void cv::ocl::oclMat::copyTo( oclMat &m ) const
{
CV_DbgAssert(!this->empty());
m.create(size(), type());
openCLCopyBuffer2D(clCxt, m.data, m.step, m.offset,
data, step, cols * elemSize(), rows, offset, clMemcpyDeviceToDevice);
}
////////////////////////////////////////////////////////////////////////
// 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);
}
void cv::ocl::device::mog::getBackgroundImage2_ocl(int cn, const oclMat& modesUsed, const oclMat& weight, const oclMat& mean, oclMat& dst, int nmixtures)
{
Context* clCxt = Context::getContext();
size_t local_thread[] = {32, 8, 1};
size_t global_thread[] = {modesUsed.cols, modesUsed.rows, 1};
int weight_step = (int)(weight.step/weight.elemSize());
int modesUsed_step = (int)(modesUsed.step/modesUsed.elemSize());
int mean_step = (int)(mean.step/mean.elemSize());
int dst_step = (int)(dst.step/dst.elemSize());
int dst_y = (int)(dst.offset/dst.step);
int dst_x = (int)(dst.offset%dst.step);
dst_x = dst_x/(int)dst.elemSize();
String kernel_name = "getBackgroundImage2_kernel";
std::vector<std::pair<size_t, const void*> > args;
char build_option[50];
if(cn == 1)
{
snprintf(build_option, 50, "-D CN1 -D NMIXTURES=%d", nmixtures);
}else
{
snprintf(build_option, 50, "-D NMIXTURES=%d", nmixtures);
}
args.push_back(std::make_pair(sizeof(cl_mem), (void*)&modesUsed.data));
args.push_back(std::make_pair(sizeof(cl_mem), (void*)&weight.data));
args.push_back(std::make_pair(sizeof(cl_mem), (void*)&mean.data));
args.push_back(std::make_pair(sizeof(cl_mem), (void*)&dst.data));
args.push_back(std::make_pair(sizeof(cl_float), (void*)&c_TB));
args.push_back(std::make_pair(sizeof(cl_int), (void*)&modesUsed.rows));
args.push_back(std::make_pair(sizeof(cl_int), (void*)&modesUsed.cols));
args.push_back(std::make_pair(sizeof(cl_int), (void*)&modesUsed_step));
args.push_back(std::make_pair(sizeof(cl_int), (void*)&weight_step));
args.push_back(std::make_pair(sizeof(cl_int), (void*)&mean_step));
args.push_back(std::make_pair(sizeof(cl_int), (void*)&dst_step));
args.push_back(std::make_pair(sizeof(cl_int), (void*)&dst_x));
args.push_back(std::make_pair(sizeof(cl_int), (void*)&dst_y));
openCLExecuteKernel(clCxt, &bgfg_mog, kernel_name, global_thread, local_thread, args, -1, -1, build_option);
}
void cv::ocl::FAST_OCL::operator ()(const oclMat& image, const oclMat& mask, std::vector<KeyPoint>& keypoints)
{
if (image.empty())
return;
(*this)(image, mask, d_keypoints_);
downloadKeypoints(d_keypoints_, keypoints);
}
void cv::ocl::FAST_OCL::downloadKeypoints(const oclMat& d_keypoints, std::vector<KeyPoint>& keypoints)
{
if (d_keypoints.empty())
return;
Mat h_keypoints(d_keypoints);
convertKeypoints(h_keypoints, keypoints);
}
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());
}
int cv::ocl::FAST_OCL::calcKeypointsOCL(const oclMat& img, const oclMat& mask, int maxKeypoints)
{
size_t localThreads[3] = {16, 16, 1};
size_t globalThreads[3] = {divUp(img.cols - 6, localThreads[0]) * localThreads[0],
divUp(img.rows - 6, localThreads[1]) * localThreads[1],
1
};
Context *clCxt = Context::getContext();
String kernelName = (mask.empty()) ? "calcKeypoints" : "calcKeypointsWithMask";
std::vector< std::pair<size_t, const void *> > args;
int counter = 0;
int err = CL_SUCCESS;
cl_mem counterCL = clCreateBuffer(*(cl_context*)clCxt->getOpenCLContextPtr(),
CL_MEM_COPY_HOST_PTR, sizeof(int),
&counter, &err);
int kpLocStep = kpLoc_.step / kpLoc_.elemSize();
int scoreStep = score_.step / score_.elemSize();
int nms = (nonmaxSupression) ? 1 : 0;
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&img.data));
