Open Source Computer Vision Library https://opencv.org/
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
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// If you do not agree to this license, do not download, install,
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//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
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//
// Redistribution and use in source and binary forms, with or without modification,
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#include "test_precomp.hpp"
#define VARNAME(A) #A
using namespace std;
using namespace cv;
using namespace cvtest;
namespace cvtest {
//std::string generateVarList(int first,...)
//{
// vector<std::string> varname;
//
// va_list argp;
// string s;
// stringstream ss;
// va_start(argp,first);
// int i=first;
// while(i!=-1)
// {
// ss<<i<<",";
// i=va_arg(argp,int);
// };
// s=ss.str();
// va_end(argp);
// return s;
//};
//std::string generateVarList(int& p1,int& p2)
//{
// stringstream ss;
// ss<<VARNAME(p1)<<":"<<src1x<<","<<VARNAME(p2)<<":"<<src1y;
// return ss.str();
//};
cv::ocl::oclMat createMat_ocl(cv::RNG& rng, Size size, int type, bool useRoi)
{
Size size0 = size;
if (useRoi)
{
size0.width += rng.uniform(5, 15);
size0.height += rng.uniform(5, 15);
}
cv::ocl::oclMat d_m(size0, type);
if (size0 != size)
d_m = d_m(Rect((size0.width - size.width) / 2, (size0.height - size.height) / 2, size.width, size.height));
return d_m;
}
cv::ocl::oclMat loadMat_ocl(cv::RNG& rng, const Mat& m, bool useRoi)
{
CV_Assert(m.type() == CV_8UC1 || m.type() == CV_8UC3);
cv::ocl::oclMat d_m;
d_m = createMat_ocl(rng, m.size(), m.type(), useRoi);
Size ls;
Point pt;
d_m.locateROI(ls, pt);
Rect roi(pt.x, pt.y, d_m.size().width, d_m.size().height);
cv::ocl::oclMat m_ocl(m);
cv::ocl::oclMat d_m_roi(d_m, roi);
m_ocl.copyTo(d_m);
return d_m;
}
vector<MatType> types(int depth_start, int depth_end, int cn_start, int cn_end)
{
vector<MatType> v;
v.reserve((depth_end - depth_start + 1) * (cn_end - cn_start + 1));
for (int depth = depth_start; depth <= depth_end; ++depth)
{
for (int cn = cn_start; cn <= cn_end; ++cn)
{
v.push_back(CV_MAKETYPE(depth, cn));
}
}
return v;
}
const vector<MatType> &all_types()
{
static vector<MatType> v = types(CV_8U, CV_64F, 1, 4);
return v;
}
Mat readImage(const string &fileName, int flags)
{
return imread(string(cvtest::TS::ptr()->get_data_path()) + fileName, flags);
}
Mat readImageType(const string &fname, int type)
{
Mat src = readImage(fname, CV_MAT_CN(type) == 1 ? IMREAD_GRAYSCALE : IMREAD_COLOR);
if (CV_MAT_CN(type) == 4)
{
Mat temp;
cvtColor(src, temp, cv::COLOR_BGR2BGRA);
swap(src, temp);
}
src.convertTo(src, CV_MAT_DEPTH(type));
return src;
}
double checkNorm(const Mat &m)
{
return norm(m, NORM_INF);
}
double checkNorm(const Mat &m1, const Mat &m2)
{
return norm(m1, m2, NORM_INF);
}
double checkSimilarity(const Mat &m1, const Mat &m2)
{
Mat diff;
matchTemplate(m1, m2, diff, TM_CCORR_NORMED);
return std::abs(diff.at<float>(0, 0) - 1.f);
}
/*
void cv::ocl::PrintTo(const DeviceInfo& info, ostream* os)
{
(*os) << info.name();
}
*/
void PrintTo(const Inverse &inverse, std::ostream *os)
{
if (inverse)
(*os) << "inverse";
else
(*os) << "direct";
}
double checkRectSimilarity(Size sz, std::vector<Rect>& ob1, std::vector<Rect>& ob2)
{
double final_test_result = 0.0;
size_t sz1 = ob1.size();
size_t sz2 = ob2.size();
if(sz1 != sz2)
{
return sz1 > sz2 ? (double)(sz1 - sz2) : (double)(sz2 - sz1);
}
else
{
if(sz1==0 && sz2==0)
return 0;
cv::Mat cpu_result(sz, CV_8UC1);
cpu_result.setTo(0);
for(vector<Rect>::const_iterator r = ob1.begin(); r != ob1.end(); r++)
{
cv::Mat cpu_result_roi(cpu_result, *r);
cpu_result_roi.setTo(1);
cpu_result.copyTo(cpu_result);
}
int cpu_area = cv::countNonZero(cpu_result > 0);
cv::Mat gpu_result(sz, CV_8UC1);
gpu_result.setTo(0);
for(vector<Rect>::const_iterator r2 = ob2.begin(); r2 != ob2.end(); r2++)
{
cv::Mat gpu_result_roi(gpu_result, *r2);
gpu_result_roi.setTo(1);
gpu_result.copyTo(gpu_result);
}
cv::Mat result_;
multiply(cpu_result, gpu_result, result_);
