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

Merge pull request #811 from pengx17:2.4_ocl_bfmatcher_newtype

Browse Source
pull/814/merge
Andrey Kamaev 12 years ago committed by OpenCV Buildbot
parent abe2ea59ed 6dd6013546
commit 0df6dc16a5
3 changed files with 172 additions and 188 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. 142
      modules/ocl/src/brute_force_matcher.cpp
  2. 194
      modules/ocl/src/opencl/brute_force_match.cl
  3. 24
      modules/ocl/test/test_brute_force_matcher.cpp

142
modules/ocl/src/brute_force_matcher.cpp
Unescape View File

@ -64,11 +64,19 @@ namespace cv
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)
{
assert(query.type() == CV_32F);
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};
@ -78,7 +86,9 @@ void matchUnrolledCached(const oclMat &query, const oclMat &train, const oclMat
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt, "-D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d", distType, block_size, m_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)
{
@ -96,7 +106,7 @@ void matchUnrolledCached(const oclMat &query, const oclMat &train, const oclMat
std::string kernelName = "BruteForceMatch_UnrollMatch";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
@ -110,7 +120,6 @@ 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)
{
assert(query.type() == CV_32F);
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};
@ -119,8 +128,9 @@ void match(const oclMat &query, const oclMat &train, const oclMat &/*mask*/,
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt, "-D DIST_TYPE=%d -D BLOCK_SIZE=%d", distType, block_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 ));
@ -137,7 +147,7 @@ void match(const oclMat &query, const oclMat &train, const oclMat &/*mask*/,
std::string kernelName = "BruteForceMatch_Match";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
@ -152,7 +162,6 @@ 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)
{
assert(query.type() == CV_32F);
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};
@ -162,7 +171,9 @@ void matchUnrolledCached(const oclMat &query, const oclMat &train, float maxDist
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt, "-D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d", distType, block_size, m_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)
{
@ -184,7 +195,7 @@ void matchUnrolledCached(const oclMat &query, const oclMat &train, float maxDist
std::string kernelName = "BruteForceMatch_RadiusUnrollMatch";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
@ -193,7 +204,6 @@ 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)
{
assert(query.type() == CV_32F);
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};
@ -202,7 +212,9 @@ void radius_match(const oclMat &query, const oclMat &train, float maxDistance, c
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt, "-D DIST_TYPE=%d -D BLOCK_SIZE=%d", distType, block_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)
{
@ -224,7 +236,7 @@ void radius_match(const oclMat &query, const oclMat &train, float maxDistance, c
std::string kernelName = "BruteForceMatch_RadiusMatch";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
@ -300,7 +312,9 @@ void knn_matchUnrolledCached(const oclMat &query, const oclMat &train, const ocl
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt, "-D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d", distType, block_size, m_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)
{
@ -318,7 +332,7 @@ void knn_matchUnrolledCached(const oclMat &query, const oclMat &train, const ocl
std::string kernelName = "BruteForceMatch_knnUnrollMatch";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
@ -334,7 +348,9 @@ void knn_match(const oclMat &query, const oclMat &train, const oclMat &/*mask*/,
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt, "-D DIST_TYPE=%d -D BLOCK_SIZE=%d", distType, block_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)
