mirror of https://github.com/opencv/opencv.git
Merge pull request #12965 from terfendail:medianBlur_wintr
Alexander Alekhin
6 years ago
commit
1c04a5ec47
3 changed files with 767 additions and 767 deletions
@ -0,0 +1,759 @@ |
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, 2018, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Copyright (C) 2014-2015, Itseez Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp" |
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#include <vector> |
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#include "opencv2/core/hal/intrin.hpp" |
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#include "opencl_kernels_imgproc.hpp" |
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/****************************************************************************************\
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Bilateral Filtering |
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\****************************************************************************************/ |
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namespace cv |
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{ |
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class BilateralFilter_8u_Invoker : |
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public ParallelLoopBody |
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{ |
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public: |
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BilateralFilter_8u_Invoker(Mat& _dest, const Mat& _temp, int _radius, int _maxk, |
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int* _space_ofs, float *_space_weight, float *_color_weight) : |
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temp(&_temp), dest(&_dest), radius(_radius), |
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maxk(_maxk), space_ofs(_space_ofs), space_weight(_space_weight), color_weight(_color_weight) |
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{ |
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} |
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virtual void operator() (const Range& range) const CV_OVERRIDE |
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{ |
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int i, j, cn = dest->channels(), k; |
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Size size = dest->size(); |
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for( i = range.start; i < range.end; i++ ) |
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{ |
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const uchar* sptr = temp->ptr(i+radius) + radius*cn; |
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uchar* dptr = dest->ptr(i); |
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if( cn == 1 ) |
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{ |
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AutoBuffer<float> buf(alignSize(size.width, CV_SIMD_WIDTH) + size.width + CV_SIMD_WIDTH - 1); |
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memset(buf.data(), 0, buf.size() * sizeof(float)); |
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float *sum = alignPtr(buf.data(), CV_SIMD_WIDTH); |
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float *wsum = sum + alignSize(size.width, CV_SIMD_WIDTH); |
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for( k = 0; k < maxk; k++ ) |
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{ |
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const uchar* ksptr = sptr + space_ofs[k]; |
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j = 0; |
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#if CV_SIMD |
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v_float32 kweight = vx_setall_f32(space_weight[k]); |
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for (; j <= size.width - v_float32::nlanes; j += v_float32::nlanes) |
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{ |
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v_uint32 val = vx_load_expand_q(ksptr + j); |
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v_float32 w = kweight * v_lut(color_weight, v_reinterpret_as_s32(v_absdiff(val, vx_load_expand_q(sptr + j)))); |
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v_store_aligned(wsum + j, vx_load_aligned(wsum + j) + w); |
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v_store_aligned(sum + j, v_muladd(v_cvt_f32(v_reinterpret_as_s32(val)), w, vx_load_aligned(sum + j))); |
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} |
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#endif |
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for (; j < size.width; j++) |
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{ |
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int val = ksptr[j]; |
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float w = space_weight[k] * color_weight[std::abs(val - sptr[j])]; |
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wsum[j] += w; |
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sum[j] += val * w; |
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} |
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} |
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j = 0; |
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#if CV_SIMD |
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for (; j <= size.width - 2*v_float32::nlanes; j += 2*v_float32::nlanes) |
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v_pack_u_store(dptr + j, v_pack(v_round(vx_load_aligned(sum + j ) / vx_load_aligned(wsum + j )), |
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v_round(vx_load_aligned(sum + j + v_float32::nlanes) / vx_load_aligned(wsum + j + v_float32::nlanes)))); |
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#endif |
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for (; j < size.width; j++) |
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{ |
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// overflow is not possible here => there is no need to use cv::saturate_cast
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CV_DbgAssert(fabs(wsum[j]) > 0); |
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dptr[j] = (uchar)cvRound(sum[j]/wsum[j]); |
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} |
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} |
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else |
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{ |
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assert( cn == 3 ); |
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AutoBuffer<float> buf(alignSize(size.width, CV_SIMD_WIDTH)*3 + size.width + CV_SIMD_WIDTH - 1); |
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memset(buf.data(), 0, buf.size() * sizeof(float)); |
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float *sum_b = alignPtr(buf.data(), CV_SIMD_WIDTH); |
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float *sum_g = sum_b + alignSize(size.width, CV_SIMD_WIDTH); |
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float *sum_r = sum_g + alignSize(size.width, CV_SIMD_WIDTH); |
