423 lines
14 KiB
C++
423 lines
14 KiB
C++
|
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||
|
|
//
|
||
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||
|
|
//
|
||
|
|
// By downloading, copying, installing or using the software you agree to this license.
|
||
|
|
// If you do not agree to this license, do not download, install,
|
||
|
|
// copy or use the software.
|
||
|
|
//
|
||
|
|
//
|
||
|
|
// Intel License Agreement
|
||
|
|
// For Open Source Computer Vision Library
|
||
|
|
//
|
||
|
|
// Copyright (C) 2000, Intel Corporation, all rights reserved.
|
||
|
|
// Third party copyrights are property of their respective owners.
|
||
|
|
//
|
||
|
|
// Redistribution and use in source and binary forms, with or without modification,
|
||
|
|
// are permitted provided that the following conditions are met:
|
||
|
|
//
|
||
|
|
// * Redistribution's of source code must retain the above copyright notice,
|
||
|
|
// this list of conditions and the following disclaimer.
|
||
|
|
//
|
||
|
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||
|
|
// this list of conditions and the following disclaimer in the documentation
|
||
|
|
// and/or other materials provided with the distribution.
|
||
|
|
//
|
||
|
|
// * The name of Intel Corporation may not be used to endorse or promote products
|
||
|
|
// derived from this software without specific prior written permission.
|
||
|
|
//
|
||
|
|
// This software is provided by the copyright holders and contributors "as is" and
|
||
|
|
// any express or implied warranties, including, but not limited to, the implied
|
||
|
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||
|
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
||
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
||
|
|
// loss of use, data, or profits; or business interruption) however caused
|
||
|
|
// and on any theory of liability, whether in contract, strict liability,
|
||
|
|
// or tort (including negligence or otherwise) arising in any way out of
|
||
|
|
// the use of this software, even if advised of the possibility of such damage.
|
||
|
|
//
|
||
|
|
//M*/
|
||
|
|
|
||
|
|
#include "test_precomp.hpp"
|
||
|
|
|
||
|
|
namespace opencv_test { namespace {
|
||
|
|
|
||
|
|
class CV_CannyTest : public cvtest::ArrayTest
|
||
|
|
{
|
||
|
|
public:
|
||
|
|
CV_CannyTest(bool custom_deriv = false);
|
||
|
|
|
||
|
|
protected:
|
||
|
|
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
|
||
|
|
double get_success_error_level( int test_case_idx, int i, int j );
|
||
|
|
int prepare_test_case( int test_case_idx );
|
||
|
|
void run_func();
|
||
|
|
void prepare_to_validation( int );
|
||
|
|
int validate_test_results( int /*test_case_idx*/ );
|
||
|
|
|
||
|
|
int aperture_size;
|
||
|
|
bool use_true_gradient;
|
||
|
|
double threshold1, threshold2;
|
||
|
|
bool test_cpp;
|
||
|
|
bool test_custom_deriv;
|
||
|
|
|
||
|
|
Mat img;
|
||
|
|
};
|
||
|
|
|
||
|
|
|
||
|
|
CV_CannyTest::CV_CannyTest(bool custom_deriv)
|
||
|
|
{
|
||
|
|
test_array[INPUT].push_back(NULL);
