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test_filter.cpp
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2121 lines (1712 loc) · 64.1 KB
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/*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_FilterBaseTest : public cvtest::ArrayTest
{
public:
CV_FilterBaseTest( bool _fp_kernel );
protected:
int prepare_test_case( int test_case_idx );
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 );
CvSize aperture_size;
CvPoint anchor;
int max_aperture_size;
bool fp_kernel;
bool inplace;
int border;
};
CV_FilterBaseTest::CV_FilterBaseTest( bool _fp_kernel ) : fp_kernel(_fp_kernel)
{
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);
max_aperture_size = 13;
inplace = false;
aperture_size = cvSize(0,0);
anchor = cvPoint(0,0);
element_wise_relative_error = false;
}
int CV_FilterBaseTest::read_params( CvFileStorage* fs )
{
int code = cvtest::ArrayTest::read_params( fs );
if( code < 0 )
return code;
max_aperture_size = cvReadInt( find_param( fs, "max_aperture_size" ), max_aperture_size );
max_aperture_size = cvtest::clipInt( max_aperture_size, 1, 100 );
return code;
}
void CV_FilterBaseTest::get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high )
{
cvtest::ArrayTest::get_minmax_bounds( i, j, type, low, high );
if( i == INPUT )
{
if( j == 1 )
{
if( fp_kernel )
{
RNG& rng = ts->get_rng();
double val = exp( cvtest::randReal(rng)*10 - 4 );
low = Scalar::all(-val);
high = Scalar::all(val);
}
else
{
low = Scalar::all(0);
high = Scalar::all(2);
}
}
else if( CV_MAT_DEPTH(type) == CV_16U )
{
low = Scalar::all(0.);
high = Scalar::all(40000.);
}
else if( CV_MAT_DEPTH(type) == CV_32F )
{
low = Scalar::all(-10.);
high = Scalar::all(10.);
}
}
}
void CV_FilterBaseTest::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) % CV_32F;
int cn = cvtest::randInt(rng) % 3 + 1;
cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
depth += depth == CV_8S;
cn += cn == 2;
types[INPUT][0] = types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_MAKETYPE(depth, cn);
aperture_size.width = cvtest::randInt(rng) % max_aperture_size + 1;
aperture_size.height = cvtest::randInt(rng) % max_aperture_size + 1;
anchor.x = cvtest::randInt(rng) % aperture_size.width;
anchor.y = cvtest::randInt(rng) % aperture_size.height;
types[INPUT][1] = fp_kernel ? CV_32FC1 : CV_8UC1;
sizes[INPUT][1] = aperture_size;
inplace = cvtest::randInt(rng) % 2 != 0;
border = BORDER_REPLICATE;
}
int CV_FilterBaseTest::prepare_test_case( int test_case_idx )
{
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
if( code > 0 )
{
if( inplace && test_mat[INPUT][0].type() == test_mat[OUTPUT][0].type())
cvtest::copy( test_mat[INPUT][0], test_mat[OUTPUT][0] );
else
inplace = false;
}
return code;
}
/////////////////////////
class CV_MorphologyBaseTest : public CV_FilterBaseTest
{
public:
CV_MorphologyBaseTest();
protected:
void prepare_to_validation( int test_case_idx );
int prepare_test_case( int test_case_idx );
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
double get_success_error_level( int test_case_idx, int i, int j );
int optype, optype_min, optype_max;
int shape;
IplConvKernel* element;
};
