potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-31 00:03:03 +04:00
Code Packages Releases Wiki Activity

Merge remote-tracking branch 'upstream/3.4' into merge-3.4

Browse Source
This commit is contained in:
Alexander Alekhin
2020-06-29 21:00:18 +00:00
parent ab4065fa1c a84afb6334
commit a711e2aa41
47 changed files with 852 additions and 719 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/core/include/opencv2/core/cvdef.h
+14 -2
View File
@@ -86,12 +86,24 @@ namespace cv { namespace debug_build_guard { } using namespace debug_build_guard
#define __CV_VA_NUM_ARGS_HELPER(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, N, ...) N
#define __CV_VA_NUM_ARGS(...) __CV_EXPAND(__CV_VA_NUM_ARGS_HELPER(__VA_ARGS__, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0))
#if defined __GNUC__
#ifdef CV_Func
// keep current value (through OpenCV port file)
#elif defined __GNUC__ || (defined (__cpluscplus) && (__cpluscplus >= 201103))
#define CV_Func __func__
#elif defined __clang__ && (__clang_minor__ * 100 + __clang_major >= 305)
#define CV_Func __func__
#elif defined(__STDC_VERSION__) && (__STDC_VERSION >= 199901)
#define CV_Func __func__
#elif defined _MSC_VER
#define CV_Func __FUNCTION__
#elif defined(__INTEL_COMPILER) && (_INTEL_COMPILER >= 600)
#define CV_Func __FUNCTION__
#elif defined __IBMCPP__ && __IBMCPP__ >=500
#define CV_Func __FUNCTION__
#elif defined __BORLAND__ && (__BORLANDC__ >= 0x550)
#define CV_Func __FUNC__
#else
#define CV_Func ""
#define CV_Func "<unknown>"
#endif
//! @cond IGNORED
modules/features2d/include/opencv2/features2d.hpp
+6
View File
@@ -261,6 +261,10 @@ public:
@param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform
(low-contrast) regions. The larger the threshold, the less features are produced by the detector.
@note The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When
nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set
this argument to 0.09.
@param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning
is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are
filtered out (more features are retained).
@@ -271,6 +275,8 @@ public:
CV_WRAP static Ptr<SIFT> create(int nfeatures = 0, int nOctaveLayers = 3,
double contrastThreshold = 0.04, double edgeThreshold = 10,
double sigma = 1.6);
CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
};
typedef SIFT SiftFeatureDetector;
modules/features2d/perf/perf_feature2d.hpp
+4 -1
View File
@@ -21,7 +21,8 @@ namespace opencv_test
ORB_DEFAULT, ORB_1500_13_1, \
AKAZE_DEFAULT, AKAZE_DESCRIPTOR_KAZE, \
BRISK_DEFAULT, \
KAZE_DEFAULT
KAZE_DEFAULT, \
SIFT_DEFAULT
#define CV_ENUM_EXPAND(name, ...) CV_ENUM(name, __VA_ARGS__)
@@ -77,6 +78,8 @@ static inline Ptr<Feature2D> getFeature2D(Feature2DType type)
return KAZE::create();
case MSER_DEFAULT:
return MSER::create();
case SIFT_DEFAULT:
return SIFT::create();
default:
return Ptr<Feature2D>();
}
modules/features2d/perf/perf_sift.cpp
-85
View File
@@ -1,85 +0,0 @@
// 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 "perf_precomp.hpp"
namespace opencv_test { namespace {
typedef perf::TestBaseWithParam<std::string> SIFT_detect;
typedef perf::TestBaseWithParam<std::string> SIFT_extract;
typedef perf::TestBaseWithParam<std::string> SIFT_full;
#define SIFT_IMAGES \
"cv/detectors_descriptors_evaluation/images_datasets/leuven/img1.png",\
"stitching/a3.png"
PERF_TEST_P_(SIFT_detect, SIFT)
{
string filename = getDataPath(GetParam());
Mat frame = imread(filename, IMREAD_GRAYSCALE);
ASSERT_FALSE(frame.empty()) << "Unable to load source image " << filename;
