mirror of
https://github.com/opencv/opencv.git
synced 2024-12-24 00:17:59 +08:00
337 lines
12 KiB
C++
337 lines
12 KiB
C++
|
/*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;
|
||
|
|
|
||
|
|
class CV_TemplMatchTest : public cvtest::ArrayTest
|
||
|
|
{
|
||
|
|
public:
|
||
|
|
CV_TemplMatchTest();
|
||
|
|
|
||
|
|
protected:
|
||
|
|
int read_params( CvFileStorage* fs );
|
||
|
|
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();
|
||
|
|
void prepare_to_validation( int );
|
||
|
|
|
||
|
|
int max_template_size;
|
||
|
|
int method;
|
||
|
|
bool test_cpp;
|
||
|
|
};
|
||
|
|
|
||
|
|
|
||
|
|
CV_TemplMatchTest::CV_TemplMatchTest()
|
||
|
|
{
|
||
|
|
test_array[INPUT].push_back(NULL);
|
||
|
|
test_array[INPUT].push_back(NULL);
|
||
|
|
test_array[OUTPUT].push_back(NULL);
|
||
|
|
test_array[REF_OUTPUT].push_back(NULL);
|
||
|
|
element_wise_relative_error = false;
|
||
|
|
max_template_size = 100;
|
||
|
|
method = 0;
|
||
|
|
test_cpp = false;
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
int CV_TemplMatchTest::read_params( CvFileStorage* fs )
|
||
|
|
{
|
||
|
|
int code = cvtest::ArrayTest::read_params( fs );
|
||
|
|
if( code < 0 )
|
||
|
|
return code;
|
||
|
|
|
||
|
|
max_template_size = cvReadInt( find_param( fs, "max_template_size" ), max_template_size );
|
||
|
|
max_template_size = cvtest::clipInt( max_template_size, 1, 100 );
|
||
|
|
|
||
|
|
return code;
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
void CV_TemplMatchTest::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_32F )
|
||
|
|
{
|
||
|
|
low = Scalar::all(-10.);
|
||
|
|
high = Scalar::all(10.);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
void CV_TemplMatchTest::get_test_array_types_and_sizes( int test_case_idx,
|
||
|
|
vector<vector<Size> >& sizes, vector<vector<int> >& types )
|
||
|
|
{
|
||
|
|
RNG& rng = ts->get_rng();
|
||
|
|
int depth = cvtest::randInt(rng) % 2, cn = cvtest::randInt(rng) & 1 ? 3 : 1;
|
||
|
|
cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
|
||
|
|
depth = depth == 0 ? CV_8U : CV_32F;
|
||
|
|
|
||
|
|
types[INPUT][0] = types[INPUT][1] = CV_MAKETYPE(depth,cn);
|
||
|
|
types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_32FC1;
|
||
|
|
|
||
|
|
sizes[INPUT][1].width = cvtest::randInt(rng)%MIN(sizes[INPUT][1].width,max_template_size) + 1;
|
||
|
|
sizes[INPUT][1].height = cvtest::randInt(rng)%MIN(sizes[INPUT][1].height,max_template_size) + 1;
|
||
|
|
sizes[OUTPUT][0].width = sizes[INPUT][0].width - sizes[INPUT][1].width + 1;
|
||
|
|
sizes[OUTPUT][0].height = sizes[INPUT][0].height - sizes[INPUT][1].height + 1;
|
||
|
|
sizes[REF_OUTPUT][0] = sizes[OUTPUT][0];
|
||
|
|
|
||
|
|
method = cvtest::randInt(rng)%6;
|
||
|
|
test_cpp = (cvtest::randInt(rng) & 256) == 0;
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
double CV_TemplMatchTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
|
||
|
|
{
|
||
|
|
if( test_mat[INPUT][1].depth() == CV_8U ||
|
||
|
|
(method >= CV_TM_CCOEFF && test_mat[INPUT][1].cols*test_mat[INPUT][1].rows <= 2) )
|
||
|
|
return 1e-2;
|
||
|
|
else
|
||
|
|
return 1e-3;
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
void CV_TemplMatchTest::run_func()
|
||
|
|
{
|
||
|
|
if(!test_cpp)
|
||
|
|
