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opencv/modules/ocl/src/brute_force_matcher.cpp

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/*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
// Nathan, liujun@multicorewareinc.com
// 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 "precomp.hpp"
#include <functional>
#include <iterator>
#include <vector>
#include <algorithm>
#include "opencl_kernels.hpp"
using namespace cv;
using namespace cv::ocl;
using namespace std;
static const int OPT_SIZE = 100;
static const char * T_ARR [] = {
"uchar",
"char",
"ushort",
"short",
"int",
"float -D T_FLOAT",
"double"};
template < int BLOCK_SIZE, int MAX_DESC_LEN/*, typename Mask*/ >
void matchUnrolledCached(const oclMat &query, const oclMat &train, const oclMat &/*mask*/,
const oclMat &trainIdx, const oclMat &distance, int distType)
{
cv::ocl::Context *ctx = query.clCxt;
size_t globalSize[] = {(query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
const size_t smemSize = (BLOCK_SIZE * (MAX_DESC_LEN >= 2 * BLOCK_SIZE ? MAX_DESC_LEN : 2 * BLOCK_SIZE) + BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
int block_size = BLOCK_SIZE;
int m_size = MAX_DESC_LEN;
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt,
"-D T=%s -D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d",
T_ARR[query.depth()], distType, block_size, m_size);
if(globalSize[0] != 0)
{
args.push_back( make_pair( sizeof(cl_mem), (void *)&query.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&train.data ));
//args.push_back( make_pair( sizeof(cl_mem), (void *)&mask.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&trainIdx.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&distance.data ));
args.push_back( make_pair( smemSize, (void *)NULL));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.rows ));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.cols ));
args.push_back( make_pair( sizeof(cl_int), (void *)&train.rows ));
args.push_back( make_pair( sizeof(cl_int), (void *)&train.cols ));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.step ));
std::string kernelName = "BruteForceMatch_UnrollMatch";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
template < int BLOCK_SIZE, int MAX_DESC_LEN/*, typename Mask*/ >
void matchUnrolledCached(const oclMat /*query*/, const oclMat * /*trains*/, int /*n*/, const oclMat /*mask*/,
const oclMat &/*bestTrainIdx*/, const oclMat & /*bestImgIdx*/, const oclMat & /*bestDistance*/, int /*distType*/)
{
}
template < int BLOCK_SIZE/*, typename Mask*/ >
void match(const oclMat &query, const oclMat &train, const oclMat &/*mask*/,
const oclMat &trainIdx, const oclMat &distance, int distType)
{
cv::ocl::Context *ctx = query.clCxt;
size_t globalSize[] = {(query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
const size_t smemSize = (2 * BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
int block_size = BLOCK_SIZE;
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt,
"-D T=%s -D DIST_TYPE=%d -D BLOCK_SIZE=%d",
T_ARR[query.depth()], distType, block_size);
if(globalSize[0] != 0)
{
args.push_back( make_pair( sizeof(cl_mem), (void *)&query.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&train.data ));
//args.push_back( make_pair( sizeof(cl_mem), (void *)&mask.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&trainIdx.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&distance.data ));
args.push_back( make_pair( smemSize, (void *)NULL));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.rows ));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.cols ));
args.push_back( make_pair( sizeof(cl_int), (void *)&train.rows ));
args.push_back( make_pair( sizeof(cl_int), (void *)&train.cols ));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.step ));
std::string kernelName = "BruteForceMatch_Match";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
template < int BLOCK_SIZE/*, typename Mask*/ >
void match(const oclMat /*query*/, const oclMat * /*trains*/, int /*n*/, const oclMat /*mask*/,
const oclMat &/*bestTrainIdx*/, const oclMat & /*bestImgIdx*/, const oclMat & /*bestDistance*/, int /*distType*/)
{
}
//radius_matchUnrolledCached
template < int BLOCK_SIZE, int MAX_DESC_LEN/*, typename Mask*/ >
void matchUnrolledCached(const oclMat &query, const oclMat &train, float maxDistance, const oclMat &/*mask*/,
const oclMat &trainIdx, const oclMat &distance, const oclMat &nMatches, int distType)
{
cv::ocl::Context *ctx = query.clCxt;
size_t globalSize[] = {(train.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, (query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, 1};
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
const size_t smemSize = (2 * BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
int block_size = BLOCK_SIZE;
int m_size = MAX_DESC_LEN;
