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提交 4fb15ae1 编写于 作者: A Alexander Mordvintesv
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Merge branch 'master' of code.opencv.org:opencv

上级 d9185ec2 bbf67926
展开全部 隐藏空白更改
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Showing with 12554 addition and 8378 deletion
3rdparty/libtiff/CMakeLists.txt
浏览文件 @ 4fb15ae1
......@@ -89,7 +89,7 @@ endif(WIN32)
ocv_warnings_disable(CMAKE_C_FLAGS -Wno-unused-but-set-variable -Wmissing-prototypes -Wmissing-declarations -Wundef -Wunused -Wsign-compare
-Wcast-align -Wshadow -Wno-maybe-uninitialized -Wno-pointer-to-int-cast -Wno-int-to-pointer-cast)
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wmissing-declarations -Wunused-parameter /wd4100 /wd4244 /wd4706 /wd4127 /wd4701 /wd4018 /wd4267 /wd4306 /wd4305 /wd4312 /wd4311)
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wmissing-declarations -Wunused-parameter /wd4100 /wd4244 /wd4706 /wd4127 /wd4701 /wd4018 /wd4267 /wd4306 /wd4305 /wd4312 /wd4311 /wd4703)
if(UNIX AND (CMAKE_COMPILER_IS_GNUCXX OR CV_ICC))
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fPIC")
......
CMakeLists.txt
浏览文件 @ 4fb15ae1
......@@ -189,11 +189,11 @@ OCV_OPTION(ENABLE_PROFILING "Enable profiling in the GCC compiler (Add
OCV_OPTION(ENABLE_OMIT_FRAME_POINTER "Enable -fomit-frame-pointer for GCC" ON IF CMAKE_COMPILER_IS_GNUCXX )
OCV_OPTION(ENABLE_POWERPC "Enable PowerPC for GCC" ON IF (CMAKE_COMPILER_IS_GNUCXX AND CMAKE_SYSTEM_PROCESSOR MATCHES powerpc.*) )
OCV_OPTION(ENABLE_FAST_MATH "Enable -ffast-math (not recommended for GCC 4.6.x)" OFF IF (CMAKE_COMPILER_IS_GNUCXX AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_SSE "Enable SSE instructions" ON IF (MSVC OR CMAKE_COMPILER_IS_GNUCXX AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_SSE2 "Enable SSE2 instructions" ON IF (MSVC OR CMAKE_COMPILER_IS_GNUCXX AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_SSE3 "Enable SSE3 instructions" OFF IF (CV_ICC OR CMAKE_COMPILER_IS_GNUCXX AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_SSE "Enable SSE instructions" ON IF ((MSVC OR CMAKE_COMPILER_IS_GNUCXX) AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_SSE2 "Enable SSE2 instructions" ON IF ((MSVC OR CMAKE_COMPILER_IS_GNUCXX) AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_SSE3 "Enable SSE3 instructions" ON IF ((CV_ICC OR CMAKE_COMPILER_IS_GNUCXX) AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_SSSE3 "Enable SSSE3 instructions" OFF IF (CMAKE_COMPILER_IS_GNUCXX AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_SSE41 "Enable SSE4.1 instructions" OFF IF (CV_ICC OR CMAKE_COMPILER_IS_GNUCXX AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_SSE41 "Enable SSE4.1 instructions" OFF IF ((CV_ICC OR CMAKE_COMPILER_IS_GNUCXX) AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_SSE42 "Enable SSE4.2 instructions" OFF IF (CMAKE_COMPILER_IS_GNUCXX AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_NOISY_WARNINGS "Show all warnings even if they are too noisy" OFF )
OCV_OPTION(OPENCV_WARNINGS_ARE_ERRORS "Treat warnings as errors" OFF )
......
cmake/OpenCVDetectCUDA.cmake
浏览文件 @ 4fb15ae1
......@@ -88,7 +88,11 @@ if(CUDA_FOUND)
if(APPLE)
set (CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} -Xcompiler -fno-finite-math-only)
endif()
string(REPLACE "-Wsign-promo" "" CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS}")
# disabled because of multiple warnings during building nvcc auto generated files
if(CMAKE_COMPILER_IS_GNUCXX AND CMAKE_GCC_REGEX_VERSION VERSION_GREATER "4.6.0")
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wunused-but-set-variable)
endif()
# we remove -ggdb3 flag as it leads to preprocessor errors when compiling CUDA files (CUDA 4.1)
set(CMAKE_CXX_FLAGS_DEBUG_ ${CMAKE_CXX_FLAGS_DEBUG})
......
cmake/OpenCVGenConfig.cmake
浏览文件 @ 4fb15ae1
......@@ -64,9 +64,14 @@ macro(ocv_generate_dependencies_map_configcmake suffix configuration)
string(REGEX REPLACE "${CMAKE_SHARED_LIBRARY_SUFFIX}$" "${OPENCV_LINK_LIBRARY_SUFFIX}" __libname "${__libname}")
endif()
string(REPLACE " " "\\ " __mod_deps "${${__ocv_lib}_MODULE_DEPS_${suffix}}")
string(REPLACE " " "\\ " __ext_deps "${${__ocv_lib}_EXTRA_DEPS_${suffix}}")
string(REPLACE "\"" "\\\"" __mod_deps "${__mod_deps}")
string(REPLACE "\"" "\\\"" __ext_deps "${__ext_deps}")
set(OPENCV_DEPENDENCIES_MAP_${suffix} "${OPENCV_DEPENDENCIES_MAP_${suffix}}set(OpenCV_${__ocv_lib}_LIBNAME_${suffix} \"${__libname}\")\n")
set(OPENCV_DEPENDENCIES_MAP_${suffix} "${OPENCV_DEPENDENCIES_MAP_${suffix}}set(OpenCV_${__ocv_lib}_DEPS_${suffix} ${${__ocv_lib}_MODULE_DEPS_${suffix}})\n")
set(OPENCV_DEPENDENCIES_MAP_${suffix} "${OPENCV_DEPENDENCIES_MAP_${suffix}}set(OpenCV_${__ocv_lib}_EXTRA_DEPS_${suffix} ${${__ocv_lib}_EXTRA_DEPS_${suffix}})\n")
set(OPENCV_DEPENDENCIES_MAP_${suffix} "${OPENCV_DEPENDENCIES_MAP_${suffix}}set(OpenCV_${__ocv_lib}_DEPS_${suffix} ${__mod_deps})\n")
set(OPENCV_DEPENDENCIES_MAP_${suffix} "${OPENCV_DEPENDENCIES_MAP_${suffix}}set(OpenCV_${__ocv_lib}_EXTRA_DEPS_${suffix} ${__ext_deps})\n")
list(APPEND OPENCV_PROCESSED_LIBS ${__ocv_lib})
list(APPEND OPENCV_LIBS_TO_PROCESS ${${__ocv_lib}_MODULE_DEPS_${suffix}})
......
cmake/OpenCVModule.cmake
浏览文件 @ 4fb15ae1
......@@ -509,8 +509,6 @@ endmacro()
macro(ocv_add_precompiled_headers the_target)
if("${the_target}" MATCHES "^opencv_test_.*$")
SET(pch_path "test/test_")
elseif("${the_target}" MATCHES "opencv_perf_gpu_cpu")
SET(pch_path "perf_cpu/perf_cpu_")
elseif("${the_target}" MATCHES "^opencv_perf_.*$")
SET(pch_path "perf/perf_")
else()
......
data/lbpcascades/lbpcascade_profileface.xml 0 → 100755
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此差异已折叠。
data/lbpcascades/lbpcascade_silverware.xml 0 → 100755
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此差异已折叠。
ios/cmake/Modules/Platform/iOS.cmake
浏览文件 @ 4fb15ae1
......@@ -42,6 +42,8 @@ set (CMAKE_CXX_OSX_CURRENT_VERSION_FLAG "${CMAKE_C_OSX_CURRENT_VERSION_FLAG}")
set (CMAKE_C_FLAGS "")
set (CMAKE_CXX_FLAGS "-headerpad_max_install_names -fvisibility=hidden -fvisibility-inlines-hidden")
set (CMAKE_CXX_FLAGS_RELEASE "-DNDEBUG -O3 -fomit-frame-pointer -ffast-math")
if (HAVE_FLAG_SEARCH_PATHS_FIRST)
set (CMAKE_C_LINK_FLAGS "-Wl,-search_paths_first ${CMAKE_C_LINK_FLAGS}")
set (CMAKE_CXX_LINK_FLAGS "-Wl,-search_paths_first ${CMAKE_CXX_LINK_FLAGS}")
......
modules/core/include/opencv2/core/core.hpp
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......@@ -440,7 +440,7 @@ template<typename _Tp, int m, int n> class CV_EXPORTS Matx
{
public:
typedef _Tp value_type;
typedef Matx<_Tp, MIN(m, n), 1> diag_type;
typedef Matx<_Tp, (m < n ? m : n), 1> diag_type;
typedef Matx<_Tp, m, n> mat_type;
enum { depth = DataDepth<_Tp>::value, rows = m, cols = n, channels = rows*cols,
type = CV_MAKETYPE(depth, channels) };
......
modules/core/include/opencv2/core/gpumat.hpp
浏览文件 @ 4fb15ae1
......@@ -72,9 +72,11 @@ namespace cv { namespace gpu
FEATURE_SET_COMPUTE_13 = 13,
FEATURE_SET_COMPUTE_20 = 20,
FEATURE_SET_COMPUTE_21 = 21,
FEATURE_SET_COMPUTE_30 = 30,
GLOBAL_ATOMICS = FEATURE_SET_COMPUTE_11,
SHARED_ATOMICS = FEATURE_SET_COMPUTE_12,
NATIVE_DOUBLE = FEATURE_SET_COMPUTE_13
NATIVE_DOUBLE = FEATURE_SET_COMPUTE_13,
WARP_SHUFFLE_FUNCTIONS = FEATURE_SET_COMPUTE_30
};
// Gives information about what GPU archs this OpenCV GPU module was
......
modules/core/src/cuda/matrix_operations.cu
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......@@ -44,7 +44,7 @@
#include "opencv2/gpu/device/transform.hpp"
#include "opencv2/gpu/device/functional.hpp"
namespace cv { namespace gpu { namespace device
namespace cv { namespace gpu { namespace device
{
template <typename T> struct shift_and_sizeof;
template <> struct shift_and_sizeof<signed char> { enum { shift = 0 }; };
......@@ -272,7 +272,7 @@ namespace cv { namespace gpu { namespace device
template <typename T, typename D> struct TransformFunctorTraits< Convertor<T, D> > : detail::ConvertTraits< Convertor<T, D> >
{
};
template<typename T, typename D>
void cvt_(DevMem2Db src, DevMem2Db dst, double alpha, double beta, cudaStream_t stream)
{
......@@ -282,6 +282,11 @@ namespace cv { namespace gpu { namespace device
cv::gpu::device::transform((DevMem2D_<T>)src, (DevMem2D_<D>)dst, op, WithOutMask(), stream);
}
#if defined __clang__
# pragma clang diagnostic push
# pragma clang diagnostic ignored "-Wmissing-declarations"
#endif
void convert_gpu(DevMem2Db src, int sdepth, DevMem2Db dst, int ddepth, double alpha, double beta, cudaStream_t stream)
{
typedef void (*caller_t)(DevMem2Db src, DevMem2Db dst, double alpha, double beta, cudaStream_t stream);
......@@ -318,4 +323,8 @@ namespace cv { namespace gpu { namespace device
func(src, dst, alpha, beta, stream);
}
#if defined __clang__
# pragma clang diagnostic pop
#endif
}}} // namespace cv { namespace gpu { namespace device
modules/core/src/gpumat.cpp
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......@@ -1199,10 +1199,6 @@ namespace
void setTo(GpuMat& m, Scalar s, const GpuMat& mask) const
{
NppiSize sz;
sz.width = m.cols;
sz.height = m.rows;
if (mask.empty())
{
if (s[0] == 0.0 && s[1] == 0.0 && s[2] == 0.0 && s[3] == 0.0)
......
modules/features2d/src/features2d_init.cpp
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......@@ -59,7 +59,7 @@ CV_INIT_ALGORITHM(BriefDescriptorExtractor, "Feature2D.BRIEF",
CV_INIT_ALGORITHM(FastFeatureDetector, "Feature2D.FAST",
obj.info()->addParam(obj, "threshold", obj.threshold);
obj.info()->addParam(obj, "nonmaxSuppression", obj.nonmaxSuppression);
obj.info()->addParam(obj, "type", obj.type, FastFeatureDetector::TYPE_9_16));
obj.info()->addParam(obj, "type", obj.type));
///////////////////////////////////////////////////////////////////////////////////////////////////////////
......
modules/features2d/test/test_fast.cpp
浏览文件 @ 4fb15ae1
......@@ -75,8 +75,8 @@ void CV_FastTest::run( int )
vector<KeyPoint> keypoints1;
vector<KeyPoint> keypoints2;
FAST(gray1, keypoints1, 30, type);
FAST(gray2, keypoints2, 30, type);
FAST(gray1, keypoints1, 30, true, type);
FAST(gray2, keypoints2, 30, true, type);
for(size_t i = 0; i < keypoints1.size(); ++i)
{
......
