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#include "test_precomp.hpp"
using namespace cv;
using namespace std;
class CV_DisTransTest : public cvtest::ArrayTest
{
public:
CV_DisTransTest();
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 );
void run_func();
void prepare_to_validation( int );
void get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high );
int prepare_test_case( int test_case_idx );
int mask_size;
int dist_type;
int fill_labels;
float mask[3];
};
CV_DisTransTest::CV_DisTransTest()
{
test_array[INPUT].push_back(NULL);
test_array[OUTPUT].push_back(NULL);
test_array[OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
optional_mask = false;
element_wise_relative_error = true;
}
void CV_DisTransTest::get_test_array_types_and_sizes( int test_case_idx,
vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
types[INPUT][0] = CV_8UC1;
types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_32FC1;
types[OUTPUT][1] = types[REF_OUTPUT][1] = CV_32SC1;
if( cvtest::randInt(rng) & 1 )
{
mask_size = 3;
}
else
{
mask_size = 5;
}
dist_type = cvtest::randInt(rng) % 3;
dist_type = dist_type == 0 ? CV_DIST_C : dist_type == 1 ? CV_DIST_L1 : CV_DIST_L2;
// for now, check only the "labeled" distance transform mode
fill_labels = 0;
if( !fill_labels )
sizes[OUTPUT][1] = sizes[REF_OUTPUT][1] = cvSize(0,0);
}
double CV_DisTransTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
Size sz = test_mat[INPUT][0].size();
return dist_type == CV_DIST_C || dist_type == CV_DIST_L1 ? 0 : 0.01*MAX(sz.width, sz.height);
}
void CV_DisTransTest::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 && CV_MAT_DEPTH(type) == CV_8U )
{
low = Scalar::all(0);
high = Scalar::all(10);
}
}
int CV_DisTransTest::prepare_test_case( int test_case_idx )
{
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
if( code > 0 )
{
// the function's response to an "all-nonzeros" image is not determined,
// so put at least one zero point
Mat& mat = test_mat[INPUT][0];
RNG& rng = ts->get_rng();
int i = cvtest::randInt(rng) % mat.rows;
int j = cvtest::randInt(rng) % mat.cols;
mat.at<uchar>(i,j) = 0;
}
return code;
}
void CV_DisTransTest::run_func()
{
cvDistTransform( test_array[INPUT][0], test_array[OUTPUT][0], dist_type, mask_size,
dist_type == CV_DIST_USER ? mask : 0, test_array[OUTPUT][1] );
}
static void
cvTsDistTransform( const CvMat* _src, CvMat* _dst, int dist_type,
int mask_size, float* _mask, CvMat* /*_labels*/ )
{
int i, j, k;
int width = _src->cols, height = _src->rows;
const float init_val = 1e6;
float mask[3];
CvMat* temp;
int ofs[16];
float delta[16];
int tstep, count;
assert( mask_size == 3 || mask_size == 5 );
if( dist_type == CV_DIST_USER )
memcpy( mask, _mask, sizeof(mask) );
else if( dist_type == CV_DIST_C )
{
mask_size = 3;
mask[0] = mask[1] = 1.f;
}
else if( dist_type == CV_DIST_L1 )
{
mask_size = 3;
mask[0] = 1.f;
mask[1] = 2.f;
}
else if( mask_size == 3 )
{
mask[0] = 0.955f;
mask[1] = 1.3693f;
}
else
{
mask[0] = 1.0f;
mask[1] = 1.4f;
mask[2] = 2.1969f;
}
temp = cvCreateMat( height + mask_size-1, width + mask_size-1, CV_32F );
tstep = temp->step / sizeof(float);
if( mask_size == 3 )
{
count = 4;
ofs[0] = -1; delta[0] = mask[0];
ofs[1] = -tstep-1; delta[1] = mask[1];
ofs[2] = -tstep; delta[2] = mask[0];
ofs[3] = -tstep+1; delta[3] = mask[1];
}
else
{
count = 8;
ofs[0] = -1; delta[0] = mask[0];
ofs[1] = -tstep-2; delta[1] = mask[2];
ofs[2] = -tstep-1; delta[2] = mask[1];
ofs[3] = -tstep; delta[3] = mask[0];
ofs[4] = -tstep+1; delta[4] = mask[1];
ofs[5] = -tstep+2; delta[5] = mask[2];
ofs[6] = -tstep*2-1; delta[6] = mask[2];
ofs[7] = -tstep*2+1; delta[7] = mask[2];
}
for( i = 0; i < mask_size/2; i++ )
{
float* t0 = (float*)(temp->data.ptr + i*temp->step);
float* t1 = (float*)(temp->data.ptr + (temp->rows - i - 1)*temp->step);
for( j = 0; j < width + mask_size - 1; j++ )
t0[j] = t1[j] = init_val;
}
for( i = 0; i < height; i++ )
{
uchar* s = _src->data.ptr + i*_src->step;
float* tmp = (float*)(temp->data.ptr + temp->step*(i + (mask_size/2))) + (mask_size/2);
for( j = 0; j < mask_size/2; j++ )
tmp[-j-1] = tmp[j + width] = init_val;
for( j = 0; j < width; j++ )
{
if( s[j] == 0 )
tmp[j] = 0;
else
{
float min_dist = init_val;
for( k = 0; k < count; k++ )
{
float t = tmp[j+ofs[k]] + delta[k];
if( min_dist > t )
min_dist = t;
}
tmp[j] = min_dist;
}
}
}
for( i = height - 1; i >= 0; i-- )
{
float* d = (float*)(_dst->data.ptr + i*_dst->step);
float* tmp = (float*)(temp->data.ptr + temp->step*(i + (mask_size/2))) + (mask_size/2);
for( j = width - 1; j >= 0; j-- )
{
float min_dist = tmp[j];
if( min_dist > mask[0] )
{
for( k = 0; k < count; k++ )
{
float t = tmp[j-ofs[k]] + delta[k];
if( min_dist > t )
min_dist = t;
}
tmp[j] = min_dist;
}
d[j] = min_dist;
}
}
cvReleaseMat( &temp );
}
void CV_DisTransTest::prepare_to_validation( int /*test_case_idx*/ )
{
CvMat _input = test_mat[INPUT][0], _output = test_mat[REF_OUTPUT][0];
cvTsDistTransform( &_input, &_output, dist_type, mask_size, mask, 0 );
}
TEST(Imgproc_DistanceTransform, accuracy) { CV_DisTransTest test; test.safe_run(); }