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Commit 6c99b5c9 authored by Andrey Kamaev's avatar Andrey Kamaev Committed by OpenCV Buildbot
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Merge pull request #705 from bitwangyaoyao:2.4_oclFix

parents 6a6ae355 d6f1ad8c
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Showing with 863 additions and 1444 deletions
cmake/OpenCVDetectOpenCL.cmake
View file @ 6c99b5c9
......@@ -43,7 +43,7 @@ if(OPENCL_FOUND)
set(OPENCL_LIBRARIES ${OPENCL_LIBRARY})
if (X86_64)
set(CLAMD_POSSIBLE_LIB_SUFFIXES lib32/import)
set(CLAMD_POSSIBLE_LIB_SUFFIXES lib64/import)
elseif (X86)
set(CLAMD_POSSIBLE_LIB_SUFFIXES lib32/import)
endif()
......
cmake/cl2cpp.cmake
View file @ 6c99b5c9
......@@ -18,6 +18,7 @@ foreach(cl ${cl_list})
string(REPLACE "\t" " " lines "${lines}")
string(REGEX REPLACE "/\\*([^*]/|\\*[^/]|[^*/])*\\*/" "" lines "${lines}") # multiline comments
string(REGEX REPLACE "/\\*([^\n])*\\*/" "" lines "${lines}") # single-line comments
string(REGEX REPLACE "[ ]*//[^\n]*\n" "\n" lines "${lines}") # single-line comments
string(REGEX REPLACE "\n[ ]*(\n[ ]*)*" "\n" lines "${lines}") # empty lines & leading whitespace
string(REGEX REPLACE "^\n" "" lines "${lines}") # leading new line
......
modules/ocl/src/brute_force_matcher.cpp
View file @ 6c99b5c9
......@@ -44,6 +44,7 @@
//M*/
#include "precomp.hpp"
#include <functional>
#include <iterator>
#include <vector>
......@@ -60,10 +61,11 @@ namespace cv
}
}
template < int BLOCK_SIZE, int MAX_DESC_LEN, typename T/*, typename Mask*/ >
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};
......@@ -91,20 +93,21 @@ 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, -1);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth());
}
}
template < int BLOCK_SIZE, int MAX_DESC_LEN, typename T/*, typename Mask*/ >
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 T/*, typename Mask*/ >
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};
......@@ -130,21 +133,22 @@ 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, -1);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth());
}
}
template < int BLOCK_SIZE, typename T/*, typename Mask*/ >
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 T/*, typename Mask*/ >
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};
......@@ -176,15 +180,16 @@ 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, -1);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth());
}
}
//radius_match
template < int BLOCK_SIZE, typename T/*, typename Mask*/ >
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};
......@@ -214,263 +219,70 @@ 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, -1);
//float *dis = (float *)clEnqueueMapBuffer(ctx->impl->clCmdQueue, (cl_mem)distance.data, CL_TRUE, CL_MAP_READ, 0, 8, 0, NULL, NULL, NULL);
//printf("%f, %f\n", dis[0], dis[1]);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth());
}
}
// with mask
template < typename T/*, typename Mask*/ >
void matchDispatcher(const oclMat &query, const oclMat &train, const oclMat &mask,
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;
if (query.cols <= 64)
{
matchUnrolledCached<16, 64, T>(query, train, mask, trainIdx, distance, distType);
}
else if (query.cols <= 128)
{
matchUnrolledCached<16, 128, T>(query, train, mask, trainIdx, distance, distType);
}
/*else if (query.cols <= 256)
{
matchUnrolled<16, 256, Dist>(query, train, mask, trainIdx, distance, stream);
}
else if (query.cols <= 512)
{
matchUnrolled<16, 512, Dist>(query, train, mask, trainIdx, distance, stream);
}
else if (query.cols <= 1024)
{
matchUnrolled<16, 1024, Dist>(query, train, mask, trainIdx, distance, stream);
}*/
else
{
match<16, T>(query, train, mask, trainIdx, distance, distType);
}
}
// without mask
template < typename T/*, typename Mask*/ >
void matchDispatcher(const oclMat &query, const oclMat &train, const oclMat &trainIdx, const oclMat &distance, int distType)
{
oclMat mask;
if (query.cols <= 64)
{
matchUnrolledCached<16, 64, T>(query, train, mask, trainIdx, distance, distType);
matchUnrolledCached<16, 64>(query, train, tempMask, trainIdx, distance, distType);
}
else if (query.cols <= 128)
{
matchUnrolledCached<16, 128, T>(query, train, mask, trainIdx, distance, distType);
}
/*else if (query.cols <= 256)
{
matchUnrolled<16, 256, Dist>(query, trains, n, mask, trainIdx, imgIdx, distance);
}
else if (query.cols <= 512)
{
matchUnrolled<16, 512, Dist>(query, trains, n, mask, trainIdx, imgIdx, distance);
matchUnrolledCached<16, 128>(query, train, tempMask, trainIdx, distance, distType);
}
else if (query.cols <= 1024)
{
matchUnrolled<16, 1024, Dist>(query, trains, n, mask, trainIdx, imgIdx, distance);
}*/
else
{
match<16, T>(query, train, mask, trainIdx, distance, distType);
match<16>(query, train, tempMask, trainIdx, distance, distType);
}
}
template < typename T/*, typename Mask*/ >
void matchDispatcher(const oclMat &query, const oclMat *trains, int n, const oclMat &mask,
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;
if (query.cols <= 64)
{
matchUnrolledCached<16, 64, T>(query, trains, n, mask, trainIdx, imgIdx, distance, distType);
matchUnrolledCached<16, 64>(query, trains, n, tempMask, trainIdx, imgIdx, distance, distType);
}
else if (query.cols <= 128)
{
matchUnrolledCached<16, 128, T>(query, trains, n, mask, trainIdx, imgIdx, distance, distType);
}
/*else if (query.cols <= 256)
{
matchUnrolled<16, 256, Dist>(query, trains, n, mask, trainIdx, imgIdx, distance, stream);
matchUnrolledCached<16, 128>(query, trains, n, tempMask, trainIdx, imgIdx, distance, distType);
}
else if (query.cols <= 512)
{
matchUnrolled<16, 512, Dist>(query, trains, n, mask, trainIdx, imgIdx, distance, stream);
}
else if (query.cols <= 1024)
{
matchUnrolled<16, 1024, Dist>(query, trains, n, mask, trainIdx, imgIdx, distance, stream);
}*/
else
{
match<16, T>(query, trains, n, mask, trainIdx, imgIdx, distance, distType);
}
}
template < typename T/*, typename Mask*/ >
void matchDispatcher(const oclMat &query, const oclMat *trains, int n, const oclMat &trainIdx,
const oclMat &imgIdx, const oclMat &distance, int distType)
{
oclMat mask;
if (query.cols <= 64)
{
matchUnrolledCached<16, 64, T>(query, trains, n, mask, trainIdx, imgIdx, distance, distType);
}
else if (query.cols <= 128)
{
matchUnrolledCached<16, 128, T>(query, trains, n, mask, trainIdx, imgIdx, distance, distType);
}
/*else if (query.cols <= 256)
{
matchUnrolled<16, 256, Dist>(query, trains, n, mask, trainIdx, imgIdx, distance, stream);
}
else if (query.cols <= 512)
{
matchUnrolled<16, 512, Dist>(query, trains, n, mask, trainIdx, imgIdx, distance, stream);
}
else if (query.cols <= 1024)
{
matchUnrolled<16, 1024, Dist>(query, trains, n, mask, trainIdx, imgIdx, distance, stream);
}*/
else
{
match<16, T>(query, trains, n, mask, trainIdx, imgIdx, distance, distType);
match<16>(query, trains, n, tempMask, trainIdx, imgIdx, distance, distType);
}
}
//radius matchDispatcher
// with mask
template < typename T/*, typename Mask*/ >
void matchDispatcher(const oclMat &query, const oclMat &train, float maxDistance, const oclMat &mask,
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;
if (query.cols <= 64)
{
matchUnrolledCached<16, 64, T>(query, train, maxDistance, mask, trainIdx, distance, nMatches, distType);
matchUnrolledCached<16, 64>(query, train, maxDistance, tempMask, trainIdx, distance, nMatches, distType);
}
else if (query.cols <= 128)
{
matchUnrolledCached<16, 128, T>(query, train, maxDistance, mask, trainIdx, distance, nMatches, distType);
}
/*else if (query.cols <= 256)
{
matchUnrolled<16, 256, Dist>(query, train, maxDistance, mask, trainIdx, distance, nMatches, stream);
matchUnrolledCached<16, 128>(query, train, maxDistance, tempMask, trainIdx, distance, nMatches, distType);
}
else if (query.cols <= 512)
{
matchUnrolled<16, 512, Dist>(query, train, maxDistance, mask, trainIdx, distance, nMatches, stream);
}
else if (query.cols <= 1024)
{
matchUnrolled<16, 1024, Dist>(query, train, maxDistance, mask, trainIdx, distance, nMatches, stream);
}*/
else
{
radius_match<16, T>(query, train, maxDistance, mask, trainIdx, distance, nMatches, distType);
}
}
// without mask
template < typename T/*, typename Mask*/ >
void matchDispatcher(const oclMat &query, const oclMat &train, float maxDistance, const oclMat &trainIdx,
const oclMat &distance, const oclMat &nMatches, int distType)
{
oclMat mask;
if (query.cols <= 64)
{
matchUnrolledCached<16, 64, T>(query, train, maxDistance, mask, trainIdx, distance, nMatches, distType);
}
else if (query.cols <= 128)
{
matchUnrolledCached<16, 128, T>(query, train, maxDistance, mask, trainIdx, distance, nMatches, distType);
}
/*else if (query.cols <= 256)
{
matchUnrolled<16, 256, Dist>(query, train, maxDistance, mask, trainIdx, distance, nMatches, stream);
}
else if (query.cols <= 512)
{
matchUnrolled<16, 512, Dist>(query, train, maxDistance, mask, trainIdx, distance, nMatches, stream);
}
else if (query.cols <= 1024)
{
matchUnrolled<16, 1024, Dist>(query, train, maxDistance, mask, trainIdx, distance, nMatches, stream);
}*/
else
{
radius_match<16, T>(query, train, maxDistance, mask, trainIdx, distance, nMatches, distType);
}
}
template < typename T/*, typename Mask*/ >
void matchDispatcher(const oclMat &query, const oclMat &train, int n, float maxDistance, const oclMat &mask,
const oclMat &trainIdx, const oclMat &distance, const oclMat &nMatches, int distType)
{
if (query.cols <= 64)
{
matchUnrolledCached<16, 64, T>(query, train, n, maxDistance, mask, trainIdx, distance, nMatches, distType);
}
else if (query.cols <= 128)
{
matchUnrolledCached<16, 128, T>(query, train, n, maxDistance, mask, trainIdx, distance, nMatches, distType);
}
/*else if (query.cols <= 256)
{
matchUnrolled<16, 256, Dist>(query, trains, n, maxDistance, masks, trainIdx, imgIdx, distance, nMatches, stream);
}
else if (query.cols <= 512)
{
matchUnrolled<16, 512, Dist>(query, trains, n, maxDistance, masks, trainIdx, imgIdx, distance, nMatches, stream);
}
else if (query.cols <= 1024)
{
matchUnrolled<16, 1024, Dist>(query, trains, n, maxDistance, masks, trainIdx, imgIdx, distance, nMatches, stream);
}*/
else
{
match<16, T>(query, train, n, maxDistance, mask, trainIdx, distance, nMatches, distType);
}
}
// without mask
template < typename T/*, typename Mask*/ >
void matchDispatcher(const oclMat &query, const oclMat &train, int n, float maxDistance, const oclMat &trainIdx,
const oclMat &distance, const oclMat &nMatches, int distType)
{
oclMat mask;
if (query.cols <= 64)
{
matchUnrolledCached<16, 64, T>(query, train, n, maxDistance, mask, trainIdx, distance, nMatches, distType);
}
else if (query.cols <= 128)
{
matchUnrolledCached<16, 128, T>(query, train, n, maxDistance, mask, trainIdx, distance, nMatches, distType);
}
/*else if (query.cols <= 256)
{
matchUnrolled<16, 256, Dist>(query, trains, n, maxDistance, masks, trainIdx, imgIdx, distance, nMatches, stream);
}
else if (query.cols <= 512)
{
matchUnrolled<16, 512, Dist>(query, trains, n, maxDistance, masks, trainIdx, imgIdx, distance, nMatches, stream);
}
else if (query.cols <= 1024)
{
matchUnrolled<16, 1024, Dist>(query, trains, n, maxDistance, masks, trainIdx, imgIdx, distance, nMatches, stream);
}*/
else
{
match<16, T>(query, train, n, maxDistance, mask, trainIdx, distance, nMatches, distType);
radius_match<16>(query, train, maxDistance, tempMask, trainIdx, distance, nMatches, distType);
}
}
//knn match Dispatcher
template < int BLOCK_SIZE, int MAX_DESC_LEN, typename T/*, typename Mask*/ >
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)
{
......@@ -501,11 +313,11 @@ 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, -1);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth());
}
}
template < int BLOCK_SIZE, typename T/*, typename Mask*/ >
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)
{
......@@ -534,11 +346,11 @@ 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, -1);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth());
}
}
template < int BLOCK_SIZE, int MAX_DESC_LEN, typename T/*, typename Mask*/ >
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;
......@@ -567,11 +379,11 @@ 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, -1);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth());
}
}
template < int BLOCK_SIZE, typename T/*, typename Mask*/ >
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;
......@@ -598,69 +410,43 @@ 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, -1);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth());
}
}
///////////////////////////////////////////////////////////////////////////////
// Calc Distance dispatcher
template < typename T/*, typename Mask*/ >
void calcDistanceDispatcher(const oclMat &query, const oclMat &train, const oclMat &mask,
static void calcDistanceDispatcher(const oclMat &query, const oclMat &train, const oclMat &mask,
const oclMat &allDist, int distType)
{
if (query.cols <= 64)
{
calcDistanceUnrolled<16, 64, T>(query, train, mask, allDist, distType);
calcDistanceUnrolled<16, 64>(query, train, mask, allDist, distType);
}
else if (query.cols <= 128)
{
calcDistanceUnrolled<16, 128, T>(query, train, mask, allDist, distType);
calcDistanceUnrolled<16, 128>(query, train, mask, allDist, distType);
}
/*else if (query.cols <= 256)
{
calcDistanceUnrolled<16, 256, Dist>(query, train, mask, allDist, stream);
}
else if (query.cols <= 512)
{
calcDistanceUnrolled<16, 512, Dist>(query, train, mask, allDist, stream);
}
else if (query.cols <= 1024)
{
calcDistanceUnrolled<16, 1024, Dist>(query, train, mask, allDist, stream);
}*/
else
{
calcDistance<16, T>(query, train, mask, allDist, distType);
calcDistance<16>(query, train, mask, allDist, distType);
}
}
template < typename T/*, typename Mask*/ >
void match2Dispatcher(const oclMat &query, const oclMat &train, const oclMat &mask,
static void match2Dispatcher(const oclMat &query, const oclMat &train, const oclMat &mask,
const oclMat &trainIdx, const oclMat &distance, int distType)
{
if (query.cols <= 64)
{
knn_matchUnrolledCached<16, 64, T>(query, train, mask, trainIdx, distance, distType);
knn_matchUnrolledCached<16, 64>(query, train, mask, trainIdx, distance, distType);
}
else if (query.cols <= 128)
{
knn_matchUnrolledCached<16, 128, T>(query, train, mask, trainIdx, distance, distType);
}
/*else if (query.cols <= 256)
{
matchUnrolled<16, 256, Dist>(query, train, mask, static_cast< DevMem2D_<int2> >(trainIdx), static_cast< DevMem2D_<float2> > (distance), stream);
knn_matchUnrolledCached<16, 128>(query, train, mask, trainIdx, distance, distType);
}
else if (query.cols <= 512)
{
matchUnrolled<16, 512, Dist>(query, train, mask, static_cast< DevMem2D_<int2> >(trainIdx), static_cast< DevMem2D_<float2> > (distance), stream);
}
else if (query.cols <= 1024)
{
matchUnrolled<16, 1024, Dist>(query, train, mask, static_cast< DevMem2D_<int2> >(trainIdx), static_cast< DevMem2D_<float2> > (distance), stream);
}*/
else
{
knn_match<16, T>(query, train, mask, trainIdx, distance, distType);
knn_match<16>(query, train, mask, trainIdx, distance, distType);
}
}
......@@ -686,7 +472,7 @@ 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, -1, -1);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, trainIdx.depth(), -1);
}
}
......@@ -695,206 +481,22 @@ static void findKnnMatchDispatcher(int k, const oclMat &trainIdx, const oclMat &
findKnnMatch<256>(k, trainIdx, distance, allDist, distType);
}
//with mask
template < typename T/*, typename Mask*/ >
void kmatchDispatcher(const oclMat &query, const oclMat &train, int k, const oclMat &mask,
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<T>(query, train, mask, trainIdx, distance, distType);
match2Dispatcher(query, train, tempMask, trainIdx, distance, distType);
}
else
{
calcDistanceDispatcher<T>(query, train, mask, allDist, distType);
calcDistanceDispatcher(query, train, tempMask, allDist, distType);
findKnnMatchDispatcher(k, trainIdx, distance, allDist, distType);
}
}
//without mask
template < typename T/*, typename Mask*/ >
void kmatchDispatcher(const oclMat &query, const oclMat &train, int k,
const oclMat &trainIdx, const oclMat &distance, const oclMat &allDist, int distType)
{
oclMat mask;
if (k == 2)
{
match2Dispatcher<T>(query, train, mask, trainIdx, distance, distType);
}
else
{
calcDistanceDispatcher<T>(query, train, mask, allDist, distType);
findKnnMatchDispatcher(k, trainIdx, distance, allDist, distType);
}
}
template <typename T>
void ocl_matchL1_gpu(const oclMat &query, const oclMat &train, const oclMat &mask,
const oclMat &trainIdx, const oclMat &distance)
{
int distType = 0;
if (mask.data)
{
matchDispatcher<T>(query, train, mask, trainIdx, distance, distType);
}
else
{
matchDispatcher< T >(query, train, trainIdx, distance, distType);
}
}
template <typename T>
void ocl_matchL1_gpu(const oclMat &query, const oclMat &trains, const oclMat &masks,
const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance)
{
int distType = 0;
if (masks.data)
{
matchDispatcher<T>(query, (const oclMat *)trains.ptr(), trains.cols, masks, trainIdx, imgIdx, distance, distType);
}
else
{
matchDispatcher<T>(query, (const oclMat *)trains.ptr(), trains.cols, trainIdx, imgIdx, distance, distType);
}
}
template <typename T>
void ocl_matchL2_gpu(const oclMat &query, const oclMat &train, const oclMat &mask,
const oclMat &trainIdx, const oclMat &distance)
{
int distType = 1;
if (mask.data)
{
matchDispatcher<T>(query, train, mask, trainIdx, distance, distType);
}
else
{
matchDispatcher<T >(query, train, trainIdx, distance, distType);
}
}
template <typename T>
void ocl_matchL2_gpu(const oclMat &query, const oclMat &trains, const oclMat &masks,
const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance)
{
int distType = 1;
if (masks.data)
{
matchDispatcher<T>(query, (const oclMat *)trains.ptr(), trains.cols, masks, trainIdx, imgIdx, distance, distType);
}
else
{
matchDispatcher<T>(query, (const oclMat *)trains.ptr(), trains.cols, trainIdx, imgIdx, distance, distType);
}
}
template <typename T>
void ocl_matchHamming_gpu(const oclMat &query, const oclMat &train, const oclMat &mask,
const oclMat &trainIdx, const oclMat &distance)
{
int distType = 2;
if (mask.data)
{
matchDispatcher<T>(query, train, mask, trainIdx, distance, distType);
}
else
{
matchDispatcher< T >(query, train, trainIdx, distance, distType);
}
}
template <typename T>
void ocl_matchHamming_gpu(const oclMat &query, const oclMat &trains, const oclMat &masks,
const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance)
{
int distType = 2;
if (masks.data)
{
matchDispatcher<T>(query, (const oclMat *)trains.ptr(), trains.cols, masks, trainIdx, imgIdx, distance, distType);
}
else
{
matchDispatcher<T>(query, (const oclMat *)trains.ptr(), trains.cols, trainIdx, imgIdx, distance, distType);
}
}
// knn caller
template <typename T>
void ocl_matchL1_gpu(const oclMat &query, const oclMat &train, int k, const oclMat &mask,
const oclMat &trainIdx, const oclMat &distance, const oclMat &allDist)
{
int distType = 0;
if (mask.data)
kmatchDispatcher<T>(query, train, k, mask, trainIdx, distance, allDist, distType);
else
kmatchDispatcher<T>(query, train, k, trainIdx, distance, allDist, distType);
}
template <typename T>
void ocl_matchL2_gpu(const oclMat &query, const oclMat &train, int k, const oclMat &mask,
const oclMat &trainIdx, const oclMat &distance, const oclMat &allDist)
{
int distType = 1;
if (mask.data)
kmatchDispatcher<T>(query, train, k, mask, trainIdx, distance, allDist, distType);
else
kmatchDispatcher<T>(query, train, k, trainIdx, distance, allDist, distType);
}
template <typename T>
void ocl_matchHamming_gpu(const oclMat &query, const oclMat &train, int k, const oclMat &mask,
const oclMat &trainIdx, const oclMat &distance, const oclMat &allDist)
{
int distType = 2;
if (mask.data)
kmatchDispatcher<T>(query, train, k, mask, trainIdx, distance, allDist, distType);
else
kmatchDispatcher<T>(query, train, k, trainIdx, distance, allDist, distType);
}
//radius caller
template <typename T>
void ocl_matchL1_gpu(const oclMat &query, const oclMat &train, float maxDistance, const oclMat &mask,
const oclMat &trainIdx, const oclMat &distance, const oclMat &nMatches)
{
int distType = 0;
if (mask.data)
matchDispatcher<T>(query, train, maxDistance, mask, trainIdx, distance, nMatches, distType);
else
