mirror of https://github.com/opencv/opencv.git
Vladislav Vinogradov
12 years ago
parent
0773ab4d07
commit
b7e6b5af1b
34 changed files with 2617 additions and 2500 deletions
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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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// Intel License Agreement
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// For Open Source Computer Vision Library
|
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//
|
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// Copyright (C) 2000, Intel Corporation, 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 materials provided with the distribution.
|
||||
//
|
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// * The name of Intel Corporation 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 implied warranties, including, but not limited to, the implied
|
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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
|
||||
// (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 "test_precomp.hpp" |
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#ifdef HAVE_CUDA |
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//////////////////////////////////////////////////////
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// FGDStatModel
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namespace cv |
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{ |
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template<> void Ptr<CvBGStatModel>::delete_obj() |
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{ |
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cvReleaseBGStatModel(&obj); |
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} |
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} |
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PARAM_TEST_CASE(FGDStatModel, cv::gpu::DeviceInfo, std::string, Channels) |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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std::string inputFile; |
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int out_cn; |
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virtual void SetUp() |
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{ |
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devInfo = GET_PARAM(0); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + GET_PARAM(1); |
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out_cn = GET_PARAM(2); |
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} |
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}; |
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GPU_TEST_P(FGDStatModel, Update) |
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{ |
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cv::VideoCapture cap(inputFile); |
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ASSERT_TRUE(cap.isOpened()); |
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cv::Mat frame; |
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cap >> frame; |
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ASSERT_FALSE(frame.empty()); |
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IplImage ipl_frame = frame; |
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cv::Ptr<CvBGStatModel> model(cvCreateFGDStatModel(&ipl_frame)); |
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cv::gpu::GpuMat d_frame(frame); |
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cv::gpu::FGDStatModel d_model(out_cn); |
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d_model.create(d_frame); |
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cv::Mat h_background; |
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cv::Mat h_foreground; |
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cv::Mat h_background3; |
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cv::Mat backgroundDiff; |
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cv::Mat foregroundDiff; |
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for (int i = 0; i < 5; ++i) |
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{ |
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cap >> frame; |
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ASSERT_FALSE(frame.empty()); |
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ipl_frame = frame; |
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int gold_count = cvUpdateBGStatModel(&ipl_frame, model); |
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d_frame.upload(frame); |
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int count = d_model.update(d_frame); |
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ASSERT_EQ(gold_count, count); |
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cv::Mat gold_background(model->background); |
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cv::Mat gold_foreground(model->foreground); |
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if (out_cn == 3) |
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d_model.background.download(h_background3); |
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else |
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{ |