modules/features2d/test/test_nearestneighbors.cpp
浏览文件 @ 4fb15ae1
......@@ -200,7 +200,7 @@ int CV_KDTreeTest_CPP::checkGetPoins( const Mat& data )
int CV_KDTreeTest_CPP::checkFindBoxed()
{
vector<float> min( dims, minValue), max(dims, maxValue);
vector<float> min( dims, static_cast<float>(minValue)), max(dims, static_cast<float>(maxValue));
vector<int> indices;
tr->findOrthoRange( min, max, indices );
// TODO check indices
......@@ -214,8 +214,8 @@ int CV_KDTreeTest_CPP::findNeighbors( Mat& points, Mat& neighbors )
const int emax = 20;
Mat neighbors2( neighbors.size(), CV_32SC1 );
int j;
vector<float> min(points.cols, minValue);
vector<float> max(points.cols, maxValue);
vector<float> min(points.cols, static_cast<float>(minValue));
vector<float> max(points.cols, static_cast<float>(maxValue));
for( int pi = 0; pi < points.rows; pi++ )
{
// 1st way
......
modules/features2d/test/test_rotation_and_scale_invariance.cpp
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......@@ -54,7 +54,7 @@ static
Mat generateHomography(float angle)
{
// angle - rotation around Oz in degrees
float angleRadian = angle * CV_PI / 180.;
float angleRadian = static_cast<float>(angle * CV_PI / 180);
Mat H = Mat::eye(3, 3, CV_32FC1);
H.at<float>(0,0) = H.at<float>(1,1) = std::cos(angleRadian);
H.at<float>(0,1) = -std::sin(angleRadian);
......@@ -69,8 +69,8 @@ Mat rotateImage(const Mat& srcImage, float angle, Mat& dstImage, Mat& dstMask)
// angle - rotation around Oz in degrees
float diag = std::sqrt(static_cast<float>(srcImage.cols * srcImage.cols + srcImage.rows * srcImage.rows));
Mat LUShift = Mat::eye(3, 3, CV_32FC1); // left up
LUShift.at<float>(0,2) = -srcImage.cols/2;
LUShift.at<float>(1,2) = -srcImage.rows/2;
LUShift.at<float>(0,2) = static_cast<float>(-srcImage.cols/2);
LUShift.at<float>(1,2) = static_cast<float>(-srcImage.rows/2);
Mat RDShift = Mat::eye(3, 3, CV_32FC1); // right down
RDShift.at<float>(0,2) = diag/2;
RDShift.at<float>(1,2) = diag/2;
......@@ -114,7 +114,7 @@ void scaleKeyPoints(const vector<KeyPoint>& src, vector<KeyPoint>& dst, float sc
static
float calcCirclesIntersectArea(const Point2f& p0, float r0, const Point2f& p1, float r1)
{
float c = norm(p0 - p1), sqr_c = c * c;
float c = static_cast<float>(norm(p0 - p1)), sqr_c = c * c;
float sqr_r0 = r0 * r0;
float sqr_r1 = r1 * r1;
......@@ -125,7 +125,7 @@ float calcCirclesIntersectArea(const Point2f& p0, float r0, const Point2f& p1, f
float minR = std::min(r0, r1);
float maxR = std::max(r0, r1);
if(c + minR <= maxR)
return CV_PI * minR * minR;
return static_cast<float>(CV_PI * minR * minR);
float cos_halfA0 = (sqr_r0 + sqr_c - sqr_r1) / (2 * r0 * c);
float cos_halfA1 = (sqr_r1 + sqr_c - sqr_r0) / (2 * r1 * c);
......@@ -133,15 +133,15 @@ float calcCirclesIntersectArea(const Point2f& p0, float r0, const Point2f& p1, f
float A0 = 2 * acos(cos_halfA0);
float A1 = 2 * acos(cos_halfA1);
return 0.5 * sqr_r0 * (A0 - sin(A0)) +
0.5 * sqr_r1 * (A1 - sin(A1));
return 0.5f * sqr_r0 * (A0 - sin(A0)) +
0.5f * sqr_r1 * (A1 - sin(A1));
}
static
float calcIntersectRatio(const Point2f& p0, float r0, const Point2f& p1, float r1)
{
float intersectArea = calcCirclesIntersectArea(p0, r0, p1, r1);
float unionArea = CV_PI * (r0 * r0 + r1 * r1) - intersectArea;
float unionArea = static_cast<float>(CV_PI) * (r0 * r0 + r1 * r1) - intersectArea;
return intersectArea / unionArea;
}
......@@ -160,7 +160,7 @@ void matchKeyPoints(const vector<KeyPoint>& keypoints0, const Mat& H,
matches.clear();
vector<uchar> usedMask(keypoints1.size(), 0);
for(size_t i0 = 0; i0 < keypoints0.size(); i0++)
for(int i0 = 0; i0 < static_cast<int>(keypoints0.size()); i0++)
{
int nearestPointIndex = -1;
float maxIntersectRatio = 0.f;
......@@ -176,7 +176,7 @@ void matchKeyPoints(const vector<KeyPoint>& keypoints0, const Mat& H,
if(intersectRatio > maxIntersectRatio)
{
maxIntersectRatio = intersectRatio;
nearestPointIndex = i1;
nearestPointIndex = static_cast<int>(i1);
}
}
......@@ -222,7 +222,7 @@ protected:
const int maxAngle = 360, angleStep = 15;
for(int angle = 0; angle < maxAngle; angle += angleStep)
{
Mat H = rotateImage(image0, angle, image1, mask1);
Mat H = rotateImage(image0, static_cast<float>(angle), image1, mask1);
vector<KeyPoint> keypoints1;
featureDetector->detect(image1, keypoints1, mask1);
......@@ -339,10 +339,10 @@ protected:
const int maxAngle = 360, angleStep = 15;
for(int angle = 0; angle < maxAngle; angle += angleStep)
{
Mat H = rotateImage(image0, angle, image1, mask1);
Mat H = rotateImage(image0, static_cast<float>(angle), image1, mask1);
vector<KeyPoint> keypoints1;
rotateKeyPoints(keypoints0, H, angle, keypoints1);
rotateKeyPoints(keypoints0, H, static_cast<float>(angle), keypoints1);
Mat descriptors1;
descriptorExtractor->compute(image1, keypoints1, descriptors1);
......@@ -457,7 +457,7 @@ protected:
keyPointMatchesCount++;
// Check does this inlier have consistent sizes
const float maxSizeDiff = 0.8;//0.9f; // grad
const float maxSizeDiff = 0.8f;//0.9f; // grad
float size0 = keypoints0[matches[m].trainIdx].size;
float size1 = osiKeypoints1[matches[m].queryIdx].size;
CV_Assert(size0 > 0 && size1 > 0);
......@@ -545,7 +545,7 @@ protected:
resize(image0, image1, Size(), 1./scale, 1./scale);
vector<KeyPoint> keypoints1;
scaleKeyPoints(keypoints0, keypoints1, 1./scale);
scaleKeyPoints(keypoints0, keypoints1, 1.0f/scale);
Mat descriptors1;
descriptorExtractor->compute(image1, keypoints1, descriptors1);
......
modules/gpu/CMakeLists.txt
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......@@ -111,43 +111,3 @@ ocv_add_accuracy_tests(FILES "Include" ${test_hdrs}
FILES "Src" ${test_srcs}
${nvidia})
ocv_add_perf_tests()
set(perf_cpu_path "${CMAKE_CURRENT_SOURCE_DIR}/perf_cpu")
if(BUILD_PERF_TESTS AND EXISTS "${perf_cpu_path}")
# opencv_highgui is required for imread/imwrite
set(perf_deps ${the_module} opencv_ts opencv_highgui opencv_imgproc opencv_calib3d opencv_objdetect opencv_video opencv_nonfree)
ocv_check_dependencies(${perf_deps})
if(OCV_DEPENDENCIES_FOUND)
set(the_target "opencv_perf_gpu_cpu")
ocv_module_include_directories(${perf_deps} "${perf_cpu_path}")
if(NOT OPENCV_PERF_${the_module}_CPU_SOURCES)
file(GLOB perf_srcs "${perf_cpu_path}/*.cpp")
file(GLOB perf_hdrs "${perf_cpu_path}/*.hpp" "${perf_cpu_path}/*.h")
source_group("Src" FILES ${perf_srcs})
source_group("Include" FILES ${perf_hdrs})
set(OPENCV_PERF_${the_module}_CPU_SOURCES ${perf_srcs} ${perf_hdrs})
endif()
add_executable(${the_target} ${OPENCV_PERF_${the_module}_CPU_SOURCES})
target_link_libraries(${the_target} ${OPENCV_MODULE_${the_module}_DEPS} ${perf_deps} ${OPENCV_LINKER_LIBS})
# Additional target properties
set_target_properties(${the_target} PROPERTIES
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
RUNTIME_OUTPUT_DIRECTORY "${EXECUTABLE_OUTPUT_PATH}"
)
if(ENABLE_SOLUTION_FOLDERS)
set_target_properties(${the_target} PROPERTIES FOLDER "tests performance")
endif()
ocv_add_precompiled_headers(${the_target})
else(OCV_DEPENDENCIES_FOUND)
#TODO: warn about unsatisfied dependencies
endif(OCV_DEPENDENCIES_FOUND)
endif()
modules/gpu/doc/object_detection.rst
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......@@ -204,7 +204,7 @@ gpu::CascadeClassifier_GPU
--------------------------
.. ocv:class:: gpu::CascadeClassifier_GPU
Cascade classifier class used for object detection. ::
Cascade classifier class used for object detection. Supports HAAR and LBP cascades. ::
class CV_EXPORTS CascadeClassifier_GPU
{
......@@ -219,6 +219,7 @@ Cascade classifier class used for object detection. ::
/* Returns number of detected objects */
int detectMultiScale( const GpuMat& image, GpuMat& objectsBuf, double scaleFactor=1.2, int minNeighbors=4, Size minSize=Size());
int detectMultiScale( const GpuMat& image, GpuMat& objectsBuf, Size maxObjectSize, Size minSize = Size(), double scaleFactor = 1.1, int minNeighbors = 4);
/* Finds only the largest object. Special mode if training is required.*/
bool findLargestObject;
......@@ -233,11 +234,11 @@ Cascade classifier class used for object detection. ::
gpu::CascadeClassifier_GPU::CascadeClassifier_GPU
-----------------------------------------------------
Loads the classifier from a file.
Loads the classifier from a file. Cascade type is detected automatically by constructor parameter.
.. ocv:function:: gpu::CascadeClassifier_GPU::CascadeClassifier_GPU(const string& filename)
:param filename: Name of the file from which the classifier is loaded. Only the old ``haar`` classifier (trained by the ``haar`` training application) and NVIDIA's ``nvbin`` are supported.
:param filename: Name of the file from which the classifier is loaded. Only the old ``haar`` classifier (trained by the ``haar`` training application) and NVIDIA's ``nvbin`` are supported for HAAR and only new type of OpenCV XML cascade supported for LBP.
......@@ -255,8 +256,7 @@ Loads the classifier from a file. The previous content is destroyed.
.. ocv:function:: bool gpu::CascadeClassifier_GPU::load(const string& filename)
:param filename: Name of the file from which the classifier is loaded. Only the old ``haar`` classifier (trained by the ``haar`` training application) and NVIDIA's ``nvbin`` are supported.
:param filename: Name of the file from which the classifier is loaded. Only the old ``haar`` classifier (trained by the ``haar`` training application) and NVIDIA's ``nvbin`` are supported for HAAR and only new type of OpenCV XML cascade supported for LBP.
gpu::CascadeClassifier_GPU::release
......@@ -273,13 +273,17 @@ Detects objects of different sizes in the input image.
.. ocv:function:: int gpu::CascadeClassifier_GPU::detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, double scaleFactor=1.2, int minNeighbors=4, Size minSize=Size())
.. ocv:function:: int gpu::CascadeClassifier_GPU::detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, Size maxObjectSize, Size minSize = Size(), double scaleFactor = 1.1, int minNeighbors = 4)
:param image: Matrix of type ``CV_8U`` containing an image where objects should be detected.
:param objectsBuf: Buffer to store detected objects (rectangles). If it is empty, it is allocated with the default size. If not empty, the function searches not more than N objects, where ``N = sizeof(objectsBufer's data)/sizeof(cv::Rect)``.
:param scaleFactor: Value to specify how much the image size is reduced at each image scale.
:param maxObjectSize: Maximum possible object size. Objects larger than that are ignored. Used for second signature and supported only for LBP cascades.
:param scaleFactor: Parameter specifying how much the image size is reduced at each image scale.
:param minNeighbors: Value to specify how many neighbours each candidate rectangle has to retain.
:param minNeighbors: Parameter specifying how many neighbors each candidate rectangle should have to retain it.
:param minSize: Minimum possible object size. Objects smaller than that are ignored.
