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Commit 922ac1a1 authored by Alexander Alekhin's avatar Alexander Alekhin Committed by GitHub
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Merge pull request #9303 from alalek:akaze_update

parents c95a9738 2a8322dd
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Showing with 129 additions and 142 deletions
modules/core/include/opencv2/core/utility.hpp
View file @ 922ac1a1
......@@ -444,6 +444,23 @@ static inline size_t alignSize(size_t sz, int n)
return (sz + n-1) & -n;
}
/** @brief Integer division with result round up.
Use this function instead of `ceil((float)a / b)` expressions.
@sa alignSize
*/
static inline int divUp(int a, unsigned int b)
{
CV_DbgAssert(a >= 0);
return (a + b - 1) / b;
}
/** @overload */
static inline size_t divUp(size_t a, unsigned int b)
{
return (a + b - 1) / b;
}
/** @brief Enables or disables the optimized code.
The function can be used to dynamically turn on and off optimized code (code that uses SSE2, AVX,
......
modules/core/src/matrix.cpp
View file @ 922ac1a1
......@@ -3406,11 +3406,6 @@ static TransposeInplaceFunc transposeInplaceTab[] =
#ifdef HAVE_OPENCL
static inline int divUp(int a, int b)
{
return (a + b - 1) / b;
}
static bool ocl_transpose( InputArray _src, OutputArray _dst )
{
const ocl::Device & dev = ocl::Device::getDefault();
......
modules/features2d/src/akaze.cpp
View file @ 922ac1a1
......@@ -113,12 +113,12 @@ namespace cv
if (descriptor_size == 0)
{
int t = (6 + 36 + 120) * descriptor_channels;
return (int)ceil(t / 8.);
return divUp(t, 8);
}
else
{
// We use the random bit selection length binary descriptor
return (int)ceil(descriptor_size / 8.);
return divUp(descriptor_size, 8);
}
default:
......
modules/features2d/src/kaze/AKAZEFeatures.h
View file @ 922ac1a1
......@@ -25,6 +25,8 @@ struct Evolution
octave = 0;
sublevel = 0;
sigma_size = 0;
octave_ratio = 0.0f;
border = 0;
}
UMat Lx, Ly; ///< First order spatial derivatives
......
modules/features2d/src/kaze/KAZEFeatures.cpp
View file @ 922ac1a1
......@@ -68,7 +68,7 @@ void KAZEFeatures::Allocate_Memory_Evolution(void) {
aux.Ldet = Mat::zeros(options_.img_height, options_.img_width, CV_32F);
aux.esigma = options_.soffset*pow((float)2.0f, (float)(j) / (float)(options_.nsublevels)+i);
aux.etime = 0.5f*(aux.esigma*aux.esigma);
aux.sigma_size = fRound(aux.esigma);
aux.sigma_size = cvRound(aux.esigma);
aux.octave = i;
aux.sublevel = j;
evolution_.push_back(aux);
......@@ -363,10 +363,10 @@ void KAZEFeatures::Determinant_Hessian(std::vector<KeyPoint>& kpts)
if (is_extremum == true) {
// Check that the point is under the image limits for the descriptor computation