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d_model.background.download(h_background); |
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cv::cvtColor(h_background, h_background3, cv::COLOR_BGRA2BGR); |
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} |
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d_model.foreground.download(h_foreground); |
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ASSERT_MAT_NEAR(gold_background, h_background3, 1.0); |
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ASSERT_MAT_NEAR(gold_foreground, h_foreground, 0.0); |
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} |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_Video, FGDStatModel, testing::Combine( |
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ALL_DEVICES, |
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testing::Values(std::string("768x576.avi")), |
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testing::Values(Channels(3), Channels(4)))); |
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//////////////////////////////////////////////////////
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// MOG
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namespace |
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{ |
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IMPLEMENT_PARAM_CLASS(UseGray, bool) |
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IMPLEMENT_PARAM_CLASS(LearningRate, double) |
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} |
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PARAM_TEST_CASE(MOG, cv::gpu::DeviceInfo, std::string, UseGray, LearningRate, UseRoi) |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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std::string inputFile; |
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bool useGray; |
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double learningRate; |
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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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cv::gpu::setDevice(devInfo.deviceID()); |
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inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + GET_PARAM(1); |
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useGray = GET_PARAM(2); |
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learningRate = GET_PARAM(3); |
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useRoi = GET_PARAM(4); |
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} |
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}; |
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GPU_TEST_P(MOG, Update) |
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{ |
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cv::VideoCapture cap(inputFile); |
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ASSERT_TRUE(cap.isOpened()); |
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cv::Mat frame; |
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cap >> frame; |
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ASSERT_FALSE(frame.empty()); |
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cv::gpu::MOG_GPU mog; |
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cv::gpu::GpuMat foreground = createMat(frame.size(), CV_8UC1, useRoi); |
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cv::BackgroundSubtractorMOG mog_gold; |
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cv::Mat foreground_gold; |
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for (int i = 0; i < 10; ++i) |
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{ |
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cap >> frame; |
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ASSERT_FALSE(frame.empty()); |
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if (useGray) |
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{ |
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cv::Mat temp; |
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cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY); |
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cv::swap(temp, frame); |
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} |
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mog(loadMat(frame, useRoi), foreground, (float)learningRate); |
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mog_gold(frame, foreground_gold, learningRate); |
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ASSERT_MAT_NEAR(foreground_gold, foreground, 0.0); |
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} |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_Video, MOG, testing::Combine( |
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ALL_DEVICES, |
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testing::Values(std::string("768x576.avi")), |
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testing::Values(UseGray(true), UseGray(false)), |
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testing::Values(LearningRate(0.0), LearningRate(0.01)), |
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WHOLE_SUBMAT)); |