......
modules/gpu/include/opencv2/gpu/gpu.hpp
浏览文件 @ 4fb15ae1
......@@ -820,6 +820,7 @@ private:
int nLayers_;
};
//! HoughLines
CV_EXPORTS void HoughLines(const GpuMat& src, GpuMat& lines, float rho, float theta, int threshold, bool doSort = false, int maxLines = 4096);
CV_EXPORTS void HoughLines(const GpuMat& src, GpuMat& lines, GpuMat& accum, GpuMat& buf, float rho, float theta, int threshold, bool doSort = false, int maxLines = 4096);
CV_EXPORTS void HoughLinesTransform(const GpuMat& src, GpuMat& accum, GpuMat& buf, float rho, float theta);
......
modules/gpu/misc/mark_nvidia.py
浏览文件 @ 4fb15ae1
import sys, re
spaces = '[\s]*'
symbols = '[\s\w\d,.=:|]*'
symbols = '[\s\w\d,.:|]*'
def pattern1(prefix, test):
return re.compile(spaces + 'perf::' + prefix + '/' + test + '::' + '\(' + symbols + '\)' + spaces)
return re.compile(spaces + prefix + '_' + test + '::' + symbols + '::' + '\(' + symbols + '\)' + spaces)
def pattern2(prefix, test, cvtype):
return re.compile(spaces + 'perf::' + prefix + '/' + test + '::' + '\(' + symbols + cvtype + symbols + '\)' + spaces)
def pattern2(prefix, test, param1):
return re.compile(spaces + prefix + '_' + test + '::' + symbols + '::' + '\(' + symbols + param1 + symbols + '\)' + spaces)
def pattern3(prefix, test, cvtype, param1):
return re.compile(spaces + 'perf::' + prefix + '/' + test + '::' + '\(' + symbols + cvtype + symbols + param1 + symbols + '\)' + spaces)
def pattern3(prefix, test, param1, param2):
return re.compile(spaces + prefix + '_' + test + '::' + symbols + '::' + '\(' + symbols + param1 + symbols + param2 + symbols + '\)' + spaces)
def pattern4(prefix, test, cvtype, param1, param2):
return re.compile(spaces + 'perf::' + prefix + '/' + test + '::' + '\(' + symbols + cvtype + symbols + param1 + symbols + param2 + symbols + '\)' + spaces)
def pattern4(prefix, test, param1, param2, param3):
return re.compile(spaces + prefix + '_' + test + '::' + symbols + '::' + '\(' + symbols + param1 + symbols + param2 + symbols + param3 + symbols + '\)' + spaces)
def pattern5(prefix, test, param1, param2, param3, param5):
return re.compile(spaces + prefix + '_' + test + '::' + symbols + '::' + '\(' + symbols + param1 + symbols + param2 + symbols + param3 + symbols + param4 + symbols + '\)' + spaces)
npp_patterns = [
##############################################################
# Core
# Core/Add_Mat (CV_8U | CV_16U | CV_32F)
pattern2('Core', 'Add_Mat', '8U'),
pattern2('Core', 'Add_Mat', '16U'),
pattern2('Core', 'Add_Mat', '32F'),
# Core/Add_Scalar (CV_8U | CV_16U | CV_32F)
pattern2('Core', 'Add_Scalar', '8U'),
pattern2('Core', 'Add_Scalar', '16U'),
pattern2('Core', 'Add_Scalar', '32F'),
# Core/Subtract_Mat (CV_8U | CV_16U | CV_32F)
pattern2('Core', 'Subtract_Mat', '8U'),
pattern2('Core', 'Subtract_Mat', '16U'),
pattern2('Core', 'Subtract_Mat', '32F'),
# Core/Subtract_Scalar (CV_8U | CV_16U | CV_32F)
pattern2('Core', 'Subtract_Scalar', '8U'),
pattern2('Core', 'Subtract_Scalar', '16U'),
pattern2('Core', 'Subtract_Scalar', '32F'),
# Core/Multiply_Mat (CV_8U | CV_16U | CV_32F)
pattern2('Core', 'Multiply_Mat', '8U'),
pattern2('Core', 'Multiply_Mat', '16U'),
pattern2('Core', 'Multiply_Mat', '32F'),
# Core/Multiply_Scalar (CV_8U | CV_16U | CV_32F)
pattern2('Core', 'Multiply_Scalar', '8U'),
pattern2('Core', 'Multiply_Scalar', '16U'),
pattern2('Core', 'Multiply_Scalar', '32F'),
# Core/Divide_Mat (CV_8U | CV_16U | CV_32F)
pattern2('Core', 'Divide_Mat', '8U'),
pattern2('Core', 'Divide_Mat', '16U'),
pattern2('Core', 'Divide_Mat', '32F'),
# Core/Divide_Scalar (CV_8U | CV_16U | CV_32F)
pattern2('Core', 'Divide_Scalar', '8U'),
pattern2('Core', 'Divide_Scalar', '16U'),
pattern2('Core', 'Divide_Scalar', '32F'),
# Core/AbsDiff_Mat (CV_8U | CV_16U | CV_32F)
pattern2('Core', 'AbsDiff_Mat', '8U'),
pattern2('Core', 'AbsDiff_Mat', '16U'),
pattern2('Core', 'AbsDiff_Mat', '32F'),
# Core/AbsDiff_Scalar (CV_8U | CV_16U | CV_32F)
pattern2('Core', 'AbsDiff_Scalar', '8U'),
pattern2('Core', 'AbsDiff_Scalar', '16U'),
pattern2('Core', 'AbsDiff_Scalar', '32F'),
# Core/Abs
# Core_AddMat (CV_8U | CV_16U | CV_32F)
pattern2('Core', 'AddMat', '8U'),
pattern2('Core', 'AddMat', '16U'),
pattern2('Core', 'AddMat', '32F'),
# Core_AddScalar (CV_8U | CV_16U | CV_32F)
pattern2('Core', 'AddScalar', '8U'),
pattern2('Core', 'AddScalar', '16U'),
pattern2('Core', 'AddScalar', '32F'),
# Core_SubtractMat (CV_8U | CV_16U | CV_32F)
pattern2('Core', 'SubtractMat', '8U'),
pattern2('Core', 'SubtractMat', '16U'),
pattern2('Core', 'SubtractMat', '32F'),
# Core_SubtractScalar (CV_8U | CV_16U | CV_32F)
pattern2('Core', 'SubtractScalar', '8U'),
pattern2('Core', 'SubtractScalar', '16U'),
pattern2('Core', 'SubtractScalar', '32F'),
# Core_MultiplyMat (CV_8U | CV_16U | CV_32F)
pattern2('Core', 'MultiplyMat', '8U'),
pattern2('Core', 'MultiplyMat', '16U'),
pattern2('Core', 'MultiplyMat', '32F'),
# Core_MultiplyScalar (CV_8U | CV_16U | CV_32F)
pattern2('Core', 'MultiplyScalar', '8U'),
pattern2('Core', 'MultiplyScalar', '16U'),
pattern2('Core', 'MultiplyScalar', '32F'),
# Core_DivideMat (CV_8U | CV_16U | CV_32F)
pattern2('Core', 'DivideMat', '8U'),
pattern2('Core', 'DivideMat', '16U'),
pattern2('Core', 'DivideMat', '32F'),
# Core_Divide_Scalar (CV_8U | CV_16U | CV_32F)
pattern2('Core', 'DivideScalar', '8U'),
pattern2('Core', 'DivideScalar', '16U'),
pattern2('Core', 'DivideScalar', '32F'),
# Core_AbsDiff_Mat (CV_8U | CV_16U | CV_32F)
pattern2('Core', 'AbsDiffMat', '8U'),
pattern2('Core', 'AbsDiffMat', '16U'),
pattern2('Core', 'AbsDiffMat', '32F'),
# Core_AbsDiffScalar (CV_8U | CV_16U | CV_32F)
pattern2('Core', 'AbsDiffScalar', '8U'),
pattern2('Core', 'AbsDiffScalar', '16U'),
pattern2('Core', 'AbsDiffScalar', '32F'),
# Core_Abs
pattern1('Core', 'Abs'),
# Core/Sqr
# Core_Sqr
pattern1('Core', 'Sqr'),
# Core/Sqrt
# Core_Sqrt
pattern1('Core', 'Sqrt'),
# Core/Log
# Core_Log
pattern1('Core', 'Log'),
# Core/Exp
# Core_Exp
pattern1('Core', 'Exp'),
# Core/Bitwise_And_Scalar
pattern1('Core', 'Bitwise_And_Scalar'),
# Core_BitwiseAndScalar
pattern1('Core', 'BitwiseAndScalar'),
# Core/Bitwise_Or_Scalar
pattern1('Core', 'Bitwise_Or_Scalar'),
# Core_BitwiseOrScalar
pattern1('Core', 'BitwiseOrScalar'),
# Core/Bitwise_Xor_Scalar
pattern1('Core', 'Bitwise_Xor_Scalar'),
# Core_BitwiseXorScalar
pattern1('Core', 'BitwiseXorScalar'),
# Core/RShift
# Core_RShift
pattern1('Core', 'RShift'),
# Core/LShift
# Core_LShift
pattern1('Core', 'LShift'),
# Core/Transpose
# Core_Transpose
pattern1('Core', 'Transpose'),
# Core/Flip
# Core_Flip
pattern1('Core', 'Flip'),
# Core/LUT_OneChannel
pattern1('Core', 'LUT_OneChannel'),
# Core_LutOneChannel
pattern1('Core', 'LutOneChannel'),
# Core/LUT_MultiChannel
pattern1('Core', 'LUT_MultiChannel'),
# Core_LutMultiChannel
pattern1('Core', 'LutMultiChannel'),
# Core/Magnitude_Complex
pattern1('Core', 'Magnitude_Complex'),
# Core_MagnitudeComplex
pattern1('Core', 'MagnitudeComplex'),
# Core/Magnitude_Sqr_Complex
pattern1('Core', 'Magnitude_Sqr_Complex'),
# Core_MagnitudeSqrComplex
pattern1('Core', 'MagnitudeSqrComplex'),
# Core/MeanStdDev
# Core_MeanStdDev
pattern1('Core', 'MeanStdDev'),
# Core/NormDiff
# Core_NormDiff
pattern1('Core', 'NormDiff'),
##############################################################
# Filters
# Filters/Blur
# Filters_Blur
pattern1('Filters', 'Blur'),
# Filters/Erode
# Filters_Erode
pattern1('Filters', 'Erode'),
# Filters/Dilate
# Filters_Dilate
pattern1('Filters', 'Dilate'),
# Filters/MorphologyEx
# Filters_MorphologyEx
pattern1('Filters', 'MorphologyEx'),
##############################################################
# ImgProc
# ImgProc/Resize (8UC1 | 8UC4, INTER_NEAREST | INTER_LINEAR)
pattern3('ImgProc', 'Resize', '8UC1', 'INTER_NEAREST'),
pattern3('ImgProc', 'Resize', '8UC4', 'INTER_NEAREST'),
pattern3('ImgProc', 'Resize', '8UC1', 'INTER_LINEAR'),
pattern3('ImgProc', 'Resize', '8UC4', 'INTER_LINEAR'),
# ImgProc/Resize (8UC4, INTER_CUBIC)
pattern3('ImgProc', 'Resize', '8UC4', 'INTER_CUBIC'),
