// radiusMatchSingle
void cv::ocl::BruteForceMatcher_OCL_base::radiusMatchSingle(const oclMat &query, const oclMat &train,
oclMat &trainIdx, oclMat &distance, oclMat &nMatches, float maxDistance, const oclMat &mask)
{
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[] = "radiusMatchSingle";
if (callType != 5)
CV_ERROR(CV_UNSUPPORTED_FORMAT_ERR, "BruteForceMatch OpenCL only support float type query!\n");
if ((distType == 0 && callType == 1 ) || (distType == 1 && callType != 5) || (distType == 2 && (callType != 0
|| callType != 2 || callType != 4)))
{
CV_ERROR(CV_UNSUPPORTED_DEPTH_ERR, "BruteForceMatch OpenCL only support float type query!\n");
}
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
CV_Assert(train.type() == query.type() && train.cols == query.cols);
CV_Assert(trainIdx.empty() || (trainIdx.rows == query.rows && trainIdx.size() == distance.size()));
nMatches.create(1, query.rows, CV_32SC1);
if (trainIdx.empty())
{
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));
matchDispatcher(query, train, maxDistance, mask, trainIdx, distance, nMatches, distType);
exit:
return;
}
void cv::ocl::BruteForceMatcher_OCL_base::matchCollection(const oclMat &query, const oclMat &trainCollection, oclMat &trainIdx,
oclMat &imgIdx, oclMat &distance, const oclMat &masks)
{
if (query.empty() || trainCollection.empty())
return;
// match1 doesn't support signed char type, match2 only support float, hamming support uchar, ushort and int
int callType = query.depth();
char cvFuncName[] = "matchCollection";
if (callType != 5)
CV_ERROR(CV_UNSUPPORTED_FORMAT_ERR, "BruteForceMatch OpenCL only support float type query!\n");
if ((distType == 0 && callType == 1 ) || (distType == 1 && callType != 5) || (distType == 2 && (callType != 0
|| callType != 2 || callType != 4)))
{
CV_ERROR(CV_UNSUPPORTED_DEPTH_ERR, "BruteForceMatch OpenCL only support float type query!\n");
}
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
trainIdx.create(1, query.rows, CV_32S);
imgIdx.create(1, query.rows, CV_32S);
distance.create(1, query.rows, CV_32F);
matchDispatcher(query, (const oclMat *)trainCollection.ptr(), trainCollection.cols, masks, trainIdx, imgIdx, distance, distType);
exit:
return;
}
///////////////////////////////////////////////////////////////////////////
//////////////////////////////// ConvertTo ////////////////////////////////
///////////////////////////////////////////////////////////////////////////
static void convert_run(const oclMat &src, oclMat &dst, double alpha, double beta)
{
String kernelName = "convert_to_S";
std::stringstream idxStr;
idxStr << src.depth();
kernelName = kernelName + idxStr.str().c_str();
float alpha_f = alpha, beta_f = beta;
CV_DbgAssert(src.rows == dst.rows && src.cols == dst.cols);
std::vector<std::pair<size_t , const void *> > args;
size_t localThreads[3] = {16, 16, 1};
size_t globalThreads[3];
globalThreads[0] = (dst.cols + localThreads[0] - 1) / localThreads[0] * localThreads[0];
globalThreads[1] = (dst.rows + localThreads[1] - 1) / localThreads[1] * localThreads[1];
globalThreads[2] = 1;
int dststep_in_pixel = dst.step / dst.elemSize(), dstoffset_in_pixel = dst.offset / dst.elemSize();
int srcstep_in_pixel = src.step / src.elemSize(), srcoffset_in_pixel = src.offset / src.elemSize();
if(dst.type() == CV_8UC1)
{
globalThreads[0] = ((dst.cols + 4) / 4 + localThreads[0]) / localThreads[0] * localThreads[0];
}
args.push_back( std::make_pair( sizeof(cl_mem) , (void *)&src.data ));
args.push_back( std::make_pair( sizeof(cl_mem) , (void *)&dst.data ));
args.push_back( std::make_pair( sizeof(cl_int) , (void *)&src.cols ));
args.push_back( std::make_pair( sizeof(cl_int) , (void *)&src.rows ));
args.push_back( std::make_pair( sizeof(cl_int) , (void *)&srcstep_in_pixel ));
args.push_back( std::make_pair( sizeof(cl_int) , (void *)&srcoffset_in_pixel ));
args.push_back( std::make_pair( sizeof(cl_int) , (void *)&dststep_in_pixel ));
args.push_back( std::make_pair( sizeof(cl_int) , (void *)&dstoffset_in_pixel ));
args.push_back( std::make_pair( sizeof(cl_float) , (void *)&alpha_f ));
args.push_back( std::make_pair( sizeof(cl_float) , (void *)&beta_f ));
openCLExecuteKernel(dst.clCxt , &operator_convertTo, kernelName, globalThreads,
localThreads, args, dst.oclchannels(), dst.depth());
}
void cv::ocl::distanceToCenters(const oclMat &src, const oclMat ¢ers, Mat &dists, Mat &labels, int distType)
{
CV_Assert(src.cols * src.channels() == centers.cols * centers.channels());
CV_Assert(src.depth() == CV_32F && centers.depth() == CV_32F);
CV_Assert(distType == NORM_L1 || distType == NORM_L2SQR);
dists.create(src.rows, 1, CV_32FC1);
labels.create(src.rows, 1, CV_32SC1);
std::stringstream build_opt_ss;
build_opt_ss << (distType == NORM_L1 ? "-D L1_DIST" : "-D L2SQR_DIST");
int src_step = src.step / src.elemSize1();
int centers_step = centers.step / centers.elemSize1();
int feature_width = centers.cols * centers.oclchannels();
int src_offset = src.offset / src.elemSize1();
int centers_offset = centers.offset / centers.elemSize1();
int all_dist_count = src.rows * centers.rows;
oclMat all_dist(1, all_dist_count, CV_32FC1);
