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Raised bilateralFilter processing precision for CV_32F matrices containing NaNs

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This commit is contained in:
Vitaly Tuzov 2018-11-12 20:42:58 +03:00
parent 1c04a5ec47
commit f5b6bea2d4
6 changed files with 104 additions and 34 deletions
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5
modules/core/include/opencv2/core/hal/intrin_avx.hpp
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@ -905,6 +905,11 @@ OPENCV_HAL_IMPL_AVX_CMP_OP_64BIT(v_int64x4)
OPENCV_HAL_IMPL_AVX_CMP_OP_FLT(v_float32x8, ps)
OPENCV_HAL_IMPL_AVX_CMP_OP_FLT(v_float64x4, pd)
inline v_float32x8 v_not_nan(const v_float32x8& a)
{ return v_float32x8(_mm256_cmp_ps(a.val, a.val, _CMP_ORD_Q)); }
inline v_float64x4 v_not_nan(const v_float64x4& a)
{ return v_float64x4(_mm256_cmp_pd(a.val, a.val, _CMP_ORD_Q)); }
/** min/max **/
OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_uint8x32, _mm256_min_epu8)
OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_uint8x32, _mm256_max_epu8)

19
modules/core/include/opencv2/core/hal/intrin_cpp.hpp
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@ -683,6 +683,25 @@ OPENCV_HAL_IMPL_CMP_OP(==)
For all types except 64-bit integer values. */
OPENCV_HAL_IMPL_CMP_OP(!=)
template<int n>
inline v_reg<float, n> v_not_nan(const v_reg<float, n>& a)
{
typedef typename V_TypeTraits<float>::int_type itype;
v_reg<float, n> c;
for (int i = 0; i < n; i++)
c.s[i] = V_TypeTraits<float>::reinterpret_from_int((itype)-(int)(a.s[i] == a.s[i]));
return c;
}
template<int n>
inline v_reg<double, n> v_not_nan(const v_reg<double, n>& a)
{
typedef typename V_TypeTraits<double>::int_type itype;
v_reg<double, n> c;
for (int i = 0; i < n; i++)
c.s[i] = V_TypeTraits<double>::reinterpret_from_int((itype)-(int)(a.s[i] == a.s[i]));
return c;
}
//! @brief Helper macro
//! @ingroup core_hal_intrin_impl
#define OPENCV_HAL_IMPL_ARITHM_OP(func, bin_op, cast_op, _Tp2) \

7
modules/core/include/opencv2/core/hal/intrin_neon.hpp
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@ -764,6 +764,13 @@ OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int64x2, vreinterpretq_s64_u64, s64, u64)
OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_float64x2, vreinterpretq_f64_u64, f64, u64)
#endif
inline v_float32x4 v_not_nan(const v_float32x4& a)
{ return v_float32x4(vreinterpretq_f32_u32(vceqq_f32(a.val, a.val))); }
#if CV_SIMD128_64F
inline v_float64x2 v_not_nan(const v_float64x2& a)
{ return v_float64x2(vreinterpretq_f64_u64(vceqq_f64(a.val, a.val))); }
#endif
OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_add_wrap, vaddq_u8)
OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_add_wrap, vaddq_s8)
OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_add_wrap, vaddq_u16)

5
modules/core/include/opencv2/core/hal/intrin_sse.hpp
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@ -1041,6 +1041,11 @@ inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \
OPENCV_HAL_IMPL_SSE_64BIT_CMP_OP(v_uint64x2, v_reinterpret_as_u64)
OPENCV_HAL_IMPL_SSE_64BIT_CMP_OP(v_int64x2, v_reinterpret_as_s64)
inline v_float32x4 v_not_nan(const v_float32x4& a)
{ return v_float32x4(_mm_cmpord_ps(a.val, a.val)); }
inline v_float64x2 v_not_nan(const v_float64x2& a)
{ return v_float64x2(_mm_cmpord_pd(a.val, a.val)); }
OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_add_wrap, _mm_add_epi8)
OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int8x16, v_add_wrap, _mm_add_epi8)
OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint16x8, v_add_wrap, _mm_add_epi16)

5
modules/core/include/opencv2/core/hal/intrin_vsx.hpp
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@ -607,6 +607,11 @@ OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_float64x2)
OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint64x2)
OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int64x2)
inline v_float32x4 v_not_nan(const v_float32x4& a)
{ return v_float32x4(vec_cmpeq(a.val, a.val)); }
inline v_float64x2 v_not_nan(const v_float64x2& a)
{ return v_float64x2(vec_cmpeq(a.val, a.val)); }
/** min/max **/
OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_min, vec_min)
OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_max, vec_max)

97
modules/imgproc/src/bilateral_filter.cpp
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@ -430,36 +430,44 @@ public:
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_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));
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, w, vx_load_aligned(sum + j)));
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 alpha = std::abs(val - sptr[j]) * scale_index;
float rval = sptr[j];
float alpha = std::abs(val - rval) * 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;
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_store(dptr + j, vx_load_aligned(sum + j) / vx_load_aligned(wsum + j));
{
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] = sum[j] / wsum[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
@ -488,45 +496,68 @@ public:
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_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));
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, 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)));
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];
float alpha = (std::abs(b - rsptr[0]) + std::abs(g - rsptr[1]) + std::abs(r - rsptr[2])) * scale_index;
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;
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;
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, dptr += 3*v_float32::nlanes)
for (; j <= size.width - v_float32::nlanes; j += v_float32::nlanes, sptr += 3*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);
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);
wsum[j] = 1.f / wsum[j];
*(dptr++) = sum_b[j] * wsum[j];
*(dptr++) = sum_g[j] * wsum[j];
*(dptr++) = sum_r[j] * wsum[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];
}
}
}
}
@ -585,9 +616,7 @@ bilateralFilter_32f( const Mat& src, Mat& dst, int d,
// 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);
@ -620,7 +649,7 @@ bilateralFilter_32f( const Mat& src, Mat& dst, int d,
for( j = -radius; j <= radius; j++ )
{
double r = std::sqrt((double)i*i + (double)j*j);
if( r > radius )
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);