 * v_lut(color_weight, v_reinterpret_as_s32(valh));
v_store_aligned(wsum + j + 2 * v_float32::nlanes, w0 + vx_load_aligned(wsum + j + 2 * v_float32::nlanes));
v_store_aligned(wsum + j + 3 * v_float32::nlanes, w1 + vx_load_aligned(wsum + j + 3 * v_float32::nlanes));
v_expand(val2, vall, valh);
v_store_aligned(sum_b + j + 2 * v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_b + j + 2 * v_float32::nlanes)));
v_store_aligned(sum_b + j + 3 * v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_b + j + 3 * v_float32::nlanes)));
v_expand(val3, vall, valh);
v_store_aligned(sum_g + j + 2 * v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_g + j + 2 * v_float32::nlanes)));
v_store_aligned(sum_g + j + 3 * v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_g + j + 3 * v_float32::nlanes)));
v_expand(val4, vall, valh);
v_store_aligned(sum_r + j + 2*v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_r + j + 2*v_float32::nlanes)));
v_store_aligned(sum_r + j + 3*v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_r + j + 3*v_float32::nlanes)));
v_load_deinterleave(ksptr1, kb, kg, kr);
v_expand(v_absdiff(kb, rb), val0, val1);
v_expand(v_absdiff(kg, rg), val2, val3);
val0 += val2; val1 += val3;
v_expand(v_absdiff(kr, rr), val2, val3);
val0 += val2; val1 += val3;
v_expand(val0, vall, valh);
w0 = kweight1 * v_lut(color_weight, v_reinterpret_as_s32(vall));
w1 = kweight1 * v_lut(color_weight, v_reinterpret_as_s32(valh));
v_store_aligned(wsum + j, w0 + vx_load_aligned(wsum + j));
v_store_aligned(wsum + j + v_float32::nlanes, w1 + vx_load_aligned(wsum + j + v_float32::nlanes));
v_expand(kb, val0, val2);
v_expand(val0, vall, valh);
v_store_aligned(sum_b + j, v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_b + j)));
v_store_aligned(sum_b + j + v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_b + j + v_float32::nlanes)));
v_expand(kg, val0, val3);
v_expand(val0, vall, valh);
v_store_aligned(sum_g + j, v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_g + j)));
v_store_aligned(sum_g + j + v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_g + j + v_float32::nlanes)));
v_expand(kr, val0, val4);
v_expand(val0, vall, valh);
v_store_aligned(sum_r + j, v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_r + j)));
v_store_aligned(sum_r + j + v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_r + j + v_float32::nlanes)));
v_expand(val1, vall, valh);
w0 = kweight1 * v_lut(color_weight, v_reinterpret_as_s32(vall));
w1 = kweight1 * v_lut(color_weight, v_reinterpret_as_s32(valh));
v_store_aligned(wsum + j + 2 * v_float32::nlanes, w0 + vx_load_aligned(wsum + j + 2 * v_float32::nlanes));
v_store_aligned(wsum + j + 3 * v_float32::nlanes, w1 + vx_load_aligned(wsum + j + 3 * v_float32::nlanes));
v_expand(val2, vall, valh);
v_store_aligned(sum_b + j + 2 * v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_b + j + 2 * v_float32::nlanes)));
v_store_aligned(sum_b + j + 3 * v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_b + j + 3 * v_float32::nlanes)));
v_expand(val3, vall, valh);
v_store_aligned(sum_g + j + 2 * v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_g + j + 2 * v_float32::nlanes)));
v_store_aligned(sum_g + j + 3 * v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_g + j + 3 * v_float32::nlanes)));
v_expand(val4, vall, valh);
v_store_aligned(sum_r + j + 2 * v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_r + j + 2 * v_float32::nlanes)));
v_store_aligned(sum_r + j + 3 * v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_r + j + 3 * v_float32::nlanes)));
v_load_deinterleave(ksptr2, kb, kg, kr);
v_expand(v_absdiff(kb, rb), val0, val1);
v_expand(v_absdiff(kg, rg), val2, val3);
val0 += val2; val1 += val3;
v_expand(v_absdiff(kr, rr), val2, val3);
val0 += val2; val1 += val3;
v_expand(val0, vall, valh);
w0 = kweight2 * v_lut(color_weight, v_reinterpret_as_s32(vall));
w1 = kweight2 * v_lut(color_weight, v_reinterpret_as_s32(valh));
v_store_aligned(wsum + j, w0 + vx_load_aligned(wsum + j));
v_store_aligned(wsum + j + v_float32::nlanes, w1 + vx_load_aligned(wsum + j + v_float32::nlanes));
v_expand(kb, val0, val2);
v_expand(val0, vall, valh);
v_store_aligned(sum_b + j, v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_b + j)));
v_store_aligned(sum_b + j + v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_b + j + v_float32::nlanes)));
v_expand(kg, val0, val3);
v_expand(val0, vall, valh);
v_store_aligned(sum_g + j, v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_g + j)));
v_store_aligned(sum_g + j + v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_g + j + v_float32::nlanes)));
v_expand(kr, val0, val4);
v_expand(val0, vall, valh);
v_store_aligned(sum_r + j, v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_r + j)));
v_store_aligned(sum_r + j + v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_r + j + v_float32::nlanes)));
v_expand(val1, vall, valh);
w0 = kweight2 * v_lut(color_weight, v_reinterpret_as_s32(vall));
w1 = kweight2 * v_lut(color_weight, v_reinterpret_as_s32(valh));
v_store_aligned(wsum + j + 2 * v_float32::nlanes, w0 + vx_load_aligned(wsum + j + 2 * v_float32::nlanes));
v_store_aligned(wsum + j + 3 * v_float32::nlanes, w1 + vx_load_aligned(wsum + j + 3 * v_float32::nlanes));
v_expand(val2, vall, valh);
v_store_aligned(sum_b + j + 2 * v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_b + j + 2 * v_float32::nlanes)));
v_store_aligned(sum_b + j + 3 * v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_b + j + 3 * v_float32::nlanes)));
v_expand(val3, vall, valh);
v_store_aligned(sum_g + j + 2 * v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_g + j + 2 * v_float32::nlanes)));
v_store_aligned(sum_g + j + 3 * v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_g + j + 3 * v_float32::nlanes)));
