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());
}
// 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;
}
// 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;
}
// 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;
}
void cv::ocl::BruteForceMatcher_OCL_base::matchDownload(const oclMat &trainIdx, const oclMat &distance, std::vector<DMatch> &matches)
{
if (trainIdx.empty() || distance.empty())
return;
Mat trainIdxCPU(trainIdx);
Mat distanceCPU(distance);
matchConvert(trainIdxCPU, distanceCPU, matches);
}
void cv::ocl::BruteForceMatcher_OCL_base::knnMatchDownload(const oclMat &trainIdx, const oclMat &distance, std::vector< std::vector<DMatch> > &matches, bool compactResult)
{
if (trainIdx.empty() || distance.empty())
return;
Mat trainIdxCPU(trainIdx);
Mat distanceCPU(distance);
knnMatchConvert(trainIdxCPU, distanceCPU, matches, compactResult);
}
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));*/
}
void cv::ocl::BruteForceMatcher_OCL_base::radiusMatchDownload(const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance,
const oclMat &nMatches, std::vector< std::vector<DMatch> > &matches, bool compactResult)
{
if (trainIdx.empty() || imgIdx.empty() || distance.empty() || nMatches.empty())
return;
Mat trainIdxCPU(trainIdx);
Mat imgIdxCPU(imgIdx);
Mat distanceCPU(distance);
Mat nMatchesCPU(nMatches);
radiusMatchConvert(trainIdxCPU, imgIdxCPU, distanceCPU, nMatchesCPU, matches, compactResult);
}
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);
}
}
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);
}
}
void cv::ocl::BruteForceMatcher_OCL_base::match(const oclMat &query, const oclMat &train, std::vector<DMatch> &matches, const oclMat &mask)
{
assert(mask.empty()); // mask is not supported at the moment
oclMat trainIdx, distance;
matchSingle(query, train, trainIdx, distance, mask);
matchDownload(trainIdx, distance, matches);
}
int findCorners_caller(
const TextureCL& eig,
const float threshold,
const oclMat& mask,
oclMat& corners,
const int max_count)
{
std::vector<int> k;
Context * cxt = Context::getContext();
std::vector< std::pair<size_t, const void*> > args;
std::string kernelname = "findCorners";
const int mask_strip = mask.step / mask.elemSize1();
oclMat g_counter(1, 1, CV_32SC1);
g_counter.setTo(0);
args.push_back(make_pair( sizeof(cl_mem), (void*)&eig ));
args.push_back(make_pair( sizeof(cl_mem), (void*)&mask.data ));
args.push_back(make_pair( sizeof(cl_mem), (void*)&corners.data ));
args.push_back(make_pair( sizeof(cl_int), (void*)&mask_strip));
args.push_back(make_pair( sizeof(cl_float), (void*)&threshold ));
args.push_back(make_pair( sizeof(cl_int), (void*)&eig.rows ));
args.push_back(make_pair( sizeof(cl_int), (void*)&eig.cols ));
args.push_back(make_pair( sizeof(cl_int), (void*)&max_count ));
args.push_back(make_pair( sizeof(cl_mem), (void*)&g_counter.data ));
size_t globalThreads[3] = {eig.cols, eig.rows, 1};
size_t localThreads[3] = {16, 16, 1};
const char * opt = mask.empty() ? "" : "-D WITH_MASK";
openCLExecuteKernel(cxt, &imgproc_gftt, kernelname, globalThreads, localThreads, args, -1, -1, opt);
return std::min(Mat(g_counter).at<int>(0), max_count);
}
static void copyTo(const oclMat &src, oclMat &m )
{
CV_DbgAssert(!src.empty());
m.create(src.size(), src.type());
openCLCopyBuffer2D(src.clCxt, m.data, m.step, m.offset,
src.data, src.step, src.cols * src.elemSize(), src.rows, src.offset);
}
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;
}
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 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());