{
@ -352,7 +368,7 @@ void knn_match(const oclMat &query, const oclMat &train, const oclMat &/*mask*/,
std::string kernelName = "BruteForceMatch_knnMatch";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
@ -368,7 +384,10 @@ void calcDistanceUnrolled(const oclMat &query, const oclMat &train, const oclMat
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt, "-D DIST_TYPE=%d", distType);
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 ));
@ -386,7 +405,7 @@ void calcDistanceUnrolled(const oclMat &query, const oclMat &train, const oclMat
std::string kernelName = "BruteForceMatch_calcDistanceUnrolled";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
@ -401,7 +420,10 @@ void calcDistance(const oclMat &query, const oclMat &train, const oclMat &/*mask
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt, "-D DIST_TYPE=%d", distType);
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 ));
@ -418,7 +440,7 @@ void calcDistance(const oclMat &query, const oclMat &train, const oclMat &/*mask
std::string kernelName = "BruteForceMatch_calcDistance";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
@ -480,7 +502,7 @@ void findKnnMatch(int k, const oclMat &trainIdx, const oclMat &distance, const o
//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, trainIdx.depth(), -1);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1);
}
}
@ -540,17 +562,6 @@ void cv::ocl::BruteForceMatcher_OCL_base::matchSingle(const oclMat &query, const
if (query.empty() || train.empty())
return;
// match1 doesn't support signed char type, match2 only support float, hamming support uchar, ushort and int
int callType = query.depth();
if (callType != 5)
CV_Error(CV_UNSUPPORTED_FORMAT_ERR, "BruteForceMatch OpenCL only support float type query!\n");
if ((distType == 0 && callType == 1 ) || (distType == 1 && callType != 5) || (distType == 2 && (callType != 0
|| callType != 2 || callType != 4)))
{
CV_Error(CV_UNSUPPORTED_DEPTH_ERR, "BruteForceMatch OpenCL only support float type query!\n");
}
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
CV_Assert(train.cols == query.cols && train.type() == query.type());
@ -605,7 +616,7 @@ void cv::ocl::BruteForceMatcher_OCL_base::matchConvert(const Mat &trainIdx, cons
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
assert(mask.empty()); // mask is not supported at the moment
oclMat trainIdx, distance;
matchSingle(query, train, trainIdx, distance, mask);
matchDownload(trainIdx, distance, matches);
@ -661,26 +672,14 @@ void cv::ocl::BruteForceMatcher_OCL_base::matchCollection(const oclMat &query, c
if (query.empty() || trainCollection.empty())
return;
// match1 doesn't support signed char type, match2 only support float, hamming support uchar, ushort and int
int callType = query.depth();
if (callType != 5)
CV_Error(CV_UNSUPPORTED_FORMAT_ERR, "BruteForceMatch OpenCL only support float type query!\n");
if ((distType == 0 && callType == 1 ) || (distType == 1 && callType != 5) || (distType == 2 && (callType != 0
|| callType != 2 || callType != 4)))
{
CV_Error(CV_UNSUPPORTED_DEPTH_ERR, "BruteForceMatch OpenCL only support float type query!\n");
}
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
const int nQuery = query.rows;
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, (const oclMat *)trainCollection.ptr(), trainCollection.cols, masks, trainIdx, imgIdx, distance, distType);
return;
@ -752,18 +751,6 @@ void cv::ocl::BruteForceMatcher_OCL_base::knnMatchSingle(const oclMat &query, co
if (query.empty() || train.empty())
return;
// match1 doesn't support signed char type, match2 only support float, hamming support uchar, ushort and int
int callType = query.depth();
if (callType != 5)
CV_Error(CV_UNSUPPORTED_FORMAT_ERR, "BruteForceMatch OpenCL only support float type query!\n");
if ((distType == 0 && callType == 1 ) || (distType == 1 && callType != 5) || (distType == 2 && (callType != 0
