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 cv::ocl::HoughCircles(const oclMat& src, oclMat& circles, HoughCirclesBuf& buf, int method,
float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles)
{
CV_Assert(src.type() == CV_8UC1);
CV_Assert(src.cols < std::numeric_limits<unsigned short>::max());
CV_Assert(src.rows < std::numeric_limits<unsigned short>::max());
CV_Assert(method == CV_HOUGH_GRADIENT);
CV_Assert(dp > 0);
CV_Assert(minRadius > 0 && maxRadius > minRadius);
CV_Assert(cannyThreshold > 0);
CV_Assert(votesThreshold > 0);
CV_Assert(maxCircles > 0);
const float idp = 1.0f / dp;
cv::ocl::Canny(src, buf.cannyBuf, buf.edges, std::max(cannyThreshold / 2, 1), cannyThreshold);
ensureSizeIsEnough(1, src.size().area(), CV_32SC1, buf.srcPoints);
const int pointsCount = hough::buildPointList_gpu(buf.edges, buf.srcPoints);
if (pointsCount == 0)
{
circles.release();
return;
}
ensureSizeIsEnough(cvCeil(src.rows * idp) + 2, cvCeil(src.cols * idp) + 2, CV_32SC1, buf.accum);
buf.accum.setTo(Scalar::all(0));
hough::circlesAccumCenters_gpu(buf.srcPoints, pointsCount, buf.cannyBuf.dx, buf.cannyBuf.dy, buf.accum, minRadius, maxRadius, idp);
ensureSizeIsEnough(1, src.size().area(), CV_32SC1, buf.centers);
int centersCount = hough::buildCentersList_gpu(buf.accum, buf.centers, votesThreshold);
if (centersCount == 0)
{
circles.release();
return;
}
if (minDist > 1)
{
cv::AutoBuffer<unsigned int> oldBuf_(centersCount);
cv::AutoBuffer<unsigned int> newBuf_(centersCount);
int newCount = 0;
unsigned int* oldBuf = oldBuf_;
unsigned int* newBuf = newBuf_;
openCLSafeCall(clEnqueueReadBuffer(buf.centers.clCxt->impl->clCmdQueue,
(cl_mem)buf.centers.data,
CL_TRUE,
0,
centersCount * sizeof(unsigned int),
oldBuf,
0,
NULL,
NULL));
const int cellSize = cvRound(minDist);
const int gridWidth = (src.cols + cellSize - 1) / cellSize;
const int gridHeight = (src.rows + cellSize - 1) / cellSize;
std::vector< std::vector<unsigned int> > grid(gridWidth * gridHeight);
const float minDist2 = minDist * minDist;
for (int i = 0; i < centersCount; ++i)
{
unsigned int p = oldBuf[i];
const int px = p & 0xFFFF;
const int py = (p >> 16) & 0xFFFF;
bool good = true;
int xCell = static_cast<int>(px / cellSize);
int yCell = static_cast<int>(py / cellSize);
int x1 = xCell - 1;
int y1 = yCell - 1;
int x2 = xCell + 1;
int y2 = yCell + 1;
// boundary check
x1 = std::max(0, x1);
y1 = std::max(0, y1);
x2 = std::min(gridWidth - 1, x2);
y2 = std::min(gridHeight - 1, y2);
for (int yy = y1; yy <= y2; ++yy)
{
for (int xx = x1; xx <= x2; ++xx)
{
vector<unsigned int>& m = grid[yy * gridWidth + xx];
for(size_t j = 0; j < m.size(); ++j)
{
const int val = m[j];
const int jx = val & 0xFFFF;
const int jy = (val >> 16) & 0xFFFF;
float dx = (float)(px - jx);
float dy = (float)(py - jy);
if (dx * dx + dy * dy < minDist2)
{
good = false;
goto break_out;
}
}
}
}
break_out:
if(good)
{
grid[yCell * gridWidth + xCell].push_back(p);
newBuf[newCount++] = p;
}
}
openCLSafeCall(clEnqueueWriteBuffer(buf.centers.clCxt->impl->clCmdQueue,
(cl_mem)buf.centers.data,
CL_TRUE,
0,
newCount * sizeof(unsigned int),
newBuf,
0,
0,
0));
centersCount = newCount;
}
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;
}
