mirror of
https://github.com/opencv/opencv_contrib.git
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126 lines
4.1 KiB
C++
126 lines
4.1 KiB
C++
/*
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* Copyright (c) 2024-2025 Qualcomm Innovation Center, Inc. All rights reserved.
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* SPDX-License-Identifier: Apache-2.0
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*/
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#include "test_precomp.hpp"
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namespace opencv_test { namespace {
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typedef std::tuple<int /*rows1*/, int /*cols1*/, int /*cols2*/> MatMulTestParams;
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class MatMulTest : public ::testing::TestWithParam<MatMulTestParams> {};
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typedef std::tuple<Size, int /*depth*/, int /*op type*/> ArithmOpTestParams;
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class ArithmOpTest : public ::testing::TestWithParam<ArithmOpTestParams> {};
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TEST_P(MatMulTest, accuracy)
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{
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auto p = GetParam();
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int rows1 = std::get<0>(p);
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int cols1 = std::get<1>(p);
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int cols2 = std::get<2>(p);
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RNG& rng = cv::theRNG();
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Mat src1(rows1, cols1, CV_8SC1), src2(cols1, cols2, CV_8SC1);
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cvtest::randUni(rng, src1, Scalar::all(-128), Scalar::all(128));
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cvtest::randUni(rng, src2, Scalar::all(-128), Scalar::all(128));
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Mat dst;
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cv::fastcv::matmuls8s32(src1, src2, dst);
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Mat fdst;
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dst.convertTo(fdst, CV_32F);
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Mat fsrc1, fsrc2;
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src1.convertTo(fsrc1, CV_32F);
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src2.convertTo(fsrc2, CV_32F);
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Mat ref;
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cv::gemm(fsrc1, fsrc2, 1.0, noArray(), 0, ref, 0);
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double normInf = cvtest::norm(ref, fdst, cv::NORM_INF);
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double normL2 = cvtest::norm(ref, fdst, cv::NORM_L2);
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EXPECT_EQ(normInf, 0);
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EXPECT_EQ(normL2, 0);
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if (cvtest::debugLevel > 0 && (normInf > 0 || normL2 > 0))
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{
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std::ofstream of(cv::format("out_%d_%d_%d.txt", rows1, cols1, cols2));
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of << ref << std::endl;
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of << dst << std::endl;
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of.close();
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}
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}
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TEST_P(ArithmOpTest, accuracy)
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{
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auto p = GetParam();
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Size sz = std::get<0>(p);
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int depth = std::get<1>(p);
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int op = std::get<2>(p);
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RNG& rng = cv::theRNG();
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Mat src1(sz, depth), src2(sz, depth);
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cvtest::randUni(rng, src1, Scalar::all(0), Scalar::all(128));
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cvtest::randUni(rng, src2, Scalar::all(0), Scalar::all(128));
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Mat dst;
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cv::fastcv::arithmetic_op(src1, src2, dst, op);
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Mat ref;
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if(op == 0)
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cv::add(src1, src2, ref);
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else if(op == 1)
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cv::subtract(src1, src2, ref);
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double normInf = cvtest::norm(ref, dst, cv::NORM_INF);
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double normL2 = cvtest::norm(ref, dst, cv::NORM_L2);
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EXPECT_EQ(normInf, 0);
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EXPECT_EQ(normL2, 0);
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}
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typedef testing::TestWithParam<tuple<Size>> IntegrateYUVTest;
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TEST_P(IntegrateYUVTest, accuracy)
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{
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auto p = GetParam();
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Size srcSize = std::get<0>(p);
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int depth = CV_8U;
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cv::Mat Y(srcSize, depth), CbCr(srcSize.height/2, srcSize.width, depth);
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cv::Mat IY, ICb, ICr;
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RNG& rng = cv::theRNG();
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cvtest::randUni(rng, Y, Scalar::all(0), Scalar::all(255));
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cvtest::randUni(rng, CbCr, Scalar::all(0), Scalar::all(255));
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cv::fastcv::integrateYUV(Y, CbCr, IY, ICb, ICr);
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CbCr = CbCr.reshape(2,0);
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std::vector<cv::Mat> ref;
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cv::fastcv::split(CbCr, ref);
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cv::Mat IY_ref, ICb_ref, ICr_ref;
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cv::integral(Y,IY_ref,CV_32S);
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cv::integral(ref[0],ICb_ref,CV_32S);
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cv::integral(ref[1],ICr_ref,CV_32S);
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EXPECT_EQ(IY_ref.at<int>(IY_ref.rows - 1, IY_ref.cols - 1), IY.at<int>(IY.rows - 1, IY.cols - 1));
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EXPECT_EQ(ICb_ref.at<int>(ICb_ref.rows - 1, ICb_ref.cols - 1), ICb.at<int>(ICb.rows - 1, ICb.cols - 1));
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EXPECT_EQ(ICr_ref.at<int>(ICr_ref.rows - 1, ICr_ref.cols - 1), ICr.at<int>(ICr.rows - 1, ICr.cols - 1));
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}
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INSTANTIATE_TEST_CASE_P(FastCV_Extension, MatMulTest,
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::testing::Combine(::testing::Values(8, 16, 128, 256), // rows1
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::testing::Values(8, 16, 128, 256), // cols1
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::testing::Values(8, 16, 128, 256))); // cols2
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INSTANTIATE_TEST_CASE_P(FastCV_Extension, ArithmOpTest,
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::testing::Combine(::testing::Values(perf::szVGA, perf::sz720p, perf::sz1080p), // sz
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::testing::Values(CV_8U, CV_16S), // depth
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::testing::Values(0,1))); // op type
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INSTANTIATE_TEST_CASE_P(FastCV_Extension, IntegrateYUVTest,
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Values(perf::szVGA, perf::sz720p, perf::sz1080p)); // sz
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}} // namespaces opencv_test, ::
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