444 lines
13 KiB
C++
444 lines
13 KiB
C++
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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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//
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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 "test_precomp.hpp"
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#include "opencv2/ts/ocl_test.hpp"
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namespace opencv_test { namespace {
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#define OCL_TUNING_MODE 0
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#if OCL_TUNING_MODE
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#define OCL_TUNING_MODE_ONLY(code) code
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#else
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#define OCL_TUNING_MODE_ONLY(code)
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#endif
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// image moments
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class CV_MomentsTest : public cvtest::ArrayTest
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{
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public:
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CV_MomentsTest(bool try_umat);
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protected:
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enum { MOMENT_COUNT = 25 };
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int prepare_test_case( int test_case_idx );
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void prepare_to_validation( int /*test_case_idx*/ );
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void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
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void get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high );
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double get_success_error_level( int test_case_idx, int i, int j );
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void run_func();
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bool is_binary;
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bool try_umat_;
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};
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CV_MomentsTest::CV_MomentsTest(bool try_umat) :
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try_umat_(try_umat)
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{
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test_array[INPUT].push_back(NULL);
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test_array[OUTPUT].push_back(NULL);
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test_array[REF_OUTPUT].push_back(NULL);
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is_binary = false;
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OCL_TUNING_MODE_ONLY(test_case_count = 10);
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//element_wise_relative_error = false;
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}
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void CV_MomentsTest::get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high )
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{
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cvtest::ArrayTest::get_minmax_bounds( i, j, type, low, high );
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int depth = CV_MAT_DEPTH(type);
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if( depth == CV_16U )
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{
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low = Scalar::all(0);
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high = Scalar::all(1000);
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}
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else if( depth == CV_16S )
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{
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low = Scalar::all(-1000);
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high = Scalar::all(1000);
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}
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else if( depth == CV_32F )
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{
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low = Scalar::all(-1);
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high = Scalar::all(1);
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}
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}
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void CV_MomentsTest::get_test_array_types_and_sizes( int test_case_idx,
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vector<vector<Size> >& sizes, vector<vector<int> >& types )
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{
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RNG& rng = ts->get_rng();
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cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
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int depth = cvtest::randInt(rng) % 4;
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depth = depth == 0 ? CV_8U : depth == 1 ? CV_16U : depth == 2 ? CV_16S : CV_32F;
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is_binary = cvtest::randInt(rng) % 2 != 0;
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OCL_TUNING_MODE_ONLY(
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depth = CV_8U;
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is_binary = false;
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sizes[INPUT][0] = Size(1024,768)
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);
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types[INPUT][0] = CV_MAKETYPE(depth, 1);
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types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_64FC1;
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sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = cvSize(MOMENT_COUNT,1);
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if(CV_MAT_DEPTH(types[INPUT][0])>=CV_32S)
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sizes[INPUT][0].width = MAX(sizes[INPUT][0].width, 3);
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cvmat_allowed = true;
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}
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double CV_MomentsTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
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{
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int depth = test_mat[INPUT][0].depth();
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return depth != CV_32F ? FLT_EPSILON*10 : FLT_EPSILON*100;
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}
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int CV_MomentsTest::prepare_test_case( int test_case_idx )
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{
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int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
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return code;
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}
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void CV_MomentsTest::run_func()
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{
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CvMoments* m = (CvMoments*)test_mat[OUTPUT][0].ptr<double>();
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double* others = (double*)(m + 1);
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if (try_umat_)
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{
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UMat u;
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test_mat[INPUT][0].clone().copyTo(u);
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OCL_TUNING_MODE_ONLY(
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static double ttime = 0;
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static int ncalls = 0;
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moments(u, is_binary != 0);
