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replacing white noise with fruits picture

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This commit is contained in:
apavlenko 2016-12-09 15:49:08 +03:00
parent a99118c4c7
commit 3aedc134c2
1 changed files with 23 additions and 6 deletions
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29
modules/imgproc/test/test_canny.cpp
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@ -62,6 +62,8 @@ protected:
double threshold1, threshold2;
bool test_cpp;
bool test_custom_deriv;
Mat img;
};
@ -77,6 +79,9 @@ CV_CannyTest::CV_CannyTest(bool custom_deriv)
test_cpp = false;
test_custom_deriv = custom_deriv;
const char imgPath[] = "shared/fruits.png";
img = cv::imread(cvtest::TS::ptr()->get_data_path() + imgPath, IMREAD_GRAYSCALE);
}
@ -112,8 +117,21 @@ 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);
//GaussianBlur(src, src, Size(11, 11), 5, 5);
if(src.cols > img.cols || src.rows > img.rows)
resize(img, src, src.size());
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;
@ -302,9 +320,8 @@ int CV_CannyTest::validate_test_results( int test_case_idx )
return code;
}
// disabling, since testing on a white noise seems having too few sense...
TEST(DISABLED_Imgproc_Canny, accuracy) { CV_CannyTest test; test.safe_run(); }
TEST(DISABLED_Imgproc_Canny, accuracy_deriv) { CV_CannyTest test(true); test.safe_run(); }
TEST(Imgproc_Canny, accuracy) { CV_CannyTest test; test.safe_run(); }
TEST(Imgproc_Canny, accuracy_deriv) { CV_CannyTest test(true); test.safe_run(); }
/*
@ -366,7 +383,7 @@ TEST_P(CannyVX, Accuracy)
Mat cannyVX;
cv::Canny(src, cannyVX, 100, 150, 3);
setUseOpenVX(false);
// 'smart' diff check (excluding isolated pixels)
Mat diff, diff1;
absdiff(canny, cannyVX, diff);
boxFilter(diff, diff1, -1, Size(3,3));
@ -374,7 +391,7 @@ TEST_P(CannyVX, Accuracy)
diff1 = diff1 > 255/9 * minPixelsAroud;
erode(diff1, diff1, Mat());
double error = cv::norm(diff1, NORM_L1) / 255;
const int maxError = 10; // empirical number
const int maxError = std::min(10, diff.size().area()/100); // empirical number
if(error > maxError)
{
string outPath =