matchDispatcher<T>(query, train, maxDistance, trainIdx, distance, nMatches, distType);
}
template <typename T>
void ocl_matchL2_gpu(const oclMat &query, const oclMat &train, float maxDistance, const oclMat &mask,
const oclMat &trainIdx, const oclMat &distance, const oclMat &nMatches)
{
int distType = 1;
if (mask.data)
matchDispatcher<T>(query, train, maxDistance, mask, trainIdx, distance, nMatches, distType);
else
matchDispatcher<T>(query, train, maxDistance, trainIdx, distance, nMatches, distType);
}
template <typename T>
void ocl_matchHamming_gpu(const oclMat &query, const oclMat &train, float maxDistance, const oclMat &mask,
const oclMat &trainIdx, const oclMat &distance, const oclMat &nMatches)
{
int distType = 2;
if (mask.data)
matchDispatcher<T>(query, train, maxDistance, mask, trainIdx, distance, nMatches, distType);
else
matchDispatcher<T>(query, train, maxDistance, trainIdx, distance, nMatches, distType);
}
cv::ocl::BruteForceMatcher_OCL_base::BruteForceMatcher_OCL_base(DistType distType_) : distType(distType_)
{
}
......@@ -929,38 +531,28 @@ 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();
char cvFuncName[] = "singleMatch";
if (callType != 5)
CV_ERROR(CV_UNSUPPORTED_FORMAT_ERR, "BruteForceMatch OpenCL only support float type query!\n");
typedef void (*caller_t)(const oclMat & query, const oclMat & train, const oclMat & mask,
const oclMat & trainIdx, const oclMat & distance);
static const caller_t callers[3][6] =
if ((distType == 0 && callType == 1 ) || (distType == 1 && callType != 5) || (distType == 2 && (callType != 0
|| callType != 2 || callType != 4)))
{
{
ocl_matchL1_gpu<unsigned char>, 0/*ocl_matchL1_gpu<signed char>*/,
ocl_matchL1_gpu<unsigned short>, ocl_matchL1_gpu<short>,
ocl_matchL1_gpu<int>, ocl_matchL1_gpu<float>
},
{
0/*ocl_matchL2_gpu<unsigned char>*/, 0/*ocl_matchL2_gpu<signed char>*/,
0/*ocl_matchL2_gpu<unsigned short>*/, 0/*ocl_matchL2_gpu<short>*/,
0/*ocl_matchL2_gpu<int>*/, ocl_matchL2_gpu<float>
},
{
ocl_matchHamming_gpu<unsigned char>, 0/*ocl_matchHamming_gpu<signed char>*/,
ocl_matchHamming_gpu<unsigned short>, 0/*ocl_matchHamming_gpu<short>*/,
ocl_matchHamming_gpu<int>, 0/*ocl_matchHamming_gpu<float>*/
}
};
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());
const int nQuery = query.rows;
trainIdx.create(1, nQuery, CV_32S);
distance.create(1, nQuery, CV_32F);
trainIdx.create(1, query.rows, CV_32S);
distance.create(1, query.rows, CV_32F);
caller_t func = callers[distType][query.depth()];
func(query, train, mask, trainIdx, distance);
matchDispatcher(query, train, mask, trainIdx, distance, distType);
exit:
return;
}
void cv::ocl::BruteForceMatcher_OCL_base::matchDownload(const oclMat &trainIdx, const oclMat &distance, vector<DMatch> &matches)
......@@ -1062,40 +654,27 @@ void cv::ocl::BruteForceMatcher_OCL_base::matchCollection(const oclMat &query, c
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);
// match1 doesn't support signed char type, match2 only support float, hamming support uchar, ushort and int
int callType = query.depth();
char cvFuncName[] = "matchCollection";
if (callType != 5)
CV_ERROR(CV_UNSUPPORTED_FORMAT_ERR, "BruteForceMatch OpenCL only support float type query!\n");
static const caller_t callers[3][6] =
if ((distType == 0 && callType == 1 ) || (distType == 1 && callType != 5) || (distType == 2 && (callType != 0
|| callType != 2 || callType != 4)))
{
{
ocl_matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
ocl_matchL1_gpu<unsigned short>, ocl_matchL1_gpu<short>,
ocl_matchL1_gpu<int>, ocl_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>*/
}
};
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;
trainIdx.create(1, nQuery, CV_32S);
imgIdx.create(1, nQuery, CV_32S);
distance.create(1, nQuery, CV_32F);
caller_t func = callers[distType][query.depth()];
CV_Assert(func != 0);
trainIdx.create(1, query.rows, CV_32S);
imgIdx.create(1, query.rows, CV_32S);
distance.create(1, query.rows, CV_32F);
func(query, trainCollection, masks, trainIdx, imgIdx, distance);
matchDispatcher(query, (const oclMat *)trainCollection.ptr(), trainCollection.cols, masks, trainIdx, imgIdx, distance, distType);
exit:
return;
}
void cv::ocl::BruteForceMatcher_OCL_base::matchDownload(const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance, vector<DMatch> &matches)
......@@ -1164,52 +743,39 @@ void cv::ocl::BruteForceMatcher_OCL_base::knnMatchSingle(const oclMat &query, co
if (query.empty() || train.empty())
return;
typedef void (*caller_t)(const oclMat & query, const oclMat & train, int k, const oclMat & mask,
const oclMat & trainIdx, const oclMat & distance, const oclMat & allDist);
// match1 doesn't support signed char type, match2 only support float, hamming support uchar, ushort and int
int callType = query.depth();
static const caller_t callers[3][6] =
char cvFuncName[] = "knnMatchSingle";
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)))
{
{
ocl_matchL1_gpu<unsigned char>, 0/*ocl_matchL1_gpu<signed char>*/,
ocl_matchL1_gpu<unsigned short>, ocl_matchL1_gpu<short>,
ocl_matchL1_gpu<int>, ocl_matchL1_gpu<float>
},
{
0/*ocl_matchL2_gpu<unsigned char>*/, 0/*ocl_matchL2_gpu<signed char>*/,
0/*ocl_matchL2_gpu<unsigned short>*/, 0/*ocl_matchL2_gpu<short>*/,
0/*ocl_matchL2_gpu<int>*/, ocl_matchL2_gpu<float>
},
{
ocl_matchHamming_gpu<unsigned char>, 0/*ocl_matchHamming_gpu<signed char>*/,
ocl_matchHamming_gpu<unsigned short>, 0/*ocl_matchHamming_gpu<short>*/,
ocl_matchHamming_gpu<int>, 0/*ocl_matchHamming_gpu<float>*/
}
};
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);
const int nQuery = query.rows;
const int nTrain = train.rows;
if (k == 2)
{
trainIdx.create(1, nQuery, CV_32SC2);
distance.create(1, nQuery, CV_32FC2);
trainIdx.create(1, query.rows, CV_32SC2);
distance.create(1, query.rows, CV_32FC2);
}
else
{
trainIdx.create(nQuery, k, CV_32S);
distance.create(nQuery, k, CV_32F);
allDist.create(nQuery, nTrain, CV_32FC1);
trainIdx.create(query.rows, k, CV_32S);
distance.create(query.rows, k, CV_32F);
allDist.create(query.rows, train.rows, CV_32FC1);
}
trainIdx.setTo(Scalar::all(-1));
caller_t func = callers[distType][query.depth()];
CV_Assert(func != 0);
func(query, train, k, mask, trainIdx, distance, allDist);
kmatchDispatcher(query, train, k, mask, trainIdx, distance, allDist, distType);
exit:
return;
}
void cv::ocl::BruteForceMatcher_OCL_base::knnMatchDownload(const oclMat &trainIdx, const oclMat &distance, vector< vector<DMatch> > &matches, bool compactResult)
......@@ -1394,8 +960,6 @@ namespace
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;
......@@ -1455,50 +1019,34 @@ void cv::ocl::BruteForceMatcher_OCL_base::radiusMatchSingle(const oclMat &query,
if (query.empty() || train.empty())
return;
typedef void (*caller_t)(const oclMat & query, const oclMat & train, float maxDistance, const oclMat & mask,
const oclMat & trainIdx, const oclMat & distance, const oclMat & nMatches);
// match1 doesn't support signed char type, match2 only support float, hamming support uchar, ushort and int
int callType = query.depth();
char cvFuncName[] = "radiusMatchSingle";
if (callType != 5)
CV_ERROR(CV_UNSUPPORTED_FORMAT_ERR, "BruteForceMatch OpenCL only support float type query!\n");
//#if 0
static const caller_t callers[3][6] =
if ((distType == 0 && callType == 1 ) || (distType == 1 && callType != 5) || (distType == 2 && (callType != 0
|| callType != 2 || callType != 4)))
{
{
ocl_matchL1_gpu<unsigned char>, 0/*ocl_matchL1_gpu<signed char>*/,
ocl_matchL1_gpu<unsigned short>, ocl_matchL1_gpu<short>,
ocl_matchL1_gpu<int>, ocl_matchL1_gpu<float>
},
{
0/*ocl_matchL2_gpu<unsigned char>*/, 0/*ocl_matchL2_gpu<signed char>*/,
0/*ocl_matchL2_gpu<unsigned short>*/, 0/*ocl_matchL2_gpu<short>*/,
0/*ocl_matchL2_gpu<int>*/, ocl_matchL2_gpu<float>
},
{
ocl_matchHamming_gpu<unsigned char>, 0/*ocl_matchHamming_gpu<signed char>*/,
ocl_matchHamming_gpu<unsigned short>, 0/*ocl_matchHamming_gpu<short>*/,
ocl_matchHamming_gpu<int>, 0/*ocl_matchHamming_gpu<float>*/
}
};
//#endif
const int nQuery = query.rows;
const int nTrain = train.rows;
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);
CV_Assert(trainIdx.empty() || (trainIdx.rows == nQuery && trainIdx.size() == distance.size()));
CV_Assert(trainIdx.empty() || (trainIdx.rows == query.rows && trainIdx.size() == distance.size()));
nMatches.create(1, nQuery, CV_32SC1);
nMatches.create(1, query.rows, CV_32SC1);
if (trainIdx.empty())
{
trainIdx.create(nQuery, std::max((nTrain / 100), 10), CV_32SC1);
distance.create(nQuery, std::max((nTrain / 100), 10), CV_32FC1);
trainIdx.create(query.rows, std::max((train.rows/ 100), 10), CV_32SC1);
distance.create(query.rows, std::max((train.rows/ 100), 10), CV_32FC1);
}
nMatches.setTo(Scalar::all(0));
caller_t func = callers[distType][query.depth()];
//CV_Assert(func != 0);
//func(query, train, maxDistance, mask, trainIdx, distance, nMatches, cc, StreamAccessor::getStream(stream));
func(query, train, maxDistance, mask, trainIdx, distance, nMatches);
matchDispatcher(query, train, maxDistance, mask, trainIdx, distance, nMatches, distType);
exit:
return;
}
void cv::ocl::BruteForceMatcher_OCL_base::radiusMatchDownload(const oclMat &trainIdx, const oclMat &distance, const oclMat &nMatches,
......@@ -1697,5 +1245,3 @@ void cv::ocl::BruteForceMatcher_OCL_base::radiusMatch(const oclMat &query, vecto
radiusMatchCollection(query, trainIdx, imgIdx, distance, nMatches, maxDistance, masks);
radiusMatchDownload(trainIdx, imgIdx, distance, nMatches, matches, compactResult);
}
modules/ocl/src/haar.cpp
View file @ 6c99b5c9
......@@ -953,8 +953,8 @@ CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemS
//int flag = 0;
oclMat gimg1(gimg.rows, gimg.cols, CV_8UC1);
oclMat gsum(totalheight, gimg.cols + 1, CV_32SC1);
oclMat gsqsum(totalheight, gimg.cols + 1, CV_32FC1);
oclMat gsum(totalheight + 4, gimg.cols + 1, CV_32SC1);
oclMat gsqsum(totalheight + 4, gimg.cols + 1, CV_32FC1);
//cl_mem cascadebuffer;
cl_mem stagebuffer;
......
modules/ocl/src/match_template.cpp
View file @ 6c99b5c9
......@@ -71,6 +71,9 @@ namespace cv
void matchTemplate_SQDIFF_NORMED(
const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &buf);
void convolve_32F(
const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &buf);
void matchTemplate_CCORR(
const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &buf);
......@@ -90,41 +93,65 @@ namespace cv
void matchTemplateNaive_CCORR(
const oclMat &image, const oclMat &templ, oclMat &result, int cn);
void extractFirstChannel_32F(
const oclMat &image, oclMat &result);
// Evaluates optimal template's area threshold. If
// template's area is less than the threshold, we use naive match
// template version, otherwise FFT-based (if available)
static int getTemplateThreshold(int method, int depth)
static bool useNaive(int , int , Size )
{
switch (method)
{
case CV_TM_CCORR:
if (depth == CV_32F) return 250;
if (depth == CV_8U) return 300;
break;
case CV_TM_SQDIFF:
if (depth == CV_32F) return 0x7fffffff; // do naive SQDIFF for CV_32F
if (depth == CV_8U) return 300;
break;
}
CV_Error(CV_StsBadArg, "getTemplateThreshold: unsupported match template mode");
return 0;
// FIXME!
// always use naive until convolve is imported
return true;
}
//////////////////////////////////////////////////////////////////////
// SQDIFF
void matchTemplate_SQDIFF(
const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &)
const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf & buf)
{
result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F);
if (templ.size().area() < getTemplateThreshold(CV_TM_SQDIFF, image.depth()))
if (useNaive(CV_TM_SQDIFF, image.depth(), templ.size()))
{
matchTemplateNaive_SQDIFF(image, templ, result, image.oclchannels());
return;
}
else
{
// TODO
CV_Error(CV_StsBadArg, "Not supported yet for this size template");
buf.image_sqsums.resize(1);
// TODO, add double support for ocl::integral
// use CPU integral temporarily
Mat sums, sqsums;
cv::integral(Mat(image.reshape(1)), sums, sqsums);
buf.image_sqsums[0] = sqsums;
unsigned long long templ_sqsum = (unsigned long long)sqrSum(templ.reshape(1))[0];
matchTemplate_CCORR(image, templ, result, buf);
//port CUDA's matchTemplatePrepared_SQDIFF_8U
Context *clCxt = image.clCxt;
string kernelName = "matchTemplate_Prepared_SQDIFF";
vector< pair<size_t, const void *> > args;
args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sqsums[0].data));
args.push_back( make_pair( sizeof(cl_mem), (void *)&result.data));
args.push_back( make_pair( sizeof(cl_ulong), (void *)&templ_sqsum));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.rows));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.cols));
args.push_back( make_pair( sizeof(cl_int), (void *)&templ.rows));
args.push_back( make_pair( sizeof(cl_int), (void *)&templ.cols));
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sqsums[0].offset));
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sqsums[0].step));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.offset));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
size_t globalThreads[3] = {result.cols, result.rows, 1};
size_t localThreads[3] = {16, 16, 1};
const char * build_opt = image.oclchannels() == 4 ? "-D CN4" : "";
openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, 1, CV_8U, build_opt);
}
}
......@@ -134,7 +161,6 @@ namespace cv
matchTemplate_CCORR(image, templ, result, buf);
buf.image_sums.resize(1);
integral(image.reshape(1), buf.image_sums[0]);
unsigned long long templ_sqsum = (unsigned long long)sqrSum(templ.reshape(1))[0];
......@@ -156,7 +182,7 @@ namespace cv
args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
size_t globalThreads[3] = {result.cols, result.rows, 1};
size_t localThreads[3] = {32, 8, 1};
size_t localThreads[3] = {16, 16, 1};
openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, 1, CV_8U);
}
......@@ -191,33 +217,39 @@ namespace cv
args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
size_t globalThreads[3] = {result.cols, result.rows, 1};
size_t localThreads[3] = {32, 8, 1};
size_t localThreads[3] = {16, 16, 1};
openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, image.oclchannels(), image.depth());
}
//////////////////////////////////////////////////////////////////////
// CCORR
void convolve_32F(
const oclMat &, const oclMat &, oclMat &, MatchTemplateBuf &)
{
CV_Error(-1, "convolve is not fully implemented yet");
}
void matchTemplate_CCORR(
const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &buf)
{
result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F);
if (templ.size().area() < getTemplateThreshold(CV_TM_SQDIFF, image.depth()))
if (useNaive(CV_TM_CCORR, image.depth(), templ.size()))
{
matchTemplateNaive_CCORR(image, templ, result, image.oclchannels());
return;
}
else
{
CV_Error(CV_StsBadArg, "Not supported yet for this size template");
if(image.depth() == CV_8U && templ.depth() == CV_8U)
{
image.convertTo(buf.imagef, CV_32F);
templ.convertTo(buf.templf, CV_32F);
convolve_32F(buf.imagef, buf.templf, result, buf);
}
else
{
convolve_32F(image, templ, result, buf);
}
CV_Assert(image.oclchannels() == 1);
oclMat o_result(image.size(), CV_MAKETYPE(CV_32F, image.oclchannels()));
filter2D(buf.imagef, o_result, CV_32F, buf.templf, Point(0, 0));
result = o_result(Rect(0, 0, image.rows - templ.rows + 1, image.cols - templ.cols + 1));
}
}
......@@ -249,7 +281,7 @@ namespace cv
args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
size_t globalThreads[3] = {result.cols, result.rows, 1};
size_t localThreads[3] = {32, 8, 1};
size_t localThreads[3] = {16, 16, 1};
openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, 1, CV_8U);
}
......@@ -284,7 +316,7 @@ namespace cv
args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
size_t globalThreads[3] = {result.cols, result.rows, 1};
size_t localThreads[3] = {32, 8, 1};
size_t localThreads[3] = {16, 16, 1};
openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, image.oclchannels(), image.depth());
}
//////////////////////////////////////////////////////////////////////
......@@ -301,7 +333,7 @@ namespace cv
kernelName = "matchTemplate_Prepared_CCOFF";
size_t globalThreads[3] = {result.cols, result.rows, 1};
size_t localThreads[3] = {32, 8, 1};
size_t localThreads[3] = {16, 16, 1};
vector< pair<size_t, const void *> > args;
args.push_back( make_pair( sizeof(cl_mem), (void *)&result.data) );
......@@ -313,22 +345,22 @@ namespace cv
args.push_back( make_pair( sizeof(cl_int), (void *)&result.cols) );
args.push_back( make_pair( sizeof(cl_int), (void *)&result.offset));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
Vec4f templ_sum = Vec4f::all(0);
// to be continued in the following section
if(image.oclchannels() == 1)
{
buf.image_sums.resize(1);
integral(image, buf.image_sums[0]);
float templ_sum = 0;
templ_sum = (float)sum(templ)[0] / templ.size().area();
templ_sum[0] = (float)sum(templ)[0] / templ.size().area();
args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sums[0].data) );
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sums[0].offset) );
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sums[0].step) );
args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sum) );
args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sum[0]) );
}
else
{
Vec4f templ_sum = Vec4f::all(0);
split(image, buf.images);
templ_sum = sum(templ) / templ.size().area();
buf.image_sums.resize(buf.images.size());
......@@ -374,7 +406,7 @@ namespace cv
kernelName = "matchTemplate_Prepared_CCOFF_NORMED";
size_t globalThreads[3] = {result.cols, result.rows, 1};
size_t localThreads[3] = {32, 8, 1};
size_t localThreads[3] = {16, 16, 1};
vector< pair<size_t, const void *> > args;
args.push_back( make_pair( sizeof(cl_mem), (void *)&result.data) );
......@@ -387,20 +419,22 @@ namespace cv
args.push_back( make_pair( sizeof(cl_int), (void *)&result.offset));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
args.push_back( make_pair( sizeof(cl_float), (void *)&scale) );
Vec4f templ_sum = Vec4f::all(0);
Vec4f templ_sqsum = Vec4f::all(0);
// to be continued in the following section
if(image.oclchannels() == 1)
{
buf.image_sums.resize(1);
buf.image_sqsums.resize(1);
integral(image, buf.image_sums[0], buf.image_sqsums[0]);
float templ_sum = 0;
float templ_sqsum = 0;
templ_sum = (float)sum(templ)[0];
templ_sqsum = sqrSum(templ)[0];
templ_sum[0] = (float)sum(templ)[0];
templ_sqsum -= scale * templ_sum * templ_sum;
templ_sum *= scale;
templ_sqsum[0] = sqrSum(templ)[0];
templ_sqsum[0] -= scale * templ_sum[0] * templ_sum[0];
templ_sum[0] *= scale;
args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sums[0].data) );
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sums[0].offset) );
......@@ -408,13 +442,11 @@ namespace cv
args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sqsums[0].data) );
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sqsums[0].offset) );
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sqsums[0].step) );
args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sum) );
args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sqsum) );
args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sum[0]) );
args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sqsum[0]) );
}
else
{
Vec4f templ_sum = Vec4f::all(0);
Vec4f templ_sqsum = Vec4f::all(0);
split(image, buf.images);
templ_sum = sum(templ);
......@@ -465,7 +497,27 @@ namespace cv
}
openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, image.oclchannels(), image.depth());
}
void extractFirstChannel_32F(const oclMat &image, oclMat &result)
{
Context *clCxt = image.clCxt;
string kernelName;
kernelName = "extractFirstChannel";
size_t globalThreads[3] = {result.cols, result.rows, 1};
size_t localThreads[3] = {16, 16, 1};
vector< pair<size_t, const void *> > args;
args.push_back( make_pair( sizeof(cl_mem), (void *)&image.data) );
args.push_back( make_pair( sizeof(cl_mem), (void *)&result.data) );
args.push_back( make_pair( sizeof(cl_int), (void *)&result.rows) );
args.push_back( make_pair( sizeof(cl_int), (void *)&result.cols) );
args.push_back( make_pair( sizeof(cl_int), (void *)&image.offset));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.offset));
args.push_back( make_pair( sizeof(cl_int), (void *)&image.step));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, -1, -1);
}
}/*ocl*/
} /*cv*/
......
