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opencv/modules/imgproc/test/test_imgwarp.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
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// 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,
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// 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.
//
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//M*/
#include "test_precomp.hpp"
namespace opencv_test { namespace {
class CV_ImgWarpBaseTest : public cvtest::ArrayTest
{
public:
CV_ImgWarpBaseTest( bool warp_matrix );
protected:
int read_params( const cv::FileStorage& fs );
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 );
void get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high );
void fill_array( int test_case_idx, int i, int j, Mat& arr );
int interpolation;
int max_interpolation;
double spatial_scale_zoom, spatial_scale_decimate;
};
CV_ImgWarpBaseTest::CV_ImgWarpBaseTest( bool warp_matrix )
{
test_array[INPUT].push_back(NULL);
if( warp_matrix )
test_array[INPUT].push_back(NULL);
test_array[INPUT_OUTPUT].push_back(NULL);
test_array[REF_INPUT_OUTPUT].push_back(NULL);
max_interpolation = 5;
interpolation = 0;
element_wise_relative_error = false;
spatial_scale_zoom = 0.01;
spatial_scale_decimate = 0.005;
}
int CV_ImgWarpBaseTest::read_params( const cv::FileStorage& fs )
{
int code = cvtest::ArrayTest::read_params( fs );
return code;
}
void CV_ImgWarpBaseTest::get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high )
{
cvtest::ArrayTest::get_minmax_bounds( i, j, type, low, high );
if( CV_MAT_DEPTH(type) == CV_32F )
{
low = Scalar::all(-10.);
high = Scalar::all(10);
}
}
void CV_ImgWarpBaseTest::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) % 3;
int cn = cvtest::randInt(rng) % 3 + 1;
cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
depth = depth == 0 ? CV_8U : depth == 1 ? CV_16U : CV_32F;
cn += cn == 2;
types[INPUT][0] = types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = CV_MAKETYPE(depth, cn);
if( test_array[INPUT].size() > 1 )
types[INPUT][1] = cvtest::randInt(rng) & 1 ? CV_32FC1 : CV_64FC1;
interpolation = cvtest::randInt(rng) % max_interpolation;
}
void CV_ImgWarpBaseTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
{
if( i != INPUT || j != 0 )
cvtest::ArrayTest::fill_array( test_case_idx, i, j, arr );
}
int CV_ImgWarpBaseTest::prepare_test_case( int test_case_idx )
{
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
Mat& img = test_mat[INPUT][0];
int i, j, cols = img.cols;
int type = img.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
double scale = depth == CV_16U ? 1000. : 255.*0.5;
double space_scale = spatial_scale_decimate;
vector<float> buffer(img.cols*cn);
if( code <= 0 )
return code;
if( test_mat[INPUT_OUTPUT][0].cols >= img.cols &&
test_mat[INPUT_OUTPUT][0].rows >= img.rows )
space_scale = spatial_scale_zoom;
for( i = 0; i < img.rows; i++ )
{
uchar* ptr = img.ptr(i);
switch( cn )
{
case 1:
for( j = 0; j < cols; j++ )
buffer[j] = (float)((sin((i+1)*space_scale)*sin((j+1)*space_scale)+1.)*scale);
break;
case 2:
for( j = 0; j < cols; j++ )
{
buffer[j*2] = (float)((sin((i+1)*space_scale)+1.)*scale);
buffer[j*2+1] = (float)((sin((i+j)*space_scale)+1.)*scale);
}
break;
case 3:
for( j = 0; j < cols; j++ )
{
buffer[j*3] = (float)((sin((i+1)*space_scale)+1.)*scale);
buffer[j*3+1] = (float)((sin(j*space_scale)+1.)*scale);
buffer[j*3+2] = (float)((sin((i+j)*space_scale)+1.)*scale);
}
break;
case 4:
for( j = 0; j < cols; j++ )
{
buffer[j*4] = (float)((sin((i+1)*space_scale)+1.)*scale);
buffer[j*4+1] = (float)((sin(j*space_scale)+1.)*scale);
buffer[j*4+2] = (float)((sin((i+j)*space_scale)+1.)*scale);
buffer[j*4+3] = (float)((sin((i-j)*space_scale)+1.)*scale);
}
break;
default:
assert(0);
}
/*switch( depth )
{
case CV_8U:
for( j = 0; j < cols*cn; j++ )
ptr[j] = (uchar)cvRound(buffer[j]);
break;
case CV_16U:
for( j = 0; j < cols*cn; j++ )
((ushort*)ptr)[j] = (ushort)cvRound(buffer[j]);
break;
case CV_32F:
for( j = 0; j < cols*cn; j++ )
((float*)ptr)[j] = (float)buffer[j];
break;
default:
assert(0);
}*/
cv::Mat src(1, cols*cn, CV_32F, &buffer[0]);
cv::Mat dst(1, cols*cn, depth, ptr);
src.convertTo(dst, dst.type());
}
return code;
}
/////////////////////////
class CV_ResizeTest : public CV_ImgWarpBaseTest
{
public:
CV_ResizeTest();
protected:
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
void run_func();
void prepare_to_validation( int /*test_case_idx*/ );
double get_success_error_level( int test_case_idx, int i, int j );
};
CV_ResizeTest::CV_ResizeTest() : CV_ImgWarpBaseTest( false )
{
}
void CV_ResizeTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
Size sz;
sz.width = (cvtest::randInt(rng) % sizes[INPUT][0].width) + 1;
sz.height = (cvtest::randInt(rng) % sizes[INPUT][0].height) + 1;
if( cvtest::randInt(rng) & 1 )
{
int xfactor = cvtest::randInt(rng) % 10 + 1;
int yfactor = cvtest::randInt(rng) % 10 + 1;
if( cvtest::randInt(rng) & 1 )
yfactor = xfactor;
sz.width = sizes[INPUT][0].width / xfactor;
sz.width = MAX(sz.width,1);
sz.height = sizes[INPUT][0].height / yfactor;
sz.height = MAX(sz.height,1);
sizes[INPUT][0].width = sz.width * xfactor;