if (!mask.empty()) args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mask.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&kpLoc_.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&score_.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&counterCL));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&nms));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&maxKeypoints));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&threshold));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&img.step));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&img.rows));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&img.cols));
if (!mask.empty()) args.push_back( std::make_pair( sizeof(cl_int), (void *)&mask.step));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&kpLocStep));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&scoreStep));
openCLExecuteKernel(clCxt, &featdetect_fast, kernelName, globalThreads, localThreads, args, -1, -1);
openCLSafeCall(clEnqueueReadBuffer(*(cl_command_queue*)clCxt->getOpenCLCommandQueuePtr(),
counterCL, CL_TRUE, 0, sizeof(int), &counter, 0, NULL, NULL));
openCLSafeCall(clReleaseMemObject(counterCL));
return counter;
}
// 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 mog_withoutLearning(const oclMat& frame, int cn, oclMat& fgmask, oclMat& weight, oclMat& mean, oclMat& var,
int nmixtures, float varThreshold, float backgroundRatio)
{
Context* clCxt = Context::getContext();
size_t local_thread[] = {32, 8, 1};
size_t global_thread[] = {frame.cols, frame.rows, 1};
int frame_step = (int)(frame.step/frame.elemSize());
int fgmask_step = (int)(fgmask.step/fgmask.elemSize());
int weight_step = (int)(weight.step/weight.elemSize());
int mean_step = (int)(mean.step/mean.elemSize());
int var_step = (int)(var.step/var.elemSize());
int fgmask_offset_y = (int)(fgmask.offset/fgmask.step);
int fgmask_offset_x = (int)(fgmask.offset%fgmask.step);
fgmask_offset_x = fgmask_offset_x/(int)fgmask.elemSize();
int frame_offset_y = (int)(frame.offset/frame.step);
int frame_offset_x = (int)(frame.offset%frame.step);
frame_offset_x = frame_offset_x/(int)frame.elemSize();
char build_option[50];
if(cn == 1)
{
snprintf(build_option, 50, "-D CN1 -D NMIXTURES=%d", nmixtures);
}else
{
snprintf(build_option, 50, "-D NMIXTURES=%d", nmixtures);
}
String kernel_name = "mog_withoutLearning_kernel";
std::vector<std::pair<size_t, const void*> > args;
args.push_back(std::make_pair(sizeof(cl_mem), (void*)&frame.data));
args.push_back(std::make_pair(sizeof(cl_mem), (void*)&fgmask.data));
args.push_back(std::make_pair(sizeof(cl_mem), (void*)&weight.data));
args.push_back(std::make_pair(sizeof(cl_mem), (void*)&mean.data));
args.push_back(std::make_pair(sizeof(cl_mem), (void*)&var.data));
args.push_back(std::make_pair(sizeof(cl_int), (void*)&frame.rows));
args.push_back(std::make_pair(sizeof(cl_int), (void*)&frame.cols));
args.push_back(std::make_pair(sizeof(cl_int), (void*)&frame_step));
args.push_back(std::make_pair(sizeof(cl_int), (void*)&fgmask_step));
args.push_back(std::make_pair(sizeof(cl_int), (void*)&weight_step));
args.push_back(std::make_pair(sizeof(cl_int), (void*)&mean_step));
args.push_back(std::make_pair(sizeof(cl_int), (void*)&var_step));
args.push_back(std::make_pair(sizeof(cl_float), (void*)&varThreshold));
args.push_back(std::make_pair(sizeof(cl_float), (void*)&backgroundRatio));
args.push_back(std::make_pair(sizeof(cl_int), (void*)&fgmask_offset_x));
args.push_back(std::make_pair(sizeof(cl_int), (void*)&fgmask_offset_y));
args.push_back(std::make_pair(sizeof(cl_int), (void*)&frame_offset_x));
args.push_back(std::make_pair(sizeof(cl_int), (void*)&frame_offset_y));
openCLExecuteKernel(clCxt, &bgfg_mog, kernel_name, global_thread, local_thread, args, -1, -1, build_option);
}
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());
}
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;
result.create(img1.size(), CV_MAKE_TYPE(depth,img1.channels()));
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);
