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opencv/modules/imgproc/test/test_moments.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
// 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.
//
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// (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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//M*/
#include "test_precomp.hpp"
using namespace cv;
using namespace std;
#define OCL_TUNING_MODE 0
#if OCL_TUNING_MODE
#define OCL_TUNING_MODE_ONLY(code) code
#else
#define OCL_TUNING_MODE_ONLY(code)
#endif
// image moments
class CV_MomentsTest : public cvtest::ArrayTest
{
public:
CV_MomentsTest();
protected:
enum { MOMENT_COUNT = 25 };
int prepare_test_case( int test_case_idx );
void prepare_to_validation( int /*test_case_idx*/ );
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 );
void run_func();
int coi;
bool is_binary;
bool try_umat;
};
CV_MomentsTest::CV_MomentsTest()
{
test_array[INPUT].push_back(NULL);
test_array[OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
coi = -1;
is_binary = false;
OCL_TUNING_MODE_ONLY(test_case_count = 10);
//element_wise_relative_error = false;
}
void CV_MomentsTest::get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high )
{
cvtest::ArrayTest::get_minmax_bounds( i, j, type, low, high );
int depth = CV_MAT_DEPTH(type);
if( depth == CV_16U )
{
low = Scalar::all(0);
high = Scalar::all(1000);
}
else if( depth == CV_16S )
{
low = Scalar::all(-1000);
high = Scalar::all(1000);
}
else if( depth == CV_32F )
{
low = Scalar::all(-1);
high = Scalar::all(1);
}
}
void CV_MomentsTest::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 );
int cn = (cvtest::randInt(rng) % 4) + 1;
int depth = cvtest::randInt(rng) % 4;
depth = depth == 0 ? CV_8U : depth == 1 ? CV_16U : depth == 2 ? CV_16S : CV_32F;
is_binary = cvtest::randInt(rng) % 2 != 0;
if( depth == 0 && !is_binary )
try_umat = cvtest::randInt(rng) % 5 != 0;
else
try_umat = cvtest::randInt(rng) % 2 != 0;
if( cn == 2 || try_umat )
cn = 1;
OCL_TUNING_MODE_ONLY(
cn = 1;
depth = CV_8U;
try_umat = true;
is_binary = false;
sizes[INPUT][0] = Size(1024,768)
);
types[INPUT][0] = CV_MAKETYPE(depth, cn);
types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_64FC1;
sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = cvSize(MOMENT_COUNT,1);
if(CV_MAT_DEPTH(types[INPUT][0])>=CV_32S)
sizes[INPUT][0].width = MAX(sizes[INPUT][0].width, 3);
coi = 0;
cvmat_allowed = true;
if( cn > 1 )
{
coi = cvtest::randInt(rng) % cn;
cvmat_allowed = false;
}
}
double CV_MomentsTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
int depth = test_mat[INPUT][0].depth();
return depth != CV_32F ? FLT_EPSILON*10 : FLT_EPSILON*100;
}
int CV_MomentsTest::prepare_test_case( int test_case_idx )
{
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
if( code > 0 )
{
int cn = test_mat[INPUT][0].channels();
if( cn > 1 )
cvSetImageCOI( (IplImage*)test_array[INPUT][0], coi + 1 );
}
return code;
}
void CV_MomentsTest::run_func()
{
CvMoments* m = (CvMoments*)test_mat[OUTPUT][0].ptr<double>();
double* others = (double*)(m + 1);
if( try_umat )
{
UMat u;
test_mat[INPUT][0].clone().copyTo(u);
OCL_TUNING_MODE_ONLY(
static double ttime = 0;
static int ncalls = 0;
moments(u, is_binary != 0);
double t = (double)getTickCount());
Moments new_m = moments(u, is_binary != 0);
OCL_TUNING_MODE_ONLY(
ttime += (double)getTickCount() - t;
ncalls++;
printf("%g\n", ttime/ncalls/u.total()));
*m = new_m;
}
else
cvMoments( test_array[INPUT][0], m, is_binary );
others[0] = cvGetNormalizedCentralMoment( m, 2, 0 );
others[1] = cvGetNormalizedCentralMoment( m, 1, 1 );
others[2] = cvGetNormalizedCentralMoment( m, 0, 2 );