CV_MorphologyBaseTest::CV_MorphologyBaseTest() : CV_FilterBaseTest( false )
{
shape = -1;
element = 0;
optype = optype_min = optype_max = -1;
}
void CV_MorphologyBaseTest::get_test_array_types_and_sizes( int test_case_idx,
vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
CV_FilterBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
int depth = cvtest::randInt(rng) % 4;
depth = depth == 0 ? CV_8U : depth == 1 ? CV_16U : depth == 2 ? CV_16S : CV_32F;
int cn = CV_MAT_CN(types[INPUT][0]);
types[INPUT][0] = types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_MAKETYPE(depth, cn);
shape = cvtest::randInt(rng) % 4;
if( shape >= 3 )
shape = CV_SHAPE_CUSTOM;
else
sizes[INPUT][1] = cvSize(0,0);
optype = cvtest::randInt(rng) % (optype_max - optype_min + 1) + optype_min;
}
double CV_MorphologyBaseTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
return test_mat[INPUT][0].depth() < CV_32F ||
(optype == CV_MOP_ERODE || optype == CV_MOP_DILATE ||
optype == CV_MOP_OPEN || optype == CV_MOP_CLOSE) ? 0 : 1e-5;
}
int CV_MorphologyBaseTest::prepare_test_case( int test_case_idx )
{
int code = CV_FilterBaseTest::prepare_test_case( test_case_idx );
vector<int> eldata;
if( code <= 0 )
return code;
if( shape == CV_SHAPE_CUSTOM )
{
eldata.resize(aperture_size.width*aperture_size.height);
const uchar* src = test_mat[INPUT][1].ptr();
int srcstep = (int)test_mat[INPUT][1].step;
int i, j, nonzero = 0;
for( i = 0; i < aperture_size.height; i++ )
{
for( j = 0; j < aperture_size.width; j++ )
{
eldata[i*aperture_size.width + j] = src[i*srcstep + j];
nonzero += src[i*srcstep + j] != 0;
}
}
if( nonzero == 0 )
eldata[anchor.y*aperture_size.width + anchor.x] = 1;
}
cvReleaseStructuringElement( &element );
element = cvCreateStructuringElementEx( aperture_size.width, aperture_size.height,
anchor.x, anchor.y, shape, eldata.empty() ? 0 : &eldata[0] );
return code;
}
void CV_MorphologyBaseTest::prepare_to_validation( int /*test_case_idx*/ )
{
Mat& src = test_mat[INPUT][0], &dst = test_mat[REF_OUTPUT][0];
Mat _ielement(element->nRows, element->nCols, CV_32S, element->values);
Mat _element;
_ielement.convertTo(_element, CV_8U);
Point _anchor(element->anchorX, element->anchorY);
int _border = BORDER_REPLICATE;
if( optype == CV_MOP_ERODE )
{
cvtest::erode( src, dst, _element, _anchor, _border );
}
else if( optype == CV_MOP_DILATE )
{
cvtest::dilate( src, dst, _element, _anchor, _border );
}
else
{
Mat temp;
if( optype == CV_MOP_OPEN )
{
cvtest::erode( src, temp, _element, _anchor, _border );
cvtest::dilate( temp, dst, _element, _anchor, _border );
}
else if( optype == CV_MOP_CLOSE )
{
cvtest::dilate( src, temp, _element, _anchor, _border );
cvtest::erode( temp, dst, _element, _anchor, _border );
}
else if( optype == CV_MOP_GRADIENT )
{
cvtest::erode( src, temp, _element, _anchor, _border );
cvtest::dilate( src, dst, _element, _anchor, _border );
cvtest::add( dst, 1, temp, -1, Scalar::all(0), dst, dst.type() );
}
else if( optype == CV_MOP_TOPHAT )
{
cvtest::erode( src, temp, _element, _anchor, _border );
cvtest::dilate( temp, dst, _element, _anchor, _border );
cvtest::add( src, 1, dst, -1, Scalar::all(0), dst, dst.type() );
}