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt,
"-D T=%s -D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d",
T_ARR[query.depth()], distType, block_size, m_size);
if(globalSize[0] != 0)
{
args.push_back( make_pair( sizeof(cl_mem), (void *)&query.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&train.data ));
args.push_back( make_pair( sizeof(cl_float), (void *)&maxDistance ));
//args.push_back( make_pair( sizeof(cl_mem), (void *)&mask.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&trainIdx.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&distance.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&nMatches.data ));
args.push_back( make_pair( smemSize, (void *)NULL));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.rows ));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.cols ));
args.push_back( make_pair( sizeof(cl_int), (void *)&train.rows ));
args.push_back( make_pair( sizeof(cl_int), (void *)&train.cols ));
args.push_back( make_pair( sizeof(cl_int), (void *)&trainIdx.cols ));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.step ));
args.push_back( make_pair( sizeof(cl_int), (void *)&trainIdx.step ));
std::string kernelName = "BruteForceMatch_RadiusUnrollMatch";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
//radius_match
template < int BLOCK_SIZE/*, typename Mask*/ >
void radius_match(const oclMat &query, const oclMat &train, float maxDistance, const oclMat &/*mask*/,
const oclMat &trainIdx, const oclMat &distance, const oclMat &nMatches, int distType)
{
cv::ocl::Context *ctx = query.clCxt;
size_t globalSize[] = {(train.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, (query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, 1};
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
const size_t smemSize = (2 * BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
int block_size = BLOCK_SIZE;
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt,
"-D T=%s -D DIST_TYPE=%d -D BLOCK_SIZE=%d",
T_ARR[query.depth()], distType, block_size);
if(globalSize[0] != 0)
{
args.push_back( make_pair( sizeof(cl_mem), (void *)&query.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&train.data ));
args.push_back( make_pair( sizeof(cl_float), (void *)&maxDistance ));
//args.push_back( make_pair( sizeof(cl_mem), (void *)&mask.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&trainIdx.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&distance.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&nMatches.data ));
args.push_back( make_pair( smemSize, (void *)NULL));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.rows ));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.cols ));
args.push_back( make_pair( sizeof(cl_int), (void *)&train.rows ));
args.push_back( make_pair( sizeof(cl_int), (void *)&train.cols ));
args.push_back( make_pair( sizeof(cl_int), (void *)&trainIdx.cols ));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.step ));
args.push_back( make_pair( sizeof(cl_int), (void *)&trainIdx.step ));
std::string kernelName = "BruteForceMatch_RadiusMatch";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
static void matchDispatcher(const oclMat &query, const oclMat &train, const oclMat &mask,
const oclMat &trainIdx, const oclMat &distance, int distType)
{
const oclMat zeroMask;
const oclMat &tempMask = mask.data ? mask : zeroMask;
bool is_cpu = isCpuDevice();
if (query.cols <= 64)
{
matchUnrolledCached<16, 64>(query, train, tempMask, trainIdx, distance, distType);
}
else if (query.cols <= 128 && !is_cpu)
{
matchUnrolledCached<16, 128>(query, train, tempMask, trainIdx, distance, distType);
}
else
{
match<16>(query, train, tempMask, trainIdx, distance, distType);
}
}
static void matchDispatcher(const oclMat &query, const oclMat *trains, int n, const oclMat &mask,
const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance, int distType)
{
const oclMat zeroMask;
const oclMat &tempMask = mask.data ? mask : zeroMask;
bool is_cpu = isCpuDevice();
if (query.cols <= 64)
{
matchUnrolledCached<16, 64>(query, trains, n, tempMask, trainIdx, imgIdx, distance, distType);
}
else if (query.cols <= 128 && !is_cpu)
{
matchUnrolledCached<16, 128>(query, trains, n, tempMask, trainIdx, imgIdx, distance, distType);
}
else
{
match<16>(query, trains, n, tempMask, trainIdx, imgIdx, distance, distType);
}
}
//radius matchDispatcher
static void matchDispatcher(const oclMat &query, const oclMat &train, float maxDistance, const oclMat &mask,
const oclMat &trainIdx, const oclMat &distance, const oclMat &nMatches, int distType)
{
const oclMat zeroMask;
const oclMat &tempMask = mask.data ? mask : zeroMask;
bool is_cpu = isCpuDevice();
if (query.cols <= 64)
{
matchUnrolledCached<16, 64>(query, train, maxDistance, tempMask, trainIdx, distance, nMatches, distType);
}
else if (query.cols <= 128 && !is_cpu)
{