# ImgProc/WarpAffine (8UC1 | 8UC3 | 8UC4 | 32FC1 | 32FC3 | 32FC4, INTER_NEAREST | INTER_LINEAR | INTER_CUBIC, BORDER_CONSTANT)
pattern4('ImgProc', 'WarpAffine', '8UC1', 'INTER_NEAREST', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpAffine', '8UC1', 'INTER_LINEAR', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpAffine', '8UC1', 'INTER_CUBIC', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpAffine', '8UC3', 'INTER_NEAREST', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpAffine', '8UC3', 'INTER_LINEAR', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpAffine', '8UC3', 'INTER_CUBIC', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpAffine', '8UC4', 'INTER_NEAREST', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpAffine', '8UC4', 'INTER_LINEAR', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpAffine', '8UC4', 'INTER_CUBIC', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpAffine', '32FC1', 'INTER_NEAREST', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpAffine', '32FC1', 'INTER_LINEAR', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpAffine', '32FC1', 'INTER_CUBIC', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpAffine', '32FC3', 'INTER_NEAREST', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpAffine', '32FC3', 'INTER_LINEAR', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpAffine', '32FC3', 'INTER_CUBIC', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpAffine', '32FC4', 'INTER_NEAREST', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpAffine', '32FC4', 'INTER_LINEAR', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpAffine', '32FC4', 'INTER_CUBIC', 'BORDER_CONSTANT'),
# ImgProc/WarpPerspective (8UC1 | 8UC3 | 8UC4 | 32FC1 | 32FC3 | 32FC4, INTER_NEAREST | INTER_LINEAR | INTER_CUBIC, BORDER_CONSTANT)
pattern4('ImgProc', 'WarpPerspective', '8UC1', 'INTER_NEAREST', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpPerspective', '8UC1', 'INTER_LINEAR', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpPerspective', '8UC1', 'INTER_CUBIC', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpPerspective', '8UC3', 'INTER_NEAREST', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpPerspective', '8UC3', 'INTER_LINEAR', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpPerspective', '8UC3', 'INTER_CUBIC', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpPerspective', '8UC4', 'INTER_NEAREST', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpPerspective', '8UC4', 'INTER_LINEAR', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpPerspective', '8UC4', 'INTER_CUBIC', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpPerspective', '32FC1', 'INTER_NEAREST', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpPerspective', '32FC1', 'INTER_LINEAR', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpPerspective', '32FC1', 'INTER_CUBIC', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpPerspective', '32FC3', 'INTER_NEAREST', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpPerspective', '32FC3', 'INTER_LINEAR', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpPerspective', '32FC3', 'INTER_CUBIC', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpPerspective', '32FC4', 'INTER_NEAREST', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpPerspective', '32FC4', 'INTER_LINEAR', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpPerspective', '32FC4', 'INTER_CUBIC', 'BORDER_CONSTANT'),
# ImgProc/CopyMakeBorder (8UC1 | 8UC4 | 32SC1 | 32FC1, BORDER_CONSTANT)
pattern3('ImgProc', 'CopyMakeBorder', '8UC1', 'BORDER_CONSTANT'),
pattern3('ImgProc', 'CopyMakeBorder', '8UC4', 'BORDER_CONSTANT'),
pattern3('ImgProc', 'CopyMakeBorder', '32SC1', 'BORDER_CONSTANT'),
pattern3('ImgProc', 'CopyMakeBorder', '32FC1', 'BORDER_CONSTANT'),
# ImgProc/Threshold (32F, THRESH_TRUNC)
# ImgProc_Resize (8U, 1 | 4, INTER_NEAREST | INTER_LINEAR)
pattern4('ImgProc', 'Resize', '8U', '1', 'INTER_NEAREST'),
pattern4('ImgProc', 'Resize', '8U', '4', 'INTER_NEAREST'),
pattern4('ImgProc', 'Resize', '8U', '1', 'INTER_LINEAR'),
pattern4('ImgProc', 'Resize', '8U', '4', 'INTER_LINEAR'),
# ImgProc_Resize (8U, 4, INTER_CUBIC)
pattern4('ImgProc', 'Resize', '8U', '4', 'INTER_CUBIC'),
# ImgProc_WarpAffine (8U | 32F, INTER_NEAREST | INTER_LINEAR | INTER_CUBIC, BORDER_CONSTANT)
pattern4('ImgProc', 'WarpAffine', '8U' , 'INTER_NEAREST', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpAffine', '8U' , 'INTER_LINEAR', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpAffine', '8U' , 'INTER_CUBIC', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpAffine', '32F', 'INTER_NEAREST', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpAffine', '32F', 'INTER_LINEAR', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpAffine', '32F', 'INTER_CUBIC', 'BORDER_CONSTANT'),
# ImgProc_WarpPerspective (8U | 32F, INTER_NEAREST | INTER_LINEAR | INTER_CUBIC, BORDER_CONSTANT)
pattern4('ImgProc', 'WarpPerspective', '8U' , 'INTER_NEAREST', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpPerspective', '8U' , 'INTER_LINEAR', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpPerspective', '8U' , 'INTER_CUBIC', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpPerspective', '32F', 'INTER_NEAREST', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpPerspective', '32F', 'INTER_LINEAR', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'WarpPerspective', '32F', 'INTER_CUBIC', 'BORDER_CONSTANT'),
# ImgProc_CopyMakeBorder (8UC1 | 8UC4 | 32SC1 | 32FC1, BORDER_CONSTANT)
pattern4('ImgProc', 'CopyMakeBorder', '8U' , '1', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'CopyMakeBorder', '8U' , '4', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'CopyMakeBorder', '32S', '1', 'BORDER_CONSTANT'),
pattern4('ImgProc', 'CopyMakeBorder', '32F', '1', 'BORDER_CONSTANT'),
# ImgProc_Threshold (32F, THRESH_TRUNC)
pattern3('ImgProc', 'Threshold', '32F', 'THRESH_TRUNC'),
# ImgProc/Integral_Sqr
pattern1('ImgProc', 'Integral_Sqr'),
# ImgProc_IntegralSqr
pattern1('ImgProc', 'IntegralSqr'),
# ImgProc/HistEven_OneChannel
pattern1('ImgProc', 'HistEven_OneChannel'),
# ImgProc_HistEven_OneChannel
pattern1('ImgProc', 'HistEvenOneChannel'),
# ImgProc/HistEven_FourChannel
pattern1('ImgProc', 'HistEven_FourChannel'),
# ImgProc_HistEven_FourChannel
pattern1('ImgProc', 'HistEvenFourChannel'),
# ImgProc/Rotate
# ImgProc_Rotate
pattern1('ImgProc', 'Rotate'),
# ImgProc/SwapChannels
# ImgProc_SwapChannels
pattern1('ImgProc', 'SwapChannels'),
# ImgProc/AlphaComp
# ImgProc_AlphaComp
pattern1('ImgProc', 'AlphaComp'),
# ImgProc/ImagePyramid_build
pattern1('ImgProc', 'ImagePyramid_build'),
# ImgProc_ImagePyramidBuild
pattern1('ImgProc', 'ImagePyramidBuild'),
# ImgProc_ImagePyramid_getLayer
pattern1('ImgProc', 'ImagePyramidGetLayer'),
# ImgProc/ImagePyramid_getLayer
pattern1('ImgProc', 'ImagePyramid_getLayer'),
##############################################################
# MatOp
# MatOp/SetTo (8UC4 | 16UC1 | 16UC4 | 32FC1 | 32FC4)
pattern2('MatOp', 'SetTo', '8UC4'),
pattern2('MatOp', 'SetTo', '16UC1'),
pattern2('MatOp', 'SetTo', '16UC4'),
pattern2('MatOp', 'SetTo', '32FC1'),
pattern2('MatOp', 'SetTo', '32FC4'),
# MatOp/SetToMasked (8UC4 | 16UC1 | 16UC4 | 32FC1 | 32FC4)
pattern2('MatOp', 'SetToMasked', '8UC4'),
pattern2('MatOp', 'SetToMasked', '16UC1'),
pattern2('MatOp', 'SetToMasked', '16UC4'),
pattern2('MatOp', 'SetToMasked', '32FC1'),
pattern2('MatOp', 'SetToMasked', '32FC4'),
# MatOp/CopyToMasked (8UC1 | 8UC3 |8UC4 | 16UC1 | 16UC3 | 16UC4 | 32FC1 | 32FC3 | 32FC4)
pattern2('MatOp', 'CopyToMasked', '8UC1'),
pattern2('MatOp', 'CopyToMasked', '8UC3'),
pattern2('MatOp', 'CopyToMasked', '8UC4'),
pattern2('MatOp', 'CopyToMasked', '16UC1'),
pattern2('MatOp', 'CopyToMasked', '16UC3'),
pattern2('MatOp', 'CopyToMasked', '16UC4'),
pattern2('MatOp', 'CopyToMasked', '32FC1'),
pattern2('MatOp', 'CopyToMasked', '32FC3'),
pattern2('MatOp', 'CopyToMasked', '32FC4'),
# MatOp_SetTo (8UC4 | 16UC1 | 16UC4 | 32FC1 | 32FC4)
pattern3('MatOp', 'SetTo', '8U' , '4'),
pattern3('MatOp', 'SetTo', '16U', '1'),
pattern3('MatOp', 'SetTo', '16U', '4'),
pattern3('MatOp', 'SetTo', '32F', '1'),
pattern3('MatOp', 'SetTo', '32F', '4'),
# MatOp_SetToMasked (8UC4 | 16UC1 | 16UC4 | 32FC1 | 32FC4)
pattern3('MatOp', 'SetToMasked', '8U' , '4'),
pattern3('MatOp', 'SetToMasked', '16U', '1'),
pattern3('MatOp', 'SetToMasked', '16U', '4'),
pattern3('MatOp', 'SetToMasked', '32F', '1'),
pattern3('MatOp', 'SetToMasked', '32F', '4'),
# MatOp_CopyToMasked (8UC1 | 8UC3 |8UC4 | 16UC1 | 16UC3 | 16UC4 | 32FC1 | 32FC3 | 32FC4)
pattern3('MatOp', 'CopyToMasked', '8U' , '1'),
pattern3('MatOp', 'CopyToMasked', '8U' , '3'),
pattern3('MatOp', 'CopyToMasked', '8U' , '4'),
pattern3('MatOp', 'CopyToMasked', '16U', '1'),
pattern3('MatOp', 'CopyToMasked', '16U', '3'),
pattern3('MatOp', 'CopyToMasked', '16U', '4'),
pattern3('MatOp', 'CopyToMasked', '32F', '1'),
pattern3('MatOp', 'CopyToMasked', '32F', '3'),
pattern3('MatOp', 'CopyToMasked', '32F', '4'),
]
cublasPattern = pattern1('Core', 'GEMM')
......@@ -260,7 +239,7 @@ if __name__ == "__main__":
inputFile = open(sys.argv[1], 'r')
lines = inputFile.readlines()
inputFile.close()
for i in range(len(lines)):
if cublasPattern.match(lines[i]):
......
