375 lines
10 KiB
C++
375 lines
10 KiB
C++
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// 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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/**
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* @file test_hdf5.cpp
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* @author Fangjun Kuang <csukuangfj dot at gmail dot com>
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* @date December 2017
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*/
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#include "test_precomp.hpp"
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namespace opencv_test { namespace {
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struct HDF5_Test : public testing::Test
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{
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virtual void SetUp()
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{
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m_filename = "test.h5";
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// 0 1 2
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// 3 4 5
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m_single_channel.create(2, 3, CV_32F);
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for (size_t i = 0; i < m_single_channel.total(); i++)
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{
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((float*)m_single_channel.data)[i] = i;
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}
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// 0 1 2 3 4 5
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// 6 7 8 9 10 11
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m_two_channels.create(2, 3, CV_32SC2);
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for (size_t i = 0; i < m_two_channels.total()*m_two_channels.channels(); i++)
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{
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((int*)m_two_channels.data)[i] = (int)i;
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}
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}
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//! Remove the hdf5 file
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void reset()
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{
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remove(m_filename.c_str());
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}
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String m_filename; //!< filename for testing
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Ptr<hdf::HDF5> m_hdf_io; //!< HDF5 file pointer
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Mat m_single_channel; //!< single channel matrix for test
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Mat m_two_channels; //!< two-channel matrix for test
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};
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TEST_F(HDF5_Test, create_a_single_group)
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{
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reset();
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String group_name = "parent";
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m_hdf_io = hdf::open(m_filename);
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m_hdf_io->grcreate(group_name);
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EXPECT_EQ(m_hdf_io->hlexists(group_name), true);
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EXPECT_EQ(m_hdf_io->hlexists("child"), false);
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// It should fail since it creates a group with an existing name
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EXPECT_ANY_THROW(m_hdf_io->grcreate(group_name));
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m_hdf_io->close();
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}
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TEST_F(HDF5_Test, create_a_child_group)
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{
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reset();
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String parent = "parent";
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String child = parent + "/child";
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m_hdf_io = hdf::open(m_filename);
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m_hdf_io->grcreate(parent);
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m_hdf_io->grcreate(child);
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EXPECT_EQ(m_hdf_io->hlexists(parent), true);
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EXPECT_EQ(m_hdf_io->hlexists(child), true);
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m_hdf_io->close();
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}
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TEST_F(HDF5_Test, create_dataset)
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{
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reset();
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String dataset_single_channel = "/single";
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String dataset_two_channels = "/dual";
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m_hdf_io = hdf::open(m_filename);
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m_hdf_io->dscreate(m_single_channel.rows,
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m_single_channel.cols,
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m_single_channel.type(),
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dataset_single_channel);
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m_hdf_io->dscreate(m_two_channels.rows,
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m_two_channels.cols,
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m_two_channels.type(),
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dataset_two_channels);
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EXPECT_EQ(m_hdf_io->hlexists(dataset_single_channel), true);
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EXPECT_EQ(m_hdf_io->hlexists(dataset_two_channels), true);
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std::vector<int> dims;
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dims = m_hdf_io->dsgetsize(dataset_single_channel, hdf::HDF5::H5_GETDIMS);
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EXPECT_EQ(dims.size(), (size_t)2);
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EXPECT_EQ(dims[0], m_single_channel.rows);
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EXPECT_EQ(dims[1], m_single_channel.cols);
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dims = m_hdf_io->dsgetsize(dataset_two_channels, hdf::HDF5::H5_GETDIMS);
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EXPECT_EQ(dims.size(), (size_t)2);
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EXPECT_EQ(dims[0], m_two_channels.rows);
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EXPECT_EQ(dims[1], m_two_channels.cols);
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int type;
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type = m_hdf_io->dsgettype(dataset_single_channel);
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EXPECT_EQ(type, m_single_channel.type());
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type = m_hdf_io->dsgettype(dataset_two_channels);
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EXPECT_EQ(type, m_two_channels.type());
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m_hdf_io->close();
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}
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TEST_F(HDF5_Test, write_read_dataset_1)
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{
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reset();
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String dataset_single_channel = "/single";
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String dataset_two_channels = "/dual";
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m_hdf_io = hdf::open(m_filename);
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// since the dataset is under the root group, it is created by dswrite() automatically.