vector<pair<size_t, const void *> > args;
args.push_back(make_pair(sizeof(cl_mem), (void *)&src.data));
args.push_back(make_pair(sizeof(cl_mem), (void *)¢ers.data));
args.push_back(make_pair(sizeof(cl_mem), (void *)&all_dist.data));
args.push_back(make_pair(sizeof(cl_int), (void *)&feature_width));
args.push_back(make_pair(sizeof(cl_int), (void *)&src_step));
args.push_back(make_pair(sizeof(cl_int), (void *)¢ers_step));
args.push_back(make_pair(sizeof(cl_int), (void *)&src.rows));
args.push_back(make_pair(sizeof(cl_int), (void *)¢ers.rows));
args.push_back(make_pair(sizeof(cl_int), (void *)&src_offset));
args.push_back(make_pair(sizeof(cl_int), (void *)¢ers_offset));
size_t globalThreads[3] = { all_dist_count, 1, 1 };
openCLExecuteKernel(Context::getContext(), &kmeans_kernel,
"distanceToCenters", globalThreads, NULL, args, -1, -1, build_opt_ss.str().c_str());
Mat all_dist_cpu;
all_dist.download(all_dist_cpu);
for (int i = 0; i < src.rows; ++i)
{
Point p;
double minVal;
Rect roi(i * centers.rows, 0, centers.rows, 1);
Mat hdr(all_dist_cpu, roi);
cv::minMaxLoc(hdr, &minVal, NULL, &p);
dists.at<float>(i, 0) = static_cast<float>(minVal);
labels.at<int>(i, 0) = p.x;
}
}
static void lkSparse_run(oclMat &I, oclMat &J,
const oclMat &prevPts, oclMat &nextPts, oclMat &status, oclMat& err, bool /*GET_MIN_EIGENVALS*/, int ptcount,
int level, /*dim3 block, */dim3 patch, Size winSize, int iters)
{
Context *clCxt = I.clCxt;
String kernelName = "lkSparse";
size_t localThreads[3] = { 8, 8, 1 };
size_t globalThreads[3] = { 8 * ptcount, 8, 1};
int cn = I.oclchannels();
char calcErr = level==0?1:0;
std::vector<std::pair<size_t , const void *> > args;
cl_mem ITex = bindTexture(I);
cl_mem JTex = bindTexture(J);
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&ITex ));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&JTex ));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&prevPts.data ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&prevPts.step ));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&nextPts.data ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&nextPts.step ));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&status.data ));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&err.data ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&level ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&I.rows ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&I.cols ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&patch.x ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&patch.y ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&cn ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&winSize.width ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&winSize.height ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&iters ));
args.push_back( std::make_pair( sizeof(cl_char), (void *)&calcErr ));
bool is_cpu = isCpuDevice();
if (is_cpu)
{
openCLExecuteKernel(clCxt, &pyrlk, kernelName, globalThreads, localThreads, args, I.oclchannels(), I.depth(), (char*)" -D CPU");
}
else
{
std::stringstream idxStr;
idxStr << kernelName << "_C" << I.oclchannels() << "_D" << I.depth();
cl_kernel kernel = openCLGetKernelFromSource(clCxt, &pyrlk, idxStr.str());
int wave_size = (int)queryWaveFrontSize(kernel);
openCLSafeCall(clReleaseKernel(kernel));
static char opt[32] = {0};
sprintf(opt, "-D WAVE_SIZE=%d", wave_size);
openCLExecuteKernel(clCxt, &pyrlk, kernelName, globalThreads, localThreads,
args, I.oclchannels(), I.depth(), opt);
}
releaseTexture(ITex);
releaseTexture(JTex);
}
static void set_to_withoutmask_run(const oclMat &dst, const Scalar &scalar, String kernelName)
{
std::vector<std::pair<size_t , const void *> > args;
size_t localThreads[3] = {16, 16, 1};
size_t globalThreads[3] = { dst.cols, dst.rows, 1 };
int step_in_pixel = dst.step / dst.elemSize(), offset_in_pixel = dst.offset / dst.elemSize();
if (dst.type() == CV_8UC1)
globalThreads[0] = ((dst.cols + 4) / 4 + localThreads[0] - 1) / localThreads[0] * localThreads[0];
const char * const typeMap[] = { "uchar", "char", "ushort", "short", "int", "float", "double" };
const char channelMap[] = { ' ', ' ', '2', '4', '4' };
std::string buildOptions = format("-D GENTYPE=%s%c", typeMap[dst.depth()], channelMap[dst.channels()]);
Mat mat(1, 1, dst.type(), scalar);
#ifdef CL_VERSION_1_2
// this enables backwards portability to
// run on OpenCL 1.1 platform if library binaries are compiled with OpenCL 1.2 support
if (Context::getContext()->supportsFeature(Context::CL_VER_1_2) &&
dst.offset == 0 && dst.cols == dst.wholecols)
{
const int sizeofMap[][7] =
{
{ sizeof(cl_uchar) , sizeof(cl_char) , sizeof(cl_ushort) , sizeof(cl_short) , sizeof(cl_int) , sizeof(cl_float) , sizeof(cl_double) },
{ sizeof(cl_uchar2), sizeof(cl_char2), sizeof(cl_ushort2), sizeof(cl_short2), sizeof(cl_int2), sizeof(cl_float2), sizeof(cl_double2) },
{ 0 , 0 , 0 , 0 , 0 , 0 , 0 },
{ sizeof(cl_uchar4), sizeof(cl_char4), sizeof(cl_ushort4), sizeof(cl_short4), sizeof(cl_int4), sizeof(cl_float4), sizeof(cl_double4) },
};