sizes[INPUT][0].height = sz.height * yfactor;
}
if( cvtest::randInt(rng) & 1 )
sizes[INPUT_OUTPUT][0] = sizes[REF_INPUT_OUTPUT][0] = sz;
else
{
sizes[INPUT_OUTPUT][0] = sizes[REF_INPUT_OUTPUT][0] = sizes[INPUT][0];
sizes[INPUT][0] = sz;
}
if( interpolation == 4 &&
(MIN(sizes[INPUT][0].width,sizes[INPUT_OUTPUT][0].width) < 4 ||
MIN(sizes[INPUT][0].height,sizes[INPUT_OUTPUT][0].height) < 4))
interpolation = 2;
}
void CV_ResizeTest::run_func()
{
cvResize( test_array[INPUT][0], test_array[INPUT_OUTPUT][0], interpolation );
}
double CV_ResizeTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
int depth = test_mat[INPUT][0].depth();
return depth == CV_8U ? 16 : depth == CV_16U ? 1024 : 1e-1;
}
void CV_ResizeTest::prepare_to_validation( int /*test_case_idx*/ )
{
CvMat _src = cvMat(test_mat[INPUT][0]), _dst = cvMat(test_mat[REF_INPUT_OUTPUT][0]);
CvMat *src = &_src, *dst = &_dst;
int i, j, k;
CvMat* x_idx = cvCreateMat( 1, dst->cols, CV_32SC1 );
CvMat* y_idx = cvCreateMat( 1, dst->rows, CV_32SC1 );
int* x_tab = x_idx->data.i;
int elem_size = CV_ELEM_SIZE(src->type);
int drows = dst->rows, dcols = dst->cols;
if( interpolation == CV_INTER_NN )
{
for( j = 0; j < dcols; j++ )
{
int t = (j*src->cols*2 + MIN(src->cols,dcols) - 1)/(dcols*2);
t -= t >= src->cols;
x_idx->data.i[j] = t*elem_size;
}
for( j = 0; j < drows; j++ )
{
int t = (j*src->rows*2 + MIN(src->rows,drows) - 1)/(drows*2);
t -= t >= src->rows;
y_idx->data.i[j] = t;
}
}
else
{
double scale_x = (double)src->cols/dcols;
double scale_y = (double)src->rows/drows;
for( j = 0; j < dcols; j++ )
{
double f = ((j+0.5)*scale_x - 0.5);
i = cvRound(f);
x_idx->data.i[j] = (i < 0 ? 0 : i >= src->cols ? src->cols - 1 : i)*elem_size;
}
for( j = 0; j < drows; j++ )
{
double f = ((j+0.5)*scale_y - 0.5);
i = cvRound(f);
y_idx->data.i[j] = i < 0 ? 0 : i >= src->rows ? src->rows - 1 : i;
}
}
for( i = 0; i < drows; i++ )
{
uchar* dptr = dst->data.ptr + dst->step*i;
const uchar* sptr0 = src->data.ptr + src->step*y_idx->data.i[i];
for( j = 0; j < dcols; j++, dptr += elem_size )
{
const uchar* sptr = sptr0 + x_tab[j];
for( k = 0; k < elem_size; k++ )
dptr[k] = sptr[k];
}
}
cvReleaseMat( &x_idx );
cvReleaseMat( &y_idx );
}
class CV_ResizeExactTest : public CV_ResizeTest
{
public:
CV_ResizeExactTest();
protected:
void get_test_array_types_and_sizes(int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types);
};
CV_ResizeExactTest::CV_ResizeExactTest() : CV_ResizeTest()
{
max_interpolation = 2;
}
void CV_ResizeExactTest::get_test_array_types_and_sizes(int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types)
{
CV_ResizeTest::get_test_array_types_and_sizes(test_case_idx, sizes, types);
switch (interpolation)
{
case 0:
interpolation = INTER_LINEAR_EXACT;
break;
case 1:
interpolation = INTER_NEAREST_EXACT;
break;
default:
CV_Assert(interpolation < max_interpolation);
}
if (CV_MAT_DEPTH(types[INPUT][0]) == CV_32F ||
CV_MAT_DEPTH(types[INPUT][0]) == CV_64F)
types[INPUT][0] = types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = CV_MAKETYPE(CV_8U, CV_MAT_CN(types[INPUT][0]));
}
/////////////////////////
static void test_remap( const Mat& src, Mat& dst, const Mat& mapx, const Mat& mapy,
Mat* mask=0, int interpolation=CV_INTER_LINEAR )
{
int x, y, k;
int drows = dst.rows, dcols = dst.cols;
int srows = src.rows, scols = src.cols;
const uchar* sptr0 = src.ptr();
int depth = src.depth(), cn = src.channels();
int elem_size = (int)src.elemSize();
int step = (int)(src.step / CV_ELEM_SIZE(depth));
int delta;
if( interpolation != CV_INTER_CUBIC )
{
delta = 0;
scols -= 1; srows -= 1;
}
else
{
delta = 1;
scols = MAX(scols - 3, 0);
srows = MAX(srows - 3, 0);
}
int scols1 = MAX(scols - 2, 0);
int srows1 = MAX(srows - 2, 0);
if( mask )
*mask = Scalar::all(0);
for( y = 0; y < drows; y++ )
{
uchar* dptr = dst.ptr(y);
const float* mx = mapx.ptr<float>(y);
const float* my = mapy.ptr<float>(y);
uchar* m = mask ? mask->ptr(y) : 0;
for( x = 0; x < dcols; x++, dptr += elem_size )
{
float xs = mx[x];
float ys = my[x];
int ixs = cvFloor(xs);
int iys = cvFloor(ys);
if( (unsigned)(ixs - delta - 1) >= (unsigned)scols1 ||
(unsigned)(iys - delta - 1) >= (unsigned)srows1 )
{
if( m )
m[x] = 1;
if( (unsigned)(ixs - delta) >= (unsigned)scols ||
(unsigned)(iys - delta) >= (unsigned)srows )
continue;
}
xs -= ixs;
ys -= iys;
switch( depth )
{
case CV_8U:
{
const uchar* sptr = sptr0 + iys*step + ixs*cn;
for( k = 0; k < cn; k++ )
{
float v00 = sptr[k];
float v01 = sptr[cn + k];
float v10 = sptr[step + k];
float v11 = sptr[step + cn + k];
v00 = v00 + xs*(v01 - v00);
v10 = v10 + xs*(v11 - v10);
v00 = v00 + ys*(v10 - v00);
dptr[k] = (uchar)cvRound(v00);
}
}
break;
case CV_16U:
{
const ushort* sptr = (const ushort*)sptr0 + iys*step + ixs*cn;
for( k = 0; k < cn; k++ )
{
float v00 = sptr[k];
float v01 = sptr[cn + k];
float v10 = sptr[step + k];
float v11 = sptr[step + cn + k];
v00 = v00 + xs*(v01 - v00);
v10 = v10 + xs*(v11 - v10);
v00 = v00 + ys*(v10 - v00);
((ushort*)dptr)[k] = (ushort)cvRound(v00);
}
}
break;
case CV_32F:
{
const float* sptr = (const float*)sptr0 + iys*step + ixs*cn;
for( k = 0; k < cn; k++ )
{
float v00 = sptr[k];
float v01 = sptr[cn + k];
float v10 = sptr[step + k];