modules/gpu/perf/main.cpp 0 → 100644
浏览文件 @ 4fb15ae1
#include "perf_precomp.hpp"
using namespace std;
using namespace cv;
using namespace cv::gpu;
using namespace cvtest;
using namespace testing;
void printOsInfo()
{
#if defined _WIN32
# if defined _WIN64
cout << "OS: Windows x64 \n" << endl;
# else
cout << "OS: Windows x32 \n" << endl;
# endif
#elif defined linux
# if defined _LP64
cout << "OS: Linux x64 \n" << endl;
# else
cout << "OS: Linux x32 \n" << endl;
# endif
#elif defined __APPLE__
# if defined _LP64
cout << "OS: Apple x64 \n" << endl;
# else
cout << "OS: Apple x32 \n" << endl;
# endif
#endif
}
void printCudaInfo()
{
#ifndef HAVE_CUDA
cout << "OpenCV was built without CUDA support \n" << endl;
#else
int driver;
cudaDriverGetVersion(&driver);
cout << "CUDA Driver version: " << driver << '\n';
cout << "CUDA Runtime version: " << CUDART_VERSION << '\n';
cout << endl;
cout << "GPU module was compiled for the following GPU archs:" << endl;
cout << " BIN: " << CUDA_ARCH_BIN << '\n';
cout << " PTX: " << CUDA_ARCH_PTX << '\n';
cout << endl;
int deviceCount = getCudaEnabledDeviceCount();
cout << "CUDA device count: " << deviceCount << '\n';
cout << endl;
for (int i = 0; i < deviceCount; ++i)
{
DeviceInfo info(i);
cout << "Device [" << i << "] \n";
cout << "\t Name: " << info.name() << '\n';
cout << "\t Compute capability: " << info.majorVersion() << '.' << info.minorVersion()<< '\n';
cout << "\t Multi Processor Count: " << info.multiProcessorCount() << '\n';
cout << "\t Total memory: " << static_cast<int>(static_cast<int>(info.totalMemory() / 1024.0) / 1024.0) << " Mb \n";
cout << "\t Free memory: " << static_cast<int>(static_cast<int>(info.freeMemory() / 1024.0) / 1024.0) << " Mb \n";
if (!info.isCompatible())
cout << "\t !!! This device is NOT compatible with current GPU module build \n";
cout << endl;
}
#endif
}
int main(int argc, char** argv)
{
CommandLineParser cmd(argc, (const char**) argv,
"{ print_info_only | print_info_only | false | Print information about system and exit }"
"{ device | device | 0 | Device on which tests will be executed }"
"{ cpu | cpu | false | Run tests on cpu }"
);
printOsInfo();
printCudaInfo();
if (cmd.get<bool>("print_info_only"))
return 0;
int device = cmd.get<int>("device");
bool cpu = cmd.get<bool>("cpu");
#ifndef HAVE_CUDA
cpu = true;
#endif
if (cpu)
{
runOnGpu = false;
cout << "Run tests on CPU \n" << endl;
}
else
{
runOnGpu = true;
if (device < 0 || device >= getCudaEnabledDeviceCount())
{
cerr << "Incorrect device index - " << device << endl;
return -1;
}
DeviceInfo info(device);
if (!info.isCompatible())
{
cerr << "Device " << device << " [" << info.name() << "] is NOT compatible with current GPU module build" << endl;
return -1;
}
setDevice(device);
cout << "Run tests on device " << device << " [" << info.name() << "] \n" << endl;
}
InitGoogleTest(&argc, argv);
perf::TestBase::Init(argc, argv);
return RUN_ALL_TESTS();
}
modules/gpu/perf/perf_calib3d.cpp
浏览文件 @ 4fb15ae1
#include "perf_precomp.hpp"
#ifdef HAVE_CUDA
using namespace std;
using namespace testing;
namespace {
//////////////////////////////////////////////////////////////////////
// StereoBM
GPU_PERF_TEST_1(StereoBM, cv::gpu::DeviceInfo)
typedef pair<string, string> pair_string;
DEF_PARAM_TEST_1(ImagePair, pair_string);
PERF_TEST_P(ImagePair, Calib3D_StereoBM, Values(make_pair<string, string>("gpu/perf/aloe.jpg", "gpu/perf/aloeR.jpg")))
{
cv::gpu::DeviceInfo devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
declare.time(5.0);
cv::Mat img_l_host = readImage("gpu/perf/aloe.jpg", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img_l_host.empty());
const cv::Mat imgLeft = readImage(GetParam().first, cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(imgLeft.empty());
cv::Mat img_r_host = readImage("gpu/perf/aloeR.jpg", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img_r_host.empty());
const cv::Mat imgRight = readImage(GetParam().second, cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(imgRight.empty());
cv::gpu::StereoBM_GPU bm(0, 256);
cv::gpu::GpuMat img_l(img_l_host);
cv::gpu::GpuMat img_r(img_r_host);
cv::gpu::GpuMat dst;
const int preset = 0;
const int ndisp = 256;
bm(img_l, img_r, dst);
if (runOnGpu)
{
cv::gpu::StereoBM_GPU d_bm(preset, ndisp);
declare.time(5.0);
cv::gpu::GpuMat d_imgLeft(imgLeft);
cv::gpu::GpuMat d_imgRight(imgRight);
cv::gpu::GpuMat d_dst;
d_bm(d_imgLeft, d_imgRight, d_dst);
TEST_CYCLE()
TEST_CYCLE()
{
d_bm(d_imgLeft, d_imgRight, d_dst);
}
}
else
{
bm(img_l, img_r, dst);
cv::StereoBM bm(preset, ndisp);
cv::Mat dst;
bm(imgLeft, imgRight, dst);
TEST_CYCLE()
{
bm(imgLeft, imgRight, dst);
}
}
}
INSTANTIATE_TEST_CASE_P(Calib3D, StereoBM, ALL_DEVICES);
//////////////////////////////////////////////////////////////////////
// StereoBeliefPropagation
GPU_PERF_TEST_1(StereoBeliefPropagation, cv::gpu::DeviceInfo)
PERF_TEST_P(ImagePair, Calib3D_StereoBeliefPropagation, Values(make_pair<string, string>("gpu/stereobp/aloe-L.png", "gpu/stereobp/aloe-R.png")))
{
cv::gpu::DeviceInfo devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
declare.time(10.0);
cv::Mat img_l_host = readImage("gpu/stereobp/aloe-L.png");
ASSERT_FALSE(img_l_host.empty());
const cv::Mat imgLeft = readImage(GetParam().first);
ASSERT_FALSE(imgLeft.empty());
cv::Mat img_r_host = readImage("gpu/stereobp/aloe-R.png");
ASSERT_FALSE(img_r_host.empty());
const cv::Mat imgRight = readImage(GetParam().second);
ASSERT_FALSE(imgRight.empty());
cv::gpu::StereoBeliefPropagation bp(64);
cv::gpu::GpuMat img_l(img_l_host);
cv::gpu::GpuMat img_r(img_r_host);
cv::gpu::GpuMat dst;
const int ndisp = 64;
bp(img_l, img_r, dst);
if (runOnGpu)
{
cv::gpu::StereoBeliefPropagation d_bp(ndisp);
declare.time(10.0);
cv::gpu::GpuMat d_imgLeft(imgLeft);
cv::gpu::GpuMat d_imgRight(imgRight);
cv::gpu::GpuMat d_dst;
TEST_CYCLE()
d_bp(d_imgLeft, d_imgRight, d_dst);
TEST_CYCLE()
{
d_bp(d_imgLeft, d_imgRight, d_dst);
}
}
else
{
bp(img_l, img_r, dst);
FAIL();
}
}
INSTANTIATE_TEST_CASE_P(Calib3D, StereoBeliefPropagation, ALL_DEVICES);
//////////////////////////////////////////////////////////////////////
// StereoConstantSpaceBP
GPU_PERF_TEST_1(StereoConstantSpaceBP, cv::gpu::DeviceInfo)
PERF_TEST_P(ImagePair, Calib3D_StereoConstantSpaceBP, Values(make_pair<string, string>("gpu/stereobm/aloe-L.png", "gpu/stereobm/aloe-R.png")))
{
cv::gpu::DeviceInfo devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
declare.time(10.0);
cv::Mat img_l_host = readImage("gpu/stereobm/aloe-L.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img_l_host.empty());
const cv::Mat imgLeft = readImage(GetParam().first, cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(imgLeft.empty());
cv::Mat img_r_host = readImage("gpu/stereobm/aloe-R.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img_r_host.empty());
const cv::Mat imgRight = readImage(GetParam().second, cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(imgRight.empty());
cv::gpu::StereoConstantSpaceBP csbp(128);
cv::gpu::GpuMat img_l(img_l_host);
cv::gpu::GpuMat img_r(img_r_host);
cv::gpu::GpuMat dst;
const int ndisp = 128;
csbp(img_l, img_r, dst);
if (runOnGpu)
{
cv::gpu::StereoConstantSpaceBP d_csbp(ndisp);
declare.time(10.0);
cv::gpu::GpuMat d_imgLeft(imgLeft);
cv::gpu::GpuMat d_imgRight(imgRight);
cv::gpu::GpuMat d_dst;
d_csbp(d_imgLeft, d_imgRight, d_dst);
TEST_CYCLE()
TEST_CYCLE()
{
d_csbp(d_imgLeft, d_imgRight, d_dst);
}
}
else
{
csbp(img_l, img_r, dst);
FAIL();
}
}
INSTANTIATE_TEST_CASE_P(Calib3D, StereoConstantSpaceBP, ALL_DEVICES);
//////////////////////////////////////////////////////////////////////
// DisparityBilateralFilter
GPU_PERF_TEST_1(DisparityBilateralFilter, cv::gpu::DeviceInfo)
PERF_TEST_P(ImagePair, Calib3D_DisparityBilateralFilter, Values(make_pair<string, string>("gpu/stereobm/aloe-L.png", "gpu/stereobm/aloe-disp.png")))
{
cv::gpu::DeviceInfo devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
const cv::Mat img = readImage(GetParam().first, cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img.empty());
const cv::Mat disp = readImage(GetParam().second, cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(disp.empty());
cv::Mat img_host = readImage("gpu/stereobm/aloe-L.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img_host.empty());
const int ndisp = 128;
cv::Mat disp_host = readImage("gpu/stereobm/aloe-disp.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(disp_host.empty());
if (runOnGpu)
{
cv::gpu::DisparityBilateralFilter d_filter(ndisp);
cv::gpu::DisparityBilateralFilter f(128);
cv::gpu::GpuMat img(img_host);
cv::gpu::GpuMat disp(disp_host);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat d_img(img);
cv::gpu::GpuMat d_disp(disp);
cv::gpu::GpuMat d_dst;
f(disp, img, dst);
d_filter(d_disp, d_img, d_dst);
TEST_CYCLE()
TEST_CYCLE()
{
d_filter(d_disp, d_img, d_dst);
}
}
else
{
f(disp, img, dst);
FAIL();
}
}
INSTANTIATE_TEST_CASE_P(Calib3D, DisparityBilateralFilter, ALL_DEVICES);
//////////////////////////////////////////////////////////////////////
// TransformPoints
IMPLEMENT_PARAM_CLASS(Count, int)
DEF_PARAM_TEST_1(Count, int);
GPU_PERF_TEST(TransformPoints, cv::gpu::DeviceInfo, Count)
PERF_TEST_P(Count, Calib3D_TransformPoints, Values(5000, 10000, 20000))
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
const int count = GetParam();
int count = GET_PARAM(1);
cv::Mat src(1, count, CV_32FC3);
fillRandom(src, -100, 100);
cv::Mat src_host(1, count, CV_32FC3);
fill(src_host, -100, 100);
const cv::Mat rvec = cv::Mat::ones(1, 3, CV_32FC1);
const cv::Mat tvec = cv::Mat::ones(1, 3, CV_32FC1);
cv::gpu::GpuMat src(src_host);
cv::Mat rvec = cv::Mat::ones(1, 3, CV_32FC1);
cv::Mat tvec = cv::Mat::ones(1, 3, CV_32FC1);
cv::gpu::GpuMat dst;
if (runOnGpu)
{
cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat d_dst;
cv::gpu::transformPoints(src, rvec, tvec, dst);
cv::gpu::transformPoints(d_src, rvec, tvec, d_dst);
TEST_CYCLE()
TEST_CYCLE()
{
cv::gpu::transformPoints(d_src, rvec, tvec, d_dst);
}
}
else
{
cv::gpu::transformPoints(src, rvec, tvec, dst);
FAIL();
}
}
INSTANTIATE_TEST_CASE_P(Calib3D, TransformPoints, testing::Combine(
ALL_DEVICES,
testing::Values<Count>(5000, 10000, 20000)));
//////////////////////////////////////////////////////////////////////
// ProjectPoints
GPU_PERF_TEST(ProjectPoints, cv::gpu::DeviceInfo, Count)
PERF_TEST_P(Count, Calib3D_ProjectPoints, Values(5000, 10000, 20000))
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
const int count = GetParam();
int count = GET_PARAM(1);
cv::Mat src(1, count, CV_32FC3);
fillRandom(src, -100, 100);
cv::Mat src_host(1, count, CV_32FC3);
fill(src_host, -100, 100);
const cv::Mat rvec = cv::Mat::ones(1, 3, CV_32FC1);
const cv::Mat tvec = cv::Mat::ones(1, 3, CV_32FC1);
const cv::Mat camera_mat = cv::Mat::ones(3, 3, CV_32FC1);
cv::gpu::GpuMat src(src_host);
cv::Mat rvec = cv::Mat::ones(1, 3, CV_32FC1);
cv::Mat tvec = cv::Mat::ones(1, 3, CV_32FC1);
cv::Mat camera_mat = cv::Mat::ones(3, 3, CV_32FC1);
cv::gpu::GpuMat dst;
if (runOnGpu)
{
cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat d_dst;
cv::gpu::projectPoints(src, rvec, tvec, camera_mat, cv::Mat(), dst);
cv::gpu::projectPoints(d_src, rvec, tvec, camera_mat, cv::Mat(), d_dst);
TEST_CYCLE()
TEST_CYCLE()
{
cv::gpu::projectPoints(d_src, rvec, tvec, camera_mat, cv::Mat(), d_dst);
}
}
else
{
cv::gpu::projectPoints(src, rvec, tvec, camera_mat, cv::Mat(), dst);
cv::Mat dst;
cv::projectPoints(src, rvec, tvec, camera_mat, cv::noArray(), dst);