int sizeofGeneric = sizeofMap[dst.oclchannels() - 1][dst.depth()];
clEnqueueFillBuffer((cl_command_queue)dst.clCxt->oclCommandQueue(),
(cl_mem)dst.data, (void*)mat.data, sizeofGeneric,
0, dst.step * dst.rows, 0, NULL, NULL);
}
else
#endif
{
oclMat m(mat);
args.push_back( std::make_pair( sizeof(cl_mem) , (void*)&m.data ));
args.push_back( std::make_pair( sizeof(cl_mem) , (void *)&dst.data ));
args.push_back( std::make_pair( sizeof(cl_int) , (void *)&dst.cols ));
args.push_back( std::make_pair( sizeof(cl_int) , (void *)&dst.rows ));
args.push_back( std::make_pair( sizeof(cl_int) , (void *)&step_in_pixel ));
args.push_back( std::make_pair( sizeof(cl_int) , (void *)&offset_in_pixel ));
openCLExecuteKernel(dst.clCxt , &operator_setTo, kernelName, globalThreads,
localThreads, args, -1, -1, buildOptions.c_str());
}
}
void cv::ocl::blendLinear(const oclMat &src1, const oclMat &src2, const oclMat &weights1, const oclMat &weights2,
oclMat &dst)
{
CV_Assert(src1.depth() <= CV_32F);
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert(weights1.size() == weights2.size() && weights1.size() == src1.size() &&
weights1.type() == CV_32FC1 && weights2.type() == CV_32FC1);
dst.create(src1.size(), src1.type());
size_t globalSize[] = { (size_t)dst.cols, (size_t)dst.rows, 1};
size_t localSize[] = { 16, 16, 1 };
int depth = dst.depth(), ocn = dst.oclchannels();
int src1_step = src1.step / src1.elemSize(), src1_offset = src1.offset / src1.elemSize();
int src2_step = src2.step / src2.elemSize(), src2_offset = src2.offset / src2.elemSize();
int weight1_step = weights1.step / weights1.elemSize(), weight1_offset = weights1.offset / weights1.elemSize();
int weight2_step = weights2.step / weights2.elemSize(), weight2_offset = weights2.offset / weights2.elemSize();
int dst_step = dst.step / dst.elemSize(), dst_offset = dst.offset / dst.elemSize();
const char * const channelMap[] = { "", "", "2", "4", "4" };
const char * const typeMap[] = { "uchar", "char", "ushort", "short", "int", "float", "double" };
std::string buildOptions = format("-D T=%s%s -D convertToT=convert_%s%s%s -D FT=float%s -D convertToFT=convert_float%s",
typeMap[depth], channelMap[ocn], typeMap[depth], channelMap[ocn],
depth >= CV_32S ? "" : "_sat_rte", channelMap[ocn], channelMap[ocn]);
vector< pair<size_t, const void *> > args;
args.push_back( make_pair( sizeof(cl_mem), (void *)&src1.data ));
args.push_back( make_pair( sizeof(cl_int), (void *)&src1_offset ));
args.push_back( make_pair( sizeof(cl_int), (void *)&src1_step ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&src2.data ));
args.push_back( make_pair( sizeof(cl_int), (void *)&src2_offset ));
args.push_back( make_pair( sizeof(cl_int), (void *)&src2_step ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&weights1.data ));
args.push_back( make_pair( sizeof(cl_int), (void *)&weight1_offset ));
args.push_back( make_pair( sizeof(cl_int), (void *)&weight1_step ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&weights2.data ));
args.push_back( make_pair( sizeof(cl_int), (void *)&weight2_offset ));
args.push_back( make_pair( sizeof(cl_int), (void *)&weight2_step ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&dst.data ));
args.push_back( make_pair( sizeof(cl_int), (void *)&dst_offset ));
args.push_back( make_pair( sizeof(cl_int), (void *)&dst_step ));
args.push_back( make_pair( sizeof(cl_int), (void *)&dst.rows ));
args.push_back( make_pair( sizeof(cl_int), (void *)&dst.cols ));
openCLExecuteKernel(src1.clCxt, &blend_linear, "blendLinear", globalSize, localSize, args,
-1, -1, buildOptions.c_str());
}
void matchUnrolledCached(const oclMat &query, const oclMat &train, const oclMat &/*mask*/,
const oclMat &trainIdx, const oclMat &distance, int distType)
{
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};
const size_t smemSize = (BLOCK_SIZE * (MAX_DESC_LEN >= 2 * BLOCK_SIZE ? MAX_DESC_LEN : 2 * BLOCK_SIZE) + BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
int block_size = BLOCK_SIZE;
int m_size = MAX_DESC_LEN;
std::vector< std::pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt,
"-D T=%s -D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d",
T_ARR[query.depth()], distType, block_size, m_size);
if(globalSize[0] != 0)
{
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&query.data ));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&train.data ));
//args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mask.data ));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&trainIdx.data ));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&distance.data ));
args.push_back( std::make_pair( smemSize, (void *)NULL));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&query.rows ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&query.cols ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&train.rows ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&train.cols ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&query.step ));
String kernelName = "BruteForceMatch_UnrollMatch";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
void convertTo( const oclMat &src, oclMat &dst, int rtype, double alpha, double beta )
