mirror/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2024-12-11 22:59:16 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity
306a11a7c5
opencv/modules/gpu/test/test_dft_routines.cpp
Alexey Spizhevoy 6f788ff8db ported GPU test to GTest framework
2011-02-17 14:01:28 +00:00

400 lines
15 KiB
C++
Raw Blame History

/*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"
using namespace cv;
using namespace cv::gpu;
using namespace std;
struct CV_GpuMulSpectrumsTest: cvtest::BaseTest
{
CV_GpuMulSpectrumsTest() {}
void run(int)
{
test(0);
testConj(0);
testScaled(0);
testScaledConj(0);
test(DFT_ROWS);
testConj(DFT_ROWS);
testScaled(DFT_ROWS);
testScaledConj(DFT_ROWS);
}
void gen(int cols, int rows, Mat& mat)
{
RNG rng;
mat.create(rows, cols, CV_32FC2);
rng.fill(mat, RNG::UNIFORM, Scalar::all(0.f), Scalar::all(10.f));
}
bool cmp(const Mat& gold, const Mat& mine, float max_err=1e-3f)
{
if (gold.size() != mine.size())
{
ts->printf(cvtest::TS::CONSOLE, "bad sizes: gold: %d d%, mine: %d %d\n", gold.cols, gold.rows, mine.cols, mine.rows);
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
return false;
}
if (gold.type() != mine.type())
{
ts->printf(cvtest::TS::CONSOLE, "bad types: gold=%d, mine=%d\n", gold.type(), mine.type());
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
return false;
}
for (int i = 0; i < gold.rows; ++i)
{
for (int j = 0; j < gold.cols * 2; ++j)
{
float gold_ = gold.at<float>(i, j);
float mine_ = mine.at<float>(i, j);
if (fabs(gold_ - mine_) > max_err)
{
ts->printf(cvtest::TS::CONSOLE, "bad values at %d %d: gold=%f, mine=%f\n", j, i, gold_, mine_);
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
return false;
}
}
}
return true;
}
bool cmpScaled(const Mat& gold, const Mat& mine, float scale, float max_err=1e-3f)
{
if (gold.size() != mine.size())
{
ts->printf(cvtest::TS::CONSOLE, "bad sizes: gold: %d d%, mine: %d %d\n", gold.cols, gold.rows, mine.cols, mine.rows);
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
return false;
}
if (gold.type() != mine.type())
{
ts->printf(cvtest::TS::CONSOLE, "bad types: gold=%d, mine=%d\n", gold.type(), mine.type());
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
return false;
}
for (int i = 0; i < gold.rows; ++i)
{
for (int j = 0; j < gold.cols * 2; ++j)
{
float gold_ = gold.at<float>(i, j) * scale;
float mine_ = mine.at<float>(i, j);
if (fabs(gold_ - mine_) > max_err)
{
ts->printf(cvtest::TS::CONSOLE, "bad values at %d %d: gold=%f, mine=%f\n", j, i, gold_, mine_);
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
return false;
}
}
}
return true;
}
void test(int flags)
{
int cols = 1 + rand() % 100, rows = 1 + rand() % 1000;
Mat a, b;
gen(cols, rows, a);
gen(cols, rows, b);
Mat c_gold;
mulSpectrums(a, b, c_gold, flags, false);
GpuMat d_c;
mulSpectrums(GpuMat(a), GpuMat(b), d_c, flags, false);
if (!cmp(c_gold, Mat(d_c)))
ts->printf(cvtest::TS::CONSOLE, "test failed: cols=%d, rows=%d, flags=%d\n", cols, rows, flags);
}
void testConj(int flags)
{
int cols = 1 + rand() % 100, rows = 1 + rand() % 1000;
Mat a, b;
gen(cols, rows, a);
gen(cols, rows, b);
Mat c_gold;
mulSpectrums(a, b, c_gold, flags, true);
GpuMat d_c;
mulSpectrums(GpuMat(a), GpuMat(b), d_c, flags, true);
if (!cmp(c_gold, Mat(d_c)))
ts->printf(cvtest::TS::CONSOLE, "testConj failed: cols=%d, rows=%d, flags=%d\n", cols, rows, flags);
}