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float *wsum = sum_r + alignSize(size.width, CV_SIMD_WIDTH); |
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for(k = 0; k < maxk; k++ ) |
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{ |
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const uchar* ksptr = sptr + space_ofs[k]; |
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const uchar* rsptr = sptr; |
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j = 0; |
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#if CV_SIMD |
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v_float32 kweight = vx_setall_f32(space_weight[k]); |
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for (; j <= size.width - v_uint8::nlanes; j += v_uint8::nlanes, ksptr += 3*v_uint8::nlanes, rsptr += 3*v_uint8::nlanes) |
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{ |
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v_uint8 kb, kg, kr, rb, rg, rr; |
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v_load_deinterleave(ksptr, kb, kg, kr); |
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v_load_deinterleave(rsptr, rb, rg, rr); |
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v_uint16 b_l, b_h, g_l, g_h, r_l, r_h; |
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v_expand(v_absdiff(kb, rb), b_l, b_h); |
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v_expand(v_absdiff(kg, rg), g_l, g_h); |
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v_expand(v_absdiff(kr, rr), r_l, r_h); |
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v_uint32 val0, val1, val2, val3; |
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v_expand(b_l + g_l + r_l, val0, val1); |
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v_expand(b_h + g_h + r_h, val2, val3); |
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v_expand(kb, b_l, b_h); |
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v_expand(kg, g_l, g_h); |
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v_expand(kr, r_l, r_h); |
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v_float32 w0 = kweight * v_lut(color_weight, v_reinterpret_as_s32(val0)); |
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v_float32 w1 = kweight * v_lut(color_weight, v_reinterpret_as_s32(val1)); |
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v_float32 w2 = kweight * v_lut(color_weight, v_reinterpret_as_s32(val2)); |
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v_float32 w3 = kweight * v_lut(color_weight, v_reinterpret_as_s32(val3)); |
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v_store_aligned(wsum + j , w0 + vx_load_aligned(wsum + j)); |
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v_store_aligned(wsum + j + v_float32::nlanes, w1 + vx_load_aligned(wsum + j + v_float32::nlanes)); |
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v_store_aligned(wsum + j + 2*v_float32::nlanes, w2 + vx_load_aligned(wsum + j + 2*v_float32::nlanes)); |
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v_store_aligned(wsum + j + 3*v_float32::nlanes, w3 + vx_load_aligned(wsum + j + 3*v_float32::nlanes)); |
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v_expand(b_l, val0, val1); |
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v_expand(b_h, val2, val3); |
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v_store_aligned(sum_b + j , v_muladd(v_cvt_f32(v_reinterpret_as_s32(val0)), w0, vx_load_aligned(sum_b + j))); |
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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))); |
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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))); |
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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))); |
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v_expand(g_l, val0, val1); |
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v_expand(g_h, val2, val3); |
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v_store_aligned(sum_g + j , v_muladd(v_cvt_f32(v_reinterpret_as_s32(val0)), w0, vx_load_aligned(sum_g + j))); |
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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))); |
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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))); |
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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))); |
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v_expand(r_l, val0, val1); |
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v_expand(r_h, val2, val3); |
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v_store_aligned(sum_r + j , v_muladd(v_cvt_f32(v_reinterpret_as_s32(val0)), w0, vx_load_aligned(sum_r + j))); |
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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))); |
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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))); |
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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))); |
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} |
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#endif |
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for(; j < size.width; j++, ksptr += 3, rsptr += 3) |
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{ |
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int b = ksptr[0], g = ksptr[1], r = ksptr[2]; |
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float w = space_weight[k]*color_weight[std::abs(b - rsptr[0]) + std::abs(g - rsptr[1]) + std::abs(r - rsptr[2])]; |
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wsum[j] += w; |
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sum_b[j] += b*w; sum_g[j] += g*w; sum_r[j] += r*w; |
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} |
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} |
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j = 0; |
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#if CV_SIMD |
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v_float32 v_one = vx_setall_f32(1.f); |
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for(; j <= size.width - v_uint8::nlanes; j += v_uint8::nlanes, dptr += 3*v_uint8::nlanes) |
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{ |
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v_float32 w0 = v_one / vx_load_aligned(wsum + j); |
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v_float32 w1 = v_one / vx_load_aligned(wsum + j + v_float32::nlanes); |
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v_float32 w2 = v_one / vx_load_aligned(wsum + j + 2*v_float32::nlanes); |
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v_float32 w3 = v_one / vx_load_aligned(wsum + j + 3*v_float32::nlanes); |
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|
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v_store_interleave(dptr, v_pack_u(v_pack(v_round(w0 * vx_load_aligned(sum_b + j)), |
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v_round(w1 * vx_load_aligned(sum_b + j + v_float32::nlanes))), |
