Fixed cmake and build issues when using Visual Studio 2015
This commit is contained in:
Maksim Shabunin
parent
4552ca98c4
commit
f49936a849
1
3rdparty/libtiff/CMakeLists.txt
vendored
1
3rdparty/libtiff/CMakeLists.txt
vendored
@ -93,6 +93,7 @@ ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4018 /wd4100 /wd4127 /wd4311 /wd4701 /wd
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ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4244) # vs2008
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ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4267 /wd4305 /wd4306) # vs2008 Win64
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ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4703) # vs2012
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ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4456 /wd4457 /wd4312) # vs2015
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ocv_warnings_disable(CMAKE_C_FLAGS /wd4267 /wd4244 /wd4018)
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1
3rdparty/openexr/CMakeLists.txt
vendored
1
3rdparty/openexr/CMakeLists.txt
vendored
@ -42,6 +42,7 @@ ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4018 /wd4099 /wd4100 /wd4101 /wd4127 /wd
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ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4334) # vs2005 Win64
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ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4244) # vs2008
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ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4267) # vs2008 Win64
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ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4456 /wd4457 /wd4312) # vs2015
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if(UNIX AND (CMAKE_COMPILER_IS_GNUCXX OR CV_ICC))
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC")
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@ -14,8 +14,6 @@ if(WIN32)
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link_directories(${CMAKE_CURRENT_BINARY_DIR})
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endif()
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link_libraries(${OPENCV_HAARTRAINING_DEPS} opencv_haartraining_engine)
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# -----------------------------------------------------------
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# Library
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# -----------------------------------------------------------
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@ -35,6 +33,7 @@ set(cvhaartraining_lib_src
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)
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add_library(opencv_haartraining_engine STATIC ${cvhaartraining_lib_src})
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target_link_libraries(opencv_haartraining_engine ${OPENCV_HAARTRAINING_DEPS})
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set_target_properties(opencv_haartraining_engine PROPERTIES
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DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
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ARCHIVE_OUTPUT_DIRECTORY ${LIBRARY_OUTPUT_PATH}
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@ -47,6 +46,7 @@ set_target_properties(opencv_haartraining_engine PROPERTIES
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# -----------------------------------------------------------
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add_executable(opencv_haartraining cvhaartraining.h haartraining.cpp)
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target_link_libraries(opencv_haartraining ${OPENCV_HAARTRAINING_DEPS} opencv_haartraining_engine)
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set_target_properties(opencv_haartraining PROPERTIES
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DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
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OUTPUT_NAME "opencv_haartraining")
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@ -56,6 +56,7 @@ set_target_properties(opencv_haartraining PROPERTIES
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# -----------------------------------------------------------
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add_executable(opencv_createsamples cvhaartraining.h createsamples.cpp)
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target_link_libraries(opencv_createsamples ${OPENCV_HAARTRAINING_DEPS} opencv_haartraining_engine)
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set_target_properties(opencv_createsamples PROPERTIES
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DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
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OUTPUT_NAME "opencv_createsamples")
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@ -64,6 +65,7 @@ set_target_properties(opencv_createsamples PROPERTIES
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# performance
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# -----------------------------------------------------------
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add_executable(opencv_performance performance.cpp)
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target_link_libraries(opencv_performance ${OPENCV_HAARTRAINING_DEPS} opencv_haartraining_engine)
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set_target_properties(opencv_performance PROPERTIES
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DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
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OUTPUT_NAME "opencv_performance")
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@ -320,5 +320,8 @@ if(MSVC)
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if(NOT ENABLE_NOISY_WARNINGS)
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /wd4251") #class 'std::XXX' needs to have dll-interface to be used by clients of YYY
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /wd4275") # non dll-interface class 'std::exception' used as base for dll-interface class 'cv::Exception'
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /wd4589") # Constructor of abstract class ... ignores initializer for virtual base class 'cv::Algorithm'
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /wd4359") # Alignment specifier is less than actual alignment (4), and will be ignored
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endif()
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endif()
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@ -100,15 +100,15 @@ TEST_F(fisheyeTest, projectPoints)
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TEST_F(fisheyeTest, undistortImage)
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{
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cv::Matx33d K = this->K;
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cv::Mat D = cv::Mat(this->D);
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cv::Matx33d theK = this->K;
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cv::Mat theD = cv::Mat(this->D);
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std::string file = combine(datasets_repository_path, "/calib-3_stereo_from_JY/left/stereo_pair_014.jpg");
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cv::Matx33d newK = K;
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cv::Matx33d newK = theK;
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cv::Mat distorted = cv::imread(file), undistorted;
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{
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newK(0, 0) = 100;
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newK(1, 1) = 100;
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cv::fisheye::undistortImage(distorted, undistorted, K, D, newK);
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cv::fisheye::undistortImage(distorted, undistorted, theK, theD, newK);
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cv::Mat correct = cv::imread(combine(datasets_repository_path, "new_f_100.png"));
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if (correct.empty())
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CV_Assert(cv::imwrite(combine(datasets_repository_path, "new_f_100.png"), undistorted));
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@ -117,8 +117,8 @@ TEST_F(fisheyeTest, undistortImage)
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}
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{
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double balance = 1.0;
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cv::fisheye::estimateNewCameraMatrixForUndistortRectify(K, D, distorted.size(), cv::noArray(), newK, balance);
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cv::fisheye::undistortImage(distorted, undistorted, K, D, newK);
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cv::fisheye::estimateNewCameraMatrixForUndistortRectify(theK, theD, distorted.size(), cv::noArray(), newK, balance);
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cv::fisheye::undistortImage(distorted, undistorted, theK, theD, newK);
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cv::Mat correct = cv::imread(combine(datasets_repository_path, "balance_1.0.png"));
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if (correct.empty())
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CV_Assert(cv::imwrite(combine(datasets_repository_path, "balance_1.0.png"), undistorted));
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@ -128,8 +128,8 @@ TEST_F(fisheyeTest, undistortImage)
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{
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double balance = 0.0;
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cv::fisheye::estimateNewCameraMatrixForUndistortRectify(K, D, distorted.size(), cv::noArray(), newK, balance);
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cv::fisheye::undistortImage(distorted, undistorted, K, D, newK);
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cv::fisheye::estimateNewCameraMatrixForUndistortRectify(theK, theD, distorted.size(), cv::noArray(), newK, balance);
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cv::fisheye::undistortImage(distorted, undistorted, theK, theD, newK);
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cv::Mat correct = cv::imread(combine(datasets_repository_path, "balance_0.0.png"));
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if (correct.empty())
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CV_Assert(cv::imwrite(combine(datasets_repository_path, "balance_0.0.png"), undistorted));
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@ -142,7 +142,7 @@ TEST_F(fisheyeTest, jacobians)
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{
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int n = 10;
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cv::Mat X(1, n, CV_64FC3);
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cv::Mat om(3, 1, CV_64F), T(3, 1, CV_64F);
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cv::Mat om(3, 1, CV_64F), theT(3, 1, CV_64F);
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cv::Mat f(2, 1, CV_64F), c(2, 1, CV_64F);
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cv::Mat k(4, 1, CV_64F);
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double alpha;
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@ -155,8 +155,8 @@ TEST_F(fisheyeTest, jacobians)
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r.fill(om, cv::RNG::NORMAL, 0, 1);
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om = cv::abs(om);
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r.fill(T, cv::RNG::NORMAL, 0, 1);
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T = cv::abs(T); T.at<double>(2) = 4; T *= 10;
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r.fill(theT, cv::RNG::NORMAL, 0, 1);
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theT = cv::abs(theT); theT.at<double>(2) = 4; theT *= 10;
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r.fill(f, cv::RNG::NORMAL, 0, 1);
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f = cv::abs(f) * 1000;
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@ -170,19 +170,19 @@ TEST_F(fisheyeTest, jacobians)
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alpha = 0.01*r.gaussian(1);
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cv::Mat x1, x2, xpred;
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cv::Matx33d K(f.at<double>(0), alpha * f.at<double>(0), c.at<double>(0),
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cv::Matx33d theK(f.at<double>(0), alpha * f.at<double>(0), c.at<double>(0),
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0, f.at<double>(1), c.at<double>(1),
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0, 0, 1);
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cv::Mat jacobians;
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cv::fisheye::projectPoints(X, x1, om, T, K, k, alpha, jacobians);
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cv::fisheye::projectPoints(X, x1, om, theT, theK, k, alpha, jacobians);
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//test on T:
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cv::Mat dT(3, 1, CV_64FC1);
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r.fill(dT, cv::RNG::NORMAL, 0, 1);
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dT *= 1e-9*cv::norm(T);
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cv::Mat T2 = T + dT;
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cv::fisheye::projectPoints(X, x2, om, T2, K, k, alpha, cv::noArray());
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dT *= 1e-9*cv::norm(theT);
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cv::Mat T2 = theT + dT;
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cv::fisheye::projectPoints(X, x2, om, T2, theK, k, alpha, cv::noArray());
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xpred = x1 + cv::Mat(jacobians.colRange(11,14) * dT).reshape(2, 1);
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CV_Assert (cv::norm(x2 - xpred) < 1e-10);
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@ -191,7 +191,7 @@ TEST_F(fisheyeTest, jacobians)
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r.fill(dom, cv::RNG::NORMAL, 0, 1);
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dom *= 1e-9*cv::norm(om);
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cv::Mat om2 = om + dom;
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cv::fisheye::projectPoints(X, x2, om2, T, K, k, alpha, cv::noArray());
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cv::fisheye::projectPoints(X, x2, om2, theT, theK, k, alpha, cv::noArray());
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xpred = x1 + cv::Mat(jacobians.colRange(8,11) * dom).reshape(2, 1);
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CV_Assert (cv::norm(x2 - xpred) < 1e-10);
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@ -199,8 +199,8 @@ TEST_F(fisheyeTest, jacobians)
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cv::Mat df(2, 1, CV_64FC1);
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r.fill(df, cv::RNG::NORMAL, 0, 1);
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df *= 1e-9*cv::norm(f);
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cv::Matx33d K2 = K + cv::Matx33d(df.at<double>(0), df.at<double>(0) * alpha, 0, 0, df.at<double>(1), 0, 0, 0, 0);
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cv::fisheye::projectPoints(X, x2, om, T, K2, k, alpha, cv::noArray());
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cv::Matx33d K2 = theK + cv::Matx33d(df.at<double>(0), df.at<double>(0) * alpha, 0, 0, df.at<double>(1), 0, 0, 0, 0);
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cv::fisheye::projectPoints(X, x2, om, theT, K2, k, alpha, cv::noArray());
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xpred = x1 + cv::Mat(jacobians.colRange(0,2) * df).reshape(2, 1);
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CV_Assert (cv::norm(x2 - xpred) < 1e-10);
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@ -208,8 +208,8 @@ TEST_F(fisheyeTest, jacobians)
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cv::Mat dc(2, 1, CV_64FC1);
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r.fill(dc, cv::RNG::NORMAL, 0, 1);
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dc *= 1e-9*cv::norm(c);
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K2 = K + cv::Matx33d(0, 0, dc.at<double>(0), 0, 0, dc.at<double>(1), 0, 0, 0);
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cv::fisheye::projectPoints(X, x2, om, T, K2, k, alpha, cv::noArray());
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K2 = theK + cv::Matx33d(0, 0, dc.at<double>(0), 0, 0, dc.at<double>(1), 0, 0, 0);
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cv::fisheye::projectPoints(X, x2, om, theT, K2, k, alpha, cv::noArray());
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xpred = x1 + cv::Mat(jacobians.colRange(2,4) * dc).reshape(2, 1);
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CV_Assert (cv::norm(x2 - xpred) < 1e-10);
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@ -218,7 +218,7 @@ TEST_F(fisheyeTest, jacobians)
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r.fill(dk, cv::RNG::NORMAL, 0, 1);
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dk *= 1e-9*cv::norm(k);
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cv::Mat k2 = k + dk;
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cv::fisheye::projectPoints(X, x2, om, T, K, k2, alpha, cv::noArray());
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cv::fisheye::projectPoints(X, x2, om, theT, theK, k2, alpha, cv::noArray());
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xpred = x1 + cv::Mat(jacobians.colRange(4,8) * dk).reshape(2, 1);
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CV_Assert (cv::norm(x2 - xpred) < 1e-10);
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@ -227,8 +227,8 @@ TEST_F(fisheyeTest, jacobians)
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r.fill(dalpha, cv::RNG::NORMAL, 0, 1);
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dalpha *= 1e-9*cv::norm(f);
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double alpha2 = alpha + dalpha.at<double>(0);
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K2 = K + cv::Matx33d(0, f.at<double>(0) * dalpha.at<double>(0), 0, 0, 0, 0, 0, 0, 0);
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cv::fisheye::projectPoints(X, x2, om, T, K, k, alpha2, cv::noArray());
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K2 = theK + cv::Matx33d(0, f.at<double>(0) * dalpha.at<double>(0), 0, 0, 0, 0, 0, 0, 0);
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cv::fisheye::projectPoints(X, x2, om, theT, theK, k, alpha2, cv::noArray());
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xpred = x1 + cv::Mat(jacobians.col(14) * dalpha).reshape(2, 1);
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CV_Assert (cv::norm(x2 - xpred) < 1e-10);
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}
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@ -258,14 +258,14 @@ TEST_F(fisheyeTest, Calibration)
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flag |= cv::fisheye::CALIB_CHECK_COND;
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flag |= cv::fisheye::CALIB_FIX_SKEW;
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cv::Matx33d K;
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cv::Vec4d D;
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cv::Matx33d theK;
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cv::Vec4d theD;
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cv::fisheye::calibrate(objectPoints, imagePoints, imageSize, K, D,
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cv::fisheye::calibrate(objectPoints, imagePoints, imageSize, theK, theD,
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cv::noArray(), cv::noArray(), flag, cv::TermCriteria(3, 20, 1e-6));
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EXPECT_MAT_NEAR(K, this->K, 1e-10);
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EXPECT_MAT_NEAR(D, this->D, 1e-10);
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EXPECT_MAT_NEAR(theK, this->K, 1e-10);
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EXPECT_MAT_NEAR(theD, this->D, 1e-10);
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}
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TEST_F(fisheyeTest, Homography)
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@ -302,15 +302,15 @@ TEST_F(fisheyeTest, Homography)
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int Np = imagePointsNormalized.cols;
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cv::calcCovarMatrix(_objectPoints, covObjectPoints, objectPointsMean, CV_COVAR_NORMAL | CV_COVAR_COLS);
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cv::SVD svd(covObjectPoints);
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cv::Mat R(svd.vt);
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cv::Mat theR(svd.vt);
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if (cv::norm(R(cv::Rect(2, 0, 1, 2))) < 1e-6)
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R = cv::Mat::eye(3,3, CV_64FC1);
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if (cv::determinant(R) < 0)
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R = -R;
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if (cv::norm(theR(cv::Rect(2, 0, 1, 2))) < 1e-6)
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theR = cv::Mat::eye(3,3, CV_64FC1);
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if (cv::determinant(theR) < 0)
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theR = -theR;
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cv::Mat T = -R * objectPointsMean;
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cv::Mat X_new = R * _objectPoints + T * cv::Mat::ones(1, Np, CV_64FC1);
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cv::Mat theT = -theR * objectPointsMean;
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cv::Mat X_new = theR * _objectPoints + theT * cv::Mat::ones(1, Np, CV_64FC1);
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cv::Mat H = cv::internal::ComputeHomography(imagePointsNormalized, X_new.rowRange(0, 2));
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cv::Mat M = cv::Mat::ones(3, X_new.cols, CV_64FC1);
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@ -354,19 +354,19 @@ TEST_F(fisheyeTest, EtimateUncertainties)
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flag |= cv::fisheye::CALIB_CHECK_COND;
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flag |= cv::fisheye::CALIB_FIX_SKEW;
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cv::Matx33d K;
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cv::Vec4d D;
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cv::Matx33d theK;
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cv::Vec4d theD;
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std::vector<cv::Vec3d> rvec;
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std::vector<cv::Vec3d> tvec;
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cv::fisheye::calibrate(objectPoints, imagePoints, imageSize, K, D,
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cv::fisheye::calibrate(objectPoints, imagePoints, imageSize, theK, theD,
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rvec, tvec, flag, cv::TermCriteria(3, 20, 1e-6));
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cv::internal::IntrinsicParams param, errors;
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cv::Vec2d err_std;
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double thresh_cond = 1e6;
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int check_cond = 1;
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param.Init(cv::Vec2d(K(0,0), K(1,1)), cv::Vec2d(K(0,2), K(1, 2)), D);
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param.Init(cv::Vec2d(theK(0,0), theK(1,1)), cv::Vec2d(theK(0,2), theK(1, 2)), theD);
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param.isEstimate = std::vector<int>(9, 1);
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param.isEstimate[4] = 0;
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@ -397,12 +397,12 @@ TEST_F(fisheyeTest, rectify)
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cv::Matx33d K1 = this->K, K2 = K1;
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cv::Mat D1 = cv::Mat(this->D), D2 = D1;
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cv::Vec3d T = this->T;
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cv::Matx33d R = this->R;
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cv::Vec3d theT = this->T;
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cv::Matx33d theR = this->R;
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double balance = 0.0, fov_scale = 1.1;
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cv::Mat R1, R2, P1, P2, Q;
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cv::fisheye::stereoRectify(K1, D1, K2, D2, calibration_size, R, T, R1, R2, P1, P2, Q,
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cv::fisheye::stereoRectify(K1, D1, K2, D2, calibration_size, theR, theT, R1, R2, P1, P2, Q,
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cv::CALIB_ZERO_DISPARITY, requested_size, balance, fov_scale);
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cv::Mat lmapx, lmapy, rmapx, rmapy;
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@ -466,8 +466,8 @@ TEST_F(fisheyeTest, stereoCalibrate)
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fs_object[cv::format("image_%d", i )] >> objectPoints[i];
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fs_object.release();
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cv::Matx33d K1, K2, R;
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cv::Vec3d T;