void testScaled(int flags)
{
int cols = 1 + rand() % 100, rows = 1 + rand() % 1000;
Mat a, b;
gen(cols, rows, a);
gen(cols, rows, b);
float scale = 1.f / a.size().area();
Mat c_gold;
mulSpectrums(a, b, c_gold, flags, false);
GpuMat d_c;
mulAndScaleSpectrums(GpuMat(a), GpuMat(b), d_c, flags, scale, false);
if (!cmpScaled(c_gold, Mat(d_c), scale))
ts->printf(cvtest::TS::CONSOLE, "testScaled failed: cols=%d, rows=%d, flags=%d\n", cols, rows, flags);
}
void testScaledConj(int flags)
{
int cols = 1 + rand() % 100, rows = 1 + rand() % 1000;
Mat a, b;
gen(cols, rows, a);
gen(cols, rows, b);
float scale = 1.f / a.size().area();
Mat c_gold;
mulSpectrums(a, b, c_gold, flags, true);
GpuMat d_c;
mulAndScaleSpectrums(GpuMat(a), GpuMat(b), d_c, flags, scale, true);
if (!cmpScaled(c_gold, Mat(d_c), scale))
ts->printf(cvtest::TS::CONSOLE, "testScaledConj failed: cols=%d, rows=%d, flags=%D\n", cols, rows, flags);
}
} CV_GpuMulSpectrumsTest_inst;
struct CV_GpuDftTest: cvtest::BaseTest
{
CV_GpuDftTest() {}
void run(int)
{
srand(0);
int cols = 2 + rand() % 100, rows = 2 + rand() % 100;
for (int i = 0; i < 2; ++i)
{
bool inplace = i != 0;
testC2C("no flags", cols, rows, 0, inplace);
testC2C("no flags 0 1", cols, rows + 1, 0, inplace);
testC2C("no flags 1 0", cols, rows + 1, 0, inplace);
testC2C("no flags 1 1", cols + 1, rows, 0, inplace);
testC2C("DFT_INVERSE", cols, rows, DFT_INVERSE, inplace);
testC2C("DFT_ROWS", cols, rows, DFT_ROWS, inplace);
testC2C("single col", 1, rows, 0, inplace);
testC2C("single row", cols, 1, 0, inplace);
testC2C("single col inversed", 1, rows, DFT_INVERSE, inplace);
testC2C("single row inversed", cols, 1, DFT_INVERSE, inplace);
testC2C("single row DFT_ROWS", cols, 1, DFT_ROWS, inplace);
testC2C("size 1 2", 1, 2, 0, inplace);
testC2C("size 2 1", 2, 1, 0, inplace);
}
testR2CThenC2R("sanity", cols, rows);
testR2CThenC2R("sanity 0 1", cols, rows + 1);
testR2CThenC2R("sanity 1 0", cols + 1, rows);
testR2CThenC2R("sanity 1 1", cols + 1, rows + 1);
testR2CThenC2R("single col", 1, rows);
testR2CThenC2R("single col 1", 1, rows + 1);
testR2CThenC2R("single row", cols, 1);
testR2CThenC2R("single row 1", cols + 1, 1);
testR2CThenC2R("sanity", cols, rows, true);
testR2CThenC2R("sanity 0 1", cols, rows + 1, true);
testR2CThenC2R("sanity 1 0", cols + 1, rows, true);
testR2CThenC2R("sanity 1 1", cols + 1, rows + 1, true);
testR2CThenC2R("single row", cols, 1, true);
testR2CThenC2R("single row 1", cols + 1, 1, true);
}
void gen(int cols, int rows, int cn, Mat& mat)
{
RNG rng(1);
mat.create(rows, cols, CV_MAKETYPE(CV_32F, cn));
rng.fill(mat, RNG::UNIFORM, Scalar::all(0.f), Scalar::all(10.f));
}
bool cmp(const Mat& gold, const Mat& mine, float max_err=1e-3f)
{
if (gold.size() != mine.size())
{
ts->printf(cvtest::TS::CONSOLE, "bad sizes: gold: %d %d, mine: %d %d\n", gold.cols, gold.rows, mine.cols, mine.rows);
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
return false;
}
if (gold.depth() != mine.depth())
{
ts->printf(cvtest::TS::CONSOLE, "bad depth: gold=%d, mine=%d\n", gold.depth(), mine.depth());
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
return false;
}
if (gold.channels() != mine.channels())
{
ts->printf(cvtest::TS::CONSOLE, "bad channel count: gold=%d, mine=%d\n", gold.channels(), mine.channels());
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
return false;
}
