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moved part of video to contrib/{outflow, bgsegm}; moved matlab to contrib

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This commit is contained in:
Vadim Pisarevsky
2014-08-10 23:24:16 +04:00
parent 4de4ff5682
commit d0137b6d2d
62 changed files with 54 additions and 159912 deletions
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modules/video/test/ocl/test_motempl.cpp
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
// Copyright (C) 2014, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
#include "../test_precomp.hpp"
#include "opencv2/ts/ocl_test.hpp"
#ifdef HAVE_OPENCL
namespace cvtest {
namespace ocl {
PARAM_TEST_CASE(UpdateMotionHistory, bool)
{
double timestamp, duration;
bool use_roi;
TEST_DECLARE_INPUT_PARAMETER(silhouette);
TEST_DECLARE_OUTPUT_PARAMETER(mhi);
virtual void SetUp()
{
use_roi = GET_PARAM(0);
}
virtual void generateTestData()
{
Size roiSize = randomSize(1, MAX_VALUE);
Border silhouetteBorder = randomBorder(0, use_roi ? MAX_VALUE : 0);
randomSubMat(silhouette, silhouette_roi, roiSize, silhouetteBorder, CV_8UC1, -11, 11);
Border mhiBorder = randomBorder(0, use_roi ? MAX_VALUE : 0);
randomSubMat(mhi, mhi_roi, roiSize, mhiBorder, CV_32FC1, 0, 1);
timestamp = randomDouble(0, 1);
duration = randomDouble(0, 1);
if (timestamp < duration)
std::swap(timestamp, duration);
UMAT_UPLOAD_INPUT_PARAMETER(silhouette);
UMAT_UPLOAD_OUTPUT_PARAMETER(mhi);
}
};
OCL_TEST_P(UpdateMotionHistory, Mat)
{
for (int j = 0; j < test_loop_times; j++)
{
generateTestData();
OCL_OFF(cv::updateMotionHistory(silhouette_roi, mhi_roi, timestamp, duration));
OCL_ON(cv::updateMotionHistory(usilhouette_roi, umhi_roi, timestamp, duration));
OCL_EXPECT_MATS_NEAR(mhi, 0);
}
}
//////////////////////////////////////// Instantiation /////////////////////////////////////////
OCL_INSTANTIATE_TEST_CASE_P(Video, UpdateMotionHistory, Values(false, true));
} } // namespace cvtest::ocl
#endif // HAVE_OPENCL
modules/video/test/test_backgroundsubtractor_gbh.cpp
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/*
* BackgroundSubtractorGBH_test.cpp
*
* Created on: Jun 14, 2012
* Author: andrewgodbehere
*/
#include "test_precomp.hpp"
using namespace cv;
class CV_BackgroundSubtractorTest : public cvtest::BaseTest
{
public:
CV_BackgroundSubtractorTest();
protected:
void run(int);
};
CV_BackgroundSubtractorTest::CV_BackgroundSubtractorTest()
{
}
/**
* This test checks the following:
* (i) BackgroundSubtractorGMG can operate with matrices of various types and sizes
* (ii) Training mode returns empty fgmask
* (iii) End of training mode, and anomalous frame yields every pixel detected as FG
*/
void CV_BackgroundSubtractorTest::run(int)
{
int code = cvtest::TS::OK;
RNG& rng = ts->get_rng();
int type = ((unsigned int)rng)%7; //!< pick a random type, 0 - 6, defined in types_c.h
int channels = 1 + ((unsigned int)rng)%4; //!< random number of channels from 1 to 4.
