mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
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4397 lines
109 KiB
4397 lines
109 KiB
#include <iomanip> |
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#include <stdexcept> |
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#include <string> |
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#include <iostream> |
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#include <cstdio> |
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#include <vector> |
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#include <numeric> |
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#include <opencv2/core/utility.hpp> |
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#include "opencv2/imgproc.hpp" |
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#include "opencv2/highgui.hpp" |
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#include "opencv2/calib3d.hpp" |
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#include "opencv2/video.hpp" |
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#include "opencv2/nonfree.hpp" |
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#include "opencv2/objdetect.hpp" |
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#include "opencv2/features2d.hpp" |
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#define USE_OPENCL |
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#ifdef USE_OPENCL |
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#include "opencv2/ocl.hpp" |
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#include "opencv2/nonfree/ocl.hpp" |
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#endif |
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#define TAB " " |
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using namespace std; |
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using namespace cv; |
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// This program test most of the functions in ocl module and generate data metrix of x-factor in .csv files |
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// All images needed in this test are in samples/gpu folder. |
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// For haar template, haarcascade_frontalface_alt.xml shouold be in working directory |
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void gen(Mat &mat, int rows, int cols, int type, Scalar low, Scalar high); |
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string abspath(const string &relpath); |
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int CV_CDECL cvErrorCallback(int, const char *, const char *, const char *, int, void *); |
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typedef struct |
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{ |
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short x; |
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short y; |
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} COOR; |
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COOR do_meanShift(int x0, int y0, uchar *sptr, uchar *dptr, int sstep, |
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cv::Size size, int sp, int sr, int maxIter, float eps, int *tab); |
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void meanShiftProc_(const Mat &src_roi, Mat &dst_roi, Mat &dstCoor_roi, |
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int sp, int sr, cv::TermCriteria crit); |
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class Runnable |
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{ |
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public: |
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explicit Runnable(const std::string &runname): name_(runname) {} |
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virtual ~Runnable() {} |
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const std::string &name() const |
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{ |
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return name_; |
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} |
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virtual void run() = 0; |
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private: |
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std::string name_; |
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}; |
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class TestSystem |
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{ |
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public: |
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static TestSystem &instance() |
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{ |
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static TestSystem me; |
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return me; |
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} |
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void setWorkingDir(const std::string &val) |
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{ |
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working_dir_ = val; |
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} |
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const std::string &workingDir() const |
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{ |
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return working_dir_; |
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} |
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void setTestFilter(const std::string &val) |
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{ |
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test_filter_ = val; |
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} |
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const std::string &testFilter() const |
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{ |
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return test_filter_; |
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} |
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void setNumIters(int num_iters) |
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{ |
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num_iters_ = num_iters; |
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} |
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void setGPUWarmupIters(int num_iters) |
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{ |
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gpu_warmup_iters_ = num_iters; |
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} |
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void setCPUIters(int num_iters) |
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{ |
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cpu_num_iters_ = num_iters; |
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} |
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void setTopThreshold(double top) |
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{ |
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top_ = top; |
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} |
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void setBottomThreshold(double bottom) |
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{ |
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bottom_ = bottom; |
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} |
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void addInit(Runnable *init) |
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{ |
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inits_.push_back(init); |
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} |
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void addTest(Runnable *test) |
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{ |
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tests_.push_back(test); |
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} |
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void run(); |
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// It's public because OpenCV callback uses it |
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void printError(const std::string &msg); |
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std::stringstream &startNewSubtest() |
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{ |
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finishCurrentSubtest(); |
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return cur_subtest_description_; |
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} |
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bool stop() const |
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{ |
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return cur_iter_idx_ >= num_iters_; |
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} |
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bool cpu_stop() const |
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{ |
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return cur_iter_idx_ >= cpu_num_iters_; |
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} |
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bool warmupStop() |
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{ |
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return cur_warmup_idx_++ >= gpu_warmup_iters_; |
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} |
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void warmupComplete() |
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{ |
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cur_warmup_idx_ = 0; |
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} |
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void cpuOn() |
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{ |
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cpu_started_ = cv::getTickCount(); |
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} |
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void cpuOff() |
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{ |
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int64 delta = cv::getTickCount() - cpu_started_; |
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cpu_times_.push_back(delta); |
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++cur_iter_idx_; |
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} |
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void cpuComplete() |
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{ |
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cpu_elapsed_ += meanTime(cpu_times_); |
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cur_subtest_is_empty_ = false; |
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cur_iter_idx_ = 0; |
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} |
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void gpuOn() |
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{ |
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gpu_started_ = cv::getTickCount(); |
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} |
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void gpuOff() |
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{ |
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int64 delta = cv::getTickCount() - gpu_started_; |
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gpu_times_.push_back(delta); |
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++cur_iter_idx_; |
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} |
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void gpuComplete() |
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{ |
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gpu_elapsed_ += meanTime(gpu_times_); |
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cur_subtest_is_empty_ = false; |
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cur_iter_idx_ = 0; |
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} |
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void gpufullOn() |
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{ |
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gpu_full_started_ = cv::getTickCount(); |
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} |
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void gpufullOff() |
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{ |
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int64 delta = cv::getTickCount() - gpu_full_started_; |
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gpu_full_times_.push_back(delta); |
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++cur_iter_idx_; |
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} |
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void gpufullComplete() |
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{ |
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gpu_full_elapsed_ += meanTime(gpu_full_times_); |
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cur_subtest_is_empty_ = false; |
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cur_iter_idx_ = 0; |
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} |
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bool isListMode() const |
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{ |
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return is_list_mode_; |
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} |
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void setListMode(bool value) |
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{ |
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is_list_mode_ = value; |
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} |
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void setRecordName(const std::string &name) |
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{ |
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recordname_ = name; |
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} |
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void setCurrentTest(const std::string &name) |
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{ |
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itname_ = name; |
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itname_changed_ = true; |
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} |
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private: |
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TestSystem(): |
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cur_subtest_is_empty_(true), cpu_elapsed_(0), |
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gpu_elapsed_(0), gpu_full_elapsed_(0), speedup_total_(0.0), |