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m_hdf_io->dswrite(m_single_channel, dataset_single_channel);
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m_hdf_io->dswrite(m_two_channels, dataset_two_channels);
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EXPECT_EQ(m_hdf_io->hlexists(dataset_single_channel), true);
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EXPECT_EQ(m_hdf_io->hlexists(dataset_two_channels), true);
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// read single channel matrix
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Mat single;
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m_hdf_io->dsread(single, dataset_single_channel);
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EXPECT_EQ(single.type(), m_single_channel.type());
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EXPECT_EQ(single.size(), m_single_channel.size());
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EXPECT_LE(cvtest::norm(single, m_single_channel, NORM_L2), 1e-10);
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// read dual channel matrix
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Mat dual;
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m_hdf_io->dsread(dual, dataset_two_channels);
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EXPECT_EQ(dual.type(), m_two_channels.type());
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EXPECT_EQ(dual.size(), m_two_channels.size());
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EXPECT_LE(cvtest::norm(dual, m_two_channels, NORM_L2), 1e-10);
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m_hdf_io->close();
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}
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TEST_F(HDF5_Test, write_read_dataset_2)
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{
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reset();
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// create the dataset manually if it is not inside
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// the root group
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String parent = "/parent";
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String dataset_single_channel = parent + "/single";
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String dataset_two_channels = parent + "/dual";
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m_hdf_io = hdf::open(m_filename);
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m_hdf_io->grcreate(parent);
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EXPECT_EQ(m_hdf_io->hlexists(parent), true);
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m_hdf_io->dscreate(m_single_channel.rows,
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m_single_channel.cols,
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m_single_channel.type(),
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dataset_single_channel);
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m_hdf_io->dscreate(m_two_channels.rows,
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m_two_channels.cols,
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m_two_channels.type(),
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dataset_two_channels);
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EXPECT_EQ(m_hdf_io->hlexists(dataset_single_channel), true);
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EXPECT_EQ(m_hdf_io->hlexists(dataset_two_channels), true);
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m_hdf_io->dswrite(m_single_channel, dataset_single_channel);
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m_hdf_io->dswrite(m_two_channels, dataset_two_channels);
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EXPECT_EQ(m_hdf_io->hlexists(dataset_single_channel), true);
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EXPECT_EQ(m_hdf_io->hlexists(dataset_two_channels), true);
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// read single channel matrix
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Mat single;
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m_hdf_io->dsread(single, dataset_single_channel);
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EXPECT_EQ(single.type(), m_single_channel.type());
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EXPECT_EQ(single.size(), m_single_channel.size());
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EXPECT_LE(cvtest::norm(single, m_single_channel, NORM_L2), 1e-10);
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// read dual channel matrix
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Mat dual;
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m_hdf_io->dsread(dual, dataset_two_channels);
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EXPECT_EQ(dual.type(), m_two_channels.type());
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EXPECT_EQ(dual.size(), m_two_channels.size());
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EXPECT_LE(cvtest::norm(dual, m_two_channels, NORM_L2), 1e-10);
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m_hdf_io->close();
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}
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TEST_F(HDF5_Test, test_attribute)
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{
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reset();
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String attr_name = "test attribute name";
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int attr_value = 0x12345678;
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m_hdf_io = hdf::open(m_filename);
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EXPECT_EQ(m_hdf_io->atexists(attr_name), false);
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m_hdf_io->atwrite(attr_value, attr_name);
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EXPECT_ANY_THROW(m_hdf_io->atwrite(attr_value, attr_name)); // error! it already exists
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EXPECT_EQ(m_hdf_io->atexists(attr_name), true);
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int expected_attr_value;
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m_hdf_io->atread(&expected_attr_value, attr_name);
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EXPECT_EQ(attr_value, expected_attr_value);
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m_hdf_io->atdelete(attr_name);
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EXPECT_ANY_THROW(m_hdf_io->atdelete(attr_name)); // error! Delete non-existed attribute
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EXPECT_EQ(m_hdf_io->atexists(attr_name), false);
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m_hdf_io->close();
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}
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TEST_F(HDF5_Test, test_attribute_int)
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{
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reset();
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String attr_name = "test int";
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int attr_value = 0x12345678;
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m_hdf_io = hdf::open(m_filename);
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m_hdf_io->atwrite(attr_value, attr_name);