TEST_CYCLE()
{
cv::projectPoints(src, rvec, tvec, camera_mat, cv::noArray(), dst);
}
}
}
INSTANTIATE_TEST_CASE_P(Calib3D, ProjectPoints, testing::Combine(
ALL_DEVICES,
testing::Values<Count>(5000, 10000, 20000)));
//////////////////////////////////////////////////////////////////////
// SolvePnPRansac
GPU_PERF_TEST(SolvePnPRansac, cv::gpu::DeviceInfo, Count)
PERF_TEST_P(Count, Calib3D_SolvePnPRansac, Values(5000, 10000, 20000))
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
declare.time(10.0);
int count = GET_PARAM(1);
const int count = GetParam();
cv::Mat object(1, count, CV_32FC3);
fill(object, -100, 100);
fillRandom(object, -100, 100);
cv::Mat camera_mat(3, 3, CV_32FC1);
fill(camera_mat, 0.5, 1);
fillRandom(camera_mat, 0.5, 1);
camera_mat.at<float>(0, 1) = 0.f;
camera_mat.at<float>(1, 0) = 0.f;
camera_mat.at<float>(2, 0) = 0.f;
camera_mat.at<float>(2, 1) = 0.f;
cv::Mat dist_coef(1, 8, CV_32F, cv::Scalar::all(0));
const cv::Mat dist_coef(1, 8, CV_32F, cv::Scalar::all(0));
std::vector<cv::Point2f> image_vec;
cv::Mat rvec_gold(1, 3, CV_32FC1);
fill(rvec_gold, 0, 1);
fillRandom(rvec_gold, 0, 1);
cv::Mat tvec_gold(1, 3, CV_32FC1);
fill(tvec_gold, 0, 1);
fillRandom(tvec_gold, 0, 1);
cv::projectPoints(object, rvec_gold, tvec_gold, camera_mat, dist_coef, image_vec);
cv::Mat image(1, count, CV_32FC2, &image_vec[0]);
......@@ -221,82 +265,92 @@ GPU_PERF_TEST(SolvePnPRansac, cv::gpu::DeviceInfo, Count)
cv::Mat rvec;
cv::Mat tvec;
cv::gpu::solvePnPRansac(object, image, camera_mat, dist_coef, rvec, tvec);
declare.time(3.0);
TEST_CYCLE()
if (runOnGpu)
{
cv::gpu::solvePnPRansac(object, image, camera_mat, dist_coef, rvec, tvec);
TEST_CYCLE()
{
cv::gpu::solvePnPRansac(object, image, camera_mat, dist_coef, rvec, tvec);
}
}
}
else
{
cv::solvePnPRansac(object, image, camera_mat, dist_coef, rvec, tvec);
INSTANTIATE_TEST_CASE_P(Calib3D, SolvePnPRansac, testing::Combine(
ALL_DEVICES,
testing::Values<Count>(5000, 10000, 20000)));
TEST_CYCLE()
{
cv::solvePnPRansac(object, image, camera_mat, dist_coef, rvec, tvec);
}
}
}
//////////////////////////////////////////////////////////////////////
// ReprojectImageTo3D
GPU_PERF_TEST(ReprojectImageTo3D, cv::gpu::DeviceInfo, cv::Size, MatDepth)
PERF_TEST_P(Sz_Depth, Calib3D_ReprojectImageTo3D, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8U, CV_16S)))
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
cv::Size size = GET_PARAM(1);
int depth = GET_PARAM(2);
cv::Mat src_host(size, depth);
fill(src_host, 5.0, 30.0);
cv::Mat src(size, depth);
fillRandom(src, 5.0, 30.0);
cv::Mat Q(4, 4, CV_32FC1);
fill(Q, 0.1, 1.0);
fillRandom(Q, 0.1, 1.0);
cv::gpu::GpuMat src(src_host);
cv::gpu::GpuMat dst;
if (runOnGpu)
{
cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat d_dst;
cv::gpu::reprojectImageTo3D(src, dst, Q);
cv::gpu::reprojectImageTo3D(d_src, d_dst, Q);
TEST_CYCLE()
TEST_CYCLE()
{
cv::gpu::reprojectImageTo3D(d_src, d_dst, Q);
}
}
else
{
cv::gpu::reprojectImageTo3D(src, dst, Q);
cv::Mat dst;
cv::reprojectImageTo3D(src, dst, Q);
TEST_CYCLE()
{
cv::reprojectImageTo3D(src, dst, Q);
}
}
}
INSTANTIATE_TEST_CASE_P(Calib3D, ReprojectImageTo3D, testing::Combine(
ALL_DEVICES,
GPU_TYPICAL_MAT_SIZES,
testing::Values<MatDepth>(CV_8U, CV_16S)));
//////////////////////////////////////////////////////////////////////
// DrawColorDisp
GPU_PERF_TEST(DrawColorDisp, cv::gpu::DeviceInfo, cv::Size, MatDepth)
PERF_TEST_P(Sz_Depth, Calib3D_DrawColorDisp, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8U, CV_16S)))
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
cv::Size size = GET_PARAM(1);
int type = GET_PARAM(2);
const cv::Size size = GET_PARAM(0);
const int type = GET_PARAM(1);
cv::Mat src_host(size, type);
fill(src_host, 0, 255);
cv::Mat src(size, type);
fillRandom(src, 0, 255);
cv::gpu::GpuMat src(src_host);
cv::gpu::GpuMat dst;
if (runOnGpu)
{
cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat d_dst;
cv::gpu::drawColorDisp(src, dst, 255);
cv::gpu::drawColorDisp(d_src, d_dst, 255);
TEST_CYCLE()
TEST_CYCLE()
{
cv::gpu::drawColorDisp(d_src, d_dst, 255);
}
}
else
{
cv::gpu::drawColorDisp(src, dst, 255);
FAIL();
}
}
INSTANTIATE_TEST_CASE_P(Calib3D, DrawColorDisp, testing::Combine(
ALL_DEVICES,
GPU_TYPICAL_MAT_SIZES,
testing::Values(MatDepth(CV_8U), MatDepth(CV_16S))));
#endif
} // namespace
modules/gpu/perf/perf_core.cpp
浏览文件 @ 4fb15ae1
此差异已折叠。
modules/gpu/perf/perf_features2d.cpp
浏览文件 @ 4fb15ae1
#include "perf_precomp.hpp"
#ifdef HAVE_CUDA
using namespace std;
using namespace testing;
namespace {
//////////////////////////////////////////////////////////////////////
// SURF
GPU_PERF_TEST_1(SURF, cv::gpu::DeviceInfo)
{
cv::gpu::DeviceInfo devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
DEF_PARAM_TEST_1(Image, string);
cv::Mat img_host = readImage("gpu/perf/aloe.jpg", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img_host.empty());
PERF_TEST_P(Image, Features2D_SURF, Values<string>("gpu/perf/aloe.jpg"))
{
declare.time(50.0);
cv::gpu::SURF_GPU surf;
cv::Mat img = readImage(GetParam(), cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img.empty());
cv::gpu::GpuMat img(img_host);
cv::gpu::GpuMat keypoints, descriptors;
if (runOnGpu)
{
cv::gpu::SURF_GPU d_surf;
surf(img, cv::gpu::GpuMat(), keypoints, descriptors);
cv::gpu::GpuMat d_img(img);
cv::gpu::GpuMat d_keypoints, d_descriptors;
declare.time(2.0);
d_surf(d_img, cv::gpu::GpuMat(), d_keypoints, d_descriptors);
TEST_CYCLE()
TEST_CYCLE()
{
d_surf(d_img, cv::gpu::GpuMat(), d_keypoints, d_descriptors);
}
}
else
{
surf(img, cv::gpu::GpuMat(), keypoints, descriptors);
cv::SURF surf;
std::vector<cv::KeyPoint> keypoints;
cv::Mat descriptors;
surf(img, cv::noArray(), keypoints, descriptors);
TEST_CYCLE()
{
keypoints.clear();
surf(img, cv::noArray(), keypoints, descriptors);
}
}
}
INSTANTIATE_TEST_CASE_P(Features2D, SURF, ALL_DEVICES);
//////////////////////////////////////////////////////////////////////
// FAST
GPU_PERF_TEST_1(FAST, cv::gpu::DeviceInfo)
PERF_TEST_P(Image, Features2D_FAST, Values<string>("gpu/perf/aloe.jpg"))
{
cv::gpu::DeviceInfo devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
cv::Mat img = readImage(GetParam(), cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img.empty());
cv::Mat img_host = readImage("gpu/perf/aloe.jpg", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img_host.empty());
cv::gpu::FAST_GPU fast(20);
if (runOnGpu)
{
cv::gpu::FAST_GPU d_fast(20);
cv::gpu::GpuMat img(img_host);
cv::gpu::GpuMat keypoints;
cv::gpu::GpuMat d_img(img);
cv::gpu::GpuMat d_keypoints;
fast(img, cv::gpu::GpuMat(), keypoints);
d_fast(d_img, cv::gpu::GpuMat(), d_keypoints);
TEST_CYCLE()
TEST_CYCLE()
{
d_fast(d_img, cv::gpu::GpuMat(), d_keypoints);
}
}
else
{
fast(img, cv::gpu::GpuMat(), keypoints);
std::vector<cv::KeyPoint> keypoints;
cv::FAST(img, keypoints, 20);
TEST_CYCLE()
{
keypoints.clear();
cv::FAST(img, keypoints, 20);
}
}
}
INSTANTIATE_TEST_CASE_P(Features2D, FAST, ALL_DEVICES);
//////////////////////////////////////////////////////////////////////
// ORB
GPU_PERF_TEST_1(ORB, cv::gpu::DeviceInfo)
PERF_TEST_P(Image, Features2D_ORB, Values<string>("gpu/perf/aloe.jpg"))
{
cv::gpu::DeviceInfo devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
cv::Mat img = readImage(GetParam(), cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img.empty());
cv::Mat img_host = readImage("gpu/perf/aloe.jpg", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img_host.empty());
if (runOnGpu)
{
cv::gpu::ORB_GPU d_orb(4000);
cv::gpu::ORB_GPU orb(4000);
cv::gpu::GpuMat d_img(img);
cv::gpu::GpuMat d_keypoints, d_descriptors;
cv::gpu::GpuMat img(img_host);
cv::gpu::GpuMat keypoints, descriptors;
d_orb(d_img, cv::gpu::GpuMat(), d_keypoints, d_descriptors);
TEST_CYCLE()
TEST_CYCLE()
{
d_orb(d_img, cv::gpu::GpuMat(), d_keypoints, d_descriptors);
}
}
else
{
orb(img, cv::gpu::GpuMat(), keypoints, descriptors);
cv::ORB orb(4000);
std::vector<cv::KeyPoint> keypoints;
cv::Mat descriptors;
orb(img, cv::noArray(), keypoints, descriptors);
TEST_CYCLE()
{
keypoints.clear();
orb(img, cv::noArray(), keypoints, descriptors);
}
}
}
INSTANTIATE_TEST_CASE_P(Features2D, ORB, ALL_DEVICES);
//////////////////////////////////////////////////////////////////////
// BruteForceMatcher_match
// BFMatch
IMPLEMENT_PARAM_CLASS(DescriptorSize, int)
DEF_PARAM_TEST(DescSize_Norm, int, NormType);
GPU_PERF_TEST(BruteForceMatcher_match, cv::gpu::DeviceInfo, DescriptorSize, NormType)
PERF_TEST_P(DescSize_Norm, Features2D_BFMatch, Combine(Values(64, 128, 256), Values(NormType(cv::NORM_L1), NormType(cv::NORM_L2), NormType(cv::NORM_HAMMING))))
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
declare.time(20.0);
int desc_size = GET_PARAM(1);
int normType = GET_PARAM(2);
int desc_size = GET_PARAM(0);
int normType = GET_PARAM(1);
int type = normType == cv::NORM_HAMMING ? CV_8U : CV_32F;
cv::Mat query_host(3000, desc_size, type);
fill(query_host, 0.0, 10.0);
cv::Mat train_host(3000, desc_size, type);
fill(train_host, 0.0, 10.0);
cv::Mat query(3000, desc_size, type);
fillRandom(query);
cv::gpu::BFMatcher_GPU matcher(normType);
cv::Mat train(3000, desc_size, type);
fillRandom(train);
cv::gpu::GpuMat query(query_host);
cv::gpu::GpuMat train(train_host);
cv::gpu::GpuMat trainIdx, distance;
if (runOnGpu)
{
cv::gpu::BFMatcher_GPU d_matcher(normType);
matcher.matchSingle(query, train, trainIdx, distance);
cv::gpu::GpuMat d_query(query);
cv::gpu::GpuMat d_train(train);
cv::gpu::GpuMat d_trainIdx, d_distance;
declare.time(3.0);
d_matcher.matchSingle(d_query, d_train, d_trainIdx, d_distance);
TEST_CYCLE()
TEST_CYCLE()
{
d_matcher.matchSingle(d_query, d_train, d_trainIdx, d_distance);
}
}
else
{
matcher.matchSingle(query, train, trainIdx, distance);
cv::BFMatcher matcher(normType);
std::vector<cv::DMatch> matches;
matcher.match(query, train, matches);
TEST_CYCLE()
{
matcher.match(query, train, matches);
}
}
}
INSTANTIATE_TEST_CASE_P(Features2D, BruteForceMatcher_match, testing::Combine(
ALL_DEVICES,
testing::Values(DescriptorSize(64), DescriptorSize(128), DescriptorSize(256)),
testing::Values(NormType(cv::NORM_L1), NormType(cv::NORM_L2), NormType(cv::NORM_HAMMING))));
//////////////////////////////////////////////////////////////////////
// BruteForceMatcher_knnMatch
// BFKnnMatch
IMPLEMENT_PARAM_CLASS(K, int)
DEF_PARAM_TEST(DescSize_K_Norm, int, int, NormType);
GPU_PERF_TEST(BruteForceMatcher_knnMatch, cv::gpu::DeviceInfo, DescriptorSize, K, NormType)
PERF_TEST_P(DescSize_K_Norm, Features2D_BFKnnMatch, Combine(
Values(64, 128, 256),
Values(2, 3),
Values(NormType(cv::NORM_L1), NormType(cv::NORM_L2), NormType(cv::NORM_HAMMING))))
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
declare.time(30.0);
int desc_size = GET_PARAM(1);
int k = GET_PARAM(2);
int normType = GET_PARAM(3);
int desc_size = GET_PARAM(0);
int k = GET_PARAM(1);
int normType = GET_PARAM(2);
int type = normType == cv::NORM_HAMMING ? CV_8U : CV_32F;
cv::Mat query_host(3000, desc_size, type);
fill(query_host, 0.0, 10.0);
cv::Mat train_host(3000, desc_size, type);
fill(train_host, 0.0, 10.0);
cv::Mat query(3000, desc_size, type);
fillRandom(query);
cv::gpu::BFMatcher_GPU matcher(normType);
cv::Mat train(3000, desc_size, type);
fillRandom(train);
cv::gpu::GpuMat query(query_host);
cv::gpu::GpuMat train(train_host);
cv::gpu::GpuMat trainIdx, distance, allDist;
if (runOnGpu)
{
cv::gpu::BFMatcher_GPU d_matcher(normType);
matcher.knnMatchSingle(query, train, trainIdx, distance, allDist, k);