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num_subtests_called_(0), |
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speedup_faster_count_(0), speedup_slower_count_(0), speedup_equal_count_(0), |
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speedup_full_faster_count_(0), speedup_full_slower_count_(0), speedup_full_equal_count_(0), is_list_mode_(false), |
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num_iters_(10), cpu_num_iters_(2), |
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gpu_warmup_iters_(1), cur_iter_idx_(0), cur_warmup_idx_(0), |
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record_(0), recordname_("performance"), itname_changed_(true) |
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{ |
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cpu_times_.reserve(num_iters_); |
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gpu_times_.reserve(num_iters_); |
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gpu_full_times_.reserve(num_iters_); |
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} |
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void finishCurrentSubtest(); |
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void resetCurrentSubtest() |
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{ |
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cpu_elapsed_ = 0; |
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gpu_elapsed_ = 0; |
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gpu_full_elapsed_ = 0; |
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cur_subtest_description_.str(""); |
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cur_subtest_is_empty_ = true; |
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cur_iter_idx_ = 0; |
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cpu_times_.clear(); |
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gpu_times_.clear(); |
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gpu_full_times_.clear(); |
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} |
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double meanTime(const std::vector<int64> &samples); |
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void printHeading(); |
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void printSummary(); |
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void printMetrics(double cpu_time, double gpu_time, double gpu_full_time, double speedup, double fullspeedup); |
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void writeHeading(); |
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void writeSummary(); |
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void writeMetrics(double cpu_time, double gpu_time, double gpu_full_time, |
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double speedup, double fullspeedup, |
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double gpu_min, double gpu_max, double std_dev); |
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std::string working_dir_; |
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std::string test_filter_; |
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std::vector<Runnable *> inits_; |
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std::vector<Runnable *> tests_; |
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std::stringstream cur_subtest_description_; |
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bool cur_subtest_is_empty_; |
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int64 cpu_started_; |
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int64 gpu_started_; |
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int64 gpu_full_started_; |
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double cpu_elapsed_; |
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double gpu_elapsed_; |
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double gpu_full_elapsed_; |
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double speedup_total_; |
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double speedup_full_total_; |
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int num_subtests_called_; |
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int speedup_faster_count_; |
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int speedup_slower_count_; |
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int speedup_equal_count_; |
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int speedup_full_faster_count_; |
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int speedup_full_slower_count_; |
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int speedup_full_equal_count_; |
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bool is_list_mode_; |
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double top_; |
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double bottom_; |
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int num_iters_; |
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int cpu_num_iters_; //there's no need to set cpu running same times with gpu |
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int gpu_warmup_iters_; //gpu warm up times, default is 1 |
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int cur_iter_idx_; |
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int cur_warmup_idx_; //current gpu warm up times |
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std::vector<int64> cpu_times_; |
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std::vector<int64> gpu_times_; |
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std::vector<int64> gpu_full_times_; |
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FILE *record_; |
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std::string recordname_; |
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std::string itname_; |
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bool itname_changed_; |
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}; |
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#define GLOBAL_INIT(name) \ |
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struct name##_init: Runnable { \ |
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name##_init(): Runnable(#name) { \ |
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TestSystem::instance().addInit(this); \ |
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} \ |
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void run(); \ |
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} name##_init_instance; \ |
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void name##_init::run() |
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#define TEST(name) \ |
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struct name##_test: Runnable { \ |
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name##_test(): Runnable(#name) { \ |
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TestSystem::instance().addTest(this); \ |
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} \ |
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void run(); \ |
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} name##_test_instance; \ |
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void name##_test::run() |
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#define SUBTEST TestSystem::instance().startNewSubtest() |
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#define CPU_ON \ |
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while (!TestSystem::instance().cpu_stop()) { \ |
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TestSystem::instance().cpuOn() |
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#define CPU_OFF \ |
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TestSystem::instance().cpuOff(); \ |
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} TestSystem::instance().cpuComplete() |
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#define GPU_ON \ |
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while (!TestSystem::instance().stop()) { \ |
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TestSystem::instance().gpuOn() |
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#define GPU_OFF \ |
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TestSystem::instance().gpuOff(); \ |
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} TestSystem::instance().gpuComplete() |
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#define GPU_FULL_ON \ |
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while (!TestSystem::instance().stop()) { \ |
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TestSystem::instance().gpufullOn() |
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#define GPU_FULL_OFF \ |
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TestSystem::instance().gpufullOff(); \ |
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} TestSystem::instance().gpufullComplete() |
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#define WARMUP_ON \ |
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while (!TestSystem::instance().warmupStop()) { |
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#define WARMUP_OFF \ |
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} TestSystem::instance().warmupComplete() |
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void TestSystem::run() |
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{ |
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if (is_list_mode_) |
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{ |
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for (vector<Runnable *>::iterator it = tests_.begin(); it != tests_.end(); ++it) |
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{ |
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cout << (*it)->name() << endl; |
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} |
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return; |
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} |
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// Run test initializers |
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for (vector<Runnable *>::iterator it = inits_.begin(); it != inits_.end(); ++it) |
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{ |
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if ((*it)->name().find(test_filter_, 0) != string::npos) |
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{ |
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(*it)->run(); |
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} |
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} |
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printHeading(); |
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writeHeading(); |
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// Run tests |
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for (vector<Runnable *>::iterator it = tests_.begin(); it != tests_.end(); ++it) |
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{ |
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try |
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{ |
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if ((*it)->name().find(test_filter_, 0) != string::npos) |
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{ |
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cout << endl << (*it)->name() << ":\n"; |
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setCurrentTest((*it)->name()); |
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//fprintf(record_,"%s\n",(*it)->name().c_str()); |
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(*it)->run(); |
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finishCurrentSubtest(); |
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} |
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} |
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catch (const Exception &) |
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{ |
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// Message is printed via callback |
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resetCurrentSubtest(); |
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} |
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catch (const runtime_error &e) |
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{ |
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printError(e.what()); |
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resetCurrentSubtest(); |
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} |
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} |
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#ifdef USE_OPENCL |
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printSummary(); |
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writeSummary(); |
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#endif |
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} |
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void TestSystem::finishCurrentSubtest() |
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{ |
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if (cur_subtest_is_empty_) |
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// There is no need to print subtest statistics |
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{ |
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return; |
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} |
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double cpu_time = cpu_elapsed_ / getTickFrequency() * 1000.0; |
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double gpu_time = gpu_elapsed_ / getTickFrequency() * 1000.0; |
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double gpu_full_time = gpu_full_elapsed_ / getTickFrequency() * 1000.0; |
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double speedup = static_cast<double>(cpu_elapsed_) / std::max(1.0, gpu_elapsed_); |
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speedup_total_ += speedup; |
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double fullspeedup = static_cast<double>(cpu_elapsed_) / std::max(1.0, gpu_full_elapsed_); |
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speedup_full_total_ += fullspeedup; |
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if (speedup > top_) |
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{ |
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speedup_faster_count_++; |
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} |
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else if (speedup < bottom_) |
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{ |
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speedup_slower_count_++; |
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} |
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else |