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v_pack(v_round(w2 * vx_load_aligned(sum_b + j + 2*v_float32::nlanes)), |
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v_round(w3 * vx_load_aligned(sum_b + j + 3*v_float32::nlanes)))), |
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v_pack_u(v_pack(v_round(w0 * vx_load_aligned(sum_g + j)), |
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v_round(w1 * vx_load_aligned(sum_g + j + v_float32::nlanes))), |
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v_pack(v_round(w2 * vx_load_aligned(sum_g + j + 2*v_float32::nlanes)), |
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v_round(w3 * vx_load_aligned(sum_g + j + 3*v_float32::nlanes)))), |
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v_pack_u(v_pack(v_round(w0 * vx_load_aligned(sum_r + j)), |
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v_round(w1 * vx_load_aligned(sum_r + j + v_float32::nlanes))), |
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v_pack(v_round(w2 * vx_load_aligned(sum_r + j + 2*v_float32::nlanes)), |
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v_round(w3 * vx_load_aligned(sum_r + j + 3*v_float32::nlanes))))); |
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} |
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#endif |
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for(; j < size.width; j++) |
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{ |
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CV_DbgAssert(fabs(wsum[j]) > 0); |
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wsum[j] = 1.f/wsum[j]; |
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*(dptr++) = (uchar)cvRound(sum_b[j]*wsum[j]); |
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*(dptr++) = (uchar)cvRound(sum_g[j]*wsum[j]); |
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*(dptr++) = (uchar)cvRound(sum_r[j]*wsum[j]); |
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} |
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} |
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} |
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#if CV_SIMD |
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vx_cleanup(); |
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#endif |
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} |
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private: |
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const Mat *temp; |
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Mat *dest; |
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int radius, maxk, *space_ofs; |
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float *space_weight, *color_weight; |
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}; |
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#ifdef HAVE_OPENCL |
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static bool ocl_bilateralFilter_8u(InputArray _src, OutputArray _dst, int d, |
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double sigma_color, double sigma_space, |
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int borderType) |
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{ |
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#ifdef __ANDROID__ |
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if (ocl::Device::getDefault().isNVidia()) |
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return false; |
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#endif |
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int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type); |
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int i, j, maxk, radius; |
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if (depth != CV_8U || cn > 4) |
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return false; |
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if (sigma_color <= 0) |
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sigma_color = 1; |
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if (sigma_space <= 0) |
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sigma_space = 1; |
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double gauss_color_coeff = -0.5 / (sigma_color * sigma_color); |
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double gauss_space_coeff = -0.5 / (sigma_space * sigma_space); |
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if ( d <= 0 ) |
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radius = cvRound(sigma_space * 1.5); |
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else |
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radius = d / 2; |
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radius = MAX(radius, 1); |
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d = radius * 2 + 1; |
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UMat src = _src.getUMat(), dst = _dst.getUMat(), temp; |
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if (src.u == dst.u) |
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return false; |
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copyMakeBorder(src, temp, radius, radius, radius, radius, borderType); |
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std::vector<float> _space_weight(d * d); |
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std::vector<int> _space_ofs(d * d); |
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float * const space_weight = &_space_weight[0]; |
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int * const space_ofs = &_space_ofs[0]; |
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// initialize space-related bilateral filter coefficients
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for( i = -radius, maxk = 0; i <= radius; i++ ) |
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for( j = -radius; j <= radius; j++ ) |
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{ |
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double r = std::sqrt((double)i * i + (double)j * j); |
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if ( r > radius ) |
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continue; |
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space_weight[maxk] = (float)std::exp(r * r * gauss_space_coeff); |
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space_ofs[maxk++] = (int)(i * temp.step + j * cn); |
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} |
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char cvt[3][40]; |
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String cnstr = cn > 1 ? format("%d", cn) : ""; |
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String kernelName("bilateral"); |
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size_t sizeDiv = 1; |
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if ((ocl::Device::getDefault().isIntel()) && |
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(ocl::Device::getDefault().type() == ocl::Device::TYPE_GPU)) |
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{ |
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//Intel GPU
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if (dst.cols % 4 == 0 && cn == 1) // For single channel x4 sized images.
