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Commit 8a518266 authored by Olexa Bilaniuk's avatar Olexa Bilaniuk
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Merge remote-tracking branch 'refs/remotes/upstream/master' into rho

parents f73b48b8 b2a5e66f
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Showing with 394 additions and 198 deletions
cmake/FindCUDA.cmake
View file @ 8a518266
......@@ -1098,7 +1098,7 @@ macro(CUDA_WRAP_SRCS cuda_target format generated_files)
set(nvcc_flags ${nvcc_flags} "--target-cpu-architecture=${CUDA_TARGET_CPU_ARCH}")
endif()
if(CUDA_TARGET_OS_VARIANT)
if(CUDA_TARGET_OS_VARIANT AND CUDA_VERSION VERSION_LESS "7.0")
set(nvcc_flags ${nvcc_flags} "-target-os-variant=${CUDA_TARGET_OS_VARIANT}")
endif()
......
modules/core/include/opencv2/core/private.cuda.hpp
View file @ 8a518266
......@@ -130,6 +130,12 @@ namespace cv { namespace cuda
class NppStreamHandler
{
public:
inline explicit NppStreamHandler(Stream& newStream)
{
oldStream = nppGetStream();
nppSetStream(StreamAccessor::getStream(newStream));
}
inline explicit NppStreamHandler(cudaStream_t newStream)
{
oldStream = nppGetStream();
......
modules/core/src/parallel.cpp
View file @ 8a518266
......@@ -56,7 +56,7 @@
#include <sys/types.h>
#if defined ANDROID
#include <sys/sysconf.h>
#else
#elif defined __APPLE__
#include <sys/sysctl.h>
#endif
#endif
......
modules/core/src/system.cpp
View file @ 8a518266
......@@ -163,8 +163,6 @@ std::wstring GetTempFileNameWinRT(std::wstring prefix)
#include <sys/types.h>
#if defined ANDROID
#include <sys/sysconf.h>
#else
#include <sys/sysctl.h>
#endif
#endif
......
modules/cudaimgproc/include/opencv2/cudaimgproc.hpp
View file @ 8a518266
......@@ -205,19 +205,11 @@ CV_EXPORTS void calcHist(InputArray src, OutputArray hist, Stream& stream = Stre
@param src Source image with CV_8UC1 type.
@param dst Destination image.
@param buf Optional buffer to avoid extra memory allocations (for many calls with the same sizes).
@param stream Stream for the asynchronous version.
@sa equalizeHist
*/
CV_EXPORTS void equalizeHist(InputArray src, OutputArray dst, InputOutputArray buf, Stream& stream = Stream::Null());
/** @overload */
static inline void equalizeHist(InputArray src, OutputArray dst, Stream& stream = Stream::Null())
{
GpuMat buf;
cuda::equalizeHist(src, dst, buf, stream);
}
CV_EXPORTS void equalizeHist(InputArray src, OutputArray dst, Stream& stream = Stream::Null());
/** @brief Base class for Contrast Limited Adaptive Histogram Equalization. :
*/
......@@ -248,8 +240,9 @@ CV_EXPORTS Ptr<cuda::CLAHE> createCLAHE(double clipLimit = 40.0, Size tileGridSi
@param nLevels Number of computed levels. nLevels must be at least 2.
@param lowerLevel Lower boundary value of the lowest level.
@param upperLevel Upper boundary value of the greatest level.
@param stream Stream for the asynchronous version.
*/
CV_EXPORTS void evenLevels(OutputArray levels, int nLevels, int lowerLevel, int upperLevel);
CV_EXPORTS void evenLevels(OutputArray levels, int nLevels, int lowerLevel, int upperLevel, Stream& stream = Stream::Null());
/** @brief Calculates a histogram with evenly distributed bins.
......@@ -259,27 +252,11 @@ a four-channel image, all channels are processed separately.
@param histSize Size of the histogram.
@param lowerLevel Lower boundary of lowest-level bin.
@param upperLevel Upper boundary of highest-level bin.
@param buf Optional buffer to avoid extra memory allocations (for many calls with the same sizes).
@param stream Stream for the asynchronous version.
*/
CV_EXPORTS void histEven(InputArray src, OutputArray hist, InputOutputArray buf, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null());
/** @overload */
static inline void histEven(InputArray src, OutputArray hist, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null())
{
GpuMat buf;
cuda::histEven(src, hist, buf, histSize, lowerLevel, upperLevel, stream);
}
/** @overload */
CV_EXPORTS void histEven(InputArray src, GpuMat hist[4], InputOutputArray buf, int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null());
CV_EXPORTS void histEven(InputArray src, OutputArray hist, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null());
/** @overload */
static inline void histEven(InputArray src, GpuMat hist[4], int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null())
{
GpuMat buf;
cuda::histEven(src, hist, buf, histSize, lowerLevel, upperLevel, stream);
}
CV_EXPORTS void histEven(InputArray src, GpuMat hist[4], int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null());
/** @brief Calculates a histogram with bins determined by the levels array.
......@@ -287,27 +264,11 @@ static inline void histEven(InputArray src, GpuMat hist[4], int histSize[4], int
For a four-channel image, all channels are processed separately.
@param hist Destination histogram with one row, (levels.cols-1) columns, and the CV_32SC1 type.
@param levels Number of levels in the histogram.
@param buf Optional buffer to avoid extra memory allocations (for many calls with the same sizes).
@param stream Stream for the asynchronous version.
*/
CV_EXPORTS void histRange(InputArray src, OutputArray hist, InputArray levels, InputOutputArray buf, Stream& stream = Stream::Null());
/** @overload */
static inline void histRange(InputArray src, OutputArray hist, InputArray levels, Stream& stream = Stream::Null())
{
GpuMat buf;
cuda::histRange(src, hist, levels, buf, stream);
}
/** @overload */
CV_EXPORTS void histRange(InputArray src, GpuMat hist[4], const GpuMat levels[4], InputOutputArray buf, Stream& stream = Stream::Null());
CV_EXPORTS void histRange(InputArray src, OutputArray hist, InputArray levels, Stream& stream = Stream::Null());
/** @overload */
static inline void histRange(InputArray src, GpuMat hist[4], const GpuMat levels[4], Stream& stream = Stream::Null())
{
GpuMat buf;
cuda::histRange(src, hist, levels, buf, stream);
}
CV_EXPORTS void histRange(InputArray src, GpuMat hist[4], const GpuMat levels[4], Stream& stream = Stream::Null());
//! @} cudaimgproc_hist
......@@ -321,15 +282,17 @@ public:
/** @brief Finds edges in an image using the @cite Canny86 algorithm.
@param image Single-channel 8-bit input image.
@param edges Output edge map. It has the same size and type as image .
@param edges Output edge map. It has the same size and type as image.
@param stream Stream for the asynchronous version.
*/
virtual void detect(InputArray image, OutputArray edges) = 0;
virtual void detect(InputArray image, OutputArray edges, Stream& stream = Stream::Null()) = 0;
/** @overload
@param dx First derivative of image in the vertical direction. Support only CV_32S type.
@param dy First derivative of image in the horizontal direction. Support only CV_32S type.
@param edges Output edge map. It has the same size and type as image .
@param edges Output edge map. It has the same size and type as image.
@param stream Stream for the asynchronous version.