}
}
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;
}
////////////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////split/////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////////////
void split_vector_run_no_roi(const oclMat &mat_src, oclMat *mat_dst)
{
Context *clCxt = mat_src.clCxt;
int channels = mat_src.channels();
int depth = mat_src.depth();
string kernelName = "split_vector";
int indexes[4][7] = {{0, 0, 0, 0, 0, 0, 0},
{8, 8, 8, 8, 4, 4, 2},
{8, 8, 8, 8 , 4, 4, 4},
{4, 4, 2, 2, 1, 1, 1}
};
size_t index = indexes[channels-1][mat_dst[0].depth()];
int cols = divUp(mat_src.cols, index);
size_t localThreads[3] = { 64, 4, 1 };
size_t globalThreads[3] = { divUp(cols, localThreads[0]) * localThreads[0],
divUp(mat_src.rows, localThreads[1]) * localThreads[1],
1
};
vector<pair<size_t , const void *> > args;
args.push_back( make_pair( sizeof(cl_mem), (void *)&mat_src.data));
args.push_back( make_pair( sizeof(cl_int), (void *)&mat_src.step));
args.push_back( make_pair( sizeof(cl_int), (void *)&mat_src.rows));
args.push_back( make_pair( sizeof(cl_int), (void *)&cols));
args.push_back( make_pair( sizeof(cl_mem), (void *)&mat_dst[0].data));
args.push_back( make_pair( sizeof(cl_int), (void *)&mat_dst[0].step));
args.push_back( make_pair( sizeof(cl_mem), (void *)&mat_dst[1].data));
args.push_back( make_pair( sizeof(cl_int), (void *)&mat_dst[1].step));
if(channels >= 3)
{
args.push_back( make_pair( sizeof(cl_mem), (void *)&mat_dst[2].data));
args.push_back( make_pair( sizeof(cl_int), (void *)&mat_dst[2].step));
}
if(channels >= 4)
{
args.push_back( make_pair( sizeof(cl_mem), (void *)&mat_dst[3].data));
args.push_back( make_pair( sizeof(cl_int), (void *)&mat_dst[3].step));
}
openCLExecuteKernel(clCxt, &split_mat, kernelName, globalThreads, localThreads, args, channels, depth);
}
void cv::ocl::PyrLKOpticalFlow::dense(const oclMat &prevImg, const oclMat &nextImg, oclMat &u, oclMat &v, oclMat *err)
{
CV_Assert(prevImg.type() == CV_8UC1);
CV_Assert(prevImg.size() == nextImg.size() && prevImg.type() == nextImg.type());
CV_Assert(maxLevel >= 0);
CV_Assert(winSize.width > 2 && winSize.height > 2);
if (err)
err->create(prevImg.size(), CV_32FC1);
prevPyr_.resize(maxLevel + 1);
nextPyr_.resize(maxLevel + 1);
prevPyr_[0] = prevImg;
//nextImg.convertTo(nextPyr_[0], CV_32F);
convertTo(nextImg, nextPyr_[0], CV_32F);
for (int level = 1; level <= maxLevel; ++level)
{
pyrDown_cus(prevPyr_[level - 1], prevPyr_[level]);
pyrDown_cus(nextPyr_[level - 1], nextPyr_[level]);
}
ensureSizeIsEnough(prevImg.size(), CV_32FC1, uPyr_[0]);
ensureSizeIsEnough(prevImg.size(), CV_32FC1, vPyr_[0]);
ensureSizeIsEnough(prevImg.size(), CV_32FC1, uPyr_[1]);
ensureSizeIsEnough(prevImg.size(), CV_32FC1, vPyr_[1]);
//uPyr_[1].setTo(Scalar::all(0));
//vPyr_[1].setTo(Scalar::all(0));
setTo(uPyr_[1], Scalar::all(0));
setTo(vPyr_[1], Scalar::all(0));
Size winSize2i(winSize.width, winSize.height);
int idx = 0;
for (int level = maxLevel; level >= 0; level--)
{
int idx2 = (idx + 1) & 1;
lkDense_run(prevPyr_[level], nextPyr_[level], uPyr_[idx], vPyr_[idx], uPyr_[idx2], vPyr_[idx2],
level == 0 ? err : 0, winSize2i, iters);
if (level > 0)
idx = idx2;
}
//uPyr_[idx].copyTo(u);
//vPyr_[idx].copyTo(v);
copyTo(uPyr_[idx], u);
copyTo(vPyr_[idx], v);
clFinish(prevImg.clCxt->impl->clCmdQueue);
}
////////////////////////////////////////////////////////////////////////
// convert_C4C3
void convert_C4C3(const oclMat &src, cl_mem &dst, int dstStep)
{
int srcStep = src.step1() / src.channels();
Context *clCxt = src.clCxt;
string kernelName = "convertC4C3";
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));
args.push_back( make_pair( sizeof(cl_int), (void *)&dstStep));
size_t globalThreads[3] = {(src.wholecols *src.wholerows + 255) / 256 * 256, 1, 1};