int result = cv::countNonZero(result_ > 0);
if(cpu_area!=0 && result!=0)
final_test_result = 1.0 - (double)result/(double)cpu_area;
else if(cpu_area==0 && result!=0)
final_test_result = -1;
}
return final_test_result;
}
void showDiff(const Mat& src, const Mat& gold, const Mat& actual, double eps, bool alwaysShow)
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{
Mat diff, diff_thresh;
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absdiff(gold, actual, diff);
diff.convertTo(diff, CV_32F);
threshold(diff, diff_thresh, eps, 255.0, cv::THRESH_BINARY);
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if (alwaysShow || cv::countNonZero(diff_thresh.reshape(1)) > 0)
{
#if 0
std::cout << "Src: " << std::endl << src << std::endl;
std::cout << "Reference: " << std::endl << gold << std::endl;
std::cout << "OpenCL: " << std::endl << actual << std::endl;
#endif
namedWindow("src", WINDOW_NORMAL);
namedWindow("gold", WINDOW_NORMAL);
namedWindow("actual", WINDOW_NORMAL);
namedWindow("diff", WINDOW_NORMAL);
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imshow("src", src);
imshow("gold", gold);
imshow("actual", actual);
imshow("diff", diff);
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waitKey();
}
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}
namespace
{
bool keyPointsEquals(const cv::KeyPoint& p1, const cv::KeyPoint& p2)
{
const double maxPtDif = 1.0;
const double maxSizeDif = 1.0;
const double maxAngleDif = 2.0;
const double maxResponseDif = 0.1;
double dist = cv::norm(p1.pt - p2.pt);
if (dist < maxPtDif &&
fabs(p1.size - p2.size) < maxSizeDif &&
abs(p1.angle - p2.angle) < maxAngleDif &&
abs(p1.response - p2.response) < maxResponseDif &&
p1.octave == p2.octave &&
p1.class_id == p2.class_id)
{
return true;
}
return false;
}
struct KeyPointLess : std::binary_function<cv::KeyPoint, cv::KeyPoint, bool>
{
bool operator()(const cv::KeyPoint& kp1, const cv::KeyPoint& kp2) const
{
return kp1.pt.y < kp2.pt.y || (kp1.pt.y == kp2.pt.y && kp1.pt.x < kp2.pt.x);
}
};
}
testing::AssertionResult assertKeyPointsEquals(const char* gold_expr, const char* actual_expr, std::vector<cv::KeyPoint>& gold, std::vector<cv::KeyPoint>& actual)
{
if (gold.size() != actual.size())
{
return testing::AssertionFailure() << "KeyPoints size mistmach\n"
<< "\"" << gold_expr << "\" : " << gold.size() << "\n"
<< "\"" << actual_expr << "\" : " << actual.size();
}
std::sort(actual.begin(), actual.end(), KeyPointLess());
std::sort(gold.begin(), gold.end(), KeyPointLess());
for (size_t i = 0; i < gold.size(); ++i)
{
const cv::KeyPoint& p1 = gold[i];
const cv::KeyPoint& p2 = actual[i];
if (!keyPointsEquals(p1, p2))
{
return testing::AssertionFailure() << "KeyPoints differ at " << i << "\n"
<< "\"" << gold_expr << "\" vs \"" << actual_expr << "\" : \n"
<< "pt : " << testing::PrintToString(p1.pt) << " vs " << testing::PrintToString(p2.pt) << "\n"
<< "size : " << p1.size << " vs " << p2.size << "\n"
<< "angle : " << p1.angle << " vs " << p2.angle << "\n"
<< "response : " << p1.response << " vs " << p2.response << "\n"
<< "octave : " << p1.octave << " vs " << p2.octave << "\n"
<< "class_id : " << p1.class_id << " vs " << p2.class_id;
}
}
return ::testing::AssertionSuccess();
}
int getMatchedPointsCount(std::vector<cv::KeyPoint>& gold, std::vector<cv::KeyPoint>& actual)
{
std::sort(actual.begin(), actual.end(), KeyPointLess());
std::sort(gold.begin(), gold.end(), KeyPointLess());
int validCount = 0;
size_t sz = std::min(gold.size(), actual.size());
for (size_t i = 0; i < sz; ++i)
{
const cv::KeyPoint& p1 = gold[i];
const cv::KeyPoint& p2 = actual[i];
if (keyPointsEquals(p1, p2))
++validCount;
}
return validCount;
}
int getMatchedPointsCount(const std::vector<cv::KeyPoint>& keypoints1, const std::vector<cv::KeyPoint>& keypoints2, const std::vector<cv::DMatch>& matches)
{
int validCount = 0;
for (size_t i = 0; i < matches.size(); ++i)
{
const cv::DMatch& m = matches[i];
const cv::KeyPoint& p1 = keypoints1[m.queryIdx];
const cv::KeyPoint& p2 = keypoints2[m.trainIdx];
if (keyPointsEquals(p1, p2))
++validCount;
}
return validCount;
}
} // namespace cvtest