cv::gpu::GpuMat d_query(query);
cv::gpu::GpuMat d_train(train);
cv::gpu::GpuMat d_trainIdx, d_distance, d_allDist;
declare.time(3.0);
d_matcher.knnMatchSingle(d_query, d_train, d_trainIdx, d_distance, d_allDist, k);
TEST_CYCLE()
TEST_CYCLE()
{
d_matcher.knnMatchSingle(d_query, d_train, d_trainIdx, d_distance, d_allDist, k);
}
}
else
{
matcher.knnMatchSingle(query, train, trainIdx, distance, allDist, k);
cv::BFMatcher matcher(normType);
std::vector< std::vector<cv::DMatch> > matches;
matcher.knnMatch(query, train, matches, k);
TEST_CYCLE()
{
matcher.knnMatch(query, train, matches, k);
}
}
}
INSTANTIATE_TEST_CASE_P(Features2D, BruteForceMatcher_knnMatch, testing::Combine(
ALL_DEVICES,
testing::Values(DescriptorSize(64), DescriptorSize(128), DescriptorSize(256)),
testing::Values(K(2), K(3)),
testing::Values(NormType(cv::NORM_L1), NormType(cv::NORM_L2), NormType(cv::NORM_HAMMING))));
//////////////////////////////////////////////////////////////////////
// BruteForceMatcher_radiusMatch
// BFRadiusMatch
GPU_PERF_TEST(BruteForceMatcher_radiusMatch, cv::gpu::DeviceInfo, DescriptorSize, NormType)
PERF_TEST_P(DescSize_Norm, Features2D_BFRadiusMatch, Combine(Values(64, 128, 256), Values(NormType(cv::NORM_L1), NormType(cv::NORM_L2), NormType(cv::NORM_HAMMING))))
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
declare.time(30.0);
int desc_size = GET_PARAM(1);
int normType = GET_PARAM(2);
int desc_size = GET_PARAM(0);
int normType = GET_PARAM(1);
int type = normType == cv::NORM_HAMMING ? CV_8U : CV_32F;
cv::Mat query_host(3000, desc_size, type);
fill(query_host, 0.0, 1.0);
cv::Mat train_host(3000, desc_size, type);
fill(train_host, 0.0, 1.0);
cv::Mat query(3000, desc_size, type);
fillRandom(query, 0.0, 1.0);
cv::gpu::BFMatcher_GPU matcher(normType);
cv::Mat train(3000, desc_size, type);
fillRandom(train, 0.0, 1.0);
cv::gpu::GpuMat query(query_host);
cv::gpu::GpuMat train(train_host);
cv::gpu::GpuMat trainIdx, nMatches, distance;
if (runOnGpu)
{
cv::gpu::BFMatcher_GPU d_matcher(normType);
matcher.radiusMatchSingle(query, train, trainIdx, distance, nMatches, 2.0);
cv::gpu::GpuMat d_query(query);
cv::gpu::GpuMat d_train(train);
cv::gpu::GpuMat d_trainIdx, d_nMatches, d_distance;
declare.time(3.0);
d_matcher.radiusMatchSingle(d_query, d_train, d_trainIdx, d_distance, d_nMatches, 2.0);
TEST_CYCLE()
TEST_CYCLE()
{
d_matcher.radiusMatchSingle(d_query, d_train, d_trainIdx, d_distance, d_nMatches, 2.0);
}
}
else
{
matcher.radiusMatchSingle(query, train, trainIdx, distance, nMatches, 2.0);
cv::BFMatcher matcher(normType);
std::vector< std::vector<cv::DMatch> > matches;
matcher.radiusMatch(query, train, matches, 2.0);
TEST_CYCLE()
{
matcher.radiusMatch(query, train, matches, 2.0);
}
}
}
INSTANTIATE_TEST_CASE_P(Features2D, BruteForceMatcher_radiusMatch, testing::Combine(
ALL_DEVICES,
testing::Values(DescriptorSize(64), DescriptorSize(128), DescriptorSize(256)),
testing::Values(NormType(cv::NORM_L1), NormType(cv::NORM_L2), NormType(cv::NORM_HAMMING))));
#endif
} // namespace
modules/gpu/perf/perf_filters.cpp
浏览文件 @ 4fb15ae1
#include "perf_precomp.hpp"
#ifdef HAVE_CUDA
using namespace std;
using namespace testing;
namespace {
//////////////////////////////////////////////////////////////////////
// Blur
IMPLEMENT_PARAM_CLASS(KernelSize, int)
DEF_PARAM_TEST(Sz_Type_KernelSz, cv::Size, MatType, int);
GPU_PERF_TEST(Blur, cv::gpu::DeviceInfo, cv::Size, MatType, KernelSize)
PERF_TEST_P(Sz_Type_KernelSz, Filters_Blur, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8UC4), Values(3, 5, 7)))
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
declare.time(20.0);
cv::Size size = GET_PARAM(1);
int type = GET_PARAM(2);
int ksize = GET_PARAM(3);
cv::Size size = GET_PARAM(0);
int type = GET_PARAM(1);
int ksize = GET_PARAM(2);
cv::Mat src_host(size, type);
fill(src_host, 0.0, 255.0);
cv::Mat src(size, type);
fillRandom(src);
cv::gpu::GpuMat src(src_host);
cv::gpu::GpuMat dst;
if (runOnGpu)
{
cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat d_dst;
cv::gpu::blur(src, dst, cv::Size(ksize, ksize));
cv::gpu::blur(d_src, d_dst, cv::Size(ksize, ksize));
TEST_CYCLE()
TEST_CYCLE()
{
cv::gpu::blur(d_src, d_dst, cv::Size(ksize, ksize));
}
}
else
{
cv::gpu::blur(src, dst, cv::Size(ksize, ksize));
cv::Mat dst;
cv::blur(src, dst, cv::Size(ksize, ksize));
TEST_CYCLE()
{
cv::blur(src, dst, cv::Size(ksize, ksize));
}
}
}
INSTANTIATE_TEST_CASE_P(Filters, Blur, testing::Combine(
ALL_DEVICES,
GPU_TYPICAL_MAT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC4)),
testing::Values(KernelSize(3), KernelSize(5), KernelSize(7))));
//////////////////////////////////////////////////////////////////////
// Sobel
GPU_PERF_TEST(Sobel, cv::gpu::DeviceInfo, cv::Size, MatType, KernelSize)
PERF_TEST_P(Sz_Type_KernelSz, Filters_Sobel, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8UC4, CV_32FC1), Values(3, 5, 7, 9, 11, 13, 15)))
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
declare.time(20.0);
cv::Size size = GET_PARAM(1);
int type = GET_PARAM(2);
int ksize = GET_PARAM(3);
cv::Size size = GET_PARAM(0);
int type = GET_PARAM(1);
int ksize = GET_PARAM(2);
cv::Mat src_host(size, type);
fill(src_host, 0.0, 255.0);
cv::Mat src(size, type);
fillRandom(src);
cv::gpu::GpuMat src(src_host);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat buf;
if (runOnGpu)
{
cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat d_dst;
cv::gpu::GpuMat d_buf;
cv::gpu::Sobel(src, dst, -1, 1, 1, buf, ksize);
cv::gpu::Sobel(d_src, d_dst, -1, 1, 1, d_buf, ksize);
TEST_CYCLE()
TEST_CYCLE()
{
cv::gpu::Sobel(d_src, d_dst, -1, 1, 1, d_buf, ksize);
}
}
else
{
cv::gpu::Sobel(src, dst, -1, 1, 1, buf, ksize);
cv::Mat dst;
cv::Sobel(src, dst, -1, 1, 1, ksize);
TEST_CYCLE()
{
cv::Sobel(src, dst, -1, 1, 1, ksize);
}
}
}
INSTANTIATE_TEST_CASE_P(Filters, Sobel, testing::Combine(
ALL_DEVICES,
GPU_TYPICAL_MAT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC4), MatType(CV_32FC1)),
testing::Values(KernelSize(3), KernelSize(5), KernelSize(7), KernelSize(9), KernelSize(11), KernelSize(13), KernelSize(15))));
//////////////////////////////////////////////////////////////////////
// Scharr
GPU_PERF_TEST(Scharr, cv::gpu::DeviceInfo, cv::Size, MatType)
PERF_TEST_P(Sz_Type, Filters_Scharr, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8UC4, CV_32FC1)))
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
declare.time(20.0);
cv::Size size = GET_PARAM(1);
int type = GET_PARAM(2);
cv::Size size = GET_PARAM(0);
int type = GET_PARAM(1);
cv::Mat src_host(size, type);
fill(src_host, 0.0, 255.0);
cv::Mat src(size, type);
fillRandom(src);
cv::gpu::GpuMat src(src_host);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat buf;
if (runOnGpu)
{
cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat d_dst;
cv::gpu::GpuMat d_buf;
cv::gpu::Scharr(src, dst, -1, 1, 0, buf);
cv::gpu::Scharr(d_src, d_dst, -1, 1, 0, d_buf);
TEST_CYCLE()
TEST_CYCLE()
{
cv::gpu::Scharr(d_src, d_dst, -1, 1, 0, d_buf);
}
}
else
{
cv::gpu::Scharr(src, dst, -1, 1, 0, buf);
cv::Mat dst;
cv::Scharr(src, dst, -1, 1, 0);
TEST_CYCLE()
{
cv::Scharr(src, dst, -1, 1, 0);
}
}
}
INSTANTIATE_TEST_CASE_P(Filters, Scharr, testing::Combine(
ALL_DEVICES,
GPU_TYPICAL_MAT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC4), MatType(CV_32FC1))));
//////////////////////////////////////////////////////////////////////
// GaussianBlur
GPU_PERF_TEST(GaussianBlur, cv::gpu::DeviceInfo, cv::Size, MatType, KernelSize)
PERF_TEST_P(Sz_Type_KernelSz, Filters_GaussianBlur, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8UC4, CV_32FC1), Values(3, 5, 7, 9, 11, 13, 15)))
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
declare.time(20.0);
cv::Size size = GET_PARAM(1);
int type = GET_PARAM(2);
int ksize = GET_PARAM(3);
cv::Size size = GET_PARAM(0);
int type = GET_PARAM(1);
int ksize = GET_PARAM(2);
cv::Mat src_host(size, type);
fill(src_host, 0.0, 255.0);
cv::Mat src(size, type);
fillRandom(src);
cv::gpu::GpuMat src(src_host);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat buf;
if (runOnGpu)
{
cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat d_dst;
cv::gpu::GpuMat d_buf;
cv::gpu::GaussianBlur(src, dst, cv::Size(ksize, ksize), buf, 0.5);
cv::gpu::GaussianBlur(d_src, d_dst, cv::Size(ksize, ksize), d_buf, 0.5);
TEST_CYCLE()
TEST_CYCLE()
{
cv::gpu::GaussianBlur(d_src, d_dst, cv::Size(ksize, ksize), d_buf, 0.5);
}
}
else
{
cv::gpu::GaussianBlur(src, dst, cv::Size(ksize, ksize), buf, 0.5);
cv::Mat dst;
cv::GaussianBlur(src, dst, cv::Size(ksize, ksize), 0.5);
TEST_CYCLE()
{
cv::GaussianBlur(src, dst, cv::Size(ksize, ksize), 0.5);
}
}
}
INSTANTIATE_TEST_CASE_P(Filters, GaussianBlur, testing::Combine(
ALL_DEVICES,
GPU_TYPICAL_MAT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC4), MatType(CV_32FC1)),
testing::Values(KernelSize(3), KernelSize(5), KernelSize(7), KernelSize(9), KernelSize(11), KernelSize(13), KernelSize(15))));
//////////////////////////////////////////////////////////////////////
// Laplacian
GPU_PERF_TEST(Laplacian, cv::gpu::DeviceInfo, cv::Size, MatType, KernelSize)
PERF_TEST_P(Sz_Type_KernelSz, Filters_Laplacian, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8UC4, CV_32FC1, CV_32FC4), Values(1, 3)))
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
declare.time(20.0);
cv::Size size = GET_PARAM(1);
int type = GET_PARAM(2);
int ksize = GET_PARAM(3);
cv::Size size = GET_PARAM(0);
int type = GET_PARAM(1);
int ksize = GET_PARAM(2);
cv::Mat src_host(size, type);
fill(src_host, 0.0, 255.0);
cv::Mat src(size, type);
fillRandom(src);
cv::gpu::GpuMat src(src_host);
cv::gpu::GpuMat dst;
if (runOnGpu)
{
cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat d_dst;
cv::gpu::Laplacian(src, dst, -1, ksize);
cv::gpu::Laplacian(d_src, d_dst, -1, ksize);
TEST_CYCLE()
TEST_CYCLE()
{
cv::gpu::Laplacian(d_src, d_dst, -1, ksize);
}
}
else
{
cv::gpu::Laplacian(src, dst, -1, ksize);
cv::Mat dst;
cv::Laplacian(src, dst, -1, ksize);
TEST_CYCLE()
{
cv::Laplacian(src, dst, -1, ksize);
}
}
}
INSTANTIATE_TEST_CASE_P(Filters, Laplacian, testing::Combine(
ALL_DEVICES,
GPU_TYPICAL_MAT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC4), MatType(CV_32FC1), MatType(CV_32FC4)),
testing::Values(KernelSize(1), KernelSize(3))));
//////////////////////////////////////////////////////////////////////
// Erode
GPU_PERF_TEST(Erode, cv::gpu::DeviceInfo, cv::Size, MatType)
PERF_TEST_P(Sz_Type, Filters_Erode, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8UC4)))
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
declare.time(20.0);
cv::Size size = GET_PARAM(1);
int type = GET_PARAM(2);
cv::Size size = GET_PARAM(0);
int type = GET_PARAM(1);
cv::Mat src_host(size, type);
fill(src_host, 0.0, 255.0);
cv::Mat src(size, type);
fillRandom(src);
cv::Mat ker = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3));
cv::gpu::GpuMat src(src_host);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat buf;
if (runOnGpu)
{
cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat d_dst;
cv::gpu::GpuMat d_buf;
cv::gpu::erode(src, dst, ker, buf);
cv::gpu::erode(d_src, d_dst, ker, d_buf);
TEST_CYCLE()
TEST_CYCLE()
{
cv::gpu::erode(d_src, d_dst, ker, d_buf);
}
}
else
{
cv::gpu::erode(src, dst, ker, buf);
cv::Mat dst;
cv::erode(src, dst, ker);
TEST_CYCLE()
{
cv::erode(src, dst, ker);
}
}
}
INSTANTIATE_TEST_CASE_P(Filters, Erode, testing::Combine(
ALL_DEVICES,
GPU_TYPICAL_MAT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC4))));