v_expand(val4, vall, valh);
v_store_aligned(sum_r + j + 2 * v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_r + j + 2 * v_float32::nlanes)));
v_store_aligned(sum_r + j + 3 * v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_r + j + 3 * v_float32::nlanes)));
v_load_deinterleave(ksptr3, kb, kg, kr);
v_expand(v_absdiff(kb, rb), val0, val1);
v_expand(v_absdiff(kg, rg), val2, val3);
val0 += val2; val1 += val3;
v_expand(v_absdiff(kr, rr), val2, val3);
val0 += val2; val1 += val3;
v_expand(val0, vall, valh);
w0 = kweight3 * v_lut(color_weight, v_reinterpret_as_s32(vall));
w1 = kweight3 * v_lut(color_weight, v_reinterpret_as_s32(valh));
v_store_aligned(wsum + j, w0 + vx_load_aligned(wsum + j));
v_store_aligned(wsum + j + v_float32::nlanes, w1 + vx_load_aligned(wsum + j + v_float32::nlanes));
v_expand(kb, val0, val2);
v_expand(val0, vall, valh);
v_store_aligned(sum_b + j, v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_b + j)));
v_store_aligned(sum_b + j + v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_b + j + v_float32::nlanes)));
v_expand(kg, val0, val3);
v_expand(val0, vall, valh);
v_store_aligned(sum_g + j, v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_g + j)));
v_store_aligned(sum_g + j + v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_g + j + v_float32::nlanes)));
v_expand(kr, val0, val4);
v_expand(val0, vall, valh);
v_store_aligned(sum_r + j, v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_r + j)));
v_store_aligned(sum_r + j + v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_r + j + v_float32::nlanes)));
v_expand(val1, vall, valh);
w0 = kweight3 * v_lut(color_weight, v_reinterpret_as_s32(vall));
w1 = kweight3 * v_lut(color_weight, v_reinterpret_as_s32(valh));
v_store_aligned(wsum + j + 2 * v_float32::nlanes, w0 + vx_load_aligned(wsum + j + 2 * v_float32::nlanes));
v_store_aligned(wsum + j + 3 * v_float32::nlanes, w1 + vx_load_aligned(wsum + j + 3 * v_float32::nlanes));
v_expand(val2, vall, valh);
v_store_aligned(sum_b + j + 2 * v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_b + j + 2 * v_float32::nlanes)));
v_store_aligned(sum_b + j + 3 * v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_b + j + 3 * v_float32::nlanes)));
v_expand(val3, vall, valh);
v_store_aligned(sum_g + j + 2 * v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_g + j + 2 * v_float32::nlanes)));
v_store_aligned(sum_g + j + 3 * v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_g + j + 3 * v_float32::nlanes)));
v_expand(val4, vall, valh);
v_store_aligned(sum_r + j + 2 * v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(vall)), w0, vx_load_aligned(sum_r + j + 2 * v_float32::nlanes)));
v_store_aligned(sum_r + j + 3 * v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(valh)), w1, vx_load_aligned(sum_r + j + 3 * v_float32::nlanes)));
}
#endif
#if CV_SIMD128
v_float32x4 kweight4 = v_load(space_weight + k);
#endif
for(; j < size.width; j++, rsptr += 3, ksptr0 += 3, ksptr1 += 3, ksptr2 += 3, ksptr3 += 3)
{
#if CV_SIMD128
v_uint32x4 rb = v_setall_u32(rsptr[0]);
v_uint32x4 rg = v_setall_u32(rsptr[1]);
v_uint32x4 rr = v_setall_u32(rsptr[2]);
v_uint32x4 b(ksptr0[0], ksptr1[0], ksptr2[0], ksptr3[0]);
v_uint32x4 g(ksptr0[1], ksptr1[1], ksptr2[1], ksptr3[1]);
v_uint32x4 r(ksptr0[2], ksptr1[2], ksptr2[2], ksptr3[2]);
v_float32x4 w = kweight4 * v_lut(color_weight, v_reinterpret_as_s32(v_absdiff(b, rb) + v_absdiff(g, rg) + v_absdiff(r, rr)));
wsum[j] += v_reduce_sum(w);
sum_b[j] += v_reduce_sum(v_cvt_f32(v_reinterpret_as_s32(b)) * w);
sum_g[j] += v_reduce_sum(v_cvt_f32(v_reinterpret_as_s32(g)) * w);
sum_r[j] += v_reduce_sum(v_cvt_f32(v_reinterpret_as_s32(r)) * w);
#else
int rb = rsptr[0], rg = rsptr[1], rr = rsptr[2];
int b = ksptr0[0], g = ksptr0[1], r = ksptr0[2];
float w = space_weight[k]*color_weight[std::abs(b - rb) + std::abs(g - rg) + std::abs(r - rr)];
wsum[j] += w;
sum_b[j] += b*w; sum_g[j] += g*w; sum_r[j] += r*w;
b = ksptr1[0]; g = ksptr1[1]; r = ksptr1[2];
w = space_weight[k+1] * color_weight[std::abs(b - rb) + std::abs(g - rg) + std::abs(r - rr)];
wsum[j] += w;
sum_b[j] += b*w; sum_g[j] += g*w; sum_r[j] += r*w;
b = ksptr2[0]; g = ksptr2[1]; r = ksptr2[2];
w = space_weight[k+2] * color_weight[std::abs(b - rb) + std::abs(g - rg) + std::abs(r - rr)];
wsum[j] += w;
sum_b[j] += b*w; sum_g[j] += g*w; sum_r[j] += r*w;
b = ksptr3[0]; g = ksptr3[1]; r = ksptr3[2];
w = space_weight[k+3] * color_weight[std::abs(b - rb) + std::abs(g - rg) + std::abs(r - rr)];
wsum[j] += w;
sum_b[j] += b*w; sum_g[j] += g*w; sum_r[j] += r*w;
#endif
}
}
for(; k < maxk; k++)
{
const uchar* ksptr = sptr + space_ofs[k];
const uchar* rsptr = sptr;
j = 0;
#if CV_SIMD
v_float32 kweight = vx_setall_f32(space_weight[k]);
for (; j <= size.width - v_uint8::nlanes; j += v_uint8::nlanes, ksptr += 3*v_uint8::nlanes, rsptr += 3*v_uint8::nlanes)
{
v_uint8 kb, kg, kr, rb, rg, rr;
v_load_deinterleave(ksptr, kb, kg, kr);
v_load_deinterleave(rsptr, rb, rg, rr);
v_uint16 b_l, b_h, g_l, g_h, r_l, r_h;
v_expand(v_absdiff(kb, rb), b_l, b_h);
v_expand(v_absdiff(kg, rg), g_l, g_h);
v_expand(v_absdiff(kr, rr), r_l, r_h);
v_uint32 val0, val1, val2, val3;
v_expand(b_l + g_l + r_l, val0, val1);
v_expand(b_h + g_h + r_h, val2, val3);
v_expand(kb, b_l, b_h);
v_expand(kg, g_l, g_h);
v_expand(kr, r_l, r_h);
v_float32 w0 = kweight * v_lut(color_weight, v_reinterpret_as_s32(val0));
v_float32 w1 = kweight * v_lut(color_weight, v_reinterpret_as_s32(val1));
v_float32 w2 = kweight * v_lut(color_weight, v_reinterpret_as_s32(val2));
v_float32 w3 = kweight * v_lut(color_weight, v_reinterpret_as_s32(val3));
v_store_aligned(wsum + j , w0 + vx_load_aligned(wsum + j));
