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Commit 2dd98e7c authored by Vitaly Tuzov's avatar Vitaly Tuzov
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bilateralFilter implementation moved to separate file

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modules/imgproc/src/bilateral_filter.cpp 0 → 100644
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#include "precomp.hpp"
#include <vector>
#include "opencv2/core/hal/intrin.hpp"
#include "opencl_kernels_imgproc.hpp"
/****************************************************************************************\
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);
for( k = 0; 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);
for(k = 0; 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;
};
#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;
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 )
{
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);
}
}
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));
}
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
for( k = 0; 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 alpha = v_absdiff(val, vx_load(sptr + j)) * sindex;
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));
v_store_aligned(wsum + j, vx_load_aligned(wsum + j) + w);
v_store_aligned(sum + j, v_muladd(val, w, vx_load_aligned(sum + j)));
}
#endif
for (; j < size.width; j++)
{
float val = ksptr[j];
float alpha = std::abs(val - sptr[j]) * scale_index;
int idx = cvFloor(alpha);
alpha -= idx;
float w = space_weight[k] * (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_store(dptr + j, vx_load_aligned(sum + j) / vx_load_aligned(wsum + j));
#endif
for (; j < size.width; j++)
{
CV_DbgAssert(fabs(wsum[j]) > 0);
dptr[j] = sum[j] / wsum[j];
}
}
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
for (k = 0; 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 alpha = (v_absdiff(kb, rb) + v_absdiff(kg, rg) + v_absdiff(kr, rr)) * sindex;
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));
v_store_aligned(wsum + j, vx_load_aligned(wsum + j) + w);
v_store_aligned(sum_b + j, v_muladd(kb, w, vx_load_aligned(sum_b + j)));
v_store_aligned(sum_g + j, v_muladd(kg, w, vx_load_aligned(sum_g + j)));
v_store_aligned(sum_r + j, v_muladd(kr, 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];
float alpha = (std::abs(b - rsptr[0]) + std::abs(g - rsptr[1]) + std::abs(r - rsptr[2])) * scale_index;
int idx = cvFloor(alpha);
alpha -= idx;
float w = space_weight[k] * (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, dptr += 3*v_float32::nlanes)
{
v_float32 w = v_one / vx_load_aligned(wsum + j);
v_store_interleave(dptr, vx_load_aligned(sum_b + j) * w, vx_load_aligned(sum_g + j) * w, vx_load_aligned(sum_r + j) * w);
}
#endif
for (; j < size.width; j++)
{
CV_DbgAssert(fabs(wsum[j]) > 0);
wsum[j] = 1.f / wsum[j];
*(dptr++) = sum_b[j] * wsum[j];
*(dptr++) = sum_g[j] * wsum[j];
*(dptr++) = sum_r[j] * 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 )
{
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 );
minValSrc -= 5. * sigma_color;
patchNaNs( temp, minValSrc ); // this replacement of NaNs makes the assumption that depth values are nonnegative
// TODO: make replacement parameter avalible in the outside function interface
// 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 )
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
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));
}
#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. */
modules/imgproc/src/smooth.cpp
View file @ 2dd98e7c
......@@ -2505,714 +2505,6 @@ void cv::GaussianBlur( InputArray _src, OutputArray _dst, Size ksize,
sepFilter2D(src, dst, sdepth, kx, ky, Point(-1, -1), 0, borderType);
}
/****************************************************************************************\
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);
for( k = 0; 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);
for(k = 0; 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;
};
#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;
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 )
{
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);
}
}
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));
}
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
for( k = 0; 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 alpha = v_absdiff(val, vx_load(sptr + j)) * sindex;
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));
v_store_aligned(wsum + j, vx_load_aligned(wsum + j) + w);
v_store_aligned(sum + j, v_muladd(val, w, vx_load_aligned(sum + j)));
}
#endif
for (; j < size.width; j++)
{
float val = ksptr[j];
float alpha = std::abs(val - sptr[j]) * scale_index;
int idx = cvFloor(alpha);
alpha -= idx;
float w = space_weight[k] * (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_store(dptr + j, vx_load_aligned(sum + j) / vx_load_aligned(wsum + j));
#endif
for (; j < size.width; j++)
{
CV_DbgAssert(fabs(wsum[j]) > 0);
dptr[j] = sum[j] / wsum[j];
}
}
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
for (k = 0; 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 alpha = (v_absdiff(kb, rb) + v_absdiff(kg, rg) + v_absdiff(kr, rr)) * sindex;
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));
v_store_aligned(wsum + j, vx_load_aligned(wsum + j) + w);
v_store_aligned(sum_b + j, v_muladd(kb, w, vx_load_aligned(sum_b + j)));
v_store_aligned(sum_g + j, v_muladd(kg, w, vx_load_aligned(sum_g + j)));
v_store_aligned(sum_r + j, v_muladd(kr, 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];
float alpha = (std::abs(b - rsptr[0]) + std::abs(g - rsptr[1]) + std::abs(r - rsptr[2])) * scale_index;
int idx = cvFloor(alpha);
alpha -= idx;
float w = space_weight[k] * (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, dptr += 3*v_float32::nlanes)
{
v_float32 w = v_one / vx_load_aligned(wsum + j);
v_store_interleave(dptr, vx_load_aligned(sum_b + j) * w, vx_load_aligned(sum_g + j) * w, vx_load_aligned(sum_r + j) * w);
}
#endif
for (; j < size.width; j++)
{
CV_DbgAssert(fabs(wsum[j]) > 0);
wsum[j] = 1.f / wsum[j];
*(dptr++) = sum_b[j] * wsum[j];
*(dptr++) = sum_g[j] * wsum[j];
*(dptr++) = sum_r[j] * 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 )
{
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 );
const double insteadNaNValue = -5. * sigma_color;
patchNaNs( temp, insteadNaNValue ); // this replacement of NaNs makes the assumption that depth values are nonnegative
// TODO: make insteadNaNValue avalible in the outside function interface to control the cases breaking the assumption
// 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 )
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
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));
}
#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" );
}
//////////////////////////////////////////////////////////////////////////////////////////
CV_IMPL void
......
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