*/
virtual void detect(InputArray dx, InputArray dy, OutputArray edges) = 0;
virtual void detect(InputArray dx, InputArray dy, OutputArray edges, Stream& stream = Stream::Null()) = 0;
virtual void setLowThreshold(double low_thresh) = 0;
virtual double getLowThreshold() const = 0;
......@@ -376,18 +339,20 @@ public:
\f$(\rho, \theta)\f$ . \f$\rho\f$ is the distance from the coordinate origin \f$(0,0)\f$ (top-left corner of
the image). \f$\theta\f$ is the line rotation angle in radians (
\f$0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\f$ ).
@param stream Stream for the asynchronous version.
@sa HoughLines
*/
virtual void detect(InputArray src, OutputArray lines) = 0;
virtual void detect(InputArray src, OutputArray lines, Stream& stream = Stream::Null()) = 0;
/** @brief Downloads results from cuda::HoughLinesDetector::detect to host memory.
@param d_lines Result of cuda::HoughLinesDetector::detect .
@param h_lines Output host array.
@param h_votes Optional output array for line's votes.
@param stream Stream for the asynchronous version.
*/
virtual void downloadResults(InputArray d_lines, OutputArray h_lines, OutputArray h_votes = noArray()) = 0;
virtual void downloadResults(InputArray d_lines, OutputArray h_lines, OutputArray h_votes = noArray(), Stream& stream = Stream::Null()) = 0;
virtual void setRho(float rho) = 0;
virtual float getRho() const = 0;
......@@ -431,10 +396,11 @@ public:
@param lines Output vector of lines. Each line is represented by a 4-element vector
\f$(x_1, y_1, x_2, y_2)\f$ , where \f$(x_1,y_1)\f$ and \f$(x_2, y_2)\f$ are the ending points of each detected
line segment.
@param stream Stream for the asynchronous version.
@sa HoughLinesP
*/
virtual void detect(InputArray src, OutputArray lines) = 0;
virtual void detect(InputArray src, OutputArray lines, Stream& stream = Stream::Null()) = 0;
virtual void setRho(float rho) = 0;
virtual float getRho() const = 0;
......@@ -475,10 +441,11 @@ public:
@param src 8-bit, single-channel grayscale input image.
@param circles Output vector of found circles. Each vector is encoded as a 3-element
floating-point vector \f$(x, y, radius)\f$ .
@param stream Stream for the asynchronous version.
@sa HoughCircles
*/
virtual void detect(InputArray src, OutputArray circles) = 0;
virtual void detect(InputArray src, OutputArray circles, Stream& stream = Stream::Null()) = 0;
virtual void setDp(float dp) = 0;
virtual float getDp() const = 0;
......@@ -593,8 +560,9 @@ public:
positions).
@param mask Optional region of interest. If the image is not empty (it needs to have the type
CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
@param stream Stream for the asynchronous version.
*/
virtual void detect(InputArray image, OutputArray corners, InputArray mask = noArray()) = 0;
virtual void detect(InputArray image, OutputArray corners, InputArray mask = noArray(), Stream& stream = Stream::Null()) = 0;
};
/** @brief Creates implementation for cuda::CornersDetector .
......@@ -630,7 +598,7 @@ as src .
@param sp Spatial window radius.
@param sr Color window radius.
@param criteria Termination criteria. See TermCriteria.
@param stream
@param stream Stream for the asynchronous version.
It maps each point of the source image into another point. As a result, you have a new color and new
position of each point.
......@@ -650,7 +618,7 @@ src size. The type is CV_16SC2 .
@param sp Spatial window radius.
@param sr Color window radius.
@param criteria Termination criteria. See TermCriteria.
@param stream
@param stream Stream for the asynchronous version.
@sa cuda::meanShiftFiltering
*/
......@@ -666,9 +634,11 @@ CV_EXPORTS void meanShiftProc(InputArray src, OutputArray dstr, OutputArray dsts
@param sr Color window radius.
@param minsize Minimum segment size. Smaller segments are merged.
@param criteria Termination criteria. See TermCriteria.
@param stream Stream for the asynchronous version.
*/
CV_EXPORTS void meanShiftSegmentation(InputArray src, OutputArray dst, int sp, int sr, int minsize,
TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1),
Stream& stream = Stream::Null());
/////////////////////////// Match Template ////////////////////////////
......
modules/cudaimgproc/perf/perf_histogram.cpp
View file @ 8a518266
......@@ -63,9 +63,8 @@ PERF_TEST_P(Sz_Depth, HistEvenC1,
{
const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
cv::cuda::GpuMat d_buf;
TEST_CYCLE() cv::cuda::histEven(d_src, dst, d_buf, 30, 0, 180);
TEST_CYCLE() cv::cuda::histEven(d_src, dst, 30, 0, 180);
CUDA_SANITY_CHECK(dst);
}
......@@ -106,9 +105,8 @@ PERF_TEST_P(Sz_Depth, HistEvenC4,
{
const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat d_hist[4];
cv::cuda::GpuMat d_buf;
TEST_CYCLE() cv::cuda::histEven(d_src, d_hist, d_buf, histSize, lowerLevel, upperLevel);
TEST_CYCLE() cv::cuda::histEven(d_src, d_hist, histSize, lowerLevel, upperLevel);
cv::Mat cpu_hist0, cpu_hist1, cpu_hist2, cpu_hist3;
d_hist[0].download(cpu_hist0);
......@@ -167,9 +165,8 @@ PERF_TEST_P(Sz, EqualizeHist,
{
const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
cv::cuda::GpuMat d_buf;
TEST_CYCLE() cv::cuda::equalizeHist(d_src, dst, d_buf);
TEST_CYCLE() cv::cuda::equalizeHist(d_src, dst);
CUDA_SANITY_CHECK(dst);
}
......
