4397 lines
109 KiB
C++
4397 lines
109 KiB
C++
#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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#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_
|
|
<< "\nGPUTOTAL failed: "
|
|
<< setprecision(3) << speedup_full_slower_count_
|
|
<< endl;
|
|
cout << "\nGPUTOTAL exceeded rate: "
|
|
<< setprecision(3) << (float)speedup_full_faster_count_ / std::max(1, num_subtests_called_) * 100
|
|
<< "%"
|
|
<< "\nGPUTOTAL passed rate: "
|
|
<< setprecision(3) << (float)speedup_full_equal_count_ / std::max(1, num_subtests_called_) * 100
|
|
<< "%"
|
|
<< "\nGPUTOTAL failed rate: "
|
|
<< setprecision(3) << (float)speedup_full_slower_count_ / std::max(1, num_subtests_called_) * 100
|
|
<< "%"
|
|
<< endl;
|
|
cout << resetiosflags(ios_base::fixed);
|
|
}
|
|
|
|
|
|
void TestSystem::printMetrics(double cpu_time, double gpu_time, double gpu_full_time, double speedup, double fullspeedup)
|
|
{
|
|
cout << TAB << setiosflags(ios_base::left);
|
|
stringstream stream;
|
|
|
|
stream << cpu_time;
|
|
cout << setw(10) << stream.str();
|
|
#ifdef USE_OPENCL
|
|
stream.str("");
|
|
stream << gpu_time;
|
|
cout << setw(10) << stream.str();
|
|
|
|
stream.str("");
|
|
stream << "x" << setprecision(3) << speedup;
|
|
cout << setw(14) << stream.str();
|
|
|
|
stream.str("");
|
|
stream << gpu_full_time;
|
|
cout << setw(14) << stream.str();
|
|
|
|
stream.str("");
|
|
stream << "x" << setprecision(3) << fullspeedup;
|
|
cout << setw(14) << stream.str();
|
|
#endif
|
|
cout << cur_subtest_description_.str();
|
|
cout << resetiosflags(ios_base::left) << endl;
|
|
}
|
|
|
|
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)
|
|
{
|
|
if (!record_)
|
|
{
|
|
recordname_ += ".csv";
|
|
record_ = fopen(recordname_.c_str(), "w");
|
|
}
|
|
|
|
#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;
|
|
}
|