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vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e

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Gitea Mirror Bot
2026-08-22 00:11:13 +08:00
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modules/fastcv/test/test_pyramid.cpp
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/*
* Copyright (c) 2024-2025 Qualcomm Innovation Center, Inc. All rights reserved.
* SPDX-License-Identifier: Apache-2.0
*/
#include "test_precomp.hpp"
namespace opencv_test { namespace {
typedef std::tuple<bool /*useFloat*/, int /*nLevels*/, bool /*scaleBy2*/> PyramidTestParams;
class PyramidTest : public ::testing::TestWithParam<PyramidTestParams> { };
TEST_P(PyramidTest, accuracy)
{
auto par = GetParam();
bool useFloat = std::get<0>(par);
int nLevels = std::get<1>(par);
bool scaleBy2 = std::get<2>(par);
cv::Mat src = imread(cvtest::findDataFile("cv/shared/baboon.png"), cv::IMREAD_GRAYSCALE);
if (useFloat)
{
cv::Mat f;
src.convertTo(f, CV_32F);
src = f;
}
std::vector<cv::Mat> pyr;
cv::fastcv::buildPyramid(src, pyr, nLevels, scaleBy2);
ASSERT_EQ(pyr.size(), (size_t)nLevels);
std::vector<cv::Mat> refPyr;
if (scaleBy2)
{
cv::buildPyramid(src, refPyr, nLevels - 1);
}
else // ORB downscaling
{
for (int i = 0; i < nLevels; i++)
{
// we don't know how exactly the bit-accurate size is calculated
cv::Mat level;
cv::resize(src, level, pyr[i].size(), 0, 0, cv::INTER_AREA);
refPyr.push_back(level);
}
}
for (int i = 0; i < nLevels; i++)
{
cv::Mat ref = refPyr[i];
cv::Mat m = pyr[i];
ASSERT_EQ(m.size(), ref.size());
double l2diff = cv::norm(m, ref, cv::NORM_L2);
double linfdiff = cv::norm(m, ref, cv::NORM_INF);
double l2Thresh = scaleBy2 ? 178.0 : 5216.0;
double linfThresh = scaleBy2 ? 16.0 : 116.0;
EXPECT_LE(l2diff, l2Thresh);
EXPECT_LE(linfdiff, linfThresh);
}
if (cvtest::debugLevel > 0)
{
for (int i = 0; i < nLevels; i++)
{
char tchar = useFloat ? 'f' : 'i';
std::string scaleStr = scaleBy2 ? "x2" : "xORB";
cv::imwrite(cv::format("pyr_diff_%c_%d_%s_l%d.png", tchar, nLevels, scaleStr.c_str(), i), cv::abs(pyr[i] - refPyr[i]));
}
}
}
INSTANTIATE_TEST_CASE_P(FastCV_Extension, PyramidTest,
// useFloat, nLevels, scaleBy2
::testing::Values(
PyramidTestParams { true, 2, true}, PyramidTestParams { true, 3, true}, PyramidTestParams { true, 4, true},
PyramidTestParams {false, 2, true}, PyramidTestParams {false, 3, true}, PyramidTestParams {false, 4, true},
PyramidTestParams {false, 2, false}, PyramidTestParams {false, 3, false}, PyramidTestParams {false, 4, false}
));
typedef std::tuple<MatType, size_t> SobelPyramidTestParams;
class SobelPyramidTest : public ::testing::TestWithParam<SobelPyramidTestParams> {};
TEST_P(SobelPyramidTest, accuracy)
{
auto p = GetParam();
int type = std::get<0>(p);
size_t nLevels = std::get<1>(p);
// NOTE: test files should be manually loaded to folder on a device, for example like this:
// adb push fastcv/misc/bilateral_recursive/ /sdcard/testdata/fastcv/bilateral/
cv::Mat src = imread(cvtest::findDataFile("cv/shared/baboon.png"), cv::IMREAD_GRAYSCALE);
std::vector<cv::Mat> pyr;
cv::fastcv::buildPyramid(src, pyr, nLevels);
std::vector<cv::Mat> pyrDx, pyrDy;
cv::fastcv::sobelPyramid(pyr, pyrDx, pyrDy, type);
ASSERT_EQ(pyrDx.size(), nLevels);
ASSERT_EQ(pyrDy.size(), nLevels);
for (size_t i = 0; i < nLevels; i++)
{
ASSERT_EQ(pyrDx[i].type(), type);
ASSERT_EQ(pyrDx[i].size(), pyr[i].size());
ASSERT_EQ(pyrDy[i].type(), type);
ASSERT_EQ(pyrDy[i].size(), pyr[i].size());
}
std::vector<cv::Mat> refPyrDx(nLevels), refPyrDy(nLevels);
for (size_t i = 0; i < nLevels; i++)
{
int stype = (type == CV_8S) ? CV_16S : type;
cv::Mat dx, dy;
cv::Sobel(pyr[i], dx, stype, 1, 0);
cv::Sobel(pyr[i], dy, stype, 0, 1);
dx.convertTo(refPyrDx[i], type, 1.0/8.0, 0.0);
dy.convertTo(refPyrDy[i], type, 1.0/8.0, 0.0);
}
for (size_t i = 0; i < nLevels; i++)
{
cv::Mat ref, dst;
double normInf, normL2;
cv::Rect roi(1, 1, pyr[i].cols - 2, pyr[i].rows - 2);
ref = refPyrDx[i](roi);
dst = pyrDx[i](roi);
normInf = cvtest::norm(dst, ref, cv::NORM_INF);
normL2 = cvtest::norm(dst, ref, cv::NORM_L2) / dst.total();
EXPECT_LE(normInf, 76.1);
EXPECT_LT(normL2, 0.4);
ref = refPyrDy[i](roi);
dst = pyrDy[i](roi);
normInf = cvtest::norm(dst, ref, cv::NORM_INF);
normL2 = cvtest::norm(dst, ref, cv::NORM_L2) / dst.total();
EXPECT_LE(normInf, 66.6);
EXPECT_LT(normL2, 0.4);
}
if (cvtest::debugLevel > 0)
{
std::map<int, std::string> typeToString =
{
{CV_8U, "8u"}, {CV_8S, "8s"}, {CV_16U, "16u"}, {CV_16S, "16s"},
{CV_32S, "32s"}, {CV_32F, "32f"}, {CV_64F, "64f"}, {CV_16F, "16f"},
};
for (size_t i = 0; i < nLevels; i++)
{
cv::imwrite(cv::format("pyr_l%zu.png", i), pyr[i]);
cv::imwrite(cv::format("pyr_sobel_x_t%s_l%zu.png", typeToString.at(type).c_str(), i), pyrDx[i] + 128);
cv::imwrite(cv::format("pyr_sobel_y_t%s_l%zu.png", typeToString.at(type).c_str(), i), pyrDy[i] + 128);
cv::imwrite(cv::format("ref_pyr_sobel_x_t%s_l%zu.png", typeToString.at(type).c_str(), i), refPyrDx[i] + 128);
cv::imwrite(cv::format("ref_pyr_sobel_y_t%s_l%zu.png", typeToString.at(type).c_str(), i), refPyrDy[i] + 128);
}
}
}
INSTANTIATE_TEST_CASE_P(FastCV_Extension, SobelPyramidTest, ::testing::Combine(
::testing::Values(CV_8S, CV_16S, CV_32F), // depth
::testing::Values(3, 6))); // nLevels
}} // namespaces opencv_test, ::