added more assertion on device features to gpu functions and tests
moved TargerArchs and DeviceInfo to core fixed bug in GpuMat::copy with mask (incorrect index in function tab)
This commit is contained in:
Vladislav Vinogradov
@@ -43,6 +43,138 @@
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namespace {
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////////////////////////////////////////////////////////////////////////////////
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// Merge
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PARAM_TEST_CASE(Merge, cv::gpu::DeviceInfo, cv::Size, MatDepth, Channels, UseRoi)
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{
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cv::gpu::DeviceInfo devInfo;
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cv::Size size;
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int depth;
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int channels;
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bool useRoi;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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size = GET_PARAM(1);
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depth = GET_PARAM(2);
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channels = GET_PARAM(3);
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useRoi = GET_PARAM(4);
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cv::gpu::setDevice(devInfo.deviceID());
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}
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};
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TEST_P(Merge, Accuracy)
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{
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std::vector<cv::Mat> src;
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src.reserve(channels);
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for (int i = 0; i < channels; ++i)
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src.push_back(cv::Mat(size, depth, cv::Scalar::all(i)));
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std::vector<cv::gpu::GpuMat> d_src;
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for (int i = 0; i < channels; ++i)
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d_src.push_back(loadMat(src[i], useRoi));
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if (depth == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
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{
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try
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{
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cv::gpu::GpuMat dst;
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cv::gpu::merge(d_src, dst);
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}
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catch (const cv::Exception& e)
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{
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ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
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}
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}
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else
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{
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cv::gpu::GpuMat dst;
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cv::gpu::merge(d_src, dst);
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cv::Mat dst_gold;
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cv::merge(src, dst_gold);
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EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
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}
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}
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INSTANTIATE_TEST_CASE_P(GPU_Core, Merge, testing::Combine(
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ALL_DEVICES,
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DIFFERENT_SIZES,
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ALL_DEPTH,
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testing::Values(1, 2, 3, 4),
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WHOLE_SUBMAT));
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////////////////////////////////////////////////////////////////////////////////
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// Split
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PARAM_TEST_CASE(Split, cv::gpu::DeviceInfo, cv::Size, MatDepth, Channels, UseRoi)
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{
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cv::gpu::DeviceInfo devInfo;
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cv::Size size;
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int depth;
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int channels;
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bool useRoi;
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int type;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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size = GET_PARAM(1);
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depth = GET_PARAM(2);
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channels = GET_PARAM(3);
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useRoi = GET_PARAM(4);
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cv::gpu::setDevice(devInfo.deviceID());
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type = CV_MAKE_TYPE(depth, channels);
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}
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};
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TEST_P(Split, Accuracy)
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{
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cv::Mat src = randomMat(size, type);
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if (depth == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
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{
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try
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{
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std::vector<cv::gpu::GpuMat> dst;
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cv::gpu::split(loadMat(src), dst);
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}
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catch (const cv::Exception& e)
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{
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ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
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}
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}
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else
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{
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std::vector<cv::gpu::GpuMat> dst;
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cv::gpu::split(loadMat(src, useRoi), dst);
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std::vector<cv::Mat> dst_gold;
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cv::split(src, dst_gold);
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ASSERT_EQ(dst_gold.size(), dst.size());