|| callType != 2 || callType != 4)))
{
CV_Error(CV_UNSUPPORTED_DEPTH_ERR, "BruteForceMatch OpenCL only support float type query!\n");
}
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
CV_Assert(train.type() == query.type() && train.cols == query.cols);
@ -860,26 +847,7 @@ void cv::ocl::BruteForceMatcher_OCL_base::knnMatch2Collection(const oclMat &quer
typedef void (*caller_t)(const oclMat & query, const oclMat & trains, const oclMat & masks,
const oclMat & trainIdx, const oclMat & imgIdx, const oclMat & distance);
#if 0
static const caller_t callers[3][6] =
{
{
ocl_match2L1_gpu<unsigned char>, 0/*match2L1_gpu<signed char>*/,
ocl_match2L1_gpu<unsigned short>, ocl_match2L1_gpu<short>,
ocl_match2L1_gpu<int>, ocl_match2L1_gpu<float>
},
{
0/*match2L2_gpu<unsigned char>*/, 0/*match2L2_gpu<signed char>*/,
0/*match2L2_gpu<unsigned short>*/, 0/*match2L2_gpu<short>*/,
0/*match2L2_gpu<int>*/, ocl_match2L2_gpu<float>
},
{
ocl_match2Hamming_gpu<unsigned char>, 0/*match2Hamming_gpu<signed char>*/,
ocl_match2Hamming_gpu<unsigned short>, 0/*match2Hamming_gpu<short>*/,
ocl_match2Hamming_gpu<int>, 0/*match2Hamming_gpu<float>*/
}
};
#endif
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
const int nQuery = query.rows;
@ -1025,23 +993,11 @@ void cv::ocl::BruteForceMatcher_OCL_base::knnMatch(const oclMat &query, vector<
// 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)
oclMat &trainIdx, oclMat &distance, oclMat &nMatches, float maxDistance, const oclMat &mask)
{
if (query.empty() || train.empty())
return;
// match1 doesn't support signed char type, match2 only support float, hamming support uchar, ushort and int
int callType = query.depth();
if (callType != 5)
CV_Error(CV_UNSUPPORTED_FORMAT_ERR, "BruteForceMatch OpenCL only support float type query!\n");
if ((distType == 0 && callType == 1 ) || (distType == 1 && callType != 5) || (distType == 2 && (callType != 0
|| callType != 2 || callType != 4)))
{
CV_Error(CV_UNSUPPORTED_DEPTH_ERR, "BruteForceMatch OpenCL only support float type query!\n");
}
const int nQuery = query.rows;
const int nTrain = train.rows;

194
modules/ocl/src/opencl/brute_force_match.cl
Unescape View File

@ -47,6 +47,10 @@
#pragma OPENCL EXTENSION cl_khr_global_int32_base_atomics:enable
#define MAX_FLOAT 3.40282e+038f
#ifndef T
#define T float
#endif
#ifndef BLOCK_SIZE
#define BLOCK_SIZE 16
#endif
@ -54,68 +58,85 @@
#define MAX_DESC_LEN 64
#endif
int bit1Count(float x)
{
int c = 0;
int ix = (int)x;
for (int i = 0 ; i < 32 ; i++)
{
c += ix & 0x1;
ix >>= 1;
}
return (float)c;
}
#ifndef DIST_TYPE
#define DIST_TYPE 0
#endif
#if (DIST_TYPE == 0)
#define DIST(x, y) fabs((x) - (y))
#elif (DIST_TYPE == 1)
#define DIST(x, y) (((x) - (y)) * ((x) - (y)))
#elif (DIST_TYPE == 2)
#define DIST(x, y) bit1Count((uint)(x) ^ (uint)(y))
#endif
//http://graphics.stanford.edu/~seander/bithacks.html#CountBitsSetParallel
int bit1Count(int v)
{
v = v - ((v >> 1) & 0x55555555); // reuse input as temporary
v = (v & 0x33333333) + ((v >> 2) & 0x33333333); // temp
return ((v + (v >> 4) & 0xF0F0F0F) * 0x1010101) >> 24; // count
}
// dirty fix for non-template support
#if (DIST_TYPE == 0) // L1Dist
# ifdef T_FLOAT
# define DIST(x, y) fabs((x) - (y))
typedef float value_type;
typedef float result_type;
# else
# define DIST(x, y) abs((x) - (y))
typedef int value_type;
typedef int result_type;
# endif
#define DIST_RES(x) (x)
#elif (DIST_TYPE == 1) // L2Dist
#define DIST(x, y) (((x) - (y)) * ((x) - (y)))
typedef float value_type;
typedef float result_type;
#define DIST_RES(x) sqrt(x)
#elif (DIST_TYPE == 2) // Hamming
#define DIST(x, y) bit1Count( (x) ^ (y) )
typedef int value_type;
typedef int result_type;
#define DIST_RES(x) (x)
#endif
float reduce_block(__local float *s_query,
__local float *s_train,
int lidx,
int lidy
)
result_type reduce_block(
__local value_type *s_query,
__local value_type *s_train,
int lidx,
int lidy
)
{
float result = 0;
result_type result = 0;
#pragma unroll
