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() callpull/10764/head
Ya-Chiu Wu
7 years ago
committed by
Alexander Alekhin
parent
92fb3fb33f
commit
c8d8b1fbcd
3 changed files with 583 additions and 37 deletions
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#include "../perf_precomp.hpp" |
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#include "opencv2/ts/ocl_perf.hpp" |
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#ifdef HAVE_OPENCL |
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#ifdef HAVE_VIDEO_INPUT |
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#include "../perf_bgfg_utils.hpp" |
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namespace cvtest { |
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namespace ocl { |
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//////////////////////////// KNN//////////////////////////
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typedef tuple<string, int> VideoKNNParamType; |
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typedef TestBaseWithParam<VideoKNNParamType> KNN_Apply; |
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typedef TestBaseWithParam<VideoKNNParamType> KNN_GetBackgroundImage; |
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using namespace opencv_test; |
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OCL_PERF_TEST_P(KNN_Apply, KNN, Combine(Values("gpu/video/768x576.avi", "gpu/video/1920x1080.avi"), Values(1,3))) |
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{ |
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VideoKNNParamType params = GetParam(); |
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const string inputFile = getDataPath(get<0>(params)); |
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const int cn = get<1>(params); |
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int nFrame = 5; |
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vector<Mat> frame_buffer(nFrame); |
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cv::VideoCapture cap(inputFile); |
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ASSERT_TRUE(cap.isOpened()); |
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prepareData(cap, cn, frame_buffer); |
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UMat u_foreground; |
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OCL_TEST_CYCLE() |
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{ |
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Ptr<cv::BackgroundSubtractorKNN> knn = createBackgroundSubtractorKNN(); |
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knn->setDetectShadows(false); |
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u_foreground.release(); |
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for (int i = 0; i < nFrame; i++) |
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{ |
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knn->apply(frame_buffer[i], u_foreground); |
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} |
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} |
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SANITY_CHECK_NOTHING(); |
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} |
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OCL_PERF_TEST_P(KNN_GetBackgroundImage, KNN, Values( |
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std::make_pair<string, int>("gpu/video/768x576.avi", 5), |
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std::make_pair<string, int>("gpu/video/1920x1080.avi", 5))) |
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{ |
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VideoKNNParamType params = GetParam(); |
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const string inputFile = getDataPath(get<0>(params)); |
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const int cn = 3; |
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const int skipFrames = get<1>(params); |
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int nFrame = 10; |
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vector<Mat> frame_buffer(nFrame); |
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cv::VideoCapture cap(inputFile); |
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ASSERT_TRUE(cap.isOpened()); |
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prepareData(cap, cn, frame_buffer, skipFrames); |
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UMat u_foreground, u_background; |
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OCL_TEST_CYCLE() |
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{ |
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Ptr<cv::BackgroundSubtractorKNN> knn = createBackgroundSubtractorKNN(); |
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knn->setDetectShadows(false); |
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u_foreground.release(); |
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u_background.release(); |
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for (int i = 0; i < nFrame; i++) |
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{ |
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knn->apply(frame_buffer[i], u_foreground); |
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} |
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knn->getBackgroundImage(u_background); |
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} |
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#ifdef DEBUG_BGFG |
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imwrite(format("fg_%d_%d_knn_ocl.png", frame_buffer[0].rows, cn), u_foreground.getMat(ACCESS_READ)); |
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imwrite(format("bg_%d_%d_knn_ocl.png", frame_buffer[0].rows, cn), u_background.getMat(ACCESS_READ)); |
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#endif |
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SANITY_CHECK_NOTHING(); |
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} |
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}}// namespace cvtest::ocl
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#endif |
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#endif |
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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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