|
||
|
|
test_array[OUTPUT].push_back(NULL);
|
||
|
|
test_array[REF_OUTPUT].push_back(NULL);
|
||
|
|
element_wise_relative_error = true;
|
||
|
|
aperture_size = 0;
|
||
|
|
use_true_gradient = false;
|
||
|
|
threshold1 = threshold2 = 0;
|
||
|
|
test_custom_deriv = custom_deriv;
|
||
|
|
|
||
|
|
const char imgPath[] = "shared/fruits.png";
|
||
|
|
img = cv::imread(cvtest::TS::ptr()->get_data_path() + imgPath, IMREAD_GRAYSCALE);
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
void CV_CannyTest::get_test_array_types_and_sizes( int test_case_idx,
|
||
|
|
vector<vector<Size> >& sizes,
|
||
|
|
vector<vector<int> >& types )
|
||
|
|
{
|
||
|
|
RNG& rng = ts->get_rng();
|
||
|
|
double thresh_range;
|
||
|
|
|
||
|
|
cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
|
||
|
|
types[INPUT][0] = types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_8U;
|
||
|
|
|
||
|
|
aperture_size = cvtest::randInt(rng) % 2 ? 5 : 3;
|
||
|
|
thresh_range = aperture_size == 3 ? 300 : 1000;
|
||
|
|
|
||
|
|
threshold1 = cvtest::randReal(rng)*thresh_range;
|
||
|
|
threshold2 = cvtest::randReal(rng)*thresh_range*0.3;
|
||
|
|
|
||
|
|
if( cvtest::randInt(rng) % 2 )
|
||
|
|
CV_SWAP( threshold1, threshold2, thresh_range );
|
||
|
|
|
||
|
|
use_true_gradient = cvtest::randInt(rng) % 2 != 0;
|
||
|
|
test_cpp = (cvtest::randInt(rng) & 256) == 0;
|
||
|
|
|
||
|
|
ts->printf(cvtest::TS::LOG, "Canny(size = %d x %d, aperture_size = %d, threshold1 = %g, threshold2 = %g, L2 = %s) test_cpp = %s (test case #%d)\n",
|
||
|
|
sizes[0][0].width, sizes[0][0].height, aperture_size, threshold1, threshold2, use_true_gradient ? "TRUE" : "FALSE", test_cpp ? "TRUE" : "FALSE", test_case_idx);
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
int CV_CannyTest::prepare_test_case( int test_case_idx )
|
||
|
|
{
|
||
|
|
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
|
||
|
|
if( code > 0 )
|
||
|
|
{
|
||
|
|
RNG& rng = ts->get_rng();
|
||
|
|
Mat& src = test_mat[INPUT][0];
|
||
|
|
//GaussianBlur(src, src, Size(11, 11), 5, 5);
|
||
|
|
if(src.cols > img.cols || src.rows > img.rows)
|
||
|
|
resize(img, src, src.size(), 0, 0, INTER_LINEAR_EXACT);
|
||
|
|
else
|
||
|
|
img(
|
||
|
|
Rect(
|
||
|
|
cvtest::randInt(rng) % (img.cols-src.cols),
|
||
|
|
cvtest::randInt(rng) % (img.rows-src.rows),
|
||
|
|
src.cols,
|
||
|
|
src.rows
|
||
|
|
)
|
||
|
|
).copyTo(src);
|
||
|
|
GaussianBlur(src, src, Size(5, 5), 0);
|
||
|
|
}
|
||
|
|
|
||
|
|
return code;
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
double CV_CannyTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
|
||
|
|
{
|
||
|
|
return 0;
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
void CV_CannyTest::run_func()
|
||
|
|
{
|
||
|
|
if (test_custom_deriv)
|
||
|
|
{
|
||
|
|
cv::Mat _out = cv::cvarrToMat(test_array[OUTPUT][0]);
|
||
|
|
cv::Mat src = cv::cvarrToMat(test_array[INPUT][0]);
|
||
|
|
cv::Mat dx, dy;
|
||
|
|
int m = aperture_size;
|
||
|
|
Point anchor(m/2, m/2);
|
||
|
|
Mat dxkernel = cvtest::calcSobelKernel2D( 1, 0, m, 0 );
|
||
|