for (int j = 0 ; j < BLOCK_SIZE ; j++)
{
result += DIST(s_query[lidy * BLOCK_SIZE + j], s_train[j * BLOCK_SIZE + lidx]);
result += DIST(
s_query[lidy * BLOCK_SIZE + j],
s_train[j * BLOCK_SIZE + lidx]);
}
return result;
return DIST_RES(result);
}
float reduce_multi_block(__local float *s_query,
__local float *s_train,
int block_index,
int lidx,
int lidy
)
result_type reduce_multi_block(
__local value_type *s_query,
__local value_type *s_train,
int block_index,
int lidx,
int lidy
)
{
float result = 0;
result_type result = 0;
#pragma unroll
for (int j = 0 ; j < BLOCK_SIZE ; j++)
{
result += DIST(s_query[lidy * MAX_DESC_LEN + block_index * BLOCK_SIZE + j], s_train[j * BLOCK_SIZE + lidx]);
result += DIST(
s_query[lidy * MAX_DESC_LEN + block_index * BLOCK_SIZE + j],
s_train[j * BLOCK_SIZE + lidx]);
}
return result;
return DIST_RES(result);
}
/* 2dim launch, global size: dim0 is (query rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, dim1 is BLOCK_SIZE
local size: dim0 is BLOCK_SIZE, dim1 is BLOCK_SIZE.
*/
__kernel void BruteForceMatch_UnrollMatch_D5(
__global float *query,
__global float *train,
__kernel void BruteForceMatch_UnrollMatch(
__global T *query,
__global T *train,
//__global float *mask,
__global int *bestTrainIdx,
__global float *bestDistance,
@ -127,13 +148,12 @@ __kernel void BruteForceMatch_UnrollMatch_D5(
int step
)
{
const int lidx = get_local_id(0);
const int lidy = get_local_id(1);
const int groupidx = get_group_id(0);
__local float *s_query = sharebuffer;
__local float *s_train = sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
__local value_type *s_query = (__local value_type *)sharebuffer;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
int queryIdx = groupidx * BLOCK_SIZE + lidy;
// load the query into local memory.
@ -151,7 +171,7 @@ __kernel void BruteForceMatch_UnrollMatch_D5(
volatile int imgIdx = 0;
for (int t = 0, endt = (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE; t < endt; t++)
{
float result = 0;
result_type result = 0;
#pragma unroll
for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE ; i++)
{
@ -207,9 +227,9 @@ __kernel void BruteForceMatch_UnrollMatch_D5(
}
}
__kernel void BruteForceMatch_Match_D5(
__global float *query,
__global float *train,
__kernel void BruteForceMatch_Match(
__global T *query,
__global T *train,
//__global float *mask,
__global int *bestTrainIdx,
__global float *bestDistance,
@ -230,14 +250,13 @@ __kernel void BruteForceMatch_Match_D5(
float myBestDistance = MAX_FLOAT;
int myBestTrainIdx = -1;
__local float *s_query = sharebuffer;
__local float *s_train = sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
__local value_type *s_query = (__local value_type *)sharebuffer;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
// loop
for (int t = 0 ; t < (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE ; t++)
{
//Dist dist;
float result = 0;
result_type result = 0;
for (int i = 0 ; i < (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE ; i++)
{
const int loadx = lidx + i * BLOCK_SIZE;
@ -299,9 +318,9 @@ __kernel void BruteForceMatch_Match_D5(
}
//radius_unrollmatch
__kernel void BruteForceMatch_RadiusUnrollMatch_D5(
__global float *query,
__global float *train,
__kernel void BruteForceMatch_RadiusUnrollMatch(
__global T *query,
__global T *train,
float maxDistance,
//__global float *mask,
__global int *bestTrainIdx,
@ -325,10 +344,10 @@ __kernel void BruteForceMatch_RadiusUnrollMatch_D5(
const int queryIdx = groupidy * BLOCK_SIZE + lidy;
const int trainIdx = groupidx * BLOCK_SIZE + lidx;
__local float *s_query = sharebuffer;
__local float *s_train = sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
__local value_type *s_query = (__local value_type *)sharebuffer;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
float result = 0;
result_type result = 0;
for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE ; ++i)
{
//load a BLOCK_SIZE * BLOCK_SIZE block into local train.