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double t = (double)getTickCount());
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Moments new_m = moments(u, is_binary != 0);
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OCL_TUNING_MODE_ONLY(
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ttime += (double)getTickCount() - t;
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ncalls++;
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printf("%g\n", ttime/ncalls/u.total()));
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*m = cvMoments(new_m);
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}
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else
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cvMoments( test_array[INPUT][0], m, is_binary );
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others[0] = cvGetNormalizedCentralMoment( m, 2, 0 );
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others[1] = cvGetNormalizedCentralMoment( m, 1, 1 );
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others[2] = cvGetNormalizedCentralMoment( m, 0, 2 );
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others[3] = cvGetNormalizedCentralMoment( m, 3, 0 );
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others[4] = cvGetNormalizedCentralMoment( m, 2, 1 );
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others[5] = cvGetNormalizedCentralMoment( m, 1, 2 );
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others[6] = cvGetNormalizedCentralMoment( m, 0, 3 );
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}
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void CV_MomentsTest::prepare_to_validation( int /*test_case_idx*/ )
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{
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Mat& src = test_mat[INPUT][0];
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CvMoments m = cvMoments();
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double* mdata = test_mat[REF_OUTPUT][0].ptr<double>();
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int depth = src.depth();
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int cn = src.channels();
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int i, y, x, cols = src.cols;
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double xc = 0., yc = 0.;
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int coi = 0;
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for( y = 0; y < src.rows; y++ )
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{
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double s0 = 0, s1 = 0, s2 = 0, s3 = 0;
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uchar* ptr = src.ptr(y);
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for( x = 0; x < cols; x++ )
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{
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double val;
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if( depth == CV_8U )
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val = ptr[x*cn + coi];
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else if( depth == CV_16U )
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val = ((ushort*)ptr)[x*cn + coi];
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else if( depth == CV_16S )
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val = ((short*)ptr)[x*cn + coi];
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else
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val = ((float*)ptr)[x*cn + coi];
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if( is_binary )
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val = val != 0;
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s0 += val;
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s1 += val*x;
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s2 += val*x*x;
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s3 += ((val*x)*x)*x;
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}
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m.m00 += s0;
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m.m01 += s0*y;
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m.m02 += (s0*y)*y;
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m.m03 += ((s0*y)*y)*y;
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m.m10 += s1;
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m.m11 += s1*y;
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m.m12 += (s1*y)*y;
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m.m20 += s2;
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m.m21 += s2*y;
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m.m30 += s3;
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}
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if( m.m00 != 0 )
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{
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xc = m.m10/m.m00, yc = m.m01/m.m00;
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m.inv_sqrt_m00 = 1./sqrt(fabs(m.m00));
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}
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for( y = 0; y < src.rows; y++ )
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{
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double s0 = 0, s1 = 0, s2 = 0, s3 = 0, y1 = y - yc;
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uchar* ptr = src.ptr(y);
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for( x = 0; x < cols; x++ )
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{
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double val, x1 = x - xc;
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if( depth == CV_8U )
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val = ptr[x*cn + coi];
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else if( depth == CV_16U )
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val = ((ushort*)ptr)[x*cn + coi];
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else if( depth == CV_16S )
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val = ((short*)ptr)[x*cn + coi];
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else
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val = ((float*)ptr)[x*cn + coi];
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if( is_binary )
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val = val != 0;
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s0 += val;
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s1 += val*x1;
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s2 += val*x1*x1;
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s3 += ((val*x1)*x1)*x1;
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}
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m.mu02 += s0*y1*y1;
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m.mu03 += ((s0*y1)*y1)*y1;
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m.mu11 += s1*y1;
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m.mu12 += (s1*y1)*y1;
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m.mu20 += s2;
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m.mu21 += s2*y1;
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m.mu30 += s3;
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}
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memcpy( mdata, &m, sizeof(m));
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mdata += sizeof(m)/sizeof(m.m00);
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/* calc normalized moments */
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{
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double inv_m00 = m.inv_sqrt_m00*m.inv_sqrt_m00;
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double s2 = inv_m00*inv_m00; /* 1./(m00 ^ (2/2 + 1)) */
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double s3 = s2*m.inv_sqrt_m00; /* 1./(m00 ^ (3/2 + 1)) */
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mdata[0] = m.mu20 * s2;
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mdata[1] = m.mu11 * s2;