|
Mat dykernel = cvtest::calcSobelKernel2D( 0, 1, m, 0 );
|
||
|
|
cvtest::filter2D(src, dx, CV_16S, dxkernel, anchor, 0, BORDER_REPLICATE);
|
||
|
|
cvtest::filter2D(src, dy, CV_16S, dykernel, anchor, 0, BORDER_REPLICATE);
|
||
|
|
cv::Canny(dx, dy, _out, threshold1, threshold2, use_true_gradient);
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
cv::Mat _out = cv::cvarrToMat(test_array[OUTPUT][0]);
|
||
|
|
cv::Canny(cv::cvarrToMat(test_array[INPUT][0]), _out, threshold1, threshold2,
|
||
|
|
aperture_size + (use_true_gradient ? CV_CANNY_L2_GRADIENT : 0));
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
static void
|
||
|
|
cannyFollow( int x, int y, float lowThreshold, const Mat& mag, Mat& dst )
|
||
|
|
{
|
||
|
|
static const int ofs[][2] = {{1,0},{1,-1},{0,-1},{-1,-1},{-1,0},{-1,1},{0,1},{1,1}};
|
||
|
|
int i;
|
||
|
|
|
||
|
|
dst.at<uchar>(y, x) = (uchar)255;
|
||
|
|
|
||
|
|
for( i = 0; i < 8; i++ )
|
||
|
|
{
|
||
|
|
int x1 = x + ofs[i][0];
|
||
|
|
int y1 = y + ofs[i][1];
|
||
|
|
if( (unsigned)x1 < (unsigned)mag.cols &&
|
||
|
|
(unsigned)y1 < (unsigned)mag.rows &&
|
||
|
|
mag.at<float>(y1, x1) > lowThreshold &&
|
||
|
|
!dst.at<uchar>(y1, x1) )
|
||
|
|
cannyFollow( x1, y1, lowThreshold, mag, dst );
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
static void
|
||
|
|
test_Canny( const Mat& src, Mat& dst,
|
||
|
|
double threshold1, double threshold2,
|
||
|
|
int aperture_size, bool use_true_gradient )
|
||
|
|
{
|
||
|
|
int m = aperture_size;
|
||
|
|
Point anchor(m/2, m/2);
|
||
|
|
const double tan_pi_8 = tan(CV_PI/8.);
|
||
|
|
const double tan_3pi_8 = tan(CV_PI*3/8);
|
||
|
|
float lowThreshold = (float)MIN(threshold1, threshold2);
|
||
|
|
float highThreshold = (float)MAX(threshold1, threshold2);
|
||
|
|
|
||
|
|
int x, y, width = src.cols, height = src.rows;
|
||
|
|
|
||
|
|
Mat dxkernel = cvtest::calcSobelKernel2D( 1, 0, m, 0 );
|
||
|
|
Mat dykernel = cvtest::calcSobelKernel2D( 0, 1, m, 0 );
|
||
|
|
Mat dx, dy, mag(height, width, CV_32F);
|
||
|
|
cvtest::filter2D(src, dx, CV_32S, dxkernel, anchor, 0, BORDER_REPLICATE);
|
||
|
|
cvtest::filter2D(src, dy, CV_32S, dykernel, anchor, 0, BORDER_REPLICATE);
|
||
|
|
|
||
|
|
// calc gradient magnitude
|
||
|
|
for( y = 0; y < height; y++ )
|
||
|
|
{
|
||
|
|
for( x = 0; x < width; x++ )
|
||
|
|
{
|
||
|
|
int dxval = dx.at<int>(y, x), dyval = dy.at<int>(y, x);
|
||
|
|
mag.at<float>(y, x) = use_true_gradient ?
|
||
|
|
(float)sqrt((double)(dxval*dxval + dyval*dyval)) :
|
||
|
|
(float)(fabs((double)dxval) + fabs((double)dyval));
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
// calc gradient direction, do nonmaxima suppression
|
||
|
|
for( y = 0; y < height; y++ )
|
||
|
|
{
|
||
|
|
for( x = 0; x < width; x++ )
|
||
|
|
{
|
||
|
|
|
||
|
|
float a = mag.at<float>(y, x), b = 0, c = 0;
|
||
|
|
int y1 = 0, y2 = 0, x1 = 0, x2 = 0;
|
||
|
|
|
||
|
|
if( a <= lowThreshold )
|
||
|
|
continue;
|
||
|
|
|
||
|
|
int dxval = dx.at<int>(y, x);
|
||
|
|
int dyval = dy.at<int>(y, x);
|
||
|
|
|
||
|
|