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for (size_t i = 0; i < dst_gold.size(); ++i)
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{
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EXPECT_MAT_NEAR(dst_gold[i], dst[i], 0.0);
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}
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}
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}
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INSTANTIATE_TEST_CASE_P(GPU_Core, Split, testing::Combine(
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ALL_DEVICES,
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DIFFERENT_SIZES,
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ALL_DEPTH,
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testing::Values(1, 2, 3, 4),
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WHOLE_SUBMAT));
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////////////////////////////////////////////////////////////////////////////////
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// Add_Array
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@@ -1974,7 +2106,7 @@ TEST_P(AddWeighted, Accuracy)
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cv::Mat dst_gold;
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cv::addWeighted(src1, alpha, src2, beta, gamma, dst_gold, dst_depth);
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EXPECT_MAT_NEAR(dst_gold, dst, dst_depth < CV_32F ? 1.0 : 1e-12);
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EXPECT_MAT_NEAR(dst_gold, dst, dst_depth < CV_32F ? 1.0 : 1e-3);
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}
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}
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@@ -2487,16 +2619,32 @@ TEST_P(MeanStdDev, Accuracy)
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{
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cv::Mat src = randomMat(size, CV_8UC1);
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cv::Scalar mean;
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cv::Scalar stddev;
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cv::gpu::meanStdDev(loadMat(src, useRoi), mean, stddev);
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if (!supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_13))
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{
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try
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{
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cv::Scalar mean;
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cv::Scalar stddev;
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cv::gpu::meanStdDev(loadMat(src, useRoi), mean, stddev);
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}
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catch (const cv::Exception& e)
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{
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ASSERT_EQ(CV_StsNotImplemented, e.code);
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}
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}
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else
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{
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cv::Scalar mean;
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cv::Scalar stddev;
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cv::gpu::meanStdDev(loadMat(src, useRoi), mean, stddev);
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cv::Scalar mean_gold;
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cv::Scalar stddev_gold;
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cv::meanStdDev(src, mean_gold, stddev_gold);
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cv::Scalar mean_gold;
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cv::Scalar stddev_gold;
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cv::meanStdDev(src, mean_gold, stddev_gold);
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EXPECT_SCALAR_NEAR(mean_gold, mean, 1e-5);
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EXPECT_SCALAR_NEAR(stddev_gold, stddev, 1e-5);
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EXPECT_SCALAR_NEAR(mean_gold, mean, 1e-5);
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EXPECT_SCALAR_NEAR(stddev_gold, stddev, 1e-5);
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}
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}
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INSTANTIATE_TEST_CASE_P(GPU_Core, MeanStdDev, testing::Combine(
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325
modules/gpu/test/test_gpumat.cpp
Normal file
325
modules/gpu/test/test_gpumat.cpp
Normal file
@@ -0,0 +1,325 @@
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other GpuMaterials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or bpied warranties, including, but not limited to, the bpied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
|
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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namespace {
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////////////////////////////////////////////////////////////////////////////////
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// SetTo
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PARAM_TEST_CASE(SetTo, cv::gpu::DeviceInfo, cv::Size, MatType, UseRoi)
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{
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cv::gpu::DeviceInfo devInfo;
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cv::Size size;
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int type;
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bool useRoi;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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size = GET_PARAM(1);
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type = GET_PARAM(2);
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useRoi = GET_PARAM(3);
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cv::gpu::setDevice(devInfo.deviceID());
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}
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};
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TEST_P(SetTo, Zero)
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{
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cv::Scalar zero = cv::Scalar::all(0);
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cv::gpu::GpuMat mat = createMat(size, type, useRoi);
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mat.setTo(zero);
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EXPECT_MAT_NEAR(cv::Mat::zeros(size, type), mat, 0.0);
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}
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TEST_P(SetTo, SameVal)
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{
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cv::Scalar val = cv::Scalar::all(randomDouble(0.0, 255.0));
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if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
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{
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try
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{