{
//cout << "cv::ocl::oclMat::convertTo()" << endl;
bool noScale = fabs(alpha - 1) < std::numeric_limits<double>::epsilon()
&& fabs(beta) < std::numeric_limits<double>::epsilon();
if( rtype < 0 )
rtype = src.type();
else
rtype = CV_MAKETYPE(CV_MAT_DEPTH(rtype), src.oclchannels());
int sdepth = src.depth(), ddepth = CV_MAT_DEPTH(rtype);
if( sdepth == ddepth && noScale )
{
src.copyTo(dst);
return;
}
oclMat temp;
const oclMat *psrc = &src;
if( sdepth != ddepth && psrc == &dst )
psrc = &(temp = src);
dst.create( src.size(), rtype );
convert_run_cus(*psrc, dst, alpha, beta);
}
// knn match
void cv::ocl::BruteForceMatcher_OCL_base::knnMatchSingle(const oclMat &query, const oclMat &train, oclMat &trainIdx,
oclMat &distance, oclMat &allDist, int k, const oclMat &mask)
{
if (query.empty() || train.empty())
return;
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)
{
ensureSizeIsEnough(1, nQuery, CV_32SC2, trainIdx);
ensureSizeIsEnough(1, nQuery, CV_32FC2, distance);
}
else
{
ensureSizeIsEnough(nQuery, k, CV_32S, trainIdx);
ensureSizeIsEnough(nQuery, k, CV_32F, distance);
ensureSizeIsEnough(nQuery, nTrain, CV_32FC1, allDist);
}
trainIdx.setTo(Scalar::all(-1));
kmatchDispatcher(query, train, k, mask, trainIdx, distance, allDist, distType);
return;
}
static void toHSV_caller(const oclMat &src, oclMat &dst, int bidx, const std::string & kernelName,
const std::string & additionalOptions = std::string(),
const oclMat & data1 = oclMat(), const oclMat & data2 = oclMat())
{
int src_offset = src.offset / src.elemSize1(), src_step = src.step1();
int dst_offset = dst.offset / dst.elemSize1(), dst_step = dst.step1();
std::string build_options = format("-D DEPTH_%d -D scn=%d -D bidx=%d", src.depth(), src.oclchannels(), bidx);
if (!additionalOptions.empty())
build_options += additionalOptions;
vector<pair<size_t , const void *> > args;
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst.cols));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst.rows));
args.push_back( make_pair( sizeof(cl_int) , (void *)&src_step));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst_step));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&src.data));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst.data));
args.push_back( make_pair( sizeof(cl_int) , (void *)&src_offset ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst_offset ));
if (!data1.empty())
args.push_back( make_pair( sizeof(cl_mem) , (void *)&data1.data ));
if (!data2.empty())
args.push_back( make_pair( sizeof(cl_mem) , (void *)&data2.data ));
size_t gt[3] = { dst.cols, dst.rows, 1 };
#ifdef ANDROID
size_t lt[3] = { 16, 10, 1 };
#else
size_t lt[3] = { 16, 16, 1 };
#endif
openCLExecuteKernel(src.clCxt, &cvt_color, kernelName.c_str(), gt, lt, args, -1, -1, build_options.c_str());
}
static void convert_C3C4(const cl_mem &src, oclMat &dst)
{
Context *clCxt = dst.clCxt;
int pixel_end = dst.wholecols * dst.wholerows - 1;
int dstStep_in_pixel = dst.step1() / dst.oclchannels();
const char * const typeMap[] = { "uchar", "char", "ushort", "short", "int", "float", "double" };
std::string buildOptions = format("-D GENTYPE4=%s4", typeMap[dst.depth()]);
std::vector< std::pair<size_t, const void *> > args;
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&src));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&dst.data));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&dst.wholecols));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&dst.wholerows));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&dstStep_in_pixel));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&pixel_end));
size_t globalThreads[3] = { divUp(dst.wholecols * dst.wholerows, 4), 1, 1 };
#ifdef ANDROID
openCLExecuteKernel(clCxt, &convertC3C4, "convertC3C4", globalThreads, NULL,
args, -1, -1, buildOptions.c_str());
#else
size_t localThreads[3] = { 256, 1, 1 };
openCLExecuteKernel(clCxt, &convertC3C4, "convertC3C4", globalThreads, localThreads,
args, -1, -1, buildOptions.c_str());
#endif
}
void cv::ocl::oclMat::convertTo( oclMat &dst, int rtype, double alpha, double beta ) const
{
if (!clCxt->supportsFeature(FEATURE_CL_DOUBLE) &&
(depth() == CV_64F || dst.depth() == CV_64F))
{
CV_Error(Error::OpenCLDoubleNotSupported, "Selected device doesn't support double");
return;
}
bool noScale = fabs(alpha - 1) < std::numeric_limits<double>::epsilon()
&& fabs(beta) < std::numeric_limits<double>::epsilon();
if( rtype < 0 )
rtype = type();
else
rtype = CV_MAKETYPE(CV_MAT_DEPTH(rtype), channels());
int sdepth = depth(), ddepth = CV_MAT_DEPTH(rtype);
if( sdepth == ddepth && noScale )
{
copyTo(dst);
return;
}
oclMat temp;
const oclMat *psrc = this;
if( sdepth != ddepth && psrc == &dst )
psrc = &(temp = *this);
dst.create( size(), rtype );
convert_run(*psrc, dst, alpha, beta);
}
static void set_to_withmask_run(const oclMat &dst, const Scalar &scalar, const oclMat &mask, String kernelName)
{
CV_DbgAssert( dst.rows == mask.rows && dst.cols == mask.cols);
std::vector<std::pair<size_t , const void *> > args;
size_t localThreads[3] = { 16, 16, 1 };