modules/cudaimgproc/src/canny.cpp
View file @ 8a518266
......@@ -53,16 +53,16 @@ Ptr<CannyEdgeDetector> cv::cuda::createCannyEdgeDetector(double, double, int, bo
namespace canny
{
void calcMagnitude(PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, bool L2Grad);
void calcMagnitude(PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, bool L2Grad);
void calcMagnitude(PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, bool L2Grad, cudaStream_t stream);
void calcMagnitude(PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, bool L2Grad, cudaStream_t stream);
void calcMap(PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, PtrStepSzi map, float low_thresh, float high_thresh);
void calcMap(PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, PtrStepSzi map, float low_thresh, float high_thresh, cudaStream_t stream);
void edgesHysteresisLocal(PtrStepSzi map, short2* st1);
void edgesHysteresisLocal(PtrStepSzi map, short2* st1, cudaStream_t stream);
void edgesHysteresisGlobal(PtrStepSzi map, short2* st1, short2* st2);
void edgesHysteresisGlobal(PtrStepSzi map, short2* st1, short2* st2, cudaStream_t stream);
void getEdges(PtrStepSzi map, PtrStepSzb dst);
void getEdges(PtrStepSzi map, PtrStepSzb dst, cudaStream_t stream);
}
namespace
......@@ -76,8 +76,8 @@ namespace
old_apperture_size_ = -1;
}
void detect(InputArray image, OutputArray edges);
void detect(InputArray dx, InputArray dy, OutputArray edges);
void detect(InputArray image, OutputArray edges, Stream& stream);
void detect(InputArray dx, InputArray dy, OutputArray edges, Stream& stream);
void setLowThreshold(double low_thresh) { low_thresh_ = low_thresh; }
double getLowThreshold() const { return low_thresh_; }
......@@ -111,7 +111,7 @@ namespace
private:
void createBuf(Size image_size);
void CannyCaller(GpuMat& edges);
void CannyCaller(GpuMat& edges, Stream& stream);
double low_thresh_;
double high_thresh_;
......@@ -128,7 +128,7 @@ namespace
int old_apperture_size_;
};
void CannyImpl::detect(InputArray _image, OutputArray _edges)
void CannyImpl::detect(InputArray _image, OutputArray _edges, Stream& stream)
{
GpuMat image = _image.getGpuMat();
......@@ -150,24 +150,24 @@ namespace
image.locateROI(wholeSize, ofs);
GpuMat srcWhole(wholeSize, image.type(), image.datastart, image.step);
canny::calcMagnitude(srcWhole, ofs.x, ofs.y, dx_, dy_, mag_, L2gradient_);
canny::calcMagnitude(srcWhole, ofs.x, ofs.y, dx_, dy_, mag_, L2gradient_, StreamAccessor::getStream(stream));
}
else
{
#ifndef HAVE_OPENCV_CUDAFILTERS
throw_no_cuda();
#else
filterDX_->apply(image, dx_);
filterDY_->apply(image, dy_);
filterDX_->apply(image, dx_, stream);
filterDY_->apply(image, dy_, stream);
canny::calcMagnitude(dx_, dy_, mag_, L2gradient_);
canny::calcMagnitude(dx_, dy_, mag_, L2gradient_, StreamAccessor::getStream(stream));
#endif
}
CannyCaller(edges);
CannyCaller(edges, stream);
}
void CannyImpl::detect(InputArray _dx, InputArray _dy, OutputArray _edges)
void CannyImpl::detect(InputArray _dx, InputArray _dy, OutputArray _edges, Stream& stream)
{
GpuMat dx = _dx.getGpuMat();
GpuMat dy = _dy.getGpuMat();
......@@ -176,8 +176,8 @@ namespace
CV_Assert( dy.type() == dx.type() && dy.size() == dx.size() );
CV_Assert( deviceSupports(SHARED_ATOMICS) );
dx.copyTo(dx_);
dy.copyTo(dy_);
dx.copyTo(dx_, stream);
dy.copyTo(dy_, stream);
if (low_thresh_ > high_thresh_)
std::swap(low_thresh_, high_thresh_);
......@@ -187,9 +187,9 @@ namespace
_edges.create(dx.size(), CV_8UC1);
GpuMat edges = _edges.getGpuMat();
canny::calcMagnitude(dx_, dy_, mag_, L2gradient_);
canny::calcMagnitude(dx_, dy_, mag_, L2gradient_, StreamAccessor::getStream(stream));
CannyCaller(edges);
CannyCaller(edges, stream);
}
void CannyImpl::createBuf(Size image_size)
......@@ -215,16 +215,16 @@ namespace
ensureSizeIsEnough(1, image_size.area(), CV_16SC2, st2_);
}
void CannyImpl::CannyCaller(GpuMat& edges)
void CannyImpl::CannyCaller(GpuMat& edges, Stream& stream)
{
map_.setTo(Scalar::all(0));
canny::calcMap(dx_, dy_, mag_, map_, static_cast<float>(low_thresh_), static_cast<float>(high_thresh_));
canny::calcMap(dx_, dy_, mag_, map_, static_cast<float>(low_thresh_), static_cast<float>(high_thresh_), StreamAccessor::getStream(stream));
canny::edgesHysteresisLocal(map_, st1_.ptr<short2>());
canny::edgesHysteresisLocal(map_, st1_.ptr<short2>(), StreamAccessor::getStream(stream));
canny::edgesHysteresisGlobal(map_, st1_.ptr<short2>(), st2_.ptr<short2>());
canny::edgesHysteresisGlobal(map_, st1_.ptr<short2>(), st2_.ptr<short2>(), StreamAccessor::getStream(stream));
canny::getEdges(map_, edges);
canny::getEdges(map_, edges, StreamAccessor::getStream(stream));
}
}
......
modules/cudaimgproc/src/cuda/canny.cu
View file @ 8a518266
......@@ -120,7 +120,7 @@ namespace canny
mag(y, x) = norm(dxVal, dyVal);
}
void calcMagnitude(PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, bool L2Grad)
void calcMagnitude(PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, bool L2Grad, cudaStream_t stream)
{
const dim3 block(16, 16);
const dim3 grid(divUp(mag.cols, block.x), divUp(mag.rows, block.y));
......@@ -131,30 +131,31 @@ namespace canny
if (L2Grad)
{
L2 norm;
calcMagnitudeKernel<<<grid, block>>>(src, dx, dy, mag, norm);
calcMagnitudeKernel<<<grid, block, 0, stream>>>(src, dx, dy, mag, norm);
}
else
{
L1 norm;
calcMagnitudeKernel<<<grid, block>>>(src, dx, dy, mag, norm);
calcMagnitudeKernel<<<grid, block, 0, stream>>>(src, dx, dy, mag, norm);
}
cudaSafeCall( cudaGetLastError() );
cudaSafeCall(cudaThreadSynchronize());
if (stream == NULL)
cudaSafeCall( cudaDeviceSynchronize() );
}
void calcMagnitude(PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, bool L2Grad)
void calcMagnitude(PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, bool L2Grad, cudaStream_t stream)
{
if (L2Grad)
{
L2 norm;
transform(dx, dy, mag, norm, WithOutMask(), 0);
transform(dx, dy, mag, norm, WithOutMask(), stream);
}
else
{
L1 norm;
transform(dx, dy, mag, norm, WithOutMask(), 0);
transform(dx, dy, mag, norm, WithOutMask(), stream);
}
}
}
......@@ -217,17 +218,18 @@ namespace canny
map(y, x) = edge_type;
}
void calcMap(PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, PtrStepSzi map, float low_thresh, float high_thresh)
void calcMap(PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, PtrStepSzi map, float low_thresh, float high_thresh, cudaStream_t stream)
{
const dim3 block(16, 16);
const dim3 grid(divUp(dx.cols, block.x), divUp(dx.rows, block.y));
bindTexture(&tex_mag, mag);
calcMapKernel<<<grid, block>>>(dx, dy, map, low_thresh, high_thresh);