v_store_aligned(wsum + j + v_float32::nlanes, w1 + vx_load_aligned(wsum + j + v_float32::nlanes));
v_store_aligned(wsum + j + 2*v_float32::nlanes, w2 + vx_load_aligned(wsum + j + 2*v_float32::nlanes));
v_store_aligned(wsum + j + 3*v_float32::nlanes, w3 + vx_load_aligned(wsum + j + 3*v_float32::nlanes));
v_expand(b_l, val0, val1);
v_expand(b_h, val2, val3);
v_store_aligned(sum_b + j , v_muladd(v_cvt_f32(v_reinterpret_as_s32(val0)), w0, vx_load_aligned(sum_b + j)));
v_store_aligned(sum_b + j + v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(val1)), w1, vx_load_aligned(sum_b + j + v_float32::nlanes)));
v_store_aligned(sum_b + j + 2*v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(val2)), w2, vx_load_aligned(sum_b + j + 2*v_float32::nlanes)));
v_store_aligned(sum_b + j + 3*v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(val3)), w3, vx_load_aligned(sum_b + j + 3*v_float32::nlanes)));
v_expand(g_l, val0, val1);
v_expand(g_h, val2, val3);
v_store_aligned(sum_g + j , v_muladd(v_cvt_f32(v_reinterpret_as_s32(val0)), w0, vx_load_aligned(sum_g + j)));
v_store_aligned(sum_g + j + v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(val1)), w1, vx_load_aligned(sum_g + j + v_float32::nlanes)));
v_store_aligned(sum_g + j + 2*v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(val2)), w2, vx_load_aligned(sum_g + j + 2*v_float32::nlanes)));
v_store_aligned(sum_g + j + 3*v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(val3)), w3, vx_load_aligned(sum_g + j + 3*v_float32::nlanes)));
v_expand(r_l, val0, val1);
v_expand(r_h, val2, val3);
v_store_aligned(sum_r + j , v_muladd(v_cvt_f32(v_reinterpret_as_s32(val0)), w0, vx_load_aligned(sum_r + j)));
v_store_aligned(sum_r + j + v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(val1)), w1, vx_load_aligned(sum_r + j + v_float32::nlanes)));
v_store_aligned(sum_r + j + 2*v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(val2)), w2, vx_load_aligned(sum_r + j + 2*v_float32::nlanes)));
v_store_aligned(sum_r + j + 3*v_float32::nlanes, v_muladd(v_cvt_f32(v_reinterpret_as_s32(val3)), w3, vx_load_aligned(sum_r + j + 3*v_float32::nlanes)));
}
#endif
for(; j < size.width; j++, ksptr += 3, rsptr += 3)
{
int b = ksptr[0], g = ksptr[1], r = ksptr[2];
float w = space_weight[k]*color_weight[std::abs(b - rsptr[0]) + std::abs(g - rsptr[1]) + std::abs(r - rsptr[2])];
wsum[j] += w;
sum_b[j] += b*w; sum_g[j] += g*w; sum_r[j] += r*w;
}
}
j = 0;
#if CV_SIMD
v_float32 v_one = vx_setall_f32(1.f);
for(; j <= size.width - v_uint8::nlanes; j += v_uint8::nlanes, dptr += 3*v_uint8::nlanes)
{
v_float32 w0 = v_one / vx_load_aligned(wsum + j);
v_float32 w1 = v_one / vx_load_aligned(wsum + j + v_float32::nlanes);
v_float32 w2 = v_one / vx_load_aligned(wsum + j + 2*v_float32::nlanes);
v_float32 w3 = v_one / vx_load_aligned(wsum + j + 3*v_float32::nlanes);
v_store_interleave(dptr, v_pack_u(v_pack(v_round(w0 * vx_load_aligned(sum_b + j)),
v_round(w1 * vx_load_aligned(sum_b + j + v_float32::nlanes))),
v_pack(v_round(w2 * vx_load_aligned(sum_b + j + 2*v_float32::nlanes)),
v_round(w3 * vx_load_aligned(sum_b + j + 3*v_float32::nlanes)))),
v_pack_u(v_pack(v_round(w0 * vx_load_aligned(sum_g + j)),
v_round(w1 * vx_load_aligned(sum_g + j + v_float32::nlanes))),
v_pack(v_round(w2 * vx_load_aligned(sum_g + j + 2*v_float32::nlanes)),
v_round(w3 * vx_load_aligned(sum_g + j + 3*v_float32::nlanes)))),
v_pack_u(v_pack(v_round(w0 * vx_load_aligned(sum_r + j)),
v_round(w1 * vx_load_aligned(sum_r + j + v_float32::nlanes))),
v_pack(v_round(w2 * vx_load_aligned(sum_r + j + 2*v_float32::nlanes)),
v_round(w3 * vx_load_aligned(sum_r + j + 3*v_float32::nlanes)))));
}
#endif
for(; j < size.width; j++)
{
CV_DbgAssert(fabs(wsum[j]) > 0);
wsum[j] = 1.f/wsum[j];
*(dptr++) = (uchar)cvRound(sum_b[j]*wsum[j]);
*(dptr++) = (uchar)cvRound(sum_g[j]*wsum[j]);
*(dptr++) = (uchar)cvRound(sum_r[j]*wsum[j]);
}
}
}
#if CV_SIMD
vx_cleanup();
#endif
}
private:
const Mat *temp;
Mat *dest;
int radius, maxk, *space_ofs;
float *space_weight, *color_weight;
};
namespace cv {
#ifdef HAVE_OPENCL
......@@ -542,6 +63,7 @@ static bool ocl_bilateralFilter_8u(InputArray _src, OutputArray _dst, int d,
double sigma_color, double sigma_space,
int borderType)
{
CV_INSTRUMENT_REGION();
#ifdef __ANDROID__
if (ocl::Device::getDefault().isNVidia())
return false;
......@@ -628,16 +150,18 @@ static bool ocl_bilateralFilter_8u(InputArray _src, OutputArray _dst, int d,
size_t globalsize[2] = { (size_t)dst.cols / sizeDiv, (size_t)dst.rows };
return k.run(2, globalsize, NULL, false);
}
#endif
static void
bilateralFilter_8u( const Mat& src, Mat& dst, int d,
double sigma_color, double sigma_space,
int borderType )
{
CV_INSTRUMENT_REGION();
int cn = src.channels();
int i, j, maxk, radius;
Size size = src.size();
CV_Assert( (src.type() == CV_8UC1 || src.type() == CV_8UC3) && src.data != dst.data );
......@@ -686,479 +210,18 @@ bilateralFilter_8u( const Mat& src, Mat& dst, int d,
}
}
BilateralFilter_8u_Invoker body(dst, temp, radius, maxk, space_ofs, space_weight, color_weight);
parallel_for_(Range(0, size.height), body, dst.total()/(double)(1<<16));
CV_CPU_DISPATCH(bilateralFilterInvoker_8u, (dst, temp, radius, maxk, space_ofs, space_weight, color_weight),
CV_CPU_DISPATCH_MODES_ALL);
}
class BilateralFilter_32f_Invoker :
public ParallelLoopBody
{
public:
BilateralFilter_32f_Invoker(int _cn, int _radius, int _maxk, int *_space_ofs,
const Mat& _temp, Mat& _dest, float _scale_index, float *_space_weight, float *_expLUT) :
cn(_cn), radius(_radius), maxk(_maxk), space_ofs(_space_ofs),
temp(&_temp), dest(&_dest), scale_index(_scale_index), space_weight(_space_weight), expLUT(_expLUT)
{
}
virtual void operator() (const Range& range) const CV_OVERRIDE
{
int i, j, k;
Size size = dest->size();
