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fdfb875208
opencv/modules/imgproc/test/test_imgwarp.cpp
Vincent Rabaud fdfb875208
Merge pull request #23922 from vrabaud:imgwarp 
Fix imgwarp at borders when transparent. #23922

I believe this is a proper fix to #23562

The PR #23754 overwrites data while that should not be the case with transparent data. The original test is failing because points at the border do not get computed because they do not have 4 neighbors to be computed. Still ,we can approximate their computation with whatever neighbors that are available.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2023-07-12 15:20:01 +03:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
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// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// this list of conditions and the following disclaimer.
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// * The name of Intel Corporation may not be used to endorse or promote products
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// (including, but not limited to, procurement of substitute goods or services;
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// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
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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;
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:
CV_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:
CV_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:
CV_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_Warp, regression_19566) // valgrind should detect problem if any
{
const Size imgSize(8192, 8);
Mat inMat = Mat::zeros(imgSize, CV_8UC4);
Mat outMat = Mat::zeros(imgSize, CV_8UC4);
warpAffine(
inMat,
outMat,
getRotationMatrix2D(Point2f(imgSize.width / 2.0f, imgSize.height / 2.0f), 45.0, 1.0),
imgSize,
INTER_LINEAR,
cv::BORDER_CONSTANT,
cv::Scalar(0.0, 0.0, 0.0, 255.0)
);
}
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
}
TEST(Imgproc_Remap, issue_23562)
{
cv::RNG rng(17);
Mat_<float> mapx({3, 3}, {0, 1, 2, 0, 1, 2, 0, 1, 2});
Mat_<float> mapy({3, 3}, {0, 0, 0, 1, 1, 1, 2, 2, 2});
for (int cn = 1; cn <= 4; ++cn) {
Mat src(3, 3, CV_32FC(cn));
rng.fill(src, cv::RNG::UNIFORM, -1, 1);
Mat dst = Mat::zeros(3, 3, CV_32FC(cn));
Mat ref = src.clone();
remap(src, dst, mapx, mapy, INTER_LINEAR, BORDER_TRANSPARENT);
ASSERT_EQ(0.0, cvtest::norm(ref, dst, NORM_INF)) << "channels=" << cn;
}
mapx = Mat1f({3, 3}, {0, 1, 2, 0, 1, 2, 0, 1, 2});
mapy = Mat1f({3, 3}, {0, 0, 0, 1, 1, 1, 2, 2, 1.5});
for (int cn = 1; cn <= 4; ++cn) {
Mat src = cv::Mat(3, 3, CV_32FC(cn));
Mat dst = 10 * Mat::ones(3, 3, CV_32FC(cn));
for(int y = 0; y < 3; ++y) {
for(int x = 0; x < 3; ++x) {
for(int k = 0; k < cn; ++k) {
src.ptr<float>(y,x)[k] = 10.f * y + x;
}
}
}
Mat ref = src.clone();
for(int k = 0; k < cn; ++k) {
ref.ptr<float>(2,2)[k] = (src.ptr<float>(1, 2)[k] + src.ptr<float>(2, 2)[k]) / 2.f;
}
remap(src, dst, mapx, mapy, INTER_LINEAR, BORDER_TRANSPARENT);
ASSERT_EQ(0.0, cvtest::norm(ref, dst, NORM_INF)) << "channels=" << cn;
}
}
}} // namespace
/* End of file. */
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