}
-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)
{
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)
{
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)
{
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)
{
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)
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)
{
}
//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)
{
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;
}