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Commit fc847654 authored by Vladislav Vinogradov's avatar Vladislav Vinogradov
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switched to Input/Output Array in Histogram Processing

parent 9eea9835
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Showing with 320 additions and 298 deletions
modules/gpuimgproc/include/opencv2/gpuimgproc.hpp
View file @ fc847654
......@@ -98,54 +98,77 @@ CV_EXPORTS void alphaComp(InputArray img1, InputArray img2, OutputArray dst, int
////////////////////////////// Histogram ///////////////////////////////
//! Calculates histogram for 8u one channel image
//! Output hist will have one row, 256 cols and CV32SC1 type.
CV_EXPORTS void calcHist(InputArray src, OutputArray hist, Stream& stream = Stream::Null());
//! normalizes the grayscale image brightness and contrast by normalizing its histogram
CV_EXPORTS void equalizeHist(InputArray src, OutputArray dst, InputOutputArray buf, Stream& stream = Stream::Null());
static inline void equalizeHist(InputArray src, OutputArray dst, Stream& stream = Stream::Null())
{
GpuMat buf;
gpu::equalizeHist(src, dst, buf, stream);
}
class CV_EXPORTS CLAHE : public cv::CLAHE
{
public:
using cv::CLAHE::apply;
virtual void apply(InputArray src, OutputArray dst, Stream& stream) = 0;
};
CV_EXPORTS Ptr<gpu::CLAHE> createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8));
//! Compute levels with even distribution. levels will have 1 row and nLevels cols and CV_32SC1 type.
CV_EXPORTS void evenLevels(GpuMat& levels, int nLevels, int lowerLevel, int upperLevel);
CV_EXPORTS void evenLevels(OutputArray levels, int nLevels, int lowerLevel, int upperLevel);
//! Calculates histogram with evenly distributed bins for signle channel source.
//! Supports CV_8UC1, CV_16UC1 and CV_16SC1 source types.
//! Output hist will have one row and histSize cols and CV_32SC1 type.
CV_EXPORTS void histEven(const GpuMat& src, GpuMat& hist, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null());
CV_EXPORTS void histEven(const GpuMat& src, GpuMat& hist, GpuMat& buf, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null());
CV_EXPORTS void histEven(InputArray src, OutputArray hist, InputOutputArray buf, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null());
static inline void histEven(InputArray src, OutputArray hist, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null())
{
GpuMat buf;
gpu::histEven(src, hist, buf, histSize, lowerLevel, upperLevel, stream);
}
//! Calculates histogram with evenly distributed bins for four-channel source.
//! All channels of source are processed separately.
//! Supports CV_8UC4, CV_16UC4 and CV_16SC4 source types.
//! Output hist[i] will have one row and histSize[i] cols and CV_32SC1 type.
CV_EXPORTS void histEven(const GpuMat& src, GpuMat hist[4], int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null());
CV_EXPORTS void histEven(const GpuMat& src, GpuMat hist[4], GpuMat& buf, int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null());
CV_EXPORTS void histEven(InputArray src, GpuMat hist[4], InputOutputArray buf, int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null());
static inline void histEven(InputArray src, GpuMat hist[4], int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null())
{
GpuMat buf;
gpu::histEven(src, hist, buf, histSize, lowerLevel, upperLevel, stream);
}
//! Calculates histogram with bins determined by levels array.
//! levels must have one row and CV_32SC1 type if source has integer type or CV_32FC1 otherwise.
//! Supports CV_8UC1, CV_16UC1, CV_16SC1 and CV_32FC1 source types.
//! Output hist will have one row and (levels.cols-1) cols and CV_32SC1 type.
CV_EXPORTS void histRange(const GpuMat& src, GpuMat& hist, const GpuMat& levels, Stream& stream = Stream::Null());
CV_EXPORTS void histRange(const GpuMat& src, GpuMat& hist, const GpuMat& levels, GpuMat& buf, Stream& stream = Stream::Null());
CV_EXPORTS void histRange(InputArray src, OutputArray hist, InputArray levels, InputOutputArray buf, Stream& stream = Stream::Null());
static inline void histRange(InputArray src, OutputArray hist, InputArray levels, Stream& stream = Stream::Null())
{
GpuMat buf;
gpu::histRange(src, hist, levels, buf, stream);
}
//! Calculates histogram with bins determined by levels array.
//! All levels must have one row and CV_32SC1 type if source has integer type or CV_32FC1 otherwise.
//! All channels of source are processed separately.
//! Supports CV_8UC4, CV_16UC4, CV_16SC4 and CV_32FC4 source types.
//! Output hist[i] will have one row and (levels[i].cols-1) cols and CV_32SC1 type.