left_x = fRound(kpts_par_[i][j].pt.x - smax*kpts_par_[i][j].size);
right_x = fRound(kpts_par_[i][j].pt.x + smax*kpts_par_[i][j].size);
up_y = fRound(kpts_par_[i][j].pt.y - smax*kpts_par_[i][j].size);
down_y = fRound(kpts_par_[i][j].pt.y + smax*kpts_par_[i][j].size);
left_x = cvRound(kpts_par_[i][j].pt.x - smax*kpts_par_[i][j].size);
right_x = cvRound(kpts_par_[i][j].pt.x + smax*kpts_par_[i][j].size);
up_y = cvRound(kpts_par_[i][j].pt.y - smax*kpts_par_[i][j].size);
down_y = cvRound(kpts_par_[i][j].pt.y + smax*kpts_par_[i][j].size);
if (left_x < 0 || right_x >= evolution_[level].Ldet.cols ||
up_y < 0 || down_y >= evolution_[level].Ldet.rows) {
......@@ -587,14 +587,14 @@ void KAZEFeatures::Compute_Main_Orientation(KeyPoint &kpt, const std::vector<TEv
xf = kpt.pt.x;
yf = kpt.pt.y;
level = kpt.class_id;
s = fRound(kpt.size / 2.0f);
s = cvRound(kpt.size / 2.0f);
// Calculate derivatives responses for points within radius of 6*scale
for (int i = -6; i <= 6; ++i) {
for (int j = -6; j <= 6; ++j) {
if (i*i + j*j < 36) {
iy = fRound(yf + j*s);
ix = fRound(xf + i*s);
iy = cvRound(yf + j*s);
ix = cvRound(xf + i*s);
if (iy >= 0 && iy < options.img_height && ix >= 0 && ix < options.img_width) {
gweight = gaussian(iy - yf, ix - xf, 2.5f*s);
......@@ -606,7 +606,7 @@ void KAZEFeatures::Compute_Main_Orientation(KeyPoint &kpt, const std::vector<TEv
resY[idx] = 0.0;
}
Ang[idx] = getAngle(resX[idx], resY[idx]);
Ang[idx] = fastAtan2(resX[idx], resY[idx]) * (float)(CV_PI / 180.0f);
++idx;
}
}
......@@ -638,7 +638,7 @@ void KAZEFeatures::Compute_Main_Orientation(KeyPoint &kpt, const std::vector<TEv
if (sumX*sumX + sumY*sumY > max) {
// store largest orientation
max = sumX*sumX + sumY*sumY;
kpt.angle = getAngle(sumX, sumY) * 180.f / static_cast<float>(CV_PI);
kpt.angle = fastAtan2(sumX, sumY);
}
}
}
......@@ -676,7 +676,7 @@ void KAZE_Descriptor_Invoker::Get_KAZE_Upright_Descriptor_64(const KeyPoint &kpt
// Get the information from the keypoint
yf = kpt.pt.y;
xf = kpt.pt.x;
scale = fRound(kpt.size / 2.0f);
scale = cvRound(kpt.size / 2.0f);
level = kpt.class_id;
i = -8;
......@@ -804,8 +804,8 @@ void KAZE_Descriptor_Invoker::Get_KAZE_Descriptor_64(const KeyPoint &kpt, float
// Get the information from the keypoint
yf = kpt.pt.y;
xf = kpt.pt.x;
scale = fRound(kpt.size / 2.0f);
angle = (kpt.angle * static_cast<float>(CV_PI)) / 180.f;
scale = cvRound(kpt.size / 2.0f);
angle = kpt.angle * static_cast<float>(CV_PI / 180.f);
level = kpt.class_id;
co = cos(angle);
si = sin(angle);
......@@ -843,13 +843,13 @@ void KAZE_Descriptor_Invoker::Get_KAZE_Descriptor_64(const KeyPoint &kpt, float