float v11 = sptr[step + cn + k];
v00 = v00 + xs*(v01 - v00);
v10 = v10 + xs*(v11 - v10);
v00 = v00 + ys*(v10 - v00);
((float*)dptr)[k] = (float)v00;
}
}
break;
default:
assert(0);
}
}
}
}
/////////////////////////
class CV_WarpAffineTest : public CV_ImgWarpBaseTest
{
public:
CV_WarpAffineTest();
protected:
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
void run_func();
int prepare_test_case( int test_case_idx );
void prepare_to_validation( int /*test_case_idx*/ );
double get_success_error_level( int test_case_idx, int i, int j );
};
CV_WarpAffineTest::CV_WarpAffineTest() : CV_ImgWarpBaseTest( true )
{
//spatial_scale_zoom = spatial_scale_decimate;
spatial_scale_decimate = spatial_scale_zoom;
}
void CV_WarpAffineTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
Size sz = sizes[INPUT][0];
// run for the second time to get output of a different size
CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
sizes[INPUT][0] = sz;
sizes[INPUT][1] = Size( 3, 2 );
}
void CV_WarpAffineTest::run_func()
{
CvMat mtx = cvMat(test_mat[INPUT][1]);
cvWarpAffine( test_array[INPUT][0], test_array[INPUT_OUTPUT][0], &mtx, interpolation );
}
double CV_WarpAffineTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
int depth = test_mat[INPUT][0].depth();
return depth == CV_8U ? 16 : depth == CV_16U ? 1024 : 5e-2;
}
int CV_WarpAffineTest::prepare_test_case( int test_case_idx )
{
RNG& rng = ts->get_rng();
int code = CV_ImgWarpBaseTest::prepare_test_case( test_case_idx );
const Mat& src = test_mat[INPUT][0];
const Mat& dst = test_mat[INPUT_OUTPUT][0];
Mat& mat = test_mat[INPUT][1];
Point2f center;
double scale, angle;
if( code <= 0 )
return code;
double buffer[6];
Mat tmp( 2, 3, mat.type(), buffer );
center.x = (float)((cvtest::randReal(rng)*1.2 - 0.1)*src.cols);
center.y = (float)((cvtest::randReal(rng)*1.2 - 0.1)*src.rows);
angle = cvtest::randReal(rng)*360;
scale = ((double)dst.rows/src.rows + (double)dst.cols/src.cols)*0.5;
getRotationMatrix2D(center, angle, scale).convertTo(mat, mat.depth());
rng.fill( tmp, CV_RAND_NORMAL, Scalar::all(1.), Scalar::all(0.01) );
cv::max(tmp, 0.9, tmp);
cv::min(tmp, 1.1, tmp);
cv::multiply(tmp, mat, mat, 1.);
return code;
}
void CV_WarpAffineTest::prepare_to_validation( int /*test_case_idx*/ )
{
const Mat& src = test_mat[INPUT][0];
Mat& dst = test_mat[REF_INPUT_OUTPUT][0];
Mat& dst0 = test_mat[INPUT_OUTPUT][0];
Mat mapx(dst.size(), CV_32F), mapy(dst.size(), CV_32F);
double m[6];
Mat srcAb, dstAb( 2, 3, CV_64FC1, m );
//cvInvert( &tM, &M, CV_LU );
// [R|t] -> [R^-1 | -(R^-1)*t]
test_mat[INPUT][1].convertTo( srcAb, CV_64F );
Mat A = srcAb.colRange(0, 2);
Mat b = srcAb.col(2);
Mat invA = dstAb.colRange(0, 2);
Mat invAb = dstAb.col(2);
cv::invert(A, invA, CV_SVD);
cv::gemm(invA, b, -1, Mat(), 0, invAb);
for( int y = 0; y < dst.rows; y++ )
for( int x = 0; x < dst.cols; x++ )
{
mapx.at<float>(y, x) = (float)(x*m[0] + y*m[1] + m[2]);
mapy.at<float>(y, x) = (float)(x*m[3] + y*m[4] + m[5]);
}
Mat mask( dst.size(), CV_8U );
test_remap( src, dst, mapx, mapy, &mask );
dst.setTo(Scalar::all(0), mask);
dst0.setTo(Scalar::all(0), mask);
}
/////////////////////////
class CV_WarpPerspectiveTest : public CV_ImgWarpBaseTest
{
public:
CV_WarpPerspectiveTest();
protected:
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
void run_func();
int prepare_test_case( int test_case_idx );
void prepare_to_validation( int /*test_case_idx*/ );
double get_success_error_level( int test_case_idx, int i, int j );
};
CV_WarpPerspectiveTest::CV_WarpPerspectiveTest() : CV_ImgWarpBaseTest( true )
{
//spatial_scale_zoom = spatial_scale_decimate;
spatial_scale_decimate = spatial_scale_zoom;
}
void CV_WarpPerspectiveTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
Size sz = sizes[INPUT][0];
// run for the second time to get output of a different size
CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
sizes[INPUT][0] = sz;
sizes[INPUT][1] = Size( 3, 3 );
}
void CV_WarpPerspectiveTest::run_func()
{
CvMat mtx = cvMat(test_mat[INPUT][1]);
cvWarpPerspective( test_array[INPUT][0], test_array[INPUT_OUTPUT][0], &mtx, interpolation );
}
double CV_WarpPerspectiveTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
int depth = test_mat[INPUT][0].depth();
return depth == CV_8U ? 16 : depth == CV_16U ? 1024 : 5e-2;
}
int CV_WarpPerspectiveTest::prepare_test_case( int test_case_idx )
{
RNG& rng = ts->get_rng();
int code = CV_ImgWarpBaseTest::prepare_test_case( test_case_idx );
const CvMat src = cvMat(test_mat[INPUT][0]);
const CvMat dst = cvMat(test_mat[INPUT_OUTPUT][0]);
Mat& mat = test_mat[INPUT][1];
Point2f s[4], d[4];
int i;
if( code <= 0 )
return code;
s[0] = Point2f(0,0);
d[0] = Point2f(0,0);
s[1] = Point2f(src.cols-1.f,0);
d[1] = Point2f(dst.cols-1.f,0);
s[2] = Point2f(src.cols-1.f,src.rows-1.f);
d[2] = Point2f(dst.cols-1.f,dst.rows-1.f);
s[3] = Point2f(0,src.rows-1.f);
d[3] = Point2f(0,dst.rows-1.f);
float bufer[16];
Mat tmp( 1, 16, CV_32FC1, bufer );
rng.fill( tmp, CV_RAND_NORMAL, Scalar::all(0.), Scalar::all(0.1) );
for( i = 0; i < 4; i++ )
{
s[i].x += bufer[i*4]*src.cols/2;
s[i].y += bufer[i*4+1]*src.rows/2;
d[i].x += bufer[i*4+2]*dst.cols/2;
d[i].y += bufer[i*4+3]*dst.rows/2;
