mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
290 lines
7.6 KiB
290 lines
7.6 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
|
// |
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
|
// |
|
// By downloading, copying, installing or using the software you agree to this license. |
|
// If you do not agree to this license, do not download, install, |
|
// copy or use the software. |
|
// |
|
// |
|
// Intel License Agreement |
|
// For Open Source Computer Vision Library |
|
// |
|
// Copyright (C) 2000, Intel Corporation, all rights reserved. |
|
// Third party copyrights are property of their respective owners. |
|
// |
|
// Redistribution and use in source and binary forms, with or without modification, |
|
// are permitted provided that the following conditions are met: |
|
// |
|
// * Redistribution's of source code must retain the above copyright notice, |
|
// this list of conditions and the following disclaimer. |
|
// |
|
// * Redistribution's in binary form must reproduce the above copyright notice, |
|
// this list of conditions and the following disclaimer in the documentation |
|
// and/or other materials provided with the distribution. |
|
// |
|
// * The name of Intel Corporation may not be used to endorse or promote products |
|
// derived from this software without specific prior written permission. |
|
// |
|
// This software is provided by the copyright holders and contributors "as is" and |
|
// any express or implied warranties, including, but not limited to, the implied |
|
// warranties of merchantability and fitness for a particular purpose are disclaimed. |
|
// In no event shall the Intel Corporation or contributors be liable for any direct, |
|
// indirect, incidental, special, exemplary, or consequential damages |
|
// (including, but not limited to, procurement of substitute goods or services; |
|
// loss of use, data, or profits; or business interruption) however caused |
|
// and on any theory of liability, whether in contract, strict liability, |
|
// or tort (including negligence or otherwise) arising in any way out of |
|
// the use of this software, even if advised of the possibility of such damage. |
|
// |
|
//M*/ |
|
|
|
#include "test_precomp.hpp" |
|
#define VARNAME(A) #A |
|
using namespace std; |
|
using namespace cv; |
|
using 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(); |
|
//}; |
|
|
|
int randomInt(int minVal, int maxVal) |
|
{ |
|
RNG &rng = TS::ptr()->get_rng(); |
|
return rng.uniform(minVal, maxVal); |
|
} |
|
|
|
double randomDouble(double minVal, double maxVal) |
|
{ |
|
RNG &rng = TS::ptr()->get_rng(); |
|
return rng.uniform(minVal, maxVal); |
|
} |
|
|
|
Size randomSize(int minVal, int maxVal) |
|
{ |
|
return cv::Size(randomInt(minVal, maxVal), randomInt(minVal, maxVal)); |
|
} |
|
|
|
Scalar randomScalar(double minVal, double maxVal) |
|
{ |
|
return Scalar(randomDouble(minVal, maxVal), randomDouble(minVal, maxVal), randomDouble(minVal, maxVal), randomDouble(minVal, maxVal)); |
|
} |
|
|
|
Mat randomMat(Size size, int type, double minVal, double maxVal) |
|
{ |
|
return randomMat(TS::ptr()->get_rng(), size, type, minVal, maxVal, false); |
|
} |
|
|
|
cv::ocl::oclMat createMat_ocl(Size size, int type, bool useRoi) |
|
{ |
|
Size size0 = size; |
|
|
|
if (useRoi) |
|
{ |
|
size0.width += randomInt(5, 15); |
|
size0.height += randomInt(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(const Mat& m, bool useRoi) |
|
{ |
|
CV_Assert(m.type() == CV_8UC1 || m.type() == CV_8UC3); |
|
cv::ocl::oclMat d_m; |
|
d_m = createMat_ocl(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; |
|
} |
|
/* |
|
void showDiff(InputArray gold_, InputArray actual_, double eps) |
|
{ |
|
Mat gold; |
|
if (gold_.kind() == _InputArray::MAT) |
|
gold = gold_.getMat(); |
|
else |
|
gold_.getGpuMat().download(gold); |
|
|
|
Mat actual; |
|
if (actual_.kind() == _InputArray::MAT) |
|
actual = actual_.getMat(); |
|
else |
|
actual_.getGpuMat().download(actual); |
|
|
|
Mat diff; |
|
absdiff(gold, actual, diff); |
|
threshold(diff, diff, eps, 255.0, cv::THRESH_BINARY); |
|
|
|
namedWindow("gold", WINDOW_NORMAL); |
|
namedWindow("actual", WINDOW_NORMAL); |
|
namedWindow("diff", WINDOW_NORMAL); |
|
|
|
imshow("gold", gold); |
|
imshow("actual", actual); |
|
imshow("diff", diff); |
|
|
|
waitKey(); |
|
} |
|
*/ |
|
|
|
|
|
|
|
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; |
|
}
|
|
|