mirror of
https://github.com/opencv/opencv.git
synced 2024-11-30 06:10:02 +08:00
ade7394e77
wrote more complicated tests for them implemented own version of warpAffine and warpPerspective for different border interpolation types refactored some gpu tests
497 lines
15 KiB
C++
497 lines
15 KiB
C++
/*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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//
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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
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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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#ifdef HAVE_CUDA
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using namespace cvtest;
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using namespace testing;
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//#define DUMP
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#define OPTICAL_FLOW_DUMP_FILE "opticalflow/opticalflow_gold.bin"
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#define OPTICAL_FLOW_DUMP_FILE_CC20 "opticalflow/opticalflow_gold_cc20.bin"
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#define INTERPOLATE_FRAMES_DUMP_FILE "opticalflow/interpolate_frames_gold.bin"
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#define INTERPOLATE_FRAMES_DUMP_FILE_CC20 "opticalflow/interpolate_frames_gold_cc20.bin"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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// BroxOpticalFlow
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struct BroxOpticalFlow : TestWithParam<cv::gpu::DeviceInfo>
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{
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cv::gpu::DeviceInfo devInfo;
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cv::Mat frame0;
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cv::Mat frame1;
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cv::Mat u_gold;
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cv::Mat v_gold;
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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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frame0 = readImage("opticalflow/frame0.png", cv::IMREAD_GRAYSCALE);
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ASSERT_FALSE(frame0.empty());
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frame0.convertTo(frame0, CV_32F, 1.0 / 255.0);
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frame1 = readImage("opticalflow/frame1.png", cv::IMREAD_GRAYSCALE);
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ASSERT_FALSE(frame1.empty());
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frame1.convertTo(frame1, CV_32F, 1.0 / 255.0);
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#ifndef DUMP
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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 += OPTICAL_FLOW_DUMP_FILE_CC20;
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else
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fname += OPTICAL_FLOW_DUMP_FILE;
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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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u_gold.create(rows, cols, CV_32FC1);
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for (int i = 0; i < u_gold.rows; ++i)
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f.read((char*)u_gold.ptr(i), u_gold.cols * sizeof(float));
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v_gold.create(rows, cols, CV_32FC1);
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for (int i = 0; i < v_gold.rows; ++i)
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f.read((char*)v_gold.ptr(i), v_gold.cols * sizeof(float));
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#endif
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}
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};
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TEST_P(BroxOpticalFlow, Regression)
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{
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cv::Mat u;
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cv::Mat v;
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cv::gpu::BroxOpticalFlow d_flow(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 d_u;
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cv::gpu::GpuMat d_v;
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d_flow(cv::gpu::GpuMat(frame0), cv::gpu::GpuMat(frame1), d_u, d_v);
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d_u.download(u);
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d_v.download(v);
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#ifndef DUMP
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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::string fname(cvtest::TS::ptr()->get_data_path());
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if (devInfo.majorVersion() >= 2)
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fname += OPTICAL_FLOW_DUMP_FILE_CC20;
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else
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fname += OPTICAL_FLOW_DUMP_FILE;
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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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for (int i = 0; i < u.rows; ++i)
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f.write((char*)u.ptr(i), u.cols * sizeof(float));
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for (int i = 0; i < v.rows; ++i)
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f.write((char*)v.ptr(i), v.cols * sizeof(float));
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#endif
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}
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INSTANTIATE_TEST_CASE_P(Video, BroxOpticalFlow, ALL_DEVICES);
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/////////////////////////////////////////////////////////////////////////////////////////////////
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// InterpolateFrames
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struct InterpolateFrames : TestWithParam<cv::gpu::DeviceInfo>
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{
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cv::gpu::DeviceInfo devInfo;
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cv::Mat frame0;
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cv::Mat frame1;
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cv::Mat newFrame_gold;
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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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frame0 = readImage("opticalflow/frame0.png", cv::IMREAD_GRAYSCALE);
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ASSERT_FALSE(frame0.empty());
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frame0.convertTo(frame0, CV_32F, 1.0 / 255.0);
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frame1 = readImage("opticalflow/frame1.png", cv::IMREAD_GRAYSCALE);
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ASSERT_FALSE(frame1.empty());
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frame1.convertTo(frame1, CV_32F, 1.0 / 255.0);
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#ifndef DUMP
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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 += INTERPOLATE_FRAMES_DUMP_FILE_CC20;
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else
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fname += INTERPOLATE_FRAMES_DUMP_FILE;
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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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newFrame_gold.create(rows, cols, CV_32FC1);
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for (int i = 0; i < newFrame_gold.rows; ++i)
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f.read((char*)newFrame_gold.ptr(i), newFrame_gold.cols * sizeof(float));
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#endif
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}
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};
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TEST_P(InterpolateFrames, Regression)
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{
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cv::Mat newFrame;