modules/ocl/src/moments.cpp
View file @ 6c99b5c9
......@@ -143,7 +143,7 @@ static void icvContourMoments( CvSeq* contour, CvMoments* mom )
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst_step ));
openCLExecuteKernel(dst_a.clCxt, &moments, "icvContourMoments", globalThreads, localThreads, args, -1, -1);
cv::Mat dst(dst_a);
a00 = a10 = a01 = a20 = a11 = a02 = a30 = a21 = a12 = a03 = 0.0;
if (!cv::ocl::Context::getContext()->supportsFeature(Context::CL_DOUBLE))
......@@ -277,16 +277,7 @@ static void ocl_cvMoments( const void* array, CvMoments* mom, int binary )
blocky = size.height/TILE_SIZE;
else
blocky = size.height/TILE_SIZE + 1;
cv::ocl::oclMat dst_m00(blocky, blockx, CV_64FC1);
cv::ocl::oclMat dst_m10(blocky, blockx, CV_64FC1);
cv::ocl::oclMat dst_m01(blocky, blockx, CV_64FC1);
cv::ocl::oclMat dst_m20(blocky, blockx, CV_64FC1);
cv::ocl::oclMat dst_m11(blocky, blockx, CV_64FC1);
cv::ocl::oclMat dst_m02(blocky, blockx, CV_64FC1);
cv::ocl::oclMat dst_m30(blocky, blockx, CV_64FC1);
cv::ocl::oclMat dst_m21(blocky, blockx, CV_64FC1);
cv::ocl::oclMat dst_m12(blocky, blockx, CV_64FC1);
cv::ocl::oclMat dst_m03(blocky, blockx, CV_64FC1);
cv::ocl::oclMat dst_m(blocky * 10, blockx, CV_64FC1);
cl_mem sum = openCLCreateBuffer(src.clCxt,CL_MEM_READ_WRITE,10*sizeof(double));
int tile_width = std::min(size.width,TILE_SIZE);
int tile_height = std::min(size.height,TILE_SIZE);
......@@ -299,25 +290,17 @@ static void ocl_cvMoments( const void* array, CvMoments* mom, int binary )
args.push_back( make_pair( sizeof(cl_int) , (void *)&src.step ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&tileSize.width ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&tileSize.height ));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_m00.data ));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_m10.data ));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_m01.data ));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_m20.data ));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_m11.data ));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_m02.data ));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_m30.data ));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_m21.data ));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_m12.data ));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_m03.data ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst_m00.cols ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst_m00.step ));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_m.data ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst_m.cols ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst_m.step ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&blocky ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&type ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&depth ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&cn ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&coi ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&binary ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&TILE_SIZE ));
openCLExecuteKernel(dst_m00.clCxt, &moments, "CvMoments", globalThreads, localThreads, args, -1, depth);
openCLExecuteKernel(dst_m.clCxt, &moments, "CvMoments", globalThreads, localThreads, args, -1, depth);
size_t localThreadss[3] = { 128, 1, 1};
size_t globalThreadss[3] = { 128, 1, 1};
......@@ -327,20 +310,12 @@ static void ocl_cvMoments( const void* array, CvMoments* mom, int binary )
args_sum.push_back( make_pair( sizeof(cl_int) , (void *)&tile_width ));
args_sum.push_back( make_pair( sizeof(cl_int) , (void *)&TILE_SIZE ));
args_sum.push_back( make_pair( sizeof(cl_mem) , (void *)&sum ));
args_sum.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_m00.data ));
args_sum.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_m10.data ));
args_sum.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_m01.data ));
args_sum.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_m20.data ));
args_sum.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_m11.data ));
args_sum.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_m02.data ));
args_sum.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_m30.data ));
args_sum.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_m21.data ));
args_sum.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_m12.data ));
args_sum.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_m03.data ));
openCLExecuteKernel(dst_m00.clCxt, &moments, "dst_sum", globalThreadss, localThreadss, args_sum, -1, -1);
args_sum.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_m.data ));
args_sum.push_back( make_pair( sizeof(cl_int) , (void *)&dst_m.step ));
openCLExecuteKernel(dst_m.clCxt, &moments, "dst_sum", globalThreadss, localThreadss, args_sum, -1, -1);
double* dstsum = new double[10];
memset(dstsum,0,10*sizeof(double));
openCLReadBuffer(dst_m00.clCxt,sum,(void *)dstsum,10*sizeof(double));
openCLReadBuffer(dst_m.clCxt,sum,(void *)dstsum,10*sizeof(double));
mom->m00 = dstsum[0];
mom->m10 = dstsum[1];
mom->m01 = dstsum[2];
......@@ -351,6 +326,7 @@ static void ocl_cvMoments( const void* array, CvMoments* mom, int binary )
mom->m21 = dstsum[7];
mom->m12 = dstsum[8];
mom->m03 = dstsum[9];
delete [] dstsum;
icvCompleteMomentState( mom );
}
......
modules/ocl/src/opencl/brute_force_match.cl
View file @ 6c99b5c9
......@@ -5,19 +5,93 @@ 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;
}
float reduce_block(__local float *s_query,
__local float *s_train,
int block_size,
int lidx,
int lidy,
int distType
)
{
/* there are threee types in the reducer. the first is L1Dist, which to sum the abs(v1, v2), the second is L2Dist, which to
sum the (v1 - v2) * (v1 - v2), the third is humming, which to popc(v1 ^ v2), popc is to count the bits are set to 1*/
float result = 0;
switch(distType)
{
case 0:
for (int j = 0 ; j < block_size ; j++)
{
result += fabs(s_query[lidy * block_size + j] - s_train[j * block_size + lidx]);
}
break;
case 1:
for (int j = 0 ; j < block_size ; j++)
{
float qr = s_query[lidy * block_size + j] - s_train[j * block_size + lidx];
result += qr * qr;
}
break;
case 2:
for (int j = 0 ; j < block_size ; j++)
{
result += bit1Count((uint)s_query[lidy * block_size + j] ^ (uint)s_train[(uint)j * block_size + lidx]);
}
break;
}
return result;
}
float reduce_multi_block(__local float *s_query,
__local float *s_train,
int max_desc_len,
int block_size,
int block_index,
int lidx,
int lidy,
int distType
)
{
/* there are threee types in the reducer. the first is L1Dist, which to sum the abs(v1, v2), the second is L2Dist, which to
sum the (v1 - v2) * (v1 - v2), the third is humming, which to popc(v1 ^ v2), popc is to count the bits are set to 1*/
float result = 0;
switch(distType)
{
case 0:
for (int j = 0 ; j < block_size ; j++)
{
result += fabs(s_query[lidy * max_desc_len + block_index * block_size + j] - s_train[j * block_size + lidx]);
}
break;
case 1:
for (int j = 0 ; j < block_size ; j++)
{
float qr = s_query[lidy * max_desc_len + block_index * block_size + j] - s_train[j * block_size + lidx];
result += qr * qr;
}
break;
case 2:
for (int j = 0 ; j < block_size ; j++)
{
//result += popcount((uint)s_query[lidy * max_desc_len + block_index * block_size + j] ^ (uint)s_train[j * block_size + lidx]);
result += bit1Count((uint)s_query[lidy * max_desc_len + block_index * block_size + j] ^ (uint)s_train[j * block_size + lidx]);
}
break;
}
return 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(
__kernel void BruteForceMatch_UnrollMatch_D5(
__global float *query,
__global float *train,
//__global float *mask,
......@@ -42,7 +116,6 @@ __kernel void BruteForceMatch_UnrollMatch(
__local float *s_train = sharebuffer + block_size * max_desc_len;
int queryIdx = groupidx * block_size + lidy;
// load the query into local memory.
for (int i = 0 ; i < max_desc_len / block_size; i ++)
{
......@@ -55,11 +128,9 @@ __kernel void BruteForceMatch_UnrollMatch(
// loopUnrolledCached to find the best trainIdx and best distance.
volatile int imgIdx = 0;
for (int t = 0 ; t < (train_rows + block_size - 1) / block_size ; t++)
{
float result = 0;
for (int i = 0 ; i < max_desc_len / block_size ; i++)
{
//load a block_size * block_size block into local train.
......@@ -69,38 +140,7 @@ __kernel void BruteForceMatch_UnrollMatch(
//synchronize to make sure each elem for reduceIteration in share memory is written already.
barrier(CLK_LOCAL_MEM_FENCE);
/* there are threee types in the reducer. the first is L1Dist, which to sum the abs(v1, v2), the second is L2Dist, which to
sum the (v1 - v2) * (v1 - v2), the third is humming, which to popc(v1 ^ v2), popc is to count the bits are set to 1*/
switch (distType)
{
case 0:
for (int j = 0 ; j < block_size ; j++)
{
result += fabs(s_query[lidy * max_desc_len + i * block_size + j] - s_train[j * block_size + lidx]);
}
break;
case 1:
for (int j = 0 ; j < block_size ; j++)
{
float qr = s_query[lidy * max_desc_len + i * block_size + j] - s_train[j * block_size + lidx];
result += qr * qr;
}
break;
case 2:
for (int j = 0 ; j < block_size ; j++)
{
//result += popcount((uint)s_query[lidy * max_desc_len + i * block_size + j] ^ (uint)s_train[j * block_size + lidx]);
result += bit1Count((uint)s_query[lidy * max_desc_len + i * block_size + j] ^(uint)s_train[j * block_size + lidx]);
}
break;
}
result += reduce_multi_block(s_query, s_train, max_desc_len, block_size, i, lidx, lidy, distType);
barrier(CLK_LOCAL_MEM_FENCE);
}
......@@ -116,8 +156,8 @@ __kernel void BruteForceMatch_UnrollMatch(
}
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;
......@@ -144,7 +184,7 @@ __kernel void BruteForceMatch_UnrollMatch(
}
}
__kernel void BruteForceMatch_Match(
__kernel void BruteForceMatch_Match_D5(
__global float *query,
__global float *train,
//__global float *mask,
......@@ -177,7 +217,6 @@ __kernel void BruteForceMatch_Match(
{
//Dist dist;
float result = 0;
for (int i = 0 ; i < (query_cols + block_size - 1) / block_size ; i++)
{
const int loadx = lidx + i * block_size;
......@@ -193,38 +232,7 @@ __kernel void BruteForceMatch_Match(
barrier(CLK_LOCAL_MEM_FENCE);
/* there are threee types in the reducer. the first is L1Dist, which to sum the abs(v1, v2), the second is L2Dist, which to
sum the (v1 - v2) * (v1 - v2), the third is humming, which to popc(v1 ^ v2), popc is to count the bits are set to 1*/
switch (distType)
{
case 0:
for (int j = 0 ; j < block_size ; j++)
{
result += fabs(s_query[lidy * block_size + j] - s_train[j * block_size + lidx]);
}
break;
case 1:
for (int j = 0 ; j < block_size ; j++)
{
float qr = s_query[lidy * block_size + j] - s_train[j * block_size + lidx];
result += qr * qr;
}
break;
case 2:
for (int j = 0 ; j < block_size ; j++)
{
//result += popcount((uint)s_query[lidy * block_size + j] ^ (uint)s_train[j * block_size + lidx]);
result += bit1Count((uint)s_query[lidy * block_size + j] ^(uint)s_train[(uint)j * block_size + lidx]);
}
break;
}
result += reduce_block(s_query, s_train, block_size, lidx, lidy, distType);
barrier(CLK_LOCAL_MEM_FENCE);
}
......@@ -270,7 +278,7 @@ __kernel void BruteForceMatch_Match(
}
//radius_unrollmatch
__kernel void BruteForceMatch_RadiusUnrollMatch(
__kernel void BruteForceMatch_RadiusUnrollMatch_D5(
__global float *query,
__global float *train,
float maxDistance,
......@@ -303,7 +311,6 @@ __kernel void BruteForceMatch_RadiusUnrollMatch(
__local float *s_train = sharebuffer + block_size * block_size;
float result = 0;
for (int i = 0 ; i < max_desc_len / block_size ; ++i)
{
//load a block_size * block_size block into local train.
......@@ -315,37 +322,7 @@ __kernel void BruteForceMatch_RadiusUnrollMatch(
//synchronize to make sure each elem for reduceIteration in share memory is written already.
barrier(CLK_LOCAL_MEM_FENCE);
/* there are three types in the reducer. the first is L1Dist, which to sum the abs(v1, v2), the second is L2Dist, which to
sum the (v1 - v2) * (v1 - v2), the third is humming, which to popc(v1 ^ v2), popc is to count the bits are set to 1*/
switch (distType)
{
case 0:
for (int j = 0 ; j < block_size ; ++j)
{
result += fabs(s_query[lidy * block_size + j] - s_train[j * block_size + lidx]);
}
break;
case 1:
for (int j = 0 ; j < block_size ; ++j)
{
float qr = s_query[lidy * block_size + j] - s_train[j * block_size + lidx];
result += qr * qr;
}
break;
case 2:
for (int j = 0 ; j < block_size ; ++j)
{
result += bit1Count((uint)s_query[lidy * block_size + j] ^(uint)s_train[j * block_size + lidx]);
}
break;
}
result += reduce_block(s_query, s_train, block_size, lidx, lidy, distType);
barrier(CLK_LOCAL_MEM_FENCE);
}
......@@ -354,7 +331,7 @@ __kernel void BruteForceMatch_RadiusUnrollMatch(
{
unsigned int ind = atom_inc(nMatches + queryIdx/*, (unsigned int) -1*/);
if (ind < bestTrainIdx_cols)
if(ind < bestTrainIdx_cols)
{
//bestImgIdx = imgIdx;
bestTrainIdx[queryIdx * (ostep / sizeof(int)) + ind] = trainIdx;
......@@ -364,7 +341,7 @@ __kernel void BruteForceMatch_RadiusUnrollMatch(
}
//radius_match
__kernel void BruteForceMatch_RadiusMatch(
__kernel void BruteForceMatch_RadiusMatch_D5(
__global float *query,
__global float *train,
float maxDistance,
......@@ -396,7 +373,6 @@ __kernel void BruteForceMatch_RadiusMatch(
__local float *s_train = sharebuffer + block_size * block_size;
float 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.
