718c41634f
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
279 lines
9.5 KiB
C++
279 lines
9.5 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#include "test_precomp.hpp"
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namespace opencv_test { namespace {
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CV_ENUM(MatchTemplType, CV_TM_CCORR, CV_TM_CCORR_NORMED,
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CV_TM_SQDIFF, CV_TM_SQDIFF_NORMED,
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CV_TM_CCOEFF, CV_TM_CCOEFF_NORMED)
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class Imgproc_MatchTemplateWithMask : public TestWithParam<std::tuple<MatType,MatType>>
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{
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protected:
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// Member functions inherited from ::testing::Test
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void SetUp() override;
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// Matrices for test calculations (always CV_32)
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Mat img_;
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Mat templ_;
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Mat mask_;
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Mat templ_masked_;
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Mat img_roi_masked_;
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// Matrices for call to matchTemplate (have test type)
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Mat img_testtype_;
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Mat templ_testtype_;
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Mat mask_testtype_;
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Mat result_;
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// Constants
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static const Size IMG_SIZE;
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static const Size TEMPL_SIZE;
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static const Point TEST_POINT;
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};
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// Arbitraryly chosen test constants
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const Size Imgproc_MatchTemplateWithMask::IMG_SIZE(160, 100);
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const Size Imgproc_MatchTemplateWithMask::TEMPL_SIZE(21, 13);
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const Point Imgproc_MatchTemplateWithMask::TEST_POINT(8, 9);
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void Imgproc_MatchTemplateWithMask::SetUp()
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{
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int type = std::get<0>(GetParam());
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int type_mask = std::get<1>(GetParam());
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// Matrices are created with the depth to test (for the call to matchTemplate()), but are also
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// converted to CV_32 for the test calculations, because matchTemplate() also only operates on
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// and returns CV_32.
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img_testtype_.create(IMG_SIZE, type);
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templ_testtype_.create(TEMPL_SIZE, type);
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mask_testtype_.create(TEMPL_SIZE, type_mask);
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randu(img_testtype_, 0, 10);
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randu(templ_testtype_, 0, 10);
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randu(mask_testtype_, 0, 5);
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img_testtype_.convertTo(img_, CV_32F);
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templ_testtype_.convertTo(templ_, CV_32F);
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mask_testtype_.convertTo(mask_, CV_32F);
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if (CV_MAT_DEPTH(type_mask) == CV_8U)
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{
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// CV_8U masks are interpreted as binary masks
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mask_.setTo(Scalar::all(1), mask_ != 0);
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}
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if (mask_.channels() != templ_.channels())
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{
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std::vector<Mat> mask_channels(templ_.channels(), mask_);
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merge(mask_channels.data(), templ_.channels(), mask_);
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}
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Rect roi(TEST_POINT, TEMPL_SIZE);
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img_roi_masked_ = img_(roi).mul(mask_);
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templ_masked_ = templ_.mul(mask_);
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}
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TEST_P(Imgproc_MatchTemplateWithMask, CompareNaiveImplSQDIFF)
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{
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matchTemplate(img_testtype_, templ_testtype_, result_, CV_TM_SQDIFF, mask_testtype_);
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// Naive implementation for one point
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Mat temp = img_roi_masked_ - templ_masked_;
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Scalar temp_s = sum(temp.mul(temp));
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double val = temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
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EXPECT_NEAR(val, result_.at<float>(TEST_POINT), TEMPL_SIZE.area()*abs(val)*FLT_EPSILON);
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}
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TEST_P(Imgproc_MatchTemplateWithMask, CompareNaiveImplSQDIFF_NORMED)
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{
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matchTemplate(img_testtype_, templ_testtype_, result_, CV_TM_SQDIFF_NORMED, mask_testtype_);
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// Naive implementation for one point
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Mat temp = img_roi_masked_ - templ_masked_;
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Scalar temp_s = sum(temp.mul(temp));
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double val = temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
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// Normalization