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cv::Matx33d K1, K2, theR;
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cv::Vec3d theT;
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cv::Vec4d D1, D2;
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int flag = 0;
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@ -477,7 +477,7 @@ TEST_F(fisheyeTest, stereoCalibrate)
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// flag |= cv::fisheye::CALIB_FIX_INTRINSIC;
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cv::fisheye::stereoCalibrate(objectPoints, leftPoints, rightPoints,
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K1, D1, K2, D2, imageSize, R, T, flag,
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K1, D1, K2, D2, imageSize, theR, theT, flag,
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cv::TermCriteria(3, 12, 0));
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|
||||
cv::Matx33d R_correct( 0.9975587205950972, 0.06953016383322372, 0.006492709911733523,
|
||||
@ -495,8 +495,8 @@ TEST_F(fisheyeTest, stereoCalibrate)
|
||||
cv::Vec4d D1_correct (-7.44253716539556e-05, -0.00702662033932424, 0.00737569823650885, -0.00342230256441771);
|
||||
cv::Vec4d D2_correct (-0.0130785435677431, 0.0284434505383497, -0.0360333869900506, 0.0144724062347222);
|
||||
|
||||
EXPECT_MAT_NEAR(R, R_correct, 1e-10);
|
||||
EXPECT_MAT_NEAR(T, T_correct, 1e-10);
|
||||
EXPECT_MAT_NEAR(theR, R_correct, 1e-10);
|
||||
EXPECT_MAT_NEAR(theT, T_correct, 1e-10);
|
||||
|
||||
EXPECT_MAT_NEAR(K1, K1_correct, 1e-10);
|
||||
EXPECT_MAT_NEAR(K2, K2_correct, 1e-10);
|
||||
@ -534,8 +534,8 @@ TEST_F(fisheyeTest, stereoCalibrateFixIntrinsic)
|
||||
fs_object[cv::format("image_%d", i )] >> objectPoints[i];
|
||||
fs_object.release();
|
||||
|
||||
cv::Matx33d R;
|
||||
cv::Vec3d T;
|
||||
cv::Matx33d theR;
|
||||
cv::Vec3d theT;
|
||||
|
||||
int flag = 0;
|
||||
flag |= cv::fisheye::CALIB_RECOMPUTE_EXTRINSIC;
|
||||
@ -555,7 +555,7 @@ TEST_F(fisheyeTest, stereoCalibrateFixIntrinsic)
|
||||
cv::Vec4d D2 (-0.0130785435677431, 0.0284434505383497, -0.0360333869900506, 0.0144724062347222);
|
||||
|
||||
cv::fisheye::stereoCalibrate(objectPoints, leftPoints, rightPoints,
|
||||
K1, D1, K2, D2, imageSize, R, T, flag,
|
||||
K1, D1, K2, D2, imageSize, theR, theT, flag,
|
||||
cv::TermCriteria(3, 12, 0));
|
||||
|
||||
cv::Matx33d R_correct( 0.9975587205950972, 0.06953016383322372, 0.006492709911733523,
|
||||
@ -564,8 +564,8 @@ TEST_F(fisheyeTest, stereoCalibrateFixIntrinsic)
|
||||
cv::Vec3d T_correct(-0.099402724724121, 0.00270812139265413, 0.00129330292472699);
|
||||
|
||||
|
||||
EXPECT_MAT_NEAR(R, R_correct, 1e-10);
|
||||
EXPECT_MAT_NEAR(T, T_correct, 1e-10);
|
||||
EXPECT_MAT_NEAR(theR, R_correct, 1e-10);
|
||||
EXPECT_MAT_NEAR(theT, T_correct, 1e-10);
|
||||
}
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
@ -189,7 +189,7 @@ namespace colormap
|
||||
|
||||
void init(int n) {
|
||||
float r[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
|
||||
float g[] = { 0, 0.01587301587301587, 0.03174603174603174, 0.04761904761904762, 0.06349206349206349, 0.07936507936507936, 0.09523809523809523, 0.1111111111111111, 0.126984126984127, 0.1428571428571428, 0.1587301587301587, 0.1746031746031746, 0.1904761904761905, 0.2063492063492063, 0.2222222222222222, 0.2380952380952381, 0.253968253968254, 0.2698412698412698, 0.2857142857142857, 0.3015873015873016, 0.3174603174603174, 0.3333333333333333, 0.3492063492063492, 0.3650793650793651, 0.3809523809523809, 0.3968253968253968, 0.4126984126984127, 0.4285714285714285, 0.4444444444444444, 0.4603174603174603, 0.4761904761904762, 0.492063492063492, 0.5079365079365079, 0.5238095238095238, 0.5396825396825397, 0.5555555555555556, 0.5714285714285714, 0.5873015873015873, 0.6031746031746031, 0.6190476190476191, 0.6349206349206349, 0.6507936507936508, 0.6666666666666666, 0.6825396825396826, 0.6984126984126984, 0.7142857142857143, 0.7301587301587301, 0.746031746031746, 0.7619047619047619, 0.7777777777777778, 0.7936507936507936, 0.8095238095238095, 0.8253968253968254, 0.8412698412698413, 0.8571428571428571, 0.873015873015873, 0.8888888888888888, 0.9047619047619048, 0.9206349206349206, 0.9365079365079365, 0.9523809523809523, 0.9682539682539683, 0.9841269841269841, 1};
|
||||
float g[] = { 0, 0.01587301587301587f, 0.03174603174603174f, 0.04761904761904762f, 0.06349206349206349f, 0.07936507936507936f, 0.09523809523809523f, 0.1111111111111111f, 0.126984126984127f, 0.1428571428571428f, 0.1587301587301587f, 0.1746031746031746f, 0.1904761904761905f, 0.2063492063492063f, 0.2222222222222222f, 0.2380952380952381f, 0.253968253968254f, 0.2698412698412698f, 0.2857142857142857f, 0.3015873015873016f, 0.3174603174603174f, 0.3333333333333333f, 0.3492063492063492f, 0.3650793650793651f, 0.3809523809523809f, 0.3968253968253968f, 0.4126984126984127f, 0.4285714285714285f, 0.4444444444444444f, 0.4603174603174603f, 0.4761904761904762f, 0.492063492063492f, 0.5079365079365079f, 0.5238095238095238f, 0.5396825396825397f, 0.5555555555555556f, 0.5714285714285714f, 0.5873015873015873f, 0.6031746031746031f, 0.6190476190476191f, 0.6349206349206349f, 0.6507936507936508f, 0.6666666666666666f, 0.6825396825396826f, 0.6984126984126984f, 0.7142857142857143f, 0.7301587301587301f, 0.746031746031746f, 0.7619047619047619f, 0.7777777777777778f, 0.7936507936507936f, 0.8095238095238095f, 0.8253968253968254f, 0.8412698412698413f, 0.8571428571428571f, 0.873015873015873f, 0.8888888888888888f, 0.9047619047619048f, 0.9206349206349206f, 0.9365079365079365f, 0.9523809523809523f, 0.9682539682539683f, 0.9841269841269841f, 1};
|
||||
float b[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
|
||||
Mat X = linspace(0,1,64);
|
||||
this->_lut = ColorMap::linear_colormap(X,
|
||||
@ -212,9 +212,9 @@ namespace colormap
|
||||
}
|
||||
|
||||
void init(int n) {
|
||||
float r[] = { 0, 0.01388888888888889, 0.02777777777777778, 0.04166666666666666, 0.05555555555555555, 0.06944444444444445, 0.08333333333333333, 0.09722222222222221, 0.1111111111111111, 0.125, 0.1388888888888889, 0.1527777777777778, 0.1666666666666667, 0.1805555555555556, 0.1944444444444444, 0.2083333333333333, 0.2222222222222222, 0.2361111111111111, 0.25, 0.2638888888888889, 0.2777777777777778, 0.2916666666666666, 0.3055555555555555, 0.3194444444444444, 0.3333333333333333, 0.3472222222222222, 0.3611111111111111, 0.375, 0.3888888888888888, 0.4027777777777777, 0.4166666666666666, 0.4305555555555555, 0.4444444444444444, 0.4583333333333333, 0.4722222222222222, 0.4861111111111112, 0.5, 0.5138888888888888, 0.5277777777777778, 0.5416666666666667, 0.5555555555555556, 0.5694444444444444, 0.5833333333333333, 0.5972222222222222, 0.611111111111111, 0.6249999999999999, 0.6388888888888888, 0.6527777777777778, 0.6726190476190474, 0.6944444444444442, 0.7162698412698412, 0.7380952380952381, 0.7599206349206349, 0.7817460317460316, 0.8035714285714286, 0.8253968253968254, 0.8472222222222221, 0.8690476190476188, 0.8908730158730158, 0.9126984126984128, 0.9345238095238095, 0.9563492063492063, 0.978174603174603, 1};
|
||||
float g[] = { 0, 0.01388888888888889, 0.02777777777777778, 0.04166666666666666, 0.05555555555555555, 0.06944444444444445, 0.08333333333333333, 0.09722222222222221, 0.1111111111111111, 0.125, 0.1388888888888889, 0.1527777777777778, 0.1666666666666667, 0.1805555555555556, 0.1944444444444444, 0.2083333333333333, 0.2222222222222222, 0.2361111111111111, 0.25, 0.2638888888888889, 0.2777777777777778, 0.2916666666666666, 0.3055555555555555, 0.3194444444444444, 0.3353174603174602, 0.3544973544973544, 0.3736772486772486, 0.3928571428571428, 0.412037037037037, 0.4312169312169312, 0.4503968253968254, 0.4695767195767195, 0.4887566137566137, 0.5079365079365078, 0.5271164021164021, 0.5462962962962963, 0.5654761904761904, 0.5846560846560845, 0.6038359788359787, 0.623015873015873, 0.6421957671957671, 0.6613756613756612, 0.6805555555555555, 0.6997354497354497, 0.7189153439153438, 0.7380952380952379, 0.7572751322751322, 0.7764550264550264, 0.7916666666666666, 0.8055555555555555, 0.8194444444444444, 0.8333333333333334, 0.8472222222222222, 0.861111111111111, 0.875, 0.8888888888888888, 0.9027777777777777, 0.9166666666666665, 0.9305555555555555, 0.9444444444444444, 0.9583333333333333, 0.9722222222222221, 0.986111111111111, 1};
|
||||
float b[] = { 0, 0.01917989417989418, 0.03835978835978836, 0.05753968253968253, 0.07671957671957672, 0.09589947089947089, 0.1150793650793651, 0.1342592592592592, 0.1534391534391534, 0.1726190476190476, 0.1917989417989418, 0.210978835978836, 0.2301587301587301, 0.2493386243386243, 0.2685185185185185, 0.2876984126984127, 0.3068783068783069, 0.326058201058201, 0.3452380952380952, 0.3644179894179894, 0.3835978835978835, 0.4027777777777777, 0.4219576719576719, 0.4411375661375661, 0.4583333333333333, 0.4722222222222222, 0.4861111111111111, 0.5, 0.5138888888888888, 0.5277777777777777, 0.5416666666666666, 0.5555555555555556, 0.5694444444444444, 0.5833333333333333, 0.5972222222222222, 0.6111111111111112, 0.625, 0.6388888888888888, 0.6527777777777778, 0.6666666666666667, 0.6805555555555556, 0.6944444444444444, 0.7083333333333333, 0.7222222222222222, 0.736111111111111, 0.7499999999999999, 0.7638888888888888, 0.7777777777777778, 0.7916666666666666, 0.8055555555555555, 0.8194444444444444, 0.8333333333333334, 0.8472222222222222, 0.861111111111111, 0.875, 0.8888888888888888, 0.9027777777777777, 0.9166666666666665, 0.9305555555555555, 0.9444444444444444, 0.9583333333333333, 0.9722222222222221, 0.986111111111111, 1};
|
||||
float r[] = { 0, 0.01388888888888889f, 0.02777777777777778f, 0.04166666666666666f, 0.05555555555555555f, 0.06944444444444445f, 0.08333333333333333f, 0.09722222222222221f, 0.1111111111111111f, 0.125f, 0.1388888888888889f, 0.1527777777777778f, 0.1666666666666667f, 0.1805555555555556f, 0.1944444444444444f, 0.2083333333333333f, 0.2222222222222222f, 0.2361111111111111f, 0.25f, 0.2638888888888889f, 0.2777777777777778f, 0.2916666666666666f, 0.3055555555555555f, 0.3194444444444444f, 0.3333333333333333f, 0.3472222222222222f, 0.3611111111111111f, 0.375f, 0.3888888888888888f, 0.4027777777777777f, 0.4166666666666666f, 0.4305555555555555f, 0.4444444444444444f, 0.4583333333333333f, 0.4722222222222222f, 0.4861111111111112f, 0.5f, 0.5138888888888888f, 0.5277777777777778f, 0.5416666666666667f, 0.5555555555555556f, 0.5694444444444444f, 0.5833333333333333f, 0.5972222222222222f, 0.611111111111111f, 0.6249999999999999f, 0.6388888888888888f, 0.6527777777777778f, 0.6726190476190474f, 0.6944444444444442f, 0.7162698412698412f, 0.7380952380952381f, 0.7599206349206349f, 0.7817460317460316f, 0.8035714285714286f, 0.8253968253968254f, 0.8472222222222221f, 0.8690476190476188f, 0.8908730158730158f, 0.9126984126984128f, 0.9345238095238095f, 0.9563492063492063f, 0.978174603174603f, 1};
|
||||
float g[] = { 0, 0.01388888888888889f, 0.02777777777777778f, 0.04166666666666666f, 0.05555555555555555f, 0.06944444444444445f, 0.08333333333333333f, 0.09722222222222221f, 0.1111111111111111f, 0.125f, 0.1388888888888889f, 0.1527777777777778f, 0.1666666666666667f, 0.1805555555555556f, 0.1944444444444444f, 0.2083333333333333f, 0.2222222222222222f, 0.2361111111111111f, 0.25f, 0.2638888888888889f, 0.2777777777777778f, 0.2916666666666666f, 0.3055555555555555f, 0.3194444444444444f, 0.3353174603174602f, 0.3544973544973544f, 0.3736772486772486f, 0.3928571428571428f, 0.412037037037037f, 0.4312169312169312f, 0.4503968253968254f, 0.4695767195767195f, 0.4887566137566137f, 0.5079365079365078f, 0.5271164021164021f, 0.5462962962962963f, 0.5654761904761904f, 0.5846560846560845f, 0.6038359788359787f, 0.623015873015873f, 0.6421957671957671f, 0.6613756613756612f, 0.6805555555555555f, 0.6997354497354497f, 0.7189153439153438f, 0.7380952380952379f, 0.7572751322751322f, 0.7764550264550264f, 0.7916666666666666f, 0.8055555555555555f, 0.8194444444444444f, 0.8333333333333334f, 0.8472222222222222f, 0.861111111111111f, 0.875f, 0.8888888888888888f, 0.9027777777777777f, 0.9166666666666665f, 0.9305555555555555f, 0.9444444444444444f, 0.9583333333333333f, 0.9722222222222221f, 0.986111111111111f, 1};
|
||||
float b[] = { 0, 0.01917989417989418f, 0.03835978835978836f, 0.05753968253968253f, 0.07671957671957672f, 0.09589947089947089f, 0.1150793650793651f, 0.1342592592592592f, 0.1534391534391534f, 0.1726190476190476f, 0.1917989417989418f, 0.210978835978836f, 0.2301587301587301f, 0.2493386243386243f, 0.2685185185185185f, 0.2876984126984127f, 0.3068783068783069f, 0.326058201058201f, 0.3452380952380952f, 0.3644179894179894f, 0.3835978835978835f, 0.4027777777777777f, 0.4219576719576719f, 0.4411375661375661f, 0.4583333333333333f, 0.4722222222222222f, 0.4861111111111111f, 0.5f, 0.5138888888888888f, 0.5277777777777777f, 0.5416666666666666f, 0.5555555555555556f, 0.5694444444444444f, 0.5833333333333333f, 0.5972222222222222f, 0.6111111111111112f, 0.625f, 0.6388888888888888f, 0.6527777777777778f, 0.6666666666666667f, 0.6805555555555556f, 0.6944444444444444f, 0.7083333333333333f, 0.7222222222222222f, 0.736111111111111f, 0.7499999999999999f, 0.7638888888888888f, 0.7777777777777778f, 0.7916666666666666f, 0.8055555555555555f, 0.8194444444444444f, 0.8333333333333334f, 0.8472222222222222f, 0.861111111111111f, 0.875f, 0.8888888888888888f, 0.9027777777777777f, 0.9166666666666665f, 0.9305555555555555f, 0.9444444444444444f, 0.9583333333333333f, 0.9722222222222221f, 0.986111111111111f, 1};
|
||||
Mat X = linspace(0,1,64);
|
||||
this->_lut = ColorMap::linear_colormap(X,
|
||||
Mat(64,1, CV_32FC1, r).clone(), // red
|
||||
@ -242,9 +242,9 @@ namespace colormap
|
||||
// breakpoints
|
||||
Mat X = linspace(0,1,256);
|
||||
// define the basemap
|
||||
float r[] = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00588235294117645,0.02156862745098032,0.03725490196078418,0.05294117647058827,0.06862745098039214,0.084313725490196,0.1000000000000001,0.115686274509804,0.1313725490196078,0.1470588235294117,0.1627450980392156,0.1784313725490196,0.1941176470588235,0.2098039215686274,0.2254901960784315,0.2411764705882353,0.2568627450980392,0.2725490196078431,0.2882352941176469,0.303921568627451,0.3196078431372549,0.3352941176470587,0.3509803921568628,0.3666666666666667,0.3823529411764706,0.3980392156862744,0.4137254901960783,0.4294117647058824,0.4450980392156862,0.4607843137254901,0.4764705882352942,0.4921568627450981,0.5078431372549019,0.5235294117647058,0.5392156862745097,0.5549019607843135,0.5705882352941174,0.5862745098039217,0.6019607843137256,0.6176470588235294,0.6333333333333333,0.6490196078431372,0.664705882352941,0.6803921568627449,0.6960784313725492,0.7117647058823531,0.7274509803921569,0.7431372549019608,0.7588235294117647,0.7745098039215685,0.7901960784313724,0.8058823529411763,0.8215686274509801,0.8372549019607844,0.8529411764705883,0.8686274509803922,0.884313725490196,0.8999999999999999,0.9156862745098038,0.9313725490196076,0.947058823529412,0.9627450980392158,0.9784313725490197,0.9941176470588236,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.9862745098039216,0.9705882352941178,0.9549019607843139,0.93921568627451,0.9235294117647062,0.9078431372549018,0.892156862745098,0.8764705882352941,0.8607843137254902,0.8450980392156864,0.8294117647058825,0.8137254901960786,0.7980392156862743,0.7823529411764705,0.7666666666666666,0.7509803921568627,0.7352941176470589,0.719607843137255,0.7039215686274511,0.6882352941176473,0.6725490196078434,0.6568627450980391,0.6411764705882352,0.6254901960784314,0.6098039215686275,0.5941176470588236,0.5784313725490198,0.5627450980392159,0.5470588235294116,0.5313725490196077,0.5156862745098039,0.5};
|
||||
float g[] = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.001960784313725483,0.01764705882352935,0.03333333333333333,0.0490196078431373,0.06470588235294117,0.08039215686274503,0.09607843137254901,0.111764705882353,0.1274509803921569,0.1431372549019607,0.1588235294117647,0.1745098039215687,0.1901960784313725,0.2058823529411764,0.2215686274509804,0.2372549019607844,0.2529411764705882,0.2686274509803921,0.2843137254901961,0.3,0.3156862745098039,0.3313725490196078,0.3470588235294118,0.3627450980392157,0.3784313725490196,0.3941176470588235,0.4098039215686274,0.4254901960784314,0.4411764705882353,0.4568627450980391,0.4725490196078431,0.4882352941176471,0.503921568627451,0.5196078431372548,0.5352941176470587,0.5509803921568628,0.5666666666666667,0.5823529411764705,0.5980392156862746,0.6137254901960785,0.6294117647058823,0.6450980392156862,0.6607843137254901,0.6764705882352942,0.692156862745098,0.7078431372549019,0.723529411764706,0.7392156862745098,0.7549019607843137,0.7705882352941176,0.7862745098039214,0.8019607843137255,0.8176470588235294,0.8333333333333333,0.8490196078431373,0.8647058823529412,0.8803921568627451,0.8960784313725489,0.9117647058823528,0.9274509803921569,0.9431372549019608,0.9588235294117646,0.9745098039215687,0.9901960784313726,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.9901960784313726,0.9745098039215687,0.9588235294117649,0.943137254901961,0.9274509803921571,0.9117647058823528,0.8960784313725489,0.8803921568627451,0.8647058823529412,0.8490196078431373,0.8333333333333335,0.8176470588235296,0.8019607843137253,0.7862745098039214,0.7705882352941176,0.7549019607843137,0.7392156862745098,0.723529411764706,0.7078431372549021,0.6921568627450982,0.6764705882352944,0.6607843137254901,0.6450980392156862,0.6294117647058823,0.6137254901960785,0.5980392156862746,0.5823529411764707,0.5666666666666669,0.5509803921568626,0.5352941176470587,0.5196078431372548,0.503921568627451,0.4882352941176471,0.4725490196078432,0.4568627450980394,0.4411764705882355,0.4254901960784316,0.4098039215686273,0.3941176470588235,0.3784313725490196,0.3627450980392157,0.3470588235294119,0.331372549019608,0.3156862745098041,0.2999999999999998,0.284313725490196,0.2686274509803921,0.2529411764705882,0.2372549019607844,0.2215686274509805,0.2058823529411766,0.1901960784313728,0.1745098039215689,0.1588235294117646,0.1431372549019607,0.1274509803921569,0.111764705882353,0.09607843137254912,0.08039215686274526,0.06470588235294139,0.04901960784313708,0.03333333333333321,0.01764705882352935,0.001960784313725483,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};
|
||||
float b[] = {0.5,0.5156862745098039,0.5313725490196078,0.5470588235294118,0.5627450980392157,0.5784313725490196,0.5941176470588235,0.6098039215686275,0.6254901960784314,0.6411764705882352,0.6568627450980392,0.6725490196078432,0.6882352941176471,0.7039215686274509,0.7196078431372549,0.7352941176470589,0.7509803921568627,0.7666666666666666,0.7823529411764706,0.7980392156862746,0.8137254901960784,0.8294117647058823,0.8450980392156863,0.8607843137254902,0.8764705882352941,0.892156862745098,0.907843137254902,0.9235294117647059,0.9392156862745098,0.9549019607843137,0.9705882352941176,0.9862745098039216,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.9941176470588236,0.9784313725490197,0.9627450980392158,0.9470588235294117,0.9313725490196079,0.915686274509804,0.8999999999999999,0.884313725490196,0.8686274509803922,0.8529411764705883,0.8372549019607844,0.8215686274509804,0.8058823529411765,0.7901960784313726,0.7745098039215685,0.7588235294117647,0.7431372549019608,0.7274509803921569,0.7117647058823531,0.696078431372549,0.6803921568627451,0.6647058823529413,0.6490196078431372,0.6333333333333333,0.6176470588235294,0.6019607843137256,0.5862745098039217,0.5705882352941176,0.5549019607843138,0.5392156862745099,0.5235294117647058,0.5078431372549019,0.4921568627450981,0.4764705882352942,0.4607843137254903,0.4450980392156865,0.4294117647058826,0.4137254901960783,0.3980392156862744,0.3823529411764706,0.3666666666666667,0.3509803921568628,0.335294117647059,0.3196078431372551,0.3039215686274508,0.2882352941176469,0.2725490196078431,0.2568627450980392,0.2411764705882353,0.2254901960784315,0.2098039215686276,0.1941176470588237,0.1784313725490199,0.1627450980392156,0.1470588235294117,0.1313725490196078,0.115686274509804,0.1000000000000001,0.08431372549019622,0.06862745098039236,0.05294117647058805,0.03725490196078418,0.02156862745098032,0.00588235294117645,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};
|
||||
float r[] = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00588235294117645f,0.02156862745098032f,0.03725490196078418f,0.05294117647058827f,0.06862745098039214f,0.084313725490196f,0.1000000000000001f,0.115686274509804f,0.1313725490196078f,0.1470588235294117f,0.1627450980392156f,0.1784313725490196f,0.1941176470588235f,0.2098039215686274f,0.2254901960784315f,0.2411764705882353f,0.2568627450980392f,0.2725490196078431f,0.2882352941176469f,0.303921568627451f,0.3196078431372549f,0.3352941176470587f,0.3509803921568628f,0.3666666666666667f,0.3823529411764706f,0.3980392156862744f,0.4137254901960783f,0.4294117647058824f,0.4450980392156862f,0.4607843137254901f,0.4764705882352942f,0.4921568627450981f,0.5078431372549019f,0.5235294117647058f,0.5392156862745097f,0.5549019607843135f,0.5705882352941174f,0.5862745098039217f,0.6019607843137256f,0.6176470588235294f,0.6333333333333333f,0.6490196078431372f,0.664705882352941f,0.6803921568627449f,0.6960784313725492f,0.7117647058823531f,0.7274509803921569f,0.7431372549019608f,0.7588235294117647f,0.7745098039215685f,0.7901960784313724f,0.8058823529411763f,0.8215686274509801f,0.8372549019607844f,0.8529411764705883f,0.8686274509803922f,0.884313725490196f,0.8999999999999999f,0.9156862745098038f,0.9313725490196076f,0.947058823529412f,0.9627450980392158f,0.9784313725490197f,0.9941176470588236f,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.9862745098039216f,0.9705882352941178f,0.9549019607843139f,0.93921568627451f,0.9235294117647062f,0.9078431372549018f,0.892156862745098f,0.8764705882352941f,0.8607843137254902f,0.8450980392156864f,0.8294117647058825f,0.8137254901960786f,0.7980392156862743f,0.7823529411764705f,0.7666666666666666f,0.7509803921568627f,0.7352941176470589f,0.719607843137255f,0.7039215686274511f,0.6882352941176473f,0.6725490196078434f,0.6568627450980391f,0.6411764705882352f,0.6254901960784314f,0.6098039215686275f,0.5941176470588236f,0.5784313725490198f,0.5627450980392159f,0.5470588235294116f,0.5313725490196077f,0.5156862745098039f,0.5f};
|
||||
float g[] = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.001960784313725483f,0.01764705882352935f,0.03333333333333333f,0.0490196078431373f,0.06470588235294117f,0.08039215686274503f,0.09607843137254901f,0.111764705882353f,0.1274509803921569f,0.1431372549019607f,0.1588235294117647f,0.1745098039215687f,0.1901960784313725f,0.2058823529411764f,0.2215686274509804f,0.2372549019607844f,0.2529411764705882f,0.2686274509803921f,0.2843137254901961f,0.3f,0.3156862745098039f,0.3313725490196078f,0.3470588235294118f,0.3627450980392157f,0.3784313725490196f,0.3941176470588235f,0.4098039215686274f,0.4254901960784314f,0.4411764705882353f,0.4568627450980391f,0.4725490196078431f,0.4882352941176471f,0.503921568627451f,0.5196078431372548f,0.5352941176470587f,0.5509803921568628f,0.5666666666666667f,0.5823529411764705f,0.5980392156862746f,0.6137254901960785f,0.6294117647058823f,0.6450980392156862f,0.6607843137254901f,0.6764705882352942f,0.692156862745098f,0.7078431372549019f,0.723529411764706f,0.7392156862745098f,0.7549019607843137f,0.7705882352941176f,0.7862745098039214f,0.8019607843137255f,0.8176470588235294f,0.8333333333333333f,0.8490196078431373f,0.8647058823529412f,0.8803921568627451f,0.8960784313725489f,0.9117647058823528f,0.9274509803921569f,0.9431372549019608f,0.9588235294117646f,0.9745098039215687f,0.9901960784313726f,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.9901960784313726f,0.9745098039215687f,0.9588235294117649f,0.943137254901961f,0.9274509803921571f,0.9117647058823528f,0.8960784313725489f,0.8803921568627451f,0.8647058823529412f,0.8490196078431373f,0.8333333333333335f,0.8176470588235296f,0.8019607843137253f,0.7862745098039214f,0.7705882352941176f,0.7549019607843137f,0.7392156862745098f,0.723529411764706f,0.7078431372549021f,0.6921568627450982f,0.6764705882352944f,0.6607843137254901f,0.6450980392156862f,0.6294117647058823f,0.6137254901960785f,0.5980392156862746f,0.5823529411764707f,0.5666666666666669f,0.5509803921568626f,0.5352941176470587f,0.5196078431372548f,0.503921568627451f,0.4882352941176471f,0.4725490196078432f,0.4568627450980394f,0.4411764705882355f,0.4254901960784316f,0.4098039215686273f,0.3941176470588235f,0.3784313725490196f,0.3627450980392157f,0.3470588235294119f,0.331372549019608f,0.3156862745098041f,0.2999999999999998f,0.284313725490196f,0.2686274509803921f,0.2529411764705882f,0.2372549019607844f,0.2215686274509805f,0.2058823529411766f,0.1901960784313728f,0.1745098039215689f,0.1588235294117646f,0.1431372549019607f,0.1274509803921569f,0.111764705882353f,0.09607843137254912f,0.08039215686274526f,0.06470588235294139f,0.04901960784313708f,0.03333333333333321f,0.01764705882352935f,0.001960784313725483f,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};
|
||||
float b[] = {0.5f,0.5156862745098039f,0.5313725490196078f,0.5470588235294118f,0.5627450980392157f,0.5784313725490196f,0.5941176470588235f,0.6098039215686275f,0.6254901960784314f,0.6411764705882352f,0.6568627450980392f,0.6725490196078432f,0.6882352941176471f,0.7039215686274509f,0.7196078431372549f,0.7352941176470589f,0.7509803921568627f,0.7666666666666666f,0.7823529411764706f,0.7980392156862746f,0.8137254901960784f,0.8294117647058823f,0.8450980392156863f,0.8607843137254902f,0.8764705882352941f,0.892156862745098f,0.907843137254902f,0.9235294117647059f,0.9392156862745098f,0.9549019607843137f,0.9705882352941176f,0.9862745098039216f,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.9941176470588236f,0.9784313725490197f,0.9627450980392158f,0.9470588235294117f,0.9313725490196079f,0.915686274509804f,0.8999999999999999f,0.884313725490196f,0.8686274509803922f,0.8529411764705883f,0.8372549019607844f,0.8215686274509804f,0.8058823529411765f,0.7901960784313726f,0.7745098039215685f,0.7588235294117647f,0.7431372549019608f,0.7274509803921569f,0.7117647058823531f,0.696078431372549f,0.6803921568627451f,0.6647058823529413f,0.6490196078431372f,0.6333333333333333f,0.6176470588235294f,0.6019607843137256f,0.5862745098039217f,0.5705882352941176f,0.5549019607843138f,0.5392156862745099f,0.5235294117647058f,0.5078431372549019f,0.4921568627450981f,0.4764705882352942f,0.4607843137254903f,0.4450980392156865f,0.4294117647058826f,0.4137254901960783f,0.3980392156862744f,0.3823529411764706f,0.3666666666666667f,0.3509803921568628f,0.335294117647059f,0.3196078431372551f,0.3039215686274508f,0.2882352941176469f,0.2725490196078431f,0.2568627450980392f,0.2411764705882353f,0.2254901960784315f,0.2098039215686276f,0.1941176470588237f,0.1784313725490199f,0.1627450980392156f,0.1470588235294117f,0.1313725490196078f,0.115686274509804f,0.1000000000000001f,0.08431372549019622f,0.06862745098039236f,0.05294117647058805f,0.03725490196078418f,0.02156862745098032f,0.00588235294117645f,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};