else if( optype == CV_MOP_BLACKHAT )
{
cvtest::dilate( src, temp, _element, _anchor, _border );
cvtest::erode( temp, dst, _element, _anchor, _border );
cvtest::add( dst, 1, src, -1, Scalar::all(0), dst, dst.type() );
}
else
CV_Error( CV_StsBadArg, "Unknown operation" );
}
cvReleaseStructuringElement( &element );
}
/////////////// erode ///////////////
class CV_ErodeTest : public CV_MorphologyBaseTest
{
public:
CV_ErodeTest();
protected:
void run_func();
};
CV_ErodeTest::CV_ErodeTest()
{
optype_min = optype_max = CV_MOP_ERODE;
}
void CV_ErodeTest::run_func()
{
cvErode( inplace ? test_array[OUTPUT][0] : test_array[INPUT][0],
test_array[OUTPUT][0], element, 1 );
}
/////////////// dilate ///////////////
class CV_DilateTest : public CV_MorphologyBaseTest
{
public:
CV_DilateTest();
protected:
void run_func();
};
CV_DilateTest::CV_DilateTest()
{
optype_min = optype_max = CV_MOP_DILATE;
}
void CV_DilateTest::run_func()
{
cvDilate( inplace ? test_array[OUTPUT][0] : test_array[INPUT][0],
test_array[OUTPUT][0], element, 1 );
}
/////////////// morphEx ///////////////
class CV_MorphExTest : public CV_MorphologyBaseTest
{
public:
CV_MorphExTest();
protected:
void run_func();
};
CV_MorphExTest::CV_MorphExTest()
{
optype_min = CV_MOP_ERODE;
optype_max = CV_MOP_BLACKHAT;
}
void CV_MorphExTest::run_func()
{
cvMorphologyEx( test_array[inplace ? OUTPUT : INPUT][0],
test_array[OUTPUT][0], 0, element, optype, 1 );
}
/////////////// generic filter ///////////////
class CV_FilterTest : public CV_FilterBaseTest
{
public:
CV_FilterTest();
protected:
void prepare_to_validation( int test_case_idx );
void run_func();
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
double get_success_error_level( int test_case_idx, int i, int j );
};
CV_FilterTest::CV_FilterTest() : CV_FilterBaseTest( true )
{
}
void CV_FilterTest::get_test_array_types_and_sizes( int test_case_idx,
vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
CV_FilterBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
RNG& rng = ts->get_rng();
int depth = cvtest::randInt(rng)%3;
int cn = CV_MAT_CN(types[INPUT][0]);
depth = depth == 0 ? CV_8U : depth == 1 ? CV_16U : CV_32F;
types[INPUT][0] = types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_MAKETYPE(depth, cn);
}
double CV_FilterTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
int depth = test_mat[INPUT][0].depth();
return depth <= CV_8S ? 2 : depth <= CV_32S ? 32 :
depth == CV_32F ? 1e-4 : 1e-10;
}
void CV_FilterTest::run_func()
{
CvMat kernel = test_mat[INPUT][1];
cvFilter2D( test_array[inplace ? OUTPUT : INPUT][0],
test_array[OUTPUT][0], &kernel, anchor );
}
void CV_FilterTest::prepare_to_validation( int /*test_case_idx*/ )
{
cvtest::filter2D( test_mat[INPUT][0], test_mat[REF_OUTPUT][0], test_mat[REF_OUTPUT][0].type(),
test_mat[INPUT][1], anchor, 0, BORDER_REPLICATE );
}
////////////////////////
class CV_DerivBaseTest : public CV_FilterBaseTest
{
public:
CV_DerivBaseTest();
protected:
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
double get_success_error_level( int test_case_idx, int i, int j );
int _aperture_size;
};
CV_DerivBaseTest::CV_DerivBaseTest() : CV_FilterBaseTest( true )
{
max_aperture_size = 7;
}
