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#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), a_sum2(0);
CvScalar ccorr(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(); }