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Merge pull request #10553 from GlueCrow:bgfg_knn_opencl
Add ocl version BackgroundSubtractorKNN (#10553) * Add ocl version bgfg_knn * Add ocl KNN perf test * ocl KNN: Avoid unnecessary initializing when non-UMat parameters are used * video: turn off OpenCL for color KNN on Intel devices due performance degradation * video: turn off KNN OpenCL on Apple devices with Intel iGPU due process freeze during clBuildProgram() call
This commit is contained in:
Ya-Chiu Wu
committed by
Alexander Alekhin
Alexander Alekhin
parent
92fb3fb33f
commit
c8d8b1fbcd
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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) 2018 Ya-Chiu Wu, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// @Authors
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// Ya-Chiu Wu, yacwu@cs.nctu.edu.tw
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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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#if CN==1
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#define T_MEAN float
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#define F_ZERO (0.0f)
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#define frameToMean(a, b) (b) = *(a);
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#define meanToFrame(a, b) *b = convert_uchar_sat(a);
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#else
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#define T_MEAN float4
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#define F_ZERO (0.0f, 0.0f, 0.0f, 0.0f)
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#define meanToFrame(a, b)\
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b[0] = convert_uchar_sat(a.x); \
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b[1] = convert_uchar_sat(a.y); \
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b[2] = convert_uchar_sat(a.z);
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#define frameToMean(a, b)\
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b.x = a[0]; \
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b.y = a[1]; \
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b.z = a[2]; \
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b.w = 0.0f;
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#endif
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__kernel void knn_kernel(__global const uchar* frame, int frame_step, int frame_offset, int frame_row, int frame_col,
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__global const uchar* nNextLongUpdate,
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__global const uchar* nNextMidUpdate,
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__global const uchar* nNextShortUpdate,
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__global uchar* aModelIndexLong,
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__global uchar* aModelIndexMid,
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__global uchar* aModelIndexShort,
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__global uchar* flag,
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__global uchar* sample,
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__global uchar* fgmask, int fgmask_step, int fgmask_offset,
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int nLongCounter, int nMidCounter, int nShortCounter,
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float c_Tb, int c_nkNN, float c_tau
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#ifdef SHADOW_DETECT
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, uchar c_shadowVal
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#endif
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)
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{
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int x = get_global_id(0);
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int y = get_global_id(1);
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if( x < frame_col && y < frame_row)
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{
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__global const uchar* _frame = (frame + mad24(y, frame_step, mad24(x, CN, frame_offset)));
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T_MEAN pix;
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frameToMean(_frame, pix);
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uchar foreground = 255; // 0 - the pixel classified as background
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int Pbf = 0;
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int Pb = 0;
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uchar include = 0;
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int pt_idx = mad24(y, frame_col, x);
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int idx_step = frame_row * frame_col;
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__global T_MEAN* _sample = (__global T_MEAN*)(sample);
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for (uchar n = 0; n < (NSAMPLES) * 3 ; ++n)
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{
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int n_idx = mad24(n, idx_step, pt_idx);
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T_MEAN c_mean = _sample[n_idx];
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uchar c_flag = flag[n_idx];
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T_MEAN diff = c_mean - pix;
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float dist2 = dot(diff, diff);
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if (dist2 < c_Tb)
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{
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Pbf++;
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if (c_flag)
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{
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Pb++;
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if (Pb >= c_nkNN)
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{
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include = 1;
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foreground = 0;
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break;
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}
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}
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}
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}
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if (Pbf >= c_nkNN)
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{
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include = 1;
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}
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#ifdef SHADOW_DETECT
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if (foreground)
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{
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int Ps = 0;
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for (uchar n = 0; n < (NSAMPLES) * 3 ; ++n)
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{
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int n_idx = mad24(n, idx_step, pt_idx);
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uchar c_flag = flag[n_idx];