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//////////////////////////////////////////////////////
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// MOG2
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PARAM_TEST_CASE(MOG2, cv::gpu::DeviceInfo, std::string, UseGray, UseRoi) |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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std::string inputFile; |
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bool useGray; |
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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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cv::gpu::setDevice(devInfo.deviceID()); |
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inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + GET_PARAM(1); |
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useGray = GET_PARAM(2); |
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useRoi = GET_PARAM(3); |
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} |
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}; |
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GPU_TEST_P(MOG2, Update) |
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{ |
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cv::VideoCapture cap(inputFile); |
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ASSERT_TRUE(cap.isOpened()); |
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cv::Mat frame; |
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cap >> frame; |
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ASSERT_FALSE(frame.empty()); |
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cv::gpu::MOG2_GPU mog2; |
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cv::gpu::GpuMat foreground = createMat(frame.size(), CV_8UC1, useRoi); |
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cv::BackgroundSubtractorMOG2 mog2_gold; |
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cv::Mat foreground_gold; |
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for (int i = 0; i < 10; ++i) |
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{ |
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cap >> frame; |
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ASSERT_FALSE(frame.empty()); |
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if (useGray) |
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{ |
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cv::Mat temp; |
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cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY); |
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cv::swap(temp, frame); |
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} |
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mog2(loadMat(frame, useRoi), foreground); |
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mog2_gold(frame, foreground_gold); |
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double norm = cv::norm(foreground_gold, cv::Mat(foreground), cv::NORM_L1); |
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norm /= foreground_gold.size().area(); |
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ASSERT_LE(norm, 0.09); |
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} |
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} |
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GPU_TEST_P(MOG2, getBackgroundImage) |
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{ |
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if (useGray) |
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return; |
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cv::VideoCapture cap(inputFile); |
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ASSERT_TRUE(cap.isOpened()); |
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cv::Mat frame; |
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cv::gpu::MOG2_GPU mog2; |
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cv::gpu::GpuMat foreground; |
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cv::BackgroundSubtractorMOG2 mog2_gold; |
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cv::Mat foreground_gold; |
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for (int i = 0; i < 10; ++i) |
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{ |
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cap >> frame; |
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ASSERT_FALSE(frame.empty()); |
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mog2(loadMat(frame, useRoi), foreground); |
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mog2_gold(frame, foreground_gold); |
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} |
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cv::gpu::GpuMat background = createMat(frame.size(), frame.type(), useRoi); |
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mog2.getBackgroundImage(background); |
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cv::Mat background_gold; |
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mog2_gold.getBackgroundImage(background_gold); |
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ASSERT_MAT_NEAR(background_gold, background, 0); |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_Video, MOG2, testing::Combine( |