|
||||
// now build lookup table
|
||||
this->_lut = ColorMap::linear_colormap(X,
|
||||
Mat(256,1, CV_32FC1, r).clone(), // red
|
||||
@ -266,9 +266,9 @@ namespace colormap
|
||||
}
|
||||
|
||||
void init(int n) {
|
||||
float r[] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
|
||||
float g[] = {0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0};
|
||||
float b[] = {1.0, 0.95, 0.9, 0.85, 0.8, 0.75, 0.7, 0.65, 0.6, 0.55, 0.5};
|
||||
float r[] = {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f};
|
||||
float g[] = {0.0f, 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.7f, 0.8f, 0.9f, 1.0f,};
|
||||
float b[] = {1.0, 0.95f, 0.9f, 0.85f, 0.8f, 0.75f, 0.7f, 0.65f, 0.6f, 0.55f, 0.5f};
|
||||
Mat X = linspace(0,1,11);
|
||||
this->_lut = ColorMap::linear_colormap(X,
|
||||
Mat(11,1, CV_32FC1, r).clone(), // red
|
||||
@ -290,9 +290,9 @@ namespace colormap
|
||||
}
|
||||
|
||||
void init(int n) {
|
||||
float r[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9365079365079367, 0.8571428571428572, 0.7777777777777777, 0.6984126984126986, 0.6190476190476191, 0.53968253968254, 0.4603174603174605, 0.3809523809523814, 0.3015873015873018, 0.2222222222222223, 0.1428571428571432, 0.06349206349206415, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.03174603174603208, 0.08465608465608465, 0.1375661375661377, 0.1904761904761907, 0.2433862433862437, 0.2962962962962963, 0.3492063492063493, 0.4021164021164023, 0.4550264550264553, 0.5079365079365079, 0.5608465608465609, 0.6137566137566139, 0.666666666666667};
|
||||
float g[] = { 0, 0.03968253968253968, 0.07936507936507936, 0.119047619047619, 0.1587301587301587, 0.1984126984126984, 0.2380952380952381, 0.2777777777777778, 0.3174603174603174, 0.3571428571428571, 0.3968253968253968, 0.4365079365079365, 0.4761904761904762, 0.5158730158730158, 0.5555555555555556, 0.5952380952380952, 0.6349206349206349, 0.6746031746031745, 0.7142857142857142, 0.753968253968254, 0.7936507936507936, 0.8333333333333333, 0.873015873015873, 0.9126984126984127, 0.9523809523809523, 0.992063492063492, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9841269841269842, 0.9047619047619047, 0.8253968253968256, 0.7460317460317465, 0.666666666666667, 0.587301587301587, 0.5079365079365079, 0.4285714285714288, 0.3492063492063493, 0.2698412698412698, 0.1904761904761907, 0.1111111111111116, 0.03174603174603208, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
|
||||
float b[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01587301587301582, 0.09523809523809534, 0.1746031746031744, 0.2539682539682535, 0.333333333333333, 0.412698412698413, 0.4920634920634921, 0.5714285714285712, 0.6507936507936507, 0.7301587301587302, 0.8095238095238093, 0.8888888888888884, 0.9682539682539679, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
|
||||
float r[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9365079365079367f, 0.8571428571428572f, 0.7777777777777777f, 0.6984126984126986f, 0.6190476190476191f, 0.53968253968254f, 0.4603174603174605f, 0.3809523809523814f, 0.3015873015873018f, 0.2222222222222223f, 0.1428571428571432f, 0.06349206349206415f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.03174603174603208f, 0.08465608465608465f, 0.1375661375661377f, 0.1904761904761907f, 0.2433862433862437f, 0.2962962962962963f, 0.3492063492063493f, 0.4021164021164023f, 0.4550264550264553f, 0.5079365079365079f, 0.5608465608465609f, 0.6137566137566139f, 0.666666666666667f};
|
||||
float g[] = { 0, 0.03968253968253968f, 0.07936507936507936f, 0.119047619047619f, 0.1587301587301587f, 0.1984126984126984f, 0.2380952380952381f, 0.2777777777777778f, 0.3174603174603174f, 0.3571428571428571f, 0.3968253968253968f, 0.4365079365079365f, 0.4761904761904762f, 0.5158730158730158f, 0.5555555555555556f, 0.5952380952380952f, 0.6349206349206349f, 0.6746031746031745f, 0.7142857142857142f, 0.753968253968254f, 0.7936507936507936f, 0.8333333333333333f, 0.873015873015873f, 0.9126984126984127f, 0.9523809523809523f, 0.992063492063492f, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9841269841269842f, 0.9047619047619047f, 0.8253968253968256f, 0.7460317460317465f, 0.666666666666667f, 0.587301587301587f, 0.5079365079365079f, 0.4285714285714288f, 0.3492063492063493f, 0.2698412698412698f, 0.1904761904761907f, 0.1111111111111116f, 0.03174603174603208f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
|
||||
float b[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01587301587301582f, 0.09523809523809534f, 0.1746031746031744f, 0.2539682539682535f, 0.333333333333333f, 0.412698412698413f, 0.4920634920634921f, 0.5714285714285712f, 0.6507936507936507f, 0.7301587301587302f, 0.8095238095238093f, 0.8888888888888884f, 0.9682539682539679f, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
|
||||
Mat X = linspace(0,1,64);
|
||||
this->_lut = ColorMap::linear_colormap(X,
|
||||
Mat(64,1, CV_32FC1, r).clone(), // red
|
||||
@ -314,9 +314,9 @@ namespace colormap
|
||||
}
|
||||
|
||||
void init(int n) {
|
||||
float r[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04761904761904762, 0.09523809523809523, 0.1428571428571428, 0.1904761904761905, 0.2380952380952381, 0.2857142857142857, 0.3333333333333333, 0.3809523809523809, 0.4285714285714285, 0.4761904761904762, 0.5238095238095238, 0.5714285714285714, 0.6190476190476191, 0.6666666666666666, 0.7142857142857143, 0.7619047619047619, 0.8095238095238095, 0.8571428571428571, 0.9047619047619048, 0.9523809523809523, 1};
|
||||
float g[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.02380952380952381, 0.04761904761904762, 0.07142857142857142, 0.09523809523809523, 0.119047619047619, 0.1428571428571428, 0.1666666666666667, 0.1904761904761905, 0.2142857142857143, 0.2380952380952381, 0.2619047619047619, 0.2857142857142857, 0.3095238095238095, 0.3333333333333333, 0.3571428571428572, 0.3809523809523809, 0.4047619047619048, 0.4285714285714285, 0.4523809523809524, 0.4761904761904762, 0.5, 0.5238095238095238, 0.5476190476190477, 0.5714285714285714, 0.5952380952380952, 0.6190476190476191, 0.6428571428571429, 0.6666666666666666, 0.6904761904761905, 0.7142857142857143, 0.7380952380952381, 0.7619047619047619, 0.7857142857142857, 0.8095238095238095, 0.8333333333333334, 0.8571428571428571, 0.8809523809523809, 0.9047619047619048, 0.9285714285714286, 0.9523809523809523, 0.9761904761904762, 1};
|
||||
float b[] = { 0, 0.01587301587301587, 0.03174603174603174, 0.04761904761904762, 0.06349206349206349, 0.07936507936507936, 0.09523809523809523, 0.1111111111111111, 0.126984126984127, 0.1428571428571428, 0.1587301587301587, 0.1746031746031746, 0.1904761904761905, 0.2063492063492063, 0.2222222222222222, 0.2380952380952381, 0.253968253968254, 0.2698412698412698, 0.2857142857142857, 0.3015873015873016, 0.3174603174603174, 0.3333333333333333, 0.3492063492063492, 0.3650793650793651, 0.3809523809523809, 0.3968253968253968, 0.4126984126984127, 0.4285714285714285, 0.4444444444444444, 0.4603174603174603, 0.4761904761904762, 0.492063492063492, 0.5079365079365079, 0.5238095238095238, 0.5396825396825397, 0.5555555555555556, 0.5714285714285714, 0.5873015873015873, 0.6031746031746031, 0.6190476190476191, 0.6349206349206349, 0.6507936507936508, 0.6666666666666666, 0.6825396825396826, 0.6984126984126984, 0.7142857142857143, 0.7301587301587301, 0.746031746031746, 0.7619047619047619, 0.7777777777777778, 0.7936507936507936, 0.8095238095238095, 0.8253968253968254, 0.8412698412698413, 0.8571428571428571, 0.873015873015873, 0.8888888888888888, 0.9047619047619048, 0.9206349206349206, 0.9365079365079365, 0.9523809523809523, 0.9682539682539683, 0.9841269841269841, 1};
|
||||
float r[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04761904761904762f, 0.09523809523809523f, 0.1428571428571428f, 0.1904761904761905f, 0.2380952380952381f, 0.2857142857142857f, 0.3333333333333333f, 0.3809523809523809f, 0.4285714285714285f, 0.4761904761904762f, 0.5238095238095238f, 0.5714285714285714f, 0.6190476190476191f, 0.6666666666666666f, 0.7142857142857143f, 0.7619047619047619f, 0.8095238095238095f, 0.8571428571428571f, 0.9047619047619048f, 0.9523809523809523f, 1};
|
||||
float g[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.02380952380952381f, 0.04761904761904762f, 0.07142857142857142f, 0.09523809523809523f, 0.119047619047619f, 0.1428571428571428f, 0.1666666666666667f, 0.1904761904761905f, 0.2142857142857143f, 0.2380952380952381f, 0.2619047619047619f, 0.2857142857142857f, 0.3095238095238095f, 0.3333333333333333f, 0.3571428571428572f, 0.3809523809523809f, 0.4047619047619048f, 0.4285714285714285f, 0.4523809523809524f, 0.4761904761904762f, 0.5f, 0.5238095238095238f, 0.5476190476190477f, 0.5714285714285714f, 0.5952380952380952f, 0.6190476190476191f, 0.6428571428571429f, 0.6666666666666666f, 0.6904761904761905f, 0.7142857142857143f, 0.7380952380952381f, 0.7619047619047619f, 0.7857142857142857f, 0.8095238095238095f, 0.8333333333333334f, 0.8571428571428571f, 0.8809523809523809f, 0.9047619047619048f, 0.9285714285714286f, 0.9523809523809523f, 0.9761904761904762f, 1};
|
||||
float b[] = { 0, 0.01587301587301587f, 0.03174603174603174f, 0.04761904761904762f, 0.06349206349206349f, 0.07936507936507936f, 0.09523809523809523f, 0.1111111111111111f, 0.126984126984127f, 0.1428571428571428f, 0.1587301587301587f, 0.1746031746031746f, 0.1904761904761905f, 0.2063492063492063f, 0.2222222222222222f, 0.2380952380952381f, 0.253968253968254f, 0.2698412698412698f, 0.2857142857142857f, 0.3015873015873016f, 0.3174603174603174f, 0.3333333333333333f, 0.3492063492063492f, 0.3650793650793651f, 0.3809523809523809f, 0.3968253968253968f, 0.4126984126984127f, 0.4285714285714285f, 0.4444444444444444f, 0.4603174603174603f, 0.4761904761904762f, 0.492063492063492f, 0.5079365079365079f, 0.5238095238095238f, 0.5396825396825397f, 0.5555555555555556f, 0.5714285714285714f, 0.5873015873015873f, 0.6031746031746031f, 0.6190476190476191f, 0.6349206349206349f, 0.6507936507936508f, 0.6666666666666666f, 0.6825396825396826f, 0.6984126984126984f, 0.7142857142857143f, 0.7301587301587301f, 0.746031746031746f, 0.7619047619047619f, 0.7777777777777778f, 0.7936507936507936f, 0.8095238095238095f, 0.8253968253968254f, 0.8412698412698413f, 0.8571428571428571f, 0.873015873015873f, 0.8888888888888888f, 0.9047619047619048f, 0.9206349206349206f, 0.9365079365079365f, 0.9523809523809523f, 0.9682539682539683f, 0.9841269841269841f, 1};
|
||||
Mat X = linspace(0,1,64);
|
||||
this->_lut = ColorMap::linear_colormap(X,
|
||||
Mat(64,1, CV_32FC1, r).clone(), // red
|
||||
@ -338,9 +338,9 @@ namespace colormap
|
||||
}
|
||||
|
||||
void init(int n) {
|
||||
float r[] = { 0, 0.01587301587301587, 0.03174603174603174, 0.04761904761904762, 0.06349206349206349, 0.07936507936507936, 0.09523809523809523, 0.1111111111111111, 0.126984126984127, 0.1428571428571428, 0.1587301587301587, 0.1746031746031746, 0.1904761904761905, 0.2063492063492063, 0.2222222222222222, 0.2380952380952381, 0.253968253968254, 0.2698412698412698, 0.2857142857142857, 0.3015873015873016, 0.3174603174603174, 0.3333333333333333, 0.3492063492063492, 0.3650793650793651, 0.3809523809523809, 0.3968253968253968, 0.4126984126984127, 0.4285714285714285, 0.4444444444444444, 0.4603174603174603, 0.4761904761904762, 0.492063492063492, 0.5079365079365079, 0.5238095238095238, 0.5396825396825397, 0.5555555555555556, 0.5714285714285714, 0.5873015873015873, 0.6031746031746031, 0.6190476190476191, 0.6349206349206349, 0.6507936507936508, 0.6666666666666666, 0.6825396825396826, 0.6984126984126984, 0.7142857142857143, 0.7301587301587301, 0.746031746031746, 0.7619047619047619, 0.7777777777777778, 0.7936507936507936, 0.8095238095238095, 0.8253968253968254, 0.8412698412698413, 0.8571428571428571, 0.873015873015873, 0.8888888888888888, 0.9047619047619048, 0.9206349206349206, 0.9365079365079365, 0.9523809523809523, 0.9682539682539683, 0.9841269841269841, 1};
|
||||
float g[] = { 0.5, 0.5079365079365079, 0.5158730158730158, 0.5238095238095238, 0.5317460317460317, 0.5396825396825397, 0.5476190476190477, 0.5555555555555556, 0.5634920634920635, 0.5714285714285714, 0.5793650793650793, 0.5873015873015873, 0.5952380952380952, 0.6031746031746031, 0.6111111111111112, 0.6190476190476191, 0.626984126984127, 0.6349206349206349, 0.6428571428571428, 0.6507936507936508, 0.6587301587301587, 0.6666666666666666, 0.6746031746031746, 0.6825396825396826, 0.6904761904761905, 0.6984126984126984, 0.7063492063492063, 0.7142857142857143, 0.7222222222222222, 0.7301587301587301, 0.7380952380952381, 0.746031746031746, 0.753968253968254, 0.7619047619047619, 0.7698412698412698, 0.7777777777777778, 0.7857142857142857, 0.7936507936507937, 0.8015873015873016, 0.8095238095238095, 0.8174603174603174, 0.8253968253968254, 0.8333333333333333, 0.8412698412698413, 0.8492063492063492, 0.8571428571428572, 0.8650793650793651, 0.873015873015873, 0.8809523809523809, 0.8888888888888888, 0.8968253968253967, 0.9047619047619048, 0.9126984126984127, 0.9206349206349207, 0.9285714285714286, 0.9365079365079365, 0.9444444444444444, 0.9523809523809523, 0.9603174603174602, 0.9682539682539683, 0.9761904761904762, 0.9841269841269842, 0.9920634920634921, 1};
|
||||
float b[] = { 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4};
|
||||
float r[] = { 0, 0.01587301587301587f, 0.03174603174603174f, 0.04761904761904762f, 0.06349206349206349f, 0.07936507936507936f, 0.09523809523809523f, 0.1111111111111111f, 0.126984126984127f, 0.1428571428571428f, 0.1587301587301587f, 0.1746031746031746f, 0.1904761904761905f, 0.2063492063492063f, 0.2222222222222222f, 0.2380952380952381f, 0.253968253968254f, 0.2698412698412698f, 0.2857142857142857f, 0.3015873015873016f, 0.3174603174603174f, 0.3333333333333333f, 0.3492063492063492f, 0.3650793650793651f, 0.3809523809523809f, 0.3968253968253968f, 0.4126984126984127f, 0.4285714285714285f, 0.4444444444444444f, 0.4603174603174603f, 0.4761904761904762f, 0.492063492063492f, 0.5079365079365079f, 0.5238095238095238f, 0.5396825396825397f, 0.5555555555555556f, 0.5714285714285714f, 0.5873015873015873f, 0.6031746031746031f, 0.6190476190476191f, 0.6349206349206349f, 0.6507936507936508f, 0.6666666666666666f, 0.6825396825396826f, 0.6984126984126984f, 0.7142857142857143f, 0.7301587301587301f, 0.746031746031746f, 0.7619047619047619f, 0.7777777777777778f, 0.7936507936507936f, 0.8095238095238095f, 0.8253968253968254f, 0.8412698412698413f, 0.8571428571428571f, 0.873015873015873f, 0.8888888888888888f, 0.9047619047619048f, 0.9206349206349206f, 0.9365079365079365f, 0.9523809523809523f, 0.9682539682539683f, 0.9841269841269841f, 1};
|
||||
float g[] = { 0.5f, 0.5079365079365079f, 0.5158730158730158f, 0.5238095238095238f, 0.5317460317460317f, 0.5396825396825397f, 0.5476190476190477f, 0.5555555555555556f, 0.5634920634920635f, 0.5714285714285714f, 0.5793650793650793f, 0.5873015873015873f, 0.5952380952380952f, 0.6031746031746031f, 0.6111111111111112f, 0.6190476190476191f, 0.626984126984127f, 0.6349206349206349f, 0.6428571428571428f, 0.6507936507936508f, 0.6587301587301587f, 0.6666666666666666f, 0.6746031746031746f, 0.6825396825396826f, 0.6904761904761905f, 0.6984126984126984f, 0.7063492063492063f, 0.7142857142857143f, 0.7222222222222222f, 0.7301587301587301f, 0.7380952380952381f, 0.746031746031746f, 0.753968253968254f, 0.7619047619047619f, 0.7698412698412698f, 0.7777777777777778f, 0.7857142857142857f, 0.7936507936507937f, 0.8015873015873016f, 0.8095238095238095f, 0.8174603174603174f, 0.8253968253968254f, 0.8333333333333333f, 0.8412698412698413f, 0.8492063492063492f, 0.8571428571428572f, 0.8650793650793651f, 0.873015873015873f, 0.8809523809523809f, 0.8888888888888888f, 0.8968253968253967f, 0.9047619047619048f, 0.9126984126984127f, 0.9206349206349207f, 0.9285714285714286f, 0.9365079365079365f, 0.9444444444444444f, 0.9523809523809523f, 0.9603174603174602f, 0.9682539682539683f, 0.9761904761904762f, 0.9841269841269842f, 0.9920634920634921f, 1};
|
||||
float b[] = { 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f};
|
||||
Mat X = linspace(0,1,64);
|
||||
this->_lut = ColorMap::linear_colormap(X,
|
||||
Mat(64,1, CV_32FC1, r).clone(), // red
|
||||
@ -363,8 +363,8 @@ namespace colormap
|
||||
|
||||
void init(int n) {
|
||||
float r[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
|
||||
float g[] = { 0, 0.01587301587301587, 0.03174603174603174, 0.04761904761904762, 0.06349206349206349, 0.07936507936507936, 0.09523809523809523, 0.1111111111111111, 0.126984126984127, 0.1428571428571428, 0.1587301587301587, 0.1746031746031746, 0.1904761904761905, 0.2063492063492063, 0.2222222222222222, 0.2380952380952381, 0.253968253968254, 0.2698412698412698, 0.2857142857142857, 0.3015873015873016, 0.3174603174603174, 0.3333333333333333, 0.3492063492063492, 0.3650793650793651, 0.3809523809523809, 0.3968253968253968, 0.4126984126984127, 0.4285714285714285, 0.4444444444444444, 0.4603174603174603, 0.4761904761904762, 0.492063492063492, 0.5079365079365079, 0.5238095238095238, 0.5396825396825397, 0.5555555555555556, 0.5714285714285714, 0.5873015873015873, 0.6031746031746031, 0.6190476190476191, 0.6349206349206349, 0.6507936507936508, 0.6666666666666666, 0.6825396825396826, 0.6984126984126984, 0.7142857142857143, 0.7301587301587301, 0.746031746031746, 0.7619047619047619, 0.7777777777777778, 0.7936507936507936, 0.8095238095238095, 0.8253968253968254, 0.8412698412698413, 0.8571428571428571, 0.873015873015873, 0.8888888888888888, 0.9047619047619048, 0.9206349206349206, 0.9365079365079365, 0.9523809523809523, 0.9682539682539683, 0.9841269841269841, 1};
|
||||
float b[] = { 1, 0.9841269841269842, 0.9682539682539683, 0.9523809523809523, 0.9365079365079365, 0.9206349206349207, 0.9047619047619048, 0.8888888888888888, 0.873015873015873, 0.8571428571428572, 0.8412698412698413, 0.8253968253968254, 0.8095238095238095, 0.7936507936507937, 0.7777777777777778, 0.7619047619047619, 0.746031746031746, 0.7301587301587302, 0.7142857142857143, 0.6984126984126984, 0.6825396825396826, 0.6666666666666667, 0.6507936507936508, 0.6349206349206349, 0.6190476190476191, 0.6031746031746033, 0.5873015873015873, 0.5714285714285714, 0.5555555555555556, 0.5396825396825398, 0.5238095238095238, 0.5079365079365079, 0.4920634920634921, 0.4761904761904762, 0.4603174603174603, 0.4444444444444444, 0.4285714285714286, 0.4126984126984127, 0.3968253968253969, 0.3809523809523809, 0.3650793650793651, 0.3492063492063492, 0.3333333333333334, 0.3174603174603174, 0.3015873015873016, 0.2857142857142857, 0.2698412698412699, 0.253968253968254, 0.2380952380952381, 0.2222222222222222, 0.2063492063492064, 0.1904761904761905, 0.1746031746031746, 0.1587301587301587, 0.1428571428571429, 0.126984126984127, 0.1111111111111112, 0.09523809523809523, 0.07936507936507942, 0.06349206349206349, 0.04761904761904767, 0.03174603174603174, 0.01587301587301593, 0};
|
||||
float g[] = { 0, 0.01587301587301587f, 0.03174603174603174f, 0.04761904761904762f, 0.06349206349206349f, 0.07936507936507936f, 0.09523809523809523f, 0.1111111111111111f, 0.126984126984127f, 0.1428571428571428f, 0.1587301587301587f, 0.1746031746031746f, 0.1904761904761905f, 0.2063492063492063f, 0.2222222222222222f, 0.2380952380952381f, 0.253968253968254f, 0.2698412698412698f, 0.2857142857142857f, 0.3015873015873016f, 0.3174603174603174f, 0.3333333333333333f, 0.3492063492063492f, 0.3650793650793651f, 0.3809523809523809f, 0.3968253968253968f, 0.4126984126984127f, 0.4285714285714285f, 0.4444444444444444f, 0.4603174603174603f, 0.4761904761904762f, 0.492063492063492f, 0.5079365079365079f, 0.5238095238095238f, 0.5396825396825397f, 0.5555555555555556f, 0.5714285714285714f, 0.5873015873015873f, 0.6031746031746031f, 0.6190476190476191f, 0.6349206349206349f, 0.6507936507936508f, 0.6666666666666666f, 0.6825396825396826f, 0.6984126984126984f, 0.7142857142857143f, 0.7301587301587301f, 0.746031746031746f, 0.7619047619047619f, 0.7777777777777778f, 0.7936507936507936f, 0.8095238095238095f, 0.8253968253968254f, 0.8412698412698413f, 0.8571428571428571f, 0.873015873015873f, 0.8888888888888888f, 0.9047619047619048f, 0.9206349206349206f, 0.9365079365079365f, 0.9523809523809523f, 0.9682539682539683f, 0.9841269841269841f, 1};
|
||||
float b[] = { 1, 0.9841269841269842f, 0.9682539682539683f, 0.9523809523809523f, 0.9365079365079365f, 0.9206349206349207f, 0.9047619047619048f, 0.8888888888888888f, 0.873015873015873f, 0.8571428571428572f, 0.8412698412698413f, 0.8253968253968254f, 0.8095238095238095f, 0.7936507936507937f, 0.7777777777777778f, 0.7619047619047619f, 0.746031746031746f, 0.7301587301587302f, 0.7142857142857143f, 0.6984126984126984f, 0.6825396825396826f, 0.6666666666666667f, 0.6507936507936508f, 0.6349206349206349f, 0.6190476190476191f, 0.6031746031746033f, 0.5873015873015873f, 0.5714285714285714f, 0.5555555555555556f, 0.5396825396825398f, 0.5238095238095238f, 0.5079365079365079f, 0.4920634920634921f, 0.4761904761904762f, 0.4603174603174603f, 0.4444444444444444f, 0.4285714285714286f, 0.4126984126984127f, 0.3968253968253969f, 0.3809523809523809f, 0.3650793650793651f, 0.3492063492063492f, 0.3333333333333334f, 0.3174603174603174f, 0.3015873015873016f, 0.2857142857142857f, 0.2698412698412699f, 0.253968253968254f, 0.2380952380952381f, 0.2222222222222222f, 0.2063492063492064f, 0.1904761904761905f, 0.1746031746031746f, 0.1587301587301587f, 0.1428571428571429f, 0.126984126984127f, 0.1111111111111112f, 0.09523809523809523f, 0.07936507936507942f, 0.06349206349206349f, 0.04761904761904767f, 0.03174603174603174f, 0.01587301587301593f, 0};
|
||||
Mat X = linspace(0,1,64);
|
||||
this->_lut = ColorMap::linear_colormap(X,
|
||||
Mat(64,1, CV_32FC1, r).clone(), // red
|
||||
@ -386,8 +386,8 @@ namespace colormap
|
||||
}
|
||||
|
||||
void init(int n) {
|
||||
float r[] = { 0, 0.01587301587301587, 0.03174603174603174, 0.04761904761904762, 0.06349206349206349, 0.07936507936507936, 0.09523809523809523, 0.1111111111111111, 0.126984126984127, 0.1428571428571428, 0.1587301587301587, 0.1746031746031746, 0.1904761904761905, 0.2063492063492063, 0.2222222222222222, 0.2380952380952381, 0.253968253968254, 0.2698412698412698, 0.2857142857142857, 0.3015873015873016, 0.3174603174603174, 0.3333333333333333, 0.3492063492063492, 0.3650793650793651, 0.3809523809523809, 0.3968253968253968, 0.4126984126984127, 0.4285714285714285, 0.4444444444444444, 0.4603174603174603, 0.4761904761904762, 0.492063492063492, 0.5079365079365079, 0.5238095238095238, 0.5396825396825397, 0.5555555555555556, 0.5714285714285714, 0.5873015873015873, 0.6031746031746031, 0.6190476190476191, 0.6349206349206349, 0.6507936507936508, 0.6666666666666666, 0.6825396825396826, 0.6984126984126984, 0.7142857142857143, 0.7301587301587301, 0.746031746031746, 0.7619047619047619, 0.7777777777777778, 0.7936507936507936, 0.8095238095238095, 0.8253968253968254, 0.8412698412698413, 0.8571428571428571, 0.873015873015873, 0.8888888888888888, 0.9047619047619048, 0.9206349206349206, 0.9365079365079365, 0.9523809523809523, 0.9682539682539683, 0.9841269841269841, 1};
|
||||
float g[] = { 1, 0.9841269841269842, 0.9682539682539683, 0.9523809523809523, 0.9365079365079365, 0.9206349206349207, 0.9047619047619048, 0.8888888888888888, 0.873015873015873, 0.8571428571428572, 0.8412698412698413, 0.8253968253968254, 0.8095238095238095, 0.7936507936507937, 0.7777777777777778, 0.7619047619047619, 0.746031746031746, 0.7301587301587302, 0.7142857142857143, 0.6984126984126984, 0.6825396825396826, 0.6666666666666667, 0.6507936507936508, 0.6349206349206349, 0.6190476190476191, 0.6031746031746033, 0.5873015873015873, 0.5714285714285714, 0.5555555555555556, 0.5396825396825398, 0.5238095238095238, 0.5079365079365079, 0.4920634920634921, 0.4761904761904762, 0.4603174603174603, 0.4444444444444444, 0.4285714285714286, 0.4126984126984127, 0.3968253968253969, 0.3809523809523809, 0.3650793650793651, 0.3492063492063492, 0.3333333333333334, 0.3174603174603174, 0.3015873015873016, 0.2857142857142857, 0.2698412698412699, 0.253968253968254, 0.2380952380952381, 0.2222222222222222, 0.2063492063492064, 0.1904761904761905, 0.1746031746031746, 0.1587301587301587, 0.1428571428571429, 0.126984126984127, 0.1111111111111112, 0.09523809523809523, 0.07936507936507942, 0.06349206349206349, 0.04761904761904767, 0.03174603174603174, 0.01587301587301593, 0};
|
||||
float r[] = { 0, 0.01587301587301587f, 0.03174603174603174f, 0.04761904761904762f, 0.06349206349206349f, 0.07936507936507936f, 0.09523809523809523f, 0.1111111111111111f, 0.126984126984127f, 0.1428571428571428f, 0.1587301587301587f, 0.1746031746031746f, 0.1904761904761905f, 0.2063492063492063f, 0.2222222222222222f, 0.2380952380952381f, 0.253968253968254f, 0.2698412698412698f, 0.2857142857142857f, 0.3015873015873016f, 0.3174603174603174f, 0.3333333333333333f, 0.3492063492063492f, 0.3650793650793651f, 0.3809523809523809f, 0.3968253968253968f, 0.4126984126984127f, 0.4285714285714285f, 0.4444444444444444f, 0.4603174603174603f, 0.4761904761904762f, 0.492063492063492f, 0.5079365079365079f, 0.5238095238095238f, 0.5396825396825397f, 0.5555555555555556f, 0.5714285714285714f, 0.5873015873015873f, 0.6031746031746031f, 0.6190476190476191f, 0.6349206349206349f, 0.6507936507936508f, 0.6666666666666666f, 0.6825396825396826f, 0.6984126984126984f, 0.7142857142857143f, 0.7301587301587301f, 0.746031746031746f, 0.7619047619047619f, 0.7777777777777778f, 0.7936507936507936f, 0.8095238095238095f, 0.8253968253968254f, 0.8412698412698413f, 0.8571428571428571f, 0.873015873015873f, 0.8888888888888888f, 0.9047619047619048f, 0.9206349206349206f, 0.9365079365079365f, 0.9523809523809523f, 0.9682539682539683f, 0.9841269841269841f, 1};
|
||||