cvMatchTemplate( test_array[INPUT][0], test_array[INPUT][1], test_array[OUTPUT][0], method );
|
||
|
|
else
|
||
|
|
{
|
||
|
|
cv::Mat _out = cv::cvarrToMat(test_array[OUTPUT][0]);
|
||
|
|
cv::matchTemplate(cv::cvarrToMat(test_array[INPUT][0]), cv::cvarrToMat(test_array[INPUT][1]), _out, method);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
static void cvTsMatchTemplate( const CvMat* img, const CvMat* templ, CvMat* result, int method )
|
||
|
|
{
|
||
|
|
int i, j, k, l;
|
||
|
|
int depth = CV_MAT_DEPTH(img->type), cn = CV_MAT_CN(img->type);
|
||
|
|
int width_n = templ->cols*cn, height = templ->rows;
|
||
|
|
int a_step = img->step / CV_ELEM_SIZE(img->type & CV_MAT_DEPTH_MASK);
|
||
|
|
int b_step = templ->step / CV_ELEM_SIZE(templ->type & CV_MAT_DEPTH_MASK);
|
||
|
|
CvScalar b_mean, b_sdv;
|
||
|
|
double b_denom = 1., b_sum2 = 0;
|
||
|
|
int area = templ->rows*templ->cols;
|
||
|
|
|
||
|
|
cvAvgSdv(templ, &b_mean, &b_sdv);
|
||
|
|
|
||
|
|
for( i = 0; i < cn; i++ )
|
||
|
|
b_sum2 += (b_sdv.val[i]*b_sdv.val[i] + b_mean.val[i]*b_mean.val[i])*area;
|
||
|
|
|
||
|
|
if( b_sdv.val[0]*b_sdv.val[0] + b_sdv.val[1]*b_sdv.val[1] +
|
||
|
|
b_sdv.val[2]*b_sdv.val[2] + b_sdv.val[3]*b_sdv.val[3] < DBL_EPSILON &&
|
||
|
|
method == CV_TM_CCOEFF_NORMED )
|
||
|
|
{
|
||
|
|
cvSet( result, cvScalarAll(1.) );
|
||
|
|
return;
|
||
|
|
}
|
||
|
|
|
||
|
|
if( method & 1 )
|
||
|
|
{
|
||
|
|
b_denom = 0;
|
||
|
|
if( method != CV_TM_CCOEFF_NORMED )
|
||
|
|
{
|
||
|
|
b_denom = b_sum2;
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
for( i = 0; i < cn; i++ )
|
||
|
|
b_denom += b_sdv.val[i]*b_sdv.val[i]*area;
|
||
|
|
}
|
||
|
|
b_denom = sqrt(b_denom);
|
||
|
|
if( b_denom == 0 )
|
||
|
|
b_denom = 1.;
|
||
|
|
}
|
||
|
|
|
||
|
|
assert( CV_TM_SQDIFF <= method && method <= CV_TM_CCOEFF_NORMED );
|
||
|
|
|
||
|
|
for( i = 0; i < result->rows; i++ )
|
||
|
|
{
|
||
|
|
for( j = 0; j < result->cols; j++ )
|
||
|
|
{
|
||
|
|
CvScalar a_sum = {{ 0, 0, 0, 0 }}, a_sum2 = {{ 0, 0, 0, 0 }};
|
||
|
|
CvScalar ccorr = {{ 0, 0, 0, 0 }};
|
||
|
|
double value = 0.;
|
||
|
|
|
||
|
|
if( depth == CV_8U )
|
||
|
|
{
|
||
|
|
const uchar* a = img->data.ptr + i*img->step + j*cn;
|
||
|
|
const uchar* b = templ->data.ptr;
|
||
|
|
|
||
|
|
if( cn == 1 || method < CV_TM_CCOEFF )
|
||
|
|
{
|
||
|
|
for( k = 0; k < height; k++, a += a_step, b += b_step )
|
||
|
|
for( l = 0; l < width_n; l++ )
|
||
|
|
{
|
||
|
|
ccorr.val[0] += a[l]*b[l];
|
||
|
|
a_sum.val[0] += a[l];
|
||
|
|
a_sum2.val[0] += a[l]*a[l];
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
for( k = 0; k < height; k++, a += a_step, b += b_step )
|
||
|
|
for( l = 0; l < width_n; l += 3 )
|
||
|
|
{
|
||
|
|
ccorr.val[0] += a[l]*b[l];
|
||
|
|
ccorr.val[1] += a[l+1]*b[l+1];
|
||
|
|
ccorr.val[2] += a[l+2]*b[l+2];
|
||
|
|
a_sum.val[0] += a[l];
|
||
|
|
a_sum.val[1] += a[l+1];
|
||
|
|
a_sum.val[2] += a[l+2];
|
||
|
|
a_sum2.val[0] += a[l]*a[l];
|
||
|
|
a_sum2.val[1] += a[l+1]*a[l+1];
|
||
|
|
a_sum2.val[2] += a[l+2]*a[l+2];
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
const float* a = (const float*)(img->data.ptr + i*img->step) + j*cn;
|
||
|
|
const float* b = (const float*)templ->data.ptr;
|
||
|
|
|
||
|
|
if( cn == 1 || method < CV_TM_CCOEFF )
|
||
|
|
{
|
||
|
|
for( k = 0; k < height; k++, a += a_step, b += b_step )