matchUnrolledCached<16, 128>(query, train, maxDistance, tempMask, trainIdx, distance, nMatches, distType);
}
else
{
radius_match<16>(query, train, maxDistance, tempMask, trainIdx, distance, nMatches, distType);
}
}
//knn match Dispatcher
template < int BLOCK_SIZE, int MAX_DESC_LEN/*, typename Mask*/ >
void knn_matchUnrolledCached(const oclMat &query, const oclMat &train, const oclMat &/*mask*/,
const oclMat &trainIdx, const oclMat &distance, int distType)
{
cv::ocl::Context *ctx = query.clCxt;
size_t globalSize[] = {(query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
const size_t smemSize = (BLOCK_SIZE * (MAX_DESC_LEN >= BLOCK_SIZE ? MAX_DESC_LEN : BLOCK_SIZE) + BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
int block_size = BLOCK_SIZE;
int m_size = MAX_DESC_LEN;
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt,
"-D T=%s -D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d",
T_ARR[query.depth()], distType, block_size, m_size);
if(globalSize[0] != 0)
{
args.push_back( make_pair( sizeof(cl_mem), (void *)&query.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&train.data ));
//args.push_back( make_pair( sizeof(cl_mem), (void *)&mask.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&trainIdx.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&distance.data ));
args.push_back( make_pair( smemSize, (void *)NULL));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.rows ));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.cols ));
args.push_back( make_pair( sizeof(cl_int), (void *)&train.rows ));
args.push_back( make_pair( sizeof(cl_int), (void *)&train.cols ));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.step ));
std::string kernelName = "BruteForceMatch_knnUnrollMatch";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
template < int BLOCK_SIZE/*, typename Mask*/ >
void knn_match(const oclMat &query, const oclMat &train, const oclMat &/*mask*/,
const oclMat &trainIdx, const oclMat &distance, int distType)
{
cv::ocl::Context *ctx = query.clCxt;
size_t globalSize[] = {(query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
const size_t smemSize = (2 * BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
int block_size = BLOCK_SIZE;
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt,
"-D T=%s -D DIST_TYPE=%d -D BLOCK_SIZE=%d",
T_ARR[query.depth()], distType, block_size);
if(globalSize[0] != 0)
{
args.push_back( make_pair( sizeof(cl_mem), (void *)&query.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&train.data ));
//args.push_back( make_pair( sizeof(cl_mem), (void *)&mask.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&trainIdx.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&distance.data ));
args.push_back( make_pair( smemSize, (void *)NULL));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.rows ));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.cols ));
args.push_back( make_pair( sizeof(cl_int), (void *)&train.rows ));
args.push_back( make_pair( sizeof(cl_int), (void *)&train.cols ));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.step ));
std::string kernelName = "BruteForceMatch_knnMatch";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
template < int BLOCK_SIZE, int MAX_DESC_LEN/*, typename Mask*/ >
void calcDistanceUnrolled(const oclMat &query, const oclMat &train, const oclMat &/*mask*/, const oclMat &allDist, int distType)
{
cv::ocl::Context *ctx = query.clCxt;
size_t globalSize[] = {(query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
const size_t smemSize = (2 * BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
int block_size = BLOCK_SIZE;
int m_size = MAX_DESC_LEN;
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt,
"-D T=%s -D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d",
T_ARR[query.depth()], distType, block_size, m_size);
if(globalSize[0] != 0)
{
args.push_back( make_pair( sizeof(cl_mem), (void *)&query.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&train.data ));
//args.push_back( make_pair( sizeof(cl_mem), (void *)&mask.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&allDist.data ));
args.push_back( make_pair( smemSize, (void *)NULL));
args.push_back( make_pair( sizeof(cl_int), (void *)&block_size ));
args.push_back( make_pair( sizeof(cl_int), (void *)&m_size ));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.rows ));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.cols ));
args.push_back( make_pair( sizeof(cl_int), (void *)&train.rows ));
args.push_back( make_pair( sizeof(cl_int), (void *)&train.cols ));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.step ));
std::string kernelName = "BruteForceMatch_calcDistanceUnrolled";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
template < int BLOCK_SIZE/*, typename Mask*/ >
void calcDistance(const oclMat &query, const oclMat &train, const oclMat &/*mask*/, const oclMat &allDist, int distType)