}
cv::getPerspectiveTransform( s, d ).convertTo( mat, mat.depth() );
return code;
}
void CV_WarpPerspectiveTest::prepare_to_validation( int /*test_case_idx*/ )
{
Mat& src = test_mat[INPUT][0];
Mat& dst = test_mat[REF_INPUT_OUTPUT][0];
Mat& dst0 = test_mat[INPUT_OUTPUT][0];
Mat mapx(dst.size(), CV_32F), mapy(dst.size(), CV_32F);
double m[9];
Mat srcM, dstM(3, 3, CV_64F, m);
//cvInvert( &tM, &M, CV_LU );
// [R|t] -> [R^-1 | -(R^-1)*t]
test_mat[INPUT][1].convertTo( srcM, CV_64F );
cv::invert(srcM, dstM, CV_SVD);
for( int y = 0; y < dst.rows; y++ )
{
for( int x = 0; x < dst.cols; x++ )
{
double xs = x*m[0] + y*m[1] + m[2];
double ys = x*m[3] + y*m[4] + m[5];
double ds = x*m[6] + y*m[7] + m[8];
ds = ds ? 1./ds : 0;
xs *= ds;
ys *= ds;
mapx.at<float>(y, x) = (float)xs;
mapy.at<float>(y, x) = (float)ys;
}
}
Mat mask( dst.size(), CV_8U );
test_remap( src, dst, mapx, mapy, &mask );
dst.setTo(Scalar::all(0), mask);
dst0.setTo(Scalar::all(0), mask);
}
/////////////////////////
class CV_RemapTest : public CV_ImgWarpBaseTest
{
public:
CV_RemapTest();
protected:
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
void run_func();
int prepare_test_case( int test_case_idx );
void prepare_to_validation( int /*test_case_idx*/ );
double get_success_error_level( int test_case_idx, int i, int j );
void fill_array( int test_case_idx, int i, int j, Mat& arr );
};
CV_RemapTest::CV_RemapTest() : CV_ImgWarpBaseTest( false )
{
//spatial_scale_zoom = spatial_scale_decimate;
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
spatial_scale_decimate = spatial_scale_zoom;
}
void CV_RemapTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
types[INPUT][1] = types[INPUT][2] = CV_32FC1;
interpolation = CV_INTER_LINEAR;
}
void CV_RemapTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
{
if( i != INPUT )
CV_ImgWarpBaseTest::fill_array( test_case_idx, i, j, arr );
}
void CV_RemapTest::run_func()
{
cvRemap( test_array[INPUT][0], test_array[INPUT_OUTPUT][0],
test_array[INPUT][1], test_array[INPUT][2], interpolation );
}
double CV_RemapTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
int depth = test_mat[INPUT][0].depth();
return depth == CV_8U ? 16 : depth == CV_16U ? 1024 : 5e-2;
}
int CV_RemapTest::prepare_test_case( int test_case_idx )
{
RNG& rng = ts->get_rng();
int code = CV_ImgWarpBaseTest::prepare_test_case( test_case_idx );
const Mat& src = test_mat[INPUT][0];
double a[9] = {0,0,0,0,0,0,0,0,1}, k[4];
Mat _a( 3, 3, CV_64F, a );
Mat _k( 4, 1, CV_64F, k );
double sz = MAX(src.rows, src.cols);
if( code <= 0 )
return code;
double aspect_ratio = cvtest::randReal(rng)*0.6 + 0.7;
a[2] = (src.cols - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
a[5] = (src.rows - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
a[0] = sz/(0.9 - cvtest::randReal(rng)*0.6);
a[4] = aspect_ratio*a[0];
k[0] = cvtest::randReal(rng)*0.06 - 0.03;
k[1] = cvtest::randReal(rng)*0.06 - 0.03;
if( k[0]*k[1] > 0 )
k[1] = -k[1];
k[2] = cvtest::randReal(rng)*0.004 - 0.002;
k[3] = cvtest::randReal(rng)*0.004 - 0.002;
cvtest::initUndistortMap( _a, _k, Mat(), Mat(), test_mat[INPUT][1].size(), test_mat[INPUT][1], test_mat[INPUT][2], CV_32F );
return code;
}
void CV_RemapTest::prepare_to_validation( int /*test_case_idx*/ )
{
Mat& dst = test_mat[REF_INPUT_OUTPUT][0];
Mat& dst0 = test_mat[INPUT_OUTPUT][0];
Mat mask( dst.size(), CV_8U );
test_remap(test_mat[INPUT][0], dst, test_mat[INPUT][1],
test_mat[INPUT][2], &mask, interpolation );
dst.setTo(Scalar::all(0), mask);
dst0.setTo(Scalar::all(0), mask);
}
////////////////////////////// GetRectSubPix /////////////////////////////////
static void
test_getQuadrangeSubPix( const Mat& src, Mat& dst, double* a )
{
int sstep = (int)(src.step / sizeof(float));
int scols = src.cols, srows = src.rows;
CV_Assert( src.depth() == CV_32F && src.type() == dst.type() );
int cn = dst.channels();
for( int y = 0; y < dst.rows; y++ )
for( int x = 0; x < dst.cols; x++ )
{
float* d = dst.ptr<float>(y) + x*cn;
float sx = (float)(a[0]*x + a[1]*y + a[2]);
float sy = (float)(a[3]*x + a[4]*y + a[5]);
int ix = cvFloor(sx), iy = cvFloor(sy);
int dx = cn, dy = sstep;
const float* s;
sx -= ix; sy -= iy;
if( (unsigned)ix >= (unsigned)(scols-1) )
ix = ix < 0 ? 0 : scols - 1, sx = 0, dx = 0;
if( (unsigned)iy >= (unsigned)(srows-1) )
iy = iy < 0 ? 0 : srows - 1, sy = 0, dy = 0;
s = src.ptr<float>(iy) + ix*cn;
for( int k = 0; k < cn; k++, s++ )
{
float t0 = s[0] + sx*(s[dx] - s[0]);
float t1 = s[dy] + sx*(s[dy + dx] - s[dy]);
d[k] = t0 + sy*(t1 - t0);
}
}
}
class CV_GetRectSubPixTest : public CV_ImgWarpBaseTest
{
public:
CV_GetRectSubPixTest();
protected:
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
void run_func();
int prepare_test_case( int test_case_idx );
void prepare_to_validation( int /*test_case_idx*/ );
double get_success_error_level( int test_case_idx, int i, int j );
void fill_array( int test_case_idx, int i, int j, Mat& arr );
CvPoint2D32f center;
bool test_cpp;
};
CV_GetRectSubPixTest::CV_GetRectSubPixTest() : CV_ImgWarpBaseTest( false )
{
//spatial_scale_zoom = spatial_scale_decimate;
spatial_scale_decimate = spatial_scale_zoom;
test_cpp = false;
}
void CV_GetRectSubPixTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