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{ |
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speedup_equal_count_++; |
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} |
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if (fullspeedup > top_) |
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{ |
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speedup_full_faster_count_++; |
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} |
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else if (fullspeedup < bottom_) |
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{ |
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speedup_full_slower_count_++; |
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} |
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else |
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{ |
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speedup_full_equal_count_++; |
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} |
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// compute min, max and |
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std::sort(gpu_times_.begin(), gpu_times_.end()); |
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double gpu_min = gpu_times_.front() / getTickFrequency() * 1000.0; |
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double gpu_max = gpu_times_.back() / getTickFrequency() * 1000.0; |
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double deviation = 0; |
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if (gpu_times_.size() > 1) |
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{ |
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double sum = 0; |
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for (size_t i = 0; i < gpu_times_.size(); i++) |
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{ |
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int64 diff = gpu_times_[i] - static_cast<int64>(gpu_elapsed_); |
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double diff_time = diff * 1000 / getTickFrequency(); |
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sum += diff_time * diff_time; |
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} |
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deviation = std::sqrt(sum / gpu_times_.size()); |
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} |
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printMetrics(cpu_time, gpu_time, gpu_full_time, speedup, fullspeedup); |
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writeMetrics(cpu_time, gpu_time, gpu_full_time, speedup, fullspeedup, gpu_min, gpu_max, deviation); |
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num_subtests_called_++; |
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resetCurrentSubtest(); |
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} |
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double TestSystem::meanTime(const vector<int64> &samples) |
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{ |
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double sum = accumulate(samples.begin(), samples.end(), 0.); |
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return sum / samples.size(); |
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} |
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void TestSystem::printHeading() |
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{ |
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cout << endl; |
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cout << setiosflags(ios_base::left); |
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#ifdef USE_OPENCL |
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cout << TAB << setw(10) << "CPU, ms" << setw(10) << "GPU, ms" |
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<< setw(14) << "SPEEDUP" << setw(14) << "GPUTOTAL, ms" << setw(14) << "TOTALSPEEDUP" |
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<< "DESCRIPTION\n"; |
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#else |
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cout << TAB << setw(10) << "CPU, ms\n"; |
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#endif |
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cout << resetiosflags(ios_base::left); |
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} |
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void TestSystem::writeHeading() |
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{ |
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if (!record_) |
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{ |
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#ifdef USE_OPENCL |
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recordname_ += "_OCL.csv"; |
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#else |
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recordname_ += "_CPU.csv"; |
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#endif |
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record_ = fopen(recordname_.c_str(), "w"); |
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} |
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#ifdef USE_OPENCL |
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fprintf(record_, "NAME,DESCRIPTION,CPU (ms),GPU (ms),SPEEDUP,GPUTOTAL (ms),TOTALSPEEDUP,GPU Min (ms),GPU Max (ms), Standard deviation (ms)\n"); |
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#else |
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fprintf(record_, "NAME,DESCRIPTION,CPU (ms)\n"); |
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#endif |
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fflush(record_); |
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} |
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void TestSystem::printSummary() |
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{ |
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cout << setiosflags(ios_base::fixed); |
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cout << "\naverage GPU speedup: x" |
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<< setprecision(3) << speedup_total_ / std::max(1, num_subtests_called_) |
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<< endl; |
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cout << "\nGPU exceeded: " |
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<< setprecision(3) << speedup_faster_count_ |
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<< "\nGPU passed: " |
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<< setprecision(3) << speedup_equal_count_ |
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<< "\nGPU failed: " |
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<< setprecision(3) << speedup_slower_count_ |
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<< endl; |
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cout << "\nGPU exceeded rate: " |
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<< setprecision(3) << (float)speedup_faster_count_ / std::max(1, num_subtests_called_) * 100 |
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<< "%" |
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<< "\nGPU passed rate: " |
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<< setprecision(3) << (float)speedup_equal_count_ / std::max(1, num_subtests_called_) * 100 |
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<< "%" |
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<< "\nGPU failed rate: " |
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<< setprecision(3) << (float)speedup_slower_count_ / std::max(1, num_subtests_called_) * 100 |
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<< "%" |
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<< endl; |
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cout << "\naverage GPUTOTAL speedup: x" |
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<< setprecision(3) << speedup_full_total_ / std::max(1, num_subtests_called_) |
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<< endl; |
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cout << "\nGPUTOTAL exceeded: " |
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<< setprecision(3) << speedup_full_faster_count_ |
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<< "\nGPUTOTAL passed: " |
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<< setprecision(3) << speedup_full_equal_count_ |
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<< "\nGPUTOTAL failed: " |
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<< setprecision(3) << speedup_full_slower_count_ |
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<< endl; |
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cout << "\nGPUTOTAL exceeded rate: " |
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<< setprecision(3) << (float)speedup_full_faster_count_ / std::max(1, num_subtests_called_) * 100 |
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<< "%" |
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<< "\nGPUTOTAL passed rate: " |
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<< setprecision(3) << (float)speedup_full_equal_count_ / std::max(1, num_subtests_called_) * 100 |
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<< "%" |
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<< "\nGPUTOTAL failed rate: " |
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<< setprecision(3) << (float)speedup_full_slower_count_ / std::max(1, num_subtests_called_) * 100 |
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<< "%" |
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<< endl; |
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cout << resetiosflags(ios_base::fixed); |
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} |
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void TestSystem::printMetrics(double cpu_time, double gpu_time, double gpu_full_time, double speedup, double fullspeedup) |
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{ |
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cout << TAB << setiosflags(ios_base::left); |
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stringstream stream; |
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stream << cpu_time; |
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cout << setw(10) << stream.str(); |
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#ifdef USE_OPENCL |
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stream.str(""); |
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stream << gpu_time; |
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cout << setw(10) << stream.str(); |
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stream.str(""); |
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stream << "x" << setprecision(3) << speedup; |
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cout << setw(14) << stream.str(); |
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stream.str(""); |
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stream << gpu_full_time; |
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cout << setw(14) << stream.str(); |
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stream.str(""); |
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stream << "x" << setprecision(3) << fullspeedup; |
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cout << setw(14) << stream.str(); |
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#endif |
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cout << cur_subtest_description_.str(); |
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cout << resetiosflags(ios_base::left) << endl; |
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} |
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void TestSystem::writeMetrics(double cpu_time, double gpu_time, double gpu_full_time, double speedup, double fullspeedup, double gpu_min, double gpu_max, double std_dev) |
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{ |
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if (!record_) |
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{ |
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recordname_ += ".csv"; |
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record_ = fopen(recordname_.c_str(), "w"); |
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} |
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#ifdef USE_OPENCL |
|
fprintf(record_, "%s,%s,%.3f,%.3f,%.3f,%.3f,%.3f,%.3f,%.3f,%.3f\n", itname_changed_ ? itname_.c_str() : "", |
|
cur_subtest_description_.str().c_str(), |
|
cpu_time, gpu_time, speedup, gpu_full_time, fullspeedup, |
|
gpu_min, gpu_max, std_dev); |
|
#else |
|
fprintf(record_, "%s,%s,%.3f\n", |
|
itname_changed_ ? itname_.c_str() : "", cur_subtest_description_.str().c_str(), cpu_time); |
|
#endif |
|
|
|
if (itname_changed_) |
|
{ |
|
itname_changed_ = false; |
|
} |
|
|
|
fflush(record_); |
|
} |
|
|
|
void TestSystem::writeSummary() |
|
{ |
|
if (!record_) |
|
{ |
|
recordname_ += ".csv"; |
|
record_ = fopen(recordname_.c_str(), "w"); |
|
} |
|
|
|
fprintf(record_, "\nAverage GPU speedup: %.3f\n" |
|
"exceeded: %d (%.3f%%)\n" |
|
"passed: %d (%.3f%%)\n" |
|
"failed: %d (%.3f%%)\n" |
|
"\nAverage GPUTOTAL speedup: %.3f\n" |
|
"exceeded: %d (%.3f%%)\n" |
|
"passed: %d (%.3f%%)\n" |
|
"failed: %d (%.3f%%)\n", |
|
speedup_total_ / std::max(1, num_subtests_called_), |
|
speedup_faster_count_, (float)speedup_faster_count_ / std::max(1, num_subtests_called_) * 100, |
|
speedup_equal_count_, (float)speedup_equal_count_ / std::max(1, num_subtests_called_) * 100, |
|
speedup_slower_count_, (float)speedup_slower_count_ / std::max(1, num_subtests_called_) * 100, |
|
speedup_full_total_ / std::max(1, num_subtests_called_), |
|
speedup_full_faster_count_, (float)speedup_full_faster_count_ / std::max(1, num_subtests_called_) * 100, |
|
speedup_full_equal_count_, (float)speedup_full_equal_count_ / std::max(1, num_subtests_called_) * 100, |
|
speedup_full_slower_count_, (float)speedup_full_slower_count_ / std::max(1, num_subtests_called_) * 100 |
|
); |
|
fflush(record_); |
|
} |
|
|
|
void TestSystem::printError(const std::string &msg) |
|
{ |
|
cout << TAB << "[error: " << msg << "] " << cur_subtest_description_.str() << endl; |
|
} |
|
|
|
void gen(Mat &mat, int rows, int cols, int type, Scalar low, Scalar high) |
|
{ |
|
mat.create(rows, cols, type); |
|
RNG rng(0); |
|
rng.fill(mat, RNG::UNIFORM, low, high); |
|
} |
|
|
|
|
|
string abspath(const string &relpath) |
|
{ |
|
return TestSystem::instance().workingDir() + relpath; |
|
} |
|
|
|
|
|
int CV_CDECL cvErrorCallback(int /*status*/, const char * /*func_name*/, |
|
const char *err_msg, const char * /*file_name*/, |