{
cv::ocl::Context *ctx = query.clCxt;
size_t globalSize[] = {(query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
const size_t smemSize = (2 * BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
int block_size = BLOCK_SIZE;
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt,
"-D T=%s -D DIST_TYPE=%d -D BLOCK_SIZE=%d",
T_ARR[query.depth()], distType, block_size);
if(globalSize[0] != 0)
{
args.push_back( make_pair( sizeof(cl_mem), (void *)&query.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&train.data ));
//args.push_back( make_pair( sizeof(cl_mem), (void *)&mask.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&allDist.data ));
args.push_back( make_pair( smemSize, (void *)NULL));
args.push_back( make_pair( sizeof(cl_int), (void *)&block_size ));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.rows ));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.cols ));
args.push_back( make_pair( sizeof(cl_int), (void *)&train.rows ));
args.push_back( make_pair( sizeof(cl_int), (void *)&train.cols ));
args.push_back( make_pair( sizeof(cl_int), (void *)&query.step ));
std::string kernelName = "BruteForceMatch_calcDistance";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
///////////////////////////////////////////////////////////////////////////////
// Calc Distance dispatcher
static void calcDistanceDispatcher(const oclMat &query, const oclMat &train, const oclMat &mask,
const oclMat &allDist, int distType)
{
if (query.cols <= 64)
{
calcDistanceUnrolled<16, 64>(query, train, mask, allDist, distType);
}
else if (query.cols <= 128)
{
calcDistanceUnrolled<16, 128>(query, train, mask, allDist, distType);
}
else
{
calcDistance<16>(query, train, mask, allDist, distType);
}
}
static void match2Dispatcher(const oclMat &query, const oclMat &train, const oclMat &mask,
const oclMat &trainIdx, const oclMat &distance, int distType)
{
bool is_cpu = isCpuDevice();
if (query.cols <= 64)
{
knn_matchUnrolledCached<16, 64>(query, train, mask, trainIdx, distance, distType);
}
else if (query.cols <= 128 && !is_cpu)
{
knn_matchUnrolledCached<16, 128>(query, train, mask, trainIdx, distance, distType);
}
else
{
knn_match<16>(query, train, mask, trainIdx, distance, distType);
}
}
template <int BLOCK_SIZE>
void findKnnMatch(int k, const oclMat &trainIdx, const oclMat &distance, const oclMat &allDist, int /*distType*/)
{
cv::ocl::Context *ctx = trainIdx.clCxt;
size_t globalSize[] = {trainIdx.rows * BLOCK_SIZE, 1, 1};
size_t localSize[] = {BLOCK_SIZE, 1, 1};
int block_size = BLOCK_SIZE;
std::string kernelName = "BruteForceMatch_findBestMatch";
for (int i = 0; i < k; ++i)
{
vector< pair<size_t, const void *> > args;
args.push_back( make_pair( sizeof(cl_mem), (void *)&allDist.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&trainIdx.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&distance.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&i));
args.push_back( make_pair( sizeof(cl_int), (void *)&block_size ));
//args.push_back( make_pair( sizeof(cl_int), (void *)&train.rows ));
//args.push_back( make_pair( sizeof(cl_int), (void *)&train.cols ));
//args.push_back( make_pair( sizeof(cl_int), (void *)&query.step ));
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1);
}
}
static void findKnnMatchDispatcher(int k, const oclMat &trainIdx, const oclMat &distance, const oclMat &allDist, int distType)
{
findKnnMatch<256>(k, trainIdx, distance, allDist, distType);
}
static void kmatchDispatcher(const oclMat &query, const oclMat &train, int k, const oclMat &mask,
const oclMat &trainIdx, const oclMat &distance, const oclMat &allDist, int distType)
{
const oclMat zeroMask;
const oclMat &tempMask = mask.data ? mask : zeroMask;
if (k == 2)
{
match2Dispatcher(query, train, tempMask, trainIdx, distance, distType);
}
else
{
calcDistanceDispatcher(query, train, tempMask, allDist, distType);
findKnnMatchDispatcher(k, trainIdx, distance, allDist, distType);
}
}
cv::ocl::BruteForceMatcher_OCL_base::BruteForceMatcher_OCL_base(DistType distType_) : distType(distType_)
{
}
void cv::ocl::BruteForceMatcher_OCL_base::add(const vector<oclMat> &descCollection)
{
trainDescCollection.insert(trainDescCollection.end(), descCollection.begin(), descCollection.end());
}
const vector<oclMat> &cv::ocl::BruteForceMatcher_OCL_base::getTrainDescriptors() const
{
return trainDescCollection;
}
void cv::ocl::BruteForceMatcher_OCL_base::clear()
{
trainDescCollection.clear();
}
bool cv::ocl::BruteForceMatcher_OCL_base::empty() const
{
return trainDescCollection.empty();
}
bool cv::ocl::BruteForceMatcher_OCL_base::isMaskSupported() const
{
return true;
}
void cv::ocl::BruteForceMatcher_OCL_base::matchSingle(const oclMat &query, const oclMat &train,
oclMat &trainIdx, oclMat &distance, const oclMat &mask)
{