float g[] = { 1, 0.9841269841269842f, 0.9682539682539683f, 0.9523809523809523f, 0.9365079365079365f, 0.9206349206349207f, 0.9047619047619048f, 0.8888888888888888f, 0.873015873015873f, 0.8571428571428572f, 0.8412698412698413f, 0.8253968253968254f, 0.8095238095238095f, 0.7936507936507937f, 0.7777777777777778f, 0.7619047619047619f, 0.746031746031746f, 0.7301587301587302f, 0.7142857142857143f, 0.6984126984126984f, 0.6825396825396826f, 0.6666666666666667f, 0.6507936507936508f, 0.6349206349206349f, 0.6190476190476191f, 0.6031746031746033f, 0.5873015873015873f, 0.5714285714285714f, 0.5555555555555556f, 0.5396825396825398f, 0.5238095238095238f, 0.5079365079365079f, 0.4920634920634921f, 0.4761904761904762f, 0.4603174603174603f, 0.4444444444444444f, 0.4285714285714286f, 0.4126984126984127f, 0.3968253968253969f, 0.3809523809523809f, 0.3650793650793651f, 0.3492063492063492f, 0.3333333333333334f, 0.3174603174603174f, 0.3015873015873016f, 0.2857142857142857f, 0.2698412698412699f, 0.253968253968254f, 0.2380952380952381f, 0.2222222222222222f, 0.2063492063492064f, 0.1904761904761905f, 0.1746031746031746f, 0.1587301587301587f, 0.1428571428571429f, 0.126984126984127f, 0.1111111111111112f, 0.09523809523809523f, 0.07936507936507942f, 0.06349206349206349f, 0.04761904761904767f, 0.03174603174603174f, 0.01587301587301593f, 0};
|
||||
float b[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
|
||||
Mat X = linspace(0,1,64);
|
||||
this->_lut = ColorMap::linear_colormap(X,
|
||||
@ -410,9 +410,9 @@ namespace colormap
|
||||
}
|
||||
|
||||
void init(int n) {
|
||||
float r[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9523809523809526, 0.8571428571428568, 0.7619047619047614, 0.6666666666666665, 0.5714285714285716, 0.4761904761904763, 0.3809523809523805, 0.2857142857142856, 0.1904761904761907, 0.0952380952380949, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.09523809523809557, 0.1904761904761905, 0.2857142857142854, 0.3809523809523809, 0.4761904761904765, 0.5714285714285714, 0.6666666666666663, 0.7619047619047619, 0.8571428571428574, 0.9523809523809523, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
|
||||
float g[] = { 0, 0.09523809523809523, 0.1904761904761905, 0.2857142857142857, 0.3809523809523809, 0.4761904761904762, 0.5714285714285714, 0.6666666666666666, 0.7619047619047619, 0.8571428571428571, 0.9523809523809523, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9523809523809526, 0.8571428571428577, 0.7619047619047619, 0.6666666666666665, 0.5714285714285716, 0.4761904761904767, 0.3809523809523814, 0.2857142857142856, 0.1904761904761907, 0.09523809523809579, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
|
||||
float b[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.09523809523809523, 0.1904761904761905, 0.2857142857142857, 0.3809523809523809, 0.4761904761904762, 0.5714285714285714, 0.6666666666666666, 0.7619047619047619, 0.8571428571428571, 0.9523809523809523, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9523809523809526, 0.8571428571428577, 0.7619047619047614, 0.6666666666666665, 0.5714285714285716, 0.4761904761904767, 0.3809523809523805, 0.2857142857142856, 0.1904761904761907, 0.09523809523809579, 0};
|
||||
float r[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9523809523809526f, 0.8571428571428568f, 0.7619047619047614f, 0.6666666666666665f, 0.5714285714285716f, 0.4761904761904763f, 0.3809523809523805f, 0.2857142857142856f, 0.1904761904761907f, 0.0952380952380949f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.09523809523809557f, 0.1904761904761905f, 0.2857142857142854f, 0.3809523809523809f, 0.4761904761904765f, 0.5714285714285714f, 0.6666666666666663f, 0.7619047619047619f, 0.8571428571428574f, 0.9523809523809523f, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
|
||||
float g[] = { 0, 0.09523809523809523f, 0.1904761904761905f, 0.2857142857142857f, 0.3809523809523809f, 0.4761904761904762f, 0.5714285714285714f, 0.6666666666666666f, 0.7619047619047619f, 0.8571428571428571f, 0.9523809523809523f, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9523809523809526f, 0.8571428571428577f, 0.7619047619047619f, 0.6666666666666665f, 0.5714285714285716f, 0.4761904761904767f, 0.3809523809523814f, 0.2857142857142856f, 0.1904761904761907f, 0.09523809523809579f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
|
||||
float b[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.09523809523809523f, 0.1904761904761905f, 0.2857142857142857f, 0.3809523809523809f, 0.4761904761904762f, 0.5714285714285714f, 0.6666666666666666f, 0.7619047619047619f, 0.8571428571428571f, 0.9523809523809523f, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9523809523809526f, 0.8571428571428577f, 0.7619047619047614f, 0.6666666666666665f, 0.5714285714285716f, 0.4761904761904767f, 0.3809523809523805f, 0.2857142857142856f, 0.1904761904761907f, 0.09523809523809579f, 0};
|
||||
Mat X = linspace(0,1,64);
|
||||
this->_lut = ColorMap::linear_colormap(X,
|
||||
Mat(64,1, CV_32FC1, r).clone(), // red
|
||||
@ -434,9 +434,9 @@ namespace colormap
|
||||
}
|
||||
|
||||
void init(int n) {
|
||||
float r[] = { 0, 0.1571348402636772, 0.2222222222222222, 0.2721655269759087, 0.3142696805273544, 0.3513641844631533, 0.3849001794597505, 0.415739709641549, 0.4444444444444444, 0.4714045207910317, 0.4969039949999532, 0.5211573066470477, 0.5443310539518174, 0.5665577237325317, 0.5879447357921312, 0.6085806194501846, 0.6285393610547089, 0.6478835438717, 0.6666666666666666, 0.6849348892187751, 0.7027283689263065, 0.7200822998230956, 0.7370277311900888, 0.753592220347252, 0.7663560447348133, 0.7732293307186413, 0.7800420555749596, 0.7867957924694432, 0.7934920476158722, 0.8001322641986387, 0.8067178260046388, 0.8132500607904444, 0.8197302434079591, 0.8261595987094034, 0.8325393042503717, 0.8388704928078611, 0.8451542547285166, 0.8513916401208816, 0.8575836609041332, 0.8637312927246217, 0.8698354767504924, 0.8758971213537393, 0.8819171036881968, 0.8878962711712378, 0.8938354428762595, 0.8997354108424372, 0.9055969413076769, 0.9114207758701963, 0.9172076325837248, 0.9229582069908971, 0.9286731730990523, 0.9343531843023135, 0.9399988742535192, 0.9456108576893002, 0.9511897312113418, 0.9567360740266436, 0.9622504486493763, 0.9677334015667416, 0.9731854638710686, 0.9786071518602129, 0.9839989676081821, 0.9893613995077727, 0.9946949227868761, 1};
|
||||
float g[] = { 0, 0.1028688999747279, 0.1454785934906616, 0.1781741612749496, 0.2057377999494559, 0.2300218531141181, 0.2519763153394848, 0.2721655269759087, 0.2909571869813232, 0.3086066999241838, 0.3253000243161777, 0.3411775438127727, 0.3563483225498992, 0.3708990935094579, 0.3849001794597505, 0.3984095364447979, 0.4114755998989117, 0.4241393401869012, 0.4364357804719847, 0.4483951394230328, 0.4600437062282361, 0.4714045207910317, 0.4824979096371639, 0.4933419132673033, 0.5091750772173156, 0.5328701692569688, 0.5555555555555556, 0.5773502691896257, 0.5983516452371671, 0.6186404847588913, 0.6382847385042254, 0.6573421981221795, 0.6758625033664688, 0.6938886664887108, 0.7114582486036499, 0.7286042804780002, 0.7453559924999299, 0.7617394000445604, 0.7777777777777778, 0.7934920476158723, 0.8089010988089465, 0.8240220541217402, 0.8388704928078611, 0.8534606386520677, 0.8678055195451838, 0.8819171036881968, 0.8958064164776166, 0.9094836413191612, 0.9172076325837248, 0.9229582069908971, 0.9286731730990523, 0.9343531843023135, 0.9399988742535192, 0.9456108576893002, 0.9511897312113418, 0.9567360740266436, 0.9622504486493763, 0.9677334015667416, 0.9731854638710686, 0.9786071518602129, 0.9839989676081821, 0.9893613995077727, 0.9946949227868761, 1};
|
||||
float b[] = { 0, 0.1028688999747279, 0.1454785934906616, 0.1781741612749496, 0.2057377999494559, 0.2300218531141181, 0.2519763153394848, 0.2721655269759087, 0.2909571869813232, 0.3086066999241838, 0.3253000243161777, 0.3411775438127727, 0.3563483225498992, 0.3708990935094579, 0.3849001794597505, 0.3984095364447979, 0.4114755998989117, 0.4241393401869012, 0.4364357804719847, 0.4483951394230328, 0.4600437062282361, 0.4714045207910317, 0.4824979096371639, 0.4933419132673033, 0.5039526306789697, 0.5143444998736397, 0.5245305283129621, 0.5345224838248488, 0.5443310539518174, 0.5539659798925444, 0.563436169819011, 0.5727497953228163, 0.5819143739626463, 0.5909368402852788, 0.5998236072282915, 0.6085806194501846, 0.6172133998483676, 0.6257270902992705, 0.6341264874742278, 0.642416074439621, 0.6506000486323554, 0.6586823467062358, 0.6666666666666666, 0.6745564876468501, 0.6823550876255453, 0.6900655593423541, 0.6976908246297114, 0.7052336473499384, 0.7237468644557459, 0.7453559924999298, 0.7663560447348133, 0.7867957924694432, 0.8067178260046388, 0.8261595987094034, 0.8451542547285166, 0.8637312927246217, 0.8819171036881968, 0.8997354108424372, 0.9172076325837248, 0.9343531843023135, 0.9511897312113418, 0.9677334015667416, 0.9839989676081821, 1};
|
||||
float r[] = { 0, 0.1571348402636772f, 0.2222222222222222f, 0.2721655269759087f, 0.3142696805273544f, 0.3513641844631533f, 0.3849001794597505f, 0.415739709641549f, 0.4444444444444444f, 0.4714045207910317f, 0.4969039949999532f, 0.5211573066470477f, 0.5443310539518174f, 0.5665577237325317f, 0.5879447357921312f, 0.6085806194501846f, 0.6285393610547089f, 0.6478835438717f, 0.6666666666666666f, 0.6849348892187751f, 0.7027283689263065f, 0.7200822998230956f, 0.7370277311900888f, 0.753592220347252f, 0.7663560447348133f, 0.7732293307186413f, 0.7800420555749596f, 0.7867957924694432f, 0.7934920476158722f, 0.8001322641986387f, 0.8067178260046388f, 0.8132500607904444f, 0.8197302434079591f, 0.8261595987094034f, 0.8325393042503717f, 0.8388704928078611f, 0.8451542547285166f, 0.8513916401208816f, 0.8575836609041332f, 0.8637312927246217f, 0.8698354767504924f, 0.8758971213537393f, 0.8819171036881968f, 0.8878962711712378f, 0.8938354428762595f, 0.8997354108424372f, 0.9055969413076769f, 0.9114207758701963f, 0.9172076325837248f, 0.9229582069908971f, 0.9286731730990523f, 0.9343531843023135f, 0.9399988742535192f, 0.9456108576893002f, 0.9511897312113418f, 0.9567360740266436f, 0.9622504486493763f, 0.9677334015667416f, 0.9731854638710686f, 0.9786071518602129f, 0.9839989676081821f, 0.9893613995077727f, 0.9946949227868761f, 1};
|
||||
float g[] = { 0, 0.1028688999747279f, 0.1454785934906616f, 0.1781741612749496f, 0.2057377999494559f, 0.2300218531141181f, 0.2519763153394848f, 0.2721655269759087f, 0.2909571869813232f, 0.3086066999241838f, 0.3253000243161777f, 0.3411775438127727f, 0.3563483225498992f, 0.3708990935094579f, 0.3849001794597505f, 0.3984095364447979f, 0.4114755998989117f, 0.4241393401869012f, 0.4364357804719847f, 0.4483951394230328f, 0.4600437062282361f, 0.4714045207910317f, 0.4824979096371639f, 0.4933419132673033f, 0.5091750772173156f, 0.5328701692569688f, 0.5555555555555556f, 0.5773502691896257f, 0.5983516452371671f, 0.6186404847588913f, 0.6382847385042254f, 0.6573421981221795f, 0.6758625033664688f, 0.6938886664887108f, 0.7114582486036499f, 0.7286042804780002f, 0.7453559924999299f, 0.7617394000445604f, 0.7777777777777778f, 0.7934920476158723f, 0.8089010988089465f, 0.8240220541217402f, 0.8388704928078611f, 0.8534606386520677f, 0.8678055195451838f, 0.8819171036881968f, 0.8958064164776166f, 0.9094836413191612f, 0.9172076325837248f, 0.9229582069908971f, 0.9286731730990523f, 0.9343531843023135f, 0.9399988742535192f, 0.9456108576893002f, 0.9511897312113418f, 0.9567360740266436f, 0.9622504486493763f, 0.9677334015667416f, 0.9731854638710686f, 0.9786071518602129f, 0.9839989676081821f, 0.9893613995077727f, 0.9946949227868761f, 1};
|
||||
float b[] = { 0, 0.1028688999747279f, 0.1454785934906616f, 0.1781741612749496f, 0.2057377999494559f, 0.2300218531141181f, 0.2519763153394848f, 0.2721655269759087f, 0.2909571869813232f, 0.3086066999241838f, 0.3253000243161777f, 0.3411775438127727f, 0.3563483225498992f, 0.3708990935094579f, 0.3849001794597505f, 0.3984095364447979f, 0.4114755998989117f, 0.4241393401869012f, 0.4364357804719847f, 0.4483951394230328f, 0.4600437062282361f, 0.4714045207910317f, 0.4824979096371639f, 0.4933419132673033f, 0.5039526306789697f, 0.5143444998736397f, 0.5245305283129621f, 0.5345224838248488f, 0.5443310539518174f, 0.5539659798925444f, 0.563436169819011f, 0.5727497953228163f, 0.5819143739626463f, 0.5909368402852788f, 0.5998236072282915f, 0.6085806194501846f, 0.6172133998483676f, 0.6257270902992705f, 0.6341264874742278f, 0.642416074439621f, 0.6506000486323554f, 0.6586823467062358f, 0.6666666666666666f, 0.6745564876468501f, 0.6823550876255453f, 0.6900655593423541f, 0.6976908246297114f, 0.7052336473499384f, 0.7237468644557459f, 0.7453559924999298f, 0.7663560447348133f, 0.7867957924694432f, 0.8067178260046388f, 0.8261595987094034f, 0.8451542547285166f, 0.8637312927246217f, 0.8819171036881968f, 0.8997354108424372f, 0.9172076325837248f, 0.9343531843023135f, 0.9511897312113418f, 0.9677334015667416f, 0.9839989676081821f, 1};
|
||||
Mat X = linspace(0,1,64);
|
||||
this->_lut = ColorMap::linear_colormap(X,
|
||||
Mat(64,1, CV_32FC1, r).clone(), // red
|
||||
@ -458,9 +458,9 @@ namespace colormap
|
||||
}
|
||||
|
||||
void init(int n) {
|
||||
float r[] = { 0, 0.03968253968253968, 0.07936507936507936, 0.119047619047619, 0.1587301587301587, 0.1984126984126984, 0.2380952380952381, 0.2777777777777778, 0.3174603174603174, 0.3571428571428571, 0.3968253968253968, 0.4365079365079365, 0.4761904761904762, 0.5158730158730158, 0.5555555555555556, 0.5952380952380952, 0.6349206349206349, 0.6746031746031745, 0.7142857142857142, 0.753968253968254, 0.7936507936507936, 0.8333333333333333, 0.873015873015873, 0.9126984126984127, 0.9523809523809523, 0.992063492063492, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
|
||||
float g[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.03174603174603163, 0.0714285714285714, 0.1111111111111112, 0.1507936507936507, 0.1904761904761905, 0.23015873015873, 0.2698412698412698, 0.3095238095238093, 0.3492063492063491, 0.3888888888888888, 0.4285714285714284, 0.4682539682539679, 0.5079365079365079, 0.5476190476190477, 0.5873015873015872, 0.6269841269841268, 0.6666666666666665, 0.7063492063492065, 0.746031746031746, 0.7857142857142856, 0.8253968253968254, 0.8650793650793651, 0.9047619047619047, 0.9444444444444442, 0.984126984126984, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
|
||||
float b[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04761904761904745, 0.1269841269841265, 0.2063492063492056, 0.2857142857142856, 0.3650793650793656, 0.4444444444444446, 0.5238095238095237, 0.6031746031746028, 0.6825396825396828, 0.7619047619047619, 0.8412698412698409, 0.92063492063492, 1};
|
||||
float r[] = { 0, 0.03968253968253968f, 0.07936507936507936f, 0.119047619047619f, 0.1587301587301587f, 0.1984126984126984f, 0.2380952380952381f, 0.2777777777777778f, 0.3174603174603174f, 0.3571428571428571f, 0.3968253968253968f, 0.4365079365079365f, 0.4761904761904762f, 0.5158730158730158f, 0.5555555555555556f, 0.5952380952380952f, 0.6349206349206349f, 0.6746031746031745f, 0.7142857142857142f, 0.753968253968254f, 0.7936507936507936f, 0.8333333333333333f, 0.873015873015873f, 0.9126984126984127f, 0.9523809523809523f, 0.992063492063492f, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
|
||||
float g[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.03174603174603163f, 0.0714285714285714f, 0.1111111111111112f, 0.1507936507936507f, 0.1904761904761905f, 0.23015873015873f, 0.2698412698412698f, 0.3095238095238093f, 0.3492063492063491f, 0.3888888888888888f, 0.4285714285714284f, 0.4682539682539679f, 0.5079365079365079f, 0.5476190476190477f, 0.5873015873015872f, 0.6269841269841268f, 0.6666666666666665f, 0.7063492063492065f, 0.746031746031746f, 0.7857142857142856f, 0.8253968253968254f, 0.8650793650793651f, 0.9047619047619047f, 0.9444444444444442f, 0.984126984126984f, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
|
||||
float b[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04761904761904745f, 0.1269841269841265f, 0.2063492063492056f, 0.2857142857142856f, 0.3650793650793656f, 0.4444444444444446f, 0.5238095238095237f, 0.6031746031746028f, 0.6825396825396828f, 0.7619047619047619f, 0.8412698412698409f, 0.92063492063492f, 1};
|
||||
Mat X = linspace(0,1,64);
|
||||
this->_lut = ColorMap::linear_colormap(X,
|
||||
Mat(64,1, CV_32FC1, r).clone(), // red
|
||||
|
||||
@ -41,6 +41,7 @@
|
||||
//M*/
|
||||
|
||||
#include "perf_precomp.hpp"
|
||||
#include "perf_util.hpp"
|
||||
|
||||
using namespace std;
|
||||
using namespace testing;
|
||||
|
||||
@ -41,6 +41,7 @@
|
||||
//M*/
|
||||
|
||||
#include "perf_precomp.hpp"
|
||||
#include "perf_util.hpp"
|
||||
|
||||
using namespace std;
|
||||
using namespace testing;
|
||||
|
||||
@ -41,6 +41,7 @@
|
||||
//M*/
|
||||
|
||||
#include "perf_precomp.hpp"
|
||||
#include "perf_util.hpp"
|
||||
|
||||
using namespace std;
|
||||
using namespace testing;
|
||||
|
||||
@ -41,6 +41,7 @@
|
||||
//M*/
|
||||
|
||||
#include "perf_precomp.hpp"
|
||||
#include "perf_util.hpp"
|
||||
|
||||
using namespace std;
|
||||
using namespace testing;
|
||||
|
||||
@ -41,6 +41,7 @@
|
||||
//M*/
|
||||
|
||||
#include "perf_precomp.hpp"
|
||||
#include "perf_util.hpp"
|
||||
|
||||
using namespace std;
|
||||
using namespace testing;
|
||||
|
||||
53
modules/gpu/perf/perf_util.hpp
Normal file
53
modules/gpu/perf/perf_util.hpp
Normal file
@ -0,0 +1,53 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#ifndef __OPENCV_PERF_UTIL_HPP__
|
||||
#define __OPENCV_PERF_UTIL_HPP__
|
||||
|
||||
namespace perf {
|
||||
DEF_PARAM_TEST_1(Sz, cv::Size);
|
||||
typedef ::perf::Size_MatType Sz_Type;
|
||||
DEF_PARAM_TEST(Sz_Depth, cv::Size, ::perf::MatDepth);
|
||||
DEF_PARAM_TEST(Sz_Depth_Cn, cv::Size, ::perf::MatDepth, MatCn);
|
||||
}
|
||||
|
||||
#endif // __OPENCV_PERF_UTIL_HPP__
|
||||
@ -1119,7 +1119,7 @@ extern "C" {
|
||||
("jdoubleArray _da_retval_ = env->NewDoubleArray(%(cnt)i); " +
|
||||
"jdouble _tmp_retval_[%(cnt)i] = {%(args)s}; " +
|
||||
"env->SetDoubleArrayRegion(_da_retval_, 0, %(cnt)i, _tmp_retval_);") %
|
||||
{ "cnt" : len(fields), "args" : ", ".join(["_retval_" + f[1] for f in fields]) } )
|
||||
{ "cnt" : len(fields), "args" : ", ".join(["(jdouble)_retval_" + f[1] for f in fields]) } )
|
||||
if fi.classname and fi.ctype and not fi.static: # non-static class method except c-tor
|
||||
# adding 'self'
|
||||
jn_args.append ( ArgInfo([ "__int64", "nativeObj", "", [], "" ]) )
|
||||
@ -1167,7 +1167,7 @@ extern "C" {
|
||||
j_prologue.append( "double[] %s_out = new double[%i];" % (a.name, len(fields)) )
|
||||
c_epilogue.append( \
|
||||
"jdouble tmp_%(n)s[%(cnt)i] = {%(args)s}; env->SetDoubleArrayRegion(%(n)s_out, 0, %(cnt)i, tmp_%(n)s);" %
|
||||
{ "n" : a.name, "cnt" : len(fields), "args" : ", ".join([a.name + f[1] for f in fields]) } )
|
||||
{ "n" : a.name, "cnt" : len(fields), "args" : ", ".join(["(jdouble)" + a.name + f[1] for f in fields]) } )
|
||||
if a.ctype in ('bool', 'int', 'long', 'float', 'double'):
|
||||
j_epilogue.append('if(%(n)s!=null) %(n)s[0] = (%(t)s)%(n)s_out[0];' % {'n':a.name,'t':a.ctype})
|
||||
else:
|
||||
|
||||
@ -1069,7 +1069,7 @@ JNIEXPORT void JNICALL Java_org_opencv_core_Mat_locateROI_10
|
||||
Size wholeSize;
|
||||
Point ofs;
|
||||
me->locateROI( wholeSize, ofs );
|
||||
jdouble tmp_wholeSize[2] = {wholeSize.width, wholeSize.height}; env->SetDoubleArrayRegion(wholeSize_out, 0, 2, tmp_wholeSize); jdouble tmp_ofs[2] = {ofs.x, ofs.y}; env->SetDoubleArrayRegion(ofs_out, 0, 2, tmp_ofs);
|
||||
jdouble tmp_wholeSize[2] = {(jdouble)wholeSize.width, (jdouble)wholeSize.height}; env->SetDoubleArrayRegion(wholeSize_out, 0, 2, tmp_wholeSize); jdouble tmp_ofs[2] = {(jdouble)ofs.x, (jdouble)ofs.y}; env->SetDoubleArrayRegion(ofs_out, 0, 2, tmp_ofs);
|
||||
} catch(const std::exception &e) {
|
||||
throwJavaException(env, &e, method_name);
|
||||
} catch (...) {
|
||||
@ -1488,7 +1488,7 @@ JNIEXPORT jdoubleArray JNICALL Java_org_opencv_core_Mat_n_1size
|
||||
Mat* me = (Mat*) self; //TODO: check for NULL
|
||||
Size _retval_ = me->size( );
|
||||
jdoubleArray _da_retval_ = env->NewDoubleArray(2);
|
||||
jdouble _tmp_retval_[2] = {_retval_.width, _retval_.height};
|
||||
jdouble _tmp_retval_[2] = {(jdouble)_retval_.width, (jdouble)_retval_.height};
|
||||
env->SetDoubleArrayRegion(_da_retval_, 0, 2, _tmp_retval_);
|
||||
return _da_retval_;
|
||||
} catch(const std::exception &e) {
|
||||
|
||||
@ -451,9 +451,9 @@ JNIEXPORT void JNICALL Java_org_opencv_gpu_DeviceInfo_queryMemory_10
|
||||
size_t totalMemory;
|
||||
size_t freeMemory;
|
||||
me->queryMemory( totalMemory, freeMemory );
|
||||
jdouble tmp_totalMemory[1] = {totalMemory};
|
||||
jdouble tmp_totalMemory[1] = {(jdouble)totalMemory};
|
||||
env->SetDoubleArrayRegion(totalMemory_out, 0, 1, tmp_totalMemory);
|
||||
jdouble tmp_freeMemory[1] = {freeMemory};
|
||||
jdouble tmp_freeMemory[1] = {(jdouble)freeMemory};
|
||||
env->SetDoubleArrayRegion(freeMemory_out, 0, 1, tmp_freeMemory);
|
||||
return;
|
||||
} catch(const std::exception &e) {
|
||||
|
||||
@ -119,8 +119,8 @@ public:
|
||||
void compute_descriptors_gpu(const oclMat &descriptors, const oclMat &keypoints, int nFeatures);
|
||||
// end of kernel callers declarations
|
||||
|
||||
SURF_OCL_Invoker(SURF_OCL &surf, const oclMat &img, const oclMat &mask) :
|
||||
surf_(surf),
|
||||
SURF_OCL_Invoker(SURF_OCL &theSurf, const oclMat &img, const oclMat &mask) :
|
||||
surf_(theSurf),
|
||||
img_cols(img.cols), img_rows(img.rows),
|
||||
use_mask(!mask.empty()), counters(oclMat()),
|
||||
imgTex(NULL), sumTex(NULL), maskSumTex(NULL), _img(img)
|
||||
@ -139,7 +139,7 @@ public:
|
||||
CV_Assert(layer_rows - 2 * min_margin > 0);
|
||||
CV_Assert(layer_cols - 2 * min_margin > 0);
|
||||
|
||||
maxFeatures = std::min(static_cast<int>(img.size().area() * surf.keypointsRatio), 65535);
|
||||
maxFeatures = std::min(static_cast<int>(img.size().area() * theSurf.keypointsRatio), 65535);
|
||||
maxCandidates = std::min(static_cast<int>(1.5 * maxFeatures), 65535);
|
||||
|
||||
CV_Assert(maxFeatures > 0);
|
||||
@ -396,9 +396,9 @@ void cv::ocl::SURF_OCL::operator()(const oclMat &img, const oclMat &mask, oclMat
|
||||
{
|
||||
if (!img.empty())
|
||||
{
|
||||
SURF_OCL_Invoker surf(*this, img, mask);
|
||||
SURF_OCL_Invoker theSurf(*this, img, mask);
|
||||
|
||||
surf.detectKeypoints(keypoints);
|
||||
theSurf.detectKeypoints(keypoints);
|
||||
}
|
||||
}
|
||||
|
||||
@ -407,16 +407,16 @@ void cv::ocl::SURF_OCL::operator()(const oclMat &img, const oclMat &mask, oclMat
|
||||
{
|
||||
if (!img.empty())
|
||||
{
|
||||
SURF_OCL_Invoker surf(*this, img, mask);
|
||||
SURF_OCL_Invoker theSurf(*this, img, mask);
|
||||
|
||||
if (!useProvidedKeypoints)
|
||||
surf.detectKeypoints(keypoints);
|
||||
theSurf.detectKeypoints(keypoints);
|
||||
else if (!upright)
|
||||
{
|
||||
surf.findOrientation(keypoints);
|
||||
theSurf.findOrientation(keypoints);
|
||||
}
|
||||
|
||||
surf.computeDescriptors(keypoints, descriptors, descriptorSize());
|
||||
theSurf.computeDescriptors(keypoints, descriptors, descriptorSize());
|
||||
}
|
||||
}
|
||||
|
||||
@ -482,23 +482,23 @@ void cv::ocl::SURF_OCL::operator()(InputArray img, InputArray mask, vector<KeyPo
|
||||
_mask.upload(mask.getMat());
|
||||
}
|
||||
|
||||
SURF_OCL_Invoker surf((SURF_OCL&)*this, _img, _mask);
|
||||
SURF_OCL_Invoker theSurf((SURF_OCL&)*this, _img, _mask);
|
||||
oclMat keypointsGPU;
|
||||
|
||||
if (!useProvidedKeypoints || !upright)
|
||||
((SURF_OCL*)this)->uploadKeypoints(keypoints, keypointsGPU);
|
||||
|
||||
if (!useProvidedKeypoints)
|
||||
surf.detectKeypoints(keypointsGPU);
|
||||
theSurf.detectKeypoints(keypointsGPU);
|
||||
else if (!upright)
|
||||
surf.findOrientation(keypointsGPU);
|
||||
theSurf.findOrientation(keypointsGPU);
|
||||
if(keypointsGPU.cols*keypointsGPU.rows != 0)
|
||||
((SURF_OCL*)this)->downloadKeypoints(keypointsGPU, keypoints);
|
||||
|
||||
if( descriptors.needed() )
|
||||
{
|
||||
oclMat descriptorsGPU;
|
||||
surf.computeDescriptors(keypointsGPU, descriptorsGPU, descriptorSize());
|
||||
theSurf.computeDescriptors(keypointsGPU, descriptorsGPU, descriptorSize());
|
||||
Size sz = descriptorsGPU.size();
|
||||
if( descriptors.kind() == _InputArray::STD_VECTOR )
|
||||
{
|
||||
|
||||
@ -108,7 +108,7 @@ static void arithmetic_run_generic(const oclMat &src1, const oclMat &src2, const
|
||||
#else
|
||||
size_t localThreads[3] = { 16, 16, 1 };
|
||||
#endif
|
||||
size_t globalThreads[3] = { dst.cols, dst.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)dst.cols, (size_t)dst.rows, 1 };
|
||||
|
||||
std::string kernelName = "arithm_binary_op";
|
||||
|
||||
@ -266,7 +266,7 @@ static void compare_run(const oclMat &src1, const oclMat &src2, oclMat &dst, int
|
||||
|
||||
int depth = src1.depth();
|
||||
size_t localThreads[3] = { 64, 4, 1 };
|
||||
size_t globalThreads[3] = { dst.cols, dst.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)dst.cols, (size_t)dst.rows, 1 };
|
||||
|
||||
int src1step1 = src1.step1(), src1offset1 = src1.offset / src1.elemSize1();
|
||||
int src2step1 = src2.step1(), src2offset1 = src2.offset / src2.elemSize1();
|
||||
@ -336,7 +336,7 @@ static void arithmetic_sum_buffer_run(const oclMat &src, cl_mem &dst, int groupn
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&total ));
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&groupnum ));
|
||||
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst ));
|
||||
size_t globalThreads[3] = { groupnum * 256, 1, 1 };
|
||||
size_t globalThreads[3] = { (size_t)groupnum * 256, 1, 1 };
|
||||
|
||||
#ifdef ANDROID
|
||||
openCLExecuteKernel(src.clCxt, &arithm_sum, "arithm_op_sum", globalThreads, NULL,
|
||||
@ -514,7 +514,7 @@ static void arithmetic_minMax_run(const oclMat &src, const oclMat & mask, cl_mem
|
||||
buildOptions += " -D WITH_MASK";
|
||||
}
|
||||
|
||||
size_t globalThreads[3] = { groupnum * 256, 1, 1 };
|
||||
size_t globalThreads[3] = { (size_t)groupnum * 256, 1, 1 };
|
||||
size_t localThreads[3] = { 256, 1, 1 };
|
||||
|
||||
// kernel use fixed grid size, replace lt on NULL is impossible without kernel changes
|
||||
@ -638,7 +638,7 @@ static void arithm_absdiff_nonsaturate_run(const oclMat & src1, const oclMat & s
|
||||
#else
|
||||
size_t localThreads[3] = { 16, 16, 1 };
|
||||
#endif
|
||||
size_t globalThreads[3] = { diff.cols, diff.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)diff.cols, (size_t)diff.rows, 1 };
|
||||
|
||||
const char * const typeMap[] = { "uchar", "char", "ushort", "short", "int", "float", "double" };
|
||||