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if (c_flag)
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{
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T_MEAN c_mean = _sample[n_idx];
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float numerator = dot(pix, c_mean);
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float denominator = dot(c_mean, c_mean);
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if (denominator == 0)
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break;
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if (numerator <= denominator && numerator >= c_tau * denominator)
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{
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float a = numerator / denominator;
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T_MEAN dD = mad(a, c_mean, -pix);
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if (dot(dD, dD) < c_Tb * a * a)
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{
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Ps++;
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if (Ps >= c_nkNN)
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{
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foreground = c_shadowVal;
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break;
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}
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}
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}
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}
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}
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}
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#endif
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__global uchar* _fgmask = fgmask + mad24(y, fgmask_step, x + fgmask_offset);
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*_fgmask = (uchar)foreground;
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__global const uchar* _nNextLongUpdate = nNextLongUpdate + pt_idx;
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__global const uchar* _nNextMidUpdate = nNextMidUpdate + pt_idx;
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__global const uchar* _nNextShortUpdate = nNextShortUpdate + pt_idx;
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__global uchar* _aModelIndexLong = aModelIndexLong + pt_idx;
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__global uchar* _aModelIndexMid = aModelIndexMid + pt_idx;
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__global uchar* _aModelIndexShort = aModelIndexShort + pt_idx;
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uchar nextLongUpdate = _nNextLongUpdate[0];
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uchar nextMidUpdate = _nNextMidUpdate[0];
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uchar nextShortUpdate = _nNextShortUpdate[0];
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uchar modelIndexLong = _aModelIndexLong[0];
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uchar modelIndexMid = _aModelIndexMid[0];
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uchar modelIndexShort = _aModelIndexShort[0];
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int offsetLong = mad24(mad24(2, (NSAMPLES), modelIndexLong), idx_step, pt_idx);
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int offsetMid = mad24((NSAMPLES)+modelIndexMid, idx_step, pt_idx);
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int offsetShort = mad24(modelIndexShort, idx_step, pt_idx);
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if (nextLongUpdate == nLongCounter)
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{
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_sample[offsetLong] = _sample[offsetMid];
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flag[offsetLong] = flag[offsetMid];
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_aModelIndexLong[0] = (modelIndexLong >= ((NSAMPLES)-1)) ? 0 : (modelIndexLong + 1);
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}
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if (nextMidUpdate == nMidCounter)
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{
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_sample[offsetMid] = _sample[offsetShort];
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flag[offsetMid] = flag[offsetShort];
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_aModelIndexMid[0] = (modelIndexMid >= ((NSAMPLES)-1)) ? 0 : (modelIndexMid + 1);
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}
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if (nextShortUpdate == nShortCounter)
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{
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_sample[offsetShort] = pix;
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flag[offsetShort] = include;
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_aModelIndexShort[0] = (modelIndexShort >= ((NSAMPLES)-1)) ? 0 : (modelIndexShort + 1);
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}
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}
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}
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__kernel void getBackgroundImage2_kernel(__global const uchar* flag,
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__global const uchar* sample,
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__global uchar* dst, int dst_step, int dst_offset, int dst_row, int dst_col)
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{
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int x = get_global_id(0);
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int y = get_global_id(1);
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if(x < dst_col && y < dst_row)
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{
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int pt_idx = mad24(y, dst_col, x);
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T_MEAN meanVal = (T_MEAN)F_ZERO;
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__global T_MEAN* _sample = (__global T_MEAN*)(sample);
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int idx_step = dst_row * dst_col;
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for (uchar n = 0; n < (NSAMPLES) * 3 ; ++n)
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{
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int n_idx = mad24(n, idx_step, pt_idx);
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uchar c_flag = flag[n_idx];
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if(c_flag)
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{
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meanVal = _sample[n_idx];
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break;
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}
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}
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__global uchar* _dst = dst + mad24(y, dst_step, mad24(x, CN, dst_offset));
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meanToFrame(meanVal, _dst);
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}
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}
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