void CV_DerivBaseTest::get_test_array_types_and_sizes( int test_case_idx,
vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
CV_FilterBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
int depth = cvtest::randInt(rng) % 2;
depth = depth == 0 ? CV_8U : CV_32F;
types[INPUT][0] = CV_MAKETYPE(depth,1);
types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_MAKETYPE(depth==CV_8U?CV_16S:CV_32F,1);
_aperture_size = (cvtest::randInt(rng)%5)*2 - 1;
sizes[INPUT][1] = aperture_size = cvSize(_aperture_size, _aperture_size);
}
double CV_DerivBaseTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
int depth = test_mat[INPUT][0].depth();
return depth <= CV_8S ? 2 : 5e-4;
}
/////////////// sobel ///////////////
class CV_SobelTest : public CV_DerivBaseTest
{
public:
CV_SobelTest();
protected:
void prepare_to_validation( int test_case_idx );
void run_func();
void get_test_array_types_and_sizes( int test_case_idx,
vector<vector<Size> >& sizes, vector<vector<int> >& types );
int dx, dy, origin;
};
CV_SobelTest::CV_SobelTest() {}
void CV_SobelTest::get_test_array_types_and_sizes( int test_case_idx,
vector<vector<Size> >& sizes,
vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
CV_DerivBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
int max_d = _aperture_size > 0 ? 2 : 1;
origin = cvtest::randInt(rng) % 2;
dx = cvtest::randInt(rng) % (max_d + 1);
dy = cvtest::randInt(rng) % (max_d + 1 - dx);
if( dx == 0 && dy == 0 )
dx = 1;
if( cvtest::randInt(rng) % 2 )
{
int t;
CV_SWAP( dx, dy, t );
}
if( _aperture_size < 0 )
aperture_size = cvSize(3, 3);
else if( _aperture_size == 1 )
{
if( dx == 0 )
aperture_size = cvSize(1, 3);
else if( dy == 0 )
aperture_size = cvSize(3, 1);
else
{
_aperture_size = 3;
aperture_size = cvSize(3, 3);
}
}
else
aperture_size = cvSize(_aperture_size, _aperture_size);
sizes[INPUT][1] = aperture_size;
anchor.x = aperture_size.width / 2;
anchor.y = aperture_size.height / 2;
}
void CV_SobelTest::run_func()
{
cvSobel( test_array[inplace ? OUTPUT : INPUT][0],
test_array[OUTPUT][0], dx, dy, _aperture_size );
/*cv::Sobel( test_mat[inplace ? OUTPUT : INPUT][0],
test_mat[OUTPUT][0], test_mat[OUTPUT][0].depth(),
dx, dy, _aperture_size, 1, 0, border );*/
}
void CV_SobelTest::prepare_to_validation( int /*test_case_idx*/ )
{
Mat kernel = cvtest::calcSobelKernel2D( dx, dy, _aperture_size, 0 );
cvtest::filter2D( test_mat[INPUT][0], test_mat[REF_OUTPUT][0], test_mat[REF_OUTPUT][0].depth(),
kernel, anchor, 0, BORDER_REPLICATE);
}
/////////////// spatialGradient ///////////////
class CV_SpatialGradientTest : public CV_DerivBaseTest
{
public:
CV_SpatialGradientTest();
protected:
void prepare_to_validation( int test_case_idx );
void run_func();
void get_test_array_types_and_sizes( int test_case_idx,
vector<vector<Size> >& sizes, vector<vector<int> >& types );
int ksize;
};
CV_SpatialGradientTest::CV_SpatialGradientTest() {
test_array[OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
inplace = false;
}
void CV_SpatialGradientTest::get_test_array_types_and_sizes( int test_case_idx,
vector<vector<Size> >& sizes,
vector<vector<int> >& types )
{
CV_DerivBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