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cv::gpu::GpuMat mat = createMat(size, type, useRoi);
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mat.setTo(val);
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}
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catch (const cv::Exception& e)
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{
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ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
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}
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}
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else
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{
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cv::gpu::GpuMat mat = createMat(size, type, useRoi);
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mat.setTo(val);
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EXPECT_MAT_NEAR(cv::Mat(size, type, val), mat, 0.0);
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}
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}
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TEST_P(SetTo, DifferentVal)
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{
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cv::Scalar val = randomScalar(0.0, 255.0);
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if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
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{
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try
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{
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cv::gpu::GpuMat mat = createMat(size, type, useRoi);
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mat.setTo(val);
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}
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catch (const cv::Exception& e)
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{
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ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
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}
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}
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else
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{
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cv::gpu::GpuMat mat = createMat(size, type, useRoi);
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mat.setTo(val);
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EXPECT_MAT_NEAR(cv::Mat(size, type, val), mat, 0.0);
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}
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}
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TEST_P(SetTo, Masked)
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{
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cv::Scalar val = randomScalar(0.0, 255.0);
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cv::Mat mat_gold = randomMat(size, type);
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cv::Mat mask = randomMat(size, CV_8UC1, 0.0, 2.0);
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if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
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{
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try
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{
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cv::gpu::GpuMat mat = createMat(size, type, useRoi);
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mat.setTo(val, loadMat(mask));
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}
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catch (const cv::Exception& e)
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{
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ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
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}
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}
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else
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{
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cv::gpu::GpuMat mat = loadMat(mat_gold, useRoi);
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mat.setTo(val, loadMat(mask, useRoi));
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mat_gold.setTo(val, mask);
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EXPECT_MAT_NEAR(mat_gold, mat, 0.0);
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}
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}
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INSTANTIATE_TEST_CASE_P(GPU_GpuMat, SetTo, testing::Combine(
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ALL_DEVICES,
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DIFFERENT_SIZES,
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ALL_TYPES,
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WHOLE_SUBMAT));
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////////////////////////////////////////////////////////////////////////////////
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// CopyTo
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PARAM_TEST_CASE(CopyTo, cv::gpu::DeviceInfo, cv::Size, MatType, UseRoi)
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{
|
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cv::gpu::DeviceInfo devInfo;
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cv::Size size;
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int type;
|
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bool useRoi;
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|
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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size = GET_PARAM(1);
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type = GET_PARAM(2);
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useRoi = GET_PARAM(3);
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cv::gpu::setDevice(devInfo.deviceID());
|
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}
|
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};
|
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|
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TEST_P(CopyTo, WithOutMask)
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{
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cv::Mat src = randomMat(size, type);
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cv::gpu::GpuMat d_src = loadMat(src, useRoi);
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cv::gpu::GpuMat dst = createMat(size, type, useRoi);
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d_src.copyTo(dst);
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EXPECT_MAT_NEAR(src, dst, 0.0);
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}
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|
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TEST_P(CopyTo, Masked)
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{
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cv::Mat src = randomMat(size, type);
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cv::Mat mask = randomMat(size, CV_8UC1, 0.0, 2.0);
|
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|
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if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
