Open Source Computer Vision Library https://opencv.org/
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 
 
 
 

244 lines
8.0 KiB

/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// 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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
// Peng Xiao, pengxiao@outlook.com
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * 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.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors as is and
// 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,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include <map>
#include <functional>
#include "test_precomp.hpp"
using namespace std;
using namespace cvtest;
using namespace testing;
using namespace cv;
namespace
{
IMPLEMENT_PARAM_CLASS(IsGreaterThan, bool)
IMPLEMENT_PARAM_CLASS(InputSize, int)
IMPLEMENT_PARAM_CLASS(SortMethod, int)
template<class T>
struct KV_CVTYPE{ static int toType() {return 0;} };
template<> struct KV_CVTYPE<int> { static int toType() {return CV_32SC1;} };
template<> struct KV_CVTYPE<float>{ static int toType() {return CV_32FC1;} };
template<> struct KV_CVTYPE<Vec2i>{ static int toType() {return CV_32SC2;} };
template<> struct KV_CVTYPE<Vec2f>{ static int toType() {return CV_32FC2;} };
template<class key_type, class val_type>
bool kvgreater(pair<key_type, val_type> p1, pair<key_type, val_type> p2)
{
return p1.first > p2.first;
}
template<class key_type, class val_type>
bool kvless(pair<key_type, val_type> p1, pair<key_type, val_type> p2)
{
return p1.first < p2.first;
}
template<class key_type, class val_type>
void toKVPair(
MatConstIterator_<key_type> kit,
MatConstIterator_<val_type> vit,
int vecSize,
vector<pair<key_type, val_type> >& kvres
)
{
kvres.clear();
for(int i = 0; i < vecSize; i ++)
{
kvres.push_back(make_pair(*kit, *vit));
++kit;
++vit;
}
}
template<class key_type, class val_type>
void kvquicksort(Mat& keys, Mat& vals, bool isGreater = false)
{
vector<pair<key_type, val_type> > kvres;
toKVPair(keys.begin<key_type>(), vals.begin<val_type>(), keys.cols, kvres);
if(isGreater)
{
std::sort(kvres.begin(), kvres.end(), kvgreater<key_type, val_type>);
}
else
{
std::sort(kvres.begin(), kvres.end(), kvless<key_type, val_type>);
}
key_type * kptr = keys.ptr<key_type>();
val_type * vptr = vals.ptr<val_type>();
for(int i = 0; i < keys.cols; i ++)
{
kptr[i] = kvres[i].first;
vptr[i] = kvres[i].second;
}
}
class SortByKey_STL
{
public:
static void sort(cv::Mat&, cv::Mat&, bool is_gt);
private:
typedef void (*quick_sorter)(cv::Mat&, cv::Mat&, bool);
SortByKey_STL();
quick_sorter quick_sorters[CV_64FC4][CV_64FC4];
static SortByKey_STL instance;
};
SortByKey_STL SortByKey_STL::instance = SortByKey_STL();
SortByKey_STL::SortByKey_STL()
{
memset(instance.quick_sorters, 0, sizeof(quick_sorters));
#define NEW_SORTER(KT, VT) \
instance.quick_sorters[KV_CVTYPE<KT>::toType()][KV_CVTYPE<VT>::toType()] = kvquicksort<KT, VT>;
NEW_SORTER(int, int);
NEW_SORTER(int, Vec2i);
NEW_SORTER(int, float);
NEW_SORTER(int, Vec2f);
NEW_SORTER(float, int);
NEW_SORTER(float, Vec2i);
NEW_SORTER(float, float);
NEW_SORTER(float, Vec2f);
#undef NEW_SORTER
}
void SortByKey_STL::sort(cv::Mat& keys, cv::Mat& vals, bool is_gt)
{
instance.quick_sorters[keys.type()][vals.type()](keys, vals, is_gt);
}
bool checkUnstableSorterResult(const Mat& gkeys_, const Mat& gvals_,
const Mat& /*dkeys_*/, const Mat& dvals_)
{
int cn_val = gvals_.channels();
int count = gkeys_.cols;
//for convenience we convert depth to float and channels to 1
Mat gkeys, gvals, dkeys, dvals;
gkeys_.reshape(1).convertTo(gkeys, CV_32F);
gvals_.reshape(1).convertTo(gvals, CV_32F);
//dkeys_.reshape(1).convertTo(dkeys, CV_32F);
dvals_.reshape(1).convertTo(dvals, CV_32F);
float * gkptr = gkeys.ptr<float>();
float * gvptr = gvals.ptr<float>();
//float * dkptr = dkeys.ptr<float>();
float * dvptr = dvals.ptr<float>();
for(int i = 0; i < count - 1; ++i)
{
int iden_count = 0;
// firstly calculate the number of identical keys
while(gkptr[i + iden_count] == gkptr[i + 1 + iden_count])
{
++ iden_count;
}
// sort dv and gv
int num_of_val = (iden_count + 1) * cn_val;
std::sort(gvptr + i * cn_val, gvptr + i * cn_val + num_of_val);
std::sort(dvptr + i * cn_val, dvptr + i * cn_val + num_of_val);
// then check if [i, i + iden_count) is the same
for(int j = 0; j < num_of_val; ++j)
{
if(gvptr[i + j] != dvptr[i + j])
{
return false;
}
}
i += iden_count;
}
return true;
}
}
#define INPUT_SIZES Values(InputSize(0x10), InputSize(0x100), InputSize(0x10000)) //2^4, 2^8, 2^16
#define KEY_TYPES Values(MatType(CV_32SC1), MatType(CV_32FC1))
#define VAL_TYPES Values(MatType(CV_32SC1), MatType(CV_32SC2), MatType(CV_32FC1), MatType(CV_32FC2))
#define SORT_METHODS Values(SortMethod(cv::ocl::SORT_BITONIC),SortMethod(cv::ocl::SORT_MERGE),SortMethod(cv::ocl::SORT_RADIX)/*,SortMethod(cv::ocl::SORT_SELECTION)*/)
#define F_OR_T Values(IsGreaterThan(false), IsGreaterThan(true))
PARAM_TEST_CASE(SortByKey, InputSize, MatType, MatType, SortMethod, IsGreaterThan)
{
InputSize input_size;
MatType key_type, val_type;
SortMethod method;
IsGreaterThan is_gt;
Mat mat_key, mat_val;
virtual void SetUp()
{
input_size = GET_PARAM(0);
key_type = GET_PARAM(1);
val_type = GET_PARAM(2);
method = GET_PARAM(3);
is_gt = GET_PARAM(4);
using namespace cv;
// fill key and val
mat_key = randomMat(Size(input_size, 1), key_type, INT_MIN, INT_MAX);
mat_val = randomMat(Size(input_size, 1), val_type, INT_MIN, INT_MAX);
}
};
OCL_TEST_P(SortByKey, Accuracy)
{
using namespace cv;
ocl::oclMat oclmat_key(mat_key);
ocl::oclMat oclmat_val(mat_val);
ocl::sortByKey(oclmat_key, oclmat_val, method, is_gt);
SortByKey_STL::sort(mat_key, mat_val, is_gt);
EXPECT_MAT_NEAR(mat_key, oclmat_key, 0.0);
EXPECT_TRUE(checkUnstableSorterResult(mat_key, mat_val, oclmat_key, oclmat_val));
}
INSTANTIATE_TEST_CASE_P(OCL_SORT, SortByKey, Combine(INPUT_SIZES, KEY_TYPES, VAL_TYPES, SORT_METHODS, F_OR_T));