mirror of
https://github.com/opencv/opencv.git
synced 2024-12-11 22:59:16 +08:00
f9e9567870
features2d: reduce accuracy test avg memory consumption #25424
**Merge with contrib**: https://github.com/opencv/opencv_contrib/pull/3722
I've observed high memory consumption in the `opencv_test_features2d` (x86_64, Ubuntu 22.04, Debug):
![image](https://github.com/opencv/opencv/assets/3304494/419d65d9-d727-4d1e-bdec-dbde6681c188)
It's always more than 180 MiB with peak at 535 MiB
This was caused by pointers to the algorithm object instances stored in the tests parameters. I've replaced them with factory functions/lambdas with the following result:
![image](https://github.com/opencv/opencv/assets/3304494/bd4ff0ea-3db4-4ab8-8e6d-192a3826e99c)
Now peak is at 355 MiB and permanent consumption level is ~ 1-2 MiB
**Note:** current peak is caused by KAZE features allocating 8x image size utility buffers. Not sure if we can or should do anything about it: 66fb5021e9/modules/features2d/src/kaze/KAZEFeatures.cpp (L61-L68)
204 lines
6.9 KiB
C++
204 lines
6.9 KiB
C++
// This file is part of OpenCV project.
|
|
// It is subject to the license terms in the LICENSE file found in the top-level directory
|
|
// of this distribution and at http://opencv.org/license.html
|
|
|
|
#include "test_invariance_utils.hpp"
|
|
#include <functional>
|
|
|
|
namespace opencv_test { namespace {
|
|
|
|
#define SHOW_DEBUG_LOG 1
|
|
|
|
// NOTE: using factory function (function<Ptr<Type>()>) instead of object instance (Ptr<Type>) as a
|
|
// test parameter, because parameters exist during whole test program run and consume a lot of memory
|
|
typedef std::function<cv::Ptr<cv::FeatureDetector>()> DetectorFactory;
|
|
typedef std::function<cv::Ptr<cv::DescriptorExtractor>()> ExtractorFactory;
|
|
typedef tuple<std::string, DetectorFactory, ExtractorFactory, float>
|
|
String_FeatureDetector_DescriptorExtractor_Float_t;
|
|
|
|
|
|
static
|
|
void SetSuitableSIFTOctave(vector<KeyPoint>& keypoints,
|
|
int firstOctave = -1, int nOctaveLayers = 3, double sigma = 1.6)
|
|
{
|
|
for (size_t i = 0; i < keypoints.size(); i++ )
|
|
{
|
|
int octv, layer;
|
|
KeyPoint& kpt = keypoints[i];
|
|
double octv_layer = std::log(kpt.size / sigma) / std::log(2.) - 1;
|
|
octv = cvFloor(octv_layer);
|
|
layer = cvRound( (octv_layer - octv) * nOctaveLayers );
|
|
if (octv < firstOctave)
|
|
{
|
|
octv = firstOctave;
|
|
layer = 0;
|
|
}
|
|
kpt.octave = (layer << 8) | (octv & 255);
|
|
}
|
|
}
|
|
|
|
static
|
|
void rotateKeyPoints(const vector<KeyPoint>& src, const Mat& H, float angle, vector<KeyPoint>& dst)
|
|
{
|
|
// suppose that H is rotation given from rotateImage() and angle has value passed to rotateImage()
|
|
vector<Point2f> srcCenters, dstCenters;
|
|
KeyPoint::convert(src, srcCenters);
|
|
|
|
perspectiveTransform(srcCenters, dstCenters, H);
|
|
|
|
dst = src;
|
|
for(size_t i = 0; i < dst.size(); i++)
|
|
{
|
|
dst[i].pt = dstCenters[i];
|
|
float dstAngle = src[i].angle + angle;
|
|
if(dstAngle >= 360.f)
|
|
dstAngle -= 360.f;
|
|
dst[i].angle = dstAngle;
|
|
}
|
|
}
|
|
|
|
class DescriptorInvariance : public TestWithParam<String_FeatureDetector_DescriptorExtractor_Float_t>
|
|
{
|
|
protected:
|
|
virtual void SetUp() {
|
|
// Read test data
|
|
const std::string filename = cvtest::TS::ptr()->get_data_path() + get<0>(GetParam());
|
|
image0 = imread(filename);
|
|
ASSERT_FALSE(image0.empty()) << "couldn't read input image";
|
|
|
|
featureDetector = get<1>(GetParam())();
|
|
descriptorExtractor = get<2>(GetParam())();
|
|
minInliersRatio = get<3>(GetParam());
|
|
}
|
|
|
|
Ptr<FeatureDetector> featureDetector;
|
|
Ptr<DescriptorExtractor> descriptorExtractor;
|
|
float minInliersRatio;
|
|
Mat image0;
|
|
};
|
|
|
|
typedef DescriptorInvariance DescriptorScaleInvariance;
|
|
typedef DescriptorInvariance DescriptorRotationInvariance;
|
|
|
|
TEST_P(DescriptorRotationInvariance, rotation)
|
|
{
|
|
Mat image1, mask1;
|
|
const int borderSize = 16;
