struct TensorWrapper pyrUpCuda(struct cutorchInfo info,
struct TensorWrapper src, struct TensorWrapper dst)
{
if (dst.isNull()) {
cuda::GpuMat retval;
cuda::pyrUp(src.toGpuMat(), retval, prepareStream(info));
return TensorWrapper(retval, info.state);
} else if (dst.tensorPtr == src.tensorPtr) {
// in-place
cuda::GpuMat source = src.toGpuMat();
cuda::pyrUp(source, source, prepareStream(info));
} else {
cuda::pyrUp(src.toGpuMat(), dst.toGpuMat(), prepareStream(info));
}
return dst;
}
bool RtspStreamWorker::setup()
{
ASSERT_WORKER_THREAD();
m_ctx = avformat_alloc_context();
m_ctx->interrupt_callback.callback = rtspStreamInterruptCallback;
m_ctx->interrupt_callback.opaque = this;
AVDictionary *options = createOptions();
bool prepared = prepareStream(&m_ctx, options);
av_dict_free(&options);
if (prepared)
{
m_frameFormatter.reset(new RtspStreamFrameFormatter(m_ctx->streams[m_videoStreamIndex]));
m_frameFormatter->setAutoDeinterlacing(m_autoDeinterlacing);
}
else if (m_ctx)
{
avformat_close_input(&m_ctx);
m_ctx = 0;
}
return prepared;
}
struct TensorWrapper evenLevels(struct cutorchInfo info,
struct TensorWrapper levels, int nLevels, int lowerLevel, int upperLevel)
{
cuda::GpuMat retval;
if (!levels.isNull()) retval = levels.toGpuMat();
cuda::evenLevels(retval, nLevels, lowerLevel, upperLevel, prepareStream(info));
}
struct TensorWrapper DenseOpticalFlow_calcCuda(struct cutorchInfo info,
struct DenseOpticalFlowPtr ptr, struct TensorWrapper I0, struct TensorWrapper I1,
struct TensorWrapper flow)
{
cuda::GpuMat retval = flow.toGpuMat();
ptr->calc(I0.toGpuMat(), I1.toGpuMat(), retval, prepareStream(info));
return TensorWrapper(retval, info.state);
}
struct TensorWrapper HOG_compute(
struct cutorchInfo info, struct HOGPtr ptr, struct TensorWrapper img,
struct TensorWrapper descriptors)
{
GpuMatT descriptorsMat = descriptors.toGpuMatT();
ptr->compute(img.toGpuMat(), descriptorsMat, prepareStream(info));
return TensorWrapper(descriptorsMat, info.state);
}
struct TensorWrapper gammaCorrection(struct cutorchInfo info,
struct TensorWrapper src, struct TensorWrapper dst, bool forward)
{
cuda::GpuMat retval;
if (!dst.isNull()) retval = dst.toGpuMat();
cuda::gammaCorrection(src.toGpuMat(), retval, forward, prepareStream(info));
return TensorWrapper(retval, info.state);
}
struct TensorWrapper Feature2DAsync_detectAsyncCuda(
struct cutorchInfo info, struct Feature2DAsyncPtr ptr, struct TensorWrapper image,
struct TensorWrapper keypoints, struct TensorWrapper mask)
{
cuda::GpuMat retval = keypoints.toGpuMat();
ptr->detectAsync(image.toGpuMat(), retval, TO_GPUMAT_OR_NOARRAY(mask), prepareStream(info));
return TensorWrapper(retval, info.state);
}
struct TensorWrapper calcHist(
struct cutorchInfo info, struct TensorWrapper src, struct TensorWrapper hist)
{
cuda::GpuMat retval;
if (!hist.isNull()) retval = hist.toGpuMat();
cuda::calcHist(src.toGpuMat(), retval, prepareStream(info));
return TensorWrapper(retval, info.state);
}
struct TensorWrapper cvtColor(struct cutorchInfo info,
struct TensorWrapper src, struct TensorWrapper dst, int code, int dstCn)
{
cuda::GpuMat retval;
if (!dst.isNull()) retval = dst.toGpuMat();
cuda::cvtColor(src.toGpuMat(), retval, code, dstCn, prepareStream(info));
return TensorWrapper(retval, info.state);
}
struct TensorWrapper CornernessCriteria_compute(
struct cutorchInfo info, struct CornernessCriteriaPtr ptr,
struct TensorWrapper src, struct TensorWrapper dst)
{
cuda::GpuMat retval;
if (!dst.isNull()) retval = dst.toGpuMat();
ptr->compute(src.toGpuMat(), retval, prepareStream(info));