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ALL_DEVICES, |
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testing::Values(std::string("768x576.avi")), |
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testing::Values(UseGray(true), UseGray(false)), |
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WHOLE_SUBMAT)); |
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//////////////////////////////////////////////////////
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// VIBE
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PARAM_TEST_CASE(VIBE, cv::gpu::DeviceInfo, cv::Size, MatType, UseRoi) |
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{ |
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}; |
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GPU_TEST_P(VIBE, Accuracy) |
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{ |
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const cv::gpu::DeviceInfo devInfo = GET_PARAM(0); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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const cv::Size size = GET_PARAM(1); |
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const int type = GET_PARAM(2); |
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const bool useRoi = GET_PARAM(3); |
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const cv::Mat fullfg(size, CV_8UC1, cv::Scalar::all(255)); |
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cv::Mat frame = randomMat(size, type, 0.0, 100); |
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cv::gpu::GpuMat d_frame = loadMat(frame, useRoi); |
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cv::gpu::VIBE_GPU vibe; |
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cv::gpu::GpuMat d_fgmask = createMat(size, CV_8UC1, useRoi); |
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vibe.initialize(d_frame); |
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for (int i = 0; i < 20; ++i) |
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vibe(d_frame, d_fgmask); |
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frame = randomMat(size, type, 160, 255); |
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d_frame = loadMat(frame, useRoi); |
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vibe(d_frame, d_fgmask); |
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// now fgmask should be entirely foreground
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ASSERT_MAT_NEAR(fullfg, d_fgmask, 0); |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_Video, VIBE, testing::Combine( |
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ALL_DEVICES, |
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DIFFERENT_SIZES, |
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testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4)), |
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WHOLE_SUBMAT)); |
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//////////////////////////////////////////////////////
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// GMG
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PARAM_TEST_CASE(GMG, cv::gpu::DeviceInfo, cv::Size, MatDepth, Channels, UseRoi) |
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{ |
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}; |
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GPU_TEST_P(GMG, Accuracy) |
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{ |
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const cv::gpu::DeviceInfo devInfo = GET_PARAM(0); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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const cv::Size size = GET_PARAM(1); |
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const int depth = GET_PARAM(2); |
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const int channels = GET_PARAM(3); |
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const bool useRoi = GET_PARAM(4); |
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const int type = CV_MAKE_TYPE(depth, channels); |
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const cv::Mat zeros(size, CV_8UC1, cv::Scalar::all(0)); |
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const cv::Mat fullfg(size, CV_8UC1, cv::Scalar::all(255)); |
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cv::Mat frame = randomMat(size, type, 0, 100); |
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cv::gpu::GpuMat d_frame = loadMat(frame, useRoi); |
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cv::gpu::GMG_GPU gmg; |
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gmg.numInitializationFrames = 5; |
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gmg.smoothingRadius = 0; |
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gmg.initialize(d_frame.size(), 0, 255); |
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cv::gpu::GpuMat d_fgmask = createMat(size, CV_8UC1, useRoi); |
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for (int i = 0; i < gmg.numInitializationFrames; ++i) |
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{ |