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int expected_attr_value;
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m_hdf_io->atread(&expected_attr_value, attr_name);
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EXPECT_EQ(attr_value, expected_attr_value);
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m_hdf_io->close();
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}
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TEST_F(HDF5_Test, test_attribute_double)
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{
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reset();
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String attr_name = "test double";
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double attr_value = 123.456789;
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m_hdf_io = hdf::open(m_filename);
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m_hdf_io->atwrite(attr_value, attr_name);
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double expected_attr_value;
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m_hdf_io->atread(&expected_attr_value, attr_name);
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EXPECT_NEAR(attr_value, expected_attr_value, 1e-9);
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m_hdf_io->close();
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}
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TEST_F(HDF5_Test, test_attribute_String)
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{
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reset();
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String attr_name = "test-String";
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String attr_value = "----_______----Hello HDF5----_______----\n";
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m_hdf_io = hdf::open(m_filename);
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m_hdf_io->atwrite(attr_value, attr_name);
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String got_attr_value;
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m_hdf_io->atread(&got_attr_value, attr_name);
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EXPECT_EQ(attr_value, got_attr_value);
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m_hdf_io->close();
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}
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TEST_F(HDF5_Test, test_attribute_String_empty)
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{
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reset();
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String attr_name = "test-empty-string";
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String attr_value;
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m_hdf_io = hdf::open(m_filename);
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m_hdf_io->atwrite(attr_value, attr_name);
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String got_attr_value;
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m_hdf_io->atread(&got_attr_value, attr_name);
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EXPECT_EQ(attr_value, got_attr_value);
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m_hdf_io->close();
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}
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TEST_F(HDF5_Test, test_attribute_InutArray_OutputArray_2d)
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{
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reset();
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String attr_name = "test-InputArray-OutputArray-2d";
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cv::Mat attr_value;
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std::vector<int> depth_vec;
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depth_vec.push_back(CV_8U); depth_vec.push_back(CV_8S);
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depth_vec.push_back(CV_16U); depth_vec.push_back(CV_16S);
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depth_vec.push_back(CV_32S); depth_vec.push_back(CV_32F);
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depth_vec.push_back(CV_64F);
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std::vector<int> channel_vec;
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channel_vec.push_back(1); channel_vec.push_back(2);
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channel_vec.push_back(3); channel_vec.push_back(4);
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channel_vec.push_back(5); channel_vec.push_back(6);
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channel_vec.push_back(7); channel_vec.push_back(8);
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channel_vec.push_back(9); channel_vec.push_back(10);
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std::vector<std::vector<int> > dim_vec;
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std::vector<int> dim_2d;
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dim_2d.push_back(2); dim_2d.push_back(3);
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dim_vec.push_back(dim_2d);
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std::vector<int> dim_3d;
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dim_3d.push_back(2);
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dim_3d.push_back(3);
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dim_3d.push_back(4);
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dim_vec.push_back(dim_3d);
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std::vector<int> dim_4d;
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dim_4d.push_back(2); dim_4d.push_back(3);
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dim_4d.push_back(4); dim_4d.push_back(5);
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dim_vec.push_back(dim_4d);
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Mat expected_attr_value;
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m_hdf_io = hdf::open(m_filename);
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for (size_t i = 0; i < depth_vec.size(); i++)
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for (size_t j = 0; j < channel_vec.size(); j++)
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for (size_t k = 0; k < dim_vec.size(); k++)
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{
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if (m_hdf_io->atexists(attr_name))
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m_hdf_io->atdelete(attr_name);
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attr_value.create(dim_vec[k], CV_MAKETYPE(depth_vec[i], channel_vec[j]));
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randu(attr_value, 0, 255);
|
||
|
|
|
||
|
|
m_hdf_io->atwrite(attr_value, attr_name);
|
||
|
|
m_hdf_io->atread(expected_attr_value, attr_name);
|
||
|
|
|
||
|
|
double diff = cvtest::norm(attr_value, expected_attr_value, NORM_L2);
|
||
|
|
EXPECT_LE(diff, 1e-6);
|
||
|
|
|
||
|
|
EXPECT_EQ(attr_value.size, expected_attr_value.size);
|
||
|
|
EXPECT_EQ(attr_value.type(), expected_attr_value.type());
|
||
|
|
}
|
||
|
|
|
||
|
|
m_hdf_io->close();
|
||
|
|
}
|
||
|
|
|
||
|
|
}} // namespace
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