if (query.empty() || train.empty())
return;
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
CV_Assert(train.cols == query.cols && train.type() == query.type());
ensureSizeIsEnough(1, query.rows, CV_32S, trainIdx);
ensureSizeIsEnough(1, query.rows, CV_32F, distance);
matchDispatcher(query, train, mask, trainIdx, distance, distType);
return;
}
void cv::ocl::BruteForceMatcher_OCL_base::matchDownload(const oclMat &trainIdx, const oclMat &distance, vector<DMatch> &matches)
{
if (trainIdx.empty() || distance.empty())
return;
Mat trainIdxCPU(trainIdx);
Mat distanceCPU(distance);
matchConvert(trainIdxCPU, distanceCPU, matches);
}
void cv::ocl::BruteForceMatcher_OCL_base::matchConvert(const Mat &trainIdx, const Mat &distance, vector<DMatch> &matches)
{
if (trainIdx.empty() || distance.empty())
return;
CV_Assert(trainIdx.type() == CV_32SC1);
CV_Assert(distance.type() == CV_32FC1 && distance.cols == trainIdx.cols);
const int nQuery = trainIdx.cols;
matches.clear();
matches.reserve(nQuery);
const int *trainIdx_ptr = trainIdx.ptr<int>();
const float *distance_ptr = distance.ptr<float>();
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx, ++trainIdx_ptr, ++distance_ptr)
{
int trainIdx = *trainIdx_ptr;
if (trainIdx == -1)
continue;
float distance = *distance_ptr;
DMatch m(queryIdx, trainIdx, 0, distance);
matches.push_back(m);
}
}
void cv::ocl::BruteForceMatcher_OCL_base::match(const oclMat &query, const oclMat &train, vector<DMatch> &matches, const oclMat &mask)
{
assert(mask.empty()); // mask is not supported at the moment
oclMat trainIdx, distance;
matchSingle(query, train, trainIdx, distance, mask);
matchDownload(trainIdx, distance, matches);
}
void cv::ocl::BruteForceMatcher_OCL_base::makeGpuCollection(oclMat &trainCollection, oclMat &maskCollection, const vector<oclMat> &masks)
{
if (empty())
return;
if (masks.empty())
{
Mat trainCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(oclMat)));
oclMat *trainCollectionCPU_ptr = trainCollectionCPU.ptr<oclMat>();
for (size_t i = 0, size = trainDescCollection.size(); i < size; ++i, ++trainCollectionCPU_ptr)
*trainCollectionCPU_ptr = trainDescCollection[i];
trainCollection.upload(trainCollectionCPU);
maskCollection.release();
}
else
{
CV_Assert(masks.size() == trainDescCollection.size());
Mat trainCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(oclMat)));
Mat maskCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(oclMat)));
oclMat *trainCollectionCPU_ptr = trainCollectionCPU.ptr<oclMat>();
oclMat *maskCollectionCPU_ptr = maskCollectionCPU.ptr<oclMat>();
for (size_t i = 0, size = trainDescCollection.size(); i < size; ++i, ++trainCollectionCPU_ptr, ++maskCollectionCPU_ptr)
{
const oclMat &train = trainDescCollection[i];
const oclMat &mask = masks[i];
CV_Assert(mask.empty() || (mask.type() == CV_8UC1 && mask.cols == train.rows));
*trainCollectionCPU_ptr = train;
*maskCollectionCPU_ptr = mask;
}
trainCollection.upload(trainCollectionCPU);
maskCollection.upload(maskCollectionCPU);
}
}
void cv::ocl::BruteForceMatcher_OCL_base::matchCollection(const oclMat &query, const oclMat &trainCollection, oclMat &trainIdx,
oclMat &imgIdx, oclMat &distance, const oclMat &masks)
{
if (query.empty() || trainCollection.empty())
return;
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
const int nQuery = query.rows;
ensureSizeIsEnough(1, nQuery, CV_32S, trainIdx);
ensureSizeIsEnough(1, nQuery, CV_32S, imgIdx);
ensureSizeIsEnough(1, nQuery, CV_32F, distance);
matchDispatcher(query, &trainCollection, trainCollection.cols, masks, trainIdx, imgIdx, distance, distType);
return;
}
void cv::ocl::BruteForceMatcher_OCL_base::matchDownload(const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance, vector<DMatch> &matches)
{
if (trainIdx.empty() || imgIdx.empty() || distance.empty())
return;
Mat trainIdxCPU(trainIdx);
Mat imgIdxCPU(imgIdx);
Mat distanceCPU(distance);
matchConvert(trainIdxCPU, imgIdxCPU, distanceCPU, matches);
}
void cv::ocl::BruteForceMatcher_OCL_base::matchConvert(const Mat &trainIdx, const Mat &imgIdx, const Mat &distance, vector<DMatch> &matches)
{
if (trainIdx.empty() || imgIdx.empty() || distance.empty())
return;
CV_Assert(trainIdx.type() == CV_32SC1);
CV_Assert(imgIdx.type() == CV_32SC1 && imgIdx.cols == trainIdx.cols);
CV_Assert(distance.type() == CV_32FC1 && distance.cols == trainIdx.cols);
const int nQuery = trainIdx.cols;
matches.clear();
matches.reserve(nQuery);
const int *trainIdx_ptr = trainIdx.ptr<int>();
const int *imgIdx_ptr = imgIdx.ptr<int>();
const float *distance_ptr = distance.ptr<float>();
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx, ++trainIdx_ptr, ++imgIdx_ptr, ++distance_ptr)
{
int trainIdx = *trainIdx_ptr;
if (trainIdx == -1)
continue;
int imgIdx = *imgIdx_ptr;
float distance = *distance_ptr;
DMatch m(queryIdx, trainIdx, imgIdx, distance);
matches.push_back(m);
}
}