sizes[OUTPUT][1] = sizes[REF_OUTPUT][1] = sizes[OUTPUT][0];
// Inputs are only CV_8UC1 for now
types[INPUT][0] = CV_8UC1;
// Outputs are only CV_16SC1 for now
types[OUTPUT][0] = types[OUTPUT][1] = types[REF_OUTPUT][0]
= types[REF_OUTPUT][1] = CV_16SC1;
ksize = 3;
border = BORDER_DEFAULT; // TODO: Add BORDER_REPLICATE
}
void CV_SpatialGradientTest::run_func()
{
spatialGradient( test_mat[INPUT][0], test_mat[OUTPUT][0],
test_mat[OUTPUT][1], ksize, border );
}
void CV_SpatialGradientTest::prepare_to_validation( int /*test_case_idx*/ )
{
int dx, dy;
dx = 1; dy = 0;
Sobel( test_mat[INPUT][0], test_mat[REF_OUTPUT][0], CV_16SC1, dx, dy, ksize,
1, 0, border );
dx = 0; dy = 1;
Sobel( test_mat[INPUT][0], test_mat[REF_OUTPUT][1], CV_16SC1, dx, dy, ksize,
1, 0, border );
}
/////////////// laplace ///////////////
class CV_LaplaceTest : public CV_DerivBaseTest
{
public:
CV_LaplaceTest();
protected:
int prepare_test_case( int test_case_idx );
void prepare_to_validation( int test_case_idx );
void run_func();
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
};
CV_LaplaceTest::CV_LaplaceTest()
{
}
void CV_LaplaceTest::get_test_array_types_and_sizes( int test_case_idx,
vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
CV_DerivBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
if( _aperture_size <= 1 )
{
if( _aperture_size < 0 )
_aperture_size = 1;
aperture_size = cvSize(3, 3);
}
else
aperture_size = cvSize(_aperture_size, _aperture_size);
sizes[INPUT][1] = aperture_size;
anchor.x = aperture_size.width / 2;
anchor.y = aperture_size.height / 2;
}
void CV_LaplaceTest::run_func()
{
cvLaplace( test_array[inplace ? OUTPUT : INPUT][0],
test_array[OUTPUT][0], _aperture_size );
}
int CV_LaplaceTest::prepare_test_case( int test_case_idx )
{
int code = CV_DerivBaseTest::prepare_test_case( test_case_idx );
return _aperture_size < 0 ? 0 : code;
}
void CV_LaplaceTest::prepare_to_validation( int /*test_case_idx*/ )
{
Mat kernel = cvtest::calcLaplaceKernel2D( _aperture_size );
cvtest::filter2D( test_mat[INPUT][0], test_mat[REF_OUTPUT][0], test_mat[REF_OUTPUT][0].depth(),
kernel, anchor, 0, BORDER_REPLICATE );
}
////////////////////////////////////////////////////////////
class CV_SmoothBaseTest : public CV_FilterBaseTest
{
public:
CV_SmoothBaseTest();
protected:
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
double get_success_error_level( int test_case_idx, int i, int j );
const char* smooth_type;
};
CV_SmoothBaseTest::CV_SmoothBaseTest() : CV_FilterBaseTest( true )
{
smooth_type = "";
}
void CV_SmoothBaseTest::get_test_array_types_and_sizes( int test_case_idx,
vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
CV_FilterBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
int depth = cvtest::randInt(rng) % 2;
int cn = CV_MAT_CN(types[INPUT][0]);
depth = depth == 0 ? CV_8U : CV_32F;
types[INPUT][0] = types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_MAKETYPE(depth,cn);
anchor.x = cvtest::randInt(rng)%(max_aperture_size/2+1);
anchor.y = cvtest::randInt(rng)%(max_aperture_size/2+1);
aperture_size.width = anchor.x*2 + 1;
aperture_size.height = anchor.y*2 + 1;
sizes[INPUT][1] = aperture_size;
}