calcMapKernel<<<grid, block, 0, stream>>>(dx, dy, map, low_thresh, high_thresh);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
if (stream == NULL)
cudaSafeCall( cudaDeviceSynchronize() );
}
}
......@@ -328,20 +330,21 @@ namespace canny
}
}
void edgesHysteresisLocal(PtrStepSzi map, short2* st1)
void edgesHysteresisLocal(PtrStepSzi map, short2* st1, cudaStream_t stream)
{
void* counter_ptr;
cudaSafeCall( cudaGetSymbolAddress(&counter_ptr, counter) );
cudaSafeCall( cudaMemset(counter_ptr, 0, sizeof(int)) );
cudaSafeCall( cudaMemsetAsync(counter_ptr, 0, sizeof(int), stream) );
const dim3 block(16, 16);
const dim3 grid(divUp(map.cols, block.x), divUp(map.rows, block.y));
edgesHysteresisLocalKernel<<<grid, block>>>(map, st1);
edgesHysteresisLocalKernel<<<grid, block, 0, stream>>>(map, st1);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
if (stream == NULL)
cudaSafeCall( cudaDeviceSynchronize() );
}
}
......@@ -441,27 +444,30 @@ namespace canny
}
}
void edgesHysteresisGlobal(PtrStepSzi map, short2* st1, short2* st2)
void edgesHysteresisGlobal(PtrStepSzi map, short2* st1, short2* st2, cudaStream_t stream)
{
void* counter_ptr;
cudaSafeCall( cudaGetSymbolAddress(&counter_ptr, canny::counter) );
int count;
cudaSafeCall( cudaMemcpy(&count, counter_ptr, sizeof(int), cudaMemcpyDeviceToHost) );
cudaSafeCall( cudaMemcpyAsync(&count, counter_ptr, sizeof(int), cudaMemcpyDeviceToHost, stream) );
cudaSafeCall( cudaStreamSynchronize(stream) );
while (count > 0)
{
cudaSafeCall( cudaMemset(counter_ptr, 0, sizeof(int)) );
cudaSafeCall( cudaMemsetAsync(counter_ptr, 0, sizeof(int), stream) );
const dim3 block(128);
const dim3 grid(::min(count, 65535u), divUp(count, 65535), 1);
edgesHysteresisGlobalKernel<<<grid, block>>>(map, st1, st2, count);
edgesHysteresisGlobalKernel<<<grid, block, 0, stream>>>(map, st1, st2, count);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
if (stream == NULL)
cudaSafeCall( cudaDeviceSynchronize() );
cudaSafeCall( cudaMemcpy(&count, counter_ptr, sizeof(int), cudaMemcpyDeviceToHost) );
cudaSafeCall( cudaMemcpyAsync(&count, counter_ptr, sizeof(int), cudaMemcpyDeviceToHost, stream) );
cudaSafeCall( cudaStreamSynchronize(stream) );
count = min(count, map.cols * map.rows);
......@@ -499,9 +505,9 @@ namespace cv { namespace cuda { namespace device
namespace canny
{
void getEdges(PtrStepSzi map, PtrStepSzb dst)
void getEdges(PtrStepSzi map, PtrStepSzb dst, cudaStream_t stream)
{
transform(map, dst, GetEdges(), WithOutMask(), 0);
transform(map, dst, GetEdges(), WithOutMask(), stream);
}
}
......
modules/cudaimgproc/src/gftt.cpp
View file @ 8a518266
......@@ -68,7 +68,7 @@ namespace
GoodFeaturesToTrackDetector(int srcType, int maxCorners, double qualityLevel, double minDistance,
int blockSize, bool useHarrisDetector, double harrisK);
void detect(InputArray image, OutputArray corners, InputArray mask = noArray());
void detect(InputArray image, OutputArray corners, InputArray mask, Stream& stream);
private:
int maxCorners_;
......@@ -96,8 +96,11 @@ namespace
cuda::createMinEigenValCorner(srcType, blockSize, 3);
}
void GoodFeaturesToTrackDetector::detect(InputArray _image, OutputArray _corners, InputArray _mask)
void GoodFeaturesToTrackDetector::detect(InputArray _image, OutputArray _corners, InputArray _mask, Stream& stream)
{
// TODO : implement async version
(void) stream;
using namespace cv::cuda::device::gfft;
GpuMat image = _image.getGpuMat();
......
modules/cudaimgproc/src/histogram.cpp
View file @ 8a518266
......@@ -49,11 +49,11 @@ using namespace cv::cuda;
void cv::cuda::calcHist(InputArray, OutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::equalizeHist(InputArray, OutputArray, InputOutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::equalizeHist(InputArray, OutputArray, Stream&) { throw_no_cuda(); }
cv::Ptr<cv::cuda::CLAHE> cv::cuda::createCLAHE(double, cv::Size) { throw_no_cuda(); return cv::Ptr<cv::cuda::CLAHE>(); }
void cv::cuda::evenLevels(OutputArray, int, int, int) { throw_no_cuda(); }
void cv::cuda::evenLevels(OutputArray, int, int, int, Stream&) { throw_no_cuda(); }
void cv::cuda::histEven(InputArray, OutputArray, InputOutputArray, int, int, int, Stream&) { throw_no_cuda(); }
void cv::cuda::histEven(InputArray, GpuMat*, InputOutputArray, int*, int*, int*, Stream&) { throw_no_cuda(); }
......@@ -93,7 +93,7 @@ namespace hist
void equalizeHist(PtrStepSzb src, PtrStepSzb dst, const int* lut, cudaStream_t stream);
}
void cv::cuda::equalizeHist(InputArray _src, OutputArray _dst, InputOutputArray _buf, Stream& _stream)
void cv::cuda::equalizeHist(InputArray _src, OutputArray _dst, Stream& _stream)
{
GpuMat src = _src.getGpuMat();
......@@ -107,8 +107,8 @@ void cv::cuda::equalizeHist(InputArray _src, OutputArray _dst, InputOutputArray
size_t bufSize = intBufSize + 2 * 256 * sizeof(int);
ensureSizeIsEnough(1, static_cast<int>(bufSize), CV_8UC1, _buf);
GpuMat buf = _buf.getGpuMat();
BufferPool pool(_stream);
GpuMat buf = pool.getBuffer(1, static_cast<int>(bufSize), CV_8UC1);
GpuMat hist(1, 256, CV_32SC1, buf.data);
GpuMat lut(1, 256, CV_32SC1, buf.data + 256 * sizeof(int));
......@@ -288,7 +288,7 @@ namespace
{
typedef typename NppHistogramEvenFuncC1<SDEPTH>::src_t src_t;
static void hist(const GpuMat& src, OutputArray _hist, InputOutputArray _buf, int histSize, int lowerLevel, int upperLevel, cudaStream_t stream)
static void hist(const GpuMat& src, OutputArray _hist, int histSize, int lowerLevel, int upperLevel, Stream& stream)
{
const int levels = histSize + 1;
......@@ -302,15 +302,15 @@ namespace
int buf_size;
get_buf_size(sz, levels, &buf_size);
ensureSizeIsEnough(1, buf_size, CV_8UC1, _buf);
GpuMat buf = _buf.getGpuMat();
BufferPool pool(stream);
GpuMat buf = pool.getBuffer(1, buf_size, CV_8UC1);
NppStreamHandler h(stream);
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), sz, hist.ptr<Npp32s>(), levels,
lowerLevel, upperLevel, buf.ptr<Npp8u>()) );
if (stream == 0)
if (!stream)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
......@@ -319,7 +319,7 @@ namespace
{
typedef typename NppHistogramEvenFuncC4<SDEPTH>::src_t src_t;
static void hist(const GpuMat& src, GpuMat hist[4],InputOutputArray _buf, int histSize[4], int lowerLevel[4], int upperLevel[4], cudaStream_t stream)
static void hist(const GpuMat& src, GpuMat hist[4], int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream)
{
int levels[] = {histSize[0] + 1, histSize[1] + 1, histSize[2] + 1, histSize[3] + 1};
hist[0].create(1, histSize[0], CV_32S);
......@@ -336,14 +336,14 @@ namespace
int buf_size;
get_buf_size(sz, levels, &buf_size);
ensureSizeIsEnough(1, buf_size, CV_8U, _buf);
GpuMat buf = _buf.getGpuMat();
