mirror of
https://github.com/opencv/opencv.git
synced 2024-11-25 19:50:38 +08:00
296 lines
13 KiB
C++
296 lines
13 KiB
C++
/*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.
|
|
//
|
|
//
|
|
// License Agreement
|
|
// For Open Source Computer Vision Library
|
|
//
|
|
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
|
// Copyright (C) 2009, Willow Garage Inc., 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 GpuMaterials provided with the distribution.
|
|
//
|
|
// * The name of the copyright holders 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 bpied warranties, including, but not limited to, the bpied
|
|
// 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"
|
|
#include <string>
|
|
#include <iostream>
|
|
|
|
//#define SHOW_TIME
|
|
|
|
#ifdef SHOW_TIME
|
|
#include <ctime>
|
|
#define F(x) x
|
|
#else
|
|
#define F(x)
|
|
#endif
|
|
|
|
using namespace cv;
|
|
using namespace std;
|
|
|
|
struct CV_GpuMatchTemplateTest: cvtest::BaseTest
|
|
{
|
|
CV_GpuMatchTemplateTest() {}
|
|
|
|
void run(int)
|
|
{
|
|
bool double_ok = gpu::TargetArchs::builtWith(gpu::NATIVE_DOUBLE) &&
|
|
gpu::DeviceInfo().supports(gpu::NATIVE_DOUBLE);
|
|
if (!double_ok)
|
|
{
|
|
// For sqrIntegral
|
|
ts->printf(cvtest::TS::CONSOLE, "\nCode and device double support is required (CC >= 1.3)");
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_GENERIC);
|
|
return;
|
|
}
|
|
|
|
Mat image, templ;
|
|
Mat dst_gold;
|
|
gpu::GpuMat dst;
|
|
int n, m, h, w;
|
|
F(clock_t t;)
|
|
|
|
RNG& rng = ts->get_rng();
|
|
|
|
for (int cn = 1; cn <= 4; ++cn)
|
|
{
|
|
F(ts->printf(cvtest::TS::CONSOLE, "cn: %d\n", cn);)
|
|
for (int i = 0; i <= 0; ++i)
|
|
{
|
|
n = rng.uniform(30, 100);
|
|
m = rng.uniform(30, 100);
|
|
h = rng.uniform(5, n - 1);
|
|
w = rng.uniform(5, m - 1);
|
|
|
|
gen(image, n, m, CV_8U, cn);
|
|
gen(templ, h, w, CV_8U, cn);
|
|
F(t = clock();)
|
|
matchTemplate(image, templ, dst_gold, CV_TM_SQDIFF);
|
|
F(cout << "depth: 8U cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;)
|
|
F(cout << "cpu:" << clock() - t << endl;)
|
|
F(t = clock();)
|
|
gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_SQDIFF);
|
|
F(cout << "gpu_block: " << clock() - t << endl;)
|
|
if (!check(dst_gold, Mat(dst), 5 * h * w * 1e-4f, "SQDIFF 8U")) return;
|
|
|
|
gen(image, n, m, CV_8U, cn);
|
|
gen(templ, h, w, CV_8U, cn);
|
|
F(t = clock();)
|
|
matchTemplate(image, templ, dst_gold, CV_TM_SQDIFF_NORMED);
|
|
F(cout << "depth: 8U cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;)
|
|
F(cout << "cpu:" << clock() - t << endl;)
|
|
F(t = clock();)
|
|
gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_SQDIFF_NORMED);
|
|
F(cout << "gpu_block: " << clock() - t << endl;)
|
|
if (!check(dst_gold, Mat(dst), h * w * 1e-5f, "SQDIFF_NOREMD 8U")) return;
|
|
|
|
gen(image, n, m, CV_8U, cn);
|
|
gen(templ, h, w, CV_8U, cn);
|
|
F(t = clock();)
|
|
matchTemplate(image, templ, dst_gold, CV_TM_CCORR);
|
|
F(cout << "depth: 8U cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;)
|
|
F(cout << "cpu:" << clock() - t << endl;)
|
|
F(t = clock();)
|
|
gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_CCORR);