void cv::ocl::BruteForceMatcher_OCL_base::match(const oclMat &query, vector<DMatch> &matches, const vector<oclMat> &masks)
{
oclMat trainCollection;
oclMat maskCollection;
makeGpuCollection(trainCollection, maskCollection, masks);
oclMat trainIdx, imgIdx, distance;
matchCollection(query, trainCollection, trainIdx, imgIdx, distance, maskCollection);
matchDownload(trainIdx, imgIdx, distance, matches);
}
// knn match
void cv::ocl::BruteForceMatcher_OCL_base::knnMatchSingle(const oclMat &query, const oclMat &train, oclMat &trainIdx,
oclMat &distance, oclMat &allDist, int k, const oclMat &mask)
{
if (query.empty() || train.empty())
return;
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
CV_Assert(train.type() == query.type() && train.cols == query.cols);
const int nQuery = query.rows;
const int nTrain = train.rows;
if (k == 2)
{
ensureSizeIsEnough(1, nQuery, CV_32SC2, trainIdx);
ensureSizeIsEnough(1, nQuery, CV_32FC2, distance);
}
else
{
ensureSizeIsEnough(nQuery, k, CV_32S, trainIdx);
ensureSizeIsEnough(nQuery, k, CV_32F, distance);
ensureSizeIsEnough(nQuery, nTrain, CV_32FC1, allDist);
}
trainIdx.setTo(Scalar::all(-1));
kmatchDispatcher(query, train, k, mask, trainIdx, distance, allDist, distType);
return;
}
void cv::ocl::BruteForceMatcher_OCL_base::knnMatchDownload(const oclMat &trainIdx, const oclMat &distance, vector< vector<DMatch> > &matches, bool compactResult)
{
if (trainIdx.empty() || distance.empty())
return;
Mat trainIdxCPU(trainIdx);
Mat distanceCPU(distance);
knnMatchConvert(trainIdxCPU, distanceCPU, matches, compactResult);
}
void cv::ocl::BruteForceMatcher_OCL_base::knnMatchConvert(const Mat &trainIdx, const Mat &distance, vector< vector<DMatch> > &matches, bool compactResult)
{
if (trainIdx.empty() || distance.empty())
return;
CV_Assert(trainIdx.type() == CV_32SC2 || trainIdx.type() == CV_32SC1);
CV_Assert(distance.type() == CV_32FC2 || distance.type() == CV_32FC1);
CV_Assert(distance.size() == trainIdx.size());
CV_Assert(trainIdx.isContinuous() && distance.isContinuous());
const int nQuery = trainIdx.type() == CV_32SC2 ? trainIdx.cols : trainIdx.rows;
const int k = trainIdx.type() == CV_32SC2 ? 2 : trainIdx.cols;
matches.clear();
matches.reserve(nQuery);
const int *trainIdx_ptr = trainIdx.ptr<int>();
const float *distance_ptr = distance.ptr<float>();
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx)
{
matches.push_back(vector<DMatch>());
vector<DMatch> &curMatches = matches.back();
curMatches.reserve(k);
for (int i = 0; i < k; ++i, ++trainIdx_ptr, ++distance_ptr)
{
int trainIdx = *trainIdx_ptr;
if (trainIdx != -1)
{
float distance = *distance_ptr;
DMatch m(queryIdx, trainIdx, 0, distance);
curMatches.push_back(m);
}
}
if (compactResult && curMatches.empty())
matches.pop_back();
}
}
void cv::ocl::BruteForceMatcher_OCL_base::knnMatch(const oclMat &query, const oclMat &train, vector< vector<DMatch> > &matches
, int k, const oclMat &mask, bool compactResult)
{
oclMat trainIdx, distance, allDist;
knnMatchSingle(query, train, trainIdx, distance, allDist, k, mask);
knnMatchDownload(trainIdx, distance, matches, compactResult);
}
void cv::ocl::BruteForceMatcher_OCL_base::knnMatch2Collection(const oclMat &query, const oclMat &trainCollection,
oclMat &trainIdx, oclMat &imgIdx, oclMat &distance, const oclMat &/*maskCollection*/)
{
if (query.empty() || trainCollection.empty())
return;
// typedef void (*caller_t)(const oclMat & query, const oclMat & trains, const oclMat & masks,
// const oclMat & trainIdx, const oclMat & imgIdx, const oclMat & distance);
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
const int nQuery = query.rows;
ensureSizeIsEnough(1, nQuery, CV_32SC2, trainIdx);
ensureSizeIsEnough(1, nQuery, CV_32SC2, imgIdx);
ensureSizeIsEnough(1, nQuery, CV_32FC2, distance);
trainIdx.setTo(Scalar::all(-1));
//caller_t func = callers[distType][query.depth()];
//CV_Assert(func != 0);
//func(query, trainCollection, maskCollection, trainIdx, imgIdx, distance, cc, StreamAccessor::getStream(stream));
}
void cv::ocl::BruteForceMatcher_OCL_base::knnMatch2Download(const oclMat &trainIdx, const oclMat &imgIdx,
const oclMat &distance, vector< vector<DMatch> > &matches, bool compactResult)
{
if (trainIdx.empty() || imgIdx.empty() || distance.empty())
return;
Mat trainIdxCPU(trainIdx);
Mat imgIdxCPU(imgIdx);
Mat distanceCPU(distance);
knnMatch2Convert(trainIdxCPU, imgIdxCPU, distanceCPU, matches, compactResult);
}
void cv::ocl::BruteForceMatcher_OCL_base::knnMatch2Convert(const Mat &trainIdx, const Mat &imgIdx, const Mat &distance,
vector< vector<DMatch> > &matches, bool compactResult)
{
if (trainIdx.empty() || imgIdx.empty() || distance.empty())
return;
CV_Assert(trainIdx.type() == CV_32SC2);
CV_Assert(imgIdx.type() == CV_32SC2 && imgIdx.cols == trainIdx.cols);
CV_Assert(distance.type() == CV_32FC2 && distance.cols == trainIdx.cols);