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cv::gpu::BroxOpticalFlow d_flow(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 d_frame0(frame0);
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cv::gpu::GpuMat d_frame1(frame1);
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cv::gpu::GpuMat d_fu;
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cv::gpu::GpuMat d_fv;
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cv::gpu::GpuMat d_bu;
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cv::gpu::GpuMat d_bv;
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d_flow(d_frame0, d_frame1, d_fu, d_fv);
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d_flow(d_frame1, d_frame0, d_bu, d_bv);
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cv::gpu::GpuMat d_newFrame;
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cv::gpu::GpuMat d_buf;
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cv::gpu::interpolateFrames(d_frame0, d_frame1, d_fu, d_fv, d_bu, d_bv, 0.5f, d_newFrame, d_buf);
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d_newFrame.download(newFrame);
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#ifndef DUMP
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EXPECT_MAT_NEAR(newFrame_gold, newFrame, 1e-3);
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#else
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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 += INTERPOLATE_FRAMES_DUMP_FILE_CC20;
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else
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fname += INTERPOLATE_FRAMES_DUMP_FILE;
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std::ofstream f(fname.c_str(), std::ios_base::binary);
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f.write((char*)&newFrame.rows, sizeof(newFrame.rows));
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f.write((char*)&newFrame.cols, sizeof(newFrame.cols));
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for (int i = 0; i < newFrame.rows; ++i)
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f.write((char*)newFrame.ptr(i), newFrame.cols * sizeof(float));
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#endif
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}
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INSTANTIATE_TEST_CASE_P(Video, InterpolateFrames, ALL_DEVICES);
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/////////////////////////////////////////////////////////////////////////////////////////////////
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// GoodFeaturesToTrack
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PARAM_TEST_CASE(GoodFeaturesToTrack, cv::gpu::DeviceInfo, double)
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{
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cv::gpu::DeviceInfo devInfo;
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cv::Mat image;
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int maxCorners;
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double qualityLevel;
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double minDistance;
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std::vector<cv::Point2f> pts_gold;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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minDistance = GET_PARAM(1);
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cv::gpu::setDevice(devInfo.deviceID());
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image = readImage("opticalflow/frame0.png", cv::IMREAD_GRAYSCALE);
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ASSERT_FALSE(image.empty());
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maxCorners = 1000;
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qualityLevel= 0.01;
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cv::goodFeaturesToTrack(image, pts_gold, maxCorners, qualityLevel, minDistance);
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}
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};
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TEST_P(GoodFeaturesToTrack, Accuracy)
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{
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cv::gpu::GoodFeaturesToTrackDetector_GPU detector(maxCorners, qualityLevel, minDistance);
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cv::gpu::GpuMat d_pts;
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detector(loadMat(image), d_pts);
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std::vector<cv::Point2f> pts(d_pts.cols);
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cv::Mat pts_mat(1, d_pts.cols, CV_32FC2, (void*)&pts[0]);
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d_pts.download(pts_mat);
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ASSERT_EQ(pts_gold.size(), pts.size());
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size_t mistmatch = 0;
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for (size_t i = 0; i < pts.size(); ++i)
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{
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cv::Point2i a = pts_gold[i];
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cv::Point2i b = pts[i];
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bool eq = std::abs(a.x - b.x) < 1 && std::abs(a.y - b.y) < 1;
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if (!eq)
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++mistmatch;
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}
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double bad_ratio = static_cast<double>(mistmatch) / pts.size();
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ASSERT_LE(bad_ratio, 0.01);
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}
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INSTANTIATE_TEST_CASE_P(Video, GoodFeaturesToTrack, Combine(ALL_DEVICES, Values(0.0, 3.0)));
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/////////////////////////////////////////////////////////////////////////////////////////////////
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// PyrLKOpticalFlow
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PARAM_TEST_CASE(PyrLKOpticalFlowSparse, cv::gpu::DeviceInfo, bool)
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{
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cv::gpu::DeviceInfo devInfo;
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cv::Mat frame0;
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cv::Mat frame1;
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std::vector<cv::Point2f> pts;
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std::vector<cv::Point2f> nextPts_gold;
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std::vector<unsigned char> status_gold;
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std::vector<float> err_gold;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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bool useGray = GET_PARAM(1);
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cv::gpu::setDevice(devInfo.deviceID());
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frame0 = readImage("opticalflow/frame0.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
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ASSERT_FALSE(frame0.empty());
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frame1 = readImage("opticalflow/frame1.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
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ASSERT_FALSE(frame1.empty());
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cv::Mat gray_frame;
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if (useGray)
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gray_frame = frame0;
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else
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cv::cvtColor(frame0, gray_frame, cv::COLOR_BGR2GRAY);
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cv::goodFeaturesToTrack(gray_frame, pts, 1000, 0.01, 0.0);
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cv::calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts_gold, status_gold, err_gold, cv::Size(21, 21), 3,
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cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 30, 0.01), 0.5);
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}
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};
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TEST_P(PyrLKOpticalFlowSparse, Accuracy)