int channelsAndType = CV_MAKETYPE(type,channels);
int width = 2 + ((unsigned int)rng)%98; //!< Mat will be 2 to 100 in width and height
int height = 2 + ((unsigned int)rng)%98;
Ptr<BackgroundSubtractorGMG> fgbg = createBackgroundSubtractorGMG();
Mat fgmask;
if (!fgbg)
CV_Error(Error::StsError,"Failed to create Algorithm\n");
/**
* Set a few parameters
*/
fgbg->setSmoothingRadius(7);
fgbg->setDecisionThreshold(0.7);
fgbg->setNumFrames(120);
/**
* Generate bounds for the values in the matrix for each type
*/
double maxd = 0, mind = 0;
/**
* Max value for simulated images picked randomly in upper half of type range
* Min value for simulated images picked randomly in lower half of type range
*/
if (type == CV_8U)
{
uchar half = UCHAR_MAX/2;
maxd = (unsigned char)rng.uniform(half+32, UCHAR_MAX);
mind = (unsigned char)rng.uniform(0, half-32);
}
else if (type == CV_8S)
{
maxd = (char)rng.uniform(32, CHAR_MAX);
mind = (char)rng.uniform(CHAR_MIN, -32);
}
else if (type == CV_16U)
{
ushort half = USHRT_MAX/2;
maxd = (unsigned int)rng.uniform(half+32, USHRT_MAX);
mind = (unsigned int)rng.uniform(0, half-32);
}
else if (type == CV_16S)
{
maxd = rng.uniform(32, SHRT_MAX);
mind = rng.uniform(SHRT_MIN, -32);
}
else if (type == CV_32S)
{
maxd = rng.uniform(32, INT_MAX);
mind = rng.uniform(INT_MIN, -32);
}
else if (type == CV_32F)
{
maxd = rng.uniform(32.0f, FLT_MAX);
mind = rng.uniform(-FLT_MAX, -32.0f);
}
else if (type == CV_64F)
{
maxd = rng.uniform(32.0, DBL_MAX);
mind = rng.uniform(-DBL_MAX, -32.0);
}
fgbg->setMinVal(mind);
fgbg->setMaxVal(maxd);
Mat simImage = Mat::zeros(height, width, channelsAndType);
int numLearningFrames = 120;
for (int i = 0; i < numLearningFrames; ++i)
{
/**
* Genrate simulated "image" for any type. Values always confined to upper half of range.
*/
rng.fill(simImage, RNG::UNIFORM, (mind + maxd)*0.5, maxd);
/**
* Feed simulated images into background subtractor
*/
fgbg->apply(simImage,fgmask);
Mat fullbg = Mat::zeros(simImage.rows, simImage.cols, CV_8U);
//! fgmask should be entirely background during training
code = cvtest::cmpEps2( ts, fgmask, fullbg, 0, false, "The training foreground mask" );
if (code < 0)
ts->set_failed_test_info( code );
}
//! generate last image, distinct from training images
rng.fill(simImage, RNG::UNIFORM, mind, maxd);
fgbg->apply(simImage,fgmask);
//! now fgmask should be entirely foreground
Mat fullfg = 255*Mat::ones(simImage.rows, simImage.cols, CV_8U);
code = cvtest::cmpEps2( ts, fgmask, fullfg, 255, false, "The final foreground mask" );
if (code < 0)
{
ts->set_failed_test_info( code );
}
}
TEST(VIDEO_BGSUBGMG, accuracy) { CV_BackgroundSubtractorTest test; test.safe_run(); }
modules/video/test/test_motiontemplates.cpp
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/*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 "test_precomp.hpp"
using namespace cv;
using namespace std;
///////////////////// base MHI class ///////////////////////
class CV_MHIBaseTest : public cvtest::ArrayTest
{
public:
CV_MHIBaseTest();
protected:
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
void get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high );
int prepare_test_case( int test_case_idx );
double timestamp, duration, max_log_duration;
int mhi_i, mhi_ref_i;
double silh_ratio;
};
CV_MHIBaseTest::CV_MHIBaseTest()
{
timestamp = duration = 0;
max_log_duration = 9;
mhi_i = mhi_ref_i = -1;
silh_ratio = 0.25;
}
void CV_MHIBaseTest::get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high )
{
cvtest::ArrayTest::get_minmax_bounds( i, j, type, low, high );
if( i == INPUT && CV_MAT_DEPTH(type) == CV_8U )
{