|
||||
{
|
||||
try
|
||||
{
|
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cv::gpu::GpuMat d_src = loadMat(src);
|
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cv::gpu::GpuMat dst;
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d_src.copyTo(dst, loadMat(mask, useRoi));
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}
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catch (const cv::Exception& e)
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{
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ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
|
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}
|
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}
|
||||
else
|
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{
|
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cv::gpu::GpuMat d_src = loadMat(src, useRoi);
|
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cv::gpu::GpuMat dst = loadMat(cv::Mat::zeros(size, type), useRoi);
|
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d_src.copyTo(dst, loadMat(mask, useRoi));
|
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|
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cv::Mat dst_gold = cv::Mat::zeros(size, type);
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src.copyTo(dst_gold, mask);
|
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|
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EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
|
||||
}
|
||||
}
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(GPU_GpuMat, CopyTo, testing::Combine(
|
||||
ALL_DEVICES,
|
||||
DIFFERENT_SIZES,
|
||||
ALL_TYPES,
|
||||
WHOLE_SUBMAT));
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
// ConvertTo
|
||||
|
||||
PARAM_TEST_CASE(ConvertTo, cv::gpu::DeviceInfo, cv::Size, MatDepth, MatDepth, UseRoi)
|
||||
{
|
||||
cv::gpu::DeviceInfo devInfo;
|
||||
cv::Size size;
|
||||
int depth1;
|
||||
int depth2;
|
||||
bool useRoi;
|
||||
|
||||
virtual void SetUp()
|
||||
{
|
||||
devInfo = GET_PARAM(0);
|
||||
size = GET_PARAM(1);
|
||||
depth1 = GET_PARAM(2);
|
||||
depth2 = GET_PARAM(3);
|
||||
useRoi = GET_PARAM(4);
|
||||
|
||||
cv::gpu::setDevice(devInfo.deviceID());
|
||||
}
|
||||
};
|
||||
|
||||
TEST_P(ConvertTo, WithOutScaling)
|
||||
{
|
||||
cv::Mat src = randomMat(size, depth1);
|
||||
|
||||
if ((depth1 == CV_64F || depth2 == CV_64F) && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
|
||||
{
|
||||
try
|
||||
{
|
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cv::gpu::GpuMat d_src = loadMat(src);
|
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cv::gpu::GpuMat dst;
|
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d_src.convertTo(dst, depth2);
|
||||
}
|
||||
catch (const cv::Exception& e)
|
||||
{
|
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ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
cv::gpu::GpuMat d_src = loadMat(src, useRoi);
|
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cv::gpu::GpuMat dst = createMat(size, depth2, useRoi);
|
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d_src.convertTo(dst, depth2);
|
||||
|
||||
cv::Mat dst_gold;
|
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src.convertTo(dst_gold, depth2);
|
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|
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EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
|
||||
}
|
||||
}
|
||||
|
||||
TEST_P(ConvertTo, WithScaling)
|
||||
{
|
||||
cv::Mat src = randomMat(size, depth1);
|
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double a = randomDouble(0.0, 1.0);
|
||||
double b = randomDouble(-10.0, 10.0);
|
||||
|
||||
if ((depth1 == CV_64F || depth2 == CV_64F) && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
|
||||
{
|
||||
try
|
||||
{
|
||||
cv::gpu::GpuMat d_src = loadMat(src);
|
||||
cv::gpu::GpuMat dst;
|
||||
d_src.convertTo(dst, depth2, a, b);
|
||||
}
|
||||
catch (const cv::Exception& e)
|
||||
{
|
||||
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
cv::gpu::GpuMat d_src = loadMat(src, useRoi);
|
||||
cv::gpu::GpuMat dst = createMat(size, depth2, useRoi);
|
||||
d_src.convertTo(dst, depth2, a, b);
|
||||
|
||||
cv::Mat dst_gold;
|
||||
src.convertTo(dst_gold, depth2, a, b);
|
||||
|
||||
EXPECT_MAT_NEAR(dst_gold, dst, depth2 < CV_32F ? 0.0 : 1e-4);
|
||||
}
|
||||
}
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(GPU_GpuMat, ConvertTo, testing::Combine(
|
||||
ALL_DEVICES,
|
||||
DIFFERENT_SIZES,
|
||||
ALL_DEPTH,
|
||||
ALL_DEPTH,
|
||||
WHOLE_SUBMAT));
|
||||
|
||||
} // namespace
|
||||
@@ -1,323 +0,0 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// Intel License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000, Intel Corporation, all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of Intel Corporation may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
|
||||
#ifdef HAVE_CUDA
|
||||
|
||||
using namespace cvtest;
|
||||
using namespace testing;
|
||||
|
||||
//#define DUMP
|
||||
|
||||
struct CV_GpuHogDetectTestRunner : cv::gpu::HOGDescriptor
|
||||
{
|
||||
void run()
|
||||
{
|
||||
cv::Mat img_rgb = readImage("hog/road.png");
|
||||
ASSERT_FALSE(img_rgb.empty());
|
||||
|
||||
#ifdef DUMP
|
||||
f.open((std::string(cvtest::TS::ptr()->get_data_path()) + "hog/expected_output.bin").c_str(), std::ios_base::binary);
|
||||
ASSERT_TRUE(f.is_open());
|
||||
#else
|
||||
f.open((std::string(cvtest::TS::ptr()->get_data_path()) + "hog/expected_output.bin").c_str(), std::ios_base::binary);
|
||||
ASSERT_TRUE(f.is_open());
|
||||
#endif
|
||||
|
||||
// Test on color image
|
||||
cv::Mat img;
|
||||
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
|
||||
test(img);
|
||||
|
||||
// Test on gray image
|
||||
cv::cvtColor(img_rgb, img, CV_BGR2GRAY);
|
||||
test(img);
|
||||
|
||||
f.close();
|
||||
}
|
||||
|
||||
#ifdef DUMP
|
||||
void dump(const cv::Mat& block_hists, const std::vector<cv::Point>& locations)
|
||||
{
|
||||
f.write((char*)&block_hists.rows, sizeof(block_hists.rows));
|
||||
f.write((char*)&block_hists.cols, sizeof(block_hists.cols));
|
||||
for (int i = 0; i < block_hists.rows; ++i)
|
||||
{
|
||||
for (int j = 0; j < block_hists.cols; ++j)
|
||||
{
|
||||
float val = block_hists.at<float>(i, j);
|
||||
f.write((char*)&val, sizeof(val));
|
||||
}
|
||||
}
|
||||
int nlocations = locations.size();
|
||||
f.write((char*)&nlocations, sizeof(nlocations));
|
||||
for (int i = 0; i < locations.size(); ++i)
|
||||
f.write((char*)&locations[i], sizeof(locations[i]));
|
||||
}
|
||||
#else
|
||||
void compare(const cv::Mat& block_hists, const std::vector<cv::Point>& locations)
|
||||
{
|
||||
int rows, cols;
|
||||
int nlocations;
|
||||
|
||||
f.read((char*)&rows, sizeof(rows));
|
||||
f.read((char*)&cols, sizeof(cols));
|
||||
ASSERT_EQ(rows, block_hists.rows);
|
||||
ASSERT_EQ(cols, block_hists.cols);
|
||||
for (int i = 0; i < block_hists.rows; ++i)
|
||||
{
|
||||
for (int j = 0; j < block_hists.cols; ++j)
|
||||
{
|
||||
float val;
|
||||
f.read((char*)&val, sizeof(val));
|
||||
ASSERT_NEAR(val, block_hists.at<float>(i, j), 1e-3);
|
||||
}
|
||||
}
|
||||
f.read((char*)&nlocations, sizeof(nlocations));
|
||||
ASSERT_EQ(nlocations, static_cast<int>(locations.size()));
|
||||
for (int i = 0; i < nlocations; ++i)
|
||||
{
