mirror of https://github.com/opencv/opencv.git
Alexander Mordvintesv
13 years ago
commit
2ea3dd391a
32 changed files with 2329 additions and 1278 deletions
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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, Intel Corporation, all rights reserved.
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// Copyright (C) 2009-2011, Willow Garage 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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#ifdef HAVE_CONCURRENCY |
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# include <ppl.h> |
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#elif defined HAVE_OPENMP |
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# include <omp.h> |
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#elif defined HAVE_GCD |
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# include <dispatch/dispatch.h> |
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#elif defined HAVE_TBB |
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# include "tbb/tbb_stddef.h" |
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# if TBB_VERSION_MAJOR*100 + TBB_VERSION_MINOR >= 202 |
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# include "tbb/tbb.h" |
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# include "tbb/task.h" |
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# undef min |
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# undef max |
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# else |
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# undef HAVE_TBB |
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# endif // end TBB version
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#endif // HAVE_CONCURRENCY
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/*
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HAVE_TBB - using TBB |
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HAVE_GCD - using GCD |
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HAVE_OPENMP - using OpenMP |
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HAVE_CONCURRENCY - using visual studio 2010 concurrency |
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*/ |
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namespace cv |
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{ |
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ParallelLoopBody::~ParallelLoopBody() { } |
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#ifdef HAVE_TBB |
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class TbbProxyLoopBody |
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{ |
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public: |
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TbbProxyLoopBody(const ParallelLoopBody& _body) : |
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body(&_body) |
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{ } |
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void operator ()(const tbb::blocked_range<int>& range) const |
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{ |
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body->operator()(Range(range.begin(), range.end())); |
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} |
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private: |
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const ParallelLoopBody* body; |
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}; |
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#endif // end HAVE_TBB
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#ifdef HAVE_GCD |
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static |
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void block_function(void* context, size_t index) |
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{ |
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ParallelLoopBody* ptr_body = static_cast<ParallelLoopBody*>(context); |
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ptr_body->operator()(Range(index, index + 1)); |
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} |
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#endif // HAVE_GCD
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void parallel_for_(const Range& range, const ParallelLoopBody& body) |
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{ |
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#ifdef HAVE_TBB |
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tbb::parallel_for(tbb::blocked_range<int>(range.start, range.end), TbbProxyLoopBody(body)); |
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#elif defined HAVE_CONCURRENCY |
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Concurrency::parallel_for(range.start, range.end, body); |
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#elif defined HAVE_OPENMP |
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#pragma omp parallel for schedule(dynamic) |
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for (int i = range.start; i < range.end; ++i) |
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body(Range(i, i + 1)); |
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#elif defined (HAVE_GCD) |
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dispatch_queue_t concurrent_queue = dispatch_get_global_queue(DISPATCH_QUEUE_PRIORITY_DEFAULT, 0); |
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dispatch_apply_f(range.end - range.start, concurrent_queue, &const_cast<ParallelLoopBody&>(body), block_function); |
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#else |
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body(range); |
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#endif // end HAVE_TBB
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} |
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} // namespace cv
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File diff suppressed because it is too large
Load Diff
@ -0,0 +1,38 @@ |
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#include "perf_precomp.hpp" |
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using namespace std; |
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using namespace cv; |
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using namespace perf; |
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using namespace testing; |
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using std::tr1::make_tuple; |
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using std::tr1::get; |
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CV_ENUM(Mat_Type, CV_8UC1, CV_8UC3, CV_32FC1, CV_32FC3) |
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typedef TestBaseWithParam< tr1::tuple<Size, int, Mat_Type> > TestBilateralFilter; |
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PERF_TEST_P( TestBilateralFilter, BilateralFilter, |
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Combine( |
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Values( szVGA, sz1080p ), // image size
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Values( 3, 5 ), // d
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ValuesIn( Mat_Type::all() ) // image type
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) |
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) |
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{ |
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Size sz; |
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int d, type; |
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const double sigmaColor = 1., sigmaSpace = 1.; |
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sz = get<0>(GetParam()); |
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d = get<1>(GetParam()); |
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type = get<2>(GetParam()); |
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Mat src(sz, type); |
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Mat dst(sz, type); |
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declare.in(src, WARMUP_RNG).out(dst).time(20); |
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TEST_CYCLE() bilateralFilter(src, dst, d, sigmaColor, sigmaSpace, BORDER_DEFAULT); |
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SANITY_CHECK(dst); |
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} |
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@ -0,0 +1,290 @@ |
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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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||||||
|
//
|
||||||
|
// By downloading, copying, installing or using the software you agree to this license.
|
||||||
|
// If you do not agree to this license, do not download, install,
|
||||||
|
// copy or use the software.