others[3] = cvGetNormalizedCentralMoment( m, 3, 0 );
others[4] = cvGetNormalizedCentralMoment( m, 2, 1 );
others[5] = cvGetNormalizedCentralMoment( m, 1, 2 );
others[6] = cvGetNormalizedCentralMoment( m, 0, 3 );
}
void CV_MomentsTest::prepare_to_validation( int /*test_case_idx*/ )
{
Mat& src = test_mat[INPUT][0];
CvMoments m;
double* mdata = test_mat[REF_OUTPUT][0].ptr<double>();
int depth = src.depth();
int cn = src.channels();
int i, y, x, cols = src.cols;
double xc = 0., yc = 0.;
memset( &m, 0, sizeof(m));
for( y = 0; y < src.rows; y++ )
{
double s0 = 0, s1 = 0, s2 = 0, s3 = 0;
uchar* ptr = src.ptr(y);
for( x = 0; x < cols; x++ )
{
double val;
if( depth == CV_8U )
val = ptr[x*cn + coi];
else if( depth == CV_16U )
val = ((ushort*)ptr)[x*cn + coi];
else if( depth == CV_16S )
val = ((short*)ptr)[x*cn + coi];
else
val = ((float*)ptr)[x*cn + coi];
if( is_binary )
val = val != 0;
s0 += val;
s1 += val*x;
s2 += val*x*x;
s3 += ((val*x)*x)*x;
}
m.m00 += s0;
m.m01 += s0*y;
m.m02 += (s0*y)*y;
m.m03 += ((s0*y)*y)*y;
m.m10 += s1;
m.m11 += s1*y;
m.m12 += (s1*y)*y;
m.m20 += s2;
m.m21 += s2*y;
m.m30 += s3;
}
if( m.m00 != 0 )
{
xc = m.m10/m.m00, yc = m.m01/m.m00;
m.inv_sqrt_m00 = 1./sqrt(fabs(m.m00));
}
for( y = 0; y < src.rows; y++ )
{
double s0 = 0, s1 = 0, s2 = 0, s3 = 0, y1 = y - yc;
uchar* ptr = src.ptr(y);
for( x = 0; x < cols; x++ )
{
double val, x1 = x - xc;
if( depth == CV_8U )
val = ptr[x*cn + coi];
else if( depth == CV_16U )
val = ((ushort*)ptr)[x*cn + coi];
else if( depth == CV_16S )
val = ((short*)ptr)[x*cn + coi];
else
val = ((float*)ptr)[x*cn + coi];
if( is_binary )
val = val != 0;
s0 += val;
s1 += val*x1;
s2 += val*x1*x1;
s3 += ((val*x1)*x1)*x1;
}
m.mu02 += s0*y1*y1;
m.mu03 += ((s0*y1)*y1)*y1;
m.mu11 += s1*y1;
m.mu12 += (s1*y1)*y1;
m.mu20 += s2;
m.mu21 += s2*y1;
m.mu30 += s3;
}
memcpy( mdata, &m, sizeof(m));
mdata += sizeof(m)/sizeof(m.m00);
/* calc normalized moments */
{
double inv_m00 = m.inv_sqrt_m00*m.inv_sqrt_m00;
double s2 = inv_m00*inv_m00; /* 1./(m00 ^ (2/2 + 1)) */
double s3 = s2*m.inv_sqrt_m00; /* 1./(m00 ^ (3/2 + 1)) */
mdata[0] = m.mu20 * s2;
mdata[1] = m.mu11 * s2;
mdata[2] = m.mu02 * s2;
mdata[3] = m.mu30 * s3;
mdata[4] = m.mu21 * s3;
mdata[5] = m.mu12 * s3;
mdata[6] = m.mu03 * s3;
}
double* a = test_mat[REF_OUTPUT][0].ptr<double>();
double* b = test_mat[OUTPUT][0].ptr<double>();
for( i = 0; i < MOMENT_COUNT; i++ )
{
if( fabs(a[i]) < 1e-3 )
a[i] = 0;
if( fabs(b[i]) < 1e-3 )
b[i] = 0;
}
}
// Hu invariants
class CV_HuMomentsTest : public cvtest::ArrayTest
{
public:
CV_HuMomentsTest();
protected:
enum { MOMENT_COUNT = 18, HU_MOMENT_COUNT = 7 };
int prepare_test_case( int test_case_idx );
void prepare_to_validation( int /*test_case_idx*/ );
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 );
void run_func();
};
CV_HuMomentsTest::CV_HuMomentsTest()
{
test_array[INPUT].push_back(NULL);
test_array[OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
}
void CV_HuMomentsTest::get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high )
{
cvtest::ArrayTest::get_minmax_bounds( i, j, type, low, high );
low = Scalar::all(-10000);
high = Scalar::all(10000);
}
void CV_HuMomentsTest::get_test_array_types_and_sizes( int test_case_idx,
vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
types[INPUT][0] = types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_64FC1;
sizes[INPUT][0] = cvSize(MOMENT_COUNT,1);
sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = cvSize(HU_MOMENT_COUNT,1);
}
double CV_HuMomentsTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
return FLT_EPSILON;
}
int CV_HuMomentsTest::prepare_test_case( int test_case_idx )
{
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
if( code > 0 )
{
// ...