for( i = range.start; i < range.end; i++ )
{
const float* sptr = temp->ptr<float>(i+radius) + radius*cn;
float* dptr = dest->ptr<float>(i);
if( cn == 1 )
{
AutoBuffer<float> buf(alignSize(size.width, CV_SIMD_WIDTH) + size.width + CV_SIMD_WIDTH - 1);
memset(buf.data(), 0, buf.size() * sizeof(float));
float *sum = alignPtr(buf.data(), CV_SIMD_WIDTH);
float *wsum = sum + alignSize(size.width, CV_SIMD_WIDTH);
#if CV_SIMD
v_float32 v_one = vx_setall_f32(1.f);
v_float32 sindex = vx_setall_f32(scale_index);
#endif
k = 0;
for(; k <= maxk - 4; k+=4)
{
const float* ksptr0 = sptr + space_ofs[k];
const float* ksptr1 = sptr + space_ofs[k + 1];
const float* ksptr2 = sptr + space_ofs[k + 2];
const float* ksptr3 = sptr + space_ofs[k + 3];
j = 0;
#if CV_SIMD
v_float32 kweight0 = vx_setall_f32(space_weight[k]);
v_float32 kweight1 = vx_setall_f32(space_weight[k+1]);
v_float32 kweight2 = vx_setall_f32(space_weight[k+2]);
v_float32 kweight3 = vx_setall_f32(space_weight[k+3]);
for (; j <= size.width - v_float32::nlanes; j += v_float32::nlanes)
{
v_float32 rval = vx_load(sptr + j);
v_float32 val = vx_load(ksptr0 + j);
v_float32 knan = v_not_nan(val);
v_float32 alpha = (v_absdiff(val, rval) * sindex) & v_not_nan(rval) & knan;
v_int32 idx = v_trunc(alpha);
alpha -= v_cvt_f32(idx);
v_float32 w = (kweight0 * v_muladd(v_lut(expLUT + 1, idx), alpha, v_lut(expLUT, idx) * (v_one-alpha))) & knan;
v_float32 v_wsum = vx_load_aligned(wsum + j) + w;
v_float32 v_sum = v_muladd(val & knan, w, vx_load_aligned(sum + j));
val = vx_load(ksptr1 + j);
knan = v_not_nan(val);
alpha = (v_absdiff(val, rval) * sindex) & v_not_nan(rval) & knan;
idx = v_trunc(alpha);
alpha -= v_cvt_f32(idx);
w = (kweight1 * v_muladd(v_lut(expLUT + 1, idx), alpha, v_lut(expLUT, idx) * (v_one - alpha))) & knan;
v_wsum += w;
v_sum = v_muladd(val & knan, w, v_sum);
val = vx_load(ksptr2 + j);
knan = v_not_nan(val);
alpha = (v_absdiff(val, rval) * sindex) & v_not_nan(rval) & knan;
idx = v_trunc(alpha);
alpha -= v_cvt_f32(idx);
w = (kweight2 * v_muladd(v_lut(expLUT + 1, idx), alpha, v_lut(expLUT, idx) * (v_one - alpha))) & knan;
v_wsum += w;
v_sum = v_muladd(val & knan, w, v_sum);
val = vx_load(ksptr3 + j);
knan = v_not_nan(val);
alpha = (v_absdiff(val, rval) * sindex) & v_not_nan(rval) & knan;
idx = v_trunc(alpha);
alpha -= v_cvt_f32(idx);
w = (kweight3 * v_muladd(v_lut(expLUT + 1, idx), alpha, v_lut(expLUT, idx) * (v_one - alpha))) & knan;
v_wsum += w;
v_sum = v_muladd(val & knan, w, v_sum);
v_store_aligned(wsum + j, v_wsum);
v_store_aligned(sum + j, v_sum);
}
#endif
#if CV_SIMD128
v_float32x4 v_one4 = v_setall_f32(1.f);
v_float32x4 sindex4 = v_setall_f32(scale_index);
v_float32x4 kweight4 = v_load(space_weight + k);
#endif
for (; j < size.width; j++)
{
#if CV_SIMD128
v_float32x4 rval = v_setall_f32(sptr[j]);
v_float32x4 val(ksptr0[j], ksptr1[j], ksptr2[j], ksptr3[j]);
v_float32x4 knan = v_not_nan(val);
v_float32x4 alpha = (v_absdiff(val, rval) * sindex4) & v_not_nan(rval) & knan;
v_int32x4 idx = v_trunc(alpha);
alpha -= v_cvt_f32(idx);
v_float32x4 w = (kweight4 * v_muladd(v_lut(expLUT + 1, idx), alpha, v_lut(expLUT, idx) * (v_one4 - alpha))) & knan;
wsum[j] += v_reduce_sum(w);
sum[j] += v_reduce_sum((val & knan) * w);
#else
float rval = sptr[j];
float val = ksptr0[j];
float alpha = std::abs(val - rval) * scale_index;
int idx = cvFloor(alpha);
alpha -= idx;
if (!cvIsNaN(val))
{
float w = space_weight[k] * (cvIsNaN(rval) ? 1.f : (expLUT[idx] + alpha*(expLUT[idx + 1] - expLUT[idx])));
wsum[j] += w;
sum[j] += val * w;
}
val = ksptr1[j];
alpha = std::abs(val - rval) * scale_index;
idx = cvFloor(alpha);
alpha -= idx;
if (!cvIsNaN(val))
{
float w = space_weight[k+1] * (cvIsNaN(rval) ? 1.f : (expLUT[idx] + alpha*(expLUT[idx + 1] - expLUT[idx])));
wsum[j] += w;
sum[j] += val * w;
}
val = ksptr2[j];
alpha = std::abs(val - rval) * scale_index;
idx = cvFloor(alpha);
alpha -= idx;
if (!cvIsNaN(val))
{
float w = space_weight[k+2] * (cvIsNaN(rval) ? 1.f : (expLUT[idx] + alpha*(expLUT[idx + 1] - expLUT[idx])));
wsum[j] += w;
sum[j] += val * w;
}
val = ksptr3[j];
alpha = std::abs(val - rval) * scale_index;
idx = cvFloor(alpha);
alpha -= idx;
if (!cvIsNaN(val))
{
float w = space_weight[k+3] * (cvIsNaN(rval) ? 1.f : (expLUT[idx] + alpha*(expLUT[idx + 1] - expLUT[idx])));
wsum[j] += w;
sum[j] += val * w;
}
#endif
}
}
for(; k < maxk; k++)
{
const float* ksptr = sptr + space_ofs[k];
j = 0;
#if CV_SIMD
v_float32 kweight = vx_setall_f32(space_weight[k]);
for (; j <= size.width - v_float32::nlanes; j += v_float32::nlanes)
{
v_float32 val = vx_load(ksptr + j);
v_float32 rval = vx_load(sptr + j);
v_float32 knan = v_not_nan(val);
v_float32 alpha = (v_absdiff(val, rval) * sindex) & v_not_nan(rval) & knan;
v_int32 idx = v_trunc(alpha);
alpha -= v_cvt_f32(idx);
v_float32 w = (kweight * v_muladd(v_lut(expLUT + 1, idx), alpha, v_lut(expLUT, idx) * (v_one-alpha))) & knan;
v_store_aligned(wsum + j, vx_load_aligned(wsum + j) + w);
v_store_aligned(sum + j, v_muladd(val & knan, w, vx_load_aligned(sum + j)));
}
#endif
for (; j < size.width; j++)
{
float val = ksptr[j];
float rval = sptr[j];
float alpha = std::abs(val - rval) * scale_index;
int idx = cvFloor(alpha);
alpha -= idx;
if (!cvIsNaN(val))
{
float w = space_weight[k] * (cvIsNaN(rval) ? 1.f : (expLUT[idx] + alpha*(expLUT[idx + 1] - expLUT[idx])));
wsum[j] += w;
sum[j] += val * w;
}
}
}
j = 0;
#if CV_SIMD
for (; j <= size.width - v_float32::nlanes; j += v_float32::nlanes)
{
v_float32 v_val = vx_load(sptr + j);
v_store(dptr + j, (vx_load_aligned(sum + j) + (v_val & v_not_nan(v_val))) / (vx_load_aligned(wsum + j) + (v_one & v_not_nan(v_val))));
}
#endif
for (; j < size.width; j++)
{
CV_DbgAssert(fabs(wsum[j]) >= 0);
dptr[j] = cvIsNaN(sptr[j]) ? sum[j] / wsum[j] : (sum[j] + sptr[j]) / (wsum[j] + 1.f);