}
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;
}
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;
}
void cv::ocl::BruteForceMatcher_OCL_base::knnMatch2Collection(const oclMat &query, const oclMat &trainCollection,
oclMat &trainIdx, oclMat &imgIdx, oclMat &distance, const oclMat &/*maskCollection*/)
{
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);
#if 0
static const caller_t callers[3][6] =
{
{
ocl_match2L1_gpu<unsigned char>, 0/*match2L1_gpu<signed char>*/,
ocl_match2L1_gpu<unsigned short>, ocl_match2L1_gpu<short>,
ocl_match2L1_gpu<int>, ocl_match2L1_gpu<float>
},
{
0/*match2L2_gpu<unsigned char>*/, 0/*match2L2_gpu<signed char>*/,
0/*match2L2_gpu<unsigned short>*/, 0/*match2L2_gpu<short>*/,
0/*match2L2_gpu<int>*/, ocl_match2L2_gpu<float>
},
{
ocl_match2Hamming_gpu<unsigned char>, 0/*match2Hamming_gpu<signed char>*/,
ocl_match2Hamming_gpu<unsigned short>, 0/*match2Hamming_gpu<short>*/,
ocl_match2Hamming_gpu<int>, 0/*match2Hamming_gpu<float>*/
}
};
#endif
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
const int nQuery = query.rows;
trainIdx.create(1, nQuery, CV_32SC2);
imgIdx.create(1, nQuery, CV_32SC2);
distance.create(1, nQuery, CV_32SC2);
trainIdx.setTo(Scalar::all(-1));
//caller_t func = callers[distType][query.depth()];
//CV_Assert(func != 0);
//func(query, trainCollection, maskCollection, trainIdx, imgIdx, distance, cc, StreamAccessor::getStream(stream));
}
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());
}
}
}
void cv::ocl::oclMat::copyTo( oclMat &mat, const oclMat &mask) const
{
if (mask.empty())
{
copyTo(mat);
}
else
{
mat.create(size(), type());
copy_to_with_mask(*this, mat, mask, "copy_to_with_mask");
}
}
// 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::oclMat::copyTo( oclMat &mat, const oclMat &mask) const
{
if (mask.empty())
{
CV_DbgAssert(!this->empty());
mat.create(size(), type());
openCLCopyBuffer2D(clCxt, mat.data, mat.step, mat.offset,
data, step, cols * elemSize(), rows, offset);
}
else
{
mat.create(size(), type());
copy_to_with_mask(*this, mat, mask, "copy_to_with_mask");
}
}
void cv::ocl::BruteForceMatcher_OCL_base::knnMatch2Collection(const oclMat &query, const oclMat &trainCollection,
oclMat &trainIdx, oclMat &imgIdx, oclMat &distance, const oclMat &/*maskCollection*/)
{
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);
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
const int nQuery = query.rows;
ensureSizeIsEnough(1, nQuery, CV_32SC2, trainIdx);
ensureSizeIsEnough(1, nQuery, CV_32SC2, imgIdx);
ensureSizeIsEnough(1, nQuery, CV_32FC2, distance);
trainIdx.setTo(Scalar::all(-1));
//caller_t func = callers[distType][query.depth()];
//CV_Assert(func != 0);
//func(query, trainCollection, maskCollection, trainIdx, imgIdx, distance, cc, StreamAccessor::getStream(stream));
}
int cv::ocl::FAST_OCL::calcKeyPointsLocation(const oclMat& img, const oclMat& mask)
{
CV_Assert(img.type() == CV_8UC1);
CV_Assert(mask.empty() || (mask.type() == CV_8UC1 && mask.size() == img.size()));
int maxKeypoints = static_cast<int>(keypointsRatio * img.size().area());
ensureSizeIsEnough(ROWS_COUNT, maxKeypoints, CV_32SC1, kpLoc_);
kpLoc_.setTo(Scalar::all(0));
if (nonmaxSupression)
{
ensureSizeIsEnough(img.size(), CV_32SC1, score_);
score_.setTo(Scalar::all(0));
}
count_ = calcKeypointsOCL(img, mask, maxKeypoints);
count_ = std::min(count_, maxKeypoints);
return count_;
}
void cv::ocl::OpticalFlowDual_TVL1_OCL::procOneScale(const oclMat &I0, const oclMat &I1, oclMat &u1, oclMat &u2)
{
using namespace ocl_tvl1flow;