int src_depth = cvtest::randInt(rng) % 2, dst_depth;
int cn = cvtest::randInt(rng) % 2 ? 3 : 1;
Size src_size, dst_size;
dst_depth = src_depth = src_depth == 0 ? CV_8U : CV_32F;
if( src_depth < CV_32F && cvtest::randInt(rng) % 2 )
dst_depth = CV_32F;
types[INPUT][0] = CV_MAKETYPE(src_depth,cn);
types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = CV_MAKETYPE(dst_depth,cn);
src_size = sizes[INPUT][0];
dst_size.width = cvRound(sqrt(cvtest::randReal(rng)*src_size.width) + 1);
dst_size.height = cvRound(sqrt(cvtest::randReal(rng)*src_size.height) + 1);
dst_size.width = MIN(dst_size.width,src_size.width);
dst_size.height = MIN(dst_size.width,src_size.height);
sizes[INPUT_OUTPUT][0] = sizes[REF_INPUT_OUTPUT][0] = dst_size;
center.x = (float)(cvtest::randReal(rng)*src_size.width);
center.y = (float)(cvtest::randReal(rng)*src_size.height);
interpolation = CV_INTER_LINEAR;
test_cpp = (cvtest::randInt(rng) & 256) == 0;
}
void CV_GetRectSubPixTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
{
if( i != INPUT )
CV_ImgWarpBaseTest::fill_array( test_case_idx, i, j, arr );
}
void CV_GetRectSubPixTest::run_func()
{
if(!test_cpp)
cvGetRectSubPix( test_array[INPUT][0], test_array[INPUT_OUTPUT][0], center );
else
{
cv::Mat _out = cv::cvarrToMat(test_array[INPUT_OUTPUT][0]);
cv::getRectSubPix( cv::cvarrToMat(test_array[INPUT][0]), _out.size(), center, _out, _out.type());
}
}
double CV_GetRectSubPixTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
int in_depth = test_mat[INPUT][0].depth();
int out_depth = test_mat[INPUT_OUTPUT][0].depth();
return in_depth >= CV_32F ? 1e-3 : out_depth >= CV_32F ? 1e-2 : 1;
}
int CV_GetRectSubPixTest::prepare_test_case( int test_case_idx )
{
return CV_ImgWarpBaseTest::prepare_test_case( test_case_idx );
}
void CV_GetRectSubPixTest::prepare_to_validation( int /*test_case_idx*/ )
{
Mat& src0 = test_mat[INPUT][0];
Mat& dst0 = test_mat[REF_INPUT_OUTPUT][0];
Mat src = src0, dst = dst0;
int ftype = CV_MAKETYPE(CV_32F,src0.channels());
double a[] = { 1, 0, center.x - dst.cols*0.5 + 0.5,
0, 1, center.y - dst.rows*0.5 + 0.5 };
if( src.depth() != CV_32F )
src0.convertTo(src, CV_32F);
if( dst.depth() != CV_32F )
dst.create(dst0.size(), ftype);
test_getQuadrangeSubPix( src, dst, a );
if( dst.data != dst0.data )
dst.convertTo(dst0, dst0.depth());
}
class CV_GetQuadSubPixTest : public CV_ImgWarpBaseTest
{
public:
CV_GetQuadSubPixTest();
protected:
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
void run_func();
int prepare_test_case( int test_case_idx );
void prepare_to_validation( int /*test_case_idx*/ );
double get_success_error_level( int test_case_idx, int i, int j );
};
CV_GetQuadSubPixTest::CV_GetQuadSubPixTest() : CV_ImgWarpBaseTest( true )
{
//spatial_scale_zoom = spatial_scale_decimate;
spatial_scale_decimate = spatial_scale_zoom;
}
void CV_GetQuadSubPixTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
int min_size = 4;
CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
Size sz = sizes[INPUT][0], dsz;
RNG& rng = ts->get_rng();
int msz, src_depth = cvtest::randInt(rng) % 2, dst_depth;
int cn = cvtest::randInt(rng) % 2 ? 3 : 1;
dst_depth = src_depth = src_depth == 0 ? CV_8U : CV_32F;
if( src_depth < CV_32F && cvtest::randInt(rng) % 2 )
dst_depth = CV_32F;
types[INPUT][0] = CV_MAKETYPE(src_depth,cn);
types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = CV_MAKETYPE(dst_depth,cn);
sz.width = MAX(sz.width,min_size);
sz.height = MAX(sz.height,min_size);
sizes[INPUT][0] = sz;
msz = MIN( sz.width, sz.height );
dsz.width = cvRound(sqrt(cvtest::randReal(rng)*msz) + 1);
dsz.height = cvRound(sqrt(cvtest::randReal(rng)*msz) + 1);
dsz.width = MIN(dsz.width,msz);
dsz.height = MIN(dsz.width,msz);
dsz.width = MAX(dsz.width,min_size);
dsz.height = MAX(dsz.height,min_size);
sizes[INPUT_OUTPUT][0] = sizes[REF_INPUT_OUTPUT][0] = dsz;
sizes[INPUT][1] = cvSize( 3, 2 );
}
void CV_GetQuadSubPixTest::run_func()
{
CvMat mtx = cvMat(test_mat[INPUT][1]);
cvGetQuadrangleSubPix( test_array[INPUT][0], test_array[INPUT_OUTPUT][0], &mtx );
}
double CV_GetQuadSubPixTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
int in_depth = test_mat[INPUT][0].depth();
//int out_depth = test_mat[INPUT_OUTPUT][0].depth();
return in_depth >= CV_32F ? 1e-2 : 4;
}
int CV_GetQuadSubPixTest::prepare_test_case( int test_case_idx )
{
RNG& rng = ts->get_rng();
int code = CV_ImgWarpBaseTest::prepare_test_case( test_case_idx );
const Mat& src = test_mat[INPUT][0];
Mat& mat = test_mat[INPUT][1];
Point2f center;
double scale, angle;
if( code <= 0 )
return code;
double a[6];
Mat A( 2, 3, CV_64FC1, a );
center.x = (float)((cvtest::randReal(rng)*1.2 - 0.1)*src.cols);
center.y = (float)((cvtest::randReal(rng)*1.2 - 0.1)*src.rows);
angle = cvtest::randReal(rng)*360;
scale = cvtest::randReal(rng)*0.2 + 0.9;
// y = Ax + b -> x = A^-1(y - b) = A^-1*y - A^-1*b
scale = 1./scale;
angle = angle*(CV_PI/180.);
a[0] = a[4] = cos(angle)*scale;
a[1] = sin(angle)*scale;
a[3] = -a[1];
a[2] = center.x - a[0]*center.x - a[1]*center.y;
a[5] = center.y - a[3]*center.x - a[4]*center.y;
A.convertTo( mat, mat.depth() );
return code;
}
void CV_GetQuadSubPixTest::prepare_to_validation( int /*test_case_idx*/ )