CV_EXPORTS void histRange(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], Stream& stream = Stream::Null());
CV_EXPORTS void histRange(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], GpuMat& buf, Stream& stream = Stream::Null());
//! Calculates histogram for 8u one channel image
//! Output hist will have one row, 256 cols and CV32SC1 type.
CV_EXPORTS void calcHist(const GpuMat& src, GpuMat& hist, Stream& stream = Stream::Null());
CV_EXPORTS void calcHist(const GpuMat& src, GpuMat& hist, GpuMat& buf, Stream& stream = Stream::Null());
CV_EXPORTS void histRange(InputArray src, GpuMat hist[4], const GpuMat levels[4], InputOutputArray buf, Stream& stream = Stream::Null());
//! normalizes the grayscale image brightness and contrast by normalizing its histogram
CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, Stream& stream = Stream::Null());
CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, GpuMat& buf, Stream& stream = Stream::Null());
class CV_EXPORTS CLAHE : public cv::CLAHE
static inline void histRange(InputArray src, GpuMat hist[4], const GpuMat levels[4], Stream& stream = Stream::Null())
{
public:
using cv::CLAHE::apply;
virtual void apply(InputArray src, OutputArray dst, Stream& stream) = 0;
};
CV_EXPORTS Ptr<cv::gpu::CLAHE> createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8));
GpuMat buf;
gpu::histRange(src, hist, levels, buf, stream);
}
//////////////////////////////// Canny ////////////////////////////////
......
modules/gpuimgproc/perf/perf_histogram.cpp
View file @ fc847654
......@@ -167,10 +167,9 @@ PERF_TEST_P(Sz, EqualizeHist,
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat d_hist;
cv::gpu::GpuMat d_buf;
TEST_CYCLE() cv::gpu::equalizeHist(d_src, dst, d_hist, d_buf);
TEST_CYCLE() cv::gpu::equalizeHist(d_src, dst, d_buf);
GPU_SANITY_CHECK(dst);
}
......
modules/gpuimgproc/src/histogram.cpp
View file @ fc847654
......@@ -47,27 +47,219 @@ using namespace cv::gpu;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
void cv::gpu::evenLevels(GpuMat&, int, int, int) { throw_no_cuda(); }
void cv::gpu::calcHist(InputArray, OutputArray, Stream&) { throw_no_cuda(); }
void cv::gpu::histEven(const GpuMat&, GpuMat&, int, int, int, Stream&) { throw_no_cuda(); }
void cv::gpu::histEven(const GpuMat&, GpuMat&, GpuMat&, int, int, int, Stream&) { throw_no_cuda(); }
void cv::gpu::histEven(const GpuMat&, GpuMat*, int*, int*, int*, Stream&) { throw_no_cuda(); }
void cv::gpu::histEven(const GpuMat&, GpuMat*, GpuMat&, int*, int*, int*, Stream&) { throw_no_cuda(); }
void cv::gpu::equalizeHist(InputArray, OutputArray, InputOutputArray, Stream&) { throw_no_cuda(); }
void cv::gpu::histRange(const GpuMat&, GpuMat&, const GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::histRange(const GpuMat&, GpuMat&, const GpuMat&, GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::histRange(const GpuMat&, GpuMat*, const GpuMat*, Stream&) { throw_no_cuda(); }
void cv::gpu::histRange(const GpuMat&, GpuMat*, const GpuMat*, GpuMat&, Stream&) { throw_no_cuda(); }
cv::Ptr<cv::gpu::CLAHE> cv::gpu::createCLAHE(double, cv::Size) { throw_no_cuda(); return cv::Ptr<cv::gpu::CLAHE>(); }
void cv::gpu::calcHist(const GpuMat&, GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::evenLevels(OutputArray, int, int, int) { throw_no_cuda(); }
void cv::gpu::equalizeHist(const GpuMat&, GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::equalizeHist(const GpuMat&, GpuMat&, GpuMat&, GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::histEven(InputArray, OutputArray, InputOutputArray, int, int, int, Stream&) { throw_no_cuda(); }
void cv::gpu::histEven(InputArray, GpuMat*, InputOutputArray, int*, int*, int*, Stream&) { throw_no_cuda(); }