|
||||
cv::Point location;
|
||||
f.read((char*)&location, sizeof(location));
|
||||
ASSERT_EQ(location, locations[i]);
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
void test(const cv::Mat& img)
|
||||
{
|
||||
cv::gpu::GpuMat d_img(img);
|
||||
|
||||
gamma_correction = false;
|
||||
setSVMDetector(cv::gpu::HOGDescriptor::getDefaultPeopleDetector());
|
||||
//cpu detector may be updated soon
|
||||
//hog.setSVMDetector(cv::HOGDescriptor::getDefaultPeopleDetector());
|
||||
|
||||
std::vector<cv::Point> locations;
|
||||
|
||||
// Test detect
|
||||
detect(d_img, locations, 0);
|
||||
|
||||
#ifdef DUMP
|
||||
dump(block_hists, locations);
|
||||
#else
|
||||
compare(cv::Mat(block_hists), locations);
|
||||
#endif
|
||||
|
||||
// Test detect on smaller image
|
||||
cv::Mat img2;
|
||||
cv::resize(img, img2, cv::Size(img.cols / 2, img.rows / 2));
|
||||
detect(cv::gpu::GpuMat(img2), locations, 0);
|
||||
|
||||
#ifdef DUMP
|
||||
dump(block_hists, locations);
|
||||
#else
|
||||
compare(cv::Mat(block_hists), locations);
|
||||
#endif
|
||||
|
||||
// Test detect on greater image
|
||||
cv::resize(img, img2, cv::Size(img.cols * 2, img.rows * 2));
|
||||
detect(cv::gpu::GpuMat(img2), locations, 0);
|
||||
|
||||
#ifdef DUMP
|
||||
dump(block_hists, locations);
|
||||
#else
|
||||
compare(cv::Mat(block_hists), locations);
|
||||
#endif
|
||||
}
|
||||
|
||||
#ifdef DUMP
|
||||
std::ofstream f;
|
||||
#else
|
||||
std::ifstream f;
|
||||
#endif
|
||||
};
|
||||
|
||||
struct Detect : TestWithParam<cv::gpu::DeviceInfo>
|
||||
{
|
||||
cv::gpu::DeviceInfo devInfo;
|
||||
|
||||
virtual void SetUp()
|
||||
{
|
||||
devInfo = GetParam();
|
||||
|
||||
cv::gpu::setDevice(devInfo.deviceID());
|
||||
}
|
||||
};
|
||||
|
||||
TEST_P(Detect, Accuracy)
|
||||
{
|
||||
CV_GpuHogDetectTestRunner runner;
|
||||
runner.run();
|
||||
}
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(HOG, Detect, ALL_DEVICES);
|
||||
|
||||
struct CV_GpuHogGetDescriptorsTestRunner : cv::gpu::HOGDescriptor
|
||||
{
|
||||
CV_GpuHogGetDescriptorsTestRunner(): cv::gpu::HOGDescriptor(cv::Size(64, 128)) {}
|
||||
|
||||
void run()
|
||||
{
|
||||
// Load image (e.g. train data, composed from windows)
|
||||
cv::Mat img_rgb = readImage("hog/train_data.png");
|
||||
ASSERT_FALSE(img_rgb.empty());
|
||||
|
||||
// Convert to C4
|
||||
cv::Mat img;
|
||||
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
|
||||
|
||||
cv::gpu::GpuMat d_img(img);
|
||||
|
||||
// Convert train images into feature vectors (train table)
|
||||
cv::gpu::GpuMat descriptors, descriptors_by_cols;
|
||||
getDescriptors(d_img, win_size, descriptors, DESCR_FORMAT_ROW_BY_ROW);
|
||||
getDescriptors(d_img, win_size, descriptors_by_cols, DESCR_FORMAT_COL_BY_COL);
|
||||
|
||||
// Check size of the result train table
|
||||
wins_per_img_x = 3;
|
||||
wins_per_img_y = 2;
|
||||
blocks_per_win_x = 7;
|
||||
blocks_per_win_y = 15;
|
||||
block_hist_size = 36;
|
||||
cv::Size descr_size_expected = cv::Size(blocks_per_win_x * blocks_per_win_y * block_hist_size,
|
||||
wins_per_img_x * wins_per_img_y);
|
||||
ASSERT_EQ(descr_size_expected, descriptors.size());
|
||||
|
||||
// Check both formats of output descriptors are handled correctly
|
||||
cv::Mat dr(descriptors);
|
||||
cv::Mat dc(descriptors_by_cols);
|
||||
for (int i = 0; i < wins_per_img_x * wins_per_img_y; ++i)
|
||||
{
|
||||
const float* l = dr.rowRange(i, i + 1).ptr<float>();
|
||||
const float* r = dc.rowRange(i, i + 1).ptr<float>();
|
||||
for (int y = 0; y < blocks_per_win_y; ++y)
|
||||
for (int x = 0; x < blocks_per_win_x; ++x)
|
||||
for (int k = 0; k < block_hist_size; ++k)
|
||||
ASSERT_EQ(l[(y * blocks_per_win_x + x) * block_hist_size + k],
|
||||
r[(x * blocks_per_win_y + y) * block_hist_size + k]);
|
||||
}
|
||||
|
||||
/* Now we want to extract the same feature vectors, but from single images. NOTE: results will
|
||||
be defferent, due to border values interpolation. Using of many small images is slower, however we
|
||||
wont't call getDescriptors and will use computeBlockHistograms instead of. computeBlockHistograms
|
||||
works good, it can be checked in the gpu_hog sample */
|
||||
|
||||
img_rgb = readImage("hog/positive1.png");
|
||||
ASSERT_TRUE(!img_rgb.empty());
|
||||
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
|
||||
computeBlockHistograms(cv::gpu::GpuMat(img));
|
||||
// Everything is fine with interpolation for left top subimage
|
||||
ASSERT_EQ(0.0, cv::norm((cv::Mat)block_hists, (cv::Mat)descriptors.rowRange(0, 1)));
|
||||
|
||||
img_rgb = readImage("hog/positive2.png");
|
||||
ASSERT_TRUE(!img_rgb.empty());
|
||||
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
|
||||
computeBlockHistograms(cv::gpu::GpuMat(img));
|
||||
compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(1, 2)));
|
||||
|
||||
img_rgb = readImage("hog/negative1.png");
|
||||
ASSERT_TRUE(!img_rgb.empty());
|
||||
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
|
||||
computeBlockHistograms(cv::gpu::GpuMat(img));
|
||||
compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(2, 3)));
|
||||
|
||||
img_rgb = readImage("hog/negative2.png");
|
||||
ASSERT_TRUE(!img_rgb.empty());
|
||||
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
|
||||
computeBlockHistograms(cv::gpu::GpuMat(img));
|
||||
compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(3, 4)));
|
||||
|
||||
img_rgb = readImage("hog/positive3.png");
|
||||
ASSERT_TRUE(!img_rgb.empty());
|
||||
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
|
||||
computeBlockHistograms(cv::gpu::GpuMat(img));
|
||||
compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(4, 5)));
|
||||
|
||||
img_rgb = readImage("hog/negative3.png");
|
||||
ASSERT_TRUE(!img_rgb.empty());
|
||||
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
|
||||
computeBlockHistograms(cv::gpu::GpuMat(img));
|
||||
compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(5, 6)));
|
||||
}
|
||||
|
||||
// Does not compare border value, as interpolation leads to delta
|
||||
void compare_inner_parts(cv::Mat d1, cv::Mat d2)
|
||||
{
|
||||
for (int i = 1; i < blocks_per_win_y - 1; ++i)
|
||||
for (int j = 1; j < blocks_per_win_x - 1; ++j)
|
||||
for (int k = 0; k < block_hist_size; ++k)
|
||||
{
|
||||
float a = d1.at<float>(0, (i * blocks_per_win_x + j) * block_hist_size);
|
||||
float b = d2.at<float>(0, (i * blocks_per_win_x + j) * block_hist_size);
|
||||
ASSERT_FLOAT_EQ(a, b);
|
||||
}
|
||||
}
|
||||
|
||||
int wins_per_img_x;
|
||||
int wins_per_img_y;
|
||||
int blocks_per_win_x;
|
||||
int blocks_per_win_y;
|
||||
int block_hist_size;
|
||||
};
|
||||
|
||||
struct GetDescriptors : TestWithParam<cv::gpu::DeviceInfo>
|
||||
{
|
||||
cv::gpu::DeviceInfo devInfo;
|
||||
|
||||
virtual void SetUp()
|
||||
{
|
||||
devInfo = GetParam();
|
||||
|
||||
cv::gpu::setDevice(devInfo.deviceID());
|
||||
}
|
||||
};
|
||||
|
||||
TEST_P(GetDescriptors, Accuracy)
|
||||
{
|
||||
CV_GpuHogGetDescriptorsTestRunner runner;
|
||||
runner.run();
|
||||
}
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(HOG, GetDescriptors, ALL_DEVICES);
|
||||
|
||||
#endif // HAVE_CUDA
|
||||
@@ -1,559 +0,0 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other GpuMaterials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or bpied warranties, including, but not limited to, the bpied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