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temp_s = sum(templ_masked_.mul(templ_masked_));
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double norm = temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
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temp_s = sum(img_roi_masked_.mul(img_roi_masked_));
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norm *= temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
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norm = sqrt(norm);
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val /= norm;
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EXPECT_NEAR(val, result_.at<float>(TEST_POINT), TEMPL_SIZE.area()*abs(val)*FLT_EPSILON);
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}
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TEST_P(Imgproc_MatchTemplateWithMask, CompareNaiveImplCCORR)
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{
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matchTemplate(img_testtype_, templ_testtype_, result_, CV_TM_CCORR, mask_testtype_);
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// Naive implementation for one point
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Scalar temp_s = sum(templ_masked_.mul(img_roi_masked_));
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double val = temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
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EXPECT_NEAR(val, result_.at<float>(TEST_POINT), TEMPL_SIZE.area()*abs(val)*FLT_EPSILON);
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}
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TEST_P(Imgproc_MatchTemplateWithMask, CompareNaiveImplCCORR_NORMED)
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{
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matchTemplate(img_testtype_, templ_testtype_, result_, CV_TM_CCORR_NORMED, mask_testtype_);
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// Naive implementation for one point
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Scalar temp_s = sum(templ_masked_.mul(img_roi_masked_));
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double val = temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
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// Normalization
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temp_s = sum(templ_masked_.mul(templ_masked_));
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double norm = temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
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temp_s = sum(img_roi_masked_.mul(img_roi_masked_));
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norm *= temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
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norm = sqrt(norm);
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val /= norm;
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EXPECT_NEAR(val, result_.at<float>(TEST_POINT), TEMPL_SIZE.area()*abs(val)*FLT_EPSILON);
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}
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TEST_P(Imgproc_MatchTemplateWithMask, CompareNaiveImplCCOEFF)
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{
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matchTemplate(img_testtype_, templ_testtype_, result_, CV_TM_CCOEFF, mask_testtype_);
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// Naive implementation for one point
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Scalar temp_s = sum(mask_);
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for (int i = 0; i < 4; i++)
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{
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if (temp_s[i] != 0.0)
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temp_s[i] = 1.0 / temp_s[i];
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else
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temp_s[i] = 1.0;
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}
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Mat temp = mask_.clone(); temp = temp_s; // Workaround to multiply Mat by Scalar
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Mat temp2 = mask_.clone(); temp2 = sum(templ_masked_); // Workaround to multiply Mat by Scalar
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Mat templx = templ_masked_ - mask_.mul(temp).mul(temp2);
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temp2 = sum(img_roi_masked_); // Workaround to multiply Mat by Scalar
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Mat imgx = img_roi_masked_ - mask_.mul(temp).mul(temp2);
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temp_s = sum(templx.mul(imgx));
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double val = temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
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EXPECT_NEAR(val, result_.at<float>(TEST_POINT), TEMPL_SIZE.area()*abs(val)*FLT_EPSILON);
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}
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TEST_P(Imgproc_MatchTemplateWithMask, CompareNaiveImplCCOEFF_NORMED)
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{
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matchTemplate(img_testtype_, templ_testtype_, result_, CV_TM_CCOEFF_NORMED, mask_testtype_);
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// Naive implementation for one point
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Scalar temp_s = sum(mask_);
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for (int i = 0; i < 4; i++)
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{
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if (temp_s[i] != 0.0)
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temp_s[i] = 1.0 / temp_s[i];
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else
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temp_s[i] = 1.0;
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}
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Mat temp = mask_.clone(); temp = temp_s; // Workaround to multiply Mat by Scalar
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Mat temp2 = mask_.clone(); temp2 = sum(templ_masked_); // Workaround to multiply Mat by Scalar
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Mat templx = templ_masked_ - mask_.mul(temp).mul(temp2);
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temp2 = sum(img_roi_masked_); // Workaround to multiply Mat by Scalar
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Mat imgx = img_roi_masked_ - mask_.mul(temp).mul(temp2);
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temp_s = sum(templx.mul(imgx));
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double val = temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
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// Normalization
