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removed unnecessary functions and variables

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This commit is contained in:
Konstantin Matskevich 2014-01-17 10:09:44 +04:00
parent 3b7683e72f
commit 2f8c29a1f0
1 changed files with 17 additions and 162 deletions
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179
modules/features2d/src/matchers.cpp
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@ -80,7 +80,7 @@ static void ensureSizeIsEnough(int rows, int cols, int type, UMat &m)
}
template < int BLOCK_SIZE, int MAX_DESC_LEN/*, typename Mask*/ >
template < int BLOCK_SIZE, int MAX_DESC_LEN >
static bool ocl_matchUnrolledCached(InputArray _query, InputArray _train,
const UMat &trainIdx, const UMat &distance, int distType)
{
@ -117,7 +117,7 @@ static bool ocl_matchUnrolledCached(InputArray _query, InputArray _train,
return true;
}
template < int BLOCK_SIZE/*, typename Mask*/ >
template < int BLOCK_SIZE >
static bool ocl_match(InputArray _query, InputArray _train,
const UMat &trainIdx, const UMat &distance, int distType)
{
@ -232,7 +232,7 @@ static bool ocl_matchDownload(const UMat &trainIdx, const UMat &distance, std::v
return ocl_matchConvert(trainIdxCPU, distanceCPU, matches);
}
template < int BLOCK_SIZE, int MAX_DESC_LEN/*, typename Mask*/ >
template < int BLOCK_SIZE, int MAX_DESC_LEN >
static bool ocl_knn_matchUnrolledCached(InputArray _query, InputArray _train,
const UMat &trainIdx, const UMat &distance, int distType)
{
@ -269,7 +269,7 @@ static bool ocl_knn_matchUnrolledCached(InputArray _query, InputArray _train,
return true;
}
template < int BLOCK_SIZE/*, typename Mask*/ >
template < int BLOCK_SIZE >
static bool ocl_knn_match(InputArray _query, InputArray _train,
const UMat &trainIdx, const UMat &distance, int distType)
{
@ -327,173 +327,26 @@ static bool ocl_match2Dispatcher(InputArray query, InputArray train, const UMat
return true;
}
template < int BLOCK_SIZE, int MAX_DESC_LEN/*, typename Mask*/ >
static bool ocl_calcDistanceUnrolled(InputArray _query, InputArray _train, const UMat &allDist, int distType)
static bool ocl_kmatchDispatcher(InputArray query, InputArray train, const UMat &trainIdx,
const UMat &distance, int distType)
{
int depth = _query.depth();
cv::String opts;
opts = format("-D T=%s %s -D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d",
ocl::typeToStr(depth), depth == CV_32F ? "-D T_FLOAT" : "", distType, (int)BLOCK_SIZE, (int)MAX_DESC_LEN);
ocl::Kernel k("BruteForceMatch_calcDistanceUnrolled", ocl::features2d::brute_force_match_oclsrc, opts);
if(k.empty())
return false;
size_t globalSize[] = {(_query.size().width + 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);
if(globalSize[0] != 0)
{
UMat query = _query.getUMat(), train = _train.getUMat();
int idx = 0;
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(query));
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(train));
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(allDist));
idx = k.set(idx, (void*)NULL, smemSize);
idx = k.set(idx, query.rows);
idx = k.set(idx, query.cols);
idx = k.set(idx, train.rows);
idx = k.set(idx, train.cols);
idx = k.set(idx, (int)query.step);
k.run(2, globalSize, localSize, false);
}
return false;// TODO in KERNEL
}
template < int BLOCK_SIZE/*, typename Mask*/ >
static bool ocl_calcDistance(InputArray _query, InputArray _train, const UMat &allDist, int distType)
{
int depth = _query.depth();
cv::String opts;
opts = format("-D T=%s %s -D DIST_TYPE=%d -D BLOCK_SIZE=%d",
ocl::typeToStr(depth), depth == CV_32F ? "-D T_FLOAT" : "", distType, (int)BLOCK_SIZE);
ocl::Kernel k("BruteForceMatch_calcDistance", ocl::features2d::brute_force_match_oclsrc, opts);
if(k.empty())
return false;
size_t globalSize[] = {(_query.size().width + 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);
if(globalSize[0] != 0)
{
UMat query = _query.getUMat(), train = _train.getUMat();
int idx = 0;
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(query));
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(train));
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(allDist));
idx = k.set(idx, (void*)NULL, smemSize);
idx = k.set(idx, query.rows);
idx = k.set(idx, query.cols);
idx = k.set(idx, train.rows);
idx = k.set(idx, train.cols);
idx = k.set(idx, (int)query.step);
k.run(2, globalSize, localSize, false);
}
return false;// TODO in KERNEL