size_t localThreads[3] = {256, 1, 1};
openCLExecuteKernel(clCxt, &convertC3C4, kernelName, globalThreads, localThreads, args, -1, src.elemSize1() >> 1);
}
void cv::ocl::BruteForceMatcher_OCL_base::makeGpuCollection(oclMat &trainCollection, oclMat &maskCollection, const std::vector<oclMat> &masks)
{
if (empty())
return;
if (masks.empty())
{
Mat trainCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(oclMat)));
oclMat *trainCollectionCPU_ptr = trainCollectionCPU.ptr<oclMat>();
for (size_t i = 0, size = trainDescCollection.size(); i < size; ++i, ++trainCollectionCPU_ptr)
*trainCollectionCPU_ptr = trainDescCollection[i];
trainCollection.upload(trainCollectionCPU);
maskCollection.release();
}
else
{
CV_Assert(masks.size() == trainDescCollection.size());
Mat trainCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(oclMat)));
Mat maskCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(oclMat)));
oclMat *trainCollectionCPU_ptr = trainCollectionCPU.ptr<oclMat>();
oclMat *maskCollectionCPU_ptr = maskCollectionCPU.ptr<oclMat>();
for (size_t i = 0, size = trainDescCollection.size(); i < size; ++i, ++trainCollectionCPU_ptr, ++maskCollectionCPU_ptr)
{
const oclMat &train = trainDescCollection[i];
const oclMat &mask = masks[i];
CV_Assert(mask.empty() || (mask.type() == CV_8UC1 && mask.cols == train.rows));
*trainCollectionCPU_ptr = train;
*maskCollectionCPU_ptr = mask;
}
trainCollection.upload(trainCollectionCPU);
maskCollection.upload(maskCollectionCPU);
}
}
void detectKeypoints(oclMat &keypoints)
{
// create image pyramid buffers
// different layers have same sized buffers, but they are sampled from gaussin kernel.
ensureSizeIsEnough(img_rows * (surf_.nOctaveLayers + 2), img_cols, CV_32FC1, surf_.det);
ensureSizeIsEnough(img_rows * (surf_.nOctaveLayers + 2), img_cols, CV_32FC1, surf_.trace);
ensureSizeIsEnough(1, maxCandidates, CV_32SC4, surf_.maxPosBuffer);
ensureSizeIsEnough(SURF_OCL::ROWS_COUNT, maxFeatures, CV_32FC1, keypoints);
keypoints.setTo(Scalar::all(0));
for (int octave = 0; octave < surf_.nOctaves; ++octave)
{
const int layer_rows = img_rows >> octave;
const int layer_cols = img_cols >> octave;
//loadOctaveConstants(octave, layer_rows, layer_cols);
icvCalcLayerDetAndTrace_gpu(surf_.det, surf_.trace, octave, surf_.nOctaveLayers, layer_rows);
icvFindMaximaInLayer_gpu(surf_.det, surf_.trace, surf_.maxPosBuffer, counters, 1 + octave,
octave, use_mask, surf_.nOctaveLayers, layer_rows, layer_cols);
unsigned int maxCounter = Mat(counters).at<unsigned int>(1 + octave);
maxCounter = std::min(maxCounter, static_cast<unsigned int>(maxCandidates));
if (maxCounter > 0)
{
icvInterpolateKeypoint_gpu(surf_.det, surf_.maxPosBuffer, maxCounter,
keypoints, counters, octave, layer_rows, maxFeatures);
}
}
unsigned int featureCounter = Mat(counters).at<unsigned int>(0);
featureCounter = std::min(featureCounter, static_cast<unsigned int>(maxFeatures));
keypoints.cols = featureCounter;
if (surf_.upright)
keypoints.row(SURF_OCL::ANGLE_ROW).setTo(Scalar::all(90.0));
else
findOrientation(keypoints);
}
static void matmul_rbf(oclMat& src, oclMat& src_e, oclMat& dst, int src_rows, int src2_cols, int var_count, double gamma1, bool flag)
{
Context *clCxt = Context::getContext();
String kernelName = "svm_rbf";
int width = var_count;
int src_step = (int)src.step / src.elemSize();
int src_e_step = (int)src_e.step / src_e.elemSize();
int dst_step = (int)dst.step / dst.elemSize();
int x = MIN(16, src_rows);
int y = MIN(16, src2_cols);
size_t localThreads[] = {x, y, 1};
size_t globalThreads[] = {src2_cols, src_rows, 1};
char build_options[50];
if(flag)
sprintf(build_options, "-D ADDEXP");
std::vector< std::pair<size_t, const void *> > args;
args.push_back(std::make_pair(sizeof(cl_mem), (void* )&src.data));
args.push_back(std::make_pair(sizeof(cl_int), (void* )&src_step));