|
int /*line*/, void * /*userdata*/) |
|
{ |
|
TestSystem::instance().printError(err_msg); |
|
return 0; |
|
} |
|
|
|
/////////// matchTemplate //////////////////////// |
|
//void InitMatchTemplate() |
|
//{ |
|
// Mat src; gen(src, 500, 500, CV_32F, 0, 1); |
|
// Mat templ; gen(templ, 500, 500, CV_32F, 0, 1); |
|
//#ifdef USE_OPENCL |
|
// ocl::oclMat d_src(src), d_templ(templ), d_dst; |
|
// ocl::matchTemplate(d_src, d_templ, d_dst, CV_TM_CCORR); |
|
//#endif |
|
//} |
|
TEST(matchTemplate) |
|
{ |
|
//InitMatchTemplate(); |
|
|
|
Mat src, templ, dst; |
|
int templ_size = 5; |
|
|
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
int all_type[] = {CV_32FC1, CV_32FC4}; |
|
std::string type_name[] = {"CV_32FC1", "CV_32FC4"}; |
|
|
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
for(templ_size = 5; templ_size <= 5; templ_size *= 5) |
|
{ |
|
gen(src, size, size, all_type[j], 0, 1); |
|
|
|
SUBTEST << src.cols << 'x' << src.rows << "; " << type_name[j] << "; templ " << templ_size << 'x' << templ_size << "; CCORR"; |
|
|
|
gen(templ, templ_size, templ_size, all_type[j], 0, 1); |
|
|
|
matchTemplate(src, templ, dst, CV_TM_CCORR); |
|
|
|
CPU_ON; |
|
matchTemplate(src, templ, dst, CV_TM_CCORR); |
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src(src), d_templ, d_dst; |
|
|
|
d_templ.upload(templ); |
|
|
|
WARMUP_ON; |
|
ocl::matchTemplate(d_src, d_templ, d_dst, CV_TM_CCORR); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::matchTemplate(d_src, d_templ, d_dst, CV_TM_CCORR); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
d_templ.upload(templ); |
|
ocl::matchTemplate(d_src, d_templ, d_dst, CV_TM_CCORR); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
} |
|
|
|
int all_type_8U[] = {CV_8UC1}; |
|
std::string type_name_8U[] = {"CV_8UC1"}; |
|
|
|
for (size_t j = 0; j < sizeof(all_type_8U) / sizeof(int); j++) |
|
{ |
|
for(templ_size = 5; templ_size <= 5; templ_size *= 5) |
|
{ |
|
SUBTEST << src.cols << 'x' << src.rows << "; " << type_name_8U[j] << "; templ " << templ_size << 'x' << templ_size << "; CCORR_NORMED"; |
|
|
|
gen(src, size, size, all_type_8U[j], 0, 255); |
|
|
|
gen(templ, templ_size, templ_size, all_type_8U[j], 0, 255); |
|
|
|
matchTemplate(src, templ, dst, CV_TM_CCORR_NORMED); |
|
|
|
CPU_ON; |
|
matchTemplate(src, templ, dst, CV_TM_CCORR_NORMED); |
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src(src); |
|
ocl::oclMat d_templ(templ), d_dst; |
|
|
|
WARMUP_ON; |
|
ocl::matchTemplate(d_src, d_templ, d_dst, CV_TM_CCORR_NORMED); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::matchTemplate(d_src, d_templ, d_dst, CV_TM_CCORR_NORMED); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
d_templ.upload(templ); |
|
ocl::matchTemplate(d_src, d_templ, d_dst, CV_TM_CCORR_NORMED); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
} |
|
} |
|
} |
|
|
|
///////////// PyrLKOpticalFlow //////////////////////// |
|
TEST(PyrLKOpticalFlow) |
|
{ |
|
std::string images1[] = {"rubberwhale1.png", "aloeL.jpg"}; |
|
std::string images2[] = {"rubberwhale2.png", "aloeR.jpg"}; |
|
|
|
for (size_t i = 0; i < sizeof(images1) / sizeof(std::string); i++) |
|
{ |
|
Mat frame0 = imread(abspath(images1[i]), i == 0 ? IMREAD_COLOR : IMREAD_GRAYSCALE); |
|
|
|
if (frame0.empty()) |
|
{ |
|
std::string errstr = "can't open " + images1[i]; |
|
throw runtime_error(errstr); |
|
} |
|
|
|
Mat frame1 = imread(abspath(images2[i]), i == 0 ? IMREAD_COLOR : IMREAD_GRAYSCALE); |
|
|
|
if (frame1.empty()) |
|
{ |
|
std::string errstr = "can't open " + images2[i]; |
|
throw runtime_error(errstr); |
|
} |
|
|
|
Mat gray_frame; |
|
|
|
if (i == 0) |
|
{ |
|
cvtColor(frame0, gray_frame, COLOR_BGR2GRAY); |
|
} |
|
|
|
for (int points = 1000; points <= 4000; points *= 2) |
|
{ |
|
if (i == 0) |
|
SUBTEST << frame0.cols << "x" << frame0.rows << "; color; " << points << " points"; |
|
else |
|
SUBTEST << frame0.cols << "x" << frame0.rows << "; gray; " << points << " points"; |
|
Mat nextPts_cpu; |
|
Mat status_cpu; |
|
|
|
vector<Point2f> pts; |
|
goodFeaturesToTrack(i == 0 ? gray_frame : frame0, pts, points, 0.01, 0.0); |
|
|
|
vector<Point2f> nextPts; |
|
vector<unsigned char> status; |
|
|
|
vector<float> err; |
|
|
|
calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts, status, err); |
|
|
|
CPU_ON; |
|
calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts, status, err); |
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
ocl::PyrLKOpticalFlow d_pyrLK; |
|
|
|
ocl::oclMat d_frame0(frame0); |
|
ocl::oclMat d_frame1(frame1); |
|
|
|
ocl::oclMat d_pts; |
|
Mat pts_mat(1, (int)pts.size(), CV_32FC2, (void *)&pts[0]); |
|
d_pts.upload(pts_mat); |
|
|
|
ocl::oclMat d_nextPts; |
|
ocl::oclMat d_status; |
|
ocl::oclMat d_err; |
|
|
|
WARMUP_ON; |
|
d_pyrLK.sparse(d_frame0, d_frame1, d_pts, d_nextPts, d_status, &d_err); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
d_pyrLK.sparse(d_frame0, d_frame1, d_pts, d_nextPts, d_status, &d_err); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_frame0.upload(frame0); |
|
d_frame1.upload(frame1); |
|
d_pts.upload(pts_mat); |
|
d_pyrLK.sparse(d_frame0, d_frame1, d_pts, d_nextPts, d_status, &d_err); |
|
|
|
if (!d_nextPts.empty()) |
|
{ |
|
d_nextPts.download(nextPts_cpu); |
|
} |
|
|
|
if (!d_status.empty()) |
|
{ |
|
d_status.download(status_cpu); |
|
} |
|
|
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
|
|
|
|
///////////// pyrDown ////////////////////// |
|
TEST(pyrDown) |
|
{ |
|
Mat src, dst; |
|
int all_type[] = {CV_8UC1, CV_8UC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src, size, size, all_type[j], 0, 256); |
|
|
|
pyrDown(src, dst); |
|
|
|
CPU_ON; |
|
pyrDown(src, dst); |
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src(src); |
|
ocl::oclMat d_dst; |
|
|
|
WARMUP_ON; |
|
ocl::pyrDown(d_src, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::pyrDown(d_src, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::pyrDown(d_src, d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
} |
|
} |
|
|
|
///////////// pyrUp //////////////////////// |
|
TEST(pyrUp) |
|
{ |
|
Mat src, dst; |
|
int all_type[] = {CV_8UC1, CV_8UC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4"}; |
|
|
|
for (int size = 500; size <= 2000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src, size, size, all_type[j], 0, 256); |
|
|
|
pyrUp(src, dst); |
|
|
|
CPU_ON; |
|
pyrUp(src, dst); |
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src(src); |
|
ocl::oclMat d_dst; |
|
|
|
WARMUP_ON; |
|
ocl::pyrUp(d_src, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::pyrUp(d_src, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::pyrUp(d_src, d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
} |
|
} |
|
|
|
///////////// Canny //////////////////////// |
|
TEST(Canny) |
|
{ |
|
Mat img = imread(abspath("aloeL.jpg"), CV_LOAD_IMAGE_GRAYSCALE); |
|
|
|
if (img.empty()) |
|
{ |
|
throw runtime_error("can't open aloeL.jpg"); |
|
} |
|
|
|
SUBTEST << img.cols << 'x' << img.rows << "; aloeL.jpg" << "; edges" << "; CV_8UC1"; |
|
|
|
Mat edges(img.size(), CV_8UC1); |
|
|
|
CPU_ON; |
|
Canny(img, edges, 50.0, 100.0); |
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_img(img); |
|
ocl::oclMat d_edges; |
|
ocl::CannyBuf d_buf; |
|
|
|
WARMUP_ON; |
|
ocl::Canny(d_img, d_buf, d_edges, 50.0, 100.0); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::Canny(d_img, d_buf, d_edges, 50.0, 100.0); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_img.upload(img); |
|
ocl::Canny(d_img, d_buf, d_edges, 50.0, 100.0); |
|
d_edges.download(edges); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
///////////// Haar //////////////////////// |
|
#ifdef USE_OPENCL |
|
namespace cv |
|
{ |
|
namespace ocl |
|
{ |
|
|
|
struct getRect |
|
{ |
|
Rect operator()(const CvAvgComp &e) const |
|
{ |
|
return e.rect; |
|
} |
|
}; |
|
|
|
class CascadeClassifier_GPU : public OclCascadeClassifier |
|
{ |
|
public: |
|
void detectMultiScale(oclMat &image, |
|
std::vector<cv::Rect>& faces, |
|
double scaleFactor = 1.1, |
|
int minNeighbors = 3, int flags = 0, |
|
Size minSize = Size(), |
|
Size maxSize = Size()) |
|
{ |
|
(void)maxSize; |
|
MemStorage storage(cvCreateMemStorage(0)); |
|
//CvMat img=image; |
|
CvSeq *objs = oclHaarDetectObjects(image, storage, scaleFactor, minNeighbors, flags, minSize); |
|
vector<CvAvgComp> vecAvgComp; |
|
Seq<CvAvgComp>(objs).copyTo(vecAvgComp); |
|
faces.resize(vecAvgComp.size()); |
|
std::transform(vecAvgComp.begin(), vecAvgComp.end(), faces.begin(), getRect()); |
|
} |
|
|
|
}; |
|
|
|
} |
|
} |
|
#endif |
|
TEST(Haar) |
|
{ |
|
Mat img = imread(abspath("basketball1.png"), CV_LOAD_IMAGE_GRAYSCALE); |
|
|
|
if (img.empty()) |
|
{ |
|
throw runtime_error("can't open basketball1.png"); |
|
} |
|
|
|
CascadeClassifier faceCascadeCPU; |
|
|
|
if (!faceCascadeCPU.load(abspath("haarcascade_frontalface_alt.xml"))) |
|
{ |
|
throw runtime_error("can't load haarcascade_frontalface_alt.xml"); |
|
} |
|
|
|
vector<Rect> faces; |
|
|
|
SUBTEST << img.cols << "x" << img.rows << "; scale image"; |
|
CPU_ON; |
|
faceCascadeCPU.detectMultiScale(img, faces, |
|
1.1, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(30, 30)); |
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
ocl::CascadeClassifier_GPU faceCascade; |
|
|
|
if (!faceCascade.load(abspath("haarcascade_frontalface_alt.xml"))) |
|
{ |
|
throw runtime_error("can't load haarcascade_frontalface_alt.xml"); |
|
} |
|
|
|
ocl::oclMat d_img(img); |
|
|
|
faces.clear(); |
|
|
|
WARMUP_ON; |
|
faceCascade.detectMultiScale(d_img, faces, |
|
1.1, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(30, 30)); |
|
WARMUP_OFF; |
|
|
|
faces.clear(); |
|
|
|
GPU_ON; |
|
faceCascade.detectMultiScale(d_img, faces, |
|
1.1, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(30, 30)); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_img.upload(img); |
|
faceCascade.detectMultiScale(d_img, faces, |
|
1.1, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(30, 30)); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
///////////// blend //////////////////////// |
|
template <typename T> |
|
void blendLinearGold(const cv::Mat &img1, const cv::Mat &img2, const cv::Mat &weights1, const cv::Mat &weights2, cv::Mat &result_gold) |
|
{ |
|
result_gold.create(img1.size(), img1.type()); |
|
|
|
int cn = img1.channels(); |
|
|
|
for (int y = 0; y < img1.rows; ++y) |
|
{ |
|
const float *weights1_row = weights1.ptr<float>(y); |
|
const float *weights2_row = weights2.ptr<float>(y); |
|
const T *img1_row = img1.ptr<T>(y); |
|
const T *img2_row = img2.ptr<T>(y); |
|
T *result_gold_row = result_gold.ptr<T>(y); |
|
|
|
for (int x = 0; x < img1.cols * cn; ++x) |
|
{ |
|
float w1 = weights1_row[x / cn]; |
|
float w2 = weights2_row[x / cn]; |
|
result_gold_row[x] = static_cast<T>((img1_row[x] * w1 + img2_row[x] * w2) / (w1 + w2 + 1e-5f)); |
|
} |
|
} |
|
} |
|
TEST(blend) |
|
{ |
|
Mat src1, src2, weights1, weights2, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src1, d_src2, d_weights1, d_weights2, d_dst; |
|
#endif |
|
int all_type[] = {CV_8UC1, CV_8UC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] << " and CV_32FC1"; |
|
|
|
gen(src1, size, size, all_type[j], 0, 256); |
|
gen(src2, size, size, all_type[j], 0, 256); |
|
gen(weights1, size, size, CV_32FC1, 0, 1); |
|
gen(weights2, size, size, CV_32FC1, 0, 1); |
|
|
|
blendLinearGold<uchar>(src1, src2, weights1, weights2, dst); |
|
|
|
CPU_ON; |
|
blendLinearGold<uchar>(src1, src2, weights1, weights2, dst); |
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
d_weights1.upload(weights1); |
|
d_weights2.upload(weights2); |
|
|
|
WARMUP_ON; |
|
ocl::blendLinear(d_src1, d_src2, d_weights1, d_weights2, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::blendLinear(d_src1, d_src2, d_weights1, d_weights2, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
d_weights1.upload(weights1); |
|
d_weights2.upload(weights2); |
|
ocl::blendLinear(d_src1, d_src2, d_weights1, d_weights2, d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
} |
|
} |
|
///////////// columnSum//////////////////////// |
|
TEST(columnSum) |
|
{ |
|
Mat src, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_dst; |
|
#endif |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
SUBTEST << size << 'x' << size << "; CV_32FC1"; |
|
|
|
gen(src, size, size, CV_32FC1, 0, 256); |
|
|
|
CPU_ON; |
|
dst.create(src.size(), src.type()); |
|
|
|
for (int i = 1; i < src.rows; ++i) |
|
{ |
|
for (int j = 0; j < src.cols; ++j) |
|
{ |
|
dst.at<float>(i, j) = src.at<float>(i, j) += src.at<float>(i - 1, j); |
|
} |
|
} |
|
|
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
WARMUP_ON; |
|
ocl::columnSum(d_src, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::columnSum(d_src, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::columnSum(d_src, d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
} |
|
|
|
///////////// HOG//////////////////////// |
|
TEST(HOG) |
|
{ |
|
Mat src = imread(abspath("road.png"), cv::IMREAD_GRAYSCALE); |
|
|
|
if (src.empty()) |
|
{ |
|
throw runtime_error("can't open road.png"); |
|
} |
|
|
|
|
|
cv::HOGDescriptor hog; |
|
hog.setSVMDetector(hog.getDefaultPeopleDetector()); |
|
std::vector<cv::Rect> found_locations; |
|
|
|
SUBTEST << 768 << 'x' << 576 << "; road.png"; |
|
|
|
hog.detectMultiScale(src, found_locations); |
|
|
|
CPU_ON; |
|
hog.detectMultiScale(src, found_locations); |
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
cv::ocl::HOGDescriptor ocl_hog; |
|
ocl_hog.setSVMDetector(ocl_hog.getDefaultPeopleDetector()); |
|
ocl::oclMat d_src; |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl_hog.detectMultiScale(d_src, found_locations); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl_hog.detectMultiScale(d_src, found_locations); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl_hog.detectMultiScale(d_src, found_locations); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
///////////// SURF //////////////////////// |
|
|
|
TEST(SURF) |
|
{ |
|
Mat keypoints_cpu; |
|
Mat descriptors_cpu; |
|
|
|
Mat src = imread(abspath("aloeL.jpg"), CV_LOAD_IMAGE_GRAYSCALE); |
|
|
|
if (src.empty()) |
|
{ |
|
throw runtime_error("can't open aloeL.jpg"); |
|
} |
|
|
|
SUBTEST << src.cols << "x" << src.rows << "; aloeL.jpg"; |
|
SURF surf; |
|
vector<KeyPoint> keypoints; |
|
Mat descriptors; |
|
|
|
surf(src, Mat(), keypoints, descriptors); |
|
|
|
CPU_ON; |
|
keypoints.clear(); |
|
surf(src, Mat(), keypoints, descriptors); |
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
ocl::SURF_OCL d_surf; |
|
ocl::oclMat d_src(src); |
|
ocl::oclMat d_keypoints; |
|
ocl::oclMat d_descriptors; |
|
|
|
WARMUP_ON; |
|
d_surf(d_src, ocl::oclMat(), d_keypoints, d_descriptors); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
d_surf(d_src, ocl::oclMat(), d_keypoints, d_descriptors); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