......@@ -408,46 +384,16 @@ __kernel void BruteForceMatch_RadiusMatch(
//synchronize to make sure each elem for reduceIteration in share memory is written already.
barrier(CLK_LOCAL_MEM_FENCE);
/* there are three types in the reducer. the first is L1Dist, which to sum the abs(v1, v2), the second is L2Dist, which to
sum the (v1 - v2) * (v1 - v2), the third is humming, which to popc(v1 ^ v2), popc is to count the bits are set to 1*/
switch (distType)
{
case 0:
for (int j = 0 ; j < block_size ; ++j)
{
result += fabs(s_query[lidy * block_size + j] - s_train[j * block_size + lidx]);
}
break;
case 1:
for (int j = 0 ; j < block_size ; ++j)
{
float qr = s_query[lidy * block_size + j] - s_train[j * block_size + lidx];
result += qr * qr;
}
break;
case 2:
for (int j = 0 ; j < block_size ; ++j)
{
result += bit1Count((uint)s_query[lidy * block_size + j] ^(uint)s_train[j * block_size + lidx]);
}
break;
}
result += reduce_block(s_query, s_train, block_size, lidx, lidy, distType);
barrier(CLK_LOCAL_MEM_FENCE);
}
if (queryIdx < query_rows && trainIdx < train_rows && result < maxDistance/* && mask(queryIdx, trainIdx)*/)
{
unsigned int ind = atom_inc(nMatches + queryIdx/*, (unsigned int) -1*/);
unsigned int ind = atom_inc(nMatches + queryIdx);
if (ind < bestTrainIdx_cols)
if(ind < bestTrainIdx_cols)
{
//bestImgIdx = imgIdx;
bestTrainIdx[queryIdx * (ostep / sizeof(int)) + ind] = trainIdx;
......@@ -457,7 +403,7 @@ __kernel void BruteForceMatch_RadiusMatch(
}
__kernel void BruteForceMatch_knnUnrollMatch(
__kernel void BruteForceMatch_knnUnrollMatch_D5(
__global float *query,
__global float *train,
//__global float *mask,
......@@ -496,11 +442,9 @@ __kernel void BruteForceMatch_knnUnrollMatch(
//loopUnrolledCached
volatile int imgIdx = 0;
for (int t = 0 ; t < (train_rows + block_size - 1) / block_size ; t++)
{
float result = 0;
for (int i = 0 ; i < max_desc_len / block_size ; i++)
{
const int loadX = lidx + i * block_size;
......@@ -511,38 +455,7 @@ __kernel void BruteForceMatch_knnUnrollMatch(
//synchronize to make sure each elem for reduceIteration in share memory is written already.
barrier(CLK_LOCAL_MEM_FENCE);
/* there are threee types in the reducer. the first is L1Dist, which to sum the abs(v1, v2), the second is L2Dist, which to
sum the (v1 - v2) * (v1 - v2), the third is humming, which to popc(v1 ^ v2), popc is to count the bits are set to 1*/
switch (distType)
{
case 0:
for (int j = 0 ; j < block_size ; j++)
{
result += fabs(s_query[lidy * max_desc_len + i * block_size + j] - s_train[j * block_size + lidx]);
}
break;
case 1:
for (int j = 0 ; j < block_size ; j++)
{
float qr = s_query[lidy * max_desc_len + i * block_size + j] - s_train[j * block_size + lidx];
result += qr * qr;
}
break;
case 2:
for (int j = 0 ; j < block_size ; j++)
{
//result += popcount((uint)s_query[lidy * max_desc_len + i * block_size + j] ^ (uint)s_train[j * block_size + lidx]);
result += bit1Count((uint)s_query[lidy * max_desc_len + i * block_size + j] ^(uint)s_train[j * block_size + lidx]);
}
break;
}
result += reduce_multi_block(s_query, s_train, max_desc_len, block_size, i, lidx, lidy, distType);
barrier(CLK_LOCAL_MEM_FENCE);
}
......@@ -589,7 +502,6 @@ __kernel void BruteForceMatch_knnUnrollMatch(
for (int i = 0 ; i < block_size ; i++)
{
float val = s_distance[i];
if (val < bestDistance1)
{
bestDistance2 = bestDistance1;
......@@ -640,7 +552,7 @@ __kernel void BruteForceMatch_knnUnrollMatch(
}
}
__kernel void BruteForceMatch_knnMatch(
__kernel void BruteForceMatch_knnMatch_D5(
__global float *query,
__global float *train,
//__global float *mask,
......@@ -673,8 +585,7 @@ __kernel void BruteForceMatch_knnMatch(
for (int t = 0 ; t < (train_rows + block_size - 1) / block_size ; t++)
{
float result = 0.0f;
for (int i = 0 ; i < (query_cols + block_size - 1) / block_size ; i++)
for (int i = 0 ; i < (query_cols + block_size -1) / block_size ; i++)
{
const int loadx = lidx + i * block_size;
//load query and train into local memory
......@@ -689,38 +600,7 @@ __kernel void BruteForceMatch_knnMatch(
barrier(CLK_LOCAL_MEM_FENCE);
/* there are threee types in the reducer. the first is L1Dist, which to sum the abs(v1, v2), the second is L2Dist, which to
sum the (v1 - v2) * (v1 - v2), the third is humming, which to popc(v1 ^ v2), popc is to count the bits are set to 1*/
switch (distType)
{
case 0:
for (int j = 0 ; j < block_size ; j++)
{
result += fabs(s_query[lidy * block_size + j] - s_train[j * block_size + lidx]);
}
break;
case 1:
for (int j = 0 ; j < block_size ; j++)
{
float qr = s_query[lidy * block_size + j] - s_train[j * block_size + lidx];
result += qr * qr;
}
break;
case 2:
for (int j = 0 ; j < block_size ; j++)
{
//result += popcount((uint)s_query[lidy * block_size + j] ^ (uint)s_train[j * block_size + lidx]);
result += bit1Count((uint)s_query[lidy * block_size + j] ^(uint)s_train[(uint)j * block_size + lidx]);
}
break;
}
result += reduce_block(s_query, s_train, block_size, lidx, lidy, distType);
barrier(CLK_LOCAL_MEM_FENCE);
}
......@@ -767,7 +647,6 @@ __kernel void BruteForceMatch_knnMatch(
for (int i = 0 ; i < block_size ; i++)
{
float val = s_distance[i];
if (val < bestDistance1)
{
bestDistance2 = bestDistance1;
......@@ -818,7 +697,7 @@ __kernel void BruteForceMatch_knnMatch(
}
}
kernel void BruteForceMatch_calcDistanceUnrolled(
kernel void BruteForceMatch_calcDistanceUnrolled_D5(
__global float *query,
__global float *train,
//__global float *mask,
......@@ -836,7 +715,7 @@ kernel void BruteForceMatch_calcDistanceUnrolled(
/* Todo */
}
kernel void BruteForceMatch_calcDistance(
kernel void BruteForceMatch_calcDistance_D5(
__global float *query,
__global float *train,
//__global float *mask,
......@@ -853,7 +732,7 @@ kernel void BruteForceMatch_calcDistance(
/* Todo */
}
kernel void BruteForceMatch_findBestMatch(
kernel void BruteForceMatch_findBestMatch_D5(
__global float *allDist,
__global int *bestTrainIdx,
__global float *bestDistance,
......
modules/ocl/src/opencl/haarobjectdetect.cl
View file @ 6c99b5c9
......@@ -211,10 +211,14 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
int4 data = *(__global int4*)&sum[glb_off];
int lcl_off = mad24(lcl_y, readwidth, lcl_x<<2);
#if OFF
lcldata[lcl_off] = data.x;
lcldata[lcl_off+1] = data.y;
lcldata[lcl_off+2] = data.z;
lcldata[lcl_off+3] = data.w;
#else
vstore4(data, 0, &lcldata[lcl_off]);
#endif
}
lcloutindex[lcl_id] = 0;
......@@ -559,3 +563,7 @@ if(result)
}
}
*/
modules/ocl/src/opencl/match_template.cl
View file @ 6c99b5c9
......@@ -45,22 +45,28 @@
#pragma OPENCL EXTENSION cl_amd_printf : enable
#if defined (__ATI__)
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#if defined (DOUBLE_SUPPORT)
#elif defined (__NVIDIA__)
#ifdef cl_khr_fp64
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#elif defined (cl_amd_fp64)
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#endif
#if !defined(USE_SQR_INTEGRAL) && (defined (__ATI__) || defined (__NVIDIA__))
#define TYPE_IMAGE_SQSUM double
#else
#define TYPE_IMAGE_SQSUM ulong
#define TYPE_IMAGE_SQSUM float
#endif
#ifndef CN4
#define CN4 1
#else
#define CN4 4
#endif
//////////////////////////////////////////////////
// utilities
#define SQSUMS_PTR(ox, oy) mad24(gidy + oy, img_sqsums_step, gidx + img_sqsums_offset + ox)
#define SQSUMS_PTR(ox, oy) mad24(gidy + oy, img_sqsums_step, (gidx + img_sqsums_offset + ox) * CN4)
#define SUMS_PTR(ox, oy) mad24(gidy + oy, img_sums_step, gidx + img_sums_offset + ox)
// normAcc* are accurate normalization routines which make GPU matchTemplate
// consistent with CPU one
......@@ -95,7 +101,7 @@ float normAcc_SQDIFF(float num, float denum)
__kernel
void normalizeKernel_C1_D0
(
__global const TYPE_IMAGE_SQSUM * img_sqsums,
__global const float * img_sqsums,
__global float * res,
ulong tpl_sqsum,
int res_rows,
......@@ -119,8 +125,8 @@ void normalizeKernel_C1_D0
if(gidx < res_cols && gidy < res_rows)
{
float image_sqsum_ = (float)(
(img_sqsums[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums[SQSUMS_PTR(0, tpl_rows)] - img_sqsums[SQSUMS_PTR(0, 0)]));
(img_sqsums[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums[SQSUMS_PTR(0, tpl_rows)] - img_sqsums[SQSUMS_PTR(0, 0)]));
res[res_idx] = normAcc(res[res_idx], sqrt(image_sqsum_ * tpl_sqsum));
}
}
......@@ -152,8 +158,8 @@ void matchTemplate_Prepared_SQDIFF_C1_D0
if(gidx < res_cols && gidy < res_rows)
{
float image_sqsum_ = (float)(
(img_sqsums[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums[SQSUMS_PTR(0, tpl_rows)] - img_sqsums[SQSUMS_PTR(0, 0)]));
(img_sqsums[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums[SQSUMS_PTR(0, tpl_rows)] - img_sqsums[SQSUMS_PTR(0, 0)]));
res[res_idx] = image_sqsum_ - 2.f * res[res_idx] + tpl_sqsum;
}
}
......@@ -161,7 +167,7 @@ void matchTemplate_Prepared_SQDIFF_C1_D0
__kernel
void matchTemplate_Prepared_SQDIFF_NORMED_C1_D0
(
__global const TYPE_IMAGE_SQSUM * img_sqsums,
__global const float * img_sqsums,
__global float * res,
ulong tpl_sqsum,
int res_rows,
......@@ -185,10 +191,10 @@ void matchTemplate_Prepared_SQDIFF_NORMED_C1_D0
if(gidx < res_cols && gidy < res_rows)
{
float image_sqsum_ = (float)(
(img_sqsums[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums[SQSUMS_PTR(0, tpl_rows)] - img_sqsums[SQSUMS_PTR(0, 0)]));
(img_sqsums[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums[SQSUMS_PTR(0, tpl_rows)] - img_sqsums[SQSUMS_PTR(0, 0)]));
res[res_idx] = normAcc_SQDIFF(image_sqsum_ - 2.f * res[res_idx] + tpl_sqsum,
sqrt(image_sqsum_ * tpl_sqsum));
sqrt(image_sqsum_ * tpl_sqsum));
}
}
......@@ -628,8 +634,8 @@ void matchTemplate_Prepared_CCOFF_C1_D0
if(gidx < res_cols && gidy < res_rows)
{
float sum = (float)(
(img_sums[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums[SUMS_PTR(tpl_cols, 0)])
- (img_sums[SUMS_PTR(0, tpl_rows)] - img_sums[SUMS_PTR(0, 0)]));
(img_sums[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums[SUMS_PTR(tpl_cols, 0)])
- (img_sums[SUMS_PTR(0, tpl_rows)] - img_sums[SUMS_PTR(0, 0)]));
res[res_idx] -= sum * tpl_sum;
}
}
......@@ -671,17 +677,17 @@ void matchTemplate_Prepared_CCOFF_C4_D0
{
float ccorr = res[res_idx];
ccorr -= tpl_sum_c0*(float)(
(img_sums_c0[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c0[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c0[SUMS_PTR(0, tpl_rows)] - img_sums_c0[SUMS_PTR(0, 0)]));
(img_sums_c0[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c0[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c0[SUMS_PTR(0, tpl_rows)] - img_sums_c0[SUMS_PTR(0, 0)]));
ccorr -= tpl_sum_c1*(float)(
(img_sums_c1[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c1[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c1[SUMS_PTR(0, tpl_rows)] - img_sums_c1[SUMS_PTR(0, 0)]));
(img_sums_c1[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c1[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c1[SUMS_PTR(0, tpl_rows)] - img_sums_c1[SUMS_PTR(0, 0)]));
ccorr -= tpl_sum_c2*(float)(
(img_sums_c2[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c2[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c2[SUMS_PTR(0, tpl_rows)] - img_sums_c2[SUMS_PTR(0, 0)]));
(img_sums_c2[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c2[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c2[SUMS_PTR(0, tpl_rows)] - img_sums_c2[SUMS_PTR(0, 0)]));
ccorr -= tpl_sum_c3*(float)(
(img_sums_c3[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c3[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c3[SUMS_PTR(0, tpl_rows)] - img_sums_c3[SUMS_PTR(0, 0)]));
(img_sums_c3[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c3[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c3[SUMS_PTR(0, tpl_rows)] - img_sums_c3[SUMS_PTR(0, 0)]));
res[res_idx] = ccorr;
}
}
......@@ -702,7 +708,7 @@ void matchTemplate_Prepared_CCOFF_NORMED_C1_D0
__global const uint * img_sums,
int img_sums_offset,
int img_sums_step,
__global const TYPE_IMAGE_SQSUM * img_sqsums,
__global const float * img_sqsums,
int img_sqsums_offset,
int img_sqsums_step,
float tpl_sum,
......@@ -725,12 +731,12 @@ void matchTemplate_Prepared_CCOFF_NORMED_C1_D0
if(gidx < res_cols && gidy < res_rows)
{
float image_sum_ = (float)(
(img_sums[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums[SUMS_PTR(tpl_cols, 0)])
- (img_sums[SUMS_PTR(0, tpl_rows)] - img_sums[SUMS_PTR(0, 0)]));
(img_sums[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums[SUMS_PTR(tpl_cols, 0)])
- (img_sums[SUMS_PTR(0, tpl_rows)] - img_sums[SUMS_PTR(0, 0)]));
float image_sqsum_ = (float)(
(img_sqsums[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums[SQSUMS_PTR(0, tpl_rows)] - img_sqsums[SQSUMS_PTR(0, 0)]));
(img_sqsums[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums[SQSUMS_PTR(0, tpl_rows)] - img_sqsums[SQSUMS_PTR(0, 0)]));
res[res_idx] = normAcc(res[res_idx] - image_sum_ * tpl_sum,
sqrt(tpl_sqsum * (image_sqsum_ - weight * image_sum_ * image_sum_)));
}
......@@ -754,10 +760,10 @@ void matchTemplate_Prepared_CCOFF_NORMED_C4_D0
__global const uint * img_sums_c3,
int img_sums_offset,
int img_sums_step,
__global const TYPE_IMAGE_SQSUM * img_sqsums_c0,
__global const TYPE_IMAGE_SQSUM * img_sqsums_c1,
__global const TYPE_IMAGE_SQSUM * img_sqsums_c2,
__global const TYPE_IMAGE_SQSUM * img_sqsums_c3,
__global const float * img_sqsums_c0,
__global const float * img_sqsums_c1,
__global const float * img_sqsums_c2,
__global const float * img_sqsums_c3,
int img_sqsums_offset,
int img_sqsums_step,
float tpl_sum_c0,
......@@ -782,42 +788,71 @@ void matchTemplate_Prepared_CCOFF_NORMED_C4_D0
if(gidx < res_cols && gidy < res_rows)
{
float image_sum_c0 = (float)(
(img_sums_c0[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c0[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c0[SUMS_PTR(0, tpl_rows)] - img_sums_c0[SUMS_PTR(0, 0)]));
(img_sums_c0[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c0[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c0[SUMS_PTR(0, tpl_rows)] - img_sums_c0[SUMS_PTR(0, 0)]));
float image_sum_c1 = (float)(
(img_sums_c1[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c1[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c1[SUMS_PTR(0, tpl_rows)] - img_sums_c1[SUMS_PTR(0, 0)]));
(img_sums_c1[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c1[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c1[SUMS_PTR(0, tpl_rows)] - img_sums_c1[SUMS_PTR(0, 0)]));
float image_sum_c2 = (float)(
(img_sums_c2[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c2[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c2[SUMS_PTR(0, tpl_rows)] - img_sums_c2[SUMS_PTR(0, 0)]));
(img_sums_c2[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c2[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c2[SUMS_PTR(0, tpl_rows)] - img_sums_c2[SUMS_PTR(0, 0)]));
float image_sum_c3 = (float)(
(img_sums_c3[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c3[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c3[SUMS_PTR(0, tpl_rows)] - img_sums_c3[SUMS_PTR(0, 0)]));
(img_sums_c3[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c3[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c3[SUMS_PTR(0, tpl_rows)] - img_sums_c3[SUMS_PTR(0, 0)]));
float image_sqsum_c0 = (float)(
(img_sqsums_c0[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums_c0[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums_c0[SQSUMS_PTR(0, tpl_rows)] - img_sqsums_c0[SQSUMS_PTR(0, 0)]));
(img_sqsums_c0[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums_c0[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums_c0[SQSUMS_PTR(0, tpl_rows)] - img_sqsums_c0[SQSUMS_PTR(0, 0)]));
float image_sqsum_c1 = (float)(
(img_sqsums_c1[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums_c1[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums_c1[SQSUMS_PTR(0, tpl_rows)] - img_sqsums_c1[SQSUMS_PTR(0, 0)]));
(img_sqsums_c1[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums_c1[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums_c1[SQSUMS_PTR(0, tpl_rows)] - img_sqsums_c1[SQSUMS_PTR(0, 0)]));
float image_sqsum_c2 = (float)(