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gmg(d_frame, d_fgmask); |
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// fgmask should be entirely background during training
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ASSERT_MAT_NEAR(zeros, d_fgmask, 0); |
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} |
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frame = randomMat(size, type, 160, 255); |
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d_frame = loadMat(frame, useRoi); |
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gmg(d_frame, d_fgmask); |
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// now fgmask should be entirely foreground
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ASSERT_MAT_NEAR(fullfg, d_fgmask, 0); |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_Video, GMG, testing::Combine( |
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ALL_DEVICES, |
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DIFFERENT_SIZES, |
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testing::Values(MatType(CV_8U), MatType(CV_16U), MatType(CV_32F)), |
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testing::Values(Channels(1), Channels(3), Channels(4)), |
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WHOLE_SUBMAT)); |
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#endif // HAVE_CUDA
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File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,404 @@ |
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// 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
|
||||
//
|
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// 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
|
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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 "test_precomp.hpp" |
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#ifdef HAVE_CUDA |
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//////////////////////////////////////////////////////
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// BroxOpticalFlow
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//#define BROX_DUMP
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struct BroxOpticalFlow : testing::TestWithParam<cv::gpu::DeviceInfo> |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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virtual void SetUp() |
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{ |
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devInfo = GetParam(); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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} |
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}; |
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GPU_TEST_P(BroxOpticalFlow, Regression) |
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{ |
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cv::Mat frame0 = readImageType("opticalflow/frame0.png", CV_32FC1); |
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ASSERT_FALSE(frame0.empty()); |
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cv::Mat frame1 = readImageType("opticalflow/frame1.png", CV_32FC1); |
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ASSERT_FALSE(frame1.empty()); |
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cv::gpu::BroxOpticalFlow brox(0.197f /*alpha*/, 50.0f /*gamma*/, 0.8f /*scale_factor*/, |
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10 /*inner_iterations*/, 77 /*outer_iterations*/, 10 /*solver_iterations*/); |
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cv::gpu::GpuMat u; |
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cv::gpu::GpuMat v; |
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brox(loadMat(frame0), loadMat(frame1), u, v); |
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std::string fname(cvtest::TS::ptr()->get_data_path()); |
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if (devInfo.majorVersion() >= 2) |
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fname += "opticalflow/brox_optical_flow_cc20.bin"; |
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else |
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fname += "opticalflow/brox_optical_flow.bin"; |
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#ifndef BROX_DUMP |
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std::ifstream f(fname.c_str(), std::ios_base::binary); |
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int rows, cols; |
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f.read((char*) &rows, sizeof(rows)); |
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f.read((char*) &cols, sizeof(cols)); |
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cv::Mat u_gold(rows, cols, CV_32FC1); |
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for (int i = 0; i < u_gold.rows; ++i) |
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f.read(u_gold.ptr<char>(i), u_gold.cols * sizeof(float)); |
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cv::Mat v_gold(rows, cols, CV_32FC1); |
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for (int i = 0; i < v_gold.rows; ++i) |