const char * const channelMap[] = { "", "", "2", "4", "4" };
|
||||
@ -744,7 +744,7 @@ static void arithmetic_flip_run(const oclMat &src, oclMat &dst, string kernelNam
|
||||
std::string buildOptions = format("-D T=%s%s", typeMap[dst.depth()], channelMap[dst.oclchannels()]);
|
||||
|
||||
size_t localThreads[3] = { 64, 4, 1 };
|
||||
size_t globalThreads[3] = { cols, rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)cols, (size_t)rows, 1 };
|
||||
|
||||
int elemSize = src.elemSize();
|
||||
int src_step = src.step / elemSize, src_offset = src.offset / elemSize;
|
||||
@ -797,7 +797,7 @@ static void arithmetic_lut_run(const oclMat &src, const oclMat &lut, oclMat &dst
|
||||
int cols1 = src.cols * src.oclchannels();
|
||||
|
||||
size_t localSize[] = { 16, 16, 1 };
|
||||
size_t globalSize[] = { lut.channels() == 1 ? cols1 : src.cols, src.rows, 1 };
|
||||
size_t globalSize[] = { (size_t)(lut.channels() == 1 ? cols1 : src.cols), (size_t)src.rows, 1 };
|
||||
|
||||
const char * const typeMap[] = { "uchar", "char", "ushort", "short", "int", "float", "double" };
|
||||
std::string buildOptions = format("-D srcT=%s -D dstT=%s", typeMap[sdepth], typeMap[dst.depth()]);
|
||||
@ -862,7 +862,7 @@ static void arithmetic_exp_log_run(const oclMat &src, oclMat &dst, string kernel
|
||||
#else
|
||||
size_t localThreads[3] = { 64, 4, 1 };
|
||||
#endif
|
||||
size_t globalThreads[3] = { dst.cols, dst.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)dst.cols, (size_t)dst.rows, 1 };
|
||||
|
||||
std::string buildOptions = format("-D srcT=%s",
|
||||
ddepth == CV_32F ? "float" : "double");
|
||||
@ -904,7 +904,7 @@ static void arithmetic_magnitude_phase_run(const oclMat &src1, const oclMat &src
|
||||
#else
|
||||
size_t localThreads[3] = { 64, 4, 1 };
|
||||
#endif
|
||||
size_t globalThreads[3] = { dst.cols, dst.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)dst.cols, (size_t)dst.rows, 1 };
|
||||
|
||||
int src1_step = src1.step / src1.elemSize(), src1_offset = src1.offset / src1.elemSize();
|
||||
int src2_step = src2.step / src2.elemSize(), src2_offset = src2.offset / src2.elemSize();
|
||||
@ -956,7 +956,7 @@ static void arithmetic_phase_run(const oclMat &src1, const oclMat &src2, oclMat
|
||||
#else
|
||||
size_t localThreads[3] = { 64, 4, 1 };
|
||||
#endif
|
||||
size_t globalThreads[3] = { cols1, dst.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)cols1, (size_t)dst.rows, 1 };
|
||||
|
||||
vector<pair<size_t , const void *> > args;
|
||||
args.push_back( make_pair( sizeof(cl_mem), (void *)&src1.data ));
|
||||
@ -1006,7 +1006,7 @@ static void arithmetic_cartToPolar_run(const oclMat &src1, const oclMat &src2, o
|
||||
#else
|
||||
size_t localThreads[3] = { 64, 4, 1 };
|
||||
#endif
|
||||
size_t globalThreads[3] = { cols, src1.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)cols, (size_t)src1.rows, 1 };
|
||||
|
||||
int src1_step = src1.step / src1.elemSize1(), src1_offset = src1.offset / src1.elemSize1();
|
||||
int src2_step = src2.step / src2.elemSize1(), src2_offset = src2.offset / src2.elemSize1();
|
||||
@ -1064,7 +1064,7 @@ static void arithmetic_ptc_run(const oclMat &src1, const oclMat &src2, oclMat &d
|
||||
#else
|
||||
size_t localThreads[3] = { 64, 4, 1 };
|
||||
#endif
|
||||
size_t globalThreads[3] = { cols, rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)cols, (size_t)rows, 1 };
|
||||
|
||||
int src1_step = src1.step / src1.elemSize1(), src1_offset = src1.offset / src1.elemSize1();
|
||||
int src2_step = src2.step / src2.elemSize1(), src2_offset = src2.offset / src2.elemSize1();
|
||||
@ -1138,7 +1138,7 @@ static void arithmetic_minMaxLoc_run(const oclMat &src, cl_mem &dst, int vlen ,
|
||||
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst ));
|
||||
char build_options[50];
|
||||
sprintf(build_options, "-D DEPTH_%d -D REPEAT_S%d -D REPEAT_E%d", src.depth(), repeat_s, repeat_e);
|
||||
size_t gt[3] = {groupnum * 256, 1, 1}, lt[3] = {256, 1, 1};
|
||||
size_t gt[3] = {(size_t)groupnum * 256, 1, 1}, lt[3] = {256, 1, 1};
|
||||
|
||||
// kernel use fixed grid size, replace lt on NULL is impossible without kernel changes
|
||||
openCLExecuteKernel(src.clCxt, &arithm_minMaxLoc, "arithm_op_minMaxLoc", gt, lt, args, -1, -1, build_options);
|
||||
@ -1147,7 +1147,7 @@ static void arithmetic_minMaxLoc_run(const oclMat &src, cl_mem &dst, int vlen ,
|
||||
static void arithmetic_minMaxLoc_mask_run(const oclMat &src, const oclMat &mask, cl_mem &dst, int vlen, int groupnum)
|
||||
{
|
||||
vector<pair<size_t , const void *> > args;
|
||||
size_t gt[3] = {groupnum * 256, 1, 1}, lt[3] = {256, 1, 1};
|
||||
size_t gt[3] = {(size_t)groupnum * 256, 1, 1}, lt[3] = {256, 1, 1};
|
||||
char build_options[50];
|
||||
if (src.oclchannels() == 1)
|
||||
{
|
||||
@ -1284,7 +1284,7 @@ static void arithmetic_countNonZero_run(const oclMat &src, cl_mem &dst, int grou
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&groupnum ));
|
||||
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst ));
|
||||
|
||||
size_t globalThreads[3] = { groupnum * 256, 1, 1 };
|
||||
size_t globalThreads[3] = { (size_t)groupnum * 256, 1, 1 };
|
||||
|
||||
#ifdef ANDROID
|
||||
openCLExecuteKernel(src.clCxt, &arithm_nonzero, "arithm_op_nonzero", globalThreads, NULL,
|
||||
@ -1409,7 +1409,7 @@ static void bitwise_run(const oclMat & src1, const oclMat & src2, const Scalar &
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&dst.rows ));
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&cols ));
|
||||
|
||||
size_t globalsize[3] = { dst.cols * ocn / kercn, dst.rows, 1 };
|
||||
size_t globalsize[3] = { (size_t)dst.cols * ocn / kercn, (size_t)dst.rows, 1 };
|
||||
globalsize[0] = divUp(globalsize[0], 256) * 256;
|
||||
openCLExecuteKernel(src1.clCxt, &arithm_bitwise, "arithm_bitwise", globalsize, NULL,
|
||||
args, -1, -1, buildOptions.c_str());
|
||||
@ -1543,7 +1543,7 @@ static void transpose_run(const oclMat &src, oclMat &dst, string kernelName, boo
|
||||
channelsString[src.channels()]);
|
||||
|
||||
size_t localThreads[3] = { TILE_DIM, BLOCK_ROWS, 1 };
|
||||
size_t globalThreads[3] = { src.cols, inplace ? src.rows : divUp(src.rows, TILE_DIM) * BLOCK_ROWS, 1 };
|
||||
size_t globalThreads[3] = { (size_t)src.cols, inplace ? (size_t)src.rows : divUp(src.rows, TILE_DIM) * BLOCK_ROWS, 1 };
|
||||
|
||||
int srcstep1 = src.step / src.elemSize(), dststep1 = dst.step / dst.elemSize();
|
||||
int srcoffset1 = src.offset / src.elemSize(), dstoffset1 = dst.offset / dst.elemSize();
|
||||
@ -1610,7 +1610,7 @@ void cv::ocl::addWeighted(const oclMat &src1, double alpha, const oclMat &src2,
|
||||
typeMap[depth], hasDouble ? "double" : "float", typeMap[depth],
|
||||
depth >= CV_32F ? "" : "_sat_rte");
|
||||
|
||||
size_t globalThreads[3] = { cols1, dst.rows, 1};
|
||||
size_t globalThreads[3] = { (size_t)cols1, (size_t)dst.rows, 1};
|
||||
|
||||
float alpha_f = static_cast<float>(alpha),
|
||||
beta_f = static_cast<float>(beta),
|
||||
@ -1663,7 +1663,7 @@ static void arithmetic_pow_run(const oclMat &src, double p, oclMat &dst, string
|
||||
int depth = dst.depth();
|
||||
|
||||
size_t localThreads[3] = { 64, 4, 1 };
|
||||
size_t globalThreads[3] = { dst.cols, dst.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)dst.cols, (size_t)dst.rows, 1 };
|
||||
|
||||
const char * const typeStr = depth == CV_32F ? "float" : "double";
|
||||
const char * const channelMap[] = { "", "", "2", "4", "4" };
|
||||
@ -1721,7 +1721,7 @@ void cv::ocl::setIdentity(oclMat& src, const Scalar & scalar)
|
||||
|
||||
int src_step1 = src.step / src.elemSize(), src_offset1 = src.offset / src.elemSize();
|
||||
size_t local_threads[] = { 16, 16, 1 };
|
||||
size_t global_threads[] = { src.cols, src.rows, 1 };
|
||||
size_t global_threads[] = { (size_t)src.cols, (size_t)src.rows, 1 };
|
||||
|
||||
const char * const typeMap[] = { "uchar", "char", "ushort", "short", "int", "float", "double" };
|
||||
const char * const channelMap[] = { "", "", "2", "4", "4" };
|
||||
|
||||
@ -199,7 +199,7 @@ static void mog_withoutLearning(const oclMat& frame, int cn, oclMat& fgmask, ocl
|
||||
Context* clCxt = Context::getContext();
|
||||
|
||||
size_t local_thread[] = {32, 8, 1};
|
||||
size_t global_thread[] = {frame.cols, frame.rows, 1};
|
||||
size_t global_thread[] = {(size_t)frame.cols, (size_t)frame.rows, 1};
|
||||
|
||||
int frame_step = (int)(frame.step/frame.elemSize());
|
||||
int fgmask_step = (int)(fgmask.step/fgmask.elemSize());
|
||||
@ -261,7 +261,7 @@ static void mog_withLearning(const oclMat& frame, int cn, oclMat& fgmask_raw, oc
|
||||
Context* clCxt = Context::getContext();
|
||||
|
||||
size_t local_thread[] = {32, 8, 1};
|
||||
size_t global_thread[] = {frame.cols, frame.rows, 1};
|
||||
size_t global_thread[] = {(size_t)frame.cols, (size_t)frame.rows, 1};
|
||||
|
||||
oclMat fgmask(fgmask_raw.size(), CV_32SC1);
|
||||
|
||||
@ -342,7 +342,7 @@ void cv::ocl::device::mog::getBackgroundImage_ocl(int cn, const oclMat& weight,
|
||||
Context* clCxt = Context::getContext();
|
||||
|
||||
size_t local_thread[] = {32, 8, 1};
|
||||
size_t global_thread[] = {dst.cols, dst.rows, 1};
|
||||
size_t global_thread[] = {(size_t)dst.cols, (size_t)dst.rows, 1};
|
||||
|
||||
int weight_step = (int)(weight.step/weight.elemSize());
|
||||
int mean_step = (int)(mean.step/mean.elemSize());
|
||||
@ -411,7 +411,7 @@ void cv::ocl::device::mog::mog2_ocl(const oclMat& frame, int cn, oclMat& fgmaskR
|
||||
detectShadows_flag = 1;
|
||||
|
||||
size_t local_thread[] = {32, 8, 1};
|
||||
size_t global_thread[] = {frame.cols, frame.rows, 1};
|
||||
size_t global_thread[] = {(size_t)frame.cols, (size_t)frame.rows, 1};
|
||||
|
||||
int frame_step = (int)(frame.step/frame.elemSize());
|
||||
int fgmask_step = (int)(fgmask.step/fgmask.elemSize());
|
||||
@ -481,7 +481,7 @@ void cv::ocl::device::mog::getBackgroundImage2_ocl(int cn, const oclMat& modesUs
|
||||
Context* clCxt = Context::getContext();
|
||||
|
||||
size_t local_thread[] = {32, 8, 1};
|
||||
size_t global_thread[] = {modesUsed.cols, modesUsed.rows, 1};
|
||||
size_t global_thread[] = {(size_t)modesUsed.cols, (size_t)modesUsed.rows, 1};
|
||||
|
||||
int weight_step = (int)(weight.step/weight.elemSize());
|
||||
int modesUsed_step = (int)(modesUsed.step/modesUsed.elemSize());
|
||||
|
||||
@ -59,7 +59,7 @@ void cv::ocl::blendLinear(const oclMat &src1, const oclMat &src2, const oclMat &
|
||||
|
||||
dst.create(src1.size(), src1.type());
|
||||
|
||||
size_t globalSize[] = { dst.cols, dst.rows, 1};
|
||||
size_t globalSize[] = { (size_t)dst.cols, (size_t)dst.rows, 1};
|
||||
size_t localSize[] = { 16, 16, 1 };
|
||||
|
||||
int depth = dst.depth(), ocn = dst.oclchannels();
|
||||
|
||||
@ -71,7 +71,7 @@ void matchUnrolledCached(const oclMat &query, const oclMat &train, const oclMat
|
||||
const oclMat &trainIdx, const oclMat &distance, int distType)
|
||||
{
|
||||
cv::ocl::Context *ctx = query.clCxt;
|
||||
size_t globalSize[] = {(query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
|
||||
size_t globalSize[] = {((size_t)query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
|
||||
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
|
||||
const size_t smemSize = (BLOCK_SIZE * (MAX_DESC_LEN >= 2 * BLOCK_SIZE ? MAX_DESC_LEN : 2 * BLOCK_SIZE) + BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
|
||||
int block_size = BLOCK_SIZE;
|
||||
@ -114,7 +114,7 @@ void match(const oclMat &query, const oclMat &train, const oclMat &/*mask*/,
|
||||
const oclMat &trainIdx, const oclMat &distance, int distType)
|
||||
{
|
||||
cv::ocl::Context *ctx = query.clCxt;
|
||||
size_t globalSize[] = {(query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
|
||||
size_t globalSize[] = {((size_t)query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
|
||||
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
|
||||
const size_t smemSize = (2 * BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
|
||||
int block_size = BLOCK_SIZE;
|
||||
@ -156,7 +156,7 @@ void matchUnrolledCached(const oclMat &query, const oclMat &train, float maxDist
|
||||
const oclMat &trainIdx, const oclMat &distance, const oclMat &nMatches, int distType)
|
||||
{
|
||||
cv::ocl::Context *ctx = query.clCxt;
|
||||
size_t globalSize[] = {(train.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, (query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, 1};
|
||||
size_t globalSize[] = {((size_t)train.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, ((size_t)query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, 1};
|
||||
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
|
||||
const size_t smemSize = (2 * BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
|
||||
int block_size = BLOCK_SIZE;
|
||||
@ -198,7 +198,7 @@ void radius_match(const oclMat &query, const oclMat &train, float maxDistance, c
|
||||
const oclMat &trainIdx, const oclMat &distance, const oclMat &nMatches, int distType)
|
||||
{
|
||||
cv::ocl::Context *ctx = query.clCxt;
|
||||
size_t globalSize[] = {(train.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, (query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, 1};
|
||||
size_t globalSize[] = {((size_t)train.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, ((size_t)query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, 1};
|
||||
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
|
||||
const size_t smemSize = (2 * BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
|
||||
int block_size = BLOCK_SIZE;
|
||||
@ -300,7 +300,7 @@ void knn_matchUnrolledCached(const oclMat &query, const oclMat &train, const ocl
|
||||
const oclMat &trainIdx, const oclMat &distance, int distType)
|
||||
{
|
||||
cv::ocl::Context *ctx = query.clCxt;
|
||||
size_t globalSize[] = {(query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
|
||||
size_t globalSize[] = {((size_t)query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
|
||||
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
|
||||
const size_t smemSize = (BLOCK_SIZE * (MAX_DESC_LEN >= BLOCK_SIZE ? MAX_DESC_LEN : BLOCK_SIZE) + BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
|
||||
int block_size = BLOCK_SIZE;
|
||||
@ -337,7 +337,7 @@ void knn_match(const oclMat &query, const oclMat &train, const oclMat &/*mask*/,
|
||||
const oclMat &trainIdx, const oclMat &distance, int distType)
|
||||
{
|
||||
cv::ocl::Context *ctx = query.clCxt;
|
||||
size_t globalSize[] = {(query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
|
||||
size_t globalSize[] = {((size_t)query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
|
||||
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
|
||||
const size_t smemSize = (2 * BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
|
||||
int block_size = BLOCK_SIZE;
|
||||
@ -372,7 +372,7 @@ template < int BLOCK_SIZE, int MAX_DESC_LEN/*, typename Mask*/ >
|
||||
void calcDistanceUnrolled(const oclMat &query, const oclMat &train, const oclMat &/*mask*/, const oclMat &allDist, int distType)
|
||||
{
|
||||
cv::ocl::Context *ctx = query.clCxt;
|
||||
size_t globalSize[] = {(query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
|
||||
size_t globalSize[] = {((size_t)query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
|
||||
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
|
||||
const size_t smemSize = (2 * BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
|
||||
int block_size = BLOCK_SIZE;
|
||||
@ -409,7 +409,7 @@ template < int BLOCK_SIZE/*, typename Mask*/ >
|
||||
void calcDistance(const oclMat &query, const oclMat &train, const oclMat &/*mask*/, const oclMat &allDist, int distType)
|
||||
{
|
||||
cv::ocl::Context *ctx = query.clCxt;
|
||||
size_t globalSize[] = {(query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
|
||||
size_t globalSize[] = {((size_t)query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
|
||||
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
|
||||
const size_t smemSize = (2 * BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
|
||||
int block_size = BLOCK_SIZE;
|
||||
@ -481,7 +481,7 @@ template <int BLOCK_SIZE>
|
||||
void findKnnMatch(int k, const oclMat &trainIdx, const oclMat &distance, const oclMat &allDist, int /*distType*/)
|
||||
{
|
||||
cv::ocl::Context *ctx = trainIdx.clCxt;
|
||||
size_t globalSize[] = {trainIdx.rows * BLOCK_SIZE, 1, 1};
|
||||
size_t globalSize[] = {(size_t)trainIdx.rows * BLOCK_SIZE, 1, 1};
|
||||
size_t localSize[] = {BLOCK_SIZE, 1, 1};
|
||||
int block_size = BLOCK_SIZE;
|
||||
std::string kernelName = "BruteForceMatch_findBestMatch";
|
||||
@ -598,14 +598,14 @@ void cv::ocl::BruteForceMatcher_OCL_base::matchConvert(const Mat &trainIdx, cons
|
||||
const float *distance_ptr = distance.ptr<float>();
|
||||
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx, ++trainIdx_ptr, ++distance_ptr)
|
||||
{
|
||||
int trainIdx = *trainIdx_ptr;
|
||||
int oneTrainIdx = *trainIdx_ptr;
|
||||
|
||||
if (trainIdx == -1)
|
||||
if (oneTrainIdx == -1)
|
||||
continue;
|
||||
|
||||
float distance = *distance_ptr;
|
||||
float oneDistance = *distance_ptr;
|
||||
|
||||
DMatch m(queryIdx, trainIdx, 0, distance);
|
||||
DMatch m(queryIdx, oneTrainIdx, 0, oneDistance);
|
||||
|
||||
matches.push_back(m);
|
||||
}
|
||||
@ -713,16 +713,16 @@ void cv::ocl::BruteForceMatcher_OCL_base::matchConvert(const Mat &trainIdx, cons
|
||||
const float *distance_ptr = distance.ptr<float>();
|
||||
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx, ++trainIdx_ptr, ++imgIdx_ptr, ++distance_ptr)
|
||||
{
|
||||
int trainIdx = *trainIdx_ptr;
|
||||
int oneTrainIdx = *trainIdx_ptr;
|
||||
|
||||
if (trainIdx == -1)
|
||||
if (oneTrainIdx == -1)
|
||||
continue;
|
||||
|
||||
int imgIdx = *imgIdx_ptr;
|
||||
int oneImgIdx = *imgIdx_ptr;
|
||||
|
||||
float distance = *distance_ptr;
|
||||
float oneDistance = *distance_ptr;
|
||||
|
||||
DMatch m(queryIdx, trainIdx, imgIdx, distance);
|
||||
DMatch m(queryIdx, oneTrainIdx, oneImgIdx, oneDistance);
|
||||
|
||||
matches.push_back(m);
|
||||
}
|
||||
@ -811,13 +811,13 @@ void cv::ocl::BruteForceMatcher_OCL_base::knnMatchConvert(const Mat &trainIdx, c
|
||||
|
||||
for (int i = 0; i < k; ++i, ++trainIdx_ptr, ++distance_ptr)
|
||||
{
|
||||
int trainIdx = *trainIdx_ptr;
|
||||
int oneTrainIdx = *trainIdx_ptr;
|
||||
|
||||
if (trainIdx != -1)
|
||||
if (oneTrainIdx != -1)
|
||||
{
|
||||
float distance = *distance_ptr;
|
||||
float oneDistance = *distance_ptr;
|
||||
|
||||
DMatch m(queryIdx, trainIdx, 0, distance);
|
||||
DMatch m(queryIdx, oneTrainIdx, 0, oneDistance);
|
||||
|
||||
curMatches.push_back(m);
|
||||
}
|
||||
@ -901,15 +901,15 @@ void cv::ocl::BruteForceMatcher_OCL_base::knnMatch2Convert(const Mat &trainIdx,
|
||||
|
||||
for (int i = 0; i < 2; ++i, ++trainIdx_ptr, ++imgIdx_ptr, ++distance_ptr)
|
||||
{
|
||||
int trainIdx = *trainIdx_ptr;
|
||||
int oneTrainIdx = *trainIdx_ptr;
|
||||
|
||||
if (trainIdx != -1)
|
||||
if (oneTrainIdx != -1)
|
||||
{
|
||||
int imgIdx = *imgIdx_ptr;
|
||||
int oneImgIdx = *imgIdx_ptr;
|
||||
|
||||
float distance = *distance_ptr;
|
||||
float oneDistance = *distance_ptr;
|
||||
|
||||
DMatch m(queryIdx, trainIdx, imgIdx, distance);
|
||||
DMatch m(queryIdx, oneTrainIdx, oneImgIdx, oneDistance);
|
||||
|
||||
curMatches.push_back(m);
|
||||
}
|
||||
@ -1051,25 +1051,25 @@ void cv::ocl::BruteForceMatcher_OCL_base::radiusMatchConvert(const Mat &trainIdx
|
||||
const int *trainIdx_ptr = trainIdx.ptr<int>(queryIdx);
|
||||
const float *distance_ptr = distance.ptr<float>(queryIdx);
|
||||
|
||||
const int nMatches = std::min(nMatches_ptr[queryIdx], trainIdx.cols);
|
||||
const int matchesNum = std::min(nMatches_ptr[queryIdx], trainIdx.cols);
|
||||
|
||||
if (nMatches == 0)
|
||||
if (matchesNum == 0)
|
||||
{
|
||||
if (!compactResult)
|
||||
matches.push_back(vector<DMatch>());
|
||||
continue;
|
||||
}
|
||||
|
||||
matches.push_back(vector<DMatch>(nMatches));
|
||||
matches.push_back(vector<DMatch>(matchesNum));
|
||||
vector<DMatch> &curMatches = matches.back();
|
||||
|
||||
for (int i = 0; i < nMatches; ++i, ++trainIdx_ptr, ++distance_ptr)
|
||||
for (int i = 0; i < matchesNum; ++i, ++trainIdx_ptr, ++distance_ptr)
|
||||
{
|
||||
int trainIdx = *trainIdx_ptr;
|
||||
int oneTrainIdx = *trainIdx_ptr;
|
||||
|
||||
float distance = *distance_ptr;
|
||||
float oneDistance = *distance_ptr;
|
||||
|
||||
DMatch m(queryIdx, trainIdx, 0, distance);
|
||||
DMatch m(queryIdx, oneTrainIdx, 0, oneDistance);
|
||||
|
||||
curMatches[i] = m;
|
||||
}
|
||||
@ -1177,9 +1177,9 @@ void cv::ocl::BruteForceMatcher_OCL_base::radiusMatchConvert(const Mat &trainIdx
|
||||
const int *imgIdx_ptr = imgIdx.ptr<int>(queryIdx);
|
||||
const float *distance_ptr = distance.ptr<float>(queryIdx);
|
||||
|
||||
const int nMatches = std::min(nMatches_ptr[queryIdx], trainIdx.cols);
|
||||
const int matchesNum = std::min(nMatches_ptr[queryIdx], trainIdx.cols);
|
||||
|
||||
if (nMatches == 0)
|
||||
if (matchesNum == 0)
|
||||
{
|
||||
if (!compactResult)
|
||||
matches.push_back(vector<DMatch>());
|
||||
@ -1188,15 +1188,15 @@ void cv::ocl::BruteForceMatcher_OCL_base::radiusMatchConvert(const Mat &trainIdx
|
||||
|
||||
matches.push_back(vector<DMatch>());
|
||||
vector<DMatch> &curMatches = matches.back();
|
||||
curMatches.reserve(nMatches);
|
||||
curMatches.reserve(matchesNum);
|
||||
|
||||
for (int i = 0; i < nMatches; ++i, ++trainIdx_ptr, ++imgIdx_ptr, ++distance_ptr)
|
||||
for (int i = 0; i < matchesNum; ++i, ++trainIdx_ptr, ++imgIdx_ptr, ++distance_ptr)
|
||||
{
|
||||
int trainIdx = *trainIdx_ptr;
|
||||
int imgIdx = *imgIdx_ptr;
|
||||
float distance = *distance_ptr;
|
||||
int oneTrainIdx = *trainIdx_ptr;
|
||||
int oneImgIdx = *imgIdx_ptr;
|
||||
float oneDistance = *distance_ptr;
|
||||
|
||||
DMatch m(queryIdx, trainIdx, imgIdx, distance);
|
||||
DMatch m(queryIdx, oneTrainIdx, oneImgIdx, oneDistance);
|
||||
|
||||
curMatches.push_back(m);
|
||||
}
|
||||
|
||||
@ -91,7 +91,7 @@ void cv::ocl::buildWarpPlaneMaps(Size /*src_size*/, Rect dst_roi, const Mat &K,
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&ymap_offset));
|
||||
args.push_back( make_pair( sizeof(cl_float), (void *)&scale));
|
||||
|
||||
size_t globalThreads[3] = { xmap.cols, xmap.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)xmap.cols, (size_t)xmap.rows, 1 };
|
||||
#ifdef ANDROID
|
||||
size_t localThreads[3] = {32, 4, 1};
|
||||
#else
|
||||
@ -137,7 +137,7 @@ void cv::ocl::buildWarpCylindricalMaps(Size /*src_size*/, Rect dst_roi, const Ma
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&ymap_offset));
|
||||
args.push_back( make_pair( sizeof(cl_float), (void *)&scale));
|
||||
|
||||
size_t globalThreads[3] = { xmap.cols, xmap.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)xmap.cols, (size_t)xmap.rows, 1 };
|
||||
#ifdef ANDROID
|
||||
size_t localThreads[3] = {32, 1, 1};
|
||||
#else
|
||||
@ -183,7 +183,7 @@ void cv::ocl::buildWarpSphericalMaps(Size /*src_size*/, Rect dst_roi, const Mat
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&ymap_offset));
|
||||
args.push_back( make_pair( sizeof(cl_float), (void *)&scale));
|
||||
|
||||
size_t globalThreads[3] = { xmap.cols, xmap.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)xmap.cols, (size_t)xmap.rows, 1 };
|
||||
#ifdef ANDROID
|
||||
size_t localThreads[3] = {32, 4, 1};
|
||||
#else
|
||||
@ -231,7 +231,7 @@ void cv::ocl::buildWarpAffineMaps(const Mat &M, bool inverse, Size dsize, oclMat
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&xmap_offset));
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&ymap_offset));
|
||||
|
||||
size_t globalThreads[3] = { xmap.cols, xmap.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)xmap.cols, (size_t)xmap.rows, 1 };
|
||||
#ifdef ANDROID
|
||||
size_t localThreads[3] = {32, 4, 1};
|
||||
#else
|
||||
@ -279,7 +279,7 @@ void cv::ocl::buildWarpPerspectiveMaps(const Mat &M, bool inverse, Size dsize, o
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&xmap_offset));
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&ymap_offset));
|
||||
|
||||
size_t globalThreads[3] = { xmap.cols, xmap.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)xmap.cols, (size_t)xmap.rows, 1 };
|
||||
|
||||
openCLExecuteKernel(Context::getContext(), &build_warps, "buildWarpPerspectiveMaps", globalThreads, NULL, args, -1, -1);
|
||||
}
|
||||
|
||||
@ -221,7 +221,7 @@ void canny::calcSobelRowPass_gpu(const oclMat &src, oclMat &dx_buf, oclMat &dy_b
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&dy_buf.step));
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&dy_buf.offset));
|
||||
|
||||
size_t globalThreads[3] = {cols, rows, 1};
|
||||
size_t globalThreads[3] = {(size_t)cols, (size_t)rows, 1};
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
openCLExecuteKernel(clCxt, &imgproc_canny, kernelName, globalThreads, localThreads, args, -1, -1);
|
||||
}
|
||||
@ -250,7 +250,7 @@ void canny::calcMagnitude_gpu(const oclMat &dx_buf, const oclMat &dy_buf, oclMat
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&mag.step));
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&mag.offset));
|
||||
|
||||
size_t globalThreads[3] = {cols, rows, 1};
|
||||
size_t globalThreads[3] = {(size_t)cols, (size_t)rows, 1};
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
|
||||
const char * build_options = L2Grad ? "-D L2GRAD":"";
|
||||
@ -274,7 +274,7 @@ void canny::calcMagnitude_gpu(const oclMat &dx, const oclMat &dy, oclMat &mag, i
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&mag.step));
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&mag.offset));
|
||||
|
||||
size_t globalThreads[3] = {cols, rows, 1};
|
||||
size_t globalThreads[3] = {(size_t)cols, (size_t)rows, 1};
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
|
||||
const char * build_options = L2Grad ? "-D L2GRAD":"";
|
||||
@ -305,7 +305,7 @@ void canny::calcMap_gpu(oclMat &dx, oclMat &dy, oclMat &mag, oclMat &map, int ro
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&map.offset));
|
||||
|
||||
|
||||
size_t globalThreads[3] = {cols, rows, 1};
|
||||
size_t globalThreads[3] = {(size_t)cols, (size_t)rows, 1};
|
||||
string kernelName = "calcMap";
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
|
||||
