mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
465 lines
14 KiB
465 lines
14 KiB
/*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; |
|
|
|
#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( test_mat[INPUT][0].ptr<CvMoments>(), |
|
test_mat[OUTPUT][0].ptr<CvHuMoments>() ); |
|
} |
|
|
|
|
|
void CV_HuMomentsTest::prepare_to_validation( int /*test_case_idx*/ ) |
|
{ |
|
CvMoments* m = test_mat[INPUT][0].ptr<CvMoments>(); |
|
CvHuMoments* hu = test_mat[REF_OUTPUT][0].ptr<CvHuMoments>(); |
|
|
|
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(); }
|
|
|