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f.read(v_gold.ptr<char>(i), v_gold.cols * sizeof(float)); |
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EXPECT_MAT_NEAR(u_gold, u, 0); |
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EXPECT_MAT_NEAR(v_gold, v, 0); |
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#else |
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std::ofstream f(fname.c_str(), std::ios_base::binary); |
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f.write((char*) &u.rows, sizeof(u.rows)); |
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f.write((char*) &u.cols, sizeof(u.cols)); |
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cv::Mat h_u(u); |
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cv::Mat h_v(v); |
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|
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for (int i = 0; i < u.rows; ++i) |
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f.write(h_u.ptr<char>(i), u.cols * sizeof(float)); |
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|
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for (int i = 0; i < v.rows; ++i) |
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f.write(h_v.ptr<char>(i), v.cols * sizeof(float)); |
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#endif |
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} |
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|
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GPU_TEST_P(BroxOpticalFlow, OpticalFlowNan) |
||||
{ |
||||
cv::Mat frame0 = readImageType("opticalflow/frame0.png", CV_32FC1); |
||||
ASSERT_FALSE(frame0.empty()); |
||||
|
||||
cv::Mat frame1 = readImageType("opticalflow/frame1.png", CV_32FC1); |
||||
ASSERT_FALSE(frame1.empty()); |
||||
|
||||
cv::Mat r_frame0, r_frame1; |
||||
cv::resize(frame0, r_frame0, cv::Size(1380,1000)); |
||||
cv::resize(frame1, r_frame1, cv::Size(1380,1000)); |
||||
|
||||
cv::gpu::BroxOpticalFlow brox(0.197f /*alpha*/, 50.0f /*gamma*/, 0.8f /*scale_factor*/, |
||||
5 /*inner_iterations*/, 150 /*outer_iterations*/, 10 /*solver_iterations*/); |
||||
|
||||
cv::gpu::GpuMat u; |
||||
cv::gpu::GpuMat v; |
||||
brox(loadMat(r_frame0), loadMat(r_frame1), u, v); |
||||
|
||||
cv::Mat h_u, h_v; |
||||
u.download(h_u); |
||||
v.download(h_v); |
||||
|
||||
EXPECT_TRUE(cv::checkRange(h_u)); |
||||
EXPECT_TRUE(cv::checkRange(h_v)); |
||||
}; |
||||
|
||||
INSTANTIATE_TEST_CASE_P(GPU_Video, BroxOpticalFlow, ALL_DEVICES); |
||||
|
||||
//////////////////////////////////////////////////////
|
||||
// GoodFeaturesToTrack
|
||||
|
||||
namespace |
||||
{ |
||||
IMPLEMENT_PARAM_CLASS(MinDistance, double) |
||||
} |
||||
|
||||
PARAM_TEST_CASE(GoodFeaturesToTrack, cv::gpu::DeviceInfo, MinDistance) |
||||
{ |
||||
cv::gpu::DeviceInfo devInfo; |
||||
double minDistance; |
||||
|
||||
virtual void SetUp() |
||||
{ |
||||
devInfo = GET_PARAM(0); |
||||
minDistance = GET_PARAM(1); |
||||
|
||||
cv::gpu::setDevice(devInfo.deviceID()); |
||||
} |
||||
}; |
||||
|
||||
GPU_TEST_P(GoodFeaturesToTrack, Accuracy) |
||||
{ |
||||
cv::Mat image = readImage("opticalflow/frame0.png", cv::IMREAD_GRAYSCALE); |
||||
ASSERT_FALSE(image.empty()); |
||||
|
||||
int maxCorners = 1000; |
||||
double qualityLevel = 0.01; |
||||
|
||||
cv::gpu::GoodFeaturesToTrackDetector_GPU detector(maxCorners, qualityLevel, minDistance); |
||||
|
||||
cv::gpu::GpuMat d_pts; |
||||
detector(loadMat(image), d_pts); |
||||
|
||||
ASSERT_FALSE(d_pts.empty()); |
||||
|
||||
std::vector<cv::Point2f> pts(d_pts.cols); |
||||
cv::Mat pts_mat(1, d_pts.cols, CV_32FC2, (void*) &pts[0]); |
||||
d_pts.download(pts_mat); |
||||
|
||||
std::vector<cv::Point2f> pts_gold; |
||||
cv::goodFeaturesToTrack(image, pts_gold, maxCorners, qualityLevel, minDistance); |
||||
|
||||
ASSERT_EQ(pts_gold.size(), pts.size()); |
||||
|
||||
size_t mistmatch = 0; |
||||
for (size_t i = 0; i < pts.size(); ++i) |
||||
{ |
||||
cv::Point2i a = pts_gold[i]; |
||||
cv::Point2i b = pts[i]; |
||||
|
||||
bool eq = std::abs(a.x - b.x) < 1 && std::abs(a.y - b.y) < 1; |
||||
|
||||
if (!eq) |
||||
++mistmatch; |
||||
} |
||||
|
||||
double bad_ratio = static_cast<double>(mistmatch) / pts.size(); |
||||
|
||||
ASSERT_LE(bad_ratio, 0.01); |
||||
} |
||||
|
||||
GPU_TEST_P(GoodFeaturesToTrack, EmptyCorners) |
||||
{ |
||||
int maxCorners = 1000; |
||||
double qualityLevel = 0.01; |
||||
|
||||
cv::gpu::GoodFeaturesToTrackDetector_GPU detector(maxCorners, qualityLevel, minDistance); |
||||
|
||||
cv::gpu::GpuMat src(100, 100, CV_8UC1, cv::Scalar::all(0)); |
||||
cv::gpu::GpuMat corners(1, maxCorners, CV_32FC2); |
||||
|
||||
detector(src, corners); |
||||
|
||||
ASSERT_TRUE(corners.empty()); |
||||
} |
||||
|
||||
INSTANTIATE_TEST_CASE_P(GPU_Video, GoodFeaturesToTrack, testing::Combine( |
||||
ALL_DEVICES, |
||||
testing::Values(MinDistance(0.0), MinDistance(3.0)))); |
||||
|
||||
//////////////////////////////////////////////////////
|
||||
// PyrLKOpticalFlow
|
||||
|
||||
namespace |
||||
{ |
||||
IMPLEMENT_PARAM_CLASS(UseGray, bool) |
||||
} |
||||
|
||||
PARAM_TEST_CASE(PyrLKOpticalFlow, cv::gpu::DeviceInfo, UseGray) |
||||
{ |
||||
cv::gpu::DeviceInfo devInfo; |
||||
bool useGray; |
||||
|
||||