args.push_back(std::make_pair(sizeof(cl_mem), (void* )&src_e.data));
args.push_back(std::make_pair(sizeof(cl_int), (void* )&src_e_step));
args.push_back(std::make_pair(sizeof(cl_mem), (void* )&dst.data));
args.push_back(std::make_pair(sizeof(cl_int), (void* )&dst_step));
args.push_back(std::make_pair(sizeof(cl_int), (void* )&src_rows));
args.push_back(std::make_pair(sizeof(cl_int), (void* )&src2_cols));
args.push_back(std::make_pair(sizeof(cl_int), (void* )&width));
float gamma = 0.0f;
if(!Context::getContext()->supportsFeature(FEATURE_CL_DOUBLE))
{
gamma = (float)gamma1;
args.push_back(std::make_pair(sizeof(cl_float), (void* )&gamma));
}
else
args.push_back(std::make_pair(sizeof(cl_double), (void* )&gamma1));
openCLExecuteKernel(clCxt, &svm, kernelName, globalThreads, localThreads, args, -1, -1, build_options);
}
void cv::ocl::BruteForceMatcher_OCL_base::knnMatch(const oclMat &query, std::vector< std::vector<DMatch> > &matches, int k,
const std::vector<oclMat> &masks, bool compactResult)
{
if (k == 2)
{
oclMat trainCollection;
oclMat maskCollection;
makeGpuCollection(trainCollection, maskCollection, masks);
oclMat trainIdx, imgIdx, distance;
knnMatch2Collection(query, trainCollection, trainIdx, imgIdx, distance, maskCollection);
knnMatch2Download(trainIdx, imgIdx, distance, matches);
}
else
{
if (query.empty() || empty())
return;
std::vector< std::vector<DMatch> > curMatches;
std::vector<DMatch> temp;
temp.reserve(2 * k);
matches.resize(query.rows);
std::for_each(matches.begin(), matches.end(), std::bind2nd(std::mem_fun_ref(&std::vector<DMatch>::reserve), k));
for (size_t imgIdx = 0, size = trainDescCollection.size(); imgIdx < size; ++imgIdx)
{
knnMatch(query, trainDescCollection[imgIdx], curMatches, k, masks.empty() ? oclMat() : masks[imgIdx]);
for (int queryIdx = 0; queryIdx < query.rows; ++queryIdx)
{
std::vector<DMatch> &localMatch = curMatches[queryIdx];
std::vector<DMatch> &globalMatch = matches[queryIdx];
for_each(localMatch.begin(), localMatch.end(), ImgIdxSetter(static_cast<int>(imgIdx)));
temp.clear();
merge(globalMatch.begin(), globalMatch.end(), localMatch.begin(), localMatch.end(), back_inserter(temp));
globalMatch.clear();
const size_t count = std::min((size_t)k, temp.size());
copy(temp.begin(), temp.begin() + count, back_inserter(globalMatch));
}
}
if (compactResult)
{
std::vector< std::vector<DMatch> >::iterator new_end = std::remove_if(matches.begin(), matches.end(), std::mem_fun_ref(&std::vector<DMatch>::empty));
matches.erase(new_end, matches.end());
}
}
}
static void split(const oclMat &mat_src, oclMat *mat_dst)
{
CV_Assert(mat_dst);
int depth = mat_src.depth();
int num_channels = mat_src.oclchannels();
Size size = mat_src.size();
if(num_channels == 1)
{
mat_src.copyTo(mat_dst[0]);
return;
}
int i;
for(i = 0; i < num_channels; i++)
mat_dst[i].create(size, CV_MAKETYPE(depth, 1));
split_vector_run(mat_src, mat_dst);
}
void cv::ocl::buildWarpPlaneMaps(Size /*src_size*/, Rect dst_roi, const Mat &K, const Mat &R, const Mat &T,
float scale, oclMat &map_x, oclMat &map_y)
{
CV_Assert(K.size() == Size(3, 3) && K.type() == CV_32F);
CV_Assert(R.size() == Size(3, 3) && R.type() == CV_32F);
CV_Assert((T.size() == Size(3, 1) || T.size() == Size(1, 3)) && T.type() == CV_32F && T.isContinuous());
Mat K_Rinv = K * R.t();
CV_Assert(K_Rinv.isContinuous());
Mat KRT_mat(1, 12, CV_32FC1); // 9 + 3
KRT_mat(Range::all(), Range(0, 8)) = K_Rinv.reshape(1, 1);
KRT_mat(Range::all(), Range(9, 11)) = T;
oclMat KRT_oclMat(KRT_mat);
// transfer K_Rinv and T into a single cl_mem
map_x.create(dst_roi.size(), CV_32F);
map_y.create(dst_roi.size(), CV_32F);
int tl_u = dst_roi.tl().x;
int tl_v = dst_roi.tl().y;
Context *clCxt = Context::getContext();
string kernelName = "buildWarpPlaneMaps";