const int nQuery = trainIdx.cols;
matches.clear();
matches.reserve(nQuery);
const int *trainIdx_ptr = trainIdx.ptr<int>();
const int *imgIdx_ptr = imgIdx.ptr<int>();
const float *distance_ptr = distance.ptr<float>();
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx)
{
matches.push_back(vector<DMatch>());
vector<DMatch> &curMatches = matches.back();
curMatches.reserve(2);
for (int i = 0; i < 2; ++i, ++trainIdx_ptr, ++imgIdx_ptr, ++distance_ptr)
{
int trainIdx = *trainIdx_ptr;
if (trainIdx != -1)
{
int imgIdx = *imgIdx_ptr;
float distance = *distance_ptr;
DMatch m(queryIdx, trainIdx, imgIdx, distance);
curMatches.push_back(m);
}
}
if (compactResult && curMatches.empty())
matches.pop_back();
}
}
namespace
{
struct ImgIdxSetter
{
explicit inline ImgIdxSetter(int imgIdx_) : imgIdx(imgIdx_) {}
inline void operator()(DMatch &m) const
{
m.imgIdx = imgIdx;
}
int imgIdx;
};
}
void cv::ocl::BruteForceMatcher_OCL_base::knnMatch(const oclMat &query, vector< vector<DMatch> > &matches, int k,
const vector<oclMat> &masks, bool compactResult)
{
if (k == 2)
{
oclMat trainCollection;
oclMat maskCollection;
makeGpuCollection(trainCollection, maskCollection, masks);
oclMat trainIdx, imgIdx, distance;
knnMatch2Collection(query, trainCollection, trainIdx, imgIdx, distance, maskCollection);
knnMatch2Download(trainIdx, imgIdx, distance, matches);
}
else
{
if (query.empty() || empty())
return;
vector< vector<DMatch> > curMatches;
vector<DMatch> temp;
temp.reserve(2 * k);
matches.resize(query.rows);
for_each(matches.begin(), matches.end(), bind2nd(mem_fun_ref(&vector<DMatch>::reserve), k));
for (size_t imgIdx = 0, size = trainDescCollection.size(); imgIdx < size; ++imgIdx)
{
knnMatch(query, trainDescCollection[imgIdx], curMatches, k, masks.empty() ? oclMat() : masks[imgIdx]);
for (int queryIdx = 0; queryIdx < query.rows; ++queryIdx)
{
vector<DMatch> &localMatch = curMatches[queryIdx];
vector<DMatch> &globalMatch = matches[queryIdx];
for_each(localMatch.begin(), localMatch.end(), ImgIdxSetter(static_cast<int>(imgIdx)));
temp.clear();
merge(globalMatch.begin(), globalMatch.end(), localMatch.begin(), localMatch.end(), back_inserter(temp));
globalMatch.clear();
const size_t count = std::min((size_t)k, temp.size());
copy(temp.begin(), temp.begin() + count, back_inserter(globalMatch));
}
}
if (compactResult)
{
vector< vector<DMatch> >::iterator new_end = remove_if(matches.begin(), matches.end(), mem_fun_ref(&vector<DMatch>::empty));
matches.erase(new_end, matches.end());
}
}
}
// radiusMatchSingle
void cv::ocl::BruteForceMatcher_OCL_base::radiusMatchSingle(const oclMat &query, const oclMat &train,
oclMat &trainIdx, oclMat &distance, oclMat &nMatches, float maxDistance, const oclMat &mask)
{
if (query.empty() || train.empty())
return;
const int nQuery = query.rows;
const int nTrain = train.rows;
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
CV_Assert(train.type() == query.type() && train.cols == query.cols);
CV_Assert(trainIdx.empty() || (trainIdx.rows == query.rows && trainIdx.size() == distance.size()));
ensureSizeIsEnough(1, nQuery, CV_32SC1, nMatches);
if (trainIdx.empty())
{
ensureSizeIsEnough(nQuery, std::max((nTrain / 100), 10), CV_32SC1, trainIdx);
ensureSizeIsEnough(nQuery, std::max((nTrain / 100), 10), CV_32FC1, distance);
}
nMatches.setTo(Scalar::all(0));
matchDispatcher(query, train, maxDistance, mask, trainIdx, distance, nMatches, distType);
return;
}
void cv::ocl::BruteForceMatcher_OCL_base::radiusMatchDownload(const oclMat &trainIdx, const oclMat &distance, const oclMat &nMatches,
vector< vector<DMatch> > &matches, bool compactResult)
{
if (trainIdx.empty() || distance.empty() || nMatches.empty())
return;
Mat trainIdxCPU(trainIdx);
Mat distanceCPU(distance);
Mat nMatchesCPU(nMatches);
radiusMatchConvert(trainIdxCPU, distanceCPU, nMatchesCPU, matches, compactResult);
}
void cv::ocl::BruteForceMatcher_OCL_base::radiusMatchConvert(const Mat &trainIdx, const Mat &distance, const Mat &nMatches,
vector< vector<DMatch> > &matches, bool compactResult)
{
if (trainIdx.empty() || distance.empty() || nMatches.empty())
return;
CV_Assert(trainIdx.type() == CV_32SC1);
CV_Assert(distance.type() == CV_32FC1 && distance.size() == trainIdx.size());
CV_Assert(nMatches.type() == CV_32SC1 && nMatches.cols == trainIdx.rows);
const int nQuery = trainIdx.rows;
matches.clear();
matches.reserve(nQuery);
const int *nMatches_ptr = nMatches.ptr<int>();
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx)
{
const int *trainIdx_ptr = trainIdx.ptr<int>(queryIdx);
const float *distance_ptr = distance.ptr<float>(queryIdx);
const int nMatches = std::min(nMatches_ptr[queryIdx], trainIdx.cols);
if (nMatches == 0)
{
if (!compactResult)
matches.push_back(vector<DMatch>());
continue;
}
matches.push_back(vector<DMatch>(nMatches));
vector<DMatch> &curMatches = matches.back();