low = Scalar::all(cvRound(-1./silh_ratio)+2.);
high = Scalar::all(2);
}
else if( i == mhi_i || i == mhi_ref_i )
{
low = Scalar::all(-exp(max_log_duration));
high = Scalar::all(0.);
}
}
void CV_MHIBaseTest::get_test_array_types_and_sizes( int test_case_idx,
vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
types[INPUT][0] = CV_8UC1;
types[mhi_i][0] = types[mhi_ref_i][0] = CV_32FC1;
duration = exp(cvtest::randReal(rng)*max_log_duration);
timestamp = duration + cvtest::randReal(rng)*30.-10.;
}
int CV_MHIBaseTest::prepare_test_case( int test_case_idx )
{
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
if( code > 0 )
{
Mat& mat = test_mat[mhi_i][0];
mat += Scalar::all(duration);
cv::max(mat, 0, mat);
if( mhi_i != mhi_ref_i )
{
Mat& mat0 = test_mat[mhi_ref_i][0];
cvtest::copy( mat, mat0 );
}
}
return code;
}
///////////////////// update motion history ////////////////////////////
static void test_updateMHI( const Mat& silh, Mat& mhi, double timestamp, double duration )
{
int i, j;
float delbound = (float)(timestamp - duration);
for( i = 0; i < mhi.rows; i++ )
{
const uchar* silh_row = silh.ptr(i);
float* mhi_row = mhi.ptr<float>(i);
for( j = 0; j < mhi.cols; j++ )
{
if( silh_row[j] )
mhi_row[j] = (float)timestamp;
else if( mhi_row[j] < delbound )
mhi_row[j] = 0.f;
}
}
}
class CV_UpdateMHITest : public CV_MHIBaseTest
{
public:
CV_UpdateMHITest();
protected:
double get_success_error_level( int test_case_idx, int i, int j );
void run_func();
void prepare_to_validation( int );
};
CV_UpdateMHITest::CV_UpdateMHITest()
{
test_array[INPUT].push_back(NULL);
test_array[INPUT_OUTPUT].push_back(NULL);
test_array[REF_INPUT_OUTPUT].push_back(NULL);
mhi_i = INPUT_OUTPUT; mhi_ref_i = REF_INPUT_OUTPUT;
}
double CV_UpdateMHITest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
return 0;
}
void CV_UpdateMHITest::run_func()
{
cv::updateMotionHistory( test_mat[INPUT][0], test_mat[INPUT_OUTPUT][0], timestamp, duration);
}
void CV_UpdateMHITest::prepare_to_validation( int /*test_case_idx*/ )
{
//CvMat m0 = test_mat[REF_INPUT_OUTPUT][0];
test_updateMHI( test_mat[INPUT][0], test_mat[REF_INPUT_OUTPUT][0], timestamp, duration );
}
///////////////////// calc motion gradient ////////////////////////////
static void test_MHIGradient( const Mat& mhi, Mat& mask, Mat& orientation,
double delta1, double delta2, int aperture_size )
{
Point anchor( aperture_size/2, aperture_size/2 );
double limit = 1e-4*aperture_size*aperture_size;
Mat dx, dy, min_mhi, max_mhi;
Mat kernel = cvtest::calcSobelKernel2D( 1, 0, aperture_size );
cvtest::filter2D( mhi, dx, CV_32F, kernel, anchor, 0, BORDER_REPLICATE );
kernel = cvtest::calcSobelKernel2D( 0, 1, aperture_size );
cvtest::filter2D( mhi, dy, CV_32F, kernel, anchor, 0, BORDER_REPLICATE );
kernel = Mat::ones(aperture_size, aperture_size, CV_8U);
cvtest::erode(mhi, min_mhi, kernel, anchor, 0, BORDER_REPLICATE);
cvtest::dilate(mhi, max_mhi, kernel, anchor, 0, BORDER_REPLICATE);
if( delta1 > delta2 )
{
std::swap( delta1, delta2 );
}
for( int i = 0; i < mhi.rows; i++ )
{
uchar* mask_row = mask.ptr(i);
float* orient_row = orientation.ptr<float>(i);
const float* dx_row = dx.ptr<float>(i);
const float* dy_row = dy.ptr<float>(i);
const float* min_row = min_mhi.ptr<float>(i);
const float* max_row = max_mhi.ptr<float>(i);
for( int j = 0; j < mhi.cols; j++ )
{
double delta = max_row[j] - min_row[j];
double _dx = dx_row[j], _dy = dy_row[j];
if( delta1 <= delta && delta <= delta2 &&
(fabs(_dx) > limit || fabs(_dy) > limit) )
{
mask_row[j] = 1;
double angle = atan2( _dy, _dx ) * (180/CV_PI);
if( angle < 0 )
angle += 360.;
orient_row[j] = (float)angle;
}
else
{