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{
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cv::gpu::PyrLKOpticalFlow d_pyrLK;
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cv::gpu::GpuMat d_pts;
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cv::Mat pts_mat(1, pts.size(), CV_32FC2, (void*)&pts[0]);
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d_pts.upload(pts_mat);
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cv::gpu::GpuMat d_nextPts;
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cv::gpu::GpuMat d_status;
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cv::gpu::GpuMat d_err;
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d_pyrLK.sparse(loadMat(frame0), loadMat(frame1), d_pts, d_nextPts, d_status, &d_err);
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std::vector<cv::Point2f> nextPts(d_nextPts.cols);
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cv::Mat nextPts_mat(1, d_nextPts.cols, CV_32FC2, (void*)&nextPts[0]);
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d_nextPts.download(nextPts_mat);
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std::vector<unsigned char> status(d_status.cols);
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cv::Mat status_mat(1, d_status.cols, CV_8UC1, (void*)&status[0]);
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d_status.download(status_mat);
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std::vector<float> err(d_err.cols);
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cv::Mat err_mat(1, d_err.cols, CV_32FC1, (void*)&err[0]);
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d_err.download(err_mat);
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ASSERT_EQ(nextPts_gold.size(), nextPts.size());
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ASSERT_EQ(status_gold.size(), status.size());
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ASSERT_EQ(err_gold.size(), err.size());
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size_t mistmatch = 0;
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for (size_t i = 0; i < nextPts.size(); ++i)
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{
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if (status[i] != status_gold[i])
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{
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++mistmatch;
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continue;
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}
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if (status[i])
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{
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cv::Point2i a = nextPts[i];
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cv::Point2i b = nextPts_gold[i];
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bool eq = std::abs(a.x - b.x) < 1 && std::abs(a.y - b.y) < 1;
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float errdiff = std::abs(err[i] - err_gold[i]);
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if (!eq || errdiff > 1e-1)
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++mistmatch;
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}
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}
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double bad_ratio = static_cast<double>(mistmatch) / nextPts.size();
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ASSERT_LE(bad_ratio, 0.01);
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}
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INSTANTIATE_TEST_CASE_P(Video, PyrLKOpticalFlowSparse, Combine(ALL_DEVICES, Bool()));
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PARAM_TEST_CASE(FarnebackOpticalFlowTest, cv::gpu::DeviceInfo, double, int, int, bool)
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{
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cv::Mat frame0, frame1;
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double pyrScale;
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int polyN;
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double polySigma;
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int flags;
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bool useInitFlow;
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virtual void SetUp()
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{
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frame0 = readImage("opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
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frame1 = readImage("opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
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ASSERT_FALSE(frame0.empty()); ASSERT_FALSE(frame1.empty());
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cv::gpu::setDevice(GET_PARAM(0).deviceID());
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pyrScale = GET_PARAM(1);
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polyN = GET_PARAM(2);
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polySigma = polyN <= 5 ? 1.1 : 1.5;
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flags = GET_PARAM(3);
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useInitFlow = GET_PARAM(4);
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}
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};
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TEST_P(FarnebackOpticalFlowTest, Accuracy)
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{
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using namespace cv;
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gpu::FarnebackOpticalFlow calc;
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calc.pyrScale = pyrScale;
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calc.polyN = polyN;
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calc.polySigma = polySigma;
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calc.flags = flags;
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gpu::GpuMat d_flowx, d_flowy;
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calc(gpu::GpuMat(frame0), gpu::GpuMat(frame1), d_flowx, d_flowy);
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Mat flow;
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if (useInitFlow)
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{
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Mat flowxy[] = {(Mat)d_flowx, (Mat)d_flowy};
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merge(flowxy, 2, flow);
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}
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if (useInitFlow)
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{
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calc.flags |= OPTFLOW_USE_INITIAL_FLOW;
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calc(gpu::GpuMat(frame0), gpu::GpuMat(frame1), d_flowx, d_flowy);
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}
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calcOpticalFlowFarneback(
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frame0, frame1, flow, calc.pyrScale, calc.numLevels, calc.winSize,
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calc.numIters, calc.polyN, calc.polySigma, calc.flags);
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std::vector<Mat> flowxy; split(flow, flowxy);
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/*std::cout << checkSimilarity(flowxy[0], (Mat)d_flowx) << " "
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<< checkSimilarity(flowxy[1], (Mat)d_flowy) << std::endl;*/
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EXPECT_LT(checkSimilarity(flowxy[0], (Mat)d_flowx), 0.1);
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EXPECT_LT(checkSimilarity(flowxy[1], (Mat)d_flowy), 0.1);
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}
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INSTANTIATE_TEST_CASE_P(Video, FarnebackOpticalFlowTest,
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Combine(ALL_DEVICES,
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Values(0.3, 0.5, 0.8),
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Values(5, 7),
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Values(0, (int)cv::OPTFLOW_FARNEBACK_GAUSSIAN),
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Values(false, true)));
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#endif // HAVE_CUDA
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