// Get the gaussian weighted x and y responses
gauss_s1 = gaussian(xs - sample_x, ys - sample_y, 2.5f*scale);
y1 = fRound(sample_y - 0.5f);
x1 = fRound(sample_x - 0.5f);
y1 = cvFloor(sample_y);
x1 = cvFloor(sample_x);
checkDescriptorLimits(x1, y1, options_.img_width, options_.img_height);
y2 = (int)(sample_y + 0.5f);
x2 = (int)(sample_x + 0.5f);
y2 = y1 + 1;
x2 = x1 + 1;
checkDescriptorLimits(x2, y2, options_.img_width, options_.img_height);
......@@ -935,7 +935,7 @@ void KAZE_Descriptor_Invoker::Get_KAZE_Upright_Descriptor_128(const KeyPoint &kp
// Get the information from the keypoint
yf = kpt.pt.y;
xf = kpt.pt.x;
scale = fRound(kpt.size / 2.0f);
scale = cvRound(kpt.size / 2.0f);
level = kpt.class_id;
i = -8;
......@@ -1087,8 +1087,8 @@ void KAZE_Descriptor_Invoker::Get_KAZE_Descriptor_128(const KeyPoint &kpt, float
// Get the information from the keypoint
yf = kpt.pt.y;
xf = kpt.pt.x;
scale = fRound(kpt.size / 2.0f);
angle = (kpt.angle * static_cast<float>(CV_PI)) / 180.f;
scale = cvRound(kpt.size / 2.0f);
angle = kpt.angle * static_cast<float>(CV_PI / 180.f);
level = kpt.class_id;
co = cos(angle);
si = sin(angle);
......@@ -1129,13 +1129,13 @@ void KAZE_Descriptor_Invoker::Get_KAZE_Descriptor_128(const KeyPoint &kpt, float
// Get the gaussian weighted x and y responses
gauss_s1 = gaussian(xs - sample_x, ys - sample_y, 2.5f*scale);
y1 = fRound(sample_y - 0.5f);
x1 = fRound(sample_x - 0.5f);
y1 = cvFloor(sample_y);
x1 = cvFloor(sample_x);
checkDescriptorLimits(x1, y1, options_.img_width, options_.img_height);
y2 = (int)(sample_y + 0.5f);
x2 = (int)(sample_x + 0.5f);
y2 = y1 + 1;
x2 = x1 + 1;
checkDescriptorLimits(x2, y2, options_.img_width, options_.img_height);
......
modules/features2d/src/kaze/fed.cpp
View file @ 922ac1a1
......@@ -72,7 +72,7 @@ int fed_tau_by_cycle_time(const float& t, const float& tau_max,
float scale = 0.0; // Ratio of t we search to maximal t
// Compute necessary number of time steps
n = (int)(ceilf(sqrtf(3.0f*t/tau_max+0.25f)-0.5f-1.0e-8f)+ 0.5f);
n = cvCeil(sqrtf(3.0f*t/tau_max+0.25f)-0.5f-1.0e-8f);
scale = 3.0f*t/(tau_max*(float)(n*(n+1)));
// Call internal FED time step creation routine
......
modules/features2d/src/kaze/nldiffusion_functions.cpp
View file @ 922ac1a1
......@@ -49,7 +49,7 @@ void gaussian_2D_convolution(const cv::Mat& src, cv::Mat& dst, int ksize_x, int
// Compute an appropriate kernel size according to the specified sigma
if (sigma > ksize_x || sigma > ksize_y || ksize_x == 0 || ksize_y == 0) {
ksize_x_ = (int)ceil(2.0f*(1.0f + (sigma - 0.8f) / (0.3f)));
ksize_x_ = cvCeil(2.0f*(1.0f + (sigma - 0.8f) / (0.3f)));
ksize_y_ = ksize_x_;
}
......