cv::Ptr<cv::gpu::CLAHE> cv::gpu::createCLAHE(double, cv::Size) { throw_no_cuda(); return cv::Ptr<cv::gpu::CLAHE>(); }
void cv::gpu::histRange(InputArray, OutputArray, InputArray, InputOutputArray, Stream&) { throw_no_cuda(); }
void cv::gpu::histRange(InputArray, GpuMat*, const GpuMat*, InputOutputArray, Stream&) { throw_no_cuda(); }
#else /* !defined (HAVE_CUDA) */
////////////////////////////////////////////////////////////////////////
// calcHist
namespace hist
{
void histogram256(PtrStepSzb src, int* hist, cudaStream_t stream);
}
void cv::gpu::calcHist(InputArray _src, OutputArray _hist, Stream& stream)
{
GpuMat src = _src.getGpuMat();
CV_Assert( src.type() == CV_8UC1 );
_hist.create(1, 256, CV_32SC1);
GpuMat hist = _hist.getGpuMat();
hist.setTo(Scalar::all(0), stream);
hist::histogram256(src, hist.ptr<int>(), StreamAccessor::getStream(stream));
}
////////////////////////////////////////////////////////////////////////
// equalizeHist
namespace hist
{
void equalizeHist(PtrStepSzb src, PtrStepSzb dst, const int* lut, cudaStream_t stream);
}
void cv::gpu::equalizeHist(InputArray _src, OutputArray _dst, InputOutputArray _buf, Stream& _stream)
{
GpuMat src = _src.getGpuMat();
CV_Assert( src.type() == CV_8UC1 );
_dst.create(src.size(), src.type());
GpuMat dst = _dst.getGpuMat();
int intBufSize;
nppSafeCall( nppsIntegralGetBufferSize_32s(256, &intBufSize) );
size_t bufSize = intBufSize + 2 * 256 * sizeof(int);
ensureSizeIsEnough(1, static_cast<int>(bufSize), CV_8UC1, _buf);
GpuMat buf = _buf.getGpuMat();
GpuMat hist(1, 256, CV_32SC1, buf.data);
GpuMat lut(1, 256, CV_32SC1, buf.data + 256 * sizeof(int));
GpuMat intBuf(1, intBufSize, CV_8UC1, buf.data + 2 * 256 * sizeof(int));
gpu::calcHist(src, hist, _stream);
cudaStream_t stream = StreamAccessor::getStream(_stream);
NppStreamHandler h(stream);
nppSafeCall( nppsIntegral_32s(hist.ptr<Npp32s>(), lut.ptr<Npp32s>(), 256, intBuf.ptr<Npp8u>()) );
hist::equalizeHist(src, dst, lut.ptr<int>(), stream);
}
////////////////////////////////////////////////////////////////////////
// CLAHE
namespace clahe
{
void calcLut(PtrStepSzb src, PtrStepb lut, int tilesX, int tilesY, int2 tileSize, int clipLimit, float lutScale, cudaStream_t stream);
void transform(PtrStepSzb src, PtrStepSzb dst, PtrStepb lut, int tilesX, int tilesY, int2 tileSize, cudaStream_t stream);
}
namespace
{
class CLAHE_Impl : public cv::gpu::CLAHE
{
public:
CLAHE_Impl(double clipLimit = 40.0, int tilesX = 8, int tilesY = 8);
cv::AlgorithmInfo* info() const;
void apply(cv::InputArray src, cv::OutputArray dst);
void apply(InputArray src, OutputArray dst, Stream& stream);
void setClipLimit(double clipLimit);
double getClipLimit() const;
void setTilesGridSize(cv::Size tileGridSize);
cv::Size getTilesGridSize() const;
void collectGarbage();
private:
double clipLimit_;
int tilesX_;
int tilesY_;
GpuMat srcExt_;
GpuMat lut_;
};
CLAHE_Impl::CLAHE_Impl(double clipLimit, int tilesX, int tilesY) :
clipLimit_(clipLimit), tilesX_(tilesX), tilesY_(tilesY)
{
}
CV_INIT_ALGORITHM(CLAHE_Impl, "CLAHE_GPU",
obj.info()->addParam(obj, "clipLimit", obj.clipLimit_);
obj.info()->addParam(obj, "tilesX", obj.tilesX_);
obj.info()->addParam(obj, "tilesY", obj.tilesY_))
void CLAHE_Impl::apply(cv::InputArray _src, cv::OutputArray _dst)
{
apply(_src, _dst, Stream::Null());
}
void CLAHE_Impl::apply(InputArray _src, OutputArray _dst, Stream& s)
{
GpuMat src = _src.getGpuMat();
CV_Assert( src.type() == CV_8UC1 );
_dst.create( src.size(), src.type() );
GpuMat dst = _dst.getGpuMat();
const int histSize = 256;
ensureSizeIsEnough(tilesX_ * tilesY_, histSize, CV_8UC1, lut_);
cudaStream_t stream = StreamAccessor::getStream(s);