Mat mask;
declare.in(frame).time(90);
Ptr<SIFT> detector = SIFT::create();
vector<KeyPoint> points;
PERF_SAMPLE_BEGIN();
detector->detect(frame, points, mask);
PERF_SAMPLE_END();
SANITY_CHECK_NOTHING();
}
PERF_TEST_P_(SIFT_extract, SIFT)
{
string filename = getDataPath(GetParam());
Mat frame = imread(filename, IMREAD_GRAYSCALE);
ASSERT_FALSE(frame.empty()) << "Unable to load source image " << filename;
Mat mask;
declare.in(frame).time(90);
Ptr<SIFT> detector = SIFT::create();
vector<KeyPoint> points;
Mat descriptors;
detector->detect(frame, points, mask);
PERF_SAMPLE_BEGIN();
detector->compute(frame, points, descriptors);
PERF_SAMPLE_END();
SANITY_CHECK_NOTHING();
}
PERF_TEST_P_(SIFT_full, SIFT)
{
string filename = getDataPath(GetParam());
Mat frame = imread(filename, IMREAD_GRAYSCALE);
ASSERT_FALSE(frame.empty()) << "Unable to load source image " << filename;
Mat mask;
declare.in(frame).time(90);
Ptr<SIFT> detector = SIFT::create();
vector<KeyPoint> points;
Mat descriptors;
PERF_SAMPLE_BEGIN();
detector->detectAndCompute(frame, mask, points, descriptors, false);
PERF_SAMPLE_END();
SANITY_CHECK_NOTHING();
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, SIFT_detect,
testing::Values(SIFT_IMAGES)
);
INSTANTIATE_TEST_CASE_P(/*nothing*/, SIFT_extract,
testing::Values(SIFT_IMAGES)
);
INSTANTIATE_TEST_CASE_P(/*nothing*/, SIFT_full,
testing::Values(SIFT_IMAGES)
);
}} // namespace
modules/features2d/src/sift.dispatch.cpp
+5
View File
@@ -126,6 +126,11 @@ Ptr<SIFT> SIFT::create( int _nfeatures, int _nOctaveLayers,
return makePtr<SIFT_Impl>(_nfeatures, _nOctaveLayers, _contrastThreshold, _edgeThreshold, _sigma);
}
String SIFT::getDefaultName() const
{
return (Feature2D::getDefaultName() + ".SIFT");
}
static inline void
unpackOctave(const KeyPoint& kpt, int& octave, int& layer, float& scale)
{
modules/features2d/test/test_descriptors_invariance.cpp
+2 -3
View File
@@ -36,9 +36,8 @@ INSTANTIATE_TEST_CASE_P(AKAZE_DESCRIPTOR_KAZE, DescriptorRotationInvariance,
* Descriptor's scale invariance check
*/
// TODO: Expected: (descInliersRatio) >= (minInliersRatio), actual: 0.330378 vs 0.78
INSTANTIATE_TEST_CASE_P(DISABLED_SIFT, DescriptorScaleInvariance,
Value(IMAGE_BIKES, SIFT::create(), SIFT::create(), 0.78f));
INSTANTIATE_TEST_CASE_P(SIFT, DescriptorScaleInvariance,
Value(IMAGE_BIKES, SIFT::create(0, 3, 0.09), SIFT::create(0, 3, 0.09), 0.78f));
INSTANTIATE_TEST_CASE_P(AKAZE, DescriptorScaleInvariance,
Value(IMAGE_BIKES, AKAZE::create(), AKAZE::create(), 0.6f));
modules/features2d/test/test_descriptors_invariance.impl.hpp
+24
View File
@@ -12,6 +12,26 @@ typedef tuple<std::string, Ptr<FeatureDetector>, Ptr<DescriptorExtractor>, 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)
{
@@ -129,6 +149,10 @@ TEST_P(DescriptorScaleInvariance, scale)
vector<KeyPoint> keypoints1;
scaleKeyPoints(keypoints0, keypoints1, 1.0f/scale);
if (featureDetector->getDefaultName() == "Feature2D.SIFT")
{
SetSuitableSIFTOctave(keypoints1);
}
Mat descriptors1;
descriptorExtractor->compute(image1, keypoints1, descriptors1);
modules/features2d/test/test_detectors_invariance.cpp
+2 -3
View File
@@ -36,9 +36,8 @@ INSTANTIATE_TEST_CASE_P(AKAZE_DESCRIPTOR_KAZE, DetectorRotationInvariance,
* Detector's scale invariance check
*/
// TODO: Expected: (keyPointMatchesRatio) >= (minKeyPointMatchesRatio), actual: 0.596752 vs 0.69
INSTANTIATE_TEST_CASE_P(DISABLED_SIFT, DetectorScaleInvariance,
Value(IMAGE_BIKES, SIFT::create(), 0.69f, 0.98f));
INSTANTIATE_TEST_CASE_P(SIFT, DetectorScaleInvariance,
Value(IMAGE_BIKES, SIFT::create(0, 3, 0.09), 0.65f, 0.98f));