|
||||||
|
//
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|
//
|
||||||
|
// License Agreement
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|
// For Open Source Computer Vision Library
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||||||
|
//
|
||||||
|
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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||||||
|
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
||||||
|
// Third party copyrights are property of their respective owners.
|
||||||
|
//
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||||||
|
// Redistribution and use in source and binary forms, with or without modification,
|
||||||
|
// are permitted provided that the following conditions are met:
|
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|
//
|
||||||
|
// * Redistribution's of source code must retain the above copyright notice,
|
||||||
|
// this list of conditions and the following disclaimer.
|
||||||
|
//
|
||||||
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||||
|
// this list of conditions and the following disclaimer in the documentation
|
||||||
|
// 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
|
||||||
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||||
|
// 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 "test_precomp.hpp" |
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using namespace cv; |
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namespace cvtest
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{
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class CV_BilateralFilterTest :
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public cvtest::BaseTest |
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{ |
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public: |
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enum
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{ |
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MAX_WIDTH = 1920, MIN_WIDTH = 1, |
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MAX_HEIGHT = 1080, MIN_HEIGHT = 1
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}; |
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CV_BilateralFilterTest(); |
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~CV_BilateralFilterTest(); |
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protected: |
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virtual void run_func(); |
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virtual int prepare_test_case(int test_case_index); |
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virtual int validate_test_results(int test_case_index); |
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private: |
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void reference_bilateral_filter(const Mat& src, Mat& dst, int d, double sigma_color, |
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double sigma_space, int borderType = BORDER_DEFAULT); |
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int getRandInt(RNG& rng, int min_value, int max_value) const; |
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double _sigma_color; |
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double _sigma_space; |
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Mat _src; |
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Mat _parallel_dst; |
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int _d; |
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}; |
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CV_BilateralFilterTest::CV_BilateralFilterTest() :
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cvtest::BaseTest(), _src(), _parallel_dst(), _d() |
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{ |
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test_case_count = 1000; |
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} |
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CV_BilateralFilterTest::~CV_BilateralFilterTest() |
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{ |
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} |
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int CV_BilateralFilterTest::getRandInt(RNG& rng, int min_value, int max_value) const |
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{ |
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double rand_value = rng.uniform(log((double)min_value), log((double)max_value + 1)); |
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return cvRound(exp((double)rand_value)); |
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} |
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void CV_BilateralFilterTest::reference_bilateral_filter(const Mat &src, Mat &dst, int d,
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double sigma_color, double sigma_space, int borderType) |
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{ |
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int cn = src.channels(); |
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int i, j, k, maxk, radius; |
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double minValSrc = -1, maxValSrc = 1; |
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const int kExpNumBinsPerChannel = 1 << 12; |
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int kExpNumBins = 0; |
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float lastExpVal = 1.f; |
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float len, scale_index; |
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Size size = src.size(); |
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dst.create(size, src.type()); |
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CV_Assert( (src.type() == CV_32FC1 || src.type() == CV_32FC3) && |
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src.type() == dst.type() && src.size() == dst.size() && |
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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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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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// compute the min/max range for the input image (even if multichannel)
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minMaxLoc( src.reshape(1), &minValSrc, &maxValSrc ); |
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if(std::abs(minValSrc - maxValSrc) < FLT_EPSILON) |
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{ |
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src.copyTo(dst); |
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return; |
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} |
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// temporary copy of the image with borders for easy processing
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Mat temp; |
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copyMakeBorder( src, temp, radius, radius, radius, radius, borderType ); |
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patchNaNs(temp); |
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// allocate lookup tables
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vector<float> _space_weight(d*d); |
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vector<int> _space_ofs(d*d); |
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float* space_weight = &_space_weight[0]; |
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int* space_ofs = &_space_ofs[0]; |
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// assign a length which is slightly more than needed
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len = (float)(maxValSrc - minValSrc) * cn; |
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kExpNumBins = kExpNumBinsPerChannel * cn; |
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vector<float> _expLUT(kExpNumBins+2); |
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float* expLUT = &_expLUT[0]; |