@ -345,7 +364,8 @@ __kernel void BruteForceMatch_RadiusUnrollMatch_D5(
barrier(CLK_LOCAL_MEM_FENCE);
}
if (queryIdx < query_rows && trainIdx < train_rows && result < maxDistance/* && mask(queryIdx, trainIdx)*/)
if (queryIdx < query_rows && trainIdx < train_rows &&
convert_float(result) < maxDistance/* && mask(queryIdx, trainIdx)*/)
{
unsigned int ind = atom_inc(nMatches + queryIdx/*, (unsigned int) -1*/);
@ -359,9 +379,9 @@ __kernel void BruteForceMatch_RadiusUnrollMatch_D5(
}
//radius_match
__kernel void BruteForceMatch_RadiusMatch_D5(
__global float *query,
__global float *train,
__kernel void BruteForceMatch_RadiusMatch(
__global T *query,
__global T *train,
float maxDistance,
//__global float *mask,
__global int *bestTrainIdx,
@ -385,10 +405,10 @@ __kernel void BruteForceMatch_RadiusMatch_D5(
const int queryIdx = groupidy * BLOCK_SIZE + lidy;
const int trainIdx = groupidx * BLOCK_SIZE + lidx;
__local float *s_query = sharebuffer;
__local float *s_train = sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
__local value_type *s_query = (__local value_type *)sharebuffer;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
float result = 0;
result_type result = 0;
for (int i = 0 ; i < (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE ; ++i)
{
//load a BLOCK_SIZE * BLOCK_SIZE block into local train.
@ -405,7 +425,8 @@ __kernel void BruteForceMatch_RadiusMatch_D5(
barrier(CLK_LOCAL_MEM_FENCE);
}
if (queryIdx < query_rows && trainIdx < train_rows && result < maxDistance/* && mask(queryIdx, trainIdx)*/)
if (queryIdx < query_rows && trainIdx < train_rows &&
convert_float(result) < maxDistance/* && mask(queryIdx, trainIdx)*/)
{
unsigned int ind = atom_inc(nMatches + queryIdx);
@ -419,9 +440,9 @@ __kernel void BruteForceMatch_RadiusMatch_D5(
}
__kernel void BruteForceMatch_knnUnrollMatch_D5(
__global float *query,
__global float *train,
__kernel void BruteForceMatch_knnUnrollMatch(
__global T *query,
__global T *train,
//__global float *mask,
__global int2 *bestTrainIdx,
__global float2 *bestDistance,
@ -438,8 +459,8 @@ __kernel void BruteForceMatch_knnUnrollMatch_D5(
const int groupidx = get_group_id(0);
const int queryIdx = groupidx * BLOCK_SIZE + lidy;
local float *s_query = sharebuffer;
local float *s_train = sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
__local value_type *s_query = (__local value_type *)sharebuffer;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
// load the query into local memory.
for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE; i ++)
@ -457,10 +478,9 @@ __kernel void BruteForceMatch_knnUnrollMatch_D5(
volatile int imgIdx = 0;
for (int t = 0 ; t < (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE ; t++)
{
float result = 0;
result_type result = 0;
for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE ; i++)
{
const int loadX = lidx + i * BLOCK_SIZE;
//load a BLOCK_SIZE * BLOCK_SIZE block into local train.