vector< pair<size_t, const void *> > args;
args.push_back( make_pair( sizeof(cl_mem), (void *)&map_x.data));
args.push_back( make_pair( sizeof(cl_mem), (void *)&map_y.data));
args.push_back( make_pair( sizeof(cl_mem), (void *)&KRT_mat.data));
args.push_back( make_pair( sizeof(cl_int), (void *)&tl_u));
args.push_back( make_pair( sizeof(cl_int), (void *)&tl_v));
args.push_back( make_pair( sizeof(cl_int), (void *)&map_x.cols));
args.push_back( make_pair( sizeof(cl_int), (void *)&map_x.rows));
args.push_back( make_pair( sizeof(cl_int), (void *)&map_x.step));
args.push_back( make_pair( sizeof(cl_int), (void *)&map_y.step));
args.push_back( make_pair( sizeof(cl_float), (void *)&scale));
size_t globalThreads[3] = {map_x.cols, map_x.rows, 1};
size_t localThreads[3] = {32, 8, 1};
openCLExecuteKernel(clCxt, &build_warps, kernelName, globalThreads, localThreads, args, -1, -1);
}
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);
}
///////////////////////////////////////////////////////////////////////////
////////////////////////////////// CopyTo /////////////////////////////////
///////////////////////////////////////////////////////////////////////////
static 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
&& mask.type() == CV_8UC1);
vector<pair<size_t , const void *> > args;
std::string string_types[4][7] = {{"uchar", "char", "ushort", "short", "int", "float", "double"},
{"uchar2", "char2", "ushort2", "short2", "int2", "float2", "double2"},
{"uchar3", "char3", "ushort3", "short3", "int3", "float3", "double3"},
{"uchar4", "char4", "ushort4", "short4", "int4", "float4", "double4"}
};
char compile_option[32];
sprintf(compile_option, "-D GENTYPE=%s", string_types[dst.oclchannels() - 1][dst.depth()].c_str());
size_t localThreads[3] = {16, 16, 1};
size_t globalThreads[3];
globalThreads[0] = divUp(dst.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, -1, -1, compile_option);
}
///////////////////////////////////////////////////////////////////////////
//////////////////////////////// ConvertTo ////////////////////////////////
///////////////////////////////////////////////////////////////////////////
static void convert_run_cus(const oclMat &src, oclMat &dst, double alpha, double beta)
{
std::string kernelName = "convert_to_S";
std::stringstream idxStr;
idxStr << src.depth();
kernelName += idxStr.str();
float alpha_f = (float)alpha, beta_f = (float)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 ));
openCLExecuteKernel2(dst.clCxt , &operator_convertTo, kernelName, globalThreads,
localThreads, args, dst.oclchannels(), dst.depth(), CLFLUSH);
}
static void matmul_linear(oclMat & src, oclMat & src2, oclMat & dst, int src_rows, int src2_cols, int var_count, double alpha1, double beta1)
{
Context *clCxt = Context::getContext();
String kernelName = "svm_linear";
int src_step = (int)src.step / src.elemSize();
int src2_step = (int)src2.step / src2.elemSize();
int dst_step = (int)dst.step / dst.elemSize();
int x = MIN(16, src_rows);
int y = MIN(16, src2_cols);
size_t localThreads[] = {x, y, 1};
size_t globalThreads[] = {src2_cols, src_rows, 1};
int width = var_count;
std::vector< std::pair<size_t, const void *> > args;
args.push_back(std::make_pair(sizeof(cl_mem), (void* )&src.data));
args.push_back(std::make_pair(sizeof(cl_int), (void* )&src_step));
args.push_back(std::make_pair(sizeof(cl_mem), (void* )&src2.data));
args.push_back(std::make_pair(sizeof(cl_int), (void* )&src2_step));
args.push_back(std::make_pair(sizeof(cl_mem), (void* )&dst.data));
args.push_back(std::make_pair(sizeof(cl_int), (void* )&dst_step));
args.push_back(std::make_pair(sizeof(cl_int), (void* )&src_rows));
args.push_back(std::make_pair(sizeof(cl_int), (void* )&src2_cols));
args.push_back(std::make_pair(sizeof(cl_int), (void* )&width));
float alpha = 0.0f, beta = 0.0f;