cv::Size tileSize;
GpuMat srcForLut;
if (src.cols % tilesX_ == 0 && src.rows % tilesY_ == 0)
{
tileSize = cv::Size(src.cols / tilesX_, src.rows / tilesY_);
srcForLut = src;
}
else
{
#ifndef HAVE_OPENCV_GPUARITHM
throw_no_cuda();
#else
cv::gpu::copyMakeBorder(src, srcExt_, 0, tilesY_ - (src.rows % tilesY_), 0, tilesX_ - (src.cols % tilesX_), cv::BORDER_REFLECT_101, cv::Scalar(), s);
#endif
tileSize = cv::Size(srcExt_.cols / tilesX_, srcExt_.rows / tilesY_);
srcForLut = srcExt_;
}
const int tileSizeTotal = tileSize.area();
const float lutScale = static_cast<float>(histSize - 1) / tileSizeTotal;
int clipLimit = 0;
if (clipLimit_ > 0.0)
{
clipLimit = static_cast<int>(clipLimit_ * tileSizeTotal / histSize);
clipLimit = std::max(clipLimit, 1);
}
clahe::calcLut(srcForLut, lut_, tilesX_, tilesY_, make_int2(tileSize.width, tileSize.height), clipLimit, lutScale, stream);
clahe::transform(src, dst, lut_, tilesX_, tilesY_, make_int2(tileSize.width, tileSize.height), stream);
}
void CLAHE_Impl::setClipLimit(double clipLimit)
{
clipLimit_ = clipLimit;
}
double CLAHE_Impl::getClipLimit() const
{
return clipLimit_;
}
void CLAHE_Impl::setTilesGridSize(cv::Size tileGridSize)
{
tilesX_ = tileGridSize.width;
tilesY_ = tileGridSize.height;
}
cv::Size CLAHE_Impl::getTilesGridSize() const
{
return cv::Size(tilesX_, tilesY_);
}
void CLAHE_Impl::collectGarbage()
{
srcExt_.release();
lut_.release();
}
}
cv::Ptr<cv::gpu::CLAHE> cv::gpu::createCLAHE(double clipLimit, cv::Size tileGridSize)
{
return new CLAHE_Impl(clipLimit, tileGridSize.width, tileGridSize.height);
}
////////////////////////////////////////////////////////////////////////
// NPP Histogram
......@@ -96,10 +288,12 @@ namespace
{
typedef typename NppHistogramEvenFuncC1<SDEPTH>::src_t src_t;
static void hist(const GpuMat& src, GpuMat& hist, GpuMat& buffer, int histSize, int lowerLevel, int upperLevel, cudaStream_t stream)
static void hist(const GpuMat& src, OutputArray _hist, InputOutputArray _buf, int histSize, int lowerLevel, int upperLevel, cudaStream_t stream)
{
int levels = histSize + 1;
hist.create(1, histSize, CV_32S);
const int levels = histSize + 1;
_hist.create(1, histSize, CV_32S);
GpuMat hist = _hist.getGpuMat();
NppiSize sz;
sz.width = src.cols;
......@@ -108,12 +302,13 @@ namespace
int buf_size;
get_buf_size(sz, levels, &buf_size);
ensureSizeIsEnough(1, buf_size, CV_8U, buffer);
ensureSizeIsEnough(1, buf_size, CV_8UC1, _buf);
GpuMat buf = _buf.getGpuMat();
NppStreamHandler h(stream);
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), sz, hist.ptr<Npp32s>(), levels,
lowerLevel, upperLevel, buffer.ptr<Npp8u>()) );
lowerLevel, upperLevel, buf.ptr<Npp8u>()) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -124,7 +319,7 @@ namespace
{
typedef typename NppHistogramEvenFuncC4<SDEPTH>::src_t src_t;
static void hist(const GpuMat& src, GpuMat hist[4], GpuMat& buffer, int histSize[4], int lowerLevel[4], int upperLevel[4], cudaStream_t stream)
static void hist(const GpuMat& src, GpuMat hist[4],InputOutputArray _buf, int histSize[4], int lowerLevel[4], int upperLevel[4], cudaStream_t stream)
{
int levels[] = {histSize[0] + 1, histSize[1] + 1, histSize[2] + 1, histSize[3] + 1};
hist[0].create(1, histSize[0], CV_32S);
......@@ -141,11 +336,12 @@ namespace
int buf_size;
get_buf_size(sz, levels, &buf_size);
ensureSizeIsEnough(1, buf_size, CV_8U, buffer);
ensureSizeIsEnough(1, buf_size, CV_8U, _buf);
GpuMat buf = _buf.getGpuMat();
NppStreamHandler h(stream);
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), sz, pHist, levels, lowerLevel, upperLevel, buffer.ptr<Npp8u>()) );