}
static bool ocl_calcDistanceDispatcher(InputArray query, InputArray train, const UMat &allDist, int distType)
{
if (query.size().width <= 64)
{
if(!ocl_calcDistanceUnrolled<16, 64>(query, train, allDist, distType)) return false;
}
else if (query.size().width <= 128)
{
if(!ocl_calcDistanceUnrolled<16, 128>(query, train, allDist, distType)) return false;
}
else
{
if(!ocl_calcDistance<16>(query, train, allDist, distType)) return false;
}
return true;
}
template <int BLOCK_SIZE>
static bool ocl_findKnnMatch(int k, const UMat &trainIdx, const UMat &distance, const UMat &allDist, int /*distType*/)
{
return false;// TODO in KERNEL
std::vector<ocl::Kernel> kernels;
for (int i = 0; i < k; ++i)
{
ocl::Kernel kernel("BruteForceMatch_findBestMatch", ocl::features2d::brute_force_match_oclsrc);
if(kernel.empty())
return false;
kernels.push_back(kernel);
}
size_t globalSize[] = {trainIdx.rows * BLOCK_SIZE, 1, 1};
size_t localSize[] = {BLOCK_SIZE, 1, 1};
int block_size = BLOCK_SIZE;
for (int i = 0; i < k; ++i)
{
int idx = 0;
idx = kernels[i].set(idx, ocl::KernelArg::PtrReadOnly(allDist));
idx = kernels[i].set(idx, ocl::KernelArg::PtrWriteOnly(trainIdx));
idx = kernels[i].set(idx, ocl::KernelArg::PtrWriteOnly(distance));
idx = kernels[i].set(idx, i);
idx = kernels[i].set(idx, block_size);
// idx = kernels[i].set(idx, train.rows);
// idx = kernels[i].set(idx, train.cols);
// idx = kernels[i].set(idx, query.step);
if(!kernels[i].run(2, globalSize, localSize, false))
return false;
}
return true;
}
static bool ocl_findKnnMatchDispatcher(int k, const UMat &trainIdx, const UMat &distance, const UMat &allDist, int distType)
{
return ocl_findKnnMatch<256>(k, trainIdx, distance, allDist, distType);
}
static bool ocl_kmatchDispatcher(InputArray query, InputArray train, int k, const UMat &trainIdx,
const UMat &distance, const UMat &allDist, int distType)
{
if(k == 2)
{
if( !ocl_match2Dispatcher(query, train, trainIdx, distance, distType) ) return false;
}
else
{
if( !ocl_calcDistanceDispatcher(query, train, allDist, distType) ) return false;
if( !ocl_findKnnMatchDispatcher(k, trainIdx, distance, allDist, distType) ) return false;
}
return true;
return ocl_match2Dispatcher(query, train, trainIdx, distance, distType);
}
static bool ocl_knnMatchSingle(InputArray query, InputArray train, UMat &trainIdx,
UMat &distance, UMat &allDist, int k, int dstType)
UMat &distance, int dstType)
{
if (query.empty() || train.empty())
return false;
const int nQuery = query.size().height;
const int nTrain = train.size().height;
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);
}
ensureSizeIsEnough(1, nQuery, CV_32SC2, trainIdx);
ensureSizeIsEnough(1, nQuery, CV_32FC2, distance);
trainIdx.setTo(Scalar::all(-1));
return ocl_kmatchDispatcher(query, train, k, trainIdx, distance, allDist, dstType);
return ocl_kmatchDispatcher(query, train, trainIdx, distance, dstType);
}
static bool ocl_knnMatchConvert(const Mat &trainIdx, const Mat &distance, std::vector< std::vector<DMatch> > &matches, bool compactResult)
@ -554,7 +407,7 @@ static bool ocl_knnMatchDownload(const UMat &trainIdx, const UMat &distance, std
return false;
}
template < int BLOCK_SIZE, int MAX_DESC_LEN/*, typename Mask*/ >
template < int BLOCK_SIZE, int MAX_DESC_LEN >
static bool ocl_matchUnrolledCached(InputArray _query, InputArray _train, float maxDistance,
const UMat &trainIdx, const UMat &distance, const UMat &nMatches, int distType)
{
@ -596,7 +449,7 @@ static bool ocl_matchUnrolledCached(InputArray _query, InputArray _train, float
}
//radius_match
template < int BLOCK_SIZE/*, typename Mask*/ >
template < int BLOCK_SIZE >
static bool ocl_radius_match(InputArray _query, InputArray _train, float maxDistance,
const UMat &trainIdx, const UMat &distance, const UMat &nMatches, int distType)
{
@ -1048,8 +901,10 @@ bool BFMatcher::ocl_match(InputArray query, InputArray _train, std::vector< std:
bool BFMatcher::ocl_knnMatch(InputArray query, InputArray _train, std::vector< std::vector<DMatch> > &matches, int k, int dstType, bool compactResult)
{
UMat trainIdx, distance, allDist;
if (!ocl_knnMatchSingle(query, _train, trainIdx, distance, allDist, k, dstType)) return false;
UMat trainIdx, distance;
if (k != 2)
return false;
if (!ocl_knnMatchSingle(query, _train, trainIdx, distance, dstType)) return false;
if( !ocl_knnMatchDownload(trainIdx, distance, matches, compactResult) ) return false;
return true;
}