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scale_index = kExpNumBins/len; |
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// initialize the exp LUT
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for( i = 0; i < kExpNumBins+2; i++ ) |
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{ |
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if( lastExpVal > 0.f ) |
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{ |
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double val = i / scale_index; |
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expLUT[i] = (float)std::exp(val * val * gauss_color_coeff); |
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lastExpVal = expLUT[i]; |
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} |
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else |
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expLUT[i] = 0.f; |
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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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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/sizeof(float)) + j*cn); |
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} |
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for( i = 0; i < size.height; i++ ) |
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{ |
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const float* sptr = (const float*)(temp.data + (i+radius)*temp.step) + radius*cn; |
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float* dptr = (float*)(dst.data + i*dst.step); |
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if( cn == 1 ) |
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{ |
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for( j = 0; j < size.width; j++ ) |
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{ |
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float sum = 0, wsum = 0; |
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float val0 = sptr[j]; |
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for( k = 0; k < maxk; k++ ) |
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{ |
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float val = sptr[j + space_ofs[k]]; |
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float alpha = (float)(std::abs(val - val0)*scale_index); |
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int idx = cvFloor(alpha); |
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alpha -= idx; |
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float w = space_weight[k]*(expLUT[idx] + alpha*(expLUT[idx+1] - expLUT[idx])); |
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sum += val*w; |
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wsum += w; |
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} |
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dptr[j] = (float)(sum/wsum); |
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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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for( j = 0; j < size.width*3; j += 3 ) |
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{ |
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float sum_b = 0, sum_g = 0, sum_r = 0, wsum = 0; |
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float b0 = sptr[j], g0 = sptr[j+1], r0 = sptr[j+2]; |
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for( k = 0; k < maxk; k++ ) |
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{ |
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const float* sptr_k = sptr + j + space_ofs[k]; |
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float b = sptr_k[0], g = sptr_k[1], r = sptr_k[2]; |
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float alpha = (float)((std::abs(b - b0) + |
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std::abs(g - g0) + std::abs(r - r0))*scale_index); |
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int idx = cvFloor(alpha); |
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alpha -= idx; |
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float w = space_weight[k]*(expLUT[idx] + alpha*(expLUT[idx+1] - expLUT[idx])); |
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sum_b += b*w; sum_g += g*w; sum_r += r*w; |
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wsum += w; |
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} |
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wsum = 1.f/wsum; |
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b0 = sum_b*wsum; |
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g0 = sum_g*wsum; |
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r0 = sum_r*wsum; |
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dptr[j] = b0; dptr[j+1] = g0; dptr[j+2] = r0; |
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} |
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} |
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} |
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} |
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int CV_BilateralFilterTest::prepare_test_case(int /* test_case_index */) |
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{ |
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const static int types[] = { CV_32FC1, CV_32FC3, CV_8UC1, CV_8UC3 }; |
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RNG& rng = ts->get_rng(); |
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Size size(getRandInt(rng, MIN_WIDTH, MAX_WIDTH), getRandInt(rng, MIN_HEIGHT, MAX_HEIGHT)); |
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int type = types[rng(sizeof(types) / sizeof(types[0]))]; |
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_d = rng.uniform(0., 1.) > 0.5 ? 5 : 3;
|
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_src.create(size, type); |
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rng.fill(_src, RNG::UNIFORM, 0, 256); |
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|
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_sigma_color = _sigma_space = 1.; |
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|
|
||||||
|
return 1; |
||||||
|
} |
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|
|
||||||
|
int CV_BilateralFilterTest::validate_test_results(int test_case_index) |
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|
{ |
||||||
|
static const double eps = 1; |
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|
|
||||||
|
Mat reference_dst, reference_src; |
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|
if (_src.depth() == CV_32F) |
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|
reference_bilateral_filter(_src, reference_dst, _d, _sigma_color, _sigma_space); |
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|
else |
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|
{ |
||||||
|
int type = _src.type(); |
||||||
|
_src.convertTo(reference_src, CV_32F); |
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|
reference_bilateral_filter(reference_src, reference_dst, _d, _sigma_color, _sigma_space); |
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|
reference_dst.convertTo(reference_dst, type); |
||||||
|
} |
||||||
|
|
||||||
|
double e = norm(reference_dst, _parallel_dst); |
||||||
|
if (e > eps) |
||||||
|
{ |
||||||
|
ts->printf(cvtest::TS::CONSOLE, "actual error: %g, expected: %g", e, eps); |
||||||
|
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); |
||||||
|
} |
||||||
|
else |
||||||
|
ts->set_failed_test_info(cvtest::TS::OK); |
||||||
|
|
||||||
|
return BaseTest::validate_test_results(test_case_index); |
||||||
|
} |
||||||
|
|
||||||
|
void CV_BilateralFilterTest::run_func() |
||||||
|
{ |
||||||
|
bilateralFilter(_src, _parallel_dst, _d, _sigma_color, _sigma_space); |
||||||
|
} |
||||||
|
|
||||||
|
TEST(Imgproc_BilateralFilter, accuracy) |
||||||
|
{
|
||||||
|
CV_BilateralFilterTest test;
|
||||||
|
test.safe_run();
|
||||||
|
} |
||||||
|
|
||||||
|
} // end of namespace cvtest
|
||||||
Loading…
Reference in new issue