}
}
else
{
CV_Assert( cn == 3 );
AutoBuffer<float> buf(alignSize(size.width, CV_SIMD_WIDTH)*3 + size.width + CV_SIMD_WIDTH - 1);
memset(buf.data(), 0, buf.size() * sizeof(float));
float *sum_b = alignPtr(buf.data(), CV_SIMD_WIDTH);
float *sum_g = sum_b + alignSize(size.width, CV_SIMD_WIDTH);
float *sum_r = sum_g + alignSize(size.width, CV_SIMD_WIDTH);
float *wsum = sum_r + alignSize(size.width, CV_SIMD_WIDTH);
#if CV_SIMD
v_float32 v_one = vx_setall_f32(1.f);
v_float32 sindex = vx_setall_f32(scale_index);
#endif
k = 0;
for (; k <= maxk-4; k+=4)
{
const float* ksptr0 = sptr + space_ofs[k];
const float* ksptr1 = sptr + space_ofs[k+1];
const float* ksptr2 = sptr + space_ofs[k+2];
const float* ksptr3 = sptr + space_ofs[k+3];
const float* rsptr = sptr;
j = 0;
#if CV_SIMD
v_float32 kweight0 = vx_setall_f32(space_weight[k]);
v_float32 kweight1 = vx_setall_f32(space_weight[k+1]);
v_float32 kweight2 = vx_setall_f32(space_weight[k+2]);
v_float32 kweight3 = vx_setall_f32(space_weight[k+3]);
for (; j <= size.width - v_float32::nlanes; j += v_float32::nlanes, rsptr += 3 * v_float32::nlanes,
ksptr0 += 3 * v_float32::nlanes, ksptr1 += 3 * v_float32::nlanes, ksptr2 += 3 * v_float32::nlanes, ksptr3 += 3 * v_float32::nlanes)
{
v_float32 kb, kg, kr, rb, rg, rr;
v_load_deinterleave(rsptr, rb, rg, rr);
v_load_deinterleave(ksptr0, kb, kg, kr);
v_float32 knan = v_not_nan(kb) & v_not_nan(kg) & v_not_nan(kr);
v_float32 alpha = ((v_absdiff(kb, rb) + v_absdiff(kg, rg) + v_absdiff(kr, rr)) * sindex) & v_not_nan(rb) & v_not_nan(rg) & v_not_nan(rr) & knan;
v_int32 idx = v_trunc(alpha);
alpha -= v_cvt_f32(idx);
v_float32 w = (kweight0 * v_muladd(v_lut(expLUT + 1, idx), alpha, v_lut(expLUT, idx) * (v_one - alpha))) & knan;
v_float32 v_wsum = vx_load_aligned(wsum + j) + w;
v_float32 v_sum_b = v_muladd(kb & knan, w, vx_load_aligned(sum_b + j));
v_float32 v_sum_g = v_muladd(kg & knan, w, vx_load_aligned(sum_g + j));
v_float32 v_sum_r = v_muladd(kr & knan, w, vx_load_aligned(sum_r + j));
v_load_deinterleave(ksptr1, kb, kg, kr);
knan = v_not_nan(kb) & v_not_nan(kg) & v_not_nan(kr);
alpha = ((v_absdiff(kb, rb) + v_absdiff(kg, rg) + v_absdiff(kr, rr)) * sindex) & v_not_nan(rb) & v_not_nan(rg) & v_not_nan(rr) & knan;
idx = v_trunc(alpha);
alpha -= v_cvt_f32(idx);
w = (kweight1 * v_muladd(v_lut(expLUT + 1, idx), alpha, v_lut(expLUT, idx) * (v_one - alpha))) & knan;
v_wsum += w;
v_sum_b = v_muladd(kb & knan, w, v_sum_b);
v_sum_g = v_muladd(kg & knan, w, v_sum_g);
v_sum_r = v_muladd(kr & knan, w, v_sum_r);
v_load_deinterleave(ksptr2, kb, kg, kr);
knan = v_not_nan(kb) & v_not_nan(kg) & v_not_nan(kr);
alpha = ((v_absdiff(kb, rb) + v_absdiff(kg, rg) + v_absdiff(kr, rr)) * sindex) & v_not_nan(rb) & v_not_nan(rg) & v_not_nan(rr) & knan;
idx = v_trunc(alpha);
alpha -= v_cvt_f32(idx);
w = (kweight2 * v_muladd(v_lut(expLUT + 1, idx), alpha, v_lut(expLUT, idx) * (v_one - alpha))) & knan;
v_wsum += w;
v_sum_b = v_muladd(kb & knan, w, v_sum_b);
v_sum_g = v_muladd(kg & knan, w, v_sum_g);
v_sum_r = v_muladd(kr & knan, w, v_sum_r);
v_load_deinterleave(ksptr3, kb, kg, kr);
knan = v_not_nan(kb) & v_not_nan(kg) & v_not_nan(kr);
alpha = ((v_absdiff(kb, rb) + v_absdiff(kg, rg) + v_absdiff(kr, rr)) * sindex) & v_not_nan(rb) & v_not_nan(rg) & v_not_nan(rr) & knan;
idx = v_trunc(alpha);
alpha -= v_cvt_f32(idx);
w = (kweight3 * v_muladd(v_lut(expLUT + 1, idx), alpha, v_lut(expLUT, idx) * (v_one - alpha))) & knan;
v_wsum += w;
v_sum_b = v_muladd(kb & knan, w, v_sum_b);
v_sum_g = v_muladd(kg & knan, w, v_sum_g);
v_sum_r = v_muladd(kr & knan, w, v_sum_r);
v_store_aligned(wsum + j, v_wsum);
v_store_aligned(sum_b + j, v_sum_b);
v_store_aligned(sum_g + j, v_sum_g);
v_store_aligned(sum_r + j, v_sum_r);
}
#endif
#if CV_SIMD128
v_float32x4 v_one4 = v_setall_f32(1.f);
v_float32x4 sindex4 = v_setall_f32(scale_index);
v_float32x4 kweight4 = v_load(space_weight + k);
#endif
for (; j < size.width; j++, rsptr += 3, ksptr0 += 3, ksptr1 += 3, ksptr2 += 3, ksptr3 += 3)
{
#if CV_SIMD128
v_float32x4 rb = v_setall_f32(rsptr[0]);
v_float32x4 rg = v_setall_f32(rsptr[1]);
v_float32x4 rr = v_setall_f32(rsptr[2]);
v_float32x4 kb(ksptr0[0], ksptr1[0], ksptr2[0], ksptr3[0]);
v_float32x4 kg(ksptr0[1], ksptr1[1], ksptr2[1], ksptr3[1]);
v_float32x4 kr(ksptr0[2], ksptr1[2], ksptr2[2], ksptr3[2]);
v_float32x4 knan = v_not_nan(kb) & v_not_nan(kg) & v_not_nan(kr);
v_float32x4 alpha = ((v_absdiff(kb, rb) + v_absdiff(kg, rg) + v_absdiff(kr, rr)) * sindex4) & v_not_nan(rb) & v_not_nan(rg) & v_not_nan(rr) & knan;
v_int32x4 idx = v_trunc(alpha);
alpha -= v_cvt_f32(idx);
v_float32x4 w = (kweight4 * v_muladd(v_lut(expLUT + 1, idx), alpha, v_lut(expLUT, idx) * (v_one4 - alpha))) & knan;
wsum[j] += v_reduce_sum(w);
sum_b[j] += v_reduce_sum((kb & knan) * w);
sum_g[j] += v_reduce_sum((kg & knan) * w);
sum_r[j] += v_reduce_sum((kr & knan) * w);
#else
float rb = rsptr[0], rg = rsptr[1], rr = rsptr[2];
bool r_NAN = cvIsNaN(rb) || cvIsNaN(rg) || cvIsNaN(rr);
float b = ksptr0[0], g = ksptr0[1], r = ksptr0[2];
bool v_NAN = cvIsNaN(b) || cvIsNaN(g) || cvIsNaN(r);
float alpha = (std::abs(b - rb) + std::abs(g - rg) + std::abs(r - rr)) * scale_index;
int idx = cvFloor(alpha);
alpha -= idx;
if (!v_NAN)
{
float w = space_weight[k] * (r_NAN ? 1.f : (expLUT[idx] + alpha*(expLUT[idx + 1] - expLUT[idx])));
wsum[j] += w;
sum_b[j] += b*w;
sum_g[j] += g*w;
sum_r[j] += r*w;
}
b = ksptr1[0]; g = ksptr1[1]; r = ksptr1[2];
v_NAN = cvIsNaN(b) || cvIsNaN(g) || cvIsNaN(r);
alpha = (std::abs(b - rb) + std::abs(g - rg) + std::abs(r - rr)) * scale_index;
idx = cvFloor(alpha);
alpha -= idx;
if (!v_NAN)
{
float w = space_weight[k+1] * (r_NAN ? 1.f : (expLUT[idx] + alpha*(expLUT[idx + 1] - expLUT[idx])));
wsum[j] += w;
sum_b[j] += b*w;
sum_g[j] += g*w;