for (int i = 0; i < nMatches; ++i, ++trainIdx_ptr, ++distance_ptr)
{
int trainIdx = *trainIdx_ptr;
float distance = *distance_ptr;
DMatch m(queryIdx, trainIdx, 0, distance);
curMatches[i] = m;
}
std::sort(curMatches.begin(), curMatches.end());
}
}
void cv::ocl::BruteForceMatcher_OCL_base::radiusMatch(const oclMat &query, const oclMat &train, vector< vector<DMatch> > &matches,
float maxDistance, const oclMat &mask, bool compactResult)
{
oclMat trainIdx, distance, nMatches;
radiusMatchSingle(query, train, trainIdx, distance, nMatches, maxDistance, mask);
radiusMatchDownload(trainIdx, distance, nMatches, matches, compactResult);
}
void cv::ocl::BruteForceMatcher_OCL_base::radiusMatchCollection(const oclMat &query, oclMat &trainIdx, oclMat &imgIdx, oclMat &distance,
oclMat &nMatches, float /*maxDistance*/, const vector<oclMat> &masks)
{
if (query.empty() || empty())
return;
#if 0
typedef void (*caller_t)(const oclMat & query, const oclMat * trains, int n, float maxDistance, const oclMat * masks,
const oclMat & trainIdx, const oclMat & imgIdx, const oclMat & distance, const oclMat & nMatches);
static const caller_t callers[3][6] =
{
{
ocl_matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
ocl_matchL1_gpu<unsigned short>, matchL1_gpu<short>,
ocl_matchL1_gpu<int>, matchL1_gpu<float>
},
{
0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
0/*matchL2_gpu<int>*/, ocl_matchL2_gpu<float>
},
{
ocl_matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
ocl_matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
ocl_matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
}
};
#endif
const int nQuery = query.rows;
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
CV_Assert(trainIdx.empty() || (trainIdx.rows == nQuery && trainIdx.size() == distance.size() && trainIdx.size() == imgIdx.size()));
nMatches.create(1, nQuery, CV_32SC1);
if (trainIdx.empty())
{
trainIdx.create(nQuery, std::max((nQuery / 100), 10), CV_32SC1);
imgIdx.create(nQuery, std::max((nQuery / 100), 10), CV_32SC1);
distance.create(nQuery, std::max((nQuery / 100), 10), CV_32FC1);
}
nMatches.setTo(Scalar::all(0));
//caller_t func = callers[distType][query.depth()];
//CV_Assert(func != 0);
vector<oclMat> trains_(trainDescCollection.begin(), trainDescCollection.end());
vector<oclMat> masks_(masks.begin(), masks.end());
/* func(query, &trains_[0], static_cast<int>(trains_.size()), maxDistance, masks_.size() == 0 ? 0 : &masks_[0],
trainIdx, imgIdx, distance, nMatches));*/
}
void cv::ocl::BruteForceMatcher_OCL_base::radiusMatchDownload(const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance,
const oclMat &nMatches, vector< vector<DMatch> > &matches, bool compactResult)
{
if (trainIdx.empty() || imgIdx.empty() || distance.empty() || nMatches.empty())
return;
Mat trainIdxCPU(trainIdx);
Mat imgIdxCPU(imgIdx);
Mat distanceCPU(distance);
Mat nMatchesCPU(nMatches);
radiusMatchConvert(trainIdxCPU, imgIdxCPU, distanceCPU, nMatchesCPU, matches, compactResult);
}
void cv::ocl::BruteForceMatcher_OCL_base::radiusMatchConvert(const Mat &trainIdx, const Mat &imgIdx, const Mat &distance, const Mat &nMatches,
vector< vector<DMatch> > &matches, bool compactResult)
{
if (trainIdx.empty() || imgIdx.empty() || distance.empty() || nMatches.empty())
return;
CV_Assert(trainIdx.type() == CV_32SC1);
CV_Assert(imgIdx.type() == CV_32SC1 && imgIdx.size() == trainIdx.size());
CV_Assert(distance.type() == CV_32FC1 && distance.size() == trainIdx.size());
CV_Assert(nMatches.type() == CV_32SC1 && nMatches.cols == trainIdx.rows);
const int nQuery = trainIdx.rows;
matches.clear();
matches.reserve(nQuery);
const int *nMatches_ptr = nMatches.ptr<int>();
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx)
{
const int *trainIdx_ptr = trainIdx.ptr<int>(queryIdx);
const int *imgIdx_ptr = imgIdx.ptr<int>(queryIdx);
const float *distance_ptr = distance.ptr<float>(queryIdx);
const int nMatches = std::min(nMatches_ptr[queryIdx], trainIdx.cols);
if (nMatches == 0)
{
if (!compactResult)
matches.push_back(vector<DMatch>());
continue;
}
matches.push_back(vector<DMatch>());
vector<DMatch> &curMatches = matches.back();
curMatches.reserve(nMatches);
for (int i = 0; i < nMatches; ++i, ++trainIdx_ptr, ++imgIdx_ptr, ++distance_ptr)
{
int trainIdx = *trainIdx_ptr;
int imgIdx = *imgIdx_ptr;
float distance = *distance_ptr;
DMatch m(queryIdx, trainIdx, imgIdx, distance);
curMatches.push_back(m);
}
std::sort(curMatches.begin(), curMatches.end());
}
}
void cv::ocl::BruteForceMatcher_OCL_base::radiusMatch(const oclMat &query, vector< vector<DMatch> > &matches, float maxDistance,
const vector<oclMat> &masks, bool compactResult)
{
oclMat trainIdx, imgIdx, distance, nMatches;
radiusMatchCollection(query, trainIdx, imgIdx, distance, nMatches, maxDistance, masks);
radiusMatchDownload(trainIdx, imgIdx, distance, nMatches, matches, compactResult);
}