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), sz, pHist, levels, lowerLevel, upperLevel, buf.ptr<Npp8u>()) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -196,11 +392,12 @@ namespace
typedef typename NppHistogramRangeFuncC1<SDEPTH>::level_t level_t;
enum {LEVEL_TYPE_CODE=NppHistogramRangeFuncC1<SDEPTH>::LEVEL_TYPE_CODE};
static void hist(const GpuMat& src, GpuMat& hist, const GpuMat& levels, GpuMat& buffer, cudaStream_t stream)
static void hist(const GpuMat& src, OutputArray _hist, const GpuMat& levels, InputOutputArray _buf, cudaStream_t stream)
{
CV_Assert(levels.type() == LEVEL_TYPE_CODE && levels.rows == 1);
CV_Assert( levels.type() == LEVEL_TYPE_CODE && levels.rows == 1 );
hist.create(1, levels.cols - 1, CV_32S);
_hist.create(1, levels.cols - 1, CV_32S);
GpuMat hist = _hist.getGpuMat();
NppiSize sz;
sz.width = src.cols;
......@@ -209,11 +406,12 @@ namespace
int buf_size;
get_buf_size(sz, levels.cols, &buf_size);
ensureSizeIsEnough(1, buf_size, CV_8U, buffer);
ensureSizeIsEnough(1, buf_size, CV_8U, _buf);
GpuMat buf = _buf.getGpuMat();
NppStreamHandler h(stream);
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), sz, hist.ptr<Npp32s>(), levels.ptr<level_t>(), levels.cols, buffer.ptr<Npp8u>()) );
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), sz, hist.ptr<Npp32s>(), levels.ptr<level_t>(), levels.cols, buf.ptr<Npp8u>()) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -226,12 +424,12 @@ namespace
typedef typename NppHistogramRangeFuncC1<SDEPTH>::level_t level_t;
enum {LEVEL_TYPE_CODE=NppHistogramRangeFuncC1<SDEPTH>::LEVEL_TYPE_CODE};
static void hist(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], GpuMat& buffer, cudaStream_t stream)
static void hist(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4],InputOutputArray _buf, cudaStream_t stream)
{
CV_Assert(levels[0].type() == LEVEL_TYPE_CODE && levels[0].rows == 1);
CV_Assert(levels[1].type() == LEVEL_TYPE_CODE && levels[1].rows == 1);
CV_Assert(levels[2].type() == LEVEL_TYPE_CODE && levels[2].rows == 1);
CV_Assert(levels[3].type() == LEVEL_TYPE_CODE && levels[3].rows == 1);
CV_Assert( levels[0].type() == LEVEL_TYPE_CODE && levels[0].rows == 1 );
CV_Assert( levels[1].type() == LEVEL_TYPE_CODE && levels[1].rows == 1 );
CV_Assert( levels[2].type() == LEVEL_TYPE_CODE && levels[2].rows == 1 );
CV_Assert( levels[3].type() == LEVEL_TYPE_CODE && levels[3].rows == 1 );
hist[0].create(1, levels[0].cols - 1, CV_32S);
hist[1].create(1, levels[1].cols - 1, CV_32S);
......@@ -249,11 +447,12 @@ namespace
int buf_size;
get_buf_size(sz, nLevels, &buf_size);
ensureSizeIsEnough(1, buf_size, CV_8U, buffer);
ensureSizeIsEnough(1, buf_size, CV_8U, _buf);
GpuMat buf = _buf.getGpuMat();
NppStreamHandler h(stream);
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), sz, pHist, pLevels, nLevels, buffer.ptr<Npp8u>()) );
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), sz, pHist, pLevels, nLevels, buf.ptr<Npp8u>()) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -261,24 +460,27 @@ namespace
};
}
void cv::gpu::evenLevels(GpuMat& levels, int nLevels, int lowerLevel, int upperLevel)
void cv::gpu::evenLevels(OutputArray _levels, int nLevels, int lowerLevel, int upperLevel)
{
Mat host_levels(1, nLevels, CV_32SC1);
const int kind = _levels.kind();
_levels.create(1, nLevels, CV_32SC1);
Mat host_levels;
if (kind == _InputArray::GPU_MAT)
host_levels.create(1, nLevels, CV_32SC1);
else
host_levels = _levels.getMat();
nppSafeCall( nppiEvenLevelsHost_32s(host_levels.ptr<Npp32s>(), nLevels, lowerLevel, upperLevel) );
levels.upload(host_levels);
}
void cv::gpu::histEven(const GpuMat& src, GpuMat& hist, int histSize, int lowerLevel, int upperLevel, Stream& stream)
{
GpuMat buf;
histEven(src, hist, buf, histSize, lowerLevel, upperLevel, stream);