double CV_SmoothBaseTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
int depth = test_mat[INPUT][0].depth();
return depth < CV_32F ? 1 : 1e-5;
}
/////////////// blur ///////////////
class CV_BlurTest : public CV_SmoothBaseTest
{
public:
CV_BlurTest();
protected:
int prepare_test_case( int test_case_idx );
void prepare_to_validation( int test_case_idx );
void run_func();
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
bool normalize;
};
CV_BlurTest::CV_BlurTest()
{
}
void CV_BlurTest::get_test_array_types_and_sizes( int test_case_idx,
vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
CV_SmoothBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
int depth = cvtest::randInt(rng) % 4;
int cn = (cvtest::randInt(rng) % 4) + 1;
depth = depth == 0 ? CV_8U : depth == 1 ? CV_16U : depth == 2 ? CV_16S : CV_32F;
types[OUTPUT][0] = types[REF_OUTPUT][0] = types[INPUT][0] = CV_MAKETYPE(depth, cn);
normalize = cvtest::randInt(rng) % 2 != 0;
if( !normalize )
{
types[INPUT][0] = CV_MAKETYPE(depth, 1);
types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_MAKETYPE(depth==CV_8U?CV_16S:CV_32F,1);
}
}
void CV_BlurTest::run_func()
{
cvSmooth( inplace ? test_array[OUTPUT][0] : test_array[INPUT][0],
test_array[OUTPUT][0], normalize ? CV_BLUR : CV_BLUR_NO_SCALE,
aperture_size.width, aperture_size.height );
}
int CV_BlurTest::prepare_test_case( int test_case_idx )
{
int code = CV_SmoothBaseTest::prepare_test_case( test_case_idx );
return code > 0 && !normalize && test_mat[INPUT][0].channels() > 1 ? 0 : code;
}
void CV_BlurTest::prepare_to_validation( int /*test_case_idx*/ )
{
Mat kernel(aperture_size, CV_64F);
kernel.setTo(Scalar::all(normalize ? 1./(aperture_size.width*aperture_size.height) : 1.));
cvtest::filter2D( test_mat[INPUT][0], test_mat[REF_OUTPUT][0], test_mat[REF_OUTPUT][0].depth(),
kernel, anchor, 0, BORDER_REPLICATE );
}
/////////////// gaussian ///////////////
class CV_GaussianBlurTest : public CV_SmoothBaseTest
{
public:
CV_GaussianBlurTest();
protected:
void prepare_to_validation( int test_case_idx );
void run_func();
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
double get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ );
double sigma;
int param1, param2;
};
CV_GaussianBlurTest::CV_GaussianBlurTest() : CV_SmoothBaseTest()
{
sigma = 0.;
smooth_type = "Gaussian";
}
double CV_GaussianBlurTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
int depth = test_mat[INPUT][0].depth();
return depth <= CV_8S ? 8 : 1e-5;
}
void CV_GaussianBlurTest::get_test_array_types_and_sizes( int test_case_idx,
vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
int kernel_case = cvtest::randInt(rng) % 2;
CV_SmoothBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
anchor = cvPoint(aperture_size.width/2,aperture_size.height/2);
sigma = exp(cvtest::randReal(rng)*5-2);
param1 = aperture_size.width;
param2 = aperture_size.height;
if( kernel_case == 0 )
sigma = 0.;
}
void CV_GaussianBlurTest::run_func()
{
cvSmooth( test_array[inplace ? OUTPUT : INPUT][0],
test_array[OUTPUT][0], CV_GAUSSIAN,
param1, param2, sigma, sigma );
}
// !!! Copied from cvSmooth, if the code is changed in cvSmooth,
// make sure to update this one too.