size_t globalThreads[3] = { dst.cols, dst.rows, 1 };
int step_in_pixel = dst.step / dst.elemSize(), offset_in_pixel = dst.offset / dst.elemSize();
const char * const typeMap[] = { "uchar", "char", "ushort", "short", "int", "float", "double" };
const char channelMap[] = { ' ', ' ', '2', '4', '4' };
std::string buildOptions = format("-D GENTYPE=%s%c", typeMap[dst.depth()], channelMap[dst.channels()]);
oclMat m(Mat(1, 1, dst.type(), scalar));
args.push_back( std::make_pair( sizeof(cl_mem) , (void *)&m.data ));
args.push_back( std::make_pair( sizeof(cl_mem) , (void *)&dst.data ));
args.push_back( std::make_pair( sizeof(cl_int) , (void *)&dst.cols ));
args.push_back( std::make_pair( sizeof(cl_int) , (void *)&dst.rows ));
args.push_back( std::make_pair( sizeof(cl_int) , (void *)&step_in_pixel ));
args.push_back( std::make_pair( sizeof(cl_int) , (void *)&offset_in_pixel ));
args.push_back( std::make_pair( sizeof(cl_mem) , (void *)&mask.data ));
args.push_back( std::make_pair( sizeof(cl_int) , (void *)&mask.step ));
args.push_back( std::make_pair( sizeof(cl_int) , (void *)&mask.offset ));
openCLExecuteKernel(dst.clCxt , &operator_setToM, kernelName, globalThreads,
localThreads, args, -1, -1, buildOptions.c_str());
}
static void pyrDown_cus(const oclMat &src, oclMat &dst)
{
CV_Assert(src.depth() <= CV_32F && src.channels() <= 4);
dst.create((src.rows + 1) / 2, (src.cols + 1) / 2, src.type());
pyrdown_run_cus(src, dst);
}
void cv::ocl::blendLinear(const oclMat &img1, const oclMat &img2, const oclMat &weights1, const oclMat &weights2,
oclMat &result)
{
cv::ocl::Context *ctx = img1.clCxt;
assert(ctx == img2.clCxt && ctx == weights1.clCxt && ctx == weights2.clCxt);
int channels = img1.oclchannels();
int depth = img1.depth();
int rows = img1.rows;
int cols = img1.cols;
int istep = img1.step1();
int wstep = weights1.step1();
size_t globalSize[] = {cols * channels / 4, rows, 1};
size_t localSize[] = {256, 1, 1};
vector< pair<size_t, const void *> > args;
if(globalSize[0] != 0)
{
args.push_back( make_pair( sizeof(cl_mem), (void *)&result.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&img1.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&img2.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&weights1.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&weights2.data ));
args.push_back( make_pair( sizeof(cl_int), (void *)&rows ));
args.push_back( make_pair( sizeof(cl_int), (void *)&cols ));
args.push_back( make_pair( sizeof(cl_int), (void *)&istep ));
args.push_back( make_pair( sizeof(cl_int), (void *)&wstep ));
std::string kernelName = "BlendLinear";
openCLExecuteKernel(ctx, &blend_linear, kernelName, globalSize, localSize, args, channels, depth);
}
}
static void toRGB5x5_caller(const oclMat &src, oclMat &dst, int bidx, int greenbits, const std::string & kernelName)
{
std::string build_options = format("-D DEPTH_%d -D greenbits=%d -D scn=%d -D bidx=%d",
src.depth(), greenbits, src.channels(), bidx);
int src_offset = (int)src.offset, src_step = (int)src.step;
int dst_offset = dst.offset >> 1, dst_step = dst.step >> 1;
vector<pair<size_t , const void *> > args;
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst.cols));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst.rows));
args.push_back( make_pair( sizeof(cl_int) , (void *)&src_step));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst_step));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&src.data));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst.data));
args.push_back( make_pair( sizeof(cl_int) , (void *)&src_offset ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst_offset ));
size_t gt[3] = { dst.cols, dst.rows, 1 };
#ifdef ANDROID
size_t lt[3] = { 16, 10, 1 };
#else
size_t lt[3] = { 16, 16, 1 };
#endif
openCLExecuteKernel(src.clCxt, &cvt_color, kernelName.c_str(), gt, lt, args, -1, -1, build_options.c_str());
}
// radiusMatchSingle
void cv::ocl::BruteForceMatcher_OCL_base::radiusMatchSingle(const oclMat &query, const oclMat &train,
oclMat &trainIdx, oclMat &distance, oclMat &nMatches, float maxDistance, const oclMat &mask)
{
if (query.empty() || train.empty())
return;
const int nQuery = query.rows;
const int nTrain = train.rows;
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 == query.rows && trainIdx.size() == distance.size()));
ensureSizeIsEnough(1, nQuery, CV_32SC1, nMatches);
if (trainIdx.empty())
{
ensureSizeIsEnough(nQuery, std::max((nTrain / 100), 10), CV_32SC1, trainIdx);
ensureSizeIsEnough(nQuery, std::max((nTrain / 100), 10), CV_32FC1, distance);
}
nMatches.setTo(Scalar::all(0));
matchDispatcher(query, train, maxDistance, mask, trainIdx, distance, nMatches, distType);
return;
}
static void RGB_caller(const oclMat &src, oclMat &dst, bool reverse)
{
int src_offset = src.offset / src.elemSize1(), src_step = src.step1();
int dst_offset = dst.offset / dst.elemSize1(), dst_step = dst.step1();
std::string build_options = format("-D DEPTH_%d -D dcn=%d -D scn=%d -D %s",
src.depth(), dst.channels(), src.channels(), reverse ? "REVERSE" : "ORDER");
vector<pair<size_t , const void *> > args;