(img_sqsums_c2[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums_c2[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums_c2[SQSUMS_PTR(0, tpl_rows)] - img_sqsums_c2[SQSUMS_PTR(0, 0)]));
(img_sqsums_c2[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums_c2[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums_c2[SQSUMS_PTR(0, tpl_rows)] - img_sqsums_c2[SQSUMS_PTR(0, 0)]));
float image_sqsum_c3 = (float)(
(img_sqsums_c3[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums_c3[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums_c3[SQSUMS_PTR(0, tpl_rows)] - img_sqsums_c3[SQSUMS_PTR(0, 0)]));
(img_sqsums_c3[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums_c3[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums_c3[SQSUMS_PTR(0, tpl_rows)] - img_sqsums_c3[SQSUMS_PTR(0, 0)]));
float num = res[res_idx] -
image_sum_c0 * tpl_sum_c0 -
image_sum_c1 * tpl_sum_c1 -
image_sum_c2 * tpl_sum_c2 -
image_sum_c3 * tpl_sum_c3;
image_sum_c0 * tpl_sum_c0 -
image_sum_c1 * tpl_sum_c1 -
image_sum_c2 * tpl_sum_c2 -
image_sum_c3 * tpl_sum_c3;
float denum = sqrt( tpl_sqsum * (
image_sqsum_c0 - weight * image_sum_c0 * image_sum_c0 +
image_sqsum_c1 - weight * image_sum_c1 * image_sum_c1 +
image_sqsum_c2 - weight * image_sum_c2 * image_sum_c2 +
image_sqsum_c3 - weight * image_sum_c0 * image_sum_c3)
);
image_sqsum_c0 - weight * image_sum_c0 * image_sum_c0 +
image_sqsum_c1 - weight * image_sum_c1 * image_sum_c1 +
image_sqsum_c2 - weight * image_sum_c2 * image_sum_c2 +
image_sqsum_c3 - weight * image_sum_c0 * image_sum_c3)
);
res[res_idx] = normAcc(num, denum);
}
}
//////////////////////////////////////////////////////////////////////
// extractFirstChannel
__kernel
void extractFirstChannel
(
const __global float4* img,
__global float* res,
int rows,
int cols,
int img_offset,
int res_offset,
int img_step,
int res_step
)
{
img_step /= sizeof(float4);
res_step /= sizeof(float);
img_offset /= sizeof(float4);
res_offset /= sizeof(float);
img += img_offset;
res += res_offset;
int gidx = get_global_id(0);
int gidy = get_global_id(1);
if(gidx < cols && gidy < rows)
{
res[gidx + gidy * res_step] = img[gidx + gidy * img_step].x;
}
}
modules/ocl/src/opencl/moments.cl
View file @ 6c99b5c9
......@@ -6,25 +6,27 @@
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#endif
typedef double T;
typedef double F;
typedef double4 F4;
#define convert_F4 convert_double4
#else
typedef float double;
typedef float4 double4;
typedef float F;
typedef float4 F4;
typedef long T;
#define convert_double4 convert_float4
#define convert_F4 convert_float4
#endif
//#pragma OPENCL EXTENSION cl_amd_printf:enable
//#if defined (DOUBLE_SUPPORT)
#define DST_ROW_A00 0
#define DST_ROW_A10 1
#define DST_ROW_A01 2
#define DST_ROW_A20 3
#define DST_ROW_A11 4
#define DST_ROW_A02 5
#define DST_ROW_A30 6
#define DST_ROW_A21 7
#define DST_ROW_A12 8
#define DST_ROW_A03 9
#define DST_ROW_00 0
#define DST_ROW_10 1
#define DST_ROW_01 2
#define DST_ROW_20 3
#define DST_ROW_11 4
#define DST_ROW_02 5
#define DST_ROW_30 6
#define DST_ROW_21 7
#define DST_ROW_12 8
#define DST_ROW_03 9
__kernel void icvContourMoments(int contour_total,
__global float* reader_oclmat_data,
......@@ -60,36 +62,76 @@ __kernel void icvContourMoments(int contour_total,
yii_1 = yi_1 + yi;
dst_step /= sizeof(T);
*( dst_a + DST_ROW_A00 * dst_step + idx) = dxy;
*( dst_a + DST_ROW_A10 * dst_step + idx) = dxy * xii_1;
*( dst_a + DST_ROW_A01 * dst_step + idx) = dxy * yii_1;
*( dst_a + DST_ROW_A20 * dst_step + idx) = dxy * (xi_1 * xii_1 + xi2);
*( dst_a + DST_ROW_A11 * dst_step + idx) = dxy * (xi_1 * (yii_1 + yi_1) + xi * (yii_1 + yi));
*( dst_a + DST_ROW_A02 * dst_step + idx) = dxy * (yi_1 * yii_1 + yi2);
*( dst_a + DST_ROW_A30 * dst_step + idx) = dxy * xii_1 * (xi_12 + xi2);
*( dst_a + DST_ROW_A03 * dst_step + idx) = dxy * yii_1 * (yi_12 + yi2);
*( dst_a + DST_ROW_A21 * dst_step + idx) =
*( dst_a + DST_ROW_00 * dst_step + idx) = dxy;
*( dst_a + DST_ROW_10 * dst_step + idx) = dxy * xii_1;
*( dst_a + DST_ROW_01 * dst_step + idx) = dxy * yii_1;
*( dst_a + DST_ROW_20 * dst_step + idx) = dxy * (xi_1 * xii_1 + xi2);
*( dst_a + DST_ROW_11 * dst_step + idx) = dxy * (xi_1 * (yii_1 + yi_1) + xi * (yii_1 + yi));
*( dst_a + DST_ROW_02 * dst_step + idx) = dxy * (yi_1 * yii_1 + yi2);
*( dst_a + DST_ROW_30 * dst_step + idx) = dxy * xii_1 * (xi_12 + xi2);
*( dst_a + DST_ROW_03 * dst_step + idx) = dxy * yii_1 * (yi_12 + yi2);
*( dst_a + DST_ROW_21 * dst_step + idx) =
dxy * (xi_12 * (3 * yi_1 + yi) + 2 * xi * xi_1 * yii_1 +
xi2 * (yi_1 + 3 * yi));
*( dst_a + DST_ROW_A12 * dst_step + idx) =
*( dst_a + DST_ROW_12 * dst_step + idx) =
dxy * (yi_12 * (3 * xi_1 + xi) + 2 * yi * yi_1 * xii_1 +
yi2 * (xi_1 + 3 * xi));
}
//#endif
//#if defined (DOUBLE_SUPPORT)
__kernel void dst_sum(int src_rows, int src_cols, int tile_height, int tile_width, int TILE_SIZE,
__global F* sum, __global F* dst_m, int dst_step)
{
int gidy = get_global_id(0);
int gidx = get_global_id(1);
int block_y = src_rows/tile_height;
int block_x = src_cols/tile_width;
int block_num;
if(src_rows > TILE_SIZE && src_rows % TILE_SIZE != 0)
block_y ++;
if(src_cols > TILE_SIZE && src_cols % TILE_SIZE != 0)
block_x ++;
block_num = block_y * block_x;
__local F dst_sum[10][128];
if(gidy<128-block_num)
for(int i=0; i<10; i++)
dst_sum[i][gidy+block_num]=0;
barrier(CLK_LOCAL_MEM_FENCE);
dst_step /= sizeof(F);
if(gidy<block_num)
{
dst_sum[0][gidy] = *(dst_m + mad24(DST_ROW_00 * block_y, dst_step, gidy));
dst_sum[1][gidy] = *(dst_m + mad24(DST_ROW_10 * block_y, dst_step, gidy));
dst_sum[2][gidy] = *(dst_m + mad24(DST_ROW_01 * block_y, dst_step, gidy));
dst_sum[3][gidy] = *(dst_m + mad24(DST_ROW_20 * block_y, dst_step, gidy));
dst_sum[4][gidy] = *(dst_m + mad24(DST_ROW_11 * block_y, dst_step, gidy));
dst_sum[5][gidy] = *(dst_m + mad24(DST_ROW_02 * block_y, dst_step, gidy));
dst_sum[6][gidy] = *(dst_m + mad24(DST_ROW_30 * block_y, dst_step, gidy));
dst_sum[7][gidy] = *(dst_m + mad24(DST_ROW_21 * block_y, dst_step, gidy));
dst_sum[8][gidy] = *(dst_m + mad24(DST_ROW_12 * block_y, dst_step, gidy));
dst_sum[9][gidy] = *(dst_m + mad24(DST_ROW_03 * block_y, dst_step, gidy));
}
barrier(CLK_LOCAL_MEM_FENCE);
for(int lsize=64; lsize>0; lsize>>=1)
{
if(gidy<lsize)
{
int lsize2 = gidy + lsize;
for(int i=0; i<10; i++)
dst_sum[i][gidy] += dst_sum[i][lsize2];
}
barrier(CLK_LOCAL_MEM_FENCE);
}
if(gidy==0)
for(int i=0; i<10; i++)
sum[i] = dst_sum[i][0];
}
__kernel void CvMoments_D0(__global uchar16* src_data, int src_rows, int src_cols, int src_step, int tileSize_width, int tileSize_height,
__global double* dst_m00,
__global double* dst_m10,
__global double* dst_m01,
__global double* dst_m20,
__global double* dst_m11,
__global double* dst_m02,
__global double* dst_m30,
__global double* dst_m21,
__global double* dst_m12,
__global double* dst_m03,
int dst_cols, int dst_step, int type, int depth, int cn, int coi, int binary, int TILE_SIZE)
__global F* dst_m,
int dst_cols, int dst_step, int blocky,
int type, int depth, int cn, int coi, int binary, int TILE_SIZE)
{
uchar tmp_coi[16]; // get the coi data
uchar16 tmp[16];
......@@ -127,7 +169,7 @@ __kernel void CvMoments_D0(__global uchar16* src_data, int src_rows, int src_col
if( binary )
for(int i=0; i < tileSize_width; i+=VLEN_C)
tmp[i/VLEN_C] = (tmp[i/VLEN_C]!=zero)?full:zero;
double mom[10];
F mom[10];
__local int m[10][128];
if(lidy == 0)
for(int i=0; i<10; i++)
......@@ -197,119 +239,53 @@ __kernel void CvMoments_D0(__global uchar16* src_data, int src_rows, int src_col
if(lidy == 0&&lidx == 0)
{
for( int mt = 0; mt < 10; mt++ )
mom[mt] = (double)lm[mt];
mom[mt] = (F)lm[mt];
if(binary)
{
double s = 1./255;
F s = 1./255;
for( int mt = 0; mt < 10; mt++ )
mom[mt] *= s;
}
double xm = x * mom[0], ym = y * mom[0];
F xm = x * mom[0], ym = y * mom[0];
// accumulate moments computed in each tile
dst_step /= sizeof(F);
// + m00 ( = m00' )
dst_m00[wgidy*dst_cols+wgidx] = mom[0];
*(dst_m + mad24(DST_ROW_00 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[0];
// + m10 ( = m10' + x*m00' )
dst_m10[wgidy*dst_cols+wgidx] = mom[1] + xm;
*(dst_m + mad24(DST_ROW_10 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[1] + xm;
// + m01 ( = m01' + y*m00' )
dst_m01[wgidy*dst_cols+wgidx] = mom[2] + ym;
*(dst_m + mad24(DST_ROW_01 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[2] + ym;
// + m20 ( = m20' + 2*x*m10' + x*x*m00' )
dst_m20[wgidy*dst_cols+wgidx] = mom[3] + x * (mom[1] * 2 + xm);
*(dst_m + mad24(DST_ROW_20 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[3] + x * (mom[1] * 2 + xm);
// + m11 ( = m11' + x*m01' + y*m10' + x*y*m00' )
dst_m11[wgidy*dst_cols+wgidx] = mom[4] + x * (mom[2] + ym) + y * mom[1];
*(dst_m + mad24(DST_ROW_11 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[4] + x * (mom[2] + ym) + y * mom[1];
// + m02 ( = m02' + 2*y*m01' + y*y*m00' )
dst_m02[wgidy*dst_cols+wgidx] = mom[5] + y * (mom[2] * 2 + ym);
*(dst_m + mad24(DST_ROW_02 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[5] + y * (mom[2] * 2 + ym);
// + m30 ( = m30' + 3*x*m20' + 3*x*x*m10' + x*x*x*m00' )
dst_m30[wgidy*dst_cols+wgidx] = mom[6] + x * (3. * mom[3] + x * (3. * mom[1] + xm));
*(dst_m + mad24(DST_ROW_30 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[6] + x * (3. * mom[3] + x * (3. * mom[1] + xm));
// + m21 ( = m21' + x*(2*m11' + 2*y*m10' + x*m01' + x*y*m00') + y*m20')
dst_m21[wgidy*dst_cols+wgidx] = mom[7] + x * (2 * (mom[4] + y * mom[1]) + x * (mom[2] + ym)) + y * mom[3];
*(dst_m + mad24(DST_ROW_21 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[7] + x * (2 * (mom[4] + y * mom[1]) + x * (mom[2] + ym)) + y * mom[3];
// + m12 ( = m12' + y*(2*m11' + 2*x*m01' + y*m10' + x*y*m00') + x*m02')
dst_m12[wgidy*dst_cols+wgidx] = mom[8] + y * (2 * (mom[4] + x * mom[2]) + y * (mom[1] + xm)) + x * mom[5];
*(dst_m + mad24(DST_ROW_12 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[8] + y * (2 * (mom[4] + x * mom[2]) + y * (mom[1] + xm)) + x * mom[5];
// + m03 ( = m03' + 3*y*m02' + 3*y*y*m01' + y*y*y*m00' )
dst_m03[wgidy*dst_cols+wgidx] = mom[9] + y * (3. * mom[5] + y * (3. * mom[2] + ym));
*(dst_m + mad24(DST_ROW_03 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[9] + y * (3. * mom[5] + y * (3. * mom[2] + ym));
}
}
//#endif
//#if defined (DOUBLE_SUPPORT)
__kernel void dst_sum(int src_rows, int src_cols, int tile_height, int tile_width, int TILE_SIZE, __global double* sum, __global double* dst_m00,
__global double* dst_m10,
__global double* dst_m01,
__global double* dst_m20,
__global double* dst_m11,
__global double* dst_m02,
__global double* dst_m30,
__global double* dst_m21,
__global double* dst_m12,
__global double* dst_m03)
{
int gidy = get_global_id(0);
int gidx = get_global_id(1);
int block_y = src_rows/tile_height;
int block_x = src_cols/tile_width;
int block_num;
if(src_rows > TILE_SIZE && src_rows % TILE_SIZE != 0)
block_y ++;
if(src_cols > TILE_SIZE && src_cols % TILE_SIZE != 0)
block_x ++;
block_num = block_y * block_x;
__local double dst_sum[10][128];
if(gidy<128-block_num)
for(int i=0; i<10; i++)
dst_sum[i][gidy+block_num]=0;
barrier(CLK_LOCAL_MEM_FENCE);
if(gidy<block_num)
{
dst_sum[0][gidy] = dst_m00[gidy];
dst_sum[1][gidy] = dst_m10[gidy];
dst_sum[2][gidy] = dst_m01[gidy];
dst_sum[3][gidy] = dst_m20[gidy];
dst_sum[4][gidy] = dst_m11[gidy];
dst_sum[5][gidy] = dst_m02[gidy];
dst_sum[6][gidy] = dst_m30[gidy];
dst_sum[7][gidy] = dst_m21[gidy];
dst_sum[8][gidy] = dst_m12[gidy];
dst_sum[9][gidy] = dst_m03[gidy];
}
barrier(CLK_LOCAL_MEM_FENCE);
for(int lsize=64; lsize>0; lsize>>=1)
{
if(gidy<lsize)
{
int lsize2 = gidy + lsize;
for(int i=0; i<10; i++)
dst_sum[i][gidy] += dst_sum[i][lsize2];
}
barrier(CLK_LOCAL_MEM_FENCE);
}
if(gidy==0)
for(int i=0; i<10; i++)
sum[i] = dst_sum[i][0];
}
//#endif
//#if defined (DOUBLE_SUPPORT)
__kernel void CvMoments_D2(__global ushort8* src_data, int src_rows, int src_cols, int src_step, int tileSize_width, int tileSize_height,
__global double* dst_m00,
__global double* dst_m10,
__global double* dst_m01,
__global double* dst_m20,
__global double* dst_m11,
__global double* dst_m02,
__global double* dst_m30,
__global double* dst_m21,
__global double* dst_m12,
__global double* dst_m03,
int dst_cols, int dst_step,
__global F* dst_m,
int dst_cols, int dst_step, int blocky,
int type, int depth, int cn, int coi, int binary, const int TILE_SIZE)
{
ushort tmp_coi[8]; // get the coi data
......@@ -345,7 +321,7 @@ __kernel void CvMoments_D2(__global ushort8* src_data, int src_rows, int src_col
if( binary )
for(int i=0; i < tileSize_width; i+=VLEN_US)
tmp[i/VLEN_US] = (tmp[i/VLEN_US]!=zero)?full:zero;
double mom[10];
F mom[10];
__local long m[10][128];
if(lidy == 0)
for(int i=0; i<10; i++)
......@@ -415,64 +391,55 @@ __kernel void CvMoments_D2(__global ushort8* src_data, int src_rows, int src_col
if(lidy == 0&&lidx == 0)
{
for(int mt = 0; mt < 10; mt++ )
mom[mt] = (double)lm[mt];
mom[mt] = (F)lm[mt];
if(binary)
{
double s = 1./255;
F s = 1./255;
for( int mt = 0; mt < 10; mt++ )
mom[mt] *= s;
}
double xm = x *mom[0], ym = y * mom[0];
F xm = x *mom[0], ym = y * mom[0];
// accumulate moments computed in each tile
dst_step /= sizeof(F);
// + m00 ( = m00' )
dst_m00[wgidy*dst_cols+wgidx] = mom[0];
*(dst_m + mad24(DST_ROW_00 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[0];
// + m10 ( = m10' + x*m00' )
dst_m10[wgidy*dst_cols+wgidx] = mom[1] + xm;
*(dst_m + mad24(DST_ROW_10 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[1] + xm;
// + m01 ( = m01' + y*m00' )
dst_m01[wgidy*dst_cols+wgidx] = mom[2] + ym;
*(dst_m + mad24(DST_ROW_01 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[2] + ym;
// + m20 ( = m20' + 2*x*m10' + x*x*m00' )
dst_m20[wgidy*dst_cols+wgidx] = mom[3] + x * (mom[1] * 2 + xm);
*(dst_m + mad24(DST_ROW_20 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[3] + x * (mom[1] * 2 + xm);
// + m11 ( = m11' + x*m01' + y*m10' + x*y*m00' )
dst_m11[wgidy*dst_cols+wgidx] = mom[4] + x * (mom[2] + ym) + y * mom[1];
*(dst_m + mad24(DST_ROW_11 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[4] + x * (mom[2] + ym) + y * mom[1];
// + m02 ( = m02' + 2*y*m01' + y*y*m00' )
dst_m02[wgidy*dst_cols+wgidx] = mom[5] + y * (mom[2] * 2 + ym);
*(dst_m + mad24(DST_ROW_02 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[5] + y * (mom[2] * 2 + ym);
// + m30 ( = m30' + 3*x*m20' + 3*x*x*m10' + x*x*x*m00' )
dst_m30[wgidy*dst_cols+wgidx] = mom[6] + x * (3. * mom[3] + x * (3. * mom[1] + xm));
*(dst_m + mad24(DST_ROW_30 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[6] + x * (3. * mom[3] + x * (3. * mom[1] + xm));
// + m21 ( = m21' + x*(2*m11' + 2*y*m10' + x*m01' + x*y*m00') + y*m20')
dst_m21[wgidy*dst_cols+wgidx] = mom[7] + x * (2 * (mom[4] + y * mom[1]) + x * (mom[2] + ym)) + y * mom[3];
*(dst_m + mad24(DST_ROW_21 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[7] + x * (2 * (mom[4] + y * mom[1]) + x * (mom[2] + ym)) + y * mom[3];
// + m12 ( = m12' + y*(2*m11' + 2*x*m01' + y*m10' + x*y*m00') + x*m02')
dst_m12[wgidy*dst_cols+wgidx] = mom[8] + y * (2 * (mom[4] + x * mom[2]) + y * (mom[1] + xm)) + x * mom[5];
*(dst_m + mad24(DST_ROW_12 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[8] + y * (2 * (mom[4] + x * mom[2]) + y * (mom[1] + xm)) + x * mom[5];
// + m03 ( = m03' + 3*y*m02' + 3*y*y*m01' + y*y*y*m00' )
dst_m03[wgidy*dst_cols+wgidx] = mom[9] + y * (3. * mom[5] + y * (3. * mom[2] + ym));
*(dst_m + mad24(DST_ROW_03 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[9] + y * (3. * mom[5] + y * (3. * mom[2] + ym));
}
}
//#endif
//#if defined (DOUBLE_SUPPORT)
__kernel void CvMoments_D3(__global short8* src_data, int src_rows, int src_cols, int src_step, int tileSize_width, int tileSize_height,
__global double* dst_m00,
__global double* dst_m10,
__global double* dst_m01,
__global double* dst_m20,
__global double* dst_m11,
__global double* dst_m02,
__global double* dst_m30,
__global double* dst_m21,
__global double* dst_m12,
__global double* dst_m03,
int dst_cols, int dst_step,
__global F* dst_m,
int dst_cols, int dst_step, int blocky,