BufferPool pool(stream);
GpuMat buf = pool.getBuffer(1, buf_size, CV_8UC1);
NppStreamHandler h(stream);
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), sz, pHist, levels, lowerLevel, upperLevel, buf.ptr<Npp8u>()) );
if (stream == 0)
if (!stream)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
......@@ -392,7 +392,7 @@ namespace
typedef typename NppHistogramRangeFuncC1<SDEPTH>::level_t level_t;
enum {LEVEL_TYPE_CODE=NppHistogramRangeFuncC1<SDEPTH>::LEVEL_TYPE_CODE};
static void hist(const GpuMat& src, OutputArray _hist, const GpuMat& levels, InputOutputArray _buf, cudaStream_t stream)
static void hist(const GpuMat& src, OutputArray _hist, const GpuMat& levels, Stream& stream)
{
CV_Assert( levels.type() == LEVEL_TYPE_CODE && levels.rows == 1 );
......@@ -406,8 +406,8 @@ namespace
int buf_size;
get_buf_size(sz, levels.cols, &buf_size);
ensureSizeIsEnough(1, buf_size, CV_8U, _buf);
GpuMat buf = _buf.getGpuMat();
BufferPool pool(stream);
GpuMat buf = pool.getBuffer(1, buf_size, CV_8UC1);
NppStreamHandler h(stream);
......@@ -424,7 +424,7 @@ 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],InputOutputArray _buf, cudaStream_t stream)
static void hist(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], Stream& 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 );
......@@ -447,8 +447,8 @@ namespace
int buf_size;
get_buf_size(sz, nLevels, &buf_size);
ensureSizeIsEnough(1, buf_size, CV_8U, _buf);
GpuMat buf = _buf.getGpuMat();
BufferPool pool(stream);
GpuMat buf = pool.getBuffer(1, buf_size, CV_8UC1);
NppStreamHandler h(stream);
......@@ -460,7 +460,7 @@ namespace
};
}
void cv::cuda::evenLevels(OutputArray _levels, int nLevels, int lowerLevel, int upperLevel)
void cv::cuda::evenLevels(OutputArray _levels, int nLevels, int lowerLevel, int upperLevel, Stream& stream)
{
const int kind = _levels.kind();
......@@ -475,7 +475,7 @@ void cv::cuda::evenLevels(OutputArray _levels, int nLevels, int lowerLevel, int
nppSafeCall( nppiEvenLevelsHost_32s(host_levels.ptr<Npp32s>(), nLevels, lowerLevel, upperLevel) );
if (kind == _InputArray::CUDA_GPU_MAT)
_levels.getGpuMatRef().upload(host_levels);
_levels.getGpuMatRef().upload(host_levels, stream);
}
namespace hist
......@@ -493,9 +493,9 @@ namespace
}
}
void cv::cuda::histEven(InputArray _src, OutputArray hist, InputOutputArray buf, int histSize, int lowerLevel, int upperLevel, Stream& stream)
void cv::cuda::histEven(InputArray _src, OutputArray hist, int histSize, int lowerLevel, int upperLevel, Stream& stream)
{
typedef void (*hist_t)(const GpuMat& src, OutputArray hist, InputOutputArray buf, int levels, int lowerLevel, int upperLevel, cudaStream_t stream);
typedef void (*hist_t)(const GpuMat& src, OutputArray hist, int levels, int lowerLevel, int upperLevel, Stream& stream);
static const hist_t hist_callers[] =
{
NppHistogramEvenC1<CV_8U , nppiHistogramEven_8u_C1R , nppiHistogramEvenGetBufferSize_8u_C1R >::hist,
......@@ -514,12 +514,12 @@ void cv::cuda::histEven(InputArray _src, OutputArray hist, InputOutputArray buf,
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));
hist_callers[src.depth()](src, hist, histSize, lowerLevel, upperLevel, stream);
}
void cv::cuda::histEven(InputArray _src, GpuMat hist[4], InputOutputArray buf, int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream)
void cv::cuda::histEven(InputArray _src, GpuMat hist[4], int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& 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);
typedef void (*hist_t)(const GpuMat& src, GpuMat hist[4], int levels[4], int lowerLevel[4], int upperLevel[4], Stream& stream);
static const hist_t hist_callers[] =
{
NppHistogramEvenC4<CV_8U , nppiHistogramEven_8u_C4R , nppiHistogramEvenGetBufferSize_8u_C4R >::hist,
......@@ -532,12 +532,12 @@ void cv::cuda::histEven(InputArray _src, GpuMat hist[4], InputOutputArray buf, i
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));
hist_callers[src.depth()](src, hist, histSize, lowerLevel, upperLevel, stream);
}
void cv::cuda::histRange(InputArray _src, OutputArray hist, InputArray _levels, InputOutputArray buf, Stream& stream)
void cv::cuda::histRange(InputArray _src, OutputArray hist, InputArray _levels, Stream& stream)
{
typedef void (*hist_t)(const GpuMat& src, OutputArray hist, const GpuMat& levels, InputOutputArray buf, cudaStream_t stream);
typedef void (*hist_t)(const GpuMat& src, OutputArray hist, const GpuMat& levels, Stream& stream);
static const hist_t hist_callers[] =
{
NppHistogramRangeC1<CV_8U , nppiHistogramRange_8u_C1R , nppiHistogramRangeGetBufferSize_8u_C1R >::hist,
......@@ -553,12 +553,12 @@ void cv::cuda::histRange(InputArray _src, OutputArray hist, InputArray _levels,
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));
hist_callers[src.depth()](src, hist, levels, stream);
}
void cv::cuda::histRange(InputArray _src, GpuMat hist[4], const GpuMat levels[4], InputOutputArray buf, Stream& stream)
void cv::cuda::histRange(InputArray _src, GpuMat hist[4], const GpuMat levels[4], Stream& stream)
{
typedef void (*hist_t)(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], InputOutputArray buf, cudaStream_t stream);
typedef void (*hist_t)(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], Stream& stream);
static const hist_t hist_callers[] =
{
NppHistogramRangeC4<CV_8U , nppiHistogramRange_8u_C4R , nppiHistogramRangeGetBufferSize_8u_C4R >::hist,
......@@ -573,7 +573,7 @@ void cv::cuda::histRange(InputArray _src, GpuMat hist[4], const GpuMat levels[4]
CV_Assert( src.type() == CV_8UC4 || src.type() == CV_16UC4 || src.type() == CV_16SC4 || src.type() == CV_32FC4 );
hist_callers[src.depth()](src, hist, levels, buf, StreamAccessor::getStream(stream));
hist_callers[src.depth()](src, hist, levels, stream);
}
#endif /* !defined (HAVE_CUDA) */
modules/cudaimgproc/src/hough_circles.cpp
View file @ 8a518266
......@@ -74,7 +74,7 @@ namespace
public:
HoughCirclesDetectorImpl(float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles);
void detect(InputArray src, OutputArray circles);
void detect(InputArray src, OutputArray circles, Stream& stream);
void setDp(float dp) { dp_ = dp; }
float getDp() const { return dp_; }
......@@ -154,8 +154,11 @@ namespace
filterDy_ = cuda::createSobelFilter(CV_8UC1, CV_32S, 0, 1);
}
void HoughCirclesDetectorImpl::detect(InputArray _src, OutputArray circles)
void HoughCirclesDetectorImpl::detect(InputArray _src, OutputArray circles, Stream& stream)
{
// TODO : implement async version
(void) stream;
using namespace cv::cuda::device::hough;
using namespace cv::cuda::device::hough_circles;
......
