[opencv] 248/251: features2d(test): more AKAZE tests
Nobuhiro Iwamatsu
iwamatsu at moszumanska.debian.org
Sun Aug 27 23:27:48 UTC 2017
This is an automated email from the git hooks/post-receive script.
iwamatsu pushed a commit to annotated tag 3.3.0
in repository opencv.
commit 94dbc35d922503d42b1d1f0ee31b3937ed9147d4
Author: Alexander Alekhin <alexander.a.alekhin at gmail.com>
Date: Thu Aug 3 22:02:47 2017 +0000
features2d(test): more AKAZE tests
---
modules/features2d/src/kaze/AKAZEFeatures.cpp | 3 +-
.../test/test_descriptors_regression.cpp | 99 +++++++++++++---------
modules/ts/misc/run_long.py | 6 +-
3 files changed, 64 insertions(+), 44 deletions(-)
diff --git a/modules/features2d/src/kaze/AKAZEFeatures.cpp b/modules/features2d/src/kaze/AKAZEFeatures.cpp
index a4d336b..024a5ca 100644
--- a/modules/features2d/src/kaze/AKAZEFeatures.cpp
+++ b/modules/features2d/src/kaze/AKAZEFeatures.cpp
@@ -2144,7 +2144,8 @@ void generateDescriptorSubsample(Mat& sampleList, Mat& comparisons, int nbits,
}
ssz *= nchannels;
- CV_Assert(nbits <= ssz); // Descriptor size can't be bigger than full descriptor
+ CV_Assert(ssz == 162*nchannels);
+ CV_Assert(nbits <= ssz && "Descriptor size can't be bigger than full descriptor (486 = 162*3 - 3 channels)");
// Since the full descriptor is usually under 10k elements, we pick
// the selection from the full matrix. We take as many samples per
diff --git a/modules/features2d/test/test_descriptors_regression.cpp b/modules/features2d/test/test_descriptors_regression.cpp
index 0862649..7540d1d 100644
--- a/modules/features2d/test/test_descriptors_regression.cpp
+++ b/modules/features2d/test/test_descriptors_regression.cpp
@@ -43,6 +43,7 @@
using namespace std;
using namespace cv;
+using namespace testing;
const string FEATURES2D_DIR = "features2d";
const string IMAGE_FILENAME = "tsukuba.png";
@@ -417,68 +418,82 @@ TEST( Features2d_DescriptorExtractor, batch )
}
}
-TEST( Features2d_Feature2d, no_crash )
+class DescriptorImage : public TestWithParam<std::string>
+{
+protected:
+ virtual void SetUp() {
+ pattern = GetParam();
+ }
+
+ std::string pattern;
+};
+
+TEST_P(DescriptorImage, no_crash)
{
- const String& pattern = string(cvtest::TS::ptr()->get_data_path() + "shared/*.png");
vector<String> fnames;
- glob(pattern, fnames, false);
+ glob(cvtest::TS::ptr()->get_data_path() + pattern, fnames, false);
sort(fnames.begin(), fnames.end());
- Ptr<AKAZE> akaze = AKAZE::create();
+ Ptr<AKAZE> akaze_mldb = AKAZE::create(AKAZE::DESCRIPTOR_MLDB);
+ Ptr<AKAZE> akaze_mldb_upright = AKAZE::create(AKAZE::DESCRIPTOR_MLDB_UPRIGHT);
+ Ptr<AKAZE> akaze_mldb_256 = AKAZE::create(AKAZE::DESCRIPTOR_MLDB, 256);
+ Ptr<AKAZE> akaze_mldb_upright_256 = AKAZE::create(AKAZE::DESCRIPTOR_MLDB_UPRIGHT, 256);
+ Ptr<AKAZE> akaze_kaze = AKAZE::create(AKAZE::DESCRIPTOR_KAZE);
+ Ptr<AKAZE> akaze_kaze_upright = AKAZE::create(AKAZE::DESCRIPTOR_KAZE_UPRIGHT);
Ptr<ORB> orb = ORB::create();
Ptr<KAZE> kaze = KAZE::create();
Ptr<BRISK> brisk = BRISK::create();
- size_t i, n = fnames.size();
+ size_t n = fnames.size();
vector<KeyPoint> keypoints;
Mat descriptors;
orb->setMaxFeatures(5000);
- for( i = 0; i < n; i++ )
+ for(size_t i = 0; i < n; i++ )
{
printf("%d. image: %s:\n", (int)i, fnames[i].c_str());
if( strstr(fnames[i].c_str(), "MP.png") != 0 )
+ {
+ printf("\tskip\n");
continue;
+ }
bool checkCount = strstr(fnames[i].c_str(), "templ.png") == 0;