d_surf(d_src, ocl::oclMat(), d_keypoints, d_descriptors); |
|
|
|
if (!d_keypoints.empty()) |
|
{ |
|
d_keypoints.download(keypoints_cpu); |
|
} |
|
|
|
if (!d_descriptors.empty()) |
|
{ |
|
d_descriptors.download(descriptors_cpu); |
|
} |
|
|
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
//////////////////// BruteForceMatch ///////////////// |
|
TEST(BruteForceMatcher) |
|
{ |
|
Mat trainIdx_cpu; |
|
Mat distance_cpu; |
|
Mat allDist_cpu; |
|
Mat nMatches_cpu; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
// Init CPU matcher |
|
int desc_len = 64; |
|
|
|
BFMatcher matcher(NORM_L2); |
|
|
|
Mat query; |
|
gen(query, size, desc_len, CV_32F, 0, 1); |
|
|
|
Mat train; |
|
gen(train, size, desc_len, CV_32F, 0, 1); |
|
// Output |
|
vector< vector<DMatch> > matches(2); |
|
#ifdef USE_OPENCL |
|
// Init GPU matcher |
|
ocl::BruteForceMatcher_OCL_base d_matcher(ocl::BruteForceMatcher_OCL_base::L2Dist); |
|
|
|
ocl::oclMat d_query(query); |
|
ocl::oclMat d_train(train); |
|
|
|
ocl::oclMat d_trainIdx, d_distance, d_allDist, d_nMatches; |
|
#endif |
|
SUBTEST << size << "; match"; |
|
|
|
matcher.match(query, train, matches[0]); |
|
|
|
CPU_ON; |
|
matcher.match(query, train, matches[0]); |
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
WARMUP_ON; |
|
d_matcher.matchSingle(d_query, d_train, d_trainIdx, d_distance); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
d_matcher.matchSingle(d_query, d_train, d_trainIdx, d_distance); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_query.upload(query); |
|
d_train.upload(train); |
|
d_matcher.match(d_query, d_train, matches[0]); |
|
GPU_FULL_OFF; |
|
#endif |
|
|
|
SUBTEST << size << "; knnMatch"; |
|
|
|
matcher.knnMatch(query, train, matches, 2); |
|
|
|
CPU_ON; |
|
matcher.knnMatch(query, train, matches, 2); |
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
WARMUP_ON; |
|
d_matcher.knnMatchSingle(d_query, d_train, d_trainIdx, d_distance, d_allDist, 2); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
d_matcher.knnMatchSingle(d_query, d_train, d_trainIdx, d_distance, d_allDist, 2); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_query.upload(query); |
|
d_train.upload(train); |
|
d_matcher.knnMatch(d_query, d_train, matches, 2); |
|
GPU_FULL_OFF; |
|
#endif |
|
SUBTEST << size << "; radiusMatch"; |
|
|
|
float max_distance = 2.0f; |
|
|
|
matcher.radiusMatch(query, train, matches, max_distance); |
|
|
|
CPU_ON; |
|
matcher.radiusMatch(query, train, matches, max_distance); |
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
d_trainIdx.release(); |
|
|
|
WARMUP_ON; |
|
d_matcher.radiusMatchSingle(d_query, d_train, d_trainIdx, d_distance, d_nMatches, max_distance); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
d_matcher.radiusMatchSingle(d_query, d_train, d_trainIdx, d_distance, d_nMatches, max_distance); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_query.upload(query); |
|
d_train.upload(train); |
|
d_matcher.radiusMatch(d_query, d_train, matches, max_distance); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
} |
|
///////////// Lut //////////////////////// |
|
TEST(lut) |
|
{ |
|
Mat src, lut, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_lut, d_dst; |
|
#endif |
|
int all_type[] = {CV_8UC1, CV_8UC3}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC3"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j]; |
|
|
|
gen(src, size, size, all_type[j], 0, 256); |
|
gen(lut, 1, 256, CV_8UC1, 0, 1); |
|
gen(dst, size, size, all_type[j], 0, 256); |
|
|
|
LUT(src, lut, dst); |
|
|
|
CPU_ON; |
|
LUT(src, lut, dst); |
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
d_lut.upload(lut); |
|
|
|
WARMUP_ON; |
|
ocl::LUT(d_src, d_lut, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::LUT(d_src, d_lut, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
d_lut.upload(lut); |
|
ocl::LUT(d_src, d_lut, d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
///////////// Exp //////////////////////// |
|
TEST(Exp) |
|
{ |
|
Mat src, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_dst; |
|
#endif |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
SUBTEST << size << 'x' << size << "; CV_32FC1"; |
|
|
|
gen(src, size, size, CV_32FC1, 0, 256); |
|
gen(dst, size, size, CV_32FC1, 0, 256); |
|
|
|
exp(src, dst); |
|
|
|
CPU_ON; |
|
exp(src, dst); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl::exp(d_src, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::exp(d_src, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::exp(d_src, d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
} |
|
|
|
///////////// LOG //////////////////////// |
|
TEST(Log) |
|
{ |
|
Mat src, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_dst; |
|
#endif |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
SUBTEST << size << 'x' << size << "; 32F"; |
|
|
|
gen(src, size, size, CV_32F, 1, 10); |
|
|
|
log(src, dst); |
|
|
|
CPU_ON; |
|
log(src, dst); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl::log(d_src, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::log(d_src, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::log(d_src, d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
} |
|
|
|
///////////// Add //////////////////////// |
|
|
|
TEST(Add) |
|
{ |
|
Mat src1, src2, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src1, d_src2, d_dst; |
|
#endif |
|
int all_type[] = {CV_8UC1, CV_32FC1}; |
|
std::string type_name[] = {"CV_8UC1", "CV_32FC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j]; |
|
|
|
gen(src1, size, size, all_type[j], 0, 1); |
|
gen(src2, size, size, all_type[j], 0, 1); |
|
|
|
add(src1, src2, dst); |
|
|
|
CPU_ON; |
|
add(src1, src2, dst); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
|
|
WARMUP_ON; |
|
ocl::add(d_src1, d_src2, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::add(d_src1, d_src2, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
ocl::add(d_src1, d_src2, d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
///////////// Mul //////////////////////// |
|
TEST(Mul) |
|
{ |
|
Mat src1, src2, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src1, d_src2, d_dst; |
|
#endif |
|
int all_type[] = {CV_8UC1, CV_8UC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src1, size, size, all_type[j], 0, 256); |
|
gen(src2, size, size, all_type[j], 0, 256); |
|
gen(dst, size, size, all_type[j], 0, 256); |
|
|
|
|
|
multiply(src1, src2, dst); |
|
|
|
CPU_ON; |
|
multiply(src1, src2, dst); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
|
|
WARMUP_ON; |
|
ocl::multiply(d_src1, d_src2, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::multiply(d_src1, d_src2, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
ocl::multiply(d_src1, d_src2, d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
|
|
///////////// Div //////////////////////// |
|
TEST(Div) |
|
{ |
|
Mat src1, src2, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src1, d_src2, d_dst; |
|
#endif |
|
int all_type[] = {CV_8UC1, CV_8UC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j]; |
|
|
|
gen(src1, size, size, all_type[j], 0, 256); |
|
gen(src2, size, size, all_type[j], 0, 256); |
|
gen(dst, size, size, all_type[j], 0, 256); |
|
|
|
|
|
divide(src1, src2, dst); |
|
|
|
CPU_ON; |
|
divide(src1, src2, dst); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
|
|
WARMUP_ON; |
|
ocl::divide(d_src1, d_src2, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::divide(d_src1, d_src2, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
ocl::divide(d_src1, d_src2, d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
|
|
///////////// Absdiff //////////////////////// |
|
TEST(Absdiff) |
|
{ |
|
Mat src1, src2, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src1, d_src2, d_dst; |
|
#endif |
|
int all_type[] = {CV_8UC1, CV_8UC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src1, size, size, all_type[j], 0, 256); |
|
gen(src2, size, size, all_type[j], 0, 256); |
|
gen(dst, size, size, all_type[j], 0, 256); |
|
|
|
|
|
absdiff(src1, src2, dst); |
|
|
|
CPU_ON; |
|
absdiff(src1, src2, dst); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
|
|
WARMUP_ON; |
|
ocl::absdiff(d_src1, d_src2, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::absdiff(d_src1, d_src2, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
ocl::absdiff(d_src1, d_src2, d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
|
|
///////////// CartToPolar //////////////////////// |
|
TEST(CartToPolar) |
|
{ |
|
Mat src1, src2, dst, dst1; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src1, d_src2, d_dst, d_dst1; |
|
#endif |
|
int all_type[] = {CV_32FC1}; |
|
std::string type_name[] = {"CV_32FC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j]; |
|
|
|
gen(src1, size, size, all_type[j], 0, 256); |
|
gen(src2, size, size, all_type[j], 0, 256); |
|
gen(dst, size, size, all_type[j], 0, 256); |
|
gen(dst1, size, size, all_type[j], 0, 256); |
|
|
|
|
|
cartToPolar(src1, src2, dst, dst1, 1); |
|
|
|
CPU_ON; |
|
cartToPolar(src1, src2, dst, dst1, 1); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
|
|
WARMUP_ON; |
|
ocl::cartToPolar(d_src1, d_src2, d_dst, d_dst1, 1); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::cartToPolar(d_src1, d_src2, d_dst, d_dst1, 1); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
ocl::cartToPolar(d_src1, d_src2, d_dst, d_dst1, 1); |
|
d_dst.download(dst); |
|
d_dst1.download(dst1); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
|
|
///////////// PolarToCart //////////////////////// |
|
TEST(PolarToCart) |
|
{ |
|
Mat src1, src2, dst, dst1; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src1, d_src2, d_dst, d_dst1; |
|
#endif |
|
int all_type[] = {CV_32FC1}; |
|
std::string type_name[] = {"CV_32FC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src1, size, size, all_type[j], 0, 256); |
|
gen(src2, size, size, all_type[j], 0, 256); |
|
gen(dst, size, size, all_type[j], 0, 256); |
|
gen(dst1, size, size, all_type[j], 0, 256); |
|
|
|
|
|
polarToCart(src1, src2, dst, dst1, 1); |
|
|
|
CPU_ON; |
|
polarToCart(src1, src2, dst, dst1, 1); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
|
|
WARMUP_ON; |
|
ocl::polarToCart(d_src1, d_src2, d_dst, d_dst1, 1); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::polarToCart(d_src1, d_src2, d_dst, d_dst1, 1); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
ocl::polarToCart(d_src1, d_src2, d_dst, d_dst1, 1); |
|
d_dst.download(dst); |
|
d_dst1.download(dst1); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
|
|
///////////// Magnitude //////////////////////// |
|
TEST(magnitude) |
|
{ |
|
Mat x, y, mag; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_x, d_y, d_mag; |
|
#endif |
|
int all_type[] = {CV_32FC1}; |
|
std::string type_name[] = {"CV_32FC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j]; |
|
|
|
gen(x, size, size, all_type[j], 0, 1); |
|
gen(y, size, size, all_type[j], 0, 1); |
|
|
|
magnitude(x, y, mag); |
|
|
|
CPU_ON; |
|
magnitude(x, y, mag); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_x.upload(x); |
|
d_y.upload(y); |
|
|
|
WARMUP_ON; |
|
ocl::magnitude(d_x, d_y, d_mag); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::magnitude(d_x, d_y, d_mag); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_x.upload(x); |
|
d_y.upload(y); |
|
ocl::magnitude(d_x, d_y, d_mag); |
|
d_mag.download(mag); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
|
|
///////////// Transpose //////////////////////// |
|
TEST(Transpose) |
|
{ |
|
Mat src, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_dst; |
|
#endif |
|
int all_type[] = {CV_8UC1, CV_8UC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j]; |
|
|
|
gen(src, size, size, all_type[j], 0, 256); |
|
gen(dst, size, size, all_type[j], 0, 256); |
|
|
|
transpose(src, dst); |
|
|
|
CPU_ON; |
|
transpose(src, dst); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl::transpose(d_src, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::transpose(d_src, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::transpose(d_src, d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
|
|
///////////// Flip //////////////////////// |
|
TEST(Flip) |
|
{ |
|
Mat src, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_dst; |
|
#endif |
|
int all_type[] = {CV_8UC1, CV_8UC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] << " ; FLIP_BOTH"; |
|
|
|
gen(src, size, size, all_type[j], 0, 256); |
|
gen(dst, size, size, all_type[j], 0, 256); |
|
|
|
flip(src, dst, 0); |
|
|
|
CPU_ON; |
|
flip(src, dst, 0); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl::flip(d_src, d_dst, 0); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::flip(d_src, d_dst, 0); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::flip(d_src, d_dst, 0); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
|
|
///////////// minMax //////////////////////// |
|
TEST(minMax) |
|
{ |
|
Mat src; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src; |
|
#endif |
|
double min_val, max_val; |
|
Point min_loc, max_loc; |
|
int all_type[] = {CV_8UC1, CV_32FC1}; |
|
std::string type_name[] = {"CV_8UC1", "CV_32FC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j]; |
|
|
|
gen(src, size, size, all_type[j], 0, 256); |
|
|
|
CPU_ON; |
|
minMaxLoc(src, &min_val, &max_val, &min_loc, &max_loc); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl::minMax(d_src, &min_val, &max_val); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::minMax(d_src, &min_val, &max_val); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::minMax(d_src, &min_val, &max_val); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
|
|
///////////// minMaxLoc //////////////////////// |
|
TEST(minMaxLoc) |
|
{ |
|
Mat src; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src; |
|
#endif |
|
double min_val, max_val; |
|
Point min_loc, max_loc; |
|
int all_type[] = {CV_8UC1, CV_32FC1}; |
|
std::string type_name[] = {"CV_8UC1", "CV_32FC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src, size, size, all_type[j], 0, 1); |
|
|
|
CPU_ON; |
|
minMaxLoc(src, &min_val, &max_val, &min_loc, &max_loc); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl::minMaxLoc(d_src, &min_val, &max_val, &min_loc, &max_loc); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::minMaxLoc(d_src, &min_val, &max_val, &min_loc, &max_loc); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::minMaxLoc(d_src, &min_val, &max_val, &min_loc, &max_loc); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
///////////// Sum //////////////////////// |
|
TEST(Sum) |
|
{ |
|
Mat src; |
|
Scalar cpures, gpures; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src; |
|
#endif |
|
int all_type[] = {CV_8UC1, CV_32SC1}; |
|
std::string type_name[] = {"CV_8UC1", "CV_32SC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src, size, size, all_type[j], 0, 256); |
|
|
|
cpures = sum(src); |
|
|
|
CPU_ON; |
|