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{ |
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kernelName = "bilateral_float4"; |
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sizeDiv = 4; |
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} |
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} |
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ocl::Kernel k(kernelName.c_str(), ocl::imgproc::bilateral_oclsrc, |
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format("-D radius=%d -D maxk=%d -D cn=%d -D int_t=%s -D uint_t=uint%s -D convert_int_t=%s" |
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" -D uchar_t=%s -D float_t=%s -D convert_float_t=%s -D convert_uchar_t=%s -D gauss_color_coeff=(float)%f", |
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radius, maxk, cn, ocl::typeToStr(CV_32SC(cn)), cnstr.c_str(), |
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ocl::convertTypeStr(CV_8U, CV_32S, cn, cvt[0]), |
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ocl::typeToStr(type), ocl::typeToStr(CV_32FC(cn)), |
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ocl::convertTypeStr(CV_32S, CV_32F, cn, cvt[1]), |
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ocl::convertTypeStr(CV_32F, CV_8U, cn, cvt[2]), gauss_color_coeff)); |
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if (k.empty()) |
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return false; |
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Mat mspace_weight(1, d * d, CV_32FC1, space_weight); |
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Mat mspace_ofs(1, d * d, CV_32SC1, space_ofs); |
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UMat ucolor_weight, uspace_weight, uspace_ofs; |
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mspace_weight.copyTo(uspace_weight); |
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mspace_ofs.copyTo(uspace_ofs); |
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k.args(ocl::KernelArg::ReadOnlyNoSize(temp), ocl::KernelArg::WriteOnly(dst), |
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ocl::KernelArg::PtrReadOnly(uspace_weight), |
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ocl::KernelArg::PtrReadOnly(uspace_ofs)); |
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size_t globalsize[2] = { (size_t)dst.cols / sizeDiv, (size_t)dst.rows }; |
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return k.run(2, globalsize, NULL, false); |
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} |
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#endif |
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static void |
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bilateralFilter_8u( const Mat& src, Mat& dst, int d, |
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double sigma_color, double sigma_space, |
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int borderType ) |
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{ |
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int cn = src.channels(); |
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int i, j, maxk, radius; |
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Size size = src.size(); |
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CV_Assert( (src.type() == CV_8UC1 || src.type() == CV_8UC3) && src.data != dst.data ); |
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if( sigma_color <= 0 ) |
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sigma_color = 1; |
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if( sigma_space <= 0 ) |
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sigma_space = 1; |
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double gauss_color_coeff = -0.5/(sigma_color*sigma_color); |
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double gauss_space_coeff = -0.5/(sigma_space*sigma_space); |
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|
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if( d <= 0 ) |
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radius = cvRound(sigma_space*1.5); |
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else |
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radius = d/2; |
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radius = MAX(radius, 1); |
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d = radius*2 + 1; |
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Mat temp; |
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copyMakeBorder( src, temp, radius, radius, radius, radius, borderType ); |
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std::vector<float> _color_weight(cn*256); |
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std::vector<float> _space_weight(d*d); |
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std::vector<int> _space_ofs(d*d); |
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float* color_weight = &_color_weight[0]; |
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float* space_weight = &_space_weight[0]; |
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int* space_ofs = &_space_ofs[0]; |
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|
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// initialize color-related bilateral filter coefficients
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|
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for( i = 0; i < 256*cn; i++ ) |
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color_weight[i] = (float)std::exp(i*i*gauss_color_coeff); |
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|
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// initialize space-related bilateral filter coefficients
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for( i = -radius, maxk = 0; i <= radius; i++ ) |
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{ |
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j = -radius; |
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|
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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. */ |
||||
Loading…
Reference in new issue