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst.cols));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst.rows));
args.push_back( make_pair( sizeof(cl_int) , (void *)&src_step));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst_step));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&src.data));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst.data));
args.push_back( make_pair( sizeof(cl_int) , (void *)&src_offset ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst_offset ));
size_t gt[3] = { dst.cols, dst.rows, 1 };
#ifdef ANDROID
size_t lt[3] = { 16, 10, 1 };
#else
size_t lt[3] = { 16, 16, 1 };
#endif
openCLExecuteKernel(src.clCxt, &cvt_color, "RGB", gt, lt, args, -1, -1, build_options.c_str());
}
void set_to_withmask_run(const oclMat &dst, const Scalar &scalar, const oclMat &mask, string kernelName)
{
CV_DbgAssert( dst.rows == mask.rows && dst.cols == mask.cols);
vector<pair<size_t , const void *> > args;
cl_float4 val;
val.s[0] = scalar.val[0];
val.s[1] = scalar.val[1];
val.s[2] = scalar.val[2];
val.s[3] = scalar.val[3];
size_t localThreads[3] = {16, 16, 1};
size_t globalThreads[3];
globalThreads[0] = (dst.cols + localThreads[0] - 1) / localThreads[0] * localThreads[0];
globalThreads[1] = (dst.rows + localThreads[1] - 1) / localThreads[1] * localThreads[1];
globalThreads[2] = 1;
if(dst.type() == CV_8UC1)
{
globalThreads[0] = ((dst.cols + 4) / 4 + localThreads[0] - 1) / localThreads[0] * localThreads[0];
}
int step_in_pixel = dst.step / dst.elemSize(), offset_in_pixel = dst.offset / dst.elemSize();
args.push_back( make_pair( sizeof(cl_float4) , (void *)&val ));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst.data ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst.cols ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst.rows ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&step_in_pixel ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&offset_in_pixel ));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&mask.data ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&mask.step ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&mask.offset ));
openCLExecuteKernel(dst.clCxt , &operator_setToM, kernelName, globalThreads,
localThreads, args, dst.channels(), dst.depth());
}
////////////////////////////////////////////////////////////////////////////
////////////////////merge//////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////
void merge_vector_run_no_roi(const oclMat *mat_src, size_t n, oclMat &mat_dst)
{
Context *clCxt = mat_dst.clCxt;
int channels = mat_dst.channels();
int depth = mat_dst.depth();
string kernelName = "merge_vector";
int indexes[4][7] = {{0, 0, 0, 0, 0, 0, 0},
{4, 4, 2, 2, 1, 1, 1},
{4, 4, 2, 2 , 1, 1, 1},
{4, 4, 2, 2, 1, 1, 1}
};
size_t index = indexes[channels-1][mat_dst.depth()];
int cols = divUp(mat_dst.cols, index);
size_t localThreads[3] = { 64, 4, 1 };
size_t globalThreads[3] = { divUp(cols, localThreads[0]) * localThreads[0],
divUp(mat_dst.rows, localThreads[1]) * localThreads[1],
1
};
vector<pair<size_t , const void *> > args;
args.push_back( make_pair( sizeof(cl_int), (void *)&mat_dst.rows));
args.push_back( make_pair( sizeof(cl_int), (void *)&cols));
args.push_back( make_pair( sizeof(cl_mem), (void *)&mat_dst.data));
args.push_back( make_pair( sizeof(cl_int), (void *)&mat_dst.step));
args.push_back( make_pair( sizeof(cl_mem), (void *)&mat_src[0].data));
args.push_back( make_pair( sizeof(cl_int), (void *)&mat_src[0].step));
args.push_back( make_pair( sizeof(cl_mem), (void *)&mat_src[1].data));
args.push_back( make_pair( sizeof(cl_int), (void *)&mat_src[1].step));
if(n >= 3)
{
args.push_back( make_pair( sizeof(cl_mem), (void *)&mat_src[2].data));
args.push_back( make_pair( sizeof(cl_int), (void *)&mat_src[2].step));
}
if(n >= 4)
{
args.push_back( make_pair( sizeof(cl_mem), (void *)&mat_src[3].data));
args.push_back( make_pair( sizeof(cl_int), (void *)&mat_src[3].step));
}
openCLExecuteKernel(clCxt, &merge_mat, kernelName, globalThreads, localThreads, args, channels, depth);
}
void cv::ocl::pyrDown(const oclMat &src, oclMat &dst)
{
int depth = src.depth(), channels = src.channels();
CV_Assert(depth == CV_8U || depth == CV_16U || depth == CV_16S || depth == CV_32F);
CV_Assert(channels == 1 || channels == 3 || channels == 4);
dst.create((src.rows + 1) / 2, (src.cols + 1) / 2, src.type());
pyrdown_run(src, dst);
}
///////////////////////////////////////////////////////////////////////////
////////////////////////////////// CopyTo /////////////////////////////////
///////////////////////////////////////////////////////////////////////////
void copy_to_with_mask(const oclMat &src, oclMat &dst, const oclMat &mask, string kernelName)
{
CV_DbgAssert( dst.rows == mask.rows && dst.cols == mask.cols &&
src.rows == dst.rows && src.cols == dst.cols);
vector<pair<size_t , const void *> > args;
int vector_lengths[4][7] = {{4, 4, 2, 2, 1, 1, 1},
{2, 2, 1, 1, 1, 1, 1},
{8, 8, 8, 8 , 4, 4, 4}, //vector length is undefined when channels = 3
{1, 1, 1, 1, 1, 1, 1}
};
size_t localThreads[3] = {16, 16, 1};
size_t globalThreads[3];