double tg = dxval ? (double)dyval/dxval : DBL_MAX*CV_SIGN(dyval);
|
||
|
|
|
||
|
|
if( fabs(tg) < tan_pi_8 )
|
||
|
|
{
|
||
|
|
y1 = y2 = y; x1 = x + 1; x2 = x - 1;
|
||
|
|
}
|
||
|
|
else if( tan_pi_8 <= tg && tg <= tan_3pi_8 )
|
||
|
|
{
|
||
|
|
y1 = y + 1; y2 = y - 1; x1 = x + 1; x2 = x - 1;
|
||
|
|
}
|
||
|
|
else if( -tan_3pi_8 <= tg && tg <= -tan_pi_8 )
|
||
|
|
{
|
||
|
|
y1 = y - 1; y2 = y + 1; x1 = x + 1; x2 = x - 1;
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
CV_Assert( fabs(tg) > tan_3pi_8 );
|
||
|
|
x1 = x2 = x; y1 = y + 1; y2 = y - 1;
|
||
|
|
}
|
||
|
|
|
||
|
|
if( (unsigned)y1 < (unsigned)height && (unsigned)x1 < (unsigned)width )
|
||
|
|
b = (float)fabs(mag.at<float>(y1, x1));
|
||
|
|
|
||
|
|
if( (unsigned)y2 < (unsigned)height && (unsigned)x2 < (unsigned)width )
|
||
|
|
c = (float)fabs(mag.at<float>(y2, x2));
|
||
|
|
|
||
|
|
if( (a > b || (a == b && ((x1 == x+1 && y1 == y) || (x1 == x && y1 == y+1)))) && a > c )
|
||
|
|
;
|
||
|
|
else
|
||
|
|
mag.at<float>(y, x) = -a;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
dst = Scalar::all(0);
|
||
|
|
|
||
|
|
// hysteresis threshold
|
||
|
|
for( y = 0; y < height; y++ )
|
||
|
|
{
|
||
|
|
for( x = 0; x < width; x++ )
|
||
|
|
if( mag.at<float>(y, x) > highThreshold && !dst.at<uchar>(y, x) )
|
||
|
|
cannyFollow( x, y, lowThreshold, mag, dst );
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
void CV_CannyTest::prepare_to_validation( int )
|
||
|
|
{
|
||
|
|
Mat src = test_mat[INPUT][0], dst = test_mat[REF_OUTPUT][0];
|
||
|
|
test_Canny( src, dst, threshold1, threshold2, aperture_size, use_true_gradient );
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
int CV_CannyTest::validate_test_results( int test_case_idx )
|
||
|
|
{
|
||
|
|
int code = cvtest::TS::OK, nz0;
|
||
|
|
prepare_to_validation(test_case_idx);
|
||
|
|
|
||
|
|
double err = cvtest::norm(test_mat[OUTPUT][0], test_mat[REF_OUTPUT][0], CV_L1);
|
||
|
|
if( err == 0 )
|
||
|
|
return code;
|
||
|
|
|
||
|
|
if( err != cvRound(err) || cvRound(err)%255 != 0 )
|
||
|
|
{
|
||
|
|
ts->printf( cvtest::TS::LOG, "Some of the pixels, produced by Canny, are not 0's or 255's; the difference is %g\n", err );
|
||
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
||
|
|
return code;
|
||
|
|
}
|
||
|
|
|
||
|
|
nz0 = cvRound(cvtest::norm(test_mat[REF_OUTPUT][0], CV_L1)/255);
|
||
|
|
err = (err/255/MAX(nz0,100))*100;
|
||
|
|
if( err > 1 )
|
||
|
|
{
|
||
|
|
ts->printf( cvtest::TS::LOG, "Too high percentage of non-matching edge pixels = %g%%\n", err);
|
||
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||
|
|
}
|
||
|
|
|
||
|
|
return code;
|
||
|
|
}
|
||
|
|
|
||
|
|
TEST(Imgproc_Canny, accuracy) { CV_CannyTest test; test.safe_run(); }
|
||
|
|
TEST(Imgproc_Canny, accuracy_deriv) { CV_CannyTest test(true); test.safe_run(); }
|
||
|
|
|
||
|
|
|
||
|
|
/*
|
||
|
|
* Comparing OpenVX based implementation with the main one
|
||
|
|
*/
|
||
|
|
|
||
|
|
#ifndef IMPLEMENT_PARAM_CLASS
|
||
|
|
#define IMPLEMENT_PARAM_CLASS(name, type) \
|
||
|
|
class name \
|
||