int type, int depth, int cn, int coi, int binary, const int TILE_SIZE)
{
short tmp_coi[8]; // get the coi data
......@@ -509,7 +476,7 @@ __kernel void CvMoments_D3(__global short8* src_data, int src_rows, int src_cols
for(int i=0; i < tileSize_width; i+=(VLEN_S))
tmp[i/VLEN_S] = (tmp[i/VLEN_S]!=zero)?full:zero;
double mom[10];
F mom[10];
__local long m[10][128];
if(lidy == 0)
for(int i=0; i<10; i++)
......@@ -579,64 +546,55 @@ __kernel void CvMoments_D3(__global short8* src_data, int src_rows, int src_cols
if(lidy ==0 &&lidx ==0)
{
for(int mt = 0; mt < 10; mt++ )
mom[mt] = (double)lm[mt];
mom[mt] = (F)lm[mt];
if(binary)
{
double s = 1./255;
F s = 1./255;
for( int mt = 0; mt < 10; mt++ )
mom[mt] *= s;
}
double xm = x * mom[0], ym = y*mom[0];
F xm = x * mom[0], ym = y*mom[0];
// accumulate moments computed in each tile
dst_step /= sizeof(F);
// + m00 ( = m00' )
dst_m00[wgidy*dst_cols+wgidx] = mom[0];
*(dst_m + mad24(DST_ROW_00 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[0];
// + m10 ( = m10' + x*m00' )
dst_m10[wgidy*dst_cols+wgidx] = mom[1] + xm;
*(dst_m + mad24(DST_ROW_10 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[1] + xm;
// + m01 ( = m01' + y*m00' )
dst_m01[wgidy*dst_cols+wgidx] = mom[2] + ym;
*(dst_m + mad24(DST_ROW_01 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[2] + ym;
// + m20 ( = m20' + 2*x*m10' + x*x*m00' )
dst_m20[wgidy*dst_cols+wgidx] = mom[3] + x * (mom[1] * 2 + xm);
*(dst_m + mad24(DST_ROW_20 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[3] + x * (mom[1] * 2 + xm);
// + m11 ( = m11' + x*m01' + y*m10' + x*y*m00' )
dst_m11[wgidy*dst_cols+wgidx] = mom[4] + x * (mom[2] + ym) + y * mom[1];
*(dst_m + mad24(DST_ROW_11 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[4] + x * (mom[2] + ym) + y * mom[1];
// + m02 ( = m02' + 2*y*m01' + y*y*m00' )
dst_m02[wgidy*dst_cols+wgidx] = mom[5] + y * (mom[2] * 2 + ym);
*(dst_m + mad24(DST_ROW_02 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[5] + y * (mom[2] * 2 + ym);
// + m30 ( = m30' + 3*x*m20' + 3*x*x*m10' + x*x*x*m00' )
dst_m30[wgidy*dst_cols+wgidx] = mom[6] + x * (3. * mom[3] + x * (3. * mom[1] + xm));
*(dst_m + mad24(DST_ROW_30 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[6] + x * (3. * mom[3] + x * (3. * mom[1] + xm));
// + m21 ( = m21' + x*(2*m11' + 2*y*m10' + x*m01' + x*y*m00') + y*m20')
dst_m21[wgidy*dst_cols+wgidx] = mom[7] + x * (2 * (mom[4] + y * mom[1]) + x * (mom[2] + ym)) + y * mom[3];
*(dst_m + mad24(DST_ROW_21 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[7] + x * (2 * (mom[4] + y * mom[1]) + x * (mom[2] + ym)) + y * mom[3];
// + m12 ( = m12' + y*(2*m11' + 2*x*m01' + y*m10' + x*y*m00') + x*m02')
dst_m12[wgidy*dst_cols+wgidx] = mom[8] + y * (2 * (mom[4] + x * mom[2]) + y * (mom[1] + xm)) + x * mom[5];
*(dst_m + mad24(DST_ROW_12 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[8] + y * (2 * (mom[4] + x * mom[2]) + y * (mom[1] + xm)) + x * mom[5];
// + m03 ( = m03' + 3*y*m02' + 3*y*y*m01' + y*y*y*m00' )
dst_m03[wgidy*dst_cols+wgidx] = mom[9] + y * (3. * mom[5] + y * (3. * mom[2] + ym));
*(dst_m + mad24(DST_ROW_03 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[9] + y * (3. * mom[5] + y * (3. * mom[2] + ym));
}
}
//#endif
//#if defined (DOUBLE_SUPPORT)
__kernel void CvMoments_D5( __global float* src_data, int src_rows, int src_cols, int src_step, int tileSize_width, int tileSize_height,
__global double* dst_m00,
__global double* dst_m10,
__global double* dst_m01,
__global double* dst_m20,
__global double* dst_m11,
__global double* dst_m02,
__global double* dst_m30,
__global double* dst_m21,
__global double* dst_m12,
__global double* dst_m03,
int dst_cols, int dst_step,
__global F* dst_m,
int dst_cols, int dst_step, int blocky,
int type, int depth, int cn, int coi, int binary, const int TILE_SIZE)
{
float tmp_coi[4]; // get the coi data
......@@ -672,23 +630,23 @@ __kernel void CvMoments_D5( __global float* src_data, int src_rows, int src_cols
if( binary )
for(int i=0; i < tileSize_width; i+=4)
tmp[i/VLEN_F] = (tmp[i/VLEN_F]!=zero)?full:zero;
double mom[10];
__local double m[10][128];
F mom[10];
__local F m[10][128];
if(lidy == 0)
for(int i = 0; i < 10; i ++)
for(int j = 0; j < 128; j ++)
m[i][j] = 0;
barrier(CLK_LOCAL_MEM_FENCE);
double lm[10] = {0};
double4 x0 = (double4)(0);
double4 x1 = (double4)(0);
double4 x2 = (double4)(0);
double4 x3 = (double4)(0);
F lm[10] = {0};
F4 x0 = (F4)(0);
F4 x1 = (F4)(0);
F4 x2 = (F4)(0);
F4 x3 = (F4)(0);
for( int xt = 0 ; xt < tileSize_width; xt+=VLEN_F )
{
double4 v_xt = (double4)(xt, xt+1, xt+2, xt+3);
double4 p = convert_double4(tmp[xt/VLEN_F]);
double4 xp = v_xt * p, xxp = xp * v_xt;
F4 v_xt = (F4)(xt, xt+1, xt+2, xt+3);
F4 p = convert_F4(tmp[xt/VLEN_F]);
F4 xp = v_xt * p, xxp = xp * v_xt;
x0 += p;
x1 += xp;
x2 += xxp;
......@@ -698,14 +656,14 @@ __kernel void CvMoments_D5( __global float* src_data, int src_rows, int src_cols
x1.s0 += x1.s1 + x1.s2 + x1.s3;
x2.s0 += x2.s1 + x2.s2 + x2.s3;
x3.s0 += x3.s1 + x3.s2 + x3.s3;
/*
double py = lidy * x0.s0, sy = lidy*lidy;
F py = lidy * x0.s0, sy = lidy*lidy;
int bheight = min(tileSize_height, TILE_SIZE/2);
if(bheight >= TILE_SIZE/2&&lidy > bheight-1&&lidy < tileSize_height)
{
m[9][lidy-bheight] = ((double)py) * sy; // m03
m[8][lidy-bheight] = ((double)x1.s0) * sy; // m12
m[7][lidy-bheight] = ((double)x2.s0) * lidy; // m21
m[9][lidy-bheight] = ((F)py) * sy; // m03
m[8][lidy-bheight] = ((F)x1.s0) * sy; // m12
m[7][lidy-bheight] = ((F)x2.s0) * lidy; // m21
m[6][lidy-bheight] = x3.s0; // m30
m[5][lidy-bheight] = x0.s0 * sy; // m02
m[4][lidy-bheight] = x1.s0 * lidy; // m11
......@@ -714,11 +672,12 @@ __kernel void CvMoments_D5( __global float* src_data, int src_rows, int src_cols
m[1][lidy-bheight] = x1.s0; // m10
m[0][lidy-bheight] = x0.s0; // m00
}
else if(lidy < bheight)
{
lm[9] = ((double)py) * sy; // m03
lm[8] = ((double)x1.s0) * sy; // m12
lm[7] = ((double)x2.s0) * lidy; // m21
lm[9] = ((F)py) * sy; // m03
lm[8] = ((F)x1.s0) * sy; // m12
lm[7] = ((F)x2.s0) * lidy; // m21
lm[6] = x3.s0; // m30
lm[5] = x0.s0 * sy; // m02
lm[4] = x1.s0 * lidy; // m11
......@@ -741,69 +700,59 @@ __kernel void CvMoments_D5( __global float* src_data, int src_rows, int src_cols
}
if(lidy == 0&&lidx == 0)
{
for(int mt = 0; mt < 10; mt++ )
mom[mt] = (double)lm[mt];
for( int mt = 0; mt < 10; mt++ )
mom[mt] = (F)lm[mt];
if(binary)
{
double s = 1./255;
F s = 1./255;
for( int mt = 0; mt < 10; mt++ )
mom[mt] *= s;
}
double xm = x * mom[0], ym = y * mom[0];
F xm = x * mom[0], ym = y * mom[0];
// accumulate moments computed in each tile
dst_step /= sizeof(F);
// + m00 ( = m00' )
dst_m00[wgidy*dst_cols+wgidx]= mom[0];
*(dst_m + mad24(DST_ROW_00 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[0];
// + m10 ( = m10' + x*m00' )
dst_m10[wgidy*dst_cols+wgidx] = mom[1] + xm;
*(dst_m + mad24(DST_ROW_10 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[1] + xm;
// + m01 ( = m01' + y*m00' )
dst_m01[wgidy*dst_cols+wgidx] = mom[2] + ym;
*(dst_m + mad24(DST_ROW_01 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[2] + ym;
// + m20 ( = m20' + 2*x*m10' + x*x*m00' )
dst_m20[wgidy*dst_cols+wgidx] = mom[3] + x * (mom[1] * 2 + xm);
*(dst_m + mad24(DST_ROW_20 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[3] + x * (mom[1] * 2 + xm);
// + m11 ( = m11' + x*m01' + y*m10' + x*y*m00' )
dst_m11[wgidy*dst_cols+wgidx] = mom[4] + x * (mom[2] + ym) + y * mom[1];
*(dst_m + mad24(DST_ROW_11 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[4] + x * (mom[2] + ym) + y * mom[1];
// + m02 ( = m02' + 2*y*m01' + y*y*m00' )
dst_m02[wgidy*dst_cols+wgidx]= mom[5] + y * (mom[2] * 2 + ym);
*(dst_m + mad24(DST_ROW_02 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[5] + y * (mom[2] * 2 + ym);
// + m30 ( = m30' + 3*x*m20' + 3*x*x*m10' + x*x*x*m00' )
dst_m30[wgidy*dst_cols+wgidx]= mom[6] + x * (3. * mom[3] + x * (3. * mom[1] + xm));
*(dst_m + mad24(DST_ROW_30 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[6] + x * (3. * mom[3] + x * (3. * mom[1] + xm));
// + m21 ( = m21' + x*(2*m11' + 2*y*m10' + x*m01' + x*y*m00') + y*m20')
dst_m21[wgidy*dst_cols+wgidx] = mom[7] + x * (2 * (mom[4] + y * mom[1]) + x * (mom[2] + ym)) + y * mom[3];
*(dst_m + mad24(DST_ROW_21 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[7] + x * (2 * (mom[4] + y * mom[1]) + x * (mom[2] + ym)) + y * mom[3];
// + m12 ( = m12' + y*(2*m11' + 2*x*m01' + y*m10' + x*y*m00') + x*m02')
dst_m12[wgidy*dst_cols+wgidx] = mom[8] + y * (2 * (mom[4] + x * mom[2]) + y * (mom[1] + xm)) + x * mom[5];
*(dst_m + mad24(DST_ROW_12 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[8] + y * (2 * (mom[4] + x * mom[2]) + y * (mom[1] + xm)) + x * mom[5];
// + m03 ( = m03' + 3*y*m02' + 3*y*y*m01' + y*y*y*m00' )
dst_m03[wgidy*dst_cols+wgidx]= mom[9] + y * (3. * mom[5] + y * (3. * mom[2] + ym));
}*/
*(dst_m + mad24(DST_ROW_03 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[9] + y * (3. * mom[5] + y * (3. * mom[2] + ym));
}
}
//#endif
//#if defined (DOUBLE_SUPPORT)
__kernel void CvMoments_D6(__global double* src_data, int src_rows, int src_cols, int src_step, int tileSize_width, int tileSize_height,
__global double* dst_m00,
__global double* dst_m10,
__global double* dst_m01,
__global double* dst_m20,
__global double* dst_m11,
__global double* dst_m02,
__global double* dst_m30,
__global double* dst_m21,
__global double* dst_m12,
__global double* dst_m03,
int dst_cols, int dst_step,
__kernel void CvMoments_D6(__global F* src_data, int src_rows, int src_cols, int src_step, int tileSize_width, int tileSize_height,
__global F* dst_m,
int dst_cols, int dst_step, int blocky,
int type, int depth, int cn, int coi, int binary, const int TILE_SIZE)
{
double tmp_coi[4]; // get the coi data
double4 tmp[64];
F tmp_coi[4]; // get the coi data
F4 tmp[64];
int VLEN_D = 4; // length of vetor
int gidy = get_global_id(0);
int gidx = get_global_id(1);
......@@ -820,39 +769,39 @@ __kernel void CvMoments_D6(__global double* src_data, int src_rows, int src_col
if(tileSize_width < TILE_SIZE)
for(int i = tileSize_width; i < rstep; i++ )
*((__global double*)src_data+(y+lidy)*src_step/8+x+i) = 0;
*((__global F*)src_data+(y+lidy)*src_step/8+x+i) = 0;
if( coi > 0 )
for(int i=0; i < tileSize_width; i+=VLEN_D)
{
for(int j=0; j<4; j++)
tmp_coi[j] = *(src_data+(y+lidy)*src_step/8+(x+i+j)*kcn+coi-1);
tmp[i/VLEN_D] = (double4)(tmp_coi[0],tmp_coi[1],tmp_coi[2],tmp_coi[3]);
tmp[i/VLEN_D] = (F4)(tmp_coi[0],tmp_coi[1],tmp_coi[2],tmp_coi[3]);
}
else
for(int i=0; i < tileSize_width; i+=VLEN_D)
tmp[i/VLEN_D] = (double4)(*(src_data+(y+lidy)*src_step/8+x+i),*(src_data+(y+lidy)*src_step/8+x+i+1),*(src_data+(y+lidy)*src_step/8+x+i+2),*(src_data+(y+lidy)*src_step/8+x+i+3));
double4 zero = (double4)(0);
double4 full = (double4)(255);
tmp[i/VLEN_D] = (F4)(*(src_data+(y+lidy)*src_step/8+x+i),*(src_data+(y+lidy)*src_step/8+x+i+1),*(src_data+(y+lidy)*src_step/8+x+i+2),*(src_data+(y+lidy)*src_step/8+x+i+3));
F4 zero = (F4)(0);
F4 full = (F4)(255);
if( binary )
for(int i=0; i < tileSize_width; i+=VLEN_D)
tmp[i/VLEN_D] = (tmp[i/VLEN_D]!=zero)?full:zero;
double mom[10];
__local double m[10][128];
F mom[10];
__local F m[10][128];
if(lidy == 0)
for(int i=0; i<10; i++)
for(int j=0; j<128; j++)
m[i][j]=0;
barrier(CLK_LOCAL_MEM_FENCE);
double lm[10] = {0};
double4 x0 = (double4)(0);
double4 x1 = (double4)(0);
double4 x2 = (double4)(0);
double4 x3 = (double4)(0);
F lm[10] = {0};
F4 x0 = (F4)(0);
F4 x1 = (F4)(0);
F4 x2 = (F4)(0);
F4 x3 = (F4)(0);
for( int xt = 0 ; xt < tileSize_width; xt+=VLEN_D )
{
double4 v_xt = (double4)(xt, xt+1, xt+2, xt+3);
double4 p = tmp[xt/VLEN_D];
double4 xp = v_xt * p, xxp = xp * v_xt;
F4 v_xt = (F4)(xt, xt+1, xt+2, xt+3);
F4 p = tmp[xt/VLEN_D];
F4 xp = v_xt * p, xxp = xp * v_xt;
x0 += p;
x1 += xp;
x2 += xxp;
......@@ -863,13 +812,13 @@ __kernel void CvMoments_D6(__global double* src_data, int src_rows, int src_col
x2.s0 += x2.s1 + x2.s2 + x2.s3;
x3.s0 += x3.s1 + x3.s2 + x3.s3;
double py = lidy * x0.s0, sy = lidy*lidy;
F py = lidy * x0.s0, sy = lidy*lidy;
int bheight = min(tileSize_height, TILE_SIZE/2);
if(bheight >= TILE_SIZE/2&&lidy > bheight-1&&lidy < tileSize_height)
{
m[9][lidy-bheight] = ((double)py) * sy; // m03
m[8][lidy-bheight] = ((double)x1.s0) * sy; // m12
m[7][lidy-bheight] = ((double)x2.s0) * lidy; // m21
m[9][lidy-bheight] = ((F)py) * sy; // m03
m[8][lidy-bheight] = ((F)x1.s0) * sy; // m12
m[7][lidy-bheight] = ((F)x2.s0) * lidy; // m21
m[6][lidy-bheight] = x3.s0; // m30
m[5][lidy-bheight] = x0.s0 * sy; // m02
m[4][lidy-bheight] = x1.s0 * lidy; // m11
......@@ -881,9 +830,9 @@ __kernel void CvMoments_D6(__global double* src_data, int src_rows, int src_col
else if(lidy < bheight)
{
lm[9] = ((double)py) * sy; // m03
lm[8] = ((double)x1.s0) * sy; // m12
lm[7] = ((double)x2.s0) * lidy; // m21
lm[9] = ((F)py) * sy; // m03
lm[8] = ((F)x1.s0) * sy; // m12
lm[7] = ((F)x2.s0) * lidy; // m21
lm[6] = x3.s0; // m30
lm[5] = x0.s0 * sy; // m02
lm[4] = x1.s0 * lidy; // m11
......@@ -907,47 +856,47 @@ __kernel void CvMoments_D6(__global double* src_data, int src_rows, int src_col
if(lidy == 0&&lidx == 0)
{
for( int mt = 0; mt < 10; mt++ )
mom[mt] = (double)lm[mt];
mom[mt] = (F)lm[mt];
if(binary)
{
double s = 1./255;
F s = 1./255;
for( int mt = 0; mt < 10; mt++ )
mom[mt] *= s;
}
double xm = x * mom[0], ym = y * mom[0];
F xm = x * mom[0], ym = y * mom[0];
// accumulate moments computed in each tile
dst_step /= sizeof(F);
// + m00 ( = m00' )
dst_m00[wgidy*dst_cols+wgidx] = mom[0];
*(dst_m + mad24(DST_ROW_00 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[0];
// + m10 ( = m10' + x*m00' )
dst_m10[wgidy*dst_cols+wgidx] = mom[1] + xm;
*(dst_m + mad24(DST_ROW_10 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[1] + xm;
// + m01 ( = m01' + y*m00' )
dst_m01[wgidy*dst_cols+wgidx] = mom[2] + ym;
*(dst_m + mad24(DST_ROW_01 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[2] + ym;
// + m20 ( = m20' + 2*x*m10' + x*x*m00' )
dst_m20[wgidy*dst_cols+wgidx] = mom[3] + x * (mom[1] * 2 + xm);
*(dst_m + mad24(DST_ROW_20 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[3] + x * (mom[1] * 2 + xm);
// + m11 ( = m11' + x*m01' + y*m10' + x*y*m00' )
dst_m11[wgidy*dst_cols+wgidx] = mom[4] + x * (mom[2] + ym) + y * mom[1];
*(dst_m + mad24(DST_ROW_11 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[4] + x * (mom[2] + ym) + y * mom[1];
// + m02 ( = m02' + 2*y*m01' + y*y*m00' )
dst_m02[wgidy*dst_cols+wgidx] = mom[5] + y * (mom[2] * 2 + ym);
*(dst_m + mad24(DST_ROW_02 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[5] + y * (mom[2] * 2 + ym);
// + m30 ( = m30' + 3*x*m20' + 3*x*x*m10' + x*x*x*m00' )
dst_m30[wgidy*dst_cols+wgidx] = mom[6] + x * (3. * mom[3] + x * (3. * mom[1] + xm));
*(dst_m + mad24(DST_ROW_30 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[6] + x * (3. * mom[3] + x * (3. * mom[1] + xm));
// + m21 ( = m21' + x*(2*m11' + 2*y*m10' + x*m01' + x*y*m00') + y*m20')
dst_m21[wgidy*dst_cols+wgidx] = mom[7] + x * (2 * (mom[4] + y * mom[1]) + x * (mom[2] + ym)) + y * mom[3];
*(dst_m + mad24(DST_ROW_21 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[7] + x * (2 * (mom[4] + y * mom[1]) + x * (mom[2] + ym)) + y * mom[3];
// + m12 ( = m12' + y*(2*m11' + 2*x*m01' + y*m10' + x*y*m00') + x*m02')
dst_m12[wgidy*dst_cols+wgidx] = mom[8] + y * (2 * (mom[4] + x * mom[2]) + y * (mom[1] + xm)) + x * mom[5];
*(dst_m + mad24(DST_ROW_12 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[8] + y * (2 * (mom[4] + x * mom[2]) + y * (mom[1] + xm)) + x * mom[5];
// + m03 ( = m03' + 3*y*m02' + 3*y*y*m01' + y*y*y*m00' )
dst_m03[wgidy*dst_cols+wgidx] = mom[9] + y * (3. * mom[5] + y * (3. * mom[2] + ym));
*(dst_m + mad24(DST_ROW_03 * blocky, dst_step, mad24(wgidy, dst_cols, wgidx))) = mom[9] + y * (3. * mom[5] + y * (3. * mom[2] + ym));
}
}
//#endif
}
\ No newline at end of file
modules/ocl/src/opencl/pyr_up.cl
View file @ 6c99b5c9
......@@ -16,6 +16,8 @@
//
// @Authors
// Zhang Chunpeng chunpeng@multicorewareinc.com
// Dachuan Zhao, dachuan@multicorewareinc.com
// Yao Wang, yao@multicorewareinc.com