|
||
|
|
for( l = 0; l < width_n; l++ )
|
||
|
|
{
|
||
|
|
ccorr.val[0] += a[l]*b[l];
|
||
|
|
a_sum.val[0] += a[l];
|
||
|
|
a_sum2.val[0] += a[l]*a[l];
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
for( k = 0; k < height; k++, a += a_step, b += b_step )
|
||
|
|
for( l = 0; l < width_n; l += 3 )
|
||
|
|
{
|
||
|
|
ccorr.val[0] += a[l]*b[l];
|
||
|
|
ccorr.val[1] += a[l+1]*b[l+1];
|
||
|
|
ccorr.val[2] += a[l+2]*b[l+2];
|
||
|
|
a_sum.val[0] += a[l];
|
||
|
|
a_sum.val[1] += a[l+1];
|
||
|
|
a_sum.val[2] += a[l+2];
|
||
|
|
a_sum2.val[0] += a[l]*a[l];
|
||
|
|
a_sum2.val[1] += a[l+1]*a[l+1];
|
||
|
|
a_sum2.val[2] += a[l+2]*a[l+2];
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
switch( method )
|
||
|
|
{
|
||
|
|
case CV_TM_CCORR:
|
||
|
|
case CV_TM_CCORR_NORMED:
|
||
|
|
value = ccorr.val[0];
|
||
|
|
break;
|
||
|
|
case CV_TM_SQDIFF:
|
||
|
|
case CV_TM_SQDIFF_NORMED:
|
||
|
|
value = (a_sum2.val[0] + b_sum2 - 2*ccorr.val[0]);
|
||
|
|
break;
|
||
|
|
default:
|
||
|
|
value = (ccorr.val[0] - a_sum.val[0]*b_mean.val[0]+
|
||
|
|
ccorr.val[1] - a_sum.val[1]*b_mean.val[1]+
|
||
|
|
ccorr.val[2] - a_sum.val[2]*b_mean.val[2]);
|
||
|
|
}
|
||
|
|
|
||
|
|
if( method & 1 )
|
||
|
|
{
|
||
|
|
double denom;
|
||
|
|
|
||
|
|
// calc denominator
|
||
|
|
if( method != CV_TM_CCOEFF_NORMED )
|
||
|
|
{
|
||
|
|
denom = a_sum2.val[0] + a_sum2.val[1] + a_sum2.val[2];
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
denom = a_sum2.val[0] - (a_sum.val[0]*a_sum.val[0])/area;
|
||
|
|
denom += a_sum2.val[1] - (a_sum.val[1]*a_sum.val[1])/area;
|
||
|
|
denom += a_sum2.val[2] - (a_sum.val[2]*a_sum.val[2])/area;
|
||
|
|
}
|
||
|
|
denom = sqrt(MAX(denom,0))*b_denom;
|
||
|
|
if( fabs(value) < denom )
|
||
|
|
value /= denom;
|
||
|
|
else if( fabs(value) < denom*1.125 )
|
||
|
|
value = value > 0 ? 1 : -1;
|
||
|
|
else
|
||
|
|
value = method != CV_TM_SQDIFF_NORMED ? 0 : 1;
|
||
|
|
}
|
||
|
|
|
||
|
|
((float*)(result->data.ptr + result->step*i))[j] = (float)value;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
void CV_TemplMatchTest::prepare_to_validation( int /*test_case_idx*/ )
|
||
|
|
{
|
||
|
|
CvMat _input = test_mat[INPUT][0], _templ = test_mat[INPUT][1];
|
||
|
|
CvMat _output = test_mat[REF_OUTPUT][0];
|
||
|
|
cvTsMatchTemplate( &_input, &_templ, &_output, method );
|
||
|
|
|
||
|
|
//if( ts->get_current_test_info()->test_case_idx == 0 )
|
||
|
|
/*{
|
||
|
|
CvFileStorage* fs = cvOpenFileStorage( "_match_template.yml", 0, CV_STORAGE_WRITE );
|
||
|
|
cvWrite( fs, "image", &test_mat[INPUT][0] );
|
||
|
|
cvWrite( fs, "template", &test_mat[INPUT][1] );
|
||
|
|
cvWrite( fs, "ref", &test_mat[REF_OUTPUT][0] );
|
||
|
|
cvWrite( fs, "opencv", &test_mat[OUTPUT][0] );
|
||
|
|
cvWriteInt( fs, "method", method );
|
||
|
|
cvReleaseFileStorage( &fs );
|
||
|
|
}*/
|
||
|
|
|
||
|
|
if( method >= CV_TM_CCOEFF )
|
||
|
|
{
|
||
|
|
// avoid numerical stability problems in singular cases (when the results are near to 0)
|
||
|
|
const double delta = 10.;
|
||
|
|
test_mat[REF_OUTPUT][0] += Scalar::all(delta);
|
||
|
|
test_mat[OUTPUT][0] += Scalar::all(delta);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
TEST(Imgproc_MatchTemplate, accuracy) { CV_TemplMatchTest test; test.safe_run(); }
|