if (kind == _InputArray::GPU_MAT)
_levels.getGpuMatRef().upload(host_levels);
}
void cv::gpu::histEven(const GpuMat& src, GpuMat& hist, GpuMat& buf, int histSize, int lowerLevel, int upperLevel, Stream& stream)
void cv::gpu::histEven(InputArray _src, OutputArray hist, InputOutputArray buf, int histSize, int lowerLevel, int upperLevel, Stream& stream)
{
CV_Assert(src.type() == CV_8UC1 || src.type() == CV_16UC1 || src.type() == CV_16SC1 );
typedef void (*hist_t)(const GpuMat& src, GpuMat& hist, GpuMat& buf, int levels, int lowerLevel, int upperLevel, cudaStream_t stream);
typedef void (*hist_t)(const GpuMat& src, OutputArray hist, InputOutputArray buf, int levels, int lowerLevel, int upperLevel, cudaStream_t stream);
static const hist_t hist_callers[] =
{
NppHistogramEvenC1<CV_8U , nppiHistogramEven_8u_C1R , nppiHistogramEvenGetBufferSize_8u_C1R >::hist,
......@@ -287,20 +489,16 @@ void cv::gpu::histEven(const GpuMat& src, GpuMat& hist, GpuMat& buf, int histSiz
NppHistogramEvenC1<CV_16S, nppiHistogramEven_16s_C1R, nppiHistogramEvenGetBufferSize_16s_C1R>::hist
};
hist_callers[src.depth()](src, hist, buf, histSize, lowerLevel, upperLevel, StreamAccessor::getStream(stream));
}
GpuMat src = _src.getGpuMat();
void cv::gpu::histEven(const GpuMat& src, GpuMat hist[4], int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream)
{
GpuMat buf;
histEven(src, hist, buf, histSize, lowerLevel, upperLevel, stream);
CV_Assert( src.type() == CV_8UC1 || src.type() == CV_16UC1 || src.type() == CV_16SC1 );
hist_callers[src.depth()](src, hist, buf, histSize, lowerLevel, upperLevel, StreamAccessor::getStream(stream));
}
void cv::gpu::histEven(const GpuMat& src, GpuMat hist[4], GpuMat& buf, int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream)
void cv::gpu::histEven(InputArray _src, GpuMat hist[4], InputOutputArray buf, int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream)
{
CV_Assert(src.type() == CV_8UC4 || src.type() == CV_16UC4 || src.type() == CV_16SC4 );
typedef void (*hist_t)(const GpuMat& src, GpuMat hist[4], GpuMat& buf, int levels[4], int lowerLevel[4], int upperLevel[4], cudaStream_t stream);
typedef void (*hist_t)(const GpuMat& src, GpuMat hist[4], InputOutputArray buf, int levels[4], int lowerLevel[4], int upperLevel[4], cudaStream_t stream);
static const hist_t hist_callers[] =
{
NppHistogramEvenC4<CV_8U , nppiHistogramEven_8u_C4R , nppiHistogramEvenGetBufferSize_8u_C4R >::hist,
......@@ -309,20 +507,16 @@ void cv::gpu::histEven(const GpuMat& src, GpuMat hist[4], GpuMat& buf, int histS
NppHistogramEvenC4<CV_16S, nppiHistogramEven_16s_C4R, nppiHistogramEvenGetBufferSize_16s_C4R>::hist
};
hist_callers[src.depth()](src, hist, buf, histSize, lowerLevel, upperLevel, StreamAccessor::getStream(stream));
}
GpuMat src = _src.getGpuMat();
void cv::gpu::histRange(const GpuMat& src, GpuMat& hist, const GpuMat& levels, Stream& stream)
{
GpuMat buf;
histRange(src, hist, levels, buf, stream);
CV_Assert( src.type() == CV_8UC4 || src.type() == CV_16UC4 || src.type() == CV_16SC4 );
hist_callers[src.depth()](src, hist, buf, histSize, lowerLevel, upperLevel, StreamAccessor::getStream(stream));
}
void cv::gpu::histRange(const GpuMat& src, GpuMat& hist, const GpuMat& levels, GpuMat& buf, Stream& stream)
void cv::gpu::histRange(InputArray _src, OutputArray hist, InputArray _levels, InputOutputArray buf, Stream& stream)
{
CV_Assert(src.type() == CV_8UC1 || src.type() == CV_16UC1 || src.type() == CV_16SC1 || src.type() == CV_32FC1);
typedef void (*hist_t)(const GpuMat& src, GpuMat& hist, const GpuMat& levels, GpuMat& buf, cudaStream_t stream);