INSTANTIATE_TEST_CASE_P(BRISK, DetectorScaleInvariance,
Value(IMAGE_BIKES, BRISK::create(), 0.08f, 0.49f));
modules/flann/include/opencv2/flann/dist.h
+2 -2
View File
@@ -708,7 +708,7 @@ struct KL_Divergence
Iterator1 last = a + size;
while (a < last) {
if (* b != 0) {
if ( *a != 0 && *b != 0 ) {
ResultType ratio = (ResultType)(*a / *b);
if (ratio>0) {
result += *a * log(ratio);
@@ -731,7 +731,7 @@ struct KL_Divergence
inline ResultType accum_dist(const U& a, const V& b, int) const
{
ResultType result = ResultType();
if( *b != 0 ) {
if( a != 0 && b != 0 ) {
ResultType ratio = (ResultType)(a / b);
if (ratio>0) {
result = a * log(ratio);
modules/flann/include/opencv2/flann/kdtree_single_index.h
+14 -6
View File
@@ -461,7 +461,7 @@ private:
DistanceType span = bbox[i].high-bbox[i].low;
if (span>(DistanceType)((1-EPS)*max_span)) {
ElementType min_elem, max_elem;
computeMinMax(ind, count, cutfeat, min_elem, max_elem);
computeMinMax(ind, count, (int)i, min_elem, max_elem);
DistanceType spread = (DistanceType)(max_elem-min_elem);
if (spread>max_spread) {
cutfeat = (int)i;
@@ -548,11 +548,19 @@ private:
/* If this is a leaf node, then do check and return. */
if ((node->child1 == NULL)&&(node->child2 == NULL)) {
DistanceType worst_dist = result_set.worstDist();
for (int i=node->left; i<node->right; ++i) {
int index = reorder_ ? i : vind_[i];
DistanceType dist = distance_(vec, data_[index], dim_, worst_dist);
if (dist<worst_dist) {
result_set.addPoint(dist,vind_[i]);
if (reorder_) {
for (int i=node->left; i<node->right; ++i) {
DistanceType dist = distance_(vec, data_[i], dim_, worst_dist);
if (dist<worst_dist) {
result_set.addPoint(dist,vind_[i]);
}
}
} else {
for (int i=node->left; i<node->right; ++i) {
DistanceType dist = distance_(vec, data_[vind_[i]], dim_, worst_dist);
if (dist<worst_dist) {
result_set.addPoint(dist,vind_[i]);
}
}
}
return;
modules/flann/include/opencv2/flann/kmeans_index.h
+3 -2
View File
@@ -650,7 +650,8 @@ private:
*
* Params:
* node = the node to use
* indices = the indices of the points belonging to the node
* indices = array of indices of the points belonging to the node
* indices_length = number of indices in the array
*/
void computeNodeStatistics(KMeansNodePtr node, int* indices, int indices_length)
{
@@ -662,7 +663,7 @@ private:
memset(mean,0,veclen_*sizeof(DistanceType));
for (size_t i=0; i<size_; ++i) {
for (size_t i=0; i<(size_t)indices_length; ++i) {
ElementType* vec = dataset_[indices[i]];
for (size_t j=0; j<veclen_; ++j) {
mean[j] += vec[j];
modules/flann/include/opencv2/flann/lsh_index.h
+13 -13
View File
@@ -60,20 +60,20 @@ struct LshIndexParams : public IndexParams
{
LshIndexParams(unsigned int table_number = 12, unsigned int key_size = 20, unsigned int multi_probe_level = 2)
{
(* this)["algorithm"] = FLANN_INDEX_LSH;
(*this)["algorithm"] = FLANN_INDEX_LSH;
// The number of hash tables to use
(*this)["table_number"] = table_number;
(*this)["table_number"] = static_cast<int>(table_number);
// The length of the key in the hash tables
(*this)["key_size"] = key_size;
(*this)["key_size"] = static_cast<int>(key_size);
// Number of levels to use in multi-probe (0 for standard LSH)
(*this)["multi_probe_level"] = multi_probe_level;
(*this)["multi_probe_level"] = static_cast<int>(multi_probe_level);
}
};
/**
* Randomized kd-tree index
* Locality-sensitive hashing index
*
* Contains the k-d trees and other information for indexing a set of points
* Contains the tables and other information for indexing a set of points
* for nearest-neighbor matching.