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temp_s = sum(templx.mul(templx));
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double norm = temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
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temp_s = sum(imgx.mul(imgx));
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norm *= temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
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norm = sqrt(norm);
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val /= norm;
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EXPECT_NEAR(val, result_.at<float>(TEST_POINT), TEMPL_SIZE.area()*abs(val)*FLT_EPSILON);
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}
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INSTANTIATE_TEST_CASE_P(SingleChannelMask, Imgproc_MatchTemplateWithMask,
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Combine(
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Values(CV_32FC1, CV_32FC3, CV_8UC1, CV_8UC3),
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Values(CV_32FC1, CV_8UC1)));
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INSTANTIATE_TEST_CASE_P(MultiChannelMask, Imgproc_MatchTemplateWithMask,
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Combine(
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Values(CV_32FC3, CV_8UC3),
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Values(CV_32FC3, CV_8UC3)));
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class Imgproc_MatchTemplateWithMask2 : public TestWithParam<std::tuple<MatType,MatType,
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MatchTemplType>>
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{
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protected:
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// Member functions inherited from ::testing::Test
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void SetUp() override;
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// Data members
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Mat img_;
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Mat templ_;
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Mat mask_;
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Mat result_withoutmask_;
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Mat result_withmask_;
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// Constants
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static const Size IMG_SIZE;
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static const Size TEMPL_SIZE;
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};
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// Arbitraryly chosen test constants
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const Size Imgproc_MatchTemplateWithMask2::IMG_SIZE(160, 100);
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const Size Imgproc_MatchTemplateWithMask2::TEMPL_SIZE(21, 13);
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void Imgproc_MatchTemplateWithMask2::SetUp()
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{
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int type = std::get<0>(GetParam());
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int type_mask = std::get<1>(GetParam());
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img_.create(IMG_SIZE, type);
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templ_.create(TEMPL_SIZE, type);
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mask_.create(TEMPL_SIZE, type_mask);
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randu(img_, 0, 100);
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randu(templ_, 0, 100);
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if (CV_MAT_DEPTH(type_mask) == CV_8U)
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{
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// CV_8U implies binary mask, so all nonzero values should work
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randu(mask_, 1, 255);
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}
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else
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{
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mask_ = Scalar(1, 1, 1, 1);
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}
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}
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TEST_P(Imgproc_MatchTemplateWithMask2, CompareWithAndWithoutMask)
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{
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int method = std::get<2>(GetParam());
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matchTemplate(img_, templ_, result_withmask_, method, mask_);
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matchTemplate(img_, templ_, result_withoutmask_, method);
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// Get maximum result for relative error calculation
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double min_val, max_val;
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minMaxLoc(abs(result_withmask_), &min_val, &max_val);
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// Get maximum of absolute diff for comparison
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double mindiff, maxdiff;
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minMaxLoc(abs(result_withmask_ - result_withoutmask_), &mindiff, &maxdiff);
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EXPECT_LT(maxdiff, max_val*TEMPL_SIZE.area()*FLT_EPSILON);
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}
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INSTANTIATE_TEST_CASE_P(SingleChannelMask, Imgproc_MatchTemplateWithMask2,
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Combine(
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Values(CV_32FC1, CV_32FC3, CV_8UC1, CV_8UC3),
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Values(CV_32FC1, CV_8UC1),
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Values(CV_TM_SQDIFF, CV_TM_SQDIFF_NORMED, CV_TM_CCORR, CV_TM_CCORR_NORMED,
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CV_TM_CCOEFF, CV_TM_CCOEFF_NORMED)));
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INSTANTIATE_TEST_CASE_P(MultiChannelMask, Imgproc_MatchTemplateWithMask2,
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Combine(
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Values(CV_32FC3, CV_8UC3),
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Values(CV_32FC3, CV_8UC3),
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Values(CV_TM_SQDIFF, CV_TM_SQDIFF_NORMED, CV_TM_CCORR, CV_TM_CCORR_NORMED,
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CV_TM_CCOEFF, CV_TM_CCOEFF_NORMED)));
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}} // namespace
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