}
struct TensorWrapper TemplateMatching_match(
struct cutorchInfo info, struct TemplateMatchingPtr ptr, struct TensorWrapper image,
struct TensorWrapper templ, struct TensorWrapper result)
{
cuda::GpuMat retval;
if (!result.isNull()) retval = result.toGpuMat();
ptr->match(image.toGpuMat(), templ.toGpuMat(), retval, prepareStream(info));
return TensorWrapper(retval, info.state);
}
struct TensorWrapper alphaComp(struct cutorchInfo info,
struct TensorWrapper img1, struct TensorWrapper img2,
struct TensorWrapper dst, int alpha_op)
{
cuda::GpuMat retval;
if (!dst.isNull()) retval = dst.toGpuMat();
cuda::alphaComp(img1.toGpuMat(), img2.toGpuMat(), retval, alpha_op, prepareStream(info));
return TensorWrapper(retval, info.state);
}
struct TensorWrapper resizeCuda(struct cutorchInfo info,
struct TensorWrapper src, struct TensorWrapper dst,
struct SizeWrapper dsize, double fx, double fy,
int interpolation)
{
GpuMatT dstMat = dst.toGpuMatT();
cuda::resize(src.toGpuMat(), dstMat, dsize, fx, fy, interpolation, prepareStream(info));
return TensorWrapper(dstMat, info.state);
}
struct TensorWrapper rotateCuda(
struct cutorchInfo info, struct TensorWrapper src, struct TensorWrapper dst,
struct SizeWrapper dsize, double angle, double xShift, double yShift, int interpolation)
{
cuda::GpuMat dstMat = dst.toGpuMat();
cuda::rotate(src.toGpuMat(), dstMat, dsize, angle, xShift, yShift,
interpolation, prepareStream(info));
return TensorWrapper(dstMat, info.state);
}
struct TensorArray Feature2DAsync_computeAsyncCuda(
struct cutorchInfo info, struct Feature2DAsyncPtr ptr, struct TensorWrapper image,
struct TensorWrapper keypoints, struct TensorWrapper descriptors)
{
std::vector<cuda::GpuMat> retval(2);
retval[0] = keypoints.toGpuMat();
retval[1] = descriptors.toGpuMat();
ptr->computeAsync(image.toGpuMat(), retval[0], retval[1], prepareStream(info));
return TensorArray(retval, info.state);
}
struct TensorWrapper blendLinear(struct cutorchInfo info,
struct TensorWrapper img1, struct TensorWrapper img2, struct TensorWrapper weights1,
struct TensorWrapper weights2, struct TensorWrapper result)
{
cuda::GpuMat retval;
if (!result.isNull()) retval = result.toGpuMat();
cuda::blendLinear(
img1.toGpuMat(), img2.toGpuMat(), weights1.toGpuMat(), weights2.toGpuMat(),
retval, prepareStream(info));
return TensorWrapper(retval, info.state);
}
struct TensorWrapper bilateralFilter(struct cutorchInfo info,
struct TensorWrapper src, struct TensorWrapper dst, int kernel_size,
float sigma_color, float sigma_spatial, int borderMode)
{
cuda::GpuMat retval;
if (!dst.isNull()) retval = dst.toGpuMat();
cuda::bilateralFilter(
src.toGpuMat(), retval, kernel_size, sigma_color,
sigma_color, borderMode, prepareStream(info));
return TensorWrapper(retval, info.state);
}
struct TensorWrapper CascadeClassifier_detectMultiScale(
struct cutorchInfo info, struct CascadeClassifierPtr ptr,
struct TensorWrapper image, struct TensorWrapper objects)
{
GpuMatT objectsMat = objects.toGpuMatT();
cuda::GpuMat imageMat = image.toGpuMat();
cuda::GpuMat imageByte;
imageMat.convertTo(imageByte, CV_8U, 255.0); // Sorry guys :(
ptr->detectMultiScale(imageByte, objectsMat, prepareStream(info));
return TensorWrapper(objectsMat, info.state);
}
struct TensorWrapper DescriptorMatcher_knnMatch_masksCuda(
struct cutorchInfo info, struct DescriptorMatcherPtr ptr,
struct TensorWrapper queryDescriptors, struct TensorWrapper trainDescriptors,
struct TensorWrapper matches, int k, struct TensorArray masks)
{
cuda::GpuMat retval = matches.toGpuMat();
ptr->knnMatchAsync(