const int loadx = lidx + i * BLOCK_SIZE;
s_train[lidx * BLOCK_SIZE + lidy] = loadx < train_cols ? train[min(t * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(float)) + loadx] : 0;
@ -494,8 +514,8 @@ __kernel void BruteForceMatch_knnUnrollMatch_D5(
barrier(CLK_LOCAL_MEM_FENCE);
local float *s_distance = (local float *)sharebuffer;
local int *s_trainIdx = (local int *)(sharebuffer + BLOCK_SIZE * BLOCK_SIZE);
__local float *s_distance = (local float *)sharebuffer;
__local int *s_trainIdx = (local int *)(sharebuffer + BLOCK_SIZE * BLOCK_SIZE);
// find BestMatch
s_distance += lidy * BLOCK_SIZE;
@ -565,9 +585,9 @@ __kernel void BruteForceMatch_knnUnrollMatch_D5(
}
}
__kernel void BruteForceMatch_knnMatch_D5(
__global float *query,
__global float *train,
__kernel void BruteForceMatch_knnMatch(
__global T *query,
__global T *train,
//__global float *mask,
__global int2 *bestTrainIdx,
__global float2 *bestDistance,
@ -584,8 +604,8 @@ __kernel void BruteForceMatch_knnMatch_D5(
const int groupidx = get_group_id(0);
const int queryIdx = groupidx * BLOCK_SIZE + lidy;
local float *s_query = sharebuffer;
local float *s_train = sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
__local value_type *s_query = (__local value_type *)sharebuffer;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
float myBestDistance1 = MAX_FLOAT;
float myBestDistance2 = MAX_FLOAT;
@ -595,7 +615,7 @@ __kernel void BruteForceMatch_knnMatch_D5(
//loop
for (int t = 0 ; t < (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE ; t++)
{
float result = 0.0f;
result_type result = 0.0f;
for (int i = 0 ; i < (query_cols + BLOCK_SIZE -1) / BLOCK_SIZE ; i++)
{
const int loadx = lidx + i * BLOCK_SIZE;
@ -708,9 +728,9 @@ __kernel void BruteForceMatch_knnMatch_D5(
}
}
kernel void BruteForceMatch_calcDistanceUnrolled_D5(
__global float *query,
__global float *train,
kernel void BruteForceMatch_calcDistanceUnrolled(
__global T *query,
__global T *train,
//__global float *mask,
__global float *allDist,
__local float *sharebuffer,
@ -723,9 +743,9 @@ kernel void BruteForceMatch_calcDistanceUnrolled_D5(
/* Todo */
}
kernel void BruteForceMatch_calcDistance_D5(
__global float *query,
__global float *train,
kernel void BruteForceMatch_calcDistance(
__global T *query,
__global T *train,
//__global float *mask,
__global float *allDist,
__local float *sharebuffer,
@ -738,7 +758,7 @@ kernel void BruteForceMatch_calcDistance_D5(
/* Todo */
}
kernel void BruteForceMatch_findBestMatch_D5(
kernel void BruteForceMatch_findBestMatch(
__global float *allDist,
__global int *bestTrainIdx,
__global float *bestDistance,
@ -746,4 +766,4 @@ kernel void BruteForceMatch_findBestMatch_D5(
)
{
/* Todo */
}
}

24
modules/ocl/test/test_brute_force_matcher.cpp
Unescape View File

@ -158,11 +158,7 @@ namespace
TEST_P(BruteForceMatcher, RadiusMatch_Single)
{
float radius;
if(distType == cv::ocl::BruteForceMatcher_OCL_base::L2Dist)
radius = 1.f / countFactor / countFactor;
else
radius = 1.f / countFactor;
float radius = 1.f / countFactor;
cv::ocl::BruteForceMatcher_OCL_base matcher(distType);
@ -191,8 +187,20 @@ namespace
INSTANTIATE_TEST_CASE_P(OCL_Features2D, BruteForceMatcher,
testing::Combine(
testing::Values(DistType(cv::ocl::BruteForceMatcher_OCL_base::L1Dist), DistType(cv::ocl::BruteForceMatcher_OCL_base::L2Dist)),
testing::Values(DescriptorSize(57), DescriptorSize(64), DescriptorSize(83), DescriptorSize(128), DescriptorSize(179), DescriptorSize(256), DescriptorSize(304))));
testing::Values(
DistType(cv::ocl::BruteForceMatcher_OCL_base::L1Dist),
DistType(cv::ocl::BruteForceMatcher_OCL_base::L2Dist)/*,
DistType(cv::ocl::BruteForceMatcher_OCL_base::HammingDist)*/
),
testing::Values(
DescriptorSize(57),
DescriptorSize(64),
DescriptorSize(83),
DescriptorSize(128),
DescriptorSize(179),
DescriptorSize(256),
DescriptorSize(304))
)
);
} // namespace
#endif

Write Preview
Loading…
Cancel
Save