Mat img = imread(fnames[i], -1);
- printf("\tAKAZE ... "); fflush(stdout);
- akaze->detectAndCompute(img, noArray(), keypoints, descriptors);
- printf("(%d keypoints) ", (int)keypoints.size()); fflush(stdout);
- if( checkCount )
- {
- EXPECT_GT((int)keypoints.size(), 0);
- }
- ASSERT_EQ(descriptors.rows, (int)keypoints.size());
- printf("ok\n");
- printf("\tKAZE ... "); fflush(stdout);
- kaze->detectAndCompute(img, noArray(), keypoints, descriptors);
- printf("(%d keypoints) ", (int)keypoints.size()); fflush(stdout);
- if( checkCount )
- {
- EXPECT_GT((int)keypoints.size(), 0);
- }
+ printf("\t%dx%d\n", img.cols, img.rows);
+
+#define TEST_DETECTOR(name, descriptor) \
+ keypoints.clear(); descriptors.release(); \
+ printf("\t" name "\n"); fflush(stdout); \
+ descriptor->detectAndCompute(img, noArray(), keypoints, descriptors); \
+ printf("\t\t\t(%d keypoints, descriptor size = %d)\n", (int)keypoints.size(), descriptors.cols); fflush(stdout); \
+ if (checkCount) \
+ { \
+ EXPECT_GT((int)keypoints.size(), 0); \
+ } \
ASSERT_EQ(descriptors.rows, (int)keypoints.size());
- printf("ok\n");
- printf("\tORB ... "); fflush(stdout);
- orb->detectAndCompute(img, noArray(), keypoints, descriptors);
- printf("(%d keypoints) ", (int)keypoints.size()); fflush(stdout);
- if( checkCount )
- {
- EXPECT_GT((int)keypoints.size(), 0);
- }
- ASSERT_EQ(descriptors.rows, (int)keypoints.size());
- printf("ok\n");
-
- printf("\tBRISK ... "); fflush(stdout);
- brisk->detectAndCompute(img, noArray(), keypoints, descriptors);
- printf("(%d keypoints) ", (int)keypoints.size()); fflush(stdout);
- if( checkCount )
- {
- EXPECT_GT((int)keypoints.size(), 0);
- }
- ASSERT_EQ(descriptors.rows, (int)keypoints.size());
- printf("ok\n");
+ TEST_DETECTOR("AKAZE:MLDB", akaze_mldb);
+ TEST_DETECTOR("AKAZE:MLDB_UPRIGHT", akaze_mldb_upright);
+ TEST_DETECTOR("AKAZE:MLDB_256", akaze_mldb_256);
+ TEST_DETECTOR("AKAZE:MLDB_UPRIGHT_256", akaze_mldb_upright_256);
+ TEST_DETECTOR("AKAZE:KAZE", akaze_kaze);
+ TEST_DETECTOR("AKAZE:KAZE_UPRIGHT", akaze_kaze_upright);
+ TEST_DETECTOR("KAZE", kaze);
+ TEST_DETECTOR("ORB", orb);
+ TEST_DETECTOR("BRISK", brisk);
}
}
+
+INSTANTIATE_TEST_CASE_P(Features2d, DescriptorImage,
+ testing::Values(
+ "shared/lena.png",
+ "shared/box*.png",
+ "shared/fruits*.png",
+ "shared/airplane.png",
+ "shared/graffiti.png",
+ "shared/1_itseez-0001*.png",
+ "shared/pic*.png",
+ "shared/templ.png"
+ )
+);
diff --git a/modules/ts/misc/run_long.py b/modules/ts/misc/run_long.py
index d820f97..5640ea3 100644
--- a/modules/ts/misc/run_long.py
+++ b/modules/ts/misc/run_long.py
@@ -8,7 +8,11 @@ from pprint import PrettyPrinter as PP
LONG_TESTS_DEBUG_VALGRIND = [
('calib3d', 'Calib3d_InitUndistortRectifyMap.accuracy', 2017.22),
('dnn', 'Reproducibility*', 1000), # large DNN models
- ('features2d', 'Features2d_Feature2d.no_crash', 1235.68),
+ ('features2d', 'Features2d/DescriptorImage.no_crash/3', 1000),
+ ('features2d', 'Features2d/DescriptorImage.no_crash/4', 1000),
+ ('features2d', 'Features2d/DescriptorImage.no_crash/5', 1000),
+ ('features2d', 'Features2d/DescriptorImage.no_crash/6', 1000),
+ ('features2d', 'Features2d/DescriptorImage.no_crash/7', 1000),
('imgcodecs', 'Imgcodecs_Png.write_big', 1000), # memory limit
('imgcodecs', 'Imgcodecs_Tiff.decode_tile16384x16384', 1000), # memory limit
('ml', 'ML_RTrees.regression', 1423.47),
--
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