modules/cudaimgproc/src/hough_lines.cpp
View file @ 8a518266
......@@ -75,8 +75,8 @@ namespace
{
}
void detect(InputArray src, OutputArray lines);
void downloadResults(InputArray d_lines, OutputArray h_lines, OutputArray h_votes = noArray());
void detect(InputArray src, OutputArray lines, Stream& stream);
void downloadResults(InputArray d_lines, OutputArray h_lines, OutputArray h_votes, Stream& stream);
void setRho(float rho) { rho_ = rho; }
float getRho() const { return rho_; }
......@@ -125,8 +125,11 @@ namespace
GpuMat result_;
};
void HoughLinesDetectorImpl::detect(InputArray _src, OutputArray lines)
void HoughLinesDetectorImpl::detect(InputArray _src, OutputArray lines, Stream& stream)
{
// TODO : implement async version
(void) stream;
using namespace cv::cuda::device::hough;
using namespace cv::cuda::device::hough_lines;
......@@ -170,7 +173,7 @@ namespace
result_.copyTo(lines);
}
void HoughLinesDetectorImpl::downloadResults(InputArray _d_lines, OutputArray h_lines, OutputArray h_votes)
void HoughLinesDetectorImpl::downloadResults(InputArray _d_lines, OutputArray h_lines, OutputArray h_votes, Stream& stream)
{
GpuMat d_lines = _d_lines.getGpuMat();
......@@ -184,12 +187,18 @@ namespace
CV_Assert( d_lines.rows == 2 && d_lines.type() == CV_32FC2 );
d_lines.row(0).download(h_lines);
if (stream)
d_lines.row(0).download(h_lines, stream);
else
d_lines.row(0).download(h_lines);
if (h_votes.needed())
{
GpuMat d_votes(1, d_lines.cols, CV_32SC1, d_lines.ptr<int>(1));
d_votes.download(h_votes);
if (stream)
d_votes.download(h_votes, stream);
else
d_votes.download(h_votes);
}
}
}
......
modules/cudaimgproc/src/hough_segments.cpp
View file @ 8a518266
......@@ -79,7 +79,7 @@ namespace
{
}
void detect(InputArray src, OutputArray lines);
void detect(InputArray src, OutputArray lines, Stream& stream);
void setRho(float rho) { rho_ = rho; }
float getRho() const { return rho_; }
......@@ -128,8 +128,11 @@ namespace
GpuMat result_;
};
void HoughSegmentDetectorImpl::detect(InputArray _src, OutputArray lines)
void HoughSegmentDetectorImpl::detect(InputArray _src, OutputArray lines, Stream& stream)
{
// TODO : implement async version
(void) stream;
using namespace cv::cuda::device::hough;
using namespace cv::cuda::device::hough_lines;
using namespace cv::cuda::device::hough_segments;
......
modules/cudaimgproc/src/mssegmentation.cpp
View file @ 8a518266
......@@ -43,7 +43,7 @@
#if !defined HAVE_CUDA || defined(CUDA_DISABLER)
void cv::cuda::meanShiftSegmentation(InputArray, OutputArray, int, int, int, TermCriteria) { throw_no_cuda(); }
void cv::cuda::meanShiftSegmentation(InputArray, OutputArray, int, int, int, TermCriteria, Stream&) { throw_no_cuda(); }
#else
......@@ -222,7 +222,7 @@ inline int dist2(const cv::Vec2s& lhs, const cv::Vec2s& rhs)
} // anonymous namespace
void cv::cuda::meanShiftSegmentation(InputArray _src, OutputArray _dst, int sp, int sr, int minsize, TermCriteria criteria)
void cv::cuda::meanShiftSegmentation(InputArray _src, OutputArray _dst, int sp, int sr, int minsize, TermCriteria criteria, Stream& stream)
{
GpuMat src = _src.getGpuMat();
......@@ -235,7 +235,10 @@ void cv::cuda::meanShiftSegmentation(InputArray _src, OutputArray _dst, int sp,
// Perform mean shift procedure and obtain region and spatial maps
GpuMat d_rmap, d_spmap;
cuda::meanShiftProc(src, d_rmap, d_spmap, sp, sr, criteria);
cuda::meanShiftProc(src, d_rmap, d_spmap, sp, sr, criteria, stream);
stream.waitForCompletion();
Mat rmap(d_rmap);
Mat spmap(d_spmap);
......
modules/ml/src/ann_mlp.cpp
View file @ 8a518266
......@@ -103,6 +103,7 @@ public:
ANN_MLPImpl( const Params& p )
{
clear();
setParams(p);
}
......@@ -126,6 +127,7 @@ public:
rng = RNG((uint64)-1);
weights.clear();
trained = false;
max_buf_sz = 1 << 12;
}
int layer_count() const { return (int)layer_sizes.size(); }
......@@ -1241,7 +1243,7 @@ public:
clear();
vector<int> _layer_sizes;
fn["layer_sizes"] >> _layer_sizes;
readVectorOrMat(fn["layer_sizes"], _layer_sizes);
create( _layer_sizes );
int i, l_count = layer_count();
......
modules/ml/src/boost.cpp
View file @ 8a518266
......@@ -434,13 +434,17 @@ public:
bparams.priors = params0.priors;
FileNode tparams_node = fn["training_params"];
String bts = (String)tparams_node["boosting_type"];
// check for old layout
String bts = (String)(fn["boosting_type"].empty() ?
tparams_node["boosting_type"] : fn["boosting_type"]);
bparams.boostType = (bts == "DiscreteAdaboost" ? Boost::DISCRETE :
bts == "RealAdaboost" ? Boost::REAL :
bts == "LogitBoost" ? Boost::LOGIT :
bts == "GentleAdaboost" ? Boost::GENTLE : -1);
_isClassifier = bparams.boostType == Boost::DISCRETE;
bparams.weightTrimRate = (double)tparams_node["weight_trimming_rate"];
// check for old layout
bparams.weightTrimRate = (double)(fn["weight_trimming_rate"].empty() ?
tparams_node["weight_trimming_rate"] : fn["weight_trimming_rate"]);
}
void read( const FileNode& fn )
......
modules/ml/src/data.cpp
View file @ 8a518266
......@@ -898,7 +898,7 @@ public:
CV_Assert( m > 0 ); // if m==0, vi is an ordered variable
const int* cmap = &catMap.at<int>(ofs[0]);
bool fastMap = (m == cmap[m] - cmap[0]);
bool fastMap = (m == cmap[m - 1] - cmap[0] + 1);
if( fastMap )
{
......