{
Mat& src0 = test_mat[INPUT][0];
Mat& dst0 = test_mat[REF_INPUT_OUTPUT][0];
Mat src = src0, dst = dst0;
int ftype = CV_MAKETYPE(CV_32F,src0.channels());
double a[6], dx = (dst0.cols - 1)*0.5, dy = (dst0.rows - 1)*0.5;
Mat A( 2, 3, CV_64F, a );
if( src.depth() != CV_32F )
src0.convertTo(src, CV_32F);
if( dst.depth() != CV_32F )
dst.create(dst0.size(), ftype);
test_mat[INPUT][1].convertTo( A, CV_64F );
a[2] -= a[0]*dx + a[1]*dy;
a[5] -= a[3]*dx + a[4]*dy;
test_getQuadrangeSubPix( src, dst, a );
if( dst.data != dst0.data )
dst.convertTo(dst0, dst0.depth());
}
////////////////////////////// resizeArea /////////////////////////////////
template <typename T>
static void check_resize_area(const Mat& expected, const Mat& actual, double tolerance = 1.0)
{
ASSERT_EQ(actual.type(), expected.type());
ASSERT_EQ(actual.size(), expected.size());
Mat diff;
absdiff(actual, expected, diff);
Mat one_channel_diff = diff; //.reshape(1);
Size dsize = actual.size();
bool next = true;
for (int dy = 0; dy < dsize.height && next; ++dy)
{
const T* eD = expected.ptr<T>(dy);
const T* aD = actual.ptr<T>(dy);
for (int dx = 0; dx < dsize.width && next; ++dx)
if (fabs(static_cast<double>(aD[dx] - eD[dx])) > tolerance)
{
cvtest::TS::ptr()->printf(cvtest::TS::SUMMARY, "Inf norm: %f\n", static_cast<float>(cvtest::norm(actual, expected, NORM_INF)));
cvtest::TS::ptr()->printf(cvtest::TS::SUMMARY, "Error in : (%d, %d)\n", dx, dy);
const int radius = 3;
int rmin = MAX(dy - radius, 0), rmax = MIN(dy + radius, dsize.height);
int cmin = MAX(dx - radius, 0), cmax = MIN(dx + radius, dsize.width);
std::cout << "Abs diff:" << std::endl << diff << std::endl;
std::cout << "actual result:\n" << actual(Range(rmin, rmax), Range(cmin, cmax)) << std::endl;
std::cout << "expected result:\n" << expected(Range(rmin, rmax), Range(cmin, cmax)) << std::endl;
next = false;
}
}
ASSERT_EQ(0, cvtest::norm(one_channel_diff, cv::NORM_INF));
}
///////////////////////////////////////////////////////////////////////////
TEST(Imgproc_cvWarpAffine, regression)
{
IplImage* src = cvCreateImage(cvSize(100, 100), IPL_DEPTH_8U, 1);
IplImage* dst = cvCreateImage(cvSize(100, 100), IPL_DEPTH_8U, 1);
cvZero(src);
float m[6];
CvMat M = cvMat( 2, 3, CV_32F, m );
int w = src->width;
int h = src->height;
cv2DRotationMatrix(cvPoint2D32f(w*0.5f, h*0.5f), 45.0, 1.0, &M);
cvWarpAffine(src, dst, &M);
cvReleaseImage(&src);
cvReleaseImage(&dst);
}
TEST(Imgproc_fitLine_vector_3d, regression)
{
std::vector<Point3f> points_vector;
Point3f p21(4,4,4);
Point3f p22(8,8,8);
points_vector.push_back(p21);
points_vector.push_back(p22);
std::vector<float> line;
cv::fitLine(points_vector, line, CV_DIST_L2, 0 ,0 ,0);
ASSERT_EQ(line.size(), (size_t)6);
}
TEST(Imgproc_fitLine_vector_2d, regression)
{
std::vector<Point2f> points_vector;
Point2f p21(4,4);
Point2f p22(8,8);
Point2f p23(16,16);
points_vector.push_back(p21);
points_vector.push_back(p22);
points_vector.push_back(p23);
std::vector<float> line;
cv::fitLine(points_vector, line, CV_DIST_L2, 0 ,0 ,0);
ASSERT_EQ(line.size(), (size_t)4);
}
TEST(Imgproc_fitLine_Mat_2dC2, regression)
{
cv::Mat mat1 = Mat::zeros(3, 1, CV_32SC2);
std::vector<float> line1;
cv::fitLine(mat1, line1, CV_DIST_L2, 0 ,0 ,0);
ASSERT_EQ(line1.size(), (size_t)4);
}
TEST(Imgproc_fitLine_Mat_2dC1, regression)
{
cv::Matx<int, 3, 2> mat2;
std::vector<float> line2;
cv::fitLine(mat2, line2, CV_DIST_L2, 0 ,0 ,0);
ASSERT_EQ(line2.size(), (size_t)4);
}
TEST(Imgproc_fitLine_Mat_3dC3, regression)
{
cv::Mat mat1 = Mat::zeros(2, 1, CV_32SC3);
std::vector<float> line1;
cv::fitLine(mat1, line1, CV_DIST_L2, 0 ,0 ,0);
ASSERT_EQ(line1.size(), (size_t)6);
}
TEST(Imgproc_fitLine_Mat_3dC1, regression)
{
cv::Mat mat2 = Mat::zeros(2, 3, CV_32SC1);
std::vector<float> line2;
cv::fitLine(mat2, line2, CV_DIST_L2, 0 ,0 ,0);
ASSERT_EQ(line2.size(), (size_t)6);
}
TEST(Imgproc_resize_area, regression)
{
static ushort input_data[16 * 16] = {
90, 94, 80, 3, 231, 2, 186, 245, 188, 165, 10, 19, 201, 169, 8, 228,
86, 5, 203, 120, 136, 185, 24, 94, 81, 150, 163, 137, 88, 105, 132, 132,
236, 48, 250, 218, 19, 52, 54, 221, 159, 112, 45, 11, 152, 153, 112, 134,
78, 133, 136, 83, 65, 76, 82, 250, 9, 235, 148, 26, 236, 179, 200, 50,
99, 51, 103, 142, 201, 65, 176, 33, 49, 226, 177, 109, 46, 21, 67, 130,
54, 125, 107, 154, 145, 51, 199, 189, 161, 142, 231, 240, 139, 162, 240, 22,
231, 86, 79, 106, 92, 47, 146, 156, 36, 207, 71, 33, 2, 244, 221, 71,
44, 127, 71, 177, 75, 126, 68, 119, 200, 129, 191, 251, 6, 236, 247, 6,
133, 175, 56, 239, 147, 221, 243, 154, 242, 82, 106, 99, 77, 158, 60, 229,
2, 42, 24, 174, 27, 198, 14, 204, 246, 251, 141, 31, 114, 163, 29, 147,
121, 53, 74, 31, 147, 189, 42, 98, 202, 17, 228, 123, 209, 40, 77, 49,
112, 203, 30, 12, 205, 25, 19, 106, 145, 185, 163, 201, 237, 223, 247, 38,
33, 105, 243, 117, 92, 179, 204, 248, 160, 90, 73, 126, 2, 41, 213, 204,
6, 124, 195, 201, 230, 187, 210, 167, 48, 79, 123, 159, 145, 218, 105, 209,
240, 152, 136, 235, 235, 164, 157, 9, 152, 38, 27, 209, 120, 77, 238, 196,
240, 233, 10, 241, 90, 67, 12, 79, 0, 43, 58, 27, 83, 199, 190, 182};
static ushort expected_data[5 * 5] = {
120, 100, 151, 101, 130,
106, 115, 141, 130, 127,