typedef void (*hist_t)(const GpuMat& src, OutputArray hist, const GpuMat& levels, InputOutputArray buf, cudaStream_t stream);
static const hist_t hist_callers[] =
{
NppHistogramRangeC1<CV_8U , nppiHistogramRange_8u_C1R , nppiHistogramRangeGetBufferSize_8u_C1R >::hist,
......@@ -333,20 +527,17 @@ void cv::gpu::histRange(const GpuMat& src, GpuMat& hist, const GpuMat& levels, G
NppHistogramRangeC1<CV_32F, nppiHistogramRange_32f_C1R, nppiHistogramRangeGetBufferSize_32f_C1R>::hist
};
hist_callers[src.depth()](src, hist, levels, buf, StreamAccessor::getStream(stream));
}
GpuMat src = _src.getGpuMat();
GpuMat levels = _levels.getGpuMat();
void cv::gpu::histRange(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], Stream& stream)
{
GpuMat buf;
histRange(src, hist, levels, buf, stream);
CV_Assert( src.type() == CV_8UC1 || src.type() == CV_16UC1 || src.type() == CV_16SC1 || src.type() == CV_32FC1 );
hist_callers[src.depth()](src, hist, levels, buf, StreamAccessor::getStream(stream));
}
void cv::gpu::histRange(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], GpuMat& buf, Stream& stream)
void cv::gpu::histRange(InputArray _src, GpuMat hist[4], const GpuMat levels[4], InputOutputArray buf, Stream& stream)
{
CV_Assert(src.type() == CV_8UC4 || src.type() == CV_16UC4 || src.type() == CV_16SC4 || src.type() == CV_32FC4);
typedef void (*hist_t)(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], GpuMat& buf, cudaStream_t stream);
typedef void (*hist_t)(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], InputOutputArray buf, cudaStream_t stream);
static const hist_t hist_callers[] =
{
NppHistogramRangeC4<CV_8U , nppiHistogramRange_8u_C4R , nppiHistogramRangeGetBufferSize_8u_C4R >::hist,
......@@ -357,201 +548,11 @@ void cv::gpu::histRange(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4
NppHistogramRangeC4<CV_32F, nppiHistogramRange_32f_C4R, nppiHistogramRangeGetBufferSize_32f_C4R>::hist
};
hist_callers[src.depth()](src, hist, levels, buf, StreamAccessor::getStream(stream));
}
////////////////////////////////////////////////////////////////////////
// calcHist
namespace hist
{
void histogram256(PtrStepSzb src, int* hist, cudaStream_t stream);
}
void cv::gpu::calcHist(const GpuMat& src, GpuMat& hist, Stream& stream)
{
CV_Assert(src.type() == CV_8UC1);
hist.create(1, 256, CV_32SC1);
hist.setTo(Scalar::all(0));
hist::histogram256(src, hist.ptr<int>(), StreamAccessor::getStream(stream));
}
////////////////////////////////////////////////////////////////////////
// equalizeHist
namespace hist
{
void equalizeHist(PtrStepSzb src, PtrStepSzb dst, const int* lut, cudaStream_t stream);
}
void cv::gpu::equalizeHist(const GpuMat& src, GpuMat& dst, Stream& stream)
{
GpuMat hist;
GpuMat buf;
equalizeHist(src, dst, hist, buf, stream);
}
void cv::gpu::equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, GpuMat& buf, Stream& s)
{
CV_Assert(src.type() == CV_8UC1);
dst.create(src.size(), src.type());
int intBufSize;
nppSafeCall( nppsIntegralGetBufferSize_32s(256, &intBufSize) );
ensureSizeIsEnough(1, intBufSize + 256 * sizeof(int), CV_8UC1, buf);
GpuMat intBuf(1, intBufSize, CV_8UC1, buf.ptr());
GpuMat lut(1, 256, CV_32S, buf.ptr() + intBufSize);
GpuMat src = _src.getGpuMat();
calcHist(src, hist, s);
CV_Assert( src.type() == CV_8UC4 || src.type() == CV_16UC4 || src.type() == CV_16SC4 || src.type() == CV_32FC4 );
cudaStream_t stream = StreamAccessor::getStream(s);
NppStreamHandler h(stream);
nppSafeCall( nppsIntegral_32s(hist.ptr<Npp32s>(), lut.ptr<Npp32s>(), 256, intBuf.ptr<Npp8u>()) );
hist::equalizeHist(src, dst, lut.ptr<int>(), stream);
}
////////////////////////////////////////////////////////////////////////
// CLAHE
namespace clahe
{