sum_r[j] += r*w;
}
b = ksptr2[0]; g = ksptr2[1]; r = ksptr2[2];
v_NAN = cvIsNaN(b) || cvIsNaN(g) || cvIsNaN(r);
alpha = (std::abs(b - rb) + std::abs(g - rg) + std::abs(r - rr)) * scale_index;
idx = cvFloor(alpha);
alpha -= idx;
if (!v_NAN)
{
float w = space_weight[k+2] * (r_NAN ? 1.f : (expLUT[idx] + alpha*(expLUT[idx + 1] - expLUT[idx])));
wsum[j] += w;
sum_b[j] += b*w;
sum_g[j] += g*w;
sum_r[j] += r*w;
}
b = ksptr3[0]; g = ksptr3[1]; r = ksptr3[2];
v_NAN = cvIsNaN(b) || cvIsNaN(g) || cvIsNaN(r);
alpha = (std::abs(b - rb) + std::abs(g - rg) + std::abs(r - rr)) * scale_index;
idx = cvFloor(alpha);
alpha -= idx;
if (!v_NAN)
{
float w = space_weight[k+3] * (r_NAN ? 1.f : (expLUT[idx] + alpha*(expLUT[idx + 1] - expLUT[idx])));
wsum[j] += w;
sum_b[j] += b*w;
sum_g[j] += g*w;
sum_r[j] += r*w;
}
#endif
}
}
for (; k < maxk; k++)
{
const float* ksptr = sptr + space_ofs[k];
const float* rsptr = sptr;
j = 0;
#if CV_SIMD
v_float32 kweight = vx_setall_f32(space_weight[k]);
for (; j <= size.width - v_float32::nlanes; j += v_float32::nlanes, ksptr += 3*v_float32::nlanes, rsptr += 3*v_float32::nlanes)
{
v_float32 kb, kg, kr, rb, rg, rr;
v_load_deinterleave(ksptr, kb, kg, kr);
v_load_deinterleave(rsptr, rb, rg, rr);
v_float32 knan = v_not_nan(kb) & v_not_nan(kg) & v_not_nan(kr);
v_float32 alpha = ((v_absdiff(kb, rb) + v_absdiff(kg, rg) + v_absdiff(kr, rr)) * sindex) & v_not_nan(rb) & v_not_nan(rg) & v_not_nan(rr) & knan;
v_int32 idx = v_trunc(alpha);
alpha -= v_cvt_f32(idx);
v_float32 w = (kweight * v_muladd(v_lut(expLUT + 1, idx), alpha, v_lut(expLUT, idx) * (v_one - alpha))) & knan;
v_store_aligned(wsum + j, vx_load_aligned(wsum + j) + w);
v_store_aligned(sum_b + j, v_muladd(kb & knan, w, vx_load_aligned(sum_b + j)));
v_store_aligned(sum_g + j, v_muladd(kg & knan, w, vx_load_aligned(sum_g + j)));
v_store_aligned(sum_r + j, v_muladd(kr & knan, w, vx_load_aligned(sum_r + j)));
}
#endif
for (; j < size.width; j++, ksptr += 3, rsptr += 3)
{
float b = ksptr[0], g = ksptr[1], r = ksptr[2];
bool v_NAN = cvIsNaN(b) || cvIsNaN(g) || cvIsNaN(r);
float rb = rsptr[0], rg = rsptr[1], rr = rsptr[2];
bool r_NAN = cvIsNaN(rb) || cvIsNaN(rg) || cvIsNaN(rr);
float alpha = (std::abs(b - rb) + std::abs(g - rg) + std::abs(r - rr)) * scale_index;
int idx = cvFloor(alpha);
alpha -= idx;
if (!v_NAN)
{
float w = space_weight[k] * (r_NAN ? 1.f : (expLUT[idx] + alpha*(expLUT[idx + 1] - expLUT[idx])));
wsum[j] += w;
sum_b[j] += b*w;
sum_g[j] += g*w;
sum_r[j] += r*w;
}
}
}
j = 0;
#if CV_SIMD
for (; j <= size.width - v_float32::nlanes; j += v_float32::nlanes, sptr += 3*v_float32::nlanes, dptr += 3*v_float32::nlanes)
{
v_float32 b, g, r;
v_load_deinterleave(sptr, b, g, r);
v_float32 mask = v_not_nan(b) & v_not_nan(g) & v_not_nan(r);
v_float32 w = v_one / (vx_load_aligned(wsum + j) + (v_one & mask));
v_store_interleave(dptr, (vx_load_aligned(sum_b + j) + (b & mask)) * w, (vx_load_aligned(sum_g + j) + (g & mask)) * w, (vx_load_aligned(sum_r + j) + (r & mask)) * w);
}
#endif
for (; j < size.width; j++)
{
CV_DbgAssert(fabs(wsum[j]) >= 0);
float b = *(sptr++);
float g = *(sptr++);
float r = *(sptr++);
if (cvIsNaN(b) || cvIsNaN(g) || cvIsNaN(r))
{
wsum[j] = 1.f / wsum[j];
*(dptr++) = sum_b[j] * wsum[j];
*(dptr++) = sum_g[j] * wsum[j];
*(dptr++) = sum_r[j] * wsum[j];
}
else
{
wsum[j] = 1.f / (wsum[j] + 1.f);
*(dptr++) = (sum_b[j] + b) * wsum[j];
*(dptr++) = (sum_g[j] + g) * wsum[j];
*(dptr++) = (sum_r[j] + r) * wsum[j];
}
}
}
}
#if CV_SIMD
vx_cleanup();
#endif
}
private:
int cn, radius, maxk, *space_ofs;
const Mat* temp;
Mat *dest;
float scale_index, *space_weight, *expLUT;
};
static void
bilateralFilter_32f( const Mat& src, Mat& dst, int d,
double sigma_color, double sigma_space,
int borderType )
{
CV_INSTRUMENT_REGION();
int cn = src.channels();
int i, j, maxk, radius;
double minValSrc=-1, maxValSrc=1;
......@@ -1166,7 +229,6 @@ bilateralFilter_32f( const Mat& src, Mat& dst, int d,
int kExpNumBins = 0;
float lastExpVal = 1.f;
float len, scale_index;
Size size = src.size();
CV_Assert( (src.type() == CV_32FC1 || src.type() == CV_32FC3) && src.data != dst.data );
......@@ -1236,9 +298,8 @@ bilateralFilter_32f( const Mat& src, Mat& dst, int d,
}
// parallel_for usage
BilateralFilter_32f_Invoker body(cn, radius, maxk, space_ofs, temp, dst, scale_index, space_weight, expLUT);
parallel_for_(Range(0, size.height), body, dst.total()/(double)(1<<16));
CV_CPU_DISPATCH(bilateralFilterInvoker_32f, (cn, radius, maxk, space_ofs, temp, dst, scale_index, space_weight, expLUT),
CV_CPU_DISPATCH_MODES_ALL);
}
#ifdef HAVE_IPP
......@@ -1339,9 +400,7 @@ static bool ipp_bilateralFilter(Mat &src, Mat &dst, int d, double sigmaColor, do
}
#endif
}
void cv::bilateralFilter( InputArray _src, OutputArray _dst, int d,
void bilateralFilter( InputArray _src, OutputArray _dst, int d,
double sigmaColor, double sigmaSpace,
int borderType )
{
......@@ -1365,4 +424,4 @@ void cv::bilateralFilter( InputArray _src, OutputArray _dst, int d,
"Bilateral filtering is only implemented for 8u and 32f images" );
}
/* End of file. */
} // namespace
modules/imgproc/src/bilateral_filter.simd.hpp
View file @ ce3c92eb
......@@ -43,18 +43,25 @@
#include "precomp.hpp"
#include <vector>
#include "opencv2/core/hal/intrin.hpp"
#include "opencl_kernels_imgproc.hpp"
/****************************************************************************************\
Bilateral Filtering
\****************************************************************************************/
namespace cv
{
namespace cv {
CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN
// forward declarations
void bilateralFilterInvoker_8u(
Mat& dst, const Mat& temp, int radius, int maxk,
int* space_ofs, float *space_weight, float *color_weight);
void bilateralFilterInvoker_32f(