|
||||
#ifdef HAVE_CUDA
|
||||
|
||||
using namespace cvtest;
|
||||
using namespace testing;
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
// merge
|
||||
|
||||
PARAM_TEST_CASE(Merge, cv::gpu::DeviceInfo, MatType, UseRoi)
|
||||
{
|
||||
cv::gpu::DeviceInfo devInfo;
|
||||
int type;
|
||||
bool useRoi;
|
||||
|
||||
cv::Size size;
|
||||
std::vector<cv::Mat> src;
|
||||
|
||||
cv::Mat dst_gold;
|
||||
|
||||
virtual void SetUp()
|
||||
{
|
||||
devInfo = GET_PARAM(0);
|
||||
type = GET_PARAM(1);
|
||||
useRoi = GET_PARAM(2);
|
||||
|
||||
cv::gpu::setDevice(devInfo.deviceID());
|
||||
|
||||
cv::RNG& rng = TS::ptr()->get_rng();
|
||||
|
||||
size = cv::Size(rng.uniform(20, 150), rng.uniform(20, 150));
|
||||
|
||||
int depth = CV_MAT_DEPTH(type);
|
||||
int num_channels = CV_MAT_CN(type);
|
||||
src.reserve(num_channels);
|
||||
for (int i = 0; i < num_channels; ++i)
|
||||
src.push_back(cv::Mat(size, depth, cv::Scalar::all(i)));
|
||||
|
||||
cv::merge(src, dst_gold);
|
||||
}
|
||||
};
|
||||
|
||||
TEST_P(Merge, Accuracy)
|
||||
{
|
||||
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
|
||||
return;
|
||||
|
||||
cv::Mat dst;
|
||||
|
||||
std::vector<cv::gpu::GpuMat> dev_src;
|
||||
cv::gpu::GpuMat dev_dst;
|
||||
|
||||
for (size_t i = 0; i < src.size(); ++i)
|
||||
dev_src.push_back(loadMat(src[i], useRoi));
|
||||
|
||||
cv::gpu::merge(dev_src, dev_dst);
|
||||
|
||||
dev_dst.download(dst);
|
||||
|
||||
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
|
||||
}
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(MatOp, Merge, Combine(
|
||||
ALL_DEVICES,
|
||||
ALL_TYPES,
|
||||
WHOLE_SUBMAT));
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
// split
|
||||
|
||||
PARAM_TEST_CASE(Split, cv::gpu::DeviceInfo, MatType, UseRoi)
|
||||
{
|
||||
cv::gpu::DeviceInfo devInfo;
|
||||
int type;
|
||||
bool useRoi;
|
||||
|
||||
cv::Size size;
|
||||
cv::Mat src;
|
||||
|
||||
std::vector<cv::Mat> dst_gold;
|
||||
|
||||
virtual void SetUp()
|
||||
{
|
||||
devInfo = GET_PARAM(0);
|
||||
type = GET_PARAM(1);
|
||||
useRoi = GET_PARAM(2);
|
||||
|
||||
cv::gpu::setDevice(devInfo.deviceID());
|
||||
|
||||
cv::RNG& rng = TS::ptr()->get_rng();
|
||||
|
||||
size = cv::Size(rng.uniform(20, 150), rng.uniform(20, 150));
|
||||
|
||||
src.create(size, type);
|
||||
src.setTo(cv::Scalar(1.0, 2.0, 3.0, 4.0));
|
||||
cv::split(src, dst_gold);
|
||||
}
|
||||
};
|
||||
|
||||
TEST_P(Split, Accuracy)
|
||||
{
|
||||
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
|
||||
return;
|
||||
|
||||
std::vector<cv::Mat> dst;
|
||||
|
||||
std::vector<cv::gpu::GpuMat> dev_dst;
|
||||
|
||||
cv::gpu::split(loadMat(src, useRoi), dev_dst);
|
||||
|
||||
dst.resize(dev_dst.size());
|
||||
|
||||
for (size_t i = 0; i < dev_dst.size(); ++i)
|
||||
dev_dst[i].download(dst[i]);
|
||||
|
||||
ASSERT_EQ(dst_gold.size(), dst.size());
|
||||
|
||||
for (size_t i = 0; i < dst_gold.size(); ++i)
|
||||
{
|
||||
EXPECT_MAT_NEAR(dst_gold[i], dst[i], 0.0);
|
||||
}
|
||||
}
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(MatOp, Split, Combine(
|
||||
ALL_DEVICES,
|
||||
ALL_TYPES,
|
||||
WHOLE_SUBMAT));
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
// split_merge_consistency
|
||||
|
||||
PARAM_TEST_CASE(SplitMerge, cv::gpu::DeviceInfo, MatType)
|
||||
{
|
||||
cv::gpu::DeviceInfo devInfo;
|
||||
int type;
|
||||
|
||||
cv::Size size;
|
||||
cv::Mat orig;
|
||||
|
||||
virtual void SetUp()
|
||||
{
|
||||
devInfo = GET_PARAM(0);
|
||||
type = GET_PARAM(1);
|
||||
|
||||
cv::gpu::setDevice(devInfo.deviceID());
|
||||
|
||||
cv::RNG& rng = TS::ptr()->get_rng();
|
||||
|
||||
size = cv::Size(rng.uniform(20, 150), rng.uniform(20, 150));
|
||||
|
||||
orig.create(size, type);
|
||||
orig.setTo(cv::Scalar(1.0, 2.0, 3.0, 4.0));
|
||||
}
|
||||
};
|
||||
|
||||
TEST_P(SplitMerge, Consistency)
|
||||
{
|
||||
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
|
||||
return;
|
||||
|
||||
cv::Mat final;
|
||||
|
||||
std::vector<cv::gpu::GpuMat> dev_vec;
|
||||
cv::gpu::GpuMat dev_final;
|
||||
|
||||
cv::gpu::split(loadMat(orig), dev_vec);
|
||||
cv::gpu::merge(dev_vec, dev_final);
|
||||
|
||||
dev_final.download(final);
|
||||
|
||||
EXPECT_MAT_NEAR(orig, final, 0.0);
|
||||
}
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(MatOp, SplitMerge, Combine(
|
||||
ALL_DEVICES,
|
||||
ALL_TYPES));
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
// setTo
|
||||
|
||||
PARAM_TEST_CASE(SetTo, cv::gpu::DeviceInfo, MatType, UseRoi)
|
||||
{
|
||||
cv::gpu::DeviceInfo devInfo;
|
||||
int type;
|
||||
bool useRoi;
|
||||
|
||||
cv::Size size;
|
||||
cv::Mat mat_gold;
|
||||
|
||||
virtual void SetUp()
|
||||
{
|
||||
devInfo = GET_PARAM(0);
|
||||
type = GET_PARAM(1);
|
||||
useRoi = GET_PARAM(2);
|
||||
|
||||
cv::gpu::setDevice(devInfo.deviceID());
|
||||
|
||||
cv::RNG& rng = TS::ptr()->get_rng();
|
||||
|
||||
size = cv::Size(rng.uniform(20, 150), rng.uniform(20, 150));
|
||||
|
||||
mat_gold.create(size, type);
|
||||
}
|
||||
};
|
||||
|
||||
TEST_P(SetTo, Zero)
|
||||
{
|
||||
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
|
||||
return;
|
||||
|
||||
cv::Scalar zero = cv::Scalar::all(0);
|
||||
|
||||
cv::Mat mat;
|
||||
|
||||
cv::gpu::GpuMat dev_mat = loadMat(mat_gold, useRoi);
|
||||
|
||||
mat_gold.setTo(zero);
|
||||
dev_mat.setTo(zero);
|
||||
|
||||
dev_mat.download(mat);
|
||||
|
||||
EXPECT_MAT_NEAR(mat_gold, mat, 0.0);
|
||||
}
|
||||
|
||||
TEST_P(SetTo, SameVal)
|
||||
{
|
||||
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
|
||||
return;
|
||||
|
||||
cv::Scalar s = cv::Scalar::all(1);
|
||||
|
||||
cv::Mat mat;
|
||||
|
||||
cv::gpu::GpuMat dev_mat(mat_gold);
|
||||
|
||||
mat_gold.setTo(s);
|
||||
dev_mat.setTo(s);
|
||||
|
||||
dev_mat.download(mat);
|
||||
|
||||
EXPECT_MAT_NEAR(mat_gold, mat, 0.0);
|
||||
}
|
||||
|
||||
TEST_P(SetTo, DifferentVal)
|
||||
{
|
||||
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
|
||||
return;
|
||||
|
||||
cv::Scalar s = cv::Scalar(1, 2, 3, 4);
|
||||
|
||||
cv::Mat mat;
|
||||
|
||||
cv::gpu::GpuMat dev_mat = loadMat(mat_gold, useRoi);
|
||||
|
||||
mat_gold.setTo(s);
|
||||
dev_mat.setTo(s);
|
||||
|
||||
dev_mat.download(mat);
|
||||
|
||||
EXPECT_MAT_NEAR(mat_gold, mat, 0.0);
|
||||
}
|
||||
|
||||
TEST_P(SetTo, Masked)
|
||||
{
|
||||
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
|
||||
return;
|
||||
|
||||
cv::Scalar s = cv::Scalar(1, 2, 3, 4);
|
||||
|
||||
cv::RNG& rng = TS::ptr()->get_rng();
|
||||
cv::Mat mask = randomMat(rng, mat_gold.size(), CV_8UC1, 0.0, 1.5, false);
|
||||
|
||||
cv::Mat mat;
|
||||
|
||||
cv::gpu::GpuMat dev_mat = loadMat(mat_gold, useRoi);
|
||||
|
||||
mat_gold.setTo(s, mask);
|
||||
dev_mat.setTo(s, loadMat(mask, useRoi));
|
||||
|
||||
dev_mat.download(mat);
|
||||
|
||||
EXPECT_MAT_NEAR(mat_gold, mat, 0.0);
|
||||
}
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(MatOp, SetTo, Combine(
|
||||
ALL_DEVICES,
|
||||
ALL_TYPES,
|
||||
WHOLE_SUBMAT));
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
// copyTo
|
||||
|
||||
PARAM_TEST_CASE(CopyTo, cv::gpu::DeviceInfo, MatType, UseRoi)
|
||||
{
|
||||
cv::gpu::DeviceInfo devInfo;
|
||||
int type;
|
||||
bool useRoi;
|
||||
|
||||
cv::Size size;
|
||||
cv::Mat src;
|
||||
|
||||
virtual void SetUp()
|
||||
{
|
||||
devInfo = GET_PARAM(0);