virtual void SetUp() |
||||
{ |
||||
devInfo = GET_PARAM(0); |
||||
useGray = GET_PARAM(1); |
||||
|
||||
cv::gpu::setDevice(devInfo.deviceID()); |
||||
} |
||||
}; |
||||
|
||||
GPU_TEST_P(PyrLKOpticalFlow, Sparse) |
||||
{ |
||||
cv::Mat frame0 = readImage("opticalflow/frame0.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR); |
||||
ASSERT_FALSE(frame0.empty()); |
||||
|
||||
cv::Mat frame1 = readImage("opticalflow/frame1.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR); |
||||
ASSERT_FALSE(frame1.empty()); |
||||
|
||||
cv::Mat gray_frame; |
||||
if (useGray) |
||||
gray_frame = frame0; |
||||
else |
||||
cv::cvtColor(frame0, gray_frame, cv::COLOR_BGR2GRAY); |
||||
|
||||
std::vector<cv::Point2f> pts; |
||||
cv::goodFeaturesToTrack(gray_frame, pts, 1000, 0.01, 0.0); |
||||
|
||||
cv::gpu::GpuMat d_pts; |
||||
cv::Mat pts_mat(1, (int) pts.size(), CV_32FC2, (void*) &pts[0]); |
||||
d_pts.upload(pts_mat); |
||||
|
||||
cv::gpu::PyrLKOpticalFlow pyrLK; |
||||
|
||||
cv::gpu::GpuMat d_nextPts; |
||||
cv::gpu::GpuMat d_status; |
||||
pyrLK.sparse(loadMat(frame0), loadMat(frame1), d_pts, d_nextPts, d_status); |
||||
|
||||
std::vector<cv::Point2f> nextPts(d_nextPts.cols); |
||||
cv::Mat nextPts_mat(1, d_nextPts.cols, CV_32FC2, (void*) &nextPts[0]); |
||||
d_nextPts.download(nextPts_mat); |
||||
|
||||
std::vector<unsigned char> status(d_status.cols); |
||||
cv::Mat status_mat(1, d_status.cols, CV_8UC1, (void*) &status[0]); |
||||
d_status.download(status_mat); |
||||
|
||||
std::vector<cv::Point2f> nextPts_gold; |
||||
std::vector<unsigned char> status_gold; |
||||
cv::calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts_gold, status_gold, cv::noArray()); |
||||
|
||||
ASSERT_EQ(nextPts_gold.size(), nextPts.size()); |
||||
ASSERT_EQ(status_gold.size(), status.size()); |
||||
|
||||
size_t mistmatch = 0; |
||||
for (size_t i = 0; i < nextPts.size(); ++i) |
||||
{ |
||||
cv::Point2i a = nextPts[i]; |
||||
cv::Point2i b = nextPts_gold[i]; |
||||
|
||||
if (status[i] != status_gold[i]) |
||||
{ |
||||
++mistmatch; |
||||
continue; |
||||
} |
||||
|
||||
if (status[i]) |
||||
{ |
||||
bool eq = std::abs(a.x - b.x) <= 1 && std::abs(a.y - b.y) <= 1; |
||||
|
||||
if (!eq) |
||||
++mistmatch; |
||||
} |
||||
} |
||||
|
||||
double bad_ratio = static_cast<double>(mistmatch) / nextPts.size(); |
||||
|
||||
ASSERT_LE(bad_ratio, 0.01); |
||||
} |
||||
|
||||
INSTANTIATE_TEST_CASE_P(GPU_Video, PyrLKOpticalFlow, testing::Combine( |
||||
ALL_DEVICES, |
||||
testing::Values(UseGray(true), UseGray(false)))); |
||||
|
||||
//////////////////////////////////////////////////////
|
||||
// FarnebackOpticalFlow
|
||||
|
||||
namespace |
||||
{ |
||||
IMPLEMENT_PARAM_CLASS(PyrScale, double) |
||||
IMPLEMENT_PARAM_CLASS(PolyN, int) |
||||
CV_FLAGS(FarnebackOptFlowFlags, 0, cv::OPTFLOW_FARNEBACK_GAUSSIAN) |
||||
IMPLEMENT_PARAM_CLASS(UseInitFlow, bool) |
||||
} |
||||
|
||||
PARAM_TEST_CASE(FarnebackOpticalFlow, cv::gpu::DeviceInfo, PyrScale, PolyN, FarnebackOptFlowFlags, UseInitFlow) |
||||
{ |
||||
cv::gpu::DeviceInfo devInfo; |
||||
double pyrScale; |
||||
int polyN; |
||||
int flags; |
||||
bool useInitFlow; |
||||
|
||||
virtual void SetUp() |
||||
{ |
||||
devInfo = GET_PARAM(0); |
||||
pyrScale = GET_PARAM(1); |
||||
polyN = GET_PARAM(2); |
||||
flags = GET_PARAM(3); |
||||
useInitFlow = GET_PARAM(4); |
||||
|
||||
cv::gpu::setDevice(devInfo.deviceID()); |
||||
} |
||||
}; |
||||
|
||||
GPU_TEST_P(FarnebackOpticalFlow, Accuracy) |
||||
{ |
||||
cv::Mat frame0 = readImage("opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE); |
||||
ASSERT_FALSE(frame0.empty()); |
||||
|
||||
cv::Mat frame1 = readImage("opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE); |
||||
ASSERT_FALSE(frame1.empty()); |
||||
|
||||
double polySigma = polyN <= 5 ? 1.1 : 1.5; |
||||
|
||||
cv::gpu::FarnebackOpticalFlow farn; |
||||
farn.pyrScale = pyrScale; |
||||
farn.polyN = polyN; |
||||
farn.polySigma = polySigma; |
||||
farn.flags = flags; |
||||
|
||||
cv::gpu::GpuMat d_flowx, d_flowy; |
||||
farn(loadMat(frame0), loadMat(frame1), d_flowx, d_flowy); |
||||
|
||||
cv::Mat flow; |
||||
if (useInitFlow) |
||||
{ |
||||
cv::Mat flowxy[] = {cv::Mat(d_flowx), cv::Mat(d_flowy)}; |
||||
cv::merge(flowxy, 2, flow); |
||||
|
||||
farn.flags |= cv::OPTFLOW_USE_INITIAL_FLOW; |
||||
farn(loadMat(frame0), loadMat(frame1), d_flowx, d_flowy); |
||||
} |
||||
|
||||
cv::calcOpticalFlowFarneback( |
||||
frame0, frame1, flow, farn.pyrScale, farn.numLevels, farn.winSize, |
||||
farn.numIters, farn.polyN, farn.polySigma, farn.flags); |
||||
|
||||
std::vector<cv::Mat> flowxy; |
||||
cv::split(flow, flowxy); |
||||
|
||||
EXPECT_MAT_SIMILAR(flowxy[0], d_flowx, 0.1); |
||||
EXPECT_MAT_SIMILAR(flowxy[1], d_flowy, 0.1); |
||||
} |
||||
|
||||
INSTANTIATE_TEST_CASE_P(GPU_Video, FarnebackOpticalFlow, testing::Combine( |
||||
ALL_DEVICES, |
||||
testing::Values(PyrScale(0.3), PyrScale(0.5), PyrScale(0.8)), |
||||
testing::Values(PolyN(5), PolyN(7)), |
||||
testing::Values(FarnebackOptFlowFlags(0), FarnebackOptFlowFlags(cv::OPTFLOW_FARNEBACK_GAUSSIAN)), |
||||
testing::Values(UseInitFlow(false), UseInitFlow(true)))); |
||||
|
||||
#endif // HAVE_CUDA
|
||||
Loading…
Reference in new issue