const double scaledEpsilon = epsilon * epsilon * I0.size().area();
CV_DbgAssert( I1.size() == I0.size() );
CV_DbgAssert( I1.type() == I0.type() );
CV_DbgAssert( u1.empty() || u1.size() == I0.size() );
CV_DbgAssert( u2.size() == u1.size() );
if (u1.empty())
{
u1.create(I0.size(), CV_32FC1);
u1.setTo(Scalar::all(0));
u2.create(I0.size(), CV_32FC1);
u2.setTo(Scalar::all(0));
}
oclMat I1x = I1x_buf(Rect(0, 0, I0.cols, I0.rows));
oclMat I1y = I1y_buf(Rect(0, 0, I0.cols, I0.rows));
centeredGradient(I1, I1x, I1y);
oclMat I1w = I1w_buf(Rect(0, 0, I0.cols, I0.rows));
oclMat I1wx = I1wx_buf(Rect(0, 0, I0.cols, I0.rows));
oclMat I1wy = I1wy_buf(Rect(0, 0, I0.cols, I0.rows));
oclMat grad = grad_buf(Rect(0, 0, I0.cols, I0.rows));
oclMat rho_c = rho_c_buf(Rect(0, 0, I0.cols, I0.rows));
oclMat p11 = p11_buf(Rect(0, 0, I0.cols, I0.rows));
oclMat p12 = p12_buf(Rect(0, 0, I0.cols, I0.rows));
oclMat p21 = p21_buf(Rect(0, 0, I0.cols, I0.rows));
oclMat p22 = p22_buf(Rect(0, 0, I0.cols, I0.rows));
p11.setTo(Scalar::all(0));
p12.setTo(Scalar::all(0));
p21.setTo(Scalar::all(0));
p22.setTo(Scalar::all(0));
oclMat diff = diff_buf(Rect(0, 0, I0.cols, I0.rows));
const float l_t = static_cast<float>(lambda * theta);
const float taut = static_cast<float>(tau / theta);
for (int warpings = 0; warpings < warps; ++warpings)
{
warpBackward(I0, I1, I1x, I1y, u1, u2, I1w, I1wx, I1wy, grad, rho_c);
double error = numeric_limits<double>::max();
double prev_error = 0;
for (int n = 0; error > scaledEpsilon && n < iterations; ++n)
{
// some tweaks to make sum operation less frequently
char calc_error = (n & 0x1) && (prev_error < scaledEpsilon);
estimateU(I1wx, I1wy, grad, rho_c, p11, p12, p21, p22,
u1, u2, diff, l_t, static_cast<float>(theta), calc_error);
if(calc_error)
{
error = ocl::sum(diff)[0];
prev_error = error;
}
else
{
error = numeric_limits<double>::max();
prev_error -= scaledEpsilon;
}
estimateDualVariables(u1, u2, p11, p12, p21, p22, taut);
}
}
}
void cv::ocl::PyrLKOpticalFlow::sparse(const oclMat &prevImg, const oclMat &nextImg, const oclMat &prevPts, oclMat &nextPts, oclMat &status, oclMat *err)
{
if (prevPts.empty())
{
nextPts.release();
status.release();
//if (err) err->release();
return;
}
derivLambda = std::min(std::max(derivLambda, 0.0), 1.0);
iters = std::min(std::max(iters, 0), 100);
const int cn = prevImg.oclchannels();
dim3 block, patch;
calcPatchSize(winSize, cn, block, patch, isDeviceArch11_);
CV_Assert(derivLambda >= 0);
CV_Assert(maxLevel >= 0 && winSize.width > 2 && winSize.height > 2);
CV_Assert(prevImg.size() == nextImg.size() && prevImg.type() == nextImg.type());
CV_Assert(patch.x > 0 && patch.x < 6 && patch.y > 0 && patch.y < 6);
CV_Assert(prevPts.rows == 1 && prevPts.type() == CV_32FC2);
if (useInitialFlow)
CV_Assert(nextPts.size() == prevPts.size() && nextPts.type() == CV_32FC2);
else
ensureSizeIsEnough(1, prevPts.cols, prevPts.type(), nextPts);
oclMat temp1 = (useInitialFlow ? nextPts : prevPts).reshape(1);
oclMat temp2 = nextPts.reshape(1);
//oclMat scalar(temp1.rows, temp1.cols, temp1.type(), Scalar(1.0f / (1 << maxLevel) / 2.0f));
multiply_cus(temp1, temp2, 1.0f / (1 << maxLevel) / 2.0f);
//::multiply(temp1, 1.0f / (1 << maxLevel) / 2.0f, temp2);
ensureSizeIsEnough(1, prevPts.cols, CV_8UC1, status);
//status.setTo(Scalar::all(1));
setTo(status, Scalar::all(1));
bool errMat = false;
if (!err)
{
err = new oclMat(1, prevPts.cols, CV_32FC1);
errMat = true;
}
else
ensureSizeIsEnough(1, prevPts.cols, CV_32FC1, *err);
//ensureSizeIsEnough(1, prevPts.cols, CV_32FC1, err);
// build the image pyramids.