int cn, int radius, int maxk, int *space_ofs,
const Mat& temp, Mat& dst, float scale_index, float *space_weight, float *expLUT);
#ifndef CV_CPU_OPTIMIZATION_DECLARATIONS_ONLY
namespace {
class BilateralFilter_8u_Invoker :
public ParallelLoopBody
{
......@@ -68,6 +75,8 @@ public:
virtual void operator() (const Range& range) const CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
int i, j, cn = dest->channels(), k;
Size size = dest->size();
......@@ -536,161 +545,20 @@ private:
float *space_weight, *color_weight;
};
#ifdef HAVE_OPENCL
static bool ocl_bilateralFilter_8u(InputArray _src, OutputArray _dst, int d,
double sigma_color, double sigma_space,
int borderType)
{
#ifdef __ANDROID__
if (ocl::Device::getDefault().isNVidia())
return false;
#endif
int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
int i, j, maxk, radius;
if (depth != CV_8U || cn > 4)
return false;
if (sigma_color <= 0)
sigma_color = 1;
if (sigma_space <= 0)
sigma_space = 1;
double gauss_color_coeff = -0.5 / (sigma_color * sigma_color);
double gauss_space_coeff = -0.5 / (sigma_space * sigma_space);
if ( d <= 0 )
radius = cvRound(sigma_space * 1.5);
else
radius = d / 2;
radius = MAX(radius, 1);
d = radius * 2 + 1;
UMat src = _src.getUMat(), dst = _dst.getUMat(), temp;
if (src.u == dst.u)
return false;
} // namespace anon
copyMakeBorder(src, temp, radius, radius, radius, radius, borderType);
std::vector<float> _space_weight(d * d);
std::vector<int> _space_ofs(d * d);
float * const space_weight = &_space_weight[0];
int * const space_ofs = &_space_ofs[0];
// initialize space-related bilateral filter coefficients
for( i = -radius, maxk = 0; i <= radius; i++ )
for( j = -radius; j <= radius; j++ )
{
double r = std::sqrt((double)i * i + (double)j * j);
if ( r > radius )
continue;
space_weight[maxk] = (float)std::exp(r * r * gauss_space_coeff);
space_ofs[maxk++] = (int)(i * temp.step + j * cn);
}
char cvt[3][40];
String cnstr = cn > 1 ? format("%d", cn) : "";
String kernelName("bilateral");
size_t sizeDiv = 1;
if ((ocl::Device::getDefault().isIntel()) &&
(ocl::Device::getDefault().type() == ocl::Device::TYPE_GPU))
{
//Intel GPU
if (dst.cols % 4 == 0 && cn == 1) // For single channel x4 sized images.
{
kernelName = "bilateral_float4";
sizeDiv = 4;
}
}
ocl::Kernel k(kernelName.c_str(), ocl::imgproc::bilateral_oclsrc,
format("-D radius=%d -D maxk=%d -D cn=%d -D int_t=%s -D uint_t=uint%s -D convert_int_t=%s"
" -D uchar_t=%s -D float_t=%s -D convert_float_t=%s -D convert_uchar_t=%s -D gauss_color_coeff=(float)%f",
radius, maxk, cn, ocl::typeToStr(CV_32SC(cn)), cnstr.c_str(),
ocl::convertTypeStr(CV_8U, CV_32S, cn, cvt[0]),
ocl::typeToStr(type), ocl::typeToStr(CV_32FC(cn)),
ocl::convertTypeStr(CV_32S, CV_32F, cn, cvt[1]),
ocl::convertTypeStr(CV_32F, CV_8U, cn, cvt[2]), gauss_color_coeff));
if (k.empty())
return false;
Mat mspace_weight(1, d * d, CV_32FC1, space_weight);
Mat mspace_ofs(1, d * d, CV_32SC1, space_ofs);
UMat ucolor_weight, uspace_weight, uspace_ofs;
mspace_weight.copyTo(uspace_weight);
mspace_ofs.copyTo(uspace_ofs);
k.args(ocl::KernelArg::ReadOnlyNoSize(temp), ocl::KernelArg::WriteOnly(dst),
ocl::KernelArg::PtrReadOnly(uspace_weight),
ocl::KernelArg::PtrReadOnly(uspace_ofs));
size_t globalsize[2] = { (size_t)dst.cols / sizeDiv, (size_t)dst.rows };
return k.run(2, globalsize, NULL, false);
}
#endif
static void
bilateralFilter_8u( const Mat& src, Mat& dst, int d,
double sigma_color, double sigma_space,
int borderType )
void bilateralFilterInvoker_8u(
Mat& dst, const Mat& temp, int radius, int maxk,
int* space_ofs, float *space_weight, float *color_weight)
{
int cn = src.channels();
int i, j, maxk, radius;
Size size = src.size();
CV_Assert( (src.type() == CV_8UC1 || src.type() == CV_8UC3) && src.data != dst.data );
if( sigma_color <= 0 )
sigma_color = 1;
if( sigma_space <= 0 )
sigma_space = 1;
double gauss_color_coeff = -0.5/(sigma_color*sigma_color);
double gauss_space_coeff = -0.5/(sigma_space*sigma_space);
if( d <= 0 )
radius = cvRound(sigma_space*1.5);
else
radius = d/2;
radius = MAX(radius, 1);
d = radius*2 + 1;
Mat temp;
copyMakeBorder( src, temp, radius, radius, radius, radius, borderType );
std::vector<float> _color_weight(cn*256);
std::vector<float> _space_weight(d*d);
std::vector<int> _space_ofs(d*d);
float* color_weight = &_color_weight[0];
float* space_weight = &_space_weight[0];
int* space_ofs = &_space_ofs[0];
// initialize color-related bilateral filter coefficients
for( i = 0; i < 256*cn; i++ )
color_weight[i] = (float)std::exp(i*i*gauss_color_coeff);
// initialize space-related bilateral filter coefficients
for( i = -radius, maxk = 0; i <= radius; i++ )
{
j = -radius;
for( ; j <= radius; j++ )
{
double r = std::sqrt((double)i*i + (double)j*j);
if( r > radius )
continue;
space_weight[maxk] = (float)std::exp(r*r*gauss_space_coeff);
space_ofs[maxk++] = (int)(i*temp.step + j*cn);
}
}
CV_INSTRUMENT_REGION();
BilateralFilter_8u_Invoker body(dst, temp, radius, maxk, space_ofs, space_weight, color_weight);
parallel_for_(Range(0, size.height), body, dst.total()/(double)(1<<16));
parallel_for_(Range(0, dst.rows), body, dst.total()/(double)(1<<16));
}
namespace {
class BilateralFilter_32f_Invoker :
public ParallelLoopBody
{
......@@ -705,6 +573,8 @@ public:
virtual void operator() (const Range& range) const CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
int i, j, k;
Size size = dest->size();
......@@ -1153,216 +1023,18 @@ private:
float scale_index, *space_weight, *expLUT;
};
} // namespace anon
static void
bilateralFilter_32f( const Mat& src, Mat& dst, int d,
double sigma_color, double sigma_space,
int borderType )
void bilateralFilterInvoker_32f(
int cn, int radius, int maxk, int *space_ofs,