*/
template<typename Distance>
@@ -94,9 +94,9 @@ public:
{
// cv::flann::IndexParams sets integer params as 'int', so it is used with get_param
// in place of 'unsigned int'
table_number_ = (unsigned int)get_param<int>(index_params_,"table_number",12);
key_size_ = (unsigned int)get_param<int>(index_params_,"key_size",20);
multi_probe_level_ = (unsigned int)get_param<int>(index_params_,"multi_probe_level",2);
table_number_ = get_param(index_params_,"table_number",12);
key_size_ = get_param(index_params_,"key_size",20);
multi_probe_level_ = get_param(index_params_,"multi_probe_level",2);
feature_size_ = (unsigned)dataset_.cols;
fill_xor_mask(0, key_size_, multi_probe_level_, xor_masks_);
@@ -112,7 +112,7 @@ public:
void buildIndex() CV_OVERRIDE
{
tables_.resize(table_number_);
for (unsigned int i = 0; i < table_number_; ++i) {
for (int i = 0; i < table_number_; ++i) {
lsh::LshTable<ElementType>& table = tables_[i];
table = lsh::LshTable<ElementType>(feature_size_, key_size_);
@@ -378,11 +378,11 @@ private:
IndexParams index_params_;
/** table number */
unsigned int table_number_;
int table_number_;
/** key size */
unsigned int key_size_;
int key_size_;
/** How far should we look for neighbors in multi-probe LSH */
unsigned int multi_probe_level_;
int multi_probe_level_;
/** The XOR masks to apply to a key to get the neighboring buckets */
std::vector<lsh::BucketKey> xor_masks_;
modules/flann/include/opencv2/flann/lsh_table.h
+1 -1
View File
@@ -245,7 +245,7 @@ public:
{
std::cerr << "LSH is not implemented for that type" << std::endl;
assert(0);
return 1;
return 0;
}
/** Get statistics about the table
modules/flann/include/opencv2/flann/result_set.h
+1 -3
View File
@@ -196,12 +196,10 @@ public:
#endif
{
// Check for duplicate indices
int j = i - 1;
while ((j >= 0) && (dists[j] == dist)) {
for (int j = i; dists[j] == dist && j--;) {
if (indices[j] == index) {
return;
}
--j;
}
break;
}
modules/flann/test/test_lshtable_badarg.cpp
+6
View File
@@ -90,4 +90,10 @@ void CV_LshTableBadArgTest::run( int /* start_from */ )
TEST(Flann_LshTable, badarg) { CV_LshTableBadArgTest test; test.safe_run(); }
TEST(Flann_LshTable, bad_any_cast) {
Mat features = Mat::ones(1, 64, CV_8U);
EXPECT_NO_THROW(flann::GenericIndex<cvflann::Hamming2<unsigned char> >(
features, cvflann::LshIndexParams()));
}
}} // namespace
modules/imgcodecs/src/ios_conversions.mm
+15
View File
@@ -112,6 +112,21 @@ void UIImageToMat(const UIImage* image,
m.step[0], colorSpace,
bitmapInfo);
}
else if (CGColorSpaceGetModel(colorSpace) == kCGColorSpaceModelIndexed)
{
// CGBitmapContextCreate() does not support indexed color spaces.
colorSpace = CGColorSpaceCreateDeviceRGB();
m.create(rows, cols, CV_8UC4); // 8 bits per component, 4 channels
if (!alphaExist)
bitmapInfo = kCGImageAlphaNoneSkipLast |
kCGBitmapByteOrderDefault;
else
m = cv::Scalar(0);
contextRef = CGBitmapContextCreate(m.data, m.cols, m.rows, 8,
m.step[0], colorSpace,
bitmapInfo);
CGColorSpaceRelease(colorSpace);
}
else
{
m.create(rows, cols, CV_8UC4); // 8 bits per component, 4 channels