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
......@@ -53,20 +55,22 @@ uchar get_valid_uchar(uchar data)
////////////////////////// CV_8UC1 //////////////////////////////////
///////////////////////////////////////////////////////////////////////
__kernel void pyrUp_C1_D0(__global uchar* src,__global uchar* dst,
int srcRows,int dstRows,int srcCols,int dstCols,
int srcOffset,int dstOffset,int srcStep,int dstStep)
int srcRows,int dstRows,int srcCols,int dstCols,
int srcOffset,int dstOffset,int srcStep,int dstStep)
{
const int x = get_global_id(0);
const int y = get_global_id(1);
__local float s_srcPatch[10][10];
__local float s_dstPatch[20][16];
const int tidx = get_local_id(0);
const int tidy = get_local_id(1);
const int lsizex = get_local_size(0);
const int lsizey = get_local_size(1);
if( get_local_id(0) < 10 && get_local_id(1) < 10 )
if( tidx < 10 && tidy < 10 )
{
int srcx = (int)(get_group_id(0) * get_local_size(0) / 2 + get_local_id(0)) - 1;
int srcy = (int)(get_group_id(1) * get_local_size(1) / 2 + get_local_id(1)) - 1;
int srcx = mad24((int)get_group_id(0), (lsizex>>1), tidx) - 1;
int srcy = mad24((int)get_group_id(1), (lsizey>>1), tidy) - 1;
srcx = abs(srcx);
srcx = min(srcCols - 1,srcx);
......@@ -74,25 +78,24 @@ __kernel void pyrUp_C1_D0(__global uchar* src,__global uchar* dst,
srcy = abs(srcy);
srcy = min(srcRows -1 ,srcy);
s_srcPatch[get_local_id(1)][get_local_id(0)] = (float)(src[srcx + srcy * srcStep]);
s_srcPatch[tidy][tidx] = (float)(src[srcx + srcy * srcStep]);
}
barrier(CLK_LOCAL_MEM_FENCE);
float sum = 0;
const int evenFlag = (int)((get_local_id(0) & 1) == 0);
const int oddFlag = (int)((get_local_id(0) & 1) != 0);
const bool eveny = ((get_local_id(1) & 1) == 0);
const int tidx = get_local_id(0);
const int evenFlag = (int)((tidx & 1) == 0);
const int oddFlag = (int)((tidx & 1) != 0);
const bool eveny = ((tidy & 1) == 0);
if(eveny)
{
sum = sum + (evenFlag * 0.0625f) * s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx - 1) >> 1)];
sum = sum + (evenFlag * 0.375f ) * s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx ) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx + 1) >> 1)];
sum = sum + (evenFlag * 0.0625f) * s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx + 2) >> 1)];
sum = (evenFlag * 0.0625f) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx - 1) >> 1)];
sum = sum + (evenFlag * 0.375f ) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx ) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx + 1) >> 1)];
sum = sum + (evenFlag * 0.0625f) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx + 2) >> 1)];
}
s_dstPatch[2 + get_local_id(1)][get_local_id(0)] = sum;
......@@ -103,42 +106,40 @@ __kernel void pyrUp_C1_D0(__global uchar* src,__global uchar* dst,
if (eveny)
{
sum = sum + (evenFlag * 0.0625f) * s_srcPatch[0][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[0][1 + ((tidx - 1) >> 1)];
sum = sum + (evenFlag * 0.375f ) * s_srcPatch[0][1 + ((tidx ) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[0][1 + ((tidx + 1) >> 1)];
sum = sum + (evenFlag * 0.0625f) * s_srcPatch[0][1 + ((tidx + 2) >> 1)];
}
sum = (evenFlag * 0.0625f) * s_srcPatch[lsizey - 16][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[lsizey - 16][1 + ((tidx - 1) >> 1)];
sum = sum + (evenFlag * 0.375f ) * s_srcPatch[lsizey - 16][1 + ((tidx ) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[lsizey - 16][1 + ((tidx + 1) >> 1)];
sum = sum + (evenFlag * 0.0625f) * s_srcPatch[lsizey - 16][1 + ((tidx + 2) >> 1)];
}
s_dstPatch[get_local_id(1)][get_local_id(0)] = sum;
}
s_dstPatch[get_local_id(1)][get_local_id(0)] = sum;
}
if (get_local_id(1) > 13)
{
if (get_local_id(1) > 13)
{
sum = 0;
if (eveny)
{
sum = sum + (evenFlag * 0.0625f) * s_srcPatch[9][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[9][1 + ((tidx - 1) >> 1)];
sum = sum + (evenFlag * 0.375f ) * s_srcPatch[9][1 + ((tidx ) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[9][1 + ((tidx + 1) >> 1)];
sum = (evenFlag * 0.0625f) * s_srcPatch[lsizey - 7][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[lsizey - 7][1 + ((tidx - 1) >> 1)];
sum = sum + (evenFlag * 0.375f ) * s_srcPatch[lsizey - 7][1 + ((tidx ) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[lsizey - 7][1 + ((tidx + 1) >> 1)];
sum = sum + (evenFlag * 0.0625f) * s_srcPatch[9][1 + ((tidx + 2) >> 1)];
}
s_dstPatch[4 + get_local_id(1)][get_local_id(0)] = sum;
}
s_dstPatch[4 + tidy][tidx] = sum;
}
barrier(CLK_LOCAL_MEM_FENCE);
sum = 0;
const int tidy = get_local_id(1);
sum = sum + 0.0625f * s_dstPatch[2 + tidy - 2][get_local_id(0)];
sum = sum + 0.25f * s_dstPatch[2 + tidy - 1][get_local_id(0)];
sum = sum + 0.375f * s_dstPatch[2 + tidy ][get_local_id(0)];
sum = sum + 0.25f * s_dstPatch[2 + tidy + 1][get_local_id(0)];
sum = sum + 0.0625f * s_dstPatch[2 + tidy + 2][get_local_id(0)];
sum = 0.0625f * s_dstPatch[2 + tidy - 2][tidx];
sum = sum + 0.25f * s_dstPatch[2 + tidy - 1][tidx];
sum = sum + 0.375f * s_dstPatch[2 + tidy ][tidx];
sum = sum + 0.25f * s_dstPatch[2 + tidy + 1][tidx];
sum = sum + 0.0625f * s_dstPatch[2 + tidy + 2][tidx];
if ((x < dstCols) && (y < dstRows))
dst[x + y * dstStep] = (float)(4.0f * sum);
......@@ -149,8 +150,8 @@ __kernel void pyrUp_C1_D0(__global uchar* src,__global uchar* dst,
////////////////////////// CV_16UC1 /////////////////////////////////
///////////////////////////////////////////////////////////////////////
__kernel void pyrUp_C1_D2(__global ushort* src,__global ushort* dst,
int srcRows,int dstRows,int srcCols,int dstCols,
int srcOffset,int dstOffset,int srcStep,int dstStep)
int srcRows,int dstRows,int srcCols,int dstCols,
int srcOffset,int dstOffset,int srcStep,int dstStep)
{
const int x = get_global_id(0);
const int y = get_global_id(1);
......@@ -210,13 +211,13 @@ __kernel void pyrUp_C1_D2(__global ushort* src,__global ushort* dst,
sum = sum + (evenFlag * 0.375f ) * s_srcPatch[0][1 + ((tidx ) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[0][1 + ((tidx + 1) >> 1)];
sum = sum + (evenFlag * 0.0625f) * s_srcPatch[0][1 + ((tidx + 2) >> 1)];
}
}
s_dstPatch[get_local_id(1)][get_local_id(0)] = sum;
}
s_dstPatch[get_local_id(1)][get_local_id(0)] = sum;
}
if (get_local_id(1) > 13)
{
if (get_local_id(1) > 13)
{
sum = 0;
if (eveny)
......@@ -228,7 +229,7 @@ __kernel void pyrUp_C1_D2(__global ushort* src,__global ushort* dst,
sum = sum + (evenFlag * 0.0625f) * s_srcPatch[9][1 + ((tidx + 2) >> 1)];
}
s_dstPatch[4 + get_local_id(1)][get_local_id(0)] = sum;
}
}
barrier(CLK_LOCAL_MEM_FENCE);
......@@ -251,12 +252,15 @@ __kernel void pyrUp_C1_D2(__global ushort* src,__global ushort* dst,
////////////////////////// CV_32FC1 /////////////////////////////////
///////////////////////////////////////////////////////////////////////
__kernel void pyrUp_C1_D5(__global float* src,__global float* dst,
int srcRows,int dstRows,int srcCols,int dstCols,
int srcOffset,int dstOffset,int srcStep,int dstStep)
int srcRows,int dstRows,int srcCols,int dstCols,
int srcOffset,int dstOffset,int srcStep,int dstStep)
{
const int x = get_global_id(0);
const int y = get_global_id(1);
const int tidx = get_local_id(0);
const int tidy = get_local_id(1);
const int lsizex = get_local_size(0);
const int lsizey = get_local_size(1);
__local float s_srcPatch[10][10];
__local float s_dstPatch[20][16];
......@@ -266,10 +270,10 @@ __kernel void pyrUp_C1_D5(__global float* src,__global float* dst,
dstStep = dstStep >> 2;
if( get_local_id(0) < 10 && get_local_id(1) < 10 )
if( tidx < 10 && tidy < 10 )
{
int srcx = (int)(get_group_id(0) * get_local_size(0) / 2 + get_local_id(0)) - 1;
int srcy = (int)(get_group_id(1) * get_local_size(1) / 2 + get_local_id(1)) - 1;
int srcx = mad24((int)get_group_id(0), lsizex>>1, tidx) - 1;
int srcy = mad24((int)get_group_id(1), lsizey>>1, tidy) - 1;
srcx = abs(srcx);
srcx = min(srcCols - 1,srcx);
......@@ -277,71 +281,67 @@ __kernel void pyrUp_C1_D5(__global float* src,__global float* dst,
srcy = abs(srcy);
srcy = min(srcRows -1 ,srcy);
s_srcPatch[get_local_id(1)][get_local_id(0)] = (float)(src[srcx + srcy * srcStep]);
s_srcPatch[tidy][tidx] = (float)(src[srcx + srcy * srcStep]);
}
barrier(CLK_LOCAL_MEM_FENCE);
float sum = 0;
const int evenFlag = (int)((get_local_id(0) & 1) == 0);
const int oddFlag = (int)((get_local_id(0) & 1) != 0);
const bool eveny = ((get_local_id(1) & 1) == 0);
const int tidx = get_local_id(0);
const int evenFlag = (int)((tidx & 1) == 0);
const int oddFlag = (int)((tidx & 1) != 0);
const bool eveny = ((tidy & 1) == 0);
if(eveny)
{
sum = sum + (evenFlag * 0.0625f) * s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx - 1) >> 1)];
sum = sum + (evenFlag * 0.375f ) * s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx ) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx + 1) >> 1)];
sum = sum + (evenFlag * 0.0625f) * s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx + 2) >> 1)];
sum = sum + (evenFlag * 0.0625f) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx - 1) >> 1)];
sum = sum + (evenFlag * 0.375f ) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx ) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx + 1) >> 1)];
sum = sum + (evenFlag * 0.0625f) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx + 2) >> 1)];
}
s_dstPatch[2 + get_local_id(1)][get_local_id(0)] = sum;
s_dstPatch[2 + tidy][tidx] = sum;
if (get_local_id(1) < 2)
if (tidy < 2)
{
sum = 0;
if (eveny)
{
sum = sum + (evenFlag * 0.0625f) * s_srcPatch[0][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[0][1 + ((tidx - 1) >> 1)];
sum = sum + (evenFlag * 0.375f ) * s_srcPatch[0][1 + ((tidx ) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[0][1 + ((tidx + 1) >> 1)];
sum = sum + (evenFlag * 0.0625f) * s_srcPatch[0][1 + ((tidx + 2) >> 1)];
}
sum = sum + (evenFlag * 0.0625f) * s_srcPatch[lsizey - 16][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[lsizey - 16][1 + ((tidx - 1) >> 1)];
sum = sum + (evenFlag * 0.375f ) * s_srcPatch[lsizey - 16][1 + ((tidx ) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[lsizey - 16][1 + ((tidx + 1) >> 1)];
sum = sum + (evenFlag * 0.0625f) * s_srcPatch[lsizey - 16][1 + ((tidx + 2) >> 1)];
}
s_dstPatch[get_local_id(1)][get_local_id(0)] = sum;
}
s_dstPatch[tidy][tidx] = sum;
}
if (get_local_id(1) > 13)
{
if (tidy > 13)
{
sum = 0;
if (eveny)
{
sum = sum + (evenFlag * 0.0625f) * s_srcPatch[9][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[9][1 + ((tidx - 1) >> 1)];
sum = sum + (evenFlag * 0.375f ) * s_srcPatch[9][1 + ((tidx ) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[9][1 + ((tidx + 1) >> 1)];
sum = sum + (evenFlag * 0.0625f) * s_srcPatch[9][1 + ((tidx + 2) >> 1)];
sum = sum + (evenFlag * 0.0625f) * s_srcPatch[lsizey - 7][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[lsizey - 7][1 + ((tidx - 1) >> 1)];
sum = sum + (evenFlag * 0.375f ) * s_srcPatch[lsizey - 7][1 + ((tidx ) >> 1)];
sum = sum + ( oddFlag * 0.25f ) * s_srcPatch[lsizey - 7][1 + ((tidx + 1) >> 1)];
sum = sum + (evenFlag * 0.0625f) * s_srcPatch[lsizey - 7][1 + ((tidx + 2) >> 1)];
}
s_dstPatch[4 + get_local_id(1)][get_local_id(0)] = sum;
}
s_dstPatch[4 + tidy][tidx] = sum;
}
barrier(CLK_LOCAL_MEM_FENCE);
sum = 0;
const int tidy = get_local_id(1);
sum = sum + 0.0625f * s_dstPatch[2 + tidy - 2][get_local_id(0)];
sum = sum + 0.25f * s_dstPatch[2 + tidy - 1][get_local_id(0)];
sum = sum + 0.375f * s_dstPatch[2 + tidy ][get_local_id(0)];
sum = sum + 0.25f * s_dstPatch[2 + tidy + 1][get_local_id(0)];
sum = sum + 0.0625f * s_dstPatch[2 + tidy + 2][get_local_id(0)];
sum = 0.0625f * s_dstPatch[2 + tidy - 2][tidx];
sum = sum + 0.25f * s_dstPatch[2 + tidy - 1][tidx];
sum = sum + 0.375f * s_dstPatch[2 + tidy ][tidx];
sum = sum + 0.25f * s_dstPatch[2 + tidy + 1][tidx];
sum = sum + 0.0625f * s_dstPatch[2 + tidy + 2][tidx];
if ((x < dstCols) && (y < dstRows))
dst[x + y * dstStep] = (float)(4.0f * sum);
......@@ -376,37 +376,16 @@ uchar4 convert_float4_to_uchar4(float4 data)
return u4Data;
}
float4 int_x_float4(int leftOpr,float4 rightOpr)
{
float4 result = {0,0,0,0};
result.x = rightOpr.x * leftOpr;
result.y = rightOpr.y * leftOpr;
result.z = rightOpr.z * leftOpr;
result.w = rightOpr.w * leftOpr;
return result;
}
float4 float4_x_float4(float4 leftOpr,float4 rightOpr)
{
float4 result;
result.x = leftOpr.x * rightOpr.x;
result.y = leftOpr.y * rightOpr.y;
result.z = leftOpr.z * rightOpr.z;
result.w = leftOpr.w * rightOpr.w;
return result;
}
__kernel void pyrUp_C4_D0(__global uchar4* src,__global uchar4* dst,
int srcRows,int dstRows,int srcCols,int dstCols,
int srcOffset,int dstOffset,int srcStep,int dstStep)
int srcRows,int dstRows,int srcCols,int dstCols,
int srcOffset,int dstOffset,int srcStep,int dstStep)
{
const int x = get_global_id(0);
const int y = get_global_id(1);
const int tidx = get_local_id(0);
const int tidy = get_local_id(1);
const int lsizex = get_local_size(0);
const int lsizey = get_local_size(1);
__local float4 s_srcPatch[10][10];
__local float4 s_dstPatch[20][16];
......@@ -416,10 +395,10 @@ __kernel void pyrUp_C4_D0(__global uchar4* src,__global uchar4* dst,
dstStep >>= 2;
if( get_local_id(0) < 10 && get_local_id(1) < 10 )
if( tidx < 10 && tidy < 10 )
{
int srcx = (int)(get_group_id(0) * get_local_size(0) / 2 + get_local_id(0)) - 1;
int srcy = (int)(get_group_id(1) * get_local_size(1) / 2 + get_local_id(1)) - 1;
int srcx = mad24((int)get_group_id(0), lsizex>>1, tidx) - 1;
int srcy = mad24((int)get_group_id(1), lsizey>>1, tidy) - 1;
srcx = abs(srcx);
srcx = min(srcCols - 1,srcx);
......@@ -427,17 +406,16 @@ __kernel void pyrUp_C4_D0(__global uchar4* src,__global uchar4* dst,
srcy = abs(srcy);
srcy = min(srcRows -1 ,srcy);
s_srcPatch[get_local_id(1)][get_local_id(0)] = covert_uchar4_to_float4(src[srcx + srcy * srcStep]);
s_srcPatch[tidy][tidx] = covert_uchar4_to_float4(src[srcx + srcy * srcStep]);
}
barrier(CLK_LOCAL_MEM_FENCE);
float4 sum = (float4)(0,0,0,0);
const int evenFlag = (int)((get_local_id(0) & 1) == 0);
const int oddFlag = (int)((get_local_id(0) & 1) != 0);
const bool eveny = ((get_local_id(1) & 1) == 0);
const int tidx = get_local_id(0);
const int evenFlag = (int)((tidx & 1) == 0);
const int oddFlag = (int)((tidx & 1) != 0);
const bool eveny = ((tidy & 1) == 0);
float4 co1 = (float4)(0.375f, 0.375f, 0.375f, 0.375f);
float4 co2 = (float4)(0.25f, 0.25f, 0.25f, 0.25f);
......@@ -446,63 +424,59 @@ __kernel void pyrUp_C4_D0(__global uchar4* src,__global uchar4* dst,
if(eveny)
{
sum = sum + float4_x_float4(int_x_float4( evenFlag, co3 ) , s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx - 2) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( oddFlag , co2 ) , s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx - 1) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( evenFlag, co1 ) , s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx ) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( oddFlag , co2 ) , s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx + 1) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( evenFlag, co3 ) , s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx + 2) >> 1)]);
sum = sum + ( evenFlag * co3) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * co2 ) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx - 1) >> 1)];
sum = sum + ( evenFlag * co1) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx ) >> 1)];
sum = sum + ( oddFlag * co2 ) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx + 1) >> 1)];
sum = sum + ( evenFlag * co3) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx + 2) >> 1)];
}
s_dstPatch[2 + get_local_id(1)][get_local_id(0)] = sum;
s_dstPatch[2 + tidy][tidx] = sum;
if (get_local_id(1) < 2)
if (tidy < 2)
{
sum = 0;
if (eveny)
{
sum = sum + float4_x_float4(int_x_float4(evenFlag , co3) , s_srcPatch[0][1 + ((tidx - 2) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( oddFlag , co2 ) , s_srcPatch[0][1 + ((tidx - 1) >> 1)]);
sum = sum + float4_x_float4(int_x_float4(evenFlag , co1 ) , s_srcPatch[0][1 + ((tidx ) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( oddFlag , co2 ) , s_srcPatch[0][1 + ((tidx + 1) >> 1)]);