|
|
{ \
|
||
|
|
public: \
|
||
|
|
name ( type arg = type ()) : val_(arg) {} \
|
||
|
|
operator type () const {return val_;} \
|
||
|
|
private: \
|
||
|
|
type val_; \
|
||
|
|
}; \
|
||
|
|
inline void PrintTo( name param, std::ostream* os) \
|
||
|
|
{ \
|
||
|
|
*os << #name << "(" << testing::PrintToString(static_cast< type >(param)) << ")"; \
|
||
|
|
}
|
||
|
|
#endif // IMPLEMENT_PARAM_CLASS
|
||
|
|
|
||
|
|
IMPLEMENT_PARAM_CLASS(ImagePath, string)
|
||
|
|
IMPLEMENT_PARAM_CLASS(ApertureSize, int)
|
||
|
|
IMPLEMENT_PARAM_CLASS(L2gradient, bool)
|
||
|
|
|
||
|
|
PARAM_TEST_CASE(CannyVX, ImagePath, ApertureSize, L2gradient)
|
||
|
|
{
|
||
|
|
string imgPath;
|
||
|
|
int kSize;
|
||
|
|
bool useL2;
|
||
|
|
Mat src, dst;
|
||
|
|
|
||
|
|
virtual void SetUp()
|
||
|
|
{
|
||
|
|
imgPath = GET_PARAM(0);
|
||
|
|
kSize = GET_PARAM(1);
|
||
|
|
useL2 = GET_PARAM(2);
|
||
|
|
}
|
||
|
|
|
||
|
|
void loadImage()
|
||
|
|
{
|
||
|
|
src = cv::imread(cvtest::TS::ptr()->get_data_path() + imgPath, IMREAD_GRAYSCALE);
|
||
|
|
ASSERT_FALSE(src.empty()) << "can't load image: " << imgPath;
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
TEST_P(CannyVX, Accuracy)
|
||
|
|
{
|
||
|
|
if(haveOpenVX())
|
||
|
|
{
|
||
|
|
loadImage();
|
||
|
|
|
||
|
|
setUseOpenVX(false);
|
||
|
|
Mat canny;
|
||
|
|
cv::Canny(src, canny, 100, 150, 3);
|
||
|
|
|
||
|
|
setUseOpenVX(true);
|
||
|
|
Mat cannyVX;
|
||
|
|
cv::Canny(src, cannyVX, 100, 150, 3);
|
||
|
|
|
||
|
|
// 'smart' diff check (excluding isolated pixels)
|
||
|
|
Mat diff, diff1;
|
||
|
|
absdiff(canny, cannyVX, diff);
|
||
|
|
boxFilter(diff, diff1, -1, Size(3,3));
|
||
|
|
const int minPixelsAroud = 3; // empirical number
|
||
|
|
diff1 = diff1 > 255/9 * minPixelsAroud;
|
||
|
|
erode(diff1, diff1, Mat());
|
||
|
|
double error = cv::norm(diff1, NORM_L1) / 255;
|
||
|
|
const int maxError = std::min(10, diff.size().area()/100); // empirical number
|
||
|
|
if(error > maxError)
|
||
|
|
{
|
||
|
|
string outPath =
|
||
|
|
string("CannyVX-diff-") +
|
||
|
|
imgPath + '-' +
|
||
|
|
'k' + char(kSize+'0') + '-' +
|
||
|
|
(useL2 ? "l2" : "l1");
|
||
|
|
std::replace(outPath.begin(), outPath.end(), '/', '_');
|
||
|
|
std::replace(outPath.begin(), outPath.end(), '\\', '_');
|
||
|
|
std::replace(outPath.begin(), outPath.end(), '.', '_');
|
||
|
|
imwrite(outPath+".png", diff);
|
||
|
|
}
|
||
|
|
ASSERT_LE(error, maxError);
|
||
|
|
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
INSTANTIATE_TEST_CASE_P(
|
||
|
|
ImgProc, CannyVX,
|
||
|
|
testing::Combine(
|
||
|
|
testing::Values(
|
||
|
|
string("shared/baboon.png"),
|
||
|
|
string("shared/fruits.png"),
|
||
|
|
string("shared/lena.png"),
|
||
|
|
string("shared/pic1.png"),
|
||
|
|
string("shared/pic3.png"),
|
||
|
|
string("shared/pic5.png"),
|
||
|
|
string("shared/pic6.png")
|
||
|
|
),
|
||
|
|
testing::Values(ApertureSize(3), ApertureSize(5)),
|
||
|
|
testing::Values(L2gradient(false), L2gradient(true))
|
||
|
|
)
|
||
|
|
);
|
||
|
|
|
||
|
|
}} // namespace
|
||
|
|
/* End of file. */
|