cpures = sum(src); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
gpures = ocl::sum(d_src); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
gpures = ocl::sum(d_src); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
gpures = ocl::sum(d_src); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
///////////// countNonZero //////////////////////// |
|
TEST(countNonZero) |
|
{ |
|
Mat src; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src; |
|
#endif |
|
int all_type[] = {CV_8UC1, CV_32FC1}; |
|
std::string type_name[] = {"CV_8UC1", "CV_32FC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src, size, size, all_type[j], 0, 256); |
|
|
|
countNonZero(src); |
|
|
|
CPU_ON; |
|
countNonZero(src); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl::countNonZero(d_src); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::countNonZero(d_src); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::countNonZero(d_src); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
///////////// Phase //////////////////////// |
|
TEST(Phase) |
|
{ |
|
Mat src1, src2, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src1, d_src2, d_dst; |
|
#endif |
|
int all_type[] = {CV_32FC1}; |
|
std::string type_name[] = {"CV_32FC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src1, size, size, all_type[j], 0, 256); |
|
gen(src2, size, size, all_type[j], 0, 256); |
|
gen(dst, size, size, all_type[j], 0, 256); |
|
|
|
|
|
phase(src1, src2, dst, 1); |
|
|
|
CPU_ON; |
|
phase(src1, src2, dst, 1); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
|
|
WARMUP_ON; |
|
ocl::phase(d_src1, d_src2, d_dst, 1); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::phase(d_src1, d_src2, d_dst, 1); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
ocl::phase(d_src1, d_src2, d_dst, 1); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
|
|
///////////// bitwise_and//////////////////////// |
|
TEST(bitwise_and) |
|
{ |
|
Mat src1, src2, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src1, d_src2, d_dst; |
|
#endif |
|
int all_type[] = {CV_8UC1, CV_32SC1}; |
|
std::string type_name[] = {"CV_8UC1", "CV_32SC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src1, size, size, all_type[j], 0, 256); |
|
gen(src2, size, size, all_type[j], 0, 256); |
|
gen(dst, size, size, all_type[j], 0, 256); |
|
|
|
|
|
bitwise_and(src1, src2, dst); |
|
|
|
CPU_ON; |
|
bitwise_and(src1, src2, dst); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
|
|
WARMUP_ON; |
|
ocl::bitwise_and(d_src1, d_src2, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::bitwise_and(d_src1, d_src2, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
ocl::bitwise_and(d_src1, d_src2, d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
///////////// bitwise_or//////////////////////// |
|
TEST(bitwise_or) |
|
{ |
|
Mat src1, src2, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src1, d_src2, d_dst; |
|
#endif |
|
int all_type[] = {CV_8UC1, CV_32SC1}; |
|
std::string type_name[] = {"CV_8UC1", "CV_32SC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j]; |
|
|
|
gen(src1, size, size, all_type[j], 0, 256); |
|
gen(src2, size, size, all_type[j], 0, 256); |
|
gen(dst, size, size, all_type[j], 0, 256); |
|
|
|
|
|
bitwise_or(src1, src2, dst); |
|
|
|
CPU_ON; |
|
bitwise_or(src1, src2, dst); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
|
|
WARMUP_ON; |
|
ocl::bitwise_or(d_src1, d_src2, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::bitwise_or(d_src1, d_src2, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
ocl::bitwise_or(d_src1, d_src2, d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
///////////// bitwise_xor//////////////////////// |
|
TEST(bitwise_xor) |
|
{ |
|
Mat src1, src2, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src1, d_src2, d_dst; |
|
#endif |
|
int all_type[] = {CV_8UC1, CV_32SC1}; |
|
std::string type_name[] = {"CV_8UC1", "CV_32SC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j]; |
|
|
|
gen(src1, size, size, all_type[j], 0, 256); |
|
gen(src2, size, size, all_type[j], 0, 256); |
|
gen(dst, size, size, all_type[j], 0, 256); |
|
|
|
|
|
bitwise_xor(src1, src2, dst); |
|
|
|
CPU_ON; |
|
bitwise_xor(src1, src2, dst); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
|
|
WARMUP_ON; |
|
ocl::bitwise_xor(d_src1, d_src2, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::bitwise_xor(d_src1, d_src2, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
ocl::bitwise_xor(d_src1, d_src2, d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
///////////// bitwise_not//////////////////////// |
|
TEST(bitwise_not) |
|
{ |
|
Mat src1, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src1, d_dst; |
|
#endif |
|
int all_type[] = {CV_8UC1, CV_32SC1}; |
|
std::string type_name[] = {"CV_8UC1", "CV_32SC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src1, size, size, all_type[j], 0, 256); |
|
gen(dst, size, size, all_type[j], 0, 256); |
|
|
|
|
|
bitwise_not(src1, dst); |
|
|
|
CPU_ON; |
|
bitwise_not(src1, dst); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src1.upload(src1); |
|
|
|
WARMUP_ON; |
|
ocl::bitwise_not(d_src1, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::bitwise_not(d_src1, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src1.upload(src1); |
|
ocl::bitwise_not(d_src1, d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
|
|
///////////// compare//////////////////////// |
|
TEST(compare) |
|
{ |
|
Mat src1, src2, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src1, d_src2, d_dst; |
|
#endif |
|
int CMP_EQ = 0; |
|
int all_type[] = {CV_8UC1, CV_32FC1}; |
|
std::string type_name[] = {"CV_8UC1", "CV_32FC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src1, size, size, all_type[j], 0, 256); |
|
gen(src2, size, size, all_type[j], 0, 256); |
|
gen(dst, size, size, all_type[j], 0, 256); |
|
|
|
|
|
compare(src1, src2, dst, CMP_EQ); |
|
|
|
CPU_ON; |
|
compare(src1, src2, dst, CMP_EQ); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
|
|
WARMUP_ON; |
|
ocl::compare(d_src1, d_src2, d_dst, CMP_EQ); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::compare(d_src1, d_src2, d_dst, CMP_EQ); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
ocl::compare(d_src1, d_src2, d_dst, CMP_EQ); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
|
|
///////////// pow //////////////////////// |
|
TEST(pow) |
|
{ |
|
Mat src, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_dst; |
|
#endif |
|
int all_type[] = {CV_32FC1}; |
|
std::string type_name[] = {"CV_32FC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src, size, size, all_type[j], 0, 100); |
|
gen(dst, size, size, all_type[j], 0, 100); |
|
|
|
pow(src, -2.0, dst); |
|
|
|
CPU_ON; |
|
pow(src, -2.0, dst); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
d_dst.upload(dst); |
|
|
|
WARMUP_ON; |
|
ocl::pow(d_src, -2.0, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::pow(d_src, -2.0, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::pow(d_src, -2.0, d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
|
|
///////////// MagnitudeSqr//////////////////////// |
|
TEST(MagnitudeSqr) |
|
{ |
|
Mat src1, src2, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src1, d_src2, d_dst; |
|
#endif |
|
int all_type[] = {CV_32FC1}; |
|
std::string type_name[] = {"CV_32FC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t t = 0; t < sizeof(all_type) / sizeof(int); t++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[t]; |
|
|
|
gen(src1, size, size, all_type[t], 0, 256); |
|
gen(src2, size, size, all_type[t], 0, 256); |
|
gen(dst, size, size, all_type[t], 0, 256); |
|
|
|
|
|
for (int i = 0; i < src1.rows; ++i) |
|
|
|
for (int j = 0; j < src1.cols; ++j) |
|
{ |
|
float val1 = src1.at<float>(i, j); |
|
float val2 = src2.at<float>(i, j); |
|
|
|
((float *)(dst.data))[i * dst.step / 4 + j] = val1 * val1 + val2 * val2; |
|
|
|
} |
|
|
|
CPU_ON; |
|
|
|
for (int i = 0; i < src1.rows; ++i) |
|
for (int j = 0; j < src1.cols; ++j) |
|
{ |
|
float val1 = src1.at<float>(i, j); |
|
float val2 = src2.at<float>(i, j); |
|
|
|
((float *)(dst.data))[i * dst.step / 4 + j] = val1 * val1 + val2 * val2; |
|
|
|
} |
|
|
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
|
|
WARMUP_ON; |
|
ocl::magnitudeSqr(d_src1, d_src2, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::magnitudeSqr(d_src1, d_src2, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
ocl::magnitudeSqr(d_src1, d_src2, d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
|
|
///////////// AddWeighted//////////////////////// |
|
TEST(AddWeighted) |
|
{ |
|
Mat src1, src2, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src1, d_src2, d_dst; |
|
#endif |
|
double alpha = 2.0, beta = 1.0, gama = 3.0; |
|
int all_type[] = {CV_8UC1, CV_32FC1}; |
|
std::string type_name[] = {"CV_8UC1", "CV_32FC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src1, size, size, all_type[j], 0, 256); |
|
gen(src2, size, size, all_type[j], 0, 256); |
|
gen(dst, size, size, all_type[j], 0, 256); |
|
|
|
|
|
addWeighted(src1, alpha, src2, beta, gama, dst); |
|
|
|
CPU_ON; |
|
addWeighted(src1, alpha, src2, beta, gama, dst); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
|
|
WARMUP_ON; |
|
ocl::addWeighted(d_src1, alpha, d_src2, beta, gama, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::addWeighted(d_src1, alpha, d_src2, beta, gama, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
ocl::addWeighted(d_src1, alpha, d_src2, beta, gama, d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
///////////// Blur//////////////////////// |
|
TEST(Blur) |
|
{ |
|
Mat src1, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src1, d_dst; |
|
#endif |
|
Size ksize = Size(3, 3); |
|
int bordertype = BORDER_CONSTANT; |
|
int all_type[] = {CV_8UC1, CV_8UC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src1, size, size, all_type[j], 0, 256); |
|
gen(dst, size, size, all_type[j], 0, 256); |
|
|
|
|
|
blur(src1, dst, ksize, Point(-1, -1), bordertype); |
|
|
|
CPU_ON; |
|
blur(src1, dst, ksize, Point(-1, -1), bordertype); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src1.upload(src1); |
|
|
|
WARMUP_ON; |
|
ocl::blur(d_src1, d_dst, ksize, Point(-1, -1), bordertype); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::blur(d_src1, d_dst, ksize, Point(-1, -1), bordertype); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src1.upload(src1); |
|
ocl::blur(d_src1, d_dst, ksize, Point(-1, -1), bordertype); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
///////////// Laplacian//////////////////////// |
|
TEST(Laplacian) |
|
{ |
|
Mat src1, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src1, d_dst; |
|
#endif |
|
int ksize = 3; |
|
int all_type[] = {CV_8UC1, CV_8UC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src1, size, size, all_type[j], 0, 256); |
|
gen(dst, size, size, all_type[j], 0, 256); |
|
|
|
|
|
Laplacian(src1, dst, -1, ksize, 1); |
|
|
|
CPU_ON; |
|
Laplacian(src1, dst, -1, ksize, 1); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src1.upload(src1); |
|
|
|
WARMUP_ON; |
|
ocl::Laplacian(d_src1, d_dst, -1, ksize, 1); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::Laplacian(d_src1, d_dst, -1, ksize, 1); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src1.upload(src1); |
|
ocl::Laplacian(d_src1, d_dst, -1, ksize, 1); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
|
|
///////////// Erode //////////////////// |
|
TEST(Erode) |
|
{ |
|
Mat src, dst, ker; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_dst; |
|
#endif |
|
int all_type[] = {CV_8UC1, CV_8UC4, CV_32FC1, CV_32FC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4", "CV_32FC1", "CV_32FC4"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src, size, size, all_type[j], Scalar::all(0), Scalar::all(256)); |
|
ker = getStructuringElement(MORPH_RECT, Size(3, 3)); |
|
|
|
erode(src, dst, ker); |
|
|
|
CPU_ON; |
|
erode(src, dst, ker); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl::erode(d_src, d_dst, ker); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::erode(d_src, d_dst, ker); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::erode(d_src, d_dst, ker); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
|
|
///////////// Sobel //////////////////////// |
|
TEST(Sobel) |
|
{ |
|
Mat src, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_dst; |
|
#endif |
|
int dx = 1; |
|
int dy = 1; |
|
int all_type[] = {CV_8UC1, CV_8UC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src, size, size, all_type[j], 0, 256); |
|
|
|
Sobel(src, dst, -1, dx, dy); |
|
|
|
CPU_ON; |
|
Sobel(src, dst, -1, dx, dy); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl::Sobel(d_src, d_dst, -1, dx, dy); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::Sobel(d_src, d_dst, -1, dx, dy); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::Sobel(d_src, d_dst, -1, dx, dy); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
///////////// Scharr //////////////////////// |
|
TEST(Scharr) |
|
{ |
|
Mat src, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_dst; |
|
#endif |
|
int dx = 1; |
|
int dy = 0; |
|
int all_type[] = {CV_8UC1, CV_8UC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src, size, size, all_type[j], 0, 256); |
|
|
|
Scharr(src, dst, -1, dx, dy); |
|
|
|
CPU_ON; |
|
Scharr(src, dst, -1, dx, dy); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl::Scharr(d_src, d_dst, -1, dx, dy); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::Scharr(d_src, d_dst, -1, dx, dy); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::Scharr(d_src, d_dst, -1, dx, dy); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
|
|
///////////// GaussianBlur //////////////////////// |
|
TEST(GaussianBlur) |
|
{ |
|
Mat src, dst; |
|
int all_type[] = {CV_8UC1, CV_8UC4, CV_32FC1, CV_32FC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4", "CV_32FC1", "CV_32FC4"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src, size, size, all_type[j], 0, 256); |
|
|
|
GaussianBlur(src, dst, Size(9, 9), 0); |
|
|
|
CPU_ON; |
|
GaussianBlur(src, dst, Size(9, 9), 0); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src(src); |
|
ocl::oclMat d_dst(src.size(), src.type()); |
|
ocl::oclMat d_buf; |
|
|
|
WARMUP_ON; |
|
ocl::GaussianBlur(d_src, d_dst, Size(9, 9), 0); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::GaussianBlur(d_src, d_dst, Size(9, 9), 0); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::GaussianBlur(d_src, d_dst, Size(9, 9), 0); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
///////////// equalizeHist //////////////////////// |
|
TEST(equalizeHist) |
|
{ |
|
Mat src, dst; |
|
int all_type[] = {CV_8UC1}; |
|
std::string type_name[] = {"CV_8UC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src, size, size, all_type[j], 0, 256); |
|
|
|
equalizeHist(src, dst); |
|
|
|
CPU_ON; |
|
equalizeHist(src, dst); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src(src); |