modules/ml/src/inner_functions.cpp
View file @ 8a518266
......@@ -115,6 +115,7 @@ void StatModel::save(const String& filename) const
{
FileStorage fs(filename, FileStorage::WRITE);
fs << getDefaultModelName() << "{";
fs << "format" << (int)3;
write(fs);
fs << "}";
}
......
modules/ml/src/precomp.hpp
View file @ 8a518266
......@@ -263,11 +263,27 @@ namespace ml
vector<int> subsets;
vector<int> classLabels;
vector<float> missingSubst;
vector<int> varMapping;
bool _isClassifier;
Ptr<WorkData> w;
};
template <typename T>
static inline void readVectorOrMat(const FileNode & node, std::vector<T> & v)
{
if (node.type() == FileNode::MAP)
{
Mat m;
node >> m;
m.copyTo(v);
}
else if (node.type() == FileNode::SEQ)
{
node >> v;
}
}
}}
#endif /* __OPENCV_ML_PRECOMP_HPP__ */
modules/ml/src/rtrees.cpp
View file @ 8a518266
......@@ -346,7 +346,7 @@ public:
oobError = (double)fn["oob_error"];
int ntrees = (int)fn["ntrees"];
fn["var_importance"] >> varImportance;
readVectorOrMat(fn["var_importance"], varImportance);
readParams(fn);
......
modules/ml/src/svm.cpp
View file @ 8a518266
......@@ -2038,7 +2038,8 @@ public:
{
Params _params;
String svm_type_str = (String)fn["svmType"];
// check for old naming
String svm_type_str = (String)(fn["svm_type"].empty() ? fn["svmType"] : fn["svm_type"]);
int svmType =
svm_type_str == "C_SVC" ? C_SVC :
svm_type_str == "NU_SVC" ? NU_SVC :
......
modules/ml/src/tree.cpp
View file @ 8a518266
......@@ -1597,7 +1597,10 @@ void DTreesImpl::writeParams(FileStorage& fs) const
fs << "}";
if( !varIdx.empty() )
{
fs << "global_var_idx" << 1;
fs << "var_idx" << varIdx;
}
fs << "var_type" << varType;
......@@ -1726,9 +1729,8 @@ void DTreesImpl::readParams( const FileNode& fn )
if( !tparams_node.empty() ) // training parameters are not necessary
{
params0.useSurrogates = (int)tparams_node["use_surrogates"] != 0;
params0.maxCategories = (int)tparams_node["max_categories"];
params0.maxCategories = (int)(tparams_node["max_categories"].empty() ? 16 : tparams_node["max_categories"]);
params0.regressionAccuracy = (float)tparams_node["regression_accuracy"];
params0.maxDepth = (int)tparams_node["max_depth"];
params0.minSampleCount = (int)tparams_node["min_sample_count"];
params0.CVFolds = (int)tparams_node["cross_validation_folds"];
......@@ -1741,13 +1743,83 @@ void DTreesImpl::readParams( const FileNode& fn )
tparams_node["priors"] >> params0.priors;
}
fn["var_idx"] >> varIdx;
readVectorOrMat(fn["var_idx"], varIdx);
fn["var_type"] >> varType;
fn["cat_ofs"] >> catOfs;
fn["cat_map"] >> catMap;
fn["missing_subst"] >> missingSubst;
fn["class_labels"] >> classLabels;
int format = 0;
fn["format"] >> format;
bool isLegacy = format < 3;
int varAll = (int)fn["var_all"];
if (isLegacy && (int)varType.size() <= varAll)
{
std::vector<uchar> extendedTypes(varAll + 1, 0);
int i = 0, n;
if (!varIdx.empty())
{
n = (int)varIdx.size();
for (; i < n; ++i)
{
int var = varIdx[i];
extendedTypes[var] = varType[i];
}
}
else
{
n = (int)varType.size();
for (; i < n; ++i)
{
extendedTypes[i] = varType[i];
}
}
extendedTypes[varAll] = (uchar)(_isClassifier ? VAR_CATEGORICAL : VAR_ORDERED);
extendedTypes.swap(varType);
}
readVectorOrMat(fn["cat_map"], catMap);
if (isLegacy)
{
// generating "catOfs" from "cat_count"
catOfs.clear();
classLabels.clear();
std::vector<int> counts;
readVectorOrMat(fn["cat_count"], counts);
unsigned int i = 0, j = 0, curShift = 0, size = (int)varType.size() - 1;
for (; i < size; ++i)
{
Vec2i newOffsets(0, 0);
if (varType[i] == VAR_CATEGORICAL) // only categorical vars are represented in catMap
{
newOffsets[0] = curShift;
curShift += counts[j];
newOffsets[1] = curShift;
++j;
}
catOfs.push_back(newOffsets);
}
// other elements in "catMap" are "classLabels"
if (curShift < catMap.size())
{
classLabels.insert(classLabels.end(), catMap.begin() + curShift, catMap.end());
catMap.erase(catMap.begin() + curShift, catMap.end());
}
}
else
{
fn["cat_ofs"] >> catOfs;
fn["missing_subst"] >> missingSubst;
fn["class_labels"] >> classLabels;
}
// init var mapping for node reading (var indexes or varIdx indexes)
bool globalVarIdx = false;
fn["global_var_idx"] >> globalVarIdx;
if (globalVarIdx || varIdx.empty())
setRangeVector(varMapping, (int)varType.size());
else
varMapping = varIdx;
initCompVarIdx();
setDParams(params0);
......@@ -1759,6 +1831,7 @@ int DTreesImpl::readSplit( const FileNode& fn )
int vi = (int)fn["var"];
CV_Assert( 0 <= vi && vi <= (int)varType.size() );
vi = varMapping[vi]; // convert to varIdx if needed
split.varIdx = vi;
if( varType[vi] == VAR_CATEGORICAL ) // split on categorical var
......