|
||||
type = GET_PARAM(1);
|
||||
useRoi = GET_PARAM(2);
|
||||
|
||||
cv::gpu::setDevice(devInfo.deviceID());
|
||||
|
||||
cv::RNG& rng = TS::ptr()->get_rng();
|
||||
|
||||
size = cv::Size(rng.uniform(20, 150), rng.uniform(20, 150));
|
||||
|
||||
src = randomMat(rng, size, type, 0.0, 127.0, false);
|
||||
}
|
||||
};
|
||||
|
||||
TEST_P(CopyTo, WithoutMask)
|
||||
{
|
||||
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
|
||||
return;
|
||||
|
||||
cv::Mat dst_gold;
|
||||
src.copyTo(dst_gold);
|
||||
|
||||
cv::Mat dst;
|
||||
|
||||
cv::gpu::GpuMat dev_src = loadMat(src, useRoi);
|
||||
cv::gpu::GpuMat dev_dst = loadMat(src, useRoi);
|
||||
|
||||
dev_src.copyTo(dev_dst);
|
||||
|
||||
dev_dst.download(dst);
|
||||
|
||||
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
|
||||
}
|
||||
|
||||
TEST_P(CopyTo, Masked)
|
||||
{
|
||||
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
|
||||
return;
|
||||
|
||||
cv::RNG& rng = TS::ptr()->get_rng();
|
||||
|
||||
cv::Mat mask = randomMat(rng, src.size(), CV_8UC1, 0.0, 2.0, false);
|
||||
|
||||
cv::Mat zeroMat(src.size(), src.type(), cv::Scalar::all(0));
|
||||
|
||||
cv::Mat dst_gold = zeroMat.clone();
|
||||
src.copyTo(dst_gold, mask);
|
||||
|
||||
cv::Mat dst;
|
||||
|
||||
cv::gpu::GpuMat dev_src = loadMat(src, useRoi);
|
||||
cv::gpu::GpuMat dev_dst = loadMat(zeroMat, useRoi);
|
||||
|
||||
dev_src.copyTo(dev_dst, loadMat(mask, useRoi));
|
||||
|
||||
dev_dst.download(dst);
|
||||
|
||||
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
|
||||
}
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(MatOp, CopyTo, Combine(
|
||||
ALL_DEVICES,
|
||||
ALL_TYPES,
|
||||
WHOLE_SUBMAT));
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
// convertTo
|
||||
|
||||
PARAM_TEST_CASE(ConvertTo, cv::gpu::DeviceInfo, MatType, MatType, UseRoi)
|
||||
{
|
||||
cv::gpu::DeviceInfo devInfo;
|
||||
int depth1;
|
||||
int depth2;
|
||||
bool useRoi;
|
||||
|
||||
cv::Size size;
|
||||
cv::Mat src;
|
||||
|
||||
virtual void SetUp()
|
||||
{
|
||||
devInfo = GET_PARAM(0);
|
||||
depth1 = GET_PARAM(1);
|
||||
depth2 = GET_PARAM(2);
|
||||
useRoi = GET_PARAM(3);
|
||||
|
||||
cv::gpu::setDevice(devInfo.deviceID());
|
||||
|
||||
cv::RNG& rng = TS::ptr()->get_rng();
|
||||
|
||||
size = cv::Size(rng.uniform(20, 150), rng.uniform(20, 150));
|
||||
|
||||
src = randomMat(rng, size, depth1, 0.0, 127.0, false);
|
||||
}
|
||||
};
|
||||
|
||||
TEST_P(ConvertTo, WithoutScaling)
|
||||
{
|
||||
if ((depth1 == CV_64F || depth2 == CV_64F) && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
|
||||
return;
|
||||
|
||||
cv::Mat dst_gold;
|
||||
src.convertTo(dst_gold, depth2);
|
||||
|
||||
cv::Mat dst;
|
||||
|
||||
cv::gpu::GpuMat dev_src = loadMat(src, useRoi);
|
||||
cv::gpu::GpuMat dev_dst;
|
||||
|
||||
dev_src.convertTo(dev_dst, depth2);
|
||||
|
||||
dev_dst.download(dst);
|
||||
|
||||
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
|
||||
}
|
||||
|
||||
TEST_P(ConvertTo, WithScaling)
|
||||
{
|
||||
if ((depth1 == CV_64F || depth2 == CV_64F) && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
|
||||
return;
|
||||
|
||||
cv::RNG& rng = TS::ptr()->get_rng();
|
||||
|
||||
const double a = rng.uniform(0.0, 1.0);
|
||||
const double b = rng.uniform(-10.0, 10.0);
|
||||
|
||||
cv::Mat dst_gold;
|
||||
src.convertTo(dst_gold, depth2, a, b);
|
||||
|
||||
cv::Mat dst;
|
||||
|
||||
cv::gpu::GpuMat dev_src = loadMat(src, useRoi);
|
||||
|
||||
cv::gpu::GpuMat dev_dst;
|
||||
|
||||
dev_src.convertTo(dev_dst, depth2, a, b);
|
||||
|
||||
dev_dst.download(dst);
|
||||
|
||||
const double eps = depth2 < CV_32F ? 1 : 1e-4;
|
||||
|
||||
EXPECT_MAT_NEAR(dst_gold, dst, eps);
|
||||
}
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(MatOp, ConvertTo, Combine(
|
||||
ALL_DEVICES,
|
||||
TYPES(CV_8U, CV_64F, 1, 1),
|
||||
TYPES(CV_8U, CV_64F, 1, 1),
|
||||
WHOLE_SUBMAT));
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
// async
|
||||
|
||||
struct Async : TestWithParam<cv::gpu::DeviceInfo>
|
||||
{
|
||||
cv::gpu::DeviceInfo devInfo;
|
||||
|
||||
cv::gpu::CudaMem src;
|
||||
|
||||
cv::Mat dst_gold0;
|
||||
cv::Mat dst_gold1;
|
||||
|
||||
virtual void SetUp()
|
||||
{
|
||||
devInfo = GetParam();
|
||||
|
||||
cv::gpu::setDevice(devInfo.deviceID());
|
||||
|
||||
cv::RNG& rng = TS::ptr()->get_rng();
|
||||
|
||||
int rows = rng.uniform(100, 200);
|
||||
int cols = rng.uniform(100, 200);
|
||||
|
||||
src = cv::gpu::CudaMem(cv::Mat::zeros(rows, cols, CV_8UC1));
|
||||
|
||||
dst_gold0 = cv::Mat(rows, cols, CV_8UC1, cv::Scalar::all(255));
|
||||
dst_gold1 = cv::Mat(rows, cols, CV_8UC1, cv::Scalar::all(128));
|
||||
}
|
||||
};
|
||||
|
||||
TEST_P(Async, Accuracy)
|
||||
{
|
||||
cv::Mat dst0, dst1;
|
||||
|
||||
cv::gpu::CudaMem cpudst0;
|
||||
cv::gpu::CudaMem cpudst1;
|
||||
|
||||
cv::gpu::GpuMat gpusrc;
|
||||
cv::gpu::GpuMat gpudst0;
|
||||
cv::gpu::GpuMat gpudst1(src.rows, src.cols, CV_8UC1);
|
||||
|
||||
cv::gpu::Stream stream0;
|
||||
cv::gpu::Stream stream1;
|
||||
|
||||
stream0.enqueueUpload(src, gpusrc);
|
||||
cv::gpu::bitwise_not(gpusrc, gpudst0, cv::gpu::GpuMat(), stream0);
|
||||
stream0.enqueueDownload(gpudst0, cpudst0);
|
||||
|
||||
stream1.enqueueMemSet(gpudst1, cv::Scalar::all(128));
|
||||
stream1.enqueueDownload(gpudst1, cpudst1);
|
||||
|
||||
stream0.waitForCompletion();
|
||||
stream1.waitForCompletion();
|
||||
|
||||
dst0 = cpudst0.createMatHeader();
|
||||
dst1 = cpudst1.createMatHeader();
|
||||
|
||||
EXPECT_MAT_NEAR(dst_gold0, dst0, 0.0);
|
||||
EXPECT_MAT_NEAR(dst_gold1, dst1, 0.0);
|
||||
}
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(MatOp, Async, ALL_DEVICES);
|
||||
|
||||
#endif // HAVE_CUDA
|
||||
287
modules/gpu/test/test_objdetect.cpp
Normal file
287
modules/gpu/test/test_objdetect.cpp
Normal file
@@ -0,0 +1,287 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// Intel License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000, Intel Corporation, all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of Intel Corporation may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
|
||||
namespace {
|
||||
|
||||
//#define DUMP
|
||||
|
||||
struct HOG : testing::TestWithParam<cv::gpu::DeviceInfo>, cv::gpu::HOGDescriptor
|
||||
{
|
||||
cv::gpu::DeviceInfo devInfo;
|
||||
|
||||
#ifdef DUMP
|
||||
std::ofstream f;
|
||||
#else
|
||||
std::ifstream f;
|
||||
#endif
|
||||
|
||||
int wins_per_img_x;
|
||||
int wins_per_img_y;
|
||||
int blocks_per_win_x;
|
||||
int blocks_per_win_y;
|
||||
int block_hist_size;
|
||||
|
||||
virtual void SetUp()
|
||||
{
|
||||
devInfo = GetParam();
|
||||
|
||||
cv::gpu::setDevice(devInfo.deviceID());
|
||||
}
|
||||
|
||||
#ifdef DUMP
|
||||
void dump(const cv::Mat& block_hists, const std::vector<cv::Point>& locations)
|
||||
{
|
||||
f.write((char*)&block_hists.rows, sizeof(block_hists.rows));
|
||||
f.write((char*)&block_hists.cols, sizeof(block_hists.cols));
|
||||
|
||||
for (int i = 0; i < block_hists.rows; ++i)
|
||||
{
|
||||
for (int j = 0; j < block_hists.cols; ++j)
|
||||
{
|
||||
float val = block_hists.at<float>(i, j);
|
||||
f.write((char*)&val, sizeof(val));
|
||||
}
|
||||
}
|
||||
|
||||
int nlocations = locations.size();
|
||||
f.write((char*)&nlocations, sizeof(nlocations));
|
||||
|
||||
for (int i = 0; i < locations.size(); ++i)
|
||||
f.write((char*)&locations[i], sizeof(locations[i]));