if(!Context::getContext()->supportsFeature(FEATURE_CL_DOUBLE))
{
alpha = (float)alpha1;
beta = (float)beta1;
args.push_back(std::make_pair(sizeof(cl_float), (void* )&alpha));
args.push_back(std::make_pair(sizeof(cl_float), (void* )&beta));
}
else
{
args.push_back(std::make_pair(sizeof(cl_double), (void* )&alpha1));
args.push_back(std::make_pair(sizeof(cl_double), (void* )&beta1));
}
openCLExecuteKernel(clCxt, &svm, kernelName, globalThreads, localThreads, args, -1, -1);
}
// FIXME:
// This function cannot sort arrays with duplicated keys
static void sortByKey(oclMat& keys, oclMat& vals, size_t vecSize, bool isGreaterThan)
{
CV_Error(-1, "This function is incorrect at the moment.");
Context * cxt = Context::getContext();
size_t globalThreads[3] = {vecSize, 1, 1};
std::vector< std::pair<size_t, const void *> > args;
char build_opt_buf [100];
genSortBuildOption(keys, vals, isGreaterThan, build_opt_buf);
//local
String kernelname = "selectionSortLocal";
#ifdef ANDROID
int lds_size = cxt->getDeviceInfo().maxWorkGroupSize * keys.elemSize();
#else
int lds_size = GROUP_SIZE * keys.elemSize();
#endif
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&keys.data));
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&vals.data));
args.push_back(std::make_pair(sizeof(cl_int), (void *)&vecSize));
args.push_back(std::make_pair(lds_size, (void*)NULL));
#ifdef ANDROID
openCLExecuteKernel(cxt, &kernel_sort_by_key, kernelname, globalThreads, NULL, args, -1, -1, build_opt_buf);
#else
size_t localThreads[3] = {GROUP_SIZE, 1, 1};
openCLExecuteKernel(cxt, &kernel_sort_by_key, kernelname, globalThreads, localThreads, args, -1, -1, build_opt_buf);
#endif
//final
kernelname = "selectionSortFinal";
args.pop_back();
#ifdef ANDROID
openCLExecuteKernel(cxt, &kernel_sort_by_key, kernelname, globalThreads, NULL, args, -1, -1, build_opt_buf);
#else
openCLExecuteKernel(cxt, &kernel_sort_by_key, kernelname, globalThreads, localThreads, args, -1, -1, build_opt_buf);
#endif
}
SURF_OCL_Invoker(SURF_OCL &surf, const oclMat &img, const oclMat &mask) :
surf_(surf),
img_cols(img.cols), img_rows(img.rows),
use_mask(!mask.empty()), counters(oclMat()),
imgTex(NULL), sumTex(NULL), maskSumTex(NULL), _img(img)
{
CV_Assert(!img.empty() && img.type() == CV_8UC1);
CV_Assert(mask.empty() || (mask.size() == img.size() && mask.type() == CV_8UC1));
CV_Assert(surf_.nOctaves > 0 && surf_.nOctaveLayers > 0);
const int min_size = calcSize(surf_.nOctaves - 1, 0);
CV_Assert(img_rows - min_size >= 0);
CV_Assert(img_cols - min_size >= 0);
const int layer_rows = img_rows >> (surf_.nOctaves - 1);
const int layer_cols = img_cols >> (surf_.nOctaves - 1);
const int min_margin = ((calcSize((surf_.nOctaves - 1), 2) >> 1) >> (surf_.nOctaves - 1)) + 1;
CV_Assert(layer_rows - 2 * min_margin > 0);
CV_Assert(layer_cols - 2 * min_margin > 0);
maxFeatures = std::min(static_cast<int>(img.size().area() * surf.keypointsRatio), 65535);
maxCandidates = std::min(static_cast<int>(1.5 * maxFeatures), 65535);
CV_Assert(maxFeatures > 0);
counters.create(1, surf_.nOctaves + 1, CV_32SC1);
counters.setTo(Scalar::all(0));
integral(img, surf_.sum);
if(support_image2d())
{
bindImgTex(img, imgTex);
bindImgTex(surf_.sum, sumTex);
}
maskSumTex = 0;
if (use_mask)
{
CV_Error(CV_StsBadFunc, "Masked SURF detector is not implemented yet");
//!FIXME
// temp fix for missing min overload
//oclMat temp(mask.size(), mask.type());
//temp.setTo(Scalar::all(1.0));
////cv::ocl::min(mask, temp, surf_.mask1); ///////// disable this
//integral(surf_.mask1, surf_.maskSum);
//bindImgTex(surf_.maskSum, maskSumTex);
}
}
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;
const char * const typeMap[] = { "uchar", "char", "ushort", "short", "int", "float", "double" };
std::string buildOptions = format("-D GENTYPE4=%s4", typeMap[src.depth()]);