modules/ml/test/test_save_load.cpp
View file @ 8a518266
......@@ -158,6 +158,109 @@ TEST(ML_Boost, save_load) { CV_SLMLTest test( CV_BOOST ); test.safe_run(); }
TEST(ML_RTrees, save_load) { CV_SLMLTest test( CV_RTREES ); test.safe_run(); }
TEST(DISABLED_ML_ERTrees, save_load) { CV_SLMLTest test( CV_ERTREES ); test.safe_run(); }
class CV_LegacyTest : public cvtest::BaseTest
{
public:
CV_LegacyTest(const std::string &_modelName, const std::string &_suffixes = std::string())
: cvtest::BaseTest(), modelName(_modelName), suffixes(_suffixes)
{
}
virtual ~CV_LegacyTest() {}
protected:
void run(int)
{
unsigned int idx = 0;
for (;;)
{
if (idx >= suffixes.size())
break;
int found = (int)suffixes.find(';', idx);
string piece = suffixes.substr(idx, found - idx);
if (piece.empty())
break;
oneTest(piece);
idx += (unsigned int)piece.size() + 1;
}
}
void oneTest(const string & suffix)
{
using namespace cv::ml;
int code = cvtest::TS::OK;
string filename = ts->get_data_path() + "legacy/" + modelName + suffix;
bool isTree = modelName == CV_BOOST || modelName == CV_DTREE || modelName == CV_RTREES;
Ptr<StatModel> model;
if (modelName == CV_BOOST)
model = StatModel::load<Boost>(filename);
else if (modelName == CV_ANN)
model = StatModel::load<ANN_MLP>(filename);
else if (modelName == CV_DTREE)
model = StatModel::load<DTrees>(filename);
else if (modelName == CV_NBAYES)
model = StatModel::load<NormalBayesClassifier>(filename);
else if (modelName == CV_SVM)
model = StatModel::load<SVM>(filename);
else if (modelName == CV_RTREES)
model = StatModel::load<RTrees>(filename);
if (!model)
{
code = cvtest::TS::FAIL_INVALID_TEST_DATA;
}
else
{
Mat input = Mat(isTree ? 10 : 1, model->getVarCount(), CV_32F);
ts->get_rng().fill(input, RNG::UNIFORM, 0, 40);
if (isTree)
randomFillCategories(filename, input);
Mat output;
model->predict(input, output, StatModel::RAW_OUTPUT | (isTree ? DTrees::PREDICT_SUM : 0));
// just check if no internal assertions or errors thrown
}
ts->set_failed_test_info(code);
}
void randomFillCategories(const string & filename, Mat & input)
{
Mat catMap;
Mat catCount;
std::vector<uchar> varTypes;
FileStorage fs(filename, FileStorage::READ);
FileNode root = fs.getFirstTopLevelNode();
root["cat_map"] >> catMap;
root["cat_count"] >> catCount;
root["var_type"] >> varTypes;
int offset = 0;
int countOffset = 0;
uint var = 0, varCount = (uint)varTypes.size();
for (; var < varCount; ++var)
{
if (varTypes[var] == ml::VAR_CATEGORICAL)
{
int size = catCount.at<int>(0, countOffset);
for (int row = 0; row < input.rows; ++row)
{
int randomChosenIndex = offset + ((uint)ts->get_rng()) % size;
int value = catMap.at<int>(0, randomChosenIndex);
input.at<float>(row, var) = (float)value;
}
offset += size;
++countOffset;
}
}
}
string modelName;
string suffixes;
};
TEST(ML_ANN, legacy_load) { CV_LegacyTest test(CV_ANN, "_waveform.xml"); test.safe_run(); }
TEST(ML_Boost, legacy_load) { CV_LegacyTest test(CV_BOOST, "_adult.xml;_1.xml;_2.xml;_3.xml"); test.safe_run(); }
TEST(ML_DTree, legacy_load) { CV_LegacyTest test(CV_DTREE, "_abalone.xml;_mushroom.xml"); test.safe_run(); }
TEST(ML_NBayes, legacy_load) { CV_LegacyTest test(CV_NBAYES, "_waveform.xml"); test.safe_run(); }
TEST(ML_SVM, legacy_load) { CV_LegacyTest test(CV_SVM, "_poletelecomm.xml;_waveform.xml"); test.safe_run(); }
TEST(ML_RTrees, legacy_load) { CV_LegacyTest test(CV_RTREES, "_waveform.xml"); test.safe_run(); }
/*TEST(ML_SVM, throw_exception_when_save_untrained_model)
{
......
modules/objdetect/src/opencl/objdetect_hog.cl
View file @ 8a518266
......@@ -48,7 +48,7 @@
#define CELLS_PER_BLOCK_X 2
#define CELLS_PER_BLOCK_Y 2
#define NTHREADS 256
#define CV_PI_F 3.1415926535897932384626433832795f
#define CV_PI_F M_PI_F
#ifdef INTEL_DEVICE
#define QANGLE_TYPE int
......@@ -606,23 +606,23 @@ __kernel void compute_gradients_8UC4_kernel(
barrier(CLK_LOCAL_MEM_FENCE);
if (x < width)
{
float3 a = (float3) (sh_row[tid], sh_row[tid + (NTHREADS + 2)],
sh_row[tid + 2 * (NTHREADS + 2)]);
float3 b = (float3) (sh_row[tid + 2], sh_row[tid + 2 + (NTHREADS + 2)],
sh_row[tid + 2 + 2 * (NTHREADS + 2)]);
float4 a = (float4) (sh_row[tid], sh_row[tid + (NTHREADS + 2)],
sh_row[tid + 2 * (NTHREADS + 2)], 0);
float4 b = (float4) (sh_row[tid + 2], sh_row[tid + 2 + (NTHREADS + 2)],
sh_row[tid + 2 + 2 * (NTHREADS + 2)], 0);
float3 dx;
float4 dx;
if (correct_gamma == 1)
dx = sqrt(b) - sqrt(a);
else
dx = b - a;
float3 dy = (float3) 0.f;
float4 dy = (float4) 0.f;
if (gidY > 0 && gidY < height - 1)
{
a = convert_float3(img[(gidY - 1) * img_step + x].xyz);
b = convert_float3(img[(gidY + 1) * img_step + x].xyz);
a = convert_float4(img[(gidY - 1) * img_step + x].xyzw);
b = convert_float4(img[(gidY + 1) * img_step + x].xyzw);
if (correct_gamma == 1)
dy = sqrt(b) - sqrt(a);
......@@ -630,28 +630,25 @@ __kernel void compute_gradients_8UC4_kernel(
dy = b - a;
}
float4 mag = hypot(dx, dy);
float best_dx = dx.x;
float best_dy = dy.x;
float mag0 = dx.x * dx.x + dy.x * dy.x;
float mag1 = dx.y * dx.y + dy.y * dy.y;
if (mag0 < mag1)
float mag0 = mag.x;
if (mag0 < mag.y)
{
best_dx = dx.y;
best_dy = dy.y;
mag0 = mag1;
mag0 = mag.y;
}
mag1 = dx.z * dx.z + dy.z * dy.z;
if (mag0 < mag1)
if (mag0 < mag.z)
{
best_dx = dx.z;
best_dy = dy.z;
mag0 = mag1;
mag0 = mag.z;
}
mag0 = sqrt(mag0);
float ang = (atan2(best_dy, best_dx) + CV_PI_F) * angle_scale - 0.5f;
int hidx = (int)floor(ang);
ang -= hidx;
......@@ -710,7 +707,7 @@ __kernel void compute_gradients_8UC1_kernel(
else
dy = a - b;
}
float mag = sqrt(dx * dx + dy * dy);
float mag = hypot(dx, dy);
float ang = (atan2(dy, dx) + CV_PI_F) * angle_scale - 0.5f;
int hidx = (int)floor(ang);
......
modules/videoio/src/cap_ffmpeg_impl.hpp
View file @ 8a518266
......@@ -139,8 +139,10 @@ extern "C" {
#include <unistd.h>
#include <stdio.h>
#include <sys/types.h>
#if defined __APPLE__
#include <sys/sysctl.h>
#endif
#endif
#ifndef MIN
#define MIN(a, b) ((a) < (b) ? (a) : (b))
......
samples/gpu/performance/tests.cpp
View file @ 8a518266
......@@ -1053,12 +1053,11 @@ TEST(equalizeHist)
cuda::GpuMat d_src(src);
cuda::GpuMat d_dst;
cuda::GpuMat d_buf;
cuda::equalizeHist(d_src, d_dst, d_buf);
cuda::equalizeHist(d_src, d_dst);
CUDA_ON;
cuda::equalizeHist(d_src, d_dst, d_buf);
cuda::equalizeHist(d_src, d_dst);
CUDA_OFF;
}
}
......
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