mask_row[j] = 0;
orient_row[j] = 0.f;
}
}
}
}
class CV_MHIGradientTest : public CV_MHIBaseTest
{
public:
CV_MHIGradientTest();
protected:
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
double get_success_error_level( int test_case_idx, int i, int j );
void run_func();
void prepare_to_validation( int );
double delta1, delta2, delta_range_log;
int aperture_size;
};
CV_MHIGradientTest::CV_MHIGradientTest()
{
mhi_i = mhi_ref_i = INPUT;
test_array[INPUT].push_back(NULL);
test_array[OUTPUT].push_back(NULL);
test_array[OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
delta1 = delta2 = 0;
aperture_size = 0;
delta_range_log = 4;
}
void CV_MHIGradientTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
CV_MHIBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_8UC1;
types[OUTPUT][1] = types[REF_OUTPUT][1] = CV_32FC1;
delta1 = exp(cvtest::randReal(rng)*delta_range_log + 1.);
delta2 = exp(cvtest::randReal(rng)*delta_range_log + 1.);
aperture_size = (cvtest::randInt(rng)%3)*2+3;
//duration = exp(cvtest::randReal(rng)*max_log_duration);
//timestamp = duration + cvtest::randReal(rng)*30.-10.;
}
double CV_MHIGradientTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int j )
{
return j == 0 ? 0 : 2e-1;
}
void CV_MHIGradientTest::run_func()
{
cv::calcMotionGradient(test_mat[INPUT][0], test_mat[OUTPUT][0],
test_mat[OUTPUT][1], delta1, delta2, aperture_size );
//cvCalcMotionGradient( test_array[INPUT][0], test_array[OUTPUT][0],
// test_array[OUTPUT][1], delta1, delta2, aperture_size );
}
void CV_MHIGradientTest::prepare_to_validation( int /*test_case_idx*/ )
{
test_MHIGradient( test_mat[INPUT][0], test_mat[REF_OUTPUT][0],
test_mat[REF_OUTPUT][1], delta1, delta2, aperture_size );
test_mat[REF_OUTPUT][0] += Scalar::all(1);
test_mat[OUTPUT][0] += Scalar::all(1);
}
////////////////////// calc global orientation /////////////////////////
static double test_calcGlobalOrientation( const Mat& orient, const Mat& mask,
const Mat& mhi, double timestamp, double duration )
{
const int HIST_SIZE = 12;
int y, x;
int histogram[HIST_SIZE];
int max_bin = 0;
double base_orientation = 0, delta_orientation = 0, weight = 0;
double low_time, global_orientation;
memset( histogram, 0, sizeof( histogram ));
timestamp = 0;
for( y = 0; y < orient.rows; y++ )
{
const float* orient_data = orient.ptr<float>(y);
const uchar* mask_data = mask.ptr(y);
const float* mhi_data = mhi.ptr<float>(y);
for( x = 0; x < orient.cols; x++ )
if( mask_data[x] )
{
int bin = cvFloor( (orient_data[x]*HIST_SIZE)/360 );
histogram[bin < 0 ? 0 : bin >= HIST_SIZE ? HIST_SIZE-1 : bin]++;
if( mhi_data[x] > timestamp )
timestamp = mhi_data[x];
}
}
low_time = timestamp - duration;
for( x = 1; x < HIST_SIZE; x++ )
{
if( histogram[x] > histogram[max_bin] )
max_bin = x;
}
base_orientation = ((double)max_bin*360)/HIST_SIZE;
for( y = 0; y < orient.rows; y++ )
{
const float* orient_data = orient.ptr<float>(y);
const float* mhi_data = mhi.ptr<float>(y);
const uchar* mask_data = mask.ptr(y);
for( x = 0; x < orient.cols; x++ )
{
if( mask_data[x] && mhi_data[x] > low_time )
{
double diff = orient_data[x] - base_orientation;
double delta_weight = (((mhi_data[x] - low_time)/duration)*254 + 1)/255;
if( diff < -180 ) diff += 360;
if( diff > 180 ) diff -= 360;
if( delta_weight > 0 && fabs(diff) < 45 )
{
delta_orientation += diff*delta_weight;
weight += delta_weight;
}
}
}
}
if( weight == 0 )
global_orientation = base_orientation;
else
{
global_orientation = base_orientation + delta_orientation/weight;
if( global_orientation < 0 ) global_orientation += 360;
if( global_orientation > 360 ) global_orientation -= 360;
}