|
||||
}
|
||||
#else
|
||||
void compare(const cv::Mat& block_hists, const std::vector<cv::Point>& locations)
|
||||
{
|
||||
int rows, cols;
|
||||
f.read((char*)&rows, sizeof(rows));
|
||||
f.read((char*)&cols, sizeof(cols));
|
||||
ASSERT_EQ(rows, block_hists.rows);
|
||||
ASSERT_EQ(cols, block_hists.cols);
|
||||
|
||||
for (int i = 0; i < block_hists.rows; ++i)
|
||||
{
|
||||
for (int j = 0; j < block_hists.cols; ++j)
|
||||
{
|
||||
float val;
|
||||
f.read((char*)&val, sizeof(val));
|
||||
ASSERT_NEAR(val, block_hists.at<float>(i, j), 1e-3);
|
||||
}
|
||||
}
|
||||
|
||||
int nlocations;
|
||||
f.read((char*)&nlocations, sizeof(nlocations));
|
||||
ASSERT_EQ(nlocations, static_cast<int>(locations.size()));
|
||||
|
||||
for (int i = 0; i < nlocations; ++i)
|
||||
{
|
||||
cv::Point location;
|
||||
f.read((char*)&location, sizeof(location));
|
||||
ASSERT_EQ(location, locations[i]);
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
void testDetect(const cv::Mat& img)
|
||||
{
|
||||
gamma_correction = false;
|
||||
setSVMDetector(cv::gpu::HOGDescriptor::getDefaultPeopleDetector());
|
||||
|
||||
std::vector<cv::Point> locations;
|
||||
|
||||
// Test detect
|
||||
detect(loadMat(img), locations, 0);
|
||||
|
||||
#ifdef DUMP
|
||||
dump(cv::Mat(block_hists), locations);
|
||||
#else
|
||||
compare(cv::Mat(block_hists), locations);
|
||||
#endif
|
||||
|
||||
// Test detect on smaller image
|
||||
cv::Mat img2;
|
||||
cv::resize(img, img2, cv::Size(img.cols / 2, img.rows / 2));
|
||||
detect(loadMat(img2), locations, 0);
|
||||
|
||||
#ifdef DUMP
|
||||
dump(cv::Mat(block_hists), locations);
|
||||
#else
|
||||
compare(cv::Mat(block_hists), locations);
|
||||
#endif
|
||||
|
||||
// Test detect on greater image
|
||||
cv::resize(img, img2, cv::Size(img.cols * 2, img.rows * 2));
|
||||
detect(loadMat(img2), locations, 0);
|
||||
|
||||
#ifdef DUMP
|
||||
dump(cv::Mat(block_hists), locations);
|
||||
#else
|
||||
compare(cv::Mat(block_hists), locations);
|
||||
#endif
|
||||
}
|
||||
|
||||
// Does not compare border value, as interpolation leads to delta
|
||||
void compare_inner_parts(cv::Mat d1, cv::Mat d2)
|
||||
{
|
||||
for (int i = 1; i < blocks_per_win_y - 1; ++i)
|
||||
for (int j = 1; j < blocks_per_win_x - 1; ++j)
|
||||
for (int k = 0; k < block_hist_size; ++k)
|
||||
{
|
||||
float a = d1.at<float>(0, (i * blocks_per_win_x + j) * block_hist_size);
|
||||
float b = d2.at<float>(0, (i * blocks_per_win_x + j) * block_hist_size);
|
||||
ASSERT_FLOAT_EQ(a, b);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
TEST_P(HOG, Detect)
|
||||
{
|
||||
cv::Mat img_rgb = readImage("hog/road.png");
|
||||
ASSERT_FALSE(img_rgb.empty());
|
||||
|
||||
#ifdef DUMP
|
||||
f.open((std::string(cvtest::TS::ptr()->get_data_path()) + "hog/expected_output.bin").c_str(), std::ios_base::binary);
|
||||
ASSERT_TRUE(f.is_open());
|
||||
#else
|
||||
f.open((std::string(cvtest::TS::ptr()->get_data_path()) + "hog/expected_output.bin").c_str(), std::ios_base::binary);
|
||||
ASSERT_TRUE(f.is_open());
|
||||
#endif
|
||||
|
||||
// Test on color image
|
||||
cv::Mat img;
|
||||
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
|
||||
testDetect(img);
|
||||
|
||||
// Test on gray image
|
||||
cv::cvtColor(img_rgb, img, CV_BGR2GRAY);
|
||||
testDetect(img);
|
||||
|
||||
f.close();
|
||||
}
|
||||
|
||||
TEST_P(HOG, GetDescriptors)
|
||||
{
|
||||
// Load image (e.g. train data, composed from windows)
|
||||
cv::Mat img_rgb = readImage("hog/train_data.png");
|
||||
ASSERT_FALSE(img_rgb.empty());
|
||||
|
||||
// Convert to C4
|
||||
cv::Mat img;
|
||||
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
|
||||
|
||||
cv::gpu::GpuMat d_img(img);
|
||||
|
||||
// Convert train images into feature vectors (train table)
|
||||
cv::gpu::GpuMat descriptors, descriptors_by_cols;
|
||||
getDescriptors(d_img, win_size, descriptors, DESCR_FORMAT_ROW_BY_ROW);
|
||||
getDescriptors(d_img, win_size, descriptors_by_cols, DESCR_FORMAT_COL_BY_COL);
|
||||
|
||||
// Check size of the result train table
|
||||
wins_per_img_x = 3;
|
||||
wins_per_img_y = 2;
|
||||
blocks_per_win_x = 7;
|
||||
blocks_per_win_y = 15;
|
||||
block_hist_size = 36;
|
||||
cv::Size descr_size_expected = cv::Size(blocks_per_win_x * blocks_per_win_y * block_hist_size,
|
||||
wins_per_img_x * wins_per_img_y);
|
||||
ASSERT_EQ(descr_size_expected, descriptors.size());
|
||||
|
||||
// Check both formats of output descriptors are handled correctly
|
||||
cv::Mat dr(descriptors);
|
||||
cv::Mat dc(descriptors_by_cols);
|
||||
for (int i = 0; i < wins_per_img_x * wins_per_img_y; ++i)
|
||||
{
|
||||
const float* l = dr.rowRange(i, i + 1).ptr<float>();
|
||||
const float* r = dc.rowRange(i, i + 1).ptr<float>();
|
||||
for (int y = 0; y < blocks_per_win_y; ++y)
|
||||
for (int x = 0; x < blocks_per_win_x; ++x)
|
||||
for (int k = 0; k < block_hist_size; ++k)
|
||||
ASSERT_EQ(l[(y * blocks_per_win_x + x) * block_hist_size + k],
|
||||
r[(x * blocks_per_win_y + y) * block_hist_size + k]);
|
||||
}
|
||||
|
||||
/* Now we want to extract the same feature vectors, but from single images. NOTE: results will
|
||||
be defferent, due to border values interpolation. Using of many small images is slower, however we
|
||||
wont't call getDescriptors and will use computeBlockHistograms instead of. computeBlockHistograms
|
||||
works good, it can be checked in the gpu_hog sample */
|
||||
|
||||
img_rgb = readImage("hog/positive1.png");
|
||||
ASSERT_TRUE(!img_rgb.empty());
|
||||
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
|
||||
computeBlockHistograms(cv::gpu::GpuMat(img));
|
||||
// Everything is fine with interpolation for left top subimage
|
||||
ASSERT_EQ(0.0, cv::norm((cv::Mat)block_hists, (cv::Mat)descriptors.rowRange(0, 1)));
|
||||
|
||||
img_rgb = readImage("hog/positive2.png");
|
||||
ASSERT_TRUE(!img_rgb.empty());
|
||||
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
|
||||
computeBlockHistograms(cv::gpu::GpuMat(img));
|
||||
compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(1, 2)));
|
||||
|
||||
img_rgb = readImage("hog/negative1.png");
|
||||
ASSERT_TRUE(!img_rgb.empty());
|
||||
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
|
||||
computeBlockHistograms(cv::gpu::GpuMat(img));
|
||||
compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(2, 3)));
|
||||
|
||||
img_rgb = readImage("hog/negative2.png");
|
||||
ASSERT_TRUE(!img_rgb.empty());
|
||||
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
|
||||
computeBlockHistograms(cv::gpu::GpuMat(img));
|
||||
compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(3, 4)));
|
||||
|
||||
img_rgb = readImage("hog/positive3.png");
|
||||
ASSERT_TRUE(!img_rgb.empty());
|
||||
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
|
||||
computeBlockHistograms(cv::gpu::GpuMat(img));
|
||||
compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(4, 5)));
|
||||
|
||||
img_rgb = readImage("hog/negative3.png");
|
||||
ASSERT_TRUE(!img_rgb.empty());
|
||||
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
|
||||
computeBlockHistograms(cv::gpu::GpuMat(img));
|
||||
compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(5, 6)));
|
||||
}
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(GPU_ObjDetect, HOG, ALL_DEVICES);
|
||||
|
||||
} // namespace
|
||||
Reference in New Issue
Block a user