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Merge remote-tracking branch 'upstream/3.4' into merge-3.4

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This commit is contained in:
Alexander Alekhin
2018-09-04 19:35:38 +03:00
committed by Alexander Alekhin
parent e0bfe44cac 351ee2e3a5
commit d74b98c3d9
117 changed files with 6913 additions and 3713 deletions
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modules/core/test/test_ippasync.cpp
-171
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@@ -1,171 +0,0 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
#include "opencv2/ts/ocl_test.hpp"
#ifdef HAVE_IPP_A
#include "opencv2/core/ippasync.hpp"
using namespace cv;
using namespace std;
using namespace opencv_test;
namespace opencv_test {
namespace ocl {
PARAM_TEST_CASE(IPPAsync, MatDepth, Channels, hppAccelType)
{
int type;
int cn;
int depth;
hppAccelType accelType;
Mat matrix, result;
hppiMatrix * hppMat;
hppAccel accel;
hppiVirtualMatrix * virtMatrix;
hppStatus sts;
virtual void SetUp()
{
type = CV_MAKE_TYPE(GET_PARAM(0), GET_PARAM(1));
depth = GET_PARAM(0);
cn = GET_PARAM(1);
accelType = GET_PARAM(2);
}
void generateTestData()
{
Size matrix_Size = randomSize(2, 100);
const double upValue = 100;
matrix = randomMat(matrix_Size, type, -upValue, upValue);
}
void Near(double threshold = 0.0)
{
EXPECT_MAT_NEAR(matrix, result, threshold);
}
};
TEST_P(IPPAsync, accuracy)
{
sts = hppCreateInstance(accelType, 0, &accel);
if (sts!=HPP_STATUS_NO_ERROR) printf("hppStatus = %d\n",sts);
CV_Assert(sts==HPP_STATUS_NO_ERROR);
virtMatrix = hppiCreateVirtualMatrices(accel, 2);
for (int j = 0; j < test_loop_times; j++)
{
generateTestData();
hppMat = hpp::getHpp(matrix,accel);
hppScalar a = 3;
sts = hppiAddC(accel, hppMat, a, 0, virtMatrix[0]);
CV_Assert(sts==HPP_STATUS_NO_ERROR);
sts = hppiSubC(accel, virtMatrix[0], a, 0, virtMatrix[1]);
CV_Assert(sts==HPP_STATUS_NO_ERROR);
sts = hppWait(accel, HPP_TIME_OUT_INFINITE);
CV_Assert(sts==HPP_STATUS_NO_ERROR);
result = hpp::getMat(virtMatrix[1], accel, cn);
Near(5.0e-6);
sts = hppiFreeMatrix(hppMat);
CV_Assert(sts==HPP_STATUS_NO_ERROR);
}
sts = hppiDeleteVirtualMatrices(accel, virtMatrix);
CV_Assert(sts==HPP_STATUS_NO_ERROR);
sts = hppDeleteInstance(accel);
CV_Assert(sts==HPP_STATUS_NO_ERROR);
}
PARAM_TEST_CASE(IPPAsyncShared, Channels, hppAccelType)
{
int cn;
int type;
hppAccelType accelType;
Mat matrix, result;
hppiMatrix* hppMat;
hppAccel accel;
hppiVirtualMatrix * virtMatrix;
hppStatus sts;
virtual void SetUp()
{
cn = GET_PARAM(0);
accelType = GET_PARAM(1);
type=CV_MAKE_TYPE(CV_8U, GET_PARAM(0));
}
void generateTestData()
{
Size matrix_Size = randomSize(2, 100);
hpp32u pitch, size;
const int upValue = 100;
sts = hppQueryMatrixAllocParams(accel, (hpp32u)(matrix_Size.width*cn), (hpp32u)matrix_Size.height, HPP_DATA_TYPE_8U, &pitch, &size);
matrix = randomMat(matrix_Size, type, 0, upValue);
}
void Near(double threshold = 0.0)
{
EXPECT_MAT_NEAR(matrix, result, threshold);
}
};
TEST_P(IPPAsyncShared, accuracy)
{
sts = hppCreateInstance(accelType, 0, &accel);