const Mat& temp, Mat& dst, float scale_index, float *space_weight, float *expLUT)
{
int cn = src.channels();
int i, j, maxk, radius;
double minValSrc=-1, maxValSrc=1;
const int kExpNumBinsPerChannel = 1 << 12;
int kExpNumBins = 0;
float lastExpVal = 1.f;
float len, scale_index;
Size size = src.size();
CV_Assert( (src.type() == CV_32FC1 || src.type() == CV_32FC3) && src.data != dst.data );
if( sigma_color <= 0 )
sigma_color = 1;
if( sigma_space <= 0 )
sigma_space = 1;
double gauss_color_coeff = -0.5/(sigma_color*sigma_color);
double gauss_space_coeff = -0.5/(sigma_space*sigma_space);
if( d <= 0 )
radius = cvRound(sigma_space*1.5);
else
radius = d/2;
radius = MAX(radius, 1);
d = radius*2 + 1;
// compute the min/max range for the input image (even if multichannel)
minMaxLoc( src.reshape(1), &minValSrc, &maxValSrc );
if(std::abs(minValSrc - maxValSrc) < FLT_EPSILON)
{
src.copyTo(dst);
return;
}
// temporary copy of the image with borders for easy processing
Mat temp;
copyMakeBorder( src, temp, radius, radius, radius, radius, borderType );
// allocate lookup tables
std::vector<float> _space_weight(d*d);
std::vector<int> _space_ofs(d*d);
float* space_weight = &_space_weight[0];
int* space_ofs = &_space_ofs[0];
// assign a length which is slightly more than needed
len = (float)(maxValSrc - minValSrc) * cn;
kExpNumBins = kExpNumBinsPerChannel * cn;
std::vector<float> _expLUT(kExpNumBins+2);
float* expLUT = &_expLUT[0];
scale_index = kExpNumBins/len;
// initialize the exp LUT
for( i = 0; i < kExpNumBins+2; i++ )
{
if( lastExpVal > 0.f )
{
double val = i / scale_index;
expLUT[i] = (float)std::exp(val * val * gauss_color_coeff);
lastExpVal = expLUT[i];
}
else
expLUT[i] = 0.f;
}
// initialize space-related bilateral filter coefficients
for( i = -radius, maxk = 0; i <= radius; i++ )
for( j = -radius; j <= radius; j++ )
{
double r = std::sqrt((double)i*i + (double)j*j);
if( r > radius || ( i == 0 && j == 0 ) )
continue;
space_weight[maxk] = (float)std::exp(r*r*gauss_space_coeff);
space_ofs[maxk++] = (int)(i*(temp.step/sizeof(float)) + j*cn);
}
// parallel_for usage
CV_INSTRUMENT_REGION();
BilateralFilter_32f_Invoker body(cn, radius, maxk, space_ofs, temp, dst, scale_index, space_weight, expLUT);
parallel_for_(Range(0, size.height), body, dst.total()/(double)(1<<16));
parallel_for_(Range(0, dst.rows), body, dst.total()/(double)(1<<16));
}
#ifdef HAVE_IPP
#define IPP_BILATERAL_PARALLEL 1
#ifdef HAVE_IPP_IW
class ipp_bilateralFilterParallel: public ParallelLoopBody
{
public:
ipp_bilateralFilterParallel(::ipp::IwiImage &_src, ::ipp::IwiImage &_dst, int _radius, Ipp32f _valSquareSigma, Ipp32f _posSquareSigma, ::ipp::IwiBorderType _borderType, bool *_ok):
src(_src), dst(_dst)
{
pOk = _ok;
radius = _radius;
valSquareSigma = _valSquareSigma;
posSquareSigma = _posSquareSigma;
borderType = _borderType;
*pOk = true;
}
~ipp_bilateralFilterParallel() {}
virtual void operator() (const Range& range) const CV_OVERRIDE
{
if(*pOk == false)
return;
try
{
::ipp::IwiTile tile = ::ipp::IwiRoi(0, range.start, dst.m_size.width, range.end - range.start);
CV_INSTRUMENT_FUN_IPP(::ipp::iwiFilterBilateral, src, dst, radius, valSquareSigma, posSquareSigma, ::ipp::IwDefault(), borderType, tile);
}
catch(const ::ipp::IwException &)
{
*pOk = false;
return;
}
}
private:
::ipp::IwiImage &src;
::ipp::IwiImage &dst;
int radius;
Ipp32f valSquareSigma;
Ipp32f posSquareSigma;
::ipp::IwiBorderType borderType;
bool *pOk;
const ipp_bilateralFilterParallel& operator= (const ipp_bilateralFilterParallel&);
};
#endif
static bool ipp_bilateralFilter(Mat &src, Mat &dst, int d, double sigmaColor, double sigmaSpace, int borderType)
{
#ifdef HAVE_IPP_IW
CV_INSTRUMENT_REGION_IPP();
int radius = IPP_MAX(((d <= 0)?cvRound(sigmaSpace*1.5):d/2), 1);
Ipp32f valSquareSigma = (Ipp32f)((sigmaColor <= 0)?1:sigmaColor*sigmaColor);
Ipp32f posSquareSigma = (Ipp32f)((sigmaSpace <= 0)?1:sigmaSpace*sigmaSpace);
// Acquire data and begin processing
try
{
::ipp::IwiImage iwSrc = ippiGetImage(src);
::ipp::IwiImage iwDst = ippiGetImage(dst);
::ipp::IwiBorderSize borderSize(radius);
::ipp::IwiBorderType ippBorder(ippiGetBorder(iwSrc, borderType, borderSize));
if(!ippBorder)
return false;
const int threads = ippiSuggestThreadsNum(iwDst, 2);
if(IPP_BILATERAL_PARALLEL && threads > 1) {
bool ok = true;
Range range(0, (int)iwDst.m_size.height);
ipp_bilateralFilterParallel invoker(iwSrc, iwDst, radius, valSquareSigma, posSquareSigma, ippBorder, &ok);
if(!ok)
return false;
parallel_for_(range, invoker, threads*4);
if(!ok)
return false;
} else {
CV_INSTRUMENT_FUN_IPP(::ipp::iwiFilterBilateral, iwSrc, iwDst, radius, valSquareSigma, posSquareSigma, ::ipp::IwDefault(), ippBorder);
}
}
catch (const ::ipp::IwException &)
{
return false;
}
return true;
#else
CV_UNUSED(src); CV_UNUSED(dst); CV_UNUSED(d); CV_UNUSED(sigmaColor); CV_UNUSED(sigmaSpace); CV_UNUSED(borderType);
return false;
#endif
}
#endif
}
void cv::bilateralFilter( InputArray _src, OutputArray _dst, int d,
double sigmaColor, double sigmaSpace,
int borderType )
{
CV_INSTRUMENT_REGION();
_dst.create( _src.size(), _src.type() );
CV_OCL_RUN(_src.dims() <= 2 && _dst.isUMat(),
ocl_bilateralFilter_8u(_src, _dst, d, sigmaColor, sigmaSpace, borderType))
Mat src = _src.getMat(), dst = _dst.getMat();
CV_IPP_RUN_FAST(ipp_bilateralFilter(src, dst, d, sigmaColor, sigmaSpace, borderType));
if( src.depth() == CV_8U )
bilateralFilter_8u( src, dst, d, sigmaColor, sigmaSpace, borderType );
else if( src.depth() == CV_32F )
bilateralFilter_32f( src, dst, d, sigmaColor, sigmaSpace, borderType );
else
CV_Error( CV_StsUnsupportedFormat,
"Bilateral filtering is only implemented for 8u and 32f images" );
}
/* End of file. */
CV_CPU_OPTIMIZATION_NAMESPACE_END
} // namespace
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