|
|
F(cout << "gpu_block: " << clock() - t << endl;)
|
|
if (!check(dst_gold, Mat(dst), 5 * h * w * cn * cn * 1e-5f, "CCORR 8U")) return;
|
|
|
|
gen(image, n, m, CV_8U, cn);
|
|
gen(templ, h, w, CV_8U, cn);
|
|
F(t = clock();)
|
|
matchTemplate(image, templ, dst_gold, CV_TM_CCORR_NORMED);
|
|
F(cout << "depth: 8U cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;)
|
|
F(cout << "cpu:" << clock() - t << endl;)
|
|
F(t = clock();)
|
|
gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_CCORR_NORMED);
|
|
F(cout << "gpu_block: " << clock() - t << endl;)
|
|
if (!check(dst_gold, Mat(dst), h * w * 1e-6f, "CCORR_NORMED 8U")) return;
|
|
|
|
gen(image, n, m, CV_8U, cn);
|
|
gen(templ, h, w, CV_8U, cn);
|
|
F(t = clock();)
|
|
matchTemplate(image, templ, dst_gold, CV_TM_CCOEFF);
|
|
F(cout << "depth: 8U cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;)
|
|
F(cout << "cpu:" << clock() - t << endl;)
|
|
F(t = clock();)
|
|
gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_CCOEFF);
|
|
F(cout << "gpu_block: " << clock() - t << endl;)
|
|
if (!check(dst_gold, Mat(dst), 5 * h * w * cn * cn * 1e-5f, "CCOEFF 8U")) return;
|
|
|
|
gen(image, n, m, CV_8U, cn);
|
|
gen(templ, h, w, CV_8U, cn);
|
|
F(t = clock();)
|
|
matchTemplate(image, templ, dst_gold, CV_TM_CCOEFF_NORMED);
|
|
F(cout << "depth: 8U cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;)
|
|
F(cout << "cpu:" << clock() - t << endl;)
|
|
F(t = clock();)
|
|
gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_CCOEFF_NORMED);
|
|
F(cout << "gpu_block: " << clock() - t << endl;)
|
|
if (!check(dst_gold, Mat(dst), h * w * 1e-6f, "CCOEFF_NORMED 8U")) return;
|
|
|
|
gen(image, n, m, CV_32F, cn);
|
|
gen(templ, h, w, CV_32F, cn);
|
|
F(t = clock();)
|
|
matchTemplate(image, templ, dst_gold, CV_TM_SQDIFF);
|
|
F(cout << "depth: 32F cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;)
|
|
F(cout << "cpu:" << clock() - t << endl;)
|
|
F(t = clock();)
|
|
gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_SQDIFF);
|
|
F(cout << "gpu_block: " << clock() - t << endl;)
|
|
if (!check(dst_gold, Mat(dst), 0.25f * h * w * 1e-5f, "SQDIFF 32F")) return;
|
|
|
|
gen(image, n, m, CV_32F, cn);
|
|
gen(templ, h, w, CV_32F, cn);
|
|
F(t = clock();)
|
|
matchTemplate(image, templ, dst_gold, CV_TM_CCORR);
|
|
F(cout << "depth: 32F cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;)
|
|
F(cout << "cpu:" << clock() - t << endl;)
|
|
F(t = clock();)
|
|
gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_CCORR);
|
|
F(cout << "gpu_block: " << clock() - t << endl;)
|
|
if (!check(dst_gold, Mat(dst), 0.25f * h * w * 1e-5f, "CCORR 32F")) return;
|
|
}
|
|
}
|
|
}
|
|
|
|
void gen(Mat& a, int rows, int cols, int depth, int cn)
|
|
{
|
|
RNG rng;
|
|
a.create(rows, cols, CV_MAKETYPE(depth, cn));
|
|
if (depth == CV_8U)
|
|
rng.fill(a, RNG::UNIFORM, Scalar::all(1), Scalar::all(10));
|
|
else if (depth == CV_32F)
|
|
rng.fill(a, RNG::UNIFORM, Scalar::all(0.001f), Scalar::all(1.f));
|
|
}
|
|
|
|
bool check(const Mat& a, const Mat& b, float max_err, const string& method="")
|
|
{
|
|
if (a.size() != b.size())
|
|
{
|