if (sts!=HPP_STATUS_NO_ERROR) printf("hppStatus = %d\n",sts);
CV_Assert(sts==HPP_STATUS_NO_ERROR);
virtMatrix = hppiCreateVirtualMatrices(accel, 2);
for (int j = 0; j < test_loop_times; j++)
{
generateTestData();
hppMat = hpp::getHpp(matrix,accel);
hppScalar a = 3;
sts = hppiAddC(accel, hppMat, a, 0, virtMatrix[0]);
CV_Assert(sts==HPP_STATUS_NO_ERROR);
sts = hppiSubC(accel, virtMatrix[0], a, 0, virtMatrix[1]);
CV_Assert(sts==HPP_STATUS_NO_ERROR);
sts = hppWait(accel, HPP_TIME_OUT_INFINITE);
CV_Assert(sts==HPP_STATUS_NO_ERROR);
result = hpp::getMat(virtMatrix[1], accel, cn);
Near(0);
sts = hppiFreeMatrix(hppMat);
CV_Assert(sts==HPP_STATUS_NO_ERROR);
}
sts = hppiDeleteVirtualMatrices(accel, virtMatrix);
CV_Assert(sts==HPP_STATUS_NO_ERROR);
sts = hppDeleteInstance(accel);
CV_Assert(sts==HPP_STATUS_NO_ERROR);
}
INSTANTIATE_TEST_CASE_P(IppATest, IPPAsyncShared, Combine(Values(1, 2, 3, 4),
Values( HPP_ACCEL_TYPE_CPU, HPP_ACCEL_TYPE_GPU)));
INSTANTIATE_TEST_CASE_P(IppATest, IPPAsync, Combine(Values(CV_8U, CV_16U, CV_16S, CV_32F),
Values(1, 2, 3, 4),
Values( HPP_ACCEL_TYPE_CPU, HPP_ACCEL_TYPE_GPU)));
}
}
#endif
modules/core/test/test_lpsolver.cpp
+10
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@@ -141,4 +141,14 @@ TEST(Core_LPSolver, regression_cycling){
#endif
}
TEST(Core_LPSolver, issue_12337)
{
Mat A=(cv::Mat_<double>(3,1)<<3,1,2);
Mat B=(cv::Mat_<double>(3,4)<<1,1,3,30,2,2,5,24,4,1,2,36);
EXPECT_ANY_THROW(Mat1f z_float; cv::solveLP(A, B, z_float));
EXPECT_NO_THROW(Mat1d z_double; cv::solveLP(A, B, z_double));
EXPECT_ANY_THROW(Mat1i z_int; cv::solveLP(A, B, z_int));
//need to update interface: EXPECT_ANY_THROW(Mat1b z_8u; cv::solveLP(A, B, z_8u));
}
}} // namespace
modules/core/test/test_mat.cpp
+59
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@@ -1872,4 +1872,63 @@ TEST(Core_Split, crash_12171)
EXPECT_EQ(2, dst2.ptr<uchar>(1)[1]);
}
struct CustomType // like cv::Keypoint
{
Point2f pt;
float size;
float angle;
float response;
int octave;
int class_id;
};
static void test_CustomType(InputArray src_, OutputArray dst_)
{
Mat src = src_.getMat();
ASSERT_EQ(sizeof(CustomType), src.elemSize());
CV_CheckTypeEQ(src.type(), CV_MAKETYPE(CV_8U, sizeof(CustomType)), "");
CustomType* kpt = NULL;
{
Mat dst = dst_.getMat();
for (size_t i = 0; i < dst.total(); i++)
{
kpt = dst.ptr<CustomType>(0) + i;
kpt->octave = (int)i;
}
}
const int N = (int)src.total();
dst_.create(1, N * 2, rawType<CustomType>());
Mat dst = dst_.getMat();
for (size_t i = N; i < dst.total(); i++)
{
kpt = dst.ptr<CustomType>(0) + i;
kpt->octave = -(int)i;
}
#if 0 // Compilation error
CustomType& kpt = dst.at<CustomType>(0, 5);
#endif
}
TEST(Core_InputArray, support_CustomType)
{
std::vector<CustomType> kp1(5);
std::vector<CustomType> kp2(3);
test_CustomType(rawIn(kp1), rawOut(kp2));
ASSERT_EQ((size_t)10, kp2.size());
for (int i = 0; i < 3; i++)
{
EXPECT_EQ(i, kp2[i].octave);
}
for (int i = 3; i < 5; i++)
{
EXPECT_EQ(0, kp2[i].octave);
}
for (int i = 5; i < 10; i++)
{
EXPECT_EQ(-i, kp2[i].octave);
}
}
}} // namespace