|
|
Mat mask0(image0.size(), CV_8UC1, Scalar(0));
|
|
mask0(Rect(borderSize, borderSize, mask0.cols - 2*borderSize, mask0.rows - 2*borderSize)).setTo(Scalar(255));
|
|
|
|
vector<KeyPoint> keypoints0;
|
|
Mat descriptors0;
|
|
featureDetector->detect(image0, keypoints0, mask0);
|
|
std::cout << "Keypoints: " << keypoints0.size() << std::endl;
|
|
EXPECT_GE(keypoints0.size(), 15u);
|
|
descriptorExtractor->compute(image0, keypoints0, descriptors0);
|
|
|
|
BFMatcher bfmatcher(descriptorExtractor->defaultNorm());
|
|
|
|
const float minIntersectRatio = 0.5f;
|
|
const int maxAngle = 360, angleStep = 15;
|
|
for(int angle = 0; angle < maxAngle; angle += angleStep)
|
|
{
|
|
Mat H = rotateImage(image0, mask0, static_cast<float>(angle), image1, mask1);
|
|
|
|
vector<KeyPoint> keypoints1;
|
|
rotateKeyPoints(keypoints0, H, static_cast<float>(angle), keypoints1);
|
|
Mat descriptors1;
|
|
descriptorExtractor->compute(image1, keypoints1, descriptors1);
|
|
|
|
vector<DMatch> descMatches;
|
|
bfmatcher.match(descriptors0, descriptors1, descMatches);
|
|
|
|
int descInliersCount = 0;
|
|
for(size_t m = 0; m < descMatches.size(); m++)
|
|
{
|
|
const KeyPoint& transformed_p0 = keypoints1[descMatches[m].queryIdx];
|
|
const KeyPoint& p1 = keypoints1[descMatches[m].trainIdx];
|
|
if(calcIntersectRatio(transformed_p0.pt, 0.5f * transformed_p0.size,
|
|
p1.pt, 0.5f * p1.size) >= minIntersectRatio)
|
|
{
|
|
descInliersCount++;
|
|
}
|
|
}
|
|
|
|
float descInliersRatio = static_cast<float>(descInliersCount) / keypoints0.size();
|
|
EXPECT_GE(descInliersRatio, minInliersRatio);
|
|
#if SHOW_DEBUG_LOG
|
|
std::cout
|
|
<< "angle = " << angle
|
|
<< ", inliers = " << descInliersCount
|
|
<< ", descInliersRatio = " << static_cast<float>(descInliersCount) / keypoints0.size()
|
|
<< std::endl;
|
|
#endif
|
|
}
|
|
}
|
|
|
|
|
|
TEST_P(DescriptorScaleInvariance, scale)
|
|
{
|
|
vector<KeyPoint> keypoints0;
|
|
featureDetector->detect(image0, keypoints0);
|
|
std::cout << "Keypoints: " << keypoints0.size() << std::endl;
|
|
EXPECT_GE(keypoints0.size(), 15u);
|
|
Mat descriptors0;
|
|
descriptorExtractor->compute(image0, keypoints0, descriptors0);
|
|
|
|
BFMatcher bfmatcher(descriptorExtractor->defaultNorm());
|
|
for(int scaleIdx = 1; scaleIdx <= 3; scaleIdx++)
|
|
{
|
|
float scale = 1.f + scaleIdx * 0.5f;
|
|
|
|
Mat image1;
|
|
resize(image0, image1, Size(), 1./scale, 1./scale, INTER_LINEAR_EXACT);
|
|
|
|
vector<KeyPoint> keypoints1;
|
|
scaleKeyPoints(keypoints0, keypoints1, 1.0f/scale);
|
|
if (featureDetector->getDefaultName() == "Feature2D.SIFT")
|
|
{
|
|
SetSuitableSIFTOctave(keypoints1);
|
|
}
|
|
Mat descriptors1;
|
|
descriptorExtractor->compute(image1, keypoints1, descriptors1);
|
|
|
|
vector<DMatch> descMatches;
|
|
bfmatcher.match(descriptors0, descriptors1, descMatches);
|
|
|
|
const float minIntersectRatio = 0.5f;
|
|
int descInliersCount = 0;
|
|
for(size_t m = 0; m < descMatches.size(); m++)
|
|
{
|
|
const KeyPoint& transformed_p0 = keypoints0[descMatches[m].queryIdx];
|
|
const KeyPoint& p1 = keypoints0[descMatches[m].trainIdx];
|
|
if(calcIntersectRatio(transformed_p0.pt, 0.5f * transformed_p0.size,
|
|
p1.pt, 0.5f * p1.size) >= minIntersectRatio)
|
|
{
|
|
descInliersCount++;
|
|
}
|
|
}
|
|
|
|
float descInliersRatio = static_cast<float>(descInliersCount) / keypoints0.size();
|
|
EXPECT_GE(descInliersRatio, minInliersRatio);
|
|
#if SHOW_DEBUG_LOG
|
|
std::cout
|
|
<< "scale = " << scale
|
|
<< ", inliers = " << descInliersCount
|
|
<< ", descInliersRatio = " << static_cast<float>(descInliersCount) / keypoints0.size()
|
|
<< std::endl;
|
|
#endif
|
|
}
|
|
}
|
|
|
|
#undef SHOW_DEBUG_LOG
|
|
}} // namespace
|
|
|
|
namespace std {
|
|
using namespace opencv_test;
|
|
static inline void PrintTo(const String_FeatureDetector_DescriptorExtractor_Float_t& v, std::ostream* os)
|
|
{
|
|
*os << "(\"" << get<0>(v)
|
|
<< "\", " << get<3>(v)
|
|
<< ")";
|
|
}
|
|
} // namespace
|