void calcLut(PtrStepSzb src, PtrStepb lut, int tilesX, int tilesY, int2 tileSize, int clipLimit, float lutScale, cudaStream_t stream);
void transform(PtrStepSzb src, PtrStepSzb dst, PtrStepb lut, int tilesX, int tilesY, int2 tileSize, cudaStream_t stream);
}
namespace
{
class CLAHE_Impl : public cv::gpu::CLAHE
{
public:
CLAHE_Impl(double clipLimit = 40.0, int tilesX = 8, int tilesY = 8);
cv::AlgorithmInfo* info() const;
void apply(cv::InputArray src, cv::OutputArray dst);
void apply(InputArray src, OutputArray dst, Stream& stream);
void setClipLimit(double clipLimit);
double getClipLimit() const;
void setTilesGridSize(cv::Size tileGridSize);
cv::Size getTilesGridSize() const;
void collectGarbage();
private:
double clipLimit_;
int tilesX_;
int tilesY_;
GpuMat srcExt_;
GpuMat lut_;
};
CLAHE_Impl::CLAHE_Impl(double clipLimit, int tilesX, int tilesY) :
clipLimit_(clipLimit), tilesX_(tilesX), tilesY_(tilesY)
{
}
CV_INIT_ALGORITHM(CLAHE_Impl, "CLAHE_GPU",
obj.info()->addParam(obj, "clipLimit", obj.clipLimit_);
obj.info()->addParam(obj, "tilesX", obj.tilesX_);
obj.info()->addParam(obj, "tilesY", obj.tilesY_))
void CLAHE_Impl::apply(cv::InputArray _src, cv::OutputArray _dst)
{
apply(_src, _dst, Stream::Null());
}
void CLAHE_Impl::apply(InputArray _src, OutputArray _dst, Stream& s)
{
GpuMat src = _src.getGpuMat();
CV_Assert( src.type() == CV_8UC1 );
_dst.create( src.size(), src.type() );
GpuMat dst = _dst.getGpuMat();
const int histSize = 256;
ensureSizeIsEnough(tilesX_ * tilesY_, histSize, CV_8UC1, lut_);
cudaStream_t stream = StreamAccessor::getStream(s);
cv::Size tileSize;
GpuMat srcForLut;
if (src.cols % tilesX_ == 0 && src.rows % tilesY_ == 0)
{
tileSize = cv::Size(src.cols / tilesX_, src.rows / tilesY_);
srcForLut = src;
}
else
{
#ifndef HAVE_OPENCV_GPUARITHM
throw_no_cuda();
#else
cv::gpu::copyMakeBorder(src, srcExt_, 0, tilesY_ - (src.rows % tilesY_), 0, tilesX_ - (src.cols % tilesX_), cv::BORDER_REFLECT_101, cv::Scalar(), s);
#endif
tileSize = cv::Size(srcExt_.cols / tilesX_, srcExt_.rows / tilesY_);
srcForLut = srcExt_;
}
const int tileSizeTotal = tileSize.area();
const float lutScale = static_cast<float>(histSize - 1) / tileSizeTotal;
int clipLimit = 0;
if (clipLimit_ > 0.0)
{
clipLimit = static_cast<int>(clipLimit_ * tileSizeTotal / histSize);
clipLimit = std::max(clipLimit, 1);
}
clahe::calcLut(srcForLut, lut_, tilesX_, tilesY_, make_int2(tileSize.width, tileSize.height), clipLimit, lutScale, stream);
clahe::transform(src, dst, lut_, tilesX_, tilesY_, make_int2(tileSize.width, tileSize.height), stream);
}
void CLAHE_Impl::setClipLimit(double clipLimit)
{
clipLimit_ = clipLimit;
}
double CLAHE_Impl::getClipLimit() const
{
return clipLimit_;
}
void CLAHE_Impl::setTilesGridSize(cv::Size tileGridSize)
{
tilesX_ = tileGridSize.width;
tilesY_ = tileGridSize.height;
}
cv::Size CLAHE_Impl::getTilesGridSize() const
{
return cv::Size(tilesX_, tilesY_);
}
void CLAHE_Impl::collectGarbage()
{
srcExt_.release();
lut_.release();
}
}
cv::Ptr<cv::gpu::CLAHE> cv::gpu::createCLAHE(double clipLimit, cv::Size tileGridSize)
{
return new CLAHE_Impl(clipLimit, tileGridSize.width, tileGridSize.height);
hist_callers[src.depth()](src, hist, levels, buf, StreamAccessor::getStream(stream));
}
#endif /* !defined (HAVE_CUDA) */
samples/gpu/performance/tests.cpp
View file @ fc847654
......@@ -1047,13 +1047,12 @@ TEST(equalizeHist)
gpu::GpuMat d_src(src);
gpu::GpuMat d_dst;
gpu::GpuMat d_hist;
gpu::GpuMat d_buf;
gpu::equalizeHist(d_src, d_dst, d_hist, d_buf);
gpu::equalizeHist(d_src, d_dst, d_buf);
GPU_ON;
gpu::equalizeHist(d_src, d_dst, d_hist, d_buf);
gpu::equalizeHist(d_src, d_dst, d_buf);
GPU_OFF;
}
}
......
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