queryDescriptors.toGpuMat(), trainDescriptors.toGpuMat(), retval,
k, TO_GPUMAT_LIST_OR_NOARRAY(masks), prepareStream(info));
return TensorWrapper(retval, info.state);
}
struct TensorWrapper DescriptorMatcher_radiusMatchCuda(
struct cutorchInfo info, struct DescriptorMatcherPtr ptr,
struct TensorWrapper queryDescriptors, struct TensorWrapper trainDescriptors,
struct TensorWrapper matches, float maxDistance, struct TensorWrapper mask)
{
cuda::GpuMat retval = matches.toGpuMat();
ptr->radiusMatchAsync(
queryDescriptors.toGpuMat(), trainDescriptors.toGpuMat(),
retval, maxDistance, TO_GPUMAT_OR_NOARRAY(mask), prepareStream(info));
return TensorWrapper(retval, info.state);
}
struct TensorWrapper remapCuda(struct cutorchInfo info,
struct TensorWrapper src, struct TensorWrapper map1,
struct TensorWrapper map2, int interpolation, struct TensorWrapper dst,
int borderMode, struct ScalarWrapper borderValue)
{
if (dst.isNull()) {
cuda::GpuMat retval;
cuda::remap(src.toGpuMat(), retval, map1.toGpuMat(), map2.toGpuMat(),
interpolation, borderMode, borderValue, prepareStream(info));
return TensorWrapper(retval, info.state);
} else if (dst.tensorPtr == src.tensorPtr) {
// in-place
cuda::GpuMat source = src.toGpuMat();
cuda::remap(source, source, map1.toGpuMat(), map2.toGpuMat(), interpolation,
borderMode, borderValue, prepareStream(info));
} else {
cuda::remap(src.toGpuMat(), dst.toGpuMat(), map1.toGpuMat(), map2.toGpuMat(),
interpolation, borderMode, borderValue, prepareStream(info));
}
return dst;
}
struct TensorWrapper warpPerspectiveCuda(struct cutorchInfo info,
struct TensorWrapper src, struct TensorWrapper dst,
struct TensorWrapper M, struct SizeWrapper dsize,
int flags, int borderMode, struct ScalarWrapper borderValue)
{
if (dst.isNull()) {
cuda::GpuMat retval;
cuda::warpPerspective(src.toGpuMat(), retval, M.toGpuMat(), dsize,
flags, borderMode, borderValue, prepareStream(info));
return TensorWrapper(retval, info.state);
} else if (dst.tensorPtr == src.tensorPtr) {
// in-place
cuda::GpuMat source = src.toGpuMat();
cuda::warpPerspective(source, source, M.toGpuMat(), dsize, flags,
borderMode, borderValue, prepareStream(info));
} else {
cuda::warpPerspective(src.toGpuMat(), dst.toGpuMat(), M.toGpuMat(),
dsize, flags, borderMode, borderValue, prepareStream(info));
}
return dst;
}
struct TensorArray buildWarpPerspectiveMapsCuda(
struct cutorchInfo info, struct TensorWrapper M, bool inverse,
struct SizeWrapper dsize, struct TensorWrapper xmap, struct TensorWrapper ymap)
{
std::vector<cuda::GpuMat> retval(2);
retval[0] = xmap.toGpuMat();
retval[1] = ymap.toGpuMat();
cuda::buildWarpPerspectiveMaps(M.toGpuMat(), inverse, dsize,
retval[0], retval[1], prepareStream(info));
return TensorArray(retval, info.state);
}
struct TensorArray SparseOpticalFlow_calcCuda(struct cutorchInfo info,
struct SparseOpticalFlowPtr ptr, struct TensorWrapper prevImg, struct TensorWrapper nextImg,
struct TensorWrapper prevPts, struct TensorWrapper nextPts, struct TensorWrapper status,
bool outputErr, struct TensorWrapper err)
{
std::vector<cuda::GpuMat> retval(3);
retval[0] = nextPts.toGpuMat();
retval[1] = status.toGpuMat();
retval[2] = err.toGpuMat();
ptr->calc(prevImg.toGpuMat(), nextImg.toGpuMat(), prevPts.toGpuMat(),
retval[0], retval[1], outputErr ? retval[2] : cv::noArray(), prepareStream(info));
return TensorArray(retval, info.state);
}
void swapChannels(
struct cutorchInfo info, struct TensorWrapper image,
struct Vec4iWrapper dstOrder)
{
cuda::swapChannels(image.toGpuMat(), &dstOrder.v0, prepareStream(info));
}