modules/features2d/src/kaze/utils.h
View file @ 922ac1a1
#ifndef __OPENCV_FEATURES_2D_KAZE_UTILS_H__
#define __OPENCV_FEATURES_2D_KAZE_UTILS_H__
/* ************************************************************************* */
/**
* @brief This function computes the angle from the vector given by (X Y). From 0 to 2*Pi
*/
inline float getAngle(float x, float y) {
if (x >= 0 && y >= 0) {
return atanf(y / x);
}
if (x < 0 && y >= 0) {
return static_cast<float>(CV_PI)-atanf(-y / x);
}
if (x < 0 && y < 0) {
return static_cast<float>(CV_PI)+atanf(y / x);
}
if (x >= 0 && y < 0) {
return static_cast<float>(2.0 * CV_PI) - atanf(-y / x);
}
return 0;
}
/* ************************************************************************* */
/**
* @brief This function computes the value of a 2D Gaussian function
......@@ -64,34 +39,4 @@ inline void checkDescriptorLimits(int &x, int &y, int width, int height) {
}
}
/* ************************************************************************* */
/**
* @brief This funtion rounds float to nearest integer
* @param flt Input float
* @return dst Nearest integer
*/
inline int fRound(float flt) {
return (int)(flt + 0.5f);
}
/* ************************************************************************* */
/**
* @brief Exponentiation by squaring
* @param flt Exponentiation base
* @return dst Exponentiation value
*/
inline int fastpow(int base, int exp) {
int res = 1;
while(exp > 0) {
if(exp & 1) {
exp--;
res *= base;
} else {
exp /= 2;
base *= base;
}
}
return res;
}
#endif
modules/features2d/test/test_descriptors_invariance.cpp
View file @ 922ac1a1
......@@ -11,7 +11,7 @@ using std::tr1::make_tuple;
using std::tr1::get;
using namespace testing;
#define SHOW_DEBUG_LOG 0
#define SHOW_DEBUG_LOG 1
typedef std::tr1::tuple<std::string, Ptr<FeatureDetector>, Ptr<DescriptorExtractor>, float>
String_FeatureDetector_DescriptorExtractor_Float_t;
......@@ -72,7 +72,7 @@ TEST_P(DescriptorRotationInvariance, rotation)
vector<KeyPoint> keypoints0;
Mat descriptors0;
featureDetector->detect(image0, keypoints0, mask0);
std::cout << "Intial keypoints: " << keypoints0.size() << std::endl;
std::cout << "Keypoints: " << keypoints0.size() << std::endl;
EXPECT_GE(keypoints0.size(), 15u);
descriptorExtractor->compute(image0, keypoints0, descriptors0);
......@@ -109,7 +109,7 @@ TEST_P(DescriptorRotationInvariance, rotation)
#if SHOW_DEBUG_LOG
std::cout
<< "angle = " << angle
<< ", keypoints = " << keypoints1.size()
<< ", inliers = " << descInliersCount
<< ", descInliersRatio = " << static_cast<float>(descInliersCount) / keypoints0.size()
<< std::endl;
#endif
......@@ -121,6 +121,7 @@ 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);
......@@ -159,6 +160,7 @@ TEST_P(DescriptorScaleInvariance, scale)
#if SHOW_DEBUG_LOG
std::cout
<< "scale = " << scale
<< ", inliers = " << descInliersCount
<< ", descInliersRatio = " << static_cast<float>(descInliersCount) / keypoints0.size()
<< std::endl;
#endif
......
modules/features2d/test/test_descriptors_regression.cpp
View file @ 922ac1a1
......@@ -56,6 +56,7 @@ static void writeMatInBin( const Mat& mat, const string& filename )
FILE* f = fopen( filename.c_str(), "wb");
if( f )
{
CV_Assert(4 == sizeof(int));
int type = mat.type();
fwrite( (void*)&mat.rows, sizeof(int), 1, f );
fwrite( (void*)&mat.cols, sizeof(int), 1, f );
......@@ -72,6 +73,7 @@ static Mat readMatFromBin( const string& filename )
FILE* f = fopen( filename.c_str(), "rb" );
if( f )
{
CV_Assert(4 == sizeof(int));
int rows, cols, type, dataSize;
size_t elements_read1 = fread( (void*)&rows, sizeof(int), 1, f );
size_t elements_read2 = fread( (void*)&cols, sizeof(int), 1, f );