#define SMALL_GAUSSIAN_SIZE 7
static void
calcGaussianKernel( int n, double sigma, vector<float>& kernel )
{
static const float small_gaussian_tab[][SMALL_GAUSSIAN_SIZE] =
{
{1.f},
{0.25f, 0.5f, 0.25f},
{0.0625f, 0.25f, 0.375f, 0.25f, 0.0625f},
{0.03125, 0.109375, 0.21875, 0.28125, 0.21875, 0.109375, 0.03125}
};
kernel.resize(n);
if( n <= SMALL_GAUSSIAN_SIZE && sigma <= 0 )
{
assert( n%2 == 1 );
memcpy( &kernel[0], small_gaussian_tab[n>>1], n*sizeof(kernel[0]));
}
else
{
double sigmaX = sigma > 0 ? sigma : (n/2 - 1)*0.3 + 0.8;
double scale2X = -0.5/(sigmaX*sigmaX);
double sum = 1.;
int i;
sum = kernel[n/2] = 1.f;
for( i = 1; i <= n/2; i++ )
{
kernel[n/2+i] = kernel[n/2-i] = (float)exp(scale2X*i*i);
sum += kernel[n/2+i]*2;
}
sum = 1./sum;
for( i = 0; i <= n/2; i++ )
kernel[n/2+i] = kernel[n/2-i] = (float)(kernel[n/2+i]*sum);
}
}
static Mat calcGaussianKernel2D( Size ksize, double sigma )
{
vector<float> kx, ky;
Mat kernel(ksize, CV_32F);
calcGaussianKernel( kernel.cols, sigma, kx );
calcGaussianKernel( kernel.rows, sigma, ky );
for( int i = 0; i < kernel.rows; i++ )
for( int j = 0; j < kernel.cols; j++ )
kernel.at<float>(i, j) = kx[j]*ky[i];
return kernel;
}
void CV_GaussianBlurTest::prepare_to_validation( int /*test_case_idx*/ )
{
Mat kernel = calcGaussianKernel2D( aperture_size, sigma );
cvtest::filter2D( test_mat[INPUT][0], test_mat[REF_OUTPUT][0], test_mat[REF_OUTPUT][0].depth(),
kernel, anchor, 0, border & ~BORDER_ISOLATED );
}
/////////////// median ///////////////
class CV_MedianBlurTest : public CV_SmoothBaseTest
{
public:
CV_MedianBlurTest();
protected:
void prepare_to_validation( int test_case_idx );
double get_success_error_level( int test_case_idx, int i, int j );
void run_func();
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
};
CV_MedianBlurTest::CV_MedianBlurTest()
{
smooth_type = "Median";
}
void CV_MedianBlurTest::get_test_array_types_and_sizes( int test_case_idx,
vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
CV_SmoothBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
int depth = CV_8U;
int cn = CV_MAT_CN(types[INPUT][0]);
types[INPUT][0] = types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_MAKETYPE(depth,cn);
types[INPUT][1] = CV_MAKETYPE(depth,1);
aperture_size.height = aperture_size.width;
anchor.x = anchor.y = aperture_size.width / 2;
sizes[INPUT][1] = cvSize(aperture_size.width,aperture_size.height);
sizes[OUTPUT][0] = sizes[INPUT][0];
sizes[REF_OUTPUT][0] = sizes[INPUT][0];
inplace = false;
border = BORDER_REPLICATE | BORDER_ISOLATED;
}
double CV_MedianBlurTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
return 0;
}
void CV_MedianBlurTest::run_func()
{
cvSmooth( test_array[INPUT][0], test_array[OUTPUT][0],
CV_MEDIAN, aperture_size.width );
}
struct median_pair
{
int col;
int val;
median_pair() { }
median_pair( int _col, int _val ) : col(_col), val(_val) { }
};
static void test_medianFilter( const Mat& src, Mat& dst, int m )
{
int i, j, k, l, m2 = m*m, n;
vector<int> col_buf(m+1);
vector<median_pair> _buf0(m*m+1), _buf1(m*m+1);
median_pair *buf0 = &_buf0[0], *buf1 = &_buf1[0];
int step = (int)(src.step/src.elemSize());
assert( src.rows == dst.rows + m - 1 && src.cols == dst.cols + m - 1 &&
src.type() == dst.type() && src.type() == CV_8UC1 );
for( i = 0; i < dst.rows; i++ )
{
uchar* dst1 = dst.ptr<uchar>(i);
for( k = 0; k < m; k++ )
{
const uchar* src1 = src.ptr<uchar>(i+k);
for( j = 0; j < m-1; j++ )
*buf0++ = median_pair(j, src1[j]);
}
n = m2 - m;
buf0 -= n;
for( k = n-1; k > 0; k-- )
{
int f = 0;
for( j = 0; j < k; j++ )
{
if( buf0[j].val > buf0[j+1].val )
{
median_pair t;
CV_SWAP( buf0[j], buf0[j+1], t );
f = 1;