|
ocl::oclMat d_dst; |
|
ocl::oclMat d_hist; |
|
ocl::oclMat d_buf; |
|
|
|
WARMUP_ON; |
|
ocl::equalizeHist(d_src, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::equalizeHist(d_src, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::equalizeHist(d_src, d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
/////////// CopyMakeBorder ////////////////////// |
|
TEST(CopyMakeBorder) |
|
{ |
|
Mat src, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_dst; |
|
#endif |
|
int bordertype = BORDER_CONSTANT; |
|
int all_type[] = {CV_8UC1, CV_8UC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
|
|
gen(src, size, size, all_type[j], 0, 256); |
|
|
|
copyMakeBorder(src, dst, 7, 5, 5, 7, bordertype, cv::Scalar(1.0)); |
|
|
|
CPU_ON; |
|
copyMakeBorder(src, dst, 7, 5, 5, 7, bordertype, cv::Scalar(1.0)); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src(src); |
|
|
|
WARMUP_ON; |
|
ocl::copyMakeBorder(d_src, d_dst, 7, 5, 5, 7, bordertype, cv::Scalar(1.0)); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::copyMakeBorder(d_src, d_dst, 7, 5, 5, 7, bordertype, cv::Scalar(1.0)); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::copyMakeBorder(d_src, d_dst, 7, 5, 5, 7, bordertype, cv::Scalar(1.0)); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
///////////// cornerMinEigenVal //////////////////////// |
|
TEST(cornerMinEigenVal) |
|
{ |
|
Mat src, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_dst; |
|
#endif |
|
int blockSize = 7, apertureSize = 1 + 2 * (rand() % 4); |
|
int borderType = BORDER_REFLECT; |
|
int all_type[] = {CV_8UC1, CV_32FC1}; |
|
std::string type_name[] = {"CV_8UC1", "CV_32FC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
|
|
gen(src, size, size, all_type[j], 0, 256); |
|
|
|
cornerMinEigenVal(src, dst, blockSize, apertureSize, borderType); |
|
|
|
CPU_ON; |
|
cornerMinEigenVal(src, dst, blockSize, apertureSize, borderType); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src(src); |
|
|
|
WARMUP_ON; |
|
ocl::cornerMinEigenVal(d_src, d_dst, blockSize, apertureSize, borderType); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::cornerMinEigenVal(d_src, d_dst, blockSize, apertureSize, borderType); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::cornerMinEigenVal(d_src, d_dst, blockSize, apertureSize, borderType); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
///////////// cornerHarris //////////////////////// |
|
TEST(cornerHarris) |
|
{ |
|
Mat src, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_dst; |
|
#endif |
|
int all_type[] = {CV_8UC1, CV_32FC1}; |
|
std::string type_name[] = {"CV_8UC1", "CV_32FC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] << " ; BORDER_REFLECT"; |
|
|
|
gen(src, size, size, all_type[j], 0, 1); |
|
|
|
cornerHarris(src, dst, 5, 7, 0.1, BORDER_REFLECT); |
|
|
|
CPU_ON; |
|
cornerHarris(src, dst, 5, 7, 0.1, BORDER_REFLECT); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl::cornerHarris(d_src, d_dst, 5, 7, 0.1, BORDER_REFLECT); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::cornerHarris(d_src, d_dst, 5, 7, 0.1, BORDER_REFLECT); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::cornerHarris(d_src, d_dst, 5, 7, 0.1, BORDER_REFLECT); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
|
|
} |
|
} |
|
///////////// integral //////////////////////// |
|
TEST(integral) |
|
{ |
|
Mat src, sum; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_sum, d_buf; |
|
#endif |
|
int all_type[] = {CV_8UC1}; |
|
std::string type_name[] = {"CV_8UC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src, size, size, all_type[j], 0, 256); |
|
|
|
integral(src, sum); |
|
|
|
CPU_ON; |
|
integral(src, sum); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl::integral(d_src, d_sum); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::integral(d_src, d_sum); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::integral(d_src, d_sum); |
|
d_sum.download(sum); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
///////////// WarpAffine //////////////////////// |
|
TEST(WarpAffine) |
|
{ |
|
Mat src, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_dst; |
|
#endif |
|
static const double coeffs[2][3] = |
|
{ |
|
{cos(3.14 / 6), -sin(3.14 / 6), 100.0}, |
|
{sin(3.14 / 6), cos(3.14 / 6), -100.0} |
|
}; |
|
Mat M(2, 3, CV_64F, (void *)coeffs); |
|
int interpolation = INTER_NEAREST; |
|
|
|
int all_type[] = {CV_8UC1, CV_8UC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4"}; |
|
|
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src, size, size, all_type[j], 0, 256); |
|
gen(dst, size, size, all_type[j], 0, 256); |
|
Size size1 = Size(size, size); |
|
|
|
warpAffine(src, dst, M, size1, interpolation); |
|
|
|
CPU_ON; |
|
warpAffine(src, dst, M, size1, interpolation); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl::warpAffine(d_src, d_dst, M, size1, interpolation); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::warpAffine(d_src, d_dst, M, size1, interpolation); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::warpAffine(d_src, d_dst, M, size1, interpolation); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
///////////// WarpPerspective //////////////////////// |
|
TEST(WarpPerspective) |
|
{ |
|
Mat src, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_dst; |
|
#endif |
|
static const double coeffs[3][3] = |
|
{ |
|
{cos(3.14 / 6), -sin(3.14 / 6), 100.0}, |
|
{sin(3.14 / 6), cos(3.14 / 6), -100.0}, |
|
{0.0, 0.0, 1.0} |
|
}; |
|
Mat M(3, 3, CV_64F, (void *)coeffs); |
|
int interpolation = INTER_NEAREST; |
|
|
|
int all_type[] = {CV_8UC1, CV_8UC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src, size, size, all_type[j], 0, 256); |
|
gen(dst, size, size, all_type[j], 0, 256); |
|
Size size1 = Size(size, size); |
|
|
|
warpPerspective(src, dst, M, size1, interpolation); |
|
|
|
CPU_ON; |
|
warpPerspective(src, dst, M, size1, interpolation); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl::warpPerspective(d_src, d_dst, M, size1, interpolation); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::warpPerspective(d_src, d_dst, M, size1, interpolation); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::warpPerspective(d_src, d_dst, M, size1, interpolation); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
|
|
///////////// resize //////////////////////// |
|
TEST(resize) |
|
{ |
|
Mat src, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_dst; |
|
#endif |
|
|
|
int all_type[] = {CV_8UC1, CV_8UC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] << " ; up"; |
|
|
|
gen(src, size, size, all_type[j], 0, 256); |
|
|
|
resize(src, dst, Size(), 2.0, 2.0); |
|
|
|
CPU_ON; |
|
resize(src, dst, Size(), 2.0, 2.0); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl::resize(d_src, d_dst, Size(), 2.0, 2.0); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::resize(d_src, d_dst, Size(), 2.0, 2.0); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::resize(d_src, d_dst, Size(), 2.0, 2.0); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] << " ; down"; |
|
|
|
gen(src, size, size, all_type[j], 0, 256); |
|
|
|
resize(src, dst, Size(), 0.5, 0.5); |
|
|
|
CPU_ON; |
|
resize(src, dst, Size(), 0.5, 0.5); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl::resize(d_src, d_dst, Size(), 0.5, 0.5); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::resize(d_src, d_dst, Size(), 0.5, 0.5); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::resize(d_src, d_dst, Size(), 0.5, 0.5); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
///////////// threshold//////////////////////// |
|
TEST(threshold) |
|
{ |
|
Mat src, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_dst; |
|
#endif |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
SUBTEST << size << 'x' << size << "; 8UC1; THRESH_BINARY"; |
|
|
|
gen(src, size, size, CV_8U, 0, 100); |
|
|
|
threshold(src, dst, 50.0, 0.0, THRESH_BINARY); |
|
|
|
CPU_ON; |
|
threshold(src, dst, 50.0, 0.0, THRESH_BINARY); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl::threshold(d_src, d_dst, 50.0, 0.0, THRESH_BINARY); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::threshold(d_src, d_dst, 50.0, 0.0, THRESH_BINARY); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::threshold(d_src, d_dst, 50.0, 0.0, THRESH_BINARY); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
SUBTEST << size << 'x' << size << "; 32FC1; THRESH_TRUNC [NPP]"; |
|
|
|
gen(src, size, size, CV_32FC1, 0, 100); |
|
|
|
threshold(src, dst, 50.0, 0.0, THRESH_TRUNC); |
|
|
|
CPU_ON; |
|
threshold(src, dst, 50.0, 0.0, THRESH_TRUNC); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl::threshold(d_src, d_dst, 50.0, 0.0, THRESH_TRUNC); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::threshold(d_src, d_dst, 50.0, 0.0, THRESH_TRUNC); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::threshold(d_src, d_dst, 50.0, 0.0, THRESH_TRUNC); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
} |
|
///////////// meanShiftFiltering//////////////////////// |
|
TEST(meanShiftFiltering) |
|
{ |
|
int sp = 10, sr = 10; |
|
|
|
Mat src, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_dst; |
|
#endif |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
SUBTEST << size << 'x' << size << "; 8UC3 vs 8UC4"; |
|
|
|
gen(src, size, size, CV_8UC3, Scalar::all(0), Scalar::all(256)); |
|
|
|
pyrMeanShiftFiltering(src, dst, sp, sr); |
|
|
|
CPU_ON; |
|
pyrMeanShiftFiltering(src, dst, sp, sr); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
gen(src, size, size, CV_8UC4, Scalar::all(0), Scalar::all(256)); |
|
|
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl::meanShiftFiltering(d_src, d_dst, sp, sr); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::meanShiftFiltering(d_src, d_dst, sp, sr); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::meanShiftFiltering(d_src, d_dst, sp, sr); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
} |
|
///////////// meanShiftProc//////////////////////// |
|
COOR do_meanShift(int x0, int y0, uchar *sptr, uchar *dptr, int sstep, cv::Size size, int sp, int sr, int maxIter, float eps, int *tab) |
|
{ |
|
|
|
int isr2 = sr * sr; |
|
int c0, c1, c2, c3; |
|
int iter; |
|
uchar *ptr = NULL; |
|
uchar *pstart = NULL; |
|
int revx = 0, revy = 0; |
|
c0 = sptr[0]; |
|
c1 = sptr[1]; |
|
c2 = sptr[2]; |
|
c3 = sptr[3]; |
|
|
|
// iterate meanshift procedure |
|
for (iter = 0; iter < maxIter; iter++) |
|
{ |
|
int count = 0; |
|
int s0 = 0, s1 = 0, s2 = 0, sx = 0, sy = 0; |
|
|
|
//mean shift: process pixels in window (p-sigmaSp)x(p+sigmaSp) |
|
int minx = x0 - sp; |
|
int miny = y0 - sp; |
|
int maxx = x0 + sp; |
|
int maxy = y0 + sp; |
|
|
|
//deal with the image boundary |
|
if (minx < 0) |
|
{ |
|
minx = 0; |
|
} |
|
|
|
if (miny < 0) |
|
{ |
|
miny = 0; |
|
} |
|
|
|
if (maxx >= size.width) |
|
{ |
|
maxx = size.width - 1; |
|
} |
|
|
|
if (maxy >= size.height) |
|
{ |
|
maxy = size.height - 1; |
|
} |
|
|
|
if (iter == 0) |
|
{ |
|
pstart = sptr; |
|
} |
|
else |
|
{ |
|
pstart = pstart + revy * sstep + (revx << 2); //point to the new position |
|
} |
|
|
|
ptr = pstart; |
|
ptr = ptr + (miny - y0) * sstep + ((minx - x0) << 2); //point to the start in the row |
|
|
|
for (int y = miny; y <= maxy; y++, ptr += sstep - ((maxx - minx + 1) << 2)) |
|
{ |
|
int rowCount = 0; |
|
int x = minx; |
|
#if CV_ENABLE_UNROLLED |
|
|
|
for (; x + 4 <= maxx; x += 4, ptr += 16) |
|
{ |
|
int t0, t1, t2; |
|
t0 = ptr[0], t1 = ptr[1], t2 = ptr[2]; |
|
|
|
if (tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2) |
|
{ |
|
s0 += t0; |
|
s1 += t1; |
|
s2 += t2; |
|
sx += x; |
|
rowCount++; |
|
} |
|
|
|
t0 = ptr[4], t1 = ptr[5], t2 = ptr[6]; |
|
|
|
if (tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2) |
|
{ |
|
s0 += t0; |
|
s1 += t1; |
|
s2 += t2; |
|
sx += x + 1; |
|
rowCount++; |
|
} |
|
|
|
t0 = ptr[8], t1 = ptr[9], t2 = ptr[10]; |
|
|
|
if (tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2) |
|
{ |
|
s0 += t0; |
|
s1 += t1; |
|
s2 += t2; |
|
sx += x + 2; |
|
rowCount++; |
|
} |
|
|
|
t0 = ptr[12], t1 = ptr[13], t2 = ptr[14]; |
|
|
|
if (tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2) |
|
{ |
|
s0 += t0; |
|
s1 += t1; |
|
s2 += t2; |
|
sx += x + 3; |
|
rowCount++; |
|
} |
|
} |
|
|
|
#endif |
|
|
|
for (; x <= maxx; x++, ptr += 4) |
|
{ |
|
int t0 = ptr[0], t1 = ptr[1], t2 = ptr[2]; |
|
|
|
if (tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2) |
|
{ |
|
s0 += t0; |
|
s1 += t1; |
|
s2 += t2; |
|
sx += x; |
|
rowCount++; |
|
} |
|
} |
|
|
|
if (rowCount == 0) |
|
{ |
|
continue; |
|
} |
|
|
|
count += rowCount; |
|
sy += y * rowCount; |
|
} |
|
|
|
if (count == 0) |
|
{ |
|
break; |
|
} |
|
|
|
int x1 = sx / count; |
|
int y1 = sy / count; |
|
s0 = s0 / count; |
|
s1 = s1 / count; |
|
s2 = s2 / count; |
|
|
|
bool stopFlag = (x0 == x1 && y0 == y1) || (abs(x1 - x0) + abs(y1 - y0) + |
|
tab[s0 - c0 + 255] + tab[s1 - c1 + 255] + tab[s2 - c2 + 255] <= eps); |
|
|
|
//revise the pointer corresponding to the new (y0,x0) |
|
revx = x1 - x0; |
|
revy = y1 - y0; |
|
|
|
x0 = x1; |
|
y0 = y1; |
|
c0 = s0; |
|
c1 = s1; |
|
c2 = s2; |
|
|
|
if (stopFlag) |
|
{ |
|
break; |
|
} |
|
} //for iter |
|
|
|
dptr[0] = (uchar)c0; |
|
dptr[1] = (uchar)c1; |
|
dptr[2] = (uchar)c2; |
|
dptr[3] = (uchar)c3; |
|
|
|
COOR coor; |
|
coor.x = static_cast<short>(x0); |
|
coor.y = static_cast<short>(y0); |
|
return coor; |
|
} |
|
|
|
void meanShiftProc_(const Mat &src_roi, Mat &dst_roi, Mat &dstCoor_roi, int sp, int sr, cv::TermCriteria crit) |
|
{ |
|
|
|
if (src_roi.empty()) |
|
{ |
|
CV_Error(CV_StsBadArg, "The input image is empty"); |
|
} |
|
|
|
if (src_roi.depth() != CV_8U || src_roi.channels() != 4) |
|
{ |
|
CV_Error(CV_StsUnsupportedFormat, "Only 8-bit, 4-channel images are supported"); |
|
} |
|
|
|
CV_Assert((src_roi.cols == dst_roi.cols) && (src_roi.rows == dst_roi.rows) && |
|
(src_roi.cols == dstCoor_roi.cols) && (src_roi.rows == dstCoor_roi.rows)); |
|
CV_Assert(!(dstCoor_roi.step & 0x3)); |
|
|
|
if (!(crit.type & cv::TermCriteria::MAX_ITER)) |
|
{ |
|
crit.maxCount = 5; |
|
} |
|
|
|
int maxIter = std::min(std::max(crit.maxCount, 1), 100); |
|
float eps; |
|
|
|
if (!(crit.type & cv::TermCriteria::EPS)) |
|
{ |
|
eps = 1.f; |
|
} |
|
|
|
eps = (float)std::max(crit.epsilon, 0.0); |
|
|
|
int tab[512]; |
|
|
|
for (int i = 0; i < 512; i++) |
|
{ |
|
tab[i] = (i - 255) * (i - 255); |
|
} |
|
|
|
uchar *sptr = src_roi.data; |
|
uchar *dptr = dst_roi.data; |
|
short *dCoorptr = (short *)dstCoor_roi.data; |
|
int sstep = (int)src_roi.step; |
|
int dstep = (int)dst_roi.step; |
|
int dCoorstep = (int)dstCoor_roi.step >> 1; |
|
cv::Size size = src_roi.size(); |
|
|
|
for (int i = 0; i < size.height; i++, sptr += sstep - (size.width << 2), |
|
dptr += dstep - (size.width << 2), dCoorptr += dCoorstep - (size.width << 1)) |
|
{ |
|
for (int j = 0; j < size.width; j++, sptr += 4, dptr += 4, dCoorptr += 2) |