91, 136, 170, 114, 140,
104, 122, 131, 147, 133,
161, 163, 70, 107, 182
};
cv::Mat src(16, 16, CV_16UC1, input_data);
cv::Mat expected(5, 5, CV_16UC1, expected_data);
cv::Mat actual(expected.size(), expected.type());
cv::resize(src, actual, cv::Size(), 0.3, 0.3, INTER_AREA);
check_resize_area<ushort>(expected, actual, 1.0);
}
TEST(Imgproc_resize_area, regression_half_round)
{
static uchar input_data[32 * 32];
for(int i = 0; i < 32 * 32; ++i)
input_data[i] = (uchar)(i % 2 + 253 + i / (16 * 32));
static uchar expected_data[16 * 16];
for(int i = 0; i < 16 * 16; ++i)
expected_data[i] = (uchar)(254 + i / (16 * 8));
cv::Mat src(32, 32, CV_8UC1, input_data);
cv::Mat expected(16, 16, CV_8UC1, expected_data);
cv::Mat actual(expected.size(), expected.type());
cv::resize(src, actual, cv::Size(), 0.5, 0.5, INTER_AREA);
check_resize_area<uchar>(expected, actual, 0.5);
}
TEST(Imgproc_resize_area, regression_quarter_round)
{
static uchar input_data[32 * 32];
for(int i = 0; i < 32 * 32; ++i)
input_data[i] = (uchar)(i % 2 + 253 + i / (16 * 32));
static uchar expected_data[8 * 8];
for(int i = 0; i < 8 * 8; ++i)
expected_data[i] = 254;
cv::Mat src(32, 32, CV_8UC1, input_data);
cv::Mat expected(8, 8, CV_8UC1, expected_data);
cv::Mat actual(expected.size(), expected.type());
cv::resize(src, actual, cv::Size(), 0.25, 0.25, INTER_AREA);
check_resize_area<uchar>(expected, actual, 0.5);
}
//////////////////////////////////////////////////////////////////////////
TEST(Imgproc_Resize, accuracy) { CV_ResizeTest test; test.safe_run(); }
TEST(Imgproc_ResizeExact, accuracy) { CV_ResizeExactTest test; test.safe_run(); }
TEST(Imgproc_WarpAffine, accuracy) { CV_WarpAffineTest test; test.safe_run(); }
TEST(Imgproc_WarpPerspective, accuracy) { CV_WarpPerspectiveTest test; test.safe_run(); }
TEST(Imgproc_Remap, accuracy) { CV_RemapTest test; test.safe_run(); }
TEST(Imgproc_GetRectSubPix, accuracy) { CV_GetRectSubPixTest test; test.safe_run(); }
TEST(Imgproc_GetQuadSubPix, accuracy) { CV_GetQuadSubPixTest test; test.safe_run(); }
//////////////////////////////////////////////////////////////////////////
template <typename T, typename WT>
struct IntCast
{
T operator() (WT val) const
{
return cv::saturate_cast<T>(val >> 2);
}
};
template <typename T, typename WT>
struct FltCast
{
T operator() (WT val) const
{
return cv::saturate_cast<T>(val * 0.25);
}
};
template <typename T, typename WT, int one, typename CastOp>
void resizeArea(const cv::Mat & src, cv::Mat & dst)
{
int cn = src.channels();
CastOp castOp;
for (int y = 0; y < dst.rows; ++y)
{
const T * sptr0 = src.ptr<T>(y << 1);
const T * sptr1 = src.ptr<T>((y << 1) + 1);
T * dptr = dst.ptr<T>(y);
for (int x = 0; x < dst.cols * cn; x += cn)
{
int x1 = x << 1;
for (int c = 0; c < cn; ++c)
{
WT sum = WT(sptr0[x1 + c]) + WT(sptr0[x1 + c + cn]);
sum += WT(sptr1[x1 + c]) + WT(sptr1[x1 + c + cn]) + (WT)(one);
dptr[x + c] = castOp(sum);
}
}
}
}
TEST(Resize, Area_half)
{
const int size = 1000;
int types[] = { CV_8UC1, CV_8UC4,
CV_16UC1, CV_16UC4,
CV_16SC1, CV_16SC3, CV_16SC4,
CV_32FC1, CV_32FC4 };
cv::RNG rng(17);
for (int i = 0, _size = sizeof(types) / sizeof(types[0]); i < _size; ++i)
{
int type = types[i], depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
const float eps = depth <= CV_32S ? 0 : 7e-5f;
SCOPED_TRACE(depth);
SCOPED_TRACE(cn);
cv::Mat src(size, size, type), dst_actual(size >> 1, size >> 1, type),
dst_reference(size >> 1, size >> 1, type);
rng.fill(src, cv::RNG::UNIFORM, -1000, 1000, true);
if (depth == CV_8U)
resizeArea<uchar, ushort, 2, IntCast<uchar, ushort> >(src, dst_reference);
else if (depth == CV_16U)
resizeArea<ushort, uint, 2, IntCast<ushort, uint> >(src, dst_reference);
else if (depth == CV_16S)
resizeArea<short, int, 2, IntCast<short, int> >(src, dst_reference);
else if (depth == CV_32F)
resizeArea<float, float, 0, FltCast<float, float> >(src, dst_reference);
else
CV_Assert(0);
cv::resize(src, dst_actual, dst_actual.size(), 0, 0, cv::INTER_AREA);
ASSERT_GE(eps, cvtest::norm(dst_reference, dst_actual, cv::NORM_INF));
}
}
TEST(Resize, lanczos4_regression_16192)
{
Size src_size(11, 17);
Size dst_size(11, 153);
Mat src(src_size, CV_8UC3, Scalar::all(128));
Mat dst(dst_size, CV_8UC3, Scalar::all(255));
cv::resize(src, dst, dst_size, 0, 0, INTER_LANCZOS4);
Mat expected(dst_size, CV_8UC3, Scalar::all(128));
EXPECT_EQ(cvtest::norm(dst, expected, NORM_INF), 0) << dst(Rect(0,0,8,8));
}
TEST(Resize, nearest_regression_15075)
{
const int C = 5;
const int i1 = 5, j1 = 5;
Size src_size(12, 12);
Size dst_size(11, 11);
cv::Mat src = cv::Mat::zeros(src_size, CV_8UC(C)), dst;
for (int j = 0; j < C; j++)
src.col(i1).row(j1).data[j] = 1;
cv::resize(src, dst, dst_size, 0, 0, INTER_NEAREST);
EXPECT_EQ(C, cvtest::norm(dst, NORM_L1)) << src.size;
}
TEST(Imgproc_Warp, multichannel)
{
static const int inter_types[] = {INTER_NEAREST, INTER_AREA, INTER_CUBIC,
INTER_LANCZOS4, INTER_LINEAR};
static const int inter_n = sizeof(inter_types) / sizeof(int);
static const int border_types[] = {BORDER_CONSTANT, BORDER_DEFAULT,
BORDER_REFLECT, BORDER_REPLICATE,
BORDER_WRAP, BORDER_WRAP};
static const int border_n = sizeof(border_types) / sizeof(int);
RNG& rng = theRNG();