@ -331,7 +331,7 @@ void canny::edgesHysteresisLocal_gpu(oclMat &map, oclMat &st1, oclMat& counter,
|
||||
cl_int offsetBytes = map.offset;
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&offsetBytes));
|
||||
|
||||
size_t globalThreads[3] = {cols, rows, 1};
|
||||
size_t globalThreads[3] = {(size_t)cols, (size_t)rows, 1};
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
|
||||
openCLExecuteKernel(clCxt, &imgproc_canny, "edgesHysteresisLocal", globalThreads, localThreads, args, -1, -1);
|
||||
@ -355,7 +355,7 @@ void canny::edgesHysteresisGlobal_gpu(oclMat &map, oclMat &st1, oclMat &st2, ocl
|
||||
counter.upload(counterMat);
|
||||
|
||||
args.clear();
|
||||
size_t globalThreads[3] = {std::min((unsigned)count, 65535u) * 128, divUp(count, 65535), 1};
|
||||
size_t globalThreads[3] = {(size_t)std::min((unsigned)count, 65535u) * 128, divUp(count, 65535), 1};
|
||||
args.push_back( make_pair( sizeof(cl_mem), (void *)&map.data));
|
||||
args.push_back( make_pair( sizeof(cl_mem), (void *)&st1.data));
|
||||
args.push_back( make_pair( sizeof(cl_mem), (void *)&st2.data));
|
||||
@ -386,7 +386,7 @@ void canny::getEdges_gpu(oclMat &map, oclMat &dst, int rows, int cols)
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&dst.step));
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&dst.offset));
|
||||
|
||||
size_t globalThreads[3] = {cols, rows, 1};
|
||||
size_t globalThreads[3] = {(size_t)cols, (size_t)rows, 1};
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
|
||||
openCLExecuteKernel(clCxt, &imgproc_canny, kernelName, globalThreads, localThreads, args, -1, -1);
|
||||
|
||||
@ -167,28 +167,28 @@ struct DeviceInfoImpl: public DeviceInfo
|
||||
parseOpenCLVersion(this->deviceVersion,
|
||||
this->deviceVersionMajor, this->deviceVersionMinor);
|
||||
|
||||
size_t maxWorkGroupSize = 0;
|
||||
openCLSafeCall(getScalarInfo(clGetDeviceInfo, device, CL_DEVICE_MAX_WORK_GROUP_SIZE, maxWorkGroupSize));
|
||||
this->maxWorkGroupSize = maxWorkGroupSize;
|
||||
size_t maxWGS = 0;
|
||||
openCLSafeCall(getScalarInfo(clGetDeviceInfo, device, CL_DEVICE_MAX_WORK_GROUP_SIZE, maxWGS));
|
||||
this->maxWorkGroupSize = maxWGS;
|
||||
|
||||
cl_uint maxDimensions = 0;
|
||||
openCLSafeCall(getScalarInfo(clGetDeviceInfo, device, CL_DEVICE_MAX_WORK_ITEM_DIMENSIONS, maxDimensions));
|
||||
std::vector<size_t> maxWorkItemSizes(maxDimensions);
|
||||
std::vector<size_t> maxWIS(maxDimensions);
|
||||
openCLSafeCall(clGetDeviceInfo(device, CL_DEVICE_MAX_WORK_ITEM_SIZES, sizeof(size_t) * maxDimensions,
|
||||
(void *)&maxWorkItemSizes[0], 0));
|
||||
this->maxWorkItemSizes = maxWorkItemSizes;
|
||||
(void *)&maxWIS[0], 0));
|
||||
this->maxWorkItemSizes = maxWIS;
|
||||
|
||||
cl_uint maxComputeUnits = 0;
|
||||
openCLSafeCall(getScalarInfo(clGetDeviceInfo, device, CL_DEVICE_MAX_COMPUTE_UNITS, maxComputeUnits));
|
||||
this->maxComputeUnits = maxComputeUnits;
|
||||
cl_uint maxCU = 0;
|
||||
openCLSafeCall(getScalarInfo(clGetDeviceInfo, device, CL_DEVICE_MAX_COMPUTE_UNITS, maxCU));
|
||||
this->maxComputeUnits = maxCU;
|
||||
|
||||
cl_ulong localMemorySize = 0;
|
||||
openCLSafeCall(getScalarInfo(clGetDeviceInfo, device, CL_DEVICE_LOCAL_MEM_SIZE, localMemorySize));
|
||||
this->localMemorySize = (size_t)localMemorySize;
|
||||
cl_ulong localMS = 0;
|
||||
openCLSafeCall(getScalarInfo(clGetDeviceInfo, device, CL_DEVICE_LOCAL_MEM_SIZE, localMS));
|
||||
this->localMemorySize = (size_t)localMS;
|
||||
|
||||
cl_ulong maxMemAllocSize = 0;
|
||||
openCLSafeCall(getScalarInfo(clGetDeviceInfo, device, CL_DEVICE_MAX_MEM_ALLOC_SIZE, maxMemAllocSize));
|
||||
this->maxMemAllocSize = (size_t)maxMemAllocSize;
|
||||
cl_ulong maxMAS = 0;
|
||||
openCLSafeCall(getScalarInfo(clGetDeviceInfo, device, CL_DEVICE_MAX_MEM_ALLOC_SIZE, maxMAS));
|
||||
this->maxMemAllocSize = (size_t)maxMAS;
|
||||
|
||||
cl_bool unifiedMemory = false;
|
||||
openCLSafeCall(getScalarInfo(clGetDeviceInfo, device, CL_DEVICE_HOST_UNIFIED_MEMORY, unifiedMemory));
|
||||
|
||||
@ -397,16 +397,16 @@ struct ProgramFileCache
|
||||
sizeof(size_t),
|
||||
&binarySize, NULL));
|
||||
|
||||
std::vector<char> binary(binarySize);
|
||||
std::vector<char> localBinary(binarySize);
|
||||
|
||||
char* ptr = &binary[0];
|
||||
char* ptr = &localBinary[0];
|
||||
openCLSafeCall(clGetProgramInfo(program,
|
||||
CL_PROGRAM_BINARIES,
|
||||
sizeof(char*),
|
||||
&ptr,
|
||||
NULL));
|
||||
|
||||
if (!writeConfigurationToFile(options, binary))
|
||||
if (!writeConfigurationToFile(options, localBinary))
|
||||
{
|
||||
std::cerr << "Can't write data to file: " << fileName_ << std::endl;
|
||||
}
|
||||
@ -463,7 +463,7 @@ cl_program ProgramCache::getProgram(const Context *ctx, const cv::ocl::ProgramEn
|
||||
}
|
||||
|
||||
{
|
||||
cv::AutoLock lockCache(mutexCache);
|
||||
cv::AutoLock localLockCache(mutexCache);
|
||||
cl_program program = ProgramCache::getProgramCache()->progLookup(src_sign.str());
|
||||
if (!!program)
|
||||
{
|
||||
@ -478,7 +478,7 @@ cl_program ProgramCache::getProgram(const Context *ctx, const cv::ocl::ProgramEn
|
||||
// second check
|
||||
if (source->name)
|
||||
{
|
||||
cv::AutoLock lockCache(mutexCache);
|
||||
cv::AutoLock localLockCache(mutexCache);
|
||||
cl_program program = ProgramCache::getProgramCache()->progLookup(src_sign.str());
|
||||
if (!!program)
|
||||
{
|
||||
@ -504,7 +504,7 @@ cl_program ProgramCache::getProgram(const Context *ctx, const cv::ocl::ProgramEn
|
||||
//Cache the binary for future use if build_options is null
|
||||
if (source->name)
|
||||
{
|
||||
cv::AutoLock lockCache(mutexCache);
|
||||
cv::AutoLock localLockCache(mutexCache);
|
||||
this->addProgram(src_sign.str(), program);
|
||||
}
|
||||
return program;
|
||||
|
||||
@ -31,11 +31,11 @@
|
||||
opencl_module = GetModuleHandleA("OpenCL.dll");
|
||||
if (!opencl_module)
|
||||
{
|
||||
const char* name = "OpenCL.dll";
|
||||
const char* libName = "OpenCL.dll";
|
||||
const char* envOpenCLBinary = getenv("OPENCV_OPENCL_BINARY");
|
||||
if (envOpenCLBinary)
|
||||
name = envOpenCLBinary;
|
||||
opencl_module = LoadLibraryA(name);
|
||||
libName = envOpenCLBinary;
|
||||
opencl_module = LoadLibraryA(libName);
|
||||
if (!opencl_module)
|
||||
return NULL;
|
||||
}
|
||||
|
||||
@ -87,7 +87,7 @@ static void fromRGB_caller(const oclMat &src, oclMat &dst, int bidx, const std::
|
||||
if (!data2.empty())
|
||||
args.push_back( make_pair( sizeof(cl_mem) , (void *)&data2.data ));
|
||||
|
||||
size_t gt[3] = { dst.cols/pixels_per_work_item, dst.rows, 1 };
|
||||
size_t gt[3] = { (size_t)dst.cols/pixels_per_work_item, (size_t)dst.rows, 1 };
|
||||
#ifdef ANDROID
|
||||
size_t lt[3] = { 16, 10, 1 };
|
||||
#else
|
||||
@ -122,7 +122,7 @@ static void toHSV_caller(const oclMat &src, oclMat &dst, int bidx, const std::st
|
||||
if (!data2.empty())
|
||||
args.push_back( make_pair( sizeof(cl_mem) , (void *)&data2.data ));
|
||||
|
||||
size_t gt[3] = { dst.cols, dst.rows, 1 };
|
||||
size_t gt[3] = { (size_t)dst.cols, (size_t)dst.rows, 1 };
|
||||
#ifdef ANDROID
|
||||
size_t lt[3] = { 16, 10, 1 };
|
||||
#else
|
||||
@ -154,7 +154,7 @@ static void fromGray_caller(const oclMat &src, oclMat &dst, int bidx, const std:
|
||||
if (!data.empty())
|
||||
args.push_back( make_pair( sizeof(cl_mem) , (void *)&data.data ));
|
||||
|
||||
size_t gt[3] = { dst.cols, dst.rows, 1 };
|
||||
size_t gt[3] = { (size_t)dst.cols, (size_t)dst.rows, 1 };
|
||||
#ifdef ANDROID
|
||||
size_t lt[3] = { 16, 10, 1 };
|
||||
#else
|
||||
@ -197,7 +197,7 @@ static void toRGB_caller(const oclMat &src, oclMat &dst, int bidx, const std::st
|
||||
if (!data.empty())
|
||||
args.push_back( make_pair( sizeof(cl_mem) , (void *)&data.data ));
|
||||
|
||||
size_t gt[3] = { dst.cols/pixels_per_work_item, dst.rows, 1 };
|
||||
size_t gt[3] = { (size_t)dst.cols/pixels_per_work_item, (size_t)dst.rows, 1 };
|
||||
#ifdef ANDROID
|
||||
size_t lt[3] = { 16, 10, 1 };
|
||||
#else
|
||||
@ -229,7 +229,7 @@ static void toRGB_NV12_caller(const oclMat &src, oclMat &dst, int bidx, const st
|
||||
if (!data.empty())
|
||||
args.push_back( make_pair( sizeof(cl_mem) , (void *)&data.data ));
|
||||
|
||||
size_t gt[3] = {src.cols, src.rows, 1};
|
||||
size_t gt[3] = {(size_t)src.cols, (size_t)src.rows, 1};
|
||||
#ifdef ANDROID
|
||||
size_t lt[3] = {16, 10, 1};
|
||||
#else
|
||||
@ -261,7 +261,7 @@ static void fromHSV_caller(const oclMat &src, oclMat &dst, int bidx, const std::
|
||||
if (!data.empty())
|
||||
args.push_back( make_pair( sizeof(cl_mem) , (void *)&data.data ));
|
||||
|
||||
size_t gt[3] = { dst.cols, dst.rows, 1 };
|
||||
size_t gt[3] = { (size_t)dst.cols, (size_t)dst.rows, 1 };
|
||||
#ifdef ANDROID
|
||||
size_t lt[3] = { 16, 10, 1 };
|
||||
#else
|
||||
@ -288,7 +288,7 @@ static void RGB_caller(const oclMat &src, oclMat &dst, bool reverse)
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&src_offset ));
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst_offset ));
|
||||
|
||||
size_t gt[3] = { dst.cols, dst.rows, 1 };
|
||||
size_t gt[3] = { (size_t)dst.cols, (size_t)dst.rows, 1 };
|
||||
#ifdef ANDROID
|
||||
size_t lt[3] = { 16, 10, 1 };
|
||||
#else
|
||||
@ -314,7 +314,7 @@ static void fromRGB5x5_caller(const oclMat &src, oclMat &dst, int bidx, int gree
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&src_offset ));
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst_offset ));
|
||||
|
||||
size_t gt[3] = { dst.cols, dst.rows, 1 };
|
||||
size_t gt[3] = { (size_t)dst.cols, (size_t)dst.rows, 1 };
|
||||
#ifdef ANDROID
|
||||
size_t lt[3] = { 16, 10, 1 };
|
||||
#else
|
||||
@ -340,7 +340,7 @@ static void toRGB5x5_caller(const oclMat &src, oclMat &dst, int bidx, int greenb
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&src_offset ));
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst_offset ));
|
||||
|
||||
size_t gt[3] = { dst.cols, dst.rows, 1 };
|
||||
size_t gt[3] = { (size_t)dst.cols, (size_t)dst.rows, 1 };
|
||||
#ifdef ANDROID
|
||||
size_t lt[3] = { 16, 10, 1 };
|
||||
#else
|
||||
|
||||
@ -67,7 +67,7 @@ void cv::ocl::columnSum(const oclMat &src, oclMat &dst)
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&src_offset));
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&dst_offset));
|
||||
|
||||
size_t globalThreads[3] = {dst.cols, 1, 1};
|
||||
size_t globalThreads[3] = {(size_t)dst.cols, 1, 1};
|
||||
size_t localThreads[3] = {256, 1, 1};
|
||||
|
||||
openCLExecuteKernel(src.clCxt, &imgproc_columnsum, "columnSum", globalThreads, localThreads, args, src.oclchannels(), src.depth());
|
||||
|
||||
@ -690,20 +690,20 @@ static void GPUFilter2D(const oclMat &src, oclMat &dst, const Mat &kernel,
|
||||
size_t lt[3] = {BLOCK_SIZE, 1, 1};
|
||||
size_t gt[3] = {divUp(dst.cols, BLOCK_SIZE - (ksize.width - 1)) * BLOCK_SIZE, divUp(dst.rows, BLOCK_SIZE_Y), 1};
|
||||
|
||||
cl_kernel kernel = openCLGetKernelFromSource(src.clCxt, &filtering_filter2D, "filter2D", -1, -1, build_options);
|
||||
cl_kernel localKernel = openCLGetKernelFromSource(src.clCxt, &filtering_filter2D, "filter2D", -1, -1, build_options);
|
||||
|
||||
size_t kernelWorkGroupSize;
|
||||
openCLSafeCall(clGetKernelWorkGroupInfo(kernel, getClDeviceID(src.clCxt),
|
||||
openCLSafeCall(clGetKernelWorkGroupInfo(localKernel, getClDeviceID(src.clCxt),
|
||||
CL_KERNEL_WORK_GROUP_SIZE, sizeof(size_t), &kernelWorkGroupSize, 0));
|
||||
if (lt[0] > kernelWorkGroupSize)
|
||||
{
|
||||
clReleaseKernel(kernel);
|
||||
clReleaseKernel(localKernel);
|
||||
CV_Assert(BLOCK_SIZE > kernelWorkGroupSize);
|
||||
tryWorkItems = kernelWorkGroupSize;
|
||||
continue;
|
||||
}
|
||||
|
||||
openCLExecuteKernel(src.clCxt, kernel, gt, lt, args); // kernel will be released here
|
||||
openCLExecuteKernel(src.clCxt, localKernel, gt, lt, args); // kernel will be released here
|
||||
} while (false);
|
||||
}
|
||||
|
||||
@ -1170,7 +1170,7 @@ void linearRowFilter_gpu(const oclMat &src, const oclMat &dst, oclMat mat_kernel
|
||||
#else
|
||||
size_t localThreads[3] = { 16, 16, 1 };
|
||||
#endif
|
||||
size_t globalThreads[3] = { dst.cols, dst.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)dst.cols, (size_t)dst.rows, 1 };
|
||||
|
||||
const char * const borderMap[] = { "BORDER_CONSTANT", "BORDER_REPLICATE", "BORDER_REFLECT", "BORDER_WRAP", "BORDER_REFLECT_101" };
|
||||
std::string buildOptions = format("-D RADIUSX=%d -D LSIZE0=%d -D LSIZE1=%d -D CN=%d -D %s",
|
||||
|
||||
@ -75,8 +75,8 @@ static void sortCorners_caller(oclMat& corners, const int count)
|
||||
Context * cxt = Context::getContext();
|
||||
int GS = count/2;
|
||||
int LS = min(255,GS);
|
||||
size_t globalThreads[3] = {GS, 1, 1};
|
||||
size_t localThreads[3] = {LS, 1, 1};
|
||||
size_t globalThreads[3] = {(size_t)GS, 1, 1};
|
||||
size_t localThreads[3] = {(size_t)LS, 1, 1};
|
||||
|
||||
// 2^numStages should be equal to count or the output is invalid
|
||||
int numStages = 0;
|
||||
@ -130,7 +130,7 @@ static void findCorners_caller(
|
||||
args.push_back(make_pair( sizeof(cl_int), (void*)&corners.cols ));
|
||||
args.push_back(make_pair( sizeof(cl_mem), (void*)&counter.data ));
|
||||
|
||||
size_t globalThreads[3] = {eig_mat.cols, eig_mat.rows, 1};
|
||||
size_t globalThreads[3] = {(size_t)eig_mat.cols, (size_t)eig_mat.rows, 1};
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
if(!mask.empty())
|
||||
opt += " -D WITH_MASK=1";
|
||||
@ -164,7 +164,7 @@ static void minMaxEig_caller(const oclMat &src, oclMat &dst, oclMat & tozero)
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&total));
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&groupnum));
|
||||
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst_data ));
|
||||
size_t globalThreads[3] = {groupnum * 256, 1, 1};
|
||||
size_t globalThreads[3] = {(size_t)groupnum * 256, 1, 1};
|
||||
size_t localThreads[3] = {256, 1, 1};
|
||||
openCLExecuteKernel(src.clCxt, &arithm_minMax, "arithm_op_minMax", globalThreads, localThreads,
|
||||
args, -1, -1, "-D T=float -D DEPTH_5 -D vlen=1");
|
||||
|
||||
@ -902,9 +902,9 @@ CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemS
|
||||
// pack node info to have less memory loads on the device side
|
||||
oclMat oclNodesPK(1,sizeof(cl_int) * NODE_SIZE * nodenum,CV_8U);
|
||||
{
|
||||
cl_int status;
|
||||
cl_int* pNodesPK = (cl_int*)clEnqueueMapBuffer(getClCommandQueue(oclNodesPK.clCxt),(cl_mem)oclNodesPK.datastart,true,CL_MAP_WRITE, 0, oclNodesPK.step, 0,0,0,&status);
|
||||
openCLVerifyCall(status);
|
||||
cl_int localStatus;
|
||||
cl_int* pNodesPK = (cl_int*)clEnqueueMapBuffer(getClCommandQueue(oclNodesPK.clCxt),(cl_mem)oclNodesPK.datastart,true,CL_MAP_WRITE, 0, oclNodesPK.step, 0,0,0,&localStatus);
|
||||
openCLVerifyCall(localStatus);
|
||||
//use known local data stride to precalulate indexes
|
||||
int DATA_SIZE_X = (localThreads[0]+cascade->orig_window_size.width);
|
||||
// check that maximal value is less than maximal unsigned short
|
||||
@ -921,11 +921,11 @@ CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemS
|
||||
struct NodePK * pOut = (struct NodePK *)(pNodesPK + NODE_SIZE*i);
|
||||
for(int k=0;k<3;++k)
|
||||
{// calc 4 short indexes in shared local mem for each rectangle instead of 2 (x,y) pair.
|
||||
int* p = &(node[i].p[k][0]);
|
||||
pOut->slm_index[k][0] = (unsigned short)(p[1]*DATA_SIZE_X+p[0]);
|
||||
pOut->slm_index[k][1] = (unsigned short)(p[1]*DATA_SIZE_X+p[2]);
|
||||
pOut->slm_index[k][2] = (unsigned short)(p[3]*DATA_SIZE_X+p[0]);
|
||||
pOut->slm_index[k][3] = (unsigned short)(p[3]*DATA_SIZE_X+p[2]);
|
||||
int* lp = &(node[i].p[k][0]);
|
||||
pOut->slm_index[k][0] = (unsigned short)(lp[1]*DATA_SIZE_X+lp[0]);
|
||||
pOut->slm_index[k][1] = (unsigned short)(lp[1]*DATA_SIZE_X+lp[2]);
|
||||
pOut->slm_index[k][2] = (unsigned short)(lp[3]*DATA_SIZE_X+lp[0]);
|
||||
pOut->slm_index[k][3] = (unsigned short)(lp[3]*DATA_SIZE_X+lp[2]);
|
||||
}
|
||||
//store used float point values for each node
|
||||
pOut->weight[0] = node[i].weight[0];
|
||||
@ -959,13 +959,13 @@ CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemS
|
||||
if(WGNumTotal>WGNumSampled)
|
||||
{// small images and each pixel is processed
|
||||
// setup global sizes to have linear array of workgroups with WGNum size
|
||||
int pixelstep = 1;
|
||||
size_t LS[3]={localThreads[0]/pixelstep,localThreads[1]/pixelstep,1};
|
||||
int pstep = 1;
|
||||
size_t LS[3]={localThreads[0]/pstep,localThreads[1]/pstep,1};
|
||||
globalThreads[0] = LS[0]*(WGNumTotal-WGNumSampled);
|
||||
globalThreads[1] = LS[1];
|
||||
globalThreads[2] = 1;
|
||||
string options1 = options;
|
||||
options1 += format(" -D PIXEL_STEP=%d",pixelstep);
|
||||
options1 += format(" -D PIXEL_STEP=%d",pstep);
|
||||
options1 += format(" -D WGSTART=%d",WGNumSampled);
|
||||
options1 += format(" -D LSx=%d",LS[0]);
|
||||
options1 += format(" -D LSy=%d",LS[1]);
|
||||
@ -975,13 +975,13 @@ CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemS
|
||||
if(WGNumSampled>0)
|
||||
{// large images each 4th pixel is processed
|
||||
// setup global sizes to have linear array of workgroups with WGNum size
|
||||
int pixelstep = 2;
|
||||
size_t LS[3]={localThreads[0]/pixelstep,localThreads[1]/pixelstep,1};
|
||||
int pstep = 2;
|
||||
size_t LS[3]={localThreads[0]/pstep,localThreads[1]/pstep,1};
|
||||
globalThreads[0] = LS[0]*WGNumSampled;
|
||||
globalThreads[1] = LS[1];
|
||||
globalThreads[2] = 1;
|
||||
string options2 = options;
|
||||
options2 += format(" -D PIXEL_STEP=%d",pixelstep);
|
||||
options2 += format(" -D PIXEL_STEP=%d",pstep);
|
||||
options2 += format(" -D WGSTART=%d",0);
|
||||
options2 += format(" -D LSx=%d",LS[0]);
|
||||
options2 += format(" -D LSy=%d",LS[1]);
|
||||
@ -1117,7 +1117,7 @@ CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemS
|
||||
args1.push_back ( make_pair(sizeof(cl_float) , (void *)&correction[i] ));
|
||||
args1.push_back ( make_pair(sizeof(cl_int) , (void *)&startnodenum ));
|
||||
|
||||
size_t globalThreads2[3] = {nodenum, 1, 1};
|
||||
size_t globalThreads2[3] = {(size_t)nodenum, 1, 1};
|
||||
openCLExecuteKernel(gsum.clCxt, &haarobjectdetect_scaled2, "gpuscaleclassifier", globalThreads2, NULL/*localThreads2*/, args1, -1, -1);
|
||||
}
|
||||
|
||||
@ -1255,7 +1255,7 @@ void cv::ocl::OclCascadeClassifierBuf::detectMultiScale(oclMat &gimg, CV_OUT std
|
||||
{
|
||||
int blocksize = 8;
|
||||
int grp_per_CU = 12;
|
||||
size_t localThreads[3] = { blocksize, blocksize, 1 };
|
||||
size_t localThreads[3] = { (size_t)blocksize, (size_t)blocksize, 1 };
|
||||
size_t globalThreads[3] = { grp_per_CU * cv::ocl::Context::getContext()->getDeviceInfo().maxComputeUnits *localThreads[0],
|
||||
localThreads[1],
|
||||
1 };
|
||||
@ -1531,7 +1531,7 @@ void cv::ocl::OclCascadeClassifierBuf::Init(const int rows, const int cols,
|
||||
args1.push_back ( make_pair(sizeof(cl_float) , (void *)&correction[i] ));
|
||||
args1.push_back ( make_pair(sizeof(cl_int) , (void *)&startnodenum ));
|
||||
|
||||
size_t globalThreads2[3] = {m_nodenum, 1, 1};
|
||||
size_t globalThreads2[3] = {(size_t)m_nodenum, 1, 1};
|
||||
|
||||
openCLExecuteKernel(Context::getContext(), &haarobjectdetect_scaled2, "gpuscaleclassifier", globalThreads2, NULL/*localThreads2*/, args1, -1, -1);
|
||||
}
|
||||
|
||||
@ -242,7 +242,7 @@ void cv::ocl::HOGDescriptor::init_buffer(const oclMat &img, Size win_stride)
|
||||
gauss_w_lut.upload(gaussian_lut);
|
||||
}
|
||||
|
||||
void cv::ocl::HOGDescriptor::computeGradient(const oclMat &img, oclMat &grad, oclMat &qangle)
|
||||
void cv::ocl::HOGDescriptor::computeGradient(const oclMat &img, oclMat &lgrad, oclMat &lqangle)
|
||||
{
|
||||
CV_Assert(img.type() == CV_8UC1 || img.type() == CV_8UC4);
|
||||
|
||||
@ -251,11 +251,11 @@ void cv::ocl::HOGDescriptor::computeGradient(const oclMat &img, oclMat &grad, oc
|
||||
{
|
||||
case CV_8UC1:
|
||||
hog::compute_gradients_8UC1(effect_size.height, effect_size.width, img,
|
||||
angleScale, grad, qangle, gamma_correction);
|
||||
angleScale, lgrad, lqangle, gamma_correction);
|
||||
break;
|
||||
case CV_8UC4:
|
||||
hog::compute_gradients_8UC4(effect_size.height, effect_size.width, img,
|
||||
angleScale, grad, qangle, gamma_correction);
|
||||
angleScale, lgrad, lqangle, gamma_correction);
|
||||
break;
|
||||
}
|
||||
}
|
||||
@ -1646,7 +1646,7 @@ void cv::ocl::device::hog::compute_hists(int nbins,
|
||||
int qangle_step = qangle.step >> qangle_step_shift;
|
||||
|
||||
int blocks_in_group = 4;
|
||||
size_t localThreads[3] = { blocks_in_group * 24, 2, 1 };
|
||||
size_t localThreads[3] = { (size_t)blocks_in_group * 24, 2, 1 };
|
||||
size_t globalThreads[3] = {
|
||||
divUp(img_block_width * img_block_height, blocks_in_group) * localThreads[0], 2, 1 };
|
||||
|
||||
@ -1791,8 +1791,8 @@ void cv::ocl::device::hog::classify_hists(int win_height, int win_width,
|
||||
int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) /
|
||||
block_stride_x;
|
||||
|
||||
size_t globalThreads[3] = { img_win_width * nthreads, img_win_height, 1 };
|
||||
size_t localThreads[3] = { nthreads, 1, 1 };
|
||||
size_t globalThreads[3] = { (size_t)img_win_width * nthreads, (size_t)img_win_height, 1 };
|
||||
size_t localThreads[3] = { (size_t)nthreads, 1, 1 };
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&cblock_hist_size));
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&img_win_width));
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&img_block_width));
|
||||
@ -1837,7 +1837,7 @@ void cv::ocl::device::hog::extract_descrs_by_rows(int win_height, int win_width,
|
||||
block_stride_x;
|
||||
int descriptors_quadstep = descriptors.step >> 2;
|
||||
|
||||
size_t globalThreads[3] = { img_win_width * NTHREADS, img_win_height, 1 };
|
||||
size_t globalThreads[3] = { (size_t)img_win_width * NTHREADS, (size_t)img_win_height, 1 };
|
||||
size_t localThreads[3] = { NTHREADS, 1, 1 };
|
||||
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&cblock_hist_size));
|
||||
@ -1873,7 +1873,7 @@ void cv::ocl::device::hog::extract_descrs_by_cols(int win_height, int win_width,
|
||||
block_stride_x;
|
||||
int descriptors_quadstep = descriptors.step >> 2;
|
||||
|
||||
size_t globalThreads[3] = { img_win_width * NTHREADS, img_win_height, 1 };
|
||||
size_t globalThreads[3] = { (size_t)img_win_width * NTHREADS, (size_t)img_win_height, 1 };
|
||||
size_t localThreads[3] = { NTHREADS, 1, 1 };
|
||||
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&cblock_hist_size));
|
||||
@ -1903,7 +1903,7 @@ void cv::ocl::device::hog::compute_gradients_8UC1(int height, int width,
|
||||
vector< pair<size_t, const void *> > args;
|
||||
|
||||
size_t localThreads[3] = { NTHREADS, 1, 1 };
|
||||
size_t globalThreads[3] = { width, height, 1 };
|
||||
size_t globalThreads[3] = { (size_t)width, (size_t)height, 1 };
|
||||
char correctGamma = (correct_gamma) ? 1 : 0;
|
||||
int img_step = img.step;
|
||||
int grad_quadstep = grad.step >> 3;
|
||||
@ -1937,7 +1937,7 @@ void cv::ocl::device::hog::compute_gradients_8UC4(int height, int width,
|
||||
vector< pair<size_t, const void *> > args;
|
||||
|
||||
size_t localThreads[3] = { NTHREADS, 1, 1 };
|
||||
size_t globalThreads[3] = { width, height, 1 };
|
||||
size_t globalThreads[3] = { (size_t)width, (size_t)height, 1 };
|
||||
|
||||
char correctGamma = (correct_gamma) ? 1 : 0;
|
||||
int img_step = img.step >> 2;
|
||||
|
||||
@ -164,7 +164,7 @@ namespace cv
|
||||
args.push_back( make_pair(sizeof(cl_int), (void *)&cols));
|
||||
|
||||
size_t localThreads[3] = { 16, 16, 1 };
|
||||
size_t globalThreads[3] = { cols, dst.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)cols, (size_t)dst.rows, 1 };
|
||||
|
||||
openCLExecuteKernel(src.clCxt, &imgproc_threshold, "threshold", globalThreads, localThreads, args,
|
||||
-1, -1, buildOptions.c_str());
|
||||
@ -229,7 +229,7 @@ namespace cv
|
||||
CV_Error(CV_StsBadArg, "Unsupported map types");
|
||||
|
||||
int ocn = dst.oclchannels();
|
||||
size_t globalThreads[3] = { dst.cols, dst.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)dst.cols, (size_t)dst.rows, 1 };
|
||||
|
||||
Mat scalar(1, 1, CV_MAKE_TYPE(dst.depth(), ocn), borderValue);
|
||||
std::string buildOptions = format("-D %s -D %s -D T=%s%s", interMap[interpolation],
|
||||
@ -422,7 +422,7 @@ namespace cv
|
||||
}
|
||||
}
|
||||
|
||||
size_t globalThreads[3] = { glbSizeX, dst.rows, 1 };
|
||||
size_t globalThreads[3] = { glbSizeX, (size_t)dst.rows, 1 };
|
||||
size_t localThreads[3] = { blkSizeX, blkSizeY, 1 };
|
||||
|
||||
std::vector< std::pair<size_t, const void *> > args;
|
||||
@ -521,7 +521,7 @@ namespace cv
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&srcStep));
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&dstStep));
|
||||
|
||||
size_t globalThreads[3] = {(src.cols + 18) / 16 * 16, (src.rows + 15) / 16 * 16, 1};
|
||||
size_t globalThreads[3] = {((size_t)src.cols + 18) / 16 * 16, ((size_t)src.rows + 15) / 16 * 16, 1};
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
|
||||
if (m == 3)
|
||||
@ -589,7 +589,7 @@ namespace cv
|
||||
CV_Error(CV_StsBadArg, "Unsupported border type");
|
||||
|
||||
size_t localThreads[3] = { 16, 16, 1 };
|
||||
size_t globalThreads[3] = { dst.cols, dst.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)dst.cols, (size_t)dst.rows, 1 };
|
||||
|
||||
vector< pair<size_t, const void *> > args;
|
||||
args.push_back( make_pair( sizeof(cl_mem), (void *)&_src.data));
|
||||
@ -936,7 +936,7 @@ namespace cv
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&src.cols ));
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&src.step ));
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.step));
|
||||
size_t gt[3] = {((vcols + 1) / 2) * 256, 1, 1}, lt[3] = {256, 1, 1};
|
||||
size_t gt[3] = {(((size_t)vcols + 1) / 2) * 256, 1, 1}, lt[3] = {256, 1, 1};
|
||||
openCLExecuteKernel(src.clCxt, &imgproc_integral, "integral_cols", gt, lt, args, -1, depth);
|
||||
|
||||
args.clear();
|
||||
@ -951,7 +951,7 @@ namespace cv
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&sqsum.step));
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&sum_offset));
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&sqsum_offset));
|
||||
size_t gt2[3] = {t_sum.cols * 32, 1, 1}, lt2[3] = {256, 1, 1};
|
||||
size_t gt2[3] = {(size_t)t_sum.cols * 32, 1, 1}, lt2[3] = {256, 1, 1};
|
||||