return global_orientation;
}
class CV_MHIGlobalOrientTest : public CV_MHIBaseTest
{
public:
CV_MHIGlobalOrientTest();
protected:
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
void get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high );
double get_success_error_level( int test_case_idx, int i, int j );
int validate_test_results( int test_case_idx );
void run_func();
double angle, min_angle, max_angle;
};
CV_MHIGlobalOrientTest::CV_MHIGlobalOrientTest()
{
mhi_i = mhi_ref_i = INPUT;
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
min_angle = max_angle = 0;
}
void CV_MHIGlobalOrientTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
CV_MHIBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
Size size = sizes[INPUT][0];
size.width = MAX( size.width, 16 );
size.height = MAX( size.height, 16 );
sizes[INPUT][0] = sizes[INPUT][1] = sizes[INPUT][2] = size;
types[INPUT][1] = CV_8UC1; // mask
types[INPUT][2] = CV_32FC1; // orientation
min_angle = cvtest::randReal(rng)*359.9;
max_angle = cvtest::randReal(rng)*359.9;
if( min_angle >= max_angle )
{
std::swap( min_angle, max_angle);
}
max_angle += 0.1;
duration = exp(cvtest::randReal(rng)*max_log_duration);
timestamp = duration + cvtest::randReal(rng)*30.-10.;
}
void CV_MHIGlobalOrientTest::get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high )
{
CV_MHIBaseTest::get_minmax_bounds( i, j, type, low, high );
if( i == INPUT && j == 2 )
{
low = Scalar::all(min_angle);
high = Scalar::all(max_angle);
}
}
double CV_MHIGlobalOrientTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
return 15;
}
void CV_MHIGlobalOrientTest::run_func()
{
//angle = cvCalcGlobalOrientation( test_array[INPUT][2], test_array[INPUT][1],
// test_array[INPUT][0], timestamp, duration );
angle = cv::calcGlobalOrientation(test_mat[INPUT][2], test_mat[INPUT][1],
test_mat[INPUT][0], timestamp, duration );
}
int CV_MHIGlobalOrientTest::validate_test_results( int test_case_idx )
{
//printf("%d. rows=%d, cols=%d, nzmask=%d\n", test_case_idx, test_mat[INPUT][1].rows, test_mat[INPUT][1].cols,
// cvCountNonZero(test_array[INPUT][1]));
double ref_angle = test_calcGlobalOrientation( test_mat[INPUT][2], test_mat[INPUT][1],
test_mat[INPUT][0], timestamp, duration );
double err_level = get_success_error_level( test_case_idx, 0, 0 );
int code = cvtest::TS::OK;
int nz = countNonZero( test_mat[INPUT][1] );
if( nz > 32 && !(min_angle - err_level <= angle &&
max_angle + err_level >= angle) &&
!(min_angle - err_level <= angle+360 &&
max_angle + err_level >= angle+360) )
{
ts->printf( cvtest::TS::LOG, "The angle=%g is outside (%g,%g) range\n",
angle, min_angle - err_level, max_angle + err_level );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
else if( fabs(angle - ref_angle) > err_level &&
fabs(360 - fabs(angle - ref_angle)) > err_level )
{
ts->printf( cvtest::TS::LOG, "The angle=%g differs too much from reference value=%g\n",
angle, ref_angle );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
if( code < 0 )
ts->set_failed_test_info( code );
return code;
}
TEST(Video_MHIUpdate, accuracy) { CV_UpdateMHITest test; test.safe_run(); }
TEST(Video_MHIGradient, accuracy) { CV_MHIGradientTest test; test.safe_run(); }
TEST(Video_MHIGlobalOrient, accuracy) { CV_MHIGlobalOrientTest test; test.safe_run(); }
modules/video/test/test_simpleflow.cpp
-190
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@@ -1,190 +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 "test_precomp.hpp"
#include <string>
using namespace std;
/* ///////////////////// simpleflow_test ///////////////////////// */
class CV_SimpleFlowTest : public cvtest::BaseTest
{