......@@ -123,24 +125,37 @@ protected:
CV_Assert( DataType<ValueType>::type == validDescriptors.type() );
int dimension = validDescriptors.cols;
DistanceType curMaxDist = std::numeric_limits<DistanceType>::min();
DistanceType curMaxDist = 0;
size_t exact_count = 0, failed_count = 0;
for( int y = 0; y < validDescriptors.rows; y++ )
{
DistanceType dist = distance( validDescriptors.ptr<ValueType>(y), calcDescriptors.ptr<ValueType>(y), dimension );
if (dist == 0)
exact_count++;
if( dist > curMaxDist )
{
if (dist > maxDist)
failed_count++;
curMaxDist = dist;
}
#if 0
if (dist > 0)
{
std::cout << "i=" << y << " fail_count=" << failed_count << " dist=" << dist << std::endl;
std::cout << "valid: " << validDescriptors.row(y) << std::endl;
std::cout << " calc: " << calcDescriptors.row(y) << std::endl;
}
#endif
}
float exact_percents = (100 * (float)exact_count / validDescriptors.rows);
float failed_percents = (100 * (float)failed_count / validDescriptors.rows);
stringstream ss;
ss << "Max distance between valid and computed descriptors " << curMaxDist;
if( curMaxDist <= maxDist )
ss << "." << endl;
else
{
ss << ">" << maxDist << " - bad accuracy!"<< endl;
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
}
ts->printf(cvtest::TS::LOG, ss.str().c_str() );
ss << "Exact count (dist == 0): " << exact_count << " (" << (int)exact_percents << "%)" << std::endl
<< "Failed count (dist > " << maxDist << "): " << failed_count << " (" << (int)failed_percents << "%)" << std::endl
<< "Max distance between valid and computed descriptors (" << validDescriptors.size() << "): " << curMaxDist;
EXPECT_LE(failed_percents, 20.0f);
std::cout << ss.str() << std::endl;
}
void emptyDataTest()
......@@ -202,22 +217,57 @@ protected:
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
const std::string keypoints_filename = string(ts->get_data_path()) +
(detector.empty()
? (FEATURES2D_DIR + "/" + std::string("keypoints.xml.gz"))
: (DESCRIPTOR_DIR + "/" + name + "_keypoints.xml.gz"));
FileStorage fs(keypoints_filename, FileStorage::READ);
vector<KeyPoint> keypoints;
FileStorage fs( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::READ );
if(!detector.empty()) {
detector->detect(img, keypoints);
} else {
read( fs.getFirstTopLevelNode(), keypoints );
EXPECT_TRUE(fs.isOpened()) << "Keypoint testdata is missing. Re-computing and re-writing keypoints testdata...";
if (!fs.isOpened())
{
fs.open(keypoints_filename, FileStorage::WRITE);
ASSERT_TRUE(fs.isOpened()) << "File for writting keypoints can not be opened.";
if (detector.empty())
{
Ptr<ORB> fd = ORB::create();
fd->detect(img, keypoints);
}
else
{
detector->detect(img, keypoints);
}
write(fs, "keypoints", keypoints);
fs.release();
}
if(!keypoints.empty())
else
{
read(fs.getFirstTopLevelNode(), keypoints);
fs.release();
}
if(!detector.empty())
{
vector<KeyPoint> calcKeypoints;
detector->detect(img, calcKeypoints);
// TODO validate received keypoints
int diff = abs((int)calcKeypoints.size() - (int)keypoints.size());
if (diff > 0)
{
std::cout << "Keypoints difference: " << diff << std::endl;
EXPECT_LE(diff, (int)(keypoints.size() * 0.03f));
}
}
ASSERT_FALSE(keypoints.empty());
{
Mat calcDescriptors;
double t = (double)getTickCount();
dextractor->compute( img, keypoints, calcDescriptors );
dextractor->compute(img, keypoints, calcDescriptors);
t = getTickCount() - t;
ts->printf(cvtest::TS::LOG, "\nAverage time of computing one descriptor = %g ms.\n", t/((double)getTickFrequency()*1000.)/calcDescriptors.rows);
if( calcDescriptors.rows != (int)keypoints.size() )
if (calcDescriptors.rows != (int)keypoints.size())
{