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mdata[2] = m.mu02 * s2;
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mdata[3] = m.mu30 * s3;
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mdata[4] = m.mu21 * s3;
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mdata[5] = m.mu12 * s3;
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mdata[6] = m.mu03 * s3;
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}
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double* a = test_mat[REF_OUTPUT][0].ptr<double>();
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double* b = test_mat[OUTPUT][0].ptr<double>();
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for( i = 0; i < MOMENT_COUNT; i++ )
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{
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if( fabs(a[i]) < 1e-3 )
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a[i] = 0;
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if( fabs(b[i]) < 1e-3 )
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b[i] = 0;
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}
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}
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// Hu invariants
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class CV_HuMomentsTest : public cvtest::ArrayTest
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{
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public:
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CV_HuMomentsTest();
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protected:
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enum { MOMENT_COUNT = 18, HU_MOMENT_COUNT = 7 };
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int prepare_test_case( int test_case_idx );
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void prepare_to_validation( int /*test_case_idx*/ );
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void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
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void get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high );
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double get_success_error_level( int test_case_idx, int i, int j );
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void run_func();
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};
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CV_HuMomentsTest::CV_HuMomentsTest()
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{
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test_array[INPUT].push_back(NULL);
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test_array[OUTPUT].push_back(NULL);
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test_array[REF_OUTPUT].push_back(NULL);
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}
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void CV_HuMomentsTest::get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high )
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{
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cvtest::ArrayTest::get_minmax_bounds( i, j, type, low, high );
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low = Scalar::all(-10000);
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high = Scalar::all(10000);
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}
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void CV_HuMomentsTest::get_test_array_types_and_sizes( int test_case_idx,
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vector<vector<Size> >& sizes, vector<vector<int> >& types )
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{
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cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
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types[INPUT][0] = types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_64FC1;
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sizes[INPUT][0] = cvSize(MOMENT_COUNT,1);
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sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = cvSize(HU_MOMENT_COUNT,1);
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}
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double CV_HuMomentsTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
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{
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return FLT_EPSILON;
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}
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int CV_HuMomentsTest::prepare_test_case( int test_case_idx )
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{
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int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
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if( code > 0 )
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{
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// ...
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}
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||
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return code;
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}
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||
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void CV_HuMomentsTest::run_func()
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{
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cvGetHuMoments( test_mat[INPUT][0].ptr<CvMoments>(),
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test_mat[OUTPUT][0].ptr<CvHuMoments>() );
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}
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||
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void CV_HuMomentsTest::prepare_to_validation( int /*test_case_idx*/ )
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{
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CvMoments* m = test_mat[INPUT][0].ptr<CvMoments>();
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CvHuMoments* hu = test_mat[REF_OUTPUT][0].ptr<CvHuMoments>();
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double inv_m00 = m->inv_sqrt_m00*m->inv_sqrt_m00;
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double s2 = inv_m00*inv_m00; /* 1./(m00 ^ (2/2 + 1)) */
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double s3 = s2*m->inv_sqrt_m00; /* 1./(m00 ^ (3/2 + 1)) */
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double nu20 = m->mu20 * s2;
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double nu11 = m->mu11 * s2;
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double nu02 = m->mu02 * s2;
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double nu30 = m->mu30 * s3;
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double nu21 = m->mu21 * s3;
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double nu12 = m->mu12 * s3;
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double nu03 = m->mu03 * s3;
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||
|
|
|
||
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#undef sqr
|
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|
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#define sqr(a) ((a)*(a))
|
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|
|
|
||
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hu->hu1 = nu20 + nu02;
|
||
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hu->hu2 = sqr(nu20 - nu02) + 4*sqr(nu11);
|
||
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hu->hu3 = sqr(nu30 - 3*nu12) + sqr(3*nu21 - nu03);
|
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hu->hu4 = sqr(nu30 + nu12) + sqr(nu21 + nu03);
|
||
|
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hu->hu5 = (nu30 - 3*nu12)*(nu30 + nu12)*(sqr(nu30 + nu12) - 3*sqr(nu21 + nu03)) +
|
||
|
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(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(false); test.safe_run(); }
|
||
|
|
OCL_TEST(Imgproc_Moments, accuracy) { CV_MomentsTest test(true); 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(); }
|
||
|
|
|
||
|
|
}} // namespace
|