Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

removed unnecessary functions and variables

Browse Source
pull/2142/head
Konstantin Matskevich 11 years ago
parent 3b7683e72f
commit 2f8c29a1f0
1 changed files with 17 additions and 162 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 179
      modules/features2d/src/matchers.cpp

179
modules/features2d/src/matchers.cpp
Unescape View File

@ -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;
}

Write Preview
Loading…
Cancel
Save