int vector_length = vector_lengths[dst.channels() -1][dst.depth()];
int offset_cols = divUp(dst.offset, dst.elemSize()) & (vector_length - 1);
int cols = vector_length == 1 ? divUp(dst.cols, vector_length) : divUp(dst.cols + offset_cols, vector_length);
globalThreads[0] = divUp(cols, localThreads[0]) * localThreads[0];
globalThreads[1] = divUp(dst.rows, localThreads[1]) * localThreads[1];
globalThreads[2] = 1;
int dststep_in_pixel = dst.step / dst.elemSize(), dstoffset_in_pixel = dst.offset / dst.elemSize();
int srcstep_in_pixel = src.step / src.elemSize(), srcoffset_in_pixel = src.offset / src.elemSize();
args.push_back( make_pair( sizeof(cl_mem) , (void *)&src.data ));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst.data ));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&mask.data ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&src.cols ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&src.rows ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&srcstep_in_pixel ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&srcoffset_in_pixel ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dststep_in_pixel ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dstoffset_in_pixel ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&mask.step ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&mask.offset ));
openCLExecuteKernel(dst.clCxt , &operator_copyToM, kernelName, globalThreads,
localThreads, args, dst.channels(), dst.depth());
}
// knn match
void cv::ocl::BruteForceMatcher_OCL_base::knnMatchSingle(const oclMat &query, const oclMat &train, oclMat &trainIdx,
oclMat &distance, oclMat &allDist, int k, const oclMat &mask)
{
if (query.empty() || train.empty())
return;
// match1 doesn't support signed char type, match2 only support float, hamming support uchar, ushort and int
int callType = query.depth();
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)))
{
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);
if (k == 2)
{
trainIdx.create(1, query.rows, CV_32SC2);
distance.create(1, query.rows, CV_32FC2);
}
else
{
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));
kmatchDispatcher(query, train, k, mask, trainIdx, distance, allDist, distType);
exit:
return;
}
void cv::ocl::BruteForceMatcher_OCL_base::radiusMatchCollection(const oclMat &query, oclMat &trainIdx, oclMat &imgIdx, oclMat &distance,
oclMat &nMatches, float /*maxDistance*/, const std::vector<oclMat> &masks)
{
if (query.empty() || empty())
return;
typedef void (*caller_t)(const oclMat & query, const oclMat * trains, int n, float maxDistance, const oclMat * masks,
const oclMat & trainIdx, const oclMat & imgIdx, const oclMat & distance, const oclMat & nMatches);
#if 0
static const caller_t callers[3][6] =
{
{
ocl_matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
ocl_matchL1_gpu<unsigned short>, matchL1_gpu<short>,
ocl_matchL1_gpu<int>, 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>*/
}
};
#endif
const int nQuery = query.rows;
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
CV_Assert(trainIdx.empty() || (trainIdx.rows == nQuery && trainIdx.size() == distance.size() && trainIdx.size() == imgIdx.size()));
nMatches.create(1, nQuery, CV_32SC1);
if (trainIdx.empty())
{
trainIdx.create(nQuery, std::max((nQuery / 100), 10), CV_32SC1);
imgIdx.create(nQuery, std::max((nQuery / 100), 10), CV_32SC1);
distance.create(nQuery, std::max((nQuery / 100), 10), CV_32FC1);
}
nMatches.setTo(Scalar::all(0));
//caller_t func = callers[distType][query.depth()];
//CV_Assert(func != 0);
std::vector<oclMat> trains_(trainDescCollection.begin(), trainDescCollection.end());
std::vector<oclMat> masks_(masks.begin(), masks.end());
/* func(query, &trains_[0], static_cast<int>(trains_.size()), maxDistance, masks_.size() == 0 ? 0 : &masks_[0],
trainIdx, imgIdx, distance, nMatches));*/
}
static void convert_run(const oclMat &src, oclMat &dst, double alpha, double beta)
{
String kernelName = "convert_to";
float alpha_f = alpha, beta_f = beta;
int sdepth = src.depth(), ddepth = dst.depth();
int sstep1 = (int)src.step1(), dstep1 = (int)dst.step1();
int cols1 = src.cols * src.oclchannels();
char buildOptions[150], convertString[50];
const char * typeMap[] = { "uchar", "char", "ushort", "short", "int", "float", "double" };
sprintf(convertString, "convert_%s_sat_rte", typeMap[ddepth]);
sprintf(buildOptions, "-D srcT=%s -D dstT=%s -D convertToDstType=%s", typeMap[sdepth],
typeMap[ddepth], CV_32F == ddepth || ddepth == CV_64F ? "" : convertString);
CV_DbgAssert(src.rows == dst.rows && src.cols == dst.cols);
std::vector<std::pair<size_t , const void *> > args;
size_t localThreads[3] = { 16, 16, 1 };
size_t globalThreads[3] = { divUp(cols1, localThreads[0]) * localThreads[0],
divUp(dst.rows, localThreads[1]) * localThreads[1], 1
};
int doffset1 = dst.offset / dst.elemSize1();
int soffset1 = src.offset / src.elemSize1();
args.push_back( std::make_pair( sizeof(cl_mem) , (void *)&src.data ));
args.push_back( std::make_pair( sizeof(cl_mem) , (void *)&dst.data ));
args.push_back( std::make_pair( sizeof(cl_int) , (void *)&cols1 ));