sum = sum + float4_x_float4(int_x_float4(evenFlag , co3) , s_srcPatch[0][1 + ((tidx + 2) >> 1)]);
}
s_dstPatch[get_local_id(1)][get_local_id(0)] = sum;
}
if (get_local_id(1) > 13)
{
sum = sum + (evenFlag * co3) * s_srcPatch[lsizey-16][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * co2) * s_srcPatch[lsizey-16][1 + ((tidx - 1) >> 1)];
sum = sum + (evenFlag * co1) * s_srcPatch[lsizey-16][1 + ((tidx ) >> 1)];
sum = sum + ( oddFlag * co2) * s_srcPatch[lsizey-16][1 + ((tidx + 1) >> 1)];
sum = sum + (evenFlag * co3) * s_srcPatch[lsizey-16][1 + ((tidx + 2) >> 1)];
}
s_dstPatch[tidy][tidx] = sum;
}
if (tidy > 13)
{
sum = 0;
if (eveny)
{
sum = sum + float4_x_float4(int_x_float4(evenFlag , co3) , s_srcPatch[9][1 + ((tidx - 2) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( oddFlag , co2) , s_srcPatch[9][1 + ((tidx - 1) >> 1)]);
sum = sum + float4_x_float4(int_x_float4(evenFlag , co1) , s_srcPatch[9][1 + ((tidx ) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( oddFlag , co2) , s_srcPatch[9][1 + ((tidx + 1) >> 1)]);
sum = sum + float4_x_float4(int_x_float4(evenFlag , co3) , s_srcPatch[9][1 + ((tidx + 2) >> 1)]);
sum = sum + (evenFlag * co3) * s_srcPatch[lsizey-7][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * co2) * s_srcPatch[lsizey-7][1 + ((tidx - 1) >> 1)];
sum = sum + (evenFlag * co1) * s_srcPatch[lsizey-7][1 + ((tidx ) >> 1)];
sum = sum + ( oddFlag * co2) * s_srcPatch[lsizey-7][1 + ((tidx + 1) >> 1)];
sum = sum + (evenFlag * co3) * s_srcPatch[lsizey-7][1 + ((tidx + 2) >> 1)];
}
s_dstPatch[4 + get_local_id(1)][get_local_id(0)] = sum;
}
s_dstPatch[4 + tidy][tidx] = sum;
}
barrier(CLK_LOCAL_MEM_FENCE);
sum = 0;
const int tidy = get_local_id(1);
sum = sum + float4_x_float4(co3 , s_dstPatch[2 + tidy - 2][get_local_id(0)]);
sum = sum + float4_x_float4(co2 , s_dstPatch[2 + tidy - 1][get_local_id(0)]);
sum = sum + float4_x_float4(co1 , s_dstPatch[2 + tidy ][get_local_id(0)]);
sum = sum + float4_x_float4(co2 , s_dstPatch[2 + tidy + 1][get_local_id(0)]);
sum = sum + float4_x_float4(co3 , s_dstPatch[2 + tidy + 2][get_local_id(0)]);
sum = co3 * s_dstPatch[2 + tidy - 2][tidx];
sum = sum + co2 * s_dstPatch[2 + tidy - 1][tidx];
sum = sum + co1 * s_dstPatch[2 + tidy ][tidx];
sum = sum + co2 * s_dstPatch[2 + tidy + 1][tidx];
sum = sum + co3 * s_dstPatch[2 + tidy + 2][tidx];
if ((x < dstCols) && (y < dstRows))
{
dst[x + y * dstStep] = convert_float4_to_uchar4(int_x_float4(4.0f,sum));
dst[x + y * dstStep] = convert_float4_to_uchar4(4.0f * sum);
}
}
///////////////////////////////////////////////////////////////////////
......@@ -535,8 +509,8 @@ ushort4 convert_float4_to_ushort4(float4 data)
__kernel void pyrUp_C4_D2(__global ushort4* src,__global ushort4* dst,
int srcRows,int dstRows,int srcCols,int dstCols,
int srcOffset,int dstOffset,int srcStep,int dstStep)
int srcRows,int dstRows,int srcCols,int dstCols,
int srcOffset,int dstOffset,int srcStep,int dstStep)
{
const int x = get_global_id(0);
const int y = get_global_id(1);
......@@ -580,11 +554,11 @@ __kernel void pyrUp_C4_D2(__global ushort4* src,__global ushort4* dst,
if(eveny)
{
sum = sum + float4_x_float4(int_x_float4( evenFlag, co3 ) , s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx - 2) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( oddFlag , co2 ) , s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx - 1) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( evenFlag, co1 ) , s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx ) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( oddFlag , co2 ) , s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx + 1) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( evenFlag, co3 ) , s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx + 2) >> 1)]);
sum = sum + ( evenFlag* co3 ) * s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * co2 ) * s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx - 1) >> 1)];
sum = sum + ( evenFlag* co1 ) * s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx ) >> 1)];
sum = sum + ( oddFlag * co2 ) * s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx + 1) >> 1)];
sum = sum + ( evenFlag* co3 ) * s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx + 2) >> 1)];
}
......@@ -596,31 +570,31 @@ __kernel void pyrUp_C4_D2(__global ushort4* src,__global ushort4* dst,
if (eveny)
{
sum = sum + float4_x_float4(int_x_float4(evenFlag , co3) , s_srcPatch[0][1 + ((tidx - 2) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( oddFlag , co2 ) , s_srcPatch[0][1 + ((tidx - 1) >> 1)]);
sum = sum + float4_x_float4(int_x_float4(evenFlag , co1 ) , s_srcPatch[0][1 + ((tidx ) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( oddFlag , co2 ) , s_srcPatch[0][1 + ((tidx + 1) >> 1)]);
sum = sum + float4_x_float4(int_x_float4(evenFlag , co3) , s_srcPatch[0][1 + ((tidx + 2) >> 1)]);
}
s_dstPatch[get_local_id(1)][get_local_id(0)] = sum;
}
if (get_local_id(1) > 13)
{
sum = sum + (evenFlag * co3) * s_srcPatch[0][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * co2 ) * s_srcPatch[0][1 + ((tidx - 1) >> 1)];
sum = sum + (evenFlag * co1 ) * s_srcPatch[0][1 + ((tidx ) >> 1)];
sum = sum + ( oddFlag * co2 ) * s_srcPatch[0][1 + ((tidx + 1) >> 1)];
sum = sum + (evenFlag * co3) * s_srcPatch[0][1 + ((tidx + 2) >> 1)];
}
s_dstPatch[get_local_id(1)][get_local_id(0)] = sum;
}
if (get_local_id(1) > 13)
{
sum = 0;
if (eveny)
{
sum = sum + float4_x_float4(int_x_float4(evenFlag , co3) , s_srcPatch[9][1 + ((tidx - 2) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( oddFlag , co2) , s_srcPatch[9][1 + ((tidx - 1) >> 1)]);
sum = sum + float4_x_float4(int_x_float4(evenFlag , co1) , s_srcPatch[9][1 + ((tidx ) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( oddFlag , co2) , s_srcPatch[9][1 + ((tidx + 1) >> 1)]);
sum = sum + float4_x_float4(int_x_float4(evenFlag , co3) , s_srcPatch[9][1 + ((tidx + 2) >> 1)]);
sum = sum + (evenFlag * co3) * s_srcPatch[9][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * co2) * s_srcPatch[9][1 + ((tidx - 1) >> 1)];
sum = sum + (evenFlag * co1) * s_srcPatch[9][1 + ((tidx ) >> 1)];
sum = sum + ( oddFlag * co2) * s_srcPatch[9][1 + ((tidx + 1) >> 1)];
sum = sum + (evenFlag * co3) * s_srcPatch[9][1 + ((tidx + 2) >> 1)];
}
s_dstPatch[4 + get_local_id(1)][get_local_id(0)] = sum;
}
}
barrier(CLK_LOCAL_MEM_FENCE);
......@@ -628,15 +602,15 @@ __kernel void pyrUp_C4_D2(__global ushort4* src,__global ushort4* dst,
const int tidy = get_local_id(1);
sum = sum + float4_x_float4(co3 , s_dstPatch[2 + tidy - 2][get_local_id(0)]);
sum = sum + float4_x_float4(co2 , s_dstPatch[2 + tidy - 1][get_local_id(0)]);
sum = sum + float4_x_float4(co1 , s_dstPatch[2 + tidy ][get_local_id(0)]);
sum = sum + float4_x_float4(co2 , s_dstPatch[2 + tidy + 1][get_local_id(0)]);
sum = sum + float4_x_float4(co3 , s_dstPatch[2 + tidy + 2][get_local_id(0)]);
sum = sum + co3 * s_dstPatch[2 + tidy - 2][get_local_id(0)];
sum = sum + co2 * s_dstPatch[2 + tidy - 1][get_local_id(0)];
sum = sum + co1 * s_dstPatch[2 + tidy ][get_local_id(0)];
sum = sum + co2 * s_dstPatch[2 + tidy + 1][get_local_id(0)];
sum = sum + co3 * s_dstPatch[2 + tidy + 2][get_local_id(0)];
if ((x < dstCols) && (y < dstRows))
{
dst[x + y * dstStep] = convert_float4_to_ushort4(int_x_float4(4.0f,sum));
dst[x + y * dstStep] = convert_float4_to_ushort4(4.0f * sum);
}
}
......@@ -644,12 +618,15 @@ __kernel void pyrUp_C4_D2(__global ushort4* src,__global ushort4* dst,
////////////////////////// CV_32FC4 //////////////////////////////////
///////////////////////////////////////////////////////////////////////
__kernel void pyrUp_C4_D5(__global float4* src,__global float4* dst,
int srcRows,int dstRows,int srcCols,int dstCols,
int srcOffset,int dstOffset,int srcStep,int dstStep)
int srcRows,int dstRows,int srcCols,int dstCols,
int srcOffset,int dstOffset,int srcStep,int dstStep)
{
const int x = get_global_id(0);
const int y = get_global_id(1);
const int tidx = get_local_id(0);
const int tidy = get_local_id(1);
const int lsizex = get_local_size(0);
const int lsizey = get_local_size(1);
__local float4 s_srcPatch[10][10];
__local float4 s_dstPatch[20][16];
......@@ -659,10 +636,10 @@ __kernel void pyrUp_C4_D5(__global float4* src,__global float4* dst,
dstStep >>= 4;
if( get_local_id(0) < 10 && get_local_id(1) < 10 )
if( tidx < 10 && tidy < 10 )
{
int srcx = (int)(get_group_id(0) * get_local_size(0) / 2 + get_local_id(0)) - 1;
int srcy = (int)(get_group_id(1) * get_local_size(1) / 2 + get_local_id(1)) - 1;
int srcx = (int)(get_group_id(0) * get_local_size(0) / 2 + tidx) - 1;
int srcy = (int)(get_group_id(1) * get_local_size(1) / 2 + tidy) - 1;
srcx = abs(srcx);
srcx = min(srcCols - 1,srcx);
......@@ -670,17 +647,16 @@ __kernel void pyrUp_C4_D5(__global float4* src,__global float4* dst,
srcy = abs(srcy);
srcy = min(srcRows -1 ,srcy);
s_srcPatch[get_local_id(1)][get_local_id(0)] = (float4)(src[srcx + srcy * srcStep]);
s_srcPatch[tidy][tidx] = (float4)(src[srcx + srcy * srcStep]);
}
barrier(CLK_LOCAL_MEM_FENCE);
float4 sum = (float4)(0,0,0,0);
const int evenFlag = (int)((get_local_id(0) & 1) == 0);
const int oddFlag = (int)((get_local_id(0) & 1) != 0);
const bool eveny = ((get_local_id(1) & 1) == 0);
const int tidx = get_local_id(0);
const int evenFlag = (int)((tidx & 1) == 0);
const int oddFlag = (int)((tidx & 1) != 0);
const bool eveny = ((tidy & 1) == 0);
float4 co1 = (float4)(0.375f, 0.375f, 0.375f, 0.375f);
float4 co2 = (float4)(0.25f, 0.25f, 0.25f, 0.25f);
......@@ -689,59 +665,55 @@ __kernel void pyrUp_C4_D5(__global float4* src,__global float4* dst,
if(eveny)
{
sum = sum + float4_x_float4(int_x_float4( evenFlag, co3 ) , s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx - 2) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( oddFlag , co2 ) , s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx - 1) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( evenFlag, co1 ) , s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx ) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( oddFlag , co2 ) , s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx + 1) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( evenFlag, co3 ) , s_srcPatch[1 + (get_local_id(1) >> 1)][1 + ((tidx + 2) >> 1)]);
sum = sum + ( evenFlag* co3 ) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * co2 ) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx - 1) >> 1)];
sum = sum + ( evenFlag* co1 ) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx ) >> 1)];
sum = sum + ( oddFlag * co2 ) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx + 1) >> 1)];
sum = sum + ( evenFlag* co3 ) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx + 2) >> 1)];
}
s_dstPatch[2 + get_local_id(1)][get_local_id(0)] = sum;
s_dstPatch[2 + tidy][tidx] = sum;
if (get_local_id(1) < 2)
if (tidy < 2)
{
sum = 0;
if (eveny)
{
sum = sum + float4_x_float4(int_x_float4(evenFlag , co3) , s_srcPatch[0][1 + ((tidx - 2) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( oddFlag , co2 ) , s_srcPatch[0][1 + ((tidx - 1) >> 1)]);
sum = sum + float4_x_float4(int_x_float4(evenFlag , co1 ) , s_srcPatch[0][1 + ((tidx ) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( oddFlag , co2 ) , s_srcPatch[0][1 + ((tidx + 1) >> 1)]);
sum = sum + float4_x_float4(int_x_float4(evenFlag , co3) , s_srcPatch[0][1 + ((tidx + 2) >> 1)]);
}
s_dstPatch[get_local_id(1)][get_local_id(0)] = sum;
}
if (get_local_id(1) > 13)
{
sum = sum + (evenFlag * co3) * s_srcPatch[lsizey-16][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * co2 ) * s_srcPatch[lsizey-16][1 + ((tidx - 1) >> 1)];
sum = sum + (evenFlag * co1 ) * s_srcPatch[lsizey-16][1 + ((tidx ) >> 1)];
sum = sum + ( oddFlag * co2 ) * s_srcPatch[lsizey-16][1 + ((tidx + 1) >> 1)];
sum = sum + (evenFlag * co3) * s_srcPatch[lsizey-16][1 + ((tidx + 2) >> 1)];
}
s_dstPatch[tidy][tidx] = sum;
}
if (tidy > 13)
{
sum = 0;
if (eveny)
{
sum = sum + float4_x_float4(int_x_float4(evenFlag , co3) , s_srcPatch[9][1 + ((tidx - 2) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( oddFlag , co2) , s_srcPatch[9][1 + ((tidx - 1) >> 1)]);
sum = sum + float4_x_float4(int_x_float4(evenFlag , co1) , s_srcPatch[9][1 + ((tidx ) >> 1)]);
sum = sum + float4_x_float4(int_x_float4( oddFlag , co2) , s_srcPatch[9][1 + ((tidx + 1) >> 1)]);
sum = sum + float4_x_float4(int_x_float4(evenFlag , co3) , s_srcPatch[9][1 + ((tidx + 2) >> 1)]);
sum = sum + (evenFlag * co3) * s_srcPatch[lsizey-7][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * co2) * s_srcPatch[lsizey-7][1 + ((tidx - 1) >> 1)];
sum = sum + (evenFlag * co1) * s_srcPatch[lsizey-7][1 + ((tidx ) >> 1)];
sum = sum + ( oddFlag * co2) * s_srcPatch[lsizey-7][1 + ((tidx + 1) >> 1)];
sum = sum + (evenFlag * co3) * s_srcPatch[lsizey-7][1 + ((tidx + 2) >> 1)];
}
s_dstPatch[4 + get_local_id(1)][get_local_id(0)] = sum;
}
s_dstPatch[4 + tidy][tidx] = sum;
}
barrier(CLK_LOCAL_MEM_FENCE);
sum = 0;
const int tidy = get_local_id(1);
sum = sum + float4_x_float4(co3 , s_dstPatch[2 + tidy - 2][get_local_id(0)]);
sum = sum + float4_x_float4(co2 , s_dstPatch[2 + tidy - 1][get_local_id(0)]);
sum = sum + float4_x_float4(co1 , s_dstPatch[2 + tidy ][get_local_id(0)]);
sum = sum + float4_x_float4(co2 , s_dstPatch[2 + tidy + 1][get_local_id(0)]);
sum = sum + float4_x_float4(co3 , s_dstPatch[2 + tidy + 2][get_local_id(0)]);
sum = co3 * s_dstPatch[2 + tidy - 2][tidx];
sum = sum + co2 * s_dstPatch[2 + tidy - 1][tidx];
sum = sum + co1 * s_dstPatch[2 + tidy ][tidx];
sum = sum + co2 * s_dstPatch[2 + tidy + 1][tidx];
sum = sum + co3 * s_dstPatch[2 + tidy + 2][tidx];
if ((x < dstCols) && (y < dstRows))
{
......
modules/ocl/src/opencl/stereobm.cl
View file @ 6c99b5c9
......@@ -323,7 +323,7 @@ float sobel(__global unsigned char *input, int x, int y, int rows, int cols)
float conv = 0;
int y1 = y==0? 0 : y-1;
int x1 = x==0? 0 : x-1;
if(x < cols && y < rows)
if(x < cols && y < rows && x > 0 && y > 0)
{
conv = (float)input[(y1) * cols + (x1)] * (-1) + (float)input[(y1) * cols + (x+1)] * (1) +
(float)input[(y) * cols + (x1)] * (-2) + (float)input[(y) * cols + (x+1)] * (2) +
......
modules/ocl/test/test_brute_force_matcher.cpp
View file @ 6c99b5c9
......@@ -110,7 +110,7 @@ namespace
}
};
TEST_P(BruteForceMatcher, DISABLED_Match_Single)
TEST_P(BruteForceMatcher, Match_Single)
{
cv::ocl::BruteForceMatcher_OCL_base matcher(distType);
......@@ -130,7 +130,7 @@ namespace
ASSERT_EQ(0, badCount);
}
TEST_P(BruteForceMatcher, DISABLED_KnnMatch_2_Single)
TEST_P(BruteForceMatcher, KnnMatch_2_Single)
{
const int knn = 2;
......
modules/ocl/test/test_match_template.cpp
View file @ 6c99b5c9
......@@ -75,7 +75,7 @@ PARAM_TEST_CASE(MatchTemplate8U, cv::Size, TemplateSize, Channels, TemplateMetho
}
};
TEST_P(MatchTemplate8U, DISABLED_Accuracy)
TEST_P(MatchTemplate8U, Accuracy)
{
std::cout << "Method: " << TEMPLATE_METHOD_NAMES[method] << std::endl;
......@@ -138,18 +138,18 @@ TEST_P(MatchTemplate32F, Accuracy)
EXPECT_MAT_NEAR(dst_gold, mat_dst, templ_size.area() * 1e-1, sss);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate8U,
INSTANTIATE_TEST_CASE_P(OCL_ImgProc, MatchTemplate8U,
testing::Combine(
MTEMP_SIZES,
testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16))/*, TemplateSize(cv::Size(30, 30))*/),
testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16)), TemplateSize(cv::Size(30, 30))),
testing::Values(Channels(1), Channels(3), Channels(4)),
ALL_TEMPLATE_METHODS
)
);
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate32F, testing::Combine(
INSTANTIATE_TEST_CASE_P(OCL_ImgProc, MatchTemplate32F, testing::Combine(
MTEMP_SIZES,
testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16))/*, TemplateSize(cv::Size(30, 30))*/),
testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16)), TemplateSize(cv::Size(30, 30))),
testing::Values(Channels(1), Channels(3), Channels(4)),
testing::Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_CCORR))));
#endif
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