//////////////////////////////////////////////////////////////////////
// Dilate
GPU_PERF_TEST(Dilate, cv::gpu::DeviceInfo, cv::Size, MatType)
PERF_TEST_P(Sz_Type, Filters_Dilate, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8UC4)))
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
declare.time(20.0);
cv::Size size = GET_PARAM(1);
int type = GET_PARAM(2);
cv::Size size = GET_PARAM(0);
int type = GET_PARAM(1);
cv::Mat src_host(size, type);
fill(src_host, 0.0, 255.0);
cv::Mat src(size, type);
fillRandom(src);
cv::Mat ker = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3));
cv::gpu::GpuMat src(src_host);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat buf;
if (runOnGpu)
{
cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat d_dst;
cv::gpu::GpuMat d_buf;
cv::gpu::dilate(src, dst, ker, buf);
cv::gpu::dilate(d_src, d_dst, ker, d_buf);
TEST_CYCLE()
TEST_CYCLE()
{
cv::gpu::dilate(d_src, d_dst, ker, d_buf);
}
}
else
{
cv::gpu::dilate(src, dst, ker, buf);
cv::Mat dst;
cv::dilate(src, dst, ker);
TEST_CYCLE()
{
cv::dilate(src, dst, ker);
}
}
}
INSTANTIATE_TEST_CASE_P(Filters, Dilate, testing::Combine(
ALL_DEVICES,
GPU_TYPICAL_MAT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC4))));
//////////////////////////////////////////////////////////////////////
// MorphologyEx
CV_ENUM(MorphOp, cv::MORPH_OPEN, cv::MORPH_CLOSE, cv::MORPH_GRADIENT, cv::MORPH_TOPHAT, cv::MORPH_BLACKHAT)
#define ALL_MORPH_OPS testing::Values(MorphOp(cv::MORPH_OPEN), MorphOp(cv::MORPH_CLOSE), MorphOp(cv::MORPH_GRADIENT), MorphOp(cv::MORPH_TOPHAT), MorphOp(cv::MORPH_BLACKHAT))
#define ALL_MORPH_OPS ValuesIn(MorphOp::all())
GPU_PERF_TEST(MorphologyEx, cv::gpu::DeviceInfo, cv::Size, MatType, MorphOp)
DEF_PARAM_TEST(Sz_Type_Op, cv::Size, MatType, MorphOp);
PERF_TEST_P(Sz_Type_Op, Filters_MorphologyEx, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8UC4), ALL_MORPH_OPS))
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
declare.time(20.0);
cv::Size size = GET_PARAM(1);
int type = GET_PARAM(2);
int morphOp = GET_PARAM(3);
cv::Size size = GET_PARAM(0);
int type = GET_PARAM(1);
int morphOp = GET_PARAM(2);
cv::Mat src_host(size, type);
fill(src_host, 0.0, 255.0);
cv::Mat src(size, type);
fillRandom(src);
cv::Mat ker = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3));
cv::gpu::GpuMat src(src_host);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat buf1;
cv::gpu::GpuMat buf2;
if (runOnGpu)
{
cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat d_dst;
cv::gpu::GpuMat d_buf1;
cv::gpu::GpuMat d_buf2;
cv::gpu::morphologyEx(src, dst, morphOp, ker, buf1, buf2);
cv::gpu::morphologyEx(d_src, d_dst, morphOp, ker, d_buf1, d_buf2);
TEST_CYCLE()
TEST_CYCLE()
{
cv::gpu::morphologyEx(d_src, d_dst, morphOp, ker, d_buf1, d_buf2);
}
}
else
{
cv::gpu::morphologyEx(src, dst, morphOp, ker, buf1, buf2);
cv::Mat dst;
cv::morphologyEx(src, dst, morphOp, ker);
TEST_CYCLE()
{
cv::morphologyEx(src, dst, morphOp, ker);
}
}
}
INSTANTIATE_TEST_CASE_P(Filters, MorphologyEx, testing::Combine(
ALL_DEVICES,
GPU_TYPICAL_MAT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC4)),
ALL_MORPH_OPS));
//////////////////////////////////////////////////////////////////////
// Filter2D
GPU_PERF_TEST(Filter2D, cv::gpu::DeviceInfo, cv::Size, MatType, KernelSize)
PERF_TEST_P(Sz_Type_KernelSz, Filters_Filter2D, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8UC4, CV_32FC1, CV_32FC4), Values(3, 5, 7, 9, 11, 13, 15)))
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
declare.time(20.0);
cv::Size size = GET_PARAM(1);
int type = GET_PARAM(2);
int ksize = GET_PARAM(3);
cv::Size size = GET_PARAM(0);
int type = GET_PARAM(1);
int ksize = GET_PARAM(2);
cv::Mat src_host(size, type);
fill(src_host, 0.0, 255.0);
cv::Mat src(size, type);
fillRandom(src);
cv::Mat kernel(ksize, ksize, CV_32FC1);
fill(kernel, 0.0, 1.0);
fillRandom(kernel, 0.0, 1.0);
cv::gpu::GpuMat src(src_host);
cv::gpu::GpuMat dst;
if (runOnGpu)
{
cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat d_dst;
cv::gpu::filter2D(src, dst, -1, kernel);
cv::gpu::filter2D(d_src, d_dst, -1, kernel);
TEST_CYCLE()
TEST_CYCLE()
{
cv::gpu::filter2D(d_src, d_dst, -1, kernel);
}
}
else
{
cv::gpu::filter2D(src, dst, -1, kernel);
cv::Mat dst;
cv::filter2D(src, dst, -1, kernel);
TEST_CYCLE()
{
cv::filter2D(src, dst, -1, kernel);
}
}
}
INSTANTIATE_TEST_CASE_P(Filters, Filter2D, testing::Combine(
ALL_DEVICES,
GPU_TYPICAL_MAT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC4), MatType(CV_32FC1), MatType(CV_32FC4)),
testing::Values(KernelSize(3), KernelSize(5), KernelSize(7), KernelSize(9), KernelSize(11), KernelSize(13), KernelSize(15))));
#endif
} // namespace
modules/gpu/perf/perf_imgproc.cpp
浏览文件 @ 4fb15ae1
此差异已折叠。
modules/gpu/perf/perf_labeling.cpp
浏览文件 @ 4fb15ae1
此差异已折叠。
modules/gpu/perf/perf_main.cpp 已删除 100644 → 0
浏览文件 @ d9185ec2
#include "perf_precomp.hpp"
#ifdef HAVE_CUDA
int main(int argc, char **argv)
{
testing::InitGoogleTest(&argc, argv);
perf::TestBase::Init(argc, argv);
return RUN_ALL_TESTS();
}
#else
int main()
{
printf("OpenCV was built without CUDA support\n");
return 0;
}
#endif
modules/gpu/perf/perf_matop.cpp
浏览文件 @ 4fb15ae1
此差异已折叠。
modules/gpu/perf/perf_objdetect.cpp
浏览文件 @ 4fb15ae1
此差异已折叠。
modules/gpu/perf/perf_precomp.hpp
浏览文件 @ 4fb15ae1
......@@ -11,6 +11,10 @@
#include "cvconfig.h"
#ifdef HAVE_CUDA
#include <cuda_runtime.h>
#endif
#include "opencv2/ts/ts.hpp"
#include "opencv2/ts/ts_perf.hpp"
......@@ -18,8 +22,12 @@
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/gpu/gpu.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/video/video.hpp"
#include "opencv2/nonfree/nonfree.hpp"
#include "opencv2/legacy/legacy.hpp"
#include "perf_utility.hpp"
#include "utility.hpp"
#ifdef GTEST_CREATE_SHARED_LIBRARY
#error no modules except ts should have GTEST_CREATE_SHARED_LIBRARY defined
......
modules/gpu/perf/perf_video.cpp
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modules/gpu/perf_cpu/perf_features2d.cpp 已删除 100644 → 0
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modules/gpu/perf_cpu/perf_imgproc.cpp 已删除 100644 → 0
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modules/gpu/perf_cpu/perf_labeling.cpp 已删除 100644 → 0
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modules/gpu/perf_cpu/perf_main.cpp 已删除 100644 → 0
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modules/gpu/perf_cpu/perf_matop.cpp 已删除 100644 → 0
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modules/gpu/perf_cpu/perf_objdetect.cpp 已删除 100644 → 0
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modules/gpu/perf_cpu/perf_utility.cpp 已删除 100644 → 0
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modules/gpu/perf_cpu/perf_utility.hpp 已删除 100644 → 0
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modules/gpu/perf_cpu/perf_video.cpp 已删除 100644 → 0
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modules/gpu/src/calib3d.cpp
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modules/gpu/src/cuda/ccomponetns.cu
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modules/gpu/src/cuda/hough.cu
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modules/gpu/src/cuda/integral_image.cu 0 → 100644
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modules/gpu/src/cuda/lbp.cu
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modules/gpu/src/hog.cpp
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modules/gpu/src/hough.cpp
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modules/gpu/src/imgproc.cpp
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modules/gpu/src/match_template.cpp
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modules/gpu/src/split_merge.cpp
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modules/gpu/src/video_decoder.cpp
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modules/gpu/test/main.cpp
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modules/gpu/test/main_test_nvidia.h
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modules/gpu/test/test_calib3d.cpp
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modules/gpu/test/test_color.cpp
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modules/gpu/test/test_core.cpp
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modules/gpu/test/test_filters.cpp
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modules/gpu/test/test_gpumat.cpp
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modules/gpu/test/test_imgproc.cpp
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modules/gpu/test/test_labeling.cpp
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modules/gpu/test/test_nvidia.cpp
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modules/gpu/test/test_objdetect.cpp
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modules/gpu/test/test_pyramids.cpp
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modules/gpu/test/test_remap.cpp
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modules/gpu/test/test_resize.cpp
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modules/gpu/test/test_threshold.cpp
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modules/gpu/test/test_video.cpp
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modules/gpu/test/utility.cpp
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modules/gpu/test/utility.hpp
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modules/highgui/CMakeLists.txt
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modules/highgui/include/opencv2/highgui/cap_ios.h 0 → 100644
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modules/highgui/src/cap_ios_abstract_camera.mm 0 → 100644
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modules/highgui/src/cap_ios_photo_camera.mm 0 → 100644
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modules/highgui/src/cap_ios_video_camera.mm 0 → 100644
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modules/imgproc/perf/perf_remap.cpp 0 → 100644
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modules/imgproc/src/imgwarp.cpp
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modules/imgproc/src/smooth.cpp
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modules/objdetect/src/hog.cpp
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modules/ocl/src/initialization.cpp
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modules/ocl/src/threadsafe.cpp
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modules/ocl/src/threadsafe.h
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modules/photo/doc/denoising.rst 0 → 100644
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modules/photo/doc/photo.rst
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modules/photo/include/opencv2/photo/denoising.hpp 0 → 100644
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modules/photo/src/arrays.hpp 0 → 100644
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modules/photo/src/denoising.cpp 0 → 100644
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modules/photo/src/fast_nlmeans_denoising_invoker.hpp 0 → 100644
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modules/photo/test/test_denoising.cpp 0 → 100644
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samples/c/facedetect.cpp
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samples/gpu/bgfg_segm.cpp
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samples/gpu/performance/tests.cpp
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