args.push_back( std::make_pair( sizeof(cl_int) , (void *)&src.rows ));
args.push_back( std::make_pair( sizeof(cl_int) , (void *)&sstep1 ));
args.push_back( std::make_pair( sizeof(cl_int) , (void *)&soffset1 ));
args.push_back( std::make_pair( sizeof(cl_int) , (void *)&dstep1 ));
args.push_back( std::make_pair( sizeof(cl_int) , (void *)&doffset1 ));
args.push_back( std::make_pair( sizeof(cl_float) , (void *)&alpha_f ));
args.push_back( std::make_pair( sizeof(cl_float) , (void *)&beta_f ));
openCLExecuteKernel(dst.clCxt , &operator_convertTo, kernelName, globalThreads,
localThreads, args, -1, -1, buildOptions);
}
void cv::ocl::BruteForceMatcher_OCL_base::matchSingle(const oclMat &query, const oclMat &train,
oclMat &trainIdx, oclMat &distance, const oclMat &mask)
{
if (query.empty() || train.empty())
return;
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
CV_Assert(train.cols == query.cols && train.type() == query.type());
ensureSizeIsEnough(1, query.rows, CV_32S, trainIdx);
ensureSizeIsEnough(1, query.rows, CV_32F, distance);
matchDispatcher(query, train, mask, trainIdx, distance, distType);
return;
}
////////////////////////////////////////////////////////////////////////
// convert_C4C3
static void convert_C4C3(const oclMat &src, cl_mem &dst)
{
int srcStep_in_pixel = src.step1() / src.oclchannels();
int pixel_end = src.wholecols * src.wholerows - 1;
Context *clCxt = src.clCxt;
string kernelName = "convertC4C3";
char compile_option[32];
switch(src.depth())
{
case 0:
sprintf(compile_option, "-D GENTYPE4=uchar4");
break;
case 1:
sprintf(compile_option, "-D GENTYPE4=char4");
break;
case 2:
sprintf(compile_option, "-D GENTYPE4=ushort4");
break;
case 3:
sprintf(compile_option, "-D GENTYPE4=short4");
break;
case 4:
sprintf(compile_option, "-D GENTYPE4=int4");
break;
case 5:
sprintf(compile_option, "-D GENTYPE4=float4");
break;
case 6:
sprintf(compile_option, "-D GENTYPE4=double4");
break;
default:
CV_Error(CV_StsUnsupportedFormat, "unknown depth");
}
vector< pair<size_t, const void *> > args;
args.push_back( make_pair( sizeof(cl_mem), (void *)&src.data));
args.push_back( make_pair( sizeof(cl_mem), (void *)&dst));
args.push_back( make_pair( sizeof(cl_int), (void *)&src.wholecols));
args.push_back( make_pair( sizeof(cl_int), (void *)&src.wholerows));
args.push_back( make_pair( sizeof(cl_int), (void *)&srcStep_in_pixel));
args.push_back( make_pair( sizeof(cl_int), (void *)&pixel_end));
size_t globalThreads[3] = {((src.wholecols * src.wholerows + 3) / 4 + 255) / 256 * 256, 1, 1};
size_t localThreads[3] = {256, 1, 1};
openCLExecuteKernel(clCxt, &convertC3C4, kernelName, globalThreads, localThreads, args, -1, -1, compile_option);
}
static void fromRGB_caller(const oclMat &src, oclMat &dst, int bidx, const std::string & kernelName,
const std::string & additionalOptions = std::string(),
const oclMat & data1 = oclMat(), const oclMat & data2 = oclMat())
{
int src_offset = src.offset / src.elemSize1(), src_step = src.step1();
int dst_offset = dst.offset / dst.elemSize1(), dst_step = dst.step1();
int pixels_per_work_item = 1;
if (Context::getContext()->supportsFeature(FEATURE_CL_INTEL_DEVICE))
{
if ((src.cols % 4 == 0) && (src.depth() == CV_8U))
pixels_per_work_item = 4;
else if (src.cols % 2 == 0)
pixels_per_work_item = 2;
else
pixels_per_work_item = 1;
}
std::string build_options = format("-D DEPTH_%d -D scn=%d -D bidx=%d -D pixels_per_work_item=%d", src.depth(), src.oclchannels(), bidx, pixels_per_work_item);
if (!additionalOptions.empty())
build_options += additionalOptions;
vector<pair<size_t , const void *> > args;
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst.cols));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst.rows));
args.push_back( make_pair( sizeof(cl_int) , (void *)&src_step));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst_step));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&src.data));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst.data));
args.push_back( make_pair( sizeof(cl_int) , (void *)&src_offset ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst_offset ));
if (!data1.empty())
args.push_back( make_pair( sizeof(cl_mem) , (void *)&data1.data ));
if (!data2.empty())
args.push_back( make_pair( sizeof(cl_mem) , (void *)&data2.data ));
size_t gt[3] = { dst.cols/pixels_per_work_item, dst.rows, 1 };
#ifdef ANDROID
size_t lt[3] = { 16, 10, 1 };
#else
size_t lt[3] = { 16, 16, 1 };
#endif
openCLExecuteKernel(src.clCxt, &cvt_color, kernelName.c_str(), gt, lt, args, -1, -1, build_options.c_str());
}
void cv::ocl::BruteForceMatcher_OCL_base::matchCollection(const oclMat &query, const oclMat &trainCollection, oclMat &trainIdx,
oclMat &imgIdx, oclMat &distance, const oclMat &masks)
{
if (query.empty() || trainCollection.empty())
return;
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
const int nQuery = query.rows;
ensureSizeIsEnough(1, nQuery, CV_32S, trainIdx);
ensureSizeIsEnough(1, nQuery, CV_32S, imgIdx);
ensureSizeIsEnough(1, nQuery, CV_32F, distance);
matchDispatcher(query, &trainCollection, trainCollection.cols, masks, trainIdx, imgIdx, distance, distType);
return;
}