openCLExecuteKernel(src.clCxt, &imgproc_integral, "integral_rows", gt2, lt2, args, -1, depth);
|
||||
}
|
||||
|
||||
@ -981,7 +981,7 @@ namespace cv
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&src.cols ));
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&src.step ));
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.step));
|
||||
size_t gt[3] = {((vcols + 1) / 2) * 256, 1, 1}, lt[3] = {256, 1, 1};
|
||||
size_t gt[3] = {(((size_t)vcols + 1) / 2) * 256, 1, 1}, lt[3] = {256, 1, 1};
|
||||
openCLExecuteKernel(src.clCxt, &imgproc_integral_sum, "integral_sum_cols", gt, lt, args, -1, depth);
|
||||
|
||||
args.clear();
|
||||
@ -992,7 +992,7 @@ namespace cv
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.step ));
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&sum.step));
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&sum_offset));
|
||||
size_t gt2[3] = {t_sum.cols * 32, 1, 1}, lt2[3] = {256, 1, 1};
|
||||
size_t gt2[3] = {(size_t)t_sum.cols * 32, 1, 1}, lt2[3] = {256, 1, 1};
|
||||
openCLExecuteKernel(src.clCxt, &imgproc_integral_sum, "integral_sum_rows", gt2, lt2, args, -1, depth);
|
||||
}
|
||||
|
||||
@ -1025,7 +1025,7 @@ namespace cv
|
||||
|
||||
CV_Assert(Dx.rows == Dy.rows && Dx.cols == Dy.cols);
|
||||
|
||||
size_t lt2[3] = {sobel_lsz, sobel_lsz, 1};
|
||||
size_t lt2[3] = {(size_t)sobel_lsz, (size_t)sobel_lsz, 1};
|
||||
size_t gt2[3] = {lt2[0]*(1 + (src.cols-1) / lt2[0]), lt2[1]*(1 + (src.rows-1) / lt2[1]), 1};
|
||||
|
||||
unsigned int src_pitch = src.step;
|
||||
@ -1247,8 +1247,8 @@ namespace cv
|
||||
if (src.rows % lty != 0)
|
||||
row = (row / lty + 1) * lty;
|
||||
|
||||
size_t globalThreads[3] = {col, row, 1};
|
||||
size_t localThreads[3] = {ltx, lty, 1};
|
||||
size_t globalThreads[3] = {(size_t)col, (size_t)row, 1};
|
||||
size_t localThreads[3] = {(size_t)ltx, (size_t)lty, 1};
|
||||
|
||||
//set args
|
||||
vector<pair<size_t , const void *> > args;
|
||||
@ -1306,8 +1306,8 @@ namespace cv
|
||||
if (src.rows % lty != 0)
|
||||
row = (row / lty + 1) * lty;
|
||||
|
||||
size_t globalThreads[3] = {col, row, 1};
|
||||
size_t localThreads[3] = {ltx, lty, 1};
|
||||
size_t globalThreads[3] = {(size_t)col, (size_t)row, 1};
|
||||
size_t localThreads[3] = {(size_t)ltx, (size_t)lty, 1};
|
||||
|
||||
//set args
|
||||
vector<pair<size_t , const void *> > args;
|
||||
@ -1567,7 +1567,7 @@ namespace cv
|
||||
args.push_back( std::make_pair( sizeof(cl_int), (void *)&lut.offset ));
|
||||
|
||||
size_t localThreads[3] = { 32, 8, 1 };
|
||||
size_t globalThreads[3] = { src.cols, src.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)src.cols, (size_t)src.rows, 1 };
|
||||
|
||||
openCLExecuteKernel(Context::getContext(), &imgproc_clahe, "transform", globalThreads, localThreads, args, -1, -1);
|
||||
}
|
||||
@ -1754,7 +1754,7 @@ namespace cv
|
||||
#else
|
||||
size_t localThreads[3] = { 16, 16, 1 };
|
||||
#endif
|
||||
size_t globalThreads[3] = { dst.cols, dst.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)dst.cols, (size_t)dst.rows, 1 };
|
||||
|
||||
if ((dst.type() == CV_8UC1) && ((dst.offset & 3) == 0) && ((dst.cols & 3) == 0))
|
||||
{
|
||||
@ -1799,7 +1799,7 @@ static void convolve_run(const oclMat &src, const oclMat &temp1, oclMat &dst, st
|
||||
dst.create(src.size(), src.type());
|
||||
|
||||
size_t localThreads[3] = { 16, 16, 1 };
|
||||
size_t globalThreads[3] = { dst.cols, dst.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)dst.cols, (size_t)dst.rows, 1 };
|
||||
|
||||
int src_step = src.step / src.elemSize(), src_offset = src.offset / src.elemSize();
|
||||
int dst_step = dst.step / dst.elemSize(), dst_offset = dst.offset / dst.elemSize();
|
||||
|
||||
@ -141,7 +141,7 @@ void interpolate::memsetKernel(float val, oclMat &img, int height, int offset)
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&step));
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&offset));
|
||||
|
||||
size_t globalThreads[3] = {img.cols, height, 1};
|
||||
size_t globalThreads[3] = {(size_t)img.cols, (size_t)height, 1};
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
openCLExecuteKernel(clCxt, &interpolate_frames, kernelName, globalThreads, localThreads, args, -1, -1);
|
||||
}
|
||||
@ -161,7 +161,7 @@ void interpolate::normalizeKernel(oclMat &buffer, int height, int factor_offset,
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&factor_offset));
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&dst_offset));
|
||||
|
||||
size_t globalThreads[3] = {buffer.cols, height, 1};
|
||||
size_t globalThreads[3] = {(size_t)buffer.cols, (size_t)height, 1};
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
openCLExecuteKernel(clCxt, &interpolate_frames, kernelName, globalThreads, localThreads, args, -1, -1);
|
||||
}
|
||||
@ -190,7 +190,7 @@ void interpolate::forwardWarpKernel(const oclMat &src, oclMat &buffer, const ocl
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&d_offset));
|
||||
args.push_back( make_pair( sizeof(cl_float), (void *)&time_scale));
|
||||
|
||||
size_t globalThreads[3] = {src.cols, src.rows, 1};
|
||||
size_t globalThreads[3] = {(size_t)src.cols, (size_t)src.rows, 1};
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
openCLExecuteKernel(clCxt, &interpolate_frames, kernelName, globalThreads, localThreads, args, -1, -1);
|
||||
}
|
||||
@ -220,7 +220,7 @@ void interpolate::blendFrames(const oclMat &frame0, const oclMat &/*frame1*/, co
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&step));
|
||||
args.push_back( make_pair( sizeof(cl_float), (void *)&pos));
|
||||
|
||||
size_t globalThreads[3] = {frame0.cols, frame0.rows, 1};
|
||||
size_t globalThreads[3] = {(size_t)frame0.cols, (size_t)frame0.rows, 1};
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
openCLExecuteKernel(clCxt, &interpolate_frames, kernelName, globalThreads, localThreads, args, -1, -1);
|
||||
}
|
||||
|
||||
@ -195,7 +195,7 @@ void cv::ocl::distanceToCenters(const oclMat &src, const oclMat ¢ers, Mat &d
|
||||
args.push_back(make_pair(sizeof(cl_int), (void *)&src_offset));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void *)¢ers_offset));
|
||||
|
||||
size_t globalThreads[3] = { all_dist_count, 1, 1 };
|
||||
size_t globalThreads[3] = { (size_t)all_dist_count, 1, 1 };
|
||||
|
||||
openCLExecuteKernel(Context::getContext(), &kmeans_kernel,
|
||||
"distanceToCenters", globalThreads, NULL, args, -1, -1, build_opt_ss.str().c_str());
|
||||
|
||||
@ -93,13 +93,13 @@ bool KNearestNeighbour::train(const Mat& trainData, Mat& labels, Mat& sampleIdx,
|
||||
return cv_knn_train;
|
||||
}
|
||||
|
||||
void KNearestNeighbour::find_nearest(const oclMat& samples, int k, oclMat& lables)
|
||||
void KNearestNeighbour::find_nearest(const oclMat& lsamples, int k, oclMat& lables)
|
||||
{
|
||||
CV_Assert(!samples_ocl.empty());
|
||||
lables.create(samples.rows, 1, CV_32FC1);
|
||||
lables.create(lsamples.rows, 1, CV_32FC1);
|
||||
|
||||
CV_Assert(samples.cols == CvKNearest::var_count);
|
||||
CV_Assert(samples.type() == CV_32FC1);
|
||||
CV_Assert(lsamples.cols == CvKNearest::var_count);
|
||||
CV_Assert(lsamples.type() == CV_32FC1);
|
||||
CV_Assert(k >= 1 && k <= max_k);
|
||||
|
||||
int k1 = KNearest::get_sample_count();
|
||||
@ -112,8 +112,8 @@ void KNearestNeighbour::find_nearest(const oclMat& samples, int k, oclMat& lable
|
||||
nThreads = 256;
|
||||
|
||||
int smem_size = nThreads * k * 4 * 2;
|
||||
size_t local_thread[] = {1, nThreads, 1};
|
||||
size_t global_thread[] = {1, samples.rows, 1};
|
||||
size_t local_thread[] = {1, (size_t)nThreads, 1};
|
||||
size_t global_thread[] = {1, (size_t)lsamples.rows, 1};
|
||||
|
||||
char build_option[50];
|
||||
if(!Context::getContext()->supportsFeature(FEATURE_CL_DOUBLE))
|
||||
@ -125,16 +125,16 @@ void KNearestNeighbour::find_nearest(const oclMat& samples, int k, oclMat& lable
|
||||
std::vector< std::pair<size_t, const void*> > args;
|
||||
|
||||
int samples_ocl_step = samples_ocl.step/samples_ocl.elemSize();
|
||||
int samples_step = samples.step/samples.elemSize();
|
||||
int samples_step = lsamples.step/lsamples.elemSize();
|
||||
int lables_step = lables.step/lables.elemSize();
|
||||
|
||||
int _regression = 0;
|
||||
if(CvKNearest::regression)
|
||||
_regression = 1;
|
||||
|
||||
args.push_back(make_pair(sizeof(cl_mem), (void*)&samples.data));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&samples.rows));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&samples.cols));
|
||||
args.push_back(make_pair(sizeof(cl_mem), (void*)&lsamples.data));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&lsamples.rows));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&lsamples.cols));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&samples_step));
|
||||
args.push_back(make_pair(sizeof(cl_int), (void*)&k));
|
||||
args.push_back(make_pair(sizeof(cl_mem), (void*)&samples_ocl.data));
|
||||
|
||||
@ -136,7 +136,7 @@ namespace cv
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&result.offset));
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
|
||||
|
||||
size_t globalThreads[3] = {result.cols, result.rows, 1};
|
||||
size_t globalThreads[3] = {(size_t)result.cols, (size_t)result.rows, 1};
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
|
||||
const char * build_opt = image.oclchannels() == 4 ? "-D CN4" : "";
|
||||
@ -170,7 +170,7 @@ namespace cv
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&result.offset));
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
|
||||
|
||||
size_t globalThreads[3] = {result.cols, result.rows, 1};
|
||||
size_t globalThreads[3] = {(size_t)result.cols, (size_t)result.rows, 1};
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, 1, CV_8U);
|
||||
}
|
||||
@ -205,7 +205,7 @@ namespace cv
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&templ.step));
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
|
||||
|
||||
size_t globalThreads[3] = {result.cols, result.rows, 1};
|
||||
size_t globalThreads[3] = {(size_t)result.cols, (size_t)result.rows, 1};
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, image.oclchannels(), image.depth());
|
||||
}
|
||||
@ -269,7 +269,7 @@ namespace cv
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&result.offset));
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
|
||||
|
||||
size_t globalThreads[3] = {result.cols, result.rows, 1};
|
||||
size_t globalThreads[3] = {(size_t)result.cols, (size_t)result.rows, 1};
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, 1, CV_8U);
|
||||
}
|
||||
@ -304,7 +304,7 @@ namespace cv
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&templ.step));
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
|
||||
|
||||
size_t globalThreads[3] = {result.cols, result.rows, 1};
|
||||
size_t globalThreads[3] = {(size_t)result.cols, (size_t)result.rows, 1};
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, image.oclchannels(), image.depth());
|
||||
}
|
||||
@ -321,7 +321,7 @@ namespace cv
|
||||
string kernelName;
|
||||
|
||||
kernelName = "matchTemplate_Prepared_CCOFF";
|
||||
size_t globalThreads[3] = {result.cols, result.rows, 1};
|
||||
size_t globalThreads[3] = {(size_t)result.cols, (size_t)result.rows, 1};
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
|
||||
vector< pair<size_t, const void *> > args;
|
||||
@ -394,7 +394,7 @@ namespace cv
|
||||
string kernelName;
|
||||
|
||||
kernelName = "matchTemplate_Prepared_CCOFF_NORMED";
|
||||
size_t globalThreads[3] = {result.cols, result.rows, 1};
|
||||
size_t globalThreads[3] = {(size_t)result.cols, (size_t)result.rows, 1};
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
|
||||
vector< pair<size_t, const void *> > args;
|
||||
@ -492,7 +492,7 @@ namespace cv
|
||||
string kernelName;
|
||||
|
||||
kernelName = "extractFirstChannel";
|
||||
size_t globalThreads[3] = {result.cols, result.rows, 1};
|
||||
size_t globalThreads[3] = {(size_t)result.cols, (size_t)result.rows, 1};
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
|
||||
vector< pair<size_t, const void *> > args;
|
||||
|
||||
@ -238,7 +238,7 @@ static void copy_to_with_mask(const oclMat &src, oclMat &dst, const oclMat &mask
|
||||
char compile_option[32];
|
||||
sprintf(compile_option, "-D GENTYPE=%s", string_types[dst.oclchannels() - 1][dst.depth()].c_str());
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
size_t globalThreads[3] = { dst.cols, dst.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)dst.cols, (size_t)dst.rows, 1 };
|
||||
|
||||
int dststep_in_pixel = dst.step / dst.elemSize(), dstoffset_in_pixel = dst.offset / dst.elemSize();
|
||||
int srcstep_in_pixel = src.step / src.elemSize(), srcoffset_in_pixel = src.offset / src.elemSize();
|
||||
@ -426,7 +426,7 @@ static void set_to_withoutmask_run(const oclMat &dst, const Scalar &scalar, stri
|
||||
vector<pair<size_t , const void *> > args;
|
||||
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
size_t globalThreads[3] = { dst.cols, dst.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)dst.cols, (size_t)dst.rows, 1 };
|
||||
int step_in_pixel = dst.step / dst.elemSize(), offset_in_pixel = dst.offset / dst.elemSize();
|
||||
|
||||
if (dst.type() == CV_8UC1)
|
||||
@ -469,7 +469,7 @@ static void set_to_withmask_run(const oclMat &dst, const Scalar &scalar, const o
|
||||
CV_DbgAssert( dst.rows == mask.rows && dst.cols == mask.cols);
|
||||
vector<pair<size_t , const void *> > args;
|
||||
size_t localThreads[3] = { 16, 16, 1 };
|
||||
size_t globalThreads[3] = { dst.cols, dst.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)dst.cols, (size_t)dst.rows, 1 };
|
||||
int step_in_pixel = dst.step / dst.elemSize(), offset_in_pixel = dst.offset / dst.elemSize();
|
||||
|
||||
const char * const typeMap[] = { "uchar", "char", "ushort", "short", "int", "float", "double" };
|
||||
|
||||
@ -184,14 +184,14 @@ namespace cv
|
||||
&err);
|
||||
}
|
||||
size_t origin[] = { 0, 0, 0 };
|
||||
size_t region[] = { mat.cols, mat.rows, 1 };
|
||||
size_t region[] = { (size_t)mat.cols, (size_t)mat.rows, 1 };
|
||||
|
||||
cl_mem devData;
|
||||
if (mat.cols * mat.elemSize() != mat.step)
|
||||
{
|
||||
devData = clCreateBuffer(*(cl_context*)mat.clCxt->getOpenCLContextPtr(), CL_MEM_READ_ONLY, mat.cols * mat.rows
|
||||
* mat.elemSize(), NULL, NULL);
|
||||
const size_t regin[3] = {mat.cols * mat.elemSize(), mat.rows, 1};
|
||||
const size_t regin[3] = {(size_t)mat.cols * mat.elemSize(), (size_t)mat.rows, 1};
|
||||
clEnqueueCopyBufferRect(*(cl_command_queue*)mat.clCxt->getOpenCLCommandQueuePtr(), (cl_mem)mat.data, devData, origin, origin,
|
||||
regin, mat.step, 0, mat.cols * mat.elemSize(), 0, 0, NULL, NULL);
|
||||
clFlush(*(cl_command_queue*)mat.clCxt->getOpenCLCommandQueuePtr());
|
||||
|
||||
@ -54,42 +54,42 @@ namespace cv
|
||||
namespace ocl
|
||||
{
|
||||
// The function calculates center of gravity and the central second order moments
|
||||
static void icvCompleteMomentState( CvMoments* moments )
|
||||
static void icvCompleteMomentState( CvMoments* lmoments )
|
||||
{
|
||||
double cx = 0, cy = 0;
|
||||
double mu20, mu11, mu02;
|
||||
|
||||
assert( moments != 0 );
|
||||
moments->inv_sqrt_m00 = 0;
|
||||
assert( lmoments != 0 );
|
||||
lmoments->inv_sqrt_m00 = 0;
|
||||
|
||||
if( fabs(moments->m00) > DBL_EPSILON )
|
||||
if( fabs(lmoments->m00) > DBL_EPSILON )
|
||||
{
|
||||
double inv_m00 = 1. / moments->m00;
|
||||
cx = moments->m10 * inv_m00;
|
||||
cy = moments->m01 * inv_m00;
|
||||
moments->inv_sqrt_m00 = std::sqrt( fabs(inv_m00) );
|
||||
double inv_m00 = 1. / lmoments->m00;
|
||||
cx = lmoments->m10 * inv_m00;
|
||||
cy = lmoments->m01 * inv_m00;
|
||||
lmoments->inv_sqrt_m00 = std::sqrt( fabs(inv_m00) );
|
||||
}
|
||||
|
||||
// mu20 = m20 - m10*cx
|
||||
mu20 = moments->m20 - moments->m10 * cx;
|
||||
mu20 = lmoments->m20 - lmoments->m10 * cx;
|
||||
// mu11 = m11 - m10*cy
|
||||
mu11 = moments->m11 - moments->m10 * cy;
|
||||
mu11 = lmoments->m11 - lmoments->m10 * cy;
|
||||
// mu02 = m02 - m01*cy
|
||||
mu02 = moments->m02 - moments->m01 * cy;
|
||||
mu02 = lmoments->m02 - lmoments->m01 * cy;
|
||||
|
||||
moments->mu20 = mu20;
|
||||
moments->mu11 = mu11;
|
||||
moments->mu02 = mu02;
|
||||
lmoments->mu20 = mu20;
|
||||
lmoments->mu11 = mu11;
|
||||
lmoments->mu02 = mu02;
|
||||
|
||||
// mu30 = m30 - cx*(3*mu20 + cx*m10)
|
||||
moments->mu30 = moments->m30 - cx * (3 * mu20 + cx * moments->m10);
|
||||
lmoments->mu30 = lmoments->m30 - cx * (3 * mu20 + cx * lmoments->m10);
|
||||
mu11 += mu11;
|
||||
// mu21 = m21 - cx*(2*mu11 + cx*m01) - cy*mu20
|
||||
moments->mu21 = moments->m21 - cx * (mu11 + cx * moments->m01) - cy * mu20;
|
||||
lmoments->mu21 = lmoments->m21 - cx * (mu11 + cx * lmoments->m01) - cy * mu20;
|
||||
// mu12 = m12 - cy*(2*mu11 + cy*m10) - cx*mu02
|
||||
moments->mu12 = moments->m12 - cy * (mu11 + cy * moments->m10) - cx * mu02;
|
||||
lmoments->mu12 = lmoments->m12 - cy * (mu11 + cy * lmoments->m10) - cx * mu02;
|
||||
// mu03 = m03 - cy*(3*mu02 + cy*m01)
|
||||
moments->mu03 = moments->m03 - cy * (3 * mu02 + cy * moments->m01);
|
||||
lmoments->mu03 = lmoments->m03 - cy * (3 * mu02 + cy * lmoments->m01);
|
||||
}
|
||||
|
||||
|
||||
@ -135,8 +135,8 @@ namespace cv
|
||||
cv::ocl::oclMat dst_a(10, lpt, CV_64FC1);
|
||||
cv::ocl::oclMat reader_oclmat(reader_mat);
|
||||
int llength = std::min(lpt,128);
|
||||
size_t localThreads[3] = { llength, 1, 1};
|
||||
size_t globalThreads[3] = { lpt, 1, 1};
|
||||
size_t localThreads[3] = { (size_t)llength, 1, 1};
|
||||
size_t globalThreads[3] = { (size_t)lpt, 1, 1};
|
||||
vector<pair<size_t , const void *> > args;
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&contour->total ));
|
||||
args.push_back( make_pair( sizeof(cl_mem) , (void *)&reader_oclmat.data ));
|
||||
@ -252,8 +252,8 @@ namespace cv
|
||||
oclMat dst_m;
|
||||
int tile_height = TILE_SIZE;
|
||||
|
||||
size_t localThreads[3] = {1, tile_height, 1};
|
||||
size_t globalThreads[3] = {blockx, size.height, 1};
|
||||
size_t localThreads[3] = {1, (size_t)tile_height, 1};
|
||||
size_t globalThreads[3] = {(size_t)blockx, (size_t)size.height, 1};
|
||||
|
||||
if(Context::getContext()->supportsFeature(FEATURE_CL_DOUBLE))
|
||||
{
|
||||
|
||||
@ -66,7 +66,7 @@ static void updateMatricesOcl(const oclMat &flowx, const oclMat &flowy, const oc
|
||||
#else
|
||||
size_t localThreads[3] = { 32, 8, 1 };
|
||||
#endif
|
||||
size_t globalThreads[3] = { flowx.cols, flowx.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)flowx.cols, (size_t)flowx.rows, 1 };
|
||||
|
||||
std::vector< std::pair<size_t, const void *> > args;
|
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&M.data));
|
||||
@ -95,7 +95,7 @@ static void boxFilter5Ocl(const oclMat &src, int ksizeHalf, oclMat &dst)
|
||||
#else
|
||||
size_t localThreads[3] = { 256, 1, 1 };
|
||||
#endif
|
||||
size_t globalThreads[3] = { src.cols, height, 1 };
|
||||
size_t globalThreads[3] = { (size_t)src.cols, (size_t)height, 1 };
|
||||
int smem_size = (localThreads[0] + 2*ksizeHalf) * 5 * sizeof(float);
|
||||
|
||||
std::vector< std::pair<size_t, const void *> > args;
|
||||
@ -121,7 +121,7 @@ static void updateFlowOcl(const oclMat &M, oclMat &flowx, oclMat &flowy)
|
||||
#else
|
||||
size_t localThreads[3] = { 32, 8, 1 };
|
||||
#endif
|
||||
size_t globalThreads[3] = { cols, flowx.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)cols, (size_t)flowx.rows, 1 };
|
||||
|
||||
std::vector< std::pair<size_t, const void *> > args;
|
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&flowx.data));
|
||||
@ -161,7 +161,7 @@ void cv::ocl::FarnebackOpticalFlow::gaussianBlurOcl(const oclMat &src, int ksize
|
||||
#else
|
||||
size_t localThreads[3] = { 256, 1, 1 };
|
||||
#endif
|
||||
size_t globalThreads[3] = { src.cols, src.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)src.cols, (size_t)src.rows, 1 };
|
||||
int smem_size = (localThreads[0] + 2*ksizeHalf) * sizeof(float);
|
||||
|
||||
CV_Assert(dst.size() == src.size());
|
||||
@ -180,7 +180,7 @@ void cv::ocl::FarnebackOpticalFlow::gaussianBlurOcl(const oclMat &src, int ksize
|
||||
globalThreads, localThreads, args, -1, -1);
|
||||
}
|
||||
|
||||
void cv::ocl::FarnebackOpticalFlow::polynomialExpansionOcl(const oclMat &src, int polyN, oclMat &dst)
|
||||
void cv::ocl::FarnebackOpticalFlow::polynomialExpansionOcl(const oclMat &src, int lpolyN, oclMat &dst)
|
||||
{
|
||||
string kernelName("polynomialExpansion");
|
||||
|
||||
@ -189,7 +189,7 @@ void cv::ocl::FarnebackOpticalFlow::polynomialExpansionOcl(const oclMat &src, in
|
||||
#else
|
||||
size_t localThreads[3] = { 256, 1, 1 };
|
||||
#endif
|
||||
size_t globalThreads[3] = { divUp(src.cols, localThreads[0] - 2*polyN) * localThreads[0], src.rows, 1 };
|
||||
size_t globalThreads[3] = { divUp(src.cols, localThreads[0] - 2*lpolyN) * localThreads[0], (size_t)src.rows, 1 };
|
||||
int smem_size = 3 * localThreads[0] * sizeof(float);
|
||||
|
||||
std::vector< std::pair<size_t, const void *> > args;
|
||||
@ -206,7 +206,7 @@ void cv::ocl::FarnebackOpticalFlow::polynomialExpansionOcl(const oclMat &src, in
|
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&src.step));
|
||||
|
||||
char opt [128];
|
||||
sprintf(opt, "-D polyN=%d", polyN);
|
||||
sprintf(opt, "-D polyN=%d", lpolyN);
|
||||
|
||||
openCLExecuteKernel(Context::getContext(), &optical_flow_farneback, kernelName,
|
||||
globalThreads, localThreads, args, -1, -1, opt);
|
||||
@ -221,7 +221,7 @@ void cv::ocl::FarnebackOpticalFlow::gaussianBlur5Ocl(const oclMat &src, int ksiz
|
||||
#else
|
||||
size_t localThreads[3] = { 256, 1, 1 };
|
||||
#endif
|
||||
size_t globalThreads[3] = { src.cols, height, 1 };
|
||||
size_t globalThreads[3] = { (size_t)src.cols, (size_t)height, 1 };
|
||||
int smem_size = (localThreads[0] + 2*ksizeHalf) * 5 * sizeof(float);
|
||||
|
||||
std::vector< std::pair<size_t, const void *> > args;
|
||||
|
||||
@ -63,7 +63,7 @@ static void pyrdown_run(const oclMat &src, const oclMat &dst)
|
||||
string kernelName = "pyrDown";
|
||||
|
||||
size_t localThreads[3] = { 256, 1, 1 };
|
||||
size_t globalThreads[3] = { src.cols, dst.rows, 1};
|
||||
size_t globalThreads[3] = { (size_t)src.cols, (size_t)dst.rows, 1};
|
||||
|
||||
vector<pair<size_t , const void *> > args;
|
||||
args.push_back( make_pair( sizeof(cl_mem), (void *)&src.data ));
|
||||
|
||||
@ -84,7 +84,7 @@ static void lkSparse_run(oclMat &I, oclMat &J,
|
||||
Context *clCxt = I.clCxt;
|
||||
string kernelName = "lkSparse";
|
||||
size_t localThreads[3] = { 8, 8, 1 };
|
||||
size_t globalThreads[3] = { 8 * ptcount, 8, 1};
|
||||
size_t globalThreads[3] = { 8 * (size_t)ptcount, 8, 1};
|
||||
int cn = I.oclchannels();
|
||||
char calcErr = level==0?1:0;
|
||||
|
||||
@ -217,7 +217,7 @@ static void lkDense_run(oclMat &I, oclMat &J, oclMat &u, oclMat &v,
|
||||
string kernelName = "lkDense";
|
||||
|
||||
size_t localThreads[3] = { 16, 16, 1 };
|
||||
size_t globalThreads[3] = { I.cols, I.rows, 1};
|
||||
size_t globalThreads[3] = { (size_t)I.cols, (size_t)I.rows, 1};
|
||||
|
||||
cl_char calcErr = err ? 1 : 0;
|
||||
|
||||
|
||||
@ -93,7 +93,7 @@ namespace cv
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&srcstep));
|
||||
args.push_back( make_pair( sizeof(cl_int), (void *)&dststep));
|
||||
|
||||
size_t globalThreads[3] = {dst.cols, dst.rows, 1};
|
||||
size_t globalThreads[3] = {(size_t)dst.cols, (size_t)dst.rows, 1};
|
||||
size_t localThreads[3] = {16, 16, 1};
|
||||
|
||||
|
||||
|
||||
@ -358,18 +358,18 @@ static void sortByKey(oclMat& keys, oclMat& vals, size_t vecSize, bool isGreater
|
||||
{
|
||||
Context * cxt = Context::getContext();
|
||||
|
||||
const size_t GROUP_SIZE = cxt->getDeviceInfo().maxWorkGroupSize >= 256 ? 256: 128;
|
||||
const size_t groupSize = cxt->getDeviceInfo().maxWorkGroupSize >= 256 ? 256: 128;
|
||||
|
||||
size_t globalThreads[3] = {vecSize, 1, 1};
|
||||
size_t localThreads[3] = {GROUP_SIZE, 1, 1};
|
||||
size_t localThreads[3] = {groupSize, 1, 1};
|
||||
|
||||
std::vector< std::pair<size_t, const void *> > args;
|
||||
char build_opt_buf [100];
|
||||
genSortBuildOption(keys, vals, isGreaterThan, build_opt_buf);
|
||||
|
||||
String kernelname[] = {String("blockInsertionSort"), String("merge")};
|
||||
int keylds_size = GROUP_SIZE * keys.elemSize();
|
||||
int vallds_size = GROUP_SIZE * vals.elemSize();
|
||||
int keylds_size = groupSize * keys.elemSize();
|
||||
int vallds_size = groupSize * vals.elemSize();
|
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&keys.data));
|
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&vals.data));
|
||||
args.push_back(std::make_pair(sizeof(cl_uint), (void *)&vecSize));
|
||||
@ -379,7 +379,7 @@ static void sortByKey(oclMat& keys, oclMat& vals, size_t vecSize, bool isGreater
|
||||
openCLExecuteKernel(cxt, &kernel_stablesort_by_key, kernelname[0], globalThreads, localThreads, args, -1, -1, build_opt_buf);
|
||||
|
||||
// Early exit for the case of no merge passes, values are already in destination vector
|
||||
if(vecSize <= GROUP_SIZE)
|
||||
if(vecSize <= groupSize)
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
@ -80,7 +80,7 @@ namespace cv
|
||||
int cols = divUp(mat_dst.cols + offset_cols, vector_length);
|
||||
|
||||
size_t localThreads[3] = { 64, 4, 1 };
|
||||
size_t globalThreads[3] = { cols, mat_dst.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)cols, (size_t)mat_dst.rows, 1 };
|
||||
|
||||
int dst_step1 = mat_dst.cols * mat_dst.elemSize();
|
||||
vector<pair<size_t , const void *> > args;
|
||||
@ -254,7 +254,7 @@ namespace cv
|
||||
|| devInfo.deviceVersion.find("Build 78712") != std::string::npos))
|
||||
build_options += " -D BYPASS_VSTORE=true";
|
||||
|
||||
size_t globalThreads[3] = { divUp(src.cols, VEC_SIZE), src.rows, 1 };
|
||||
size_t globalThreads[3] = { divUp(src.cols, VEC_SIZE), (size_t)src.rows, 1 };
|
||||
openCLExecuteKernel(clCtx, &split_mat, kernelName, globalThreads, NULL, args, -1, -1, build_options.c_str());
|
||||
}
|
||||
static void split(const oclMat &mat_src, oclMat *mat_dst)
|
||||
|
||||
@ -133,7 +133,7 @@ namespace cv
|
||||
|
||||
const int threadsNum = 256;
|
||||
//size_t blockSize = threadsNum;
|
||||
size_t localThreads[3] = {win_size, 1, threadsNum / win_size};
|
||||