prevPyr_.resize(maxLevel + 1);
nextPyr_.resize(maxLevel + 1);
if (cn == 1 || cn == 4)
{
//prevImg.convertTo(prevPyr_[0], CV_32F);
//nextImg.convertTo(nextPyr_[0], CV_32F);
convertTo(prevImg, prevPyr_[0], CV_32F);
convertTo(nextImg, nextPyr_[0], CV_32F);
}
else
{
//oclMat buf_;
// cvtColor(prevImg, buf_, COLOR_BGR2BGRA);
// buf_.convertTo(prevPyr_[0], CV_32F);
// cvtColor(nextImg, buf_, COLOR_BGR2BGRA);
// buf_.convertTo(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]);
}
// dI/dx ~ Ix, dI/dy ~ Iy
for (int level = maxLevel; level >= 0; level--)
{
lkSparse_run(prevPyr_[level], nextPyr_[level],
prevPts, nextPts, status, *err, getMinEigenVals, prevPts.cols,
level, /*block, */patch, winSize, iters);
}
clFinish(prevImg.clCxt->impl->clCmdQueue);
if(errMat)
delete err;
}
void cv::ocl::gemm(const oclMat &src1, const oclMat &src2, double alpha,
const oclMat &src3, double beta, oclMat &dst, int flags)
{
CV_Assert(src1.cols == src2.rows &&
(src3.empty() || (src1.rows == src3.rows && src2.cols == src3.cols)));
CV_Assert(!(cv::GEMM_3_T & flags)); // cv::GEMM_3_T is not supported
if(!src3.empty())
{
src3.copyTo(dst);
}
else
{
dst.create(src1.rows, src2.cols, src1.type());
dst.setTo(Scalar::all(0));
}
clBlasSetup();
const clAmdBlasTranspose transA = (cv::GEMM_1_T & flags) ? clAmdBlasTrans : clAmdBlasNoTrans;
const clAmdBlasTranspose transB = (cv::GEMM_2_T & flags) ? clAmdBlasTrans : clAmdBlasNoTrans;
const clAmdBlasOrder order = clAmdBlasRowMajor;
const int M = src1.rows;
const int N = src2.cols;
const int K = src1.cols;
int lda = src1.step;
int ldb = src2.step;
int ldc = dst.step;
int offa = src1.offset;
int offb = src2.offset;
int offc = dst.offset;
cl_command_queue clq = *(cl_command_queue*)src1.clCxt->getOpenCLCommandQueuePtr();
switch(src1.type())
{
case CV_32FC1:
lda /= sizeof(float);
ldb /= sizeof(float);
ldc /= sizeof(float);
offa /= sizeof(float);
offb /= sizeof(float);
offc /= sizeof(float);
openCLSafeCall
(
clAmdBlasSgemmEx(order, transA, transB, M, N, K,
alpha, (const cl_mem)src1.data, offa, lda, (const cl_mem)src2.data, offb, ldb,
beta, (cl_mem)dst.data, offc, ldc, 1, &clq, 0, NULL, NULL)
);
break;
case CV_64FC1:
lda /= sizeof(double);
ldb /= sizeof(double);
ldc /= sizeof(double);
offa /= sizeof(double);
offb /= sizeof(double);
offc /= sizeof(double);
openCLSafeCall
(
clAmdBlasDgemmEx(order, transA, transB, M, N, K,
alpha, (const cl_mem)src1.data, offa, lda, (const cl_mem)src2.data, offb, ldb,
beta, (cl_mem)dst.data, offc, ldc, 1, &clq, 0, NULL, NULL)
);
break;
case CV_32FC2:
{
lda /= (2*sizeof(float));
ldb /= (2*sizeof(float));
ldc /= (2*sizeof(float));
offa /= (2*sizeof(float));
offb /= (2*sizeof(float));
offc /= (2*sizeof(float));
cl_float2 alpha_2 = {{alpha, 0}};
cl_float2 beta_2 = {{beta, 0}};
openCLSafeCall
(
clAmdBlasCgemmEx(order, transA, transB, M, N, K,
alpha_2, (const cl_mem)src1.data, offa, lda, (const cl_mem)src2.data, offb, ldb,
beta_2, (cl_mem)dst.data, offc, ldc, 1, &clq, 0, NULL, NULL)
);
}
break;
case CV_64FC2:
{
lda /= (2*sizeof(double));
ldb /= (2*sizeof(double));
ldc /= (2*sizeof(double));
offa /= (2*sizeof(double));
offb /= (2*sizeof(double));
offc /= (2*sizeof(double));
cl_double2 alpha_2 = {{alpha, 0}};
cl_double2 beta_2 = {{beta, 0}};
openCLSafeCall
(
clAmdBlasZgemmEx(order, transA, transB, M, N, K,
alpha_2, (const cl_mem)src1.data, offa, lda, (const cl_mem)src2.data, offb, ldb,
beta_2, (cl_mem)dst.data, offc, ldc, 1, &clq, 0, NULL, NULL)
);
}
break;
}
}