|
{ |
|
*((COOR *)dCoorptr) = do_meanShift(j, i, sptr, dptr, sstep, size, sp, sr, maxIter, eps, tab); |
|
} |
|
} |
|
|
|
} |
|
TEST(meanShiftProc) |
|
{ |
|
Mat src, dst, dstCoor_roi; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_dst, d_dstCoor_roi; |
|
#endif |
|
TermCriteria crit(TermCriteria::COUNT + TermCriteria::EPS, 5, 1); |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
SUBTEST << size << 'x' << size << "; 8UC4 and CV_16SC2 "; |
|
|
|
gen(src, size, size, CV_8UC4, Scalar::all(0), Scalar::all(256)); |
|
gen(dst, size, size, CV_8UC4, Scalar::all(0), Scalar::all(256)); |
|
gen(dstCoor_roi, size, size, CV_16SC2, Scalar::all(0), Scalar::all(256)); |
|
|
|
meanShiftProc_(src, dst, dstCoor_roi, 5, 6, crit); |
|
|
|
CPU_ON; |
|
meanShiftProc_(src, dst, dstCoor_roi, 5, 6, crit); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl::meanShiftProc(d_src, d_dst, d_dstCoor_roi, 5, 6, crit); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::meanShiftProc(d_src, d_dst, d_dstCoor_roi, 5, 6, crit); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::meanShiftProc(d_src, d_dst, d_dstCoor_roi, 5, 6, crit); |
|
d_dst.download(dst); |
|
d_dstCoor_roi.download(dstCoor_roi); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
} |
|
///////////// ConvertTo//////////////////////// |
|
TEST(ConvertTo) |
|
{ |
|
Mat src, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_dst; |
|
#endif |
|
int all_type[] = {CV_8UC1, CV_8UC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] << " to 32FC1"; |
|
|
|
gen(src, size, size, all_type[j], 0, 256); |
|
//gen(dst, size, size, all_type[j], 0, 256); |
|
|
|
//d_dst.upload(dst); |
|
|
|
src.convertTo(dst, CV_32FC1); |
|
|
|
CPU_ON; |
|
src.convertTo(dst, CV_32FC1); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
d_src.convertTo(d_dst, CV_32FC1); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
d_src.convertTo(d_dst, CV_32FC1); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
d_src.convertTo(d_dst, CV_32FC1); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
///////////// copyTo//////////////////////// |
|
TEST(copyTo) |
|
{ |
|
Mat src, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_dst; |
|
#endif |
|
int all_type[] = {CV_8UC1, CV_8UC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src, size, size, all_type[j], 0, 256); |
|
//gen(dst, size, size, all_type[j], 0, 256); |
|
|
|
//d_dst.upload(dst); |
|
|
|
src.copyTo(dst); |
|
|
|
CPU_ON; |
|
src.copyTo(dst); |
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
d_src.copyTo(d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
d_src.copyTo(d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
d_src.copyTo(d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
///////////// setTo//////////////////////// |
|
TEST(setTo) |
|
{ |
|
Mat src, dst; |
|
Scalar val(1, 2, 3, 4); |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_dst; |
|
#endif |
|
int all_type[] = {CV_8UC1, CV_8UC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
gen(src, size, size, all_type[j], 0, 256); |
|
|
|
src.setTo(val); |
|
|
|
CPU_ON; |
|
src.setTo(val); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
d_src.setTo(val); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
d_src.setTo(val); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
d_src.setTo(val); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
///////////// Merge//////////////////////// |
|
TEST(Merge) |
|
{ |
|
Mat dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_dst; |
|
#endif |
|
int channels = 4; |
|
int all_type[] = {CV_8UC1, CV_32FC1}; |
|
std::string type_name[] = {"CV_8UC1", "CV_32FC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ; |
|
Size size1 = Size(size, size); |
|
std::vector<Mat> src(channels); |
|
|
|
for (int i = 0; i < channels; ++i) |
|
{ |
|
src[i] = Mat(size1, all_type[j], cv::Scalar::all(i)); |
|
} |
|
|
|
merge(src, dst); |
|
|
|
CPU_ON; |
|
merge(src, dst); |
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
std::vector<ocl::oclMat> d_src(channels); |
|
|
|
for (int i = 0; i < channels; ++i) |
|
{ |
|
d_src[i] = ocl::oclMat(size1, all_type[j], cv::Scalar::all(i)); |
|
} |
|
|
|
WARMUP_ON; |
|
ocl::merge(d_src, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::merge(d_src, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
|
|
for (int i = 0; i < channels; ++i) |
|
{ |
|
d_src[i] = ocl::oclMat(size1, CV_8U, cv::Scalar::all(i)); |
|
} |
|
|
|
ocl::merge(d_src, d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
|
|
///////////// Split//////////////////////// |
|
TEST(Split) |
|
{ |
|
//int channels = 4; |
|
int all_type[] = {CV_8UC1, CV_32FC1}; |
|
std::string type_name[] = {"CV_8UC1", "CV_32FC1"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j]; |
|
Size size1 = Size(size, size); |
|
|
|
Mat src(size1, CV_MAKE_TYPE(all_type[j], 4), cv::Scalar(1, 2, 3, 4)); |
|
|
|
std::vector<cv::Mat> dst; |
|
|
|
split(src, dst); |
|
|
|
CPU_ON; |
|
split(src, dst); |
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src(size1, CV_MAKE_TYPE(all_type[j], 4), cv::Scalar(1, 2, 3, 4)); |
|
std::vector<cv::ocl::oclMat> d_dst; |
|
|
|
WARMUP_ON; |
|
ocl::split(d_src, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::split(d_src, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::split(d_src, d_dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
|
|
|
|
///////////// norm//////////////////////// |
|
TEST(norm) |
|
{ |
|
Mat src, buf; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_buf; |
|
#endif |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
SUBTEST << size << 'x' << size << "; CV_8UC1; NORM_INF"; |
|
|
|
gen(src, size, size, CV_8UC1, Scalar::all(0), Scalar::all(1)); |
|
gen(buf, size, size, CV_8UC1, Scalar::all(0), Scalar::all(1)); |
|
|
|
norm(src, NORM_INF); |
|
|
|
CPU_ON; |
|
norm(src, NORM_INF); |
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
d_buf.upload(buf); |
|
|
|
WARMUP_ON; |
|
ocl::norm(d_src, d_buf, NORM_INF); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::norm(d_src, d_buf, NORM_INF); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::norm(d_src, d_buf, NORM_INF); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
} |
|
///////////// remap//////////////////////// |
|
TEST(remap) |
|
{ |
|
Mat src, dst, xmap, ymap; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_dst, d_xmap, d_ymap; |
|
#endif |
|
int all_type[] = {CV_8UC1, CV_8UC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4"}; |
|
|
|
int interpolation = INTER_LINEAR; |
|
int borderMode = BORDER_CONSTANT; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t t = 0; t < sizeof(all_type) / sizeof(int); t++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; src " << type_name[t] << "; map CV_32FC1"; |
|
|
|
gen(src, size, size, all_type[t], 0, 256); |
|
|
|
xmap.create(size, size, CV_32FC1); |
|
dst.create(size, size, CV_32FC1); |
|
ymap.create(size, size, CV_32FC1); |
|
|
|
for (int i = 0; i < size; ++i) |
|
{ |
|
float *xmap_row = xmap.ptr<float>(i); |
|
float *ymap_row = ymap.ptr<float>(i); |
|
|
|
for (int j = 0; j < size; ++j) |
|
{ |
|
xmap_row[j] = (j - size * 0.5f) * 0.75f + size * 0.5f; |
|
ymap_row[j] = (i - size * 0.5f) * 0.75f + size * 0.5f; |
|
} |
|
} |
|
|
|
|
|
remap(src, dst, xmap, ymap, interpolation, borderMode); |
|
|
|
CPU_ON; |
|
remap(src, dst, xmap, ymap, interpolation, borderMode); |
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
d_dst.upload(dst); |
|
d_xmap.upload(xmap); |
|
d_ymap.upload(ymap); |
|
|
|
WARMUP_ON; |
|
ocl::remap(d_src, d_dst, d_xmap, d_ymap, interpolation, borderMode); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::remap(d_src, d_dst, d_xmap, d_ymap, interpolation, borderMode); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::remap(d_src, d_dst, d_xmap, d_ymap, interpolation, borderMode); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
///////////// cvtColor//////////////////////// |
|
TEST(cvtColor) |
|
{ |
|
Mat src, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_dst; |
|
#endif |
|
int all_type[] = {CV_8UC4}; |
|
std::string type_name[] = {"CV_8UC4"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
gen(src, size, size, all_type[j], 0, 256); |
|
SUBTEST << size << "x" << size << "; " << type_name[j] << " ; CV_RGBA2GRAY"; |
|
|
|
cvtColor(src, dst, CV_RGBA2GRAY, 4); |
|
|
|
CPU_ON; |
|
cvtColor(src, dst, CV_RGBA2GRAY, 4); |
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl::cvtColor(d_src, d_dst, CV_RGBA2GRAY, 4); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::cvtColor(d_src, d_dst, CV_RGBA2GRAY, 4); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::cvtColor(d_src, d_dst, CV_RGBA2GRAY, 4); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
|
|
} |
|
|
|
|
|
} |
|
///////////// filter2D//////////////////////// |
|
TEST(filter2D) |
|
{ |
|
Mat src; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
int all_type[] = {CV_8UC1, CV_8UC4}; |
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4"}; |
|
|
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
gen(src, size, size, all_type[j], 0, 256); |
|
|
|
for (int ksize = 3; ksize <= 15; ksize = 2*ksize+1) |
|
{ |
|
SUBTEST << "ksize = " << ksize << "; " << size << 'x' << size << "; " << type_name[j] ; |
|
|
|
Mat kernel; |
|
gen(kernel, ksize, ksize, CV_32FC1, 0.0, 1.0); |
|
|
|
Mat dst; |
|
cv::filter2D(src, dst, -1, kernel); |
|
|
|
CPU_ON; |
|
cv::filter2D(src, dst, -1, kernel); |
|
CPU_OFF; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src(src); |
|
ocl::oclMat d_dst; |
|
|
|
WARMUP_ON; |
|
ocl::filter2D(d_src, d_dst, -1, kernel); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::filter2D(d_src, d_dst, -1, kernel); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::filter2D(d_src, d_dst, -1, kernel); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
|
|
|
|
} |
|
} |
|
|
|
|
|
///////////// dft //////////////////////// |
|
TEST(dft) |
|
{ |
|
Mat src, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src, d_dst; |
|
#endif |
|
|
|
int all_type[] = {CV_32FC1, CV_32FC2}; |
|
std::string type_name[] = {"CV_32FC1", "CV_32FC2"}; |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++) |
|
{ |
|
SUBTEST << size << 'x' << size << "; " << type_name[j] << " ; complex-to-complex"; |
|
|
|
gen(src, size, size, all_type[j], Scalar::all(0), Scalar::all(1)); |
|
|
|
dft(src, dst); |
|
|
|
CPU_ON; |
|
dft(src, dst); |
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
d_src.upload(src); |
|
|
|
WARMUP_ON; |
|
ocl::dft(d_src, d_dst, Size(size, size)); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::dft(d_src, d_dst, Size(size, size)); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src.upload(src); |
|
ocl::dft(d_src, d_dst, Size(size, size)); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
|
|
} |
|
} |
|
|
|
///////////// gemm //////////////////////// |
|
TEST(gemm) |
|
{ |
|
Mat src1, src2, src3, dst; |
|
#ifdef USE_OPENCL |
|
ocl::oclMat d_src1, d_src2, d_src3, d_dst; |
|
#endif |
|
|
|
for (int size = 1000; size <= 4000; size *= 2) |
|
{ |
|
SUBTEST << size << 'x' << size; |
|
|
|
gen(src1, size, size, CV_32FC1, Scalar::all(-10), Scalar::all(10)); |
|
gen(src2, size, size, CV_32FC1, Scalar::all(-10), Scalar::all(10)); |
|
gen(src3, size, size, CV_32FC1, Scalar::all(-10), Scalar::all(10)); |
|
|
|
gemm(src1, src2, 1.0, src3, 1.0, dst); |
|
|
|
CPU_ON; |
|
gemm(src1, src2, 1.0, src3, 1.0, dst); |
|
CPU_OFF; |
|
|
|
#ifdef USE_OPENCL |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
d_src3.upload(src3); |
|
|
|
WARMUP_ON; |
|
ocl::gemm(d_src1, d_src2, 1.0, d_src3, 1.0, d_dst); |
|
WARMUP_OFF; |
|
|
|
GPU_ON; |
|
ocl::gemm(d_src1, d_src2, 1.0, d_src3, 1.0, d_dst); |
|
GPU_OFF; |
|
|
|
GPU_FULL_ON; |
|
d_src1.upload(src1); |
|
d_src2.upload(src2); |
|
d_src3.upload(src3); |
|
ocl::gemm(d_src1, d_src2, 1.0, d_src3, 1.0, d_dst); |
|
d_dst.download(dst); |
|
GPU_FULL_OFF; |
|
#endif |
|
} |
|
} |
|
|
|
int main(int argc, const char *argv[]) |
|
{ |
|
#ifdef USE_OPENCL |
|
vector<ocl::Info> oclinfo; |
|
int num_devices = getDevice(oclinfo); |
|
|
|
if (num_devices < 1) |
|
{ |
|
cerr << "no device found\n"; |
|
return -1; |
|
} |
|
|
|
int devidx = 0; |
|
|
|
for (size_t i = 0; i < oclinfo.size(); i++) |
|
{ |
|
for (size_t j = 0; j < oclinfo[i].DeviceName.size(); j++) |
|
{ |
|
printf("device %d: %s\n", devidx++, oclinfo[i].DeviceName[j].c_str()); |
|
} |
|
} |
|
|
|
#endif |
|
redirectError(cvErrorCallback); |
|
|
|
const char *keys = |
|
"{ h help | false | print help message }" |
|
"{ f filter | | filter for test }" |
|
"{ w workdir | | set working directory }" |
|
"{ l list | false | show all tests }" |
|
"{ d device | 0 | device id }" |
|
"{ i iters | 10 | iteration count }" |
|
"{ m warmup | 1 | gpu warm up iteration count}" |
|
"{ t xtop | 1.1 | xfactor top boundary}" |
|
"{ b xbottom | 0.9 | xfactor bottom boundary}" |
|
"{ v verify | false | only run gpu once to verify if problems occur}"; |
|
|
|
CommandLineParser cmd(argc, argv, keys); |
|
|
|
if (cmd.get<bool>("help")) |
|
{ |
|
cout << "Avaible options:" << endl; |
|
cmd.printMessage(); |
|
return 0; |
|
} |
|
|
|
#ifdef USE_OPENCL |
|
int device = cmd.get<int>("device"); |
|
|
|
if (device < 0 || device >= num_devices) |
|
{ |
|
cerr << "Invalid device ID" << endl; |
|
return -1; |
|
} |
|
|
|
if (cmd.get<bool>("verify")) |
|
{ |
|
TestSystem::instance().setNumIters(1); |
|
TestSystem::instance().setGPUWarmupIters(0); |
|
TestSystem::instance().setCPUIters(0); |
|
} |
|
|
|
devidx = 0; |
|
|
|
for (size_t i = 0; i < oclinfo.size(); i++) |
|
{ |
|
for (size_t j = 0; j < oclinfo[i].DeviceName.size(); j++, devidx++) |
|
{ |
|
if (device == devidx) |
|
{ |
|
ocl::setDevice(oclinfo[i], (int)j); |
|
TestSystem::instance().setRecordName(oclinfo[i].DeviceName[j]); |
|
printf("\nuse %d: %s\n", devidx, oclinfo[i].DeviceName[j].c_str()); |
|
goto END_DEV; |
|
} |
|
} |
|
} |
|
|
|
END_DEV: |
|
|
|
#endif |
|
string filter = cmd.get<string>("filter"); |
|
string workdir = cmd.get<string>("workdir"); |
|
bool list = cmd.get<bool>("list"); |
|
int iters = cmd.get<int>("iters"); |
|
int wu_iters = cmd.get<int>("warmup"); |
|
double x_top = cmd.get<double>("xtop"); |
|
double x_bottom = cmd.get<double>("xbottom"); |
|
|
|
TestSystem::instance().setTopThreshold(x_top); |
|
TestSystem::instance().setBottomThreshold(x_bottom); |
|
|
|
if (!filter.empty()) |
|
{ |
|
TestSystem::instance().setTestFilter(filter); |
|
} |
|
|
|
if (!workdir.empty()) |
|
{ |
|
if (workdir[workdir.size() - 1] != '/' && workdir[workdir.size() - 1] != '\\') |
|
{ |
|
workdir += '/'; |
|
} |
|
|
|
TestSystem::instance().setWorkingDir(workdir); |
|
} |
|
|
|
if (list) |
|
{ |
|
TestSystem::instance().setListMode(true); |
|
} |
|
|
|
TestSystem::instance().setNumIters(iters); |
|
TestSystem::instance().setGPUWarmupIters(wu_iters); |
|
|
|
TestSystem::instance().run(); |
|
|
|
return 0; |
|
}
|
|
|