ts->printf( cvtest::TS::LOG, "Count of computed descriptors and keypoints count must be equal.\n" );
ts->printf( cvtest::TS::LOG, "Count of keypoints is %d.\n", (int)keypoints.size() );
......@@ -226,7 +276,7 @@ protected:
return;
}
if( calcDescriptors.cols != dextractor->descriptorSize() || calcDescriptors.type() != dextractor->descriptorType() )
if (calcDescriptors.cols != dextractor->descriptorSize() || calcDescriptors.type() != dextractor->descriptorType())
{
ts->printf( cvtest::TS::LOG, "Incorrect descriptor size or descriptor type.\n" );
ts->printf( cvtest::TS::LOG, "Expected size is %d.\n", dextractor->descriptorSize() );
......@@ -239,33 +289,14 @@ protected:
// TODO read and write descriptor extractor parameters and check them
Mat validDescriptors = readDescriptors();
if( !validDescriptors.empty() )
compareDescriptors( validDescriptors, calcDescriptors );
else
EXPECT_FALSE(validDescriptors.empty()) << "Descriptors testdata is missing. Re-writing descriptors testdata...";
if (!validDescriptors.empty())
{
if( !writeDescriptors( calcDescriptors ) )
{
ts->printf( cvtest::TS::LOG, "Descriptors can not be written.\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
}
}
if(!fs.isOpened())
{
ts->printf( cvtest::TS::LOG, "Compute and write keypoints.\n" );
fs.open( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::WRITE );
if( fs.isOpened() )
{
Ptr<ORB> fd = ORB::create();
fd->detect(img, keypoints);
write( fs, "keypoints", keypoints );
compareDescriptors(validDescriptors, calcDescriptors);
}
else
{
ts->printf(cvtest::TS::LOG, "File for writting keypoints can not be opened.\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
ASSERT_TRUE(writeDescriptors(calcDescriptors)) << "Descriptors can not be written.";
}
}
}
......@@ -344,7 +375,7 @@ TEST( Features2d_DescriptorExtractor_KAZE, regression )
TEST( Features2d_DescriptorExtractor_AKAZE, regression )
{
CV_DescriptorExtractorTest<Hamming> test( "descriptor-akaze",
(CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
(CV_DescriptorExtractorTest<Hamming>::DistanceType)(486*0.05f),
AKAZE::create(),
Hamming(), AKAZE::create());
test.safe_run();
......
modules/features2d/test/test_detectors_invariance.cpp
View file @ 922ac1a1
......@@ -11,7 +11,7 @@ using std::tr1::make_tuple;
using std::tr1::get;
using namespace testing;
#define SHOW_DEBUG_LOG 0
#define SHOW_DEBUG_LOG 1
typedef std::tr1::tuple<std::string, Ptr<FeatureDetector>, float, float> String_FeatureDetector_Float_Float_t;
const static std::string IMAGE_TSUKUBA = "features2d/tsukuba.png";
......
modules/photo/src/fast_nlmeans_denoising_opencl.hpp
View file @ 922ac1a1
......@@ -23,11 +23,6 @@ enum
CTA_SIZE_DEFAULT = 256
};
static int divUp(int a, int b)
{
return (a + b - 1) / b;
}
template <typename FT, typename ST, typename WT>
static bool ocl_calcAlmostDist2Weight(UMat & almostDist2Weight,
int searchWindowSize, int templateWindowSize,
......
modules/stitching/perf/perf_stich.cpp
View file @ 922ac1a1
......@@ -102,7 +102,7 @@ PERF_TEST_P(stitchDatasets, affine, testing::Combine(AFFINE_DATASETS, TEST_DETEC
Mat pano;
vector<Mat> imgs;
int width, height, allowed_diff = 10;
int width, height, allowed_diff = 20;
Ptr<detail::FeaturesFinder> featuresFinder = getFeatureFinder(detector);
if(dataset == "budapest")
......@@ -117,7 +117,7 @@ PERF_TEST_P(stitchDatasets, affine, testing::Combine(AFFINE_DATASETS, TEST_DETEC
height = 1158;
// this dataset is big, the results between surf and orb differ slightly,
// but both are still good
allowed_diff = 27;
allowed_diff = 50;
}
else if (dataset == "newspaper")
{
......
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