size_t localThreads[3] = {(size_t)win_size, 1, (size_t)threadsNum / win_size};
|
||||
size_t globalThreads[3] = { w *localThreads[0],
|
||||
h * divUp(rthis.ndisp, localThreads[2]) *localThreads[1], 1 * localThreads[2]
|
||||
};
|
||||
@ -316,7 +316,7 @@ namespace cv
|
||||
|
||||
const size_t threadsNum = 256;
|
||||
//size_t blockSize = threadsNum;
|
||||
size_t localThreads[3] = { win_size, 1, threadsNum / win_size };
|
||||
size_t localThreads[3] = { (size_t)win_size, 1, (size_t)threadsNum / win_size };
|
||||
size_t globalThreads[3] = { w *localThreads[0],
|
||||
h * divUp(nr_plane, localThreads[2]) *localThreads[1], 1 * localThreads[2]
|
||||
};
|
||||
|
||||
@ -65,7 +65,7 @@ static void prefilter_xsobel(const oclMat &input, oclMat &output, int prefilterC
|
||||
string kernelName = "prefilter_xsobel";
|
||||
|
||||
size_t blockSize = 1;
|
||||
size_t globalThreads[3] = { input.cols, input.rows, 1 };
|
||||
size_t globalThreads[3] = { (size_t)input.cols, (size_t)input.rows, 1 };
|
||||
size_t localThreads[3] = { blockSize, blockSize, 1 };
|
||||
|
||||
std::vector< std::pair<size_t, const void *> > args;
|
||||
@ -103,7 +103,7 @@ static void stereo_bm(const oclMat &left, const oclMat &right, oclMat &disp,
|
||||
sizeof(cl_uint);
|
||||
//size_t blockSize = 1;
|
||||
size_t localThreads[] = { BLOCK_W, 1, 1 };
|
||||
size_t globalThreads[] = { left.cols - maxdisp - 2 * winsz2,
|
||||
size_t globalThreads[] = { (size_t)left.cols - maxdisp - 2 * winsz2,
|
||||
divUp(left.rows - 2 * winsz2, ROWSperTHREAD),
|
||||
1 };
|
||||
|
||||
@ -135,7 +135,7 @@ static void postfilter_textureness(oclMat &left, int winSize,
|
||||
|
||||
size_t blockSize = 1;
|
||||
size_t localThreads[] = { BLOCK_W, blockSize ,1};
|
||||
size_t globalThreads[] = { left.cols,
|
||||
size_t globalThreads[] = { (size_t)left.cols,
|
||||
divUp(left.rows, 2 * ROWSperTHREAD),
|
||||
1 };
|
||||
|
||||
|
||||
@ -116,7 +116,7 @@ namespace cv
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&data.step));
|
||||
args.push_back( make_pair( sizeof(cl_mem) , (void *)&cl_con_struct));
|
||||
|
||||
size_t gt[3] = {left.cols, left.rows, 1}, lt[3] = {16, 16, 1};
|
||||
size_t gt[3] = {(size_t)left.cols, (size_t)left.rows, 1}, lt[3] = {16, 16, 1};
|
||||
|
||||
const int OPT_SIZE = 50;
|
||||
char cn_opt [OPT_SIZE] = "";
|
||||
@ -148,7 +148,7 @@ namespace cv
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst.step));
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&disp));
|
||||
|
||||
size_t gt[3] = {dst_cols, dst_rows, 1}, lt[3] = {16, 16, 1};
|
||||
size_t gt[3] = {(size_t)dst_cols, (size_t)dst_rows, 1}, lt[3] = {16, 16, 1};
|
||||
const char* t_opt = data_type == CV_16S ? "-D T_SHORT":"-D T_FLOAT";
|
||||
openCLExecuteKernel(clCxt, &stereobp, kernelName, gt, lt, args, -1, -1, t_opt);
|
||||
}
|
||||
@ -173,7 +173,7 @@ namespace cv
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst.step));
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&ndisp));
|
||||
|
||||
size_t gt[3] = {dst_cols, dst_rows, 1}, lt[3] = {16, 16, 1};
|
||||
size_t gt[3] = {(size_t)dst_cols, (size_t)dst_rows, 1}, lt[3] = {16, 16, 1};
|
||||
const char* t_opt = data_type == CV_16S ? "-D T_SHORT":"-D T_FLOAT";
|
||||
openCLExecuteKernel(clCxt, &stereobp, kernelName, gt, lt, args, -1, -1, t_opt);
|
||||
}
|
||||
@ -223,7 +223,7 @@ namespace cv
|
||||
args.push_back( make_pair( sizeof(cl_float) , (void *)&cmax_disc_term));
|
||||
args.push_back( make_pair( sizeof(cl_float) , (void *)&cdisc_single_jump));
|
||||
|
||||
size_t gt[3] = {cols, rows, 1}, lt[3] = {16, 16, 1};
|
||||
size_t gt[3] = {(size_t)cols, (size_t)rows, 1}, lt[3] = {16, 16, 1};
|
||||
char opt[80] = "";
|
||||
sprintf(opt, "-D %s -D CNDISP=%d", data_type == CV_16S ? "T_SHORT":"T_FLOAT", cndisp);
|
||||
openCLExecuteKernel(clCxt, &stereobp, kernelName, gt, lt, args, -1, -1, opt);
|
||||
@ -263,7 +263,7 @@ namespace cv
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&disp.step));
|
||||
args.push_back( make_pair( sizeof(cl_int) , (void *)&ndisp));
|
||||
|
||||
size_t gt[3] = {disp.cols, disp.rows, 1}, lt[3] = {16, 16, 1};
|
||||
size_t gt[3] = {(size_t)disp.cols, (size_t)disp.rows, 1}, lt[3] = {16, 16, 1};
|
||||
const char* t_opt = data_type == CV_16S ? "-D T_SHORT":"-D T_FLOAT";
|
||||
openCLExecuteKernel(clCxt, &stereobp, kernelName, gt, lt, args, -1, -1, t_opt);
|
||||
}
|
||||
|
||||
@ -443,8 +443,8 @@ static void matmul_sigmod(oclMat & src, oclMat & src2, oclMat & dst, int src_row
|
||||
int dst_step = (int)dst.step / dst.elemSize();
|
||||
int x = MIN(16, src_rows);
|
||||
int y = MIN(16, src2_cols);
|
||||
size_t localThreads[] = {x, y, 1};
|
||||
size_t globalThreads[] = {src2_cols, src_rows, 1};
|
||||
size_t localThreads[] = {(size_t)x, (size_t)y, 1};
|
||||
size_t globalThreads[] = {(size_t)src2_cols, (size_t)src_rows, 1};
|
||||
int width = var_count;
|
||||
|
||||
vector< pair<size_t, const void *> > args;
|
||||
@ -483,8 +483,8 @@ static void matmul_poly(oclMat & src, oclMat & src2, oclMat & dst, int src_rows,
|
||||
int dst_step = (int)dst.step / dst.elemSize();
|
||||
int x = MIN(16, src_rows);
|
||||
int y = MIN(16, src2_cols);
|
||||
size_t localThreads[] = {x, y, 1};
|
||||
size_t globalThreads[] = {src2_cols, src_rows, 1};
|
||||
size_t localThreads[] = {(size_t)x, (size_t)y, 1};
|
||||
size_t globalThreads[] = {(size_t)src2_cols, (size_t)src_rows, 1};
|
||||
int width = var_count;
|
||||
|
||||
char build_options[50];
|
||||
@ -532,8 +532,8 @@ static void matmul_linear(oclMat & src, oclMat & src2, oclMat & dst, int src_row
|
||||
int dst_step = (int)dst.step / dst.elemSize();
|
||||
int x = MIN(16, src_rows);
|
||||
int y = MIN(16, src2_cols);
|
||||
size_t localThreads[] = {x, y, 1};
|
||||
size_t globalThreads[] = {src2_cols, src_rows, 1};
|
||||
size_t localThreads[] = {(size_t)x, (size_t)y, 1};
|
||||
size_t globalThreads[] = {(size_t)src2_cols, (size_t)src_rows, 1};
|
||||
int width = var_count;
|
||||
|
||||
vector< pair<size_t, const void *> > args;
|
||||
@ -579,8 +579,8 @@ static void matmul_rbf(oclMat& src, oclMat& src_e, oclMat& dst, int src_rows, in
|
||||
|
||||
int x = MIN(16, src_rows);
|
||||
int y = MIN(16, src2_cols);
|
||||
size_t localThreads[] = {x, y, 1};
|
||||
size_t globalThreads[] = {src2_cols, src_rows, 1};
|
||||
size_t localThreads[] = {(size_t)x, (size_t)y, 1};
|
||||
size_t globalThreads[] = {(size_t)src2_cols, (size_t)src_rows, 1};
|
||||
char build_options[50];
|
||||
|
||||
if(flag)
|
||||
@ -788,11 +788,11 @@ bool CvSVMSolver_ocl::solve_generic( CvSVMSolutionInfo& si )
|
||||
}
|
||||
Mat src1 = Mat(sample_count, var_count, CV_32FC1);
|
||||
|
||||
for(int i = 0; i < sample_count; ++i)
|
||||
for(int i2 = 0; i2 < sample_count; ++i2)
|
||||
{
|
||||
for(int j = 0; j < var_count; ++j)
|
||||
for(int j2 = 0; j2 < var_count; ++j2)
|
||||
{
|
||||
src1.at<float>(i, j) = samples[i][j];
|
||||
src1.at<float>(i2, j2) = samples[i2][j2];
|
||||
}
|
||||
}
|
||||
oclMat src, src_e;
|
||||
|
||||
@ -273,7 +273,7 @@ void ocl_tvl1flow::centeredGradient(const oclMat &src, oclMat &dx, oclMat &dy)
|
||||
{
|
||||
Context *clCxt = src.clCxt;
|
||||
size_t localThreads[3] = {32, 8, 1};
|
||||
size_t globalThreads[3] = {src.cols, src.rows, 1};
|
||||
size_t globalThreads[3] = {(size_t)src.cols, (size_t)src.rows, 1};
|
||||
|
||||
int srcElementSize = src.elemSize();
|
||||
int src_step = src.step/srcElementSize;
|
||||
@ -301,8 +301,8 @@ void ocl_tvl1flow::estimateDualVariables(oclMat &u1, oclMat &u2, oclMat &p11, oc
|
||||
size_t localThread[] = {32, 8, 1};
|
||||
size_t globalThread[] =
|
||||
{
|
||||
u1.cols,
|
||||
u1.rows,
|
||||
(size_t)u1.cols,
|
||||
(size_t)u1.rows,
|
||||
1
|
||||
};
|
||||
|
||||
@ -355,8 +355,8 @@ void ocl_tvl1flow::estimateU(oclMat &I1wx, oclMat &I1wy, oclMat &grad,
|
||||
size_t localThread[] = {32, 8, 1};
|
||||
size_t globalThread[] =
|
||||
{
|
||||
I1wx.cols,
|
||||
I1wx.rows,
|
||||
(size_t)I1wx.cols,
|
||||
(size_t)I1wx.rows,
|
||||
1
|
||||
};
|
||||
|
||||
@ -433,8 +433,8 @@ void ocl_tvl1flow::warpBackward(const oclMat &I0, const oclMat &I1, oclMat &I1x,
|
||||
size_t localThread[] = {32, 8, 1};
|
||||
size_t globalThread[] =
|
||||
{
|
||||
I0.cols,
|
||||
I0.rows,
|
||||
(size_t)I0.cols,
|
||||
(size_t)I0.rows,
|
||||
1
|
||||
};
|
||||
|
||||
|
||||
@ -64,7 +64,7 @@ TEST(TestAPI, openCLExecuteKernelInterop)
|
||||
args.push_back( make_pair( sizeof(cl_mem), (void *) &gpuMat.data ));
|
||||
args.push_back( make_pair( sizeof(cl_mem), (void *) &gpuMatDst.data ));
|
||||
|
||||
size_t globalThreads[3] = { sz.width, 1, 1 };
|
||||
size_t globalThreads[3] = { (size_t)sz.width, 1, 1 };
|
||||
cv::ocl::openCLExecuteKernelInterop(
|
||||
gpuMat.clCxt,
|
||||
program,
|
||||
|
||||
@ -717,12 +717,12 @@ OCL_TEST_P(MinMax, MAT)
|
||||
{
|
||||
minVal = std::numeric_limits<double>::max();
|
||||
maxVal = -std::numeric_limits<double>::max();
|
||||
for (int i = 0; i < src1_roi.rows; ++i)
|
||||
for (int j = 0; j < src1_roi.cols; ++j)
|
||||
for (int i2 = 0; i2 < src1_roi.rows; ++i2)
|
||||
for (int j2 = 0; j2 < src1_roi.cols; ++j2)
|
||||
{
|
||||
signed char val = src1_roi.at<signed char>(i, j);
|
||||
if (val < minVal) minVal = val;
|
||||
if (val > maxVal) maxVal = val;
|
||||
signed char oneVal = src1_roi.at<signed char>(i2, j2);
|
||||
if (oneVal < minVal) minVal = oneVal;
|
||||
if (oneVal > maxVal) maxVal = oneVal;
|
||||
}
|
||||
}
|
||||
|
||||
@ -739,10 +739,10 @@ OCL_TEST_P(MinMax, MASK)
|
||||
enum { MAX_IDX = 0, MIN_IDX };
|
||||
static const double minMaxGolds[2][7] =
|
||||
{
|
||||
{ std::numeric_limits<uchar>::min(), std::numeric_limits<char>::min(), std::numeric_limits<ushort>::min(),
|
||||
std::numeric_limits<short>::min(), std::numeric_limits<int>::min(), -std::numeric_limits<float>::max(), -std::numeric_limits<double>::max() },
|
||||
{ std::numeric_limits<uchar>::max(), std::numeric_limits<char>::max(), std::numeric_limits<ushort>::max(),
|
||||
std::numeric_limits<short>::max(), std::numeric_limits<int>::max(), std::numeric_limits<float>::max(), std::numeric_limits<double>::max() },
|
||||
{ (double)std::numeric_limits<uchar>::min(), (double)std::numeric_limits<char>::min(), (double)std::numeric_limits<ushort>::min(),
|
||||
(double)std::numeric_limits<short>::min(), (double)std::numeric_limits<int>::min(), (double)-std::numeric_limits<float>::max(), (double)-std::numeric_limits<double>::max() },
|
||||
{ (double)std::numeric_limits<uchar>::max(), (double)std::numeric_limits<char>::max(), (double)std::numeric_limits<ushort>::max(),
|
||||
(double)std::numeric_limits<short>::max(), (double)std::numeric_limits<int>::max(), (double)std::numeric_limits<float>::max(), (double)std::numeric_limits<double>::max() },
|
||||
};
|
||||
|
||||
for (int j = 0; j < LOOP_TIMES; j++)
|
||||
@ -758,13 +758,13 @@ OCL_TEST_P(MinMax, MASK)
|
||||
{
|
||||
minVal = std::numeric_limits<double>::max();
|
||||
maxVal = -std::numeric_limits<double>::max();
|
||||
for (int i = 0; i < src1_roi.rows; ++i)
|
||||
for (int j = 0; j < src1_roi.cols; ++j)
|
||||
for (int i2 = 0; i2 < src1_roi.rows; ++i2)
|
||||
for (int j2 = 0; j2 < src1_roi.cols; ++j2)
|
||||
{
|
||||
signed char val = src1_roi.at<signed char>(i, j);
|
||||
unsigned char m = mask_roi.at<unsigned char>(i, j);
|
||||
if (val < minVal && m) minVal = val;
|
||||
if (val > maxVal && m) maxVal = val;
|
||||
signed char oneVal = src1_roi.at<signed char>(i2, j2);
|
||||
unsigned char m = mask_roi.at<unsigned char>(i2, j2);
|
||||
if (oneVal < minVal && m) minVal = oneVal;
|
||||
if (oneVal > maxVal && m) maxVal = oneVal;
|
||||
}
|
||||
}
|
||||
|
||||
@ -796,29 +796,29 @@ OCL_TEST_P(MinMaxLoc, MAT)
|
||||
|
||||
double minVal, maxVal;
|
||||
cv::Point minLoc, maxLoc;
|
||||
int depth = src1.depth();
|
||||
int oneDepth = src1.depth();
|
||||
|
||||
if (depth != CV_8S)
|
||||
if (oneDepth != CV_8S)
|
||||
cv::minMaxLoc(src1_roi, &minVal, &maxVal, &minLoc, &maxLoc);
|
||||
else
|
||||
{
|
||||
minVal = std::numeric_limits<double>::max();
|
||||
maxVal = -std::numeric_limits<double>::max();
|
||||
for (int i = 0; i < src1_roi.rows; ++i)
|
||||
for (int j = 0; j < src1_roi.cols; ++j)
|
||||
for (int i2 = 0; i2 < src1_roi.rows; ++i2)
|
||||
for (int j2 = 0; j2 < src1_roi.cols; ++j2)
|
||||
{
|
||||
signed char val = src1_roi.at<signed char>(i, j);
|
||||
if (val < minVal)
|
||||
signed char oneVal = src1_roi.at<signed char>(i2, j2);
|
||||
if (oneVal < minVal)
|
||||
{
|
||||
minVal = val;
|
||||
minLoc.x = j;
|
||||
minLoc.y = i;
|
||||
minVal = oneVal;
|
||||
minLoc.x = j2;
|
||||
minLoc.y = i2;
|
||||
}
|
||||
if (val > maxVal)
|
||||
if (oneVal > maxVal)
|
||||
{
|
||||
maxVal = val;
|
||||
maxLoc.x = j;
|
||||
maxLoc.y = i;
|
||||
maxVal = oneVal;
|
||||
maxLoc.x = j2;
|
||||
maxLoc.y = i2;
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -828,7 +828,7 @@ OCL_TEST_P(MinMaxLoc, MAT)
|
||||
cv::ocl::minMaxLoc(gsrc1_roi, &minVal_, &maxVal_, &minLoc_, &maxLoc_, cv::ocl::oclMat());
|
||||
|
||||
double error0 = 0., error1 = 0., minlocVal = 0., minlocVal_ = 0., maxlocVal = 0., maxlocVal_ = 0.;
|
||||
if (depth == 0)
|
||||
if (oneDepth == 0)
|
||||
{
|
||||
minlocVal = src1_roi.at<unsigned char>(minLoc);
|
||||
minlocVal_ = src1_roi.at<unsigned char>(minLoc_);
|
||||
@ -837,7 +837,7 @@ OCL_TEST_P(MinMaxLoc, MAT)
|
||||
error0 = ::abs(src1_roi.at<unsigned char>(minLoc_) - src1_roi.at<unsigned char>(minLoc));
|
||||
error1 = ::abs(src1_roi.at<unsigned char>(maxLoc_) - src1_roi.at<unsigned char>(maxLoc));
|
||||
}
|
||||
if (depth == 1)
|
||||
if (oneDepth == 1)
|
||||
{
|
||||
minlocVal = src1_roi.at<signed char>(minLoc);
|
||||
minlocVal_ = src1_roi.at<signed char>(minLoc_);
|
||||
@ -846,7 +846,7 @@ OCL_TEST_P(MinMaxLoc, MAT)
|
||||
error0 = ::abs(src1_roi.at<signed char>(minLoc_) - src1_roi.at<signed char>(minLoc));
|
||||
error1 = ::abs(src1_roi.at<signed char>(maxLoc_) - src1_roi.at<signed char>(maxLoc));
|
||||
}
|
||||
if (depth == 2)
|
||||
if (oneDepth == 2)
|
||||
{
|
||||
minlocVal = src1_roi.at<unsigned short>(minLoc);
|
||||
minlocVal_ = src1_roi.at<unsigned short>(minLoc_);
|
||||
@ -855,7 +855,7 @@ OCL_TEST_P(MinMaxLoc, MAT)
|
||||
error0 = ::abs(src1_roi.at<unsigned short>(minLoc_) - src1_roi.at<unsigned short>(minLoc));
|
||||
error1 = ::abs(src1_roi.at<unsigned short>(maxLoc_) - src1_roi.at<unsigned short>(maxLoc));
|
||||
}
|
||||
if (depth == 3)
|
||||
if (oneDepth == 3)
|
||||
{
|
||||
minlocVal = src1_roi.at<signed short>(minLoc);
|
||||
minlocVal_ = src1_roi.at<signed short>(minLoc_);
|
||||
@ -864,7 +864,7 @@ OCL_TEST_P(MinMaxLoc, MAT)
|
||||
error0 = ::abs(src1_roi.at<signed short>(minLoc_) - src1_roi.at<signed short>(minLoc));
|
||||
error1 = ::abs(src1_roi.at<signed short>(maxLoc_) - src1_roi.at<signed short>(maxLoc));
|
||||
}
|
||||
if (depth == 4)
|
||||
if (oneDepth == 4)
|
||||
{
|
||||
minlocVal = src1_roi.at<int>(minLoc);
|
||||
minlocVal_ = src1_roi.at<int>(minLoc_);
|
||||
@ -873,7 +873,7 @@ OCL_TEST_P(MinMaxLoc, MAT)
|
||||
error0 = ::abs(src1_roi.at<int>(minLoc_) - src1_roi.at<int>(minLoc));
|
||||
error1 = ::abs(src1_roi.at<int>(maxLoc_) - src1_roi.at<int>(maxLoc));
|
||||
}
|
||||
if (depth == 5)
|
||||
if (oneDepth == 5)
|
||||
{
|
||||
minlocVal = src1_roi.at<float>(minLoc);
|
||||
minlocVal_ = src1_roi.at<float>(minLoc_);
|
||||
@ -882,7 +882,7 @@ OCL_TEST_P(MinMaxLoc, MAT)
|
||||
error0 = ::abs(src1_roi.at<float>(minLoc_) - src1_roi.at<float>(minLoc));
|
||||
error1 = ::abs(src1_roi.at<float>(maxLoc_) - src1_roi.at<float>(maxLoc));
|
||||
}
|
||||
if (depth == 6)
|
||||
if (oneDepth == 6)
|
||||
{
|
||||
minlocVal = src1_roi.at<double>(minLoc);
|
||||
minlocVal_ = src1_roi.at<double>(minLoc_);
|
||||
@ -909,29 +909,29 @@ OCL_TEST_P(MinMaxLoc, MASK)
|
||||
random_roi();
|
||||
double minVal, maxVal;
|
||||
cv::Point minLoc, maxLoc;
|
||||
int depth = src1.depth();
|
||||
if (depth != CV_8S)
|
||||
int oneDepth = src1.depth();
|
||||
if (oneDepth != CV_8S)
|
||||
cv::minMaxLoc(src1_roi, &minVal, &maxVal, &minLoc, &maxLoc, mask_roi);
|
||||
else
|
||||
{
|
||||
minVal = std::numeric_limits<double>::max();
|
||||
maxVal = -std::numeric_limits<double>::max();
|
||||
for (int i = 0; i < src1_roi.rows; ++i)
|
||||
for (int j = 0; j < src1_roi.cols; ++j)
|
||||
for (int i2 = 0; i2 < src1_roi.rows; ++i2)
|
||||
for (int j2 = 0; j2 < src1_roi.cols; ++j2)
|
||||
{
|
||||
signed char val = src1_roi.at<signed char>(i, j);
|
||||
unsigned char m = mask_roi.at<unsigned char>(i , j);
|
||||
if (val < minVal && m)
|
||||
signed char oneVal = src1_roi.at<signed char>(i2, j2);
|
||||
unsigned char m = mask_roi.at<unsigned char>(i2 , j2);
|
||||
if (oneVal < minVal && m)
|
||||
{
|
||||
minVal = val;
|
||||
minLoc.x = j;
|
||||
minLoc.y = i;
|
||||
minVal = oneVal;
|
||||
minLoc.x = j2;
|
||||
minLoc.y = i2;
|
||||
}
|
||||
if (val > maxVal && m)
|
||||
if (oneVal > maxVal && m)
|
||||
{
|
||||
maxVal = val;
|
||||
maxLoc.x = j;
|
||||
maxLoc.y = i;
|
||||
maxVal = oneVal;
|
||||
maxLoc.x = j2;
|
||||
maxLoc.y = i2;
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -942,7 +942,7 @@ OCL_TEST_P(MinMaxLoc, MASK)
|
||||
|
||||
double error0 = 0., error1 = 0., minlocVal = 0., minlocVal_ = 0., maxlocVal = 0., maxlocVal_ = 0.;
|
||||
if (minLoc_.x == -1 || minLoc_.y == -1 || maxLoc_.x == -1 || maxLoc_.y == -1) continue;
|
||||
if (depth == 0)
|
||||
if (oneDepth == 0)
|
||||
{
|
||||
minlocVal = src1_roi.at<unsigned char>(minLoc);
|
||||
minlocVal_ = src1_roi.at<unsigned char>(minLoc_);
|
||||
@ -951,7 +951,7 @@ OCL_TEST_P(MinMaxLoc, MASK)
|
||||
error0 = ::abs(src1_roi.at<unsigned char>(minLoc_) - src1_roi.at<unsigned char>(minLoc));
|
||||
error1 = ::abs(src1_roi.at<unsigned char>(maxLoc_) - src1_roi.at<unsigned char>(maxLoc));
|
||||
}
|
||||
if (depth == 1)
|
||||
if (oneDepth == 1)
|
||||
{
|
||||
minlocVal = src1_roi.at<signed char>(minLoc);
|
||||
minlocVal_ = src1_roi.at<signed char>(minLoc_);
|
||||
@ -960,7 +960,7 @@ OCL_TEST_P(MinMaxLoc, MASK)
|
||||
error0 = ::abs(src1_roi.at<signed char>(minLoc_) - src1_roi.at<signed char>(minLoc));
|
||||
error1 = ::abs(src1_roi.at<signed char>(maxLoc_) - src1_roi.at<signed char>(maxLoc));
|
||||
}
|
||||
if (depth == 2)
|
||||
if (oneDepth == 2)
|
||||
{
|
||||
minlocVal = src1_roi.at<unsigned short>(minLoc);
|
||||
minlocVal_ = src1_roi.at<unsigned short>(minLoc_);
|
||||
@ -969,7 +969,7 @@ OCL_TEST_P(MinMaxLoc, MASK)
|
||||
error0 = ::abs(src1_roi.at<unsigned short>(minLoc_) - src1_roi.at<unsigned short>(minLoc));
|
||||
error1 = ::abs(src1_roi.at<unsigned short>(maxLoc_) - src1_roi.at<unsigned short>(maxLoc));
|
||||
}
|
||||
if (depth == 3)
|
||||
if (oneDepth == 3)
|
||||
{
|
||||
minlocVal = src1_roi.at<signed short>(minLoc);
|
||||
minlocVal_ = src1_roi.at<signed short>(minLoc_);
|
||||
@ -978,7 +978,7 @@ OCL_TEST_P(MinMaxLoc, MASK)
|
||||
error0 = ::abs(src1_roi.at<signed short>(minLoc_) - src1_roi.at<signed short>(minLoc));
|
||||
error1 = ::abs(src1_roi.at<signed short>(maxLoc_) - src1_roi.at<signed short>(maxLoc));
|
||||
}
|
||||
if (depth == 4)
|
||||
if (oneDepth == 4)
|
||||
{
|
||||
minlocVal = src1_roi.at<int>(minLoc);
|
||||
minlocVal_ = src1_roi.at<int>(minLoc_);
|
||||
@ -987,7 +987,7 @@ OCL_TEST_P(MinMaxLoc, MASK)
|
||||
error0 = ::abs(src1_roi.at<int>(minLoc_) - src1_roi.at<int>(minLoc));
|
||||
error1 = ::abs(src1_roi.at<int>(maxLoc_) - src1_roi.at<int>(maxLoc));
|
||||
}
|
||||
if (depth == 5)
|
||||
if (oneDepth == 5)
|
||||
{
|
||||
minlocVal = src1_roi.at<float>(minLoc);
|
||||
minlocVal_ = src1_roi.at<float>(minLoc_);
|
||||
@ -996,7 +996,7 @@ OCL_TEST_P(MinMaxLoc, MASK)
|
||||
error0 = ::abs(src1_roi.at<float>(minLoc_) - src1_roi.at<float>(minLoc));
|
||||
error1 = ::abs(src1_roi.at<float>(maxLoc_) - src1_roi.at<float>(maxLoc));
|
||||
}
|
||||
if (depth == 6)
|
||||
if (oneDepth == 6)
|
||||
{
|
||||
minlocVal = src1_roi.at<double>(minLoc);
|
||||
minlocVal_ = src1_roi.at<double>(minLoc_);
|
||||
|
||||
@ -77,10 +77,10 @@ OCL_TEST_P(Canny, Accuracy)
|
||||
cv::ocl::oclMat edges;
|
||||
cv::ocl::Canny(ocl_img, edges, low_thresh, high_thresh, apperture_size, useL2gradient);
|
||||
|
||||
cv::Mat edges_gold;
|
||||
cv::Canny(img, edges_gold, low_thresh, high_thresh, apperture_size, useL2gradient);
|
||||
cv::Mat local_edges_gold;
|
||||
cv::Canny(img, local_edges_gold, low_thresh, high_thresh, apperture_size, useL2gradient);
|
||||
|
||||
EXPECT_MAT_SIMILAR(edges_gold, edges, 1e-2);
|
||||
EXPECT_MAT_SIMILAR(local_edges_gold, edges, 1e-2);
|
||||
}
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(OCL_ImgProc, Canny, testing::Combine(
|
||||
|
||||
@ -133,10 +133,10 @@ OCL_TEST_P(Kmeans, Mat){
|
||||
// verify lables with ground truth resutls
|
||||
int label = labels.at<int>(row_idx);
|
||||
int header_label = dd_labels.at<int>(row_idx);
|
||||
for(int j = 0; (j < MHEIGHT/K)||(i == K-1 && j < MHEIGHT/K+MHEIGHT%K); j++)
|
||||
for(int j2 = 0; (j2 < MHEIGHT/K)||(i == K-1 && j2 < MHEIGHT/K+MHEIGHT%K); j2++)
|
||||
{
|
||||
ASSERT_NEAR(labels.at<int>(row_idx+j), label, 0);
|
||||
ASSERT_NEAR(dd_labels.at<int>(row_idx+j), header_label, 0);
|
||||
ASSERT_NEAR(labels.at<int>(row_idx+j2), label, 0);
|
||||
ASSERT_NEAR(dd_labels.at<int>(row_idx+j2), header_label, 0);
|
||||
}
|
||||
|
||||
// verify centers
|
||||
|
||||
@ -142,12 +142,12 @@ PARAM_TEST_CASE(SVM_OCL, int, int, int)
|
||||
kernel_type = GET_PARAM(0);
|
||||
svm_type = GET_PARAM(1);
|
||||
K = GET_PARAM(2);
|
||||
cv::Size size = cv::Size(MWIDTH, MHEIGHT);
|
||||
src.create(size, CV_32FC1);
|
||||
labels.create(1, size.height, CV_32SC1);
|
||||
cv::Size sz = cv::Size(MWIDTH, MHEIGHT);
|
||||
src.create(sz, CV_32FC1);
|
||||
labels.create(1, sz.height, CV_32SC1);
|
||||
int row_idx = 0;
|
||||
const int max_number = size.height / K - 1;
|
||||
CV_Assert(K <= size.height);
|
||||
const int max_number = sz.height / K - 1;
|
||||
CV_Assert(K <= sz.height);
|
||||
for(int i = 0; i < K; i++ )
|
||||
{
|
||||
Mat center_row_header = src.row(row_idx);
|
||||
@ -159,7 +159,7 @@ PARAM_TEST_CASE(SVM_OCL, int, int, int)
|
||||
}
|
||||
labels.at<int>(0, row_idx) = i;
|
||||
for(int j = 0; (j < max_number) ||
|
||||
(i == K - 1 && j < max_number + size.height % K); j ++)
|
||||
(i == K - 1 && j < max_number + sz.height % K); j ++)
|
||||
{
|
||||
Mat cur_row_header = src.row(row_idx + 1 + j);
|
||||
center_row_header.copyTo(cur_row_header);
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
#if defined(_MSC_VER) && (_MSC_VER >= 1800)
|
||||
// eliminating duplicated round() declaration
|
||||
#define HAVE_ROUND
|
||||
#define HAVE_ROUND 1
|
||||
#endif
|
||||
|
||||
#include <Python.h>
|
||||
|
||||
@ -96,7 +96,7 @@ void btv_l1_device_ocl::buildMotionMaps(const oclMat& forwardMotionX, const oclM
|
||||
Context* clCxt = Context::getContext();
|
||||
|
||||
size_t local_thread[] = {32, 8, 1};
|
||||
size_t global_thread[] = {forwardMapX.cols, forwardMapX.rows, 1};
|
||||
size_t global_thread[] = {(size_t)forwardMapX.cols, (size_t)forwardMapX.rows, 1};
|
||||
|
||||
int forwardMotionX_step = (int)(forwardMotionX.step/forwardMotionX.elemSize());
|
||||
int forwardMotionY_step = (int)(forwardMotionY.step/forwardMotionY.elemSize());
|
||||
@ -139,7 +139,7 @@ void btv_l1_device_ocl::upscale(const oclMat& src, oclMat& dst, int scale)
|
||||
Context* clCxt = Context::getContext();
|
||||
|
||||
size_t local_thread[] = {32, 8, 1};
|
||||
size_t global_thread[] = {src.cols, src.rows, 1};
|
||||
size_t global_thread[] = {(size_t)src.cols, (size_t)src.rows, 1};
|
||||
|
||||
int src_step = (int)(src.step/src.elemSize());
|
||||
int dst_step = (int)(dst.step/dst.elemSize());
|
||||
@ -175,7 +175,7 @@ void btv_l1_device_ocl::diffSign(const oclMat& src1, const oclMat& src2, oclMat&
|
||||
int dst_step = (int)(dst_.step/dst_.elemSize());
|
||||
|
||||
size_t local_thread[] = {32, 8, 1};
|
||||
size_t global_thread[] = {src1_.cols, src1_.rows, 1};
|
||||
size_t global_thread[] = {(size_t)src1_.cols, (size_t)src1_.rows, 1};
|
||||
|
||||
String kernel_name = "diffSignKernel";
|
||||
vector< pair<size_t, const void*> > args;
|
||||
@ -201,7 +201,7 @@ void btv_l1_device_ocl::calcBtvRegularization(const oclMat& src, oclMat& dst, in
|
||||
oclMat dst_ = dst.reshape(1);
|
||||
|
||||
size_t local_thread[] = {32, 8, 1};
|
||||
size_t global_thread[] = {src.cols, src.rows, 1};
|
||||
size_t global_thread[] = {(size_t)src.cols, (size_t)src.rows, 1};
|
||||
|
||||
int src_step = (int)(src_.step/src_.elemSize());
|
||||
int dst_step = (int)(dst_.step/dst_.elemSize());
|
||||
|
||||
@ -226,11 +226,11 @@ namespace
|
||||
|
||||
static const double maxVals[] =
|
||||
{
|
||||
numeric_limits<uchar>::max(),
|
||||
numeric_limits<schar>::max(),
|
||||
numeric_limits<ushort>::max(),
|
||||
numeric_limits<short>::max(),
|
||||
numeric_limits<int>::max(),
|
||||
(double)numeric_limits<uchar>::max(),
|
||||
(double)numeric_limits<schar>::max(),
|
||||
(double)numeric_limits<ushort>::max(),
|
||||
(double)numeric_limits<short>::max(),
|
||||
(double)numeric_limits<int>::max(),
|
||||
1.0,
|
||||
1.0,
|
||||
};
|
||||
@ -329,11 +329,11 @@ namespace
|
||||
|
||||
static const double maxVals[] =
|
||||
{
|
||||
std::numeric_limits<uchar>::max(),
|
||||
std::numeric_limits<schar>::max(),
|
||||
std::numeric_limits<ushort>::max(),
|
||||
std::numeric_limits<short>::max(),
|
||||
std::numeric_limits<int>::max(),
|
||||
(double)std::numeric_limits<uchar>::max(),
|
||||
(double)std::numeric_limits<schar>::max(),
|
||||
(double)std::numeric_limits<ushort>::max(),
|
||||
(double)std::numeric_limits<short>::max(),
|
||||
(double)std::numeric_limits<int>::max(),
|
||||
1.0,
|
||||
1.0,
|
||||
};
|
||||
|
||||
@ -73,11 +73,6 @@ namespace perf
|
||||
#define DEF_PARAM_TEST(name, ...) typedef ::perf::TestBaseWithParam< std::tr1::tuple< __VA_ARGS__ > > name
|
||||
#define DEF_PARAM_TEST_1(name, param_type) typedef ::perf::TestBaseWithParam< param_type > name
|
||||
|
||||
DEF_PARAM_TEST_1(Sz, cv::Size);
|
||||
typedef perf::Size_MatType Sz_Type;
|
||||
DEF_PARAM_TEST(Sz_Depth, cv::Size, perf::MatDepth);
|
||||
DEF_PARAM_TEST(Sz_Depth_Cn, cv::Size, perf::MatDepth, MatCn);
|
||||
|
||||
#define GPU_TYPICAL_MAT_SIZES testing::Values(perf::sz720p, perf::szSXGA, perf::sz1080p)
|
||||
|
||||
#define FAIL_NO_CPU() FAIL() << "No such CPU implementation analogy"
|
||||
|
||||
Loading…
Reference in New Issue
Block a user