for (int i = 0; i < gold.rows; ++i)
{
for (int j = 0; j < gold.cols * gold.channels(); ++j)
{
float gold_ = gold.at<float>(i, j);
float mine_ = mine.at<float>(i, j);
if (fabs(gold_ - mine_) > max_err)
{
ts->printf(cvtest::TS::CONSOLE, "bad values at %d %d: gold=%f, mine=%f\n", j / gold.channels(), i, gold_, mine_);
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
return false;
}
}
}
return true;
}
void testC2C(const std::string& hint, int cols, int rows, int flags, bool inplace=false)
{
Mat a;
gen(cols, rows, 2, a);
Mat b_gold;
dft(a, b_gold, flags);
GpuMat d_b;
GpuMat d_b_data;
if (inplace)
{
d_b_data.create(1, a.size().area(), CV_32FC2);
d_b = GpuMat(a.rows, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize());
}
dft(GpuMat(a), d_b, Size(cols, rows), flags);
bool ok = true;
if (ok && inplace && d_b.ptr() != d_b_data.ptr())
{
ts->printf(cvtest::TS::CONSOLE, "unnecessary reallocation was done\n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
ok = false;
}
if (ok && d_b.depth() != CV_32F)
{
ts->printf(cvtest::TS::CONSOLE, "bad depth: %d\n", d_b.depth());
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
ok = false;
}
if (ok && d_b.channels() != 2)
{
ts->printf(cvtest::TS::CONSOLE, "bad channel count: %d\n", d_b.channels());
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
ok = false;
}
if (ok) ok = cmp(b_gold, Mat(d_b), rows * cols * 1e-4f);
if (!ok)
ts->printf(cvtest::TS::CONSOLE, "testC2C failed: hint=%s, cols=%d, rows=%d, flags=%d, inplace=%d\n",
hint.c_str(), cols, rows, flags, inplace);
}
void testR2CThenC2R(const std::string& hint, int cols, int rows, bool inplace=false)
{
Mat a;
gen(cols, rows, 1, a);
bool ok = true;
GpuMat d_b, d_c;
GpuMat d_b_data, d_c_data;
if (inplace)
{
if (a.cols == 1)
{
d_b_data.create(1, (a.rows / 2 + 1) * a.cols, CV_32FC2);
d_b = GpuMat(a.rows / 2 + 1, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize());
}
else
{
d_b_data.create(1, a.rows * (a.cols / 2 + 1), CV_32FC2);
d_b = GpuMat(a.rows, a.cols / 2 + 1, CV_32FC2, d_b_data.ptr(), (a.cols / 2 + 1) * d_b_data.elemSize());
}
d_c_data.create(1, a.size().area(), CV_32F);
d_c = GpuMat(a.rows, a.cols, CV_32F, d_c_data.ptr(), a.cols * d_c_data.elemSize());
}
dft(GpuMat(a), d_b, Size(cols, rows), 0);
dft(d_b, d_c, Size(cols, rows), DFT_REAL_OUTPUT | DFT_SCALE);
if (ok && inplace && d_b.ptr() != d_b_data.ptr())
{
ts->printf(cvtest::TS::CONSOLE, "unnecessary reallocation was done for b\n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
ok = false;
}
if (ok && inplace && d_c.ptr() != d_c_data.ptr())
{
ts->printf(cvtest::TS::CONSOLE, "unnecessary reallocation was done for c\n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
ok = false;
}
if (ok && d_c.depth() != CV_32F)
{
ts->printf(cvtest::TS::CONSOLE, "bad depth: %d\n", d_c.depth());
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
ok = false;
}
if (ok && d_c.channels() != 1)
{
ts->printf(cvtest::TS::CONSOLE, "bad channel count: %d\n", d_c.channels());
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
ok = false;
}
if (ok) ok = cmp(a, Mat(d_c), rows * cols * 1e-5f);
if (!ok)
ts->printf(cvtest::TS::CONSOLE, "testR2CThenC2R failed: hint=%s, cols=%d, rows=%d, inplace=%d\n",
hint.c_str(), cols, rows, inplace);
}
};
TEST(dft, accuracy) { CV_GpuDftTest test; test.safe_run(); }
Reference in New Issue View Git Blame Copy Permalink