|
ts->printf(cvtest::TS::CONSOLE, "bad size, method=%s\n", method.c_str());
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
|
return false;
|
|
}
|
|
|
|
//for (int i = 0; i < a.rows; ++i)
|
|
//{
|
|
// for (int j = 0; j < a.cols; ++j)
|
|
// {
|
|
// float a_ = a.at<float>(i, j);
|
|
// float b_ = b.at<float>(i, j);
|
|
// if (fabs(a_ - b_) > max_err)
|
|
// {
|
|
// ts->printf(cvtest::TS::CONSOLE, "a=%f, b=%f, i=%d, j=%d\n", a_, b_, i, j);
|
|
// cin.get();
|
|
// }
|
|
// }
|
|
//}
|
|
|
|
float err = (float)norm(a, b, NORM_INF);
|
|
if (err > max_err)
|
|
{
|
|
ts->printf(cvtest::TS::CONSOLE, "bad accuracy: %f, method=%s\n", err, method.c_str());
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
|
return false;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
};
|
|
|
|
TEST(matchTemplate, accuracy) { CV_GpuMatchTemplateTest test; test.safe_run(); }
|
|
|
|
struct CV_GpuMatchTemplateFindPatternInBlackTest: cvtest::BaseTest
|
|
{
|
|
CV_GpuMatchTemplateFindPatternInBlackTest() {}
|
|
|
|
void run(int)
|
|
{
|
|
bool double_ok = gpu::TargetArchs::builtWith(gpu::NATIVE_DOUBLE) &&
|
|
gpu::DeviceInfo().supports(gpu::NATIVE_DOUBLE);
|
|
if (!double_ok)
|
|
{
|
|
// For sqrIntegral
|
|
ts->printf(cvtest::TS::CONSOLE, "\nCode and device double support is required (CC >= 1.3)");
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_GENERIC);
|
|
return;
|
|
}
|
|
|
|
Mat image = imread(std::string(ts->get_data_path()) + "matchtemplate/black.png");
|
|
if (image.empty())
|
|
{
|
|
ts->printf(cvtest::TS::CONSOLE, "can't open file '%s'", (std::string(ts->get_data_path())
|
|
+ "matchtemplate/black.png").c_str());
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
|
|
return;
|
|
}
|
|
|
|
Mat pattern = imread(std::string(ts->get_data_path()) + "matchtemplate/cat.png");
|
|
if (pattern.empty())
|
|
{
|
|
ts->printf(cvtest::TS::CONSOLE, "can't open file '%s'", (std::string(ts->get_data_path())
|
|
+ "matchtemplate/cat.png").c_str());
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
|
|
return;
|
|
}
|
|
|
|
gpu::GpuMat d_image(image);
|
|
gpu::GpuMat d_pattern(pattern);
|
|
gpu::GpuMat d_result;
|
|
|
|
double maxValue;
|
|
Point maxLoc;
|
|
Point maxLocGold(284, 12);
|
|
|
|
gpu::matchTemplate(d_image, d_pattern, d_result, CV_TM_CCOEFF_NORMED);
|
|
gpu::minMaxLoc(d_result, NULL, &maxValue, NULL, &maxLoc );
|
|
if (maxLoc != maxLocGold)
|
|
{
|
|
ts->printf(cvtest::TS::CONSOLE, "bad match (CV_TM_CCOEFF_NORMED): %d %d, must be at: %d %d",
|
|
maxLoc.x, maxLoc.y, maxLocGold.x, maxLocGold.y);
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
|
return;
|
|
}
|
|
|
|
gpu::matchTemplate(d_image, d_pattern, d_result, CV_TM_CCORR_NORMED);
|
|
gpu::minMaxLoc(d_result, NULL, &maxValue, NULL, &maxLoc );
|
|
if (maxLoc != maxLocGold)
|
|
{
|
|
ts->printf(cvtest::TS::CONSOLE, "bad match (CV_TM_CCORR_NORMED): %d %d, must be at: %d %d",
|
|
maxLoc.x, maxLoc.y, maxLocGold.x, maxLocGold.y);
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
|
return;
|
|
}
|
|
}
|
|
};
|
|
|
|
TEST(matchTemplate, find_pattern_in_black) { CV_GpuMatchTemplateFindPatternInBlackTest test; test.safe_run(); }
|