public:
CV_SimpleFlowTest();
protected:
void run(int);
};
CV_SimpleFlowTest::CV_SimpleFlowTest() {}
static bool readOpticalFlowFromFile(FILE* file, cv::Mat& flow) {
char header[5];
if (fread(header, 1, 4, file) < 4 && (string)header != "PIEH") {
return false;
}
int cols, rows;
if (fread(&cols, sizeof(int), 1, file) != 1||
fread(&rows, sizeof(int), 1, file) != 1) {
return false;
}
flow = cv::Mat::zeros(rows, cols, CV_32FC2);
for (int i = 0; i < rows; ++i) {
for (int j = 0; j < cols; ++j) {
cv::Vec2f flow_at_point;
if (fread(&(flow_at_point[0]), sizeof(float), 1, file) != 1 ||
fread(&(flow_at_point[1]), sizeof(float), 1, file) != 1) {
return false;
}
flow.at<cv::Vec2f>(i, j) = flow_at_point;
}
}
return true;
}
static bool isFlowCorrect(float u) {
return !cvIsNaN(u) && (fabs(u) < 1e9);
}
static float calc_rmse(cv::Mat flow1, cv::Mat flow2) {
float sum = 0;
int counter = 0;
const int rows = flow1.rows;
const int cols = flow1.cols;
for (int y = 0; y < rows; ++y) {
for (int x = 0; x < cols; ++x) {
cv::Vec2f flow1_at_point = flow1.at<cv::Vec2f>(y, x);
cv::Vec2f flow2_at_point = flow2.at<cv::Vec2f>(y, x);
float u1 = flow1_at_point[0];
float v1 = flow1_at_point[1];
float u2 = flow2_at_point[0];
float v2 = flow2_at_point[1];
if (isFlowCorrect(u1) && isFlowCorrect(u2) && isFlowCorrect(v1) && isFlowCorrect(v2)) {
sum += (u1-u2)*(u1-u2) + (v1-v2)*(v1-v2);
counter++;
}
}
}
return (float)sqrt(sum / (1e-9 + counter));
}
void CV_SimpleFlowTest::run(int) {
const float MAX_RMSE = 0.6f;
const string frame1_path = ts->get_data_path() + "optflow/RubberWhale1.png";
const string frame2_path = ts->get_data_path() + "optflow/RubberWhale2.png";
const string gt_flow_path = ts->get_data_path() + "optflow/RubberWhale.flo";
cv::Mat frame1 = cv::imread(frame1_path);
cv::Mat frame2 = cv::imread(frame2_path);
if (frame1.empty()) {
ts->printf(cvtest::TS::LOG, "could not read image %s\n", frame2_path.c_str());
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
return;
}
if (frame2.empty()) {
ts->printf(cvtest::TS::LOG, "could not read image %s\n", frame2_path.c_str());
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
return;
}
if (frame1.rows != frame2.rows && frame1.cols != frame2.cols) {
ts->printf(cvtest::TS::LOG, "images should be of equal sizes (%s and %s)",
frame1_path.c_str(), frame2_path.c_str());
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
return;
}
if (frame1.type() != 16 || frame2.type() != 16) {
ts->printf(cvtest::TS::LOG, "images should be of equal type CV_8UC3 (%s and %s)",
frame1_path.c_str(), frame2_path.c_str());
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
return;
}
cv::Mat flow_gt;
FILE* gt_flow_file = fopen(gt_flow_path.c_str(), "rb");
if (gt_flow_file == NULL) {
ts->printf(cvtest::TS::LOG, "could not read ground-thuth flow from file %s",
gt_flow_path.c_str());
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
return;
}
if (!readOpticalFlowFromFile(gt_flow_file, flow_gt)) {
ts->printf(cvtest::TS::LOG, "error while reading flow data from file %s",
gt_flow_path.c_str());
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
return;
}
fclose(gt_flow_file);
cv::Mat flow;
cv::calcOpticalFlowSF(frame1, frame2, flow, 3, 2, 4);
float rmse = calc_rmse(flow_gt, flow);
ts->printf(cvtest::TS::LOG, "Optical flow estimation RMSE for SimpleFlow algorithm : %lf\n",
rmse);
if (rmse > MAX_RMSE) {
ts->printf( cvtest::TS::LOG,
"Too big rmse error : %lf ( >= %lf )\n", rmse, MAX_RMSE);
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
return;
}
}
TEST(Video_OpticalFlowSimpleFlow, accuracy) { CV_SimpleFlowTest test; test.safe_run(); }
/* End of file. */