std::vector< std::pair<size_t, const void *> > args;
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&src.data));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&dst));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&src.wholecols));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&src.wholerows));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&srcStep_in_pixel));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&pixel_end));
size_t globalThreads[3] = { divUp(src.wholecols * src.wholerows, 4), 1, 1};
size_t localThreads[3] = { 256, 1, 1 };
openCLExecuteKernel(clCxt, &convertC3C4, "convertC4C3", globalThreads, localThreads, args, -1, -1, buildOptions.c_str());
}
void cv::ocl::device::mog::getBackgroundImage_ocl(int cn, const oclMat& weight, const oclMat& mean, oclMat& dst, int nmixtures, float backgroundRatio)
{
Context* clCxt = Context::getContext();
size_t local_thread[] = {32, 8, 1};
size_t global_thread[] = {(size_t)dst.cols, (size_t)dst.rows, 1};
int weight_step = (int)(weight.step/weight.elemSize());
int mean_step = (int)(mean.step/mean.elemSize());
int dst_step = (int)(dst.step/dst.elemSize());
char build_option[50];
if(cn == 1)
{
snprintf(build_option, 50, "-D CN1 -D NMIXTURES=%d", nmixtures);
}else
{
snprintf(build_option, 50, "-D NMIXTURES=%d", nmixtures);
}
String kernel_name = "getBackgroundImage_kernel";
vector< pair<size_t, const void*> > args;
args.push_back(make_pair(sizeof(cl_mem), (void*)&weight.data));
args.push_back(make_pair(sizeof(cl_mem), (void*)&mean.data));
args.push_back(make_pair(sizeof(cl_mem), (void*)&dst.data));
args.push_back(make_pair(sizeof(cl_int), (void*)&dst.rows));
args.push_back(make_pair(sizeof(cl_int), (void*)&dst.cols));
args.push_back(make_pair(sizeof(cl_int), (void*)&weight_step));
args.push_back(make_pair(sizeof(cl_int), (void*)&mean_step));
args.push_back(make_pair(sizeof(cl_int), (void*)&dst_step));
args.push_back(make_pair(sizeof(cl_float), (void*)&backgroundRatio));
openCLExecuteKernel(clCxt, &bgfg_mog, kernel_name, global_thread, local_thread, args, -1, -1, build_option);
}
SURF_OCL_Invoker(SURF_OCL &surf, const oclMat &img, const oclMat &mask) :
surf_(surf),
img_cols(img.cols), img_rows(img.rows),
use_mask(!mask.empty()),
imgTex(NULL), sumTex(NULL), maskSumTex(NULL)
{
CV_Assert(!img.empty() && img.type() == CV_8UC1);
CV_Assert(mask.empty() || (mask.size() == img.size() && mask.type() == CV_8UC1));
CV_Assert(surf_.nOctaves > 0 && surf_.nOctaveLayers > 0);
const int min_size = calcSize(surf_.nOctaves - 1, 0);
CV_Assert(img_rows - min_size >= 0);
CV_Assert(img_cols - min_size >= 0);
const int layer_rows = img_rows >> (surf_.nOctaves - 1);
const int layer_cols = img_cols >> (surf_.nOctaves - 1);
const int min_margin = ((calcSize((surf_.nOctaves - 1), 2) >> 1) >> (surf_.nOctaves - 1)) + 1;
CV_Assert(layer_rows - 2 * min_margin > 0);
CV_Assert(layer_cols - 2 * min_margin > 0);
maxFeatures = std::min(static_cast<int>(img.size().area() * surf.keypointsRatio), 65535);
maxCandidates = std::min(static_cast<int>(1.5 * maxFeatures), 65535);
CV_Assert(maxFeatures > 0);
counters.create(1, surf_.nOctaves + 1, CV_32SC1);
counters.setTo(Scalar::all(0));
//loadGlobalConstants(maxCandidates, maxFeatures, img_rows, img_cols, surf_.nOctaveLayers, static_cast<float>(surf_.hessianThreshold));
bindImgTex(img, imgTex);
integral(img, surf_.sum); // the two argumented integral version is incorrect
bindImgTex(surf_.sum, sumTex);
maskSumTex = 0;
if (use_mask)
{
throw std::exception();
//!FIXME
// temp fix for missing min overload
//oclMat temp(mask.size(), mask.type());
//temp.setTo(Scalar::all(1.0));
////cv::ocl::min(mask, temp, surf_.mask1); ///////// disable this
//integral(surf_.mask1, surf_.maskSum);
//bindImgTex(surf_.maskSum, maskSumTex);
}
}