for( int iter = 0; iter < 100; iter++ )
{
int inter = inter_types[rng.uniform(0, inter_n)];
int border = border_types[rng.uniform(0, border_n)];
int width = rng.uniform(3, 333);
int height = rng.uniform(3, 333);
int cn = rng.uniform(1, 15);
if(inter == INTER_CUBIC || inter == INTER_LANCZOS4)
cn = rng.uniform(1, 5);
Mat src(height, width, CV_8UC(cn)), dst;
//randu(src, 0, 256);
src.setTo(0.);
Mat rot = getRotationMatrix2D(Point2f(0.f, 0.f), 1.0, 1.0);
warpAffine(src, dst, rot, src.size(), inter, border);
ASSERT_EQ(0.0, cvtest::norm(dst, NORM_INF));
Mat rot2 = Mat::eye(3, 3, rot.type());
rot.copyTo(rot2.rowRange(0, 2));
warpPerspective(src, dst, rot2, src.size(), inter, border);
ASSERT_EQ(0.0, cvtest::norm(dst, NORM_INF));
}
}
TEST(Imgproc_GetAffineTransform, singularity)
{
Point2f A_sample[3];
A_sample[0] = Point2f(8.f, 9.f);
A_sample[1] = Point2f(40.f, 41.f);
A_sample[2] = Point2f(47.f, 48.f);
Point2f B_sample[3];
B_sample[0] = Point2f(7.37465f, 11.8295f);
B_sample[1] = Point2f(15.0113f, 12.8994f);
B_sample[2] = Point2f(38.9943f, 9.56297f);
Mat trans = getAffineTransform(A_sample, B_sample);
ASSERT_EQ(0.0, cvtest::norm(trans, NORM_INF));
}
TEST(Imgproc_Remap, DISABLED_memleak)
{
Mat src;
const int N = 400;
src.create(N, N, CV_8U);
randu(src, 0, 256);
Mat map_x, map_y, dst;
dst.create( src.size(), src.type() );
map_x.create( src.size(), CV_32FC1 );
map_y.create( src.size(), CV_32FC1 );
randu(map_x, 0., N+0.);
randu(map_y, 0., N+0.);
for( int iter = 0; iter < 10000; iter++ )
{
if(iter % 100 == 0)
{
putchar('.');
fflush(stdout);
}
remap(src, dst, map_x, map_y, CV_INTER_LINEAR);
}
}
//** @deprecated */
TEST(Imgproc_linearPolar, identity)
{
const int N = 33;
Mat in(N, N, CV_8UC3, Scalar(255, 0, 0));
in(cv::Rect(N/3, N/3, N/3, N/3)).setTo(Scalar::all(255));
cv::blur(in, in, Size(5, 5));
cv::blur(in, in, Size(5, 5));
Mat src = in.clone();
Mat dst;
Rect roi = Rect(0, 0, in.cols - ((N+19)/20), in.rows);
for (int i = 1; i <= 5; i++)
{
linearPolar(src, dst,
Point2f((N-1) * 0.5f, (N-1) * 0.5f), N * 0.5f,
CV_WARP_FILL_OUTLIERS | CV_INTER_LINEAR | CV_WARP_INVERSE_MAP);
linearPolar(dst, src,
Point2f((N-1) * 0.5f, (N-1) * 0.5f), N * 0.5f,
CV_WARP_FILL_OUTLIERS | CV_INTER_LINEAR);
double psnr = cvtest::PSNR(in(roi), src(roi));
EXPECT_LE(25, psnr) << "iteration=" << i;
}
#if 0
Mat all(N*2+2,N*2+2, src.type(), Scalar(0,0,255));
in.copyTo(all(Rect(0,0,N,N)));
src.copyTo(all(Rect(0,N+1,N,N)));
src.copyTo(all(Rect(N+1,0,N,N)));
dst.copyTo(all(Rect(N+1,N+1,N,N)));
imwrite("linearPolar.png", all);
imshow("input", in); imshow("result", dst); imshow("restore", src); imshow("all", all);
cv::waitKey();
#endif
}
//** @deprecated */
TEST(Imgproc_logPolar, identity)
{
const int N = 33;
Mat in(N, N, CV_8UC3, Scalar(255, 0, 0));
in(cv::Rect(N/3, N/3, N/3, N/3)).setTo(Scalar::all(255));
cv::blur(in, in, Size(5, 5));
cv::blur(in, in, Size(5, 5));
Mat src = in.clone();
Mat dst;
Rect roi = Rect(0, 0, in.cols - ((N+19)/20), in.rows);
double M = N/log(N * 0.5f);
for (int i = 1; i <= 5; i++)
{
logPolar(src, dst,
Point2f((N-1) * 0.5f, (N-1) * 0.5f), M,
CV_WARP_FILL_OUTLIERS | CV_INTER_LINEAR | CV_WARP_INVERSE_MAP);
logPolar(dst, src,
Point2f((N-1) * 0.5f, (N-1) * 0.5f), M,
CV_WARP_FILL_OUTLIERS | CV_INTER_LINEAR);
double psnr = cvtest::PSNR(in(roi), src(roi));
EXPECT_LE(25, psnr) << "iteration=" << i;
}
#if 0
Mat all(N*2+2,N*2+2, src.type(), Scalar(0,0,255));
in.copyTo(all(Rect(0,0,N,N)));
src.copyTo(all(Rect(0,N+1,N,N)));
src.copyTo(all(Rect(N+1,0,N,N)));
dst.copyTo(all(Rect(N+1,N+1,N,N)));
imwrite("logPolar.png", all);
imshow("input", in); imshow("result", dst); imshow("restore", src); imshow("all", all);
cv::waitKey();
#endif
}
TEST(Imgproc_warpPolar, identity)
{
const int N = 33;
Mat in(N, N, CV_8UC3, Scalar(255, 0, 0));
in(cv::Rect(N / 3, N / 3, N / 3, N / 3)).setTo(Scalar::all(255));
cv::blur(in, in, Size(5, 5));
cv::blur(in, in, Size(5, 5));
Mat src = in.clone();
Mat dst;
Rect roi = Rect(0, 0, in.cols - ((N + 19) / 20), in.rows);
Point2f center = Point2f((N - 1) * 0.5f, (N - 1) * 0.5f);
double radius = N * 0.5;
int flags = CV_WARP_FILL_OUTLIERS | CV_INTER_LINEAR;
// test linearPolar
for (int ki = 1; ki <= 5; ki++)
{
warpPolar(src, dst, src.size(), center, radius, flags + WARP_POLAR_LINEAR + CV_WARP_INVERSE_MAP);
warpPolar(dst, src, src.size(), center, radius, flags + WARP_POLAR_LINEAR);
double psnr = cv::PSNR(in(roi), src(roi));
EXPECT_LE(25, psnr) << "iteration=" << ki;
}
// test logPolar
src = in.clone();
for (int ki = 1; ki <= 5; ki++)
{
warpPolar(src, dst, src.size(),center, radius, flags + WARP_POLAR_LOG + CV_WARP_INVERSE_MAP );
warpPolar(dst, src, src.size(),center, radius, flags + WARP_POLAR_LOG);
double psnr = cv::PSNR(in(roi), src(roi));
EXPECT_LE(25, psnr) << "iteration=" << ki;
}
#if 0
Mat all(N*2+2,N*2+2, src.type(), Scalar(0,0,255));
in.copyTo(all(Rect(0,0,N,N)));
src.copyTo(all(Rect(0,N+1,N,N)));
src.copyTo(all(Rect(N+1,0,N,N)));
dst.copyTo(all(Rect(N+1,N+1,N,N)));
imwrite("linearPolar.png", all);
imshow("input", in); imshow("result", dst); imshow("restore", src); imshow("all", all);
cv::waitKey();
#endif
}
}} // namespace
/* End of file. */
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