}
return code;
}
void CV_HuMomentsTest::run_func()
{
cvGetHuMoments( (CvMoments*)test_mat[INPUT][0].data,
(CvHuMoments*)test_mat[OUTPUT][0].data );
}
void CV_HuMomentsTest::prepare_to_validation( int /*test_case_idx*/ )
{
CvMoments* m = (CvMoments*)test_mat[INPUT][0].data;
CvHuMoments* hu = (CvHuMoments*)test_mat[REF_OUTPUT][0].data;
double inv_m00 = m->inv_sqrt_m00*m->inv_sqrt_m00;
double s2 = inv_m00*inv_m00; /* 1./(m00 ^ (2/2 + 1)) */
double s3 = s2*m->inv_sqrt_m00; /* 1./(m00 ^ (3/2 + 1)) */
double nu20 = m->mu20 * s2;
double nu11 = m->mu11 * s2;
double nu02 = m->mu02 * s2;
double nu30 = m->mu30 * s3;
double nu21 = m->mu21 * s3;
double nu12 = m->mu12 * s3;
double nu03 = m->mu03 * s3;
#undef sqr
#define sqr(a) ((a)*(a))
hu->hu1 = nu20 + nu02;
hu->hu2 = sqr(nu20 - nu02) + 4*sqr(nu11);
hu->hu3 = sqr(nu30 - 3*nu12) + sqr(3*nu21 - nu03);
hu->hu4 = sqr(nu30 + nu12) + sqr(nu21 + nu03);
hu->hu5 = (nu30 - 3*nu12)*(nu30 + nu12)*(sqr(nu30 + nu12) - 3*sqr(nu21 + nu03)) +
(3*nu21 - nu03)*(nu21 + nu03)*(3*sqr(nu30 + nu12) - sqr(nu21 + nu03));
hu->hu6 = (nu20 - nu02)*(sqr(nu30 + nu12) - sqr(nu21 + nu03)) +
4*nu11*(nu30 + nu12)*(nu21 + nu03);
hu->hu7 = (3*nu21 - nu03)*(nu30 + nu12)*(sqr(nu30 + nu12) - 3*sqr(nu21 + nu03)) +
(3*nu12 - nu30)*(nu21 + nu03)*(3*sqr(nu30 + nu12) - sqr(nu21 + nu03));
}
TEST(Imgproc_Moments, accuracy) { CV_MomentsTest test; test.safe_run(); }
TEST(Imgproc_HuMoments, accuracy) { CV_HuMomentsTest test; test.safe_run(); }
class CV_SmallContourMomentTest : public cvtest::BaseTest
{
public:
CV_SmallContourMomentTest() {}
~CV_SmallContourMomentTest() {}
protected:
void run(int)
{
try
{
vector<Point> points;
points.push_back(Point(50, 56));
points.push_back(Point(53, 53));
points.push_back(Point(46, 54));
points.push_back(Point(49, 51));
Moments m = moments(points, false);
double area = contourArea(points);
CV_Assert( m.m00 == 0 && m.m01 == 0 && m.m10 == 0 && area == 0 );
}
catch(...)
{
ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
}
}
};
TEST(Imgproc_ContourMoment, small) { CV_SmallContourMomentTest test; test.safe_run(); }
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